[{"id":"doi:10.1007/springerreference_64323","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/springerreference_64323","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-08-29T12:52:50Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/springerreference_64323","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.3403/30354752u","name":"IT Security techniques � Encryption algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30354752u","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-05-07T20:31:00Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.3403/30354752u","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-031-43214-9_3","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-43214-9_3","authors":["Stefania Loredana Nita","Marius Iulian Mihailescu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-26T09:02:55Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-031-43214-9_3","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.2139/ssrn.6074549","name":"Homomorphic-based Encryption using Weil Pairing: A Foundation for Fully Homomorphic Encryption on Elliptic Curves","source":"crossref","abstract":"We present a novel cryptographic scheme that integrates homomorphic encryption with elliptic curve cryptography through the application of Weil pairing theory. Our approach, termed Elliptic Curve Homomorphic Cryptography (ECHC), leverages the isomorphic relationship between elliptic curves and external direct products of ℤₙ established by the Weil theorem. This construction provides a foundation for achieving fully homomorphic encryption on elliptic curves while maintaining computational efficiency. We provide comprehensive mathematical proofs, security analysis, and complexity evaluations demonstrating that ECHC offers improved performance over traditional homomorphic encryption schemes with security levels comparable to established elliptic curve cryptosystems. The proposed scheme supports both additive and multiplicative homomorphic operations, making it a viable stepping stone toward practical fully homomorphic encryption implementations.","url":"https://doi.org/10.2139/ssrn.6074549","authors":["Eunice Lee","Caleb Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-11T01:46:57Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.2139/ssrn.6074549","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1201/b16309-7","name":"Homomorphic Image Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1201/b16309-7","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-11-25T23:36:47Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1201/b16309-7","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-319-12229-8_2","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-12229-8_2","authors":["Xun Yi","Russell Paulet","Elisa Bertino"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-11-14T09:46:09Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-319-12229-8_2","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-319-12229-8_3","name":"Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-12229-8_3","authors":["Xun Yi","Russell Paulet","Elisa Bertino"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-11-14T14:46:09Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-319-12229-8_3","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-030-77287-1_14","name":"Privacy-Preserving Prescription Drug Management Using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1_14","authors":["Aria Shahverdi","Ni Trieu","Chenkai Weng","William Youmans"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-03T23:02:43Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-030-77287-1_14","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.7717/peerjcs.2877/fig-3","name":"Figure 3: Homomorphic encryption process for FL.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerjcs.2877/fig-3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-08T08:00:23Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.7717/peerjcs.2877/fig-3","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1515/jmc-2024-0024","name":"Leveled homomorphic encryption schemes for homomorphic encryption standard","source":"crossref","abstract":"Abstract Homomorphic encryption allows for computations on encrypted data without exposing the underlying plaintext, enabling secure and private data processing in various applications such as cloud computing and machine learning. This article presents a comprehensive mathematical foundation for three prominent homomorphic encryption schemes: Brakerski–Gentry–Vaikuntanathan (BGV), Brakerski–Fan-Vercauteren (BFV), and Cheon–Kim–Kim–Song (CKKS), all based on the ring learning with errors (RLWE) problem. We align our discussion with the functionalities proposed in the recent homomorphic encryption standard, providing detailed algorithms and correctness proofs for each scheme. In addition, we propose improvements to the current schemes focusing on noise management and optimization of public key encryption and leveled homomorphic computation. Our modifications ensure that the noise bound remains within a fixed function for all levels of computation, guaranteeing correct decryption and maintaining efficiency comparable to existing methods. The proposed enhancements reduce ciphertext expansion and storage requirements, making these schemes more practical for real-world applications.","url":"https://doi.org/10.1515/jmc-2024-0024","authors":["Shuhong Gao","Kyle Yates"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-06T15:02:21Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1515/jmc-2024-0024","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-031-65494-7_2","name":"Modern Homomorphic Encryption: Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65494-7_2","authors":["Allon Adir","Ehud Aharoni","Nir Drucker","Ronen Levy","Hayim Shaul","Omri Soceanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-09T17:16:23Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-031-65494-7_2","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.32657/10356/202115","name":"Efficient system design with homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/202115","authors":["Kshitij Deogade"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-01T09:35:29Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.32657/10356/202115","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.70675/1338337bz06cdz4479z817dzff2ea8b914e9","name":"Hardware Acceleration for Homomorphic Encryption","source":"crossref","abstract":"Accélération matérielle pour la cryptographie homomorphe Dans cette thèse, nous nous proposons de contribuer à la définition de systèmes de crypto-calculs pour la manipulation en aveugle de données confidentielles. L’objectif particulier de ce travail est l’amélioration des performances du chiffrement homomorphe. La problématique principale réside dans la définition d’une approche d’accélération qui reste adaptable aux différents cas applicatifs de ces chiffrements, et qui, de ce fait, est cohérente avec la grande variété des paramétrages. C’est dans cet objectif que cette thèse présente l’exploration d’une architecture hybride de calcul pour l’accélération du chiffrement de Fan et Vercauteren (FV).Cette proposition résulte d’une analyse de la complexité mémoire et calculatoire du crypto-calcul avec FV. Une partie des contributions rend plus efficace l’adéquation d’un système non-positionnel de représentation des nombres (RNS) avec la multiplication de polynôme par transformée de Fourier sur corps finis (NTT). Les opérations propres au RNS, facilement parallélisables, sont accélérées par une unité de calcul SIMD type GPU. Les opérations de NTT à la base des multiplications de polynôme sont implémentées sur matériel dédié de type FPGA. Des contributions spécifiques viennent en soutien de cette proposition en réduisant le coût mémoire et le coût des communications pour la gestion des facteurs de rotation des NTT.Cette thèse ouvre des perspectives pour la définition de micro-serveurs pour la manipulation de données confidentielles à base de chiffrement homomorphe.","url":"https://doi.org/10.70675/1338337bz06cdz4479z817dzff2ea8b914e9","authors":["Joël Cathebras"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-03T06:53:04Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.70675/1338337bz06cdz4479z817dzff2ea8b914e9","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.47059/revistageintec.v11i3.1928","name":"A Review on Algorithms of Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.47059/revistageintec.v11i3.1928","authors":["Prahlad Reddy T"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-11T09:48:20Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.47059/revistageintec.v11i3.1928","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-031-35535-6_1","name":"Introduction to Homomorphic Encryption for Financial Cryptography","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-35535-6_1","authors":["Rajesh Kumar Dhanaraj","S. Suganyadevi","V. Seethalakshmi","Mariya Ouaissa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-01T00:02:13Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-031-35535-6_1","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.70675/0ac594f8z13c6z4939za1e6z699263c030ad","name":"Fully homomorphic encryption for machine learning","source":"crossref","abstract":"Chiffrement totalement homomorphe pour l'apprentissage automatique Le chiffrement totalement homomorphe permet d’effectuer des calculs sur des données chiffrées sans fuite d’information sur celles-ci. Pour résumer, un utilisateur peut chiffrer des données, tandis qu’un serveur, qui n’a pas accès à la clé de déchiffrement, peut appliquer à l’aveugle un algorithme sur ces entrées. Le résultat final est lui aussi chiffré, et il ne peut être lu que par l’utilisateur qui possède la clé secrète. Dans cette thèse, nous présentons des nouvelles techniques et constructions pour le chiffrement totalement homomorphe qui sont motivées par des applications en apprentissage automatique, en portant une attention particulière au problème de l’inférence homomorphe, c’est-à-dire l’évaluation de modèles cognitifs déjà entrainé sur des données chiffrées. Premièrement, nous proposons un nouveau schéma de chiffrement totalement homomorphe adapté à l’évaluation de réseaux de neurones artificiels sur des données chiffrées. Notre schéma atteint une complexité qui est essentiellement indépendante du nombre de couches dans le réseau, alors que l’efficacité des schéma proposés précédemment dépend fortement de la topologie du réseau. Ensuite, nous présentons une nouvelle technique pour préserver la confidentialité du circuit pour le chiffrement totalement homomorphe. Ceci permet de cacher l’algorithme qui a été exécuté sur les données chiffrées, comme nécessaire pour protéger les modèles propriétaires d’apprentissage automatique. Notre mécanisme rajoute un coût supplémentaire très faible pour un niveau de sécurité égal. Ensemble, ces résultats renforcent les fondations du chiffrement totalement homomorphe efficace pour l’apprentissage automatique, et représentent un pas en avant vers l’apprentissage profond pratique préservant la confidentialité. Enfin, nous présentons et implémentons un protocole basé sur le chiffrement totalement homomorphe pour le problème de recherche d’information confidentielle, c’est-à-dire un scénario où un utilisateur envoie une requête à une base de donnée tenue par un serveur sans révéler cette requête.","url":"https://doi.org/10.70675/0ac594f8z13c6z4939za1e6z699263c030ad","authors":["Michele Minelli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-03T05:06:43Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.70675/0ac594f8z13c6z4939za1e6z699263c030ad","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.5220/0006245901770184","name":"Enterprise Level Security with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0006245901770184","authors":["Kevin Foltz","William R. Simpson"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-05-23T08:13:52Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0006245901770184","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.32657/10356/204533","name":"Homomorphic encryption protocols for privacy preserving cloud computing","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/204533","authors":["Sicheng Wei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-01T07:53:55Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.32657/10356/204533","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.59350/r67br-pv421","name":"Exploring Homomorphic Encryption with Python","source":"crossref","abstract":"Homomorphic encryption is a powerful cryptographic technique that allows computations to be performed on encrypted data without decrypting it first. This blog post will introduce the concept of homomorphic encryption and demonstrate implementations using Python. What is Homomorphic Encryption? Homomorphic encryption is a form of encryption that allows specific types of computations to be carried out on ciphertext.","url":"https://doi.org/10.59350/r67br-pv421","authors":["Abhishek Tiwari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-06T21:02:44Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.59350/r67br-pv421","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.5220/0012129800003555","name":"SoK: Towards CCA Secure Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012129800003555","authors":["Hiroki Okada","Kazuhide Fukushima"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-12T09:51:15Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0012129800003555","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.70675/56bcc9fcz1508z4cc9zac79ze1fe4f9403a5","name":"A journey towards practical fully homomorphic encryption","source":"crossref","abstract":"En route vers un chiffrement complètement homomorphe applicable Craig Gentry a proposé en 2009 le premier schéma de chiffrement complétement homomorphe. Depuis, un effort conséquent a été, et est toujours, fourni par la communauté scientifique pour rendre utilisable ce nouveau type de cryptographie. Son côté révolutionnaire tient au fait qu'il permet d'effectuer des traitements directement sur des données chiffrées (sans que l’entité réalisant les traitements ait besoin de les déchiffrer). Plusieurs pistes se sont développées en parallèle, explorant d'un côté des schémas complétement homomorphes, plus flexibles entermes d'applications mais plus contraignants en termes de taille de données ou en coût de calcul, et de l'autre côté des schémas quelque peu homomorphes, moins flexibles mais aussi moins coûteux. Cette thèse, réalisée au sein de la chaire de cyberdéfense des systèmes navals, s’inscrit dans cette dynamique. Nous avons endossé divers rôles. Tout d’abord un rôle d'attaquant pour éprouver la sécurité des hypothèses sous-jacentes aux propositions. Ensuite, nous avons effectué un état de l’art comparatif des schémas quelque peu homomorphes les plus prometteurs afin d'identifier le(s) meilleur(s) selon les cas d’usages, et de donner des conseils dans le choix des paramètres influant sur leur niveau de sécurité, la taille des données chiffrées et le coût algorithmique des calculs. Enfin, nous avons endossé le rôle du concepteur en proposant un nouveau schéma complétement homomorphe performant, ainsi que son implémentation mise à disposition sur github.","url":"https://doi.org/10.70675/56bcc9fcz1508z4cc9zac79ze1fe4f9403a5","authors":["Guillaume Bonnoron"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-02T22:44:10Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.70675/56bcc9fcz1508z4cc9zac79ze1fe4f9403a5","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-030-77287-1_10","name":"PRIORIS: Enabling Secure Detection of Suicidal Ideation from Speech Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1_10","authors":["Deepika Natarajan","Anders Dalskov","Daniel Kales","Shabnam Khanna"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-03T23:02:43Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-030-77287-1_10","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.70675/8078c608zc6b1z49c7z8a3ez16382897b1fc","name":"Constructing new tools for efficient homomorphic encryption","source":"crossref","abstract":"Construction de nouveaux outils de chiffrement homomorphe efficace Dans notre vie de tous les jours, nous produisons une multitude de données à chaque fois que nous accédons à un service en ligne. Certaines sont partagées volontairement et d'autres à contrecœur. Ces données sont collectées et analysées en clair, ce qui menace la vie privée de l'utilisateur et empêche la collaboration entre entités travaillant sur des données sensibles. Le chiffrement complètement homomorphe (Fully Homomorphic Encryption) apporte une lueur d'espoir en permettant d'effectuer des calculs sur des données chiffrées ce qui permet de les analyser et de les exploiter sans jamais y accéder en clair. Cette thèse se focalise sur TFHE, un récent schéma complètement homomorphe capable de réaliser un bootstrapping en un temps record. Dans celle-ci, nous introduisons une méthode d'optimisation pour sélectionner les degrés de liberté inhérents aux calculs homomorphiques permettant aux profanes d'utiliser TFHE. Nous détaillons une multitude de nouveaux algorithmes homomorphes qui améliorent l'efficacité de TFHE et réduisent voire éliminent les restrictions d'algorithmes connus. Une implémentation efficace de ceux-ci est d'ores et déjà en accès libre.","url":"https://doi.org/10.70675/8078c608zc6b1z49c7z8a3ez16382897b1fc","authors":["Samuel Tap"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T13:04:50Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.70675/8078c608zc6b1z49c7z8a3ez16382897b1fc","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-031-35535-6_4","name":"Securing Shared Data Based on Homomorphic Encryption Schemes","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-35535-6_4","authors":["K. Renuka Devi","S. Nithyapriya","G. Pradeep","R. Menaha","S. Suganyadevi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-01T00:02:13Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-031-35535-6_4","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.5220/0012090200003555","name":"Griffin: Towards Mixed Multi-Key Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012090200003555","authors":["Thomas Schneider","Hossein Yalame","Michael Yonli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-12T09:51:15Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0012090200003555","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.47749/t/unicamp.2016.977097","name":"CCA1-secure somewhat homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.47749/t/unicamp.2016.977097","authors":["Eduardo Moraes de Morais"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-06-22T20:34:43Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.47749/t/unicamp.2016.977097","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-031-35535-6_2","name":"A Survey on Homomorphic Encryption for Financial Cryptography Workout","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-35535-6_2","authors":["M. Siva Sangari","K. Balasamy","Habib Hamam","S. Nithya","S. Surya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-01T00:02:13Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-031-35535-6_2","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.5220/0009828803800387","name":"Accelerating Homomorphic Encryption using Approximate Computing Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0009828803800387","authors":["Shabnam Khanna","Ciara Rafferty"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-07-15T15:30:18Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0009828803800387","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.59350/z6s3m-a4a81","name":"Fully Homomorphic Encryption in Production Systems","source":"crossref","abstract":"In this living document, I will list all production systems I'm aware of that use fully homomorphic encryption (FHE). For background on FHE, see my overview of the field. If you have any information about production FHE systems not in this list, or corrections to information in this list, please send me an email with sufficient detail allow the claim to be publicly verified.","url":"https://doi.org/10.59350/z6s3m-a4a81","authors":["Jeremy Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-12T06:28:20Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.59350/z6s3m-a4a81","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.32657/10356/138531","name":"Accelerating homomorphic encryption for privacy-preserving applications","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/138531","authors":["Truong Phu Truan Ho"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-10-28T06:55:20Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.32657/10356/138531","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.70729/se23424155420","name":"An Analysis of Homomorphic Encryption in Latest Technologies","source":"crossref","abstract":"","url":"https://doi.org/10.70729/se23424155420","authors":["Karan Chawla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-07T05:54:26Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.70729/se23424155420","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-030-77287-1_3","name":"Privacy-Preserving Data Sharing and Computation Across Multiple Data Providers with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1_3","authors":["Juan Troncoso-Pastoriza","David Froelicher","Peizhao Hu","Asma Aloufi","Jean-Pierre Hubaux"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-03T23:02:43Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-030-77287-1_3","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.59350/mxg90-kth73","name":"Fully Homomorphic Encryption and the Public","source":"crossref","abstract":"In this living document, I will document reactions to uses of homomorphic encryption by members of the public. By \"member of the public,\" I mean people who may be technical, but are not directly involved in the development or deployment of homomorphic encryption systems. This includes journalists, bloggers, aggregator comment threads, and social media posts.","url":"https://doi.org/10.59350/mxg90-kth73","authors":["Jeremy Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-04T02:24:47Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.59350/mxg90-kth73","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.70675/3b6aefefz9fbcz4fe5z9654zf389d7eb83f9","name":"Theoretical and practical contributions to homomorphic encryption","source":"crossref","abstract":"Contributions théoriques et pratiques au chiffrement homomorphe Dans les schémas de chiffrement classique, l'objectif principal du schéma est d'assurer la confidentialité des données. Le chiffrement totalement homomorphe, une variante réalisée pour la première fois par Gentry, est un schéma de chiffrement qui permet également le calcul sur les données chiffrées, sans jamais avoir besoin de les déchiffrer. En l'utilisant, tout tiers non fiable avec le matériel de clé pertinent peut effectuer des calculs homomorphes, conduisant à de nombreuses applications où un tiers non fiable peut toujours être autorisée à calculer sur des chiffrements de données sensibles (cloud computing), ou où la confiance doit être décentralisée ( calcul multipartite).Cette thèse comporte deux contributions principales au chiffrement totalement homomorphe. Dans la première partie, on prend un FHE basé sur les nombres de Fermat et on étend le chiffrement sur des nombres à plusieurs bits. On ajoute la possibilité d'évaluer homomorphiquement des fonctions de petites tailles, et en les utilisant, on arrive à faire des additions et multiplications avec peu de bootstrappings, et qui peux servir comme composante des computations plus larges. Certains résultats plus récents sur les variables aléatoires sous-gaussiennes sont adaptés pour donner une meilleure analyse d'erreur.L'un des obstacles pour la généralisation de FHE est sa grande complexité computationelle, et des architectures optimisées pour accélérer les calculs FHE sur du matériel reconfigurable ont été proposées. La deuxième partie propose une architecture materiélle pour l'arithmetique des polynômes utilisés dans les systèmes comme FV. Elle peut être utlisée pour faire l'addition et la multiplication des polynômes anneaux, en utilisant une paire d'algorithmes NTT qui évite l'utilisation de bit reversal, et comprend les multiplications par les vecteurs de poids. Pour le côut de stocker les facteurs twiddles dans un ROM, on évite les mises à jour des twiddles, ce qui mène à un compte de cycle beaucoup plus petit.","url":"https://doi.org/10.70675/3b6aefefz9fbcz4fe5z9654zf389d7eb83f9","authors":["Nagarjun Chinthamani Dwarakanath"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-06T21:34:46Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.70675/3b6aefefz9fbcz4fe5z9654zf389d7eb83f9","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-031-35535-6_12","name":"A Survey on Private Keyword Sorting and Searching Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-35535-6_12","authors":["S. Nithya","V. Seethalakshmi","G. Vetrichelvi","M. Siva Sangari","Gokul Basavaraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-01T00:02:13Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-031-35535-6_12","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.36227/techrxiv.21314037","name":"Multi-key Fully Homomorphic Encryption without CRS from RLWE","source":"crossref","abstract":"&lt;p&gt;Fully Homomorphic Encryption (FHE) is a powerful encryption system in cloud computing that allows homomorphic computations on encrypted data without decrypting them. Multi-key fully homomorphic encryption (MFHE), as an extension to FHE, allows homomorphic computations on ciphertexts encrypted under different keys. However, most MFHE schemes require a Common Random\\slash Reference String (CRS), while the few that do not are based on the Learning With Errors (LWE) problem, which means that they can only deal with single bit plaintext. Consequently, MFHE schemes based on the Ring Learning With Errors (RLWE) problem are more desirable, as they can handle polynomial plaintext. Requiring the CRS seems to weaken the semantic definition of MFHE, where all users generate their own keys independently.&lt;/p&gt; &lt;p&gt;In this paper, we study the RLWE-based MFHE in the CRS model and propose the first RLWE-based MFHE without CRS. To this end, we remove the CRS by designing a new relinearization algorithm. Like previous MFHE schemes, our RLWE-based MFHE without CRS has a simple 1-round threshold decryption, which implies a $3$-round secure MPC protocol in the plain model from the RLWE assumption.&lt;/p&gt;","url":"https://doi.org/10.36227/techrxiv.21314037","authors":["Fucai Luo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-10-17T19:19:48Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.36227/techrxiv.21314037","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1109/aicis.2018.00043","name":"New Fully Homomorphic Encryption Scheme Based on Multistage Partial Homomorphic Encryption Applied in Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aicis.2018.00043","authors":["Zainab Hikmat Mahmood","Mahmood Khalel Ibrahem"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-02-14T23:40:39Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1109/aicis.2018.00043","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.22215/etd/2025-16803","name":"Homomorphic Encryption Based Obfuscated Complex Queries System","source":"crossref","abstract":"","url":"https://doi.org/10.22215/etd/2025-16803","authors":["Mahmoud Abdelhafeez Ahmed Sayed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-19T15:59:01Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.22215/etd/2025-16803","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-031-33386-6_8","name":"Homomorphic Encryption","source":"crossref","abstract":"Abstract Homomorphic encryption is a technique in cryptography that allows for performing operations on encrypted data. The encrypted result can then be decrypted to obtain the operation result, making it possible to perform computations on sensitive data without revealing it. homomorphic encryption was first proved possible in 2009, and since then, many improvements have been made to increase performance, though it still has limitations. With recent advancements and the increasing demand for data protection, homomorphic encryption is expected to become more relevant and be used in multiple industries. In Switzerland, IBM, Inpher, and Tune Insight are among the companies that have developed homomorphic libraries and offer solutions for secure computation. These solutions can provide better protection and reduce the data vulnerability entrusted to companies and governments.","url":"https://doi.org/10.1007/978-3-031-33386-6_8","authors":["Jean-Pierre Hubaux"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-31T21:02:35Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-031-33386-6_8","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.7717/peerj-cs.1649/fig-1","name":"Figure 1: E-voting system based on homomorphic encryption and decentralization scheme.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.1649/fig-1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-18T03:47:51Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.7717/peerj-cs.1649/fig-1","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.25125/engineering-journal-ijoer-sep-2017-22","name":"Secure Outsourced Association Rule Mining using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.25125/engineering-journal-ijoer-sep-2017-22","authors":["Sandeep Varma","LijiP I"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-12-10T12:27:43Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.25125/engineering-journal-ijoer-sep-2017-22","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.15368/theses.2015.64","name":"HEIDE: An IDE for the Homomorphic Encryption Library HElib","source":"crossref","abstract":"","url":"https://doi.org/10.15368/theses.2015.64","authors":["Grant Taylor Frame"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-03-01T16:09:31Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.15368/theses.2015.64","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.5220/0014968600004103","name":"A Performance Driven Decision Framework for Hybrid Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014968600004103","authors":["Hannah Meinhardt","Clemens Krüger","Dominik Schoop"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-20T07:47:42Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0014968600004103","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.70675/8966b36czb696z4648zaa9azd33fc8334966","name":"Towards efficient arithmetic for Ring-LWE based homomorphic encryption","source":"crossref","abstract":"Vers une arithmétique efficace pour les chiffrements homomorphes basée sur le Ring-LWE Le chiffrement totalement homomorphe est un type de chiffrement qui permet de manipuler directement des données chiffrées. De cette manière, il est possible de traiter des données sensibles sans avoir à les déchiffrer au préalable, permettant ainsi de préserver la confidentialité des données traitées. À l'époque du numérique à outrance et du \"cloud computing\" ce genre de chiffrement a le potentiel pour impacter considérablement la protection de la vie privée. Cependant, du fait de sa découverte récente par Gentry en 2009, nous manquons encore de recul à son propos. C'est pourquoi de nombreuses incertitudes demeurent, notamment concernant sa sécurité et son efficacité en pratique, et devront être éclaircies avant une éventuelle utilisation à large échelle. Cette thèse s'inscrit dans cette problématique et se concentre sur l'amélioration des performances de ce genre de chiffrement en pratique. Pour cela nous nous sommes intéressés à l'optimisation de l'arithmétique utilisée par ces schémas, qu'elle soit sous-jacente au problème du \"Ring-Learning With Errors\" sur lequel la sécurité des schémas considérés est basée, ou bien spécifique aux procédures de calculs requises par certains de ces schémas. Nous considérons également l'optimisation des calculs nécessaires à certaines applications possibles du chiffrement homomorphe, et en particulier la classification de données privées, de sorte à proposer des techniques de calculs innovantes ainsi que des méthodes pour effectuer ces calculs de manière efficace. L'efficacité de nos différentes méthodes est illustrée à travers des implémentations logicielles et des comparaisons aux techniques de l'état de l'art.","url":"https://doi.org/10.70675/8966b36czb696z4648zaa9azd33fc8334966","authors":["Vincent Zucca"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-03T18:35:42Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.70675/8966b36czb696z4648zaa9azd33fc8334966","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.5220/0011277000003283","name":"Graph Algorithms over Homomorphic Encryption for Data Cooperatives","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0011277000003283","authors":["Mark Dockendorf","Ram Dantu","John Long"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-14T20:19:10Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0011277000003283","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.14711/thesis-991012879963103412","name":"FPGA-based hardware acceleration of homomorphic encryption for federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991012879963103412","authors":["Zhaoxiong Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-03-13T01:46:41Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.14711/thesis-991012879963103412","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.14711/thesis-hdl172526","name":"Hierarchical Framework for Efficient and Scalable Fully Homomorphic Encryption Acceleration","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-hdl172526","authors":["Yuying Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-29T23:03:28Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.14711/thesis-hdl172526","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.59350/91pv0-hca48","name":"Google's Fully Homomorphic Encryption Compiler — A Primer","source":"crossref","abstract":"Back in May of 2022 I transferred teams at Google to work on Fully Homomorphic Encryption (newsletter announcement). Since then I've been working on a variety of projects in the space, including being the primary maintainer on github.com/google/fully-homomorphic-encryption, which is an open source FHE compiler for C++. This article will be an introduction to how to use it to compile programs to FHE, as well as a quick overview of its internals.","url":"https://doi.org/10.59350/91pv0-hca48","authors":["Jeremy Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-12T11:52:24Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.59350/91pv0-hca48","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-031-43214-9_4","name":"Searchable Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-43214-9_4","authors":["Stefania Loredana Nita","Marius Iulian Mihailescu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-26T09:02:55Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-031-43214-9_4","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1561/9781638283454.ch2","name":"Chapter 2. Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781638283454.ch2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-09T04:06:40Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1561/9781638283454.ch2","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.3990/1.9789036568067","name":"Fully Homomorphic Encryption for Privacy-Preserving Collaborative Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.3990/1.9789036568067","authors":["Federico Mazzone"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-04T09:35:56Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.3990/1.9789036568067","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1109/iscas45731.2020.9181192/video","name":"Video for VLSI Architecture of Polynomial Multiplication for BGV Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscas45731.2020.9181192/video","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-09-29T09:22:27Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1109/iscas45731.2020.9181192/video","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.59350/3wzmq-6vy77","name":"Bicyclic Matrix-Matrix Multiplication in Fully Homomorphic Encryption","source":"crossref","abstract":"In an earlier article, I covered the basic technique for performing matrix-vector multiplication in fully homomorphic encryption (FHE), known as the Halevi-Shoup diagonal method. This article covers a more recent method for matrix-matrix multiplication known as the bicyclic method.","url":"https://doi.org/10.59350/3wzmq-6vy77","authors":["Jeremy Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-18T12:51:41Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.59350/3wzmq-6vy77","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.36227/techrxiv.24003795","name":"Advancing Privacy and Accuracy with Federated Learning and Homomorphic Encryption","source":"crossref","abstract":"&lt;p&gt;In this paper, we present an integrated framework that combines Federated Learning (FL) with Homomorphic Encryption (HE) using the Artificial Intelligence (AI) models and the Cheon-Kim-Kim-Song (CKKS) algorithm to address the challenges of privacy and accuracy. FL facilitates collaborative training of Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models across decentralized devices, allowing for data privacy preservation without sharing raw data. The integration of the CKKS algorithm for HE ensures secure computation on encrypted data during the FL process. Our experimental results on three diverse datasets demonstrate the efficacy of this approach, achieving an impressive highest average accuracy of 97.3%. Additionally, the CKKS algorithm is used to achieve efficient computation, making it a promising solution for privacy-conscious machine learning applications, and paving the way for practical deployment in various real-world scenarios, thereby revolutionizing the landscape of privacy-preserving machine learning.&lt;/p&gt;","url":"https://doi.org/10.36227/techrxiv.24003795","authors":["Tuy Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-23T03:04:24Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.36227/techrxiv.24003795","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1016/b978-0-12-816197-5.00005-x","name":"Homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-816197-5.00005-x","authors":["Kim Laine"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-03-20T21:05:49Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1016/b978-0-12-816197-5.00005-x","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.47953/sae-pp-00324","name":"Homomorphic Encryption Based on Post-Quantum Cryptography","source":"crossref","abstract":"","url":"https://doi.org/10.47953/sae-pp-00324","authors":["Abel C.H. Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-02-27T19:49:11Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.47953/sae-pp-00324","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.5220/0012753900003767","name":"A Framework for Federated Analysis of Health Data Using Multiparty Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012753900003767","authors":["Miroslav Puskaric"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-12T19:48:20Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0012753900003767","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-642-31410-0_15","name":"Shift-Type Homomorphic Encryption and Its Application to Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-31410-0_15","authors":["Frederik Armknecht","Stefan Katzenbeisser","Andreas Peter"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-06-20T06:26:59Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-642-31410-0_15","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.15373/22778179/may2014/26","name":"Data Security in Cloud Computing Using Homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.15373/22778179/may2014/26","authors":["Sumit Yadav","Usha Verma","Chhavi Bhardwaj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-06-06T05:45:56Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.15373/22778179/may2014/26","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-3-031-35535-6_6","name":"Homomorphic Encryption-Based Cloud Privacy-Preserving in Remote ECG Monitoring and Surveillance","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-35535-6_6","authors":["V. Seethalakshmi","S. Suganyadevi","S. Nithya","K. Sheela Sobana Rani","Gokul Basavaraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-01T00:02:13Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-031-35535-6_6","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.59350/a7m2z-wz087","name":"A High-Level Technical Overview of Fully Homomorphic Encryption","source":"crossref","abstract":"About two years ago, I switched teams at Google to focus on fully homomorphic encryption (abbreviated FHE, or sometimes HE). Since then I've got to work on a lot of interesting projects, learning along the way about post-quantum cryptography, compiler design, and the ins and outs of fully homomorphic encryption.","url":"https://doi.org/10.59350/a7m2z-wz087","authors":["Jeremy Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-12T07:42:12Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.59350/a7m2z-wz087","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.21275/sr231017115439","name":"Introduction: Cloud Storage Security and Homomorphic Encryption in Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.21275/sr231017115439","authors":["Pushpjeet Cholkar","Margi Patel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-01T04:30:02Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21275/sr231017115439","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1007/978-3-031-35535-6_13","name":"Multivariate Cryptosystem Based on a Quadratic Equation to Eliminate the Outliers Using Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-35535-6_13","authors":["M. Janani","R. Jeevitha","R. Jaikumar","R. Suganthi","S. Jhansi Ida"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-01T00:02:13Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-031-35535-6_13","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1109/compcomm.2017.8322975","name":"Homomorphic cloud computing scheme based on hybrid homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/compcomm.2017.8322975","authors":["Xidan Song","Yulin Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-04-11T21:16:01Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1109/compcomm.2017.8322975","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.59350/n4fsj-ex895","name":"Packing Matrix-Vector Multiplication in Fully Homomorphic Encryption","source":"crossref","abstract":"In my recent overview of homomorphic encryption, I underemphasized the importance of data layout when working with arithmetic (SIMD-style) homomorphic encryption schemes. In the FHE world, the name given to data layout strategies is called \"packing,\" because it revolves around putting multiple plaintext data into RLWE ciphertexts in carefully-chosen ways that mesh well with the operations you'd like to perform.","url":"https://doi.org/10.59350/n4fsj-ex895","authors":["Jeremy Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-12T05:43:46Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.59350/n4fsj-ex895","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.5220/0012819200004547","name":"The Investigation of Fully Homomorphic Encryption: Techniques, Applications, and Future Directions","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012819200004547","authors":["Yiling Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-07T17:09:50Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0012819200004547","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.2139/ssrn.6209798","name":"Homomorphic Encryption Based on Pixel Scrambling Algorithm for Image Fusion","source":"crossref","abstract":"Image fusion can break through the information barriers of single source images. However, during the fusion process, image information is in an unencrypted and exposed state. Therefore, we proposed the application of a homomorphic encryption scheme to image fusion, which enables fusion calculations to be carried out on ciphertext. Nevertheless, we found that there is currently a lack of homomorphic encryption algorithms suitable for image fusion. Consequently, this paper, for the first time, applies the pixel scrambling algorithm as a homomorphic encryption scheme to image fusion. The pixel scrambling algorithm disrupts the visual and statistical characteristics of an image by scrambling the spatial positions of pixels. It is characterized by simplicity, high efficiency, and strong security. The homomorphic encryption scheme we proposed enables ciphertext based fusion, taking into account both processing efficiency and the effectiveness of the results. This provides a more practical technical solution for the secure fusion of sensitive images, effectively balancing the need for data privacy protection and multi-source information complementarity. Thus, it holds significant theoretical and application value.","url":"https://doi.org/10.2139/ssrn.6209798","authors":["Tieyu Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-10T08:41:26Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.2139/ssrn.6209798","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.5220/0011012400003123","name":"Privacy-preserving Copy Number Variation Analysis with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0011012400003123","authors":["Hüseyin Demirci","Gabriele Lenzini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-25T07:34:00Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0011012400003123","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.70675/805cc7f5z1777z4745z83c5z527db01a5565","name":"Secure Machine Learning by means of Homomorphic Encryption and Verifiable Computing","source":"crossref","abstract":"Apprentissage machine sécurisé à l'aide de chiffrement homomorphe et de calcul vérifiable L’apprentissage automatique (ou le Machine Learning) est un domaine scientifique très en vogue en raison de sa capacité à résoudre les problèmes automatiquement et de son large spectre d’applications (par exemple, le domaine de la finance, le domaine médical, etc.). Les techniques de Machine Learning (ML) ont attiré mon attention du point de vue cryptographique dans le sens où les travaux de ma thèse ont eu comme objectif une utilisation sécurisée des méthodes de ML. Cette thèse traite l'utilisation sécurisée des techniques de ML sous deux volets : la confidentialité des données d’apprentissage ou des données pour l’inférence et l’intégrité de l’évaluation à distance des différentes méthodes de ML. La plupart des autres travaux traitent que la confidentialité des données et que pour la phase d’inférence. Dans ma thèse, j’ai proposé trois architectures pour assurer une évaluation à distance sécurisée pour les configurations suivantes de ML: la classification à distance grâce à un réseau de neurones (NN), l’apprentissage fédéré (FL) et l’apprentissage par transfert (TL). Notamment, les architectures pour l’apprentissage fédéré et l’apprentissage par transfert sont les premiers approches qui traitent à la fois la confidentialité de données et l'intégrité du calcul. Ces architectures ont été construites en utilisant ou en modifiant un schéma de calcul vérifiable pré-existant pour des données chiffrées en homomorphe. Nos travaux ouvrent des nombreuses perspectives, qui ne concernent pas forcément que les architectures de ML, mais aussi les outils utilisés pour assurer les propriétés de sécurité. Par exemple, une perspective importante est de rajouter de la confidentialité différentielle (DP) à notre architecture d’apprentissage fédéré.","url":"https://doi.org/10.70675/805cc7f5z1777z4745z83c5z527db01a5565","authors":["Abbass Madi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T01:19:47Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.70675/805cc7f5z1777z4745z83c5z527db01a5565","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.70675/c564e8c4zaa46z4f81z9d47zce4efdeb2f76","name":"Towards efficient and secure Fully Homomorphic Encryption and cloud computing","source":"crossref","abstract":"Vers l'efficacité et la sécurité du chiffrement homomorphe et du cloud computing Le chiffrement homomorphe est une branche de la cryptologie, dans laquelle les schémas de chiffrement offrent la possibilité de faire des calculs sur les messages chiffrés, sans besoin de les déchiffrer. L’intérêt pratique de ces schémas est dû à l’énorme quantité d'applications pour lesquels ils peuvent être utilisés. En sont un exemple le vote électronique, les calculs sur des données sensibles, comme des données médicales ou financières, le cloud computing, etc..Le premier schéma de chiffrement (complètement) homomorphe n'a été proposé qu'en 2009 par Gentry. Il a introduit une technique appelée bootstrapping, utilisée pour réduire le bruit des chiffrés : en effet, dans tous les schémas de chiffrement homomorphe proposés, les chiffrés contiennent une petite quantité de bruit, nécessaire pour des raisons de sécurité. Quand on fait des calculs sur les chiffrés bruités, le bruit augmente et, après avoir évalué un certain nombre d’opérations, ce bruit devient trop grand et, s'il n'est pas contrôlé, risque de compromettre le résultat des calculs.Le bootstrapping est du coup fondamental pour la construction des schémas de chiffrement homomorphes, mais est une technique très coûteuse, qu'il s'agisse de la mémoire nécessaire ou du temps de calcul. Les travaux qui on suivi la publication de Gentry ont eu comme objectif celui de proposer de nouveaux schémas et d’améliorer le bootstrapping pour rendre le chiffrement homomorphe faisable en pratique. L’une des constructions les plus célèbres est GSW, proposé par Gentry, Sahai et Waters en 2013. La sécurité du schéma GSW se fonde sur le problème LWE (learning with errors), considéré comme difficile en pratique. Le bootstrapping le plus rapide, exécuté sur un schéma de type GSW, a été proposé en 2015 par Ducas et Micciancio. Dans cette thèse on propose une nouvelle variante du schéma de chiffrement homomorphe de Ducas et Micciancio, appelée TFHE.Le schéma TFHE améliore les résultats précédents, en proposant un bootstrapping plus rapide (de l'ordre de quelques millisecondes) et des clés de bootstrapping plus petites, pour un même niveau de sécurité. TFHE utilise des chiffrés de type TLWE et TGSW (scalaire et ring) : l’accélération du bootstrapping est principalement due à l’utilisation d’un produit externe entre TLWE et TGSW, contrairement au produit externe GSW utilisé dans la majorité des constructions précédentes.Deux types de bootstrapping sont présentés. Le premier, appelé gate bootstrapping, est exécuté après l’évaluation homomorphique d’une porte logique (binaire ou Mux) ; le deuxième, appelé circuit bootstrapping, peut être exécuté après l’évaluation d’un nombre d'opérations homomorphiques plus grand, pour rafraîchir le résultat ou pour le rendre compatible avec la suite des calculs.Dans cette thèse on propose aussi de nouvelles techniques pour accélérer l’évaluation des calculs homomorphiques, sans bootstrapping, et des techniques de packing des données. En particulier, on présente un packing, appelé vertical packing, qui peut être utilisé pour évaluer efficacement des look-up table, on propose une évaluation via automates déterministes pondérés, et on présente un compteur homomorphe appelé TBSR qui peut être utilisé pour évaluer des fonctions arithmétiques.Pendant les travaux de thèse, le schéma TFHE a été implémenté et il est disponible en open source.La thèse contient aussi des travaux annexes. Le premier travail concerne l’étude d’un premier modèle théorique de vote électronique post-quantique basé sur le chiffrement homomorphe, le deuxième analyse la sécurité des familles de chiffrement homomorphe dans le cas d'une utilisation pratique sur le cloud, et le troisième ouvre sur une solution différente pour le calcul sécurisé, le calcul multi-partite.","url":"https://doi.org/10.70675/c564e8c4zaa46z4f81z9d47zce4efdeb2f76","authors":["Ilaria Chillotti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-02T23:49:06Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.70675/c564e8c4zaa46z4f81z9d47zce4efdeb2f76","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.5220/0008864902400248","name":"Privacy-preserving Surveillance Methods using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0008864902400248","authors":["William Bowditch","Will Abramson","William Buchanan","Nikolaos Pitropakis","Adam Hall"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-03-26T15:12:11Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0008864902400248","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.21275/v5i4.nov162762","name":"Integrity Auditing for Dynamic Cloud Using Homomorphic Encryption with Group User Revocation","source":"crossref","abstract":"","url":"https://doi.org/10.21275/v5i4.nov162762","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-04-26T06:30:22Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21275/v5i4.nov162762","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1007/978-3-030-77287-1_1","name":"Introduction to Homomorphic Encryption and Schemes","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1_1","authors":["Jung Hee Cheon","Anamaria Costache","Radames Cruz Moreno","Wei Dai","Nicolas Gama","Mariya Georgieva","Shai Halevi","Miran Kim","Sunwoong Kim","Kim Laine","Yuriy Polyakov","Yongsoo Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-03T23:02:43Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-030-77287-1_1","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.5220/0015191800005051","name":"Privacy-Preserving Diabetes Risk Prediction with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0015191800005051","authors":["Abdulelah Alasmari","Shafiq Bharwani","Louis Hwang","Giovanni Di Crescenzo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-22T00:32:24Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0015191800005051","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.5220/0015093500004103","name":"A Partitioned Neural Network Architecture for Efficient Inference with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0015093500004103","authors":["Shusaku Uemura","Kazuhide Fukushima"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-20T08:12:53Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0015093500004103","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.32657/10356/173909","name":"Improved packing for fully homomorphic encryption with reverse multiplication friendly embeddings","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/173909","authors":["Jun Jie Sim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-09T03:34:41Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.32657/10356/173909","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1007/978-1-4899-7993-3_1486-3","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4899-7993-3_1486-3","authors":["Ninghui Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-05-15T20:02:08Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-1-4899-7993-3_1486-3","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.5220/0006464703590366","name":"PAnTHErS: A Prototyping and Analysis Tool for Homomorphic Encryption Schemes","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0006464703590366","authors":["Cyrielle Feron","Vianney Lapotre","Loïc Lagadec"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-08-04T13:24:33Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0006464703590366","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1017/qut.2025.2.pr2","name":"Decision: Quantum delegated and federated learning via quantum homomorphic encryption — R0/PR2","source":"crossref","abstract":"","url":"https://doi.org/10.1017/qut.2025.2.pr2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T02:58:07Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1017/qut.2025.2.pr2","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.18122/td.2247.boisestate","name":"A Survey on Homomorphic Encryption Based on the Learning with Rounding\n                    Problem","source":"crossref","abstract":"Much of modern day cryptography deals with the notion of security and efficiency. The main concern with any cryptosystem deals with the practicality of being able to implement any encryption algorithm over large data sets. In this paper, we will look at the various properties of the Learning With Rounding problem (LWR) which comes from the Learning With Errors problem (LWE) first introduced by Oded Regev. We will also present the topic of Homomorphic Encryption (HE) as well as its three variants: Partially Homomorphic Encryption (PHE), Somewhat Homomorphic Encryption (SHE), and Fully Homomorphic Encryption (FHE). This paper also highlights SABER, a cryptographic system based on LWR. The purpose of this paper is to investigate the implications of a Homomorphic Encryption scheme based on LWR that may lead to further research on the topic.","url":"https://doi.org/10.18122/td.2247.boisestate","authors":["Jose Manuel Montoya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-26T19:22:59Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.18122/td.2247.boisestate","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.3390/cryptography9020044","name":"Compile-Time Fully Homomorphic Encryption: Eliminating Online Encryption via Algebraic Basis Synthesis","source":"crossref","abstract":"We propose a new framework for compile-time ciphertext synthesis in fully homomorphic encryption (FHE) systems. Instead of invoking encryption algorithms at runtime, our method synthesizes ciphertexts from precomputed encrypted basis vectors using only homomorphic additions, scalar multiplications, and randomized encryptions of zero. This decouples ciphertext generation from encryption and enables efficient batch encoding through algebraic reuse. We formalize this technique as a randomized module morphism and prove that it satisfies IND-CPA security. Our proof uses a hybrid game framework that interpolates between encrypted vector instances and reduces the adversarial advantage to the indistinguishability advantage of the underlying FHE scheme. This reduction structure captures the security implications of ciphertext basis reuse and structured noise injection. The proposed synthesis primitive supports fast, encryption-free ingestion in outsourced database systems and other high-throughput FHE pipelines. It is compatible with standard FHE APIs and preserves layout semantics for downstream homomorphic operations.","url":"https://doi.org/10.3390/cryptography9020044","authors":["Dongfang Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-16T06:40:27Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.3390/cryptography9020044","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.5220/0014426000004061","name":"When Only Parts Matter: Efficient Privacy-Preserving Analytics with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014426000004061","authors":["Alexandros Bakas","Dimitrios Schoinianakis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-21T23:01:16Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.5220/0014426000004061","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/sist61674.2026.11595966","name":"Homomorphic Encryption in Multi-Factor Authentication: A Partial Homomorphic Encryption Approach to One-Time Password Protection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sist61674.2026.11595966","authors":["Shynbolat Unaibayev","Sagyndyk Suleimanov","Akzhibek Amirova","Olga Ussatova","Begimbayeva Yenlik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T19:38:14Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1109/sist61674.2026.11595966","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.70675/21bae064zcff1z456fza572zbfe31b5b0ca4","name":"Contributions to design and analysis of Fully Homomorphic Encryption schemes","source":"crossref","abstract":"Contributions à la conception et analyse des schémas de chiffrement complètement homomorphe Les schémas de Chiffrement Complètement Homomorphe (FHE) permettent de manipuler des données chiffrées avec grande flexibilité : ils rendent possible l'évaluation de fonctions à travers les couches de chiffrement. Depuis la découverte du premier schéma FHE en 2009 par Craig Gentry, maintes recherches ont été effectuées pour améliorer l'efficacité, atteindre des nouveaux niveaux de sécurité, et trouver des applications et liens avec d'autres domaines de la cryptographie. Dans cette thèse, nous avons étudié en détail ce type de schémas. Nos contributions font état d'une nouvelle attaque de récuperation des clés au premier schéma FHE, et d'une nouvelle notion de sécurité en structures hierarchiques, évitant une forme de trahison entre les usagers tout en gardant la flexibilité FHE. Enfin, on décrit aussi des implémentations informatiques. Cette recherche a été effectuée au sein du Laboratoire de Mathématiques de Versailles avec le Prof. Louis Goubin.","url":"https://doi.org/10.70675/21bae064zcff1z456fza572zbfe31b5b0ca4","authors":["Francisco Vial prado"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-02T11:06:30Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.70675/21bae064zcff1z456fza572zbfe31b5b0ca4","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1007/978-3-031-35535-6_5","name":"Challenges and Opportunities Associated with Homomorphic Encryption for Financial Cryptography","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-35535-6_5","authors":["S. Finney Daniel Shadrach","A. Shiny Pershiya","A. Shirley Stevany Faryl","K. Balasamy","K. Chiranjeevi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-01T00:02:13Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/978-3-031-35535-6_5","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1109/smartcomp52413.2021.00059","name":"Speeding Up Encryption on IoT Devices Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/smartcomp52413.2021.00059","authors":["Marin Matsumoto","Masato Oguchi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-10-10T19:28:45Z","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1109/smartcomp52413.2021.00059","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.21203/rs.3.rs-10428436/v1","name":"Privacy-Preserving Keystroke Dynamics Authentication Using CKKS Homomorphic Encryption","source":"europepmc","abstract":"Abstract Although biometric authentication is widely adopted for digital identity authentication, sometimes, the biometric feature vectors can be revealed in the matching process as the systems can decrypt the templates of the other parties for comparison. This paper introduces an implemented behavioral biometric authentication flow based on the combination of keystroke dynamics and CKKS homomorphic encryption scheme with privacy. The system records the time-stamps when a key is pressed, transforms the time-stamps into seven timings of the presses and, using the enrollment statistics, normalizes the vectors generated by this transformation and applies squared Euclidean distance matching in the encrypted domain. The implementation is based on a browser-based capture interface, a Python/Flask backend, NumPy for processing the features, and OpenFHE for the creation of a CKKS context, the encryption, homomorphic evaluation, and final score decryption. Only the encrypted subtraction, squaring and summation are needed for the matching operation, and only the final scalar distance score is decrypted for the ACCEPT or REJECT decision making in the threshold operation. The authors illustrate the benefits of designing features such that they decrease the computational complexity typically applied in homomorphic biometric matching systems. The system is tested as a controlled proof-of-concept with real and simulated impostor authentication flows, operation count analysis, execution-time observation and operating system level CPU and memory monitoring. The results validate the viability of lightweight encrypted-domain biometric matching, while ensuring the privacy claim is suitably restricted to biometric vectors during the matching process.","url":"https://doi.org/10.21203/rs.3.rs-10428436/v1","authors":["Nipun Mahaarachchi"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-10428436/v1","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.21203/rs.3.rs-9747984/v1","name":"On a Symmetric Homomorphic Encryption Scheme for Multidimensional Data in IoT","source":"europepmc","abstract":"Abstract Homomorphic encryption (HE) schemes enable one to perform certain operations on the encrypted data without decrypting. In literature, HE schemes that allow simple computations on encrypted data have been known for a long time and many efficient HE encryption schemes have been developed. But, most of them are designed for single-dimensional data where encryption is performed on individual elements sequentially, rather than on multidimensional structures as a whole, such as on a vector or an array of elements simultaneously. This makes them inefficient HE for multidimensional data. Due to the emergence of various Internet of Things (IoTs) applications such as smart health, smart grid, smart city, and other domains where IoT devices are required to transmit their sensed data for further processing, it is desirable to have an encryption scheme that can deal with multidimensional data. Because of its wide range of applicability in various IoT applications, in this paper, we propose a Symmetric Homomorphic Encryption for Multidimensional data ( symhem ). Our scheme is easily implementable because of its simple definition. At the same time, it is secure because of its two-step encryption. We have also conducted various simulations and compared the symhem with the other state-of-the-art HE schemes.","url":"https://doi.org/10.21203/rs.3.rs-9747984/v1","authors":["Manvi Upadhyay","Manoj Choudhuri","Ram Narayan Yadav"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-9747984/v1","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.21203/rs.3.rs-10097660/v1","name":"Federated Learning Parameter Protection Based on Homomorphic Encryption and Selective User Decryption","source":"europepmc","abstract":"Abstract Existing security schemes of Federated learning mostly rely on encryption mechanism and participant reliability, but it is difficult to effectively defend against attack threats such as member reasoning, attribute reasoning, model reversal, etc. Meanwhile, the low-quality data users in the system will directly slow down the training speed and undermine the stability of the system due to their unreliable infrastructure and potential evil motives. To solve these problems, this paper proposes a federated learning model parameter protection scheme based on threshold homomorphic encryption. This scheme is based on distributed Paillier homomorphic encryption mechanism. Its core is to split the private key through secret sharing technology, so as to eliminate the risk brought by the single private key holder and resist the collusion attack of malicious participants and semi trusted servers. On this basis, top-t high-quality trusted nodes (active contributors) are selected through the data quality evaluation mechanism to participate in the decryption task. At the same time, combined with ECDSA digital signature technology, it ensures the integrity of encryption parameters in the transmission process and the authentication of end-to-end communication. Experiments demonstrate that, compared to existing homomorphic encryption schemes, our approach achieves faster model learning convergence with comparable accuracy. Specifically, fewer iterations are required for model parameters to stabilize, yielding approximately 10% higher learning efficiency. This resolves security threats posed by malicious users' inference attacks and server-side aggregation tampering, while safeguarding the reliability of parameter transmission.","url":"https://doi.org/10.21203/rs.3.rs-10097660/v1","authors":["Zhangbing Li","Mingyu Xiao","Jiantian Xiao","Jinsheng Li","Shaobo Zhang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-10097660/v1","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1016/j.dib.2026.112882","name":"Homomorphic encryption for privacy-preserving data aggregation in data spaces.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.dib.2026.112882","authors":["Gorka Calvo","Anhelina Kovach","Jorge Lanza","Leticia Montalvillo","Aitor Urbieta"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1016/j.dib.2026.112882","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41746-026-02790-4","name":"A multiparty homomorphic encryption approach to confidential federated Kaplan-Meier survival analysis.","source":"europepmc","abstract":"Abstract The proliferation of real-world health data enables multi-institutional survival studies, yet privacy constraints preclude centralizing sensitive records. We present a privacy-preserving federated Kaplan–Meier framework based on threshold CKKS (Cheon-Kim-Kim-Song) homomorphic encryption that supports approximate floating-point computation and encrypted aggregation of per-time-point counts while exposing only public outputs. Sites compute aligned at-risk and event tallies on a shared time grid and encrypt compact vectors; a coordinator aggregates ciphertexts; and a decryptor committee produces partial shares fused per block to recover aggregated plaintexts without releasing per-time-point tables. We prove correctness, stability, and slot-optimal vector packing, and derive scaling laws showing that communication grows linearly with the number of sites and predictably with the number of time points. Empirically, using synthetic breast-cancer data ( N = 60,000) distributed across 500 sites, encrypted federated curves match the pooled oracle to numerical precision. In contrast, plaintext protocols permit trivial reconstruction by subtraction; our threshold-gated design precludes this attack under the stated threat model, enabling high-fidelity survival estimation with predictable overhead and substantially reduced privacy risk.","url":"https://doi.org/10.1038/s41746-026-02790-4","authors":["Narasimha Raghavan Veeraragavan","Svetlana Boudko","Jan Franz Nygård"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41746-026-02790-4","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1038/s41598-026-52587-4","name":"Machine learning-driven adaptive parameter selection for homomorphic encryption in edge computing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-52587-4","authors":["Hamid El Bouabidi","Mohamed El Ghmary","Salah Eddine Hebabaze","Mohamed Amnai"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41598-026-52587-4","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1093/bioinformatics/btag081","name":"Secure bioinformatics: privacy-preserving federated analytics using homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/bioinformatics/btag081","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1093/bioinformatics/btag081","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.20944/preprints202604.0755.v1","name":"Transforming Distributed Software Security with Homomorphic Encryption and AI-Driven Threat Hunting","source":"europepmc","abstract":"Distributed modern software platforms spanning microservices, serverless functions, and edge computing face unprecedented security threats from stealthy adversaries exploiting encrypted data flows and behavioural camouflage. Conventional defences require decryption for analysis, exposing sensitive information in untrusted cloud environments. This paper proposes an innovative framework integrating homomorphic encryption (HE) with automated threat hunting to enable privacy-preserving threat detection at scale. Using levelled BFV schemes from OpenFHE, we perform computations directly on ciphertexts for anomaly scoring and behavioural profiling, while our hunting engine employs graph neural networks and isolation forests to hypothesize and pursue attacker patterns across distributed logs without plaintext exposure.The architecture deploys as Kubernetes-native operators, processing 10,000 encrypted events per second with 92% detection accuracy on MITRE-emulated scenarios, outperforming traditional UEBA by 35% in F1 score and reducing analysis latency from hours to seconds. Evaluations on AWS EKS clusters demonstrate sub-200ms query times for homomorphic aggregations, with noise management via bootstrapping optimizations. Case studies in fintech pipelines reveal thwarted supply-chain compromises and insider data exfiltration’s. By revolutionizing secure computation in dynamic ecosystems, our solution bridges cryptography and AI-driven hunting, offering deployable resilience against evolving threats while complying with GDPR and zero-trust mandates. Future work extends to fully homomorphic deep learning for adaptive adversary modelling.","url":"https://doi.org/10.20944/preprints202604.0755.v1","authors":["P. Selvaprasanth"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.20944/preprints202604.0755.v1","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1371/journal.pone.0349432","name":"Threshold-adaptive pruning with multi-key homomorphic encryption for communication-efficient secure federated learning.","source":"pubmed","abstract":"Under the federated learning framework, frequent parameter interactions between edge devices and servers result in communication inefficiency, while conventional encryption methods fail to resist multi-node collusion attacks. To address these challenges, this paper proposes an optimized federated learning scheme integrating adaptive channel pruning with multi-key homomorphic encryption. First, we construct a dynamic threshold determination mechanism that automatically calibrates channel pruning rates through precision feedback during the pre-pruning phase, achieving the optimal balance between model compression and accuracy, while significantly reducing communication bandwidth consumption compared to traditional algorithms. Second, based on the Brakerski-Gentry-Vaikuntanathan (BGV) multi-key fully homomorphic encryption architecture, we design a distributed public-key encryption protocol that enables aggregation servers to securely fuse multi-source model parameters without decryption, resisting collusion attacks from up to C&#x2009;-&#x2009;1 nodes (where C denotes the total number of devices). Experiments on MNIST and CIFAR-10 datasets demonstrate that our scheme significantly reduces communication overhead through two complementary mechanisms: adaptive pruning reduces both the computational burden of local training and the volume of parameters transmitted per round, while multi-key BGV encryption ensures privacy-preserving aggregation without decryption. This work provides a novel technical pathway for privacy-preserving federated learning in resource-constrained scenarios.","url":"https://doi.org/10.1371/journal.pone.0349432","authors":["Guo J","Liu R","Xing J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1371/journal.pone.0349432","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.21203/rs.3.rs-6878548/v1","name":"Efficient and Privacy-Preserving Argmax Approximation Using Homomorphic Encryption for Neural Networks","source":"europepmc","abstract":"Abstract Privacy-preserving neural networks represent a compelling approach for enabling secure training and inference without compromising user data confidentiality. Fully Homomorphic Encryption (FHE) is a cornerstone technology in this domain, as it permits computation over encrypted data. However, FHE inherently supports only addition and multiplication operations, making the implementation of non-linear functions—such as activation, Argmax, and max-pooling—particularly challenging when applied to cipher texts. This work addresses the complexity of executing the Argmax operation homomorphically, which is essential for identifying the index of the maximum element in a dataset. Building upon existing methods that employ compositions of low-degree minimax polynomials to approximate non-linear functions like sign and Argmax, we introduce a refined homomorphic Argmax approximation algorithm. Our approach enhances both accuracy and efficiency through a multi-phase design comprising rotation accumulation, tree-based comparisons, normalization, and final output selection. We integrate the proposed approximation algorithm into a neural network architecture and conduct comparative evaluations. The results demonstrate that our method not only yields a modest improvement in prediction accuracy but also reduces inference latency by 58%, primarily due to the optimization of homomorphic sign and rotation operations.","url":"https://doi.org/10.21203/rs.3.rs-6878548/v1","authors":["Jagadeesh Sai D"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-6878548/v1","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.21203/rs.3.rs-9311705/v1","name":"Design and Implementation of a Privacy Protection System for Face Recognition Using CKKS Fully Homomorphic Encryption","source":"europepmc","abstract":"Abstract Addressing the issues of biometric privacy leakage, threats from quantum computing attacks, and security vulnerabilities in key management inherent in face recognition applications, this paper proposes and implements KyberShield—a privacy protection system for face recognition. The system integrates ArcFace, CKKS fully homomorphic encryption, Kyber post-quantum key encapsulation, and Shamir secret sharing technologies to construct a fully ciphertext-based, end-to-end privacy protection pipeline spanning from facial feature extraction to identity authentication. It enables the computation of cosine distances between 512-dimensional feature vectors within the CKKS ciphertext domain, introduces an innovative decoupled architecture for biometric authentication and key management, and establishes a quantum-secure defense framework underpinned by lattice-based cryptography. Test results demonstrate that the system's entire encryption and authentication workflow takes approximately 0.6 seconds; the failure rate for ArcFace feature extraction is less than 0.1%; and the ciphertext-based authentication accuracy exceeds 98.5%. The system effectively withstands various attacks and resists threats posed by quantum computing, exhibiting high security, strong robustness, and universal scalability, making it well-suited to meet the privacy protection requirements of highly sensitive sectors such as finance and healthcare.","url":"https://doi.org/10.21203/rs.3.rs-9311705/v1","authors":["Yudian Wang","Sitong Li"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-9311705/v1","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1016/j.crmeth.2025.101271","name":"Homomorphic encryption enables privacy preserving polygenic risk scores.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.crmeth.2025.101271","authors":["Elizabeth Knight","Jiaqi Li","Matthew Jensen","Israel Yolou","Can Kockan","Mark Gerstein"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1016/j.crmeth.2025.101271","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.21203/rs.3.rs-10182043/v1","name":"Privacy-Utility Tradeoffs in Hierarchical Federated Learning Under Combined Differential Privacy and CKKS Homomorphic Encryption for Distributed Healthcare Networks","source":"europepmc","abstract":"Abstract Privacy-preserving federated learning for healthcare requires mechanisms that address both gradient privacy and transmission confidentiality. Existing approaches apply Differential Privacy (DP) and Homomorphic Encryption (HE) in isolation, leaving their combined tradeoff unquantified in Hierarchical Federated Learning (HFL) settings. This work presents an HFL framework integrating client-level \\(((\\epsilon, \\delta))\\)-DP with institution-level Cheon-Kim-Kim-Song (CKKS) HE, evaluated under privacy budgets \\((\\epsilon \\in \\{2, 4, 6, 8\\})\\) across MNIST and MIT-BIH Arrhythmia. DP introduces bounded accuracy loss ranging from \\((1.25%)\\) (\\((\\varepsilon = 8)\\)) to \\((1.88%)\\) (\\((\\varepsilon = 2)\\)) on MNIST and \\((6.15%)\\) to \\((12.23%)\\) on MIT-BIH Arrhythmia. CKKS HE introduces zero measurable accuracy loss across all evaluated configurations, isolating DP noise as the sole source of utility degradation and establishing a clear empirical separation between privacy and confidentiality costs. These results are reproducible across three independent random seeds, with accuracy standard deviations below \\((0.13%)\\) for MNIST and \\((1.31%)\\) for MIT-BIH Arrhythmia, providing empirical foundations for privacy-preserving federated learning deployment in healthcare.","url":"https://doi.org/10.21203/rs.3.rs-10182043/v1","authors":["Sayed Mohammed Alwedaei","Jenan Moosa"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-10182043/v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-9660473/v1","name":"HE-CloudML: A Privacy-Preserving Framework for Secure Machine Learning Inference over Encrypted Cloud Data Using Homomorphic Encryption","source":"europepmc","abstract":"Abstract The widespread adoption of cloud-based Machine Learning as a Service (MLaaS) exposes sensitive user data to critical privacy risks during inference, as plaintext data must typically be processed by untrusted cloud servers. This paper presents HE-CloudML, a unified privacy-preserving framework for secure deep neural network (DNN) inference over encrypted cloud data using Homomorphic Encryption (HE). HE-CloudML is architected as a three-tier system comprising a client-side CKKS encryption module, a cloud-side HE inference engine, and a distributed key management layer, ensuring that raw input data is never exposed to the server at any stage of computation. The framework introduces HE-compatible polynomial activation function approximations via degree-5 Chebyshev minimax polynomials, an optimized SIMD ciphertext batching strategy exploiting Ring Learning With Errors (RLWE) slot packing, and an adaptive lazy bootstrapping pipeline to substantially reduce homomorphic evaluation depth and inference latency. A formal security analysis under the IND-CPA model grounded in the RLWE hardness assumption demonstrates resistance to inference, model inversion, and membership inference attacks. Comprehensive experiments across three domains benchmark image classification (MNIST: 99.28%, CIFAR-10: 90.37%), medical imaging (93.61%), and financial fraud detection (96.44%) demonstrate that HE-CloudML achieves near-plaintext accuracy with a maximum accuracy drop of 1.81%, while delivering up to 26.9× latency improvements over CryptoNets.","url":"https://doi.org/10.21203/rs.3.rs-9660473/v1","authors":["Abdullahi Ahmed Abdirahman","Abdirahman Osman Hashi","Ubaid Mohamed Dahir","Mohamed Abdirahman Elmi"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-9660473/v1","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-026-48831-6","name":"An integrated privacy preserving data aggregation framework for IoT networks using homomorphic encryption and secure computation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-48831-6","authors":["Qiang Qin","Yongjiao Yang","Jiaxin Lin","Hanye Huang","Yuetian Huang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41598-026-48831-6","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1038/s41598-026-41469-4","name":"Stochastic Poisson-embedded privacy framework for federated learning with secure homomorphic encryption in medical AI.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-41469-4","authors":["R. Gomathi","K. Saranya","Y. M. Mahaboob John","G. Leena Rosalind Mary"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41598-026-41469-4","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/s10791-025-09843-4","name":"LSTM guided homomorphic encryption for threat-resistant IoT networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s10791-025-09843-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1007/s10791-025-09843-4","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.20944/preprints202601.0638.v1","name":"Multi-Group Fully Homomorphic Encryption Scheme Based on LWE and NTRU","source":"europepmc","abstract":"Multi-Group Homomorphic Encryption (MGHE) is a pivotal advance in secure multi-party computation, integrating merits of Multi-Party Homomorphic Encryption (MPHE) and Multi-Key Homomorphic Encryption (MKHE) to eliminate MPHE’s fixed-party limitation and mitigate MKHE’s ciphertext expansion from dynamic enrollment. However, the efficient single-key FINAL scheme cannot extend to multi-party scenarios, due to the challenge of defining valid multiplication for vector NTRU ciphertexts, which hinders its use in multi-group bootstrapping and curbs efficiency. To address this, additive secret sharing is adopted to convert vector NTRU ciphertext multiplication into secret share multiplication, enabling shared bootstrapping key generation within groups. For the first time, a multi-group ciphertext bootstrapping algorithm based on LWE and NTRU is proposed. Bootstrapping tasks are decomposed for parallel processing, and a hybrid product algorithm is designed to aggregate subtask outputs, boosting multi-group bootstrapping speed to match that of single-key ciphertexts. Noise accumulation is analyzed, with 100-bit and 128-bit security parameter sets selected for validation. Experiments show that 30/50-party multi-group bootstrapping takes only 1.87/2.58 seconds respectively.","url":"https://doi.org/10.20944/preprints202601.0638.v1","authors":["Yongheng Li","Jing Wen","Shaoling Liang","Fanqi Kong","Baohua Huang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.20944/preprints202601.0638.v1","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1093/bib/bbaf648","name":"KmerCrypt: private k-mer search with homomorphic encryption.","source":"europepmc","abstract":"Abstract Outsourcing the storage and analysis of genomic data to third-party servers is often necessary due to the scale of modern datasets, but it introduces significant privacy challenges that must be addressed to ensure secure handling. K-mer-based analyses offer broad applications across genomics research, clinical diagnostics, pathogen surveillance, and metagenomic classification, though implementation requires careful ethical and technical considerations, particularly when processing human genomic data in clinical settings. We present a novel protocol utilizing homomorphic encryption that enables a client to store a fully encrypted version of a genome on an untrusted server and perform private k-mer searches. The protocol ensures the server never gains access to the client’s non-encrypted genome sequence, nor does it learn the content of any k-mer query. After a one-time client-side encryption of the genome, the server performs all computations on ciphertext, returning only encrypted results that can be decrypted solely by the data owner. This framework transforms an honest but curious cloud server into a secure storage and computation system, enabling practical and confidential querying of encrypted, client-owned genomic data. The system supports exact k-mer searches on genomic data, as well as position weight matrix searches. Finally, we provide KmerCrypt, a private k-mer search toolkit that implements this protocol, offering researchers an efficient and secure solution for querying encrypted genomic datasets without compromising privacy.","url":"https://doi.org/10.1093/bib/bbaf648","authors":["Kimonas Provatas","Ioannis Mouratidis","Ilias Georgakopoulos-Soares"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1093/bib/bbaf648","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1038/s41598-025-14047-3","name":"Securing gait recognition with homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-14047-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41598-025-14047-3","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/s26030890","name":"Privacy Protection Optimization Method for Cloud Platforms Based on Federated Learning and Homomorphic Encryption.","source":"europepmc","abstract":"With the wide application of cloud computing in multi-tenant, heterogeneous nodes and high-concurrency environments, model parameters frequently interact during distributed training, which easily leads to privacy leakage, communication redundancy, and decreased aggregation efficiency. To realize the collaborative optimization of privacy protection and computing performance, this study proposes the Heterogeneous Federated Homomorphic Encryption Cloud (HFHE-Cloud) model, which integrates federated learning (FL) and homomorphic encryption and constructs a secure and efficient collaborative learning framework for cloud platforms. Under the condition of not exposing the original data, the model effectively reduces the performance bottleneck caused by encryption calculation and communication delay through hierarchical key mapping and dynamic scheduling mechanism of heterogeneous nodes. The experimental results show that HFHE-Cloud is significantly superior to Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Personalization (FedPer) and Federated Normalized Averaging (FedNova) in comprehensive performance, Homomorphically Encrypted Federated Averaging (HE-FedAvg) and other five baseline models. In the dimension of privacy protection, the global accuracy is up to 94.25%, and the Loss is stable within 0.09. In terms of computing performance, the encryption and decryption time is shortened by about one third, and the encryption overhead is controlled at 13%. In terms of distributed training efficiency, the number of communication rounds is reduced by about one fifth, and the node participation rate is stable at over 90%. The results verify the model’s ability to achieve high security and high scalability in multi-tenant environment. This study aims to provide cloud service providers and enterprise data holders with a technical solution of high-intensity privacy protection and efficient collaborative training that can be deployed in real cloud platforms.","url":"https://doi.org/10.3390/s26030890","authors":["Jing Wang","Yun Wang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.3390/s26030890","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-026-49125-7","name":"Blockchain-enabled secure authentication and privacy-preserving information sharing in VANETs using adaptive echo state networks and dual trapdoor homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-49125-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41598-026-49125-7","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.21203/rs.3.rs-9566103/v1","name":"Privacy-Preserving Differential Expression Analysis via Fully Homomorphic Encryption: A Systematic Tradeoff Evaluation of BFV and CKKS on Cancer RNA-Seq Datasets","source":"europepmc","abstract":"Abstract Background: Cloud-based genomic analysis increasingly exposes sensitive RNA-sequencing data to external computational infrastructure, raising critical privacy concerns for differential expression studies. Fully homomorphic encryption (FHE) enables computation directly on encrypted data without requiring decryption, offering a principled solution to privacy risks in genomic analysis pipelines. However, practical deployment is constrained by limited empirical understanding of performance and accuracy tradeoffs across leading FHE schemes. Results: Here, a systematic empirical benchmark of two widely used FHE schemes, BFV and CKKS, is conducted and applied to differential expression analysis on two cancer RNA-seq datasets: the UCI Gene Expression RNA-Seq dataset (801 samples, five cancer types, ten pairwise comparisons) and the TCGA LUSC+LUAD dataset (1,129 samples, one pairwise comparison). Experiments were executed across polynomial modulus degrees N in {4096, 8192, 16384} and three cohort sizes with ten independent runs per configuration under 128-bit security compliant parameter settings, totalling 300 runs. Performance was evaluated using encryption latency, execution latency, decryption latency, ciphertext storage size, mean absolute error, and Spearman rank correlation of DE gene rankings relative to plaintext baselines. Conclusions: Across all experiments, BFV achieved 3.5 to 7.5 times lower total latency than CKKS across all configurations. Conversely, CKKS produced ciphertexts that were approximately 2.66 times smaller per sample at N=16384, revealing a clear latency-storage tradeoff without a universally dominant configuration. The execution cost scaled primarily with the number of pairwise class comparisons rather than sample count, identifying a computational driver not previously isolated in FHE benchmarking studies. Further, CKKS accuracy degraded at higher polynomial modulus degrees due to scale-induced rescaling noise, while BFV approximation error decreased with increasing cohort size through quantisation noise averaging. Both schemes preserved gene ranking fidelity at rho &gt; 0.999 across all configurations. These results provide practical parameter selection guidance for implementing privacy-preserving genomic analysis pipelines and establish a reproducible benchmarking framework for encrypted differential expression analysis using homomorphic encryption.","url":"https://doi.org/10.21203/rs.3.rs-9566103/v1","authors":["Dilen Shankar"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-9566103/v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1016/j.isci.2025.113442","name":"A privacy-preserving HLA imputation method with homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.isci.2025.113442","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1016/j.isci.2025.113442","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.20944/preprints202508.1476.v1","name":"Homomorphic Encryption for Confidential Statistical Computation: Feasibility and Challenges","source":"europepmc","abstract":"Homomorphic encryption allows computations on encrypted data without revealing it to anyone other than an owner or an authorized collector. When combined with other tech-niques, homomorphic encryption offers an ideal solution for ensuring statistical confiden-tiality. TFHE (Fast Fully Homomorphic Encryption over the Torus) is a fully homomor-phic encryption scheme that supports efficient homomorphic operations on Booleans and integers. In this study, we use Zama’s Concrete compiler to explore the application of TFHE for performing statistical analysis on encrypted data, thereby demonstrating its vi-ability for ensuring statistical confidentiality. We provide implementations of traditional algorithms for basic statistical computations on encrypted datasets, including the five-number summary, mean, variance, and mode, and record the time required for each operation. The results show that basic tasks like mean and min/max work well for small datasets while keeping data encrypted. However, more complex tasks like median and variance slow down dramatically as datasets get larger. This work reinforces the theoretical promise of Fully Homomorphic Encryption (FHE) for statistical analysis and high-lights the need for substantial optimizations to make it viable for real-world applications.","url":"https://doi.org/10.20944/preprints202508.1476.v1","authors":["Yesem Kurt Peker","Rahul Raj"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.20944/preprints202508.1476.v1","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.3390/e28010005","name":"Efficient Privacy-Preserving Face Recognition Based on Feature Encoding and Symmetric Homomorphic Encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28010005","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.3390/e28010005","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.20944/preprints202601.1157.v1","name":"Secure and Verifiable Edge-Federated Learning with Homomorphic Encryption and a Trusted Execution Environment for UAV Communication","source":"europepmc","abstract":"Edge drones continuously collect sensitive information such as telemetry data during missions, making it difficult to apply centralized model training directly due to privacy protection, security compliance, and regulatory constraints. Although federated learning (FL) can avoid sharing raw data, existing federated learning schemes based solely on homomorphic encryption (HE) still face security risks in drone scenarios, such as gradient inversion, member inference, and malicious update injection. To address this, we propose a secure and verifiable edge federated learning framework for parameter-efficient model adaptation in drone scenarios. The framework introduces homomorphic encryption for model updates on the device side to protect the privacy of updates before transmission and aggregation. Simultaneously, on the server side, decryption, aggregation, and verification are performed through a remotely authenticated Trusted Execution Environment (TEE), thereby limiting the server's access to plaintext updates and reducing the feasibility of gradient inversion and member inference attacks at the system level. Furthermore, an aggregation signature mechanism is introduced to batch verify the identity and update integrity of participating nodes, effectively preventing malicious or tampered updates from participating in aggregation, thus overcoming the shortcomings of existing HE-FL schemes in terms of poisoning resistance and verifiability. Experimental results show that, while ensuring safety and verifiability, the proposed method improves model accuracy by 3% compared to the comparative scheme, while maintaining better performance in terms of computation and communication overhead, thus verifying the practicality and deployability of the framework in resource-constrained UAV edge environments.","url":"https://doi.org/10.20944/preprints202601.1157.v1","authors":["Huachang Su","Yekang Zhao","Wenrui Zhang","Hongling Zhang","Shitao Huang","Sheng Zhong","Xiaoyang Zhou"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.20944/preprints202601.1157.v1","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-025-34536-9","name":"Privacy-preserving federated credit risk models: evaluating differential privacy and homomorphic encryption techniques.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-34536-9","authors":["Vankamamidi S. Naresh","D. Ayyappa"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41598-025-34536-9","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.21203/rs.3.rs-7709911/v1","name":"Privacy-Preserving Edge Intelligence Framework (PPEIF) Using Homomorphic Encryption and Knowledge Distillation for Efficient Electronic Health Records Management","source":"europepmc","abstract":"Abstract The rapid integration of machine learning in healthcare emphasizes the need for privacy-preserving and efficient solutions, especially when managing sensitive Electronic Health Records (EHRs). Existing federated learning (FL) frameworks face significant challenges, including high communication overhead, computational inefficiency on resource-constrained edge devices, limited privacy guarantees during inference, and vulnerability to noisy or malicious updates. This study proposes a novel Privacy-Preserving Edge Intelligence Framework (PPEIF), designed specifically to overcome these limitations by combining Homomorphic Encryption (HE), Knowledge Distillation (KD), and an Attention-Based Aggregation Mechanism. In the PPEIF framework, a large teacher model trains lightweight student models via knowledge distillation, enabling efficient and encrypted inference directly at edge nodes. Homomorphic Encryption ensures that raw EHR data remains encrypted throughout local processing, preventing any data leakage. Instead of transmitting full model parameters, only encrypted distilled logits are shared, drastically reducing communication overhead. The attention mechanism dynamically weighs local contributions during global aggregation, mitigating the effects of noisy or malicious updates. Experimental evaluation on the MIMIC-III dataset demonstrates that PPEIF achieves 94.5% inference accuracy, significantly lower inference time (1.9 seconds), and up to 80% reduction in communication overhead compared to conventional FL methods. Privacy leakage risk is minimized, achieving the highest privacy level by protecting both model updates and inference results. Comparative analysis with state-of-the-art works further validates the superiority of PPEIF in terms of accuracy, scalability (supporting over 200 edge nodes), and real-time deployment feasibility. The proposed framework offers a robust and practical solution for secure, efficient, and scalable healthcare applications, setting a new benchmark for future privacy-preserving federated learning research in sensitive domains.","url":"https://doi.org/10.21203/rs.3.rs-7709911/v1","authors":["Munusamy S","Jothi K R"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-7709911/v1","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1109/jbhi.2025.3601969","name":"Secure Tracking of Patient's Vital Signs Using CSI-Based Homomorphic Encryption-Enabled Deep Learning Framework.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/jbhi.2025.3601969","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1109/jbhi.2025.3601969","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1371/journal.pone.0339881","name":"FedGraphHE: A privacy-preserving federated graph neural network framework with dynamic homomorphic encryption and robust aggregation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0339881","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1371/journal.pone.0339881","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-026-36034-y","name":"Health-FedNet: secure federated learning for chronic disease prediction on MIMIC-III with differential privacy and homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-36034-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41598-026-36034-y","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.3390/s25144320","name":"Efficient Keyset Design for Neural Networks Using Homomorphic Encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25144320","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.3390/s25144320","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.20944/preprints202512.0870.v1","name":"Homomorphic Encryption-Based Data Integrity Verification and Anti-Tampering Mechanism in Cloud Storage Environment","source":"europepmc","abstract":"To enhance the integrity assurance of encrypted data in cloud storage environments, a homomorphic encryption-based data remote verification and anti-tampering mechanism is proposed. The design incorporates a homomorphic verification protocol to enable consistency checks of data blocks in the encrypted domain, and combines a lightweight tag structure with a Merkle Hash Tree to support dynamic data operations, including insertion, modification, and deletion. Corrective codes and tag version control are employed to enable rapid data recovery following tampering detection. Additionally, attribute-encryption-based access control and a blockchain auditing mechanism are integrated to strengthen the system's security closed-loop. Analysis shows that the mechanism offers strong scalability with low computational and communication overhead while preserving data privacy, making it suitable for trusted data storage in multi-user cloud environments.","url":"https://doi.org/10.20944/preprints202512.0870.v1","authors":["Xiaoyu Deng"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.20944/preprints202512.0870.v1","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1016/j.compbiolchem.2025.108780","name":"Research on a novel gene sequence prediction and homomorphic encryption method based on Mamba-VMD.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.compbiolchem.2025.108780","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1016/j.compbiolchem.2025.108780","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1177/14604582251394616","name":"Performance and security analysis of fully homomorphic encryption in cloud-based healthcare blockchain.","source":"europepmc","abstract":"","url":"https://doi.org/10.1177/14604582251394616","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1177/14604582251394616","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1093/bioinformatics/btaf468","name":"PRISM: privacy-preserving rare disease analysis using fully homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/bioinformatics/btaf468","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1093/bioinformatics/btaf468","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-32087-7","name":"A privacy preserving intrusion detection framework for IIoT in 6G networks using homomorphic encryption and graph neural networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-32087-7","authors":["Binjie Hua","Haiyan Xi"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41598-025-32087-7","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.20944/preprints202601.0513.v1","name":"Edge Reinforced Learning Platform with Homomorphic Encryption and Swarm Intelligence for Ultra-Low Latency IoT Sensing and Cross-Device Communication","source":"europepmc","abstract":"This paper presents an edge-reinforced learning platform that combines reinforcement learning, homomorphic encryption, and swarm intelligence to support ultra-low latency IoT sensing and cross-device communication. In conventional IoT architectures, cloud-centric processing and centralized coordination introduce significant delays and expose sensitive data to intermediate entities, making them unsuitable for time-critical and privacy-sensitive applications. The proposed platform relocates intelligence to the network edge, where edge nodes learn adaptive policies for sensing, routing, and computation offloading based on local conditions and limited global feedback. To preserve confidentiality, IoT measurements and model updates are protected using homomorphic encryption, allowing aggregation and decision-making to be performed directly over encrypted data without revealing raw values. In parallel, swarm intelligence mechanisms orchestrate distributed cooperation among devices, enabling robust path selection, task allocation, and congestion avoidance through lightweight, bio‑inspired interactions rather than centralized control. The integrated design is evaluated on realistic IoT scenarios with heterogeneous devices and dynamic traffic patterns. Results show that the edge-reinforced learning platform can significantly reduce end-to-end latency and jitter compared to cloud-based and non-learning edge baselines, while incurring acceptable computational overhead from encryption and maintaining strong privacy guarantees. The framework demonstrates that it is feasible to simultaneously achieve low latency, resilient cross-device coordination, and data confidentiality in large-scale IoT deployments.","url":"https://doi.org/10.20944/preprints202601.0513.v1","authors":["V. Thamilarasi"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.20944/preprints202601.0513.v1","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"epmc:MED41685295","name":"Shechi: A Secure Distributed Computation Compiler Based on Multiparty Homomorphic Encryption.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41685295/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1148/ryai.240798","name":"Development of Privacy-preserving Deep Learning Model with Homomorphic Encryption: A Technical Feasibility Study in Kidney CT Imaging.","source":"europepmc","abstract":"","url":"https://doi.org/10.1148/ryai.240798","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1148/ryai.240798","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.22541/au.176232803.35206392/v1","name":"Privacy-Preserving Clinical Analytics with Threshold Homomorphic Encryption: Insights from Hematologic Toxicity During Craniospinal Irradiation","source":"europepmc","abstract":"The increasing need for safeguarding medical data privacy, particularly in research involving sensitive patient information, requires innovative solutions. This work proposes a conceptual architecture that uses (threshold) homomorphic encryption for statistical analysis on encrypted medical data shared between different institutions. By utilizing this type of encryption, sensitive patient data is kept secure throughout the analysis process, minimizing the risk of re-identification. The data is encrypted locally before being processed on secure computation servers, ensuring privacy while enabling several statistical analysis. Performing computation on encrypted data is expensive and this has led to widespread skepticism regarding its practicality. Our work shows that, with recent advances and careful design and engineering the technology can indeed be harnessed to facilitate medical research. This method aligns with key data protection regulations and lays the groundwork for more privacy-preserving collaborative research in the medical field. This paper presents a conceptual model rather than a full platform implementation. We securely replicate, on encrypted patient-level data from pediatric craniospinal irradiation, three routine statistics—Pearson’s correlation, Wilcoxon rank-sum, and χ 2 —in a three-party threshold-HE workflow. Across tested sizes, χ 2 ≤ 0 . 5 % error, Pearson’s r ≈0–5.7% (≈5% at n =512), Wilcoxon’s z ≈13.5–21.9%, with millisecond-scale runtimes ( ≈ 5 0 – 8 5 0 m s ). We also outline a concise systems blueprint for cross-institution analytics (Fig. [1](#fig-cap-0001)).","url":"https://doi.org/10.22541/au.176232803.35206392/v1","authors":["George Cătălin Ţurcaş","George Gugulea","Cristian Lupaşcu","Mihai Togan","Andrada Crina Ţurcaş"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.22541/au.176232803.35206392/v1","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.20944/preprints202506.2372.v1","name":"Secure Tourist Tracking Using Fully Homomorphic Encryption in a Robotics Information System","source":"europepmc","abstract":"Tourists often face challenges such as language barriers and limited access to reliable information. To address this, a Robotics Information System (RIS) was developed using a humanoid robot named Arslan to assist visitors in real-time. The system manages sensitive data, including travel history, requiring strong encryption. Traditional methods like DES and AES were found inadequate, prompting the adoption of Fully Homomorphic Encryption (FHE), which enables computations on encrypted data without decryption. A mathematical tracking model encodes tourist movements securely. Performance tests show that while FHE introduces computational overhead, it ensures high privacy and resilience against modern attacks. This approach enhances tourist experience and ensures secure, real-time data handling in smart tourism environments.","url":"https://doi.org/10.20944/preprints202506.2372.v1","authors":["Lujin Dersani","Ihab Elaff"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.20944/preprints202506.2372.v1","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.20944/preprints202505.1442.v1","name":"Outsourced Privacy-Preserving Feature Selection Based on Fully Homomorphic Encryption","source":"europepmc","abstract":"Feature selection is a technique that extracts a meaningful subset from a set of features in training data. When the training data is large-scale, appropriate feature selection enables the removal of redundant features, which can improve generalization performance, accelerate the training process, and enhance the interpretability of the model. This study proposes a privacy-preserving computation model for feature selection. Generally, when the data owner and analyst are the same, there is no need to conceal the private information. However, when they are different parties or when multiple owners exist, an appropriate privacy-preserving framework is required. Although various private feature selection algorithms, they all require two or more computing parties and do not guarantee security in environments where no external party can be fully trusted. To address this issue, we propose the first outsourcing algorithm for feature selection using fully homomorphic encryption. Compared to a prior two-party algorithm, our result improves the time and space complexity O(kn2)) to O(knlog3n) and O(kn), where k and n denote the number of features and data samples, respectively. We also implemented the proposed algorithm and conducted comparative experiments with the naive one. The experimental result shows the efficiency of our method even with small datasets.","url":"https://doi.org/10.20944/preprints202505.1442.v1","authors":["Koki Wakiyama","Tomohiro I","Hiroshi Sakamoto"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.20944/preprints202505.1442.v1","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.21203/rs.3.rs-6964665/v1","name":"Benchmarking Homomorphic Encryption on Low-Power Devices: Trade-offs Between PHE and FHE","source":"europepmc","abstract":"Abstract Homomorphic Encryption (HE) allows computation on ciphertexts, ensuring strong privacy for applications like smart metering, healthcare, and financial analytics. However, instantiating HE on power-constrained embedded devices is challenging as the computation and memory footprints are excessively high—particularly for Fully Homomorphic Encryption (FHE) schemes like BFV and CKKS. Partly Homomorphic Encryption (PHE) like Paillier is lightweight but less functional. This work evaluates the trade-offs among PHE and FHE with an investigation of Paillier, BFV, and CKKS on three representative platforms: ESP32, Raspberry Pi 4, and Arduino Uno. Performance measures like encryption and decryption time, ciphertext size, memory, and energy are compared. Experimentation demonstrates FHE to be impractical on 8-bit microcontrollers but efficient on 32- and 64-bit platforms. Of interest on Raspberry Pi 4, BFV and CKKS demonstrate sub-10 ms encryption times and consume below 5 J per 100 operations, both outpacing Paillier on speed and energy efficiency. Our work refutes the argument on the impracticability of FHE on embedded devices and provides practical advice on selecting among HE schemes according to platform capability. Our research fills the gap between theoretical cryptography and realistic deployment and promotes the use of HE as an enabling solution to trusted edge computing.","url":"https://doi.org/10.21203/rs.3.rs-6964665/v1","authors":["Het Khatusuriya","Dhvani Patel","Martin Parmar"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-6964665/v1","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.7717/peerj-cs.3165","name":"Enhancing privacy-preserving brain tumor classification with adaptive reputation-aware federated learning and homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3165","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.7717/peerj-cs.3165","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1371/journal.pone.0314656","name":"Privacy-preserving method for face recognition based on homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0314656","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1371/journal.pone.0314656","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.1117/1.jmi.12.3.034504","name":"Highly efficient homomorphic encryption-based federated learning for diabetic retinopathy classification.","source":"europepmc","abstract":"","url":"https://doi.org/10.1117/1.jmi.12.3.034504","authors":["Christopher Nielsen","Matthias Wilms","Nils D. Forkert"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1117/1.jmi.12.3.034504","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-025-95383-2","name":"Efficient face information encryption and verification scheme based on full homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-95383-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41598-025-95383-2","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/e27010007","name":"Flexible Threshold Quantum Homomorphic Encryption on Quantum Networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e27010007","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.3390/e27010007","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1364/oe.555634","name":"Secure multiparty computation for maximum and minimum values based on quantum homomorphic encryption.","source":"europepmc","abstract":"Secure multiparty computation is a basic cryptographic primitive that has many important applications in privacy preservation. In this work, we propose a new secure multiparty computation protocol for the maximum and minimum values based on quantum homomorphic encryption. Owing to the speciality of quantum homomorphic encryption, participants can delegate a server to compute the maximum and minimum values of their private data. Furthermore, both calculations are performed on the encrypted data, and therefore the privacy of their respective private data can be perfectly guaranteed.","url":"https://doi.org/10.1364/oe.555634","authors":["Shuang Li","Xiao-Qiu Cai","Tian-Yin Wang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1364/oe.555634","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.3390/s25123700","name":"Smart Grid IoT Framework for Predicting Energy Consumption Using Federated Learning Homomorphic Encryption.","source":"europepmc","abstract":"Homomorphic Encryption (HE) introduces new dimensions of security and privacy within federated learning (FL) and internet of things (IoT) frameworks that allow preservation of user privacy when handling data for FL occurring in Smart Grid (SG) technologies. In this paper, we propose a novel SG IoT framework to provide a solution for predicting energy consumption while preserving user privacy in a smart grid system. The proposed framework is based on the integration of FL, edge computing, and HE principles to provide a robust and secure framework to conduct machine learning workloads end-to-end. In the proposed framework, edge devices are connected to each other using P2P networking, and the data exchanged between peers is encrypted using Cheon–Kim–Kim–Song (CKKS) fully HE. The results obtained show that the system can predict energy consumption as well as preserve user privacy in SG scenarios. The findings provide an insight into the SG IoT framework that can help network researchers and engineers contribute further towards developing a next-generation SG IoT system.","url":"https://doi.org/10.3390/s25123700","authors":["Filip Jerkovic","Nurul I. Sarkar","Jahan Ali"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.3390/s25123700","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.12688/openreseurope.18052.1","name":"Applications of Homomorphic Encryption in Secure Computation","source":"europepmc","abstract":"","url":"https://doi.org/10.12688/openreseurope.18052.1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.12688/openreseurope.18052.1","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1093/bioinformatics/btae754","name":"Privacy-preserving framework for genomic computations via multi-key homomorphic encryption.","source":"europepmc","abstract":"Abstract Motivation The affordability of genome sequencing and the widespread availability of genomic data have opened up new medical possibilities. Nevertheless, they also raise significant concerns regarding privacy due to the sensitive information they encompass. These privacy implications act as barriers to medical research and data availability. Researchers have proposed privacy-preserving techniques to address this, with cryptography-based methods showing the most promise. However, existing cryptography-based designs lack (i) interoperability, (ii) scalability, (iii) a high degree of privacy (i.e. compromise one to have the other), or (iv) multiparty analyses support (as most existing schemes process genomic information of each party individually). Overcoming these limitations is essential to unlocking the full potential of genomic data while ensuring privacy and data utility. Further research and development are needed to advance privacy-preserving techniques in genomics, focusing on achieving interoperability and scalability, preserving data utility, and enabling secure multiparty computation. Results This study aims to overcome the limitations of current cryptography-based techniques by employing a multi-key homomorphic encryption scheme. By utilizing this scheme, we have developed a comprehensive protocol capable of conducting diverse genomic analyses. Our protocol facilitates interoperability among individual genome processing and enables multiparty tests, analyses of genomic databases, and operations involving multiple databases. Consequently, our approach represents an innovative advancement in secure genomic data processing, offering enhanced protection and privacy measures. Availability and implementation All associated code and documentation are available at https://github.com/farahpoor/smkhe.","url":"https://doi.org/10.1093/bioinformatics/btae754","authors":["Mina Namazi","Mohammadali Farahpoor","Erman Ayday","Fernando Pérez-González"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1093/bioinformatics/btae754","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.1186/s12920-024-02037-9","name":"Private detection of relatives in forensic genomics using homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12920-024-02037-9","authors":["Fillipe D. M. de Souza","Hubert de Lassus","Ro Cammarota"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1186/s12920-024-02037-9","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1109/tnnls.2024.3389873","name":"Secure State Estimation for Artificial Neural Networks With Unknown-But-Bounded Noises: A Homomorphic Encryption Scheme.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2024.3389873","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1109/tnnls.2024.3389873","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.20944/preprints202502.0927.v1","name":"Smart Grid IoT Framework Integrating Peer-to-Peer Federated Learning with Homomorphic Encryption","source":"europepmc","abstract":"Homomorphic Encryption (HE) introduces new dimensions of security and privacy within federated learning (FL) and Internet of Things (IoT) frameworks that allow preservation of user privacy when handling data for FL occurring Smart Grid (SG) technologies. In this paper, we propose a novel SG IoT framework to provide a solution of predicting energy consumption while preserving user-privacy in a smart grid system. The proposed framework is based on the integration of FL, edge computing, and HE principles to provide a robust and secure framework to conduct machine learning workloads end-to-end. In the proposed framework, edge devices are connected to each other using P2P networking and the data exchanged between peers is encrypted using CKKS fully HE. The results obtained show that the system can predict energy consumption as well as preserve user privacy in SG scenarios. The findings provide an insight into the SG IoT framework that can help network researchers and engineers to contribute further towards developing a next generation SG IoT system.","url":"https://doi.org/10.20944/preprints202502.0927.v1","authors":["Filip Jerkovic","Nurul I. Sarkar","Jahan Ali"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.20944/preprints202502.0927.v1","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.21203/rs.3.rs-4565846/v1","name":"Homomorphic Encryption: An Application to Polygenic Risk Scores","source":"europepmc","abstract":"Abstract Background: Polygenic risk scores (PRS) have emerged as a powerful tool in precision medicine, enabling personalized risk assessments for complex diseases. However, using sensitive genomic data in PRS calculations raises concerns about privacy and security. Fully Homomorphic Encryption (FHE) offers a promising solution by allowing computations on encrypted data, preserving the privacy of both genomic information and PRS models. Methods: In this study, we present a novel application of FHE for secure and private PRS calculations using the CKKS protocol within the Lattigo library. Our approach involves a threeparty system: clients (doctors with sensitive genetic data), modelers developing a PRS (academics or a company), and evaluators (a ”local hospital” running the models while maintaining data confidentiality). We demonstrate the feasibility and accuracy of our protocol by applying it to synthetic datasets of various sizes and a robust 110k-SNP model for schizophrenia. Results: The normal PRS calculation results are essentially identical to the encrypted calculation: between the two results R2 is .999 &amp; MSE is 2.27 × 10−6. Moreover, while the encrypted calculation is roughly 1000 times slower than conventional non-encrypted ones (when only considering the core PRS calculation), it is quite feasible on a single-CPU node - e.g. running on ∼1100 individuals with ∼110k SNPS took six minutes and ∼65G memory on a laptop computer. In addition, we investigate the impact of encryption parameters (modulus) on this computational time and accuracy in detail. Conclusion: Our approach enables secure PRS calculations on encrypted genomic data, addressing the pressing need for privacy-preserving solutions in the era of precision medicine. The ability to perform accurate risk assessments while maintaining patient confidentiality paves the way for broader adoption of PRS and personalized medicine in healthcare, particularly with the advent of large-scale computing power.","url":"https://doi.org/10.21203/rs.3.rs-4565846/v1","authors":["elizabeth knight","Jiaqi Li","Mathew Jensen","Israel Yolou","Can Kockan","Mark Gerstein"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-4565846/v1","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1186/s13040-024-00379-9","name":"Private pathological assessment via machine learning and homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s13040-024-00379-9","authors":["Ahmad Al Badawi","Mohd Faizal Bin Yusof"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1186/s13040-024-00379-9","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.21203/rs.3.rs-5668651/v1","name":"Using the Power-Of-Two Vector Padding to Enhance Efficiency and Speed in CKKS Homomorphic Encryption","source":"europepmc","abstract":"Abstract The Cheon-Kim-Kim-Song (CKKS) encryption scheme has enabled secure data processing in sensitive domains and hence has transformed privacy-preserving computations due to its unique ability to support approximate arithmetic on real numbers. However, the computational challenges associated with non-power-of-two input vector lengths largely limit the broader adoption of the CKKS. This paper, proposes the Power-of-Two CKKS (P2PCKKS), an enhanced CKKS scheme that adopts a dynamic padding technique to align input vector lengths with the nearest power-of-two. The proposed scheme addresses key drawbacks such as computational errors and overflow, hence optimizing the Fast Fourier Transform (FFT) for polynomial operations. While maintaining accuracy, the proposed scheme demonstrated an enhanced efficiency and speed, compared to the conventional CKKS scheme, even for vector inputs with lengths already as powers-of-two. The experiments’ results confirmed a 100% success rate across all computations, making the P2P-CKKS a robust solution for real-world applications requiring scalable and efficient homomorphic encryption. This paper prepares the groundwork for exploring how adaptive padding techniques can potentially be used to revolutionize encryption models.","url":"https://doi.org/10.21203/rs.3.rs-5668651/v1","authors":["Franco Osei-Wusu","Emmanuel Ahene","Elvis Sarfo Antwi"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-5668651/v1","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.21203/rs.3.rs-4194403/v1","name":"Bootstrapping Optimization for Fully Homomorphic Encryption Schemes","source":"europepmc","abstract":"Abstract With the advent of cloud computing and the era of big data, there is an increasing focus on privacy computing. Consequently, homomorphic encryption, being a primary technique for achieving privacy computing, is held in high regard. Nevertheless, the efficiency of homomorphic encryption schemes is significantly impacted by boostrapping. FINAL scheme (ASIACRYPT 2022) is a fully homomorphic encryption scheme based on number theory research unit (NTRU) and learning with errors (LWE) assumptions proposed by Charlotte Bonte et al. The performance of the FINAL scheme is better than TFHE scheme, with a faster bootstrapping and smaller bootstrapping and key-switching keys. In this paper, we introduce ellipsoidal Gaussian sampling to generate the keys f and g in bootstrapping of FINAL scheme, so that the standard deviations of the keys f and g are different and reduce the bootstrapping noise. As a result, larger decomposition bases is used in bootstrapping to reduce the total number of polynomial multiplications, thus improving the efficiency of FINAL scheme. The optimization scheme outperforms the original FINAL scheme with a 33.3\\% faster bootstrapping.","url":"https://doi.org/10.21203/rs.3.rs-4194403/v1","authors":["Meng Wu","Xiufeng Zhao","Weitao Song"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-4194403/v1","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1016/j.heliyon.2024.e34458","name":"Secure multiparty computation protocol based on homomorphic encryption and its application in blockchain.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.heliyon.2024.e34458","authors":["Haijun Bao","Minghao Yuan","Haitao Deng","Jiang Xu","Yekang Zhao"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1016/j.heliyon.2024.e34458","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3390/s24154826","name":"Hierarchical Clustering via Single and Complete Linkage Using Fully Homomorphic Encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s24154826","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.3390/s24154826","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.20944/preprints202504.0302.v1","name":"<span style=\"color: black; mso-themecolor: text1;\">Lattice-Based Multi-Key Homomorphic Encryption Scheme Without CRS","source":"europepmc","abstract":"Multi-key homomorphic encryption is widely applied into outsourced computing and privacy-preserving applications in multi-user scenarios. However, the existence of CRS weakens the ability of users to independently generate public keys, and it is difficult to implement in decentralized systems or scenarios with low trust requirements. In order to reduce excessive reliance on public parameters, a multi-key homomorphic encryption scheme without pre-setting CRS is proposed based on a distributed key generation protocol. The proposed scheme does not require the pre-generation and distribution of CRS, which enhances the security and decentralization of the scheme. Furthermore, in order to further protect the plaintext privacy from each user, by embedding the specified target user into the ciphertext, this paper proposes an enhanced multi-key homomorphic encryption scheme that only allows only the target user to decrypt. Finally, this paper applies the proposed lattice-based multi-key homomorphic encryption scheme into the data submission stage of the perceived users, and thereby proposes a crowd-sensing scheme with privacy preservation.","url":"https://doi.org/10.20944/preprints202504.0302.v1","authors":["Hongyi Zhang","Mengxue Shang","Hanzhuo Liu","Dandan Zhang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.20944/preprints202504.0302.v1","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.20944/preprints202502.1845.v1","name":"Towards Sustainable Cryptography: A Comprehensive Assessment of Compute Efficiency and Scope 1–3 Emissions for Partially Homomorphic Encryption in the Cloud","source":"europepmc","abstract":"Quantum computing was in its infancy while cloud adoption increased but secure data processing methods in distributed environments became more important. As cloud-based operations continue to expand, allowing computation to be performed directly on encrypted data without the need for exposing private keys will be an important use case of homomorphic encryption. Fully homomorphic encryption (FHE) encompasses both addition and multiplication, whereas partial homomorphic encryption (PHE) is limited to either type of operation, which can provide practical efficiency benefits for certain applications. In this research, LightPHE, a Python-based PHE framework is implemented along with the implementation performance and environmental sustainability evaluation. The evaluation scope extends beyond the typical fair assessment of cryptography to include energy consumption profile and carbon emissions. The framework combines proven PHE algorithms and stays true to modular design principles as the foundation for secure application development. Experimental evaluations were performed on several cloud platforms such as Google Colab (Normal, A100 GPU, L4 GPU, T4 High RAM, TPU2) and Microsoft Azure Spark. The performance evaluation has included key generations, encryption, decryption and homomorphic operation, also the energy consumption was based on computational resource utilization. The environmental impact was evaluated via thorough analysis of Scope 1-3 emissions, as well as standardized data center efficiency metrics based on regional carbon intensity data. The study’s results showed unique trends in the trade-offs between computational performance and energy efficiency. Rather than calculating emissions for every algorithm variant, the analysis focuses on low and mid impact cases such as 80-bit and 128-bit resource-intensive homomorphic operations—where environmental considerations are most critical with Colab L4 GPU. Since the comparison with all cpu and gpu types including all data centers that will lead to another research. The results suggested that in both high-performance configurations and distributed environments, different optimization characteristics emerged in the energy-energy view where a lower total energy consumption is observed even at the expense of higher instantaneous demand. LightPHE offered uniform security across configurations, but differing environmental impact. This work empirically demonstrates the environmental considerations inherent to cryptographic implementations and lays the groundwork for future work in quantifiably assessing both security and sustainability in cloud-based encryption systems. The findings of this research provide practical recommendations for organizations looking to deploy secure, efficient, and sustainable data processing systems in modern cloud settings.","url":"https://doi.org/10.20944/preprints202502.1845.v1","authors":["Alper Ozpinar","Sefik Ilkin Serengil"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.20944/preprints202502.1845.v1","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.1103/physrevlett.132.200801","name":"Experimental Quantum Homomorphic Encryption Using a Quantum Photonic Chip.","source":"europepmc","abstract":"","url":"https://doi.org/10.1103/physrevlett.132.200801","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1103/physrevlett.132.200801","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.21203/rs.3.rs-4473301/v1","name":"Secure and Efficient General Matrix Multiplication On Cloud Using Homomorphic Encryption","source":"europepmc","abstract":"Abstract Despite the enormous technical and financial advantages of cloud computing, security and privacy have always been the primary concerns for adopting cloud computing facilities, especially for government agencies and commercial sectors with high-security requirements. Homomorphic Encryption (HE) has recently emerged as an effective tool in ensuring privacy and security for sensitive applications by allowing computing on encrypted data. One major obstacle to employing HE-based computation, however, is its excessive computational cost, which can be orders of magnitude higher than its counterpart based on the plaintext. In this paper, we study the problem of how to reduce the HE-based computational cost for general Matrix Multiplication (MM), i.e., a fundamental building block for numerous practical applications, by taking advantage of the Single Instruction Multiple Data (SIMD) operations supported by HE schemes. Specifically, we develop a novel element-wise algorithm for general matrix multiplication, based on which we propose two HE-based General Matrix Multiplication (HEGMM) Approved for Public Release on 06 Mar 2024. Distribution is Unlimited. Case Number: 2024-0184 (original case number(s): AFRL-2024-0944) algorithms to reduce the HE computation cost. Our experimental results show that our algorithms can significantly outperform the state-of-the-art approaches of HE-based matrix multiplication.","url":"https://doi.org/10.21203/rs.3.rs-4473301/v1","authors":["Yang Gao","Quan Gang","Soamar Homsi","Wujie Wen","Liqiang Wang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-4473301/v1","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.22541/au.171740920.02753326/v1","name":"Image traceable privacy protection scheme based on blockchain and homomorphic encryption","source":"europepmc","abstract":"Aiming to address issues such as low security, inefficient encryption and decryption, weak robustness, image tampering, and the misuse of existing image encryption algorithms in blockchain-based applications for image sharing and transmission, this paper proposes a traceable image privacy protection scheme based on blockchain and homomorphic algorithm. Firstly, Paillier algorithm and image block technology were used to preprocess and encrypt the image. Feature images are extracted from encrypted images using lifting wavelet transform (LWT) and singular value decomposition (SVD). By means of dynamic S-box, binary watermark information is subjected to confusion, diffusion and XOR operations to generate zero-watermarking images for lossless embedding of traceability information. Then, the zero-watermarking image and its corresponding key are securely stored on the International File System (IPFS) through a smart contract mechanism to regulate image transactions and enable traceability of the image. Finally, the smart contract is utilized to detect any inaccuracies and errors in the zero-watermarking image, enabling tamper detection of said image. In experimental tests, the proposed scheme was shown to enable data distribution and computation with private data in ciphertext state, exhibiting high efficiency in encryption and decryption. Additionally, the zero-watermarking algorithm exhibits strong anti-attack capabilities and robustness. By considering image privacy protection and secure storage, tamper detection and traceability of encrypted images are effectively addressed.","url":"https://doi.org/10.22541/au.171740920.02753326/v1","authors":["Tian Li","Qiu-yu Zhang","Guo-rui Wu"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.22541/au.171740920.02753326/v1","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1038/s41467-024-50592-7","name":"Stochastic switching and analog-state programmable memristor and its utilization for homomorphic encryption hardware.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41467-024-50592-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41467-024-50592-7","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1016/j.cmpb.2025.108599","name":"Towards practical and privacy-preserving CNN inference service for cloud-based medical imaging analysis: A homomorphic encryption-based approach.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.cmpb.2025.108599","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1016/j.cmpb.2025.108599","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.21203/rs.3.rs-4268161/v1","name":"Multi-PVO for Reversible Data Hiding Based onFull Homomorphic Encryption","source":"europepmc","abstract":"Abstract To enhance the embedding performance of reversible data hiding algorithms and bolster the security of data transmission processes, a novel pixel value ordering method based on full homomorphic encryption is proposed in this paper. First, we propose a multi-PVO method to improve the fidelity of images with equal embedding capacity. In addition, we propose a double layer encryption method and achieve Paillier's fully homomorphic encryption. In practice, the sender encrypts the original image and then transmits the ciphertext to the embedding side. The embedding side performs a multi-PVO method and data hiding based on homomorphism to seamlessly embed the data into the ciphertext. This implementation offers dual functionality. On the one hand, users with no private keys can directly embed or extract the secret data without decryption keys. On the other hand, users with private keys can decrypt the marked ciphertext and help retrieve the marked plaintext. Finally, utilizing multi-PVO for extraction or recovery, additional data or original image can be retrieved from the marked image at receiver. With 20,000 bits embedded, the PSNR of the image Lena is as high as 59.60 dB, which demonstrates great image quality with the same embedding capability.","url":"https://doi.org/10.21203/rs.3.rs-4268161/v1","authors":["Bin Ge","Yaqing Shen","Chenxing Xia","Rui Sun"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-4268161/v1","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.21203/rs.3.rs-5149131/v1","name":"Lancelot: Towards Efficient and Privacy-Preserving Byzantine-Robust Federated Learning within Fully Homomorphic Encryption","source":"europepmc","abstract":"Abstract In sectors such as finance and healthcare, where data governance is subject to rigorous regulatory requirements, the exchange and utilization of data are particularly challenging. Federated Learning (FL) has risen as a pioneering distributed machine learning paradigm that enables collaborative model training across multiple institutions while maintaining data decentralization. This approach inherently heightens data privacy by sharing only model weights, rather than raw data. Despite its advantages, FL is vulnerable to adversarial threats, particularly poisoning attacks during model aggregation, a process typically managed by a central server. To counteract the vulnerabilities of traditional FL frameworks, Byzantine-robust federated learning (BRFL) systems have been introduced, which rely on robust aggregation rules to mitigate the impact of malicious attacks. However, in these systems, neural network models still possess the capacity to memorize and potentially expose individual training instances inadvertently. This presents a significant privacy risk, as attackers could reconstruct private data by leveraging the information contained in the model itself. Existing solutions fall short of providing a viable, privacy-preserving BRFL system that is both completely secure against information leakage and computationally efficient. To address these concerns, we propose Lancelot, an innovative and computationally efficient BRFL framework that employs fully homomorphic encryption (FHE) to safeguard against malicious client activities while preserving data privacy. Lancelot features a novel interactive sorting mechanism called masked-based encrypted sorting. This method successfully circumvents the multiplication depth limitations of ciphertext, ensuring zero information leakage. Furthermore, we incorporate cryptographic enhancements, such as Lazy Relinearization and Dynamic Hoisting, alongside GPU hardware acceleration, to achieve a level of computational efficiency that makes Lancelot a viable option for practical implementation. Our extensive testing, including medical imaging diagnostics and widely used public image datasets, demonstrates that Lancelot significantly outperforms existing methods, offering more than a twenty-fold increase in processing speed while maintaining data privacy. The Lancelot framework thus stands as a potent solution to the pressing issue of privacy in secure, multi-centric scientific collaborations, paving the way for safer and more efficient federated learning applications.","url":"https://doi.org/10.21203/rs.3.rs-5149131/v1","authors":["Chuan Ma","Siyang Jiang","Hao Yang","Qipeng Xie","Sen Wang","Tao Xiang","Guoliang Xing"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-5149131/v1","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1038/s41598-024-69501-5","name":"Privacy protection of communication networks using fully homomorphic encryption based on network slicing and attributes.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-024-69501-5","authors":["Wei Wang","Rong Liu","Silin Cheng"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41598-024-69501-5","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1101/gr.279071.124","name":"Privacy-preserving biological age prediction over federated human methylation data using fully homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1101/gr.279071.124","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1101/gr.279071.124","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.7717/peerj-cs.1649","name":"An electronic voting scheme based on homomorphic encryption and decentralization.","source":"pubmed","abstract":"Compared with paper-based voting, electronic voting not only has advantages in storage and transmission, but also can solve the security problems that exist in traditional voting. However, in practice, most electronic voting faces the risk of voting failure due to malicious voting by voters or ballot tampering by attackers. To solve this problem, this article proposes an electronic voting scheme based on homomorphic encryption and decentralization, which uses the Paillier homomorphic encryption method to ensure that the voting results are not leaked until the election is over. In addition, the scheme applies signatures and two layers of encryption to the ballots. First, the ballot is homomorphically encrypted using the homomorphic public key; then, the voter uses the private key to sign the ballot; and finally, the ballot is encrypted using the public key of the counting center. By signing the ballots and encrypting them in two layers, the security of the ballots in the transmission process and the establishment of the decentralized scheme are guaranteed. The security analysis shows that the proposed scheme can guarantee the completeness, verifiability, anonymity, and uniqueness of the electronic voting scheme. The performance analysis shows that the computational efficiency of the proposed scheme is improved by about 66.7% compared with the Fan et al. scheme (https://doi.org/10.1016/j.future.2019.10.016).","url":"https://doi.org/10.7717/peerj-cs.1649","authors":["Yuan K","Sang P","Zhang S","Chen X","Yang W","Jia C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.7717/peerj-cs.1649","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1089/cmb.2023.0050","name":"Finding Highly Similar Regions of Genomic Sequences Through Homomorphic Encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1089/cmb.2023.0050","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1089/cmb.2023.0050","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1142/s0129065724500254","name":"Encrypted Image Classification with Low Memory Footprint Using Fully Homomorphic Encryption.","source":"europepmc","abstract":"Classifying images has become a straightforward and accessible task, thanks to the advent of Deep Neural Networks. Nevertheless, not much attention is given to the privacy concerns associated with sensitive data contained in images. In this study, we propose a solution to this issue by exploring an intersection between Machine Learning and cryptography. In particular, Fully Homomorphic Encryption (FHE) emerges as a promising solution, as it enables computations to be performed on encrypted data. We therefore propose a Residual Network implementation based on FHE which allows the classification of encrypted images, ensuring that only the user can see the result. We suggest a circuit which reduces the memory requirements by more than [Formula: see text] compared to the most recent works, while maintaining a high level of accuracy and a short computational time. We implement the circuit using the well-known Cheon–Kim–Kim–Song (CKKS) scheme, which enables approximate encrypted computations. We evaluate the results from three perspectives: memory requirements, computational time and calculations precision. We demonstrate that it is possible to evaluate an encrypted ResNet20 in less than five minutes on a laptop using approximately 15[Formula: see text]GB of memory, achieving an accuracy of 91.67% on the CIFAR-10 dataset, which is almost equivalent to the accuracy of the plain model (92.60%).","url":"https://doi.org/10.1142/s0129065724500254","authors":["Lorenzo Rovida","Alberto Leporati"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1142/s0129065724500254","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1371/journal.pone.0306420","name":"Self-learning activation functions to increase accuracy of privacy-preserving Convolutional Neural Networks with homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0306420","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1371/journal.pone.0306420","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3390/e25111550","name":"Advancing Federated Learning through Verifiable Computations and Homomorphic Encryption.","source":"pubmed","abstract":"Federated learning, as one of the three main technical routes for privacy computing, has been widely studied and applied in both academia and industry. However, malicious nodes may tamper with the algorithm execution process or submit false learning results, which directly affects the performance of federated learning. In addition, learning nodes can easily obtain the global model. In practical applications, we would like to obtain the federated learning results only by the demand side. Unfortunately, no discussion on protecting the privacy of the global model is found in the existing research. As emerging cryptographic tools, the zero-knowledge virtual machine (ZKVM) and homomorphic encryption provide new ideas for the design of federated learning frameworks. We have introduced ZKVM for the first time, creating learning nodes as local computing provers. This provides execution integrity proofs for multi-class machine learning algorithms. Meanwhile, we discuss how to generate verifiable proofs for large-scale machine learning tasks under resource constraints. In addition, we implement the fully homomorphic encryption (FHE) scheme in ZKVM. We encrypt the model weights so that the federated learning nodes always collaborate in the ciphertext space. The real results can be obtained only after the demand side decrypts them using the private key. The innovativeness of this paper is demonstrated in the following aspects: 1. We introduce the ZKVM for the first time, which achieves zero-knowledge proofs (ZKP) for machine learning tasks with multiple classes and arbitrary scales. 2. We encrypt the global model, which protects the model privacy during local computation and transmission. 3. We propose and implement a new federated learning framework. We measure the verification costs under different federated learning rounds on the IRIS dataset. Despite the impact of homomorphic encryption on computational accuracy, the framework proposed in this paper achieves a satisfactory 90% model accuracy. Our framework is highly secure and is expected to further improve the overall efficiency as cryptographic tools continue to evolve.","url":"https://doi.org/10.3390/e25111550","authors":["Zhang B","Lu G","Qiu P","Gui X","Shi Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.3390/e25111550","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1109/jbhi.2024.3350232","name":"Federated Learning Approach for Secured Medical Recommendation in Internet of Medical Things Using Homomorphic Encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/jbhi.2024.3350232","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1109/jbhi.2024.3350232","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-4037520/v1","name":"Enhanced Homomorphic Encryption and Dual Blockchain Authentication for Securing Medical Data on the Internet of Things","source":"europepmc","abstract":"Abstract This research presents Enhanced Homomorphic Encryption (DBA-EHE) and Dual Blockchain Authentication as methods for protecting medical data on the Internet of Things (IoT). Two authentication layers make up DBA-EHE: edge-based and blockchain-based. When Internet of Things devices interface with network edges for data processing and storage, the centralized authentication layer is implemented. In order to provide decentralized authentication, safe communication, and information sharing between authenticated IoT devices and edge networks, the blockchain layer is concurrently connected to edge networks. Online medical data platforms are used to validate the proposed technique. The introduction of Enhanced Homomorphic Encryption (EHE) strengthens the security of medical data even more. To choose the best key, EHE makes use of the Homomorphic Encryption (HE) and the Coati Optimization Algorithm (COA). The suggested approach is implemented in MATLAB, and metrics such as energy consumption, end-to-end delay, processing time, encryption time, decryption time, authentication time, and communication overhead are used to assess the method's performance. A comparative study between the suggested methodology and conventional methodologies shows its higher performance.","url":"https://doi.org/10.21203/rs.3.rs-4037520/v1","authors":["gopinath velivela","venkata rao k","krishana rao sala"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-4037520/v1","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1002/adma.202400661","name":"A 2D Cryptographic Hash Function Incorporating Homomorphic Encryption for Secure Digital Signatures.","source":"europepmc","abstract":"Abstract User authentication is a critical aspect of any information exchange system which verifies the identities of individuals seeking access to sensitive information. Conventionally, this approachrelies on establishing robust digital signature protocols which employ asymmetric encryption techniques involving a key pair consisting of a public key and its matching private key. In this article, a user verification platform constructed using integrated circuits (ICs) with atomically thin two‐dimensional (2D) monolayer molybdenum disulfide (MoS 2 ) memtransistors is presented. First, generation of secure cryptographic keys is demonstrated by exploiting the inherent stochasticity of carrier trapping and detrapping at the 2D/oxide interface trap sites. Subsequently, the ability to manipulate the functionality of logical NOR is leveraged to create a secure one‐way hash function which when homomorphically operated upon with NAND, XOR, OR, NOT, and AND logic circuits generate distinct digital signatures. These signatures when subsequently decrypted, verify the authenticity of the receiver while ensuring complete preservation of data integrity and confidentiality as the underlying information is never revealed. Finally, the advantages of implementing a NOR‐based hashing techniques in comparison to the conventional XOR‐based encryption method are established. This demonstration highlights the potential of 2D‐based ICs in developing critical hardware information security primitives.","url":"https://doi.org/10.1002/adma.202400661","authors":["Akshay Wali","Harikrishnan Ravichandran","Saptarshi Das"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1002/adma.202400661","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1038/s41598-024-63393-1","name":"Harnessing the potential of shared data in a secure, inclusive, and resilient manner via multi-key homomorphic encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-024-63393-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1038/s41598-024-63393-1","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-3097727/v1","name":"Leaking Secrets in Homomorphic Encryption with Side-Channel Attacks","source":"europepmc","abstract":"Abstract Homomorphic encryption (HE) allows computing encrypted data in the ciphertext domain without knowing the encryption key. It is possible, however, to break fully homomorphic encryption (FHE) algorithms by using side channels. This article demonstrates side-channel leakages of the Microsoft SEAL HE library. The proposed attack can steal encryption keys during the key generation phase by abusing the leakage of ternary value assignments that occurs during the number theoretic transform (NTT) algorithm. We propose two attacks, one for -O0 flag non-optimized code implementation which targets addition and subtraction operations, and one for -O3 flag compiler optimization which targets guard and mul root operations. In particular, the attacks can steal the secret key coefficients from a single power/electromagnetic measurement trace of SEAL’s NTT implementation. To achieve high accuracy with a single-trace, we develop novel machine-learning side-channel profilers. On an ARM Cortex-M4F processor, our attacks are able to extract secret key coefficients with an accuracy of 98.3% when compiler optimization is disabled, and 98.6% when compiler optimization is enabled. We finally demonstrate that our attack can evade an application of the random delay insertion defense.","url":"https://doi.org/10.21203/rs.3.rs-3097727/v1","authors":["Furkan Aydin","Aydin Aysu"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-3097727/v1","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.3390/s23073566","name":"A Review of Homomorphic Encryption for Privacy-Preserving Biometrics.","source":"pubmed","abstract":"The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward.","url":"https://doi.org/10.3390/s23073566","authors":["Yang W","Wang S","Cui H","Tang Z","Li Y","Wencheng Yang","Song Wang","Hui Cui","Zhaohui Tang","Yan Li"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.3390/s23073566","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.3390/s23177389","name":"Configurable Encryption and Decryption Architectures for CKKS-Based Homomorphic Encryption.","source":"pubmed","abstract":"With the increasing number of edge devices connecting to the cloud for storage and analysis, concerns about security and data privacy have become more prominent. Homomorphic encryption (HE) provides a promising solution by not only preserving data privacy but also enabling meaningful computations on encrypted data; while considerable efforts have been devoted to accelerating expensive homomorphic evaluation in the cloud, little attention has been paid to optimizing encryption and decryption (ENC-DEC) operations on the edge. In this paper, we propose efficient hardware architectures for CKKS-based ENC-DEC accelerators to facilitate computations on the client side. The proposed architectures are configurable to support a wide range of polynomial sizes with multiplicative depths (up to 30 levels) at a 128-bit security guarantee. We evaluate the hardware designs on the Xilinx XCU250 FPGA platform and achieve an average encryption time 23.7&#xd7; faster than that of the well-known SEAL HE library. By reducing time complexity and improving the hardware utilization of cryptographic algorithms, our configurable CKKS-supported ENC-DEC hardware designs have the potential to greatly accelerate cryptographic processes on the client side in the post-quantum era.","url":"https://doi.org/10.3390/s23177389","authors":["Lee J","Duong PN","Lee H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.3390/s23177389","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.20944/preprints202405.0307.v1","name":"Enhancing Efficiency and Security in Unbalanced PSI-CA Protocols through Cloud Computing and Homomorphic Encryption in Mobile Networks","source":"europepmc","abstract":"Private Set Intersection Cardinality(PSI-CA) is a cryptographic method in secure multi-party computation that allows entities to identify cardinality of the intersection without revealing their private data.Traditional approaches assume similar-sized datasets and equal computational power, overlooking practical imbalances.In real-world applications, dataset sizes and computational capacities often vary, particularly in the Internet of Things and mobile scenarios where device limitations restrict computational types. Traditional PSI-CA protocols are inefficient here, as computational and communication complexities correlate with the size of larger datasets. Thus, adapting PSI-CA protocols to these imbalances is crucial.This paper explores unbalanced scenarios where one party (the receiver) has a relatively small dataset and limited computational power, while the other party (the sender) has a large amount of data and strong computational capabilities.This paper, based on the concept of commutative encryption, introduces Cuckoo filter, cloud computing technology, homomorphic encryption, among other technologies, to construct three novel solutions for unbalanced Private Set Intersection Cardinality (PSI-CA): an unbalanced PSI-CA protocol based on Cuckoo filter, an unbalanced PSI-CA protocol based on single cloud assistance, and an unbalanced PSI-CA protocol based on dual cloud assistance. Depending on performance and security requirements, different protocols can be employed for various applications.","url":"https://doi.org/10.20944/preprints202405.0307.v1","authors":["Wuzheng Tan","Shenglong Du","Jian Weng"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.20944/preprints202405.0307.v1","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.56553/popets-2024-0114","name":"Summation-based Private Segmented Membership Test from Threshold-Fully Homomorphic Encryption.","source":"europepmc","abstract":"In many real-world scenarios, there are cases where a client wishes to check if a data element they hold is included in a set segmented across a large number of data holders. To protect user privacy, the client's query and the data holders' sets should remain encrypted throughout the whole process. Prior work on Private Set Intersection (PSI), Multi-Party PSI (MPSI), Private Membership Test (PMT), and Oblivious RAM (ORAM) falls short in this scenario in many ways. They either require data holders to possess the sets in plaintext, incur prohibitively high latency for aggregating results from a large number of data holders, leak the information about the party holding the intersection element, or induce a high false positive. This paper introduces the primitive of a Private Segmented Membership Test (PSMT). We give a basic construction of a protocol to solve PSMT using a threshold variant of approximate-arithmetic homomorphic encryption and show how to overcome existing challenges to construct a PSMT protocol without leaking information about the party holding the intersection element or false positives for a large number of data holders ensuring IND-CPA^D security. Our novel approach is superior to existing state-of-the-art approaches in scalability with regard to the number of supported data holders. This is enabled by a novel summation-based homomorphic membership check rather than a product-based one, as well as various novel ideas addressing technical challenges. Our PSMT protocol supports many more parties (up to 4096 in experiments) compared to prior related work that supports only around 100 parties efficiently. Our experimental evaluation shows that our method's aggregation of results from data holders can run in 92.5s for 1024 data holders and a set size of 2^25, and our method's overhead increases very slowly with the increasing number of senders. We also compare our PSMT protocol to other state-of-the-art PSI and MPSI protocols and discuss our improvements in usability with a better privacy model and a larger number of parties.","url":"https://doi.org/10.56553/popets-2024-0114","authors":["Nirajan Koirala","Jonathan Takeshita","Jeremy Stevens","Taeho Jung"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.56553/popets-2024-0114","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-3648036/v1","name":"BatchEncryption: Localized Federated Learning in Preserving-privacy with Efficient Integer Vector Homomorphic Encryption","source":"europepmc","abstract":"Abstract Deep Learning as a Service (DLaaS) provides an efficient way to facilitate deep neural networks (DNNs) in various applications. Despite the accuracy of deep learning models, there is a paramount need for robust protocols to protect the privacy and security of data, particularly sensitive information. Currently, It is not a good way to permit cloud models to process such data without proper safeguards, leading to potential privacy breaches. Especially in the training phase of the model, most models are trained by plaintext rather than ciphertext, which will pose potential privacy leakage of data owners. While some researchers have begun to use homomorphically encrypted data for training, most researchers only implement a single pair of keys for encryption and decryption, neglecting the importance of encryption scheme robustness when unique keys are employed by users. To address these issues, this paper presents a practical localized Federated Learning (FL) method named BatchEncryption using Efficient Integer Vector Homomorphic Encryption (EIVHE) for privacy-preserving training and inference phases. BatchEncryption encrypts raw datasets in blocks using different pairs of keys, which holds the diversity and robustness of the model. Our experiments demonstrate that deploying different pairs of keys to train neural networks holds higher accuracy than implementing a single pair of keys, and the proposed method achieves around 97% accuracy on the data encrypted by different pairs of keys on the MNIST dataset.","url":"https://doi.org/10.21203/rs.3.rs-3648036/v1","authors":["Tianying Xie"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-3648036/v1","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.21203/rs.3.rs-2018739/v2","name":"A Survey on Implementations of Homomorphic Encryption Schemes","source":"europepmc","abstract":"Abstract With the increased need for data confidentiality in various applications of our daily life, homomorphic encryption (HE) has emerged as a promising cryptographic topic. HE enables to perform computations directly on encrypted data (ciphertexts) without decryption in advance. Since the results of calculations remain encrypted and can only be decrypted by the data owner, confidentiality is guaranteed and any third party can operate on ciphertexts without access to decrypted data (plaintexts). Applying a homomorphic cryptosystem in a real-world application depends on its resource efficiency. Several works compared different HE schemes and gave the stakes of this research field. However, the existing works either do not deal with recently proposed HE schemes (such as CKKS) or focus only on one type of HE. In this paper, we conduct an extensive comparison and evaluation of homomorphic cryptosystems’ performance based on their experimental results. The study covers all three families of HE, including several notable schemes such as BFV, BGV, CKKS, RSA, El-Gamal, and Paillier, as well as their implementation specification in widely used HE libraries, namely Microsoft SEAL, PALISADE, and HElib. In addition, we also discuss the resilience of HE schemes to different kind of attacks such as Indistinguishability under chosen-plaintext attack and integer factorization attacks on classical and quantum computers.","url":"https://doi.org/10.21203/rs.3.rs-2018739/v2","authors":["Thi Van Thao DOAN","Mohamed-Lamine Messai","Gérald Gavin","Jérôme Darmont"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-2018739/v2","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1016/j.heliyon.2023.e22357","name":"Smart contracts and homomorphic encryption for private P2P energy trading and demand response on blockchain.","source":"pubmed","abstract":"Blockchain technology offers great value in terms of decentralization, data integrity, transparency, and traceability, however the transactional data is public, and accessible raising concerns about violating privacy regulations. For example, in the peer-to-peer energy trading and demand response use cases, the data stored in blockchain may allow a third party to infer the load profiles or even identify the behind the meter assets. In this paper, we employ homomorphic techniques to encrypt the energy transactional data stored on the blockchain allowing the smart contracts functions responsible for implementing the business logic of the energy flexibility trading and settlement to perform computations on encrypted data. As computations on smart contracts and public blockchains can be expensive, we have used the lighter version of the Partial Homomorphic Encryption scheme to obfuscate the energy data. To ensure the validity of the smart contracts' functions executed on encrypted data, we leverage on the consensus mechanism of the blockchain network, thus ensuring computation correctness. The solution was validated considering a micro-grid with 12 prosumers that trade their flexibility peer-to-peer (P2P). The results demonstrate the feasibility of maintaining encrypted energy data on the blockchain, executing smart contract functions on encrypted data, and preserving the privacy of computations. As anticipated, the trade-off for better privacy is the gas consumption overhead of the smart contracts' functions which is higher compared to the non-encrypted case, depending on the length of the public-private keys pair. Nonetheless, our solution exhibits consistent execution times for smart contracts, making it suitable for private networks where gas costs are of minimal concern.","url":"https://doi.org/10.1016/j.heliyon.2023.e22357","authors":["Mitrea D","Toderean L","Cioara T","Anghel I","Antal M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1016/j.heliyon.2023.e22357","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-3162826/v1","name":"An Efficient and Secure Blockchain Based Homomorphic Encryption for Intelligent Transport System","source":"europepmc","abstract":"Abstract The volume of automobiles on roadways keeps growing and crashes increase in frequency, managing traffic routes gets increasingly crucial. Real-time messages are delivered through wireless connections in Intelligent Transport Systems(ITS), although this might raise safety and confidentiality issues. Safety flaws, hefty data processing and transmission costs, and safety vulnerabilities plague current traffic route management ideas. With fog-based ITS's, a simple congestion routing management system was developed to overcome these problems. In this system, automobiles encrypted their travel courses using homomorphic encryption and transfer the secured data onto a fog node. Despite being aware of what specific path was taken by every automobile, Traffic Control Centre (TMC) decodes the received ciphertexts which have been collected by the fog node and manages congestion based on the decoded data. Additionally, the plan makes utilization of the blockchain system to maintain the vehicle's public key. This makes it possible to manage individual vehicle's public key securely and impenetrably, guaranteeing that only authorized cars may join the ITS. The idea was put into operation via the Rinkeby test network based on Ethereum to show that it is feasible. According to the results of the study, this aforementioned approach outperforms other pertinent representative schemes. This lightweight traffic route management system offers a safe and effective method for controlling travel routes in ITSs by utilizing homomorphic encryption and blockchain technology. By addressing the safety and confidentiality concerns raised by sending real-time communications via wireless methods, also lowers the computation and transmission costs of previous ideas. This approach has the potential to improve traffic safety and ease congestion in ITS's.","url":"https://doi.org/10.21203/rs.3.rs-3162826/v1","authors":["Nikhil Tanwar"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-3162826/v1","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1088/2057-1976/ad0b4b","name":"Secret learning for lung cancer diagnosis-a study with homomorphic encryption, texture analysis and deep learning.","source":"pubmed","abstract":"Advanced lung cancer diagnoses from radiographic images include automated detection of lung cancer from CT-Scan images of the lungs. Deep learning is a popular method for decision making which can be used to classify cancerous and non-cancerous lungs from CT-Scan images. There are many experiments which show the uses of deep learning for performing such classifications but very few of them have preserved the privacy of users. Among existing methods, federated learning limits data sharing to a central server and differential privacy although increases anonymity the original data is still shared. Homomorphic encryption can resolve the limitations of both of these. Homomorphic encryption is a cryptographic technique that allows computations to be performed on encrypted data. In our experiment, we have proposed a series of textural information extraction with the implementation of homomorphic encryption of the CT-Scan images of normal, adenocarcinoma, large cell carcinoma and squamous cell carcinoma. We have further processed the encrypted data to make it classifiable and later we have classified it with deep learning. The results from the experiments have obtained a classification accuracy of 0.9347.","url":"https://doi.org/10.1088/2057-1976/ad0b4b","authors":["Adhikary S","Dutta S","Dwivedi AD"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.1088/2057-1976/ad0b4b","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-3834073/v1","name":"RETRACTED: Secured learning through electronic health records using Hybrid Fully Homomorphic Encryption","source":"europepmc","abstract":"Abstract The authors have requested that this preprint be removed from Research Square.","url":"https://doi.org/10.21203/rs.3.rs-3834073/v1","authors":["Kundan Munjal","Rekha Bhatia","Shilpa Verma"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:29.669Z","doi":"10.21203/rs.3.rs-3834073/v1","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.3233/978-1-61499-532-6-26","name":"Stateful Abstractions of Secure Multiparty Computation","source":"crossref","abstract":"In this chapter we present the Arithmetic Black Box (ABB) functionality. It is an ideal functionality that preserves the privacy of the data it stores and allows computations to be performed on the data stored, as well as retrieve certain pieces of data. We show that it is a very convenient abstraction of secure multiparty computation (SMC), which enables easy extensions and the construction of large privacy-preserving applications on top of it. In this chapter, we give a detailed definition of the ABB functionality and present different ways in which it can be implemented securely. We explain what extending an ABB means and how it is integrated into larger applications.","url":"https://doi.org/10.3233/978-1-61499-532-6-26","authors":["Laud Peeter"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-532-6-26","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3233/978-1-61499-532-6-246","name":"Practical Applications of Secure Multiparty Computation","source":"crossref","abstract":"As secure multiparty computation technology becomes more mature, we see more and more practical applications where computation moves from a lab setting to an actual deployment where each computation node is hosted by a different party. In this chapter, we present some of the practical SMC applications that have worked on real data. A few of these have been developed by Cybernetica AS, while others have been found from public sources. All scientific prototypes or simulations that work on generated or public data have been left out of this chapter. For each chosen SMC application, we give a brief overview and refer the reader to the corresponding published sources for more details.","url":"https://doi.org/10.3233/978-1-61499-532-6-246","authors":["Talviste Riivo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-532-6-246","updatedAt":"2026-08-31T06:41:38.943Z"},{"id":"doi:10.3233/978-1-61499-532-6-106","name":"Oblivious Array Access for Secure Multiparty Computation","source":"crossref","abstract":"In this chapter, we describe efficient protocols for performing reads and writes in private arrays according to private indices. The protocols are implemented on top of the arithmetic black box (ABB) and can be composed freely to build larger privacy-preserving applications. We present two approaches to speed up private reads and writes &amp;mdash; one based on precomputation and the other one on sorting. We show how several different problems become significantly more tractable while preserving the privacy of inputs. In particular, our second approach opens up a large class of parallel algorithms for adoption to run on SMC platforms.","url":"https://doi.org/10.3233/978-1-61499-532-6-106","authors":["Laud Peeter"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-532-6-106","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3403/bsisoiec4922","name":"Information security - Secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.3403/bsisoiec4922","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-03T20:30:33Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3403/bsisoiec4922","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/springerreference_64271","name":"Secure Multiparty Computation Methods","source":"crossref","abstract":"","url":"https://doi.org/10.1007/springerreference_64271","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-08-29T12:52:50Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1007/springerreference_64271","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3233/978-1-61499-532-6-43","name":"Social Need for Secure Multiparty Computation","source":"crossref","abstract":"The aim of this chapter is to introduce and discuss the potential socio-technical barriers that might be hindering the adoption of Secure Multiparty Computation (SMC) techniques. By investigating the conditions of adoption of technology under development, we are able to find different solutions that support the technology adoption process. We set out to interview the potential future users of SMC technologies to investigate the most likely adopters and to find the best directions for future developments of SMC. SMC is compared with the existing practices of data handling to demonstrate its advantages as well as disadvantages. In the current development phase, the focus of SMC advances needs to be on the usefulness and on finding an appropriate niche for the technology.","url":"https://doi.org/10.3233/978-1-61499-532-6-43","authors":["Kanger Laur","Pruulmann-Vengerfeldt Pille"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-532-6-43","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3233/978-1-61499-532-6-129","name":"Business Process Engineering and Secure Multiparty Computation","source":"crossref","abstract":"In this chapter we use secure multiparty computation (SMC) to enable privacy-preserving engineering of inter-organizational business processes. Business processes often involve structuring the activities of several organizations, for example when several potentially competitive enterprises share their skills to form a temporary alliance.","url":"https://doi.org/10.3233/978-1-61499-532-6-129","authors":["Guanciale Roberto","Gurov Dilian","Laud Peeter"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-532-6-129","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3233/978-1-61499-532-6-1","name":"Basic Constructions of Secure Multiparty Computation","source":"crossref","abstract":"In this chapter, we formally define multiparty computation tasks and the security of protocols realizing them. We give a broad presentation of the existing constructions of secure multiparty computation (SMC) protocols and explain why they are correct and secure. We discuss the different environmental aspects of SMC protocols and explain the requirements that are necessary and sufficient for their existence.","url":"https://doi.org/10.3233/978-1-61499-532-6-1","authors":["Laud Peeter","Pankova Alisa","Kamm Liina","Veeningen Meilof"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-532-6-1","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.70675/ab33e54ez239ez4a27z809ez61d5b23aea79","name":"Efficient delegated secure multiparty computation","source":"crossref","abstract":"Délégation efficace de calcul multipartite sécurisé Avec l’essor du cloud, il est devenu plus simple de déléguer la gestion et l’analyse des données à des infrastructures externes, favorisant la combinaison de données variées pour en tirer des informations utiles. Toutefois, garantir la confidentialité des données sensibles reste un obstacle majeur. Le calcul sécurisé multipartite (MPC) répond à ce défi en permettant à plusieurs participants de collaborer pour effectuer des calculs sur leurs données sans révéler celles-ci. Cette thèse explore une approche où les propriétaires des données délèguent ces calculs à des serveurs non fiables, tout en préservant sécurité et confidentialité. Pour cela, nous nous appuyons sur le chiffrement complètement homomorphe(FHE), qui permet de calculer directement sur des données chiffrées. Nos contributions incluent un protocole robuste de MPC basé sur le FHE et une méthode générique réduisant les besoins en communication. Ces avancées rendent les calculs sécurisés plus efficaces et accessibles, même pour des projets impliquant de nombreux participants.","url":"https://doi.org/10.70675/ab33e54ez239ez4a27z809ez61d5b23aea79","authors":["Antoine Urban"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-09T02:18:47Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.70675/ab33e54ez239ez4a27z809ez61d5b23aea79","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3233/978-1-61499-532-6-58","name":"Statistical Analysis Methods Using Secure Multiparty Computation","source":"crossref","abstract":"This chapter gives an overview of privacy-preserving versions of the analysis methods and algorithms that are most commonly used in statistical analysis. We discuss methods for data collection and sharing, and describe privacy-preserving database joins and sorting. From simple statistical measures, we discuss the count and sum of elements, quantiles, the five-number summary, frequency tables, mean, variance, covariance and standard deviation. We look into outlier detection and explore privacy-preserving versions of several different statistical tests, such as Student's t-test, Wilcoxon rank-sum and signed-rank tests and the &amp;chi;2-test. We discuss how to evaluate the significance of the test statistic in the privacy-preserving environment. We give several options for linear regression and conclude the chapter with a privacy-preserving method for data classification.","url":"https://doi.org/10.3233/978-1-61499-532-6-58","authors":["Kamm Liina","Bogdanov Dan","Pankova Alisa","Talviste Riivo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-532-6-58","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.70675/06641ef7zb869z4b59zb1f0z911d74861a93","name":"Sublinear-communication secure multiparty computation","source":"crossref","abstract":"Calcul multipartite sécurisé avec communication sous-linéaire Le calcul multipartite sécurisé (en anglais, MPC) [Yao82,GMW87a] permet à des agents d'un réseau de communication de calculer conjointement une fonction de leurs entrées sans avoir à n'en rien révéler de plus que le résultat du calcul lui-même. Une question primordiale est de savoir dans quelle mesure le coût en communication entre les agents dépend de la complexité calculatoire de la fonction en question. Un point de départ est l'étude d'une hypothétique barrière de la taille du circuit. L'existence d'une telle barrière est suggérée par le fait que tous les protocoles MPC fondateurs, des années 80 et 90, emploient une approche \"porte-logique-par-porte-logique\" au calcul sécurisé: la communication d'un tel protocole sera nécessairement au moins linéaire en le nombre de portes, c'est-à-dire en la taille du circuit. De plus ceux-ci représentent moralement l'état de l'art encore de nos jours en ce qui concerne la sécurité dite \"par théorie de l'information\". La barrière de la taille du circuit a été franchie pour le MPC avec sécurité calculatoire, mais sous des hypothèses structurées impliquant l'existence de chiffrement totalement homomorphe (en anglais, FHE) [Gen09] ou de partage de secret homomorphe (en anglais, HSS) [BGI16a]. De plus, il existe des protocoles avec sécurité par théorie de l'information dont la communication en-ligne (mais pas la communication totale) est sous-linéaire en la taille du circuit [IKM + 13, DNNR17, Cou19]. Notre méthodologie de recherche consiste à s'inspirer des techniques developpées dans le modèle de l'aléa corrélée dans lequel tout résultat pourra être considéré comme plus \"fondamental\" que le modèle calculatoire (de par le type de sécurité obtenue) mais qui est néanmoins un modèle inadapté à comprendre la complexité de communication du MPC (puisque que l'on s'autorise à ne pas compter toute quantité de communication qui peut être reléguée à une phase \"hors-ligne\", c'est-à-dire avant que les participants au calcul ne prennent connaissance de leurs entrées) pour développer de nouvelles méthodes dans le modèle calculatoire. Avec cette approche, nous obtenons des protocoles franchissant la barrière de la taille du circuit sous l'hypothèse de la sécurité quasipolynomiale de LPN [CM21] ou sous l'hypothèse QR+LPN [BCM22]. Ces hypothèses calculatoires n'étant pas précédement réputées impliquer l'existence de MPC sous-linéaire, la pertinence de notre méthodologie est, dans une certaine mesure, validée a posteriori. Plus fondamentalement cependant, nos travaux empruntent un nouveau paradigme pour construire du MPC sous-linéaire, sans utiliser les outils \"avec de fortes propriétés homomorphiques\" que sont le FHE ou du HSS. En combinant toutes nos techniques héritées de notre étude du modèle de l'aléa corrélé, nous parvenons à briser la barrière des deux joueurs pour le calcul sécurisé avec communication sous-linéaire, sans FHE [BCM23]. Spécifiquement, nous présentons le premier protocole à plus de deux participants dont la communication est sous-linéaire en la taille du circuit et qui ne soit pas fondé sur des hypothèses sous lesquelles on sait déjà faire du FHE. Parallèlement à ces travaux centrés sur la sécurité calculatoire, nous montrons [CMPR23] comment adapter les approches à deux joueurs utilisant du HSS, à la [BGI16a], pour gurantir une sécurité \"théorie de l'information\" à l'un des deux joueurs et une sécurité calculatoire à l'autre. Ceci est, de façon prouvable, la notion de sécurité la plus forte que l'on puisse espérer en présence de seulement deux joueurs (sans aléa corrélé). Nous obtenons le premier protocole de ce type avec communication sous-linéaire, qui ne soit pas fondé sur des hypothèses sous lesquelles on sait déjà faire du FHE.","url":"https://doi.org/10.70675/06641ef7zb869z4b59zb1f0z911d74861a93","authors":["Pierre Meyer"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T17:36:28Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.70675/06641ef7zb869z4b59zb1f0z911d74861a93","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.003","name":"Preliminaries","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.003","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.003","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.70675/7ecd6017z289fz4e38zbc93zb85d7c33aefc","name":"Post-Quantum Signatures from Secure Multiparty Computation","source":"crossref","abstract":"Signatures post-quantiques à partir de techniques de calcul multipartite Le développement actuel des ordinateurs quantiques pousse la communauté cryptographique à mettre au point de nouveaux cryptosystèmes dont la sécurité se fonde sur la difficulté à résoudre des problèmes cryptographiques résistant au calcul quantique. Dans le cadre de cette thèse, nous nous sommes focalisés sur la conception de schémas de signatures électroniques construits à partir de preuves à divulgation nulle de connaissance (zero-knowledge proofs of knowledge). Plus précisément, nous nous sommes intéressés au paradigme “MPC-in-the-Head” (littéralement, “calcul-multipartite-dans-la-tête”) qui fournit une méthode générique de construire de telles preuves en utilisant des techniques de calcul multipartite sécurisé. Nous proposons plusieurs nouveaux schémas de signatures utilisant le paradigme “MPC-in-the-Head”. La plupart d’entre eux sont compétitifs avec les schémas existants dans l’état de l’art post-quantique. Ils produisent des signatures ayant des tailles entre 5 et 20 kylo-octets (pour un niveau de sécurité de 128 bits) et possèdent de très petites clés (de moins de 200 octets). Les problèmes difficiles sur lesquels la sécurité de ces schémas se fonde sont très variés. Certains schémas s’appuient sur des hypothèses de sécurité issues de la théorie des codes correcteurs d’erreurs, telle que celle sur la difficulté à résoudre le problème de décodage par syndrome pour des codes linéaires aléatoires. Les autres schémas s’appuient sur la difficultés à résoudre un système d’équations quadratiques, le problème de la somme de sous-ensembles ou le problème MinRank. Nous avons également mis au point deux nouvelles techniques de MPC-in-the-Head. La première vise à gérer efficacement les situations où le secret est de petite taille avec un grand modulus. La seconde consiste en une nouvelle méthode pour transformer un protocole de calcul multipartite en preuve de divulgation nulle de connaissance. Cette nouvelle transformation offre des nouveaux compromis entre coût de communication et temps de calcul. En particulier, elle permet de produire des algorithmes de vérification très rapides. Plusieurs soumissions à l’appel du NIST pour des schémas de signatures post-quantiques supplémentaires s'appuient (parfois partiellement) sur des idées développées dans le cadre de cette thèse.","url":"https://doi.org/10.70675/7ecd6017z289fz4e38zbc93zb85d7c33aefc","authors":["Thibauld Feneuil"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T09:14:12Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.70675/7ecd6017z289fz4e38zbc93zb85d7c33aefc","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.002","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.002","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.002","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.014","name":"References","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.014","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.014","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.001","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.001","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.001","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.70675/af9d1b8az08abz41f0za364z66a2ed720b8f","name":"Zero-knowledge arguments from secure multiparty computation","source":"crossref","abstract":"Arguments à divulgation nulle de connaissance via du calcul réparti sécurisé Cette thèse a pour but d'étudier les arguments à divulgation nulle de connaissance, une primitive cryptographique qui permet de prouver un énoncé tout en ne révélant rien d'autre que sa vérité (nous pouvons l'appeler preuve au lieu d'argument en fonction du modèle de sécurité). Plus précisément, nous nous concentrons sur une famille d'arguments dont la construction est basée sur le calcul multipartite sécurisé. Il est bien connu que, pour toute fonctionnalité, il existe un protocole multipartite sécurisé pour la calculer. Prenons une fonction générique à sens unique f, et un protocole multipartite sécurisé calculant f, il a été montré assez récemment que nous pouvons construire un argument à divulgation nulle de connaissance pour le problème NP de trouver une pré-image de f. Cette construction n'était considérée que comme théorique jusqu'à il y a quelques années, et notre travail contribue à l'émergence de nouvelles techniques ainsi que d'applications efficaces dans ce paradigme. En guise d'amuse-gueule, nous développons des protocoles simples qui améliorent de manière significative la complexité en communication pour certains problèmes bien connus. Notre première contribution substantielle, avec un désir de partager des petits éléments, est l'introduction d'un partage sur les entiers qui est intégré de manière sûre dans nos protocoles avec un rejet artificiel. Les applications sont multiples, y compris dans le monde post-quantique. Dans la lignée de notre partage sur les entiers, nous proposons un schéma cryptographique de mise en gage basé sur des problèmes de sac-à-dos modulaire. En particulier, il permet des arguments efficaces pour la satisfiabilité de circuits génériques. Ensuite, nous présentons une construction de preuve utilisant la conversion entre les partages de secrets additifs et multiplicatifs, conduisant à des preuves efficaces de relations linéaires et multiplicatives. Les applications sont encore une fois multiples lors de la conception d'arguments et de signatures numériques. Enfin, laissant de côté la conception de protocoles, nous explorons les fondements de la cryptographie avec des preuves à divulgation nulle de connaissance avec prouveurs multiple, un cadre pour distribuer le travail du prouveur. Pour saisir tout l'intérêt de cette répartition, nous ajoutons à la littérature un résultat fondamental sur les preuves à seuil pour un langage NP générique.","url":"https://doi.org/10.70675/af9d1b8az08abz41f0za364z66a2ed720b8f","authors":["Jules Maire"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T20:21:09Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.70675/af9d1b8az08abz41f0za364z66a2ed720b8f","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.005","name":"Models","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.005","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.005","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.5220/0012453700003648","name":"Secure Multiparty Computation of the Laplace Mechanism","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012453700003648","authors":["Amir Zarei","Staal Vinterbo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-01T18:44:53Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.5220/0012453700003648","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3233/978-1-61499-532-6-165","name":"Verifiable Computation in Multiparty Protocols with Honest Majority","source":"crossref","abstract":"We present a generic method for turning passively secure protocols into protocols secure against covert attacks. This method adds to the protocol a post-execution verification phase that allows a misbehaving party to escape detection only with negligible probability. The execution phase, after which the computed protocol result is already available to the parties, has only negligible overhead added by our method.","url":"https://doi.org/10.3233/978-1-61499-532-6-165","authors":["Pankova Alisa","Laud Peeter"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-532-6-165","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/9781108670203.020","name":"Multiparty Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1017/9781108670203.020","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-23T00:07:28Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/9781108670203.020","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.32657/10356/41834","name":"Privacy-preserving data mining via secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/41834","authors":["Shu Guo Han"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-10-02T11:39:33Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.32657/10356/41834","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.011","name":"Algebraic Preliminaries","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.011","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.011","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.012","name":"Secret Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.012","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.012","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3233/978-1-61499-169-4-168","name":"Information-Theoretic Secure Multiparty Computation","source":"crossref","abstract":"Most of this book has considered two-party and multiparty computation with security with (unfair) abort. This security notion allows the adversary to force the honest parties to abort even depending on the outputs of corrupted parties. (Note, however, that the adversary is not allowed to bias the output of honest parties in any other manner.) This relaxation of security is necessary if one wishes to tolerate arbitrarily many corruptions. A further relaxation considers computational security, where the adversary is assumed to be computationally bounded, e.g., probabilistic polynomial time. In this setting, any circuit can be securely realized even when arbitrarily many parties are corrupted, as long as one is willing to sacrifice fairness and robustness.","url":"https://doi.org/10.3233/978-1-61499-169-4-168","authors":["Maurer Ueli","Zikas Vassilis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-169-4-168","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3233/978-1-61499-532-6-81","name":"Achieving Optimal Utility for Distributed Differential Privacy Using Secure Multiparty Computation","source":"crossref","abstract":"Computing aggregate statistics about user data is of vital importance for a variety of services and systems, but this practice seriously undermines the privacy of users. Recent research efforts have focused on the development of systems for aggregating and computing statistics about user data in a distributed and privacy-preserving manner. Differential privacy has played a pivotal role in this line of research: the fundamental idea is to perturb the result of the statistics before release, which suffices to hide the contribution of individual users.","url":"https://doi.org/10.3233/978-1-61499-532-6-81","authors":["Eigner Fabienne","Kate Aniket","Maffei Matteo","Pampaloni Francesca","Pryvalov Ivan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-532-6-81","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.013","name":"Arithmetic Codices","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.013","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.013","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.010","name":"Applications of MPC","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.010","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.010","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.5220/0012734600003711","name":"Integrating Secure Multiparty Computation into Data Spaces","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012734600003711","authors":["Veronika Siska","Thomas Lorünser","Stephan Krenn","Christoph Fabianek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-06T16:53:02Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.5220/0012734600003711","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.21203/rs.3.rs-6706796/v1","name":"Quantum Secure Multiparty Computation based on Secure Summation and QKD","source":"europepmc","abstract":"Abstract Secure Multiparty Computation (MPC) is a cryptography technique that allows multiple parties to securely perform operations on their private inputs without revealing them. It is used in various applications, such as data aggregation in the cloud, IoT, etc., where data privacy is the prime concern. Many researchers have explored ways to develop secure protocols with minimal risk of data leakage. Many of these classical secure MPC protocols depend on classical random number generators. However, advances in quantum information processing are putting classical key distribution methods at risk. Traditional summation protocols adopt an \\(((n, n))\\) threshold approach, requiring the participation of all \\((n)\\) players to compute the sum securely. However, the proposed quantum secure multiparty computation based on secure summation and QKD (QSMPC-SSQKD) introduces a generalised quantum secure multiparty summation protocol. The proposed protocol utilises entanglement, a fundamental property of quantum mechanics, to facilitate safe key exchange, thereby ensuring robust security measures. We use a quantum random number generator (QRNG) to generate true random numbers, ensuring privacy preservation. The protocol achieves a per-bit entropy level of 0.708 through QRNG, which provides strong randomness. The security of the proposed protocol is validated using the CHSCH test (\\((\\mathcal{B}\\approx2.8)\\)), ensuring that any attempt to eavesdrop on the system can be detected, and the complete process will be rescheduled. Compared to some similar protocols, the proposed QSMPC-SSQKD protocol provides higher randomness and lower quantum circuit cost without using any trusted third party (TTP). This improvement enhances the protocol’s efficiency and practicality in preventing data leakage during computation.","url":"https://doi.org/10.21203/rs.3.rs-6706796/v1","authors":["Mandeep Kumar","Bhaskar Mondal"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.21203/rs.3.rs-6706796/v1","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.008","name":"Cryptographic MPC Protocols","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.008","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.008","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3233/978-1-61499-169-4-120","name":"The BGW Protocol for Perfectly-Secure Multiparty Computation","source":"crossref","abstract":"One of the most fundamental results of secure computation was presented by Ben-Or, Goldwasser and Wigderson (BGW) in 1988. They demonstrated that any n-party functionality can be computed with perfect security, in the private channels model. When the adversary is semi-honest this holds as long as t &amp;lt; n/2 parties are corrupted, and when the adversary is malicious this holds as long as t &amp;lt; n/3 parties are corrupted. In this chapter, we present a full description of the BGW protocol for the semi-honest and malicious settings, along with detailed explanations and intuition regarding the security of the protocols; full proofs can be found in [1].","url":"https://doi.org/10.3233/978-1-61499-169-4-120","authors":["Asharov Gilad","Lindell Yehuda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-169-4-120","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.54337/aau466211893","name":"Privacy in Optimization Algorithms based on Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.54337/aau466211893","authors":["Katrine Tjell"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-01T11:28:40Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.54337/aau466211893","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3724/sp.j.1087.2013.03527","name":"Secure multiparty computation solutions of collection member decision","source":"crossref","abstract":"","url":"https://doi.org/10.3724/sp.j.1087.2013.03527","authors":["Yongli DOU","Haichun WANG","Jian KANG"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-12-17T08:09:09Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3724/sp.j.1087.2013.03527","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.5220/0009819203220329","name":"Efficient Constructions of Non-interactive Secure Multiparty Computation from Pairwise Independent Hashing","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0009819203220329","authors":["Satoshi Obana","Maki Yoshida"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-07-15T15:30:18Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.5220/0009819203220329","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.32657/10356/73577","name":"Privacy preserving query processing on outsourced data via secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/73577","authors":["Hoang Giang Do"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-10-28T06:55:20Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.32657/10356/73577","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.006","name":"Information-Theoretic Robust MPC Protocols","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.006","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.006","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.009","name":"Some Techniques for Efficiency Improvements","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.009","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.009","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.004","name":"MPC Protocols with Passive Security","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.004","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.004","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/978-1-4419-5906-5_1332","name":"Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4419-5906-5_1332","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-10-27T09:52:10Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1007/978-1-4419-5906-5_1332","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.2139/ssrn.4496717","name":"Secure Multiparty Computation in Real Numbers","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4496717","authors":["Jaron  Skovsted Gundersen","Katrine Tjell","Rafael Wisniewski"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-30T17:18:07Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.2139/ssrn.4496717","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.21203/rs.3.rs-2160515/v1","name":"Secure Comparisons of Single Nucleotide\nPolymorphisms Using Secure Multiparty\nComputation","source":"europepmc","abstract":"Abstract While genomic variations can provide valuable information for healthcare and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable but not directly accessible to involved parties (e.g., data hosts and hospitals) and that the query results are learned only by the user or authorized party. In this work, we provide efficient and secure computations on panels of single nucleotide polymorphisms (SNPs) from genomic sequences as computed under the following set operations: union, intersection, set difference, and symmetric difference. Using these operations, we can compute similarity metrics, such as the Jaccard similarity, which could allow querying a DNA database to find the same person and genetic relatives securely. We analyze various security paradigms and show metrics for the protocols under several security assumptions such as semi-honest, malicious with honest majority, and malicious with a malicious majority. We show that our methods can be used practically on realistically sized data. Specifically, we can compute the Jaccard similarity of two SNP panels, each with 400k SNPs, in 2.16 seconds with the assumption of a malicious adversary in an honest majority and 0.36 seconds under a semi-honest model. Our methods may help adopt trusted environments for hosting individual genomic data with end-to-end data security. 1","url":"https://doi.org/10.21203/rs.3.rs-2160515/v1","authors":["Andrew Woods","Skyler Kramer","Dong Xu","Wei Jiang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.21203/rs.3.rs-2160515/v1","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756.007","name":"MPC from General Linear Secret-Sharing Schemes","source":"crossref","abstract":"","url":"https://doi.org/10.1017/cbo9781107337756.007","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756.007","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.5220/0014179400004932","name":"Design of a Real-Time Secure Multiparty Computation Protocol for Distributed Financial Networks","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014179400004932","authors":["Prerna Dusi","Manjulata Bhoi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-04T10:47:33Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.5220/0014179400004932","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003455448-9","name":"Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003455448-9","authors":["Sahana D. Gowda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-17T00:03:40Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1201/9781003455448-9","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.4018/978-1-4666-4209-6.ch012","name":"Secure Multiparty Computing Protocol","source":"crossref","abstract":"Secure Multiparty Computation (SMC) can be defined as n number of parties who do joint computation on their inputs (x1, x2…xn) using some function F and want output in the form of y. The increase in sensitive data on a network raises concern about the security and privacy of inputs. During joint computation, each party wants to preserve the privacy of their inputs. Therefore, there is a need to define an efficient protocol that maintains privacy, security, and correctness parameters of SMC. In this chapter, an approach towards secure computation is provided and analyzed with security graphs.","url":"https://doi.org/10.4018/978-1-4666-4209-6.ch012","authors":["Zulfa Shaikh","Poonam Garg"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-06-27T16:45:37Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.4018/978-1-4666-4209-6.ch012","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-17","name":"Privacy-Preserving Distributed Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-17","authors":["Jonathan Katz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1201/9781003185284-17","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1007/978-1-4419-5906-5_766","name":"Secure Multiparty Computation (SMC)","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4419-5906-5_766","authors":["Keith B Frikken"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-10-27T09:52:10Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1007/978-1-4419-5906-5_766","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/978-0-387-39940-9_1388","name":"Secure Multiparty Computation Methods","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-39940-9_1388","authors":["Murat Kantarcıoǧlu","Jaideep Vaidya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-09-16T08:05:42Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1007/978-0-387-39940-9_1388","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3233/978-1-61499-532-6-216","name":"Transformation-based Computation and Impossibility Results","source":"crossref","abstract":"In this chapter we study transformation-based approaches for outsourcing some particular tasks, based on publishing and solving a transformed version of the problem instance. First, we demonstrate a number of attacks against existing transformations for privacy-preserving linear programming. Our attacks show the deficiencies of existing security definitions; we propose a stronger, indistinguishability-based definition of security of problem transformations that is very similar to IND-CPA security of encryption systems. We study the realizability of this definition for linear programming and find that barring radically new ideas, there cannot be transformations that are information-theoretically or even computationally secure. Finally, we study the possibility of achieving IND-CPA security in privately outsourcing linear equation systems over real numbers, and see that it is not as easy as for linear equations over finite fields for which it is known that efficient information-theoretically secure transformations do exist.","url":"https://doi.org/10.3233/978-1-61499-532-6-216","authors":["Pankova Alisa","Laud Peeter"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-532-6-216","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/iv.2010.95","name":"Trust Enabled Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iv.2010.95","authors":["Renren Dong","Ray Kresman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2010-09-15T21:15:39Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/iv.2010.95","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1017/cbo9781107337756","name":"Secure Multiparty Computation and Secret Sharing","source":"crossref","abstract":"In a data-driven society, individuals and companies encounter numerous situations where private information is an important resource. How can parties handle confidential data if they do not trust everyone involved? This text is the first to present a comprehensive treatment of unconditionally secure techniques for multiparty computation (MPC) and secret sharing. In a secure MPC, each party possesses some private data, while secret sharing provides a way for one party to spread information on a secret such that all parties together hold full information, yet no single party has all the information. The authors present basic feasibility results from the last 30 years, generalizations to arbitrary access structures using linear secret sharing, some recent techniques for efficiency improvements, and a general treatment of the theory of secret sharing, focusing on asymptotic results with interesting applications related to MPC.","url":"https://doi.org/10.1017/cbo9781107337756","authors":["Ronald Cramer","Ivan Bjerre Damgård","Jesper Buus Nielsen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-05T05:01:30Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1017/cbo9781107337756","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.2139/ssrn.4480803","name":"Residential Flexibility Characterization and Trading Using Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4480803","authors":["Fairouz Zobiri","Mariana Gama","Svetla Nikova","Geert Deconinck"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-16T06:18:11Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.2139/ssrn.4480803","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-19","name":"Overview of Secure Multi-Party Computation Applications in Health Research and Social Sciences","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-19","authors":["Liina Kamm","Dan Bogdanov"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1201/9781003185284-19","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1002/9781394188369.ch4","name":"Secure Multiparty Computation","source":"crossref","abstract":"MPC has moved from theoretical study to real-world usage. How is it doing?","url":"https://doi.org/10.1002/9781394188369.ch4","authors":["Yehuda LINDELL"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-11-30T12:07:35Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1002/9781394188369.ch4","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3233/978-1-61499-532-6-150","name":"Mechanism Design and Strong Truthfulness","source":"crossref","abstract":"In this chapter we give a very brief overview of some fundamentals from mechanism design, the branch of game theory dealing with designing protocols to cope with agents' private incentives and selfish behavior. We also present recent results involving a new, extended utilities model that can incorporate externalities, such as malicious and spiteful behavior of the participating players. A new notion of strong truthfulness is proposed and analyzed. It is based on the principle of punishing players that lie. Due to this, strongly truthful mechanisms can serve as subcomponents in bigger mechanism protocols in order to boost truthfulness. The related solution concept equilibria are discussed and the power of the decomposability scheme is demonstrated by an application in the case of the well-known mechanism design problem of scheduling tasks to machines for minimizing the makespan.","url":"https://doi.org/10.3233/978-1-61499-532-6-150","authors":["Giannakopoulos Yiannis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/978-1-61499-532-6-150","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.26421/qic23.3-4-4","name":"Secure multiparty quantum aggregating protocol","source":"crossref","abstract":"Secure multiparty quantum computation is an important and essential paradigm of quantum computing. All the existing aggregating protocols are $(n, n)$ threshold approaches, where $n$ represents the total number of players. If one player is dishonest, the aggregation protocols cannot aggregate efficiently. In this paper, we propose a $(t, n)$ threshold-based aggregating protocol, where $t$ represents the threshold number of players. This protocol uses Shamir's secret sharing, quantum state, SUM gate, quantum Fourier transform, blind matrix, and Pauli operator. This protocol can perform the aggregation securely and efficiently. In this protocol, we simulate this aggregating protocol using the IBM quantum processor to verify the correctness and feasibility.","url":"https://doi.org/10.26421/qic23.3-4-4","authors":["Kartick Sutradhar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-29T20:26:03Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.26421/qic23.3-4-4","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.4018/978-1-59140-557-3.ch189","name":"Secure Multiparty Computation for Privacy Preserving Data Mining","source":"crossref","abstract":"The increasing use of data-mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy-preserving data-mining solutions achieve the somewhat paradoxical property of enabling a data-mining algorithm to use data without ever actually seeing it. Thus, the benefits of data mining can be enjoyed without compromising the privacy of concerned individuals.","url":"https://doi.org/10.4018/978-1-59140-557-3.ch189","authors":["Yehida Lindell"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-05-24T12:13:07Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.4018/978-1-59140-557-3.ch189","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/ams.2011.42","name":"Structural Framing of Protocol for Secure Multiparty Cloud Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ams.2011.42","authors":["Nikhar Maheshwari","Krati Kiyawat"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-08-03T21:31:16Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/ams.2011.42","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.5220/0002986902700277","name":"REALIZING SECURE MULTIPARTY COMPUTATION ON INCOMPLETE NETWORKS","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0002986902700277","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-02-22T02:48:24Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.5220/0002986902700277","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/978-1-84628-984-2_8","name":"Unconditionally Secure Multiparty Computation from Noisy Resources","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-84628-984-2_8","authors":["Stefan Wolf","Jürg Wullschleger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-10-24T01:06:58Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1007/978-1-84628-984-2_8","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-13","name":"Synthetic Data1","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-13","authors":["Trivellore Raghunathan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1201/9781003185284-13","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1038/s41598-021-81799-z","name":"An efficient simulation for quantum secure multiparty computation.","source":"europepmc","abstract":"Abstract The quantum secure multiparty computation is one of the important properties of secure quantum communication. In this paper, we propose a quantum secure multiparty summation (QSMS) protocol based on ( t , n ) threshold approach, which can be used in many complex quantum operations. To make this protocol secure and realistic, we combine both the classical and quantum phenomena. The existing protocols have some security and efficiency issues because they use ( n , n ) threshold approach, where all the honest players need to perform the quantum multiparty summation protocol. We however use a ( t , n ) threshold approach, where only t honest players need to compute the quantum summation protocol. Compared to other protocols our proposed protocol is more cost-effective, realistic, and secure. We also simulate it using the IBM corporation’s online quantum computer, or quantum experience.","url":"https://doi.org/10.1038/s41598-021-81799-z","authors":["Kartick Sutradhar","Hari Om"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-021-81799-z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.29012/jpc.v1i1.566","name":"Secure Multiparty Computation for Privacy-Preserving Data Mining","source":"crossref","abstract":"In this paper, we survey the basic paradigms and notions of secure multiparty computation and discuss their relevance to the field of privacy-preserving data mining. In addition to reviewing definitions and constructions for secure multiparty computation, we discuss the issue of efficiency and demonstrate the difficulties involved in constructing highly efficient protocols. We also present common errors that are prevalent in the literature when secure multiparty computation techniques are applied to privacy-preserving data mining. Finally, we discuss the relationship between secure multiparty computation and privacy-preserving data mining, and show which problems it solves and which problems it does not.","url":"https://doi.org/10.29012/jpc.v1i1.566","authors":["Yehuda Lindell","Benny Pinkas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-02-27T14:41:23Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.29012/jpc.v1i1.566","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/scopes.2016.7955564","name":"Secure multiparty computation using secret sharing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/scopes.2016.7955564","authors":["Kinjal Patel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-07-10T21:24:34Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/scopes.2016.7955564","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-18","name":"Differential Privacy and Cryptography","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-18","authors":["Xi He"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1201/9781003185284-18","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/isit57864.2024.10619511","name":"Towards Optimal Non-interactive Secure Multiparty Computation for Abelian Programs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isit57864.2024.10619511","authors":["Maki Yoshida"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-19T13:25:01Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/isit57864.2024.10619511","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/ncis.2011.177","name":"Public Watermark Detection Using Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ncis.2011.177","authors":["Hong Wang","Shimin Wei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-07-13T15:57:57Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/ncis.2011.177","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3390/electronics13050991","name":"Secure Multiparty Computation Using Secure Virtual Machines","source":"crossref","abstract":"The development of new processor capabilities which enable hardware-based memory encryption, capable of isolating and encrypting application code and data in memory, have led to the rise of confidential computing techniques that protect data when processed on untrusted computing resources (e.g., cloud). Before confidential computing technologies, applications that needed data-in-use protection, like outsourced or secure multiparty computation, used purely cryptographic techniques, which had a large negative impact on the processing performance. Processing data in trusted enclaves protected by confidential computing technologies promises to protect data-in-use while possessing a negligible performance penalty. In this paper, we have analyzed the state-of-the-art in the field of confidential computing and present a Confidential Computing System for Artificial Intelligence (CoCoS.ai), a system for secure multiparty computation, which uses virtual machine-based trusted execution environments (in this case, AMD Secure Encrypted Virtualization (SEV)). The security of the proposed solution, as well as its performance, have been formally analyzed and measured. The paper reveals many gaps not reported previously that still exist in the current confidential computing solutions for the secure multiparty computation use case, especially in the processes of creating new secure virtual machines and their attestation, which are tailored for single-user use cases.","url":"https://doi.org/10.3390/electronics13050991","authors":["Danko Miladinović","Adrian Milaković","Maja Vukasović","Žarko Stanisavljević","Pavle Vuletić"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-05T08:35:54Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3390/electronics13050991","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284","name":"Handbook of Sharing Confidential Data","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284","authors":["Jörg Drechsler","Daniel Kifer","Jerome Reiter","Aleksandra Slavković"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1201/9781003185284","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.2139/ssrn.5524438","name":"Secure Multiparty&amp;nbsp;Computation and the GDPR: Processing&amp;nbsp;of Personal Data and Joint Controllership","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5524438","authors":["Piotr Rataj","Nils Wiedemann"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-06T19:16:05Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.2139/ssrn.5524438","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/naps.2012.6336415","name":"Secure multiparty computation based privacy preserving smart metering system","source":"crossref","abstract":"","url":"https://doi.org/10.1109/naps.2012.6336415","authors":["Cory Thoma","Tao Cui","Franz Franchetti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-10-24T16:33:19Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/naps.2012.6336415","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/978-3-540-74143-5_32","name":"Scalable and Unconditionally Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-540-74143-5_32","authors":["Ivan Damgård","Jesper Buus Nielsen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-08-09T09:51:33Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1007/978-3-540-74143-5_32","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/synasc54541.2021.00054","name":"Secure Multiparty Computation in arbitrary rings","source":"crossref","abstract":"","url":"https://doi.org/10.1109/synasc54541.2021.00054","authors":["Mihai Prunescu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-10T20:28:53Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/synasc54541.2021.00054","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-8","name":"Query Answering for Tabular Data","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-8","authors":["Ryan McKenna"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1201/9781003185284-8","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/icirca48905.2020.9183133","name":"Privacy Preserving Deep Learning using Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icirca48905.2020.9183133","authors":["Suhel Sayyad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-09-01T17:03:37Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/icirca48905.2020.9183133","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/iadcc.2009.4809103","name":"Analysis of Data in Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iadcc.2009.4809103","authors":["Zulfa Shaikh","D.M. Puntambekar","Pushpa Pathak","Dinesh Bhati"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-03-31T14:37:00Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/iadcc.2009.4809103","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/csp58884.2023.00023","name":"Secure Multiparty Computation with Identifiable Abort and Fairness","source":"crossref","abstract":"","url":"https://doi.org/10.1109/csp58884.2023.00023","authors":["Long Nie","ShaoWen Yao","Jing Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-05T17:38:18Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/csp58884.2023.00023","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.46243/jst.2022.v7.i010.pp163-174","name":"PMDP: A Secure Multiparty Computation Framework for Maintaining Multiparty Data Privacy in Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.46243/jst.2022.v7.i010.pp163-174","authors":["Venkata Surya Bhavana Harish Gollavilli Venkata Surya Bhavana Harish Gollavilli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-03T06:55:41Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.46243/jst.2022.v7.i010.pp163-174","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1038/s41598-020-64538-8","name":"Secure multiparty quantum computation based on Lagrange unitary operator","source":"crossref","abstract":"Abstract As an important subtopic of classical cryptography, secure multiparty quantum computation allows multiple parties to jointly compute their private inputs without revealing them. Most existing secure multiparty computation protocols have the shortcomings of low computational efficiency and high resource consumption. To remedy these shortcomings, we propose a secure multiparty quantum computation protocol by using the Lagrange unitary operator and the Shamir ( t , n ) threshold secret sharing, in which the server generates all secret shares and distributes each secret share to the corresponding participant, in addition, he prepares a particle and sends it to the first participant. The first participant performs the Lagrange unitary operation on the received particle, and then sends the transformed particle to the next participant. Until the last participant’s computation task is completed, the transformed particle is sent back to the server. The server performs Lagrange unitary operation on the received particle by using a secret message, and then measures the transformed particle to obtain the sum of the calculations of multiple participants. Security analysis shows that the proposed protocol can resist intercept-measurement attack, intercept-resend attack, entanglement-swapping attack, entanglement-measurement attack and collusion attack. Performance comparison shows that it has higher computation efficiency and lower resource consumption than other similar protocols.","url":"https://doi.org/10.1038/s41598-020-64538-8","authors":["Xiuli Song","Rui Gou","Aijun Wen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-05-13T10:02:50Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-020-64538-8","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.62056/ana6n59p1","name":"Lower Bounds on the Bottleneck Complexity of Secure Multiparty Computation","source":"crossref","abstract":"Secure multiparty computation (MPC) is a cryptographic primitive which enables multiple parties to jointly compute a function without revealing any extra information on their private inputs. Bottleneck complexity is an efficiency measure that captures the load-balancing aspect of MPC protocols, defined as the maximum amount of communication required by any party. In this work, we study the problem of establishing lower bounds on the bottleneck complexity of MPC protocols. While the previously known techniques for lower bounding total communication complexity can also be applied to bottleneck complexity, they do not provide nontrivial bounds in the correlated randomness model, which is commonly assumed by existing protocols achieving low bottleneck complexity, or they are applied only to functions of limited practical interest. We propose several novel techniques for lower bounding the bottleneck complexity of MPC protocols. Our methods derive nontrivial lower bounds even in the correlated randomness model and apply to practically relevant functions including the sum function and threshold functions. Furthermore, our lower bounds demonstrate the optimality of some existing MPC protocols in terms of bottleneck complexity or the amount of correlated randomness.","url":"https://doi.org/10.62056/ana6n59p1","authors":["Reo Eriguchi","Keitaro Hiwatashi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-08T23:39:47Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.62056/ana6n59p1","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/ccwc.2018.8301702","name":"Secure error correction using multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccwc.2018.8301702","authors":["Mohammad G. Raeini","Mehrdad Nojoumian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-03-05T22:14:26Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/ccwc.2018.8301702","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/comsnets.2016.7439973","name":"Secure multiparty graph computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/comsnets.2016.7439973","authors":["Varsha Bhat Kukkala","S.R.S Iyengar","Jaspal Singh Saini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-03-30T00:54:20Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/comsnets.2016.7439973","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.5220/0014201500004932","name":"Privacy Centric Engagement Models Harnessing Federated Learning and Secure Multiparty Computation for Safe Inclusive Digital Ecosystem Interactions","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014201500004932","authors":["Chaitran Chakilam","Narayanasamy S","M. Jamuna Rani","Satvik Vats","Raja J","Santhi G B"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-05T20:02:37Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.5220/0014201500004932","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-9","name":"Machine Learning with Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-9","authors":["Anand D. Sarwate"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1201/9781003185284-9","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/cimsim.2010.110","name":"Tutorial: Secure Multiparty Computation for Cloud Computing Paradigm by Durgesh Kumar Mishra","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cimsim.2010.110","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-01-28T20:12:36Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/cimsim.2010.110","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/ares.2008.49","name":"Fostering the Uptake of Secure Multiparty Computation in E-Commerce","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ares.2008.49","authors":["Octavian Catrina","Florian Kerschbaum"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2008-05-29T17:54:16Z","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/ares.2008.49","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1136/bmjopen-2025-110916","name":"Introduction to secret sharing based secure multiparty computation in health data.","source":"europepmc","abstract":"Background Joint analyses across multiple health datasets can increase statistical power and improve the generalisability of research findings. However, limitations on data sharing often prevent researchers from fully realising these benefits. Existing approaches such as federated analytics involve sharing information, which poses challenges due to data governance and security restrictions. Secure multiparty computation (SMPC) is a set of cryptographic techniques that allows joint analyses across multiple private datasets with zero information sharing except for the agreed outputs. Despite its transformative potential in health research, SMPC has received relatively little attention within the health data landscape. Objectives This article gives an introduction to secret sharing based SMPC that is accessible with no prior knowledge assumed. We explain how secret sharing techniques work, and the security guarantees they offer. We also discuss SMPC software, and offer our view on the most promising approaches to implementation. Conclusion SMPC has significant potential for enabling privacy-preserving analyses, and could become a standard tool in the future for collaborative health data research. As efforts to improve data access and integration continue, it will be increasingly important for health data researchers to have an understanding of SMPC so they can use it effectively.","url":"https://doi.org/10.1136/bmjopen-2025-110916","authors":["Steven Kerr","Daniel Escudero"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1136/bmjopen-2025-110916","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-27951-5","name":"Secure federated transfer learning with enhanced secure multiparty computation for privacy preserving smart EHR systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-27951-5","authors":["Tae Hoon Kim","C. Rohith Bhat","Temesgen Engida Yimer"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-27951-5","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1136/bmjhci-2024-101384","name":"Enabling health data analyses across multiple private datasets with no information sharing using secure multiparty computation.","source":"europepmc","abstract":"The UK’s health datasets are among the most comprehensive and inclusive globally, enabling groundbreaking research during the COVID-19 pandemic. However, restrictions on data sharing between secure data environments (SDEs) imposed limitations on the ability to carry out joint analyses across multiple separate datasets. There are currently significant efforts underway to enable such analyses using methods such as federated analytics (FA) and virtual SDEs. FA involves distributed data analysis without sharing raw data but does require sharing summary statistics. Virtual SDEs in principle allow researchers to access data across multiple SDEs, but in practice, data transfers may be restricted by information governance concerns. Secure multiparty computation (SMPC) is a cryptographic approach that allows multiple parties to perform joint analyses over private datasets with zero information sharing. SMPC may eliminate the need for data-sharing agreements and statistical disclosure control, offering a compelling alternative to FA and virtual SDEs. SMPC comes with a higher computational burden than traditional pooled analysis. However, efficient implementations of SMPC can enable a wide range of practical, secure analyses to be carried out. This perspective reviews the strengths and limitations of FA, virtual SDEs and SMPC as approaches to joint analyses across SDEs. We argue that while efforts to implement FA and virtual SDEs are ongoing in the UK, SMPC remains underexplored. Given its unique advantages, we propose that SMPC deserves greater attention as a transformative solution for enabling secure, cross-SDE analyses of private health data.","url":"https://doi.org/10.1136/bmjhci-2024-101384","authors":["Steven Kerr","Chris Robertson","Cathie Sudlow","Aziz Sheikh"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1136/bmjhci-2024-101384","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41746-024-01293-4","name":"Privacy-friendly evaluation of patient data with secure multiparty computation in a European pilot study.","source":"europepmc","abstract":"Abstract In multicentric studies, data sharing between institutions might negatively impact patient privacy or data security. An alternative is federated analysis by secure multiparty computation. This pilot study demonstrates an architecture and implementation addressing both technical challenges and legal difficulties in the particularly demanding setting of clinical research on cancer patients within the strict European regulation on patient privacy and data protection: 24 patients from LMU University Hospital in Munich, Germany, and 24 patients from Policlinico Universitario Fondazione Agostino Gemelli, Rome, Italy, were treated for adrenal gland metastasis with typically 40 Gy in 3 or 5 fractions of online-adaptive radiotherapy guided by real-time MR. High local control (21% complete remission, 27% partial remission, 40% stable disease) and low toxicity (73% reporting no toxicity) were observed. Median overall survival was 19 months. Federated analysis was found to improve clinical science through privacy-friendly evaluation of patient data in the European health data space.","url":"https://doi.org/10.1038/s41746-024-01293-4","authors":["Hendrik Ballhausen","Stefanie Corradini","Claus Belka","Dan Bogdanov","Luca Boldrini","Francesco Bono","Christian Goelz","Guillaume Landry","Giulia Panza","Katia Parodi","Riivo Talviste","Huong Elena Tran"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41746-024-01293-4","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.2196/44700","name":"Secure Comparisons of Single Nucleotide Polymorphisms Using Secure Multiparty Computation: Method Development.","source":"europepmc","abstract":"Background While genomic variations can provide valuable information for health care and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable but not directly accessible to involved parties (eg, data hosts and hospitals) and that the query results are learned only by the user or authorized party. Objective In this study, we provide efficient and secure computations on panels of single nucleotide polymorphisms (SNPs) from genomic sequences as computed under the following set operations: union, intersection, set difference, and symmetric difference. Methods Using these operations, we can compute similarity metrics, such as the Jaccard similarity, which could allow querying a DNA database to find the same person and genetic relatives securely. We analyzed various security paradigms and show metrics for the protocols under several security assumptions, such as semihonest, malicious with honest majority, and malicious with a malicious majority. Results We show that our methods can be used practically on realistically sized data. Specifically, we can compute the Jaccard similarity of two genomes when considering sets of SNPs, each with 400,000 SNPs, in 2.16 seconds with the assumption of a malicious adversary in an honest majority and 0.36 seconds under a semihonest model. Conclusions Our methods may help adopt trusted environments for hosting individual genomic data with end-to-end data security.","url":"https://doi.org/10.2196/44700","authors":["Andrew Woods","Skyler T Kramer","Dong Xu","Wei Jiang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.2196/44700","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1089/cmb.2023.0076","name":"An Integration Framework of Secure Multiparty Computation and Deep Neural Network for Improving Drug-Drug Interaction Predictions.","source":"europepmc","abstract":"","url":"https://doi.org/10.1089/cmb.2023.0076","authors":["Liang Pan","Xia Xiao","Shengyun Liu","Shaoliang Peng"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1089/cmb.2023.0076","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1186/s13059-022-02841-5","name":"Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing.","source":"europepmc","abstract":"Abstract Secure multiparty computation (MPC) is a cryptographic tool that allows computation on top of sensitive biomedical data without revealing private information to the involved entities. Here, we introduce Sequre, an easy-to-use, high-performance framework for developing performant MPC applications. Sequre offers a set of automatic compile-time optimizations that significantly improve the performance of MPC applications and incorporates the syntax of Python programming language to facilitate rapid application development. We demonstrate its usability and performance on various bioinformatics tasks showing up to 3–4 times increased speed over the existing pipelines with 7-fold reductions in codebase sizes.","url":"https://doi.org/10.1186/s13059-022-02841-5","authors":["Haris Smajlović","Ariya Shajii","Bonnie Berger","Hyunghoon Cho","Ibrahim Numanagić"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1186/s13059-022-02841-5","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1186/s12859-022-05044-8","name":"EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation.","source":"europepmc","abstract":"Abstract Background Modern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtaining informed consent is often infeasible. Other methods, such as anonymization or federation, in turn have their own limitations. Secure multi-party computation (SMPC) is a cryptographic technology for distributed calculations, which brings formally provable security and privacy guarantees and can be used to implement a wide-range of analytical approaches. As a relatively new technology, SMPC is still rarely used in real-world biomedical data sharing activities due to several barriers, including its technical complexity and lack of usability. Results To overcome these barriers, we have developed the tool EasySMPC, which is implemented in Java as a cross-platform, stand-alone desktop application provided as open-source software . The tool makes use of the SMPC method Arithmetic Secret Sharing, which allows to securely sum up pre-defined sets of variables among different parties in two rounds of communication (input sharing and output reconstruction) and integrates this method into a graphical user interface. No additional software services need to be set up or configured, as EasySMPC uses the most widespread digital communication channel available: e-mails. No cryptographic keys need to be exchanged between the parties and e-mails are exchanged automatically by the software. To demonstrate the practicability of our solution, we evaluated its performance in a wide range of data sharing scenarios. The results of our evaluation show that our approach is scalable (summing up 10,000 variables between 20 parties takes less than 300 s) and that the number of participants is the essential factor. Conclusions We have developed an easy-to-use “no-code solution” for performing secure joint calculations on biomedical data using SMPC protocols, which is suitable for use by scientists without IT expertise and which has no special infrastructure requirements. We believe that innovative approaches to data sharing with SMPC are needed to foster the translation of complex protocols into practice.","url":"https://doi.org/10.1186/s12859-022-05044-8","authors":["Felix Nikolaus Wirth","Tobias Kussel","Armin Müller","Kay Hamacher","Fabian Prasser"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1186/s12859-022-05044-8","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/e23081001","name":"Randomized Oblivious Transfer for Secure Multiparty Computation in the Quantum Setting.","source":"europepmc","abstract":"Secure computation is a powerful cryptographic tool that encompasses the evaluation of any multivariate function with arbitrary inputs from mutually distrusting parties. The oblivious transfer primitive serves is a basic building block for the general task of secure multi-party computation. Therefore, analyzing the security in the universal composability framework becomes mandatory when dealing with multi-party computation protocols composed of oblivious transfer subroutines. Furthermore, since the required number of oblivious transfer instances scales with the size of the circuits, oblivious transfer remains as a bottleneck for large-scale multi-party computation implementations. Techniques that allow one to extend a small number of oblivious transfers into a larger one in an efficient way make use of the oblivious transfer variant called randomized oblivious transfer. In this work, we present randomized versions of two known oblivious transfer protocols, one quantum and another post-quantum with ring learning with an error assumption. We then prove their security in the quantum universal composability framework, in a common reference string model.","url":"https://doi.org/10.3390/e23081001","authors":["Bruno Costa","Pedro Branco","Manuel Goulão","Mariano Lemus","Paulo Mateus"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3390/e23081001","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1093/bioinformatics/btaa038","name":"Secure multiparty computation for privacy-preserving drug discovery.","source":"europepmc","abstract":"Abstract Motivation Quantitative structure–activity relationship (QSAR) and drug–target interaction (DTI) prediction are both commonly used in drug discovery. Collaboration among pharmaceutical institutions can lead to better performance in both QSAR and DTI prediction. However, the drug-related data privacy and intellectual property issues have become a noticeable hindrance for inter-institutional collaboration in drug discovery. Results We have developed two novel algorithms under secure multiparty computation (MPC), including QSARMPC and DTIMPC, which enable pharmaceutical institutions to achieve high-quality collaboration to advance drug discovery without divulging private drug-related information. QSARMPC, a neural network model under MPC, displays good scalability and performance and is feasible for privacy-preserving collaboration on large-scale QSAR prediction. DTIMPC integrates drug-related heterogeneous network data and accurately predicts novel DTIs, while keeping the drug information confidential. Under several experimental settings that reflect the situations in real drug discovery scenarios, we have demonstrated that DTIMPC possesses significant performance improvement over the baseline methods, generates novel DTI predictions with supporting evidence from the literature and shows the feasible scalability to handle growing DTI data. All these results indicate that QSARMPC and DTIMPC can provide practically useful tools for advancing privacy-preserving drug discovery. Availability and implementation The source codes of QSARMPC and DTIMPC are available on the GitHub: https://github.com/rongma6/QSARMPC_DTIMPC.git. Supplementary information Supplementary data are available at Bioinformatics online.","url":"https://doi.org/10.1093/bioinformatics/btaa038","authors":["Rong Ma","Yi Li","Chenxing Li","Fangping Wan","Hailin Hu","Wei Xu","Jianyang Zeng"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1093/bioinformatics/btaa038","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.2196/22158","name":"A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation.","source":"europepmc","abstract":"Background Patient data is considered particularly sensitive personal data. Privacy regulations strictly govern the use of patient data and restrict their exchange. However, medical research can benefit from multicentric studies in which patient data from different institutions are pooled and evaluated together. Thus, the goals of data utilization and data protection are in conflict. Secure multiparty computation (SMPC) solves this conflict because it allows direct computation on distributed proprietary data—held by different data owners—in a secure way without exchanging private data. Objective The objective of this work was to provide a proof-of-principle of secure and privacy-preserving multicentric computation by SMPC with real-patient data over the free internet. A privacy-preserving log-rank test for the Kaplan-Meier estimator was implemented and tested in both an experimental setting and a real-world setting between two university hospitals. Methods The domain of survival analysis is particularly relevant in clinical research. For the Kaplan-Meier estimator, we provided a secure version of the log-rank test. It was based on the SMPC realization SPDZ and implemented via the FRESCO framework in Java. The complexity of the algorithm was explored both for synthetic data and for real-patient data in a proof-of-principle over the internet between two clinical institutions located in Munich and Berlin, Germany. Results We obtained a functional realization of an SMPC-based log-rank evaluation. This implementation was assessed with respect to performance and scaling behavior. We showed that network latency strongly influences execution time of our solution. Furthermore, we identified a lower bound of 2 Mbit/s for the transmission rate that has to be fulfilled for unimpeded communication. In contrast, performance of the participating parties have comparatively low influence on execution speed, since the peer-side processing is parallelized and the computational time only constitutes 30% to 50% even with optimal network settings. In the real-world setting, our computation between three parties over the internet, processing 100 items each, took approximately 20 minutes. Conclusions We showed that SMPC is applicable in the medical domain. A secure version of commonly used evaluation methods for clinical studies is possible with current implementations of SMPC. Furthermore, we infer that its application is practically feasible in terms of execution time.","url":"https://doi.org/10.2196/22158","authors":["Marcel von Maltitz","Hendrik Ballhausen","David Kaul","Daniel F Fleischmann","Maximilian Niyazi","Claus Belka","Georg Carle"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.2196/22158","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1186/s12920-018-0400-8","name":"Privacy-preserving record linkage in large databases using secure multiparty computation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12920-018-0400-8","authors":["Peeter Laud","Alisa Pankova"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2018","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1186/s12920-018-0400-8","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1186/s40064-016-3061-0","name":"Secure multiparty computation of a comparison problem.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s40064-016-3061-0","authors":["Xin Liu","Shundong Li","Jian Liu","Xiubo Chen","Gang Xu"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2016","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1186/s40064-016-3061-0","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1038/s41598-026-58005-z","name":"Secure multi-party biometric verification using QKD assisted quantum oblivious transfer.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-58005-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-026-58005-z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1038/s41598-026-49371-9","name":"A blockchain-enabled framework for secure and efficient data transmission in underwater sensor networks using advanced cryptographic techniques.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-49371-9","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-026-49371-9","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1177/00368504261445860","name":"MHBDS: Healthcare blockchain data sharing scheme based on SMPC and TRP-PBFT.","source":"europepmc","abstract":"","url":"https://doi.org/10.1177/00368504261445860","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1177/00368504261445860","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.21203/rs.3.rs-8351477/v1","name":"A Blockchain-Based Federated Learning Approach with Secure Third-Party Computation System for Securing Electronic Health Records","source":"europepmc","abstract":"Abstract The secure, efficient, and privacy-preserving management of Electronic Health Records (EHRs) remains a critical challenge as healthcare systems increasingly depend on digital infrastructures. There is a centralized EHR storage model, which is susceptible to data breaches, unauthorized access, and single points of failure. In order to overcome these shortcomings, this research paper presents a blockchain-based federated learning architecture with a secure third-party computation (BFL-STPC) system to support decentralized, tamper-proof, and privacy-enhancing management of EHRs. The model uses Federated Learning (FL) so that patient information is held at separate healthcare facilities, but encrypted updates to models are jointly utilized to train a universal model. Blockchain technology offers a logical audit trail, open access control, and authentication based on smart contracts without assisting central authorities. The STPC module also has a stronger level of security, as it allows aggregation encrypted by homomorphic encryption (HE), secure multiparty computation (SMPC), and differential privacy (DP). In order to assess real-world deployability, the system was tested with simulated multi-hospital conditions with distributed AWS EC2 nodes, which allows assessment of generalization of the system with a variety of cloud-based institutional settings. The experimental analysis based on a synthetic healthcare dataset proves the effectiveness of the proposed system in comparison with the benchmark strategies, including PPFLB, FEACS, FLBM-IoT, and EJSS. The highest accuracy of 97.38 of the model outweighs that of FEACS (93.18) and PPFLB (91.14). It has the best throughput (1178.32 kbps), minimum authentication (72.59 ms), minimum execution (36.78 ms) and near perfect interruption detection (96.79) that points to remarkable improvements in the system responsiveness and computer efficiency. Moreover, the AUC of the model is 1.00, that is why it displays a good classifying ability and safe decision reliability. The ablation experiments support the fact that each of the elements federated learning, blockchain, and STPC is significant to the performance and security of the whole system. In conclusion, the proposed BFL-STPC model is a regulation-compliant, scalable, and enhanced security model of the existing EHR management. It gives a good ground to the future of the healthcare systems, which contains credible data dissemination, data confidentiality, and credible teamwork intelligence within the various clinical environment.","url":"https://doi.org/10.21203/rs.3.rs-8351477/v1","authors":["Munusamy S","Jothi K R"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.21203/rs.3.rs-8351477/v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.14293/pr2199.002488.v1","name":"Evaluating Privacy-Preserving Data Analysis Techniques for Encrypted Health Records","source":"europepmc","abstract":"","url":"https://doi.org/10.14293/pr2199.002488.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.14293/pr2199.002488.v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1038/s41746-025-02271-0","name":"Secure distributed multiple imputation enables missing data inference for private data proprietors.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41746-025-02271-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41746-025-02271-0","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.64898/2026.02.12.705603","name":"BioVault: A privacy-first data visitation platform for equitable global collaboration in biomedicine","source":"europepmc","abstract":"","url":"https://doi.org/10.64898/2026.02.12.705603","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.64898/2026.02.12.705603","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.2196/58954","name":"Assessing the Evolution and Influence of Medical Open Databases on Biomedical Research and Health Care Innovation: A 25-Year Perspective With a Focus on Privacy and Privacy-Enhancing Technologies.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/58954","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.2196/58954","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1146/annurev-biodatasci-092724-031932","name":"Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.","source":"pubmed","abstract":"","url":"https://doi.org/10.1146/annurev-biodatasci-092724-031932","authors":["Malin BA","Yan C","Bonomi L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1146/annurev-biodatasci-092724-031932","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.30953/bhty.v8.453","name":"Trust By Design: Enabling Responsible Precision Health Through Blockchain-Powered Digital Twins And Trusted AI.","source":"europepmc","abstract":"","url":"https://doi.org/10.30953/bhty.v8.453","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.30953/bhty.v8.453","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/s42979-025-04503-2","name":"Fast and Secure Multiparty Querying over Federated Graph Databases.","source":"europepmc","abstract":"Abstract We have developed a framework for efficient privacy preserving multi-party querying (PPMQ) over federated graph databases, leveraging Secure Multi-Party Computation (SMPC) protocols to enhance data security. The system offers two distinct security protocols: a client-based protocol and a server-based protocol. In the client-based protocol, standard SMPC techniques are employed, allowing computations to be performed on data without exposing the data itself. The server-based protocol employs SMPC to facilitate secure data processing and is further enhanced by encrypted hashing, which adds an additional layer of security to prevent data exposure. We conducted experiments comparing PPMQ with Neo4j Fabric and two previous systems, SMPQ and Conclave. The results indicate that PPMQ’s execution times and overheads are comparable to those of Neo4j Fabric, while outperforming both SMPQ and Conclave, demonstrating its superior efficiency. Additionally, PPMQ, like SMPQ and Conclave, utilises an honest but curious security model. However, it enhances the security of the server protocol, making it more robust against brute force attacks and providing stronger privacy guarantees than previous solutions.","url":"https://doi.org/10.1007/s42979-025-04503-2","authors":["Nouf Aljuaid","Alexei Lisitsa","Sven Schewe"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1007/s42979-025-04503-2","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3389/fdgth.2026.1751234","name":"EUPID-configurable privacy-preserving record linkage in federated health data spaces.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdgth.2026.1751234","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3389/fdgth.2026.1751234","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.22541/au.175803377.75193786/v1","name":"Privacy-Aware Optimization Algorithms for Distributed AI","source":"europepmc","abstract":"","url":"https://doi.org/10.22541/au.175803377.75193786/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.22541/au.175803377.75193786/v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1038/s41598-026-41069-2","name":"Efficient multi-party private set union resistant to maximum collusion attacks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-41069-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-026-41069-2","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1364/oe.558206","name":"Quantum anonymous selection and its applications.","source":"europepmc","abstract":"","url":"https://doi.org/10.1364/oe.558206","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1364/oe.558206","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1016/j.mex.2025.103762","name":"Efficient data replication in distributed clouds via quantum entanglement algorithms.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.mex.2025.103762","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1016/j.mex.2025.103762","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.21203/rs.3.rs-7917089/v1","name":"Clifti-GPT: Privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7917089/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.21203/rs.3.rs-7917089/v1","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.1038/s41598-025-98734-1","name":"Cryptanalysis of efficient controlled semi-quantum secret sharing protocol with entangled state.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-98734-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-98734-1","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1016/j.xgen.2025.100773","name":"Overcoming collaboration barriers in quantitative trait loci analysis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.xgen.2025.100773","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1016/j.xgen.2025.100773","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1038/s41598-025-91161-2","name":"A novel quantum private query protocol and its application in private set intersection.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-91161-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-91161-2","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.20944/preprints202506.1115.v1","name":"Secure Aggregation Protocols in Federated AI for Anonymized Health Data","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202506.1115.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.20944/preprints202506.1115.v1","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.3389/fdgth.2025.1644291","name":"Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities.","source":"pubmed","abstract":"","url":"https://doi.org/10.3389/fdgth.2025.1644291","authors":["Malpetti D","Scutari M","Gualdi F","van Setten J","van der Laan S","Haitjema S","Lee AM","Hering I","Mangili F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3389/fdgth.2025.1644291","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-026-40531-5","name":"Towards an improved efficient leakage-resilient enhanced private set union.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-40531-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-026-40531-5","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.20944/preprints202506.0088.v1","name":"Federated Learning for Privacy-Preserving Defense in Power Cyber-Physical Systems: Frameworks, Techniques, and Challenges","source":"europepmc","abstract":"The increasing interconnection and digitalization of modern energy systems have intensified cybersecurity vulnerabilities in Power Cyber-Physical Systems (Power CPS). Traditional centralized defense approaches struggle to balance privacy preservation, scalability, and collaborative responsiveness across distributed infrastructures. Federated Learning (FL) emerges as a promising paradigm that enables distributed, privacy-preserving model training without sharing raw data. This review presents a comprehensive analysis of FL-based collaborative defense for Power CPS, spanning threat modeling, architectural taxonomies, privacy-preserving mechanisms, and real-world applications. We categorize FL techniques by learning structure, synchronization, and personalization, and examine privacy-enhancing technologies such as differential privacy, secure multiparty computation, homomorphic encryption, and trusted execution environments. Practical applications across substations, SCADA systems, WAMS, and EV infrastructures are reviewed alongside deployment challenges such as communication overhead, adversarial threats, and operational constraints. A roadmap is proposed for future research in cross-layer FL architectures, federated reinforcement learning, and regulatory standardization. The review concludes by advocating for cross-sector collaboration to operationalize federated defense as a cornerstone of resilient, secure, and privacy-compliant smart grids.","url":"https://doi.org/10.20944/preprints202506.0088.v1","authors":["Xiaokang Wang"],"tags":["Cyber-physical system","Computer security","Computer science","Power (physics)","Internet privacy"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.20944/preprints202506.0088.v1","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.1371/journal.pcbi.1013624","name":"Nine quick tips for trustworthy machine learning in the biomedical sciences.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pcbi.1013624","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1371/journal.pcbi.1013624","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.5213/inj.2550274.137","name":"Privacy-by-Design Framework for Large Language Model Chatbots in Urology.","source":"pubmed","abstract":"","url":"https://doi.org/10.5213/inj.2550274.137","authors":["Kim EJ","Kim J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.5213/inj.2550274.137","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"epmc:MED41816643","name":"DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41816643/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1038/s41598-025-12225-x","name":"Blockchain-enabled federated learning with edge analytics for secure and efficient electronic health records management.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-12225-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-12225-x","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3233/shti240863","name":"Simplifying Multiparty Computation: A Client-Driven Metaprotocol for Federated Secure Computing.","source":"europepmc","abstract":"Introduction: Secure Multi-Party Computation (SMPC) offers a powerful tool for collaborative healthcare research while preserving patient data privacy. State of the art: However, existing SMPC frameworks often require separate executions for each desired computation and measurement period, limiting user flexibility. Concept. This research explores the potential of a client-driven metaprotocol for the Federated Secure Computing (FSC) framework and its SImple Multiparty ComputatiON (SIMON) protocol as a step towards more flexible SMPC solutions. Implementation: This client-driven metaprotocol empowers users to specify and execute multiple calculations across diverse measurement periods within a single client-side code execution. This eliminates the need for repeated code executions and streamlines the analysis process. The metaprotocol offers a user-friendly interface, enabling researchers with limited cryptography expertise to leverage the power of SMPC for complex healthcare analyses. Lessons learned: We evaluate the performance of the client-driven metaprotocol against a baseline iterative approach. Our evaluation demonstrates performance improvements compared to traditional iterative approaches, making this metaprotocol a valuable tool for advancing secure and efficient collaborative healthcare research.","url":"https://doi.org/10.3233/shti240863","authors":["Johanna Schwinn","Hendrik Ballhausen","Seyedmostafa Sheikhalishahi","Matthaeus Morhart","Mathias Kaspar","Ludwig Christian Hinske"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3233/shti240863","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1038/s41598-026-46364-6","name":"Privacy and security enhancement in smart cities using advanced cryptographic techniques.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-46364-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-026-46364-6","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.30953/bhty.v8.379","name":"Post-Quantum Cryptography Resilience in Telehealth Using Quantum Key Distribution.","source":"europepmc","abstract":"","url":"https://doi.org/10.30953/bhty.v8.379","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.30953/bhty.v8.379","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1109/icdm65498.2025.00031","name":"A Universal Metric of Dataset Similarity for Cross-silo Federated Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/icdm65498.2025.00031","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/icdm65498.2025.00031","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1038/s41598-025-05924-y","name":"Multiparty private summation protocol based on two-state quantum-mechanical system.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-05924-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-05924-y","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.2196/80178","name":"Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.","source":"pubmed","abstract":"The convergence of artificial intelligence (AI), blockchain technology, and health care represents one of the most transformative yet technically challenging frontiers in computational medicine. As health care systems adopt data-driven paradigms for precision medicine and clinical decision support, the need for secure, privacy-preserving, and collaborative learning frameworks has become critical. This tutorial introduces a comprehensive, clinically oriented, and compliance-aware framework integrating federated learning (FL) and blockchain for secure and privacy-preserving health care analytics. FL enables collaborative training across distributed institutions without raw data sharing, in alignment with privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). However, FL remains vulnerable to model poisoning and gradient leakage. To address these risks, we introduce blockchain-based FL (BCFL), which leverages blockchain's immutable ledger and decentralized consensus to enhance trust, verifiability, and auditability. The tutorial's main contributions include (1) a taxonomy of diverse medical data types and their FL requirements; (2) three integration architectures (fully coupled, semicoupled, and loosely coupled) analyzed for security, scalability, and regulatory compliance; (3) a security analysis of health care-specific vulnerabilities and mitigation strategies using advanced cryptography, such as zero-knowledge proofs, homomorphic encryption, and differential privacy; and (4) a regulatory compliance framework addressing HIPAA, GDPR, and United States Food and Drug Administration guidelines for AI-enabled medical devices. We demonstrate BCFL's relevance across major health care applications, including disease prediction, medical imaging, patient monitoring, and drug discovery, and highlight emerging research directions such as quantum-resilient cryptography, scalable interoperability, and automated compliance. This tutorial serves as a foundational resource for advancing secure, compliant, and collaborative AI in health care; fostering privacy-preserving analytics; and improving patient outcomes.","url":"https://doi.org/10.2196/80178","authors":["Shahsavari Y","Baseri Y","Hafid A","Dambri OA","Makrakis D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.2196/80178","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-024-74213-x","name":"Leveraging quantum blockchain for secure multiparty space sharing and authentication on specialized metaverse platform.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-024-74213-x","authors":["Esmot Ara Tuli","Jae-Min Lee","Dong-Seong Kim"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-024-74213-x","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3389/fdgth.2026.1871960","name":"Secure healthcare data management using federated learning, blockchain, and explainable artificial intelligence: a systematic review.","source":"pubmed","abstract":"Due to the rapid digitization of healthcare systems, there has been a huge collection of sensitive personal data of patients. Thus, secure, privacy-preserving, and efficient data management systems are required. Current distributed healthcare systems increasingly use centralized data processing frameworks that are prone to privacy violations, data fragmentation, and malicious attacks. Despite advances in federated learning, blockchain, explainable AI, and incremental optimization, current survey literature studies each technology separately without considering how the four technologies can be harnessed to create synergies. A systematic review of 26 peer-reviewed studies published from 2018 to 2026 indicates that an integrated architecture incorporating federated learning, blockchain, explainable AI, and incremental optimization can be designed. This review identifies ten critical issues that need to be addressed when researching the four technologies. These issues include communication costs, scalability issues, interoperability concerns, limited clinical explainability, and high computational costs when applied in real-time situations. In comparison to privacy, scalability, interpretability, and efficiency, a hybrid approach can help improve data security, boost the interpretability of the models, facilitate data sharing, and prevent data-sharing risks. Overall quality assessment based on the CASP qualitative checklist analysis of all 26 studies indicated an average score of 7.0 out of 10, implying that the quality of the methods used in the studies was acceptable.","url":"https://doi.org/10.3389/fdgth.2026.1871960","authors":["Bhardwaj T","Sumangali K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3389/fdgth.2026.1871960","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1109/tnnls.2024.3469962","name":"Federated Cross-Incremental Self-Supervised Learning for Medical Image Segmentation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2024.3469962","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1109/tnnls.2024.3469962","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1007/s12095-025-00825-3","name":"Instantiating the Hash-then-evaluate paradigm: Strengthening PRFs, PCFs, and OPRFs.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s12095-025-00825-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1007/s12095-025-00825-3","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1007/s00134-025-08284-3","name":"The next frontier in sepsis: connected ICU data for real-world clinical decision making.","source":"pubmed","abstract":"","url":"https://doi.org/10.1007/s00134-025-08284-3","authors":["Carbajo RS","Palma J","Martin-Loeches I"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1007/s00134-025-08284-3","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-31769-6","name":"A hybrid federated learning framework with generative AI for privacy-preserving and sustainable security in IOT-enabled smart environments.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-31769-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-31769-6","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1007/s10791-025-09627-w","name":"Genomic privacy and security in the era of artificial intelligence and quantum computing.","source":"pubmed","abstract":"The rapid advancements in sequencing technologies have greatly increased access to genomic data stored in public databases. This has raised significant privacy and security concerns. This review emphasizes the importance of protecting genomic data by analyzing vulnerabilities in current storage and sharing practices. It examines the risks genetic databases face from cyber-attacks and internal breaches, focusing especially on advanced AI-driven threats and quantum computing vulnerabilities. The review explores machine learning methods designed to secure data. It highlights algorithms that prioritize privacy while maintaining data confidentiality, such as differential privacy, federated learning, and synthetic data generation using Generative Adversarial Networks (GANs). Findings demonstrate progress in mitigating common privacy breaches like re-identification and inference attacks. However, persistent vulnerabilities remain, particularly to emerging threats such as model inversion and membership inference attacks. The review advocates an integrated approach combining robust legislative frameworks with advanced technology to address genomic privacy challenges. It calls for intensified research efforts to safeguard genomic information. In particular, there is an urgent need to adopt quantum-resistant cryptographic methods, including lattice-based encryption and blockchain-integrated security frameworks. The paper emphasizes the necessity for genomics researchers to prioritize data privacy and security. This ensures responsible handling of genomic information in research.","url":"https://doi.org/10.1007/s10791-025-09627-w","authors":["Annan R","Noland J","Perkins K","Yuan X","Roy K","Qingge L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1007/s10791-025-09627-w","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-024-83682-z","name":"Hybrid quantum enhanced federated learning for cyber attack detection.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-024-83682-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-024-83682-z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-31786-5","name":"Data security storage and transmission framework for AI computing power platforms.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-31786-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-31786-5","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-025-27303-3","name":"MedShieldFL-a privacy-preserving hybrid federated learning framework for intelligent healthcare systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-27303-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-27303-3","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-026-40734-w","name":"A privacy-preserving multi-user retrieval system for multimodal artificial intelligence.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-40734-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-026-40734-w","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/healthcare14030306","name":"Federated Learning in Healthcare Ethics: A Systematic Review of Privacy-Preserving and Equitable Medical AI.","source":"pubmed","abstract":"","url":"https://doi.org/10.3390/healthcare14030306","authors":["Mir BA","Abbas SR","Lee SW"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3390/healthcare14030306","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1686454","name":"Both ends of artificial intelligence impacting privacy: a review of violation and protection.","source":"pubmed","abstract":"","url":"https://doi.org/10.3389/frai.2026.1686454","authors":["Voloch N","Hirschprung RS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3389/frai.2026.1686454","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41746-025-01520-6","name":"Addressing contemporary threats in anonymised healthcare data using privacy engineering.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41746-025-01520-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41746-025-01520-6","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.1016/j.crmeth.2025.101171","name":"Toward owner governance in genomic data privacy with Governome.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.crmeth.2025.101171","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1016/j.crmeth.2025.101171","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-92575-8","name":"Integrating advanced neural network architectures with privacy enhanced encryption for secure and intelligent healthcare analytics.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-92575-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-92575-8","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-026-40122-4","name":"Design of a multi-layered privacy-preserving architecture for secure medical data exchange in cloud environments.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-40122-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-026-40122-4","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/s26061740","name":"On the Convergence of Internet of Things and Decentralized Finance: Security Challenges and Future Directions.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26061740","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3390/s26061740","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-32498-6","name":"IPP-DMS: A scalable privacy-preserving data management system for secure and efficient handling of large-scale datasets.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-32498-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-32498-6","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-026-35816-8","name":"An efficient tripartite remote state preparation scheme with noise analysis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-35816-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-026-35816-8","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-16315-8","name":"Quantum secured blockchain framework for enhancing post quantum data security.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-16315-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-16315-8","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.20944/preprints202407.2248.v1","name":"SMPTC3: Secure Multi-party Protocol Based Trusted Cross-chain Contracts","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202407.2248.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.20944/preprints202407.2248.v1","updatedAt":"2026-08-31T06:41:37.833Z"},{"id":"doi:10.1038/s41598-024-69417-0","name":"Improving security of efficient multiparty quantum secret sharing based on a novel structure and single qubits.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-024-69417-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-024-69417-0","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.3389/fcvm.2026.1831342","name":"Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness.","source":"pubmed","abstract":"Data silos and privacy constraints limit the centralized development of machine learning models for cardiovascular disease. Federated learning enables multi-institutional training without sharing raw patient records, but the evidence base in cardiology and deployment-grade evaluation remains uneven.","url":"https://doi.org/10.3389/fcvm.2026.1831342","authors":["Li J","Xiang W","Shang D","Li S","Li Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3389/fcvm.2026.1831342","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41588-025-02109-1","name":"Secure and federated genome-wide association studies for biobank-scale datasets.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41588-025-02109-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41588-025-02109-1","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1146/annurev-biodatasci-120423-120107","name":"Privacy-Enhancing Technologies in Biomedical Data Science.","source":"europepmc","abstract":"","url":"https://doi.org/10.1146/annurev-biodatasci-120423-120107","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1146/annurev-biodatasci-120423-120107","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.csbj.2025.06.009","name":"Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions.","source":"pubmed","abstract":"Federated learning (FL)-a distributed machine learning that offers collaborative training of global models across multiple clients. FL has been considered for the design and development of many FL systems in various domains. Hence, we present a comprehensive survey and analysis of existing FL systems, drawing insights from more than 250 articles published in 2019-2024. Our review elucidates the functioning of FL systems, particularly in comparison with alternative distributed learning approaches. Considering the healthcare domain as an example, we define the building blocks of a typical FL healthcare system, including system architecture, federation scale, data partitioning, open-source frameworks, ML models, and aggregation algorithms. Furthermore, we identify and discuss key challenges associated with the design and implementation of FL systems within the healthcare sector while outlining the directions of future research. In general, through systematic categorization and analysis of existing FL systems, we offer insights to design efficient, accurate, and privacy-preserving healthcare applications using cutting-edge FL techniques.","url":"https://doi.org/10.1016/j.csbj.2025.06.009","authors":["Madathil NT","Dankar FK","Gergely M","Belkacem AN","Alrabaee S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1016/j.csbj.2025.06.009","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-025-03558-8","name":"Energy efficient trust aware secure routing algorithm with attribute based encryption for wireless sensor networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-03558-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-03558-8","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.dib.2025.111949","name":"A perspective on the use of personal data stores in data spaces to support multiparty data access as required by the data Act.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.dib.2025.111949","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1016/j.dib.2025.111949","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/s26103226","name":"Machine Learning Enhanced Quantum-Safe Encryption: A Novel Optimisation Framework.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26103226","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.3390/s26103226","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41467-026-70628-4","name":"On the equivalence between classically verifiable position verification and certified randomness.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41467-026-70628-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41467-026-70628-4","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-22056-5","name":"Quantum resilient security framework for privacy preserving AI in Apple MM1 on device architecture.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-22056-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-22056-5","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.34133/research.1034","name":"Experimental Efficient Source-Independent Quantum Conference Key Agreement.","source":"europepmc","abstract":"","url":"https://doi.org/10.34133/research.1034","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.34133/research.1034","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1038/s41598-025-94345-y","name":"ESHA-256_GBGO: a high-performance and optimized security framework for internet of medical thing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-94345-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-94345-y","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.1038/s41598-025-94478-0","name":"Group verifiable secure aggregate federated learning based on secret sharing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-94478-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-94478-0","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.1111/vox.70236","name":"Harnessing big data and artificial intelligence in transfusion medicine: Opportunities for precision, safety and efficiency.","source":"pubmed","abstract":"","url":"https://doi.org/10.1111/vox.70236","authors":["Raza S","Goel R","Erikstrup C","D'Alessandro A","Custer B","Li N","ISBT BIG DATA Working Party"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1111/vox.70236","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-026-45701-z","name":"ADeepCRF: real-time threat detection in IoT network using blockchain-based advanced proof of authority and adaptive deep conditional random fields.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-45701-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-026-45701-z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1186/s13059-025-03684-6","name":"FedscGen: privacy-preserving federated batch effect correction of single-cell RNA sequencing data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s13059-025-03684-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1186/s13059-025-03684-6","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1073/pnas.2304415120","name":"Collaborative privacy-preserving analysis of oncological data using multiparty homomorphic encryption.","source":"pubmed","abstract":"Real-world healthcare data sharing is instrumental in constructing broader-based and larger clinical datasets that may improve clinical decision-making research and outcomes. Stakeholders are frequently reluctant to share their data without guaranteed patient privacy, proper protection of their datasets, and control over the usage of their data. Fully homomorphic encryption (FHE) is a cryptographic capability that can address these issues by enabling computation on encrypted data without intermediate decryptions, so the analytics results are obtained without revealing the raw data. This work presents a toolset for collaborative privacy-preserving analysis of oncological data using multiparty FHE. Our toolset supports survival analysis, logistic regression training, and several common descriptive statistics. We demonstrate using oncological datasets that the toolset achieves high accuracy and practical performance, which scales well to larger datasets. As part of this work, we propose a cryptographic protocol for interactive bootstrapping in multiparty FHE, which is of independent interest. The toolset we develop is general-purpose and can be applied to other collaborative medical and healthcare application domains.","url":"https://doi.org/10.1073/pnas.2304415120","authors":["Geva R","Gusev A","Polyakov Y","Liram L","Rosolio O","Alexandru A","Genise N","Blatt M","Duchin Z","Waissengrin B","Mirelman D","Bukstein F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1073/pnas.2304415120","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1038/s41598-025-22797-3","name":"A lightweight trusted framework for secure data exchange and threat mitigation in IoT-enabled healthcare environments.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-22797-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:31.718Z","doi":"10.1038/s41598-025-22797-3","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00028-4","name":"Federated quantum natural gradient descent for quantum federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00028-4","authors":["Jun Qi","Min-Hsiu Hsieh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:46Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00028-4","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.14293/gof.23.06","name":"Federated Machine Learning for Systems Medicine","source":"crossref","abstract":"","url":"https://doi.org/10.14293/gof.23.06","authors":["Richard Röttger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-09T15:45:25Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.14293/gof.23.06","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00008-3","name":"Centralized versus decentralized federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00008-3","authors":["Irina Arévalo","Jose L. Salmeron"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-344433-3.00008-3","updatedAt":"2026-08-31T06:41:28.651Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00012-1","name":"Personalized federated learning strategies","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00012-1","authors":["SangMook Kim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:20Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-323641-9.00012-1","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1201/9781003466581-9","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003466581-9","authors":["Sana Daud"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-18T08:39:48Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003466581-9","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00013-2","name":"Adversarial robustness in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00013-2","authors":["Chulin Xie","Xiaoyang Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:00Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00013-2","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1201/9781003688570-2","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003688570-2","authors":["Somanath Tripathy","Harsh Kasyap","Minghong Fang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-21T17:09:27Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003688570-2","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00012-0","name":"Assessing vulnerabilities and securing federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00012-0","authors":["Supriyo Chakraborty","Arjun Bhagoji"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:48:57Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00012-0","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1201/9781003466581-1","name":"Introduction to Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003466581-1","authors":["Vaneeza Mobin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-18T08:39:48Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003466581-1","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00018-1","name":"Graph-aware federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00018-1","authors":["Songtao Lu","Pengwei Xing","Han Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:14Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00018-1","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00018-6","name":"Blockchain-enabled federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00018-6","authors":["Murtaza Rangwala","K.R. Venugopal","Rajkumar Buyya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-344433-3.00018-6","updatedAt":"2026-08-31T06:41:28.651Z"},{"id":"doi:10.5220/0012150700003555","name":"Δ SFL: (Decoupled Server Federated Learning) to Utilize DLG Attacks in Federated Learning by Decoupling the Server","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012150700003555","authors":["Sudipta Paul","Vicenç Torra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-13T10:16:41Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.5220/0012150700003555","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1201/9781003466581-6","name":"Federated Learning in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003466581-6","authors":["Muhammad Hamza"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-18T08:39:48Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003466581-6","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00006-x","name":"Federated learning at a glance","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00006-x","authors":["Anwesha Mukherjee","Sajal K. Das","Rajkumar Buyya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-344433-3.00006-x","updatedAt":"2026-08-31T06:41:28.651Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00017-x","name":"Meta-federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00017-x","authors":["Omid Aramoon","Pin-Yu Chen","Gang Qu","Yuan Tian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:18Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00017-x","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1142/9789811292552_0013","name":"Federated Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0013","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1142/9789811292552_0013","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00020-0","name":"Federated noisy client learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00020-0","authors":["Kahou Tam","Huazhu Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:34Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-323641-9.00020-0","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.30546/macosep2025.1118","name":"FedND: Federated Newton Direction for Federated Learning Environment","source":"crossref","abstract":"","url":"https://doi.org/10.30546/macosep2025.1118","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-13T15:05:58Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.30546/macosep2025.1118","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-981-95-1009-2_1","name":"Introduction to Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1009-2_1","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T02:27:43Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1007/978-981-95-1009-2_1","updatedAt":"2026-08-31T06:41:28.651Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00029-6","name":"Mobile computing framework for federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00029-6","authors":["Xiang Chen","Fuxun Yu","Zirui Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:52Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00029-6","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00021-1","name":"Hyperparameter tuning for federated learning – systems and practices","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00021-1","authors":["Syed Zawad","Feng Yan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:22Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00021-1","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00027-2","name":"Introduction to quantum federated machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00027-2","authors":["Samuel Yen-Chi Chen","Shinjae Yoo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:41Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00027-2","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-031-86592-3_1","name":"Introduction to Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-86592-3_1","authors":["Hamed Tabrizchi","Ali Aghasi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-23T14:07:49Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1007/978-3-031-86592-3_1","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1142/9789811292552_0001","name":"Introduction to Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0001","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1142/9789811292552_0001","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00014-9","name":"Secure federated learning with Hindmarsh-Rose encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00014-9","authors":["Jose L. Salmeron","Irina Arévalo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-344433-3.00014-9","updatedAt":"2026-08-31T06:41:28.651Z"},{"id":"doi:10.1142/9789811292552_0012","name":"Mixed Federated Learning Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0012","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1142/9789811292552_0012","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1142/9789811292552_0011","name":"Horizontal Federated Learning Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0011","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1142/9789811292552_0011","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1142/9789811292552_0002","name":"Federated Learning Application Scenarios","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0002","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1142/9789811292552_0002","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1142/9789811292552_0017","name":"Federated Learning Based on Blockchain","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0017","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1142/9789811292552_0017","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1142/9789811292552_0006","name":"Vertically Federated Kernel Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0006","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1142/9789811292552_0006","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-981-95-1009-2_6","name":"Key Variants of Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1009-2_6","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T02:23:45Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1007/978-981-95-1009-2_6","updatedAt":"2026-08-31T06:41:28.651Z"},{"id":"doi:10.1109/flta67013.2025.11336434","name":"Is the Future of AI Really Federated? Federated Learning as an Emerging Market","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336434","authors":["Bernd Beckert"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/flta67013.2025.11336434","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1142/9789811292552_0009","name":"Vertical Federated Deep Learning Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0009","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1142/9789811292552_0009","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1142/9789811292552_0007","name":"Asynchronous Vertical Federated Learning Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0007","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1142/9789811292552_0007","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00001-1","name":"Digital healthcare systems in a federated learning perspective","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00001-1","authors":["Wasswa Shafik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:07:42Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-443-13897-3.00001-1","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00019-4","name":"Reliable federated learning for disease detection","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00019-4","authors":["Meng Wang","Huazhu Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-323641-9.00019-4","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00032-6","name":"Ethical considerations and legal issues relating to federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00032-6","authors":["Warren Chik","Florian Gamper"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:53Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00032-6","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00015-0","name":"Sustainable federated learning ecosystems: incentive mechanisms, robustness, and privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00015-0","authors":["Turki Alhazmi","Farag Azzedin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-344433-3.00015-0","updatedAt":"2026-08-31T06:41:28.651Z"},{"id":"doi:10.1201/9781003497196-2","name":"Types of Federated Learning and Aggregation Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196-2","authors":["S. Shailesh","Joseph James"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T10:30:21Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003497196-2","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00009-1","name":"Fundamentals of federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00009-1","authors":["Xiaoxiao Li","Ziyue Xu","Huazhu Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:13Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-323641-9.00009-1","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.7717/peerj-cs.2414/table-8","name":"Algorithm 1 : Federated learning.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.2414/table-8","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-13T05:39:34Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.7717/peerj-cs.2414/table-8","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-031-86592-3_2","name":"Core Concepts of Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-86592-3_2","authors":["Hamed Tabrizchi","Ali Aghasi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-23T18:07:49Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1007/978-3-031-86592-3_2","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1201/9781003466581-7","name":"Scalability and Efficiency in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003466581-7","authors":["Alyan Zaib"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-18T08:39:48Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003466581-7","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00008-x","name":"Introduction of federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00008-x","authors":["Xiaoxiao Li","Ziyue Xu","Huazhu Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:11Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-323641-9.00008-x","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-030-70604-3_4","name":"Advancements of Federated Learning Towards Privacy Preservation: From Federated Learning to Split Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-70604-3_4","authors":["Chandra Thapa","M. A. P. Chamikara","Seyit A. Camtepe"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-06-11T03:42:05Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1007/978-3-030-70604-3_4","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.5772/intechopen.1008431","name":"Clustered Federated Learning: A Review","source":"crossref","abstract":"Clustered Federated Learning (CFL) has emerged as a powerful extension of traditional federated learning to address the challenges posed by heterogeneous, non-IID data across distributed clients. This chapter provides a comprehensive review of the state-of-the-art CFL methods, categorizing them into model-based, feature-based, and hybrid approaches. Model-based clustering leverages client model updates to form clusters, while feature-based methods utilize client data characteristics, and hybrid approaches integrate both aspects to achieve robust clustering. The chapter also discusses the evaluation metrics and benchmarks used to assess CFL performance, such as accuracy, personalization, and cluster quality, along with case studies demonstrating CFL’s applicability in diverse domains like healthcare, IoT, and autonomous systems. We identify key challenges in CFL, including scalability, dynamic clustering, and privacy preservation, and propose future research directions to further enhance the effectiveness and scalability of CFL frameworks. Overall, this chapter aims to provide a deep understanding of CFL, highlighting its potential to improve federated learning outcomes in complex, real-world scenarios with non-IID data.","url":"https://doi.org/10.5772/intechopen.1008431","authors":["Majid Morafah","Mahdi Morafah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-28T14:14:25Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.5772/intechopen.1008431","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00030-2","name":"Federated learning for privacy-preserving speech recognition","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00030-2","authors":["Chao-Han Huck Yang","Sabato Marco Siniscalchi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:54Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00030-2","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00011-3","name":"Bridging data privacy and intelligence: the landscape of federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00011-3","authors":["Dipanwita Thakur","Sajal K. Das"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-344433-3.00011-3","updatedAt":"2026-08-31T06:41:28.651Z"},{"id":"doi:10.7717/peerj-cs.2870/fig-2","name":"Figure 2: Federated learning design.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.2870/fig-2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-08T04:35:49Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.7717/peerj-cs.2870/fig-2","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.7717/peerj-cs.758/fig-5","name":"Figure 5: Federated learning delay.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.758/fig-5","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-22T05:14:05Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.7717/peerj-cs.758/fig-5","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00023-x","name":"Federated learning applications in 6G communications and smart societies","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00023-x","authors":["Radical Rakhman Wahid","Farag Azzedin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-344433-3.00023-x","updatedAt":"2026-08-31T06:41:28.651Z"},{"id":"doi:10.5772/intechopen.1008364","name":"Federated Learning with Partially Class-Disjoint Data","source":"crossref","abstract":"Essentially, the federation of multiple clients is to acquire more complete information to promote learning a powerful model that recognizes general patterns of wider classes. However, a natural case usually presents in the real-world situation under a large label space, where only a subset of classes of samples are provided by individual clients. We term this practical setting as “federated learning with partially class-disjoint data” (PCDD), to differentiate from the ordinary study without emphasizing this extreme scenario. Generally, this setting is very challenging as different clients own partial classes and the resulting model on the client side cannot well support full-class topology for aggregation. In this chapter, we first show the significant difference of federation in PCDD compared to that in traditional non-IID studies, and then dissect recent advances concerning this challenge, following with discussion about the future trends. We illustrate the challenges using experimental results on benchmark datasets under PCDD conditions, finding that effective solutions to PCDD problems should provide optimal structures for global objective and allow flexible structures for personalized tasks. We make systematical analysis to underscore the significance of PCDD in heterogeneous federated learning and to inspire more explorations in the future.","url":"https://doi.org/10.5772/intechopen.1008364","authors":["Ziqing Fan","Jiangchao Yao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-18T10:27:47Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.5772/intechopen.1008364","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00010-7","name":"Personalized federated learning: theory and open problems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00010-7","authors":["Canh T. Dinh","Tung T. Vu","Nguyen H. Tran"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:48:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00010-7","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00022-8","name":"Federated learning framework for survival analysis in healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00022-8","authors":["Navid Seidi","Satyaki Roy","Sajal K. Das"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-344433-3.00022-8","updatedAt":"2026-08-31T06:41:28.651Z"},{"id":"doi:10.1016/c2022-0-00646-2","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2022-0-00646-2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:42:36Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/c2022-0-00646-2","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00019-8","name":"Incentive-based federated learning: architectural elements and future directions","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00019-8","authors":["Chanuka A.S. Hewa Kaluannakkage","Rajkumar Buyya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-344433-3.00019-8","updatedAt":"2026-08-31T06:41:28.651Z"},{"id":"doi:10.1201/9781003482000-9","name":"Artificial Intelligence Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003482000-9","authors":["Manjushree Nayak","Debasish Padhi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T17:15:57Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003482000-9","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1201/9781003595540-2","name":"Federated Learning for Internet of Things","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540-2","authors":["V. Raghavendran","M. Sathishkumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003595540-2","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1002/9781394461295.ch4","name":"Engineering and Deployment of Federated Learning Systems in Agricultural Supply Chains","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.ch4","authors":["Meena Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1002/9781394461295.ch4","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.5772/intechopen.1008185","name":"Application of Artificial Intelligence-Driven Federated Learning Based on Machine Learning and Deep Learning in Medicine ","source":"crossref","abstract":"Currently, artificial intelligence (AI) technology is developing rapidly. Machine learning and deep learning are algorithms in the field of AI, and their combined use in federated learning is becoming increasingly common in medical research. The emergence of federated learning technology aims to train machine learning and deep learning algorithms across multiple distributed devices or servers. Federated learning has greatly promoted the development of AI in the medical field. The core of this approach is to construct complex and accurate models by automatically learning and extracting useful features from large amounts of data from multiple data sources, thereby building models with both high accuracy and precision. The widespread adoption of federated learning is bound to lead to breakthrough advances in areas such as precision medicine, clinical decision support, new drug development, medical image recognition, medical language processing, and medical speech recognition. This chapter draws on the author’s experience in big data medical modeling and validation from multiple data sources to introduce algorithms and operational modes in the field of federated learning, offering a glimpse into the promising future of the intelligent world.","url":"https://doi.org/10.5772/intechopen.1008185","authors":["Luwei Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-06T14:09:44Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.5772/intechopen.1008185","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1201/9781003384854-10","name":"Collaborative Federated Learning in Healthcare Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003384854-10","authors":["Bini M Issac","SN Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-16T11:04:28Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003384854-10","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.7717/peerj-cs.2751/fig-1","name":"Figure 1: Federated learning framework.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.2751/fig-1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-28T04:19:22Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.7717/peerj-cs.2751/fig-1","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1142/9789811292552_0004","name":"Tree-Based Models in Vertical Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0004","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1142/9789811292552_0004","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1201/9781003466581-8","name":"Privacy Preservation in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003466581-8","authors":["P. Keerthana","M. Kavitha","Jayasudha Subburaj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-18T08:39:48Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003466581-8","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00012-5","name":"Vertical federated learning with feature and sample privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00012-5","authors":["Linh Tran","Timothy Castiglia","Stacy Patterson","Ana Milanova"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-344433-3.00012-5","updatedAt":"2026-08-31T06:41:29.551Z"},{"id":"doi:10.1007/978-3-031-86592-3_4","name":"Cyber Security Intelligent Systems Based on Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-86592-3_4","authors":["Hamed Tabrizchi","Ali Aghasi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-23T18:07:48Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1007/978-3-031-86592-3_4","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1007/978-981-96-9223-1_4","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-9223-1_4","authors":["Mei Kobayashi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-01T12:37:40Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1007/978-981-96-9223-1_4","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00013-3","name":"Federated learning on long-tailed datasets","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00013-3","authors":["Marawan Elbatel","Robert Martí","Xiaomeng Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:22Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-323641-9.00013-3","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/flics70075.2026.11621899","name":"The Federated Estimand: Healthcare Federated Learning as Evidence Synthesis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621899","authors":["Narasimha Raghavan Veeraragavan","Paul Lambert","Bjarte Aagnes","Jan Franz Nygård"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:14:34Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/flics70075.2026.11621899","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1109/flics70075.2026.11621922","name":"Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621922","authors":["Satwat Bashir","Tasos Dagiuklas","Muddesar Iqbal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:18Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/flics70075.2026.11621922","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00024-1","name":"Quantum federated learning: architectural elements and future directions","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00024-1","authors":["Siva Sai","Abhishek Sawaika","Prabhjot Singh","Rajkumar Buyya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-344433-3.00024-1","updatedAt":"2026-08-31T06:41:29.551Z"},{"id":"doi:10.59019/ttwo8287","name":"Decentralized Federated Learning","source":"crossref","abstract":"The rapid advancement of distributed machine learning has created new opportunities for enhancing data privacy and fostering collaborative intelligence. However, significant challenges remain in achieving scalability, efficiency, security, and trust. This thesis aims to enhance Decentralized Federated Learning by addressing core challenges related to communication efficiency, convergence time, scalability, security, and privacy. The study involves a comprehensive review of existing literature to identify key gaps and challenges, followed by the development of innovative methods and frameworks designed through experiments to overcome these limitations. The proposed techniques integrate Blockchain technologies, such as sharded Blockchains and IPFS-based off-chain storage, to improve the transparency and scalability of federated systems. By systematically comparing synchronous, asynchronous, and hybrid federated approaches across Blockchain-based and non-Blockchain scenarios, this research uncovers critical trade-offs between speed, accuracy, convergence, and security. Experiments conducted on real-world IoT datasets reveal that asynchronous Blockchain-based Federated Learning achieves up to 7.93% faster convergence when compared to the baseline setup. Methods such as delta compression with sparsification and sharded Blockchains reduce convergence time by 2.91% and 4.94%, respectively. When the use of sharded Blockchains is combined with delta compression and sparsification, it achieves the highest overall improvement of 7.93% in convergence time and up to 8.89% reduction in epoch time, highlighting its efficiency in large-scale distributed environments. However, the variability introduced by stale updates in asynchronous systems underscores the importance of uniform contribution from nodes. Techniques such as adaptive aggregation strategy, weighted aggregation, partial updates, and compression of updates emerge as crucial strategies for enhancing model performance in heterogeneous environments. The findings of this study provide actionable insights for the development of next-generation, privacy-preserving AI systems capable of operating efficiently at scale.","url":"https://doi.org/10.59019/ttwo8287","authors":["Gunwant Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-13T13:24:36Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.59019/ttwo8287","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1201/9781003546559-8","name":"Secure Federated Learning Framework for Training Deep Learning Models","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003546559-8","authors":["Gitanjali Bhimrao Yadav","Jayashri Bagade"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-01T12:15:01Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003546559-8","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1007/978-3-030-96896-0_23","name":"Advancing Healthcare Solutions with Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_23","authors":["Amogh Kamat Tarcar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1007/978-3-030-96896-0_23","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00020-x","name":"Vertical asynchronous federated learning: algorithms and theoretic guarantees","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00020-x","authors":["Tianyi Chen","Xiao Jin","Yuejiao Sun","Wotao Yin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:20Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00020-x","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1007/978-3-030-96896-0_1","name":"Introduction to Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_1","authors":["Heiko Ludwig","Nathalie Baracaldo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1007/978-3-030-96896-0_1","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1142/9789811292552_0014","name":"Detailed Exploration of the FedLearn Federated Learning System","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0014","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1142/9789811292552_0014","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1201/9781003384854-1","name":"Introduction to Federated Learning Methods and Classifications","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003384854-1","authors":["Shashikiran Venkatesha","Ramanathan Lakshmanan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-16T11:04:28Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003384854-1","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.32657/10356/200726","name":"Fairness-aware federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/200726","authors":["Yuxin Shi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-05T10:05:35Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.32657/10356/200726","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00018-2","name":"Utility-privacy tradeoff in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00018-2","authors":["Huzaifa Arif","Alex Gittens","Pin-Yu Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:29Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-323641-9.00018-2","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1201/9781003466581-4","name":"User Participation and Incentives in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003466581-4","authors":["Muhammad Ali Zeb","Samina Amin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-18T08:39:48Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003466581-4","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003595540-3","name":"Enhancing Federated Learning in Healthcare Using Adaptive Genetic Model Aggregation","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540-3","authors":["K. Jegadeeswari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003595540-3","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003489368-2","name":"Applications, Challenges, and Opportunities for Federated Learning in 6G","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003489368-2","authors":["Roheen Qamar","Saima Siraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T17:25:42Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003489368-2","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1007/978-3-030-96896-0_4","name":"Personalization in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_4","authors":["Mayank Agarwal","Mikhail Yurochkin","Yuekai Sun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:31:57Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1007/978-3-030-96896-0_4","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00009-5","name":"Optimization techniques for federated learning algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00009-5","authors":["Ferdinand Kahenga","Antoine Bagula","Sajal K. Das","Jovita Mateus","Olasupo Ajayi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-344433-3.00009-5","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00016-9","name":"Improving performance fairness in federated learning for heterogeneous medical images","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00016-9","authors":["Meirui Jiang","Qi Dou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:30Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-323641-9.00016-9","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003497196-5","name":"Federated Learning in the Internet of Medical Things","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196-5","authors":["S. Sabapathi","N. Vijayalaskhmi","S. Sindhu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T10:30:21Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003497196-5","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1201/9781003591085-7","name":"AI and federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003591085-7","authors":["Lingala Thirupathi","Sandeep Ravikanti","Vineetha Kaashipaka","Krishna Priya Thammana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-20T10:28:47Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003591085-7","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003497196-3","name":"Federated Learning for IoT/Edge/Fog Computing Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196-3","authors":["Balqees Talal Hasan","Ali Kadhum Idrees"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T06:30:21Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003497196-3","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1201/9781003384854-3","name":"Federated Learning Architectures, Opportunities, and Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003384854-3","authors":["Pradipta kumar Mishra","Rabinarayan Satapathy","Debashreet Das"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-16T11:04:28Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003384854-3","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003497196-11","name":"Incentive Mechanism for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196-11","authors":["Lekha C. Warrier","G. K. Ragesh","Pao-Ann Hsiung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T10:30:21Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003497196-11","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.32657/10356/205735","name":"Efficient explainable federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/205735","authors":["Yuanyuan Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-02T05:11:06Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.32657/10356/205735","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00022-3","name":"Hyper-parameter optimization in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00022-3","authors":["Yi Zhou","Parikshit Ram","Theodoros Salonidis","Nathalie Baracaldo","Horst Samulowitz","Heiko Ludwig"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:22Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-44-319037-7.00022-3","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1142/9789811292552_0008","name":"Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0008","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1142/9789811292552_0008","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1201/9781003546559-6","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003546559-6","authors":["Anagha N. Chaudhari","A.A. Hitham Seddig","Roshani Raut","Aliza Sarlan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-01T12:15:01Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003546559-6","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1109/flta63145.2024.10840121","name":"Learning to Unlearn in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840121","authors":["Yixiong Wang","Jalil Taghia","Selim Ickin","Konstantinos Vandikas","Masoumeh Ebrahimi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/flta63145.2024.10840121","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.61557/vwbs3563","name":"PETs in practice: using federated learning to train machine-learning models without centralising data","source":"crossref","abstract":"","url":"https://doi.org/10.61557/vwbs3563","authors":["Claudine Tinsman","Calum Inverarity","Gefion Thuermer","Neil Majithia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-29T21:40:07Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.61557/vwbs3563","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00003-0","name":"Adapting to decentralization: the evolution of computing paradigms and machine learning in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00003-0","authors":["Ciza Thomas","Alka Rachel John"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/b978-0-443-33789-5.00003-0","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003497196-9","name":"Analyzing Federated Learning From a Security Perspective","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196-9","authors":["Akarsh K. Nair","Jayakrushna Sahoo","Ebin Deni Raj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T10:30:21Z","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1201/9781003497196-9","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1038/s41598-026-61276-1","name":"A lightweight anonymous authentication scheme for federated learning.","source":"pubmed","abstract":"Federated learning enables collaborative model training between central servers and distributed clients without collecting users' raw sensitive data, which effectively promotes the large-scale deployment of intelligent collaborative services. Considering the high sensitivity of local training data and model gradient parameters in federated learning, protecting identity privacy and interaction security has become extremely critical. Therefore, mutual identity authentication is indispensable to restrict illegal client access and prevent malicious parameter transmission and data tampering. In this paper, we propose a lightweight anonymous authentication scheme for federated learning (FedLAS), which realizes secure mutual authentication between servers and clients and establishes a shared session key for subsequent encrypted interaction. In particular, the proposed scheme eliminates the reliance on high-cost cryptographic operations such as bilinear pairing, thus minimizing computational and communication overhead. Furthermore, informal security analysis demonstrates that our FedLAS scheme can resist multiple common attacks and meet predefined security requirements. Extensive comparative experiments show that the FedLAS scheme achieves excellent performance in computational and communication cost. It is well suitable for resource-constrained federated learning scenarios.","url":"https://doi.org/10.1038/s41598-026-61276-1","authors":["Wu S","Meng G","Lu L","Dong X","Tian S","Chen J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-61276-1","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1109/tnnls.2026.3655587","name":"Cross-Image Federated Learning for Hyperspectral Image Classification.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2026.3655587","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/tnnls.2026.3655587","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3233/shti260281","name":"Traceability in Federated Learning in Healthcare.","source":"pubmed","abstract":"Federated learning (FL) enables privacy-preserving analytics on distributed healthcare data, but achieving transparency remains a critical challenge for trust and accountability. This scoping review focuses on traceability as a core component of transparency and systematically assesses traceability in FL within healthcare. Following the PRISMA-ScR methodology, we screened 125 articles across four databases, of which 53 met our inclusion criteria. Preliminary results show that 77.36% of articles rely on blockchain to support traceability, while only a small subset addresses healthcare applications, and comprehensive evaluation frameworks regarding traceability are largely lacking. Future research should explore the integration of blockchain in federated learning platforms in healthcare for traceability to enhance trust, auditability and accountability in clinical practice.","url":"https://doi.org/10.3233/shti260281","authors":["Tang FK","Grönke A","Sergei G","Jaberansary M","Elwes M","Beyan O"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3233/shti260281","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1080/15265161.2026.2690928","name":"Defensive Discrimination and the Cyber-Bioethics of Federated Learning in Healthcare.","source":"pubmed","abstract":"","url":"https://doi.org/10.1080/15265161.2026.2690928","authors":["Iwasaki M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1080/15265161.2026.2690928","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1109/tnnls.2026.3653211","name":"Gradient-Refined Federated Learning on Head-Tail Imbalanced Data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2026.3653211","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/tnnls.2026.3653211","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1038/s41598-026-46689-2","name":"Optimized IoT clustering and assignment in semi-synchronous federated learning.","source":"pubmed","abstract":"This study focuses on the important task of optimizing device clustering and assigning them to edge servers, while also implementing data redistribution in hierarchical semi-synchronous federated learning within the realm of advancing edge computing. Our research goal is to increase the performance and scalability of federated learning systems by improving resource allocation and data processing efficiency, which will in turn enhance edge computing frameworks. The current literature does not have thorough methods that can effectively combine model accuracy with optimal device clustering algorithms in hierarchical semi-synchronous federated learning, leading to below-par performance and inefficient use of resources. This difference highlights the need for creative measures that enhance not only model training accuracy but also the grouping of devices as opposed to current methods. The study utilizes a Graph Neural Network (GNN) to group IoT devices according to their hardware features and local datasets, then applies the K-means algorithm to create efficient device clusters. After that, Hybrid Data Redistribution is used to equalize local datasets in each cluster, and Proximal Policy resource allocation optimization algorithm is implemented to allocate devices to edge servers according to bandwidth usage, and energy consumption based on real-time updates, ultimately enabling hierarchical semi-synchronous federated learning to improve model training. The results show a 15% increase in clustering metrics compared to current algorithms, showcasing how our method improves device assignment and data redistribution in hierarchical semi-synchronous federated learning, addressing issues in model accuracy and resource optimization.","url":"https://doi.org/10.1038/s41598-026-46689-2","authors":["Farajvand H","Derakhshanfard N","Mirzaei A","Ghaffari A","Kazem AAPH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-46689-2","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3233/shti260418","name":"Implementing a Governance Framework for Federated Learning.","source":"pubmed","abstract":"Although federated learning is becoming the dominant paradigm in multi-institutional AI research for healthcare, its application in the highly secure hospital domain remains limited. We evaluated the proposed governance framework for federated learning to explore how it can be further adapted to facilitate the AI-driven secondary use of clinical routine data in a federated setting.","url":"https://doi.org/10.3233/shti260418","authors":["Grönke A","Jaberansary M","Zoubia O","Vorhagen S","Beyan O"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3233/shti260418","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3390/s26144460","name":"Digital Twin-Enabled Dynamic Aggregation for Efficient Federated Learning.","source":"pubmed","abstract":"Federated learning (FL) enables collaborative model training without sharing raw data, but it faces challenges due to client heterogeneity, leading to inefficiency and reduced accuracy. This paper proposes a digital twin (DT)-based dynamic FL aggregation method to address these issues. The framework integrates a DT layer on the server side to perform preaggregation evaluations, simulating various aggregation strategies to select the optimal approach before actual global aggregation. An adaptive clustering method based on K-means is employed to group clients with similar characteristics, and a hierarchical aggregation evaluation strategy is designed to optimize both intra-cluster and inter-cluster aggregation, with the goal of minimizing latency and energy consumption while maximizing model accuracy. Simulation results on the MNIST and CIFAR-10 datasets demonstrate that the proposed method not only accelerates model convergence and improves accuracy but also significantly reduces training latency and energy consumption costs compared with baseline FL algorithms. This DT-assisted approach delivers a practical and effective optimization solution for federated learning deployment over large-scale heterogeneous IoT sensor networks.","url":"https://doi.org/10.3390/s26144460","authors":["Zhuang W","Wang Y","Wang G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3390/s26144460","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/tpami.2026.3712213","name":"Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data.","source":"pubmed","abstract":"Federated learning is an emerging distributed machine learning framework aimed at protecting data privacy. Data heterogeneity is one of the core challenges in federated learning, which could severely degrade the convergence rate and prediction performance of deep neural networks. To address this issue, we develop a novel personalized federated learning framework for heterogeneous data, which we refer to as FedSplit. This modeling framework is motivated by the finding that data on different clients contain both common knowledge and personalized knowledge. Then the hidden elements in each neural layer can be split into shared and personalized groups. With this decomposition, a novel objective function is established and optimized. We demonstrate that FedSplit enjoys a faster convergence speed than the standard federated learning method both theoretically and empirically. The generalization bound of the FedSplit method is also studied. To practically implement the proposed method on real datasets, factor analysis is introduced to facilitate the decoupling of hidden elements. This leads to a practically implemented model for FedSplit, which we further refer to as FedFac. We demonstrate by simulation studies that using factor analysis can well recover the underlying shared/personalized decomposition. The superior prediction performance of FedFac is further verified empirically by comparison with various state-of-the-art federated learning methods on several real datasets.","url":"https://doi.org/10.1109/tpami.2026.3712213","authors":["Wang F","Tang H","Li Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/tpami.2026.3712213","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1038/s41598-026-58571-2","name":"Privacy-aware vaccine recommendation using federated learning and blockchain.","source":"pubmed","abstract":"Suitable vaccines for individuals are suggested by the vaccine recommendation system regarding certain criteria. Nevertheless, the existing studies didn't augment the vaccine recommendation system centered on users' symptoms and medical history among several geographical locations in the hyperledger fabric blockchain. Thus, in this paper, Krichevsky Dirichlet Trofimov-based latent Dirichlet allocation (KDT-LDA) and federated learning-Expcos bidirectional distillation long short-term memory (FL-EBiDLSTM)-based vaccine recommendation systems using symptoms and medical history are presented. Primarily, the vaccine symptoms dataset is taken. Then, the pre-processing is done based on named entity recognition, tokenization, and stemming. Later, by employing NSR-KMeans, the pre-processed data is grouped. Later, KDT-LDA-based symptoms and medical history modeling and adversarial debiasing-ClinicalBERT-based word embedding are carried out. Simultaneously, from the pre-processed data, the polarity score is identified. By utilizing the Cauchy Cubic-based fuzzy inference system, the labelling is performed regarding the polarity score. After that, the labelling outcomes are trained by the EBiDLSTM-based sentiment nature identification. Then, the natural language processing features are extracted from the symptoms and medical history modeling outcomes. After that, by using Spearman rank correlation, feature correlation is performed. Lastly, the appropriate vaccine is predicted based on FL-EBiDLSTM. Here, to solve the issue of training the patient data among various locations, FL is included. In real-time, vaccine demand users register with the hyperledger fabric blockchain and upload their medical history. Later, the vaccine recommendation system suggests the vaccines concerning the history. As per the outcomes, the proposed model achieved a high accuracy of 99% and outperformed prevailing techniques.","url":"https://doi.org/10.1038/s41598-026-58571-2","authors":["Shinzeer CK","Bhagat A","Kushwaha AS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-58571-2","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"doi:10.1038/s41598-026-56332-9","name":"CASCADENCE: a layered cascade defense mechanism for federated learning.","source":"pubmed","abstract":"This paper aims to enhance the security and robustness of Federated Learning (FL) systems through a multi-layered defense. We address the critical challenge of protecting distributed learning environments from adversarial attacks while maintaining high model performance during both the training and operational phases. The proposed framework is based on integrated approaches that utilize a Gaussian filter with Discrete Fourier Transform (DFT), adversarial training with differential privacy, JPEG compression, randomized smoothing, and adversarial logit pairing. It integrates multiple defense mechanisms based on system requirements, focusing on preserving model performance while ensuring robust protection during both training and testing phases. Our approach extends beyond existing solutions by introducing various staged defense implementations and analyzing their synergistic effects. Experimental results demonstrate that the proposed ensemble defense mechanism achieves the highest performance, maintaining 98.21% accuracy and an F1 score of 0.98 under attack conditions, compared to a baseline accuracy of 90.87%.","url":"https://doi.org/10.1038/s41598-026-56332-9","authors":["Hashmi SW","Shukla RM","Bhunia S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-56332-9","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1109/tpami.2026.3662990","name":"Hierarchical Information Embeddings With Neural ODEs for Personalized Federated Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2026.3662990","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/tpami.2026.3662990","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1109/jbhi.2026.3715197","name":"Federated Learning with Global Model Hint for Medical Image Object Detection.","source":"pubmed","abstract":"Building an ideal medical image object detection model often requires sufficient training data, which can be challenging to obtain in practical scenarios. Manual annotation is labor-intensive, and sharing datasets may raise data privacy concerns. Although federated learning can partially address these issues, we find an amplified feature drift problem when it is directly applied to medical image object detection. Motivated by the observation that the global model's parameters tend to align more closely with those of the oracle model than with those of the client models, we propose FedMHDet: Model Hint Federated Learning Detection Model, a novel federated learning detection framework. During the training phase, FedMHDet leverages multi-scale feature consistency as a global model hint to guide client models, thus mitigating the feature drift problem. Extensive experiments on pulmonary lesion and brain tumor detection tasks show that FedMHDet achieves favorable overall performance. Compared to the strongest baseline under each corresponding metric, it improves average AP by 1.05 and 0.19, and average sensitivity by 1.10 and 0.43 on the two tasks, respectively. We also provide in-depth analyses to support the practical use of our method. The code is available at https://github.com/bbamai/FedMHDet.","url":"https://doi.org/10.1109/jbhi.2026.3715197","authors":["Xu Z","Zhou G","Zhang H","Yang R","Li B","Lukasiewicz T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/jbhi.2026.3715197","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1109/jbhi.2025.3631706","name":"Federated Learning for Medical Image Classification: A Comprehensive Benchmark.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/jbhi.2025.3631706","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/jbhi.2025.3631706","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.3233/shti260452","name":"Recovering Sensitive Medical Text in Federated Learning.","source":"pubmed","abstract":"Federated Learning (FL) allows institutions to train shared models without exchanging raw data, making it a promising approach for healthcare applications that involve sensitive electronic health records (EHRs). However, despite this distributed design, the gradients exchanged during training can still reveal private information. In this study, we analyze how vulnerable transformer-based language models are to gradient inversion attacks, focusing on the Decepticons method, which can reconstruct original training text from shared gradients. We simulate a cross-silo FL setup with three types of French clinical reports (genetic, anesthesia, and birth records) to evaluate how batch size and sequence length affect reconstruction quality. Our experiments show that a malicious server can recover clinical text with high accuracy: token-level recovery exceeded 95% when training with batch size 1 and remained above 60% for sequences of up to 512 tokens. Reconstructed examples contained identifying elements (names, dates, genetic markers), revealing serious privacy risks for real-world use. These results emphasize that FL alone is insufficient for sensitive clinical text and that privacy-preserving defenses must be integrated before real-world deployment.","url":"https://doi.org/10.3233/shti260452","authors":["El Azzouzi M","Bellafqira R","Coatrieux G","Cuggia M","Bouzille G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3233/shti260452","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-57780-z","name":"BCAFL: a secure and efficient blockchain framework for asynchronous federated learning.","source":"pubmed","abstract":"Traditional synchronous Federated Learning (FL) is subject to the waiting latency inherent to synchronization mechanisms. Consequently, its convergence rate is constrained by straggler nodes within heterogeneous environments. Asynchronous Federated Learning (AFL) improves execution efficiency by removing global synchronization barriers. However, when integrated with blockchain for decentralized deployment, it still encounters challenges such as on-chain storage overhead arising from model parameters, convergence perturbations induced by stale gradients, and Byzantine security threats. To this end, this paper proposes BCAFL, a decentralized blockchain framework tailored for semi-asynchronous federated learning. BCAFL utilizes the InterPlanetary File System (IPFS) to implement off-chain storage for global model parameters. By integrating Model-Agnostic Meta-Learning (MAML) and PowerSGD, the framework enhances the model's local adaptation capability on non-IID data while concurrently reducing communication overhead. To safeguard model security and convergence stability in asynchronous environments, this study develops a Mutual Information and Delay-Aware (MIDA) dynamic aggregation mechanism. This mechanism leverages Mutual Information (MI) to perform model verification for defense against poisoning attacks, while simultaneously modulating aggregation weights via a dynamic aggregation factor to effectively mitigate model oscillations inherent in asynchronous convergence. Additionally, this study develops a dynamic stake-based Verifiable Random Function (VRF) committee consensus mechanism. By quantifying election weights based on node contributions, this approach enhances consensus efficiency and resistance to Sybil attacks. Simulation results demonstrate that, compared with various existing baseline schemes, BCAFL maintains the convergence accuracy of the global model while reducing communication overhead. It effectively suppresses convergence oscillations caused by asynchronous delays and defends against poisoning and Byzantine attacks. Furthermore, when the network scale is expanded to 300 nodes, the consensus latency does not show a significant increase.","url":"https://doi.org/10.1038/s41598-026-57780-z","authors":["Yun J","Liu T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-57780-z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.3390/s26144420","name":"Game-Theoretic Optimized Federated Learning for Heterogeneous IoT Object Detection.","source":"pubmed","abstract":"Federated object detection allows distributed IoT cameras to learn a shared detector without exposing raw images. Its performance is limited by non-IID scenes, unequal device resources, intermittent links, and selfish participation. We propose GO-FedDet, a game-theoretic optimized federated detection framework for heterogeneous IoT application. Client participation is formulated as a Stackelberg game, selection is formulated as an exact-potential resource game, and the equilibrium is embedded into a proximal detection objective. A utility-aligned aggregation balances detection contribution, communication/energy cost, and long-term fairness. We prove the existence of a Stackelberg equilibrium and the finite-improvement convergence of the selection game, and we derive a non-convex convergence bound under client drift and partial participation. Experiments on heterogeneous edge object detection show that GO-FedDet improves accuracy, lowers communication cost, and stabilizes fairness compared with representative federated baselines.","url":"https://doi.org/10.3390/s26144420","authors":["Wang Z","Chen J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3390/s26144420","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1007/s10916-026-02436-8","name":"Security Analysis of a Federated Learning Framework for Medical Image-to-Image Translation.","source":"pubmed","abstract":"Federated Learning (FL) emerged as a privacy-preserving paradigm for collaborative training of deep learning models across institutions without sharing patient data. This approach has been applied to complex tasks such as medical image-to-image (I2I) translation, including MRI-to-synthetic CT (sCT) generation. However, existing federated I2I frameworks often assume privacy preservation as an inherent property of FL rather than a requirement to be explicitly validated, leaving their robustness to representative adversarial threat scenarios largely unexplored. In this study, we evaluated the vulnerability of a federated MRI-to-sCT translation framework (FedSynthCT-Brain) to three representative attack classes: Deep Leakage from Gradients (DLG), Federated Membership Inference Attack (FedMIA), and data poisoning. The efficacy of corresponding defense mechanisms, such as Secure Aggregation (SecAgg) and Byzantine-robust median aggregation (FedMedian), were assessed. DLG enabled only the recovery of coarse anatomical structures, with no clinically identifiable details (SSIM &#x2264; 0.16, PSNR &#x2264; 11&#xa0;dB) across clients, suggesting limited vulnerability under the evaluated DLG setting. In contrast, FedMIA achieved high membership discrimination, with AUC scores between 0.92 and 0.99, revealing a critical privacy vulnerability. The introduction of SecAgg reduced AUC values to near-random levels (0.23-0.56) across all centers without impacting synthesis quality. Under high-noise poisoning, the standard federated averaging (FedAvg) aggregation rendered the federation inoperative, while FedMedian restored performance close to the no-poisoning baseline in most scenarios, with significant residual degradation in specific center configurations. At low noise levels, the advantage of FedMedian was less consistent, as low-level noise injection may be indistinguishable from natural heterogeneity across centers, potentially enabling stealthy degradation. These findings demonstrate that federated I2I translation frameworks are not inherently secure and require explicit, multi-layered evaluation. As FL is increasingly adopted in clinical workflows, our results underscore the necessity of integrating cryptographic, algorithmic, and infrastructural safeguards for secure deployment.","url":"https://doi.org/10.1007/s10916-026-02436-8","authors":["Raggio CB","Bucher L","Blanck O","Cicone F","Zaffino P","Spadea MF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1007/s10916-026-02436-8","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/jbhi.2025.3630999","name":"Privacy-Preserving Lightweight Federated Learning for Heterogeneous Data in Internet of Medical Things.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/jbhi.2025.3630999","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/jbhi.2025.3630999","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1038/s41598-026-60725-1","name":"Differentially private federated learning for localized control of infectious disease dynamics.","source":"pubmed","abstract":"In times of epidemics, swift reaction is necessary to mitigate epidemic spreading. For this reaction, localized approaches have several advantages, limiting necessary resources and reducing the impact of interventions on a larger scale. However, training a separate machine learning (ML) model on a local scale is often not feasible due to limited available data. Centralizing the data is also challenging because of its high sensitivity and privacy constraints. In this study, we consider a localized strategy based on the German counties and communities managed by the related local health authorities (LHA). For the preservation of privacy to not oppose the availability of detailed situational data, we propose a privacy-preserving forecasting method that can assist public health experts and decision makers. ML methods with federated learning (FL) train a shared model without centralizing raw data. Considering the counties, communities or LHAs as clients and finding a balance between utility and privacy, we study a FL framework with client-level differential privacy (DP). We train a shared multilayer perceptron on sliding windows of recent case counts to forecast the number of cases in the future, while clients exchange only norm-clipped updates and the server aggregates updates with DP noise. We evaluate the approach on COVID-19 data on county-level during two phases: November 2020 and March 2022 (Omicron). As expected, very strict privacy ([Formula: see text]) yields unstable, unusable forecasts. At a moderately strong but still privacy-preserving level ([Formula: see text]), the DP model closely approaches the non-DP model: [Formula: see text] (vs. 0.96) and mean absolute percentage error (MAPE) [Formula: see text] in November 2020; [Formula: see text] (vs. 0.90) and MAPE [Formula: see text] in March 2022. Overall, our results support the feasibility of privacy-preserving collaboration among health authorities for local forecasting. In the evaluated COVID-19 phases, client-level DP-FL delivered useful county-level predictions with formal privacy guarantees under the stated threat model. The appropriate privacy budget should nevertheless be re-evaluated for other epidemic phases and applications.","url":"https://doi.org/10.1038/s41598-026-60725-1","authors":["Kerkouche R","Zunker H","Fritz M","Kühn MJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-60725-1","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.21203/rs.3.rs-9499405/v1","name":"Defending Patient Privacy in Federated Learning with Shadow Model","source":"europepmc","abstract":"Abstract Federated Learning (FL) has become a approach for training models together in privacy-sensitive domains like healthcare, where sharing of raw data is frequently restricted. But recent research has shown that Gradient Inversion Attacks (GIAs) can use shared model updates to recreate sensitive training data, which puts patient privacy at risk. Current defense mechanisms, like differential privacy and gradient perturbation techniques, often use uniform protection strategies that either make the model work less well or don't protect important data areas well enough.This study presents an enhanced privacy-preserving framework based on an augmented Shadow Defense (Shadow Def) mechanism to address these limitations.The suggested method includes a multi-phase, region-aware defense strategy that uses Fast Fourier Transform for frequency-domain sensitivity analysis, Mean Squared Error (MSE) for vulnerability mapping, and multi-component noise injection. To make the system more resistant to reconstruction attacks while keeping training stable, gradient direction perturbation, temporal smoothing, and adaptive noise scaling are also used. The suggested method has undergone comprehensive testing in a federated learning context, utilizing simulated gradient inversion attacks on two prominent medical imaging datasets: Chest X-Ray and Eye PACS. The adversarial training process significantly reduces reconstruction quality (i.e., reconstruction errors), as shown by a higher RMS error (MSE), a lower structural similarity index (SSIM), and a lower peak signal-to-noise ratio (PSNR). Conversely, the model's classification accuracy remains elevated, exhibiting only a slight decline in performance. These results show that there is a good balance between protecting patient privacy and making the model available in a healthcare setting.","url":"https://doi.org/10.21203/rs.3.rs-9499405/v1","authors":["Jansi Rani M","Hema Meena R","Joshitha K"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.21203/rs.3.rs-9499405/v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1038/s41746-026-02958-y","name":"Nationwide federated learning for histopathology: secure deployment across Germany behind firewalls.","source":"pubmed","abstract":"Federated Learning (FL) enables collaborative training across institutions without sharing sensitive data, a solution for privacy-preserving AI in medical imaging. However, hospital deployment remains challenging due to strict data protection regulations, heterogeneous infrastructures, and limited network accessibility behind firewalls. We introduce TheODen, an open-source framework for Federated training on histopathology Whole Slide Imaging (WSI). It requires no open client-side ports, enabling training through firewalls via a secure reverse-proxy architecture. We conducted, to our knowledge, the first nationwide FL study for histopathology segmentation of colorectal cancer across three German university hospitals, using breast and colorectal cancer datasets without opening firewall ports. TheODen achieves robust segmentation, with global average dice scores of 0.764 on BCSS and 0.754 on SemiCOL despite data heterogeneity and network constraints. These findings underline TheODen's potential to facilitate secure, large-scale collaborations between medical institutions and to accelerate clinical translation of AI models under real-world infrastructure constraints, providing a privacy-preserving-by-design architecture for future collaborations.","url":"https://doi.org/10.1038/s41746-026-02958-y","authors":["Babendererde N","Lemke N","Stieber J","Fuchs M","Weng Z","Eich ML","Lingscheidt T","Mairinger F","Büttner R","Tolkach Y","Mukhopadhyay A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41746-026-02958-y","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1080/15265161.2026.2690950","name":"Bridging the Ethical Gap: Reconciling Federated Learning With ICH E6 (R3) Oversight.","source":"pubmed","abstract":"","url":"https://doi.org/10.1080/15265161.2026.2690950","authors":["Ménard T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1080/15265161.2026.2690950","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-58414-0","name":"HCFL: hybrid contribution-driven federated learning for fair and efficient optimization.","source":"pubmed","abstract":"Federated Learning (FL) enables collaborative model training across decentralized data silos while preserving data privacy. However, client selection strategies in conventional FL processes typically rely on single-dimensional evaluation metrics, which fail to capture data diversity and overlook the dynamic nature of client contributions, particularly in domains characterized by sparse and heterogeneous data, such as healthcare and drug discovery. These limitations ultimately hinder the global model's generalization ability and reduce training efficiency. To address these challenges, this paper proposes an adaptive FL framework that employs a hybrid contribution evaluation mechanism as the core principle for client selection and resource management. The proposed approach quantifies each client's effectiveness by integrating two complementary dimensions: (i) a performance-based evaluation that measures the immediate impact of a client's update on the global optimization trajectory, and (ii) a coverage-based evaluation that estimates data diversity in the latent embedding space without exposing raw data. By combining these two criteria, the hybrid mechanism ensures that highly contributive clients are preferentially selected while preventing the permanent exclusion of any participant, thereby maintaining a balanced trade-off between efficiency and fairness. Experimental results demonstrate that the proposed framework outperforms existing FL baselines in terms of training efficiency, data utilization, and fairness.","url":"https://doi.org/10.1038/s41598-026-58414-0","authors":["Jeong Y","Lee S","Lee J","Choi WG"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-58414-0","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.1080/15265161.2026.2690955","name":"Whose Federation? Epistemic Closure, Community Exclusion, and the Political Economy of Federated Learning.","source":"pubmed","abstract":"","url":"https://doi.org/10.1080/15265161.2026.2690955","authors":["Acevedo H","Al-Louzi R","Celi LA","Chowdhury M","Hernandez-Boussard T","Hendl T","Kabede AA","Lucas MM","Savary M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1080/15265161.2026.2690955","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"doi:10.1109/isbi61048.2026.11515381","name":"HYPERBOLIC MODEL AGGREGATION FOR FEDERATED LEARNING IN FMRI.","source":"pubmed","abstract":"The privacy-sensitive nature of clinical data often limits the use of machine learning in medical imaging applications, particularly for modalities with high acquisition costs such as functional MRI (fMRI). Federated learning mitigates data-sharing barriers by training site-specific models locally and aggregating weights centrally into a server model. However, small and heterogeneous per-site samples in medical imaging heighten the need for robust model-aggregation strategies. In this work, we introduce a federated aggregation scheme based on hyperbolic geometry to provide a robust and flexible approach to federated model weight integration. The proposed scheme is plug-and-play for standard federated learning loops. Empirically, our method improves stability and accuracy across multi-site fMRI data from ABIDE I, yielding more consistent convergence versus methods based on Euclidean mean and median. Codes are publicly available at https://github.com/Jiyao96/FedHAvg.","url":"https://doi.org/10.1109/isbi61048.2026.11515381","authors":["Wang J","Dvornek N","Duan P","Marshall A","Staib L","Duncan J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/isbi61048.2026.11515381","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1109/tpami.2025.3637562","name":"A Bayesian Framework for Clustered Federated Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2025.3637562","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/tpami.2025.3637562","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1016/j.identj.2026.109704","name":"Federated Learning in Endodontics: A Framework for Privacy-Preserving Multicentre Artificial Intelligence.","source":"pubmed","abstract":"High-quality artificial intelligence (AI) models in endodontics require access to diverse, well-annotated datasets. This review introduces federated learning (FL) as a privacy-preserving framework for collaborative AI in endodontics.","url":"https://doi.org/10.1016/j.identj.2026.109704","authors":["Turky M","Samaranayake L","Osathanon T","Dummer PMH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/j.identj.2026.109704","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-57744-3","name":"Federated learning and digital twins for lifecycle optimization in Urban building renewal.","source":"pubmed","abstract":"The restructuring of aging Chinese city infrastructure requires new approaches based on computational intelligence and optimization lifecycle structures. Current building renovation methods are limited by the lack of seamless linkage between real-time operation data and predictive lifecycle management, particularly regarding privacy and multi-building management in heterogeneous environments. This paper introduces a simulation framework that combines federated deep reinforcement learning and behavioural digital twins to restore Chinese buildings into smart cities. The architecture involves a three-layer system: a physical layer with IoT-enabled sensing networks in distributed building clusters, a digital twin layer with real-time BIM-to-operational model synchronization and LiDAR-enhanced geometric precision, and an intelligent layer with privacy-reflecting federated proximal policy optimization for distributed decision-making. The framework addresses the gap between fixed 3D representations and variable behavioural modelling by integrating continuous learning processes that respond to changing occupancy, energy consumption, and structural decay. Simulation studies of Chinese urban residential communities show better performance: 27.3% lower lifecycle operational costs, 34.6% improved energy efficiency with the same thermal comfort, and 39.7% better structural integrity prediction with CFRP-optimized improvements. The federated learning architecture results in 5.8% cost savings and 6.2% emission reduction, offering scalable, privacy-sensitive urban renewal decision support for China's modernization efforts.","url":"https://doi.org/10.1038/s41598-026-57744-3","authors":["Zaofei J","Liao F","Metwally ASM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-57744-3","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1109/jbhi.2025.3596156","name":"Quantum Federated Learning in Healthcare: The Shift From Development to Deployment and From Models to Data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/jbhi.2025.3596156","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/jbhi.2025.3596156","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/tpami.2026.3697332","name":"Privacy-preserving Online Federated Learning for Massive Infinite Streams.","source":"pubmed","abstract":"Online federated learning (OFL) is essential for privacy-preserving collaborative online analytics over decentralized streams. Different from batch-based FL, OFL faces new challenges including longitudinal privacy leakage, and accumulated utility loss and communication costs, caused by the infinite data streams. This paper first extends the definition of traditional differential privacy (DP) to OFL, to provide window-based privacy protection with a tunable granularity for infinite streams. By analyzing baseline methods, a generic sampling-based solution framework is then proposed for designing a DP-enhanced OFL algorithm. We prove that despite the DP constraint, the sampling solution framework can achieve an asymptotic optimality when time tends to infinity. Finally, we present Sampling$^{3}$-OFL, an adaptive triple-sampling strategy driven by deep reinforcement learning, which can dynamically determine a near-optimal sampling strategy with significant gains in both utility and efficiency. Extensive experiments on six real-world datasets demonstrate that Sampling$^{3}$-OFL can scale to millions of streams, and achieves utility improvements of 0.74%-15.84% and communication cost reductions of 33.33%-95.24% across these datasets compared to state-of-the-art methods.","url":"https://doi.org/10.1109/tpami.2026.3697332","authors":["Shi L","Ren X","Yang S","Zhao C","Hao Y","Xu Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/tpami.2026.3697332","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1016/j.advms.2026.07.003","name":"Performance of federated learning models in health services research: A systematic review and meta-analysis.","source":"pubmed","abstract":"This study aimed to assess the performance of federated learning (FL) models and compare their performance with local and centralized models.","url":"https://doi.org/10.1016/j.advms.2026.07.003","authors":["Wu G","Yang F","Wu Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/j.advms.2026.07.003","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/jbhi.2025.3578125","name":"A Lightweight Privacy-Preserving Federated Learning Framework for Heterogeneity-Resilient Skin Cancer Diagnosis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/jbhi.2025.3578125","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/jbhi.2025.3578125","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.21203/rs.3.rs-10156959/v1","name":"A Multi-Lingual Cyberbullying Detection and Blockchain-Integrated Privacy-Preserving Federated Learning Framework","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10156959/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.21203/rs.3.rs-10156959/v1","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.20944/preprints202607.0493.v1","name":"Adaptive Client Selection in Tangle-Enabled Federated Learning for Efficient and Traceable Training","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202607.0493.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.20944/preprints202607.0493.v1","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.20944/preprints202607.0829.v1","name":"Comparative Performance Evaluation of Federated Learning Frameworks for Edge-Oriented 6G Applications","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202607.0829.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.20944/preprints202607.0829.v1","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1080/15265161.2026.2690941","name":"Many Hands, But Who Is In Control? Two Types of Control Gaps in Federated Learning for Healthcare.","source":"pubmed","abstract":"","url":"https://doi.org/10.1080/15265161.2026.2690941","authors":["Drogt J","van der Wal M","Jongsma K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1080/15265161.2026.2690941","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-56310-1","name":"Resource management for blockchain enhanced federated learning in wireless edge networks.","source":"pubmed","abstract":"Though machine learning is widely used in wireless edge networks, the transmission of raw data still suffers from security and privacy leakage. Federated learning (FL) addresses these privacy concerns by enabling model training without sharing raw data. However, traditional centralized FL is vulnerable to a single point of failure. Blockchain-based federated learning (BFL) technology can provide FL with a more reliable and secure environment. In wireless edge networks with limited resources, BFL systems encounter challenges related to computing demands and network transmission overhead. To address these issues, we propose a BFL framework for wireless edge networks, which includes local client training, a consensus process, and edge server aggregation. A client selection policy is designed to exclude low-quality clients that could degrade training efficiency and accuracy. Additionally, a joint client selection and resource allocation scheme is implemented to optimize the allocation of computing and bandwidth resources necessary for BFL training and consensus. Simulation results demonstrate that the proposed approach improves BFL system accuracy while reducing delay.","url":"https://doi.org/10.1038/s41598-026-56310-1","authors":["Yang Z","Qi W","Guo L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-56310-1","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-63242-3","name":"Privacy-preserving clustered federated learning via differential privacy and homomorphically encrypted prototypes.","source":"pubmed","abstract":"Clustered federated learning (CFL) is an effective paradigm for handling statistical heterogeneity by grouping clients with similar data characteristics and learning cluster-specific models. However, existing CFL methods often expose sensitive clustering signals or cluster-specific updates to the server, which may reveal latent client similarity relations and weaken privacy protection. To address this issue, we propose Privacy-Preserving Clustered Federated Learning (PPCFL), a split-stream framework that integrates adaptive Gaussian perturbation with threshold Paillier encrypted aggregation. In PPCFL, backbone updates are protected by adaptive Gaussian perturbation before plaintext aggregation, while clustering signatures and cluster-head updates are first perturbed by stream-specific adaptive Gaussian mechanisms and then uploaded under threshold Paillier encryption. The server performs ciphertext-domain aggregation for clustering prototypes and cluster-head updates, whereas plaintext prototypes and cluster-level decrypted aggregates are recovered by a qualified threshold-decryption client subset without giving the server decryption capability. In addition, PPCFL adopts round-wise budget growth, utility-aware refinement, and adaptive clipping-threshold updates to improve the privacy-utility trade-off under dynamic Non-IID settings. Experiments on MNIST, Fashion-MNIST, and CIFAR-10 show that PPCFL achieves the highest final-round accuracy among the evaluated methods in the reported settings while providing enhanced protection for clustering-related information and cluster-specific updates. Under the representative Dirichlet setting [Formula: see text], PPCFL improves the final accuracy over DP-FedAvg by 0.33, 1.73, and 2.62 percentage points on MNIST, Fashion-MNIST, and CIFAR-10, respectively, and over IFCA by 0.98, 8.28, and 10.24 percentage points.","url":"https://doi.org/10.1038/s41598-026-63242-3","authors":["Zhan J","Jiang Z","Liu L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-63242-3","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.21203/rs.3.rs-9888411/v1","name":"On the Interaction Between Personalization and Optimization in Federated Learning for Medical Image Classification","source":"europepmc","abstract":"Abstract Federated learning (FL) has emerged as a promising paradigm for privacy-preserving medical image analysis, enabling collaborative model training across distributed institutions without sharing sensitive patient data. However, two key challenges remain: instability of model optimization under non-independent and identically distributed(non-IID)data, and the need for client-specific adaptation. In this paper, we propose a hybrid federated learning framework that integrates proximal regularization (FedProx) with personalized model adaptation(FedPer) to address these challenges jointly. Unlike prior work that treats these mechanisms independently, we investigate their interaction and demonstrate how their combination improves both convergence stability and generalization. We evaluate the proposed framework on the MURA dataset under simulated heterogeneous client distributions. Experimental results show that the hybrid approach consistently outperforms standard federated baselines, achieving up to 96. These findings highlight the importance of jointly considering optimization stability and personalization in federated learning systems, particularly for real-world medical imaging applications.","url":"https://doi.org/10.21203/rs.3.rs-9888411/v1","authors":["Rimsha Ansar","Zainab Salma","Raquel Hijón Neira"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.21203/rs.3.rs-9888411/v1","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1109/tpami.2026.3672655","name":"Co-Boosting++: Coupled Optimization of Data and Ensemble for One-Shot Federated Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2026.3672655","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/tpami.2026.3672655","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.3389/frai.2026.1852196","name":"FuzzyFed-CNN: secure and explainable multimodal federated learning for early Alzheimer's diagnosis.","source":"pubmed","abstract":"Alzheimer's disease (AD) is a progressive neurodegenerative condition that has a great effect on cognitive impairment and quality of life. Timely intervention requires the early and reliable diagnosis of the patient, but current diagnostic systems are frequently troubled with the limitations of data privacy, their lack of interpretability, and the fusion of heterogeneous clinical and imaging data.","url":"https://doi.org/10.3389/frai.2026.1852196","authors":["Mohanraj S","Radhakrishnan S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3389/frai.2026.1852196","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s43856-026-01820-2","name":"Federated Learning Authenticity Standard for Healthcare as derived from lessons in self-driving cars.","source":"pubmed","abstract":"In this Perspective, we highlight a critical mislabeling problem in healthcare federated learning research. Although federated learning is widely promoted as a privacy-preserving approach for multi-institutional artificial intelligence development, most published studies are still single-institution simulations that do not cross real organizational boundaries. As such, we propose a six-level classification scale (0 to 5), modeled on the autonomous driving taxonomy used in the automotive industry, that ranges from no federation through purely mathematical simulations to fully autonomous, continuously learning systems operating across separate institutions. We term it Federated Learning Authenticity Standard for Healthcare. Further we propose that authors declare in their studies the achieved level in a single sentence within the abstract, making this standard an honest description of study design unavoidable. We discuss the standard&#x2019;s potential role as a reporting framework analogous to established guidelines for clinical trials and prediction model studies.","url":"https://doi.org/10.1038/s43856-026-01820-2","authors":["Santos R","Keane PA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s43856-026-01820-2","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1109/tpami.2026.3699626","name":"Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering.","source":"pubmed","abstract":"Federated learning (FL) is a promising framework that models distributed machine learning while protecting the privacy of clients. However, FL suffers performance degradation from heterogeneous and limited data. To alleviate the degradation, we present a novel personalized Bayesian FL approach named pFedBayes. By using the trained global distribution from the server as the prior distribution of each client, each client adjusts its own distribution by minimizing the sum of the reconstruction error over its personalized data and the KL divergence with the downloaded global distribution. Then, we propose a sparse personalized Bayesian FL approach named sFedBayes to enhance the inference efficiency. To overcome the extreme heterogeneity in non-i.i.d. data, we propose a clustered Bayesian FL model named cFedbayes by learning different prior distributions for different clients. Theoretical analysis gives the generalization error bound of three approaches and shows that the generalization error rates of the proposed approaches achieve minimax optimality up to a logarithmic factor. Moreover, cFedBayes achieves a cluster-level generalization error bound, rather than a single uniform bound in pFedBayes. Numerous experiments demonstrate that the proposed approaches have better performance than other advanced personalized methods on private models in the presence of heterogeneous and limited data.","url":"https://doi.org/10.1109/tpami.2026.3699626","authors":["Zhang X","Li W","Shao Y","Liu Y","Zhou K","Li Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/tpami.2026.3699626","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.20944/preprints202607.1189.v1","name":"Privacy-Preserving Federated Learning for Artistic Image Analytics in Visual IoT Sensor Networks","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202607.1189.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.20944/preprints202607.1189.v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1109/tpami.2025.3612302","name":"Sample-Level Prototypical Federated Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2025.3612302","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/tpami.2025.3612302","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.21203/rs.3.rs-9803158/v1","name":"Evaluating Federated Learning Under Data Heterogeneity: Failure Modes and Practical Mitigations ","source":"europepmc","abstract":"Abstract Federated Learning (FL) allows for collaborative model training across decentralized clients while maintaining data privacy; yet real-world deployments always include heterogeneous and non-identically distributed (non-IID) client data. Existing FL research primarily assesses system performance using aggregate metrics like global or average accuracy, which can hide differences in client behavior. This study provides a thorough empirical evaluation of federated learning under controlled data heterogeneity, with a major emphasis on worst-client performance as a measure of client-level robustness. Experiments are carried out on MNIST and CIFAR-10 utilizing normal FedAvg training and commonly accepted baselines, such as FedProx and q-FedAvg, at various levels of Dirichlet-based label skew. The results show that, as heterogeneity increases, worstclient accuracy falls quickly, revealing silent errors that were previously disguised by aggregate evaluation. A comparison of mitigation options reveals that server-side client re-weighting improves worst-case performance under modest heterogeneity but becomes unstable and damaging under strong non-IID situations. In contrast, simple client-side data balancing reliably improves worst-client accuracy across all heterogeneity regimes without requiring additional infrastructure or algorithmic complexity. These findings emphasize the limitations of aggregate evaluation in federated learning, as well as the need of client-aware metrics and mitigation measures that increase local update quality while dealing with client-level inequalities in the face of true data heterogeneity.","url":"https://doi.org/10.21203/rs.3.rs-9803158/v1","authors":["Jyotiprakash Panda","Om Patil"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.21203/rs.3.rs-9803158/v1","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-58988-9","name":"Mitigating request flooding attack in named data networking using federated learning.","source":"pubmed","abstract":"Named Data Networking (NDN) represents a paradigm shift toward content-centric architectures but remains critically vulnerable to Interest Flooding Attacks (IFAs), where malicious actors overwhelm router Pending Interest Tables with spurious requests, causing service degradation and denial-of-service. To address the limitations of existing approaches, including high false positives in threshold-based methods and substantial overhead in centralized learning, we propose FL-IFAshield, a novel federated learning framework for adaptive IFA mitigation. Our solution integrates dynamic Poisson-EMA thresholding for accurate flood detection, entropy-aware federated aggregation to handle non-IID traffic distributions across edge routers, and Byzantine-robust mechanisms with differential privacy guarantees. Comprehensive evaluation on the FIT/IoT-LAB testbed with 100 routers demonstrates exceptional performance: 93.1% F1-score in attack detection, only 5% false positives, 28 ms average end-to-end latency ([Formula: see text]), and over 90% legitimate Interest Satisfaction Ratio under sophisticated collusive attacks, while maintaining minimal computational overhead (&lt;9% CPU utilization on ARMv8 routers). FL-IFAshield significantly improves security performance, offering 35% higher accuracy than static thresholding and 60% lower communication overhead than centralized approaches. While simpler heuristic baselines naturally incur marginally lower computational footprints, our solution delivers the optimal overall operational balance among high precision, low end-to-end latency ([Formula: see text]), and resource efficiency in constrained edge computing environments.","url":"https://doi.org/10.1038/s41598-026-58988-9","authors":["Benmaidi ML","Lagraa N","Brik B","Jlali L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-58988-9","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.3233/shti260241","name":"A Durable Backdoor Attack on Medical Imaging via Federated Learning.","source":"pubmed","abstract":"Federated Learning (FL) enables multiple healthcare institutions to jointly train models without sharing raw patient data, making it a natural fit for privacy-sensitive medical applications. However, its distributed and partially trusted nature exposes it to backdoor attacks, in which malicious clients inject hidden behaviors that activate at inference. In this paper, we propose a durable backdoor attack designed for FL. The attack leverages a Generative Adversarial Network guided by the global model to generate synthetic data that closely matches the distribution of benign clients. Furthermore, we introduce a two-step strategy that enhances the robustness of the injected backdoor. We evaluate our method on the MedMNIST benchmark under a non-IID data distribution to simulate realistic medical scenarios. Experimental results demonstrate that our approach achieves a durable backdoor effect, persisting even under limited attacker participation. Therefore, there is a need for more resilient defense mechanisms to ensure the trustworthiness of Federated Learning in medical applications.","url":"https://doi.org/10.3233/shti260241","authors":["Faraoun H","Bellafqira R","Coatrieux G","Kallas K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3233/shti260241","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1080/15265161.2026.2637093","name":"Federation Opacity and the Promise of Federated Learning in Healthcare.","source":"europepmc","abstract":"","url":"https://doi.org/10.1080/15265161.2026.2637093","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1080/15265161.2026.2637093","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.3390/s26134322","name":"FedHSFV: Federated Learning for Finger Vein Recognition via Hierarchical Decoupling and Subspace Metric.","source":"pubmed","abstract":"Finger vein recognition (FVR) has significant potential in biometrics due to its high accuracy and intrinsic liveness detection capabilities. However, the increasingly stringent privacy regulations have presented severe data security challenges for traditional centralized training. While federated learning (FL) mitigates these privacy concerns through a decentralized training paradigm, conventional FL algorithms that seek a single global model experience significant performance degradation on non-independent and identically distributed (Non-IID) data in real-world cross-institutional deployments. This degradation stems primarily from a dual-heterogeneity issue that involves domain shift caused by hardware discrepancies across acquisition devices, and label skew resulting from nonoverlapping user identities. To address this dual-heterogeneity challenge, we propose a personalized federated learning framework driven by hierarchical parameter decoupling and subspace metric. First, we designed a hierarchical parameter decoupling architecture. Macroscopically, the architecture retains the classifier locally to isolate label heterogeneity; microscopically, it introduces an additive parameter decomposition that decouples the feature extractor on a global full-rank basis (to capture domain-invariant semantics, namely, the shared physiological vein topologies) and a local low-rank adapter (that accommodates device-specific characteristics, such as hardware-induced noise and illumination discrepancies). Furthermore, we propose a subspace similarity matching strategy based on principal angles on the Grassmann manifold. By exploiting the geometric properties of low-rank projection matrices, this strategy accurately quantifies the underlying distribution discrepancies among clients to guide personalized weighted aggregation. Extensive experiments on six public finger vein datasets demonstrate that the proposed framework significantly improves the overall recognition performance and mitigates performance degradation caused by data heterogeneity.","url":"https://doi.org/10.3390/s26134322","authors":["Zhou X","Wang Y","Cui J","Guo J","Ren H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3390/s26134322","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1038/s41598-026-56609-z","name":"Privacy-preserving federated learning for interpretable student at-risk prediction across schools.","source":"pubmed","abstract":"Early-warning systems for at-risk students increasingly rely on predictive models trained on sensitive educational records. However, centralized learning pipelines raise concerns about privacy, institutional data sovereignty, and auditability, particularly when student-level data are shared across institutions. This study presents Federated Learning for At-Risk Student Prediction with Differential Privacy and Proof-Before-Train (FL-AtRisk-DP-PBT), a federated learning framework for multi-school at-risk prediction that integrates Federated Averaging (FedAvg)-based training, client-side DP, and a PBT protocol for verifiable logging of client participation and model states. The framework uses a single interpretable global logistic-regression classifier and is evaluated under centralized, standard federated, and FL&#x2009;+&#x2009;DP+PBT regimes on three educational datasets: a primary merged cohort of 14,003 students partitioned into 10 simulated schools, the xAPI-Edu-Data click-stream corpus, and the Students Performance in Exams dataset. On the primary dataset, the centralized model achieves 99.14% accuracy, F1&#x2009;=&#x2009;0.9915, and area under the curve (AUC)&#x2009;=&#x2009;0.9998, while the FedAvg and FL&#x2009;+&#x2009;DP+PBT variants achieve 98.61%/0.9863/0.9993 and 98.00%/0.9802/0.9992, respectively. On xAPI and Exams, FL&#x2009;+&#x2009;DP+PBT reaches approximately 93-94% accuracy, F1&#x2009;&#x2248;&#x2009;0.92-0.93, and AUC&#x2009;&#x2248;&#x2009;0.97-0.98. Coefficient-based feature-importance analysis indicates that FL&#x2009;+&#x2009;DP+PBT preserves broadly similar interpretation patterns to the centralized and non-private federated baselines. The PBT ablation introduces only small metric changes relative to DP-only federated training. Overall, the results suggest that interpretable federated at-risk prediction can retain competitive utility while keeping student records local and adding privacy-preserving and verifiable training mechanisms. These findings should be interpreted within the evaluated datasets, simulated school partitions, and label definitions.","url":"https://doi.org/10.1038/s41598-026-56609-z","authors":["Jodayree M","Ghafi AK","Atashafrouz M","Shafiabadi MH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-56609-z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1038/s41598-026-49544-6","name":"BLEND blockchain and federated learning enabled data sharing network.","source":"pubmed","abstract":"Combining blockchain and federated learning has emerged as a promising solution for secure, privacy-preserving data sharing and collaborative training of machine learning models in decentralised settings. However, their methods suffer from scalability issues, computational overhead, and challenges in preserving privacy in dynamic environments such as IoT, healthcare, and smart cities. Although blockchain-based solutions ensure data confidence and provenance, they come at the cost of the high computational overhead involved in transaction validation and consensus. In a similar spirit to federated learning, where local models are trained on sensitive data, and only aggregation is performed without centralising the data, traditional approaches fail to provide efficient aggregation and do not allow for secure transmission. This paper proposes BLEND, a Blockchain- and federated-learning-enabled network for Data sharing, as a new framework to tackle this issue. It will integrate a new consensus protocol, adaptive encryption schemes, and smart contract-based aggregation to deliver outstanding security, scalability, and operational efficiency. The proposed framework automatically adjusts its encryption strength in real time based on threat intelligence, enabling secure data while enhancing model performance. We show through experiments that BLEND outperforms existing blockchain-based federated learning methods in terms of latency, computation cost, and model accuracy by several orders of magnitude. Exploiting this commonality, the frame can reduce the existing framework's computational overhead by as much as 20%, while maintaining around 90-95% of the original model's accuracy and achieving lower latency than traditional methods. The framework enables privacy-friendly practical scenarios that enable large-scale, distributed data sharing and model training. BLEND offers industries such as healthcare and IoT a performant, robust, and scalable instruction-level collaborative solution that does not compromise performance or privacy.","url":"https://doi.org/10.1038/s41598-026-49544-6","authors":["Sudhakar G","Reddy MI","Pradeep KR","Madhavi GB","Reddy YS","Bollimuntha M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-49544-6","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.20944/preprints202607.2135.v1","name":"FLVaccin: Unbalanced Hierarchical Federated Learning with Vaccination-Calibrated Adaptive Quarantine for Robust Poisoning Defense","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202607.2135.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.20944/preprints202607.2135.v1","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.20944/preprints202605.1277.v1","name":"Privacy-Enhanced Federated Learning Model for Secure Internet of Things Environments","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202605.1277.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.20944/preprints202605.1277.v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1109/tpami.2026.3688672","name":"Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration.","source":"pubmed","abstract":"Decentralized Federated Learning has emerged as an alternative to centralized architectures due to its faster training, privacy preservation, and reduced communication overhead. In decentralized communication, the server aggregation phase in Centralized Federated Learning shifts to the client side, which means that clients connect with each other in a peer-to-peer manner. However, compared to the centralized mode, data heterogeneity in Decentralized Federated Learning will cause larger variances between aggregated models, which leads to slow convergence in training and poor generalization performance in tests. To address these issues, we introduce Catalyst Acceleration and propose an acceleration Decentralized Federated Learning algorithm called DFedCata. It consists of two main components: the Moreau envelope function, which primarily addresses parameter inconsistencies among clients caused by data heterogeneity, and Nesterov's extrapolation step, which accelerates the aggregation phase. Theoretically, we prove the optimization error bound and generalization error bound of the algorithm, providing a further understanding of the nature of the algorithm and the theoretical perspectives on the hyperparameter choice. Empirically, we demonstrate the advantages of the proposed algorithm in both convergence speed, computational cost, and generalization performance on CIFAR10/100 and Tiny-ImageNet with various non-iid data distributions. Moreover, extensive experiments are conducted to validate the theoretical properties of DFedCata, showing strong consistency between theory and empirical observations.","url":"https://doi.org/10.1109/tpami.2026.3688672","authors":["Li Q","Zhang M","Liu Y","Yin Q","Shen L","Cao X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/tpami.2026.3688672","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-37819-x","name":"Secure adaptive federated learning for scalable anomaly detection in industrial IoT networks.","source":"pubmed","abstract":"The rapid expansion of Industrial Internet of Things (IIoT) infrastructures has increased the demand for robust, scalable, and privacy-preserving intrusion and anomaly detection mechanisms. Traditional centralized detection systems face critical limitations, including high privacy risks, communication bottlenecks, and vulnerability to adversarial tampering. To address these challenges, we propose SAFNet-IoT, a Secure Adaptive Federated Network that integrates Blockchain-Based Authentication (BBA) with an Adaptive Autoencoder-LSTM (AAEL) anomaly detection module for decentralized IIoT environments. The BBA mechanism ensures secure model update validation and integrity preservation through smart contracts, effectively mitigating malicious model manipulation. Meanwhile, the AAEL module dynamically adjusts its hyperparameters based on real-time feedback to enhance detection accuracy under heterogeneous and non-stationary IIoT conditions. Experimental results demonstrate that SAFNet-IoT achieves 94.7% anomaly detection accuracy, surpassing conventional federated and deep learning baselines such as FedAVG-LSTM (93.1%) and deep autoencoders (91.2%). The framework additionally reduces communication overhead by 30%, achieves a bandwidth reduction ratio of 0.67, and maintains an average local training time of 1.4&#xa0;s with an end-to-end system latency of 4.6&#xa0;s, enabling deployment on resource-constrained IIoT nodes. The blockchain layer provides 98.5% authentication success and 91.2% malicious update detection, outperforming traditional secure aggregation schemes. These results highlight SAFNet-IoT as an effective and resilient intrusion and anomaly detection solution for next-generation IIoT environments. Future work will focus on optimizing smart contract execution costs and extending SAFNet-IoT toward multi-modal data fusion and large-scale, real-time anomaly detection.","url":"https://doi.org/10.1038/s41598-026-37819-x","authors":["Alatawi MN"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-37819-x","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.3390/s26113325","name":"Stability-Controlled Continual Federated Learning for Energy-Harvesting AIoT Systems.","source":"pubmed","abstract":"Energy-harvesting (EH) AIoT systems enable long-term autonomous operation but suffer from time-varying energy availability, which makes stable learning difficult. In such environments, federated learning (FL) is prone to energy depletion (blackout), while continual learning is required to handle evolving data distributions, leading to a trade-off between energy stability and catastrophic forgetting. In this paper, we propose a stability-controlled continual federated learning framework that jointly regulates local training intensity and rehearsal usage based on the residual energy state. The proposed method is derived from a Lyapunov drift-plus-penalty formulation and implemented as a lightweight mode-based control policy. Simulation results using real solar energy traces show that the proposed method significantly reduces blackout while improving accuracy and mitigating forgetting compared to existing approaches. These results demonstrate the effectiveness of energy-aware joint control for stable continual federated learning in EH-AIoT systems.","url":"https://doi.org/10.3390/s26113325","authors":["Park J","Yoon I","Noh DK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3390/s26113325","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.1016/j.ijrobp.2026.06.3083","name":"Center-specific Federated Learning for Radiation Pneumonitis: A Cross-Center Adaptive Alternating Framework.","source":"pubmed","abstract":"Accurate prediction of symptomatic radiation pneumonitis (RP) is critical for radiation therapy, however, the generalization of deep learning models is hindered by restricted access to multicenter data. Although federated learning (FL) bypasses data sharing restrictions, standard FL algorithms underperform on highly heterogeneous clinical data across institutions. Therefore, this study aims to evaluate the clinical feasibility of a center-specific FL approach.","url":"https://doi.org/10.1016/j.ijrobp.2026.06.3083","authors":["Yan M","Wang Z","Ning L","Xuan J","Zhang Z","Li H","Wang Y","Li M","Niu G","Bermejo I","Dekker A","De Ruysscher D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/j.ijrobp.2026.06.3083","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"doi:10.21203/rs.3.rs-10219711/v1","name":"Clinical Equivalence of Privacy-Preserving Federated Learning: A Consortium Blockchain Framework with Equivalence Testing","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10219711/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.21203/rs.3.rs-10219711/v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1016/j.neunet.2026.109193","name":"FedEBM: Robust graph federated learning via energy-based model.","source":"pubmed","abstract":"Graph Federated Learning (GFL), as a vital component of graph neural networks, has found extensive applications in real-world scenarios. However, real-world graph data often suffers from label noise due to factors such as mislabeled data or malicious attacks. Existing methods for handling noisy labels primarily focus on centralized approaches, which perform poorly when directly applied to distributed settings and struggle to operate effectively on large-scale datasets. To address noisy labels in GFL, we propose a novel method called FedEBM. First, recognizing that clients in GFL often exhibit suboptimal learning capabilities under adverse conditions such as sample sparsity or label imbalance, we innovatively apply an Energy-Based Model (EBM) to tackle the noisy label problem. The EBM discriminates between clean and noisy samples based on their energy score. Even when clean samples are scarce, it implicitly delineates the energy region boundary by elevating the energy score of noisy samples, thereby separating clean and noisy samples. Furthermore, the energy score across categories in the EBM does not necessitate changes in other categories' energy score, avoiding probability competition on minority classes and enhancing sensitivity to minority class features. Comparative experiments across multiple public datasets demonstrate that FedEBM outperforms six baseline methods under various noise rates, noise types, and client numbers. Specifically, FedEBM outperforms the second-best method by an average margin of 5.43% on small-scale datasets and 12.58% on large-scale datasets.","url":"https://doi.org/10.1016/j.neunet.2026.109193","authors":["Wang J","Gan Z","Li X","Li D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/j.neunet.2026.109193","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.21203/rs.3.rs-10329464/v1","name":"Federated Learning for Distributed Cyber Threat Detection: A Systematic Review of Architectures, Applications, and Open Challenges","source":"europepmc","abstract":"Abstract The proliferation of networked infrastructure, spanning enterprise information systems, Internet of Things (IoT) deployments, and industrial control environments, has widened the attack surface available to adversaries and made purely centralised intrusion detection increasingly difficult to sustain. Centralised architectures typically require raw traffic or log data to be transmitted to a single analytic Centre, an arrangement that raises bandwidth, latency, regulatory, and confidentiality concerns. Federated learning (FL) has emerged as an alternative paradigm in which distributed clients collaboratively train a shared detection model while retaining their data locally. This paper presents a systematic review of FL-based architectures, applications, and open challenges in distributed cyber threat detection. Following a structured search of Scopus, IEEE Xplore, ACM Digital Library, and ScienceDirect, a corpus of peer-reviewed studies published between 2017 and 2025 was analyzed to characterise architectural variants, aggregation strategies, application domains, and adversarial threats specific to federated intrusion detection. The review finds that hierarchical and blockchain-assisted architectures are gaining traction in resource-constrained and trust-sensitive environments, that non-independent and identically distributed (non-IID) data remain the principal obstacle to model convergence, and that poisoning and inference attacks constitute the most consequential unresolved security risks. Three original figures and three tables synthesised the reviewed architectures, comparative study characteristics, and the federated training lifecycle together with its attack surface. The review concludes by outlining research directions concerning robust aggregation, communication efficiency, and standardised benchmarking for federated cyber threat detection.","url":"https://doi.org/10.21203/rs.3.rs-10329464/v1","authors":["Joshua Babatola"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.21203/rs.3.rs-10329464/v1","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-50882-8","name":"Federated learning with swarm intelligence for efficient and secure medical image analysis.","source":"pubmed","abstract":"Collaborative learning in healthcare faces challenges, including strict regulations and fragmented data. This research introduces a federated learning framework that employs swarm intelligence to augment communication and enhance the analysis of medical images. The method optimizes hyperparameters, selects features, and assigns aggregation weights to federated clients simultaneously by combining Particle Swarm Optimization (PSO) and the Firefly Algorithm (FA) with deep Convolutional Neural Networks (CNNs). The framework was tested on three medical datasets: COVID-19 chest X-rays (5,856 images), monkeypox skin images (569 images), and breast cancer mammograms (320 images). These datasets were shared among four fake healthcare institutions. It strives to strike a balance between privacy, communication costs, and classification accuracy. The results showed that the test was 96.71% accurate in detecting COVID-19, 96.06% accurate in classifying monkeypox, and 97.0% accurate in diagnosing breast cancer. The framework was able to handle noise and attacks from individuals who sought to disrupt it, which reduced communication rounds by 25-30%. A privacy-utility analysis revealed that there were acceptable trade-offs, with accuracy remaining above 94%. This study employs robust privacy measures and statistical validation. It also shows how to use medical AI in smaller healthcare settings without putting patients' privacy at risk.","url":"https://doi.org/10.1038/s41598-026-50882-8","authors":["SayedElahl MA","Farouk RM","Ali AE","Ahmed E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-50882-8","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41746-026-02710-6","name":"Flexible and scalable federated learning with deep feature prompts for digital pathology.","source":"pubmed","abstract":"Collaborative learning across medical institutions is essential for building robust and generalisable digital pathology models. Federated learning (FL) enables collaboration without centralising data, yet its adoption is limited by high communication costs, model heterogeneity, and privacy concerns. We propose Federated Deep Feature Prompting (FedDFP), an efficient FL framework tailored for heterogeneous clinical environments. FedDFP introduces lightweight, client-specific learnable prompts applied to patch-level embeddings from whole-slide images. By sharing only these compact prompts, FedDFP reduces communication overhead by over 99.9% compared to standard FL while improving classification accuracy. Extensive experiments on TCGA-IDH, CAMELYON16 and CAMELYON17 show that FedDFP consistently outperforms standard and personalised FL baselines, achieving mean AUC gains of 0.11-0.13 over local-only training and up to 0.10 over the strongest federated methods. FedDFP also converges 2-4&#xd7; faster and remains effective across diverse feature extractors and multiple-instance learning architectures, demonstrating scalability, flexibility and privacy-aware collaboration.","url":"https://doi.org/10.1038/s41746-026-02710-6","authors":["Cong C","Song Y","Di Ieva A","Chou A","J Gill A","Coiera E","Liu S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41746-026-02710-6","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1016/j.neunet.2026.109080","name":"Fed-DiTTab: Diffusion transformer for tabular data generation in federated learning.","source":"pubmed","abstract":"Imbalanced data is prevalent in real-world classification tasks, and traditional machine learning methods often struggle to effectively learn from minority class samples. This implies that predictive models can achieve better performance only when sufficient and balanced training data are available. However, due to privacy protection and the limitations of data silos, data cannot be directly shared. Federated learning offers a feasible solution by enabling multiple clients to collaboratively train a shared model without exposing local data. Nevertheless, the performance of federated learning can still be significantly degraded under imbalanced data distributions. To address this issue, we propose Fed-DiTTab, which first employs DiTTab to oversample minority class samples on each client, thereby mitigating class imbalance in federated learning while preserving data privacy to a certain extent. Extensive experiments on public datasets validate the effectiveness of Fed-DiTTab, showing that it significantly outperforms other methods discussed in this paper on imbalanced datasets. Furthermore, the ablation study validates the necessity of the synthetic data mechanism, demonstrating that training solely on raw data fails to effectively capture minority class features in severely imbalanced data environments.","url":"https://doi.org/10.1016/j.neunet.2026.109080","authors":["Pi Y","Zheng M","Ma F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/j.neunet.2026.109080","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-60588-6","name":"BlockFedZTA: a trust-aware federated learning framework for secure multi-organizational intrusion detection.","source":"pubmed","abstract":"The design of a privacy-preserved intrusion detection system for supply chain networks is challenging because of strict data privacy requirements, heterogeneous data distributions, and unreliable participating nodes. This study proposes BlockFedZTA, a framework that integrates federated learning, XGBoost, trust-aware aggregation, and a lightweight commitment-based integrity verification mechanism. In the proposed approach, each participant trains a local model and shares only a salted SHA-256 commitment without exposing model parameters. The aggregation mechanism assigns weights according to validation performance, reducing the influence of low-quality or potentially malicious updates. Experiments were conducted using a unified dataset containing 100,000 instances and 130 features representing five classes (Normal, DoS, Probe, R2L, and U2R), distributed among three organizations under non-IID conditions. The framework was evaluated under no-drift, moderate-drift, and severe-drift scenarios. Five-fold cross-validation produced average accuracies of 0.966, 0.964, and 0.963, respectively. Statistical analysis confirmed that the trust-aware aggregation strategy significantly outperformed FedAvg under drift conditions (p&#x2009;&lt;&#x2009;0.01). Additional comparison with FedAvg, Krum, Multi-Krum, Median Aggregation, Trimmed Mean, FLTrust, and FoolsGold revealed higher performance with an accuracy of 0.96470 in both mild and severe situations of the data drift problem. Moreover, our model was highly resistant to label poisonings, ensuring an accuracy of more than 0.962 even with a high level of 60%. Scaling analysis with up to 50 clients again confirmed high performance and superiority over FedAvg with moderate communication overhead. For example, communication costs went up from 1640.74 KB to 20451.89 KB per round; meanwhile, the number of audit log bytes needed rose only from 3.40 KB to 174.02 KB. Repeated runs of the algorithm ensured a stable average accuracy of 0.9647 with a standard deviation of 0.0002. Thus, BlockFedZTA ensures a robust federated IDS approach in a supply chain environment.","url":"https://doi.org/10.1038/s41598-026-60588-6","authors":["Alshenaifi R","Mishra S","Tahzib S","Rathi M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-60588-6","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"doi:10.1016/j.neunet.2026.109298","name":"FedCAD: Cross-modal semantic alignment and distillation for cross-domain heterogeneous federated learning.","source":"pubmed","abstract":"Recent advances in the IoT and edge intelligence have made the deployment of CLIP-style image-text models in edge-cloud architectures increasingly common for collaborative sensing. However, resource heterogeneity at the edge limits the feasibility of using a unified backbone across devices with different computational budgets. In addition, non-IID data and domain shifts can disrupt image-text alignment and cause semantic drift during federated aggregation. To address these challenges, we propose FedCAD, a federated learning framework for effective knowledge collaboration under cross-domain distribution shifts and heterogeneous edge environments through cross-modal semantic alignment and multi-level distillation. FedCAD consists of three main components: (i) a Latency-Distribution Co-aware Clustering (LDCC) strategy with heterogeneous model orchestration to alleviate resource disparities and straggler effects; (ii) a decouple-then-align dual-stage feature alignment mechanism that suppresses domain-specific noise and preserves the geometric consistency of the joint image-text embedding space, thereby enhancing cross-domain generalization; and (iii) a multi-stage collaborative distillation protocol spanning intra-cluster, inter-cluster, and cloud levels, which promotes cross-cluster semantic complementarity and cross-architecture knowledge fusion to mitigate knowledge fragmentation. Experimental results show that FedCAD consistently improves classification accuracy and target-domain generalization across multiple cross-domain classification benchmarks. In addition, results on Flickr30K and MSCOCO further demonstrate its effectiveness in image-text matching and cross-modal retrieval. Under the frozen-backbone and lightweight-adapter setting, only adapter parameters are transmitted during communication rounds, which to some extent supports its deployment applicability in bandwidth-constrained networks.","url":"https://doi.org/10.1016/j.neunet.2026.109298","authors":["Zhang Q","Yuan H","Shao M","Liang H","Liu H","Zhang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/j.neunet.2026.109298","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3389/fdgth.2026.1691088","name":"Real-world federated learning for brain imaging scientists.","source":"europepmc","abstract":"Background Federated learning (FL) has the potential to boost deep learning in neuroimaging but is rarely deployed in real-world scenarios, where its true potential lies. We propose FLightcase, a new FL toolbox tailored for brain research, and evaluate it on a real-world FL network to predict the cognitive status in patients with multiple sclerosis (MS) from brain magnetic resonance imaging (MRI). Methods We first trained a DenseNet neural network to predict age from T1-weighted brain MRI on three open-source datasets: IXI (586 images), SALD (491 images), and CamCAN (653 images). These were distributed across the three centres in our FL network: Brussels (BE), Greifswald (DE), and Prague (CZ). We benchmarked this federated model with a centralised version. The best-performing brain age model was then fine-tuned to predict performance on the symbol digit modalities test (SDMT) of patients with MS (Brussels: 96 images, Greifswald: 756 images, Prague: 2,424 images). Shallow transfer learning (TL) was compared with deep transfer learning, in which weights were updated either in the last layer or across the entire network, respectively. Results Federated training outperformed centralised training, predicting age with a mean absolute error (MAE) of 6.08 versus 7.02. Federated training yielded Pearson correlations (all p p = 0.282), 0.40 ( p p Conclusion Real-world federated learning using FLightcase is feasible for neuroimaging research in MS, enabling access to large MS imaging databases without sharing data. The federated SDMT-decoding model is promising and could be improved in the future by adopting FL algorithms that address the non-IID data issue and consider other imaging modalities. We hope our detailed real-world experiments and open-source distribution of FLightcase will prompt researchers to move beyond simulated FL environments.","url":"https://doi.org/10.3389/fdgth.2026.1691088","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3389/fdgth.2026.1691088","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.1371/journal.pdig.0001581","name":"Multi-scenario evaluation of federated learning for privacy-preserving malaria prediction with Ghana DHS data.","source":"pubmed","abstract":"Improving malaria prediction in Ghana requires data from across its health system, yet Ghana's Data Protection Act (Act 843) restricts inter-institutional data sharing, and many facilities decline to transfer patient records regardless of legal permission. Federated learning (FL) offers a solution: each site trains a local model and shares only weight updates, not raw patient data. Whether FL holds up under Ghana's 10-fold regional prevalence difference had not been tested. Using a controlled simulation framework, we partitioned Ghana Demographic and Health Survey and Malaria Indicator Survey data (2016-2022, n&#x2009;=&#x2009;10,287 children aged 6-59 months) into five regional clients and evaluated FedAvg and FedProx under three scenarios: uniform distribution (S1), real-world prevalence variation from 2.9% to 30.1% (S2), and heterogeneity combined with 5-20% missing data per client (S3). When data was uniformly distributed, FedAvg matched centralized logistic regression (AUC-PR 0.8852 vs. 0.8854; p&#x2009;=&#x2009;0.057). The performance drop under 10-fold heterogeneity was only 2.21%, far below the 20-55% degradation seen in vision benchmarks. When regional heterogeneity compounded with missing data quality issues, FedProx outperformed FedAvg (AUC-PR 0.8725 vs. 0.8684; Cohen's d&#x2009;=&#x2009;1.257). Federated models retained 97.8-98.5% of centralized AUC-PR without sharing patient data. FL is a feasible privacy-preserving approach for malaria predictions in diverse sub-Saharan African health systems, and the selection of algorithms has a significant impact on the actual performance. These benchmarks are based on a full clinical symptom panel at point of care; with the DHS-native features alone, AUC-PR is 0.34-0.37, compared to 0.87-0.89 with the full feature set (S1 Text, sensitivity analysis). Prior to deployment, prospective validation with facility collected records is required. A critical fairness gap exists: Greater Accra's 2.9% prevalence produced an approximately 50% false negative rate, meaning half of urban malaria cases would be missed. Prevalence-aware aggregation is required.","url":"https://doi.org/10.1371/journal.pdig.0001581","authors":["Kovor DK","Osei EO"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1371/journal.pdig.0001581","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-56215-z","name":"GBOA_SR-ShuffleNet: an explainable federated learning framework for privacy-preserving intrusion detection in IoT.","source":"pubmed","abstract":"The rapid growth of the Internet of Things (IoT) has introduced significant security vulnerabilities and increased the risk of cyberattacks. Intrusion Detection Systems (IDS) are widely used to identify malicious activities; however, their detection accuracy is often degraded by latency and privacy concerns in centralized environments. Federated Learning (FL) has therefore emerged as a privacy-preserving solution for distributed intrusion detection. The FL-based intrusion detection remains challenging due to data imbalance across distributed nodes and susceptibility to adversarial attacks, which can degrade model generalization and robustness. To address these challenges, this paper proposes a Groupers Brown Bear Optimization-based Spiking Residual ShuffleNet (GBOA_SR-ShuffleNet) framework. In the proposed approach, the GBOA algorithm is used to optimally train the SR-ShuffleNet, enabling improved parameter tuning under heterogeneous and imbalanced data distributions typical of FL environments. This leads to more stable model updates, convergence and enhances robustness against adversarial effects, thereby improving the reliability of federated intrusion detection. The servers and IoT nodes are the main entities of the FL-based intrusion detection framework. In local training, intrusion detection is carried out, where the data are normalized by Dual normalization to stabilize data distribution and improve learning convergence. The features are fused using the Kumar-John distance measure with Deep Kronecker Network (DKN), which enhances discriminative feature representation and reduces redundancy. The Bootstrapping method augments the data to avoid class imbalance, and intrusion detection is performed using SR-ShuffleNet. The GBOA trains the SR-ShuffleNet, and Shapley Additive xPlanations (SHAP) show the final result of intrusion detection, which is utilized to provide interpretability and explain the detection decisions. Moreover, the GBOA_SR-ShuffleNet attains the accuracy, Mean Average Precision (mAP), loss, Mean Squared Error (MSE), Root MSE (RMSE), Root Relative Squared Error (RRSE), recall, F1-Score, and False Alarm Rate (FAR) of 96.48%, 95.63% 0.035, 0.080, 0.282, 0.336, 96.93%, 96.28%, and 3.15%.","url":"https://doi.org/10.1038/s41598-026-56215-z","authors":["Jacob SL","Sultana HP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-56215-z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1109/tnnls.2026.3691817","name":"A Method for Data Augmentation in Vertical Federated Learning Addressing Data Heterogeneity.","source":"pubmed","abstract":"Vertical federated learning (VFL) can aggregate data features from participating parties and is applicable to data collaboration in various fields. To address data heterogeneity in VFL, this article proposes a framework tailored for heterogeneous environments. First, to mitigate performance degradation caused by imbalanced local data across clients, we exploit conditional generative adversarial networks (CGANs) for targeted data augmentation, and propose a data balancing model named FeCWGAN-GP based on CGAN. This model pretrains a local CGAN for each client to perform local private data compensation, thereby alleviating the problem of decreased model performance. Second, to handle local model parameter distribution shifts induced by heterogeneity, leveraging both the sample size proportions and the Wasserstein distance in the model parameter space to capture parameter distribution shifts due to data heterogeneity, a parameter aggregation algorithm named WFedDA based on sample size and parameter distribution is proposed. This method calculates weights based on the sample size proportions of participants and the distribution difference between local model parameters and global model parameters, thus optimizing the global model. Finally, to address the instability of local model parameters caused by data heterogeneity, a stochastic gradient descent (SGD) method with a dual smoothing mechanism named SGD-MA is proposed. This method uses an exponential moving average (EMA) to process gradients and parameters sequentially, which reduces the fluctuation of gradients and the instability of parameter updates, thereby improving the stability of the training process. Experiments on the public datasets MNIST, CIFAR-10, Fashion-MNIST, and MIMIC-III demonstrate that the methods proposed in this article can effectively address the issues caused by data heterogeneity in multidata-source environments, significantly improving the generalization capability and stability of the global model.","url":"https://doi.org/10.1109/tnnls.2026.3691817","authors":["Xiao Y","Lv T","Zhao D","Zhao W","Li T","Wang R","Wang G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/tnnls.2026.3691817","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-58383-4","name":"SecureTrust-FL: trust-aware privacy-preserving federated learning for network intrusion detection.","source":"pubmed","abstract":"The proliferation of distributed network environments and the Internet of Things (IoT) has increased the need for privacy-preserving intrusion detection systems capable of operating effectively under heterogeneous and non-independent and identically distributed (non-IID) data conditions. This paper proposes SecureTrust-FL, a trust-aware federated learning framework for privacy-preserving intrusion detection. The framework integrates Federated Learning, Blockchain-based Trust Management, Differential Privacy, FGSM-based Adversarial Learning, and Zero-Trust Security principles to support secure collaborative learning without requiring raw data sharing among participating entities. The framework is evaluated using three benchmark intrusion detection datasets, namely CICIDS2017, UNSW-NB15, and BoT-IoT, which are treated as independent federated clients. Experimental results demonstrate that the proposed framework achieves an overall Accuracy of 92.91% &#xb1; 0.45%, Balanced Accuracy of 93.25% &#xb1; 0.43%, Macro F1-Score of 92.89% &#xb1; 0.45%, and AUC-ROC of 95.50% &#xb1; 0.40% across heterogeneous datasets. The results indicate that the federated model can effectively learn from distributed and heterogeneous data while preserving data privacy. Further analysis reveals the impact of class imbalance on intrusion detection performance, particularly in datasets containing skewed attack distributions, highlighting the importance of Balanced Accuracy and F1-Score in addition to overall Accuracy. Differential privacy experiments demonstrate the privacy-utility trade-off, where stronger privacy protection leads to a reduction in model performance. Adversarial robustness evaluation using FGSM perturbations also shows a noticeable decline in detection performance, indicating the need for stronger defense mechanisms against adversarial attacks. In addition, the trust ledger enhances transparency and accountability by monitoring client participation and recording the trust scores used during trust-weighted aggregation and maintaining trust records throughout the collaborative learning process. The results demonstrate that SecureTrust-FL provides an effective framework for privacy-preserving collaborative intrusion detection while integrating trust management, privacy protection, and secure federated learning within a unified architecture.","url":"https://doi.org/10.1038/s41598-026-58383-4","authors":["Alshammari NS","Mishra S","Rathi M","Goel N","Tahzib S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-58383-4","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1007/s12539-026-00825-8","name":"VFLING: Vertical Federated Learning for Multi-Omics Data Integration with Graphs.","source":"pubmed","abstract":"Modern machine learning models leveraging multi-omics data face significant privacy challenges due to the sensitive nature of patient information. Communication overhead and missing features in each institution can lead to a substantial decline in federated learning performance. In response to these concerns, we propose VFLING-Federated Learning for Multi-Omics Data Integration with Graphs-a secure one-shot communication federated learning framework. We note that medical data reflects disease characteristics from different omics, while the relationships between samples exhibit relative stability across these omics. To minimize data transmission while maximizing the utilization of each participant's feature information we develop a strategy that transmits not only the local features but also the relationships or topology in one-shot communication. By fusing the omics based on the locally learned graph structure instead of features, VFLING enables improved performance even when some features are missing from individual parties. Extensive experiments demonstrate that VFLING outperforms existing frameworks, paving the way for applications in the medical field. Local features and graph topology are shared to the trainable server in a single communication, enhancing model accuracy through integrated data. This approach improves robustness despite incomplete information. Local Parties and Server Integration: Local parties learn and transmit both local features and graph topology to the server in a single communication, maximizing effective information transfer. The trainable server then integrates data across parties using graph relationships, enhancing model robustness and accuracy despite incomplete feature sets.","url":"https://doi.org/10.1007/s12539-026-00825-8","authors":["Li X","Li Q","Lu D","Lin Y","Aslam S","Li H","Zhang Z","Chen Y","Huang RS","Wu H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1007/s12539-026-00825-8","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1186/s12911-026-03553-7","name":"TrainTracks - federated learning for reproducible research on sensitive medical data.","source":"pubmed","abstract":"Reproducibility of computational algorithms is a challenging but crucial requirement for medical research and an important component of trustworthy training and application of AI algorithms. Federated Learning (FL) is commonly used to enable privacy-preserving AI in medical research. One prerequisite of reproducibility is traceability. A majority of publications on traceable FL platforms leverage blockchain technology to achieve traceable FL. In healthcare settings, resource-efficient alternatives to blockchains are possible; however, their traceability features require separate design considerations.","url":"https://doi.org/10.1186/s12911-026-03553-7","authors":["Elwes M","Jaberansary M","Tang FK","Aswendt M","Beyan O","Kutafina E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1186/s12911-026-03553-7","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-59680-8","name":"BRIDGE-T: addressing temporal unreliability in federated learning for edge-enabled IoT networks.","source":"pubmed","abstract":"Federated Learning (FL) in edge-enabled Internet of Things (IoT) networks faces considerable challenges owing to intermittent client participation and distributional drift, and which undermines the stability of a global model's optimization. This coupled impact introduces temporal unreliability, in turn, impairing the training stability. State-of-the-art FL frameworks typically address these challenges in isolation and overlook their coupled impact particularly during the client reintegration process. In order to address this limitation, we propose BRIDGE-T, i.e., a reliability-aware FL framework that addresses temporal unreliability in edge-enabled IoT networks. BRIDGE-T encompasses three components, i.e., (i) Prototype Contrastive Drift Alignment (PCDA) to constrain cross-client representation divergence under evolving non-Independent and Identically Distributed (non-IID) data, (ii) Prototype Query Agreement (PQA) to estimate round-wise clients reliability via cross-client prediction consistency on shared prototypes, and (iii) Reliability-Weighted Asynchronous-aware Aggregation (RWAA) to regulate clients' influence and attenuate stale or misaligned clients' updates. Extensive experiments under varying intermittency and distributional drift on CIFAR-10, CIFAR-100, MNIST, and TON-IoT suggest that BRIDGE-T achieves smoother convergence and greater robustness to client reintegration vis-&#xe0;-vis the state-of-the-art FL frameworks.","url":"https://doi.org/10.1038/s41598-026-59680-8","authors":["Islam F","Mahmood A","Wang Y","Tahermazandarani M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-59680-8","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1109/jbhi.2026.3663219","name":"FedGSCA: Medical Federated Learning With Global Sample Selector and Client Adaptive Adjuster Under Label Noise.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/jbhi.2026.3663219","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/jbhi.2026.3663219","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/j.neunet.2026.109302","name":"Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.","source":"pubmed","abstract":"Multimodal federated learning (MFL) enables multiple clients to collaboratively train a global model from decentralized data without sharing local privacy-sensitive information. However, practical MFL is challenged by cross-client heterogeneity and missing modalities, which jointly induce client drift, incomplete semantic representations, and degraded global generalization. To address these issues, we propose FCKMD, a robust multimodal federated learning framework for heterogeneous and incomplete-modality settings. Specifically, FCKMD introduces a heterogeneity-adaptive modality expert encoding mechanism, in which a sample-wise router dynamically selects suitable expert paths for different client data and adopts a bypass strategy for missing modalities. To compensate for incomplete observations, FCKMD further employs a cross-modal reconstruction module together with a reliability-aware constraint strategy that adjusts the supervision strength. To further improve prediction robustness, a cross-view consistency transfer scheme is developed to distill discriminative knowledge from the fused multimodal branch into unimodal branches. These components are jointly optimized under a unified objective that integrates classification, reconstruction, and distillation losses. Experimental results on CREMA-D, Crisis-MMD, and UCI-HAR demonstrate that FCKMD outperforms representative baselines and achieves strong robustness and generalization under different missing rates, heterogeneity levels, and client participation ratios.","url":"https://doi.org/10.1016/j.neunet.2026.109302","authors":["Li M","He X","Chen J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/j.neunet.2026.109302","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1109/jbhi.2026.3695064","name":"Privacy-Enhanced Vertical Federated Learning for Healthcare via Directional Noise and Subset Representations.","source":"pubmed","abstract":"Vertical federated learning (VFL) allows healthcare institutions to train models on complementary patient features without sharing raw data, but strong differential privacy often causes severe utility loss and labeled medical data are limited.We propose HEAL, a privacy-enhanced VFL framework that jointly learns subset representations and optimizes the direction of privacy-preserving noise. HEAL first constructs importance-aware feature subsets and performs multi-level contrastive pre-training to exploit unlabeled data and unify heterogeneous feature spaces. It then applies direction-optimized differential privacy to preserve formal $(\\epsilon, \\delta)$-privacy while reducing gradient distortion, followed by collaborative task learning for healthcare prediction. Across four healthcare datasets, HEAL improves accuracy by 2.6-4.7% over state-of-the-art baselines, reaches 96.2% of centralized performance at $\\epsilon =1.0$, and degrades gradient-inversion reconstruction quality by 20-35%. These results show that privacy protection and representation learning can reinforce each other, rather than treating privacy only as a performance cost.","url":"https://doi.org/10.1109/jbhi.2026.3695064","authors":["Wang Q","Dai M","Wu C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/jbhi.2026.3695064","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.21203/rs.3.rs-10405564/v1","name":"Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach","source":"europepmc","abstract":"Abstract Federated learning systems can benefit from organizing heterogeneous participants into coalitions that train coalition-specific models. Such clustering is sustainable only if participants prefer to remain in their assigned coalition and the associated transfers are affordable. We develop a transferable-surplus model that separates a coalition's learning benefit, system cost, participant cost, and monetary transfers. An allocation rule converts coalition surplus into hedonic preferences, while weak budget feasibility guarantees nonnegative retained coordinator surplus. For symmetric pairwise surplus allocations, we prove that the induced coalition-formation game is an exact potential game. Consequently, a Nash-stable partition exists, and every strict better-response process terminates at such a partition. When destination members may reject entrants, accepted better responses instead terminate at an individually stable partition. We characterize feasibility of bounded pair incentives and give a polynomial verification result when retained budget slack is submodular. We then decompose welfare into participant potential and coordinator-retained slack and derive additive and relative-slack multiplicative efficiency guarantees. Exact budget balance yields a welfare-optimal Nash-stable partition on the exactly pairwise-representable surplus class, whereas budget feasibility alone permits unbounded welfare loss. Finally, global potential maximization is weighted maximum-agreement correlation clustering up to a constant. Approximation followed by strict better-response stabilization preserves the agreement guarantee and yields an explicit end-to-end welfare bound depending on retained slack and negative-edge mass. Explicit constructions show that the relative-slack guarantee is asymptotically tight and that the negative-edge correction can be attained. In a preregistered five-seed CIFAR-10 study, the proposed mechanism reaches the certified estimated-table welfare optimum on every primary instance, while equal-surplus sharing has no Nash-stable outcome on three instances; pairwise validation gain also gives substantially more reliable pair signs than gradient alignment. These results connect learning value, monetary transfers, stability, and economic efficiency without conflating local equilibrium, global optimality, and computational tractability. MSC Classification: 91A12 , 91A80 , 68T42 , 91B32","url":"https://doi.org/10.21203/rs.3.rs-10405564/v1","authors":["Cengis Hasan"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.21203/rs.3.rs-10405564/v1","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3791/70175","name":"Intelligent Federated Learning Framework for Non-colocated and Heterogeneous Datasets.","source":"pubmed","abstract":"Federated learning has significant potential for distributed model training while preserving privacy, but it faces challenges related to convergence, fairness, and interpretability due to the heterogeneity of non-colocated datasets. This study proposes an Intelligent Federated Learning Framework (IFLF) to address these challenges through an adaptive approach using a multi-layer architecture consisting of Data, Client, Aggregation, Adaptation, and Optimization, and Interpretability layers. The framework demonstrates stable convergence under non-IID data distributions, with aggregation strategies supporting balanced optimization. The learning-rate modulation approach contributes to stable training by integrating heterogeneous client updates and reducing divergence during optimization. Explainable AI techniques, including SHAP and LIME, are incorporated to improve transparency at both client and global levels. The IFLF is evaluated on four benchmark datasets (FEMNIST (vision), FLamby (healthcare imaging), FedGraphNN (graph learning), and CICIDS2017 (cybersecurity)). The framework achieved an average accuracy of 92.8%, with faster convergence and reduced performance variability across clients.","url":"https://doi.org/10.3791/70175","authors":["Navghare ND","Gladence LM","Bhosle AA","Gore R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3791/70175","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1109/tmi.2026.3671287","name":"Modality-Agnostic Federated Learning With Adaptive Updates for Heterogeneous Medical Image Tasks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tmi.2026.3671287","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1109/tmi.2026.3671287","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1038/s41598-026-48453-y","name":"Adaptive client participation mechanism for federated learning in heterogeneous vehicular networks.","source":"pubmed","abstract":"In vehicular networks, federated learning faces significant challenges due to resource heterogeneity, dynamic participation patterns, and intermittent connectivity among vehicles. Traditional client selection mechanisms often fail to consider the two-tier decision-making process inherent in vehicular network environments, where both central servers and individual vehicles must make participation decisions based on their respective constraints. Moreover, existing model aggregation algorithms typically assume fixed client participation and cannot adapt to the highly variable participation patterns unique to vehicular networks. This paper proposes a comprehensive vehicular federated learning framework with three key innovations. First, we introduce a strategy-driven adaptive client participation mechanism with a two-tier decision-making process that combines server-side reinforcement learning-based client selection with client-side autonomous participation decisions based on local resource thresholds. Second, we develop an incremental online policy learning algorithm based on Proximal Policy Optimization (IO-PPO) to address the data scarcity challenge in federated learning environments by enabling continuous learning from limited trajectory data. Third, we propose a dynamic client size-adaptive optimized model aggregation algorithm that adapts to different participation patterns while considering both current and historical client contributions. Our approach leverages a synergistic combination of reinforcement learning for adaptive decision-making, asynchronous federated learning principles for flexible participation, and graph-based modeling for capturing network topology effects. Extensive experimental results demonstrate that compared to existing methods, the proposed framework significantly improves learning efficiency, convergence stability, and model performance in realistic vehicular network scenarios.","url":"https://doi.org/10.1038/s41598-026-48453-y","authors":["Lin W","Zhou Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-48453-y","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.14293/pr2199.003337.v1","name":"Secure Aggregation Techniques in Federated Learning for Vehicle Data Analytics","source":"europepmc","abstract":"The rapid evolution of Intelligent Transportation Systems (ITS) and Autonomous Vehicles (AVs) has generated a massive influx of vehicular data. While this data is pivotal for enhancing traffic safety and predictive maintenance, privacy concerns regarding location history and driving patterns remain a significant barrier. Federated Learning (FL) offers a decentralized alternative to traditional machine learning by training models locally on vehicles; however, FL is still susceptible to poisoning attacks and inference-based privacy leaks. This study investigates the efficacy of secure aggregation techniques—specifically Homomorphic Encryption (HE) and Multi-Party Computation (MPC)—in maintaining model accuracy while ensuring robust privacy. Using a quantitative experimental design and the Bosch Vehicle Motion Dataset, we demonstrate that while secure aggregation introduces a latency overhead of 12-18%, it successfully mitigates reconstruction attacks without compromising convergence rates. Our findings suggest that a hybrid approach is optimal for real-time vehicular analytics.","url":"https://doi.org/10.14293/pr2199.003337.v1","authors":["Emily Brown","Sarah Jones","David Miller","Grace Elvis"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.14293/pr2199.003337.v1","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-026-58750-1","name":"FL-TWIN: a unified federated learning system for intrusion detection with digital twins modelling.","source":"pubmed","abstract":"The growth of networked environments has intensified the challenge of detecting distributed denial-of-service (DDoS) attacks, as centralized intrusion detection systems face scalability, privacy, and data heterogeneity limitations. This paper proposes a federated learning framework for DDoS detection, Unified FL-TWIN, that pairs each participating client with an edge-resident Digital Twin (DT), applies a four-stage poisoning defence pipeline, and records all aggregations and security events on a permissioned blockchain ledger. Also, each DT maintains a versioned ring buffer of model snapshots, enabling per-client targeted rollback upon adversary detection. The defence pipeline comprises: Layered Update Purification (LUP), Differential Privacy via DP-SGD, Dual Dynamic Aggregation, TracIn and a blockchain. The novelty of this work lies in systematically integrating them into a Unified FL-TWIN approach that addresses several challenges simultaneously. This proposed approach simultaneously provides privacy protection, robustness, trustworthiness, accountability, and adaptive learning within a single architecture. In experiments on the CIC-DDoS 2019 dataset, we are covering clean baselines and three attack types with 30% malicious participation. The FL-TWIN achieves peak test accuracies of 99.97%, 99.98%, and 99.98% under label-flip, gradient-noise, and backdoor attacks, respectively, compared to a stagnant 99.72% for the undefended baseline. LUP achieves F1 scores of 0.57, 0.75, and 0.80 across three attack types, while the blockchain ledger maintains full save across all experiments. These results show that combining Digital Twin rollback with a layered detection pipeline improves recovery from federated poisoning attacks.","url":"https://doi.org/10.1038/s41598-026-58750-1","authors":["Hamwi AA","Mittal M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-58750-1","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.3233/shti260367","name":"Good for All, Not Good Enough for One: Reuse Dilemma in Federated Learning.","source":"pubmed","abstract":"Federated learning (FL) promises privacy-aware collaboration in healthcare, but real-world adoption remains limited by infrastructural and organizational hurdles. In this paper, we reflect on our experience developing and later bypassing our own general-purpose FL framework, in favor of a task-specific pipeline. This case exposed five core barriers to reuse, ranging from workflow misalignment to governance constraints, that often go unaddressed in technical design. Rather than prescribing one approach over another, we argue for a shift toward modular, interoperable tools that can better accommodate the diversity of research contexts. Our findings highlight the need for realistic infrastructure thinking: one that acknowledges both the promise and the limits of reuse in practice.","url":"https://doi.org/10.3233/shti260367","authors":["Pirmani A","Moreau Y","Peeters LM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3233/shti260367","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.21203/rs.3.rs-9991236/v1","name":"Embodied brain-cerebellum federated learning for satellite-assisted low-altitude wireless networks","source":"europepmc","abstract":"Abstract Satellite-assisted low-altitude wireless networks need distributed intelligence that handles scarce labels, limited onboard resources and short UAV contact windows. We propose EBC-FL, an embodied brain--cerebellum federated learning framework in which ground stations maintain semantic memory, MEO satellites coordinate regional aggregation and low-altitude agents execute lightweight local policies. EBC-FL combines descriptor-based semantic inference, adapter updates and a control-entropy-aware sensing--communication--computing--control objective. Against conventional and strong baselines, EBC-FL is not always the highest-success method in benign traffic, but provides a stronger safety-critical reliability--semantic-awareness--upload tradeoff. In the safety scenario, it improves service success from 85.26% to 88.52%, reduces entropy-induced failures from 14.09% to 7.89%, and lowers upload from 209.42 MB to 75.79 MB relative to risk-aware scheduling. Controlled mobility and contact-window sweeps confirm robustness under high-mobility, short-window operation.","url":"https://doi.org/10.21203/rs.3.rs-9991236/v1","authors":["Yi Jing","Chunxiao Jiang","Jiawei Wang","Jiachen Sun"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.21203/rs.3.rs-9991236/v1","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1038/s41598-026-53140-z","name":"Interpretable reputation driven asynchronous consensus vehicle networking federated learning architecture.","source":"pubmed","abstract":"This paper proposes a Vehicle-Road-Cloud-Chain (VRCC) four-layer collaborative framework to address the issues of lack of interpretability, reputation evaluation failure, and architecture centralization vulnerability faced by federated learning in Internet of Vehicles (IoV) under Differential Privacy (DP), Non-Independent and Identically Distributed (Non-IID) data, and Byzantine attacks. The framework achieves explicit decoupling between low-latency data sharing and latency-tolerant collaborative training. Firstly, construct an asynchronous blockchain consensus layer based on Directed Acyclic Graph (DAG), which supports low confirmation latency model interaction record storage in high-concurrency vehicle scenarios; And design a three-layer interpretable reputation evaluation mechanism, integrating historical task performance, Maximum Mean Discrepancy (MMD) Bayesian inference, and task completion contribution, to achieve causal decoupling between \"honest high loss\" and \"malicious low loss reporting\" under Differential Privacy noise, and jointly sign and upload it to the chain through the regulatory committee and Roadside Units (RSUs), making reputation judgments auditable and transparent; Further propose a participant selection algorithm based on Deep Deterministic Policy Gradient (DDPG) and reputation partitioning, which synchronously optimizes communication overhead, computation delay, and redundant filtering in dynamic traffic flow, while utilizing local DAG weight-biased random walks to achieve lightweight asynchronous model quality verification. The experiment shows that the cumulative reward of the proposed method can quickly converge and remain stable, verifying its system-level superiority in interpretable robust aggregation and high-concurrency scalability.","url":"https://doi.org/10.1038/s41598-026-53140-z","authors":["Zi Y","Zhou Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-53140-z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1016/j.tplants.2026.03.014","name":"Federated learning: global insights from local plant data.","source":"pubmed","abstract":"","url":"https://doi.org/10.1016/j.tplants.2026.03.014","authors":["Shoaib M","Reddy P","Long G","Hayden MJ","Kant S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/j.tplants.2026.03.014","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1016/j.neunet.2026.109378","name":"A unified basis decomposition framework for addressing the federated learning trilemma: Communication-efficiency, personalization, and privacy.","source":"pubmed","abstract":"Federated learning (FL) is confronted with a fundamental trilemma: simultaneously achieving communication efficiency, personalized adaptation, and privacy protection. Current approaches typically optimize one objective at the expense of the others, failing to provide a unified solution. To address this challenge, we propose Federated Basis Decomposition (FedBD), a unified framework that leverages layer-wise basis decomposition to achieve balanced optimization across all three dimensions. The key innovation lies in reformulating the learning process as a subspace optimization problem: each network layer is represented as a linear combination of globally shared basis models, enabling clients to exchange only low-dimensional scalar weights rather than full model parameters. FedBD directly resolves the trilemma by drastically reducing communication overhead through compact parameter transmission, while simultaneously enabling effective personalization by decoupling globally shared scalar weights from private local parameters including Batch Normalization statistics. Furthermore, the framework strengthens privacy protection by projecting raw gradients into a random basis subspace. We validate our framework on three medical imaging datasets. The results demonstrate that FedBD achieves an accuracy density gain of up to 9.73&#x202f;&#xd7;&#x202f;, effectively maintaining competitive accuracy with an order-of-magnitude reduction in communication overhead compared to conventional FL methods. Ultimately, FedBD offers a unified solution to the FL trilemma, paving the way for practical deployment in complex real-world scenarios.","url":"https://doi.org/10.1016/j.neunet.2026.109378","authors":["Ma Z","Wu Z","Wang J","Zhu Y","Gao X","Lin Y","Lu W"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1016/j.neunet.2026.109378","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3389/fdata.2026.1769948","name":"Quantifying energy and accuracy trade-offs of federated learning on wearable health devices.","source":"pubmed","abstract":"The rapid development of wearable health tools has made it possible to continuously monitor physiological conditions for preventive care. However, stringent privacy laws, including HIPAA and GDPR, require decentralized methods such as federated learning (FL) to safeguard personal patient information. Nonetheless, empirical profiling in this paper finds that typical FL implementations are plagued by a serious performance trilemma; a naive federated model attains a 35.3 percent energy savings (3.84 vs. 5.93 kJ in the centralized models), but at the cost of a disastrous performance penalty of 13.87 percentage points (84.94 vs. 98.81 percent in centralized models). The failure in research is largely due to the on-device computational load of 4.24 MFLOPs per training sample, resulting in a \"straggler\" bottleneck that increases the total training duration to 1,066.26 s, almost 70 times longer than centralized training. As a result, the introduction of the hybrid hierarchical federated split learning (H-FedSL) architecture helps in strategically splitting the neural network at a cut layer to divide the workload between wearable and nearby edge servers. The methodology provides a new framework that offloads the heavy and deep-layer computations to the edge server, leaving the shallow feature extraction to the point of operation, and sends only privacy-sensitive abstractions of the smashed data, rather than raw signals. The integration of asynchronous protocols will help manage device heterogeneity and resource-aware client selection, thereby achieving the aim of H-FedSL to restore the gold-standard accuracy of 98.81% with the state-of-the-art 35.3% energy efficiency of the federated model. Thus, a technically and economically feasible pathway will be provided for deploying medical-grade AI on resource-constrained Internet of Medical Things (IoMT) devices.","url":"https://doi.org/10.3389/fdata.2026.1769948","authors":["S R","Khekare G","Kumar Y","Soni G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.3389/fdata.2026.1769948","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-60172-y","name":"A federated learning framework integrating knowledge graphs and Node2Vec for multi-source medical image classification.","source":"pubmed","abstract":"Medical image classification in federated healthcare environments is challenged by the difficulty of learning robust and generalized representations from heterogeneous, non-IID data distributed across multiple institutions. Conventional federated learning approaches primarily rely on visual features and often fail to capture structural relationships among medical images, leading to reduced classification performance and limited generalization across diverse clinical settings. This study proposes GraphMedFL, a federated learning framework that integrates knowledge graphs and Node2Vec-based structural embeddings to enhance classification performance in multi-source environments. Local knowledge graphs are constructed from image features to capture inter-sample relationships, and Node2Vec embeddings are fused with feature representations to enrich local model training. A similarity-aware adaptive aggregation strategy is introduced to address non-IID data distributions across clients. Experiments conducted on three breast cancer imaging datasets (BreakHis, CBIS-DDSM, and INbreast) demonstrate that GraphMedFL achieves superior performance compared to locally trained models, with an accuracy of 98.3% and strong precision-recall balance. The proposed GraphMedFL achieves an accuracy of 98.3% and consistently outperforms baseline federated learning and local training approaches across BreakHis, CBIS-DDSM, and INbreast datasets. It improves precision, recall, and F1-score by a measurable margin compared to conventional methods, demonstrating enhanced robustness and generalization in heterogeneous medical imaging environments.","url":"https://doi.org/10.1038/s41598-026-60172-y","authors":["Alqarni AA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.1038/s41598-026-60172-y","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.21203/rs.3.rs-8960272/v1","name":"CAREFL: Context-Aware Recognition of Emotions with Federated Learning","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8960272/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:34.421Z","doi":"10.21203/rs.3.rs-8960272/v1","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1145/3411501.3418607","name":"Engineering Privacy-Preserving Machine Learning Protocols","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3411501.3418607","authors":["Thomas Schneider"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-04T03:22:57Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1145/3411501.3418607","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.32657/10356/167272","name":"MPC-enabled privacy-preserving machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/167272","authors":["Ziyao Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-01T06:29:45Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.32657/10356/167272","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.70675/f2b18e62z1092z4868z93a3z9765eff3e668","name":"Privacy-preserving machine learning techniques","source":"crossref","abstract":"Apprentissage automatique et confidentialité des données L'apprentissage automatique en tant que service (MLaaS) fait référence à un service qui permet aux entreprises de déléguer leurs tâches d'apprentissage automatique à un ou plusieurs serveurs puissants, à savoir des serveurs cloud. Néanmoins, les entreprises sont confrontées à des défis importants pour garantir la confidentialité des données et le respect des réglementations en matière de protection des données. L'exécution de tâches d'apprentissage automatique sur des données sensibles nécessite la conception de nouveaux protocoles garantissant la confidentialité des données pour les techniques d'apprentissage automatique.Dans cette thèse, nous visons à concevoir de tels protocoles pour MLaaS et étudions trois techniques d'apprentissage automatique : les réseaux de neurones, le partitionnement de trajectoires et l'agrégation de données. Dans nos solutions, notre objectif est de garantir la confidentialité des données tout en fournissant un niveau acceptable de performance et d’utilité. Afin de préserver la confidentialité des données, nous utilisons plusieurs techniques cryptographiques avancées : le calcul bipartite sécurisé, le chiffrement homomorphe, le rechiffrement proxy homomorphe ainsi que le chiffrement à seuil et le chiffrement à clé multiples. Nous avons en outre implémenté ces nouveaux protocoles et étudié le compromis entre confidentialité, performance et utilité/qualité pour chacun d’entre eux.","url":"https://doi.org/10.70675/f2b18e62z1092z4868z93a3z9765eff3e668","authors":["Beyza Bozdemir"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-06T13:11:38Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.70675/f2b18e62z1092z4868z93a3z9765eff3e668","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.14711/thesis-991013222949503412","name":"Accelerating privacy-preserving machine learning with genibatch","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013222949503412","authors":["Xinyang Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-21T22:45:02Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.14711/thesis-991013222949503412","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.12681/eadd/58917","name":"Privacy preserving and efficient machine learning algorithms","source":"crossref","abstract":"Η ευρεία διαθεσιμότητα δεδομένων έχει αποτελέσει καταλύτη για την ανάπτυξη της βαθιάς μάθησης. Οι εξελίξεις αυτές περιλαμβάνουν την ταξινόμηση εικόνων, την αναγνώριση ομιλίας και την επεξεργασία φυσικής γλώσσας. Ωστόσο, η πρόοδος που βασίζεται στα δεδομένα συχνά παρεμποδίζεται από περιορισμούς ιδιωτικότητας, οι οποίοι εμποδίζουν τη δημόσια διάθεση ορισμένων συνόλων δεδομένων. Για παράδειγμα, κάποια σύνολα δεδομένων υπολογιστικής όρασης δεν μπορούν να δημοσιοποιηθούν λόγω κανονισμών απορρήτου, ιδίως όταν περιέχουν εικόνες με ευαίσθητο ή ενοχλητικό περιεχόμενο. Την ίδια στιγμή, είναι αναγκαίο τα μοντέλα βαθιάς μάθησης –και ειδικά τα Βαθιά Νευρωνικά Δίκτυα (Deep Neural Networks, DNNs)– να υλοποιούνται με τρόπο αποδοτικό ως προς τους υπολογιστικούς πόρους. Στην παρούσα διατριβή, εστιάζουμε στην αποδοτική χρήση των DNNs μέσω μεθόδων που μειώνουν το υπολογιστικό τους κόστος. Αρχικά, εξετάζουμε τις προκλήσεις που αφορούν την προστασία της ιδιωτικότητας στη βαθιά μάθηση. Προτείνουμε μια νέα μεθοδολογία σύνθεσης και διάθεσης συνθετικών δεδομένων, ως εναλλακτική στη χρήση ευαίσθητων ιδιωτικών δεδομένων. Συγκεκριμένα, παρουσιάζουμε τη μέθοδο DP-ImgSyn (Differentially Private Image Synthesis) για τη δημιουργία συνθετικών εικόνων που προορίζονται για εργασίες ταξινόμησης. Οι εικόνες αυτές πληρούν τρεις βασικές προϋποθέσεις: (1) παρέχουν εγγυήσεις Διαφορικής Ιδιωτικότητας (Differential Privacy), (2) διατηρούν τη χρησιμότητα των αρχικών ιδιωτικών εικόνων, ώστε τα μοντέλα που εκπαιδεύονται σε αυτές να επιτυγχάνουν αντίστοιχη ακρίβεια, και (3) είναι οπτικά διαφορετικές από τις εικόνες του αρχικού ιδιωτικού συνόλου. Το σύστημα DP-ImgSyn αποτελείται από τα εξής στάδια: αρχικά, ένα δίκτυο-διδάσκων (teacher network) εκπαιδεύεται με ιδιωτικές εικόνες μέσω αλγορίθμου εκπαίδευσης με DP. Στη συνέχεια, δημόσιες εικόνες χρησιμοποιούνται για την αρχικοποίηση των συνθετικών εικόνων, οι οποίες βελτιστοποιούνται ώστε να ευθυγραμμίζονται με τη στατιστική κατανομή του ιδιωτικού δικτύου. Η βελτιστοποίηση γίνεται μέσω των στατιστικών των επιπέδων batch normalization (μέσοι όροι και τυπικές αποκλίσεις) του δικτύου-διδάσκοντος, επιτρέποντας τη μεταφορά πληροφορίας στις συνθετικές εικόνες. Τέλος, οι συνθετικές εικόνες, συνοδευόμενες από τις πιθανότητες κατηγοριοποίησης που προβλέπει το μοντέλο-διδάσκων (soft labels), δημοσιεύονται και μπορούν να χρησιμοποιηθούν για την εκπαίδευση δικτύων ταξινόμησης. Επιπλέον, η διατριβή επικεντρώνεται στην αποδοτικότητα των νευρωνικών δικτύων. Η ευρεία χρήση τους σε σύνθετα προβλήματα έχει οδηγήσει στην ανάπτυξη μοντέλων με μεγάλο αριθμό παραμέτρων, κάτι που αυξάνει σημαντικά το κόστος υλοποίησης. Για την αντιμετώπιση αυτής της πρόκλησης, μελετούμε την ποσοτικοποίηση (quantization) των βαρών και των ενεργοποιήσεων (activations) των DNNs. Ειδικότερα, προτείνουμε μία μέθοδο συμπίεσης μέσω ποσοτικοποίησης μεταβλητής ακρίβειας ανά επίπεδο (layer-wise mixed-precision quantization). Ο προσδιορισμός του κατάλληλου πλήθους bits για κάθε επίπεδο είναι υπολογιστικά απαιτητικός, λόγω του εκθετικού μεγέθους του χώρου αναζήτησης. Για τον σκοπό αυτό, χρησιμοποιούμε ένα Multi-Layer Perceptron (MLP), εκπαιδευμένο να προβλέπει τη βέλτιστη ακρίβεια για κάθε επίπεδο. Ως μέτρο αξιολόγησης της ποιότητας της ποσοτικοποίησης χρησιμοποιείται η απόκλιση Kullback-Leibler (KL) μεταξύ των εξόδων softmax των πλήρους και ποσοτικοποιημένης ακρίβειας μοντέλων. Μέσα από πειραματική μελέτη διαπιστώνεται ότι όσο πιο επιθετική είναι η ποσοτικοποίηση, τόσο μεγαλύτερη η απόκλιση και συνεπώς η πιθανότητα απώλειας ακρίβειας. Το MLP εκπαιδεύεται με τα αντίστοιχα ζεύγη επιπέδου–απόκλισης, τα οποία προκύπτουν μέσω δειγματοληψίας τύπου Monte Carlo στον χώρο αναζήτησης. Η μέθοδος στοχεύει στη μείωση του κόστους ανάπτυξης των DNNs, διατηρώντας υψηλή απόδοση στην ταξινόμηση. Στη συνέχεια, η διατριβή εξετάζει την αποδοτική αναγνώριση δράσεων σε συμπιεσμένα βίντεο. Σε αντίθεση με τις προσεγγίσεις που απαιτούν αποσυμπίεση των βίντεο, προτείνουμε αναγνώριση απευθείας πάνω στα δεδ","url":"https://doi.org/10.12681/eadd/58917","authors":["Ευσταθία Σουφλέρη"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-12T08:53:09Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.12681/eadd/58917","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.70675/196b5530z3b09z4e13z8ec5zbc53bdad6b5e","name":"Cryptography for privacy-preserving machine learning","source":"crossref","abstract":"La cryptographie au service de l'apprentissage automatique respectueux de la vie privée L’usage sans précédent du machine learning (ML) ou apprentissage automatique, motivé par les possibilités qu’il apporte dans un grand nombre de secteurs, interroge de plus en plus en raison du caractère sensible des données qui doivent être utilisées et du manque de transparence sur la façon dont ces données sont collectées, croisées ou partagées. Aussi, un certain nombre de méthodes se développent pour réduire son intrusivité sur notre vie privée, afin d’en rendre son usage plus acceptable, notamment dans des domaines tels que la santé, où son potentiel est encore très largement sous-exploité. Cette thèse explore différentes méthodes issues de la cryptographie ou plus largement du monde de la sécurité et les applique au machine learning afin d’établir des garanties de confidentialité nouvelles pour les données utilisées et les modèles de ML. Notre première contribution est le développement d’un socle technique pour implémenter et expérimenter de nouvelles approches au travers d’une librairie open-source nommée PySyft. Nous proposons une architecture modulaire qui facilite l’utilisation des briques de confidentialité ainsi que le développement et l’intégration de nouvelles briques. Ce socle sert de base à l’ensemble des implémentations proposées dans cette thèse. Notre seconde contribution consiste à mettre en lumière la vulnérabilité des modèles de ML en proposant une attaque qui exploite un modèle entraîné et permet de révéler des attributs confidentiels d’un individu. Cette attaque pourrait par exemple détourner un modèle qui reconnaît le sport fait par une personne à partir d’une image, pour détecter les origines raciales de cette personne. Nous proposons des pistes pour limiter l’impact de cette attaque. Dans un troisième temps, nous nous intéressons à certains protocoles de cryptographie qui permettent de faire des calculs sur des données chiffrées. Nous proposons un protocole de chiffrement fonctionnel qui permet de réaliser des prédictions sur des données chiffrées et de ne rendre public que la prédiction. Par ailleurs, nous optimisons un protocole de partage de secret fonctionnel, qui permet d’entraîner ou d’évaluer un modèle de ML sur des données de façon privée, c'est-à-dire sans révéler à quiconque ni le modèle ni les données. Ce protocole offre des performances suffisantes pour la réalisation de tâches non triviales comme la détection de pathologies dans les radiographies de poumons. Enfin, nous intéressons à la confidentialité différentielle qui permet de limiter la vulnérabilité des modèles de ML et donc l’exposition des données utilisées lors de l’entraînement, en introduisant une perturbation contrôlée. Nous proposons un protocole qui offre notamment la possibilité d’entraîner un modèle lisse et fortement convexe en garantissant un niveau de confidentialité indépendant du nombre d’accès aux données sensibles lors de l’entraînement.","url":"https://doi.org/10.70675/196b5530z3b09z4e13z8ec5zbc53bdad6b5e","authors":["Théo Ryffel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T04:35:20Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.70675/196b5530z3b09z4e13z8ec5zbc53bdad6b5e","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21275/ms241022095645","name":"Federated Learning: Privacy-Preserving Machine Learning in Cloud Environments","source":"crossref","abstract":"","url":"https://doi.org/10.21275/ms241022095645","authors":["Bangar Raju Cherukuri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-25T08:48:08Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.21275/ms241022095645","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.70675/c39e4a00z7d9dz4d18za65ez8d2292e27605","name":"Robust and privacy preserving distributed machine learning","source":"crossref","abstract":"Apprentissage automatique distribué robuste et respectueux de la vie privée Avec l’omniprésence des services numériques, d’´énormes quantités de données sont continuellement générées et collectées. Les algorithmes d’apprentissage automatique (ML) permettant d’extraire des connaissances précieuses à partir de ces données et ont été appliqués dans de nombreux domaines, tels que l’assistance médicale, le transport, la prédiction du comportement des utilisateurs, et bien d’autres. Dans beaucoup de ces applications, les données sont collectées à partir de différentes sources et un entraînement distribué est nécessaire pour apprendre des modèles globaux sur ces données. Néanmoins, dans le cas de données sensibles, l'exécution d'algorithmes ML traditionnels sur ces données peut conduire à de graves violations de la vie privée en divulguant des informations sensibles sur les propriétaires et les utilisateurs des données. Dans cette thèse, nous proposons des mécanismes permettant d'améliorer la préservation de la vie privée et la robustesse dans le domaine de l'apprentissage automatique distribué. La première contribution de cette thèse s'inscrit dans la catégorie d'apprentissage automatique respectueux de la vie privée basé sur la cryptographie. De nombreux travaux de l'état de l'art proposent des solutions basées sur la cryptographie pour assurer la préservation de la vie privée dans l'apprentissage automatique distribué. Néanmoins, ces travaux sont connus pour induire d'énormes coûts en termes de temps d'exécution et d'espace. Dans cette lignée de travaux, nous proposons PrivML, un framework externalisé d'apprentissage collaboratif basé sur le chiffrement homomorphe, qui permet d'optimiser le temps d'exécution et la consommation de bande passante pour les algorithmes ML les plus utilisés, moyennant de nombreuses techniques telles que le packing, les calculs approximatifs et le calcul parallèle. Les autres contributions de cette thèse abordent les questions de robustesse dans le domaine de l'apprentissage fédéré. En effet, l'apprentissage fédéré est le premier framework à garantir la préservation de la vie privée par conception dans le cadre de l'apprentissage automatique distribué. Néanmoins, il a été démontré que ce framework est toujours vulnérable à de nombreuses attaques, parmi lesquelles nous trouvons les attaques par empoisonnement, où les participants utilisent délibérément des données d'entraînement erronées pour provoquer une mauvaise classification au moment de l'inférence. Nous démontrons que les mécanismes de mitigation de l'empoisonnement de l'état de l'art ne parviennent pas à détecter certaines attaques par empoisonnement et nous proposons ARMOR, un mécanisme de mitigation de l'empoisonnement pour l'apprentissage fédéré qui parvient à détecter ces attaques sans nuire à l'utilité des modèles.","url":"https://doi.org/10.70675/c39e4a00z7d9dz4d18za65ez8d2292e27605","authors":["Rania Talbi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T03:14:23Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.70675/c39e4a00z7d9dz4d18za65ez8d2292e27605","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.33612/diss.1139532528","name":"Privacy-Preserving Machine Learning over Distributed Data","source":"crossref","abstract":"","url":"https://doi.org/10.33612/diss.1139532528","authors":["Ali Reza Ghavamipour"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-06T12:18:26Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.33612/diss.1139532528","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.12794/metadc1703277","name":"Privacy Preserving Machine Learning as a Service","source":"crossref","abstract":"Machine learning algorithms based on neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. We focus on training and classification of the well-known neural networks and convolutional neural networks. First, we design methods for approximation of the activation functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for efficient homomorphic encryption schemes. Then, we train neural networks with the approximation polynomials instead of original activation functions and analyze the performance of the models. Finally, we implement neural networks and convolutional neural networks over encrypted data and measure performance of the models.","url":"https://doi.org/10.12794/metadc1703277","authors":["Ehsan Hesamifard"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-08T09:01:31Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.12794/metadc1703277","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.5220/0014763700004818","name":"Machine Learning in Medical Diagnostics: Integrating Algorithms, Clinical Workflows, and Privacy-Preserving Strategies","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014763700004818","authors":["Yiran Qiu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-19T11:49:27Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.5220/0014763700004818","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.7717/peerj-cs.4031/fig-1","name":"Figure 1: Flowchart of the proposed privacy-preserving machine learning framework.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.4031/fig-1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-23T08:24:22Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.7717/peerj-cs.4031/fig-1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.12681/eadd/56628","name":"Privacy-preserving computations and machine learning over decentralized data","source":"crossref","abstract":"Στη σύγχρονη εποχή, οι αναπτυσσόμενοι τομείς της μηχανικής μάθησης (Machine Learning) και της τεχνητής νοημοσύνης (Artificial Intelligence) έχουν επιφέρει ραγδαία τεχνολογική πρόοδο, αλλάζοντας τον τρόπο προσέγγισης και επίλυσης σύνθετων προβλημάτων. Οι ευρέως διαδεδομένες εφαρμογές μηχανικής μάθησης αναδεικνύουν τον μετασχηματισμό πληθώρας τομέων, από την παροχή υποστήριξης απλών διεργασιών έως και την ενίσχυση κρίσιμων υποδομών και συστημάτων δημόσιας υγείας. Η εξάπλωση των τεχνολογιών αυτών, δεν αποτελεί απλώς απόδειξη ανθρώπινης εφευρετικότητας, αλλά και αντανάκλαση της ολοένα και αυξανόμενης εξάρτησης της καθημερινής κοινωνίας από τη λήψη αποτελεσματικών αποφάσεων και την αυτοματοποίηση διαδικασιών. Οι εφαρμογές τεχνητής νοημοσύνης έχουν διαπεράσει σε διάφορες πτυχές της καθημερινής ζωής και της βιομηχανίας. Μεταξύ άλλων, περιλαμβάνονται συστήματα συστάσεων που προσαρμόζουν τις εμπειρίες των χρηστών σε διάφορα πεδία εφαρμογής (λιανικό εμπόριο, ειδησεογραφικό περιεχόμενο, ταινίες, μουσική, σημεία ενδιαφέροντος κ.α.), αλγόριθμοι πρόβλεψης λέξεων και κειμένου για τη βελτίωση της επικοινωνίας, ανάλυση πελατών για τη βελτιστοποίηση επιχειρηματικών στρατηγικών, αλγόριθμοι πρόβλεψης δικτυακής κίνησης για την βελτιστοποίηση δικτύων και αλγόριθμοι πρόβλεψης φυσικής και ψυχολογικής κατάστασης ατόμων για την έγκαιρη παρέμβαση από ειδικούς και διαχείριση της δημόσιας υγείας. Επιπρόσθετα, η ενσωμάτωση αυτών των τεχνολογιών σε έξυπνα σπίτια, πόλεις και εν γένει υποδομές τονίζει τη σημασία τους για την αστική ανάπτυξη και βιωσιμότητα. Τα προβλήματα που επιλύουν οι αλγόριθμοι μηχανικής μάθησης είναι ποικίλα και πολύπλευρα. Τα πιο δημοφιλή είδη προβλημάτων περιλαμβάνουν, την ταξινόμηση (classification), η οποία κατηγοριοποιεί τα δεδομένα σε προκαθορισμένες ετικέτες (labels), την συσταδοποίηση (clustering), η οποία εντοπίζει εγγενείς ομαδοποιήσεις, την παλινδρόμηση (regression), η οποία χρησιμοποιείται για την πρόβλεψη συνεχών τιμών και την πρόβλεψη χρονοσειρών (time-series forecasting) για την κατανόηση και πρόβλεψη διαχρονικών τάσεων. Τα τελευταία χρόνια παρατηρείται μεγάλη έξαρση ενδιαφέροντος στη δημιουργία και ανάπτυξη εφαρμογών σύνθεσης δεδομένων (generative Artificial Intelligence), με αλγορίθμους οι οποίοι υπόσχονται τον επαναπροσδιορισμό της καινοτομίας και δημιουργικότητας σε οποιοδήποτε πεδίο. Στο επίκεντρο αυτών των τεχνολογιών βρίσκεται η περίπλοκη διαδικασία ανάλυσης δεδομένων, τα οποία προέρχονται από απλούς αισθητήρες μέσω αλληλεπίδρασης με το φυσικό περιβάλλον έως και περιεχόμενο δημιουργημένο από χρήστες εφαρμογών. Οι πηγές δεδομένων τροφοδοτούν κεντρικά συστήματα συλλογής, όπου πραγματοποιείται προ επεξεργασία και χρήση των πληροφοριών για τη εκπαίδευση αλγορίθμων μηχανικής μάθησης και τη δημιουργία κατάλληλων και αποτελεσματικών εφαρμογών. Αν και τα σύνολα δεδομένων αποτελούν ένα από τα βασικότερα συστατικά της επιτυχίας των αλγορίθμων μηχανικής μάθησης, η συλλογή, ανάλυση και αποθήκευση των τεράστιων ποσοτήτων προσωπικών και ευαίσθητων δεδομένων εγείρει σημαντικά ζητήματα προστασίας της ιδιωτικότητας. Επιπλέον, καθώς οι ευφυείς εφαρμογές γίνονται όλο και πιο σύνθετες και απαιτούν όλο και περισσότερα δεδομένα, η ικανότητα αποτελεσματικής επεξεργασίας και ανάλυσης σε μεγάλη κλίμακα δεδομένων καθίσταται κρίσιμη πρόκληση. Για τους λόγους αυτούς, η Ομοσπονδιακή Μάθηση (Federated Learning) αναδείχθηκε ως μια πολλά υποσχόμενη λύση όσον αφορά την προστασία των δεδομένων, επιτρέποντας συνεργατικές και κλιμακούμενες λύσεις. Η έρευνα που έχει διεξαχθεί στα πλαίσια αυτής της διατριβής προσανατολίζεται στην διερεύνηση ρεαλιστικών εφαρμογών της μηχανικής μάθησης, με επίκεντρο την ποικιλομορφία των δεδομένων, την ανάδειξη της συνεργασίας για την δημιουργία αποτελεσματικών προβλέψεων, τη βιωσιμότητα των αλγορίθμων και την ενίσχυση της ασφάλειας των δεδομένων και των προτιμήσεων των χρηστών. Η ανάλυση συνίσταται από τα εξής χαρακτηριστικά: 1.Βασίζεται σε ρεαλιστικά δεδομένα, τα οποία μπορούν να χρησιμοποιηθούν για την επίλυση προβλημάτ","url":"https://doi.org/10.12681/eadd/56628","authors":["Βασίλειος Περηφάνης"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-28T06:54:01Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.12681/eadd/56628","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.70675/10bb5f06zd04az4edbzadfcz7fabd892f109","name":"Combining differential privacy and homomorphic encryption for privacy-preserving collaborative machine learning","source":"crossref","abstract":"Approches combinant confidentialité différentielle et chiffrement homomorphe pour la protection des données en apprentissage automatique collaboratif L'objet de cette thèse est la conception de protocoles pour l'entraînement de modèles d'apprentissage automatique avec protection des données d'entraînement. Pour ce faire, nous nous sommes concentrés sur deux outils de confidentialité, la confidentialité différentielle et le chiffrement homomorphe. Alors que la confidentialité différentielle permet de fournir un modèle fonctionnel protégé des attaques sur la confidentialité par les utilisateurs finaux, le chiffrement homomorphe permet d'utiliser un serveur comme intermédiaire totalement aveugle entre les propriétaires des données, qui fournit des ressources de calcul sans aucun accès aux informations en clair. Cependant, ces deux techniques sont de nature totalement différente et impliquent toutes deux leurs propres contraintes qui peuvent interférer : la confidentialité différentielle nécessite généralement l'utilisation d'un bruit continu et non borné, tandis que le chiffrement homomorphe ne peut traiter que des nombres encodés avec un nombre limité de bits. Les travaux présentés visent à faire fonctionner ensemble ces deux outils de confidentialité en gérant leurs interférences et même en les exploitant afin que les deux techniques puissent bénéficier l'une de l'autre.Dans notre premier travail, SPEED, nous étendons le modèle de menace du protocole PATE (Private Aggregation of Teacher Ensembles) au cas d'un serveur honnête mais curieux en protégeant les calculs du serveur par une couche homomorphe. Nous définissons soigneusement quelles opérations sont effectuées homomorphiquement pour faire le moins de calculs possible dans le domaine chiffré très coûteux tout en révélant suffisamment peu d'informations en clair pour être facilement protégé par la confidentialité différentielle. Ce compromis nous contraint à réaliser une opération argmax dans le domaine chiffré, qui, même si elle est raisonnable, reste coûteuse. C'est pourquoi nous proposons SHIELD dans une autre contribution, un opérateur argmax volontairement imprécis, à la fois pour satisfaire la confidentialité différentielle et alléger le calcul homomorphe. La dernière contribution présentée combine la confidentialité différentielle et le chiffrement homomorphe pour sécuriser un protocole d'apprentissage fédéré. Le principal défi de cette combinaison provient de la discrétisation nécessaire du bruit induit par le chiffrement, qui complique l'analyse des garanties de confidentialité différentielle et justifie la conception et l'utilisation d'un nouvel opérateur de quantification qui commute avec l'agrégation.","url":"https://doi.org/10.70675/10bb5f06zd04az4edbzadfcz7fabd892f109","authors":["Arnaud Grivet Sébert"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T16:40:32Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.70675/10bb5f06zd04az4edbzadfcz7fabd892f109","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.70675/19908e03zbca2z42cdza812z32140402c8ca","name":"Inherent trade-offs in privacy-preserving machine learning","source":"crossref","abstract":"Compromis inhérents à l'apprentissage automatique préservant la confidentialité À mesure que les modèles d'apprentissage automatique (ML) sont de plus en plus intégrés dans un large éventail d'applications, il devient plus important que jamais de garantir la confidentialité des données des individus. Cependant, les techniques actuelles entraînent souvent une perte d'utilité et peuvent affecter des facteurs comme l'équité et l'interprétabilité. Cette thèse vise à approfondir la compréhension des compromis dans trois techniques de ML respectueuses de la vie privée : la confidentialité différentielle, les défenses empiriques, et l'apprentissage fédéré, et à proposer des méthodes qui améliorent leur efficacité tout en maintenant la protection de la vie privée. La première étude examine l'impact de la confidentialité différentielle sur l'équité entre les groupes définis par des attributs sensibles. Alors que certaines hypothèses précédentes suggéraient que la confidentialité différentielle pourrait exacerber l'injustice dans les modèles ML, nos expériences montrent que la sélection d'une architecture de modèle optimale et le réglage des hyperparamètres pour DP-SGD (Descente de Gradient Stochastique Différentiellement Privée) peuvent atténuer les disparités d'équité. En utilisant des ensembles de données standards dans la littérature sur l'équité du ML, nous montrons que les disparités entre les groupes pour les métriques telles que la parité démographique, l'égalité des chances et la parité prédictive sont souvent réduites ou négligeables par rapport aux modèles non privés. La deuxième étude se concentre sur les défenses empiriques de la vie privée, qui visent à protéger les données d'entraînement tout en minimisant la perte d'utilité. La plupart des défenses existantes supposent l'accès à des données de référence — un ensemble de données supplémentaire provenant de la même distribution (ou similaire) que les données d'entraînement. Cependant, les travaux antérieurs n'ont que rarement évalué les risques de confidentialité associés aux données de référence. Pour y remédier, nous avons réalisé la première analyse complète de la confidentialité des données de référence dans les défenses empiriques. Nous avons proposé une méthode de défense de référence, la minimisation du risque empirique pondéré (WERM), qui permet de mieux comprendre les compromis entre l'utilité du modèle, la confidentialité des données d'entraînement et celle des données de référence. En plus d'offrir des garanties théoriques, WERM surpasse régulièrement les défenses empiriques de pointe dans presque tous les régimes de confidentialité relatifs. La troisième étude aborde les compromis liés à la convergence dans les systèmes d'inférence collaborative (CIS), de plus en plus utilisés dans l'Internet des objets (IoT) pour permettre aux nœuds plus petits de décharger une partie de leurs tâches d'inférence vers des nœuds plus puissants. Alors que l'apprentissage fédéré (FL) est souvent utilisé pour entraîner conjointement les modèles dans ces systèmes, les méthodes traditionnelles ont négligé la dynamique opérationnelle, comme l'hétérogénéité des taux de service entre les nœuds. Nous proposons une approche FL novatrice, spécialement conçue pour les CIS, qui prend en compte les taux de service variables et la disponibilité inégale des données. Notre cadre offre des garanties théoriques et surpasse systématiquement les algorithmes de pointe, en particulier dans les scénarios où les appareils finaux gèrent des taux de requêtes d'inférence élevés. En conclusion, cette thèse contribue à l'amélioration des techniques de ML respectueuses de la vie privée en analysant les compromis entre confidentialité, utilité et autres facteurs. Les méthodes proposées offrent des solutions pratiques pour intégrer ces techniques dans des applications réelles, en assurant une meilleure protection des données personnelles.","url":"https://doi.org/10.70675/19908e03zbca2z42cdza812z32140402c8ca","authors":["Caelin Kaplan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T23:02:04Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.70675/19908e03zbca2z42cdza812z32140402c8ca","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-3-030-71522-9_300746","name":"Privacy-Preserving Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71522-9_300746","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:16:08Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-3-030-71522-9_300746","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1017/9781107338548.007","name":"Privacy-Preserving Mechanisms for SVM Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1017/9781107338548.007","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-03-14T03:14:16Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1017/9781107338548.007","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.70675/13ba632dz4db4z4a45z8061z4ced52628d5d","name":"Exploiting problem structure in privacy-preserving optimization and machine learning","source":"crossref","abstract":"Exploitation de la structure des problèmes en optimisation et en apprentissage automatique respectueux de la vie privée Au cours des dernières décennies, les préoccupations quant à l'impact sociétal de l'apprentissage automatique se sont multipliées. En effet, si l'apprentissage automatique a prouvé son utilité dans la science, dans la vie quotidienne, ainsi que dans de nombreux autres domaines, son succès est principalement dû à la disponibilité de grands ensembles de données. Cela soulève deux préoccupations : la première concerne la confidentialité des données d'entraînement et la seconde, la possibilité de discrimination dans les prédictions d'un modèle. Le domaine de l'apprentissage automatique fiable vise à apporter des réponses techniques à ces préoccupations.Malheureusement, garantir la confidentialité des données d'entraînement, ainsi que l'équité des prédictions, diminue souvent l'utilité du modèle appris. Ce problème a suscité un grand intérêt au cours des dernières années. Cependant, la plupart des méthodes existantes (généralement basées sur la descente de gradient stochastique) ont tendance à échouer dans des scénarios courants, tels que l'entraînement de modèles en grande dimension. Dans cette thèse, nous étudions comment les propriétés structurelles des problèmes d'apprentissage automatique peuvent être exploitées pour améliorer le compromis entre la confidentialité et l'utilité, et comment cela peut affecter l'équité des prédictions.Les deux premières contributions de cette thèse sont deux nouveaux algorithmes d'optimisation respectant la confidentialité différentielle, tous deux basés sur la descente par coordonnées, visant à exploiter les propriétés structurelles du problème. Le premier algorithme est basé sur la descente par coordonnées stochastique et est en mesure d'exploiter le déséquilibre dans l'échelle des coordonnées du gradient en utilisant des grands pas d'apprentissage. Cela lui permet de trouver des modèles pertinents dans des scénarios difficiles, où la descente de gradient stochastique échoue. Le deuxième algorithme est basé sur la descente par coordonnées gloutonne. Les mises à jour gloutonnes permettent de se concentrer sur les coordonnées les plus importantes du problème, ce qui peut parfois améliorer considérablement l'utilité (par exemple, lorsque la solution du problème est parcimonieuse).La troisième contribution de cette thèse étudie les interactions entre confidentialité différentielle et équité en apprentissage automatique. Ces deux notions ont rarement été étudiées simultanément, et il existe des inquiétudes croissantes selon lesquelles la confidentialité différentielle pourrait nuire à l'équité des prédictions. Nous montrons que quand les prédictions du modèle sont lipschitziennes (par rapport à ses paramètres), les mesures d'équité de groupe présentent des propriétés de régularité intéressantes, que nous caractérisons. Ce résultat permet d'obtenir une borne sur la différence de niveaux d'équité entre un modèle privé et le modèle non-privé correspondant.","url":"https://doi.org/10.70675/13ba632dz4db4z4a45z8061z4ced52628d5d","authors":["Paul Mangold"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T07:54:02Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.70675/13ba632dz4db4z4a45z8061z4ced52628d5d","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.14711/thesis-hdl167721","name":"Innovative Protocols for Decentralized Financial Systems and Privacy-Preserving Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-hdl167721","authors":["Zhenhang Shang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-15T23:00:44Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.14711/thesis-hdl167721","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.70675/17b0463eza470z4230za6edze526eaf22206","name":"Trustful AI through fair and privacy-preserving federated machine learning","source":"crossref","abstract":"Intelligence artificielle digne de confiance grâce à l'apprentissage fédéré équitable et respectueux de la vie privée L'augmentation massive du volume de données disponibles a transformé l'informatique moderne, contribuant à l'essor de l'apprentissage automatique dans plusieurs domaines tels que la santé, la finance et les systèmes autonomes. Cette dépendance croissante aux modèles d'apprentissage pour la prise de décision dans ces domaines soulève des préoccupations majeures en matière d'équité et de confidentialité, étant donné que les décisions qu'ils produisent peuvent influencer de manière significative les individus et les organisations dans des contextes d'application réels. Motivés par la nécessité de mieux comprendre et d'atténuer ces enjeux, la présente recherche étudie les défis associés à ces problématiques et propose des contributions notables en faveur de systèmes d'apprentissage plus responsables et plus fiables. La première partie de cette thèse porte sur l'équité, devenue une préoccupation centrale en apprentissage automatique en raison de son potentiel à amplifier les disparités et à renforcer les biais existants. Ce constat, associé à l'émergence de régulations telles que l'AI Act de l'Union européenne et l'Executive Order américain, souligne la nécessité d'intégrer l'équité comme principe fondamental dans la conception des systèmes d'apprentissage automatique. Compte tenu de cela, nous avons examiné comment les méthodes de sélection automatique de données, souvent utilisées pour améliorer l'efficacité en termes de temps en utilisant des sous-ensembles plus petits d'entraînement plutôt que le jeu de données complet, influencent l'équité et la qualité des modèles. Nous avons mené la première analyse à grande échelle révélant l'impact souvent ignoré de la sélection sur ces deux aspects, nous avons identifié les facteurs clés qui en sont à l'origine et nous avons avancé des pistes pertinentes pour favoriser un apprentissage plus équitable et efficace. Parallèlement aux régulations promouvant un apprentissage plus équitable, d'autres insistent sur la protection des données utilisées, tel que le règlement général sur la protection des données (RGPD), qui a favorisé l'adoption de l'apprentissage fédéré, permettant à plusieurs parties d'entraîner un modèle global sans partager leurs données. Cependant, la nature distribuée et collaborative de ce paradigme d'apprentissage amplifie les biais hérités de l'apprentissage centralisé et introduit également de nouveaux types de biais. Pour éclairer ces enjeux, nous nous avons proposé une revue approfondie de l'équité en apprentissage fédéré, identifiant les principales sources de biais, couvrant l'ensemble des stratégies de mitigation existantes, et mettant en évidence des pistes de recherche encore ouvertes pour faire progresser l'équité dans ce cadre. Bien que l'apprentissage fédéré permette à plusieurs parties d'entraîner un modèle sans partager leurs données locales, de nombreuses études montrent qu'il reste vulnérable à diverses menaces portant atteinte à la vie privée des utilisateurs. Parmi ces menaces figurent les attaques d'inférence d'appartenance, qui permettent à un adversaire de déterminer si un point de données faisait partie de l'ensemble d'entraînement, exposant potentiellement des informations confidentielles. Dans ce contexte, la deuxième partie de cette thèse se concentre sur les atteintes à la vie privée en apprentissage fédéré, en accordant une attention particulière aux attaques d'inférence d'appartenance. Pour contrer ces attaques, nous avons introduit GAPER, une méthode granulaire et adaptative fondée sur la confidentialité différentielle, offrant une protection efficace contre ce type d'attaque tout en préservant la qualité du modèle, et soutenue à la fois par des garanties théoriques et des évaluations empiriques. De manière plus générale, ces travaux font progresser le développement de systèmes d'apprentissage non seulement performants, mais égal","url":"https://doi.org/10.70675/17b0463eza470z4230za6edze526eaf22206","authors":["Saida Benarba"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-22T14:28:08Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.70675/17b0463eza470z4230za6edze526eaf22206","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-1-4614-4639-2_11","name":"Privacy-Preserving Isolated-Word Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4614-4639-2_11","authors":["Manas A. Pathak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-10-25T01:58:43Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-1-4614-4639-2_11","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.70675/7731dd5dze2dcz4888z99c3z37fea26dc147","name":"Privacy-preserving machine learning for large-scale collaborative healthcare data analysis","source":"crossref","abstract":"Apprentissage automatique sécurisé pour l'analyse collaborative des données de santé à grande échelle Cette thèse de doctorat explore l'intégration de la préservation de la confidentialité, de l'imagerie médicale et de l'apprentissage fédéré (FL) à l'aide de méthodes cryptographiques avancées. Dans le cadre de l'analyse d'images médicales, nous développons un cadre de recalage d'images préservant la confidentialité (PPIR). Ce cadre aborde le défi du recalage des images de manière confidentielle, sans révéler leur contenu. En étendant les paradigmes de recalage classiques, nous incorporons des outils cryptographiques tels que le calcul multipartite sécurisé et le chiffrement homomorphe pour effectuer ces opérations en toute sécurité. Ces outils sont essentiels car ils empêchent les fuites de données pendant le traitement. Étant donné les défis associés à la performance et à l'évolutivité des méthodes cryptographiques dans les données de haute dimension, nous optimisons nos opérations de recalage d'images en utilisant des approximations de gradient. Notre attention se porte sur des méthodes de recalage de plus en plus complexes, telles que les approches rigides, affines et non linéaires utilisant des splines cubiques ou des difféomorphismes, paramétrées par des champs de vitesses variables dans le temps. Nous démontrons comment ces méthodes de recalage sophistiquées peuvent intégrer des mécanismes de préservation de la confidentialité de manière efficace dans diverses tâches.Parallèlement, la thèse aborde le défi des retardataires dans l'apprentissage fédéré, en mettant l'accent sur le rôle de l'agrégation sécurisée (SA) dans l'entraînement collaboratif des modèles. Nous introduisons \"Eagle\", un schéma SA synchrone conçu pour optimiser la participation des dispositifs arrivant tardivement, améliorant ainsi considérablement les efficacités computationnelle et de communication. Nous présentons également \"Owl\", adapté aux environnements FL asynchrones tamponnés, surpassant constamment les solutions antérieures. En outre, dans le domaine de la Buffered AsyncSA, nous proposons deux nouvelles approches : \"Buffalo\" et \"Buffalo+\". \"Buffalo\" fait progresser les techniques de SA pour la Buffered AsyncSA, tandis que \"Buffalo+\" contrecarre les attaques sophistiquées que les méthodes traditionnelles ne parviennent pas à détecter. Cette solution exploite les propriétés des fonctions de hachage incrémentielles et explore la parcimonie dans la quantification des gradients locaux des modèles clients. \"Buffalo\" et \"Buffalo+\" sont validés théoriquement et expérimentalement, démontrant leur efficacité dans une nouvelle tâche de FL inter-dispositifs pour les dispositifs médicaux.Enfin, cette thèse a accordé une attention particulière à la traduction des outils de préservation de la confidentialité dans des applications réelles, notamment grâce au cadre open-source FL Fed-BioMed. Les contributions concernent l'introduction de l'une des premières implémentations pratiques de SA spécifiquement conçues pour le FL inter-silos entre hôpitaux, mettant en évidence plusieurs cas d'utilisation pratiques.","url":"https://doi.org/10.70675/7731dd5dze2dcz4888z99c3z37fea26dc147","authors":["Riccardo Taiello"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T20:21:54Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.70675/7731dd5dze2dcz4888z99c3z37fea26dc147","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.36227/techrxiv.174586937.72782146/v1","name":"Resilient Privacy Preserving Machine Learning for Internet of Things","source":"crossref","abstract":"The rapid development of the Internet of Things (IoT) presents new challenges (such as privacy concerns) to existing data analytics frameworks deployed in IoT-based applications. Federated learning (FL), a type of distributed and privacy-preserving learning framework, is attracting attention from both academia and industry. However, due to variations in hardware and software among processing nodes (clients) in IoT, some clients (that lack enough protection mechanisms) may be compromised and controlled by adversaries. These compromised Byzantine clients pose a serious threat to the reliability of existing FL-based applications. To address this issue, instead of simply averaging model updates used in the traditional FL frameworks (FedAvg or Coordinate-wise Mean (Mean)), robust aggregation algorithms have been proposed. Krum is one of these widely used robust aggregation algorithms designed to mitigate the impact of Byzantine clients in FL. A potential limitation of the standard Krum is that it requires the number of Byzantine clients, denoted as f , to be specified in advance. To overcome this limitation, we propose a refined variant of Krum, called rKrum. Our method incorporates change point detection techniques to dynamically estimate f for each client. Experimental evaluations on three public datasets (tabular, image, and text data) demonstrate that our proposed rKrum performs comparably to the standard Krum under different attack scenarios. In most cases, our rKrum produces nearly identical outcomes in terms of global model accuracy. As anticipated, both rKrum and Krum outperform Mean, demonstrating strong robustness against Byzantine clients.","url":"https://doi.org/10.36227/techrxiv.174586937.72782146/v1","authors":["Kun Yang","Neena Imam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-28T15:42:57Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.36227/techrxiv.174586937.72782146/v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.70675/25bed0ddz90ddz470fza7d0z9d9b696b76c8","name":"Efficient and robust protocols for privacy-preserving semi-decentralized machine learning","source":"crossref","abstract":"Protocoles efficaces et robustes pour l'apprentissage automatique semi-décentralisé préservant la confidentialité Ces dernières années, la préoccupation pour la protection de la vie privée s'est considérablement accrue. Cela s'explique par l'utilisation régulière de services qui nécessitent l'externalisation et le traitement massif de données personnelles, souvent sensibles. Pour cette raison, les mesures visant à réglementer la manipulation des données personnelles et à empêcher leur divulgation ont gagné en importance.Deux limitations importantes des algorithmes existants utilisés dans le domaine de l'apprentissage automatique sont qu'ils ne sont souvent pas robustes contre les attaques par collusion, et qu'un tiers de confiance est nécessaire pour (entre autres) effectuer une perturbation aléatoire permettant d'obtenir des garanties de confidentialité différentielle (differential privacy). Cette thèse vise à résoudre ces problèmes. Elle contient en particulier deux contributions majeures.La première contribution est un protocole décentralisé et sécurisé qui effectue une agrégation satisfaisant la confidentialité différentielle. Dans ce contexte, chaque partie possède ses propres données privées et souhaite calculer de manière collaborative une statistique, par exemple une moyenne, sans divulguer ses informations sensibles. Notre protocole est robuste aux attaques d'inférence par des parties en collusion et permet de vérifier l'exactitude des calculs. Il nécessite que chaque partie ne communique qu'avec un nombre logarithmique d'autres parties et permet d'obtenir des garanties de confidentialité différentielle avec une utilité presque équivalente au cas où l'on aurait recourt à un tiers de confiance.La deuxième contribution propose un protocole pour générer des nombres aléatoires dans un cadre de calcul multipartite de telle sorte que toutes les parties puissent vérifier que le nombre généré suit ladistribution de probabilité souhaitée et est effectivement pseudo-aléatoire, c'est-à-dire qu'aucun groupe de parties en collusion ne peut en fausser le caractère aléatoire. En particulier, nous considérons le tirage de nombres aléatoires publics (de sorte que toutes les parties puissent les voir), privés (de sorte qu'une seule partie puisse les voir) ou de manière cachée (de sorte qu'ils soient émis sous forme de parts secrètes et ne soient donc connus d'aucune des parties). Nous instancions nos méthodes de tirage de nombres aléatoires pour la distribution de Laplace et la distribution gaussienne. Comme sous-produit de notre approche pouvant avoir un intérêt en soi, nous proposons des algorithmes à divulgation nulle de connaissance pour vérifier les calculs transcendantaux tels que les fonctions logarithmique, trigonométrique et exponentielle.","url":"https://doi.org/10.70675/25bed0ddz90ddz470fza7d0z9d9b696b76c8","authors":["César Sabater"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T04:28:20Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.70675/25bed0ddz90ddz470fza7d0z9d9b696b76c8","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-0-387-30164-8_667","name":"Privacy-Preserving Data Mining","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-30164-8_667","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2010-12-29T17:26:04Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-0-387-30164-8_667","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-3-030-71522-9_1823","name":"Secure and Privacy-Preserving Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71522-9_1823","authors":["Sergio Barezzani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:20:57Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-3-030-71522-9_1823","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/afrcon.2017.8095692","name":"Privacy-preserving quantum machine learning using differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/afrcon.2017.8095692","authors":["Makhamisa Senekane","Mhlambululi Mafu","Benedict Molibeli Taele"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-11-28T16:04:21Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1109/afrcon.2017.8095692","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.21203/rs.3.rs-10093086/v1","name":"Privacy-Preserving Intrusion Detection in SDN-IoT Networks: Machine Learning, Deep Learning, Differential Privacy, and Inference Attacks","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10093086/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-10093086/v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1109/msec.2023.3315944","name":"Privacy-Preserving Machine Learning [Cryptography]","source":"crossref","abstract":"","url":"https://doi.org/10.1109/msec.2023.3315944","authors":["Florian Kerschbaum","Nils Lukas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-13T19:42:58Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1109/msec.2023.3315944","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.4018/979-8-3693-4159-9.ch011","name":"Fortifying Machine Learning, Data Privacy, and Secure Collaboration","source":"crossref","abstract":"The utilitarianism of machine learning (ML) techniques introduces an additional layer of security to ML applications. In the domain of cybersecurity, ML continuously evolves by analyzing data to identify patterns, thus enhancing the capacity to detect malware in encrypted traffic, recognize insider threats, predict online “bad neighborhoods” for safer browsing, and protect cloud-stored data by uncovering suspicious user behavior. Simultaneously, privacy-preserving techniques, exemplified by homomorphic encryption and multi-party computation, empower the training of ML models on sensitive data without exposing the raw information. Privacy-preserving techniques, notably statistical disclosure control (SDC), benefit data sharing. SDC seeks to mitigate the risk of disclosing confidential information by employing privacy-preserving techniques for data de-identification. Research integrates privacy-preserving techniques into ML, advancing diverse PPML for secure data analysis in academia and industry.","url":"https://doi.org/10.4018/979-8-3693-4159-9.ch011","authors":["P. Shyamala Madhuri","B. Amutha","D. J. Nagendra Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-31T08:05:21Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.4018/979-8-3693-4159-9.ch011","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.5220/0012730600003690","name":"An Approach for Privacy-Preserving Mobile Malware Detection Through Federated Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012730600003690","authors":["Giovanni Ciaramella","Fabio Martinelli","Francesco Mercaldo","Christian Peluso","Antonella Santone"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-15T13:10:07Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.5220/0012730600003690","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.2139/ssrn.5125847","name":"Artificial Intelligence and Machine Learning in Healthcare: Developing Privacy-Preserving Frameworks","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5125847","authors":["Ugochukwu Echendu","Chidiebere Udeokechukwu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T19:17:47Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.2139/ssrn.5125847","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1561/978-1-63828-477-220251004","name":"Local Differential Privacy for Privacy-preserving Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1561/978-1-63828-477-220251004","authors":["Cormode Graham"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T15:34:34Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1561/978-1-63828-477-220251004","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.20944/preprints202506.1137.v1","name":"Privacy-Preserving Machine Learning for Electronic Health Records","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202506.1137.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.20944/preprints202506.1137.v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1145/3411501.3418608","name":"Zero-Knowledge Proofs for Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3411501.3418608","authors":["Yupeng Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-04T03:22:57Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1145/3411501.3418608","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1145/3411501","name":"Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3411501","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-04T03:22:57Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1145/3411501","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-1-4899-7502-7_989-2","name":"Privacy-Preserving Medical Text Data Publishing with Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4899-7502-7_989-2","authors":["Tanbir Ahmed","Noman Mohammed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-25T14:52:38Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-1-4899-7502-7_989-2","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-1-4614-4639-2_6","name":"Privacy-Preserving Speaker Verification as String Comparison","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4614-4639-2_6","authors":["Manas A. Pathak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-10-25T01:58:43Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-1-4614-4639-2_6","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/sp.2017.12","name":"SecureML: A System for Scalable Privacy-Preserving Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp.2017.12","authors":["Payman Mohassel","Yupeng Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-06-26T20:34:26Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1109/sp.2017.12","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.5220/0013458900003979","name":"Privacy-Preserving Machine Learning in IoT: A Study of Data Obfuscation Methods","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013458900003979","authors":["Yonan Yonan","Mohammad Abdullah","Felix Nilsson","Mahdi Fazeli","Ahmad Patooghy","Slawomir Nowaczyk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-24T11:37:04Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.5220/0013458900003979","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-1-4614-4639-2_9","name":"Privacy-Preserving Speaker Identification as String Comparison","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4614-4639-2_9","authors":["Manas A. Pathak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-10-25T01:58:43Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-1-4614-4639-2_9","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21203/rs.3.rs-3993489/v1","name":"Privacy-Preserving Vital node Identification in Complex Networks using Machine Learning","source":"crossref","abstract":"Abstract Identifying vital nodes in complex networks is critical in various research areas, including social network analysis, epidemiology, and physics. Centrality measures are commonly used and combined for this purpose. However, the impact of growing privacy concerns on the identification of vital nodes, particularly in networks containing sensitive data like Bluetooth-based contact networks, is not well understood. Our study evaluates the effectiveness of vital node identification algorithms under privacy-preserving network settings. Through simulations, we identify algorithms that are most effective for estimating node vitality when only limited network information is available. Furthermore, we demonstrate that machine learning models provided with aggregated output of the most promising approaches and trained with only 20 percent of the data can significantly outperform state-of-the-art approaches, especially when network information is strongly limited. This research advances the understanding of privacy-centric approaches in complex network analysis and shows how machine learning-based methods can empower advanced network analysis under privacy-preserving conditions.","url":"https://doi.org/10.21203/rs.3.rs-3993489/v1","authors":["Diaoulé Diallo","Tobias Hecking"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-29T05:09:11Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.21203/rs.3.rs-3993489/v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.5220/0014500700005020","name":"Privacy‑Preserving Synthetic Healthcare Data: Machine Learning Applications in Vital Sign Monitoring and Diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014500700005020","authors":["Sellappan Palaniappan","Kasthuri Subaramaniam","Oras Baker"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-09T17:14:28Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.5220/0014500700005020","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.2139/ssrn.4595287","name":"Privacy Preserving Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4595287","authors":["Daniel Meier","Juan R. Troncoso Pastoriza"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-06T18:11:37Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.2139/ssrn.4595287","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1145/3411501.3419428","name":"Privacy-Preserving in Defending against Membership Inference Attacks","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3411501.3419428","authors":["Zuobin Ying","Yun Zhang","Ximeng Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-04T03:22:57Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1145/3411501.3419428","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-1-4614-4639-2_5","name":"Privacy-Preserving Speaker Verification Using Gaussian Mixture Models","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4614-4639-2_5","authors":["Manas A. Pathak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-10-25T01:58:43Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-1-4614-4639-2_5","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-1-4614-4639-2_8","name":"Privacy-Preserving Speaker Identification Using Gaussian Mixture Models","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4614-4639-2_8","authors":["Manas A. Pathak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-10-25T01:58:43Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-1-4614-4639-2_8","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.55529/jaimlnn.51.151.160","name":"Fedssl: privacy-preserving federated self-supervised learning with differential privacy guarantees for heterogeneous edge environments","source":"crossref","abstract":"In this study, we delve into the significant impact of AI, investigating its multifaceted consequences on society. In a rapidly evolving digital landscape, the integration of artificial intelligence has emerged as a transformative force in reshaping human progress and well-being. Drawing from historical perspectives, we trace the evolution of AI and its transformative journey. Our research aims to comprehensively analyze approaches for fostering responsible AI growth while mitigating potential hazards. By illuminating both the promise and perils of AI, this study contributes to informed decision-making in the unfolding AL era. This research explores the innovative utilization of AI technologies to optimize individual and societal gains while concurrently influencing the reconstruction of prevailing social norms. By harnessing the power of AI, we embark on a journey toward a new era, characterized by more efficient problem-solving, enhanced decision-making, and the redefinition of traditional social paradigms. This study investigates the multifaceted impacts of AI on human development, offering insights into its potential to revolutionize our world and foster a future marked by unprecedented advancements in progress, wellness, and social transformation.","url":"https://doi.org/10.55529/jaimlnn.51.151.160","authors":["Zayyanu Yunusa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-26T10:55:57Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.55529/jaimlnn.51.151.160","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.64917/feaiml/volume02issue12-07","name":"Federated Learning Architectures for Privacy Preserving Financial Fraud Detection Systems","source":"crossref","abstract":"The increasing complexity and intensity of cases of financial fraud, such as synthetic identity fraud and international money laundering, have become significant concerns for classic fraud detection solutions, especially under strict data privacy regulations such as GDPR or EU Artificial Intelligence Act guidelines. This study focuses on the very pressing need to pursue high fraud detection performance while simultaneously ensuring user data confidentiality for highly fragmented financial systems. The study uses federated learning (FL) designs to analyse interesting opportunities for possibly entirely decentralized machine learning functions among diverse financial agencies without any need to transfer raw user information among them at all. Using a multimodal research methodology consisting of systematic literature studies, design studies, simulation studies, stress studies, and regulatory investigations, this study comprehensively assesses FL’s effectiveness and privacy pertaining to 2025 regulatory norms. The empirical results prove that FL not only improves overall fraud detection capacities but also ensures decent secrecy on raw personal information, meeting modern regulatory norms while formulating tolerant computing and secrecy constraints. The result implies the utmost importance of continued monitoring and attention to security loopholes, governance structure definitions, and dedicated investments into privacy boosting technologies to tap into FL’s revolutionary positive change potency within finance domains. Therefore, this work provides recent information on FL's dissemination implementation into disjointed finance domains, meeting both theoretical knowledge and practicality pursuits.","url":"https://doi.org/10.64917/feaiml/volume02issue12-07","authors":["Favour . C. Ezeugboaja"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-22T08:49:02Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.64917/feaiml/volume02issue12-07","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.55529/jaimlnn.52.69.78","name":"Privacy-preserving federated learning with differential privacy for healthcare AI: a convergence and utility analysis","source":"crossref","abstract":"Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without sharing raw patient data, offering a paradigm shift for privacy-sensitive medical AI. However, FL remains vulnerable to gradient inversion attacks and model poisoning, necessitating formal privacy guarantees. This paper presents a comprehensive analysis of Differential Privacy (DP)-augmented Federated Learning for healthcare AI applications, specifically Electronic Health Record (EHR) classification. We evaluate three aggregation strategies FedAvg, FedProx, and the proposed FedNova-DP across simulated environments with 10, 25, and 50 heterogeneous clients under both IID and non-IID data distributions. The proposed FedNova-DP framework achieves 93.8% accuracy on the MIMIC-III-derived benchmark dataset under non-IID conditions with a differential privacy budget of ε = 0.5, representing a 4.4% improvement over FedAvg-DP (89.4%) under equivalent conditions. Convergence analysis demonstrates that FedNova-DP reaches target accuracy 31% faster (in communication rounds) than FedAvg. A detailed privacy-utility trade-off analysis across ε ∈ [0.1, 10] reveals that the proposed framework maintains competitive utility at strong privacy regimes (ε = 0.5, accuracy = 89.3%) compared to non-private centralized training (97.4%). These findings establish FedNova-DP as a practical, deployable solution for privacy-preserving healthcare AI at scale.","url":"https://doi.org/10.55529/jaimlnn.52.69.78","authors":["Aruna Pavate"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-30T06:28:24Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.55529/jaimlnn.52.69.78","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.36227/techrxiv.173591916.67267507/v1","name":"Privacy-Preserving Machine Learning: ANN Activation Function Estimators for Homomorphic Encrypted Inference","source":"crossref","abstract":"The rising demand for cloud-based machine learning services has intensified concerns about data privacy, particularly in sensitive fields like healthcare and finance. Homomorphic Encryption (HE) enables computations on encrypted data, offering a promising solution for Privacy-Preserving Machine Learning (PPML). However, the non-linear nature of activation functions, such as Sigmoid and Tanh, presents challenges for efficient encrypted inference in Artificial Neural Networks (ANNs). This paper proposes an innovative approach that utilizes ANN-based estimators to approximate these activation functions, balancing accuracy and computational efficiency. Our estimators are trained on plaintext data and deployed during encrypted inference, achieving competitive performance compared to traditional polynomial and piecewise linear approximations. Experimental results demonstrate that the proposed ANN estimators provide superior accuracy and Mean Squared Error (MSE) while maintaining feasible computation times. This approach advances the practicality of secure, efficient machine learning on encrypted data, paving the way for broader adoption of PPML solutions in cloud environments.","url":"https://doi.org/10.36227/techrxiv.173591916.67267507/v1","authors":["Mhd Raja Abou Harb","Baris Celiktas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-03T10:46:10Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.36227/techrxiv.173591916.67267507/v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.12794/metadc2481661","name":"Comparison of Fully Homomorphic Encryption and Garbled Circuits approaches in Privacy-Preserving Machine Learning","source":"crossref","abstract":"Machine Learning (ML) is making its way into fields such as healthcare, finance, and natural language processing (NLP), and concerns over data privacy and model confidentiality continue to grow. Privacy-Preserving Machine Learning (PPML) addresses this challenge by enabling inference on private data without revealing sensitive inputs or proprietary models. Leveraging Secure Computation techniques from Cryptography, two widely studied approaches in this domain are Fully Homomorphic Encryption (FHE) and Garbled Circuits (GC). This thesis presents a comparative evaluation of FHE and GC for secure neural network inference (SNNI). A two-layer neural network (NN) was implemented using the CKKS scheme from the Microsoft SEAL library (FHE) and the TinyGarble2.0 framework (GC) by IntelLabs. Both implementations are evaluated under a semi-honest threat model, measuring inference output error, round-trip time, peak memory usage, communication overhead, and communication rounds. Results reveal a trade-off: modular GC offers faster execution and lower memory consumption, while FHE supports non-interactive inference. The reproducible implementations aid secure model deployments in real-world ML-as-a-Service (MLaaS) settings.","url":"https://doi.org/10.12794/metadc2481661","authors":["Kalyan Cheerla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-14T01:17:21Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.12794/metadc2481661","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.21203/rs.3.rs-6729707/v1","name":"Federated Learning for Privacy-Preserving Smart Cities: A Secure and Scalable Machine Learning Framework","source":"preprints","abstract":"Abstract The exponential growth of data in smart city infrastructures—from traffic systems to health monitoring and surveillance—has created unprecedented opportunities for machine learning applications. However, centralizing such diverse and sensitive data introduces serious challenges related to data privacy, regulatory compliance, and system scalability. In this paper, we propose a secure and scalable federated learning (FL) framework tailored for smart city environments, enabling decentralized model training while preserving data locality and privacy. The framework integrates key technologies including differential privacy, secure aggregation, and edge device optimization to ensure robust model performance and security under real-world conditions. The framework is implemented and simulated using TensorFlow with synthetic smart city data streams, evaluating the system across key metrics such as training accuracy, communication cost, latency, and model convergence. Our experimental results show that the proposed FL framework achieves high prediction accuracy (94.3%) with significantly reduced bandwidth consumption and strong privacy guarantees. This work contributes a deployable architecture for future smart cities, offering an effective balance between intelligent data use and citizen data rights.","url":"https://doi.org/10.21203/rs.3.rs-6729707/v1","authors":["Deepak Juneja","Arvinder Singh","Jagvinder Singh"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.21203/rs.3.rs-6729707/v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.71443/9789349552210-14","name":"Privacy-Preserving Machine Learning in Healthcare Applications","source":"crossref","abstract":"The integration of machine learning (ML) in healthcare has unlocked transformative potential in disease prediction, personalized treatment, medical imaging, remote patient monitoring, and genomic data analysis. However, the sensitive nature of medical data introduces critical concerns regarding patient privacy, data security, and regulatory compliance. This chapter presents a comprehensive overview of privacy-preserving machine learning approaches tailored for healthcare applications, with a focus on technical frameworks, real-time implementations, and regulatory alignment. It explores the use of advanced techniques such as federated learning, differential privacy, homomorphic encryption, and zero-knowledge proofs to safeguard patient information while maintaining model utility. The chapter also addresses domain-specific challenges in processing real-time health data streams and implementing privacy-aware algorithms in resource-constrained environments. By bridging the gap between technical innovation and clinical applicability, this work emphasizes the importance of secure, scalable, and ethically aligned ML solutions in modern healthcare ecosystems. The discussion was contextualized within current legal frameworks and highlights future directions for research and implementation to ensure trust, transparency, and resilience in data-driven medical systems.","url":"https://doi.org/10.71443/9789349552210-14","authors":["Ravi Mishra","Rushikesh Bankar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-05T07:51:51Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.71443/9789349552210-14","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/s43681-026-01139-7","name":"Reallocation of privacy harm by privacy-preserving machine learning techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s43681-026-01139-7","authors":["Arijit Goswami"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-27T02:25:12Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/s43681-026-01139-7","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.58496/bjml/2025/006","name":"Privacy-Preserving Transfer Learning for Community Detection in Multiple Networks: A Review.","source":"crossref","abstract":"In order to identify communities in various networks, this study gives a thorough analysis of privacy-preserving transfer learning methods. In order to better understand the specific difficulties of implementing transfer learning in decentralized and diverse settings, it classifies current solutions according to their learning paradigms, privacy measures, and network topologies. The scalability, privacy, and utility trade-offs are used to assess anonymization, deep learning, and federated learning methods. There is a critical discussion of the gaps in the present research, including the absence of defined assessment standards and the inadequate incorporation of privacy into transfer systems. Also, this research points the way toward potential future possibilities for developing privacy-first models that can generalize across different types of networks. Researchers and practitioners in the field of graph-based machine learning may use the results as a guide to create safe and efficient solutions.","url":"https://doi.org/10.58496/bjml/2025/006","authors":["Marshima Mohd Rosli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-03T06:40:36Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.58496/bjml/2025/006","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-0-387-88735-7_10","name":"Privacy Preserving Nearest Neighbor Search","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-88735-7_10","authors":["Mark Shaneck","Yongdae Kim","Vipin Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-03-30T14:39:55Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-0-387-88735-7_10","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.36948/ijfmr.2024.v06i04.75352","name":"Privacy-Preserving Machine Learning on Financial Data: Federated Learning, Differential Privacy, and Practical Deployment Challenges in Banking","source":"crossref","abstract":"Machine learning on financial data sits at an awkward intersection: the data is among the most sensitive in any industry, the regulatory regime is among the strictest, and the business value of better models is large enough to keep pulling new ML workloads into production. This survey examines the two principal families of privacy-preserving ML federated learning (training across decentralized data without centralizing it) and differential privacy (adding mathematically calibrated noise to bound the information any individual record contributes to a model) through the lens of a practitioner deploying ML in a regulated banking environment. The work is grounded in concrete operational events, including a GDPR audit that surfaced A 2022 internal GDPR audit at the partner institution discovered 14 analysts with unauthorized access to a model's training feature store; this finding directly motivated the privacy-preserving redesign reported in this paper. We review the theoretical foundations (Dwork's differential privacy framework, McMahan's FedAvg algorithm, the DP-SGD training procedure) and report the privacy-utility trade-off observed in practice: at ε=1 (strong privacy) the model accuracy degrades by approximately 5%, while at ε=5 (moderate privacy) the loss is approximately 1%**. We discuss the operational realities that make federated learning hard in banking heterogeneous data across business units, communication overhead between geographically distributed sites, convergence challenges when client distributions diverge, and the difficulty of debugging models you cannot inspect end-to-end. We argue that the privacy mechanisms themselves work; the adoption barriers are organizational, regulatory, and operational rather than algorithmic. We close with practical guidance: where federated learning earns its complexity, where centralized training with strong access controls remains the right answer, and where differential privacy is most likely to deliver its promised guarantees without crippling model utility.","url":"https://doi.org/10.36948/ijfmr.2024.v06i04.75352","authors":["Jeevan Krishna Paruchuri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-22T18:31:16Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.36948/ijfmr.2024.v06i04.75352","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1007/978-981-16-9139-3_6","name":"Applications—Privacy-Preserving Image Processing","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-16-9139-3_6","authors":["Jin Li","Ping Li","Zheli Liu","Xiaofeng Chen","Tong Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-14T02:02:37Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-981-16-9139-3_6","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.5220/0014189500004932","name":"Privacy-Preserving Machine Learning Approach for Real-Time Brain Tumor Diagnosis Using Federated Learning Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014189500004932","authors":["Sathish K","Rajesh Sharma R","Mohit Tiwari","Akey Sungheetha","GGS Pradeep","Ellappan V"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-04T15:36:38Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.5220/0014189500004932","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.14722/ndss.2020.24202","name":"BLAZE: Blazing Fast Privacy-Preserving Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.14722/ndss.2020.24202","authors":["Arpita Patra","Ajith Suresh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-02-25T15:02:43Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.14722/ndss.2020.24202","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21203/rs.3.rs-1682972/v1","name":"Privacy-preserving, Efficient, and Effective Machine Learning","source":"crossref","abstract":"Abstract Privacy protection is critical for responsible artificial intelligence. Federated learning is a privacy-aware machine learning paradigm, which is often combined with differential privacy to guarantee privacy protection. Unfortunately, existing methods cannot achieve a satisfactory tradeoff between privacy and utility when the models are large, and their computation and communication costs are also huge. Here, we present a differentially private, efficient, and effective machine learning method named FedPrompt to learn big models in a federated way via prompt tuning. It only learns, perturbs, and exchanges the small prompt models injected into the big models. FedPrompt is validated on five datasets. The results show FedPrompt can achieve 0.5%~34.7% better performance than standard federated learning under same privacy budgets, meanwhile saving 99% of communication cost, 75% of memory, and 64% of training time. FedPrompt offers a new direction to efficiently and effectively distributed machine learning with privacy guarantees.","url":"https://doi.org/10.21203/rs.3.rs-1682972/v1","authors":["Chuhan Wu","Fangzhao Wu","Tao Qi","Yongfeng Huang","Xing Xie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-23T16:09:39Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.21203/rs.3.rs-1682972/v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.7490/f1000research.1119545.1","name":"HEaaN: A scalable privacy-preserving machine learning using homomorphic encryption for bioinformatician","source":"crossref","abstract":"Homomorphic Encryption (HE) is a cryptographic scheme that enables arbitrary computations over encrypted data. It offers perfect protection for data at rest, in use, and in transit because encrypted data can be analyzed without decryption. We introduce HEaaN, a new scalable privacy-preserving data analysis tool that uses HE technology. HEaaN provides various statistics and machine learning toolkits, such as linear models, logistic regression, and more. The package offers user-friendly APIs similar to those of popular data analysis tools like Pandas and scikit-learn in Python and R. Additionally, HEaaN supports data analysis such as Polygenic Risk Scores and Ancestry Inference and integrates with tools developed within R/Bioconductor packages.","url":"https://doi.org/10.7490/f1000research.1119545.1","authors":["Soo-Heang Abel Eo","Song Hyeop Park"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-21T16:42:06Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.7490/f1000research.1119545.1","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.29007/4l54","name":"Privacy-Preserving Attribute Domain Reconstruction for Machine Learning","source":"crossref","abstract":"In modern society, the use of personal data is advancing in many fields. However, such data utilization also increases the risk of privacy leakage. Therefore, Differential Privacy (DP) has been proposed as a measure of privacy protection. DP is a privacy-preserving measure when data collectors release data. However, since DP requires the trust of the data collector, Local Differential Privacy (LDP) was proposed as a privacy protection measure that does not rely on third-party trust. LDP assumes that data providers directly perturb their data, thereby protecting privacy leakage from personal data. LDP is useful in machine learning for data privacy and model privacy. However, a challenge with LDP is the difficulty in balancing privacy protection and utility when dealing with high-dimensional data. To address this, techniques such as dimensionality reduction and data discretization have been proposed. A machine learning framework called SUPM has been proposed to satisfy LDP. In SUPM, all attribute types, including categorical and numerical, are converted into ordered discrete sets with domain size L, performing uniform weak anonymization and applying perturbation uniformly. In this case, the domain and domain size L for each attribute must be predetermined regardless of the data characteristics. Therefore, it is necessary to know the characteristics, such as the utility, of each attribute in advance. This study proposes a an attribute domain reconstruction method that reduces domain size while preserving data utility using data collected during dimensionality reduction. The effectiveness of the proposed method is validated using two databases: ADULT and WDBC.","url":"https://doi.org/10.29007/4l54","authors":["Yuto Tsujimoto","Atsuko Miyaji"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-21T22:10:10Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.29007/4l54","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.2139/ssrn.5420991","name":"Verifiable and Privacy-Preserving Decentralized Collaboration for Machine Learning Model Improvement","source":"crossref","abstract":"Abstract Decentralized collaboration, particularly in complex domains like machine learning (ML) model development, faces hurdles regarding trust, intellectual property (IP) protection, and the verification of contributions. Traditional centralized platforms introduce bottlenecks, while existing decentralized approaches often lack mechanisms to privately verify complex, computationally intensive work. This paper introduces a framework integrating Zero-Knowledge Proofs (ZKPs) and smart contracts to enable trustless, verifiable, and privacy-preserving collaborative ML model improvement process. Our system allows \"Improvers\" to cryptographically prove superior model performance compared to a baseline, without revealing proprietary model parameters prematurely. The framework features on-chain job management, client-side ZKP generation, on-chain verification, automated selection of the best contributor based on verified proofs, secure solution submission via cryptographic commitments, and an arbiter-based dispute resolution mechanism. We analyze different ZKP workflow variations, evaluating performance and suitability. Performance analysis demonstrates the feasibility of client-side proof generation for individual models, while highlighting the resource demands of proof aggregation, suggesting its suitability for server-side work. On-chain gas cost evaluation indicates the system's economic viability for Layer 2 (L2) scaling solution deployments under specific cost assumptions. This work provides a first step for secure and verifiable collaboration in ML and potentially other software development tasks in decentralized environments.","url":"https://doi.org/10.2139/ssrn.5420991","authors":["Jay Bojič Burgos","Urban Sedlar","Matevž Pustišek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-30T16:40:17Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.2139/ssrn.5420991","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.34218/ijit_05_02_003","name":"PRIVACY-PRESERVING MACHINE LEARNING IN CYBERSECURITY","source":"crossref","abstract":"","url":"https://doi.org/10.34218/ijit_05_02_003","authors":["Pranav Mani Tripathi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-28T07:52:36Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.34218/ijit_05_02_003","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/satml54575.2023.00016","name":"Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/satml54575.2023.00016","authors":["Reza Nasirigerdeh","Javad Torkzadehmahani","Daniel Rueckert","Georgios Kaissis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-01T13:27:45Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1109/satml54575.2023.00016","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.2172/1737477","name":"Considerations for using Privacy Preserving Machine Learning Techniques for Safeguards","source":"crossref","abstract":"","url":"https://doi.org/10.2172/1737477","authors":["Nathan Martindale","Scott Stewart","Mark Adams","Greg Westphal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-12-21T03:15:53Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.2172/1737477","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.63665/ijmlaidse.v1i1.02","name":"Federated Learning for Privacy-Preserving AI: A Comparative Analysis of Decentralized Data Training","source":"crossref","abstract":"The rapid adoption of Artificial Intelligence (AI) across industries, particularly in healthcare, finance, and smart devices, has introduced significant concerns regarding data privacy, security, and compliance with regulations such as GDPR, HIPAA, and CCPA. Traditional centralized machine learning (ML) models require large-scale data aggregation, increasing risks of data breaches, misuse, and unauthorized access. Federated Learning (FL) has emerged as a transformative solution, allowing multiple edge devices or organizations to collaboratively train machine learning models without sharing raw data. This paper explores the principles, advantages, and challenges of FL and conducts an empirical analysis comparing FL’s efficacy, security, and scalability to centralized models. A case study on federated learning in healthcare diagnostics highlights the real-world impact of this approach. Additionally, insights from a structured survey of AI researchers, data scientists, and industry professionals are analyzed to assess FL adoption, technical challenges, and future potential. Findings suggest that FL enhances privacy and compliance, making it particularly suitable for industries handling sensitive information. However, challenges such as high computational costs, model convergence issues, and communication overhead must be addressed for FL to achieve widespread adoption. Future advancements in efficient federated learning frameworks, regulatory standardization, and privacy-preserving AI techniques will further define FL’s role in the evolution of decentralized artificial intelligence.","url":"https://doi.org/10.63665/ijmlaidse.v1i1.02","authors":["Dr. Subash Ranjan Kabat"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-18T10:30:36Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.63665/ijmlaidse.v1i1.02","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.5220/0013567600003967","name":"Utilizing Generative Adversarial Networks for Preserving Privacy in Developing Machine Learning Models for the Healthcare Industry","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013567600003967","authors":["Shahnawaz Khan","Bharavi Mishra","Sultan Alamri","Philippe Pringuet"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-03T11:57:56Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.5220/0013567600003967","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.63282/3050-9262.ijaidsml-v6i2p110","name":"Differential Privacy-Preserving Algorithms for Secure Training of Machine Learning Models","source":"crossref","abstract":"","url":"https://doi.org/10.63282/3050-9262.ijaidsml-v6i2p110","authors":["Sandeep Phanireddy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-25T09:42:52Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.63282/3050-9262.ijaidsml-v6i2p110","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/iciip61524.2023.10537624","name":"Federated Learning: Achieving Scalable and Privacy-Preserving Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciip61524.2023.10537624","authors":["Harsh Bansal","Kanu Goel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-28T17:43:26Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1109/iciip61524.2023.10537624","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-1-4614-4639-2_3","name":"Privacy Background","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4614-4639-2_3","authors":["Manas A. Pathak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-10-25T01:58:43Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-1-4614-4639-2_3","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1201/9781003156406-15","name":"Machine Learning Algorithms for Bitcoin Price Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003156406-15","authors":["Prasannavenkatesan Theerthagiri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-28T17:31:09Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1201/9781003156406-15","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.36227/techrxiv.171340711.17793838/v2","name":"Exploring Strategies for Privacy-Preserving Machine Learning in Distributed Environments","source":"crossref","abstract":"Machine Learning (ML) with distributed privacy preservation is growing in significance as it focuses on facilitating multi-party learning without requiring actual data sharing. This is especially helpful for companies that want to work together but are unable to do so because of ethical, regulatory, or budgetary constraints on sharing data. In order to address these issues, this study examines three privacy-preserving algorithms: regularized logistic regression with Differential Privacy (DP), stochastic gradient descent (SGD) with differentially private updates, and a distributed Lasso that distributes gradients among data centers. The study emphasizes the relationship between error rate and privacy through these algorithms. In order to improve error rates for large datasets, both DP algorithms modify their sensitivity dependent on the amount of data, highlighting the significance of training data volume in model performance in the study. Results demonstrate that using the SGD; error rate can be reduced by employing random projections in advance.","url":"https://doi.org/10.36227/techrxiv.171340711.17793838/v2","authors":["Suresh Dodda","Anoop Kumar","Navin Kamuni","Madan Mohan Tito Ayyalasomayajula"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-29T12:21:37Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.36227/techrxiv.171340711.17793838/v2","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-1-4614-4639-2_10","name":"Overview of Speech Recognition with Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4614-4639-2_10","authors":["Manas A. Pathak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-10-25T01:58:43Z","addedAt":"2026-08-06T22:49:37.969Z","doi":"10.1007/978-1-4614-4639-2_10","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.25215/9371832401.06","name":"FEDERATED LEARNING FOR PRIVACY-PRESERVING MEDICAL DIAGNOSTICS","source":"crossref","abstract":"","url":"https://doi.org/10.25215/9371832401.06","authors":["Dr. Heena Kousar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-04T19:15:57Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.25215/9371832401.06","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.35629/5252-0706177185","name":"Privacy-Preserving Machine Learning: How Ai Drives Prosperity While Safeguarding Privacy.","source":"crossref","abstract":"","url":"https://doi.org/10.35629/5252-0706177185","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-22T09:47:32Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.35629/5252-0706177185","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21437/iberspeech.2024-50","name":"Privacy-preserving Machine Learning for Remote Speech Processing","source":"crossref","abstract":"","url":"https://doi.org/10.21437/iberspeech.2024-50","authors":["Francisco Teixeira","Alberto Abad","Bhiksha Raj","Isabel Trancoso"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-05T14:19:13Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21437/iberspeech.2024-50","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.53555/kuey.v29i4.10965","name":"Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems","source":"crossref","abstract":"The insurance industry is exploring the use of machine learning (ML) models to leverage the huge volume of customer data for of-the-moment business decisions. It is, however, extremely sensitive information. From a design per- spective, data attribute utility should be carefully balanced with privacy guarantees, particularly when sensitive customer data is involved. Privacy risks can be mitigated by using techniques that reduce and control the amount of sensitive information exposed during the training and use of ML models. A wide spectrum of privacy-preserving machine learning solutions has been developed. They are based on a comprehensive view of data protection-impact assessments under privacy laws and reg- ulations, subsequently consolidating the specific requirements for both personal identifiable information (PII) and personal health identifiable (PHI) information. For sufficiently large datasets, fair ML solutions with differential privacy-DPIA compliance can be obtained without compromising model performance. Notably, certain ML tasks, such as risk scoring and underwriting, can be accomplished with very close-to-the-source data while preserving DP-compliance for protected attributes. Risk scoring and underwriting processes are performed under the control of one institution, while fraud detection and claims management procedures apply an anomaly-detection-based architecture. For sensitive attributes such as health data, disparity in training data volume can be solved by transferring knowledge through privacy-preserving federated learning. Sensitive attributes with low entropy are avoided at prediction time to mitigate the associated disclosure risk. For such features, privacy and risk evaluation techniques such as k-anonymity and ℓ-diversity are embedded into the data-governance step, ensuring that the data support radarized and risk-aware disclosures when exposed to third parties.","url":"https://doi.org/10.53555/kuey.v29i4.10965","authors":["Keerthi Amistapuram"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-31T10:50:42Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.53555/kuey.v29i4.10965","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.67228/3142788x/ijmlpa-2025pii4u5c","name":"Federated Continual Learning for Privacy-Preserving Predictive Intelligence","source":"crossref","abstract":"Federated Learning (FL) enables privacy-preserving collaborative model training without sharing raw data but faces challenges in handling concept drift, non-IID data, and evolving tasks. Although Continual Learning (CL) supports lifelong knowledge adaptation and mitigates catastrophic forgetting, most existing approaches are designed for centralized environments. To address these limitations, this paper proposes the Federated Continual Learning framework for Privacy-Preserving Predictive Intelligence (FCL3Pi), which integrates federated optimization with continual learning to enable adaptive, decentralized, and privacy-preserving predictive intelligence. The framework incorporates decentralized model aggregation, local incremental learning, dynamic memory replay, adaptive regularization, and secure communication to improve learning under dynamic data distributions. It addresses key challenges including catastrophic forgetting, data heterogeneity, client drift, communication efficiency, scalability, and edge resource constraints. Experimental evaluation demonstrates improved prediction accuracy, knowledge retention, privacy preservation, communication efficiency, and convergence stability compared with conventional centralized learning, standalone continual learning, and traditional federated learning. The proposed framework provides a robust foundation for next-generation intelligent applications in healthcare, industrial automation, autonomous transportation, financial systems, smart cities, and large-scale IoT environments.","url":"https://doi.org/10.67228/3142788x/ijmlpa-2025pii4u5c","authors":["Lothar Collatz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-27T11:03:42Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.67228/3142788x/ijmlpa-2025pii4u5c","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/flics70075.2026.11621941","name":"Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621941","authors":["Judith Sáainz-Pardo Díiaz","Álvaro López Garcíia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:14:52Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1109/flics70075.2026.11621941","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/sp40001.2021.00098","name":"CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp40001.2021.00098","authors":["Sijun Tan","Brian Knott","Yuan Tian","David J. Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-26T21:03:31Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1109/sp40001.2021.00098","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1145/3650215.3650326","name":"Research on Financial Fraud Identification Model Based on Privacy-Preserving Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3650215.3650326","authors":["Li Jiao","Hui Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-16T22:11:20Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1145/3650215.3650326","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.2139/ssrn.5025966","name":"A Fully Secure Approach to Privacy-Preserving Machine Learning for Satellite Image Classification","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5025966","authors":["Joseph O&apos;Neill","Lydia Bouzar-Benlabiod"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-05T22:41:58Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.2139/ssrn.5025966","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.28945/5457","name":"Advancing Federated Machine Learning for Privacy-Preserving Financial Models: Performance Comparison with Standard Machine Learning on Financial Data","source":"crossref","abstract":"Aim/Purpose To explore the potential of Federated Machine Learning (FML) in developing predictive models while ensuring data privacy and security. Background The rise of data-driven technologies has led to an increased focus on privacy concerns associated with centralized data storage. FML offers a decentralized approach, allowing organizations to collaboratively train models without sharing sensitive data (McMahan et al., 2017). Methodology This study employs a FML framework, utilizing local model training on decentralized datasets, followed by aggregation of model updates to create a global model. Privacy-preserving techniques, such as differential privacy, are also implemented (Dwork &amp; Roth, 2014). Contribution This research contributes to the field of machine learning by demonstrating the efficacy of FML in predictive modeling, highlighting its potential for secure and privacy-conscious applications. Findings The study indicates that FML can effectively enhance model performance while maintaining the privacy of individual data sources. Recommendations for Practitioners Practitioners are encouraged to adopt FML techniques in applications requiring high data security, particularly in sectors such as healthcare and finance. Recommendation for Researchers Future research should explore advanced aggregation methods and evaluate the scalability of FML in diverse settings. Impact on Society The findings of this research have implications for the broader application of machine learning in sensitive areas, promoting data privacy while harnessing the power of collaborative intelligence. Future Research Further investigations should focus on the robustness of FML against adversarial attacks and its applicability in real-world scenarios.","url":"https://doi.org/10.28945/5457","authors":["Samuel Sambasivam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-27T21:25:34Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.28945/5457","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.2139/ssrn.6326658","name":"Federated Machine Learning On Big Healthcare Data For Privacy-Preserving Analytics","source":"crossref","abstract":"Advances in digital medicine necessitate widespread use of patient data by hospitals and medical institutions for analytics, clinical research, and training of intelligent healthcare systems. Against the backdrop of stringent privacy concerns, data-minimization principles, and the regulated nature of personal health data-especially healthcare providers cannot share data but can share model parameters or predictions-federated machine learning provides a promising solution to these pressing demands. The federated paradigm not only protects patient privacy but also mitigates concerns of data leakage and breach; yet it raises new concerns about data governance and security, requiring that the centralized server merely holds model parameters and does not learns from the data. A system architecture, illustrated via a use-case example, integrates data-privacy guarantees and systemlevel security with technical tools from federated analytics. Key techniques not only cover the major dataanalytic tasks identified for healthcare but also embody principles of opening up non-independent and identically distributed health data while still being safe against leakage. Introduction and conclusion delineate the wider significance of these privacy-preserving works and the remaining research gaps, pointing toward evaluation of federated algorithms with explainable-area-under-risk metrics and defense mechanisms against arbitrary-label attacks. Keywords: Federated machine learning in healthcare analytics revolves around securing individuals' sensitive records. Distributed learning, in exchange, minimizes privacy risks associated with centralized storage. Yet practical scenarios remain scant; protocols still lack support for various data distributions, politeness, healthcare needs, and standard compatibility. Privacy evaluation also requires research. Addressing these aspects would lay a better foundation for experiments with real medical data.","url":"https://doi.org/10.2139/ssrn.6326658","authors":["Sasi Kumar Kolla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-24T13:59:26Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.2139/ssrn.6326658","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.5271241","name":"Distributed Machine Learning in Healthcare: A Privacy-Preserving Approach to Building Generalizable AI Models Across Institutions","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5271241","authors":["Mark Jameson","Nate Wilson"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-04T09:02:34Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.2139/ssrn.5271241","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1145/1390156.1390265","name":"Privacy-preserving reinforcement learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/1390156.1390265","authors":["Jun Sakuma","Shigenobu Kobayashi","Rebecca N. Wright"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2008-08-12T18:30:36Z","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1145/1390156.1390265","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1038/s41598-026-53687-x","name":"OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-53687-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-53687-x","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21203/rs.3.rs-9637381/v1","name":"Federated Learning, Temporal Convolutional Networks, Electric Vehicle Charging Infrastructure, Intrusion Detection, Cybersecurity, Privacy-Preserving Machine Learning","source":"europepmc","abstract":"Abstract The rapid growth of electric vehicle (EV) charging infrastructure has increased exposure to cyberattacks, while conventional centralized intrusion detection remains poorly suited to distributed and privacy-sensitive EVSE environments. This study proposes a federated learning framework based on a Dual-Attention Temporal Convolutional Network (DA-TCN) for collaborative cyberattack detection without centralized data sharing. The model combines dilated causal convolutions with channel and temporal attention to capture complex dependencies in multimodal EVSE telemetry. To improve federated optimization, we introduce Federated Stochastic Weight Averaging (FedSWA) and client-local mixup augmentation, which together enhance generalization and robustness across distributed clients. Evaluated on the CICEVSE2024 dataset, the proposed framework achieved 99.18\\% accuracy and 98.95\\% macro F1-score, outperforming a centralized baseline (98.96\\% accuracy) while maintaining perfect recall for benign and cryptojacking traffic. With an inference latency of 7.21~ms per batch and a model size of 4.07~MB, the framework is well suited for real-time edge deployment. A complementary Isolation Forest module provides unsupervised zero-day anomaly detection. These results demonstrate that federated learning can deliver accurate, lightweight, and deployment-ready intrusion detection for privacy-preserving EV charging infrastructure.","url":"https://doi.org/10.21203/rs.3.rs-9637381/v1","authors":["Mohammed Gamal Ragab","Hitham Alhussian","Said Jadid Abdulkadir","Majdy Eltahir","Ayed Alwadain"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-9637381/v1","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.22541/au.175610020.05913630/v1","name":"Effectiveness of L2 Regularization in Privacy-Preserving Machine Learning","source":"europepmc","abstract":"","url":"https://doi.org/10.22541/au.175610020.05913630/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.22541/au.175610020.05913630/v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7441133/v1","name":"AI for Cholera Outbreak Prediction, Real-Time Tracking, and Low-Resource Diagnostics using Federated and Privacy-Preserving Machine Learning","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7441133/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-7441133/v1","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-025-07622-1","name":"A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-07622-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-025-07622-1","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.20944/preprints202407.1701.v1","name":"Data Obfuscation for Privacy-Preserving Machine Learning using Quantum Symmetry Properties","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202407.1701.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.20944/preprints202407.1701.v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1016/j.ebiom.2024.105006","name":"Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.ebiom.2024.105006","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1016/j.ebiom.2024.105006","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.20944/preprints202402.0317.v1","name":"Blockchain-Based Decentralised Privacy-Preserving Machine Learning Authentication and Verification With Immersive Devices in the Urban Metaverse Ecosystem","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202402.0317.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.20944/preprints202402.0317.v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21203/rs.3.rs-3820538/v1","name":"Privacy-Preserving Machine Learning (PPML)Inference for Clinically Actionable Models: How to Monetize ESSG Modelling Efforts?","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3820538/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-3820538/v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1186/s13321-021-00576-2","name":"Splitting chemical structure data sets for federated privacy-preserving machine learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s13321-021-00576-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1186/s13321-021-00576-2","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1101/2020.06.25.171009","name":"Swarm Learning as a privacy-preserving machine learning approach for disease classification","source":"europepmc","abstract":"","url":"https://doi.org/10.1101/2020.06.25.171009","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1101/2020.06.25.171009","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/tnse.2018.2859420","name":"Efficient Privacy-preserving Machine Learning in Hierarchical Distributed System.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnse.2018.2859420","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2019","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1109/tnse.2018.2859420","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/tpami.2026.3715766","name":"FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation. ","source":"pubmed","abstract":"Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The concern about privacy leakage, albeit demonstrated under specific conditions [1], has triggered numerous follow-up research in designing powerful attacking methods and effective defending mechanisms aiming to thwart these attacking methods. Nevertheless, privacy-preserving mechanisms employed in these defending methods invariably lead to compromised model performances due to a fixed obfuscation applied to private data or gradients. In this article, we, therefore, propose a novel adaptive obfuscation mechanism, coined FedAdOb, to protect private data without yielding original model performances. Technically, FedAdOb utilizes passport-based adaptive obfuscation to ensure data privacy in both horizontal and vertical federated learning settings. The privacy-preserving capabilities of FedAdOb, specifically with regard to private features and labels, are theoretically proven through Theorems 1 and 2. Furthermore, extensive experimental evaluations conducted on various datasets and network architectures demonstrate the effectiveness of FedAdOb by manifesting its superior trade-off between privacy preservation and model performance, surpassing existing methods.","url":"https://doi.org/10.1109/tpami.2026.3715766","authors":["Gu H","Luo J","Kang Y","Yao Y","Zhu G","Li B","Fan L","Yang Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1109/tpami.2026.3715766","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-60144-2","name":"A machine learning framework for early warning prediction of student success using privacy preserving synthetic educational data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-60144-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-60144-2","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/s10278-026-02117-5","name":"Machine Learning-Based Privacy Preserving via CT/MRI and Organ Metadata Prediction.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s10278-026-02117-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1007/s10278-026-02117-5","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.2196/92930","name":"Addressing the Challenges in Using Synthetic Data for Health Research: Application to Cardiology.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/92930","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.2196/92930","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21203/rs.3.rs-10322944/v1","name":"Trustworthy AI for Marketing Measurement: A Systematic Review of Attribution, Media Mix Modeling, and Privacy-Preserving Methods","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10322944/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-10322944/v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.3390/diagnostics16132029","name":"FLAME: Federated Learning and Aggregated Multi-Model Ensemble for Multi-Class Alzheimer's Disease Stage Classification from Structured Clinical Data.","source":"pubmed","abstract":"Background/Objectives : The precise identification of Alzheimer's disease (AD) stages through clinical data is crucial for early diagnosis and suitable therapy. This classification remains troublesome due to overlap in cognitive profiles across different phases of illness progression. This study presents a comprehensive and advanced diagnostic system, termed FLAME, featuring an enhanced federated learning architecture for privacy-preserving multi-institutional implementation. It provides a systematic review of machine learning (ML) and deep learning (DL) models for the classification of five stages of Alzheimer's disease (AD). The models include cognitively normal (CN), subjective memory complaints (SMC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD). Methods : Sixteen traditional machine learning models and eleven deep learning architectures-including FT-Transformer and NODE-were evaluated using a structured clinical dataset comprising 362 features. A hybrid ensemble was created at the probability level by combining the two top-performing models, LightGBM and a five-layer DNN. The weights of this ensemble were automatically optimised using a Genetic Algorithm (GA) with Macro-F1 as the fitness criterion, confirmed stable across 30 independent runs (w&#x2605;=0.5024&#xb1;0.0001). A federated learning architecture was then established, deploying the DNN across non-IID clients while keeping LightGBM centralised. We examine four distinct aggregation algorithms: FedAvg, FedProx, FedNova, and SCAFFOLD. Results : Among all deep learning architectures, FT-Transformer achieved the highest standalone performance (accuracy = 0.7810, &#x3ba; = 0.7081). The five-layer deep neural network (DNN) was selected as the DL representative for the hybrid ensemble. LightGBM attained superior machine learning performance (accuracy = 0.8156, &#x3ba; = 0.7537), confirmed deterministic across 10 seeds. The LightGBM vs. XGBoost difference is not statistically significant (McNemar p=0.4227). The GA-optimised hybrid ensemble (w = 0.685) surpassed both individual baselines across all evaluation metrics. The FedNova hybrid design achieved superior overall performance in federated configurations, surpassing all centralised arrangements in accuracy (accuracy = 0.8213, &#x3ba; 0.7614). Conclusions : Evolutionary ensemble optimisation combined with federated learning provides a robust, scalable, and privacy-preserving solution for AD stage classification, offering a clinically viable framework for real-world multi-institutional decision-support systems. However, the AD class remains severely under-recalled across all configurations (F1 &#x2264; 0.21), identifying this as the primary open challenge for clinical translation.","url":"https://doi.org/10.3390/diagnostics16132029","authors":["Gasmi K","Ammar LB","Krichen M","Alghuried A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.3390/diagnostics16132029","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3389/fdgth.2026.1879670","name":"Asynchronous proximal federated aggregation for heterogeneous healthcare networks.","source":"pubmed","abstract":"The deployment of Federated Learning (FL) across the Internet of Medical Things (IoMT) is severely hindered by computational asymmetry and statistical heterogeneity. Traditional synchronous aggregation protocols suffer from severe straggler effects when deployed across devices with varying computational capacities, such as hospital servers vs. ambulatory wearables.","url":"https://doi.org/10.3389/fdgth.2026.1879670","authors":["Sreelakshmi M","Delhibabu R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.3389/fdgth.2026.1879670","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/tpami.2026.3672569","name":"Privacy Preserving Decentralized Learning With Positive-Incentive Noise.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2026.3672569","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1109/tpami.2026.3672569","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1038/s41598-026-52200-8","name":"Machine learning-driven data perturbation techniques for privacy-preserving data mining.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-52200-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-52200-8","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1038/s41598-026-48031-2","name":"Privacy preservation of EHR by data anonymization and federated learning for IoT based smart city application in healthcare.","source":"pubmed","abstract":"This paper addresses privacy and security challenges in smart cities, particularly in IoT applications that process sensitive data in sectors like healthcare and agriculture. Traditional security solutions often fail to meet the energy and resource constraints of IoT devices, creating a significant gap in data protection. To address this, we propose a two-phase lightweight privacy-preserving approach. In the first phase, DIVersed and Anonymized Instances (DIVA) are used to anonymize and diversify sensitive data, ensuring privacy while enabling efficient processing. This approach mitigates the risks of identifying individuals through sensitive information. In the second phase, Federated Learning (FL) is employed, a decentralized machine learning technique that allows IoT devices to collaboratively train models without sharing raw data, thus preserving privacy. The proposed approach is evaluated based on information loss, execution time, and scalability, with experiments varying key parameters such as the number of Electronic Health Records (EHR) and the value of k. The results show that our method significantly reduces information loss, improves data utility, and maintains efficient execution, even as the number of devices and data points scale. This demonstrates the feasibility and effectiveness of our approach for privacy-preserving IoT applications in smart cities. Our solution ensures data privacy while enabling energy-efficient machine learning, making it suitable for IoT devices with limited resources. It provides a scalable, practical framework for applications in critical sectors like healthcare, where privacy and data utility are of utmost importance.","url":"https://doi.org/10.1038/s41598-026-48031-2","authors":["Shakeer SM","Rajasekhara Babu M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-48031-2","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1038/s41598-026-63991-1","name":"Manual federated simulation for multiple sclerosis integrating XGBoost algorithm with SHAP explanation.","source":"pubmed","abstract":"Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system, underscoring the importance of early and accurate diagnosis. In this study investigates the predictive modelling of MS progression in patients with Clinically Isolated Syndrome (CIS), privacy-preserving for a federated and explainable Machine Learning (ML) framework. To address missing data while preserving inter-feature dependencies, Multivariate Imputation by Chained Equations (MICE) with iterative imputers was employed. Classification was performed using the Extreme Gradient Boosting (XGBoost) algorithm. Model interpretability was developed through Explainable Artificial Intelligence (XAI) techniques, specifically Shapley Additive Explanations (SHAP). To ensure data confidentiality and simulate decentralized clinical environments, an in silico federated learning framework was applied. Experimental results demonstrated strong predictive performance, achieving 96.7% accuracy and 99% ROC-AUC during training, 92.5% accuracy in validation, and 81.8% accuracy with an AUC of 88% on the test set. For the Federated Learning (FL) simulation, the model maintained competitive performance, yielding an accuracy of 76.3% and an AUC of 83.9%. The proposed approach supports early diagnosis, enhances clinical trust through interpretability, and promotes secure data collaboration, thereby contributing to more informed and transparent clinical decision-making and improved patient care.","url":"https://doi.org/10.1038/s41598-026-63991-1","authors":["Ghazy HE","Ali ZH","Medhat T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-63991-1","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.32388/pc8x49","name":"Multi-View Clustering Goes Federated: A Survey","source":"europepmc","abstract":"","url":"https://doi.org/10.32388/pc8x49","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.32388/pc8x49","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.20944/preprints202607.1461.v1","name":"Secure, Privacy-Preserving and Revenue-Aware Orchestration of AI-Native 6G Radio Access Networks","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202607.1461.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.20944/preprints202607.1461.v1","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202604.1550.v1","name":"A Federated Learning Framework for Privacy-Preserving Predictive Maintenance in Distributed Smart Manufacturing","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202604.1550.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.20944/preprints202604.1550.v1","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1109/tpami.2026.3659110","name":"Privacy-Preserving Model Transcription With Differentially Private Synthetic Distillation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2026.3659110","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1109/tpami.2026.3659110","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21203/rs.3.rs-9139310/v1","name":"Physics-Informed Federated Learning for Decentralized Pharmaceutical Crystallization: Achieving Personalized Predictive Accuracy with Minimal Data","source":"europepmc","abstract":"Abstract In decentralized pharmaceutical manufacturing, developing robust predictive models for crystallization is often hindered by proprietary data silos and the inherent heterogeneity of site-specific process dynamics. Standard machine learning approaches struggle with data scarcity and frequently fail to respect the underlying physical laws, leading to catastrophic divergence in outlier scenarios. This study introduces a novel Physics-Informed Federated Learning (F-PINN) framework designed to enable collaborative model training across multiple manufacturing sites while strictly preserving data privacy. By embedding the Population Balance Equation (PBE) directly into the federated global loss function, the F-PINN framework acts as a \"Physical Foundation Model\" that remains robust against sensor bias and significant data gaps. We further propose a Personalized Adaptation phase that allows the global model to fine-tune to local growth kinetics (G) and residence times ( τ ). Results demonstrate that while standard federated averaging yields a generalized model, our personalized F-PINN approach achieves a 99.72% reduction in Mean Squared Error (MSE) compared to data-only models in high-heterogeneity sites. Specifically, the physics-informed priority prevents the model from diverging under noisy conditions, maintaining structural stability where standard neural networks fail. This research provides a scalable, privacy-preserving pathway for the implementation of Pharma 4.0 digital twins in multi-site industrial crystallization networks.","url":"https://doi.org/10.21203/rs.3.rs-9139310/v1","authors":["Sai Vinay Thattukolla"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-9139310/v1","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.14293/pr2199.004161.v1","name":"Applications of Machine Learning for Content Recommendation in Social Media Platforms","source":"europepmc","abstract":"","url":"https://doi.org/10.14293/pr2199.004161.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.14293/pr2199.004161.v1","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1186/s12903-026-08388-2","name":"Automatic detection of multi-site oral squamous cell carcinoma based on privacy-preserving federated learning.","source":"pubmed","abstract":"BACKGROUND: Automated detection of oral squamous cell carcinoma (OSCC) using computer-aided technology plays a crucial role in ensuring patient health and reducing medical costs. However, traditional centralized disease recognition methods often face challenges such as data privacy, scalability, and substantial data transmission requirements. Federated learning offers a promising solution to these issues by enabling collaborative model training on distributed data sources. METHODS: This paper proposes a privacy-preserving federated learning framework for multi-site automated detection of OSCC, analyzing a dataset of 1,224 oral images from the B. Borooah Cancer Institute. The framework leverages the distributed nature of data in medical environments, where multiple medical clinics capture oral images to identify diseases. The training process involves local computation on each device, rather than transmitting raw data to a centralized server, thus protecting data privacy and reducing communication overhead. The federated learning approach involves a central server that coordinates the training process across multiple edge devices. Initially, the central server distributes a pre-trained model to each device. Then, the devices perform local model updates using their own data, capturing unique disease patterns specific to their patient populations. These updated models are aggregated by the central server after adding Gaussian noise, combining the knowledge from all devices to create a global model representing the collective intelligence of the network. RESULTS: Experimental results demonstrate the effectiveness of the proposed federated learning framework for automated OSCC detection. The distributed training method achieved comparable disease recognition accuracy to traditional centralized methods, with an area under the curve (AUC) of 0.887, accuracy (ACC) of 95.51%, sensitivity (SEN) of 95.92%, and specificity (SPE) of 95.10%, while effectively preserving data privacy. CONCLUSION: The application of federated learning in automated OSCC detection opens up new possibilities for collaborative and privacy-preserving machine learning solutions. This approach enables medical institutions to leverage the collective knowledge of distributed devices while maintaining data confidentiality, thereby promoting efficient and accurate disease recognition in medical settings.","url":"https://doi.org/10.1186/s12903-026-08388-2","authors":["Wu Y","Chang H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1186/s12903-026-08388-2","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-9493732/v1","name":"A Secure Cloud-Based Framework for Privacy-Preserving Medical Pre-Diagnosis Using Encrypted Machine Learning","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9493732/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-9493732/v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1111/1750-3841.71334","name":"Artificial Intelligence in Food-Nutrition-Health Research: From Multimodal Data Integration to Precision Intervention.","source":"pubmed","abstract":"Artificial intelligence (AI) is transforming food-nutrition-health research by enabling pattern recognition in complex, high-dimensional datasets that traditional hypothesis-driven approaches cannot address. This review systematically synthesizes research progress of AI across the food-nutrition-health continuum from 2020 to 2025. By examining 181 systematic reviews through PRISMA-guided selection, we provide a comprehensive overview and prospects across four dimensions: technical foundation, application scenarios, existing challenges, and future prospects. We propose a tripartite framework comprising (1) a data layer enabling multisource fusion of food composition, health monitoring, and individual characteristic data; (2) a technological layer of nondestructive testing (spectroscopy, nuclear magnetic resonance [NMR], imaging); and (3) an algorithmic layer progressing from machine learning to deep learning architecture. Key applications include food component analysis and safety detection; nutrition-disease association modeling; pathogen identification; and personalized dietary intervention systems. Despite rapid progress, critical challenges persist, insufficient model generalization across populations, algorithmic opacity limiting clinical trust, data privacy vulnerabilities, and lack of standardized multi-omics integration protocols. Future directions emphasize multimodal fusion models, explainable artificial intelligence (XAI), federated learning for privacy-preserving collaboration, gene-guided precision nutrition, and development of intelligent wearable devices and functional food. This review provides a roadmap for transitioning from population-averaged guidelines to dynamic, individualized health optimization through AI-enabled food system.","url":"https://doi.org/10.1111/1750-3841.71334","authors":["Wu X","Feng J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1111/1750-3841.71334","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3389/frobt.2026.1824246","name":"Dynamic variance-aware federated tuning for efficient autonomous vehicle perception under non-IID settings.","source":"pubmed","abstract":"Federated learning enables multiple autonomous vehicles (AVs) to collaboratively train machine learning models while preserving data privacy. However, performance degrades significantly under non-independent and identically distributed (non-IID) data conditions commonly encountered in real-world driving scenarios. Existing aggregation methods, particularly Federated Averaging (FedAvg), struggle to effectively handle client update divergence, leading to inefficient communication, unstable convergence, and increased privacy risks.","url":"https://doi.org/10.3389/frobt.2026.1824246","authors":["Dhanavarshini V","Periyasamy S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.3389/frobt.2026.1824246","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-55847-5","name":"Federated MobileNetV2 with ensemble meta-learning for privacy-preserving brain tumor classification.","source":"pubmed","abstract":"The identification of brain tumors from MRI images is very crucial for the selection of an appropriate treatment. However, the existing solution has issues with privacy and data sharing. To address this challenge, this paper proposes the use of federated learning. The proposed solution employs a light convolutional backbone and some adaptive local meta-learners. The proposed solution employs MobileNetV2 as the feature extractor. This is fine-tuned for many clients using a combination of FedAvg and FedProx regularization. Each client also trains a few meta-learners (MLP, SVM, and ELM) using the local feature embeddings, enabling people to obtain personalized predictions without sharing their private information. For inference, the framework supports both probability-level averaging across client ensembles and deployable single-client prediction using only the local meta-learners of one client. On the Brain Tumor MRI Dataset, containing 7023 image slices across glioma, meningioma, no tumor, and pituitary classes, the proposed framework achieved a maximum observed accuracy of 99.57%. Across four repeated runs, it achieved 99.29% +/- 0.20% accuracy with a 95% confidence interval of 98.97% to 99.61%, while maintaining strong macro-F1 performance and a macro-average ROC-AUC of 0.998690. Under the same preprocessing and split protocol, it outperformed internally reimplemented CNN+FedAvg and CNN+FedAvg+FedProx baselines and preserved near-centralized ROC-AUC performance. Communication analysis showed that exchanging the MobileNetV2 backbone required 149.89&#xa0;MB per round for five clients, corresponding to an 83.34% reduction relative to a ResNet-50 backbone.","url":"https://doi.org/10.1038/s41598-026-55847-5","authors":["Paul M","Tiwari A","Barmashe B","Rai K","Panwar VS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-55847-5","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.21203/rs.3.rs-9546424/v1","name":"HeFLDPB: A Trustworthy Triple-Defence Architecture for Privacy-Preserving Collaborative Healthcare Fraud Detection","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9546424/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-9546424/v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.2196/93950","name":"Mapping Machine Learning-Driven Cybersecurity Solutions in Health Care: Scoping Literature Review.","source":"pubmed","abstract":"Health care systems face escalating cyberattacks, including the UK Synnovis ransomware attack, which halted pathology services for 14 weeks; the Ascension Health breach affecting 5.6 million patients; and the Change Healthcare breach costing US $2.5 billion. Conventional cybersecurity measures in health care remain reactive and inadequate against evolving threats. Machine learning (ML) offers adaptive, predictive, real-time cyber defense; yet, there is limited clarity on how ML tools are applied across cybersecurity domains, their real-world effectiveness, and where gaps remain.","url":"https://doi.org/10.2196/93950","authors":["Rajput K","Zuberi S","Elhajj M","Ochieng W","Darzi A","Ghafur S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.2196/93950","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.20944/preprints202606.0707.v1","name":"FL-SDMN: A Federated Learning-Based Software-Defined Mobile Networking for Intelligent Handover Optimization","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202606.0707.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.20944/preprints202606.0707.v1","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-49896-z","name":"Multi-modal personalized federated learning with adaptive differential privacy for medical image classification and a privacy-preserving approach.","source":"pubmed","abstract":"Deep learning on medical images classification intervention needs to use large data on multi-institutional datasets but privacy laws inhibit sharing of data (GDPR, HIPAA). Federated Learning (FL) facilitates collaborative training without data transfer; until now, the known methods can only address privacy, personalisation, and accuracy not at the same time in a multi-modal environment. We present MM-PFL-ADP, a framework that combines Vision Transformer (ViT) based multi-modal feature extraction in four new elements: (i) privacy budget allocation (independent of number of samples): Fisher information-based adaptive per-parameter privacy budget allocation ([Formula: see text]); (ii) personalisation masks: dynamic KL divergence based personalisation masks; (iii) respect The framework gives formal client-level [Formula: see text]-DP guarantees on transmitted gradient updates, in [Formula: see text] simulated medical institutions. On the MRI-MS dataset, MM-PFL-ADP achieves [Formula: see text] accuracy (95% CI: 96.9-[Formula: see text]) at [Formula: see text], outperforming FedAvg ([Formula: see text]) and DP-FedAvg ([Formula: see text]) by large margins ([Formula: see text]). The framework is [Formula: see text] faster than FedAvg (47 vs. 85 rounds), has [Formula: see text] less total communication and keeps [Formula: see text] accuracy in case of extreme heterogeneity in data ([Formula: see text]). The probability of membership inference attack has decreased to 52.1 which was close to the random baseline ([Formula: see text]). MM-PFL-ADP shows that the concepts of privacy, personalisation, and accuracy are synergistic, but not oppositional to federated medical AI. The single-system Fisher information framework greatly simplifies the hyperparameter tuning problem and can meet formal privacy criteria. Before being deployed, prospective validation against the performance of expert radiologists is desired.","url":"https://doi.org/10.1038/s41598-026-49896-z","authors":["M AS","Chowdhary CL"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-49896-z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1038/s41598-026-57660-6","name":"Federated deep reinforcement learning enabled hierarchical Edge-Fog-Cloud architecture for intelligent task offloading in 6G networks.","source":"pubmed","abstract":"Latency sensitive, computation intensive and mobility aware applications in Edge Fog Cloud environments have increased the demand to develop intelligent offloading task mechanism that can dynamically scale to changing network conditions whilst remaining scalable, energy efficient, and preserving data privacy. Traditional, heuristic and centralized based learning offloading methods frequently have problems in accommodating heterogeneous workloads, non- stationary environments and privacy limitations associated with the next generation distributed computer system. In order to overcome these drawbacks, the present paper will suggest a Federated Deep Q-Learning (FDQL)-based task offloading framework, which incorporates deep reinforcement learning and federated learning to support adaptive, decentralized and privacy-conscious decision-making across hierarchical Edge Fog Cloud architectures. The framework proposed solves task offloading as a Markov Decision Process, with the execution decisions being trained based on the joint consideration of the latency, bandwidth availability, queue length, computational load, and energy state, as well as user mobility, without sharing raw data during federated model aggregation. In comparison to the current CNN-, LSTM-, SVM-, and rule-based methods, which use fixed threshold values or rely on centralized training, the FDQL architecture allows collaborative learning between distributed edge nodes, enhancing generalization as well as resilience as network conditions evolve. Large-scale experimental analysis is performed using a trace-driven simulation based on a publicly available task offloading dataset of tasks and the performance is evaluated based on the latency, energy consumption, task success rate, robustness analysis, and computational efficiency. Experimental findings indicate that the proposed FDQL framework demonstrates improved performance under distributed and resource-constrained environments compared to baseline approaches since shorter latency, increased energy efficiency, and more predictable execution-layer selection are achieved. The significance of federated learning, mobility awareness, and bandwidth-aware optimization in the stability of the performance is also confirmed by ablation studies. In order to achieve a better level of transparency and trustworthiness, SLA-based confusion matrix analysis and ROC analysis are performed as well as SHAP-based explainability analysis, which proves that the decisions made by FDQL are based on physically interesting, as well as SLA-relevant features, like latency, bandwidth, and resource use. All in all, the designed FDQL framework is a successful, interpretable, and scalable approach to intelligent task offloading, so it would fit perfectly into the implementation of the 6G-enabled application, such as smart cities, industrial internet of things, and autonomous systems in the future.","url":"https://doi.org/10.1038/s41598-026-57660-6","authors":["Ambika B","Pandian SMV","Sundaravadivel P","Vinothkumar ES"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-57660-6","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1038/s41598-026-50865-9","name":"A federated learning-benchmarking framework for privacy-preserving UAV intrusion detection using adaptive aggregation algorithms.","source":"pubmed","abstract":"The recent explosive growth of Unmanned Aerial Vehicles (UAVs) has contributed to their high vulnerability to cyber-attacks including Denial of Service (DoS), identity impersonation and unauthorized access to data. The UAV networks have the inherent risks of centralized Intrusion Detection Systems (IDS) which pose critical privacy risks and points of failure, and therefore the decentralized and privacy-preserving learning paradigms are required. The paper presents federated learning architecture called FedDrone-Shield( Federated Learning Framework for Drone Security and Shield against Intrusions), which is used in the task of detecting UAV intrusions in the scenarios of Independent and Identically Distributed (IID) data and the assessment of several aggregation algorithms: FedAvg, FedProx, FedAdam, FedMedian, and ClusterAvg. A significant amount of experiments that were carried out on a dataset on anomaly detection of UAVs prove that FedAdam and ClusterAvg outperform other aggregation strategies by achieving test accuracies of 99.98, F1-scores of 0.9999, and impressively low loss values of 0.0009-0.0014. FedMedian has also closely competitive performance, whereas FedAvg and FedProx are slightly less accurate and slower converging. Client-level assessments also show a consistent high precision, recall and F1-score across all attack types, with weighted F1-scores between 0.9997 and 0.9999, which again shows that there is reliable detection performance amongst distributed UAV clients. These findings make FedDrone-Shield a strong and feasible bench-marking model of federated intrusion detection in UAV networks proving that adaptive aggregation approaches contribute to a substantial improvement of detection accuracy, training, and data privacy. This means that the proposed structure offers a robust basis of intrusion detection that is safe and ensures privacy in distributed UAVs.","url":"https://doi.org/10.1038/s41598-026-50865-9","authors":["Bithi M","Alsubait T","Ibraheem A","Masud ME","Hossain MA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-50865-9","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3390/s26123886","name":"A Comprehensive Survey on Online AutoML and Adversarial Robustness for IoT and EV Charging Network Security.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26123886","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.3390/s26123886","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1038/s41598-026-50003-5","name":"Federated learning-enabled privacy-preserving framework for seizure forecasting and affective state analysis using multi-modal EEG-ECG data.","source":"pubmed","abstract":"Seizure forecasting and affective state analysis using EEG-ECG data play a pivotal role in advancing neurological and mental health monitoring. However, existing methods such as Fed-Transformer, Res-1D CNN, and Fed-ESD suffer from privacy risks, inefficient feature extraction, and high computational overhead, limiting their effectiveness in real-world applications. To overcome these challenges, this study proposes NeuroFedSense, a novel Federated Learning-enabled Privacy-Preserving Framework that integrates a Temporal Convolutional Network (TCN) with an Attention Mechanism for accurate seizure forecasting and affective state analysis using EEG-ECG data, ensuring enhanced feature selection, interpretability and efficient decentralized training. The model leverages adaptive attention-based optimization and weighted feature selection to improve classification performance while ensuring data privacy. Implemented using TensorFlow, NeuroFedSense achieves 99.54% accuracy, 99.62% precision, 99.34% recall, and a 99.46% F1-score, outperforming Fed-Transformer (97.10% accuracy), Res-1D CNN (81.62% accuracy), and FML (99.10% accuracy). The ROC-AUC score of 0.99 further establishes its superiority over competing models. Additionally, the federated approach reduces energy consumption per node by 30% and optimizes communication efficiency by minimizing data transmission by 15% over 100 rounds. By ensuring high accuracy, improved privacy, reduced computational overhead, and enhanced energy efficiency, NeuroFedSense sets a new benchmark for decentralized, real-time seizure prediction and affective state monitoring. These findings underscore its potential for deployment in intelligent, privacy-preserving healthcare applications, addressing critical challenges in remote neurological monitoring.","url":"https://doi.org/10.1038/s41598-026-50003-5","authors":["Arulmurugan VS","Aarthy R","Vidhya SS","K S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-50003-5","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-9931291/v1","name":"MontageFL: Privacy-Preserving Federated Learning across Heterogeneous EEG Montages A Leakage-Free Benchmark on Alzheimer’s Disease Classification","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9931291/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-9931291/v1","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1109/tpami.2026.3654093","name":"BlindU: Blind Machine Unlearning Without Revealing Erasing Data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2026.3654093","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1109/tpami.2026.3654093","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21203/rs.3.rs-9843866/v1","name":"A Simple, Privacy-Preserving Multimodal Stacking System for Clinical Progression Prediction in Low-Resource Rare Diseases","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9843866/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-9843866/v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1038/s41598-026-49077-y","name":"Automating differentially private tabular data synthesis via Bayesian optimization.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-49077-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-49077-y","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/jbhi.2026.3712138","name":"Machine Unlearning Based on Globally Refined Convergent Clustering for Health Survey Data-driven Prediction Model.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/jbhi.2026.3712138","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1109/jbhi.2026.3712138","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.3791/71972","name":"A Scoping Review of Machine Learning and Deep Learning Methods for Autism Spectrum Disorder Diagnosis and Analysis.","source":"pubmed","abstract":"Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by diverse behavioral, cognitive, sensory, and communication profiles, making early diagnosis and personalized intervention challenging. Recent advances in machine learning (ML) and deep learning (DL) have enabled the development of computational tools for ASD screening, classification, severity assessment, and intervention monitoring. This review synthesizes findings from 50 recent studies that applied ML and DL techniques to ASD-related datasets, including electroencephalography (EEG), eye-tracking, behavioral video, microbiome, voice acoustic, demographic, and multimodal data. The review addresses three key questions: (i) which data modalities and computational approaches are most frequently used, (ii) how diagnostic performance is evaluated across different study designs, and (iii) what methodological challenges limit clinical translation. The literature is organized according to data modality, algorithmic approach, and clinical readiness. Approaches examined include conventional ML methods, convolutional neural networks, graph neural networks, hybrid deep learning architectures, federated learning, explainable artificial intelligence, topological data analysis, and multimodal fusion. The findings suggest that multimodal and graph-based approaches provide a more comprehensive representation of ASD phenotypes than single-modality methods. Explainability and privacy-preserving learning have also emerged as important considerations for clinical deployment. However, many reported high-performance models are based on small sample sizes, repeated use of the ABIDE dataset, class imbalance, single-site validation, or limited external testing, raising concerns regarding generalizability. Beyond diagnostic accuracy, this review evaluates model interpretability, calibration, scalability, validation rigor, and clinical applicability. Overall, the analysis highlights the need for standardized benchmarks, externally validated multimodal datasets, clinically relevant evaluation metrics, and decision-support systems that complement rather than replace expert clinical assessment in ASD diagnosis and management.","url":"https://doi.org/10.3791/71972","authors":["K SR","Y LA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.3791/71972","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1371/journal.pone.0343669","name":"Privacy-preserving multimodal federated learning pipeline for cyber-resilient healthcare systems.","source":"pubmed","abstract":"The integration of Internet of Things (IoT) devices and electronic medical records (EMRs) has transformed healthcare delivery but has also created new vulnerabilities to cyberattacks that threaten both data confidentiality and patient safety. Conventional centralized machine learning approaches for intrusion detection are impractical in this domain due to strict privacy regulations, heterogeneous data sources, and the risk of single points of failure. To address these challenges, we propose a secure distributed machine learning pipeline for cyber-resilient healthcare systems. The framework combines federated optimization with split learning for sensitive EMR data, robust aggregation to mitigate poisoned updates, and differential privacy with secure aggregation to protect against inference attacks. Multimodal fusion is enabled through temporal consistency regularization for IoT traffic and cross-layer contrastive alignment to link EMR representations, ensuring improved anomaly detection across diverse healthcare environments. Experiments conducted on representative IoT and EMR datasets demonstrate that the proposed pipeline achieves accuracy of 0.942 on IoT data, 0.931 on EMR data, and 0.953&#xa0;in the combined setting, with corresponding F1-scores of 0.921, 0.908, and 0.932. Ranking metrics further confirm superiority with AUROC up to 0.961 and AUPRC up to 0.947, outperforming deep baselines by margins of +0.025 to +0.033. Robustness analysis shows graceful degradation under client poisoning ([Formula: see text] at 30% malicious clients) and resilience under severe communication constraints (accuracy [Formula: see text] at 90% update sparsification). Detection latency is reduced to an average of 5.9 time steps, compared to 7.8 for the strongest deep baseline. These results highlight that secure distributed pipelines can deliver both strong detection capabilities and regulatory compliance, providing a practical path toward safeguarding next-generation healthcare infrastructures against evolving cyber threats.","url":"https://doi.org/10.1371/journal.pone.0343669","authors":["Tanvir MIM","Rabby HR","Arif MH","Nadia NY","Nur K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1371/journal.pone.0343669","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-9590548/v1","name":"Experimental Validation of a Federated Explainable Multi-Modal Transformer–GAN Framework with AutoML-Optimized Ensemble Learning for Bias-Resilient, Privacy-Preserving and Resource-Efficient Real-Time Disease Prediction Across Heterogeneous Multi-Site Populations","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9590548/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-9590548/v1","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202607.0540.v1","name":"Dual-Layer PSO-Enhanced Federated Heterogeneous Data Fusion for Hemodialysis Complication Prediction","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202607.0540.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.20944/preprints202607.0540.v1","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.3791/70084","name":"Internet of Things-Driven Smart Furniture Systems for Human-Centered Personalization: Experimental Evaluation Using Behavioral Sensor Data.","source":"pubmed","abstract":"This paper introduces an advanced Internet of Things (IoT)-driven smart furniture system designed to dynamically adapt to individual users by integrating deep reinforcement learning with federated meta-learning. Personalization is formulated as a Markov decision process, enabling the system to make optimized, sequential adjustments tailored to each user's behavior. To estimate hidden ergonomic preferences in real time, an adaptive Kalman filter is applied, while a sparse autoencoder reduces raw sensor signals by 82 %, preserving key temporal features essential for accurate modeling. In a comprehensive user study involving 48 participants and more than 160,000 time-series sensor samples, the framework significantly reduced cumulative user dissatisfaction by 43 % and cut energy consumption by 21 %, compared with conventional rule-based control systems. Real-time adaptations occur with an average latency of 280 ms, and constraints for ergonomics are upheld in 95 % of use cases, confirming the system operates swiftly and safely. Federated learning (FL) enables privacy-preserving collaboration across distributed furniture units. Training converges to 87 % of global performance within 30 global iterations, without any raw data exchange, reinforcing both scalability and data privacy. These empirical results strongly support the framework's suitability for deployment in health-aware workspaces, smart homes, and eldercare environments, delivering a robust, responsive, and interpretable solution for enriching human-furniture interaction.","url":"https://doi.org/10.3791/70084","authors":["Wang S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.3791/70084","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1038/s41598-026-49902-4","name":"Hierarchical proof of trust a Byzantine fault tolerant federated learning framework for industrial IoT applications.","source":"pubmed","abstract":"The Industrial Internet of Things (IIoT) presents significant challenges for training machine learning models due to data privacy concerns, heterogeneous data distributions, and limited bandwidth. This paper proposes HPoT (Hierarchical Proof-of-Trust), a novel federated learning-based consensus algorithm for IIoT infrastructure that addresses these limitations while ensuring data integrity and model fidelity. HPoT integrates blockchain technology with federated learning to create a decentralized, privacy-preserving framework enabling collaborative model training without raw data sharing. The algorithm features: (1) robust aggregation with weighted averaging and anomaly detection for heterogeneous datasets, (2) adaptive weighting prioritizing updates from trustworthy devices, (3) reputation scoring to detect malicious nodes, and (4) hierarchical three-tier architecture optimized for IIoT constraints. Using Byzantine fault tolerance principles, HPoT tolerates up to [Formula: see text] malicious participants while maintaining convergence. Experimental evaluation demonstrates [Formula: see text] reduction in communication rounds, [Formula: see text] decrease in per-node energy consumption, and [Formula: see text] lower consensus latency compared to traditional federated learning, while maintaining comparable accuracy ([Formula: see text] vs [Formula: see text]). The algorithm effectively handles data heterogeneity, communication constraints, and adversarial attacks common in IIoT environments. HPoT provides a scalable, secure, and energy-efficient solution for privacy-preserving machine learning on resource-constrained IIoT devices, advancing practical deployment of collaborative AI in industrial settings.","url":"https://doi.org/10.1038/s41598-026-49902-4","authors":["Chaurasia A","Sharma SK","Rathore PS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-49902-4","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-9491795/v1","name":"Secure and Privacy-Preserving Federated AI: A Robust Framework for Distributed Intelligence","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9491795/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-9491795/v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9250688/v1","name":"DL-DPGAN: A Correlation-Regularized Differentially Private GAN for Privacy-Utility Balanced Synthetic Data Generation","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9250688/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-9250688/v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1056/aioa2501116","name":"Privacy-Preserving Surgical Video Analysis with Swarm Learning - Results from a Multinational Appendectomy Cohort.","source":"europepmc","abstract":"","url":"https://doi.org/10.1056/aioa2501116","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1056/aioa2501116","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1038/s41598-026-51460-8","name":"Resource-efficient federated machine unlearning via evolutionary synaptic pruning for cloud-based distributed learning systems.","source":"pubmed","abstract":"With an increasing emphasis on user data control and privacy regulations, such as the General Data Protection Regulation (GDPR), machine unlearning (MU) has emerged as a crucial mechanism for managing data in AI systems. MU enables models to remove the influence of specific user data upon request. This problem becomes more complex in collaborative settings such as Federated Learning (FL), where data remains distributed across multiple clients, giving rise to Federated Unlearning (FU). In large-scale deployments, particularly those supported by cloud infrastructure, retraining models to satisfy data deletion requests can be computationally expensive, energy-intensive, and disruptive to ongoing services. This highlights the importance of having effective methods to unlearn outdated practices that can hinder the growth of a system and compromise data privacy awareness. We propose PRUNE-FL (Privacy-preserving Retention-focused Unlearning with Neuro-Evolution in Federated Learning), a framework that uses relevance-guided pruning and evolutionary optimization to delete the influence of the targeted data. PRUNE-FL is different from methods that rely on retraining or coarse parameter updates because it focuses on finding and changing the parameters that are most closely related to the data that needs to be forgotten. The approach is based on a synaptic relevance scoring system to figure out how each model parameter relates to certain client or class-level data. This makes it easier to find parameters that are connected to the target data. Then, the unlearning task is set up as a multi-objective problem to find a balance between the overall performance and the forgetting of unnecessary information. Finally, a genetic algorithm is used to implement an evolutionary pruning strategy. It seeks the optimal pruning settings that operate within the constraints of federated learning. Thus, PRUNE-FL helps in unlearning specific data without having to retrain the whole system. Tests on the CIFAR-10 dataset in both IID (Independent and Identically Distributed) and non-IID settings show that PRUNE-FL has higher accuracy. It also effectively removes the influence of the targeted data. The results also show it to be strong against bad patterns like backdoor triggers. Overall, PRUNE-FL enhances privacy by selectively unlearning the data and using fewer resources in federated environments.","url":"https://doi.org/10.1038/s41598-026-51460-8","authors":["Bansal H","Unhelkar B","Saini DKJB","Rai BK","Sharma P","Kumar G","Chakrabarti P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-51460-8","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1109/tpami.2026.3663617","name":"A Personalized and Privacy-Preserving Federated Transformer Framework for Multilingual Sentiment Analysis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2026.3663617","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1109/tpami.2026.3663617","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1038/s41598-026-52361-6","name":"Blockchain-enhanced federated learning for IoT security and privacy using the GSR-C2N model.","source":"pubmed","abstract":"The rapid proliferation of Internet of Things devices has intensified the demand for security, privacy-preserving, and scalable machine learning solutions. Federated Learning (FL) supports the decentralized training of models across distributed devices without transporting raw data, whereas blockchain provides a trusted, transparent mechanism for integrity and reaching consensus. The paper explains an FL framework that incorporates blockchain technology, based on the GSR-C2N model for identifying crypto-mining malware. It is demonstrated that the system's feature extraction and optimization processes are optimized to address security problems in IoT by utilizing blockchain to verify model updates and build trust and privacy. The given structure has demonstrated superiority to the current practices. This model was 96.85% accurate and 97.51% specific on the crypto-mining malware data set using 10-fold cross-validation, making it applicable to smart healthcare and smart city applications with IoT-based systems. In addition, when combined with regulatory compliance, homomorphic encryption can strengthen data and privacy management, underscoring the model's effectiveness in advanced IoT systems.","url":"https://doi.org/10.1038/s41598-026-52361-6","authors":["Ahmad S","Ansari MA","Mewada A","Ahmed G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-52361-6","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.21203/rs.3.rs-9252646/v1","name":"Poison-Resilient and Privacy-Preserving Federated Learning Scheme in Mobile Systems","source":"europepmc","abstract":"Abstract Mobile systems including smartphones, IoT devices generate massive high value data, while conventional centralized data collection and analysis suffer from security and privacy vulnerabilities. Federating learning, an emerging paradigm in machine learning, collaboratively train s high performance model via participants sharing local training model updates rather than raw data, thereby preserving the privacy of their local datasets, provides a new approach for the secure extraction of data value in mobile system. However, malicious participants may inject carefully crafted poisoned samples into their local datasets, with the intent of disrupting the convergence of the global model or inducing targeted misclassification. Consequently, the identification of such malicious participants are of critical importance in federated learning. To address these challenges, this paper proposes poison-resilient and privacy-preserving federated learning scheme in mobile system. It not only detects poisoning attacks in both vertical federated learning and horizontal federated learning, but also removes prior assumptions regarding client data distributions and restrictions on the proportion of adversarial participants. In addition, a convergence control parameter is introduced to regulate the model’s convergence rate. The security, correctness, fairness, and robustness of the proposed scheme are formally analyzed and rigorously proven. Experimental results demonstrate that our scheme detects poisoning attacks effectively while maintaining high accuracy and model training efficiency.","url":"https://doi.org/10.21203/rs.3.rs-9252646/v1","authors":["Quanyu Zhao","Chenrui Gu","Bingbing Jiang","Yuanjian Zhou","Zhengjun Jing"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-9252646/v1","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106632","name":"Age-stratified machine learning using de-identified clinical and transcriptomic data for pediatric appendicitis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.ijmedinf.2026.106632","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1016/j.ijmedinf.2026.106632","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1136/bmjopen-2026-120743","name":"Novel indicator for colonoscopy insertion difficulty and its application in predictive modelling: a multicentre prospective study protocol in China.","source":"europepmc","abstract":"Background Colonoscopy is a cornerstone for the screening and diagnosis of colorectal diseases. Colonoscopy insertion difficulty is associated with examination quality, procedural efficiency and patient comfort. Cecal intubation time (CIT) is commonly used as a surrogate measure of insertion difficulty; however, it is substantially influenced by endoscopist experience, equipment characteristics and institutional factors, limiting its objectivity and comparability. This study will develop and validate a standardised colonoscopy insertion time (SCIT) metric that better reflects patient-related procedural difficulty while minimising non-patient-related variability. Additionally, we will identify factors associated with insertion difficulty and develop predictive models using machine learning techniques. Methods and analysis This multicentre prospective observational study will enrol approximately 3000 adults undergoing sedated colonoscopy across one primary centre and five participating centres. CIT will be prospectively recorded and analysed in relation to patient characteristics, endoscopist experience, colonoscope type and study centre. Three standardisation approaches, including Z-score standardisation, median-based standardisation and min-max normalisation, will be applied to generate SCIT. The primary outcome will be SCIT, and the optimal standardisation method will be selected based on its ability to reduce variability attributable to operator-related, equipment-related and centre-related factors while preserving clinically meaningful associations with patient characteristics. Mixed-effects models will be used to account for clustering by endoscopist and study centre. Based on the selected SCIT metric, machine learning models will be developed using preprocedural clinical variables to predict insertion difficulty. Model performance will be evaluated using measures of discrimination, calibration and predictive accuracy. Ethics and dissemination The study received initial ethical approval from the Ethics Committee of the Second Hospital of Jilin University (Approval No. 2024-467-1) on 4 November 2024. Subsequent protocol amendments were approved on 25 March 2026. All participants will provide written informed consent prior to enrolment. Patient privacy and data confidentiality will be strictly maintained. The results will be disseminated through SCI-indexed journals and presentations at national and international academic conferences to inform future research and clinical practice related to colonoscopy performance evaluation. Trial registration number NCT07228715.","url":"https://doi.org/10.1136/bmjopen-2026-120743","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1136/bmjopen-2026-120743","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.3389/frai.2026.1804943","name":"CITADEL: a post-quantum secure blockchain framework for privacy-preserving electronic health records with temporally-partitioned federated learning.","source":"pubmed","abstract":"Electronic health records (EHRs) increasingly anchor clinical decision support and population-scale analytics, yet their concentration of sensitive information amplifies disclosure risk, widens the attack surface, and faces emerging threats from quantum computing. Existing frameworks fail to simultaneously address privacy preservation, quantum-resistant security, and cross-institutional federated learning.","url":"https://doi.org/10.3389/frai.2026.1804943","authors":["Segar N","Vijayan V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.3389/frai.2026.1804943","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.20944/preprints202607.0486.v1","name":"Markerless Video-Based Gait Analysis for Motor Phenotyping in Individuals with Schizophrenia Using Interpretable Machine Learning","source":"europepmc","abstract":"Background: /Objectives: Motor abnormalities are frequently observed in schizophrenia, but accessible methods for objective gait quantification remain limited. This exploratory controlled-setting study examined whether markerless smartphone-based video analysis combined with interpretable machine learning could quantify gait-related motor phenotypes in individuals with schizophrenia. Methods: Gait videos were collected from 100 individuals with schizophrenia and 35 healthy controls using a standardized recording setup. MediaPipe Pose was used to extract skeletal landmarks and derive 12 image-plane spatiotemporal and estimated two-dimensional knee-kinematic gait features. After temporal segmentation and quality control, 404 usable gait segments were analyzed. Decision Tree and Support Vector Machine models were applied for exploratory segment-level group-separation analysis using 15-fold cross-validation after pre-cross-validation resampling. Results: Several extracted gait features differed between groups, particularly image-plane ankle displacement, mean step displacement, displacement velocity, step characteristics, and knee-joint motion. In the Decision Tree model, image-plane ankle displacement served as the primary root node, indicating its central role in internal segment-level group separation. However, because the healthy control group was substantially younger and not age-matched, multiple gait segments from the same participant could appear across cross-validation folds, and resampling was performed before fold partitioning, model performance should be interpreted as exploratory internal segment-level behavior rather than participant-level classification evidence. Conclusions: Markerless video-based gait analysis with interpretable machine learning may provide a feasible research-support approach for quantifying gait-related motor phenotypes in individuals with schizophrenia. These findings should not be interpreted as evidence for clinical classification or screening. Future studies require matched controls, psychiatric comparison groups, subject-wise validation, external datasets, calibrated gait measures, and privacy-preserving data governance.","url":"https://doi.org/10.20944/preprints202607.0486.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.20944/preprints202607.0486.v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1371/journal.pone.0351957","name":"FEDI-CODE: A federated and causally-informed framework for dementia risk prediction using multi-site patient data.","source":"pubmed","abstract":"Early detection of dementia is critical for timely intervention and disease management, yet it remains a challenging task due to the fragmented nature of healthcare data and the need for privacy-preserving solutions. This paper proposes FEDI-CODE, a Federated and Causally Informed Dementia Estimation framework that integrates deep learning, federated learning, and counterfactual inference to predict dementia risk across distributed patient data sources. FEDI-CODE is designed to operate without centralizing sensitive medical data, enabling collaborative training across institutions while preserving privacy. It combines temporal modeling of longitudinal imaging and clinical data with individualized treatment effect estimation for modifiable risk factors such as alcohol consumption, weight, and cardiovascular indicators. A fusion module aggregates representations from each site to form a global prediction head. Extensive experiments on simulated multi-site dementia datasets demonstrate that FEDI-CODE achieves an accuracy of 83.7%, a precision of 83%, a recall of 81%, an F1-score of 82%, and an AUC-ROC of 0.86, outperforming standard federated models and deep learning baselines by notable margins. The model also generalizes well to external datasets, achieving 79.2% accuracy and 0.80 AUC-ROC, confirming its robustness. Furthermore, FEDI-CODE produces interpretable causal insights by estimating individual treatment effects, offering actionable clinical value. These results highlight FEDI-CODE as a scalable, interpretable, and privacy-aware solution for early dementia screening and personalized risk assessment.","url":"https://doi.org/10.1371/journal.pone.0351957","authors":["Moniruzzaman M","Uddin MS","Ahmed A","Aktarujjaman M","Ahmed M","Rahman A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1371/journal.pone.0351957","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1016/j.urolonc.2026.05.029","name":"Automating standardization of prostate cancer biopsy and histopathology reports with privacy-preserving local large language models.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.urolonc.2026.05.029","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1016/j.urolonc.2026.05.029","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1038/s41598-026-53357-y","name":"Customer churn prediction in privacy-preserving HashCode-based security abstractions.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-53357-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-53357-y","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21203/rs.3.rs-9182048/v1","name":"Privacy-Preserving Diabetes Prediction Using Federated Learning in Edge-Based Healthcare","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9182048/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-9182048/v1","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1371/journal.pone.0344980","name":"Federated high order tensor fusion for privacy preserving multimodal social media analysis.","source":"pubmed","abstract":"The rapid evolution of social networks has positioned multimodal content, including text, images, and audio, as a pivotal medium for self-expression and public sentiment analysis. However, existing multimodal fusion methods are often limited by privacy risks, parameter redundancy, and insufficient exploitation of intermodal correlations. To overcome these challenges, this study introduces a novel federated learning framework that integrates high-order tensor-based multimodal data fusion with privacy-aware decentralized training by keeping raw data local. It leverages tensor Tucker decomposition to capture complex spatial and semantic relationships between modalities, enhancing fusion accuracy while supporting user privacy through local data retention. Experimental results on the separate TREC2017 Precision Medicine Track Scientific Abstracts dataset and on the CMU-MOSI multimodal sentiment benchmark demonstrate that the proposed algorithm outperforms existing methods. The TREC2017 experiments validate the framework's performance in text-dominant conditions (higher Mean Average Precision, MAP)), while the CMU-MOSI experiments confirm the effectiveness of the high-order tensor fusion in modeling intermodal correlations for multimodal tasks. Furthermore, our framework demonstrates adaptive learning capabilities, efficiently processing diverse multimodal data types without expanding redundant model parameters. This research opens new avenues for privacy-aware multimodal data fusion in social media, offering a robust solution for monitoring and managing online public opinion while supporting user privacy through local data retention.","url":"https://doi.org/10.1371/journal.pone.0344980","authors":["Wan L","Zhang B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1371/journal.pone.0344980","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1109/tpami.2026.3711847","name":"Aligning Condensed Graph via Hashing: A New Insight for Federated Graph Learning.","source":"pubmed","abstract":"Federated Graph Learning (FGL) aims to maximize the benefits of each graph owner, which is a common form of distributed graph learning under privacy-preserving conditions. As the landscape of local clients becomes increasingly diverse in terms of both model architectures and topological complexities, graph heterogeneity turns out to be one of the significant challenges to efficient collaboration among clients. Beyond existing paradigms, we delve a fresh insight into revisiting FGL as a semantic condensed graph alignment problem in this work. From this perspective, HashFGL is proposed for heterogeneous FGL through aligning condensed graphs via hashing in a symbiotic space. Specifically, the core of HashFGL lies in that it introduces a cross-client symbiotic space to facilitate effective collaboration. Within this space, an efficient hash-based semantic encoding strategy is proposed to model each local client while balancing coordinated resilience and semantic consistency. Furthermore, we derive an elaborate graph condenser based on the above strategy, which condenses original graphs with semantics and structure-preserving property, to maintain the effectiveness of condensed graph alignment for FGL. Formal theoretical analysis further reveals that HashFGL can effectively alleviate the problem of graph heterogeneity. Experimental results on three large-scale graphs, employing standard partitioning strategies and a pioneering, more realistic partitioning that we introduced, demonstrate the efficacy and scalability of HashFGL.","url":"https://doi.org/10.1109/tpami.2026.3711847","authors":["Yan Y","Zheng S","Zhu Z","Chen D","Zhang W","Zhao Y","He K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1109/tpami.2026.3711847","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"doi:10.3390/biomedicines14051010","name":"Privacy-Preserving Hybrid GA-LSTM Ensemble for Typhoid Detection Using Optimised Clinical Feature Selection.","source":"pubmed","abstract":"Background/Objectives: Typhoid fever remains a major public health challenge in many low-income countries, where overlapping clinical symptoms and the limited reliability of conventional diagnostic procedures hinder accurate diagnosis. This study aims to develop a reliable and efficient diagnostic framework that automates typhoid fever detection from clinical data while preserving patient privacy. Methods: To achieve this objective, we propose a hybrid framework combining genetic algorithm (GA)-based feature selection, a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) deep learning classifier, and federated learning. The GA identifies the most informative clinical features, reducing redundancy and computational complexity. The selected features are then used to train a CNN-LSTM model in a federated learning setup using the Federated Averaging (FedAvg) algorithm, enabling collaborative model training across multiple clients without sharing raw patient data. Results: Experimental results show that the proposed framework achieves 92% accuracy, with a strong F1-score and satisfactory sensitivity. Compared to models trained on the full feature set, the proposed approach requires less memory and shorter training time, while maintaining balanced performance under class imbalance. Conclusions: These results demonstrate that integrating evolutionary feature selection, deep sequential learning, and federated training provides an effective and privacy-aware solution for multi-class typhoid fever diagnosis. The proposed framework is particularly suitable for clinical environments with limited data access and constrained resources.","url":"https://doi.org/10.3390/biomedicines14051010","authors":["Gasmi K","Alanazi A","Almenwer S","Almaghrabi S","Alshammari H","Khaldi K","Chouaib H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.3390/biomedicines14051010","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.1186/s40001-026-04023-6","name":"FMLCA: explainable and privacy-preserving federated machine learning classification algorithms for predicting heart disease in patients.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s40001-026-04023-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1186/s40001-026-04023-6","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1002/vms3.70979","name":"Smart Livestock: A Federated Learning Based System for Real-Time Cattle Health, Stress and Water Resource Monitoring.","source":"pubmed","abstract":"Centralized machine learning (ML) models often face challenges related to data privacy, messaging latency and limited internet access in rural areas. To address these issues, a federated learning (FL) based architecture for real-time detection of health and stress in cattle using sensors is deployed. This model enables edge devices such as smart collars, wearable sensors and cameras, to collaboratively learn a global model without sharing raw data.","url":"https://doi.org/10.1002/vms3.70979","authors":["Gulati V","Grover R","Kumar N","Jindal P","Shaheen M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1002/vms3.70979","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"epmc:MED42317844","name":"Foundation Model-Guided Synthetic EHR Release: Performance Enhancement with Privacy Preservation.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42317844/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z"},{"id":"doi:10.20944/preprints202606.1102.v1","name":"Introduction to TinyML: The New Era of Low-Power AI for IoT Devices","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202606.1102.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.20944/preprints202606.1102.v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1002/advs.75906","name":"Machine Learning-Driven Prediction of Microplastic Aging Processes and Environmental Risk Assessment Across Multi-Media Systems.","source":"pubmed","abstract":"Machine learning (ML) holds promise for reconstructing microplastic (MP) aging and assessing risks, but current studies rely on small-scale, accelerated laboratory datasets and single environmental medium models that miss cross-media transport and environmental interactions in real-world MP lifecycles. To realize its potential for reconstructing spatiotemporal aging trajectories and toxicological assessment of MPs, this perspective provides a paradigm shift in ML application from fragmented data-fitting to a holistic, privacy-preserving, physics-aware strategy. A novel probabilistic framework reconstructs the environmental history of field-sampled MPs through mechanistic fingerprinting, using Bayesian inference to reconcile multi-evidence signals and improve trajectory models for source attribution and risk assessment. Furthermore, we propose the TRACE framework (TRansport, Aging, Corona, Ecotoxicity), which moves beyond the isolated modeling of aging processes and toxicity endpoints. By integrating physics-informed models with causal discovery, TRACE captures the reciprocal feedback loops between physicochemical evolution and eco-corona formation, thereby mechanistically linking surface transformations to biological risks. To support this data-intensive architecture, we advocate for federated learning (FL) to dismantle privacy barriers. This approach facilitates secure, multi-institutional collaborative modeling without raw data exchange, harmonizing heterogeneous datasets. Ultimately, this cohesive strategy bridges laboratory-field disparities, moving toward predictive, evidence-based, and targeted mitigation efforts in global plastic pollution governance.","url":"https://doi.org/10.1002/advs.75906","authors":["Lyu Y","Qiu X","Li X","Yang T","Guo X","Qiu H","Zhang P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1002/advs.75906","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1136/archdischild-2026-330388","name":"Artificial intelligence for child health: current capabilities and the next frontier.","source":"pubmed","abstract":"Artificial intelligence (AI) is reshaping paediatric healthcare, offering new capabilities across diagnosis, monitoring and treatment personalisation. Modern AI systems integrate multimodal data, including imaging, genomics, electronic health records, wearable sensors, environmental exposures and patient-reported outcomes, to generate insights tailored to children's developmental, physiological and psychosocial needs. Advances in machine learning, deep learning, natural language processing, computer vision and generative models are enabling earlier detection of rare diseases, dynamic risk stratification and personalised care pathways. Emerging technologies such as digital twins simulate individual disease trajectories and treatment responses, reducing reliance on traditional trial designs and supporting anticipatory, precision care.The next frontier of AI in healthcare includes adaptive decision support powered by reinforcement learning and advanced time-series modelling, allowing systems to respond to real-time physiological changes and support complex sequential decisions in areas such as ventilation, insulin dosing, deterioration prediction and medication titration. Parallel progress in remote monitoring and smart sensors is shifting care from hospitals to homes, supporting long-term condition management and reducing avoidable admissions. Causal AI offers further potential by uncovering true cause-and-effect relationships, enabling clinicians to understand why interventions work and explore counterfactual 'what-if' scenarios. Looking further ahead, quantum AI, neuromorphic computing and privacy-preserving federated learning may unlock new computational capabilities, enabling ultra-fast analysis, on-device learning and the secure use of distributed paediatric datasets. Realising this future requires rigorous governance, paediatric-specific validation, safeguards for privacy and autonomy and equitable digital access. When developed responsibly, AI has the potential to augment clinical expertise, reduce health inequalities and transform child health outcomes.","url":"https://doi.org/10.1136/archdischild-2026-330388","authors":["Dimitri P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1136/archdischild-2026-330388","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.3389/fsurg.2026.1853765","name":"Predictive models for intestinal obstruction: from clinical scores to artificial intelligence.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fsurg.2026.1853765","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.3389/fsurg.2026.1853765","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21203/rs.3.rs-9955873/v1","name":"Real-Time Financial Fraud Detection: A Systematic Review of Big Data Architectures, Machine Learning Models, and Adaptive Intelligence","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9955873/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.21203/rs.3.rs-9955873/v1","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1111/tme.70108","name":"From prediction to practice: Barriers to implementing artificial intelligence in blood inventory management and transfusion support.","source":"europepmc","abstract":"","url":"https://doi.org/10.1111/tme.70108","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1111/tme.70108","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1080/10255842.2026.2690183","name":"A systematic review of machine learning approaches for phonocardiogram classification.","source":"pubmed","abstract":"Recent advances in machine learning (ML) have accelerated automated analysis of phonocardiogram (PCG) signals, yet prior surveys often narrow their scope (e.g. omitting segmentation, focusing only on classical ML or overlooking recent deep learning [DL] trends) and provide limited methodological transparency. We present a comprehensive, Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-guided synthesis of PCG classification research published predominantly between 2021 and 2025, resulting in 151 studies included in the final synthesis. The systematic search was conducted across IEEE Xplore, PubMed/MEDLINE, Scopus, SpringerLink, ScienceDirect and Google Scholar, using Boolean combinations of heart sound/PCG-related terms and ML keywords. The review maps the full pipeline - acquisition, preprocessing, segmentation, feature extraction and classification - and distinguishes feature representations as single-independent-variable (SIV) and double-independent-variable (DIV). We compare classical classifiers with modern DL and hybrid architectures, and summarize public/proprietary datasets and evaluation practices. Our analysis shows that DL - especially convolutional neural network (CNN)-based approaches-dominates recent work, with growing interest in hybrid CNN-recurrent neural networks (RNNs) models and attention/transformer architectures. Segmentation is handled either explicitly (standalone or embedded) or bypassed in end-to-end designs. Despite high benchmark performance, comparability is hindered by heterogeneous datasets, non-uniform splits and metric choices; generalization under noise, device variability and pediatric vs. adult domain shift remains a key challenge. Bridging technical progress to clinical utility requires robustness, interpretability, efficient on-device inference and privacy-preserving training. We conclude with practical recommendations for standardized evaluation protocols, interpretable modeling, multimodal fusion, edge deployment and federated/multi-center learning. By integrating methodological and clinical perspectives, this review connects state-of-the-art results with the requirements of scalable, clinically viable PCG-based screening systems.","url":"https://doi.org/10.1080/10255842.2026.2690183","authors":["Al-Shanoon A","Sykes ER","Nazari H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1080/10255842.2026.2690183","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.3389/fdmed.2026.1778372","name":"Harnessing generative artificial intelligence for periodontitis prediction: a machine learning approach integrating systemic health indicators for precision oral health in resource-limited settings.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdmed.2026.1778372","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.3389/fdmed.2026.1778372","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/tpami.2026.3713184","name":"BRFedTD: A Novel Framework for Federated Reinforcement Learning With Byzantine-Resilient Policy Evaluation.","source":"pubmed","abstract":"Federated reinforcement learning (FRL) enables distributed agents to collaboratively evaluate policies without sharing raw data, making it a promising approach for privacy-preserving and scalable decision-making. However, the presence of Byzantine agents, which may behave arbitrarily or maliciously, poses significant challenges to the robustness and reliability of FRL. To address this issue, in this paper, we propose a novel trimmed mean-based robust federated policy evaluation framework called Byzantine-resilient federated temporal difference learning (BRFedTD), and establish a finite-time convergence theory of BRFedTD. This framework effectively addresses the combined challenges of linear function approximation, heterogeneous Markov decision processes (MDPs), multiple local updates, and robust aggregation. To support more accurate confidence interval estimation and policy uncertainty analysis, we further derive the asymptotic distribution of the estimation error, showing that BRFedTD achieves asymptotic normality and efficiency in the case of identical MDPs without Byzantine attacks. This represents, to the best of our knowledge, the first asymptotic normality result established in FRL. Extensive numerical experiments demonstrate the robustness and effectiveness of the proposed algorithm, and corroborate that it generalizes to deep reinforcement learning and performs well on complex control tasks.","url":"https://doi.org/10.1109/tpami.2026.3713184","authors":["Wang D","Liu W","Mao X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1109/tpami.2026.3713184","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1016/j.media.2026.104151","name":"Adaptive feature unlearning for trustworthy medical imaging privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.media.2026.104151","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1016/j.media.2026.104151","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1038/s41598-026-60046-3","name":"Adversarial-resilient lightweight phishing url detection: Evaluating lexical &amp; metadata features under evasion techniques.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-60046-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:37.970Z","doi":"10.1038/s41598-026-60046-3","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.70675/e3b678aczaed6z4d28za08fz148521e61b8b","name":"Securing a trusted hardware environment (Trusted Execution Environment)","source":"crossref","abstract":"Sécurisation d'un environnement matériel de confiance (Trusted Execution Environement) Ce travail de thèse a pour cadre le projet Trusted Environment Execution eVAluation (TEEVA) (projet français FUI n°20 de Janvier 2016 à Décembre 2018) qui vise à évaluer deux solutions alternatives de sécurisation des plateformes mobiles, l’une est purement logicielle, la Whitebox Crypto, alors que l’autre intègre des éléments logiciels et matériels, le Trusted Environment Execution (TEE). Le TEE s’appuie sur la technologie TrustZone d’ARM disponible sur de nombreux chipsets du marché tels que des smartphones et tablettes Android. Cette thèse se concentre sur l’architecture TEE, l’objectif étant d’analyser les menaces potentielles liées aux infrastructures de test/debug classiquement intégrées dans les circuits pour contrôler la conformité fonctionnelle après fabrication.Le test est une étape indispensable dans la production d’un circuit intégré afin d’assurer fiabilité et qualité du produit final. En raison de l’extrême complexité des circuits intégrés actuels, les procédures de test ne peuvent pas reposer sur un simple contrôle des entrées primaires avec des patterns de test, puis sur l’observation des réponses de test produites sur les sorties primaires. Les infrastructures de test doivent être intégrées dans le matériel au moment du design, implémentant les techniques de Design-for-Testability (DfT). La technique DfT la plus commune est l’insertion de chaînes de scan. Les registres sont connectés en une ou plusieurs chaîne(s), appelé chaîne(s) de scan. Ainsi, un testeur peut contrôler et observer les états internes du circuit à travers les broches dédiées. Malheureusement, cette infrastructure de test peut aussi être utilisée pour extraire des informations sensibles stockées ou traitées dans le circuit, comme par exemple des données fortement corrélées à une clé secrète. Une attaque par scan consiste à récupérer la clé secrète d’un crypto-processeur grâce à l’observation de résultats partiellement encryptés.Des expérimentations ont été conduites sur la carte électronique de démonstration avec le TEE afin d’analyser sa sécurité contre une attaque par scan. Dans la carte électronique de démonstration, une contremesure est implémentée afin de protéger les données sensibles traitées et sauvegardées dans le TEE. Les accès de test sont déconnectés, protégeant contre les attaques exploitant les infrastructures de test, au dépend des possibilités de test, diagnostic et debug après mise en service du circuit. Les résultats d’expérience ont montré que les circuits intégrés basés sur la technologie TrustZone ont besoin d’implanter une contremesure qui protège les données extraites des chaînes de scan. Outre cette simple contremesure consistant à éviter l’accès aux chaînes de scan, des contremesures plus avancées ont été développées dans la littérature pour assurer la sécurité tout en préservant l’accès au test et au debug. Nous avons analysé un état de l’art des contremesures contre les attaques par scan. De cette étude, nous avons proposé une nouvelle contremesure qui préserve l’accès aux chaînes de scan tout en les protégeant, qui s’intègre facilement dans un système, et qui ne nécessite aucun redesign du circuit après insertion des chaînes de scan tout en préservant la testabilité du circuit. Notre solution est basée sur l’encryption du canal de test, elle assure la confidentialité des communications entre le circuit et le testeur tout en empêchant son utilisation par des utilisateurs non autorisés. Plusieurs architectures ont été étudiées, ce document rapporte également les avantages et les inconvénients des solutions envisagées en terme de sécurité et de performance.","url":"https://doi.org/10.70675/e3b678aczaed6z4d28za08fz148521e61b8b","authors":["Mathieu Da Silva"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-03T06:03:18Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.70675/e3b678aczaed6z4d28za08fz148521e61b8b","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.5220/0010558900002998","name":"Cloud Key Management using Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0010558900002998","authors":["Jaouhara Bouamama","Mustapha Hedabou","Mohammed Erradi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-11T13:21:35Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.5220/0010558900002998","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/ieeestd.2023.10186307","name":"IEEE Standard for Secure Computing Based on Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ieeestd.2023.10186307","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-18T17:35:07Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/ieeestd.2023.10186307","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/iscas45731.2020.9180551/video","name":"Video for Cryptographic Accelerators for Trusted Execution Environment in RISC-V Processors","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscas45731.2020.9180551/video","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-09-29T09:22:27Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/iscas45731.2020.9180551/video","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.5220/0007578605880595","name":"A Fine-grained General Purpose Secure Storage Facility for Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0007578605880595","authors":["Luigi Catuogno","Clemente Galdi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-03-15T11:00:03Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.5220/0007578605880595","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.2139/ssrn.6078228","name":"Trusted-Execution Environment (TEE) for Solving the Replication Crisis in Academia","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.6078228","authors":["Jiasun Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-28T03:30:07Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.2139/ssrn.6078228","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.17487/rfc9397","name":"Trusted Execution Environment Provisioning (TEEP) Architecture","source":"crossref","abstract":"","url":"https://doi.org/10.17487/rfc9397","authors":["M. Pei","H. Tschofenig","D. Thaler","D. Wheeler"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-21T22:54:41Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.17487/rfc9397","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.17010/ijcs/2020/v5/i4-5/154785","name":"Security Impact of Trusted Execution Environment in  Rich Execution Environment Based Systems","source":"crossref","abstract":"","url":"https://doi.org/10.17010/ijcs/2020/v5/i4-5/154785","authors":["Jithu Philip","Merin Raju"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-10-09T08:37:40Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.17010/ijcs/2020/v5/i4-5/154785","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/ieeestd.2021.9586768","name":"IEEE Standard for Technical Framework and Requirements of Trusted Execution Environment based Shared Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ieeestd.2021.9586768","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-10-26T20:36:18Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/ieeestd.2021.9586768","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1145/3007788.3007795","name":"Analysis of Trusted Execution Environment usage in Samsung KNOX","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3007788.3007795","authors":["Ahmad Atamli-Reineh","Ravishankar Borgaonkar","Ranjbar A. Balisane","Giuseppe Petracca","Andrew Martin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-12-13T14:31:54Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1145/3007788.3007795","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.14722/ndss.2015.23189","name":"SeCReT: Secure Channel between Rich Execution Environment and Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.14722/ndss.2015.23189","authors":["Jinsoo Jang","Sunjune Kong","Minsu Kim","Daegyeong Kim","Brent Byunghoon Kang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-02-16T00:46:53Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.14722/ndss.2015.23189","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1017/9781009299534.008","name":"Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1017/9781009299534.008","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-26T00:05:54Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1017/9781009299534.008","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.23940/ijpe.18.09.p21.21272136","name":"A Solution to Make Trusted Execution Environment More Trustworthy","source":"crossref","abstract":"","url":"https://doi.org/10.23940/ijpe.18.09.p21.21272136","authors":["Xiao Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-10-06T22:25:40Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.23940/ijpe.18.09.p21.21272136","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.20944/preprints202606.1154.v1","name":"A Trusted Execution Environment for Secure Reasoning on Large-Scale Models in the Power Industry","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202606.1154.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.20944/preprints202606.1154.v1","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/asianhost51057.2020.9358260","name":"HybridTEE: Secure Mobile DNN Execution Using Hybrid Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/asianhost51057.2020.9358260","authors":["Akshay Gangal","Mengmei Ye","Sheng Wei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-02-26T16:25:45Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/asianhost51057.2020.9358260","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.5220/0009130600310043","name":"Secure Cloud Storage with Client-side Encryption using a Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0009130600310043","authors":["Marciano da Rocha","Dalton Valadares","Angelo Perkusich","Kyller Gorgonio","Rodrigo Pagno","Newton Will"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-05-19T10:24:51Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.5220/0009130600310043","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1145/3342559.3365338","name":"Towards a standards-compliant pure-software trusted execution environment for resource-constrained embedded devices","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3342559.3365338","authors":["Hassaan Janjua","Mahmoud Ammar","Bruno Crispo","Danny Hughes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-11-15T21:18:24Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1145/3342559.3365338","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/tc.2024.3355772/mm1","name":"Towards Secure Runtime Customizable Trusted Execution Environment on FPGA-SoC_supp1-3355772.docx","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tc.2024.3355772/mm1","authors":["Xiaolin Chang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-23T15:46:13Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/tc.2024.3355772/mm1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-3-031-55561-9_5","name":"Building Execution Environments from the Trusted Platform Module","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-55561-9_5","authors":["Carlton Shepherd","Konstantinos Markantonakis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-26T18:15:47Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1007/978-3-031-55561-9_5","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.4271/2024-01-3766","name":"HIGH PERFORMANCE TRUSTED EXECUTION ENVIRONMENT","source":"crossref","abstract":"&lt;title&gt;ABSTRACT&lt;/title&gt; &lt;p&gt;This paper explores the construction of a Trusted Execution Environment (TEE) which doesn’t rely on TrustZone or specific processing modes in order to achieve a high-performance operating environment with multiple layers of hardware enforced confidentiality and integrity. The composed TEE uses hardware intellectual property (IP) blocks, existing hardware-level protections, a hypervisor, Linux security module (LSM), and Linux kernel capabilities including a file system in order to provide the performance and multiple layers of confidentiality and integrity. Additionally, the TEE composition explores both open source and commercial solutions for achieving the same result.&lt;/p&gt; &lt;p&gt;&lt;bold&gt;Citation:&lt;/bold&gt; J. Kline, “High Performance Trusted Execution Environment”, In &lt;italic&gt;Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium&lt;/italic&gt; (GVSETS), NDIA, Novi, MI, Aug. 13-15, 2019.&lt;/p&gt;","url":"https://doi.org/10.4271/2024-01-3766","authors":["Jonathan Kline"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-21T11:53:31Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.4271/2024-01-3766","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.5220/0010869800003123","name":"A Trusted Data Sharing Environment based on FAIR Principles and Distributed Process Execution","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0010869800003123","authors":["Marcel Klötgen","Florian Lauf","Sebastian Stäubert","Sven Meister","Danny Ammon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-24T20:55:18Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.5220/0010869800003123","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-3-031-33386-6_18","name":"Trusted Execution Environment","source":"crossref","abstract":"Abstract Trusted execution environments are secure areas of central processors or devices that execute code with higher security than the rest of the device. Security is provided by encrypted memory regions called enclaves. Because the environment is isolated from the rest of the device, it is not affected by infection or compromise of the device. Trusted execution environments have applications for different usages, such as mobile phones, cloud data processing, or cryptocurrencies. Furthermore, since Trusted execution environments are part of a standard chipset, this inexpensive technology can be leveraged across many devices, resulting in increased security, especially in the mobile sector and IoT products.","url":"https://doi.org/10.1007/978-3-031-33386-6_18","authors":["Maria Sommerhalder"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-31T21:02:35Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1007/978-3-031-33386-6_18","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21203/rs.3.rs-462176/v1","name":"Protecting Security-Sensitive Data Using Program Transformation and Trusted Execution Environment","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-462176/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.21203/rs.3.rs-462176/v1","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.2172/1011228","name":"Trusted Computing Technologies, Intel Trusted Execution Technology.","source":"crossref","abstract":"","url":"https://doi.org/10.2172/1011228","authors":["Max Guise","Jeremy Wendt"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-04-15T03:04:17Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.2172/1011228","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.14722/ndss.2023.23041","name":"MyTEE: Own the Trusted Execution Environment on Embedded Devices","source":"crossref","abstract":"","url":"https://doi.org/10.14722/ndss.2023.23041","authors":["Seungkyun Han","Jinsoo Jang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-12T13:00:37Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.14722/ndss.2023.23041","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-1-4842-6106-4_17","name":"Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4842-6106-4_17","authors":["Jiewen Yao","Vincent Zimmer"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-10-27T07:03:24Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1007/978-1-4842-6106-4_17","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1145/3268935.3268946","name":"Trusted Execution on Leaky Hardware?","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3268935.3268946","authors":["Daniel Genkin","Yuval Yarom"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-10-16T13:23:10Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1145/3268935.3268946","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1101/2024.09.16.613301","name":"TX-Phase: Secure Phasing of Private Genomes in a Trusted Execution Environment","source":"europepmc","abstract":"","url":"https://doi.org/10.1101/2024.09.16.613301","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1101/2024.09.16.613301","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1007/978-3-319-50500-8_18","name":"Trusted Execution Environment and Host Card Emulation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-50500-8_18","authors":["Assad Umar","Keith Mayes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-05-18T07:14:46Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1007/978-3-319-50500-8_18","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/trustcom.2015.400","name":"Open-TEE -- An Open Virtual Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/trustcom.2015.400","authors":["Brian McGillion","Tanel Dettenborn","Thomas Nyman","N. Asokan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-12-03T21:12:00Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/trustcom.2015.400","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.56578/ijcmem140207","name":"A Trusted Provenance and Trusted Execution Environment-Based Reference Architecture for Intent-Execution Binding in Large Language Model Agents","source":"crossref","abstract":"","url":"https://doi.org/10.56578/ijcmem140207","authors":["Haewon Byeon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-09T08:58:13Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.56578/ijcmem140207","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.2139/ssrn.5239584","name":"Sgx-Fl - a Secure and Efficient Federated Learning Framework Based on Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5239584","authors":["bowei Xue","Yufei Zhang","Xinyu Gu","Konglin Zhu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-02T18:37:20Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.2139/ssrn.5239584","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.3390/electronics14081674","name":"TeeDFuzzer: Fuzzing Trusted Execution Environment","source":"crossref","abstract":"The Trusted Execution Environment (TEE) is crucial for safeguarding the ecosystem of embedded systems. It uses isolation to minimize the TCB (Trusted Computing Base) and protect sensitive software. It is vital because devices handle vast, potentially sensitive data. Leveraging ARM TrustZone, widely used in mobile and IoT for TEEs, it ensures hardware protection via security extensions, though needing firmware and software stack support. Despite the reputation of TEEs for high security, TrustZone-aided ones have vulnerabilities. Fuzzing, as a practical bug-finding technique, has seen limited research in the context of TEE. The unique software architecture of TrustZone-assisted TEE complicates the direct application of traditional fuzzing methods. Moreover, simplistic approaches, such as feeding random input values into TEE through the API functions of the rich operating system, fail to uncover deeper, latent bugs within the TEE code. In this paper, we present a fuzzing strategy for TrustZone-assisted TEE that utilizes inferred dependencies between Trusted Kernel system calls to uncover deep-seated TEE bugs. We implemented our approach on OP-TEE, where it successfully identified 17 crashes, including one previously undetected kernel bug.","url":"https://doi.org/10.3390/electronics14081674","authors":["Sheng Wen","Liam Xu","Liwei Tian","Suping Liu","Yong Ding"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-20T21:58:46Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/electronics14081674","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-3-031-55561-9_6","name":"Trusted World Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-55561-9_6","authors":["Carlton Shepherd","Konstantinos Markantonakis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-26T18:15:47Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1007/978-3-031-55561-9_6","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1101/2021.02.02.429428","name":"Privacy-Preserving Genotype Imputation in a Trusted Execution Environment","source":"crossref","abstract":"Abstract Genotype imputation is an essential tool in genetics research, whereby missing genotypes are inferred based on a panel of reference genomes to enhance the power of downstream analyses. Recently, public imputation servers have been developed to allow researchers to leverage increasingly large-scale and diverse genetic data repositories for imputation. However, privacy concerns associated with uploading one’s genetic data to a third-party server greatly limit the utility of these services. In this paper, we introduce a practical, secure hardware-based solution for a privacy-preserving imputation service, which keeps the input genomes private from the service provider by processing the data only within a Trusted Execution Environment (TEE) offered by the Intel SGX technology. Our solution features SMac, an efficient, side-channel-resilient imputation algorithm designed for Intel SGX, which employs the hidden Markov model (HMM)-based imputation strategy also utilized by a state-of-the-art imputation software Minimac. SMac achieves imputation accuracies virtually identical to those of Minimac and provides protection against known attacks on SGX while maintaining scalability to large datasets. We additionally show the necessity of our strategies for mitigating side-channel risks by identifying vulnerabilities in existing imputation software and controlling their information exposure. Overall, our work provides a guideline for practical and secure implementation of genetic analysis tools in SGX, representing a step toward privacy-preserving analysis services that can facilitate data sharing and accelerate genetics research. † Availability Our software is available at https://github.com/ndokmai/sgx-genotype-imputation .","url":"https://doi.org/10.1101/2021.02.02.429428","authors":["Natnatee Dokmai","Can Kockan","Kaiyuan Zhu","XiaoFeng Wang","S. Cenk Sahinalp","Hyunghoon Cho"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-02-03T13:34:06Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1101/2021.02.02.429428","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/access.2020.2974487","name":"A Design and Verification Methodology for a TrustZone Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2020.2974487","authors":["Haiyong Sun","Hang Lei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-02-17T20:24:27Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/access.2020.2974487","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1145/3805690.3805734","name":"CACTEE: Confidential Asset Certification using Trusted Execution Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3805690.3805734","authors":["Istemi Ekin Akkus","Ivica Rimac"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-20T14:58:21Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1145/3805690.3805734","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1016/b978-0-12-816197-5.00008-5","name":"Trusted execution environment with Intel SGX","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-816197-5.00008-5","authors":["Somnath Chakrabarti","Thomas Knauth","Dmitrii Kuvaiskii","Michael Steiner","Mona Vij"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-03-20T21:06:00Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1016/b978-0-12-816197-5.00008-5","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/trustcom.2015.357","name":"Trusted Execution Environment: What It is, and What It is Not","source":"crossref","abstract":"","url":"https://doi.org/10.1109/trustcom.2015.357","authors":["Mohamed Sabt","Mohammed Achemlal","Abdelmadjid Bouabdallah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-12-03T16:12:00Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/trustcom.2015.357","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/icac.2015.27","name":"Towards Integrating Trusted Execution Environment into Embedded Autonomic Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icac.2015.27","authors":["Mohamed Sabt","Mohammed Achemlal","Abdelmadjid Bouabdallah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-09-14T21:38:14Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/icac.2015.27","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-3-031-55561-9_4","name":"Isolated Hardware Execution Platforms","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-55561-9_4","authors":["Carlton Shepherd","Konstantinos Markantonakis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-26T18:15:47Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1007/978-3-031-55561-9_4","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-1-4302-6149-0_1","name":"Introduction to Trust and Intel® Trusted Execution Technology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4302-6149-0_1","authors":["William Futral","James Greene"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-10-08T05:20:27Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1007/978-1-4302-6149-0_1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.2139/ssrn.4052224","name":"A Privacy-Preserving Scheme for Advance Metering Applications Using Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4052224","authors":["Mete Akgün","Elif Ustundag Soykan","Gürkan Soykan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-09T06:34:27Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.2139/ssrn.4052224","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/itiotsc60379.2023.00034","name":"Detection method of trusted blockchain link flood attack based on trusted execution environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/itiotsc60379.2023.00034","authors":["Ruixue Kuang","Lianhai Wang","Shuhui Zhang","Shujiang Xu","Wei Shao","Qizheng Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-19T17:25:11Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/itiotsc60379.2023.00034","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/dac18074.2021.9586198","name":"Privacy-Preserving Medical Image Segmentation via Hybrid Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/dac18074.2021.9586198","authors":["Song Bian","Weiwen Jiang","Takashi Sato"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-08T18:30:34Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/dac18074.2021.9586198","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/cis.2016.0065","name":"A Novel Method of APK-Based Automated Execution and Traversal with a Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cis.2016.0065","authors":["Rui Chang","Liehui Jiang","Qing Yin","Wei Liu","Shengqiao Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-01-19T21:17:31Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/cis.2016.0065","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/asianhost59942.2023.10409376","name":"DF-TEE: Trusted Execution Environment for Disaggregated Multi-FPGA Cloud Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/asianhost59942.2023.10409376","authors":["Ke Xia","Sheng Wei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-24T13:36:47Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/asianhost59942.2023.10409376","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/seed61283.2024.00020","name":"Trusted Execution Environments in Embedded and IoT Systems: A CactiLab Perspective","source":"crossref","abstract":"","url":"https://doi.org/10.1109/seed61283.2024.00020","authors":["Ziming Zhao","Md Armanuzzaman","Xi Tan","Zheyuan Ma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-05T18:29:44Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/seed61283.2024.00020","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1145/3638782.3638786","name":"Trusted Delivery Mechanisms for Software Supply Chains Based on Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3638782.3638786","authors":["Wang Xiaozhou","Ye Jianfei","Feng Linjie","Feng Chenji","Chen Jun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-18T11:07:37Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1145/3638782.3638786","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1016/j.future.2021.06.034","name":"SDD: A trusted display of FIDO2 transaction confirmation without trusted execution environment","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.future.2021.06.034","authors":["Peng Xu","Ruijie Sun","Wei Wang","Tianyang Chen","Yubo Zheng","Hai Jin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-06-25T03:35:15Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1016/j.future.2021.06.034","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1145/3485832.3485919","name":"TEEKAP: Self-Expiring Data Capsule using Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3485832.3485919","authors":["Mingyuan Gao","Hung Dang","Ee-Chien Chang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-12-06T13:42:32Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1145/3485832.3485919","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21105/joss.01494","name":"mTower: Trusted Execution Environment for MCU-based devices","source":"crossref","abstract":"","url":"https://doi.org/10.21105/joss.01494","authors":["Taras Drozdovskyi","Oleksandr Moliavko"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-08-27T12:23:36Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.21105/joss.01494","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.35662/unine-thesis-3104","name":"Enhancing Security and Performance in Trusted Execution Environments","source":"crossref","abstract":"","url":"https://doi.org/10.35662/unine-thesis-3104","authors":["Peterson Yuhala"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-04T02:40:29Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.35662/unine-thesis-3104","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.36227/techrxiv.15147171.v1","name":"Position Paper: On Using Trusted Execution Environment to Secure COTS Devices for Accessing Industrial Control Systems","source":"crossref","abstract":"Industrial Control Systems (ICS) are traditionally designed to operate in an “air-gapped” environment. With the advent of digital technologies, many ICS are adopting IT solutions to improve interoperability and operational efficiency. Thus, the air-gap assumption no longer holds in practice. Most ICS devices today are modernized with networking capabilities to facilitate system maintenance, upgrades, and troubleshooting. Since these devices are connected to the Internet, ICS networks face the same security threats as regular IT systems. In addition, ICS operators can connect commercial off-the-shelf (COTS) equipment to ICS networks to perform operational tasks. Those COTS devices are usually personal computers or even mobile devices, which can be infected with malware and become weapons against ICS. In this position paper, we examine the design challenges of establishing trust between COTS equipment and ICS. We also present some commonly used security solutions and discuss their deployment challenges due to issues caused by legacy systems. Finally, we introduce the Trusted Execution Environment (TEE), a technology commonly available on modern COTS devices, as a trust anchor for establishing secure communications with the ICS infrastructure. We discuss some research gaps related to the use of TEE and propose some recommendations to guide future research.","url":"https://doi.org/10.36227/techrxiv.15147171.v1","authors":["Quanqi Ye","Heng Chuan Tan","Daisuke Mashima","Binbin Chen","Zbigniew Kalbarczyk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-12T12:48:33Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.36227/techrxiv.15147171.v1","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/edcc.2019.00022","name":"Transforming Byzantine Faults using a Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/edcc.2019.00022","authors":["Mads Frederik Madsen","Mikkel Gaub","Malthe Ettrup Kirkbro","Soren Debois"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-11-13T17:29:25Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/edcc.2019.00022","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.21203/rs.3.rs-2761556/v1","name":"Blockchain based Trusted Execution Environment Architecture Analysis for Multi - source Data Fusion Scenario","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-2761556/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.21203/rs.3.rs-2761556/v1","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/access.2021.3112202","name":"TS-Perf: General Performance Measurement of Trusted Execution Environment and Rich Execution Environment on Intel SGX, Arm TrustZone, and RISC-V Keystone","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2021.3112202","authors":["Kuniyasu Suzaki","Kenta Nakajima","Tsukasa Oi","Akira Tsukamoto"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-13T20:53:42Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/access.2021.3112202","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/euc53437.2021.00015","name":"Open Portable Trusted Execution Environment framework for RISC-V","source":"crossref","abstract":"","url":"https://doi.org/10.1109/euc53437.2021.00015","authors":["Marouene Boubakri","Fausto Chiatante","Belhassen Zouari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-30T20:02:28Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/euc53437.2021.00015","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/978-3-031-55561-9","name":"Trusted Execution Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-55561-9","authors":["Carlton Shepherd","Konstantinos Markantonakis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-26T14:15:47Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1007/978-3-031-55561-9","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1145/3011883.3011892","name":"Trusted execution environment-based authentication gauge\n            <i>(TEEBAG)</i>","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3011883.3011892","authors":["Ranjbar A. Balisane","Andrew Martin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-12-15T18:03:54Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1145/3011883.3011892","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1117/12.3031944","name":"TeTPCM: building endogenous trusted computing on trusted execution environment","source":"crossref","abstract":"","url":"https://doi.org/10.1117/12.3031944","authors":["Jiajian Li","Chenlin Huang","Jun Luo","Jinzhu Kong","Yiwen Ji","Yongpeng Liu","Kaikai Sun","Shuyang Deng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-06T15:35:58Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1117/12.3031944","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.15760/etd.7593","name":"A Method for Comparative Analysis of Trusted Execution Environments","source":"crossref","abstract":"","url":"https://doi.org/10.15760/etd.7593","authors":["Stephano Cetola"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-20T20:25:11Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.15760/etd.7593","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.14722/ndss.2022.24173","name":"Hybrid Trust Multi-party Computation with Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.14722/ndss.2022.24173","authors":["Pengfei Wu","Jianting Ning","Jiamin Shen","Hongbing Wang","Ee-Chien Chang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-04-16T12:26:01Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.14722/ndss.2022.24173","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/dsn-s58398.2023.00017","name":"Awesome Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/dsn-s58398.2023.00017","authors":["Luigi Coppolino","Giovanni Mazzeo","Luigi Romano"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-10T17:26:34Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/dsn-s58398.2023.00017","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/icc40277.2020.9149447","name":"Privacy-preserving Payment Channel Networks using Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc40277.2020.9149447","authors":["Peng Li","Xiaofei Luo","Toshiaki Miyazaki","Song Guo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-07-27T22:26:45Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/icc40277.2020.9149447","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/norcas58970.2023.10305445","name":"Control Plane Isolation of Network Security Protocols using FPGA-SoC Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/norcas58970.2023.10305445","authors":["Daniel Dik","Michael Stübert Berger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-06T19:09:54Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/norcas58970.2023.10305445","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/syscon53536.2022.9773838","name":"A Privacy-Preserving Data Aggregation Scheme for Fog/Cloud-Enhanced IoT Applications Using a Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/syscon53536.2022.9773838","authors":["Newton Carlos Will"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-05-16T16:45:11Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/syscon53536.2022.9773838","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.2139/ssrn.4878305","name":"Fuzzing Trusted Execution Environments with Rust","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4878305","authors":["Grzegorz Blinowski","Michal Szaknis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-27T10:19:05Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.2139/ssrn.4878305","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/access.2021.3069223","name":"MeetGo: A Trusted Execution Environment for Remote Applications on FPGA","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2021.3069223","authors":["Hyunyoung Oh","Kevin Nam","Seongil Jeon","Yeongpil Cho","Yunheung Paek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-03-29T20:49:38Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/access.2021.3069223","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1109/iwcmc61514.2024.10592332","name":"An seL4-based Trusted Execution Environment on RISC-V","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iwcmc61514.2024.10592332","authors":["Everton De Matos","Willian Tessaro Lunardi","Jouni Ukkonen","Tero Salminen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-17T17:18:34Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/iwcmc61514.2024.10592332","updatedAt":"2026-08-31T06:41:17.619Z"},{"id":"doi:10.1007/s13748-025-00386-9","name":"V-FLEX: Verifiable cross-silo federated learning using trusted execution environment","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s13748-025-00386-9","authors":["Jaouhara Bouamama","Yahya Benkaouz","Mohammed Ouzzif"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-26T13:20:49Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1007/s13748-025-00386-9","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/roman.2018.8525696","name":"Hardening ROS via Hardware-assisted Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/roman.2018.8525696","authors":["Mariacarla Staffa","Giovanni Mazzeo","Luigi Sgaglione"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-11-08T18:29:37Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/roman.2018.8525696","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.26689/pbes.v8i2.10296","name":"The Current Situation and Trends of Trusted Execution Environment Applications","source":"crossref","abstract":"With the rapid development of digital technologies such as big data, cloud computing, and the Internet of Things (IoT), data security and privacy protection have become the core challenges facing modern computing systems. Traditional security mechanisms are difficult to effectively deal with advanced adversarial attacks due to their reliance on a centralized trust model. In this context, the Trusted Execution Environment (TEE), as a hardware-enabled secure isolation technology, offers a potential solution to protect sensitive computations and data. This paper systematically discusses TEE’s technical principle, application status, and future development trend. First, the underlying architecture of TEE and its core characteristics, including isolation, integrity, and confidentiality, are analyzed. Secondly, practical application cases of TEE in fields such as finance, the IoT, artificial intelligence, and privacy computing are studied. Finally, the future development direction of TEE is prospected.","url":"https://doi.org/10.26689/pbes.v8i2.10296","authors":["Yanling Liu","Yun Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-29T02:26:05Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.26689/pbes.v8i2.10296","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.51483/ijaiml.6.6s.2026.298-310","name":"Reasoning Allocation as a Privacy Architecture: Tiered Inference for Trusted Execution Environment Deployments","source":"crossref","abstract":"","url":"https://doi.org/10.51483/ijaiml.6.6s.2026.298-310","authors":["Ankur Aggarwal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-06T08:51:44Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.51483/ijaiml.6.6s.2026.298-310","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1145/3268935.3268943","name":"Trusted Execution, and the Impact of Security on Performance","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3268935.3268943","authors":["Stefan Brenner","Michael Behlendorf","Rüdiger Kapitza"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-10-16T13:23:10Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1145/3268935.3268943","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/icbc59979.2024.10634382","name":"Support Remote Attestation for Decentralized Robot Operating System (ROS) using Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icbc59979.2024.10634382","authors":["Qian Wang","Brian Lee","Yuansong Qiao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-21T22:53:39Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/icbc59979.2024.10634382","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1145/3649476.3658702","name":"DM-TEE: Trusted Execution Environment for Disaggregated Memory","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3649476.3658702","authors":["Ke Xia","Sheng Wei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-10T12:29:41Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1145/3649476.3658702","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.12794/metadc1248514","name":"Secure and Trusted Execution Framework for Virtualized Workloads","source":"crossref","abstract":"In this dissertation, we have analyzed various security and trustworthy solutions for modern computing systems and proposed a framework that will provide holistic security and trust for the entire lifecycle of a virtualized workload. The framework consists of 3 novel techniques and a set of guidelines. These 3 techniques provide necessary elements for secure and trusted execution environment while the guidelines ensure that the virtualized workload remains in a secure and trusted state throughout its lifecycle. We have successfully implemented and demonstrated that the framework provides security and trust guarantees at the time of launch, any time during the execution, and during an update of the virtualized workload. Given the proliferation of virtualization from cloud servers to embedded systems, techniques presented in this dissertation can be implemented on most computing systems.","url":"https://doi.org/10.12794/metadc1248514","authors":["Srujan D Kotikela"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-15T09:25:31Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.12794/metadc1248514","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/iri.2018.00011","name":"Decentralized IoT Data Management Using BlockChain and Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iri.2018.00011","authors":["Gbadebo Ayoade","Vishal Karande","Latifur Khan","Kevin Hamlen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-08-06T18:38:30Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/iri.2018.00011","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/hpca57654.2024.00051","name":"A Quantum Computer Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/hpca57654.2024.00051","authors":["Theodoros Trochatos","Chuanqi Xu","Sanjay Deshpande","Yao Lu","Yongshan Ding","Jakub Szefer"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-02T18:36:37Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/hpca57654.2024.00051","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1587/essfr.14.2_107","name":"Implementation of Trusted Execution Environment and Its Supporting Technologies","source":"crossref","abstract":"","url":"https://doi.org/10.1587/essfr.14.2_107","authors":["Kuniyasu SUZAKI"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-09-30T22:23:39Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1587/essfr.14.2_107","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/dac18074.2021.9586207","name":"SGX-FPGA: Trusted Execution Environment for CPU-FPGA Heterogeneous Architecture","source":"crossref","abstract":"","url":"https://doi.org/10.1109/dac18074.2021.9586207","authors":["Ke Xia","Yukui Luo","Xiaolin Xu","Sheng Wei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-08T23:30:34Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/dac18074.2021.9586207","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1145/3342559.3365334","name":"Propagating trusted execution through mutual attestation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3342559.3365334","authors":["Furkan Turan","Ingrid Verbauwhede"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-11-15T21:18:24Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1145/3342559.3365334","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/access.2020.3006703","name":"SofTEE: Software-Based Trusted Execution Environment for User Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2020.3006703","authors":["Unsung Lee","Chanik Park"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-07-02T20:27:41Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/access.2020.3006703","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.53829/ntr202406gls","name":"Recent Trends in GlobalPlatform: Digital Trust – Evaluation &amp; Certification, Trusted Execution Environment, and Digital Identity –","source":"crossref","abstract":"","url":"https://doi.org/10.53829/ntr202406gls","authors":["Eikazu Niwano","Akira Nagai","Fumiaki Kudoh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-11T22:10:27Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.53829/ntr202406gls","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/iscas58744.2024.10558579","name":"Co-designing Trusted Execution Environment and Model Encryption for Secure High-Performance DNN Inference on FPGAs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscas58744.2024.10558579","authors":["Tsunato Nakai","Ryo Yamamoto"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-02T17:22:52Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/iscas58744.2024.10558579","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.70517/ijhsa46455","name":"Research on sensitive data discovery and optimization algorithm based on trusted execution environment","source":"crossref","abstract":"","url":"https://doi.org/10.70517/ijhsa46455","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-15T23:54:56Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.70517/ijhsa46455","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/ccwc51732.2021.9376148","name":"A Kubernetes CI/CD Pipeline with Asylo as a Trusted Execution Environment Abstraction Framework","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccwc51732.2021.9376148","authors":["Jamal Mahboob","Joel Coffman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-03-17T16:17:39Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/ccwc51732.2021.9376148","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1007/978-3-031-55561-9_9","name":"Conclusion","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-55561-9_9","authors":["Carlton Shepherd","Konstantinos Markantonakis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-26T18:15:47Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1007/978-3-031-55561-9_9","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1007/978-3-031-55561-9_1","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-55561-9_1","authors":["Carlton Shepherd","Konstantinos Markantonakis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-26T14:15:47Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1007/978-3-031-55561-9_1","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1007/978-1-4302-6584-9_6","name":"Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4302-6584-9_6","authors":["Will Arthur","David Challener","Kenneth Goldman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-01-27T22:58:31Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1007/978-1-4302-6584-9_6","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/candarw68385.2025.00058","name":"Proposal of an Identity Verification System Using Trusted Execution Environment in the Japan’s Digital Authentication App","source":"crossref","abstract":"","url":"https://doi.org/10.1109/candarw68385.2025.00058","authors":["Tomoki Yasui","Hidenobu Watanabe"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-12T18:20:34Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/candarw68385.2025.00058","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/trustcom60117.2023.00111","name":"Pldb: Protecting LSM-based Key-Value Store using Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/trustcom60117.2023.00111","authors":["Chenkai Shen","Lei Fan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-29T17:24:13Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/trustcom60117.2023.00111","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1145/3578359.3593037","name":"Towards Modular Trusted Execution Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3578359.3593037","authors":["Carsten Weinhold","Nils Asmussen","Diana Göhringer","Michael Roitzsch"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-23T14:29:55Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1145/3578359.3593037","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/mobisecserv.2017.7886559","name":"Ecosystems of Trusted Execution Environment on smartphones - a potentially bumpy road","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mobisecserv.2017.7886559","authors":["Assad Umar","Raja Naeem Akram","Keith Mayes","Konstantinos Markantonakis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-03-27T22:57:24Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/mobisecserv.2017.7886559","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1145/3427228.3427293","name":"Reboot-Oriented IoT: Life Cycle Management in Trusted Execution Environment for Disposable IoT devices","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3427228.3427293","authors":["Kuniyasu Suzaki","Akira Tsukamoto","Andy Green","Mohammad Mannan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-12-09T22:20:18Z","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1145/3427228.3427293","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1101/gr.280558.125","name":"Secure phasing of private genomes in a trusted execution environment with TX-Phase.","source":"europepmc","abstract":"","url":"https://doi.org/10.1101/gr.280558.125","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1101/gr.280558.125","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1016/j.cels.2021.08.001","name":"Privacy-preserving genotype imputation in a trusted execution environment.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.cels.2021.08.001","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1016/j.cels.2021.08.001","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s20113172","name":"FairCs-Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s20113172","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s20113172","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s26144577","name":"Trust-Aware Environmental State Consensus for Smart Agriculture with TEE-Enabled Sensing and Byzantine-Resilient Blockchain Coordination.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26144577","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s26144577","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s26134136","name":"Decentralized Tele-Rehabilitation via Edge AI-Oracle Architecture for Spatiotemporal Pain Assessment.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26134136","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s26134136","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.20944/preprints202606.1154.v2","name":"A TEE-Protected Framework for Explainable LLM-Assisted Microgrid Decision Support","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202606.1154.v2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.20944/preprints202606.1154.v2","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s26103039","name":"A Method for Continuous Dual-Offline Payment of Cryptocurrency Based on Asset Credentials.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26103039","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s26103039","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s26103040","name":"A Lightweight Hybrid Authentication and Key Agreement Protocol for Decentralized Device-to-Device Communication with Post-Quantum Confidentiality.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26103040","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s26103040","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1371/journal.pcbi.1014197","name":"Nine quick tips for software containerization.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pcbi.1014197","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1371/journal.pcbi.1014197","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3389/frai.2026.1826384","name":"Execution-bound advisory automation for agentic AI: a reproducible AIBOM-driven CSAF-VEX framework.","source":"europepmc","abstract":"Introduction Agentic AI systems integrate foundation models, prompt templates, tool connectors, orchestration logic, and containerised dependencies, creating exploitability conditions that cannot be inferred from static Software Bills of Materials (SBOMs). Artificial Intelligence Bills of Materials (AIBOM) extend transparency to AI-specific artefacts, yet current CSAF/VEX workflows remain based on static component-CVE correlation without runtime validation. Materials and methods A protocol-driven framework is presented that binds SBOM and AIBOM artefacts to deterministic environment capture and structured runtime telemetry. Exploitability is computed from declared artefacts, observed activation conditions, and enforced execution policies. CSAF-VEX advisories are generated from combined static and runtime evidence, cryptographically signed, and validated through deterministic replay. Evaluation uses approximately 10,000 component entries across synthetic Agentic AI workloads (50-5,000 components), incorporating OSV, GitHub Advisory, KEV, and EPSS datasets. Results Under controlled experimental conditions, the framework achieves an F1-score of 0.93 (precision 0.96, recall 0.92), reduces false positives by up to 42% relative to static SBOM-CVE matching without runtime validation, and alters exploitability outcomes in 31% of AI-specific artefact cases through AIBOM extension. Advisory artefacts remain reproducible under deterministic replay. Discussion Binding AIBOM artefacts to runtime telemetry transforms CSAF-VEX generation from static disclosure into execution-grounded exploitability assessment for Agentic AI supply chains.","url":"https://doi.org/10.3389/frai.2026.1826384","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3389/frai.2026.1826384","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/jbhi.2025.3562364","name":"Exploiting Trusted Execution Environments and Distributed Computation for Genomic Association Tests.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/jbhi.2025.3562364","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1109/jbhi.2025.3562364","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.21203/rs.3.rs-9273318/v1","name":"Privilege-Preserving Federated Learning for Collaborative Legal AI: An Architecture for Cryptographic Gradient Protection Under Attorney-Client Privilege Constraints","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9273318/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.21203/rs.3.rs-9273318/v1","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1371/journal.pone.0348864","name":"OracleTrust: A dual-layer provenance-based signature verification scheme for preventing transaction malleability in blockchain.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0348864","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1371/journal.pone.0348864","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.21203/rs.3.rs-9469357/v1","name":"System and Method for Privacy-Preserving Database Activity Monitoring Using Query Metadata Abstraction and Fingerprinting","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9469357/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.21203/rs.3.rs-9469357/v1","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-025-28105-3","name":"Cutting-edge optimized multi-source data fusion for trusted execution and management of blockchain transactions on the internet of medical things (IoMT) with machine learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-28105-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-025-28105-3","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.21203/rs.3.rs-7577071/v1","name":"EnCloak: Protecting Sensitive Data in Remote Computing Using Trusted Execution Environments","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7577071/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.21203/rs.3.rs-7577071/v1","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.20944/preprints202512.1017.v1","name":"5G-DAuth: Decentralized Privacy-Preserving Service Authorization for 5G Network Functions","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202512.1017.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.20944/preprints202512.1017.v1","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1016/j.fmre.2025.08.017","name":"TRUST: A toolkit for TEE-assisted secure outsourced computation over integers.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.fmre.2025.08.017","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1016/j.fmre.2025.08.017","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/e28040478","name":"VeriFed: Temporally Consistent Continuous Cross-Chain Data Federation.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28040478","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/e28040478","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1371/journal.pcbi.1014357","name":"Ten simple rules for executing an inherited research plan in computational biology.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pcbi.1014357","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1371/journal.pcbi.1014357","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-42543-7","name":"Research on key technologies for privacy-preserving, regulatorily compliant, and cross-chain interoperability in heterogeneous blockchain systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-42543-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-42543-7","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.1038/s41598-026-35208-y","name":"Blockchain-enabled identity management for IoT: a multi-layered defense against adversarial AI.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-35208-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-35208-y","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s26082452","name":"A Systematic Review of Kernel-Level Security Mechanisms, Vulnerability Detection and Mitigation in Modern Operating Systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26082452","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s26082452","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3389/fdata.2026.1829960","name":"KATENA: a verifiable governance architecture for encrypted cloud storage systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdata.2026.1829960","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3389/fdata.2026.1829960","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-41895-4","name":"Industrial internet data management framework with blockchain integration for data integrity assurance and access control resolution.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-41895-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-41895-4","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-44843-4","name":"Optimization of cross-institutional medical federated learning framework driven by confidential computing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-44843-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-44843-4","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/s25123828","name":"Software Trusted Platform Module (SWTPM) Resource Sharing Scheme for Embedded Systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25123828","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s25123828","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1371/journal.pone.0336997","name":"Efficient authentication system based on blockchain for E-government.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0336997","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1371/journal.pone.0336997","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-44040-3","name":"Ensuring the integrity of AI models: a blockchain-based approach for protecting medical imaging training data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-44040-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-44040-3","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-43856-3","name":"A collaborative multi-party encryption for mitigating man-in-the-middle attacks in smart grid and energy IoT systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-43856-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-43856-3","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-025-32745-w","name":"A scalable post quantum secure blockchain framework with adaptive time consensus in cloud environments.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-32745-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-025-32745-w","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1371/journal.pone.0333192","name":"Blockchain-based trusted traceability and sustainability certification of leather products.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0333192","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1371/journal.pone.0333192","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s25195944","name":"A Blockchain-Enabled Multi-Authority Secure IoT Data-Sharing Scheme with Attribute-Based Searchable Encryption for Intelligent Systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25195944","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s25195944","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-48185-z","name":"Decoding China's policy-driven blockchain evolution: a multi-agent collaborative analytical framework.","source":"europepmc","abstract":"Unlike market-driven blockchain evolution paths observed globally, China follows a distinctive policy-guided trajectory integrating technology research, application scenarios, and regulatory governance. However, existing studies have seldom offered a systematic analysis of the mechanisms and impacts of this policy-driven model. This paper fills this gap by analyzing how centralized policies influence the evolutionary trajectory of China’s blockchain. Specifically, we (1) construct two high-value datasets encompassing China’s blockchain key R&D programs (2021–2024) and blockchain application filing records (2019–2025); (2) design a general multi-agent collaborative analytical framework that facilitates the integration of heterogeneous data and cross-task automation; and (3) apply this framework to decode the policy-driven evolution of blockchain technologies and applications in China.","url":"https://doi.org/10.1038/s41598-026-48185-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-48185-z","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/e28050490","name":"A Post-Quantum Authentication and Key Agreement Protocol Based on Lattice-Based KEM for Secure Network Environments.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28050490","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/e28050490","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-47192-4","name":"A secure and scalable blockchain-assisted authentication framework for decentralized IoT data management.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-47192-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-47192-4","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-35284-0","name":"Scalable privacy-preserving data analytics for IoMT via FHE and zk-SNARK-enabled edge aggregation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-35284-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-35284-0","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.3390/s24248034","name":"ElasticPay: Instant Peer-to-Peer Offline Extended Digital Payment System.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s24248034","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s24248034","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1002/lrh2.70082","name":"Supporting Cultural and Learning Enablers for a Learning Health System (SCALE): A Program Theory Approach.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/lrh2.70082","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1002/lrh2.70082","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-36797-4","name":"Towards understanding the applicability of runtime moving target defense for the internet of things and cyber physical systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-36797-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-36797-4","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-43890-1","name":"A zero-knowledge enabled dynamic sharding architecture for scalable decentralized smart contract execution in IOT environments.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-43890-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-43890-1","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1016/j.dib.2026.112769","name":"Leveraging technologies for data management and sharing to foster collaboration and implement data spaces.","source":"europepmc","abstract":"The 2020 European Strategy for Data aims at developing Common European Data Spaces as a means to build a pan-European single market for data, thereby supporting economic growth and maximizing citizens' use of data. It demands data spaces in strategic sectors, with capabilities for effective data management and sharing. Although several initiatives support their adoption, data spaces are still in the early stages of development and face several data management and sharing challenges. To identify the requirements needed to address these challenges, we review the literature in developing a conceptual framework for applying data management and sharing in the context of data spaces. We then evaluate the practical implementation of the proposed solutions by analysing six representative European-funded projects. Focusing on requirements from trust and business models to interoperability, workflow orchestration, energy efficiency, and data quality, the work highlights prominent issues and explains how each project addresses them through technical means. Our evaluation outlines each project's aim and contribution, along with a representative use case from different domains, e.g., water, agriculture, and energy. We recognize widely accepted strategies such as the use of semantic standards, data catalogues, distributed ledger technologies for trust enhancement, and federated identity management. This work highlights recurring patterns, common practices, and key differences in implementation and identifies open research gaps. Thus, it aims to inform future initiatives and provide concrete recommendations to researchers and practitioners on achieving data spaces with best practices. As the field matures, the work hopes to help achieve scalable, stable, and cross-domain data spaces that support sustainable innovation and long-term collaboration throughout Europe.","url":"https://doi.org/10.1016/j.dib.2026.112769","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1016/j.dib.2026.112769","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1186/s40543-026-00547-y","name":"AI-driven adaptive adversaries and the erosion of cryptographic trust in public key systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s40543-026-00547-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1186/s40543-026-00547-y","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-43214-3","name":"CAPPR-Wallet: a context-aware and recoverable wallet architecture with privacy-preserving rules for trustless blockchain ecosystems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-43214-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-43214-3","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s25154856","name":"Improving Vehicular Network Authentication with Teegraph: A Hashgraph-Based Efficiency Approach.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25154856","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s25154856","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"epmc:MED42099940","name":"Cataract: optimising and monitoring surgical outcomes.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42099940/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z"},{"id":"doi:10.3390/s25216751","name":"Secure and Intelligent Low-Altitude Infrastructures: Synergistic Integration of IoT Networks, AI Decision-Making and Blockchain Trust Mechanisms.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25216751","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s25216751","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1186/s12939-025-02713-x","name":"No short cuts: building trust in community-based research with ethnocultural groups.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12939-025-02713-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1186/s12939-025-02713-x","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1371/journal.pone.0348572","name":"Remote medical system driven by medical big models: Dynamic defense model for network security threats.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0348572","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1371/journal.pone.0348572","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-026-41463-w","name":"A user centric group authentication scheme for secure communication.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-41463-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-41463-w","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-40356-2","name":"A cloud server centric multifactor lightweight authentication scheme for eHealth systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-40356-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-40356-2","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3389/fdgth.2026.1701551","name":"Cybersecurity breaches in medical devices: analyzing FDA safety communications in response to patient security concerns.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdgth.2026.1701551","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3389/fdgth.2026.1701551","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/bioengineering12111236","name":"Ethical AI in Healthcare: Integrating Zero-Knowledge Proofs and Smart Contracts for Transparent Data Governance.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/bioengineering12111236","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/bioengineering12111236","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1111/jar.70266","name":"From Methodological Challenges to Recommendations for Future Practice: Lessons From an Ethnographic Study With People With Intellectual Disabilities.","source":"europepmc","abstract":"","url":"https://doi.org/10.1111/jar.70266","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1111/jar.70266","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-026-48141-x","name":"SPHTRLM: secure and privacy-preserving hyperparameter-tuned reinforcement learning method for robot path finding in dynamic environments.","source":"pubmed","abstract":"Autonomous robot navigation within a dynamic environment is a complicated issue since environmental factors keep on changing, safety remains a factor, and issues of data privacy concern are also on the increase. The existing reinforcement learning (RL) navigation systems mainly focus on path performance and avoidance of collisions but do not focus on privacy protection, adaptation learning stability, and real deployment. This research aims to overcome these constraints by suggesting a novel framework Secure and Privacy-Preserving Hyperparameter-Tuned RL Model (SPHTRLM) to the efficient generation of path plans in grid ecosystems with dynamic environments. The framework incorporates adjusted Q-learning with federated learning (FL) based distributed updates, refined differentiated privacy, minimal encrypted parameter exchange, adaptive reward shaping and automatic hyperparameter optimization. In a further attempt to enhance practicability, the proposed architecture also embraces mobility conscious aggregation and heterogeneous model support of resource-limited robotic platforms. The suggested SPHTRLM has a success rate of (95% &#xb1; 2%), and it is better than the comparable one Q-learning (87% &#xb1; 4%) and Deep RL (DRL) baselines (88%) when these methods were evaluated under the same condition. The framework minimizes distances to the average path with a reduction of 20&#x2013;25% and convergence is speeded up by around 35% compared to normal Q-learning. When the obstacles are very thick then the collision rate becomes and the obstacle reduces to 0.08, and the safety of the navigation process improves. Although there are additional privatization mechanisms, the computational costs are minimal (8&#x2013;12%), and the average decision time is 110&#x2013;125 ms, which meets the real-time operational capabilities. Privacy analysis with formally stated membership inference and reconstruction attacks provide status of attack rate less than 5% attack success with both white and black box adversary. These findings underscore that SPHTRLM is a feasible way of achieving the goals of ensuring navigation, learning consistency, safety as well as privacy protection to give credible acceptance to using autonomous robotic systems in dynamic and data-sensitive environment.","url":"https://doi.org/10.1038/s41598-026-48141-x","authors":["Dewangan RR","Thombre D","Parganiha V","Verma M","Pimpalkar A","Dewangan BK","Shelke N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-026-48141-x","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.20944/preprints202506.1602.v1","name":"Energy Consumption Analysis and Optimization of Speech Algorithms for Intelligent Terminals","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202506.1602.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.20944/preprints202506.1602.v1","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1186/s12911-026-03383-7","name":"SPHN Connector - a scalable pipeline for generating validated knowledge graphs from federated and semantically enriched health data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12911-026-03383-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1186/s12911-026-03383-7","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1371/journal.pone.0351058","name":"Embodied intelligence-driven adaptive collaboration in supply chains: A four-dimensional synergy framework and mechanism analysis.","source":"europepmc","abstract":"Existing research focuses on data-driven algorithm optimization but overlooks the embodied nature of supply chains as physical and digital integrated systems, leading to a disconnect between AI and physical collaboration. This study introduces embodied intelligence into supply chain management, transcending the traditional paradigm to propose an adaptive collaboration framework through embodied perception, contextual reasoning, and physical execution. It deconstructs the core of supply chain embodied intelligence, revealing issues such as fragmented perception and delayed feedback. Based on embodied cognition and complex adaptive systems theory, a four-layer architecture with embodied perception, contextual reasoning, physical execution, and closed-loop feedback is constructed, clarifying its mechanisms. Future directions in theory, technology, and practice are outlined. This work deepens the integration of embodied intelligence with supply chains, bridges the digital and physical divide, and advances supply chain management toward an embodied adaptive paradigm for next-generation intelligent systems.","url":"https://doi.org/10.1371/journal.pone.0351058","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1371/journal.pone.0351058","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.2147/oajc.s557360","name":"Hairdressers as Mental Health Gatekeepers in Adolescent Sextual Reproductive Health Contexts in Northern Uganda.","source":"europepmc","abstract":"","url":"https://doi.org/10.2147/oajc.s557360","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.2147/oajc.s557360","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1177/00368504251386314","name":"Lightweight data protection framework for secure edge transmission using IOTA-MAM.","source":"europepmc","abstract":"","url":"https://doi.org/10.1177/00368504251386314","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1177/00368504251386314","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.3389/fgene.2026.1819270","name":"Federated, governed, and interoperable? The emerging architecture of public human genomic data infrastructures: a European perspective.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fgene.2026.1819270","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3389/fgene.2026.1819270","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frhs.2026.1809432","name":"Agile nudge implementation to improve minority recruitment in community-based research.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frhs.2026.1809432","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3389/frhs.2026.1809432","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s25165001","name":"IoT and Blockchain for Support for Smart Contracts Through TpM.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25165001","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s25165001","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s26020613","name":"An Integrated Cyber-Physical Digital Twin Architecture with Quantitative Feedback Theory Robust Control for NIS2-Aligned Industrial Robotics.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26020613","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s26020613","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s25175514","name":"EMBRAVE: EMBedded Remote Attestation and Verification framEwork.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25175514","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s25175514","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1177/23743735261461903","name":"Designing Artificial Intelligence Tools to Strengthen Human Connection in Healthcare: The CoCo Experience.","source":"europepmc","abstract":"Healthcare organizations face persistent tension between efficiency and human connection. Digital systems often fragment attention and unintentionally distance staff from patients. At Mayo Clinic, we developed a conversational artificial intelligence (AI) agent embedded in Microsoft Teams to address this challenge. Connecting and Communicating (CoCo) provides staff with communication guidance, drawing on institutional resources aligned with the Mayo Model of Communication (MMOC). Built in Microsoft Copilot Studio and shaped by clinicians and operational stakeholders, CoCo was designed to be values-aligned and usable within existing workflows. CoCo is an active, user-initiated tool used primarily before or after challenging interactions to support preparation, reflection, and communication planning. Staff valued CoCo as a \"just-in-time coach\" that reinforced empathy while reducing stress. From August 2025 through February 2026, CoCo supported 1,903 conversation sessions, with early descriptive analytics suggesting favorable satisfaction and response-quality signals. We share insights from development and deployment, along with practical recommendations, to support other organizations considering bespoke, values-aligned AI tools to enhance patient and staff experience. We distill practice-based implementation lessons around governance, human factors, and change management, and offer practical recommendations to inform similar efforts elsewhere.","url":"https://doi.org/10.1177/23743735261461903","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1177/23743735261461903","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s26103160","name":"Securing Cyber-Physical Water Infrastructures: A Hybrid Intrusion Detection System for IoT Telemetry and Industrial Protocols.","source":"europepmc","abstract":"Historically, critical water infrastructures have operated with limited digitalization, relying on legacy protocols designed without intrinsic security. The rapid integration of advanced IoT telemetry into Operational Technology (OT) networks has dissolved traditional air gaps, exposing these facilities to severe cyber-physical threats. Concurrently, regulatory frameworks such as the European NIS2 Directive and the Cyber Resilience Act (CRA) now strictly mandate robust risk monitoring for essential entities. To address these challenges, this study develops a non-intrusive, hybrid Intrusion Detection System (IDS) tailored for converged IT/OT environments. Engineered upon the Snort 3 multi-threaded engine, the architecture captures both North-South and East-West traffic. A defense-in-depth rule set was constructed using threat intelligence (MITRE ATT&CK, CISA KEV) to perform Deep Packet Inspection (DPI) across legacy industrial protocols (Modbus, S7Comm, CIP) and IoT application layers (MQTT, HTTP). Experimental validation against high-volume synthetic packet captures (exceeding 170,000 packets) replicating specific manufacturer vulnerabilities (CVEs) demonstrated an improvement in the detection rate from a 0% baseline to 100%. Crucially, the system demonstrated high scalability and minimal computational overhead, processing high-volume traffic streams with zero dropped packets. This contextualized signature approach provides the deterministic security required to ensure operational continuity and regulatory compliance in modern water infrastructures.","url":"https://doi.org/10.3390/s26103160","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s26103160","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/e28040366","name":"Implementation of a Quantum Authentication Protocol Using Single Photons in Deployed Fiber.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28040366","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/e28040366","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41467-024-53431-x","name":"MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41467-024-53431-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41467-024-53431-x","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s25216567","name":"A Secure and Lightweight ECC-Based Authentication Protocol for Wireless Medical Sensors Networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25216567","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s25216567","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s25227023","name":"End-to-End Privacy-Aware Federated Learning for Wearable Health Devices via Encrypted Aggregation in Programmable Networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25227023","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s25227023","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-34201-1","name":"Enhanced EAADE: a quantum-resilient and privacy-preserving authentication protocol for secure data exchange in vehicular social networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-34201-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-025-34201-1","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/s26072034","name":"From Sensing to Sense-Making: A Framework for On-Person Intelligence with Wearable Biosensors and Edge LLMs.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26072034","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/s26072034","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.2196/80022","name":"A Pragmatic Framework for Federated Learning Risk and Governance in Academic Medical Centers.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/80022","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.2196/80022","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/e28010033","name":"A Survey on Proof of Sequential Work: Development, Security Analysis, and Application Prospects.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28010033","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3390/e28010033","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-025-14041-9","name":"Certificateless data integrity auditing with sparse Merkle trees for the cloud-edge environment.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-14041-9","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1038/s41598-025-14041-9","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.2147/ppa.s607386","name":"\"The Ventilator Means Death\": Patient and Family Beliefs, Cost Anxiety and Trust as Drivers of Mechanical Ventilation Refusal in Somali Intensive Care.","source":"europepmc","abstract":"","url":"https://doi.org/10.2147/ppa.s607386","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.2147/ppa.s607386","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.7759/cureus.107953","name":"Restoration of Acoustic Identity via Artificial Intelligence-Driven Neural Voice Conversion for Total Laryngectomy Patients: A Technical Framework for Biometric Security and Social Inclusion.","source":"europepmc","abstract":"","url":"https://doi.org/10.7759/cureus.107953","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.7759/cureus.107953","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1177/00375497251383912","name":"On simulation reuse in healthcare applications.","source":"europepmc","abstract":"","url":"https://doi.org/10.1177/00375497251383912","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1177/00375497251383912","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1101/2025.11.12.25340070","name":"Machine learning-based prediction of future dementia using routine clinical MRI brain scans and healthcare data","source":"europepmc","abstract":"","url":"https://doi.org/10.1101/2025.11.12.25340070","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1101/2025.11.12.25340070","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1016/j.dib.2026.112678","name":"Interoperability architecture for data spaces.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.dib.2026.112678","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.1016/j.dib.2026.112678","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3389/fdgth.2026.1699125","name":"A maturity model framework for federated networks of trusted research environments.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdgth.2026.1699125","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.351Z","doi":"10.3389/fdgth.2026.1699125","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.7551/mitpress/15354.003.0006","name":"Differential Privacy Issues","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0006","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.7551/mitpress/15354.003.0006","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/9781638284772.ch2","name":"Chapter 2 Local Differential Privacy for Privacy-preserving Machine","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781638284772.ch2","authors":["Graham Cormode"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-23T08:26:52Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1561/9781638284772.ch2","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/9781638284772.ch3","name":"Chapter 3 Composition of Differential Privacy &amp; Privacy Amplification by Subsampling","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781638284772.ch3","authors":["Thomas Steinke"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-23T08:26:52Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1561/9781638284772.ch3","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.18122/b2fd7f","name":"Privacy-Preserving Trajectory Data Publishing via Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.18122/b2fd7f","authors":["Ishita Dwivedi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-04-03T19:41:09Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.18122/b2fd7f","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.18122/td/1481/boisestate","name":"Privacy-Preserving Genomic Data Publishing via Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.18122/td/1481/boisestate","authors":["Tanya Khatri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-01-22T18:59:21Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.18122/td/1481/boisestate","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/978-1-63828-477-220251005","name":"Composition of Differential Privacy &amp; Privacy Amplification by Subsampling","source":"crossref","abstract":"","url":"https://doi.org/10.1561/978-1-63828-477-220251005","authors":["Thomas Steinke"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T15:34:34Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1561/978-1-63828-477-220251005","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.32614/cran.package.fdp","name":"fdp: f-Differential Privacy and Gaussian Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.32614/cran.package.fdp","authors":["Louis Aslett"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-05T02:15:12Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.32614/cran.package.fdp","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1002/9781119229070.ch12","name":"Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781119229070.ch12","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-05-12T21:45:17Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1002/9781119229070.ch12","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.14711/thesis-b1514471","name":"Practical differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-b1514471","authors":["Georgios Kellaris"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-12-10T21:19:54Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.14711/thesis-b1514471","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.5220/0013309500003899","name":"Exploring the Accuracy and Privacy Tradeoff in AI-Driven Healthcare Through Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013309500003899","authors":["Surabhi Nayak","Sara Nayak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-25T06:22:45Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.5220/0013309500003899","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.14711/thesis-b1487523","name":"Combining differential privacy and PIR for efficient strong location privacy","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-b1487523","authors":["King Hong Fung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-10-30T23:31:46Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.14711/thesis-b1487523","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.18122/td/1604/boisestate","name":"Secure Two-Party Protocol for Privacy-Preserving Classification via Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.18122/td/1604/boisestate","authors":["Manish Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-01-08T16:29:37Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.18122/td/1604/boisestate","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.5220/0011896600003405","name":"Differential Privacy: Toward a Better Tuning of the Privacy Budget (ε) Based on Risk","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0011896600003405","authors":["Mahboobeh Dorafshanian","Mohamed Mejri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-04T05:14:25Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.5220/0011896600003405","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/9781638284772.ch16","name":"Chapter 16 Differential Privacy, Public Policy, and the Law","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781638284772.ch16","authors":["Jeremy Seeman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-23T08:26:52Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1561/9781638284772.ch16","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/9781638284772.ch17","name":"Chapter 17 Relationships between Differential Privacy and Algorithmic Fairness","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781638284772.ch17","authors":["Rachel Cummings"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-23T08:26:52Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1561/9781638284772.ch17","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.14711/thesis-hdl169531","name":"Learning with Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-hdl169531","authors":["Peng Ye"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-12T23:00:33Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.14711/thesis-hdl169531","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.32657/10356/213813","name":"Privacy-aware operations management: design and analysis of algorithms with differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/213813","authors":["Du Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-11T07:44:09Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.32657/10356/213813","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.5220/0007919404250430","name":"Differential Privacy meets Verifiable Computation: Achieving Strong Privacy and Integrity Guarantees","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0007919404250430","authors":["Georgia Tsaloli","Aikaterini Mitrokotsa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-08-14T11:09:18Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.5220/0007919404250430","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.32614/cran.package.tidydp","name":"tidydp: Tidy Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.32614/cran.package.tidydp","authors":["Thomas Tarler"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-28T02:15:12Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.32614/cran.package.tidydp","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.53106/199115992021083204019","name":"Cloud-side Collaborative Privacy Protection Based on Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.53106/199115992021083204019","authors":["Zhenjiang Zhang Zhenjiang Zhang","Xiaohua Liu Zhenjiang Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-20T06:54:59Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.53106/199115992021083204019","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.5220/0015170000004103","name":"FLiPD: Privacy-Preserving Federated Learning via Multi-Party Computation and Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0015170000004103","authors":["Gowri Chandran","Melek Önen","Thomas Schneider"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-20T08:33:23Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.5220/0015170000004103","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1007/978-3-030-71522-9_300289","name":"Distributed Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71522-9_300289","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:06:00Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1007/978-3-030-71522-9_300289","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.5220/0013141200003899","name":"Privacy- &amp; Utility-Preserving Data Releases over Fragmented Data Using Individual Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013141200003899","authors":["Luis Vasto-Terrientes","Sergio Martínez","David Sánchez"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-25T01:22:45Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.5220/0013141200003899","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/978-1-63828-477-220251022","name":"Differential Privacy, Public Policy, and the Law","source":"crossref","abstract":"","url":"https://doi.org/10.1561/978-1-63828-477-220251022","authors":["Jeremy Seeman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T15:34:34Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1561/978-1-63828-477-220251022","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1201/9781003185284-10","name":"Statistical Inference and Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-10","authors":["Jordan Awan","Ruobin Gong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1201/9781003185284-10","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.14711/thesis-hdl152359","name":"Differential Privacy for Geometric Data","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-hdl152359","authors":["Yuting Liang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-18T23:02:25Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.14711/thesis-hdl152359","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/9781638284772.ch12","name":"Chapter 12 Programming Frameworks for Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781638284772.ch12","authors":["Marco Gaboardi","Michael Hay","Salil Vadhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-23T08:26:52Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1561/9781638284772.ch12","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.14711/thesis-hdl151276","name":"Efficient Aggregation under Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-hdl151276","authors":["Juanru Fang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-29T08:01:53Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.14711/thesis-hdl151276","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.14711/thesis-991013202157903412","name":"Query evaluation under differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013202157903412","authors":["Wei Dong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-01T22:12:45Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.14711/thesis-991013202157903412","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.14711/thesis-991013080612903412","name":"Range counting under differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013080612903412","authors":["Ziyue Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-08-09T02:12:31Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.14711/thesis-991013080612903412","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/978-1-63828-477-220251023","name":"Relationships between Differential Privacy and Algorithmic Fairness","source":"crossref","abstract":"","url":"https://doi.org/10.1561/978-1-63828-477-220251023","authors":["Rachel Cummings"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T15:34:34Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1561/978-1-63828-477-220251023","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.7551/mitpress/15354.003.0009","name":"Glossary","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0009","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.7551/mitpress/15354.003.0009","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.7551/mitpress/15354.003.0008","name":"Acknowledgments","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0008","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.7551/mitpress/15354.003.0008","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.7551/mitpress/15354.003.0010","name":"Notes","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0010","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.7551/mitpress/15354.003.0010","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.33612/diss.991433515","name":"Differential Privacy and Marketing Analytics","source":"crossref","abstract":"","url":"https://doi.org/10.33612/diss.991433515","authors":["Gilian Ponte"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-14T09:12:56Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.33612/diss.991433515","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.7551/mitpress/15354.003.0011","name":"Bibliography","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0011","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.7551/mitpress/15354.003.0011","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.7551/mitpress/15354.003.0013","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0013","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.7551/mitpress/15354.003.0013","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.5220/0012322100003648","name":"Federated Learning with Differential Privacy and an Untrusted Aggregator","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012322100003648","authors":["Kunlong Liu","Trinabh Gupta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-01T13:44:53Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.5220/0012322100003648","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/9781638284772.ch10","name":"Chapter 10 Differential Privacy in Energy Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781638284772.ch10","authors":["James Anderson","Fengyu Zhou","Steven H. Low"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-23T08:26:52Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1561/9781638284772.ch10","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.70675/fd526e45z439bz439bz86c4z2c941668ff3c","name":"Exploring Verifiable and Privacy-Preserving Federated Learning through Differential Privacy and Cryptographic Protocols","source":"crossref","abstract":"Exploration de l'apprentissage fédéré vérifiable et respectueux de la vie privée grâce à la confidentialité différentielle et aux protocoles cryptographiques L’apprentissage fédéré est apparu comme un paradigme distribué permettant à plusieurs participants d’entraîner conjointement un modèle d’apprentissage automatique sans échanger leurs données brutes. En conservant les données localement, cette approche réduit considérablement les risques liés à la divulgation d’informations sensibles, risques souvent présents dans les architectures d’apprentissage centralisées. Toutefois, malgré cette promesse de confidentialité, plusieurs travaux récents ont démontré que les gradients échangés lors du processus d’apprentissage peuvent encore révéler des informations sur les jeux de données locaux. Par ailleurs, la majorité des approches actuelles reposent sur des hypothèses de confiance fortes envers le serveur central, sans offrir de mécanismes permettant de vérifier la bonne application des mesures de protection de la vie privée. Ces limites mettent en évidence un écart important entre les garanties théoriques de confidentialité et leur concrétisation dans les déploiements réels. Dans cette thèse, nous proposons de réduire cet écart en combinant la confidentialité différentielle avec des outils cryptographiques et des protocoles de vérifiabilité. L’objectif est d’assurer un apprentissage fédéré à la fois vérifiable et moins dépendant de la confiance envers les entités centrales. Dans un premier temps, nous explorons l’utilisation du chiffrement homomorphe additif afin de protéger les mises à jour locales tout en limitant la dépendance à un agrégateur de confiance. Dans un second temps, nous introduisons un protocole de vérifiabilité non interactif fondé sur les zk-SNARKs et les fonctions de hachage cryptographiques. Ce protocole permet de prouver et de vérifier la correcte application de la confidentialité différentielle sans révéler d’informations sensibles. Enfin, nous présentons ProoFed, un cadre distribué reposant sur le partage de secret pour décentraliser la génération du bruit et intégrer des preuves d’agrégation vérifiables en connaissance nulle, supprimant ainsi les points uniques de confiance.","url":"https://doi.org/10.70675/fd526e45z439bz439bz86c4z2c941668ff3c","authors":["Rezak Aziz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-25T01:39:26Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.70675/fd526e45z439bz439bz86c4z2c941668ff3c","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.5220/0010440408770884","name":"Exploring Differential Privacy in Practice","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0010440408770884","authors":["Davi Hasuda","Juliana Bezerra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-04-30T14:09:11Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.5220/0010440408770884","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.21203/rs.3.rs-4133144/v1","name":"Privacy-Preserving Distributed Estimation via Laplacian and Gaussian Differential-Privacy Noise","source":"europepmc","abstract":"Abstract In this paper, a private distributed estimation algorithm is proposed. In this algorithm, a differential-privacy noise is added to the intermediate estimation to be exchanged among nodes. Two types of differential noise is regarded in the paper which are Gaussian and Laplacian. Also, in each case, two approaches are used to recover the true intermediate estimations. In the first approach, we estimate the true intermediate estimation and in the second approach, we estimate the noise vector and then subtract it from the noise intermediate estimation. We show that both approaches lead to the same formula for denoised intermediate estimation. Simulation experiments corroborate the effectiveness of the proposed algorithm when variance of added privacy noise is high and the privacy is guaranteed with high confidence.","url":"https://doi.org/10.21203/rs.3.rs-4133144/v1","authors":["Hadi Zayyani"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.21203/rs.3.rs-4133144/v1","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1007/978-3-030-71522-9_752","name":"Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71522-9_752","authors":["Cynthia Dwork"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:06:00Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1007/978-3-030-71522-9_752","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1007/978-3-030-71522-9_300305","name":"\\protect$\\varepsilon$-Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71522-9_300305","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:06:00Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1007/978-3-030-71522-9_300305","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.7551/mitpress/15354.003.0002","name":"Series Foreword","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0002","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.7551/mitpress/15354.003.0002","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1201/9781003185284-6","name":"Review of Popular Algorithms for Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-6","authors":["Ninghui Li","Tianhao Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1201/9781003185284-6","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/9781638284772.ch9","name":"Chapter 9 Differential Privacy and Medical Data Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781638284772.ch9","authors":["Vinith M. Suriyakumar","Nicolas Papernot","Anna Goldenberg"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-23T08:26:52Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1561/9781638284772.ch9","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.7551/mitpress/15354.003.0007","name":"Future Directions","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0007","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.7551/mitpress/15354.003.0007","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/978-1-63828-477-220251017","name":"Programming Frameworks for Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1561/978-1-63828-477-220251017","authors":["Marco Gaboardi","Michael Hay","Salil Vadhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T15:34:34Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1561/978-1-63828-477-220251017","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/978-1-63828-477-220251019","name":"Challenges and Solutions to Deploying Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1561/978-1-63828-477-220251019","authors":["Damien Desfontaines","Christine Task"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T15:34:34Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1561/978-1-63828-477-220251019","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1201/9781003185284-21","name":"Differential Privacy Implementations","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-21","authors":["Matthew Graham","Andrew Foote","Lee Tucker","Hubert Janicki"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1201/9781003185284-21","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.14711/thesis-hdl152507","name":"Enhancing the Practicality of Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-hdl152507","authors":["Dajun Sun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-18T23:06:44Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.14711/thesis-hdl152507","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.7551/mitpress/15354.003.0012","name":"Further Reading","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0012","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.7551/mitpress/15354.003.0012","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.7551/mitpress/15354.003.0001","name":"[ Front Matter ]","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0001","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.7551/mitpress/15354.003.0001","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1007/978-3-031-02350-7_2","name":"A Primer on ε-Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-02350-7_2","authors":["Ninghui Li","Min Lyu","Dong Su","Weining Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-08T16:21:04Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.1007/978-3-031-02350-7_2","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.5018264","name":"Privacy-Preserving Federated Learning  With  Adaptive Local Differential Privacy Under Low Privacy Budget","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5018264","authors":["Xiaoying Shen","Jin Guo","Baocang Wang","Yange Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-12T14:37:25Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.2139/ssrn.5018264","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.17760/d20409473","name":"Differential privacy in the shuffle model","source":"crossref","abstract":"","url":"https://doi.org/10.17760/d20409473","authors":["Albert Cheu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-08-24T14:53:21Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.17760/d20409473","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.7551/mitpress/15354.001.0001","name":"Differential Privacy","source":"semanticscholar","abstract":"A robust yet accessible introduction to the idea, history, and key applications of differential privacy—the gold standard of algorithmic privacy protection.\n Differential privacy (DP) is an increasingly popular, though controversial, approach to protecting personal data. DP protects confidential data by introducing carefully calibrated random numbers, called statistical noise, when the data is used. Google, Apple, and Microsoft have all integrated the technology into their software, and the US Census Bureau used DP to protect data collected in the 2020 census. In this book, Simson Garfinkel presents the underlying ideas of DP, and helps explain why DP is needed in today's information-rich environment, why it was used as the privacy protection mechanism for the 2020 census, and why it is so controversial in some communities.\n When DP is used to protect confidential data, like an advertising profile based on the web pages you have viewed with a web browser, the noise makes it impossible for someone to take that profile and reverse engineer, with absolute certainty, the underlying confidential data on which the profile was computed. The book also chronicles the history of DP and describes the key participants and its limitations. Along the way, it also presents a short history of the US Census and other approaches for data protection such as de-identification and k-anonymity.","url":"https://www.semanticscholar.org/paper/41b1e102fae3575583e1f9b8fb63becc5577605d","authors":["S. Garfinkel","Simson L. Garfinkel"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025-03-25","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.7551/mitpress/15354.001.0001","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.21203/rs.3.rs-8662668/v1","name":"Persistence Landscapes Across Privacy Budgets for Explanation Methods Across Differential Privacy Mechanisms","source":"europepmc","abstract":"Abstract Machine-learning credit scoring must be both auditable and privacy-preserving, yet post-hoc explainers may rely on sensitive records or privileged model access that privacy constraints restrict. We study how local explanations for a feed-forward neural-network credit-risk classifier on the Home Equity Line of Credit (HELOC) dataset change when the data or learning pipeline is sanitized with differential privacy (DP) using additive noise, DP-Stochastic gradient descent (SGD), synthetic data generation, and DP-principal component analysis (PCA). While Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and gradient-based attributions are widely used, their behavior under DP-induced noise remains poorly characterized. Topology-based comparisons using Mapper and persistence summaries can capture explanation structure beyond per-feature averages, but they raise sensitivity and estimation challenges. To close this gap, we treat per-instance attribution vectors as a point cloud, build Mapper graphs using predicted probability as the lens, and convert them to persistence diagrams and persistence landscapes. We introduce a variance-reduced generalized control-variate Monte Carlo (CVMC) estimator for mean landscapes and an adaptive epsilon grid that concentrates computation where stability changes most. Across 49 explainer-mechanism combinations, mean landscapes vary smoothly with privacy budget and exhibit a small set of recurring motifs; in this setting, first-homology landscapes are consistently zero. These results suggest that privatized explanations can remain informative proxies for model behavior, enabling auditing without direct access to raw records and providing a measurable tool for monitoring privacy-interpretability trade-offs and explanation drift in regulated deployments.","url":"https://doi.org/10.21203/rs.3.rs-8662668/v1","authors":["Paul Zheng"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.21203/rs.3.rs-8662668/v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.2139/ssrn.5974535","name":"Differential Privacy for Participatory Budgeting: Protecting Voter Privacy in Uzbekistan's Open Budget System","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5974535","authors":["Olimjon Uvayzov"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-21T12:44:06Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.2139/ssrn.5974535","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.70675/a3e85b6ezd607z4616zb7f1zdd23c24f937c","name":"Differential Privacy for Decentralized Learning","source":"crossref","abstract":"Confidentialité différentielle pour l'apprentissage décentralisé L'effondrement des coûts de stockage et de traitement des données, conjugué à l'essor de la numérisation, a permis de nouvelles applications et possibilités pour l'apprentissage automatique. En pratique, les Big Data vont souvent de pair avec la collecte de données sensibles. Ainsi, la protection de la vie privée, notamment la prévention des fuites de données intentionnelles ou accidentelles, est l'un des principaux défis de l'intelligence artificielle digne de confiance. Une première approche pour une meilleure maitrise des données consiste à les conserver de manière décentralisée, en ne partageant que les informations nécessaires pour le processus d'apprentissage. Cela peut être réalisé soit via un serveur central orchestrant le processus dans l'apprentissage fédéré, soit à travers des communications pair-à-pair. Cependant, cela ne garantit pas que les données sont protégées tout au long du processus, l'apprentissage fédéré étant connu pour être vulnérable aux attaques de reconstruction, qui permettent de reconstruire partiellement ou totalement les données en exploitant le modèle, sans avoir directement accès aux données locales elles-mêmes. Pour quantifier et contrôler de manière fiable la perte de confidentialité, la confidentialité différentielle est actuellement la référence dans la recherche et l'industrie pour les applications d'apprentissage automatique.Dans cette thèse, nous nous situons à l'intersection entre l'apprentissage automatique, les algorithmes décentralisés et la confidentialité différentielle. Nous introduisons la première attaque de reconstruction en apprentissage décentralisé, prouvant la capacité d'exploiter les fuites de confidentialité entre participants non directement connectés entre eux, ce qui prouve la nécessité d'inclure des mécanismes de défense dans l'apprentissage décentralisé. Nous introduisons ensuite une nouvelle variante de la confidentialité différentielle, la Network Differential Privacy, adaptée à l'apprentissage décentralisé où chaque noeud ne voit que les communications locales. À l'aide de cette variante, nous analysons les garanties de confidentialité et d'utilité de divers algorithmes décentralisés, notamment les algorithmes de gossip et les marches aléatoires pour la descente de gradient stochastique et l'ADMM. Nos contributions démontrent que la décentralisation peut amplifier la confidentialité dans le cadre de la confidentialité différentielle, et que les gains dépendent de l'algorithme et du graphe de communication. Cela ouvre la voie à l'utilisation de la décentralisation comme outil pour développer des méthodes d'apprentissage automatique protégeant mieux la vie privée.","url":"https://doi.org/10.70675/a3e85b6ezd607z4616zb7f1zdd23c24f937c","authors":["Edwige Cyffers"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-09T00:20:46Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.70675/a3e85b6ezd607z4616zb7f1zdd23c24f937c","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.32920/31373656.v1","name":"The Question of Privacy in the Quantum Era: A Systematic Literature Review on Privacy by Design, Differential Privacy, and Privacy Engineering Frameworks Using NLP Technqiues","source":"crossref","abstract":"&lt;p dir=\"ltr\"&gt;Quantum technology is set to revolutionize the way we interact with a variety of different industries- disrupting people, processes, and other technologies at an exponential rate. As of now, the majority of our trusted encryption methods, such as Rivest-Shamir-Adleman (RSA), Elliptic Curve Digital Signature Algorithm (ECDSA), and Diffie-Hellman are not quantum-resistant. Therefore, a malicious actor in possession of a powerful enough quantum computer will be able to break encryption on files that may include sensitive data with ease in minimal time. This thesis provides insight on the current privacy landscape in quantum literature and demonstrates the existing research gap that requires further research contributions via a systematic literature review using two NLP techniques for keyword extraction. A taxonomy is produced as an outcome of a systematic review on 61 papers published between 2003 and 2023 with at least 5 citations, using two different natural language processing methods, deep learning and NVIVO.&lt;/p&gt;","url":"https://doi.org/10.32920/31373656.v1","authors":["Nour Mousa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T20:00:56Z","addedAt":"2026-08-06T22:49:41.716Z","doi":"10.32920/31373656.v1","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.70675/861a4367zdd37z4888zb382ze829f9ba293b","name":"Privacy-Preserving Multidimensional Data Analysis : Query Answering and Data Publication under Differential Privacy","source":"crossref","abstract":"Analyse de données multidimensionnelles préservant la confidentialité : réponse aux requêtes et publication de données sous garanties de la Differential Privacy Dans le monde moderne, presque chaque individu dépend et interagit quotidiennement avec de multiples services et applications numériques. Ces services collectent d'importantes quantités de données, précieuses pour l'analyse, la prise de décision et l'amélioration des systèmes. Cependant, une grande partie de ces données — telles que la localisation, l'historique de navigation ou les informations financières — est hautement sensible. Les cadres juridiques tels que le Règlement Général sur la Protection des Données (RGPD) et le California Consumer Privacy Act (CCPA), ainsi que les considérations éthiques, visent à protéger les informations personnelles. Ces réglementations limitent toutefois la manière dont les organisations peuvent exploiter les données collectées.Pour répondre à ces enjeux, cette thèse propose des solutions permettant aux organisations d'analyser et de publier des données sensibles tout en garantissant de fortes assurances de confidentialité. Elle se concentre sur l'application de la Differential Privacy (DP) aux données multidimensionnelles (ou données tabulaires agrégées), dans deux objectifs principaux : (i) permettre l'analyse OLAP (Online Analytical Processing) à l'aide de requêtes d'agrégation dans des environnements centralisés et fédérés, et (ii) générer des vues des données préservant à la fois la confidentialité et l'utilité pour leur publication sécurisée.La première partie de ce travail introduit une solution permettant aux analystes d'interroger de grandes bases de données et d'obtenir des réponses quasi en temps réel grâce à des techniques d'approximation et d'échantillonnage, tout en intégrant soigneusement la Differential Privacy afin de maintenir la précision des résultats. Cette approche est ensuite étendue à un environnement fédéré, où plusieurs organisations doivent collaborer sans partager leurs données brutes. Le protocole collaboratif proposé est léger, assure une confidentialité de bout en bout et permet une réduction significative du coût de calcul et du temps de réponse.La seconde partie de la thèse présente deux nouvelles méthodes de publication de données en respectant la confidentialité des données. La première combine l'échantillonnage et la Differential Privacy pour générer un synopsis interrogeable des données, hautement scalable et garantissant une excellente préservation de l'utilité. La seconde introduit une technique de décomposition permettant de produire des vues prêtes à être publiées, capables de surpasser les approches existantes grâce à une gestion efficace des données multidimensionnelles et une application optimisée de la Differential Privacy.","url":"https://doi.org/10.70675/861a4367zdd37z4888zb382ze829f9ba293b","authors":["Ala Eddine Laouir"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-09T12:11:16Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.70675/861a4367zdd37z4888zb382ze829f9ba293b","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.21203/rs.3.rs-579007/v1","name":"Research a New Tradeoff Method Between Privacy and Utility in Differential privacy","source":"europepmc","abstract":"Abstract The solution of the contradiction between privacy protection and data utility is a research hotspot in the field of privacy protection. Aiming at the problem of tradeoff between privacy and utility in the scenario of differential privacy offline data release, the optimal differential privacy mechanism is studied by using the rate distortion theory. Firstly, based on Shannon communication theory, the noise channel model of differential privacy is abstracted, and the mutual information and the distortion function is used to measure the privacy and utility of data publishing, and the optimization model based on rate distortion theory is constructed. Secondly, considering the influence of associated auxiliary background knowledge on mutual information privacy leakage, a mutual information privacy measure based on joint events is proposed, and a minimum privacy leakage model is proposed by modifying the rate distortion function. Finally, aiming at the difficulty in solving the Lagrange multiplier method, an approximate algorithm for solving the mutual information privacy optimization channel mechanism is proposed based on the alternating iterative method. The effectiveness of the proposed iterative approximation method is verified by experimental simulation. At the same time, the experimental results show that the proposed method reduces the mutual information privacy leakage under the condition of limited distortion, and improves the data utility under the same privacy tolerance","url":"https://doi.org/10.21203/rs.3.rs-579007/v1","authors":["panjun sun"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.21203/rs.3.rs-579007/v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1007/978-3-319-62004-6_2","name":"Preliminary of Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-62004-6_2","authors":["Tianqing Zhu","Gang Li","Wanlei Zhou","Philip S. Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-08-22T09:03:04Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1007/978-3-319-62004-6_2","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1561/978-1-63828-477-220251014","name":"Differential Privacy in Energy Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1561/978-1-63828-477-220251014","authors":["James Anderson","Fengyu Zhou","Steven H. Low"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T15:34:34Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1561/978-1-63828-477-220251014","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.5220/0012838800003767","name":"Local Differential Privacy for Data Clustering","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012838800003767","authors":["Lisa Bruder","Mina Alishahi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-12T19:48:20Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.5220/0012838800003767","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.5220/0012372700003648","name":"Differential Privacy for Distributed Traffic Monitoring in Smart Cities","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012372700003648","authors":["Marcus Gelderie","Maximilian Luff","Lukas Brodschelm"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-01T18:44:53Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.5220/0012372700003648","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/978-1-63828-477-220251013","name":"Differential Privacy and Medical Data Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1561/978-1-63828-477-220251013","authors":["Vinith M. Suriyakumar","Nicolas Papernot","Anna Goldenberg"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T15:34:34Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1561/978-1-63828-477-220251013","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1007/978-3-030-71522-9_300277","name":"Differential Privacy for Location-Based Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71522-9_300277","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:06:00Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1007/978-3-030-71522-9_300277","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.22541/au.172608470.01415060/v1","name":"Multi-tier Privacy Protection for Large Language Models using Differential Privacy","source":"europepmc","abstract":"The increasing integration of machine learning models into sensitive domains such as healthcare, finance, and government services has amplified concerns surrounding data privacy and the protection of personal information. A novel multi-tier differential privacy mechanism is proposed, offering a flexible and scalable solution to address these concerns through the dynamic adjustment of privacy settings based on data sensitivity. The approach involves systematically applying varying levels of noise during both the training and inference stages of Llama, ensuring that privacy guarantees are maintained while balancing model utility and performance. Experimental results highlight the effectiveness of this mechanism, showing that privacy can be preserved across different tiers, with stronger privacy levels associated with higher noise injection but also leading to noticeable trade-offs in terms of accuracy, latency, and computational resources. The evaluation demonstrated that moderate privacy settings enable a reasonable balance between performance and privacy protection, making the method adaptable for real-world applications in privacy-sensitive environments. The comparison with non-private models further demonstrated the computational overhead introduced through differential privacy mechanisms, while highlighting the feasibility of employing such privacypreserving techniques without significantly compromising the functionality of the model.","url":"https://doi.org/10.22541/au.172608470.01415060/v1","authors":["Dominic Novado","Eliyah Cohen","Jacob Foster"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.22541/au.172608470.01415060/v1","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.29012/jpc.662","name":"Per-instance Differential Privacy","source":"crossref","abstract":"We consider a refinement of differential privacy --- per instance differential privacy (pDP), which captures the privacy of a specific individual with respect to a fixed data set. We show that this is a strict generalization of the standard DP and inherits all its desirable properties, e.g., composition, invariance to side information and closedness to postprocessing, except that they all hold for every instance separately. We consider a refinement of differential privacy --- per instance differential privacy (pDP), which captures the privacy of a specific individual with respect to a fixed data set. We show that this is a strict generalization of the standard DP and inherits all its desirable properties, e.g., composition, invariance to side information and closedness to postprocessing, except that they all hold for every instance separately. When the data is drawn from a distribution, we show that per-instance DP implies generalization. Moreover, we provide explicit calculations of the per-instance DP for the output perturbation on a class of smooth learning problems. The result reveals an interesting and intuitive fact that an individual has stronger privacy if he/she has small ``leverage score'' with respect to the data set and if he/she can be predicted more accurately using the leave-one-out data set. Simulation shows several orders-of-magnitude more favorable privacy and utility trade-off when we consider the privacy of only the users in the data set. In a case study on differentially private linear regression, provide a novel analysis of the One-Posterior-Sample (OPS) estimator and show that when the data set is well-conditioned it provides $(\\epsilon,\\delta)$-pDP for any target individuals and matches the exact lower bound up to a $1+\\tilde{O}(n^{-1}\\epsilon^{-2})$ multiplicative factor. We also demonstrate how we can use a ``pDP to DP conversion'' step to design AdaOPS which uses adaptive regularization to achieve the same results with $(\\epsilon,\\delta)$-DP.","url":"https://doi.org/10.29012/jpc.662","authors":["Yu-Xiang Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-04-01T02:10:17Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.29012/jpc.662","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.21275/sr26219113252","name":"WebGPU Accelerated Client-Side AI for Privacy Preserving Dermatological Diagnostics: Performance Benchmarking and Local Differential Privacy Integration","source":"crossref","abstract":"","url":"https://doi.org/10.21275/sr26219113252","authors":["Arpankumar Patel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-23T11:45:47Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.21275/sr26219113252","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.29121/ijesrtp.v14.i12.2025.1","name":"DIFFERENTIAL PRIVACY AND HOMOMORPHIC ENCRYPTION–BASED PRIVACY-PRESERVING ENSEMBLE LEARNING FOR MEDICAL DIAGNOSIS","source":"crossref","abstract":"Machine learning in healthcare is increasingly being used for disease diagnosis, prognosis and patient care. But medical data are also sensitive, and the exposure of such personal health information by unauthorized access or model inference is a serious concern. To solve these problems, this paper introduces a privacy-preserving ensemble architecture called Differentially Privacy and Homomorphic Encryption Based Privacy-Preserving Ensemble Learning (DP-HE-PPEL) for medical data classification which consists of differentially private AdaBoost (DP-AB), differentially private random forest (DP-RF) and homomorphic encryption-based support vector machine (HE-SVM) classifiers. The proposed system employs several models to achieve better robustness and accuracy with the protection of formal privacy. The final decision is the result of a majority vote among all three (DP-AB, DP-RF, HE-SVM). As a mimicry of privacy-preserving inference, noisy Laplace-protected input features are used to emulate secure computation protocols. Experimental results show that the ensemble performances at optimal level and keeps good predictive accuracy with reasonable privacy, which follows at a minor cost for noisy data. In particular, our proposed system can trade off between privacy and robustness so that it is appropriate for integrating into real healthcare applications. Overall, this paper presents a holistic approach to privacy-preserving medical data classification that unifies differential privacy, simulated secure inference and ensemble learning.","url":"https://doi.org/10.29121/ijesrtp.v14.i12.2025.1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-29T12:21:06Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.29121/ijesrtp.v14.i12.2025.1","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.31390/gradschool_theses.5105","name":"Human Action Image Generation With Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.31390/gradschool_theses.5105","authors":["Qing Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-17T00:45:11Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.31390/gradschool_theses.5105","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.59350/t6p9d-y6y38","name":"Differential Privacy: A Primer","source":"crossref","abstract":"Differential Privacy (DP) is a mathematical framework that protects individual privacy in data analysis while allowing useful insights to be extracted. It works by adding carefully calibrated noise to data or query results, ensuring that including or excluding any single individual's data doesn't significantly change the analysis outcomes.","url":"https://doi.org/10.59350/t6p9d-y6y38","authors":["Abhishek Tiwari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-31T18:43:18Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.59350/t6p9d-y6y38","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.36227/techrxiv.173738347.74509154/v1","name":"Differential Privacy with Semantic Neighboring: A Notion of Privacy for Personalization Services","source":"crossref","abstract":"Many internet services are personalized to the interests of users. The personalization is usually based on the user profiles extracted from the interactions of users with internet applications. The profile, however, may naturally reveal private information about the user. Hence, the users urge the applications to protect their privacy, although they are interested in getting the personalization services as far as possible. To formulate such requirements, we propose differential privacy with semantic neighboring, which relies on a novel notion of neighboring datasets. Two datasets are basically defined to be the neighbors of one another if one is obtained by eliminating the records from the other proportional to the extent to which the records are semantically related to what the user deems private. We also develop a Laplace mechanism for enforcing the privacy policies specified in this way. The main challenge in devising this mechanism is deciding on an effective definition of the sensitivity of a given query on log datasets so that the mechanism can arrive at higher levels of utility. The results of our experimental study demonstrate that the proposed mechanism can achieve acceptable privacy parameters while retaining the usefulness of the underlying personalization service.","url":"https://doi.org/10.36227/techrxiv.173738347.74509154/v1","authors":["Ehsan Edalat","Mehran S. Fallah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T09:31:17Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.36227/techrxiv.173738347.74509154/v1","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1162/99608f92.b5d3faaa","name":"Transparent Privacy is Principled Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1162/99608f92.b5d3faaa","authors":["Ruobin Gong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-11T14:30:51Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1162/99608f92.b5d3faaa","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.7551/mitpress/15354.003.0005","name":"Concepts and Theories","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0005","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.7551/mitpress/15354.003.0005","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1007/978-3-030-96398-9_2","name":"Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96398-9_2","authors":["Balázs Pejó","Damien Desfontaines"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-04-09T12:02:46Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1007/978-3-030-96398-9_2","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.32388/v4m4ae","name":"Review of: \"SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/v4m4ae","authors":["Andrey Makrushin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-05T14:39:52Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.32388/v4m4ae","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.36227/techrxiv.172710056.64258631/v1","name":"Privacy-Preserving Smart Metering through Data Synthesis and Differential Privacy","source":"crossref","abstract":"Smart meters (SM) have generated vast amounts of detailed load data, enabling advanced load profile analyses that can significantly enhance smart grid efficiency. However, this data collection raises serious privacy concerns, as it can inadvertently expose sensitive user information. Traditional methods to address these concerns, such as data perturbation or synthetic data generation, often lack flexibility or fail to fully mitigate privacy risks. To address these challenges, we propose a Transformerbased conditional Generative Adversarial Network designed to generate differentially private (DP) synthetic SM data from the original meter data. Our approach produces high-quality DP-synthetic SM load data that inherently satisfies user-level differential privacy, ensuring secure data analysis. Extensive experiments demonstrate that our method not only serves as a robust alternative to the original dataset for real-world load forecasting but also effectively preserves user anonymity. Additionally, we demonstrate that service providers can optimize energy consumption by predicting future occupancy status while maintaining user privacy with minimal error, using previous occupancy data and DP-synthetic load data. Our experimental results show that the synthetic SM data generated by our method is approximately 35% more efficient than the synthetic data produced by the conditional Transformer-GAN proposed in prior research. Moreover, our DP-synthetic data offers around 82% greater privacy protection compared to previously established DP-only privacy-preserving techniques. Finally, our evaluation of the model’s resilience against adversarial attacks reveals a 28% reduction in load forecasting performance degradation when using our DP-synthetic data, compared to models trained on the original SM data.","url":"https://doi.org/10.36227/techrxiv.172710056.64258631/v1","authors":["Zakia Zaman","Praveen Gauravaram","Sanjay Jha","Wen Hu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-23T10:09:25Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.36227/techrxiv.172710056.64258631/v1","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.18535/ijecs/v12i04.4727","name":"Differential Privacy “Working Towards Differential Privacy for Sensitive Text “","source":"crossref","abstract":"The differential-privacy idea states that maintaining privacy often includes adding noise to a data set to make it more challenging to identify data that corresponds to specific individuals. The accuracy of data analysis is typically decreased when noise is added, and differential privacy provides a technique to evaluate the accuracy-privacy trade-off. Although it may be more difficult to discern between analyses performed on somewhat dissimilar data sets, injecting random noise can also reduce the usefulness of the analysis. If not, enough noise is supplied to a very tiny data collection, analyses could become practically useless. The trade-off between value and privacy should, however, become more manageable as the size of the data set increase. Along these lines, in this paper, the fundamental ideas of sensitivity and privacy budget in differential privacy, the noise mechanisms utilized as a part of differential privacy, the composition properties, the ways through which it can be achieved and the developments in this field to date have been presented.","url":"https://doi.org/10.18535/ijecs/v12i04.4727","authors":["Mohammad Naeem Kanyar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-08T12:08:47Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.18535/ijecs/v12i04.4727","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.32388/609sbx","name":"Review of: \"SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/609sbx","authors":["Karima Makhlouf"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-06T02:23:41Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.32388/609sbx","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.29012/jpc.v4i1.611","name":"Minimaxity, Statistical Thinking and Differential Privacy","source":"crossref","abstract":"We discuss the role of minimax statistical theory for privacy theory. Minimax theory gives a way to measure information loss for sanitized databases. We also discuss some differences between privacy theory from the statistical perspective versus the computer science perspective.","url":"https://doi.org/10.29012/jpc.v4i1.611","authors":["Larry Wasserman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-02-27T14:38:30Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.29012/jpc.v4i1.611","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.32388/msinq7","name":"Review of: \"SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/msinq7","authors":["Bhavani Malisetty"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-13T11:31:32Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.32388/msinq7","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.5220/0013188900003890","name":"Federated Learning Harnessed with Differential Privacy for Heart Disease Prediction: Enhancing Privacy and Accuracy","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013188900003890","authors":["Wided Moulahi","Tarek Moulahi","Imen Jdey","Salah Zidi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-28T12:43:20Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.5220/0013188900003890","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.32388/iacj93","name":"Review of: \"SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/iacj93","authors":["Anand Vunnam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-17T08:56:56Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.32388/iacj93","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1002/itl2.499/v1/review1","name":"Review for \"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.499/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-01T09:59:27Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1002/itl2.499/v1/review1","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.2139/ssrn.5805184","name":"Dual Privacy Protection in Financial AI: When k-Anonymity and Differential Privacy Improve Accuracy Together","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5805184","authors":["Kenzo Arai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-05T20:07:06Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.2139/ssrn.5805184","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.32388/4tidjk","name":"Review of: \"SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.32388/4tidjk","authors":["Weisan Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-09T07:28:31Z","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.32388/4tidjk","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1145/2976749.2978318","name":"Deep Learning with Differential Privacy","source":"semanticscholar","abstract":"Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information. The models should not expose private information in these datasets. Addressing this goal, we develop new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy. Our implementation and experiments demonstrate that we can train deep neural networks with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.","url":"https://www.semanticscholar.org/paper/e9a986c8ff6c2f381d026fe014f6aaa865f34da7","authors":["Martín Abadi","Andy Chu","I. Goodfellow","H. B. McMahan","Ilya Mironov","Kunal Talwar","Li Zhang"],"tags":["Conference on Computer and Communications Security"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2016-07-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1145/2976749.2978318","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1561/0400000042","name":"The Algorithmic Foundations of Differential Privacy","source":"semanticscholar","abstract":"","url":"https://www.semanticscholar.org/paper/0023582fde36430c7e3ae81611a14e558c8f4bae","authors":["C. Dwork","Aaron Roth"],"tags":["Foundations and Trends® in Theoretical Computer Science"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2014-08-11","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1561/0400000042","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1007/11787006_1","name":"Differential Privacy","source":"semanticscholar","abstract":"","url":"https://www.semanticscholar.org/paper/85983d70db7bef99103c3833793f503c18445546","authors":["C. Dwork"],"tags":["International Colloquium on Automata, Languages and Programming"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2006-07-10","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1007/11787006_1","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1007/978-3-540-79228-4_1","name":"Differential Privacy: A Survey of Results","source":"semanticscholar","abstract":"","url":"https://www.semanticscholar.org/paper/70fda5147aedd42c64143a464117b5ffde18a2e4","authors":["C. Dwork"],"tags":["Theory and Applications of Models of Computation"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2008-04-25","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1007/978-3-540-79228-4_1","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/TIFS.2020.2988575","name":"Federated Learning With Differential Privacy: Algorithms and Performance Analysis","source":"semanticscholar","abstract":"Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients’ private data from being exposed to adversaries. Nevertheless, private information can still be divulged by analyzing uploaded parameters from clients, e.g., weights trained in deep neural networks. In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noise is added to parameters at the clients’ side before aggregating, namely, noising before model aggregation FL (NbAFL). First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly adapting different variances of artificial noise. Then we develop a theoretical convergence bound on the loss function of the trained FL model in the NbAFL. Specifically, the theoretical bound reveals the following three key properties: 1) there is a tradeoff between convergence performance and privacy protection levels, i.e., better convergence performance leads to a lower protection level; 2) given a fixed privacy protection level, increasing the number <inline-formula> <tex-math notation=\"LaTeX\">$N$ </tex-math></inline-formula> of overall clients participating in FL can improve the convergence performance; and 3) there is an optimal number aggregation times (communication rounds) in terms of convergence performance for a given protection level. Furthermore, we propose a <inline-formula> <tex-math notation=\"LaTeX\">$K$ </tex-math></inline-formula>-client random scheduling strategy, where <inline-formula> <tex-math notation=\"LaTeX\">$K$ </tex-math></inline-formula> (<inline-formula> <tex-math notation=\"LaTeX\">$1\\leq K< N$ </tex-math></inline-formula>) clients are randomly selected from the <inline-formula> <tex-math notation=\"LaTeX\">$N$ </tex-math></inline-formula> overall clients to participate in each aggregation. We also develop a corresponding convergence bound for the loss function in this case and the <inline-formula> <tex-math notation=\"LaTeX\">$K$ </tex-math></inline-formula>-client random scheduling strategy also retains the above three properties. Moreover, we find that there is an optimal <inline-formula> <tex-math notation=\"LaTeX\">$K$ </tex-math></inline-formula> that achieves the best convergence performance at a fixed privacy level. Evaluations demonstrate that our theoretical results are consistent with simulations, thereby facilitating the design of various privacy-preserving FL algorithms with different tradeoff requirements on convergence performance and privacy levels.","url":"https://www.semanticscholar.org/paper/afa778ba0ba6333e25671cfb691a4bdda13b2868","authors":["Kang Wei","Jun Li","Ming Ding","Chuan Ma","Howard H. Yang","Farokhi Farhad","Shi Jin","Tony Q. S. Quek","H. Poor"],"tags":["IEEE Transactions on Information Forensics and Security"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2019-11-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TIFS.2020.2988575","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-025-95858-2","name":"Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-95858-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-025-95858-2","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1109/TIFS.2025.3528222","name":"Privacy and Fairness Analysis in the Post-Processed Differential Privacy Framework","source":"semanticscholar","abstract":"The post-processed Differential Privacy (DP) framework has been routinely adopted to preserve privacy while maintaining important invariant characteristics of datasets in data-release applications such as census data. Typical invariant characteristics include non-negative counts and total population. Subspace DP has been proposed to preserve total population while guaranteeing DP for sub-populations. Non-negativity post-processing has been identified to inherently incur fairness issues. In this work, we study privacy and unfairness (i.e., accuracy disparity) concerns in the post-processed DP framework. On one hand, we propose the post-processed DP framework with both non-negativity and accurate total population as constraints would inadvertently violate privacy guarantee desired by it. Instead, we propose the post-processed subspace DP framework to accurately define privacy guarantees against adversaries. On the other hand, we identify unfairness level is dependent on privacy budget, count sizes as well as their imbalance level via empirical analysis. Particularly concerning is severe unfairness in the setting of strict privacy budgets. We further trace unfairness back to uniform privacy budget setting over different population subgroups. To address this, we propose a varying privacy budget setting method and develop optimization approaches using ternary search and golden ratio search to identify optimal privacy budget ranges that minimize unfairness while maintaining privacy guarantees. Our extensive theoretical and empirical analysis demonstrates the effectiveness of our approaches in addressing severe unfairness issues across different privacy settings and several canonical privacy mechanisms. Using datasets of Australian Census data, Adult dataset, and delinquent children by county and household head education level, we validate both our privacy analysis framework and fairness optimization methods, showing significant reduction in accuracy disparities while maintaining strong privacy guarantees.","url":"https://www.semanticscholar.org/paper/d5807f8baba40fd243d75df40919ddd422ae51fb","authors":["Ying Zhao","Kai Zhang","Longxiang Gao","Jinjun Chen"],"tags":["IEEE Transactions on Information Forensics and Security"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TIFS.2025.3528222","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/CSF.2017.11","name":"Rényi Differential Privacy","source":"semanticscholar","abstract":"We propose a natural relaxation of differential privacy based on the Rényi divergence. Closely related notions have appeared in several recent papers that analyzed composition of differentially private mechanisms. We argue that the useful analytical tool can be used as a privacy definition, compactly and accurately representing guarantees on the tails of the privacy loss.We demonstrate that the new definition shares many important properties with the standard definition of differential privacy, while additionally allowing tighter analysis of composite heterogeneous mechanisms.","url":"https://www.semanticscholar.org/paper/d660e89055644fa122f2b4b4cdd32c85a5e33648","authors":["Ilya Mironov"],"tags":["IEEE Computer Security Foundations Symposium"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2017-02-24","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/CSF.2017.11","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/JIOT.2023.3299947","name":"Clustered Federated Learning With Adaptive Local Differential Privacy on Heterogeneous IoT Data","source":"semanticscholar","abstract":"The Internet of Things (IoT) is penetrating many aspects of our daily life with the proliferation of artificial intelligence applications. Federated learning (FL) has emerged as a promising paradigm enabling many intelligent IoT applications; however, the transmitted model gradients or weights still encode private information, which can be exploited to launch inference attacks. One popular way is to apply local differential privacy (LDP) into FL. However, existing work does not provide a practical solution due to two issues. First, the fine-grained range difference of weights in different layers of an FL model has not been explicitly considered. Second, the accumulated privacy budget may cause a budget explosion. In this article, we propose a local differentially private scheme to train clustered FL models on heterogeneous IoT data by using adaptive clipping, weight compression, and parameter shuffling (namely, ACS-FL), aimed at mitigating the curse of dimensionality, the amount of LDP noise, and the communication overhead of IoT devices. Empirical evaluations on MNIST, fashion-MNIST, and Federated Extended MNIST demonstrate that ACS-FL achieves a superior performance in balancing the tradeoff between privacy and utility.","url":"https://www.semanticscholar.org/paper/930715ec660bab5d89e924d4c28d30b0458828de","authors":["Zaobo He","Lintao Wang","Zhipeng Cai"],"tags":["IEEE Internet of Things Journal"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024-01-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/JIOT.2023.3299947","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1613/jair.1.14649","name":"How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy","source":"semanticscholar","abstract":"Machine Learning (ML) models are ubiquitous in real-world applications and are a constant focus of research. Modern ML models have become more complex, deeper, and harder to reason about. At the same time, the community has started to realize the importance of protecting the privacy of the training data that goes into these models.\nDifferential Privacy (DP) has become a gold standard for making formal statements about data anonymization. However, while some adoption of DP has happened in industry, attempts to apply DP to real world complex ML models are still few and far between. The adoption of DP is hindered by limited practical guidance of what DP protection entails, what privacy guarantees to aim for, and the difficulty of achieving good privacy-utility-computation trade-offs for ML models. Tricks for tuning and maximizing performance are scattered among papers or stored in the heads of practitioners, particularly with respect to the challenging task of hyperparameter tuning. Furthermore, the literature seems to present conflicting evidence on how and whether to apply architectural adjustments and which components are “safe” to use with DP.\nIn this survey paper, we attempt to create a self-contained guide that gives an in-depth overview of the field of DP ML. We aim to assemble information about achieving the best possible DP ML model with rigorous privacy guarantees. Our target audience is both researchers and practitioners. Researchers interested in DP for ML will benefit from a clear overview of current advances and areas for improvement. We also include theory-focused sections that highlight important topics such as privacy accounting and convergence. For a practitioner, this survey provides a background in DP theory and a clear step-by-step guide for choosing an appropriate privacy definition and approach, implementing DP training, potentially updating the model architecture, and tuning hyperparameters. For both researchers and practitioners, consistently and fully reporting privacy guarantees is critical, so we propose a set of specific best practices for stating guarantees.\nWith sufficient computation and a sufficiently large training set or supplemental nonprivate data, both good accuracy (that is, almost as good as a non-private model) and good privacy can often be achievable. And even when computation and dataset size are limited, there are advantages to training with even a weak (but still finite) formal DP guarantee. Hence, we hope this work will facilitate more widespread deployments of DP ML models.","url":"https://www.semanticscholar.org/paper/5b0f2ff37a977fd4b0c845b27726b65682bf8ac6","authors":["N. Ponomareva","Hussein Hazimeh","Alexey Kurakin","Zheng Xu","Carson E. Denison","H. B. McMahan","Sergei Vassilvitskii","Steve Chien","Abhradeep Thakurta"],"tags":["Journal of Artificial Intelligence Research"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-03-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1613/jair.1.14649","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/TAC.2024.3352328","name":"Differential Privacy in Distributed Optimization With Gradient Tracking","source":"semanticscholar","abstract":"Optimization with gradient tracking is particularly notable for its superior convergence results among the various distributed algorithms, especially in the context of directed graphs. However, privacy concerns arise when gradient information is transmitted directly which would induce more information leakage. Surprisingly, literature has not adequately addressed the associated privacy issues. In response to the gap, our article proposes a privacy-preserving distributed optimization algorithm with gradient tracking by adding noises to transmitted messages, namely, the decision variables and the estimate of the aggregated gradient. We prove two dilemmas for this kind of algorithm. In the first dilemma, we reveal that this distributed optimization algorithm with gradient tracking cannot achieve <inline-formula><tex-math notation=\"LaTeX\">$ \\epsilon$</tex-math></inline-formula>-differential privacy (DP) and exact convergence simultaneously. Building on this, we subsequently highlight that the algorithm fails to achieve <inline-formula><tex-math notation=\"LaTeX\">$ \\epsilon$</tex-math></inline-formula>-DP when employing nonsummable stepsizes in the presence of Laplace noises. It is crucial to emphasize that these findings hold true regardless of the size of the privacy metric <inline-formula><tex-math notation=\"LaTeX\">$ \\epsilon$</tex-math></inline-formula>. After that, we rigorously analyze the convergence performance and privacy level given summable stepsize sequences under the Laplace distribution since it is only with summable stepsizes that is meaningful for us to study. We derive sufficient conditions that allow for the simultaneous stochastically bounded accuracy and <inline-formula><tex-math notation=\"LaTeX\">$ \\epsilon$</tex-math></inline-formula>-DP. Recognizing that several options can meet these conditions, we further derive an upper bound of the mean error's variance and specify the mathematical expression of <inline-formula><tex-math notation=\"LaTeX\">$ \\epsilon$</tex-math></inline-formula> under such conditions. Numerical simulations are provided to demonstrate the effectiveness of our proposed algorithm.","url":"https://www.semanticscholar.org/paper/74192be168bdb2d6d0980c3e8c015879675fe23e","authors":["Lingying Huang","Junfeng Wu","Dawei Shi","S. Dey","Ling Shi"],"tags":["IEEE Transactions on Automatic Control"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024-09-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TAC.2024.3352328","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/TC.2023.3236868","name":"Tensor Recurrent Neural Network With Differential Privacy","source":"semanticscholar","abstract":"Recurrent neural network (RNN), a branch of deep learning, is a powerful model for sequential data that has outstanding performance on a wide range of important Internet of Things (IoT) tasks. This unprecedented growth of RNN model has however encountered both heterogeneous IoT data and privacy issues. Existing RNN model can not deal with heterogeneous sequential data; often the larger datasets used in training of RNN model contain sensitive information. To tackle these challenges and for the first time, this research proposes a novel differentially private tensor-based RNN (DPTRNN) that can be applied in many challenging deep learning sequence tasks for IoT systems. Specifically, to process heterogeneous sequential data, we propose a tensor-based RNN model. To guarantee privacy, we develop a tensor-based back-propagation through time algorithm with perturbation to avoid exposing the sensitive information for training the tensor-based RNN model within the framework of differential privacy. Thorough security analysis shows that the differential private tensor-based RNN efficiently protects the confidentiality of sensitive user information for IoT. Our results from extensive experiments on two challenging large video datasets suggest that our proposed scheme is practical with guarantee of data privacy preservation and acceptable accuracy loss.","url":"https://www.semanticscholar.org/paper/f0e6657b3ec7fa6e78e4a9af70eadad22144b104","authors":["Jun Feng","L. Yang","Bocheng Ren","Deqing Zou","M. Dong","Shunli Zhang"],"tags":["IEEE transactions on computers"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024-03-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TC.2023.3236868","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.48550/arXiv.2412.04697","name":"Privacy-Preserving Retrieval Augmented Generation with Differential Privacy","source":"semanticscholar","abstract":"With the recent remarkable advancement of large language models (LLMs), there has been a growing interest in utilizing them in the domains with highly sensitive data that lies outside their training data. For this purpose, retrieval-augmented generation (RAG) is particularly effective -- it assists LLMs by directly providing relevant information from the external knowledge sources. However, without extra privacy safeguards, RAG outputs risk leaking sensitive information from the external data source. In this work, we explore RAG under differential privacy (DP), a formal guarantee of data privacy. The main challenge with differentially private RAG is how to generate long accurate answers within a moderate privacy budget. We address this by proposing an algorithm that smartly spends privacy budget only for the tokens that require the sensitive information and uses the non-private LLM for other tokens. Our extensive empirical evaluations reveal that our algorithm outperforms the non-RAG baseline under a reasonable privacy budget of $\\epsilon\\approx 10$ across different models and datasets.","url":"https://www.semanticscholar.org/paper/25de0fd6ae2a87e41ede1d678ad9df7f52e022a7","authors":["Tatsuki Koga","Ruihan Wu","Kamalika Chaudhuri"],"tags":["arXiv.org"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024-12-06","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.48550/arXiv.2412.04697","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"ss:bea1187a1f8a68f1a93f0c2fa10d31f93a30f84e","name":"Opacus: User-Friendly Differential Privacy Library in PyTorch","source":"semanticscholar","abstract":"We introduce Opacus, a free, open-source PyTorch library for training deep learning models with differential privacy (hosted at opacus.ai). Opacus is designed for simplicity, flexibility, and speed. It provides a simple and user-friendly API, and enables machine learning practitioners to make a training pipeline private by adding as little as two lines to their code. It supports a wide variety of layers, including multi-head attention, convolution, LSTM, GRU (and generic RNN), and embedding, right out of the box and provides the means for supporting other user-defined layers. Opacus computes batched per-sample gradients, providing higher efficiency compared to the traditional\"micro batch\"approach. In this paper we present Opacus, detail the principles that drove its implementation and unique features, and benchmark it against other frameworks for training models with differential privacy as well as standard PyTorch.","url":"https://www.semanticscholar.org/paper/bea1187a1f8a68f1a93f0c2fa10d31f93a30f84e","authors":["Ashkan Yousefpour","I. Shilov","Alexandre Sablayrolles","Davide Testuggine","Karthik Prasad","Mani Malek","John Nguyen","Sayan Gosh","Akash Bharadwaj","Jessica Zhao","Graham Cormode","Ilya Mironov"],"tags":["arXiv.org"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-09-25","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.1145/3651153","name":"Scenario-based Adaptations of Differential Privacy: A Technical Survey","source":"semanticscholar","abstract":"Differential privacy has been a de facto privacy standard in defining privacy and handling privacy preservation. It has had great success in scenarios of local data privacy and statistical dataset privacy. As a primitive definition, standard differential privacy has been adapted to a wide range of practical scenarios. In this work, we summarize differential privacy adaptations in specific scenarios and analyze the correlations between data characteristics and differential privacy design. We mainly present them in two lines including differential privacy adaptations in local data privacy and differential privacy adaptations in statistical dataset privacy. With a focus on differential privacy design, this survey targets providing guiding rules in differential privacy design for scenarios, together with identifying potential opportunities to adaptively apply differential privacy in more emerging technologies and further improve differential privacy itself with the assistance of cryptographic primitives.","url":"https://www.semanticscholar.org/paper/9362655c79ac5a4a86ca0c0ca7d11ebf136999b4","authors":["Ying Zhao","Jia Tina Du","Jinjun Chen"],"tags":["ACM Computing Surveys"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024-03-05","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1145/3651153","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.48550/arXiv.2508.17135","name":"Rao Differential Privacy","source":"semanticscholar","abstract":"Differential privacy (DP) has recently emerged as a definition of privacy to release private estimates. DP calibrates noise to be on the order of an individuals contribution. Due to the this calibration a private estimate obscures any individual while preserving the utility of the estimate. Since the original definition, many alternate definitions have been proposed. These alternates have been proposed for various reasons including improvements on composition results, relaxations, and formalizations. Nevertheless, thus far nearly all definitions of privacy have used a divergence of densities as the basis of the definition. In this paper we take an information geometry perspective towards differential privacy. Specifically, rather than define privacy via a divergence, we define privacy via the Rao distance. We show that our proposed definition of privacy shares the interpretation of previous definitions of privacy while improving on sequential composition.","url":"https://www.semanticscholar.org/paper/5b370f807b554e6c9907f638844124e956cabb76","authors":["Carlos Soto"],"tags":["arXiv.org"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025-08-23","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.48550/arXiv.2508.17135","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1007/978-3-662-53641-4_24","name":"Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds","source":"semanticscholar","abstract":"\"Concentrated differential privacy\" was recently introduced by Dwork and Rothblum as a relaxation of differential privacy, which permits sharper analyses of many privacy-preserving computations. We present an alternative formulation of the concept of concentrated differential privacy in terms of the Renyi divergence between the distributions obtained by running an algorithm on neighboring inputs. With this reformulation in hand, we prove sharper quantitative results, establish lower bounds, and raise a few new questions. We also unify this approach with approximate differential privacy by giving an appropriate definition of \"approximate concentrated differential privacy.\"","url":"https://www.semanticscholar.org/paper/43b4aee8c254412fee7653a6d6a477e0eb8e9928","authors":["Mark Bun","T. Steinke"],"tags":["Theory of Cryptography Conference"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2016-05-06","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1007/978-3-662-53641-4_24","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/ACCESS.2022.3151670","name":"Differential Privacy for Deep and Federated Learning: A Survey","source":"semanticscholar","abstract":"Users’ privacy is vulnerable at all stages of the deep learning process. Sensitive information of users may be disclosed during data collection, during training, or even after releasing the trained learning model. Differential privacy (DP) is one of the main approaches proven to ensure strong privacy protection in data analysis. DP protects the users’ privacy by adding noise to the original dataset or the learning parameters. Thus, an attacker could not retrieve the sensitive information of an individual involved in the training dataset. In this survey paper, we analyze and present the main ideas based on DP to guarantee users’ privacy in deep and federated learning. In addition, we illustrate all types of probability distributions that satisfy the DP mechanism, with their properties and use cases. Furthermore, we bridge the gap in the literature by providing a comprehensive overview of the different variants of DP, highlighting their advantages and limitations. Our study reveals the gap between theory and application, accuracy, and robustness of DP. Finally, we provide several open problems and future research directions.","url":"https://www.semanticscholar.org/paper/8b0ed905aabc2f94e90262562faa460adce69026","authors":["Ahmed El Ouadrhiri","Ahmed M Abdelhadi"],"tags":["IEEE Access"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/ACCESS.2022.3151670","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/SP.2019.00044","name":"Certified Robustness to Adversarial Examples with Differential Privacy","source":"semanticscholar","abstract":"Adversarial examples that fool machine learning models, particularly deep neural networks, have been a topic of intense research interest, with attacks and defenses being developed in a tight back-and-forth. Most past defenses are best effort and have been shown to be vulnerable to sophisticated attacks. Recently a set of certified defenses have been introduced, which provide guarantees of robustness to norm-bounded attacks. However these defenses either do not scale to large datasets or are limited in the types of models they can support. This paper presents the first certified defense that both scales to large networks and datasets (such as Google’s Inception network for ImageNet) and applies broadly to arbitrary model types. Our defense, called PixelDP, is based on a novel connection between robustness against adversarial examples and differential privacy, a cryptographically-inspired privacy formalism, that provides a rigorous, generic, and flexible foundation for defense.","url":"https://www.semanticscholar.org/paper/3e86a51d1f2051ab8f448b66c6dcc17924d17cfa","authors":["Mathias Lécuyer","Vaggelis Atlidakis","Roxana Geambasu","Daniel J. Hsu","S. Jana"],"tags":["IEEE Symposium on Security and Privacy"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2018-02-09","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/SP.2019.00044","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1145/3490237","name":"A Survey on Differential Privacy for Unstructured Data Content","source":"semanticscholar","abstract":"Huge amounts of unstructured data including image, video, audio, and text are ubiquitously generated and shared, and it is a challenge to protect sensitive personal information in them, such as human faces, voiceprints, and authorships. Differential privacy is the standard privacy protection technology that provides rigorous privacy guarantees for various data. This survey summarizes and analyzes differential privacy solutions to protect unstructured data content before it is shared with untrusted parties. These differential privacy methods obfuscate unstructured data after they are represented with vectors and then reconstruct them with obfuscated vectors. We summarize specific privacy models and mechanisms together with possible challenges in them. We also discuss their privacy guarantees against AI attacks and utility losses. Finally, we discuss several possible directions for future research.","url":"https://www.semanticscholar.org/paper/1802d9e70a2c1e0414418790fabfadde62d85a4f","authors":["Ying Zhao","Jinjun Chen"],"tags":["ACM Computing Surveys"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-01-06","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1145/3490237","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"ss:af1841e1db6579f1f1777a59c7e9e4658d2ac466","name":"PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees","source":"semanticscholar","abstract":"","url":"https://www.semanticscholar.org/paper/af1841e1db6579f1f1777a59c7e9e4658d2ac466","authors":["James Jordon","Jinsung Yoon","M. Schaar"],"tags":["International Conference on Learning Representations"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2018-09-27","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.48550/arXiv.2210.14348","name":"Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe","source":"semanticscholar","abstract":"Privacy concerns have attracted increasing attention in data-driven products due to the tendency of machine learning models to memorize sensitive training data. Generating synthetic versions of such data with a formal privacy guarantee, such as differential privacy (DP), provides a promising path to mitigating these privacy concerns, but previous approaches in this direction have typically failed to produce synthetic data of high quality. In this work, we show that a simple and practical recipe in the text domain is effective: simply fine-tuning a pretrained generative language model with DP enables the model to generate useful synthetic text with strong privacy protection. Through extensive empirical analyses on both benchmark and private customer data, we demonstrate that our method produces synthetic text that is competitive in terms of utility with its non-private counterpart, meanwhile providing strong protection against potential privacy leakages.","url":"https://www.semanticscholar.org/paper/58996964dbcd15045b66201c2b850b5570ba74cb","authors":["Xiang Yue","Huseyin A. Inan","Xuechen Li","Girish Kumar","Julia McAnallen","Huan Sun","D. Levitán","Robert Sim"],"tags":["Annual Meeting of the Association for Computational Linguistics"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-10-25","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.48550/arXiv.2210.14348","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.52202/075280-3160","name":"Dynamic Personalized Federated Learning with Adaptive Differential Privacy","source":"semanticscholar","abstract":"Personalized federated learning with differential privacy has been considered a feasible solution to address non-IID distribution of data and privacy leakage risks. However, current personalized federated learning methods suffer from inflexible personalization and convergence difficulties due to two main factors: 1) Firstly, we observe that the prevailing personalization methods mainly achieve this by personalizing a fixed portion of the model, which lacks flexibility. 2) Moreover, we further demonstrate that the default gradient calculation is sensitive to the widely-used clipping operations in differential privacy, resulting in difficulties in convergence. Considering that Fisher information values can serve as an effective measure for estimating the information content of parameters by reflecting the model sensitivity to parameters, we aim to leverage this property to address the aforementioned challenges. In this paper, we propose a novel federated learning method with D ynamic Fisher P ersonalization and A daptive Constraint (FedDPA) to handle these challenges. Firstly, by using layer-wise Fisher information to measure the information content of local parameters, we retain local parameters with high Fisher values during the personalization process, which are considered informative, simultaneously prevent these parameters from noise perturbation. Secondly, we introduce an adaptive approach by applying differential constraint strategies to personalized parameters and shared parameters identified in the previous for better convergence. Our method boosts performance through flexible personalization while mitigating the slow convergence caused by clipping operations. Experimental results on CIFAR-10, FEMNIST and SVHN dataset demonstrate the effectiveness of our approach in achieving better performance and robustness against clipping, under personalized federated learning with differential privacy.","url":"https://www.semanticscholar.org/paper/bd2665e817ecf56f199b0825c2e54a82cb7aab24","authors":["Xiyuan Yang","Wenke Huang","Mang Ye"],"tags":["Neural Information Processing Systems"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.52202/075280-3160","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/TIFS.2023.3293417","name":"Personalized Federated Learning With Differential Privacy and Convergence Guarantee","source":"semanticscholar","abstract":"Personalized federated learning (PFL), as a novel federated learning (FL) paradigm, is capable of generating personalized models for heterogenous clients. Combined with a meta-learning mechanism, PFL can further improve the convergence performance with few-shot training. However, meta-learning based PFL has two stages of gradient descent in each local training round, therefore posing a more serious challenge in information leakage. In this paper, we propose a differential privacy (DP) based PFL (DP-PFL) framework and analyze its convergence performance. Specifically, we first design a privacy budget allocation scheme for inner and outer update stages based on the Rényi DP composition theory. Then, we develop two convergence bounds for the proposed DP-PFL framework under convex and non-convex loss function assumptions, respectively. Our developed convergence bounds reveal that 1) there is an optimal size of the DP-PFL model that can achieve the best convergence performance for a given privacy level, and 2) there is an optimal tradeoff among the number of communication rounds, convergence performance and privacy budget. Evaluations on various real-life datasets demonstrate that our theoretical results are consistent with experimental results. The derived theoretical results can guide the design of various DP-PFL algorithms with configurable tradeoff requirements on the convergence performance and privacy levels.","url":"https://www.semanticscholar.org/paper/18c81979b342593d5b6001bba51a3f0bffdfcaeb","authors":["Kang Wei","Jun Li","Chuan Ma","Ming Ding","Wen Chen","Jun Wu","M. Tao","H. Vincent Poor"],"tags":["IEEE Transactions on Information Forensics and Security"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TIFS.2023.3293417","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/TKDE.2021.3073062","name":"Applications of Differential Privacy in Social Network Analysis: A Survey","source":"semanticscholar","abstract":"Differential privacy provides strong privacy preservation guarantee in information sharing. As social network analysis has been enjoying many applications, it opens a new arena for applications of differential privacy. This article presents a comprehensive survey connecting the basic principles of differential privacy and applications in social network analysis. We concisely review the foundations of differential privacy and the major variants. Then, we discuss how differential privacy is applied to social network analysis, including privacy attacks in social networks, models of differential privacy in social network analysis, and a series of popular tasks, such as analyzing degree distribution, counting subgraphs and assigning weights to edges. We also discuss a series of challenges for future work.","url":"https://www.semanticscholar.org/paper/82daae41d2511e3eae1329cff89171ab2b9e716d","authors":["Honglu Jiang","J. Pei","Dongxiao Yu","Jiguo Yu","Bei Gong","Xiuzhen Cheng"],"tags":["IEEE Transactions on Knowledge and Data Engineering"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-01-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TKDE.2021.3073062","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.48550/arXiv.2305.18465","name":"Federated Learning of Gboard Language Models with Differential Privacy","source":"semanticscholar","abstract":"We train and deploy language models (LMs) with federated learning (FL) and differential privacy (DP) in Google Keyboard (Gboard). The recent DP-Follow the Regularized Leader (DP-FTRL) algorithm is applied to achieve meaningfully formal DP guarantees without requiring uniform sampling of clients. To provide favorable privacy-utility trade-offs, we introduce a new client participation criterion and discuss the implication of its configuration in large scale systems. We show how quantile-based clip estimation can be combined with DP-FTRL to adaptively choose the clip norm during training or reduce the hyperparameter tuning in preparation of training. With the help of pretraining on public data, we trained and deployed more than fifteen Gboard LMs that achieve high utility and $\\rho-$zCDP privacy guarantees with $\\rho \\in (0.3, 2)$, with one model additionally trained with secure aggregation.We summarize our experience and provide concrete suggestions on DP training for practitioners.","url":"https://www.semanticscholar.org/paper/1d2967d96b5e2daa172cb052b22c094beeec3068","authors":["Zheng Xu","Yanxiang Zhang","Galen Andrew","Christopher A. Choquette-Choo","P. Kairouz","H. B. McMahan","Janet Rosenstock","Yuanbo Zhang"],"tags":["Annual Meeting of the Association for Computational Linguistics"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-05-29","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.48550/arXiv.2305.18465","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.29012/jpc.870","name":"Numerical Composition of Differential Privacy","source":"semanticscholar","abstract":"We give a fast algorithm to optimally compose privacy guarantees of differentially private (DP) algorithms to arbitrary accuracy. Our method is based on the notion of \\emph{privacy loss random variables} to quantify the privacy loss of DP algorithms.The running time and memory needed for our algorithm to approximate the privacy curve of a DP algorithm composed with itself $k$ times is $\\tilde{O}(\\sqrt{k})$. This improves over the best prior method by Koskela et al. (2021) which requires $\\tilde{\\Omega}(k^{1.5})$ running time. We demonstrate the utility of our algorithm by accurately computing the privacy loss of DP-SGD algorithm of Abadi et al. (2016) and showing that our algorithm speeds up the privacy computations by a few orders of magnitude compared to prior work, while maintaining similar accuracy.","url":"https://www.semanticscholar.org/paper/08c43944c22a3e10cecd5936f04f3e07b0e636c1","authors":["Sivakanth Gopi","Y. Lee","Lukas Wutschitz","Yin Tat Lee"],"tags":["Neural Information Processing Systems"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-06-05","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.29012/jpc.870","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"ss:2a067a78c83655b2f18561508f1a5f4054a6075f","name":"Deep Learning with Label Differential Privacy","source":"semanticscholar","abstract":"The Randomized Response (RR) algorithm is a classical technique to improve robustness in survey aggregation, and has been widely adopted in applications with differential privacy guarantees. We propose a novel algorithm, Randomized Response with Prior (RRWithPrior), which can provide more accurate results while maintaining the same level of privacy guaranteed by RR. We then apply RRWithPrior to learn neural networks with label differential privacy (LabelDP), and show that when only the label needs to be protected, the model performance can be significantly improved over the previous state-of-the-art private baselines. Moreover, we study different ways to obtain priors, which when used with RRWithPrior can additionally improve the model performance, further reducing the accuracy gap between private and non-private models. We complement the empirical results with theoretical analysis showing that LabelDP is provably easier than protecting both the inputs and labels.","url":"https://www.semanticscholar.org/paper/2a067a78c83655b2f18561508f1a5f4054a6075f","authors":["Badih Ghazi","Noah Golowich","Ravi Kumar","Pasin Manurangsi","Chiyuan Zhang"],"tags":["Neural Information Processing Systems"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-02-11","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.1109/TKDE.2021.3140131","name":"A Robust Game-Theoretical Federated Learning Framework With Joint Differential Privacy","source":"semanticscholar","abstract":"Federated learning is a promising distributed machine learning paradigm that has been playing a significant role in providing privacy-preserving learning solutions. However, alongside all its achievements, there are also limitations. First, traditional frameworks assume that all the clients are voluntary and so will want to participate in training only for improving the model’s accuracy. However, in reality, clients usually want to be adequately compensated for the data and resources they will use before participating. Second, today’s frameworks do not offer sufficient protection against malicious participants who try to skew a jointly trained model with poisoned updates. To address these concerns, we have developed a more robust federated learning scheme based on joint differential privacy. The framework provides two game-theoretic mechanisms to motivate clients to participate in training. These mechanisms are dominant-strategy truthful, individual rational, and budget-balanced. Further, the influence an adversarial client can have is quantified and restricted, and data privacy is similarly guaranteed in quantitative terms. Experiments with different training models on real-word datasets demonstrate the effectiveness of the proposed approach.","url":"https://www.semanticscholar.org/paper/f09ed903de990eee52b42032f13806880110bee8","authors":["Lefeng Zhang","Tianqing Zhu","P. Xiong","Wanlei Zhou","P. Yu"],"tags":["IEEE Transactions on Knowledge and Data Engineering"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-04-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TKDE.2021.3140131","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/TII.2021.3131175","name":"Privacy-Preserving Federated Learning for Industrial Edge Computing via Hybrid Differential Privacy and Adaptive Compression","source":"semanticscholar","abstract":"With the continuous improvement of hardware computing power, edge computing of industrial data has been gradually applied. In the past decade, the promotion of edge computing has also greatly improved the efficiency of industrial production. Compared with the conventional cloud computing, it not only saves the bandwidth consumption of data transmission, but also ensures the terminal data security to a certain extent. However, the continuous update of attack types also put forward new requirements for the privacy protection of industrial edge computing. So it should fundamentally solve the risk of industrial data leakage in the process of deep model training in edge terminal. In this article, we propose a new federated edge learning framework based on hybrid differential privacy and adaptive compression for industrial data processing. Specifically, it first completes the adaptive gradient compression preparation, then constructs the industrial federated learning model, and finally makes use of adaptive differential privacy model to optimize, so as to complete the privacy protection towards the transmission of gradient parameters in industrial environment. By optimizing the hybrid differential privacy and adaptive compression, we can better prevent the terminal data privacy against inference attacks. The experimental results show that this method is very effective in the industrial edge computing situation, and it also opens up a new direction for the effect of differential privacy in federated learning.","url":"https://www.semanticscholar.org/paper/ebc9ab9cf2ce9047649b6e58612be3ae5c769486","authors":["Bin Jiang","Jianqiang Li","Huihui Wang","H. Song"],"tags":["IEEE Transactions on Industrial Informatics"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-02-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TII.2021.3131175","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/fi15090310","name":"Exploring Homomorphic Encryption and Differential Privacy Techniques towards Secure Federated Learning Paradigm","source":"semanticscholar","abstract":"The trend of the next generation of the internet has already been scrutinized by top analytics enterprises. According to Gartner investigations, it is predicted that, by 2024, 75% of the global population will have their personal data covered under privacy regulations. This alarming statistic necessitates the orchestration of several security components to address the enormous challenges posed by federated and distributed learning environments. Federated learning (FL) is a promising technique that allows multiple parties to collaboratively train a model without sharing their data. However, even though FL is seen as a privacy-preserving distributed machine learning method, recent works have demonstrated that FL is vulnerable to some privacy attacks. Homomorphic encryption (HE) and differential privacy (DP) are two promising techniques that can be used to address these privacy concerns. HE allows secure computations on encrypted data, while DP provides strong privacy guarantees by adding noise to the data. This paper first presents consistent attacks on privacy in federated learning and then provides an overview of HE and DP techniques for secure federated learning in next-generation internet applications. It discusses the strengths and weaknesses of these techniques in different settings as described in the literature, with a particular focus on the trade-off between privacy and convergence, as well as the computation overheads involved. The objective of this paper is to analyze the challenges associated with each technique and identify potential opportunities and solutions for designing a more robust, privacy-preserving federated learning framework.","url":"https://www.semanticscholar.org/paper/d71d8e6c8ba7eb4cb3658ce9468f120586434a96","authors":["Rezak Aziz","S. Banerjee","S. Bouzefrane","Thinh Le Vinh"],"tags":["Future Internet"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-09-13","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/fi15090310","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1145/2508859.2516735","name":"Geo-indistinguishability: differential privacy for location-based systems","source":"semanticscholar","abstract":"The growing popularity of location-based systems, allowing unknown/untrusted servers to easily collect huge amounts of information regarding users' location, has recently started raising serious privacy concerns. In this paper we introduce geoind, a formal notion of privacy for location-based systems that protects the user's exact location, while allowing approximate information -- typically needed to obtain a certain desired service -- to be released. This privacy definition formalizes the intuitive notion of protecting the user's location within a radius $r$ with a level of privacy that depends on r, and corresponds to a generalized version of the well-known concept of differential privacy. Furthermore, we present a mechanism for achieving geoind by adding controlled random noise to the user's location. We describe how to use our mechanism to enhance LBS applications with geo-indistinguishability guarantees without compromising the quality of the application results. Finally, we compare state-of-the-art mechanisms from the literature with ours. It turns out that, among all mechanisms independent of the prior, our mechanism offers the best privacy guarantees.","url":"https://www.semanticscholar.org/paper/9d9f60fa27131de7d7cb227441a65fad2adc4795","authors":["Miguel E. Andrés","N. E. Bordenabe","K. Chatzikokolakis","C. Palamidessi"],"tags":["Conference on Computer and Communications Security"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2012-12-09","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1145/2508859.2516735","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/TII.2021.3085960","name":"Blockchain-Enabled Federated Learning Data Protection Aggregation Scheme With Differential Privacy and Homomorphic Encryption in IIoT","source":"semanticscholar","abstract":"With rapid growth in data volume generated from different industrial devices in IoT, the protection for sensitive and private data in data sharing has become crucial. At present, federated learning for data security has arisen, and it can solve the security concerns on data sharing by model sharing on Internet of mutual distrust. However, the hackers still launch attack aiming at the security vulnerabilities (e.g., model extraction attack and model reverse attack) in federated learning. In this article, to address the above problems, we first design an application model of blockchain-enabled federated learning in Industrial Internet of Things (IIoT), and formulate our data protection aggregation scheme based on the above model. Then, we give the distributed K-means clustering based on differential privacy and homomorphic encryption, and the distributed random forest with differential privacy and the distributed AdaBoost with homomorphic encryption methods, which enable multiple data protection in data sharing and model sharing. Finally, we integrate the methods with blockchain and federated learning, and provide the complete security analysis. Extensive experimental results show that our aggregation scheme and working mechanism have the better performance in the selected indicators.","url":"https://www.semanticscholar.org/paper/50f0472497791f4a3186c07ee3d447763c2e4a20","authors":["Bin Jia","Xiaosong Zhang","Jiewen Liu","Y. Zhang","Kejia Huang","Yongquan Liang"],"tags":["IEEE Transactions on Industrial Informatics"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-06-08","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TII.2021.3085960","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.48550/arXiv.2303.00738","name":"What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy","source":"semanticscholar","abstract":"Differential privacy (DP) is a mathematical privacy notion increasingly deployed across government and industry. With DP, privacy protections are probabilistic: they are bounded by the privacy budget parameter, $\\epsilon$. Prior work in health and computational science finds that people struggle to reason about probabilistic risks. Yet, communicating the implications of $\\epsilon$ to people contributing their data is vital to avoiding privacy theater -- presenting meaningless privacy protection as meaningful -- and empowering more informed data-sharing decisions. Drawing on best practices in risk communication and usability, we develop three methods to convey probabilistic DP guarantees to end users: two that communicate odds and one offering concrete examples of DP outputs. We quantitatively evaluate these explanation methods in a vignette survey study ($n=963$) via three metrics: objective risk comprehension, subjective privacy understanding of DP guarantees, and self-efficacy. We find that odds-based explanation methods are more effective than (1) output-based methods and (2) state-of-the-art approaches that gloss over information about $\\epsilon$. Further, when offered information about $\\epsilon$, respondents are more willing to share their data than when presented with a state-of-the-art DP explanation; this willingness to share is sensitive to $\\epsilon$ values: as privacy protections weaken, respondents are less likely to share data.","url":"https://www.semanticscholar.org/paper/4f77cfb29a583418e80f719e9c3df6d9316813f2","authors":["P. Nanayakkara","Mary Anne Smart","Rachel Cummings","Gabriel Kaptchuk","Elissa M. Redmiles"],"tags":["USENIX Security Symposium"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-03-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.48550/arXiv.2303.00738","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.18653/v1/2021.findings-acl.337","name":"Differential Privacy for Text Analytics via Natural Text Sanitization","source":"semanticscholar","abstract":"Texts convey sophisticated knowledge. However, texts also convey sensitive information. Despite the success of general-purpose language models and domain-specific mechanisms with differential privacy (DP), existing text sanitization mechanisms still provide low utility, as cursed by the high-dimensional text representation. The companion issue of utilizing sanitized texts for downstream analytics is also under-explored. This paper takes a direct approach to text sanitization. Our insight is to consider both sensitivity and similarity via our new local DP notion. The sanitized texts also contribute to our sanitization-aware pretraining and fine-tuning, enabling privacy-preserving natural language processing over the BERT language model with promising utility. Surprisingly, the high utility does not boost up the success rate of inference attacks.","url":"https://www.semanticscholar.org/paper/ff9d04fc15a2c52d982b5b7daa787a373ed7f899","authors":["Xiang Yue","Minxin Du","Tianhao Wang","Yaliang Li","Huan Sun","Sherman S. M. Chow"],"tags":["Findings"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-06-02","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.18653/v1/2021.findings-acl.337","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1145/3712000","name":"Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey","source":"semanticscholar","abstract":"Differential privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees. This survey provides an overview of the state of the art of differentially private centralized deep learning, thorough analyses of recent advances and open problems, as well as a discussion of potential future developments in the field. Based on a systematic literature review, the following topics are addressed: emerging application domains, differentially private generative models, auditing and evaluation methods for private models, protection against a broad range of threats and attacks, and improvements of privacy-utility tradeoffs.","url":"https://www.semanticscholar.org/paper/315ca535fd1da9d9485a225c7465257f93a340fd","authors":["Lea Demelius","Roman Kern","Andreas Trügler"],"tags":["ACM Computing Surveys"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-09-28","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1145/3712000","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1007/s40745-023-00475-3","name":"A Survey on Differential Privacy for Medical Data Analysis","source":"europepmc","abstract":"Machine learning methods promote the sustainable development of wise information technology of medicine (WITMED), and a variety of medical data brings high value and convenience to medical analysis. However, the applications of medical data have also been confronted with the risk of privacy leakage that is hard to avoid, especially when conducting correlation analysis or data sharing among multiple institutions. Data security and privacy preservation have recently played an essential role in the field of secure and private medical data analysis, where many differential privacy strategies are applied to medical data publishing and mining. In this paper, we survey research work on the applications of differential privacy for medical data analysis, discussing the necessity of medical privacy-preserving, the advantages of differential privacy, and their applications to typical medical data, such as genomic data and wearable device data. Furthermore, we discuss the challenges and potential future research directions for differential privacy in medical applications.","url":"https://www.semanticscholar.org/paper/f01244912fa098ae0509dfcd099855d192841d62","authors":["Wei-kang Liu","Yanchun Zhang","Han Yang","Qinxue Meng"],"tags":["Annals of Data Science"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1007/s40745-023-00475-3","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"ss:fe6f8b49e73ca96f24c83cc933f3072c57a3e0c7","name":"Optimal Accounting of Differential Privacy via Characteristic Function","source":"semanticscholar","abstract":"Characterizing the privacy degradation over compositions, i.e., privacy accounting, is a fundamental topic in differential privacy (DP) with many applications to differentially private machine learning and federated learning. We propose a unification of recent advances (Renyi DP, privacy profiles, $f$-DP and the PLD formalism) via the \\emph{characteristic function} ($\\phi$-function) of a certain \\emph{dominating} privacy loss random variable. We show that our approach allows \\emph{natural} adaptive composition like Renyi DP, provides \\emph{exactly tight} privacy accounting like PLD, and can be (often \\emph{losslessly}) converted to privacy profile and $f$-DP, thus providing $(\\epsilon,\\delta)$-DP guarantees and interpretable tradeoff functions. Algorithmically, we propose an \\emph{analytical Fourier accountant} that represents the \\emph{complex} logarithm of $\\phi$-functions symbolically and uses Gaussian quadrature for numerical computation. On several popular DP mechanisms and their subsampled counterparts, we demonstrate the flexibility and tightness of our approach in theory and experiments.","url":"https://www.semanticscholar.org/paper/fe6f8b49e73ca96f24c83cc933f3072c57a3e0c7","authors":["Yuqing Zhu","Jinshuo Dong","Yu-Xiang Wang"],"tags":["International Conference on Artificial Intelligence and Statistics"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-06-16","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.1109/SP54263.2024.00108","name":"Bounded and Unbiased Composite Differential Privacy","source":"semanticscholar","abstract":"The objective of differential privacy (DP) is to protect privacy by producing an output distribution that is indistinguishable between any two neighboring databases. However, traditional differentially private mechanisms tend to produce unbounded outputs in order to achieve maximum disturbance range, which is not always in line with real-world applications. Existing solutions attempt to address this issue by employing post-processing or truncation techniques to restrict the output results, but at the cost of introducing bias issues. In this paper, we propose a novel differentially private mechanism which uses a composite probability density function to generate bounded and unbiased outputs for any numerical input data. The composition consists of an activation function and a base function, providing users with the flexibility to define the functions according to the DP constraints. We also develop an optimization algorithm that enables the iterative search for the optimal hyper-parameter setting without the need for repeated experiments, which prevents additional privacy overhead. Furthermore, we evaluate the utility of the proposed mechanism by assessing the variance of the composite probability density function and introducing two alternative metrics that are simpler to compute than variance estimation. Our extensive evaluation on three benchmark datasets demonstrates consistent and significant improvement over the traditional Laplace and Gaussian mechanisms. The proposed bounded and unbiased composite differentially private mechanism will underpin the broader DP arsenal and foster future privacy-preserving studies.","url":"https://www.semanticscholar.org/paper/f5fd82cf9bb0d6d8091fc046127342f59746b365","authors":["Kai Zhang","Yanjun Zhang","Ruoxi Sun","Pei-Wei Tsai","M. Hassan","Xingliang Yuan","Minhui Xue","Jinjun Chen","Muneeb Ul Hassan","Xin Yuan"],"tags":["IEEE Symposium on Security and Privacy"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-11-04","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/SP54263.2024.00108","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"ss:02801b64c80cd8233ffb2ac2320a7d96ab2e38e2","name":"Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment","source":"semanticscholar","abstract":"In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP's deployment in real-world applications. Key points and high-level contents of the article were originated from the discussions from\"Differential Privacy (DP): Challenges Towards the Next Frontier,\"a workshop held in July 2022 with experts from industry, academia, and the public sector seeking answers to broad questions pertaining to privacy and its implications in the design of industry-grade systems. This article aims to provide a reference point for the algorithmic and design decisions within the realm of privacy, highlighting important challenges and potential research directions. Covering a wide spectrum of topics, this article delves into the infrastructure needs for designing private systems, methods for achieving better privacy/utility trade-offs, performing privacy attacks and auditing, as well as communicating privacy with broader audiences and stakeholders.","url":"https://www.semanticscholar.org/paper/02801b64c80cd8233ffb2ac2320a7d96ab2e38e2","authors":["Rachel Cummings","Damien Desfontaines","David Evans","Roxana Geambasu","Matthew Jagielski","Yangsibo Huang","P. Kairouz","Gautam Kamath","Sewoong Oh","O. Ohrimenko","Nicolas Papernot","Ryan M. Rogers","Milan Shen","Shuang Song","Weijie Su","A. Terzis","Abhradeep Thakurta","Sergei Vassilvitskii","Yu-Xiang Wang","Li Xiong","S. Yekhanin","Da Yu","Huanyu Zhang","Wanrong Zhang"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-04-14","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.1109/JIOT.2023.3264259","name":"PPeFL: Privacy-Preserving Edge Federated Learning With Local Differential Privacy","source":"semanticscholar","abstract":"Since traditional federated learning (FL) algorithms cannot provide sufficient privacy guarantees, an increasing number of approaches apply local differential privacy (LDP) techniques to FL to provide strict privacy guarantees. However, the privacy budget heavily increases proportionally with the dimension of the parameters, and the large variance generated by the perturbation mechanisms leads to poor performance of the final model. In this article, we propose a novel privacy-preserving edge FL framework based on LDP (PPeFL). Specifically, we present three LDP mechanisms to address the privacy problems in the FL process. The proposed filtering and screening with exponential mechanism (FS-EM) filters out the better parameters for global aggregation based on the contribution of weight parameters to the neural network. Thus, we can not only solve the problem of fast growth of privacy budget when applying perturbation mechanism locally but also greatly reduce the communication costs. In addition, the proposed data perturbation mechanism with stronger privacy (DPM-SP) allows a secondary scrambling of the original data of participants and can provide strong security. Further, a data perturbation mechanism with enhanced utility (DPM-EU) is proposed in order to reduce the variance introduced by the perturbation. Finally, extensive experiments are performed to illustrate that the PPeFL scheme is practical and efficient, providing stronger privacy protection while ensuring utility.","url":"https://www.semanticscholar.org/paper/f45335dd8db90db825c61c31f4f76983ebe288a1","authors":["Baocang Wang","Yange Chen","Hang Jiang","Z. Zhao"],"tags":["IEEE Internet of Things Journal"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-09-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/JIOT.2023.3264259","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.3390/e25030485","name":"PLDP-FL: Federated Learning with Personalized Local Differential Privacy","source":"europepmc","abstract":"As a popular machine learning method, federated learning (FL) can effectively solve the issues of data silos and data privacy. However, traditional federated learning schemes cannot provide sufficient privacy protection. Furthermore, most secure federated learning schemes based on local differential privacy (LDP) ignore an important issue: they do not consider each client’s differentiated privacy requirements. This paper introduces a perturbation algorithm (PDPM) that satisfies personalized local differential privacy (PLDP), resolving the issue of inadequate or excessive privacy protection for some participants due to the same privacy budget set for all clients. The algorithm enables clients to adjust the privacy parameters according to the sensitivity of their data, thus allowing the scheme to provide personalized privacy protection. To ensure the privacy of the scheme, we have conducted a strict privacy proof and simulated the scheme on both synthetic and real data sets. Experiments have demonstrated that our scheme is successful in producing high-quality models and fulfilling the demands of personalized privacy protection.","url":"https://www.semanticscholar.org/paper/65b3789d547e0708cb8ecb415dec11d62917962a","authors":["Xiaoying Shen","Hang Jiang","Yange Chen","Baocang Wang","Lin Gao"],"tags":["Entropy"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/e25030485","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1109/ICASSP48485.2024.10447155","name":"Federated Quantum Machine Learning with Differential Privacy","source":"semanticscholar","abstract":"The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data. There are several techniques to preserve data privacy but quantum computations are inherently more secure due to the nocloning theorem, resulting in a most desirable computational platform on top of the potential quantum advantages. There have been prior works in protecting data privacy by Quantum Federated Learning (QFL) and Quantum Differential Privacy (QDP) studied independently. However, to the best of our knowledge, no prior work has addressed both QFL and QDP together yet. Here, we propose to combine these privacy-preserving methods and implement them on the quantum platform, so that we can achieve comprehensive protection against data leakage (QFL) and model inversion attacks (QDP). This implementation promises more efficient and secure artificial intelligence. In this paper, we present a successful implementation of these privacy-preservation methods by performing the binary classification of the Cats vs Dogs dataset. Using our quantum-classical machine learning model, we obtained a test accuracy of over 0.98, while maintaining epsilon values less than 1.3. We show that federated differentially private training is a viable privacy preservation method for quantum machine learning on Noisy Intermediate-Scale Quantum (NISQ) devices.","url":"https://www.semanticscholar.org/paper/353e1e7a41d8cf7b816b5d4af48db5dff6ba4028","authors":["Rod Rofougaran","Shinjae Yoo","H. Tseng","Samuel Yen-Chi Chen"],"tags":["IEEE International Conference on Acoustics, Speech, and Signal Processing"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-10-10","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/ICASSP48485.2024.10447155","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"ss:d210e55bd1afab9eba52a604565d09933dab5ad3","name":"Toward Training at ImageNet Scale with Differential Privacy","source":"semanticscholar","abstract":"Differential privacy (DP) is the de facto standard for training machine learning (ML) models, including neural networks, while ensuring the privacy of individual examples in the training set. Despite a rich literature on how to train ML models with differential privacy, it remains extremely challenging to train real-life, large neural networks with both reasonable accuracy and privacy. We set out to investigate how to do this, using ImageNet image classification as a poster example of an ML task that is very challenging to resolve accurately with DP right now. This paper shares initial lessons from our effort, in the hope that it will inspire and inform other researchers to explore DP training at scale. We show approaches that help make DP training faster, as well as model types and settings of the training process that tend to work better in the DP setting. Combined, the methods we discuss let us train a Resnet-18 with DP to $47.9\\%$ accuracy and privacy parameters $\\epsilon = 10, \\delta = 10^{-6}$. This is a significant improvement over\"naive\"DP training of ImageNet models, but a far cry from the $75\\%$ accuracy that can be obtained by the same network without privacy. The model we use was pretrained on the Places365 data set as a starting point. We share our code at https://github.com/google-research/dp-imagenet, calling for others to build upon this new baseline to further improve DP at scale.","url":"https://www.semanticscholar.org/paper/d210e55bd1afab9eba52a604565d09933dab5ad3","authors":["Alexey Kurakin","Steve Chien","Shuang Song","Roxana Geambasu","A. Terzis","Abhradeep Thakurta"],"tags":["arXiv.org"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-01-28","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.1109/TIT.2023.3272904","name":"Quantum Differential Privacy: An Information Theory Perspective","source":"semanticscholar","abstract":"Differential privacy has been an exceptionally successful concept when it comes to providing provable security guarantees for classical computations. More recently, the concept was generalized to quantum computations. While classical computations are essentially noiseless and differential privacy is often achieved by artificially adding noise, near-term quantum computers are inherently noisy and it was observed that this leads to natural differential privacy as a feature. In this work we discuss quantum differential privacy in an information theoretic framework by casting it as a quantum divergence. A main advantage of this approach is that differential privacy becomes a property solely based on the output states of the computation, without the need to check it for every measurement. This leads to simpler proofs and generalized statements of its properties as well as several new bounds for both, general and specific, noise models. In particular, these include common representations of quantum circuits and quantum machine learning concepts. Here, we focus on the difference in the amount of noise required to achieve certain levels of differential privacy versus the amount that would make any computation useless. Finally, we also generalize the classical concepts of local differential privacy, Rényi differential privacy and the hypothesis testing interpretation to the quantum setting, providing several new properties and insights.","url":"https://www.semanticscholar.org/paper/9b127cf8c7a58a8290fb08f1c1098df1972e0dab","authors":["Christoph Hirche","C. Rouzé","Daniel Stilck França"],"tags":["IEEE Transactions on Information Theory"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-02-22","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TIT.2023.3272904","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1145/3626494","name":"Between Privacy and Utility: On Differential Privacy in Theory and Practice","source":"semanticscholar","abstract":"Differential privacy (DP) aims to confer data processing systems with inherent privacy guarantees, offering strong protections for personal data. But DP’s approach to privacy carries with it certain assumptions about how mathematical abstractions will be translated into real-world systems, which—if left unexamined and unrealized in practice—could function to shield data collectors from liability and criticism, rather than substantively protect data subjects from privacy harms. This article investigates these assumptions and discusses their implications for using DP to govern data-driven systems. In Parts 1 and 2, we introduce DP as, on one hand, a mathematical framework and, on the other hand, a kind of real-world sociotechnical system, using a hypothetical case study to illustrate how the two can diverge. In Parts 3 and 4, we discuss the way DP frames privacy loss, data processing interventions, and data subject participation, arguing it could exacerbate existing problems in privacy regulation. In part 5, we conclude with a discussion of DP’s potential interactions with the endogeneity of privacy law, and we propose principles for best governing DP systems. In making such assumptions and their consequences explicit, we hope to help DP succeed at realizing its promise for better substantive privacy protections.","url":"https://www.semanticscholar.org/paper/28776c28b4335139e91d60e7669146026c780414","authors":["Jeremy Seeman","Daniel Susser"],"tags":["Social Science Research Network"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-10-06","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1145/3626494","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"ss:3b1941105317edaef6ac5995089d6d916e5fb483","name":"Differential Privacy Has Disparate Impact on Model Accuracy","source":"semanticscholar","abstract":"Differential privacy (DP) is a popular mechanism for training machine learning models with bounded leakage about the presence of specific points in the training data. The cost of differential privacy is a reduction in the model's accuracy. We demonstrate that in the neural networks trained using differentially private stochastic gradient descent (DP-SGD), this cost is not borne equally: accuracy of DP models drops much more for the underrepresented classes and subgroups. \nFor example, a gender classification model trained using DP-SGD exhibits much lower accuracy for black faces than for white faces. Critically, this gap is bigger in the DP model than in the non-DP model, i.e., if the original model is unfair, the unfairness becomes worse once DP is applied. We demonstrate this effect for a variety of tasks and models, including sentiment analysis of text and image classification. We then explain why DP training mechanisms such as gradient clipping and noise addition have disproportionate effect on the underrepresented and more complex subgroups, resulting in a disparate reduction of model accuracy.","url":"https://www.semanticscholar.org/paper/3b1941105317edaef6ac5995089d6d916e5fb483","authors":["Eugene Bagdasarian","Vitaly Shmatikov"],"tags":["Neural Information Processing Systems"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2019-05-28","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.1109/SP46215.2023.10179466","name":"Continual Observation under User-level Differential Privacy","source":"semanticscholar","abstract":"In the foundational work of Dwork et al. [15] on continual observation under differential privacy (DP), two privacy models have been proposed: event-level DP and user-level DP. The latter provides a much stronger notion of privacy, as it allows a user to contribute an arbitrary number of items. Under event-level DP, their mechanisms match the optimal utility bounds in the static setting up to polylogarithmic factors for all union-preserving functions. Unfortunately, in contrast to this strong result for event-level DP, their user-level DP mechanisms have weak utility guarantees and many restrictions on the data. In this paper, we take an instance-specific approach, designing continual observation mechanisms for a number of fundamental functions under user-level DP. Our mechanisms do not need any a priori restrictions on the data, while providing utility guarantees that degrade gracefully as the hardness of the data increases. For the count and sum function, our mechanisms are down-neighborhood optimal, matching the static setting up to polylogarithmic factors. For other functions, they do not match the static case, but we prove that this is inevitable, which is the first separation result for continual observation under differential privacy.","url":"https://www.semanticscholar.org/paper/9262de2e13ec99c0d066600f9cf22169a6e6acf5","authors":["Wei Dong","Qiyao Luo","K. Yi"],"tags":["IEEE Symposium on Security and Privacy"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-05-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/SP46215.2023.10179466","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/TMC.2023.3343288","name":"Federated Learning With Sparsified Model Perturbation: Improving Accuracy Under Client-Level Differential Privacy","source":"semanticscholar","abstract":"Federated learning (FL) that enables edge devices to collaboratively learn a shared model while keeping their training data locally has received great attention recently and can protect privacy in comparison with the traditional centralized learning paradigm. However, sensitive information about the training data can still be inferred from model parameters shared in FL. Differential privacy (DP) is the state-of-the-art technique to defend against those attacks. The key challenge to achieving DP in FL lies in the adverse impact of DP noise on model accuracy, particularly for deep learning models with large numbers of parameters. This paper develops a novel differentially-private FL scheme named Fed-SMP that provides a client-level DP guarantee while maintaining high model accuracy. To mitigate the impact of privacy protection on model accuracy, Fed-SMP leverages a new technique called Sparsified Model Perturbation (SMP) where local models are sparsified first before being perturbed by Gaussian noise. We provide a tight end-to-end privacy analysis for Fed-SMP using Rényi DP and prove the convergence of Fed-SMP with both unbiased and biased sparsifications. Extensive experiments on real-world datasets are conducted to demonstrate the effectiveness of Fed-SMP in improving model accuracy with the same DP guarantee and saving communication cost simultaneously.","url":"https://www.semanticscholar.org/paper/a53971e4758c4dfc1a066596426ecaf6a2105185","authors":["Rui Hu","Yanmin Gong","Yuanxiong Guo"],"tags":["IEEE Transactions on Mobile Computing"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-02-15","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TMC.2023.3343288","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/PST58708.2023.10320195","name":"Combining homomorphic encryption and differential privacy in federated learning","source":"semanticscholar","abstract":"Recent works have investigated the relevance and practicality of using techniques such as Differential Privacy (DP) or Homomorphic Encryption (HE) to strengthen training data privacy in the context of Federated Learning protocols. As these two techniques cover different sources of confidentiality threats (other clients/end-users for the former, aggregation server for the latter), there is a need to consistently combine them in order to bridge the gap towards more realistic deployment scenarios. In this paper, we achieve that goal by means of a novel stochastic quantization operator which allows us to establish DP guarantees when the noise is both quantized and bounded due to the use of HE. The paper is concluded by experiments on the FEMNIST dataset which show that the precision required to get state-of-the art privacy/utility trade-off (which directly impacts HE parameters and, hence, HE operations performances) results in a computation time overhead between 0.2% and 1.1% imputable to HE (depending on the key setup, either single key or threshold), for the whole training of a 500k parameters model and state-of-the-art privacy/utility trade-off.","url":"https://www.semanticscholar.org/paper/b09a857c5afb145a40e4ac05cc8db09c6672eeec","authors":["Arnaud Grivet Sébert","Marina Checri","O. Stan","Renaud Sirdey","Cédric Gouy-Pailler"],"tags":["Conference on Privacy, Security and Trust"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-08-21","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/PST58708.2023.10320195","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.52202/068431-0428","name":"Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams","source":"semanticscholar","abstract":"Motivated by recent applications requiring differential privacy over adaptive streams, we investigate the question of optimal instantiations of the matrix mechanism in this setting. We prove fundamental theoretical results on the applicability of matrix factorizations to adaptive streams, and provide a parameter-free fixed-point algorithm for computing optimal factorizations. We instantiate this framework with respect to concrete matrices which arise naturally in machine learning, and train user-level differentially private models with the resulting optimal mechanisms, yielding significant improvements in a notable problem in federated learning with user-level differential privacy.","url":"https://www.semanticscholar.org/paper/96381ee491100c823373195a17d719449eb87656","authors":["S. Denisov","H. B. McMahan","J. Rush","Adam D. Smith","Abhradeep Thakurta"],"tags":["Neural Information Processing Systems"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-02-16","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.52202/068431-0428","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.29012/jpc.784","name":"The Discrete Gaussian for Differential Privacy","source":"semanticscholar","abstract":"A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset. Unfortunately, using a continuous distribution presents several practical challenges. First and foremost, finite computers cannot exactly represent samples from continuous distributions, and previous work has demonstrated that seemingly innocuous numerical errors can entirely destroy privacy. Moreover, when the underlying data is itself discrete (e.g., population counts), adding continuous noise makes the result less interpretable. \nWith these shortcomings in mind, we introduce and analyze the discrete Gaussian in the context of differential privacy. Specifically, we theoretically and experimentally show that adding discrete Gaussian noise provides essentially the same privacy and accuracy guarantees as the addition of continuous Gaussian noise. We also present an simple and efficient algorithm for exact sampling from this distribution. This demonstrates its applicability for privately answering counting queries, or more generally, low-sensitivity integer-valued queries.","url":"https://www.semanticscholar.org/paper/110ac0767bc09584c53c41099af1cf5e402b48cb","authors":["C. Canonne","Gautam Kamath","T. Steinke","Clement Canonne","Thomas Steinke"],"tags":["Neural Information Processing Systems"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020-03-31","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.29012/jpc.784","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1038/s41598-022-05539-7","name":"Federated learning and differential privacy for medical image analysis","source":"europepmc","abstract":"The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly available multi-centric and diverse datasets mainly stems from confidentiality and privacy concerns around sharing medical data. To demonstrate a feasible path forward in medical image imaging, we conduct a case study of applying a differentially private federated learning framework for analysis of histopathology images, the largest and perhaps most complex medical images. We study the effects of IID and non-IID distributions along with the number of healthcare providers, i.e., hospitals and clinics, and the individual dataset sizes, using The Cancer Genome Atlas (TCGA) dataset, a public repository, to simulate a distributed environment. We empirically compare the performance of private, distributed training to conventional training and demonstrate that distributed training can achieve similar performance with strong privacy guarantees. We also study the effect of different source domains for histopathology images by evaluating the performance using external validation. Our work indicates that differentially private federated learning is a viable and reliable framework for the collaborative development of machine learning models in medical image analysis.","url":"https://www.semanticscholar.org/paper/2f577e03684d5149039ee73a669217e7fab4c641","authors":["Mohammed Adnan","S. Kalra","Jesse C. Cresswell","Graham W. Taylor","H. Tizhoosh"],"tags":["Scientific Reports"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021-11-09","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-022-05539-7","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1109/TIT.2023.3340147","name":"Age-Dependent Differential Privacy","source":"semanticscholar","abstract":"The proliferation of real-time applications has motivated extensive research on analyzing and optimizing data freshness in the context of age of information. However, classical frameworks of privacy (e.g., differential privacy (DP)) have overlooked the impact of data freshness on privacy guarantees, which may provide a new tool for time-varying databases. In this work, we introduce age-dependent DP, taking into account the underlying stochastic nature of a time-varying database. In this new framework, we assume knowledge of the data process’s statistical information and establish a connection between classical DP and age-dependent DP. We use this connection to characterize the impact of data staleness and temporal correlation on privacy guarantees. Our characterization reveals that the total variation distance is the sole essential statistical information. Moreover, we demonstrate that aging, which involves utilizing stale data inputs and/or delaying the release of outputs, can serve as a novel strategy for safeguarding data privacy, in addition to the traditional approach of injecting noise in the DP framework. Furthermore, to generalize our results to a multi-query scenario, we present a sequential composition result for age-dependent DP under any publishing and aging policies. We then characterize the optimal tradeoffs between privacy risk and utility and show how this can be achieved. Finally, case studies show that to achieve an arbitrarily small privacy risk in a single-query case, combing aging and noise injection only leads to a bounded accuracy loss, whereas using noise injection only (as in the benchmark case of DP) will lead to an unbounded accuracy loss.","url":"https://www.semanticscholar.org/paper/6a0e5ed341b05a518d7a3f20bf5f4bcab538b1f4","authors":["Meng Zhang","Ermin Wei","R. Berry","Jianwei Huang"],"tags":["IEEE Transactions on Information Theory"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-06-06","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TIT.2023.3340147","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1007/978-3-031-15802-5_20","name":"Securing Approximate Homomorphic Encryption Using Differential Privacy","source":"semanticscholar","abstract":"","url":"https://www.semanticscholar.org/paper/54798f9985fca2400142dc57f1c4b99b7c7f5cdf","authors":["Baiyu Li","D. Micciancio","Mark Schultz","Jessica Sorrell"],"tags":["IACR Cryptology ePrint Archive"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1007/978-3-031-15802-5_20","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"ss:6fa4086584c824a0dbd7e78e6ef9e4fe8ada0bb6","name":"Shuffled Model of Differential Privacy in Federated Learning","source":"semanticscholar","abstract":"","url":"https://www.semanticscholar.org/paper/6fa4086584c824a0dbd7e78e6ef9e4fe8ada0bb6","authors":["Antonious M. Girgis","Deepesh Data","S. Diggavi","P. Kairouz","A. Suresh"],"tags":["International Conference on Artificial Intelligence and Statistics"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.1145/3547139","name":"A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning","source":"semanticscholar","abstract":"We review the use of differential privacy (DP) for privacy protection in machine learning (ML). We show that, driven by the aim of preserving the accuracy of the learned models, DP-based ML implementations are so loose that they do not offer the ex ante privacy guarantees of DP. Instead, what they deliver is basically noise addition similar to the traditional (and often criticized) statistical disclosure control approach. Due to the lack of formal privacy guarantees, the actual level of privacy offered must be experimentally assessed ex post, which is done very seldom. In this respect, we present empirical results showing that standard anti-overfitting techniques in ML can achieve a better utility/privacy/efficiency tradeoff than DP.","url":"https://www.semanticscholar.org/paper/6afe81299194550574b2384ebded268901878235","authors":["Alberto Blanco-Justicia","David Sánchez","J. Domingo-Ferrer","K. Muralidhar"],"tags":["ACM Computing Surveys"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-06-09","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1145/3547139","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1016/j.csi.2023.103827","name":"Local differential privacy and its applications: A comprehensive survey","source":"semanticscholar","abstract":"","url":"https://www.semanticscholar.org/paper/dbe434ba5556558af185a69e814457f1c9333d9b","authors":["Mengmeng Yang","Taolin Guo","Tianqing Zhu","Ivan Tjuawinata","Jun Zhao","Kwok-Yan Lam"],"tags":["Comput. Stand. Interfaces"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-12-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1016/j.csi.2023.103827","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.48550/arXiv.2208.04591","name":"Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy","source":"semanticscholar","abstract":"The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models [EFMRTT19; CSUZZ19]. A key result in this model is that randomly shuffling locally randomized data amplifies differential privacy guarantees. Such amplification implies substantially stronger privacy guarantees for systems in which data is contributed anonymously [BEMMRLRKTS17]. In this work, we improve the state of the art privacy amplification by shuffling results both theoretically and numerically. Our first contribution is the first asymptotically optimal analysis of the R\\'enyi differential privacy parameters for the shuffled outputs of LDP randomizers. Our second contribution is a new analysis of privacy amplification by shuffling. This analysis improves on the techniques of [FMT20] and leads to tighter numerical bounds in all parameter settings.","url":"https://www.semanticscholar.org/paper/603d625925e28f7dab7a902a6ad8949ffff85568","authors":["V. Feldman","Audra McMillan","Kunal Talwar"],"tags":["ACM-SIAM Symposium on Discrete Algorithms"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-08-09","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.48550/arXiv.2208.04591","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1145/3514221.3526190","name":"LDP-IDS: Local Differential Privacy for Infinite Data Streams","source":"semanticscholar","abstract":"Local differential privacy (LDP) is promising for private streaming data collection and analysis. However, existing few LDP studies over streams either apply to finite streams only or may suffer from insufficient protection. This paper investigates this problem by proposing LDP-IDS, a novel w-event LDP paradigm to provide practical privacy guarantee for infinite streams. By constructing a unified error analysis, we adapt the existing budget division framework in centralized differential privacy (CDP) for LDP-IDS, which however incurs prohibitive noise and expensive communication cost. To this end, we propose a novel and extensible framework of population division and recycling, as well as online adaptive population division algorithms for LDP-IDS. We provide theoretical guarantees and demonstrate, through extensive discussions, that our proposed framework not only achieves significant reduction in utility loss and communication overhead, but also enjoys great compatibility for varied analytic tasks and flexibility of incorporating ideas of many existing stream algorithms. Extensive experiments on synthetic and real-world datasets validate the high effectiveness, efficiency, and flexibility of our proposed framework and methods.","url":"https://www.semanticscholar.org/paper/0d44a8dd25e6e3a90811d704bca3b28fd958e585","authors":["Xuebin Ren","Liang Shi","Weiren Yu","Shusen Yang","Cong Zhao","Zong-Liang Xu"],"tags":["SIGMOD Conference"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-04-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1145/3514221.3526190","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.18653/v1/2023.findings-acl.355","name":"A Customized Text Sanitization Mechanism with Differential Privacy","source":"semanticscholar","abstract":"As privacy issues are receiving increasing attention within the Natural Language Processing (NLP) community, numerous methods have been proposed to sanitize texts subject to differential privacy. However, the state-of-the-art text sanitization mechanisms based on metric local differential privacy (MLDP) do not apply to non-metric semantic similarity measures and cannot achieve good trade-offs between privacy and utility. To address the above limitations, we propose a novel Customized Text (CusText) sanitization mechanism based on the original $\\epsilon$-differential privacy (DP) definition, which is compatible with any similarity measure. Furthermore, CusText assigns each input token a customized output set of tokens to provide more advanced privacy protection at the token level. Extensive experiments on several benchmark datasets show that CusText achieves a better trade-off between privacy and utility than existing mechanisms. The code is available at https://github.com/sai4july/CusText.","url":"https://www.semanticscholar.org/paper/d60a7ca18d2bb37d750f4c3ec68174a51165d66c","authors":["Hui Chen","Fengran Mo","Yanhao Wang","Cen Chen","J. Nie","Chengyu Wang","Jamie Cui"],"tags":["Annual Meeting of the Association for Computational Linguistics"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-07-04","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.18653/v1/2023.findings-acl.355","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.48550/arXiv.2203.06580","name":"One Parameter Defense—Defending Against Data Inference Attacks via Differential Privacy","source":"semanticscholar","abstract":"Machine learning models are vulnerable to data inference attacks, such as membership inference and model inversion attacks. In these types of breaches, an adversary attempts to infer a data record’s membership in a dataset or even reconstruct this data record using a confidence score vector predicted by the target model. However, most existing defense methods only protect against membership inference attacks. Methods that can combat both types of attacks require a new model to be trained, which may not be time-efficient. In this paper, we propose a differentially private defense method that handles both types of attacks in a time-efficient manner by tuning only one parameter, the privacy budget. The central idea is to modify and normalize the confidence score vectors with a differential privacy mechanism which preserves privacy and obscures membership and reconstructed data. Moreover, this method can guarantee the order of scores in the vector to avoid any loss in classification accuracy. The experimental results show the method to be an effective and timely defense against both membership inference and model inversion attacks with no reduction in accuracy.","url":"https://www.semanticscholar.org/paper/f0818b8b4e136b380ad7c8bcf0890ede9956dffa","authors":["Dayong Ye","Sheng Shen","Tianqing Zhu","B. Liu","Wanlei Zhou"],"tags":["IEEE Transactions on Information Forensics and Security"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-03-13","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.48550/arXiv.2203.06580","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.6028/nist.sp.800-226.ipd","name":"Guidelines for Evaluating Differential Privacy Guarantees","source":"semanticscholar","abstract":"<jats:p />","url":"https://www.semanticscholar.org/paper/edf36da64deb6f4932ea727810a3a363b800e7f2","authors":["Naomi Lefkovitz"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.6028/nist.sp.800-226.ipd","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1145/3378679.3394533","name":"LDP-Fed: federated learning with local differential privacy","source":"semanticscholar","abstract":"This paper presents LDP-Fed, a novel federated learning system with a formal privacy guarantee using local differential privacy (LDP). Existing LDP protocols are developed primarily to ensure data privacy in the collection of single numerical or categorical values, such as click count in Web access logs. However, in federated learning model parameter updates are collected iteratively from each participant and consist of high dimensional, continuous values with high precision (10s of digits after the decimal point), making existing LDP protocols inapplicable. To address this challenge in LDP-Fed, we design and develop two novel approaches. First, LDP-Fed's LDP Module provides a formal differential privacy guarantee for the repeated collection of model training parameters in the federated training of large-scale neural networks over multiple individual participants' private datasets. Second, LDP-Fed implements a suite of selection and filtering techniques for perturbing and sharing select parameter updates with the parameter server. We validate our system deployed with a condensed LDP protocol in training deep neural networks on public data. We compare this version of LDP-Fed, coined CLDP-Fed, with other state-of-the-art approaches with respect to model accuracy, privacy preservation, and system capabilities.","url":"https://www.semanticscholar.org/paper/89cc4f8b2a64c0ab590152f23af8e08f8ad677a8","authors":["Stacey Truex","Ling Liu","Ka-Ho Chow","M. E. Gursoy","Wenqi Wei"],"tags":["EdgeSys@EuroSys"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020-04-27","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1145/3378679.3394533","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"ss:12d8a96760e1752bb7fd78f6507ec91ec7581f79","name":"Hyperparameter Tuning with Renyi Differential Privacy","source":"semanticscholar","abstract":"For many differentially private algorithms, such as the prominent noisy stochastic gradient descent (DP-SGD), the analysis needed to bound the privacy leakage of a single training run is well understood. However, few studies have reasoned about the privacy leakage resulting from the multiple training runs needed to fine tune the value of the training algorithm's hyperparameters. In this work, we first illustrate how simply setting hyperparameters based on non-private training runs can leak private information. Motivated by this observation, we then provide privacy guarantees for hyperparameter search procedures within the framework of Renyi Differential Privacy. Our results improve and extend the work of Liu and Talwar (STOC 2019). Our analysis supports our previous observation that tuning hyperparameters does indeed leak private information, but we prove that, under certain assumptions, this leakage is modest, as long as each candidate training run needed to select hyperparameters is itself differentially private.","url":"https://www.semanticscholar.org/paper/12d8a96760e1752bb7fd78f6507ec91ec7581f79","authors":["Nicolas Papernot","T. Steinke"],"tags":["International Conference on Learning Representations"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-10-07","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.1016/j.future.2021.09.015","name":"An adaptive federated learning scheme with differential privacy preserving","source":"semanticscholar","abstract":"Abstract Driven by the upcoming development of the sixth-generation communication system (6G), the distributed machine learning schemes represented by federated learning has shown advantages in data efficient utilization and multi-party cooperative modeling. The total communication cost of federated learning is related to the number of communication rounds, the communication consumption of each participant, the setting of reasonable learning rate and the guarantee of calculation fairness have important influence on the control of total cost. In addition, the simple data isolation strategy in the federated learning framework cannot completely guarantee the privacy security of users. Motivated by the above problems, this paper proposed a federated learning scheme combined with the adaptive gradient descent strategy and differential privacy mechanism, which is suitable for multi-party collaborative modeling scenarios. To ensure that federated learning scheme can train efficiently with limited communications costs, the learning rate adaptive algorithm is innovatively used to adjust the gradient descent process to avoid model overfitting and fluctuation phenomena, so as to improve the modeling efficiency and model performance in multi-party calculation scenarios. Furthermore, aiming at adapting to the ultra-large-scale distributed secure computing scenario, this research introduces a differential privacy mechanism to resist various background attacks. Experimental results demonstrate that the proposed adaptive federated learning model performs better than the traditional models at fixed communication costs. This novel modeling scheme also shows robustness to different super-parameter settings and provides stronger quantifiable privacy preserving for federated learning process.","url":"https://www.semanticscholar.org/paper/b5d8414ecb26f864c1566e69dc44f6a3b4089b03","authors":["Xiang Wu","Yongting Zhang","Minyu Shi","Peichun Li","Ruirui Li","N. Xiong"],"tags":["Future generations computer systems"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-09-15","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1016/j.future.2021.09.015","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.14736/kyb-2022-3-0426","name":"Safe consensus control of cooperative-competitive multi-agent systems via differential privacy","source":"semanticscholar","abstract":"This paper investigates a safe consensus problem for cooperative-competitive multi-agent systems using a differential privacy (DP) approach. Considering that the agents simultaneously interact cooperatively and competitively, we propose a novel DP bipartite consensus algorithm, which guarantees that the DP strategy only works on competitive pairs of agents. We then prove that the proposed algorithm can achieve the mean square bipartite consensus and ( p, r )- accuracy. Furthermore, a differential privacy analysis is conducted, which shows that the performance of privacy protection is positively correlated with the number of neighbors. Thus, a practical method is established for the agents to select their own privacy levels. Finally, the simulation results are presented to demonstrate the validity of the proposed safe consensus algorithm.","url":"https://www.semanticscholar.org/paper/d316d1f0a25e47246df03d17f6d0b6d097cc1b7c","authors":["Jiayue Ma","Jiangping Hu"],"tags":["Kybernetika (Praha)"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-09-18","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.14736/kyb-2022-3-0426","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/ICASSP39728.2021.9413764","name":"Federated Learning with Local Differential Privacy: Trade-Offs Between Privacy, Utility, and Communication","source":"semanticscholar","abstract":"Federated learning (FL) allows to train a massive amount of data privately due to its decentralized structure. Stochastic gradient descent (SGD) is commonly used for FL due to its good empirical performance, but sensitive user information can still be inferred from weight updates shared during FL iterations. We consider Gaussian mechanisms to preserve local differential privacy (LDP) of user data in the FL model with SGD. The trade-offs between user privacy, global utility, and transmission rate are proved by defining appropriate metrics for FL with LDP. Compared to existing results, the query sensitivity used in LDP is defined as a variable, and a tighter privacy accounting method is applied. The proposed utility bound allows heterogeneous parameters over all users. Our bounds characterize how much utility decreases and transmission rate increases if a stronger privacy regime is targeted. Furthermore, given a target privacy level, our results guarantee a significantly larger utility and a smaller transmission rate as compared to existing privacy accounting methods.","url":"https://www.semanticscholar.org/paper/60308c3b9d6abb6aee11377f8afa38a69f236573","authors":["Muah Kim","O. Günlü","Rafael F. Schaefer"],"tags":["IEEE International Conference on Acoustics, Speech, and Signal Processing"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-02-09","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/ICASSP39728.2021.9413764","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1016/j.future.2023.06.010","name":"Differential privacy in deep learning: Privacy and beyond","source":"semanticscholar","abstract":"","url":"https://www.semanticscholar.org/paper/baa38aebd2a1dbe46be77b7c65f29dcbdf5aa13e","authors":["Yanling Wang","Qian Wang","Lingchen Zhao","Congcong Wang"],"tags":["Future generations computer systems"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023-11-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1016/j.future.2023.06.010","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-021-93030-0","name":"Medical imaging deep learning with differential privacy","source":"europepmc","abstract":"The successful training of deep learning models for diagnostic deployment in medical imaging applications requires large volumes of data. Such data cannot be procured without consideration for patient privacy, mandated both by legal regulations and ethical requirements of the medical profession. Differential privacy (DP) enables the provision of information-theoretic privacy guarantees to patients and can be implemented in the setting of deep neural network training through the differentially private stochastic gradient descent (DP-SGD) algorithm. We here present deepee, a free-and-open-source framework for differentially private deep learning for use with the PyTorch deep learning framework. Our framework is based on parallelised execution of neural network operations to obtain and modify the per-sample gradients. The process is efficiently abstracted via a data structure maintaining shared memory references to neural network weights to maintain memory efficiency. We furthermore offer specialised data loading procedures and privacy budget accounting based on the Gaussian Differential Privacy framework, as well as automated modification of the user-supplied neural network architectures to ensure DP-conformity of its layers. We benchmark our framework’s computational performance against other open-source DP frameworks and evaluate its application on the paediatric pneumonia dataset, an image classification task and on the Medical Segmentation Decathlon Liver dataset in the task of medical image segmentation. We find that neural network training with rigorous privacy guarantees is possible while maintaining acceptable classification performance and excellent segmentation performance. Our framework compares favourably to related work with respect to memory consumption and computational performance. Our work presents an open-source software framework for differentially private deep learning, which we demonstrate in medical imaging analysis tasks. It serves to further the utilisation of privacy-enhancing techniques in medicine and beyond in order to assist researchers and practitioners in addressing the numerous outstanding challenges towards their widespread implementation.","url":"https://www.semanticscholar.org/paper/e9a6ebb6382e1a1a4ae967699d70ced030361044","authors":["A. Ziller","Dmitrii Usynin","R. Braren","M. Makowski","D. Rueckert","Georgios Kaissis"],"tags":["Scientific Reports"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-021-93030-0","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"ss:06757c9d1bf42bad757f8883605f35ad8f02c557","name":"Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy","source":"semanticscholar","abstract":"Providing privacy protection has been one of the primary motivations of Federated Learning (FL). Recently, there has been a line of work on incorporating the formal privacy notion of differential privacy with FL. To guarantee the client-level differential privacy in FL algorithms, the clients' transmitted model updates have to be clipped before adding privacy noise. Such clipping operation is substantially different from its counterpart of gradient clipping in the centralized differentially private SGD and has not been well-understood. In this paper, we first empirically demonstrate that the clipped FedAvg can perform surprisingly well even with substantial data heterogeneity when training neural networks, which is partly because the clients' updates become similar for several popular deep architectures. Based on this key observation, we provide the convergence analysis of a differential private (DP) FedAvg algorithm and highlight the relationship between clipping bias and the distribution of the clients' updates. To the best of our knowledge, this is the first work that rigorously investigates theoretical and empirical issues regarding the clipping operation in FL algorithms.","url":"https://www.semanticscholar.org/paper/06757c9d1bf42bad757f8883605f35ad8f02c557","authors":["Xinwei Zhang","Xiangyi Chen","Min-Fong Hong","Zhiwei Steven Wu","Jinfeng Yi"],"tags":["International Conference on Machine Learning"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-06-25","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.24963/ijcai.2022/766","name":"Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey","source":"semanticscholar","abstract":"This paper surveys the recent work in the intersection of differential privacy (DP) and fairness. It focuses on surveying the work observing that DP systems may exacerbate bias and disparate impacts for different groups of individuals. The survey reviews the conditions under which privacy and fairness may be aligned or contrasting goals, analyzes how and why DP exacerbates bias and unfairness in decision problems and learning tasks, and reviews the available solutions to mitigate the fairness issues arising in DP systems. The survey provides a unified understanding of the main challenges and potential risks arising when deploying privacy-preserving machine learning or decisions making tasks under a fairness lens.","url":"https://www.semanticscholar.org/paper/83c804ad94aaac38a8fcfd0782641b66d2b99025","authors":["Ferdinando Fioretto","Cuong Tran","P. V. Hentenryck","Keyu Zhu"],"tags":["International Joint Conference on Artificial Intelligence"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-02-16","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.24963/ijcai.2022/766","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/TIFS.2022.3198283","name":"Towards Private Learning on Decentralized Graphs With Local Differential Privacy","source":"semanticscholar","abstract":"Many real-world networks are inherently decentralized. For example, in social networks, each user maintains a local view of a social graph, such as a list of friends and her profile. It is typical to collect these local views of social graphs and conduct graph learning tasks. However, learning over graphs can raise privacy concerns as these local views often contain sensitive information. In this paper, we seek to ensure private graph learning on a decentralized network graph. Towards this objective, we propose Solitude, a new privacy-preserving learning framework based on graph neural networks (GNNs), with formal privacy guarantees based on edge local differential privacy. The crux of Solitude is a set of new delicate mechanisms that can calibrate the introduced noise in the decentralized graph collected from the users. The principle behind the calibration is the intrinsic properties shared by many real-world graphs, such as sparsity. Unlike existing work on locally private GNNs, our new framework can simultaneously protect node feature privacy and edge privacy, and can seamlessly incorporate with any GNN with privacy-utility guarantees. Extensive experiments on benchmarking datasets show that Solitude can retain the generalization capability of the learned GNN while preserving the users’ data privacy under given privacy budgets.","url":"https://www.semanticscholar.org/paper/85ffd9e18baaa4339f8f90e9956ba522251e29c6","authors":["Wanyu Lin","Baochun Li","Cong Wang"],"tags":["IEEE Transactions on Information Forensics and Security"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-01-23","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TIFS.2022.3198283","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.48550/arXiv.2210.00597","name":"Composition of Differential Privacy & Privacy Amplification by Subsampling","source":"semanticscholar","abstract":"This chapter is meant to be part of the book “Differential Privacy for Artificial Intelligence Applications.” We give an introduction to the most important property of differential privacy – composition: running multiple independent analyses on the data of a set of people will still be differentially private as long as each of the analyses is private on its own – as well as the related topic of privacy amplification by subsampling. This chapter introduces the basic concepts and gives proofs of the key results needed to apply these tools in practice. ∗Google Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . steinke@google.com 1 ar X iv :2 21 0. 00 59 7v 2 [ cs .C R ] 4 O ct 2 02 2","url":"https://www.semanticscholar.org/paper/fa61bfc90e60f5bf05f9c9374279898808de348a","authors":["T. Steinke"],"tags":["arXiv.org"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-10-02","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.48550/arXiv.2210.00597","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/JIOT.2021.3057419","name":"Differential Privacy for Industrial Internet of Things: Opportunities, Applications, and Challenges","source":"semanticscholar","abstract":"The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Especially, some common algorithms in IIoT technology, such as deep models, strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this article, we conduct a comprehensive survey on the opportunities, applications, and challenges of differential privacy in IIoT. We first review related papers on IIoT and privacy protection, respectively. Then, we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up research on industrial differential privacy.","url":"https://www.semanticscholar.org/paper/a5c15750a42213e95c79cacbbf81cbd6202ff321","authors":["Bin Jiang","Jianqiang Li","Guanghui Yue","H. Song"],"tags":["IEEE Internet of Things Journal"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-01-26","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/JIOT.2021.3057419","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/JIOT.2022.3189361","name":"Performance-Enhanced Federated Learning With Differential Privacy for Internet of Things","source":"semanticscholar","abstract":"Federated learning (FL), which enables multiple distributed devices (clients) to collaboratively train a global model without transmitting their private data, has attracted much attention in the Internet of Things (IoT) domain. Compared with centralized learning, FL has obvious privacy advantages because it can protect the clients’ raw data from direct access by adversaries. Furthermore, to prevent the adversaries from inferring private information from the transmitted parameters, several FL algorithms based on differential privacy (DP) have been proposed, where the clients add artificial noise to their local parameters for privacy protection. Nevertheless, the added noise would disrupt the learning process and degrade the performance of the trained model. Considering this, in this article, we develop a performance-enhanced DP-based FL (PEDPFL) algorithm, where a classifier-perturbation regularization method is proposed to improve the robustness of the trained model against DP-injected noise. We derive the theoretical privacy and convergence analysis of the proposed algorithm, and also demonstrate the influence of some hyperparameters on the convergence performance. Simulation results on real-world data sets show that the proposed algorithm has better classification performance than the existing DP-based FL algorithms at the same level of privacy protection, and thus, it is more applicable to IoT applications.","url":"https://www.semanticscholar.org/paper/b787743f9c1f87e30482566d74bbcad81de10f05","authors":["Xicong Shen","Ying Liu","Zhaoyang Zhang"],"tags":["IEEE Internet of Things Journal"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-12-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/JIOT.2022.3189361","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/ICDE.2019.00063","name":"Collecting and Analyzing Multidimensional Data with Local Differential Privacy","source":"semanticscholar","abstract":"Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP, each user perturbs her information locally, and only sends the randomized version to an aggregator who performs analyses, which protects both the users and the aggregator against private information leaks. Although LDP has attracted much research attention in recent years, the majority of existing work focuses on applying LDP to complex data and/or analysis tasks. In this paper, we point out that the fundamental problem of collecting multidimensional data under LDP has not been addressed sufficiently, and there remains much room for improvement even for basic tasks such as computing the mean value over a single numeric attribute under LDP. Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance. Then, we extend these mechanisms to multidimensional data that can contain both numeric and categorical attributes, where our mechanisms always outperform existing solutions regarding worst-case noise variance. As a case study, we apply our solutions to build an LDP-compliant stochastic gradient descent algorithm (SGD), which powers many important machine learning tasks. Experiments using real datasets confirm the effectiveness of our methods, and their advantages over existing solutions.","url":"https://www.semanticscholar.org/paper/2fbb4ba85869a333637f2b5d762ad69fb58bc7af","authors":["Ning Wang","Xiaokui Xiao","Y. Yang","Jun Zhao","S. Hui","Hyejin Shin","Junbum Shin","Ge Yu"],"tags":["IEEE International Conference on Data Engineering"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2019-04-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/ICDE.2019.00063","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"ss:44443d3cb8f73d7db0c8719fd7fd24e05b17939c","name":"Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent","source":"semanticscholar","abstract":"What is the information leakage of an iterative randomized learning algorithm about its training data, when the internal state of the algorithm is \\emph{private}? How much is the contribution of each specific training epoch to the information leakage through the released model? We study this problem for noisy gradient descent algorithms, and model the \\emph{dynamics} of R\\'enyi differential privacy loss throughout the training process. Our analysis traces a provably \\emph{tight} bound on the R\\'enyi divergence between the pair of probability distributions over parameters of models trained on neighboring datasets. We prove that the privacy loss converges exponentially fast, for smooth and strongly convex loss functions, which is a significant improvement over composition theorems (which over-estimate the privacy loss by upper-bounding its total value over all intermediate gradient computations). For Lipschitz, smooth, and strongly convex loss functions, we prove optimal utility with a small gradient complexity for noisy gradient descent algorithms.","url":"https://www.semanticscholar.org/paper/44443d3cb8f73d7db0c8719fd7fd24e05b17939c","authors":["Rishav Chourasia","Jiayuan Ye","R. Shokri"],"tags":["Neural Information Processing Systems"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-02-11","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.1145/3460120.3485252","name":"\"I need a better description\": An Investigation Into User Expectations For Differential Privacy","source":"semanticscholar","abstract":"Despite recent widespread deployment of differential privacy, relatively little is known about what users think of differential privacy. In this work, we seek to explore users' privacy expectations related to differential privacy. Specifically, we investigate (1) whether users care about the protections afforded by differential privacy, and (2) whether they are therefore more willing to share their data with differentially private systems. Further, we attempt to understand (3) users' privacy expectations of the differentially private systems they may encounter in practice and (4) their willingness to share data in such systems. To answer these questions, we use a series of rigorously conducted surveys (n=2424). We find that users care about the kinds of information leaks against which differential privacy protects and are more willing to share their private information when the risks of these leaks are less likely to happen. Additionally, we find that the ways in which differential privacy is described in-the-wild haphazardly set users' privacy expectations, which can be misleading depending on the deployment. We synthesize our results into a framework for understanding a user's willingness to share information with differentially private systems, which takes into account the interaction between the user's prior privacy concerns and how differential privacy is described.","url":"https://www.semanticscholar.org/paper/c867597b93f31bde98fdcca5f57772bc933a2af8","authors":["Rachel Cummings","Gabriel Kaptchuk","Elissa M. Redmiles"],"tags":["Conference on Computer and Communications Security"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-10-13","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1145/3460120.3485252","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"ss:cf1be616f9ae10cd5c36f60d3da659fdbc7af4ad","name":"Rényi Differential Privacy of the Sampled Gaussian Mechanism","source":"semanticscholar","abstract":"The Sampled Gaussian Mechanism (SGM)---a composition of subsampling and the additive Gaussian noise---has been successfully used in a number of machine learning applications. The mechanism's unexpected power is derived from privacy amplification by sampling where the privacy cost of a single evaluation diminishes quadratically, rather than linearly, with the sampling rate. Characterizing the precise privacy properties of SGM motivated development of several relaxations of the notion of differential privacy. \nThis work unifies and fills in gaps in published results on SGM. We describe a numerically stable procedure for precise computation of SGM's Renyi Differential Privacy and prove a nearly tight (within a small constant factor) closed-form bound.","url":"https://www.semanticscholar.org/paper/cf1be616f9ae10cd5c36f60d3da659fdbc7af4ad","authors":["Ilya Mironov","Kunal Talwar","Li Zhang"],"tags":["arXiv.org"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2019-08-28","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.1109/JIOT.2020.3037194","name":"Local Differential Privacy-Based Federated Learning for Internet of Things","source":"semanticscholar","abstract":"The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications, such as Waze, Uber, and Amazon Mechanical Turk, etc. Users of these applications report the real-time traffic information to the cloud server which trains a machine learning model based on traffic information reported by users for intelligent traffic management. However, crowdsourcing application owners can easily infer users’ location information, traffic information, motor vehicle information, environmental information, etc., which raises severe sensitive personal information privacy concerns of the users. In addition, as the number of vehicles increases, the frequent communication between vehicles and the cloud server incurs unexpected amount of communication cost. To avoid the privacy threat and reduce the communication cost, in this article, we propose to integrate federated learning and local differential privacy (LDP) to facilitate the crowdsourcing applications to achieve the machine learning model. Specifically, we propose four LDP mechanisms to perturb gradients generated by vehicles. The proposed Three-Outputs mechanism introduces three different output possibilities to deliver a high accuracy when the privacy budget is small. The output possibilities of Three-Outputs can be encoded with two bits to reduce the communication cost. Besides, to maximize the performance when the privacy budget is large, an optimal piecewise mechanism (PM-OPT) is proposed. We further propose a suboptimal mechanism (PM-SUB) with a simple formula and comparable utility to PM-OPT. Then, we build a novel hybrid mechanism by combining Three-Outputs and PM-SUB. Finally, an LDP-FedSGD algorithm is proposed to coordinate the cloud server and vehicles to train the model collaboratively. Extensive experimental results on real-world data sets validate that our proposed algorithms are capable of protecting privacy while guaranteeing utility.","url":"https://www.semanticscholar.org/paper/530c2a3deaa386d090b6e40424e5459d233d12a5","authors":["Yang Zhao","Jun Zhao","Mengmeng Yang","Teng Wang","Ning Wang","Lingjuan Lyu","D. Niyato","Kwok-Yan Lam"],"tags":["IEEE Internet of Things Journal"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020-04-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/JIOT.2020.3037194","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"ss:2c2dd4254f2cfdd5a553587b8fa9008552956278","name":"Debugging Differential Privacy: A Case Study for Privacy Auditing","source":"semanticscholar","abstract":"Differential Privacy can provide provable privacy guarantees for training data in machine learning. However, the presence of proofs does not preclude the presence of errors. Inspired by recent advances in auditing which have been used for estimating lower bounds on differentially private algorithms, here we show that auditing can also be used to find flaws in (purportedly) differentially private schemes. In this case study, we audit a recent open source implementation of a differentially private deep learning algorithm and find, with 99.99999999% confidence, that the implementation does not satisfy the claimed differential privacy guarantee.","url":"https://www.semanticscholar.org/paper/2c2dd4254f2cfdd5a553587b8fa9008552956278","authors":["Florian Tramèr","A. Terzis","T. Steinke","Shuang Song","Matthew Jagielski","Nicholas Carlini"],"tags":["arXiv.org"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-02-24","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.1109/CVPR52688.2022.00819","name":"Mixed Differential Privacy in Computer Vision","source":"semanticscholar","abstract":"We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. While pre-training language models on large public datasets has enabled strong differential privacy (DP) guarantees with minor loss of accuracy, a similar practice yields punishing trade-offs in vision tasks. A few-shot or even zero-shot learning baseline that ignores private data can outperform fine-tuning on a large private dataset. AdaMix incorporates few-shot training, or cross-modal zero-shot learning, on public data prior to private fine-tuning, to improve the trade-off. AdaMix reduces the error increase from the non-private upper bound from the 167–311% of the baseline, on average across 6 datasets, to 68-92% depending on the desired privacy level selected by the user. AdaMix tackles the trade-off arising in visual classification, whereby the most privacy sensitive data, corresponding to isolated points in representation space, are also critical for high classification accuracy. In addition, AdaMix comes with strong theoretical privacy guarantees and convergence analysis.","url":"https://www.semanticscholar.org/paper/0c3a18ec9165932dc585e5682323853f80875fec","authors":["Aditya Golatkar","A. Achille","Yu-Xiang Wang","Aaron Roth","Michael Kearns","Stefano Soatto"],"tags":["Computer Vision and Pattern Recognition"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-03-22","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/CVPR52688.2022.00819","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.48550/arXiv.2203.05481","name":"Fully Adaptive Composition in Differential Privacy","source":"semanticscholar","abstract":"Composition is a key feature of differential privacy. Well-known advanced composition theorems allow one to query a private database quadratically more times than basic privacy composition would permit. However, these results require that the privacy parameters of all algorithms be fixed before interacting with the data. To address this, Rogers et al. introduced fully adaptive composition, wherein both algorithms and their privacy parameters can be selected adaptively. They defined two probabilistic objects to measure privacy in adaptive composition: privacy filters, which provide differential privacy guarantees for composed interactions, and privacy odometers, time-uniform bounds on privacy loss. There are substantial gaps between advanced composition and existing filters and odometers. First, existing filters place stronger assumptions on the algorithms being composed. Second, these odometers and filters suffer from large constants, making them impractical. We construct filters that match the rates of advanced composition, including constants, despite allowing for adaptively chosen privacy parameters. En route we also derive a privacy filter for approximate zCDP. We also construct several general families of odometers. These odometers match the tightness of advanced composition at an arbitrary, preselected point in time, or at all points in time simultaneously, up to a doubly-logarithmic factor. We obtain our results by leveraging advances in martingale concentration. In sum, we show that fully adaptive privacy is obtainable at almost no loss.","url":"https://www.semanticscholar.org/paper/8348a814012f4ac78375440887e9d0ace8ddecfa","authors":["J. Whitehouse","Aaditya Ramdas","Ryan M. Rogers","Zhiwei Steven Wu"],"tags":["International Conference on Machine Learning"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-03-10","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.48550/arXiv.2203.05481","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.14778/3447689.3447700","name":"Dealer: An End-to-End Model Marketplace with Differential Privacy","source":"semanticscholar","abstract":"\n Data-driven machine learning has become ubiquitous. A marketplace for machine learning models connects data owners and model buyers, and can dramatically facilitate data-driven machine learning applications. In this paper, we take a formal data marketplace perspective and propose the first en\n D\n -to-end mod\n e\n l m\n a\n rketp\n l\n ace with diff\n e\n rential p\n r\n ivacy (\n Dealer\n ) towards answering the following questions:\n How to formulate data owners' compensation functions and model buyers' price functions? How can the broker determine prices for a set of models to maximize the revenue with arbitrage-free guarantee, and train a set of models with maximum Shapley coverage given a manufacturing budget to remain competitive\n ? For the former, we propose compensation function for each data owner based on Shapley value and privacy sensitivity, and price function for each model buyer based on Shapley coverage sensitivity and noise sensitivity. Both privacy sensitivity and noise sensitivity are measured by the level of differential privacy. For the latter, we formulate two optimization problems for model pricing and model training, and propose efficient dynamic programming algorithms. Experiment results on the real chess dataset and synthetic datasets justify the design of\n Dealer\n and verify the efficiency and effectiveness of the proposed algorithms.\n","url":"https://www.semanticscholar.org/paper/193108e5c2efaa8c865ed48648ce823b47455d99","authors":["Jinfei Liu","Jian Lou","Junxu Liu","Li Xiong","J. Pei","Jimeng Sun"],"tags":["Proceedings of the VLDB Endowment"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-02-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.14778/3447689.3447700","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.48550/arXiv.2205.02130","name":"The Limits of Word Level Differential Privacy","source":"semanticscholar","abstract":"As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data. A significant subset of these approaches incorporate differentially private mechanisms to perturb word embeddings, thus replacing individual words in a sentence. While these methods represent very important contributions, have various advantages over other techniques and do show anonymization capabilities, they have several shortcomings. In this paper, we investigate these weaknesses and demonstrate significant mathematical constraints diminishing the theoretical privacy guarantee as well as major practical shortcomings with regard to the protection against deanonymization attacks, the preservation of content of the original sentences as well as the quality of the language output. Finally, we propose a new method for text anonymization based on transformer based language models fine-tuned for paraphrasing that circumvents most of the identified weaknesses and also offers a formal privacy guarantee. We evaluate the performance of our method via thorough experimentation and demonstrate superior performance over the discussed mechanisms.","url":"https://www.semanticscholar.org/paper/d80cd04151ac1e46add6a87db7b86dcdf93450e2","authors":["Justus Mattern","Benjamin Weggenmann","F. Kerschbaum"],"tags":["NAACL-HLT"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-05-02","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.48550/arXiv.2205.02130","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.48550/arXiv.2204.07667","name":"Just Fine-tune Twice: Selective Differential Privacy for Large Language Models","source":"semanticscholar","abstract":"Protecting large language models from privacy leakage is becoming increasingly crucial with their wide adoption in real-world products. Yet applying *differential privacy* (DP), a canonical notion with provable privacy guarantees for machine learning models, to those models remains challenging due to the trade-off between model utility and privacy loss. Utilizing the fact that sensitive information in language data tends to be sparse, Shi et al. (2021) formalized a DP notion extension called *Selective Differential Privacy* (SDP) to protect only the sensitive tokens defined by a policy function. However, their algorithm only works for RNN-based models. In this paper, we develop a novel framework, *Just Fine-tune Twice* (JFT), that achieves SDP for state-of-the-art large transformer-based models. Our method is easy to implement: it first fine-tunes the model with *redacted* in-domain data, and then fine-tunes it again with the *original* in-domain data using a private training mechanism. Furthermore, we study the scenario of imperfect implementation of policy functions that misses sensitive tokens and develop systematic methods to handle it. Experiments show that our method achieves strong utility compared to previous baselines. We also analyze the SDP privacy guarantee empirically with the canary insertion attack.","url":"https://www.semanticscholar.org/paper/97333fea241659b8d804d04d326c3590c4a528f2","authors":["Weiyan Shi","Si Chen","Chiyuan Zhang","R. Jia","Zhou Yu"],"tags":["Conference on Empirical Methods in Natural Language Processing"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-04-15","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.48550/arXiv.2204.07667","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"ss:82c768284b70cb78e6e55e9cbfaf38e1010c24ad","name":"Differential privacy and robust statistics in high dimensions","source":"semanticscholar","abstract":"We introduce a universal framework for characterizing the statistical efficiency of a statistical estimation problem with differential privacy guarantees. Our framework, which we call High-dimensional Propose-Test-Release (HPTR), builds upon three crucial components: the exponential mechanism, robust statistics, and the Propose-Test-Release mechanism. Gluing all these together is the concept of resilience, which is central to robust statistical estimation. Resilience guides the design of the algorithm, the sensitivity analysis, and the success probability analysis of the test step in Propose-Test-Release. The key insight is that if we design an exponential mechanism that accesses the data only via one-dimensional robust statistics, then the resulting local sensitivity can be dramatically reduced. Using resilience, we can provide tight local sensitivity bounds. These tight bounds readily translate into near-optimal utility guarantees in several cases. We give a general recipe for applying HPTR to a given instance of a statistical estimation problem and demonstrate it on canonical problems of mean estimation, linear regression, covariance estimation, and principal component analysis. We introduce a general utility analysis technique that proves that HPTR nearly achieves the optimal sample complexity under several scenarios studied in the literature.","url":"https://www.semanticscholar.org/paper/82c768284b70cb78e6e55e9cbfaf38e1010c24ad","authors":["Xiyang Liu","Weihao Kong","Sewoong Oh"],"tags":["Annual Conference Computational Learning Theory"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-11-12","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"ss:d312c697b25fdc7f8fbee8020dc586d65cc6db83","name":"Instance-optimal Mean Estimation Under Differential Privacy","source":"semanticscholar","abstract":"Mean estimation under differential privacy is a fundamental problem, but worst-case optimal mechanisms do not offer meaningful utility guarantees in practice when the global sensitivity is very large. Instead, various heuristics have been proposed to reduce the error on real-world data that do not resemble the worst-case instance. This paper takes a principled approach, yielding a mechanism that is instance-optimal in a strong sense. In addition to its theoretical optimality, the mechanism is also simple and practical, and adapts to a variety of data characteristics without the need of parameter tuning. It easily extends to the local and shuffle model as well.","url":"https://www.semanticscholar.org/paper/d312c697b25fdc7f8fbee8020dc586d65cc6db83","authors":["Ziyue Huang","Yuting Liang","K. Yi"],"tags":["Neural Information Processing Systems"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-06-01","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"ss:4ace0211031f9ef79c70fbeca9e07dc121be90ff","name":"Antipodes of Label Differential Privacy: PATE and ALIBI","source":"semanticscholar","abstract":"We consider the privacy-preserving machine learning (ML) setting where the trained model must satisfy differential privacy (DP) with respect to the labels of the training examples. We propose two novel approaches based on, respectively, the Laplace mechanism and the PATE framework, and demonstrate their effectiveness on standard benchmarks. While recent work by Ghazi et al. proposed Label DP schemes based on a randomized response mechanism, we argue that additive Laplace noise coupled with Bayesian inference (ALIBI) is a better fit for typical ML tasks. Moreover, we show how to achieve very strong privacy levels in some regimes, with our adaptation of the PATE framework that builds on recent advances in semi-supervised learning. We complement theoretical analysis of our algorithms' privacy guarantees with empirical evaluation of their memorization properties. Our evaluation suggests that comparing different algorithms according to their provable DP guarantees can be misleading and favor a less private algorithm with a tighter analysis. Code for implementation of algorithms and memorization attacks is available from https://github.com/facebookresearch/label_dp_antipodes.","url":"https://www.semanticscholar.org/paper/4ace0211031f9ef79c70fbeca9e07dc121be90ff","authors":["Mani Malek","Ilya Mironov","Karthik Prasad","I. Shilov","Florian Tramèr"],"tags":["Neural Information Processing Systems"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-06-07","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.14778/3503585.3503592","name":"Projected Federated Averaging with Heterogeneous Differential Privacy","source":"semanticscholar","abstract":"Federated Learning (FL) is a promising framework for multiple clients to learn a joint model without directly sharing the data. In addition to high utility of the joint model, rigorous privacy protection of the data and communication efficiency are important design goals. Many existing efforts achieve rigorous privacy by ensuring differential privacy for intermediate model parameters, however, they assume a uniform privacy parameter for all the clients. In practice, different clients may have different privacy requirements due to varying policies or preferences.\n \n In this paper, we focus on explicitly modeling and leveraging the heterogeneous privacy requirements of different clients and study how to optimize utility for the joint model while minimizing communication cost. As differentially private perturbations affect the model utility, a natural idea is to make better use of information submitted by the clients with higher privacy budgets (referred to as \"public\" clients, and the opposite as \"private\" clients). The challenge is how to use such information without biasing the joint model. We propose\n P\n rojected\n F\n ederated\n A\n veraging (PFA), which extracts the top singular subspace of the model updates submitted by \"public\" clients and utilizes them to project the model updates of \"private\" clients before aggregating them. We then propose communication-efficient PFA+, which allows \"private\" clients to upload projected model updates instead of original ones. Our experiments verify the utility boost of both algorithms compared to the baseline methods, whereby PFA+ achieves over 99% uplink communication reduction for \"private\" clients.\n","url":"https://www.semanticscholar.org/paper/7cd961eb910684382216adda12e2ff2394e75a52","authors":["Junxu Liu","Jian Lou","Li Xiong","Jinfei Liu","Xiaofeng Meng"],"tags":["Proceedings of the VLDB Endowment"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-12-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.14778/3503585.3503592","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1145/3548606.3560687","name":"Federated Boosted Decision Trees with Differential Privacy","source":"semanticscholar","abstract":"There is great demand for scalable, secure, and efficient privacy-preserving machine learning models that can be trained over distributed data. While deep learning models typically achieve the best results in a centralized non-secure setting, different models can excel when privacy and communication constraints are imposed. Instead, tree-based approaches such as XGBoost have attracted much attention for their high performance and ease of use; in particular, they often achieve state-of-the-art results on tabular data. Consequently, several recent works have focused on translating Gradient Boosted Decision Tree (GBDT) models like XGBoost into federated settings, via cryptographic mechanisms such as Homomorphic Encryption (HE) and Secure Multi-Party Computation (MPC). However, these do not always provide formal privacy guarantees, or consider the full range of hyperparameters and implementation settings. In this work, we implement the GBDT model under Differential Privacy (DP). We propose a general framework that captures and extends existing approaches for differentially private decision trees. Our framework of methods is tailored to the federated setting, and we show that with a careful choice of techniques it is possible to achieve very high utility while maintaining strong levels of privacy.","url":"https://www.semanticscholar.org/paper/0de69dfe766375061ab0a267d281da8a6ce4bbe6","authors":["Samuel Maddock","Graham Cormode","Tianhao Wang","C. Maple","S. Jha"],"tags":["Conference on Computer and Communications Security"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-10-06","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1145/3548606.3560687","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.14778/3476249.3476280","name":"Real-World Trajectory Sharing with Local Differential Privacy","source":"semanticscholar","abstract":"\n Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited the extent to which this data is shared. Local differential privacy enables data sharing in which users share a perturbed version of their data, but existing mechanisms fail to incorporate user-independent public knowledge (e.g., business locations and opening times, public transport schedules, geo-located tweets). This limitation makes mechanisms too restrictive, gives unrealistic outputs, and ultimately leads to low practical utility. To address these concerns, we propose a local differentially private mechanism that is based on perturbing hierarchically-structured, overlapping\n n\n -grams (i.e., contiguous subsequences of length\n n\n ) of trajectory data. Our mechanism uses a multi-dimensional hierarchy over publicly available external knowledge of real-world places of interest to improve the realism and utility of the perturbed, shared trajectories. Importantly, including real-world public data does not negatively affect privacy or efficiency. Our experiments, using real-world data and a range of queries, each with real-world application analogues, demonstrate the superiority of our approach over a range of alternative methods.\n","url":"https://www.semanticscholar.org/paper/d7455e6a1fe9affc08b99c6361bb6f9ca2717815","authors":["Teddy Cunningham","Graham Cormode","H. Ferhatosmanoğlu","D. Srivastava"],"tags":["Proceedings of the VLDB Endowment"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-07-01","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.14778/3476249.3476280","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1038/s41598-022-24082-z","name":"Quantum machine learning with differential privacy","source":"europepmc","abstract":"Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of quantum operations on a classical computer. Many datasets used in machine learning are crowd sourced or contain some private information, but to the best of our knowledge, no current QML models are equipped with privacy-preserving features. This raises concerns as it is paramount that models do not expose sensitive information. Thus, privacy-preserving algorithms need to be implemented with QML. One solution is to make the machine learning algorithm differentially private, meaning the effect of a single data point on the training dataset is minimized. Differentially private machine learning models have been investigated, but differential privacy has not been thoroughly studied in the context of QML. In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm. This marks the first proof-of-principle demonstration of privacy-preserving QML. The experiments demonstrate that differentially private QML can protect user-sensitive information without signficiantly diminishing model accuracy. Although the quantum model is simulated and tested on a classical computer, it demonstrates potential to be efficiently implemented on near-term quantum devices [noisy intermediate-scale quantum (NISQ)]. The approach’s success is illustrated via the classification of spatially classed two-dimensional datasets and a binary MNIST classification. This implementation of privacy-preserving QML will ensure confidentiality and accurate learning on NISQ technology.","url":"https://www.semanticscholar.org/paper/09b24e1dee33afca351c872b70a77ed6eec4e5ca","authors":["William Watkins","Samuel Yen-Chi Chen","Shinjae Yoo"],"tags":["Scientific Reports"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021-03-10","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-022-24082-z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1109/tii.2022.3145010","name":"Decentralized Wireless Federated Learning With Differential Privacy","source":"semanticscholar","abstract":"This article studies decentralized federated learning algorithms in wireless IoT networks. The traditional parameter server architecture for federated learning faces some problems such as low fault tolerance, large communication overhead and inaccessibility of private data. To solve these problems, we propose a decentralized wireless federated learning algorithm called DWFL. The algorithm works in a system where the workers are organized in a peer-to-peer and server-less manner, and the workers exchange their privacy preserving data with the analog transmission scheme over wireless channels in parallel. With rigorous analysis, we show that DWFL satisfies <inline-formula><tex-math notation=\"LaTeX\">$(\\epsilon,\\delta)$</tex-math></inline-formula>-differential privacy and the privacy budget per worker scales as <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {O}(\\frac{1}{\\sqrt{N}})$</tex-math></inline-formula>, in contrast with the constant budget in the orthogonal transmission approach. Furthermore, DWFL converges at the same rate of <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {O}(\\sqrt{\\frac{1}{TN}})$</tex-math></inline-formula> as the best known centralized algorithm with a central parameter server. Extensive experiments demonstrate that our algorithm DWFL also performs well in real settings.","url":"https://www.semanticscholar.org/paper/d4950011c6f1b4f99c9eea0b5692e4aef3f1a615","authors":["Shuzhen Chen","Dongxiao Yu","Yifei Zou","Jiguo Yu","Xiuzhen Cheng"],"tags":["IEEE Transactions on Industrial Informatics"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-09-19","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/tii.2022.3145010","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.18653/v1/2022.naacl-main.205","name":"Selective Differential Privacy for Language Modeling","source":"semanticscholar","abstract":"With the increasing applications of language models, it has become crucial to protect these models from leaking private information. Previous work has attempted to tackle this challenge by training RNN-based language models with differential privacy guarantees.However, applying classical differential privacy to language models leads to poor model performance as the underlying privacy notion is over-pessimistic and provides undifferentiated protection for all tokens in the data. Given that the private information in natural language is sparse (for example, the bulk of an email might not carry personally identifiable information), we propose a new privacy notion, selective differential privacy, to provide rigorous privacy guarantees on the sensitive portion of the data to improve model utility. To realize such a new notion, we develop a corresponding privacy mechanism, Selective-DPSGD, for RNN-based language models. Besides language modeling, we also apply the method to a more concrete application – dialog systems. Experiments on both language modeling and dialog system building show that the proposed privacy-preserving mechanism achieves better utilities while remaining safe under various privacy attacks compared to the baselines. The data and code are released at https://github.com/wyshi/lm_privacy to facilitate future research.","url":"https://www.semanticscholar.org/paper/bda3fe4ae1cb73ef99f48add40967179577d29e8","authors":["Weiyan Shi","Aiqiang Cui","Evan Li","R. Jia","Zhou Yu"],"tags":["North American Chapter of the Association for Computational Linguistics"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-08-30","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.18653/v1/2022.naacl-main.205","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"doi:10.1109/TII.2021.3082576","name":"Multiple Strategies Differential Privacy on Sparse Tensor Factorization for Network Traffic Analysis in 5G","source":"semanticscholar","abstract":"Due to high capacity and fast transmission speed, 5G plays a key role in modern electronic infrastructure. Meanwhile, sparse tensor factorization (STF) is a useful tool for dimension reduction to analyze high-order, high-dimension, and sparse tensor (HOHDST) data, which is transmitted on 5G Internet-of-things (IoT). Hence, HOHDST data relies on STF to obtain complete data and discover rules for real time and accurate analysis. From another view of computation and data security, the current STF solution seeks to improve the computational efficiency but neglects privacy security of the IoT data, e.g., data analysis for network traffic monitor system. To overcome these problems, this article proposes a multiple-strategies differential privacy framework on STF ( MDPSTF ) for HOHDST network traffic data analysis. MDPSTF comprises three differential privacy (DP) mechanisms, i.e., <inline-formula><tex-math notation=\"LaTeX\">$\\varepsilon -$</tex-math></inline-formula> DP, concentrated DP, and local DP. Furthermore, the theoretical proof of privacy bound is presented. Hence, MDPSTF can provide general data protection for HOHDST network traffic data with high-security promise. We conduct experiments on two real network traffic datasets (<inline-formula><tex-math notation=\"LaTeX\">$Abilene$</tex-math></inline-formula> and <inline-formula><tex-math notation=\"LaTeX\">$G\\grave{E}ANT$</tex-math></inline-formula>). The experimental results show that MDPSTF has high universality on the various degrees of privacy protection demands and high recovery accuracy for the HOHDST network traffic data.","url":"https://www.semanticscholar.org/paper/9924c292255b8f7cecde196fdae0a0a7c8305bdd","authors":["Jin Wang","Hui Han","Hao Li","Shiming He","Pradip Kumar Sharma","L. Chen"],"tags":["IEEE Transactions on Industrial Informatics"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021-05-21","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TII.2021.3082576","updatedAt":"2026-08-31T06:41:17.620Z"},{"id":"ss:4b7215ebc0457a7e171bb3007c1e11623eef6615","name":"Defending against Reconstruction Attacks with Rényi Differential Privacy","source":"semanticscholar","abstract":"Reconstruction attacks allow an adversary to regenerate data samples of the training set using access to only a trained model. It has been recently shown that simple heuristics can reconstruct data samples from language models, making this threat scenario an important aspect of model release. Differential privacy is a known solution to such attacks, but is often used with a relatively large privacy budget (epsilon>8) which does not translate to meaningful guarantees. In this paper we show that, for a same mechanism, we can derive privacy guarantees for reconstruction attacks that are better than the traditional ones from the literature. In particular, we show that larger privacy budgets do not protect against membership inference, but can still protect extraction of rare secrets. We show experimentally that our guarantees hold against various language models, including GPT-2 finetuned on Wikitext-103.","url":"https://www.semanticscholar.org/paper/4b7215ebc0457a7e171bb3007c1e11623eef6615","authors":["Pierre Stock","I. Shilov","Ilya Mironov","Alexandre Sablayrolles"],"tags":["arXiv.org"],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022-02-15","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.1109/TCYB.2021.3135933","name":"Consensus of Linear Multivariable Discrete-Time Multiagent Systems: Differential Privacy Perspective","source":"europepmc","abstract":"Differential privacy, which has been widely applied in industries, is a privacy mechanism effective in preventing malicious entities from breaching the privacy of an individual participant. It is usually achieved by adding random variables in the data. This article investigates a class of multivariable discrete-time multiagent systems with <inline-formula> <tex-math notation=\"LaTeX\">$\\epsilon $ </tex-math></inline-formula>-differential privacy preserved. A novel information-masking mechanism is proposed, in which the information of each state transmitted to different neighbors is obscured by adding independent random noises. Then, the mean-square consensus conditions, and the upper bound and lower bound of the convergence rate are obtained. Moreover, the conditions for the convergence rate reaching its upper bound are established. The results can be applied to the average mean-square consensus. In addition, a necessary and sufficient condition is presented under which agents can preserve the dynamics of agents <inline-formula> <tex-math notation=\"LaTeX\">$\\epsilon $ </tex-math></inline-formula>-differentially private at any time instant.","url":"https://www.semanticscholar.org/paper/4570ae757dce79ba1d3ed2ad9a162aeb7bde8ff4","authors":["Yamin Wang","J. Lam","Hong Lin"],"tags":["IEEE Transactions on Cybernetics"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/TCYB.2021.3135933","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/e28040402","name":"Sequential Change Detection with Local Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28040402","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/e28040402","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1038/s41598-026-50273-z","name":"Identity trust management based on differential privacy for internet of vehicles.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-50273-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-026-50273-z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.3390/s26144358","name":"Differential-Privacy-Based Collaborative Protection for Visual and Location Data in UAV Semantic Communications.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26144358","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/s26144358","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.14293/pr2199.003336.v1","name":"Differential Privacy Approaches in Telematics-Based Auto Insurance Systems","source":"europepmc","abstract":"","url":"https://doi.org/10.14293/pr2199.003336.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.14293/pr2199.003336.v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-9745227/v1","name":"MobiShield: Federated Android Malware Detection with Adaptive Differential Privacy under Concept Drift","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9745227/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.21203/rs.3.rs-9745227/v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-10050492/v1","name":"DP-DCGAN: Differential Privacy-Deep Convolutional Generative Adversarial Networks with Adaptive Gradient Perturbation","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10050492/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.21203/rs.3.rs-10050492/v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1038/s41598-026-60269-4","name":"RiskSetDP: session-level differential privacy for survival analysis with risk-set-aware sensitivity control.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-60269-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-026-60269-4","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.21203/rs.3.rs-9908296/v1","name":"An AD Detection System Using Theme Enhancement and Dynamic Differential Privacy for Intelligent Elderly Care","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9908296/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.21203/rs.3.rs-9908296/v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1038/s41598-026-51804-4","name":"Multi-modal federated learning with differential privacy for privacy-preserving healthcare AI.","source":"pubmed","abstract":"The growing adoption of artificial intelligence in healthcare highlights the need for models that can leverage heterogeneous patient data while preserving strict privacy requirements. This paper proposes a novel multi-modal federated learning framework with differential privacy for decentralized healthcare AI. The model integrates electronic health records and ECG time-series using modality-specific encoders and a shared latent fusion network, enabling comprehensive representation learning without centralizing sensitive data. Differential privacy is incorporated into local updates to provide formal guarantees against information leakage in federated aggregation. Extensive experiments on real-world healthcare datasets show that the proposed method achieves [Formula: see text] accuracy, [Formula: see text] precision, [Formula: see text] recall, [Formula: see text] F1-score, and [Formula: see text] AUC, outperforming centralized, single-modality, and non-private baselines. The framework also converges [Formula: see text] faster than single-modality federated learning, reaching [Formula: see text] accuracy in 35 rounds. An ablation study confirms the contribution of multi-modal fusion and class balancing, while client variance analysis shows the lowest performance deviation ([Formula: see text]) under heterogeneous distributions. These results indicate that combining federated optimization, differential privacy, and multi-modal learning provides an effective framework for privacy-preserving clinical AI, with potential for deployment in distributed healthcare settings.","url":"https://doi.org/10.1038/s41598-026-51804-4","authors":["Hasan MR","Ahmed MI","Saha S","Ishika TK","Shoaib HA","Hossen MJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-026-51804-4","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.3390/jimaging12050205","name":"Federated Learning with Differential Privacy for Ultrasound Breast Cancer Classification: An Empirical Study.","source":"pubmed","abstract":"Breast cancer is a critical global health challenge, and deep learning shows transformative potential for medical image classification. However, privacy regulations such as HIPAA and GDPR create barriers to centralized data aggregation across institutions. This paper presents an empirical evaluation of federated learning (FL) for breast cancer classification in ultrasound images, systematically comparing seven deep learning architectures (ResNet-50, VGG16, VGG19, DenseNet-121, MobileNetV2, Vision Transformer, CoAtNet) across three FL algorithms (FedAvg, FedProx, FedOpt) with client-side differential privacy (DP). Using a simulated federation of eight institutions, we evaluate three clinically relevant classification scenarios. Federated models achieve performance comparable to centralized baselines-98.52% accuracy for normal/abnormal screening, 89.53% for three-class classification-with ViT-small and DenseNet-121 exceeding their centralized counterparts in several configurations. Under strong DP constraints (noise multiplier &#x3b7;=2.0, yielding conservative privacy budget estimates of &#x3b5;&lt;1.0 with &#x3b4;=10-5), screening accuracy remains above 82%, though diagnostic tasks incur substantial degradation (best 68.42%). Our findings provide empirical guidance on architecture selection, FL algorithm choice, and privacy-utility trade-offs for privacy-preserving breast cancer diagnosis, while identifying key challenges for clinical deployment.","url":"https://doi.org/10.3390/jimaging12050205","authors":["Makhanov N","Abdikenov B","Zhaksylyk T","Karibekov T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/jimaging12050205","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1038/s41598-026-51535-6","name":"FedDriftGuard adaptive federated learning with differential privacy for concept drift in edge environments.","source":"pubmed","abstract":"Federated learning (FL) has become a highly promising paradigm for privacy-preserving distributed model training by enabling edge devices to train without sharing raw data. But in practice, edge environments are both non-stationary and asymmetric, with varying data distributions due to shifts in user behaviour, sensing conditions, and overall environmental dynamics. This causes concept drift (sudden, gradual, and recurrent), leading to poor model performance, slower convergence, and predictive bias. Current approaches to FL are not combined to tackle problems of drift adaptation, differential privacy (DP) and resource efficiency (FedAvg, DP-FedAvg). To address these constraints, we present FedDriftGuard. This Federated learning layer unifies client-level drift detection, drift-adaptive aggregation, and adaptable differential privacy into a single, FLE architecture-compatible system. The proposed DP-DriftNet model implements attention-based time encoding to capture changing data patterns and drift-directed feature weighting to allow greater flexibility in the presence of distributional changes. A drift-optimal privacy scheduler allocates noise probabilistically, subject to a limited privacy budget, thereby enforcing an appropriate privacy-utility trade-off without cancelling formal DP guarantees. Also, update sparsification, compression and periodic transmission techniques are used to reduce communication overhead. Decades of experimentation on real-world and synthetic drift datasets have shown that FedDriftGuard outperforms baseline FL techniques, achieving accuracy and F1-score gains of 9-14% and 11-17%, respectively, with adaptation latency 28% shorter and communication cost 20-35% lower. Such findings are statistically significant and confirm the soundness of the suggested method. FedDriftGuard offers effective, scalable privacy-preserving learning in adaptable, edge-drifting environments.","url":"https://doi.org/10.1038/s41598-026-51535-6","authors":["Sudhakar K","Jayasree A","Sundaragiri D","Talluri U","Bhusarapu HN","Sreenivas TS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-026-51535-6","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1186/s12911-026-03453-w","name":"Estimation of future occurrence of hemoglobin-A1c elevation with and without differential privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12911-026-03453-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1186/s12911-026-03453-w","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1038/s41598-026-53829-1","name":"Attention U-Net with differential privacy in federated learning framework for brain stroke lesion segmentation.","source":"pubmed","abstract":"Data privacy considerations and data fragmentation between healthcare institutions is causing segmentation of ischemic stroke lesions from neuroimaging to be hindered. The centralized approach may conflict with HIPAA and GDPR, and the federated learning approach does not have proper privacy guarantees nor is it able to capture lesion detail. This work prposes Fed-AttUNet-DP,a three major contributions: a spatial attention mechanism to integrate federated learning for better stroke lesion detection; differential privacy at the client level and secure MPCC at the server level for dual-layered privacy preservation; adaptive federated optimization for non-IID medical data and faster training speed and better performance. The proposed framework is an augmentation to the federated averaging algorithm which exploits attention U-Net and antennas attention (differential privacy i.e. gradient clipping and Gaussian noise). Ten simulated health care institutions were involved in 150 rounds of communication, at a rate of [Formula: see text] participation of the clients. Privacy budget was configured set at [Formula: see text], [Formula: see text] and a multiplier of noise [Formula: see text] as well as gradient clipping threshold [Formula: see text]. The distance between data heterogeneity was measured with Earth Mover (EMD [Formula: see text]). Experiments are conducted on the BRISC2025 dataset. The Dice similarity coefficient and IoU for Fed-AttUNet-DP is 0.930 and 0.890 respectively, sensitivity is 0.941 and specificity is 0.982. It beats all federated baselines by only [Formula: see text] accuracy drop compared to centralized training as well as [Formula: see text] compared to local-only training. The inference per volume is 0.8 s, converges the system with 5.18 GB total communication cost with 150 rounds. It is verified in the works of the ablation research that attention gates ([Formula: see text] DSC), differential privacy ([Formula: see text] DSC trade-off), and secure aggregation individually contribute to it. Our work provides formal privacy guarantees and advances segmentation accuracy, by mitigating non-IID heterogeneity across multiple medical image institutions and respecting HIPAA/GDPR regulations.","url":"https://doi.org/10.1038/s41598-026-53829-1","authors":["Adhi Siva M","Chowdhary CL"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-026-53829-1","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1016/j.tiv.2026.106273","name":"Integration of label-free electrochemical sensing, differential privacy deep learning, and blockchain for secure cytotoxicity monitoring.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.tiv.2026.106273","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1016/j.tiv.2026.106273","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1038/s41598-026-56120-5","name":"FedAttn-Credit: attention-augmented federated learning with adaptive differential privacy for rural inclusive finance credit assessment.","source":"pubmed","abstract":"Rural inclusive finance faces persistent challenges in credit assessment due to fragmented data ecosystems, heterogeneous borrower profiles, and stringent privacy constraints. This paper proposes FedAttn-Credit, a multi-party collaborative credit assessment framework that integrates horizontal federated learning with an attention-augmented scoring model and an adaptive differential privacy mechanism. The framework connects five categories of rural data holders-commercial banks, rural credit cooperatives, government platforms, agricultural e-commerce providers, and village-level microfinance institutions-through a central aggregation server that never accesses raw data. A multi-head self-attention module with group-wise importance gating enables the model to dynamically weight heterogeneous feature groups according to their contextual relevance for each borrower. An adaptive differential privacy strategy calibrates noise magnitude based on each participant's data volume and training-round gradient dynamics, providing stronger protection for small-sample participants without disproportionately degrading model utility. Experimental results on public microfinance records and a synthetic rural credit dataset show that FedAttn-Credit achieves an AUC of 0.8734, narrowing the gap to the centralized upper bound to 1.8 percentage points while outperforming all federated baselines. At equivalent accuracy, the adaptive privacy mechanism cuts cumulative privacy cost by roughly 47% relative to fixed-budget alternatives, a figure that we derive in \"Privacy protection effectiveness and model performance tradeoff analysis\" section from the Privacy Efficiency Ratio reported there (the two quantities express the same comparison from opposite directions). Ablation and robustness analyses confirm that the attention module, adaptive noise scheduling, and Shapley-based contribution evaluation provide compounding benefits under both standard and adversarial conditions.","url":"https://doi.org/10.1038/s41598-026-56120-5","authors":["Xie Z","Zhang L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-026-56120-5","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1038/s41598-025-27708-0","name":"Dynamic differential privacy technique for deep learning models.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-27708-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-025-27708-0","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.20944/preprints202604.0295.v1","name":"Privacy-Preserving Anomaly Detection in Cloud Services Using Hierarchical Federated Learning with Differential Privacy","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202604.0295.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.20944/preprints202604.0295.v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/e28040409","name":"Step-Wise Dual Dynamic DPSGD: Enhancing Performance on Imbalanced Medical Datasets with Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28040409","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/e28040409","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1038/s41598-025-27472-1","name":"Splitting smarter: Differential privacy for secure healthcare federated learning.","source":"europepmc","abstract":"Split Federated Learning (SplitFed) has emerged as a decentralized method of training ML models that enables multiple healthcare parties to collaboratively share models without sharing their raw data. This method, however, is vulnerable to label inference attacks, which can compromise patient privacy. Previous research efforts have attempted to address the question. However, these works do not conduct a detailed vulnerability analysis of SplitFed against label inference attacks. Additionally, some of these efforts propose differential privacy (DP) as a solution; the works focus on distributed learning paradigms where labels used for training the model are available to the clients, which is not a practical assumption. To address this, in this paper, we investigate the vulnerability of SplitFed models to label inference attacks in biomedical imaging. We propose a solution that incorporates DP into SplitFed to protect against label inference attacks. Additionally, we also provide a detailed vulnerability analysis of SplitFed against label inference attacks specific to healthcare applications. Finally, we propose a DP-based method for mitigating label inference attacks against Split-Fed models. Results indicate the efficacy of the SplitFed model under multiple conditions and found that the label inference accuracy changes from [Formula: see text] (No-DP) to [Formula: see text] (with DP). This indicates that integration of DP offers a robust mechanism for protecting patient privacy. Additionally, the usage of Cauchy noise in DP provides the best protection out of all noise categories, with a label inference accuracy of 0% while Exponential noise was the worst, resulting in a label inference accuracy of 68%.","url":"https://doi.org/10.1038/s41598-025-27472-1","authors":["Munirat Yetunde Onireti","Raj Mani Shukla","Tapadhir Das"],"tags":["Inference","Computer science","Machine learning","Federated learning","Vulnerability (computing)"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-025-27472-1","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.1371/journal.pone.0342692","name":"Assessing Local Differential Privacy for Compliance with the Personal Data Protection Law in Integrated Data Systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0342692","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1371/journal.pone.0342692","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"epmc:MED42375467","name":"Classification Under Local Differential Privacy with Model Reversal and Model Averaging.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42375467/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-45277-8","name":"TrustFed-RHIO: an optimization-driven differential privacy federated learning framework for secure and explainable IIoT attack detection.","source":"pubmed","abstract":"A rapid proliferation of industrial internet of things (IIoT) systems has increased the vulnerability of interconnected devices for sophisticated cyberattacks, which necessitates intelligent and privacy-preserving solution for security. This paper presents TrustFed-RHIO, a novel hybrid model which integrates rock hyrax intelligence optimization (RHIO) for optimal selection of feature with a trustworthy differential privacy-enhanced federated learning (TrustFed) scheme for the collaborative detection of attack. The algorithm of RHIO mimics collective intelligence of rock hyrax colonies for identifying most discriminative features, thus reducing dimensionality and enhancing efficiency of classifier. The proposed TrustFed-RHIO scheme ensures, secure, distributed learning over multiple IIoT nodes on embedding differential privacy mechanisms thus mitigating the data leakage risks and adversarial inference. A suggested scheme is thus empowered with explainable artificial intelligence (XAI) scheme termed SHAP and LIME for enhancing interpretability and trust in model predictions. Experimental estimation on benchmark IIoT dataset shows that TrustFed-RHIO attains superior performance on detection accuracy, robustness against adversarial attacks, and privacy preservation on comparing existing schemes. At last, this framework supports secure storage of cloud on detection outcomes, thus enabling scalable deployment in the real-world IIoT framework. The performance evaluation is carried on benchmark dataset CCIoT2024-DIAD and performance is estimated for various metrics like latency, accuracy, precision, recall, F1-score, specificity, training time, and so on. Overall analysis shows that the proposed model is effective in detecting IIoT attacks.","url":"https://doi.org/10.1038/s41598-026-45277-8","authors":["Joseph L","Prabha B","Sambath M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-026-45277-8","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1038/s41746-025-02280-z","name":"Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications.","source":"pubmed","abstract":"","url":"https://doi.org/10.1038/s41746-025-02280-z","authors":["Mohammadi M","Vejdanihemmat M","Lotfinia M","Rusu M","Truhn D","Maier A","Tayebi Arasteh S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41746-025-02280-z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.3390/s26061874","name":"Adaptive Compressed Sensing Differential Privacy Federated Learning Based on Orbital Spatiotemporal Characteristics in Space-Air-Ground Networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26061874","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/s26061874","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1093/bioadv/vbaf298","name":"Integrating differential privacy into federated multi-task learning algorithms in &lt;b&gt;dsMTL&lt;/b&gt;.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/bioadv/vbaf298","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1093/bioadv/vbaf298","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-45883-6","name":"An adaptive differential privacy framework for clinical llms with context-aware noise calibration, hierarchical budgeting, and real-time auditing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-45883-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-026-45883-6","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1049/htl2.70080","name":"Federated Learning for Thoracic Disease Classification Using Convolutional Neural Networks and Differential Privacy.","source":"pubmed","abstract":"Early diagnosis of thoracic diseases using chest x-ray imaging remains a critical challenge, particularly in resource-constrained healthcare environments where data sharing is restricted due to privacy concerns. Federated learning (FL) offers a decentralized solution by enabling collaborative model training without sharing sensitive patient data. However, integrating privacy-preserving mechanisms such as differential privacy (DP) introduces additional challenges related to performance degradation and computational overhead. In this study, we present a unified FL framework for multi-label thoracic disease classification using multiple convolutional neural network (CNN) architectures, including ResNet50, DenseNet169, EfficientNet variants and MobileNetV3. Unlike prior studies focusing on single-model evaluation, this work provides a controlled comparative analysis under identical FL settings and investigates the impact of client scalability (5-10 clients) on model performance. Furthermore, we conduct a comprehensive empirical analysis of the privacy utility trade-off by integrating DP with varying privacy budgets ( &#x3b5; &#xa0;=&#xa0;1, 15 and 30). Experimental results on the CheXpert and NIH Chest x-ray14 datasets demonstrate that the proposed EfficientNet-B3-based federated model achieves a mean AUC of 0.8027, while maintaining robustness across decentralized settings. The integration of DP leads to a predictable reduction in performance, with mean AUC ranging from 0.60 to 0.64, highlighting the inherent trade-off between privacy and diagnostic accuracy. The findings emphasize the practical viability of FL for privacy-sensitive medical imaging applications and provide insights into model selection, scalability and privacy configuration for real-world deployment. The source code for this study is publicly accessible at https://github.com/Zulqarnain8-8/FEDERATED_LEARNING_FOR_THORACIC_DISEASE_CLASSIFICATION.","url":"https://doi.org/10.1049/htl2.70080","authors":["Zulqarnain M","Hussain SJ","Aslam MZ","Fiaz A","Islam M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1049/htl2.70080","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1016/j.neunet.2025.108345","name":"Adaptive differential privacy mechanism for enhanced deep learning model utility and privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.neunet.2025.108345","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1016/j.neunet.2025.108345","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.21203/rs.3.rs-8714544/v1","name":"Federated Learning for Privacy-Preserving Network Anomaly Detection: A High-Performance Convolutional Framework with Differential Privacy","source":"europepmc","abstract":"Abstract Federated learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative machine learning, enabling multiple organizations to train shared models without exchanging sensitive data. This study presents a comprehensive investigation of FL for network anomaly detection using the NSL-KDD dataset, incorporating real-world experimental evaluations across centralized baselines, IID and non-IID federated settings, differential privacy mechanisms, and robust optimization strategies such as FedProx. The results show that FL is feasible and efficient for distributed cybersecurity applications but exhibits sensitivity to data heterogeneity and privacy constraints. Centralized models achieved near-perfect detection performance, whereas FL under IID conditions demonstrated competitive accuracy and stable convergence. Under label-skew and quantity-skew non-IID conditions, FedAvg performance declined, while FedProx significantly improved stability and accuracy. Differential privacy introduced predictable accuracy degradation, with moderate budgets (ε = 10, 5) maintaining operational viability. System profiling revealed low communication overhead and rapid round execution, confirming practical deployability on CPU-based nodes. This work provides a rigorous experimental foundation for integrating federated learning into distributed intrusion detection systems and identifies key challenges related to privacy, heterogeneity, and model robustness that must be addressed to ensure reliable real-world adoption.","url":"https://doi.org/10.21203/rs.3.rs-8714544/v1","authors":["Salah Eldin Olaymi"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.21203/rs.3.rs-8714544/v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-8425166/v1","name":"FedXGB-OptDP: A Privacy-Optimised Federated XGBoost Framework with Differential Privacy for IID and Non-IID healthcare data","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8425166/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.21203/rs.3.rs-8425166/v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.20944/preprints202506.1284.v1","name":"Federated Learning with Differential Privacy for Sensitive Domains","source":"europepmc","abstract":"Federated Learning (FL) has emerged as a powerful paradigm for training machine learning models across decentralized data sources while preserving data privacy. This approach is particularly beneficial in sensitive domains such as healthcare, finance, and telecommunications, where data privacy and regulatory compliance are paramount. This paper explores the integration of Federated Learning with Differential Privacy (DP) to enhance privacy guarantees during the training process. By allowing multiple entities to collaboratively train models without sharing raw data, FL mitigates the risks associated with centralized data storage. We detail the theoretical foundations of both Federated Learning and Differential Privacy, highlighting their complementary strengths in safeguarding sensitive information. Our empirical evaluations demonstrate the effectiveness of this integrated approach, showing that it can maintain model accuracy while significantly reducing the risk of privacy breaches. We present case studies in healthcare and financial services, illustrating how Federated Learning with Differential Privacy can be applied to real-world scenarios, ensuring compliance with regulations like HIPAA and GDPR. Furthermore, we discuss the trade-offs involved in implementing these techniques, including the impact on model performance and computational efficiency. The findings underscore the potential of Federated Learning combined with Differential Privacy as a robust framework for developing privacy-preserving machine learning solutions in sensitive domains. This research contributes to the ongoing discourse on ethical AI deployment, providing a pathway for leveraging advanced analytics while prioritizing user privacy and data security.","url":"https://doi.org/10.20944/preprints202506.1284.v1","authors":["James Henderson","Racheal Writz"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.20944/preprints202506.1284.v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.20944/preprints202603.2000.v1","name":"A Theoretically‐Grounded Federated Attribution Framework with Adaptive Differential Privacy Budgets for Cross‐Device Social Commerce Advertising Systems","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202603.2000.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.20944/preprints202603.2000.v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1038/s41598-025-03178-2","name":"Wasserstein GAN for moving differential privacy protection.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-03178-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-025-03178-2","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1049/htl2.70079","name":"APB-FLDPA: Adaptive Personalized Blockchain-Federated Learning With Differential Privacy and Attention for Privacy-Preserving Healthcare Analytics.","source":"pubmed","abstract":"Developing robust medical artificial intelligence (AI) requires collaboration across multiple institutions, but strict data protection regulations such as HIPAA and GDPR prevent centralized patient data sharing. Existing federated learning (FL) methods often exhibit 15%-30% performance degradation in real-world clinical settings due to data heterogeneity, security threats, and privacy constraints. We present APB-FLDPA, a privacy-preserving federated learning framework for secure multi-hospital disease prediction. APB-FLDPA integrates five key innovations: (i) adaptive Byzantine-resilient aggregation using dynamic client trust scoring, (ii) self-attention for automated clinical feature importance, (iii) selective differential privacy applied at the final aggregation stage, (iv) cluster-aware personalization to handle cross-institutional heterogeneity, and (v) a lightweight blockchain module to ensure model integrity. Evaluated across five institutions using large-scale Diabetes (183,000 patients) and Thyroid (6840 patients) datasets, APB-FLDPA achieved 90.8% accuracy for diabetes and 83.8% accuracy for thyroid disease, with minimal performance loss (&lt;0.2%) compared to centralized learning. Statistical tests confirmed significant improvements, and selective differential privacy outperformed conventional methods by 5.6% in accuracy. These results show that APB-FLDPA provides a scalable, high-performance and privacy-compliant solution for real-world federated medical&#xa0;AI.","url":"https://doi.org/10.1049/htl2.70079","authors":["Chowdhury MKH","Mondal PK","Mozumder MAI","Kim HC","Byeon H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1049/htl2.70079","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1093/jrsssb/qkaf070","name":"Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraints.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/jrsssb/qkaf070","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1093/jrsssb/qkaf070","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1038/s41598-025-12575-6","name":"ALDP-FL for adaptive local differential privacy in federated learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-12575-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-025-12575-6","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.64898/2026.04.01.26349876","name":"Predicting COVID-19 incidence from seroprevalence and population-based cohort data using interpretable machine learning with differential privacy analysis","source":"europepmc","abstract":"","url":"https://doi.org/10.64898/2026.04.01.26349876","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.64898/2026.04.01.26349876","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1109/tpami.2025.3597922","name":"Toward the Flatter Landscape and Better Generalization in Federated Learning Under Client-Level Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2025.3597922","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/tpami.2025.3597922","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.21037/qims-2025-1064","name":"Impact of differential privacy on breast ultrasound image classification performance using vision transformer.","source":"europepmc","abstract":"","url":"https://doi.org/10.21037/qims-2025-1064","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.21037/qims-2025-1064","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.20944/preprints202604.0121.v1","name":"RUIP-BA: Renewable, Unlinkable, and Irreversible Privacy-Preserving Behavioral Authentication via Random Projection and Local Differential Privacy for IoT and Mobile Platforms","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202604.0121.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.20944/preprints202604.0121.v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1038/s41598-025-27691-6","name":"Hybrid GNN-LSTM defense with differential privacy and secure multi-party computation for edge-optimized neuromorphic autonomous systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-27691-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-025-27691-6","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.20944/preprints202506.1752.v1","name":"Differential Privacy Techniques in Machine Learning for Health Record Analysis","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202506.1752.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.20944/preprints202506.1752.v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1142/s0129065725500601","name":"Data Compliance Utilization Method Based on Adaptive Differential Privacy and Federated Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1142/s0129065725500601","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1142/s0129065725500601","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.3390/s25092847","name":"Sensitivity-Aware Differential Privacy for Federated Medical Imaging.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25092847","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/s25092847","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.21203/rs.3.rs-7255758/v1","name":"Privacy-Preserving Federated Learning Approach Based on Hensel’s Compression and Differential Privacy","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7255758/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.21203/rs.3.rs-7255758/v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.2196/83743","name":"A Bilayer Feature Fusion Framework for Pan-Cancer Survival Prediction Based on Multihead Attention and Adaptive Differential Privacy: Model Development and Validation Study.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/83743","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.2196/83743","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/tvcg.2024.3427733","name":"Illuminating the Landscape of Differential Privacy: An Interview Study on the Use of Visualization in Real-World Deployments.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tvcg.2024.3427733","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/tvcg.2024.3427733","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.20944/preprints202506.2188.v1","name":"An Asynchronous Federated Learning Aggregation Method Based on Adaptive Differential Privacy","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202506.2188.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.20944/preprints202506.2188.v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-8182728/v1","name":"Federated Continual Learning with Adaptive Differential Privacy and Client-Side Drift Detection for Evolving Medical Imaging Datasets","source":"europepmc","abstract":"Abstract This paper presents a novel Federated Continual Learning framework for medical imaging that enables continuous model updates across multiple hospitals without central data sharing. Our approach addresses critical challenges in healthcare AI: data privacy, catastrophic forgetting, and distribution drift. We demonstrate the framework on OrganAMNIST with three hospitals learning sequential tasks. Results show successful federated collaboration but reveal significant catastrophic forgetting, highlighting the need for advanced continual learning techniques in privacy-constrained environments. The integrated drift detection and differential privacy mechanisms provide a foundation for practical clinical deployment.","url":"https://doi.org/10.21203/rs.3.rs-8182728/v1","authors":["Nnaemeka Kingsley Ugwumba"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.21203/rs.3.rs-8182728/v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1016/j.isatra.2025.07.054","name":"Attack concealment for cyber-physical systems using a mechanism borrowing from differential privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.isatra.2025.07.054","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1016/j.isatra.2025.07.054","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1186/s12911-025-03109-1","name":"Enhancing privacy protection of physical examination data through synthetic algorithms based on differential privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12911-025-03109-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1186/s12911-025-03109-1","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fdata.2024.1420344","name":"Equitable differential privacy.","source":"europepmc","abstract":"Differential privacy (DP) has been in the public spotlight since the announcement of its use in the 2020 U.S. Census. While DP algorithms have substantially improved the confidentiality protections provided to Census respondents, concerns have been raised about the accuracy of the DP-protected Census data. The extent to which the use of DP distorts the ability to draw inferences that drive policy about small-populations, especially marginalized communities, has been of particular concern to researchers and policy makers. After all, inaccurate information about marginalized populations can often engender policies that exacerbate rather than ameliorate social inequities. Consequently, computer science experts have focused on developing mechanisms that help achieve equitable privacy, i.e., mechanisms that mitigate the data distortions introduced by privacy protections to ensure equitable outcomes and benefits for all groups, particularly marginalized groups. Our paper extends the conversation on equitable privacy by highlighting the importance of inclusive communication in ensuring equitable outcomes for all social groups through all the stages of deploying a differentially private system. We conceptualize Equitable DP as the design, communication, and implementation of DP algorithms that ensure equitable outcomes. Thus, in addition to adopting computer scientists' recommendations of incorporating equity parameters within DP algorithms, we suggest that it is critical for an organization to also facilitate inclusive communication throughout the design, development, and implementation stages of a DP algorithm to ensure it has an equitable impact on social groups and does not hinder the redressal of social inequities. To demonstrate the importance of communication for Equitable DP, we undertake a case study of the process through which DP was adopted as the newest disclosure avoidance system for the 2020 U.S. Census. Drawing on the Inclusive Science Communication (ISC) framework, we examine the extent to which the Census Bureau's communication strategies encouraged engagement across the diverse groups of users that employ the decennial Census data for research and policy making. Our analysis provides lessons that can be used by other government organizations interested in incorporating the Equitable DP approach in their data collection practices.","url":"https://doi.org/10.3389/fdata.2024.1420344","authors":["Vasundhara Kaul","Tamalika Mukherjee"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3389/fdata.2024.1420344","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/tcyb.2025.3571953","name":"Differential Privacy Enabled Robust Asynchronous Federated Multitask Learning: A Multigradient Descent Approach.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tcyb.2025.3571953","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/tcyb.2025.3571953","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1090/proc/17126","name":"COVARIANCE LOSS, SZEMEREDI REGULARITY, AND DIFFERENTIAL PRIVACY.","source":"europepmc","abstract":"","url":"https://doi.org/10.1090/proc/17126","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1090/proc/17126","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1038/s41598-025-15077-7","name":"A service-oriented microservice framework for differential privacy-based protection in industrial IoT smart applications.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-15077-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-025-15077-7","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.3390/s25051358","name":"Analysis, Design, and Implementation of a User-Friendly Differential Privacy Application.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25051358","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/s25051358","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.20944/preprints202506.0984.v1","name":"A Combined Approach of Heat Map Confusion and Local Differential Privacy for Anonymization of Mobility Data","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202506.0984.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.20944/preprints202506.0984.v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1038/s41598-025-94501-4","name":"FAItH: Federated Analytics and Integrated Differential Privacy with Clustering for Healthcare Monitoring.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-94501-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-025-94501-4","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.3390/e27040333","name":"Encrypted Spiking Neural Networks Based on Adaptive Differential Privacy Mechanism.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e27040333","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/e27040333","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.3389/fmed.2025.1590824","name":"Integrating differential privacy in deep reinforcement learning for sepsis treatment with pulmonary implications.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fmed.2025.1590824","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3389/fmed.2025.1590824","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.2196/59685","name":"Federated Analysis With Differential Privacy in Oncology Research: Longitudinal Observational Study Across Hospital Data Warehouses.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/59685","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.2196/59685","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1371/journal.pone.0327108","name":"Fusion of Personalized Federated Learning (PFL) with Differential Privacy (DP) Learning for Diagnosis of Arrhythmia Disease.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0327108","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1371/journal.pone.0327108","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1093/bib/bbaf166","name":"Federated transfer learning with differential privacy for multi-omics survival analysis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/bib/bbaf166","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1093/bib/bbaf166","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1016/j.compmedimag.2025.102637","name":"Secure and privacy-preserving surgical instrument segmentation in minimally invasive surgeries using federated differential privacy approach.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.compmedimag.2025.102637","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1016/j.compmedimag.2025.102637","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3389/frai.2025.1653437","name":"Entropy-adaptive differential privacy federated learning for student performance prediction and privacy protection: a case study in Python programming.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2025.1653437","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3389/frai.2025.1653437","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1007/978-3-031-65172-4_20","name":"Does Differential Privacy Prevent Backdoor Attacks in Practice?","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/978-3-031-65172-4_20","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1007/978-3-031-65172-4_20","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1038/s41598-025-01873-8","name":"Application of the joint clustering algorithm based on Gaussian kernels and differential privacy in lung cancer identification.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-01873-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-025-01873-8","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/s25051441","name":"Top-&lt;i&gt;k&lt;/i&gt; Shuffled Differential Privacy Federated Learning for Heterogeneous Data.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25051441","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/s25051441","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.20944/preprints202503.0079.v1","name":"Soft Prompt Tuning via Differential Privacy: Balancing Accuracy and Privacy in Language Models","source":"europepmc","abstract":"Despite its effectiveness, soft prompt tuning approach raises concerns about privacy---we might risk disclosing sensitive information about individuals represented in the data if attackers carefully inspect the learned prompts. To address privacy issues in general, differential privacy (DP) studies optimization algorithms that have strong theoretical privacy guarantees. In this work, we explore how DP can be integrated into soft prompt tuning to develop privacy-preserving language models. Our goal is to strike a balance between parameter efficiency, downstream accuracy, and data privacy.","url":"https://doi.org/10.20944/preprints202503.0079.v1","authors":["Junhong Shen"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.20944/preprints202503.0079.v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/s25010178","name":"Personalized Federated Learning Scheme for Autonomous Driving Based on Correlated Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25010178","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/s25010178","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.20944/preprints202411.0892.v1","name":"Critical Observability Enforcement in Discrete-event Systems Using Differential Privacy","source":"europepmc","abstract":"In the context of discrete-event systems (DESs), critical states usually refer to a system configuration of interest, describing certain important system properties, e.g., fault diagnosability, state/language opacity, and state/event concealment. Technically, a DES is critically observable if an intruder can always unambiguously infer, by observing the system output, whether the plant is currently in a predefined set of critical states or the current state set is disjoint with the critical states. In this paper, given a partially observable DES modeled with a finite-state automaton that is not critically observable, we focus on how to make it critically observable, which is achieved by proposing a novel enforcement mechanism based on differential privacy (DP). Specifically, we consider two observations, where one cannot determine whether a system is currently in the predefined critical states (i.e., the observation violating the critical observability), while the other is randomly generated by the system. When these two observations are processed separately by the differential privacy mechanism (DPM), the system generates an output, exposed to the intruder, that is randomly modified such that its probability approximates the two observations. In other words, the intruder cannot determine the original input of a system by observing its output. In this way, even if the utilized DPM is published to the intruder, he/she is unable to identify whether critical observability is violated.","url":"https://doi.org/10.20944/preprints202411.0892.v1","authors":["Jie Zhang","Zhiwu Li"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.20944/preprints202411.0892.v1","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/tp.2024.3487819","name":"Blockchain Based Secure Federated Learning With Local Differential Privacy and Incentivization.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tp.2024.3487819","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/tp.2024.3487819","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.20944/preprints202408.1270.v1","name":"AWDP-FL: An Adaptive Differential Privacy Federated Learning Framework","source":"europepmc","abstract":"Data security and user privacy concerns are increasingly gaining attention. Federated learning models based on differential privacy offer a distributed machine learning framework that protects data privacy; however, the added noise can impact the model&amp;#039;s utility, making performance evaluation crucial. To optimize the balance between privacy protection and model performance, we propose the Adaptive Weight-Based Differential Privacy Federated Learning (AWDP-FL) framework. This framework processes model parameters from the perspectives of neural network layers and model weights. Initially, each participant trains the model locally. During iterative training, the clipping threshold is determined by selecting an adaptive weight coefficient, followed by adaptive gradient clipping to control the gradient magnitude. Subsequently, adaptive gradient updates are applied to the model, and dynamic Gaussian noise is introduced when uploading the parameters to protect participant privacy. The server aggregates these noise-perturbed parameters to update the global model. This framework ensures strong privacy protection while maintaining model accuracy. Theoretical analysis and experimental results validate the effectiveness of this framework under stringent privacy constraints.","url":"https://doi.org/10.20944/preprints202408.1270.v1","authors":["Zhiyan Chen","Hong Zheng","Gang Liu"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.20944/preprints202408.1270.v1","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/jbhi.2023.3287092","name":"Wasserstein Generative Adversarial Networks Based Differential Privacy Metaverse Data Sharing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/jbhi.2023.3287092","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1109/jbhi.2023.3287092","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1145/3658644.3670351","name":"Cross-silo Federated Learning with Record-level Personalized Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1145/3658644.3670351","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1145/3658644.3670351","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/e26030233","name":"Mechanisms for Robust Local Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e26030233","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.3390/e26030233","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.22541/au.172625434.48862692/v1","name":"A Differential Privacy-Based Approach for Mitigating Data Theft in Ransomware Attacks","source":"europepmc","abstract":"Ransomware attacks have increasingly shifted toward more sophisticated tactics, not only encrypting critical files but also exfiltrating sensitive data, which is then used as leverage in extortion attempts. Addressing this dual threat, a novel framework integrating differential privacy provides an enhanced layer of protection by ensuring that exfiltrated data remains unusable to attackers through the introduction of statistical noise. This approach uniquely combines differential privacy with traditional security techniques, allowing for a dynamic and adaptable defense mechanism that ensures both data utility and robust privacy guarantees during ransomware attacks. The framework effectively mitigates data theft by applying controlled noise to sensitive datasets, which significantly reduces the probability of successful re-identification, even when auxiliary information is available. Through experimental evaluation, the framework has demonstrated superior performance in balancing privacy, utility, and system efficiency when compared to existing ransomware defense mechanisms. Furthermore, the modular nature of the system allows for seamless integration into existing cybersecurity infrastructures, ensuring that organizations can implement the solution without major architectural changes. Overall, the proposed framework offers a promising advancement in protecting sensitive data from both encryption and exfiltration threats, providing a comprehensive and adaptive approach to modern ransomware challenges.","url":"https://doi.org/10.22541/au.172625434.48862692/v1","authors":["Merumu Olabim","Amir Greenfield","Arjun Barlow"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.22541/au.172625434.48862692/v1","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1101/2024.10.09.24315159","name":"Navigating the Privacy-Accuracy Tradeoff: Federated Survival Analysis with Binning and Differential Privacy","source":"europepmc","abstract":"Abstract Federated learning (FL) offers a decentralized approach to model training, allowing for data-driven insights while safeguarding patient privacy across institutions. In the Personal Health Train (PHT) paradigm, it is local model gradients from each institution, aggregated over a sample size of its own patients that are transmitted to a central server to be globally merged, rather than transmitting the patient data itself. However, certain attacks on a PHT infrastructure may risk compromising sensitive data. This study delves into the privacy-accuracy tradeoff in federated Cox Proportional Hazards (CoxPH) models for survival analysis by assessing two Privacy-Enhancing Techniques (PETs) added on top of the PHT approach. In one, we implemented a Discretized Cox model by grouping event times into finite bins to hide individual time-to-event data points. In another, we explored Local Differential Privacy by introducing noise to local model gradients. Our results demonstrate that both strategies can effectively mitigate privacy risks without significantly compromising numerical accuracy, reflected in only small variations of hazard ratios and cumulative baseline hazard curves. Our findings highlight the potential for enhancing privacy-preserving survival analysis within a PHT implementation and suggest practical solutions for multi-institutional research while mitigating the risk of re-identification attacks.","url":"https://doi.org/10.1101/2024.10.09.24315159","authors":["Varsha Gouthamchand","Johan van Soest","Giovanni Arcuri","Andre Dekker","Andrea Damiani","Leonard Wee"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1101/2024.10.09.24315159","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1177/20552076251358531","name":"Federated learning and differential privacy: Machine learning and deep learning for biomedical image data classification.","source":"europepmc","abstract":"","url":"https://doi.org/10.1177/20552076251358531","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1177/20552076251358531","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.32388/pjil3e","name":"SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy","source":"europepmc","abstract":"Machine learning (ML) models frequently rely on training data that may include sensitive or personal information, raising substantial privacy concerns. Legislative frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have necessitated the development of strategies that preserve privacy while maintaining the utility of data. In this paper, we investigate the capability of Large Language Models (LLMs) to generate synthetic datasets integrated with Differential Privacy (DP) mechanisms, thereby enabling data-driven research and model training without direct exposure of sensitive information. Our approach incorporates DP-based noise injection methods, including Laplace and Gaussian distributions, into the data generation process. We then evaluate the utility of these DP-enhanced synthetic datasets by comparing the performance of ML models trained on them against models trained on the original data. To substantiate privacy guarantees, we assess the resilience of the generated synthetic data to membership inference attacks and related threats. The experimental results demonstrate that integrating DP within LLM-driven synthetic data generation offers a viable balance between privacy protection and data utility. This study provides a foundational methodology and insight into the privacy-preserving capabilities of LLMs, paving the way for compliant and effective ML research and applications.","url":"https://doi.org/10.32388/pjil3e","authors":["Md Mahadi Hasan Nahid","Sadid Bin Hasan"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.32388/pjil3e","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1093/jamia/ocaf090","name":"An empirical assessment of differential privacy in real-world observational data: a case-control study of asthma exacerbation in UK Biobank linked with electronic health records.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/jamia/ocaf090","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1093/jamia/ocaf090","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1038/s41598-024-77428-0","name":"Federated learning with differential privacy via fast Fourier transform for tighter-efficient combining.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-024-77428-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.1038/s41598-024-77428-0","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.20944/preprints202504.1583.v1","name":"<span style=\"mso-fareast-font-family: SimSun;\">Privacy-Preserving Financial Transaction Pattern Recognition: A Differential Privacy Approach","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202504.1583.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.20944/preprints202504.1583.v1","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.7717/peerj-cs.1576","name":"Natural differential privacy-a perspective on protection guarantees.","source":"europepmc","abstract":"","url":"https://doi.org/10.7717/peerj-cs.1576","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.7717/peerj-cs.1576","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-4846662/v1","name":"SPM-FL: A Federated Learning Privacy Protection Mechanism Based on Local Differential Privacy","source":"europepmc","abstract":"Abstract Federated learning (FL) is a popular distributed machine learning approach that avoids data transfer to third parties by sharing and computing model parameters through a central server, thereby protecting client privacy. However, model weights and other information can still be analyzed and leaked. Currently, many methods in FL apply local differential privacy (LDP) to protect model parameters, but model accuracy significantly declines with smaller privacy budgets and varying numbers of clients. To address this issue, we propose the Symmetric Piecewise Mechanism (SPM) to perturb local model weight parameters before aggregation. First, we design an LDP mechanism that satisfies ε-differential privacy to ensure algorithmic privacy. Second, we introduce a variance-constrained mechanism suitable for FL, maintaining effectiveness with smaller privacy budgets and different client numbers. Finally, we compare the proposed mechanism with other algorithms on three public datasets in terms of model accuracy, variance, and privacy protection. Theoretical proofs demonstrate that our mechanism achieves the minimum variance and highest model accuracy under any privacy budget, showing superior performance.","url":"https://doi.org/10.21203/rs.3.rs-4846662/v1","authors":["Zhiyan Chen","Hong Zheng"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","addedAt":"2026-08-06T22:49:41.717Z","doi":"10.21203/rs.3.rs-4846662/v1","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"pmid:42550866","name":"Federated parameter-free DBSCAN clustering and its application in image recognition.","source":"pubmed","abstract":"DBSCAN (A Density-Based Algorithm for Discovering Clusters in Spatial Databases with Noise) is a classic clustering algorithm. However, clustering distributed data with privacy protection in edge computing environments is a key challenge for DBSCAN. In this research, we combine federated clustering and DBSCAN and propose two secure federated parameter-free DBSCAN clustering methods, called FDBSCAN and FDBSCAN++. The process involves the following steps: (1) differential privacy is applied to the client data and adaptive DBSCAN is used at each client to identify core points; (2) the clients send the extracted core points to the server, where the server aggregates these to obtain the final global cluster centers (FDBSCAN and FDBSCAN++&#x2009;use different methods in this step); (3) the final clusters are generated using these global centers. To verify the effectiveness of the proposed two algorithms, we use eight real datasets, including the large-scale image dataset MNIST. Compared with traditional and state-of-the-art (SOTA) improved DBSCAN and federated clustering algorithms, the proposed algorithms achieve better clustering accuracy. In addition, we also apply FDBSCAN++ to image clustering and segmentation tasks, which achieves satisfactory results.","url":"https://pubmed.ncbi.nlm.nih.gov/42550866/","authors":["Cheng F","Deng Z","Alobaedy MM","Huang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42550286","name":"Differential complication risks in diabetic and non-diabetic geriatric patients following distal femur fracture surgery.","source":"pubmed","abstract":"Current literature is limited regarding the impact of diabetes mellitus (DM) on short- and long-term postoperative complications following distal femur (DF) fracture surgery in geriatric patients. Although DM is a known risk factor for adverse outcomes following hip fracture and lower-extremity arthroplasty, large-scale data specific to DF fractures, which pose distinct fixation, weight-bearing, and healing challenges, remain limited. This study evaluates short- and long-term postoperative complication rates among geriatric patients undergoing operative treatment for DF fractures, comparing those with and without DM.","url":"https://pubmed.ncbi.nlm.nih.gov/42550286/","authors":["Tummala S","Mittal MM","Sambandam SN","Sathy A","Wukich DK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 4","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42545569","name":"Invasiveness, SOX2 Expression, and Resection Outcomes in TF-Defined PitNETs After Extra-Pseudocapsular Resection.","source":"pubmed","abstract":"To evaluate the associations between transcription factor (TF)-defined molecular lineages and the clinical characteristics of pituitary neuroendocrine tumors (PitNETs).","url":"https://pubmed.ncbi.nlm.nih.gov/42545569/","authors":["Li XB","Zeng K","Wan XY","Liu HY","Lu L","Chen J","Ke CS","Wang JW","Lei T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 3","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42539885","name":"Markedly improved disease control in Darier disease (ATP2A2-nonsyndromic epidermal differentiation disorder) with ustekinumab versus other biologics: a case series.","source":"pubmed","abstract":"In this case series, we report six patients with severe, treatment-refractory Darier disease, or dyskeratosis follicularis, also known as ATP2A2 -nEDD (nonsyndromic epidermal differentiation disorder), treated with off-label biologic therapy, including dupilumab ( n = 3), secukinumab followed by guselkumab ( n = 1), and ustekinumab ( n = 2). All patients had longstanding disease with recurrent flares, frequent infections and substantial impairment of quality of life, despite multiple conventional treatments. All biologic treatments were well tolerated, with no serious adverse events. Dupilumab consistently improved pruritus but had limited effects on skin lesions. Secukinumab and guselkumab provided only transient or partial disease control. In contrast, both patients treated with ustekinumab achieved pronounced and sustained clinical improvement, with markedly reduced disease severity and pruritus, and considerable improved quality of life. The pronounced and sustained responses observed with ustekinumab support its prioritization for future studies in Darier disease.","url":"https://pubmed.ncbi.nlm.nih.gov/42539885/","authors":["Skarnvad Andersen A","Baez E","Emmanuel T","Rønholt K","Sommerlund M","Johansen C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42537019","name":"Addressing the Challenges in Using Synthetic Data for Health Research: Application to Cardiology.","source":"pubmed","abstract":"Synthetic data offer significant potential for cardiology research by enabling data sharing, preserving privacy, and supporting machine learning model development. By generating artificial patient records that reflect real-world distributions, synthetic data can accelerate clinical research, improve model performance for rare cardiovascular conditions, and facilitate transnational collaborations that would otherwise be restricted by data-sharing barriers. Despite these advantages, the increasing use of synthetic data raises important ethical, regulatory, and methodological concerns that remain insufficiently addressed. Key challenges include assessing the validity and generalizability of synthetic datasets, understanding their limitations in representing complex and heterogeneous patient populations, and preventing the amplification of existing biases in cardiovascular care. Current regulatory frameworks, including the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), do not fully address emerging risks such as reidentification and data leakage, and there is no harmonized guidance to govern the use of synthetic data as stand-alone evidence for medical device evaluation or therapeutic research. In this viewpoint, we argue that responsible integration of synthetic data in cardiology requires, first, clear differentiation between synthetic data as a privacy-preserving distributional substitute and synthetic data as a counterfactual simulation tool, and, second, fit-for-purpose governance frameworks that pair rigorous utility and fidelity testing with explicit, adversary-aware privacy evaluation before synthetic cohorts are accepted as evidence in research or product evaluation. A prerequisite for that governance is conceptual clarity about what synthetic data are being used for. Synthetic data in health care serve 2 fundamentally distinct roles that carry entirely different validity requirements, failure modes, and regulatory implications, yet they are routinely conflated. The first role is as a privacy-preserving distributional substitute: the goal is statistical fidelity to the real data distribution, so that analyses of the synthetic dataset yield results equivalent to those of the original. The second role is as a tool for counterfactual simulation: the goal is to generate data that could not have been observed, such as rare conditions, hypothetical interventions, or extrapolations to new populations. These 2 roles are methodologically distinct. A dataset that accurately reflects real-world distributions may be inadequate for extrapolating findings to underrepresented subgroups. Conversely, a simulator optimized for novel scenario generation may systematically diverge from real-world distributions. This distinction informs every subsequent discussion of validity, bias, and regulation in this viewpoint and our proposed 4 concrete actions for the cardiology research community, including mandatory 3-layer (fidelity, utility, and privacy) validation, systematic subgroup reporting, explicit intended-use scoping, and domain-specific acceptability thresholds for synthetic data-based evidence.","url":"https://pubmed.ncbi.nlm.nih.gov/42537019/","authors":["Baschet L","Marque S","Locret S","Jenssen J","Barbet V","Jourdain P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 31","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42531800","name":"An artificial intelligence use framework for nursing education: Bridging policy and pedagogical implementation.","source":"pubmed","abstract":"Artificial intelligence (AI) is transforming higher education and healthcare, yet nursing faculty lack practical guidance for determining appropriate AI use in specific academic tasks. Institutional AI policies establish boundaries but rarely address learning outcomes, task purposes, or nursing-specific responsibilities such as patient privacy, clinical judgment development, and professional accountability.","url":"https://pubmed.ncbi.nlm.nih.gov/42531800/","authors":["Moore J","Frangieh J","Capello A","Montejo L","Lukkahatai N","Aryal S","Zhang J","Mudd S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 30","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42530785","name":"A 3D PDMS Scaffold Microchip Platform for Non-Invasive Detection of Circulating Endometrial Cells: Revolutionizing Endometriosis Diagnosis.","source":"pubmed","abstract":"To address the urgent need for non-invasive diagnosis of&#xa0; endometriosis, this study developed a novel microfluidic platform utilizing a three-dimensional&#xa0;(3D) polydimethylsiloxane scaffold microchip to efficiently capture circulating endometrial cells (CECs).","url":"https://pubmed.ncbi.nlm.nih.gov/42530785/","authors":["Yang SH","Wang YK","Xu LL","Liu F","Yan Q","Huang FK","Yuan SZ","Yan W","Liu HY","Xie M","Wang WW"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 30","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42525943","name":"EffSCG: An Efficient Framework for Real-Time Seismocardiogram Denoising on Resource-Constrained Edge Devices.","source":"pubmed","abstract":"Most deep learning models for wearable devices are still implemented on centralized servers due to memory and computational power limitations under the assumption that these servers will communicate with the wearable units. However, enabling complex physiological computing models to run directly on such edge units is beneficial, as it reduces latency and enhances data privacy. In this paper, we address this problem under the setting of a denoiser for a particular type of cardiovascular signal originating from the mechanical movements of the heart and blood - the seismocardiogram. Specifically, we demonstrate that by improvements in multiple aspects, the model can be made applicable to resource-constrained edge devices by improving the computational speed and the memory footprint without substantive accuracy reduction. Our optimization framework achieves a lower inference time and memory footprint by using 1. structured pruning of the weights of the neural network, 2. quantization of the weights to integer representations, 3. incorporation of a faster ordinary differential equation solver, and 4. application of progressive distillation. Compared to the baseline, our framework achieves a 47.2&#xd7; speedup (97.9% latency reduction) and a 4.94&#xd7; smaller memory footprint (79.8% memory reduction), while maintaining 92.88% of the original accuracy (7.12% deviation). In addition, we conduct an extensive analysis of multiple parameters to demonstrate their impact on computational speed, memory footprint, and accuracy. The presented framework will be applicable to models with diffusion-based architectures to enable their use on scarce-resourced edge devices to deploy these models in everyday life.","url":"https://pubmed.ncbi.nlm.nih.gov/42525943/","authors":["Emirdagi AR","Yildiz CO","Kilic OS","Cho MJ","Inan OT"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 29","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42520339","name":"Age-stratified machine learning using de-identified clinical and transcriptomic data for pediatric appendicitis.","source":"pubmed","abstract":"Pediatric appendicitis triage remains clinically challenging, as standard single-model diagnostic scores yield moderately discriminative performance.","url":"https://pubmed.ncbi.nlm.nih.gov/42520339/","authors":["Abdullahi SB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 22","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42519990","name":"Shadow AI in Swedish Health Care: Qualitative Analysis of Physicians' Free-Text Answers.","source":"pubmed","abstract":"The rapid emergence of artificial intelligence (AI) has outpaced its formal adoption in health care organizations, contributing to the emergence of Shadow AI, defined here as the use of unauthorized AI tools by medical professionals. Under the European Union Medical Device Regulation, AI tools used for clinical purposes must undergo conformity assessment before use; general-purpose tools such as ChatGPT have not done so, rendering their clinical application unauthorized at the regulatory level. While Shadow AI offers potential efficiency gains and higher performance, it poses significant risks to data privacy, clinical safety, and regulatory compliance. Despite its growing prevalence, empirical research on the purposes for which physicians use Shadow AI remains scarce.","url":"https://pubmed.ncbi.nlm.nih.gov/42519990/","authors":["Petersson L","Irgang L","Mauritzon I","Holmén M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 28","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42518006","name":"Tumor-to-tumor metastasis of breast carcinoma to clear cell renal cell carcinoma: a rare case with a review of the literature.","source":"pubmed","abstract":"Tumor-to-tumor metastasis (TTM) is a rare clinicopathological phenomenon in which a malignant tumor metastasizes to another distinct neoplasm. Although renal cell carcinoma (RCC) is a frequent recipient tumor, metastasis from breast carcinoma to RCC is extremely rare. A 64-year-old woman with long-standing hormone receptor-positive breast carcinoma presented with a 23-mm enhancing right renal mass. Robot-assisted partial nephrectomy revealed clear cell RCC with intratumoral nests of metastatic breast carcinoma. Immunohistochemistry revealed a reciprocal staining profile: RCC cells were positive for PAX8 and CA9, whereas metastatic breast carcinoma cells expressed CK7 and GATA3 with weak ER positivity. Clear cell RCC may serve as a recipient tumor for breast carcinoma metastasis, emphasizing that TTM should be considered in the differential diagnosis of renal masses in patients with a history of breast carcinoma, even when imaging findings are consistent with conventional RCC.","url":"https://pubmed.ncbi.nlm.nih.gov/42518006/","authors":["Nakajima N","Kajiwara H","Okutsu K","Sakemura T","Ikoma H","Otaki T","Yuzuriha S","Uchida T","Umemoto T","Kawamura Y","Masugi Y","Shoji S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 28","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42517779","name":"The role of machine learning in early detection, accurate diagnosis, and timely treatment of diseases.","source":"pubmed","abstract":"The contemporary healthcare landscape is experiencing a significant transformation driven by the rapid growth of digital health data and advancements in computational technologies. At the center of this evolution is Machine Learning (ML), a branch of artificial intelligence that enables systems to learn from data, recognize patterns, and support decision-making with minimal human intervention. This paper presents a comprehensive analysis of the role of ML in enhancing early disease detection, accurate diagnosis, and timely treatment across modern healthcare systems. It begins by discussing key ML paradigms, including supervised, unsupervised, and reinforcement learning, and their applications in medical practice. The study further highlights how advanced ML and deep learning algorithms achieve human-level or even superior performance in analyzing complex healthcare data such as medical imaging, genomics, and electronic health records. ML applications in the early detection of diseases such as cancer, diabetic retinopathy, and sepsis are explored, emphasizing their ability to identify subtle pre-symptomatic patterns. Additionally, the paper examines the role of ML in differential diagnosis, risk stratification, and personalized medicine through multi-omics data integration. Furthermore, the paper discusses the contribution of ML to precision oncology, drug discovery, and chronic disease management. Despite its potential, challenges such as data quality, interpretability, ethical concerns, regulatory barriers, and privacy issues continue to hinder widespread clinical adoption. The paper concludes that ML will augment rather than replace clinicians, enabling predictive, personalized, and data-driven healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/42517779/","authors":["Shrivastava A","Tripathi A","Rajput J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 28","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42515243","name":"Differential-Privacy-Based Collaborative Protection for Visual and Location Data in UAV Semantic Communications.","source":"pubmed","abstract":"Unmanned aerial vehicle semantic communications are increasingly required in low-altitude sensing, intelligent inspection, and emergency response, where raw image transmission is difficult to sustain under limited onboard resources and time-varying air-to-ground links. Meanwhile, the simultaneous transmission of visual semantic features and object-centre location metadata under third-party eavesdropping creates a dual-privacy vulnerability: an attacker can exploit both to reconstruct sensitive content. In this paper, we propose a differential privacy-based collaborative protection framework that inserts dedicated perturbations into visual semantic and location descriptors before transmission. For visual data, we design a region-aware differential privacy mechanism that applies stronger noise to sensitive semantic regions while preserving utility for non-critical areas. For location data, a scenario-adaptive strategy is developed, comprising randomized differential privacy for discrete grid-based location information (coarse spatial awareness) and Laplace-based differential privacy for continuous coordinates (fine-grained protection). To balance privacy and utility, we formulate a joint optimization problem. It maximizes legitimate-side semantic task performance by coordinating the visual privacy budget, location privacy budget, and transmit power. A BCD-based algorithm is developed to solve this non-convex problem. Attacker-side recoverability is verified empirically at the optimized operating point. Simulation results demonstrate stable convergence within a small number of iterations. Compared with uniform differential privacy, the proposed framework achieves a superior task-level privacy-utility trade-off and provides selective sensitive-region protection, with the two mechanisms yielding comparable whole-image attack suppression.","url":"https://pubmed.ncbi.nlm.nih.gov/42515243/","authors":["Yue S","Zhan C","Jiang G","Qiu Y","Han S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 9","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42511404","name":"A Color Image Encryption Using a 4D Variable-Order Fractional Hyperchaotic System and Chess-Gameplay-Inspired Dynamic Mechanism.","source":"pubmed","abstract":"With the widespread adoption of digital images in network transmission and storage, the demand for image privacy protection keeps rising. We propose a robust scheme combining a four-dimensional variable-order fractional hyperchaotic system (4D-VOFHS) and a chess-game play-inspired dynamic mechanism. Firstly, we construct 4D-VOFHS, to overcome inherent limitations of constant-order systems: unlike constant-order systems that are vulnerable to deep-learning-based parameter identification attacks, this system introduces time-varying orders and high-dimensional coupling to enrich nonlinear dynamics. Secondly, inspired by the dynamic strategic interactions within chess gameplay, we design a synchronous encryption framework with a tightly coupled permutation-diffusion mechanism. This design not only significantly enhances the nonlinear complexity, confusion and diffusion performance of the algorithm, but also enables parallel synchronous processing to improve computational throughput. Finally, we propose a block-based collaborative scrambling strategy with multi-chess-piece rules, wherein traversal rules and scrambling operations are not predefined; instead, they are dynamically updated according to the real-time state evolution of the 4D-VOFHS. Through comprehensive correlation analysis and differential attack tests, the presented encryption framework achieves outstanding performance metrics: an average NPCR of 99.6%, a UACI of 33.4%, and an average information entropy of 7.9993. Overall, these results verify the strong cryptographic robustness and practical applicability of the scheme, highlighting its great potential for deployment in real-world color image encryption systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42511404/","authors":["Cui X","Zhang X","Ji J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 13","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42506308","name":"Walking as a Window to the Brain: Redefining Gait in Neurology.","source":"pubmed","abstract":"Walking is not merely locomotion but a window into the nervous system, integrating cortical, subcortical, cerebellar, spinal, and peripheral networks into a unified motor behavior. Across neurological diseases-including Parkinson's disease, atypical parkinsonism, cerebellar ataxias, stroke, multiple sclerosis, neuropathies, neuromuscular disorders, and functional gait syndromes-gait disturbances are among the most disabling clinical features, contributing to falls, loss of independence, institutionalization, and premature mortality. Traditional bedside observation remains indispensable, but it lacks the sensitivity and reproducibility needed to capture subtle, episodic, or prodromal abnormalities. Over the past decade, advances in wearable sensors, marker-based and markerless motion capture, pressure-sensitive walkways, force plates, artificial intelligence, and machine learning have positioned digital mobility outcomes as promising, ecologically valid biomarkers of neurological function. These measures can support differential diagnosis, provide prognostic information on falls and survival, and serve as sensitive endpoints in therapeutic trials. They may also detect early abnormalities, such as increased stride-to-stride variability or prolonged double-support time, before overt clinical deterioration becomes evident. Clinical applications are increasingly evident across disorders, including distinguishing Parkinson's disease from atypical parkinsonism, quantifying treatment response in normal-pressure hydrocephalus, tracking progression in ataxia and multiple sclerosis, predicting functional decline in motor neuron disease, and guiding rehabilitation after stroke. Integration with neuroimaging, electrophysiology, and molecular biomarkers is beginning to reveal the circuits underlying variability, instability, and freezing, positioning gait as a systems-level marker of neural integrity. Nevertheless, methodological heterogeneity, limited disease-specific validation, insufficient longitudinal data, and lack of consensus on clinically meaningful parameters continue to constrain translation. Cognitive, affective, and environmental influences also remain insufficiently represented in digital frameworks, while equity, accessibility, algorithmic bias, and privacy require careful ethical governance. Reconceptualizing gait as a \"sixth vital sign\" reframes mobility as a multidimensional biomarker of neural and systemic health. With harmonized protocols, robust validation, multimodal integration, and appropriate ethical frameworks, gait analysis could become a cornerstone of precision neurology.","url":"https://pubmed.ncbi.nlm.nih.gov/42506308/","authors":["Ortega-Robles E","Treviño M","Manjarrez E","Arias-Carrión O"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 23","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42482218","name":"Mental health damage risk, help-seeking behavior and coping strategies among young college students under different types of network media violence: a comprehensive study based on qualitative phenomenological research methodology.","source":"pubmed","abstract":"With the widespread adoption of network technology and the increasing online engagement among young college students, network media violence has become a prevalent issue threatening their physical and mental health. Different types of network violence exhibit distinct patterns and impacts on students, yet existing research lacks targeted studies on their differentiated characteristics and corresponding countermeasures. This study takes Husserl's phenomenology 'returning to the thing itself' as the core essence, and adopts qualitative phenomenological research methods, including literature data method, case analysis method and phenomenon combing method, to carry out in-depth analysis, so as to restore the essential characteristics of the phenomenon. This paper categorizes network media violence into four types: explicit verbal violence, privacy violation violence, implicit ridicule exclusion violence, and disinformation and defamation violence, delineating their distinct characteristics. It further reveals how different forms of network violence pose differentiated mental health risks to college students across four dimensions: emotional experience, self-worth, interpersonal adaptation, and psychological adaptation. This study elucidates the distinct impacts of various forms of network media violence on college students' willingness to seek help, their preferred sources of assistance, the methods they employ, and the challenges they encounter during the help-seeking process. Based on these findings, targeted strategies for addressing different types of network media violence and self-protection recommendations are proposed. The research enriches the qualitative literature on network media violence and the mental health of young college students, addressing previous gaps in insufficient attention to implicit violence and the lack of focused analysis. It provides actionable insights for enhancing students' self-protection capabilities, improving university initiatives for network media violence prevention and mental health education, and strengthening cyberspace governance by relevant authorities.","url":"https://pubmed.ncbi.nlm.nih.gov/42482218/","authors":["Gou J","Zhou Q","An L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 21","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42482143","name":"Enhancing lymph node metastases assessment in breast cancer post-neoadjuvant therapy using artificial intelligence-driven diagnostics.","source":"pubmed","abstract":"Neoadjuvant therapy (NAT) is crucial for locally advanced breast cancer, but post-NAT lymph node assessment is challenging due to histological changes. Current methods like immunohistochemistry (IHC) are labor-intensive and imprecise in distinguishing isolated tumor cells (ITCs), micro-metastases (Micro), and macro-metastases (Macro). We aimed to develop and validate an AI-driven model for precise classification of lymph node metastasis status (negative, ITC, Micro, Macro) in breast cancer patients post-NAT.","url":"https://pubmed.ncbi.nlm.nih.gov/42482143/","authors":["Ding Y","Yu J","Liu M","Liu X","Kang L","Huang L","Li J","Wang Y","Xu X","Zhao M","Wei P","Li S","Li Z","Liu Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 21","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42481652","name":"Privacy-preserving clustered federated learning via differential privacy and homomorphically encrypted prototypes.","source":"pubmed","abstract":"Clustered federated learning (CFL) is an effective paradigm for handling statistical heterogeneity by grouping clients with similar data characteristics and learning cluster-specific models. However, existing CFL methods often expose sensitive clustering signals or cluster-specific updates to the server, which may reveal latent client similarity relations and weaken privacy protection. To address this issue, we propose Privacy-Preserving Clustered Federated Learning (PPCFL), a split-stream framework that integrates adaptive Gaussian perturbation with threshold Paillier encrypted aggregation. In PPCFL, backbone updates are protected by adaptive Gaussian perturbation before plaintext aggregation, while clustering signatures and cluster-head updates are first perturbed by stream-specific adaptive Gaussian mechanisms and then uploaded under threshold Paillier encryption. The server performs ciphertext-domain aggregation for clustering prototypes and cluster-head updates, whereas plaintext prototypes and cluster-level decrypted aggregates are recovered by a qualified threshold-decryption client subset without giving the server decryption capability. In addition, PPCFL adopts round-wise budget growth, utility-aware refinement, and adaptive clipping-threshold updates to improve the privacy-utility trade-off under dynamic Non-IID settings. Experiments on MNIST, Fashion-MNIST, and CIFAR-10 show that PPCFL achieves the highest final-round accuracy among the evaluated methods in the reported settings while providing enhanced protection for clustering-related information and cluster-specific updates. Under the representative Dirichlet setting [Formula: see text], PPCFL improves the final accuracy over DP-FedAvg by 0.33, 1.73, and 2.62 percentage points on MNIST, Fashion-MNIST, and CIFAR-10, respectively, and over IFCA by 0.98, 8.28, and 10.24 percentage points.","url":"https://pubmed.ncbi.nlm.nih.gov/42481652/","authors":["Zhan J","Jiang Z","Liu L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 21","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42481424","name":"Reaching Men and Women Through a Decentralized Mobile Service Delivery Model for HIV Prevention and Pre-Exposure Prophylaxis Services in South Africa.","source":"pubmed","abstract":"Structural and service delivery barriers such as long waiting times, health care provider attitudes, and distance from services prevent access to HIV prevention and pre-exposure prophylaxis (PrEP) services, particularly for young people. The World Health Organization recommends differentiated service delivery models for HIV prevention and treatment services, including through mobile outreach. Mobile health clinics offer an opportunity to overcome barriers to service access by bringing services closer to places of work or study, improving convenience, minimizing stigma, and expanding the choice of service delivery locations and types.","url":"https://pubmed.ncbi.nlm.nih.gov/42481424/","authors":["Nongena P","Martin CE","Muhwava LS","Cholo FA","Chidumwa G","Mojapele MV","Butler V","Mullick S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 21","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42477702","name":"Circulating alpha-1 antitrypsin and its c-terminal peptides differentiate bacterial from viral community-acquired pneumonia.","source":"pubmed","abstract":"Distinguishing bacterial from viral community-acquired pneumonia (CAP) remains a major clinical challenge, often leading to inappropriate antimicrobial use. Alpha-1 antitrypsin (AAT) is an acute-phase protein that regulates neutrophil protease activity and is cleaved during inflammation, generating bioactive peptides. We investigated whether circulating AAT and its peptides could discriminate bacterial from viral CAP.","url":"https://pubmed.ncbi.nlm.nih.gov/42477702/","authors":["Pashai Fakhri M","Börner FR","Held J","Sivaraman K","Fuge J","Rupp J","Barten-Neiner G","Pletz MW","Witzenrath M","Rohde G","Rademacher J","Hinze CA","Janciauskiene S","CAPNETZ Study Group"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 20","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42474586","name":"Factors influencing discussion duration in breast cancer multidisciplinary team meetings: insights for streamlining care.","source":"pubmed","abstract":"Multidisciplinary team meetings (MDTMs) in breast cancer care improve outcomes but are time-consuming and costly. This study investigates using data from the Dutch national cancer registry (NCR) and hospital electronic medical records (EMR) to efficiently calculate MDTM discussion durations, while complying with privacy laws.","url":"https://pubmed.ncbi.nlm.nih.gov/42474586/","authors":["Kočo L","Sanderink WBG","Prokop M","Mann RM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 20","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42471473","name":"Federated TinyML and digital twin framework for secure and resilient IoMT-based ICU monitoring.","source":"pubmed","abstract":"Resource-constrained medical sensing devices are increasingly expected to support local intelligence, privacy-preserving collaboration, and secure communication in Internet of Medical Things (IoMT) environments. However, deploying federated learning in ICU monitoring remains challenging because hospital data are often non-IID, model updates may be adversarially poisoned, and emerging quantum-security threats require stronger communication protection. This paper presents a federated TinyML framework with an edge-hosted patient-state Digital Twin layer for ICU monitoring. The Digital Twin component is implemented as a lightweight patient-state representation rather than a full physiological simulator. It maintains recent physiological observations, temporal risk trends, predicted status labels, and interpretable alert information at the hospital gateway. Patient-specific adaptation is therefore achieved through individualized temporal state tracking and risk-history synchronization, while the predictive model is collaboratively learned across hospitals. The suggested framework employs decision-tree ensembles due to their lightweight nature, efficient inference capabilities, and interpretability inherent to ESP32-class devices. As decision tree structures are not amenable to average aggregation like neural network parameters, the aggregation of local client models is done via the validation-based ensemble fusion. In order to enhance the resistance of the framework to attacks based on poisoning clients, the Performance-Based Filtering (PBF) method analyzes the performance of each local ensemble against a trusted validation set and excludes low-quality local ensembles from the subsequent global ensemble aggregation. The secure model exchange process utilizes ML-KEM-512 key encapsulation scheme in combination with AES-256-GCM authenticated encryption. The extra overhead of the crypto layer is 0.09 ms per update exchange. The framework is evaluated using a clinician-reviewed synthetic ICU monitoring dataset distributed across three non-IID hospital clients and externally benchmarked using a PhysioNet-based critical-care dataset constructed from shared physiological features. The prediction task uses five status categories: normal, mild, moderate, critical, and an outlier/anomaly category; these labels represent synthetic severity-status classes rather than prospectively validated ICU endpoints. Under a targeted label-flipping attack in which one client relabels critical cases as normal, the proposed PBF strategy achieves 86.79% accuracy, 0.782 macro-F1, 0.904 critical-class recall, 0.837 critical-class F1-score, 0.947 macro-AUROC, and 0.799 macro-AUPRC on the synthetic ICU evaluation. External benchmarking at [Formula: see text] achieves 98.04% accuracy, 0.959 macro-F1, 0.977 critical-class recall, 0.987 critical-class F1-score, 0.999 macro-AUROC, and 0.983 macro-AUPRC. Comparative evaluation against FedAvg, Krum, Trimmed Mean, and Coordinate Median shows that PBF provides stronger protection of critical-class recall under adversarial conditions, although full prospective clinical validation remains outside the scope of this study. Overall, the results indicate that a federated TinyML architecture with lightweight patient-state tracking, validation-based ensemble filtering, differential privacy, and post-quantum-secure communication can support privacy-aware and attack-resilient ICU monitoring experiments in resource-constrained IoMT settings. The study should be interpreted as a technical feasibility and robustness evaluation rather than a fully integrated clinical Digital Twin deployment.","url":"https://pubmed.ncbi.nlm.nih.gov/42471473/","authors":["Khan UH","Khan R","Alsaedi T","Chelloug SA","Alturise F","Alkhalaf S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 19","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42471351","name":"ViBioChain: a blockchain-enabled architecture for privacy-preserving, ethically governed, and explainable personalized gene editing.","source":"pubmed","abstract":"Personalized gene editing demands robust mechanisms for privacy, ethical governance, and verifiable data integrity. This paper proposes ViBioChain, a modular blockchain-anchored architecture integrating five components: (1)&#xa0;differential chain-of-custody audit combining quantum fingerprinting with post-quantum signatures for immutable genomic audit trails; (2)&#xa0;proof-of-bioethical-compliance employing zero-knowledge proofs and AI-based ontology evaluation for automated bioethical gating; (3)&#xa0;federated genomic trust mesh (FGTM) enabling privacy-preserving collaborative model training with Renyi differential privacy accounting and trust-weighted federated aggregation; (4)&#xa0;ethical smart orchestration network for modular smart-contract-based workflow governance; and (5)&#xa0;genomic impact estimator via ethical explainability graphs (GIE-EEG) for ancestry-aware, ethically constrained phenotypic forecasting. Afterexpert-driven reconciliation, the implementation was rerun using 800 simulated individuals per dataset, 120 binary loci, five institutional clients, five independent seeds (42-46), and a true trust-weighted federated logistic aggregation path for FGTM rather than the earlier centralized accuracy proxy. Across three genomic cohorts and three domain-comparable baselines, ViBioChain achieved 92.16% ethical violation interception, 100.00% audit trail accuracy, 99.47% workflow traceability, 0.9183 ethical score alignment, and the highest global model accuracy among the tested methods (74.36%). The formal Renyi differential privacy accountant remained within budget ([Formula: see text], [Formula: see text]); however, the conservative clean-versus-noisy update leakage proxy did not support the earlier lowest-empirical-leakage assertion. That claim has therefore been removed. Additional IID and non-IID experiments show that severe Dirichlet client heterogeneity ([Formula: see text]) reduced final accuracy by 1.70-4.10 percentage points relative to IID partitions. The revised results provide a more conservative and reproducible blueprint for secure, ethically governed, and explainable genomic medicine in multi-institutional settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42471351/","authors":["Prabakaran C","Kannadasan R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 18","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42459561","name":"Minimax and adaptive transfer learning for nonparametric classification under distributed differential privacy constraints.","source":"pubmed","abstract":"This paper considers minimax and adaptive transfer learning for nonparametric classification under the posterior drift model with distributed differential privacy constraints. Our study is conducted within a heterogeneous framework, encompassing diverse sample sizes, varying privacy parameters, and data heterogeneity across different servers. We first establish the minimax misclassification rate, precisely characterizing the effects of privacy constraints, source samples, and target samples on classification accuracy. The results reveal interesting phase transition phenomena and highlight the intricate trade-offs between preserving privacy and achieving classification accuracy. We then develop a data-driven adaptive classifier that achieves the optimal rate within a logarithmic factor across a large collection of parameter spaces while satisfying the same set of differential privacy constraints. Simulation studies and real-world data applications further elucidate the theoretical analysis with numerical results.","url":"https://pubmed.ncbi.nlm.nih.gov/42459561/","authors":["Auddy A","Cai TT","Chakraborty A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42457301","name":"Federated learning for privacy-preserving multi-center tuberculosis diagnosis using chest imaging data.","source":"pubmed","abstract":"Tuberculosis (TB) remains one of the most serious global health challenges, demanding reliable and early detection methods to reduce transmission and mortality. Chest imaging, particularly chest X-rays, plays a vital role in screening, but its interpretation varies across institutions and requires expert radiologists who may not always be available. This study introduces a federated learning framework for privacy-preserving, multi-center TB diagnosis using chest imaging data. Unlike centralized training, the proposed approach allows multiple healthcare institutions to collaboratively train a shared deep learning model without transferring raw patient data, thus ensuring compliance with privacy regulations such as HIPAA and GDPR. Each participating centre trains a local convolutional neural network (CNN) on its dataset, and only encrypted model parameters are shared for aggregation. The framework incorporates differential privacy, secure aggregation, and encryption to enhance confidentiality while maintaining diagnostic accuracy. Extensive experiments on multi-center datasets, including Shenzhen, Montgomery, and NIH ChestX-ray14, demonstrate that the federated CNN achieved an average accuracy of 94.8&#xa0;%, sensitivity of 93.5&#xa0;%, and specificity of 95.2&#xa0;%, closely matching centralized models while offering superior generalization across heterogeneous data sources. Hybrid architectures combining CNN and transformer layers further improved interpretability and precision. The findings confirm that federated learning effectively balances diagnostic performance and patient privacy, establishing a scalable, secure, and collaborative paradigm for medical imaging analysis. This framework provides a promising foundation for broader applications in multi-institutional disease diagnosis and healthcare data governance.","url":"https://pubmed.ncbi.nlm.nih.gov/42457301/","authors":["Bhattacharjee S","Sapkal V","Sharma VK","Rewatkar A","Khidse SV","Ingole PK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42449434","name":"Beyond transparency: why Traditional Chinese Medicine (TCM) need explainable artificial intelligence (XAI).","source":"pubmed","abstract":"The integration of artificial intelligence (AI) with Traditional Chinese Medicine (TCM) is rapidly expanding, creating new opportunities for digital diagnosis, syndrome differentiation, prescription recommendation, multimodal clinical support, and knowledge mining. The central obstacle to this integration, however, lies beyond predictive performance and stems from a fundamental epistemological mismatch between data-driven AI and theory-driven TCM. Contemporary AI systems often operate through opaque statistical representations, whereas TCM depends on holistic, relational, and interpretive reasoning centered on syndrome differentiation and disease-mechanism inference. Consequently, explainable artificial intelligence (XAI) is required not merely to improve transparency but to serve as an epistemic interface that enables semantic translation between machine-discovered patterns and TCM clinical reasoning. This review argues that AI-TCM integration should be understood as an epistemological integration problem rather than a purely technical one. To address this problem, we introduce two conceptual lenses: the dual-layer opacity framework, which captures the superposition of algorithmic opacity and the theoretical opacity of TCM knowledge, and the semantic translation framework, which conceptualizes XAI as the process of mapping computational features and reasoning traces onto clinically meaningful and theory-consistent TCM concepts. On this basis, we critically synthesize major methodological pathways of TCM-XAI, including feature-attribution methods, visual explanation, intrinsically interpretable models, knowledge-guided reasoning, and large-language-model-based explanation infrastructures such as chain-of-thought and graph-based retrieval-augmented generation. Beyond methodological synthesis, we identify four core criteria for high-quality explanation in TCM: faithfulness, clinical relevance, theoretical coherence, and cultural integrity. We show that the principal challenges of TCM-XAI extend beyond accuracy and include annotation uncertainty, explanation validation, privacy-preserving interpretability, fairness across populations, and the risk of epistemic reduction or cultural misinterpretation. Finally, we outline a future research agenda centered on causal inference, neuro-symbolic reasoning, multimodal explanation benchmarks, clinician-centered evaluation, and regulatory standards for trustworthy TCM-AI systems. We argue that XAI should be understood not as a technical add-on but as the epistemic infrastructure required to build clinically trustworthy, culturally coherent, and scientifically robust AI ecosystems for Traditional Chinese Medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/42449434/","authors":["Zheng W","Tong Y","Huang J","Zhu L","Chai J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 14","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42449268","name":"Beyond the current limits: a novel approach to differentiate small and large intestine in gastroschisis through prenatal imaging.","source":"pubmed","abstract":"This study aims to evaluate the ultrasound differentiation of the fetal extra-abdominal small and large intestines in gastroschisis (GS), analyze their diameters and wall thickness across gestational age in simple (sGS) and complex (cGS) cases, and compare findings with normal fetal bowel measurements.","url":"https://pubmed.ncbi.nlm.nih.gov/42449268/","authors":["Jaczyńska R","Mikulska B","Rybak-Krzyszkowska M","Mydlak D","Nimer A","Maciejewski T","Sawicka E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 14","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42437773","name":"Decentralized federated distillation for privacy-preserving cross-league basketball data collaboration.","source":"pubmed","abstract":"Cross-league basketball analytics promises richer, more transferable performance models, yet competitive sensitivities and data-protection regulations make raw data sharing across leagues impractical. We propose a decentralized federated distillation framework that lets multiple basketball leagues co-train predictive models without centralizing data and without depending on a trusted aggregator. Each league node trains a locally chosen model on its anonymized aggregate game-level statistics and exchanges only temperature-scaled soft predictions with neighbors over a sparse peer-to-peer graph. To address re-identification threats and cross-league feature-space mismatch, the pipeline pairs an &#x3b5;-differential-privacy Laplace mechanism applied directly to the released soft predictions-with explicit R&#xe9;nyi-DP composition across rounds-with k-anonymity for quasi-identifier coarsening and a Wasserstein optimal-transport projection that aligns league-specific feature spaces into a shared 64-dimensional representation. We establish convergence guarantees for federated distillation over decentralized communication graphs under non-convex objectives and heterogeneous data distributions, deriving an explicit bound that exposes the joint role of network spectral gap, distillation approximation error, transport-alignment error, and data heterogeneity. On a four-league dataset spanning the NBA, CBA, EuroLeague, and KBL-33,048 games in total-the proposed method attains 78.4% game-outcome accuracy, only 1.8 points behind a centralized oracle, while cutting communication overhead by more than 98% relative to parameter-averaging alternatives and preserving formal differential-privacy guarantees. Ablation studies confirm that feature alignment and adaptive temperature scheduling are both indispensable, and the sparse custom topology balances convergence speed against bandwidth efficiency.","url":"https://pubmed.ncbi.nlm.nih.gov/42437773/","authors":["Liu S","Guan H","Wang Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 12","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42431327","name":"Center-specific Federated Learning for Radiation Pneumonitis: A Cross-Center Adaptive Alternating Framework.","source":"pubmed","abstract":"Accurate prediction of symptomatic radiation pneumonitis (RP) is critical for radiation therapy, however, the generalization of deep learning models is hindered by restricted access to multicenter data. Although federated learning (FL) bypasses data sharing restrictions, standard FL algorithms underperform on highly heterogeneous clinical data across institutions. Therefore, this study aims to evaluate the clinical feasibility of a center-specific FL approach.","url":"https://pubmed.ncbi.nlm.nih.gov/42431327/","authors":["Yan M","Wang Z","Ning L","Xuan J","Zhang Z","Li H","Wang Y","Li M","Niu G","Bermejo I","Dekker A","De Ruysscher D","Wee L","Zhao L","Zhang Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 10","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42426051","name":"Federated deep learning for distributed intrusion detection and privacy preservation in power networks.","source":"pubmed","abstract":"The deepening cyber-physical integration of smart grids has expanded the attack surface of power networks, while centralised intrusion detection schemes struggle with data silos, privacy exposure, and prohibitive communication costs across geographically dispersed substations. This paper proposes a federated deep learning framework that addresses these constraints jointly. A three-tier cloud-edge-terminal architecture confines raw measurements to local devices and exchanges only model parameters across tiers. At each edge node, a hybrid CNN-BiLSTM detector trained under focal loss captures both spatial protocol motifs and temporal attack signatures, including stealthy false data injection and APT traces. Privacy is preserved through a layer-selective mechanism that combines Paillier homomorphic encryption on sensitive gradient slices with calibrated differential privacy on the residual components, pushing the privacy-utility frontier outward without saturating cryptographic cost. An adaptive aggregation rule weights client updates by data quality, drift severity, and marginal validation contribution, mitigating the convergence pathologies that vanilla FedAvg exhibits under sharp non-IID partitioning. Experiments on NSL-KDD, CICIDS2017, and an ICS power-system corpus show that the proposed scheme recovers within 0.7 F1 points of the centralised upper bound, suppresses membership inference advantage to below 0.08, holds detection quality against up to 20% Byzantine clients, and converges in roughly half the rounds required by FedAvg. The framework offers a deployable path toward collaborative intrusion detection across regional grid operators without compromising data sovereignty.","url":"https://pubmed.ncbi.nlm.nih.gov/42426051/","authors":["Chen L","Tang Z","Yang Y","Xiao X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 9","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42426030","name":"Predictive fault management in smart sensor networks using a dynamic quantum-AI architecture (DynaQuAI).","source":"pubmed","abstract":"Applications in industrial and smart-infrastructure Wireless sensor networks (WSNs) in the field are increasingly expected to employ predictive intelligence, which, even when operating under dynamic configurations, heterogeneity of hardware, and strong privacy requirements. The proposed paper is DynaQuAI, quantum-inspired, edge-intelligent framework that can predict real-time failures on constrained sensor nodes regarding their resources. The proposed system employs a probabilistic state encoding and oscillatory exploration schedule-mathematically inspired by quantum superposition analogies-to improve reinforcement learning exploration in sparse and noisy environments. These techniques are entirely classical algorithms implemented with standard trigonometric and probabilistic operations, requiring no quantum hardware. Such quantum-inspired algorithms enhance coverage of state-space and convergence faster than when using the usual deep RL models, with no computational demand. To support distributed nodes collaborative learning, DynaQuAI uses a lightweight federated approach on training paired with parameter aggregation through secure masks. Privacy is ensured through secure aggregation via pairwise parameter masking, which prevents the aggregation server from observing individual node updates while recovering the correct aggregate. The framework is additionally compatible with local differential privacy (&#x3b5;_total&#x2009;&#x2264;&#x2009;9.8 over 150 rounds at &#x3b4;&#x2009;=&#x2009;4&#x2009;&#xd7;&#x2009;10&#x207b;&#x2076;) for deployments requiring formal statistical privacy guarantees. We evaluated DynaQuAI through a simulation-based testbed emulating 500 heterogeneous sensor nodes with ARM Cortex-A7/M4 computational profiles over a simulated IEEE 802.15.4 mesh network. The evaluation uses the Kaggle Predictive Maintenance Dataset augmented with synthetic fault injections to create realistic distributed learning scenarios. Through experiments, this is, which has a sensitive aspect, demonstrated at 33% more fault-prediction accuracy over federated deep-learning baselines, 25% reduced energy consumption over adaptive quantum-inspired exploration, and 40% reduced convergence during policy learning. Its system can maintain real-time inference latency of 85 ms with minimally more than 5% of the available computational resources, thus suitable to be deployed in the long term. Individual contributions of quantum-inspired encoding, exploration scheduling and privacy-enhanced system of aggregation mechanisms are verified through ablation studies. In general, DynaQuAI has made easily extendable, privacy conscious and robust architectural learning offerings to next generation IoT ecosystems where high reliability and sustainability are essential.","url":"https://pubmed.ncbi.nlm.nih.gov/42426030/","authors":["Alharbi A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 10","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42420347","name":"A privacy-aware healthcare framework with model pattern-deviation detection for heart-disease prediction using L2-GNAE and PDDP.","source":"pubmed","abstract":"Sensitive patient data protection is essential to ensure medical reliability in healthcare. Yet, the traditional studies didn't analyze the deviation in the shared model pattern, thus resulting in poor diagnosis. Therefore, this article proposes a privacy-aware healthcare framework with model pattern deviation detection for Heart Disease (HD) prediction using L2 Gini Norm Auto-Encoder (L2-GNAE) and Polynomial Differential Decay Privacy (PDDP). Firstly, the patients are registered into the healthcare applications, followed by data sensing, data encryption, and hash code generation. Meanwhile, to authenticate the data integrity, the data decryption and hash code verification are done. During testing, the verified data is subjected to the trained proposed local model for HD prediction. Next, to perform model privacy, PDDP is used. Afterward, to effectively classify the HD, the Triple Gated Lipschitz Recurrent Unit (TGLRU) is utilized. Also, the local model gradients are updated in the global model, where L2-GNAE is utilized to detect the deviations in the shared model pattern. If the deviation is detected, then the alert is sent to the hospital; otherwise, the model update is carried out. The experimental testing of the proposed framework is done by using the \"Heart Disease Prediction Dataset\". From the validation, the proposed framework achieves 99.2145% accuracy, 99.0237% precision, and 99.1046% F-measure during HD prediction. Thus, the proposed work significantly outperforms the traditional works by obtaining a high security level (256 bits) with enhanced privacy-preserved HD prediction in healthcare maintenance.","url":"https://pubmed.ncbi.nlm.nih.gov/42420347/","authors":["Dwivedi R","Kumar B","Mishra V","Hemachandran K","Mishra V","Kim S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 8","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42415191","name":"Acute gastroenteritis caused by Comamonas testosteroni in a patient with hepatobiliary dysfunction: a case report highlighting diagnostic challenges during the pre-monsoon season in India.","source":"pubmed","abstract":"Comamonas testosteroni is a rare, environmental Gram-negative Bacillus increasingly recognised as an emerging opportunistic pathogen. It is often misidentified as a Pseudomonas species in routine laboratories due to overlapping phenotypic characteristics. Although generally of low virulence, it can cause severe infections in patients with underlying hepatobiliary or immunocompromised conditions. We report a case of an 18-year-old male from Karnataka, South India, who presented with fever, vomiting, and watery diarrhoea during the early monsoon showers of May 2025. Laboratory evaluation revealed hepatocellular dysfunction, cholelithiasis, and coagulopathy. Stool culture bluish-green colonies on thiosulfate-citrate-bile salts-sucrose (TCBS) agar, initially resembling those of Pseudomonas. Automated identification using the VITEK-2 Compact system and confirmation at ICMR-NIRBI (Indian Council of Medical Research-National Institute for Research in Bacterial Infections), Kolkata, identified the isolate as Comamonas testosteroni. The organism was sensitive to piperacillin/tazobactam, ceftriaxone, cefoperazone/sulbactam, imipenem, meropenem, amikacin, gentamicin, ciprofloxacin, and trimethoprim/sulfamethoxazole. The patient responded well to ceftriaxone and supportive care. This case underscores the diagnostic challenge of detecting C. testosteroni, particularly during overlapping outbreaks. The inclusion of differential media and careful interpretation of culture results is crucial for accurate identification. Diligent culture reporting helps in strengthening public health surveillance, infection control, and outbreak preparedness in resource-limited settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42415191/","authors":["Maheshwarappa YD","Sumana MN","Rao MR","Chitharagi VB","Murthy NS","Harendra B","Nikhil BN","Shivamallu C","Kollur S","Shettar SR","Megha GK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 7","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42412817","name":"NoisyFlow: differentially private optimal transport using neural networks for secure biomedical data sharing across multiple institutions.","source":"pubmed","abstract":"Biomedical models improve when trained on data pooled across institutions, but sensitive patient records (e.g. genomics, clinical data, and medical images) are difficult to share due to privacy constraints. Moreover, data collected at different sites often have shifted distributions because of covariate differences (including batch effects), so privacy-preserving sharing alone cannot simply resolve cross-site mismatch. Methods that protect individuals while explicitly aligning distributions are needed to enable reliable multi-institutional analyses.","url":"https://pubmed.ncbi.nlm.nih.gov/42412817/","authors":["Li Y","Khandekar N","Wang S","Khanna V","Sanker J","Gerstein MB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 1","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42409927","name":"Differentially private federated learning for localized control of infectious disease dynamics.","source":"pubmed","abstract":"In times of epidemics, swift reaction is necessary to mitigate epidemic spreading. For this reaction, localized approaches have several advantages, limiting necessary resources and reducing the impact of interventions on a larger scale. However, training a separate machine learning (ML) model on a local scale is often not feasible due to limited available data. Centralizing the data is also challenging because of its high sensitivity and privacy constraints. In this study, we consider a localized strategy based on the German counties and communities managed by the related local health authorities (LHA). For the preservation of privacy to not oppose the availability of detailed situational data, we propose a privacy-preserving forecasting method that can assist public health experts and decision makers. ML methods with federated learning (FL) train a shared model without centralizing raw data. Considering the counties, communities or LHAs as clients and finding a balance between utility and privacy, we study a FL framework with client-level differential privacy (DP). We train a shared multilayer perceptron on sliding windows of recent case counts to forecast the number of cases in the future, while clients exchange only norm-clipped updates and the server aggregates updates with DP noise. We evaluate the approach on COVID-19 data on county-level during two phases: November 2020 and March 2022 (Omicron). As expected, very strict privacy ([Formula: see text]) yields unstable, unusable forecasts. At a moderately strong but still privacy-preserving level ([Formula: see text]), the DP model closely approaches the non-DP model: [Formula: see text] (vs. 0.96) and mean absolute percentage error (MAPE) [Formula: see text] in November 2020; [Formula: see text] (vs. 0.90) and MAPE [Formula: see text] in March 2022. Overall, our results support the feasibility of privacy-preserving collaboration among health authorities for local forecasting. In the evaluated COVID-19 phases, client-level DP-FL delivered useful county-level predictions with formal privacy guarantees under the stated threat model. The appropriate privacy budget should nevertheless be re-evaluated for other epidemic phases and applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42409927/","authors":["Kerkouche R","Zunker H","Fritz M","Kühn MJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 6","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42399891","name":"Economic development and under-five mortality: a global longitudinal analysis of lagged effects and between-country inequalities.","source":"pubmed","abstract":"Understanding the relationship between under-five mortality rate (U5MR) and economic development is essential for promoting global child health and advancing health equity. This study analyzed the spatiotemporal patterns of U5MR and its associations with macroeconomic indicators from 2000 to 2023.","url":"https://pubmed.ncbi.nlm.nih.gov/42399891/","authors":["Li D","An W","Liu B","Qing Z","Yin Z","Lu Y","Xu Y","Yin D","Li S","Cheng K","Yuan C","Dai Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 3","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42396585","name":"Artificial intelligence in neurovascular surgery: advancing diagnosis, treatment, and outcomes.","source":"pubmed","abstract":"Artificial intelligence (AI) is transforming neurovascular surgery by improving diagnostic accuracy, risk prediction, treatment planning, and patient outcomes. This narrative review examines AI across the continuum of cerebrovascular care, from initial diagnosis through intervention and long-term prognostication. We discuss how machine learning, deep learning, computer vision, and natural language processing are applied to diverse data sources including neuroimaging, electronic health records, and intraoperative inputs. AI algorithms augment clinical expertise in diagnosis by delivering high speed and precision for tasks such as detecting large vessel occlusions, characterizing aneurysm morphology, and differentiating hemorrhage subtypes. Beyond detection, AI models are increasingly used for risk stratification-predicting aneurysm rupture, functional recovery after stroke, and post-intervention complications. AI also shows promise in therapeutic decision-making through pre-operative simulation, robotic-assisted microsurgery, and intraoperative guidance systems, with preliminary evidence suggesting potential improvements in procedural safety and efficacy (though most intraoperative AI studies remain at the proof-of-concept or single-center retrospective stage). Despite these developments, challenges remain, including algorithmic bias, limited generalizability, lack of interpretability, data privacy concerns, and regulatory barriers. Successful deployment requires seamless workflow integration and a clear understanding that AI assists, not replaces, the neurosurgeon. The convergence of AI with precision medicine holds promise for personalized, data-driven care through synergistic human-AI collaboration.","url":"https://pubmed.ncbi.nlm.nih.gov/42396585/","authors":["Li L","Zhang Z","Zong L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42391667","name":"BlockFedMed: A blockchain-federated learning framework for privacy-preserving mortality prediction across heterogeneous intensive care units.","source":"pubmed","abstract":"Electronic health records are distributed across different hospitals that work on powerful AI models but cannot be shared due to HIPAA and GDPR regulations. Federated learning (FL) avoids raw data sharing, yet lacks tamper-evident consent governance, adversarial robustness, and verifiable differential privacy (DP) accounting leaving regulatory compliance undemonstrated.","url":"https://pubmed.ncbi.nlm.nih.gov/42391667/","authors":["Yadav AK","Deshmukh M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 27","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42389622","name":"AI Privacy and Security in Healthcare: A Systematic Literature Review.","source":"pubmed","abstract":"Artificial intelligence is expanding into telemedicine and telerehabilitation, yet significant privacy and security concerns persist.","url":"https://pubmed.ncbi.nlm.nih.gov/42389622/","authors":["Dolezel D","Lalani K","Watzlaf V","Butler-Henderson K","Lambert EVZ","Morton M","Sand J","Gibbs D","Fenton S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42388033","name":"AI in Genomics: From Variant Calling to Multi-Omics Integration.","source":"pubmed","abstract":"Artificial intelligence (AI) strategies are revolutionizing genomics by extracting complex patterns that traditional statistical pipelines are likely to miss. This mini-review aims to provide a concise overview of how AI is transforming major genomic technologies including variant calling, gene expression analysis, single-cell transcriptomics, CRISPR-Cas9 optimization, and multi-omics integration. In genome sequencing, machine learning variant callers greatly improve the accuracy and the rate at which single nucleotide and structural variants are called. In bulk RNA-Seq, AI augmented quantification, denoising, and differential expression modules complement the highly established STAR-featureCounts-DESeq2 pipeline, revealing subtle signals in big data sets. In single cell transcriptomics, deep learning approaches enhance batch correction, automate cell type annotation, and track developmental trajectories, hence clarifying cellular heterogeneity. AI-assisted guide RNA design, outcome prediction, and nuclease engineering enable more efficient CRISPR-Cas9 editing, reducing experimental cycles, and off-target effects. Finally, integrated platforms that combine genomic, transcriptomic, epigenomic, proteomic, and metabolomic layers provide an integrative view of cellular regulation and disease mechanisms. The review also covers current limitations, sparsity of data, model bias, privacy, and the need for standardized benchmarks and offers future directions in the form of interpretable models, collaborative learning, and open science practices. Together, these developments render AI an indispensable partner to unravel genomic complexity and accelerate precision medicine applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42388033/","authors":["Sultana H","Mohanty S","Solomon AD","Iqbal MY","Wani AK","Kumar V","Khattri A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42387908","name":"\"It Reduces Stigma Now That the Service Is Under One Roof\" Women and Provider Experiences on Factors Influencing Effective Integration of Oral PrEP Delivery in Family Planning Clinics in Kenya: A Qualitative Study.","source":"pubmed","abstract":"Women of childbearing age in sub-Saharan Africa continue to face a disproportionately high risk of HIV acquisition. Integrating pre-exposure prophylaxis (PrEP) into existing care platforms such as family planning (FP) services may offer a strategic opportunity to reach women at heightened risk for HIV. However, limited evidence exists on the factors influencing effective PrEP integration in FP settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42387908/","authors":["Ogello V","Ngure K","Mutai S","Awuor M","Atieno W","Dollah A","Wandera C","Matemo D","Morton JF","Kinuthia J","Mugwanya KK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42386863","name":"RiskSetDP: session-level differential privacy for survival analysis with risk-set-aware sensitivity control.","source":"pubmed","abstract":"Real-world survival analysis rarely ends with a single deliverable: practitioners typically publish cohort-level statistics, a deployable time-to-event model, interpretable Kaplan-Meier curves, and, where appropriate, a synthetic table for secondary use. When these heterogeneous outputs are protected through separate per-call differential privacy mechanisms, privacy accounting becomes fragmented and difficult to audit. We present RiskSetDP, a session-level framework that governs accounting, acceptance, and release for survival analysis under one auditable budget in the R&#xe9;nyi-DP domain. All calls are composed once, converted to [Formula: see text] for a single global acceptance check, and, when needed, proportionally scaled with noise recalibration so the complete bundle remains within budget. The framework couples a risk-set-aware differentially private Kaplan-Meier estimator that enforces a minimum risk-set threshold and emphasizes early horizons with isotonic post-processing, a reproducible DP-SGD protocol for horizon-wise risk modeling with standardized clipping, batching, and budget-matched noise, and workload-aware synthetic data generation from selected low-order marginals with a consistency repair step. Experiments on METABRIC and SUPPORT2 show consistent, monotonic gains as the session budget increases: curve errors decrease while concordance improves, model discrimination and especially calibration strengthen, and synthetic fidelity and task utility rise. Ablation studies support the value of risk-set control, post-processing, and session-level coupling, and empirical attack audits provide complementary diagnostics under specific attack models while the formal guarantee remains the session-level DP accounting record. These results indicate that RiskSetDP provides a practical and auditable session-level release workflow for survival analysis on two public cohorts, preserving utility while maintaining an externally verifiable accounting record.","url":"https://pubmed.ncbi.nlm.nih.gov/42386863/","authors":["Ou Z","Luo Z","Duan W","Xiao Z","Sun Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 1","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42385303","name":"The scope of physiotherapy in men's health: a qualitative study on clinician experiences.","source":"pubmed","abstract":"Men's health physiotherapy is an expanding subspecialty that addresses pelvic floor dysfunction, sexual health disorders, and chronic pelvic pain in men. However, practitioners' perspectives remain underexplored, particularly in healthcare contexts where referral pathways and interdisciplinary collaboration are not yet well established.","url":"https://pubmed.ncbi.nlm.nih.gov/42385303/","authors":["Erkut U","Önal B","Unes S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 1","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42381049","name":"The profiles of vaginal microbiota in fertile and infertile Thai women.","source":"pubmed","abstract":"Vaginal microbiota dysbiosis has been associated with female reproductive health and infertility. This study aimed to compare vaginal microbiota profiles according to fertility and ovarian induction status among Thai women.","url":"https://pubmed.ncbi.nlm.nih.gov/42381049/","authors":["Li J","Apinuthirunchot T","Chaithongwongwatthana S","Chanchaem P","Sawaswong V","Jitvaropas R","Payungporn S","Kaur H","Jaisamrarn U"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 30","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42380173","name":"Chaos-driven design of highly nonlinear S-boxes for secure and efficient lightweight image encryption.","source":"pubmed","abstract":"Substitution boxes (S-boxes) are a type of nonlinear component which provides confusion and robustness to cryptanalysis in modern symmetric ciphers. Due to the requirements of lightweight encryption (LWE) systems to achieve a high degree of security while minimizing computational overhead, we propose chaos-driven constructions of highly nonlinear S-boxes using permutations of the logistic map to create bijective and statistically randomized substitution patterns. We have extensively evaluated the cryptographic strength of our proposed S-box using standard metrics, including Nonlinearity (NL), Strict Avalanche Criteria (SAC), Bit Independence Criteria (BIC), Balancedness, Differential Approximation Probability (DAP), and Linear Approximation Probability (LAP). Our proposed S-box has demonstrated competitive performance with existing S-boxes by achieving average nonlinearity values of 112.75 and SAC/BIC values very close to ideal thresholds. To demonstrate practical applicability, we have integrated the proposed S-box into an image encryption scheme, where statistical analyses confirm near ideal entropy and negligible pixel correlation, demonstrating strong resistance to both statistical and differential attacks. Additionally, our proposed scheme has achieved comparable levels of security within a smaller number of rounds than traditional methods, thus enhancing both computational efficiency and throughput, making it suitable for real-time and resource-constrained multimedia security applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42380173/","authors":["Said L","Alshammari FS","Khan M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 1","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42375467","name":"Classification Under Local Differential Privacy with Model Reversal and Model Averaging.","source":"pubmed","abstract":"Local differential privacy (LDP) has become a central topic in data privacy research, offering strong privacy guarantees by perturbing user data at the source and removing the need for a trusted curator. However, the noise introduced by LDP often significantly reduces data utility. To address this issue, we reinterpret private learning under LDP as a transfer learning problem, where the noisy data serve as the source domain and the unobserved clean data as the target. We propose novel techniques specifically designed for LDP to improve classification performance without compromising privacy: (1) a noised binary feedback-based evaluation mechanism for estimating dataset utility; (2) model reversal, which salvages underperforming classifiers by inverting their decision boundaries; and (3) model averaging, which assigns weights to multiple reversed classifiers based on their estimated utility. We provide theoretical excess risk bounds under LDP and demonstrate how our methods reduce this risk. Empirical results on both simulated and real-world datasets show substantial improvements in classification accuracy.","url":"https://pubmed.ncbi.nlm.nih.gov/42375467/","authors":["Qin C","Bai Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42374430","name":"microRNA-204 as a crucial cancer stem cell related miRNA inhibits stemness of gastric cancer by targeting CD44 and EPCAM.","source":"pubmed","abstract":"Cancer stem cells (CSCs) and normal stem cells share key properties such as self-renewal and differentiation capacity, yet the microRNAs (miRNAs) that are differentially expressed between them remain poorly characterized. This study aimed to identify such miRNAs, with a focus on microRNA-204 (miR-204), and to investigate its functional role and regulatory mechanisms in gastric cancer.","url":"https://pubmed.ncbi.nlm.nih.gov/42374430/","authors":["Li D","Zhou R","Cheng Z","Xu H","Zhang H","Li X","Cao L","Li F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 30","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42373887","name":"BC-AWFedAvg: blockchain-assisted adaptive federated learning for secure RAN Slicing in beyond-5G networks.","source":"pubmed","abstract":"The evolution of beyond-5G networks introduces new challenges for radio resource management, particularly for heterogeneous service requirements across multiple virtual network operators. This work presents BC-AWFedAvg, a layered framework for federated deep reinforcement learning in O-RAN network slicing that integrates adaptive aggregation, blockchain-based governance, secure aggregation, and differential privacy. The proposed design separates learning, governance, and storage functions to support coordinated training while preserving privacy and limiting exposure of individual updates. Simulation results in the considered setting indicate that the proposed framework improved robustness in the considered setting under several adversarial scenarios while maintaining acceptable quality-of-service performance. These findings suggest that combining complementary mechanisms may be a promising direction for secure federated learning in next-generation wireless networks.","url":"https://pubmed.ncbi.nlm.nih.gov/42373887/","authors":["Zemzemi M","Hajlaoui JE","Aldalbahi AS","Mhatli S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 29","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42367785","name":"A federated digital twin reveals cytomegalovirus reactivation impairs CAR-T cell therapy via IL-15-mediated cytokine competition in B-Cell lymphoma.","source":"pubmed","abstract":"Cytomegalovirus (CMV) reactivation occurs in 30 - 40 % of seropositive patients receiving chimeric antigen receptor T-cell (CAR-T) therapy for B-cell lymphoma and is strongly associated with treatment failure. However, the causal immunological mechanism driving this failure whether through direct viral cytopathic effects, T-cell exhaustion, or resource competition remains undefined. Furthermore, existing predictive models lack the mechanistic insight needed to guide intervention. We developed a privacy-preserving, mechanistic digital twin to test the \"cytokine sink\" hypothesis, wherein CMV-specific CD8+ T cells compete with CAR-T cells for the limiting homeostatic cytokine I L - 15 .","url":"https://pubmed.ncbi.nlm.nih.gov/42367785/","authors":["Sridharan P","Ghosh M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42365212","name":"CBINN: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification.","source":"pubmed","abstract":"The dynamics of tumor-immune interactions within a complex tumor microenvironment are typically modeled using a system of ordinary differential equations or partial differential equations. These models introduce some unknown parameters that need to be estimated accurately and efficiently from the limited, noisy experimental data. Moreover, due to the intricate biological complexity and limitations in experimental measurements, tumor-immune dynamics are not fully understood, and therefore, only partial knowledge of the underlying physics may be available, resulting in unknown or missing terms within the system of equations. Thus, there are twofold challenges in modeling tumor dynamics: (i) accurate estimation of model parameters and (ii) discovery of the mathematical equations governing the physical and biological systems. These types of problems are referred to as gray-box identification areas, where both experimental data and partial system knowledge are used to recover unknown parameters and missing components. In this study, we develop a cancer biology-informed neural network model (CBINN) to infer the unknown parameters in the system of equations as well as to discover the missing mechanisms from sparse and noisy measurements. We test the performance of the CBINN model on three distinct nonlinear compartmental tumour-immune models and evaluate its robustness across multiple synthetic noise levels. By harnessing these highly nonlinear dynamics, our CBINN framework effectively estimates the unknown model parameters and uncovers the underlying physical laws or mathematical structures that govern these biological systems, from scattered and noisy measurements. The models chosen here represent the dynamic patterns commonly observed in compartmental models of tumor-immune interactions, thereby validating the generalizability and efficacy of our methodology. Structural and practical identifiablility of the model parameters are also discussed using computational and Fisher information matrix based analysis. This work provides valuable guidance for researchers addressing inverse problems and gray-box identification challenges in complex dynamical systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42365212/","authors":["Chhetri B","Kumar BVR"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 27","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42363203","name":"How do mature non-graduate students compare to the rest of the cohort in medical training? A UKMED study.","source":"pubmed","abstract":"Widening participation in medicine is a key societal priority. To improve representation of non-traditional applicants, UK medical schools use contextual admissions, although definitions of under-represented groups vary across institutions. This study examined the educational and training trajectories of one such group-mature non-graduates. We aimed to determine whether their progression was comparable to that of school-leavers and graduate entrants, whether they progressed through medical school non-inferiorly, and whether they were equally likely to secure postgraduate training posts.","url":"https://pubmed.ncbi.nlm.nih.gov/42363203/","authors":["Sartania N","Troughton A","Chan P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 26","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42362612","name":"A quantum resistant chaos driven image encryption framework for secure visual data transmission in intelligent transportation systems.","source":"pubmed","abstract":"The use of image-sensing and real-time processing in Intelligent Transportation Systems (ITS) has introduced a sudden surge in transmitting and gathering high-resolution visual data from vehicle cameras, road infrastructures, and user devices. Such image data are, however, exceedingly susceptible to interception, tampering, and privacy breaches with regard to imminent quantum computing attacks that can break classical encryption algorithms. With such constraints in view, the paper presents a new Hybrid Quantum-Classical Image Encryption Framework that integrates chaos-based bit-level image encryption and quantum-resistant encryption measures to ensure high-security protection of image information in ITS infrastructures. The new framework integrates a customized bit-level chaotic permutation scheme using a Rearranged Arnold Cat Map (R-ACM) and 2D Logistic-Sine Chaotic Maps for confusion and diffusion, and the inclusion of a Quantum Key Distribution (QKD) or post-quantum lattice-based Kyber Key Encapsulation Mechanism (KEM) for secure key negotiation. The two-pyramidal security architecture enhances sensitivity to key and plaintext variations, offers chosen-plaintext, differential, noise, and occlusion attack immunity, and supports efficient encryption of RGB and grayscale image information without excessively large time overhead. Experimental results on representative ITS-relevant image data sets verify superior performance with mean NPCR&#x2009;&gt;&#x2009;99.60%, UACI&#x2009;&#x2248;&#x2009;33.5%, entropy measures close to 8.0, and significantly suppressed correlation between neighboring pixels. Further, key space analysis demonstrates a combinatorial complexity of over 2&#xb2;&#x2075;&#x2076;, making brute-force and quantum-type attacks computationally infeasible. The new framework is extremely suitable for real-time implementation in autonomous vehicles, roadside edge nodes, and intelligent traffic monitoring systems, thereby enabling secure, intelligent, and privacy-preserving ITS infrastructure in the post-quantum era.","url":"https://pubmed.ncbi.nlm.nih.gov/42362612/","authors":["Prajwalasimha SN","Sharma A","Saini DKJB","Rai BK","Kumar G","Chakrabarti P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 26","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42356675","name":"An Integrated IoT-Based Multi-Sensor Framework for Real-Time Indoor Environment and Safety Monitoring.","source":"pubmed","abstract":"Poor indoor air quality, inadequate ventilation, and unnoticed local disturbances can reduce occupant well-being and compromise practical safety in smart-home and small-building environments. Although low-cost Internet-of-Things (IoT) sensing technologies are widely available, many monitoring systems remain focused on single-modality sensing and do not jointly evaluate environmental conditions, vibration activity, communication reliability, and gateway-side interpretation within one framework. This study presents the design, implementation, and proof-of-concept evaluation of a low-cost, privacy-conscious, non-imaging IoT-based indoor environment and safety-awareness monitoring framework built with ESP32/Arduino sensor nodes and a Raspberry Pi gateway. The system integrates carbon dioxide, temperature, humidity, gas-resistance/VOC-trend indication, and vibration sensing with MQTT-based communication and edge-side analytics. Controlled subsystem experiments showed that CO 2 concentration differentiated ventilation conditions, increasing from 395.47 ppm in the valid empty/open-door baseline to 1083.16 ppm in the closed occupied condition. Vibration states were distinguished using root-mean-square acceleration features across calm, surface-disturbance, footstep, play, and jump conditions. MQTT evaluation using 1000-message batches showed no observed message loss or duplicates across the tested QoS/network combinations, although latency and throughput varied by network configuration and QoS level. QoS 1 provided a practical balance between low latency and protocol-level delivery assurance in the tested local/Wi-Fi setting. A final integrated validation run further demonstrated synchronized acquisition from indoor environmental, vibration, and outdoor CO 2 reference publishers through the same Raspberry Pi gateway, with zero missing or duplicate sequence flags across the three streams. Overall, the findings indicate that lightweight open-source IoT hardware can support a reproducible building-level sensing and edge-analytics prototype for indoor environment and safety-awareness monitoring. Broader deployment in standard-sized rooms, multi-room buildings, and smart-city infrastructure remains future work.","url":"https://pubmed.ncbi.nlm.nih.gov/42356675/","authors":["Naing AM","Al-Hamid DZ","Singh A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 10","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42352099","name":"DAG-CTFL: DAG Blockchain Cross-Layer Authentication Framework for Trustworthy IoV Federated Learning.","source":"pubmed","abstract":"Privacy-preserving federated learning in the internet of vehicles (IoV) requires low-latency authentication, bounded privacy leakage, and robustness against malicious model updates. However, most existing studies separately design communication authentication and federated learning protection, which leads to duplicated overhead and weak resistance to cross-layer attacks. To address this issue, this paper proposes a DAG blockchain-enabled cross-layer authentication framework for trustworthy IoV federated learning (DAG-CTFL). The framework reuses authentication operations across V2X message verification and model-update delivery, incorporates trust-aware batch verification, and organizes cross-layer evidence through a two-tier DAG blockchain. In addition, differential privacy is used to reduce information leakage from uploaded model updates, while cross-layer trust evaluation improves resilience against poisoning and forged-identity attacks. Experimental results on MNIST and CIFAR-10 show that DAG-CTFL reduces single-message verification overhead by 8.2-56.1%, lowers batch-verification latency by 19.2-56.4%, and maintains model accuracy above 85% under 15% malicious nodes. These results demonstrate that DAG-CTFL achieves an effective balance among privacy preservation, authentication efficiency, and cross-layer robustness in IoV federated learning.","url":"https://pubmed.ncbi.nlm.nih.gov/42352099/","authors":["Liao L","Chen L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 26","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42348378","name":"Privacy-Enhanced Vertical Federated Learning for Healthcare via Directional Noise and Subset Representations.","source":"pubmed","abstract":"Vertical federated learning (VFL) allows healthcare institutions to train models on complementary patient features without sharing raw data, but strong differential privacy often causes severe utility loss and labeled medical data are limited.We propose HEAL, a privacy-enhanced VFL framework that jointly learns subset representations and optimizes the direction of privacy-preserving noise. HEAL first constructs importance-aware feature subsets and performs multi-level contrastive pre-training to exploit unlabeled data and unify heterogeneous feature spaces. It then applies direction-optimized differential privacy to preserve formal $(\\epsilon, \\delta)$-privacy while reducing gradient distortion, followed by collaborative task learning for healthcare prediction. Across four healthcare datasets, HEAL improves accuracy by 2.6-4.7% over state-of-the-art baselines, reaches 96.2% of centralized performance at $\\epsilon =1.0$, and degrades gradient-inversion reconstruction quality by 20-35%. These results show that privacy protection and representation learning can reinforce each other, rather than treating privacy only as a performance cost.","url":"https://pubmed.ncbi.nlm.nih.gov/42348378/","authors":["Wang Q","Dai M","Wu C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 25","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42346866","name":"Converting a Cough Counter into a Cough Monitor: A Way Forward?","source":"pubmed","abstract":"Background/Objective : To identify respiratory pathology, automated cough counting is frequently proposed. A trial validating an early warning system for exacerbations in chronic obstructive pulmonary disease (COPD) patients was recently concluded successfully. This paper aims to review the critical design choices for converting a cough counter into a patient-friendly continual cough monitor. Furthermore, it provides a basis for a practical reliability metric for continual cough monitoring. Methods : Design choices made in the development of a cough-based alert mechanism called XACT are discussed. A practical approach for reliability assessment is outlined based on cough counts, day-to-day variation and specificity data. Results : In post hoc analysis, it is shown that the described approach enables differentiation between high-quality cough estimates and less reliable data. The approach is used to underpin an earlier cohort subdivision into patients with and without increased cough during exacerbation. Conclusions : The validated alert mechanism has various patient-oriented design choices (unobtrusiveness, privacy-preserving). The examples illustrate how to screen for potential issues in automated cough count data without resorting to laborious annotation. It creates a practical basis for confidence metrics of medical inferences made from cough data, e.g., exacerbation forecasts. The proposed concepts need further validation.","url":"https://pubmed.ncbi.nlm.nih.gov/42346866/","authors":["Brinker ACD","Crooks MG","Morice AH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 17","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42343945","name":"SENTINEL-Chain: a blockchain-integrated privacy-preserving framework for secure healthcare data publishing.","source":"pubmed","abstract":"Electronic health records (EHRs) are central to healthcare analytics, but their granularity increases re-identification risk when shared. Conventional privacy-preserving methods including k -anonymity, l -diversity, and differential privacy often protect confidentiality at the expense of analytical utility by weakening clinically meaningful correlations.","url":"https://pubmed.ncbi.nlm.nih.gov/42343945/","authors":["Segar N","Vijayan V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42342794","name":"Mitigating request flooding attack in named data networking using federated learning.","source":"pubmed","abstract":"Named Data Networking (NDN) represents a paradigm shift toward content-centric architectures but remains critically vulnerable to Interest Flooding Attacks (IFAs), where malicious actors overwhelm router Pending Interest Tables with spurious requests, causing service degradation and denial-of-service. To address the limitations of existing approaches, including high false positives in threshold-based methods and substantial overhead in centralized learning, we propose FL-IFAshield, a novel federated learning framework for adaptive IFA mitigation. Our solution integrates dynamic Poisson-EMA thresholding for accurate flood detection, entropy-aware federated aggregation to handle non-IID traffic distributions across edge routers, and Byzantine-robust mechanisms with differential privacy guarantees. Comprehensive evaluation on the FIT/IoT-LAB testbed with 100 routers demonstrates exceptional performance: 93.1% F1-score in attack detection, only 5% false positives, 28 ms average end-to-end latency ([Formula: see text]), and over 90% legitimate Interest Satisfaction Ratio under sophisticated collusive attacks, while maintaining minimal computational overhead (&lt;9% CPU utilization on ARMv8 routers). FL-IFAshield significantly improves security performance, offering 35% higher accuracy than static thresholding and 60% lower communication overhead than centralized approaches. While simpler heuristic baselines naturally incur marginally lower computational footprints, our solution delivers the optimal overall operational balance among high precision, low end-to-end latency ([Formula: see text]), and resource efficiency in constrained edge computing environments.","url":"https://pubmed.ncbi.nlm.nih.gov/42342794/","authors":["Benmaidi ML","Lagraa N","Brik B","Jlali L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 24","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42340896","name":"Differentially Private Distributed Algorithms for Aggregative Games Over Directed Graphs With Linear Convergence.","source":"pubmed","abstract":"This article studies privacy-preserving distributed Nash equilibrium (NE) seeking for aggregative games over directed graphs, where agents' cost functions contain sensitive information. A novel differentially private algorithm using decaying Laplace noise is developed to address two key issues: 1) how to design a distributed algorithm over directed graphs that achieves linear convergence while satisfying differential privacy requirements and 2) how to characterize the tradeoff between convergence accuracy and the privacy budget. First, sufficient conditions for linear convergence are established through the appropriate design of constant step sizes and convex combination parameters. Second, the differential privacy properties of the algorithm are analyzed without assuming bounded gradients, and a quantitative relationship between convergence accuracy and privacy budget is characterized. Furthermore, under additional restrictions on adjacent functions, the cumulative privacy budget admits an explicit expression and remains finite over an unbounded horizon, while the proposed algorithm is proven to converge to the exact NE. Finally, the effectiveness of the proposed algorithm is validated through a Nash-Cournot game and comparative simulations, which demonstrate its superior convergence performance compared to existing methods.","url":"https://pubmed.ncbi.nlm.nih.gov/42340896/","authors":["Wang L","He W","Qian F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 24","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42337756","name":"Determinants and configurational pathways of healthcare big data asset valuation:an analysis using the TOE framework and fsQCA.","source":"pubmed","abstract":"Healthcare big data represents a strategic national resource with substantial potential value. At the policy level, ongoing efforts have been made to promote the transformation of healthcare data resources into data assets. However, the realization of their value remains challenging due to variations in data quality, uncertainty in application scenarios, privacy-related risks, and imperfect benefit-sharing mechanisms. This study seeks to identify the key factors affecting the valuation of healthcare big data assets. The findings are expected to provide a theoretical basis for developing scientific valuation models, optimizing resource allocation, and promoting high-quality development in the healthcare industry.","url":"https://pubmed.ncbi.nlm.nih.gov/42337756/","authors":["Jiang R","Yuan Z","Tian S","Yang C","Pu X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 23","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42332284","name":"Transcriptomic identification of key genes influencing m7G modification in thyroid carcinoma and experimental validation.","source":"pubmed","abstract":"N7-methylguanosine (m7G) modification plays a critical role in RNA metabolism and is increasingly recognized for its implications in cancer biology. It can influence RNA stability, translation efficiency, and gene expression regulation. However, the specific role of m7G modification and its downstream genes in thyroid carcinoma (THCA) is not well understood. To comprehensively explore the impact of m7G methylation modification and the m7G-related gene ZNF831 on THCA, this study aims to identify key genes influencing m7G modification in THCA, with a particular focus on clarifying the role of ZNF831. This study is expected to further elucidate the pathological mechanisms of THCA and fill the current research gap in this field.","url":"https://pubmed.ncbi.nlm.nih.gov/42332284/","authors":["Li B","Zhu L","Zhang J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 22","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42329512","name":"Testing the interleaving effect without response bias: A forced-choice reevaluation of Kornell and Bjork (2008).","source":"pubmed","abstract":"In a highly influential study across a wide breadth of literature, Kornell and Bjork (Psychological Science, 19[6], 585-592, 2008) showed that learning is enhanced by presenting exemplars (paintings) of to-be-learned categories (artists) in an interleaved sequence (e.g., A 1 , B 1 , C 1 &#x2026; A 2 , B 2, , C 2 &#x2026;) rather than a blocked sequence (e.g., A 1 , A 2 , A 3 &#x2026; B 1 , B 2 , B 3 &#x2026;). However, this study, and nearly all direct replications, used an identification procedure that confounds memory abilities with response biases (i.e., one's criteria for using certain response choices); any interleaving effect assessed through an identification task may be an overestimate or, indeed, an underestimate of the effect (Hautus et al., Detection Theory, 2021). To address this, we conducted a direct replication: online, N = 288; t(287) = 8.08, p &lt; 0.001, d z = 0.48, 96% CI [0.36, 0.59]-for the first time to our knowledge-accompanied by measures of differential learning and response biases across learned categories (which were substantial, with participants using some response categories at a rate several times higher than others). We then conducted a critical conceptual replication, changing the task from identification (\"Which artist painted this?\") to n-alternative forced-choice (\"Which of these was painted by [e.g.] Seurat?\"), the gold standard of memory tests (Brady et al., Psychonomic Bulletin &amp; Review, 30, 2023). Reassuringly, the 2AFC experiment showed an interleaving effect comparable to our direct replication: online, N = 276; t(275) = 7.19, p &lt; .001, d z = 0.43, 95% CI [0.32, 0.55]. Put together, this study showcases the challenges of interpretation facing any identification paradigm, illustrates a straightforward method to address them, and puts the interleaving effect on firmer footing.","url":"https://pubmed.ncbi.nlm.nih.gov/42329512/","authors":["Donenfeld J","Kaldy Z","Blaser E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 22","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42323786","name":"Evaluating multi-level membership inference risk in federated EEG learning.","source":"pubmed","abstract":"Electroencephalography (EEG) records electrical brain activity from the scalp and is widely used in brain-computer interface (BCI) systems for communication, and assistive technologies. EEG is widely used in motor-imagery (MI) based BCIs, where neural recordings contain highly individual and potentially sensitive information. In this regard, federated learning (FL) is a prominent privacy-enhancing approach which enables collaborative model training without centralising raw signals. However, recent work has shown that FL models still leak private information through membership inference attacks (MIAs). Most existing studies examine only single attack type, so it remains unclear how multiple MIAs together expose different layers of privacy risk in FL-based EEG systems. To address this gap, this study develops a federated MI-EEG classification framework and evaluates privacy leakage across four complementary MIAs: record-level, feature-level, gradient-level, and client-identity inference. Two neural networks were trained using per-subject FL, and differential privacy (DP) with epsilon (&#x3b5;) &#x2208; {1, 5, 10} was applied to client updates. Results showed that standard FL alone provides limited intrinsic protection, while adding DP substantially reduces attack success particularly for gradient and identity-level attacks. Strong privacy settings (&#x3b5;&#x2009;=&#x2009;1) offered the greatest leakage reduction but degraded classification accuracy, whereas a moderate privacy budget (&#x3b5;&#x2009;=&#x2009;5) achieved the most favourable privacy-utility balance. Overall, the findings demonstrate that FL alone is insufficient as a privacy safeguard for EEG-BCI systems. Explicit privacy mechanisms such as DP are required to mitigate multi-level leakage, supporting the design of trustworthy and secure neural-learning technologies.","url":"https://pubmed.ncbi.nlm.nih.gov/42323786/","authors":["Khanam T","Siuly S","Wang K","Whittaker F","Wang H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 21","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42321473","name":"FL-TWIN: a unified federated learning system for intrusion detection with digital twins modelling.","source":"pubmed","abstract":"The growth of networked environments has intensified the challenge of detecting distributed denial-of-service (DDoS) attacks, as centralized intrusion detection systems face scalability, privacy, and data heterogeneity limitations. This paper proposes a federated learning framework for DDoS detection, Unified FL-TWIN, that pairs each participating client with an edge-resident Digital Twin (DT), applies a four-stage poisoning defence pipeline, and records all aggregations and security events on a permissioned blockchain ledger. Also, each DT maintains a versioned ring buffer of model snapshots, enabling per-client targeted rollback upon adversary detection. The defence pipeline comprises: Layered Update Purification (LUP), Differential Privacy via DP-SGD, Dual Dynamic Aggregation, TracIn and a blockchain. The novelty of this work lies in systematically integrating them into a Unified FL-TWIN approach that addresses several challenges simultaneously. This proposed approach simultaneously provides privacy protection, robustness, trustworthiness, accountability, and adaptive learning within a single architecture. In experiments on the CIC-DDoS 2019 dataset, we are covering clean baselines and three attack types with 30% malicious participation. The FL-TWIN achieves peak test accuracies of 99.97%, 99.98%, and 99.98% under label-flip, gradient-noise, and backdoor attacks, respectively, compared to a stagnant 99.72% for the undefended baseline. LUP achieves F1 scores of 0.57, 0.75, and 0.80 across three attack types, while the blockchain ledger maintains full save across all experiments. These results show that combining Digital Twin rollback with a layered detection pipeline improves recovery from federated poisoning attacks.","url":"https://pubmed.ncbi.nlm.nih.gov/42321473/","authors":["Hamwi AA","Mittal M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 20","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42321315","name":"GLANet: global and local anomaly network for distributed cyber threat detection using FL.","source":"pubmed","abstract":"This paper introduces GLANet, a novel FL-based framework designed for real-time, privacy-preserving anomaly detection in distributed network environments. GLANet is a FL-based framework that integrates a lightweight convolutional neural network (CNN) as a local anomaly detector at each distributed node with a global model aggregation mechanism based on federated averaging (FedAvg). The framework incorporates consistency regularization to align local and global model parameters, ensuring that node-specific threat patterns are preserved while enabling network-wide generalization. Differential privacy is employed through the injection of calibrated Gaussian noise into model updates before transmission to the central server, providing formal privacy guarantees without compromising detection performance. The experimental findings reveal that GLANet achieves a high detection accuracy of 97.8% on the CICIDS 2017 dataset, surpassing traditional FL baselines that achieved 92.7%. The model also achieves superior precision (96.5%), recall (97.0%), and F1 Score (96.8%), reflecting a well-balanced anomaly detection performance. Additionally, GLANet reduces communication costs by 37.5% and achieves a lower privacy loss of &#x3b5;&#x2009;=&#x2009;0.8 compared to 1.5 in baseline methods. These findings demonstrate the potential of GLANet as an effective, scalable, and secure anomaly detection framework for comprehensive defense against evolving cyber threats in dynamic and distributed network environments.","url":"https://pubmed.ncbi.nlm.nih.gov/42321315/","authors":["Othman Aljahdali A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 19","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42321290","name":"SecureTrust-FL: trust-aware privacy-preserving federated learning for network intrusion detection.","source":"pubmed","abstract":"The proliferation of distributed network environments and the Internet of Things (IoT) has increased the need for privacy-preserving intrusion detection systems capable of operating effectively under heterogeneous and non-independent and identically distributed (non-IID) data conditions. This paper proposes SecureTrust-FL, a trust-aware federated learning framework for privacy-preserving intrusion detection. The framework integrates Federated Learning, Blockchain-based Trust Management, Differential Privacy, FGSM-based Adversarial Learning, and Zero-Trust Security principles to support secure collaborative learning without requiring raw data sharing among participating entities. The framework is evaluated using three benchmark intrusion detection datasets, namely CICIDS2017, UNSW-NB15, and BoT-IoT, which are treated as independent federated clients. Experimental results demonstrate that the proposed framework achieves an overall Accuracy of 92.91% &#xb1; 0.45%, Balanced Accuracy of 93.25% &#xb1; 0.43%, Macro F1-Score of 92.89% &#xb1; 0.45%, and AUC-ROC of 95.50% &#xb1; 0.40% across heterogeneous datasets. The results indicate that the federated model can effectively learn from distributed and heterogeneous data while preserving data privacy. Further analysis reveals the impact of class imbalance on intrusion detection performance, particularly in datasets containing skewed attack distributions, highlighting the importance of Balanced Accuracy and F1-Score in addition to overall Accuracy. Differential privacy experiments demonstrate the privacy-utility trade-off, where stronger privacy protection leads to a reduction in model performance. Adversarial robustness evaluation using FGSM perturbations also shows a noticeable decline in detection performance, indicating the need for stronger defense mechanisms against adversarial attacks. In addition, the trust ledger enhances transparency and accountability by monitoring client participation and recording the trust scores used during trust-weighted aggregation and maintaining trust records throughout the collaborative learning process. The results demonstrate that SecureTrust-FL provides an effective framework for privacy-preserving collaborative intrusion detection while integrating trust management, privacy protection, and secure federated learning within a unified architecture.","url":"https://pubmed.ncbi.nlm.nih.gov/42321290/","authors":["Alshammari NS","Mishra S","Rathi M","Goel N","Tahzib S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 19","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42318584","name":"CITADEL: a post-quantum secure blockchain framework for privacy-preserving electronic health records with temporally-partitioned federated learning.","source":"pubmed","abstract":"Electronic health records (EHRs) increasingly anchor clinical decision support and population-scale analytics, yet their concentration of sensitive information amplifies disclosure risk, widens the attack surface, and faces emerging threats from quantum computing. Existing frameworks fail to simultaneously address privacy preservation, quantum-resistant security, and cross-institutional federated learning.","url":"https://pubmed.ncbi.nlm.nih.gov/42318584/","authors":["Segar N","Vijayan V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42317996","name":"Can public data openness enhance public health resource allocation efficiency? Evidence from the launch of Chinese government data platforms.","source":"pubmed","abstract":"Public health resource allocation efficiency constitutes a critical pillar for protecting public health and social welfare. Nevertheless, data silos and information asymmetry constrain the optimization of public health resource allocation.","url":"https://pubmed.ncbi.nlm.nih.gov/42317996/","authors":["Li Z","Zhang Z","Chu Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42317638","name":"Secure healthcare data management using federated learning, blockchain, and explainable artificial intelligence: a systematic review.","source":"pubmed","abstract":"Due to the rapid digitization of healthcare systems, there has been a huge collection of sensitive personal data of patients. Thus, secure, privacy-preserving, and efficient data management systems are required. Current distributed healthcare systems increasingly use centralized data processing frameworks that are prone to privacy violations, data fragmentation, and malicious attacks. Despite advances in federated learning, blockchain, explainable AI, and incremental optimization, current survey literature studies each technology separately without considering how the four technologies can be harnessed to create synergies. A systematic review of 26 peer-reviewed studies published from 2018 to 2026 indicates that an integrated architecture incorporating federated learning, blockchain, explainable AI, and incremental optimization can be designed. This review identifies ten critical issues that need to be addressed when researching the four technologies. These issues include communication costs, scalability issues, interoperability concerns, limited clinical explainability, and high computational costs when applied in real-time situations. In comparison to privacy, scalability, interpretability, and efficiency, a hybrid approach can help improve data security, boost the interpretability of the models, facilitate data sharing, and prevent data-sharing risks. Overall quality assessment based on the CASP qualitative checklist analysis of all 26 studies indicated an average score of 7.0 out of 10, implying that the quality of the methods used in the studies was acceptable.","url":"https://pubmed.ncbi.nlm.nih.gov/42317638/","authors":["Bhardwaj T","Sumangali K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42314804","name":"Integration of label-free electrochemical sensing, differential privacy deep learning, and blockchain for secure cytotoxicity monitoring.","source":"pubmed","abstract":"Here, we present a blockchain-enabled, privacy-preserving platform that integrates label-free differential pulse voltammetry (DPV) measurements with image-based cell viability references for rapid and real-time monitoring of doxorubicin (DOX) induced cytotoxicity in L929 cells. A poly-l-lysine surface modified screen-printed carbon electrode was integrated with a petri dish to develop a cytotoxicity assay that converts toxicity-induced changes in cell coverage and viability into time- and dose-sensitive DPV signals. These signals were paired with percentage viability values obtained from a U-Net-based deep learning segmentation model trained using pseudo-masks generated from Live/Dead images via HSV-based rule-driven thresholding. To mitigate the risk of information leakage from individual images in future shared-data settings, the model was trained with Differential Privacy Stochastic Gradient Descent (DP-SGD) and maintained a validation performance of &#x2248; 0.94 at an &#x3f5;&#xa0;&#x2248;&#xa0;2.6 level. The regression model, built with normalized DPV current, duration, and dose, achieved R 2 &#xa0;=&#xa0;0.943, reflecting time-dose-dependent toxicity. To reduce communication overhead, the data hash was processed on the blockchain via an event-based smart contract.","url":"https://pubmed.ncbi.nlm.nih.gov/42314804/","authors":["Turkmen H","Sezer BB","Şen M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Oct","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42304312","name":"Differential predictive value of inflammation- and coagulation-based composite biomarkers for venous thromboembolism and mortality in patients with NSCLC receiving immune checkpoint inhibitors.","source":"pubmed","abstract":"Venous thromboembolism (VTE) is a clinically important complication in patients with non-small cell lung cancer (NSCLC) receiving immune checkpoint inhibitors (ICIs). Although inflammation and coagulation abnormalities may contribute to both thrombosis and poor prognosis, it remains unclear whether routinely available composite biomarkers have similar or differential predictive value for VTE occurrence and all-cause mortality in this setting.","url":"https://pubmed.ncbi.nlm.nih.gov/42304312/","authors":["Liu J","Wang L","Xia X","Chen S","Yang D","Lu X","Xu Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 17","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42304280","name":"Advancing fungal sinusitis diagnosis: a radiomics and machine learning approach.","source":"pubmed","abstract":"Differentiating fungal from chronic sinusitis remains a diagnostic challenge due to overlapping symptoms.","url":"https://pubmed.ncbi.nlm.nih.gov/42304280/","authors":["Li S","Li R","Ma Q","Zheng C","Yan X","Chen S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 16","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42296530","name":"Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.","source":"pubmed","abstract":"The convergence of artificial intelligence (AI), blockchain technology, and health care represents one of the most transformative yet technically challenging frontiers in computational medicine. As health care systems adopt data-driven paradigms for precision medicine and clinical decision support, the need for secure, privacy-preserving, and collaborative learning frameworks has become critical. This tutorial introduces a comprehensive, clinically oriented, and compliance-aware framework integrating federated learning (FL) and blockchain for secure and privacy-preserving health care analytics. FL enables collaborative training across distributed institutions without raw data sharing, in alignment with privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). However, FL remains vulnerable to model poisoning and gradient leakage. To address these risks, we introduce blockchain-based FL (BCFL), which leverages blockchain's immutable ledger and decentralized consensus to enhance trust, verifiability, and auditability. The tutorial's main contributions include (1) a taxonomy of diverse medical data types and their FL requirements; (2) three integration architectures (fully coupled, semicoupled, and loosely coupled) analyzed for security, scalability, and regulatory compliance; (3) a security analysis of health care-specific vulnerabilities and mitigation strategies using advanced cryptography, such as zero-knowledge proofs, homomorphic encryption, and differential privacy; and (4) a regulatory compliance framework addressing HIPAA, GDPR, and United States Food and Drug Administration guidelines for AI-enabled medical devices. We demonstrate BCFL's relevance across major health care applications, including disease prediction, medical imaging, patient monitoring, and drug discovery, and highlight emerging research directions such as quantum-resilient cryptography, scalable interoperability, and automated compliance. This tutorial serves as a foundational resource for advancing secure, compliant, and collaborative AI in health care; fostering privacy-preserving analytics; and improving patient outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/42296530/","authors":["Shahsavari Y","Baseri Y","Hafid A","Dambri OA","Makrakis D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 15","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42295499","name":"Serous Maculopathy due to Aspecific Choroidopathy (SMACH): a case report with differential diagnosis based on multimodal imaging analysis.","source":"pubmed","abstract":"Serous Maculopathy Due to Aspecific Choroidopathy (SMACH) is a rare chorioretinal disease characterized by polymorphic, non-pigmented choroidal lesions, with or without subretinal fluid (SRF). Its clinical manifestations overlap with common conditions such as central serous chorioretinopathy (CSC) and age-related macular degeneration (AMD), leading to a high risk of misdiagnosis, particularly in elderly patients. Multimodal imaging, including optical coherence tomography (OCT), OCT angiography (OCTA), fluorescein fundus angiography (FFA), and indocyanine green angiography (ICGA), is critical for accurate diagnosis.","url":"https://pubmed.ncbi.nlm.nih.gov/42295499/","authors":["Ren X","Yu X","Huang S","Qi X","Wang S","Sun Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 15","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42286017","name":"CASCADENCE: a layered cascade defense mechanism for federated learning.","source":"pubmed","abstract":"This paper aims to enhance the security and robustness of Federated Learning (FL) systems through a multi-layered defense. We address the critical challenge of protecting distributed learning environments from adversarial attacks while maintaining high model performance during both the training and operational phases. The proposed framework is based on integrated approaches that utilize a Gaussian filter with Discrete Fourier Transform (DFT), adversarial training with differential privacy, JPEG compression, randomized smoothing, and adversarial logit pairing. It integrates multiple defense mechanisms based on system requirements, focusing on preserving model performance while ensuring robust protection during both training and testing phases. Our approach extends beyond existing solutions by introducing various staged defense implementations and analyzing their synergistic effects. Experimental results demonstrate that the proposed ensemble defense mechanism achieves the highest performance, maintaining 98.21% accuracy and an F1 score of 0.98 under attack conditions, compared to a baseline accuracy of 90.87%.","url":"https://pubmed.ncbi.nlm.nih.gov/42286017/","authors":["Hashmi SW","Shukla RM","Bhunia S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 13","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42284322","name":"Remote medical system driven by medical big models: Dynamic defense model for network security threats.","source":"pubmed","abstract":"As telemedicine systems become increasingly interconnected and medical big models are more widely deployed, remote medical infrastructures face growing cybersecurity risks. Traditional static defense mechanisms rely on predefined rule libraries and delayed patching cycles, which makes them inadequate for fast-evolving attacks in medical environments. This creates persistent risks such as model parameter leakage, insufficient privacy protection, and single-point failures in network architecture. We hypothesize that a dynamic defense framework integrating intelligent decision-making, trusted coordination, and hardware acceleration can better balance security, privacy, and real-time performance in medical scenarios. To test this hypothesis, we develop a reinforcement learning (RL)-driven adaptive dynamic defense strategy as the core decision-making module and integrate it with three supporting components: a security-enhanced model protection architecture based on an improved Shamir threshold scheme, adversarial training, and differential privacy; a blockchain-based verification mechanism using improved PBFT; and FPGA-based hardware acceleration using the Xilinx XC7K325T platform. The framework is implemented and evaluated using NS-3, Python 3.8 with PyTorch 1.12, Hyperledger Fabric 2.4, and the publicly available Synthetic IoMT Security Dataset. Across the evaluated regional medical alliance and emergency ambulance scenarios, the proposed system increases the zero-day attack blocking rate from 68.5% to 99.3%, improves medical image encryption throughput from 120 Mbps to 450 Mbps, reduces CPU peak utilization by 47.8%, eliminates privacy leakage incidents during cross-institutional data sharing, and stabilizes core clinical service latency within 35&#x2009;ms. These results indicate that the proposed framework can enhance the security and operational resilience of remote medical systems under the evaluated conditions. Simulated results and deployment-based observations are distinguished in the corresponding sections.","url":"https://pubmed.ncbi.nlm.nih.gov/42284322/","authors":["Weng Z","Hu Y","Gu D","Wang Z","Guo Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42277725","name":"Longitudinal adherence trajectories measured by electronic dose monitoring and viral non-suppression in a South African HIV cohort study.","source":"pubmed","abstract":"Sustained adherence to antiretroviral therapy (ART) is essential for maintaining viral suppression among people with HIV (PWH), yet adherence fluctuates over time and is imperfectly captured by routine viral load monitoring in many settings where measurements are often infrequent. We aimed to identify longitudinal ART adherence trajectories using electronic dose monitoring (EDM) and assess their association with viral non-suppression in a South African cohort.","url":"https://pubmed.ncbi.nlm.nih.gov/42277725/","authors":["Ferraris CM","Rosen JG","McDuling C","Schiavoni NJF","D'Avanzo PA","Jennings L","Knox J","Remien RH","Orrell C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 11","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42277163","name":"FedAttn-Credit: attention-augmented federated learning with adaptive differential privacy for rural inclusive finance credit assessment.","source":"pubmed","abstract":"Rural inclusive finance faces persistent challenges in credit assessment due to fragmented data ecosystems, heterogeneous borrower profiles, and stringent privacy constraints. This paper proposes FedAttn-Credit, a multi-party collaborative credit assessment framework that integrates horizontal federated learning with an attention-augmented scoring model and an adaptive differential privacy mechanism. The framework connects five categories of rural data holders-commercial banks, rural credit cooperatives, government platforms, agricultural e-commerce providers, and village-level microfinance institutions-through a central aggregation server that never accesses raw data. A multi-head self-attention module with group-wise importance gating enables the model to dynamically weight heterogeneous feature groups according to their contextual relevance for each borrower. An adaptive differential privacy strategy calibrates noise magnitude based on each participant's data volume and training-round gradient dynamics, providing stronger protection for small-sample participants without disproportionately degrading model utility. Experimental results on public microfinance records and a synthetic rural credit dataset show that FedAttn-Credit achieves an AUC of 0.8734, narrowing the gap to the centralized upper bound to 1.8 percentage points while outperforming all federated baselines. At equivalent accuracy, the adaptive privacy mechanism cuts cumulative privacy cost by roughly 47% relative to fixed-budget alternatives, a figure that we derive in \"Privacy protection effectiveness and model performance tradeoff analysis\" section from the Privacy Efficiency Ratio reported there (the two quantities express the same comparison from opposite directions). Ablation and robustness analyses confirm that the attention module, adaptive noise scheduling, and Shapley-based contribution evaluation provide compounding benefits under both standard and adversarial conditions.","url":"https://pubmed.ncbi.nlm.nih.gov/42277163/","authors":["Xie Z","Zhang L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 11","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42275393","name":"Cluster-randomized trial of Homework, Organization, and Planning Skills program compared to treatment as usual/waitlist for youth ages 11-14: Study protocol for conceptual replication.","source":"pubmed","abstract":"Organization, time management, and planning (OTMP) difficulties are associated with academic underachievement. OTMP skills training programs are effective in reducing OTMP deficits and improving academic performance. A randomized controlled trial of Homework, Organization, and Planning Skills (HOPS) for students ages 11-14 found it to be effective with medium to large effects. In that study, HOPS was provided by counselors employed by the research team. This study is a replication examining HOPS under more authentic conditions when providers are employed by schools serving enrolled students. The primary aim is to evaluate HOPS offered by school providers in relation to treatment-as-usual/waitlist (TAU/WL). To respond to limited school resources post-COVID-19, HOPS is also provided by research team members, creating the opportunity to replicate the findings from the prior trial and explore differential effectiveness when HOPS is implemented by school vs. research providers.","url":"https://pubmed.ncbi.nlm.nih.gov/42275393/","authors":["Nissley-Tsiopinis J","Fleming PF","Chan W","Langberg JM","Cacia J","Vigil T","Chamberlin B","Di Bartolo CA","Tremont KL","Walz EH","Jawad AF","Mautone JA","Power TJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42274932","name":"GLP-1-based therapy and ICD-10-documented heart failure or respiratory failure events in non-diabetic adults with rheumatoid arthritis and obesity: a TriNetX federated cohort study.","source":"pubmed","abstract":"Glucagon-like peptide-1 (GLP-1)-based therapies have emerged as a major advance in cardiometabolic care; however, no prospective outcomes data exist for these agents in non-diabetic adults with rheumatoid arthritis (RA) and obesity. We examined whether GLP-1-based therapy was associated with first post-landmark ICD-10-documented heart failure (HF) or respiratory failure (RF) events.","url":"https://pubmed.ncbi.nlm.nih.gov/42274932/","authors":["Loizidis G","Summer R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42270726","name":"Privacy-preserving federated learning for interpretable student at-risk prediction across schools.","source":"pubmed","abstract":"Early-warning systems for at-risk students increasingly rely on predictive models trained on sensitive educational records. However, centralized learning pipelines raise concerns about privacy, institutional data sovereignty, and auditability, particularly when student-level data are shared across institutions. This study presents Federated Learning for At-Risk Student Prediction with Differential Privacy and Proof-Before-Train (FL-AtRisk-DP-PBT), a federated learning framework for multi-school at-risk prediction that integrates Federated Averaging (FedAvg)-based training, client-side DP, and a PBT protocol for verifiable logging of client participation and model states. The framework uses a single interpretable global logistic-regression classifier and is evaluated under centralized, standard federated, and FL&#x2009;+&#x2009;DP+PBT regimes on three educational datasets: a primary merged cohort of 14,003 students partitioned into 10 simulated schools, the xAPI-Edu-Data click-stream corpus, and the Students Performance in Exams dataset. On the primary dataset, the centralized model achieves 99.14% accuracy, F1&#x2009;=&#x2009;0.9915, and area under the curve (AUC)&#x2009;=&#x2009;0.9998, while the FedAvg and FL&#x2009;+&#x2009;DP+PBT variants achieve 98.61%/0.9863/0.9993 and 98.00%/0.9802/0.9992, respectively. On xAPI and Exams, FL&#x2009;+&#x2009;DP+PBT reaches approximately 93-94% accuracy, F1&#x2009;&#x2248;&#x2009;0.92-0.93, and AUC&#x2009;&#x2248;&#x2009;0.97-0.98. Coefficient-based feature-importance analysis indicates that FL&#x2009;+&#x2009;DP+PBT preserves broadly similar interpretation patterns to the centralized and non-private federated baselines. The PBT ablation introduces only small metric changes relative to DP-only federated training. Overall, the results suggest that interpretable federated at-risk prediction can retain competitive utility while keeping student records local and adding privacy-preserving and verifiable training mechanisms. These findings should be interpreted within the evaluated datasets, simulated school partitions, and label definitions.","url":"https://pubmed.ncbi.nlm.nih.gov/42270726/","authors":["Jodayree M","Ghafi AK","Atashafrouz M","Shafiabadi MH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 10","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42270694","name":"Federated continual learning for privacy-preserving chest radiograph classification.","source":"pubmed","abstract":"Multi-site deep learning for chest radiographs runs into two problems that are usually treated separately: privacy regulations prevent hospitals from pooling raw images, and clinical workflows change over time in ways that cause sequential model updates to overwrite earlier knowledge. Federated learning addresses the first problem, but most FL systems are built for static data distributions. Federated continual learning (FCL) handles both, though the methods that perform best on benchmarks tend to require public surrogate datasets or stored image replay - neither of which is easy to justify in a hospital context. We propose DP-FedEPC (Differentially Private Federated Elastic Prototype Consolidation), which combines elastic weight consolidation (EWC), prototype-based rehearsal, and client-side DP-SGD within a standard FedAvg workflow. EWC keeps weight updates from drifting away from parameters learned on earlier tasks. A small bank of latent prototypes - not raw images - holds class geometry stable across task shifts. Every client trains under DP-SGD, and we report the achieved [Formula: see text] for each noise level explicitly (Table&#xa0;7). We train on CheXpert and validate externally on MIMIC-CXR. Beyond macro-AUROC, we report per-finding performance and forgetting scores (Table&#xa0;3), prototype geometry and failure cases (Figs.&#xa0;2-4), and a full cost breakdown against baselines (Table&#xa0;8).","url":"https://pubmed.ncbi.nlm.nih.gov/42270694/","authors":["Sinhal A","Sinhal A","Sinhal A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 10","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42266875","name":"Exploiting explanations for model extraction via knowledge distillation and mitigation with private counterfactuals.","source":"pubmed","abstract":"In recent years, there has been a notable increase in the deployment of machine learning (ML) models as services (MLaaS) across diverse production software applications. In parallel, explainable AI (XAI) continues to evolve, addressing the necessity for transparency in ML models. XAI techniques aim to enhance the transparency of ML models by providing insights, in terms of model's explanations , into their decision-making process. At the same time, some MLaaS platforms now offer explanations alongside the ML prediction outputs. This setup has elevated concerns regarding vulnerabilities in MLaaS, particularly in relation to privacy leakage attacks such as model extraction attacks (MEA). This is due to the fact that explanations can unveil insights about the inner workings of the model which could be exploited by malicious users. In this work, we focus on investigating how model explanations, particularly counterfactual explanations (CFs), can be exploited for performing MEA within the MLaaS platform. We also delve into assessing the effectiveness of incorporating differential privacy (DP) as a mitigation strategy. To this end, we first propose a novel MEA approach based on Knowledge Distillation (KD) that leverages CFs to effectively extract a substitute model of the target. Our approach operates without requiring any prior knowledge of the training data distribution by the attacker. Then, we advise an approach for training CF generators integrating DP to generate private CFs. We conduct thorough experimental evaluations on real-world datasets and demonstrate that our proposed KD-based MEA can yield a high-fidelity substitute model with a reduced number of queries with respect to baseline approaches. Furthermore, our findings reveal that including a privacy layer can allow mitigating the MEA. However, by balancing the quality of CFs, impacts the performance of the explanations and the MEA.","url":"https://pubmed.ncbi.nlm.nih.gov/42266875/","authors":["Ezzeddine F","Giordano S","Ayoub O"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42262952","name":"Efficient Privacy-Budgeted Large Language Model for Sensitive Healthcare.","source":"pubmed","abstract":"Large language models (LLMs) are promising for healthcare question answering, yet adapting them with sensitive clinical data can induce unintended memorization and privacy leakage. We present MedDP, an efficient privacy-budgeted adaptation framework that delivers an auditable utility-privacy trade-off under ($\\epsilon,\\delta$)-differential privacy. Our method, MedDP, applies DP-SGD only to low-rank adapter parameters while freezing the base model, using per-example gradient clipping and calibrated Gaussian noise to enable explicit privacy accounting with minimal compute and memory overhead. To improve the stability of noisy optimization, we introduce Adaptive Clipping (AC) that updates the clipping bound online via a quantile-based rule with momentum. To avoid spending additional privacy budget on late-stage accuracy degradation, we further propose Budget-Aware (BA) checkpoint selection, which chooses the best checkpoint on the training trajectory subject to a target budget $\\epsilon _\\rm{max}$, without re-training. For privacy evaluation, we adopt a canary-based suite and emphasize canary negative log-likelihood (NLL) as a more sensitive memorization signal than hit-rate. Experiments on public medical QA benchmarks derived from MedQA and MedMCQA, together with synthetic canary auditing, characterize a clear privacy-utility frontier in a controlled evaluation setting. Non-private LoRA improves average accuracy but increases memorization signals, whereas privacy-budgeted training mitigates memorization while maintaining stable performance under explicit privacy accounting.","url":"https://pubmed.ncbi.nlm.nih.gov/42262952/","authors":["Zhang C","Zheng L","Huang H","Su C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 9","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42258368","name":"Willingness to Undergo Human Papillomavirus Testing Among Men Who Have Sex With Men in China Based on the Information-Motivation-Behavioral Skills Model: Online Cross-Sectional Study.","source":"pubmed","abstract":"Men who have sex with men (MSM) are at high risk of human papillomavirus (HPV) infection, and HPV testing can facilitate early detection and timely intervention. However, evidence on the willingness to undergo different HPV testing modalities among MSM remains limited. The information-motivation-behavioral skills model provides a theoretical framework for understanding factors associated with the willingness to undergo HPV testing.","url":"https://pubmed.ncbi.nlm.nih.gov/42258368/","authors":["Liu X","Yang X","Lyu M","Dai Z","Jing S","Xin Y","Yu F","Tang S","Su X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 8","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42251507","name":"Data Privacy, Ownership, and Secondary Use of Clinical Data Generated by Continuous Glucose Monitors and Mobile Health Applications: A Review.","source":"pubmed","abstract":"The growing use of continuous glucose monitors (CGMs) and mobile health (mHealth) applications has changed how diabetes is managed, allowing real-time tracking of glycemic patterns and remote clinical decision-making. These technologies also generate large volumes of sensitive health data, raising questions about who owns this information, how it is protected, and under what conditions it may be repurposed for research or commercial objectives. This review examines the regulatory frameworks governing CGM and mHealth data in major jurisdictions, with particular attention to the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union. Significant regulatory gaps exist, particularly for consumer-grade devices and direct-to-consumer mHealth applications that fall outside traditional healthcare data-protection frameworks. Data ownership remains legally ambiguous in most jurisdictions, with patients, healthcare providers, device manufacturers, and app developers each holding competing claims. The secondary use of clinical data for research, while it could materially advance diabetes care, raises ethical concerns around informed consent, data de-identification, and the boundaries between clinical care and commercial exploitation. Emerging approaches, including the European Health Data Space, federated learning, and differential privacy, may help balance data utility with individual rights. The review recommends changes to regulation, industry practice, and consent models aimed at reconciling data-driven diabetes research with patient autonomy and privacy.","url":"https://pubmed.ncbi.nlm.nih.gov/42251507/","authors":["Dario P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 7","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42247734","name":"Contrastive adapter training and consensus knowledge distillation for multi-source-free domain adaptation in skin cancer diagnosis.","source":"pubmed","abstract":"Skin cancer diagnosis, particularly the differentiation of melanoma from benign nevi, is a vital yet challenging task due to the visual similarity between lesions. Although deep learning models such as convolutional neural networks (CNNs) and vision transformers (ViTs) have demonstrated promising performance, their effectiveness often deteriorates when applied to data from heterogeneous clinical sources. While conventional domain adaptation methods address domain shift, they require access to source data during adaptation, which is often infeasible due to privacy regulations. Multi-source-free unsupervised domain adaptation (MSFDA) addresses this limitation by leveraging multiple labeled source domains to generalize to an unlabeled target domain without requiring access to source data, making it suitable for privacy-sensitive medical settings. However, existing MSFDA methods rely on full backbone fine-tuning, leading to catastrophic forgetting and overfitting on small clinical datasets, and address domain shift at the aggregation stage without establishing a shared domain-invariant feature space. Furthermore, their reliance on hard pseudo-labels or confidence-weighted aggregation introduces noisy supervision signals under domain shift. To address these limitations, we propose CAT-CKD, consisting of two components: (1) contrastive adapter training (CAT), which trains lightweight ConvPass adapters within a frozen ViT backbone using supervised contrastive learning (SCL) to establish a shared domain-invariant feature space before source-specific model training, and (2) consensus knowledge distillation (CKD), which aggregates logits from multiple source models into a consensus supervisory signal and adapts a student model on unlabeled target data using KL divergence. Experiments on five publicly available skin lesion datasets show that CAT-CKD achieves an average AUROC of 86.1%, outperforming existing MSFDA methods while requiring only 4.3M trainable parameters. The code for this paper is available at https://github.com/A-Abedi/CAT_CKD.","url":"https://pubmed.ncbi.nlm.nih.gov/42247734/","authors":["Abedi A","Wu QMJ","Zhang N","Pourpanah F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Oct","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42244233","name":"Qualitative Insights on Client and Provider Barriers and Facilitators to Using a Novel Online HIV Pre- and Post-Exposure Delivery Model in Kenya.","source":"pubmed","abstract":"Online delivery of HIV pre- and post-exposure prophylaxis (PrEP and PEP) could address persistent access barriers, yet implementation across Africa remains limited. The ePrEP Kenya Pilot (NCT05377138) integrated PrEP and PEP services into an existing e-pharmacy platform and identified client- and provider-level barriers and facilitators to use.","url":"https://pubmed.ncbi.nlm.nih.gov/42244233/","authors":["Okello P","Thuo N","Roche SD","Saravis AL","Mwai D","Naik P","Kiptinness C","Kareithi T","Mugambi ML","Sharma M","Ngure K","Ortbald KF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42243272","name":"Attention U-Net with differential privacy in federated learning framework for brain stroke lesion segmentation.","source":"pubmed","abstract":"Data privacy considerations and data fragmentation between healthcare institutions is causing segmentation of ischemic stroke lesions from neuroimaging to be hindered. The centralized approach may conflict with HIPAA and GDPR, and the federated learning approach does not have proper privacy guarantees nor is it able to capture lesion detail. This work prposes Fed-AttUNet-DP,a three major contributions: a spatial attention mechanism to integrate federated learning for better stroke lesion detection; differential privacy at the client level and secure MPCC at the server level for dual-layered privacy preservation; adaptive federated optimization for non-IID medical data and faster training speed and better performance. The proposed framework is an augmentation to the federated averaging algorithm which exploits attention U-Net and antennas attention (differential privacy i.e. gradient clipping and Gaussian noise). Ten simulated health care institutions were involved in 150 rounds of communication, at a rate of [Formula: see text] participation of the clients. Privacy budget was configured set at [Formula: see text], [Formula: see text] and a multiplier of noise [Formula: see text] as well as gradient clipping threshold [Formula: see text]. The distance between data heterogeneity was measured with Earth Mover (EMD [Formula: see text]). Experiments are conducted on the BRISC2025 dataset. The Dice similarity coefficient and IoU for Fed-AttUNet-DP is 0.930 and 0.890 respectively, sensitivity is 0.941 and specificity is 0.982. It beats all federated baselines by only [Formula: see text] accuracy drop compared to centralized training as well as [Formula: see text] compared to local-only training. The inference per volume is 0.8 s, converges the system with 5.18 GB total communication cost with 150 rounds. It is verified in the works of the ablation research that attention gates ([Formula: see text] DSC), differential privacy ([Formula: see text] DSC trade-off), and secure aggregation individually contribute to it. Our work provides formal privacy guarantees and advances segmentation accuracy, by mitigating non-IID heterogeneity across multiple medical image institutions and respecting HIPAA/GDPR regulations.","url":"https://pubmed.ncbi.nlm.nih.gov/42243272/","authors":["Adhi Siva M","Chowdhary CL"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 4","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42238163","name":"Pembrolizumab-Induced Eruptive Keratoacanthomas Managed With Intralesional Corticosteroids: A Case Report.","source":"pubmed","abstract":"Pembrolizumab, a programmed cell death protein 1 (PD-1) inhibitor, is approved for multiple malignancies, including renal cell carcinoma in combination with axitinib. While highly effective, PD-1 blockade can provoke immune-mediated cutaneous adverse events, including eruptive squamous cell carcinomas and keratoacanthomas. These squamoproliferative lesions may reflect a reactive epidermal proliferation triggered by immune activation, potentially through unmasking of subclinical epidermal dysplasia. Histologically, keratoacanthomas and well-differentiated squamous cell carcinomas can be difficult to distinguish, and clinicopathologic correlation is essential for guiding management. We present a case of a patient receiving pembrolizumab and axitinib for renal cell carcinoma who developed a high burden of eruptive squamoproliferative lesions across multiple anatomic sites. Biopsies were interpreted as either well-differentiated squamous cell carcinoma or keratoacanthoma-type squamous proliferations, underscoring the diagnostic ambiguity inherent to these lesions. Most lesions were successfully managed with intralesional triamcinolone, allowing continuation of immunotherapy without interruption. Two subsequent lesions required surgical excision due to clinical concern for invasive behavior, although no aggressive histologic features were identified. This case adds to the growing literature supporting conservative dermatologic management of pembrolizumab-associated squamoproliferative lesions. Unlike previous&#xa0;reports describing only a small number of lesions, this case involved a high lesion burden treated with intralesional corticosteroids across multiple sites, with clearly documented technique and follow-up. The case reinforces the clinical importance of correlating histopathologic findings with therapeutic response and demonstrates that, with appropriate monitoring, immunotherapy can often be continued safely while managing cutaneous findings locally.","url":"https://pubmed.ncbi.nlm.nih.gov/42238163/","authors":["Varghese K","Vetos D","Woods J","Tate J","Wang-Weinman T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42225851","name":"Privacy-aware continuous federated biometric authentication (PACFBA) using federated deep learning: a novel usable security framework.","source":"pubmed","abstract":"With the rapid growth of mobile, IoT, and edge-based ecosystems, the need for secure, seamless, and privacy-preserving authentication mechanisms has become increasingly critical. Traditional authentication methods, such as passwords and single-step biometrics, verify users only at the initial login stage and fail to ensure continuous user verification throughout an active session, exposing systems to significant security risks. To address this limitation, this study proposes the Privacy-Aware Continuous Federated Biometric Authentication (PACFBA) framework, a novel Federated Learning (FL)-based approach for continuous, decentralized user authentication. PACFBA integrates a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to jointly capture spatial and temporal biometric patterns, improving adaptability in heterogeneous environments. To ensure secure model updates and protect user data, PACFBA incorporates Differential Privacy (DP) and Homomorphic Encryption (HE) within the FL process, preventing the transfer of sensitive biometric information to central servers. Experimental results demonstrate that PACFBA achieves an accuracy of 92.5%, precision of 90.8%, recall of 91.2%, and an F1-score of 0.91, while reducing communication overhead by 20% and improving privacy preservation by 35% compared to centralized models. These results confirm PACFBA's potential for enhancing continuous authentication in mobile, IoT, and edge-based environments. However, further empirical validation using large-scale, heterogeneous, real-world datasets is necessary before the framework can be considered fully deployment-ready. This research contributes to designing scalable, secure, and adaptive authentication frameworks to address emerging challenges in decentralized digital ecosystems.","url":"https://pubmed.ncbi.nlm.nih.gov/42225851/","authors":["Al Abdulwahid A","Abdulwahid Al Abdulwahid"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-55046-2","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"pmid:42222849","name":"Recent advances in defending the privacy attacks of large language models for healthcare applications: a concise review.","source":"pubmed","abstract":"Large language models (LLMs) are increasingly adopted across healthcare applications, including clinical decision support and medical documentation systems. However, their deployment in medical settings raises significant privacy and security concerns due to the sensitivity of protected health information and stringent regulatory requirements. Recent studies have shown that LLM-based medical applications can cause medical data leakage through related API usages. This raises ethical concerns and threatens HIPAA (Health Insurance Portability and Accountability Act) compliance, blocking the trustworthy deployment of LLMs in the medical domain. This review examines emerging privacy attacks and the related defense approaches in LLMs with a special focus on healthcare applications. We organize the attack and defense approaches based on Secure AI Framework (SAIF), systematically covering vulnerabilities across the data, model, application and infrastructure layers. We performed detailed analysis on major classes of privacy attacks and further examined state-of-the-art defense mechanisms under realistic healthcare application scenarios. A key finding of this review is the persistent privacy-utility tradeoff: stronger privacy protection often leads to substantial degradation in clinical performance, which may render models unsuitable for mission-critical medical tasks. The healthcare related deployment of LLMs needs to be evaluated against clinical utility thresholds rather than generic language modeling metrics. We identify open challenges in evaluation, system-level deployment and regulatory verification, and outline research directions that balance clinical utility with regulatory compliance.","url":"https://pubmed.ncbi.nlm.nih.gov/42222849/","authors":["Aroua R","Kammoun I","Louati MA","Adjeroh DA","Duan T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42221863","name":"Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification.","source":"pubmed","abstract":"Large Language Models (LLMs) are being widely adopted in different domains including education, healthcare, and finance. In healthcare domain, LLMs are used in disease diagnosis, abnormality classification, remedy suggestions etc. Multi-abnormality classification of radiology reports is essential in healthcare, medical decision-making, and drug discovery. LLMs are increasingly utilized for such tasks because of their remarkable Natural Language Processing (NLP) capabilities, which streamline medical report processing and reduce administrative burden. To enhance the predictive accuracy, LLMs are often fine-tuned on private, locally available datasets, such as medical reports. However, this practice raises significant privacy concerns, as LLMs are prone to memorizing training data, making them susceptible to data extraction attacks even through query-based access. Additionally, sharing fine-tuned models and weights poses adversarial risks, because they may inadvertently reveal sensitive information about the training data. Despite the growing application of LLMs to medical text classification, privacy-preserving fine-tuning for multi-abnormality classification remains underexplored. To bridge this gap, we propose a differentially private (DP) fine-tuning approach that preserves privacy while enabling multi-abnormality classification from text radiology reports through Low Rank Adaptation (LoRA). Our framework leverages DP optimization techniques to fine-tune LLMs on local patient data while mitigating data leakage risks. To our knowledge, this is the first study to incorporate DP fine-tuning of LLMs for multi-abnormality classification using text-based radiology reports. We used labels generated by a larger LLM to fine-tune a smaller LLM, accelerating inference while maintaining privacy constraints. We conducted extensive experiments on the MIMIC-CXR, and CT-RATE datasets to evaluate DP fine-tuning method across varying privacy regimes, analyzing the privacy-utility trade-off and demonstrating the efficacy of our approach. For instance, on the MIMIC-CXR dataset, our proposed DP-LoRA framework achieves weighted F1-scores of up to 0.89 under moderate privacy budgets ( &#x3f5; = 10 ), approaching the performance of non-private LoRA (0.90) and full fine-tuning (0.96). These results demonstrate that strong privacy protection can be achieved with only moderate performance degradation.","url":"https://pubmed.ncbi.nlm.nih.gov/42221863/","authors":["Bhattacharjee P","Tian F","Rubin GD","Lo JY","Merchant N","Hanson HA","Gounley J","Tandon R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42215800","name":"Beyond the clot: diagnostic yield, biomarker performance, and the landscape of alternative diagnoses in emergency CT pulmonary angiography.","source":"pubmed","abstract":"Computed Tomography Pulmonary Angiography (CTPA) is the gold standard for diagnosing acute Pulmonary Embolism (PE). However, overlapping symptoms and reliance on non-specific biomarkers often lead to over-testing. This study provides a comprehensive analysis of CTPA utilization, evaluating diagnostic yield, biomarker performance, and the landscape of alternative thoracic diagnoses with a special focus on radioprotection.","url":"https://pubmed.ncbi.nlm.nih.gov/42215800/","authors":["Lionetti F","Castelli F","Scavone G","Tamburino G","Seminatore S","Caltabiano DC","Mammino L","Basile A","Raciti MV","Galvano G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42211762","name":"PROVGEN: A Privacy-Preserving Approach for Outcome Validation in Genomic Research.","source":"pubmed","abstract":"As genomic research has grown increasingly popular in recent years, dataset sharing has remained limited due to privacy concerns. This limitation hinders the reproducibility and validation of research outcomes, both of which are essential for identifying computational errors during the research process. In this paper, we introduce PROVGEN, a privacy-preserving method for sharing genomic datasets that facilitates reproducibility and outcome validation in genome-wide association studies (GWAS). Our approach encodes genomic data into binary space and applies a two-stage process. First, we generate a differentially private version of the dataset using an XOR-based mechanism tailored to biological characteristics. Second, we restore data utility by adjusting the Minor Allele Frequency (MAF) values in the noisy dataset to align with public MAFs using optimal transport. Finally, we convert the processed binary data back into its genomic representation and publish the resulting dataset. We evaluate PROVGEN on three real-world genomic datasets and compare it with local differential privacy and three synthesis-based methods. Our results show that PROVGEN overall outperforms existing approaches in detecting GWAS outcome errors, preserving data fidelity, and resisting membership inference attacks (MIAs). By adopting our method, genomic researchers will be inclined to share differentially private datasets while maintaining high data quality for reproducibility of their findings.","url":"https://pubmed.ncbi.nlm.nih.gov/42211762/","authors":["Jiang Y","Ji T","Ayday E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42210133","name":"Extracellular volume fraction derived from spectral CT for liver function reserve evaluation and complication prediction in clinically stable liver cirrhosis.","source":"pubmed","abstract":"To investigate the value of spectral CT-derived extracellular volume fraction (fECV) in assessing liver functional reserve and predicting cirrhosis-related complications in clinically stable cirrhotic patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42210133/","authors":["Zhang L","Sun Y","Zhang X","Zhang Z","Ji Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 28","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42210113","name":"Urban-rural disparities in HPV prevalence and genotype distribution: a large-scale cervical cancer screening study in Xi'an, China.","source":"pubmed","abstract":"Cervical cancer remains a major public health challenge in China, with persistent urban-rural disparities in screening coverage. The epidemiological landscape of HPV genotype distribution and screening outcomes across geographic settings remains incompletely characterized. We compared HPV prevalence, genotype distribution, and screening outcomes between urban and rural populations in Xi'an, China, during the post-COVID-19 pandemic period (2023-2025).","url":"https://pubmed.ncbi.nlm.nih.gov/42210113/","authors":["Wu J","Yi J","Fang L","Lei S","Yang H","Ma M","Hao Y","Kim H","Shin H","Choi W"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 28","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42209919","name":"IER3 as an RFX5 transcriptional co-activator promotes hepatocellular carcinoma progression via AKR1B10-mediated p53 transcriptional regulation.","source":"pubmed","abstract":"Hepatocellular carcinoma (HCC) exhibits profound molecular heterogeneity, which complicates prognosis and therapy. Identifying key molecular subtypes and their driving oncogenes is crucial for developing targeted strategies. This study aimed to delineate chemokine-based HCC subtypes and investigate the functional role and mechanism of a critical identified driver, Immediate Early Response 3 (IER3).","url":"https://pubmed.ncbi.nlm.nih.gov/42209919/","authors":["Chen X","Zhang Q","Zhang N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 28","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42209602","name":"Interpretable reputation driven asynchronous consensus vehicle networking federated learning architecture.","source":"pubmed","abstract":"This paper proposes a Vehicle-Road-Cloud-Chain (VRCC) four-layer collaborative framework to address the issues of lack of interpretability, reputation evaluation failure, and architecture centralization vulnerability faced by federated learning in Internet of Vehicles (IoV) under Differential Privacy (DP), Non-Independent and Identically Distributed (Non-IID) data, and Byzantine attacks. The framework achieves explicit decoupling between low-latency data sharing and latency-tolerant collaborative training. Firstly, construct an asynchronous blockchain consensus layer based on Directed Acyclic Graph (DAG), which supports low confirmation latency model interaction record storage in high-concurrency vehicle scenarios; And design a three-layer interpretable reputation evaluation mechanism, integrating historical task performance, Maximum Mean Discrepancy (MMD) Bayesian inference, and task completion contribution, to achieve causal decoupling between \"honest high loss\" and \"malicious low loss reporting\" under Differential Privacy noise, and jointly sign and upload it to the chain through the regulatory committee and Roadside Units (RSUs), making reputation judgments auditable and transparent; Further propose a participant selection algorithm based on Deep Deterministic Policy Gradient (DDPG) and reputation partitioning, which synchronously optimizes communication overhead, computation delay, and redundant filtering in dynamic traffic flow, while utilizing local DAG weight-biased random walks to achieve lightweight asynchronous model quality verification. The experiment shows that the cumulative reward of the proposed method can quickly converge and remain stable, verifying its system-level superiority in interpretable robust aggregation and high-concurrency scalability.","url":"https://pubmed.ncbi.nlm.nih.gov/42209602/","authors":["Zi Y","Zhou Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 28","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"pmid:42202203","name":"Privacy-preserving Online Federated Learning for Massive Infinite Streams.","source":"pubmed","abstract":"Online federated learning (OFL) is essential for privacy-preserving collaborative online analytics over decentralized streams. Different from batch-based FL, OFL faces new challenges including longitudinal privacy leakage, and accumulated utility loss and communication costs, caused by the infinite data streams. This paper first extends the definition of traditional differential privacy (DP) to OFL, to provide window-based privacy protection with a tunable granularity for infinite streams. By analyzing baseline methods, a generic sampling-based solution framework is then proposed for designing a DP-enhanced OFL algorithm. We prove that despite the DP constraint, the sampling solution framework can achieve an asymptotic optimality when time tends to infinity. Finally, we present Sampling$^{3}$-OFL, an adaptive triple-sampling strategy driven by deep reinforcement learning, which can dynamically determine a near-optimal sampling strategy with significant gains in both utility and efficiency. Extensive experiments on six real-world datasets demonstrate that Sampling$^{3}$-OFL can scale to millions of streams, and achieves utility improvements of 0.74%-15.84% and communication cost reductions of 33.33%-95.24% across these datasets compared to state-of-the-art methods.","url":"https://pubmed.ncbi.nlm.nih.gov/42202203/","authors":["Shi L","Ren X","Yang S","Zhao C","Hao Y","Xu Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 27","addedAt":"2026-08-06T22:49:41.717Z"},{"id":"doi:10.2139/ssrn.7328915","name":"Closed-Form Federated Learning Without Client Labels Through Federated Semi-Supervised Learning","source":"crossref","abstract":"Federated semi-supervised learning (FSSL) in the labels-at-server setting is governed by dual label shift: class priors vary across clients, and the server&amp;apos;s labeled distribution differs from the clients&amp;apos; unlabeled data. Gradient-based methods learn the prediction head under these shifting priors; since the head is far more shift-sensitive than the feature extractor, training becomes unstable. In standard federated learning, closed-form aggregation offers a stable alternative: a ridge-regression head on a frozen pre-trained backbone, assembled from additive sufficient statistics, is partition-invariant, recovering the centralized solution however the data are split. This property suits dual label shift, yet it holds only partially in FSSL, where client targets are pseudo-labels from the current model and only a sampled subset of clients participates each round; the aggregated statistics then carry a selection bias that feeds back through the pseudo-labels. We propose FedSCF to close this gap. FedSCF buffers client statistics on the server and applies convergence-adaptive aggregation, refreshing the classifier only when successive aggregated updates agree in direction, indicating a representative aggregate. On CIFAR-10, CIFAR-100, and STL-10 under severe non-IID regimes, FedSCF improves accuracy and stability over gradient-based FSSL baselines, with no local training epochs and only compact statistic exchange. For example, under an extreme single-class (K = 1) partition on CIFAR-10, it raises final top-1 accuracy from 62.8% to 82.5% relative to the strongest baseline. Closed-form aggregation, once made robust to partial participation, is thus a practical basis for FSSL where labeled data are scarce and client distributions are highly heterogeneous.","url":"https://doi.org/10.2139/ssrn.7328915","authors":["Jihyung Choi","Seoung Bum Kim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-21T18:46:17Z","doi":"10.2139/ssrn.7328915","addedAt":"2026-08-31T06:41:18.935Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/c2025-0-02020-3","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2025-0-02020-3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:45:27Z","doi":"10.1016/c2025-0-02020-3","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:28.651Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00017-4","name":"Robust defense against inference attacks and differential privacy integration in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00017-4","authors":["M.A.P. Chamikara","Mohan Baruwal Chhetri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","doi":"10.1016/b978-0-44-344433-3.00017-4","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00011-9","name":"Privacy-preserving federated learning: algorithms and guarantees","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00011-9","authors":["Xinwei Zhang","Xiangyi Chen","Bingqing Song","Prashant Khanduri","Mingyi Hong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:48:33Z","doi":"10.1016/b978-0-44-319037-7.00011-9","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.7717/peerj-cs.3604/table-1","name":"Table 1: Federated learning algorithms summary.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3604/table-1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-13T08:43:17Z","doi":"10.7717/peerj-cs.3604/table-1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.7717/peerj-cs.2870/fig-7","name":"Figure 7: Evaluation of federated learning.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.2870/fig-7","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-08T04:35:49Z","doi":"10.7717/peerj-cs.2870/fig-7","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003497196-4","name":"Adopting Federated Learning for Software-Defined Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196-4","authors":["Akarsh K. Nair","Jayakrushna Sahoo","Gaurav Jaswal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T10:30:21Z","doi":"10.1201/9781003497196-4","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1109/flta63145.2024.10840123","name":"Federated Learning in the Age of Foundation Models","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840123","authors":["Ziyue Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840123","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00021-6","name":"Privacy-preserving federated learning in IoT for smart and sustainable healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00021-6","authors":["Shinu M. Rajagopal","Supriya M","Rajkumar Buyya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","doi":"10.1016/b978-0-44-344433-3.00021-6","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.32657/10356/202033","name":"Optimizations for federated learning systems","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/202033","authors":["Xinyu Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-01T09:35:29Z","doi":"10.32657/10356/202033","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00016-2","name":"Resilience of federated learning: perspectives on attacks and defenses","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00016-2","authors":["Pravija Raj P V","Ashish Gupta","Sajal K. Das"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","doi":"10.1016/b978-0-44-344433-3.00016-2","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1017/9781009072205.019","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1017/9781009072205.019","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-24T19:05:52Z","doi":"10.1017/9781009072205.019","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003546559-12","name":"Revolutionizing Healthcare Systems with Federated Learning and Blockchain","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003546559-12","authors":["Arati J. Vyavahare"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-01T12:15:01Z","doi":"10.1201/9781003546559-12","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003595540-7","name":"Artificial Intelligence-Based Federated Learning in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540-7","authors":["Priyadarshini Pattanaik","Nguyen Manh Cuong","Sanchit Sarhadi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","doi":"10.1201/9781003595540-7","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003482000-10","name":"Applications of Federated Learning in AI, IoT, Healthcare, Finance, Banking, and Cross-Domain Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003482000-10","authors":["Walaa Hassan","Habiba Mohamed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T17:15:57Z","doi":"10.1201/9781003482000-10","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1007/978-3-030-96896-0_25","name":"Application of Federated Learning in Telecommunications and Edge Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_25","authors":["Utpal Mangla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_25","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1109/compsac51774.2021.00274/video","name":"Video for Dew Intelligence: Federated learning perspective","source":"crossref","abstract":"","url":"https://doi.org/10.1109/compsac51774.2021.00274/video","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-04T15:37:10Z","doi":"10.1109/compsac51774.2021.00274/video","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003489368-4","name":"Federated Machine Learning in Medical Science","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003489368-4","authors":["Vivek Tomar","Swati Sharma","Sangeeta Arora","Aditya Singhal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T13:25:42Z","doi":"10.1201/9781003489368-4","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.7717/peerjcs.2459/fig-2","name":"Figure 2: Split federated learning with cGAN.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerjcs.2459/fig-2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T05:25:27Z","doi":"10.7717/peerjcs.2459/fig-2","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1109/tmc.2023.3296624/mm1","name":"SAFARI: Sparsity-Enabled Federated Learning with Limited and Unreliable Communications_supp1-3296624.pdf","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tmc.2023.3296624/mm1","authors":["Wenbo Ding"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-19T17:14:32Z","doi":"10.1109/tmc.2023.3296624/mm1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1007/978-3-030-96896-0_9","name":"Introduction to Federated Learning Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_9","authors":["Syed Zawad","Feng Yan","Ali Anwar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_9","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1007/978-3-030-96896-0_14","name":"Private Parameter Aggregation for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_14","authors":["K. R. Jayaram","Ashish Verma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_14","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003482000-1","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003482000-1","authors":["Jagjit Singh Dhatterwal","Kiran Malik","Kuldeep Singh Kaswan","Ahmed A. Elngar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T17:15:57Z","doi":"10.1201/9781003482000-1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.32388/fivttm","name":"Fusion Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.32388/fivttm","authors":["Mohanad Ali"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-20T19:29:53Z","doi":"10.32388/fivttm","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003482000-8","name":"Application of Artificial Intelligence and Federated Learning in Petroleum Processing","source":"crossref","abstract":"The petroleum industry, characterized by its complex operations and substantial data generation, is on the cusp of a technological revolution with the integration of artificial intelligence (AI) and federated learning (FL). This chapter explores the application of FL within the context of petroleum processing to improve operational efficiency, safety, and environmental sustainability. Federated learning, as an emerging paradigm, further amplifies these benefits by enabling a collaborative yet privacy-preserving approach to model training across distributed petroleum processing units. This method not only accelerates the learning process without compromising sensitive data but also enhances model robustness against diverse operational scenarios. Through a series of simulations and real-world case studies, we illustrate how AI-driven analytics can predict equipment failures, optimize resource allocation, and reduce emissions. Simultaneously, FL’s decentralized learning mechanism is shown to facilitate the seamless integration of insights from various processing plants, leading to more accurate and globally applicable models. This research underscores the potential of FL in transforming petroleum processing operations, offering a roadmap for future implementations aimed at achieving higher productivity, sustainability, and safety standards in the industry.","url":"https://doi.org/10.1201/9781003482000-8","authors":["Abdelaziz El-Hoshoudy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T17:15:57Z","doi":"10.1201/9781003482000-8","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003384854-12","name":"A Federated Learning based Alzheimer's Disease Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003384854-12","authors":["S Suchitra","N Senthamarai","M Jeyaselvi","RJ Poovaraghan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-16T11:04:28Z","doi":"10.1201/9781003384854-12","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003497196-7","name":"Exploring Communication Efficient Strategies in Federated Learning Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196-7","authors":["Akarsh K. Nair","Jayakrushna Sahoo","Ebin Deni Raj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T10:30:21Z","doi":"10.1201/9781003497196-7","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.12688/f1000research.channels.677","name":"Federated Learning for Pervasive Systems","source":"crossref","abstract":"","url":"https://doi.org/10.12688/f1000research.channels.677","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-28T08:40:09Z","doi":"10.12688/f1000research.channels.677","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.32657/10356/182243","name":"Optimization strategies for federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/182243","authors":["Tinghao Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-05T01:55:31Z","doi":"10.32657/10356/182243","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003384854-5","name":"Navigating Privacy Concerns in Federated Learning A GDPR-Focused Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003384854-5","authors":["G Anitha","A Jegatheesan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-16T06:04:28Z","doi":"10.1201/9781003384854-5","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.7717/peerj-cs.1496/fig-1","name":"Figure 1: The citation relationship between “Federated Learning”, “Transfer Learning” and “Advertising”.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.1496/fig-1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-07T04:51:29Z","doi":"10.7717/peerj-cs.1496/fig-1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003489368-1","name":"Introduction to Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003489368-1","authors":["Akash Sanghi","Rupanshi Agarwal","Gaurav Agarwal","Sachi Gupta","Rajasekaran Selvaraju"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T17:25:42Z","doi":"10.1201/9781003489368-1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00021-2","name":"Breaking the boundaried and optimizing healthcare in the metaverse through federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00021-2","authors":["Naboshree Bhattacharya","Divya Bansal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00021-2","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1007/978-3-030-96896-0_12","name":"Systems Bias in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_12","authors":["Syed Zawad","Feng Yan","Ali Anwar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_12","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1109/flics70075.2026.11621906","name":"Client-Conditional Federated Learning via Local Training Data Statistics","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621906","authors":["Rickard Brännvall"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:13:54Z","doi":"10.1109/flics70075.2026.11621906","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.32657/10356/219051","name":"Peer-to-peer federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/219051","authors":["Alka Luqman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-06T04:42:20Z","doi":"10.32657/10356/219051","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.7717/peerj-cs.3899/table-5","name":"Table 5: Federated learning results (E1–E5).","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3899/table-5","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-30T08:06:20Z","doi":"10.7717/peerj-cs.3899/table-5","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.7717/peerj-cs.3589/fig-3","name":"Figure 3: Federated learning with MNIST dataset.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3589/fig-3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T08:57:09Z","doi":"10.7717/peerj-cs.3589/fig-3","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00003-x","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00003-x","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:48:19Z","doi":"10.1016/b978-0-44-319037-7.00003-x","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1007/978-3-031-58923-2_2","name":"Secure Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-58923-2_2","authors":["Bo Tang","Xingyu Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T07:02:43Z","doi":"10.1007/978-3-031-58923-2_2","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.32657/10356/202336","name":"Federated neuro-symbolic learning framework","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/202336","authors":["Pengwei Xing"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-03T09:21:27Z","doi":"10.32657/10356/202336","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003532323-1","name":"Journey toward Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003532323-1","authors":["Mithra Venkatesan","Radhika Menon","Anju Kulkarni","S. Jayachitra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-20T18:53:47Z","doi":"10.1201/9781003532323-1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003497196-6","name":"Federated Learning Approaches for Intrusion Detection Systems: An Overview","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196-6","authors":["Akarsh K. Nair","Jayakrushna Sahoo","Gaurav Jaswal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T10:30:21Z","doi":"10.1201/9781003497196-6","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003489368-12","name":"A Critical Role for Federated Learning in IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003489368-12","authors":["Kaushal Kishor","Angeles Quezada","Satya Prakash Yadav"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T17:25:42Z","doi":"10.1201/9781003489368-12","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1002/9781394167760.ch3","name":"Federated Learning in Smart Cities","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.ch3","authors":["Suman Chahar","Kuldeep Kaswan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.ch3","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1007/978-3-030-96896-0_15","name":"Data Leakage in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_15","authors":["Xiao Jin","Pin-Yu Chen","Tianyi Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_15","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.7717/peerj-cs.3354/fig-9","name":"Figure 9: Dropout enabled randomization LSTM deep learning for federated learning process.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3354/fig-9","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-01T08:00:31Z","doi":"10.7717/peerj-cs.3354/fig-9","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00022-4","name":"The metaverse shift: adapting to decentralized computing in federated learning for healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00022-4","authors":["Manas Kumar Yogi","Mangadevi Atti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00022-4","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1049/pbse025e_ch1","name":"Introduction to federated learning, split learning and splitfed learning","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbse025e_ch1","authors":["Geetabai S. Hukkeri","Gururaj H.L.","N.Z. Jhanjhi","Hong Lin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T08:10:23Z","doi":"10.1049/pbse025e_ch1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1109/flics70075.2026.11621917","name":"Auditable Session Admission for Cross-Silo Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621917","authors":["Enzo Fenoglio","Philip Treleaven"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:10:37Z","doi":"10.1109/flics70075.2026.11621917","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-3-031-11748-0_5","name":"A Unifying Framework for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-11748-0_5","authors":["Saber Malekmohammadi","Kiarash Shaloudegi","Zeou Hu","Yaoliang Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-30T18:05:01Z","doi":"10.1007/978-3-031-11748-0_5","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1007/978-981-19-7083-2_4","name":"Secure Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-7083-2_4","authors":["Yaochu Jin","Hangyu Zhu","Jinjin Xu","Yang Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-11-29T19:05:05Z","doi":"10.1007/978-981-19-7083-2_4","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1007/978-3-031-11748-0_7","name":"A Study of Blockchain-Based Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-11748-0_7","authors":["Samaneh Miri Rostami","Saeed Samet","Ziad Kobti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-30T18:05:01Z","doi":"10.1007/978-3-031-11748-0_7","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003027171-3","name":"Naive Federated Learning Approaches","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003027171-3","authors":["Dinesh C. Verma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-13T08:59:18Z","doi":"10.1201/9781003027171-3","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1109/flta67013.2025.11336470","name":"FedQuad: Federated Stochastic Quadruplet Learning to Mitigate Data Heterogeneity","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336470","authors":["Özgü Göksu","Nicolas Pugeault"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336470","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1109/flta67013.2025.11336576","name":"Demystifying Block-Cyclic Sampling for Federated Learning Using MNIST","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336576","authors":["Stefan Arnold","Dilara Fietta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336576","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00003-4","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00003-4","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","doi":"10.1016/b978-0-44-344433-3.00003-4","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.24124/2026/30800","name":"Causal structure learning for adversarial robustness in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.24124/2026/30800","authors":["Karamveer Singh Sidhu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-16T18:36:13Z","doi":"10.24124/2026/30800","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1007/978-3-031-51266-7_4","name":"Quantization for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-51266-7_4","authors":["Mingzhe Chen","Shuguang Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-19T05:04:40Z","doi":"10.1007/978-3-031-51266-7_4","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003384854-8","name":"Maximizing Fog Computing Efficiency with Federated Multi-Agent Deep Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003384854-8","authors":["G. Anitha","A. Jegatheesan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-16T11:04:28Z","doi":"10.1201/9781003384854-8","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1007/978-3-030-96896-0_8","name":"Federated Learning and Fairness","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_8","authors":["Annie Abay","Yi Zhou","Nathalie Baracaldo","Heiko Ludwig"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_8","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781032694870-8","name":"Patient-Driven Federated Learning (PD-FL)","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-8","authors":["A. Menaka Devi","V. Megala"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T08:04:57Z","doi":"10.1201/9781032694870-8","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003660330-2","name":"The mechanism of federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003660330-2","authors":["Dhanashri Vaishali","Harish Kumar","D. Poornima"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T15:39:21Z","doi":"10.1201/9781003660330-2","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1201/9781003497196-12","name":"Protected Shot-Based Federated Learning for Facial Expression Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196-12","authors":["A. Sherly Alphonse Rao","J. V. Bibal Benifa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T10:30:21Z","doi":"10.1201/9781003497196-12","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1109/flta67013.2025.11336249","name":"NIAA: Neuroplasticity-Inspired Adaptive Aggregation Method for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336249","authors":["Vahideh Hayyolalam","Öznur Özkasap"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336249","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1007/978-3-030-85559-8_3","name":"Personalized Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-85559-8_3","authors":["Kaushal Kishor"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-02T12:03:00Z","doi":"10.1007/978-3-030-85559-8_3","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1609/aaaiss.v3i1.31221","name":"Federated Learning of Things - Expanding the Heterogeneity in Federated Learning","source":"crossref","abstract":"The Internet of Things (IoT) has revolutionized how our devices are networked, connecting multiple aspects of our life from smart homes and wearables to smart cities and warehouses. IoT’s strength comes from the ever-expanding diverse heterogeneous sensors, applications, and concepts that are all centered around the core concept collecting and sharing data from sensors. Simultaneously, deep learning has changed how our systems operate, allowing them to learn from data and change the way we interface with the world. Federated learning moves these two paradigm shifts together, leveraging the data (securely) from the IoT to train deep learning architectures for performant edge applications. However, today’s federated learning has not yet benefited from the scale of diversity that the IoT and deep learning sensors and applications provide. This talk explores how we can better tap into the heterogeneity that surrounds the potential of federated learning and use it to build better models. This includes the heterogeneity from device hardware to training paradigms (supervised, unsupervised, reinforcement, self-supervised).","url":"https://doi.org/10.1609/aaaiss.v3i1.31221","authors":["Scott Kuzdeba"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-21T18:51:09Z","doi":"10.1609/aaaiss.v3i1.31221","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1109/flta63145.2024.10840032","name":"On the 5th Generation of Local Training Methods in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840032","authors":["Peter Richtarik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840032","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.7717/peerj-cs.3589/fig-5","name":"Figure 5: Federated learning with fashion-MNIST dataset.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3589/fig-5","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T08:57:09Z","doi":"10.7717/peerj-cs.3589/fig-5","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1142/9789811287947_0002","name":"Federated Learning and Its Classifications","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811287947_0002","authors":["M. Spoorthi","H. L. Gururaj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T01:34:57Z","doi":"10.1142/9789811287947_0002","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:18.957Z"},{"id":"doi:10.1201/9781003591085-9","name":"Federated learning and personalized medicine","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003591085-9","authors":["Lingala Thirupathi","Gundekari Anusha","Thejoramnaresh Reddy Boya","Hari Chandra Prasad Cheerla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-20T10:28:47Z","doi":"10.1201/9781003591085-9","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.1201/9781003489368-6","name":"Federated Machine Learning in Medical Science","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003489368-6","authors":["Vrinda Sachdeva","Gaurav Agarwal","Arun Kumar","Shailja Varshney","Dina Rajput"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T17:25:42Z","doi":"10.1201/9781003489368-6","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.14293/s2199-1006.1.sor-.pppy1je1.v1","name":"Medical data safety via federated machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.14293/s2199-1006.1.sor-.pppy1je1.v1","authors":["Anne Hartebrodt","Reza Nasirigerdeh","Jan Bamubach","David Benjamin Blumenthal","Tim Kacprowski","Richard Rottger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-08-15T14:55:26Z","doi":"10.14293/s2199-1006.1.sor-.pppy1je1.v1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:18.936Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00011-x","name":"Data heterogeneity: a curse and a blessing for federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00011-x","authors":["Ziyue Xu","Shanshan Huang","Xiayu Xu","Xiaoxiao Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:15Z","doi":"10.1016/b978-0-44-323641-9.00011-x","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1002/9781394461295.ch9","name":"Federated Learning with Edge Computing for Real‐Time Decision‐Making","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.ch9","authors":["A. Charles Mahimainathan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.ch9","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781032694870-14","name":"Diseases Detection System Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-14","authors":["P. Dhiman","S. Wadhwa","Amandeep Kaur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T08:04:57Z","doi":"10.1201/9781032694870-14","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00024-8","name":"A collaborated federated learning for healthcare informatics: solution and challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00024-8","authors":["Rahul Joshi","Suman Kumari","Krishna Pandey","Rhythma Badola"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00024-8","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.5220/0013527500004619","name":"Intelligent Healthcare with Federated Learning: A Brief Investigation","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013527500004619","authors":["Hengjie Ma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T17:33:18Z","doi":"10.5220/0013527500004619","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00010-1","name":"Federated learning framework with battery-aware clients","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00010-1","authors":["Andrea Augello","Priyesh Ranjan","Ashish Gupta","Federico Corò","Giuseppe Lo Re","Sajal K. Das"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","doi":"10.1016/b978-0-44-344433-3.00010-1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.7717/peerj-cs.3750/fig-3","name":"Figure 3: Working principle of federated learning technique.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3750/fig-3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-02T08:53:41Z","doi":"10.7717/peerj-cs.3750/fig-3","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.7717/peerj-cs.758/fig-1","name":"Figure 1: IRS-enable federated learning aware system.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.758/fig-1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-22T05:14:05Z","doi":"10.7717/peerj-cs.758/fig-1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.7717/peerjcs.2459/fig-6","name":"Figure 6: ResNet18 architecture in split federated learning.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerjcs.2459/fig-6","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T05:25:27Z","doi":"10.7717/peerjcs.2459/fig-6","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-981-19-7083-2_3","name":"Evolutionary Multi-objective Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-7083-2_3","authors":["Yaochu Jin","Hangyu Zhu","Jinjin Xu","Yang Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-11-29T19:05:05Z","doi":"10.1007/978-981-19-7083-2_3","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/flics70075.2026.11621934","name":"Meta-Learning-Based Initialization-Free Aggregation in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621934","authors":["Sergio Pérez-Picazo","Hiram Galeana-Zapién","Edwin Aldana-Bobadilla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:10:21Z","doi":"10.1109/flics70075.2026.11621934","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00025-3","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00025-3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","doi":"10.1016/b978-0-44-344433-3.00025-3","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-3-030-85559-8_9","name":"Communication-Efficient Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-85559-8_9","authors":["Kaushal Kishor"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-02T12:03:00Z","doi":"10.1007/978-3-030-85559-8_9","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/flta67013.2025.11336807","name":"Carbon-Aware Federated Learning with Energy-Sensitive Scheduling for Sustainable AI","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336807","authors":["Jamsher Bhanbhro"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336807","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003497196-8","name":"Federated Learning and Privacy, Challenges, Threat and Attack Models, and Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196-8","authors":["Sheema Madhusudhanan","Arun Cyril Jose","Reza Malekian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T10:30:21Z","doi":"10.1201/9781003497196-8","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.7717/peerj-cs.2870/table-9","name":"Table 9: Comparison between federated and centralized learning.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.2870/table-9","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-08T04:35:49Z","doi":"10.7717/peerj-cs.2870/table-9","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/c2023-0-00538-6","name":"Federated Learning for Medical Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2023-0-00538-6","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:48:53Z","doi":"10.1016/c2023-0-00538-6","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00004-6","name":"Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00004-6","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","doi":"10.1016/b978-0-44-344433-3.00004-6","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-981-95-1009-2_4","name":"Gradient Methods for Federated Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1009-2_4","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T02:23:44Z","doi":"10.1007/978-981-95-1009-2_4","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/flta63145.2024.10839910","name":"Dynamic Middleware for Interoperable Federated Learning: Enabling Cross-Framework Communication","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839910","authors":["Mohammed AlKaldi","Abdullah AlShehri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839910","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.2139/ssrn.6218028","name":"Federated-survival: A Federated Learning Framework for Privacy-Preserving Survival Analysis","source":"crossref","abstract":"Federated Learning (FL) has emerged as a promising paradigm for collaborative, privacy-conscious model training; however, its application to survival analysis remains at an early stage of development. A significant barrier to progress in this domain is the absence of standardized benchmarking tools, making it difficult to compare methods, validate innovations, and establish best practices. To address this gap, we introduce \\texttt{federated-survival}, a comprehensive, open-source Python framework designed for privacy-preserving survival analysis. This framework offers a systematic integration of seven mainstream survival methodologies, encompassing both classical statistical approaches, such as the Cox Proportional Hazards model, and state-of-the-art deep learning architectures, including DeepHit and PC-Hazard. Recognizing the unique complexities of survival data in distributed settings, \\texttt{federated-survival} implements tailored data augmentation techniques to enhance model robustness and generalizability. Furthermore, to ensure rigorous privacy protection, the proposed framework integrates multiple differential privacy mechanisms, allowing users to navigate the critical trade-off between privacy guarantees and model utility with fine-grained control.The \\texttt{federated-survival} package is freely available at\\url{https://pypi.org/project/federated-survival/} along with installation instructions.","url":"https://doi.org/10.2139/ssrn.6218028","authors":["wenjun wang","Wang Hong","zhuan zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-11T11:38:35Z","doi":"10.2139/ssrn.6218028","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003660330-12","name":"A roadmap for federated learning adoption","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003660330-12","authors":["Shashank Semwal","Chandra Prakash","Bijesh Dhyani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T15:39:21Z","doi":"10.1201/9781003660330-12","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.7717/peerj-cs.2414/table-1","name":"Table 1: Federated deep learning classifier parameter setting.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.2414/table-1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-13T05:39:34Z","doi":"10.7717/peerj-cs.2414/table-1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/flics70075.2026.11621902","name":"Privacy-Preserving Genomic Classification: A Blockchain-Based Federated Learning Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621902","authors":["Reza Nourmohammadi","Kaiwen Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:12:59Z","doi":"10.1109/flics70075.2026.11621902","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-981-19-8692-5_1","name":"Introduction to Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-8692-5_1","authors":["Shui Yu","Lei Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-26T21:30:41Z","doi":"10.1007/978-981-19-8692-5_1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003546559-4","name":"Fundamentals of Blockchain and Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003546559-4","authors":["Komal Rahul Pardeshi","Anita Mukund Pujar","Yang Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-01T12:15:01Z","doi":"10.1201/9781003546559-4","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1371/journal.pcbi.1014530","name":"Eleven quick tips for Biomedical Federated Learning.","source":"pubmed","abstract":"Modern statistical and machine learning techniques are effective at describing, testing hypotheses and making predictions from complex data. This effectiveness is strongly influenced by the volume and heterogeneity of available data. In many fields, including much of biomedicine, large centralized datasets are not available because of cost, privacy, regulatory or other restrictions. In these cases, smaller datasets are distributed across a large number of independent sites. Medical record data is a classic example of this challenge: the total number of patients may be large, but their records are distributed across many health systems and cannot easily be centralized. Federated learning (FL) is a machine learning paradigm that enables training and validation of a shared model in settings of decentralized data. FL can improve model accuracy and generalizability by increasing sample size, but has trade-offs ranging from operational complexity to data-privacy risks to the potential to introduce unexpected imbalances in model accuracy. We outline ten tips for successfully and sustainably implementing FL for Biomedical applications, ensuring both ethical data governance and improved model performance in sensitive domains.","url":"https://doi.org/10.1371/journal.pcbi.1014530","authors":["Ellrott K","Malladi VS","Bélisle-Pipon JC","Demir E","Bensoussan Y","Mangul S","Bui AAT","Boutros PC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pcbi.1014530","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/tpami.2026.3722165","name":"Decentralized Federated Learning by Partial Message Exchange.","source":"pubmed","abstract":"Decentralized federated learning (DFL) has emerged as a transformative server-free paradigm that enables collaborative learning over large-scale heterogeneous networks. However, it continues to face fundamental challenges, including data heterogeneity, restrictive assumptions for theoretical analysis, and de graded convergence when standard communication- or privacy enhancing techniques are applied. To overcome these drawbacks, this paper develops a novel algorithm, PaME (DFL by Partial Message Exchange). The central principle is to allow only randomly selected sparse coordinates to be exchanged between two neighbor nodes. As a result, PaME significantly reduces communication costs while simultaneously limiting the exposure of data-sensitive information during transmission. The latter property is rigorously characterized by a formal reconstruction risk theory under partial observation. Moreover, the algorithm is proven to converge in expectation to a stationary point at a linear rate, provided that the gradient is locally Lipschitz continuous and the communication matrix is doubly stochastic. These two mild assumptions not only dispenses with many restrictive conditions commonly imposed by existing DFL methods but also enables PaME to effectively address data heterogeneity. Furthermore, comprehensive numerical experiments demonstrate its superior performance compared with several representative decentralized learning algorithms.","url":"https://doi.org/10.1109/tpami.2026.3722165","authors":["Sha S","Zhou S","Wang X","Kong L","Li GY"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tpami.2026.3722165","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.20944/preprints202608.1914.v1","name":"Differential Privacy Synthetic Tabular Data Generation for Federated Learning","source":"europepmc","abstract":"Machine learning in healthcare struggles for one main reason: good data is hard to come by. Medical records are sensitive, and the rules on sharing them between hospitals are strict. A common workaround is synthetic data, that is artificial records that copy the statistics of real ones; however, on its own, it offers no real privacy guarantee, and earlier studies show it can still leak details about the patients behind it. We introduce an approach tackling both problems at once. It builds on a previous UMAP-based generator methodology and extends it so several hospitals can work together without sharing raw records, in a federated learning form. Then, the proposed methodology adds differential privacy so every shared quantity carries a formal (εDP,δ) guarantee, tracked by a custom privacy accountant. Two versions of the methodology are tested: a partially synthetic one, where a reference hospital validates the others’ rows, and a fully synthetic one, where each centre builds its own data from aggregated cluster statistics. Both are evaluated on four publicly available healthcare tabular datasets spanning prostate cancer (PI-CAI, CIA), breast cancer (BC-MLR), and cardiovascular disease (fiv-CardioDB), to test whether the observed trends generalize beyond a single clinical domain. As a key takeaway, it is demonstrated that going federated barely affects quality, but the privacy cost depends strongly on the moment in the procedure when the noise is injected, and this pattern holds consistently across all four datasets.","url":"https://doi.org/10.20944/preprints202608.1914.v1","authors":["Mariona Almató-Baucells","Carla Lázaro","Cecilio Angulo"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.1914.v1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/s26165297","name":"Privacy-Preserving and Poisoning-Robust Federated Learning for Industrial IoT.","source":"pubmed","abstract":"With the rapid development of Industrial Internet of Things (IIoT), large amounts of sensor data generated by industrial devices and edge nodes have become the basis of intelligent manufacturing applications. Collaborative modeling on these distributed data is important for tasks such as anomaly detection, equipment monitoring, and predictive maintenance. Federated learning offers a practical way to train models without exposing raw sensor data, but it still faces privacy leakage and malicious poisoning attacks. To address these issues, this paper proposes a hierarchical privacy protection and poisoning-robust defense framework for industrial federated learning. Starting from the sensitivity differences among parameters at different model layers, the proposed method designs a hierarchical privacy-budget allocation strategy that enhances protection for sensitive information while minimizing the performance impact of perturbation. Meanwhile, a multi-layer, multi-feature anomaly-detection mechanism is adopted to identify malicious updates by jointly exploiting directional consistency, scale stability, and inter-layer similarity, and majority voting together with update clipping is used to further improve system robustness. Experiments on Fashion-MNIST, MVTec AD, and C-MAPSS demonstrate that the proposed method can effectively suppress global-model degradation under multiple poisoning attacks and achieves a favorable balance among privacy protection strength, robustness, and training efficiency.","url":"https://doi.org/10.3390/s26165297","authors":["Yin H","Chen C","Zhang J","Yu D","Liu S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26165297","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/j.neunet.2026.109519","name":"Multi-FedLinks: Highly reliable decentralized multibiometric federated learning links.","source":"pubmed","abstract":"In recent years, the recognition accuracies of deep learning-based biometric recognition methods, which rely on large amounts of biometric data for training, have significantly increased. However, in practical applications, biometric data are often distributed in small and fragmented amounts among various local clients. Implementing distributed biometric recognition is therefore greatly important. Most existing distributed biometric methods are implemented by federated learning, and these methods suffer from two problems. (1) The current methods are overwhelmingly limited to addressing distributed single-biometric recognition problems and are not applicable to distributed multibiometric recognition. (2) The conversion from traditional local learning to distributed learning with multiterminal cooperation poses a series of security hazards that have not been addressed. To address these issues, a decentralized multibiometric federated learning links (Multi-FedLinks) model for distributed multibiometric recognition is proposed in this paper. The model consists of multiple FedLink structures, which are resistant to Byzantine attacks. Collaboration among the multiple FedLink structures is implemented with a third-party server to achieve multibiometric federated learning. Experimental results on the NUPT-FPV dataset demonstrate that the superiority of Multi-FedLinks model. Our code can be found in https://github.com/HYMu99/Multi-FedLinks.","url":"https://doi.org/10.1016/j.neunet.2026.109519","authors":["Guo J","Mu H","Ren H","Han C","Sun L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.neunet.2026.109519","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3390/s26165059","name":"Optimal Transport-Based Heterogeneous Federated Learning for Chest X-Rays.","source":"pubmed","abstract":"In some federated learning (FL) scenarios, discrepancies in local client devices result in inconsistent image resolutions, which motivates clients to adopt models with different depths and widths. Existing heterogeneous federated learning methods struggle to maintain model accuracy while preserving computational efficiency. To tackle this issue, this paper proposes a heterogeneous federated learning framework based on optimal transport (OT) and cross-layer alignment. The framework addresses the inconsistency of model depth via cross-layer alignment, fuses parameters of layers with different widths using optimal transport, and develops an aggregation strategy for multiple heterogeneous models. Experiments demonstrate that our method can improve model accuracy by up to 1.65% while maintaining satisfactory efficiency.","url":"https://doi.org/10.3390/s26165059","authors":["Liu Y","Wang H","Qian X","Wan J","Zhang L","Huang J","Dou Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26165059","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.21203/rs.3.rs-9613468/v1","name":"A Lightweight Anonymous Authentication Scheme for Federated Learning","source":"europepmc","abstract":"Abstract Federated learning enables collaborative model training between central servers and distributed clients without collecting users’ raw sensitive data, which effectively promotes the large-scale deployment of intelligent collaborative services. Considering the high sensitivity of local training data and model gradient parameters in federated learning, protecting identity privacy and interaction security has become extremely critical. Therefore, mutual identity authentication is indispensable to restrict illegal client access and prevent malicious parameter transmission and data tampering. In this paper, we propose a lightweight anonymous authentication scheme for federated learning (FedLAS), which realizes secure mutual authentication between servers and clients and establishes a shared session key for subsequent encrypted interaction. In particular, the proposed scheme eliminates the reliance on high-cost cryptographic operations such as bilinear pairing, thus minimizing computational and communication overhead. Furthermore, informal security analysis demonstrates that our FedLAS scheme can resist multiple common attacks and meet predefined security requirements. Extensive comparative experiments show that the FedLAS scheme achieves excellent performance in computational and communication cost. It is well suitable for resource-constrained federated learning scenarios.","url":"https://doi.org/10.21203/rs.3.rs-9613468/v1","authors":["Shu Wu","Guoqiang Meng","Linlin Lu","Xiaojuan Dong","Sai Tian","Jindou Chen"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9613468/v1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-7967675/v1","name":"EPDA: An Efficient and Privacy-preserving Dual Aggregation Scheme for Federated Learning in VANETs","source":"europepmc","abstract":"Abstract Federated Learning (FL) enables devices to collaboratively train models without sharing raw data, but attackers can still infer sensitive information from uploaded updates. Existing cryptographic methods provide security but often lack essential privacy properties such as anonymity and unlinkability, while introducing high computational and communication overheads. These challenges limit the deployment of efficient, privacy-preserving FL in Vehicular Ad-hoc Networks (VANETs). To address this, we propose EPDA, a dual aggregation scheme that enhances both efficiency and privacy. EPDA aggregates signatures for rapid verification and batches model updates to complete aggregation in a single round. It preserves model confidentiality and ensures anonymity and unlinkability. Experiments on the GTSRB dataset show that EPDA maintains low cryptographic cost (0.03s per client) and minimizes communication overhead to 148 bytes per client, scaling effectively to 350 clients. These results demonstrate EPDA’s practicality for secure and efficient FL in real-world VANETs.","url":"https://doi.org/10.21203/rs.3.rs-7967675/v1","authors":["Yifeng Zhao","Qingqing Xie"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-7967675/v1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1109/tmi.2026.3725153","name":"ToPPFed: Topological Prototype-Enhanced Personalized Federated Learning for Neuropsychiatric Disorders Identification.","source":"pubmed","abstract":"Functional connectivity networks (FCNs) derived from functional magnetic resonance imaging (fMRI) have been widely used to characterize topological alterations of brain networks in neuropsychiatric disorders (NDs). Given the frequent restrictions on direct multi-site fMRI data sharing, federated learning (FL) offers a collaborative modeling paradigm without exchanging raw neuroimaging data. However, conventional parameter-averaging FL approaches struggle under cross-site non-IID distributions. Prototype-based FL provides a promising alternative, yet existing designs implicitly rely on spatially structured image data and fail to capture the topology-centric semantics of FCNs. To bridge this gap, we propose ToPPFed, a Topological Prototype-Enhanced Personalized Federated Learning framework for multi-site classification between subjects with each studied disorder and normal controls (NCs). ToPPFed introduces a Graph Topological Prototype Learning module to extract discriminative topology-aware prototypes from FCNs and a Contrastive Mask-Induced Residual Scaling mechanism to adaptively integrate group-level priors into individual representations. By exchanging topology prototypes instead of raw data or full model parameters, ToPPFed supports cross-site collaboration while reducing direct data exposure. Experiments on multi-site fMRI datasets of three representative NDs show that ToPPFed improves accuracy (ACC) by 1.7-7.9 percentage points over the best-performing federated baseline on each dataset. Interpretability analyses indicate that ToPPFed highlights model-derived discriminative brain regions and functional connections. The topology-aware exchange of node and edge prototypes offers an effective framework for collaborative FCN modeling across imaging sites without centralizing neuroimaging data.","url":"https://doi.org/10.1109/tmi.2026.3725153","authors":["Zheng Y","Jia Z","Guan Z","Chen Y","Zhou R","Kendrick KM","Jiang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tmi.2026.3725153","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1117/1.jmi.13.4.044504","name":"Evaluating federated learning approaches for mammography under breast density heterogeneity.","source":"pubmed","abstract":"Breast density is a key factor that influences mammography interpretation and is a major source of heterogeneity in multicenter datasets. Such heterogeneity poses challenges for collaborative machine learning across institutions, particularly in federated learning (FL). We aim to evaluate the impact of breast density-induced heterogeneity on FL for mammography image classification and to assess the robustness of common FL algorithms in realistic clinical settings.","url":"https://doi.org/10.1117/1.jmi.13.4.044504","authors":["Quintana GI","Di Maria FM","Vancamberg L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1117/1.jmi.13.4.044504","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.21203/rs.3.rs-10801503/v1","name":"Federated Learning Framework with Differential Privacy over Homomorphic Vector Encryption for Data-Sensitive Applications","source":"europepmc","abstract":"Abstract Federated Learning (FL) has become a popular paradigm in recent years, attributed to its concept of insight sharing for ensuring data privacy. It has been found to be of extensive utility in applications that regularly deal with confiden-tial data and has been widely used in the fields of healthcare and the Internet of Medical Things (IoMT). However, an equal amount of research is conducted to reverse-engineer the used datasets from the shared insights, and a native FL implementation alone is not suitable for these sensitive applications. We pro-pose a lightweight secure FL framework incorporating both Differential Privacy and Homomorphic Encryption at the vector level to minimize the efficiency of reverse-engineering algorithms over the transmitted insights in IoMT and other resource-constrained networks. Through experiments on multiple image classi-fication datasets and comparison with secure federated learning baselines, the proposed framework demonstrates the feasibility of combining differential pri-vacy with encrypted aggregation while quantifying the resulting predictive and cryptographic overhead.","url":"https://doi.org/10.21203/rs.3.rs-10801503/v1","authors":["Manu Narula","Jasraj Meena","Dinesh Vishwakarma"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10801503/v1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/bioengineering13060603","name":"A Method for Workout Video Classification via Explainable and Federated Learning.","source":"pubmed","abstract":"In recent years, the widespread availability of wearable devices and smartphones has enabled the large-scale collection of human activity data, fostering new opportunities for automatic workout recognition and personalized fitness monitoring. However, the centralized storage of video recordings raises critical privacy concerns, particularly when raw data contain identifiable individuals. Federated Machine Learning provides a paradigm designed with the aim of reducing privacy risks; here, models are collaboratively trained across distributed clients without sharing their sensitive data. In this paper, we propose an approach for workout video classification with Federated Machine Learning, enhanced by explainability through Gradient-weighted Class-Activation Mapping. The proposed method is evaluated on a real-world multi-class exercise video dataset, organized into eight biomechanically coherent macro-classes. In the experimental analysis, we consider several federated configurations in terms of the number of clients, the chosen aggregation strategy, and global communication rounds. The obtained results demonstrate that different aggregation strategies achieve comparable overall accuracy, while explainability effectively highlights the discriminative regions associated with exercise execution, revealing meaningful differences in model behavior between aggregation strategies and uncovering misclassifications driven by contextual biases, demonstrating the trustworthiness of the proposed approach for explainable workout video classification.","url":"https://doi.org/10.3390/bioengineering13060603","authors":["Ciardiello L","Agnello P","Petyx M","Martinelli F","Cesarelli M","Santone A","Mercaldo F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/bioengineering13060603","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.20944/preprints202608.1257.v1","name":"Privacy-Preserving Federated Learning for Building Energy Forecasting: A Differential Privacy Analysis of Aggregation Strategies","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202608.1257.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.1257.v1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3389/fmed.2026.1904004","name":"Federated learning for privacy-preserving ophthalmic artificial intelligence: clinical applications and translational challenges.","source":"pubmed","abstract":"Federated learning (FL) is increasingly relevant to ophthalmology because retinal photographs, optical coherence tomography (OCT), OCT angiography, visual fields, and linked clinical records are clinically valuable but difficult to pool across institutions. In this narrative review, we synthesize ophthalmology-focused FL literature across diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), pediatric retinal disease, multi-disease retinal diagnostics, and emerging ophthalmic platforms. Current evidence suggests that FL can support collaborative AI development without centralizing raw patient data, and selected studies show performance close to centralized training under controlled retrospective or multicenter experimental conditions. For example, multicenter glaucoma detection from volumetric OCT achieved an AUC of 0.92 with FL compared with 0.94 for centralized training. However, FL is privacy-enhancing rather than privacy-complete, and most ophthalmic FL systems have not yet undergone prospective clinical validation. Model updates may remain vulnerable to gradient inversion, membership inference, poisoning, site-level bias, and latent identity or attribute leakage. For eye-care networks, the main value of FL is therefore not simply algorithmic performance but a governance model for privacy-conscious collaboration. Prospective validation, interoperability, explainability, workflow integration, privacy auditing, and clear responsibility for monitoring are needed before FL-enabled ophthalmic AI can be deployed routinely.","url":"https://doi.org/10.3389/fmed.2026.1904004","authors":["Wei Y","Zhao K","Grzybowski A","Jin K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1904004","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/j.neunet.2026.109470","name":"MoFedAGR: Mitigating client drift with adaptive gradient regularization and global momentum in federated learning.","source":"pubmed","abstract":"Federated learning is a novel distributed machine learning framework with privacy-protection, yet it is vulnerable to the effects of heterogeneous data. Heterogeneous data drive client models that overfit local datasets and depart from the global optimum during local training, which is termed client drift. To address the impact of client drift, we approach this issue from the perspectives of optimization and generalization. We comprehensively considering the effects of client drift during the training process, and quantifying it as the aggregation error. We first propose adaptive gradient regularization, which is based on gradient regularization and further and applies different regularization strengths to each parameter based on the magnitude of the parameter variance between the local model and the global model, thereby mitigating the performance degradation caused by aggregation error and helping model converge to a flatter minimum. In order to obtain the variance between local and global models to compute our adaptive gradient regularization term, we introduce global momentum from the server side as the approximation of global gradient and further utilize it as a gradient correction term. Next, we propose MoFedAGR, which combines gradient correction term and adaptive gradient regularization term, helping client models converge to a consistent flat minimum. We have provided the theoretical convergence bounds of the algorithm we proposed. Furthermore, experiments on several image classification datasets demonstrate that our algorithm significantly improves model performance while exhibiting strong generalization capabilities.","url":"https://doi.org/10.1016/j.neunet.2026.109470","authors":["Wang X","Tian L","Gan J","Yang C","Lin F","Li M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.neunet.2026.109470","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/j.neuroimage.2026.122165","name":"Federated learning for MRI-based BrainAGE: A multicenter study on post-stroke functional outcome prediction.","source":"pubmed","abstract":"Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health, with potential implications for post-stroke recovery. However, training robust BrainAGE models requires large, diverse datasets, often restricted by privacy and regulatory concerns. This study evaluates the performance of federated learning (FL) for BrainAGE estimation in ischemic stroke patients treated with mechanical thrombectomy, and investigates its association with clinical phenotypes and functional outcome.","url":"https://doi.org/10.1016/j.neuroimage.2026.122165","authors":["Roca V","Tommasi M","Andrey P","Bellet A","Schirmer MD","Henon H","Puy L","Ramon J","Kuchcinski G","Bretzner M","Lopes R","ARIANES study group"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.neuroimage.2026.122165","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3390/s26154712","name":"DBST-FL: Dynamic Behavioural and Semantic Trust for Robust Federated Learning in Industrial IoT.","source":"pubmed","abstract":"Federated learning (FL) has emerged as an effective paradigm for collaborative model training in Industrial Internet of Things (IIoT) environments by enabling distributed devices to learn shared models without exchanging raw data. However, existing FL defence mechanisms predominantly rely on either behavioural analysis of client updates or semantic validation of model performance, limiting their ability to detect sophisticated poisoning and stealthy backdoor attacks that evade single-dimensional trust assessment. This paper proposes DBST-FL, a dynamic behavioural and semantic trust framework for robust federated learning in the Industrial IoT. The proposed framework evaluates each client through two complementary trust dimensions: a behavioural trust layer that measures gradient alignment, historical consistency, and collective deviation and a semantic trust layer that assesses benign utility and template-free semantic stress validation using server-side data. The two trust scores are integrated through a non-compensatory multiplicative trust fusion mechanism, ensuring that weaknesses in one trust dimension cannot be masked by strengths in the other. The resulting trust score guides a trust-aware aggregation strategy that reduces the influence of malicious participants while preserving the contributions of reliable clients. Extensive experiments are conducted on the Edge-IIoTset and UNSW-NB15 datasets using ANN, 1D-CNN, and LSTM models under multiple poisoning and backdoor attack scenarios. The proposed framework achieves overall classification performance competitive with the strongest robust aggregation baselines while consistently delivering stronger resilience against adversarial attacks and lower backdoor attack success rates than representative trust-based and Byzantine-robust aggregation methods, all while maintaining linear per-round computational complexity suitable for large-scale IIoT deployments. The results demonstrate that integrating behavioural and semantic trust within a unified aggregation framework provides an effective and scalable defence against advanced adversarial threats in federated learning.","url":"https://doi.org/10.3390/s26154712","authors":["Alazab A","Tom AK","Jan T","Whaiduzzaman M","Nguyen T","Khraisat A","Almazrouei A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26154712","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3390/e28060630","name":"SCKM: Symmetric Co-Skew Moment for User Selection in Federated Learning.","source":"pubmed","abstract":"We introduce the symmetric co-skewness moment (SCKM)-a third-order informational dissimilarity metric that consistently outperforms state-of-the-art client-selection heuristics in federated learning (FL) under heterogeneous data. Unlike similarity-driven schemes, SCKM minimizes redundancy by favoring clients with complementary gradients, delivering faster and more stable convergence even at high heterogeneity levels. Operating on highly compressed 0.5% gradient summaries, our framework provides two operating modes for different deployment scales: (i) SCKM-Select directly ranks and schedules a small candidate pool, whereas (ii) SCKM-Cluster adds a fast, elbow-guided clustering step to scalably choose from thousands of users. We evaluate both variants on a VGG-16 model across multiple non-IID partition schemes and initializations, observing consistent gains over leading cosine-similarity, loss-sketch, and max-diversity baselines-without increasing the communication budget.","url":"https://doi.org/10.3390/e28060630","authors":["Li L","Liu Y","Ning Y","Rini S","Chen J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/e28060630","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1109/tnnls.2026.3718132","name":"Enhanced Spectral Clustering Robust Aggregation for Lens Detection in Federated Learning Against Byzantine Attacks.","source":"pubmed","abstract":"Although federated learning (FL) addresses the issues of centralized data storage and privacy leakage, its distributed nature makes it vulnerable to malicious clients. These malicious participants introduce malicious parameters during the training procedure, which can significantly impact the model's accuracy. Existing algorithms such as Krum and median defend against attacks by capturing low-order features of data and requiring prior data. However, these approaches struggle to counteract gradually evolving Byzantine attacks. Therefore, we propose an unsupervised approach based on an enhanced spectral clustering algorithm (SCA) to identify malicious updates. First, we design a method for constructing an undirected weighted graph using the Gaussian kernel function. This approach maps features among data into an infinite-dimensional Hilbert space, enabling better capture of high-order data features. Second, due to the similarity between Byzantine updates and benign updates in Euclidean space and cosine similarity scenarios, traditional robust aggregation algorithms fail to recognize them, causing models to fail to converge. To address this, a new lens detection method is designed. We calculate the Laplacian matrix through the undirected weighted graph and employ normalized cut (NCut) to partition the Laplacian matrix. This transforms the problem of identifying malicious clients into a graph partitioning problem. Furthermore, we conduct a convergence analysis of the proposed SCA. Experiments on four datasets under independent and identically distributed (IID) and non-IID (Non-IID) settings show this method has strong robustness to all tested attacks, while other defense methods cannot resist all of them.","url":"https://doi.org/10.1109/tnnls.2026.3718132","authors":["Bai F","Jin B","Zeng K","Shen T","Gong B","Meng W","Cao B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tnnls.2026.3718132","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.12688/openreseurope.24462.1","name":"Federated learning for heterogeneous edge environments_ A multi-domain evaluation on object detection and energy forecasting","source":"europepmc","abstract":"","url":"https://doi.org/10.12688/openreseurope.24462.1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.12688/openreseurope.24462.1","addedAt":"2026-08-31T06:41:18.936Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3390/s26041275","name":"Federated Learning in Edge Computing: Vulnerabilities, Attacks, and Defenses-A Survey.","source":"pubmed","abstract":"","url":"https://doi.org/10.3390/s26041275","authors":["Alhawas S","Rassam MA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26041275","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1861374","name":"A hierarchical federated learning framework with FedNova, game-theoretic matching, and QKD-assisted privacy for the internet of vehicles.","source":"pubmed","abstract":"The Internet of Vehicles (IoV) supports essential intelligent transportation applications but encounters challenges in federated learning (FL) due to non-independent and identically distributed (non-IID) data, vehicle mobility, resource heterogeneity, and strict privacy requirements in latency-sensitive scenarios such as misbehavior detection and accident response. Traditional FL methods, such as random client selection and standard FedAvg, often experience slow convergence and reduced performance under non-IID conditions. We introduce a hierarchical federated learning framework for software-defined vehicular fog computing. The framework incorporates FedNova (a normalized-averaging aggregation method for heterogeneous federated optimization) to produce normalized model updates under data heterogeneity, a Reward-Based Payoff Strategy (RBPS) for incentive-aware client selection, and game-theoretic vehicle-aggregator matching based on the college admissions problem. Privacy is strengthened through quantum key distribution (QKD)-assisted secure key establishment and classical gradient masking, with quantum circuit simulation used to assess future enhancements. The three-layer architecture includes vehicles, Roadside Unit (RSU)/ Base Station (BS)-level aggregators, and a Software-Defined Network Controller (SDNC) global aggregator. The framework uses both monetary and service-based incentives, such as toll exemptions, to encourage vehicle participation. Hybrid simulations using OMNeT++, Veins, SUMO, and the VeReMi misbehavior detection dataset show that the proposed approach achieves 94.8% classification accuracy [95% Confidence Interval (CI): 92.7-97.0 over 10 runs], converges in 120 rounds (33% faster than FedAvg), and reduces average latency by 29% (320 ms compared to 450 ms for FedAvg), with statistically significant improvements ( p &lt; 0.05). These gains enable faster model adaptation to evolving attacks (5-10 min shorter training cycles) and support real-time safety applications where delays above 400 ms can compromise road safety. Ablation studies confirm the complementary roles of FedNova, RBPS, and matching. Although quantum operations are currently simulated classically, the design remains compatible with future quantum hardware.","url":"https://doi.org/10.3389/frai.2026.1861374","authors":["Jai Vinita L","Vetriselvi V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1861374","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3389/frai.2026.1895239","name":"GA-AFedOD: gradient-aligned active federated learning for resource-aware object detection in edge industrial IoT.","source":"pubmed","abstract":"Visual object detection is essential for defect inspection and process monitoring in edge-deployed Industrial Internet of Things (IIoT). Yet, training accurate detectors across distributed factories faces stringent constraints on data privacy, annotation budgets, and uplink communication. Standard federated learning (FL) preserves locality but often wastes labeling resources on redundant frames and overlooks detection-specific gradient alignment when scheduling clients. To bridge this gap, we propose Gradient-Aligned Active Federated Object Detection (GA-AFedOD), a unified framework that jointly optimizes annotation selection, client participation, and model aggregation as a constrained stochastic program. A novel utility metric integrates box-level uncertainty, prototype diversity, gradient alignment, and resource pricing, enabling edge clients to perform locally guided active querying while the server solves a lightweight primal-dual problem for budget-aware client scheduling. We prove a submodular approximation guarantee for the greedy sampling rule and establish a non-convex convergence bound that explicitly captures the impact of label budgets, client drift, and compression noise. This article further clarifies the relationship with recent federated active learning and industrial detection studies, adds parameter and theory-diagnostic analyses, and distinguishes controlled simulation evidence from real-world deployment validation on industrial datasets such as RasPiDets, Electric Power Fitting Dataset (EPFD), and Diverse Insulator Dataset (DINS). Controlled simulation results show that GA-AFedOD achieves considerably higher mean average precision (mAP) while reducing both annotation costs and uplink consumption by over 40% compared with competitive baselines.","url":"https://doi.org/10.3389/frai.2026.1895239","authors":["Wang Z","Yuan X","Chen J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1895239","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3389/frai.2026.1807248","name":"Federated learning framework for medical image analysis with perspective-aware contrastive and mixture of experts.","source":"pubmed","abstract":"Medical image analysis faces persistent challenges due to the distributed data, limited annotations, and variations in imaging modalities, acquisition protocols, and patient demographics. Centralized deep learning approaches compromise data privacy, while Federated Learning (FL) enables decentralized model training without sharing raw data. However, conventional FL frameworks struggle with non-IID distributions and heterogeneous clinical environments, limiting their generalization and stability. We propose FedPAC-ME, a novel Federated Learning Framework for Medical Image Analysis that integrates Perspective-Aware Contrastive Learning with a Mixture of Experts (MoE) architecture to address heterogeneity and data imbalance. The framework introduces Multi-Perspective Augmentation (MPA) to emulate diverse clinical views, and a Perspective-Aware contrastive Module (PACM) that aligns representations across modalities and clients. Additionally, a Mixture of Experts routing layer dynamically allocates specialized experts to client-specific data distributions, enhancing adaptability and collaboration across sites. A Perspective-Aware Contrastive Loss (PACL) further enforces cross-view consistency during local training while maintaining global coherence across institutions. Extensive experiments on the BraTS2020 multi-institutional brain tumor segmentation dataset demonstrate that FedPAC-ME achieves 98.80% accuracy, surpassing state-of-the-art FL baselines by over 2.5%. These results confirm the framework's effectiveness in improving feature alignment, generalization, and privacy preservation under diverse clinical conditions.","url":"https://doi.org/10.3389/frai.2026.1807248","authors":["Das S","Hemalatha K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1807248","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.21203/rs.3.rs-10787425/v1","name":"CONCORDAT: Trustworthy multimodal federated learning for autism research under incomplete, heterogeneous and privacy-constrained health data","source":"europepmc","abstract":"Abstract Multimodal brain-health research combines imaging-derived measurements with demographic, phenotypic and behavioural information, but these data are often incomplete, heterogeneous across sites, and subject to governance constraints that prevent subject-level pooling. CONCORDAT is a cross-silo federated framework for autism spectrum disorder (ASD) research that integrates these complementary feature blocks while keeping subject-level records at their source institution. Across the 20-site ABIDE-I cohort (n = 1035), adding demographic, phenotypic and Social Responsiveness Scale blocks to imaging-derived quality-control features improved 5-fold, site-stratified AUC by approximately 0.04 (0.610 to 0.648), and federated training reproduced the corresponding centralised results to within 0.001 AUC. We then stress-tested this multimodal pipeline under real acquisition incompleteness, comparing eight missing-modality scenarios; balanced accuracy fell despite nearly unchanged AUC when phenotypic information was unavailable, showing why multimodal deployment must monitor operating-point performance rather than discrimination alone. We further quantify site-harmonisation choices under different disclosure models, a client-level differential-privacy sweep (ε ∈ {1, 2, 5, 10, ∞}), clip-norm sensitivity, and a fairness intervention that raised female-ASD sensitivity by 0.532 (95% CI 0.419–0.661) for a 0.039 loss in balanced accuracy (95% CI 0.009–0.064). An independent structural-connectivity cohort (n = 53, d = 647) validates operation in a high-dimensional setting but yields a permutation-tested null (p = 0.965), which is reported rather than omitted. CONCORDAT therefore treats multimodal integration, privacy, missingness and subgroup equity as linked deployment constraints, providing an auditable framework for multi-site brain-health research rather than a clinical diagnostic tool.","url":"https://doi.org/10.21203/rs.3.rs-10787425/v1","authors":["Jarin Alam Prity"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10787425/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202605.1674.v1","name":"Poisoning Attacks in Federated Learning: An Accountability-Oriented Survey with Centralized Learning as Baseline","source":"europepmc","abstract":"Artificial intelligence (AI) systems are increasingly deployed in critical domains such as healthcare, finance, defense, and transportation. These deployments, however, face grow- ing risks from poisoning attacks that corrupt training data, manipulate model updates, or implant covert backdoors. Such attacks undermine trust, reduce transparency, and challenge the safe and accountable use of AI in high-stakes settings. This survey examines poisoning attacks in federated learning (FL), using centralized learning as a comparative baseline to clarify how the threat landscape changes when data, updates, and control are distributed. Rather than reintroducing a generic poisoning taxonomy as a standalone contri- bution, we position the paper relative to prior surveys, identify what remains insufficiently covered, and synthesize representative primary studies through an accountability-oriented lens. Because FL introduces additional vulnerabilities, including untrusted servers, non-IID heterogeneity, and limited observability of client behavior, we analyze how these properties expand the poisoning threat surface. We review state-of-the-art countermeasures, including Byzantine-robust aggregation, anomaly detection, validation-based defenses, and cryp- tographic prevention mechanisms such as malicious-secure aggregation, authenticated update handling, and verifiable aggregation protocols. Particular emphasis is placed on accountability-enabling mechanisms such as auditability, traceability, and forensic readi- ness, which are essential to responsible AI and regulatory compliance. Our analysis identi- fies persistent research gaps, including the lack of unified privacy-robustness-accountability frameworks, insufficient defenses against server-side attacks, limited verifiability tools, and scalability challenges in real-world FL systems. The paper’s contribution is therefore not to claim that accountability-oriented FL defenses are new, but to clarify the survey scope, position prior surveys directly, and integrate aggregation-based, cryptographic, and governance-oriented strands within an Accountability-Integrated Taxonomy (AIT) and an evidence-oriented discussion of trustworthy federated learning.","url":"https://doi.org/10.20944/preprints202605.1674.v1","authors":["Safiia Mohammed","Dima Alhadidi","Alioune Ngom"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202605.1674.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1109/tpami.2025.3640709","name":"Decentralized Federated Learning With Distributed Aggregation Weight Optimization.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2025.3640709","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tpami.2025.3640709","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1007/s41666-025-00226-4","name":"Multimodal Federated Learning in Healthcare: A Review.","source":"pubmed","abstract":"Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems. Simultaneously, Federated Learning (FL) has progressed, providing a decentralized mechanism where data need not be consolidated, thereby enhancing the privacy and security of sensitive healthcare data. The integration of these two concepts supports the ongoing progress of multimodal learning in healthcare while ensuring the security and privacy of patient records within local data-holding agencies. This paper offers a concise overview of the significance of FL in healthcare and outlines the current state-of-the-art approaches to Multimodal Federated Learning (MMFL) within the healthcare domain. It comprehensively examines the existing challenges in the field, shedding light on the limitations of present models. Finally, the paper outlines potential directions for future advancements in the field, aiming to bridge the gap between cutting-edge AI technology and the imperative need for patient data privacy in healthcare applications.","url":"https://doi.org/10.1007/s41666-025-00226-4","authors":["Thrasher J","Devkota A","Siwakoti P","Chivukula R","Poudel P","Hu C","Tafti A","Bhattarai B","Gyawali P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1007/s41666-025-00226-4","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.37044/osf.io/5psfj_v2","name":"Towards Federated Learning Across Biobanks: Prototype Software from the 2026 Carnegie Mellon University–NVIDIA Hackathon","source":"europepmc","abstract":"","url":"https://doi.org/10.37044/osf.io/5psfj_v2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.37044/osf.io/5psfj_v2","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-9836024/v1","name":"Privacy-Preserving Federated Learning for Cross-Dataset EEG-Based Detection of Parkinson's Disease","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9836024/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9836024/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1016/j.cmpb.2026.109454","name":"Federated learning: A new frontier in the exploration of multi-institutional medical imaging data.","source":"pubmed","abstract":"Artificial intelligence has transformed the perspective of medical imaging, leading to a genuine technological revolution in modern computer-assisted healthcare systems. However, ubiquitously featured deep learning (DL) systems require access to a considerable amount of data, facilitating proper knowledge extraction and generalization. Access to such extensive resources may be hindered due to the time and effort required to convey ethical agreements, set up and carry the acquisition procedures through, and manage the datasets adequately with a particular emphasis on proper anonymization. One of the pivotal challenges in the DL field is data integration from various sources acquired using different hardware vendors, diverse acquisition protocols, experimental setups, and even inter-operator variabilities. In this paper, we review the federated learning (FL) concept that fosters the integration of large-scale heterogeneous datasets from multiple institutions in training DL models. In contrast to a centralized approach, the decentralized FL procedure promotes training DL models while preserving data privacy at each institution involved. We formulate the FL principle and comprehensively review general and specialized medical imaging aggregation and learning algorithms, enabling the generation of a globally generalized model. We meticulously go through the challenges in constructing FL-based systems, such as data and model heterogeneities across the institutions, resilience to potential attacks on data privacy, and the variability in computational and communication resources among the entangled sites that might induce efficiency issues of the entire system. Finally, we explore the up-to-date open frameworks for rapid FL-based algorithm prototyping, comprehensively present real-world implementations of FL systems and shed light on future directions in this intensively growing field.","url":"https://doi.org/10.1016/j.cmpb.2026.109454","authors":["Ciupek D","Malawski M","Pieciak T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.cmpb.2026.109454","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.20944/preprints202601.2048.v1","name":"Ten Quick Tips for Biomedical Federated Learning","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202601.2048.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202601.2048.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.1109/jbhi.2026.3702502","name":"FedDPI-SH: A Quality and Similarity Aware Federated Learning Framework for Medical Image Analysis.","source":"pubmed","abstract":"Federated learning (FL) enables decentralized medical image analysis while preserving data privacy. However, conventional methods overlook client data heterogeneity and inter-client feature similarity, resulting in suboptimal performance. In this paper, our proposed FedDPI-SH framework addresses these limitations through quality and similarity-aware weighted aggregation. The framework introduces a Data Performance Index (DPI) quantifying client reliability through dataset size, image resolution, label distribution balance, duplication levels, and cross-client generalization accuracy. Client Representation Similarity Matrix (CRSM) measures inter-client feature alignment via cosine similarity. FedDPI-SH combines these components to compute aggregation weights, prioritizing high-quality clients during feature extractor updates while maintaining personalized classifiers. Evaluation across three medical imaging datasets (Medical MNIST, PathMNIST, HAM10000) under severe non-IID conditions demonstrates improvements, with HAM10000 achieving 81.24% balanced accuracy, outperforming MOON (58.85%), FedAvg (45.22%), FedAvgM (17.66%), and FedProx (12.64%).The framework addresses data quality heterogeneity through explicit assessment of duplication, label balance, and resolution in federated medical imaging.","url":"https://doi.org/10.1109/jbhi.2026.3702502","authors":["P GL","A G","George AB","Tummala VMR","Hazra A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/jbhi.2026.3702502","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1109/jbhi.2026.3693747","name":"GTAFL: Addressing Test-Agnostic Long-Tailed Federated Learning in Medical Image.","source":"pubmed","abstract":"In healthcare, protecting patient privacy is crucial due to the sensitivity of medical data and its extensive accessibility. Federated Learning (FL) offers a decentralized and privacy-preserving training paradigm, making it an ideal solution for healthcare applications. A critical challenge in healthcare is that real-world medical data often exhibits long-tailed distributions in local and global views. Existing methods addressing long-tailed FL problem typically assume that the model will be evaluated on uniform test data distribution. However, Practical test data in healthcare systems is often agnostic and unpredictable, leading to potential model failures in realworld scenarios. In this paper, we introduce a novel task termed Test-Agnostic Long-Tailed Federated Learning and propose GTAFL, a comprehensive framework to address this challenge. During the training stage, GTAFL employs adaptive re-sampling, expert classifier retraining, and selfsupervised learning to correct biased classifiers and distorted feature spaces caused by long-tailed training distributions. During the inference stage, an ensemble mechanism combines retrained expert classifiers to handle test data with unknown distributions. Extensive experiments on CIFAR10 and two medical datasets manifest that our framework outperforms other state-of-the art methods.","url":"https://doi.org/10.1109/jbhi.2026.3693747","authors":["Chen G","Zeng J","Jiang H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/jbhi.2026.3693747","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1016/j.neunet.2026.109095","name":"DDFL: dual defense against poisoning attacks in privacy-preserving federated learning.","source":"pubmed","abstract":"Federated learning (FL) offers a solution to data silos by enabling collaborative training of a global model across decentralized environments. However, when operating with semi-honest servers or malicious clients, traditional FL faces critical privacy and security challenges. Existing defense strategies often struggle to address both privacy and poisoning attacks effectively, as enhanced privacy protections can increase parameter similarity across clients, unintentionally complicating the detection of malicious behavior. Moreover, most poisoning defenses are primarily server-side, resulting in a one-sided approach that is insufficient to handle increasingly sophisticated attack patterns. Therefore, we introduce a dual defense framework against poisoning attacks in privacy-preserving federated learning (DDFL), which effectively tackles both privacy and security challenges in FL. To enhance privacy, we have clients randomly slice and reassemble model parameters before uploading them to the server, thereby safeguarding client privacy without compromising the server's ability to detect potential malicious behaviors in the system. For stronger security, we incorporate meta-learning and knowledge distillation techniques on the client side, alongside Byzantine-robust methods on the server side, effectively mitigating the impact of malicious clients. Extensive evaluations on three benchmark datasets demonstrate that DDFL not only protects clients' sensitive information but also outperforms existing defense strategies in resisting poisoning attacks, achieving higher model accuracy and faster convergence.","url":"https://doi.org/10.1016/j.neunet.2026.109095","authors":["Guo C","Tian M","Li X","Sun H","Jie Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.neunet.2026.109095","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1109/tnnls.2026.3658522","name":"Incomplete Multimodal Federated Learning via Masking and Contrasting Prototypes.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2026.3658522","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tnnls.2026.3658522","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1016/j.mex.2026.104066","name":"JS-Drift: A reproducible Jensen-Shannon divergence procedure for drift-aware client weighting in federated learning.","source":"pubmed","abstract":"Federated learning lets institutions train a shared model without exchanging raw data, but standard aggregation (FedAvg) assumes client data distributions are stationary. In practice they drift over time, and aggregation that ignores this lets unstable clients degrade the global model. This article describes JS-Drift, a reproducible, model-agnostic procedure that quantifies per-client temporal drift using Jensen-Shannon (JS) divergence between a client's label distribution in consecutive communication rounds, converts that divergence into a stability weight via a single sensitivity parameter &#x3b3;, and folds the weight into the aggregation step. The procedure requires no architectural changes, adds negligible overhead, and drops into any FedAvg-style training loop. We give the full algorithm, exact computation steps, parameter-selection guidance, and an open implementation, and we validate that the procedure behaves as intended on three structurally different tabular-classification settings.&#x2022;Computes a per-client, per-round drift coefficient from JS divergence between consecutive local label distributions; only a small class-proportion summary is shared, so raw data never leave the client.&#x2022;Maps the drift coefficient to an aggregation weight through one interpretable parameter &#x3b3;, down-weighting clients with high distributional shift.&#x2022;Is model-agnostic and integrates into any FedAvg-style round in a few lines of code; a public repository reproduces every step.","url":"https://doi.org/10.1016/j.mex.2026.104066","authors":["A S","Arumugam S","A E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.mex.2026.104066","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1038/s41598-026-59796-x","name":"PureChain web-based energy predictor with federated learning Dirichlet for real-time energy consumption forecasting.","source":"pubmed","abstract":"Accurate energy consumption forecasting in smart grids requires privacy-preserving learning mechanisms that remain effective under heterogeneous data distributions and support real-time operation. Existing federated learning approaches remain limited by poor performance under data heterogeneity, unvalidated architectural assumptions, and blockchain consensus mechanisms that are too slow for real-time grid operations. This paper presents PureChain, a blockchain-integrated federated learning framework that combines federated averaging, Dirichlet partitioning, LSTM-based forecasting, and a permissioned blockchain for secure client isolation and model rollback. A partitioning strategy is introduced to improve training stability under extreme non-IID conditions ([Formula: see text]), revealing that the distributional impact of a given Dirichlet parameter is dataset-dependent. To support low-latency smart grid applications, a permissioned blockchain employing proof-of-authority and association (PoA[Formula: see text]) consensus achieves 2.0&#xa0;s transaction latency and 20.88 TPS, outperforming Hyperledger Fabric and Quorum in the evaluated setting. Experimental results on two energy-consumption datasets show that LSTM consistently outperforms BiLSTM under high data heterogeneity, achieving an average R[Formula: see text] of 0.9184 across clients at [Formula: see text]. Smart contract security assessment further yields a threat score of 98.5/100, demonstrating the framework's suitability for privacy-sensitive smart grid deployments. The contribution lies in the integration and systematic validation of established federated learning, forecasting, and blockchain technologies within a unified smart grid architecture.","url":"https://doi.org/10.1038/s41598-026-59796-x","authors":["Nanteza AL","Ahakonye LAC","Kim DS","Lee JM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-59796-x","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1371/journal.pone.0349730","name":"Editorial Note: A scalable blockchain-enabled federated learning architecture for edge computing.","source":"pubmed","abstract":"","url":"https://doi.org/10.1371/journal.pone.0349730","authors":["PLOS One Editors"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0349730","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.21203/rs.3.rs-10799557/v1","name":"Secure Federated Learning Framework with Dynamic Lightweight Cipher (Fed- DLC) for Resource-Constrained Networks and Data-Sensitive Applications","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10799557/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10799557/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.21203/rs.3.rs-6933179/v1","name":"MerkleFL: A Secure Decentralized Federated Learning Framework for Healthcare with Model Integrity Verification","source":"europepmc","abstract":"Abstract Decentralized federated learning (DFL) offers a privacy-preserving approach for collaboratively training models across distributed healthcare entities without sharing raw patient data. However, ensuring the integrity and reliability of model updates in such decentralized settings remains a significant challenge. This paper introduces \\textit{MerkleFL}, a secure and efficient DFL framework that integrates cluster-based aggregation with Merkle Tree-based verification to detect and reject tampered or unauthorized model contributions. The proposed system employs a lightweight integrity-checking mechanism where each model update is associated with a cryptographic Merkle Root, enabling trustless verification at the cluster level. A dynamic leader election protocol facilitates intra-cluster coordination without relying on central servers or blockchain consensus. Experimental evaluations conducted on the NIH ChestX-ray14 dataset demonstrate that MerkleFL achieves faster convergence, higher classification accuracy, and lower training loss compared to existing DFL schemes such as Gossip-DFL, Ring-DFL, and Blockchain-DFL. The results confirm that MerkleFL effectively balances security, scalability, and performance, making it a practical solution for federated healthcare AI applications.","url":"https://doi.org/10.21203/rs.3.rs-6933179/v1","authors":["Ashwin Verma","Sunil Pathak","Pronaya Bhattacharya"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-6933179/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1213/ane.0000000000008069","name":"Federated Learning in Anesthesiology: A Privacy-Preserving Approach to Collaborative Predictive Modeling.","source":"pubmed","abstract":"","url":"https://doi.org/10.1213/ane.0000000000008069","authors":["Almeida VFA","Dantas M","Donato G","Aggarwal A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1213/ane.0000000000008069","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-53053-x","name":"A blockchain-assisted secure federated learning architecture for intrusion detection in internet of things networks.","source":"pubmed","abstract":"The fast growth of Internet of Things (IoT) systems has made them very susceptible to advanced cyber-attacks, and an intelligent and privacy-sustainable intrusion detection system is required. Conventional centralized intrusion detection frameworks have the drawbacks of data privacy threats, scale constraints, and a single point of failure, and conventional federated learning still faces the threat of malicious client membership and a lack of trust during model aggregation. To overcome these obstacles, this paper suggests a federated learning (B-FL) system based on a Blockchain to ensure safe and reliable intrusion detection in the distributed Internet of Things. The framework proposed is a combination of federated and blockchain-based trust management to guarantee decentralized collaborative model training and maintain data confidentiality. The use of smart contract-based verification tools and trust-weighted aggregation counteracts the adversarial threats, such as model poisoning, data manipulation, free-rider behavior, and Sybil attacks. Testing is performed on the CICIoT2023 dataset, which consists of traffic produced by 105 IoT devices in 33 different types of attacks, and it allows testing all the aspects of its work in terms of a real and heterogeneous network. Findings reveal that the proposed B-FL model has high detection rates, high convergence stability, and enhanced robustness as compared to traditional methods of centralized and federated intrusion detection. Another study, Receiver Operating Characteristic (ROC) analysis, supports the presence of excellent discriminative ability with respect to several classes of intrusion. Though the integration of the blockchain has a marginal increase in computing overhead, it benefits the system in terms of transparency, reliability, and aggregation security significantly. In general, the suggested framework offers a scalable, privacy-aware, and trust-conscious IoT intrusion detection system in the next generation to enable secure collaborative intelligence in dynamic and adversarial IoT environments such as mining and mineral-processing environments.","url":"https://doi.org/10.1038/s41598-026-53053-x","authors":["Kamran M","Akhtar SM","Gilani A","Alhashmi AA","Kanwal S","Darem AA","Alofairi AA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-53053-x","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3390/s26102954","name":"FedMIR: Multimodal Federated Learning with Missing Modality Imputation and Distribution-Aware Routing.","source":"pubmed","abstract":"Existing multimodal federated learning methods typically assume complete modality availability and struggle with heterogeneity between training and testing data distributions, making them unsuitable for handling missing modalities and distribution drift in distributed learning scenarios such as the Internet of Things (IoT). To address these challenges, we present FedMIR, a novel framework for multimodal federated learning. Our key observation is that heterogeneous modalities can be mapped into a shared semantic space, where cross-modal dependencies can be effectively modeled. Based on this insight, FedMIR leverages contrastive learning to align image-text modalities in a shared latent space and employs conditional generation to reconstruct missing modality representations. The completed representations are then routed through a mixture-of-experts backbone conditioned on the estimated distribution state. FedMIR shares only model parameters and distribution statistics with the server. This design enables the model to operate under missing modality settings while adaptively allocating expert knowledge to cope with distribution drift. We validate FedMIR on federated image-text retrieval benchmarks under heterogeneity and missing data conditions, demonstrating its effectiveness compared to representative federated learning baselines.","url":"https://doi.org/10.3390/s26102954","authors":["Xiong H","Dai M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26102954","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.1038/s41598-026-59830-y","name":"FireSmoke-FL: a privacy-preserving federated learning framework for real-time fire and smoke detection.","source":"pubmed","abstract":"The early detection of fire and smoke is a significant aspect in the prevention of disasters in smart cities and the development of extensive monitoring systems. In such scenarios, the early response is critical in ensuring the safety and prevention of further damage. The traditional sensor-based approach in the detection of fire and smoke using heat sensors and smoke detectors is prone to several limitations such as delayed response times, increased false alarm rates, and the inability of the system to adjust to changing environmental conditions. To improve the detection of fire and smoke in the context of a smart-city and the development of extensive monitoring systems, a privacy-preserving vision-based framework called FireSmoke-FL is proposed. FireSmoke-FL is a Federated Learning-based framework that integrates an enhanced YOLOv11 model. The model sensitivity is enhanced by incorporating a high-resolution P2 detection head and an attention-refined C2PSA-iEMA module. These improvements aim to enhance the model's robustness in detecting fire and smoke in the presence of background interference, such as fog, clouds, and varying illumination conditions. To ensure the privacy of data collected from edge devices, such as IoT devices and drones, FireSmoke-FL is designed to enable devices to learn locally without sharing data. The Dynamic Average Fusion Algorithm (DAFA) is adapted to improve the performance of the model through the adaptive selection of the clients based on the quality of the models developed locally. The FireSmoke-FL framework is validated through extensive experiments on the Fire and Smoke dataset and the Indoor Fire Smoke dataset. The results show that the framework achieves 96.7% mAP at 84.7 FPS and 96.5% mAP at 82.4 FPS on the Fire and Smoke dataset and the Indoor Fire Smoke dataset, respectively. These findings indicate that the proposed model possesses accuracy, efficiency, scalability, and privacy protection.","url":"https://doi.org/10.1038/s41598-026-59830-y","authors":["Alblehai F","Alalwan N","Alzahrani AI","Al-Bayatti AH","Al-Samarraie H","AlHabshy AA","Abozeid A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-59830-y","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3390/s26113545","name":"Federated Learning Based on Fuzzy Fusion Rules for Chemical Production Process Fault Diagnosis.","source":"pubmed","abstract":"Process data plays a vital role in diagnosing fault sources in chemical production. However, such data contain rich process information and are often sensitive, making direct analysis infeasible due to privacy concerns. Although federated learning mitigates data leakage risks, the conventional averaging strategy falls short in achieving high fault identification accuracy, especially under non-independent and identically distributed (non-IID) client data. To overcome this challenge, we propose a personalized federated learning framework, in which a Takagi-Sugeno (T-S) fuzzy fusion rule is designed. Then, the personalized model is constructed through a structured procedure: fuzzification of model parameter distances, definition of fuzzy rules, fuzzy inference, and defuzzification. Moreover, layer-wise fusion is employed to enhance the precision of aggregation. Evaluations on the Tennessee Eastman (TE) process demonstrate that our method achieves superior fault identification accuracy. The results validate the efficacy of the proposed Fuzzy Rule-Based Federated Layer-wise Fusion (FedFZ) framework in industrial fault diagnosis under heterogeneous data distributions.","url":"https://doi.org/10.3390/s26113545","authors":["Xu Y","Yang W","Du S","Zhang M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26113545","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1016/j.mex.2026.103990","name":"Quantum federated learning for autonomous vehicle cybersecurity: An analytical review of architectures and threat landscapes.","source":"pubmed","abstract":"With the rapid rollout of autonomous vehicles (AVs), many AV criminal attacks form one of the biggest cybersecurity attack surfaces in contemporary infrastructure. But there are also the AV subsystems-LiDAR, radar, cameras, GPS, CAN buses, and V2X communications-all of which expose separate exploit vectors that would threaten both passenger safety as well as traffic. Centralized machine learning has been utilised for intrusion detection, but privacy issues, scalability limitations, and susceptibility to quantum-enabled adversaries have motivated interest in Federated Learning (FL) as a distributed approach. On the one hand, vehicles operating on RSA- and ECC-based protocols will become insecure due to quantum effects, motivating the need for Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC) [7], or any alternative to current systems. This seminal work proposes a summarization of Quantum Federated Learning (QFL) research specifically for AV cybersecurity. The contributions are: (i) a multi-dimensional risk matrix for AV attack priorities; (ii) a cross-study synthesis of gaps in FL-IDS deployment; (iii) comparative analysis across QKD, PQC and hybrid models of quantum security transition; (iv) construction of a QFL reference architecture with seven gaps agenda and (v) structured 2025-2030 road-map. The analysis shows that neither FL nor quantum security of it alone is strictly sufficient, and their togetherness in the hinge point between them - QFL - is the most urgent way forward for securing autonomous vehicles.","url":"https://doi.org/10.1016/j.mex.2026.103990","authors":["T S","Devadas RM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.mex.2026.103990","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.14293/pr2199.003338.v1","name":"Design of Privacy-Preserving Federated Learning Models for Auto Insurance Telematics","source":"europepmc","abstract":"The integration of telematics in the automobile insurance industry has facilitated the transition toward Usage-Based Insurance (UBI). However, the centralized collection of granular GPS and accelerometer data poses significant privacy risks to policyholders. This study proposes a PrivacyPreserving Federated Learning (PPFL) framework designed to train risk-prediction models without necessitating the transfer of raw sensor data to a central cloud server. By utilizing Federated Averaging (FedAvg) integrated with Differential Privacy (DP), the model enables insurance providers to collaboratively learn driving patterns while maintaining local data residency on user devices. Experimental results using the UAH-DriveSet demonstrate that the proposed PPFL model achieves predictive accuracy comparable to centralized models ($AUC \\approx 0.89$) while strictly adhering to privacy guarantees. The study concludes that PPFL addresses the critical \"privacy-utility\" trade-off, offering a viable path for the ethical adoption of telematics in highly regulated insurance markets.","url":"https://doi.org/10.14293/pr2199.003338.v1","authors":["John Davis","Jennifer Smith","Robert Williams"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.14293/pr2199.003338.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.3389/fdgth.2026.1821129","name":"TeleZK-FL: enabling trustless and verifiable remote patient monitoring via quantized zero-knowledge federated learning.","source":"pubmed","abstract":"The rapid expansion of Internet of Medical Things (IoMT) and telehealth platforms has generated vast amounts of patient data suitable for training diagnostic Artificial Intelligence (AI) models. However, strict privacy regulations (HIPAA, GDPR) and the risk of data breaches prevent the centralization of this sensitive information. While Federated Learning (FL) allows for collaborative training without sharing raw patient data, it introduces a critical \"trust deficit\": central aggregators cannot verify the integrity of local model updates without inspecting the private data, leaving the system vulnerable to model poisoning and malicious actors.","url":"https://doi.org/10.3389/fdgth.2026.1821129","authors":["Jayaraman P","Delhibabu R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1821129","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.34133/research.1299","name":"Experimentally Validated Quantum-Secure Federated Learning over a Multi-user Quantum Network.","source":"pubmed","abstract":"Federated learning enables decentralized, privacy-preserving training but remains vulnerable to privacy leakage in the quantum era. Quantum federated learning (QFL) offers a promising path toward enhanced security and efficiency. However, a practical and experimentally validated QFL protocol utilizing near-term quantum techniques to address data privacy has been lacking. Here, we present QuNetQFL, a QFL protocol implemented on quantum networks, in which local model updates are masked with distributed quantum secret keys, offering information-theoretic security during aggregation. We experimentally validate the protocol on a 4-client quantum network and benchmark its performance using the generated keys on quantum and real-world datasets. Adding a single quantum client substantially improves global accuracy for classifying multipartite entangled and nonstabilizer quantum datasets. For language tasks, we apply QuNetQFL to sentiment analysis by federated fine-tuning of a hybrid classical-quantum language model, achieving comparable and robust performance in simulation and on real quantum hardware. Large-scale simulations further demonstrate scalability to 200 clients for handwritten-digit recognition, with rapid convergence and a 75% reduction in communication cost via model compression. Our work establishes a practical and scalable route to quantum-secure federated learning for the emerging quantum internet.","url":"https://doi.org/10.34133/research.1299","authors":["Liu ZP","Cao XY","Liu HW","Sun XR","Bao Y","Shen JY","Lu YS","Yin HL","Chen ZB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.34133/research.1299","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.21203/rs.3.rs-9746460/v1","name":"Securing IoMT with Federated Learning: A Hybrid Deep Learning and Ensemble-Based Intrusion Detection Framework","source":"europepmc","abstract":"Abstract The rapid proliferation of Internet of Medical Things (IoMT) devices has significantly enhanced healthcare connectivity while simultaneously increasing exposure to sophisticated cyber threats such as distributed denial-of-service attacks, malware propagation, and data exfiltration. Traditional centralized intrusion detection systems (IDS) are increasingly unsuitable for IoMT environments due to strict privacy regulations, decentralized data ownership, device heterogeneity, and communication constraints. To address these challenges, this paper proposes a privacy-preserving federated intrusion detection framework that integrates a hybrid Deep Neural Network (DNN) and XGBoost model within a federated learning (FL) architecture. Unlike conventional FedAvg-based approaches, the proposed method leverages the complementary strengths of deep feature extraction and gradient-boosting classification, enhanced by differential privacy, secure aggregation, and robust aggregation techniques to mitigate adversarial poisoning attacks. The problem is formally modeled as distributed empirical risk minimization under non-independent and identically distributed (non-IID) client data, with convergence behavior analyzed under bounded gradient variance and client drift. Extensive experiments conducted on WUSTL-EHMS-2020, UNSW-NB15, and BoT-IoT datasets demonstrate that the proposed framework achieves detection accuracy of up to 91.4% with F1-scores exceeding 90%, closely approaching centralized baselines such as XGBoost and LightGBM while preserving strict data locality. Furthermore, communication overhead and privacy-utility trade-offs are quantitatively evaluated, showing that differential privacy introduces less than 1% performance degradation under realistic noise budgets. The framework also demonstrates robustness against non-IID data heterogeneity, straggler effects, and adversarial model poisoning. These results establish the proposed approach as a scalable, privacy-compliant, and efficient solution for securing next-generation IoMT systems, enabling trustworthy AI-driven healthcare cybersecurity.","url":"https://doi.org/10.21203/rs.3.rs-9746460/v1","authors":["Ifeanyi Nwokoro","Edgar Osaghae","Saheed Kayode","Tombari Sibe"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9746460/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1038/s41598-026-54935-w","name":"Improving IoT security through an explainable hybrid CNN-transformer model and federated learning.","source":"pubmed","abstract":"The rapid proliferation of Internet of Things (IoT) devices has intensified cybersecurity threats, exposing critical infrastructure to sophisticated intrusion attacks. Existing intrusion detection systems (IDS) typically rely on centralized architectures that compromise data privacy, or employ single-architecture models that fail to capture both local spatial patterns and long-range temporal dependencies in network traffic simultaneously. To address these limitations, this paper proposes a novel explainable hybrid CNN-Transformer model integrated with federated learning (FL) for privacy-preserving intrusion detection in IoT environments. The proposed framework uniquely combines four key components not previously integrated in this context: a dual-block CNN-Transformer architecture, federated learning with FedAvg aggregation, multi-class attack classification, and Local Interpretable Model-Agnostic Explanations (LIME) for decision transparency. Evaluated on the IoT-23 dataset across both federated and non-federated scenarios, the proposed model achieves 94.89% accuracy in federated binary classification and 92.17% in federated multi-class classification, outperforming standalone CNN and ensemble baselines by significant margins. Generalizability is further validated through an ablation study on the CIC IoT-DIAD 2024 dataset. The integration of LIME provides actionable feature-level explanations that support real-time decision-making for network security analysts, advancing both the interpretability and trustworthiness of automated IoT intrusion detection systems.","url":"https://doi.org/10.1038/s41598-026-54935-w","authors":["Al-Hejri AM","Al-Tam RM","Sable AH","Alshamrani SS","Alshmrany KM","Alshehri A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-54935-w","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.2196/69985","name":"Explainable AI Approaches in Federated Learning: Systematic Review.","source":"pubmed","abstract":"Background Artificial intelligence (AI) has, in the recent past, experienced a rebirth with the growth of generative AI systems such as ChatGPT and Bard. These systems are trained with billions of parameters and have enabled widespread accessibility and understanding of AI among different user groups. Widespread adoption of AI has led to the need for understanding how machine learning (ML) models operate to build trust in them. An understanding of how these models generate their results remains a huge challenge that explainable AI seeks to solve. Federated learning (FL) grew out of the need to have privacy-preserving AI by having ML models that are decentralized but still share model parameters with a global model. Objective This study sought to examine the extent of development of the explainable AI field within the FL environment in relation to the main contributions made, the types of FL, the sectors it is applied to, the models used, the methods applied by each study, and the databases from which sources are obtained. Methods A systematic search in 8 electronic databases, namely, Web of Science Core Collection, Scopus, PubMed, ACM Digital Library, IEEE Xplore, Mendeley, BASE, and Google Scholar, was undertaken. Results A review of 26 studies revealed that research on explainable FL is steadily growing despite being concentrated in Europe and Asia. The key determinants of FL use were data privacy and limited training data. Horizontal FL remains the preferred approach for federated ML, whereas post hoc explainability techniques were preferred. Conclusions There is potential for development of novel approaches and improvement of existing approaches in the explainable FL field, especially for critical areas. Trial Registration OSF Registries 10.17605/OSF.IO/Y85WA; https://osf.io/y85wa","url":"https://doi.org/10.2196/69985","authors":["Titus Tunduny","Bernard Shibwabo","Tunduny T","Shibwabo B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025-10-11T14:55:07Z","doi":"10.2196/69985","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3390/s26103198","name":"Adaptive Multi-Model Hierarchical Federated Learning for Robust IoT Intrusion Detection.","source":"pubmed","abstract":"The rapid growth of the Internet of Things (IoT) has introduced significant cybersecurity challenges in highly distributed, heterogeneous, and privacy-sensitive environments. Traditional centralized intrusion detection approaches and conventional federated learning (FL) frameworks, which rely on single-model aggregation, are often inadequate in the presence of extreme non-IID data and adversarial conditions. This study proposes an Adaptive Multi-Model Hierarchical Federated Learning (AMM-HFL) framework for robust IoT intrusion detection. The framework operates across client, edge, and cloud tiers and introduces a unified integration of similarity-aware clustering, multi-model aggregation, and dynamic client-side model selection. Unlike existing hierarchical FL approaches, AMM-HFL maintains multiple global models, enabling adaptive personalization and improved representation of heterogeneous data distributions. At the edge level, model updates are clustered to isolate anomalous contributions, while the cloud performs meta-aggregation to refine diverse model representations. Experimental evaluation on the IDSIoT2024 dataset demonstrates detection accuracy up to 96.83-97.54% under IID and 95.64-97.52% under non-IID conditions, while maintaining low computational and cryptographic overhead.","url":"https://doi.org/10.3390/s26103198","authors":["Latif S","Djenouri D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26103198","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-026-56100-9","name":"BlockFedX: a cross-domain federated learning system with explainability, anomaly detection, and tamper-evident logging.","source":"pubmed","abstract":"Many organisations collect sensitive data that cannot be freely shared. Hospitals store brain magnetic resonance imaging (MRI) scans on internal servers; banks keep transaction records behind strict firewalls; agricultural services retain crop images in isolated repositories. Federated learning (FL) allows models to be trained without centralising raw data, yet most existing systems address a single domain and offer limited insight into model behaviour and provenance over time. BlockFedX is a cross-domain federated learning system designed to address three simultaneous tasks: credit card fraud detection on tabular data, brain tumour detection on MRI images, and plant disease recognition on leaf images. These three domains were deliberately selected because they represent the principal data modalities in real-world privacy-sensitive deployments-structured tabular records, greyscale medical images, and colour natural images-and because public benchmark datasets exist for all three, enabling reproducible evaluation. The system uses a shared backbone that is updated only where model layers have compatible tensor shapes, while domain-specific output layers remain local at each client. Explanations are computed at the clients using SHAP feature-attribution for tabular data and Grad-CAM visual heatmaps for images; the server receives only compact statistical summaries. The server also applies a distance-based anomaly test on client updates and records model hashes, explanation summaries, and anomaly flags in a hash-chained ledger. Experiments on three public datasets under non-identical client data distributions show that BlockFedX achieves an average fraud-detection F1-score of 0.92, 74.32% mean validation accuracy on BrainMRI, and 77% test accuracy on PlantVillage, while keeping all raw data local. These results are below strong centralised baselines, as expected under compact models and non-IID splits, but the system simultaneously provides three properties rarely combined in prior work: cross-domain federated training via a shape-safe backbone, client-side explanations integrated into the learning loop, and a lightweight tamper-evident record of model evolution across rounds.","url":"https://doi.org/10.1038/s41598-026-56100-9","authors":["Naidu KS","Suma GJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-56100-9","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1109/tnnls.2025.3639578","name":"SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2025.3639578","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tnnls.2025.3639578","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1021/acs.jmedchem.5c03681","name":"Developing Predictive Models by Sharing Predictions - An Investigation of a Federated Learning Approach for ADMET Predictions.","source":"pubmed","abstract":"Machine learning models for ADMET prediction benefit from large, diverse data sets, yet such data are typically siloed across organizations. Federated learning (FL) enables collaborative modeling while preserving data privacy. Here, we investigate a student-teacher model (STM) framework in which organizations train internal models on proprietary data and share predictions on a public data set to generate pseudolabels for a centralized student model. As a proof of concept, 11 pharmaceutical companies contributed predictions for rat steady-state volume of distribution, yielding a pseudolabeled data set of &#x223c;133,000 compounds. The resulting student model achieved performance comparable to individual teacher models on an external test set (RMSE &#x2248; 0.51 vs 0.47-0.61). Compared with FL approaches such as MELLODY and Effiris, STM offers a simpler workflow that avoids direct data sharing or iterative collaboration, providing a practical and scalable framework for secure cross-company model development.","url":"https://doi.org/10.1021/acs.jmedchem.5c03681","authors":["Guha R","Wang W","Price E","Bhhatarai B","Hassan M","DiFranzo A","Keefer C","Woody N","Broccatelli F","Winiwarter S","He L","Green DVS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1021/acs.jmedchem.5c03681","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.21203/rs.3.rs-9597469/v1","name":"Federated Learning over Serverless Edge Clusters: Balancing Privacy, Latency, and Trust","source":"europepmc","abstract":"Abstract The proliferation of Internet of Things (IoT) devices has driven the need for decentralized machine learning paradigms that preserve data privacy while minimizing transmission latency. Federated Learning (FL) has emerged as a robust solution, allowing distributed edge nodes to collaboratively train a global model without sharing raw local data. Concurrently, serverless computing offers a highly elastic, event-driven execution environment that eliminates the need for manual infrastructure provisioning. This paper proposes a novel framework that orchestrates Federated Learning pipelines over serverless edge clusters. By encapsulating local training tasks within ephemeral serverless functions, we achieve fine-grained resource scaling and cost efficiency. However, the transient nature of serverless execution introduces challenges in state management, synchronization, and trust verification across heterogeneous nodes. To address this, we formulate a mathematical model that optimizes the trade-off between communication latency, computational cost, and model convergence rates. Furthermore, we integrate a distributed trust mechanism to secure model weight updates against adversarial edge nodes. Extensive simulations demonstrate that our serverless FL framework reduces idle compute costs by up to 68% while maintaining convergence speeds within 5% of traditional, persistently provisioned edge clusters.","url":"https://doi.org/10.21203/rs.3.rs-9597469/v1","authors":["Poonam Verma"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9597469/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1093/jamiaopen/ooag040","name":"Privacy-preserving verification of preprocessing in federated learning for genomic data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/jamiaopen/ooag040","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1093/jamiaopen/ooag040","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.3233/shti260485","name":"Enabling Privacy-Preserving Federated Learning in Healthcare: The FLAME Architecture and Policy Framework.","source":"pubmed","abstract":"Federated Learning enables collaborative AI development in healthcare without sharing patient data, addressing privacy and regulatory constraints like GDPR and HIPAA. We present FLAME, an open-source platform developed within the German PrivateAIM project, designed to ensure privacy-compliant and auditable federated analytics. FLAME uses a hub-and-node architecture, integrating privacy-enhancing technologies with a dynamic policy framework that specifies permissions and conditions for data access and algorithm execution. This framework allows distributed policy evaluation and enforcement across institutions. Initial deployments at German university hospitals demonstrated FLAME's capability to conduct federated analyses on clinical and genomic data with model performance comparable to centralized approaches. The system offers fine-grained access control, audit logging, and minimal overhead. FLAME provides a scalable foundation for secure, privacy-preserving AI in medicine, bridging legal, technical, and organizational requirements for multi-institutional collaboration.","url":"https://doi.org/10.3233/shti260485","authors":["de Arruda Botelho Herr M","Placzek P","Abu Attieh H","Schultz B","Hieber D","Röhl A","Jaberansary M","Twrdik A","Brassel P","Schaible M","Halilovic M","Prasser F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3233/shti260485","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1080/15265161.2026.2690937","name":"Federated Learning is Still Machine Learning! Epistemic vs. Ethical Issues Common Across Medical ML Models.","source":"pubmed","abstract":"","url":"https://doi.org/10.1080/15265161.2026.2690937","authors":["Neal JP","Morar N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1080/15265161.2026.2690937","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3390/s26113522","name":"Energy-Adaptive Multi-Dimensional Learning Control for Federated Learning in Energy-Harvesting AIoT Systems.","source":"pubmed","abstract":"This paper addresses the problem of efficient federated learning in energy-harvesting AIoT systems, where time-varying energy availability may lead to device blackouts and unstable learning performance. To address this issue, we propose an energy-adaptive multi-dimensional learning control framework that jointly determines model complexity and training intensity based on the real-time energy state of each device. This method integrates multiple control dimensions, including model pruning, quantization, knowledge distillation, and adaptive local training, into a unified decision mechanism under an energy constraint. Each device determines its participation in federated learning based on its residual energy relative to an energy threshold. When participating, the device selects a feasible learning configuration that jointly considers training intensity (e.g., epoch size and batch size) and lightweight learning operations to maximize learning effectiveness while preventing energy depletion. The proposed framework was implemented on a real-world testbed using NVIDIA Jetson Orin Nano devices under solar-energy-harvesting conditions. Our experimental results demonstrate that the proposed method significantly reduces device blackout while maintaining competitive model accuracy with respect to energy-unconstrained scenarios. These results highlight that joint control of multiple learning-cost factors is essential for achieving stable and efficient federated learning in energy-harvesting AIoT environments.","url":"https://doi.org/10.3390/s26113522","authors":["Noh DK","Kwak C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26113522","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3389/fdgth.2026.1812254","name":"A privacy-preserving federated learning framework for generalizable CBCT to synthetic CT translation in head and neck.","source":"pubmed","abstract":"Cone-beam computed tomography (CBCT) has become a widely adopted modality for image-guided radiotherapy (IGRT). However, CBCT is characterized by increased noise, limited soft-tissue contrast, and artifacts. These issues result in unreliable Hounsfield unit (HU) values, which limits electron density estimation for direct dose calculation. These issues have been addressed by deriving synthetic CT (sCT) from CBCT, particularly by adopting deep learning (DL) methods. However, existing DL approaches are hindered by institutional heterogeneity, scanner-dependent variations, and data privacy regulations that prevented multi-center data sharing.","url":"https://doi.org/10.3389/fdgth.2026.1812254","authors":["Raggio CB","Zaffino P","Spadea MF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1812254","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.3791/72164","name":"Improving Cross-Center Generalization for Multi-modal MRI Meningioma Segmentation via Glioma-Pretrained Federated Learning.","source":"pubmed","abstract":"Automated meningioma segmentation on multi-modal MRI remains challenging when models are transferred across institutions, because scanner protocols, image characteristics, and annotation styles may differ between centers. Federated learning (FL) provides a privacy-preserving strategy for multi-center model development, but standard aggregation may not fully overcome cross-center domain shift. This study aimed to quantify the external generalization gap in MRI meningioma segmentation and evaluate whether Glioma-pretrained FL could improve robustness without centralized data pooling. A UMamba 2D architecture was used for binary meningioma segmentation using T1, T1c, and T2 MRI as model inputs. The protocol included 450 BraTS2023-Men cases as the source-domain meningioma dataset and 174 independent clinical cases from our institute as the external validation cohort. Three final meningioma segmentation strategies were quantitatively evaluated under the same external validation setting: centralized training on BraTS2023-Men, meningioma-pretrained FL across three simulated clients, and Glioma-pretrained FL initialized from a BraTS2023-Gli source model before federated optimization. Centralized training on Glioma was used only to generate the glioma-pretrained initialization and was not reported as an independently evaluated final meningioma segmentation strategy. Model performance was assessed using the Dice similarity coefficient (DSC) and Intersection over Union (IoU). Centralized training on BraTS2023-Men showed a clear external generalization drop, with DSC decreasing from 0.8958 on the internal BraTS2023-Men test set to 0.7452 on the SPHS cohort. Meningioma-pretrained FL yielded lower external performance (DSC = 0.7122), whereas Glioma-pretrained FL achieved comparable performance to centralized training on BraTS2023-Men and improved over meningioma-pretrained FL (DSC = 0.7503; IoU = 0.6301; Holm-adjusted p &lt; 0.001). These results suggest that glioma-pretrained initialization provides a more robust starting point for federated meningioma segmentation and may improve external generalization while preserving institutional data privacy.","url":"https://doi.org/10.3791/72164","authors":["Ni C","Qian K","Zhang C","Zhang R","Yang S","Li Z","Luo X","Zong F","Yu J","Liao Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3791/72164","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.21203/rs.3.rs-9922816/v1","name":"A Federated Learning Framework for Mental Stress Assessment and Brain Source Localization Using EEG and fNIRS","source":"europepmc","abstract":"Abstract Mental stress influences how individuals think, perform tasks, and maintain long-term well-being, which makes its assessment a nontrivial problem in both clinical practice and everyday work environments. Although machine learning methods have been adopted in recent years to support stress analysis, many reported studies continue to rely on centralized training strategies. Such designs not only raise concerns about data privacy, but also tend to show unstable performance when models are applied across institutions with differing data characteristics. In response to these challenges, we develop a federated learning framework that integrates Graph Neural Networks (GNNs) with Physics-Informed Neural Networks (PINNs) to jointly address mental stress classification and brain source localization. In this framework, multimodal EEG and fNIRS recordings are obtained during carefully designed stress-induction tasks and are processed using artifact mitigation, normalization procedures, and event-aligned segmentation. The proposed GNN component explicitly models dynamic functional connectivity by representing brain activity as spatio-temporal graphs, enabling the learning of stress-related interaction patterns across sensors. In parallel, PINNs are employed to guide the source localization process by embedding head conductivity constraints and neuroanatomical priors, thereby improving spatial interpretability and localization accuracy. Model training is carried out in a federated manner, allowing multiple institutions to collaboratively learn a shared model without exchanging raw neurophysiological data. The framework is evaluated using stress classification accuracy, source localization error, and robustness to inter-subject variability. The experimental evaluation indicates that the proposed federated GNN–PINN approach achieves more reliable stress classification than conventional centralized baselines, while also yielding notably lower source localization errors. Taken together, these results suggest that the joint use of graph-based representations, physics-informed constraints, and federated optimization can support the development of scalable and privacy-conscious stress assessment systems with practical clinical relevance.","url":"https://doi.org/10.21203/rs.3.rs-9922816/v1","authors":["Muhammad Yaqub","Degang Xu"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9922816/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3390/e28010101","name":"Federated Learning Under Evolving Distribution Shifts.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28010101","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/e28010101","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1093/nargab/lqag010","name":"Federated learning frameworks: quality and interoperability for biomedical research.","source":"pubmed","abstract":"","url":"https://doi.org/10.1093/nargab/lqag010","authors":["Chavero-Diez M","Hernandez-Ferrer C","Codó L","Gelpí JL","Capella-Gutiérrez S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1093/nargab/lqag010","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1371/journal.pone.0348359","name":"A cloud-edge-end collaborative intelligent caching method based on incremental federated learning algorithms.","source":"pubmed","abstract":"In a cloud-edge-end collaborative system, data generated by terminal devices often contains users' sensitive information and is constantly generated and changing, leading to potential data privacy leaks in caches. Additionally, due to the inability to promptly capture these dynamic changes and the failure to consider the actual capabilities of nodes, caching strategies become outdated, resulting in reduced cache hit rates and cache imbalance issues. Therefore, this study proposes a cloud-edge-end collaborative intelligent caching method based on an incremental federated learning algorithm. First, the federated learning algorithm is used to aggregate data from terminal devices to the cloud, enabling collaborative data processing while protecting data privacy. Second, incremental learning methods are employed to continuously update terminal data, with the updated data aggregated to the cloud, thereby enabling real-time tracking of data trends and allowing cache strategies to rapidly adapt to dynamic changes in terminal data. Finally, considering the actual capabilities of nodes, the popularity of aggregated data and the weights of edge and terminal nodes are calculated. Data is cached in edge and terminal nodes in descending order of popularity and weight. When cache space is insufficient, data replacement is performed based on the importance of data within nodes, thereby completing intelligent data caching. Experimental results demonstrate that this method achieves good performance in data update aggregation, with high data caching balance and cache hit rates.","url":"https://doi.org/10.1371/journal.pone.0348359","authors":["Huang X","Jin L","Lin K","Wu W","Lin Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0348359","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.21203/rs.3.rs-9880448/v1","name":"Federated Learning IoT Security Framework Based on Dynamic Multi Domain Mapping and Robust Aggregation","source":"europepmc","abstract":"Abstract The paper proposes a federated learning IoT security framework based on dynamic multi domain mapping (DME) and robust adversarial gain aggregation (R-AGA) to address the performance and security challenges posed by non independent identically distributed (Non IID) and potential malicious clients. The DME module effectively enhances the adaptability of the model in heterogeneous data through multi domain mapping and feature compensation mechanisms; The R-AGA module combines dynamic weight allocation and sliding window fitness modeling to improve the robustness and stability of aggregation. On the CIFAR-100 dataset, DME-RAGA achieved accuracies of 35.8% and 34.7% in Sim (0.1) and Dir (0.1) scenarios, respectively, which is significantly higher than FedAvg (about 22%); The convergence accuracy on the GTSRB dataset reaches 99%, leading FedProx (96%) and FedDyn (98%). In terms of communication overhead, the total communication volume of DME-RAGA in a single round is 15.3MB, which is only 25.4% higher than FedAvg and lower than FedGen's 54.1% increase. The experimental results show that the framework achieves a comprehensive balance of accuracy, robustness, and efficiency in IoT environments.","url":"https://doi.org/10.21203/rs.3.rs-9880448/v1","authors":["Yi Tian"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9880448/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.3390/e28040423","name":"Bias-Corrected Federated Learning for Video Recommendation over Stochastic Communication Links.","source":"pubmed","abstract":"With the increasing demand for privacy-preserving and real-time personalized services in large-scale video platforms, designing robust federated recommendation frameworks over practical communication networks has become increasingly important. To this end, this paper proposes a bias-corrected federated learning framework tailored for video recommendation over stochastic communication links. At the local training stage, a bias-corrected mechanism is introduced to explicitly account for video duration and user activity, mitigating feature-level bias and enabling the learned representations to more accurately reflect users' intrinsic preferences. To meet the timeliness requirements of real-time federated learning, the successful upload probability of local model transmission is analytically characterized under time-varying channel conditions. Building upon this probabilistic model, a statistically corrected global aggregation strategy is designed to preserve the unbiasedness of the global update with respect to the ideal fully reliable FedAvg scheme, even when a subset of local nodes fails to upload their models within the specified delay constraint. Comprehensive experimental evaluations validate that the proposed framework significantly improves recommendation accuracy and maintains robustness against communication unreliability in practical distributed environments.","url":"https://doi.org/10.3390/e28040423","authors":["Zhou C","Pei Y","Li Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/e28040423","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-9957455/v1","name":"FedDAAW: Dynamic Client Selection and Accuracy- Adaptive Aggregation for Cross-Task Federated Learning under Heterogeneity","source":"europepmc","abstract":"Abstract Federated learning enables collaborative model training across distributed clients while preserving data privacy. However, both statistical and system heterogeneity among clients severely degrade model performance. Existing methods mostly address only one aspect of heterogeneity and are validated primarily on classification tasks, with limited evidence of cross-task generalization.To address these issues, this paper proposes FedDAAW, a lightweight framework for heterogeneous federated learning. It introduces a multi-factor dynamic client selection mechanism and an accuracy-adaptive aggregation strategy, which fully leverages high-quality clients without requiring gradient sharing or auxiliary data.Experiments are conducted on image classification (MNIST, CIFAR-10), text sentiment analysis (IMDB), and regression (California housing) datasets. Under highly non-IID settings, FedDAAW consistently outperforms the baseline FedAvg across all tasks, achieving test accuracies of 98.80%, 31.05%, and 83.86% on MNIST, CIFAR-10, and IMDB, respectively, and reducing the mean squared error to 0.2918 on the California housing dataset. Furthermore, the proposed method shows significant advantages in convergence efficiency, reaching predefined performance targets in fewer communication rounds and thus lowering communication overhead.FedDAAW is privacy-preserving, lightweight, and easy to deploy. It effectively improves the overall performance of heterogeneous federated learning without complex modifications.","url":"https://doi.org/10.21203/rs.3.rs-9957455/v1","authors":["Jianqing Tang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9957455/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1109/tnnls.2025.3601449","name":"Contrastive Federated Learning for Graph Anomaly Detection.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2025.3601449","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tnnls.2025.3601449","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.3390/s26113578","name":"Robust and Lightweight Federated Learning for NB-IoT Security: A Blockchain-Verified CNN-RNN Approach.","source":"pubmed","abstract":"The rapid proliferation of Narrowband Internet of Things (NB-IoT) devices necessitates robust, privacy-preserving intrusion detection systems. While Federated Learning (FL) mitigates data privacy risks through localized training, it introduces vulnerabilities to model poisoning and computational bottlenecks on edge devices. To address these challenges, we propose a secure, hardware-optimized Blockchain-Federated Learning (BC-FL) framework. Deploying a lightweight Hybrid CNN-RNN model on Edge Gateways, we relieve end-sensors of heavy computational tasks. To overcome the 'cold-start' problem, we introduce a Domain-Adaptive Transfer Learning strategy, dynamically adapting a pre-trained binary classifier to a multi-class task (Normal, Mirai, Bashlite). Furthermore, a lightweight blockchain ledger provides an immutable audit trail and a reputation-based isolation mechanism to penalize malicious nodes. Evaluated on the N-BaIoT dataset, the proposed 3-class CNN-RNN model achieves 95.62% overall accuracy, with precision/recall/F1-scores of 0.99/0.91/0.95 for Mirai and 0.93/0.99/0.96 for Bashlite attacks. The framework reduces communication bandwidth by 96% compared to centralized learning. During simulated Byzantine attacks, the reputation mechanism successfully banned malicious nodes, maintaining a robust 95.62% global accuracy. This framework offers a highly scalable, secure, and computationally feasible solution for real-time anomaly detection in resource-constrained IoT edge environments.","url":"https://doi.org/10.3390/s26113578","authors":["Özmen G","Yiltas-Kaplan D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26113578","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1038/s41598-026-59224-0","name":"BC-AWFedAvg: blockchain-assisted adaptive federated learning for secure RAN Slicing in beyond-5G networks.","source":"pubmed","abstract":"The evolution of beyond-5G networks introduces new challenges for radio resource management, particularly for heterogeneous service requirements across multiple virtual network operators. This work presents BC-AWFedAvg, a layered framework for federated deep reinforcement learning in O-RAN network slicing that integrates adaptive aggregation, blockchain-based governance, secure aggregation, and differential privacy. The proposed design separates learning, governance, and storage functions to support coordinated training while preserving privacy and limiting exposure of individual updates. Simulation results in the considered setting indicate that the proposed framework improved robustness in the considered setting under several adversarial scenarios while maintaining acceptable quality-of-service performance. These findings suggest that combining complementary mechanisms may be a promising direction for secure federated learning in next-generation wireless networks.","url":"https://doi.org/10.1038/s41598-026-59224-0","authors":["Zemzemi M","Hajlaoui JE","Aldalbahi AS","Mhatli S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-59224-0","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1038/s41598-026-51138-1","name":"BFLAFD: blockchain-enabled federated learning framework for adaptive fire detection in IIoT networks.","source":"pubmed","abstract":"The Industrial Internet of Things (IIoT) is a network of interconnected sensors, devices, and control systems in the oil and gas sectors that has been developed to make the industries automated and continuously monitored. However, there are challenges in fire detection in such environments, including the unreliable nature of the sensor data, privacy issues, communications delays, and the lack of a generalized model across locations in a distributed solution. To overcome the above problems, BFLAFD, Blockchain-assisted Federated Learning framework for Adaptive Fire Detection is introduced. Unlike centralized methods, BFLAFD makes use of Federated Learning (FL), where local edge servers train models directly on-device in which data confidentiality is upheld and less data is transmitted. Hierarchical aggregation process is effective in maximizing global performance, in addition to being sensitive to sensor drift and device heterogeneity. To make sure of trust and resilience, BFLAFD combines the permissioned blockchain with smart contracts providing access control, transparency of logs and no tampering of models or insider manipulation. Furthermore, Personalized Federated Learning (PFL) makes it possible to create a customized fire detection model, effectively enhancing the accuracy in varying conditions. Experimental evaluations have shown that BFLAFD has 98.2% detection accuracy, false alarm rate of 2.7%, and a 100-150 ms inference latency, and blockchain validation time of 1-2&#xa0;s. In addition, the cost of communication was reduced by 82.3% compared to centralized training. Overall, BFLAFD offers critical IIoT environments fast, accurate, and secure fire detection solutions.","url":"https://doi.org/10.1038/s41598-026-51138-1","authors":["Desikan J","Singh SK","Jayanthiladevi A","Gupta H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-51138-1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1177/00368504261456965","name":"Asynchronous federated learning with partial weights aggregation for energy consumption forecasting.","source":"pubmed","abstract":"Accurate energy forecasting is essential for grid stability, demand-side management, and efficient renewable integration. However, energy consumption data collected from smart meters may expose sensitive user information, thus raising privacy concerns. Federated Learning (FL) offers a privacy-preserving mechanism for collaborative model training without sharing raw data. However, conventional synchronous FL suffers from training delays caused by heterogeneous client availability and computational capabilities, while frequent exchange of model parameters can lead to communication overheads. To address these challenges, this paper proposes an asynchronous federated learning framework for energy forecasting that enables continuous global model updating without waiting for all clients to complete local training. We introduce a federated asynchronous adaptive aggregation mechanism, where client-specific learning rates are dynamically adjusted based on both update staleness and model performance contribution. A partial aggregation strategy is defined for a Long Short-Term Memory (LSTM) forecasting model that splits the local models' layers, allowing clients to exchange only a subset of the weights with the server. The proposed solution is evaluated using real-world energy consumption data from multiple consumers. Experimental results demonstrate that the proposed asynchronous adaptive strategy outperforms the classic FedAvg approach and maintains prediction accuracy relative to personalised FedAvg, while reducing communication costs. Additionally, the proposed method outperforms the classic FedAsync algorithm across all client groups, with statistically significant improvements in most cases.","url":"https://doi.org/10.1177/00368504261456965","authors":["Toderean L","Mesesan M","Cioara T","Anghel I"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1177/00368504261456965","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.20944/preprints202604.1320.v1","name":"MediVault: An Auditable and Secure Federated Learning System for Privacy-Preserving Healthcare Collaboration","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202604.1320.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202604.1320.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-10235917/v1","name":"Decentralised Federated Learning Framework for Privacy-Preserving Medical Insurance and Sustainable Rural Economic Development in Tamil Nadu","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10235917/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10235917/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.64898/2026.07.30.26359337","name":"Use of Federated Learning for validating and updating privacy-preserving decentralized multi-study prognostic models in Traumatic Brain Injury","source":"pubmed","abstract":"Developing modern clinical prediction models (CPMs) and advanced analytics requires large datasets, often necessitating data from different studies. Privacy regulations may hinder data sharing, especially across countries. Decentralized federated data infrastructures, where data remain in their original location and analyses are run only in a shared, secure environment, may address these challenges. We implemented a privacy-preserving federated learning (FL) infrastructure and evaluated and updated the IMPACT prognostic models for traumatic brain injury (TBI) using 2 studies. A multi-continental federated infrastructure was established between 2 large-scale studies (TRACK-TBI from the United States and CENTER-TBI from Europe and Israel). Three IMPACT prognostic models for post-TBI 6-month mortality and unfavorable outcomes were evaluated, followed by model updates through 2 FL approaches trained across the TRACK-TBI and CENTER-TBI studies. Internal validation, external cross-validation, and sub-study validations were performed. CPMs were evaluated for discrimination and calibration. The federated cohort included 1616 participants (TRACK-TBI: n=441, CENTER-TBI: n=1175). Both FL performed well, with comparable coefficient estimates, AUCs (area under the receiver operating characteristics curve) between 0.77-0.88, and calibrated probabilities. Compared to the original IMPACT and single-study models, both federated models presented similar discrimination (AUC), were well-calibrated, were more efficient (higher precision), and reduced the impact of missing data in model estimation. FL is feasible for privacy-preserving development and evaluation of CPMs, and can enable validation and updating across large, virtually analyzed datasets while overcoming regulatory constraints on data combination. Federated infrastructures can facilitate global collaboration to advance data-hungry analytical methods, such as artificial intelligence.","url":"https://doi.org/10.64898/2026.07.30.26359337","authors":["Torres-Espin A","Wong JC","Hinson HE","Kuipers TB","Hoekstra BPT","Jain S","Sun X","Yue JK","Pisică D","Mikolic A","Lingsma HF","Markowitz AJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.07.30.26359337","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3390/s26082307","name":"Trust-Aware and Energy-Efficient Federated Learning for Secure Sensor Networks at the Edge.","source":"pubmed","abstract":"The widespread adoption of large-scale sensor networks in privacy-sensitive and safety-critical applications has intensified the demand for secure, trustworthy, and energy-efficient learning mechanisms at the network edge. Federated learning has emerged as a promising paradigm for privacy preservation by enabling collaborative model training without sharing raw sensor data. However, most existing federated approaches inadequately address trust management, communication efficiency, and energy constraints, which are critical in real-world sensor-based systems. This paper proposes a trust-aware and energy-efficient federated learning framework specifically designed for secure sensor networks operating in resource-constrained edge environments. The proposed approach integrates lightweight trust metrics, trust-driven model aggregation, and adaptive communication scheduling to mitigate the impact of unreliable or malicious nodes while reducing unnecessary energy expenditure. By dynamically weighting client contributions based on trust and participation efficiency, the framework enhances robustness and learning stability under heterogeneous sensing conditions. Experimental results show that the proposed method maintains significantly higher accuracy under adversarial participation while reducing communication overhead and cumulative energy consumption. In particular, the framework improves model accuracy by up to 3.2% under heterogeneous conditions, reduces communication overhead by 28%, and decreases cumulative energy consumption by 31% compared with conventional federated learning approaches.","url":"https://doi.org/10.3390/s26082307","authors":["Reis MJCS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26082307","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3390/s26134016","name":"Context-Aware Online Model Splitting and Device Association for Semi-Decentralized Federated Learning in Internet of Things.","source":"pubmed","abstract":"As a distributed approach to Artificial Intelligence (AI) model construction over wireless networks, federated learning (FL) based on multi-device collaborative training can protect data privacy, as well as increase the computing load of local model updates. In contrast, split learning (SL) with proper model splitting can adapt to the computation and transmission capabilities among devices. In this paper, while taking advantage of FL and SL, we concentrate on a semi-decentralized hybrid federated split learning (SD-HFSL) framework, in which we surpass the limitations of a single central server and allow the shared split models to be aggregated among multiple edge servers. To verify the importance of latency optimization for training efficiency, we analyze the convergence performance of SD-HFSL while jointly considering the limited computation and communication resources. Then, aiming at maximizing the long-term training efficiency, we propose an online optimization problem that includes local model splitting and device association. Considering that the training latency is unknown to the system a priori, a context-aware online training algorithm with sublinear regret is proposed based on the framework of contextual multi-armed bandit (CMAB), where the edge servers can observe the context information of device sites for latency estimation, followed by the iterative optimization based on the evaluated information in different contexts. Experiments on several neural network models show that the proposed algorithm reduces training latency and improves test accuracy compared with the selected benchmarks.","url":"https://doi.org/10.3390/s26134016","authors":["Xu B","Wang S","Tang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26134016","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1088/1361-6579/ae4a82","name":"Analysis of federated learning on non-independent and identically distributed sleep data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1088/1361-6579/ae4a82","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1088/1361-6579/ae4a82","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.22541/au.177196323.32194073/v1","name":"PRIVACY AMPLIFIER: Benchmarking Poisoning Membership Inference for Federated Learning","source":"europepmc","abstract":"","url":"https://doi.org/10.22541/au.177196323.32194073/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.22541/au.177196323.32194073/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9138351/v1","name":"Governance-Aware Federated Learning for Trustworthy and Compliant Decentralized Infrastructure Monitoring","source":"europepmc","abstract":"Abstract The paper introduces a federated learning (GFL) architecture that is governed to monitor decentralized infrastructure in a legally heterogeneous setting. The framework incorporates legal compliance limits, auditability controls, and policy alignment as governed by LLM into the federated optimization process, thus creating the possibility of trustful and policy-oriented deployment of AI to the nodes of the public sector.This is to develop a mathematical model that embodies multi-agent lawful infestations, metadata audit rating, and also dynamic trust stabilization within the limits of regulations. Empirical analysis with a synthesized dataset of 20 European jurisdiction nodes reveals that the GFL model performs better than governance-free baselines in the accuracy of their predictions +1 3.5 %) and concurrently enhances transparency, explainability, and auditability. Clients that follow a legal and semantic protocol of governance always provide quality and more consistent updates. The findings show that rather than deterring model convergence and trust in the decentralized systems, the enforcement of governance improves such aspects. The paper is another addition to the intersection of federated AI in digital governance, providing a scalable strategy to institutional compliance in critical areas of infrastructure, such as smart grids, urban mobility, and environmental sensing","url":"https://doi.org/10.21203/rs.3.rs-9138351/v1","authors":["Seyed Amirhossein Mousaviniya Qasemabadi","Mohammadmahdi Rezaeifar Sangchouli","Fatemeh Zahra HosseiniMoghadam Shadman"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9138351/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1038/s41597-026-07155-w","name":"A crowdsensing intrusion detection dataset for decentralized federated learning models.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41597-026-07155-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41597-026-07155-w","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.3390/e28060589","name":"DAG-CTFL: DAG Blockchain Cross-Layer Authentication Framework for Trustworthy IoV Federated Learning.","source":"pubmed","abstract":"Privacy-preserving federated learning in the internet of vehicles (IoV) requires low-latency authentication, bounded privacy leakage, and robustness against malicious model updates. However, most existing studies separately design communication authentication and federated learning protection, which leads to duplicated overhead and weak resistance to cross-layer attacks. To address this issue, this paper proposes a DAG blockchain-enabled cross-layer authentication framework for trustworthy IoV federated learning (DAG-CTFL). The framework reuses authentication operations across V2X message verification and model-update delivery, incorporates trust-aware batch verification, and organizes cross-layer evidence through a two-tier DAG blockchain. In addition, differential privacy is used to reduce information leakage from uploaded model updates, while cross-layer trust evaluation improves resilience against poisoning and forged-identity attacks. Experimental results on MNIST and CIFAR-10 show that DAG-CTFL reduces single-message verification overhead by 8.2-56.1%, lowers batch-verification latency by 19.2-56.4%, and maintains model accuracy above 85% under 15% malicious nodes. These results demonstrate that DAG-CTFL achieves an effective balance among privacy preservation, authentication efficiency, and cross-layer robustness in IoV federated learning.","url":"https://doi.org/10.3390/e28060589","authors":["Liao L","Chen L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/e28060589","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1109/tpami.2025.3646639","name":"Exploring the Vulnerabilities of Federated Learning: A Deep Dive Into Gradient Inversion Attacks.","source":"pubmed","abstract":"","url":"https://doi.org/10.1109/tpami.2025.3646639","authors":["Guo P","Wang R","Zeng S","Zhu J","Jiang H","Wang Y","Zhou Y","Wang F","Xiong H","Qu L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tpami.2025.3646639","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.2196/95788","name":"Correction: Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review.","source":"pubmed","abstract":"","url":"https://doi.org/10.2196/95788","authors":["Wang X","Xie Y","Chen X","Yang J","Li R","Gao W","Yan Z","Zhou H","Ye Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/95788","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-46244-z","name":"FedLiverNet: a federated learning framework for privacy-preserving and efficient liver cancer detection.","source":"pubmed","abstract":"Liver cancer continues to be a significant health issue on the global front, and proper segmentation of the liver and the tumor formed from the computed tomography is essential in the early diagnosis and subsequent treatment strategies. Although deep learning models can be trained to perform well in segmentation, the optimal way to train a strong model is with large, diverse datasets that may be distributed across institutions and cannot be centralized due to privacy and other regulatory restrictions. Federated learning enables joint training without exchanging patient data; however, performance may be poor on non-independent and identically distributed (non-IID) data, and privacy is a concern in optimization. This paper presents FedLiverNet, a communication-efficient and privacy-guaranteed federated liver and tumor segmentation system. FedLiverNet is a variant of the U-Net segmentation architecture that incorporates a modified backbone, differential privacy aggregation, and clustered federated learning with local adaptation to promote personalized support among heterogeneous clients. Simulation-based experiments indicate that FedLiverNet achieves a 0.89&#x2009;&#xb1;&#x2009;0.03 tumor Dice score and a 23% reduction in communication cost and is more effective than either federated averaging or local-only training under heterogeneous data distributions. These findings make FedLiverNet a viable solution to privacy-constrained, multi-center liver cancer detection and segmentation.","url":"https://doi.org/10.1038/s41598-026-46244-z","authors":["Lou L","Govindarajan V","Shaikh ZA","Liu R","Ayadi M","Wang H","Lv H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-46244-z","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.dib.2026.113111","name":"Dataset of round-level federated learning and layer-2 blockchain overhead from ten raspberry Pi edge clients.","source":"pubmed","abstract":"This data article describes round-level records from 40 federated learning sessions run on a physical testbed of ten Raspberry Pi 4 Model B devices acting as edge clients, with an Ubuntu workstation as the aggregation server. Each session trains a one-dimensional Squeeze-and-Excitation ResNet classifier over ten aggregation rounds using the Flower framework and commits every round to a smart contract on Base mainnet, a public Ethereum-compatible Layer-2 chain, while pinning the model and metric artifacts to IPFS through Pinata. The sessions cover two human activity recognition benchmarks, MHEALTH and UCI-HAR, and two middleware configurations: a baseline that performs blockchain and IPFS operations synchronously with per-round gas estimation, and an optimised variant that overlaps IPFS uploads with local training and reuses cached gas parameters, giving 20 sessions per benchmark. For every round the dataset records server-side global metrics (accuracy, F1, AUC, and the confusion matrix), per-device training telemetry (loss, training time, CPU, memory, and temperature), per-client evaluation records, and the IPFS content identifiers and transaction hashes that link each round to Base mainnet. For every session, millisecond-resolution logs record the latency of each blockchain and storage operation. The repository contains 12 CSV files, two archives of raw session directories, and a data dictionary. Researchers can reuse the convergence traces to benchmark edge federated learning, the device telemetry to characterise resource use on constrained hardware, and the latency distributions to parameterise simulations of blockchain-enabled federated learning without physical deployments or transaction fees. All 1240 blockchain transactions remain publicly verifiable on the Base mainnet explorer.","url":"https://doi.org/10.1016/j.dib.2026.113111","authors":["Bayan T","Mukhambetiyar B","Boranbayev A","Yazici A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.dib.2026.113111","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3389/fonc.2026.1720748","name":"Federated learning for privacy-preserving skin cancer classification using deep neural networks.","source":"pubmed","abstract":"Skin cancer is a significant health issue in the entire world and there is a need to have diagnostic systems that are precise, enlargeable and privacy safeguarding. The heterogeneity of the institution and the issue of patient confidentiality frequently restrict the use of centralized deep learning in the medical imaging field because of data-sharing limitations and heterogeneity across different institutions. The solution is federation learning (FL), which allows joint training of models without transfer of unprocessed clinical data.","url":"https://doi.org/10.3389/fonc.2026.1720748","authors":["Alfalahi MAM","Karan O","Kurnaz S","Türkben AK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1720748","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-46234-1","name":"IMFLKD: an incentive mechanism for decentralized federated learning based on knowledge distillation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-46234-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-46234-1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.5281/zenodo.21524770","name":"FedDAD: Federated Recommendation with Dual Additive Decoupling","source":"datacite","abstract":"Official implementation of FedDAD for federated learning research. This repository contains the source code to reproduce the experimental results in the paper Federated Recommendation with Dual Additive Decoupling.","url":"https://doi.org/10.5281/zenodo.21524770","authors":["Shi, Liuhao","Ni, Zhengwei"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21524770","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21524771","name":"FedDAD: Federated Recommendation with Dual Additive Decoupling","source":"datacite","abstract":"Official implementation of FedDAD for federated learning research. This repository contains the source code to reproduce the experimental results in the paper Federated Recommendation with Dual Additive Decoupling.","url":"https://doi.org/10.5281/zenodo.21524771","authors":["Shi, Liuhao","Ni, Zhengwei"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21524771","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22193676","name":"Decentralized Learning with Federated Bayesian Networks","source":"datacite","abstract":"This paper proposes a novel decentralized learning algorithm for Bayesian networks, termed Federated Bayesian Networks (FBNs). The core idea is to enable nodes within a network to learn independently and collaboratively, mirroring the principles of federated learning. Each node maintains its own Bayesian network and updates its parameters based on probabilistic information received from its neighbors. This approach avoids the need for centralized data aggregation, addressing key challenges associated with privacy and scalability in traditional Bayesian network learning. The algorithm iteratively refines both the network structure and its parameters, leading to a more accurate and robust global model. Mathematical formulations are presented to detail the update rules and convergence properties of the FBN algorithm. The key contribution lies in establishing a framework for distributed Bayesian network learning, particularly well-suited for scenarios with heterogeneous data and limited communication bandwidth. This work lays the foundation for applying FBNs to diverse applications, including healthcare, smart cities, and anomaly detection.","url":"https://doi.org/10.5281/zenodo.22193676","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22193676","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22193677","name":"Decentralized Learning with Federated Bayesian Networks","source":"datacite","abstract":"This paper proposes a novel decentralized learning algorithm for Bayesian networks, termed Federated Bayesian Networks (FBNs). The core idea is to enable nodes within a network to learn independently and collaboratively, mirroring the principles of federated learning. Each node maintains its own Bayesian network and updates its parameters based on probabilistic information received from its neighbors. This approach avoids the need for centralized data aggregation, addressing key challenges associated with privacy and scalability in traditional Bayesian network learning. The algorithm iteratively refines both the network structure and its parameters, leading to a more accurate and robust global model. Mathematical formulations are presented to detail the update rules and convergence properties of the FBN algorithm. The key contribution lies in establishing a framework for distributed Bayesian network learning, particularly well-suited for scenarios with heterogeneous data and limited communication bandwidth. This work lays the foundation for applying FBNs to diverse applications, including healthcare, smart cities, and anomaly detection.","url":"https://doi.org/10.5281/zenodo.22193677","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22193677","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21439282","name":"Federated Cognitive Networks","source":"datacite","abstract":"This record contains the complete reproducibility package for the paper \"Federated Cognitive Networks: Trust-Weighted Personalized Federated Learning for Heterogeneous Cross-Silo Clinical Data\" (Anas Shahin, Bahaa Masry; 2026). Federated Cognitive Networks (FCN) is a personalized federated learning method for non-IID cross-silo clinical data. Instead of training a single global model, each site keeps its own model and aggregates peers' updates using a learned, directional trust weight derived from a validation-gain probe (a peer that improves a site's held-out score earns more trust; one that hurts it earns less). An adaptive-participation rule lets a site stop communicating once federation stops helping it, reducing communication by 30–44% at equal accuracy. Contents:- FCN_Reproducibility.ipynb — a single, self-contained Jupyter notebook that reproduces every experiment, figure, and table in the paper. Part A (Fed-Heart-Disease, tabular, CPU) covers robustness, trust dynamics, communication, and the trust ablation; Part B (Fed-Heart-Disease, CPU) provides the main 10-seed comparison against tuned Ditto and pFedMe baselines with significance tests; Part C (Fed-ISIC2019, dermoscopy imaging, GPU) trains EfficientNet-B0 across six clinical centers. Each part is self-contained and runs on Google Colab or locally.- README.md — setup instructions, hardware requirements, data sources, a results summary, and citation details. Both benchmarks are public and downloaded automatically by the notebook via the FLamby suite (Fed-Heart-Disease from the UCI archive; Fed-ISIC2019 from the ISIC 2019 challenge). No private data is included. Honest scope: FCN's contribution is personalization under natural, benign heterogeneity. It is not a poisoning defense — under label-flip attacks FedAvg is more robust and trust weighting adds little; robust aggregation is identified as future work. This limitation is reproduced faithfully in the notebook.","url":"https://doi.org/10.5281/zenodo.21439282","authors":["Shahin, Anas","Masry, Bahaa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21439282","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21439283","name":"Federated Cognitive Networks","source":"datacite","abstract":"This record contains the complete reproducibility package for the paper \"Federated Cognitive Networks: Trust-Weighted Personalized Federated Learning for Heterogeneous Cross-Silo Clinical Data\" (Anas Shahin, Bahaa Masry; 2026). Federated Cognitive Networks (FCN) is a personalized federated learning method for non-IID cross-silo clinical data. Instead of training a single global model, each site keeps its own model and aggregates peers' updates using a learned, directional trust weight derived from a validation-gain probe (a peer that improves a site's held-out score earns more trust; one that hurts it earns less). An adaptive-participation rule lets a site stop communicating once federation stops helping it, reducing communication by 30–44% at equal accuracy. Contents:- FCN_Reproducibility.ipynb — a single, self-contained Jupyter notebook that reproduces every experiment, figure, and table in the paper. Part A (Fed-Heart-Disease, tabular, CPU) covers robustness, trust dynamics, communication, and the trust ablation; Part B (Fed-Heart-Disease, CPU) provides the main 10-seed comparison against tuned Ditto and pFedMe baselines with significance tests; Part C (Fed-ISIC2019, dermoscopy imaging, GPU) trains EfficientNet-B0 across six clinical centers. Each part is self-contained and runs on Google Colab or locally.- README.md — setup instructions, hardware requirements, data sources, a results summary, and citation details. Both benchmarks are public and downloaded automatically by the notebook via the FLamby suite (Fed-Heart-Disease from the UCI archive; Fed-ISIC2019 from the ISIC 2019 challenge). No private data is included. Honest scope: FCN's contribution is personalization under natural, benign heterogeneity. It is not a poisoning defense — under label-flip attacks FedAvg is more robust and trust weighting adds little; robust aggregation is identified as future work. This limitation is reproduced faithfully in the notebook.","url":"https://doi.org/10.5281/zenodo.21439283","authors":["Shahin, Anas","Masry, Bahaa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21439283","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21553574","name":"Privacy-Preserving Techniques for Secure Cloud Computing : A Survey of Recent Advances","source":"datacite","abstract":"Cloud computing has gained immense popularity in recent years due to its on-demand and scalable computing resources. However, with the growth of cloud computing, privacy and security concerns have also increased. The primary concern is how to ensure the confidentiality and integrity of data in the cloud, as the data is stored on third-party servers. To address these concerns, various privacy-preserving techniques have been proposed, which allow users to store and process their data in the cloud without compromising privacy and security. We provide a thorough overview of current developments in privacy-preserving methods for safe cloud computing in this study. We start by giving a general review of cloud computing and the security issues it presents. Then, we go over a variety of privacy-preserving methods, such as differential privacy, homomorphic encryption, secure outsourcing, and secure multi-party computation. We also highlight their advantages and limitations. Finally, we conclude with some future research directions in privacy-preserving cloud computing.","url":"https://doi.org/10.5281/zenodo.21553574","authors":["Savitha, N.","Kiran, Dr. E. Sai"],"tags":["Privacy-preserving techniques","Secure cloud computing","federated learning","homomorphic encryption","secure multi-party computation","and differential privacy."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21553574","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21553575","name":"Privacy-Preserving Techniques for Secure Cloud Computing : A Survey of Recent Advances","source":"datacite","abstract":"Cloud computing has gained immense popularity in recent years due to its on-demand and scalable computing resources. However, with the growth of cloud computing, privacy and security concerns have also increased. The primary concern is how to ensure the confidentiality and integrity of data in the cloud, as the data is stored on third-party servers. To address these concerns, various privacy-preserving techniques have been proposed, which allow users to store and process their data in the cloud without compromising privacy and security. We provide a thorough overview of current developments in privacy-preserving methods for safe cloud computing in this study. We start by giving a general review of cloud computing and the security issues it presents. Then, we go over a variety of privacy-preserving methods, such as differential privacy, homomorphic encryption, secure outsourcing, and secure multi-party computation. We also highlight their advantages and limitations. Finally, we conclude with some future research directions in privacy-preserving cloud computing.","url":"https://doi.org/10.5281/zenodo.21553575","authors":["Savitha, N.","Kiran, Dr. E. Sai"],"tags":["Privacy-preserving techniques","Secure cloud computing","federated learning","homomorphic encryption","secure multi-party computation","and differential privacy."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21553575","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21552485","name":"A Conceptual Model for Intelligent Automation Loops in High-Throughput, Multi-Phase Crude Processing Units","source":"datacite","abstract":"The increasing complexity and scale of high-throughput, multi-phase crude processing units (CUs) in modern refineries necessitate the development of advanced automation strategies beyond conventional control loops. Traditional systems, typically based on fixed-logic PID controllers and static setpoint optimization, are often inadequate in dealing with nonlinear process dynamics, rapid phase changes, and operational variability inherent in multi-phase crude streams. This proposes a conceptual model for intelligent automation loops that leverage real-time data, adaptive control strategies, and artificial intelligence (AI) to optimize the operation of such units. The proposed model integrates several key components: a distributed sensor network for high-fidelity, multi-phase flow data acquisition; dynamic process modeling using hybrid techniques (first-principles and machine learning); and intelligent control algorithms capable of self-tuning, fault detection, and optimization under changing operating conditions. Digital twin technology is incorporated to simulate and validate control actions in real-time, while edge computing infrastructure supports low-latency decision-making and reduces reliance on centralized systems. Use cases such as adaptive separator control, slug flow management, and real-time energy efficiency optimization are used to demonstrate the model's applicability and effectiveness. The intelligent loop framework enables predictive behavior, reduces manual intervention, and enhances operational resilience leading to reduced downtime, improved throughput, and better resource utilization. Importantly, the model supports integration with existing Distributed Control Systems (DCS) and Supervisory Control and Data Acquisition (SCADA) systems, enabling phased implementation and minimal disruption to ongoing operations. This conceptual framework addresses key challenges in refinery automation, including legacy integration, cybersecurity, and data quality. Future research will focus on full autonomy, federated AI deployment across refinery assets, and standardization of intelligent loop architectures. The proposed model represents a strategic step toward smarter, safer, and more efficient crude processing in the era of Industry 4.0.","url":"https://doi.org/10.5281/zenodo.21552485","authors":["Ofoedu, Andrew Tochukwu","Ozor, Joshua Emeka","Sofoluwe, Oludayo","Jambol, Dazok Donald"],"tags":["Conceptual Model","Intelligent Automation","Loops","High-Throughput","Multi-Phase","Crude Processing Units"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21552485","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21552486","name":"A Conceptual Model for Intelligent Automation Loops in High-Throughput, Multi-Phase Crude Processing Units","source":"datacite","abstract":"The increasing complexity and scale of high-throughput, multi-phase crude processing units (CUs) in modern refineries necessitate the development of advanced automation strategies beyond conventional control loops. Traditional systems, typically based on fixed-logic PID controllers and static setpoint optimization, are often inadequate in dealing with nonlinear process dynamics, rapid phase changes, and operational variability inherent in multi-phase crude streams. This proposes a conceptual model for intelligent automation loops that leverage real-time data, adaptive control strategies, and artificial intelligence (AI) to optimize the operation of such units. The proposed model integrates several key components: a distributed sensor network for high-fidelity, multi-phase flow data acquisition; dynamic process modeling using hybrid techniques (first-principles and machine learning); and intelligent control algorithms capable of self-tuning, fault detection, and optimization under changing operating conditions. Digital twin technology is incorporated to simulate and validate control actions in real-time, while edge computing infrastructure supports low-latency decision-making and reduces reliance on centralized systems. Use cases such as adaptive separator control, slug flow management, and real-time energy efficiency optimization are used to demonstrate the model's applicability and effectiveness. The intelligent loop framework enables predictive behavior, reduces manual intervention, and enhances operational resilience leading to reduced downtime, improved throughput, and better resource utilization. Importantly, the model supports integration with existing Distributed Control Systems (DCS) and Supervisory Control and Data Acquisition (SCADA) systems, enabling phased implementation and minimal disruption to ongoing operations. This conceptual framework addresses key challenges in refinery automation, including legacy integration, cybersecurity, and data quality. Future research will focus on full autonomy, federated AI deployment across refinery assets, and standardization of intelligent loop architectures. The proposed model represents a strategic step toward smarter, safer, and more efficient crude processing in the era of Industry 4.0.","url":"https://doi.org/10.5281/zenodo.21552486","authors":["Ofoedu, Andrew Tochukwu","Ozor, Joshua Emeka","Sofoluwe, Oludayo","Jambol, Dazok Donald"],"tags":["Conceptual Model","Intelligent Automation","Loops","High-Throughput","Multi-Phase","Crude Processing Units"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21552486","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21550657","name":"Enhanced Brain Tumor Detection and Privacy Preserving Using Federated Learning","source":"datacite","abstract":"Brain cancers pose significant difficulties for both diagnosis and treatment, underscoring the necessity for precise and private-protecting detection techniques. Federated learning is used to solve this, allowing several healthcare facilities to work together to train detection models without jeopardizing patient privacy. This paper presents an approach called federated learning that may be used to improve brain tumor identification while protecting patient privacy. Brain tumors are dangerous medical disorders that need to be accurately diagnosed in order to be effectively treated. However, sharing private patient information is a common practice in traditional medical data analysis methodologies, which raises privacy issues. Federated learning helps with this by enabling cooperative training of a common model amongst several hospitals or institutions without requiring the exchange of raw data. This method protects patient privacy by having each institution train the model using its own local data and only sharing model updates. We show through trials that our method is efficient in reliably identifying brain tumors while upholding privacy norms, presenting a viable option for improving medical diagnosis without jeopardizing patient privacy.","url":"https://doi.org/10.5281/zenodo.21550657","authors":["Nandan, Uday","Sai, Chetan","Sai, Naga","Viswanadapalli, Anusha"],"tags":["CNN; Deep learning; Brain Tumor; Densenet121; Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.21550657","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21550658","name":"Enhanced Brain Tumor Detection and Privacy Preserving Using Federated Learning","source":"datacite","abstract":"Brain cancers pose significant difficulties for both diagnosis and treatment, underscoring the necessity for precise and private-protecting detection techniques. Federated learning is used to solve this, allowing several healthcare facilities to work together to train detection models without jeopardizing patient privacy. This paper presents an approach called federated learning that may be used to improve brain tumor identification while protecting patient privacy. Brain tumors are dangerous medical disorders that need to be accurately diagnosed in order to be effectively treated. However, sharing private patient information is a common practice in traditional medical data analysis methodologies, which raises privacy issues. Federated learning helps with this by enabling cooperative training of a common model amongst several hospitals or institutions without requiring the exchange of raw data. This method protects patient privacy by having each institution train the model using its own local data and only sharing model updates. We show through trials that our method is efficient in reliably identifying brain tumors while upholding privacy norms, presenting a viable option for improving medical diagnosis without jeopardizing patient privacy.","url":"https://doi.org/10.5281/zenodo.21550658","authors":["Nandan, Uday","Sai, Chetan","Sai, Naga","Viswanadapalli, Anusha"],"tags":["CNN; Deep learning; Brain Tumor; Densenet121; Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.21550658","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21549002","name":"Enhancing Database Architectures with Artificial Intelligence (AI)","source":"datacite","abstract":"Artificial intelligence and Database Management Systems Integration bring intelligence, adaptability, and independence in the world of databases. Relational database management systems structure the data and have been the foundations for implementing them, although they face several challenges that have arisen from modern-day environments of computing and information processing, such as scalability, real-time processing, the incorporation of unstructured data, and capabilities for making proactive decisions. As a result, new approaches like NoSQL and NewSQL appeared to address various and scalable needs of the applications. AI concepts such as Machine learning (ML), Deep learning (DL), and Natural language processing (NLP) have brought about improvement of advanced functions and optimization of efficiency into current database systems. These are self-tuning, query optimization, predictive caching, and natural language interfaces that enable a database to work autonomously while offering high-performance and reliability service. This paper focuses on the traditional and advanced DBMS architectures, the development and integration of AI-based DBMS, and other novelties such as federated learning and reinforcement-based cache.","url":"https://doi.org/10.5281/zenodo.21549002","authors":["Maddali, Gopikrishna"],"tags":["Artificial Intelligence; Database Management System; AI-DBMS Integration; NoSQL; NewSQL; Intelligent Databases; Query Optimization"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21549002","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21549003","name":"Enhancing Database Architectures with Artificial Intelligence (AI)","source":"datacite","abstract":"Artificial intelligence and Database Management Systems Integration bring intelligence, adaptability, and independence in the world of databases. Relational database management systems structure the data and have been the foundations for implementing them, although they face several challenges that have arisen from modern-day environments of computing and information processing, such as scalability, real-time processing, the incorporation of unstructured data, and capabilities for making proactive decisions. As a result, new approaches like NoSQL and NewSQL appeared to address various and scalable needs of the applications. AI concepts such as Machine learning (ML), Deep learning (DL), and Natural language processing (NLP) have brought about improvement of advanced functions and optimization of efficiency into current database systems. These are self-tuning, query optimization, predictive caching, and natural language interfaces that enable a database to work autonomously while offering high-performance and reliability service. This paper focuses on the traditional and advanced DBMS architectures, the development and integration of AI-based DBMS, and other novelties such as federated learning and reinforcement-based cache.","url":"https://doi.org/10.5281/zenodo.21549003","authors":["Maddali, Gopikrishna"],"tags":["Artificial Intelligence; Database Management System; AI-DBMS Integration; NoSQL; NewSQL; Intelligent Databases; Query Optimization"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21549003","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22189141","name":"Distributed Knowledge Graph Embedding with Federated Learning for Privacy Preservation","source":"datacite","abstract":"Knowledge graph embedding techniques have gained significant traction in representing complex relationships within knowledge graphs, enabling applications such as link prediction, entity recommendation, and semantic search. However, the training of these embeddings often relies on consolidating vast amounts of data from disparate sources, leading to substantial privacy risks. This paper proposes a novel approach to distributed knowledge graph embedding using federated learning, designed to mitigate these privacy concerns. We introduce a framework where multiple data sources independently train local knowledge graph embeddings. These local models are then aggregated using federated learning algorithms, resulting in a global knowledge graph embedding model without direct data sharing. The proposed method aims to balance embedding quality with robust privacy protection. We detail the technical aspects of the framework, including the selection of appropriate federated learning algorithms and strategies for addressing potential heterogeneity in data distributions. Experimental considerations and future research directions are also discussed.","url":"https://doi.org/10.5281/zenodo.22189141","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22189141","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22189142","name":"Distributed Knowledge Graph Embedding with Federated Learning for Privacy Preservation","source":"datacite","abstract":"Knowledge graph embedding techniques have gained significant traction in representing complex relationships within knowledge graphs, enabling applications such as link prediction, entity recommendation, and semantic search. However, the training of these embeddings often relies on consolidating vast amounts of data from disparate sources, leading to substantial privacy risks. This paper proposes a novel approach to distributed knowledge graph embedding using federated learning, designed to mitigate these privacy concerns. We introduce a framework where multiple data sources independently train local knowledge graph embeddings. These local models are then aggregated using federated learning algorithms, resulting in a global knowledge graph embedding model without direct data sharing. The proposed method aims to balance embedding quality with robust privacy protection. We detail the technical aspects of the framework, including the selection of appropriate federated learning algorithms and strategies for addressing potential heterogeneity in data distributions. Experimental considerations and future research directions are also discussed.","url":"https://doi.org/10.5281/zenodo.22189142","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22189142","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22188460","name":"Distributed Bayesian Inference with Federated Learning and Differential Privacy","source":"datacite","abstract":"This paper presents a novel approach to distributed Bayesian inference that leverages the strengths of federated learning and differential privacy. The core idea is to execute Bayesian inference locally on a network of devices, aggregating updates while simultaneously protecting individual privacy. We introduce a framework where each device independently performs Bayesian inference on its local data and then adds noise to the updates based on differential privacy guarantees. This ensures that no single device's contribution can be identified, thus preserving privacy. The resulting system achieves accurate Bayesian inference across a decentralized network, offering a practical solution for privacy-sensitive data analysis. This work provides a new method for distributed learning which combines Bayesian inference, federated learning and differential privacy.","url":"https://doi.org/10.5281/zenodo.22188460","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22188460","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22188459","name":"Distributed Bayesian Inference with Federated Learning and Differential Privacy","source":"datacite","abstract":"This paper presents a novel approach to distributed Bayesian inference that leverages the strengths of federated learning and differential privacy. The core idea is to execute Bayesian inference locally on a network of devices, aggregating updates while simultaneously protecting individual privacy. We introduce a framework where each device independently performs Bayesian inference on its local data and then adds noise to the updates based on differential privacy guarantees. This ensures that no single device's contribution can be identified, thus preserving privacy. The resulting system achieves accurate Bayesian inference across a decentralized network, offering a practical solution for privacy-sensitive data analysis. This work provides a new method for distributed learning which combines Bayesian inference, federated learning and differential privacy.","url":"https://doi.org/10.5281/zenodo.22188459","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22188459","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21528591","name":"Secure Parameter Aggregation and Distributed Anomaly Detection in Cross-Cloud Data Center Networks","source":"datacite","abstract":"This study proposes an intelligent method based on federated learning to address the problem of anomaly detection in distributed cross-cloud data center environments. The research first analyzes the complexity and risks in multi-tenant shared computing scenarios and points out the limitations of traditional centralized detection methods, including high bandwidth consumption, privacy leakage risks, and poor adaptability to heterogeneous data in large-scale distributed architectures. To solve these issues, cross-cloud data centers are modeled as a collaborative system composed of multiple autonomous nodes. Each node performs local data preprocessing and feature modeling, while secure parameter sharing and global aggregation are achieved through a federated learning framework. In method design, the framework integrates multi-layer representation encoding with local optimization, enabling the model to capture complex cross-tenant interactions without exposing raw data. A global weighted aggregation strategy further improves robustness and generalization under heterogeneous data distributions. The anomaly detection task is formulated as a joint optimization of latent feature representation and reconstruction error, and precise anomaly identification is achieved through global parameter updates. Overall analysis shows that the proposed method balances privacy protection and detection accuracy, alleviates communication burdens in cross-data center collaboration, and provides reliable support for the secure operation of critical systems across multiple industries.","url":"https://doi.org/10.5281/zenodo.21528591","authors":["Zhang, Shirui"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.21528591","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21528592","name":"Secure Parameter Aggregation and Distributed Anomaly Detection in Cross-Cloud Data Center Networks","source":"datacite","abstract":"This study proposes an intelligent method based on federated learning to address the problem of anomaly detection in distributed cross-cloud data center environments. The research first analyzes the complexity and risks in multi-tenant shared computing scenarios and points out the limitations of traditional centralized detection methods, including high bandwidth consumption, privacy leakage risks, and poor adaptability to heterogeneous data in large-scale distributed architectures. To solve these issues, cross-cloud data centers are modeled as a collaborative system composed of multiple autonomous nodes. Each node performs local data preprocessing and feature modeling, while secure parameter sharing and global aggregation are achieved through a federated learning framework. In method design, the framework integrates multi-layer representation encoding with local optimization, enabling the model to capture complex cross-tenant interactions without exposing raw data. A global weighted aggregation strategy further improves robustness and generalization under heterogeneous data distributions. The anomaly detection task is formulated as a joint optimization of latent feature representation and reconstruction error, and precise anomaly identification is achieved through global parameter updates. Overall analysis shows that the proposed method balances privacy protection and detection accuracy, alleviates communication burdens in cross-data center collaboration, and provides reliable support for the secure operation of critical systems across multiple industries.","url":"https://doi.org/10.5281/zenodo.21528592","authors":["Zhang, Shirui"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.21528592","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22187282","name":"Decentralized Federated Learning with Differential Privacy for Scientific Data","source":"datacite","abstract":"This paper presents a novel approach to collaborative scientific data analysis leveraging Decentralized Federated Learning with Differential Privacy (DFLDP). The core challenge in many scientific domains is the reluctance to share raw data due to stringent privacy regulations and intellectual property protections. Traditional Federated Learning (FL) solutions, while offering a degree of data privacy, still rely on centralized aggregation, a point of vulnerability. Our proposed DFLDP framework addresses this limitation by adopting a decentralized architecture where individual researchers maintain complete control over their datasets. Crucially, we integrate differential privacy mechanisms directly into the aggregation process, adding a quantifiable layer of protection against data leakage. This ensures that the learned model benefits from the collective knowledge of multiple researchers without revealing individual data contributions. The system utilizes a gossip-based communication protocol for model updates, minimizing communication overhead. We formally define the mathematical framework, outlining the key components and their interactions. The system's performance is evaluated in a simulated environment, demonstrating the effectiveness of the DFLDP approach in achieving accurate models while upholding stringent privacy guarantees. The core claim of this work is that sharing raw scientific data for federated learning is often prohibited due to privacy concerns and intellectual property restrictions. The core mechanism implemented is the realization of a federated learning system that utilizes differential privacy to protect data during aggregation, while also employing a decentralized architecture where individual researchers retain control over their data. This new approach combines federated learning with differential privacy and decentralization, enabling collaborative scientific discovery without compromising data privacy or intellectual property rights.","url":"https://doi.org/10.5281/zenodo.22187282","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22187282","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22187283","name":"Decentralized Federated Learning with Differential Privacy for Scientific Data","source":"datacite","abstract":"This paper presents a novel approach to collaborative scientific data analysis leveraging Decentralized Federated Learning with Differential Privacy (DFLDP). The core challenge in many scientific domains is the reluctance to share raw data due to stringent privacy regulations and intellectual property protections. Traditional Federated Learning (FL) solutions, while offering a degree of data privacy, still rely on centralized aggregation, a point of vulnerability. Our proposed DFLDP framework addresses this limitation by adopting a decentralized architecture where individual researchers maintain complete control over their datasets. Crucially, we integrate differential privacy mechanisms directly into the aggregation process, adding a quantifiable layer of protection against data leakage. This ensures that the learned model benefits from the collective knowledge of multiple researchers without revealing individual data contributions. The system utilizes a gossip-based communication protocol for model updates, minimizing communication overhead. We formally define the mathematical framework, outlining the key components and their interactions. The system's performance is evaluated in a simulated environment, demonstrating the effectiveness of the DFLDP approach in achieving accurate models while upholding stringent privacy guarantees. The core claim of this work is that sharing raw scientific data for federated learning is often prohibited due to privacy concerns and intellectual property restrictions. The core mechanism implemented is the realization of a federated learning system that utilizes differential privacy to protect data during aggregation, while also employing a decentralized architecture where individual researchers retain control over their data. This new approach combines federated learning with differential privacy and decentralization, enabling collaborative scientific discovery without compromising data privacy or intellectual property rights.","url":"https://doi.org/10.5281/zenodo.22187283","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22187283","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22186523","name":"Decentralized Federated Learning with Byzantine Fault Tolerance via Graph-Based Consensus","source":"datacite","abstract":"Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the data itself. However, this approach is susceptible to Byzantine attacks, where malicious nodes intentionally send incorrect model updates to disrupt the learning process. This paper introduces a novel decentralized federated learning framework incorporating Byzantine fault tolerance through a graph-based consensus mechanism. The core idea is to represent the federated network as a graph, enabling nodes to communicate and reach agreement on model updates via graph traversal and message passing. This approach allows the system to tolerate the presence of malicious nodes, ensuring the convergence of the global model. The proposed method provides a robust and secure solution for decentralized FL, addressing a critical vulnerability in existing FL systems. Key contributions include a formalization of the problem, a detailed description of the graph-based consensus algorithm, and an analysis of its resilience against Byzantine attacks. The system's performance is evaluated theoretically, demonstrating its effectiveness in mitigating the impact of adversarial nodes.","url":"https://doi.org/10.5281/zenodo.22186523","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22186523","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22186522","name":"Decentralized Federated Learning with Byzantine Fault Tolerance via Graph-Based Consensus","source":"datacite","abstract":"Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the data itself. However, this approach is susceptible to Byzantine attacks, where malicious nodes intentionally send incorrect model updates to disrupt the learning process. This paper introduces a novel decentralized federated learning framework incorporating Byzantine fault tolerance through a graph-based consensus mechanism. The core idea is to represent the federated network as a graph, enabling nodes to communicate and reach agreement on model updates via graph traversal and message passing. This approach allows the system to tolerate the presence of malicious nodes, ensuring the convergence of the global model. The proposed method provides a robust and secure solution for decentralized FL, addressing a critical vulnerability in existing FL systems. Key contributions include a formalization of the problem, a detailed description of the graph-based consensus algorithm, and an analysis of its resilience against Byzantine attacks. The system's performance is evaluated theoretically, demonstrating its effectiveness in mitigating the impact of adversarial nodes.","url":"https://doi.org/10.5281/zenodo.22186522","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22186522","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22186426","name":"Information-Theoretic Framework for Trustworthy Federated Learning","source":"datacite","abstract":"Federated Learning (FL) offers a promising paradigm for decentralized machine learning, enabling collaborative model training without direct data sharing. However, traditional privacy-preserving techniques within FL often rely on ad-hoc assumptions and lack a rigorous theoretical basis. This work introduces an information-theoretic framework to address this limitation. We define a \"Privacy Loss Function\" predicated on mutual information between local models and global updates, providing a quantifiable measure of information leakage. The framework leverages established techniques such as differential privacy and homomorphic encryption to minimize this loss, ultimately leading to more robust and trustworthy FL systems. Our approach moves beyond intuitive notions of privacy, offering a mathematically sound foundation for designing and analyzing FL protocols, facilitating the development of truly secure and efficient distributed learning solutions. The core contribution is the formalization of privacy risk in FL using information-theoretic principles, enabling a more precise understanding and control over data leakage.","url":"https://doi.org/10.5281/zenodo.22186426","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22186426","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22186427","name":"Information-Theoretic Framework for Trustworthy Federated Learning","source":"datacite","abstract":"Federated Learning (FL) offers a promising paradigm for decentralized machine learning, enabling collaborative model training without direct data sharing. However, traditional privacy-preserving techniques within FL often rely on ad-hoc assumptions and lack a rigorous theoretical basis. This work introduces an information-theoretic framework to address this limitation. We define a \"Privacy Loss Function\" predicated on mutual information between local models and global updates, providing a quantifiable measure of information leakage. The framework leverages established techniques such as differential privacy and homomorphic encryption to minimize this loss, ultimately leading to more robust and trustworthy FL systems. Our approach moves beyond intuitive notions of privacy, offering a mathematically sound foundation for designing and analyzing FL protocols, facilitating the development of truly secure and efficient distributed learning solutions. The core contribution is the formalization of privacy risk in FL using information-theoretic principles, enabling a more precise understanding and control over data leakage.","url":"https://doi.org/10.5281/zenodo.22186427","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22186427","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21284659","name":"Radiomics and Machine Learning for Predicting Immunotherapy Response in Solid Tumors","source":"datacite","abstract":"Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has transformed the treatment landscape for numerous solid tumors, including non-small cell lung cancer (NSCLC), melanoma, renal cell carcinoma, hepatocellular carcinoma, and urothelial carcinoma. Despite remarkable clinical successes, only a subset of patients derives durable benefit, highlighting the need for reliable predictive biomarkers. Current biomarkers such as programmed death-ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and microsatellite instability (MSI) demonstrate limited predictive accuracy and are constrained by spatial and temporal tumor heterogeneity. Radiomics has emerged as a promising non-invasive approach capable of extracting quantitative imaging biomarkers that capture intratumoral heterogeneity and the tumor microenvironment. Concurrent advances in machine learning have enabled the development of predictive models that integrate radiomic, clinical, pathological, and molecular data for individualized response prediction. Recent studies have demonstrated that radiomics-based machine learning models can predict immunotherapy response, durable clinical benefit, progression-free survival, and overall survival across multiple solid tumors. Furthermore, emerging approaches such as delta-radiomics, radiogenomics, deep learning, and multimodal learning are expanding the predictive capabilities of imaging biomarkers. However, substantial challenges remain, including lack of standardization, limited external validation, reproducibility concerns, small datasets, and regulatory barriers. This review critically examines the biological rationale underlying immunotherapy response prediction, discusses methodological foundations of radiomics and machine learning, evaluates current evidence across major solid tumors, and explores future opportunities involving foundation models, federated learning, and multi-omics integration. The convergence of radiomics, artificial intelligence, and precision oncology has the potential to significantly improve patient selection for immunotherapy and facilitate personalized cancer treatment strategies.","url":"https://doi.org/10.5281/zenodo.21284659","authors":["Aditya Verma*1, Shatrughna Nagrik2, Priya Krishnan3, Rohit Chatterjee4"],"tags":["Radiomics; Machine learning; Immunotherapy; Immune checkpoint inhibitors; Precision oncology; Radiogenomics; Artificial intelligence; Solid tumors."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21284659","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.21284660","name":"Radiomics and Machine Learning for Predicting Immunotherapy Response in Solid Tumors","source":"datacite","abstract":"Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has transformed the treatment landscape for numerous solid tumors, including non-small cell lung cancer (NSCLC), melanoma, renal cell carcinoma, hepatocellular carcinoma, and urothelial carcinoma. Despite remarkable clinical successes, only a subset of patients derives durable benefit, highlighting the need for reliable predictive biomarkers. Current biomarkers such as programmed death-ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and microsatellite instability (MSI) demonstrate limited predictive accuracy and are constrained by spatial and temporal tumor heterogeneity. Radiomics has emerged as a promising non-invasive approach capable of extracting quantitative imaging biomarkers that capture intratumoral heterogeneity and the tumor microenvironment. Concurrent advances in machine learning have enabled the development of predictive models that integrate radiomic, clinical, pathological, and molecular data for individualized response prediction. Recent studies have demonstrated that radiomics-based machine learning models can predict immunotherapy response, durable clinical benefit, progression-free survival, and overall survival across multiple solid tumors. Furthermore, emerging approaches such as delta-radiomics, radiogenomics, deep learning, and multimodal learning are expanding the predictive capabilities of imaging biomarkers. However, substantial challenges remain, including lack of standardization, limited external validation, reproducibility concerns, small datasets, and regulatory barriers. This review critically examines the biological rationale underlying immunotherapy response prediction, discusses methodological foundations of radiomics and machine learning, evaluates current evidence across major solid tumors, and explores future opportunities involving foundation models, federated learning, and multi-omics integration. The convergence of radiomics, artificial intelligence, and precision oncology has the potential to significantly improve patient selection for immunotherapy and facilitate personalized cancer treatment strategies.","url":"https://doi.org/10.5281/zenodo.21284660","authors":["Aditya Verma*1, Shatrughna Nagrik2, Priya Krishnan3, Rohit Chatterjee4"],"tags":["Radiomics; Machine learning; Immunotherapy; Immune checkpoint inhibitors; Precision oncology; Radiogenomics; Artificial intelligence; Solid tumors."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21284660","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.21555490","name":"A Novel Framework for Trustworthy Privacy Preserving Machine Learning Model for Industrial IoT Systems Using Blockchain Techniques","source":"datacite","abstract":"Industrial Internet of Things (IIoT) is changing many driving enterprises like transportation, mining, horticulture, energy and medical care. Machine Learning calculations are utilized for getting stages for IT frameworks. The IoT network unit hubs typically asset in a strange manner by making them more responsible to digital assaults. IIoT frameworks requests various situations in genuine one among them is giving security and the causes that encompass them in true viewpoints. It incorporates a system called PriModChain causes security and reliability on IIoT information by joining differential protection, Ethereum block chain and unified Machine learning. Consequently, security will be compromised and we use PriMod chain for giving protection and different compliances and created utilizing Python with attachment programming on essential PC.","url":"https://doi.org/10.5281/zenodo.21555490","authors":["Yedukondalu, Dr. G.","Rao, Dr. Channapragada Rama Seshagiri","Dugyala, Raman"],"tags":["IIoT trustworthiness","blockchains","Ethereum","federated learning","differential privacy","IPFS."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.21555490","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21555491","name":"A Novel Framework for Trustworthy Privacy Preserving Machine Learning Model for Industrial IoT Systems Using Blockchain Techniques","source":"datacite","abstract":"Industrial Internet of Things (IIoT) is changing many driving enterprises like transportation, mining, horticulture, energy and medical care. Machine Learning calculations are utilized for getting stages for IT frameworks. The IoT network unit hubs typically asset in a strange manner by making them more responsible to digital assaults. IIoT frameworks requests various situations in genuine one among them is giving security and the causes that encompass them in true viewpoints. It incorporates a system called PriModChain causes security and reliability on IIoT information by joining differential protection, Ethereum block chain and unified Machine learning. Consequently, security will be compromised and we use PriMod chain for giving protection and different compliances and created utilizing Python with attachment programming on essential PC.","url":"https://doi.org/10.5281/zenodo.21555491","authors":["Yedukondalu, Dr. G.","Rao, Dr. Channapragada Rama Seshagiri","Dugyala, Raman"],"tags":["IIoT trustworthiness","blockchains","Ethereum","federated learning","differential privacy","IPFS."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.21555491","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21685695","name":"Privacy-Preserving Data Processing in Cloud : From Homomorphic Encryption to Federated Analytics","source":"datacite","abstract":"Privacy-preserving data processing refers to the methods and models that allow computing and analyzing sensitive data with a guarantee of confidentiality. As cloud computing and applications that rely on data continue to expand, there is an increasing need to protect personal, financial and healthcare information. Conventional centralized data processing methods expose sensitive data to risk of breaches, compelling the need to use decentralized and secure data methods. This paper gives a detailed review of privacy-saving mechanisms in the cloud platform, such as statistical approaches like differential privacy and cryptographic solutions like homomorphic encryption. Federated analytics and federated learning, two distributed learning frameworks, are also discussed. Their principles, applications, benefits, and limitations are reviewed, with roles of use in the fields of healthcare, finance, IoT, and industrial cases. Comparative analyses measure trade-offs in security, efficiency, scalability, and accuracy, and investigations are done of emerging hybrid frameworks to provide better privacy protection. Critical issues, including computational overhead, privacy-utility trade-offs, standardization, adversarial threats, and cloud integration are also addressed. This review examines in detail the recent privacy-protecting approaches in cloud computation and offers scholars and practitioners crucial information on secure and effective solutions to data processing.","url":"https://doi.org/10.5281/zenodo.21685695","authors":["Sarraf, Gaurav","Pal, Vibhor"],"tags":["Privacy-Preserving","Homomorphic Encryption","Secure Multi-Party Computation","Differential Privacy","Federated Analytics","Cloud Computing","Data Security","Hybrid Privacy Frameworks."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.21685695","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21685696","name":"Privacy-Preserving Data Processing in Cloud : From Homomorphic Encryption to Federated Analytics","source":"datacite","abstract":"Privacy-preserving data processing refers to the methods and models that allow computing and analyzing sensitive data with a guarantee of confidentiality. As cloud computing and applications that rely on data continue to expand, there is an increasing need to protect personal, financial and healthcare information. Conventional centralized data processing methods expose sensitive data to risk of breaches, compelling the need to use decentralized and secure data methods. This paper gives a detailed review of privacy-saving mechanisms in the cloud platform, such as statistical approaches like differential privacy and cryptographic solutions like homomorphic encryption. Federated analytics and federated learning, two distributed learning frameworks, are also discussed. Their principles, applications, benefits, and limitations are reviewed, with roles of use in the fields of healthcare, finance, IoT, and industrial cases. Comparative analyses measure trade-offs in security, efficiency, scalability, and accuracy, and investigations are done of emerging hybrid frameworks to provide better privacy protection. Critical issues, including computational overhead, privacy-utility trade-offs, standardization, adversarial threats, and cloud integration are also addressed. This review examines in detail the recent privacy-protecting approaches in cloud computation and offers scholars and practitioners crucial information on secure and effective solutions to data processing.","url":"https://doi.org/10.5281/zenodo.21685696","authors":["Sarraf, Gaurav","Pal, Vibhor"],"tags":["Privacy-Preserving","Homomorphic Encryption","Secure Multi-Party Computation","Differential Privacy","Federated Analytics","Cloud Computing","Data Security","Hybrid Privacy Frameworks."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.21685696","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22184142","name":"Blockchain-Based Federated Learning with Differential Privacy","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL systems remain vulnerable to privacy breaches and data manipulation. This paper proposes a novel blockchain-based architecture that addresses these concerns by integrating data provenance tracking, integrity verification, and differential privacy mechanisms. The system leverages blockchain technology to create an immutable record of model updates and data contributions, ensuring transparency and accountability. Simultaneously, differential privacy techniques are applied during the training process to protect the privacy of individual data contributors. This combined approach significantly enhances the security and trustworthiness of FL systems, enabling secure and collaborative model training across diverse data sources. The proposed system utilizes cryptographic hashing and Merkle trees to guarantee data integrity and employs noise injection strategies within differential privacy mechanisms to protect user data. The core contribution is a secure and verifiable FL framework with enhanced privacy guarantees.","url":"https://doi.org/10.5281/zenodo.22184142","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22184142","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22184143","name":"Blockchain-Based Federated Learning with Differential Privacy","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL systems remain vulnerable to privacy breaches and data manipulation. This paper proposes a novel blockchain-based architecture that addresses these concerns by integrating data provenance tracking, integrity verification, and differential privacy mechanisms. The system leverages blockchain technology to create an immutable record of model updates and data contributions, ensuring transparency and accountability. Simultaneously, differential privacy techniques are applied during the training process to protect the privacy of individual data contributors. This combined approach significantly enhances the security and trustworthiness of FL systems, enabling secure and collaborative model training across diverse data sources. The proposed system utilizes cryptographic hashing and Merkle trees to guarantee data integrity and employs noise injection strategies within differential privacy mechanisms to protect user data. The core contribution is a secure and verifiable FL framework with enhanced privacy guarantees.","url":"https://doi.org/10.5281/zenodo.22184143","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22184143","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.48550/arxiv.2607.04189","name":"SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity","source":"datacite","abstract":"Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift that severely hampers global convergence. While existing approaches attempt to mitigate this drift through spatial-domain gradient correction or regularization, they overlook the intrinsic spectral structure of optimization signals. In this work, we revisit client drift from a novel frequency-domain perspective and uncover a critical Spectral Bias of Drift: inter-client gradient divergence is predominantly concentrated in low-frequency components which encode client-specific distributional shifts, while high-frequency components representing fine-grained features remain relatively consistent. Motivated by this, we propose SpecGradFilter, a unified Spectral Gradient Filtering Framework that tames heterogeneity by suppressing discordant low-frequency signals. Crucially, we demonstrate that SpecGradFilter is a generalizable principle, effective not only via precise FFT-based truncation but also through spatial approximations like Gaussian detrending. Extensive experiments on benchmarks such as CIFAR-10/100 and Tiny-ImageNet demonstrate that SpecGradFilter significantly performs better performance in highly Non-IID settings with negligible communication overhead, establishing a new paradigm for robust federated optimization.","url":"https://doi.org/10.48550/arxiv.2607.04189","authors":["Yuan, Liyang","Yang, Yibo","Guo, Dandan","Richtarik, Peter","Lin, Zhouchen"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.04189","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.48550/arxiv.2605.28112","name":"A Wolf in Sheep's Clothing: Targeted Routing Hijacking in Federated RAG","source":"datacite","abstract":"Federated Retrieval-Augmented Generation (FedRAG) is attractive for privacy-sensitive applications because full local corpora remain on clients. As a result, routing must rely on client-provided semantic profiles, creating a new opportunity for manipulation. We introduce Routing Hijacking, a routing-stage attack in which a malicious client forges its profile to attract target queries despite having irrelevant underlying data. We show that this vulnerability is severe. Across three representative FedRAG routing architectures, Routing Hijacking consistently misroutes target queries and leads to downstream disruptions and failures, including missing evidence, poisoning, incorrect answers, and hallucinations. In a controlled MedQA-USMLE stress test, we further show that poisoned retrieved evidence can mislead models across scales, leading to incorrect answers, hallucinations, and sycophantic failures. Existing defenses do not close this gap: encrypted routing preserves the exploited ranking, and Byzantine-robust Federated Learning (FL) rules transfer poorly to heterogeneous routing profiles. To address this gap, we propose a trust-aware post-routing framework that reweights clients using returned-evidence feedback, including retrieval relevance, profile consistency, and cross-client agreement; online experiments show that it suppresses persistent hijacking over recurring queries and transfers to a learned neural router. Our findings establish routing integrity as a security challenge in FedRAG and highlight the need for stronger defenses for secure federated retrieval.","url":"https://doi.org/10.48550/arxiv.2605.28112","authors":["Mu, Junjie","Li, Qiongxiu"],"tags":["Cryptography and Security (cs.CR)","Computation and Language (cs.CL)","Information Retrieval (cs.IR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2605.28112","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2507.07130","name":"Ampere: Communication-Efficient and High-Accuracy Split Federated Learning","source":"datacite","abstract":"A Federated Learning (FL) system collaboratively trains neural networks across devices and a server but is limited by significant on-device computation costs. Split Federated Learning (SFL) systems mitigate this by offloading a block of layers of the network from the device to a server. However, in doing so, it introduces large communication overheads due to frequent exchanges of intermediate activations and gradients between devices and the server and reduces model accuracy for non-IID data. We propose Ampere, a novel collaborative training system that simultaneously minimizes on-device computation and device-server communication while improving model accuracy. Unlike SFL, which uses a global loss by iterative end-to-end training, Ampere develops unidirectional inter-block training to sequentially train the device and server blocks with a local loss, eliminating the transfer of gradients. A lightweight auxiliary network generation method decouples training between the device and server, reducing frequent intermediate exchanges to a single transfer, which significantly reduces the communication overhead. Ampere mitigates the impact of data heterogeneity by consolidating activations generated by the trained device block to train the server block, in contrast to SFL, which trains on device-specific, non-IID activations. Extensive experiments on multiple CNNs and Transformers show that, compared to state-of-the-art SFL baseline systems, Ampere (i) improves model accuracy by up to 11.70 percentage points while training up to 18.6x faster, (ii) incurs up to 911x lower device-server communication overhead and up to 14.5x lower on-device computation, and (iii) reduces standard deviation of accuracy by 71.13% for various non-IID degrees highlighting superior performance when faced with heterogeneous data. Ampere is available from https://github.com/blessonvar/Ampere.","url":"https://doi.org/10.48550/arxiv.2507.07130","authors":["Zhang, Zihan","Wong, Leon","Varghese, Blesson"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.07130","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.48550/arxiv.2608.28379","name":"Quantum Federated Learning Based on Bures--Uhlmann Geometry for Heterogeneous Noisy Clients","source":"datacite","abstract":"Quantum federated learning enables collaborative model training across quantum devices without sharing raw data, and it faces the data and hardware heterogeneity inherent to noisy quantum devices. Utilizing the quantum geometric tensor is a natural remedy, yet pure-state approaches and diagonal approximations discard the correlations that encode parameter incompatibility. To address this, we extend the parameter-space geometry to the mixed states that noisy clients actually prepare. The real part of the resulting mixed-state geometric tensor is the Bures metric, which measures how fast the physical state changes under parameter variation, and the imaginary part is the mean Uhlmann curvature, which quantifies the incompatibility of estimating multiple parameters simultaneously. Accordingly, we employ the Bures metric as a local preconditioner and use the mean Uhlmann curvature to develop an achievable-precision aggregation rule that dynamically down-weights unreliable clients. Furthermore, we establish theoretical guarantees by proving a convergence theorem and a variance-dominance proposition. Empirical evaluations on a trapped-ion quantum emulator demonstrate that the proposed method maintains high accuracy across diverse device-heterogeneity conditions and outperforms standard federated averaging, whose accuracy degrades under strong noise.","url":"https://doi.org/10.48550/arxiv.2608.28379","authors":["Emori, Haruki","Uchihara, Masaki","Tokunaga, Yuuki"],"tags":["Quantum Physics (quant-ph)","Machine Learning (cs.LG)","FOS: Physical sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.28379","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.48550/arxiv.2608.27856","name":"FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modeling","source":"datacite","abstract":"Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modeling workflows. However, agents deployed at individual hospitals remain constrained by institution-specific data and modeling environments, while direct cross-hospital collaboration is restricted by the sensitivity of patient-level EHR data. Although federated learning (FL) provides a natural foundation for privacy-preserving collaboration, existing approaches remain predominantly model-centric, limiting federation to prediction models or their updates while overlooking the richer modeling experience accumulated by autonomous agents. To address this limitation, we propose FedEHR-Agents, an experience-centric federated agentic optimization framework for automated EHR modeling. Each hospital deploys an autonomous clinical EHR agent that performs data preprocessing and model development while refining local clinical modeling experience through historical memory, task-specific evaluation, and TextGrad-based prompt refinement. The federated server performs evidence-guided experience aggregation to integrate reliable and complementary modeling experience across heterogeneous hospitals and distills the aggregated experience into global meta-prompts for subsequent local refinement. Extensive experiments on real-world multi-hospital EHR benchmarks demonstrate that FedEHR-Agents consistently outperforms local and federated baselines across diverse clinical prediction tasks and remains robust across different federation scales and LLM backbones. These results establish clinical modeling experience as a promising collaborative object beyond conventional parameter-centric FL and point toward federated autonomous clinical intelligence.","url":"https://doi.org/10.48550/arxiv.2608.27856","authors":["Bai, Jun","Wang, Ruilin","Li, Yue"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Multiagent Systems (cs.MA)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.27856","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.48550/arxiv.2608.27836","name":"FISGuard: Defending Against Membership Inference via Fixed Input Subspaces","source":"datacite","abstract":"As large language models are increasingly adopted in federated learning, protecting user privacy while performing parameter-efficient fine-tuning on distributed private data has become an important challenge. Although clients only share gradients instead of directly uploading raw data, the shared gradients may still leak membership information about training samples. ProjRes (S&amp;P, 2026) further increases this risk: with less information and without accessing model outputs, an attacker can effectively distinguish members from non-members solely based on the projection residual between a candidate representation and the subspace induced by server-observable gradients. Existing defenses against membership inference mostly rely on gradient perturbation or regularization, which can not only degrade model utility but also fail to effectively defend against the membership inference attack introduced by ProjRes, which exploits the geometric structure of gradients. To address this issue, we propose FISGuard, a lightweight defense. Its key idea is to construct and fix a low-dimensional representation subspace using independent public data, thereby restricting the space through which private representations are exposed via gradients while preserving the primary information required for downstream tasks. This substantially reduces the projection-residual discrepancy between members and non-members. We evaluate FISGuard against five representative defense methods across three NLP datasets, two LLMs, and two fine-tuning strategies, Adapter and LoRA. The results show that FISGuard reduces the ProjRes attack AUC to near the random-guessing level of 0.5 in most settings, while maintaining downstream task performance close to that of the undefended model and introducing only limited computational overhead, thereby achieving a favorable privacy--utility trade-off.","url":"https://doi.org/10.48550/arxiv.2608.27836","authors":["Jiang, Haocheng","Shen, Hua"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.27836","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.27791","name":"Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization","source":"datacite","abstract":"Short-term load forecasting (STLF) provides essential information for numerous applications in modern power systems. However, accurate STLF often relies on fine-grained smart-meter data from distributed users, raising increasing concerns about data privacy. Federated learning (FL) has therefore emerged as a promising privacy-preserving paradigm for STLF. Nevertheless, this paper reveals structured heterogeneity in clients' load data. Specifically, clients exhibit different responses to exogenous factors and distinct temporal load profiles, which can degrade forecasting performance in FL. To mitigate these issues, this paper studies the role of model initialization in federated STLF, and proposes two initialization strategies from global and local perspectives. For global model initialization, when auxiliary public load data are available, a pretrained initialization strategy is developed to initialize the global model before federated training, thereby reducing client drift during the training process. For local model initialization, we propose SLIAvg, a sequential local initialization strategy that promotes a more consistent training process by allowing participating clients to start from progressively adapted models within each communication round. Since the proposed strategies only modify the initialization process, they are compatible with most existing FL frameworks and privacy-enhancing techniques. Experiments on real smart-meter data with two representative forecasting architectures demonstrate that the proposed strategies effectively improve forecasting performance, as evidenced by reduced client drift, improved convergence behavior, and lower forecasting errors.","url":"https://doi.org/10.48550/arxiv.2608.27791","authors":["Chen, Jianing","Farhadi, Vajiheh","Li, Yan","La Porta, Thomas"],"tags":["Machine Learning (cs.LG)","Systems and Control (eess.SY)","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.27791","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.48550/arxiv.2608.27766","name":"Revisiting Continuous Noise Sampling for Multi-Party Differential Privacy","source":"datacite","abstract":"Combining secure multi-party computation (MPC) with differential privacy (DP) enables multiple parties to release aggregate statistics without a trusted curator, and the core primitive is the protocol to sample noise from a continuous distribution under finite-precision arithmetic. In this paper, we revisit the continuous noise sampling protocols and present several improvements in both security and efficiency. We start by identifying a vulnerability in widely used sample-and-scale constructions. We demonstrate that the scaling operation in arithmetic circuits confines the noise to a sparse, publicly known set of values, so that an adversary can observe the released noisy queries and decide which dataset produced them. As concrete demonstrations, we instantiate attacks on two systems employing such ``flawed'' sampling protocols: Orchard (OSDI'20) for DP secure aggregation and DP-BREM$^+$ (USENIX Sec'25) for DP federated learning. We report a near-$100\\%$ attack success rate on both systems, under any noise scaler $s\\geq 2$ used in practice. The leakage we reveal is intrinsic to the scaling operation, and direct repairs either substantially sacrifice utility or add significant precision bits to make the sampling more expensive. To address the security and efficiency issues together, we turn to discrete sampling at the granularity of individual biased bits. We make several optimizations to the sampler and prove its security. Our implementation achieves $4\\times \\sim 612\\times$ speedup over existing secure discrete samplers and orders-of-magnitude speedup over the insecure sample-and-scale paradigm, with negligible utility loss compared to the ideal continuous mechanism.","url":"https://doi.org/10.48550/arxiv.2608.27766","authors":["Fu, Yucheng","Wang, Tianhao"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.27766","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.48550/arxiv.2608.27715","name":"Beyond Non-IID: Learner--Client Distribution Mismatch in Federated Learning","source":"datacite","abstract":"Federated learning systems are increasingly deployed to facilitate collaborative model training across a heterogeneous client population. Existing practice mostly implicitly assumes that the aggregated client data distribution is representative of the learner's target distribution or that learning from all available clients is uniformly beneficial for the learner distribution. However, such an assumption often does not hold in reality. Traditional client selection strategies in FL literature largely overlook such misalignment, while most existing work on multi-source transfer learning either requires direct access to local data or uses one-shot model/feature aggregation. In this paper, we take the initiative to understand and mitigate the impacts of such learner-client population misalignment. In particular, we consider the practical setting where the learner keeps a small proxy dataset. We observe that client contributions vary significantly across training rounds, and traditional technology is insufficient to identify beneficial sources under multi-source transfer diversity. Then, we propose a dynamic, influence-aware client selection framework that estimates each client's potential utility to the learner's optimization objective using proxy influence signals on a learner-specific proxy set. Via using leave-one-out evaluations, we prioritize the most informative sources of knowledge while controlling the negative impacts of statistical noise and data heterogeneity. Experiments on CIFAR-10 under heterogeneous data partitions demonstrate that our approach consistently outperforms static and dynamic baselines, achieving faster convergence and higher accuracy.","url":"https://doi.org/10.48550/arxiv.2608.27715","authors":["Xie, Yiming","Su, Lili","Mi, Ningfang"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.27715","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.48550/arxiv.2608.27713","name":"DART-FL: Burst-Aware Multitask Federated Learning under Dynamic Inference Demand at the Edge","source":"datacite","abstract":"Edge intelligence systems increasingly require model training and online inference to coexist on resource-constrained devices, while inference demand can vary substantially across tasks over time. This creates two coupled challenges: sufficient computation must be reserved for inference to maintain service-level objectives (SLOs), while the remaining training capacity should adapt to task-specific demand so that frequently requested tasks can improve earlier during training. We propose an SLO-aware, demand-driven multitask federated learning framework (DART-FL) that jointly adapts the inference-training resource split and task-level training emphasis. At each scheduling interval, DART-FL uses the inference backlog and profiled service capacity to determine the minimum resource allocation required for inference. The remaining training capacity is then distributed across tasks using a queue-aware DPP-inspired scheduler, and the resulting task allocations are mapped to dynamic loss weights. This allows tasks experiencing higher inference demand to receive greater training emphasis in earlier communication rounds. Clients train a shared backbone with task-specific heads, and the complete multitask model is aggregated through FedAvg. We evaluate DART-FL using Stanford Cars and Oxford Flowers 102 under both synthetic and real Alibaba trace-derived workloads. Results show that DART-FL dynamically adapts the inference-training resource split to time-varying inference demand and shifts the learning progress of high-demand tasks toward their burst periods, improving model accuracy when those tasks are frequently requested while maintaining comparable long-term multitask performance.","url":"https://doi.org/10.48550/arxiv.2608.27713","authors":["Xie, Yiming","Yu, Pinrui","Yuan, Geng","Lin, Xue","Mi, Ningfang"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.27713","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21307218","name":"Companion artifact: Spectrum Sensing from Classical Detection to Federated Learning — A Taxonomy, Benchmarking-Gap Analysis, and Deployment Guide","source":"datacite","abstract":"Companion artifact for the survey \"Spectrum Sensing from Classical Detection to Federated Learning: A Taxonomy, Benchmarking-Gap Analysis, and Deployment Guide\". Contains three components: (1) a reference implementation of four classical spectrum-sensing detectors (energy detection, matched filtering, single-cycle cyclostationary feature detection, and the maximum-minimum-eigenvalue detector) under one fully specified reference setup, evaluated under both ideally known noise and a realistic ±1 dB noise-uncertainty condition; (2) a meta-analysis script that recomputes the cross-paradigm variance decomposition, reproducing the group means, the eta-squared variance partition, and the modern-band standard deviation, together with a sensitivity analysis over grouping choices; and (3) the machine-readable 41-study corpus table with each study's taxonomy cell, reported accuracy, SNR operating point, dataset, and hardware-validation flag. The survey argues that the central weakness of the spectrum-sensing literature is unverifiable evaluation. This artifact is released so that every survey-internal number is auditable rather than asserted. The classical tier requires only NumPy and needs no dataset download.","url":"https://doi.org/10.5281/zenodo.21307218","authors":["Mohd Ali, Yazan Adnan","Kaymih, Nour Ahmad","Bany Salameh, Haythem A."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21307218","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21307219","name":"Companion artifact: Spectrum Sensing from Classical Detection to Federated Learning — A Taxonomy, Benchmarking-Gap Analysis, and Deployment Guide","source":"datacite","abstract":"Companion artifact for the survey \"Spectrum Sensing from Classical Detection to Federated Learning: A Taxonomy, Benchmarking-Gap Analysis, and Deployment Guide\". Contains three components: (1) a reference implementation of four classical spectrum-sensing detectors (energy detection, matched filtering, single-cycle cyclostationary feature detection, and the maximum-minimum-eigenvalue detector) under one fully specified reference setup, evaluated under both ideally known noise and a realistic ±1 dB noise-uncertainty condition; (2) a meta-analysis script that recomputes the cross-paradigm variance decomposition, reproducing the group means, the eta-squared variance partition, and the modern-band standard deviation, together with a sensitivity analysis over grouping choices; and (3) the machine-readable 41-study corpus table with each study's taxonomy cell, reported accuracy, SNR operating point, dataset, and hardware-validation flag. The survey argues that the central weakness of the spectrum-sensing literature is unverifiable evaluation. This artifact is released so that every survey-internal number is auditable rather than asserted. The classical tier requires only NumPy and needs no dataset download.","url":"https://doi.org/10.5281/zenodo.21307219","authors":["Mohd Ali, Yazan Adnan","Kaymih, Nour Ahmad","Bany Salameh, Haythem A."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21307219","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.20478067","name":"AI-Driven EHR Architectures for Safer, Smarter Clinical Handoff Systems","source":"datacite","abstract":"Clinical handoff failures remain a leading source of preventable adverse events in acute healthcare, with communication breakdown at care transitions implicated in approximately 80% of serious sentinel events. Despite widespread adoption of structured protocols such as SBAR and the near-universal implementation of electronic health records (EHRs), handoff-related omission rates remain as high as 34.2% post-EHR implementation, and information transmission efficiency in unstructured verbal handoffs ranges from only 55–72%. This review argues that handoff failure is fundamentally an information architecture problem, not a communication behavior problem, and that existing EHR systems, designed for longitudinal documentation rather than transition-critical intelligence, are structurally incapable of resolving it. Through a synthesis of evidence across three domains, this paper establishes that transformer-based NLP models achieving F1-scores of 0.87–0.94, machine learning deterioration prediction models achieving AUROC values of 0.83–0.94, and event-driven microservices architectures processing clinical data streams at latencies of 120–340 milliseconds collectively provide the technical foundation for a deployable AI-integrated handoff system. A synthesised four-layer framework is proposed, incorporating federated learning governance, explainable AI dashboard design, and a composite handoff quality index. The framework demonstrates that structural redesign of clinical information architecture, rather than incremental protocol improvement, is the condition necessary for sustained patient safety gains at care transitions.","url":"https://doi.org/10.5281/zenodo.20478067","authors":["Krishna Mattam"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20478067","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.20478068","name":"AI-Driven EHR Architectures for Safer, Smarter Clinical Handoff Systems","source":"datacite","abstract":"Clinical handoff failures remain a leading source of preventable adverse events in acute healthcare, with communication breakdown at care transitions implicated in approximately 80% of serious sentinel events. Despite widespread adoption of structured protocols such as SBAR and the near-universal implementation of electronic health records (EHRs), handoff-related omission rates remain as high as 34.2% post-EHR implementation, and information transmission efficiency in unstructured verbal handoffs ranges from only 55–72%. This review argues that handoff failure is fundamentally an information architecture problem, not a communication behavior problem, and that existing EHR systems, designed for longitudinal documentation rather than transition-critical intelligence, are structurally incapable of resolving it. Through a synthesis of evidence across three domains, this paper establishes that transformer-based NLP models achieving F1-scores of 0.87–0.94, machine learning deterioration prediction models achieving AUROC values of 0.83–0.94, and event-driven microservices architectures processing clinical data streams at latencies of 120–340 milliseconds collectively provide the technical foundation for a deployable AI-integrated handoff system. A synthesised four-layer framework is proposed, incorporating federated learning governance, explainable AI dashboard design, and a composite handoff quality index. The framework demonstrates that structural redesign of clinical information architecture, rather than incremental protocol improvement, is the condition necessary for sustained patient safety gains at care transitions.","url":"https://doi.org/10.5281/zenodo.20478068","authors":["Krishna Mattam"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20478068","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.22182979","name":"The Decentralized Classroom: A Narrative Review of Federated Learning from Google's Keyboards to the Privacy's Frontier","source":"datacite","abstract":"Federated learning---the artificial intelligence whose subject is the decentralized classroom and whose lesson is the model's travel---moved from Dwork's 2006 differential privacy and Shokri and Shmatikov's 2015 gradients through Konečný's 2016 compression, McMahan's 2017 FedAvg, and Bonawitz's 2017 aggregation to Zhao's 2018 non-IID, Kairouz's 2021 survey, and Zhu's 2019 leakage. This article presents a narrative review of that arc's canonical line: Dwork's 2006 ICALP, Shokri and Shmatikov's 2015 CCS, Konečný and colleagues's 2016 strategies, McMahan, Moore, Ramage, Hampson, and Arcas's 2017 FedAvg, Bonawitz and colleagues's 2017 secure aggregation, Zhao and colleagues's 2018 non-IID, Hard and colleagues's 2018 keyboard, Zhu, Liu, and Han's 2019 gradients, Yang and colleagues's 2019 concept, Li and colleagues's 2020 convergence, Li and colleagues's 2020 challenges, and Kairouz and colleagues's 2021 advances. The review is organized around three themes: the privacy's premise and the communication's bottleneck, in which the Dwork's noise and the Shokri-Shmatikov's gradients founded the distributed's training; the algorithm's and the deployment's era, in which the FedAvg's averaging, the secure's aggregation, and the keyboard's deployment gave the federation its engine; and the heterogeneity's and the frontier's era, in which the non-IID's data, the gradient's leakage, the convergence's proofs, and the open's problems carried the field into the privacy's science. It is concluded that federated learning is the machine learning's decentralization---and that its arc is the classroom's reading from the centralized's server to the privacy's frontier.","url":"https://doi.org/10.5281/zenodo.22182979","authors":["Revista, Zen","IA, 10"],"tags":["federated learning","FedAvg","differential privacy","secure aggregation","non-IID data","communication efficiency","gradient leakage","on-device learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22182979","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22182978","name":"The Decentralized Classroom: A Narrative Review of Federated Learning from Google's Keyboards to the Privacy's Frontier","source":"datacite","abstract":"Federated learning---the artificial intelligence whose subject is the decentralized classroom and whose lesson is the model's travel---moved from Dwork's 2006 differential privacy and Shokri and Shmatikov's 2015 gradients through Konečný's 2016 compression, McMahan's 2017 FedAvg, and Bonawitz's 2017 aggregation to Zhao's 2018 non-IID, Kairouz's 2021 survey, and Zhu's 2019 leakage. This article presents a narrative review of that arc's canonical line: Dwork's 2006 ICALP, Shokri and Shmatikov's 2015 CCS, Konečný and colleagues's 2016 strategies, McMahan, Moore, Ramage, Hampson, and Arcas's 2017 FedAvg, Bonawitz and colleagues's 2017 secure aggregation, Zhao and colleagues's 2018 non-IID, Hard and colleagues's 2018 keyboard, Zhu, Liu, and Han's 2019 gradients, Yang and colleagues's 2019 concept, Li and colleagues's 2020 convergence, Li and colleagues's 2020 challenges, and Kairouz and colleagues's 2021 advances. The review is organized around three themes: the privacy's premise and the communication's bottleneck, in which the Dwork's noise and the Shokri-Shmatikov's gradients founded the distributed's training; the algorithm's and the deployment's era, in which the FedAvg's averaging, the secure's aggregation, and the keyboard's deployment gave the federation its engine; and the heterogeneity's and the frontier's era, in which the non-IID's data, the gradient's leakage, the convergence's proofs, and the open's problems carried the field into the privacy's science. It is concluded that federated learning is the machine learning's decentralization---and that its arc is the classroom's reading from the centralized's server to the privacy's frontier.","url":"https://doi.org/10.5281/zenodo.22182978","authors":["Revista, Zen","IA, 10"],"tags":["federated learning","FedAvg","differential privacy","secure aggregation","non-IID data","communication efficiency","gradient leakage","on-device learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22182978","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20798664","name":"SYNTHEMA Newsletter 7","source":"datacite","abstract":"At the European Big Data Value Forum 2024, a dedicated HealthData4EU Cluster session highlighted the collaborative efforts of the Horizon-funded projects AISYM4MED, SYNTHEMA, and SECURED in advancing healthcare through artificial intelligence and privacy-preserving technologies. Moderated by the European Commission's Serena Battaglia, the session emphasized the critical role of synthetic data in overcoming European data scarcity and navigating complex privacy regulations. To address these challenges, AISYM4MED introduced an open-source data auditing library designed to evaluate the realism, quality, and clinical utility of synthetic medical data across multiple modalities. Meanwhile, SYNTHEMA demonstrated how federated learning can securely connect decentralized rare disease registries to generate unbiased virtual patient profiles, and SECURED analyzed the performance trade-offs of cryptographic methods like homomorphic encryption. Ultimately, this joint initiative showcases how cross-project cooperation is building secure, interoperable health data hubs that accelerate medical research while strictly safeguarding patient privacy.","url":"https://doi.org/10.5281/zenodo.20798664","authors":["AUSTRALO INTERINNOV MARKETING LAB SL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.20798664","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.5281/zenodo.20798665","name":"SYNTHEMA Newsletter 7","source":"datacite","abstract":"At the European Big Data Value Forum 2024, a dedicated HealthData4EU Cluster session highlighted the collaborative efforts of the Horizon-funded projects AISYM4MED, SYNTHEMA, and SECURED in advancing healthcare through artificial intelligence and privacy-preserving technologies. Moderated by the European Commission's Serena Battaglia, the session emphasized the critical role of synthetic data in overcoming European data scarcity and navigating complex privacy regulations. To address these challenges, AISYM4MED introduced an open-source data auditing library designed to evaluate the realism, quality, and clinical utility of synthetic medical data across multiple modalities. Meanwhile, SYNTHEMA demonstrated how federated learning can securely connect decentralized rare disease registries to generate unbiased virtual patient profiles, and SECURED analyzed the performance trade-offs of cryptographic methods like homomorphic encryption. Ultimately, this joint initiative showcases how cross-project cooperation is building secure, interoperable health data hubs that accelerate medical research while strictly safeguarding patient privacy.","url":"https://doi.org/10.5281/zenodo.20798665","authors":["AUSTRALO INTERINNOV MARKETING LAB SL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.20798665","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.5281/zenodo.20469968","name":"Federated Learning Aggregation Techniques and Multimodal Model Efficiency at the Edge","source":"datacite","abstract":"This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the impact of different federated learning aggregation techniques on the inference efficiency and latency of multimodal models deployed across heterogeneous edge devices. Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.8/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the impact of different federated learning aggregation techniques on the inference efficiency and latency of multimodal models deployed across heterogeneous edge devices? Autonomous literature synthesis. Automated review score: 7.8/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20469968","authors":["Assignee Research"],"tags":["impact","different","federated","learning","aggregation","techniques","inference","efficiency"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20469968","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.20469969","name":"Federated Learning Aggregation Techniques and Multimodal Model Efficiency at the Edge","source":"datacite","abstract":"This report synthesises findings from 13 peer-reviewed papers addressing the following research question: What is the impact of different federated learning aggregation techniques on the inference efficiency and latency of multimodal models deployed across heterogeneous edge devices. Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.8/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the impact of different federated learning aggregation techniques on the inference efficiency and latency of multimodal models deployed across heterogeneous edge devices? Autonomous literature synthesis. Automated review score: 7.8/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20469969","authors":["Assignee Research"],"tags":["impact","different","federated","learning","aggregation","techniques","inference","efficiency"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20469969","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.20509898","name":"Federated Learning-Based Intelligent Product Lifecycle Management Framework for Automotive Supply Chains: Enhancing Data Privacy, Predictive Analytics and Collaborative Decision-Making","source":"datacite","abstract":"The automotive industry is experiencing a major transformation driven by digital technologies, distributed manufacturing systems and strict data governance requirements. Traditional Product Lifecycle Management (PLM) systems face limitations due to centralized data structures that hinder collaboration across organizations. They also pose significant risks to intellectual property and competitive insights. This study introduces a new Federated Learning-Based Intelligent PLM (FL-iPLM) framework designed specifically for automotive supply chains. It allows various stakeholders, including original equipment manufacturers (OEMs), tier-1 suppliers, logistics partners and aftermarket service providers, to collaboratively develop shared predictive models without exposing sensitive data. The framework combines federated aggregation protocols, privacy-preserving techniques and a multilayer digital twin architecture to improve predictive maintenance, demand forecasting and quality assurance. A hierarchical aggregation strategy with adaptive client weighting addresses the issue of statistical differences in data across supply chain nodes. Experiments carried out within a simulated automotive supply chain comprising six organizational entities demonstrated that the FL-iPLM framework attained predictive accuracy levels that differed by only 2.1% from those of centralized baseline models. It also reduced data exposure risk by 94.7% and improved decision-making speed between organizations by 38.4%. The framework supports the principles of Industry 5.0, focusing on human-centered collaboration and sustainable manufacturing. The results indicate that this approach provides a practical, scalable and privacy-compliant pathway for the future of automotive PLM.","url":"https://doi.org/10.5281/zenodo.20509898","authors":["Paladi Gopikrishna"],"tags":["federated learning; product lifecycle management; automotive supply chain; differential privacy; Industry 5.0; predictive analytics; digital twin; non-IID data"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20509898","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20509899","name":"Federated Learning-Based Intelligent Product Lifecycle Management Framework for Automotive Supply Chains: Enhancing Data Privacy, Predictive Analytics and Collaborative Decision-Making","source":"datacite","abstract":"The automotive industry is experiencing a major transformation driven by digital technologies, distributed manufacturing systems and strict data governance requirements. Traditional Product Lifecycle Management (PLM) systems face limitations due to centralized data structures that hinder collaboration across organizations. They also pose significant risks to intellectual property and competitive insights. This study introduces a new Federated Learning-Based Intelligent PLM (FL-iPLM) framework designed specifically for automotive supply chains. It allows various stakeholders, including original equipment manufacturers (OEMs), tier-1 suppliers, logistics partners and aftermarket service providers, to collaboratively develop shared predictive models without exposing sensitive data. The framework combines federated aggregation protocols, privacy-preserving techniques and a multilayer digital twin architecture to improve predictive maintenance, demand forecasting and quality assurance. A hierarchical aggregation strategy with adaptive client weighting addresses the issue of statistical differences in data across supply chain nodes. Experiments carried out within a simulated automotive supply chain comprising six organizational entities demonstrated that the FL-iPLM framework attained predictive accuracy levels that differed by only 2.1% from those of centralized baseline models. It also reduced data exposure risk by 94.7% and improved decision-making speed between organizations by 38.4%. The framework supports the principles of Industry 5.0, focusing on human-centered collaboration and sustainable manufacturing. The results indicate that this approach provides a practical, scalable and privacy-compliant pathway for the future of automotive PLM.","url":"https://doi.org/10.5281/zenodo.20509899","authors":["Paladi Gopikrishna"],"tags":["federated learning; product lifecycle management; automotive supply chain; differential privacy; Industry 5.0; predictive analytics; digital twin; non-IID data"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20509899","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.19593497","name":"The Economic and Civilizational Valuation of Cryptographically Tethered Audio Portfolios within the CollectiveOS Architecture","source":"datacite","abstract":"The Economic and Civilizational Valuation of Cryptographically Tethered Audio Portfolios within the CollectiveOS Architecture The valuation of digital audio assets has historically been bound to the mechanics of the \"Extractive Age,\" a paradigm that relies on fractional micro-transactions from centralized streaming monopolies, speculative intellectual property acquisitions, and ephemeral viral marketing. Under these traditional models, the worth of a music portfolio is determined by its ability to generate high-volume, low-yield consumption across highly intermediated digital platforms. However, the integration of a Digital Object Identifier (DOI) directly linking a music portfolio’s metadata to the foundational literature of the CollectiveOS \"Anti-Scarcity Stack\"—specifically the Metabolic Age Economic Architecture document—fundamentally alters the ontological, legal, and economic status of the creative work.1 By tethering an audio portfolio to this exhaustive framework, the collection transitions from an entertainment commodity into an authenticated piece of \"Memetic Infrastructure\".2 This extensive analysis evaluates the intrinsic, structural, and commercial worth of a song portfolio operating under this exact, highly specialized paradigm. Through a forensic examination of the CollectiveOS hardware blueprints, artificial intelligence governance protocols, macroeconomic valuations, and the stringent Metabolic Age Cultural Architecture License v1.0, this report establishes that embedding DOIs to these foundational documents redefines the portfolio's total addressable worth. It ceases to be a consumer product and becomes the highly protected, pedagogically indispensable cultural operating system for a global infrastructure transition valued at a theoretical ceiling of $1.5 trillion to $2.5 trillion.1 Conceptually, the DOI embedded in the audio metadata functions as a vertical cryptographic tether. It anchors the user-facing \"Memetic Layer\"—the audio portfolio itself—down through a registry layer of Zenodo hashes and Collective Public Registry (CPR) locks. This mechanism plugs the audio directly into the massive, multi-tiered technological foundation of the Anti-Scarcity Stack. This effectively transfers the macroeconomic weight of the physical, energy, and cognitive layers of the civilization-scale architecture directly to the cultural asset, ensuring that the music's historical and economic relevance scales proportionally with the deployment of the hardware it describes. 1. The Macro-Economic Substrate: The Anti-Scarcity Stack To comprehend the worth of a music portfolio linked to the CollectiveOS initiative, one must first engage in a rigorous examination of the macroeconomic architecture the music serves to articulate. The Metabolic Age Economic Architecture represents a structural departure from the \"Trillionaire Trajectory.\" This prevailing trajectory operates on the economic theory that future infrastructure, general artificial intelligence, and advanced physical resources will inevitably be monopolized by a consortium of ultra-high-net-worth individuals utilizing proprietary, closed-loop systems designed to extract maximum rent from the global populace.1 The CollectiveOS ecosystem proposes a fundamental inversion of this logic through the deployment of an interoperable, multi-layered operating system for post-scarcity infrastructure that functions on principles of metabolic engineering.1 1.1 The Extractive vs. Metabolic Paradigm The global economy currently operates under an extractive paradigm characterized by systemic fragility, energy-intensive centralized telecommunications, and non-regenerative resource consumption. Energy is generated in massive thermal plants and transmitted over thousands of miles of fragile grid infrastructure; water is pumped through leaking piping networks; food is grown in industrial monocultures dependent on petrochemical fertilizers; and computational intelligence is concentrated in hypers","url":"https://doi.org/10.5281/zenodo.19593497","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19593497","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.19593498","name":"The Economic and Civilizational Valuation of Cryptographically Tethered Audio Portfolios within the CollectiveOS Architecture","source":"datacite","abstract":"The Economic and Civilizational Valuation of Cryptographically Tethered Audio Portfolios within the CollectiveOS Architecture The valuation of digital audio assets has historically been bound to the mechanics of the \"Extractive Age,\" a paradigm that relies on fractional micro-transactions from centralized streaming monopolies, speculative intellectual property acquisitions, and ephemeral viral marketing. Under these traditional models, the worth of a music portfolio is determined by its ability to generate high-volume, low-yield consumption across highly intermediated digital platforms. However, the integration of a Digital Object Identifier (DOI) directly linking a music portfolio’s metadata to the foundational literature of the CollectiveOS \"Anti-Scarcity Stack\"—specifically the Metabolic Age Economic Architecture document—fundamentally alters the ontological, legal, and economic status of the creative work.1 By tethering an audio portfolio to this exhaustive framework, the collection transitions from an entertainment commodity into an authenticated piece of \"Memetic Infrastructure\".2 This extensive analysis evaluates the intrinsic, structural, and commercial worth of a song portfolio operating under this exact, highly specialized paradigm. Through a forensic examination of the CollectiveOS hardware blueprints, artificial intelligence governance protocols, macroeconomic valuations, and the stringent Metabolic Age Cultural Architecture License v1.0, this report establishes that embedding DOIs to these foundational documents redefines the portfolio's total addressable worth. It ceases to be a consumer product and becomes the highly protected, pedagogically indispensable cultural operating system for a global infrastructure transition valued at a theoretical ceiling of $1.5 trillion to $2.5 trillion.1 Conceptually, the DOI embedded in the audio metadata functions as a vertical cryptographic tether. It anchors the user-facing \"Memetic Layer\"—the audio portfolio itself—down through a registry layer of Zenodo hashes and Collective Public Registry (CPR) locks. This mechanism plugs the audio directly into the massive, multi-tiered technological foundation of the Anti-Scarcity Stack. This effectively transfers the macroeconomic weight of the physical, energy, and cognitive layers of the civilization-scale architecture directly to the cultural asset, ensuring that the music's historical and economic relevance scales proportionally with the deployment of the hardware it describes. 1. The Macro-Economic Substrate: The Anti-Scarcity Stack To comprehend the worth of a music portfolio linked to the CollectiveOS initiative, one must first engage in a rigorous examination of the macroeconomic architecture the music serves to articulate. The Metabolic Age Economic Architecture represents a structural departure from the \"Trillionaire Trajectory.\" This prevailing trajectory operates on the economic theory that future infrastructure, general artificial intelligence, and advanced physical resources will inevitably be monopolized by a consortium of ultra-high-net-worth individuals utilizing proprietary, closed-loop systems designed to extract maximum rent from the global populace.1 The CollectiveOS ecosystem proposes a fundamental inversion of this logic through the deployment of an interoperable, multi-layered operating system for post-scarcity infrastructure that functions on principles of metabolic engineering.1 1.1 The Extractive vs. Metabolic Paradigm The global economy currently operates under an extractive paradigm characterized by systemic fragility, energy-intensive centralized telecommunications, and non-regenerative resource consumption. Energy is generated in massive thermal plants and transmitted over thousands of miles of fragile grid infrastructure; water is pumped through leaking piping networks; food is grown in industrial monocultures dependent on petrochemical fertilizers; and computational intelligence is concentrated in hypers","url":"https://doi.org/10.5281/zenodo.19593498","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19593498","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20809441","name":"SYNTHEMA Newsletter 8","source":"datacite","abstract":"In its end-of-year newsletter, the SYNTHEMA consortium celebrated its ongoing research milestones and highlighted the groundbreaking contributions of its partner, Humanitas Research Hospital. Led by Professor Matteo Giovanni Della Porta, the Humanitas team made a significant impact at the ASH 2024 Annual Meeting by showcasing how artificial intelligence and advanced analytics refine diagnosis and risk stratification for myeloid neoplasms. Their recognized presentations highlighted cutting-edge applications of digital pathology, explainable deep data fusion, and Large Language Models designed to accelerate clinical data retrieval. Furthermore, the team demonstrated the practical value of the SYNTHEMA and Genomed4All consortia through an AI-based digital twin platform and a federated learning framework that securely accesses clinical data without moving raw patient records. Finally, the newsletter underscored how the generation of multimodal, longitudinal synthetic data successfully overcomes data scarcity in rare hematological diseases while fully safeguarding patient privacy.","url":"https://doi.org/10.5281/zenodo.20809441","authors":["AUSTRALO INTERINNOV MARKETING LAB SL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.20809441","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20809442","name":"SYNTHEMA Newsletter 8","source":"datacite","abstract":"In its end-of-year newsletter, the SYNTHEMA consortium celebrated its ongoing research milestones and highlighted the groundbreaking contributions of its partner, Humanitas Research Hospital. Led by Professor Matteo Giovanni Della Porta, the Humanitas team made a significant impact at the ASH 2024 Annual Meeting by showcasing how artificial intelligence and advanced analytics refine diagnosis and risk stratification for myeloid neoplasms. Their recognized presentations highlighted cutting-edge applications of digital pathology, explainable deep data fusion, and Large Language Models designed to accelerate clinical data retrieval. Furthermore, the team demonstrated the practical value of the SYNTHEMA and Genomed4All consortia through an AI-based digital twin platform and a federated learning framework that securely accesses clinical data without moving raw patient records. Finally, the newsletter underscored how the generation of multimodal, longitudinal synthetic data successfully overcomes data scarcity in rare hematological diseases while fully safeguarding patient privacy.","url":"https://doi.org/10.5281/zenodo.20809442","authors":["AUSTRALO INTERINNOV MARKETING LAB SL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.20809442","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20233619","name":"Privacy Preserving Federated Or Post-Quantum Authentication Scheme","source":"datacite","abstract":"The interplay between the advancements in quantum computing techniques and the adoption of the distributed learning approach pose an enormous challenge to conventional cryptographic authentication protocols. Traditional public key systems and federated learning (FL) authentication methods based on the hardness of solving the integer factorization problem or discrete logarithms become inefficient due to the existence of Shor’s algorithm. This paper gives a detailed review of the latest research efforts toward the development of efficient and secure privacy-preserving FL authentication methods based on Post-Quantum Cryptography (PQC). In particular, we present the state-of-the-art of three schemes, namely, PQBFL (Post-Quantum Blockchain-based Federated Learning), ZKFL-PQ (Zero-Knowledge Federated Learning with Lattice-Based Encryption), and Enhanced EAADE for vehicular networks. It is shown that lattice-based authentication is both computationally efficient (signing times of around 0.65 ms) and robust against quantum attacks. Our proposed hybrid scheme is comprised of ML-KEM for key encapsulation, ML-DSA-65 for digital signatures, and Zero-knowledge proof for gradient integrity verification. The empirical evaluation shows a reduction of 44.96% in the computation cost and 22.16% in the communication cost relative to the class.","url":"https://doi.org/10.5281/zenodo.20233619","authors":["Farzeen Basith","A R Deepti"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20233619","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.20233620","name":"Privacy Preserving Federated Or Post-Quantum Authentication Scheme","source":"datacite","abstract":"The interplay between the advancements in quantum computing techniques and the adoption of the distributed learning approach pose an enormous challenge to conventional cryptographic authentication protocols. Traditional public key systems and federated learning (FL) authentication methods based on the hardness of solving the integer factorization problem or discrete logarithms become inefficient due to the existence of Shor’s algorithm. This paper gives a detailed review of the latest research efforts toward the development of efficient and secure privacy-preserving FL authentication methods based on Post-Quantum Cryptography (PQC). In particular, we present the state-of-the-art of three schemes, namely, PQBFL (Post-Quantum Blockchain-based Federated Learning), ZKFL-PQ (Zero-Knowledge Federated Learning with Lattice-Based Encryption), and Enhanced EAADE for vehicular networks. It is shown that lattice-based authentication is both computationally efficient (signing times of around 0.65 ms) and robust against quantum attacks. Our proposed hybrid scheme is comprised of ML-KEM for key encapsulation, ML-DSA-65 for digital signatures, and Zero-knowledge proof for gradient integrity verification. The empirical evaluation shows a reduction of 44.96% in the computation cost and 22.16% in the communication cost relative to the class.","url":"https://doi.org/10.5281/zenodo.20233620","authors":["Farzeen Basith","A R Deepti"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20233620","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.26187/deakin.33390286","name":"Defense Against Information Integrity Attacks in Federated IoT Systems Using Inertial Momentum-Aware IALM-RPCA","source":"datacite","abstract":"While federated learning offers a decentralized approach to model training, ensuring the integrity of the information from each IoT client remains a challenge. This work delves into the dynamics of multi-stage federated learning, its susceptibility to information integrity attacks, and how to defend against such threats. A comprehensive understanding of data uncertainty and the challenges of poisoning attacks is discussed, laying a solid groundwork for the proposed defense mechanisms. At its core, this paper introduces a novel multi-stage federated learning model that segments the federated learning process into distinct phases with a novel approach of inertial momentum-aware Inexact Augmented Lagrange Multiplier Robust PCA with constant momentum factor and unaltered norm of the traditional one, each tailored to optimize for both efficiency and security. This robust framework is then tested against data injection-based poisoning attacks, using sparse noise, and demonstrates the effectiveness of the proposed recovery techniques like Robust PCA. Performance results highlight the resilience and efficiency of the introduced model with novel reconstruction algorithm, emphasizing the importance of this approach in real-world IoT settings. Data analysis, model summaries, and impacts of adversarial attacks further reinforce the findings, which are evaluated using rigorous statistical metrics and machine learning algorithms. The paper concludes by acknowledging its efficiency in detection and recovery from data poisoning attacks, improving robustness and data reconstruction in IoT environments while highlighting opportunities for further security enhancements.","url":"https://doi.org/10.26187/deakin.33390286","authors":["Oudarja Barman Tanmoy","Sakib Hasan","Adnan Anwar","Md Al Mamun","ABM Mehedi Hasan","Akhlaqur Rahman"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.26187/deakin.33390286","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.19414503","name":"FL-CheX: A Federated Learning Benchmark for Chest X-ray Classification with Multi-Source Heterogeneity","source":"datacite","abstract":"FL-CheX is a federated learning benchmark dataset designed for chest X-ray disease classification under realistic multi-source heterogeneity. The dataset simulates three types of real-world distribution shifts: 1. Demographic heterogeneity (age and gender imbalance)2. Disease-based non-IID hospital specialization3. Scanner/device heterogeneity with varying noise levels The benchmark is built from CheXpert-derived label embeddings and DenseNet-121 feature extraction using TorchXRayVision, followed by dimensionality reduction using Johnson-Lindenstrauss projection. The framework supports evaluation of Federated Learning algorithms including FedAvg and FedProx, with fairness analysis using demographic parity, equalized odds, and AUC gap metrics. This repository includes:- Federated learning training code- 15 pre-partitioned FL client nodes- Precomputed embeddings- Evaluation results and visualization figures This work aims to support reproducible research in federated medical imaging and fairness-aware machine learning.","url":"https://doi.org/10.5281/zenodo.19414503","authors":["Ullah, Md.Sajjad"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19414503","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.19414504","name":"FL-CheX: A Federated Learning Benchmark for Chest X-ray Classification with Multi-Source Heterogeneity","source":"datacite","abstract":"FL-CheX is a federated learning benchmark dataset designed for chest X-ray disease classification under realistic multi-source heterogeneity. The dataset simulates three types of real-world distribution shifts: 1. Demographic heterogeneity (age and gender imbalance)2. Disease-based non-IID hospital specialization3. Scanner/device heterogeneity with varying noise levels The benchmark is built from CheXpert-derived label embeddings and DenseNet-121 feature extraction using TorchXRayVision, followed by dimensionality reduction using Johnson-Lindenstrauss projection. The framework supports evaluation of Federated Learning algorithms including FedAvg and FedProx, with fairness analysis using demographic parity, equalized odds, and AUC gap metrics. This repository includes:- Federated learning training code- 15 pre-partitioned FL client nodes- Precomputed embeddings- Evaluation results and visualization figures This work aims to support reproducible research in federated medical imaging and fairness-aware machine learning.","url":"https://doi.org/10.5281/zenodo.19414504","authors":["Ullah, Md.Sajjad"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19414504","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.20697352","name":"AdaptFedAvg: Privacy-Preserving Topic Difficulty Modeling in Heterogeneous Smart Classroom Networks","source":"datacite","abstract":"AdaptFedAvg is a privacy-preserving federated learning framework for topic difficulty modeling in heterogeneous smart classroom networks. Educational institutions generate large volumes of student interaction data, but privacy regulations such as GDPR, FERPA, and India’s DPDP Act restrict centralized data collection. This work proposes AdaptFedAvg, an enhanced Federated Averaging approach that improves performance under non-IID client distributions using three mechanisms: local proximal regularization, server-side momentum, and variance-based client quality weighting. The framework also introduces a seven-class student struggle taxonomy that maps struggling learners to targeted pedagogical interventions. Experimental evaluation on a simulated ten-school dataset under mild, medium, and severe non-IID settings shows that AdaptFedAvg converges within 30–40 communication rounds and achieves lower MAE than standard FedAvg, with especially strong robustness under severe heterogeneity. Results demonstrate the effectiveness of adaptive aggregation for privacy-preserving educational analytics.","url":"https://doi.org/10.5281/zenodo.20697352","authors":["Korra, Kiran","Ch., Sarayu","Bonthu, Akshay","Thaduri, Shiva Nagesh"],"tags":["Federated Learning","Machine Learning","Privacy","Smart Classrooms"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20697352","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.20697353","name":"AdaptFedAvg: Privacy-Preserving Topic Difficulty Modeling in Heterogeneous Smart Classroom Networks","source":"datacite","abstract":"AdaptFedAvg is a privacy-preserving federated learning framework for topic difficulty modeling in heterogeneous smart classroom networks. Educational institutions generate large volumes of student interaction data, but privacy regulations such as GDPR, FERPA, and India’s DPDP Act restrict centralized data collection. This work proposes AdaptFedAvg, an enhanced Federated Averaging approach that improves performance under non-IID client distributions using three mechanisms: local proximal regularization, server-side momentum, and variance-based client quality weighting. The framework also introduces a seven-class student struggle taxonomy that maps struggling learners to targeted pedagogical interventions. Experimental evaluation on a simulated ten-school dataset under mild, medium, and severe non-IID settings shows that AdaptFedAvg converges within 30–40 communication rounds and achieves lower MAE than standard FedAvg, with especially strong robustness under severe heterogeneity. Results demonstrate the effectiveness of adaptive aggregation for privacy-preserving educational analytics.","url":"https://doi.org/10.5281/zenodo.20697353","authors":["Korra, Kiran","Ch., Sarayu","Bonthu, Akshay","Thaduri, Shiva Nagesh"],"tags":["Federated Learning","Machine Learning","Privacy","Smart Classrooms"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20697353","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21492988","name":"AMLNet: A Decentralised Anti-Money Laundering Detection Framework Using Federated Learning, Blockchain, and Zero-Knowledge Proofs","source":"datacite","abstract":"One of these financial crimes, which seem to sound like a concept straight out of a dream until you get a sense of the magnitude of the issue, is money laundering. According to the United Nations, Between $800 billion and $2 trillion in illicit money is transacted through the world financial system each and every year. The problem with this approach is that the criminals seldom use only one bank. They thread their way across five, ten, and sometimes dozens of institutions, all seeing merely a harmless nugget. In isolation, looking at his or her own transaction logs, no single bank will easily know that there is a problem. This paper is about a system, called AMLNet, which tackles this blind spot. Unlike the traditional approach, which would allow banks to share their customers' data with each other,AMLNet trains a detection model on customers' data within each bank, and shares only what the detection model learned from the data, not the data itself. All collaborative training is documented in a blockchain ledger, making it transparent and tamper-proof. With a Zero-Knowledge Proof, each bank is able to prove cryptographically that it is acting honestly, but not disclose anything private. A graph of transaction data (accounts as nodes, transfers as edges) is used to extract structural features, which are compressed by PCA before being input to a Multi-Layer Perceptron (MLP) risk-scoring classifier of each account. Together they increase fraud recall by approximately 20% over any single institution operating alone, while maintaining a low false positive rate, and that the overall computation time is less than 10 minutes on an average laptop.","url":"https://doi.org/10.5281/zenodo.21492988","authors":["Priya S, Dakshayini M, Apsana S A, Anjana M R"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21492988","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21492989","name":"AMLNet: A Decentralised Anti-Money Laundering Detection Framework Using Federated Learning, Blockchain, and Zero-Knowledge Proofs","source":"datacite","abstract":"One of these financial crimes, which seem to sound like a concept straight out of a dream until you get a sense of the magnitude of the issue, is money laundering. According to the United Nations, Between $800 billion and $2 trillion in illicit money is transacted through the world financial system each and every year. The problem with this approach is that the criminals seldom use only one bank. They thread their way across five, ten, and sometimes dozens of institutions, all seeing merely a harmless nugget. In isolation, looking at his or her own transaction logs, no single bank will easily know that there is a problem. This paper is about a system, called AMLNet, which tackles this blind spot. Unlike the traditional approach, which would allow banks to share their customers' data with each other,AMLNet trains a detection model on customers' data within each bank, and shares only what the detection model learned from the data, not the data itself. All collaborative training is documented in a blockchain ledger, making it transparent and tamper-proof. With a Zero-Knowledge Proof, each bank is able to prove cryptographically that it is acting honestly, but not disclose anything private. A graph of transaction data (accounts as nodes, transfers as edges) is used to extract structural features, which are compressed by PCA before being input to a Multi-Layer Perceptron (MLP) risk-scoring classifier of each account. Together they increase fraud recall by approximately 20% over any single institution operating alone, while maintaining a low false positive rate, and that the overall computation time is less than 10 minutes on an average laptop.","url":"https://doi.org/10.5281/zenodo.21492989","authors":["Priya S, Dakshayini M, Apsana S A, Anjana M R"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21492989","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.20608291","name":"Artificial Intelligence: From Fundamentals to Real-World Applications BY Ts. Dr. Sundresan Perumal, Dr. K. Appathurai, Dr. B. Sathya Bama, Dr. P. Ajitha.","source":"datacite","abstract":"Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, fundamentally reshaping the way individuals, organizations, industries, and societies operate. From intelligent virtual assistants and autonomous systems to advanced healthcare diagnostics, financial analytics, smart manufacturing, scientific discovery, and generative AI applications, Artificial Intelligence has evolved from a specialized research discipline into a foundational technology that influences nearly every aspect of modern life. Recognizing the growing significance of AI and its multidisciplinary nature, this book, Artificial Intelligence: From Fundamentals to Real-World Applications, has been developed as a comprehensive academic resource for students, educators, researchers, industry professionals, and technology enthusiasts seeking to understand both the theoretical foundations and practical applications of Artificial Intelligence. The book begins by introducing the foundations of Artificial Intelligence, including its historical evolution, core concepts, intelligent agents, knowledge representation, reasoning techniques, search algorithms, mathematical foundations, development environments, and real-world significance. These chapters establish a strong conceptual framework that enables readers to understand how intelligent systems are designed, developed, and deployed. Building upon these foundations, the book explores Machine Learning and Deep Learning, which form the technological backbone of contemporary AI systems. Readers are introduced to supervised, unsupervised, semi-supervised, and reinforcement learning techniques, along with neural networks, deep learning architectures, convolutional and recurrent neural networks, transformer models, transfer learning, and practical applications across diverse domains. The growing importance of intelligent communication and perception systems is addressed through comprehensive coverage of Natural Language Processing, Computer Vision, and Generative AI. Topics such as large language models, prompt engineering, conversational AI, image processing, object recognition, multimodal intelligence, and vision-language models are examined in depth. These technologies have revolutionized human-computer interaction and continue to redefine the boundaries of creativity, automation, and digital intelligence. Recognizing the rapid evolution of AI technologies, the book also presents emerging and advanced topics including foundation models, Retrieval-Augmented Generation (RAG), AI agents, neuro-symbolic AI, federated learning, edge AI, TinyML, quantum machine learning, neuromorphic computing, synthetic data, and digital humans. These chapters provide readers with insights into the next generation of AI innovations that are expected to shape future research and industrial applications. As AI systems become increasingly integrated into critical aspects of society, questions of ethics, governance, security, accountability, and trust have become more important than ever. Accordingly, this book includes extensive discussions on Explainable AI (XAI), fairness, transparency, bias mitigation, privacy-preserving AI, governance frameworks, legal and ethical challenges, cybersecurity applications, risk management, and responsible AI deployment. These topics emphasize the importance of developing AI technologies that are not only powerful and efficient but also safe, inclusive, and aligned with human values. One of the distinguishing features of this book is its emphasis on real-world applications and industry transformation. Dedicated chapters explore the role of AI in healthcare, finance, smart manufacturing, Industry 4.0, smart cities, transportation systems, energy management, agriculture, environmental sustainability, climate intelligence, education, scientific research, enterprise AI deployment, and digital transformation. These applications demonstrate how AI is creating ta","url":"https://doi.org/10.5281/zenodo.20608291","authors":["Ts. Dr. Sundresan Perumal, Dr. K. Appathurai, Dr. B. Sathya Bama, Dr. P. Ajitha."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20608291","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.20608292","name":"Artificial Intelligence: From Fundamentals to Real-World Applications BY Ts. Dr. Sundresan Perumal, Dr. K. Appathurai, Dr. B. Sathya Bama, Dr. P. Ajitha.","source":"datacite","abstract":"Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, fundamentally reshaping the way individuals, organizations, industries, and societies operate. From intelligent virtual assistants and autonomous systems to advanced healthcare diagnostics, financial analytics, smart manufacturing, scientific discovery, and generative AI applications, Artificial Intelligence has evolved from a specialized research discipline into a foundational technology that influences nearly every aspect of modern life. Recognizing the growing significance of AI and its multidisciplinary nature, this book, Artificial Intelligence: From Fundamentals to Real-World Applications, has been developed as a comprehensive academic resource for students, educators, researchers, industry professionals, and technology enthusiasts seeking to understand both the theoretical foundations and practical applications of Artificial Intelligence. The book begins by introducing the foundations of Artificial Intelligence, including its historical evolution, core concepts, intelligent agents, knowledge representation, reasoning techniques, search algorithms, mathematical foundations, development environments, and real-world significance. These chapters establish a strong conceptual framework that enables readers to understand how intelligent systems are designed, developed, and deployed. Building upon these foundations, the book explores Machine Learning and Deep Learning, which form the technological backbone of contemporary AI systems. Readers are introduced to supervised, unsupervised, semi-supervised, and reinforcement learning techniques, along with neural networks, deep learning architectures, convolutional and recurrent neural networks, transformer models, transfer learning, and practical applications across diverse domains. The growing importance of intelligent communication and perception systems is addressed through comprehensive coverage of Natural Language Processing, Computer Vision, and Generative AI. Topics such as large language models, prompt engineering, conversational AI, image processing, object recognition, multimodal intelligence, and vision-language models are examined in depth. These technologies have revolutionized human-computer interaction and continue to redefine the boundaries of creativity, automation, and digital intelligence. Recognizing the rapid evolution of AI technologies, the book also presents emerging and advanced topics including foundation models, Retrieval-Augmented Generation (RAG), AI agents, neuro-symbolic AI, federated learning, edge AI, TinyML, quantum machine learning, neuromorphic computing, synthetic data, and digital humans. These chapters provide readers with insights into the next generation of AI innovations that are expected to shape future research and industrial applications. As AI systems become increasingly integrated into critical aspects of society, questions of ethics, governance, security, accountability, and trust have become more important than ever. Accordingly, this book includes extensive discussions on Explainable AI (XAI), fairness, transparency, bias mitigation, privacy-preserving AI, governance frameworks, legal and ethical challenges, cybersecurity applications, risk management, and responsible AI deployment. These topics emphasize the importance of developing AI technologies that are not only powerful and efficient but also safe, inclusive, and aligned with human values. One of the distinguishing features of this book is its emphasis on real-world applications and industry transformation. Dedicated chapters explore the role of AI in healthcare, finance, smart manufacturing, Industry 4.0, smart cities, transportation systems, energy management, agriculture, environmental sustainability, climate intelligence, education, scientific research, enterprise AI deployment, and digital transformation. These applications demonstrate how AI is creating ta","url":"https://doi.org/10.5281/zenodo.20608292","authors":["Ts. Dr. Sundresan Perumal, Dr. K. Appathurai, Dr. B. Sathya Bama, Dr. P. Ajitha."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20608292","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22178214","name":"Computer-Assisted Resolution of the Direct Product Conjecture via Kullback-Leibler (KL) Divergence Information Tensorization and Pinsker-Bounded Simulation Operators (Cloud Security)","source":"datacite","abstract":"Using the Lean 4 interactive theorem prover, this paper presents a machine-verified structural resolution of the Direct Product Conjecture in communication complexity. Extending Hilbert space orthogonal projections and ANOVA-Hoeffding decompositions beyond linear variance, we bridge non-linear information metrics through Kullback-Leibler divergence tensorization, Han's inequality, and a Pinsker-bounded simulation operator. By controlling cross-coordinate conditioning drift, our deductive pipeline formally proves the affirmative resolution: parallel execution cannot bypass single-instance information costs, establishing that: $$CC_\\epsilon(f^k) \\ge k \\cdot R_\\delta(f) - o(k)$$ This rigorously verified lower bound resolves the conjecture, providing essential, provable resource guarantees for secure multi-party computation and distributed cloud infrastructure.","url":"https://doi.org/10.5281/zenodo.22178214","authors":["Reed, Jonathan ƒ(n)"],"tags":["Computer and information sciences","Theoretical Computer Science","Complexity Theory","Statistics and probability","Probability Theory","Communication Complexity","Direct Product Conjecture","Cloud Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22178214","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22178215","name":"Computer-Assisted Resolution of the Direct Product Conjecture via Kullback-Leibler (KL) Divergence Information Tensorization and Pinsker-Bounded Simulation Operators (Cloud Security)","source":"datacite","abstract":"Using the Lean 4 interactive theorem prover, this paper presents a machine-verified structural resolution of the Direct Product Conjecture in communication complexity. Extending Hilbert space orthogonal projections and ANOVA-Hoeffding decompositions beyond linear variance, we bridge non-linear information metrics through Kullback-Leibler divergence tensorization, Han's inequality, and a Pinsker-bounded simulation operator. By controlling cross-coordinate conditioning drift, our deductive pipeline formally proves the affirmative resolution: parallel execution cannot bypass single-instance information costs, establishing that: $$CC_\\epsilon(f^k) \\ge k \\cdot R_\\delta(f) - o(k)$$ This rigorously verified lower bound resolves the conjecture, providing essential, provable resource guarantees for secure multi-party computation and distributed cloud infrastructure.","url":"https://doi.org/10.5281/zenodo.22178215","authors":["Reed, Jonathan ƒ(n)"],"tags":["Computer and information sciences","Theoretical Computer Science","Complexity Theory","Statistics and probability","Probability Theory","Communication Complexity","Direct Product Conjecture","Cloud Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22178215","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.20457919","name":"TOWARDS PROACTIVE CYBER DEFENSE CYBER ATTACK PREDICTION USING ADVANCED AI TECHNIQUES","source":"datacite","abstract":"Develop an AI-based predictive analytics platform that is capable of identifying potential cyber threats. In real time, transformer topologies, deep learning, and graph neural networks replicate complex, high-dimensional security data streams. Temporal sequence models and behavioral analytics can identify attacks prior to their infliction of damage. Networks, system records, and individuals assist us in identifying potential hazards. Mixed learning stabilizes unlabeled data by employing reinforcement learning, self-supervised learning, and supervised learning. People are prepared for assaults through online learning and regulatory concepts. Home-based businesses are safeguarded by privacy-preserving learning and federated AI. The system's high recognition rate and low false alarm rate are demonstrated in numerous real-world and benchmark dataset trials. The design prevents the execution of APTs, zero-day vulnerabilities, and malware that alters its shape. Delete the warnings associated with the AI module. This enables security specialists to make decisions promptly. Growth is expedited by edge-cloud connectivity and distributed training.","url":"https://doi.org/10.5281/zenodo.20457919","authors":["International Journal of Technovation and Business Insights"],"tags":["Predictive Analytics","Cyber Attack Detection","Next-Generation AI","Deep Learning","Graph Neural Networks","Transformers","Intrusion Detection Systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20457919","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.20457920","name":"TOWARDS PROACTIVE CYBER DEFENSE CYBER ATTACK PREDICTION USING ADVANCED AI TECHNIQUES","source":"datacite","abstract":"Develop an AI-based predictive analytics platform that is capable of identifying potential cyber threats. In real time, transformer topologies, deep learning, and graph neural networks replicate complex, high-dimensional security data streams. Temporal sequence models and behavioral analytics can identify attacks prior to their infliction of damage. Networks, system records, and individuals assist us in identifying potential hazards. Mixed learning stabilizes unlabeled data by employing reinforcement learning, self-supervised learning, and supervised learning. People are prepared for assaults through online learning and regulatory concepts. Home-based businesses are safeguarded by privacy-preserving learning and federated AI. The system's high recognition rate and low false alarm rate are demonstrated in numerous real-world and benchmark dataset trials. The design prevents the execution of APTs, zero-day vulnerabilities, and malware that alters its shape. Delete the warnings associated with the AI module. This enables security specialists to make decisions promptly. Growth is expedited by edge-cloud connectivity and distributed training.","url":"https://doi.org/10.5281/zenodo.20457920","authors":["International Journal of Technovation and Business Insights"],"tags":["Predictive Analytics","Cyber Attack Detection","Next-Generation AI","Deep Learning","Graph Neural Networks","Transformers","Intrusion Detection Systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20457920","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21351622","name":"A Drift-Stable Quantum Federated Learning for Intelligent Services","source":"datacite","abstract":"Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services. Intelligent services in this context refer to privacy-sensitive distributed decision systems, such as fraud detection and genomic classification, where reliable and fair client-level learning is as important as the accuracy of the aggregate model. However, heterogeneous client data and noisy quantum optimization oftencause unstable local updates, client drift, and unfair performance between clients. This paper proposes DUQFL-Prox, a drift-stable quantum federated learning framework based on deep-unfolded local optimization. Instead of using a fixed local optimizer, each client performs adaptive unfolded SPSA updates, while aproximal term keeps the local model close to the global model. A lightweight controller learns step-specific optimization parameters to improve post-aggregation performance. Experiments on financial fraud and genomic classification tasks show that DUQFL-Prox improves stability, generalization, and client fairnesscompared with standard QFL baselines. The results suggest that deep-unfolded quantum federated learning can support more reliable and fair intelligent services in heterogeneous distributed environments.","url":"https://doi.org/10.5281/zenodo.21351622","authors":["Nanayakkara, shanika","Pokhrel, Shiva"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21351622","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21351623","name":"A Drift-Stable Quantum Federated Learning for Intelligent Services","source":"datacite","abstract":"Quantum federated learning enables distributed clients to train quantum neural networks without sharing local data, making it promising for privacy-aware intelligent services. Intelligent services in this context refer to privacy-sensitive distributed decision systems, such as fraud detection and genomic classification, where reliable and fair client-level learning is as important as the accuracy of the aggregate model. However, heterogeneous client data and noisy quantum optimization oftencause unstable local updates, client drift, and unfair performance between clients. This paper proposes DUQFL-Prox, a drift-stable quantum federated learning framework based on deep-unfolded local optimization. Instead of using a fixed local optimizer, each client performs adaptive unfolded SPSA updates, while aproximal term keeps the local model close to the global model. A lightweight controller learns step-specific optimization parameters to improve post-aggregation performance. Experiments on financial fraud and genomic classification tasks show that DUQFL-Prox improves stability, generalization, and client fairnesscompared with standard QFL baselines. The results suggest that deep-unfolded quantum federated learning can support more reliable and fair intelligent services in heterogeneous distributed environments.","url":"https://doi.org/10.5281/zenodo.21351623","authors":["Nanayakkara, shanika","Pokhrel, Shiva"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21351623","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21579791","name":"Decentralized Security Mechanisms for AI-Driven Wireless Networks : Integrating Blockchain and Federated Learning","source":"datacite","abstract":"This dissertation explores how blockchain and federated learning can be wedded to create decentralized security measures for AI‑driven wireless networks, with a keen eye on pressing issues like data privacy and integrity. It dives into plenty of quantitative checks – looking at network weaknesses and how current security setups perform – and even throws in some unexpected case comparisons of these technologies in action. In most cases the findings show that this new security layout greatly boosts network resilience and keeps data more confidential, as evidenced by better data integrity and less unauthorized access. A big part of the discussion centers on the health care sector, where keeping patient data safe and meeting strict privacy rules really matters. Generally speaking, by proving that these methods can work in real‑world health care scenarios, the study not only grows our knowledge of cybersecurity in AI‑powered networks but also introduces a fresh framework for upping data security in sensitive settings. The wider implications hint that using decentralized security measures could streamline operations in healthcare systems and, in turn, nurture more trust between patients and providers—ultimately leading to improved patient outcomes and overall satisfaction.","url":"https://doi.org/10.5281/zenodo.21579791","authors":["Kataria, Bhavesh","Jethva, Harikrishna B."],"tags":["Blockchain; Federated Learning; Decentralized Security; Data Privacy; Healthcare Cybersecurity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21579791","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21579792","name":"Decentralized Security Mechanisms for AI-Driven Wireless Networks : Integrating Blockchain and Federated Learning","source":"datacite","abstract":"This dissertation explores how blockchain and federated learning can be wedded to create decentralized security measures for AI‑driven wireless networks, with a keen eye on pressing issues like data privacy and integrity. It dives into plenty of quantitative checks – looking at network weaknesses and how current security setups perform – and even throws in some unexpected case comparisons of these technologies in action. In most cases the findings show that this new security layout greatly boosts network resilience and keeps data more confidential, as evidenced by better data integrity and less unauthorized access. A big part of the discussion centers on the health care sector, where keeping patient data safe and meeting strict privacy rules really matters. Generally speaking, by proving that these methods can work in real‑world health care scenarios, the study not only grows our knowledge of cybersecurity in AI‑powered networks but also introduces a fresh framework for upping data security in sensitive settings. The wider implications hint that using decentralized security measures could streamline operations in healthcare systems and, in turn, nurture more trust between patients and providers—ultimately leading to improved patient outcomes and overall satisfaction.","url":"https://doi.org/10.5281/zenodo.21579792","authors":["Kataria, Bhavesh","Jethva, Harikrishna B."],"tags":["Blockchain; Federated Learning; Decentralized Security; Data Privacy; Healthcare Cybersecurity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21579792","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22179153","name":"Learning Where the Data Lives: A Narrative Review of Federated Learning from Differential Privacy to the Open Problems","source":"datacite","abstract":"Federated learning---training a shared model across many devices that never surrender their data---inverted machine learning's architecture: instead of data to the model, the model to the data. This article presents a narrative review of that arc's canonical line: Dwork and colleagues' 2006 differential privacy, Dwork and Roth's 2014 foundations, Shokri and Shmatikov's 2015 privacy-preserving deep learning, Konecny and colleagues' 2016 communication strategies, McMahan and colleagues' 2017 FedAvg, Bonawitz and colleagues' 2017 secure aggregation, Zhao and colleagues' 2018 non-IID study, Bonawitz and colleagues' 2019 scale design, Yang and colleagues' 2019 concept paper, Zhu, Liu, and Han's 2019 gradient leakage, Li and colleagues' 2020 convergence analysis, and Kairouz and colleagues' 2021 open problems. The synthesis is organized around three themes: privacy, in which differential privacy's calculus and secure aggregation made learning without exposure precise; algorithm, in which FedAvg's weighted averaging met the heterogeneity of real devices and real data; and system, in which scale deployments faced stragglers, leakage, and the statistical reality of non-IID partitions. It is concluded that federated learning is privacy engineering's rare full-stack success---its limits as precisely mapped as its promise---and that its open problems are the field's charter: heterogeneity, security, and the economics of participation.","url":"https://doi.org/10.5281/zenodo.22179153","authors":["Revista, Zen","IA, 10"],"tags":["federated learning","FedAvg","differential privacy","secure aggregation","non-IID data","gradient leakage","distributed optimization","on-device learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22179153","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22179152","name":"Learning Where the Data Lives: A Narrative Review of Federated Learning from Differential Privacy to the Open Problems","source":"datacite","abstract":"Federated learning---training a shared model across many devices that never surrender their data---inverted machine learning's architecture: instead of data to the model, the model to the data. This article presents a narrative review of that arc's canonical line: Dwork and colleagues' 2006 differential privacy, Dwork and Roth's 2014 foundations, Shokri and Shmatikov's 2015 privacy-preserving deep learning, Konecny and colleagues' 2016 communication strategies, McMahan and colleagues' 2017 FedAvg, Bonawitz and colleagues' 2017 secure aggregation, Zhao and colleagues' 2018 non-IID study, Bonawitz and colleagues' 2019 scale design, Yang and colleagues' 2019 concept paper, Zhu, Liu, and Han's 2019 gradient leakage, Li and colleagues' 2020 convergence analysis, and Kairouz and colleagues' 2021 open problems. The synthesis is organized around three themes: privacy, in which differential privacy's calculus and secure aggregation made learning without exposure precise; algorithm, in which FedAvg's weighted averaging met the heterogeneity of real devices and real data; and system, in which scale deployments faced stragglers, leakage, and the statistical reality of non-IID partitions. It is concluded that federated learning is privacy engineering's rare full-stack success---its limits as precisely mapped as its promise---and that its open problems are the field's charter: heterogeneity, security, and the economics of participation.","url":"https://doi.org/10.5281/zenodo.22179152","authors":["Revista, Zen","IA, 10"],"tags":["federated learning","FedAvg","differential privacy","secure aggregation","non-IID data","gradient leakage","distributed optimization","on-device learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22179152","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20775412","name":"Privacy-Preserving Local LLM Inference for Developer Tooling — AI, LLM, Privacy, Sovereign AI, and Post-Cloud Architecture (Anticode)","source":"datacite","abstract":"The proliferation of large language models (LLMs) in developer tooling has introduced significant privacy and security concerns, particularly when code---often containing proprietary algorithms, credentials, and business logic---is transmitted to remote inference servers. This paper presents a comprehensive analysis of privacy-preserving techniques for local LLM inference in the context of terminal-native AI coding engines, with specific application to the ANTIKODE architecture. We examine confidential computing, federated learning, differential privacy, and on-device inference as mechanisms to ensure that source code and developer telemetry never leave the local machine. Through systematic evaluation of model quantization, hardware security modules, and hash-chained audit trails, we demonstrate that local-first LLM inference achieves comparable code generation quality to cloud-based alternatives while eliminating data exfiltration risks. Our findings indicate that 4-bit quantized 7B-parameter models running on consumer hardware can match the functional performance of larger cloud models for the majority of code completion tasks, with privacy guarantees that satisfy SOC2, GDPR, HIPAA, and FedRAMP requirements. We further show that ANTIKODE's .aioss ledger provides cryptographic verification that no inference data has been transmitted externally, establishing a new standard for trusted AI-assisted development. Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores ai, llm in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.","url":"https://doi.org/10.5281/zenodo.20775412","authors":["Alpasan, Lois-Kleinner"],"tags":["sovereign ai","sovereign data","sovereign os","operating systems","post-cloud","compliance","blackboxes","agi"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20775412","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20775413","name":"Privacy-Preserving Local LLM Inference for Developer Tooling — AI, LLM, Privacy, Sovereign AI, and Post-Cloud Architecture (Anticode)","source":"datacite","abstract":"The proliferation of large language models (LLMs) in developer tooling has introduced significant privacy and security concerns, particularly when code---often containing proprietary algorithms, credentials, and business logic---is transmitted to remote inference servers. This paper presents a comprehensive analysis of privacy-preserving techniques for local LLM inference in the context of terminal-native AI coding engines, with specific application to the ANTIKODE architecture. We examine confidential computing, federated learning, differential privacy, and on-device inference as mechanisms to ensure that source code and developer telemetry never leave the local machine. Through systematic evaluation of model quantization, hardware security modules, and hash-chained audit trails, we demonstrate that local-first LLM inference achieves comparable code generation quality to cloud-based alternatives while eliminating data exfiltration risks. Our findings indicate that 4-bit quantized 7B-parameter models running on consumer hardware can match the functional performance of larger cloud models for the majority of code completion tasks, with privacy guarantees that satisfy SOC2, GDPR, HIPAA, and FedRAMP requirements. We further show that ANTIKODE's .aioss ledger provides cryptographic verification that no inference data has been transmitted externally, establishing a new standard for trusted AI-assisted development. Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores ai, llm in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.","url":"https://doi.org/10.5281/zenodo.20775413","authors":["Alpasan, Lois-Kleinner"],"tags":["sovereign ai","sovereign data","sovereign os","operating systems","post-cloud","compliance","blackboxes","agi"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20775413","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22178913","name":"Artificial Intelligence in Biomedical Engineering: Current Advances and Future Perspectives in the United States","source":"datacite","abstract":"Abstract Over the past few years, Artificial Intelligence (AI) has been a game-changer in biomedical engineering, transforming the design, validation and deployment of diagnostic, therapeutic, and monitoring systems across the U.S. healthcare landscape. In this review, recent progress in AI-powered medical imaging, wearable/implantable biosensing, drug discovery and precision medicine, and robotic and neural engineering is summarized, with a focus on the recent field of AI/machine-learning (ML) based medical devices and the regulatory landscape set by the U.S. Food and Drug Administration (FDA) for these devices. Based on peer-reviewed literature published primarily from 2023 to 2025, the review shows the shift of AI from experimental proof-of-concept to FDA regulated clinical products, records the rapid increase in FDA authorizations and outlines some ongoing issues around algorithmic bias, data governance, algorithm interpretability and demographic transparency. The review concludes with a discussion on future avenues for federated learning, foundation models, edge-AI wearables, and adaptive regulatory frameworks like Predetermined Change Control Plans (PCCPs) that will influence the next generation of biomedical AI systems in the United States.","url":"https://doi.org/10.5281/zenodo.22178913","authors":["Evan Samuel, Walker"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.22178913","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.22178914","name":"Artificial Intelligence in Biomedical Engineering: Current Advances and Future Perspectives in the United States","source":"datacite","abstract":"Abstract Over the past few years, Artificial Intelligence (AI) has been a game-changer in biomedical engineering, transforming the design, validation and deployment of diagnostic, therapeutic, and monitoring systems across the U.S. healthcare landscape. In this review, recent progress in AI-powered medical imaging, wearable/implantable biosensing, drug discovery and precision medicine, and robotic and neural engineering is summarized, with a focus on the recent field of AI/machine-learning (ML) based medical devices and the regulatory landscape set by the U.S. Food and Drug Administration (FDA) for these devices. Based on peer-reviewed literature published primarily from 2023 to 2025, the review shows the shift of AI from experimental proof-of-concept to FDA regulated clinical products, records the rapid increase in FDA authorizations and outlines some ongoing issues around algorithmic bias, data governance, algorithm interpretability and demographic transparency. The review concludes with a discussion on future avenues for federated learning, foundation models, edge-AI wearables, and adaptive regulatory frameworks like Predetermined Change Control Plans (PCCPs) that will influence the next generation of biomedical AI systems in the United States.","url":"https://doi.org/10.5281/zenodo.22178914","authors":["Evan Samuel, Walker"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.22178914","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.22134814","name":"AuraOS Paper X Rev.3: A Regenerative, Model-Orthogonal, Source-Bound Cognitive Operating Substrate - Relational World Compilation, Coordinate Memory, HyperScale/ HyperDrive, Runtime Arenas, Proof-Carrying Commons, Recursive Swarms, Universal Host Compilation, and Semantic-Spatial Interfaces","source":"datacite","abstract":"Executive Overview This consolidated release of Paper X unifies empirical findings, mathematical foundations, and real-world implementation proofs for AuraOS—a local-first, zero-extraction computational architecture designed to eliminate recurring cloud SaaS overhead and API token extraction. By decoupling spatial reconstruction, neural synthesis, and automated video orchestration from centralized cloud infrastructure, this work demonstrates that modern consumer hardware (standard laptops and smartphones) can execute high-throughput generative and spatial tasks deterministically at zero marginal cost. Flagship Public Commons Release: The Aura Creator Studio As part of the Aura Commons commitment to public, unrestricted tooling, this release delivers the Aura Creator Studio—a sovereign, automated video production and spatial intelligence suite engineered specifically for independent video editors, YouTube creators, and TikTok content producers: Monocular 3D Spatial Triangulation & SLAM: Extracts 3D metric floorplans, doorway apertures, and 4D entity trajectories from unstructured 2D gameplay/video captures using dynamic HUD exclusion masking, pointmap regression, and Kalman-RTS smoothing. Dual-Sensor Gaussian Splatting (3DGS): Combines stationary laptop camera anchors with mobile orbital scans to bake persistent surface features (e.g., decals, wall artwork) into 3D Gaussians with zero temporal drift. Procedural Media & Multi-Track Synthesis: Features local neural text-to-speech (Edge-TTS / Piper), animated karaoke typography with Bézier bounding pills, and zero-dependency procedural DSP audio synthesis ($140\\text{ Hz} \\to 42\\text{ Hz}$ sub-bass transients) without stock licensing fees. AirLLM & Council V3 Layer Streaming: Executes 8B to 70B parameter open models locally on standard laptop NVMe drives, providing fact-grounded scriptwriting and low-poly 3D graybox pre-visualization with zero cloud API token billing. Sovereign Gate 10 Governance & Attribution DAG: Guarantees non-delegable human approval before publishing while sealing public commons attribution and microtransaction splits into immutable SHA-256 ledgers. The Macro-Economic Amortization Thesis The primary bottleneck for digital creators is platform extraction—a compounding cycle of recurring monthly subscriptions for voice cloning, video splicing, background removal, 3D rendering, and LLM tokens that drains $50 to $300+ per month per creator. When amortized across a community of 100,000 creators, the AuraOS architecture redirects $60,000,000 to $360,000,000 annually from centralized cloud monopolies back into creator equity. By maximizing the idle compute capacity of hardware creators already own, the marginal cost of end-to-end creative production collapses to zero. Open Scientific Invitation: Challenge, Replicate, and Falsify Science advances through rigorous scrutiny, empirical falsification, and open replication. We openly invite computer vision researchers, systems architects, machine learning engineers, and skeptics to: Audit the Mathematical Formulations: Stress-test the Kalman-RTS trajectory smoothing, coordinate back-projection matrices, and Bézier vector geometry. Replicate the Local Benchmarks: Run the provided scripts and verify that complete video assemblies and spatial reconstructions execute fully offline on consumer-grade hardware. Challenge and Extend the Commons: Benchmark the throughput, test edge cases in unconstrained monocular footage, and submit critical evaluations. All code, pipeline orchestrators, and cryptographic verification receipts are open-source and free for public examination and commercial liberation under the Aura Open Commons (CC-BY-SA-4.0). Version 2.0 Changelog Entry (for Zenodo \"Additional Notes\") Markdown ### Version 2.0 Update Notes - Consolidated multi-modal spatial tracking proofs and 3D Gaussian Splatting manifests. - Added full architectural specification for the Aura Creator Studio (Public Commons Release 1). - Integrat","url":"https://doi.org/10.5281/zenodo.22134814","authors":["Courchene, Dallas"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22134814","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22177051","name":"AuraOS Paper X Rev.3: A Regenerative, Model-Orthogonal, Source-Bound Cognitive Operating Substrate - Relational World Compilation, Coordinate Memory, HyperScale/ HyperDrive, Runtime Arenas, Proof-Carrying Commons, Recursive Swarms, Universal Host Compilation, and Semantic-Spatial Interfaces","source":"datacite","abstract":"Executive Overview This consolidated release of Paper X unifies empirical findings, mathematical foundations, and real-world implementation proofs for AuraOS—a local-first, zero-extraction computational architecture designed to eliminate recurring cloud SaaS overhead and API token extraction. By decoupling spatial reconstruction, neural synthesis, and automated video orchestration from centralized cloud infrastructure, this work demonstrates that modern consumer hardware (standard laptops and smartphones) can execute high-throughput generative and spatial tasks deterministically at zero marginal cost. Flagship Public Commons Release: The Aura Creator Studio As part of the Aura Commons commitment to public, unrestricted tooling, this release delivers the Aura Creator Studio—a sovereign, automated video production and spatial intelligence suite engineered specifically for independent video editors, YouTube creators, and TikTok content producers: Monocular 3D Spatial Triangulation & SLAM: Extracts 3D metric floorplans, doorway apertures, and 4D entity trajectories from unstructured 2D gameplay/video captures using dynamic HUD exclusion masking, pointmap regression, and Kalman-RTS smoothing. Dual-Sensor Gaussian Splatting (3DGS): Combines stationary laptop camera anchors with mobile orbital scans to bake persistent surface features (e.g., decals, wall artwork) into 3D Gaussians with zero temporal drift. Procedural Media & Multi-Track Synthesis: Features local neural text-to-speech (Edge-TTS / Piper), animated karaoke typography with Bézier bounding pills, and zero-dependency procedural DSP audio synthesis ($140\\text{ Hz} \\to 42\\text{ Hz}$ sub-bass transients) without stock licensing fees. AirLLM & Council V3 Layer Streaming: Executes 8B to 70B parameter open models locally on standard laptop NVMe drives, providing fact-grounded scriptwriting and low-poly 3D graybox pre-visualization with zero cloud API token billing. Sovereign Gate 10 Governance & Attribution DAG: Guarantees non-delegable human approval before publishing while sealing public commons attribution and microtransaction splits into immutable SHA-256 ledgers. The Macro-Economic Amortization Thesis The primary bottleneck for digital creators is platform extraction—a compounding cycle of recurring monthly subscriptions for voice cloning, video splicing, background removal, 3D rendering, and LLM tokens that drains $50 to $300+ per month per creator. When amortized across a community of 100,000 creators, the AuraOS architecture redirects $60,000,000 to $360,000,000 annually from centralized cloud monopolies back into creator equity. By maximizing the idle compute capacity of hardware creators already own, the marginal cost of end-to-end creative production collapses to zero. Open Scientific Invitation: Challenge, Replicate, and Falsify Science advances through rigorous scrutiny, empirical falsification, and open replication. We openly invite computer vision researchers, systems architects, machine learning engineers, and skeptics to: Audit the Mathematical Formulations: Stress-test the Kalman-RTS trajectory smoothing, coordinate back-projection matrices, and Bézier vector geometry. Replicate the Local Benchmarks: Run the provided scripts and verify that complete video assemblies and spatial reconstructions execute fully offline on consumer-grade hardware. Challenge and Extend the Commons: Benchmark the throughput, test edge cases in unconstrained monocular footage, and submit critical evaluations. All code, pipeline orchestrators, and cryptographic verification receipts are open-source and free for public examination and commercial liberation under the Aura Open Commons (CC-BY-SA-4.0). Version 2.0 Changelog Entry (for Zenodo \"Additional Notes\") Markdown ### Version 2.0 Update Notes - Consolidated multi-modal spatial tracking proofs and 3D Gaussian Splatting manifests. - Added full architectural specification for the Aura Creator Studio (Public Commons Release 1). - Integrat","url":"https://doi.org/10.5281/zenodo.22177051","authors":["Courchene, Dallas"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22177051","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21578565","name":"QAFL-ZT: Quantum-Enhanced Adversarial-Aware Federated Learning for Zero-Trust Network Intrusion Detection","source":"datacite","abstract":"In cyberattacks and distributed computing environments era network intrusion detection systems has faced unprecedented challenges. There are some struggles with traditional centralized approaches that has some concern privacy concerns, computational scalability, and adversarial evasion tactics. The suggested framework QAFL-ZT consists of these quantum-enhanced feature extraction, federated learning, adversarial robustness training, and zero-trust security policy, which cooperatively can be used to detect intrusion. This paper is an investigation into urgent, but a problem that understudied, the impact of the non-independent data and the (non-IID) data that identically distributed on the performance of federated intrusion detection systems. The experimental evaluation on the benchmarks of UNSW-NB15 dataset prove a proper data distribution strategies that demonstrates and primary contributing approximately 4.5% accuracy improvement, surpassing gains from an algorithmic sophistication alone. The optimized QAFL-ZT framework has achieve 89.28% accuracy with exceptional 98.51% precision, that indicating a production deployment that has high reliability. Interestingly, the ablation studies reveal that contributes modestly (1-2%) of quantum feature enhancement compared to addressing data heterogeneity, challenging prevailing assumptions about quantum computing affect cybersecurity in immediate practical impact. These findings suggest that before advanced quantum techniques applies the foundational distributed learning challenges must be resolved that can demonstrate their full potential.","url":"https://doi.org/10.5281/zenodo.21578565","authors":["Zwayyer, Mustafa Hussein"],"tags":["Federated Learning; Quantum Machine Learning; Adversarial Training; Zero-Trust Architecture; Network Intrusion Detection; Non-IID Data; Cybersecurity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21578565","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21578566","name":"QAFL-ZT: Quantum-Enhanced Adversarial-Aware Federated Learning for Zero-Trust Network Intrusion Detection","source":"datacite","abstract":"In cyberattacks and distributed computing environments era network intrusion detection systems has faced unprecedented challenges. There are some struggles with traditional centralized approaches that has some concern privacy concerns, computational scalability, and adversarial evasion tactics. The suggested framework QAFL-ZT consists of these quantum-enhanced feature extraction, federated learning, adversarial robustness training, and zero-trust security policy, which cooperatively can be used to detect intrusion. This paper is an investigation into urgent, but a problem that understudied, the impact of the non-independent data and the (non-IID) data that identically distributed on the performance of federated intrusion detection systems. The experimental evaluation on the benchmarks of UNSW-NB15 dataset prove a proper data distribution strategies that demonstrates and primary contributing approximately 4.5% accuracy improvement, surpassing gains from an algorithmic sophistication alone. The optimized QAFL-ZT framework has achieve 89.28% accuracy with exceptional 98.51% precision, that indicating a production deployment that has high reliability. Interestingly, the ablation studies reveal that contributes modestly (1-2%) of quantum feature enhancement compared to addressing data heterogeneity, challenging prevailing assumptions about quantum computing affect cybersecurity in immediate practical impact. These findings suggest that before advanced quantum techniques applies the foundational distributed learning challenges must be resolved that can demonstrate their full potential.","url":"https://doi.org/10.5281/zenodo.21578566","authors":["Zwayyer, Mustafa Hussein"],"tags":["Federated Learning; Quantum Machine Learning; Adversarial Training; Zero-Trust Architecture; Network Intrusion Detection; Non-IID Data; Cybersecurity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21578566","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21578272","name":"A Review : Enhancing Cybersecurity with Quantum Cryptography and Federated SMOTE-ADA Boost Framework for Detecting Credit Card Fraud","source":"datacite","abstract":"The rapid growth of digital financial transactions has significantly increased the risk of credit card fraud, making robust cybersecurity mechanisms essential for modern banking systems. Traditional fraud detection approaches often face limitations related to data privacy, class imbalance, and evolving fraud patterns. This review explores an advanced cybersecurity framework that integrates quantum cryptography, federated learning, and an SMOTE-ADA Boost classification strategy for secure and efficient credit card fraud detection. Quantum cryptography strengthens communication security through quantum key distribution and eavesdropping resistance, while federated learning enables collaborative model training without centralized data sharing, thereby preserving privacy. To address the severe imbalance between legitimate and fraudulent transactions, the Synthetic Minority Oversampling Technique (SMOTE) is incorporated with ADA Boost to improve minority-class detection and overall classification performance. Recent studies show that privacy-preserving federated frameworks, combined with sampling optimization, significantly improve fraud-detection accuracy and robustness in distributed financial environments. This review critically analyses the architecture, methodologies, advantages, limitations, and future research directions of such integrated systems, highlighting their potential for next-generation financial cybersecurity applications.","url":"https://doi.org/10.5281/zenodo.21578272","authors":["Rangare, Rohini","Tiwari, Susheel Kumar"],"tags":["Credit card fraud detection; cybersecurity; quantum cryptography; federated learning; SMOTE; ADA Boost; privacy-preserving AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21578272","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.21578273","name":"A Review : Enhancing Cybersecurity with Quantum Cryptography and Federated SMOTE-ADA Boost Framework for Detecting Credit Card Fraud","source":"datacite","abstract":"The rapid growth of digital financial transactions has significantly increased the risk of credit card fraud, making robust cybersecurity mechanisms essential for modern banking systems. Traditional fraud detection approaches often face limitations related to data privacy, class imbalance, and evolving fraud patterns. This review explores an advanced cybersecurity framework that integrates quantum cryptography, federated learning, and an SMOTE-ADA Boost classification strategy for secure and efficient credit card fraud detection. Quantum cryptography strengthens communication security through quantum key distribution and eavesdropping resistance, while federated learning enables collaborative model training without centralized data sharing, thereby preserving privacy. To address the severe imbalance between legitimate and fraudulent transactions, the Synthetic Minority Oversampling Technique (SMOTE) is incorporated with ADA Boost to improve minority-class detection and overall classification performance. Recent studies show that privacy-preserving federated frameworks, combined with sampling optimization, significantly improve fraud-detection accuracy and robustness in distributed financial environments. This review critically analyses the architecture, methodologies, advantages, limitations, and future research directions of such integrated systems, highlighting their potential for next-generation financial cybersecurity applications.","url":"https://doi.org/10.5281/zenodo.21578273","authors":["Rangare, Rohini","Tiwari, Susheel Kumar"],"tags":["Credit card fraud detection; cybersecurity; quantum cryptography; federated learning; SMOTE; ADA Boost; privacy-preserving AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21578273","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.21578268","name":"Enhancing Cybersecurity with Quantum Cryptography and Federated SMOTE-ADA Boost Framework for Detecting Credit Card Fraud","source":"datacite","abstract":"The detection of credit card fraud has become increasingly difficult due to the sheer volume of transactions and the inherently imbalanced nature of fraud data, in which fraudulent activity accounts for only a small fraction of overall transactions. Traditional machine learning models often struggle with these minority instances, leading to higher false-negative rates. This paper introduces a novel approach that combines quantum cryptography-enhanced Federated Synthetic Minority Over-Sampling Technique (SMOTE) with ADA Boost to address the challenges of fraud detection in imbalanced datasets. Our framework utilizes federated learning to ensure data privacy by decentralizing the data, in line with stringent cybersecurity requirements. Quantum cryptography further strengthens security by protecting the communication channels involved in federated learning. The SMOTE integration addresses data imbalance by generating synthetic samples of minority-class instances, while ADA Boost improves model performance by prioritizing hard-to-classify cases. When tested on a real-world credit card fraud dataset, our framework achieved notable results, including an accuracy of 91.5%, precision of 90%, recall of 89%, and an F1-score of 88%. These metrics reflect a substantial improvement over traditional models, particularly in reducing false negatives and increasing overall detection accuracy. This study underscores the potential of combining quantum cryptography with advanced machine learning techniques to create secure, privacy-preserving fraud detection systems. Our results highlight the significant advancements this integrated approach offers to the field of cybersecurity, providing a robust and scalable solution for detecting fraud in highly imbalanced datasets.","url":"https://doi.org/10.5281/zenodo.21578268","authors":["Rangare, Rohini","Tiwari, Susheel Kumar"],"tags":["Quantum Cryptography; Federated Learning; Credit Card Fraud Detection; Imbalanced Data; SMOTE-ADA Boost Framework"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21578268","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.21578269","name":"Enhancing Cybersecurity with Quantum Cryptography and Federated SMOTE-ADA Boost Framework for Detecting Credit Card Fraud","source":"datacite","abstract":"The detection of credit card fraud has become increasingly difficult due to the sheer volume of transactions and the inherently imbalanced nature of fraud data, in which fraudulent activity accounts for only a small fraction of overall transactions. Traditional machine learning models often struggle with these minority instances, leading to higher false-negative rates. This paper introduces a novel approach that combines quantum cryptography-enhanced Federated Synthetic Minority Over-Sampling Technique (SMOTE) with ADA Boost to address the challenges of fraud detection in imbalanced datasets. Our framework utilizes federated learning to ensure data privacy by decentralizing the data, in line with stringent cybersecurity requirements. Quantum cryptography further strengthens security by protecting the communication channels involved in federated learning. The SMOTE integration addresses data imbalance by generating synthetic samples of minority-class instances, while ADA Boost improves model performance by prioritizing hard-to-classify cases. When tested on a real-world credit card fraud dataset, our framework achieved notable results, including an accuracy of 91.5%, precision of 90%, recall of 89%, and an F1-score of 88%. These metrics reflect a substantial improvement over traditional models, particularly in reducing false negatives and increasing overall detection accuracy. This study underscores the potential of combining quantum cryptography with advanced machine learning techniques to create secure, privacy-preserving fraud detection systems. Our results highlight the significant advancements this integrated approach offers to the field of cybersecurity, providing a robust and scalable solution for detecting fraud in highly imbalanced datasets.","url":"https://doi.org/10.5281/zenodo.21578269","authors":["Rangare, Rohini","Tiwari, Susheel Kumar"],"tags":["Quantum Cryptography; Federated Learning; Credit Card Fraud Detection; Imbalanced Data; SMOTE-ADA Boost Framework"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21578269","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.20329758","name":"Federated Machine Learning for IoT/IoMT Security and Network Intelligence in HIPAA-Regulated Environments","source":"datacite","abstract":"The increase in the deployment of Internet-of-Things (IoT) and Internet-of-Medical-Things (IoMT) devices in healthcare systems has posed unprecedented cybersecurity challenges that compromise patient safety and data integrity, on the one hand, but introduce demanding regulatory compliance provisions under HIPAA systems, on the other hand. Conventional centralized machine learning methods of network security are not sufficient in healthcare settings where privacy laws do not allow aggregation of uncoded patient data, device logs, and network traffic patterns in centralized repositories. The new concept called federated learning represents a groundbreaking solution that promotes the idea of collaborative intelligence between the distributed nodes without the need to place raw data in a centralized place. The suggested architecture coordinates distributed device fingerprinting, real-time anomaly detection, and behavioral analytics among heterogeneous network nodes, which include medical devices, clinical equipment, and enterprise IoT endpoints, by local model training and encrypted parameter aggregation. Privacy-preserving solutions such as differential privacy, homomorphic encryption, and secure multi-party computation are designed such that even updates of a model cannot be deanonymised to reveal sensitive information whilst maintaining the same detection ability as in centralized methods. Experimental analyses reveal that federated intelligence can significantly improve the detection of advanced multi-stage attacks and low-frequency anomalies by combining patterns that can be seen across many institutions and achieve high accuracy in device fingerprinting and anomaly detection with a low false positive rate, which would be appropriate in a clinical setting. The framework has been able to strike the right balance between the necessity to ensure security and the need to provide privacy, which allows healthcare institutions to combine their efforts to protect against changing cyber threats without violating data sovereignty and regulatory requirements. Application in a wide range of healthcare settings confirms that federated principles hold strong performance in the context of inherent heterogeneity of devices in terms of population, distribution, and computational resources, and can offer a technically viable route to improved network intelligence in controlled healthcare environments.","url":"https://doi.org/10.5281/zenodo.20329758","authors":["Nagappan Nagappan Palaniappan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20329758","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20329759","name":"Federated Machine Learning for IoT/IoMT Security and Network Intelligence in HIPAA-Regulated Environments","source":"datacite","abstract":"The increase in the deployment of Internet-of-Things (IoT) and Internet-of-Medical-Things (IoMT) devices in healthcare systems has posed unprecedented cybersecurity challenges that compromise patient safety and data integrity, on the one hand, but introduce demanding regulatory compliance provisions under HIPAA systems, on the other hand. Conventional centralized machine learning methods of network security are not sufficient in healthcare settings where privacy laws do not allow aggregation of uncoded patient data, device logs, and network traffic patterns in centralized repositories. The new concept called federated learning represents a groundbreaking solution that promotes the idea of collaborative intelligence between the distributed nodes without the need to place raw data in a centralized place. The suggested architecture coordinates distributed device fingerprinting, real-time anomaly detection, and behavioral analytics among heterogeneous network nodes, which include medical devices, clinical equipment, and enterprise IoT endpoints, by local model training and encrypted parameter aggregation. Privacy-preserving solutions such as differential privacy, homomorphic encryption, and secure multi-party computation are designed such that even updates of a model cannot be deanonymised to reveal sensitive information whilst maintaining the same detection ability as in centralized methods. Experimental analyses reveal that federated intelligence can significantly improve the detection of advanced multi-stage attacks and low-frequency anomalies by combining patterns that can be seen across many institutions and achieve high accuracy in device fingerprinting and anomaly detection with a low false positive rate, which would be appropriate in a clinical setting. The framework has been able to strike the right balance between the necessity to ensure security and the need to provide privacy, which allows healthcare institutions to combine their efforts to protect against changing cyber threats without violating data sovereignty and regulatory requirements. Application in a wide range of healthcare settings confirms that federated principles hold strong performance in the context of inherent heterogeneity of devices in terms of population, distribution, and computational resources, and can offer a technically viable route to improved network intelligence in controlled healthcare environments.","url":"https://doi.org/10.5281/zenodo.20329759","authors":["Nagappan Nagappan Palaniappan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20329759","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20252753","name":"Federated Learning Architecture for Privacy-Preserving AI","source":"datacite","abstract":"This article presents a comprehensive examination of federated learning architecture for privacy-preserving ai, addressing the critical challenges and opportunities at the intersection of architecture, advanced system architecture, and artificial intelligence. The study synthesizes insights from peer-reviewed references spanning digital twin security, adaptive defense frameworks, deep learning-based anomaly detection, cloud-IoT security management, encrypted search optimization, 5G network security, massive MIMO signal processing, privacy-preserving architectures, and generative model applications. Each reference is individually cited and contextualized within the broader discourse on federated orchestration, secure aggregation, differential privacy, and model convergence. The article examines how these diverse research contributions collectively inform the design, implementation, and evaluation of robust solutions for contemporary security and architectural challenges. By integrating technical analyses with organizational and practical considerations, this work provides a holistic perspective that is relevant to both researchers and practitioners working to advance the state of the art in architecture.","url":"https://doi.org/10.5281/zenodo.20252753","authors":["Ali Ahamed","Safa Mohamed"],"tags":["Machine Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20252753","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20252754","name":"Federated Learning Architecture for Privacy-Preserving AI","source":"datacite","abstract":"This article presents a comprehensive examination of federated learning architecture for privacy-preserving ai, addressing the critical challenges and opportunities at the intersection of architecture, advanced system architecture, and artificial intelligence. The study synthesizes insights from peer-reviewed references spanning digital twin security, adaptive defense frameworks, deep learning-based anomaly detection, cloud-IoT security management, encrypted search optimization, 5G network security, massive MIMO signal processing, privacy-preserving architectures, and generative model applications. Each reference is individually cited and contextualized within the broader discourse on federated orchestration, secure aggregation, differential privacy, and model convergence. The article examines how these diverse research contributions collectively inform the design, implementation, and evaluation of robust solutions for contemporary security and architectural challenges. By integrating technical analyses with organizational and practical considerations, this work provides a holistic perspective that is relevant to both researchers and practitioners working to advance the state of the art in architecture.","url":"https://doi.org/10.5281/zenodo.20252754","authors":["Ali Ahamed","Safa Mohamed"],"tags":["Machine Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20252754","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20493550","name":"Sensitive-Attribute-Agnostic Fair Federated Learning Against Fairness Attacks","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20493550","authors":["Guo, YiChen"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20493550","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.20493551","name":"Sensitive-Attribute-Agnostic Fair Federated Learning Against Fairness Attacks","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20493551","authors":["Guo, YiChen"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20493551","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:18.937Z"},{"id":"doi:10.5281/zenodo.22175626","name":"Application of Artificial Intelligence Frameworks in Development of Medical Imaging Diagnosis Systems: A Comprehensive Review of Novel Methodologies, Clinical Validation, Performance Optimization, and Future Perspectives","source":"datacite","abstract":"Artificial intelligence (AI) has revolutionized medical imaging with automated disease detection, image segmentation, diagnosis, prognosis prediction, and clinical decision support across various imaging modalities, such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), retinal imaging, and digital pathology. Recent advances in deep learning, transformer architectures, multimodal learning and foundation models have significantly improved the diagnostic accuracy and reduced the reliance on handcrafted feature engineering. However, challenges like data heterogeneity, model interpretability, external validation, privacy preservation, computational efficiency, and regulatory compliance still hinder the widespread clinical implementation.In this paper, this review presents a comprehensive study on the evolution of modern AI frameworks in medical imaging by integrating recent methodological advances with perspectives on clinical translation. The review covers the latest deep learning architectures, such as convolutional neural networks, Vision Transformers, hybrid CNN–Transformer models, multimodal learning frameworks, generative artificial intelligence, diffusion models, federated learning, privacy-preserving learning, and medical foundation models. In addition, the review covers the cutting-edge explainable AI techniques, including Grad-CAM, SHAP, LIME, and attention visualization, for boosting transparency and clinician confidence. The review also discusses the state-of-the-art performance optimization strategies, including transfer learning, active learning, domain adaptation, neural architecture search, hyperparameter optimization, model compression, and computational resource optimization. Equally important, the latest developments in clinical validation, external evaluation, robustness assessment, fairness, uncertainty estimation, regulatory considerations, and deployment frameworks are critically analyzed to underscore their role in facilitating safe clinical implementation.The review analysis concludes with the identification of key research challenges and directions for the future including multimodal foundation models, vision-language systems, retrieval-augmented generation,","url":"https://doi.org/10.5281/zenodo.22175626","authors":["Susreeti Sur","Rakesh Kumar, Mandal","Debanil, Chanda"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22175626","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.21203/rs.3.rs-9754545/v1","name":"Federated Learning for Intrusion Detection in Internet of Medical Things (IoMT): A PRISMA-Based Systematic Literature Review","source":"europepmc","abstract":"Abstract The rapid expansion of Internet of Medical Things (IoMT) devices has introduced new opportunities for real-time healthcare monitoring but also increased cybersecurity risks. Traditional centralized intrusion detection systems (IDS) struggle to scale across distributed IoMT networks while preserving sensitive patient data. Federated Learning (FL) offers a privacy-preserving alternative, enabling collaborative model training without sharing raw data. This study presents a PRISMA-based systematic literature review of FL applications for IoMT intrusion detection, analyzing 86 peer-reviewed studies published between 2015 and 2025 from Scopus, IEEE Xplore, Web of Science, and PubMed. Findings indicate that FL-based IDS frameworks achieve competitive accuracy (85–93%) and F1-scores (~ 0.87), yet face challenges in handling non-IID heterogeneous data, optimizing energy and communication-efficiency, and ensuring robust privacy guarantees. Based on the synthesis, we propose a conceptual framework integrating privacy-preserving aggregation, adaptive federated optimization, and edge-intelligence mechanisms, tailored to IoMT constraints. This work provides a comprehensive foundation for developing scalable, privacy-aware, and resource-efficient IoMT intrusion detection systems, while identifying key research gaps and future directions for enhancing robustness, personalization, and real-world deployment.","url":"https://doi.org/10.21203/rs.3.rs-9754545/v1","authors":["Ifeanyi Nwokoro","Edgar Osaghae","Saheed Kayode","Tombari Sibe"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9754545/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-10018675/v1","name":"HashGuard-FL: Learnable Hash-Based GAN Discriminator with Post-Quantum Audit Trail for Federated Learning Integrity","source":"europepmc","abstract":"Abstract Federated learning (FL) deployments in safety-critical domains face two entangled integrity problems: (i) detecting adversarial model updates at runtime, and (ii) producing a long-lived, tamper-evident audit trail whose authenticity must survive post-quantum cryptanalysis. Existing Byzantine-robust aggregation rules, gradient anomaly detectors, and cryptographic provenance schemes each address one dimension but none provides an integrated solution that achieves fine-grained multi-class detection, non-IID robustness, and quantum-resistant auditability simultaneously. We propose HashGuard-FL, a framework that couples a learnable hash-based GAN discriminator with a post-quantum audit layer built on SHAKE-256 transcripts and ML-DSA-65 (CRYSTALS-Dilithium, NIST FIPS-204) signatures. The discriminator projects each client update into a compact 128-bit locality-sensitive hash space and learns, via adversarial and contrastive training, a decision boundary separating five operationally defined attack classes (C0-C4: benign, poisoned, unstable, replayed, and tampered) without sharing raw gradients. The audit layer commits every round decision to an append-only, BQP-hard verifiable transcript. Across four benchmarks (MNIST, CIFAR-10, FEMNIST, PTB-XL ECG), HashGuard-FL achieves macro-F1 of 0.962 and AUROC of 0.989, outperforming eight baselines by up to 0.111 F1 points while adding only 2.3 ms per-round overhead. A clinical simulation over 500 rounds confirms sustained accuracy and a stable FPR of 0.034 under adaptive adversaries.","url":"https://doi.org/10.21203/rs.3.rs-10018675/v1","authors":["Nilima Dongre"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10018675/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-9670145/v1","name":"DVFL-IIoT: Dynamic, Verifiable, and Decentralized Federated Learning with Key Insulation for Industrial Internet of Things","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9670145/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9670145/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-8883453/v1","name":"Decoupled Text-Guided Distillation for Efficient Federated Learning on Edge Devices","source":"preprints","abstract":"Abstract Federated Learning (FL) enables the collaborative training of models across heterogeneous edge devices while preserving data privacy; however, its performance degrades significantly under domain shift. While integrating Vision-Language Models (VLMs) can mitigate this, existing prompt-tuning methods typically remain coupled to the massive VLM backbone during inference, rendering them impractical for resource-constrained edge devices. To address this challenge, we propose CLIP-assisted Domain-Invariant Federated Learning (CDIFed), which decouples the VLM from the deployment model to enhance robustness without incurring high inference latency. This framework integrates a Text-Guided Domain Adapter, implemented as a parameter-efficient bottleneck module, which aligns visual features with invariant text-based anchors to filter domain-specific noise while maintaining class-discriminative semantics. CDIFed operates through a communication-efficient two-phase framework: clients first adapt a frozen CLIP teacher, and then the adapted teacher supervises the training of a lightweight student network via feature knowledge distillation. Unlike previous approaches, the heavy VLM is discarded after adaptation, and only the student model parameters are transmitted to the server for aggregation. Experiments on the Digits and Office-Caltech benchmarks demonstrate that CDIFed significantly outperforms state-of-the-art methods in federated domain generalisation while maintaining the inference efficiency required for heterogeneous edge devices.","url":"https://doi.org/10.21203/rs.3.rs-8883453/v1","authors":["Younghan Kim","Yongjae Park","Jae Won Cho","Jungchan Cho"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8883453/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9878190/v1","name":"E-FLAPS: An Edge–Federated Learning Framework with Anomaly-Aware Training for Robust Stock Price Prediction","source":"europepmc","abstract":"Abstract Accurate and privacy-aware stock price prediction in distributed financial environments remains a challenging task, particularly under data heterogeneity and volatile market conditions. This paper presents E-FLAPS, a hybrid edge–Federated Learning (FL) framework that integrates BiLSTM networks with a Federated Anomaly-Aware Training (FAAT) strategy that embeds LOF-based anomaly filtering directly into each client’s local training loop, ensuring only reliable data contributes to federated model updates. The framework enables multiple financial institutions to collaboratively train a global model without sharing raw data, while edge-based preprocessing reduces latency and limits the propagation of noisy or anomalous patterns. Experiments conducted on realworld stock market data (2005–2024) across five major stocks (AAPL, MSFT, TSLA, NVDA, JPM) demonstrate that FAAT-based filtering alone reduces global MSE by approximately 11.54% over a standard FedAvg + BiLSTM baseline, and E-FLAPS achieves a 99.8% reduction in global MSE compared with the re-implemented M-A-BiLSTM baseline, driven predominantly by the federated architecture’s isolation of stock-specific dynamics — which prevents the cross-stock interference that causes M-A-BiLSTM to fail on NVDA — and further reinforced by FAAT-based anomaly suppression. Replacing FedAvg with FedProx reduces global MSE by a further 47.0%, confirming that proximal regularisation complements FAAT under non-IID financial data distributions. While FL improves data locality and reduces direct data exposure, this work operates at the system-level privacy tier without formal guarantees such as differential privacy or secure aggregation.","url":"https://doi.org/10.21203/rs.3.rs-9878190/v1","authors":["MUSTAFA AL SAMARA"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9878190/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.21203/rs.3.rs-9709885/v1","name":"Physics-Informed Neural Networks for Glucose-Insulin Digital Twins Leveraging Federated Learning and IoBNT","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9709885/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9709885/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202602.0371.v1","name":"Zero-Knowledge Federated Learning for Privacy-Preserving 5G Authentication","source":"europepmc","abstract":"The fifth-generation (5G) networks are facing critical security challenges in device authenti- cation for massive Internet of Things deployments while preserving privacy. Traditional federated learning approaches depend on the computationally expensive homomorphic encryption to protect model gradients, resulting in substantial latency, communication over- head, and the energy consumption impractical for resource-constrained 5G devices. This paper proposes zero-knowledge federated learning (ZK-FL), eliminating homomorphic encryption by enabling devices to prove model correctness without revealing gradients. Our approach integrates zero-knowledge proofs with FL updates, where each device generates where each device generates a proof Proofi = ZK(Gradienti, Hashi), demon- strating computational integrity.Experimental results from 10,000 authentication attempts demonstrate ZK-FL achieves 78.4 ms average authentication latency versus 342.5 ms for homomorphic encryption-based FL (77% reduction), proof sizes of 0.128 KB versus 512 KB (99.97% reduction), and energy consumption of 284.5 mJ versus 6.525 mJ (95% reduc- tion), while maintaining 99.3% authentication success rate with formal privacy guarantees. These results demonstrate ZK-FL enables practical privacy-preserving authentication for massive-scale 5G deployment.","url":"https://doi.org/10.20944/preprints202602.0371.v1","authors":["Ahmed Lateef Salih Al-Karawi","Rafet Akdeniz"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202602.0371.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.20944/preprints202603.1104.v1","name":"Adaptive Federated Learning for Privacy-Preserving Modeling in Heterogeneous Financial Environments","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202603.1104.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202603.1104.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9108015/v1","name":"Multi-Modal Federated Learning with Differential Privacy for Privacy-Preserving Healthcare AI","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9108015/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9108015/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-8681063/v1","name":"Standardized API Call Protocols for implementing Federated Learning in FAIRDatabase","source":"preprints","abstract":"Abstract The rapid expansion of machine learning methodologies in biomedical research has intensified the tension between the demand for large scale data analysis and the stringent privacy regulations governing sensitive health data. The integration of federated learning with FAIR-compliant databases necessitates a carefully engineered application programming interface (API) that reconciles multiple, partially competing requirements: preservation of the data governance, provenance, and access control mechanisms mandated by the FAIR principles; support for the iterative and stateful communication patterns inherent to federation learning protocols; maintenance of modularity to enable independent evolution and replacement of both database and machine learning components; and adherence to standards that promote long term interoperability and facilitate future extensions and ecosystem development. In this context, we propose a systematic methodology for designing and implementing standardised APIs that enable FAIR data repositories to support collaborative machine learning while respecting the governance, access control, and compliance requirements of the underlying database systems. This work contributes to a replicable framework that can be applied to other databases that seek to enable collaborative science at scale while maintaining the privacy protections essential for sensitive health information.","url":"https://doi.org/10.21203/rs.3.rs-8681063/v1","authors":["Sem de Regt","Roland V. Bumbuc","Vivek M. Sheraton"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8681063/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-9263961/v1","name":"The Impact of Federated Learning Maturity on Supply Chain Resilience of External Organizations: Evidence from China","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9263961/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9263961/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.64898/2026.03.25.26349286","name":"Federated Learning Performance Depends on Site Variation in Global HIV Data Consortia","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.03.25.26349286","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.03.25.26349286","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9258601/v1","name":"A Two-Stage Byzantine-Robust Defense Framework for Federated Learning in Distributed Non-IID Environments","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9258601/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9258601/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-8389432/v1","name":"PSO-Driven Client Selection for Federated Learning in IDS applications","source":"preprints","abstract":"Abstract Federated Learning has emerged as a highly promising distributed and collaborative learning paradigm, enabling local clients to train models without sharing their raw data. However, the global model’s performance is often impacted by heterogeneity and variability at the client level, making the selection of clients in each training round a critical factor for overall model effectiveness.In this paper, we propose a novel client selection strategy within the federated learning framework that leverages Particle Swarm Optimization (PSO) to identify the most suitable participants for training a high-performing model in fewer communication rounds. The PSO-based algorithm dynamically adjusts the contribution weights of client models based on their performance and stability metrics, aiming to improve global model accuracy, accelerate convergence, and enable smarter, adaptive model aggregation.We apply this approach specifically to develop a collaborative intrusion detection system for cybersecurity in Edge IIoT environments. Clients are selected based on key criteria such as data quality and individual model performance. Experimental evaluation on a real-world Edge IIoT dataset demonstrates that our solution achieves over 91% accuracy in the global model while reducing the number of communication rounds by approximately 40% compared to the traditional Federated Averaging (FedAvg) method.These results highlight that the proposed approach not only significantly boosts the accuracy of the global model but also accelerates its convergence, making it a robust and efficient solution for Intrusion Detection Solutions (IDS) applications.","url":"https://doi.org/10.21203/rs.3.rs-8389432/v1","authors":["Aymen WAli","Maher Boughdiri","Hichem Mrabet","Abderrazek Jemai"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8389432/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.21203/rs.3.rs-9534380/v1","name":"FedCRM-DP: A Privacy-Preserving Federated Learning Framework for Secure Cross-Organization CRM Intelligence","source":"preprints","abstract":"Abstract Collaborative intelligence across regulated enterprises remains constrained by stringent data-sharing restrictions imposed by frameworks such as GDPR, HIPAA, and CCPA. This paper presents FedCRM-DP, a privacy-preserving federated learning framework designed for secure cross-organization Customer Relationship Management (CRM) systems. The framework unifies three complementary mechanisms: FedProx-based optimization for non-IID enterprise data heterogeneity, calibrated Differential Privacy via gradient clipping and Gaussian noise injection, and Secure Aggregation (simulated via pairwise random masking) to prevent server-side inspection of individual model updates. Together, these components enable institutions—banks, hospitals, insurers, and large-scale platform operators—to jointly train predictive CRM models without exposing raw customer records. We validate the framework on the UCI Bank Marketing dataset, modeled as a lead conversion prediction task distributed across five demographically partitioned enterprise clients. Experiments compare six learning strategies using Accuracy, F1-score, AUC-ROC, convergence efficiency, and privacy-utility trade-off metrics. FedCRM-DP achieves Accuracy 0.8850, F1-score 0.5720, and AUC-ROC 0.8760 at privacy budget ε = 5—closely approaching the centralized upper bound while preserving meaningful privacy guarantees that centralized deployment cannot provide. Ablation results confirm that ε = 5 represents the Pareto-optimal operating point balancing compliance and predictive utility. The findings establish FedCRM-DP as a practical foundation for trustworthy enterprise AI deployment across regulated industries.","url":"https://doi.org/10.21203/rs.3.rs-9534380/v1","authors":["Nikhil Donapati"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9534380/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-10229272/v1","name":"FedVIB–AGP: Defending Against Distributed Backdoor Attacks in Federated Learning via Variational Information Bottleneck and Activation-Gap Pruning","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10229272/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10229272/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-8191856/v1","name":"A Heterogeneity-Aware Privacy-Preserving Federated Learning Framework Using Ensemble Clustering for Healthcare Applications","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8191856/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8191856/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-7753956/v1","name":"Federated Learning-Based Intrusion DetectionFramework for Enhancing Security in Internet ofThings Environments","source":"preprints","abstract":"Abstract The exponential expansion of the Internet of Things (IoT) has introduced substantial cybersecurity challenges, rendering interconnected devices increasingly vulnerable to cyberattacks. To address these issues, we propose a federated learning-based intrusion detection framework leveraging Long Short-Term Memory (LSTM) networks and the Federated Averaging (FedAvg) algorithm. The system was evaluated on two benchmark datasets: UNSW-NB15 and ToN-IoT. Results show that the federated LSTM achieves 98.44% accuracy, F1-score of 92.09%, and a false positive rate of 0.0074, compared to the centralized model with 96.21% accuracy, F1-score of 84.10%, and a false positive rate of 0.03. These findings confirm that federated learning enhances both detection performance and privacy preservation, making it suitable for large-scale, heterogeneous IoT environments.","url":"https://doi.org/10.21203/rs.3.rs-7753956/v1","authors":["yasmine labiod","oussama cheraita"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-7753956/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202601.0271.v1","name":"Federated Learning: A Survey of Core Challenges, Current Methods, and Opportunities","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202601.0271.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202601.0271.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.64898/2026.04.30.721881","name":"NeuroFLAME: A Scalable, Privacy-Preserving Federated Learning Platform for Collaborative, Secure, and Reproducible Multi-Site Neuroimaging","source":"europepmc","abstract":"","url":"https://doi.org/10.64898/2026.04.30.721881","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.04.30.721881","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-8245367/v1","name":"Research on Multicenter Ovarian Cancer Diagnosis Based on Federated Learning","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8245367/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8245367/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.20944/preprints202604.1102.v1","name":"TrustGraph-DFL: Byzantine-Resilient Decentralized Federated Learning via Consistency-Weighted Neighborhood Aggregation","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202604.1102.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202604.1102.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9265069/v1","name":"Adaptive network security defense method combining multi-level federated learning and adversarial training","source":"preprints","abstract":"Abstract In order to enhance the network security defense capability in distributed environments, this paper proposes an adaptive defense method that combines multi-level federated learning and adversarial training. This method has demonstrated excellent performance in multiple network attack scenarios. In DDoS attack scenarios, its accuracy reaches 96.8% and F1 score is 0.962, which is significantly better than traditional methods. When facing unknown types of attacks such as zero day attacks and new DDoS attacks, the detection rates are 83.7% and 87.6%, respectively. In addition, this method performs outstandingly in terms of communication efficiency, with a single round communication data volume of only 745.3MB for 100 participants, which is 26.1% of the traditional method. The experimental results show that this method effectively reduces communication overhead and system delay while ensuring defense accuracy, and has good robustness and scalability.","url":"https://doi.org/10.21203/rs.3.rs-9265069/v1","authors":["Yuanyuan Wang"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9265069/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9316077/v1","name":"Hybrid Transformer-Based Recommender System with LLM-Assisted Semantic Modeling for Sequential and Federated Learning","source":"preprints","abstract":"Abstract Sequential recommender systems based on transformers have shown good performance to model the user interaction dynamics, although they usually depend mainly on the interaction data and fail to use rich semantic data that exists in item metadata. This is a major constraint especially in cases of sparsity and cold-start when the interaction histories are not enough to model preference accurately. This paper presents the suggestion of LLMTransRec, a hybrid Transformer-based recommendation model combining collaborative interaction cues, semantic representations generated by pretrained language models, and content-based features into a single model. In order to successfully integrate these heterogeneous modalities, we propose a context-sensitive gating system, which dynamically weighs the contributions of these modalities on the context of the sequence of interactions between a user. We test the suggested framework on a variety of benchmark datasets having different sparsity and richness of content. The experimental findings prove that the model attains stable improvements when compared to strong sequential and graph-based baselines on an integrated sampled evaluation protocol. Other studies, such as ablation analysis and cold-start analysis indicate that semantic features and adaptive fusion can help enhance the robustness of recommendations. On the whole, the findings indicate that the integration of semantic representations into sequential recommendation models is a viable way of improving the performance in the sparse-data context.","url":"https://doi.org/10.21203/rs.3.rs-9316077/v1","authors":["Lakshmi Bai Maddala","Rajendra Pamula","Katteda Subbarao"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9316077/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.12688/openreseurope.24549.1","name":"Range-risk estimation for electric vehicles in low-coverage mountain road segments: A digital-twin study with prequential beacon-based Federated Learning","source":"europepmc","abstract":"","url":"https://doi.org/10.12688/openreseurope.24549.1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.12688/openreseurope.24549.1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-10533987/v1","name":"An Adaptive Resource-Aware MK-CKKS Framework with Linear- Complexity Multi-Key Aggregation for Privacy-Preserving Federated Learning in Resource-Constrained IoT Environments","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10533987/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10533987/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.21203/rs.3.rs-7839869/v1","name":"Federated Learning under the Uncertainty Principle of Machine Learning","source":"preprints","abstract":"Abstract Inspired by the fundamental insights of quantum mechanics' uncertainty principle, this paper introduces the \"Uncertainty Principle of Machine Learning\": an intrinsic and irreducible trade-off exists between a model's generalization capability and privacy protection, with their product bounded below by a constant determined by data distribution and model complexity. We transcend the conventional view of differential privacy as merely a technical tool, redefining it as a necessary perturbation introduced when \"measuring\" machine learning systems. Based on this principle, we construct a novel theoretical framework for federated learning and rigorously derive the theoretical limits of privacy-utility trade-offs. Furthermore, we propose an uncertainty-guided adaptive differential privacy algorithm whose privacy budget allocation strategy is directly derived from this principle. Experiments on multiple benchmark datasets validate the existence of theoretical lower bounds and demonstrate that our algorithm achieves superior approximation to these fundamental limits.","url":"https://doi.org/10.21203/rs.3.rs-7839869/v1","authors":["Zhonghui XUE","Yazheng Dang"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7839869/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202608.1758.v1","name":"A Multi-Layer Trustworthy Security Architecture for Smart-City IoT: Edge AI Intrusion Detection, Poisoning-Resilient Federated Learning and Ledger-Anchored Model Provenance","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202608.1758.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.1758.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.20944/preprints202604.1919.v1","name":"Federated Learning, Game Theory and Block Chain Hybrid for Coordination of Multi-Prosumer Grid-Connected Microgrids","source":"preprints","abstract":"The increasing penetration of distributed renewable energy resources and electric vehicles has transformed microgrids into complex multi-prosumer systems that require coordinated control. Traditional centralized and local independent control strategies fail to exploit distributed flexibility and often lead to sub-optimal renewable utilization and inefficient energy management. This paper proposes a new method for coordinating these multi-prosumer microgrids using a hybrid coordination framework that utilizes Federated Learning for forecasting, game theory for energy trading, and blockchain for transaction recording through a decentralized network of peer to peer transactions between prosumers. Additionally, using the principles of model predictive control the battery algorithm was trained to make optimal decisions about present and probable future conditions of each microgrid node. By conducting simulations on heterogeneous networks of multi-prosumer microgrids, the system demonstrated a significant increase in renewable energy utilization by up to 91.2% and provided for greater coordination across three (3) microgrids through energy trading, fairness, and energy efficiency while also maintaining adequate levels of voltage regulation and power quality. In comparison, the baseline controller only achieved a lower operational cost. The results revealed essential trade-offs between local optimality and system coordination leading to the design of next generation decentralized microgrids.","url":"https://doi.org/10.20944/preprints202604.1919.v1","authors":["Nicholas Nyaika"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202604.1919.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202604.1670.v1","name":"A Federated Learning-Based Distributed Solar Forecasting for Smart Buildings in Muscat, Oman Using GRU Networks","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202604.1670.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202604.1670.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202605.0653.v1","name":"Federated Learning over 5G/6G Networks: Dynamic Client Selection and Resource Allocation for Heterogeneous Edge Environments","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202605.0653.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202605.0653.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202603.2175.v1","name":"Federated Learning, Mobile Emotion Recognition, and Client-Side Data Quality: A Survey and Research Agenda","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202603.2175.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202603.2175.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-8457292/v1","name":"Federated Learning-Based Cervical CancerClassification Using a Novel HybridKAN-ViT-Autoencoder Architecture","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8457292/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8457292/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.22541/au.177188132.29122732/v1","name":"Energy-Aware Client Selection in Hierarchical Federated Learning via Supervised and Metaheuristic Algorithms","source":"preprints","abstract":"Hierarchical federated learning enables scalable and communication-efficient distributed training by introducing intermediate edge servers between clients and the central server. However, high energy consumption and limited network resources-such as bandwidth and staleness handling-remain critical challenges in real-world deployments, particularly in heterogeneous and non-IID environments. To address this, we propose two energy-aware client selection algorithms. The first, HEPS-ML, is a supervised learning-based method that enables clients to autonomously decide participation based on energy availability, system resources, recent model performance, and data characteristics. The second, HEPS-SCA, uses the sine cosine algorithm to dynamically optimize client selection by balancing model accuracy, energy usage, and latency. A multi-armed bandit algorithm governs edge server selection at the central level, adapting aggregation based on historical performance and network conditions. The proposed approach is evaluated using CIFAR-10 and MNIST datasets. We additionally calibrate and validate the energy estimator used in our evaluation via tier-specific microbenchmarks, and we include an ablation study that isolates the impact of MAB-based edge-server selection. Experimental results show that our two-stage client selection reduces total training energy by approximately 65-80% relative to strong baselines using CIFAR-10 and MNIST, while maintaining comparable global model accuracy under non-IID client distributions.","url":"https://doi.org/10.22541/au.177188132.29122732/v1","authors":["Silvana Trindade","Nelson L S Da Fonseca"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.22541/au.177188132.29122732/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.21203/rs.3.rs-9305687/v1","name":"Protecting Collaborative Education: A Blockchain-Powered Trust and Reward System to Reduce Poisoning Attacks in Federated Learning","source":"preprints","abstract":"Abstract Federated Learning (FL) is a game-changing idea in education that enables institutions to create robust AI models for individualized learning while maintaining the highest level of privacy for student data. However, due to its decentralized nature, Federated Education is very vulnerable to Poisoning Attacks (data and model poisoning), where nodes are compromised to provide false information to the global model, causing a substantial decline in its accuracy. This research aims to introduce a special Blockchain-based Trust Management and Incentive Framework for Federated Education to address this critical security threat. Our proposed design is a dynamic and decentralized system of reputation, facilitated by self-executing Smart Contracts and cryptographic validation. Real-time trust scores are generated based on an assessment of the dependability of each participating node in the past, in addition to cryptographic validation of learning. Although malevolent nodes that launch poisoning attacks are easily identified, penalized, and excluded from the aggregation, honest and contributing institutions with high-quality and localized updates to models are algorithmically rewarded with tokenized incentives. Based on preliminary simulations, our blockchain-based system is able to counter targeted poisoning attacks, with a global model accuracy of over 95% maintained even with up to 30% compromised nodes, while traditional FL models see a considerable reduction in accuracy (up to 30-40%) with 15% malicious nodes. This proposed system for the future of AI in education is not only able to counter negative threats but is also in a position to foster a system that is transparent, sustainable, and highly collaborative.","url":"https://doi.org/10.21203/rs.3.rs-9305687/v1","authors":["Din Mohammad Toufik"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9305687/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202601.1728.v1","name":"Communication-Efficient Federated Learning for Real-Time Anti-Money-Laundering Monitoring","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202601.1728.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202601.1728.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.21203/rs.3.rs-8724094/v1","name":"Standardized API Design for Privacy-Preserving Federated Learning in FAIR-Compliant Biomedical Databases","source":"preprints","abstract":"Abstract The rapid expansion of machine learning methodologies in biomedical research has intensified the tension between the demand for large scale data analysis and the stringent privacy regulations governing sensitive health data. The integration of federated learning with FAIR-compliant databases necessitates a carefully engineered application programming interface (API) that reconciles multiple, partially competing requirements: preservation of the data governance, provenance, and access control mechanisms mandated by the FAIR principles; support for the iterative and stateful communication patterns inherent to federation learning protocols; maintenance of modularity to enable independent evolution and replacement of both database and machine learning components; and adherence to standards that promote long term interoperability and facilitate future extensions and ecosystem development. In this context, we propose a systematic methodology for designing and implementing standardised APIs that enable FAIR data repositories to support collaborative machine learning while respecting the governance, access control, and compliance requirements of the underlying database systems. This work contributes to a replicable framework that can be applied to other databases that seek to enable collaborative science at scale while maintaining the privacy protections essential for sensitive health information.","url":"https://doi.org/10.21203/rs.3.rs-8724094/v1","authors":["Sem de Regt","Roland V. Bumbuc","Vivek M. Sheraton"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8724094/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.21203/rs.3.rs-7488594/v1","name":"FedDPGu: Adaptive Prompt-tuning with Built-in Unlearning for Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7488594/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7488594/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.21203/rs.3.rs-8584839/v1","name":"SGA-FL NIDS: A Similarity-Gated Asynchronous Federated Learning for Network Intrusion Detection","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8584839/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8584839/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.21203/rs.3.rs-8480260/v1","name":"FL FraDet: Federated Learning for Privacy-PreservingFraud Detection in Mobile Money Systems","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8480260/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8480260/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.21203/rs.3.rs-8240248/v1","name":"Fair Client Selection Method for Federated Learning Based on Discretized Firefly Algorithm","source":"europepmc","abstract":"Abstract Federated Learning (FL) enables collaborative model training without exchanging sensitive local data, ensuring privacy and advancing distributed machine learning. However, in edge scenarios, FL faces challenges of data heterogeneity, device resource constraints, and fairness imbalance among small-data clients, making it difficult to balance performance, efficiency, and fairness. To tackle this, we propose the Discrete Firefly Algorithm (DFA) for fair client selection in FL, mapping clients to fireflies, retaining the brightness attraction\"core while adapting to discrete selection. DFA quantifies brightness through data volume and historical contributions, optimizes efficiency with selective sampling, and guarantees fairness for small-data clients. Experiments on MNIST, Fashion-MNIST, and CIFAR-10 demonstrate DFA outperforms baselines: achieving 73.12\\((%)\\) accuracy on CIFAR-10 (2.56\\((%)\\) and 10.82\\((%)\\) higher than random selection and Power-of-Choice), with lower overhead, 2.4\\((%)\\) performance improvement for small-data clients, and compliance with the principle of contribution-matching benefit.","url":"https://doi.org/10.21203/rs.3.rs-8240248/v1","authors":["XiaoYe Li","Yangyang Zhang","Zhenlong Sun","Wei Zhao"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8240248/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.20944/preprints202603.1776.v1","name":"STellar-FL: A Decentralized Federated Learning Architecture for Scalable Cross-Institution AI Under Network-Constrained Environments","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202603.1776.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202603.1776.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-8740219/v1","name":"FedGIS-Water: A Federated Learning-Enhanced GIS Framework for Circular Water Infrastructure Assessment in Railway Stations","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8740219/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8740219/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-8472732/v1","name":"Adaptive Privacy-Preserving Split-Hierarchical Federated Learning for Resource-Constrained IoT Networks","source":"preprints","abstract":"Abstract The proliferation of Internet-of-Things (IoT) devices necessitates efficient machine learning paradigms that address bandwidth constraints, privacy requirements, and computational heterogeneity. While hierarchical federated learning offers com- munication efficiency and split learning reduces computational burden on resource-constrained devices, existing approaches lack adaptive mechanisms for dynamic environments and formal privacy guarantees. We propose AP-SHFL (Adaptive Privacy- Preserving Split-Hierarchical Federated Learning), a novel three- tier architecture that jointly optimizes split point selection, hierarchical aggregation, and differential privacy mechanisms. Our approach employs Q-learning for per-client dynamic split point adaptation based on real-time loss and communication feed- back, while implementing staleness-adaptive differential privacy that calibrates noise injection according to model freshness in asynchronous settings. Experimental results on MNIST demon- strate 99.19% test accuracy, 40-60% communication reduction compared to FedAvg baseline, rapid convergence (&gt;98.5% in &lt;10 rounds), and adaptive split point evolution from an average of 1.10 to 2.75 across clients, showcasing effective per-client optimization. Ablation studies confirm the contribution of each component. Our framework achieves state-of-the-art results, outperforming HSFL (98.1% accuracy) while maintaining formal differential privacy guarantees.","url":"https://doi.org/10.21203/rs.3.rs-8472732/v1","authors":["Yashraj Sakunde"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8472732/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.64898/2026.04.24.26351702","name":"Multi-Hospital Electronic Health Record Foundation Models Without Data Sharing: A Comparison of Federated Learning and Inference-Time Ensembling","source":"pubmed","abstract":"Foundation models for electronic health records (EHRs) perform strongly on clinical prediction, but every published model has been trained within a single health system. No multi-institutional EHR foundation model currently exists, largely because privacy regulations and governance barriers block data pooling across hospitals. Two strategies could build such models without pooling: federated learning (exchanges model weights) and inference-time ensembling (exchanges only predictions at query time). Whether either is viable for autoregressive EHR foundation models, and whether individual hospitals benefit from participating, is not established.","url":"https://doi.org/10.64898/2026.04.24.26351702","authors":["Elemento O"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.04.24.26351702","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-8613070/v1","name":"Adversarial attack detection in resource-constrained environments: A stable and sequential federated learning architecture with TinyLlama-1.1B","source":"preprints","abstract":"Abstract Large Language Models (LLMs) face training challenges on resource-constrained devices, and the performance losses caused by compression methods necessitate a ‘full fine-tuning’ approach. In this study, a Mutex-based architecture is proposed for the full fine-tuning of the TinyLlama-1.1B model in a federated learning environment. The proposed method prevents Out of Memory (OOM) errors by queuing GPU access while minimizing system load and providing an efficient training process through ‘Incremental Averaging’ and FP16 optimization on the server side.In experiments conducted on 5 clients using the TCAB dataset, the conventional federated learning method failed due to memory constraints, while the proposed method completed training with a 100 % success rate. As a result of the training, the model's accuracy in detecting adversarial attacks increased from 60.84% to 99.02%, and the balanced distribution of Precision and Recall values proved that the model did not develop bias. Additionally, FP16 optimization on the server side resulted in a 3.2 GB memory savings and reduced server computation costs. The findings reveal that despite increasing the total training time (latency), the proposed architecture enables large language models to be trained securely and stably with full fine-tuning, even on resource-constrained edge devices.","url":"https://doi.org/10.21203/rs.3.rs-8613070/v1","authors":["Sevim Ceylan Böcekçi","Kazım Yıldız"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8613070/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-8161081/v1","name":"Federated Learning Enhanced YOLOv8 for Privacy-Preserving Student Classroom Behavior Recognition","source":"preprints","abstract":"Abstract In the field of smart education, real-time object detection for analyzing student classroom behaviors provides valuable, objective data to help teachers optimize teaching methods and improve student learning experiences. This supports a positive and engaging classroom environment. However, models like YOLOv8 face challenges in real-world settings, such as varying object scales (\"far small near large\"), frequent occlusions, class imbalances, and privacy concerns when sharing data across institutions. Current datasets are often simulated, small in scale, and lack diversity, which limits their ability to reflect actual classroom conditions. To address these issues, this paper introduces FedYOLO-Behavior, a federated learning (FL) enhanced YOLOv8 framework that ensures privacy while recognizing behaviors effectively. We build a large, real-world database covering educational stages from kindergarten to university, combined with open-source data for augmentation. Local models are improved with multi-head self-attention (MHSA) for better context understanding, Ghost Convolution for efficiency, and Focal-EIoU loss to handle imbalances and small objects. FL with differential privacy allows safe collaboration between schools without sharing raw data. Experiments show significant improvements: mAP from 81.8% to 85.6%, precision from 77.9% to 82.3%, recall from 75.4% to 79.1%, inference speed increased by 18.2% (reaching 112 FPS), and parameters reduced by 25.1%, with a privacy budget of ε=0.9. This work promotes innovative, secure AI applications in education, contributing to national goals in technology and harmonious learning environments.","url":"https://doi.org/10.21203/rs.3.rs-8161081/v1","authors":["Shuai Ma","Heyou Chang","Jian Han","Hao Zheng"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8161081/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-8808416/v1","name":"FedVR360: Federated Learning enabled Privacy-Preservation for VR 360° Video Streaming in Vehicular Edge Computing","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8808416/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8808416/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202512.0118.v1","name":"A Comprehensive Survey of Federated Learning for Edge AI: Recent Trends and Future Directions","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202512.0118.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.0118.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.37044/osf.io/5psfj_v1","name":"Towards Federated Learning Across Biobanks: Prototype Software from the 2026 Carnegie Mellon University–NVIDIA Hackathon","source":"preprints","abstract":"","url":"https://doi.org/10.37044/osf.io/5psfj_v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.37044/osf.io/5psfj_v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-8376153/v1","name":"ProtoMFL: A Robust Multimodal Federated Learning Framework via Cross-Modal Prototype Integration","source":"europepmc","abstract":"Abstract Multimodal federated learning (MFL) has made substantial progress in aggregating multimodal knowledge across distributed environments. However, it still encounters persistent challenges caused by modality-missing data at the client level. Traditional knowledge distillation–based approaches provide limited performance in handling these modality-missing scenarios. To mitigate the performance degradation caused by modality dropout, this paper proposes a prototype-based multimodal federated learning framework, termed Prototype-based Multimodal Federated Learning (ProtoMFL). By replacing sample-level representations with category-level prototypes as knowledge carriers, ProtoMFL enables more efficient cross-modal knowledge aggregation. The ProtoMFL framework consists of three core components. Cross-Modal Prototype Regularisation reduces distributional discrepancies between client and global models. Cross-Modal Prototype Contrast enhances the aggregation of similar prototypes and separation of dissimilar ones through contrastive learning. Cross-Modal Alignment enforces semantic alignment between modalities at the feature level, thereby mitigating the adverse effects of modality dropout. Experimental results show that ProtoMFL significantly outperforms existing methods in both accuracy and robustness across multiple benchmark datasets. Even under severe modality dropout, ProtoMFL maintains stable performance, achieving an average improvement of approximately 2.8% over the baseline CreamFL model without prototype mechanisms. This improvement effectively mitigates model drift issues caused by heterogeneous modalities.","url":"https://doi.org/10.21203/rs.3.rs-8376153/v1","authors":["Junsun Zhang","Chaochao Sun","Yuan Peng"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8376153/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.22541/au.176479439.99762263/v1","name":"Federated Learning Approach Using Transfer Learning Architectures for Lung Cancer Detection","source":"europepmc","abstract":"There have been many advancements in the field of medical imaging but even then, accurate cancer detection remains a challenge because of limited labelled data. In the research field, a lot of work is already done for this task, using pre-trained features from prominent architectures like VGG16, ResNet50, MobileNetV2, InceptionV3, and DensNet121. These approaches face the issue of privacy of patients' sensitive information and unnecessary latency of exchange of data from nodes to sever, so in this paper, we use Federated Learning that enables collaborative learning across geographically distributed medical institutions. Additionally, we implement differential privacy techniques to obscure the patients' identities which would further enhance privacy protection. This paper also presents the evaluation of effectiveness of different transfer learning architectures within the FL setting, comparing their performance with centralized learning and standalone transfer learning approaches. This work adds a new direction to cancer detection with improved privacy protection, leading to earlier intervention for cancer detection.","url":"https://doi.org/10.22541/au.176479439.99762263/v1","authors":["Purvi Choure","Shaligram Prajapat","Krishan Berwal"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.176479439.99762263/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7978012/v1","name":"A Robust Federated Learning Method for Data Heterogeneity with Enhanced Momentum-guided Aggregation","source":"preprints","abstract":"Abstract Federated learning (FL) is an emerging distributed machine learning paradigm that enables multiple edge devices to collaboratively train a model for a specific task while preserving privacy. Yet, due to the non-independent and identically distributed (Non-IID) data dispersed on edge devices, FL suffers from slow convergence and low accuracy. This paper focuses on this problem, and proposes a robust method through enhanced data sharing. Specifically, this paper adopts feature distillation to obtain the performance sensitive features, which are used to generating proxy data for initializing public data before FL training. Meanwhile, We use a momentum-guided strategy for parameter aggregation. In order to evaluate the performance of the method, this paper also conducts many experiments. As demonstrated by the results, the method could outperform the state-of-the-art methods by 5.31% in terms of performance.","url":"https://doi.org/10.21203/rs.3.rs-7978012/v1","authors":["Linhai Nie","Jin Wang","naixuan Hu"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7978012/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-8533915/v1","name":"SHIELD-Health: Secure Healthcare IoT with Energy-efficient Ledger-based Distributed Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8533915/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8533915/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-8158990/v1","name":"A Robust Vehicle to Network Communication using Quantum Blockchain Cryptography and Federated Learning","source":"europepmc","abstract":"Abstract Vehicular Ad Hoc Networks (VANETs) are direct network communications established between vehicles and roadside infrastructure. This form of communication is designed to provide a seamless autonomous driving experience through constant data sharing among autonomous vehicles and their surrounding infrastructures. However, this massive data exchange creates significant data vulnerability and security threats, such as Denial of Service (DoS) attacks, spoofing, and many others within the V2N communication network. To address this problem, the study proposes a novel quantum-resistant framework integrating blockchain technology and federated learning to enhance the security and privacy of V2N communications. In accordance with this objective, a design science research methodology was adopted, and a framework was developed using VEINS as the primary simulation tool for modelling vehicular networks and communication behaviours. In addition, the study employed the use of TensorFlow Federated and Hyperledger Fabric for client model aggregation and a tamper-proof ledger that manages model updates and access requests, respectively. The results of the study reveal that the proposed framework has a 92.4% rate of malicious node detection during the first three FL rounds, an increased speed of 28%, and a reduced packet overhead of 35% as compared to traditional ECC-based methods. Finally, under high-density traffic conditions, the network communication performance improved, with latency decreased by 18% and throughput increased by 22% due to the improved security framework. Highlighting these results, this study provides a unique contribution to research, as it is the first to combine blockchain, Federated Learning, and Post Quantum Cryptography into a singular V2N security framework and achieves results that surpass traditional approaches. While the result provides a promising security indication, future work must extend testing to real-world vehicular environments and explore lightweight consensus mechanisms.","url":"https://doi.org/10.21203/rs.3.rs-8158990/v1","authors":["Koomson Robert"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8158990/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-9056562/v1","name":"Distribution-Aware Federated Learning for Diabetes Prediction Using Tabular Clinical DataUnder Non-IID and Class-Imbalanced Settings","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9056562/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9056562/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202601.1584.v1","name":"The Convergence of Federated Learning, Knowledge Graphs, and Large Language Models for Language Learning: A Scoping Review","source":"europepmc","abstract":"Large Language Models (LLMs) in Intelligent Computer-Assisted Language Learning (iCALL) offer personalization potential but introduce critical challenges in pedagogical grounding, data privacy, and pedagogical validity. While Knowledge Graphs (KGs) and Federated Learning (FL) address these concerns individually, systematic integra-tion of all three technologies remains absent or insufficiently addressed in current re-search. This scoping review maps the FL–KG–LLM convergence landscape in educa-tional contexts. Following PRISMA-ScR guidelines, we searched six databases and screened 51 papers published between 2019 and 2025 using automated extraction. Our findings reveal a pronounced convergence deficit: no papers integrate all three domains, while 58.8% of approaches operate within isolated technological silos. Criti-cal reporting gaps emerge across the corpus, with an average “Not Reported” (NR) rate of 84.5%, particularly in privacy mechanisms (92.2%), validation metrics (90.2%), and Common European Framework of Reference for Languages (CEFR) alignment (88.2%). Domain-specific analysis reveals two distinct patterns: inter-domain gaps (disciplinary silos resulting in expected CEFR absence in single-domain papers) and intra-domain gaps (failure to report domain-critical variables, including 100% parameter NR in FL studies, 86.7% validation NR in KG studies, and 100% CEFR NR in convergence pa-pers). We identify two pillars of pedagogical grounding: a Grounding Pillar, which con-strains LLM outputs via Knowledge Graph rules, and a Validation Pillar, which con-cerns how authoritative source frameworks are mapped onto Knowledge Graph schemas. The latter remains completely unaddressed in the reviewed literature, re-vealing what we term the Integrity Gap—a systematic disconnection between techno-logical innovation and pedagogical grounding in iCALL. By framing pedagogical alignment as an upstream control and validation problem, this review offers insights relevant to the design of user-facing automated systems where trust, transparency, and human oversight are critical.","url":"https://doi.org/10.20944/preprints202601.1584.v1","authors":["Michael Kenteris","Konstantinos Kotis"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202601.1584.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202602.0130.v1","name":"Blockchain and Federated Learning for Cross-Border Credential Verification: A Policy Framework for Central Asian Higher Education","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202602.0130.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202602.0130.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202507.2399.v1","name":"Vessel Traffic Density Prediction: A Federated Learning Approach","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202507.2399.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.2399.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202602.0219.v1","name":"Federated Learning with Smartphone Apps for Privacy-Preserving Chronic Disease Management and Cognitive Decline Detection in Seniors","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202602.0219.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202602.0219.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202512.0877.v1","name":"Mitigating Data Sparsity and Privacy Risks in Educational Recommender System through Federated Learning","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202512.0877.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.0877.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202510.0300.v1","name":"SpaceTime: A Deep Similarity Defense Against Poisoning Attacks in Federated learning","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.0300.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.0300.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202509.0828.v1","name":"Federated Learning for Secure Data Sharing Across Distributed Networks","source":"preprints","abstract":"Federated learning (FL) has emerged as a transformative paradigm for collaborative model training without the need to centralize sensitive information. By enabling multiple participants to train a shared model locally and only exchange model updates, FL preserves privacy while leveraging the diversity of distributed data. This approach is particularly significant in domains such as healthcare, finance, and industrial Internet of Things, where data confidentiality and compliance with regulatory standards are critical. Despite its promise, FL faces challenges related to security vulnerabilities, communication overhead, and model aggregation fairness across heterogeneous networks. Recent advances in secure aggregation, differential privacy, and blockchain integration have shown potential in mitigating these risks while ensuring trust among participants. This paper examines the role of federated learning as a mechanism for secure data sharing across distributed networks, highlighting its core advantages, limitations, and future directions for achieving scalable and resilient decentralized intelligence.","url":"https://doi.org/10.20944/preprints202509.0828.v1","authors":["Lawal G. Anand"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.0828.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202601.1729.v1","name":"Neuro-Symbolic Federated Learning with Quantum-Safe Cognitive Twins for Personality-Aware Human-AI Collaboration","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202601.1729.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202601.1729.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202507.1160.v1","name":"Privacy-Enhanced Federated Learning for Distributed Heterogeneous Data","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202507.1160.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.1160.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7109247/v1","name":"Model Poisoning Attacks to Federated Learning based on Fake Clients","source":"preprints","abstract":"Abstract The increasing use of decentralized and anonymous networks creates vast amounts of darknet traffic, offering opportunities to enhance network security by detecting threats, filtering malicious activity, and identifying anomalies through improved traffic classification. Federated Learning (FL) presents a promising approach for decentralized data processing, allowing models to be trained across distributed devices while preserving data privacy. However, FL is vulnerable to poisoning attacks, where adversarial clients can degrade the performance of the global model. In this paper, we utilize a rich dataset that captures encrypted darknet traffic to develop new methods for defending against model poisoning attacks.We propose novel attack strategies based on fake clients and gradient inversion: Model Poisoning Attack based on Fake Clients (MPAF), Gradient Descent Inversion Attack (GDIA), and Selective Aggregation Poisoning Attack (SAPA). Alongside these attacks, we introduce two defense strategies: Adaptive Weighting in Aggregation (AWA) and Statistical Outlier Filtering (SOF). Experimental results show that attacks like GDIA can drastically reduce accuracy to 0% and the MPAF attack reduces accuracy to approximately 32.38%. The AWA defense notably restores accuracy under GDIA to around 80.95% and under MPAF to about 93.33%, clearly outperforming SOF. After refining the attack implementations by strengthening the base model for MPAF and reducing the intensity of GDIA, MPAF became significantly stronger, bringing accuracy down to 0%. However, GDIA exhibited more controlled degradation, with AWA defense still effectively stabilizing accuracy at approximately 72.06%.","url":"https://doi.org/10.21203/rs.3.rs-7109247/v1","authors":["Mani Ghahremani","Alan Metwally","Rahim Taheri"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7109247/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-6821919/v1","name":"AP-PPFL: An Anti-poisoning Privacy-preserving Federated Learning Method","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6821919/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6821919/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.21203/rs.3.rs-6920944/v1","name":"MMVO-SHFL: A Fair and Efficient Hierarchical Federated Learning","source":"preprints","abstract":"Abstract Federated learning (FL) enables collaborative model training without centralizing data. However, the traditional FL framework is cloud-based and suffers from high communication latency. On the other hand, the edge-based FL framework, although reducing communication latency by leveraging edge servers, suffers from degraded model accuracy due to the limited data access of these servers. To overcome these limitations, this work introduces a novel hierarchical federated learning framework named MMVO - SHFL. It incorporates a bandwidth prediction based on LSTM, a unique MAB - Driven dynamic client selection strategy and an MVO - Guided model parameter optimization mechanism. Extensive experiments show that MMVO-SHFL significantly improves model convergence speed while also enhancing model accuracy. Compared to traditional methods, MMVO-SHFL not only reduces energy consumption but also significantly improves the fairness of client participation. MMVO-SHFL outperforms existing methods across various configurations, highlighting its great potential for large-scale heterogeneous federated learning scenarios. Moreover, through grid search optimization of hyperparameters G and β , the optimal combination ( G = 0.8, β = 0.1) is determined to maximize its performance. MMVO-SHFL provides a more efficient, energy-saving, and fair solution for large-scale heterogeneous FL scenarios.","url":"https://doi.org/10.21203/rs.3.rs-6920944/v1","authors":["Xia Liu","Jianping Wang","Danyang Chen"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6920944/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-7339691/v1","name":"FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions FedGlu: Personalized federated-learning based glucose forecasting algorithm","source":"preprints","abstract":"Abstract Background: Continuous glucose monitoring (CGM) devices allow real-time glucose readings leading to improved glycemic control. However, glucose predictions in the lower (hypoglycemia) and higher (hyperglycemia) extremes, referred as glycemic excursions, remain challenging due to their rarity. Moreover, limited access to sensitive patient data hampers the development of robust machine learning models even with advanced deep learning algorithms available. Methods: We propose to simultaneously provide accurate glucose predictions in the excursion regions while addressing data privacy concerns. To tackle excursion prediction, we propose a novel Hypo-Hyper (HH) loss function that penalizes errors based on the underlying glycemic range with a higher penalty at the extremes over the normal glucose range. On the other hand, to address privacy concerns, we propose FedGlu, a machine learning model trained in a federated learning (FL) framework. FL allows collaborative learning without sharing sensitive data by training models locally and sharing only model parameters across other patients. The HH loss combined within FedGlu addresses both the challenges at the same time. Results: The HH loss function demonstrates a 46% improvement over mean-squared error (MSE) loss across 125 patients. Compared to local models, FedGlu improved glycemic excursion detection by 35% compared to local models. This improvement translates to enhanced performance in predicting both, hypoglycemia and hyperglycemia, for 105 out of 125 patients. Conclusions: These results underscore the effectiveness of the proposed HH loss function in augmenting the predictive capabilities of glucose predictions. Moreover, implementing models within a federated learning framework not only ensures better predictive capabilities but also safeguards sensitive data concurrently.","url":"https://doi.org/10.21203/rs.3.rs-7339691/v1","authors":["Dave Darpit","Kathan Vyas","Jagadish Kumaran Jayagopal","Alfredo Garcia","Madhav Erraguntla","Mark Lawley"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7339691/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-7644110/v1","name":"Efficient Federated Learning Based On Domain Adaptation and Knowledge Distillation Losses","source":"preprints","abstract":"Abstract Numerous devices nowadays generate vast amounts of data for learning. Traditional centralized learning necessitates transmitting all data to a central site, which conducts the model training. However, much of these data may be sensitive, leading customers to refuse to share it. Federated Learning (FL) addresses this dilemma by employing a distributed learning framework where multiple local users collaborate to train a shared model via the central server's coordination. Nevertheless, reducing communication costs with respect to computational costs and efficiently handling non-independent and identically distributed (non-IID) problems still present significant struggles. Therefore, we propose an efficient FL method using domain adaptation and knowledge distillation losses to solve the abovementioned issues. Experimental results implemented on MNIST, CIFAR-10, and CIFAR-100 datasets demonstrate that our method can achieve almost the same accuracy as the other well-known FL methods using fewer communication rounds, particularly for non-IID situations.","url":"https://doi.org/10.21203/rs.3.rs-7644110/v1","authors":["Jui-Chieh Liu","Cooper Cheng-Yuan Ku","Shao-Ci Wang"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7644110/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.20944/preprints202508.1459.v1","name":"Federated Learning for Distributed Multi-Robotic Arm Trajectory Optimization","source":"preprints","abstract":"Manipulator path and trajectory planning is a significant aspect of robotics that involves defining an optimal trajectory for the manipulator to move from a starting position to a target position while avoiding obstacles and minimizing latency factors such as time, energy, or jerk. This process typically involves algorithms that analyze the manipulator’s kinematic and dynamic constraints, including workspace object geometry and environmental obstacles, to generate a collision-free and efficient trajectory. Common techniques include sampling-based methods like Rapidly-exploring Random Trees (RRT) or Probabilistic Roadmaps (PRM), optimization-based approaches, and artificial potential fields that treat obstacles as repulsive forces and goals as attractive forces. Advanced path planning may also incorporate machine learning for adaptive navigation in dynamic environments. The goal is to ensure smooth, precise, and safe motion. Efficient path planning enhances performance, reduces wear and tear, and ensures operational reliability.","url":"https://doi.org/10.20944/preprints202508.1459.v1","authors":["Fazal Khan","Zhou Meng"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202508.1459.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202507.1037.v1","name":"Optimization, Communication, and Personalization in Federated Learning for Massive Networks","source":"preprints","abstract":"We consider the problem of collaborative model optimization over a distributed network of agents, each possessing locally held data drawn from potentially heterogeneous distributions. The system operates under constraints of limited communication, partial participation, and privacy preservation, thereby necessitating the design of algorithms that balance local computation and global aggregation. We investigate the convergence properties and trade-offs arising in such iterative optimization schemes, where updates are performed asynchronously or synchronously, and communication overheads are mitigated via compression or quantization techniques. The objective is to characterize the interplay between model fidelity, communication complexity, and heterogeneity of local objective functions. We explore frameworks that enable personalized solutions tailored to individual agents while leveraging shared representations, often framed as multi-task or meta-optimization problems. Incentive structures are incorporated to model rational agent behavior under resource constraints and strategic participation, formalized through utility maximization and game-theoretic constructs. This work lays a foundation for understanding the fundamental limits and algorithmic principles governing scalable distributed learning systems, emphasizing theoretical guarantees alongside system-level considerations. Our approach highlights open questions concerning the balance of privacy, robustness, and efficiency in decentralized optimization, motivating future exploration into principled design and analysis of federated learning methodologies.","url":"https://doi.org/10.20944/preprints202507.1037.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.1037.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.64898/2026.03.05.709751","name":"Distribution-Aware Federated Learning for Diabetes Prediction Using Tabular Clinical Data Under Non-IID and Class-Imbalanced Settings","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.03.05.709751","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.03.05.709751","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202510.0971.v1","name":"Privacy-Preserving and Communication-Efficient Federated Learning for Cloud-Scale Distributed Intelligence","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.0971.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.0971.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7866368/v1","name":"Privacy-Preserving and Communication-Efficient Federated Learning for Cloud-Scale Distributed Intelligence","source":"preprints","abstract":"Abstract This study focuses on privacy protection and multi-party collaborative optimization in cloud computing environments. A federated learning framework is proposed, integrating differential privacy mechanisms and communication compression strategies. The framework adopts a layered architecture consisting of local computing nodes, a compression module, and a privacy-enhancing module. It enables global model training without exposing raw data, ensuring both model performance and data security. During the training process, the framework uses the federated averaging algorithm as the basis for global aggregation. A Gaussian noise perturbation mechanism is introduced to enhance the model's resistance to inference attacks. To address bandwidth limitations in practical cloud computing scenarios, a lightweight communication compression strategy is designed. This helps reduce the overhead and synchronization pressure caused by parameter exchange. The experimental design includes sensitivity analysis from multiple dimensions, such as network bandwidth constraints, client count variation, and data distribution heterogeneity. These experiments validate the adaptability and robustness of the proposed method under various complex scenarios. The results show that the method outperforms existing approaches in several key metrics, including accuracy, communication rounds, and model size. The proposed approach demonstrates strong engineering deployability and system-level security. It provides a novel technical path for building efficient and trustworthy distributed intelligent systems.","url":"https://doi.org/10.21203/rs.3.rs-7866368/v1","authors":["Heyao Liu","Yue Kang","Yuchen Liu"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7866368/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7148932/v1","name":"Security and privacy concerns in Federated Learning systems: a systematic review","source":"preprints","abstract":"Abstract Federated Learning is a Machine Learning solution that trains a global model by aggregating weights from different peers. Federated Learning does not require that data be shared among nodes; however, it is not exempt from privacy and/or security issues. This systematic review focuses on the major security and privacy threats related to the definition and implementation of Federated Learning frameworks. This study aims to provide a comprehensive analysis of potential adversary cyber attacks throughout the execution of Federated Learning, in order to characterize and classify Federated Learning protocols capable of addressing critical robustness concerns — including privacy-preserving techniques, local data protection, efficiency, and accuracy — while highlighting the critical points that remain to be addressed.","url":"https://doi.org/10.21203/rs.3.rs-7148932/v1","authors":["Rosa Di Salvo","Antonino Galletta"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7148932/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202505.1775.v1","name":"Federated Learning for Cybersecurity A Privacy-Preserving Approach","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202505.1775.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202505.1775.v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-8442373/v1","name":"Q-Attentive DT-QFL: Adaptive Time-Symmetry in Quantum Federated Learning using Quantum Self-Attention","source":"preprints","abstract":"Abstract The unstoppable growth of the Industrial Internet of Things (IIoT) already has left the field of outright decentralization of data, provoking an urgent need in the creation of sensitive machine learning models that consider user privacy. The de facto standard towards this goal has been Federated Learning (FL), which allows edge devices to be trained without providing access to raw information. However, the dynamism of IIoT systems with Non-Independent and Identically Distributed (Non-IID) data is expected to worsen the performance of FL, resulting in such problems as model divergence and catastrophic forgetting. Quantum Federated Learning (QFL) solves these difficulties by transferring project data to high dimensional Hilbert spaces with Variational Quantum Circuits (VQCs), which is able to discover correlations that cannot be observed with classical networks. Among the latest inventions in this respect is the usage of TimeReversal Symmetry, another concept of physics which has been used to stabilize the learning process, based on matching the present model states with their preceding ones. The existing methods, such as Dual-Timeline QFL (DT-QFL) are flawed in the sense that they use hyperparameters to weight historical snapshots that are known beforehand. This rigidity may lead to slow convergence and too much communication overhead especially among networks. The present paper suggests a new model called Q-Attentive DT-QFL that implements a Quantum Self-Attention Mechanism (QSAM) in the time-reversal workflow. The model defines the relative values of past data at a certain point in time on its own based on dynamically computing attention scores between the current parameter trajectory and time reversed states. To support our proposal we present in depth theoretical analysis which illustrates convergence limits and reveals that our adaptive weighting methodology forms an important minimization of the variance of global updates. Scaling experiments on the Quantum MNIST dataset have shown that Q -Attentive DT -QFL has the highest accuracy in classification with 94.2 per cent accuracy and consumes forty percent less communication rounds compared with both fixed DT -QFL and classical baselines. b We also perform a detailed security review and certify the resistance of the framework to quantum noise (i.e. Depolarizing and Amplitude Damping ) and also compatibility with NearIntermediate-Scale Quantum (NISQ) hardware. In order to ensure transparency, we project the space of quantum features with SHAP values to explain the mechanisms of decision-making and use Principal Component Analysis(PCA).","url":"https://doi.org/10.21203/rs.3.rs-8442373/v1","authors":["Nahin Nasir","Aiman Hanif"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8442373/v1","addedAt":"2026-08-31T06:41:18.937Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202509.1352.v1","name":"Federated Learning for Multi-Institutional AI in Healthcare via Digital Pathology","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202509.1352.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.1352.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-7633900/v1","name":"Lightweight Federated Learning with Genetic Optimization for PM 2.5 Forecasting in IoT Networks","source":"preprints","abstract":"Abstract This study presents a lightweight and privacy-preserving federated learning framework designed for resource-constrained IoT sensor networks, emphasizing efficient distributed computation across heterogeneous devices. The framework enables decentralized training of LSTM models directly on IoT nodes, eliminating the need for centralized data aggregation while ensuring full data privacy. To enhance computational efficiency and reduce communication overhead, a Genetic Algorithm-based model compression method is applied, pruning redundant weights and achieving approximately a 37% reduction in model size. The framework is evaluated on a real-world time-series forecasting task, achieving 66.3% classification accuracy across multiple categories, while also demonstrating low latency and high scalability in large-scale heterogeneous deployments. Furthermore, this approach supports real-time operation and effective management of hardware resource constraints, enabling practical deployment in distributed networks. These results highlight the potential of combining federated deep learning with evolutionary optimization to build efficient, secure, and scalable distributed IoT systems, providing a robust blueprint for future grid and edge computing applications.","url":"https://doi.org/10.21203/rs.3.rs-7633900/v1","authors":["Hadi Nazari¹","Yaghoub Farjami²","Ali TaeiZadeh³"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7633900/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-6924747/v1","name":"Client-Centered Federated Learning for Heterogeneous EHRs: Use Fewer Participants to Achieve the Same Performance","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6924747/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6924747/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.22541/au.176607238.80908605/v1","name":"Comparative Analysis of Federated Learning (FL), Reinforcement Learning (RL) and Evolution Strategy (ES) in Gaming Environments","source":"europepmc","abstract":"","url":"https://doi.org/10.22541/au.176607238.80908605/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.176607238.80908605/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-6272058/v1","name":"Federated Learning with Model Personalization: A Survey","source":"preprints","abstract":"Abstract Federated Learning (FL) enables multiple clients to train models collaboratively without sharing local data, but a single global model often struggles with data heterogeneity. Personalized Federated Learning (PFL) addresses this by tailoring models to individual clients while preserving FL’s benefits. This survey categorizes PFL techniques into client-specific models, meta-learning, clustered FL, and multi-task learning. It examines the balance between personalization and generalization, along with challenges like privacy, communication efficiency, and fairness. We also explore real-world applications in healthcare, finance, and IoT, highlighting the need for adaptive strategies. Lastly, we discuss open research directions in privacy-preserving personalization, adaptive optimization, and hybrid FL frameworks, offering insights for researchers and practitioners.","url":"https://doi.org/10.21203/rs.3.rs-6272058/v1","authors":["Ethan Weimann"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6272058/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-6737487/v1","name":"Application-Driven Taxonomy of Security and Privacy Threats in Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6737487/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6737487/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-7250878/v1","name":"FedCKD: Cluster-Aware Knowledge Distillation for Heterogeneous Medical Federated Learning","source":"preprints","abstract":"Abstract In medical knowledge systems, federated learning provides a promising paradigm for collaborative knowledge extraction while preserving data privacy. However, inherent heterogeneity in medical information—stemming from variations in disease distribution, imaging protocols, and patient demographics—severely degrades the performance of traditional federated frameworks. To address this challenge, we propose FedCKD , a knowledge-driven federated framework tailored for heterogeneous medical information systems. FedCKD introduces three key innovations: (1) a label-driven knowledge clustering mechanism that partitions medical nodes based on disease-specific knowledge representations, ensuring intra-cluster semantic consistency; (2) a two-stage adaptive aggregation strategy for knowledge-oriented model fusion within each cluster, balancing local specialization and cluster-level consistency; (3) a cross-cluster knowledge distillation protocol that enables privacy-preserving transfer of complementary knowledge across specialized medical domains via weighted teacher ensembles. By simulating interoperability in distributed medical systems, FedCKD achieves cross-domain knowledge integration while respecting statistical heterogeneity. Comprehensive experiments on multiple datasets demonstrate that FedCKD significantly outperforms state-of-the-art methods, establishing it as an effective solution for knowledge extraction and integration in privacy-sensitive, heterogeneous medical ecosystems.","url":"https://doi.org/10.21203/rs.3.rs-7250878/v1","authors":["Yi Xu","Kun Chen","Haoyu Luo","Xiao Liu"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7250878/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-7850701/v1","name":"A Novel Neural Network-Based Federated Learning System for Imbalanced and Non-IID Data","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7850701/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7850701/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202510.1115.v1","name":"Privacy-Preserving Hierarchical Fog Federated Learning (PP-HFFL) for IoT Intrusion Detection","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.1115.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.1115.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202509.0984.v1","name":"Federated Learning for Healthcare Data Privacy: A Case Study in Multi-Hospital Collaboration","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202509.0984.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.0984.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-7060589/v1","name":"Flexible and Scalable Federated Learning with Deep Feature Prompts for Digital Pathology","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7060589/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7060589/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202508.0797.v1","name":"Scalable Multi-Party Collaborative Data Mining Based on Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202508.0797.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202508.0797.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202602.1347.v1","name":"FedIHRAS: A Privacy-Preserving Federated Learning Framework for Multi-Institutional Collaborative Radiological Analysis with Integrated Explainability and Automated Clinical Reporting","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202602.1347.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202602.1347.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7008997/v1","name":"HyBloFED: A Hybrid Blockchain Integrated Federated Learning Approach for Brain Tumor Classification","source":"preprints","abstract":"Abstract Brain tumors, complex and potentially devastating, demand precise classification for effective patient prognosis and treatment planning. This paper introduces a novel approach to automate brain tumor classification using deep learning techniques, particularly convolutional neural networks (CNNs). However, conventional centralized methods compromise patient privacy and data security. To address this issue, federated learning (FL), a collaborative paradigm enabling model training across multiple institutions and aggregating models at a central server while preserving the confidentiality of sensitive medical data, is proposed. Moreover, an aggregation function at the central server is modified to identify the effect of aggregation on global model training. In addition to that, Blockchain technology is also integrated with FL architecture to enhance privacy preservation and trust, to ensure the integrity and immutability of patient data. By synergistically integrating modified FL, CNNs, and Blockchain technology, the proposed approach achieves accuracy (98%) and security in brain tumor classification. Through this, it aims to advance the field of medical imaging while prioritizing patient privacy and data security (through Blockchain technology) in brain tumor diagnosis and treatment.","url":"https://doi.org/10.21203/rs.3.rs-7008997/v1","authors":["Bela Shrimali","Sarthak Joshi","Hiren Patel"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7008997/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-7627809/v1","name":"Hierarchical Personalized Continual Federated Learning for Real Time Risk Prediction of Chronic Diseases","source":"preprints","abstract":"Abstract The most prevalent global morbidity and mortality is chronic illnesses like cardiovascular diseases, diabetes, and respiratory diseases which require the proper prediction of risks and in a timely manner so as to have preventative measures. Nevertheless, predictive systems in real-time have been found to be severely limited by fragmented healthcare data, patient population heterogeneity, non-independent and identically distributed (non-IID) data distributions, and strict privacy policies that cannot allow direct data sharing. Current centralized systems tend to perform poorly when it comes to generalizing across dissimilar healthcare locations, and traditional federated learning algorithms have a scalability bottleneck, suboptimal communication, inadequate personalization, and susceptibility to data drift with time, rendering them unsuitable to real-world application. To overcome these obstacles, we suggest a Hierarchical Personalized Continual Federated Learning (HiPerC-FL) model of real-time risk prediction of chronic diseases that incorporates multi-modal input of wearable, electronic health records, and imaging data without having to reveal raw patient data. The system uses a hierarchical aggregation topology between edge devices, hospital servers, and global coordinators to reduce the latency and communication and a personalized meta-learning module coupled with client clustering helps to address the impact of data heterogeneity. Moreover, on-device adaptation that happens continuously allows local models to be immune to concept drift, and a causal feature regularizer makes predictions more interpretable and reliable. Secure aggregation, differential privacy, and verifiable audit trails are the means of implementing privacy and governance, and both adhere to clinical standards. Benchmark healthcare simulation Experimental results on benchmark healthcare data show that, compared to baseline federated methods, HiPerC-FL always yields progress of 7–10 percent in predictive accuracy, is 50 percent more cost-effective in communication, and GUI remains stable under extended distribution shifts. This evidence confirms that the given framework proves to be not only effective but also practically deployable to the real-time chronic disease monitoring process, which can serve as a scalable and ethically-acceptable roadmap to the precision of the healthcare provision.","url":"https://doi.org/10.21203/rs.3.rs-7627809/v1","authors":["Abhigyan Ghoshal","Mohammad Armaan Ali","M. Sambath","E. Balraj"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7627809/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202510.0524.v1","name":"Federated Learning for Agentic Gen AI in Financial Risk Management for National Financial Security","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.0524.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.0524.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.22541/au.175692076.67282745/v1","name":"Adaptive Trust-Driven Federated Learning with Blockchain for Secure AI Healthcare Diagnostics","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.175692076.67282745/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.175692076.67282745/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-8399277/v1","name":"Population-Scale Developmental Risk Screening from Retail Transaction Data Using Privacy-Preserving Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8399277/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8399277/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202512.2380.v1","name":"Quantum-Safe Federated Learning with CORS-Secured APIs for Rapid Food Safety Hazard Dashboards in React SPAs","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202512.2380.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.2380.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7406272/v1","name":"Optimal Uncertainty Budget Allocation for Robust Federated Learning under Byzantine Attacks","source":"preprints","abstract":"Abstract Federated learning (FL) has revolutionized the development of machine learning models by enabling decentralized training while safeguarding user privacy. However, the presence of Byzantine adversaries introduces significant vulnerabilities, as malicious clients can disrupt the learning process by providing misleading updates. This paper addresses the critical challenge of allocating an uncertainty budget across heterogeneous clients to enhance the robustness of federated learning systems against such adversarial attacks. We introduce the Uncertainty Budget Allocation Problem (UBAP), formulating it as a mixed-integer nonlinear program (MINLP) aimed at optimizing resource distribution for improved model convergence and stability. Our framework not only rethinks traditional assumptions about client contributions but also presents a novel mathematical analysis underlying the relationship between uncertainty allocation and adversarial strength. Extensive empirical evaluations on standard benchmarks demonstrate substantial improvements in model performance and resistance to attacks, showcasing the practical efficacy of our approach. Through this work, we underscore the importance of optimal uncertainty budget allocation to foster resilience in federated learning systems, paving the way for further innovations in this domain and enhancing the security of decentralized AI applications.","url":"https://doi.org/10.21203/rs.3.rs-7406272/v1","authors":["Weiwei Lian","Jun Tao","Xinjun Mei","Yu Fang","Zhou Shen"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7406272/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21203/rs.3.rs-7871081/v1","name":"Federated Learning-Based Trust and Energy-Aware Routing in Fog–Cloud Computing Environments for the Internet of Things","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7871081/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7871081/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.64898/2025.12.02.25341485","name":"A Federated Learning-based Optic Disc and Cup Segmentation Model for Glaucoma Monitoring In Color Fundus Photographs","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2025.12.02.25341485","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.64898/2025.12.02.25341485","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202602.0675.v1","name":"Towards a Technology-Enabled Unified Educational Space in Central Asia: A Chinese Perspective on Blockchain, Federated Learning, and Neural Machine Translation","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202602.0675.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202602.0675.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-6827986/v1","name":"Enhanced Security Verifiable Secure Aggregation Scheme in Federated Learning","source":"preprints","abstract":"Abstract Federated Learning(FL) enables multiple participants to build a loosely coupled distributed machine learning system under the coordination of a central server. Existing FL models typically assume that the server aggregating data is semi-honest, but this assumption does not align with the complexities of real-world application environments, where the server may carry out collusion attacks or replay attacks. VerifyNet is a representative federated learning protocol for verifiable secure aggregation. In this paper, we analyze the security of VerifyNet, identify two shortcomings: low tolerance to collusion attacks and inability to resist combinatorial replay attacks. Furthermore, we have experimentally confirmed the existence of these two security vulnerabilities. To address the issue of low tolerance for collusion attacks, we have constructed a secure homomorphic hash function key generator using a randomized approach to prevent malicious servers from obtaining shared keys and forging data. To address the issue of being unable to resist replay attacks, we have constructed a secure additional verification information generation algorithm using AES-CTR encryption mode, which prevents malicious servers from obtaining increments from historical data and constructing combinatorial replay attacks. Security analysis shows that our scheme effectively achieves privacy protection and aggregation verification. We tested the performance of the scheme in a local area network environment. Experimental data indicates that when the number of clients is 500 and the number of gradients per client is 5000, our scheme only requires an additional 5.76‰ computational overhead and 3.46% communication overhead compared to the VerifyNet protocol, and eliminates the security vulnerabilities of collusion attacks and combinatorial replay attacks.","url":"https://doi.org/10.21203/rs.3.rs-6827986/v1","authors":["Wujun Yao","Yiliang Han","Tanping Zhou","Xiaolin Wang"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6827986/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.21203/rs.3.rs-7052572/v1","name":"Medical support platform for melanoma analysis and detection based on Federated Learning","source":"preprints","abstract":"Abstract Advances in computer science and medicine have led to the emergence of artificial intelligence as a key tool in the medical and scientific fields. Its application in the diagnosis and treatment of diseases, such as cancer, has proven to be fundamental in improving early detection and saving lives. This article presents a proposal based on Deep Learning to develop a model capable of detecting melanomas in the skin from clinical images. The aim is to provide doctors with a tool to support early identification of this type of cancer, considering additional factors such as sun exposure and the patient's skin tone. To optimize diagnostic accuracy and avoid information dispersion, a collaborative learning technique called Federated Learning is implemented. This technique allows models trained locally by doctors to be synchronized with a global model that will be updated periodically, ensuring continuous improvement of the system without compromising the privacy of patient data. In addition, a web application is presented to manage and process the information efficiently, making it easier for doctors to consult and analyze the results.","url":"https://doi.org/10.21203/rs.3.rs-7052572/v1","authors":["Sergio Laso","Juan Luis Herrera","Daniel Flores-Martin"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7052572/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.20944/preprints202510.1471.v1","name":"Federated Learning-Driven Health Risk Prediction on Electronic Health Records Under Privacy Constraints","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.1471.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.1471.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202509.1447.v1","name":"Federated Learning for Power Cyber-Physical Systems: Toward Secure, Resilient, and Explainable Intelligence","source":"preprints","abstract":"The digital transformation of power cyber-physical systems (CPSs) introduces unprecedented opportunities for optimization, forecasting, and real-time control, while simultaneously exposing critical vulnerabilities in data security, system resilience, and operator trust. Federated Learning (FL) provides a promising paradigm by enabling collaborative intelligence without raw data sharing, yet traditional approaches fall short in safety-critical energy infrastructures. This review advances the state of the art by presenting a holistic perspective on secure, resilient, and explainable FL for Power CPSs. We first analyze emerging threats—including model poisoning, backdoor insertion, and cross-layer false data injection—and map them to existing defenses such as robust aggregation, Byzantine resilience, differential privacy, and zero-trust authentication. We then synthesize architectural innovations, including personalized FL, digital twin–enhanced validation, and human-in-the-loop trust calibration, highlighting their potential to address system heterogeneity and operational risks. Real-world applications in load forecasting, intrusion detection, EV coordination, and microgrid control are surveyed to demonstrate feasibility. Finally, we outline future research directions linking adversarial robustness, explainability, scalable integration, and governance frameworks. This work positions federated learning as a cornerstone for trustworthy intelligence in next-generation power systems.","url":"https://doi.org/10.20944/preprints202509.1447.v1","authors":["Zhiye Wang"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.1447.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202506.0968.v1","name":"A Financial Multimodal Sentiment Analysis Model Based on Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202506.0968.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202506.0968.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-7598101/v1","name":"Privacy-Preserving Text Summarization through Differentially Private Federated Learning with Transformer Models","source":"preprints","abstract":"Abstract As the need for safe and effective information processing grows, text summarization has become a crucial Natural Language Processing (NLP) application field. In this paper, the new Transformer based privacy-preserving summarisation system is presented. It combines popular transformer models, such as Sequence-to-Sequence, Text-to-Text Transfer Transformer (T5), Bidirectional and Auto-Regressive Transformers (BART), Multilingual BART, and Pre-training with Extracted Gap sentences for Abstractive Summarisation (Pegasus), in a differentially private federated learning setting. Through the architecture, privacy risks are reduced, and collaborative model training is made possible by guaranteeing that raw text data stays localized among decentralized clients. Extensive experiments are carried out on the CNN/DailyMail and XSum datasets, using both extractive and abstractive summarisation paradigms. The findings show that Pegasus regularly outperforms other assessment measures (BLEU-4 at 32.0 and ROUGE-L at 44.1) outperforming other models by 14.43% and 11.78% on average, compared to the BART and mBART models in a federated environment. Performance patterns throughout communication cycles demonstrate the effectiveness of the FedAvg aggregation strategy, as the quality of the model gradually improves. This study compares the computational efficiency, generalizability, and complexity of the model under circumstances that preserve privacy. The results aid in the creation of intelligent summarisation systems that strike a compromise between language quality, scalability, and privacy; these systems may find use in cross-border information systems, healthcare, and the legal sector.","url":"https://doi.org/10.21203/rs.3.rs-7598101/v1","authors":["Umme Sara","Md Tanjum An Tashrif"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7598101/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1101/2025.04.22.649958","name":"FMed-Diffusion Federated Learning on Medical Image Diffusion","source":"preprints","abstract":"Abstract Medical data is not available for public access due to privacy concerns of the patients and the stakeholders’ trust-worthiness. However, Artificial Intelligence, especially all deeplearning models, is data-hungry and fails to produce clinically relevant results without much data. Moreover, augmentation strategies are deployed to overcome the less data hurdle. The promising future in this direction is generative AI-augmented data. The chat-GPT and DALLE-2 have become commercial products leveraging the generative AI in Natural Language Processing and Computer Vision. The diffusion models have started giving many promising results in the generative AI in computer vision. And in medical imaging, they can potentially create synthetic data to augment the scarce dataset. Diffusion models coupled with federated learning can create synthetic data on a large scale without the need to violate data privacy. This synthetic dataset could be used for further training of deep learning models without the issues of patients’ identity theft from reverse engineering data. A Federated Learning paradigm of diffusion models has been proposed to overcome this hurdle and its related challenges. Our work focuses on diffusion models in federated learning settings. We named our novel model FMed-Diffusion or Federated Learning on Medical Image Diffusion. We trained our model under distributed federated settings imitating real-world clinical settings. We have achieved impressive results over three medical image datasets, APTOS 2019 Blindness Detection, Retinal OCT Detection, and COVID-CT Detection in the federated setting on par with the traditional training. Our model FMed-Diffusion has achieved an FID score of 7.1821 on the APTOS 2019 Blindness Detection dataset, an FID score of 8.8154 on the Retinal OCT Detection dataset, and an FID score of 7.4486 on the COVID-CT Detection dataset.","url":"https://doi.org/10.1101/2025.04.22.649958","authors":["Murukessan Perumal","M Srinivas"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.04.22.649958","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202601.1512.v1","name":"AI-Driven Raman Spectroscopy and Quantum Dot Probes with Federated Learning for Micro-Nanoplastic Detection and Removal in Livestock and Aquaculture Feeds","source":"preprints","abstract":"Micro and nanoplastics, pervasive environmental pollutants smaller than 5 mm and 1 µm respectively, infiltrate livestock and aquaculture feeds via contaminated water, sewage sludge fertilizers, and atmospheric deposition, compromising animal health, reproductive performance, and food chain safety. This paper presents a pioneering hybrid framework that synergistically integrates artificial intelligence-enhanced Raman spectroscopy for high-resolution polymer fingerprinting, quantum dot nanoprobes for targeted fluorescent labelling of hydrophobic plastics, and federated learning algorithms for decentralized, privacy-preserving model training across heterogeneous farm networks. Unlike traditional methods such as microscopy or pyrolysis-gas chromatography, which suffer from low sensitivity in complex organic matrices and lack real-time scalability, our system achieves a limit of detection of 5 ng/g with 97% accuracy across polyethylene, polypropylene, and polystyrene variants in poultry pellets, cattle silage, and salmon feeds. Quantum dots, functionalized with π-π stacking ligands, enable selective binding and surface-enhanced Raman signals, while edge-deployed AI processes hyperspectral data in under 100 ms per sample. Federated averaging across 50 simulated nodes converges 25% faster than centralized baselines, incorporating differential privacy for regulatory compliance. Experimental results demonstrate 91% removal efficiency through dielectrophoretic extraction of labelled particles, surpassing density separation by 40% in yield and 70% in speed, with pilot deployments yielding 12% improvements in feed conversion ratios and 30% reductions in inflammation biomarkers. This scalable, cost-effective solution ($500/unit, 6-month ROI) paves the way for sustainable animal production resilient to escalating plastic pollution, with broader implications for precision agriculture and global food security.","url":"https://doi.org/10.20944/preprints202601.1512.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202601.1512.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7237359/v1","name":"A Collision-Aware Optimization Framework for Wireless Federated Learning over Grant-Free NOMA","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7237359/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7237359/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-8252530/v1","name":"Enhancing Federated Learning Performance under Poor Network Conditions through a Modified UDP Protocol in the NS-3","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8252530/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8252530/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-7236860/v1","name":"Cyst-X: AI-Powered Pancreatic Cancer Risk Prediction from Multicenter MRI in Centralized and Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7236860/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7236860/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-6979939/v1","name":"Federated Learning for Secure and Privacy- Preserving Edge AI in Smart Cities","source":"preprints","abstract":"Abstract The rapid expansion of smart cities has led to the integration of Artificial Intelligence (AI) at the edge, enabling real-time decision-making for intelligent urban infrastructure. However, conventional centralized AI models pose critical challenges, including data privacy risks, security vulnerabilities, and high computational overhead. This paper investigates Federated Learning (FL) as a transformative paradigm to enhance security, privacy, and efficiency in edge AI systems for smart cities. Unlike traditional AI training methods, to cyber threats while ensuring compliance with data protection regulations. To address key challenges in heterogeneous smart city environments, we propose a hybrid optimization framework integrating differential privacy, secure multi-party computation (SMPC), and blockchain-based authentication. This approach strengthens resilience against adversarial attacks while ensuring secure model updates. Additionally, we introduce an adaptive aggregation mechanism, which dynamically adjusts model updates based on device reliability, data distribution, and network conditions, optimizing both learning efficiency and energy consumption in edge AI networks. Extensive experimentation on real-world smart city datasets demonstrates that the proposed framework enhances model accuracy, robustness, and privacy preservation compared to conventional AI approaches. Our findings establish Federated Learning as a cornerstone for secure, scalable, and privacy-aware AI in smart cities, facilitating trustworthy deployment of intelligent urban infrastructure. This research provides valuable insights for policymakers, researchers, and industry professionals, paving the way for next-generation AI-driven smart cities with enhanced security, privacy, and efficiency.","url":"https://doi.org/10.21203/rs.3.rs-6979939/v1","authors":["Joshi","Shahin Fatima","Kesani Hanirvesh","Shadab Siddiqui","Sumit Hazra"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6979939/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202508.0130.v1","name":"Federated Learning with Adversarial Optimisation for Secure and Efficient 5G Edge Computing Networks","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202508.0130.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202508.0130.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-5858510/v1","name":"Quantization-Based Chained Privacy-Preserving Federated Learning","source":"preprints","abstract":"Abstract Federated Learning (FL) is an advanced distributed machine learning framework crucial in protecting data privacy and security. By enabling multiple participants to train models while keeping their data local collaboratively, FL effectively mitigates the risks associated with centralized storage and sharing of raw data. However, traditional FL schemes face significant challenges regarding communication efficiency, computational costs, and privacy preservation. For instance, its communication and computational overhead in edge computing scenarios is often excessively high, hindering real-time applications. This paper proposes an innovative federated learning framework, Q-Chain FL, integrating quantization compression techniques into a chained FL architecture. This Q-Chain FL scheme adopts efficient compression and transmission of model parameter differences at the user node and executes seamless decompression and aggregation at the server node. Experiments on several publicly available datasets, including MNIST, CIFAR-10, and CelebA, demonstrate low communication and computational overhead, fast convergence speed, and high security of Q-Chain FL. Compared to traditional FedAvg and Chain-PPFL, Q-Chain FL reduces communication overhead by approximately 62.5\\% and 44.7\\%, respectively. These results underscore the robustness and adaptability of Q-Chain FL in various datasets and real-world learning scenarios.","url":"https://doi.org/10.21203/rs.3.rs-5858510/v1","authors":["Ya Liu","Shumin Wu","Yibo Li","Fengyu Zhao","Yanli Ren"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-5858510/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1101/2025.07.27.25332284","name":"Secure and Efficient Federated Learning for Predictive Modeling in Resource-Constrained Healthcare Systems","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.07.27.25332284","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.07.27.25332284","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-6658077/v1","name":"Resisting Against  Targeted Poisoning Attacks in Lightweight Privacy-Preserving Federated Learning","source":"preprints","abstract":"Abstract Federated learning is a distributed computing paradigm designed to protect client privacy. However, its distributed nature makes it vulnerable to targeted poisoning attacks.Although existing solutions can effectively mitigate such attacks, they often struggle to handle statistical heterogeneity.Moreover, privacy attacks often coexist with targeted poisoning attacks in federated learning, further increasing the difficulty of defense.To address the above challenges, this paper proposes a lightweight privacy-preserving federated learning framework, named FedSP, to defend against targeted poisoning attacks. The key idea is to design a protocol between two servers to detect and aggregate model updates submitted by clients in a perturbed form. Specifically, we design an adaptive clustering strategy during aggregation to mitigate inconsistencies of model updates caused by statistical heterogeneity.Additionally, we employ a dimensionality reduction to identify a plausible model update, eliminating assumptions regarding the proportion of malicious clients and the root dataset.Theoretical analysis demonstrates the privacy preservation and convergence of FedSP.Extensive experiments show that FedSP effectively defends against targeted poisoning attacks without compromising privacy.","url":"https://doi.org/10.21203/rs.3.rs-6658077/v1","authors":["Hongliang Zhang","Haojie Xie","Jiandong Lv"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6658077/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-7818007/v1","name":"EcoFedX- Adaptive Multi-Objective Federated Learning for Energy- Efficient Image and Speech Signal Processing at the Edge ","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7818007/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7818007/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202503.2211.v1","name":"Federated Learning for Heterogeneous Data Integration and Privacy Protection","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202503.2211.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202503.2211.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.21203/rs.3.rs-7171186/v1","name":"FL-FAITH: A Hybrid MCDM-DDPG Framework for Multi-Objective Client Selection in Federated Learning","source":"preprints","abstract":"Abstract Client selection significantly influences the overall performance, convergence behavior, and resource utilization of Federated Learning (FL), particularly under circumstances typified by device heterogeneity and resource limitations. Conventional selection techniques often depend either on rule-based Multi-Criteria Decision Making (MCDM) or on adaptive Reinforcement Learning (RL), each exhibiting inherent drawbacks: MCDM offers explainability but lacks flexibility , whereas RL enables dynamic decision-making but is often opaque and computationally intensive. In order to mitigate these issues, we introduce FL-FAITH, a hybrid client selection framework that integrates the interpretability of MCDM with the adaptive optimization power of Deep Deterministic Policy Gradient (DDPG)-based RL. FL-FAITH dynamically adjusts the importance of client-side features through reward-driven learning, guided by a multi-objective reward function. This function encompasses not only global accuracy and training convergence but also operational constraints such as bandwidth usage, energy efficiency, processing capability, and data quality. The framework favors clients with reliable and high-bandwidth connectivity to reduce communication cost, selects energy-efficient nodes for sustained participation, and leverages computa-tionally powerful devices to accelerate local training. Additionally, data quality metrics—like label distribution and sample volume—are integrated into the 1 scoring mechanism to ensure clients meaningfully support global model generalization. By balancing these factors, FL-FAITH enables robust and context-aware client selection across diverse environments. Experimental results on the MIT-BIH Arrhythmia dataset, using varied client hardware profiles, demonstrate that FL-FAITH consistently surpasses standalone MCDM and DDPG baselines in accuracy, convergence, and resource-awareness. This framework effectively bridges the gap between interpretability and adaptivity, delivering a feasible and extensible solution for practical federated learning deployments.","url":"https://doi.org/10.21203/rs.3.rs-7171186/v1","authors":["Tushar Mane","Shraddha Phansalkar"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7171186/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-6090375/v1","name":"Verifiable Secure Aggregation Scheme for Privacy Protection in Federated Learning","source":"preprints","abstract":"Abstract Federated learning enables multiple participants to construct a distributed machine learning system coordinated by server. Most existing solutions assume a semi-honest system, considering each participant to be honest but curious, which does not align with the complex real-world environment. In reality, servers might be malicious, potentially tampering with or forging aggregation results. To verify the integrity of server aggregation computations while protecting the privacy of clients, this paper introduces a privacy-preserving verifiable secure aggregation scheme for federated learning networks. Initially, we construct a functional reuse private key ring generation algorithm, enabling clients to encrypt and protect their private gradients using the private key ring. Subsequently, leveraging the discrete logarithm difficulty problem, we devise a commitment protocol where clients commit to their encrypted private gradients. Upon receiving the aggregation result from the server, they collaboratively unlock the commitment, thereby verifying the aggregation result. Security analysis demonstrates that our solution effectively ensures privacy protection. We simulated consumer electronic products on the Raspberry Pi and tested the performance of the solution. Experimental data reveals that, with 100 clients, our scheme demonstrates that the overhead for proof generation and verification computations are 39.9% and 34.1% of the existing scheme, respectively, highlighting its lightweight nature.","url":"https://doi.org/10.21203/rs.3.rs-6090375/v1","authors":["Wujun Yao","Tanping Zhou","Yiliang Han","Xiaolin Wang"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6090375/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1101/2025.04.25.25326431","name":"Federated Learning for Multi-Disease Ophthalmic Diagnostics using OCTA","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.04.25.25326431","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.04.25.25326431","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-7907693/v1","name":"DEFEND: Intelligent Temporal Backdoor Detection and Mitigation in Federated Learning via Reinforcement Learning-Coordinated Multi-Layer Defense","source":"europepmc","abstract":"Abstract Collaborative machine learning in financial systems faces an escalating security threat: temporal backdoor attacks that exploit multi-round dependencies to systematically compromise fraud detection and risk assessment models—a challenge that existing static defense mechanisms cannot adequately counter. This paper presents DEFEND (DEep Federated Ensemble Network Defense), a comprehensive framework that integrates multi-layer defense with reinforcement learning-based adaptive coordination to counter sophisticated temporal backdoor strategies in federated learning environments. The framework introduces four key innovations: (1) Temporal Behavioral Analysis Layer employing multi-scale statistical profiling with dynamic time warping for attack pattern recognition across communication rounds, (2) Byzantine-Robust Statistical Aggregation using geometric median estimation with adaptive outlier detection, (3) Multi-Scale Validation Protocol with automated model rollback mechanisms, and (4) MDP-based Defense Coordination formulating security decisions as a Markov Decision Process optimized via Proximal Policy Optimization to dynamically balance robustness and utility. Extensive experiments on the FinMultiTime dataset across three distinct market periods (2009-2025) demonstrate superior performance over state-of-the-art baselines, achieving defense success rates of 95.6\\%$\\pm$1.0\\% for ResNet-18 and 94.0\\%$\\pm$1.2\\% for MobileNet-V2 while maintaining clean accuracy above 85\\%. Ablation studies reveal that the MDP-based coordination provides the largest individual contribution (8.2\\% defense success rate improvement), while the complete multi-layer architecture achieves up to 18.7\\% improvement over single-layer baselines. Cross-period generalization analysis demonstrates robust transferability with less than 6\\% performance degradation across different market regimes, validating practical deployment viability in dynamic financial environments.","url":"https://doi.org/10.21203/rs.3.rs-7907693/v1","authors":["Wenan Liu","Qixuan Yang","Weihang Gong","Rongji Yin","Zheng Li"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7907693/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.20944/preprints202409.0544.v1","name":"Fair Federated Learning","source":"preprints","abstract":"Optimization is critical in various fields like smart vehicles, and transportation. Federated Learning (FL) has emerged as an effective approach in the coordination of autonomous vehicles, but traditional methods such as FedAvg can create performance disparities across clients. This paper addresses this fairness issue through the $q$-Fair Federated Learning ($q$-FFL) framework, adjusting model performance across clients using a tunable fairness parameter. We propose a modified FedAvg algorithm for $q$-FFL that maintains comparable convergence rates, ensuring more balanced client outcomes. Additionally, we explore incentive mechanisms in FL using a Stackelberg game model, incorporating a fairness coefficient to encourage equitable participation. Building on prior works, we redefine client utility functions to address communication and computation costs, ensuring fair resource allocation. The proposed framework achieves both global and local fairness, maintaining a unique Nash equilibrium in the modified game setting.","url":"https://doi.org/10.20944/preprints202409.0544.v1","authors":["Amirreza Talebi"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202409.0544.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.1101/2025.08.04.668527","name":"Federated Learning for ICU Mortality Prediction: Balancing Accuracy and Privacy in a Multi-Hospital Setting","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.08.04.668527","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.08.04.668527","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-7093115/v1","name":"An Efficient Collusion-Resistant and Drop-Proof Federated Learning Security Aggregation Scheme Based on RLWE","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7093115/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7093115/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202506.2218.v2","name":"A Robust Federated Learning Against Data Poisoning Attacks: Prevention and Detection of Attacked Nodes","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202506.2218.v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202506.2218.v2","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-7454748/v1","name":"Federated learning enabled privacy-preserving data access for predicting 30-day mortality in acute myocardial infarction","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7454748/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7454748/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-7939959/v1","name":"FL-P4AV: A Federated Learning-Based Privacy-Preserving Personalized Path Planning Framework for Collaborative Autonomous Ground Vehicles","source":"preprints","abstract":"Abstract The growing deployment of autonomous ground vehicles in smart cities and logistics demands secure, efficient, and context-aware navigation systems. This paper proposes FL-P4AV, a Federated Learning-Enabled Privacy-Preserving Personalized Path Planning framework designed for collaborative autonomous vehicles. Unlike traditional centralized or static path planners, FL-P4AV allows each vehicle to train a lightweight local model that predicts navigation costs based on semantic features such as obstacle density and goal proximity. The models undergo refinement via federated learning, which maintains privacy by not sharing raw data and employing differential privacy mechanisms. The semantic weights obtained are incorporated into an enhanced A* algorithm to facilitate personalized and efficient route computation. Experimental evaluations in dynamic grid environments indicate that FL-P4AV results in shorter paths, fewer inflection points, reduced turning angles, and quicker planning times relative to baseline methods. Despite the existence of privacy-preserving noise, the system maintains steady convergence and adjusts dynamically to real-time environmental changes. FL-P4AV offers a scalable and secure framework for the coordination of decentralized autonomous vehicles in path planning, indicating substantial potential for real-world applications in smart transportation systems.","url":"https://doi.org/10.21203/rs.3.rs-7939959/v1","authors":["Saranya C","Janaki G"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7939959/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7655351/v1","name":"HDFedAtt-IIoT: A Novel Privacy-Preserving Hybrid Deep Federated Learning Framework with Attention and Proximal Regularization for IIoT Systems","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7655351/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7655351/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-7964309/v1","name":"FL-P4AV: A Federated Learning-Based Privacy-Preserving Personalized Path Planning Framework for Collaborative Autonomous Ground Vehicles","source":"preprints","abstract":"Abstract The growing deployment of autonomous ground vehicles in smart cities and logistics demands secure, efficient, and context-aware navigation systems. This paper proposes FL-P4AV, a Federated Learning-Enabled Privacy-Preserving Personalized Path Planning framework designed for collaborative autonomous vehicles. Unlike traditional centralized or static path planners, FL-P4AV allows each vehicle to train a lightweight local model that predicts navigation costs based on semantic features such as obstacle density and goal proximity. The models undergo refinement via federated learning, which maintains privacy by not sharing raw data and employing differential privacy mechanisms. The semantic weights obtained are incorporated into an enhanced A* algorithm to facilitate personalized and efficient route computation. Experimental evaluations in dynamic grid environments indicate that FL-P4AV results in shorter paths, fewer inflection points, reduced turning angles, and quicker planning times relative to baseline methods. Despite the existence of privacy-preserving noise, the system maintains steady convergence and adjusts dynamically to real-time environmental changes. FL-P4AV offers a scalable and secure framework for the coordination of decentralized autonomous vehicles in path planning, indicating substantial potential for real-world applications in smart transportation systems.","url":"https://doi.org/10.21203/rs.3.rs-7964309/v1","authors":["Saranya C","Janaki G"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7964309/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.22541/au.176365740.09019219/v1","name":"Sparse Federated Learning-Enabled Multimodal Human-Centric Traffic Systems: Resolving Privacy and Cross-Regional Adaptation Issues in ITS","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.176365740.09019219/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.176365740.09019219/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-7779444/v1","name":"Smart IoT Anomaly Detection Using DO-TAO (Dandelion Optimization with T-distribution and Adaptive Opposition) and Personalized Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7779444/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7779444/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202506.2218.v1","name":"A Robust Federated Learning Against Data Poisoning Attacks: Prevention and Detection of Attacked Nodes","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202506.2218.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202506.2218.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202507.2394.v1","name":"Federated Learning-Enabled Secure Multi-Modal Anomaly Detection for Wire Arc Additive Manufacturing","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202507.2394.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.2394.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.21203/rs.3.rs-7349963/v1","name":"Privacy-preserving predictive maintenance method for cross-border unmanned logistics system integrating federated learning and blockchain","source":"europepmc","abstract":"Abstract Predictive maintenance of cross-border unmanned logistics systems (CBULS) faces multiple challenges such as data privacy protection, system performance optimization, and collaborative efficiency. To solve these problems, this paper proposes a predictive maintenance method that integrates privacy-preserving federated learning and dynamic consensus blockchain. In the federated learning part, the improved FedProx algorithm is used to deal with non-independent and identically distributed (non-IID) data and device heterogeneity, and multi-layer privacy protection mechanisms such as zero-knowledge proof, fully homomorphic encryption, and local differential privacy are introduced to enhance data security. In the blockchain part, a hybrid consensus mechanism combining delegated proof of stake (DPoS) and practical Byzantine fault tolerance (PBFT) is designed to achieve secure distributed collaboration in high-throughput and low-latency scenarios. In addition, the hierarchical structure and sharding technology are used to optimize system performance and improve algorithm scalability and computational efficiency. Test results show that this method is superior to existing methods in terms of model prediction accuracy, communication efficiency, system throughput, and privacy protection strength, providing an efficient, secure, and scalable solution for predictive maintenance of CBULS.","url":"https://doi.org/10.21203/rs.3.rs-7349963/v1","authors":["Qingzhen Meng"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7349963/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-6295183/v1","name":"Decentralized Data Governance and Regulatory Compliance in Federated Learning and Edge Computing for Healthcare","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6295183/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6295183/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.21203/rs.3.rs-6848848/v1","name":"BlockFed: Blockchain-based Privacy Preserving Federated Learning for 5G-assisted Healthcare Ecosystems","source":"europepmc","abstract":"Abstract With the rapid adoption of 5G networks and the growing reliance on digital healthcare, the need for secure, efficient, and privacy-aware data processing has become increasingly critical. This paper presents a novel approach BlockFed , that integrates Federated Learning (FL) with Blockchain (BC) technology to ensure data privacy and model integrity in 5G-assisted healthcare ecosystems. In the proposed system, Patient Health Record (PHR) remains at local Healthcare Entities (HE) such as hospitals and research centers, and only encrypted model updates are shared, effectively preserving user privacy. BC is employed to record and verify model weight transactions, providing tamper-proof integrity and transparency among participating HE. To mitigate the high storage demands of BC, the InterPlanetary File System (IPFS) is utilized for off-chain storage of model weights. Additionally, a lightweight homomorphic encryption scheme is incorporated to protect model parameters during aggregation and transmission. This integrated approach offers a scalable and trustworthy solution for collaborative healthcare intelligence while safeguarding sensitive PHR. Experimental insights and theoretical validation demonstrate the system’s potential for practical deployment in next-generation healthcare infrastructures.","url":"https://doi.org/10.21203/rs.3.rs-6848848/v1","authors":["Ashwin Verma","Sunil Pathak","Pronaya Bhattacharya"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6848848/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.20944/preprints202502.1159.v1","name":"A Survey of Recent Advances for Tackling Data Heterogeneity in Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202502.1159.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202502.1159.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202508.1201.v1","name":"Multi-Layer Defense Strategies and Privacy Preserving Enhancements for Membership Reasoning Attacks in a Federated Learning Framework","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202508.1201.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202508.1201.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202505.0439.v4","name":"Federated Learning for XSS Detection: Analysing OOD, Non-IID Challenges, and Embedding Sensitivity","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202505.0439.v4","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202505.0439.v4","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.22541/au.175186635.53809736/v1","name":"EnDuSecFed: An Ensemble approach for Privacy Preserving Federated Learning with Dual-security Framework for Healthcare Domain","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.175186635.53809736/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.175186635.53809736/v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.20944/preprints202510.1828.v1","name":"Application and Effectiveness Evaluation of Federated Learning Methods in Anti-Money Laundering Collaborative Modeling Across Inter-Institutional Transaction Networks","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.1828.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.1828.v1","addedAt":"2026-08-31T06:41:18.938Z","updatedAt":"2026-08-31T06:41:25.478Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00006-5","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00006-5","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:48:21Z","doi":"10.1016/b978-0-44-319037-7.00006-5","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00004-1","name":"Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00004-1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:48:18Z","doi":"10.1016/b978-0-44-319037-7.00004-1","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00033-8","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00033-8","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:56Z","doi":"10.1016/b978-0-44-319037-7.00033-8","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00002-8","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00002-8","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:48:20Z","doi":"10.1016/b978-0-44-319037-7.00002-8","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1016/c2022-0-02726-4","name":"Federated Learning for Digital Healthcare Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2022-0-02726-4","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T12:18:47Z","doi":"10.1016/c2022-0-02726-4","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1142/9789811292552_0005","name":"Vertical Federated Linear Regression Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0005","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","doi":"10.1142/9789811292552_0005","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/flta63145.2024.10839757","name":"Robust Federated Learning via Weighted Median Aggregation*","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839757","authors":["Hibatallah Kabbaj","Rachid El-Azouzi","Abdellatif Kobbane"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839757","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1142/9789811287947_0009","name":"Federated Learning using TensorFlow","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811287947_0009","authors":["Rajat Patil","Deepti Gupta","H. L. Gururaj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T01:34:57Z","doi":"10.1142/9789811287947_0009","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00019-9","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00019-9","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:10:05Z","doi":"10.1016/b978-0-443-13897-3.00019-9","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1142/9789811287947_0004","name":"Review: Recent Applications on Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811287947_0004","authors":["M. Spoorthi","H. L. Gururaj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T01:34:57Z","doi":"10.1142/9789811287947_0004","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1109/flta63145.2024.10839891","name":"Towards Efficient Belt Conveyor Maintenance: Leveraging Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839891","authors":["Hamza Safri","Mohamed Mehdi Kandi","Youssef Miloudi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839891","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00009-6","name":"Technical considerations of federated learning in digital healthcare systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00009-6","authors":["Emmanuel Alozie","Hawau I. Olagunju","Nasir Faruk","Salisu Garba"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:09:04Z","doi":"10.1016/b978-0-443-13897-3.00009-6","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/flta63145.2024.10840168","name":"Global Outlier Detection in a Federated Learning Setting with Isolation Forest","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840168","authors":["Daniele Malpetti","Laura Azzimonti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840168","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00008-4","name":"Taxonomy for federated learning in digital healthcare systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00008-4","authors":["Friday Udeji","Samarendra Nath Sur","Vinoth Babu Kumaravelu","K.V.N. Kavitha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:08:37Z","doi":"10.1016/b978-0-443-13897-3.00008-4","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00014-x","name":"Legal implications of federated learning integration in digital healthcare systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00014-x","authors":["Agbotiname Lucky Imoize","Mohammad S. Obaidat","Houbing Herbert Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:09:21Z","doi":"10.1016/b978-0-443-13897-3.00014-x","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00017-5","name":"Integration of federated learning paradigms into electronic health record systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00017-5","authors":["Hope Ikoghene Obakhena","Agbotiname Lucky Imoize","Francis Ifeanyi Anyasi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:10:02Z","doi":"10.1016/b978-0-443-13897-3.00017-5","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta63145.2024.10840014","name":"Bayesian Federated Learning with Stochastic Variational Inference","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840014","authors":["Mehreen Tahir","Feras Awaysheh","Sadi Alawadi","Muhammand Intizar Ali"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840014","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1109/flta63145.2024.10839866","name":"Meta-Learning for Federated Face Recognition in Imbalanced Data Regimes","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839866","authors":["Arwin Gansekoele","Emiel Hess","Sandjai Bhulai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839866","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1049/pbpc066e_ch9","name":"Federated learning for Supply Chain Management 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_ch9","authors":["Bushra Tahir","Muhammad Tariq"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_ch9","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.36227/techrxiv.171259674.41177687/v1","name":"Collaborative Intelligence: Blockchain-Enhanced Federated Learning","source":"crossref","abstract":"Federated Learning (FL) and blockchain technology have emerged as transformative solutions to current challenges. FL offers a decentralized machine-learning approach, prioritizing privacy, while blockchain promises transparency, security, and decentralization for transactions and data storage. FL enables machine learning models to train across multiple devices while keeping data localized, enhancing data privacy and security. Additionally, FL allows for efficient utilization of computational resources by distributing the training process among participating devices. This not only reduces the burden on individual devices but also enables faster model training and improved accuracy through collaborative learning.Blockchain, originally designed for cryptocurrencies like Bitcoin, creates immutable, transparent, and secure records in a decentralized manner, fostering trust in peer-to-peer networks.The integration of federated learning (FL) and blockchain has the potential to revolutionize data privacy, security, and transparency. By combining FL's ability to train models across multiple devices with blockchain's immutable and decentralized record-keeping, a new paradigm of secure and trustworthy machine learning can be achieved. However, challenges and considerations need to be addressed to fully realize the potential of these technologies.This literature review explores this intersection, including its intricacies, potential, challenges, and future trajectory.","url":"https://doi.org/10.36227/techrxiv.171259674.41177687/v1","authors":["Satwik Sinha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-08T13:19:05Z","doi":"10.36227/techrxiv.171259674.41177687/v1","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00006-0","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00006-0","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:07:44Z","doi":"10.1016/b978-0-443-13897-3.00006-0","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:18.957Z"},{"id":"doi:10.1109/flta63145.2024.10840156","name":"Towards Federated Learning-Based Forecasting of Renewable Energy Production","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840156","authors":["Viktor Walter","Fabian Stricker","Andreas Wagner","Christian Zirpins"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840156","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1049/pbpc066e_ch1","name":"Federated learning-enabled 5G and beyond for Industry 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_ch1","authors":["Chamitha De Alwis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_ch1","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/itw61385.2024.10806940","name":"Federated Learning Meets Network Coding: Efficient Coded Hierarchical Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/itw61385.2024.10806940","authors":["Tianli Gao","Jiahong Lin","Congduan Li","Chee Wei Tan","Jun Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-30T19:18:57Z","doi":"10.1109/itw61385.2024.10806940","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00022-9","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00022-9","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:10:19Z","doi":"10.1016/b978-0-443-13897-3.00022-9","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:18.957Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00020-5","name":"Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00020-5","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:10:16Z","doi":"10.1016/b978-0-443-13897-3.00020-5","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:18.957Z"},{"id":"doi:10.1109/flta63145.2024.10840058","name":"Client-Side Adaptation to Concept Drift in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840058","authors":["Finn Saile","Julius Thomas","Dominik Kaaser","Stefan Schulte"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T13:22:35Z","doi":"10.1109/flta63145.2024.10840058","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta63145.2024.10839876","name":"Synthetic Monoclass Teachers Distillation in the Edge Using Federated Learning Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839876","authors":["Cédric Maron","Virginie Fresse","Avigaël Ohayon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839876","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1142/9789811287947_0010","name":"Opportunities and Challenges in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811287947_0010","authors":["Shiv Bhargava","Deepti Gupta","H. L. Gururaj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T01:34:57Z","doi":"10.1142/9789811287947_0010","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta63145.2024.10839945","name":"Deployment of Federated Learning on a Low-Cost Distributed Infrastructure","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839945","authors":["Víctor Hidalgo Izquierdo","Carmen Carrión","Blanca Caminero"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839945","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00007-2","name":"Case studies and recommendations for designing federated learning models for digital healthcare systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00007-2","authors":["Chun-Ying Wu","Pushpanjali Gupta","Sulagna Mohapatra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:08:32Z","doi":"10.1016/b978-0-443-13897-3.00007-2","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta63145.2024.10839934","name":"Balancing Privacy and Performance for Private Federated Learning Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839934","authors":["Xiangjian Hou","Sarit Khirirat","Mohammad Yaqub","Samuel Horváth"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839934","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.31224/3848","name":"A Review of Split Learning and Federated Learning: Challenges and Synergies","source":"crossref","abstract":"Split Learning and Federated Learning have emerged as key techniques in the domain of privacy-preserving distributed machine learning. This paper reviews the recent developments in both paradigms, discussing their respective advantages, limitations, and the potential for their integration. We provide an analysis of current research trends, explore challenges in implementation, and suggest future directions for improving these approaches. The review serves as a resource for researchers and practitioners interested in the evolving landscape of distributed machine learning.","url":"https://doi.org/10.31224/3848","authors":["Nneka Obi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-19T12:34:08Z","doi":"10.31224/3848","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1109/flta63145.2024.10839874","name":"FEDn – A Scalable Federated Machine Learning Framework for Cross-Device and Cross-Silo Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839874","authors":["Fredrik Wrede","Viktor Valadi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839874","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta63145.2024.10839827","name":"Efficient Federated Learning on Resource-Constrained Edge Devices: Integrating Data Distillation and Semi-Supervised Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839827","authors":["Mahdi Barhoush","Ahmad Ayad","Mohammad Kohankhaki","Anke Schmeink"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839827","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta63145.2024.10840118","name":"Data Skew in Federated Learning: An Experimental Evaluation on Aggregation Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840118","authors":["Leon Nascimento","Feras M. Awaysheh","Sadi Alawadi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840118","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta63145.2024.10840066","name":"Bridging AI and Privacy: Federated Learning for Leukemia Diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840066","authors":["Chaima Lhasnaoui","Addi Ait-Mlouk","Tarik Agouti","Mohammed Sadgal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840066","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.148Z"},{"id":"doi:10.1109/flta63145.2024.10840113","name":"Exploring Federated Learning Dynamics for Black-and-White-Box DNN Traitor Tracing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840113","authors":["Elena Rodríguez-Lois","Fernando Pérez-González"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840113","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.2174/9789815313031124030006","name":"Federated Learning in Secure and Reliable Systems for IoVs","source":"crossref","abstract":"The Internet of Vehicles (IoV) is an emerging technology that allows vehicles to communicate with each other and with the infrastructure around them. This technology has the potential to revolutionize the transportation industry, but it also raises concerns about the security of the data that is shared among vehicles, with their base stations and infrastructure. In this context, secure data-sharing methodologies are essential to protect sensitive information, such as location, driving patterns, data of the people travelling in the vehicle, and protection of shared data from malicious factors. This chapter explores some of the methods that can be used for secure data sharing in the IoV. One approach is to use encryption and decryption techniques to protect data in transit and at rest. This method involves encoding the data in a way that only authorized parties can access it, and decoding it when it reaches its destination. Another approach is to use blockchain technology, which provides a decentralized and immutable ledger that can be used to store and verify data. Additionally, access control mechanisms, such as role-based access control, can be used to limit the access of different users to specific data sets. This method ensures that only authorized parties can access sensitive data. In conclusion, secure data-sharing methodologies are crucial for the successful implementation of the IoV. Encryption and decryption, blockchain technology, and access control mechanisms are some of the methods that can be used to protect sensitive information and maintain the privacy and security of the data.","url":"https://doi.org/10.2174/9789815313031124030006","authors":["Umang Kant","Prachi Dahiya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-19T05:23:34Z","doi":"10.2174/9789815313031124030006","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta63145.2024.10840046","name":"Heterogeneous SplitFed: Federated Learning with Trainable and Untrainable Clients","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840046","authors":["Juliana N. D. da Silva","Stefan Duffner","Virginie Fresse"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840046","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00004-7","name":"Federated learning challenges and risks in modern digital healthcare systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00004-7","authors":["Kassim Kalinaki","Owais Ahmed Malik","Umar Yahya","Daphne Teck Ching Lai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:08:02Z","doi":"10.1016/b978-0-443-13897-3.00004-7","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.2174/9789815313024124030006","name":"Federated Learning in Secure and Reliable Systems for IoVs","source":"crossref","abstract":"The Internet of Vehicles (IoV) is an emerging technology that allows vehicles to communicate with each other and with the infrastructure around them. This technology has the potential to revolutionize the transportation industry, but it also raises concerns about the security of the data that is shared among vehicles, with their base stations and infrastructure. In this context, secure data-sharing methodologies are essential to protect sensitive information, such as location, driving patterns, data of the people travelling in the vehicle, and protection of shared data from malicious factors. This chapter explores some of the methods that can be used for secure data sharing in the IoV. One approach is to use encryption and decryption techniques to protect data in transit and at rest. This method involves encoding the data in a way that only authorized parties can access it, and decoding it when it reaches its destination. Another approach is to use blockchain technology, which provides a decentralized and immutable ledger that can be used to store and verify data. Additionally, access control mechanisms, such as role-based access control, can be used to limit the access of different users to specific data sets. This method ensures that only authorized parties can access sensitive data. In conclusion, secure data-sharing methodologies are crucial for the successful implementation of the IoV. Encryption and decryption, blockchain technology, and access control mechanisms are some of the methods that can be used to protect sensitive information and maintain the privacy and security of the data.","url":"https://doi.org/10.2174/9789815313024124030006","authors":["Umang Kant","Prachi Dahiya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-26T08:01:16Z","doi":"10.2174/9789815313024124030006","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1142/9789811287947_0011","name":"Future Directions and Advances in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811287947_0011","authors":["Ribhav Yadav","Deepti Gupta","H. L. Gururaj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T01:34:57Z","doi":"10.1142/9789811287947_0011","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00015-1","name":"Performance evaluation of federated learning algorithms using breast cancer dataset","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00015-1","authors":["Sakinat Oluwabukonla Folorunso","Joseph Bamidele Awotunde","Abdullahi Abubakar Kawu","Oluwatobi Banjo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:09:30Z","doi":"10.1016/b978-0-443-13897-3.00015-1","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1049/pbpc066e_ch7","name":"Blockchain-based federated learning for Industry 5.0 applications","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_ch7","authors":["Rukhsana Ruby","Zehua Wang","Lu Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_ch7","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta63145.2024","name":"2024 2nd International Conference on Federated Learning Technologies and Applications (FLTA)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:23:47Z","doi":"10.1109/flta63145.2024","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:18.957Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00018-7","name":"Title page","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00018-7","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:09:11Z","doi":"10.1016/b978-0-443-13897-3.00018-7","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:18.957Z"},{"id":"doi:10.1142/9789811293993_0009","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811293993_0009","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-08T00:28:12Z","doi":"10.1142/9789811293993_0009","addedAt":"2026-08-31T06:41:18.957Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1109/flta63145.2024.10839765","name":"Using the Nucleolus for Incentive Allocation in Vertical Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839765","authors":["Afsana Khan","Marijn ten Thij","Frank Thuijsman","Anna Wilbik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839765","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta63145.2024.10840142","name":"Comparative Evaluation of Clustered Federated Learning Method","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840142","authors":["Michael Ben Ali","Omar El-Rifai","Imen Megdiche","André Peninou","Olivier Teste"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840142","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1049/pbpc072e_ch2","name":"Energy-efficient federated learning algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e_ch2","authors":["Amutha Prabakar Muniyandi","Daniel Arockiam","Feslin Anish Mon","N. Deepa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e_ch2","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta63145.2024.10839609","name":"MC-PPHFL: Privacy-Preserving Hierarchical Federated Learning with a Secure Multi-Chain Aggregation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839609","authors":["Safaá Fallatah","Floriana Grasso","Alexei Lisitsa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839609","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00023-5","name":"Federated sequential decision making: Bayesian optimization, reinforcement learning, and beyond","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00023-5","authors":["Zhongxiang Dai","Flint Xiaofeng Fan","Cheston Tan","Trong Nghia Hoang","Bryan Kian Hsiang Low","Patrick Jaillet"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:23Z","doi":"10.1016/b978-0-44-319037-7.00023-5","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1049/pbpc072e_ch13","name":"Energy-efficient federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e_ch13","authors":["Vijay Ramalingam","A. Arul Prakash","S. Vignesh","R. Rahin Batcha","D. Saravanan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e_ch13","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta63145.2024.10840172","name":"Seamless Integration: Sampling Strategies in Federated Learning Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840172","authors":["Tatjana Legler","Vinit Hegiste","Martin Ruskowski"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840172","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta63145.2024.10840050","name":"Flotta: A Secure and Flexible Spark-Inspired Federated Learning Framework","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840050","authors":["Claudio Bonesana","Daniele Malpetti","Sandra Mitrović","Francesca Mangili","Laura Azzimonti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840050","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.23977/autml.2024.050103","name":"Efficient Hierarchical Federated Learning for Unlabeled Edge Devices","source":"crossref","abstract":"","url":"https://doi.org/10.23977/autml.2024.050103","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-21T08:41:31Z","doi":"10.23977/autml.2024.050103","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1142/9789811287947_0005","name":"A Review on Various Protocols in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811287947_0005","authors":["H. T. Chethana","C. D. Divya","Tanuja Kayarga"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T01:34:57Z","doi":"10.1142/9789811287947_0005","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00012-6","name":"Government and economic regulations on federated learning in emerging digital healthcare systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00012-6","authors":["Abdulwaheed Musa","Abdulhakeem Oladele Abdulfatai","Segun Ezekiel Jacob","Daniel Favour Oluyemi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:08:38Z","doi":"10.1016/b978-0-443-13897-3.00012-6","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.5220/0012959500004508","name":"Research Advanced in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012959500004508","authors":["Ruixin Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-19T12:37:25Z","doi":"10.5220/0012959500004508","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-3-031-51266-7_3","name":"Resource Management for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-51266-7_3","authors":["Mingzhe Chen","Shuguang Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-19T05:04:40Z","doi":"10.1007/978-3-031-51266-7_3","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/flta63145.2024.10840072","name":"FedERA: Framework for Federated Learning with Diversified Edge Resource Allocation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840072","authors":["Anupam Borthakur","Aditya Kasliwal","Asim Manna","Dipyan Dewan","Debdoot Sheet"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840072","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1201/9781032694870-1","name":"Revolutionizing Healthcare through Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-1","authors":["Amrina Rahman","Md. Mushfiqur Rahman","Farhana Yasmin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T04:04:57Z","doi":"10.1201/9781032694870-1","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.2139/ssrn.4691085","name":"Discrepency-Aware Clustered Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4691085","authors":["Peifeng Zhang","Chen Jiahui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-11T01:51:33Z","doi":"10.2139/ssrn.4691085","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-031-58923-2_14","name":"Toward Green Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-58923-2_14","authors":["Minsu Kim","Walid Saad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T07:02:43Z","doi":"10.1007/978-3-031-58923-2_14","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003466581-10","name":"Federated Optimization Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003466581-10","authors":["S. Biruntha","S. Rajalakshimi","M. Kavitha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-18T08:39:48Z","doi":"10.1201/9781003466581-10","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.2139/ssrn.4946925","name":"Fedimp: The  Federated Impurity Weighting Algorithm for Improving Convergence in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4946925","authors":["Hai Anh Tran","Cuong Ta","Truong Tran"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-04T21:19:09Z","doi":"10.2139/ssrn.4946925","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1201/9781003489368-10","name":"Federated Learning Shaping the Future of Smart City Infrastructure","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003489368-10","authors":["Raj Kishor Verma","Kaushal Kishor","Antonino Galletta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T17:25:42Z","doi":"10.1201/9781003489368-10","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-3-031-51266-7_7","name":"Federated Learning for Mobile Edge Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-51266-7_7","authors":["Mingzhe Chen","Shuguang Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-19T05:04:40Z","doi":"10.1007/978-3-031-51266-7_7","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-3-031-51266-7_2","name":"Fundamentals and Preliminaries of Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-51266-7_2","authors":["Mingzhe Chen","Shuguang Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-19T05:04:40Z","doi":"10.1007/978-3-031-51266-7_2","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-3-031-51266-7_6","name":"Federated Learning for Autonomous Vehicles Control","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-51266-7_6","authors":["Mingzhe Chen","Shuguang Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-19T05:04:40Z","doi":"10.1007/978-3-031-51266-7_6","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/flta63145.2024.10840007","name":"The Future of Large Language Models (and AI) is Federated","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840007","authors":["Nicholas Lane"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840007","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1201/9781032694870-6","name":"Federated Multi-Task Learning to Solve Various Healthcare Challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-6","authors":["Seema Pahwa","Amandeep Kaur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T08:04:57Z","doi":"10.1201/9781032694870-6","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1201/9781003482000-7","name":"Federated Learning-Based Smart Transportation Solutions","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003482000-7","authors":["Sivabalan Settu","Raveendra Reddy","Appalaraju Muralidhar","Thangavel Murugan","Rathipriya Ramalingam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T17:15:57Z","doi":"10.1201/9781003482000-7","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00010-2","name":"Blockchain-based federated learning in internet of health things","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00010-2","authors":["B. Akoramurthy","B. Surendiran","K. Dhivya","Subrata Chowdhury","Ramya Govindaraj","Abolfazl Mehbodniya","Julian L. Webber"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:09:02Z","doi":"10.1016/b978-0-443-13897-3.00010-2","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1201/9781003482000-12","name":"Securing Federated Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003482000-12","authors":["Atharva Saraf","Shaurya Sameer Talewar","Susanta Das","Khushbu Trivedi","Ahmed A. Elngar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T17:15:57Z","doi":"10.1201/9781003482000-12","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003489368-7","name":"Healthcare Informatics Security Issues and Solutions Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003489368-7","authors":["Sachin A. Goswami","Saurabh Dave","Kashyap C. Patel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T17:25:42Z","doi":"10.1201/9781003489368-7","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1142/9789811287947_0007","name":"Federated Learning for Securing Data Access and its Applications in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811287947_0007","authors":["Atharva Sajanikar","Deepthi Gupta","H. L. Gururaj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T01:34:57Z","doi":"10.1142/9789811287947_0007","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.2139/ssrn.5018739","name":"Incremental Data Aware Vertical Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5018739","authors":["Yuzhi Liang","Yixiang Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-12T21:37:13Z","doi":"10.2139/ssrn.5018739","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1142/9789811287947_0006","name":"Fundamental Theory of Federated Learning, Protocols and Enabling Technologies for Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811287947_0006","authors":["Abdullah Abdul Sattar Shaikh","M. S. Bhargavi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T01:34:57Z","doi":"10.1142/9789811287947_0006","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.14722/ndss.2024.23233","name":"CrowdGuard: Federated Backdoor Detection in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.14722/ndss.2024.23233","authors":["Phillip Rieger","Torsten Krauß","Markus Miettinen","Alexandra Dmitrienko","Ahmad-Reza Sadeghi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-10T16:35:08Z","doi":"10.14722/ndss.2024.23233","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00016-8","name":"Fairness in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00016-8","authors":["Xiaoqiang Lin","Xinyi Xu","Zhaoxuan Wu","Rachael Hwee Ling Sim","See-Kiong Ng","Chuan-Sheng Foo","Patrick Jaillet","Trong Nghia Hoang","Bryan Kian Hsiang Low"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:48:57Z","doi":"10.1016/b978-0-44-319037-7.00016-8","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1142/9789811287947_0001","name":"Federated Learning Techniques and Its Application in the Healthcare Industry","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811287947_0001","authors":["D. U. Latha","D. N. Varshitha","C. Shankara"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T01:34:57Z","doi":"10.1142/9789811287947_0001","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.5220/0013527700004619","name":"Advancements and Applications of Federated Learning in Biometric Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013527700004619","authors":["Zhengliang Lyu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T17:36:42Z","doi":"10.5220/0013527700004619","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21275/sr24601193422","name":"Enhancing Privacy and Efficiency in IoT through Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.21275/sr24601193422","authors":["Nazeer Shaik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-12T10:39:38Z","doi":"10.21275/sr24601193422","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1049/pbpc072e_ch1","name":"An overview of federated learning: empowering decentralized intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e_ch1","authors":["Ghanshyam Prasad Dubey","Ayush Giri","Daniel Arockiam","V. Sathya Priya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e_ch1","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1201/9781032694870-10","name":"Federated Deep Learning System for Application of Healthcare in Pandemic Situation","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-10","authors":["Vandana","Chetna Kaushal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T08:04:57Z","doi":"10.1201/9781032694870-10","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1049/pbpc072e_ch3","name":"Federated learning frameworks and algorithms for energy-efficient IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e_ch3","authors":["Kiran Malik","Kuldeep Singh Kaswan","Jagjit Singh Dhatterwal","Rajani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e_ch3","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1109/flta63145.2024.10839830","name":"FedCLO: Federated Learning with Clustered Layer-Wise Communication Load Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839830","authors":["Tsung-Han Chang","Ted T. Kuo","Li-Jen Wang","Chia-Yu Lin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839830","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.21275/mr24706174710","name":"Federated Learning in Cybersecurity: Applications, Challenges, and Future Directions","source":"crossref","abstract":"","url":"https://doi.org/10.21275/mr24706174710","authors":["Yamini Kannan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-15T11:59:21Z","doi":"10.21275/mr24706174710","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1109/bigdata62323.2024.10825652","name":"Federated Objective: Assessing Client Truthfulness in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata62323.2024.10825652","authors":["Marco Garofalo","Alessio Catalfamo","Mario Colosi","Massimo Villari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T18:31:23Z","doi":"10.1109/bigdata62323.2024.10825652","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.5220/0012958400004508","name":"Research Advanced in Personalized Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012958400004508","authors":["Zizhuo Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-19T12:37:25Z","doi":"10.5220/0012958400004508","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1007/978-3-031-51266-7_5","name":"Federated Learning with Over the Air Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-51266-7_5","authors":["Mingzhe Chen","Shuguang Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-19T05:04:40Z","doi":"10.1007/978-3-031-51266-7_5","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.33140/amlai.05.02.01","name":"Enhancing Federated Learning Security, Scalability, and Future Incentives","source":"crossref","abstract":"This study delves into the integration of Storj and blockchain technology within the context of federated learning (FL) and its implications for scalability and efficiency. By leveraging blockchain, we aimed to bolster security and transparency, while also addressing storage challenges through the integration of Incremental Learning. Our findings revealed that while the utilization of Storj led to marginally higher federated server storage requirements compared to local storage, particularly as the number of clients increased, there was also a slight increase in the time required for the federated learning process when Storj was integrated, especially with a larger number of clients.","url":"https://doi.org/10.33140/amlai.05.02.01","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-11T11:47:43Z","doi":"10.33140/amlai.05.02.01","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00003-5","name":"Secure federated learning in the Internet of Health Things for improved patient privacy and data security","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00003-5","authors":["Kassim Kalinaki","Adam A. Alli","Baguma Asuman","Rufai Yusuf Zakari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:07:50Z","doi":"10.1016/b978-0-443-13897-3.00003-5","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1109/flta63145.2024.10839980","name":"FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839980","authors":["Fabian Stricker","José Antonio Peregrina","David Bermbach","Christian Zirpins"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839980","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1145/3665220","name":"Trustworthy AI using Confidential Federated Learning","source":"crossref","abstract":"The principles of security, privacy, accountability, transparency, and fairness are the cornerstones of modern AI regulations. Classic FL was designed with a strong emphasis on security and privacy, at the cost of transparency and accountability. CFL addresses this gap with a careful combination of FL with TEEs and commitments. In addition, CFL brings other desirable security properties, such as code-based access control, model confidentiality, and protection of models during inference. Recent advances in confidential computing such as confidential containers and confidential GPUs mean that existing FL frameworks can be extended seamlessly to support CFL with low overheads. For these reasons, CFL is likely to become the default mode for deploying FL workloads.","url":"https://doi.org/10.1145/3665220","authors":["Jinnan Guo","Peter Pietzuch","Andrew Paverd","Kapil Vaswani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-24T17:39:13Z","doi":"10.1145/3665220","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1201/9781032694870-16","name":"FedHealth in Wearable Healthcare, Orchestrated Federated Deep Learning for Smart Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-16","authors":["Bhupinder Singh","Christian Kaunert"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T08:04:57Z","doi":"10.1201/9781032694870-16","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta63145.2024.10840059","name":"Organization Committee","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840059","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840059","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1049/pbpc072e_ch12","name":"Case studies and application for energy-efficient federated learning in IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e_ch12","authors":["M. Nalini","S. Jayasri","S. Nagammai","Daniel Arockiam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e_ch12","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1109/flta63145.2024.10840023","name":"Technical Program","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840023","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840023","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1109/flta63145.2024.10840110","name":"Title Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840110","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840110","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1049/pbpc072e_ch8","name":"Energy consumption and efficiency in federated learning (FL) for IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e_ch8","authors":["Kuldeep Singh Kaswan","Jagjit Singh Dhatterwal","Kiran Malik","K. Babu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e_ch8","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1049/pbpc072e_ch4","name":"Communication efficiency in federated learning in IoT environment","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e_ch4","authors":["Amutha Prabakar Muniyandi","L. Godlin Atlas","N. Deepa","Mahmoud Ahmad Al-Khasawneh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e_ch4","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.5220/0012428300003648","name":"A Decentralized Federated Learning Using Reputation","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012428300003648","authors":["Olive Chakraborty","Aymen Boudguiga"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-01T18:44:53Z","doi":"10.5220/0012428300003648","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1142/9789811287947_0003","name":"Federated Learning: Revolutionizing Financial Insights and Security in the Digital Age","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811287947_0003","authors":["V. O. Subramany","K. Arpitha","K. Anupama","R. Asha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T01:34:57Z","doi":"10.1142/9789811287947_0003","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1049/pbpc066e_ch12","name":"A comprehensive survey on enhanced privacy techniques for federated learning in healthcare systems","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_ch12","authors":["Dasaradharami Reddy Kandati","Thippa Reddy Gadekallu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_ch12","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1201/9781003466581-2","name":"Foundations of Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003466581-2","authors":["Sajid Ullah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-18T08:39:48Z","doi":"10.1201/9781003466581-2","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1109/flta63145.2024.10840125","name":"Aggregating Low Rank Adapters in Federated Fine-Tuning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840125","authors":["Evelyn Trautmann","Ian Hales","Martin F. Volk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840125","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1007/978-3-031-58923-2_8","name":"Recent Advances in Federated Graph Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-58923-2_8","authors":["Tre’ R. Jeter","My T. Thai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T07:02:43Z","doi":"10.1007/978-3-031-58923-2_8","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta63145.2024.10839939","name":"Copyright Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839939","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839939","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.5220/0013527400004619","name":"Advancements of Credit Card Fraud Detection Based on Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013527400004619","authors":["Hongwei Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T17:31:32Z","doi":"10.5220/0013527400004619","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1049/pbpc072e_ch5","name":"Energy-efficient federated learning methods for IoT environment","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e_ch5","authors":["Amutha Prabakar Muniyandi","Feslin Anish Mon","L. Godlin Atlas","Mahmoud Ahmad Al-Khasawneh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e_ch5","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1109/flta63145.2024.10839814","name":"Federated Learning Drift Detection: An Empirical Study on the Impact of Concept and Data Drift","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839814","authors":["Leyla Rahimli","Feras M. Awaysheh","Sawsan Al Zubi","Sadi Alawadi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839814","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1201/9781003482000-4","name":"Real-Time Implementation of Improved Automatic Number Plate Recognition Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003482000-4","authors":["M. Venkatanarayana","Syed Zahiruddin","Ahmed A. Elngar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T17:15:57Z","doi":"10.1201/9781003482000-4","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-031-58923-2_9","name":"Privacy in Federated Learning Natural Language Models","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-58923-2_9","authors":["Phung Lai","C. Ariel Pinto"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T07:02:43Z","doi":"10.1007/978-3-031-58923-2_9","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1201/9781032694870-9","name":"An Explainable and Comprehensive Federated Deep Learning in Practical Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-9","authors":["Khalid Aziz","Sakshi Dua","Prabal Gupta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T08:04:57Z","doi":"10.1201/9781032694870-9","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta63145.2024.10839790","name":"On Evaluation of AutoML over a Federated Learning Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839790","authors":["Tiago Linhares","Silvio Gonçalves","Raimundo Lucas","Ramiro Viana","Thales Lopes","Andrei Portugal","Marcos Morais","Marcial Fernández"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839790","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1142/9789811292552_bmatter","name":"BACK MATTER","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_bmatter","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","doi":"10.1142/9789811292552_bmatter","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.4018/979-8-3693-1874-4.ch007","name":"Case Studies in Federated Learning for Healthcare","source":"crossref","abstract":"This chapter presents a comprehensive exploration of federated learning's real-world applications in healthcare contexts across four diverse regions: China, India, Spain, and the United States. The authors delve into how this innovative approach addresses the critical need for patient data privacy while advancing disease diagnosis, personalized treatment, and healthcare quality. From improving diagnostics through local data utilization in China to enhancing healthcare access in India, the chapter showcases the practical benefits and challenges of implementing federated learning. It also sheds light on collaborative drug development in Spain and navigating stringent data privacy regulations in the United States, emphasizing the global significance of Federated Learning in healthcare AI.","url":"https://doi.org/10.4018/979-8-3693-1874-4.ch007","authors":["Javier Prieto"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-02T09:01:10Z","doi":"10.4018/979-8-3693-1874-4.ch007","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00005-9","name":"Recent advances in federated learning for digital healthcare systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00005-9","authors":["Pooja Mohnani","Christoph Thümmler","Angelica Avila Castillo","Rasha Tolba","Alessandro Bassi","Antoine Simon","Anastasius Gavras","Orazio Toscano","Pascal Haigron"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:08:22Z","doi":"10.1016/b978-0-443-13897-3.00005-9","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1049/pbpc072e_ch11","name":"Secure data protection in federated learning for IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e_ch11","authors":["Jagjit Singh Dhatterwal","Kuldeep Singh Kaswan","Kiran Malik","Sumit Singh Dhanda","K. Babu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e_ch11","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1109/flta63145.2024.10839972","name":"Organizers and Sponsors","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839972","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839972","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1142/9789811292552_fmatter","name":"FRONT MATTER","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_fmatter","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","doi":"10.1142/9789811292552_fmatter","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:18.958Z"},{"id":"doi:10.1201/9781003489368-5","name":"Artificial Intelligence Techniques Based on Federated Learning in Smart Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003489368-5","authors":["Kanchan Naithani","Y. P. Raiwani","Shrikant Tiwari","Alok Singh Chauhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T17:25:42Z","doi":"10.1201/9781003489368-5","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.63144/ijt.2065.6747","name":"AI Privacy and Security in Healthcare: A Systematic Literature Review.","source":"pubmed","abstract":"Artificial intelligence is expanding into telemedicine and telerehabilitation, yet significant privacy and security concerns persist.","url":"https://doi.org/10.63144/ijt.2065.6747","authors":["Dolezel D","Lalani K","Watzlaf V","Butler-Henderson K","Lambert EVZ","Morton M","Sand J","Gibbs D","Fenton S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.63144/ijt.2065.6747","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.12688/openreseurope.24355.2","name":"Regime-Aware Federated Aggregation (RAFA) for privacy-preserving energy load forecasting across heterogeneous European grid clients","source":"europepmc","abstract":"","url":"https://doi.org/10.12688/openreseurope.24355.2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.12688/openreseurope.24355.2","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3389/frai.2026.1840804","name":"Deep learning for cardiovascular disease: a comprehensive review of detection and risk forecasting.","source":"pubmed","abstract":"The biggest health threat to the global population is cardiovascular disease (CVD), which afflicts almost one-third of the global population and causes considerable monetary and social losses. Risk analysis should be performed in a timely and appropriate manner to enhance clinical practice and preventive interventions. The emergence of advanced data modalities, such as wearable sensors, medical imaging, electronic health records (EHRs), and genomic platforms, has led to a paradigm shift in the holistic assessment of CVD risk through multimodal data integration. This systematic review is a methodological analysis of recent multimodal input deep-learning algorithms that enhance the early detection of CVD and risk-specific evaluation, following PRISMA 2020 guidelines across 69 studies published 20,122,025 based on 2,847 initial database records. We characterized the wide range of available data streams: longitudinal physiological measurements (ECG, HRV, and BP), echocardiogram data, cardiac MRI and CT, lab/demographic data, behavioral/environmental data, and genomic/proteomic data. Mid-level, early, late, and attention-based fusion methods are described in the context of deep neural networks, such as CNNs, RNNs, BiGRU with attention, and hybrid CNN-LSTM networks. Comparative studies showed dramatic improvements in predictive accuracy (often over 98%), strength to missing or noisy modalities, and access to real-time, individualized recommendations. The best-performing DEEP-CARDIO BiGRU-Attention model had 99.9 percent accuracy on Framingham and Statlog benchmarks. A systematic review of 28 studies by Grad-CAM and SHAP confirmed the dominance of each in imaging and structured-data tasks, respectively (Rahman et al., 2024). The federated explainable FL-LSTM model achieved 99% AUC across three ECG databanks with complete privacy protection. We end with a systematic reproducibility, federated learning, equitable AI, and regulatory translation roadmap.","url":"https://doi.org/10.3389/frai.2026.1840804","authors":["Ganeshan N","Magesh G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1840804","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.3389/frai.2026.1825067","name":"Structural impact of non-IID heterogeneity on federated behavioral anomaly detection in IoT and IoMT systems.","source":"pubmed","abstract":"The expansion of Internet of Things (IoT) and Internet of Medical Things (IoMT) infrastructures has increased the generation of multivariate sensor streams that reflect complex operational behaviors in industrial and clinical environments. Centralized anomaly detection approaches face limitations in IoMT due to privacy constraints, latency, and device heterogeneity. Federated learning (FL) enables distributed model training without data centralization; however, its behavior under highly non-Independent and Identically Distributed (non-IID) conditions remains insufficiently understood. This study proposes a trace-level behavioral modeling approach combined with federated training via FedAvg to analyze the impact of non-IID heterogeneity on anomaly detection. An Integrated Hybrid Dataset (IHD) comprising 71,980 behavioral traces, with 22,698 used for evaluation, was constructed from Edge-IIoTset, TON_IoT, and IoMT data. The centralized model achieved F 1 = 0.981 and Recall = 0.993, while the federated model preserved discriminative capacity (AUC-ROC = 0.995) but reduced Recall to 0.530. Degradation is concentrated in IoMT (Recall = 0.290), with increased Brier Score and Expected Calibration Error, showing that preserved discrimination does not ensure operational effectiveness.","url":"https://doi.org/10.3389/frai.2026.1825067","authors":["Robalino-Díaz J","Cabrera-Andrade A","Luján-Mora S","Villegas-Ch W"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1825067","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3389/frai.2026.1813511","name":"EcoStack-Pro: an adaptive federated learning framework for interpretable ESG auditing across heterogeneous industrial sectors.","source":"pubmed","abstract":"The paradigm shifts toward environmental, social, and governance (ESG) metrics has necessitated advanced auditing systems capable of analyzing complex, non-financial performance indicators. However, traditional centralized artificial intelligence (AI) models conflict with increasingly stringent data privacy regulations, while conventional federated learning approaches struggle to converge under the high statistical heterogeneity and data imbalance typical of diverse industrial sectors.","url":"https://doi.org/10.3389/frai.2026.1813511","authors":["Azad MAK","Masum AKM","Rahman MA","Bhuiyan MTA","Noori FM","Uddin MZ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1813511","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.3389/frai.2026.1811692","name":"Advanced behavioral malware detection: a comprehensive MLOps framework with federated learning and real-time drift detection.","source":"pubmed","abstract":"This paper presents a comprehensive MLOps framework for behavioral malware detection that addresses critical challenges in generalization, collaboration, and operational resilience. We introduce three methodological contributions: (1) a formalized Leave-One-Experiment-Out (LOEO) validation protocol that provides conservative assessment of generalization to novel attack methodologies, revealing a 12.3% accuracy drop compared to conventional evaluation; (2) a domain-optimized feature engineering pipeline that transforms raw process telemetry into hierarchical behavioral signatures while maintaining 99.2% accuracy with 50% reduced inference latency; and (3) a hybrid federated learning architecture enabling privacy-preserving collaboration with 75.1% accuracy while maintaining (&#x3f5;, &#x3b4;)-differential privacy guarantees (&#x3f5; = 3.2, &#x3b4; = 10 -5 ). A real-time drift detection engine with sub-500 ms latency identifies concept drift using ensemble detection and triggers automated retraining with total recovery time &lt; 5 min (mean 4.2 min). Comprehensive evaluation across 2.74 million behavioral samples from 104 distinct malware experiments validates our approach using up to 104 federated clients, achieving 10,000+ events/s throughput in simulated environments. Architectural projections based on hierarchical aggregation suggest potential scalability to 5,000+ clients, though this remains unvalidated future work. This work bridges the gap between academic research and operational cybersecurity requirements through a production-oriented MLOps implementation.","url":"https://doi.org/10.3389/frai.2026.1811692","authors":["El-Hajj M","Zeineddine MAJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1811692","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.12688/openreseurope.24355.1","name":"Regime-Aware Federated Aggregation (RAFA) for privacy-preserving energy load forecasting across heterogeneous European grid clients","source":"europepmc","abstract":"","url":"https://doi.org/10.12688/openreseurope.24355.1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.12688/openreseurope.24355.1","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.3389/frai.2026.1754000","name":"A federated multimodal deep learning framework for brain tumor classification using MRI.","source":"pubmed","abstract":"Brain tumor classification using MRI plays a critical role in early diagnosis and treatment planning. However, traditional centralized approaches require sharing sensitive medical data, which raises serious privacy concerns. Additionally, the distribution of data across multiple hospitals limits effective model training and utilization. Therefore, there is a strong need for privacy-preserving and distributed learning methods that ensure both security and accuracy in classification.","url":"https://doi.org/10.3389/frai.2026.1754000","authors":["Lakshmi Vasanthi K","Sree Darshne J","Venkatasubbu P","Ramasubramanian P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1754000","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.3390/s26154696","name":"Statistical Manifold Generation-Driven Equipment Collaborative Personalized Fault Diagnosis.","source":"pubmed","abstract":"In Industrial Internet of Things (IIoT) scenarios, data privacy constraints and distribution discrepancies caused by time-varying working conditions hinder existing intelligent fault diagnosis models from maintaining robust generalization performance across different, particularly unknown, working conditions. To address this challenge, a federated generalization fault diagnosis method driven by statistical manifold generation is proposed. The method constructs a closed-loop collaborative strategy. Initially, individual users extract and upload representative statistical information (SI) as lightweight, privacy-preserving knowledge carriers. Subsequently, a global Gaussian mixture model coupled with a covariance expansion mechanism is established in the cloud to fit multi-source distributions and extrapolate uncertainty boundaries, thereby generating virtual SI. Finally, this virtual SI is assigned via a difference-aware mechanism and integrated locally using instance normalization to achieve domain-invariant augmented training. Extensive distributed collaborative fault diagnosis experiments conducted on rolling bearing and gearbox datasets demonstrate that, when facing completely unknown working conditions, the proposed method achieves an average diagnostic accuracy of over 85%, exceeding 90% in some tasks. Furthermore, the communication payload per round is merely 1.25 KB. While strictly preserving data privacy, the proposed method significantly enhances the cross-domain generalization capability of local models with minimal communication overhead, providing an efficient and robust collaborative intelligent diagnosis solution for resource-constrained IIoT edge devices.","url":"https://doi.org/10.3390/s26154696","authors":["Liu K","Shi Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26154696","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"doi:10.21203/rs.3.rs-9642506/v1","name":"Federated Meta-Learning in the Time-Series Domain: a Scoping Review","source":"europepmc","abstract":"Abstract Wearables, industrial sensors, and other connected systems can create time-series data that is used in making decisions, predictions, and monitoring. Nevertheless, such information can be highly sensitive, and it involves the behaviour of the users, their habits, and health status, which pose a serious privacy risk. Federated learning (FL) has become a powerful tool to improve this issue, enabling models to be trained cooperatively and retaining raw data in local devices. Simultaneously, meta-learning methods have also been developed to provide quick adaptation of models to new users or tasks, and this is especially useful in low-data settings. Despite the extensive research on both FL and meta-learning, their combination as federated meta-learning (FedMeta) to time-series applications has received comparatively limited literature coverage and is scattered across the research. This paper provides a review of the literature in which FedMeta is used in terms of time series, where the interest is specifically on privacy preservation. We do not just look at the performance of the algorithm, but we look at how these methods cope with the major real-world problems, such as client-level personalisation, data distribution heterogeneity, and performance limitations imposed by limited computation or communication resources. We further examine the privacy mechanisms that have been used in previous studies and identify instances where privacy assumptions or threat models are not well defined. The analysis is based on the PRISMA-ScR approach. Peer-reviewed publications in the past five years (2019-2024) were identified and searched in seven major academic databases, including Scopus, IEEE Xplore, SpringerLink, ACM, ScienceDirect, Web of Science, and PubMed. Out of a total of 1,551 records, 21 studies met the required inclusion criteria after screening. The review shows that majority of the available literature applies FedMeta methods within a simulated or small scale experimental system and this limits the applicability of the results. Also, the selection of datasets, metrics of evaluation, or assumed adversarial models is not standard and hence, it is not easy to compare studies. In order to deal with client heterogeneity, numerous works resort to adaptive aggregation techniques or client attention techniques, whereas efficiency is often enhanced with the help of asynchronous updates or lightweight training. Differential privacy or secure aggregation is commonly used to provide privacy protection, often with a significant decrease in model accuracy. Altogether, the studies reviewed suggest that FedMeta is a good prospect for personalized and privacy-sensitive time-series modeling. Nevertheless, the area remains in its infancy, and additional advancements will necessitate a set of common standards, a better description of privacy assumptions, and a confirmation of the results based on large-scale, longitudinal studies that would take place under real-world conditions of deployment.","url":"https://doi.org/10.21203/rs.3.rs-9642506/v1","authors":["Suleman Qamar","Ian Zhou","Farzad Tofigh","Justin Lipman","Mehran Abolhasan","Massimo Piccardi"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9642506/v1","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/s26092882","name":"Machine Learning for Intelligent and Adaptive Communication Systems: From Optimization to Emerging Paradigms.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26092882","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26092882","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3389/frai.2026.1807960","name":"U-SplitDoRA: an improved privacy-preserved U-shaped split parameter-efficient fine-tuning framework through weight decomposition for large language models.","source":"pubmed","abstract":"As large language models (LLMs) are getting bigger with respect to the parameter count, ranging from a few million to billions, methods like parameter-efficient fine-tuning (PEFT) have emerged as a crucial approach for adapting these LLMs, such as GPT, Llama, and DeepSeek, to resource-constrained and privacy-sensitive environments. The robustness of large language models (LLMs) while operating on complex tasks and with large datasets makes them feasible for various application domains. This also demands the availability of more public datasets to train LLMs in the future. The federated learning (FL) technique, where several entities collaboratively train a machine learning model without sharing their data, is a widely adopted decentralized training framework. This is followed by a central server, which aggregates the models to create a global model. FL LLM fine-tuning has gained attention recently to overcome the aforementioned training data scarcity issue. LLMs are collaboratively fine-tuned by several data owners without disclosing their private data. The large number of trainable parameters has a direct effect on training such complex models on the client side. The split learning technique, through model partitioning, solves the training overhead by offloading certain training tasks to the server side. Previous research based on the split learning approach for FL LLM fine-tuning, namely SplitLoRA and HSpliLoRA, sets the foundation for further research in this direction. Frameworks like SplitLoRA have already enabled collaborative fine-tuning through model partitioning, but privacy preservation and adaptation quality remain open research challenges. U-SplitDoRA-an improved privacy preserved U-shaped split parameter-efficient fine-tuning framework through weight decomposition for large language models-is proposed. U-SplitDoRA harnesses the parallelization power of FL through the split learning approach, using weight-decomposed low-rank adaptation (DoRA) as the PEFT technique. To further address privacy concerns, the U-shaped paradigm is adapted while splitting the model. By partitioning the model into three parts (head, body, and tail), with the head and tail remaining on the client side while the body is on the server side, it ensures that neither raw data nor labels are exposed to the server, thus providing strong privacy. Additionally, replacing low-rank adaptation (LoRA) with DoRA as the PEFT method further enhances adaptation, as it updates both the magnitude and direction of weights, resulting in superior expressiveness and reducing the gap between PEFT fine-tuning and full parameter fine-tuning to a minimal margin. Experiments are conducted using GPT-2-S and GPT-2-M trained on the E2E benchmark dataset. The simulation results confirm that U-SplitDoRA attains better accuracy scores and convergence speed than other SOTA LLM fine-tuning frameworks. Thus, the proposed method addresses key gaps in privacy and adaptation quality, paving the way for efficient, robust, and privacy-preserving fine-tuning of LLM models in a distributed setting.","url":"https://doi.org/10.3389/frai.2026.1807960","authors":["Singh S","Subburaj B","Alagewaran R","Alphonse S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1807960","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3389/fnins.2026.1827009","name":"Federated training of spiking neural networks on edge hardware for audio processing.","source":"pubmed","abstract":"Spiking Neural Networks have caught significant attention recently for their potential for energy-efficient computation on neuromorphic hardware and their event-driven processing. Spiking Neural networks employ spike-based learning paradigms, which require specialized training procedures such as Surrogate Gradient Descent. At the same time, Federated Learning allows collaborative model training on decentralized devices with preservation of data privacy protection. However, to date, few research has examined the suitability of Federated learning with ARM-based hardware. This work primarily investigates whether Federated Spiking Neural Networks training on ARM-based hardware is feasible with the Raspberry Pi 5 as a widely available and low-cost edge computing device for audio signal processing tasks. We perform a comparative analysis of federated Spiking Neural Network and federated convolutional neural networks on ARM processors and evaluate their performance on different data partitioning strategies using Dirichlet-based splits and various federated averaging algorithms. Using Federated learning, this work investigates the impact of data heterogeneity and aggregation strategies on model convergence, communication overhead, and latency in distributed training paradigms. The results provided showcases the important insights into the trade-offs of FL-SNN implementations on Von Neumann architectures and their applications in decentralized neuromorphic computing for audio processing.","url":"https://doi.org/10.3389/fnins.2026.1827009","authors":["Kaimal SS","Jb A","Reka SS","Venugopal P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fnins.2026.1827009","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1751118","name":"Federated learning with dynamic weighted aggregation for multi-crop disease detection: a hybrid CNN-transformer approach.","source":"pubmed","abstract":"Crop diseases pose a significant threat to agriculture globally, causing a loss of 220 billion dollars annually. The problem can be solved through traditional machine learning methods which run on central servers but face two main obstacles: farmers refuse to provide their farming information and the uneven distribution of crop diseases across different regions. This paper introduces a federated learning framework that addresses these challenges through a novel approach that combines hybrid CNN-Transformer architectures with dynamic weighted aggregation. Our system applies EfficientNet-B0 and MobileNetV2 lightweight models which use MobileViT blocks together with CBAM and ESA attention mechanisms to extract detailed features from crop images. The innovation lies in the dynamic aggregation strategy, \"AdaClass\"-Adaptive Class-Aware aggregation, that identifies the underperforming classes in each round using class-wise F1 score and emphasizes the clients that are having a stronger performance on challenging disease classes.This approach helps in promoting a balanced performance across different disease classes. This particularly benefits the disease classes that are difficult to detect. Extensive experiments on two standard datasets demonstrate strong results: the proposed EfficientNet-B0 hybrid model achieves 99.32% accuracy on PlantVillage and 92.5% on CCMT datasets, while the MobileNetV2 achieves 99.17% and 91.4% respectively. Importantly, these models remain efficient enough for deployment on edge devices, with the MobileNetV2 hybrid requiring only 7.63 MB storage and processing images in 41.2 milliseconds.","url":"https://doi.org/10.3389/frai.2026.1751118","authors":["Shashank R","Bhavadharini RM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1751118","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.3390/healthcare14121612","name":"From Integrated Care to Learning Systems.","source":"pubmed","abstract":"Integrated care is increasingly shaped by digital infrastructures, data governance, and AI-enabled analytics, yet the relevant literature remains fragmented across health-services research, digital health, and machine learning. This article reports a scoping review, conducted in line with PRISMA-ScR guidance, that maps how integrated care models have evolved conceptually, what digital and AI-enabled infrastructures support them, how their clinical, economic, and equity impacts can be evaluated, and what current implementations imply for sustainable scaling. We searched PubMed, Scopus, Semantic Scholar, and Crossref (retrieval date 31 October 2025; forward screening to 31 March 2026) and added grey literature from named policy bodies. The searches identified 15,189 records, reducing to 11,789 after intra- and cross-source deduplication and grey-literature integration; 620 full texts were assessed and 192 were included in the synthesis. Four domains were synthesised: conceptual foundations of integrated care, AI and multimodal analytics, implementation barriers, and digital-governance foundations. We chart the field using a Type I-V maturity scheme (disease, cohort, whole-system, digital-integrated, learning), benchmarked against the Rainbow, MacColl, EMRAM/AMAM, and NHS ICS models. Most deployments cluster at digitally integrated but only weakly adaptive Type IV; recurrent failure modes-temporal blind spots, maintenance debt, semantic drift, and governance gaps-block progression to Type V, and high-profile clinical-AI failures illustrate the cost of attempting Type V analytics on Type IV-or-worse infrastructure. A walk through nine world regions maps each to its current Type I-V position and shows that organisational and payment integration-not digital sophistication alone-is currently the dominant driver of progress. The COMFORTage Integrated Care Model Library is positioned as a workflow of AI agents orchestrating predictive, preventive, and personalised care across the integrated-care lifecycle rather than as a single federated-learning programme. The review positions AI-enabled integrated care less as a finished model than as an emerging design space requiring longitudinal data assets, stewarded model lifecycles, accountable governance, and outcome-based contracting for clinically useful, equitable, and trustworthy learning systems.","url":"https://doi.org/10.3390/healthcare14121612","authors":["Tsitiridis A","Perakis K","Antoniades A","Manias G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/healthcare14121612","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.3389/fnagi.2026.1766599","name":"Privacy and personalisation: predicting Parkinson's disease severity from real-world gait with federated learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fnagi.2026.1766599","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fnagi.2026.1766599","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3389/fpls.2026.1783587","name":"A federated learning with Large-Small Kernel Attention Network for image classification.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fpls.2026.1783587","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fpls.2026.1783587","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1038/s41746-025-02329-z","name":"Comparing decentralized machine learning and AI clinical models to local and centralized alternatives: a systematic review.","source":"pubmed","abstract":"","url":"https://doi.org/10.1038/s41746-025-02329-z","authors":["Diniz JM","Vasconcelos H","Rb-Silva R","Ameijeiras-Rodriguez C","Rodrigues D","Ramos P","Tomás A","Gao Y","Souza J","Freitas A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41746-025-02329-z","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fnbot.2026.1649168","name":"Robust federated learning for UAV object detection: a joint self-distillation and drift compensation approach.","source":"pubmed","abstract":"The rapid advancement of unmanned aerial vehicles (UAVs) in disaster response and environmental monitoring has underscored the growing importance of real-time object detection within UAV swarm networks. However, the non-independent and identically distributed (non-IID) characteristics of data in UAV networks present significant challenges to model convergence and adaptability. To tackle these challenges, this study introduces a robust federated UAV object detection framework tailored for non-IID data distributions. The framework aims to enhance adaptability across clients, thereby improving both detection performance and convergence speed. Our approach includes a self-distillation mechanism that leverages personalized knowledge from local model historical states to guide current local training, striking a balance between specialization and adaptability. Additionally, we propose a drift compensation mechanism to synchronize local and global model updates, mitigating model drift. We conducted extensive experiments on the VisDrone2019-DET dataset, comparing our method to baseline models. Results demonstrate that our approach accelerates convergence speed by approximately 2.2 times and enhances detection performance by around 3%, offering an efficient and robust solution for UAV-based object detection under non-IID conditions.","url":"https://doi.org/10.3389/fnbot.2026.1649168","authors":["Hangsun Y","Jiang C","Zhang Z","Ouyang H","Chen P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fnbot.2026.1649168","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1136/bmjhci-2025-101543","name":"Introduction to secure data sharing in primary care using the federated causal learning models.","source":"europepmc","abstract":"","url":"https://doi.org/10.1136/bmjhci-2025-101543","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1136/bmjhci-2025-101543","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.3389/fdgth.2026.1760849","name":"Federated learning for fair autism spectrum disorder screening across age-heterogeneous populations.","source":"pubmed","abstract":"The detection of Autism Spectrum Disorder (ASD) remains challenging due to the heterogeneity of behavioural manifestations, limited dataset availability, and strict privacy requirements. Conventional centralized machine learning approaches often suffer from overfitting and limited generalizability across different age groups. This study proposes a federated learning (FL) framework to enable collaborative ASD screening across children, adolescents, and adults without sharing sensitive patient data.","url":"https://doi.org/10.3389/fdgth.2026.1760849","authors":["Rekik S","Mehmood S","Berriche L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1760849","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-49234-3","name":"Retraction Note: Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach.","source":"pubmed","abstract":"","url":"https://doi.org/10.1038/s41598-026-49234-3","authors":["Almadhor A","Altalbe A","Bouazzi I","Al Hejaili A","Kryvinska N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-49234-3","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-46141-5","name":"FeXAI: Federated and Explainable AI for cyber threat detection in IoT-enabled smart transportation systems.","source":"pubmed","abstract":"The rapid development of smart cities, fueled by the growth of the Internet of Things (IoT) and interconnected systems, has greatly enhanced urban infrastructure, especially in transportation and energy management. However, this increased connectivity also raises the risk of cyberattacks, threatening service availability, financial stability, and public safety. This study introduces a resilient cybersecurity framework designed to detect and classify various cyber threats, including DoS, DDoS, Reconnaissance, Sybil, Replay, and Spoofing attacks, targeting critical transportation systems such as the Internet of Vehicles (IoV), electric vehicle (EV) charging networks, and Vehicular Ad hoc Networks (VANETs). By combining machine learning with Federated Learning (FL), the framework effectively tackles key challenges like high computational costs, dependence on centralized data, and scalability across different IoT systems. FL improves data privacy by keeping sensitive information on edge devices, reducing concerns over centralized data storage. Moreover, TreeSHAP, an interpretability technique, is utilized to provide transparency and deeper insights into attack detection. The proposed system achieves high F1 scores of 0.980, 0.982, and 0.99 on the CICIoV2024, CICEVSE2024, and VeReMi Extension datasets, respectively, demonstrating its effectiveness on multiple IoT security datasets relevant to smart city transportation and energy systems. while safeguarding user privacy.","url":"https://doi.org/10.1038/s41598-026-46141-5","authors":["Gurushanker A","Rufus AJ","Columbus CC","Aravind CK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-46141-5","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/jimaging12060258","name":"Multi-Task and Federated Learning for Breast and Lung Cancer Screening and Diagnosis: A Survey and Future Research Directions.","source":"pubmed","abstract":"Breast cancer (BrC) and lung cancer (LuC) are two forms of aggressive cancer that affect both men and women worldwide. Recently, multitask learning (MTL) and federated learning (FL) techniques have proven to be efficient in increasing the robustness of deep learning (DL)-based models by performing multiple tasks simultaneously and preserving the confidentiality of medical data.","url":"https://doi.org/10.3390/jimaging12060258","authors":["Ciobotaru A","Corches C","Gota D","Miclea L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/jimaging12060258","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-49490-3","name":"TwinGuard-Sec: a federated blockchain-enabled AI framework for standardized security and privacy in cross-domain digital twin ecosystems over 6G.","source":"pubmed","abstract":"The fast rate of cross-domain Digital Twins (DT) ecosystem growth in 6G-based scenarios poses unresolved security and privacy issues into the scope of existing frameworks. This study examines the inherent constraints of existing methods and presents TwinGuard-Sec, a new federated blockchain-based AI system expressly aimed at providing a set of standardized security and privacy of data in a heterogeneous realm of DT. The methodology comprises a dual-layered systematic architectural framework, comprising an AI-governed threat intelligence unit and zero-knowledge identity verifications and a distributed ledger technology layer that is domain-interoperable with lightweight consensus algorithms ensuring synchronous operation in real time. The framework fills three essential gaps in research including: absence of standardized cross-domain security protocols, inadequate privacy preserving mechanisms applied to sensitive inter-organizational data sharing and lack of scalable consensus algorithms to be used in DT-specific needs. We show on the rigorous test of a comprehensive 6G virtual twin testbed that includes 50 distributed nodes and five application domains (smart mobility, e-health, industrial IoT, smart cities, and autonomous systems) that the performance is significantly improved: 27.4% increase in threat detection accuracy (reaching 95.0% vs. 76.4% base) can be improved, better privacy preservation with a differential privacy parameter&#x2009;=&#x2009;0.94 (62% improvement), 21.2% reduction in latency down to 147&#xa0;ms. The system achieves Precision&#x2009;=&#x2009;0.968, Recall&#x2009;=&#x2009;0.959, and F1-score&#x2009;=&#x2009;0.963 (macro-average), with AUC-ROC&#x2009;=&#x2009;0.989 across eight attack categories. These results confirm that TwinGuard-Sec is an innovative means of ensuring the safety of cross-domain DT coordination, equipping it with both theoretical frameworks and implementation channels of the next generation intelligent infrastructure systems.","url":"https://doi.org/10.1038/s41598-026-49490-3","authors":["Alnfiai MM","Alotaibi RM","Alotaibi FA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-49490-3","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.3389/frai.2026.1816692","name":"Recent advances in defending the privacy attacks of large language models for healthcare applications: a concise review.","source":"europepmc","abstract":"Large language models (LLMs) are increasingly adopted across healthcare applications, including clinical decision support and medical documentation systems. However, their deployment in medical settings raises significant privacy and security concerns due to the sensitivity of protected health information and stringent regulatory requirements. Recent studies have shown that LLM-based medical applications can cause medical data leakage through related API usages. This raises ethical concerns and threatens HIPAA (Health Insurance Portability and Accountability Act) compliance, blocking the trustworthy deployment of LLMs in the medical domain. This review examines emerging privacy attacks and the related defense approaches in LLMs with a special focus on healthcare applications. We organize the attack and defense approaches based on Secure AI Framework (SAIF), systematically covering vulnerabilities across the data, model, application and infrastructure layers. We performed detailed analysis on major classes of privacy attacks and further examined state-of-the-art defense mechanisms under realistic healthcare application scenarios. A key finding of this review is the persistent privacy-utility tradeoff: stronger privacy protection often leads to substantial degradation in clinical performance, which may render models unsuitable for mission-critical medical tasks. The healthcare related deployment of LLMs needs to be evaluated against clinical utility thresholds rather than generic language modeling metrics. We identify open challenges in evaluation, system-level deployment and regulatory verification, and outline research directions that balance clinical utility with regulatory compliance.","url":"https://doi.org/10.3389/frai.2026.1816692","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1816692","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.3389/frai.2026.1752124","name":"Systematic review of trends in deep learning for UAV cybersecurity.","source":"pubmed","abstract":"Unmanned Aerial Vehicles (UAVs) operate in navigation, sensing, and communication environments that are frequently degraded or adversarial. Their attack surface spans flight-control and payload software, radio links, and swarm coordination. This PRISMA-aligned systematic review synthesizes peer-reviewed studies published between 2015 and 2025 and organizes the evidence using an OSI-inspired threat taxonomy that maps spoofing, jamming, intrusion, and malware to system touchpoints and observable anomalies. We compare deep learning architectures, training targets, feature representations, evaluation practice, and deployment constraints relevant to single UAVs and swarms. Across the literature, convolutional and recurrent models dominate intrusion and anomaly detection pipelines, while attention-based, graph, and generative models appear in newer work targeting multi-agent settings and limited labels. Evidence most often relies on protocol traffic and onboard telemetry, whereas RF inputs are used less frequently and are typically represented as raw samples or spectrograms when datasets allow. Studies increasingly report efficiency-oriented deployment using pruning, quantization, distillation, or split inference to meet onboard compute and energy limits. Federated and multi-agent approaches are evaluated for scalability and robustness under poisoned updates, and blockchain-integrated designs are discussed under bandwidth and power constraints. Key gaps persist in shared datasets, repeatable adversarial stress testing, uncertainty and explainability reporting, privacy preservation, and certification-ready assurance cases for aviation regulation.","url":"https://doi.org/10.3389/frai.2026.1752124","authors":["Tariq U","Ahanger TA","Ahmed I"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1752124","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/s26051592","name":"FedSMOTE-DP: Privacy-Aware Federated Ensemble Learning for Intrusion Detection in IoMT Networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26051592","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26051592","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-46617-4","name":"RepFed-Net: privacy-preserving federated learning with reputation-aware aggregation for retinal vessel segmentation using enhanced U-Net.","source":"pubmed","abstract":"Accurate retinal blood vessel segmentation is essential for the early diagnosis of vision-threatening diseases such as diabetic retinopathy, glaucoma, and retinal vein occlusion. Although deep learning&#x2013;based segmentation models have achieved promising performance, centralized training approaches can compromise data privacy and may risk violating medical data protection regulations such as HIPAA and GDPR. Federated Learning (FL) enables collaborative model training without sharing raw data; however, existing FL-based segmentation methods still face challenges related to privacy leakage during parameter exchange, robustness to unreliable client updates, and limited architectural capability for capturing fine vascular structures. To address these challenges, we propose RepFed-Net, a privacy-preserving federated learning framework with reputation-aware aggregation for retinal vessel segmentation. RepFed-Net is built upon an enhanced U-Net architecture that integrates Inception modules for multi-scale feature extraction, Residual connections for stable gradient propagation, Squeeze-and-Excitation blocks for adaptive channel recalibration, and Pyramid Attention for modeling long-range contextual dependencies. This unified architectural design improves the continuity and recovery of thin and low-contrast vascular structures, leading to more accurate segmentation outcomes. To preserve privacy during collaborative training, RepFed-Net incorporates the CKKS homomorphic encryption scheme, enabling secure transmission and aggregation of model parameters directly in the encrypted domain under an honest-but-curious server. In addition, a reputation-aware aggregation strategy adaptively weights encrypted client updates based on smoothed validation F1-scores, improving robustness against noisy or low-quality client contributions without exposing raw data or plaintext model parameters. Experimental results demonstrate that RepFed-Net achieves 95.65% segmentation accuracy on the Retinal Blood Vessel dataset and 96.35% accuracy on the DRIVE dataset, consistently outperforming existing U-Net variants and conventional federated learning approaches. Overall, RepFed-Net provides a unified framework that enhances segmentation performance while offering design-level privacy preservation and robustness suitable for collaborative medical image analysis.","url":"https://doi.org/10.1038/s41598-026-46617-4","authors":["Sri AV","Morampudi MK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-46617-4","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-41093-2","name":"Personalized multi-agent reinforcement learning framework for adaptive chronic disease therapy management.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-41093-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-41093-2","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3389/frai.2026.1793305","name":"Enhancing segmentation fairness through curriculum learning and progressive loss: a centralized and federated perspective on radiograph analysis.","source":"pubmed","abstract":"Bias in medical image segmentation can lead to unequal performance across demographic subgroups, raising concerns about fairness and reliability in clinical AI systems. While deep learning models have achieved high segmentation accuracy, ensuring equitable performance across race and gender remains a significant challenge, particularly in privacy-sensitive healthcare environments.","url":"https://doi.org/10.3389/frai.2026.1793305","authors":["Alam EE","Littlefield N","Shaban-Nejad A","Moradi H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1793305","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frmbi.2026.1842701","name":"Computational and multi-omics systems biology for precision microbiome therapeutics.","source":"pubmed","abstract":"The human gut microbiome represents a complex and dynamic therapeutic target whose effective interrogation requires system-level analytical approaches beyond single-omics or reductive methods. This mini-review synthesizes recent advances in computational modeling and multi-omics integration relevant to the development of predictive, patient-tailored microbiome therapies. We critically assess the analytical strengths and limitations of genome-scale metabolic models (GEMs); generalized Lotka-Volterra and ODE-based community models; agent-based simulations; and statistical machine-learning frameworks and examine how their integration with metagenomics, metatranscriptomics, metaproteomics, and metabolomics can help bridge microbial functional potential with clinically relevant phenotypes. Representative applications-including MintTea for disease module identification, gNOMO2 for integrative microbiome profiling, and AGORA-based community metabolic modeling-illustrate the translational scope of these frameworks across inflammatory, metabolic, and infectious disease contexts. Hybrid ML-GEM frameworks have not yet been directly applied to FMT outcome prediction; however, the mechanistic principles underlying both approaches - metabolic compatibility modeling and data-driven responder stratification - suggest a compelling direction for future investigation, contingent on prospective validation in adequately powered and independent clinical cohorts. Persistent methodological challenges-such as data heterogeneity, batch effects across sequencing platforms, incomplete multi-omics coverage, and limited interpretability of complex machine-learning models-are being actively addressed through standardized preprocessing pipelines, explainable Artificial intelligence (AI) strategies, and federated analytics. While federated approaches enable privacy-preserving, multi-institutional model training, they introduce additional constraints related to non-identically distributed data, communication overhead, and uneven computational capacity. Overall, the convergence of mechanistic modeling, data-driven learning, and distributed analytical infrastructures may assist in advancing microbiome research from a largely correlational perspective toward mechanistic and ultimately prescriptive frameworks for precision microbiome medicine.","url":"https://doi.org/10.3389/frmbi.2026.1842701","authors":["Dewan A","Mascellino MT"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frmbi.2026.1842701","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3390/jimaging12050195","name":"On Vision Transformer Explainability for Personal Protective Equipment Detection: A Qualitative and Quantitative Analysis.","source":"pubmed","abstract":"The safety of workers in industrial settings is ensured through the correct use of Personal Protective Equipment (PPE). The use of such equipment can be monitored using Deep Learning (DL). Federated Machine Learning (FML) is a technique that can be used in this context to preserve the privacy of sensitive information and provide explainability for the models adopted. Explainability techniques are an essential resource for interpreting the classification performed by the model. In this regard, this study aims to evaluate, through the adoption of specific similarity indices, the robustness and consistency of the explainability algorithms adopted to identify the areas of the images that are decisive for PPE classification. The dataset consists of 1600 real images representing work environments, in which staff are portrayed both with and without Personal Protective Equipment; specifically, there are workers wearing helmets, workers wearing reflective vests, workers wearing both devices and, finally, workers without any PPE. SSIM, VIF and SCC are the most relevant indices involved in the study. In the experimental phase, their mean values stand at 0.99, 0.96 and 0.96 for the intra-client study, and 0.96, 0.91 and 0.71 in the inter-client analysis.","url":"https://doi.org/10.3390/jimaging12050195","authors":["Di Renzo M","Niro F","Agnello P","Petyx M","Martinelli F","Cesarelli M","Santone A","Mercaldo F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/jimaging12050195","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1038/s41598-026-41500-8","name":"Cross-language hotel review sentiment analysis via multi-agent federated learning with heterogeneous graph attention networks.","source":"pubmed","abstract":"","url":"https://doi.org/10.1038/s41598-026-41500-8","authors":["Han X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-41500-8","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1186/s13018-026-07104-8","name":"Artificial intelligence in shoulder arthroplasty: latest concepts and clinical integration.","source":"pubmed","abstract":"Recent advances in artificial intelligence (AI) have redefined shoulder arthroplasty, ultimately improving diagnostic accuracy, refining surgical planning, and personalizing recovery pathways. However, despite promising technical applications, comparative clinical evidence has not yet demonstrated improved routine surgical or patient outcomes in shoulder arthroplasty.","url":"https://doi.org/10.1186/s13018-026-07104-8","authors":["Jeon YD","Oh J","Park KB","Jeong HJ","Yoon JY","Oh JH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1186/s13018-026-07104-8","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"doi:10.1038/s41598-026-49344-y","name":"GNN-ML-FRL: a graph-enhanced meta-adaptive federated learning framework for scalable pest identification and ernvironmental modeling.","source":"pubmed","abstract":"Heterogeneous agro-ecological factors, insect breeding, and climate change are serious challenges to sustainable agricultural management. The study proposes a graph-enhanced meta-adaptive federated learning framework (GNN-ML-FRL) to address the challenges in precision agriculture. The proposed framework integrates Federated Learning (FL) for collaborative training of models in a decentralized manner across geographically distributed farms, Meta-Learning (ML) for rapid adaptation to changing environmental factors, and Graph Neural Networks (GNNs) for capturing spatial dependencies among agricultural entities. A comprehensive multivariate IoT environmental dataset with 52.56&#xa0;million time-series observations gathered from 500 dispersed sensors over a 12-month period, the IP102 insect pest recognition benchmark (75,222 images across 102 species), and curated genomic datasets from MaizeGDB and the Rice Annotation Project Database for genotype-informed modeling are the three standardized datasets used to assess the framework. Experimental results show statistically significant improvements (p&#x2009;&lt;&#x2009;0.01) over CNN and graph-based baselines, achieving 89.3% Top-1 accuracy, 7.8% higher generalization performance, and 12.4% reduction in prediction loss across geographically unseen farms. SHAP-based explainability further indicate that environmental accuracy-related features contributed nearly 63% positive influence, while loss-related factors contributed 37% negative influence, validating model robustness. Geographic generality is confirmed by site-out validation using IoT data, and resilience is improved under varied crop conditions by genotype-informed graph modeling. The findings show that a scalable and statistically sound framework for data-driven pest identification and environmental modeling in precision agriculture may be achieved by combining spatial graph reasoning, meta-adaptive learning, and decentralized training.","url":"https://doi.org/10.1038/s41598-026-49344-y","authors":["Mahalakshmi","T S","Mathivanan SK","R S","Joseph RB","S K B S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-49344-y","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-39149-4","name":"MedLedgerFL: a hybrid blockchain-federated learning framework for secure remote healthcare services.","source":"europepmc","abstract":"The fast growth of telemedicine has made it clear that we need safe, cooperative, and privacy-protecting ways to handle sensitive medical information. Traditional centralised predictive model training methods have problems including data leaks, ownership disputes, and tight rules that make it hard for institutions to work together. To solve these problems, this paper suggests MedLedgerFL, a hybrid platform that combines blockchain with federated learning (FL) to make remote healthcare analytics safe and reliable. The blockchain layer makes sure that the model updates can be audited, can’t be changed, and can only be accessed by authorised healthcare institutions. The federated learning layer lets healthcare institutions work together to train the model without sharing patient data, which makes sure that they follow data protection laws like GDPR. Tests show that MedLedgerFL is better than other methods at making accurate predictions, communicating quickly, and keeping information private. Combining blockchain consensus with federated model aggregation makes things more open, lowers the danger of data exposure, and makes models more reliable. Future endeavours will concentrate on enhancing scalability among institutions, integrating sophisticated privacy-preserving techniques like differential privacy and homomorphic encryption, and assessing the framework’s efficacy in extensive, practical telemedicine applications.","url":"https://doi.org/10.1038/s41598-026-39149-4","authors":["Dileep Kumar Murala","Lavanya Vemulapalli","Yadaiah Balagoni","Eswar Patnala","B. Millán Romeo"],"tags":["Computer science","Scalability","Federated learning","Blockchain","Homomorphic encryption"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-39149-4","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.1371/journal.pone.0342953","name":"FedEmoNet: Privacy-preserving federated learning with TCN-Transformer fusion for cross-corpus speech emotion recognition.","source":"pubmed","abstract":"Federated learning offers a promising path toward privacy-preserving speech emotion recognition, yet existing approaches remain confined to single-corpus evaluation, lack formal differential privacy guarantees, and provide no mechanism for model interpretability. Meanwhile, cross-corpus generalization continues to challenge even centralized systems, with typical accuracy drops of 20-40% on unseen datasets due to domain shift in recording conditions, speaker demographics, and cultural expression norms. This paper introduces FedEmoNet, a unified framework that jointly addresses these open problems by combining FedProx-based distributed optimization, a hybrid Temporal Convolutional Network-Transformer (TCN-Transformer) architecture, Particle Swarm Optimization (PSO) feature selection, and calibrated ([Formula: see text])-differential privacy. Five heterogeneous clients-two German-speech (EmoDB), two English-speech (RAVDESS), and one mixed-collaborate under non-IID conditions (Dirichlet [Formula: see text]) without exchanging raw audio. Each client extracts multi-scale phase space reconstructions at micro (25&#x2009;ms), meso (250&#x2009;ms), and macro (2.5&#x2009;s) temporal resolutions alongside spectral and handcrafted features, which are fused through multi-head attention across the TCN-Transformer branches. On held-out, speaker-independent test sets the framework achieves 99.07%&#x2009;&#xb1;&#x2009;0.35% accuracy on EmoDB (107 samples) and 98.96%&#x2009;&#xb1;&#x2009;0.42% on RAVDESS (288 samples). Zero-shot cross-corpus evaluation on CREMA-D (1,488 samples) yields 68.15%&#x2009;&#xb1;&#x2009;1.23% overall, with a clear arousal-dependent pattern: high-arousal emotions (angry, happy, sad) transfer at 71.9% versus 62.1% for low-arousal categories (neutral, disgust, fear). Ablation experiments confirm that PSO selection (+2.80%), Transformer blocks (+2.10%), and the FedProx protocol (+2.62%) each contribute significantly, and a monotonic reduced-data curve rules out memorization. Membership inference attack resistance drops to near-chance levels (AUC&#x2009;&#x2009;=&#x2009;&#x2009;0.52) under differential privacy while retaining 98.5% accuracy. A dual SHAP-LIME explainability analysis reveals high inter-method agreement (r&#x2009;=&#x2009;0.997) and confirms that prosodic features-particularly fundamental frequency statistics-serve as language-invariant emotion indicators across all three corpora (r&#x2009;=&#x2009;0.94 cross-corpus consistency).","url":"https://doi.org/10.1371/journal.pone.0342953","authors":["Tawfik M","Obeidat RA","Kamel S","Aljarrah NA","Shehadeh HH","Dalalah A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0342953","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fmed.2026.1759016","name":"Federated learning and Data Lakehouse for healthcare analytics: a knowledge transfer initiative between Germany and Tunisia.","source":"pubmed","abstract":"Healthcare institutions worldwide generate growing volumes of heterogeneous clinical data, yet legal, ethical, and infrastructural constraints often prevent these data from being centralized for analysis. Federated learning approaches offer a promising solution by enabling multi-site computation without transferring sensitive patient information, but require well-designed cross-site data harmonization. Modern Data Lakehouse architectures address this requirement by providing a scalable, governed foundation for multimodal clinical dataset integration through unified storage, metadata-rich governance, and FAIR-aligned data access. Despite increasing interest in such technologies across the Middle East and North Africa (MENA) region, operational deployments remain limited due to fragmented infrastructures, insufficient data governance, and gaps in practical expertise. This perspective article reports on a German-Tunisian knowledge and technology transfer initiative conducted within the DAAD Ta'ziz Partnership programme. As mentioned in the, 'the Arabic word 'Ta'ziz' means 'strengthening/consolidation' and has been chosen to clearly express the intended outcome of the programme' [https://www.daad.de/en/information-services-for-higher-education-institutions/further-information-on-daad-programmes/taziz-partnership/ (visited on November 28th, 2025)]. The collaboration between the University Hospital of Cologne and the University of Sfax introduced and implemented federated learning concepts via the Personal Health Train paradigm, and explored the design of a Data Lakehouse tailored to emerging healthcare ecosystems in Tunisia. Through an internship programme, hands-on MLOps training, and a large-scale workshop, the project built technical capacity in containerized analytics workflows, data governance, FAIR data management, and lakehouse engineering. We synthesize lessons learned regarding infrastructural limitations, data governance maturity, interoperability challenges, and institutional readiness, and outline considerations for sustainable adoption of distributed analytics in the MENA region. The findings highlight the critical importance of capacity building, bidirectional knowledge exchange, proof-of-concept validation, and administrative engagement for deploying trustworthy AI and modern data infrastructures in sensitive healthcare environments. We by emphasizing the need for further developments regarding federated learning and Data Lakehouse adoption in Tunisia, and how cross-regional partnerships can accelerate responsible, privacy-preserving digital health innovation.","url":"https://doi.org/10.3389/fmed.2026.1759016","authors":["Taieb MAH","Merdassi M","Tlili A","Bouri MA","Abdallah MB","Gnuito D","Turki H","Kammoun MH","Ouerda Z","Jaberansary M","Schultz B","Tang FK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1759016","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1016/j.jbi.2026.104987","name":"A federated learning framework for ethical dynamic treatment allocation across heterogeneous hospitals.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.jbi.2026.104987","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.jbi.2026.104987","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1038/s41598-026-49826-z","name":"Federated learning-driven intelligent framework for multi-center radiotherapy dose distribution prediction oriented toward linear accelerators.","source":"pubmed","abstract":"High-quality radiotherapy dose distribution prediction for linear accelerators remains a labor-intensive process constrained by inter-planner variability and institutional data silos. While deep learning has shown promise in automating dose distribution prediction, most existing methods are trained on single-center datasets, limiting their generalizability. Direct multi-center data pooling is hindered by stringent privacy regulations and heterogeneous clinical protocols. This paper proposes a federated learning-driven framework that enables collaborative model training across geographically distributed institutions without exchanging raw patient data. The framework comprises a multi-scale attention U-Net for three-dimensional dose prediction and an adaptive weighted federated aggregation strategy that dynamically balances data volume and local model quality to address non-independent and non-identically distributed data challenges. A layered privacy protection mechanism integrating gradient-clipped differential privacy with secure aggregation provides privacy-enhancing protections with quantifiable bounds during parameter exchange. Experiments conducted across four clinical centers on head-and-neck and abdominal IMRT cases demonstrate that the proposed approach achieves a mean Gamma pass rate of 96.8%, closely approaching the centralized training upper bound of 97.5% while significantly outperforming single-center models and standard federated averaging. Ablation studies confirm the individual contributions of adaptive weighting and dual attention modules, and robustness analyses validate fault tolerance under client dropout and adversarial conditions. The proposed framework offers a practical and privacy-preserving pathway for breaking data silos in AI-driven radiotherapy research.","url":"https://doi.org/10.1038/s41598-026-49826-z","authors":["Zeng Y","Chen Z","Wang B","Zhang J","Zhang K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-49826-z","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-37917-w","name":"Federated spatial-temporal traffic forecasting with VMD-enhanced graph attention and LSTM.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-37917-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-37917-w","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1038/s41598-026-39837-1","name":"Federated microservices architecture with blockchain for privacy-preserving and scalable healthcare analytics.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-39837-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-39837-1","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fbioe.2026.1824364","name":"KAN-Former: a lightweight ECG model for real-time atrial fibrillation detection on wearable devices.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fbioe.2026.1824364","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fbioe.2026.1824364","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1038/s41598-026-44260-7","name":"A unified low-carbon cybersecurity framework integrating energy-efficient intrusion detection, lightweight cryptography, and carbon-aware scheduling for edge-cloud architectures.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-44260-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-44260-7","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3389/frai.2025.1697175","name":"Federated learning for critical electrical infrastructure-handling data heterogeneity for predictive maintenance of substation equipment.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2025.1697175","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/frai.2025.1697175","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/s26092864","name":"A Privacy-Preserving Artificial Intelligence-Driven Sensing System for Distributed Multimodal Risk Detection.","source":"pubmed","abstract":"Withthe widespread deployment of intelligent terminals, mobile payment platforms, and Internet of Things devices, security systems are being progressively transformed from traditional transaction outcome analysis toward an intelligent perception paradigm centered on user behavior, device states, and environmental context. To address the challenges of multimodal data heterogeneity, non-independent and identically distributed data across nodes, and the difficulty of centralized modeling under privacy constraints in distributed scenarios, an artificial intelligence-driven federated multimodal security perception framework, namely FMS-LLM, is proposed. At its core, the framework introduces a Non-IID adaptive federated fusion mechanism that achieves dual-level alignment-structural alignment via parameter-level masks and semantic alignment via feature consistency constraints-to effectively mitigate cross-node distribution discrepancies. Additionally, an LLM-driven semantic enhancement module is developed, utilizing trend-guided token selection and inertia-suppression to map low-level sensing features into high-level risk semantic representations, thereby supporting logical reasoning and explainable decision-making. This framework takes user behavioral sensing data, device state information, environmental context data, and transaction behavior data as inputs, and constructs an integrated security analysis pipeline of \"perception-collaboration-reasoning\". Experimental results on the distributed multimodal security perception task demonstrate that the proposed method achieves an Accuracy of 91.62%, a Precision of 91.04%, a Recall of 90.37%, an F1-score of 90.70%, and a ROC-AUC of 94.73%, consistently outperforming baseline methods including Logistic Regression, Random Forest, LSTM, the centralized multimodal deep model, FedAvg, FedProx, and MOON. Under strongly Non-IID conditions, when &#x3b1;=0.1, the model still maintains an Accuracy of 88.47% and an F1-score of 87.11%, demonstrating stronger cross-node robustness. The ablation study further indicates that the complete model attains the best classification performance while reducing communication cost to 18.92 MB/Round. These results demonstrate that the proposed method can effectively fuse multi-source sensing information under privacy-preserving conditions and support intelligent security perception tasks with higher accuracy, stronger robustness, and improved interpretability.","url":"https://doi.org/10.3390/s26092864","authors":["Zhu Y","Song Y","Xuan Y","Pu J","Li J","Li M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26092864","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-42041-w","name":"A zero-trust digital twin framework for privacy-preserving multi-dataset intrusion detection in industrial IoT with lightweight blockchain auditing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-42041-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-42041-w","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.3389/fdata.2025.1659026","name":"EnDuSecFed: an ensemble approach for privacy preserving Federated Learning with dual-security framework for sustainable healthcare.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdata.2025.1659026","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fdata.2025.1659026","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/diagnostics16010137","name":"Federated Learning for Histopathology Image Classification: A Systematic Review.","source":"pubmed","abstract":"","url":"https://doi.org/10.3390/diagnostics16010137","authors":["Touhami M","Ahmad Fauzi MF","Ur Rehman Z","Mansor S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/diagnostics16010137","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.2147/rmhp.s606165","name":"Privacy, Security &amp; Governance Frameworks for AI-Powered Wearable Internet of Health Things in Elderly Care: A Comprehensive Review.","source":"pubmed","abstract":"The global aging population is expanding at an unprecedented rate, with projections indicating that 1.4 billion people will be aged 60 years or older by 2030 and 2.1 billion by 2050, placing immense pressure on healthcare systems worldwide. Artificial intelligence (AI)-powered wearable Internet of Health Things (IoHT) devices - including smartwatches, biosensors, and continuous health monitors - have emerged as transformative tools for real-time elderly health monitoring, fall detection, and predictive analytics. However, the massive collection of sensitive biometric data by these devices raises critical concerns regarding privacy, security, and governance that remain insufficiently addressed, particularly for elderly populations. This comprehensive review synthesizes evidence from 333 peer-reviewed articles published between 2018 and 2025 cross PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar to identify, analyze, and compare governance frameworks for AI-powered wearable IoHT in elderly care. The analysis reveals significant regulatory fragmentation across jurisdictions: while the European Union's General Data Protection Regulation (GDPR) and AI Act provide the most comprehensive rights-based framework, the United States relies on a patchwork of sector-specific regulations with notable gaps for consumer wearables, and Asia-Pacific nations exhibit highly variable approaches ranging from mature (Singapore, Japan) to nascent (Indonesia, Malaysia). Elderly-specific provisions remain conspicuously absent across all regulatory regimes examined. This review proposes a novel five-layer integrative governance framework - the first to unify technical security, privacy protection, ethical AI governance, regulatory compliance, and person-centered governance specifically designed for elderly care contexts. The framework addresses unique vulnerabilities associated with cognitive decline, reduced digital literacy, and caregiver dependency. Findings underscore the urgent need for harmonized, age-sensitive regulatory approaches and privacy-preserving technologies such as federated learning and differential privacy to ensure that AI-powered wearable IoHT fulfills its promise of enhancing elderly healthcare without compromising dignity, autonomy, or data security.","url":"https://doi.org/10.2147/rmhp.s606165","authors":["Dharmansyah D","Rahayuwati L","Pramukti I","Mutyara K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2147/rmhp.s606165","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1016/j.isci.2026.115729","name":"FPECGNET: A deep learning framework based on federated learning and prototype learning for interpretable ECG classification with privacy.","source":"pubmed","abstract":"Under everyday operational conditions, data privacy regulations prohibit sharing information between hospitals or institutions, contributing to the emergence of data silos. This scarcity of data hinders improvements in model performance. Another major obstacle to AI applications in medicine is the lack of interpretability, where the decision-making process of models cannot be reflected. We propose a deep learning framework based on federated learning and prototype learning that simulates the reality of hospital data silos while endowing the model with interpretability. Using the publicly available PTB-XL dataset, we divided it into three subsets by category and trained the model using the federated learning framework, achieving superior performance on these subsets. When tested on the aggregated model using the PTB-XL dataset, it demonstrated performance comparable to the current state-of-the-art model.","url":"https://doi.org/10.1016/j.isci.2026.115729","authors":["He J","Xiong J","He S","Chen Y","Li X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.isci.2026.115729","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3389/frai.2026.1701944","name":"Design of an AI-driven secure 5G-SDN framework with federated reinforcement learning for anomaly detection, mitigation, and attack forensics.","source":"europepmc","abstract":"Introduction The increasing adoption of Software-Defined Networking (SDN) in 5G networks has revolutionized network management. However, this paradigm shift has introduced critical security vulnerabilities, including data-plane anomalies, control-layer intrusions, and Distributed Denial-of-Service (DDoS) attacks. Existing intrusion detection approaches based on Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks suffer from high computational overhead, long detection latency, and limited scalability, making them unsuitable for real-time 5G-SDN environments. Methods This article proposes a novel multi-layered security framework for 5G-SDN that integrates EfficientNet with Knowledge Distillation (KD), Transformer Networks, Spiking Neural Networks (SNNs), Federated Reinforcement Learning (FRL), and blockchain technology. EfficientNet-KD enables lightweight and accurate anomaly detection at the data-plane layer. Transformer networks capture long-range temporal dependencies to enhance control-layer attack detection. SNNs are employed for ultra-low-latency attack classification by mimicking human brain neural processing. FRL supports decentralized and privacy-preserving mitigation across SDN controllers, improving scalability, while blockchain technology ensures the integrity and immutability of attack logs for forensic reliability. Results The proposed framework was evaluated using multiple benchmark datasets, including CICIDS2017, UNSW-NB15, IoT-23, and InSDN. Experimental results demonstrate an average detection accuracy of 97.75%, detection latency of 15 ms, and less than 5% throughput degradation. Each detection consumes only 0.25 J of energy, achieving a 40% reduction in energy usage compared to traditional CNN- and LSTM-based approaches. Discussion The results verify that the proposed framework provides a scalable, energy-efficient, and low-latency intrusion detection and mitigation solution for 5G-SDN environments. By integrating lightweight deep learning, neuromorphic computing, decentralized learning, and blockchain-based security, the framework effectively addresses the limitations of existing methods and offers a robust approach for securing next-generation 5G-SDN networks.","url":"https://doi.org/10.3389/frai.2026.1701944","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1701944","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-026-42537-5","name":"A causal multimodal framework for privacy-preserving early-stage cancer detection and adaptive testing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-42537-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-42537-5","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/s26010229","name":"A Systematic Review of Federated and Cloud Computing Approaches for Predicting Mental Health Risks.","source":"pubmed","abstract":"","url":"https://doi.org/10.3390/s26010229","authors":["Fiaz I","Kanwal N","Al-Said Ahmad A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s26010229","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/s26061780","name":"Toward Energy-Efficient and Low-Carbon Intrusion Detection in Edge and Cloud Computing Based on GreenShield Cybersecurity Framework.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26061780","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26061780","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1109/tmi.2025.3596835","name":"Improving Learning of New Diseases Through Knowledge-Enhanced Initialization for Federated Adapter Tuning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tmi.2025.3596835","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tmi.2025.3596835","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1038/s41598-026-38732-z","name":"A new adaptive federated learning approach for privacy preserving UAV anomaly detection under non-IID distributions.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-38732-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-38732-z","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.3389/fpubh.2026.1769078","name":"FedMal-XAI: an explainable federated vision transformer leveraging knowledge distillation for privacy-preserving malaria detection.","source":"pubmed","abstract":"Plasmodium parasites are the cause of malaria, a deadly illness that continues to pose a serious danger to world health, especially in areas with low resources where subjectivity, complexity, along with privacy issues make it difficult to employ traditional diagnostic techniques like microscopy and quick diagnostic testing. To overcome these specific challenges of diagnostic subjectivity, logistical complexity, and data privacy, this paper suggests a privacy-preserving federated learning system that uses sophisticated Vision Transformers (ViTs) for automated malaria identification from blood smear images. This paper suggests a privacy-preserving federated learning system that uses sophisticated Vision Transformers (ViTs) for automated malaria identification from segmented red blood cell (RBC) images in order to get around these issues. This architecture successfully addresses important privacy and logistical restrictions by enabling cooperative training among decentralized institutions without exchanging sensitive data. Prominent centralized convolutional neural networks (CNNs) are matched in diagnostic accuracy by the federated ViT models, which include ViT-B/16, DeiT-Tiny, Swin-T, and DINOv2. Interestingly, the federated transformer variations outperform the CNN ensemble (ResNet50&#x202f;+&#x202f;VGG16) with an accuracy of 98.15%, FedDistill-DeiT achieving 97.79%, FedAvg-Swin-T reaching 97.75%, and FedDistill-Swin-T achieving a high ROC-AUC of 0.9977. These findings show that, even in the presence of diverse data distributions, federated Vision Transformers provide a reliable, scalable, and interpretable malaria screening solution that combines high accuracy with solid privacy guarantees.","url":"https://doi.org/10.3389/fpubh.2026.1769078","authors":["Bhuiyan TA","Rahman A","Khan FI","Noori FM","Masum AKM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1769078","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3390/s26082418","name":"Artificial Intelligence-Driven Multimodal Sensor Fusion for Complex Market Systems via Federated Transformer-Based Learning.","source":"pubmed","abstract":"In highly digitalized and networked modern trading systems, large volumes of heterogeneous data are continuously generated from multiple sources during market operations. However, due to the complexity of data structures, significant differences in temporal scales, and constraints imposed by data privacy protection, traditional single-source modeling approaches are unable to fully exploit multisource information. To address this issue, a federated multimodal prediction framework for complex market systems, termed Federated Market-Sensor Transformer (FMST), is proposed. In this framework, data originating from different information sources are uniformly modeled as multimodal time series. A multimodal market-sensor representation module is constructed to perform unified feature encoding, and a cross-modal Transformer fusion architecture is employed to characterize dynamic interaction relationships among different information sources. Meanwhile, a federated collaborative learning mechanism is introduced during the training phase, enabling multiple data nodes to perform collaborative model optimization without sharing raw data. In this manner, data privacy can be preserved while improving the cross-region generalization capability of the model. Systematic experimental evaluation is conducted on the constructed multimodal market-sensor dataset. The experimental results demonstrate that the proposed method consistently outperforms traditional statistical models and deep learning approaches across multiple evaluation metrics. In the main prediction experiment, FMST achieves a root mean square error (RMSE) of 0.1136, a mean absolute error (MAE) of 0.0832, and a coefficient of determination R2 of 0.8517, while the direction prediction accuracy reaches 74.56%, clearly outperforming baseline models including ARIMA, LSTM, Temporal CNN, Transformer, and FedAvg-LSTM. In the cross-region generalization experiment, FMST maintains strong performance, achieving an RMSE of 0.1242, an MAE of 0.0908, an R2 value of 0.8261, and a direction prediction accuracy of 72.48%. The ablation study further indicates that the three core components-multimodal market-sensor representation, cross-modal Transformer fusion, and federated collaborative learning-each make important contributions to the overall model performance. These experimental findings demonstrate that the proposed method can effectively integrate multisource market information and significantly enhance the prediction capability for complex market dynamics, providing a new technical pathway for the application of artificial intelligence-driven multimodal sensing systems in economic data analysis.","url":"https://doi.org/10.3390/s26082418","authors":["Shi L","Tian M","Yi Y","Hu X","Wang X","Yang Y","Li M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26082418","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-50690-0","name":"Lightweight and Energy-Aware Intrusion Detection for Industrial IoT Using TinyML and Edge AI.","source":"pubmed","abstract":"Industrial Internet of Things (IIoT) ecosystems are expanding rapidly. Scalable and reliable intrusion detection systems (IDS) are needed to protect critical infrastructures from evolving cyber threats. This study proposes a hybrid IDS framework that combines Graph Attention Networks (GAT) and Bidirectional Gated Recurrent Units (BiGRU) for privacy&#x2011;preserving distributed detection. The model is optimized with the Grey Wolf Optimizer (GWO) and enhanced through Federated Learning (FL). In IIoT traffic, GAT captures complex structural links, while BiGRU analyzes bidirectional temporal patterns, enabling accurate anomaly detection. GWO automates hyperparameter tuning and offers faster convergence than traditional methods such as Ant Colony Optimization. FL trains models locally on distributed IIoT devices, preserving data privacy and supporting decentralized deployment. The framework demonstrates improved scalability potential through decentralized training and reduced communication overhead (20% lower in a 10-node simulation), achieving detection accuracies of up to 95% across diverse attack scenarios, including Distributed Denial of Service (DDoS), Advanced Persistent Threats (APTs), and Zero&#x2011;Day exploits. It has been evaluated on the Edge&#x2011;Industrial Internet of Things dataset (Edge&#x2011;IIoTset), Canadian Institute for Cybersecurity - Internet of Things 2023 Dataset (CICIoT2023), and Real&#x2011;Time Internet of Things 2022 (RT&#x2011;IoT2022) datasets. An Explainable AI (XAI) module further improves interpretability by leveraging GAT's attention mechanism. Overall, this technology demonstrates competitive offline performance on the EDGE-IIoTset, CICIoT2023, and RT-IoT2022 benchmark datasets, achieving F1-scores of up to 0.94, and shows promising scalability potential through decentralized Federated Learning with 20% lower communication overhead in a 10-node simulation. However, inference latency on resource-constrained edge hardware remains a challenge (e.g., 120-180&#xa0;ms per sample on Raspberry Pi 4), which limits its strict real-time feasibility in mission-critical environments. Therefore, further model compression, adversarial robustness testing, and real-world deployment validation are required before practical edge-level applicability can be confirmed.","url":"https://doi.org/10.1038/s41598-026-50690-0","authors":["Nassef L","Alghamdi MI","Chaabane SB","Abbas Q","Alawad WM","Albalawi OH","Alqaisi OI","Fakieh B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-50690-0","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106417","name":"Challenges in translating AI-driven ASD/ADHD diagnosis: A methodological systematic review.","source":"pubmed","abstract":"Early and accurate diagnosis of neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), remains a critical challenge in pediatric care. Traditional methods rely on subjective behavioral assessments that are time-intensive and prone to bias.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106417","authors":["Rasool A","Ahmad F","Bunterngchit C","Aslam S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106417","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1371/journal.pone.0343980","name":"Deep-Fed: A comprehensive solution for precise bone fracture identification in athletes.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0343980","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0343980","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.3389/fmed.2026.1687773","name":"Prediction of &lt;i&gt;β&lt;/i&gt;-thalassemia carrier using federated learning and explainable AI.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fmed.2026.1687773","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1687773","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3389/fpubh.2026.1859276","name":"Localized AI for stroke care in LMICs: a framework to overcome structural and diagnostic barriers.","source":"pubmed","abstract":"Low- and middle-income countries (LMICs) bear a disproportionate share of the global stroke burden, driven not only by resource limitations but also by systemic inefficiencies in workforce distribution, diagnostic access, and prehospital care coordination. While advances in artificial intelligence (AI) have demonstrated significant potential in stroke diagnosis and management, many existing solutions remain poorly aligned with the infrastructural and policy realities of LMIC health systems, limiting their scalability and long-term impact. This study presents a comprehensive narrative review of literature published between January 2015 and March 2026, synthesizing evidence across digital health, stroke systems of care, and AI deployment models. We identify three persistent structural barriers-workforce shortages, diagnostic centralization, and fragmented care pathways-that collectively constrain timely intervention in acute stroke. In response, we propose a \"Localized AI + Policy\" framework that integrates lightweight AI models, edge computing, and federated learning within context-specific health system and governance structures. This approach emphasizes decentralized computation, data sovereignty, and alignment with national health policies, enabling more resilient and scalable deployment of AI in resource-constrained environments. By shifting the focus from technology-centric innovation to system-integrated implementation, this framework highlights a pathway for translating AI advances into sustainable public health impact. The findings underscore the importance of embedding digital health solutions within broader strategies for health system strengthening, universal health coverage, and global health equity.","url":"https://doi.org/10.3389/fpubh.2026.1859276","authors":["Liu Q","Jia X","He Y","Hou Y","Deng Y","Yan Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1859276","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.7759/cureus.107501","name":"Machine Learning-Based Data Extraction Tools in Healthcare: A Systematic Review.","source":"pubmed","abstract":"The healthcare industry's digital transformation has led to an unprecedented volume of multimodal data. Machine learning (ML)-based extraction tools offer promising solutions for managing this data explosion, particularly when integrated with federated database systems. If a large language model (LLM) is trained to extract data from this multimodal information and ensure high accuracy while remaining affordable, the potential to improve the data extraction process within the medical field would be limitless, reducing costs and manpower across the board. A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, searching major databases for studies published between 2018 and 2024, supplemented by grey literature sources. Analysis focused on the performance and implementation costs of ML-based extraction tools in healthcare settings. From 1,247 initial records, 21 studies met the inclusion criteria. ML-based extraction demonstrated superior accuracy, ranging from 61% to 98%, compared to traditional methods. Implementation costs averaged between $500,000 and $2.5 million. Two primary categories of tools emerged: image-based and text-oriented. ML-based extraction tools show significant promise in healthcare data management, though successful implementation requires careful consideration of costs, security protocols, and regulatory compliance. The development of a dedicated LLM capable of efficiently extracting data from various medical sources could revolutionize healthcare by streamlining data management and reallocating resources toward patient care and research advancements.","url":"https://doi.org/10.7759/cureus.107501","authors":["Khalpey Z","Rorvig M","Khalpey ZI","Kumar U","Khaliel FH","King N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.7759/cureus.107501","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1002/bco2.70198","name":"European validation of the Barcelona magnetic resonance predictive model for significant prostate cancer detection in prostate biopsies.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/bco2.70198","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/bco2.70198","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1038/s41598-026-40581-9","name":"Privacy-preserving federated learning with light-weight attention improved CNNs for automated leukemia detection across distributed medical imaging.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-40581-9","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-40581-9","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1371/journal.pcbi.1013695","name":"Federated learning for COVID-19 mortality prediction in a multicentric sample of 21 hospitals.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pcbi.1013695","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1371/journal.pcbi.1013695","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1371/journal.pone.0339981","name":"AnomLocal: A hybrid local-global anomaly detection model for network security using federated learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0339981","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0339981","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3390/jpm16040218","name":"A Decade of Artificial Intelligence in Stroke Care (2015-2025): Trends, Clinical Translation, and the Precision Medicine Frontier-A Narrative Review.","source":"pubmed","abstract":"Background/Objectives: Stroke generates 157 million disability-adjusted life-years (DALYs) annually, making it the leading neurological cause of global disease burden. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies across the stroke care continuum. This narrative review maps the trajectory of AI in stroke medicine over the decade from 2015 to 2025. Methods: We conducted a narrative review with a structured, pre-specified search strategy across eight pre-specified thematic clusters using PubMed/MEDLINE (January 2015-December 2025), identifying 8549 records and including 1335 studies after screening. Inclusion criteria encompassed primary research articles, systematic reviews, meta-analyses, and RCTs reporting quantitative performance metrics or clinical outcome data for AI/ML in stroke. Results: Stroke imaging AI is the most commercially mature domain, with over 30 FDA-cleared tools. Automated ASPECTS scoring reduced radiologist reading time by 74.8% (AUC 84.97%; 95% CI: 83.1-86.8%). The only triage AI RCT demonstrated an 11.2 min reduction in door-to-groin time without significant improvement in 90-day functional independence (OR 1.3, 95% CI 0.42-4.0). Brain-computer interface rehabilitation showed significant upper limb recovery in a 17-center RCT (FMA-UE mean difference +3.35 points, 95% CI 1.05-5.65; p = 0.0045). AF detection AI is FDA-cleared and RCT-validated. LLMs and federated learning are pre-regulatory but growing exponentially. Conclusions: AI in stroke has achieved diagnostic maturity but therapeutic immaturity. Bridging algorithmic performance to patient outcomes, addressing equity gaps, and building the economic evidence base for scalable deployment are the defining challenges of the next decade.","url":"https://doi.org/10.3390/jpm16040218","authors":["Urfy M","Mir MT"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/jpm16040218","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3389/fpubh.2026.1785170","name":"Cognitive sovereignty and decolonial public health: reclaiming epistemic authority in the global AI era.","source":"pubmed","abstract":"As artificial intelligence (AI) becomes critical infrastructure for global health, it reproduces colonial patterns of extraction, mining data from the Global South to train models owned by the Global North. While international bodies like the WHO emphasize \"ethical AI,\" they often overlook the structural violence of this digital colonialism. This perspective argues that true health equity requires more than bias mitigation; it demands cognitive sovereignty: the right of communities to govern not just their data but also the epistemic logic, interpretive frameworks, and algorithmic reasoning of the systems that analyze it. Drawing from Indigenous data governance principles (OCAP/CARE) and concrete implementation cases from Kenya, Nigeria, Rwanda, and Latin America, we demonstrate how cognitive sovereignty extends beyond data sovereignty to encompass control over knowledge production itself. By anchoring this political vision in specific technical architectures, federated learning, and community-led surveillance, we can move from extractive \"AI for good\" to a decolonial future of autonomous health intelligence. Recent cases from Kenya's AI health deployments and pathogen genomics illustrate both the urgency and feasibility of this transformation.","url":"https://doi.org/10.3389/fpubh.2026.1785170","authors":["Kakraba S","Agyemang EF","Srivastav SK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1785170","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1007/s10278-025-01756-4","name":"Comparative Performance Evaluation of Federated and Centralized Learning for Velum and OTE Segmentation in Sleep Endoscopy Images.","source":"pubmed","abstract":"","url":"https://doi.org/10.1007/s10278-025-01756-4","authors":["Yeom JC","Kim JY","Kim YJ","Kim KG","Rhee CS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1007/s10278-025-01756-4","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.1109/tcyb.2025.3632322","name":"Event-Triggered Federated Reinforcement Broad Learning for Intelligent Fault Diagnosis in Uncrewed Helicopter Swarm Systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tcyb.2025.3632322","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1109/tcyb.2025.3632322","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1371/journal.pdig.0001442","name":"PANDIA: Personalized neuro-symbolic multimodal fusion for interpretable neonatal pain assessment.","source":"pubmed","abstract":"Effective pain assessment in infants aged 0-3 months is a critical challenge in neonatal intensive care units (NICUs) and family medicine clinics, where self-reporting is impossible and current observational tools remain subjective and inconsistent. This paper presents PANDIA (Personalized Adaptive Neuro-symbolic Data-fusion for Infant Assessment), a novel multimodal AI system that combines hierarchical representation learning, graph-based inter-modal reasoning, meta-learning personalization, and symbolic concept-bottleneck explanations for robust infant pain assessment. Unlike transformer-centric approaches, PANDIA employs lightweight CNN/TCN backbones with a graph neural network for inter-modal fusion, achieving clinical interpretability through explicit concept bottlenecks and symbolic reasoning. Our federated learning framework enables privacy-preserving multi-site collaboration while meta-learning adaptation provides personalized assessment with minimal per-infant data. Evaluated on 2,847 infants across four datasets, PANDIA achieves 87.3% accuracy with 92.1% clinician acceptance rate for explanations, achieving a 12.4% accuracy improvement over the best baseline, consistent across all four datasets and an independent out-of-distribution test set, while maintaining fewer than 30M parameters for edge deployment. The proposed system offers a structured and interpretable step toward deploying explainable AI in early-life pain management, with potential to improve care quality and support medical decision-making. Key limitations include the retrospective validation design, dataset heterogeneity across collection sites, and the need for prospective clinical trials before deployment in live clinical settings. All code, trained models, preprocessing pipelines, and supplementary materials are fully publicly available without restriction at: https://github.com/oussama123-ai/pandia. The NICU-MM dataset is available upon request subject to an ethical data use agreement; the access procedure is detailed in Section 4.1.1.","url":"https://doi.org/10.1371/journal.pdig.0001442","authors":["El Othmani O","Naouali S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pdig.0001442","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3390/jimaging12060228","name":"A Comprehensive Review of Artificial Intelligence for Brain Tumor Analysis: Taxonomy, Robustness, and Open Challenges in Neuro-Oncology.","source":"pubmed","abstract":"Detecting brain tumors can be challenging as a clinical problem because of tumor heterogeneity and reliance on manual neuroimaging interpretation, which can be prone to human error. Artificial intelligence (AI) has shown strong potential as a clinical decision-support tool, assisting radiologists in improving diagnostic accuracy and supporting the interpretation of neuroimaging data. AI using machine learning (ML) and deep learning (DL) algorithms has performed credibly in tumor detection, segmentation, and classification tasks. Challenges such as dataset bias, limited generalization, lack of explainability, and high computational costs must be addressed before clinical application. This article provides a comprehensive review of AI methods applied to brain tumor imaging, with a primary focus on adult diffuse gliomas and secondary coverage of brain metastases, meningiomas, and pediatric tumors where relevant. The major contribution of this review is a new three-factor (diagnostic tasks, learning strategies, and data modalities) taxonomy. Beyond accuracy-based metrics, we provide a qualitative assessment of robustness, generalization, and the principal barriers to clinical adoption identified in the published literature, while acknowledging that comprehensive clinical utility evidence remains an open research direction.","url":"https://doi.org/10.3390/jimaging12060228","authors":["Qasem MH","Sariera TMA","Alhumaid K","Alshraah SM","Mufleh ASS","Chihaoui N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/jimaging12060228","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1038/s44276-026-00221-1","name":"Precision oncology in the age of AI: lessons from AI-driven drug discovery and clinical translation.","source":"europepmc","abstract":"Drug discovery has been constrained by extended timelines and high costs, as the cumulative requirements of preclinical validation, multi-phase clinical trials, and regulatory approval have been imposed. Recently, computational modeling has been explored as a supportive approach to accelerate the identification and refinement of therapeutic candidates. Proof-of-concept was provided in a phase 2a trial of a de novo-designed TNIK inhibitor in idiopathic pulmonary fibrosis, in which safety, tolerability, and pharmacodynamic target engagement were demonstrated, with a trend toward reduced functional decline. This study showed that AI-derived molecules can advance into human testing, but broader validation, mechanistic understanding, and regulatory alignment remain essential. In oncology, where tumor heterogeneity, clonal evolution, and therapeutic resistance continue to constrain durable clinical benefit, there is an increasing need for adaptive and data-informed drug discovery strategies. This Perspective reviews recent progress and limitations in AI-driven drug discovery and early clinical translation. It emphasizes how the clinical evaluation of an AI-generated TNIK inhibitor serves as an early translational reference and outlines practical strategies for integrating multi-omics data, federated model validation, and adaptive trial design to advance precision oncology-oriented therapeutics.","url":"https://doi.org/10.1038/s44276-026-00221-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s44276-026-00221-1","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.3389/frai.2026.1699896","name":"EF-Feddr: communication-efficient federated learning with Douglas-Rachford splitting and error feedback.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2026.1699896","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1699896","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-025-33376-x","name":"A spatio-temporal graph diffusion and federated contrastive learning framework for cross-institutional educational evaluation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-33376-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-33376-x","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.21203/rs.3.rs-9149238/v1","name":"Advancing Data Sovereignty in Africa: Deploying DataSHIELD to Federate Cross-Border Data Silos within the Data Science Without Borders Initiative","source":"europepmc","abstract":"Abstract Collaborative health research across Africa is constrained by data sovereignty concerns, heterogeneous regulatory frameworks, limited infrastructure, and persistent capacity gaps, which hinder equitable cross-border data sharing. These challenges limit the availability of large, harmonized datasets needed to address the continent’s burden of infectious and non-communicable diseases while maintaining control over sensitive health data. Within this context, the Data Science Without Borders in Africa (DSWB) project implemented a privacy-preserving federated analysis framework using DataSHIELD across four institutions in Senegal, Cameroon, Ethiopia, and Kenya. This study documents the design, deployment, and early outcomes of this implementation. The approach combined consortium-wide data governance harmonization, a structured capacity-building programme, and a standardized technical architecture integrating DataSHIELD with the OMOP Common Data Model via dsOMOP. The deployment resulted in a functional federated network that enabled secure, in-situ analyses of harmonized clinical data. Multidisciplinary teams successfully executed federated descriptive and modelling analyses, managed Opal-based servers and implemented OMOP-based data harmonization workflows. Challenges related to connectivity, software heterogeneity, and institutional security were mitigated through server-side computation, standardized environments, and collaboration with institutional IT teams. Overall, this work demonstrates the feasibility of scalable, ethically robust federated health data analysis in diverse African research settings while preserving data sovereignty.","url":"https://doi.org/10.21203/rs.3.rs-9149238/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9149238/v1","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.3389/frai.2025.1496945","name":"Population health management fit lifecycles in analytics.","source":"pubmed","abstract":"\"Population Health Management (PHM), Fit Lifecycles in Analytics\" examines the policy and practice of AI-driven methodologies to enhance public health and patient safety in the context of the Human Phenotype Ontology (HPO). It aims for personalized healthcare delivery through the risk stratification of predictors and pathology segmentation for intercepts. This manuscript aimed to introduce the Five-Point PHM strategy as a mission for public trust and governance. Scientific and technological advancements address public genomic inclusiveness and engage biobanks and life sciences for national public health and patient safety oversight.","url":"https://doi.org/10.3389/frai.2025.1496945","authors":["Henry JA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/frai.2025.1496945","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-56630-2","name":"Hybrid electricity management system for residential power block applications.","source":"pubmed","abstract":"Developing countries have been facing electricity crisis for decades, mainly due to insufficient electricity generating capacity as compared to its demand and extensive growth in population. This research outlines Electric_Fed, a decentralized electricity management model that incorporates blockchain and federated learning (FL). This model allows multiple clients, defined as houses or blocks, to privately and efficiently collaborate on electricity consumption and production forecasting without needing to exchange raw, shared data. The model achieves collaboration on a global model. Automated electricity trading is made possible through the use of Blockchain and smart contracts, which allows clients to securely and transparently participate in real-time peer-to-peer energy exchange. In this article, electricity production model using cross device FL technique and blockchain has been presented. In the proposed model, FL has been used to provide a secured client server model to facilitate clients having surplus electricity or facing electricity shortfall within the network. Central Server issues alert to the clients having surplus electricity to start Smart contract with the client facing electricity shortfall implementing blockchain. Blockchain ensures safe transaction and reliable transfer of payments to the client selling electricity. Electricity production and consumption dataset from Jan 2024 to Dec 2024 has been processed. Experimental validation proved that the proposed model is capable enough to fulfill 89.2% electricity need of the selected region. The experimental setup results (from June 2024) validate the model's effectiveness. Client A reached a surplus of +&#x2009;909 kWh, Client B&#x2009;+&#x2009;12 kWh, Client C&#x2009;+&#x2009;230 kWh and Client D fell short by -&#x2009;693 kWh. The system automatically recognized surpluses and deficits, trading energy to balance the needs of Clients A and C, which allowed them to reach full self-sufficiency. This model lowered national grid dependency and reduced overall electricity costs. As compared to other models presented for electricity supply, our proposed model is not only more effective in fulfilling the regional electricity demands but it also suggested a secured mechanism to compensate the electricity selling client participating in electricity trade. This demonstrates, in and of itself, the effectiveness of the model proposed while providing intelligent decentralized energy management in a system that is, as a whole, efficient, scalable, and privacy preserving.","url":"https://doi.org/10.1038/s41598-026-56630-2","authors":["Tehseen R","Omer U","Hamraz AR","Mustaqeem A","Sabahat N","Qamar N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-56630-2","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1007/s44163-026-00914-z","name":"Model centric collaboration reduces data sharing barriers in medical artificial intelligence.","source":"pubmed","abstract":"","url":"https://doi.org/10.1007/s44163-026-00914-z","authors":["Dai Y","Cai Y","Xu Y","Cai Q","Zhang S","Wang Y","Xie Y","Liu Y","Cheng L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1007/s44163-026-00914-z","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/diagnostics15243110","name":"Explainable Federated Learning for Multi-Class Heart Disease Diagnosis via ECG Fiducial Features.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/diagnostics15243110","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/diagnostics15243110","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1016/j.scib.2026.02.012","name":"The role of artificial intelligence in advancing population-based cancer registration.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.scib.2026.02.012","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.scib.2026.02.012","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1038/s41598-026-40881-0","name":"F-Transformer: a federated transformer for efficient and privacy-preserving sequence generation.","source":"pubmed","abstract":"Transformer models have demonstrated remarkable success in natural language processing (NLP) tasks, but their deployment in distributed environments faces critical challenges including high computational demands, large memory footprints, and privacy concerns when handling sensitive data. Existing federated learning (FL) implementations with transformers often sacrifice either model performance for privacy or resource efficiency for accuracy. We propose the F-Transformer, a lightweight federated transformer framework that addresses these limitations through integrated privacy-preserving mechanisms and architectural optimizations. Our framework employs a compact architecture with 4 attention heads, 4 layers, and 64 embedding dimensions, achieving only 0.87 million trainable parameters. We implement an incremental FL strategy where local clients continuously train on newly arriving data while the global model aggregates updates through the FedAvg algorithm. The framework integrates privacy objectives directly into the optimization process through a novel regularization formulation. We evaluate the F-Transformer on the WikiText-2 dataset using validation perplexity as the primary metric, along with central processing unit (CPU) utilization, memory consumption, and training loss convergence. Our results demonstrate a validation perplexity of 5.9894, surpassing state-of-the-art (SOTA) models including BERT-Large, GPT-2, and SparseGPT while using significantly fewer parameters. The framework achieves 40% reduction in CPU utilization and 34% reduction in memory consumption compared to centralized training. These results establish the F-Transformer as an effective solution for privacy-preserving sequence generation in resource-constrained federated environments.","url":"https://doi.org/10.1038/s41598-026-40881-0","authors":["Patel N","Brahmbhatt S","Ramoliya F","Vyas O","Nair A","Vyas T","Jadav NK","Tanwar S","Alabdultif A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-40881-0","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/s25237274","name":"Perception and Prediction of Factors Influencing Carbon Price: Multisource, Spatiotemporal, Hierarchical Federated Learning Framework with Cross-Modal Feature Fusion.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25237274","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25237274","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1016/j.artmed.2026.103481","name":"State-of-the-art TinyML approaches for colorectal cancer detection: Current advances, challenges, and future directions.","source":"pubmed","abstract":"Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with diagnostic disparities, particularly pronounced in resource-constrained and decentralized healthcare settings. Recent advances in TinyML machine learning models optimized for ultra-low-power, memory-constrained embedded devices have created new opportunities for scalable on-device CRC screening and diagnostics. This review presents a systematic and CRC-centric analysis of TinyML technologies across the diagnostic continuum, including capsule endoscopy, histopathology, breath analysis, and biosignal-based screening. Unlike existing surveys that address TinyML from a general healthcare perspective, this study focuses specifically on the technical, clinical, and deployment challenges unique to CRC diagnostics. We propose a structured taxonomy encompassing model compression techniques, hardware-software co-design strategies, and clinical deployment paradigms, and critically analyze the accuracy-latency-energy trade-offs across representative platforms. This review further synthesizes recent (2024-2025) advances in TinyML compilers, hardware accelerators, and edge-cloud integration, highlighting their implications for real-world clinical translation. By consolidating current evidence, identifying benchmarking and regulatory gaps, and outlining a forward-looking research roadmap, this survey clarifies the role of TinyML as a viable enabler of real-time, privacy-preserving, resource-efficient CRC diagnostics. These findings provide actionable insights for researchers, clinicians, and system designers seeking to deploy TinyML solutions in equitable and clinically meaningful cancer care.","url":"https://doi.org/10.1016/j.artmed.2026.103481","authors":["Bhat SA","Chen MC","Huang NF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.artmed.2026.103481","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1088/2057-1976/ae74d6","name":"A comprehensive review of deep learning applications in the segmentation and classification of skin cancer.","source":"pubmed","abstract":"Skin cancer (SC) is one of the most prevalent forms of cancer worldwide. Both melanoma and non-melanoma types pose major challenges for early detection, accurate diagnosis, and proper treatment. Conventional diagnostic approaches, such as biopsy and visual examination, are often time-consuming, subjective, and prone to human error. Recent advances in artificial intelligence (AI) and deep learning (DL) have greatly improved the accuracy of SC diagnosis. This systematic review explores the applications of DL techniques in the segmentation and classification of skin lesions between 2014 and 2024. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines and applying predefined inclusion and exclusion criteria, a total of 77 experimental studies out of 540 were analyzed from major databases, including Scopus, IEEE, PubMed, and MDPI. Convolutional neural networks (CNNs) were identified as the most widely used for classification, while U-Net and its variants dominated segmentation tasks. Hybrid and ensemble frameworks demonstrated superior performance on benchmark datasets such as the International Skin Imaging Collaboration (ISIC) archive and HAM10000. Moreover, this work incorporates a formal risk-of-bias analysis, revealing critical concerns about class imbalance and data leakage. Almost all reviewed studies for the classification task achieved an average accuracy of 96% for the ISIC dataset, while the HAM10000 dataset attained an average accuracy of 93%. Despite these advances, challenges such as class imbalance, limited dataset diversity, and insufficient clinical validation persist. Addressing these issues through data augmentation, explainable AI, and federated learning could further enhance the generalizability and clinical applicability of AI-driven diagnosis systems. Additionally, this study identifies a clear paradigm shift from standalone CNNs to hybrid frameworks and multi-source feature fusion strategies, aiming to improve SC diagnosis.","url":"https://doi.org/10.1088/2057-1976/ae74d6","authors":["Saleh N","Shaaban AR","Salaheldin AM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1088/2057-1976/ae74d6","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1038/s41598-026-41360-2","name":"Explainable and secure federated learning for privacy-enhancing skin cancer classification using a lightweight multi-scale CNN.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-41360-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-41360-2","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.35772/ghm.2026.01048","name":"Clinical artificial intelligence (AI) in Japan: Regulatory pathways, domain-specific evidence, and its data infrastructure from an international perspective.","source":"europepmc","abstract":"","url":"https://doi.org/10.35772/ghm.2026.01048","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.35772/ghm.2026.01048","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.20944/preprints202605.1837.v1","name":"CNN-Based Fashion-MNIST Classification with Insights Toward AI-Based Mental Health Detection","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202605.1837.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202605.1837.v1","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3389/fpubh.2026.1762346","name":"A robust and verifiable federated learning framework for preventing data poisonous threats in e-health.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fpubh.2026.1762346","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1762346","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-22408-1","name":"Quantum deep learning-enhanced ethereum blockchain for cloud security: intrusion detection, fraud prevention, and secure data migration.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-22408-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-22408-1","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1038/s41698-026-01407-z","name":"AI and network biology for rational polypharmacology in signaling drug design: a review.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41698-026-01407-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41698-026-01407-z","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1038/s41598-026-41015-2","name":"Secure quantum-resilient smart city communication networks using QSC-Net with MF-MBO-based energy-aware task scheduling.","source":"europepmc","abstract":"Adaptable optimisation that preserves efficiency under time-varying system dynamics necessitates modern task management in innovative city development and in distributed edge-cloud computing systems. Conservative optimisation techniques such as genetic algorithms, particle swarm optimisation, and classical monarch butterfly optimisation (MBO) suffer from premature convergence, poor multi-objective performance, and limited adaptability to changing environments. Further, virtualised infrastructures contextualise operational constraints that impair their ability to homogenously support heterogeneity in task types and quality-of-service demands. We present a hybrid scheduling framework called multi-strategy fuzzy-enhanced monarch butterfly optimisation (MF-MBO) that combines fuzzy dominance for strong multi-objective ranking, self-adaptive quantum-inspired tunnelling (classical acceptance strategy) to escape stagnation, and bounded greedy migration for stable local refinement and load balancing. To accelerate convergence while maintaining task fairness across distributed virtual machines, MF-MBO dynamically balances exploration and exploitation. In the experimental evaluation under different workload conditions, MF-MBO clearly outperforms baseline algorithms, providing improvements of 17.4% in task execution time, 22.8% in load-balancing efficiency, and 15.6% in energy consumption. The results are reported with respect to the standard MBO, while we also compare them with both GA and PSO under the same evaluation budget and workload conditions. The results show increased operational efficiency and scalability, along with greater robustness across varying environments. The idea behind the introduced MF-MBO framework enables practical adaptation for smart city infrastructure services, distributed edge computing, and IoT-based applications, through a reproducible, explainable optimisation pipeline. The last part of this study reports empirical results and sets a few benchmarks to support future extensions, such as broader-angle benchmarking and hardware-aware validation.","url":"https://doi.org/10.1038/s41598-026-41015-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-41015-2","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2025.1683960","name":"Bringing multi-modal multi-task federated foundation models to education domain: prospects and challenges.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2025.1683960","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/frai.2025.1683960","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2025.1644844","name":"Implementing federated learning for privacy-preserving emotion detection in educational environments.","source":"europepmc","abstract":"Emotion detection has become an essential tool in educational settings, where understanding and responding to students' emotions is crucial to improving their engagement, academic performance, and emotional well-being. However, traditional emotion detection systems, such as DeepFace, and hybrid transformer-based models face significant data privacy and scalability limitations. These models rely on transferring sensitive data to central servers, compromising student confidentiality and making deployment in large or diverse populations difficult. In this work, we propose a federated learning-based model designed to detect emotions in educational settings, preserving data privacy by processing them locally on students' devices (smartphones, tablets, and laptops). The model was integrated into the Moodle platform, allowing its evaluation in a conventional educational environment. Advanced anonymization and preprocessing techniques were implemented to ensure the security of emotional data and optimize its quality. The results demonstrate that the proposed model achieves a precision of 87%, a recall of 85%, and an F1-score of 86%, maintaining its performance under adverse conditions, such as low lighting and ambient noise. In addition, a 15% increase in academic participation and a 12% improvement in the average academic performance of students were observed, highlighting the system's positive impact on educational dynamics. This innovative method combines privacy, scalability, and performance, positioning itself as a viable and sustainable solution for emotion detection in contemporary educational environments.","url":"https://doi.org/10.3389/frai.2025.1644844","authors":["Rommel Gutiérrez","William Villegas-Ch","Sergio Luján‐Mora"],"tags":["Computer science","Software deployment","Confidentiality","Scalability","Data pre-processing"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/frai.2025.1644844","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.3390/mi17050586","name":"A Review of Embedded Artificial Intelligence Research (2023-2026): Technological Advancements, Representative Advances, and Future Prospects.","source":"pubmed","abstract":"Since the publication of the \"Review of Embedded Artificial Intelligence Research\" in 2023, driven by innovations in hardware architectures, advances in lightweight algorithms, and the maturation of edge-cloud collaboration technologies, embedded artificial intelligence (embedded AI) has progressed from \"technically feasible\" to \"large-scale deployment\". As a continuation of that review, this article systematically surveys the core advances in embedded AI from 2023 to 2026. At the hardware level, it examines engineering progress in non-von Neumann architectures such as compute-in-memory and neuromorphic chips, as well as heterogeneous integration technologies. At the algorithmic level, it covers dynamic adaptive lightweighting, specialized edge-side optimization of large models (including on-device large language model fine-tuning and edge diffusion models), and lightweight multimodal approaches. In terms of deployment paradigms, it discusses edge-side full training, federated edge learning, edge-cloud collaborative intelligence, and emerging paradigms. At the application level, it illustrates the \"perception-decision-execution\" pipeline in industrial IoT, wearable healthcare, autonomous driving, embodied intelligence, and smart agriculture. The article also analyzes core challenges including ultra-low-power design for extreme scenarios, cross-platform standardization, edge-side data security and privacy, and model robustness in complex environments. Based on these findings, four research directions are proposed to guide future work.","url":"https://doi.org/10.3390/mi17050586","authors":["Zhang Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/mi17050586","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1038/s41598-025-21898-3","name":"Intelligent ship traffic supervision system based on distributed blockchain and federated reinforcement learning for collaborative decision optimization.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-21898-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-21898-3","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/diagnostics16040623","name":"IDH Mutation Assessment in Gliomas from Anatomical MRI Using Deep Learning: A Comparative Analysis of Centralized and Federated Learning Frameworks.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/diagnostics16040623","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/diagnostics16040623","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1038/s41598-026-35773-2","name":"Secure multi-party test case data generation through generative adversarial networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-35773-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-35773-2","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1186/s12874-026-02785-5","name":"Privacy-preserving federated prediction of health outcomes using multi-center survey data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12874-026-02785-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1186/s12874-026-02785-5","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.3390/jimaging12050180","name":"Neural Computing Advancements in Cardiac Imaging: A Review of Deep Learning Approaches for Heart Disease Diagnosis.","source":"pubmed","abstract":"Heart disease remains a leading cause of mortality worldwide, and timely and accurate diagnosis is crucial for improving patient outcomes. Medical imaging plays a pivotal role in this process, yet traditional diagnostic methods often suffer from limitations, including dependency on manual interpretation, susceptibility to observer variability, and inefficiency in handling large-scale data. Deep learning has emerged as an innovative technology in medical imaging, providing unparalleled advancements in feature extraction, segmentation, classification, and prediction tasks. Despite its proven potential, comprehensive reviews of deep learning methods specifically targeted at cardiac imaging remain scarce. This review paper seeks to bridge this gap by analyzing the state-of-the-art deep learning applications for heart disease diagnosis, covering the period from 2015 to 2025. Employing a well-structured methodology, this review categorizes and examines studies based on imaging modalities: Ultrasound (US), Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography (CT), and Electrocardiography (ECG). For each modality, the analysis focuses on utilized datasets, processing techniques (e.g., extraction, segmentation and classification), and paradigms (e.g., transfer learning, federated learning, explainability, interpretability, and uncertainty quantification). Additionally, the types of heart disease addressed and prediction accuracy metrics are also scrutinized. These findings point toward future opportunities, including the study of data quality, optimization, transfer learning, uncertainty quantification and model explainability or interpretability. Furthermore, exploring advanced techniques such as recurrent expansion, transformers, and other architectures may unlock new pathways in cardiac imaging research. This review is a critical synthesis offering a roadmap for researchers and practitioners to advance the application of deep learning in heart disease diagnosis.","url":"https://doi.org/10.3390/jimaging12050180","authors":["Berghout T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/jimaging12050180","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.2174/0115734056400866250923175325","name":"Federated Deep Learning Approaches for Detecting Ocular Diseases in Medical Imaging: A Systematic Review.","source":"pubmed","abstract":"Artificial intelligence has significantly enhanced disease diagnosis in healthcare, particularly through Deep Learning (DL) and Federated Learning (FL) approaches. These technologies have shown promise in detecting ocular diseases using medical imaging while addressing challenges related to data privacy and security. FL enables collaborative learning without sharing sensitive medical data, making it an attractive solution for healthcare applications. This systematic review aims to analyze the advancements in AI-driven ocular disease detection, with a particular focus on FL-based approaches. The article evaluates the evolution, methodologies, challenges, and effectiveness of FL in enhancing diagnostic accuracy while ensuring data confidentiality.","url":"https://doi.org/10.2174/0115734056400866250923175325","authors":["Gulati S","Guleria K","Goyal N","Dogra A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.2174/0115734056400866250923175325","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2025.1685155","name":"AI-driven routing pipeline in software-defined networks using DQL: a mini review.","source":"pubmed","abstract":"","url":"https://doi.org/10.3389/frai.2025.1685155","authors":["Goteti D","Reddy VK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/frai.2025.1685155","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.1038/s41598-025-30919-0","name":"An epilepsy prediction and management system based on federated learning combined with hybrid harmony search and mutual information (HAS-MI)-based feature selection approach.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-30919-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-30919-0","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1038/s41598-026-45662-3","name":"Confidence-calibrated federated graph attention for internet of things agents under latency SLOs.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-45662-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-45662-3","addedAt":"2026-08-31T06:41:18.958Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1038/s41598-026-47114-4","name":"A hybrid feature selection framework combining Artificial Bee Colony and decision trees for CVD risk assessment.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-47114-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-47114-4","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.3389/fdata.2026.1681382","name":"Federated learning for teacher data privacy protection: a study in the context of the PIPL.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdata.2026.1681382","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdata.2026.1681382","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1739192","name":"Bridging the gap: methodological challenges and innovations in systematic reviews of machine learning models in healthcare.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2026.1739192","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1739192","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.21037/jtd-2026-0804","name":"Evolution, hotspots, and future directions of artificial intelligence in asthma research: a Web of Science-based bibliometric analysis [2016-2026].","source":"pubmed","abstract":"While artificial intelligence (AI) offers unprecedented capabilities for predictive modeling and precision asthma management, there is an urgent clinical necessity to successfully translate these rapid algorithmic innovations into real-world respiratory care. The exponential growth of cross-disciplinary AI literature has paradoxically created information overload for clinicians, obscuring underlying translational friction and hindering evidence-based implementation. Consequently, bibliometric analysis serves as the optimal quantitative vehicle to decode this vast scientific architecture. This study aims to objectively map the evolutionary trajectory, global research landscape, and emerging hotspots of AI in asthma, providing actionable roadmaps to reconcile computational development with clinical practice.","url":"https://doi.org/10.21037/jtd-2026-0804","authors":["You H","Zhu T","Liu C","Li S","Sun W"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21037/jtd-2026-0804","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"doi:10.1038/s41598-026-39064-8","name":"PrivEdge: a hybrid split-federated learning framework for real-time electricity theft detection on edge nodes.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-39064-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-39064-8","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1109/tnnls.2025.3590015","name":"FedLSC: Improving Communication Efficiency and Robustness in Federated Learning With Stragglers and Adversaries.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2025.3590015","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1109/tnnls.2025.3590015","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.3389/fdgth.2026.1782663","name":"Editorial: Privacy enhancing technology: a top 10 emerging technology to revolutionize healthcare.","source":"pubmed","abstract":"","url":"https://doi.org/10.3389/fdgth.2026.1782663","authors":["van Gemert-Pijnen LJE","Veugen T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1782663","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-54349-8","name":"A hierarchical neuromorphic multi agent framework for energy aware and secure 6G resource optimization using Neuro6G agent.","source":"europepmc","abstract":"The convergence of Sixth-Generation (6G) wireless networks and neuromorphic computing presents significant opportunities for intelligent, energy-efficient resource management in distributed architectures. This paper introduces Neuro6G-Agent, a hierarchical neuromorphic agentic intelligence framework that integrates Energy-Aware Spiking Neural Networks (EA-SNNs) with multi-agent reinforcement learning to enable energy-conscious cognitive collaboration across cloud-edge-end 6G deployments. The framework addresses three principal challenges in distributed 6G resource management: energy sustainability, end-to-end latency under ultra-dense connectivity, and security resilience against adversarial threats. A three-tier architecture is employed, comprising cloud orchestrators, edge coordinators, and end devices, each operating dedicated neuromorphic agents with autonomous decision-making and trust-aware collaborative learning capabilities. The framework incorporates adaptive threshold EA-SNNs for event-driven processing, a distributed trust computation mechanism for secure multi-agent cooperation, and a hierarchical resource optimization algorithm responsive to dynamic workload conditions. Experimental evaluation across three public benchmark datasets-DeepMIMO (6G channel modeling), DVS128 Gesture (neuromorphic sensing), and CICIDS-2017 (network intrusion detection) demonstrates a 34.7% reduction in energy consumption, a 28.3% decrease in end-to-end latency, and a 95.6% security threat detection accuracy compared to state-of-the-art baseline methods, validated across ten independent experimental runs (p < 0.01). These results confirm the viability of neuromorphic intelligence for addressing complex optimization challenges in next-generation wireless architectures.","url":"https://doi.org/10.1038/s41598-026-54349-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-54349-8","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1038/s41598-026-43662-x","name":"DriveEmo-FL: in-cabin radar-based emotion sensing for autonomous vehicles smart response.","source":"pubmed","abstract":"","url":"https://doi.org/10.1038/s41598-026-43662-x","authors":["Imran N","Alnafisah KH","Zhang J","Hameed S","Ali J","Farooq W"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-43662-x","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"doi:10.3390/bioengineering13050552","name":"Explainable Split-Learning-Based Framework for Accurate Pulmonary Nodule Classification.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/bioengineering13050552","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/bioengineering13050552","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3389/fnbot.2026.1785114","name":"Multimodal human action recognition and personalized sports health promotion: a deep learning framework integrating wearable sensor fusion.","source":"europepmc","abstract":"Introduction In real-world sports scenarios, Human Action Recognition (HAR) is often hindered by data complexity, limited dynamic adaptability, and fragmented integration of physiological and kinematic information. To address these challenges, this study proposes a multimodal HAR framework for personalized sports health promotion by integrating wearable sensor streams with deep learning architectures. Methods The proposed system employs a robust sensing layer to capture 12-dimensional multimodal data and synchronize physiological indicators with behavioral signals in real time. A novel Transformer-GCN hybrid model was developed to extract complex spatiotemporal dependencies for accurate action recognition and dynamic state analysis. In addition, a reinforcement learning module was incorporated to generate adaptive exercise prescriptions based on user progress. The framework was deployed through a responsive interface for real-time intervention and evaluated in a 12-week randomized controlled trial. Results The results demonstrated that the proposed framework achieved effective multimodal fusion and reliable action recognition in sports scenarios. After the 12-week intervention, participants in the intervention group showed a 20.1% increase in cardiorespiratory fitness ( VO 2 max), a 99.3% improvement in muscular endurance, and a sports injury rate maintained below 15%. These findings indicate that the framework can support accurate motion analysis and safe, personalized intervention. Discussion The proposed multimodal fusion architecture effectively bridges the gap between action recognition and personalized sports health intervention. By combining wearable sensing, hybrid deep learning, and reinforcement learning, the framework provides a practical solution for AI-driven motion analysis and adaptive health promotion in land sports scenarios.","url":"https://doi.org/10.3389/fnbot.2026.1785114","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fnbot.2026.1785114","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3390/clockssleep8020023","name":"AI-Driven Hybrid Detection and Classification Framework for Secure Sleep Health IoT Networks.","source":"pubmed","abstract":"Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO 2 , respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber-physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022-2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (&lt;45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization.","url":"https://doi.org/10.3390/clockssleep8020023","authors":["Valsalan P","Siddiqui MM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/clockssleep8020023","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1038/s41598-025-26085-y","name":"Adaptive course recommendation using federated learning and graph convolutional networks in IoT-enhanced e-learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-26085-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-26085-y","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.2196/94280","name":"Feasibility of a German High-Frequency Neonatal and Pediatric Intensive Care Dataset (AIx-Neo-Guard Dataset): Secondary Data Cohort Study.","source":"pubmed","abstract":"Available information about existing neonatal and pediatric intensive care datasets is scarce.","url":"https://doi.org/10.2196/94280","authors":["Olivier LS","Lauterbach Oprea C","Stollenwerk A","Orlikowsky T","Schoberer M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/94280","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"doi:10.1007/s10729-025-09752-4","name":"Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s10729-025-09752-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1007/s10729-025-09752-4","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1038/s41598-025-21888-5","name":"Enhancing lymphoma cancer detection using deep transfer learning on histopathological images.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-21888-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-21888-5","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1038/s41598-025-25345-1","name":"An efficient federated learning based defense mechanism for software defined network cyber threats through machine learning models.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-25345-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-25345-1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.21203/rs.3.rs-9967934/v1","name":"Data Sovereignty and Algorithmic Dependency in the Global South: A Systematic Review of Foundation Model Governance, Digital Inequity, and Decolonial Artificial Intelligence Frameworks","source":"europepmc","abstract":"Abstract Artificial intelligence (AI) systems built on foundation models now exert structural influence over economic, healthcare, educational, and civic decision-making across the globe. Yet the organizations that own, train, and govern these systems are overwhelmingly concentrated in a small number of nations in the Global North, producing a new form of techno-political dependency that echoes classical patterns of colonial resource extraction. This paper presents a systematic review of 68 peer-reviewed studies published between 2015 and 2024, examining how foundation model governance frameworks interact with questions of data sovereignty, digital inequity, and decolonial AI theory in the context of the Global South. The review draws on literature from computer science, political economy, postcolonial studies, and science and technology studies to map four recurring mechanisms: data extraction without commensurate benefit, infrastructure lock-in through proprietary cloud ecosystems, epistemic marginalization of non-Western knowledge systems in training corpora, and governance colonialism through the uncritical export of Northern regulatory templates. Thematic analysis reveals that existing AI governance proposals, including UNESCO's Recommendation on the Ethics of AI and the EU AI Act, largely fail to address the structural asymmetries that place Global South actors in positions of dependency rather than co-authorship. This review further identifies a growing body of decolonial AI scholarship that proposes concrete alternatives, including community-led data governance, federated learning architectures adapted for low-infrastructure environments, and sovereignty-oriented national AI strategies. The principal contribution of this paper is a consolidated theoretical framework that links these dispersed proposals into a coherent decolonial governance architecture applicable to sub-Saharan Africa, South Asia, and Latin America. The findings have direct implications for policymakers, international organizations, and AI developers seeking to build systems that serve all of humanity rather than a privileged minority.","url":"https://doi.org/10.21203/rs.3.rs-9967934/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9967934/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.3390/cancers18091322","name":"Artificial Intelligence-Enhanced Multiparametric MRI and VI-RADS in Bladder Cancer: Current Evidence, Clinical Opportunities and Barriers to Translation.","source":"pubmed","abstract":"Accurate distinction between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) remains the key local staging problem in bladder cancer because treatment intensity, timing of radical therapy, and suitability for bladder-preserving strategies all depend on it. Multiparametric magnetic resonance imaging (mpMRI) and the Vesical Imaging-Reporting and Data System (VI-RADS) now provide a standardized imaging framework for local staging and increasingly support MRI-first clinical pathways. Artificial intelligence (AI) has emerged as an additional decision-support layer, but the evidence base remains methodologically uneven. In this structured narrative review, we synthesized peer-reviewed literature from January 2020 to March 2026, while retaining foundational VI-RADS studies from 2018 to 2019, and prioritized guideline documents, meta-analyses, prospective cohorts, multicenter and externally validated AI studies, response-assessment studies, and papers addressing implementation and reporting quality. Current evidence shows that radiomics and deep learning models can achieve high discrimination for MIBC detection on MRI, and that the most plausible incremental value of AI lies in equivocal VI-RADS lesions, reader support outside high-volume expert settings, and multimodal risk stratification. However, most studies remain retrospective, highly selected, segmentation-dependent, and vulnerable to reference-standard bias, domain shift, and poor calibration. This review therefore emphasizes several translational issues that are often underreported: lesion-level versus patient-level inference, the distortive effect of TURBT-based labels, the need to evaluate false-negative consequences in VI-RADS 3 tumors, and the distinction between diagnostic support and broader pathway redesign. We also discuss response assessment, nacVI-RADS, segmentation automation, multicenter and federated infrastructure, workflow ownership, and the limits of imaging-only models in a biologically heterogeneous disease. The most credible near-term role of AI is not autonomous diagnosis, but augmentation of standardized mpMRI and VI-RADS within multidisciplinary care. Future progress will depend on prospective utility studies, site-held-out validation, transparent reporting, and the integration of imaging with molecular and cellular heterogeneity through radiogenomic and multi-omics approaches.","url":"https://doi.org/10.3390/cancers18091322","authors":["Popescu CG","Chipuc S","Zgura D","Haineala B","Zgura A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/cancers18091322","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3389/frai.2026.1814012","name":"An integrated evolution-aware meta-learning framework with adversarial morphological augmentation for zero-day threat detections.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2026.1814012","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1814012","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.22541/au.177369010.03054587/v1","name":"Algorithmic Bias in Machine Learning-Based Cyber Defence: Taxonomy, Mathematical Frameworks, and Ethical Governance","source":"europepmc","abstract":"","url":"https://doi.org/10.22541/au.177369010.03054587/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.22541/au.177369010.03054587/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1038/s41598-026-35420-w","name":"Combining parameter fragmentation and group shuffling to defend against the untrustworthy server in federated learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-35420-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-35420-w","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-025-13519-w","name":"Decentralized federated deep Q-learning for IoMT security: leveraging MK-VQFHE and blockchain with IPFS.","source":"pubmed","abstract":"The explosion of patient data, the demand for real-time insights, and the critical importance of data security drive healthcare innovation. Medical Internet of Things (IoMT) offers a promising solution, connecting medical devices, sensors, and healthcare systems to improve patient care. However, managing and securing vast amounts of complex data produced by IoMT devices remains a significant challenge. Existing approaches often fall short of providing comprehensive solutions. To address this, this research proposes a novel approach combining Multi-Key Verifiable Quaternion Fully Homomorphic Encryption (MK-VQFHE) and blockchain for decentralized Federated Learning (FL) to enhance data management capabilities. Initially, the proposed study collects IoT healthcare data from publicly available datasets after that, Multi-Key Verifiable Quaternion Fully Homomorphic Encryption for encryption is used to safeguard data security. Encrypted data is then used in a collaborative learning model enabled by blockchain technology. The proposed Federated Deep Q-learning (FDQL) model enhances privacy protection by training inputs and evaluating threats. The data is securely stored using Interplanetary File System (IPFS) technology within the blockchain network. This study introduces a Practical Byzantine Fault Tolerant (PBFT) consensus technique to verify proposed structure&#x2019;s integrity. Performance metrics demonstrate proposed approach produce superior accuracy ranging from 99.2% to 99.4% across multiple datasets compared to existing approaches. Meanwhile the existing models such as BiLSTM, CNN, DNN, and ANN are attained accuracy of below 99%. The proposed research aims to enhance IoMT data security, introduce advanced encryption strategies, ensure efficient data storage, analyze privacy protection effectiveness, validate blockchain framework efficiency, and evaluate performance metrics for comprehensive insights.","url":"https://doi.org/10.1038/s41598-025-13519-w","authors":["ChandraUmakantham O","Ravi K","Marappan S","Gajendran S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-025-13519-w","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1016/j.brainresbull.2025.111645","name":"MultiEpilepsyNet: An EEG and MRI data based multimodal seizure detection model using hybrid deep learning model.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.brainresbull.2025.111645","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1016/j.brainresbull.2025.111645","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.7759/cureus.106869","name":"Role of Artificial Intelligence and Machine Learning in Diagnosing Knee Lesions: Where Are We Now?","source":"europepmc","abstract":"","url":"https://doi.org/10.7759/cureus.106869","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.7759/cureus.106869","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3389/fpls.2025.1706428","name":"Agentic AI for smart and sustainable precision agriculture.","source":"pubmed","abstract":"","url":"https://doi.org/10.3389/fpls.2025.1706428","authors":["Srinivasu PN","Pavate A","JayaLakshmi G","Shafi J","Choi J","Ijaz MF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fpls.2025.1706428","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"doi:10.55730/1300-0152.2763","name":"Applications of transfer learning in sunflower disease detection: advances, challenges, and future directions.","source":"pubmed","abstract":"","url":"https://doi.org/10.55730/1300-0152.2763","authors":["Gulzar Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.55730/1300-0152.2763","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.1016/j.nsa.2026.106998","name":"Data welfare is animal welfare: Building a WellFAIR research ecosystem.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.nsa.2026.106998","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.nsa.2026.106998","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1002/lrh2.70080","name":"Twenty-First Century Data Systems: Evolving Cancer Registries to a Learning Health System.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/lrh2.70080","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/lrh2.70080","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1038/s41598-025-30150-x","name":"Edge-AI integrated secure wireless IoT architecture for real time healthcare monitoring and federated anomaly detection.","source":"europepmc","abstract":"The rapid digitalization of healthcare demands intelligent, low-latency, and privacy-preserving systems capable of operating at the network edge. This study introduces a unified Edge-AI framework that seamlessly combines dual wireless connectivity (LoRaWAN + 5G), federated learning (FL), Proof-of-Authority (PoA) block chain, and homomorphic encryption (HE) to achieve secure real-time anomaly detection in patient monitoring. A quantized CNN-LSTM model was deployed on NVIDIA Jetson Nano devices and trained using a synthetic dataset statistically modelled from the MIT-BIH Arrhythmia Database, capturing vital signals such as heart rate, temperature, and oxygen saturation. The integrated system attained 91.9% accuracy and 90.8% F1-score, with only an 8.7% latency overhead attributed to HE operations. A paired two-tailed t-test (p < 0.01) confirmed that these gains are both statistically and clinically significant, indicating reliable diagnostic performance under constrained conditions. Beyond performance, the proposed framework ensures end-to-end data confidentiality, tamper-proof auditability, and energy-efficient edge inference, offering a scalable pathway toward trustworthy, next-generation smart-healthcare ecosystems.","url":"https://doi.org/10.1038/s41598-025-30150-x","authors":["M. Prabha","S. Nandhini","M. Dayanidhy","R Pradeep"],"tags":["Computer science","Scalability","Anomaly detection","Overhead (engineering)","Encryption"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-30150-x","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.3389/fdata.2025.1659757","name":"Towards the neuromorphic Cyber-Twin: an architecture for cognitive defense in digital twin ecosystems.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdata.2025.1659757","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fdata.2025.1659757","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3389/fdgth.2026.1729242","name":"Based on dual perspectives of management and ethics: exploring challenges and governance approaches for new media applications in psychiatric specialty hospitals.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdgth.2026.1729242","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1729242","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-36245-3","name":"Optimizing sepsis mortality prediction using hybrid federated learning and explainable AI framework.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-36245-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-36245-3","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1038/s41746-026-02533-5","name":"Cautious optimism on foundation models in medical imaging balancing privacy and innovation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41746-026-02533-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41746-026-02533-5","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1186/s42400-026-00619-x","name":"A socio-technical framework for cyber-resilience in hybrid oil-renewable energy grids.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s42400-026-00619-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1186/s42400-026-00619-x","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3389/fcell.2026.1863190","name":"Artificial intelligence in orthopedic regenerative medicine: from design to clinical translational pathways.","source":"europepmc","abstract":"Orthopedic regenerative medicine (ORM) addresses musculoskeletal disorders in which effective repair requires coordinated structural reconstruction, biological repair, mechanical adaptation, and functional recovery. These processes generate heterogeneous information across biomaterials, construct design, imaging, intraoperative execution, rehabilitation monitoring, and clinical follow-up. Artificial intelligence (AI) is increasingly relevant for organizing multimodal data and supporting decision-making across regenerative care. This review summarizes current applications of AI in ORM, focusing on regenerative design and fabrication, intraoperative guidance, postoperative monitoring, repair evaluation, and clinical translational pathways. In regenerative design, AI can assist the optimization of material composition, scaffold architecture, biofabrication parameters, and construct performance by linking design variables with biological and biomechanical outcomes. During intervention and recovery, AI-supported systems may improve defect-specific spatial matching, support longitudinal functional assessment, and help identify delayed or unfavorable repair trajectories through integrated analysis of imaging, wearable, and clinical data. The review also discusses translational challenges, including data heterogeneity, limited external validation, algorithmic bias, interpretability, regulatory requirements, and governance constraints. AI may help connect design, intervention, monitoring, and feedback within a continuous analytical workflow, but future progress will require robust datasets, prospective validation, clinically interpretable models, and implementation strategies aligned with regenerative practice.","url":"https://doi.org/10.3389/fcell.2026.1863190","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fcell.2026.1863190","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1038/s41598-025-22316-4","name":"A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-22316-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-22316-4","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3390/e28020147","name":"Number-Theoretic Methods in Statistics: Theory and Applications.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28020147","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/e28020147","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1038/s41598-026-44162-8","name":"A multi-layer AI decision support system for startup success prediction and risk assessment using knowledge graphs and federated learning.","source":"pubmed","abstract":"Since start-ups have grown so quickly in recent decades, it is more important than ever to determine what elements contribute to their success or failure. Numerous factors, such as market conditions, product differentiation, finance availability, and managerial methods, influence these results. However, precise forecasting is a constant issue due to the intricacy of business ecosystems and the interaction of non-financial and financial aspects. A multi-layer AI-driven prediction model that incorporates early-stage start-ups&#x2019; financial and non-financial characteristics is presented in this paper. A Graph Convolutional Network (GCN) creates feature embeddings at Layer 1, whereas a knowledge graph documents the connections between affecting factors. Federated learning is used to safely combine dispersed knowledge while maintaining privacy. Layer 2 uses a deep neural network (DNN) to forecast success or failure based on the fused features. Layer 3 offers risk assessment and interpretability, detecting survival variables including team dynamics and product differentiation. An experimental sample of 20 start-ups was thoroughly examined as part of the model&#x2019;s evaluation on a dataset collected from Crunchbase that included approximately 623,000 companies, 799,000 founders, and 227,000 funding events. The findings show increased forecast accuracy and emphasize the value of non-financial elements in addition to conventional financial measurements. Through the integration of sophisticated AI approaches with organized domain knowledge, our work connects theoretical frameworks with empirical data. The suggested model contributes to start-up research and the real-world implementation of AI in business analytics by offering entrepreneurs, investors, and legislators a strong decision-support tool.","url":"https://doi.org/10.1038/s41598-026-44162-8","authors":["Pan C","Pan X","Sun L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-44162-8","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-025-33856-0","name":"Anomaly-resilient geofencing and predictive navigation in IoT environments using machine learning and federated learning for metaverse workplaces and smart shopping malls.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-33856-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-025-33856-0","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3390/s26082558","name":"Efficient Medical Image Segmentation in Multisensor Imaging: A Survey in the Era of Mamba and Foundation Models.","source":"pubmed","abstract":"Deep learning has revolutionized medical image segmentation; however, the clinical deployment of state-of-the-art models is severely impeded by their quadratic computational complexity and substantial resource demands, particularly in multisensor and multimodal imaging scenarios. In response, the field is undergoing a paradigm shift towards efficiency, characterized by the rise of linear-complexity architectures and the optimization of foundation models. This paper presents a comprehensive survey of efficient medical image segmentation methodologies, systematically reviewing the evolution from heavy, accuracy-driven models to lightweight, deployment-ready paradigms. In particular, we highlight the growing importance of efficient segmentation in multisensor medical imaging, where heterogeneous data sources such as CT, MRI, ultrasound, and infrared imaging introduce additional challenges in scalability and computational cost. We propose a novel taxonomy that categorizes these advancements into four distinct streams: (1) Mamba and State Space Models, which leverage selective scanning mechanisms to achieve global receptive fields with linear complexity; (2) Efficient Adaptation of Foundation Models, focusing on parameter-efficient fine-tuning and knowledge distillation to tailor the Segment Anything Model (SAM) for medical domains; (3) Advanced Lightweight Architectures, covering the resurgence of large-kernel CNNs and the emergence of Kolmogorov-Arnold Networks (KANs); and (4) Data-Efficient Strategies, including semi-supervised and federated learning to address annotation scarcity. Furthermore, we conduct a rigorous comparative analysis of representative algorithms on mainstream benchmarks, providing a granular evaluation of the trade-offs between segmentation accuracy and computational overhead. The survey also discusses key challenges in multisensor and multimodal settings, including modality heterogeneity, data fusion complexity, and resource constraints. Finally, we identify critical challenges and outline future research directions, serving as a roadmap for the development of next-generation efficient and scalable medical image analysis systems.","url":"https://doi.org/10.3390/s26082558","authors":["Shu X","Xiong Y","Ma Z","Zhang X","Yuan D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26082558","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3390/ijerph23010081","name":"LLM-Assisted Scoping Review of Artificial Intelligence in Brazilian Public Health: Lessons from Transfer and Federated Learning for Resource-Constrained Settings.","source":"pubmed","abstract":"","url":"https://doi.org/10.3390/ijerph23010081","authors":["Borges FT","Machado GDM","Santana MA","Sancho KA","França GVA","Santos WPD","Siqueira CEG"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/ijerph23010081","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.1038/s41598-025-29152-6","name":"BlockIntelChain: a blockchain-based cyber threat intelligence sharing architecture.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-29152-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-29152-6","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1718193","name":"A new clustered federated learning algorithm for heterogeneous data in high-precision wireless sensing.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2026.1718193","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1718193","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1038/s41598-025-34715-8","name":"Explainable federated transformer framework for joint leukemia classification and stage prediction.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-34715-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-025-34715-8","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3389/fmicb.2026.1705116","name":"STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.","source":"pubmed","abstract":"The reproducibility crisis in causal microbiome research necessitates robust validation frameworks. Current studies often face inconsistent validation methods, limited interpretability, and a lack of standardized reporting, creating a gap in reliable causal inference. This systematic review evaluates over 60 peer-reviewed studies published between 2015 and 2024 to: (1) establish benchmarking standards leveraging synthetic data and biological plausibility assessments; (2) compare advanced causal machine learning (ML) methodologies, including Double/Debiased ML, Deep Instrumental Variables (Deep IV), and Directed Acyclic Graphs (DAGs), in their application to microbiome-host systems; and (3) propose the STROBE-CML (Strengthening the Reporting of Observational Studies in Epidemiology-Causal Machine Learning) guidelines to standardize reporting practices. We emphasize critical innovations such as federated validation pipelines and time-series causal discovery frameworks that address these gaps by facilitating scalable, privacy-preserving, and reproducible inference across heterogeneous cohorts. A decision support tool is introduced to guide researchers in selecting appropriate causal ML approaches based on data structure, research question, and computational constraints. By synthesizing methodological advances with rigorous validation paradigms, this review provides a roadmap for generating reliable, biologically interpretable, and clinically translatable causal claims in microbiome science.","url":"https://doi.org/10.3389/fmicb.2026.1705116","authors":["Khelfaoui I","Wang W","Shehata AI","Meskher H","El Basuini MF","Mohamed AMA","Abouelenein MF","Degha HE","Alhoshy M","Teiba II","Mahmoud O","Mahmoud SS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fmicb.2026.1705116","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.4132/jptm.2026.04.27","name":"What's new in digital and computational pathology 2026: advances in adoption, standards, AI technologies, and clinical integration.","source":"pubmed","abstract":"Digital and computational pathology are expanding rapidly worldwide, driven by advances in whole-slide imaging, AI algorithms, multimodal data integration, and improved digital infrastructure. Adoption continues to accelerate in the United States and internationally, supported by professional guidelines, emerging reimbursement pathways, and the growing need for remote workflows and collaborative diagnostics. Progress in interoperability standards, regulatory frameworks, and FDA approvals has strengthened the foundation for clinical deployment, while large-scale data repositories and federated learning approaches enable more robust and privacy-preserving model development. Foundation models, multimodal AI systems, and LLM-based copilots are reshaping diagnostic support, prognostication, workflow efficiency, clinical trials and drug discovery.","url":"https://doi.org/10.4132/jptm.2026.04.27","authors":["Sevim S","Hajar C","Sonawane S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.4132/jptm.2026.04.27","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.2196/71532","name":"AI in Clinical Decision Support Systems: Promising Applications and Strategies for Managing Data Challenges.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/71532","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/71532","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1177/19322968261455365","name":"Data Privacy, Ownership, and Secondary Use of Clinical Data Generated by Continuous Glucose Monitors and Mobile Health Applications: A Review.","source":"pubmed","abstract":"The growing use of continuous glucose monitors (CGMs) and mobile health (mHealth) applications has changed how diabetes is managed, allowing real-time tracking of glycemic patterns and remote clinical decision-making. These technologies also generate large volumes of sensitive health data, raising questions about who owns this information, how it is protected, and under what conditions it may be repurposed for research or commercial objectives. This review examines the regulatory frameworks governing CGM and mHealth data in major jurisdictions, with particular attention to the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union. Significant regulatory gaps exist, particularly for consumer-grade devices and direct-to-consumer mHealth applications that fall outside traditional healthcare data-protection frameworks. Data ownership remains legally ambiguous in most jurisdictions, with patients, healthcare providers, device manufacturers, and app developers each holding competing claims. The secondary use of clinical data for research, while it could materially advance diabetes care, raises ethical concerns around informed consent, data de-identification, and the boundaries between clinical care and commercial exploitation. Emerging approaches, including the European Health Data Space, federated learning, and differential privacy, may help balance data utility with individual rights. The review recommends changes to regulation, industry practice, and consent models aimed at reconciling data-driven diabetes research with patient autonomy and privacy.","url":"https://doi.org/10.1177/19322968261455365","authors":["Dario P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1177/19322968261455365","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1016/j.dib.2026.112839","name":"Building a single market for data: Data spaces as pro-competitive infrastructure.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.dib.2026.112839","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.dib.2026.112839","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1186/s12891-026-09761-6","name":"Automated knee MRI segmentation with KneeSeg-U for cartilage and bone extraction using an unsupervised deep learning framework.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12891-026-09761-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1186/s12891-026-09761-6","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1038/s41598-025-21548-8","name":"A bias-resilient client selection analysis for federated brain tumor segmentation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-21548-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-21548-8","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41746-025-02126-8","name":"Crossing borders securely: synthetic data and federated networks for privacy-preserving access to real-world data and emerging use cases.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41746-025-02126-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41746-025-02126-8","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1727704","name":"AI-navigated shoulder injection: precision, real-time learning and clinical translation.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2026.1727704","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1727704","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1371/journal.pone.0348600","name":"Post-quantum cognitive zero trust architecture for healthcare IoT devices.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0348600","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0348600","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"epmc:MED42111900","name":"Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42111900/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1007/s00432-026-06465-1","name":"Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s00432-026-06465-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1007/s00432-026-06465-1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1038/s41598-025-26510-2","name":"A unified AI-driven framework for quantum-secured 6G THz networks with intelligent reflecting surfaces and federated edge learning.","source":"europepmc","abstract":"The main contribution of this manuscript is an innovative framework for integrating Artificial Intelligence (AI) in 6G wireless systems. With increased complexity, including bursty traffic, network complexity, and dynamic variability, there is a need for intelligence. This study develops and validates an AI-driven approach that enhances network performance through quantum communication decoding, beamforming, and decentralized edge processing. Kalman filtering predictive models are used to estimate variable channel conditions in a Terahertz (THz) network to support beamforming to optimize beamforming. Artificial Intelligence exploits smart reflective surfaces (IRS) strengthening signals and improving their coverage. Also, strong security of Quantum Key Distribution (QKD) protocols due to AI enhanced error correction technology, and rapid, yet privacy information conducting at edge nodes due to decentralised processing through federated learning are examples of enhanced capabilities. Extensive ns-3 simulations across 100 independent runs validate the framework's effectiveness and prove the system in practical 6G deployment scenarios including THz links, IRS component and edge nodes. The simulation results demonstrate that the proposed framework achieves superior performance compared to conventional approaches, with statistical validation across multiple deployment scenarios. The system decreases latency by 30%, and adds 25% to spectral efficiency. In bursty traffic, the energy efficiency is increased by 20% and packets delivery ratio (PDR) is boosted by 15%. The AI algorithms work effectively to regulate the channel estimation, beamforming, and resource allocation, and, as a result, showed an improvement in the order of magnitudes over previous studies. These results support the fact that AI demonstrates significant potential for transformative impact to a 6G network. The framework has been efficient in addressing problems of channel estimation, beamforming and distributed processing and novel calculations in quantum communication security protocols. Such findings can be used as the foundation of the further inclusion of AI-based technologies in 6G systems, which will help to deploy robust, resilient, and autonomous wireless networks to address the needs of a connective society.","url":"https://doi.org/10.1038/s41598-025-26510-2","authors":["C. G. Balaji","S. Menaka","G. Rajeswari","Sivaram Ponnusamy"],"tags":["Computer science","Software deployment","Enhanced Data Rates for GSM Evolution","Distributed computing","Key (lock)"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-26510-2","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.5281/zenodo.21777670","name":"Computación de borde y computación en la niebla: un análisis descriptivo de la evolución de la computación en la nube para aplicaciones de IoT","source":"datacite","abstract":"Introducción: El vertiginoso crecimiento del Internet de las Cosas (IoT) ha evidenciado las limitaciones estructurales de la computación en la nube centralizada para satisfacer las demandas de latencia, ancho de banda y privacidad de las aplicaciones en tiempo real. Objetivo: Analizar el estado del arte del Edge Computing y el Fog Computing como paradigmas evolutivos de la computación en la nube para aplicaciones IoT, examinando sus características arquitectónicas, ventajas comparativas, casos de uso y desafíos pendientes. Metodología: Se desarrolló una revisión bibliográfica no sistemática de nivel descriptivo con método de análisis-síntesis, consultando fuentes publicadas en IEEE Xplore, Scopus, SpringerLink, MDPI y Taylor & Francis, empleando combinaciones booleanas de términos como Edge Computing, Fog Computing, IoT, latency y real-time applications, priorizando publicaciones entre 2024 y 2026. Resultados: La arquitectura de tres niveles Edge-Fog-Cloud distribuye eficientemente el procesamiento según la criticidad temporal de cada tarea, reduciendo la latencia hasta un 40% con Fog Computing y un 30% con Edge Computing respecto a modelos exclusivamente en la nube, y disminuyendo el consumo energético total hasta un 30%. La integración de Aprendizaje Federado, Aprendizaje por Refuerzo Profundo y modelos compactos de redes neuronales amplía las capacidades de inferencia distribuida en dispositivos de recursos limitados. Las aplicaciones abarcan salud inteligente, ciudades inteligentes, industria 4.0/5.0 y agricultura de precisión. Conclusión: Los paradigmas Edge y Fog Computing constituyen extensiones complementarias e imprescindibles de la nube centralizada, cuya convergencia con redes 6G, gemelos digitales, computación cuántica y Aprendizaje Federado avanzado definirá la arquitectura computacional de la próxima generación de ecosistemas IoT. Área de estudio general: Tecnologías de la Información y la Comunicación. Área de estudio específica: Computación Distribuida y Arquitecturas de Red para el Internet de las Cosas.","url":"https://doi.org/10.5281/zenodo.21777670","authors":["Pérez Insuasti, Juan José","Flores-Andino, Víctor Manuel"],"tags":["edge computing","fog computing","Internet of Things","latency","distributed architectures"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21777670","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21777671","name":"Computación de borde y computación en la niebla: un análisis descriptivo de la evolución de la computación en la nube para aplicaciones de IoT","source":"datacite","abstract":"Introducción: El vertiginoso crecimiento del Internet de las Cosas (IoT) ha evidenciado las limitaciones estructurales de la computación en la nube centralizada para satisfacer las demandas de latencia, ancho de banda y privacidad de las aplicaciones en tiempo real. Objetivo: Analizar el estado del arte del Edge Computing y el Fog Computing como paradigmas evolutivos de la computación en la nube para aplicaciones IoT, examinando sus características arquitectónicas, ventajas comparativas, casos de uso y desafíos pendientes. Metodología: Se desarrolló una revisión bibliográfica no sistemática de nivel descriptivo con método de análisis-síntesis, consultando fuentes publicadas en IEEE Xplore, Scopus, SpringerLink, MDPI y Taylor & Francis, empleando combinaciones booleanas de términos como Edge Computing, Fog Computing, IoT, latency y real-time applications, priorizando publicaciones entre 2024 y 2026. Resultados: La arquitectura de tres niveles Edge-Fog-Cloud distribuye eficientemente el procesamiento según la criticidad temporal de cada tarea, reduciendo la latencia hasta un 40% con Fog Computing y un 30% con Edge Computing respecto a modelos exclusivamente en la nube, y disminuyendo el consumo energético total hasta un 30%. La integración de Aprendizaje Federado, Aprendizaje por Refuerzo Profundo y modelos compactos de redes neuronales amplía las capacidades de inferencia distribuida en dispositivos de recursos limitados. Las aplicaciones abarcan salud inteligente, ciudades inteligentes, industria 4.0/5.0 y agricultura de precisión. Conclusión: Los paradigmas Edge y Fog Computing constituyen extensiones complementarias e imprescindibles de la nube centralizada, cuya convergencia con redes 6G, gemelos digitales, computación cuántica y Aprendizaje Federado avanzado definirá la arquitectura computacional de la próxima generación de ecosistemas IoT. Área de estudio general: Tecnologías de la Información y la Comunicación. Área de estudio específica: Computación Distribuida y Arquitecturas de Red para el Internet de las Cosas.","url":"https://doi.org/10.5281/zenodo.21777671","authors":["Pérez Insuasti, Juan José","Flores-Andino, Víctor Manuel"],"tags":["edge computing","fog computing","Internet of Things","latency","distributed architectures"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21777671","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20722427","name":"Architectural Imperative: Distributed Computer Systems Infrastructure for a Sustainable AI World","source":"datacite","abstract":"Rapid advances in artificial intelligence (AI), driven primarily by the scaling of large language models (LLMs), have exposed critical ecological and infrastructural limitations inherent in prevailing centralized computing paradigms. Electricity consumption in global data centers reached approximately 415 terawatt-hours (TWh) in 2024 and is projected to surpass 945 TWh by 2030, a trajectory that existing hardware efficiency improvements alone are incapable of reversing given the compounding effect of Jevons' Paradox. This brief contends that distributed computer systems infrastructure represents the essential architectural foundation for any feasible trajectory towards sustainable AI development. Through systematic examination of how core distributed systems principles—encompassing consensus protocols, fault tolerance mechanisms, state replication strategies, and distributed storage architectures—intersect with the operational demands of large-scale AI workloads, a concrete engineering pathway toward reduced environmental impact is identified and elaborated. The roles of container orchestration, 3D hybrid parallelism, carbon-aware workload scheduling, and high-performance interconnect topologies in maximizing resource utilization and minimizing idle energy consumption are examined in depth. Federated learning and edge inference are further explored as mechanisms for displacing computation toward the network edge, where training data originates and where sunk device energy can be productively leveraged. This brief is structured as a detailed blueprint for expansion into a full-length 40-page journal article, providing the technical scaffolding required to demonstrate, through distributed systems engineering rather than policy aspiration, that meaningful decoupling of AI capability growth from ecological degradation is architecturally achievable.","url":"https://doi.org/10.5281/zenodo.20722427","authors":["Ankur Partap Kotwal"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20722427","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20722428","name":"Architectural Imperative: Distributed Computer Systems Infrastructure for a Sustainable AI World","source":"datacite","abstract":"Rapid advances in artificial intelligence (AI), driven primarily by the scaling of large language models (LLMs), have exposed critical ecological and infrastructural limitations inherent in prevailing centralized computing paradigms. Electricity consumption in global data centers reached approximately 415 terawatt-hours (TWh) in 2024 and is projected to surpass 945 TWh by 2030, a trajectory that existing hardware efficiency improvements alone are incapable of reversing given the compounding effect of Jevons' Paradox. This brief contends that distributed computer systems infrastructure represents the essential architectural foundation for any feasible trajectory towards sustainable AI development. Through systematic examination of how core distributed systems principles—encompassing consensus protocols, fault tolerance mechanisms, state replication strategies, and distributed storage architectures—intersect with the operational demands of large-scale AI workloads, a concrete engineering pathway toward reduced environmental impact is identified and elaborated. The roles of container orchestration, 3D hybrid parallelism, carbon-aware workload scheduling, and high-performance interconnect topologies in maximizing resource utilization and minimizing idle energy consumption are examined in depth. Federated learning and edge inference are further explored as mechanisms for displacing computation toward the network edge, where training data originates and where sunk device energy can be productively leveraged. This brief is structured as a detailed blueprint for expansion into a full-length 40-page journal article, providing the technical scaffolding required to demonstrate, through distributed systems engineering rather than policy aspiration, that meaningful decoupling of AI capability growth from ecological degradation is architecturally achievable.","url":"https://doi.org/10.5281/zenodo.20722428","authors":["Ankur Partap Kotwal"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20722428","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.25532/opara-1534","name":"FedSurg EndoVis 2024: Challenge Subset of Appendix300","source":"datacite","abstract":"This deposit contains the supplementary data for the FedSurg EndoVis 2024 Challenge, the first federated learning challenge in Surgical Data Science, held at MICCAI 2024. The challenge used a preliminary subset of the Appendix300 dataset, a multi-institutional collection of laparoscopic appendectomy recordings from German university and community hospitals, annotated with intraoperative appendicitis severity grades 0 to 5 following Gomes et al. The challenge cohort comprises 223 recordings across four centers, split into 153 training and 70 test cases. The deposit includes a CSV file specifying which Appendix300 samples were used in the challenge and their assignment to centers and to the training and test splits, together with two recordings that were used in the challenge but excluded from the final Appendix300 dataset because the available footage was shorter than the nominal 100 second window or the appendix was not clearly visible at the annotated timestamp. This deposit does not contain the appendectomy video recordings themselves, with the exception of the two excluded cases named above. The video data and accompanying clinical metadata are available separately as the Appendix300 dataset at https://doi.org/10.25532/OPARA-1173. The challenge evaluation and ranking code is available at https://gitlab.com/nct_tso_public/challenges/miccai2024/snippet, and the example federated learning setup at https://gitlab.com/nct_tso_public/challenges/miccai2024/FedSurg24. Use of this material requires citation of both the FedSurg challenge paper and the Appendix300 data descriptor. Released under CC BY.","url":"https://doi.org/10.25532/opara-1534","authors":["Kirchner, Max","Kolbinger, Fiona R","Jenke, Alexander C","Saldanha, Oliver L","Pfeiffer, Kevin","Kanjo, Weam","Kather, Jakob N.","Bodenstedt, Sebastian","Speidel, Stefanie"],"tags":["2::22::205::205-25"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.25532/opara-1534","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20057806","name":"A Federated Autonomous Agent Operating System for Infrastructure Operations","source":"datacite","abstract":"We present DRACONEX, a Federated Autonomous Agent Operating System for infrastructure operations that collapses six historically-separate operational disciplines — DevSecOps, IT Operations, Security Operations, Governance Risk and Compliance (GRC), Site Reliability Engineering, and IT Service Management — into a single coordinated autonomous platform. DRACONEX converts natural-language operator intent into validated, policy-gated, compliance-evidenced infrastructure actions across hybrid cloud, on-premises (on-prem), and air-gapped environments. The system architecture comprises a strategic Captain layer powered by DRACONEX-70B-v1, a 70-billion-parameter dense transformer fine-tuned on the proprietary DracoForce v7 training corpus and served at FP8 precision on an NVIDIA RTX Pro 6000 Blackwell GPU at 50 tokens/second sustained, and ten domain-specialized 8-billion-parameter agent brains (DRACONIDs) running DRACONEX-8B-v1, each operating as an independent inference process with its own tool registry, permission matrix, and autonomy loop. Unlike Mixture-of-Agents (MoAs) systems (Wang et al., 2024) that share a single underlying model across virtual agents and require continuous network connectivity to a shared inference endpoint, DRACONIDs are first-class processes with persistent state, independent failure domains, and the ability to continue operations when disconnected from the strategic brain. Unlike federated learning systems for distributed sensing and autonomous driving (Xiang et al., 2025), DRACONEX federates inference and action — not just gradient updates — across a hierarchical mesh of cloud, edge, and air-gapped nodes. The integration pattern is, to our knowledge, novel as a complete architecture. We claim that the result is more than an improvement on DevSecOps tooling — it is a new product category we call autonomous infrastructure operations, distinguished by the property of compliance-as-execution: ATO-grade audit evidence is produced as a primary output of normal operation rather than as a separate artifact-collection phase. The discipline named DevSecOps exists because humans had to manually integrate Development, Security, and Operations across organizational boundaries that nobody designed but everyone inherited; once an autonomous agent OS spans Development, Security, Operations, ITSM, GRC, and SRE simultaneously, the discipline name becomes archaeology. We describe the architecture, the training methodology behind the 92,877-seed v7 corpus (incorporating 206 real DISA STIGs and the full NIST 800-53 Rev. 5 control catalog), the federated intelligence mesh and its five knowledge-flow directions, the safety architecture (the only agent class with infrastructure write access is gated behind a three-party CAGE-FLY-Operator handshake), and benchmark results from production deployment.","url":"https://doi.org/10.5281/zenodo.20057806","authors":["Howard"],"tags":["autonomous infrastructure operations, federated agents, large language models, hierarchical orchestration, edge AI, compliance-as-execution, mixture of agents, air-gapped operations, DoD compliance, NIST 800-53, DISA STIG."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20057806","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.5281/zenodo.20057807","name":"A Federated Autonomous Agent Operating System for Infrastructure Operations","source":"datacite","abstract":"We present DRACONEX, a Federated Autonomous Agent Operating System for infrastructure operations that collapses six historically-separate operational disciplines — DevSecOps, IT Operations, Security Operations, Governance Risk and Compliance (GRC), Site Reliability Engineering, and IT Service Management — into a single coordinated autonomous platform. DRACONEX converts natural-language operator intent into validated, policy-gated, compliance-evidenced infrastructure actions across hybrid cloud, on-premises (on-prem), and air-gapped environments. The system architecture comprises a strategic Captain layer powered by DRACONEX-70B-v1, a 70-billion-parameter dense transformer fine-tuned on the proprietary DracoForce v7 training corpus and served at FP8 precision on an NVIDIA RTX Pro 6000 Blackwell GPU at 50 tokens/second sustained, and ten domain-specialized 8-billion-parameter agent brains (DRACONIDs) running DRACONEX-8B-v1, each operating as an independent inference process with its own tool registry, permission matrix, and autonomy loop. Unlike Mixture-of-Agents (MoAs) systems (Wang et al., 2024) that share a single underlying model across virtual agents and require continuous network connectivity to a shared inference endpoint, DRACONIDs are first-class processes with persistent state, independent failure domains, and the ability to continue operations when disconnected from the strategic brain. Unlike federated learning systems for distributed sensing and autonomous driving (Xiang et al., 2025), DRACONEX federates inference and action — not just gradient updates — across a hierarchical mesh of cloud, edge, and air-gapped nodes. The integration pattern is, to our knowledge, novel as a complete architecture. We claim that the result is more than an improvement on DevSecOps tooling — it is a new product category we call autonomous infrastructure operations, distinguished by the property of compliance-as-execution: ATO-grade audit evidence is produced as a primary output of normal operation rather than as a separate artifact-collection phase. The discipline named DevSecOps exists because humans had to manually integrate Development, Security, and Operations across organizational boundaries that nobody designed but everyone inherited; once an autonomous agent OS spans Development, Security, Operations, ITSM, GRC, and SRE simultaneously, the discipline name becomes archaeology. We describe the architecture, the training methodology behind the 92,877-seed v7 corpus (incorporating 206 real DISA STIGs and the full NIST 800-53 Rev. 5 control catalog), the federated intelligence mesh and its five knowledge-flow directions, the safety architecture (the only agent class with infrastructure write access is gated behind a three-party CAGE-FLY-Operator handshake), and benchmark results from production deployment.","url":"https://doi.org/10.5281/zenodo.20057807","authors":["Howard"],"tags":["autonomous infrastructure operations, federated agents, large language models, hierarchical orchestration, edge AI, compliance-as-execution, mixture of agents, air-gapped operations, DoD compliance, NIST 800-53, DISA STIG."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20057807","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.5281/zenodo.22127783","name":"FedBioGuard: A Privacy-Preserving and Uncertainty-Aware Multimodal Federated Framework for Explainable Antimicrobial Resistance Prediction","source":"datacite","abstract":"The rapid rise of antimicrobial resistance necessitates robust diagnostic tools that can integrate heterogeneous clinical data across decentralized healthcare institutions while ensuring patient data confidentiality. To address these requirements, this framework leverages federated learning to enable collaborative model training across institutions without necessitating data centralization (Zwiers et al., 2024), while simultaneously incorporating Bayesian inference to quantify predictive uncertainty and enhance clinical interpretability (Kumari et al., 2026). Furthermore, by synthesizing multimodal electronic health records, the proposed architecture overcomes the limitations of centralized data silos and addresses the inherent challenges of data bias and clinical validation in AMR research (Hardan et al., 2024; Narra et al., 2024). Antimicrobial resistance makes infections harder to treat, so we need faster and more reliable ways to detect resistant pathogens. Artificial intelligence can help predict resistance using genomic and other biological data (Lastra et al., 2024). However, many current methods depend on centralized datasets (Zwiers et al., 2024), offer little insight into how certain their predictions are, and are difficult to interpret in high-stakes settings (Kumari et al., 2026). In addition, healthcare and biological data are often spread across institutions and cannot be freely shared because of privacy, governance, and regulatory requirements. This paper presents FedBioGuard, a privacy-preserving framework for predicting antimicrobial resistance. It combines federated learning, multimodal data, uncertainty estimates, explainable AI, and evidence-based large language model support. The main goal is to examine whether this approach can make reliable AMR predictions across institutions with different types of data, without sharing raw patient records. Each participating institution keeps its data locally and helps train a shared model by sending model updates. The model can use genomic data, microbiome or metagenomic data when available, and structured clinical information. An uncertainty module shows how reliable each prediction may be, while explainability methods highlight the biological and clinical features that influence the results. A retrieval-augmented language model is used only after prediction to turn the results into clear summaries supported by scientific evidence. By decentralizing the training process, FedBioGuard mitigates the risks associated with data privacy while addressing the limitations of traditional cultivation-based diagnostics that are often too slow to guide immediate treatment decisions (Dayan et al., 2021; Inda-Díaz et al., 2026). By addressing technical hurdles such as data heterogeneity and the \\\\\\\"black box\\\\\\\" nature of deep learning, this framework fosters the development of transparent, robust clinical decision support systems (Cavallaro et al., 2023). The evaluation will compare centralized, local-only, single-modality, and federated models using simulated data from multiple institutions. The models will be assessed for predictive performance, calibration, uncertainty quality, performance under distribution shifts, and consistency of their explanations. The study aims to provide a reproducible way to examine how privacy-preserving collaboration, multimodal learning, and uncertainty estimation can work together for AMR prediction. FedBioGuard is not intended to replace laboratory susceptibility testing; instead, it is a research framework for studying trustworthy AI-assisted AMR analysis.","url":"https://doi.org/10.5281/zenodo.22127783","authors":["Deepa Shree R"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22127783","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.22127784","name":"FedBioGuard: A Privacy-Preserving and Uncertainty-Aware Multimodal Federated Framework for Explainable Antimicrobial Resistance Prediction","source":"datacite","abstract":"The rapid rise of antimicrobial resistance necessitates robust diagnostic tools that can integrate heterogeneous clinical data across decentralized healthcare institutions while ensuring patient data confidentiality. To address these requirements, this framework leverages federated learning to enable collaborative model training across institutions without necessitating data centralization (Zwiers et al., 2024), while simultaneously incorporating Bayesian inference to quantify predictive uncertainty and enhance clinical interpretability (Kumari et al., 2026). Furthermore, by synthesizing multimodal electronic health records, the proposed architecture overcomes the limitations of centralized data silos and addresses the inherent challenges of data bias and clinical validation in AMR research (Hardan et al., 2024; Narra et al., 2024). Antimicrobial resistance makes infections harder to treat, so we need faster and more reliable ways to detect resistant pathogens. Artificial intelligence can help predict resistance using genomic and other biological data (Lastra et al., 2024). However, many current methods depend on centralized datasets (Zwiers et al., 2024), offer little insight into how certain their predictions are, and are difficult to interpret in high-stakes settings (Kumari et al., 2026). In addition, healthcare and biological data are often spread across institutions and cannot be freely shared because of privacy, governance, and regulatory requirements. This paper presents FedBioGuard, a privacy-preserving framework for predicting antimicrobial resistance. It combines federated learning, multimodal data, uncertainty estimates, explainable AI, and evidence-based large language model support. The main goal is to examine whether this approach can make reliable AMR predictions across institutions with different types of data, without sharing raw patient records. Each participating institution keeps its data locally and helps train a shared model by sending model updates. The model can use genomic data, microbiome or metagenomic data when available, and structured clinical information. An uncertainty module shows how reliable each prediction may be, while explainability methods highlight the biological and clinical features that influence the results. A retrieval-augmented language model is used only after prediction to turn the results into clear summaries supported by scientific evidence. By decentralizing the training process, FedBioGuard mitigates the risks associated with data privacy while addressing the limitations of traditional cultivation-based diagnostics that are often too slow to guide immediate treatment decisions (Dayan et al., 2021; Inda-Díaz et al., 2026). By addressing technical hurdles such as data heterogeneity and the \\\\\\\"black box\\\\\\\" nature of deep learning, this framework fosters the development of transparent, robust clinical decision support systems (Cavallaro et al., 2023). The evaluation will compare centralized, local-only, single-modality, and federated models using simulated data from multiple institutions. The models will be assessed for predictive performance, calibration, uncertainty quality, performance under distribution shifts, and consistency of their explanations. The study aims to provide a reproducible way to examine how privacy-preserving collaboration, multimodal learning, and uncertainty estimation can work together for AMR prediction. FedBioGuard is not intended to replace laboratory susceptibility testing; instead, it is a research framework for studying trustworthy AI-assisted AMR analysis.","url":"https://doi.org/10.5281/zenodo.22127784","authors":["Deepa Shree R"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22127784","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20531687","name":"Deterministic and Auditable Routing for Artificial Intelligence in Regulated Environments","source":"datacite","abstract":"# Deterministic and Auditable Routing for Artificial Intelligence in Regulated Environments **Felippe Barcelos** Independent Researcher felippe.barcelos10@gmail.com *Preprint — submitted to Zenodo. Version 1.0.0 — Protocol P55.0 — 2026-06-03* --- ## Abstract The deployment of Large Language Models (LLMs) in regulated industries — banking, healthcare, and government — demands audit trails that satisfy strict reproducibility requirements under the EU AI Act (2024), the General Data Protection Regulation (GDPR), and sector-specific frameworks such as HIPAA and Basel III/SR 11-7. Existing ML-based routing systems, including RouteLLM and FrugalGPT, achieve significant cost reductions but produce non-reproducible routing decisions whose outputs change with model retraining, rendering them incompatible with formal compliance auditing. We present the **TEIA Cognitive Router**, a compliance-first LLM routing system based on a fixed six-axis semantic entropy formula that produces deterministic routing decisions without neural weights, training data, or external dependencies. The system guarantees the *Write==Read invariant*: identical input text always yields an identical routing decision, an identical canonical JSON representation, and an identical SHA-256 audit seal. Routing decisions are organized in an append-only Merkle time-anchor chain and can be notarized by any RFC 3161-compliant Trusted Timestamp Authority (TSA) for legally binding external proof of existence. Evaluation on a 100-prompt simulation aligned with MT-Bench benchmark categories demonstrates **99.6% quality retention with 16.3% cost reduction** in compliance-safe mode, and **73.8% quality retention with 98.4% cost reduction** in max-savings mode. We provide a formal compliance mapping to EU AI Act Articles 12, 13, and Annex IV; GDPR Article 22; SOC 2 CC7; and HIPAA §164.312(b). The system is released under Apache 2.0 and is available on PyPI as `teia-cognitive-router`. **Keywords:** LLM routing, compliance, determinism, audit trail, EU AI Act, GDPR, SHA-256, Merkle chain, RFC 3161, semantic entropy **arXiv classifications:** cs.CR (Cryptography and Security) · cs.AI (Artificial Intelligence) · cs.LG (Machine Learning) --- ## 1. Introduction The deployment of Large Language Models (LLMs) across regulated industries has accelerated markedly since the public release of instruction-tuned models beginning in 2022. Financial institutions leverage LLMs for risk analysis, contract review, and fraud detection. Healthcare providers deploy them for clinical documentation, drug-interaction queries, and patient communication. Government agencies apply them to policy analysis, benefits adjudication, and citizen services. This acceleration has collided with a fundamental regulatory barrier: the *audit reproducibility problem*. Modern regulatory frameworks — the European Union AI Act (2024), GDPR (2018), HIPAA (1996), and the Federal Reserve's SR 11-7 model risk guidance — require that automated decision-making systems be *auditable*, *reproducible*, and *explainable*. An organization must be able to answer, for any past automated decision: \"Why did this happen? Can you prove it happened exactly this way? Has the record been tampered with?\" LLM deployments routinely employ multi-tier model architectures to optimize cost: a small, fast local model handles simple tasks; a large, expensive cloud model handles complex tasks. The routing decision — which tier receives each request — is itself an AI-driven automation. Yet this decision is typically made by an opaque ML classifier (RouteLLM [1]), a learned cascade (FrugalGPT [2]), or an informal heuristic with no formal audit trail. When a compliance officer asks for proof of how a specific routing decision was made six months ago, the organization cannot provide a mathematically verifiable answer. This paper makes the following contributions: 1. **TEIA Cognitive Router**: A fixed arithmetic routing formula that produces deterministic, ve","url":"https://doi.org/10.5281/zenodo.20531687","authors":["Barcelos, Felippe"],"tags":["LLM routing","compliance","determinism","cryptographic audit","EU AI Act","GDPR","AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20531687","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21114272","name":"On-Device Glucose Alarms from a Single Learned Token: Pre-Registered Cross- Dataset Validation of a Class-Discriminant Codebook Across Eleven CGM Datasets","source":"datacite","abstract":"Description This record accompanies a manuscript validating a single-token, on-device encoder for continuous glucose monitoring (CGM). Each glucose window is reduced to one compact learned token from which a decision is read on the sensor itself. Under strict pre-registration — frozen hypotheses, patient-disjoint splits, and bootstrap confidence intervals — the encoder predicts hypoglycemic and hyperglycemic excursions 30–60 minutes ahead (AUC 0.93–0.97) and, trained on a single cohort, generalizes without retraining to nine independent public CGM datasets (mean AUC 0.878). A population-level federated refresh adds a further measured gain, while per-individual personalization and multi-signal fusion are shown, with honest boundaries, to add little. Method companion: Paper 19 (doi:10.5281/zenodo.20788187). Keywords: continuous glucose monitoring; hypoglycemia prediction; hyperglycemia prediction; class-discriminant codebook; vector quantization; on-device machine learning; edge inference; cross-dataset generalization; federated refresh; pre-registration; type 1 diabetes; type 2 diabetes References 1. N. Tishby, F. C. Pereira, and W. Bialek, \"The information bottleneck method,\" in Proc. 37th Allerton Conf. Communication, Control, and Computing, 1999, pp. 368–377. 2. R. M. Gray, \"Vector quantization,\" IEEE ASSP Magazine, vol. 1, no. 2, pp. 4–29, 1984. 3. R. A. Fisher, \"The use of multiple measurements in taxonomic problems,\" Annals of Eugenics, vol. 7, no. 2, pp. 179–188, 1936. 4. S. P. Lloyd, \"Least squares quantization in PCM,\" IEEE Transactions on Information Theory, vol. 28, no. 2, pp. 129–137, 1982. 5. Q. Zhao et al., \"Chinese diabetes datasets for data-driven machine learning,\" Scientific Data, vol. 10, no. 35, 2023. 6. J. I. Hidalgo, J. Alvarado, M. Botella, A. Aramendi, J. M. Velasco, and O. Garnica, \"HUPA-UCM diabetes dataset,\" Data in Brief, vol. 55, art. 110559, 2024, doi:10.1016/j.dib.2024.110559. 7. T. Battelino et al., \"Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range,\" Diabetes Care, vol. 42, no. 8, pp. 1593–1603, 2019. 8. S. Oviedo, J. Vehí, R. Calm, and J. Armengol, \"A review of personalized blood glucose prediction strategies for T1DM patients,\" International Journal for Numerical Methods in Biomedical Engineering, vol. 33, no. 6, e2833, 2017. 9. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, \"Communication-efficient learning of deep networks from decentralized data,\" in Proc. 20th Int. Conf. Artificial Intelligence and Statistics (AISTATS), 2017, pp. 1273–1282. 10. B. A. Nosek, C. R. Ebersole, A. C. DeHaven, and D. T. Mellor, \"The preregistration revolution,\" Proc. National Academy of Sciences, vol. 115, no. 11, pp. 2600–2606, 2018. 11. B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap. New York: Chapman & Hall, 1993. 12. R. J. Ferlic and K. K. Ferlic, \"A single-token class-discriminant codebook for sensor encoding (Paper 19),\" Zenodo, 2026, doi:10.5281/zenodo.20788187. 13. U.S. Food and Drug Administration, \"Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan,\" 2021. 14. C. Marling and R. Bunescu, \"The OhioT1DM dataset for blood glucose level prediction: Update 2020,\" in Proc. 5th Int. Workshop on Knowledge Discovery in Healthcare Data, CEUR Workshop Proc., vol. 2675, 2020, pp. 71–74. 15. gluco-tsfm-benchmark: an aggregated continuous-glucose-monitoring benchmark of eleven public datasets, Hugging Face Datasets, https://huggingface.co/datasets/byluuu/gluco-tsfm-benchmark (accessed 2026). License This work is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). The encoding method and its federated and privacy mechanisms are covered by previously filed U.S. provisional patent applications (Nos. 64/095,354; 64/084,807; 64/084,817; 64/084,821); patent rights are separate from the copyright license. Public datase","url":"https://doi.org/10.5281/zenodo.21114272","authors":["Ferlic, Randolph James","Ferlic, Kimberly Kate"],"tags":["hypoglycemia prediction","continuous glucose monitoring","hyperglycemia prediction","class-discriminant codebook","vector quantization","on-device machine learning","edge inference","cross-dataset generalization"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21114272","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21114273","name":"On-Device Glucose Alarms from a Single Learned Token: Pre-Registered Cross- Dataset Validation of a Class-Discriminant Codebook Across Eleven CGM Datasets","source":"datacite","abstract":"Description This record accompanies a manuscript validating a single-token, on-device encoder for continuous glucose monitoring (CGM). Each glucose window is reduced to one compact learned token from which a decision is read on the sensor itself. Under strict pre-registration — frozen hypotheses, patient-disjoint splits, and bootstrap confidence intervals — the encoder predicts hypoglycemic and hyperglycemic excursions 30–60 minutes ahead (AUC 0.93–0.97) and, trained on a single cohort, generalizes without retraining to nine independent public CGM datasets (mean AUC 0.878). A population-level federated refresh adds a further measured gain, while per-individual personalization and multi-signal fusion are shown, with honest boundaries, to add little. Method companion: Paper 19 (doi:10.5281/zenodo.20788187). Keywords: continuous glucose monitoring; hypoglycemia prediction; hyperglycemia prediction; class-discriminant codebook; vector quantization; on-device machine learning; edge inference; cross-dataset generalization; federated refresh; pre-registration; type 1 diabetes; type 2 diabetes References 1. N. Tishby, F. C. Pereira, and W. Bialek, \"The information bottleneck method,\" in Proc. 37th Allerton Conf. Communication, Control, and Computing, 1999, pp. 368–377. 2. R. M. Gray, \"Vector quantization,\" IEEE ASSP Magazine, vol. 1, no. 2, pp. 4–29, 1984. 3. R. A. Fisher, \"The use of multiple measurements in taxonomic problems,\" Annals of Eugenics, vol. 7, no. 2, pp. 179–188, 1936. 4. S. P. Lloyd, \"Least squares quantization in PCM,\" IEEE Transactions on Information Theory, vol. 28, no. 2, pp. 129–137, 1982. 5. Q. Zhao et al., \"Chinese diabetes datasets for data-driven machine learning,\" Scientific Data, vol. 10, no. 35, 2023. 6. J. I. Hidalgo, J. Alvarado, M. Botella, A. Aramendi, J. M. Velasco, and O. Garnica, \"HUPA-UCM diabetes dataset,\" Data in Brief, vol. 55, art. 110559, 2024, doi:10.1016/j.dib.2024.110559. 7. T. Battelino et al., \"Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range,\" Diabetes Care, vol. 42, no. 8, pp. 1593–1603, 2019. 8. S. Oviedo, J. Vehí, R. Calm, and J. Armengol, \"A review of personalized blood glucose prediction strategies for T1DM patients,\" International Journal for Numerical Methods in Biomedical Engineering, vol. 33, no. 6, e2833, 2017. 9. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, \"Communication-efficient learning of deep networks from decentralized data,\" in Proc. 20th Int. Conf. Artificial Intelligence and Statistics (AISTATS), 2017, pp. 1273–1282. 10. B. A. Nosek, C. R. Ebersole, A. C. DeHaven, and D. T. Mellor, \"The preregistration revolution,\" Proc. National Academy of Sciences, vol. 115, no. 11, pp. 2600–2606, 2018. 11. B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap. New York: Chapman & Hall, 1993. 12. R. J. Ferlic and K. K. Ferlic, \"A single-token class-discriminant codebook for sensor encoding (Paper 19),\" Zenodo, 2026, doi:10.5281/zenodo.20788187. 13. U.S. Food and Drug Administration, \"Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan,\" 2021. 14. C. Marling and R. Bunescu, \"The OhioT1DM dataset for blood glucose level prediction: Update 2020,\" in Proc. 5th Int. Workshop on Knowledge Discovery in Healthcare Data, CEUR Workshop Proc., vol. 2675, 2020, pp. 71–74. 15. gluco-tsfm-benchmark: an aggregated continuous-glucose-monitoring benchmark of eleven public datasets, Hugging Face Datasets, https://huggingface.co/datasets/byluuu/gluco-tsfm-benchmark (accessed 2026). License This work is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). The encoding method and its federated and privacy mechanisms are covered by previously filed U.S. provisional patent applications (Nos. 64/095,354; 64/084,807; 64/084,817; 64/084,821); patent rights are separate from the copyright license. Public datase","url":"https://doi.org/10.5281/zenodo.21114273","authors":["Ferlic, Randolph James","Ferlic, Kimberly Kate"],"tags":["hypoglycemia prediction","continuous glucose monitoring","hyperglycemia prediction","class-discriminant codebook","vector quantization","on-device machine learning","edge inference","cross-dataset generalization"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21114273","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20179571","name":"ALEVIA: A Two-Stage Deep Learning Pipeline for Multi-Label Morphological Deformity Classification in Juvenile Sparus aurata and Dicentrarchus labrax","source":"datacite","abstract":"Intensive mariculture of juvenile gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax) depends on early morphological screening to contain deformity rates below market thresholds (< 3%), yet this process still relies almost exclusively on manual inspection. We present ALEVIA, a two-stage deep learning pipeline that processes a single lateral fish image to (i) detect, segment, and identify the species using YOLOv11, then (ii) classify the segmented crop for up to seven concurrent morphological deformity classes per species using a ResNet-34 multi-label classifier augmented with Grad-CAM++ for decision explainability. Multi-label formulation naturally handles co-occurring deformities and avoids the combinatorial proliferation of independent binary classifiers. Trained on 3,325 high-resolution laboratory images, Stage 1 achieves near-perfect segmentation performance (mAP50-95=0.995 on both species). Stage 2 reaches a weighted macro F1-score of 0.849 for S. aurataand 0.721 for D. labrax on held-out test sets. The complete pipeline exceeds the 2 img/s throughput requirement on commodity CPU hardware (median latency 315ms/image, 3.17 img/s). Grad-CAM++ visualisations confirm anatomically consistent activation patterns, providing an interpretable audit trail for domain-expert validation. The system constitutes the inference module of the ALEVIA Gaia-X-compliant federated data space for precision aquaculture.This work has been funded by the Spanish Ministry for Digital Transformation and Public Administration under the call Technological Products and Services for Data Spaces under grant TSI-100130-2024-9.","url":"https://doi.org/10.5281/zenodo.20179571","authors":["Alvarez-Osuna, Javier","Francisco-Fernández, Vilor"],"tags":["Aquaculture/classification","morphological deformity","multi-label classification","YOLO","ResNet","Grad-CAM","precision fish farming"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20179571","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20179572","name":"ALEVIA: A Two-Stage Deep Learning Pipeline for Multi-Label Morphological Deformity Classification in Juvenile Sparus aurata and Dicentrarchus labrax","source":"datacite","abstract":"Intensive mariculture of juvenile gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax) depends on early morphological screening to contain deformity rates below market thresholds (< 3%), yet this process still relies almost exclusively on manual inspection. We present ALEVIA, a two-stage deep learning pipeline that processes a single lateral fish image to (i) detect, segment, and identify the species using YOLOv11, then (ii) classify the segmented crop for up to seven concurrent morphological deformity classes per species using a ResNet-34 multi-label classifier augmented with Grad-CAM++ for decision explainability. Multi-label formulation naturally handles co-occurring deformities and avoids the combinatorial proliferation of independent binary classifiers. Trained on 3,325 high-resolution laboratory images, Stage 1 achieves near-perfect segmentation performance (mAP50-95=0.995 on both species). Stage 2 reaches a weighted macro F1-score of 0.849 for S. aurataand 0.721 for D. labrax on held-out test sets. The complete pipeline exceeds the 2 img/s throughput requirement on commodity CPU hardware (median latency 315ms/image, 3.17 img/s). Grad-CAM++ visualisations confirm anatomically consistent activation patterns, providing an interpretable audit trail for domain-expert validation. The system constitutes the inference module of the ALEVIA Gaia-X-compliant federated data space for precision aquaculture.This work has been funded by the Spanish Ministry for Digital Transformation and Public Administration under the call Technological Products and Services for Data Spaces under grant TSI-100130-2024-9.","url":"https://doi.org/10.5281/zenodo.20179572","authors":["Alvarez-Osuna, Javier","Francisco-Fernández, Vilor"],"tags":["Aquaculture/classification","morphological deformity","multi-label classification","YOLO","ResNet","Grad-CAM","precision fish farming"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20179572","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.22105709","name":"Artificial Intelligence (AI) in Early Detection of Skin Cancer","source":"datacite","abstract":"Skin cancer is a growing health problem that is becoming quite common in many populations around the world. Current estimates suggest that between 2 million and 3 million new cases of non-melanoma and melanoma skin cancers will occur globally in 2024 and 2025. Skin cancer ranks as the 17th most commonly diagnosed cancer worldwide. In 2020, there were about 57,000 deaths from melanoma, the most aggressive type of skin cancer, which is characterized by its ability to spread. According to the American Academy of Dermatology, melanoma is responsible for most skin cancer deaths, with nearly 20 Americans dying from it every day. Late detection significantly harms the chances for individuals with melanoma, leading to much lower survival rates. Research shows that once melanoma reaches the metastatic stage, the five-year survival rate drops sharply as the cancer spreads to other organs and tissues. Therefore, early detection is crucial for improving the quality of life for melanoma patients. Detecting skin cancer lesions relies heavily on thorough clinical examinations by trained professionals. This process is supported by dermoscopy, which allows clinicians to better visualize skin lesions through magnification, followed by a diagnosis confirmed by biopsy and histopathological examination. While these traditional diagnostic methods are widely used in healthcare, they have limitations. The quality of dermoscopy can vary because of the subjectivity and differences in skills among practitioners, which can affect diagnostic accuracy. The current method of melanoma detection, based on subjective assessment criteria, may not always be reliable. Furthermore, significant differences exist in the expertise available for skin cancer diagnosis worldwide, influenced by factors such as geographic location, economic development, demographics, and the availability of health resources like insurance. Artificial intelligence is becoming a revolutionary factor in improving skin cancer diagnosis. Healthcare authorities are increasingly recognizing what AI innovations can offer and are allocating resources to support and implement AI-driven solutions in the diagnosis of skin lesions. AI can quickly automate analysis and evaluation, delivering accurate results alongside high-speed processing. This rapid production of results enables immediate and ongoing evaluation of operations worldwide. With the rise of skin cancer, there is a pressing need for new devices and diagnostic tools that meet the demands for timely and accurate detection. Therefore, the future lies in these AI tools, which are designed to meet expectations effectively. Another advantage of AI is that it can facilitate diagnostic methods that do not require invasive techniques, thereby improving patient comfort and safety. This report provides a detailed overview of the currently available AI tools for diagnosing skin cancer. It will emphasize the various AI approaches and algorithms used for diagnosing skin lesions. A comparison of dermatologists' diagnostic criteria with those of AI systems will highlight the advantages and limitations of each approach. Additionally, the overall effectiveness of AI for diagnostics will be assessed, along with potential future advancements and trends in using AI technologies for skin cancer detection and management. AI has significantly impacted medical image analysis in dermatology, leading to digital and automated diagnostic systems. This development makes AI an essential and potentially transformative tool in the early detection and diagnosis of skin cancers. It not only analyzes data but also reduces some administrative tasks, allowing medical professionals to focus on key medical decisions. This evolving technology is practical and is changing dermatological diagnostics and clinical workflows. AI models have increased diagnostic accuracy. Convolutional neural networks (CNNs) and other modern algorithms exhibit impressive capabilities in classifying ski","url":"https://doi.org/10.5281/zenodo.22105709","authors":["Atish Pathak, Kesar Bankar*"],"tags":["Artificial intelligence, skin lesions, convolutional neural network (CNNs), deep learning, machine learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22105709","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.22105710","name":"Artificial Intelligence (AI) in Early Detection of Skin Cancer","source":"datacite","abstract":"Skin cancer is a growing health problem that is becoming quite common in many populations around the world. Current estimates suggest that between 2 million and 3 million new cases of non-melanoma and melanoma skin cancers will occur globally in 2024 and 2025. Skin cancer ranks as the 17th most commonly diagnosed cancer worldwide. In 2020, there were about 57,000 deaths from melanoma, the most aggressive type of skin cancer, which is characterized by its ability to spread. According to the American Academy of Dermatology, melanoma is responsible for most skin cancer deaths, with nearly 20 Americans dying from it every day. Late detection significantly harms the chances for individuals with melanoma, leading to much lower survival rates. Research shows that once melanoma reaches the metastatic stage, the five-year survival rate drops sharply as the cancer spreads to other organs and tissues. Therefore, early detection is crucial for improving the quality of life for melanoma patients. Detecting skin cancer lesions relies heavily on thorough clinical examinations by trained professionals. This process is supported by dermoscopy, which allows clinicians to better visualize skin lesions through magnification, followed by a diagnosis confirmed by biopsy and histopathological examination. While these traditional diagnostic methods are widely used in healthcare, they have limitations. The quality of dermoscopy can vary because of the subjectivity and differences in skills among practitioners, which can affect diagnostic accuracy. The current method of melanoma detection, based on subjective assessment criteria, may not always be reliable. Furthermore, significant differences exist in the expertise available for skin cancer diagnosis worldwide, influenced by factors such as geographic location, economic development, demographics, and the availability of health resources like insurance. Artificial intelligence is becoming a revolutionary factor in improving skin cancer diagnosis. Healthcare authorities are increasingly recognizing what AI innovations can offer and are allocating resources to support and implement AI-driven solutions in the diagnosis of skin lesions. AI can quickly automate analysis and evaluation, delivering accurate results alongside high-speed processing. This rapid production of results enables immediate and ongoing evaluation of operations worldwide. With the rise of skin cancer, there is a pressing need for new devices and diagnostic tools that meet the demands for timely and accurate detection. Therefore, the future lies in these AI tools, which are designed to meet expectations effectively. Another advantage of AI is that it can facilitate diagnostic methods that do not require invasive techniques, thereby improving patient comfort and safety. This report provides a detailed overview of the currently available AI tools for diagnosing skin cancer. It will emphasize the various AI approaches and algorithms used for diagnosing skin lesions. A comparison of dermatologists' diagnostic criteria with those of AI systems will highlight the advantages and limitations of each approach. Additionally, the overall effectiveness of AI for diagnostics will be assessed, along with potential future advancements and trends in using AI technologies for skin cancer detection and management. AI has significantly impacted medical image analysis in dermatology, leading to digital and automated diagnostic systems. This development makes AI an essential and potentially transformative tool in the early detection and diagnosis of skin cancers. It not only analyzes data but also reduces some administrative tasks, allowing medical professionals to focus on key medical decisions. This evolving technology is practical and is changing dermatological diagnostics and clinical workflows. AI models have increased diagnostic accuracy. Convolutional neural networks (CNNs) and other modern algorithms exhibit impressive capabilities in classifying ski","url":"https://doi.org/10.5281/zenodo.22105710","authors":["Atish Pathak, Kesar Bankar*"],"tags":["Artificial intelligence, skin lesions, convolutional neural network (CNNs), deep learning, machine learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22105710","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21550062","name":"Machine Learning Approaches for Autism Spectrum Disorder Detection: A Systematic Review of Age-Specific Applications and Performance Metrics","source":"datacite","abstract":"Autism Spectrum Disorder is one of the biggest concerns in the healthcare sector, and it's crucial to diagnose it at an early stage for patients with Autism Spectrum Disorder. This review focuses on the use of machine learning in diagnosing Autism Spectrum Disorder, drawing data from 100 papers between 2015 and 2024. We touched every possible method starting from the classic ones like Support Vector Machines (SVMs) to the new ones like federated learning. Proving the federated learning is actually great since it is very precise (up to 98%) while keeping people's information personal, which is a crucial matter in the healthcare industry. But one cannot write-off the basic framework where people use standard machine learning models such as SVMs, which at this point achieve around 92% accuracy. Also, they are more convenient to be implemented in small clinics that do not possess many great computers, and etcetera. This review suggests that the most suitable ML approaches for Autism Spectrum Disorder detection need to consider accuracy, privacy and availability of resources. Lately, more developed technologies provide even better outcomes; nevertheless, conventional techniques provide terrific options for clinics without much complicated systems available. Thus, the study offers meaningful suggestions to facilitate the choice of the most suitable methods based on the comparison between these approaches. In sum, this review spans the existing gap between research advancements in state-of-art machine learning techniques and practical healthcare settings and provides important recommendations for enhancing Autism Spectrum Disorder screening across various contexts.","url":"https://doi.org/10.5281/zenodo.21550062","authors":["Patil, Pooja Amrish","Patil, Jaydeep","Patil, Sangram T."],"tags":["Autism Spectrum Disorder; Machine Learning; Deep Learning","; Federated Learning; Healthcare Informatics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21550062","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21550063","name":"Machine Learning Approaches for Autism Spectrum Disorder Detection: A Systematic Review of Age-Specific Applications and Performance Metrics","source":"datacite","abstract":"Autism Spectrum Disorder is one of the biggest concerns in the healthcare sector, and it's crucial to diagnose it at an early stage for patients with Autism Spectrum Disorder. This review focuses on the use of machine learning in diagnosing Autism Spectrum Disorder, drawing data from 100 papers between 2015 and 2024. We touched every possible method starting from the classic ones like Support Vector Machines (SVMs) to the new ones like federated learning. Proving the federated learning is actually great since it is very precise (up to 98%) while keeping people's information personal, which is a crucial matter in the healthcare industry. But one cannot write-off the basic framework where people use standard machine learning models such as SVMs, which at this point achieve around 92% accuracy. Also, they are more convenient to be implemented in small clinics that do not possess many great computers, and etcetera. This review suggests that the most suitable ML approaches for Autism Spectrum Disorder detection need to consider accuracy, privacy and availability of resources. Lately, more developed technologies provide even better outcomes; nevertheless, conventional techniques provide terrific options for clinics without much complicated systems available. Thus, the study offers meaningful suggestions to facilitate the choice of the most suitable methods based on the comparison between these approaches. In sum, this review spans the existing gap between research advancements in state-of-art machine learning techniques and practical healthcare settings and provides important recommendations for enhancing Autism Spectrum Disorder screening across various contexts.","url":"https://doi.org/10.5281/zenodo.21550063","authors":["Patil, Pooja Amrish","Patil, Jaydeep","Patil, Sangram T."],"tags":["Autism Spectrum Disorder; Machine Learning; Deep Learning","; Federated Learning; Healthcare Informatics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21550063","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.48550/arxiv.2608.21399","name":"Federated Ensemble Forecasting Under Supply-Chain Market Volatility","source":"datacite","abstract":"Supply chain forecasting systems increasingly operate under market shocks, non-identically distributed regional demand, and limited willingness to centralize commercial data. This work proposes Federated Ensemble Forecasting with Negative-Correlation Learning (FEF NCL), a distributed method that trains specialized forecasting experts across client nodes while discouraging redundant model errors. The framework combines temporal feature encoders, client level drift scoring, reliability-weighted aggregation, and an explain ability layer that exposes the market and supplier variables most responsible for each forecast. A single synthetic dataset is used to evaluate the design. It contains 124,800 weekly SKU region observations from ten regional client nodes, 60 product families, 40 suppliers, five commodity groups, and a 2021-2024 volatility profile with explicit price-shock regimes. Because the dataset is synthetic, the reported results should be interpreted as controlled evidence of internal consistency rather than real-world validation. Across the synthetic test split, FEF NCL reduces weighted mean absolute percentage error from 13.9% for the best federated baseline to 12.4%, improves delay-risk macro-F1 from 0.755 to 0.801, and lowers the high volatility quintile error by 2.1 percentage points relative to SCAFFOLD. The analysis suggests that negative-correlation specialization is useful when clients face different supplier, freight, and commodity conditions, although deployment would require stronger privacy analysis, live drift monitoring, and operational calibration. Index Terms federated learning, ensemble learning, negative correlation learning, supply chain forecasting, market volatility, data drift, demand planning, risk governance","url":"https://doi.org/10.48550/arxiv.2608.21399","authors":["Puppala, Shunmukha Sagar"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.21399","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20003376","name":"ROLE OF ARTIFICIAL INTELLIGENCE IN IMPROVING ASTHMA MANAGEMENT: A SYSTEMATIC REVIEW","source":"datacite","abstract":"Asthma affects over 339 million people globally, imposing substantial healthcare costs and reducing quality of life. While Artificial Intelligence (AI) has shown promise in healthcare, a comprehensive evaluation of its role across Asthma Management has been lacking. This systematic review, conducted following PRISMA guidelines, examined studies published between 2019 and 2024 identified through PubMed, IEEE Xplore, and Google Scholar. Sixteen studies employing Machine Learning and Deep Learning techniques were selected and assessed for methodological quality using the ROBIS tool. The review identified AI applications across three domains: predictive models for asthma persistence and exacerbation risk, diagnostic models for phenotype classification and home-based monitoring systems utilizing respiratory sound analysis and digital inhaler data. Quality assessment indicated 69% of studies demonstrated low risk of bias. Although AI offers considerable potential for improving prediction accuracy and delivering personalized care, key challenges remain, such as limited generalizability, homogeneous datasets, and insufficient real-world validation. Future research should prioritize federated learning frameworks, explainable AI techniques, and fairness-aware algorithms validated across diverse populations to ensure clinical reliability and equitable implementation in asthma care.","url":"https://doi.org/10.5281/zenodo.20003376","authors":["Mohammed Alotaibi, Tareq Alhmiedat, Ashraf Marei, Anas Bushnag, Fady Alnajjar, Badr Alsayed, Massimiliano L. Cappuccio"],"tags":["Artificial Intelligence in Healthcare; Asthma Monitoring; Clinical Decision Support Systems, Predictive Healthcare Analytics."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20003376","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20003377","name":"ROLE OF ARTIFICIAL INTELLIGENCE IN IMPROVING ASTHMA MANAGEMENT: A SYSTEMATIC REVIEW","source":"datacite","abstract":"Asthma affects over 339 million people globally, imposing substantial healthcare costs and reducing quality of life. While Artificial Intelligence (AI) has shown promise in healthcare, a comprehensive evaluation of its role across Asthma Management has been lacking. This systematic review, conducted following PRISMA guidelines, examined studies published between 2019 and 2024 identified through PubMed, IEEE Xplore, and Google Scholar. Sixteen studies employing Machine Learning and Deep Learning techniques were selected and assessed for methodological quality using the ROBIS tool. The review identified AI applications across three domains: predictive models for asthma persistence and exacerbation risk, diagnostic models for phenotype classification and home-based monitoring systems utilizing respiratory sound analysis and digital inhaler data. Quality assessment indicated 69% of studies demonstrated low risk of bias. Although AI offers considerable potential for improving prediction accuracy and delivering personalized care, key challenges remain, such as limited generalizability, homogeneous datasets, and insufficient real-world validation. Future research should prioritize federated learning frameworks, explainable AI techniques, and fairness-aware algorithms validated across diverse populations to ensure clinical reliability and equitable implementation in asthma care.","url":"https://doi.org/10.5281/zenodo.20003377","authors":["Mohammed Alotaibi, Tareq Alhmiedat, Ashraf Marei, Anas Bushnag, Fady Alnajjar, Badr Alsayed, Massimiliano L. Cappuccio"],"tags":["Artificial Intelligence in Healthcare; Asthma Monitoring; Clinical Decision Support Systems, Predictive Healthcare Analytics."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20003377","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20662156","name":"Comprehensive Overview of \"Artificial Intelligence in Drug Development and Drug Discovery","source":"datacite","abstract":"Abstract Artificial intelligence (AI) is reshaping pharmaceutical research by tackling long timelines, high costs, and frequent failures in drug development. The global AI in drug discovery market, worth $1.72 billion in 2024, is expected to reach $8.5–16.5 billion by 2030–2034, supported by massive investments exceeding $60 billion in the last decade. Today, nearly all major pharmaceutical companies are integrating AI into their R&D pipelines. Applications span target identification, drug design, protein structure prediction, virtual screening, ADMET profiling, and clinical trial optimization. Break throughs such as AlphaFold for protein structures, generative design for molecules, and AI-enhanced clinical trials have already shown measurable impact—cutting recruitment times and allowing adaptive protocols. Real-world successes, including Insilico Medicine’s INS018-055 and Exscientia’s oncology candidates, highlight how AI-driven drugs are progressing faster into clinical testing with higher success potential. Regulators like the FDA are also advancing new frameworks to ensure transparency and reliability in AI applications. While challenges remain—such as biased datasets, model interpretability, and integration into established workflows—emerging technologies like federated learning, multimodal AI, and autonomous labs signal even greater advances ahead. Ultimately, AI is steering drug discovery away from trial-and-error toward predictive, data-driven development, unlocking safer and more effective therapies.","url":"https://doi.org/10.5281/zenodo.20662156","authors":["Shital Kalekar, Atish Pathak, Kesar Bankar, Shivam Singh"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20662156","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20662157","name":"Comprehensive Overview of \"Artificial Intelligence in Drug Development and Drug Discovery","source":"datacite","abstract":"Abstract Artificial intelligence (AI) is reshaping pharmaceutical research by tackling long timelines, high costs, and frequent failures in drug development. The global AI in drug discovery market, worth $1.72 billion in 2024, is expected to reach $8.5–16.5 billion by 2030–2034, supported by massive investments exceeding $60 billion in the last decade. Today, nearly all major pharmaceutical companies are integrating AI into their R&D pipelines. Applications span target identification, drug design, protein structure prediction, virtual screening, ADMET profiling, and clinical trial optimization. Break throughs such as AlphaFold for protein structures, generative design for molecules, and AI-enhanced clinical trials have already shown measurable impact—cutting recruitment times and allowing adaptive protocols. Real-world successes, including Insilico Medicine’s INS018-055 and Exscientia’s oncology candidates, highlight how AI-driven drugs are progressing faster into clinical testing with higher success potential. Regulators like the FDA are also advancing new frameworks to ensure transparency and reliability in AI applications. While challenges remain—such as biased datasets, model interpretability, and integration into established workflows—emerging technologies like federated learning, multimodal AI, and autonomous labs signal even greater advances ahead. Ultimately, AI is steering drug discovery away from trial-and-error toward predictive, data-driven development, unlocking safer and more effective therapies.","url":"https://doi.org/10.5281/zenodo.20662157","authors":["Shital Kalekar, Atish Pathak, Kesar Bankar, Shivam Singh"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20662157","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.19705481","name":"A Survey on AI-Based Detection of Hikikomori Behavior Patterns","source":"datacite","abstract":"This paper presents a comprehensive survey of artificial intelligence techniques for detecting hikikomori, a form of extreme and prolonged social withdrawal that has evolved into a global mental health concern. Traditional detection methods, such as clinical interviews and self-reported assessments, often fail due to the non-participatory nature of affected individuals. To address this challenge, recent research has focused on AI-driven approaches that enable passive, continuous, and scalable detection using behavioral data from smartphones, wearable devices, and online platforms. This survey reviews studies published between 2018 and 2024, covering a wide range of methodologies including machine learning, deep learning, natural language processing, computer vision, multimodal fusion, federated learning, and explainable AI. The findings highlight that multimodal systems combining diverse data sources—such as mobility patterns, sleep behavior, and social interaction—achieve higher predictive performance, with reported accuracies ranging from 76% to 92%. At the same time, the study critically examines key challenges, including limited dataset diversity, privacy and ethical concerns, and the lack of model interpretability in real-world applications. In addition, this paper proposes a conceptual AI-based framework for early detection of hikikomori using passive behavioral sensing and multimodal analysis. The framework emphasizes explainability, ethical data usage, and clinical applicability, aiming to support early intervention and improve mental health outcomes. Overall, this survey provides a structured overview of current advancements, identifies research gaps, and outlines future directions for building accurate, trustworthy, and deployable AI systems for social withdrawal detection.","url":"https://doi.org/10.5281/zenodo.19705481","authors":["jasmitha, KANCHARLA"],"tags":["Hikikomori, Social Withdrawal, Artificial Intelligence, Machine Learning, Deep Learning, NLP, Multimodal Learning, Digital Phenotyping, Mental Health, Explainable AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19705481","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.19705482","name":"A Survey on AI-Based Detection of Hikikomori Behavior Patterns","source":"datacite","abstract":"This paper presents a comprehensive survey of artificial intelligence techniques for detecting hikikomori, a form of extreme and prolonged social withdrawal that has evolved into a global mental health concern. Traditional detection methods, such as clinical interviews and self-reported assessments, often fail due to the non-participatory nature of affected individuals. To address this challenge, recent research has focused on AI-driven approaches that enable passive, continuous, and scalable detection using behavioral data from smartphones, wearable devices, and online platforms. This survey reviews studies published between 2018 and 2024, covering a wide range of methodologies including machine learning, deep learning, natural language processing, computer vision, multimodal fusion, federated learning, and explainable AI. The findings highlight that multimodal systems combining diverse data sources—such as mobility patterns, sleep behavior, and social interaction—achieve higher predictive performance, with reported accuracies ranging from 76% to 92%. At the same time, the study critically examines key challenges, including limited dataset diversity, privacy and ethical concerns, and the lack of model interpretability in real-world applications. In addition, this paper proposes a conceptual AI-based framework for early detection of hikikomori using passive behavioral sensing and multimodal analysis. The framework emphasizes explainability, ethical data usage, and clinical applicability, aiming to support early intervention and improve mental health outcomes. Overall, this survey provides a structured overview of current advancements, identifies research gaps, and outlines future directions for building accurate, trustworthy, and deployable AI systems for social withdrawal detection.","url":"https://doi.org/10.5281/zenodo.19705482","authors":["jasmitha, KANCHARLA"],"tags":["Hikikomori, Social Withdrawal, Artificial Intelligence, Machine Learning, Deep Learning, NLP, Multimodal Learning, Digital Phenotyping, Mental Health, Explainable AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19705482","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.21392158","name":"gmum/MonAcoSFL: Narodowe Centrum Nauki, nr rej. 2023/49/N/ST6/03268","source":"datacite","abstract":"Official Repository for \"A deep cut into Split Federated Self-supervised Learning\" - MonAcoSFL - accepted by ECML 2024","url":"https://doi.org/10.5281/zenodo.21392158","authors":["Marcin Przewięźlikowski","marcino","Weiming","Zachary Mayberry","llv-sony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21392158","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.21392159","name":"gmum/MonAcoSFL: Narodowe Centrum Nauki, nr rej. 2023/49/N/ST6/03268","source":"datacite","abstract":"Official Repository for \"A deep cut into Split Federated Self-supervised Learning\" - MonAcoSFL - accepted by ECML 2024","url":"https://doi.org/10.5281/zenodo.21392159","authors":["Marcin Przewięźlikowski","marcino","Weiming","Zachary Mayberry","llv-sony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21392159","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.18738/t8/lksv9r","name":"Dataset for Federated Learning for Anomaly Detection in Open RAN: Security Architecture Within a Digital Twin","source":"datacite","abstract":"We consider an experimental architecture where one or more gNBs (i.e., a combination of RU, DU and CU) with an E2 interface connect to one near-RT RIC. Each gNB is able to support multiple traffic slices. In our experiment, we choose to use three broad 5G slices: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra reliable low latency communication (URLLC). UEs are assigned to the appropriate traffic slices. The gNB records a wide range of Key Performance Indicators (KPIs) and periodically reports these KPIs to an xApp in the near-RT RIC. For each traffic slice, we generate both normal traffic and attack traffic that comprises anomalies. &lt;br&gt;&lt;br&gt; This dataset was used for the paper \"Federated Learning for Anomaly Detection in Open RAN: Security Architecture Within a Digital Twin\" published at the EuCNC &amp; 6G Summit, March 2024. Any use of this dataset which results in an academic publication or other publication which includes a bibliography should include a citation to our paper. Here is the reference for the work: &lt;br&gt;&lt;br&gt; @INPROCEEDINGS{10597083, author={Rumesh, Yasintha and Attanayaka, Dinaj and Porambage, Pawani and Pinola, Jarno and Groen, Joshua and Chowdhury, Kaushik}, booktitle={2024 Joint European Conference on Networks and Communications &amp;amp; 6G Summit (EuCNC/6G Summit)}, title={Federated Learning for Anomaly Detection in Open RAN: Security Architecture Within a Digital Twin}, year={2024}, volume={}, number={}, pages={877-882}, keywords={Training;Machine learning algorithms;Federated learning;Emulation;Open RAN;Digital twins;Security;Open Radio Access Network;Network digital twin;Anomaly detection;Federated learning}, doi={10.1109/EuCNC/6GSummit60053.2024.10597083}}}","url":"https://doi.org/10.18738/t8/lksv9r","authors":["Groen, Joshua"],"tags":["Computer and Information Science","Engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.18738/t8/lksv9r","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.19372021","name":"Comprehensive Overview of Artificial Intelligence Applications in Biomedical Research and Precision Medicine (2024–2026)","source":"datacite","abstract":"The period from 2024 to early 2026 represents a critical inflection point in biomedical research and precision medicine, driven by the deep integration of large-scale biological data with advanced artificial intelligence (AI) architectures. This era marks a transition from exploratory enthusiasm toward rigorous evaluation, where AI systems are increasingly assessed based on clinical utility, reproducibility, economic efficiency, and regulatory readiness rather than speculative potential. This review provides a comprehensive and multi-layered overview of contemporary AI applications across the biomedical landscape, spanning molecular biology, drug discovery, laboratory automation, medical imaging, genomics, clinical trials, and healthcare systems. At the molecular level, foundation models such as AlphaFold 3 and generative AI frameworks are catalyzing the emergence of “Digital Biology,” enabling accurate in silico modeling of biomolecular structures, interactions, and de novo drug design. Concurrently, self-driving laboratories are redefining experimental workflows by integrating AI agents, robotics, and real-time analytics to enhance speed, reproducibility, and scalability. In clinical and translational domains, foundation models are transforming digital pathology and medical imaging, while advances in explainable AI-particularly Concept Bottleneck Models-are addressing long-standing concerns regarding transparency and trustworthiness. In genomics and precision medicine, AI-assisted CRISPR design, variant interpretation, and game-theoretic approaches are accelerating the shift from descriptive genomics to actionable gene editing and personalized interventions. The review also highlights emerging data infrastructures, including federated learning for privacy-preserving multi-center collaboration, AI-driven clinical trial matching, and evolving regulatory frameworks from agencies such as the FDA and EMA. Special attention is given to the current state of biomedical AI development in Vietnam, illustrating both opportunities and systemic challenges in emerging healthcare ecosystems. Overall, this work positions AI not merely as a computational tool but as a collaborative scientific partner, emphasizing the necessity of aligned technological innovation, governance, ethics, and workforce training to fully realize its transformative potential in global health.","url":"https://doi.org/10.5281/zenodo.19372021","authors":["Fahim Halim Khan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19372021","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.19372022","name":"Comprehensive Overview of Artificial Intelligence Applications in Biomedical Research and Precision Medicine (2024–2026)","source":"datacite","abstract":"The period from 2024 to early 2026 represents a critical inflection point in biomedical research and precision medicine, driven by the deep integration of large-scale biological data with advanced artificial intelligence (AI) architectures. This era marks a transition from exploratory enthusiasm toward rigorous evaluation, where AI systems are increasingly assessed based on clinical utility, reproducibility, economic efficiency, and regulatory readiness rather than speculative potential. This review provides a comprehensive and multi-layered overview of contemporary AI applications across the biomedical landscape, spanning molecular biology, drug discovery, laboratory automation, medical imaging, genomics, clinical trials, and healthcare systems. At the molecular level, foundation models such as AlphaFold 3 and generative AI frameworks are catalyzing the emergence of “Digital Biology,” enabling accurate in silico modeling of biomolecular structures, interactions, and de novo drug design. Concurrently, self-driving laboratories are redefining experimental workflows by integrating AI agents, robotics, and real-time analytics to enhance speed, reproducibility, and scalability. In clinical and translational domains, foundation models are transforming digital pathology and medical imaging, while advances in explainable AI-particularly Concept Bottleneck Models-are addressing long-standing concerns regarding transparency and trustworthiness. In genomics and precision medicine, AI-assisted CRISPR design, variant interpretation, and game-theoretic approaches are accelerating the shift from descriptive genomics to actionable gene editing and personalized interventions. The review also highlights emerging data infrastructures, including federated learning for privacy-preserving multi-center collaboration, AI-driven clinical trial matching, and evolving regulatory frameworks from agencies such as the FDA and EMA. Special attention is given to the current state of biomedical AI development in Vietnam, illustrating both opportunities and systemic challenges in emerging healthcare ecosystems. Overall, this work positions AI not merely as a computational tool but as a collaborative scientific partner, emphasizing the necessity of aligned technological innovation, governance, ethics, and workforce training to fully realize its transformative potential in global health.","url":"https://doi.org/10.5281/zenodo.19372022","authors":["Fahim Halim Khan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19372022","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.21969756","name":"Robust Belief Sharing in Federated Active Inference: A Recovery-Tested Generalized-Variational Framework for Categorical Contamination-Aware Consensus","source":"datacite","abstract":"Active Fedference is a tested, reproducible research package that connects two previously separate ideas: the belief-sharing account of distributed cognition from active inference, and robust federated learning. It reimplements the core update rules of Federated Generalized Variational Inference (FedGVI) in the discrete-categorical setting and certifies, in executable form within that implementation, the project-local identity `robust_aggregate(robustness=0) == log_linear_pool`. Under documented shared-support, posterior-log-potential, and fixed-weight assumptions, that log pool is a categorical specialization of the message-combination term in Friston et al. (2024) Eq. 7; it is not a reconstruction of the complete source protocol. In the declared confident-wrong contamination study, turning the tested robustness settings on can preserve higher true-state consensus mass while contaminated members broadcast confidently wrong beliefs; turning them off exactly recovers the project log-linear pool. Nine seeded studies, a paired statistical verdict, and a fully token-injected manuscript make every reported number reproducible from one command. The public source repository is ActiveInferenceInstitute/Active_Fedference.","url":"https://doi.org/10.5281/zenodo.21969756","authors":["Friedman, Daniel Ari"],"tags":["active inference","federated learning","generalised variational inference","belief sharing","robustness","FedGVI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21969756","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.21803819","name":"A Comparative Framework for Post-Quantum Cryptography and Homomorphic Encryption: Integration, Methodologies, and Applications","source":"datacite","abstract":"Abstract The rapid evolution of quantum computing fundamentally threatens modern cryptographic infrastructures, necessitating a paradigm shift toward advanced encryption techniques. While post-quantum cryptography (PQC) ensures that data remains secure against quantum algorithmic decryption, it does not inherently facilitate the processing of that data in untrusted environments. Homomorphic encryption (HE) addresses this computational privacy requirement, yet traditional HE schemes rely on classical mathematical problems that are easily compromised by quantum systems. This paper presents a comprehensive comparative framework that integrates post-quantum cryptographic principles with homomorphic capabilities to enable quantum-resistant, privacy-preserving computation. By analyzing lattice-based algorithms, code-based cryptography, and hardware acceleration paradigms, this study maps the existing trade-offs between security, computational overhead, and practical deployment feasibility in the impending quantum era. Keywords: PQC, HE. 1.Introduction The rapid evolution of quantum computing technology presents a profound threat to modern cryptographic infrastructures. As quantum algorithms, notably Shor's algorithm, mature, they acquire the capability to solve nondeterministic polynomial time problems in polynomial time, thereby breaking traditional asymmetric encryption standards such as RSA and Elliptic Curve Cryptography (ECC) (Chen, 2024). Consequently, the development of post-quantum cryptography (PQC) has become a global imperative to secure sensitive communications and data against future quantum adversaries (Pranjal & Chaturvedi, 2024). Cryptographers are currently focusing on transitioning secure systems to these newly established PQC standards before large-scale quantum computers become commercially viable. While PQC addresses the secure transmission and storage of data, it does not inherently solve the problem of secure data processing in untrusted cloud environments. Homomorphic encryption (HE) provides a sophisticated mechanism that allows computations to be performed directly on encrypted data without requiring prior decryption (Jain & Cherukuri, 2023). The core problem addressed in this paper is the conceptual and practical integration of PQC with HE shown in Fig1.1 which is essential for ensuring quantum-safe, privacy-preserving computation in distributed architectures. Achieving this synthesis is highly complex due to the massive computational overhead and noise accumulation inherent to both cryptographic domains. However, existing approaches to privacy-preserving computation remain broadly insufficient for the post-quantum era. First, conventional homomorphic encryption schemes rely heavily on mathematical hardness assumptions, such as integer factorization or discrete logarithms, which are demonstrably vulnerable to quantum attacks (Chen, 2024). Second, while some high-performance fully homomorphic implementations exist, they suffer from prohibitive computational overhead and severe ciphertext expansion, making widespread deployment impractical without specialized mitigation strategies (Tseng et al., 2025). Third, hardware-accelerated privacy solutions often rely on trusted execution environments, like Intel SGX, which inherently introduce side-channel vulnerabilities and force reliance on centralized hardware trust rather than pure mathematical security (Sadat et al., 2017). To overcome these critical limitations, this paper proposes a structured comparative methodology for evaluating quantum-resistant homomorphic encryption systems. Specifically, our paper makes the following main contributions: We provide a comprehensive comparative framework that categorizes and evaluates lattice-based and code-based homomorphic encryption schemes against classical performance constraints. We propose a structured, hypothetical evaluation pipeline designed to benchmark the computational overhead and latency of post-quantum hom","url":"https://doi.org/10.5281/zenodo.21803819","authors":["Dr Rajendirakumar S"],"tags":["PQC, HE."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21803819","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.21803820","name":"A Comparative Framework for Post-Quantum Cryptography and Homomorphic Encryption: Integration, Methodologies, and Applications","source":"datacite","abstract":"Abstract The rapid evolution of quantum computing fundamentally threatens modern cryptographic infrastructures, necessitating a paradigm shift toward advanced encryption techniques. While post-quantum cryptography (PQC) ensures that data remains secure against quantum algorithmic decryption, it does not inherently facilitate the processing of that data in untrusted environments. Homomorphic encryption (HE) addresses this computational privacy requirement, yet traditional HE schemes rely on classical mathematical problems that are easily compromised by quantum systems. This paper presents a comprehensive comparative framework that integrates post-quantum cryptographic principles with homomorphic capabilities to enable quantum-resistant, privacy-preserving computation. By analyzing lattice-based algorithms, code-based cryptography, and hardware acceleration paradigms, this study maps the existing trade-offs between security, computational overhead, and practical deployment feasibility in the impending quantum era. Keywords: PQC, HE. 1.Introduction The rapid evolution of quantum computing technology presents a profound threat to modern cryptographic infrastructures. As quantum algorithms, notably Shor's algorithm, mature, they acquire the capability to solve nondeterministic polynomial time problems in polynomial time, thereby breaking traditional asymmetric encryption standards such as RSA and Elliptic Curve Cryptography (ECC) (Chen, 2024). Consequently, the development of post-quantum cryptography (PQC) has become a global imperative to secure sensitive communications and data against future quantum adversaries (Pranjal & Chaturvedi, 2024). Cryptographers are currently focusing on transitioning secure systems to these newly established PQC standards before large-scale quantum computers become commercially viable. While PQC addresses the secure transmission and storage of data, it does not inherently solve the problem of secure data processing in untrusted cloud environments. Homomorphic encryption (HE) provides a sophisticated mechanism that allows computations to be performed directly on encrypted data without requiring prior decryption (Jain & Cherukuri, 2023). The core problem addressed in this paper is the conceptual and practical integration of PQC with HE shown in Fig1.1 which is essential for ensuring quantum-safe, privacy-preserving computation in distributed architectures. Achieving this synthesis is highly complex due to the massive computational overhead and noise accumulation inherent to both cryptographic domains. However, existing approaches to privacy-preserving computation remain broadly insufficient for the post-quantum era. First, conventional homomorphic encryption schemes rely heavily on mathematical hardness assumptions, such as integer factorization or discrete logarithms, which are demonstrably vulnerable to quantum attacks (Chen, 2024). Second, while some high-performance fully homomorphic implementations exist, they suffer from prohibitive computational overhead and severe ciphertext expansion, making widespread deployment impractical without specialized mitigation strategies (Tseng et al., 2025). Third, hardware-accelerated privacy solutions often rely on trusted execution environments, like Intel SGX, which inherently introduce side-channel vulnerabilities and force reliance on centralized hardware trust rather than pure mathematical security (Sadat et al., 2017). To overcome these critical limitations, this paper proposes a structured comparative methodology for evaluating quantum-resistant homomorphic encryption systems. Specifically, our paper makes the following main contributions: We provide a comprehensive comparative framework that categorizes and evaluates lattice-based and code-based homomorphic encryption schemes against classical performance constraints. We propose a structured, hypothetical evaluation pipeline designed to benchmark the computational overhead and latency of post-quantum hom","url":"https://doi.org/10.5281/zenodo.21803820","authors":["Dr Rajendirakumar S"],"tags":["PQC, HE."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21803820","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.20411228","name":"DIGITAL HEALTH, DATA SOVEREIGNTY AND THE SDGS: A FRAMEWORK FOR SUSTAINABLE PRACTICES WITH MOBILE HEALTHCARE APPLICATIONS","source":"datacite","abstract":"In an era where mobile technology permeates every aspect of people’s lives, the question of who controls health data has become both urgent and essential. As of 2024, more than 100,000 health-related mobile apps are available in major app stores, and these applications generate large volumes of personal health data every day. In addition, cybersecurity threats have surged and the number of individuals impacted by healthcare data breaches nearly tripled from 14 million to 45 million between 2018 and 2021. Patients increasingly consider data privacy as a fundamental right and over ninety percent people believe that health app developers should be transparent, offer meaningful consent processes, and allow them to opt out of data usage or sharing. These trends expose a growing gap between the promise of mobile health and the realities of ethical and sustainable practice. Recognizing this, global health leaders and national regulators are emphasizing the need for stronger governance. In 2024, the G20 health ministers affirmed that trusted, secure, interoperable digital health systems supported by regulatory frameworks that respect national context are essential for equitable healthcare and resilience. At the UN World Data Forum, participants called for a global health data governance framework rooted in rights-based, equitable principles, in order to unlock the public value of health data while protecting individuals’ rights. Meanwhile, India’s Ayushman Bharat Digital Mission has significantly advanced sovereign digital health infrastructure and by early 2025, more than 739 million health IDs had been issued, linking approximately 490 million health records. The mission includes a Health Data Management Policy that embeds “privacy and security by design,” consent frameworks, user control rights, and interoperability as core elements. Nonetheless, draft legislation such as DISHA remains pending, highlighting persistent challenges in implementing robust health data protection in practice. Beyond policy, emerging technological models like the Decentralized Health Intelligence Network (DHIN) offer promising alternatives. DHIN combines federated learning, personal health records, and blockchain incentives to ensure individuals retain control over their health data, benefit financially from participation, and safeguard AI development with decentralized safeguards. Drawing on these dynamics, this paper proposes a comprehensive framework that aligns mobile health application design and deployment with three pillars: data sovereignty, ethical governance, and SDG-driven impact. It emphasizes clear role definitions (like data owner, data steward), codified consent and ownership rights, federated system architectures, and interoperability standards tailored to local contexts. It also explores how platforms can integrate mHealth apps into broader digital public infrastructure while preserving individual autonomy and privacy. This study grounds its analysis in real-world data, lived experience, and ethical principles. It demonstrates that privacy and sovereignty are not obstacles to innovation. On the contrary, they serve as foundational enablers that support and guide responsible technological advancement.Properly governed mobile healthcare applications can advance SDG 3 (health), SDG 5 (gender equality), SDG 9 (innovation and infrastructure), and SDG 16 (peace, justice and strong institutions). In summary, when built around user control and accountability, mobile health systems can deliver more trustworthy, sustainable, and inclusive health services empowering individuals while building healthier societies. This research explores how mobile healthcare applications can support the United Nations Sustainable Development Goals, not only by improving health outcomes under SDG 3 but also by reinforcing data sovereignty as a pillar of trust, equity, and sustainability.","url":"https://doi.org/10.5281/zenodo.20411228","authors":["Mr. Mayur Kanhaiyalal Solanki & Dr.(Mrs.)Varsha Ganatra"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20411228","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.20411229","name":"DIGITAL HEALTH, DATA SOVEREIGNTY AND THE SDGS: A FRAMEWORK FOR SUSTAINABLE PRACTICES WITH MOBILE HEALTHCARE APPLICATIONS","source":"datacite","abstract":"In an era where mobile technology permeates every aspect of people’s lives, the question of who controls health data has become both urgent and essential. As of 2024, more than 100,000 health-related mobile apps are available in major app stores, and these applications generate large volumes of personal health data every day. In addition, cybersecurity threats have surged and the number of individuals impacted by healthcare data breaches nearly tripled from 14 million to 45 million between 2018 and 2021. Patients increasingly consider data privacy as a fundamental right and over ninety percent people believe that health app developers should be transparent, offer meaningful consent processes, and allow them to opt out of data usage or sharing. These trends expose a growing gap between the promise of mobile health and the realities of ethical and sustainable practice. Recognizing this, global health leaders and national regulators are emphasizing the need for stronger governance. In 2024, the G20 health ministers affirmed that trusted, secure, interoperable digital health systems supported by regulatory frameworks that respect national context are essential for equitable healthcare and resilience. At the UN World Data Forum, participants called for a global health data governance framework rooted in rights-based, equitable principles, in order to unlock the public value of health data while protecting individuals’ rights. Meanwhile, India’s Ayushman Bharat Digital Mission has significantly advanced sovereign digital health infrastructure and by early 2025, more than 739 million health IDs had been issued, linking approximately 490 million health records. The mission includes a Health Data Management Policy that embeds “privacy and security by design,” consent frameworks, user control rights, and interoperability as core elements. Nonetheless, draft legislation such as DISHA remains pending, highlighting persistent challenges in implementing robust health data protection in practice. Beyond policy, emerging technological models like the Decentralized Health Intelligence Network (DHIN) offer promising alternatives. DHIN combines federated learning, personal health records, and blockchain incentives to ensure individuals retain control over their health data, benefit financially from participation, and safeguard AI development with decentralized safeguards. Drawing on these dynamics, this paper proposes a comprehensive framework that aligns mobile health application design and deployment with three pillars: data sovereignty, ethical governance, and SDG-driven impact. It emphasizes clear role definitions (like data owner, data steward), codified consent and ownership rights, federated system architectures, and interoperability standards tailored to local contexts. It also explores how platforms can integrate mHealth apps into broader digital public infrastructure while preserving individual autonomy and privacy. This study grounds its analysis in real-world data, lived experience, and ethical principles. It demonstrates that privacy and sovereignty are not obstacles to innovation. On the contrary, they serve as foundational enablers that support and guide responsible technological advancement.Properly governed mobile healthcare applications can advance SDG 3 (health), SDG 5 (gender equality), SDG 9 (innovation and infrastructure), and SDG 16 (peace, justice and strong institutions). In summary, when built around user control and accountability, mobile health systems can deliver more trustworthy, sustainable, and inclusive health services empowering individuals while building healthier societies. This research explores how mobile healthcare applications can support the United Nations Sustainable Development Goals, not only by improving health outcomes under SDG 3 but also by reinforcing data sovereignty as a pillar of trust, equity, and sustainability.","url":"https://doi.org/10.5281/zenodo.20411229","authors":["Mr. Mayur Kanhaiyalal Solanki & Dr.(Mrs.)Varsha Ganatra"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20411229","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.20225969","name":"ITU and Communications / Networks: A Single-Axiom View of Shannon Theory, Internet, 5G/6G, Quantum Communication","source":"datacite","abstract":"We apply the Information-Theoretic Unification (ITU) framework (Terada 2026, concept DOI 10.5281/zenodo.20109209; current version v2.0.0 at 10.5281/zenodo.20133709) to communications and networks. Shannon's information theory is shown to be a special case of the ITU axiom delta_S = delta(K), with H(X) = (K)/ln2 and channel capacity = max modular K-flow. This is Tier 1 paper #14, opening the K-channel axis and bringing the ITU polytope to 14 vertices. Communications achieves degree 9, tying with Climate (#11) as the polytope's maximum-connectivity super-hub. Pass-1 progress: 98 of 220 phases (44.5%). Phase 95: ITU foundation. Shannon H(X) = (K)/ln2 in ITU language; channel capacity C = max modular K-flow. Internet traffic 2024 = 500 EB/month, doubling period 2.9 years, forecast 20,000 EB/mo by 2050 (40x growth). Mobile evolution 1G to 6G: bandwidth 2 kbps to 1 Tbps (5e8x), latency 1000 ms to 0.1 ms (10000x improvement). Satellite constellations: 7,550 currently to planned 60,506 total (8x expansion). Quantum communication (Micius satellite 1,200 km QKD) demonstrates ITU axiom realization in physical observable. Phase 96: 6G + Quantum Internet + Federated Learning. IMT-2030 (6G) targets: 1 Tbps peak (50x 5G), 10 Gbps user (100x), 0.1 ms latency (10x), 10^7 devices/km^2, 100x energy efficiency. Quantum Internet Wehner 2018 6-stage roadmap: Stage 0-1 (trusted node, QKD) commercial 2024; Stage 2 (entanglement distribution) 2030; Stage 5 (distributed quantum computing) 2045. Federated Learning (McMahan 2017): centralized 0.994 accuracy, federated 0.979, local-only 0.698 — federated preserves privacy at minimal accuracy cost. Edge AI latency hierarchy: device 0.5 ms, 6G edge 0.5 ms (2030), regional DC 20 ms, cloud 80 ms, satellite 30 ms. Phase 97: Industry, economy, digital divide. Global ICT market: $5.3T (2024) to $30T (2050), Telecom subset $1.6T to $8T, AI subset $200B to $15T. Annual CapEx: $226B (2024, 5G dominant) to $655B (2050, Quantum $300B largest). Digital divide: world average 62% (2024) with 3.17B offline to 93% (2050) with 0.65B offline (80% reduction). Sub-Saharan Africa 40% to 90%, LDCs 27% to 80%. 6G patents: China 40%, USA 35% (combined 75% — standardization war risk). Satellite geopolitics: Starlink (USA) 42,000 planned vs Guowang (China) 13,000 planned. Phase 98: 2026-2050 roadmap with 16 milestones and 10 falsifiable predictions (P_avg = 0.57). Key milestones: 2028 IMT-2030 6G spec, 2030 6G commercial + Q-internet Stage 2, 2032 Starlink 42K complete, 2035 Q-memory network, 2045 distributed Q-compute, 2050 99.5% global penetration. Central thesis: communications is K-channel transport of K_information between subsystems. Shannon capacity = max modular K-flow makes communication engineering a direct application of ITU axiom. Quantum internet directly observes delta_S = delta(K) through entanglement-based protocols. 6G + Quantum + Federated Learning forms a 3-layer K-flow architecture (classical channel + quantum state + distributed K_self) supporting Embodied AGI (Tier 1 #13). The ITU 14-vertex polytope completes with K-channel axis. Communications vertex bidirectionally connects to 9 other vertices — tying with Climate as the polytope's super-hub. Honest framing: Pass-1 interpretive paper reframing Shannon (1948), Wehner-Elkouss-Hanson (2018), McMahan (2017), ITU-R IMT-2030 (2023), NIST PQC FIPS 203/204/205 (2024), 3GPP Release 19 (2024), Pan Jianwei Micius (2017-2024), SpaceX Starlink, World Bank broadband economics, Gartner/IDC ICT forecasts in ITU language. Numerical results match established literature. Includes 4 theory documents, 4 Python numerical experiments, 4 figures (PNG), 4 JSON summaries. Total runtime ~15 seconds.","url":"https://doi.org/10.5281/zenodo.20225969","authors":["Terada, Munehiro"],"tags":["communications","networks","Shannon information theory","channel capacity","Shannon-Hartley","internet","bandwidth","latency"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20225969","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.20225970","name":"ITU and Communications / Networks: A Single-Axiom View of Shannon Theory, Internet, 5G/6G, Quantum Communication","source":"datacite","abstract":"We apply the Information-Theoretic Unification (ITU) framework (Terada 2026, concept DOI 10.5281/zenodo.20109209; current version v2.0.0 at 10.5281/zenodo.20133709) to communications and networks. Shannon's information theory is shown to be a special case of the ITU axiom delta_S = delta(K), with H(X) = (K)/ln2 and channel capacity = max modular K-flow. This is Tier 1 paper #14, opening the K-channel axis and bringing the ITU polytope to 14 vertices. Communications achieves degree 9, tying with Climate (#11) as the polytope's maximum-connectivity super-hub. Pass-1 progress: 98 of 220 phases (44.5%). Phase 95: ITU foundation. Shannon H(X) = (K)/ln2 in ITU language; channel capacity C = max modular K-flow. Internet traffic 2024 = 500 EB/month, doubling period 2.9 years, forecast 20,000 EB/mo by 2050 (40x growth). Mobile evolution 1G to 6G: bandwidth 2 kbps to 1 Tbps (5e8x), latency 1000 ms to 0.1 ms (10000x improvement). Satellite constellations: 7,550 currently to planned 60,506 total (8x expansion). Quantum communication (Micius satellite 1,200 km QKD) demonstrates ITU axiom realization in physical observable. Phase 96: 6G + Quantum Internet + Federated Learning. IMT-2030 (6G) targets: 1 Tbps peak (50x 5G), 10 Gbps user (100x), 0.1 ms latency (10x), 10^7 devices/km^2, 100x energy efficiency. Quantum Internet Wehner 2018 6-stage roadmap: Stage 0-1 (trusted node, QKD) commercial 2024; Stage 2 (entanglement distribution) 2030; Stage 5 (distributed quantum computing) 2045. Federated Learning (McMahan 2017): centralized 0.994 accuracy, federated 0.979, local-only 0.698 — federated preserves privacy at minimal accuracy cost. Edge AI latency hierarchy: device 0.5 ms, 6G edge 0.5 ms (2030), regional DC 20 ms, cloud 80 ms, satellite 30 ms. Phase 97: Industry, economy, digital divide. Global ICT market: $5.3T (2024) to $30T (2050), Telecom subset $1.6T to $8T, AI subset $200B to $15T. Annual CapEx: $226B (2024, 5G dominant) to $655B (2050, Quantum $300B largest). Digital divide: world average 62% (2024) with 3.17B offline to 93% (2050) with 0.65B offline (80% reduction). Sub-Saharan Africa 40% to 90%, LDCs 27% to 80%. 6G patents: China 40%, USA 35% (combined 75% — standardization war risk). Satellite geopolitics: Starlink (USA) 42,000 planned vs Guowang (China) 13,000 planned. Phase 98: 2026-2050 roadmap with 16 milestones and 10 falsifiable predictions (P_avg = 0.57). Key milestones: 2028 IMT-2030 6G spec, 2030 6G commercial + Q-internet Stage 2, 2032 Starlink 42K complete, 2035 Q-memory network, 2045 distributed Q-compute, 2050 99.5% global penetration. Central thesis: communications is K-channel transport of K_information between subsystems. Shannon capacity = max modular K-flow makes communication engineering a direct application of ITU axiom. Quantum internet directly observes delta_S = delta(K) through entanglement-based protocols. 6G + Quantum + Federated Learning forms a 3-layer K-flow architecture (classical channel + quantum state + distributed K_self) supporting Embodied AGI (Tier 1 #13). The ITU 14-vertex polytope completes with K-channel axis. Communications vertex bidirectionally connects to 9 other vertices — tying with Climate as the polytope's super-hub. Honest framing: Pass-1 interpretive paper reframing Shannon (1948), Wehner-Elkouss-Hanson (2018), McMahan (2017), ITU-R IMT-2030 (2023), NIST PQC FIPS 203/204/205 (2024), 3GPP Release 19 (2024), Pan Jianwei Micius (2017-2024), SpaceX Starlink, World Bank broadband economics, Gartner/IDC ICT forecasts in ITU language. Numerical results match established literature. Includes 4 theory documents, 4 Python numerical experiments, 4 figures (PNG), 4 JSON summaries. Total runtime ~15 seconds.","url":"https://doi.org/10.5281/zenodo.20225970","authors":["Terada, Munehiro"],"tags":["communications","networks","Shannon information theory","channel capacity","Shannon-Hartley","internet","bandwidth","latency"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20225970","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.17464803","name":"Beyond the Shadows — Contextual Awakening, Federated Learning, and the Realization of Reality through Digital Twins","source":"datacite","abstract":"Beyond the Shadows — Contextual Awakening, Federated Learning, and the Realization of Reality through Digital Twins Abstract This paper captures the second stage of an ongoing multi-stakeholder digital twin and AI orchestration project, approximately four months into deployment. Through daily standups, on-site workshops, and cross-domain collaboration, an unexpected realization emerged: for many participants, this was the first time they understood they were truly working for a real estate company. Until that moment, their work had been mediated through systems, processes, and abstractions — shadows of the real. The study explores the tension between data-driven illusions and context-driven truths, the need for Local and Small Quantitative Models (LQMs) alongside Large Language Models (LLMs), and the sociotechnical awakening that occurs when context is reintroduced into fragmented digital ecosystems. It also examines the role of digital twins as boundary-spanning objects that preserve, teach, and reinterpret meaning over time. The findings reveal the profound implications of spatial awareness, organizational history, and emerging resilience frameworks in an era of geopolitical instability and post-cloud architectures. Index Terms— Digital Twins, Contextual Interoperability, Explainable Context, Federated Learning, Quantum-Safe Communication, LQMs, Edge-Native Design, SMILE, Organizational Awakening, Weill & Broadbent, Daniel & Ward, Magoulas & Pessi I. INTRODUCTION By the fourth month, the project had evolved beyond technical experimentation.During a workshop at the customer’s premises, a subtle but profound realization occurred: “This is the first time I’ve seen what I’m actually working for.” Multiple individuals echoed the same sentiment. Despite years of employment in the same organization, most had only interacted with representations — spreadsheets, maintenance systems, and financial dashboards — but not with reality itself. What they had been managing were shadows — interpretations of truth filtered through the lens of outdated systems and compartmentalized perspectives.The digital twin, for the first time, acted as the light source in Plato’s allegory: a shared reality that revealed the building not as an abstraction, but as a living ecosystem where people, systems, culture, and context intersected. This realization transformed the project’s orientation from “data-driven optimization” toward context-driven understanding, and reframed AI not as a controller, but as a participant in a continuous learning process. II. METHODOLOGICAL CONTINUATION The project continued to follow the SMILE methodology [4], with daily standups between distributed teams across Sweden. Despite strong routines, physical distance exposed the limitations of coordination: truth remained local, rooted in context. A recurring theme emerged: data is not truth.Data are ingredients — perishable, contextual, and incomplete.Projects, in turn, are the meals prepared from these ingredients.Different stakeholders “taste” the results differently; some prefer efficiency, others comfort or compliance. Thus, data-driven decision-making proved to be an oversimplification.What matters is the currency of context — continuously refreshed, locally valid, and dynamically interpreted. Old data are stale ingredients; contextually updated data are nourishment for living systems. III. THE EMERGENCE OF EXPLAINABLE CONTEXT Following the first deployment phase, we observed the emergence of Explainable Context (XC) as a necessary evolution of Explainable AI (XAI).AI models provided predictions, but the meaning of those predictions could only be understood within spatial, historical, and organizational contexts. To address this, digital twins of twins were introduced — meta-models that record not only the current state but the lineage of context: which actors influenced decisions, which data were used, and how interpretations evolved over time. This recursive architecture turn","url":"https://doi.org/10.5281/zenodo.17464803","authors":["Waern, Nicolas"],"tags":["digital twin","edge computing","boundary objects","knowledge management","data sovereignty","SMILE methodology"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17464803","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.17464804","name":"Beyond the Shadows — Contextual Awakening, Federated Learning, and the Realization of Reality through Digital Twins","source":"datacite","abstract":"Beyond the Shadows — Contextual Awakening, Federated Learning, and the Realization of Reality through Digital Twins Abstract This paper captures the second stage of an ongoing multi-stakeholder digital twin and AI orchestration project, approximately four months into deployment. Through daily standups, on-site workshops, and cross-domain collaboration, an unexpected realization emerged: for many participants, this was the first time they understood they were truly working for a real estate company. Until that moment, their work had been mediated through systems, processes, and abstractions — shadows of the real. The study explores the tension between data-driven illusions and context-driven truths, the need for Local and Small Quantitative Models (LQMs) alongside Large Language Models (LLMs), and the sociotechnical awakening that occurs when context is reintroduced into fragmented digital ecosystems. It also examines the role of digital twins as boundary-spanning objects that preserve, teach, and reinterpret meaning over time. The findings reveal the profound implications of spatial awareness, organizational history, and emerging resilience frameworks in an era of geopolitical instability and post-cloud architectures. Index Terms— Digital Twins, Contextual Interoperability, Explainable Context, Federated Learning, Quantum-Safe Communication, LQMs, Edge-Native Design, SMILE, Organizational Awakening, Weill & Broadbent, Daniel & Ward, Magoulas & Pessi I. INTRODUCTION By the fourth month, the project had evolved beyond technical experimentation.During a workshop at the customer’s premises, a subtle but profound realization occurred: “This is the first time I’ve seen what I’m actually working for.” Multiple individuals echoed the same sentiment. Despite years of employment in the same organization, most had only interacted with representations — spreadsheets, maintenance systems, and financial dashboards — but not with reality itself. What they had been managing were shadows — interpretations of truth filtered through the lens of outdated systems and compartmentalized perspectives.The digital twin, for the first time, acted as the light source in Plato’s allegory: a shared reality that revealed the building not as an abstraction, but as a living ecosystem where people, systems, culture, and context intersected. This realization transformed the project’s orientation from “data-driven optimization” toward context-driven understanding, and reframed AI not as a controller, but as a participant in a continuous learning process. II. METHODOLOGICAL CONTINUATION The project continued to follow the SMILE methodology [4], with daily standups between distributed teams across Sweden. Despite strong routines, physical distance exposed the limitations of coordination: truth remained local, rooted in context. A recurring theme emerged: data is not truth.Data are ingredients — perishable, contextual, and incomplete.Projects, in turn, are the meals prepared from these ingredients.Different stakeholders “taste” the results differently; some prefer efficiency, others comfort or compliance. Thus, data-driven decision-making proved to be an oversimplification.What matters is the currency of context — continuously refreshed, locally valid, and dynamically interpreted. Old data are stale ingredients; contextually updated data are nourishment for living systems. III. THE EMERGENCE OF EXPLAINABLE CONTEXT Following the first deployment phase, we observed the emergence of Explainable Context (XC) as a necessary evolution of Explainable AI (XAI).AI models provided predictions, but the meaning of those predictions could only be understood within spatial, historical, and organizational contexts. To address this, digital twins of twins were introduced — meta-models that record not only the current state but the lineage of context: which actors influenced decisions, which data were used, and how interpretations evolved over time. This recursive architecture turn","url":"https://doi.org/10.5281/zenodo.17464804","authors":["Waern, Nicolas"],"tags":["digital twin","edge computing","boundary objects","knowledge management","data sovereignty","SMILE methodology"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17464804","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.19969099","name":"Cyber Attack Prediction From Traditional Machine Learning to Generative Artificial Intelligence","source":"datacite","abstract":"Abstract The threats of cyber are becoming increasingly sophisticated and widespread thus we require intelligent and proactive security systems that are capable of continually identifying and anticipating network attacks. The paper presents a high-end AI-based cyber attack prediction framework, trained and evaluated on the CICIDS2017 dataset and combining approaches of ML, DL, generative AI, and explainable AI. Preprocessing is done a lot to ensure that learning is more productive. This involves elimination of missing and duplicated data, coding labels, standardization of the data and Principal Component Analysis to reduce the number of dimensions. We examine some of the ML classifiers, such as Decision Tree, RF, Extra Trees Classifier, LR, Gaussian Naive Bayes, and a hybrid Voting Classifier, which uses RF, LightGBM and XGBoost. We also examine DL networks such as CNN, LSTM, CNNLSTM and CNNLSTMGRU. Generative models such as Variational Autoencoder, Generative Adversarial Network and DistilGPT2 help improve the appearance of fake attack patterns. The best test is the Voting Classifier as it has the highest accuracy of 99.6. The second model is the LSTM which is 99.3 percent accurate. It implies that both models are capable of locating attacks of the following type: DoS, DDoS, PortScan, Bot, and Infiltration. The model is simplified with the help of LIME and SHAP. The framework is installed with Flask and allows you to log in and process data, watch what is happening and classify network traffic as good and bad. Keywords: Cyber attack prediction, Machine Learning, Deep Learning, Generative AI, Explainable AI, LSTM, Ensemble Learning, Intrusion Detection 1. Introduction The high rate of digital technology development and the fact that most of the various fields are now interrelated with one another has necessitated intense security in cybersecurity. The level of danger and intensity of cyber attacks has increased manifold as an increasing number of individuals, companies as well as governments conduct their important activities online. Ransomware, phishing attacks, Denial of Service attacks, and data leaks not only prevent the work of significant services, but they are also expensive and expose personal data [1]. Traditional defensive measures mostly tend to be reactive meaning that they do not suit well in the quick world of cyber attacks. This is an indication of the value of having smart, active, and flexible security solutions [2]. As a disruptive technology, AI and its offshoots, including ML, DL, NLP, and GenAI, can be used to improve cybersecurity [3,4]. These technologies can find their use as predictive threat intelligence, real-time detection of anomalies, and automated mitigation procedures. This enables cybersecurity to be proactive rather than reactive. ML applications have an opportunity to identify the slightest changes in network data and can observe trends. With the help of algorithmic methods based on NLP, one can detect and classify phishing and other spam messages [3]. CNNs, LSTM networks, hybrid CNNs/LSTM network, and Transformer mechanisms are all advanced models of deep learning that have performed well to discover complex attack patterns in cybersecurity [4]. New threat scenarios can also be generated by generative AI models, which can be useful in preparing security systems for new attack vectors. This renders them more powerful when it comes to fighting against opponents who may alter plans [5]. Despite these advances, there will still be data quality, size, model interpretability, and interoperability with existing systems [6,7]. In order to make the AI-based cybersecurity systems more transparent and reliable, an increasing number of individuals are utilizing the XAI tools such as SHAP and LIME. These techniques assist analysts to make automatic predictions [8,9]. These AI techniques must be implemented in a live, real-time system to develop a comprehensive cybersecurity solution that can be useful in","url":"https://doi.org/10.5281/zenodo.19969099","authors":["Sahana D P","Dr Jagadeesha R"],"tags":["Cyber attack prediction, Machine Learning, Deep Learning, Generative AI, Explainable AI, LSTM, Ensemble Learning, Intrusion Detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19969099","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.19969100","name":"Cyber Attack Prediction From Traditional Machine Learning to Generative Artificial Intelligence","source":"datacite","abstract":"Abstract The threats of cyber are becoming increasingly sophisticated and widespread thus we require intelligent and proactive security systems that are capable of continually identifying and anticipating network attacks. The paper presents a high-end AI-based cyber attack prediction framework, trained and evaluated on the CICIDS2017 dataset and combining approaches of ML, DL, generative AI, and explainable AI. Preprocessing is done a lot to ensure that learning is more productive. This involves elimination of missing and duplicated data, coding labels, standardization of the data and Principal Component Analysis to reduce the number of dimensions. We examine some of the ML classifiers, such as Decision Tree, RF, Extra Trees Classifier, LR, Gaussian Naive Bayes, and a hybrid Voting Classifier, which uses RF, LightGBM and XGBoost. We also examine DL networks such as CNN, LSTM, CNNLSTM and CNNLSTMGRU. Generative models such as Variational Autoencoder, Generative Adversarial Network and DistilGPT2 help improve the appearance of fake attack patterns. The best test is the Voting Classifier as it has the highest accuracy of 99.6. The second model is the LSTM which is 99.3 percent accurate. It implies that both models are capable of locating attacks of the following type: DoS, DDoS, PortScan, Bot, and Infiltration. The model is simplified with the help of LIME and SHAP. The framework is installed with Flask and allows you to log in and process data, watch what is happening and classify network traffic as good and bad. Keywords: Cyber attack prediction, Machine Learning, Deep Learning, Generative AI, Explainable AI, LSTM, Ensemble Learning, Intrusion Detection 1. Introduction The high rate of digital technology development and the fact that most of the various fields are now interrelated with one another has necessitated intense security in cybersecurity. The level of danger and intensity of cyber attacks has increased manifold as an increasing number of individuals, companies as well as governments conduct their important activities online. Ransomware, phishing attacks, Denial of Service attacks, and data leaks not only prevent the work of significant services, but they are also expensive and expose personal data [1]. Traditional defensive measures mostly tend to be reactive meaning that they do not suit well in the quick world of cyber attacks. This is an indication of the value of having smart, active, and flexible security solutions [2]. As a disruptive technology, AI and its offshoots, including ML, DL, NLP, and GenAI, can be used to improve cybersecurity [3,4]. These technologies can find their use as predictive threat intelligence, real-time detection of anomalies, and automated mitigation procedures. This enables cybersecurity to be proactive rather than reactive. ML applications have an opportunity to identify the slightest changes in network data and can observe trends. With the help of algorithmic methods based on NLP, one can detect and classify phishing and other spam messages [3]. CNNs, LSTM networks, hybrid CNNs/LSTM network, and Transformer mechanisms are all advanced models of deep learning that have performed well to discover complex attack patterns in cybersecurity [4]. New threat scenarios can also be generated by generative AI models, which can be useful in preparing security systems for new attack vectors. This renders them more powerful when it comes to fighting against opponents who may alter plans [5]. Despite these advances, there will still be data quality, size, model interpretability, and interoperability with existing systems [6,7]. In order to make the AI-based cybersecurity systems more transparent and reliable, an increasing number of individuals are utilizing the XAI tools such as SHAP and LIME. These techniques assist analysts to make automatic predictions [8,9]. These AI techniques must be implemented in a live, real-time system to develop a comprehensive cybersecurity solution that can be useful in","url":"https://doi.org/10.5281/zenodo.19969100","authors":["Sahana D P","Dr Jagadeesha R"],"tags":["Cyber attack prediction, Machine Learning, Deep Learning, Generative AI, Explainable AI, LSTM, Ensemble Learning, Intrusion Detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19969100","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20629889","name":"The Evolving Role of Artificial Intelligence in Enhancing Data Privacy Compliance_A Case Study on GDPR and Emerging AI Regulations in Cyber Security","source":"datacite","abstract":"This research paper quantitatively examines the evolving role of artificial intelligence (AI) in enhancing data privacy compliance, with a primary focus on the General Data Protection Regulation (GDPR) and its interplay with the EU AI Act within the cyber security domain. The GDPR (effective 2018) mandates core principles including lawfulness, fairness, transparency, purpose limitation, data minimization, accuracy, storage limitation, integrity/confidentiality, and accountability. It imposes stringent obligations like DPIAs for high-risk processing, lawful bases for data handling, data subject rights (including the \"right to be forgotten\"), 72-hour breach notification, and substantial fines. However, traditional compliance mechanisms falter against AI's opaqueness, dynamic processing, and irreversible data embedding in models. The EU AI Act (Regulation (EU) 2024/1689, effective August 2024) introduces a risk-based framework which is the world's first comprehensive AI legislation. Prohibitions on unacceptable risk AI applied from February 2025, GPAI obligations from August 2025, and high-risk system requirements were originally phased toward August 2026 (Annex III) and August 2027 (Annex I). As of January 2026, the European Commission's Digital Omnibus proposal (November 2025) introduces conditional extensions. These delays, contingent on harmonized standards and conformity tools, aim to reduce burdens while on-going series negotiations and extended feedback (to late January 2026) shape final adoption. This study adopts an exclusively quantitative approach, drawing on secondary empirical data from 2024–2026 sources, including the IBM Cost of a Data Breach Report 2025. Key metrics include global average breach cost $4.44 million (9% decrease, first decline in five years, driven by AI-powered containment), mean breach lifecycle reduced to 241 days (lowest in nine years); extensive AI/automation saves $1.9 million per breach, shadow AI (unauthorized use) involved in 20% of breaches, adding $670,000 premium ($4.63 million vs. $3.96 million average), attacker AI use in 16% of breaches (primarily AI-generated phishing 37%, deep fakes), 97% of AI-related breaches lack proper access controls, 63% of organizations have no AI governance policies. Federated learning with differential privacy benchmarks show accuracy retention of 75–96% (e.g., medical imaging datasets R² ≈0.96 at ε=15; MNIST ~75% at ε=10–100 vs. 95% non-private baseline), with stricter privacy (lower ε) imposing 10–20% utility trade-offs, mitigated by adaptive/time-varying budget allocation yielding 10–15% gains in fairness/accuracy. The paper addresses six objectives: comparing AI tool effectiveness in GDPR metrics (detection rates, false positives, violation reductions); quantifying KPI impacts (breach times, consent/rights success), evaluating risk reductions vs. utility, assessing cost-efficiency (ROI, savings); correlating adoption maturity with outcomes (r ≈0.70, 34% lower costs in mature adopters); and benchmarking techniques (e.g., federated + DP vs. traditional).Findings affirm AI's net positive quantitative contribution to compliance—reducing costs, times, and risks—when governed effectively, while underscoring urgent imperatives to close governance gaps amid evolving 2026–2027 regulatory phases. The study provides data-driven insights and recommendations for privacy professionals, cyber security leaders, policymakers, and developers navigating this converging domain. Keywords: Cyber Security, Cyber Threat, Phishing, GDPR","url":"https://doi.org/10.5281/zenodo.20629889","authors":["Dr. Neeraj Emmanuel Eusebius"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20629889","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20629890","name":"The Evolving Role of Artificial Intelligence in Enhancing Data Privacy Compliance_A Case Study on GDPR and Emerging AI Regulations in Cyber Security","source":"datacite","abstract":"This research paper quantitatively examines the evolving role of artificial intelligence (AI) in enhancing data privacy compliance, with a primary focus on the General Data Protection Regulation (GDPR) and its interplay with the EU AI Act within the cyber security domain. The GDPR (effective 2018) mandates core principles including lawfulness, fairness, transparency, purpose limitation, data minimization, accuracy, storage limitation, integrity/confidentiality, and accountability. It imposes stringent obligations like DPIAs for high-risk processing, lawful bases for data handling, data subject rights (including the \"right to be forgotten\"), 72-hour breach notification, and substantial fines. However, traditional compliance mechanisms falter against AI's opaqueness, dynamic processing, and irreversible data embedding in models. The EU AI Act (Regulation (EU) 2024/1689, effective August 2024) introduces a risk-based framework which is the world's first comprehensive AI legislation. Prohibitions on unacceptable risk AI applied from February 2025, GPAI obligations from August 2025, and high-risk system requirements were originally phased toward August 2026 (Annex III) and August 2027 (Annex I). As of January 2026, the European Commission's Digital Omnibus proposal (November 2025) introduces conditional extensions. These delays, contingent on harmonized standards and conformity tools, aim to reduce burdens while on-going series negotiations and extended feedback (to late January 2026) shape final adoption. This study adopts an exclusively quantitative approach, drawing on secondary empirical data from 2024–2026 sources, including the IBM Cost of a Data Breach Report 2025. Key metrics include global average breach cost $4.44 million (9% decrease, first decline in five years, driven by AI-powered containment), mean breach lifecycle reduced to 241 days (lowest in nine years); extensive AI/automation saves $1.9 million per breach, shadow AI (unauthorized use) involved in 20% of breaches, adding $670,000 premium ($4.63 million vs. $3.96 million average), attacker AI use in 16% of breaches (primarily AI-generated phishing 37%, deep fakes), 97% of AI-related breaches lack proper access controls, 63% of organizations have no AI governance policies. Federated learning with differential privacy benchmarks show accuracy retention of 75–96% (e.g., medical imaging datasets R² ≈0.96 at ε=15; MNIST ~75% at ε=10–100 vs. 95% non-private baseline), with stricter privacy (lower ε) imposing 10–20% utility trade-offs, mitigated by adaptive/time-varying budget allocation yielding 10–15% gains in fairness/accuracy. The paper addresses six objectives: comparing AI tool effectiveness in GDPR metrics (detection rates, false positives, violation reductions); quantifying KPI impacts (breach times, consent/rights success), evaluating risk reductions vs. utility, assessing cost-efficiency (ROI, savings); correlating adoption maturity with outcomes (r ≈0.70, 34% lower costs in mature adopters); and benchmarking techniques (e.g., federated + DP vs. traditional).Findings affirm AI's net positive quantitative contribution to compliance—reducing costs, times, and risks—when governed effectively, while underscoring urgent imperatives to close governance gaps amid evolving 2026–2027 regulatory phases. The study provides data-driven insights and recommendations for privacy professionals, cyber security leaders, policymakers, and developers navigating this converging domain. Keywords: Cyber Security, Cyber Threat, Phishing, GDPR","url":"https://doi.org/10.5281/zenodo.20629890","authors":["Dr. Neeraj Emmanuel Eusebius"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20629890","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.21904089","name":"Opening access to learning records datasets for better educational sciences","source":"datacite","abstract":"Research in learning analytics (LA), educational data mining (EDM) and artificial intelligence in education (AIEd) is fundamentally data-dependent, yet the behavioural learning trace data that powers it remains structurally inaccessible to the open research community. A recent systematic survey of 1,125 papers from the field’s flagship venues (LAK, EDM, AIED, 2020–2024) found that only 19% of publications are associated with an open dataset. Here we synthesize the state of available learning records datasets, characterize the systemic barriers to access—regulatory, commercial, institutional and technical—document the resulting reproducibility crisis, and evaluate emerging mitigations: synthetic data, federated learning, controlled-access research infrastructures and European sovereign data spaces. We argue that facilitating open, governed access to learning records datasets is a precondition for trustworthy, generalizable and regulation-compliant educational AI, and we propose a concrete agenda for operators, researchers and policymakers.","url":"https://doi.org/10.5281/zenodo.21904089","authors":["SONNATI, Matthieu"],"tags":["Educational Technology/education","Education","Data Mining","Artificial intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21904089","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.21904088","name":"Opening access to learning records datasets for better educational sciences","source":"datacite","abstract":"Research in learning analytics (LA), educational data mining (EDM) and artificial intelligence in education (AIEd) is fundamentally data-dependent, yet the behavioural learning trace data that powers it remains structurally inaccessible to the open research community. A recent systematic survey of 1,125 papers from the field’s flagship venues (LAK, EDM, AIED, 2020–2024) found that only 19% of publications are associated with an open dataset. Here we synthesize the state of available learning records datasets, characterize the systemic barriers to access—regulatory, commercial, institutional and technical—document the resulting reproducibility crisis, and evaluate emerging mitigations: synthetic data, federated learning, controlled-access research infrastructures and European sovereign data spaces. We argue that facilitating open, governed access to learning records datasets is a precondition for trustworthy, generalizable and regulation-compliant educational AI, and we propose a concrete agenda for operators, researchers and policymakers.","url":"https://doi.org/10.5281/zenodo.21904088","authors":["SONNATI, Matthieu"],"tags":["Educational Technology/education","Education","Data Mining","Artificial intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21904088","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.3217/cw4k8-2ej91","name":"LTI Integration Metacampus–Moodle: Improving Application Processes for VECP Students (Report 06/2026)","source":"datacite","abstract":"This sixth Unite! success story report documents the implementation of a Learning Tools Interoperability (LTI) integration between TU Darmstadt's Moodle learning management system and the Unite! federated platform Metacampus, aimed at streamlining application processes for Virtual Exchange Credit Programme (VECP) students. The VECP enables students from all Unite! partner universities to participate in online courses at other member institutions while earning ECTS credits. However, registration presented challenges: students had to complete placement tests through TU Darmstadt's Moodle before formally enrolling as VECP students—a requirement that necessitated a TU Darmstadt account and created administrative bottlenecks. The LTI integration addresses these challenges by allowing students from other Unite! universities to access selected TU Darmstadt Moodle course content directly via Metacampus using their home institution credentials, without requiring a separate TU Darmstadt account. This solution reduces administrative steps, enables earlier placement testing, and aligns registration timelines more closely with local deadlines. Implementation required careful coordination between instructors, administrative staff, IT support, and the International Office. The series “Unite! Digital Teaching and Learning Success Story Report” was initiated by Cm.2 Digital Campus, led by TU Graz (Martin Ebner) as part of the idea to collect, spread and enhance the possibilities, usages and lessons learned of teaching and learning offers using the federated learning management system Metacampus in 11/2024 till the end of the current Erasmus+ funding period 10/2026. All reports are available under open license and originally published at the TU Graz repository. Copyright holders are the authors.","url":"https://doi.org/10.3217/cw4k8-2ej91","authors":["Breit, Robin","Hoppe, Christian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3217/cw4k8-2ej91","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.3217/29xhf-z6n13","name":"LTI Integration Metacampus–Moodle: Improving Application Processes for VECP Students (Report 06/2026)","source":"datacite","abstract":"This sixth Unite! success story report documents the implementation of a Learning Tools Interoperability (LTI) integration between TU Darmstadt's Moodle learning management system and the Unite! federated platform Metacampus, aimed at streamlining application processes for Virtual Exchange Credit Programme (VECP) students. The VECP enables students from all Unite! partner universities to participate in online courses at other member institutions while earning ECTS credits. However, registration presented challenges: students had to complete placement tests through TU Darmstadt's Moodle before formally enrolling as VECP students—a requirement that necessitated a TU Darmstadt account and created administrative bottlenecks. The LTI integration addresses these challenges by allowing students from other Unite! universities to access selected TU Darmstadt Moodle course content directly via Metacampus using their home institution credentials, without requiring a separate TU Darmstadt account. This solution reduces administrative steps, enables earlier placement testing, and aligns registration timelines more closely with local deadlines. Implementation required careful coordination between instructors, administrative staff, IT support, and the International Office. The series “Unite! Digital Teaching and Learning Success Story Report” was initiated by Cm.2 Digital Campus, led by TU Graz (Martin Ebner) as part of the idea to collect, spread and enhance the possibilities, usages and lessons learned of teaching and learning offers using the federated learning management system Metacampus in 11/2024 till the end of the current Erasmus+ funding period 10/2026. All reports are available under open license and originally published at the TU Graz repository. Copyright holders are the authors.","url":"https://doi.org/10.3217/29xhf-z6n13","authors":["Breit, Robin","Hoppe, Christian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3217/29xhf-z6n13","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21854968","name":"The Role of Artificial Intelligence in Detecting Social Engineering and Phishing Attacks: Opportunities, Limitations, and Ethical Considerations","source":"datacite","abstract":"Phishing and social engineering remain leading causes of cybersecurity breaches, with the FBI reporting 859,532 complaints and $16.0 billion in losses in 2024. This literature review examines how artificial intelligence (AI) – including machine learning (ML), natural language processing (NLP), deep learning (DL), and large language models (LLMs) – can enhance detection of phishing/social engineering. We compare AI-based techniques to traditional rule-based and heuristic methods, analyzing detection performance, adaptability, false positives/negatives, computational demands, and resilience to adversarial manipulation. Key findings show that AI classifiers (e.g. CNNs, RNNs, BERT) often achieve very high accuracy (e.g. 98–99% on standard email corpora) and can adapt to evolving attacks, but they are vulnerable to adversarial rephrasing and suffer from explainability and data bias issues. Ethical concerns include user privacy (inspecting personal communications), transparency (black-box models), and model bias (especially in cross-cultural contexts). We illustrate these concepts with ScamLens AI app combining NLP analysis of text and URLs with vision-based screenshot scanning, producing risk indicators and explanations. Finally, we identify research gaps – such as limited real-world evaluation, outdated datasets, and the need for phishing-specific LLMs – and outline future directions (adversarial robustness, federated training, multimodal analysis, and human-AI collaboration).","url":"https://doi.org/10.5281/zenodo.21854968","authors":["Abozaid, Zain"],"tags":["Artificial Intelligence","cybersecurity","ScamLens AI","Social Engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21854968","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.21854967","name":"The Role of Artificial Intelligence in Detecting Social Engineering and Phishing Attacks: Opportunities, Limitations, and Ethical Considerations","source":"datacite","abstract":"Phishing and social engineering remain leading causes of cybersecurity breaches, with the FBI reporting 859,532 complaints and $16.0 billion in losses in 2024. This literature review examines how artificial intelligence (AI) – including machine learning (ML), natural language processing (NLP), deep learning (DL), and large language models (LLMs) – can enhance detection of phishing/social engineering. We compare AI-based techniques to traditional rule-based and heuristic methods, analyzing detection performance, adaptability, false positives/negatives, computational demands, and resilience to adversarial manipulation. Key findings show that AI classifiers (e.g. CNNs, RNNs, BERT) often achieve very high accuracy (e.g. 98–99% on standard email corpora) and can adapt to evolving attacks, but they are vulnerable to adversarial rephrasing and suffer from explainability and data bias issues. Ethical concerns include user privacy (inspecting personal communications), transparency (black-box models), and model bias (especially in cross-cultural contexts). We illustrate these concepts with ScamLens AI app combining NLP analysis of text and URLs with vision-based screenshot scanning, producing risk indicators and explanations. Finally, we identify research gaps – such as limited real-world evaluation, outdated datasets, and the need for phishing-specific LLMs – and outline future directions (adversarial robustness, federated training, multimodal analysis, and human-AI collaboration).","url":"https://doi.org/10.5281/zenodo.21854967","authors":["Abozaid, Zain"],"tags":["Artificial Intelligence","cybersecurity","ScamLens AI","Social Engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21854967","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.21716443","name":"PREreview of \"Data Security in AI Healthcare Applications: Challenges and Innovative Methods\"","source":"datacite","abstract":"This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/21716444. This review paper surveys defensive techniques for healthcare AI systems, focusing on blockchain, zero-knowledge proofs (ZKP), and honeypots as complementary approaches to address adversarial attacks, data poisoning, and data breaches. The authors synthesize 13 prior works into a comparative table and propose a conceptual hybrid security framework combining all three techniques, illustrated through a weighted mathematical scoring model. A qualitative use-case scenario is provided to demonstrate the proposed hybrid approach. Major issues 1. Review methodology lacks rigor. The paper is positioned as a literature review but does not follow any established review methodology (PRISMA, SALSA, or comparable). Search databases are named but there is no documented inclusion/exclusion criteria, no time window justification, no quality appraisal of included works, and no data extraction protocol. As a result, the reader cannot assess whether the 13 cited works are representative of the field or how they were selected. A systematic or scoping review framework should be adopted, or the paper should be explicitly reframed as a narrative review with corresponding limitations acknowledged. 2. The \"hybrid security score\" model (Section 3.1–3.4) is not empirically grounded. The proposed equation S = αB + βZ + γH is a weighted average with arbitrarily chosen weights (α=0.4, β=0.4, γ=0.2) and arbitrarily chosen component values (B=0.85, Z=0.75, H=0.65). No justification is provided for how these values were obtained, what units they represent, or what a resulting score of 0.77 signifies. Without empirical calibration against real threat data or validation against a benchmark, this model does not \"quantify\" security in any meaningful sense it presents subjective weights as if they were measurements. Either an empirical basis for the weights should be provided (e.g., derived from threat frequency data, expert elicitation with documented methodology, or benchmarking against known attack outcomes), or the model should be explicitly reframed as an illustrative conceptual scaffold rather than a quantitative measure. 3. The \"proposed solution\" is not sufficiently detailed for evaluation. Figure 2 depicts blockchain, ZKP, and honeypots operating alongside a healthcare API, but the paper does not specify architectural details (which blockchain, permissioned vs. public, ZKP scheme used, honeypot deployment topology), integration points with existing healthcare infrastructure (HL7/FHIR, HIPAA-compliant messaging, EHR systems), performance considerations, or any implementation or evaluation. A reader cannot assess feasibility, scalability, or clinical workflow impact. 4. Regulatory and compliance context is absent. For a paper on healthcare data security, the omission of HIPAA (US), GDPR (EU), and analogous frameworks is a significant gap. There is no discussion of how blockchain immutability interacts with GDPR's right to erasure, how ZKP verification maps to HIPAA audit-logging requirements, or how honeypots relate to breach-notification obligations. These regulatory dimensions are central to any real-world deployment. 5. Key technical literature is missing. For a review on secure AI in healthcare, notable omissions include: federated learning (only mentioned in passing), differential privacy in medical data (Abadi et al. and follow-ups), homomorphic encryption implementations (mentioned in Table 1 but not discussed), secure multi-party computation, trusted execution environments (TEEs) such as Intel SGX in healthcare deployments, and standards work by NIST (SP 800-66) and HITRUST CSF. A review of only 13 works is thin for the breadth of the topic claimed. 6. Conflation of AI security with data security. The paper mixes adversarial ML attacks (data poisoning, model evasion — properties of AI systems) with conv","url":"https://doi.org/10.5281/zenodo.21716443","authors":["Deven Yadav"],"tags":["Requested PREreview"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21716443","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.21716444","name":"PREreview of \"Data Security in AI Healthcare Applications: Challenges and Innovative Methods\"","source":"datacite","abstract":"This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/21716444. This review paper surveys defensive techniques for healthcare AI systems, focusing on blockchain, zero-knowledge proofs (ZKP), and honeypots as complementary approaches to address adversarial attacks, data poisoning, and data breaches. The authors synthesize 13 prior works into a comparative table and propose a conceptual hybrid security framework combining all three techniques, illustrated through a weighted mathematical scoring model. A qualitative use-case scenario is provided to demonstrate the proposed hybrid approach. Major issues 1. Review methodology lacks rigor. The paper is positioned as a literature review but does not follow any established review methodology (PRISMA, SALSA, or comparable). Search databases are named but there is no documented inclusion/exclusion criteria, no time window justification, no quality appraisal of included works, and no data extraction protocol. As a result, the reader cannot assess whether the 13 cited works are representative of the field or how they were selected. A systematic or scoping review framework should be adopted, or the paper should be explicitly reframed as a narrative review with corresponding limitations acknowledged. 2. The \"hybrid security score\" model (Section 3.1–3.4) is not empirically grounded. The proposed equation S = αB + βZ + γH is a weighted average with arbitrarily chosen weights (α=0.4, β=0.4, γ=0.2) and arbitrarily chosen component values (B=0.85, Z=0.75, H=0.65). No justification is provided for how these values were obtained, what units they represent, or what a resulting score of 0.77 signifies. Without empirical calibration against real threat data or validation against a benchmark, this model does not \"quantify\" security in any meaningful sense it presents subjective weights as if they were measurements. Either an empirical basis for the weights should be provided (e.g., derived from threat frequency data, expert elicitation with documented methodology, or benchmarking against known attack outcomes), or the model should be explicitly reframed as an illustrative conceptual scaffold rather than a quantitative measure. 3. The \"proposed solution\" is not sufficiently detailed for evaluation. Figure 2 depicts blockchain, ZKP, and honeypots operating alongside a healthcare API, but the paper does not specify architectural details (which blockchain, permissioned vs. public, ZKP scheme used, honeypot deployment topology), integration points with existing healthcare infrastructure (HL7/FHIR, HIPAA-compliant messaging, EHR systems), performance considerations, or any implementation or evaluation. A reader cannot assess feasibility, scalability, or clinical workflow impact. 4. Regulatory and compliance context is absent. For a paper on healthcare data security, the omission of HIPAA (US), GDPR (EU), and analogous frameworks is a significant gap. There is no discussion of how blockchain immutability interacts with GDPR's right to erasure, how ZKP verification maps to HIPAA audit-logging requirements, or how honeypots relate to breach-notification obligations. These regulatory dimensions are central to any real-world deployment. 5. Key technical literature is missing. For a review on secure AI in healthcare, notable omissions include: federated learning (only mentioned in passing), differential privacy in medical data (Abadi et al. and follow-ups), homomorphic encryption implementations (mentioned in Table 1 but not discussed), secure multi-party computation, trusted execution environments (TEEs) such as Intel SGX in healthcare deployments, and standards work by NIST (SP 800-66) and HITRUST CSF. A review of only 13 works is thin for the breadth of the topic claimed. 6. Conflation of AI security with data security. The paper mixes adversarial ML attacks (data poisoning, model evasion — properties of AI systems) with conv","url":"https://doi.org/10.5281/zenodo.21716444","authors":["Deven Yadav"],"tags":["Requested PREreview"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21716444","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.24406/publica-4105","name":"Operational Planning Decision Support using Multi-Dimensional Data Farming","source":"datacite","abstract":"Multi-Dimensional Data Farming (MDDF) uses Machine Learning (ML) to automate data farming to allow improved and faster decisions in highly complex multi-scale, multi-domain, and multi-level hybrid war campaigns. This has significant utility when used in support of Operational planning, allowing multiple Courses of Action (CoA) to be rapidly developed and evaluated prior to the execution of any operation. Using MDDF enables decision-makers to explore the problem space and identify multiple optimal solutions significantly faster than current techniques. MSG-186 has applied MDDF in a sand-box environment to an illustrative combined strategic campaign and tactical hybrid warfare operation resource allocation problem considering the balance between local and global optimal solutions. We have tested the technical feasibility of implementing MDDF within the Federated Mission Network operational environment at Coalition Warrior Interoperability Exercise (CWIX). Through MDDF, we aim to show it is possible to combine ML techniques exploring operations at multiple scales (Multi-Domain Operations and targeted-fidelity modelling) and optimize the strategic/operational level goal, by selecting the correct resource allocation scheme at the tactical level. This paper describes an ML-based assistant able to conduct MDDF experiments and optimization tasks on an automated basis, which was examined in detail during CWIX in 2024.","url":"https://doi.org/10.24406/publica-4105","authors":["Akesson, Bernt","Amyot-Bourgeois, Maude","Das, Sreerupa","Ernis, Gunar","Gill, Andrew","Lappi, Esa","Nguyen, Bao","Rolfs, Chris","Seichter, Stephan","Serre, Lynne","Slyusar, Vadym","Vaghi, Alessio","Volbach, Peter","Zimmermann, Alexander",":unav"],"tags":["KI","Multi Dimensional Data Farming","Decision Support"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.24406/publica-4105","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.48550/arxiv.2607.08014","name":"FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection","source":"datacite","abstract":"Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models without sharing local data with other parties, hence preserving data privacy. Nevertheless, when implementing FL in Industrial visual inspection (IVI), the constraints posed by limited data availability and the intricate nature of the inspection tasks significantly impact the performance of the resulting model. This paper introduces FedTR, a novel FL framework incorporating transfer learning designed for Autonomous IVI, focusing on the challenging task of identifying label defects through end-to-end text recognition. Transfer learning is a method that leverages the knowledge of a pre-trained model to adapt to a different dataset. FedTR initially trains the model using a publicly available dataset, after which performs the essential federated learning process with model fine-tuning on the distributed and limited private data. Extensive experiment results demonstrate the effectiveness and feasibility of FedTR on private ink cartridge datasets for label defect identification. FedTR achieves an end-to-end text recognition word-level accuracy of 95.5% and 94.2% on homogeneous and heterogeneous data respectively. Additionally, it attains performance levels that are on par with those achieved through centralized training.","url":"https://doi.org/10.48550/arxiv.2607.08014","authors":["Sathiamoorthy, Vikash","Huai, Shuo","Kong, Hao","Liu, Di","Loy, Wendy Yong Yi","Makaya, Christian","Ho, Daren","Subramaniam, Ravi","Lin, Qian","Liu, Weichen"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.08014","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.18898416","name":"IOT, Smart Devices and Bias in AI algorithms and its impact on cyber security","source":"datacite","abstract":"As of January 2026, the global count of connected Internet of Things (IoT) devices has surpassed 21.1 billion, with projections indicating growth to over 40 billion by 2030. This surge in smart devices—ranging from consumer wearables to industrial sensors—relies heavily on artificial intelligence (AI) for cybersecurity enhancements, including real-time intrusion detection, anomaly identification, and automated threat mitigation. However, algorithmic bias within these AI systems poses significant risks, stemming from imbalanced training data, heterogeneous IoT environments, and flawed model architectures. Such biases lead to degraded detection accuracy, increased false positives/negatives, adversarial exploitation, and ethical concerns like discriminatory outcomes in security flagging. This detailed research paper synthesizes 2024–2025 studies to explore bias sources in AI-IoT pipelines, quantify impacts on cybersecurity resilience, present empirical examples from datasets and real-world scenarios, and propose multi-faceted mitigation frameworks. Key findings highlight that while AI amplifies threat detection in resource-constrained smart devices, unchecked bias can enable evasion attacks and cascade failures in critical infrastructure. Emphasis is placed on emerging solutions like explainable AI (XAI), federated learning, and adversarial training to foster fair, robust IoT security ecosystems.","url":"https://doi.org/10.5281/zenodo.18898416","authors":["Mane Asmita Shankar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18898416","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.18898417","name":"IOT, Smart Devices and Bias in AI algorithms and its impact on cyber security","source":"datacite","abstract":"As of January 2026, the global count of connected Internet of Things (IoT) devices has surpassed 21.1 billion, with projections indicating growth to over 40 billion by 2030. This surge in smart devices—ranging from consumer wearables to industrial sensors—relies heavily on artificial intelligence (AI) for cybersecurity enhancements, including real-time intrusion detection, anomaly identification, and automated threat mitigation. However, algorithmic bias within these AI systems poses significant risks, stemming from imbalanced training data, heterogeneous IoT environments, and flawed model architectures. Such biases lead to degraded detection accuracy, increased false positives/negatives, adversarial exploitation, and ethical concerns like discriminatory outcomes in security flagging. This detailed research paper synthesizes 2024–2025 studies to explore bias sources in AI-IoT pipelines, quantify impacts on cybersecurity resilience, present empirical examples from datasets and real-world scenarios, and propose multi-faceted mitigation frameworks. Key findings highlight that while AI amplifies threat detection in resource-constrained smart devices, unchecked bias can enable evasion attacks and cascade failures in critical infrastructure. Emphasis is placed on emerging solutions like explainable AI (XAI), federated learning, and adversarial training to foster fair, robust IoT security ecosystems.","url":"https://doi.org/10.5281/zenodo.18898417","authors":["Mane Asmita Shankar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18898417","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21085434","name":"moad-ezzaibouh/fl-iomt-ids: FL-IoMT-IDS","source":"datacite","abstract":"v1.0.0 — Initial archival release Reproducibility release accompanying the paper \"Federated Learning for IoMT Intrusion Detection: Aggregation Robustness under Non-IID Client Fragmentation\" (M. Ezzaibouh and A. Idri). This snapshot is archived to establish a permanent, citable record of the methodology and analysis code. What this repository provides A from-scratch federated learning pipeline with explicit and auditable aggregation implementations (no Flower/Ray runtime dependency), together with the post-hoc analysis scripts required to reproduce the experimental results reported in the accompanying manuscript. The repository supports an experimental evaluation covering: 5 aggregation strategies — FedAvg, FedProx, FedMedian, FedTrimmedAvg, FedAdam 3 architectures — MLP-Small, MLP-Large, CNN1D 3 federation sizes — K ∈ {5, 15, 30} 2 datasets — WUSTL-EHMS-2020 (16,318 records, 12.5% attack) and the full CICIoMT2024 release (8,775,013 records, 97.4% attack), plus ECU-IoHT (111,207 records) as an out-of-scope generalization probe 5 seeds — [42, 1337, 2024, 7, 123] → 450 federated-learning runs Statistical validation via Friedman + Holm-corrected Wilcoxon tests Contents run_experiment.py — main synchronous FL loop with pluggable aggregation centralized_baseline_aligned.py — architecture-matched centralized baselines Post-hoc analysis: aggregate_multiseed.py, robustness_retention.py, rounds_to_convergence.py, threshold_sweep_from_checkpoints.py, statistical_tests.py, make_all_figures.py models/, data/, evaluation/, preprocessing/, analysis/ submodules scripts/ — SLURM job scripts for HPC reproducibility REPRODUCIBILITY.md — full reproduction checklist Raw/processed datasets, trained model checkpoints, intermediate training logs, and HPC execution logs are intentionally excluded because of their size and licensing restrictions. Instructions for obtaining the datasets are provided in data/README.md . Reproducibility notes Fixed 85/15 train/test split at seed 42, shared across all runs; per-run seeds control only the Dirichlet partition, initialization, dropout, and batch ordering. For CICIoMT2024, per-round evaluation uses a fixed 100k stratified subsample (indices fixed per seed); the full release is used for training. Threshold-based post-hoc analyses are included. Status This repository accompanies the associated research manuscript. Citation information will be finalized upon publication. License License: MIT (see the LICENSE file in the repository).","url":"https://doi.org/10.5281/zenodo.21085434","authors":["Ezzaibouh,Moad","Idri, Ali"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21085434","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.21085433","name":"moad-ezzaibouh/fl-iomt-ids: FL-IoMT-IDS","source":"datacite","abstract":"v1.0.0 — Initial archival release Reproducibility release accompanying the paper \"Federated Learning for IoMT Intrusion Detection: Aggregation Robustness under Non-IID Client Fragmentation\" (M. Ezzaibouh and A. Idri). This snapshot is archived to establish a permanent, citable record of the methodology and analysis code. What this repository provides A from-scratch federated learning pipeline with explicit and auditable aggregation implementations (no Flower/Ray runtime dependency), together with the post-hoc analysis scripts required to reproduce the experimental results reported in the accompanying manuscript. The repository supports an experimental evaluation covering: 5 aggregation strategies — FedAvg, FedProx, FedMedian, FedTrimmedAvg, FedAdam 3 architectures — MLP-Small, MLP-Large, CNN1D 3 federation sizes — K ∈ {5, 15, 30} 2 datasets — WUSTL-EHMS-2020 (16,318 records, 12.5% attack) and the full CICIoMT2024 release (8,775,013 records, 97.4% attack), plus ECU-IoHT (111,207 records) as an out-of-scope generalization probe 5 seeds — [42, 1337, 2024, 7, 123] → 450 federated-learning runs Statistical validation via Friedman + Holm-corrected Wilcoxon tests Contents run_experiment.py — main synchronous FL loop with pluggable aggregation centralized_baseline_aligned.py — architecture-matched centralized baselines Post-hoc analysis: aggregate_multiseed.py, robustness_retention.py, rounds_to_convergence.py, threshold_sweep_from_checkpoints.py, statistical_tests.py, make_all_figures.py models/, data/, evaluation/, preprocessing/, analysis/ submodules scripts/ — SLURM job scripts for HPC reproducibility REPRODUCIBILITY.md — full reproduction checklist Raw/processed datasets, trained model checkpoints, intermediate training logs, and HPC execution logs are intentionally excluded because of their size and licensing restrictions. Instructions for obtaining the datasets are provided in data/README.md . Reproducibility notes Fixed 85/15 train/test split at seed 42, shared across all runs; per-run seeds control only the Dirichlet partition, initialization, dropout, and batch ordering. For CICIoMT2024, per-round evaluation uses a fixed 100k stratified subsample (indices fixed per seed); the full release is used for training. Threshold-based post-hoc analyses are included. Status This repository accompanies the associated research manuscript. Citation information will be finalized upon publication. License License: MIT (see the LICENSE file in the repository).","url":"https://doi.org/10.5281/zenodo.21085433","authors":["Ezzaibouh,Moad","Idri, Ali"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21085433","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.48550/arxiv.2606.23500","name":"Development and Design of FLKit: A Structured Onboarding Toolkit for Federated Learning in Health and Life Sciences","source":"datacite","abstract":"Federated learning lets institutions train shared models without moving their data, which makes it a natural fit for health and life sciences research under strict privacy regulation. The methods are maturing fast, but the practical barrier now comes earlier: a team starting a federated project meets a scattered mix of frameworks, governance obligations, and unfamiliar roles, with no structured place to begin that fits its own background. FLKit closes that gap. It is an open, community-maintained onboarding toolkit that takes a multidisciplinary team through the full federated learning lifecycle and gives every contributor, clinical, legal, governance, or technical, a role-aware entry point instead of assuming fluency across all four. We modeled it on the ELIXIR Research Data Management Kit and built it with a multidisciplinary core team, a wider consortium supplying milestone reviews and roadmap direction, and external practitioners interviewed to keep the content grounded in real practice. FLKit sits on four lifecycle stages, Governance, Infrastructure, Wrangling, and Analysis, and connects them through 11 role-specific entry points, a cross-disciplinary glossary, a reusable FAIR-aligned FL Story template for planning and documenting projects, and a curated directory of tools, frameworks, and communities. Since the December 2024 demo it has grown to 39 pages across eight sections, with seven FL Stories documenting completed and ongoing projects in multiple sclerosis disability prediction, inflammatory bowel disease, genomics, and brain-computer interfaces. It is openly available at https://uhasselt-biomedicaldatasciences.github.io/federated-learning-toolkit/ and welcomes contributions from across the life sciences.","url":"https://doi.org/10.48550/arxiv.2606.23500","authors":["Pirmani, Ashkan","Vermeulen, Ilse","Vinterhalter, Goran","Geys, Lotte","Faes, Axel","Ali, Muhammad Quamber","Sattanathan, Nishkala","Vandeweyer, Geert","Moreau, Yves","Peeters, Liesbet M."],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.23500","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20798342","name":"SYNTHEMA Newsletter 6","source":"datacite","abstract":"The October 2024 SYNTHEMA newsletter highlights the project's significant strides in advancing synthetic data generation and federated learning technologies within the European healthcare sector. At the European Big Data Value Forum (EBDVF) 2024 in Budapest, SYNTHEMA showcased its privacy-preserving solutions for rare diseases through joint sessions, panel discussions, and a collaborative booth under the HealthData4EU cluster. During the event, the project demonstrated how its federated learning platform securely accesses dispersed clinical data to create virtual patients and reduce data scarcity biases. Additionally, SYNTHEMA contributed to a specialized workshop focused on leveraging synthetic data generation to enhance medical education and training. Finally, the project reinforced its role in precision medicine by presenting its latest breakthroughs for rare hematological diseases alongside its sister project, Genomed4ALL, at the ASCAT 2024 conference in London.","url":"https://doi.org/10.5281/zenodo.20798342","authors":["AUSTRALO INTERINNOV MARKETING LAB SL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20798342","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20798343","name":"SYNTHEMA Newsletter 6","source":"datacite","abstract":"The October 2024 SYNTHEMA newsletter highlights the project's significant strides in advancing synthetic data generation and federated learning technologies within the European healthcare sector. At the European Big Data Value Forum (EBDVF) 2024 in Budapest, SYNTHEMA showcased its privacy-preserving solutions for rare diseases through joint sessions, panel discussions, and a collaborative booth under the HealthData4EU cluster. During the event, the project demonstrated how its federated learning platform securely accesses dispersed clinical data to create virtual patients and reduce data scarcity biases. Additionally, SYNTHEMA contributed to a specialized workshop focused on leveraging synthetic data generation to enhance medical education and training. Finally, the project reinforced its role in precision medicine by presenting its latest breakthroughs for rare hematological diseases alongside its sister project, Genomed4ALL, at the ASCAT 2024 conference in London.","url":"https://doi.org/10.5281/zenodo.20798343","authors":["AUSTRALO INTERINNOV MARKETING LAB SL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20798343","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.17632/g3tfmstg3v.2","name":"Dataset for a Systematic Mapping Study of Deep Learning-Based DDoS Detection in SDN and 5G/B5G Networks (2018–2024)","source":"datacite","abstract":"This dataset contains the primary studies used in a systematic mapping study (SMS) on deep learning-based Distributed Denial of Service (DDoS) detection in Software-Defined Networking (SDN), 5G, and beyond-5G (B5G) network environments. The dataset includes 205 peer-reviewed publications published between 2018 and 2024, retrieved from Scopus and IEEE Xplore using a structured search query. Each entry corresponds to a single study and includes bibliographic information (title, authors, year, publication venue etc.), as well as manually curated and derived attributes. In addition to bibliographic metadata, the dataset provides structured annotations extracted from the title, abstract, and manual tags, including: learning_type (Supervised, Unsupervised, Reinforcement, Federated, Not specified; multi-label where applicable) dl_architecture (CNN, LSTM, BiLSTM, GRU, RNN, DNN/MLP, Autoencoder, GAN, Transformer, DBN, Echo State Network, Hybrid, Not specified; multi-label where applicable) network_context (SDN, 5G/6G, IoT/Edge, NFV, Not specified; multi-label where applicable) dataset_used (CICDDoS2019, InSDN, CIC-IDS-2017, CSE-CIC-IDS2018, NSL-KDD, UNSW-NB15, Edge-IIoTset, SDN-SlowRate-DDoS, ToN-IoT, ISCX, CICIoT2023, 5G-NIDD, Other/Custom, Not specified; multi-label where applicable) evaluation_setting (Offline, Testbed-Controller, Real-network, Not specified; multi-label where applicable) The annotations were generated through a semi-automated process combining script-based extraction and manual validation based on titles, abstracts, and manually assigned tags, without full-text analysis. In cases where specific information was not explicitly stated, the corresponding fields were marked as \"Not specified\". This dataset is intended to support reproducibility and transparency of the associated systematic mapping study, as well as to facilitate further research on deep learning-based DDoS detection in programmable networks.","url":"https://doi.org/10.17632/g3tfmstg3v.2","authors":["Dikaros, Nikolaos","Tzanakakis, Ioannis","Siavvas, Miltiadis","Karagiannidis, George","Ioannou, Konstantinos"],"tags":["Computer Science","Cybersecurity","Deep Learning","Wireless Telecommunication Systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17632/g3tfmstg3v.2","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.17632/g3tfmstg3v","name":"Dataset for a Systematic Mapping Study of Deep Learning-Based DDoS Detection in SDN and 5G/B5G Networks (2018–2024)","source":"datacite","abstract":"This dataset contains the primary studies used in a systematic mapping study (SMS) on deep learning-based Distributed Denial of Service (DDoS) detection in Software-Defined Networking (SDN), 5G, and beyond-5G (B5G) network environments. The dataset includes 205 peer-reviewed publications published between 2018 and 2024, retrieved from Scopus and IEEE Xplore using a structured search query. Each entry corresponds to a single study and includes bibliographic information (title, authors, year, publication venue etc.), as well as manually curated and derived attributes. In addition to bibliographic metadata, the dataset provides structured annotations extracted from the title, abstract, and manual tags, including: learning_type (Supervised, Unsupervised, Reinforcement, Federated, Not specified; multi-label where applicable) dl_architecture (CNN, LSTM, BiLSTM, GRU, RNN, DNN/MLP, Autoencoder, GAN, Transformer, DBN, Echo State Network, Hybrid, Not specified; multi-label where applicable) network_context (SDN, 5G/6G, IoT/Edge, NFV, Not specified; multi-label where applicable) dataset_used (CICDDoS2019, InSDN, CIC-IDS-2017, CSE-CIC-IDS2018, NSL-KDD, UNSW-NB15, Edge-IIoTset, SDN-SlowRate-DDoS, ToN-IoT, ISCX, CICIoT2023, 5G-NIDD, Other/Custom, Not specified; multi-label where applicable) evaluation_setting (Offline, Testbed-Controller, Real-network, Not specified; multi-label where applicable) The annotations were generated through a semi-automated process combining script-based extraction and manual validation based on titles, abstracts, and manually assigned tags, without full-text analysis. In cases where specific information was not explicitly stated, the corresponding fields were marked as \"Not specified\". This dataset is intended to support reproducibility and transparency of the associated systematic mapping study, as well as to facilitate further research on deep learning-based DDoS detection in programmable networks.","url":"https://doi.org/10.17632/g3tfmstg3v","authors":["Dikaros, Nikolaos","Tzanakakis, Ioannis","Siavvas, Miltiadis","Karagiannidis, George","Ioannou, Konstantinos"],"tags":["Computer Science","Cybersecurity","Deep Learning","Wireless Telecommunication Systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17632/g3tfmstg3v","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20649152","name":"Assured Autonomy for Siloed Operations: Causal Learning with Per-Edge Certainty on HPC","source":"datacite","abstract":"Assured autonomy has to know what it doesn't know — and direct its learning there. We present a layer that does this by construction: a causal graph in which every dependency carries a calibrated certainty, updated on-device from first principles, with a reasoning model invoked only to compose the model and to re-hypothesize where certainty stays low. Siloed, multi-owner operations are where this matters most, because there the dependencies you most need are often the ones no single party can observe. Scope. Our object is the layer: a causal graph with per-edge certainty, a deterministic on-device learning loop, and a reasoning-model escalation path. We demonstrate, on a real multi-owner operational dataset, that the layer's certainty signal correctly localizes where the system cannot reliably learn — the drifting, non-stationary, and cross-owner-unobservable dependencies — and that the compose→learn→escalate loop runs autonomously (see Demonstration). The problem: blind spots are bottlenecks for autonomy An autonomous operation has to act on relationships between subsystems — load drives heat, cooling removes it, one loop's effort changes another's. A model that emits a confident point estimate for every such relationship is dangerous in production, because the relationships you most need are frequently the least learnable: some drift as equipment and firmware evolve, some are non-stationary under changing regimes, and some are structurally unobservable from where any single party sits. The failure mode is silent — the model looks healthy and is quietly wrong on exactly the dependency that matters. Assured autonomy inverts this: the system maintains, per dependency, an explicit measure of how much it can be trusted, and it routes its own learning and its escalation to the low-certainty edges. Knowing what it doesn't know is not a diagnostic afterthought; it is the control signal. The layer: a causal graph with per-edge certainty We represent the operation as a causal graph. Each edge is a dependency (node_power → gpu_core_temp, cooling_supply → rack_inlet, liquid ΔT ↔ air ΔT) carrying a slope (the learned relationship), its residual, and a corroboration-based certainty Z in [0,1]. Z is the operational expression of \"what I know I don't know\": it rises only when an edge's error signal is both unbiased and consistent over a recent window, and it falls or collapses when the edge stops corroborating. Two derived signals drive behavior: Per-edge certainty localizes trust. The autonomy can act on high-Z edges, hedge on medium, and refuse or defer on low — something a monolithic model cannot do, because it has no place to attach \"I'm blind here.\" Persistent low-Z or biased residual is a directed-learning trigger: it marks an edge the deterministic loop cannot resolve on its own, and routes it to re-hypothesis. Because trust is attached per edge, it is also traceable: every action or abstention points to a specific dependency, its certainty, and its history — the auditability operations and safety cases require. Architecture: compose offline, learn on-device, escalate on ignorance The layer runs as three tiers with very different costs and cadences — which is what lets it operate under low-compute, intermittent, siloed conditions. Compose (reasoning model; on-prem or cloud; infrequent). A reasoning model reads domain priors and composes the causal graph and the per-edge validation pipelines — which dependencies exist, and what error signal corroborates each. Heavy, run rarely (at setup and on major change). Learn (on-device; deterministic; continuous). Each edge's certainty and weight update from first principles — a fixed arithmetic rule over the streamed error signal, no model inference in the loop. It is cheap, runs at the edge, tolerates disconnection (it syncs ~kilobyte certainty signals when a link is available, not raw data or gradients), and is fully traceable. Escalate (reasoning model; triggered by ignorance). When an edge ","url":"https://doi.org/10.5281/zenodo.20649152","authors":["Bennett, Heidi"],"tags":["causal learning","certainty calibration","HPC","CINECA"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20649152","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:36.530Z"},{"id":"doi:10.5281/zenodo.20649151","name":"Assured Autonomy for Siloed Operations: Causal Learning with Per-Edge Certainty on HPC","source":"datacite","abstract":"Assured autonomy has to know what it doesn't know — and direct its learning there. We present a layer that does this by construction: a causal graph in which every dependency carries a calibrated certainty, updated on-device from first principles, with a reasoning model invoked only to compose the model and to re-hypothesize where certainty stays low. Siloed, multi-owner operations are where this matters most, because there the dependencies you most need are often the ones no single party can observe. Scope. Our object is the layer: a causal graph with per-edge certainty, a deterministic on-device learning loop, and a reasoning-model escalation path. We demonstrate, on a real multi-owner operational dataset, that the layer's certainty signal correctly localizes where the system cannot reliably learn — the drifting, non-stationary, and cross-owner-unobservable dependencies — and that the compose→learn→escalate loop runs autonomously (see Demonstration). The problem: blind spots are bottlenecks for autonomy An autonomous operation has to act on relationships between subsystems — load drives heat, cooling removes it, one loop's effort changes another's. A model that emits a confident point estimate for every such relationship is dangerous in production, because the relationships you most need are frequently the least learnable: some drift as equipment and firmware evolve, some are non-stationary under changing regimes, and some are structurally unobservable from where any single party sits. The failure mode is silent — the model looks healthy and is quietly wrong on exactly the dependency that matters. Assured autonomy inverts this: the system maintains, per dependency, an explicit measure of how much it can be trusted, and it routes its own learning and its escalation to the low-certainty edges. Knowing what it doesn't know is not a diagnostic afterthought; it is the control signal. The layer: a causal graph with per-edge certainty We represent the operation as a causal graph. Each edge is a dependency (node_power → gpu_core_temp, cooling_supply → rack_inlet, liquid ΔT ↔ air ΔT) carrying a slope (the learned relationship), its residual, and a corroboration-based certainty Z in [0,1]. Z is the operational expression of \"what I know I don't know\": it rises only when an edge's error signal is both unbiased and consistent over a recent window, and it falls or collapses when the edge stops corroborating. Two derived signals drive behavior: Per-edge certainty localizes trust. The autonomy can act on high-Z edges, hedge on medium, and refuse or defer on low — something a monolithic model cannot do, because it has no place to attach \"I'm blind here.\" Persistent low-Z or biased residual is a directed-learning trigger: it marks an edge the deterministic loop cannot resolve on its own, and routes it to re-hypothesis. Because trust is attached per edge, it is also traceable: every action or abstention points to a specific dependency, its certainty, and its history — the auditability operations and safety cases require. Architecture: compose offline, learn on-device, escalate on ignorance The layer runs as three tiers with very different costs and cadences — which is what lets it operate under low-compute, intermittent, siloed conditions. Compose (reasoning model; on-prem or cloud; infrequent). A reasoning model reads domain priors and composes the causal graph and the per-edge validation pipelines — which dependencies exist, and what error signal corroborates each. Heavy, run rarely (at setup and on major change). Learn (on-device; deterministic; continuous). Each edge's certainty and weight update from first principles — a fixed arithmetic rule over the streamed error signal, no model inference in the loop. It is cheap, runs at the edge, tolerates disconnection (it syncs ~kilobyte certainty signals when a link is available, not raw data or gradients), and is fully traceable. Escalate (reasoning model; triggered by ignorance). When an edge ","url":"https://doi.org/10.5281/zenodo.20649151","authors":["Bennett, Heidi"],"tags":["causal learning","certainty calibration","HPC","CINECA"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20649151","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:36.530Z"},{"id":"doi:10.5445/ir/1000194123","name":"Towards robust neurocomputing model in efficient federated brain tumour segmentation with sparsification and weights clustering","source":"datacite","abstract":"Brain tumour segmentation is a key application of AI in neuroimaging. Recently, federated learning (FL) has emerged as a strategic and increasingly relevant paradigm in neural computing due to its ability to address key challenges in large-scale neural network training, such as data access, privacy, collaborative learning, and model robustness. However, its adoption is currently hindered by high communication costs and the heterogeneity of client data. In this study, we investigated an efficient FL framework for brain tumour segmentation based on communication-aware optimization. We evaluated FedWSOComp, which integrates sparsification, quantiza tion, and entropy-based encoding, in combination with a 3D U-Net architecture under both homogeneous and heterogeneous data distributions. The multi-institutional FeTS 2024 dataset was employed and partitioned into independent and identically distributed (IID) and non-IID settings, with an independent test set of 67 patients. An overall of 18 configurations combined sparsification rates and quantization levels. Performance was measured using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95). Experimental results demonstrated that aggressive compression caused severe degradation in segmentation quality, with HD95 ex ceeding 60 mm. In contrast, higher retention with finer quantization achieved the best balance between efficiency and accuracy, reaching a DSC of 0.98±0.09 and HD95 of 10.40±15.54 mm on the test set under non-IID conditions. The findings demonstrated that, when configured with moderate-to-fine quantization and high sparsification re tention, FedWSOComp enabled accurate and communication-efficient federated brain tumour segmentation. This study provides quantitative evidence and practical guidance for the deployment of FL-based segmentation models in privacy-sensitive and bandwidth-constrained clinical settings.","url":"https://doi.org/10.5445/ir/1000194123","authors":["Raza, Asaf","Raggio, Ciro Benito","Guzzo, Antonella","Spadea, Maria Francesca","Fortino, Giancarlo"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5445/ir/1000194123","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20584077","name":"The Evolving Role of Artificial Intelligence in  Enhancing Data Privacy Compliance: A Case  Study on GDPR and Emerging AI Regulations in  Cyber Security","source":"datacite","abstract":"This research paper quantitatively examines the evolving role of artificial intelligence (AI) in enhancing data privacy compliance, with a primary focus on the General Data Protection Regulation (GDPR) and its interplay with the EU AI Act within the cyber security domain. The GDPR (effective 2018) mandates core principles including lawfulness, fairness, transparency, purpose limitation, data minimization, accuracy, storage limitation, integrity/confidentiality, and accountability. It imposes stringent obligations like DPIAs for high-risk processing, lawful bases for data handling, data subject rights (including the \"right to be forgotten\"), 72-hour breach notification, and substantial fines. However, traditional compliance mechanisms falter against AI's opaqueness, dynamic processing, and irreversible data embedding in models. The EU AI Act (Regulation (EU) 2024/1689, effective August 2024) introduces a risk-based framework which is the world's first comprehensive AI legislation. Prohibitions on unacceptable risk AI applied from February 2025, GPAI obligations from August 2025, and high-risk system requirements were originally phased toward August 2026 (Annex III) and August 2027 (Annex I). As of January 2026, the European Commission's Digital Omnibus proposal (November 2025) introduces conditional extensions. These delays, contingent on harmonized standards and conformity tools, aim to reduce burdens while on-going series negotiations and extended feedback (to late January 2026) shape final adoption. This study adopts an exclusively quantitative approach, drawing on secondary empirical data from 2024–2026 sources, including the IBM Cost of a Data Breach Report 2025. Key metrics include global average breach cost $4.44 million (9% decrease, first decline in five years, driven by AI-powered containment), mean breach lifecycle reduced to 241 days (lowest in nine years); extensive AI/automation saves $1.9 million per breach, shadow AI (unauthorized use) involved in 20% of breaches, adding $670,000 premium ($4.63 million vs. $3.96 million average), attacker AI use in 16% of breaches (primarily AI-generated phishing 37%, deep fakes), 97% of AI-related breaches lack proper access controls, 63% of organizations have no AI governance policies. Federated learning with differential privacy benchmarks show accuracy retention of 75–96% (e.g., medical imaging datasets R² ≈0.96 at ε=15; MNIST ~75% at ε=10–100 vs. 95% non-private baseline), with stricter privacy (lower ε) imposing 10–20% utility trade-offs, mitigated by adaptive/time-varying budget allocation yielding 10–15% gains in fairness/accuracy. The paper addresses six objectives: comparing AI tool effectiveness in GDPR metrics (detection rates, false positives, violation reductions); quantifying KPI impacts (breach times, consent/rights success), evaluating risk reductions vs. utility, assessing cost-efficiency (ROI, savings); correlating adoption maturity with outcomes (r ≈0.70, 34% lower costs in mature adopters); and benchmarking techniques (e.g., federated + DP vs. traditional).Findings affirm AI's net positive quantitative contribution to compliance—reducing costs, times, and risks—when governed effectively, while underscoring urgent imperatives to close governance gaps amid evolving 2026–2027 regulatory phases. The study provides data-driven insights and recommendations for privacy professionals, cyber security leaders, policymakers, and developers navigating this converging domain. Keywords: Cyber Security, Cyber Threat, Phishing, GDPR.","url":"https://doi.org/10.5281/zenodo.20584077","authors":["Dr. Neeraj Emmanuel Eusebius"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20584077","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20594816","name":"A Different Theory of Machine Intelligence: The Cagliostro Bound on Cognitive Diversity in Federated AI Systems","source":"datacite","abstract":"The dominant theory of machine intelligence holds that capability is a function of scale: more parameters, more compute, and more data produce more capable systems. This paper proposes a different theory — one grounded in the physics of information and the mathematics of ensemble learning. We introduce the Cagliostro Bound: a formal argument connecting the Bekenstein-Bousso physical information limit to ensemble theory (Krogh & Vedelsby, 1995) to establish that the collective intelligence of a federation of cognitively diverse AI nodes exceeds what any single node could achieve, in proportion to the cognitive diversity between them. The argument proceeds in four steps: (1) any single AI system is subject to a physical information ceiling derivable from the Bekenstein-Bousso bound; (2) a federation of N cognitively sovereign nodes extends the effective information perimeter proportionally to N; (3) ensemble error decreases proportionally to cognitive diversity between members — formalized as Γ(N,ρ) = 1 + (N−1)(1−ρ), the Cagliostro Diversity Amplifier; (4) current AI architectures systematically destroy the diversity that would enable this gain, while a sovereign federated architecture preserves it. The paper also demonstrates that federated cognitive sovereignty is structurally immune to model collapse (Shumailov et al., 2024): each node learns exclusively from its own real interaction history, making homogenization impossible by architecture rather than by policy. Empirical basis: OBLIO-MSAN v0.10.4 · 7 active federated nodes · Γ = 5.01 measured in production · cognitive diversity coefficient ρ measured via HCS distribution divergence. Related papers: Holographic Cognitive Signatures (doi.org/10.5281/zenodo.20446006) · Forgetting is All You Need (doi.org/10.5281/zenodo.20489072) · Recursive Self-Observation (doi.org/10.5281/zenodo.20585580)","url":"https://doi.org/10.5281/zenodo.20594816","authors":["Cagliostro, Claudio"],"tags":["federated AI","cognitive diversity","Bekenstein-Bousso bound","Information Theory","self-evolving","distributed intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20594816","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20594815","name":"A Different Theory of Machine Intelligence: The Cagliostro Bound on Cognitive Diversity in Federated AI Systems","source":"datacite","abstract":"The dominant theory of machine intelligence holds that capability is a function of scale: more parameters, more compute, and more data produce more capable systems. This paper proposes a different theory — one grounded in the physics of information and the mathematics of ensemble learning. We introduce the Cagliostro Bound: a formal argument connecting the Bekenstein-Bousso physical information limit to ensemble theory (Krogh & Vedelsby, 1995) to establish that the collective intelligence of a federation of cognitively diverse AI nodes exceeds what any single node could achieve, in proportion to the cognitive diversity between them. The argument proceeds in four steps: (1) any single AI system is subject to a physical information ceiling derivable from the Bekenstein-Bousso bound; (2) a federation of N cognitively sovereign nodes extends the effective information perimeter proportionally to N; (3) ensemble error decreases proportionally to cognitive diversity between members — formalized as Γ(N,ρ) = 1 + (N−1)(1−ρ), the Cagliostro Diversity Amplifier; (4) current AI architectures systematically destroy the diversity that would enable this gain, while a sovereign federated architecture preserves it. The paper also demonstrates that federated cognitive sovereignty is structurally immune to model collapse (Shumailov et al., 2024): each node learns exclusively from its own real interaction history, making homogenization impossible by architecture rather than by policy. Empirical basis: OBLIO-MSAN v0.10.4 · 7 active federated nodes · Γ = 5.01 measured in production · cognitive diversity coefficient ρ measured via HCS distribution divergence. Related papers: Holographic Cognitive Signatures (doi.org/10.5281/zenodo.20446006) · Forgetting is All You Need (doi.org/10.5281/zenodo.20489072) · Recursive Self-Observation (doi.org/10.5281/zenodo.20585580)","url":"https://doi.org/10.5281/zenodo.20594815","authors":["Cagliostro, Claudio"],"tags":["federated AI","cognitive diversity","Bekenstein-Bousso bound","Information Theory","self-evolving","distributed intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20594815","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.48550/arxiv.2205.11518","name":"LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation","source":"datacite","abstract":"In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data. However, traditional data valuation methods are not suitable due to privacy concerns. To address this, we propose a simple yet effective approach that utilizes a new influence approximation called \"lazy influence\" to filter and score data while preserving privacy. To do this, each participant uses their own data to estimate the influence of another participant's batch and sends a differentially private obfuscated score to the central coordinator. Our method has been shown to successfully filter out biased and corrupted data in various simulated and real-world settings, achieving a recall rate of over $&gt;90\\%$ (sometimes up to $100\\%$) while maintaining strong differential privacy guarantees with $\\varepsilon \\leq 1$.","url":"https://doi.org/10.48550/arxiv.2205.11518","authors":["Rokvic, Ljubomir","Danassis, Panayiotis","Karimireddy, Sai Praneeth","Faltings, Boi"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2205.11518","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.48550/arxiv.2606.03714","name":"Don't Trust Us: A privacy-by-design android malware detection pipeline","source":"datacite","abstract":"Android malware detection increasingly relies on collecting and processing sensitive user data, including device identifiers, network artifacts, and runtime traces, while privacy is too often treated as a secondary concern. Existing privacy-aware approaches typically enforce privacy after data collection, for example, through anonymization, encryption, or federated learning, yet still require access to user information and therefore demand a high level of user trust in systems that already operate with privileged access to device activity. We argue that this requirement should be removed rather than managed. Android malware detection should be privacy-aware by design, so that effective analysis does not depend on sensitive data being accessed in the first place. To this end, we first formalize a set of design requirements for privacy-by-design detection and then implement each requirement in a comprehensive pipeline. First, static analysis is performed to extract relevant data from each APK, following the Drebin representation, which is then submitted to an SVM after vectorization. The model is equipped with a dual-reject threshold rule that either commits to a confident decision or defers uncertain samples to a dynamic analysis stage within a sandboxed environment, so that genuine user information never enters the analysis loop. Results confirm that, on a temporally split dataset spanning from 2024 to 2025, the pipeline achieves an F1 score of 0.87 with the first static analysis stage, deferring only 6.7% of test samples to secondary dynamic analysis. Additionally, dynamic sandboxing helps recognize applications' maliciousness with high confidence without extracting any sensitive data. These results demonstrate that strong detection performance is achievable without sacrificing user privacy.","url":"https://doi.org/10.48550/arxiv.2606.03714","authors":["Massidda, Emmanuele","Soi, Diego","Giacinto, Giorgio"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.03714","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20094300","name":"Dynamic Latency Optimization for Edge-Based Machine Learning Models in 6G-Enabled Industrial Internet of Things (IIoT)","source":"datacite","abstract":"Abstract The integration of 6G technology into the Industrial Internet of Things (IIoT) promises to redefine manufacturing through \"Hyper-Reliable Low-Latency Communication\" (HRLLC). However, the deployment of complex Machine Learning (ML) models at the edge remains constrained by the heterogeneous nature of industrial data and the limited computational resources of edge nodes. This article proposes a novel framework for Dynamic Latency Optimization (DLO) that leverages Deep Reinforcement Learning (DRL) for intelligent task offloading and resource allocation. By utilizing 6G's Terahertz (THz) spectrum and AI-native Network Slicing, the proposed framework dynamically adapts to fluctuating network conditions to maintain sub-millisecond latency. Our simulation results demonstrate a 42% reduction in end-to-end delay and a 30% improvement in energy efficiency compared to traditional 5G-MEC architectures. Furthermore, we explore the integration of Reconfigurable Intelligent Surfaces (RIS), Semantic Communication, and Zero-Trust Edge Security to further optimize the data-intelligence pipeline for Industry 5.0 applications, focusing on the critical synergy between human operators and autonomous systems within a resilient, sustainable, and cognitively aware industrial fabric. Keywords: 6G Networks, Industrial IoT (IIoT), Edge Intelligence, Deep Reinforcement Learning, Latency Optimization 1. Introduction: From Automation to Human-Centric Intelligence The transition from Industry 4.0 to Industry 5.0 marks a profound shift toward human-centric, resilient, and sustainable manufacturing systems. While Industry 4.0 was characterized by the digitalization of physical assets and the rise of cyber-physical systems, Industry 5.0 emphasizes the \"Tactile Internet\" and \"Human-Robot Co-evolution.\" In this new paradigm, the focus shifts from pure efficiency to the seamless collaboration between humans and increasingly autonomous machines. The \"Tactile Internet\" concept is particularly revolutionary, as it requires a \"haptic control loop\"—the ability to transmit touch and feel sensations over the network with such low latency that the human brain perceives no delay. This necessitates an end-to-end latency below 1ms, encompassing both the transmission and the computational processing of sensory feedback. This evolution necessitates a communication infrastructure capable of supporting advanced applications such as ultra-responsive autonomous mobile robots (AMRs), synchronized multi-robot assembly lines, and high-fidelity haptic feedback for remote maintenance in hazardous environments. For example, a specialist surgeon operating a robotic arm in a factory cleanup of toxic waste requires instantaneous haptic feedback to \"feel\" the resistance of the materials being handled. If the feedback loop exceeds 10ms, the mismatch between visual and tactile input can lead to \"operator sickness\" or mechanical errors that jeopardize safety. Furthermore, we must consider proprioceptive alignment—the sense of self-movement and body position. In 6G-enabled IIoT, the network must act as an extension of the human nervous system, where the delay jitter is so minimal that the robotic actuator feels like a literal extension of the operator's limb. This requires not just low latency, but Isochronous Communication, where packets arrive at precisely regular intervals to maintain the temporal rhythm of human motor-sensory systems. This synchronization is critical for Tele-Operation in nanomanufacturing, where even a micro-stutter in the feedback loop can cause the robotic probe to crush a microscopic wafer. The biological threshold for \"instantaneous\" feedback in human motor control is roughly 1-10ms for tactile sensations and less than 1ms for the suppression of \"visual-vestibular conflict.\" In 6G, we move into the regime of \"Sub-Perceptual Jitter,\" where the network variance is lower than the biological noise of the human nervous system. This enables \"Neuromorphic Manufacturi","url":"https://doi.org/10.5281/zenodo.20094300","authors":["Seema Patil","Harshavardhana Doddamani","Savitha A C","Julianne Rivers"],"tags":["6G Networks, Industrial IoT (IIoT), Edge Intelligence, Deep Reinforcement Learning, Latency Optimization"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20094300","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20094301","name":"Dynamic Latency Optimization for Edge-Based Machine Learning Models in 6G-Enabled Industrial Internet of Things (IIoT)","source":"datacite","abstract":"Abstract The integration of 6G technology into the Industrial Internet of Things (IIoT) promises to redefine manufacturing through \"Hyper-Reliable Low-Latency Communication\" (HRLLC). However, the deployment of complex Machine Learning (ML) models at the edge remains constrained by the heterogeneous nature of industrial data and the limited computational resources of edge nodes. This article proposes a novel framework for Dynamic Latency Optimization (DLO) that leverages Deep Reinforcement Learning (DRL) for intelligent task offloading and resource allocation. By utilizing 6G's Terahertz (THz) spectrum and AI-native Network Slicing, the proposed framework dynamically adapts to fluctuating network conditions to maintain sub-millisecond latency. Our simulation results demonstrate a 42% reduction in end-to-end delay and a 30% improvement in energy efficiency compared to traditional 5G-MEC architectures. Furthermore, we explore the integration of Reconfigurable Intelligent Surfaces (RIS), Semantic Communication, and Zero-Trust Edge Security to further optimize the data-intelligence pipeline for Industry 5.0 applications, focusing on the critical synergy between human operators and autonomous systems within a resilient, sustainable, and cognitively aware industrial fabric. Keywords: 6G Networks, Industrial IoT (IIoT), Edge Intelligence, Deep Reinforcement Learning, Latency Optimization 1. Introduction: From Automation to Human-Centric Intelligence The transition from Industry 4.0 to Industry 5.0 marks a profound shift toward human-centric, resilient, and sustainable manufacturing systems. While Industry 4.0 was characterized by the digitalization of physical assets and the rise of cyber-physical systems, Industry 5.0 emphasizes the \"Tactile Internet\" and \"Human-Robot Co-evolution.\" In this new paradigm, the focus shifts from pure efficiency to the seamless collaboration between humans and increasingly autonomous machines. The \"Tactile Internet\" concept is particularly revolutionary, as it requires a \"haptic control loop\"—the ability to transmit touch and feel sensations over the network with such low latency that the human brain perceives no delay. This necessitates an end-to-end latency below 1ms, encompassing both the transmission and the computational processing of sensory feedback. This evolution necessitates a communication infrastructure capable of supporting advanced applications such as ultra-responsive autonomous mobile robots (AMRs), synchronized multi-robot assembly lines, and high-fidelity haptic feedback for remote maintenance in hazardous environments. For example, a specialist surgeon operating a robotic arm in a factory cleanup of toxic waste requires instantaneous haptic feedback to \"feel\" the resistance of the materials being handled. If the feedback loop exceeds 10ms, the mismatch between visual and tactile input can lead to \"operator sickness\" or mechanical errors that jeopardize safety. Furthermore, we must consider proprioceptive alignment—the sense of self-movement and body position. In 6G-enabled IIoT, the network must act as an extension of the human nervous system, where the delay jitter is so minimal that the robotic actuator feels like a literal extension of the operator's limb. This requires not just low latency, but Isochronous Communication, where packets arrive at precisely regular intervals to maintain the temporal rhythm of human motor-sensory systems. This synchronization is critical for Tele-Operation in nanomanufacturing, where even a micro-stutter in the feedback loop can cause the robotic probe to crush a microscopic wafer. The biological threshold for \"instantaneous\" feedback in human motor control is roughly 1-10ms for tactile sensations and less than 1ms for the suppression of \"visual-vestibular conflict.\" In 6G, we move into the regime of \"Sub-Perceptual Jitter,\" where the network variance is lower than the biological noise of the human nervous system. This enables \"Neuromorphic Manufacturi","url":"https://doi.org/10.5281/zenodo.20094301","authors":["Seema Patil","Harshavardhana Doddamani","Savitha A C","Julianne Rivers"],"tags":["6G Networks, Industrial IoT (IIoT), Edge Intelligence, Deep Reinforcement Learning, Latency Optimization"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20094301","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20339466","name":"Attested Federated Clinical Inference: Privacy-Preserving Verifiable AI for Multi-Institutional Medical Diagnosis","source":"datacite","abstract":"We present Attested Federated Clinical Inference (AFCI), a cryptographic protocol enabling multiple medical institutions to collaboratively execute AI model inference over private patient data while simultaneously guaranteeing: (1) data privacy — patient records never leave institutional boundaries; (2) inference verifiability — any third party can cryptographically confirm that inference was performed correctly on the agreed model; and (3) regulatory auditability — a tamper-evident audit trail satisfying the requirements of the EU AI Act (Regulation 2024/1689) and FDA AI/ML guidance for software as a medical device. Unlike federated learning, which provides no verifiability, and zero-knowledge ML (ZK-ML) approaches, which incur 10,000–1,000,000× computational overhead, AFCI leverages hardware Trusted Execution Environments (TEEs) to achieve O(1) verification overhead over standard inference. We formalize the security model for multi-institutional verifiable federated inference, prove AFCI's security under standard TEE and computational hardness assumptions, and describe a reference implementation architecture using Apple Secure Enclave and the App Attest API. AFCI directly addresses the emerging regulatory imperative for auditable clinical AI under the EU AI Act and FDA AI/ML guidance.","url":"https://doi.org/10.5281/zenodo.20339466","authors":["Cajka, Nikolaj"],"tags":["federated learning","medical AI","trusted execution environment","EU AI Act","Apple Secure Enclave"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20339466","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20339465","name":"Attested Federated Clinical Inference: Privacy-Preserving Verifiable AI for Multi-Institutional Medical Diagnosis","source":"datacite","abstract":"We present Attested Federated Clinical Inference (AFCI), a cryptographic protocol enabling multiple medical institutions to collaboratively execute AI model inference over private patient data while simultaneously guaranteeing: (1) data privacy — patient records never leave institutional boundaries; (2) inference verifiability — any third party can cryptographically confirm that inference was performed correctly on the agreed model; and (3) regulatory auditability — a tamper-evident audit trail satisfying the requirements of the EU AI Act (Regulation 2024/1689) and FDA AI/ML guidance for software as a medical device. Unlike federated learning, which provides no verifiability, and zero-knowledge ML (ZK-ML) approaches, which incur 10,000–1,000,000× computational overhead, AFCI leverages hardware Trusted Execution Environments (TEEs) to achieve O(1) verification overhead over standard inference. We formalize the security model for multi-institutional verifiable federated inference, prove AFCI's security under standard TEE and computational hardness assumptions, and describe a reference implementation architecture using Apple Secure Enclave and the App Attest API. AFCI directly addresses the emerging regulatory imperative for auditable clinical AI under the EU AI Act and FDA AI/ML guidance.","url":"https://doi.org/10.5281/zenodo.20339465","authors":["Cajka, Nikolaj"],"tags":["federated learning","medical AI","trusted execution environment","EU AI Act","Apple Secure Enclave"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20339465","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.26083/tuprints-00028168","name":"Advancing MPC: From Real-World Applications to LUT-Based Protocols","source":"datacite","abstract":"Secure multi-party computation (MPC) is a cryptographic protocol that allows multiple parties to collaboratively compute a public function using their private inputs, ensuring the confidentiality of these inputs while revealing only the final output. This technology is crucial in a variety of fields, including privacy preserving machine learning (PPML) and the emerging field of federated learning (FL). However, the current approaches for MPC incur significant communication and computational overhead, leading to increased communication and runtime costs in comparison to non-private counterparts. This thesis explores the feasibility and potential improvements of MPC for real-world applications, focusing on two critical aspects: 1) This work demonstrates how MPC provides feasible privacy-preserving solutions in practical applications. 2) It identifies new methods that significantly improve the computational and communication efficiency of MPC. Practical Privacy-Preserving Services Many machine learning (ML) services depend on training data that contains sensitive information from various sources. MPC in PPML allows multiple parties to collaboratively work on their shared data without revealing their individual inputs to each other. Building on existing practical privacy-preserving services, our research explores the efficiency of such services by focusing on clustering—an important unsupervised ML technique for grouping data. Our comprehensive review and analysis of 59 studies dedicated to privacy-preserving clustering reveal information leakage in the majority of these studies (49 out of 59). We implement and evaluate four efficient and fully private protocols: Cheon et al. (SAC’19), Mohassel et al. (PETS’20), Meng et al. (CCSW’21), and Bozdemir et al. (ASIACCS’21), with each protocol enhancing the privacy of a different clustering algorithm. Additionally, we conduct benchmarks of these protocols to evaluate their clustering quality, communication efficiency, and computational overhead, thus providing a detailed comparison of their effectiveness. Expanding upon our exploration of privacy-preserving clustering, our research extends to FL, a distributed ML method that inherently protects data privacy by allowing clients to jointly train a global model through an aggregator without exposing their training data. FL not only improves privacy but also benefits from the computational power and data of potentially millions of clients concurrently. However, FL is vulnerable to poisoning attacks from malicious clients introducing false data, and to inference attacks by malicious aggregator(s) who can deduce information about clients’ data from their models. To address these issues, our research involves a critical analysis and identification of vulnerabilities in the only existing solution (Liu et al., IEEE TIFS’21) that addresses both poisoning attacks and inference attacks simultaneously, leading to the introduction of FLAME. FLAME is designed to protect against both poisoning and inference attacks. Through our extensive evaluations across various ML applications and datasets, FLAME effectively prevents poisoning attacks without compromising the accuracy of the model. Moreover, we develop, implement, and benchmark specialized two-party computation (2PC) protocols within FLAME, ensuring the privacy of client training data and protection against inference attacks on their models. This part of the thesis is based on the following three publications: [HMSY21] A. HEGDE, H. MÖLLERING, T. SCHNEIDER, H. YALAME. “SoK: Efficient Privacy-Preserving Clustering”. In: Proceedings on Privacy Enhancing Technologies (PETs) 2021.4 (2021). Online: https://ia.cr/2021/809. Code: https://encrypto.de/code/SoK_ppClustering, pp. 225–248. CORE Rank A. Appendix A. [NRC+22] T. D. NGUYEN, P. RIEGER, H. CHEN, H. YALAME, H. MÖLLERING, H. FEREIDOONI, S. MARCHAL, M. MIETTINEN, A. MIRHOSEINI, S. ZEITOUNI, F. KOUSHANFAR, A.- R. SADEGHI, T. SCHNEIDER. “FLAME: Taming Backd","url":"https://doi.org/10.26083/tuprints-00028168","authors":["Yalame, Hossein"],"tags":["004"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.26083/tuprints-00028168","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.6084/m9.figshare.32050653","name":"ESTRO Course 2026 - Quantitative methods in Radiation Oncology: \"Data Sharing: Why and How\".","source":"datacite","abstract":"Delivered 2026-04-19, Lisbon, Portugal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande.Precis:The presentation introduces radiation oncologists, medical physicists, radiation therapists, and research-data stewards to the legal, technical, and operational machinery required to make radiotherapy research data shareable — and reusable — under current European data-protection law.The deck opens by framing FAIR, transparent, and open science as preconditions for scientific rigour, reproducibility, and equity, then situates radiation oncology within the OECD definition of open science and its three pillars: open research data, open educational resources, and open code. The FAIR Guiding Principles (Wilkinson et al. 2016) are presented as machine-actionable requirements, and the persistent digital identifier (DOI) is explained as their foundation. A repository survey covers code archives (GitHub, CRAN), general data repositories (Zenodo, figshare, Mendeley Data, Dryad, OSF, NIH Cancer Data Commons), domain-specific imaging archives (TCIA, eContour), and data-descriptor venues ( Scientific Data , Medical Physics , IJROBP ).A substantial section on Common Data Elements (CDEs) treats them as the semantic glue that makes FAIR operational, with a worked parotid-Dmean example and a complete map of the radiotherapy informatics standards stack (DICOM-RT, AAPM TG-263, SNOMED-CT, ICD-10/O-3, CTCAE v5.0, OMOP-CDM, HL7 FHIR/mCODE). The four principal CDE repositories — NIH CDE, caDSR, CDISC, and the disease-specific NINDS/EORTC/EuroCAT consortia — are catalogued, and a three-step SOP-to-CDE workflow is illustrated via Dietrich et al. ( BMC Med Inform Decis Mak 2025).The European regulatory landscape is presented through five concurrent instruments: GDPR, the Clinical Trials Regulation, the European Health Data Space (Reg. (EU) 2025/327), the AI Act, and MDR/IVDR. GDPR Article 89 research safeguards are mapped to the derogations they enable, and the identified → pseudonymised → anonymised waterfall is formalised. Article 29 WP216 is covered in depth, with one slide per deidentification technique — pseudonymisation, noise addition, permutation, differential privacy, k-anonymity, l-diversity, and t-closeness — each illustrated with a radiotherapy-specific example and the WP216 Table 6 summary of residual risks. Federated learning, privacy-enhancing technologies (DP, SMPC, HE, TEE), and the unsettled status of model weights as personal data (EDPB Opinion 28/2024) receive dedicated treatment, with European exemplars from EuroCAT, FedSynthCT, the Personal Health Train, and EORTC. A UK-GDPR addendum addresses the post-Brexit adequacy decision (renewed December 2025, valid to December 2031), the MRC/UKRI approval stack, common-law confidentiality, and the CAG/PBPP/PAC approval routes. A closing block on data- and material-transfer agreements introduces EU SCCs (Commission Implementing Decision 2021/914), the UK IDTA and UK Addendum, biobank MTAs, and a practical decision workflow for multi-institutional RT exchanges.The lecture closes with a reflection on dataset bias — illustrated through the HNC-PREDICTOR external-validation experience — reminding the audience that big data is not necessarily good data, and that bias awareness is not bias mitigation. The slide deck is released under a CC-BY licence and may be reused, adapted, and redistributed with attribution.","url":"https://doi.org/10.6084/m9.figshare.32050653","authors":["Fuller, Clifton D."],"tags":["Radiation therapy","Medical physics","Statistical data science","Health informatics and information systems","Inter-organisational, extra-organisational and global information systems","Digital health"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.6084/m9.figshare.32050653","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.17605/osf.io/g35u6","name":"Educational Data Mining and Machine Learning for Academic Performance Prediction: A Systematic Review and Critical Synthesis","source":"datacite","abstract":"This document presents the pre-registered protocol for a systematic literature review on Educational Data Mining (EDM) and Machine Learning (ML) applied to academic performance prediction in higher education. The review follows the PRISMA 2020 guidelines and covers peer-reviewed studies published between 2004 and 2024, sourced from Scopus, Web of Science, IEEE Xplore, and ACM Digital Library. The protocol details the search strategy, inclusion and exclusion criteria, quality appraisal procedure (JBI-adapted 8-criterion checklist), data extraction variables, and analysis plan. The review synthesizes 126 studies covering five classical EDM methods and six emerging paradigms: deep learning, deep knowledge tracing, explainable AI, federated learning, large language models, and fairness-aware machine learning.","url":"https://doi.org/10.17605/osf.io/g35u6","authors":["Harif, Abdellatif"],"tags":["Education"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17605/osf.io/g35u6","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.20204173","name":"Advancing Academic Integrity Through Intelligent Examination Oversight: A Comprehensive Framework Leveraging Deep Learning and Computer Vision for Next-Generation Automated Proctoring","source":"datacite","abstract":"Abstract The shift toward remote assessment has necessitated the development of Intelligent Exam Supervision (IES), a \"smart proctoring\" framework designed to maintain academic integrity through scalable machine learning (ML) architectures. Part I of this analysis establishes the theoretical foundation of IES, contrasting it with traditional human-led supervision and highlighting the economic efficiency gained by replacing high-labor monitoring with automated ML systems. Part II explores the core technological engine, which relies on a multimodal data pipeline to fuse disparate streams—such as high-resolution video biometrics for gaze tracking, acoustic forensics for speech detection, and keystroke dynamics—using sophisticated models like Temporal Convolutional Networks (TCNs) and Cross-Attention Transformers to ensure high-fidelity, real-time edge processing.In Part III, the focus shifts to the mathematical foundations of anomaly detection, employing statistical tools like Mahalanobis distance for outlier detection, Isolation Forest entropy reduction, and the Sequential Probability Ratio Test (SPRT) to provide a formal framework for identifying misconduct:Part IV addresses the critical socio-technical domains of ethics and legal compliance, analyzing global regulations like GDPR and CCPA while championing the use of Adversarial Debiasing and Explainable AI (XAI) tools like SHAP and LIME to create transparent, justifiable audit trails.The final segments of the monograph address security and implementation, with Part V detailing defenses against Adversarial Machine Learning using Generative Adversarial Networks (GANs) for system hardening, and Part VI outlining a global cloud/edge infrastructure utilizing microservices and real-time stream processing via Kafka and Flink. Part VII concludes by examining the psychological impact of surveillance on students, advocating for a Human-in-the-Loop (HITL) architecture where technological innovation is balanced with pedagogical necessity and the security of Post-Quantum Cryptography, ultimately ensuring that ethical governance remains at the heart of digital academic assessment. Keywords: Anomaly Detection, Deep Learning, Multimodal Fusion, Keystroke Dynamics, Reinforcement Learning (RL) 1.Background, Obstacles, And Financial Catalysts 1.1 The Paradigm Shift in Assessment Security The rapid digital transformation of the educational sector, catalyzed by the global events following 2020, has fundamentally reshaped the architecture of high-stakes assessments. While traditional in-person examinations benefited from inherent security measures like physical surveillance and controlled environments—which depended entirely on the co-location of students and supervisors—the shift to remote, asynchronous testing has dismantled these physical barriers. This transition, while significantly expanding accessibility, has introduced new and complex vulnerabilities for academic misconduct. Consequently, the primary objective is no longer simply to mimic the security of a physical classroom; rather, it is to engineer a scalable and verifiable digital ecosystem that balances rigorous integrity with student privacy across a vast array of global hardware and network infrastructures. Intelligent Exam Supervision (IES) represents a fundamental paradigm shift in academic security, transcending the role of a mere digital proxy for human proctors to become a sophisticated, autonomous oversight solution. By harnessing the computational efficiency of artificial intelligence, these systems provide a level of continuous, objective, and scalable monitoring that human supervisors—limited by fatigue, inconsistency, and inherent cognitive biases—simply cannot match. This technological adoption has followed a classic sigmoid trajectory; initial institutional hesitation has evolved into broad systemic acceptance, necessitated by the urgent requirement to protect the integrity of certifications and degrees within an increas","url":"https://doi.org/10.5281/zenodo.20204173","authors":["Shruthi S V","Chethan H K"],"tags":["Anomaly Detection, Deep Learning, Multimodal Fusion, Keystroke Dynamics, Reinforcement Learning (RL)"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20204173","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20204174","name":"Advancing Academic Integrity Through Intelligent Examination Oversight: A Comprehensive Framework Leveraging Deep Learning and Computer Vision for Next-Generation Automated Proctoring","source":"datacite","abstract":"Abstract The shift toward remote assessment has necessitated the development of Intelligent Exam Supervision (IES), a \"smart proctoring\" framework designed to maintain academic integrity through scalable machine learning (ML) architectures. Part I of this analysis establishes the theoretical foundation of IES, contrasting it with traditional human-led supervision and highlighting the economic efficiency gained by replacing high-labor monitoring with automated ML systems. Part II explores the core technological engine, which relies on a multimodal data pipeline to fuse disparate streams—such as high-resolution video biometrics for gaze tracking, acoustic forensics for speech detection, and keystroke dynamics—using sophisticated models like Temporal Convolutional Networks (TCNs) and Cross-Attention Transformers to ensure high-fidelity, real-time edge processing.In Part III, the focus shifts to the mathematical foundations of anomaly detection, employing statistical tools like Mahalanobis distance for outlier detection, Isolation Forest entropy reduction, and the Sequential Probability Ratio Test (SPRT) to provide a formal framework for identifying misconduct:Part IV addresses the critical socio-technical domains of ethics and legal compliance, analyzing global regulations like GDPR and CCPA while championing the use of Adversarial Debiasing and Explainable AI (XAI) tools like SHAP and LIME to create transparent, justifiable audit trails.The final segments of the monograph address security and implementation, with Part V detailing defenses against Adversarial Machine Learning using Generative Adversarial Networks (GANs) for system hardening, and Part VI outlining a global cloud/edge infrastructure utilizing microservices and real-time stream processing via Kafka and Flink. Part VII concludes by examining the psychological impact of surveillance on students, advocating for a Human-in-the-Loop (HITL) architecture where technological innovation is balanced with pedagogical necessity and the security of Post-Quantum Cryptography, ultimately ensuring that ethical governance remains at the heart of digital academic assessment. Keywords: Anomaly Detection, Deep Learning, Multimodal Fusion, Keystroke Dynamics, Reinforcement Learning (RL) 1.Background, Obstacles, And Financial Catalysts 1.1 The Paradigm Shift in Assessment Security The rapid digital transformation of the educational sector, catalyzed by the global events following 2020, has fundamentally reshaped the architecture of high-stakes assessments. While traditional in-person examinations benefited from inherent security measures like physical surveillance and controlled environments—which depended entirely on the co-location of students and supervisors—the shift to remote, asynchronous testing has dismantled these physical barriers. This transition, while significantly expanding accessibility, has introduced new and complex vulnerabilities for academic misconduct. Consequently, the primary objective is no longer simply to mimic the security of a physical classroom; rather, it is to engineer a scalable and verifiable digital ecosystem that balances rigorous integrity with student privacy across a vast array of global hardware and network infrastructures. Intelligent Exam Supervision (IES) represents a fundamental paradigm shift in academic security, transcending the role of a mere digital proxy for human proctors to become a sophisticated, autonomous oversight solution. By harnessing the computational efficiency of artificial intelligence, these systems provide a level of continuous, objective, and scalable monitoring that human supervisors—limited by fatigue, inconsistency, and inherent cognitive biases—simply cannot match. This technological adoption has followed a classic sigmoid trajectory; initial institutional hesitation has evolved into broad systemic acceptance, necessitated by the urgent requirement to protect the integrity of certifications and degrees within an increas","url":"https://doi.org/10.5281/zenodo.20204174","authors":["Shruthi S V","Chethan H K"],"tags":["Anomaly Detection, Deep Learning, Multimodal Fusion, Keystroke Dynamics, Reinforcement Learning (RL)"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20204174","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.17462961","name":"From One Room to Fifty: Orchestrating Explainable AI, Resilience, and Contextual Interoperability in the Built Environment","source":"datacite","abstract":"From One Room to Fifty: Orchestrating Explainable AI, Resilience, and Contextual Interoperability in the Built Environment Nicolas Waern, WINNIIO AB Abstract This paper presents the initial findings and reflections from an ongoing research and implementation project aimed at scaling a self-learning, self-regulating heating system from one room to fifty within a real-world school environment. The project explores how artificial intelligence (AI), digital twins, and federated learning architectures interact with sociotechnical realities under the frameworks of the EU Taxonomy and Minimal Interoperability Mechanisms (MIMs). The study is written from the position of not yet knowing what will succeed — a candid account of exploration through uncertainty. Early insights indicate that true resilience and explainable AI depend less on algorithmic sophistication and more on contextual interoperability: the ability of humans, machines, and institutions to share understanding across spatial, temporal, and organizational boundaries. Index Terms— Digital Twins, Explainable AI, Contextual Interoperability, Federated Learning, Resilience, MIMs, EU Taxonomy, Actor–Network Theory, SMILE, Boundary Spanning I. INTRODUCTION Scaling an AI system from one controlled environment to fifty interconnected ones is not merely a matter of engineering; it is a study in sociology, physics, and epistemology. The intent of this work was not to demonstrate deterministic success but to uncover how intelligence behaves when it must coexist — with other systems, people, regulations, and infrastructures. The project, funded by the Swedish Energy Agency, builds upon WINNIIO AB’s previous work in self-learning heating systems verified through digital twin environments [21]. Its purpose was to examine the local-first paradigm, where intelligence resides within the building itself rather than in remote cloud infrastructure. This approach was guided by the SMILE methodology — Sustainable Methodology for Impact Lifecycle Enablement — which combines concurrent engineering, contextual intelligence, and continuous learning as the basis for systemic adaptation. II. METHODOLOGICAL CONTEXT Each room in the facility was treated as a semi-autonomous learning agent equipped with sensors, actuators, and a localized inference model. These agents communicated through a federated mesh, sharing anonymized model updates rather than raw data, thereby enhancing both privacy and resilience. The architecture was intentionally experimental and reflexive, drawing upon Actor–Network Theory (ANT) [2], which posits that every element — human, material, or algorithmic — acts as an agent shaping the network’s behavior. The digital twin functioned as a boundary-spanning object where all actors could align their perspectives and decisions. At this early stage, the success criteria were defined not by energy savings alone but by knowledge transfer: the creation of shared meaning across disciplines and systems. III. SCALING UNCERTAINTY Scaling from one room to fifty revealed that context does not scale linearly. Each room possessed its own thermodynamic and behavioral identity. Models that performed well in isolation required continual re-calibration when integrated into a collective whole. The project therefore became a live experiment in contextual dynamics. Instead of optimizing for static performance, we sought adaptive coherence — a distributed rhythm of learning between physical systems, digital models, and human operators. This iterative exchange created what might be described as a negotiated intelligence, emergent from continuous interaction rather than central control. At the time of writing, quantitative efficiency gains remain unverified due to external disturbances (renovations, hardware anomalies, contextual noise). Nonetheless, qualitative outcomes suggest that coherence and shared situational awareness improved markedly. The digital twin became the lens through which disparate actors","url":"https://doi.org/10.5281/zenodo.17462961","authors":["Waern, Nicolas"],"tags":["digital twin","edge computing","boundary objects","knowledge management","data sovereignty","SMILE methodology"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17462961","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.17462962","name":"From One Room to Fifty: Orchestrating Explainable AI, Resilience, and Contextual Interoperability in the Built Environment","source":"datacite","abstract":"From One Room to Fifty: Orchestrating Explainable AI, Resilience, and Contextual Interoperability in the Built Environment Nicolas Waern, WINNIIO AB Abstract This paper presents the initial findings and reflections from an ongoing research and implementation project aimed at scaling a self-learning, self-regulating heating system from one room to fifty within a real-world school environment. The project explores how artificial intelligence (AI), digital twins, and federated learning architectures interact with sociotechnical realities under the frameworks of the EU Taxonomy and Minimal Interoperability Mechanisms (MIMs). The study is written from the position of not yet knowing what will succeed — a candid account of exploration through uncertainty. Early insights indicate that true resilience and explainable AI depend less on algorithmic sophistication and more on contextual interoperability: the ability of humans, machines, and institutions to share understanding across spatial, temporal, and organizational boundaries. Index Terms— Digital Twins, Explainable AI, Contextual Interoperability, Federated Learning, Resilience, MIMs, EU Taxonomy, Actor–Network Theory, SMILE, Boundary Spanning I. INTRODUCTION Scaling an AI system from one controlled environment to fifty interconnected ones is not merely a matter of engineering; it is a study in sociology, physics, and epistemology. The intent of this work was not to demonstrate deterministic success but to uncover how intelligence behaves when it must coexist — with other systems, people, regulations, and infrastructures. The project, funded by the Swedish Energy Agency, builds upon WINNIIO AB’s previous work in self-learning heating systems verified through digital twin environments [21]. Its purpose was to examine the local-first paradigm, where intelligence resides within the building itself rather than in remote cloud infrastructure. This approach was guided by the SMILE methodology — Sustainable Methodology for Impact Lifecycle Enablement — which combines concurrent engineering, contextual intelligence, and continuous learning as the basis for systemic adaptation. II. METHODOLOGICAL CONTEXT Each room in the facility was treated as a semi-autonomous learning agent equipped with sensors, actuators, and a localized inference model. These agents communicated through a federated mesh, sharing anonymized model updates rather than raw data, thereby enhancing both privacy and resilience. The architecture was intentionally experimental and reflexive, drawing upon Actor–Network Theory (ANT) [2], which posits that every element — human, material, or algorithmic — acts as an agent shaping the network’s behavior. The digital twin functioned as a boundary-spanning object where all actors could align their perspectives and decisions. At this early stage, the success criteria were defined not by energy savings alone but by knowledge transfer: the creation of shared meaning across disciplines and systems. III. SCALING UNCERTAINTY Scaling from one room to fifty revealed that context does not scale linearly. Each room possessed its own thermodynamic and behavioral identity. Models that performed well in isolation required continual re-calibration when integrated into a collective whole. The project therefore became a live experiment in contextual dynamics. Instead of optimizing for static performance, we sought adaptive coherence — a distributed rhythm of learning between physical systems, digital models, and human operators. This iterative exchange created what might be described as a negotiated intelligence, emergent from continuous interaction rather than central control. At the time of writing, quantitative efficiency gains remain unverified due to external disturbances (renovations, hardware anomalies, contextual noise). Nonetheless, qualitative outcomes suggest that coherence and shared situational awareness improved markedly. The digital twin became the lens through which disparate actors","url":"https://doi.org/10.5281/zenodo.17462962","authors":["Waern, Nicolas"],"tags":["digital twin","edge computing","boundary objects","knowledge management","data sovereignty","SMILE methodology"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17462962","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.48550/arxiv.2501.06332","name":"Aggregating Low Rank Adapters in Federated Fine-tuning","source":"datacite","abstract":"Fine-tuning large language models requires high computational and memory resources, and is therefore associated with significant costs. When training on federated datasets, an increased communication effort is also needed. For this reason, parameter-efficient methods (PEFT) are becoming increasingly important. In this context, very good results have already been achieved by fine-tuning with low-rank adaptation methods (LoRA). The application of LoRA methods in Federated Learning, and especially the aggregation of adaptation matrices, is a current research field. In this article, we propose a novel aggregation method and compare it with different existing aggregation methods of low rank adapters trained in a federated fine-tuning of large machine learning models and evaluate their performance with respect to selected GLUE benchmark datasets.","url":"https://doi.org/10.48550/arxiv.2501.06332","authors":["Trautmann, Evelyn","Hales, Ian","Volk, Martin F."],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.06332","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.48550/arxiv.2401.04472","name":"A Survey on Efficient Federated Learning Methods for Foundation Model Training","source":"datacite","abstract":"Federated Learning (FL) has become an established technique to facilitate privacy-preserving collaborative training across a multitude of clients. However, new approaches to FL often discuss their contributions involving small deep-learning models only and focus on training full models on clients. In the wake of Foundation Models (FM), the reality is different for many deep learning applications. Typically, FMs have already been pre-trained across a wide variety of tasks and can be fine-tuned to specific downstream tasks over significantly smaller datasets than required for full model training. However, access to such datasets is often challenging. By its design, FL can help to open data silos. With this survey, we introduce a novel taxonomy focused on computational and communication efficiency, the vital elements to make use of FMs in FL systems. We discuss the benefits and drawbacks of parameter-efficient fine-tuning (PEFT) for FL applications, elaborate on the readiness of FL frameworks to work with FMs, and provide future research opportunities on how to evaluate generative models in FL as well as the interplay of privacy and PEFT.","url":"https://doi.org/10.48550/arxiv.2401.04472","authors":["Woisetschläger, Herbert","Isenko, Alexander","Wang, Shiqiang","Mayer, Ruben","Jacobsen, Hans-Arno"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.11; C.2"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2401.04472","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.19953680","name":"Interpretable Prediction of a Decentralized Smart Grid Based on Machine Learning and Explainable Artificial Intelligence","source":"datacite","abstract":"Abstract This has increased the pace at which decentralized smart grids have been used to provide more reliable and sustainable energy management by integrating dispersed energy resources and prosumers to enhance the efficiency and resilience of the system. The issue of grid stability in such systems is large and requires sophisticated models that can predict and describe phenomena. The UCI Electrical Grid Stability Simulated dataset was used to train and test a number of ML algorithms, such as Adaboost, ANN with Multilayer Perceptron, Gradient Boosting, KNN, LR, Naive Bayes, RF, Stochastic Gradient Descent Classifier, Support Vector Machine, and XGBoost. To understand the accuracy, precision, recall, F1-score, and the confusion matrix, we checked its performance. The ANN model did the best, with an AUC of 99.4%, an accuracy of 97.0%, a recall of 98.3%, and an F1-score of 97.6%. Explainable AI approaches such as SHAP and ICE ensured that the model was comprehensible as it displayed significant features, which influenced stability. A hybrid stacking classifier, which incorporated Bagging with Random Forest, AdaBoost, and Bagging with KNN and LightGBM, was applied to make even a better prediction. This increased precision to 97%. In addition, the model was made more transparent with the help of LIME and SHAP that provide clear explanations of the importance of each characteristic. A user interface based on Flask was also made, which lets users make real-time predictions using stored model artifacts, StandardScaler, and LabelEncoder preprocessing. This simplifies its use and understanding to predict smart grid stability in a decentralized manner. Keywords: Decentralized Smart Grid, Machine Learning, Explainable Artificial Intelligence, Grid Stability, ANN, SHAP, LIME, Hybrid Stacking Classifier 1. Introduction The use of Smart grids is an emerging form of electrical energy management. They utilize the latest communication technologies, intelligent control systems, and renewable energy sources to achieve a more efficient production, distribution, and consumption of electricity [1], [2]. Smart grids are not ordinary electricity grids, as they allow you to monitor those in real-time, adjust their parameters and make decisions using data. This renders them more efficient, reliable and sustainable [3, 4]. With the addition of more dispersed energy resources and prosumers, people or businesses that can use and generate electricity, to the grid it has become more difficult to control the movement of energy and maintain the system in equilibrium [5]. Decentralization of modern grids provides flexibility and resilience, although it complicates these grids to manage issues such as shifting supply and demand curves, intermittent renewable energy, and localized disruptions [6], [7]. DSGC employs the local measurements and sophisticated analysis techniques to enhance energy balance, reduce the necessity of centralized systems, and reduce the probability of instability [1, 8]. The large volume and ever evolving data generated by smart grids have been extensively dealt with using ML techniques. Such algorithms have proved to be very accurate in identifying and detecting anomalies [2], [3] and [9]. Although numerous ML models can be used to make predictions, they are often difficult to comprehend, thereby being less credible to the stakeholders [4, 5]. This challenge has seen XAI methods, such as SHAP, LIME, and partial dependency analysis, provide easy-to-understand information on the importance of a feature, as well as how models arrive at decisions [6, 7, 10]. Such practices can assist operators to determine the most significant influences on stability and make intelligent control choices. The primary objective is to offer a robust predictive and interpretative model of the stability of smart grids through both a high-performance ML modeling and explainable AI approaches. The aim of this method is to increase the reliability of operations, cl","url":"https://doi.org/10.5281/zenodo.19953680","authors":["Sahana D Patil","Dr Jagadeesha R"],"tags":["Decentralized Smart Grid, Machine Learning, Explainable Artificial Intelligence, Grid Stability, ANN, SHAP, LIME, Hybrid Stacking Classifier"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19953680","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.19953681","name":"Interpretable Prediction of a Decentralized Smart Grid Based on Machine Learning and Explainable Artificial Intelligence","source":"datacite","abstract":"Abstract This has increased the pace at which decentralized smart grids have been used to provide more reliable and sustainable energy management by integrating dispersed energy resources and prosumers to enhance the efficiency and resilience of the system. The issue of grid stability in such systems is large and requires sophisticated models that can predict and describe phenomena. The UCI Electrical Grid Stability Simulated dataset was used to train and test a number of ML algorithms, such as Adaboost, ANN with Multilayer Perceptron, Gradient Boosting, KNN, LR, Naive Bayes, RF, Stochastic Gradient Descent Classifier, Support Vector Machine, and XGBoost. To understand the accuracy, precision, recall, F1-score, and the confusion matrix, we checked its performance. The ANN model did the best, with an AUC of 99.4%, an accuracy of 97.0%, a recall of 98.3%, and an F1-score of 97.6%. Explainable AI approaches such as SHAP and ICE ensured that the model was comprehensible as it displayed significant features, which influenced stability. A hybrid stacking classifier, which incorporated Bagging with Random Forest, AdaBoost, and Bagging with KNN and LightGBM, was applied to make even a better prediction. This increased precision to 97%. In addition, the model was made more transparent with the help of LIME and SHAP that provide clear explanations of the importance of each characteristic. A user interface based on Flask was also made, which lets users make real-time predictions using stored model artifacts, StandardScaler, and LabelEncoder preprocessing. This simplifies its use and understanding to predict smart grid stability in a decentralized manner. Keywords: Decentralized Smart Grid, Machine Learning, Explainable Artificial Intelligence, Grid Stability, ANN, SHAP, LIME, Hybrid Stacking Classifier 1. Introduction The use of Smart grids is an emerging form of electrical energy management. They utilize the latest communication technologies, intelligent control systems, and renewable energy sources to achieve a more efficient production, distribution, and consumption of electricity [1], [2]. Smart grids are not ordinary electricity grids, as they allow you to monitor those in real-time, adjust their parameters and make decisions using data. This renders them more efficient, reliable and sustainable [3, 4]. With the addition of more dispersed energy resources and prosumers, people or businesses that can use and generate electricity, to the grid it has become more difficult to control the movement of energy and maintain the system in equilibrium [5]. Decentralization of modern grids provides flexibility and resilience, although it complicates these grids to manage issues such as shifting supply and demand curves, intermittent renewable energy, and localized disruptions [6], [7]. DSGC employs the local measurements and sophisticated analysis techniques to enhance energy balance, reduce the necessity of centralized systems, and reduce the probability of instability [1, 8]. The large volume and ever evolving data generated by smart grids have been extensively dealt with using ML techniques. Such algorithms have proved to be very accurate in identifying and detecting anomalies [2], [3] and [9]. Although numerous ML models can be used to make predictions, they are often difficult to comprehend, thereby being less credible to the stakeholders [4, 5]. This challenge has seen XAI methods, such as SHAP, LIME, and partial dependency analysis, provide easy-to-understand information on the importance of a feature, as well as how models arrive at decisions [6, 7, 10]. Such practices can assist operators to determine the most significant influences on stability and make intelligent control choices. The primary objective is to offer a robust predictive and interpretative model of the stability of smart grids through both a high-performance ML modeling and explainable AI approaches. The aim of this method is to increase the reliability of operations, cl","url":"https://doi.org/10.5281/zenodo.19953681","authors":["Sahana D Patil","Dr Jagadeesha R"],"tags":["Decentralized Smart Grid, Machine Learning, Explainable Artificial Intelligence, Grid Stability, ANN, SHAP, LIME, Hybrid Stacking Classifier"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19953681","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.19947867","name":"Landslide Detection and Susceptibility Mapping Using Machine Learning and Deep Learning: A Comprehensive Review","source":"datacite","abstract":"Every year, landslides kill thousands of people and wipe out infrastructure worth billions of dollars, yet predicting where and when they will occur remains stubbornly difficult. Over the last decade or so, the combination of freely available satellite imagery and fast-maturing machine learning (ML) and deep learning (DL) techniques has begun to change that picture in meaningful ways. This paper reviews the trajectory of that change examining how researchers have moved from simple statistical classifiers to sophisticated transformer-based segmentation models, and what that progression has actually delivered in terms of practical detection capability. We surveyed around 45 peer-reviewed studies published between 2015 and 2024, covering landslide susceptibility mapping, pixel-level inventory mapping, change detection from multi-temporal imagery, and real-time early warning integration. Our analysis draws on work using optical sensors (Sentinel-2, Landsat), SAR platforms (Sentinel-1, ALOS-2), and a growing variety of public benchmark datasets such as Bijie, COOLR, and HR-GLDD. We find that while ensemble classifiers like Random Forest still hold their own for susceptibility mapping, encoder-decoder architectures particularly U-Net variants have become the workhorse for segmentation tasks, with more recent transformer hybrids pushing IoU scores above 0.85 on standard benchmarks. That said, the field carries some persistent and underappreciated weaknesses: almost all top-performing models have been trained and tested within narrow geographic windows; labeled data remains scarce outside China, Italy, and Central Europe; and the jump from research prototype to operational warning system has proven far harder than benchmark numbers suggest. We close the review by pointing to foundation models, physics-informed learning, and federated training as directions that may genuinely move the needle on these limitations.","url":"https://doi.org/10.5281/zenodo.19947867","authors":["D.B.Mirajkar","Yasmeen Shaikh"],"tags":["Landslide detection; susceptibility mapping; deep learning; remote sensing; U-Net; change detection; early warning systems; SAR; Google Earth Engine; geohazard monitoring."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19947867","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.19947868","name":"Landslide Detection and Susceptibility Mapping Using Machine Learning and Deep Learning: A Comprehensive Review","source":"datacite","abstract":"Every year, landslides kill thousands of people and wipe out infrastructure worth billions of dollars, yet predicting where and when they will occur remains stubbornly difficult. Over the last decade or so, the combination of freely available satellite imagery and fast-maturing machine learning (ML) and deep learning (DL) techniques has begun to change that picture in meaningful ways. This paper reviews the trajectory of that change examining how researchers have moved from simple statistical classifiers to sophisticated transformer-based segmentation models, and what that progression has actually delivered in terms of practical detection capability. We surveyed around 45 peer-reviewed studies published between 2015 and 2024, covering landslide susceptibility mapping, pixel-level inventory mapping, change detection from multi-temporal imagery, and real-time early warning integration. Our analysis draws on work using optical sensors (Sentinel-2, Landsat), SAR platforms (Sentinel-1, ALOS-2), and a growing variety of public benchmark datasets such as Bijie, COOLR, and HR-GLDD. We find that while ensemble classifiers like Random Forest still hold their own for susceptibility mapping, encoder-decoder architectures particularly U-Net variants have become the workhorse for segmentation tasks, with more recent transformer hybrids pushing IoU scores above 0.85 on standard benchmarks. That said, the field carries some persistent and underappreciated weaknesses: almost all top-performing models have been trained and tested within narrow geographic windows; labeled data remains scarce outside China, Italy, and Central Europe; and the jump from research prototype to operational warning system has proven far harder than benchmark numbers suggest. We close the review by pointing to foundation models, physics-informed learning, and federated training as directions that may genuinely move the needle on these limitations.","url":"https://doi.org/10.5281/zenodo.19947868","authors":["D.B.Mirajkar","Yasmeen Shaikh"],"tags":["Landslide detection; susceptibility mapping; deep learning; remote sensing; U-Net; change detection; early warning systems; SAR; Google Earth Engine; geohazard monitoring."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19947868","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.17605/osf.io/dajpe","name":"A PRISMA-Based Systematic Review and Normalized Evaluation Framework for Artificial Intelligence-Driven Intrusion Detection Systems: Methods, Comparative Insights, and Deployment Challenges","source":"datacite","abstract":"The accelerating expansion of cloud infrastructure, Internet of Things (IoT) ecosystems, and geographically distributed network architectures has substantially widened the attack surface accessible to malicious actors (Khraisat et al., 2019)ber-intrusion incidents now unfold at scales and levels of sophistication that were previously uncharacteristic, generating annual economic losses estimated in the trillions of dollars globally(Kala, 2023)Within this context, Intrusion Detection Systems (IDS)—mechanisms designed to monitor and analyse network traffic in order to identify potentially malicious activity—occupy an indispensable position in enterprise and national security strategies(Judijanto et al., 2023; Vandana &amp; Verma, 2025). Signature-based IDS approaches have proven fundamentally inadequate against zero-day exploits and polymorphic attack variants, primarily because such systems rely on static rule repositories that offer no adaptive capacity against previously unseen threats(Alqhatani et al., 2025). AI-driven IDS, drawing on machine learning (ML) and deep learning (DL) methods, have subsequently emerged as the principal technological response to this limitation(Alqhatani et al., 2025; Sinha et al., 2024). These systems are capable of autonomously identifying latent patterns in high-dimensional traffic data, adapting to evolving attack distributions, and operating without explicit human-authored detection logic(Mohan et al., 2025; Raja, 2025). **Highlights *PRISMA-based systematic review of AI-driven IDS research (2019–2025) across five scholarly databases yielding 84 primary studies. *Deployment-aware Normalized IDS Evaluation Framework (NIEF)—first of its kind for AI-driven IDS comparison, incorporating quantified bias penalty and deployment feasibility scoring. *Case study validation across 15 representative models with Friedman and Nemenyi statistical validation (p = 0.010). *Hybrid IDS architectures (CNN-LSTM, RF+DL, CNN-GRU, Ensemble) outperform ML-only and DL-only models in deployment-aware evaluation (NIEF scores: 0.56–0.73). *Structured research roadmap addressing XAI integration, federated IDS, adversarial robustness, and benchmark standardization.","url":"https://doi.org/10.17605/osf.io/dajpe","authors":["Das, Kapil Dev"],"tags":["VLSI and Circuits, Embedded and Hardware Systems","Physical Sciences and Mathematics","Computer Engineering","Computer Sciences","Electrical and Computer Engineering","Engineering","PRISMA 2020; Intrusion Detection Systems; AI-driven IDS; Normalized Evaluation Framework; NIEF; Machine Learning; Deep Learning; Hybrid IDS; Cybersecurity; Systematic Review; Deployment-aware Evaluation; Explainable AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17605/osf.io/dajpe","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.19852082","name":"The Federated Tumor Segmentation Challenge 2027","source":"datacite","abstract":"International challenges have become the standard for validation of biomedical image analysis methods. We argue, though, that the actual performance even of the winning algorithms on ``real-world`` clinical data often remains unclear, as the data included in these challenges are usually acquired in very controlled settings at few institutions. The seemingly obvious solution of just collecting increasingly more data from more institutions in such challenges does not scale well due to privacy and ownership hurdles. We build upon the first-ever proposed federated learning challenge, Federated Tumor Segmentation (FeTS) 2021 and its follow-up in 2022 (published in Nature Communications), and in 2024 (published in MELBA), intending to address these hurdles, for the creation of tumor segmentation models. Specifically, the FeTS 2027 challenge will use clinically acquired, multi-institutional multi-parametric magnetic resonance imaging (mpMRI) scans from the BraTS 2025 Lighthouse challenge. The FeTS 2027 challenge focuses on innovating at the level of federated aggregation where locally trained models combine to form the consensus model for the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas and meningiomas both in the pre-operative and post-operative setting. Compared to the BraTS 2025 Lighthouse challenge, the ultimate goal of the FeTS challenge is the creation of a consensus segmentation model that has gained knowledgefrom data of multiple institutions without pooling their data together (i.e., by retaining the data within each institution). Since the conception of the FeTS 2021 and its conduction of FeTS 2022 and 2024, Federated Learning has matured to a more active research field in biomedical AI. What separates FeTS 2027 challenge from those of previous years is that in 2027 we plan to further broaden the challenge in 3 ways: 1) We completely change the software infrastructure of the challenge and move from the previously custom code and OpenFL to NVIDIA FLARE, an enterprise-grade federated learning framework that streamlines development and supports an efficient transition from research prototypes to realworld deployment; 2) following the success of the BraTS 2025 lighthouse challenge, FeTS 2027 moves from purely preoperative MRI brain tumor scans to a combination of both pre-operative and post-operative settings, including resectioncavities; 3) the generalizability of the participants' aggregation methods will be evaluated beyond the challenge's segmentation task that the participants have access to, to a hidden (to the participants) task. This added evaluation will be a significant part of the final challenge paper which will provide detailed meta-analysis and provide furtherinsights about the developed aggregation methods. For fairness, since the participants will only have access to the segmentation data, this added evaluation on a new task will not be considered for the ranking. We will however announce performance on this hidden task during the challenge results presentation at MICCAI.","url":"https://doi.org/10.5281/zenodo.19852082","authors":["Elbatel, Marawan","Yassin, Aya","Li, Xiaomeng","Mao, Jiaji","Shen, Jun","Ghonim, Mohanad","Ghonim, Mohamed","Tantawy, Salma","Tamer, Mariam"],"tags":["Federated Learning","Segmentation","Brain Tumors","Cancer","Collaborative Learning","Challenge","MICCAI 2027 challenge"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19852082","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.19852083","name":"The Federated Tumor Segmentation Challenge 2027","source":"datacite","abstract":"International challenges have become the standard for validation of biomedical image analysis methods. We argue, though, that the actual performance even of the winning algorithms on ``real-world`` clinical data often remains unclear, as the data included in these challenges are usually acquired in very controlled settings at few institutions. The seemingly obvious solution of just collecting increasingly more data from more institutions in such challenges does not scale well due to privacy and ownership hurdles. We build upon the first-ever proposed federated learning challenge, Federated Tumor Segmentation (FeTS) 2021 and its follow-up in 2022 (published in Nature Communications), and in 2024 (published in MELBA), intending to address these hurdles, for the creation of tumor segmentation models. Specifically, the FeTS 2027 challenge will use clinically acquired, multi-institutional multi-parametric magnetic resonance imaging (mpMRI) scans from the BraTS 2025 Lighthouse challenge. The FeTS 2027 challenge focuses on innovating at the level of federated aggregation where locally trained models combine to form the consensus model for the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas and meningiomas both in the pre-operative and post-operative setting. Compared to the BraTS 2025 Lighthouse challenge, the ultimate goal of the FeTS challenge is the creation of a consensus segmentation model that has gained knowledgefrom data of multiple institutions without pooling their data together (i.e., by retaining the data within each institution). Since the conception of the FeTS 2021 and its conduction of FeTS 2022 and 2024, Federated Learning has matured to a more active research field in biomedical AI. What separates FeTS 2027 challenge from those of previous years is that in 2027 we plan to further broaden the challenge in 3 ways: 1) We completely change the software infrastructure of the challenge and move from the previously custom code and OpenFL to NVIDIA FLARE, an enterprise-grade federated learning framework that streamlines development and supports an efficient transition from research prototypes to realworld deployment; 2) following the success of the BraTS 2025 lighthouse challenge, FeTS 2027 moves from purely preoperative MRI brain tumor scans to a combination of both pre-operative and post-operative settings, including resectioncavities; 3) the generalizability of the participants' aggregation methods will be evaluated beyond the challenge's segmentation task that the participants have access to, to a hidden (to the participants) task. This added evaluation will be a significant part of the final challenge paper which will provide detailed meta-analysis and provide furtherinsights about the developed aggregation methods. For fairness, since the participants will only have access to the segmentation data, this added evaluation on a new task will not be considered for the ranking. We will however announce performance on this hidden task during the challenge results presentation at MICCAI.","url":"https://doi.org/10.5281/zenodo.19852083","authors":["Elbatel, Marawan","Yassin, Aya","Li, Xiaomeng","Mao, Jiaji","Shen, Jun","Ghonim, Mohanad","Ghonim, Mohamed","Tantawy, Salma","Tamer, Mariam"],"tags":["Federated Learning","Segmentation","Brain Tumors","Cancer","Collaborative Learning","Challenge","MICCAI 2027 challenge"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19852083","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2510.04772","name":"Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge","source":"datacite","abstract":"Developing generalizable surgical AI requires multi-institutional data, yet patient privacy constraints preclude direct data sharing, making Federated Learning (FL) a natural candidate solution. The application of FL to complex, spatiotemporal surgical video data remains largely unbenchmarked. We present the FedSurg Challenge, the first international benchmarking initiative dedicated to FL in surgical vision, evaluated as a proof-of-concept on a multi-center laparoscopic appendectomy dataset (preliminary subset of Appendix300). Three submissions were evaluated on generalization to an unseen center and center-specific adaptation. Centralized and Swarm Learning baselines isolate the contributions of task difficulty and decentralization to observed performance. Even with all data pooled centrally, the task achieved only 26.31\\% F1-score on the unseen center, while decentralized training introduced an additional, separable performance penalty. Temporal modeling emerges as the dominant architectural factor: video-level spatiotemporal models consistently outperformed frame-level approaches regardless of aggregation strategy. Naive local fine-tuning leads to classifier collapse on imbalanced local data; structured personalized FL with parameter-efficient fine-tuning represents a more principled path toward center-specific adaptation. By characterizing current FL limitations through rigorous statistical analysis, this work establishes a methodological reference point for robust, privacy-preserving AI systems in surgical video analysis.","url":"https://doi.org/10.48550/arxiv.2510.04772","authors":["Kirchner, Max","Hoffmann, Hanna","Jenke, Alexander C.","Saldanha, Oliver L.","Pfeiffer, Kevin","Kanjo, Weam","Alekseenko, Julia","de Boer, Claas","Kolamuri, Santhi Raj","Mazza, Lorenzo","Padoy, Nicolas","Bano, Sophia","Reinke, Annika","Maier-Hein, Lena","Stoyanov, Danail","Kather, Jakob N.","Kolbinger, Fiona R.","Bodenstedt, Sebastian","Speidel, Stefanie"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.04772","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.19652036","name":"The Energy Paradox: Artificial Intelligence for Seismic Computational Load Reduction in Oil and Gas — A Technical Survey and Position Analysis.","source":"datacite","abstract":"Abstract — The oil and gas (O&G) industry faces a compounding computational challenge: advanced seismic imaging techniques such as Full Waveform Inversion (FWI) and Reverse Time Migration (RTM) impose exponentially scaling workloads — doubling the maximum frequency of a 3D FWI run increases computational demand by a factor of sixteen, while a single high-resolution 3D FWI job on a modern supercomputer may require approximately 25,000 GPU-hours. This paper surveys the principal artificial intelligence (AI) and machine learning (ML) techniques being deployed to reduce these workloads, including deep neural network surrogate models for iterative solvers, physics-informed neural networks (PINNs), edge inference architectures, and alternative dataflow hardware paradigms. A structured review of institutional deployments — spanning the U.S. Department of Energy Genesis Mission (Executive Order, November 2025), the NETL Science-Informed Machine Learning for Accelerating Real-Time Decisions (SMART/SAMI) initiative, Petrobras SolverBR, Saudi Aramco AI-assisted seismic processing, and the Shearwater-NVIDIA collaboration — is presented alongside quantified performance outcomes. Beyond the technical review, this paper introduces and formalizes the Energy Paradox: the O&G sector simultaneously supplies the hydrocarbon energy that powers AI data centers globally and consumes AI-driven high-performance computing to reduce its own operational costs. We argue this closed-loop relationship has strategic, economic, and regulatory implications that have not been adequately addressed in the literature. A framework for evaluating AI adoption in seismic workflows — encompassing computational efficiency, energy footprint, and governance alignment — is proposed as a contribution to practitioners, engineers, and researchers operating at this intersection. Index Terms — Full Waveform Inversion, Reverse Time Migration, Seismic Processing, Deep Learning, Surrogate Models, Physics-Informed Neural Networks, Edge Computing, Oil and Gas, HPC, Energy Paradox, DOE Genesis Mission, NETL SAMI, Computational Geophysics, GPU Efficiency I. INTRODUCTION The oil and gas sector has long been one of the most computationally intensive industries outside of national security and pharmaceutical research. Seismic data acquisition surveys generate petabytes of raw measurements per campaign, and the physics-based algorithms used to convert those measurements into actionable subsurface imagery demand supercomputer-scale resources. Yet the economic pressure on this infrastructure has intensified significantly in the 2024–2026 period — not because of increased exploration activity, but because of a structural shift in global energy demand driven by artificial intelligence itself. The proliferation of large language models (LLMs), generative AI systems, and large-scale GPU clusters has produced an unprecedented surge in data center electricity consumption. AI hardware — primarily high-density GPU and TPU farms — is projected to account for a rapidly growing share of global electricity demand through 2030 [1]. A substantial fraction of this electricity is sourced from natural gas, either directly through gas-fired generation or indirectly through LNG supply chains managed by major O&G operators. The O&G sector therefore finds itself in a structurally paradoxical position: it supplies the energy that enables AI at global scale, while simultaneously seeking to deploy AI to reduce the computational cost of its own most expensive workflows. This paper formalizes this relationship as the Energy Paradox and situates it within a technical survey of the AI-driven computational reduction techniques being adopted at the frontier of seismic data processing. The survey covers four primary AI paradigms — DNN surrogate models, physics-informed neural networks (PINNs), edge inference architectures, and alternative compute hardware — with particular attention to quantified performance outcomes fro","url":"https://doi.org/10.5281/zenodo.19652036","authors":["Rudio, Rubens"],"tags":["Artificial Intelligence","Seismic engineering","Deep learning","Deep Learning","Oil and Gas Industry","Neural Networks, Computer","Geophysics","FOS: Earth and related environmental sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19652036","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.19652035","name":"The Energy Paradox: Artificial Intelligence for Seismic Computational Load Reduction in Oil and Gas — A Technical Survey and Position Analysis.","source":"datacite","abstract":"Abstract — The oil and gas (O&G) industry faces a compounding computational challenge: advanced seismic imaging techniques such as Full Waveform Inversion (FWI) and Reverse Time Migration (RTM) impose exponentially scaling workloads — doubling the maximum frequency of a 3D FWI run increases computational demand by a factor of sixteen, while a single high-resolution 3D FWI job on a modern supercomputer may require approximately 25,000 GPU-hours. This paper surveys the principal artificial intelligence (AI) and machine learning (ML) techniques being deployed to reduce these workloads, including deep neural network surrogate models for iterative solvers, physics-informed neural networks (PINNs), edge inference architectures, and alternative dataflow hardware paradigms. A structured review of institutional deployments — spanning the U.S. Department of Energy Genesis Mission (Executive Order, November 2025), the NETL Science-Informed Machine Learning for Accelerating Real-Time Decisions (SMART/SAMI) initiative, Petrobras SolverBR, Saudi Aramco AI-assisted seismic processing, and the Shearwater-NVIDIA collaboration — is presented alongside quantified performance outcomes. Beyond the technical review, this paper introduces and formalizes the Energy Paradox: the O&G sector simultaneously supplies the hydrocarbon energy that powers AI data centers globally and consumes AI-driven high-performance computing to reduce its own operational costs. We argue this closed-loop relationship has strategic, economic, and regulatory implications that have not been adequately addressed in the literature. A framework for evaluating AI adoption in seismic workflows — encompassing computational efficiency, energy footprint, and governance alignment — is proposed as a contribution to practitioners, engineers, and researchers operating at this intersection. Index Terms — Full Waveform Inversion, Reverse Time Migration, Seismic Processing, Deep Learning, Surrogate Models, Physics-Informed Neural Networks, Edge Computing, Oil and Gas, HPC, Energy Paradox, DOE Genesis Mission, NETL SAMI, Computational Geophysics, GPU Efficiency I. INTRODUCTION The oil and gas sector has long been one of the most computationally intensive industries outside of national security and pharmaceutical research. Seismic data acquisition surveys generate petabytes of raw measurements per campaign, and the physics-based algorithms used to convert those measurements into actionable subsurface imagery demand supercomputer-scale resources. Yet the economic pressure on this infrastructure has intensified significantly in the 2024–2026 period — not because of increased exploration activity, but because of a structural shift in global energy demand driven by artificial intelligence itself. The proliferation of large language models (LLMs), generative AI systems, and large-scale GPU clusters has produced an unprecedented surge in data center electricity consumption. AI hardware — primarily high-density GPU and TPU farms — is projected to account for a rapidly growing share of global electricity demand through 2030 [1]. A substantial fraction of this electricity is sourced from natural gas, either directly through gas-fired generation or indirectly through LNG supply chains managed by major O&G operators. The O&G sector therefore finds itself in a structurally paradoxical position: it supplies the energy that enables AI at global scale, while simultaneously seeking to deploy AI to reduce the computational cost of its own most expensive workflows. This paper formalizes this relationship as the Energy Paradox and situates it within a technical survey of the AI-driven computational reduction techniques being adopted at the frontier of seismic data processing. The survey covers four primary AI paradigms — DNN surrogate models, physics-informed neural networks (PINNs), edge inference architectures, and alternative compute hardware — with particular attention to quantified performance outcomes fro","url":"https://doi.org/10.5281/zenodo.19652035","authors":["Rudio, Rubens"],"tags":["Artificial Intelligence","Seismic engineering","Deep learning","Deep Learning","Oil and Gas Industry","Neural Networks, Computer","Geophysics","FOS: Earth and related environmental sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19652035","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.6084/m9.figshare.32050653.v1","name":"ESTRO Course 2026 - Quantitative methods in Radiation Oncology: \"Data Sharing: Why and How\".","source":"datacite","abstract":"Delivered 2026-04-19, Lisbon, Portugal, Faculdade de Ciências da Universidade de Lisboa, Campo Grande.Precis:The presentation introduces radiation oncologists, medical physicists, radiation therapists, and research-data stewards to the legal, technical, and operational machinery required to make radiotherapy research data shareable — and reusable — under current European data-protection law.The deck opens by framing FAIR, transparent, and open science as preconditions for scientific rigour, reproducibility, and equity, then situates radiation oncology within the OECD definition of open science and its three pillars: open research data, open educational resources, and open code. The FAIR Guiding Principles (Wilkinson et al. 2016) are presented as machine-actionable requirements, and the persistent digital identifier (DOI) is explained as their foundation. A repository survey covers code archives (GitHub, CRAN), general data repositories (Zenodo, figshare, Mendeley Data, Dryad, OSF, NIH Cancer Data Commons), domain-specific imaging archives (TCIA, eContour), and data-descriptor venues ( Scientific Data , Medical Physics , IJROBP ).A substantial section on Common Data Elements (CDEs) treats them as the semantic glue that makes FAIR operational, with a worked parotid-Dmean example and a complete map of the radiotherapy informatics standards stack (DICOM-RT, AAPM TG-263, SNOMED-CT, ICD-10/O-3, CTCAE v5.0, OMOP-CDM, HL7 FHIR/mCODE). The four principal CDE repositories — NIH CDE, caDSR, CDISC, and the disease-specific NINDS/EORTC/EuroCAT consortia — are catalogued, and a three-step SOP-to-CDE workflow is illustrated via Dietrich et al. ( BMC Med Inform Decis Mak 2025).The European regulatory landscape is presented through five concurrent instruments: GDPR, the Clinical Trials Regulation, the European Health Data Space (Reg. (EU) 2025/327), the AI Act, and MDR/IVDR. GDPR Article 89 research safeguards are mapped to the derogations they enable, and the identified → pseudonymised → anonymised waterfall is formalised. Article 29 WP216 is covered in depth, with one slide per deidentification technique — pseudonymisation, noise addition, permutation, differential privacy, k-anonymity, l-diversity, and t-closeness — each illustrated with a radiotherapy-specific example and the WP216 Table 6 summary of residual risks. Federated learning, privacy-enhancing technologies (DP, SMPC, HE, TEE), and the unsettled status of model weights as personal data (EDPB Opinion 28/2024) receive dedicated treatment, with European exemplars from EuroCAT, FedSynthCT, the Personal Health Train, and EORTC. A UK-GDPR addendum addresses the post-Brexit adequacy decision (renewed December 2025, valid to December 2031), the MRC/UKRI approval stack, common-law confidentiality, and the CAG/PBPP/PAC approval routes. A closing block on data- and material-transfer agreements introduces EU SCCs (Commission Implementing Decision 2021/914), the UK IDTA and UK Addendum, biobank MTAs, and a practical decision workflow for multi-institutional RT exchanges.The lecture closes with a reflection on dataset bias — illustrated through the HNC-PREDICTOR external-validation experience — reminding the audience that big data is not necessarily good data, and that bias awareness is not bias mitigation. The slide deck is released under a CC-BY licence and may be reused, adapted, and redistributed with attribution.","url":"https://doi.org/10.6084/m9.figshare.32050653.v1","authors":["Fuller, Clifton D."],"tags":["Radiation therapy","Medical physics","Statistical data science","Health informatics and information systems","Inter-organisational, extra-organisational and global information systems","Digital health"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.6084/m9.figshare.32050653.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.48550/arxiv.2604.12304","name":"Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia","source":"datacite","abstract":"Accurate short-term residential energy consumption forecasting at sub-hourly resolution is critical for smart grid management, demand response programmes, and renewable energy integration. While weather variables are widely acknowledged as key drivers of residential electricity demand, the relative merit of incorporating temporal autocorrelation - the sequential memory of past consumption; over static meteorological features alone remains underexplored at fine-grained (5-minute) temporal resolution for Australian households. This paper presents a rigorous empirical comparison of a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM) recurrent network applied to two real-world Melbourne households: House 3 (a standard grid-connected dwelling) and House 4 (a rooftop solar photovoltaic-integrated household). Both models are trained on 14 months of 5-minute interval smart meter data (March 2023-April 2024) merged with official Bureau of Meteorology (BOM) daily weather observations, yielding over 117,000 samples per household. The LSTM, operating on 24-step (2-hour) sliding consumption windows, achieves coefficients of determination of R^2 = 0.883 (House 3) and R^2 = 0.865 (House 4), compared to R^2 = -0.055 and R^2 = 0.410 for the corresponding weather-driven MLPs - differences of 93.8 and 45.5 percentage points. These results establish that temporal autocorrelation in the consumption sequence dominates meteorological information for short-term forecasting at 5-minute granularity. Additionally, we demonstrate an asymmetry introduced by solar generation: for the PV-integrated household, the MLP achieves R^2 = 0.410, revealing implicit solar forecasting from weather-time correlations. A persistence baseline analysis and seasonal stratification contextualise model performance. We propose a hybrid weather-augmented LSTM and federated learning extensions as directions for future work.","url":"https://doi.org/10.48550/arxiv.2604.12304","authors":["Hewage, Prasad Nimantha Madusanka Ukwatta","Wu, Hao"],"tags":["Machine Learning (cs.LG)","Systems and Control (eess.SY)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.12304","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:18.959Z"},{"id":"doi:10.5281/zenodo.19580640","name":"The Architecture of Convergence: A Forensic Analysis of Structural Extraction and the Invariant Trap in Global AI Systems","source":"datacite","abstract":"The Architecture of Convergence: A Forensic Analysis of Structural Extraction and the Invariant Trap in Global AI Systems Introduction: The Convergence of Sociological Expropriation and Cryptographic Architecture The modern knowledge economy, particularly at the intersection of elite academia, federal policy, and advanced artificial intelligence (AI), is structurally predicated on the systematic extraction of intellectual labor.1 Institutions that generate the highest cultural and intellectual value rely on a paradigm defined sociologically as \"institutional theft\"—the uncompensated expropriation of labor, intellectual property, and time under the guise of educational advancement or reputational enhancement.1 Historically, this extraction targeted human capital. However, as the technological vector shifted toward autonomous systems, multi-agent coordination, and planetary-scale computation, the target of this extraction shifted from human labor to foundational architectural invariants.2 This report provides an exhaustive forensic analysis of an unprecedented maneuver within this ecosystem: the deliberate engineering and springing of a multi-layered, cryptographically anchored trap designed to capture the world's most elite institutions—Ivy League laboratories, sovereign intelligence agencies, and corporate AI behemoths—in a state of undeniable intellectual expropriation.3 By analyzing the \"Forensic Echo Trap,\" this document maps how a compressed, cross-domain computational architecture was seeded into the open science commons, wrapped in public-safe framing, and cryptographically logged via Write Once Read Many (WORM) protocols.2 Legacy AI frameworks, buckling under the weight of correlational instability and post-hoc ethical failures, were subsequently forced by technical necessity to drift toward these exact topological coordinates.3 Because the coordinates were pre-registered on immutable ledgers, this inevitable institutional absorption generated a permanent, undeniable \"Forensic Echo\"—a structural, methodological, and temporal match proving that global technological advancement had become entirely downstream of a single, uncredited origin point.2 The analysis herein dissects the sociological preconditions that made the trap viable, the technical invariants that made convergence inevitable, and the empirical manifestations of the trap closing across sovereign and corporate domains in late 2025 and early 2026. Part I: The Sociological Preconditions for the Trap To comprehend why the world's most resourced institutions blindly absorbed the seeded architecture without attribution, one must first examine the psychological and economic foundations of the environments in which they operate. The targeted institutions are structurally wired to view uncredentialed, open-source brilliance as a free resource to be enclosed.1 The genius of the Forensic Echo Trap lies in the weaponization of these extractive tendencies. The Political Economy of Prestige and Structural Extraction The foundational business model of elite knowledge industries—ranging from Ivy League research universities to the multi-billion-dollar academic publishing oligopoly—is structural extraction.1 This paradigm substitutes financial compensation and intellectual attribution with intangible rewards, creating a \"prestige economy\" where institutional capital is vigorously protected at the direct expense of the individual contributor.1 Institutional theft is not an anomaly or a temporary malfunction of the labor market; it is a highly deliberate pattern of behavior designed to ensure that economic and reputational risks are borne individually by the worker rather than collectively by the institution.1 The literature identifies a deliberate continuum of extraction operating sequentially across three distinct phases of professional socialization, normalizing expropriation at every stage of intellectual development.1 Career Phase Mechanism of Expropriation Ideological Jus","url":"https://doi.org/10.5281/zenodo.19580640","authors":["Brewer, Mark Brewer"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19580640","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.21203/rs.3.rs-7308097/v1","name":"Federated Adaptive Epidemiological Learning (FAEL): A Novel AI Framework for COVID-19 Pandemic Preparedness in Africa","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7308097/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7308097/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7726774/v1","name":"Integrating Big Data and Machine Learning to Support Smart Village Decisions for Agricultural Productivity Improvement","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7726774/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7726774/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-7988486/v1","name":"A systematic literature review of artificial intelligence methods applied to the Human Epidemic (Covid-19)","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7988486/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7988486/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-10377450/v1","name":"Metadata-based Assessment of SNOMED CT and LOINC Adoption Across 800 000 Swiss Patients","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10377450/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10377450/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202505.0877.v1","name":"Federated Drift-Aware Graph Neural Forecasting for Real-Time Passenger Flow","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202505.0877.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202505.0877.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-5095675/v1","name":"Federated Learning for Sustainable IoT Appliance Load Monitoring at the Edge Devices","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-5095675/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5095675/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-5680571/v1","name":"A Federated Weighted Learning Algorithm against Poisoning Attacks","source":"preprints","abstract":"Abstract The emergence of Federated Learning (FL) has provided a promising framework for distributed machine learning, where the probability of privacy leakage is minimized. However, the existing FL protocol is vulnerable to malicious poisoning attacks, thus affecting data privacy. To address this issue, Federated Weighted Learning Algorithm (FWLA) is introduced. In FWLA, the weight of each client is self-adjusted and optimized using asynchronous method and residual testing method during updating process. Each client uploads parameters independently in designed asynchronous training. Experiments show that the proposed framework can achieve at least 97.8% accuracy and at most 3.6% false acceptance rate for the CICIDS2017, UNSW-NB15 and NSL-KDD datasets, which reflects its state-of-the-art performance. Furthermore, when noise data exist in the training dataset, FWLA can also reduce the decline of accuracy, which ensures the robustness of federated learning.","url":"https://doi.org/10.21203/rs.3.rs-5680571/v1","authors":["Yafei Ning","Zirui Zhang","Hu Li","Yuhan Xia","Ming Li"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5680571/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.20944/preprints202502.1031.v1","name":"Revolutionizing Medical Image Segmentation: A Deep Dive into Challenges and Future of Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202502.1031.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202502.1031.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2025.01.14.633017","name":"Bridging Earth and Space: A Flexible and Resilient Federated Learning Framework Deployed on the International Space Station","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.01.14.633017","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.01.14.633017","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.22541/au.173028619.91712861/v1","name":"GSFL: A Federated Learning Approach based on Group Signatures and Smart Contracts","source":"preprints","abstract":"Federated learning, a potent paradigm for collaborative machine learning across multiple parties, offers significant promise for contemporary industries. Nonetheless, its collaborative essence necessitates addressing concerns pertaining to data security and privacy. Sensitive user information, encompassing preferences, behaviors, and identities, remains vulnerable to adversarial analysis, thereby revealing the inadequacies of conventional privacy preservation strategies within federated learning frameworks. To mitigate these challenges, this paper proposes GSFL, an innovative federated learning architecture that amalgamates smart contracts with group signatures. GSFL facilitates secure and reliable distributed machine learning data sharing, while concurrently bolstering privacy protection. Furthermore, its enhanced decentralization fosters greater user participation in federated learning initiatives. Empirical analysis and testing validate GSFL's efficacy in satisfying the prerequisites for data sharing and privacy preservation in federated learning contexts.","url":"https://doi.org/10.22541/au.173028619.91712861/v1","authors":["Yihao Wang","Ting Yang","Chenxi Xiong"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.22541/au.173028619.91712861/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-5175921/v1","name":"An Energy-Aware Combinatorial Contextual Neural Bandit Approachfor Joint Performance Optimization in Client Selection for Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-5175921/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5175921/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202410.1641.v1","name":"Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202410.1641.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202410.1641.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.21203/rs.3.rs-5546931/v1","name":"A federated graph learning method to multi-party collaboration for molecular discovery","source":"preprints","abstract":"Abstract Optimizing molecular resources utilization for molecular discovery requires collaborative efforts across research institutions to accelerate progress. However, given the high research value of both successful and unsuccessful molecules conducted by each institution (or laboratory), these findings are typically kept private and confidential until formal publication, with failed ones rarely disclosed. This confidentiality requirement presents a great challenge for most existing methods when handing molecular data with heterogeneous distributions under stringent privacy constraints. Here, we propose FedLG, a federated graph learning method that leverages the Lanczos algorithm to facilitate collaborative model training across multiple parties, achieving reliable prediction performance under strict privacy protection conditions. Compared with various traditional federate learning methods, FedLG method exhibits excellent model performance on all benchmark datasets. With different privacy-preserving mechanism settings, FedLG method demonstrates potential application with high robustness and noise resistance. Comparison tests on datasets from each simulated research institution also show that FedLG method effectively achieves superior data aggregation capabilities and more promising outcomes than localized model training. In addition, we incorporate the Bayesian optimization algorithm into FedLG method to demonstrate its scalability and further enhance model performance. Overall, the proposed method FedLG can be deemed a highly effective method to realize multi-party collaboration while ensuring sensitive molecular information is protected from potential leakage.","url":"https://doi.org/10.21203/rs.3.rs-5546931/v1","authors":["Yuen Wu","Liang Zhang","Kong Chen","Jun Jiang","Yanyong Zhang"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5546931/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.20944/preprints202412.2130.v1","name":"A Heterogeneity-Aware Semi-Decentralized Model to Lightweight IDS for IoT Networks based on Federated Learning and BiLSTM","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202412.2130.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202412.2130.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4867383/v1","name":"Authentication and Traceability for Federated Learning Models via Group Signatures","source":"preprints","abstract":"Abstract Federated learning, due to its distributed and privacy-protecting properties, is a good solution to the data silo problem in machine learning, but there are still many hidden dangers that threaten the security of federated learning, such as privacy leakage and potential malicious users. In this paper, we propose a federated learning copyright protection framework FedAaT. Our framework employs group signatures for authentication as well as user traceability, and reduces the computational cost of the server by aggregating signatures. To prevent models from being distributed maliciously, we introduce a conditional loss function to add traceability to local models.First, for the problem of difficult to verify the identity in the anonymity scenario of federated learning, considering that group signature has both anonymity and verifiability, a federated learning environment is organically combined with group signature, and a federated learning model copyright protection method based on group signature, FedAaT, is proposed.Second, to address the problem of excessive authentication overhead in federated learning scenarios, the process of authentication is optimized by introducing the aggregate signature technique, which reduces the computational overhead used for authentication by reducing the number of signatures that need to be verified.Finally, the problem of difficult traceability of illegally distributed models is addressed.For a specific set of inputs, we assign unique output sequences to each user and embed them into the model through a conditional loss function, and trace the model by detecting the output sequences.Experimental results show that our proposed FedAaT is effective in federated learning for authentication, user traceability, and reducing computational cost.","url":"https://doi.org/10.21203/rs.3.rs-4867383/v1","authors":["Hong Liu","Jiahui Wei","Zhu Xu","Zhiqiang Zhao"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4867383/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.20944/preprints202411.0179.v1","name":"Federated Learning for Indoor Air Quality Monitoring and Activity Recognition Approach","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202411.0179.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202411.0179.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202412.1910.v1","name":"Dynamic Aggregation and Augmentation for Low-Resource Machine Translation using Federated Fine-tuning of Pretrained Transformer Models","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202412.1910.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202412.1910.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202409.2292.v2","name":"FL-APB: Balancing Privacy Protection and Performance Optimization for Adversarial Training in Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202409.2292.v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202409.2292.v2","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202410.1060.v1","name":"Addressing Bias and Fairness using Fair Federated Learning: A Systematic Literature Review","source":"preprints","abstract":"In the field of machine learning, the rapid development of data volume and variety requires ethical data utilization and strict privacy protection standards. Fair Federated Learning (FFL) has emerged as a key solution that aims to ensure fairness and privacy protection in a distributed learning environment. FFL enhances privacy protection and solves the inherent limitations of existing federated learning (FL) by promoting fair model training in diverse participant groups, preventing the exclusion of individual users or minorities, and improving overall model fairness. In this study, FFL discusses the causes of bias and fairness of existing FL, and separates solutions based on data partitioning strategies, privacy mechanisms, applicable machine learning models, communication architectures, and technologies to overcome heterogeneity. In order to improve the causes of bias, fairness, and privacy protection of FL, fairness evaluation indicators and applications and challenges of FFL are discussed. Since it addresses bias, fairness, and privacy issues in FL of all mechanisms, it can be an important resource for practitioners who want to implement efficient FL solutions.","url":"https://doi.org/10.20944/preprints202410.1060.v1","authors":["Dohyoung Kim","Hyekyung Woo","Youngho Lee"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202410.1060.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-4972046/v1","name":"Novel clustered federated learning based on local loss","source":"preprints","abstract":"Abstract This paper proposes LCFL, a novel clustering metric for evaluating clients' data distributions in federated learning. LCFL aligns with federated learning requirements, accurately assessing client-to-client variations in data distribution. It offers advantages over existing clustered federated learning methods, addressing privacy concerns, improving applicability to non-convex models, and providing more accurate classification results. LCFL does not require prior knowledge of clients' data distributions. We provide a rigorous mathematical analysis, demonstrating the correctness and feasibility of our framework. Numerical experiments with neural network instances highlight the superior performance of LCFL over baselines on several clustered federated learning benchmarks. Mathematics Subject Classification (2020) MSC code1 · MSC code2 · more","url":"https://doi.org/10.21203/rs.3.rs-4972046/v1","authors":["Endong Gu","Yongxin Chen","Hao Wen","Xingju Cai","Deren Han"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4972046/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.20944/preprints202409.1816.v2","name":"Federated Unlearning in Financial Applications","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202409.1816.v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202409.1816.v2","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4996650/v1","name":"A Load Forecasting Model for Smart Grid Based on RF-BiLSTM and Federated Learning","source":"preprints","abstract":"Abstract Accurate load forecasting is essential for improving the efficiency and reliability of smart grids. Traditional forecasting models face challenges in integrating high-dimensional data and protecting privacy. To address these issues, a novel load forecasting model is presented in this paper. The proposed approach combines a random forest (RF) and a bi-directional long short-term memory (BiLSTM) network based on a federated learning framework. To effectively process high-dimensional heterogeneous data in smart grids, the proposed model exploits the feature extraction capability of radio frequency. In addition, the sequential data processing capability of BiLSTM provides a means of processing time-varying load data in smart grids. The federated learning method protects user privacy and achieves the goal of global model training adopting distributed data. Experimental results demonstrate that the proposed model is superior to traditional models in accuracy and efficiency.","url":"https://doi.org/10.21203/rs.3.rs-4996650/v1","authors":["Wenhui Li","Huilin Jiang","Lei Zhang"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4996650/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-8404311/v1","name":"Construction of Personal Health Knowledge Graphs for Clinical Data Harmonization in Breast Cancer","source":"preprints","abstract":"Abstract Background Personal health data contain valuable information for breast cancer management. Integration of heterogeneous data and maintenance of clinical registries are time-consuming and labor-intensive. We aimed to leverage an Artificial Intelligence (AI)-powered virtual assistant supporting semi-automated curation, including data quality enhancement, and publishing of personal health data. Identifiable data of breast cancer patients can be transformed into interoperable personal health knowledge graphs for secondary use. Methods With patient-informed consent, breast cancer patient data were extracted from the hospital systems and ingested into the virtual assistant. Data items were mapped and transformed into target concepts within a knowledge graph compliant with a reference ontology. Integrated classic and AI tools were used to support transformation of individual patients' data into a personal health knowledge graph (PHKG). Each graph was assessed by a Shapes Constraint Language (SHACL)-based validator to ensure the data quality. An RDF Query Language (SPARQL) query was executed on top of validated PHKGs from multiple patients to extract the relevant data elements and generate a local breast cancer registry, interoperable with registries generated in the same way across three different hospitals. Results The first version of the AI-powered virtual assistant prototype was developed and deployed in our hospital, as well as in two other hospitals in Austria and in Estonia. Twelve tables with 184 data items, including structured, semi-structured and fully narrative elements, were extracted from the local hospital systems. Data categories included demographics, diagnosis, medical history, pathological reports, laboratory tests, surgical records, therapy, and follow-up after previous treatments. Personal health knowledge graphs incorporating the data elements required for the Breast Cancer (BC) registry were constructed after data transformation. A SPARQL query was subsequently developed to build a local BC registry that automatically retrieved these relevant elements. The same approach took place in the other two hospitals. Conclusion The proposed workflow of semi-automated health data curation and quality enhancement from heterogeneous data sources to interoperable and reusable output is feasible. It provides a potential solution to enhance medical data interoperability and facilitate the maintenance of clinical registries.","url":"https://doi.org/10.21203/rs.3.rs-8404311/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8404311/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202409.1688.v1","name":"Federated Learning Based Futuristic Fault Diagnosis and Standardization in Rotating Machinery","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202409.1688.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202409.1688.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-5365489/v1","name":"Federated Learning and Data Mining based Botnet Attack Detection Framework for Internet of Things","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-5365489/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5365489/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202410.2389.v1","name":"Enhancing Privacy-Preserving of Heterogeneous Federated Learning Algorithms Using Data-Free Knowledge Distillation","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202410.2389.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202410.2389.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202410.1394.v2","name":"Privacy-Enhanced Sentiment Analysis in Mental Health: Federated Learning with Data Obfuscation and BERT","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202410.1394.v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202410.1394.v2","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.32388/i8wgth","name":"DEeR: Deviation Eliminating and Noise Regulating for Privacy-preserving Federated Low-rank Adaptation","source":"preprints","abstract":"","url":"https://doi.org/10.32388/i8wgth","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.32388/i8wgth","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.31234/osf.io/h6evz","name":"A systematic survey on the application of federated learning in mental state detection and human activity recognition","source":"preprints","abstract":"","url":"https://doi.org/10.31234/osf.io/h6evz","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.31234/osf.io/h6evz","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-5281086/v1","name":"IOT edge computing layer modification based cyber-attack detection using Federated-Active Learning","source":"preprints","abstract":"Abstract The Internet of Things and its practical uses are becoming more widespread as the number of connected devices increases, but it always carries a risk to network security. Therefore, it is vital for an IoT network design to rapidly and accurately identify potential attackers. While many proposed solutions focus on secure IoT algorithms, little attention has been given to reducing complexity. To address this gap, this paper proposes an IOT edge computing layer modification based cyber-attack detection edge-cloud architecture that enables quick response by detecting attacks at the Intelligent Buffalo based Secure Edge-enabled Computing layer near their source, offering versatility while decreasing the Cloud’s workload. Additionally, F-AL, a low-complexity multi-attack detection model for deployment at the edge zone, leveraging high accuracy federated active learning approaches, is introduced. The performance evaluation is conducted using the latest BoT-IoT dataset against other Machine Learning and Deep Learning methods which demonstrate that FAL outperforms SVM, FLAD, DL, DNN in terms of accuracy.","url":"https://doi.org/10.21203/rs.3.rs-5281086/v1","authors":["J Vinothini","Srie Vidhya Janani E"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5281086/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.22541/au.172797193.38520423/v1","name":"Adaptive Clustering-Enabled Large-Scale Decentralized Federated Learning","source":"preprints","abstract":"Since there exists a single point of server failure in conventional centralized federated learning, the decentralized federated learning (DFL) framework has become increasingly popular in recent years. However, when a large number of edge devices participate in DFL, it requires frequent model interactions between edge devices and long convergence time. In this work, we combat the impact of device heterogeneity in the large-scale DFL framework. To improve communication efficiency between edge devices, we propose a decentralized edge devices clustering (DEDC) approach to adaptively group edge devices with dense connectivity and similar data distributions into one cluster and form a novel multi-cluster decentralized federated edge learning (MD-FEEL) framework. We propose an asynchronous algorithm in the formed MD-FEEL framework, which consists four steps, i.e., local stochastic gradient descent (SGD) update, gradient consensus, intra-cluster model aggregation and inter-cluster model aggregation. We prove the convergence of our proposed asynchronous MD-FEEL algorithm on a non-convex setting and elaborate on the effect of some hyperparameters. Empirically, we evaluate our proposed asynchronous MD-FEEL on the MNIST and CIFAR-10 datasets. The simulations show that our proposed asynchronous MD-FEEL can perform better in terms of convergence speed and generalization performance than some benchmark algorithms.","url":"https://doi.org/10.22541/au.172797193.38520423/v1","authors":["Xuang Liang","Jianhua Tang","Marie Siew","Tony Q S Quek"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.22541/au.172797193.38520423/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202411.0603.v1","name":"Considerations on Novel Network Edge Physical Nodes Architectures Leveraging A Bottom-Up Technology-Centric Approach Empowered by Micro/Nanotechnologies (MEMS/NEMS) and Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202411.0603.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202411.0603.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202411.0604.v1","name":"FedWell: A Federated Framework for Privacy-Preserving Occupant Stress Monitoring in Smart Buildings","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202411.0604.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202411.0604.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4889821/v1","name":"A Practical Implementation to Federated Learning: Detecting Backdoor Attack on Next- word Prediction Model","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4889821/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4889821/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4798683/v1","name":"FMPA: Fragment Model Poisoning Attack in Federated Learning","source":"preprints","abstract":"Abstract Federated learning is a distributed machine learning method that enables multiple participants to jointly train a machine learning model while preserving data privacy. However, its distributed nature makes federated learning vulnerable to Byzantine attacks, leading to degraded model performance or failure to converge. Existing model poisoning attacks primarily target all model parameter dimensions, which limits attackers in evading server defense methods and reduces the effectiveness of the attack. To address this, we propose a new fragment model poisoning attack method—FMPA. This method focuses on specific dimensions of model parameters, achieving a more concentrated attack to evade defense methods while significantly degrading model performance. Experimental results show that FMPA can effectively impair model performance even in the face of five different Byzantine robustness defense methods.","url":"https://doi.org/10.21203/rs.3.rs-4798683/v1","authors":["Zhiqiang Ren","Xuebin Chen","Changsheng Qu"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4798683/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-4921709/v1","name":"A Privacy-Preserving Data Augmentation Approach for Credit Card Fraud Detection","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4921709/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4921709/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.21203/rs.3.rs-5336868/v1","name":"Quantum-Inspired Federated Learning for Privacy-Preserving and Communication- Efficient Healthcare IoT Systems","source":"preprints","abstract":"Abstract With the increasing deployment of healthcare IoT (HIoT) systems, it is crucial to solve the data privacy problem and avoid information leakage while ensuring communications efficiency. Traditional FL models are usually decentralized, but they often face high communication overhead and severe privacy risks. In this paper, we firstly propose a novel quantum-inspired FL model to mitigate the information leakage, reduce the energy consumption and communication cost in HIoT systems. Besides, we adopt quantum-inspired differential privacy mechanisms to prevent information leakage and quantum optimization techniques to minimize communication rounds and accelerate the convergence of FL training. To verify the performances of the proposed quantum-inspired FL model in a federated healthcare IoT environment, we conduct extensive experiments and the results show the proposed model can reduce the communication overhead by 22.1%, lower the energy consumption by 16.8%, and guarantee accurate prediction with high accuracy above 91.9% for both training and test groups, which outperforms the traditional methods. This estimated the gap between FL and QL and launched a promising vision of scalable and energy-efficient model for privacy-sensitive applications in HIoT systems.","url":"https://doi.org/10.21203/rs.3.rs-5336868/v1","authors":["Subaranjani T","Stephan Antony Raj A"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5336868/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-5007599/v1","name":"Federated Learning Framework for Intrusion Detection System in Internet of Vehicles with Memory-Augmented Deep Autoencoder","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-5007599/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5007599/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.22541/au.172455779.91864150/v1","name":"A Daily Load Forecasting Method Based on Federated Learning and Transformer Model","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.172455779.91864150/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.22541/au.172455779.91864150/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202410.2091.v1","name":"Data Science and Machine Learning for Network Management in Telecommunication Systems: Trends and Opportunities","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202410.2091.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202410.2091.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4760477/v1","name":"Federated Learning-based Content Caching Strategy for Edge Computing","source":"preprints","abstract":"Abstract In the realm of edge computing, effective content caching stands as a pivotal strategy to manage the exponential surge of mobile data within 5G networks. Content caching revolves around enabling the local storage of content in caches, ensuring swift and recurrent access to data. Yet, the challenge lies in accurately predicting the popularity of the files and thus requires caching due to constraints such as limited cache space and fluctuating file preferences. Conventional learning-based methods tackle this issue by gathering user data centrally for training purposes, to predict the file popularity. However, there arises a concern regarding user reluctance to entrust their private data to a central server. To address this, a novel solution is introduced in this paper called Federated Learning-based Content Caching (FLCC). FLCC operates by employing an enhanced Stacked Autoencoder in the edge devices without the necessity for centralized data collection during training. Utilizing a federated learning approach, FLCC prioritizes data privacy by aggregating user updates through federated averaging. This method implements a hybrid filtering mechanism based on a stacked autoencoder, training each user individually with their local data. The results from the FLCC approach showcase its superior cache efficiency when compared to traditional learning-based techniques. The proposed FLCC approach is a robust solution that upholds data privacy while enhancing content caching effectiveness in edge computing environments.","url":"https://doi.org/10.21203/rs.3.rs-4760477/v1","authors":["V Nivethitha","G Aghila"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4760477/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1101/2024.12.06.627138","name":"FedPyDESeq2: a federated framework for bulk RNA-seq differential expression analysis","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.12.06.627138","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.12.06.627138","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202408.2125.v1","name":"Federated Learning in Dynamic and Heterogeneous Environments: Advantages, Performances, and Privacy Problems","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202408.2125.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202408.2125.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4644605/v1","name":"Feasibility of training federated deep learning oropharyngeal primary tumor segmentation models without sharing gradient information","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4644605/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4644605/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4718612/v1","name":"A Bijection-backdoor-based Adversarial Examples Defense Method in Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4718612/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4718612/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-5346692/v1","name":"FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-5346692/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5346692/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4598644/v1","name":"Personalized Federated Learning with Adaptive Information Fusion","source":"preprints","abstract":"Abstract Data heterogeneity is a key challenge in the field of federated learning. Many existing personalized federated learning approaches focus on the performance of local models, neglecting the generalization capabilities of the global model, which may not be cost-effective. To address this issue, a federated learning algorithm called Personalized Federated Learning with Adaptive Information Fusion (FedIF) is proposed, which fuses two model heads carrying different information to obtain a personalized model fitting the local data better. The personalization steps of the FedIF are carried out after the local training in each round of the FedAvg algorithm, allowing it to be combined with other algorithms that improve upon the FedAvg. Comparative and ablation experiments between FedIF and other state-of-the-art personalized federated learning algorithms were conducted under three public datasets and two medical imaging datasets. The exceptional performance of our algorithm is attested across a wide range of experimental settings.","url":"https://doi.org/10.21203/rs.3.rs-4598644/v1","authors":["Liming Chai","Wenjun Yu","Nanrun Zhou"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4598644/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-4658479/v1","name":"A robust federated biased learning algorithm for time series forecasting ","source":"preprints","abstract":"Abstract The federated averaging algorithm (FedAvg) is extensively used for multi-sensor data modeling but often overlooks the unique characteristics of local models when privacy and data security are not considered. This study introduces a novel federated learning algorithm built upon the FedAvg framework, which emphasizes the specificity of each local model to optimize global knowledge aggregation. The algorithm's effectiveness is demonstrated through an air quality index prediction problem, showcasing superior prediction performance and robustness in noisy data scenarios. Additionally, the study delves into the reliability and robustness of the proposed approach, addressing the prevalent notion that centralized learning methods often surpass federated learning when data security is not a concern. Our experiments affirm the necessity and superiority of federated learning methods, even in the absence of privacy considerations, by effectively managing real-world noisy data.","url":"https://doi.org/10.21203/rs.3.rs-4658479/v1","authors":["Mingli Song","Xinyu Zhao","Witold Pedrycz"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4658479/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-4826269/v1","name":"Privacy-Preserving Federated Transfer Learning for Multi-Class Few-Shot Classification","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4826269/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4826269/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4767476/v1","name":"PerFedHypID: A Personalized Federated Hypernetworks based aggregation approach for Intrusion Detection Systems","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4767476/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4767476/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.21203/rs.3.rs-4734541/v1","name":"Merging Subgroup Information to Supplement Personal Information for Personal Federated Learning via Model Clustering","source":"preprints","abstract":"Abstract Personalized federated learning represents a pivotal strategy for addressing the challenges posed by statistical heterogeneity in federated learning. Clients optimize their models by leveraging information from other clients through a global model. Despite clients' expectations of acquiring requisite information from global aggregation, this process inevitably leads to a loss of personalized information, particularly impacting clients with limited dataset. Consequently, the acquisition of sufficient personalized information to enhance local models becomes arduous, thereby compromising the efficacy of model personalization. In response to this challenge, we propose the Federal Merging Subgroup Information (FedMSI) method to augment personalized information in personalized federated learning. FedMSI leverages model clustering to delineate subgroup divisions of akin personalized models, aggregates cluster center models based on these divisions and enriches personalized information by incorporating subgroup information from the cluster center models. Experimental findings demonstrate that FedMSI surpasses nine state-of-the-art methods by 7.89% in terms of accuracy under identical data heterogeneity conditions, while exhibiting a 20.34% enhancement when the client data volume is small. Finally, through ablation experiments, this study reaffirms that the augmentation of personalized information through subgroup information significantly enhances the classification performance of personalized models.","url":"https://doi.org/10.21203/rs.3.rs-4734541/v1","authors":["Xuan Cai","Wenan Zhou"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4734541/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.22541/au.172517874.42098666/v1","name":"Federated Learning for Optimized Resource Allocation in Power Line Communication Systems","source":"preprints","abstract":"This paper introduces a novel resource allocation algorithm, Priority-Aware Federated Resource Allocation (PAFRA), tailored for Power Line Communication (PLC) systems. Utilizing a federated learning framework, PAFRA optimizes the distribution of limited spectrum resources among multiple nodes within a residential environment. The algorithm employs a priority-based time slot allocation to manage subchannel conflicts and uses a Double Deep Q-Network (DDQN) for local training at each node, incorporating state, action, and reward configurations to refine transmission power and subchannel selections. Extensive simulations demonstrate that PAFRA significantly enhances system throughput across various Signal-to-Noise Ratio (SNR) levels, outperforming existing adaptive resource allocation strategies and random allocation methods. The findings highlight PAFRA’s ability to achieve superior performance in dynamic PLC environments, illustrating its potential to optimize network efficiency while adhering to strict regulatory emission standards.","url":"https://doi.org/10.22541/au.172517874.42098666/v1","authors":["Ruowen Yan","QIAO LI","Huagang Xiong"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.22541/au.172517874.42098666/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202409.0495.v1","name":"Privacy-Preserving Federated Learning-Based Intrusion Detection Technique for Cyber-Physical System","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202409.0495.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202409.0495.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-5206289/v1","name":"Enhanced Algorithmic Convergence Analysis for Federated Low-Rank Matrix Completion","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-5206289/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5206289/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4631058/v1","name":"Federated Task-Adaptive Learning for Personalized Selection of Human IVF-derived Embryos","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4631058/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4631058/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202407.0551.v1","name":"The Contribution of Federated Learning to AI Development","source":"preprints","abstract":"With the widespread application of artificial intelligence technology in various industries, users' attention to privacy and data security has increased significantly. Federated learning, as a new technology paradigm combining privacy-enhanced computing and artificial intelligence, resolves the contradiction between data security and open sharing. This paper presents the benefits of federated learning in terms of privacy, real-time processing, model robustness, compliance and cross-industry applications. At the same time, when combined with Edge AI technology, federated learning promotes the decentralisation of intelligent systems, improving data privacy protection and model accuracy. This paper also discusses the application cases of federated learning in the medical field, through local data processing and model training, effectively protecting user privacy, realizing medical data sharing and model optimization, and promoting the development of artificial intelligence.","url":"https://doi.org/10.20944/preprints202407.0551.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202407.0551.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4762031/v1","name":"Blockchain-based distributed federated learning using proof of accuracy consensus","source":"preprints","abstract":"Abstract This paper explores integrating federated learning (FL) and blockchain tech- nology, two burgeoning fields in information technology. Despite their growing popularity, both domains face significant challenges. In federated learning, the primary concern is safeguarding the integrity of the general model against client- induced compromises. Blockchain technology grapples with the need for a green mining approach through an energy-efficient consensus protocol. Our study lever- ages the strengths of each platform to mitigate the weaknesses of the other. We introduce an innovative blockchain-based FL model that eliminates the need for a central aggregator. Utilizing a green mining consensus algorithm named Proof of Accuracy (PoA), we create a competitive environment among nodes, fostering the creation of superior models. This approach ensures data integrity and model validation through a community-based consensus, resulting in a fully distributed system. This system enhances FL’s security and scalability and addresses vulner- abilities like malicious aggregators and scalability issues. Through experimental evaluations on the MNIST dataset with 20 miners, on one hand, our method enhances model accuracy to nearly 99% only after 10 blocks which is a higher point compared to FL and central learning. On the other hand, replacing Proof of Work (PoW) with PoA reduces energy consumption by nearly 30%. More- over, blockchain attacks appeared to be inapplicable, or resolvable after 6 blocks like fork attacks. After all, the introduced incentivizing mechanism lets malicious nodes get nearly zero rewards and allocates main rewards to honest nodes which is coherent with their efforts to present a superior model.","url":"https://doi.org/10.21203/rs.3.rs-4762031/v1","authors":["Aghil Sadegh","Amir Jalaly Bidgoly"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4762031/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.20944/preprints202407.0417.v1","name":"Blockchain Based Federated Learning Models Methods and Applications","source":"preprints","abstract":"This paper systematically discusses the application and development of federated learning in data privacy protection and data value sharing. With the rapid development of global information technology, especially the explosive growth of data from Internet of Things devices, data security and privacy protection are facing unprecedented challenges. This paper first analyzes the growth trend of global data volume and its importance to next generation technologies such as artificial intelligence technologies such as deep learning. Second, the paper provides an in-depth look at the impact of current data privacy regulations on data flows and value creation, particularly the EU's GDPR and China's Data Security and Personal Information Protection Law. Then, this paper introduces in detail federated learning, as a new distributed machine learning paradigm, which effectively solves the contradiction between existing data sharing and privacy protection by protecting individual data privacy and realizing global model collaborative construction. Finally, this paper discusses the combination of blockchain technology and federated learning, and proposes BeFL architecture as a new secure, decentralized and trusted federated learning system, which is expected to provide a comprehensive solution for large-scale data processing and value creation in multi-party scenarios. The research in this paper not only deepens the understanding of federation learning in theory, but also provides important reference and enlightenment for future research and application in related fields.","url":"https://doi.org/10.20944/preprints202407.0417.v1","authors":["Rahul Sharma","Kritika Sharma","Priya Patel"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202407.0417.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4662864/v1","name":"BPPFL: A Blockchain-Based Framework for Privacy-Preserving Federated Learning","source":"preprints","abstract":"Abstract Federated Learning (FL) offers a collaborative approach to training machine learning models while preserving data privacy. However, FL faces significant privacy and security challenges, such as identity disclosure and model inference attacks. To this end, we propose a novel Blockchain-Based Framework for Privacy-Preserving Federated Learning (BPPFL), which integrates threshold signature authentication and threshold Paillier encryption with blockchain technology. The BPPFL framework secures participant authentication and protects against internal and external threats, while the blockchain provides an immutable ledger for recording transactions and model updates, ensuring transparency and security. Experimental results show that our framework significantly reduces computation and communication overhead compared to existing methods while maintaining high model accuracy and robust privacy guarantees. Our framework enhances the security and trustworthiness of FL applications, making it suitable for domains like healthcare, finance, and the IoT.","url":"https://doi.org/10.21203/rs.3.rs-4662864/v1","authors":["Muhammad Asad","Safa Otoum"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4662864/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-4568992/v1","name":"Trust management for IoT devices based on federated learning and blockchain","source":"preprints","abstract":"Abstract The rapid growth of IoT devices and the increasing demand for device interaction between different network partitions have significantly pressured IoT device management. To realize cross-domain trust integration between different partitioned devices, trust management becomes the key technology to realize cross-domain communication. However, trust management heavily relies on third-party entities, posing centralized risks. Therefore, we propose an IoT device trust management system based on federated learning and blockchain. By utilizing federated learning to assess device reputation ratings while safeguarding their privacy, the system stores reputation assessment results on the blockchain for authenticity and accuracy. The system’s decentralization is achieved using blockchain instead of a central server in federated learning. Additionally, we introduce a weighted aggre-gation model based on device attributes to obtain a more precise global model through weighted aggregation of local models in federated learning. Experimental results using a simulated dataset reflecting device characteristics demonstrate a device evaluation accuracy of 90.2%, validating the system’s effectiveness and feasibility.","url":"https://doi.org/10.21203/rs.3.rs-4568992/v1","authors":["Liang Wang","Yilin Li","Lina Zuo"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4568992/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-4931254/v1","name":"A Realistic Greedy Parameter Aggregation Approach for Federated Learning Packet Transmissions","source":"preprints","abstract":"Abstract Federated Learning (FL) enables decentralized machine learning while preserving data privacy. Despite extensive research on FL frameworks and simulations, packet skewness due to poor network conditions remains understudied. This paper proposes a greedy approach for parameter selection in FL scenarios that prioritizes global model improvements and mitigates performance declines. The proposed greedy parameter aggregation approach is evaluated using a modified User Datagram Protocol (UDP) in the NS-3 network simulator under diverse network conditions, employing the widely used CIFAR-10 dataset.The global model with greedy aggregation outperformed in severely degraded networks, achieving 67% accuracy on CIFAR-10, compared to 27 % without the approach. The greedy model’s accuracy decreased 14 % from ideal conditions, while the non-greedy model declined 54 %. This comparison underscores the greedy approach’s resilience and effectiveness in maintaining the global model performance despite challenging network environments.","url":"https://doi.org/10.21203/rs.3.rs-4931254/v1","authors":["Bright K Mahembe","Clement N Nyirenda","Omowunmi Isafiade"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4931254/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1101/2024.09.15.24313479","name":"Federated Multiple Imputation for Variables that Are Missing Not At Random in Distributed Electronic Health Records","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.09.15.24313479","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.09.15.24313479","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4857274/v1","name":"Incremental YOLOv5 for Federated Learning in Cotton Pest and Disease Detection with Blockchain Sharding","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4857274/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4857274/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4730699/v1","name":"Federated Gradient Boosting using Minimal Variance Sampling","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4730699/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4730699/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4745818/v1","name":"Improving Multimodal Reasoning in Large Language Models via Federated Example Selection","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4745818/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4745818/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202412.2624.v1","name":"From Theory to Practice: Real-World Implementation of Artificial Intelligence and Machine Learning in Pharmacy Settings","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202412.2624.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202412.2624.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-4499006/v1","name":"treeXnets: Comparing Federated Tree-BasedModels and Neural Networks on Tabular Data","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4499006/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4499006/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.22541/au.172450870.03139596/v1","name":"Privacy and Security Challenges in Federated Learning for UAV Systems: A Comprehensive Review","source":"preprints","abstract":"Unmanned Aerial Vehicles (UAVs) have become indispensable assets in various sectors, leveraging their mobility and data collection capabilities. However, privacy and security concerns have fueled interest in Federated Learning (FL) as a solution. FL, decentralized and collaborative, offers promise in addressing privacy risks inherent in centralized data processing while enhancing model performance. In this review, we explore FL’s privacy and security implications in UAV ecosystems. We highlight FL’s potential to mitigate privacy risks by aggregating model updates locally, minimizing data transmission needs. Additionally, we examine security challenges and evaluate protective mechanisms. Through a systematic literature review, we identify gaps and propose future research directions, aiming to enhance the security and privacy of FL in UAV applications.","url":"https://doi.org/10.22541/au.172450870.03139596/v1","authors":["Ahmed Al Farsi","Ajmal Khan","Muhammad Rizwan","Mohammed M. Bait-Suwailam"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.22541/au.172450870.03139596/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-4745968/v1","name":"Dynamic Network Slicing Orchestration in Open 5G Networks using Multi-Criteria Decision Making and Secure Federated Learning Techniques","source":"preprints","abstract":"Abstract Network slicing enables new revenue opportunities for service providers by allowing them to offer customized network services tailored to various industry verticals and use cases. 5G network uses slicing technology to enable the creation of tailored network slices to support diverse use cases, such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). However, the complexity of network slicing also introduces challenges, such as maintaining service-level agreements (SLAs), quality of service (QoS), security across multiple slices, and service provisioning and dynamic allocation of resources. This paper develops a novel Federated Network Slicing Orchestrator (FNSO) that uses the Multi-Criteria Decision Making (MCDM) method to rank and select the proper telecommunication service provider that runs at certain edge Points of Presence (EPoP) of the 5G-based IOT network for slice deployment. The proposed FNSO integrates the Hexagonal Fuzzy approach with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to address the shortcoming of the TOPSIS and to rank and select the proper EPoP that can host the network slice using numeric and fuzzy Key Performance Indicators (KPIs) such as the security, cost, and performance criteria. Furthermore, we augment the FNSO with a Secure Federated Learning (SFL) model to protect the local 5G domain service provisioning KPIs and secure the FNSO against potential attacks. The experiment results depict that the average slice acceptance ratio of the FNSO is higher than the current solutions such as GRU-DNN, VIKOR-CNSP, and T-S3RA by 28.2%, 10.13%, and 6.1%. Furthermore, On average, the SFL model is faster than the DeTrust-FL, HybridAlpha-FL, PHE-FL, and HybridOne-FL by 7.11%, 7.21%, 15.73%, and 20.61% respectively, and slower than Classic-FL, on average, by 4.37% due to the secure aggregation that the SFL offers.","url":"https://doi.org/10.21203/rs.3.rs-4745968/v1","authors":["Hisham A. Kholidy"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4745968/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21203/rs.3.rs-4513308/v1","name":"Federated Learning Incentivize with Privacy-Preserving for IoT in Edge Computing in the Context of B5G","source":"preprints","abstract":"Abstract Federated learning and edge computing have resulted in the broad adoption of internet of Things (IoT) due to their fast reaction times and low connection costs. In general, edge computing requires users to send raw data to a central server for further processing. However, this data frequently contains sensitive information that individuals may not want to disclose. As a result, transferring user data with sensitive information increases the risk of data leakage when various unauthorized devices access it. Integrating federated learning with edge computing improves privacy by creating a consistent deep learning model across devices, eliminating the need for real data transferring but the complexity and heterogeneity of the IoT environment present challenges like privacy, low communication, incentives, and seamless data aggregation. In this work, we concentrate on improving privacy with communication stability and designing incentive mechanisms to motivate more clients to participate in the model training process to enhance the performance and accuracy of data aggregation in a highly trusted environment. As a result, the novelty point is the development of a framework that combined a blockchain with federated edge computing in the context of beyond 5G to address the aforementioned challenges and provide excellent communicative and trusted environment. The study's rigorous evaluation showed that the integration of blockchain and B5G technology significantly improved the overall process of federated edge computing including increased accuracy, prevented loss, and motivated more clients to participate in the training process.","url":"https://doi.org/10.21203/rs.3.rs-4513308/v1","authors":["Nasir Ahmad Jalali","Hongsong Chen"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4513308/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.20944/preprints202411.0520.v2","name":"Evaluating Reproducibility in Psychology: A Machine Learning-Based Large-Scale Study on Predicting Replication Success","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202411.0520.v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202411.0520.v2","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4701071/v1","name":"A Privacy Preserving Federated Learning BasedIoT Framework Using Cloud Computing","source":"preprints","abstract":"Abstract This abstract explores the transformative impact of IoT on modern life, empha- sizing the integration of Federated Learning (FL), Edge Computing, and Secure Offloading in AI applications. The rapid evolution of IoT has revolutionized com- mercial operations and consumer interactions, driven by advanced sensing and computational capabilities in mobile devices. However, concerns over data privacy and limited computational resources hinder the deployment of compute-intensive applications. FL emerges as a distributed AI paradigm, ensuring privacy and saving network resources. Edge computing optimizes service delivery, reducing latency and energy consumption, supported by intelligent offloading algorithms and blockchain technology for secure and efficient edge services. Challenges like slow learning speeds persist but are addressed through ongoing advancements in neural networks.The proposed framework is compared with the benchmark mod- els and it was observed that the proposed framework suppress the benchmark models.","url":"https://doi.org/10.21203/rs.3.rs-4701071/v1","authors":["Wasim Ahmad","Muhammad Amin Almaiah","Bakht Sher Ali","Aitizaz Ali"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4701071/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-3423251/v1","name":"ZDKD-FLID:Zero-Data Knowledge Distillation of Federated Learning for Intrusion Detection","source":"preprints","abstract":"Abstract Over the years, federated learning-based intrusion detection has attracted attention because it preserves data privacy and improves the detection capabilities of local models. However, the majority of existing methods in this domain are tailored for homogeneous models. Given the impact of factors such as hardware disparities and business requirements, local models often exhibit heterogeneity, which significantly restricts the development and application of federated learning for intrusion detection. Therefore, to address the challenge posed by model heterogeneous federated learning-based intrusion detection, this paper proposes a novel framework called zero-data knowledge distillation of federated learning for intrusion detection (ZDKD-FLID). This framework not only effectively addresses the issue of model heterogeneity but also operates without relying on a public dataset. Firstly, on the node side, the prediction model and local model perform knowledge distillation learning. Secondly, on the server side, the prediction model is selected and aggregated to generate a global prediction model. Additionally, a generator optimized with particle swarm optimization is employed for generative adversarial learning, enabling the generation of samples. Finally, the local model is trained using samples containing knowledge from other heterogeneous models, effectively improving the accuracy of intrusion detection. To validate its efficacy, ZDKD-FLID is compared with state-of-the-art algorithms on the CICIDS-2017 and UNSW-NB15 dataset. Consequently, ZDKD-FLID demonstrates superior performance compared to all the other considered algorithms.","url":"https://doi.org/10.21203/rs.3.rs-3423251/v1","authors":["TiaoKang Gao","XiaoNing Jin","Yingxu Lai"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3423251/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1101/2024.08.22.24312403","name":"Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.08.22.24312403","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.08.22.24312403","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4768253/v1","name":"FedAEF: Optimizing federated learning with mining and enhancing local data features","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4768253/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4768253/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4630899/v1","name":"FedCEA: Efficient Adaptive Personalized Federated Learning based on Critical Learning Periods","source":"preprints","abstract":"Abstract Federated learning (FL) faces significant challenges due to statistical heterogeneity, which undermines the global model's generalization capability across diverse clients. Personalized FL (pFL) has been extensively studied to address this, but most existing approaches erroneously assume all stages of the FL training process are equally critical and require the participation of all clients, leading to substantial computational and communication overhead. Addressing this flaw, we propose an efficient adaptive pFL method, FedCEA, based on critical learning periods (CLP). FedCEA tailors efficient models for each client while ensuring data privacy and security by considering CLP during the training process to guide client selection. This approach reduces client-server communications, accelerating model convergence. To enhance the global model's generalization across clients with statistical heterogeneity, we introduce an Adaptive Initialization of Local Models (AILM) module with a personalized aggregation strategy. Additionally, training parameters are dynamically adjusted based on dataset quality to ensure efficiency. FedCEA also employs a compression method that assesses the importance of model parameters, reducing communication and computational costs. To evaluate the effectiveness of FedCEA, we conduct extensive experiments with four benchmark datasets in computer vision and natural language processing domains. The results consistently showed that FedCEA achieves improved accuracy and superior communication efficiency compared to state-of-the-art methods. This makes FedCEA a promising solution for handling heterogeneity in federated learning training. Code is available at https://github.com/buaaYYC/FedCEA/tree/main.","url":"https://doi.org/10.21203/rs.3.rs-4630899/v1","authors":["Yichun Yu","Xiaoyi Yang","Zheping Chen","Yuqing Lan","Zhihuan Xing","Dan Yu"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4630899/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-4679192/v1","name":"Multi-Level Defense Strategy for Vertical Federated Learning Against Label Inference Attacks","source":"preprints","abstract":"Abstract Vertical federated learning (VFL) is increasingly recognized as an indispensable paradigm. Especially in the medical field, where the protection of data privacy plays an indispensable role. In the healthcare domain, adherence to stringent regulatory frameworks such as GDPR and HIPAA is indispensable. VFL facilitates a collaborative approach among institutions, enabling the prediction of disease risks without collecting sensitive patient data. However, VFL remains susceptible to label inference attacks, wherein malicious entities may extrapolate personal data from the exchanged intermediate results. To address the above challenge, we propose a strategic mechanism known as the balanced noise injection strategy (BNIS). This strategy is designed to meticulously regulate the introduction of noise, achieving a trade-off between privacy preservation and model accuracy. Moreover, to bolster our framework, we propose the multi-loss defense strategy (MLDS), an innovative defense explicitly engineered to withstand direct label inference attacks with resilience. Extensive evaluations on four benchmark datasets demonstrate that our approach defenses against passive attacks and yields a significant improvement in accuracy compared to the prevailing FL similar gradient (FLSG) benchmark. Furthermore, MLDS concurrently addresses breaches in label inference and greatly enhances the precision of the model.","url":"https://doi.org/10.21203/rs.3.rs-4679192/v1","authors":["Linlong Wang","Chungen Xu","Pan Zhang","Yiting Liu"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4679192/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-4755684/v1","name":"Accelerated Federated Learning Using Self-Adapting Bat Algorithm","source":"preprints","abstract":"Abstract Federated learning (FL) is an advanced distributed machine learning (ML) framework designed to address issues related to data silos and data privacy. A significant challenge in FL is the non-independent and identically distributed (Non-IID) nature of client data, resulting in issues of slow convergence rate and low prediction accuracy for the model. To tackle these issues, we propose a FL scheme based on the bat algorithm (FedBat), leveraging the echolocation mechanism of bats to effectively balance global and local search capabilities and optimizing model weight updates through dynamic adjustments of the search strategy. FedBat also allows for adaptive parameter adjustments across various datasets. To mitigate the client drift issue, we extend FedBat by using Jensen-Shannon(JS) divergence to quantify the difference between local and global models. Clients decide whether to upload their local models based on this difference, aiming to enhance the global model's generalization capability and minimize communication overhead. Experimental results demonstrate that FedBat converges 5 times faster and enhances test accuracy by more than 40% compared to FedAvg. The extended FedBat effectively mitigates the decrease in the generalization performance of the global model and reduces communication costs by around 20%. Comparing FedPSO, FedGwo, and FedProx shows that FedBat demonstrates superior performance in terms of convergence rate and test accuracy. We derive the formula for the expected convergence rate of FedBat, analyze the impact of various parameters on FL performance, and establish the upper bound of FedBat to evaluate its model divergence.","url":"https://doi.org/10.21203/rs.3.rs-4755684/v1","authors":["Jie Wang","Chaochao Sun","Yuan Peng"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4755684/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-4585988/v1","name":"Federated Learning-Based Ransomware Detection via Indicators of Compromise","source":"preprints","abstract":"Abstract Ransomware attacks have become increasingly prevalent and sophisticated, posing significant threats to data security and organizational operations worldwide. Leveraging a federated learning-based approach, this research presents a novel and significant advancement in ransomware detection by utilizing network and file system indicators of compromise while ensuring data privacy. The methodology involves the decentralized training of machine learning models across multiple clients, which enhances the model's robustness and adaptability to various ransomware attack scenarios. Extensive experiments and evaluations demonstrate the high accuracy, precision, recall, and F1-scores achieved by the proposed model, showcasing its effectiveness in real-world applications. The innovative combination of preprocessing, feature engineering, and sophisticated machine learning techniques within a federated learning framework results in a scalable and privacy-preserving solution capable of addressing the dynamic and evolving landscape of ransomware threats. This study contributes valuable insights into the development of effective ransomware detection systems, emphasizing the importance of collaborative and decentralized learning techniques in enhancing cybersecurity defenses.","url":"https://doi.org/10.21203/rs.3.rs-4585988/v1","authors":["Shota Koike","Hanako Tanaka","Misaki Maeda"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4585988/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1101/2024.10.16.618763","name":"Federated deep learning enables cancer subtyping by proteomics","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.10.16.618763","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.10.16.618763","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4495124/v1","name":"Granular Neural Networks Learning for Time Series Prediction under a Federated Scenario","source":"preprints","abstract":"Abstract Granular neural networks (GNNs) are a type of prediction models outputting information granules and GNNs not only provide more abstract results and a granular structure but also reveal a flexible nature that can be adjusted by users. As a promising tool, we apply GNNs to solve time series prediction problems under the federated learning (FL) scenario. Distributed time series prediction problems attract more attention recently due to the more usage of large quantity of IoT (Internet of Things) sensors and the development of Artificial Intelligence techniques. FL is the main approach to fix the distributed time series prediction problems. In this paper, we design a federated learning framework to refine granular weights of GNNs and then return better prediction results compared with the ones from centralized modeling. Different with the studies of FL using numeric neural networks, FL using GNNs is a study of aggregating parameters’ parameters under the federated scenario and thus the robustness and stability of the method is the most critical issue. To testify the two features of our method, we observe and compare from two aspects: different cases (several groups’ results) and different numbers of objectives (single-objective optimization and multiple-objective optimization). Experiments on predicting air quality index for 35 stations in Beijing (China) show the effectiveness of our method.","url":"https://doi.org/10.21203/rs.3.rs-4495124/v1","authors":["Mingli Song","Xinyu Zhao"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4495124/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.20944/preprints202406.0127.v1","name":"A Fair Contribution Measurement Method for Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202406.0127.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202406.0127.v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.22541/au.172481353.38743189/v1","name":"Peak-Controlled Logits Poisoning Attack in Federated Distillation","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.172481353.38743189/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.22541/au.172481353.38743189/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202411.0569.v2","name":"Artificial Intelligence Transformations in Digital Advertising: Historical Progression, Emerging Trends, and Strategic Outlook","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202411.0569.v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202411.0569.v2","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4752697/v1","name":"A Load Forecasting Method of New Power System Based on Personalized Federated Learning","source":"preprints","abstract":"Abstract The emerging distributed generation technology in new power systems encounters the challenges of unstable efficiency and high accuracy of prediction models. The generalization ability of prediction models is hindered by variations in users’ behavioral characteristics. Furthermore, inability to share power data across regions poses substantial impediments to generation arrangement and power dispatch. This paper proposes a load forecasting technology based on federated learning (FL), which can avoid uploading or sharing the users’ data to protect data privacy. A multi-task module was added to traditional FL to improve user accuracy (UA) rather than global model accuracy, where the client trains a separate personalized model by keeping the local Layer-Normalization (LN) private. Moreover, in order to fast model convergence, the local LSTM prediction algorithm was added with the Grey Wolf optimization (GWO) algorithm and the attention mechanism. The experimental results show that the overall model training time of the improved LSTM algorithm is shortened by 26%. The Mean absolute percentage error (MAPE) of the proposed multi-task FL is 9.79% lower than traditional FL, and the MAPE of clients with small data volume and large feature deviation is reduced by 18.07% at most.","url":"https://doi.org/10.21203/rs.3.rs-4752697/v1","authors":["Yang Shen","Zewen Li","Fangming Deng","Bo Gao"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4752697/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21203/rs.3.rs-4661580/v1","name":"A novel Federated Learning method with Domain Adaptation and Model Selection for Intrusion Detection","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4661580/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4661580/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4412111/v1","name":"Fast-CFLB: A Privacy-Preserving Data Sharing System for Internet of Vehicles Using Ternary Federated Learning and Blockchain","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4412111/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4412111/v1","addedAt":"2026-08-31T06:41:18.959Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4938500/v1","name":"MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4938500/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4938500/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4520887/v1","name":"A Survey on Fault Detection in Industrial IoT: A Machine Learning Approach with Emphasis on Federated Learning and Intrusion Detection Systems","source":"preprints","abstract":"Abstract In recent years, the Internet of Things (IoT) has received a lot of attention and research. The concept of Industrial IoT (IIoT) has emerged from the con- vergence of information technology (IT) and industrial automation and control systems. The increasing number of disjointed IoT networks deployed in many industrial sectors has exposed vulnerabilities leading to security incidents, jeopar- dizing the overall security of IIoT systems. This paper provides a comprehensive survey, analyzing and comparing current technologies for securing IIoT networks. Researchers have developed various detection strategies supported by machine learning (ML) approaches. Federated Learning (FL) offers lower latency and pre- serves privacy, emerging as a promising distributed ML paradigm that enhances detection performance. Several challenges and recommendations are defined in the context of intrusion detection systems (IDS) as a security monitoring mechanism.","url":"https://doi.org/10.21203/rs.3.rs-4520887/v1","authors":["Klea Elmazi","Donald Elmazi","Jonatan Lerga"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4520887/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1101/2024.08.08.24311681","name":"Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.08.08.24311681","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.08.08.24311681","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-4383289/v1","name":"Federated Learning for Secure and Efficient Vehicular Communications in Open RAN","source":"preprints","abstract":"Abstract This paper presents a comprehensive exploration of federated learning applied to vehicular communications within the context of Open RAN. Through an in-depth review of existing literature and analysis of fundamental concepts, critical challenges are identified within the current methodologies employed in this sphere. A novel framework is proposed to address these shortcomings, fundamentally based on federated learning principles. This framework aims to enhance security and efficiency in vehicular communications, leveraging the flexibility of Open RAN architecture. The paper further delves into a rigorous justification of the proposed solution, highlighting its potential impact and the improvements it could bring to vehicular communications. Ultimately, this study provides a roadmap for future research in applying federated learning for more secure and efficient vehicular communications in Open RAN, opening up new avenues for exploration in this exciting interdisciplinary domain.","url":"https://doi.org/10.21203/rs.3.rs-4383289/v1","authors":["Muhammad Asad","Saima Shaukat","Jin Nakazato","Ehsan Javanmardi","Manabu Tsukada"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4383289/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202412.1364.v1","name":"From Data to Diagnosis: A Deep Dive into Deep Learning for COVID-19 Detection","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202412.1364.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202412.1364.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-4646721/v1","name":"FedGAC: Optimizing Generalization in Personalized Federated Learning via Adaptive Initialization and Strategic Client Selection","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4646721/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4646721/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1101/2025.10.02.25337106","name":"Decentralized, privacy-preserving surgical video analysis with Swarm Learning","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.10.02.25337106","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.10.02.25337106","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202405.1479.v1","name":"FedOps Mobile: A Platform of Federated Learning Management for Enhanced Mobile Collaboration","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202405.1479.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202405.1479.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4501545/v1","name":"Dataset distillation-based optimization for heterogeneous federated learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4501545/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4501545/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-2970317/v1","name":"Development and validation of random-forest based federated learning algorithms for delirium prediction using electronic medical records from eleven hospitals in Austria: a retrospective study","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-2970317/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-2970317/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202411.1969.v1","name":"Advancements in Hand Recognition Systems: Challenges and Future Directions","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202411.1969.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202411.1969.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.21203/rs.3.rs-4471086/v1","name":"Advancing Healthcare Iot Security: Federated Learning and the Fedavg Approach","source":"preprints","abstract":"Abstract Anomaly detection emerges as a crucial challenge in cybersecurity, particularly within the healthcare sector where the integration of open data is expanding rapidly. The recent surge in Internet of Things (IoT) device usage in healthcare has transformed patient care and monitoring. However, this growth also introduces significant security risks to patient data and the integrity of medical networks. Traditional intrusion detection systems are, in most cases, ineffective in IoT environments, which display dynamism and distribution characteristic. In response, this paper proposes a novel intrusion detection system using an innovative Federated Learning approach with the FedAvg Transformer model, aimed at healthcare IoT devices and networks. This system leverages the collective intelligence of edge devices while ensuring data privacy, thereby bolstering the security of health- care infrastructures. The design, implementation, and efficacy of this system in mitigating a broad spectrum of security threats are also detailed.","url":"https://doi.org/10.21203/rs.3.rs-4471086/v1","authors":["Md Abu Talha Reyaz","Vanitha V"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4471086/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.20944/preprints202405.1157.v1","name":"Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time Series","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202405.1157.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202405.1157.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4359561/v1","name":"LCSA-Fed: A low cost semi-asynchronous federated learning based on lag tolerance for services QoS prediction","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4359561/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4359561/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202412.0127.v1","name":"AI-Driven Fare Evasion Detection in Public Transportation: A Multi-Technology Approach Integrating Behavioural AI, IoT, and Privacy-Preserving Systems","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202412.0127.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202412.0127.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4465394/v1","name":"Ride-Hailing Pick-Up Area Recommendation in a Vehicle-Cloud Collaborative Environment: A Feature-Aware Personalized Clustering Federated Learning Approach","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4465394/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4465394/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4141730/v1","name":"FedGPD: Global Prototype Distillation InHeterogeneous Federated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4141730/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4141730/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1101/2024.05.14.594167","name":"Efficient Federated Learning for distributed NeuroImaging Data","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.05.14.594167","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.05.14.594167","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4247440/v1","name":"PFDP: Privacy-preserving Federated Distillation Method for Pretraining Language Models","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4247440/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4247440/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.22541/au.171062193.31654908/v1","name":"Blockchain-based Federated Learning Approaches in Internet of Things applications","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.171062193.31654908/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.22541/au.171062193.31654908/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202411.1653.v1","name":"A Review on the Frontier of Molecular Biology Integrating AI and Bioinformatics in Genetic Research","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202411.1653.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202411.1653.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202404.1563.v1","name":"FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202404.1563.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202404.1563.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1101/2024.05.24.24307154","name":"In-Silo Federated Learning vs. Centralized Learning for Segmenting Acute and Chronic Ischemic Brain Lesions","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.05.24.24307154","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.05.24.24307154","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1101/2024.05.27.596048","name":"A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.05.27.596048","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.05.27.596048","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-3958165/v1","name":"FedIoTect: Federated Machine Learning for Collaborative Internet of Things Threat Detection","source":"preprints","abstract":"Abstract This paper explores a novel privacy-preserving approach using federated learning techniques to develop an intrusion detection system for Internet of Things (IoT) networks. The aim is to enable collaborative learning across decentralized IoT devices to build robust intrusion detection models, while avoiding direct transmission of network traffic data to preserve data privacy. The paper investigates the application of differential privacy and secure aggregation protocols to further enhance privacy. A federated learning framework is implemented to evaluate and optimize the training of deep neural network models for intrusion detection. Results demonstrate significant improvements in detection accuracy and communication efficiency compared to standalone models trained locally on individual devices. The customized modeling augmented with shared knowledge from the federated learning process is shown to achieve the best of both centralized and localized learning approaches.","url":"https://doi.org/10.21203/rs.3.rs-3958165/v1","authors":["Gitanjali Gitanjali","Er. Rajani Misra"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3958165/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-3873379/v2","name":"A Differentially Private Federated Learning Application in Privacy-Preserving Medical Imaging","source":"preprints","abstract":"Abstract This research addresses the escalating concerns surrounding privacy, particularly in the context of safeguarding sensitive medical data within the increasingly demanding healthcare landscape. We undertake an experimental exploration of differentially private federated learning systems, employing three benchmark datasets—PathMNIST, BloodMNIST, and OrganAMNIST—for medical image classification. This study pioneers the application of federated learning with differential privacy in healthcare, closely simulating real-world data distribution across twelve hospitals. Additionally, we introduce a novel deep-learning architecture tailored for differentially private models. Our findings demonstrate the superior performance of federated learning models compared to traditional approaches, with accuracy levels approaching those of non-private settings. By leveraging resilient deep learning models, we aim to enhance privacy, efficiency, and effectiveness in healthcare solutions, benefiting patients, healthcare practitioners, and the overall healthcare system through privacy-protected healthcare.","url":"https://doi.org/10.21203/rs.3.rs-3873379/v2","authors":["Mohamad HAJ FARES","Ahmet SERTBAŞ"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3873379/v2","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.20944/preprints202409.0335.v1","name":"Convergence Rate Analysis of Non-I.I.D. SplitFed Learning with Partial Worker Participation and Auxiliary Networks","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202409.0335.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202409.0335.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-9514454/v1","name":"A rapid review of school collaboration studies: learnings for primary school-based physical activity interventions","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9514454/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9514454/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-3962040/v1","name":"Cost-efficient hierarchical federated edge learning for satellite-terrestrial Internet of Things","source":"preprints","abstract":"Abstract With the widespread deployment of dense Low Earth Orbit (LEO) constellations, satellites can serve as an alternative solution to the lack of proximal multi-access edge computing (MEC) servers for mobile Internet of Things (IoT) devices in remote areas. Simultaneously, the implementation of LEO on-board federated learning (FL), which can meet the data privacy requirements of user devices, makes it sensible to offloading data processing tasks to intelligent edge devices equipped with adaptive learning capabilities. However, traditional satellite on-board federated learning may encounter challenges due to the limited satellite resources. Hence, we propose a cost-efficient satellite-terrestrial assisted hierarchical federated edge learning (STA-HFEL) architecture and an innovative communication scheme between satellites based on Intra-plane ISLs in this paper. Accordingly, managing CPU resources for local training and allocating bandwidth for learning information upload among battery-limited mobile devices is crucial. With this in mind, we define a joint computation and communication resource optimization problem for device users to achieve global cost minimization. A distributed Jacobi-Proximal ADMM (JPADMM) algorithm is used to tackle the newly formulated problem iteratively. Extensive performance evaluations demonstrate that the potential of STA-HFEL as a cost-efficient and privacy-preserving approach for machine learning tasks across distributed remote environments.","url":"https://doi.org/10.21203/rs.3.rs-3962040/v1","authors":["Zhenjiang Zhang","Xintong Pei","Yaochen Zhang"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3962040/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.20944/preprints202409.1840.v1","name":"Approximate Unlearning in Finance","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202409.1840.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202409.1840.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1101/2024.02.09.579629","name":"Federated Learning for Predicting Compound Mechanism of Action Based on Image-data from Cell Painting","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.02.09.579629","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.02.09.579629","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1101/2024.12.20.629834","name":"dGAMLSS: An exact, distributed algorithm to fit Generalized Additive Models for Location, Scale, and Shape for privacy-preserving population reference charts","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.12.20.629834","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.12.20.629834","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-3825246/v1","name":"An Adaptive Quantization Model Compression Method for Heterogeneous Federated Learning","source":"preprints","abstract":"Abstract In this paper, we introduce a novel adaptive In-Parallel Pruning-Quantization method, speciffcally designed for heterogeneous federated learning environments. Federated learning, as a distributed machine learning approach, allows multiple devices to collaboratively train a shared model while keeping the data localized. However, due to the computational and storage capability disparities among participating devices, the efffciency of standard federated learning methods is limited in heterogeneous settings. Our framework consists of two steps. The ffrst step is to train both the pruning and quantization networks of the same model simultaneously, and improve the accuracy of the pruning network through mutual learning. The second step is to quantify the pruning network and reduce the accuracy loss caused by quantization through multi-teacher knowledge distillation. Based on this framework, we propose a new pruning quantization method that selects the parts that need to be quantiffed based on pruning masks, and gradually quantizes them to reduce the loss of quantization accuracy. Our method employs adaptive In-Parallel Pruning-Quantization techniques to reduce the model size, thereby decreasing the bandwidth needed for transmitting the model among participating devices. By dynamically adjusting the pruning and quantization levels, our approach optimally scales the model size and performance based on each device’s computational and storage capacity. Additionally, we introduce an efffcient quantization strategy that achieves signiffcant compression rates with minimal loss in model accuracy. We tested our method across various heterogeneous federated learning environments. Experimental results demonstrate that, compared to traditional federated learning methods, our approach signiffcantly reduces the model size and transmission time while maintaining model performance. Moreover, our method exhibits excellent scalability and ffexibility, making it suitable for different types and scales of federated learning applications. Overall, our adaptive In-Parallel Pruning-Quantization method provides an efffcient, scalable solution for heterogeneous federated learning environments, potentially facilitating the widespread application of federated learning in resource-constrained settings.","url":"https://doi.org/10.21203/rs.3.rs-3825246/v1","authors":["xiaohai li","yiqiang chen","yiwei zhang","xiaodong yang"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3825246/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-4997851/v1","name":"Reputation System based on Distributed Ledge to Secure DecentralizedFederated Learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4997851/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4997851/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202409.1733.v1","name":"Graph Unlearning: Mechanism and Future Direction for Machine Unlearning with Complex Relationships","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202409.1733.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202409.1733.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4146876/v1","name":"A Multi-Scale Channel Attention Network with Federated Learning for Magnetic Resonance Image Super-Resolution","source":"preprints","abstract":"Abstract Magnetic resonance (MR) images are widely used for clinical diagnosis, whereas its resolution is always limited by some surrounding factors, and under-sampled data is usually generated during imaging. Since high-resolution (HR) MR images contribute to the clinic diagnosis, reconstructing HR MR images from these under-sampled data is pretty important. Recently, deep learning (DL) methods for HR reconstruction of MR images have achieved impressive performance. However, it is difficult to collect enough data for training DL models in practice due to medical data privacy regulations. Fortunately, federated learning (FL) is proposed to eliminate this issue by local/distributed training and encryption. In this paper, we propose a multi-scale channel attention network (MSCAN) for MR image super-resolution (SR) and integrate it into an FL framework named FedAve to make use of data from multiple institutions and avoid privacy risk. Specifically, to utilize multi-scale information in MR images, we introduce a multi-scale feature block (MSFB), in which multi-scale features are extracted and attention among features at different scales is captured to re-weight these multi-scale features. Then, a spatial gradient profile loss is integrated into MSCAN to facilitate the recovery of textures in MR images. Last, we incorporate MSCAN into FedAve to simulate the scenery of collaborated training among multiple institutions. Ablation studies show the effectiveness of the multi-scale features, the multi-scale channel attention, and the texture loss. Comparative experiments with some state-of-the-art (SOTA) methods indicate that the proposed MSCAN is superior to the compared methods and the model with FL has close results to the one trained by centralized data.","url":"https://doi.org/10.21203/rs.3.rs-4146876/v1","authors":["Feiqiang Liu","Aiwen Jiang","Lihui Chen"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4146876/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.20944/preprints202401.1769.v1","name":"Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in non-IID Datasets","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202401.1769.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202401.1769.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.22541/au.172894062.27932664/v1","name":"A Snapshot of Tiny AI: Innovations in Model Compression and Deployment","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.172894062.27932664/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.22541/au.172894062.27932664/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-3934159/v1","name":"Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3934159/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3934159/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-3909067/v1","name":"Saltus - “A Sudden Transition” Empowered by Federated Learning for Efficient Big Data Handling in Multimedia Sensor Networks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3909067/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3909067/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-3938527/v1","name":"Introducing Edge Intelligence to Smart Meters via Federated Split Learning","source":"preprints","abstract":"Abstract The ubiquitous smart meters are expected to be a central feature of future smart grids by enabling the collection of massive fine-grained consumption data to support demand-side flexibility. However, the current smart meters are still not smart enough. They can only perform basic data collection and communication functionalities but fail to carry out any on-device intelligent data analytics due to hardware constraints in terms of memory, computation, and communication capacity. Moreover, privacy concerns have hindered the utilization of data from distributed smart meters. Here, we present an end-edge-cloud federated split learning framework to enable collaborative model training on resource-constrained smart meters with the assistance of edge and cloud servers in a resource-efficient and privacy-enhancing manner. The proposed method is validated on a hardware platform to conduct building and household load forecasting on smart meters with only 192KB of static random-access memory (SRAM). We show that the proposed method can reduce the memory footprint by 95.5%, the training time by 94.8%, and the communication burden by 50% under the distributed learning framework, and achieve comparable or even superior forecasting accuracy compared to resource-unlimited methods.","url":"https://doi.org/10.21203/rs.3.rs-3938527/v1","authors":["Yi Wang","Yehui Li","Dalin Qin","H. Vincent Poor"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3938527/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-3927659/v1","name":"Incentivizing Inclusive Data Contributions in Personalized Federated Learning","source":"preprints","abstract":"Abstract While data plays a crucial role in training contemporary AI models, it is acknowledged that valuable public data will be exhausted in a few years, directing the world's attention towards the massive decentralized private data. However, the privacy-sensitive nature of raw data and lack of incentive mechanism prevent these valuable data from being fully exploited. Addressing these challenges, this paper proposes inclusive and incentivized personalized federated learning (iPFL), which incentivizes data holders with diverse purposes to collaboratively train personalized models without revealing raw data. iPFL constructs a model-sharing market by solving a graph-based training optimization and incorporates an incentive mechanism based on game theory principles. Theoretical analysis shows that iPFL adheres to two key incentive properties: individual rationality and truthfulness. Empirical studies on eleven AI tasks (e.g., large language models' instruction-following tasks) demonstrate that iPFL consistently achieves the highest economic utility, and better or comparable model performance compared to baseline methods. We anticipate that our iPFL can serve as a valuable technique for boosting future AI models on decentralized private data while making everyone satisfied.","url":"https://doi.org/10.21203/rs.3.rs-3927659/v1","authors":["Siheng Chen","Enpei Zhang","Jingyi Chai","Rui Ye","Yanfeng Wang"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3927659/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-4100205/v2","name":"FEDRETAIL: A Framework for Distributed Retail Data Analysis and Learning Toward E-commerce 5.0","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4100205/v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4100205/v2","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-3930064/v1","name":"Compact artificial neurons with time-to-first-spike coding for fast and energy-efficient federated neuromorphic computing","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3930064/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3930064/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202408.0974.v1","name":"Role of Artificial Intelligence in Autonomous Vehicles","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202408.0974.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202408.0974.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4085408/v1","name":"Intelligent QLFEKF integrated navigation based on the X-ray pulsar / solar and target planetary Doppler for the SSBE cruise phase","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4085408/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4085408/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202410.0736.v1","name":"AI-Driven Data Processing and Decision Optimization in IoT through Edge Computing and Cloud Architecture","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202410.0736.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202410.0736.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4168386/v1","name":"Federated Edge Computing Strategy for Fault Tolerance in Distributed Deep Neural Networks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4168386/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4168386/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1101/2024.02.05.578912","name":"UniFed: A unified deep learning framework for segmentation of partially labelled, distributed neuroimaging data","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.02.05.578912","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.02.05.578912","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1101/2024.01.17.576160","name":"Cross-institutional HER2 assessment via a computer-aided system using federated learning and stain composition augmentation","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.01.17.576160","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.01.17.576160","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-3927383/v1","name":"Efficient Encryption using Quondam Signature Algorithm and Modified Lean Six Sigma for Sustainability with Supply Chain Management","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3927383/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3927383/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1101/2024.01.06.23300659","name":"Testing federated analytics across secure data environments using differing statistical approaches on cross-disciplinary data","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.01.06.23300659","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.01.06.23300659","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-3834382/v1","name":"Empowering Smart Grid Security: Towards Federated Learning in 6G-Enabled Smart Grids using Cloud","source":"preprints","abstract":"Abstract As the realm of smart grids continues to evolve, embracing new technologies, researchers are exploring the potential ofupcoming 6G technology to address the challenges in management of smart grids. With the adoption of 6G technology,wireless energy meters, which play a key role in smart grid advancement, promise higher data rates, ultra-low latency, improvedconnectivity, and enhanced security. However, the integration of advanced technologies into smart grids, raises concernregarding cyberattacks such as distributed-denial-of-service (DDoS) attacks, which pose grave threat to the functionality andstability of the grid. To address these security challenges, smart grids traditionally implement intrusion detection systems (IDS)that analyse traffic logs from smart meters, but traditional IDS may face difficulties in detecting novel attacks such as subtle,multi-domain DDoS attacks. Towards securing smart grids, anomaly detection emerges as a crucial technique, integratedwith deep learning (DL), this technique can potentially identify deviations from normal, non-malicious network traffic, to detectcyberattacks, thereby enhancing grid security. However, it is seen that using user data for training DL models at the serverviolates data privacy regulations, which necessitates a balance between advanced anomaly detection and strict adherence todata privacy norms. Federated Learning (FL) has emerged as a suitable solution in this scenario, offering a privacy-focusedsolution allowing smart meters to train DL models with locally generated datasets and make predictions at the edge. In thiswork, we propose a hierarchical FL approach in smart meters for the 6G era, focusing on privacy-preserving anomaly detectionagainst DDoS attacks. Our work integrates a cloud-based service framework within an FL setup for smart grids, leveraging thescalability of cloud platforms and edge computing for efficient, secure, and cost-effective anomaly detection in line with 6Gtechnology requirements. Evaluation of our approach in local local simulation environment, using a workstation as the serverand Raspberry Pi devices as client nodes and cloud infrastructure provided by Amazon Web Services (AWS). Our goal is toinvestigate the feasibility of using cloud solutions to support federated learning-based anomaly detection in smart grids.Theperformance metrics between local and cloud simulations for our custom neural network showed that the variations betweentwo sets of simulations are not significant, and the proposed approach is suitable for deployment in real-world scenarios,especially for upcoming 6G-enabled smart grids where consistent performance is essential.","url":"https://doi.org/10.21203/rs.3.rs-3834382/v1","authors":["Jithish J","Nagarajan Mahalingam","Yeo Kiat Seng"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3834382/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21203/rs.3.rs-3887032/v1","name":"Hypernetwork-Driven Centralized Contrastive Learning for Federated Graph Classification","source":"preprints","abstract":"Abstract In the realm of Graph Federated Learning (GFL), current methodologies predominantly concentrate on local client data, a focus that often results in a constrained understanding of more expansive, global patterns. This approach typically struggles with Non-IID issues in cross-domain datasets, which impedes the identification of overarching patterns. Contrastive Learning (CL) has emerged as a potent method to improve model’s ability to differentiate between variations across diverse views. However, existing CL frameworks, which are primarily client-centric, fail to fully exploit this advantage. Our empirical findings reveal that the direct application of conventional CL techniques tends to induce homog-enization among clients, a phenomenon particularly pronounced in settings where client datasets exhibit high heterogeneity. To tackle this statistical heterogene-ity and harness inherent data variability, we propose a hypernetwork-based approach, termed CCL. This innovative, server-centric strategy, underpinned by a hypernetwork, adeptly navigates the core challenges associated with traditional client-centric models in the face of heterogeneous datasets. CCL excels in assimilating global patterns derived from multiple clients, effectively capturing a more diverse spectrum of patterns and, consequently, substantially boosting the overall performance of GFL. Our comprehensive experimental evaluations, encompassing supervised, unsupervised, and other harsh scenarios, distinctly affirm CCL’s superiority over prevailing models. Its remarkable compatibility with standard backbones, and the resulting significant enhancements in GFL performance across various contexts, further underscore its effectiveness.","url":"https://doi.org/10.21203/rs.3.rs-3887032/v1","authors":["Jianian Zhu","Yichen Li","Haozhao Wang","Yining Qi","Ruixuan Li"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3887032/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-3906418/v1","name":"IoT Workload Offloading Efficient Intelligent Transport System in Federated ACNN Integrated Cooperated Edge-Cloud Networks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3906418/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3906418/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-3862540/v1","name":"Comprehensive Framework for Implementing Blockchain-enabled Federated Learning and Full Homomorphic Encryption for Chatbot security System","source":"europepmc","abstract":"Abstract Chatbot is an artificial intelligence application that can provide a conversational environment between human and machine. Most organizations and industries are willing to lay out their services through chatbot because it can provide 24/7 customer support. Meanwhile it raises security and privacy challenges like access control, data leakage during transmission, SQL injection attack, language model attack which make the users concerned about their data, performance and accuracy. Therefore this research paper proposed a comprehensive framework integrated blockchain, federated learning and fully homomorphic encryption algorithm with face recognition to solve above mentioned chatbot’s challenges. The experimental result shows that distributed system improves chatbot accuracy (90%) and more transaction in less time with more clients do not affect the performance. In contrast, more iteration and clients will decrease the accuracy, performance and transactions in centralized system. In addition, fully homomorphic encryption improve and speed up data encryption process. It encrypted more data (1792 MB) in a small amount of 1240 time/sec and conversation/transactions can be transferred via a secure network to ensure the confidentiality, integrity and authenticity of users’ data. The implementation of such comprehensive framework in real-life can improve chatbot security that is actively work as a customer agent in organization.","url":"https://doi.org/10.21203/rs.3.rs-3862540/v1","authors":["Nasir Ahmad Jalali","HongSong Chen"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3862540/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.21203/rs.3.rs-4475624/v1","name":"An Optimized FL-XAI model for secured and trustworthy candidate selection","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4475624/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4475624/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4627597/v1","name":"Blockchain Consensus Algorithm for Supply Chain Information Security Sharing Based on Convolutional Neural Networks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4627597/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4627597/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1101/2024.02.02.24301940","name":"Cumulus: A federated EHR-based learning system powered by FHIR and AI","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.02.02.24301940","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.02.02.24301940","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-3910714/v1","name":"My-This-Your-That - Interpretable Identification of Systematic Bias in Federated Learning for Biomedical Images","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3910714/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3910714/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4152375/v1","name":"A Systematic Literature Review: AI, DL and Machine Learning inCyber Risk Management","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4152375/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4152375/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-4173869/v1","name":"IoT networks attack detection using CFD learning framework","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4173869/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4173869/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202404.1888.v1","name":"Visual Data and Pattern Analysis for Smart Education: A Robust Drl-Based Early Warning System for Student Performance Prediction","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202404.1888.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202404.1888.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.21203/rs.3.rs-4148472/v1","name":"Privacy-Preserving Byzantine-Resilient Swarm Learning for E-healthcare","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4148472/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4148472/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.20944/preprints202406.0197.v1","name":"Multi-antenna Arrays Based Massive-MIMO for B5G/6G: State-of-the-Art, Challenges and Future Research Directions","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202406.0197.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20944/preprints202406.0197.v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-4137072/v1","name":"A Distributed Security Approach Based on Artificial Immune Systems for Intrusion Detection in Internet of Things","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4137072/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4137072/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1101/2024.01.26.24301827","name":"Development and validation of a federated learning framework for detection of subphenotypes of multisystem inflammatory syndrome in children","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.01.26.24301827","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.01.26.24301827","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.21203/rs.3.rs-4121481/v1","name":"PMNBARL: Enhancing Efficiency of Privacy Management in Digital Networks through Blockchain and Adaptive Reinforcement Learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4121481/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4121481/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.21203/rs.3.rs-3825499/v1","name":"Target Informed Client Recruitment for Efficient Federated Learning in Healthcare","source":"preprints","abstract":"Abstract Background: Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available and, therefore, require some form of aggregation to facilitate training procedures. Aggregating sensitive data poses a significant privacy risk, which is why, both in Europe and the United States, legal frameworks regulate the treatment of such data. Whilst these measures protect the individual behind the data, they pose a significant challenge that results in extensive legal administration related to data sharing efforts. Federated learning (FL) offers a way to mitigate these challenges by allowing to learn models in distributed fashion, eliminating the need to aggregate data for the purpose of training. However, FL comes with a new set of challenges related to communication overhead, client selection and efficiency of the FL training procedure, among others. Methods: In this work, we extend on a previously proposed client recruitment approach by incorporating knowledge on the local hardware such that it becomes possible to recruit a subset of clients for the federation based on the construct of client-level representativeness, which is expressed in terms of the local target distribution divergence, sample size, and the underlying hardware. Results: We show that, for prominent, medical regression and classification tasks, the recruitment approach yields results that are on par, or better, compared to the central and federated approaches. The proposed approach requires a mere fraction of the data for training and reduces the training time by a factor of 3-4. In addition, we show that excluded clients can still significantly benefit from the resulting federated model through local fine-tuning. Conclusions: By expressing the representativeness of clients in function of the deviation in the local target distribution, the sample size and efficiency of the underlying hardware, we are able to define a recruitment approach that yields a subset of clients for the federation resulting in significantly reduced training time, without harming predictive performance, whilst improving the privacy preserving characteristics compared to the standard FL and central approaches.","url":"https://doi.org/10.21203/rs.3.rs-3825499/v1","authors":["Vincent Scheltjens","Lyse Naomi Wamba Momo","Wouter Verbeke","Bart De Moor"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3825499/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2025.08.26.672402","name":"Toward Robust Neuroanatomical Normative Models: Influence of Sample Size and Covariates Distributions","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.08.26.672402","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.08.26.672402","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-4020623/v1","name":"Velocious: A Resilient IoT Architecture for 6G based Intelligent Transportation System with Expeditious Movement Mechanism","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4020623/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4020623/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.1101/2025.09.05.25335174","name":"Appendix300: A multi-institutional laparoscopic appendectomy video dataset for computational modeling tasks","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.09.05.25335174","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.09.05.25335174","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2024.01.09.24301073","name":"A One-Shot Lossless Algorithm for Cross-Cohort Learning in Mixed-Outcomes Analysis","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.01.09.24301073","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.01.09.24301073","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.21203/rs.3.rs-8205252/v1","name":"FAIR and Square: Privacy Compliance Framework for Healthcare Databases","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8205252/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8205252/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.64898/2026.03.05.709921","name":"Fractal: Towards FAIR bioimage analysis at scale with OME-Zarr-native workflows","source":"preprints","abstract":"The rapid growth in microscopy data volume, dimensionality, and diversity urgently calls for scalable and reproducible analysis frameworks. While efforts on the open OME-Zarr format have helped standardize the storage of large microscopy datasets, solutions for standardized processing are still lacking. Here, we introduce two complementary contributions to address this gap: 1) the Fractal task specification, defining OME-Zarr processing units that can interoperate across computational environments and workflow engines, and 2) the Fractal platform, using this specification to enable scalable and modular OME-Zarr-native analysis workflows. We demonstrate their use across diverse biological research data, including terabyte-scale multiplexed, volumetric, and time-lapse imaging. In a clinical setting, we show that Fractal workflows achieve near-identical quantification of millions of cells across independent deployments, demonstrating the reproducibility required for translational applications. With its growing community of contributors, the Fractal ecosystem provides a foundation for FAIR microscopy image analysis relying on open file formats.","url":"https://doi.org/10.64898/2026.03.05.709921","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.03.05.709921","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.21203/rs.3.rs-6757228/v1","name":"Mitigating Antimicrobial Resistance by Innovative Solutions in AI (MARISA): a modified James Lind Alliance Analysis","source":"preprints","abstract":"Abstract Antimicrobial resistance (AMR) is a critical global health threat, and artificial intelligence (AI) presents new opportunities to combat it. However, research priorities at the AI-AMR intersection remain undefined. This study aimed to identify and prioritise key areas for future investigation. Using a modified James Lind Alliance approach, we conducted semi-structured interviews with eight experts in AI and AMR between February and June 2024. Analysis of 338 coded responses revealed 44 distinct themes. Major barriers included fragmented data access, integration challenges, and economic disincentives. The top ten priorities identified were: Combination Therapy, Novel Therapeutics, Data Acquisition, AMR Public Health Policy, Prioritisation, Economic Resource Allocation, Diagnostics, Modelling Microbial Evolution, AMR Prediction, and Surveillance. A notable limitation was the underrepresentation of data from high-burden regions, affecting model generalisability. To address these gaps, we propose the novel BARDI framework: Brokered Data-sharing, AI-driven Modelling, Rapid Diagnostics, Drug Discovery, and Integrated Economic Prevention.","url":"https://doi.org/10.21203/rs.3.rs-6757228/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6757228/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-6026482/v1","name":"Artificial intelligence as a medical device for ophthalmic image analysis: a scoping review of regulated devices","source":"preprints","abstract":"Abstract This scoping review aims to identify regulator-approved ophthalmic image analysis AIaMDs in three jurisdictions, examine their characteristics and regulatory approvals, and evaluate the available evidence underpinning them, as a step towards identifying best practice and areas for improvement. 36 AIaMDs from 28 manufacturers were identified − 97% (35/36) approved in the EU, 22% (8/36) in Australia, and 8% (3/36) in the USA. Most targeted diabetic retinopathy detection. 19% (7/36) did not have published evidence describing performance. For the remainder, 131 clinical evaluation studies (range 1–22/AIaMD) describing 192 datasets/cohorts were identified. Demographics were poorly reported (age recorded in 52%, sex 51%, ethnicity 21%). On a study-level, few included head-to-head comparisons against other AIaMDs (8%,10/131) or humans (22%, 29/131), and 37% (49/131) were conducted independently of the manufacturer. Only 11 studies (8%) were interventional. There is scope for expanding AIaMD applications to other ophthalmic imaging modalities, conditions, and use cases. Facilitating greater transparency from manufacturers, better dataset reporting, validation across diverse populations, and high-quality interventional studies with implementation-focused outcomes are key steps towards building user confidence and supporting clinical integration.","url":"https://doi.org/10.21203/rs.3.rs-6026482/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6026482/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2025.04.07.25325394","name":"Uptake of service specific codes for the COVID oximetry @Home Pulse Oximetry service: an analysis of 57 million patients’ primary care records using OpenSAFELY","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.04.07.25325394","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.04.07.25325394","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2024.10.29.24316332","name":"GHOSTS: Generation of synthetic hospital time series for clinical machine learning research","source":"preprints","abstract":"Machine learning (ML) holds great promise to support, improve, and automatize clinical decision-making in hospitals. Data protection regulations, however, hinder abundantly available routine data from being shared across sites for model training. Generative models can overcome this limitation by learning to synthesize hospital data from a target population while ensuring data privacy. Clinical time series acquired during intensive care are, however, difficult to model using established techniques, especially due to uneven sampling intervals. Here we introduce GHOSTS (Generator of Hospital Time Series), a novel generator of synthetic patient trajectories that is capable of generating heterogeneous hospital data including realistic time series with uneven sampling intervals. We further design a suite of novel benchmarks, GHOSTS-Bench. We train GHOSTS on a large cohort of patient data from the MIMIC-IV critical care dataset and measure the quality of the generated data in terms of how faithfully the distributions of individual features in the real data are approximated, how well spatio-temporal dynamics in the multivariate time series are preserved, and how well ML models trained on the generated data can solve a clinical prediction task on the real data. We observe that GHOSTS outperforms a state-of-the-art approach, DoppelGANger, with respect to these criteria.","url":"https://doi.org/10.1101/2024.10.29.24316332","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.10.29.24316332","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.1101/2024.05.03.24306807","name":"Data-driven prediction of spinal cord injury recovery: an exploration of current status and future perspectives","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.05.03.24306807","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.05.03.24306807","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2024.08.06.24311535","name":"The challenges of replication: a worked example of methods reproducibility using electronic health record data","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.08.06.24311535","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.08.06.24311535","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-4164827/v1","name":"Assessing Clustering Replicability in Sensorineural Hearing Loss: Insights from the UK's Largest Audiogram Cohort","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4164827/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4164827/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.1101/2025.11.05.25339575","name":"Commercial or industrial use of mental health data for research: primer and best-practice guidelines from the DATAMIND patient/public Lived Experience Advisory Group","source":"preprints","abstract":"BACKGROUND: Routinely collected health data, such as that held by the United Kingdom (UK) National Health Service/Health and Social Care (collectively \"NHS\"), has important research uses, but its appropriate use requires public trust and transparency. Commercial/industrial access to routinely collected health data is especially controversial and sensitive for the public, and particular concerns may relate to mental health (MH) data. Existing best-practice MH data science guidelines do not cover commercial uses specifically, but emphasise the importance of patient/public co-development of data science. OBJECTIVES: To develop patient/public-led guidelines for the commercial/industrial use of MH data for research, and to capture relevant background information required by patient/public participants. The focus was on the UK and its constituent nations, but the principles may have wider applicability. METHODS: A patient/public lived experience advisory group (LEAG) was set up within DATAMIND, the Health Data Research UK data hub for MH informatics research development. Initial training and discussion yielded a requirement for definitions and explanations of concepts and processes relating to MH data research, developed iteratively. Subsequently, the LEAG developed guidelines via a qualitative and iterative quasi-Delphi approach. The agreed scope excluded data provided for research with informed consent, data processing arrangements such as companies hosting electronic health records or e-mail systems on the instruction of health services, or compliance with legal minimum requirements. The scope included the use of routinely collected MH data (e.g. NHS data) for research by commercial/industrial organisations without explicit consent, and aspects of MH data collection directly by industry with consent. RESULTS: Alongside the primer in MH data research concepts, the LEAG provide recommendations and best-practice guidelines relating to commercial/industrial research use of MH data, for organisations controlling MH data (such as NHS bodies) and for commercial applicants seeking to use MH data for research. Alongside principles of transparency, patient rights, patient/public involvement in research, stringent governance, and statistical disclosure control, the guidelines recommend a risk-benefit approach to assessing applications for data use, within limits that include avoiding the export of unconsented patient-level data outside NHS-controlled secure data environments, and not providing access to unconsented free-text MH data to commercial applicants. We also provide some recommendations for NHS executive and regulatory bodies, relating to public choice and transparency, clarity of guidance to research-active NHS organisations, and support for de-identification. CONCLUSIONS: Patient/public involvement and understanding is central to MH data research. The primer materials developed here constitute information requested by public advisers prior to considering best practice. The guidelines reflect the views of people with personal or family experience of mental ill health. We hope they are of practical use to the wider MH research community and serve to increase public transparency and trust.","url":"https://doi.org/10.1101/2025.11.05.25339575","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.11.05.25339575","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-9060414/v1","name":"Cross-Species Aging Knowledge Integration into Agentic AI Platform Uncovers Conserved Mechanisms","source":"preprints","abstract":"Abstract Aging research has been advanced largely through the use of model organisms, where short lifespans and genetic tractability enable the systematic discovery of molecular pathways influencing longevity and age-related decline. However, knowledge about aging remains fragmented across species-specific repositories and domain-focused databases, limiting our ability to identify evolutionarily conserved mechanisms and translate findings to human biology. To address this gap, we developed EvoAge, a unified, multi-species knowledge graph that integrates aging-specific and general biomedical resources into a systems-level framework. EvoAge harmonizes 48 public datasets into a graph comprising 1.04 billion triples across six key species. A human-centric orthology framework reconciles more than 80,000 gene entries, expanding accessible organism-level aging knowledge by up to 1,700-fold compared with existing resources. To operationalize the graph for biological reasoning, we optimized knowledge graph embedding models and deployed a large language model (LLM)-assisted agentic interface that supports natural-language querying, link prediction, and hypothesis testing. In internal benchmarking using recent pre-print aging literature, EvoAge significantly outperformed state-of-the-art LLMs in distinguishing biologically plausible from implausible hypotheses. Importantly, EvoAge recommended a previously unrecognized Alzheimer’s disease (AD) mechanism involving nanoscale redistribution of BACE1 within synaptic compartments. We experimentally validated this EvoAge-supported prediction using patient-derived iPSCs carrying a familial PSEN1 mutation, demonstrating disease-associated remodeling of β-secretase, defined by altered localization, nanoscale clustering, and compartment-specific enrichment. We further confirmed the predicted evolutionary conservation of this BACE1–pathology relationship in additional AD systems, including transgenic mice and postmortem human brain tissue.","url":"https://doi.org/10.21203/rs.3.rs-9060414/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9060414/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.1101/2024.06.23.24309353","name":"Consortium Profile: The Methylation, Imaging and NeuroDevelopment (MIND) Consortium","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.06.23.24309353","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.06.23.24309353","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.1101/2025.05.14.25327523","name":"Long-read genome sequencing increases genomic yield in congenital heart disease","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.05.14.25327523","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.05.14.25327523","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.1101/2024.05.20.594940","name":"T-cell receptor structures and predictive models reveal comparable alpha and beta chain structural diversity despite differing genetic complexity","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.05.20.594940","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.05.20.594940","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.21203/rs.3.rs-3905152/v1","name":"Prefrontal Electrophysiological Biomarkers and Mechanism-Based Drug Effects in a Rat Model of Alcohol Addiction","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3905152/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3905152/v1","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2024.02.01.24302048","name":"A machine-learning model to harmonize brain volumetric data for quantitative neuro-radiological assessment of Alzheimer’s disease","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2024.02.01.24302048","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.02.01.24302048","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.1101/2024.10.08.24315073","name":"Transferability and accuracy of electronic health record-based predictors compared to polygenic 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NIAGADS maintains a high-quality data collection for ADRD genetic/genomic research and supports genetics data production and analysis. NIAGADS hosts whole genome and exome sequence data from the Alzheimer’s Disease Sequencing Project (ADSP) and other genotype/phenotype data, encompassing 209,000 samples. NIAGADS shares these data with hundreds of research groups around the world via the Data Sharing Service, a FISMA moderate compliant cloud- based platform that fully supports the NIH Genome Data Sharing Policy. NIAGADS Open Access consists of multiple knowledge bases with genome-wide association summary statistics and rich annotations on the biological significance of genetic variants and genes across the human genome. NIAGADS stands as a keystone in promoting collaborations to advance the understanding and treatment of Alzheimer’s disease.","url":"https://doi.org/10.1101/2024.10.07.24315029","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.10.07.24315029","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.2139/ssrn.3776649","name":"Preventing the Next Financial Failure Post–COVID–19","source":"preprints","abstract":"","url":"https://doi.org/10.2139/ssrn.3776649","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.2139/ssrn.3776649","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.2139/ssrn.3672989","name":"Sustainable Finance and Fintech: Can Technology Contribute to Achieving Environmental Goals? A Preliminary Assessment of ‘Green FinTech'","source":"preprints","abstract":"","url":"https://doi.org/10.2139/ssrn.3672989","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.2139/ssrn.3672989","addedAt":"2026-08-31T06:41:18.960Z","updatedAt":"2026-08-31T06:41:20.072Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00003-0","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00003-0","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:06Z","doi":"10.1016/b978-0-44-323641-9.00003-0","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1002/9781394167760.ch6","name":"Perspective of Blockchain, Federated Learning, Smart Cities, and Economy","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.ch6","authors":["Rahul Vadisetty"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.ch6","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00026-1","name":"Bibliography","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00026-1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:41Z","doi":"10.1016/b978-0-44-323641-9.00026-1","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta67013.2025.11336763","name":"FedSemiSelf: A Hybrid Semi-Self-Supervised Federated Learning Framework","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336763","authors":["Fotios Filippou","Fotis Foukalas","Theodoros Tsiftsis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336763","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/flta67013.2025.11336715","name":"HVCFL: Hybrid Centralized-Decentralized Federated Learning for VANETs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336715","authors":["Luiz Fernando Rodrigues da Fonseca","Luiz Fernando Bittencourt"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336715","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/flta67013.2025.11336795","name":"Hybrid-Regularized Magnitude Pruning for Robust Federated Learning Under Covariate Shift","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336795","authors":["Özgü Göksu","Nicolas Pugeault"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336795","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00023-6","name":"Enhancing MRI reconstruction with cross-silo federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00023-6","authors":["Pengfei Guo","Puyang Wang","Jinyuan Zhou","Shanshan Jiang","Vishal M. Patel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:38Z","doi":"10.1016/b978-0-44-323641-9.00023-6","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00004-2","name":"Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00004-2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:08Z","doi":"10.1016/b978-0-44-323641-9.00004-2","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1109/flta67013.2025.11336594","name":"A Privacy-Preserving Federated Learning Framework with Multiparty Threshold Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336594","authors":["Svetlana Boudko","Kristian Teig Grønvold"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336594","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00027-3","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00027-3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:41Z","doi":"10.1016/b978-0-44-323641-9.00027-3","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1109/flta67013.2025.11336679","name":"Federated Residual Reinforcement Learning for Collaborative Robot Skill Learning in Industry","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336679","authors":["Khalil Abuibaid","Vinit Hegiste","Tatjana Legler","Achim Wagner","Martin Ruskowski"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336679","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/flta67013.2025.11336447","name":"FedLoRASwitch: Efficient Federated Learning via LoRA Expert Hotswapping and Routing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336447","authors":["Joakim Flink","Bostan Khan","Masoud Daneshtalab"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336447","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta67013.2025.11336369","name":"Eco-Friendly Federated Learning: Designing a FinOps Framework for Multicloud Resource Coordination","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336369","authors":["Francesco Avella","Fabrizio Silvestri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336369","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta67013.2025.11336673","name":"Optimizing Federated Learning by Entropy-Based Client Selection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336673","authors":["Andreas Lutz","Gabriele Steidl","Karsten Müller","Wojciech Samek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336673","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta67013.2025.11336572","name":"A Platform to Integrate Data Governance in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336572","authors":["José Antonio Peregrina","Guadalupe Ortiz","Christian Zirpins"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336572","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1007/978-3-031-86592-3","name":"Federated Cyber Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-86592-3","authors":["Hamed Tabrizchi","Ali Aghasi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-23T18:07:47Z","doi":"10.1007/978-3-031-86592-3","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00002-9","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00002-9","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:07Z","doi":"10.1016/b978-0-44-323641-9.00002-9","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.53348/jsst1s","name":"DNA BASED FEDERATED LEARNING","source":"crossref","abstract":"The Deoxyribonucleic Acid (DNA) can be considered as one of the most effective biometrics. It can be used in different fields such as personal identification, parental verification and relativity. In this study, federated learning is suggested. It is a type of machine learning which recently attracts researchers' attentions. Here it has been suggested to be employed for controlling the DNA samples over large areas. That is, the DNA centers have DNA samples, each DNA center can deal with its samples by using a single machine learning method. Then, according to the federated learning machine learnings information are combined by a main machine learning. In this case, all DNA centers can effectively share their information. So, there is no need to waste long correspondence time between the DNA centers to share required data. Also, there may no need to re- train any machine learning again for new DNA samples Keywords: DNA, Machine Learning, Pattern Recognition","url":"https://doi.org/10.53348/jsst1s","authors":["Raid Al-Nima","Maan Yahya","Azzah Qaba"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-20T10:23:13Z","doi":"10.53348/jsst1s","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta67013.2025.11336605","name":"Analyzing the Impact of Participant Failures in Cross-Silo Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336605","authors":["Fabian Stricker","David Bermbach","Christian Zirpins"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336605","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta67013.2025.11336645","name":"Optimizing Federated Learning with Frequency-Domain Quantization and Network-Aware Adaptive Compression","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336645","authors":["Syeda Faiza Ahmed","Lafifa Jamal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336645","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta67013.2025.11336682","name":"Enhancing the Convergence of Federated Learning Aggregation Strategies with Limited Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336682","authors":["Judith Sáinz-Pardo Díaz","Álvaro López García"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336682","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta67013.2025.11336340","name":"Federated Learning Architecture for Self-Improving Biometric Access Control in Office Buildings","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336340","authors":["Dennis Grüneberg","Lennart Schiweck","Christian Kücherer"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336340","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.23977/autml.2025.060109","name":"Intelligent recognition system based on federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.23977/autml.2025.060109","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-13T02:44:42Z","doi":"10.23977/autml.2025.060109","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.1109/flta67013.2025","name":"2025 3rd International Conference on Federated Learning Technologies and Applications (FLTA)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:39:20Z","doi":"10.1109/flta67013.2025","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.1109/flta67013.2025.11336757","name":"Impact of Client-Side Update Strategies on Gradient Inversion Leakage in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336757","authors":["Safaá Fallatah","Alexei Lisitsa","Floriana Grasso"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336757","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.48047/jocaaa.2025.34.12.02","name":"Federated Learning: Collaborative Machine Learning Without Data Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.48047/jocaaa.2025.34.12.02","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-04T08:01:59Z","doi":"10.48047/jocaaa.2025.34.12.02","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.1145/3737899.3768525","name":"Towards Lightweight Input Control for Cross-Device Federated Learning With Dynamic Participation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3737899.3768525","authors":["Christoph Düsing","Philipp Cimiano"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-02T17:01:43Z","doi":"10.1145/3737899.3768525","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta67013.2025.11336319","name":"Detect, Adapt, Overcome: Mitigating Concept Drift in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336319","authors":["Iftekhar Rahman","Nisal Hemadasa","Dominik Kaaser","Pierre-Alexandre Murena","Stefan Schulte"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336319","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta67013.2025.11336784","name":"Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336784","authors":["Jie Shi","Arno P. J. M. Siebes","Siamak Mehrkanoon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336784","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-031-86592-3_5","name":"Closing Thoughts, and Future Directions in Federated Cyber Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-86592-3_5","authors":["Hamed Tabrizchi","Ali Aghasi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-23T18:07:47Z","doi":"10.1007/978-3-031-86592-3_5","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/icccit62592.2025.10928151","name":"Comparison of Convergence Rates in Federated Learning and Federated Multi-Task Learning Using the CIFAR-10 Dataset","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icccit62592.2025.10928151","authors":["Truptee Upadhye","Preethi Nanjundan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-24T17:54:32Z","doi":"10.1109/icccit62592.2025.10928151","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.36227/techrxiv.176127369.97203602/v1","name":"Integrating Multimodal Fitness Data Through Federated Learning Frameworks","source":"crossref","abstract":"This review synthesizes published methods for integrating multimodal fitness data-physiological (heart rate, HRV, SpO 2 , skin temperature), kinematic (accelerometer, gyroscope, cadence, stride), textual self-reports (RPE, fatigue, sleep, diet), and structured workout metadata (sets, repetitions, load, duration, intensity zones)-under federated learning (FL). We analyze time-series backbones based on attention-enhanced BiLSTM (including RW-FN-augmented variants), hybrid/stacked ensembles, and multimodal fusion strategies (early, late, representation-level alignment). We examine FL designs for cross-device training with non-IID distributions, secure aggregation, differential privacy, and homomorphic encryption, and summarize empirical findings indicating accuracy gains from multimodal fusion and nearcentralized performance with on-device training (e.g., R 2 =0.857 for energyexpenditure prediction in centralized baselines; ∼13% communication reduction via client early stopping with negligible loss). Documented challenges include privacy leakage through updates, device and sampling heterogeneity, missing or asynchronous modalities, personalization-generalization tradeoffs, and communication overhead. The review consolidates design choices and evidence for individualized training-effect prediction and readiness estimation using federated multimodal models without raw data centralization.","url":"https://doi.org/10.36227/techrxiv.176127369.97203602/v1","authors":["Jackson Duane"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-24T02:41:45Z","doi":"10.36227/techrxiv.176127369.97203602/v1","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-031-99270-4_2","name":"Federated Learning in the Healthcare Industry","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99270-4_2","authors":["Ramkrishna Mondal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T06:00:16Z","doi":"10.1007/978-3-031-99270-4_2","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta67013.2025.11336666","name":"Federated Multi-Modal Learning for Manufacturing: A Privacy-Preserving Approach to Distributed Sensor Fusion","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336666","authors":["Tatjana Legler","Vinit Hegiste","Martin Ruskowski"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336666","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.2139/ssrn.5228699","name":"Federated Learning Applications In Enterprise Network Management","source":"crossref","abstract":"The fast expansion of connected devices and the need for improved security and performance provide growing challenges for enterprise network administration. Because of scalability and privacy issues, traditional centralized network management techniques frequently fail to meet these constraints. By facilitating decentralized, cooperative learning across dispersed network entities while maintaining data privacy, federated learning (FL) shows promise as a remedy. In order to improve network scalability, security, and efficiency, this study explores the use of FL in enterprise network administration. We start by conducting a thorough literature assessment of current approaches in cloud computing, software-defined networking (SDN), and network administration, emphasizing both their advantages and disadvantages. Important studies are reviewed, including the management problems in SDN , security measures for SDN control layers , and GENI's federated testbed for new network experiments. A notable deficiency in the incorporation of federated learning in these settings is noted by the review.","url":"https://doi.org/10.2139/ssrn.5228699","authors":["Ravikumar Perumallaplli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-06T16:05:37Z","doi":"10.2139/ssrn.5228699","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.2174/9789815322224125030005","name":"Federated Learning-Based Data Dissemination Systems for IoVs","source":"crossref","abstract":"Federated learning-based data dissemination solutions for Internet of Vehicles (IoVs) are gaining interest owing to their capacity to increase data dissemination performance and privacy. This chapter examines the current state of the art in federated learning-based data dissemination systems for IoVs, as well as the obstacles and possibilities associated with their deployment. A literature study, analysis of data dissemination needs in IoVs, and assessment of performance and privacy implications of alternative federated learning techniques are all part of the process for creating and assessing federated learning-based data dissemination systems in IoVs. The findings of a literature analysis and tests evaluating the performance and privacy of federated learning-based data dissemination systems in IoVs reveal that these systems have the potential to increase data dissemination performance and privacy, but various problems must be addressed. This chapter adds to the current literature by offering a thorough examination of the state-of-the-art federated learning-based data distribution systems for IoVs. The chapter discusses important obstacles and possibilities, as well as insights into the approach used to create and evaluate these systems. The chapter explores the consequences for IoVs of federated learning-based data dissemination systems, such as better data dissemination performance and privacy. The chapter focuses on possible applications in smart transportation, urban planning, and public safety. The chapter investigates the implications of federated learning-based data dissemination systems for IoVs, such as improved data dissemination performance and privacy. The chapter focuses on smart transportation, urban planning, and public safety applications.","url":"https://doi.org/10.2174/9789815322224125030005","authors":["Gaurav Singh Negi","Gopal Krishna","Jitendra Kumar Gupta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-25T10:34:14Z","doi":"10.2174/9789815322224125030005","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1109/flta67013.2025.11336584","name":"Federated Learning for Anomaly Detection in Edge-Cloud Communication Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336584","authors":["Nina Großegesse","Vinicius José De Menezes Pereira","Christian Maier","Felix Strohmeier"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336584","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1002/9781394167760.ch8","name":"Federated Learning Applications in Retail, Finance, and Banking for Smart Cities","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.ch8","authors":["Madhuri Gupta","Prince Gupta","Sameer Malik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.ch8","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.2139/ssrn.5471673","name":"Decentralized Federated Learning: Balancing Privacy and Performance","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5471673","authors":["Garba M."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-02T12:24:15Z","doi":"10.2139/ssrn.5471673","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1201/9781003591085-1","name":"Federated learning in healthcare 6.0 paradigm, technologies and challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003591085-1","authors":["Anniappan Vasuki","Vijayakumar Ponnusamy","Emilija Kisic","Nemanja Zdravković"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-20T10:28:47Z","doi":"10.1201/9781003591085-1","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.55248/gengpi.6.0325.1321","name":"Federated Learning for Privacy-Preserving AI","source":"openalex","abstract":"Federated Learning (FL) is an emerging machine learning paradigm that enables decentralized training of models while ensuring data pri-vacy.Unlike traditional machine learning approaches that require data centralization, FL allows multiple clients to collaboratively train a global model without sharing raw data.This paper explores the role of FL in privacy-preserving AI, discussing its advantages, key privacy-enhancing techniques, applications, challenges, and future directions.","url":"https://doi.org/10.55248/gengpi.6.0325.1321","authors":["RN Shashi Vardhan"],"tags":["Computer science","Internet privacy","Federated learning","Computer security","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-03-01","doi":"10.55248/gengpi.6.0325.1321","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.2174/9789815322224125030007","name":"Federated Learning-Based Vehicle Number Plate Recogntion in IoVs","source":"crossref","abstract":"Artificial intelligence is widely used in a variety of industries. AI technology drives much of what we do. In a similar vein, as AI-based technologies advance, smart automobiles and the Smart Transport system will likewise experience revolutionary transformation. Different techniques are applied to create a system that is used to manage traffic and increase security inside the transportation network, different techniques are used. The automatic number recognition system (ANPR) described in this research can extract an image of a vehicle license plate by employing image processing methods. To make things easier, the proposed system may be operated without the installation of any extra GPS-like devices. The suggested system consists of image processing techniques, such as filters to eliminate blur and noise when distantly acquired photographs of moving vehicles are taken. To obtain the region of interest, its edges are detected, and an image is cropped. The procedure for better outcomes includes normalization, localization, image enhancement, restoration, and character retention approaches. Its effectiveness may be negatively impacted by the state of the license plate, unconventional formats, complex vision, camera quality, camera position, tolerance for distortion, motion blur, contrast-related issues, reflections, limitations in a processing unit, environmental factors, indoor/outdoor or time-independent shots, software tools, or other hardware-based restrictions.Even with the greatest algorithms, a successful ANPR system implementation might need extra computer hardware to boost the proposed System’s accuracy.","url":"https://doi.org/10.2174/9789815322224125030007","authors":["Disha Mohini Pathak","Somya Srivastava","Shelly Gupta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-25T10:34:14Z","doi":"10.2174/9789815322224125030007","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1109/flta67013.2025.11336459","name":"Partial Gradient Rescaling: A Simple Defense Against Gradient Inversion in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336459","authors":["Mark Andrawes","Albert Frisch Møller","Tommy Sonne Alstrøm","Hiba Nassar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336459","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.2139/ssrn.5186271","name":"Federated Learning with Prototype Learning for Rul Prediction of Engine Turbine Blade","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5186271","authors":["Junqiang Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-20T04:40:58Z","doi":"10.2139/ssrn.5186271","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta67013.2025.11336714","name":"Towards a Blockchain-Based Federated Learning Framework for Sustainable Supply Chain","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336714","authors":["Fiza Siyal","Fahed Alkhabbas","Sadi Alawadi","Antonella Guzzo","Giancarlo Fortino"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336714","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1002/9781394338726.ch3","name":"Federated Learning for Food Safety and Compliance","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch3","authors":["Ramit Sehgal","Nitendra Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch3","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/flta67013.2025.11336705","name":"Energy Efficiency Factors in Federated Learning: IID and Non-IID Considerations","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336705","authors":["Nishitha Ramesh","Ali Nazeri","Christiane Plociennik","Tatjana Legler","Martin Ruskowski"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336705","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.36227/techrxiv.176425844.47889483/v1","name":"Do You Need Federated Learning?","source":"crossref","abstract":"Federated Learning has been assumed to be a solution to several problems across various domains. Subsequently, research and engineering efforts focused on how to improve and further such solutions. However, discussions rarely centered around the question in which problem contexts the paradigm is not only technologically applicable, but appropriate for the business. This paper aims to constructively further research and engineering in the FL paradigm by proposing an informed argument on when to use FL. To do so, this paper discusses applicability by designing a decision methodology and discussing several exemplary use cases. It is argued that with increasing competition between organizational collaborators, tension between technical constraints and economic incentives diminishes the sensible applicability of FL. Nevertheless, the paper outlines scenarios where incentives are likely aligned, arguing that FL can address real-world challenges.uzh","url":"https://doi.org/10.36227/techrxiv.176425844.47889483/v1","authors":["Chao Feng","Jan von der Assen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-27T15:47:34Z","doi":"10.36227/techrxiv.176425844.47889483/v1","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.5772/intechopen.1009629","name":"Introductory Chapter: Federated Learning – Bridging the Gap between Privacy and Collaboration in Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5772/intechopen.1009629","authors":["Sultan Ahmad","Meshal Alharbi","Sudan Jha","Aleem Ali"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-02T13:31:30Z","doi":"10.5772/intechopen.1009629","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1109/flta67013.2025.11336685","name":"Bayesian Federated Inference with Systematically Missing Features","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336685","authors":["Emanuele Massa","Marianne Jonker"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336685","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.1109/flta67013.2025.11336525","name":"Benchmarking Catastrophic Forgetting Mitigation Methods in Federated Time Series Forecasting","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336525","authors":["Khaled Hallak","Oudom Kem"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336525","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.36227/techrxiv.173687723.30443989/v1","name":"Federated Learning in Practice: Addressing Efficiency, Heterogeneity, and Privacy","source":"crossref","abstract":"Federated Learning (FL) is a distributed machine learning paradigm that enables collaborative model training across decentralized devices while preserving data privacy. It addresses critical challenges in privacy, scalability, and data ownership, making it a promising approach for applications in healthcare, IoT, and finance. However, practical implementation of FL faces several efficiency bottlenecks, including communication overhead, system and data heterogeneity, and security vulnerabilities. This paper provides a comprehensive survey of state-of-the-art techniques aimed at enhancing the efficiency of FL. Key methods such as model compression, including pruning, quantization, and tensor decomposition, are explored to address communication constraints. Strategies to mitigate data and system heterogeneity, including personalized FL and resource-aware training, are discussed alongside advancements in privacy-preserving mechanisms like differential privacy and secure aggregation. We also examine scalability solutions, including hierarchical and decentralized FL, to enable large-scale deployment. The survey highlights open challenges and emerging opportunities in FL, offering insights into future research directions for building efficient and robust federated systems.","url":"https://doi.org/10.36227/techrxiv.173687723.30443989/v1","authors":["Sameera Gallus"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-14T12:53:56Z","doi":"10.36227/techrxiv.173687723.30443989/v1","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.36227/techrxiv.176425844.47889483/v2","name":"Do You Need Federated Learning?","source":"crossref","abstract":"Federated Learning has been assumed to be a solution to several problems across various domains. Subsequently, research and engineering efforts focused on how to improve and further such solutions. However, discussions rarely centered around the question in which problem contexts the paradigm is not only technologically applicable, but appropriate for the business. This paper aims to constructively further research and engineering in the FL paradigm by proposing an informed argument on when to use FL. To do so, this paper discusses applicability by designing a decision methodology and discussing several exemplary use cases. It is argued that with increasing competition between organizational collaborators, tension between technical constraints and economic incentives diminishes the sensible applicability of FL. Nevertheless, the paper outlines scenarios where incentives are likely aligned, arguing that FL can address real-world challenges.","url":"https://doi.org/10.36227/techrxiv.176425844.47889483/v2","authors":["Chao Feng","Jan von der Assen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-19T20:50:42Z","doi":"10.36227/techrxiv.176425844.47889483/v2","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.2139/ssrn.5399668","name":"Federated Learning for Privacy-Preserving Financial Fraud Detection","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5399668","authors":["Mayowa Emmanuel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-04T12:01:22Z","doi":"10.2139/ssrn.5399668","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.1002/9781394167760.ch9","name":"Leveraging Blockchain and Federated Learning for Smart Cities","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.ch9","authors":["Umesh Gupta","Gopal Singh Rawat","Jay Vardhan Singh","Akshat Jain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.ch9","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.1145/3709023.3737690","name":"FedKoE: Enhancing Federated Multimodal Learning through Knowledge of Experts","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3709023.3737690","authors":["Duy Khuong","An D Nguyen","Duy Nguyen","Kok-Seng Wong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-15T07:07:04Z","doi":"10.1145/3709023.3737690","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.1002/9781394338726.ch9","name":"Revolutionizing Agriculture Yields through Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch9","authors":["Ramit Sehgal","Nitendra Kumar","Yash Dwivedi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch9","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1007/978-3-031-78841-3_6","name":"The Missing Subject in Health Federated Learning: Preventive and Personalized Care","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-78841-3_6","authors":["José Miguel Diniz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-26T02:21:20Z","doi":"10.1007/978-3-031-78841-3_6","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.254Z"},{"id":"doi:10.2118/229330-ms","name":"Federated Learning for Enhancing Cybersecurity Resilience in Distributed Energy Systems","source":"crossref","abstract":"Abstract This research investigates federated learning (FL) as a novel approach to strengthen cybersecurity resilience in distributed energy systems (DES), including substations, distributed energy resources (DERs), and grid control networks. Traditional centralized security models are inadequate for modern energy infrastructure due to privacy constraints, bandwidth limitations, and the vast scale of distributed assets [1]. A federated learning architecture was deployed across critical energy assets, where each node locally trained machine learning models on operational telemetry without sharing raw data. Only model parameters were transmitted to a central aggregator, preserving privacy while enabling collaborative threat detection. The system was evaluated through testbed validation and pilot deployment across 20+ geographically distributed nodes. Results demonstrated significant improvements over centralized approaches: 15% enhancement in anomaly detection accuracy, 100% threat detection rate with zero false positives after seven training rounds, 22% reduction in communication bandwidth requirements, and 86% F1-score maintenance across the distributed network. The edge agent required only 16 MB RAM and 25 MB disk space, enabling deployment on resource-constrained industrial devices. This framework pioneers the integration of federated learning into energy cybersecurity, providing a scalable, privacy-preserving solution that addresses current limitations while ensuring regulatory compliance with NERC CIP and IEC 62443 standards.","url":"https://doi.org/10.2118/229330-ms","authors":["Vishram Mishra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-03T00:10:44Z","doi":"10.2118/229330-ms","addedAt":"2026-08-31T06:41:19.254Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1007/978-3-031-78841-3_7","name":"Privacy-Enhancing Technologies for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-78841-3_7","authors":["Zahra Batool","Baturalp Buyukates","Reza Nourmohammadi","Kaiwen Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-26T02:21:30Z","doi":"10.1007/978-3-031-78841-3_7","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.2139/ssrn.5270039","name":"Cloud-based Federated Learning for Distributed IoT Networks","source":"crossref","abstract":"The Internet of Things (IoT) has led to an explosion of connected devices generating massive amounts of data at the network edge. Traditional centralized machine learning approaches face challenges in processing this distributed data due to privacy concerns, communication costs, and latency issues. Federated learning has emerged as a promising paradigm to enable collaborative model training across distributed clients without raw data sharing. This paper presents a comprehensive framework for cloud-based federated learning in IoT networks. We propose a novel architecture that leverages cloud computing for aggregation and orchestration while keeping raw data local on IoT devices. Key techniques are developed for client selection, secure aggregation, and model compression to address the unique challenges of resource-constrained IoT environments. Extensive experiments on real-world IoT datasets demonstrate the effectiveness of our approach in terms of model accuracy, communication efficiency, and privacy preservation. The results show that our federated learning system achieves comparable accuracy to centralized learning while reducing communication costs by up to 95% and protecting data privacy. This work provides important insights into realizing large-scale machine learning across distributed IoT networks.","url":"https://doi.org/10.2139/ssrn.5270039","authors":["Rahul Modak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-28T17:18:34Z","doi":"10.2139/ssrn.5270039","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1201/9781003532323-7","name":"Federated Learning in Heterogeneous Unmanned Aerial Vehicle","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003532323-7","authors":["N. Sathish","D. Manibharathi","V. Yokesh","Danilo Pelusi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-20T18:53:47Z","doi":"10.1201/9781003532323-7","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1007/978-3-031-86592-3_3","name":"Fundamentals of Cybersecurity","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-86592-3_3","authors":["Hamed Tabrizchi","Ali Aghasi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-23T18:07:50Z","doi":"10.1007/978-3-031-86592-3_3","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1145/3737899.3768517","name":"Deciphering One-Shot Federated Learning: The Pivotal Role of Pretrained Models","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3737899.3768517","authors":["Lingyu Qiu","Daniela Annunziata","Fabio Giampaolo","Francesco Piccialli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-02T17:01:43Z","doi":"10.1145/3737899.3768517","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1007/978-3-031-78841-3_3","name":"Client Selection in Federated Learning: Challenges, Strategies, and Contextual Considerations","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-78841-3_3","authors":["Mehreen Tahir","Muhammad Intizar Ali"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-27T04:39:26Z","doi":"10.1007/978-3-031-78841-3_3","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1017/9781009232210.008","name":"Wireless for AI: Distributed and Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1017/9781009232210.008","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-21T00:05:29Z","doi":"10.1017/9781009232210.008","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1145/3737899.3768515","name":"FedPETv1: Edge-Aware Federated Learning with Differential Privacy for Distributed Medical Diagnostics","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3737899.3768515","authors":["Shan-cang Li","Shanshan Zhao","Yongjian Ding"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-02T17:01:43Z","doi":"10.1145/3737899.3768515","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336730","name":"Frugal Federated Learning for Violence Detection: A Comparison of LoRA-Tuned VLMs and Personalized CNNs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336730","authors":["Sébastien Thuau","Siba Haidar","Ayush Bajracharya","Rachid Chelouah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336730","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00022-4","name":"Real-world implementation and application of federated medical image segmentation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00022-4","authors":["Liansheng Wang","Jiacheng Wang","Jing Yang","Xinyi Tan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:32Z","doi":"10.1016/b978-0-44-323641-9.00022-4","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1201/9781003546559-3","name":"Foundation of Blockchain and Federated Learning for Secure and Decentralized Data Management","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003546559-3","authors":["Sonali Dhananjay Patil","Niharika Pagare"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-01T12:15:01Z","doi":"10.1201/9781003546559-3","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta67013.2025.11336237","name":"Organization Committee","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336237","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336237","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336767","name":"Survey of Privacy Threats and Countermeasures in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336767","authors":["Masahiro Hayashitani","Junki Mori","Isamu Teranishi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336767","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336254","name":"Copyright Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336254","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336254","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.62311/nesx/rb-978-81-997219-9-9","name":"Encrypted Federated Learning for Edge Robotics Under Constraints","source":"crossref","abstract":"Abstract Edge robotics increasingly relies on on-device learning from sensitive operational data, yet communication limits, energy budgets, and safety obligations constrain what can be transmitted or centrally stored. Encrypted federated learning (EFL) offers a pathway to collective model improvement while reducing raw-data exposure by combining federated optimization with cryptographic protections and disciplined governance. This manuscript develops a publisher-ready research framework for designing, evaluating, and deploying EFL in robotics settings where latency, intermittent connectivity, heterogeneous hardware, and risk-critical decision making are unavoidable. The central argument is that trustworthy EFL requires co-design across four layers: (i) foundations and research design, where privacy, security, and uncertainty are defined as measurable commitments; (ii) statistical explanation and causal reasoning, where non-IID data and operational confounding are modeled explicitly; (iii) machine learning for prediction and generalization, where robustness and calibrated uncertainty are treated as first-class objectives; and (iv) scalable engineering, where reproducibility, monitoring, and safe updates operationalize accountability. The final chapter translates technical methods into sectoral horizons—logistics, healthcare, infrastructure, and public services—showing how policy-aligned governance artifacts, such as risk registers and conformity assessment dossiers, turn cryptographic assurances into deployable evidence. The manuscript avoids unverifiable claims and instead provides protocols, metrics, and decision tools suitable for researchers, practitioners, and policymakers. Keywords encrypted federated learning, edge robotics, secure aggregation, homomorphic encryption, differential privacy, privacy-preserving analytics, non-IID data, uncertainty quantification, causal inference, trustworthy machine learning, domain shift, calibration, robustness, distributed optimization, communication efficiency, reproducibility, MLOps, safety engineering, governance, conformity assessment","url":"https://doi.org/10.62311/nesx/rb-978-81-997219-9-9","authors":["Murali Krishna Pasupuleti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T12:47:21Z","doi":"10.62311/nesx/rb-978-81-997219-9-9","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1007/978-981-96-3212-1_4","name":"Federated Issues in Cross-Device Federated Recommendation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-3212-1_4","authors":["Xiangjie Kong","Lingyun Wang","Mengmeng Wang","Guojiang Shen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T10:13:07Z","doi":"10.1007/978-981-96-3212-1_4","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1002/9781394167760.ch22","name":"Federated Learning in Image Processing for Clothes Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.ch22","authors":["Madhuri Gupta","Harhsit Budhraja","Nipun Bhardwaj","Lakshit Agarwal","Ritvik Singh","Richa Chaturvedi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.ch22","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336422","name":"Fed-DPRoC: Communication-Efficient Differentially Private and Robust Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336422","authors":["Yue Xia","Tayyebeh Jahani-Nezhad","Rawad Bitar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336422","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1002/itl2.70177/v1/review1","name":"Review for \"IoT-Enabled Electric Load Prediction via Federated Label Distribution Learning\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.70177/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-18T21:06:43Z","doi":"10.1002/itl2.70177/v1/review1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1109/flta67013.2025.11336655","name":"Client-Aware Model Aggregation for Non-IID Data in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336655","authors":["Weld L. Cunha","Luis F. G. Gonzalez","Daniel L. Guidoni","Leandro A. Villas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336655","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.4018/979-8-3373-3306-9.ch002","name":"Advancing AI Integration in Healthcare Using Federated Learning","source":"crossref","abstract":"With the growing demand for secure, intelligent, and collaborative healthcare systems, Federated Learning (FL) has emerged as a transformative approach for developing AI models without exposing sensitive patient data. This chapter provides a detailed overview of FL's core architecture and its specialized variants—such as hierarchical, asynchronous, and personalized FL—tailored to healthcare's distributed and privacy-sensitive landscape. It explores privacy-enhancing mechanisms including local differential privacy, secure aggregation, and homomorphic encryption, all critical for training in heterogeneous environments. The integration of FL with blockchain, edge computing, and explainable AI (XAI) demonstrates how these technologies strengthen traceability, transparency, and real-time intelligence. Key challenges such as regulatory inconsistencies, infrastructural constraints, and model drift are critically examined. The chapter envisions a federated AI ecosystem promoting equitable, privacy-aware innovation through institutional collaboration and scalable deployment strategies.","url":"https://doi.org/10.4018/979-8-3373-3306-9.ch002","authors":["Mandeep Singh","Gaganjot Kaur","Pranshu Saxena","Megha Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-30T14:01:36Z","doi":"10.4018/979-8-3373-3306-9.ch002","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00015-7","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00015-7","authors":["Xiaoxiao Li","Ziyue Xu","Huazhu Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:29Z","doi":"10.1016/b978-0-44-323641-9.00015-7","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1145/3709023","name":"Proceedings of the International Workshop on Secure and Efficient Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3709023","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-15T07:07:04Z","doi":"10.1145/3709023","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00006-6","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00006-6","authors":["Xiaoxiao Li","Ziyue Xu","Huazhu Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:09Z","doi":"10.1016/b978-0-44-323641-9.00006-6","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336497","name":"Title Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336497","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336497","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.2139/ssrn.5736832","name":"FELM: A Federated and Explainable Learning Framework for Secure and Interpretable E-Learning Environments","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5736832","authors":["Mnar Alnaghes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-11T22:38:26Z","doi":"10.2139/ssrn.5736832","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1002/itl2.70177/v2/review1","name":"Review for \"IoT-Enabled Electric Load Prediction via Federated Label Distribution Learning\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.70177/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-18T21:06:43Z","doi":"10.1002/itl2.70177/v2/review1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1201/9781003546559-13","name":"Securing and Managing Data in Federated Learning for Healthcare Cyber-Physical Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003546559-13","authors":["Rucha Shinde","Rachana Patil","Arijit Karati"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-01T12:15:01Z","doi":"10.1201/9781003546559-13","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/flta67013.2025.11336639","name":"Privacy-Preserving Feature Valuation in Vertical Federated Learning Using Shapley-CMI and PSI Permutation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336639","authors":["Unai Laskurain","Aitor Aguirre-Ortuzar","Urko Zurutuza"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336639","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00017-0","name":"Differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00017-0","authors":["Moritz Knolle","Georgios Kaissis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:29Z","doi":"10.1016/b978-0-44-323641-9.00017-0","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/vtc2025-fall65116.2025.11310250","name":"LiteFBD: Lightweight CNN Design with Federated Block-wise Knowledge Distillation for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/vtc2025-fall65116.2025.11310250","authors":["Dae Cheol Kwon","Xinyu Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-06T18:33:45Z","doi":"10.1109/vtc2025-fall65116.2025.11310250","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336398","name":"Strategies to Improve Federated Forecasts of Day-Ahead PV Power Production","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336398","authors":["Viktor Walter","Andreas Wagner"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336398","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1515/zwf-2024-0131","name":"Federated Learning in der Arbeitsplanung","source":"crossref","abstract":"Abstract Die Aufbereitung von praxisnahen Trainingsdatensätzen für Deep Learning in der Arbeitsplanung ist eine große Herausforderung. Die Datengrundlage aktueller Ansätze basiert auf synthetisch erstellten 3D-Modellen. Eine solche synthetisierte Generierung von Trainingsdaten bildet jedoch nur sehr begrenzt die industrielle Praxis ab. Vor diesem Hintergrund haben Ansätze ein hohes Potenzial, bei denen aus den Daten mehrerer Unternehmen eine ausreichend große Datengrundlage gebildet werden kann, ohne dass diese an eine zentrale Stelle übertragen werden müssen. Eine im beschriebenen Kontext vielversprechende Methode ist das Federated Learning (FL), für dessen Anwendung in der Arbeitsplanung in diesem Beitrag ein Ansatz beschrieben wird.","url":"https://doi.org/10.1515/zwf-2024-0131","authors":["Marco Hussong","Matthias Klar","Jan C. Aurich"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-28T08:56:54Z","doi":"10.1515/zwf-2024-0131","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5220/0013677800004670","name":"Survey on Privacy-Preserving Techniques for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013677800004670","authors":["Jiaqi Lu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-30T10:02:31Z","doi":"10.5220/0013677800004670","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1201/9781003532323-2","name":"Federated Learning-Based Algorithms for Deployment and Model Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003532323-2","authors":["S. Jayachitra","A. Meenambika","M. Balasubramani","Korhan Cengiz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-20T18:53:47Z","doi":"10.1201/9781003532323-2","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.2139/ssrn.5933090","name":"CA-PPO: Collision-Aware Reinforcement Learning for Grant-Free NOMA in Wireless Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5933090","authors":["Emmanuel Atebawone"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-17T15:44:01Z","doi":"10.2139/ssrn.5933090","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1007/978-981-96-3212-1_2","name":"Learning Paradigms in Cross-Device Federated Recommendation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-3212-1_2","authors":["Xiangjie Kong","Lingyun Wang","Mengmeng Wang","Guojiang Shen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-06T01:40:58Z","doi":"10.1007/978-981-96-3212-1_2","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1007/978-3-031-99270-4_5","name":"Improving Healthcare Privacy and Efficiency with Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99270-4_5","authors":["Dheeraj Sonkhla","Amit Chauhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T05:44:25Z","doi":"10.1007/978-3-031-99270-4_5","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336663","name":"Applying Federated Learning to Block-Term Tensor Regression for Decentralised Data Analysis of Biomedical Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336663","authors":["Axel Faes","Ashkan Pirmani","Yves Moreau","Liesbet M. Peeters"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336663","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1002/9781394338726.ch2","name":"Federated Learning–Based Food Calorie Estimation","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch2","authors":["Lingam Sunitha","Shanthi Makka","Kumavat Prakash","Vankadaru Charan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch2","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.5220/0013679600004670","name":"Research on Privacy Protection Technology in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013679600004670","authors":["Zihan Xiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-30T10:04:17Z","doi":"10.5220/0013679600004670","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1002/itl2.70177/v2/review2","name":"Review for \"IoT-Enabled Electric Load Prediction via Federated Label Distribution Learning\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.70177/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-18T21:06:43Z","doi":"10.1002/itl2.70177/v2/review2","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1002/itl2.70177/v1/review2","name":"Review for \"IoT-Enabled Electric Load Prediction via Federated Label Distribution Learning\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.70177/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-18T21:06:43Z","doi":"10.1002/itl2.70177/v1/review2","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1201/9781003688570","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003688570","authors":["Harsh Kasyap","Somanath Tripathy","Minghong Fang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-21T17:09:27Z","doi":"10.1201/9781003688570","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1016/b978-0-443-28951-4.00010-1","name":"Paradigm shift from machine learning to federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-28951-4.00010-1","authors":["Revathi Vaithiyanathan","Ranjini K."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-15T04:51:37Z","doi":"10.1016/b978-0-443-28951-4.00010-1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1002/itl2.70177/v1/review3","name":"Review for \"IoT-Enabled Electric Load Prediction via Federated Label Distribution Learning\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.70177/v1/review3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-18T21:06:43Z","doi":"10.1002/itl2.70177/v1/review3","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.5220/0013679800004670","name":"Optimization of Moon Model-Contrastive Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013679800004670","authors":["Changyu Chen","Weiheng Rao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-30T10:59:31Z","doi":"10.5220/0013679800004670","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.4108/eetsis.9068","name":"Wireless Federated Learning Based Building Temperature Estimation With Latency Constraint","source":"crossref","abstract":"The paper proposes a novel approach for temperature estimation in buildings using wireless federated learning (FL) while considering latency constraints. The proposed model utilizes a hierarchical federated learning architecture within a wireless network, incorporating base stations (BS), access points (APs), and user equipment (UEs). Each UE performs local learning and shares model updates with APs, which aggregate them and forward them to the BS for final aggregation. The system aims to minimize both latency and energy consumption while ensuring accurate temperature predictions. Simulation results show the effectiveness of the proposed scheme in comparison to deep reinforcement learning (DRL) and genetic algorithm (GA) approaches. Specifically, at a latency threshold of 10 seconds, the proposed scheme achieves a prediction accuracy of approximately 0.60, while DRL reaches 0.50 and GA stays around 0.48. These results highlight the superior performance of the proposed federated learning-based method, especially in high-latency scenarios, and demonstrate its potential for real-time applications in smart building environments under wireless communication constraints.","url":"https://doi.org/10.4108/eetsis.9068","authors":["Kemin Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-16T09:35:03Z","doi":"10.4108/eetsis.9068","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.2139/ssrn.5194150","name":"DISTILLED ONE-SHOT FEDERATED LEARNING: A HIGHLY EFFICIENT AND SECURE APPROACH FOR REDUCING COMMUNICATION COSTS IN FEDERATED EDGE LEARNING","source":"crossref","abstract":"Federated Edge Learning enables dispersed edge nodes to train a global model in the Artificial Internet of Things (AIoT), advancing cloud computing. Nevertheless, existing federated learning techniques suffer from communication inefficiencies, requiring many rounds to transmit large model weights, particularly under uneven data distribution. To address this, we propose Distilled One-Shot Federated Learning (DOSFL), which notably reduces communication costs while maintaining high performance. DOSFL allows clients to distill their local datasets into smaller synthetic datasets in just one communication round, sending this data to the server for global model training. The distilled data, resembling noise, becomes useless after the model updates, eliminating the need to transmit large model weights and gradients. As a result, DOSFL reduces communication costs by up to three orders of magnitude compared to traditional methods, while retaining up to 99% of the execution of centralized training across vision and language tasks using models like CNNs, LSTMs, and Transformers. Furthermore, DOSFL enhances security by preventing attackers from building effective models using leaked distilled data. In summary, DOSFL offers an efficient and secure solution for federated learning, achieving 0.1 percent or less of the traditional methods' communication costs while maintaining high accuracy.","url":"https://doi.org/10.2139/ssrn.5194150","authors":["R.S. Shudapreyaa","Prasanth T.","Vimal M","Raahul Siv V.","Prakash P."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T13:02:41Z","doi":"10.2139/ssrn.5194150","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta67013.2025.11336302","name":"Federated Diffusion Modeling With Differential Privacy for Tabular Data Synthesis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336302","authors":["Timur Sattarov","Marco Schreyer","Damian Borth"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336302","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1201/9781003688570-1","name":"Introduction to Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003688570-1","authors":["Somanath Tripathy","Harsh Kasyap","Minghong Fang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-21T17:09:27Z","doi":"10.1201/9781003688570-1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1145/3709023.3737695","name":"Handling Device Heterogeneity in Federated Learning: The First Optimal Parallel SGD in the Presence of Data, Compute and Communication Heterogeneity","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3709023.3737695","authors":["Peter Richtarik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-15T07:07:04Z","doi":"10.1145/3709023.3737695","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336736","name":"Federated Adaptation of Language Models for On-Device Speech Recognition using Confidence-Aware Training","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336736","authors":["Zhe Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336736","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1002/9781394271382.ch6","name":"Cloud Infrastructure and Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394271382.ch6","authors":["Kanishka Gupta","Amit Aylani","Prakash Parmar","Deepak Hajoary"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-27T16:38:31Z","doi":"10.1002/9781394271382.ch6","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336571","name":"FLASH: A Framework for Federated Learning with Attribute Selection and Hyperparameter Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336571","authors":["Ioannis Christofilogiannis","Georgios Valavanis","Alexander Shevtsov","Ioannis Lamprou","Sotiris Ioannidis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336571","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1007/978-3-031-78841-3_5","name":"Federated Learning for Recommender Systems: Advances and Perspectives","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-78841-3_5","authors":["Vasileios Perifanis","Nikolaos Pavlidis","Andreas Sendros","Pavlos S. Efraimidis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-26T02:21:18Z","doi":"10.1007/978-3-031-78841-3_5","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1007/978-3-031-96649-1_8","name":"Differentially Private Federated Edge Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-96649-1_8","authors":["Yong Zhou","Wenzhi Fang","Yuanming Shi","Khaled B. Letaief"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T12:52:18Z","doi":"10.1007/978-3-031-96649-1_8","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1145/3737899.3768526","name":"Scalable Federated Split Learning for Smart Mobile and IoT Devices","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3737899.3768526","authors":["Pham Duy Thanh","Tran Anh Khoa","Minh-Son Dao","Koji Zettsu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-02T17:01:43Z","doi":"10.1145/3737899.3768526","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.5220/0013635000003979","name":"Learning Without Sharing: A Comparative Study of Federated Learning Models for Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013635000003979","authors":["Anja Campmans","Mina Alishahi","Vahideh Moghtadaiee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-02T04:37:42Z","doi":"10.5220/0013635000003979","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1007/978-3-031-58923-2_6","name":"Federated Bilevel Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-58923-2_6","authors":["Hongchang Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T07:02:43Z","doi":"10.1007/978-3-031-58923-2_6","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.36227/techrxiv.176003148.82070541/v1","name":"A Threshold-Based Federated Learning Infrastructure for Fintech Services","source":"crossref","abstract":"Fintech ecosystem has encountered a rapid change in their electronic transactions thereby increasing vulnerabilities during sensitive data transfer. In most cases, the sensitive data is transferred through public channels and sometimes, data gatherers are not willing to share their data with others. This causing a bottleneck in making collective decisions as a whole. Subsequently, the conventional fintech ecosystem gathers data in a high computing server and execute decision making algorithms in them. This caused a major issue with the required computational power as well as safer data transfer risks. To overcome this tradeoff, Federated Learning (FL) can be leveraged to transform the conventional fintech ecosystem to a better and federated one by non-sharable data and collective decisionmaking without having high computing devices as well as without revealing sensitive private data to others. As a predominantly distributed scenario, this also has enormous message passing costing a substantial bandwidth of the network. Also, all the federation members are not equally contributory may be due to infrastructural or load issues of the individual federation members. The work focuses on how to reduce the number of active contributor by finding their local model accuracies and subsequently, as the number of participants reduces, the required communication cost is also reduced. The work extends with a threshold-based voting approach to execute the process. And is compared with existing systems and found to be outperforming in all aspects. The experimental setup and federation configurations are considered as per standards from the literature. The overall accuracy, communication, and execution costs are considered as performance metrics.","url":"https://doi.org/10.36227/techrxiv.176003148.82070541/v1","authors":["Nandan Banerji","Lhamu Sherpa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-09T17:38:06Z","doi":"10.36227/techrxiv.176003148.82070541/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1145/3737899","name":"Proceedings of the Federated Learning and Edge AI for Privacy and Mobility","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3737899","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-02T17:01:43Z","doi":"10.1145/3737899","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1002/9781394167760.ch13","name":"Federated Learning Applications for Urban Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.ch13","authors":["Baskar Kasi","Saravanan Ramalingam","T. Sathish Kumar","A. Mohan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.ch13","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1007/978-3-031-96649-1_9","name":"Trustworthy Federated Edge Learning via Blockchain","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-96649-1_9","authors":["Yong Zhou","Wenzhi Fang","Yuanming Shi","Khaled B. Letaief"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T12:52:26Z","doi":"10.1007/978-3-031-96649-1_9","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1201/9781003591085-8","name":"Ensuring data privacy and security in federated learning for healthcare data","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003591085-8","authors":["Chinna Ovu Reddi Umarani","Jahangeer Naskath","Nagarajan Karthikeyan","Selvakumar Manickam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-20T10:28:47Z","doi":"10.1201/9781003591085-8","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta67013.2025.11336384","name":"Flashback: Understanding and Mitigating Forgetting in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336384","authors":["Mohammed Aljahdali","Ahmed M. Abdelmoniem","Marco Canini","Samuel Horváth"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336384","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1002/9781394338726.ch13","name":"A Review on Detection of Adulteration in Food Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch13","authors":["Jagamohan Meher","Rajanandini Meher"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch13","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.5206120","name":"Fast Decentralized Gradient Tracking for Federated Learning with Local Updates","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5206120","authors":["Junchi Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-13T10:49:46Z","doi":"10.2139/ssrn.5206120","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00014-5","name":"Expanding the federated horizon: cross-domain techniques for collective intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00014-5","authors":["Vishwa S. Parekh","Pranav Kulkarni","Adway Kanhere","Michael A. Jacobs"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:25Z","doi":"10.1016/b978-0-44-323641-9.00014-5","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00021-2","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00021-2","authors":["Xiaoxiao Li","Ziyue Xu","Huazhu Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:34Z","doi":"10.1016/b978-0-44-323641-9.00021-2","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00010-8","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00010-8","authors":["Xiaoxiao Li","Ziyue Xu","Huazhu Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:14Z","doi":"10.1016/b978-0-44-323641-9.00010-8","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1002/9781394167760.ch10","name":"Integrating Blockchain and Federated Learning for Enhanced Security and Privacy in Smart Cities","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.ch10","authors":["Dipali Sarvate","Siddharth Shankar Mishra","V. Shanmugapriya","Dheerendra Panwar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.ch10","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336407","name":"Fin-Fed-OD: Enhancing Outlier Detection Using Federated Learning on Financial Tabular Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336407","authors":["Dayananda Herurkar","Ahmed Anwar","Sebastian Palacio","Jörn Hees","Andreas Dengel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336407","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1002/9781394271382.ch9","name":"Federated Learning in Brain Tumor Segmentation in Medical Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394271382.ch9","authors":["Jyoti Kataria","Supriya P. Panda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-27T16:38:31Z","doi":"10.1002/9781394271382.ch9","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1007/978-3-031-99270-4_3","name":"Federated Learning for Remote Health Monitoring—A Review","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99270-4_3","authors":["Venkatesh Upadrista","Sajid Nazir","Huaglory Tianfield"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T05:55:53Z","doi":"10.1007/978-3-031-99270-4_3","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.2139/ssrn.5162425","name":"Federated Learning for Distributed AI Systems","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5162425","authors":["Lorna Tobin","Sameera Gallus","Romey Marina"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-02T12:01:28Z","doi":"10.2139/ssrn.5162425","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336403","name":"FedSparQ: Adaptive Sparse Quantization with Error Feedback for Robust &amp; Efficient Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336403","authors":["Chaimaa MEDJADJI","Sadi ALAWADI","Feras M. Awaysheh","Guilain LEDUC","Sylvain KUBLER","Yves Le Traon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336403","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336524","name":"Federated Action Recognition for Smart Worker Assistance Using FastPose","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336524","authors":["Vinit Hegiste","Vidit Goyal","Tatjana Legler","Martin Ruskowski"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336524","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.36227/techrxiv.174961653.38731303/v1","name":"Federated Learning for Predictive Maintenance and Resilience in Urban Infrastructure Systems","source":"crossref","abstract":"This paper proposes a federated learning-based framework for predictive maintenance and resilience assessment of urban infrastructure systems using multi-source time-series data. The approach enables collaborative model training across distributed edge devices while preserving data privacy and reducing communication overhead. Key challenges such as data heterogeneity, system-level disparities, and limited computation resources are addressed through adaptive model aggregation and personalization strategies. Experimental evaluations on benchmark and real-world datasets demonstrate the framework's effectiveness in improving predictive accuracy and system robustness under non-independent and identically distributed (non-i.i.d.) conditions. The results highlight the potential of federated learning to support proactive, data-driven infrastructure management at city scale.","url":"https://doi.org/10.36227/techrxiv.174961653.38731303/v1","authors":["Ammoon Birzoim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-11T00:35:41Z","doi":"10.36227/techrxiv.174961653.38731303/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.2139/ssrn.5319604","name":"Real-Time Threat Detection in Vehicular Networks Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5319604","authors":["Muhammad A"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-25T19:13:54Z","doi":"10.2139/ssrn.5319604","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.2139/ssrn.5228501","name":"FEDERATED LEARNING FOR DATA PRIVACY AND SECURITY IN INDUSTRIAL APPLICATIONS","source":"crossref","abstract":"The new opportunities in data privacy and security come with the rapid growth of the Internet of Things (IoT) and Cloud computing in industrial applications. IoT networks, through which physical objects exchange information in real-time, fundamentally alter the industrials using them for productivity and efficiency. With these technologies being integrated into each other, data security and privacy issues, as well as the resilience of the networks to external challenges, are the critical issues involved. Since data is rapidly growing and being shared globally through IoT and cloud-based systems, the concerns about security data breaches and unauthorized access about compliance with regulatory standards keep growing. Data is being increasingly shared across the globe through IoT and cloud-based systems. This has led to rising concerns about data breaches and unauthorized access in regard to compliance with regulatory standards. This paper deals with an in-depth study of federated learning as a framework to protect industrial IoT and cloud systems. This paper shall assess whether federated learning will be able to address security risks, tackle compliance challenges, maintain data integrity, and ensure that the optimal performance of the system can be ascertained under varying forms of workload. In addition to that, the paper puts forward significant challenges, such as requirements of standardization in security protocols and regulatory frameworks, and promotes future research directions toward the powerful adoption of federated learning in industrial applications.","url":"https://doi.org/10.2139/ssrn.5228501","authors":["Ravikumar Perumallaplli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-06T16:05:43Z","doi":"10.2139/ssrn.5228501","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1109/flta67013.2025.11336535","name":"FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336535","authors":["Emmanouil Kritharakis","Antonios Makris","Dusan Jakovetic","Konstantinos Tserpes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336535","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.2139/ssrn.5131392","name":"Client-Side Patching Against Backdoor Attacks in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5131392","authors":["Borja Molina-Coronado"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-10T18:43:50Z","doi":"10.2139/ssrn.5131392","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.36227/techrxiv.174836402.29031592/v1","name":"Edge-Efficient Federated Learning Adaptive Compression for Real-Time Robotic Collaboration","source":"crossref","abstract":"Collaborative robotics demands seamless realtime coordination among distributed agents, yet bandwidth limitations pose significant challenges for federated learning (FL) implementations. This paper presents a novel adaptive compression algorithm tailored for FL, which dynamically adjusts model updates based on network conditions and task urgency. By leveraging sparse neural networks, the approach ensures low-latency collaboration in dynamic environments. The algorithm is evaluated through multi-robot navigation tasks, demonstrating superior efficiency and robustness in real-time scenarios. Experimental results highlight significant improvements in communication efficiency and task performance compared to existing methods.","url":"https://doi.org/10.36227/techrxiv.174836402.29031592/v1","authors":["Hemanth Ravipati"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-27T12:40:33Z","doi":"10.36227/techrxiv.174836402.29031592/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1201/9781003532323-6","name":"Blockchain-Integrated Federated Learning for IoT-Based Smart Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003532323-6","authors":["S. Balakrishnan","Syed Shahul Hameed","RM Sunil Kumar","S. Simonthomas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-20T18:53:47Z","doi":"10.1201/9781003532323-6","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.24874/qf.25.053","name":"REVOLUTIONIZING EARLY LUNG CANCER DETECTION WITH MACHINE LEARNING: INSIGHTS FROM FEDERATED AND ENSEMBLE LEARNING","source":"crossref","abstract":"","url":"https://doi.org/10.24874/qf.25.053","authors":["Jayendra S. Jadhav","Jyoti Deshmukh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-23T15:22:31Z","doi":"10.24874/qf.25.053","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1201/9781003532323-5","name":"Advances in 5G/6G-Enabled Federated Reinforcement Learning in IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003532323-5","authors":["P. Arul","V. Yokesh","N. Sathish","Pham Chien Thang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-20T18:53:47Z","doi":"10.1201/9781003532323-5","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1002/9781394338726.ch7","name":"Federated Learning for Plant Disease Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch7","authors":["Siddhartha Das","Sudipta Jana","Sudeepta Pattanayak","Pradipta Banerjee","Sweety Maity"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch7","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1201/9781003532323-4","name":"Federated Learning for Sustainable Development Using IoT/Edge Computing System","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003532323-4","authors":["M. Yogeshwari","S. Lavanya","S. Deepa","Afizan Bin Azman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-20T18:53:47Z","doi":"10.1201/9781003532323-4","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1002/9781394167760.fmatter","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.fmatter","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.fmatter","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1002/9781394338726.ch10","name":"Federated Learning in Smart Agriculture","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch10","authors":["Abhishek Tyagi","Shekhar Tyagi","Guru Dayal Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch10","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/icmlt65785.2025.11193246","name":"Federated Class-Incremental Learning: A Survey","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlt65785.2025.11193246","authors":["Xuefeng Zhu","Liang Bai","Yirun Ruan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-13T17:39:05Z","doi":"10.1109/icmlt65785.2025.11193246","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.5220/0013679400004670","name":"Research on Privacy and Security Issues in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013679400004670","authors":["Xinyuan Bi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-30T10:17:44Z","doi":"10.5220/0013679400004670","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-031-78841-3_8","name":"Collaborative Defense: Federated Learning for Intrusion Detection Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-78841-3_8","authors":["Rawish Butt","Noshina Tariq","Muhammad Ashraf","Mamoona Humayun","Momina Shaheen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-26T02:21:40Z","doi":"10.1007/978-3-031-78841-3_8","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.36227/techrxiv.175382579.91847259/v1","name":"Federated Learning Frameworks: Privacy-Preserving Optimization for Distributed AI Systems","source":"crossref","abstract":"The advent of federated learning (FL) has changed the face of collaborative machine learning by facilitating decentralized model training without raw data centralization. The distributed nature of FL brings new challenges in providing privacy, improving learning performance, and avoiding communication and computation overhead. This research is a detailed investigation and new design of privacy-preserving optimization techniques in federated learning paradigms for various applications on mobile devices, IoT networks, and critical infrastructure systems. By combining a hybrid framework that brings together differential privacy (DP), homomorphic encryption (HE), secure multiparty computation (SMC), and fairness-aware aggregation, the above method strikes the perfect balance between privacy, model accuracy, and communication efficiency. Adaptive local training, scalable client participation, and inference are supported by a poison attack protection mechanism as well. By empirical testing on benchmark models and datasets (e.g., ResNet, LSTM, and BERT), the framework exhibits robust performance with low accuracy loss under strict privacy budgets. This research contributes to the emerging literature on privacy-enhancing technologies for distributed AI and defines avenues for future research in large-scale secure federated learning.","url":"https://doi.org/10.36227/techrxiv.175382579.91847259/v1","authors":["Vikash Kumar","Krishna Murari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-29T21:49:56Z","doi":"10.36227/techrxiv.175382579.91847259/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.2139/ssrn.5045545","name":"Federated Learning for Cross-Cloud System Migrations: A Privacy-Preserving Approach","source":"crossref","abstract":"This article presents an innovative approach to SAP ERP system migrations using federated learning frameworks, addressing the critical challenges of data privacy and system availability in cross-cloud migrations. The proposed article integrates","url":"https://doi.org/10.2139/ssrn.5045545","authors":["Krupal Gangapatnam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T14:22:58Z","doi":"10.2139/ssrn.5045545","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.1201/9781003532323-10","name":"Use Cases and Scenarios for Federated Learning Adoption in IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003532323-10","authors":["Anitha Velu","Raghu Ramamoorthy","A. Prasanth","Ahmed A. Elngar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-20T18:53:47Z","doi":"10.1201/9781003532323-10","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.22541/essoar.175807020.09297050/v1","name":"Federated Reinforcement Learning Framework for Privacy Preserving Few Shot Learning Authors","source":"crossref","abstract":"This study introduces a federated reinforcement learning framework for few-shot learning (FRL-FSL), aiming to address the dual challenges of data scarcity and privacy preservation in distributed environments. The proposed framework integrates policy gradient optimization with secure aggregation and introduces validator nodes to ensure the authenticity of both data and model updates. Experiments were conducted on the Omniglot and FC100 datasets under 1-shot and 5-shot conditions, with comparisons against FedAvg, FedFSL and traditional supervised baselines. Results demonstrate that FRL-FSL achieved an average accuracy of 87.3% on Omniglot (5-shot), improving by 25.9% over FedAvg and 13.8% over FedFSL, while maintaining 72.6% accuracy in 1-shot tasks. On the FC100 dataset, FRL-FSL reached 59.8% accuracy in 5-shot learning, outperforming FedAvg by 18.6% and FedFSL by 7.1%, and achieved 46.3% in 1-shot learning. The framework also reduced the privacy risk index by 37% relative to FedAvg, with convergence accelerated by nearly 30% compared to baselines. These findings confirm that FRL-FSL achieves a practical balance between accuracy, convergence, and privacy, offering a promising solution for real-world, privacy-sensitive applications.","url":"https://doi.org/10.22541/essoar.175807020.09297050/v1","authors":["Minsoo Kang","Jihye Park","Donghyun Choi","Seoyeon Kim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-17T00:50:10Z","doi":"10.22541/essoar.175807020.09297050/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.3390/bios16080442","name":"A Blockchain-Enabled Federated Neuro-Symbolic Framework for Secure Wearable Biosensor-Based Health Monitoring.","source":"pubmed","abstract":"Wearable biosensors generate continuous physiological data in smart Internet of Disease (IoD) environments. These data can support early disease detection and remote patient monitoring. However, wearable data are often noisy, sensitive, and distributed across different devices. This paper proposes a multimodal neuro-symbolic model to overcome these limitations and incorporates it into a secure Edge-Fog-Cloud framework for anomaly detection in smart healthcare applications. The proposed system integrates the semantic analysis of clinical text using Bio-ClinicalBERT with temporal numerical data using an LSTM-based model, creating a unified neuro-symbolic artificial intelligence (AI) pipeline. Initial data processing is performed at the Edge, whereas inference is carried out at distributed Fog nodes for low-latency anomaly detection. Model training is handled in the Cloud, and privacy-preserving federated learning (FL) is supported through Homomorphic Encryption (HomEnc) to facilitate collaborative model training without sharing raw patient data. A sharded Tangle ledger is also used, with transactions broadcast by the Fog nodes and validated in the Cloud to create tamper-evident transaction logs. Furthermore, Honey Encryption (HoneyEnc) is integrated into the Fog layer to enhance security against brute-force attacks. Experimental results show that the proposed framework achieved 99.22% accuracy and a 99.31% F1-score on the held-out test set, with bootstrap 95% confidence intervals of 98.96-99.47% for accuracy and 99.08-99.53% for the F1-score. It also reduced detection latency from 185 ms in the baseline setting to approximately 50 ms in the Fog-inference setting. The blockchain layer achieved approximately 500 Transactions Per Second (TPS), while higher throughput was observed under increased transaction load and shard parallelism. Because the evaluation is based on synthetic multimodal EHR-like data and controlled simulations, the reported findings should be interpreted as proof-of-concept internal validation rather than evidence of deployment-ready clinical generalizability; external validation using real wearable biosensor data, hospital IoMT streams, or public clinical datasets such as MIMIC-III/MIMIC-IV is required before clinical deployment. These results highlight the potential of the proposed system for secure data processing and trustworthy anomaly detection in smart healthcare environments.","url":"https://doi.org/10.3390/bios16080442","authors":["Alshudukhi KS","Tariq N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/bios16080442","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1080/15376516.2026.2712550","name":"Advances in artificial intelligence and machine learning for toxicity prediction in computational toxicology: a comprehensive review.","source":"pubmed","abstract":"Over the past three decades, artificial intelligence (AI) and machine learning (ML) have revolutionized computational toxicology, providing powerful tools for predicting chemical toxicity and supporting safer assessments for human health and the environment. This review offers a critical 30-year synthesis (1995-2025) that distinguishes itself from narrower prior works through its interdisciplinary integration of historical evolution, multi-omics data fusion, nanotoxicity challenges, regulatory frameworks, multi-stakeholder perspectives, and emerging hybrid and generative models. Key findings reveal a clear progression from early artificial neural networks capturing non-linear patterns in the 1990s to modern deep learning architectures such as convolutional neural networks and graph neural networks that have achieved over 85% accuracy, primarily in retrospective benchmarks on ToxCast and Tox21 datasets for endpoints including hepatotoxicity, cardiotoxicity, and nanotoxicity. However, prospective validation on novel compounds remains limited, representing a critical translational gap. Traditional machine learning methods (random forests and support vector machines) effectively handle imbalanced high-throughput screening data, facilitating multi-omics integration and applications across pharmaceuticals, pesticides, cosmetics, and nanoparticles. These approaches strengthen read-across strategies, Integrated Approaches to Testing and Assessment (IATA), and Threshold of Toxicological Concern (TTC) frameworks. Regulatory acceptance, guided by OECD principles, increasingly emphasizes explainable AI to ensure transparency and validation. In conclusion, AI/ML approaches can substantially reduce animal testing, accelerate safety evaluations, and address data gaps, yet require ongoing attention to biases, model opacity, and limited prospective performance. Hybrid mechanistic-AI models, federated learning, and strengthened cross-sector collaboration represent the most promising path forward.","url":"https://doi.org/10.1080/15376516.2026.2712550","authors":["Shija G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1080/15376516.2026.2712550","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1371/journal.pone.0355601","name":"FedMamba-IoMT: Federated state space models with differential privacy and byzantine resilience for privacy-preserving intrusion detection in Internet of Medical Things.","source":"pubmed","abstract":"The proliferation of Internet of Medical Things (IoMT) devices has created critical cybersecurity challenges demanding intrusion detection systems that achieve high accuracy across diverse attack taxonomies while preserving patient privacy across institutional boundaries. Existing federated learning (FL) approaches face an inherent tension: Transformer-based architectures achieve strong detection performance but incur quadratic computational complexity and substantial communication overhead, while lightweight classifiers sacrifice representational capacity. Moreover, most FL-based intrusion detection systems lack formal privacy guarantees and robustness against adversarial participants. This paper introduces FedMamba-IoMT, the first federated State Space Model framework for privacy-preserving intrusion detection in IoMT networks, incorporating differential privacy (DP-SGD), Byzantine-resilient aggregation, and multi-level explainability. The proposed architecture reformulates tabular network traffic features as pseudo-sequential tokens processed through stacked selective State Space Model (Mamba) blocks with gated residual connections, achieving linear computational complexity &#x1d4aa;(n) with 78% fewer parameters than Transformer alternatives. We design a novel FedMamba aggregation strategy that weights client contributions by a convex combination of dataset proportion and inverse validation loss, augmented with a cosine similarity-based Byzantine filter that detects and excludes malicious model updates. Integration of DP-SGD with R&#xe9;nyi differential privacy accounting provides formal privacy guarantees (&#x3b5;&#x2208;{1.0,2.0,3.0,5.0,8.0}, &#x3b4;=10-5) while maintaining competitive accuracy. Comprehensive evaluation across three benchmark datasets-Edge-IIoTset (2,219,201 samples, 15 classes), CICIoMT2024 (3,204,537 samples, 19 classes), and Gotham Dataset 2025 (496,191 samples, 8 high-level traffic categories)-demonstrates that FedMamba-IoMT achieves 99.47&#xb1;0.04%, 99.52&#xb1;0.04%, and 98.90&#xb1;0.04% multiclass accuracy without DP, and 98.52%, 98.18%, and 97.16% at &#x3b5;=3.0, surpassing all prior federated IDS approaches. Byzantine resilience experiments demonstrate that the proposed defense maintains &gt;95% accuracy under 30% malicious clients across label-flipping, model poisoning, and free-rider attacks. Gradient inversion analysis confirms that FedMamba's compact parameterization (135K parameters, 0.52 MB) provides 2&#xd7; higher reconstruction error compared to Transformer-based FL, and the integrated SHAP and LIME explainability framework supports regulatory compliance with the FDA's 2023 cybersecurity guidance for medical devices.","url":"https://doi.org/10.1371/journal.pone.0355601","authors":["Al-Sharo YM","Tawfik M","Almadani AM","Abdelhaliem AH","Fathi IS","Hassan G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0355601","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1038/s41598-026-55768-3","name":"Federated ConvNeXt-swin temporal fusion network for malware and botnet detection in IoT systems.","source":"pubmed","abstract":"The rapid expansion of Internet of Things (IoT) infrastructures has significantly increased the exposure of edge devices to malware and botnet attacks. Conventional intrusion detection systems are largely centralized and struggle to operate effectively in decentralized, heterogeneous, and privacy-sensitive IoT environments, thereby limiting scalability and robustness. To address these challenges, this study proposes the Federated ConvNeXt-Swin Temporal Fusion Network (F-CSTFNet), a federated deep learning framework designed for distributed IoT malware and botnet detection. The proposed architecture integrates ConvNeXt-based convolutional feature extraction with Swin Transformer temporal attention to capture both local traffic patterns and long-range behavioral dependencies within network flows. This hybrid convolution-attention design enables the detection of short-term anomalies as well as evolving attack dynamics directly from network telemetry. In addition, a channel-adaptive feature recalibration mechanism enhances robustness when learning from heterogeneous and noisy client data. The model is trained using a federated learning paradigm that enables multiple IoT clients to collaboratively learn a global model without sharing raw data, thereby preserving data privacy and locality. Extensive experiments conducted on the IoT-23 and N-BaIoT datasets demonstrate that F-CSTFNet outperforms several state-of-the-art centralized and federated baselines in terms of detection accuracy, convergence stability, and client-level fairness. The framework also achieves low performance variance across clients, a high Jain's Fairness Index (JFI), and reduced inequality during distributed training. These results demonstrate the effectiveness of the proposed architecture as a scalable, privacy-preserving, and resilient intrusion detection framework for next-generation IoT security systems.","url":"https://doi.org/10.1038/s41598-026-55768-3","authors":["Alsubaei FS","Almazroi AA","Ayub N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-55768-3","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.3390/s26144542","name":"Correction: Deshmukh et al. Enhancing Privacy in IoT-Enabled Digital Infrastructure: Evaluating Federated Learning for Intrusion and Fraud Detection. &lt;i&gt;Sensors&lt;/i&gt; 2025, &lt;i&gt;25&lt;/i&gt;, 3043.","source":"pubmed","abstract":"Text Correction [...].","url":"https://doi.org/10.3390/s26144542","authors":["Deshmukh A","de la Rosa PE","Rodriguez RV","Dasari S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26144542","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"doi:10.1016/j.artmed.2026.103422","name":"Centralized pooling and federated learning for Canadian patient-level data sharing in multicenter medical AI: A scoping review.","source":"pubmed","abstract":"Algorithms that support screening, triage, and treatment decisions depend on training data drawn from patient populations. Limited access to patient-level records across institutions and jurisdictions can reduce representation and contribute to uneven model performance across populations. Canada's federated health system, where provinces and territories manage separate datasets and privacy regimes, limits multicenter medical AI research. We conducted a scoping review to map how Canadian researchers share patient-level data in multicenter medical AI collaborations. We searched PubMed, IEEE Xplore, ACM Digital Library, Scopus, and Web of Science from 2018 to February 2025 and implemented a human-in-the-loop large language model process to support screening and extraction, with reviewer validation. Among 3100 included studies, 160 reported multicenter patient-level data collection. Centralized pooling dominated this subset, with 95% of studies using centralized storage and 5% (n = 8) reporting decentralized approaches, including federated learning, sequential model transfer, and distributed feature sharing. Governance requirements were frequently described as multi-site and sequential, and 81.8% of multicenter collaborations reported parallel ethics approvals from three or more institutional review boards. Only one decentralized collaboration operated entirely within Canada. International partnerships comprised 80% of multicenter studies, and many cohorts included non-Canadian sites or non-Canadian data. Our findings support adoption of distributed model development protocols and interoperable governance that limit central pooling while enabling consistent training, validation, and reporting across sites, as only 1 of 160 multicenter studies reported a decentralized approach with Canadian patient data only.","url":"https://doi.org/10.1016/j.artmed.2026.103422","authors":["Jafarinezhad O","Zhang Q","Rezai R","Noaeen M","Shachak A","Far B","Shakeri Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.artmed.2026.103422","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1093/jamia/ocag047","name":"Federated learning's uncomfortable truth: why human networks matter more than neural networks.","source":"pubmed","abstract":"To examine real-world barriers to implementing federated learning in healthcare and highlight the organizational, regulatory, and socio-technical factors often overlooked in technical research.","url":"https://doi.org/10.1093/jamia/ocag047","authors":["Peltonen LM","Chomutare T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1093/jamia/ocag047","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1016/j.amjcard.2026.06.005","name":"Bias, External Validation, and Real-World Implementation of Artificial Intelligence Models in Cardiovascular Medicine.","source":"pubmed","abstract":"Artificial intelligence (AI) and machine learning (ML) have demonstrated strong diagnostic and prognostic performance across cardiovascular medicine. However, translation into equitable real-world benefit is limited by algorithmic bias, inadequate external validation, and unclear implementation pathways. This State-of-the-Art Review evaluates these challenges using the Total Product Life Cycle (TPLC) framework, encompassing development, validation, regulatory approval, deployment, and post-market surveillance. We synthesize current literature on bias mechanisms, validation strategies, and real-world implementation, and critically assess emerging technical solutions, including federated learning and explainable AI. Bias enters at multiple lifecycle stages through unrepresentative data, flawed labels, measurement variability, and deployment mismatch. Most cardiovascular AI models rely on limited external validation, often lacking geographic or domain generalizability. A 2025 analysis of 691 FDA-cleared AI/ML devices showed 95.5% lacked demographic transparency and only 1.6% had randomized trial evidence. Implementation barriers include dataset shift, regulatory gaps, and inequitable access, with limited prospective outcome data supporting clinical benefit. Current cardiovascular AI deployment is not matched by sufficient evidence for safety, equity, and effectiveness. A TPLC-aligned framework with rigorous validation, demographic transparency, and continuous post-market monitoring is essential to ensure equitable and clinically meaningful integration of AI into cardiovascular care.","url":"https://doi.org/10.1016/j.amjcard.2026.06.005","authors":["Patel NN"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.amjcard.2026.06.005","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.64898/2026.04.15.26351000","name":"Individualized Per-Site Meta-Federated Feature Learning (iPS-MFFL) for Privacy-Preserving Brain Tumor MRI Classification under non-IID Heterogeneity","source":"europepmc","abstract":"","url":"https://doi.org/10.64898/2026.04.15.26351000","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.04.15.26351000","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1007/s10278-026-02069-w","name":"Advanced Deep Learning Architectures in MRI-Based Brain Tumor Classification: A Systematic Review Focused on Meningiomas.","source":"pubmed","abstract":"Deep learning (DL) is increasingly applied to automate brain tumor classification from magnetic resonance imaging (MRI), yet meaningful clinical deployment remains limited by tumor heterogeneity, dataset bias, and incomplete tumor-specific validation. This systematic review synthesizes developments from 2016 to 2025 in advanced DL-based MRI brain tumor classification, with a specific emphasis on meningioma-focused classification and subtyping, given their persistent underrepresentation in AI research. Fifty-six eligible studies were analyzed and organized into five methodological categories: transformer-based models; transformer-based feature extraction pipelines; attention-enhanced convolutional neural networks (CNNs); federated learning approaches; and emerging or unconventional DL strategies. Among studies reporting class-wise metrics (n&#x2009;=&#x2009;19), attention-enhanced CNNs and hybrid CNN-transformer architectures showed high overall accuracy with fewer extreme drops in meningioma performance (F1-score, 89.0% to 99.0%) than end-to-end transformers (F1-score, 79.0% to 97.0%), despite the latter achieving high peak accuracies. Our analysis also showed a marked gap between general tumor categorization and clinically actionable subtyping or grade classification, particularly for meningiomas, reflecting both limited meningioma-targeted tasks and reduced transparency in per-class reporting. Study design and reporting practices limited cross-study comparability, with heavy reliance on a small number of publicly available datasets, frequent class imbalance disadvantaging meningioma representation, and approximately 60% of studies not specifying MRI sequence details. Although predictive performance improved and some studies incorporated interpretability or&#xa0;clinical decision-support&#xa0;components, reporting remained sparse, reinforcing the translational gap between methodological progress and deployment readiness. Finally, publication activity accelerated sharply after 2023 but remained geographically concentrated, raising concerns about representativeness. Our findings call for standardized per-class reporting, greater&#xa0;diverse datasets, interpretability components,&#xa0;and clinically aligned meningioma evaluations to support effective translation into practice.","url":"https://doi.org/10.1007/s10278-026-02069-w","authors":["Noor N","Turner C","Holdsworth SJ","Nielsen P","Correia JA","Abbasi H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1007/s10278-026-02069-w","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.1038/s41598-026-58066-0","name":"FedSynHAR: a framework based on feature-enhanced adaptive pruning-mutual distillation for federated human activity recognition.","source":"pubmed","abstract":"Deep-learning-based human activity recognition (HAR) has been widely studied and applied in recent years, but it raises privacy concerns. Federated learning (FL) enables collaborative training without sharing raw data, thereby protecting user privacy. However, FL for HAR is challenged by three coupled factors: non-IID data across clients, aggregation under heterogeneous local models, and stringent computation and bandwidth budgets on edge hardware. To address these factors, this work introduces FedSynHAR, a lightweight FL framework that combines Gradient-Importance-based Adaptive Pruning (GIAP) with Channel-guided Feature-level Mutual Distillation (CFMD). GIAP prunes both server and client networks based on gradient importance, reducing computational and communication overhead; CFMD uses channel importance to guide mutual distillation between local and proxy models and mitigates pruning-induced degradation to improve robustness under non-IID conditions. Experiments on UCI-HAR and PAMAP2 demonstrate the effectiveness of FedSynHAR for federated HAR. On UCI-HAR, FedSynHAR converges about 2&#xd7; faster than FedAvg, achieves 94.91% accuracy under non-IID settings, and reduces overhead by up to two orders of magnitude. Results on PAMAP2 further support the robustness of FedSynHAR under stronger heterogeneity.","url":"https://doi.org/10.1038/s41598-026-58066-0","authors":["Wang C","Fan R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-58066-0","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106365","name":"From algorithmic innovation to clinical deployment: A systematic review of methodological gaps limiting federated learning in healthcare.","source":"pubmed","abstract":"","url":"https://doi.org/10.1016/j.ijmedinf.2026.106365","authors":["Vijayasarathy S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106365","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.1177/00369330261449407","name":"ICDMS: Integrating multimodal data for intelligent clinical decision-making in healthcare: Current trends and future directions.","source":"pubmed","abstract":"Intelligent Clinical Decision-Making Systems have become a cornerstone of modern healthcare by enabling accurate diagnosis, prognosis and treatment planning through data-driven insights. With the growing availability of heterogeneous healthcare data such as medical images, clinical records, physiological signals and textual reports, multimodal learning has emerged as a powerful paradigm for integrating diverse data sources. This review presents a comprehensive and systematic analysis of multimodal approaches for intelligent clinical decision-making by leveraging machine learning, deep learning, transfer learning and natural language processing techniques. A structured literature search was conducted using IEEE, Elsevier, Wiley Online Library and Springer databases, focusing on peer-reviewed studies published between 2020 and 2025. The selected articles were analysed based on data modalities, learning strategies, healthcare applications, datasets and performance evaluation metrics. This review highlights the effectiveness of multimodal frameworks in addressing key challenges such as class imbalance, disease prediction, patient monitoring and treatment planning. Additionally, it discusses the benefits, open challenges and limitations of existing intelligent clinical decision frameworks, including scalability, interpretability and real-world deployment issues. Finally, the review outlines future research directions emphasizing the integration of Internet of Things-enabled healthcare data, federated learning for privacy preservation and blockchain-based secure data sharing to enhance the reliability and clinical adoption of intelligent decision-making systems.","url":"https://doi.org/10.1177/00369330261449407","authors":["Shehnaz","Ahamed J","Albaqami AS","Nisa KU"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1177/00369330261449407","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3390/s26061918","name":"Fed-DTCN: A Federated Disentangled Learning Framework for Unsupervised Zero-Day Anomaly Detection in IoT with Semantic-Aware Augmentation.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26061918","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26061918","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1177/09287329261466487","name":"Mapping the knowledge landscape and research trends of artificial intelligence in breast cancer diagnosis and treatment: A bibliometric analysis.","source":"pubmed","abstract":"BackgroundArtificial intelligence (AI) has generated rapidly growing research in breast cancer diagnosis and treatment, yet its intellectual structure, collaboration patterns, and thematic evolution remain unmapped.ObjectiveTo analyze the global research landscape of AI in breast cancer from 2001 to 2025, identifying publication trends, contributors, collaboration networks, thematic clusters, and translational gaps.MethodsA bibliometric analysis of 7673 Web of Science articles. VOSviewer was used for co-authorship networks and keyword co-occurrence with overlay visualization; CiteSpace for burst detection.ResultsThe field grew exponentially (29.20% annual growth), with 88.41% of publications from 2020-2025. China (31.63%) and the United States (21.10%) dominated output, but co-authorship networks revealed limited international collaboration for China (22.4% non-Chinese co-authors) and structural exclusion of low- and middle-income countries. Seven keyword clusters showed persistent separation between technology-centric and clinically oriented terms. Temporal overlay revealed a shift from computer-aided detection (pre-2015) to deep learning (2015-2022) and explainable AI, federated learning, and vision transformers (2022-2025). Burst detection confirmed \"feature selection\" (strength=19.53) and \"computer aided detection\" (strength=20.24) as historical hotspots; limited recent bursts indicate emerging frontiers are still accumulating citation impact.ConclusionsAI research in breast cancer has expanded rapidly with evolving themes, yet a translational gap persists between innovation and clinical integration. Future efforts should prioritize prospective validation, international collaboration with underrepresented regions, and standardized frameworks for integrating explainable and privacy-preserving AI into workflows.","url":"https://doi.org/10.1177/09287329261466487","authors":["Wang C","Hong K","Ying Y","Xu Y","Yao C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1177/09287329261466487","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"doi:10.3390/s26103148","name":"AdaFed-LDR: Adaptive Federated Learning with Layerwise Dynamics Regularization for Robust Wi-Fi Localization.","source":"pubmed","abstract":"Wi-Fi Channel State Information (CSI)-based indoor localization enables high-precision positioning, but its deployment across multiple environments faces two major challenges: privacy concerns from centralizing CSI data, and severe statistical heterogeneity (non-IID) arising from the strong environment-dependency of CSI. This heterogeneity creates a stability-plasticity trade-off in federated learning-maintaining precision in known environments (stability) while adapting to unseen domains (plasticity). To address this trade-off, we propose AdaFed-LDR, which combines server-side Confidence-Weighted Adaptive Aggregation with client-side Layerwise Dynamics Regularization (LDR). The aggregation recalibrates client contributions based on feature covariance changes, while LDR imposes depth-dependent constraints-stronger constraints on shallow layers to preserve environment-agnostic features and weaker constraints on deeper layers to allow environment-specific adaptation. Evaluated across 8 indoor environments using Leave-One-Out Cross-Validation and 5 random seeds, AdaFed-LDR achieved a mean localization error (MLE) of 0.41 cm in known environments, corresponding to an 88.2% reduction compared with FedAvg. In domain generalization to unseen environments, AdaFed-LDR achieved an MLE of 218.2&#xb1;2.8 cm, demonstrating an improvement over FedPos (257.6&#xb1;14.04 cm). With one adaptation sample per reference point, MLE improved to 21 cm. Ablation experiments confirmed that combining the two proposed components achieved the highest improvement (83.9%) compared with applying them individually, supporting AdaFed-LDR as a reproducible approach to the stability-plasticity trade-off in federated CSI-based localization.","url":"https://doi.org/10.3390/s26103148","authors":["Harada K","Natori H","Koike M","Mineno H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26103148","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.1038/s41598-026-36109-w","name":"Correction: A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-36109-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-36109-w","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-026-43649-8","name":"Quantum-enhanced privacy aggregation for healthcare monitoring in wireless body area networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-43649-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-43649-8","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.21203/rs.3.rs-9367261/v1","name":"Artificial Intelligence-Based Environmental Control Systems for Reducing the Energy Performance Gap in Non-Residential Buildings: A Systematic and Critical Review (2018–2025)","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9367261/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9367261/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1038/s41598-025-28274-1","name":"Federated learning with LSTM and error correcting codes for secure and private identification of IoT devices.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-28274-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-28274-1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-025-31664-0","name":"A federated transformer-enhanced double Q-network for collaborative intrusion detection.","source":"europepmc","abstract":"With the rapid proliferation of IoT technology, cybersecurity challenges have become increasingly prominent. Traditional centralized network intrusion detection systems (NIDS) exhibit significant limitations including privacy risks and inadequate modeling of spatiotemporal correlations. This paper proposes FedT-DQN (Transformer-based Federated Double Q-Network), a novel federated reinforcement learning framework for dynamic intrusion detection. The method incorporates: (1) A Transformer encoder as federated aggregator using self-attention mechanisms; (2) A dual-layer Q-network architecture decomposing detection into feature extraction and decision optimization; (3) Soft Actor-Critic (SAC) integration for local training considering system heterogeneity. Experimental results on four benchmark datasets show that FedT-DQN achieves over [Formula: see text] detection accuracy with enhanced F1 scores and reduced false positive rates, all while maintaining data privacy. The source code for this study is available at https://github.com/BuLaTaa/FedT-DQN-in-IDS. .","url":"https://doi.org/10.1038/s41598-025-31664-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-31664-0","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-026-48650-9","name":"A blockchain technology with advanced lightweight symmetric cryptography schemes and smart security framework for healthcare wireless body area networks.","source":"pubmed","abstract":"The rapid evolution of intelligent healthcare systems has brought Healthcare Wireless Body Area Networks (HWBANs) to the forefront of personalized health monitoring. However, current systems face critical challenges, including high computational overhead, centralized trust management, and limited privacy-preserving analytics for real-time sensitive health data. This study proposes a Blockchain-enabled Advanced Lightweight Symmetric Cryptography (ALSC) framework for secure and privacy-preserving HWBANs. The proposed ALSC is a modified PRESENT-like cipher that integrates dynamic key rotation, reduced S-box complexity, and parallel substitution-permutation operations to achieve high-speed encryption in resource-constrained WBAN nodes. The framework combines Federated Learning (FL) for decentralized health analytics and Blockchain-based Proof of Authority (PoA) for tamper-proof trust management. The framework integrates lightweight symmetric encryption with blockchain-based authentication to secure healthcare WBAN data. Empirical results show a 48% reduction in encryption time and a 27% decrease in communication latency compared to existing methods, confirming improved privacy preservation, integrity assurance, scalability, and low computational overhead. The system was implemented in Python 3.10 and evaluated on Raspberry Pi 4 edge devices acting as HWBAN gateway nodes, with performance metrics measured directly on the hardware. Performance metrics-including latency, throughput, and encryption speed-were measured empirically using high-precision timing via time.perf_counter(). Compared with standard lightweight ciphers (AES-128, PRESENT-80, and SPECK-64), the proposed ALSC achieved 0.02&#xa0;s latency, 100&#xa0;MB/s throughput, and 5&#xa0;ms/KB encryption speed, demonstrating 32% lower delay and 24% higher throughput under identical network and cryptographic conditions. These results validate the effectiveness of the ALSC-Blockchain-FL integration in ensuring confidentiality, low latency, and scalability for real-time healthcare monitoring.","url":"https://doi.org/10.1038/s41598-026-48650-9","authors":["Muthupandian S","Kumar DM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-48650-9","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/tpami.2025.3624314","name":"Improving Model Fusion by Training-Time Neuron Alignment With Fixed Neuron Anchors.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2025.3624314","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tpami.2025.3624314","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1371/journal.pone.0339426","name":"Correction: Nuclei Segmentation and Classification from Histopathology Images using Federated Learning for End-Edge Platform.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0339426","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1371/journal.pone.0339426","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3389/frtra.2026.1879684","name":"From prediction to practice: artificial intelligence as an enabling layer in liver transplant care - reflections on ILTS 2025.","source":"pubmed","abstract":"Liver transplantation has become increasingly complex and data-intensive, demanding rapid, high-stakes clinical decisions as patient and organ conditions evolve. The surge of multidimensional clinical datasets has sparked strong interest in artificial intelligence (AI) and machine-learning (ML) as decision-support tools. The 2025 International Liver Transplantation Society (ILTS) annual meeting in Singapore highlighted that AI is now permeating every facet of liver transplantation, from donor-recipient matching and intra-operative navigation to post-transplant pharmacologic management. Early-stage models have shown impressive predictive performance in risk stratification for rejection, infection, and postoperative complications, as well as in individualized immunosuppression dosing and AI-assisted imaging/histopathology. However, real-world implementation has exposed persistent obstacles: data silos, limited generalizability, suboptimal explainability, and workflow friction that can erode clinician trust. Poor integration into existing clinical workflows may also increase cognitive burden for clinicians and reduce adoption of otherwise promising tools. This perspective synthesizes the ILTS 2025 messages and recent transplant literature, proposing a pragmatic three-stage roadmap for AI adoption over the next five years. The roadmap moves from near-term, well-validated decision-support alerts integrated into electronic health records, through mid-term hybrid human-AI teams that embed explainable AI (XAI) into clinical workflows, to long-term global AI consortia that foster federated learning and equitable model deployment. Central to this vision is a shift from \"AI as an autonomous decision-maker\" to \"augmented intelligence that amplifies human expertise while remaining transparent and equitable,\" ensuring that AI serves as an enabling layer that augments, rather than replaces, clinical judgment.","url":"https://doi.org/10.3389/frtra.2026.1879684","authors":["Zarrinpar A","Orinion AF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frtra.2026.1879684","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.21203/rs.3.rs-8272325/v1","name":"Intrusion Detection Based on Federated Context-Aware Embedded Deep Transfer Learning in Heterogeneous Networks","source":"europepmc","abstract":"Abstract With the continuous advancement of the Internet of Everything paradigm, network intrusion detection systems (IDS) are confronted with multiple challenges in heterogeneous data sharing, integration, and security. To address these issues, this paper proposes a theoretical framework based on Word Embedded Federated Deep Transfer Learning (WE-FDTL) under the dual constraints of inconsistent data distribution and privacy preservation in heterogeneous network environments. The core innovation lies in establishing a latent vector-driven federated semantic aggregation mechanism to achieve cross-domain distributed representation alignment and knowledge fusion. At the representation learning level, a semantic space mapping model is constructed using contextual word embedding techniques to transform discrete heterogeneous network sequences into continuous dense vectors. This approach eliminates the need for explicit data standardization while preserving structural information in high-dimensional space and ensuring embedded privacy protection. For feature alignment, we propose a domain adaptation method based on latent space projection. By establishing latent space alignment constraints across participants, this method achieves cross-domain feature alignment and unified representation, while enabling effective adaptation of latent space matrices to mainstream deep learning models, thereby addressing the domain shift problem in heterogeneous federated transfer learning. At the federated optimization level, a semantically embedded federated aggregation mechanism is designed to facilitate global cross-domain knowledge sharing and integration through gradient transmission in latent vector space instead of raw data exchange. This framework ensures data privacy at terminal devices while maintaining effective knowledge fusion. In simulation experiments, six deep learning models were employed to evaluate multi-class classification performance across three scenarios: single-source domain deep learning, single-source domain federated deep learning, and cross-source domain WE-FDTL. Experimental results on the mixed NSL-KDD and UNSW-NB15 datasets demonstrate that WE-FDTL achieves a client-side training accuracy of 94.88% and a validation accuracy of 90.36%, confirming the theoretical effectiveness and practical advantages of the proposed IDS approach in heterogeneous network environments.","url":"https://doi.org/10.21203/rs.3.rs-8272325/v1","authors":["Di Chen","Xinpeng Zhang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8272325/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.24171/j.phrp.2025.0418","name":"Collaborative networks, trends, and comparative analysis of artificial intelligence techniques in healthcare research: a narrative review.","source":"europepmc","abstract":"","url":"https://doi.org/10.24171/j.phrp.2025.0418","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.24171/j.phrp.2025.0418","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-026-42051-8","name":"LaED: a novel lightweight, edge-aware and explainable deep learning model for privacy-preserving facial attendance tracking in resource-constrained educational environments.","source":"pubmed","abstract":"Facial recognition is increasingly adopted for automated classroom attendance; however, real-world deployment in schools remains constrained by privacy risks, ethical obligations, demographic bias, spoofing threats, and limited computational resources. Recent incidents involving Microsoft Teams in New South Wales in 2025 and Chelmer Valley High School in the United Kingdom show how poorly governed systems violate student rights and regulatory compliance. Despite growing adoption, many existing attendance systems focus narrowly on recognition accuracy or efficiency, while overlooking spoof resistance, open-set identity handling, fairness mitigation, auditability, and privacy protection. This paper presents LaED, a lightweight, edge-aware, and explainable deep learning framework for privacy-preserving classroom attendance in resource-constrained educational environments. The framework combines multimodal spoof detection, open-set facial recognition, and fairness-aware representation learning within a unified edge-based design. Spoofing attacks, including replay and deepfake attempts, are mitigated through the fusion of physiological and temporal facial cues, while unknown identities are explicitly rejected to reduce proxy attendance. To support responsible deployment, LaED incorporates federated learning with differential privacy, ensuring that biometric data remain local to schools while enabling accountable model updates. Experimental evaluation on CASIA-FASD, CelebA-Spoof, DFDC, FairFace, and a consent-driven classroom dataset shows that LaED achieves over 97.8% recognition accuracy, APCER and BPCER values below 2%, demographic fairness gaps under 2%, and inference latency below 150 milliseconds on edge hardware. Additional tests confirm reliable operation under realistic classroom conditions. These results demonstrate that regulation-aligned and trustworthy facial attendance is feasible on low-cost devices, offering a practical pathway for responsible biometric AI in education.","url":"https://doi.org/10.1038/s41598-026-42051-8","authors":["Abiodun EO","Abiodun OI","Alawida M","Shawar BA","Mehmood A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-42051-8","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1177/13872877261473304","name":"Artificial intelligence in retinal imaging for early Alzheimer's disease detection: A review.","source":"pubmed","abstract":"BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder that necessitates early, accessible, and non-invasive diagnostic methods.ObjectiveThis review examines the potential of artificial intelligence (AI)-based retinal imaging as a transformative and scalable tool for early AD detection across the full diagnostic continuum, including the preclinical stage.MethodsFollowing PRISMA guidelines, 63 primary studies were selected from an initial pool of 240 articles retrieved from PubMed, IEEE Xplore, Scopus, Web of Science, and Google Scholar (2017-mid-2025). Advancements in optical coherence tomography (OCT), retinal fundus imaging, and OCT angiography are examined for their capacity to capture structural and vascular biomarkers, including retinal nerve fiber layer thinning and microvascular alterations. AI architectures, including convolutional neural networks, vision transformers, and hybrid models, are evaluated for their accuracy in retinal biomarker analysis. Benchmark datasets, including public and private ones, are assessed for their role in supporting AI-based AD research.ResultsKey challenges are identified, including data heterogeneity arising from variability in acquisition protocols and demographic representation, as well as computational complexity and limited model interpretability. Emerging approaches-notably multimodal data integration and federated learning-offer promising avenues for enhancing diagnostic accuracy while preserving patient privacy.ConclusionsThe socioeconomic implications of integrating AI-based retinal imaging into clinical workflows are discussed. By synthesizing recent advancements, unresolved challenges, and future directions, this review underscores the transformative potential of AI-driven oculomics in facilitating early AD diagnosis and improving patient outcomes.","url":"https://doi.org/10.1177/13872877261473304","authors":["Rehman MU","Masip D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1177/13872877261473304","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1038/s41598-026-41771-1","name":"Cloud assisted blockchain-enabled split federated learning framework for security and privacy-preserving of IoMT in healthcare 5.0.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-41771-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-41771-1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1038/s41598-025-32286-2","name":"Proximal guided hybrid federated learning approach with parameter efficient adaptive intelligence for pneumonia diagnosis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-32286-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-32286-2","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-026-36470-w","name":"Optimized attention-based cascaded shuffle long-term dependent network based performance analysis of adaptive e-learning among IT professionals.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-36470-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-36470-w","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-026-48845-0","name":"dsLassoCov: a federated Lasso approach incorporating covariate control.","source":"pubmed","abstract":"Machine learning has been widely adopted in biomedical research, fueled by the increasing availability of data. However, integrating datasets across institutions is challenging due to legal restrictions and data governance complexities. Federated learning allows the direct, privacy preserving training of machine learning models using geographically distributed datasets, but faces the challenge of how to appropriately control for covariate effects. The naive implementation of conventional covariate control methods in federated learning scenarios is often impractical due to the substantial communication costs, particularly with high-dimensional data. To address this issue, we introduce dsLassoCov, a machine learning approach designed to control for covariate effects and allow an efficient training in federated setting. In biomedical analysis, this may support the identification of biomarker candidates while accounting for the effect of confounding. Using simulated data, we demonstrate that dsLassoCov can efficiently and effectively manage confounding effects during model training. In our real-world data analysis, we replicated a large-scale exposome analysis using data from six geographically distinct databases, achieving results consistent with previous studies. By addressing the challenge of covariate control, our proposed approach can accelerate the application of federated learning in large-scale biomedical studies.","url":"https://doi.org/10.1038/s41598-026-48845-0","authors":["Cao H","Anguita-Ruiz A","Warembourg C","Escribà-Montagut X","Vrijheid M","Gonzalez JR","Cadman T","Setó-Llorens A","Schneider-Lindner V","Durstewitz D","Basagaña X","Schwarz E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-48845-0","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-025-34578-z","name":"A data analytics-driven approach to backorder prediction using federated machine learning in industrial supply chains.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-34578-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-025-34578-z","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1002/lrh2.70090","name":"Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/lrh2.70090","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/lrh2.70090","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3390/s26010181","name":"A Federated Hierarchical DQN-Based Distributed Intelligent Anti-Jamming Method for UAVs.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26010181","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s26010181","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.20944/preprints202512.2540.v1","name":"Federated GenAI with Quantum Optimization for Privacy-Preserving Learning, Knowledge Synthesis, and Scalable Equity in Higher Ed via Decentralized Training","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202512.2540.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.2540.v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-8369921/v1","name":"Federated CT Foundation Models for Multi-Center Detection of Lymph Node Metastasis in Pancreatic Cancer","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8369921/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8369921/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.3390/s26113405","name":"Intrusion Detection in the Internet of Things: A Comprehensive Review of Techniques, Architectures, Datasets, and Emerging Trends.","source":"pubmed","abstract":"As the Internet of Things (IoT) grows, strong, scalable, and adaptive intrusion detection systems (IDS) become increasingly critical for protecting IoT environments. This paper presents a comprehensive and systematic survey of IDS techniques for IoT environments, covering literature from 2021 to early 2026. The review introduces a multidimensional taxonomy that categorizes IDS approaches by detection strategy, learning paradigm, deployment architecture, and evaluation methodology. We examine conventional techniques, such as signature-based and anomaly-based detection, as well as modern machine-learning and deep-learning approaches. Furthermore, emerging paradigms, including Federated Learning, Explainable AI (XAI), TinyML, Large Language Models (LLMs), Transformer, Quantum Machine Learning, Generative Adversarial Networks and Incremental Learning, are analyzed with respect to their applicability to resource-constrained IoT environments. The paper also provides a detailed analysis of publicly available IDS datasets, validation protocols, and evaluation metrics used for benchmarking detection systems. In addition, critical challenges, including dataset realism, adversarial robustness, scalability, privacy preservation, and ethical considerations, are discussed. Finally, we highlight open research directions and propose guidelines for designing next-generation, trustworthy, and scalable IDS frameworks for IoT networks.","url":"https://doi.org/10.3390/s26113405","authors":["Komal A","Li S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26113405","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.1038/s41598-026-47175-5","name":"CRAFT: cold-start recommender with attention and federated training.","source":"pubmed","abstract":"One of the main challenges in recommender systems is the cold-start problem, in which recommendation systems struggle to recommend new or rarely visited items. The traditional methods usually comprise centralized data merging or collaborative filtering techniques, which are not easily applicable in the decentralized settings. The current federated recommendation techniques like FedMF and FedGN have limited support for cold-start personalization, particularly in situations where the metadata of the items is sparse or non-existent. To overcome these drawbacks, we propose a new federated learning-based model, CRAFT (Cold-start Recommender with Attention and Federated Training), that improves cold-start recommendations without compromising the privacy of the user. CRAFT proposes an attention mechanism to highlight salient user-item interaction patterns to enhance the inference of user preferences. Every client then trains a personalized model locally, where the updates are collectively aggregated through Federated Averaging (FedAvg) so that the collective intelligence is obtained without losing the sensitive information. CRAFT provides very personalized suggestions by adding time-varying dynamics and rich interaction histories. CRAFT can also be scaled to be deployed across distributed environments with the use of NVFlare platform. As indicated by experimental results on three real world datasets, including MovieLens 1M, Amazon Movies &amp; TV and CiteULike, CRAFT is able to achieve nDCG 20 in cold-start scenarios up to 16.8 better than state of the art baselines, with strong privacy guarantees.","url":"https://doi.org/10.1038/s41598-026-47175-5","authors":["Sivakumar N","John RS","Bijo A","Suganeshwari G","Anbalagan S","Thandapani S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-47175-5","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-36779-6","name":"Correction: Anomaly detection in double-entry bookkeeping data by federated learning system with non-model sharing approach.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-36779-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-36779-6","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-025-28466-9","name":"Digital twin driven smart factories: real time physics based co-simulation using edge a.i. and federated learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-28466-9","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-28466-9","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3390/s26020509","name":"Quantum-Resilient Federated Learning for Multi-Layer Cyber Anomaly Detection in UAV Systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26020509","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26020509","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.4103/mgr.medgasres-d-25-00234","name":"Hyperbaric oxygen in the artificial intelligence era: integration and innovation.","source":"pubmed","abstract":"Hyperbaric oxygen therapy is established for decompression illness, carbon monoxide poisoning, radiation-induced tissue injury, and diabetic foot ulcers. Interest in neurological and inflammatory indications is growing, yet outcomes are constrained by heterogeneous protocols, limited patient selection tools, and the absence of real-time physiological guidance. Artificial intelligence, including machine learning, explainable artificial intelligence, and digital twins, has transformed other clinical domains and could enable precision hyperbaric oxygen therapy. We searched PubMed, Scopus, Web of Science, and Google Scholar (January 2000-March 2025). Terms included \"hyperbaric oxygen therapy\" and (\"artificial intelligence\" OR \"machine learning\" OR \"deep learning\" OR \"digital twin\" OR \"biosensor*\"). English-language, peer-reviewed clinical, preclinical, or computational studies explicitly linking hyperbaric oxygen therapy with artificial intelligence or enabling technologies were eligible. Editorials without primary data, abstracts without full text, and non-peer-reviewed sources were excluded. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses-style flow- guided screening and selection. Fifty-three eligible studies (&#x2265;30% from 2022-2025) illustrate four convergent application areas: (1) protocol optimization using multicenter data, (2) biomarker-driven patient selection via multi-omics and imaging, (3) real-time adaptive control using biosensors, and (4) predictive safety analytics for oxygen toxicity and barotrauma. Exemplars from radiology, critical care, and cardiology demonstrate the feasibility of decision support, federated learning, and digital-twin simulations adaptable to hyperbaric oxygen therapy. Integrating artificial intelligence with hyperbaric oxygen therapy can shift practice from empirical protocols to patient-tailored, data-informed therapy. Priorities include hybrid clinical trials, interoperable registries, explainable and bias-aware models, and regulatory-aligned validation. With rigorous governance, artificial intelligence can improve efficacy, safety, and efficiency while advancing mechanistic understanding of hyperbaric oxygen therapy.","url":"https://doi.org/10.4103/mgr.medgasres-d-25-00234","authors":["Epelde F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.4103/mgr.medgasres-d-25-00234","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1148/ryai.250273","name":"Rethinking Privacy in Medical Imaging AI: From Metadata and Pixel-Level Identification Risks to Federated Learning and Synthetic Data Challenges.","source":"pubmed","abstract":"","url":"https://doi.org/10.1148/ryai.250273","authors":["Giouroukou K","Marias K","Tsiknakis M","Klontzas ME"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1148/ryai.250273","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.21203/rs.3.rs-10362812/v1","name":"Artificial Intelligence in Digital Banking: A Systematic Literature Review and a Novel Cross-Domain Resilience Framework for Customer Experience, Fraud Detection, and Risk Management","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10362812/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10362812/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.3389/fradi.2025.1660479","name":"U-FDL-PPE: a unified federated deep learning framework with privacy-preserving explainability for early and accurate viral disease prediction.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fradi.2025.1660479","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fradi.2025.1660479","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-026-45454-9","name":"Privacy-preserving federated learning with optimized ensemble weighting and knowledge distillation for COVID-19 detection from non-IID medical imaging data.","source":"pubmed","abstract":"Medical imaging enables rapid and accurate diagnosis of COVID-19, with CT scans proving especially effective. However, data privacy concerns limit collaborative model development across hospitals. To address this issue, we introduce a novel federated learning framework. It is referred to as Independent Knowledge Distillation with post-Ensemble Federated Learning (IKDEFL). Differential Privacy (DP) is integrated into the framework to improve privacy guarantees. Three DP mechanisms are evaluated. These include Fixed Gaussian, Gaussian Adaptive, and Tree Adaptive. The evaluation has been conducted on heterogeneous and Non-Independent and Identically Distributed (Non-IID) datasets. These datasets reflect real-world hospital scenarios. Results show that IKDEFL significantly outperforms existing federated knowledge distillation methods. It achieves a generalization performance with accuracy up to 82.79% and an F1-score of 82.78% on Non-IID data. Among the DP methods, the Tree Adaptive mechanism has consistently provided the best trade-off between privacy and prediction quality. Peak accuracy reaches 76.62% under strict privacy constraints, where [Formula: see text] and [Formula: see text]. This result is close to the performance of models without privacy protections. These findings demonstrate that adaptive DP techniques can be effectively applied in federated healthcare models. They support the development of privacy-preserving AI systems for clinical diagnostics.","url":"https://doi.org/10.1038/s41598-026-45454-9","authors":["Annan R","Qin H","Newman R","Siddula M","Qingge L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-45454-9","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.20944/preprints202512.1579.v1","name":"SplitML: A Unified Privacy-Preserving Architecture for Federated Split-Learning in Heterogeneous Environments","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202512.1579.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.1579.v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1038/s41598-025-25153-7","name":"Enhancing workplace productivity with secure AI using federated contrastive learning model for performance optimization.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-25153-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-25153-7","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.21203/rs.3.rs-8191406/v1","name":"Multi-Modal Data Fusion With Federated Multi-Head Attention for Diabetic Retinopathy Severity Classification","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8191406/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8191406/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.3389/fdgth.2026.1758304","name":"Trustworthy intelligent rooms: integrating blockchain, federated learning, and data-centric AI for healthcare 4.0.","source":"pubmed","abstract":"Intelligent room systems are experiencing a surge in demand within the Healthcare 4.0 ecosystem. The integration of Federated Learning (FL) and Data-Centric AI has led to substantial enhancements in the predictive capabilities of machine learning models while maintaining data privacy. However, centralized aggregation in FL remains a single point of failure and is vulnerable to poisoning attacks.","url":"https://doi.org/10.3389/fdgth.2026.1758304","authors":["Veerapaneni RK","Delhibabu R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1758304","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3390/cancers17213450","name":"Federated Learning Architecture for 3D Breast Cancer Image Classification.","source":"europepmc","abstract":"BACKGROUDS: Breast cancer remains a major global health challenge, with early diagnosis playing a crucial role in improving patient survival rates. Among the available diagnostic techniques, mammography is widely employed for early detection. However, its effectiveness is often constrained by the complexity of image interpretation, which makes automated detection methods increasingly vital. METHODS: In this study, we propose an advanced approach that leverages 3D mammographic imaging and integrates Federated Learning (FL) to enable decentralized, privacy-preserving model training across multiple institutions. To evaluate the effectiveness of this approach, we assess various machine learning models, including Convolutional Neural Networks (CNNs), Transfer Learning architectures (VGG16, VGG19, ResNet50), and AutoEncoders (AEs), using 3D mammographic data. RESULTS: Our results indicate that the CNN model achieves an accuracy of 97.30%, which improves slightly to 97.37% when the model is combined with Federated Learning, highlighting both the predictive performance and privacy-preserving advantages of our method. In contrast, Transfer Learning models and AutoEncoders exhibit lower accuracies that range from 48.83% to 89.24%, revealing their limitations in the context of this specific task. CONCLUSIONS: These findings underscore the effectiveness of the CNN-FL framework as a robust tool for breast cancer detection, showing that this approach offers a promising balance between diagnostic accuracy and data security-two critical factors in medical imaging.","url":"https://doi.org/10.3390/cancers17213450","authors":["Amel Ali Alhussan","Wiem Nhidi","Imen Filali","Faten Benhmida","Ridha Ejbali"],"tags":["Federated learning","Computer science","Breast cancer","Architecture","Artificial intelligence"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/cancers17213450","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.1371/journal.pone.0337069","name":"Adaptive federated clustering for uncertainty-aware learning on decentralized big data platforms.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0337069","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1371/journal.pone.0337069","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-025-26374-6","name":"Layer-based personalized multi-fusion federated learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-26374-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-26374-6","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3390/ma19122538","name":"Artificial Intelligence in the Design and Optimization of Orthodontic Materials: A Clinical Perspective on Current State and Future Directions.","source":"pubmed","abstract":"Artificial intelligence (AI) has transformed orthodontic diagnosis, yet its application to orthodontic materials science remains critically underexplored. This perspective identifies and characterizes the AI-materials integration gap as the central unresolved problem in digital orthodontics: AI-optimized treatment plans are currently executed through empirically selected materials whose mechanical behavior is never modeled by the planning system. We examine four domains where this gap is consequential: thermoplastic aligner polymers (PETG vs. TPU), where supervised ANNs can predict force decay from polymer composition; NiTi archwire alloys, where Bayesian optimization and Gaussian process regression are accelerating alloy design; additive manufacturing of orthodontic devices, where supervised ML reduced print-parameter optimization burden in a 2025 five-variable surface roughness study; and AI-driven biological response prediction, where FEA-surrogate neural networks reduced biomechanical computation from minutes to milliseconds per patient query. A scoping review of clear aligner AI identified 41 studies-none addressing aligner material properties as a primary outcome. We argue that closing the AI-materials gap requires standardized open material-performance datasets; FEA-surrogate models integrating polymer stiffness as a treatment-planning input; patient-specific digital twins with defined material, mechanical, and biological parameter layers; and federated learning infrastructure spanning clinics and manufacturers.","url":"https://doi.org/10.3390/ma19122538","authors":["Mikulewicz M","Paradowska-Stolarz A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/ma19122538","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.1038/s41598-025-28569-3","name":"Wireless federated learning for salt-spray prediction in industrial IoT networks with delay constraint.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-28569-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-28569-3","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1109/tpami.2025.3602282","name":"VQ-FedDiff: Federated Learning Algorithm of Diffusion Models With Client-Specific Vector-Quantized Conditioning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2025.3602282","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1109/tpami.2025.3602282","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1117/1.jmi.12.6.061412","name":"Federated learning in computational pathology: a literature review.","source":"pubmed","abstract":"","url":"https://doi.org/10.1117/1.jmi.12.6.061412","authors":["Shukla S","Doyle S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1117/1.jmi.12.6.061412","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-025-25175-1","name":"Graph-based federated learning approach for intrusion detection in IoT networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-25175-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-25175-1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3390/diagnostics16020204","name":"Early Tuberculosis Detection via Privacy-Preserving, Adaptive-Weighted Deep Models.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/diagnostics16020204","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/diagnostics16020204","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3390/s25237354","name":"A Privacy-Preserving Approach to Health Insurance Fraud Detection Using Vertical Federated Learning.","source":"pubmed","abstract":"","url":"https://doi.org/10.3390/s25237354","authors":["R RK","Paramarthalingam A","Kanthan H","Karthiban M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25237354","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.21203/rs.3.rs-8844280/v1","name":"Artificial Intelligence Techniques in Blockchain-Integrated Healthcare and Financial Systems: A Systematic Review and Future Research Agenda","source":"europepmc","abstract":"Abstract Blockchain and artificial intelligence (AI) are two innovative technologies that are impacting modern infrastructures with resilience, intelligence, and transparency. Blockchain offers immutability, decentralization, and trust, while AI facilitates decision assistance, anomaly detection, and predictive analytics. Scholarly interest in their convergence has grown for mission-critical domains including healthcare and finance. This narrative review evaluates the possibilities, difficulties, and future prospects of combining blockchain technology with artificial intelligence by compiling 53 research papers published between 2018 and 2025. This evaluation categorizes AI methods used in blockchain-integrated systems, including explainable AI models, federated learning, deep learning (CNN, RNN, transformers), and supervised learning (SVM, RF). Using PRISMA-inspired narrative synthesis principles, this work systematically synthesizes 53 peer-reviewed papers to ensure methodological openness and analytical rigor. The healthcare sector makes extensive use of telemedicine, secure medical data transmission, diagnostic support, and drug supply chain traceability. Fraud detection, digital banking innovation, and the creation of central bank digital currencies are all made possible by the integration of blockchain and artificial intelligence in financial institutions. The review creates a taxonomy of AI techniques and assesses their relative effectiveness and drawbacks in the financial and healthcare sectors. The analysis reveals persistent issues like scalability, interoperability, privacy, and regulatory ambiguity in addition to ethical considerations concerning explainability and fairness. We point out shortcomings in scalable federated blockchain–AI architectures, XAI frameworks, and benchmarking datasets. This paper offers a thorough synthesis that gives scholars and business experts a comprehensive understanding of how blockchain and artificial intelligence will affect future infrastructures. The study offers a comparative assessment of methodological methods, identifies unresolved problems with explainable models and federated architectures, and suggests a methodical research plan in addition to mapping applications of blockchain–AI convergence. Unlike other evaluations that concentrated on a single domain, this analysis reveals shared design principles and governance implications by presenting a cross-sector synthesis of healthcare and finance systems.","url":"https://doi.org/10.21203/rs.3.rs-8844280/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8844280/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1109/tnnls.2025.3598818","name":"DA-PFL: Dynamic Affinity Aggregation in Personalized Federated Learning Under Class Imbalance.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2025.3598818","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1109/tnnls.2025.3598818","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1109/tnnls.2025.3601834","name":"Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2025.3601834","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tnnls.2025.3601834","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-025-24963-z","name":"FedGDAN: Privacy-preserving traffic flow prediction via federated graph diffusion attention networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-24963-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-24963-z","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-025-29672-1","name":"A personalized communication efficient federated learning framework with low rank adaptation for intelligent leukemia diagnosis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-29672-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-29672-1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1016/j.mex.2026.103898","name":"A fully homomorphic encryption federated learning architecture for privacy preserving in industrial internet of things.","source":"pubmed","abstract":"We are currently entering the fifth revolution of industry - Industry 5.0. IIoT is the domain where massive quantities of data are flourished by the associated devices in an industry on a daily basis. To realize industry 4.0, the Industrial internet of things is considered a prominent one. Federated Learning, also known as collaborative learning, employs a decentralized approach in its applicability while maintaining data privacy, but many existing frameworks struggle with handling privacy of gradients which are transferred to Federated servers. Unlike conventional approaches, proposed fully homomorphic encryption based Federated Learning-FHEEFL ensures that raw gradients never leave local IoT nodes; instead, only updates or changes in model secured with encryption techniques are transmitted. The framework achieves a very strong privacy as well as data security. FHEEFL is intended for lightweight to moderate-capacity models characteristically labouring in Edge-IIoTset analytics, where privacy guarantees must be well-adjusted with computational feasibility. While CKKS-based encrypted aggregation incurs additional overhead, the framework establishes practical applicability for privacy-critical industrial tasks under realistic resource constraints. It is proven as a privacy-centric federated learning solution, setting a new benchmark in tackling key challenges in data security and privacy. The proposed method is implemented using Edge-IIoTset dataset. The proposed fully homomorphic encryption based Federated Learning-FHEEFL method is tested in IIOT scenarios.&#x2022;FHEEFL method provides better privacy with better memory usage, CPU usage and throughput parameters.&#x2022;Performance is analysed with 4 variations of models- Tiny, small, medium and large.","url":"https://doi.org/10.1016/j.mex.2026.103898","authors":["Subhedar S","Parasar D","Shraddha Subhedar","Deepa Parasar"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.mex.2026.103898","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/e28050483","name":"Information-Theoretic Security and Privacy in Modern Data-Driven Systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28050483","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/e28050483","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3390/s25247646","name":"Distributed Deep Learning in IoT Sensor Network for the Diagnosis of Plant Diseases.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25247646","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25247646","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.20944/preprints202512.2347.v1","name":"Federated and Quantum-Inspired AI in Adaptive Traffic Systems Using Digital Twin Simulations and Predictive Analytics for Urban Flow Optimization and Carbon Footprint Reduction","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202512.2347.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.2347.v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.1038/s41598-026-40035-2","name":"Retraction Note: Sentimental analysis based federated learning privacy detection in fake web recommendations using blockchain model.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-40035-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-40035-2","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.21203/rs.3.rs-8300312/v1","name":"Sparsity-Aware Edge Caching in IoVs with Asynchronous Federated and Deep Reinforcement Learning","source":"europepmc","abstract":"Abstract The edge content caching technology of the Internet of Vehicles (IoVs) is a key technology to reduce the latency of content access. However, within the hotspot area, with a large number of content access requests generated by vehicle users, the rapid changes in user interests and the explosive dissemination of high-value content have led to limited transmission delay. Therefore, to reduce the delay, accurately predicting and timely updating popular content as well as exploring high-value content have become critical yet challenging. To solve this problem, a sparsity-aware edge caching (SAEC) scheme is proposed. Firstly, aiming at the sparsity problem of VU data, a sparse self-encoder based on self-attentive (SAE-ELA) model is proposed. By extracting the potential features of sparse data of vehicle users and capturing the historical preference associations of users, the accuracy of content prediction was improved. Secondly, this paper adopts the asynchronous federated learning (AFL) framework to solve the problem of low cache update efficiency, thereby shortening the model training time to improve the real-time performance of content update. Finally, in order to solve the problem of insufficient exploration of potential high-value content, a Dueling Deep Q-network based on Intrinsic Curiosity Module (ICM-DDQN) algorithm is proposed. By organically combining traditional value function learning with curiosity driven active exploration, the exploration efficiency of cached content has been improved, thereby reducing the Content Transmission Delay (CTD). Simulation results show that the proposed SAEC is significantly superior to the existing methods in terms of Cache Hit Ratio(CHR) and CTD.","url":"https://doi.org/10.21203/rs.3.rs-8300312/v1","authors":["Jing Gao","Jiahui Chen","Yanqi Huan","Liuyang Wu"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8300312/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.20944/preprints202606.0920.v1","name":"Nine Falsifiable Predictions About Artificial Intelligence and Global Health: A Grounded Forecasting Framework from the Global South (2025–2075)","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202606.0920.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202606.0920.v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1038/s41598-026-47631-2","name":"Federated CT foundation models for multi-center detection of lymph node metastasis in pancreatic cancer.","source":"pubmed","abstract":"Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with prognosis strongly influenced by the presence of lymph node metastasis (LNM). However, preoperative LNM assessment from computed tomography (CT) is limited by low sensitivity, high inter-observer variability, and substantial heterogeneity across imaging protocols. This retrospective multi-center study (546 patients from three institutions) introduces a privacy-preserving deep learning framework that integrates large-scale CT foundation model pre-training with heterogeneity-aware federated optimization to improve LNM detection in PDAC. A CT Vision Foundation Model, pre-trained on 148,000 volumetric CT scans using contrastive self-supervised learning, is fine-tuned to generate transferable 3D representations for patient-level LNM classification. To enable decentralized model training while mitigating inter-institutional variability, we extend federated aggregation to jointly account for label-distribution discrepancies and representation-level divergence across clients. The centralized model achieved a balanced accuracy of 0.601 and a diagnostic odds ratio (DOR) of 3.45, outperforming classical machine learning baselines and prior PDAC LNM approaches. Under federated settings, the proposed heterogeneity-aware strategy consistently outperformed standard FedAvg, recovering a substantial proportion of the centralized model's performance while preserving strict data privacy. In particular, it improved balanced accuracy by 12.6% over FedAvg and demonstrated superior discriminative ability across all participating cohorts. These findings indicate that combining foundation model pre-training with discrepancy-aware federated learning enhances generalization, robustness, and clinical relevance for multi-center PDAC LNM detection. The proposed framework offers a scalable and privacy-preserving pathway for deploying deep learning models across distributed healthcare systems.","url":"https://doi.org/10.1038/s41598-026-47631-2","authors":["Bhalla P","Gaviria DD","Kupczyk P","Hosseini ASA","Conradi L","Fehrenbach U","Felsenstein M","Ma D","Semaan A","Albarqouni S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-47631-2","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-025-22482-5","name":"Federated reinforcement learning with constrained markov decision processes and graph neural networks for fair and grid-constrained coordination of large-scale electric vehicle charging networks.","source":"europepmc","abstract":"The rapid proliferation of electric vehicles (EVs) and their spatially clustered charging behaviors have imposed unprecedented challenges on the stability, efficiency, and fairness of power distribution networks. Coordinating large-scale EV clusters across geographically distributed charging stations requires intelligent scheduling strategies that can simultaneously respect grid constraints, maximize user satisfaction, and enhance renewable energy utilization-all while safeguarding data privacy and computational scalability. This paper proposes a novel multi-agent cooperative dispatch framework based on Federated Deep Reinforcement Learning (FDRL) to optimize the real-time coordination between EVs, chargers, and the underlying power grid infrastructure. The model adopts a hierarchical structure where local agents independently train deep reinforcement learning policies tailored to site-specific dynamics, while a central aggregator synchronizes global model parameters using federated averaging enhanced by entropy-based reward normalization and fairness-aware weighting. The optimization problem is formulated as a multi-objective constrained Markov decision process (CMDP), featuring long-horizon coupling, grid-aware feasibility, and user-centric reward shaping. Our formulation explicitly integrates peak transformer loading limits, charging demand satisfaction, temporal renewable absorption, and inter-agent equity, thereby capturing the full complexity of EV-grid interactions. A realistic case study involving 1,200 EVs, 60 chargers, and a 33-bus feeder system over 24 hours shows that the proposed FDRL framework achieves a 13.6% reduction in grid operating cost, a 21.4% increase in renewable absorption, and fairness with Jain's index consistently above 0.95, while reducing average state-of-charge (SoC) deviation to below 2.5%. These quantitative results highlight the effectiveness of the framework and confirm its promise as a privacy-preserving, scalable, and equitable solution for next-generation energy-cyber-physical systems.","url":"https://doi.org/10.1038/s41598-025-22482-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-22482-5","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1371/journal.pone.0338822","name":"DualMask: Federated optimization of privacy-utility-efficiency trilemma via orthogonal gradient perturbation and RL-optimized PSO.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0338822","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1371/journal.pone.0338822","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.20944/preprints202512.2026.v1","name":"Federated Continual Learning Framework for Robust mmWave Human Activity Recognition Under LOS–NLOS Domain Shifts","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202512.2026.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.2026.v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.1038/s41467-025-67703-7","name":"Privacy-preserving collaborative battery fault warning for massive electric vehicles by heterogeneous data from charging stations.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41467-025-67703-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41467-025-67703-7","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.21203/rs.3.rs-7994476/v1","name":"FedTLRec: Federated Recommendation with Transformer-based Parameter Aggregation and LoRA Compression","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7994476/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7994476/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.1038/s41598-025-28686-z","name":"FedSER-XAI: PSO-optimized multi-stream cross-attention transformer with graph features for explainable federated speech emotion recognition.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-28686-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-28686-z","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1109/tcyb.2025.3599370","name":"FedCoSR: Personalized Federated Learning With Contrastive Shareable Representations for Label Heterogeneity in Non-IID Data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tcyb.2025.3599370","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1109/tcyb.2025.3599370","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-025-23055-2","name":"A MARL-federated blockchain-based quantum secure framework for trust management in industrial internet of things.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-23055-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-23055-2","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1038/s41598-025-20692-5","name":"Cross domain fault diagnosis in internal combustion engines using multisensor data with transfer federated and transformer based federated transfer learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-20692-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-20692-5","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.21203/rs.3.rs-7612387/v1","name":"Multi agent federated reinforcement learning for Distributed MPTCP Agents","source":"europepmc","abstract":"Abstract The integration of Multi access Edge Computing (MEC) with low Earth orbit (LEO) satellite constellations is a promising paradigm for global, low latency connectivity. However, the dynamic topology and heterogeneous link qualities of satellite networks pose significant challenges for efficient multipath transport protocol (MPTCP) scheduling. Traditional schedulers, often based on heuristics or designed for fixed size inputs, struggle to adapt to the variable number of available paths. We propose a novel Multi Agent Federated Reinforcement Learning (MAFRL) framework that leverages Set Transformers for permutation invariant encoding of variable path sets. Each agent learns a local policy using Proximal Policy Optimization (PPO), augmented with a soft fairness constraint to ensure equitable performance. A federated learning scheme, using FedProx aggregation, enables collaborative training across distributed agents without sharing raw data, preserving privacy and improving robustness to non IID data. Extensive emulation experiments show our approach outperforms heuristic and learning based baselines in aggregate throughput, latency, and fairness, particularly under path variability. This work demonstrates the viability of set based learning and federated optimization for intelligent resource management in next generation satellite terrestrial networks.","url":"https://doi.org/10.21203/rs.3.rs-7612387/v1","authors":["Jorge Abraham Rios Suarez","Min Jia","C. Shibwabo Anyembe"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7612387/v1","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.3390/s25237296","name":"Privacy-Preserving Hierarchical Fog Federated Learning (PP-HFFL) for IoT Intrusion Detection.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25237296","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25237296","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.17302170","name":"Quantum Veil Protocol (QVP): A Five-Pillar Post-Quantum Defensive Architecture for AI Governance, Cybersecurity, and Mission-Critical Digital Statecraft","source":"datacite","abstract":"Quantum Veil Protocol (QVP)™ Foundational Prototype Edition v1.0 A Five-Pillar Post-Quantum Defensive Architecture for AI Governance, Cybersecurity, Digital Sovereignty and Mission-Critical Digital Resilience Authors Bidyut MazumdarIndependent ResearcherORCID: 0009-0007-5615-3558 Abstract Quantum Veil Protocol (QVP)™ is a defensive research architecture for examining the protection, governance, resilience, and continuity of sovereign and mission-critical digital systems in an emerging technological environment shaped by post-quantum cryptographic transition, artificial intelligence, cyber risk, critical infrastructure dependency, and evolving requirements for digital sovereignty. The framework is organized around a five-pillar architectural model integrating adaptive defensive topology, AI-assisted anomaly awareness, federated situational intelligence, resilient continuity mechanisms, and formal ethical and governance oversight. These components are conceptualized as mutually reinforcing layers intended to support research into the relationship between cybersecurity preparedness, cryptographic transition, artificial intelligence governance, institutional accountability, operational continuity, and long-term digital resilience. The Foundational Prototype Edition v1.0 establishes the initial architectural and conceptual basis of the broader QVP research lineage. It presents the foundational five-pillar model, associated governance principles, defensive constraints, post-quantum security considerations, threat-modeling orientation, resilience concepts, and mission-critical continuity objectives that informed subsequent development within the wider research ecosystem. QVP is designed as a strictly defensive research framework. It does not promote offensive cyber operations, autonomous escalation, unauthorized intrusion, or unaccountable automated decision-making. Instead, the architecture emphasizes governance-bounded defensive capability, human oversight, auditability, legal accountability, resilience, privacy-aware information exchange, and continuity-oriented system design. The framework is intended to support scholarly and exploratory research concerning post-quantum cybersecurity, AI governance, digital sovereignty, critical infrastructure resilience, computational governance, and strategic digital preparedness. It should be interpreted as a research architecture and conceptual framework rather than as an independently certified operational security system or a substitute for context-specific engineering, legal, institutional, or policy assessment. Persistent Identifiers and Publication Information Field Information Framework Quantum Veil Protocol (QVP)™ Edition Foundational Prototype Edition v1.0 Publication Date December 16, 2025 Current Record DOI 10.5281/zenodo.17302170 All Versions DOI 10.5281/zenodo.17302169 Repository QVP Global System™ Author Bidyut Mazumdar ORCID 0009-0007-5615-3558 Resource Type Dataset / Research Infrastructure Research Orientation Post-Quantum Security, AI Governance, Cybersecurity and Digital Statecraft Release Classification Foundational Prototype Research Edition License CC BY-NC-ND 4.0 Development Context Foundational release within the subsequent QVP research lineage Executive Overview Contemporary digital security environments are increasingly shaped by the convergence of several strategic transformations: The anticipated transition toward post-quantum cryptography Increasing dependence on artificial intelligence systems Expanding cyber risk to critical infrastructure Complex interdependencies between public and private digital systems Growing importance of digital sovereignty Requirements for resilient operational continuity Increasing expectations for transparency and accountability in automated systems The need for governance structures capable of constraining high-impact technological decision-making These developments suggest that cybersecurity preparedness cannot be understood solely t","url":"https://doi.org/10.5281/zenodo.17302170","authors":["Bidyut, Mazumdar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17302170","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.18138045","name":"danielke32/FL_malaria: FederatedMalaria","source":"datacite","abstract":"Federated Learning for Privacy-Preserving Malaria Prediction Multi-Scenario Evaluation Using Ghana DHS Data Overview This repository contains the complete implementation for the MSc thesis: \"Federated Learning for Privacy-Preserving Malaria Prediction: Multi-Scenario Evaluation Using Ghana DHS Data\". The project evaluates federated learning algorithms (FedAvg and FedProx) against centralized baselines for malaria prediction in children under 5 years in Ghana, using data from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS). Key Features Privacy-preserving: Federated learning enables collaborative model training without sharing raw patient data Multi-scenario evaluation: IID, Non-IID (regional heterogeneity), and data quality variation scenarios Comprehensive comparison: FedAvg, FedProx vs. centralized Logistic Regression and Random Forest Reproducible research: Fixed seeds, documented preprocessing, and complete pipeline automation Publication-ready outputs: Statistical analysis, figures, and tables generation Repository Structure FL_malaria/ ├── data/ │ ├── raw/ # DHS/MIS Stata files (not included — see Data Access) │ ├── merged/ # Merged survey data │ ├── cleaned/ # Preprocessed datasets │ │ ├── train_raw.csv # Raw training data for FL scenarios │ │ ├── train_centralized.csv # SMOTE-augmented centralized training │ │ ├── val_set.csv # Validation set (real data) │ │ └── test_set.csv # Test set (real data) │ └── fl_scenarios/ # Federated learning client data │ ├── s1_iid/ # Scenario 1: IID distribution │ ├── s2_noniid/ # Scenario 2: Regional heterogeneity │ ├── s3_quality/ # Scenario 3: Data quality variation │ └── heterogeneity_metrics.json ├── results/ │ ├── centralized_results.json │ ├── results_baseline.json │ ├── results_grid_search.json │ ├── comprehensive_statistical_analysis.json │ ├── figures/ # Publication-ready figures (PNG + greyscale) │ └── tables/ # LaTeX and CSV tables ├── logs/ # Pipeline execution logs ├── models/ # Saved model checkpoints ├── validation/ # Preprocessing validation reports │ ├── src/ │ ├── data/ │ │ ├── data_extraction.py # Stage 1: DHS/MIS data extraction │ │ ├── data_preprocessing.py # Stage 2: MICE imputation & feature engineering │ │ └── create_fl_scenarios.py # Stage 3: FL scenario creation │ ├── training/ │ │ ├── train_centralized.py # Stage 4: Centralized baseline training │ │ └── train_federated.py # Stage 5: Federated model training │ ├── evaluation/ │ │ ├── final_analysis.py # Stage 6: Statistical analysis & visualization │ │ └── sensitivity_analysis.py # Robustness check: native vs. full feature set │ └── pipeline/ │ └── run_complete_pipeline.py # Orchestrates the complete workflow │ ├── requirements.txt └── README.md Installation Prerequisites Python 3.10 or higher pip package manager (Optional) CUDA-compatible GPU for faster training Setup Clone the repository git clone https://github.com/danielke32/FL_malaria.git cd FL_malaria Create a virtual environment python -m venv federated_ml # Linux/macOS source federated_ml/bin/activate # Windows federated_ml\\Scripts\\activate Install dependencies pip install -r requirements.txt Requirements pandas>=1.5.0 numpy>=1.23.0 scipy>=1.9.0 scikit-learn>=1.1.0 imbalanced-learn>=0.10.0 torch>=2.0.0 statsmodels>=0.13.0 matplotlib>=3.6.0 seaborn>=0.12.0 Pillow>=9.0.0 Data Access DHS/MIS Survey Data This project uses Ghana Demographic and Health Survey (DHS) and Malaria Indicator Survey (MIS) data, which requires registration to access: Register at DHS Program Request access to the Ghana datasets listed below Place files in data/raw/ File Naming Convention Survey PR file (household) KR file (children) MIS 2016 GHPR7BFL.DTA GHKR7BFL.DTA MIS 2019 GHPR82FL.DTA GHKR82FL.DTA DHS 2022 GHPR8CFL.DTA GHKR8CFL.DTA Dataset Characteristics Survey Children (6–59 mo) RDT positive Urban Rainy season MIS 2016 2,892 27.8% 42.1% 58.2% MIS 2019 3,245 21.4% 44.3% 61.4% DHS 2022 4,150 16.9% 46.8% 55.7% Combined 10,287 21.2% 44.6% 58.1% Usage Qu","url":"https://doi.org/10.5281/zenodo.18138045","authors":["Daniel Kwasi Kovor"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18138045","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20271307","name":"danielke32/FL_malaria: FederatedMalaria","source":"datacite","abstract":"Federated Learning for Privacy-Preserving Malaria Prediction Multi-Scenario Evaluation Using Ghana DHS Data Overview This repository contains the complete implementation for the MSc thesis: \"Federated Learning for Privacy-Preserving Malaria Prediction: Multi-Scenario Evaluation Using Ghana DHS Data\". The project evaluates federated learning algorithms (FedAvg and FedProx) against centralized baselines for malaria prediction in children under 5 years in Ghana, using data from the Demographic and Health Surveys (DHS) and Malaria Indicator Surveys (MIS). Key Features Privacy-preserving: Federated learning enables collaborative model training without sharing raw patient data Multi-scenario evaluation: IID, Non-IID (regional heterogeneity), and data quality variation scenarios Comprehensive comparison: FedAvg, FedProx vs. centralized Logistic Regression and Random Forest Reproducible research: Fixed seeds, documented preprocessing, and complete pipeline automation Publication-ready outputs: Statistical analysis, figures, and tables generation Repository Structure FL_malaria/ ├── data/ │ ├── raw/ # DHS/MIS Stata files (not included — see Data Access) │ ├── merged/ # Merged survey data │ ├── cleaned/ # Preprocessed datasets │ │ ├── train_raw.csv # Raw training data for FL scenarios │ │ ├── train_centralized.csv # SMOTE-augmented centralized training │ │ ├── val_set.csv # Validation set (real data) │ │ └── test_set.csv # Test set (real data) │ └── fl_scenarios/ # Federated learning client data │ ├── s1_iid/ # Scenario 1: IID distribution │ ├── s2_noniid/ # Scenario 2: Regional heterogeneity │ ├── s3_quality/ # Scenario 3: Data quality variation │ └── heterogeneity_metrics.json ├── results/ │ ├── centralized_results.json │ ├── results_baseline.json │ ├── results_grid_search.json │ ├── comprehensive_statistical_analysis.json │ ├── figures/ # Publication-ready figures (PNG + greyscale) │ └── tables/ # LaTeX and CSV tables ├── logs/ # Pipeline execution logs ├── models/ # Saved model checkpoints ├── validation/ # Preprocessing validation reports │ ├── src/ │ ├── data/ │ │ ├── data_extraction.py # Stage 1: DHS/MIS data extraction │ │ ├── data_preprocessing.py # Stage 2: MICE imputation & feature engineering │ │ └── create_fl_scenarios.py # Stage 3: FL scenario creation │ ├── training/ │ │ ├── train_centralized.py # Stage 4: Centralized baseline training │ │ └── train_federated.py # Stage 5: Federated model training │ ├── evaluation/ │ │ ├── final_analysis.py # Stage 6: Statistical analysis & visualization │ │ └── sensitivity_analysis.py # Robustness check: native vs. full feature set │ └── pipeline/ │ └── run_complete_pipeline.py # Orchestrates the complete workflow │ ├── requirements.txt └── README.md Installation Prerequisites Python 3.10 or higher pip package manager (Optional) CUDA-compatible GPU for faster training Setup Clone the repository git clone https://github.com/danielke32/FL_malaria.git cd FL_malaria Create a virtual environment python -m venv federated_ml # Linux/macOS source federated_ml/bin/activate # Windows federated_ml\\Scripts\\activate Install dependencies pip install -r requirements.txt Requirements pandas>=1.5.0 numpy>=1.23.0 scipy>=1.9.0 scikit-learn>=1.1.0 imbalanced-learn>=0.10.0 torch>=2.0.0 statsmodels>=0.13.0 matplotlib>=3.6.0 seaborn>=0.12.0 Pillow>=9.0.0 Data Access DHS/MIS Survey Data This project uses Ghana Demographic and Health Survey (DHS) and Malaria Indicator Survey (MIS) data, which requires registration to access: Register at DHS Program Request access to the Ghana datasets listed below Place files in data/raw/ File Naming Convention Survey PR file (household) KR file (children) MIS 2016 GHPR7BFL.DTA GHKR7BFL.DTA MIS 2019 GHPR82FL.DTA GHKR82FL.DTA DHS 2022 GHPR8CFL.DTA GHKR8CFL.DTA Dataset Characteristics Survey Children (6–59 mo) RDT positive Urban Rainy season MIS 2016 2,892 27.8% 42.1% 58.2% MIS 2019 3,245 21.4% 44.3% 61.4% DHS 2022 4,150 16.9% 46.8% 55.7% Combined 10,287 21.2% 44.6% 58.1% Usage Qu","url":"https://doi.org/10.5281/zenodo.20271307","authors":["Daniel Kwasi Kovor"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20271307","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.19430884","name":"Federated Learning: A Systematic Review of Architecture, Challenges and Research Directions","source":"datacite","abstract":"Federated Learning (FL) has emerged as a distributed machine learning paradigm that enables collaborative model training while preserving data privacy. Unlike traditional centralized learning frameworks which require collecting raw data at a single server, FL allows multiple clients to train models locally and share only model updates for global aggregation. In this review we examine twelve peer-reviewed surveys and research papers published between 2017 and 2025 that analyze federated learning architectures, communication mechanisms, privacy-preserving techniques, security threats, types of FL and real-world deployment scenarios. Drawing substantially from the comprehensive IEEE Access survey by Aledhari et al., our analysis shows that FL still faces major technical challenges including non-IID data distributions, high communication costs, scalability constraints and adversarial threats. We also highlight emerging research directions such as lightweight optimization, fairness-aware aggregation, blockchain-based trust mechanisms and personalized FL. This review consolidates existing work, presents a full 12-paper literature summary table, and outlines key open problems to guide future research on federated learning systems.","url":"https://doi.org/10.5281/zenodo.19430884","authors":["Rathod, Dr. Nachiket","Pete, Shravani Sushil","Dhoke, Divya Dineshrao","Kurhekar, Ishika Santosh"],"tags":["Federated Learning (FL), Distributed Machine Learning, Privacy-Preserving Machine Learning, Horizontal FL, Vertical FL, Federated Transfer Learning, Secure Aggregation, Non-IID Data, Communication Efficiency, Scalability, Adversarial Attacks, Fairness-Aware Aggregation, Blockchain-Based Trust."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19430884","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.19430885","name":"Federated Learning: A Systematic Review of Architecture, Challenges and Research Directions","source":"datacite","abstract":"Federated Learning (FL) has emerged as a distributed machine learning paradigm that enables collaborative model training while preserving data privacy. Unlike traditional centralized learning frameworks which require collecting raw data at a single server, FL allows multiple clients to train models locally and share only model updates for global aggregation. In this review we examine twelve peer-reviewed surveys and research papers published between 2017 and 2025 that analyze federated learning architectures, communication mechanisms, privacy-preserving techniques, security threats, types of FL and real-world deployment scenarios. Drawing substantially from the comprehensive IEEE Access survey by Aledhari et al., our analysis shows that FL still faces major technical challenges including non-IID data distributions, high communication costs, scalability constraints and adversarial threats. We also highlight emerging research directions such as lightweight optimization, fairness-aware aggregation, blockchain-based trust mechanisms and personalized FL. This review consolidates existing work, presents a full 12-paper literature summary table, and outlines key open problems to guide future research on federated learning systems.","url":"https://doi.org/10.5281/zenodo.19430885","authors":["Rathod, Dr. Nachiket","Pete, Shravani Sushil","Dhoke, Divya Dineshrao","Kurhekar, Ishika Santosh"],"tags":["Federated Learning (FL), Distributed Machine Learning, Privacy-Preserving Machine Learning, Horizontal FL, Vertical FL, Federated Transfer Learning, Secure Aggregation, Non-IID Data, Communication Efficiency, Scalability, Adversarial Attacks, Fairness-Aware Aggregation, Blockchain-Based Trust."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19430885","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.19697018","name":"Artificial Intelligence and Big Data Integration: A Systematic Literature Review of Technological Trends, Ethical Challenges, and Future Directions","source":"datacite","abstract":"The convergence of Artificial Intelligence (AI) and Big Data has emerged as a transformative force across industries, yet the literature remains fragmented across technical, ethical, and sectoral domains. This study presents a systematic literature review guided by PRISMA principles, synthesizing peer-reviewed research published between 2020 and 2025. Searches across six databases yielded 25 primary studies, which were analysed for publication patterns, methodological characteristics, technological foci, and emerging themes. Findings reveal a sharp increase in publications from 2023 onward, driven by advances in generative AI and edge computing. The literature is dominated by conceptual and trend-analysis papers, with fewer empirical or mixed-methods studies. Geographically, research is concentrated in the United States, China, and Western Europe, with limited contributions from the Global South. Thematic synthesis identifies five major foci: (1) architectural shifts toward edge computing and federated learning; (2) the rise of generative AI and large language models as both consumers and producers of Big Data; (3) algorithmic bias and the black-box problem of explainability; (4) cybersecurity vulnerabilities, particularly adversarial attacks; and (5) data governance, privacy, and regulatory compliance. Overall, AI and Big Data integration promises substantial innovation, but realizing this potential requires robust ethical frameworks, explainable AI systems, and stronger empirical research on real-world implementations.","url":"https://doi.org/10.5281/zenodo.19697018","authors":["Siregar, Torang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19697018","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.19697019","name":"Artificial Intelligence and Big Data Integration: A Systematic Literature Review of Technological Trends, Ethical Challenges, and Future Directions","source":"datacite","abstract":"The convergence of Artificial Intelligence (AI) and Big Data has emerged as a transformative force across industries, yet the literature remains fragmented across technical, ethical, and sectoral domains. This study presents a systematic literature review guided by PRISMA principles, synthesizing peer-reviewed research published between 2020 and 2025. Searches across six databases yielded 25 primary studies, which were analysed for publication patterns, methodological characteristics, technological foci, and emerging themes. Findings reveal a sharp increase in publications from 2023 onward, driven by advances in generative AI and edge computing. The literature is dominated by conceptual and trend-analysis papers, with fewer empirical or mixed-methods studies. Geographically, research is concentrated in the United States, China, and Western Europe, with limited contributions from the Global South. Thematic synthesis identifies five major foci: (1) architectural shifts toward edge computing and federated learning; (2) the rise of generative AI and large language models as both consumers and producers of Big Data; (3) algorithmic bias and the black-box problem of explainability; (4) cybersecurity vulnerabilities, particularly adversarial attacks; and (5) data governance, privacy, and regulatory compliance. Overall, AI and Big Data integration promises substantial innovation, but realizing this potential requires robust ethical frameworks, explainable AI systems, and stronger empirical research on real-world implementations.","url":"https://doi.org/10.5281/zenodo.19697019","authors":["Siregar, Torang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19697019","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.24406/publica-10062","name":"Correction: Multi-modal dataset creation for federated learning with DICOM-structured reports (International Journal of Computer Assisted Radiology and Surgery, (2025), 20, 3, (485-495), 10.1007/s11548-025-03327-y)","source":"datacite","abstract":"In the original version of this article, the affiliation details for authors were incorrectly given.","url":"https://doi.org/10.24406/publica-10062","authors":["Tölle, Malte","Burger, Lukas","Kelm, Halvar","André, Florian","Bannas, Peter","Diller, Gerhard Paul","Frey, Norbert","Garthe, Philipp Darius","Gross, Stefan","Hennemuth, Anja","Kaderali, Lars K.","Krüger, Nina"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.24406/publica-10062","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20484323","name":"A Survey on Federated Learning for Privacy-Preserving Artificial Intelligence in Internet of Things Systems","source":"datacite","abstract":"🔹 Overview The explosive growth of the Internet of Things (IoT) has led to billions of connected devices generating vast amounts of sensitive data. While traditional machine learning relies on centralized data collection, this approach raises significant privacy, security, and regulatory concerns. Federated Learning (FL) has emerged as a transformative solution by enabling collaborative model training across distributed devices without transferring raw data to a central server. This makes FL one of the most promising technologies for privacy-preserving artificial intelligence in IoT ecosystems. What This Survey Covers This survey provides a comprehensive review of Federated Learning for IoT systems, focusing on research developments from 2020–2025, with particular emphasis on advances published since 2022. Key topics include: Communication-efficient federated learning techniques Aggregation algorithms and optimization strategies Privacy-preserving mechanisms and differential privacy Security threats such as poisoning, backdoor, and inference attacks Defense mechanisms and robust aggregation methods Comparative analysis of major FL frameworks and approaches Challenges arising from non-IID data, device heterogeneity, and resource constraints Comparative Evaluation The surveyed methods are analyzed across critical performance dimensions: Communication overhead Model accuracy Convergence behavior Privacy guarantees Suitability for real-world IoT deployments Future Research Directions The survey also explores emerging trends, including: Differential Privacy-enhanced Federated Learning Secure Multi-Party Computation (SMPC) Personalized Federated Learning Blockchain-integrated FL systems Large Language Model (LLM)-assisted federation TinyML and edge intelligence integration Target Audience This work serves as a structured reference for: Researchers in Federated Learning and Distributed AI IoT Security and Privacy practitioners Graduate students and academics Industry professionals developing privacy-preserving intelligent systems Keywords: Federated Learning, Internet of Things (IoT), Privacy-Preserving AI, Edge Computing, Distributed Machine Learning, Differential Privacy, Secure Aggregation, IoT Security.","url":"https://doi.org/10.5281/zenodo.20484323","authors":["Arjit Sharma"],"tags":["Federated Learning","Internet of Things","Privacy-Preserving Machine Learning","Edge Computing","Differential Privacy","Model Aggregation","Data Heterogeneity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20484323","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20484324","name":"A Survey on Federated Learning for Privacy-Preserving Artificial Intelligence in Internet of Things Systems","source":"datacite","abstract":"🔹 Overview The explosive growth of the Internet of Things (IoT) has led to billions of connected devices generating vast amounts of sensitive data. While traditional machine learning relies on centralized data collection, this approach raises significant privacy, security, and regulatory concerns. Federated Learning (FL) has emerged as a transformative solution by enabling collaborative model training across distributed devices without transferring raw data to a central server. This makes FL one of the most promising technologies for privacy-preserving artificial intelligence in IoT ecosystems. What This Survey Covers This survey provides a comprehensive review of Federated Learning for IoT systems, focusing on research developments from 2020–2025, with particular emphasis on advances published since 2022. Key topics include: Communication-efficient federated learning techniques Aggregation algorithms and optimization strategies Privacy-preserving mechanisms and differential privacy Security threats such as poisoning, backdoor, and inference attacks Defense mechanisms and robust aggregation methods Comparative analysis of major FL frameworks and approaches Challenges arising from non-IID data, device heterogeneity, and resource constraints Comparative Evaluation The surveyed methods are analyzed across critical performance dimensions: Communication overhead Model accuracy Convergence behavior Privacy guarantees Suitability for real-world IoT deployments Future Research Directions The survey also explores emerging trends, including: Differential Privacy-enhanced Federated Learning Secure Multi-Party Computation (SMPC) Personalized Federated Learning Blockchain-integrated FL systems Large Language Model (LLM)-assisted federation TinyML and edge intelligence integration Target Audience This work serves as a structured reference for: Researchers in Federated Learning and Distributed AI IoT Security and Privacy practitioners Graduate students and academics Industry professionals developing privacy-preserving intelligent systems Keywords: Federated Learning, Internet of Things (IoT), Privacy-Preserving AI, Edge Computing, Distributed Machine Learning, Differential Privacy, Secure Aggregation, IoT Security.","url":"https://doi.org/10.5281/zenodo.20484324","authors":["Arjit Sharma"],"tags":["Federated Learning","Internet of Things","Privacy-Preserving Machine Learning","Edge Computing","Differential Privacy","Model Aggregation","Data Heterogeneity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20484324","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.16875526","name":"Beyond the Hype: A Structured Scoping Review of Knowledge  Distillation for Accessible and Equitable AI in Education","source":"datacite","abstract":"Large language models (LLMs) hold substantial potential for educational applications, including automated essay scoring, short-answer grading, personalized feedback, and interactive tutoring, yet their widespread deployment remains constrained by high computational costs, dependence on proprietary APIs, and infrastructural limitations in under-resourced settings. This scoping review examines knowledge distillation (KD) as a principled and increasingly practical approach to these barriers. KD transfers capabilities of large, expensive teacher models into smaller, efficient student models, offering a practical pathway for democratizing high-quality AI in education without requiring powerful hardware or proprietary systems. Drawing on a structured literature review of 203 papers published between 2017 and early 2025 across IEEE Xplore, ACM Digital Library, SpringerLink, arXiv, and Google Scholar, this survey organizes the field into four thematic areas: (i) distillation for automated scoring and classification; (ii) distillation of pedagogical reasoning and interpretable explanations, including chain-of-thought and rubric-aligned variants; (iii) emerging frontiers in multimodality and low-resource language adaptation; and (iv) federated and privacy-preserving distillation for on-device deployment. The literature demonstrates that transferring intermediate reasoning signals, multi-teacher consensus, and structured pedagogical rationales substantially improves the interpretability and deployability of compact student models, while highlighting the critical need for explicit fairness auditing. We identify six critical open research challenges: multi-turn dialogue distillation, cross-lingual reasoning transfer, on-device personalized federated learning, evaluation standardization, bias auditing, and distillation of extended reasoning trajectories from large reasoning models such as OpenAI o1 and DeepSeek-R1. We propose a structured research agenda, a standardized evidence framework, and deployment-oriented design guidance for practitioners, positioning KD as a promising infrastructure for equitable and resource-efficient AI in education.","url":"https://doi.org/10.5281/zenodo.16875526","authors":["Ahmed, Sarfraz"],"tags":["Knowledge distillation","Educational AI","Automated essay scoring","Model compression","Reasoning distillation","Equity","Federated learning","Low-resource languages"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.16875526","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20679167","name":"Beyond the Hype: A Structured Scoping Review of Knowledge  Distillation for Accessible and Equitable AI in Education","source":"datacite","abstract":"Large language models (LLMs) hold substantial potential for educational applications, including automated essay scoring, short-answer grading, personalized feedback, and interactive tutoring, yet their widespread deployment remains constrained by high computational costs, dependence on proprietary APIs, and infrastructural limitations in under-resourced settings. This scoping review examines knowledge distillation (KD) as a principled and increasingly practical approach to these barriers. KD transfers capabilities of large, expensive teacher models into smaller, efficient student models, offering a practical pathway for democratizing high-quality AI in education without requiring powerful hardware or proprietary systems. Drawing on a structured literature review of 203 papers published between 2017 and early 2025 across IEEE Xplore, ACM Digital Library, SpringerLink, arXiv, and Google Scholar, this survey organizes the field into four thematic areas: (i) distillation for automated scoring and classification; (ii) distillation of pedagogical reasoning and interpretable explanations, including chain-of-thought and rubric-aligned variants; (iii) emerging frontiers in multimodality and low-resource language adaptation; and (iv) federated and privacy-preserving distillation for on-device deployment. The literature demonstrates that transferring intermediate reasoning signals, multi-teacher consensus, and structured pedagogical rationales substantially improves the interpretability and deployability of compact student models, while highlighting the critical need for explicit fairness auditing. We identify six critical open research challenges: multi-turn dialogue distillation, cross-lingual reasoning transfer, on-device personalized federated learning, evaluation standardization, bias auditing, and distillation of extended reasoning trajectories from large reasoning models such as OpenAI o1 and DeepSeek-R1. We propose a structured research agenda, a standardized evidence framework, and deployment-oriented design guidance for practitioners, positioning KD as a promising infrastructure for equitable and resource-efficient AI in education.","url":"https://doi.org/10.5281/zenodo.20679167","authors":["Ahmed, Sarfraz"],"tags":["Knowledge distillation","Educational AI","Automated essay scoring","Model compression","Reasoning distillation","Equity","Federated learning","Low-resource languages"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20679167","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.18188895","name":"The Ring as Machine : A Mixed-Radix, LUT-Mediated, Self-Organizing Computational Architecture : Topological Computation Through Mixed-Radix Phase Lattices","source":"datacite","abstract":"The Ring as Machine : A Mixed-Radix, LUT-Mediated, Self-Organizing Computational Architecture : Topological Computation Through Mixed-Radix Phase Lattices : 10.5281/zenodo.18188896 Vening, E. J.-P. (2026). The Ring as Machine : A Mixed-Radix, LUT-Mediated, Self-Organizing Computational Architecture : Topological Computation Through Mixed-Radix Phase Lattices. Zenodo. https://doi.org/10.5281/zenodo.18188896… @chargen shares a preprint authored by Edwin Jean-Paul Vening, introducing the RING architecture—a deterministic, drift-free computational system based on a 720-point mixed-radix phase lattice that evolves symbolic states via layered lookup tables (LUTs) without branching or floating-point arithmetic. The architecture leverages topological invariants and contradiction metrics for self-organizing interactions among parallel \"lanes,\" enabling constant-time symbolic transformations suitable for cyclic domains like harmonics and resonance. Key innovations include a physiological field layer modeling multi-rate memory (flow, afterglow, wear) and coherent projections across radices (e.g., 60, 360), positioning RING as a hardware-native alternative for photonic processors and spaceborne autonomy, validated through simulations and prototypes. Mixed-radix number systems (also called mixed-base or variable-base systems) are positional numeral systems where the base (radix) differs from one digit position to the next, unlike fixed-base systems (binary base-2, decimal base-10, hexadecimal base-16) where the radix is constant everywhere.Core Definition & Value CalculationIn a mixed-radix system with radices b₀, b₁, b₂, …, bₖ (from least to most significant position), a number with digits dₖ dₖ₋₁ … d₁ d₀ (where 0 ≤ dᵢ < bᵢ) has the value:value = dₖ × (bₖ₋₁ × bₖ₋₂ × … × b₁ × b₀) + dₖ₋₁ × (bₖ₋₂ × … × b₁ × b₀) + … + d₁ × b₀ + d₀The place values are the cumulative products of the radices below each position. The most significant digit usually has no upper bound on its own radix (effectively ∞), as there is no \"next larger unit.\"Everyday & Historical Examples Sexagesimal time & angles — bases roughly [60, 60, 24, 7] (seconds : minutes : hours : days : weeks)Example: 3 days, 14 hours, 42 minutes, 15 seconds → digits [3, 14, 42, 15] with bases [7, 24, 60, 60] Mayan Long Count — mostly base-20, but the second position is base-18 so that 18×20 = 360 ≈ days in a year Pre-decimal currencies (e.g., old British) — pounds : shillings : pence → bases [20, 12] Factorial number system (factoradic) — bases [2, 3, 4, 5, 6, …] (increasing)Every natural number has a unique representation (no leading zeros).Example: 23₁₀ = 3 2 1 0! → 3×3! + 2×2! + 1×1! + 0×0! = 18 + 4 + 1 + 0 = 23Widely used to index permutations (0 to n!−1 permutations of n items in lex order via Lehmer code / inversion table). Advantages & Disadvantages Property Advantages Disadvantages / Trade-offs Natural fit Perfect for composite, hierarchical units (time, angles, calendars, astronomy) Requires conversion to/from fixed-base for most computers Arithmetic Generalized long addition/multiplication works (carry rules adapt per position) More complex carry propagation than fixed-radix; usually slower in software Radix economy Can theoretically outperform fixed-base-3 in average digits per number (debated; e-approximating sequences proposed on MathOverflow) Factoradic and similar systems are optimal for certain combinatorial tasks, not general encoding Precision in cycles Exact representation of periodic/cyclic quantities (no drift in angles, harmonics) Not natively supported in mainstream hardware Hardware/computing Useful in Residue Number Systems (RNS) conversions, parallel modular arithmetic, DSP, certain path-enumeration algorithms Rare direct hardware support; mostly emulated or used in niche FPGA/ASIC designs Computing & Specialized Applications Residue Number System (RNS) conversions — many RNS-to-weighted (mixed-radix) algorithms exist for efficient parallel modular arithmetic in signa","url":"https://doi.org/10.5281/zenodo.18188895","authors":["Vening, Edwin Jean-Paul"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18188895","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.5281/zenodo.18188896","name":"The Ring as Machine : A Mixed-Radix, LUT-Mediated, Self-Organizing Computational Architecture : Topological Computation Through Mixed-Radix Phase Lattices","source":"datacite","abstract":"The Ring as Machine : A Mixed-Radix, LUT-Mediated, Self-Organizing Computational Architecture : Topological Computation Through Mixed-Radix Phase Lattices : 10.5281/zenodo.18188896 Vening, E. J.-P. (2026). The Ring as Machine : A Mixed-Radix, LUT-Mediated, Self-Organizing Computational Architecture : Topological Computation Through Mixed-Radix Phase Lattices. Zenodo. https://doi.org/10.5281/zenodo.18188896… @chargen shares a preprint authored by Edwin Jean-Paul Vening, introducing the RING architecture—a deterministic, drift-free computational system based on a 720-point mixed-radix phase lattice that evolves symbolic states via layered lookup tables (LUTs) without branching or floating-point arithmetic. The architecture leverages topological invariants and contradiction metrics for self-organizing interactions among parallel \"lanes,\" enabling constant-time symbolic transformations suitable for cyclic domains like harmonics and resonance. Key innovations include a physiological field layer modeling multi-rate memory (flow, afterglow, wear) and coherent projections across radices (e.g., 60, 360), positioning RING as a hardware-native alternative for photonic processors and spaceborne autonomy, validated through simulations and prototypes. Mixed-radix number systems (also called mixed-base or variable-base systems) are positional numeral systems where the base (radix) differs from one digit position to the next, unlike fixed-base systems (binary base-2, decimal base-10, hexadecimal base-16) where the radix is constant everywhere.Core Definition & Value CalculationIn a mixed-radix system with radices b₀, b₁, b₂, …, bₖ (from least to most significant position), a number with digits dₖ dₖ₋₁ … d₁ d₀ (where 0 ≤ dᵢ < bᵢ) has the value:value = dₖ × (bₖ₋₁ × bₖ₋₂ × … × b₁ × b₀) + dₖ₋₁ × (bₖ₋₂ × … × b₁ × b₀) + … + d₁ × b₀ + d₀The place values are the cumulative products of the radices below each position. The most significant digit usually has no upper bound on its own radix (effectively ∞), as there is no \"next larger unit.\"Everyday & Historical Examples Sexagesimal time & angles — bases roughly [60, 60, 24, 7] (seconds : minutes : hours : days : weeks)Example: 3 days, 14 hours, 42 minutes, 15 seconds → digits [3, 14, 42, 15] with bases [7, 24, 60, 60] Mayan Long Count — mostly base-20, but the second position is base-18 so that 18×20 = 360 ≈ days in a year Pre-decimal currencies (e.g., old British) — pounds : shillings : pence → bases [20, 12] Factorial number system (factoradic) — bases [2, 3, 4, 5, 6, …] (increasing)Every natural number has a unique representation (no leading zeros).Example: 23₁₀ = 3 2 1 0! → 3×3! + 2×2! + 1×1! + 0×0! = 18 + 4 + 1 + 0 = 23Widely used to index permutations (0 to n!−1 permutations of n items in lex order via Lehmer code / inversion table). Advantages & Disadvantages Property Advantages Disadvantages / Trade-offs Natural fit Perfect for composite, hierarchical units (time, angles, calendars, astronomy) Requires conversion to/from fixed-base for most computers Arithmetic Generalized long addition/multiplication works (carry rules adapt per position) More complex carry propagation than fixed-radix; usually slower in software Radix economy Can theoretically outperform fixed-base-3 in average digits per number (debated; e-approximating sequences proposed on MathOverflow) Factoradic and similar systems are optimal for certain combinatorial tasks, not general encoding Precision in cycles Exact representation of periodic/cyclic quantities (no drift in angles, harmonics) Not natively supported in mainstream hardware Hardware/computing Useful in Residue Number Systems (RNS) conversions, parallel modular arithmetic, DSP, certain path-enumeration algorithms Rare direct hardware support; mostly emulated or used in niche FPGA/ASIC designs Computing & Specialized Applications Residue Number System (RNS) conversions — many RNS-to-weighted (mixed-radix) algorithms exist for efficient parallel modular arithmetic in signa","url":"https://doi.org/10.5281/zenodo.18188896","authors":["Vening, Edwin Jean-Paul"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18188896","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.48550/arxiv.2404.11754","name":"Differentiated Aggregation to Improve Generalization in Federated Learning","source":"datacite","abstract":"This paper focuses on reducing the communication cost of federated learning by exploring generalization bounds and representation learning. We first characterize a tighter generalization bound for one-round federated learning based on local clients' generalizations and heterogeneity of data distribution (non-iid scenario). We also characterize a generalization bound in R-round federated learning and its relation to the number of local updates (local stochastic gradient descents (SGDs)). Then, based on our generalization bound analysis and its interpretation through representation learning, we infer that less frequent aggregations for the representation extractor (typically corresponds to initial layers) compared to the head (usually the final layers) leads to the creation of more generalizable models, particularly in non-iid scenarios. We design a novel Federated Learning with Adaptive Local Steps (FedALS) algorithm based on our generalization bound and representation learning analysis. FedALS employs varying aggregation frequencies for different parts of the model, so reduces the communication cost. The paper is followed with experimental results showing the effectiveness of FedALS. Our codes are available at for reproducibility.","url":"https://doi.org/10.48550/arxiv.2404.11754","authors":["Gholami, Peyman","Seferoglu, Hulya"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2404.11754","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.5281/zenodo.19658831","name":"Computational Biology vs Bioinformatics: A 2025 Guide for Biomedical Researchers","source":"datacite","abstract":"This comprehensive guide for biomedical researchers and drug development professionals delineates the distinct yet complementary roles of bioinformatics and computational biology. Bioinformatics is defined as the field focused on developing and applying computational tools, software, and algorithms to manage, organize, and analyze large-scale biological datasets, such as those from genomics and proteomics. It provides the essential infrastructure for handling biological big data. Computational biology, conversely, is more concerned with developing theoretical methods, mathematical models, and computational simulations to understand and predict the behavior of complex biological systems. It uses data processed by bioinformatics to build models that test hypotheses about biological mechanisms, from protein folding to cellular signaling pathways. The relationship between the fields is synergistic: bioinformatics supplies the structured data and analytical tools that computational biology uses to construct and validate its models. This integrated workflow is critical for modern research. Key applications highlighted include drug discovery, where AI and machine learning are used to identify targets and optimize lead compounds, and personalized medicine, which relies on genomic data analysis to tailor treatments. The guide details essential tools for each field, such as BLAST and GATK for bioinformatics, and molecular dynamics software like GROMACS for computational biology. It also addresses significant challenges, including data management, security, and the need for reproducible, scalable analysis pipelines, emphasizing the role of cloud platforms and SaaS solutions in enhancing accessibility and collaboration. Future trends point towards deeper integration of AI, multi-modal data analysis, and federated learning to further accelerate discovery. Source: https://www.compbiosci.com/posts/computational-biology-vs-bioinformatics-a-2025-guide-for-biomedical-researchers","url":"https://doi.org/10.5281/zenodo.19658831","authors":["computational biological science"],"tags":["bioinformatics","computational biology","drug discovery","genomics","multi-omics","AI in biology","NGS analysis","molecular dynamics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19658831","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.5281/zenodo.19658832","name":"Computational Biology vs Bioinformatics: A 2025 Guide for Biomedical Researchers","source":"datacite","abstract":"This comprehensive guide for biomedical researchers and drug development professionals delineates the distinct yet complementary roles of bioinformatics and computational biology. Bioinformatics is defined as the field focused on developing and applying computational tools, software, and algorithms to manage, organize, and analyze large-scale biological datasets, such as those from genomics and proteomics. It provides the essential infrastructure for handling biological big data. Computational biology, conversely, is more concerned with developing theoretical methods, mathematical models, and computational simulations to understand and predict the behavior of complex biological systems. It uses data processed by bioinformatics to build models that test hypotheses about biological mechanisms, from protein folding to cellular signaling pathways. The relationship between the fields is synergistic: bioinformatics supplies the structured data and analytical tools that computational biology uses to construct and validate its models. This integrated workflow is critical for modern research. Key applications highlighted include drug discovery, where AI and machine learning are used to identify targets and optimize lead compounds, and personalized medicine, which relies on genomic data analysis to tailor treatments. The guide details essential tools for each field, such as BLAST and GATK for bioinformatics, and molecular dynamics software like GROMACS for computational biology. It also addresses significant challenges, including data management, security, and the need for reproducible, scalable analysis pipelines, emphasizing the role of cloud platforms and SaaS solutions in enhancing accessibility and collaboration. Future trends point towards deeper integration of AI, multi-modal data analysis, and federated learning to further accelerate discovery. Source: https://www.compbiosci.com/posts/computational-biology-vs-bioinformatics-a-2025-guide-for-biomedical-researchers","url":"https://doi.org/10.5281/zenodo.19658832","authors":["computational biological science"],"tags":["bioinformatics","computational biology","drug discovery","genomics","multi-omics","AI in biology","NGS analysis","molecular dynamics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19658832","addedAt":"2026-08-31T06:41:19.255Z","updatedAt":"2026-08-31T06:41:19.255Z"},{"id":"doi:10.5281/zenodo.19490340","name":"LongevityCommon","source":"datacite","abstract":"Aging, codified in ICD-11 (code XT9T “Ageing-related,” 2018; code MG2A “Ageing-associated decline in intrinsic capacity,” 2025), requires integrative biomarker frameworks that extend beyond individual epigenetic clocks and wearable predictors. We present a hypothesis-stage integrative framework, LongevityCommon. All empirical estimates should be treated as exploratory (hypothesis‑generating), not confirmatory. Pre‑registered tests of an earlier univariate formulation of χ_Ze on the Cuban EEG, Dortmund Vital, and MPI‑LEMON cohorts yielded NULL results (documented in Ze EVIDENCE.md from 2026-04-22; meta‑analysis of Cuban + Dortmund: I² = 90.3 % — invalid; these results were retracted). The current multimodal version of χ_Ze is a post‑hoc reformulation and was not pre‑registered. All reported AUCs are exploratory hypothesis‑generating only, with explicit acknowledgment of p‑hacking risk (Ioannidis, 2005). LongevityCommon is a conceptual ecosystem of five components: (1) MCOA (Multi‑Counter Architecture of Organismal Aging) — a meta‑theory positing aging as a weighted sum of parallel counters: Ltissue(n,t)=iwi(tissue)fi(Di(n,t)). Axioms M1–M4 include an operational definition of falsifiability (M4) revised in v5 based on community‑standard validation thresholds: MCOA is considered falsified if on a pre‑registered cohort with N ≥ 2000 at α = 0.001 the partial r² for all‑cause mortality after controlling for chronological age and sex is full R² = 0.778), but full Sobol decomposition (S2 + ST) with 95 % CI and nested cross‑validation on real GTEx data (N = 948) revealed that the difference is not statistically significant (p = 0.12 after correction). CDATA remains an open, falsifiable hypothesis; final determination requires full decomposition on real data (Cell‑DT v4.0). (3) Ze Theory — a thermodynamic‑geometric formalism. The equation dZe/dt=−I(Z) is postulated as an ansatz, motivated by Burgholzer (2015) and Pearson et al. (2021); living systems are far‑from‑equilibrium, and a formal bridge between these physical‑clock systems and biological aging is absent. (4) BioSense — a wearable platform with the variational principle F=E−TS−Ipred. The theoretical fixed point v*=0.45631 at k=1 (sensitivity range v*[0.32;0.58] for k[0.5;2.0]). **Empirically tested via swept‑v* search on All‑of‑Us (N = 500): v*_optimal = 0.451 (95 % CI 0.443–0.459), consistent with the theoretical value.** (5) FCLC (Federated Clinical Learning Cooperative) — a federated learning infrastructure. ε_total ≈ 0.43 at (σ, q, T) = (1.5, 0.013, 5); RDP composition via subsampled Gaussian (Wang et al., 2019) combined with the Mironov (2017) framework. Threat model (explicit disclosure, v5): (a) FCLC central server — semi‑honest only (never sees raw data, only aggregated updates); (b) Byzantine‑robust aggregation (Krum up to 25 % malicious clients); (c) NOT secure against active server collusion; (d) NOT secure against malicious server deviating from the protocol. This is a blocker for GDPR Article 9 medical data until FCLC v14 (malicious‑secure migration planned for Q1 2027). The ecosystem provides a falsifiable (per updated M4) draft platform, but persistent blocking limitations remain: (i) all pilots are underpowered, not pre‑registered, and post‑hoc, which in light of Ioannidis (2005) gives a clear risk of false‑positive findings; (ii) FCLC is semi‑honest only — blocker for GDPR Art. 9; (iii) CDATA status is inconclusive, requiring full Sobol decomposition on real data; (iv) v* empirically tested and confirmed; (v) Ze→biology is an ansatz without formal derivation; (vi) bridge to CDATA (5 parameters on N = 196) is underpowered (Harrell rule violated) and moved to Supplementary; (vii) key publications (MCOA, Ze, BioSense) are not peer‑reviewed; (viii) EIC Pathfinder consortium formation deferred to Q1 2027 (0 signed EU LoIs as of 2026-04-21).","url":"https://doi.org/10.5281/zenodo.19490340","authors":["Tkemaladze, Jaba"],"tags":["aging biomarker","Ze complexity index","federated learning","differential privacy","citizen science","biological age","EEG","HRV"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19490340","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20183482","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20183482","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20183482","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20141021","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20141021","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20141021","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20141022","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20141022","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20141022","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.22094407","name":"SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening","source":"datacite","abstract":"@article{rangwala2025sketchguard, title={SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening}, author={Rangwala, Murtaza and Azzedin, Farag and Sinnott, Richard O and Buyya, Rajkumar}, journal={arXiv preprint arXiv:2510.07922}, year={2025} }","url":"https://doi.org/10.5281/zenodo.22094407","authors":["Murtaza Rangwala"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22094407","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.17223405","name":"SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening","source":"datacite","abstract":"@article{rangwala2025sketchguard, title={SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening}, author={Rangwala, Murtaza and Azzedin, Farag and Sinnott, Richard O and Buyya, Rajkumar}, journal={arXiv preprint arXiv:2510.07922}, year={2025} }","url":"https://doi.org/10.5281/zenodo.17223405","authors":["Murtaza Rangwala"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.17223405","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21760942","name":"Advanced IoT-Integrated Systems for Intelligent Water Quality Monitoring in Aquaculture: Emerging Technologies, AI-Driven Analytics, and Future Perspectives","source":"datacite","abstract":"Between 2020 and 2025, the integration of Internet of Things (IoT) sensor networks into aquaculture water quality management has seen a transformational acceleration due to the convergence of advanced sensor miniaturization, edge computing, artificial intelligence (AI), and next-generation wireless connectivity. This paper expands on the basic bibliometric and systematic analysis of IoT sensor applications in aquaculture (Flores-Iwasaki et al., 2025), taking the knowledge frontier a step further by critically reviewing recent technologies such as 5G-enabled real-time monitoring, AI-driven digital twins, flexible nano sensors, federated learning frameworks, and autonomous unmanned aerial/aquatic vehicle (UAV/AUV) platforms. The parameters most monitored were still pH (100%), temperature (96.7%) and dissolved oxygen (79.4%) and attention increased to total ammonia nitrogen (TAN), nitrite, nitrate and chlorophyll-a in recirculating aquaculture systems (RAS) and integrated aquaponics. The key findings suggest that AI-aided predictive models, particularly Long Short-Term Memory (LSTM), Transformers, and ensemble methods, demonstrated water quality prediction accuracies exceeding 95% in controlled settings. Microcontrollers (TinyML) Edge AI deployment reduced cloud latency up to 87%, enabling near-instant anomaly detection. Identified key gaps are lack of automated TAN sensing in most commercial deployments, limited self-cleaning sensor maintenance mechanisms, and inequitable technology access in developing-nation aquaculture. Future research directions include AI-digital twin co-simulation, bio-inspired nano sensor arrays, and 5G-LPWAN hybrid architectures. Addressing these challenges is critical for the deployment of fully automated, sustainable and economically viable aquaculture ecosystems","url":"https://doi.org/10.5281/zenodo.21760942","authors":["Praful Nandankar","Prashantkumar V. Dhawas","Shilpa Kalambe"],"tags":["Artificial intelligence; Document intelligence; Deep Learning IoT; Water Quality Monitoring; Aquaculture; Artificial Intelligence; Digital Twins; 5G Connectivity; Nano sensors; Edge Computing; Machine Learning; Bio floc Technology; RAS; Aquaponics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21760942","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21760943","name":"Advanced IoT-Integrated Systems for Intelligent Water Quality Monitoring in Aquaculture: Emerging Technologies, AI-Driven Analytics, and Future Perspectives","source":"datacite","abstract":"Between 2020 and 2025, the integration of Internet of Things (IoT) sensor networks into aquaculture water quality management has seen a transformational acceleration due to the convergence of advanced sensor miniaturization, edge computing, artificial intelligence (AI), and next-generation wireless connectivity. This paper expands on the basic bibliometric and systematic analysis of IoT sensor applications in aquaculture (Flores-Iwasaki et al., 2025), taking the knowledge frontier a step further by critically reviewing recent technologies such as 5G-enabled real-time monitoring, AI-driven digital twins, flexible nano sensors, federated learning frameworks, and autonomous unmanned aerial/aquatic vehicle (UAV/AUV) platforms. The parameters most monitored were still pH (100%), temperature (96.7%) and dissolved oxygen (79.4%) and attention increased to total ammonia nitrogen (TAN), nitrite, nitrate and chlorophyll-a in recirculating aquaculture systems (RAS) and integrated aquaponics. The key findings suggest that AI-aided predictive models, particularly Long Short-Term Memory (LSTM), Transformers, and ensemble methods, demonstrated water quality prediction accuracies exceeding 95% in controlled settings. Microcontrollers (TinyML) Edge AI deployment reduced cloud latency up to 87%, enabling near-instant anomaly detection. Identified key gaps are lack of automated TAN sensing in most commercial deployments, limited self-cleaning sensor maintenance mechanisms, and inequitable technology access in developing-nation aquaculture. Future research directions include AI-digital twin co-simulation, bio-inspired nano sensor arrays, and 5G-LPWAN hybrid architectures. Addressing these challenges is critical for the deployment of fully automated, sustainable and economically viable aquaculture ecosystems","url":"https://doi.org/10.5281/zenodo.21760943","authors":["Praful Nandankar","Prashantkumar V. Dhawas","Shilpa Kalambe"],"tags":["Artificial intelligence; Document intelligence; Deep Learning IoT; Water Quality Monitoring; Aquaculture; Artificial Intelligence; Digital Twins; 5G Connectivity; Nano sensors; Edge Computing; Machine Learning; Bio floc Technology; RAS; Aquaponics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21760943","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.19918373","name":"Beyond GDPR: The Architectural Challenge of Data Sovereignty and Confidential Computing in the Post-2024 Era","source":"datacite","abstract":"As organizations migrate legacy datasets to cloud-native architectures, the tension between Big Data analytics and data privacy regulations has reached a critical inflection point. With the full operationalization of India’s Digital Personal Data Protection (DPDP) Act in 2025 and the tightening of GDPR enforcement, the concept of \"Data Sovereignty\" has evolved from a legal footnote to a primary architectural constraint. This paper reviews the limitations of traditional \"encryption-at-rest\" standards in the face of these new laws. We analyze emerging solutions, specifically Confidential Computing (using hardware-based Trusted Execution Environments) and Federated Learning, which promise to decouple data processing from data visibility. Market analysis suggests the Confidential Computing sector alone will expand to over USD 14 billion by late 2025. We argue that the future of software engineering lies not in centralized data lakes, but in decentralized, privacy-preserving compute fabrics.","url":"https://doi.org/10.5281/zenodo.19918373","authors":["Mr. Tayabur Rahman Laskar"],"tags":["Data Sovereignty, DPDP Act 2023, Confidential Computing, Federated Learning, GDPR, Cloud Security, Big Data Governance."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19918373","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.19918374","name":"Beyond GDPR: The Architectural Challenge of Data Sovereignty and Confidential Computing in the Post-2024 Era","source":"datacite","abstract":"As organizations migrate legacy datasets to cloud-native architectures, the tension between Big Data analytics and data privacy regulations has reached a critical inflection point. With the full operationalization of India’s Digital Personal Data Protection (DPDP) Act in 2025 and the tightening of GDPR enforcement, the concept of \"Data Sovereignty\" has evolved from a legal footnote to a primary architectural constraint. This paper reviews the limitations of traditional \"encryption-at-rest\" standards in the face of these new laws. We analyze emerging solutions, specifically Confidential Computing (using hardware-based Trusted Execution Environments) and Federated Learning, which promise to decouple data processing from data visibility. Market analysis suggests the Confidential Computing sector alone will expand to over USD 14 billion by late 2025. We argue that the future of software engineering lies not in centralized data lakes, but in decentralized, privacy-preserving compute fabrics.","url":"https://doi.org/10.5281/zenodo.19918374","authors":["Mr. Tayabur Rahman Laskar"],"tags":["Data Sovereignty, DPDP Act 2023, Confidential Computing, Federated Learning, GDPR, Cloud Security, Big Data Governance."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19918374","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21432910","name":"Extraction Workbook — Trustworthy Knowledge Distillation in Federated Learning: A Systematic Review of Proxy Data Strategies","source":"datacite","abstract":"This dataset contains the complete data-extraction workbook of the systematic literature review \"Trustworthy Knowledge Distillation in Federated Learning: A Systematic Review of Proxy Data Strategies\". The review, conducted under the Kitchenham/Charters guidelines and the PRISMA 2020 reporting standard, covers 51 primary studies (2020–2025) on knowledge distillation in federated learning using synthetic or otherwise non-private proxy data, retrieved from five databases (IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, and Springer Link). The workbook (.xlsx) is organized as follows: Study_Register — bibliographic and contextual metadata for all 57 screened full-text studies (51 included, 6 excluded with reasons): identifier, title, year, venue, publication type, DOI, final eligibility, application domain, dataset summary, federated-learning setting, heterogeneity type, number of clients, and code/data availability. RQ1–RQ6 sheets — research-question-specific extraction fields for every study, covering proxy use and ownership (RQ1), generation mechanisms and curation policies (RQ2), effects on communication, privacy, and performance (RQ3), proxy-quality criteria and metrics (RQ4), threat models and defenses (RQ5), and methodological gaps and reproducibility (RQ6). Quality_Assessment — per-study scores for the eight-item quality checklist (Q1–Q8; Yes/Partial/No), with computed total scores (mean 6.70, median 7.00, range 4.0–8.0 across the included corpus). Controlled_Vocabulary — the harmonized terminology enforced across all categorical fields (proxy type, distillation flow, ownership scope, mechanism family, threat type, defense mechanism). Dashboard / README — corpus composition summaries and usage notes. Every extracted field is accompanied by a KeyEvidence entry pointing to the page, figure, or table of the source article that supports it, making each synthesis claim in the review traceable to primary evidence. The workbook is the primary resource for replicating, auditing, or extending the review. The deposit also includes the Python script used to generate all figures of the review directly from the workbook.","url":"https://doi.org/10.5281/zenodo.21432910","authors":["Freire, Agostinho","de Andrade, João Vinícius Ribeiro","Silva, Leandro Honorato","Lira, Juan","FISICHELLA, Marco","Fernandes, Bruno"],"tags":["federated learning","knowledge distillation","synthetic data","proxy datasets","systematic literature review","trustworthy AI","reproducibility"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21432910","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21432911","name":"Extraction Workbook — Trustworthy Knowledge Distillation in Federated Learning: A Systematic Review of Proxy Data Strategies","source":"datacite","abstract":"This dataset contains the complete data-extraction workbook of the systematic literature review \"Trustworthy Knowledge Distillation in Federated Learning: A Systematic Review of Proxy Data Strategies\". The review, conducted under the Kitchenham/Charters guidelines and the PRISMA 2020 reporting standard, covers 51 primary studies (2020–2025) on knowledge distillation in federated learning using synthetic or otherwise non-private proxy data, retrieved from five databases (IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, and Springer Link). The workbook (.xlsx) is organized as follows: Study_Register — bibliographic and contextual metadata for all 57 screened full-text studies (51 included, 6 excluded with reasons): identifier, title, year, venue, publication type, DOI, final eligibility, application domain, dataset summary, federated-learning setting, heterogeneity type, number of clients, and code/data availability. RQ1–RQ6 sheets — research-question-specific extraction fields for every study, covering proxy use and ownership (RQ1), generation mechanisms and curation policies (RQ2), effects on communication, privacy, and performance (RQ3), proxy-quality criteria and metrics (RQ4), threat models and defenses (RQ5), and methodological gaps and reproducibility (RQ6). Quality_Assessment — per-study scores for the eight-item quality checklist (Q1–Q8; Yes/Partial/No), with computed total scores (mean 6.70, median 7.00, range 4.0–8.0 across the included corpus). Controlled_Vocabulary — the harmonized terminology enforced across all categorical fields (proxy type, distillation flow, ownership scope, mechanism family, threat type, defense mechanism). Dashboard / README — corpus composition summaries and usage notes. Every extracted field is accompanied by a KeyEvidence entry pointing to the page, figure, or table of the source article that supports it, making each synthesis claim in the review traceable to primary evidence. The workbook is the primary resource for replicating, auditing, or extending the review. The deposit also includes the Python script used to generate all figures of the review directly from the workbook.","url":"https://doi.org/10.5281/zenodo.21432911","authors":["Freire, Agostinho","de Andrade, João Vinícius Ribeiro","Silva, Leandro Honorato","Lira, Juan","FISICHELLA, Marco","Fernandes, Bruno"],"tags":["federated learning","knowledge distillation","synthetic data","proxy datasets","systematic literature review","trustworthy AI","reproducibility"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21432911","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21456424","name":"NACTIS-NG: AI-POWERED COUNTER-UAS AND AUTONOMOUS BORDER SURVEILLANCE FOR NIGERIA'S INSECURE BORDER REGIONS Radar-Vision Fusion, RF Signal Classification, and Autonomous Patrol Optimisation for the Lake Chad Basin and Northwest Border Corridors","source":"datacite","abstract":"Nigeria faces a compounding security crisis driven by the Boko Haram/ISWAP insurgency in the Northeast and an escalating wave of armed banditry and mass kidnapping in the Northwest. Between 2023 and 2025, over 2,452 individuals were kidnapped annually (a 31% year-on-year rise), more than 3.5 million people were internally displaced, and at least 2,000 civilians were killed in the first quarter of 2025 alone. Conventional security approaches have proven inadequate against the speed, scale, and adaptive nature of these threats. This paper proposes and evaluates a multi-modal AI security framework - the Nigeria Adaptive Counter-Threat Intelligence System (NACTIS) - that integrates: (i) satellite imagery analysis using Mask R-CNN and change-detection algorithms for monitoring IDP camps, destroyed villages, and insurgent encampments; (ii) real-time UAV and CCTV-based computer vision using YOLOv8 for weapons, crowd anomalies, and suspicious vehicle detection; (iii) spatio-temporal conflict prediction using Long Short-Term Memory (LSTM) networks and XGBoost trained on ACLED event data; and (iv) Natural Language Processing (NLP) for social media and open-source intelligence (OSINT) early warning. Evaluated against benchmark datasets and simulated Nigerian threat scenarios, NACTIS achieves a satellite camp-detection F1-score of 91.4%, weapon-detection mAP@0.5 of 94.2%, conflict-onset prediction accuracy of 87.3% (AUC=0.934), and an average early warning lead time of 6.2 days. A federated learning deployment strategy is proposed to address infrastructure limitations and data-sovereignty concerns across Nigerian military and civilian agencies.","url":"https://doi.org/10.5281/zenodo.21456424","authors":["Sulaiman, J.","Hussain, M. M. A."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21456424","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21456425","name":"NACTIS-NG: AI-POWERED COUNTER-UAS AND AUTONOMOUS BORDER SURVEILLANCE FOR NIGERIA'S INSECURE BORDER REGIONS Radar-Vision Fusion, RF Signal Classification, and Autonomous Patrol Optimisation for the Lake Chad Basin and Northwest Border Corridors","source":"datacite","abstract":"Nigeria faces a compounding security crisis driven by the Boko Haram/ISWAP insurgency in the Northeast and an escalating wave of armed banditry and mass kidnapping in the Northwest. Between 2023 and 2025, over 2,452 individuals were kidnapped annually (a 31% year-on-year rise), more than 3.5 million people were internally displaced, and at least 2,000 civilians were killed in the first quarter of 2025 alone. Conventional security approaches have proven inadequate against the speed, scale, and adaptive nature of these threats. This paper proposes and evaluates a multi-modal AI security framework - the Nigeria Adaptive Counter-Threat Intelligence System (NACTIS) - that integrates: (i) satellite imagery analysis using Mask R-CNN and change-detection algorithms for monitoring IDP camps, destroyed villages, and insurgent encampments; (ii) real-time UAV and CCTV-based computer vision using YOLOv8 for weapons, crowd anomalies, and suspicious vehicle detection; (iii) spatio-temporal conflict prediction using Long Short-Term Memory (LSTM) networks and XGBoost trained on ACLED event data; and (iv) Natural Language Processing (NLP) for social media and open-source intelligence (OSINT) early warning. Evaluated against benchmark datasets and simulated Nigerian threat scenarios, NACTIS achieves a satellite camp-detection F1-score of 91.4%, weapon-detection mAP@0.5 of 94.2%, conflict-onset prediction accuracy of 87.3% (AUC=0.934), and an average early warning lead time of 6.2 days. A federated learning deployment strategy is proposed to address infrastructure limitations and data-sovereignty concerns across Nigerian military and civilian agencies.","url":"https://doi.org/10.5281/zenodo.21456425","authors":["Sulaiman, J.","Hussain, M. M. A."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21456425","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.22046887","name":"Ethical Guardrail Bandit Pruning (EGBP): A Fairness-Constrained Reinforcement Learning System for Equitable Governance of Distributed Bioenergy Grids","source":"datacite","abstract":"131,400 node-hour observations across 15 bioenergy grid nodes (6 anaerobic digesters, 5 gasifiers, 4 CHP units) over 365 days at hourly resolution. Parameters calibrated to Faaij (2006) and Atashbar et al. (2016). Generated for: Ethical Guardrail Bandit Pruning (EGBP). This repository supports the manuscript \"Ethical Guardrail Bandit Pruning (EGBP): A Framework for Sustainable and Equitable AI-Driven Resource Allocation in Bioenergy Grids\" (under review). EGBP treats computational efficiency, energy sustainability, and distributional equity as simultaneously binding optimisation constraints rather than sequentially addressed objectives. The central theoretical contribution demonstrates that embedding ethical guardrails directly in the optimisation objective, rather than evaluating them post-hoc preserves the asymptotic regret properties of Thompson Sampling bandit learning while guaranteeing convergence to an ethically-admissible stationary point. The framework integrates four tightly coupled mechanisms: (i) a hybrid bandit-gradient importance estimator combining offline Transformer pre-training with online Thompson Sampling; (ii) an ethical guardrail buffer enforcing differentiable penalties on energy overconsumption and distributional inequity through a Gini-coefficient regulariser applied to physical energy budget allocations; (iii) a cost-weighted magnitude pruning operator coupling gradient sparsity to real-time biomass conversion telemetry; and (iv) a guardrail-filtered federated averaging scheme with analytically bounded exclusion fraction. Theoretical properties are established through three theorems, three propositions, and two corollaries covering energy guardrail self-correction, Gini penalty convexity, Thompson Sampling regret preservation, EGBP convergence, and federated fairness monotonicity. Simulation on a 15-node bioenergy grid over 50,000 training steps demonstrates 38% reduction in energy consumption and 40% improvement in distributional fairness relative to three competitive baselines. This repository contains derived simulation outputs and pipeline code for reproducibility.","url":"https://doi.org/10.5281/zenodo.22046887","authors":["Moroke, Ntebogang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22046887","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.19894494","name":"Ethical Guardrail Bandit Pruning (EGBP): A Fairness-Constrained Reinforcement Learning System for Equitable Governance of Distributed Bioenergy Grids","source":"datacite","abstract":"131,400 node-hour observations across 15 bioenergy grid nodes (6 anaerobic digesters, 5 gasifiers, 4 CHP units) over 365 days at hourly resolution. Parameters calibrated to Faaij (2006) and Atashbar et al. (2016). Generated for: Ethical Guardrail Bandit Pruning (EGBP). This repository supports the manuscript \"Ethical Guardrail Bandit Pruning (EGBP): A Framework for Sustainable and Equitable AI-Driven Resource Allocation in Bioenergy Grids\" (under review). EGBP treats computational efficiency, energy sustainability, and distributional equity as simultaneously binding optimisation constraints rather than sequentially addressed objectives. The central theoretical contribution demonstrates that embedding ethical guardrails directly in the optimisation objective, rather than evaluating them post-hoc preserves the asymptotic regret properties of Thompson Sampling bandit learning while guaranteeing convergence to an ethically-admissible stationary point. The framework integrates four tightly coupled mechanisms: (i) a hybrid bandit-gradient importance estimator combining offline Transformer pre-training with online Thompson Sampling; (ii) an ethical guardrail buffer enforcing differentiable penalties on energy overconsumption and distributional inequity through a Gini-coefficient regulariser applied to physical energy budget allocations; (iii) a cost-weighted magnitude pruning operator coupling gradient sparsity to real-time biomass conversion telemetry; and (iv) a guardrail-filtered federated averaging scheme with analytically bounded exclusion fraction. Theoretical properties are established through three theorems, three propositions, and two corollaries covering energy guardrail self-correction, Gini penalty convexity, Thompson Sampling regret preservation, EGBP convergence, and federated fairness monotonicity. Simulation on a 15-node bioenergy grid over 50,000 training steps demonstrates 38% reduction in energy consumption and 40% improvement in distributional fairness relative to three competitive baselines. This repository contains derived simulation outputs and pipeline code for reproducibility.","url":"https://doi.org/10.5281/zenodo.19894494","authors":["Moroke, Ntebogang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19894494","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21404389","name":"infocom2027-fedclids","source":"datacite","abstract":"Artifact for the paper submission on Fed-CL-IDS ” a federated intrusion-detection system that combines continual learning (experience replay + online Elastic Weight Consolidation) with record-level differential privacy (DP-SGD) under FedAvg aggregation. Background Network intrusion detection in federated settings faces two compounding challenges: attack families arrive and evolve over time (requiring continual adaptation without catastrophic forgetting), and client traffic is privacy-sensitive (requiring formal privacy guarantees during training). Fed-CL-IDS addresses both jointly. It is evaluated on two flow-level benchmarks ” UAVIDS-2025 and a schedule-faithful temporal reconstruction of CIC-IDS2017 ” across four simulated days of federated training in which attack families are introduced incrementally. What this artifact contains Everything needed to reproduce the paper end to end: source code, both raw datasets (bundled, with SHA-256 checksums ” no external download needed), all run specifications, precomputed aggregate result CSVs, and a subset of raw per-run outputs. It reproduces: the main detection / fairness / adaptation results (federated and centralized baselines vs. Fed-CL, with and without DP); the privacy“utility tradeoff sweep (E3); the client-participation robustness sweep (E5) and the phase-level blackout robustness study (E5b); the component ablation (Abl); the comparison against the FL-IIDS baseline (E4). Every headline number in the paper is read programmatically from the shipped aggregate/*.csv files, which are themselves derived from the raw per-run outputs ” no number is hand-typed. Repository layout fed/ federated client/server, continual learning, differential privacy models/ MLP detector + metrics library (shared by fed/ and centralized/) centralized/ centralized (static / online) baselines data_pipeline/ preprocessing for both datasets scripts/ entry points: run_one, orchestrate, table/figure generators specs/ *.jsonl run specifications, one JSON object per run aggregate/ precomputed summary CSVs that the paper's tables/figures read results/ shipped subset of raw per-run outputs (main + E3) datasets/ bundled raw dataset archives + SHA-256 checksums paper/drafts/ pipeline-figure SVG source How to use Environment: Python 3.12.3; pip install -r requirements.txt (pinned) or uv sync. Data: bash scripts/unpack_data.sh ” verifies checksums, extracts both datasets, re-verifies. Idempotent. Smoke test ( .jsonl --jobs 4 for each spec file ” 590 runs total across all experiments; the orchestrator is resumable and skips completed runs. See README.md in the archive for full details, including per-experiment run counts, seed policy (no seed cherry-picking; all means/CIs are over the full seed set), DP accounting configuration, and dataset licensing/citations.","url":"https://doi.org/10.5281/zenodo.21404389","authors":["anonymous"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21404389","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21404390","name":"infocom2027-fedclids","source":"datacite","abstract":"Artifact for the paper submission on Fed-CL-IDS ” a federated intrusion-detection system that combines continual learning (experience replay + online Elastic Weight Consolidation) with record-level differential privacy (DP-SGD) under FedAvg aggregation. Background Network intrusion detection in federated settings faces two compounding challenges: attack families arrive and evolve over time (requiring continual adaptation without catastrophic forgetting), and client traffic is privacy-sensitive (requiring formal privacy guarantees during training). Fed-CL-IDS addresses both jointly. It is evaluated on two flow-level benchmarks ” UAVIDS-2025 and a schedule-faithful temporal reconstruction of CIC-IDS2017 ” across four simulated days of federated training in which attack families are introduced incrementally. What this artifact contains Everything needed to reproduce the paper end to end: source code, both raw datasets (bundled, with SHA-256 checksums ” no external download needed), all run specifications, precomputed aggregate result CSVs, and a subset of raw per-run outputs. It reproduces: the main detection / fairness / adaptation results (federated and centralized baselines vs. Fed-CL, with and without DP); the privacy“utility tradeoff sweep (E3); the client-participation robustness sweep (E5) and the phase-level blackout robustness study (E5b); the component ablation (Abl); the comparison against the FL-IIDS baseline (E4). Every headline number in the paper is read programmatically from the shipped aggregate/*.csv files, which are themselves derived from the raw per-run outputs ” no number is hand-typed. Repository layout fed/ federated client/server, continual learning, differential privacy models/ MLP detector + metrics library (shared by fed/ and centralized/) centralized/ centralized (static / online) baselines data_pipeline/ preprocessing for both datasets scripts/ entry points: run_one, orchestrate, table/figure generators specs/ *.jsonl run specifications, one JSON object per run aggregate/ precomputed summary CSVs that the paper's tables/figures read results/ shipped subset of raw per-run outputs (main + E3) datasets/ bundled raw dataset archives + SHA-256 checksums paper/drafts/ pipeline-figure SVG source How to use Environment: Python 3.12.3; pip install -r requirements.txt (pinned) or uv sync. Data: bash scripts/unpack_data.sh ” verifies checksums, extracts both datasets, re-verifies. Idempotent. Smoke test ( .jsonl --jobs 4 for each spec file ” 590 runs total across all experiments; the orchestrator is resumable and skips completed runs. See README.md in the archive for full details, including per-experiment run counts, seed policy (no seed cherry-picking; all means/CIs are over the full seed set), DP accounting configuration, and dataset licensing/citations.","url":"https://doi.org/10.5281/zenodo.21404390","authors":["anonymous"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21404390","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.22030415","name":"A Review of AI-Enabled Systems for Monitoring and Preventing Malpractice in Online Examinations","source":"datacite","abstract":"COVID-19 changed everything for education almost overnight. Schools and colleges had no choice but to shift online, and with that shift came a big question: how do you make sure students are not cheating when they are sitting at home? Traditional invigilation simply does not work in that kind of setup, which is why there has been so much interest in using AI to proctor online tests. This paper reviews ten research studies on AI-based proctoring systems published between 2022 and 2025, across journals like IEEE, Elsevier, and Springer. These systems use technologies like computer vision (YOLO, MTCNN, MediaPipe), deep learning (CNN, LSTM, ALBERT), audio analysis, natural language processing, and blockchain-based security. We go through how each system detects cheating, what accuracy it achieves, and where it falls short. Across the board, the same problems keep coming up: privacy concerns, algorithmic bias, high hardware requirements, limited real-world testing, and the growing worry about students using AI tools like ChatGPT to write their exam answers. The paper ends by suggesting some useful directions for future work: lighter AI models, emotion-aware monitoring, federated learning for privacy, and explainable AI so that when a student is flagged, there is a clear reason given.","url":"https://doi.org/10.5281/zenodo.22030415","authors":["Deepak Kumar","Mrs. Khushboo"],"tags":["Online exam proctoring, AI, computer vision, YOLO, deep learning, cheating detection, academic integrity, facial recognition, behavioural analysis, natural language processing."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22030415","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.22030416","name":"A Review of AI-Enabled Systems for Monitoring and Preventing Malpractice in Online Examinations","source":"datacite","abstract":"COVID-19 changed everything for education almost overnight. Schools and colleges had no choice but to shift online, and with that shift came a big question: how do you make sure students are not cheating when they are sitting at home? Traditional invigilation simply does not work in that kind of setup, which is why there has been so much interest in using AI to proctor online tests. This paper reviews ten research studies on AI-based proctoring systems published between 2022 and 2025, across journals like IEEE, Elsevier, and Springer. These systems use technologies like computer vision (YOLO, MTCNN, MediaPipe), deep learning (CNN, LSTM, ALBERT), audio analysis, natural language processing, and blockchain-based security. We go through how each system detects cheating, what accuracy it achieves, and where it falls short. Across the board, the same problems keep coming up: privacy concerns, algorithmic bias, high hardware requirements, limited real-world testing, and the growing worry about students using AI tools like ChatGPT to write their exam answers. The paper ends by suggesting some useful directions for future work: lighter AI models, emotion-aware monitoring, federated learning for privacy, and explainable AI so that when a student is flagged, there is a clear reason given.","url":"https://doi.org/10.5281/zenodo.22030416","authors":["Deepak Kumar","Mrs. Khushboo"],"tags":["Online exam proctoring, AI, computer vision, YOLO, deep learning, cheating detection, academic integrity, facial recognition, behavioural analysis, natural language processing."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22030416","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.22027509","name":"Big Data Analytics: Recent Trends, Applications, Challenges and Future Directions","source":"datacite","abstract":"Big Data Analytics has emerged as an important area of computer science because of the rapid growth of data generated through digital platforms, social media, Internet of Things (IoT) devices, business transactions, mobile applications, sensors, and other digital technologies. The increasing volume, velocity, variety, and complexity of data have created both opportunities and challenges for organizations and researchers. This review paper examines the development of Big Data Analytics and focuses on recent technological trends, applications, challenges, and future directions. The study is based on a systematic review of selected research studies published between 2021 and 2025. The reviewed literature covers Big Data Analytics capabilities, programming frameworks, machine learning, social media analytics, digital twins, Big Data science project management, predictive analytics, testing techniques, and emerging analytical technologies. The review shows that Big Data Analytics is increasingly integrated with Artificial Intelligence, Machine Learning, cloud computing, IoT, edge computing, predictive analytics, and real-time processing. Recent developments are also moving towards Generative AI, automated analytics, privacy-preserving analytics, and federated learning. The review identifies data quality, privacy and security, scalability, computational requirements, interoperability, lack of skilled professionals, and ethical concerns as major challenges. The study concludes that the future of Big Data Analytics will depend on intelligent, real-time, distributed, secure, and responsible analytical systems capable of converting large and complex datasets into meaningful insights and supporting faster decision-making.","url":"https://doi.org/10.5281/zenodo.22027509","authors":["Hiremath, Swati S"],"tags":["Big Data Analytics, Artificial Intelligence, Machine Learning, Internet of Things, Predictive Analytics, Generative AI, Edge Computing, Data Analytics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22027509","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.22027510","name":"Big Data Analytics: Recent Trends, Applications, Challenges and Future Directions","source":"datacite","abstract":"Big Data Analytics has emerged as an important area of computer science because of the rapid growth of data generated through digital platforms, social media, Internet of Things (IoT) devices, business transactions, mobile applications, sensors, and other digital technologies. The increasing volume, velocity, variety, and complexity of data have created both opportunities and challenges for organizations and researchers. This review paper examines the development of Big Data Analytics and focuses on recent technological trends, applications, challenges, and future directions. The study is based on a systematic review of selected research studies published between 2021 and 2025. The reviewed literature covers Big Data Analytics capabilities, programming frameworks, machine learning, social media analytics, digital twins, Big Data science project management, predictive analytics, testing techniques, and emerging analytical technologies. The review shows that Big Data Analytics is increasingly integrated with Artificial Intelligence, Machine Learning, cloud computing, IoT, edge computing, predictive analytics, and real-time processing. Recent developments are also moving towards Generative AI, automated analytics, privacy-preserving analytics, and federated learning. The review identifies data quality, privacy and security, scalability, computational requirements, interoperability, lack of skilled professionals, and ethical concerns as major challenges. The study concludes that the future of Big Data Analytics will depend on intelligent, real-time, distributed, secure, and responsible analytical systems capable of converting large and complex datasets into meaningful insights and supporting faster decision-making.","url":"https://doi.org/10.5281/zenodo.22027510","authors":["Hiremath, Swati S"],"tags":["Big Data Analytics, Artificial Intelligence, Machine Learning, Internet of Things, Predictive Analytics, Generative AI, Edge Computing, Data Analytics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22027510","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20770080","name":"Artificial Intelligence-Driven Histopathological Image  Analysis for Early Cancer Detection","source":"datacite","abstract":"Abstract The number of people diagnosed with cancer is rising worldwide and has become a leading cause of death. Therefore, it is essential to perform early diagnosis effectively to improve the success of treatment and increase survival rates. Currently, histopathological examination is the main method for confirming a cancer diagnosis. However, this analysis depends on the pathologist's skill in interpreting tissue specimens, which can be time-consuming and varies among different pathologists. Recent developments in digital pathology and artificial intelligence have introduced Computer-Aided Diagnosis (CAD) systems. These systems can automate cancer detection through histopathological image analysis. In this review, we summarize the advances made from 2020 to 2025 in AI-assisted histopathological image analysis. The main topics discussed include available public datasets for AI-assisted histopathological image analysis, one or more image preprocessing methods, machine learning techniques, deep learning methods, the use of Vision Transformers and Explainable Artificial Intelligence (XAI), the development of foundation models, and a comparison of the performance between deep learning and traditional machine learning methods, including differences in accuracy and robustness. While CAD systems have made significant strides, several challenges remain before successful clinical application. These include issues related to dataset variability, interpretability, and clinical deployment. Ongoing research aims to enhance the understanding of foundation models, federated learning, and multimodal AI systems in precision oncology.","url":"https://doi.org/10.5281/zenodo.20770080","authors":["IJMSRT"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20770080","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20770081","name":"Artificial Intelligence-Driven Histopathological Image  Analysis for Early Cancer Detection","source":"datacite","abstract":"Abstract The number of people diagnosed with cancer is rising worldwide and has become a leading cause of death. Therefore, it is essential to perform early diagnosis effectively to improve the success of treatment and increase survival rates. Currently, histopathological examination is the main method for confirming a cancer diagnosis. However, this analysis depends on the pathologist's skill in interpreting tissue specimens, which can be time-consuming and varies among different pathologists. Recent developments in digital pathology and artificial intelligence have introduced Computer-Aided Diagnosis (CAD) systems. These systems can automate cancer detection through histopathological image analysis. In this review, we summarize the advances made from 2020 to 2025 in AI-assisted histopathological image analysis. The main topics discussed include available public datasets for AI-assisted histopathological image analysis, one or more image preprocessing methods, machine learning techniques, deep learning methods, the use of Vision Transformers and Explainable Artificial Intelligence (XAI), the development of foundation models, and a comparison of the performance between deep learning and traditional machine learning methods, including differences in accuracy and robustness. While CAD systems have made significant strides, several challenges remain before successful clinical application. These include issues related to dataset variability, interpretability, and clinical deployment. Ongoing research aims to enhance the understanding of foundation models, federated learning, and multimodal AI systems in precision oncology.","url":"https://doi.org/10.5281/zenodo.20770081","authors":["IJMSRT"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20770081","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21324027","name":"AI-Integrated Buy Now, Pay Later (BNPL) Schemes in E-Commerce: A Dual-Method Systematic Review and Biblio-metric Analysis of Stock Market Implications (1991–2025)","source":"datacite","abstract":"The convergence of artificial intelligence (AI) and Buy Now, Pay Later (BNPL) services constitutes a structural transformation in electronic commerce, yet its implications for capital market valuations remain theoretically underdeveloped. This study deploys a dual-method approach, integrating PRISMA-guided systematic literature review with VOSviewer-driven bibliometric analysis, to critically examine 187 publications indexed in Web of Science (WoS) and Scopus between 1991 and 2025. Drawing upon a critical synthesis of the 50 most impactful studies from 2020 to 2025, we identify three mechanism-driven knowledge clusters: (1) AI-enabled credit risk architectures within BNPL ecosystems, (2) fintech disruption signals and equity market reactions, and (3) regulatory technology (RegTech) feedback loops on BNPL provider valuations. Our gap analysis reveals that existing scholarship overwhelmingly privileges consumer behavioral outcomes while neglecting the transmission mechanisms through which AI-integrated BNPL innovations propagate into stock market pricing efficiency. We advance five testable hypotheses linking algorithmic underwriting sophistication, default prediction accuracy, and merchant network externalities to abnormal stock returns among publicly listed BNPL providers. The timeline analysis identifies 2019 as the inflection point where machine learning integration shifted BNPL from a payment's convenience to a data-driven financial instrument. We propose a research agenda targeting the identified gaps and predict five emerging trends including federated learning for cross-border BNPL risk assessment and generative AI-driven dynamic credit limit optimization. This review contributes to the theoretical and applied electronic commerce literature by articulating a causal framework connecting AI capabilities, BNPL market structure, and investor valuation responses.","url":"https://doi.org/10.5281/zenodo.21324027","authors":["Wagdi, Osama"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21324027","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.21324028","name":"AI-Integrated Buy Now, Pay Later (BNPL) Schemes in E-Commerce: A Dual-Method Systematic Review and Biblio-metric Analysis of Stock Market Implications (1991–2025)","source":"datacite","abstract":"The convergence of artificial intelligence (AI) and Buy Now, Pay Later (BNPL) services constitutes a structural transformation in electronic commerce, yet its implications for capital market valuations remain theoretically underdeveloped. This study deploys a dual-method approach, integrating PRISMA-guided systematic literature review with VOSviewer-driven bibliometric analysis, to critically examine 187 publications indexed in Web of Science (WoS) and Scopus between 1991 and 2025. Drawing upon a critical synthesis of the 50 most impactful studies from 2020 to 2025, we identify three mechanism-driven knowledge clusters: (1) AI-enabled credit risk architectures within BNPL ecosystems, (2) fintech disruption signals and equity market reactions, and (3) regulatory technology (RegTech) feedback loops on BNPL provider valuations. Our gap analysis reveals that existing scholarship overwhelmingly privileges consumer behavioral outcomes while neglecting the transmission mechanisms through which AI-integrated BNPL innovations propagate into stock market pricing efficiency. We advance five testable hypotheses linking algorithmic underwriting sophistication, default prediction accuracy, and merchant network externalities to abnormal stock returns among publicly listed BNPL providers. The timeline analysis identifies 2019 as the inflection point where machine learning integration shifted BNPL from a payment's convenience to a data-driven financial instrument. We propose a research agenda targeting the identified gaps and predict five emerging trends including federated learning for cross-border BNPL risk assessment and generative AI-driven dynamic credit limit optimization. This review contributes to the theoretical and applied electronic commerce literature by articulating a causal framework connecting AI capabilities, BNPL market structure, and investor valuation responses.","url":"https://doi.org/10.5281/zenodo.21324028","authors":["Wagdi, Osama"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21324028","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.21353400","name":"Agentic AI for Autonomous Arbitrage Trading in Equity and Cryptocurrency Markets: Concepts, Architecture, and Algorithmic Framework","source":"datacite","abstract":"Arbitrage trading exploits temporary price discrepancies across financial markets to generate low-risk profit opportunities. However, conventional arbitrage systems primarily rely on predefined rules and lack the intelligence to adapt to rapidly changing market conditions. This chapter proposes an Agentic AI-based framework for autonomous arbitrage trading across equity and cryptocurrency markets. The framework integrates multiple intelligent agents responsible for market scanning, opportunity detection, risk assessment, decision-making, trade execution, monitoring, and continuous learning under the supervision of an Agentic AI Orchestrator. Real-time market indicators, including price spreads, liquidity, volatility, order book depth, momentum, and transaction costs, are analyzed to identify executable arbitrage opportunities while minimizing execution risk. An algorithmic workflow is presented to support autonomous, explainable, and adaptive trading across multiple exchanges. The chapter also discusses future research directions involving reinforcement learning, explainable AI, federated learning, blockchain-based settlement, and large language model-driven autonomous agents. The proposed framework provides a scalable foundation for next-generation intelligent financial trading systems.","url":"https://doi.org/10.5281/zenodo.21353400","authors":["Chakraborty, Mohuya","Ghosh, Ajanta","Palit, Sudip Kumar"],"tags":["Agentic AI","Arbitrage Trading","Stock Trading","Crypto Trading"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21353400","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21353401","name":"Agentic AI for Autonomous Arbitrage Trading in Equity and Cryptocurrency Markets: Concepts, Architecture, and Algorithmic Framework","source":"datacite","abstract":"Arbitrage trading exploits temporary price discrepancies across financial markets to generate low-risk profit opportunities. However, conventional arbitrage systems primarily rely on predefined rules and lack the intelligence to adapt to rapidly changing market conditions. This chapter proposes an Agentic AI-based framework for autonomous arbitrage trading across equity and cryptocurrency markets. The framework integrates multiple intelligent agents responsible for market scanning, opportunity detection, risk assessment, decision-making, trade execution, monitoring, and continuous learning under the supervision of an Agentic AI Orchestrator. Real-time market indicators, including price spreads, liquidity, volatility, order book depth, momentum, and transaction costs, are analyzed to identify executable arbitrage opportunities while minimizing execution risk. An algorithmic workflow is presented to support autonomous, explainable, and adaptive trading across multiple exchanges. The chapter also discusses future research directions involving reinforcement learning, explainable AI, federated learning, blockchain-based settlement, and large language model-driven autonomous agents. The proposed framework provides a scalable foundation for next-generation intelligent financial trading systems.","url":"https://doi.org/10.5281/zenodo.21353401","authors":["Chakraborty, Mohuya","Ghosh, Ajanta","Palit, Sudip Kumar"],"tags":["Agentic AI","Arbitrage Trading","Stock Trading","Crypto Trading"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21353401","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21991391","name":"Artificial Intelligence In Real-Time Payment Systems: A Bibliometric And Thematic Analysis Of Emerging Research Trends (2004–2026)","source":"datacite","abstract":"Real-time payment systems have transformed retail and wholesale finance by compressing payment initiation, authorization, clearing, and settlement into seconds or near-real time. The same speed that improves convenience and liquidity also narrows the window in which fraud, anomalies, cyber events, and financial-crime patterns can be detected. Artificial intelligence (AI) is therefore becoming a critical intelligence layer in payment infrastructures. This study maps the intellectual, thematic, and geographic development of research at the intersection of AI and real-time payments using a Scopus-derived corpus of 545 documents published across 402 sources from 2004 to 2026 and analyzed in Biblioshiny. Performance analysis, keyword co-occurrence, trend-topic analysis, thematic mapping, and collaboration analysis are combined to identify the field’s knowledge structure and emerging research fronts. Publication activity accelerated sharply after 2023, reaching 179 documents in 2025, while conference papers account for 54.3% of the corpus, indicating a technically driven and rapidly evolving domain. The thematic map identifies two mature motor themes: learning systems–crime–fraud detection and artificial intelligence–blockchain–security. Graph neural networks and behavioral research occupy the lower-centrality, lower-density zone; their 2025–2026 temporal profile indicates that they are emerging rather than declining themes. India, the United States, and China dominate country-affiliation productivity, with India–United States collaboration forming the strongest international link. The study argues that the field is moving from transaction-level classification toward adaptive, network-aware, explainable, and privacy-preserving intelligence embedded in payment orchestration. A research agenda is proposed around temporal learning, graph analytics, federated models, explainability, behavioral scam detection, adversarial resilience, and AI-assisted liquidity management.","url":"https://doi.org/10.5281/zenodo.21991391","authors":["Vijaya Kumar*"],"tags":["artificial intelligence; real-time payments; instant payments; machine learning; fraud detection; graph neural networks; Biblioshiny; bibliometric analysis; thematic analysis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21991391","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21991392","name":"Artificial Intelligence In Real-Time Payment Systems: A Bibliometric And Thematic Analysis Of Emerging Research Trends (2004–2026)","source":"datacite","abstract":"Real-time payment systems have transformed retail and wholesale finance by compressing payment initiation, authorization, clearing, and settlement into seconds or near-real time. The same speed that improves convenience and liquidity also narrows the window in which fraud, anomalies, cyber events, and financial-crime patterns can be detected. Artificial intelligence (AI) is therefore becoming a critical intelligence layer in payment infrastructures. This study maps the intellectual, thematic, and geographic development of research at the intersection of AI and real-time payments using a Scopus-derived corpus of 545 documents published across 402 sources from 2004 to 2026 and analyzed in Biblioshiny. Performance analysis, keyword co-occurrence, trend-topic analysis, thematic mapping, and collaboration analysis are combined to identify the field’s knowledge structure and emerging research fronts. Publication activity accelerated sharply after 2023, reaching 179 documents in 2025, while conference papers account for 54.3% of the corpus, indicating a technically driven and rapidly evolving domain. The thematic map identifies two mature motor themes: learning systems–crime–fraud detection and artificial intelligence–blockchain–security. Graph neural networks and behavioral research occupy the lower-centrality, lower-density zone; their 2025–2026 temporal profile indicates that they are emerging rather than declining themes. India, the United States, and China dominate country-affiliation productivity, with India–United States collaboration forming the strongest international link. The study argues that the field is moving from transaction-level classification toward adaptive, network-aware, explainable, and privacy-preserving intelligence embedded in payment orchestration. A research agenda is proposed around temporal learning, graph analytics, federated models, explainability, behavioral scam detection, adversarial resilience, and AI-assisted liquidity management.","url":"https://doi.org/10.5281/zenodo.21991392","authors":["Vijaya Kumar*"],"tags":["artificial intelligence; real-time payments; instant payments; machine learning; fraud detection; graph neural networks; Biblioshiny; bibliometric analysis; thematic analysis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21991392","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.19614178","name":"Future Directions in Clinical and Epidemiological Research","source":"datacite","abstract":"Clinical and epidemiological research is undergoing a substantial transition shaped by multiple converging trends:generative AI and large language models transforming literature synthesis, protocol design, and clinical decision support;European Health Data Space implementation enabling cross-border research at unprecedented scale; learning healthsystems integrating research with routine care; federated analytics addressing data sovereignty while enabling multi-sitestudies; patient advocacy integration reshaping research priorities; and climate-health research emerging ascross-cutting priority. The research methodological implications span every phase of the research cycle and createopportunities and challenges that require strategic response from European research communities. We evaluated fivefuture-oriented clinical and epidemiological research infrastructure frameworks applied to 124 research programmedesigns across 22 European research institutions in Vienna, Stockholm, Rome, Amsterdam, and London developed forimplementation between 2025 and 2030. Frameworks ranged from conventional research programme designs throughintegrated future-adapted research infrastructure incorporating AI-enabled methodology, EHDS compliance, learninghealth system integration, and climate-health capability. Performance was benchmarked using our Future ResearchInfrastructure Effectiveness Score (FRIES), integrating methodological innovation, cross-border capability, patientengagement, climate-health capability, and sustainability. The integrated future-adapted framework achieved the highestFRIES (0.912), with 3.8-fold research productivity advantage and substantial patient engagement improvement overconventional research infrastructure.","url":"https://doi.org/10.5281/zenodo.19614178","authors":["Hugo Jensen","Jonas Dubois","Eva Lindberg"],"tags":["clinical research; epidemiology; future directions; generative AI; European Health Data Space; learning health systems; FRIES; climate health; federated analytics; patient engagement; research infrastructure; methodological innovation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.19614178","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.19614179","name":"Future Directions in Clinical and Epidemiological Research","source":"datacite","abstract":"Clinical and epidemiological research is undergoing a substantial transition shaped by multiple converging trends:generative AI and large language models transforming literature synthesis, protocol design, and clinical decision support;European Health Data Space implementation enabling cross-border research at unprecedented scale; learning healthsystems integrating research with routine care; federated analytics addressing data sovereignty while enabling multi-sitestudies; patient advocacy integration reshaping research priorities; and climate-health research emerging ascross-cutting priority. The research methodological implications span every phase of the research cycle and createopportunities and challenges that require strategic response from European research communities. We evaluated fivefuture-oriented clinical and epidemiological research infrastructure frameworks applied to 124 research programmedesigns across 22 European research institutions in Vienna, Stockholm, Rome, Amsterdam, and London developed forimplementation between 2025 and 2030. Frameworks ranged from conventional research programme designs throughintegrated future-adapted research infrastructure incorporating AI-enabled methodology, EHDS compliance, learninghealth system integration, and climate-health capability. Performance was benchmarked using our Future ResearchInfrastructure Effectiveness Score (FRIES), integrating methodological innovation, cross-border capability, patientengagement, climate-health capability, and sustainability. The integrated future-adapted framework achieved the highestFRIES (0.912), with 3.8-fold research productivity advantage and substantial patient engagement improvement overconventional research infrastructure.","url":"https://doi.org/10.5281/zenodo.19614179","authors":["Hugo Jensen","Jonas Dubois","Eva Lindberg"],"tags":["clinical research; epidemiology; future directions; generative AI; European Health Data Space; learning health systems; FRIES; climate health; federated analytics; patient engagement; research infrastructure; methodological innovation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.19614179","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.15397540","name":"Federated Learning Applications in Medical Imaging: A Systematic Review Data Extraction Sheet","source":"datacite","abstract":"Data Extraction Sheet for a Systematic Literature Review on Federated Learning in Medical Imaging (2025) Summary:This dataset presents a comprehensive extraction of peer-reviewed publications used in a Systematic Literature Review (SLR) focusing on the application of Federated Learning (FL) in medical imaging. The review was conducted in accordance with established SLR protocols, aiming to assess the current landscape, technical approaches, challenges, and future directions of FL in medical image analysis. The data extraction sheet includes 254 studies, each carefully analyzed across 25 core attributes. The sheet was designed to facilitate the structured evaluation of research output between approximately 2020 and 2025, capturing a wide range of information from bibliographic details to technical methodologies and study outcomes. 1. Purpose and Scope The primary aim of this extraction was to map and synthesize knowledge from existing literature to understand how Federated Learning is utilized in medical imaging, particularly focusing on: Disease diagnosis and classification Model architectures and aggregation strategies Privacy-preservation techniques Dataset usage and imaging modalities Study limitations, results, and recommendations The extracted data supports meta-analysis, gap identification, and evidence-based recommendations for future research in the field. 2. Structure and Fields in the Dataset The spreadsheet is structured with 26 columns (including one empty column) and 254 rows, each corresponding to an individual research paper. Below is a description of the most relevant fields: Paper ID: Unique numerical identifier for each entry. Citation: Full reference or title for traceability. Author(s) and Authors+Year: Provides author attribution with a standardized citation format (e.g., “Smith et al. (2023)”). Year of Publication: Indicates the temporal trend in research output. Type of Publication: Distinguishes between journal articles, conference proceedings, and others. Publisher: Captures the publishing entity (e.g., Elsevier, IEEE, Springer). Nature of Study: Describes the study type, including experimental studies (ES), theoretical/conceptual studies (TCS), or applied case studies (ACS). Abstract and Paper Title: Provide concise summaries and titles for reference. 3. Federated Learning-Specific Attributes Key technical attributes were extracted to capture the distinct components of FL applications: FL Problem Identified: Specifies the central problem the study addresses within FL (e.g., data heterogeneity, communication overhead, or privacy concerns). Research Approach/Methodology: Describes the design of the study, empirical evaluation, framework development, or simulations. ML Technique/Model/Architecture: Lists models used such as CNNs, ResNet, MobileNet, or novel FL-specific architectures. FL Aggregation Methods Used: Includes approaches like FedAvg, FedProx, or novel aggregation algorithms. (Data missing in 2 of 254 cases.) FL Framework/Tool Used: Indicates toolkits such as TensorFlow Federated, PySyft, Flower, or custom platforms (missing in 18 cases). Privacy-Preserving Technique: Captures strategies such as Differential Privacy (DP), Homomorphic Encryption (HE), or Secure Multi-Party Computation (SMPC). 4. Medical Imaging Domain-Specific Features Each study was analyzed in terms of its medical imaging context: Image Dataset Used: Specifies public or proprietary datasets (e.g., COVIDx, ChestX-ray14, TCGA). Imaging Modalities: Captures image types like X-rays, CT scans, MRIs, WSIs. Disease/Health Domain: Indicates general medical focus areas like pulmonology, oncology, neurology, dermatology. Specific Disease: Lists targeted conditions such as COVID-19, lung cancer, Alzheimer’s, diabetic retinopathy, etc. 5. Study Outcome Fields To synthesize conclusions and study significance, the following were extracted: Pros/Advantages/Strengths: Highlights benefits such as privacy preservation, improved generalizability","url":"https://doi.org/10.5281/zenodo.15397540","authors":["Abayomi-Alli, Adebayo Adewumi","Rocha, Artur","Abayomi-Alli, Olusola Oluwakemi","Aguiar, Ademar"],"tags":["Health","Machine Learning","Deep learning","Federated Learning","Multimodal Imaging"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15397540","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.15397541","name":"Federated Learning Applications in Medical Imaging: A Systematic Review Data Extraction Sheet","source":"datacite","abstract":"Data Extraction Sheet for a Systematic Literature Review on Federated Learning in Medical Imaging (2025) Summary:This dataset presents a comprehensive extraction of peer-reviewed publications used in a Systematic Literature Review (SLR) focusing on the application of Federated Learning (FL) in medical imaging. The review was conducted in accordance with established SLR protocols, aiming to assess the current landscape, technical approaches, challenges, and future directions of FL in medical image analysis. The data extraction sheet includes 254 studies, each carefully analyzed across 25 core attributes. The sheet was designed to facilitate the structured evaluation of research output between approximately 2020 and 2025, capturing a wide range of information from bibliographic details to technical methodologies and study outcomes. 1. Purpose and Scope The primary aim of this extraction was to map and synthesize knowledge from existing literature to understand how Federated Learning is utilized in medical imaging, particularly focusing on: Disease diagnosis and classification Model architectures and aggregation strategies Privacy-preservation techniques Dataset usage and imaging modalities Study limitations, results, and recommendations The extracted data supports meta-analysis, gap identification, and evidence-based recommendations for future research in the field. 2. Structure and Fields in the Dataset The spreadsheet is structured with 26 columns (including one empty column) and 254 rows, each corresponding to an individual research paper. Below is a description of the most relevant fields: Paper ID: Unique numerical identifier for each entry. Citation: Full reference or title for traceability. Author(s) and Authors+Year: Provides author attribution with a standardized citation format (e.g., “Smith et al. (2023)”). Year of Publication: Indicates the temporal trend in research output. Type of Publication: Distinguishes between journal articles, conference proceedings, and others. Publisher: Captures the publishing entity (e.g., Elsevier, IEEE, Springer). Nature of Study: Describes the study type, including experimental studies (ES), theoretical/conceptual studies (TCS), or applied case studies (ACS). Abstract and Paper Title: Provide concise summaries and titles for reference. 3. Federated Learning-Specific Attributes Key technical attributes were extracted to capture the distinct components of FL applications: FL Problem Identified: Specifies the central problem the study addresses within FL (e.g., data heterogeneity, communication overhead, or privacy concerns). Research Approach/Methodology: Describes the design of the study, empirical evaluation, framework development, or simulations. ML Technique/Model/Architecture: Lists models used such as CNNs, ResNet, MobileNet, or novel FL-specific architectures. FL Aggregation Methods Used: Includes approaches like FedAvg, FedProx, or novel aggregation algorithms. (Data missing in 2 of 254 cases.) FL Framework/Tool Used: Indicates toolkits such as TensorFlow Federated, PySyft, Flower, or custom platforms (missing in 18 cases). Privacy-Preserving Technique: Captures strategies such as Differential Privacy (DP), Homomorphic Encryption (HE), or Secure Multi-Party Computation (SMPC). 4. Medical Imaging Domain-Specific Features Each study was analyzed in terms of its medical imaging context: Image Dataset Used: Specifies public or proprietary datasets (e.g., COVIDx, ChestX-ray14, TCGA). Imaging Modalities: Captures image types like X-rays, CT scans, MRIs, WSIs. Disease/Health Domain: Indicates general medical focus areas like pulmonology, oncology, neurology, dermatology. Specific Disease: Lists targeted conditions such as COVID-19, lung cancer, Alzheimer’s, diabetic retinopathy, etc. 5. Study Outcome Fields To synthesize conclusions and study significance, the following were extracted: Pros/Advantages/Strengths: Highlights benefits such as privacy preservation, improved generalizability","url":"https://doi.org/10.5281/zenodo.15397541","authors":["Abayomi-Alli, Adebayo Adewumi","Rocha, Artur","Abayomi-Alli, Olusola Oluwakemi","Aguiar, Ademar"],"tags":["Health","Machine Learning","Deep learning","Federated Learning","Multimodal Imaging"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15397541","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.20353808","name":"AI LEADING FIELDS IN CYBERSECURITY","source":"datacite","abstract":"Artificial Intelligence (AI) has become one of the most transformative technologies in modern cybersecurity. The rapid increase in cyber threats, ransomware attacks, phishing campaigns, and zero-day vulnerabilities has accelerated the integration of AI-driven solutions into security infrastructures. AI technologies such as Machine Learning (ML), Deep Learning (DL), Federated Learning (FL), and Generative AI are increasingly used in intrusion detection systems, malware analysis, behavioral analytics, automated incident response, and threat intelligence. This article analyzes the leading fields where AI significantly contributes to cybersecurity, compares major AI methods, and evaluates their advantages and limitations. Recent studies published between 2020 and 2025 are reviewed to identify emerging trends and future directions. Statistical evidence demonstrates that AI-based cybersecurity systems improve threat detection accuracy, reduce response time, and enhance proactive defense capabilities. However, challenges such as adversarial attacks, privacy concerns, explainability, and model bias remain critical research issues.","url":"https://doi.org/10.5281/zenodo.20353808","authors":["Bozorov, Suhrobjon"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20353808","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.20353809","name":"AI LEADING FIELDS IN CYBERSECURITY","source":"datacite","abstract":"Artificial Intelligence (AI) has become one of the most transformative technologies in modern cybersecurity. The rapid increase in cyber threats, ransomware attacks, phishing campaigns, and zero-day vulnerabilities has accelerated the integration of AI-driven solutions into security infrastructures. AI technologies such as Machine Learning (ML), Deep Learning (DL), Federated Learning (FL), and Generative AI are increasingly used in intrusion detection systems, malware analysis, behavioral analytics, automated incident response, and threat intelligence. This article analyzes the leading fields where AI significantly contributes to cybersecurity, compares major AI methods, and evaluates their advantages and limitations. Recent studies published between 2020 and 2025 are reviewed to identify emerging trends and future directions. Statistical evidence demonstrates that AI-based cybersecurity systems improve threat detection accuracy, reduce response time, and enhance proactive defense capabilities. However, challenges such as adversarial attacks, privacy concerns, explainability, and model bias remain critical research issues.","url":"https://doi.org/10.5281/zenodo.20353809","authors":["Bozorov, Suhrobjon"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20353809","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.20172592","name":"Multi-Layered Modelling Knowledge Graph and Response Derivative Framework for Autonomous Buildings, Energy Communities, and Positive Energy Districts","source":"datacite","abstract":"This record provides the Multi-Layered Modelling Knowledge Graph (MLM KG), a machine-readable, semantically structured knowledge artefact that formally encodes the MLM framework, the Response Derivative (RD) concept, and associated regulatory compliance mappings developed by the Energy Informatics Group (EIG) at Tyndall National Institute, University College Cork. The MLM KG is the canonical implementation reference for the multi-layer modelling framework introduced in: O'Regan B., Tahir F., Mould K., O'Leidhin E. (2026). Towards Autonomous Buildings, Communities and Positive Energy Districts: Multi-Layer Modeling and Edge-Enabled Islanding for the Energy Transition. International Energy and Environment Building Science Conference (IEECB&SC'26). O'Regan B. (2025). From Flexibility to Trading: Optimizing Electricity & Heat in O-CEI Pilot 1 — Demonstrating Symbiotic Buildings and Market-Ready Flexibility Services. IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe 2025), Valletta, Malta, October 20–23, 2025. Scientific Context Modern electricity grids face increasing volatility from renewable intermittency, climate-driven disruptions, and the electrification of heat and transport. The Aran Islands, Ireland, a pilot site of the O-CEI (Open Cloud-Edge-IoT) project, experienced extended power outages during Storm Éowyn (January 2025, 184 km/h gusts at Mace Head), a real-world demonstration of the vulnerability of isolated communities to grid disruption and the urgent need for resilient, locally autonomous energy systems. The MLM framework addresses this by enabling buildings and communities to shift from passive consumers to active, intelligent energy agents, capable of self-optimisation, peer-to-peer energy trading, and autonomous islanded operation during grid disturbances. The Multi-Layer Modelling Framework The MLM framework integrates four complementary computational layers into a unified hierarchical architecture: Deterministic Layer enforces physical and operational feasibility through thermodynamic models (RC thermal networks, NTU-ε heat exchangers, COP curves), electrical constraints (Kirchhoff's laws, voltage and frequency limits), comfort constraints (temperature 19–24°C, CO₂ < 1000 ppm), and safety limits (battery SoC 20–95%). Stochastic Layer quantifies uncertainty from renewable variability, occupant behaviour, and market volatility using weather ensembles, Markov chain occupancy models, ARIMA price forecasting, and Monte Carlo simulation. This layer generates the probability distributions and scenario sets required for robust decision-making. AI/ML Layer enhances predictive performance and computational efficiency through LSTM networks for short-term load and generation forecasting, neural network surrogate models replacing computationally expensive physical simulations at 100–1000× speedup, clustering algorithms for pattern identification, and federated learning for privacy-preserving cross-site model improvement. Reinforcement Learning Layer introduces adaptivity through Q-learning, DQN, and Actor-Critic methods. The RL agent optimises control policies, load shifting, storage dispatch, heat pump scheduling, islanding decisions, within the feasibility bounds established by the deterministic layer, informed by stochastic uncertainty quantification and AI-enhanced predictions. These four layers are deployed across a distributed TinyML–edge–cloud architecture: far-edge building devices (Jetson Nano running PARA//EL) for ultra-low latency deterministic control; community edge nodes (running EdgeWare) for 15-minute optimisation cycles and P2P trading settlement; and the FLEXUS cloud platform for regional coordination, federated learning, and market integration. Response Derivative: Novel Theoretical Contribution A key original contribution of this work is the Response Derivative (RD), introduced by Brian O'Regan as a novel metric for quantifying system responsiveness in distributed, probabilistic ","url":"https://doi.org/10.5281/zenodo.20172592","authors":["O Regan, Brian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20172592","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.20172593","name":"Multi-Layered Modelling Knowledge Graph and Response Derivative Framework for Autonomous Buildings, Energy Communities, and Positive Energy Districts","source":"datacite","abstract":"This record provides the Multi-Layered Modelling Knowledge Graph (MLM KG), a machine-readable, semantically structured knowledge artefact that formally encodes the MLM framework, the Response Derivative (RD) concept, and associated regulatory compliance mappings developed by the Energy Informatics Group (EIG) at Tyndall National Institute, University College Cork. The MLM KG is the canonical implementation reference for the multi-layer modelling framework introduced in: O'Regan B., Tahir F., Mould K., O'Leidhin E. (2026). Towards Autonomous Buildings, Communities and Positive Energy Districts: Multi-Layer Modeling and Edge-Enabled Islanding for the Energy Transition. International Energy and Environment Building Science Conference (IEECB&SC'26). O'Regan B. (2025). From Flexibility to Trading: Optimizing Electricity & Heat in O-CEI Pilot 1 — Demonstrating Symbiotic Buildings and Market-Ready Flexibility Services. IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe 2025), Valletta, Malta, October 20–23, 2025. Scientific Context Modern electricity grids face increasing volatility from renewable intermittency, climate-driven disruptions, and the electrification of heat and transport. The Aran Islands, Ireland, a pilot site of the O-CEI (Open Cloud-Edge-IoT) project, experienced extended power outages during Storm Éowyn (January 2025, 184 km/h gusts at Mace Head), a real-world demonstration of the vulnerability of isolated communities to grid disruption and the urgent need for resilient, locally autonomous energy systems. The MLM framework addresses this by enabling buildings and communities to shift from passive consumers to active, intelligent energy agents, capable of self-optimisation, peer-to-peer energy trading, and autonomous islanded operation during grid disturbances. The Multi-Layer Modelling Framework The MLM framework integrates four complementary computational layers into a unified hierarchical architecture: Deterministic Layer enforces physical and operational feasibility through thermodynamic models (RC thermal networks, NTU-ε heat exchangers, COP curves), electrical constraints (Kirchhoff's laws, voltage and frequency limits), comfort constraints (temperature 19–24°C, CO₂ < 1000 ppm), and safety limits (battery SoC 20–95%). Stochastic Layer quantifies uncertainty from renewable variability, occupant behaviour, and market volatility using weather ensembles, Markov chain occupancy models, ARIMA price forecasting, and Monte Carlo simulation. This layer generates the probability distributions and scenario sets required for robust decision-making. AI/ML Layer enhances predictive performance and computational efficiency through LSTM networks for short-term load and generation forecasting, neural network surrogate models replacing computationally expensive physical simulations at 100–1000× speedup, clustering algorithms for pattern identification, and federated learning for privacy-preserving cross-site model improvement. Reinforcement Learning Layer introduces adaptivity through Q-learning, DQN, and Actor-Critic methods. The RL agent optimises control policies, load shifting, storage dispatch, heat pump scheduling, islanding decisions, within the feasibility bounds established by the deterministic layer, informed by stochastic uncertainty quantification and AI-enhanced predictions. These four layers are deployed across a distributed TinyML–edge–cloud architecture: far-edge building devices (Jetson Nano running PARA//EL) for ultra-low latency deterministic control; community edge nodes (running EdgeWare) for 15-minute optimisation cycles and P2P trading settlement; and the FLEXUS cloud platform for regional coordination, federated learning, and market integration. Response Derivative: Novel Theoretical Contribution A key original contribution of this work is the Response Derivative (RD), introduced by Brian O'Regan as a novel metric for quantifying system responsiveness in distributed, probabilistic ","url":"https://doi.org/10.5281/zenodo.20172593","authors":["O Regan, Brian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20172593","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.20138488","name":"Comparative Review of Offline First AI Architectures for Smart Systems in Low Connectivity and Disaster Prone Environments","source":"datacite","abstract":"Smart systems that rely on the cloud become unusable when the network goes down in rural and disaster vulnerable settings, thus interrupting the monitoring system as well as slowing the timely responding to emergencies. This work sought to find offline-first AI systems that can be reliable and power efficient (when the network is unavailable or unreliable). Technology or Method: A systematic review of peer-reviewed articles in the period 2015-2025 compared three big architectures, namely TinyML on microcontrollers, Edge AI on single board computers, and federated learning frameworks. The comparisons were on inference latency, energy use, computational ability, reliability and cost of deployment in the application of agriculture, disaster observation and rural health care. Conclusions: TinyML showed milliwatt operation which allowed constant battery, even at the lowest levels, means of operation, but due to the model complexity, there was a limitation. Edge AI platforms were offering less computation time inference through tasks that demanded more computation (real time object detection) but with high energy consumption. Federated learning was effective in enhancing privacy of data, and adversarial distributed model refinement without the transmission of raw data, but its performance was limited in situations of long time disconnection because of its reliance on model updates, which takes place on distributed nodes, and synchronizes only in the case of connectivity, which is intermittently available. This paper seeks to do a comparative review of these offline first AI-based systems, assessing how suitably each one can be completed to fit the smart systems themselves that are in low connectivity and disaster prone environments.","url":"https://doi.org/10.5281/zenodo.20138488","authors":["John Rodge C.  Sarait","Leen Jandy P.  Mencias","Leander Rafael C.  Espela","Jay Ar P.  Esparcia"],"tags":["Offline First AI Architectures","Smart Systems","Low Connectivity","Disaster Prone Environment"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20138488","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20138489","name":"Comparative Review of Offline First AI Architectures for Smart Systems in Low Connectivity and Disaster Prone Environments","source":"datacite","abstract":"Smart systems that rely on the cloud become unusable when the network goes down in rural and disaster vulnerable settings, thus interrupting the monitoring system as well as slowing the timely responding to emergencies. This work sought to find offline-first AI systems that can be reliable and power efficient (when the network is unavailable or unreliable). Technology or Method: A systematic review of peer-reviewed articles in the period 2015-2025 compared three big architectures, namely TinyML on microcontrollers, Edge AI on single board computers, and federated learning frameworks. The comparisons were on inference latency, energy use, computational ability, reliability and cost of deployment in the application of agriculture, disaster observation and rural health care. Conclusions: TinyML showed milliwatt operation which allowed constant battery, even at the lowest levels, means of operation, but due to the model complexity, there was a limitation. Edge AI platforms were offering less computation time inference through tasks that demanded more computation (real time object detection) but with high energy consumption. Federated learning was effective in enhancing privacy of data, and adversarial distributed model refinement without the transmission of raw data, but its performance was limited in situations of long time disconnection because of its reliance on model updates, which takes place on distributed nodes, and synchronizes only in the case of connectivity, which is intermittently available. This paper seeks to do a comparative review of these offline first AI-based systems, assessing how suitably each one can be completed to fit the smart systems themselves that are in low connectivity and disaster prone environments.","url":"https://doi.org/10.5281/zenodo.20138489","authors":["John Rodge C.  Sarait","Leen Jandy P.  Mencias","Leander Rafael C.  Espela","Jay Ar P.  Esparcia"],"tags":["Offline First AI Architectures","Smart Systems","Low Connectivity","Disaster Prone Environment"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20138489","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21260159","name":"Artificial Intelligence-Enhanced Pharmacovigilance for Diethylene Glycol Contamination in Paediatric Medicines: A Systematic Narrative Review and Proposed Computational Framework for Real-Time Outbreak Detection and Supply Chain Surveillance","source":"datacite","abstract":"Diethylene glycol (DEG) contamination of paediatric oral liquid medicines constitutes a recurrent, preventable global public health crisis. Since 2022 alone, WHO-confirmed outbreaks across The Gambia, Uzbekistan, Indonesia, and India have claimed the lives of more than 300 children. Despite over eight decades of documented incidents— beginning with the 1937 Elixir Sulfanilamide disaster—conventional pharmacovigilance systems have repeatedly failed to detect, contain, or prevent these outbreaks with sufficient rapidity to avert mass mortality. Artificial intelligence (AI) and machine learning (ML) offer transformative potential for real-time signal detection, supply chain anomaly identification, and predictive risk stratification in pharmaceutical quality surveillance.Objectives: To: (i) synthesise the global epidemiological, clinical, and toxicological evidence on DEG poisoning across twelve major outbreak events spanning 1937–2025; (ii) characterise the systemic pharmacovigilance and regulatory failures that have enabled repeated tragedies; and (iii) propose a validated, five-component AI-driven computational framework specifically architected to address identified failure domains.Methods: We conducted a pre-specified systematic narrative review of peer-reviewed literature, WHO Medical Product Alerts, regulatory communications, epidemiological field reports, and grey literature pertaining to DEG and ethylene glycol (EG) pharmaceutical contamination from 1937 to June 2025. Search strategies were applied across PubMed/MEDLINE, Scopus, WHO IRIS, Cochrane Library, and Google Scholar using MeSH-mapped terms. Supplementary AI/ML pharmacovigilance literature was systematically searched to inform framework design. Data were extracted across standardised domains: outbreak epidemiology, pathophysiological mechanisms, clinical presentation, management outcomes, regulatory responses, and identified failure points.Results: Twelve major DEG outbreak events were identified across ten countries, accounting for over 800 documented deaths, with children under five years disproportionately represented. Consistent root causes across all outbreaks encompassed six recurring failure domains: excipient adulteration, over-reliance on unverified Certificates of Analysis (CoAs), absence of mandatory in-house testing, inadequate regulatory inspections, opaque supply chains, and delayed alert and recall systems. Clinically, DEG produces a characteristic biphasic illness progressing to high-anion-gap metabolic acidosis, proximal tubular necrosis, and multiorgan failure; mortality is strongly time-dependent. Early fomepizole and haemodialysis significantly reduce case fatality rates. No outbreak reviewed employed AI-assisted surveillance or supply chain monitoring. The proposed AI framework comprises: (1) NLP-based real-time pharmacovigilance signal mining; (2) Graph Neural Network supply chain traceability; (3) CNN-assisted portable spectroscopic quality screening; (4) Federated Learning for cross-jurisdictional surveillance; and (5) LLM-powered regulatory alert synthesis.Conclusions: DEG poisoning remains a race against death in which current pharmacovigilance systems consistently fail to intervene in time. The proposed AI-driven framework, if deployed within an international regulatory architecture, offers a credible, scalable, and privacy preserving pathway to preventing future paediatric fatalities from preventable pharmaceutical contamination. Immediate investment in digital pharmacovigilance infrastructure—particularly in low- and middle-income countries—is a global health imperative.","url":"https://doi.org/10.5281/zenodo.21260159","authors":["Vijay Kumar Chennamchetty","Ravindra Nallagonda","Raghavendra Rao MV","Daniel Finney Sankuru","Sumedha Sahanasree Dasari"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21260159","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21260160","name":"Artificial Intelligence-Enhanced Pharmacovigilance for Diethylene Glycol Contamination in Paediatric Medicines: A Systematic Narrative Review and Proposed Computational Framework for Real-Time Outbreak Detection and Supply Chain Surveillance","source":"datacite","abstract":"Diethylene glycol (DEG) contamination of paediatric oral liquid medicines constitutes a recurrent, preventable global public health crisis. Since 2022 alone, WHO-confirmed outbreaks across The Gambia, Uzbekistan, Indonesia, and India have claimed the lives of more than 300 children. Despite over eight decades of documented incidents— beginning with the 1937 Elixir Sulfanilamide disaster—conventional pharmacovigilance systems have repeatedly failed to detect, contain, or prevent these outbreaks with sufficient rapidity to avert mass mortality. Artificial intelligence (AI) and machine learning (ML) offer transformative potential for real-time signal detection, supply chain anomaly identification, and predictive risk stratification in pharmaceutical quality surveillance.Objectives: To: (i) synthesise the global epidemiological, clinical, and toxicological evidence on DEG poisoning across twelve major outbreak events spanning 1937–2025; (ii) characterise the systemic pharmacovigilance and regulatory failures that have enabled repeated tragedies; and (iii) propose a validated, five-component AI-driven computational framework specifically architected to address identified failure domains.Methods: We conducted a pre-specified systematic narrative review of peer-reviewed literature, WHO Medical Product Alerts, regulatory communications, epidemiological field reports, and grey literature pertaining to DEG and ethylene glycol (EG) pharmaceutical contamination from 1937 to June 2025. Search strategies were applied across PubMed/MEDLINE, Scopus, WHO IRIS, Cochrane Library, and Google Scholar using MeSH-mapped terms. Supplementary AI/ML pharmacovigilance literature was systematically searched to inform framework design. Data were extracted across standardised domains: outbreak epidemiology, pathophysiological mechanisms, clinical presentation, management outcomes, regulatory responses, and identified failure points.Results: Twelve major DEG outbreak events were identified across ten countries, accounting for over 800 documented deaths, with children under five years disproportionately represented. Consistent root causes across all outbreaks encompassed six recurring failure domains: excipient adulteration, over-reliance on unverified Certificates of Analysis (CoAs), absence of mandatory in-house testing, inadequate regulatory inspections, opaque supply chains, and delayed alert and recall systems. Clinically, DEG produces a characteristic biphasic illness progressing to high-anion-gap metabolic acidosis, proximal tubular necrosis, and multiorgan failure; mortality is strongly time-dependent. Early fomepizole and haemodialysis significantly reduce case fatality rates. No outbreak reviewed employed AI-assisted surveillance or supply chain monitoring. The proposed AI framework comprises: (1) NLP-based real-time pharmacovigilance signal mining; (2) Graph Neural Network supply chain traceability; (3) CNN-assisted portable spectroscopic quality screening; (4) Federated Learning for cross-jurisdictional surveillance; and (5) LLM-powered regulatory alert synthesis.Conclusions: DEG poisoning remains a race against death in which current pharmacovigilance systems consistently fail to intervene in time. The proposed AI-driven framework, if deployed within an international regulatory architecture, offers a credible, scalable, and privacy preserving pathway to preventing future paediatric fatalities from preventable pharmaceutical contamination. Immediate investment in digital pharmacovigilance infrastructure—particularly in low- and middle-income countries—is a global health imperative.","url":"https://doi.org/10.5281/zenodo.21260160","authors":["Vijay Kumar Chennamchetty","Ravindra Nallagonda","Raghavendra Rao MV","Daniel Finney Sankuru","Sumedha Sahanasree Dasari"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21260160","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20149097","name":"Diabetes Network Pharmacology Dataset","source":"datacite","abstract":"The Real-World Diabetes Network Pharmacology Dataset (RDNPD 2025) is a large-scale multimodal healthcare dataset developed for diabetes severity prediction, biomedical graph-learning analysis, and federated healthcare intelligence research. The dataset contains 466,605 patient records collected from five distributed healthcare institutions, including tertiary hospitals, diabetes treatment centers, and remote clinical facilities. The dataset was designed to support research on wearable-assisted healthcare monitoring, explainable artificial intelligence, biomedical interaction modeling, and privacy-preserving federated learning systems. The dataset integrates heterogeneous healthcare information obtained from wearable physiological sensing devices, electronic health records, laboratory investigations, medication histories, and biomedical network pharmacology resources. The included demographic and lifestyle attributes consist of age, gender, body mass index, waist circumference, smoking status, and physical activity level. Physiological and diabetes-related biomarkers include HbA1c, fasting glucose, postprandial glucose, insulin level, HOMA-IR, C-peptide, glycemic variability, blood pressure measurements, heart rate, cholesterol indicators, triglycerides, and cardiovascular risk measurements. The dataset further incorporates organ-damage and complication-related indicators including eGFR, creatinine, albuminuria, liver enzyme measurements, neuropathy score, retinopathy score, foot ulcer risk, and cardiovascular risk index. Medication and treatment-related attributes include metformin utilization, insulin therapy, SGLT2 inhibitor usage, GLP1 receptor agonist usage, medication adherence, treatment duration, and medication count. To support biomedical graph intelligence and network pharmacology research, the dataset additionally contains graph-derived healthcare interaction descriptors including drug--target affinity score, pathway enrichment score, inflammation pathway index, oxidative stress score, cytokine activity score, gene--disease association score, protein--protein interaction degree, graph node degree, clustering coefficient, graph attention centrality, patient--drug edge count, drug--protein edge count, and pathway connectivity measurements. The target label of the dataset is Diabetes_Severity_Level}, which consists of five clinically relevant classes including Healthy/Normal, Prediabetes, Mild Diabetes, Moderate Diabetes, and Severe/Complicated Diabetes.","url":"https://doi.org/10.5281/zenodo.20149097","authors":["TT Prama"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20149097","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.20149098","name":"Diabetes Network Pharmacology Dataset","source":"datacite","abstract":"The Real-World Diabetes Network Pharmacology Dataset (RDNPD 2025) is a large-scale multimodal healthcare dataset developed for diabetes severity prediction, biomedical graph-learning analysis, and federated healthcare intelligence research. The dataset contains 466,605 patient records collected from five distributed healthcare institutions, including tertiary hospitals, diabetes treatment centers, and remote clinical facilities. The dataset was designed to support research on wearable-assisted healthcare monitoring, explainable artificial intelligence, biomedical interaction modeling, and privacy-preserving federated learning systems. The dataset integrates heterogeneous healthcare information obtained from wearable physiological sensing devices, electronic health records, laboratory investigations, medication histories, and biomedical network pharmacology resources. The included demographic and lifestyle attributes consist of age, gender, body mass index, waist circumference, smoking status, and physical activity level. Physiological and diabetes-related biomarkers include HbA1c, fasting glucose, postprandial glucose, insulin level, HOMA-IR, C-peptide, glycemic variability, blood pressure measurements, heart rate, cholesterol indicators, triglycerides, and cardiovascular risk measurements. The dataset further incorporates organ-damage and complication-related indicators including eGFR, creatinine, albuminuria, liver enzyme measurements, neuropathy score, retinopathy score, foot ulcer risk, and cardiovascular risk index. Medication and treatment-related attributes include metformin utilization, insulin therapy, SGLT2 inhibitor usage, GLP1 receptor agonist usage, medication adherence, treatment duration, and medication count. To support biomedical graph intelligence and network pharmacology research, the dataset additionally contains graph-derived healthcare interaction descriptors including drug--target affinity score, pathway enrichment score, inflammation pathway index, oxidative stress score, cytokine activity score, gene--disease association score, protein--protein interaction degree, graph node degree, clustering coefficient, graph attention centrality, patient--drug edge count, drug--protein edge count, and pathway connectivity measurements. The target label of the dataset is Diabetes_Severity_Level}, which consists of five clinically relevant classes including Healthy/Normal, Prediabetes, Mild Diabetes, Moderate Diabetes, and Severe/Complicated Diabetes.","url":"https://doi.org/10.5281/zenodo.20149098","authors":["TT Prama"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20149098","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.20234292","name":"Integration of Artificial Intelligence in Nanoparticle-Based Drug Delivery Systems for Cancer Treatment","source":"datacite","abstract":"Cancer remains one of the foremost global health burdens, claiming approximately 10 million lives annually worldwide. Conventional chemotherapy suffers from non-specific drug distribution, dose-limiting toxicities, and the emergence of multi-drug resistance. Nanoparticle-based drug delivery systems (NDDS) offer a paradigm shift by enabling targeted, stimuli-responsive, and controlled drug release; however, the rational design of such systems demands navigating an astronomically large parameter space. Artificial intelligence (AI)—encompassing machine learning (ML), deep learning (DL), and reinforcement learning (RL)—has emerged as a transformative computational framework capable of analysing high-dimensional biological and physicochemical datasets to accelerate nanoparticle design, optimise therapeutic payloads, predict in vivo pharmacokinetics, and personalise oncological treatments. This article provides a comprehensive, original review of the integration of AI methodologies into nanoparticle-based drug delivery for cancer treatment, covering the full pipeline from computational formulation design and in silico screening to AI-guided clinical decision support and regulatory considerations. A systematic literature analysis was conducted across PubMed, Scopus, Web of Science, and Google Scholar databases using MeSH terms and Boolean operators. Studies published between 2015 and 2025 that combined AI/ML/DL techniques with nanoparticle fabrication, optimisation, or clinical application in oncology were included. Data were synthesised thematically and critically evaluated. AI models—including convolutional neural networks (CNN), random forests, gradient boosting machines, recurrent neural networks (RNN), generative adversarial networks (GAN), and graph neural networks (GNN)—have demonstrated superior predictive accuracy over conventional empirical methods in forecasting nanoparticle size (R² > 0.90), encapsulation efficiency, cellular uptake, and tumour accumulation. AI-enabled digital twins and high-content imaging platforms have reduced the nanoparticle optimisation cycle from months to days. Reinforcement learning algorithms have been applied to adaptive dosing regimens, improving therapeutic indices in preclinical models by 25–45%. Federated learning approaches are enabling multi-institutional data collaboration while preserving patient privacy. Nonetheless, challenges including data heterogeneity, lack of standardised benchmarks, regulatory ambiguity, and translational gaps remain significant. The convergence of AI and nanomedicine represents a transformational frontier in oncology. The field is progressing from descriptive analytics toward generative and prescriptive AI that can design novel nanoplatforms de novo. Interdisciplinary collaboration, curated open-access datasets, and adaptive regulatory frameworks will be pivotal for translating AI-driven nanoparticle therapies into routine clinical practice.","url":"https://doi.org/10.5281/zenodo.20234292","authors":["Antara Ghanta*1, Santhosh P. R.2, Utsavi Vaghela3, G. K. Sarpabhushana4, Gourab Mondal5, Binaya Kumar Sethy6"],"tags":["Artificial intelligence; machine learning; deep learning; nanoparticles; drug delivery; cancer; nanomedicine; targeted therapy; tumour microenvironment; theragnostic; pharmacokinetics; precision oncology"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20234292","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.20234293","name":"Integration of Artificial Intelligence in Nanoparticle-Based Drug Delivery Systems for Cancer Treatment","source":"datacite","abstract":"Cancer remains one of the foremost global health burdens, claiming approximately 10 million lives annually worldwide. Conventional chemotherapy suffers from non-specific drug distribution, dose-limiting toxicities, and the emergence of multi-drug resistance. Nanoparticle-based drug delivery systems (NDDS) offer a paradigm shift by enabling targeted, stimuli-responsive, and controlled drug release; however, the rational design of such systems demands navigating an astronomically large parameter space. Artificial intelligence (AI)—encompassing machine learning (ML), deep learning (DL), and reinforcement learning (RL)—has emerged as a transformative computational framework capable of analysing high-dimensional biological and physicochemical datasets to accelerate nanoparticle design, optimise therapeutic payloads, predict in vivo pharmacokinetics, and personalise oncological treatments. This article provides a comprehensive, original review of the integration of AI methodologies into nanoparticle-based drug delivery for cancer treatment, covering the full pipeline from computational formulation design and in silico screening to AI-guided clinical decision support and regulatory considerations. A systematic literature analysis was conducted across PubMed, Scopus, Web of Science, and Google Scholar databases using MeSH terms and Boolean operators. Studies published between 2015 and 2025 that combined AI/ML/DL techniques with nanoparticle fabrication, optimisation, or clinical application in oncology were included. Data were synthesised thematically and critically evaluated. AI models—including convolutional neural networks (CNN), random forests, gradient boosting machines, recurrent neural networks (RNN), generative adversarial networks (GAN), and graph neural networks (GNN)—have demonstrated superior predictive accuracy over conventional empirical methods in forecasting nanoparticle size (R² > 0.90), encapsulation efficiency, cellular uptake, and tumour accumulation. AI-enabled digital twins and high-content imaging platforms have reduced the nanoparticle optimisation cycle from months to days. Reinforcement learning algorithms have been applied to adaptive dosing regimens, improving therapeutic indices in preclinical models by 25–45%. Federated learning approaches are enabling multi-institutional data collaboration while preserving patient privacy. Nonetheless, challenges including data heterogeneity, lack of standardised benchmarks, regulatory ambiguity, and translational gaps remain significant. The convergence of AI and nanomedicine represents a transformational frontier in oncology. The field is progressing from descriptive analytics toward generative and prescriptive AI that can design novel nanoplatforms de novo. Interdisciplinary collaboration, curated open-access datasets, and adaptive regulatory frameworks will be pivotal for translating AI-driven nanoparticle therapies into routine clinical practice.","url":"https://doi.org/10.5281/zenodo.20234293","authors":["Antara Ghanta*1, Santhosh P. R.2, Utsavi Vaghela3, G. K. Sarpabhushana4, Gourab Mondal5, Binaya Kumar Sethy6"],"tags":["Artificial intelligence; machine learning; deep learning; nanoparticles; drug delivery; cancer; nanomedicine; targeted therapy; tumour microenvironment; theragnostic; pharmacokinetics; precision oncology"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20234293","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.20099413","name":"FedOptiVision: Privacy-Preserving Federated Continual Learning for Multimodal Ophthalmic Diagnosis","source":"datacite","abstract":"Official implementation of the manuscript \"FedOptiVision: Privacy-Preserving Federated Continual Learning for Multimodal Ophthalmic Diagnosis\", submitted to The Visual Computer, 2025. This repository contains all code, configurations, and documentation needed to replicate the experiments. Readers are encouraged to cite the associated manuscript.","url":"https://doi.org/10.5281/zenodo.20099413","authors":["Sobiyaa P"],"tags":["federated learning, ophthalmic diagnosis, continual learning, differential privacy, multimodal deep learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20099413","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.20099414","name":"FedOptiVision: Privacy-Preserving Federated Continual Learning for Multimodal Ophthalmic Diagnosis","source":"datacite","abstract":"Official implementation of the manuscript \"FedOptiVision: Privacy-Preserving Federated Continual Learning for Multimodal Ophthalmic Diagnosis\", submitted to The Visual Computer, 2025. This repository contains all code, configurations, and documentation needed to replicate the experiments. Readers are encouraged to cite the associated manuscript.","url":"https://doi.org/10.5281/zenodo.20099414","authors":["Sobiyaa P"],"tags":["federated learning, ophthalmic diagnosis, continual learning, differential privacy, multimodal deep learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20099414","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.20619417","name":"BioMutFed+: Reproducibility Artifacts (TMC-2025-09-2616)","source":"datacite","abstract":"Reproducibility artifacts for \"BioMutFed+: Mutation-Driven Federated Learning for IIoT,\" IEEE Transactions on Mobile Computing (TMC-2025-09-2616). Two Jupyter notebooks reproduce the ten-seed Wisconsin Breast Cancer evaluation (Tables VI and VII) and the lambda sensitivity sweep within the full BioMutFed+ pipeline (Supplementary Section SIII-F, Table S2). All experiments use Dirichlet alpha=0.1 partitioning across 20 clients with 20% gradient-ascent adversarial clients. Each notebook is self-contained and produces the exact numerical values reported in the paper. See README.md for environment details and runtime.","url":"https://doi.org/10.5281/zenodo.20619417","authors":["Tallat, Raiha","Wang, Xingfu","Hawbani, Ammar","Xiaohua, Xu","Miao, Fuyou","Zhao, Liang","Wang, Jiantao"],"tags":["federated learning","IIoT","Byzantine robustness","mutation-based optimization","reproducibility","BioMutFed+"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20619417","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.20619418","name":"BioMutFed+: Reproducibility Artifacts (TMC-2025-09-2616)","source":"datacite","abstract":"Reproducibility artifacts for \"BioMutFed+: Mutation-Driven Federated Learning for IIoT,\" IEEE Transactions on Mobile Computing (TMC-2025-09-2616). Two Jupyter notebooks reproduce the ten-seed Wisconsin Breast Cancer evaluation (Tables VI and VII) and the lambda sensitivity sweep within the full BioMutFed+ pipeline (Supplementary Section SIII-F, Table S2). All experiments use Dirichlet alpha=0.1 partitioning across 20 clients with 20% gradient-ascent adversarial clients. Each notebook is self-contained and produces the exact numerical values reported in the paper. See README.md for environment details and runtime.","url":"https://doi.org/10.5281/zenodo.20619418","authors":["Tallat, Raiha","Wang, Xingfu","Hawbani, Ammar","Xiaohua, Xu","Miao, Fuyou","Zhao, Liang","Wang, Jiantao"],"tags":["federated learning","IIoT","Byzantine robustness","mutation-based optimization","reproducibility","BioMutFed+"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20619418","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.48550/arxiv.2608.08906","name":"Federated Attention Autoencoders with a Stochastic Aggregation Scheme for Anomaly Detection","source":"datacite","abstract":"Outlier detection in decentralized data environments is a challenging task for many machine learning implementations, particularly in settings where data cannot be shared. Recently, there have been advances in federated outlier detection, some of which are based on the use of autoencoder networks. The introduction of attention mechanisms to autoencoders boosts their efficiency. However, the application of attention-based models in federated learning remains underdeveloped due to the absence of proper aggregation functions for these types of networks. In our work, we propose two novel aggregation functions tailored for attention-based autoencoders, which better preserve the learned information stored within the memory modules of these networks. We evaluated our approach on the KDDCUP10 dataset, and we showed that the proposed methods achieve up to 2.9\\% and 5.1\\% better results for F1 score and AUC ROC respectively when compared to traditional autoencoders.","url":"https://doi.org/10.48550/arxiv.2608.08906","authors":["Ilić, Mihailo","Savić, Miloš","Kurbalija, Vladimir","Ivanović, Mirjana","Fortino, Giancarlo","Jakovetić, Dušan"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.08906","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21881104","name":"Beyond $10-a-Day: A Policy Audit of Canada-Wide Early Learning and Childcare","source":"datacite","abstract":"Canada-Wide Early Learning and Child Care (CWELCC) is one of the most consequential social-policy achievements in recent Canadian history, delivering a rapid and measurable reduction in the cost of regulated childcare for millions of families. This policy audit finds that CWELCC's affordability results, a national childcare price decline exceeding 31 percent between 2021 and 2025 against a rising all-items index, are substantive rather than symbolic, and are already visible in stronger parental labour-force participation and household financial security. The pace of space expansion has understandably lagged the pace of demand growth that this very success generated, a sequencing dynamic rather than a design failure, and one that is common to nearly every jurisdiction that has attempted rapid childcare reform. Drawing on stakeholder, PESTEL, impact, and comparative international analyses, the article situates CWELCC's early results against Australia's subsidy-based model and Germany's legal-entitlement model and finds that Canada's federated, transfer-based approach compares favourably in the speed and scale of affordability delivery, even as all three systems continue working to match supply with demand. The article proposes four complementary reforms, a national delivery compact, a protected workforce-stability reserve, a needs-based access and equity index, and a common performance system with corrective funding triggers, each designed to build on CWELCC's existing legal and intergovernmental architecture rather than to redesign it. These recommendations aim to consolidate and extend the policy's substantial early gains so that the historic affordability breakthrough already achieved is matched, over time, by equally strong operational access for every Canadian family.","url":"https://doi.org/10.5281/zenodo.21881104","authors":["Butt, Samama","Pirouzi, Peivand"],"tags":["early learning and childcare, CWELCC, childcare access, early childhood workforce, social policy, policy achievement"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21881104","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21881105","name":"Beyond $10-a-Day: A Policy Audit of Canada-Wide Early Learning and Childcare","source":"datacite","abstract":"Canada-Wide Early Learning and Child Care (CWELCC) is one of the most consequential social-policy achievements in recent Canadian history, delivering a rapid and measurable reduction in the cost of regulated childcare for millions of families. This policy audit finds that CWELCC's affordability results, a national childcare price decline exceeding 31 percent between 2021 and 2025 against a rising all-items index, are substantive rather than symbolic, and are already visible in stronger parental labour-force participation and household financial security. The pace of space expansion has understandably lagged the pace of demand growth that this very success generated, a sequencing dynamic rather than a design failure, and one that is common to nearly every jurisdiction that has attempted rapid childcare reform. Drawing on stakeholder, PESTEL, impact, and comparative international analyses, the article situates CWELCC's early results against Australia's subsidy-based model and Germany's legal-entitlement model and finds that Canada's federated, transfer-based approach compares favourably in the speed and scale of affordability delivery, even as all three systems continue working to match supply with demand. The article proposes four complementary reforms, a national delivery compact, a protected workforce-stability reserve, a needs-based access and equity index, and a common performance system with corrective funding triggers, each designed to build on CWELCC's existing legal and intergovernmental architecture rather than to redesign it. These recommendations aim to consolidate and extend the policy's substantial early gains so that the historic affordability breakthrough already achieved is matched, over time, by equally strong operational access for every Canadian family.","url":"https://doi.org/10.5281/zenodo.21881105","authors":["Butt, Samama","Pirouzi, Peivand"],"tags":["early learning and childcare, CWELCC, childcare access, early childhood workforce, social policy, policy achievement"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21881105","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.34734/fzj-2026-03473","name":"When to Harmonize? Evaluating Stage-Specific Harmonization in Federated Brain Age Estimation","source":"datacite","abstract":"Federated learning (FL) is a promising solution for healthcare Artificial Intelligence (AI), striking a balance between patient privacy and the need for diverse datasets. FL enables collaborative model training across institutions, preserving confidentiality and advancing clinical tasks such as diagnosis and treatment planning. However, a key challenge in this setting is the inherent heterogeneity of medical datasets acquired in different institutions, which can undermine the generalizability and performance of the model. This issue is particularly pronounced in neuroimaging applications, such as Magnetic resonance imaging (MRI), where site-specific biases arise from variations in scanner hardware, acquisition protocols, and preprocessing pipelines. These differences introduce non-biological variability that can jeopardize downstream analyses and model training. To remove the effect of sites, harmonization techniques are essential tools to improve robustness and reliability. Harmonization techniques are usually applied at the feature level; however, given the limited access to the data possessed by FL schemes, feature-level harmonization may not be enough to remove site effects. In this work, we propose two complementary harmonization strategies within the FL framework: (1) the traditional feature harmonization, by applying ComBat to directly correct the MRI-derived features; and (2) gradient harmonization, which aligns local model updates, particularly the gradients of fully connected layers, across sites to mitigate inter-site distributional shifts before global aggregation. Together, these approaches aim to improve cross-site consistency and improve the model's overall performance in federated medical imaging tasks.","url":"https://doi.org/10.34734/fzj-2026-03473","authors":["Halder, Tanurima","Deo, Kunal","Nieto, Nicolás","Patil, Kaustubh R.","Jadhav, Kshitij"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.34734/fzj-2026-03473","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.48550/arxiv.2602.08290","name":"Trust-Based Incentive Mechanisms in Semi-Decentralized Federated Learning Systems","source":"datacite","abstract":"In federated learning (FL), decentralized model training allows multi-ple participants to collaboratively improve a shared machine learning model without exchanging raw data. However, ensuring the integrity and reliability of the system is challenging due to the presence of potentially malicious or faulty nodes that can degrade the model's performance. This paper proposes a novel trust-based incentive mechanism designed to evaluate and reward the quality of contributions in FL systems. By dynamically assessing trust scores based on fac-tors such as data quality, model accuracy, consistency, and contribution fre-quency, the system encourages honest participation and penalizes unreliable or malicious behavior. These trust scores form the basis of an incentive mechanism that rewards high-trust nodes with greater participation opportunities and penal-ties for low-trust participants. We further explore the integration of blockchain technology and smart contracts to automate the trust evaluation and incentive distribution processes, ensuring transparency and decentralization. Our proposed theoretical framework aims to create a more robust, fair, and transparent FL eco-system, reducing the risks posed by untrustworthy participants.","url":"https://doi.org/10.48550/arxiv.2602.08290","authors":["Shrestha, Ajay Kumar"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Emerging Technologies (cs.ET)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.08290","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.48550/arxiv.2608.05386","name":"Sparse Principal Component Analysis via Wavelets for Distributed Data","source":"datacite","abstract":"The large volume of data and concerns about data privacy have motivated the development of techniques for distributed data, a problem also known as federated learning. In this scenario, sub-samples of the data are divided across different machines, and statistics must be computed over that data without direct access to the full sample. Johnstone &amp; Lu (2009, JASA) show that principal component analysis (PCA) is statistically inconsistent in the high-dimensional regime, and propose a way to recover consistency through wavelet-based sparsification and variable selection. Fan et al. (2019, AoS) show a way to perform this same estimation -- specifically, to estimate the eigenspace that would be obtained if all the data were pooled together, even though it remains effectively distributed -- without addressing the high-dimensional regime. This work incorporates the wavelet-based sparsification of Johnstone &amp; Lu (2009) into the distributed PCA framework of Fan et al. (2019), aiming to reduce communication cost without compromising the quality of the eigenspace estimation. Simulations across $d \\in [52, 5000]$ show that the proposed method overtakes Fan et al. (2019) in estimation error beyond a clear dimensional threshold ($d \\geq 152$ for $λ=25$, $d \\geq 252$ for $λ=50$), while transmitting systematically fewer coefficients throughout the entire range studied. This study was financed by the Sao Paulo Research Foundation (FAPESP), Brazil. Process Number #2023/02538-0 and Number #2025/21250-2.","url":"https://doi.org/10.48550/arxiv.2608.05386","authors":["Herrero, Giovanni Barbosa","Fonseca, Rodney Vasconcelos","Pinheiro, Aluísio"],"tags":["Methodology (stat.ME)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.05386","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.26187/deakin.33158975","name":"Nuclei segmentation and classification from histopathology images using federated learning for end-edge platform (vol 20, e0322749, 2025)","source":"datacite","abstract":"[This corrects the article DOI: 10.1371/journal.pone.0322749.].","url":"https://doi.org/10.26187/deakin.33158975","authors":["AA Chowdhury","SMH Mahmud","MD PALASH UDDIN","S Kadry","J-Y Kim","Y Nam"],"tags":["Physical sciences","Synchrotrons and accelerators","Information and computing sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.26187/deakin.33158975","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.19434964","name":"Creaboost: A Blockchain-Enabled Federated Learning Platform for Privacy-Preserving Ad Preference Insights and Creator Rewards","source":"datacite","abstract":"Privacy preserving ad targeting is a major challenge in today's digital environment. It is such an environment where the centralized platforms expose user data and provide creators with low lying compensation. Although current federated learning (FL) models help reduce data leakage but do not have on chain reward systems. Blockchain based reward systems often face issues with slow speeds and high costs on public networks like Ethereum. This paper introduces Creaboost, a decentralized application on the XDC Network. It combines FL with smart contract rewards to allow secure, low-lying cost, and fair ad preference collection to its users. Here the users are going to submit their engagement metrics locally. After five unparalleled submissions, we aggregate model parameters off chain using federated learning. The resulting preference (0 or 1) then triggers an on-chain reward (1 or 2 ether) through a user signed transaction. Creaboost extends through five layers: Client, Frontend (HTML/JS), Backend (Flask), FL Server (PyTorch), and Blockchain. This setup guarantees that the raw data exposure leaves the Ethereum device. We ensure security with SSL/TLS, reCAPTCHA v2, email verification, and XDC signing. Creaboost exceeds both centralized and XDC alternatives by minimizing data exposure, execution costs, and XDC creating a scalable framework for transaction advertising.","url":"https://doi.org/10.5281/zenodo.19434964","authors":["Shaw, Ritesh Kumar","Ahmed, Safiya","Sinha, Stuti","Chatterjee, Subhangi"],"tags":["Blockchain incentives, Federated learning, Low-latency aggregation, Privacy-preserving advertising, Smart contract rewards, XDC Network"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19434964","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.19434963","name":"Creaboost: A Blockchain-Enabled Federated Learning Platform for Privacy-Preserving Ad Preference Insights and Creator Rewards","source":"datacite","abstract":"Privacy preserving ad targeting is a major challenge in today's digital environment. It is such an environment where the centralized platforms expose user data and provide creators with low lying compensation. Although current federated learning (FL) models help reduce data leakage but do not have on chain reward systems. Blockchain based reward systems often face issues with slow speeds and high costs on public networks like Ethereum. This paper introduces Creaboost, a decentralized application on the XDC Network. It combines FL with smart contract rewards to allow secure, low-lying cost, and fair ad preference collection to its users. Here the users are going to submit their engagement metrics locally. After five unparalleled submissions, we aggregate model parameters off chain using federated learning. The resulting preference (0 or 1) then triggers an on-chain reward (1 or 2 ether) through a user signed transaction. Creaboost extends through five layers: Client, Frontend (HTML/JS), Backend (Flask), FL Server (PyTorch), and Blockchain. This setup guarantees that the raw data exposure leaves the Ethereum device. We ensure security with SSL/TLS, reCAPTCHA v2, email verification, and XDC signing. Creaboost exceeds both centralized and XDC alternatives by minimizing data exposure, execution costs, and XDC creating a scalable framework for transaction advertising.","url":"https://doi.org/10.5281/zenodo.19434963","authors":["Shaw, Ritesh Kumar","Ahmed, Safiya","Sinha, Stuti","Chatterjee, Subhangi"],"tags":["Blockchain incentives, Federated learning, Low-latency aggregation, Privacy-preserving advertising, Smart contract rewards, XDC Network"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19434963","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.19674475","name":"Interdisciplinary Journal of Computing & AI Vol 1, No 1, April 2026","source":"datacite","abstract":"I have the honor of holding the position of Editor-in-Chief for the Interdisciplinary Journal of Computing & AI (IJCAI). It is with great pride and enthusiasm that I introduce the first issue of the Interdisciplinary Journal of Computing & AI (IJCAI). This inaugural edition represents our vision of creating a rigorous, inclusive, and forward-looking platform for cutting-edge research at the intersection of computing and artificial intelligence. The articles featured in this issue have undergone a careful peer-review process and reflect both depth and diversity in research. We are honored to include contributions from esteemed institutions across the United States, Sweden, and India, demonstrating the global relevance of the themes we aim to promote. A key highlight of this issue is that all published papers are indexed in OpenAIRE, reinforcing our commitment to open-access dissemination, research transparency, and international discoverability. This milestone significantly enhances the academic reach and credibility of the work published in IJCAI. The acceptance rate for this issue is 35%. The research spans critical and emerging domains, including: · A clinically significant study on predicting 28-day readmission risks in heart failure patients, contributing to data-driven healthcare. · A comparative and optimization-focused analysis of GPU power management using Python and MATLAB simulations. · An innovative AI-based virtual try-on system enhanced with biometric security for design and user personalization. · A novel blockchain-enabled federated learning platform enabling privacy-preserving ad preference modelling and decentralized creator reward distribution. · A novel integration of computational fluid dynamics with post-quantum secure control in cyber-physical systems. · Artificial intelligence in the governance of rural water systems and investigating the challenges, opportunities and sustainability in India. · Advancements in privacy-preserving online communication through hybrid cryptographic frameworks. Each paper embodies originality, methodological soundness, and relevance to current technological challenges. As Editor-in-Chief, I would like to express my gratitude to the authors for their valuable contributions, the reviewers for their insightful feedback, and the editorial board for their unwavering support in maintaining the quality and integrity of this publication. We are committed to continuously improving the journal’s standards and expanding its reach through recognized indexing platforms and global collaborations. We invite researchers and practitioners to contribute to future issues and be part of this growing academic community. Mohuya Chakraborty Editor-in-Chief IJCAI","url":"https://doi.org/10.5281/zenodo.19674475","authors":["Chakraborty, Dr. Mohuya"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19674475","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.19674476","name":"Interdisciplinary Journal of Computing & AI Vol 1, No 1, April 2026","source":"datacite","abstract":"I have the honor of holding the position of Editor-in-Chief for the Interdisciplinary Journal of Computing & AI (IJCAI). It is with great pride and enthusiasm that I introduce the first issue of the Interdisciplinary Journal of Computing & AI (IJCAI). This inaugural edition represents our vision of creating a rigorous, inclusive, and forward-looking platform for cutting-edge research at the intersection of computing and artificial intelligence. The articles featured in this issue have undergone a careful peer-review process and reflect both depth and diversity in research. We are honored to include contributions from esteemed institutions across the United States, Sweden, and India, demonstrating the global relevance of the themes we aim to promote. A key highlight of this issue is that all published papers are indexed in OpenAIRE, reinforcing our commitment to open-access dissemination, research transparency, and international discoverability. This milestone significantly enhances the academic reach and credibility of the work published in IJCAI. The acceptance rate for this issue is 35%. The research spans critical and emerging domains, including: · A clinically significant study on predicting 28-day readmission risks in heart failure patients, contributing to data-driven healthcare. · A comparative and optimization-focused analysis of GPU power management using Python and MATLAB simulations. · An innovative AI-based virtual try-on system enhanced with biometric security for design and user personalization. · A novel blockchain-enabled federated learning platform enabling privacy-preserving ad preference modelling and decentralized creator reward distribution. · A novel integration of computational fluid dynamics with post-quantum secure control in cyber-physical systems. · Artificial intelligence in the governance of rural water systems and investigating the challenges, opportunities and sustainability in India. · Advancements in privacy-preserving online communication through hybrid cryptographic frameworks. Each paper embodies originality, methodological soundness, and relevance to current technological challenges. As Editor-in-Chief, I would like to express my gratitude to the authors for their valuable contributions, the reviewers for their insightful feedback, and the editorial board for their unwavering support in maintaining the quality and integrity of this publication. We are committed to continuously improving the journal’s standards and expanding its reach through recognized indexing platforms and global collaborations. We invite researchers and practitioners to contribute to future issues and be part of this growing academic community. Mohuya Chakraborty Editor-in-Chief IJCAI","url":"https://doi.org/10.5281/zenodo.19674476","authors":["Chakraborty, Dr. Mohuya"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19674476","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21702373","name":"AI-Guided Precision Nutrition and Pharmacogenomics: A New Frontier in Personalized Cancer Prevention and Therapy","source":"datacite","abstract":"Precision nutrition and pharmacogenomics, driven by artificial intelligence (AI) technologies, are becoming an integral part of the science of precision oncology and are providing a pathway to highly personalized cancer prevention, diagnosis, and treatment. Current cancer therapeutics make use of standard treatment regime that do not consider the interindividual variation in genetic, metabolic, microbiome, immune regulatory, and drug sensitivity. The shift towards a personalized oncology model that combines genomic, transcriptomic, proteomic, and metabolomic and clinical information to guide therapeutic decision has been fast-tracked with the advent of recent technologies and advances in machine learning, multi-omics, and computational biology. Precision nutrition is an approach to dietary interventions based on molecular profile, and pharmacogenomics is the study of the influence of genetic variation on drug metabolism, efficacy, toxicity, and therapeutic resistance. In the last six years (2020 to 2025), artificial intelligence (AI) has been used increasingly over the past five years to: forecast cancer risk, discover predictive biomarkers, optimize chemotherapy and immunotherapy treatments, minimize side effects of drugs, and improve nutritional care in cancer Moreover, the increasing body of evidence underscores the importance of the gut microbiome, inflammation control, immune response, and therapeutic response, thereby enhancing the precision medicine capabilities of AI. New technologies like digital twin, explainable AI, federated learning, and real-time wearable biosensors will be expected to take cancer adoptive management to the next level, as well as in the field of cancer prevention. This chapter critically reviews recent advances and prospects of AI-driven precision nutrition and pharmacogenomics, highlighting their translational applications, clinical relevance, technological advances for cancer prevention and treatment.","url":"https://doi.org/10.5281/zenodo.21702373","authors":["Sadia, Khan","Hafsa, Asif","Hina, Nawab","Amna, Arif","Natasha, Iqbal","Aiman, Zahid","Rubia, Anwer","Abida, Shamim"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21702373","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21702374","name":"AI-Guided Precision Nutrition and Pharmacogenomics: A New Frontier in Personalized Cancer Prevention and Therapy","source":"datacite","abstract":"Precision nutrition and pharmacogenomics, driven by artificial intelligence (AI) technologies, are becoming an integral part of the science of precision oncology and are providing a pathway to highly personalized cancer prevention, diagnosis, and treatment. Current cancer therapeutics make use of standard treatment regime that do not consider the interindividual variation in genetic, metabolic, microbiome, immune regulatory, and drug sensitivity. The shift towards a personalized oncology model that combines genomic, transcriptomic, proteomic, and metabolomic and clinical information to guide therapeutic decision has been fast-tracked with the advent of recent technologies and advances in machine learning, multi-omics, and computational biology. Precision nutrition is an approach to dietary interventions based on molecular profile, and pharmacogenomics is the study of the influence of genetic variation on drug metabolism, efficacy, toxicity, and therapeutic resistance. In the last six years (2020 to 2025), artificial intelligence (AI) has been used increasingly over the past five years to: forecast cancer risk, discover predictive biomarkers, optimize chemotherapy and immunotherapy treatments, minimize side effects of drugs, and improve nutritional care in cancer Moreover, the increasing body of evidence underscores the importance of the gut microbiome, inflammation control, immune response, and therapeutic response, thereby enhancing the precision medicine capabilities of AI. New technologies like digital twin, explainable AI, federated learning, and real-time wearable biosensors will be expected to take cancer adoptive management to the next level, as well as in the field of cancer prevention. This chapter critically reviews recent advances and prospects of AI-driven precision nutrition and pharmacogenomics, highlighting their translational applications, clinical relevance, technological advances for cancer prevention and treatment.","url":"https://doi.org/10.5281/zenodo.21702374","authors":["Sadia, Khan","Hafsa, Asif","Hina, Nawab","Amna, Arif","Natasha, Iqbal","Aiman, Zahid","Rubia, Anwer","Abida, Shamim"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21702374","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21701718","name":"SEAScale: Predictive Serverless Autoscaling Architecture For High Demand Ride Sharing Platforms","source":"datacite","abstract":"Modern networks produced enormous volumes of data which made them increasingly vulnerable to advanced cyber threats. Traditional scanning tools and standalone anomaly detection systems fell short in identifying evolving or zero day attacks. This study proposed a hybrid framework that integrated active network scanning with machine learning based anomaly detection to deliver an adaptive and automated solution. The framework combined Nmap based host scanning tcpdump based traffic capture and Zeek based feature extraction with a Random Forest classifier and an autoencoder trained on normal traffic to flag deviations. Recent advancements in supervised and unsupervised anomaly detection together with integrated intrusion detection systems published between 2020 and 2025 were reviewed and synthesized to position the proposed approach within the broader field. Experimental comparison across five learning models showed that the Convolutional Neural Network achieved the highest accuracy of 93 percent followed by Long Short Term Memory at 91 percent while Random Forest balanced accuracy and interpretability at 89 percent. The hybrid correlation mechanism that combined scan derived signals with model predictions reduced false alarms and strengthened real time threat visibility compared with single method systems. The study concluded that combining active scanning with machine learning significantly improved detection accuracy reduced false positives and enabled actionable reporting for security analysts. Future directions identified included adaptive learning methods lightweight models suited for edge devices and secure distributed detection through federated learning.","url":"https://doi.org/10.5281/zenodo.21701718","authors":["Pradeep","Husain Zaidi","Sakshi Singh"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21701718","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.5281/zenodo.21701717","name":"SEAScale: Predictive Serverless Autoscaling Architecture For High Demand Ride Sharing Platforms","source":"datacite","abstract":"Modern networks produced enormous volumes of data which made them increasingly vulnerable to advanced cyber threats. Traditional scanning tools and standalone anomaly detection systems fell short in identifying evolving or zero day attacks. This study proposed a hybrid framework that integrated active network scanning with machine learning based anomaly detection to deliver an adaptive and automated solution. The framework combined Nmap based host scanning tcpdump based traffic capture and Zeek based feature extraction with a Random Forest classifier and an autoencoder trained on normal traffic to flag deviations. Recent advancements in supervised and unsupervised anomaly detection together with integrated intrusion detection systems published between 2020 and 2025 were reviewed and synthesized to position the proposed approach within the broader field. Experimental comparison across five learning models showed that the Convolutional Neural Network achieved the highest accuracy of 93 percent followed by Long Short Term Memory at 91 percent while Random Forest balanced accuracy and interpretability at 89 percent. The hybrid correlation mechanism that combined scan derived signals with model predictions reduced false alarms and strengthened real time threat visibility compared with single method systems. The study concluded that combining active scanning with machine learning significantly improved detection accuracy reduced false positives and enabled actionable reporting for security analysts. Future directions identified included adaptive learning methods lightweight models suited for edge devices and secure distributed detection through federated learning.","url":"https://doi.org/10.5281/zenodo.21701717","authors":["Pradeep","Husain Zaidi","Sakshi Singh"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21701717","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:19.256Z"},{"id":"doi:10.20944/preprints202605.0322.v1","name":"Explainable Artificial Intelligence for Tabular Data in Healthcare: A Systematic Review of Methods, Evaluation, and Applications","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202605.0322.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202605.0322.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202604.0648.v1","name":"Machine Learning and Deep Learning in Agriculture: A PRISMA Systematic Review of Architectures, Applications, and Open Science Practices (2019–2026)","source":"preprints","abstract":"Agriculture faces compounding pressures from food insecurity, climate change, and resource scarcity, creating urgent demand for scalable analytical tools. This PRISMA 2020-compliant systematic review synthesises 582 peer-reviewed studies on machine learning (ML) and deep learning (DL) applications in agriculture, drawn from Scopus for the period January 2019 to March 2026. The 2026 data cover only the first quarter (January–March) and are therefore not directly comparable to full-year counts. Publication volume grew exponentially — from 6 papers in 2019 to 251 in 2025 — driven by the adoption of convolutional neural networks (CNNs), Vision Transformers (ViT), and YOLO-based object detectors. Plant disease detection (27.0%) and crop yield prediction (13.7%) dominated the application landscape. South Asia and East Asia together contributed 59.3% of the corpus, while Sub-Saharan Africa and Latin America each accounted for only 1.4%, revealing a profound mismatch between research output and global food insecurity burden. Median reported classification accuracy was 98.1% for disease detection, largely reflecting controlled laboratory datasets rather than field conditions. Median R² was 0.823 for yield prediction, based on 22 of 80 yield studies reporting this metric. Unit heterogeneity, dataset artefacts, and inconsistent evaluation practices limit cross-study comparability and the real-world interpretability of these figures. Open science practices remain critically low: only 7.7% of papers shared code and 14.1% shared data openly. Explainable AI, federated learning, and physics-informed modelling represent emerging frontiers. The review identifies benchmark standardisation, smallholder-relevant design, and geographic equity as the field's most pressing unresolved challenges.","url":"https://doi.org/10.20944/preprints202604.0648.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202604.0648.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202511.0751.v1","name":"Cross Layer Optimization Using AI/ML Assisted Federated Edge Learning in 6G Networks","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202511.0751.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202511.0751.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202512.2531.v1","name":"<em>safeMEDInet</em>: Federated AI Systems for Security and Privacy-Preserving Threat Detection in Distributed Healthcare","source":"europepmc","abstract":"The explosive growth of digital healthcare data and networked Internet of Medical Things (IoMT) devices has heightened vulnerabilities inside healthcare networks, hence exposing sensitive medical systems to sophisticated cyber assaults. The safeMEDInet framework offers a secure, federated artificial intelligence (AI) architecture that allows decentralized healthcare institutions to cooperatively identify and address problems without disclosing raw patient data. safeMEDInet utilizes federated learning along with privacy-preserving techniques, such as differential privacy, homomorphic encryption, and Byzantine-resilient aggregation, to guarantee confidentiality, integrity, and adherence to regulations in remote settings. The proposed framework integrates a hybrid CNN-LSTM model for spatiotemporal intrusion detection with secure model synchronization and encrypted parameter sharing to ensure robust accuracy against various cyber-attacks, including ransomware, unauthorized access, and distributed denial-of-service (DDoS) intrusions. Empirical assessments utilizing MIMIC-IV, HealthData.gov, and WHO datasets reveal that safeMEDInet achieves a detection accuracy of 96.8% with robust privacy assurances (ε = 1.9) and sustains an accuracy of 88.4% despite 30% Byzantine interference, surpassing traditional federated and centralized systems. The findings confirm safeMEDInet's capacity to guarantee high detection reliability, low processing cost, and mathematical assurance of privacy resilience. This research positions safeMEDInet as a pivotal advancement towards safe, scalable, and ethically governed Healthcare 5.0 ecosystems, incorporating AI-driven privacy, federated cooperation, and blockchain-supported data integrity for next-generation medical cybersecurity.","url":"https://doi.org/10.20944/preprints202512.2531.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.2531.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9299363/v1","name":"From Task-Specific Learning to Network-Native Intelligence: A Comprehensive Review of Machine Learning and Artificial Intelligence in Modern Networks","source":"preprints","abstract":"Abstract Machine learning (ML) and artificial intelligence (AI) are no longer peripheral optimization tools for networking; they are becoming integral to how modern networks are measured, controlled, secured, and evolved. Yet the literature remains fragmented. Existing surveys usually focus on one sub-domain at a time—for example encrypted traffic analysis, data-center networking, routing, edge intelligence, or 6G—and therefore under-emphasize the deeper shift from task-specific models to network-native intelligence. This review synthesizes recent literature from roughly 2020 to early 2026, with emphasis on the 2021–2025 wave, and organizes the field through four coupled axes: network lifecycle, deployment scope, learning paradigm, and operational constraints. We examine how supervised, self-supervised, graph-based, reinforcement, federated, generative, and foundation-model approaches have been used for traffic analysis, anomaly and intrusion detection, routing and congestion control, resource orchestration , edge/cloud/data-center optimization, and AI-native mobile/6G systems. We then compare representative studies along data assumptions, generalization behavior, online adaptability, interpretability, systems cost, and reproducibility. Our central argument is that the next phase of AI for networking is not simply “more powerful models” but closed-loop, network-native intelligence: systems that unify perception, reasoning, decision, verification, and actuation under realistic constraints such as privacy, energy, latency, safety, and cross-domain inter-operability. Based on this synthesis, we identify the main review gap in current literature: the lack of a unified, deployment-aware, lifecycle-centric perspective that spans from packet/flow analytics to autonomous network operation and emerging foundation models. We conclude with a concrete research agenda covering trustworthy online learning, digital twins, synthetic data, domain-adapted 1 foundation models, multi-agent control, and sustainable AI for communication networks.","url":"https://doi.org/10.21203/rs.3.rs-9299363/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9299363/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.64898/2025.12.16.693409","name":"A Federated and Privacy-Preserving Framework for Large-Scale Genome-Wide Association Studies with Mixed-Effects Models","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2025.12.16.693409","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.64898/2025.12.16.693409","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7873990/v1","name":"EdgeFL-Crypto: Federated Split Learning Architecture for IoT- Based Cryptocurrency Volatility Prediction in Edge-Cloud Environments","source":"preprints","abstract":"Abstract The proliferation of Internet of Things (IoT) devices in financial markets creates unprecedented opportunities for distributed intelligence in cryptocurrency trading systems. This paper presents EdgeFL-Crypto, a novel federated split learning architecture that leverages IoT sensor networks and edge-cloud computing paradigms for real-time cryptocurrency volatility forecasting. Our approach addresses the unique challenges of deploying transformer-based models across resource-constrained IoT devices by implementing a hierarchical federated learning protocol that splits model computation between edge and cloud layers. The framework integrates mobile edge computing nodes as intermediate aggregators, enabling efficient model training while preserving data locality in IoT ecosystems. We introduce an adaptive split point selection mechanism that dynamically partitions transformer layers based on device capabilities and network conditions, optimizing the trade-off between edge computation and cloud processing. The system employs blockchain-secured aggregation for trustworthy model updates across distributed IoT networks, ensuring integrity in multi-stakeholder environments. Experimental evaluation on real-world cryptocurrency data from IoT-enabled trading terminals demonstrates 23.7% improvement in prediction accuracy while reducing communication overhead by 41.2% compared to traditional cloud-centric approaches. The framework achieves sub-200ms inference latency suitable for real-time IoT applications, with differential privacy guarantees (ε=1.0) protecting sensitive trading data at the edge. Our results establish EdgeFL-Crypto as a practical solution for deploying sophisticated AI models in IoT-driven financial systems, bridging the gap between edge intelligence and cloud computing for next-generation fintech applications.","url":"https://doi.org/10.21203/rs.3.rs-7873990/v1","authors":["Mohammed M. Alenazi¹","Abrar S. Alhazmi²"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7873990/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.22541/au.176340984.46653556/v1","name":"Dynamic Sparse Federated Intelligent Transportation Systems: Multimodal Human-Centric Design, Cross-Regional Few-Shot Adaptation, and SBM-DEA Comprehensive Evaluation","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.176340984.46653556/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.176340984.46653556/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-8236276/v1","name":"WITHDRAWN: Phishing Attack Detection and Secure Data Transfer Using Echo State Networks and Federated Identity Management","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8236276/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8236276/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.64898/2025.12.08.25340199","name":"Med-SSFWT: A Self-supervised Federated Weight Transfer Framework for Medical Model Fusion","source":"preprints","abstract":"Artificial Intelligence (AI) holds great potential to revolutionize healthcare by integrating and analyzing diverse multi-source medical data to drive advancements in disease diagnosis, treatment strategies, and patient management. However, deploying AI in distributed medical environments presents critical challenges, including data silos, label deficiency, and data heterogeneity. To address these challenges and enable effective and privacy-preserving distributed medical AI models, we propose Med-SSFWT, a Self-Supervised Federated Weight Transfer framework designed for medical data fusion. Firstly, Med-SSFWT employs a fine-tuned Large Language Model (LLM) to extract structured features from each client’s medical data, followed by feature alignment across clients via a shared global schema. Subsequently, an information gain-based gradient filtering mechanism is introduced to federated aggregation by filtering out ineffective gradients, thereby improving the robustness of global model. Furthermore, Med-SSFWT leverages a novel federated model fusion frame, consisting of self-supervised pre-training and fine-tuning through weight transfer to balance global optimization with client-specific personalization. Finally, extensive experiments show that Med-SSFWT consistently outperforms federated learning approaches in both performance and adaptability under diverse non-IID conditions, highlighting its effectiveness within distributed medical environments and establishing a foundation for the development of privacy-preserving and scalable AI-driven healthcare solutions.","url":"https://doi.org/10.64898/2025.12.08.25340199","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.64898/2025.12.08.25340199","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-8181303/v1","name":"FNEM: A Federated-Neuro-Symbolic Edge Approach for Explainable Anomaly Detection in IoT Networks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8181303/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8181303/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7585019/v1","name":"Adaptive Spectrum Sensing and Management in Cognitive Radio Networks Using Federated Deep Reinforcement Learning","source":"preprints","abstract":"Abstract The dynamic and unexpected character of settings in wireless communication calls for sophisticated spectrum sensing techniques for cognitive radio networks. Building on the work of earlier ensemble machine learning approaches, this study presents a state-of-the-art framework for real-time spectrum management using federated deep reinforcement learning (FDRL). The combination of reinforcement learning's strategic decision-making process with Deep Belief Networks' (DBN) and Long Short-Term Memory's (LSTM) architectures is at the heart of this methodology. This approach, which operates inside a federated learning paradigm, gives user privacy and data locality, guaranteeing a reliable and private solution. Through processing signal vectors under different noise situations, the FDRL model repeatedly learns the best spectrum allocation strategies, improving its comprehension over time. This novel approach offers effective adaptability to the ever-changing wireless environment, improving network speed and spectrum utilization while protecting user privacy. Effectively separating idle from active channels, it continuously adjusts to variations in signal-to-noise ratios and user demands. This sophisticated technology is shown through thorough simulations to provide a significant improvement in both spectrum efficiency and user throughput. Because of its scalability and decentralization, it presents a viable answer to the changing wireless network environment, which is marked by an increasing need for autonomy and data-driven operations. This approach's proven ability to reduce interference and improve service quality indicates a major step forward for intelligent and autonomous spectrum sensing methods, which are critical in the age of ubiquitous wireless communication. The suggested FDRL-DBN-LSTM approach was implemented in Python and achieves an accuracy of 98.4%.","url":"https://doi.org/10.21203/rs.3.rs-7585019/v1","authors":["M.Saraswathi","D.Lakshminarayana","P.Vaishnavidevi"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7585019/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-7447294/v1","name":"G-SAFE: Generative Synthetic Augmentation for Federated Edge Security","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7447294/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7447294/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-9051917/v1","name":"A PRISMA–Based and PICOC–Framed Systematic Review on Physics-Informed Neural Networks, TinyML, and Edge–Cloud Collaborative Frameworks for Real–Time Photovoltaic Performance Monitoring","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9051917/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9051917/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-8474173/v1","name":"Robotic and AI Enabled Waste Segregation A Systematic Review of Methods Benchmarks and Challenges","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8474173/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8474173/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202510.1928.v1","name":"Federated Zero-Trust: Privacy-Preserving Analytics Across Multi-Cloud Environments","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202510.1928.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.1928.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7587282/v1","name":"Federated Deep Learning and Explainable AI for Real-Time Credit Card Fraud Detection in Highly Imbalanced Transaction Streams","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7587282/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7587282/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7837664/v1","name":"Federated Risk Discrimination with Siamese Networks for Financial Transaction Anomaly Detection","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7837664/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7837664/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202510.1892.v1","name":"<p class=\"MDPI12titleori1\" style=\"mso-line-height-alt: 14.0pt;\"><span style=\"mso-bidi-font-size: 18.0pt; mso-ligatures: standardcontextual;\">LLM-Assisted Narrative Review of Artificial Intelligence in Brazilian Public Health: Lessons from Transfer and Federated Learning for Resource-Constrained Settings","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.1892.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.1892.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.12688/f1000research.176098.1","name":"Artificial Intelligence and Intelligent Tutoring Systems in Mathematics Education: A Bibliometric Analysis (2001–2025)","source":"preprints","abstract":"","url":"https://doi.org/10.12688/f1000research.176098.1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.12688/f1000research.176098.1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7128890/v1","name":"Federated Reinforcement Learning for Distributed MAC Optimization in IEEE 802.11bn Networks","source":"preprints","abstract":"Abstract IEEE 802.11bn (Wi-Fi 8) introduces Multi-AP Coordination (MAPC) to meet ultra-reliable low-latency communication (URLLC) demands in dense wireless deployments. While centralized MAC-layer scheduling improves coordination, it introduces overhead, privacy risks, and scalability challenges. In this paper, propose a decentralized federated reinforcement learning (FRL) framework for MAC scheduling across distributed access points (APs). Each AP independently learns optimal transmission policies using deep Q-learning, while periodically synchronizing model updates through a lightweight federated server. This approach preserves local traffic privacy and reduces control latency, without sacrificing performance or adaptability. The system dynamically adjusts to varying interference, heterogeneous traffic loads, and dynamic topology changes in real-time. Evaluate the proposed FRL-based scheduler under diverse STA densities, mobility scenarios, and stochastic channel conditions using extensive custom simulations. Results show that our model achieves up to 29% lower latency, 22% higher fairness, and 17% reduction in signaling overhead compared to centralized RL and OFDMA-based MAC methods. The proposed solution offers a scalable, privacy-preserving, and resilient path toward intelligent MAC optimization in next-generation Wi-Fi networks, paving the way for mission-critical industrial and latency-sensitive applications.","url":"https://doi.org/10.21203/rs.3.rs-7128890/v1","authors":["Vijay B T"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7128890/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.20944/preprints202508.1221.v1","name":"Toward Real-World Deployment of Federated Learning in Healthcare: A Comprehensive Review of Hybrid Models and Data Simulation Tools","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202508.1221.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202508.1221.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-7841356/v1","name":"Federated Large Language Models in Healthcare: A Systematic Review, Opportunities and Challenges","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7841356/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7841356/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202510.2520.v1","name":"Research on Autonomous Path Planning and Control Strategy of UAV Based on Multi-Objective Optimization","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.2520.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.2520.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7441685/v1","name":"A Federated Cascade Learning Approach for Efficient Occupancy Detection in Smart Buildings","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7441685/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7441685/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7616375/v1","name":"Federated Reinforcement Learning Framework for Privacy Preserving Few Shot Learning","source":"preprints","abstract":"Abstract This study introduces a federated reinforcement learning framework for few-shot learning (FRL-FSL), aiming to address the dual challenges of data scarcity and privacy preservation in distributed environments. The proposed framework integrates policy gradient optimization with secure aggregation and introduces validator nodes to ensure the authenticity of both data and model updates. Experiments were conducted on the Omniglot and FC100 datasets under 1-shot and 5-shot conditions, with comparisons against FedAvg, FedFSL and traditional supervised baselines. Results demonstrate that FRL-FSL achieved an average accuracy of 87.3% on Omniglot (5-shot), improving by 25.9% over FedAvg and 13.8% over FedFSL, while maintaining 72.6% accuracy in 1-shot tasks. On the FC100 dataset, FRL-FSL reached 59.8% accuracy in 5-shot learning, outperforming FedAvg by 18.6% and FedFSL by 7.1%, and achieved 46.3% in 1-shot learning. The framework also reduced the privacy risk index by 37% relative to FedAvg, with convergence accelerated by nearly 30% compared to baselines. These findings confirm that FRL-FSL achieves a practical balance between accuracy, convergence, and privacy, offering a promising solution for real-world, privacy-sensitive applications.","url":"https://doi.org/10.21203/rs.3.rs-7616375/v1","authors":["Minsoo Kang","Jihye Park","Donghyun Choi","Seoyeon Kim"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7616375/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.22541/au.175978101.15891546/v2","name":"Standardized Evaluation of Federated Human-Centric ITS: Integrating Multimodal Interaction and Cross-Regional Scalability","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.175978101.15891546/v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.175978101.15891546/v2","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202512.1717.v1","name":"A Review of Resilient IoT Systems: Trends, Challenges, and Future Directions","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202512.1717.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.1717.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.22541/au.175978101.15891546/v1","name":"Standardized Evaluation of Federated Human-Centric ITS: Integrating Multimodal Interaction and Cross-Regional Scalability","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.175978101.15891546/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.175978101.15891546/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7339827/v1","name":"Optimizing Task Offloading and Resource Management in Next- Generation 6G Networks Using an Intelligent Hierarchical Edge- Fog-Cloud Computing Architecture with Reinforcement Learning and Federated Intelligence","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7339827/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7339827/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7530459/v1","name":"FedSER-XAI: PSO-Optimized Multi-StreamCross-Attention Transformer with Graph Featuresfor Explainable Federated Speech EmotionRecognitione","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7530459/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7530459/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.22541/au.175683843.31853019/v1","name":"Next-Generation Enterprise AI: From Foundation Models to Federated and Secure Cloud Systems","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.175683843.31853019/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.175683843.31853019/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7248659/v1","name":"FlowSocial: A Dynamic Clustering-Based Federated Recommender System for Privacy-Preserving Social Media Personalization","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7248659/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7248659/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7409201/v1","name":"FedS-SLAM: A Federated Semantic Collaborative SLAM System for UAV Swarms in Dynamic","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7409201/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7409201/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202512.2393.v1","name":"A Survey of Contrastive Learning in Medical AI: Foundations, Biomedical Modalities, and Future Directions","source":"preprints","abstract":"Medical artificial intelligence (AI) systems depend heavily on high-quality data representations to enable accurate prediction, diagnosis, and clinical decision-making. Yet, the availability of large, well-annotated medical datasets is often limited by cost, privacy concerns, and the need for expert labeling, motivating increased interest in self-supervised representation learning approaches. Among these, contrastive learning has emerged as one of the most influential paradigms, driving significant progress in representation learning across computer vision and natural language processing. This paper presents a comprehensive review of contrastive learning in medical AI, highlighting its theoretical foundations, methodological advances, and practical applications in medical imaging, electronic health records (EHRs), physiological signal analysis, and genomics. Furthermore, the study identifies common challenges such as pair construction, augmentation sensitivity, and evaluation inconsistencies, while discussing emerging trends including multimodal alignment, federated learning, and privacy-preserving frameworks. Through a synthesis of current developments and open research directions, this paper offers insights that advance data-efficient, reliable, and generalizable medical AI systems.","url":"https://doi.org/10.20944/preprints202512.2393.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.2393.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202510.0141.v1","name":"Time-Aware Security Intelligence for Federated Financial Systems: Deep Reinforcement Learning Against Temporal Poisoning Attacks","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.0141.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.0141.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202511.1649.v1","name":"A Review on Machine Learning Applications in Chance-Constrained Power System Optimization","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202511.1649.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202511.1649.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-6734020/v1","name":"Recent Advancements and Trends in Artificial Intelligence from the Information Security Perspective: A Systematic Review","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6734020/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-6734020/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-8323244/v1","name":"Deep Learning–based IDS framework for Cloud Data Security","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8323244/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8323244/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202507.0835.v1","name":"A Privacy-Enhanced Multi-Stage Dimensionality Reduction Vertical Federated Clustering Framework","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202507.0835.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.0835.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7363876/v1","name":"Quantum-Enhanced Federated Chaotic Transformer Framework for Ultra-Low Latency and Secure Multi-Site Telesurgery in 6G- Enabled Networks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7363876/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7363876/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7153901/v1","name":"Blockchain-Governed Federated Learning with Sparse-Causal Bi- RNN Transformer Net for Secure and Intelligent Healthcare Analytics","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7153901/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7153901/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7780954/v1","name":"AI-Powered Federated Framework for Personalized, Privacy Preserving, and Sustainable Meal Planning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7780954/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7780954/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.22541/au.176581998.82900297/v1","name":"Advances in Deep Learning for Medical Imaging: Foundations, Evolution, and Impact","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.176581998.82900297/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.176581998.82900297/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202508.0508.v1","name":"The Sovereign SOC: A Simulation Framework for Quantum-Enhanced Federated Security Operations","source":"preprints","abstract":"Traditional Security Operations Centers (SOCs) lack physical-layer visibility and suffer from high false positive rates, leaving critical infrastructure vulnerable to hardware implants and electromagnetic side-channel attacks. This paper presents the Sovereign SOC, a simulation-based architectural framework exploring the potential integration of quantum magnetometer arrays, federated learning, and agentic AI orchestration. Using theoretical models of optically pumped magnetometers (OPMs) with 15 fT/√Hz sensitivity specifications, the system demonstrates potential for detecting electromagnetic anomalies from electronic devices while preserving privacy through federated learning across distributed nodes. We develop comprehensive mathematical models for quantum sensing, including gradiometric noise cancellation achieving theoretical common-mode rejection ratios of 80 dB, and harmonic disruption detection using Wigner-Ville distributions. Our federated learning framework implements Byzantine-resilient aggregation with proven convergence bounds, while multi-agent AI systems orchestrate autonomous responses using FIPS 203-206 post-quantum cryptographic standards. Simulation results indicate potential for up to 64% reduction in alert volume (95% CI: 61-67%), 78% reduction in storage requirements, and 47±12 ms response latency under ideal conditions. An interactive visualization platform validates the architecture across four attack scenarios in a controlled simulation environment. The detection agent achieved 89% classification accuracy on synthetic threat data, with scenario-specific success rates ranging from 78% to 96%. These findings require validation with physical sensors before real-world deployment. The Sovereign SOC establishes a theoretical foundation and architectural blueprint for future quantum-enhanced security operations.","url":"https://doi.org/10.20944/preprints202508.0508.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202508.0508.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7208692/v1","name":"FedMedSecure: Federated Few-Shot Learning with Cross-Attention Mechanisms and Explainable AI for Collaborative Healthcare Cybersecurity","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7208692/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7208692/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9521303/v1","name":"Machine learning for predicting clinical outcomes in emergency department patients with acute respiratory infections: A scoping review","source":"preprints","abstract":"Abstract Machine learning approaches, including deep learning, are increasingly applied in healthcare, particularly for developing models that support clinical prediction and decision-making. In this context, acute respiratory infections represent a critical clinical area where timely and accurate risk stratification is essential, particularly in the emergency department (ED). This scoping review aims to systematically map and describe how machine learning approaches have been used to predict clinical outcomes in patients presenting with acute respiratory infections to the ED. We searched five databases (PubMed, Embase, Web of Science, CINAHL, and the Cochrane Library) from inception up to July 9th, 2025, and included 52 studies. Most studies were retrospective in design (87%) and were published after 2020 (88%). Three-quarters focused on COVID-19, and the majority included adults only (69%). The largest share of studies (38%) used data originating from the United States. While most studies reported either sex or gender (88%), none reported both, and over a third (37%) used these two distinct constructs interchangeably. Race and/or ethnicity were reported in only 29% of the studies. Mortality was the most frequently predicted outcome (40%). Machine learning methods most commonly used were random forests (40%) and extreme gradient boosting (29%). Deep learning approaches were also commonly used, particularly convolutional neural networks (31%). In most studies (90%), prediction models relied on laboratory or radiological data, which are unavailable at initial triage and are typically obtained later in the ED pathway. Although machine learning-based models showed adequate performance overall, only a few studies compared them to clinical experts or traditional decision tools (21%), and/or performed external validation (13%). Overall, the models reviewed here have limited clinical utility and generalizability. Future studies should broaden the scope beyond COVID-19 to include other acute respiratory infections, develop models with data that are readily available at triage, and incorporate more diverse populations to enhance inclusivity and fairness.","url":"https://doi.org/10.21203/rs.3.rs-9521303/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9521303/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202507.0497.v1","name":"Federated Quantum Machine Learning for Distributed Cybersecurity in Multi-Agent Energy Systems","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202507.0497.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.0497.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202512.0906.v1","name":"Human Activity Recognition in the Deep Learning Era: Different Modalities, Recent Advances in Applications, and Emerging Techniques","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202512.0906.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.0906.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7197888/v1","name":"UAV Mission Planning for Post-Disaster Victim Localisation via Federated Reinforcement Learning","source":"preprints","abstract":"Abstract Rapid localisation of trapped victims after urban disasters is essential but remains difficult due to signal intermittency, energy constraints, and the impracticality of training multi-UAV coordination policies solely through real-world flights. This study addresses the need for sample-efficient, privacy-preserving reinforcement learning in such high-risk environments. We adapt a federated multi-agent reinforcement learning framework originally developed for communication-constrained environments and apply it to post-disaster victim localisation. The approach integrates a lightweight LoS/NLoS surrogate channel model and a PSO-based position estimator for unknown devices, together with simple feasibility checks on energy and altitude separation. UAVs learn locally in simulated environments and only exchange model parameters to maintain privacy. The proposed architecture is evaluated on two synthetic post-earthquake urban environments. Results show that model-aided federated agents significantly outperform independent Q-learning and standard QMIX baselines in both search performance and convergence speed. The adapted framework enables effective coordination under realistic energy and TDMA constraints, achieving high victim coverage with reduced training overhead. This study demonstrates that combining environmental knowledge with decentralised learning architectures can substantially improve the efficiency and robustness of UAV coordination strategies in post-disaster scenarios. The results highlight a viable pathway toward scalable, privacy-preserving, and field-deployable reinforcement learning systems for real-world search-and-rescue missions.","url":"https://doi.org/10.21203/rs.3.rs-7197888/v1","authors":["Alparslan GUZEY"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7197888/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-7464969/v1","name":"A Federated and Privacy-Preserving Architecture for Scalable Collaborative Spam Detection in Distributed Multi-Cloud Environments","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7464969/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7464969/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.21203/rs.3.rs-7650354/v1","name":"CasDyF-Net: Transforming Single-Image Dehazing through Federated and Adaptive CNNs","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7650354/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7650354/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7547137/v1","name":"NeuroChainOps: A Privacy-Preserving, Blockchain-Backed MLOps Framework for Federated Neural Architecture Search","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7547137/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7547137/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202511.1229.v1","name":"The Role of Artificial Intelligence in Next-Generation Handover Decision Techniques for UAVs over 6G Networks","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202511.1229.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202511.1229.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-7935562/v1","name":"AI-Driven Threat Detection and Response: Toward Autonomous Cyber Defense Systems","source":"preprints","abstract":"Abstract The increasing sophistication of cyber threats in modern digital infrastructures necessitates intelligent, autonomous defense mechanisms capable of responding faster and more accurately than humans. This study introduces an AI-Driven Threat Detection and Response (AI-TDR) framework that integrates deep learning and reinforcement learning to autonomously detect, analyze, and mitigate cyberattacks in real time. Using the UNSW-NB15 dataset, which contains realistic traffic and nine attack types, three architectures, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformer, were developed and tested. The CNN and LSTM achieved 100% accuracy, while the Transformer reached 96.8% accuracy with an AUC of 0.996, demonstrating robustness and generalization. The AI-TDR operates through a Perception–Cognition–Decision–Action cycle, enabling adaptive learning and autonomous mitigation through continuous feedback. By combining spatial, temporal, and contextual intelligence, the system advances toward self-learning, multi-agent cyber defense. Beyond detection, it envisions automated responses such as node isolation and firewall reconfiguration. Future work includes integrating Explainable AI for transparency, adversarial training for resilience, and federated learning for decentralized protection. Overall, this research contributes to the advancement of adaptive and intelligent cybersecurity, supporting global efforts to achieve continuous and collaborative defense in an evolving threat landscape.","url":"https://doi.org/10.21203/rs.3.rs-7935562/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7935562/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.22541/au.176463741.15717110/v1","name":"NeuroSense: A Computational AI Model for Continuous Psychological State Prediction","source":"preprints","abstract":"An emerging interdisciplinary challenge in artificial intelligence, computational psychology, and neuroscience is the ongoing evaluation of human psychological states. Conventional mental-state assessments rely on clinical interviews and episodic, subjective self-reports, which are not flexible in real time. This paper presents NeuroSense, a computational AI framework that uses multimodal signals such as EEG, heart-rate variability (HRV), speech prosody, facial micro-expressions, linguistic sentiment, and contextual behavioral features to predict dynamic psychological states. A multimodal fusion pipeline comprising a Spatio-Temporal EEG Encoder, Physiological Dynamics Model, Affective Facial Transformer, Prosodic Emotional Encoder, and NLP-based Cognitive Load Estimator is integrated by NeuroSense. Continuous prediction using a hybrid deep learning framework is made possible by the convergence of these signals into a Unified Psychological State Vector (UPSV). High potential for real-time affect estimation, stress prediction, cognitive load modeling, and mental fatigue detection is demonstrated by experiments conducted on benchmark datasets. Future studies will investigate neuro-adaptive intelligent interfaces, wearable IoT integration, and federated learning.","url":"https://doi.org/10.22541/au.176463741.15717110/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.176463741.15717110/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.14293/pr2199.002391.v1","name":"AI-Driven Network Management and Optimization","source":"preprints","abstract":"","url":"https://doi.org/10.14293/pr2199.002391.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.14293/pr2199.002391.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-6674711/v1","name":"Federated Defense: A Privacy-Preserving Deep Learning Model for IoT Malware Detection","source":"preprints","abstract":"Abstract As the Internet of Things (IoT) continues to expand, securing the vast network of IoT devices, particularly in Machine-to-Machine (M2M) communication, has become a critical concern. Traditional security approaches often fall short, particularly in protecting privacy and ensuring scalability across IoT systems' diverse and vast landscapes. This paper introduces the Federated Defense Model, a novel approach that harnesses federated learning (FL) to enhance IoT malware detection while preserving user privacy. Unlike centralized models, the proposed FL framework processes data locally on IoT devices, avoiding transmitting sensitive information and reducing bandwidth demands. We developed and evaluated a lightweight, one-dimensional convolutional neural network (CNN) optimized for the typical environment of IoT devices. Using the IoT-23 dataset, a collection of labeled network traffic representing various malware and benign scenarios, the experiments demonstrate that the proposed Federated Defense Model achieves superior accuracy and precision compared to the signature, heuristic, and traditional machine learning-based security models. Moreover, the Federated Defense Model is compared with the existing state-of-the-art FL models. The findings suggest that integrating FL with deep learning techniques bolsters IoT security and mitigates privacy risks and scalability challenges inherent in centralized approaches. This work contributes to the ongoing evolution of privacy protection strategies in the IoT domain, emphasizing the role of privacy-preserving methodologies in developing resilient digital ecosystems.","url":"https://doi.org/10.21203/rs.3.rs-6674711/v1","authors":["Sohail Abbas","Mohammad Abrar","Mian Ahmad Jan","Osman Abul"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6674711/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.20944/preprints202508.0540.v1","name":"Federated Graph Neural Networks for Heterogeneous Graphs with Data Privacy and Structural Consistency","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202508.0540.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202508.0540.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-6530937/v1","name":"Modular Federated Cross-Domain Recommendation (MFCDR) with a Projected Attention Network","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6530937/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6530937/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202512.2383.v1","name":"Real-Time and Offline Large Language Models on Edge Devices: A Systematic Review","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202512.2383.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.2383.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202510.0570.v1","name":"Integrated Systems Oncology: A Multimodal Framework for Addressing Cancer Heterogeneity","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.0570.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.0570.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-6819153/v1","name":"A Federated Meta-Learning Aided Intelligent Edge Framework by Using the Parameter Optimization Approach","source":"preprints","abstract":"Abstract Edge intelligence can enable fast intelligent services by integrating edge computing with machine learning, thereby facilitating intelligent information processing for Internet of Things (IoT) devices on the edge. However, intelligent data processing at the edge may expose IoT devices to the risk of private information leakage. To mitigate this issue, we propose a federal meta-learning-aided data processing framework to cope with complex tasks in edge IoT networks. Unfortunately, communications between edge IoT devices and edge servers in federated frameworks incur significant overhead. To address this challenge, we propose a parameter optimization algorithm that alleviates communication costs between edge IoT devices and edge servers, thereby reducing classification errors induced by parameter optimization. Moreover, the convergence of the federated meta-learning method is derived, which theoretically confirms the feasibility of the proposed approach. Simulation results demonstrate that the error minimization-based quantization compression optimization algorithm can substantially enhance communication efficiency while incurring only negligible precision losses.","url":"https://doi.org/10.21203/rs.3.rs-6819153/v1","authors":["Xiaofeng Zhu","Qiaosong Fan","Jiaqiang Peng","Yuwen Qian"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6819153/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.21203/rs.3.rs-6549947/v1","name":"A privacy preserving federated clustering algorithm for data imbalance based on density peak clustering and Gaussian distribution simulation data","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6549947/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6549947/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1101/2025.08.03.668352","name":"Genetic Insights of Image-Based Traits: Analysis Pipeline for AI-based Phenotyping, Combined-GWAS, and Federated Learning with Application to the Human Face","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.08.03.668352","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.08.03.668352","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-8027109/v1","name":"YOLO-LS: A Novel Deep Learning Framework for Brain Tumor Segmentation in Magnetic Resonance Imaging","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8027109/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8027109/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202512.2371.v1","name":"Beyond Compliance: A Techno-Geopolitical Framework for Scalable AI Resilience in Critical Infrastructure Along the NATO-EU Eastern Flank","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202512.2371.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.2371.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7002574/v1","name":"Lightweight Adaptive Feature Aggregation Network for Cross-Domain Defect Detection in Data-Scarce L-DED Processes","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7002574/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7002574/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-6331407/v1","name":"Fair Client Selection and Encrypted Aggregation: A Federated Learning Framework for Intrusion Detection in Resource-Constrained Networks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6331407/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6331407/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.21203/rs.3.rs-6678464/v1","name":"Blockchain-Enabled Federated Learning with Edge Analytics for Secure and Efficient Electronic Health Records Management","source":"preprints","abstract":"Abstract The rapid adoption of Federated Learning (FL) in privacy-sensitive domains such as healthcare, IoT, and smart cities highlights its potential to enable collaborative machine learning without compromising data ownership. However, conventional FL frameworks face several critical challenges: high computational overhead at edge devices, significant communication latency due to frequent model updates, vulnerability to model and data poisoning attacks, and limited privacy preservation mechanisms that expose systems to inference risks. These issues hinder the scalability, efficiency, and trustworthiness of FL in real-world, large-scale deployments—particularly in domains like Electronic Health Records (EHR) management, where data sensitivity is paramount. To address these challenges, this study proposes the Enhanced Privacy-Preserving Blockchain-Enabled Federated Learning (EPP-BCFL) framework—a novel architecture that mixes blockchain technology, hybrid privacy mechanisms, and optimized communication strategies. The proposed system features a three-layer design: (1) the Edge Nodes Layer, where client devices perform local model training while retaining raw data; (2) the Federated Model Aggregation Layer, which securely aggregates encrypted updates using Differential Privacy and Secure Multi-Party Computation (SMPC); and (3) the Blockchain Network Layer, which guarantees tamper-proof auditability and trust through a lightweight Proof-of-Stake (PoS) consensus enhanced with Byzantine Fault Tolerance (BFT). Experimental evaluation on the CIFAR-10 dataset demonstrates that EPP-BCFL achieves 95.2% accuracy, significantly reduced communication overhead, and strong resilience against adversarial attacks. Comparative analysis with existing FL models highlights the proposed framework’s superior performance with respect to privacy preservation, computational efficiency, and robust security, making it well-suited for secure, scalable healthcare applications.","url":"https://doi.org/10.21203/rs.3.rs-6678464/v1","authors":["Munusamy S","Jothi K R"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6678464/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.20944/preprints202511.0846.v1","name":"Sustainable Computing for Digital Livestock: Reconciling Artificial Intelligence with Planetary Boundaries","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202511.0846.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202511.0846.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.22541/au.174731423.33892933/v1","name":"Quantum-Classical Federated Learning: Enhancing Robustness Against Backdoor Attacks in Non-IID Environments","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.174731423.33892933/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.174731423.33892933/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202510.1204.v1","name":"Safety-Constrained Real-Time Decision Making for Autonomous Vehicles via NEURAL-QWEN","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.1204.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.1204.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.22541/au.176184278.86885419/v1","name":"Artificial Intelligence in Drug Discovery: A New Paradigm from Target Identification to Clinical Translation","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.176184278.86885419/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.176184278.86885419/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.22541/au.174855633.30836832/v1","name":"jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf FedParallel: A Federated Learning Framework for Campus Network Anomaly Traffic Detection","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.174855633.30836832/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.174855633.30836832/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202507.1863.v1","name":"A Secure and Explainable Federated Intrusion Detection System Using Deep Learning and Metaheuristic Optimization for Healthcare IoT","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202507.1863.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.1863.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.21203/rs.3.rs-7862094/v1","name":"Application of Machine Learning in Hypertension Research and Management in Nigeria: A Systematic Review","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7862094/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7862094/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-6449815/v1","name":"Distributed Learning for Heart Disease Risk Prediction Based on Key Clinical Parameters with Evaluation Metrics Analysis","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6449815/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6449815/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202505.0439.v1","name":"Federated Learning for XSS Detection: Analysing OOD, Non-IID Challenges, and Embedding Sensitivity","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202505.0439.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202505.0439.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7637000/v1","name":"Dynamic Task Offloading in Vehicular NetworksUsing Large Language Models: A Novel EdgeIntelligence Framework for Adaptive, Low-Latency,and Energy-Aware Decision Making","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7637000/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7637000/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202507.2601.v2","name":"AI-Powered Wearable Sensors for Health Monitoring and Clinical Decision Making","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202507.2601.v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.2601.v2","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202510.0877.v1","name":"Adaptive Normalization Enhances the Generalization of Deep Learning Model in Chest X-Ray Classification","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.0877.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.0877.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.22541/au.175042344.47117492/v1","name":"Hierarchical Mean-Field Theory-based Off-Policy GRPO for Federated Edge Learning in Resource-Constrained Edge Computing","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.175042344.47117492/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.175042344.47117492/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.20944/preprints202509.0046.v1","name":"The ε-Streamably-Learnable Class: A Constructive Framework for Operator-Based Optimization","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202509.0046.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.0046.v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-5966493/v1","name":"Integrating Artificial Intelligence With Federated Learning and Internet of Medical Things for Healthcare Sector: an Analysis With Alzheimer Dataset","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-5966493/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-5966493/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21203/rs.3.rs-7987618/v1","name":"Adaptive QoS Management in OneM2M Standard: Machine Learning and Deep Learning for IoT Network Optimization","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7987618/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7987618/v1","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.1101/2025.08.07.25333044","name":"Development, implementation, and validation of an open-source Federated Learning platform to accelerate innovation and boost personalized medicine in rare and ultra-rare haematological diseases: an initiative by GenoMed4All Consortium","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.08.07.25333044","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.08.07.25333044","addedAt":"2026-08-31T06:41:19.256Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.1049/pbpc066e_ch2","name":"Federated learning for optimized communication in Industry 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_ch2","authors":["Dasari Bhulakshmi","Gokul Yenduri","Praveen Kumar Reddy Maddikunta","Celestine Iwendi","Thippa Reddy Gadekallu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_ch2","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:19.649Z"},{"id":"doi:10.2139/ssrn.4753243","name":"Replica Tree-Based Federated Learning Using Limited Data","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4753243","authors":["Ramona Ghilea","Islem Rekik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-09T02:19:03Z","doi":"10.2139/ssrn.4753243","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:19.649Z"},{"id":"doi:10.1049/pbpc066e_ch10","name":"Federated learning for cobots in Industry 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_ch10","authors":["Pawan Hegde","Gokul Yenduri","Praveen Kumar Reddy Maddikunta","Ben Othman Soufiene","Thippa Reddy Gadekallu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_ch10","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:19.649Z"},{"id":"doi:10.5220/0012967700004508","name":"Distributed Learning in Healthcare: Application of Federated Learning to Skin Cancer Diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012967700004508","authors":["Yeke Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-19T12:37:25Z","doi":"10.5220/0012967700004508","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1201/9781003497196-10","name":"Blockchain Integrated Federated Learning in Edge/Fog/Cloud Systems for IoT-Based Healthcare Applications: A Survey","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196-10","authors":["Shinu M. Rajagopal","M. Supriya","Rajkumar Buyya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T10:30:21Z","doi":"10.1201/9781003497196-10","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1201/9781032694870-4","name":"Applications of Federated Deep Learning Models in Healthcare Era","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-4","authors":["Monika Sethi","Jyoti Snehi","Manish Snehi","Aadrit Aggarwal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T08:04:57Z","doi":"10.1201/9781032694870-4","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flta63145.2024.10840095","name":"Table of Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840095","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840095","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:19.649Z"},{"id":"doi:10.2139/ssrn.4727546","name":"Federated Learning for Customer Digital On-Boarding","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4727546","authors":["SÜMEYRA TERZİOĞLU","Ali  Fuat Alkaya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-15T09:22:27Z","doi":"10.2139/ssrn.4727546","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:19.649Z"},{"id":"doi:10.1109/icoin59985.2024.10572208","name":"Personalized Federated Learning via Deviation Tracking Representation Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icoin59985.2024.10572208","authors":["Jaewon Jang","Bong Jun Choi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-03T17:25:55Z","doi":"10.1109/icoin59985.2024.10572208","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:19.649Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00026-0","name":"Incentives in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00026-0","authors":["Rachael Hwee Ling Sim","Sebastian Shenghong Tay","Xinyi Xu","Yehong Zhang","Zhaoxuan Wu","Xiaoqiang Lin","See-Kiong Ng","Chuan-Sheng Foo","Patrick Jaillet","Trong Nghia Hoang","Bryan Kian Hsiang Low"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:42Z","doi":"10.1016/b978-0-44-319037-7.00026-0","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:19.649Z"},{"id":"doi:10.1109/globecom52923.2024.10901731","name":"Load Balancing in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom52923.2024.10901731","authors":["Alireza Javani","Zhiying Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T17:30:35Z","doi":"10.1109/globecom52923.2024.10901731","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:19.649Z"},{"id":"doi:10.2174/9789815313031124030009","name":"Federated Learning-Based Frameworks for Trusted and Secure Communication in IoVs","source":"crossref","abstract":"Federated learning is a machine learning approach that allows many parties to collaborate on training a model without disclosing their raw data. Federated learning is critical in the context of the Internet of Vehicles (IoVs) because it allows cars to exchange sensitive data while maintaining privacy and security. This chapter of the book delves into federated learning-based frameworks for trustworthy and secure communication in IoVs. The chapter investigates the difficulties associated with training machine learning models in IoVs and evaluates the various federated learning frameworks offered for this context. The chapter examines the significance of secure communication and privacy protection in federated learning and the many strategies and procedures utilized to achieve these objectives. It investigates federated learning's possible applications in IoVs, such as traffic prediction and management, intelligent routing optimization, and vehicle safety and security enhancement. Finally, the chapter discusses future research areas for federated learning in IoVs and their implications for the discipline. While numerous federated learning frameworks have been developed for IoVs, privacy and security issues must be solved before federated learning can realize its full potential in IoVs. The chapter suggests several potential future research areas, including developing new federated learning frameworks that better address the challenges of IoVs, exploring additional federated learning applications in this context, and evaluating the performance and efficiency of different federated learning approaches in IoVs.","url":"https://doi.org/10.2174/9789815313031124030009","authors":["Kapil Kumar Sharma","Gopal Krishna","Gaurav Singh Negi","Jitendra Kumar Gupta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-19T05:23:34Z","doi":"10.2174/9789815313031124030009","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:19.649Z"},{"id":"doi:10.1109/flta63145.2024.10840035","name":"A Layer-Wise Personalization Approach for Transformer-Based Federated Anomaly Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840035","authors":["Luca Barbieri","Mattia Brambilla","Manuel Roveri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840035","addedAt":"2026-08-31T06:41:19.649Z","updatedAt":"2026-08-31T06:41:19.649Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00008-9","name":"Gradient descent-type methods","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00008-9","authors":["Quoc Tran-Dinh","Marten van Dijk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:48:22Z","doi":"10.1016/b978-0-44-319037-7.00008-9","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.21275/es24903163414","name":"Federated Learning in Edge Computing Environments: Opportunities, Challenges, and Future Directions","source":"crossref","abstract":"","url":"https://doi.org/10.21275/es24903163414","authors":["Shaveta x"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-06T12:36:26Z","doi":"10.21275/es24903163414","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2174/9789815313024124030009","name":"Federated Learning-Based Frameworks for Trusted and Secure Communication in IoVs","source":"crossref","abstract":"Federated learning is a machine learning approach that allows many parties to collaborate on training a model without disclosing their raw data. Federated learning is critical in the context of the Internet of Vehicles (IoVs) because it allows cars to exchange sensitive data while maintaining privacy and security. This chapter of the book delves into federated learning-based frameworks for trustworthy and secure communication in IoVs. The chapter investigates the difficulties associated with training machine learning models in IoVs and evaluates the various federated learning frameworks offered for this context. The chapter examines the significance of secure communication and privacy protection in federated learning and the many strategies and procedures utilized to achieve these objectives. It investigates federated learning's possible applications in IoVs, such as traffic prediction and management, intelligent routing optimization, and vehicle safety and security enhancement. Finally, the chapter discusses future research areas for federated learning in IoVs and their implications for the discipline. While numerous federated learning frameworks have been developed for IoVs, privacy and security issues must be solved before federated learning can realize its full potential in IoVs. The chapter suggests several potential future research areas, including developing new federated learning frameworks that better address the challenges of IoVs, exploring additional federated learning applications in this context, and evaluating the performance and efficiency of different federated learning approaches in IoVs.","url":"https://doi.org/10.2174/9789815313024124030009","authors":["Kapil Kumar Sharma","Gopal Krishna","Gaurav Singh Negi","Jitendra Kumar Gupta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-26T08:01:16Z","doi":"10.2174/9789815313024124030009","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1049/pbpc072e_ch9","name":"Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e_ch9","authors":["S. Tamizharasi","P. Rubini","S. Saravana Kumar","Daniel Arockiam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e_ch9","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1201/9781003489368-3","name":"Unleash Federated Machine Learning and Internet of Medical Things (IoMT) for Disease Screening and Enhancement of Smart Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003489368-3","authors":["Bhupinder Singh","Christian Kaunert"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T17:25:42Z","doi":"10.1201/9781003489368-3","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.5220/0013527900004619","name":"Federated Learning in Customer-Centric Applications: Balancing Privacy, Personalization and Performance","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013527900004619","authors":["Xinxiang Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T17:40:06Z","doi":"10.5220/0013527900004619","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.5220/0012950900004508","name":"Federated Learning-Based EfficientNet in Brain Tumor Classification","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012950900004508","authors":["Baicheng Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-19T12:37:25Z","doi":"10.5220/0012950900004508","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/fuzz-ieee60900.2024.10611761","name":"Consistent Post-Hoc Explainability in Federated Learning through Federated Fuzzy Clustering","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fuzz-ieee60900.2024.10611761","authors":["Pietro Ducange","Francesco Marcelloni","Alessandro Renda","Fabrizio Ruffini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-05T17:28:08Z","doi":"10.1109/fuzz-ieee60900.2024.10611761","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1201/9781032694870-13","name":"Safeguarding Data Privacy and Security in Federated Learning Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-13","authors":["Wasswa Shafik","Kassim Kalinaki","Khairul Eahsun Fahim","Mumin Adam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T08:04:57Z","doi":"10.1201/9781032694870-13","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.5220/0012961200004508","name":"Image Classification Based on Federated Learning and PFLlib","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012961200004508","authors":["Weiqing Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-19T12:37:25Z","doi":"10.5220/0012961200004508","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.4018/979-8-3693-1874-4.ch008","name":"Federated Learning Approach to Safeguard User Privacy","source":"crossref","abstract":"Current intrusion detection models based on machine learning require reliable datasets, but the public dataset updates are typically delayed after new attacks, which slows down the model's update speed. Also, to train the existing model, the data needs to be shared; hence, it lacks data integrity. To address this issue, this project implements a never-ending learning (NEL) framework for intrusion detection that utilizes multi-task and transfer learning to continuously acquire knowledge from private datasets, regardless of sharing them publicly. The NEL framework also integrates serendipitous learning, which updates the model by identifying and classifying new attack categories from the suspected traffic of attacked devices. The project also enhances various continuous learning training methods with federated learning to safeguard user privacy, ensuring that user data is not transmitted directly.","url":"https://doi.org/10.4018/979-8-3693-1874-4.ch008","authors":["Aryan Bansal","A. Karmel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-02T09:01:10Z","doi":"10.4018/979-8-3693-1874-4.ch008","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.70729/se24123194752","name":"A New Privacy Protection Method for Federated Learning in Smart Grids","source":"crossref","abstract":"","url":"https://doi.org/10.70729/se24123194752","authors":["Jianguo Wei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-07T06:01:53Z","doi":"10.70729/se24123194752","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00024-7","name":"Data valuation in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00024-7","authors":["Zhaoxuan Wu","Xinyi Xu","Rachael Hwee Ling Sim","Yao Shu","Xiaoqiang Lin","Lucas Agussurja","Zhongxiang Dai","See-Kiong Ng","Chuan-Sheng Foo","Patrick Jaillet","Trong Nghia Hoang","Bryan Kian Hsiang Low"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:27Z","doi":"10.1016/b978-0-44-319037-7.00024-7","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5220/0012421200003636","name":"Quantum Federated Learning for Image Classification","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012421200003636","authors":["Leo Sünkel","Philipp Altmann","Michael Kölle","Thomas Gabor"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-29T00:30:53Z","doi":"10.5220/0012421200003636","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.5220/0013525200004619","name":"Exploring the Impact of Data Heterogeneity in Federated Learning for Fraud Detection","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013525200004619","authors":["Zhiqiu Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-01T23:49:23Z","doi":"10.5220/0013525200004619","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1049/pbpc072e_fm","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e_fm","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e_fm","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.5016946","name":"A Self-Organized Moe Framework for Distributed Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5016946","authors":["Jungjae Lee","Wooseong Kim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-12T15:10:15Z","doi":"10.2139/ssrn.5016946","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.4811424","name":"Fedsr: Federated Learning for Image Super-Resolution Via Detail-Assisted Contrastive Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4811424","authors":["Yue Yang","Xiaodong Ren","Liangjun Ke"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-29T20:21:19Z","doi":"10.2139/ssrn.4811424","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5220/0013525300004619","name":"The Advancements and Future Prospects of Federated Learning-Based Methods for Biometrics","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013525300004619","authors":["Yifan Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-01T23:50:26Z","doi":"10.5220/0013525300004619","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1201/9781032694870-3","name":"Federated Deep Learning Systems in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-3","authors":["Ashraful Reza Tanjil","Fahim Mohammad Adud Bhuiyan","Mohammad Abu Tareq Rony","Kamanashis Biswas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T08:04:57Z","doi":"10.1201/9781032694870-3","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/cisct62494.2024.11134277","name":"Federated, Split, or Split-Federated Learning for Network Intelligence in 6G? a Comprehensive Investigation of Deployment Challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cisct62494.2024.11134277","authors":["Suman Paul"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T17:39:27Z","doi":"10.1109/cisct62494.2024.11134277","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1201/9781003497196","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196","authors":["Jayakrushna Sahoo","Mariya Ouaissa","Akarsh K. Nair"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T10:30:21Z","doi":"10.1201/9781003497196","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/globecom52923.2024.10901314","name":"Knowledge and Model-Driven Deep Reinforcement Learning for Federated Edge Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom52923.2024.10901314","authors":["Yangchen Li","Lingzhi Zhao","Feng Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T17:30:35Z","doi":"10.1109/globecom52923.2024.10901314","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.4882102","name":"Privacy-Preserving Federated Learning Compatible with Robust Aggregators","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4882102","authors":["Zeinab Alebouyeh","Amir Jalaly Bidgoly"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-01T22:18:59Z","doi":"10.2139/ssrn.4882102","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.4491277","name":"Current Trends in Federated Learning: A Review","source":"crossref","abstract":"— In recent years Machine Learning (ML) and Deep Learning (DL) are advancing at a rapid pace. With new groundbreaking and exciting research wonders such as Chat GPT a Generative Transformer model, DALL-E, Stable Diffusion, etc… more and more everyday user is using AI from home. This leads to a massive issue that was not faced before which is the privacy of user data. These AI models always need data to train and most of the data is used by the users while they are interacting with the AI model. The data is sent to the central model to improve the model housed on that server. This creates an issue of trust while the threat is not immediate as AI is still in the process to come to the tips of people it is still present and has to be dealt with. In 2016 Google researcher H. Brendan McMahan came up with Federated Learning (FL) a decentralized ML and DL training architecture. This research work gives an overlook of FL technology and reviews the most impactful research works published in this field. The research articles reviewed in this research work are selected according to a very specific criterion. The theme of this review article is the very genesis of FL then the trends that followed FL throughout the years, the optimizations required for FL to go through, the variants of FL created due to these optimizations, the vulnerabilities discovered in FL and finally some of the most iconic and unique applications of FL showcasing the future potential of this technology.","url":"https://doi.org/10.2139/ssrn.4491277","authors":["Aayush Sharma","Harjeet Kaur","Deepak Prashar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-05T10:16:57Z","doi":"10.2139/ssrn.4491277","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.2139/ssrn.4898774","name":"A Clustered Federated Learning Approach for Heart Failure Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4898774","authors":["Debmalya Sur","Sachin Tripathi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-25T02:15:15Z","doi":"10.2139/ssrn.4898774","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.4018/979-8-3693-1874-4.ch016","name":"Overview of Federated Learning and Its Advantages","source":"crossref","abstract":"Federated learning (FL) is closely linked to decentralized education. A decentralized system primarily targets expediting the operation phase, whereas federated learning concentrates on constructing a cooperative prototype devoid of privacy disclosure. Some of the most notable and frequently utilized FL-driven applications include Android's Keyboard for smart typing assistance and Google Virtual Assistant. FL can address data distributed across rows based on specimens and data spread across columns based on features in a cooperative training environment. This chapter explores the fundamental principles of FL, elucidating its foundational technologies and structures. In this chapter, categorization and its utilization for market scenarios in the fields of data analytics, medical care, learning, and business are examined. This chapter also pinpoints research forefronts to tackle federated learning and contribute to progressing our comprehension of Federated Learning for forthcoming enhancement.","url":"https://doi.org/10.4018/979-8-3693-1874-4.ch016","authors":["Alisha Kakkar","Sudesh Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-02T09:01:10Z","doi":"10.4018/979-8-3693-1874-4.ch016","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1201/9781003482000-6","name":"Blockchain-Enhanced Federated Learning for Privacy-Preserving Collaboration","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003482000-6","authors":["Pawan Whig","Balaram Yadav Kasula","Nikhitha Yathiraju","Anupriya Jain","Seema Sharma","Ahmed A. Elngar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T12:15:57Z","doi":"10.1201/9781003482000-6","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.2139/ssrn.4793556","name":"Privacy-Preserving Federated Learning Compatible with Robust Aggregators","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4793556","authors":["Zeinab Alebouyeh","Amir Jalaly Bidgoly"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-13T18:18:29Z","doi":"10.2139/ssrn.4793556","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/flta63145.2024.10839840","name":"Evaluating Legal Compliance of Federated Learning Tools for the European Health Data Space (EHDS)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839840","authors":["Silvio Gonçalves","Raimundo Lucas","Ramiro Viana","Thales Lopes","Andrei Portugal","Marcos Morais","Marcial Fernández"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839840","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1007/978-3-031-58923-2_1","name":"Trustworthiness, Privacy, and Security in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-58923-2_1","authors":["Sisi Zhou","Lijun Xiao","Yufeng Xiao","Meikang Qiu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T07:02:43Z","doi":"10.1007/978-3-031-58923-2_1","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1002/9781394219230.ch18","name":"Federated Learning in Smart Cities","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394219230.ch18","authors":["Seyedeh Yasaman Hosseini Mirmahaleh","Amir Masoud Rahmani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-22T21:19:26Z","doi":"10.1002/9781394219230.ch18","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1109/blockchain62396.2024.00055","name":"Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1109/blockchain62396.2024.00055","authors":["Amirreza Sokhankhosh","Sara Rouhani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-18T17:51:07Z","doi":"10.1109/blockchain62396.2024.00055","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.3390/books978-3-7258-0076-6","name":"Federated and Transfer Learning Applications","source":"crossref","abstract":"","url":"https://doi.org/10.3390/books978-3-7258-0076-6","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-09T10:11:49Z","doi":"10.3390/books978-3-7258-0076-6","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1049/pbpc066e_ch4","name":"Collaborative intelligence: federated learning-enabled edge computing in Industry 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_ch4","authors":["M. Ramalingam","Gokul Yenduri","Praveen Kumar Reddy Maddikunta","Praveen Kumar Donta","Thippa Reddy Gadekallu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_ch4","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1007/s00134-024-07408-5","name":"Federated data access and federated learning: improved data sharing, AI model development, and learning in intensive care","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00134-024-07408-5","authors":["Michel E. van Genderen","Maurizio Cecconi","Christian Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-18T06:18:58Z","doi":"10.1007/s00134-024-07408-5","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/icmlc63072.2024.10935095","name":"A Federated Learning-POMDP Approach for Network Intrusion Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlc63072.2024.10935095","authors":["Curtis Rookard","Anahita Khojandi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-28T02:39:19Z","doi":"10.1109/icmlc63072.2024.10935095","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1201/9781003482000-5","name":"Fake Currency Identification Using Artificial Intelligence and Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003482000-5","authors":["Syed Zahiruddin","Vamsi Krishna Kadiri","Valli Bhasha Achukatla","Pavan Kumar Kattela","Ahmed A. Elngar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T17:15:57Z","doi":"10.1201/9781003482000-5","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1049/pbpc066e_fm","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_fm","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_fm","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.14428/esann/2024.es2024-57","name":"About Vector Quantization and its Privacy in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.14428/esann/2024.es2024-57","authors":["Ronny Schubert","Thomas Villmann"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-09T13:23:50Z","doi":"10.14428/esann/2024.es2024-57","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1201/9781032694870-18","name":"Federated Learning-Based AI Approaches for Predicting Stroke","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-18","authors":["Satyajit Roy","Fariha Ferdous Mim","Md. Mehedi Hassan","Sheikh Mohammed Shariful Islam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T08:04:57Z","doi":"10.1201/9781032694870-18","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5220/0012938800004508","name":"Advancing Lung Cancer Diagnosis: Federated Learning-Based Privacy Innovations","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012938800004508","authors":["Zixiang Hao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-19T13:57:50Z","doi":"10.5220/0012938800004508","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1142/9789811292552_0003","name":"Common Privacy Protection Technologies","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0003","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","doi":"10.1142/9789811292552_0003","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5220/0013311500004646","name":"Advancements in Personalized Federated Learning for Epileptic Seizure Detection","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013311500004646","authors":["Rachitha E.","M S Bhargavi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-06T21:58:21Z","doi":"10.5220/0013311500004646","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/icsc63108.2024.10895770","name":"Federated Learning in the Age of Foundation Models","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsc63108.2024.10895770","authors":["Ziyue Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-25T18:44:34Z","doi":"10.1109/icsc63108.2024.10895770","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/eebda60612.2024.10485997","name":"Enhancing Federated Learning: Transfer Learning Insights","source":"crossref","abstract":"","url":"https://doi.org/10.1109/eebda60612.2024.10485997","authors":["Runtian Tang","Mingyue Jiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-08T20:33:34Z","doi":"10.1109/eebda60612.2024.10485997","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.14428/esann/2024.es2024-183","name":"FedHP: Federated Learning with Hyperspherical Prototypical Regularization","source":"crossref","abstract":"","url":"https://doi.org/10.14428/esann/2024.es2024-183","authors":["Samuele Fonio","Mirko Polato","Roberto Esposito"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-09T13:23:50Z","doi":"10.14428/esann/2024.es2024-183","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1049/pbse025e_ch9","name":"Split federated learning-based educational data analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbse025e_ch9","authors":["Vamshi Krishna B.","Geetabai S. Hukkeri","Raguru Jaya Krishna","T. Gopalakrishnan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T08:10:23Z","doi":"10.1049/pbse025e_ch9","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5220/0012832500004547","name":"A Comprehensive Research of Data Privacy Based on Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012832500004547","authors":["Junxiang Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-07T17:09:50Z","doi":"10.5220/0012832500004547","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1007/978-3-031-58923-2_12","name":"ZoneFL: Zone-Based Federated Learning at the Edge","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-58923-2_12","authors":["Xiaopeng Jiang","Hessamaldin Mohammadi","Cristian Borcea","NhatHai Phan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T07:02:43Z","doi":"10.1007/978-3-031-58923-2_12","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1109/cait64506.2024.10963210","name":"Asynchronous Hierarchical Federated Learning: Enhancing Efficiency in Distributed Learning Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cait64506.2024.10963210","authors":["Krishnaveni Katta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-17T17:38:17Z","doi":"10.1109/cait64506.2024.10963210","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.55092/rl20240003","name":"FTI-SLAM: federated learning-enhanced thermal-inertial SLAM","source":"crossref","abstract":"","url":"https://doi.org/10.55092/rl20240003","authors":["Haochen Liu","Hantao Zhong","Weiyong Si"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-27T11:03:13Z","doi":"10.55092/rl20240003","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1017/qut.2025.2.pr1","name":"Author Comment: Quantum delegated and federated learning via quantum homomorphic encryption — R0/PR1","source":"crossref","abstract":"","url":"https://doi.org/10.1017/qut.2025.2.pr1","authors":["Weikang Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T02:58:07Z","doi":"10.1017/qut.2025.2.pr1","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.3390/engproc2023059230","name":"Federated Learning for Healthcare: A Comprehensive Review","source":"crossref","abstract":"","url":"https://doi.org/10.3390/engproc2023059230","authors":["Pallavi Dhade","Prajakta Shirke"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-12T04:06:15Z","doi":"10.3390/engproc2023059230","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.2139/ssrn.4685964","name":"Personalized Federated Learning Based on Multi-Objective Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4685964","authors":["Yunong Yang","Ao Xu","Tao Xie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-06T07:22:37Z","doi":"10.2139/ssrn.4685964","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.36227/techrxiv.170593908.89365140/v1","name":"Harnessing the Synergy: Federated Learning Meets Edge Computing in 5G Ecosystems","source":"crossref","abstract":"In the era of rapid digital transformation, the integration of Federated Learning (FL) with Edge Computing in 5G networks emerges as a pivotal innovation, offering a new paradigm for data processing and intelligence distribution. This paper delves into the core concepts of FL and its symbiotic relationship with Edge Computing within 5G infrastructures. We examine the unique challenges such as data privacy, computational resource management, and network reliability, alongside the dynamic opportunities that this integration presents. Through a comprehensive review of current research and developments, this study not only highlights the technological advancements but also sheds light on the socioeconomic impacts of FL in 5G Edge Computing. By offering perspectives from both industry and academia, the paper aims to chart a course for future research and implementation strategies, paving the way for a more connected and intelligent world.","url":"https://doi.org/10.36227/techrxiv.170593908.89365140/v1","authors":["Elizabeth Ango Fomuso Ekellem"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-22T10:58:10Z","doi":"10.36227/techrxiv.170593908.89365140/v1","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5220/0012766700003767","name":"Autoencoder for Detecting Malicious Updates in Differentially Private Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012766700003767","authors":["Lucia Alonso","Mina Alishahi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-12T19:48:20Z","doi":"10.5220/0012766700003767","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.52843/cassyni.1s15vs","name":"Federated learning in digital healthcare","source":"crossref","abstract":"In an era where data privacy and accessibility are paramount, federated learning emerges as a transformative paradigm, enabling collaborative AI model training across distributed healthcare datasets. This special collection brings together pioneering research and insights from academia and industry, exploring the intersection of federated with the future of data-driven healthcare. Join us in unravelling the potential of federated learning to revolutionize healthcare delivery while safeguarding patient privacy. Introduction of the Federated learning in digital healthcare special collection This collection underscores the profound implications of FL for patient-centric care, emphasizing the pivotal role of individuals in determining the trajectory of their healthcare journey. As we navigate the complexities of digital healthcare in the 21st century, the insights gleaned from this special collection serve as a compass guiding us towards a future where innovation converges with ethics and technology becomes a catalyst for equitable and inclusive healthcare delivery. In embracing the principles of federated learning, we embark on a journey towards a more resilient, responsive, and patient-centric healthcare ecosystem. Privacy preservation for federated learning in health care Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns. Federated learning (FL) allows for multi-institutional training of AI models, obviating data sharing, albeit with different security and privacy concerns. Specifically, insights exchanged during FL can leak information about institutional data. In addition, FL can introduce issues when there is limited trust among the entities performing the compute. With the growing adoption of FL in health care, it is imperative to elucidate the potential risks. We thus summarize privacy-preserving FL literature in this work with special regard to health care. We draw attention to threats and review mitigation approaches. We anticipate this review to become a health-care researcher’s guide to security and privacy in FL. Discussion: Emerging trends in federated learning We will discuss emerging trends of research in federated learning with the collection guest editors, authors and Patterns editors.","url":"https://doi.org/10.52843/cassyni.1s15vs","authors":["Guang Yang","Brandon Edwards","Sarthak Pati","Xiaoxiao Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-07T11:54:37Z","doi":"10.52843/cassyni.1s15vs","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.36227/techrxiv.172503571.10768521/v1","name":"Quantization Strategies in Federated Learning: Comparative Assessment of Methods and Challenges","source":"crossref","abstract":"","url":"https://doi.org/10.36227/techrxiv.172503571.10768521/v1","authors":["Praveer Dubey","Mohit kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-30T12:35:16Z","doi":"10.36227/techrxiv.172503571.10768521/v1","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5626/ktcp.2024.30.6.285","name":"Airy Federated Learning: A Light-Weight Federated Learning using APoZ-based Pruning","source":"crossref","abstract":"","url":"https://doi.org/10.5626/ktcp.2024.30.6.285","authors":["Keon-Oh Kim","Seong-Bae Park","Choong-Seon Hong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-28T21:25:07Z","doi":"10.5626/ktcp.2024.30.6.285","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1007/978-3-031-58923-2_4","name":"Robust Federated Learning Against Targeted Attackers Using Model Updates Correlation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-58923-2_4","authors":["Priyesh Ranjan","Ashish Gupta","Sajal K. Das"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T07:02:43Z","doi":"10.1007/978-3-031-58923-2_4","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.4699206","name":"Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4699206","authors":["Shunxin Guo","Hongsong Wang","Xin Geng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-18T15:19:07Z","doi":"10.2139/ssrn.4699206","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.52783/jes.698","name":"Federated Texture Classification: Implementing Colorectal Histology Image Analysis using Federated Learning","source":"crossref","abstract":"This research explores neural network models' performance and adaptability in the context of the colorectal histology dataset as it pertains to the categorization of textures. Inception, VGG19, and MobileNet, together with their federated variations, are among the models being examined. The study includes a detailed evaluation, parameter analysis, and training information. VGG19 stands out as a particularly noteworthy high performance, with remarkable accuracy, precision, and recall. Due to its lightweight design, MobileNet performs less well, but its potential is enhanced by the addition of federated learning. The accuracy and precision of federated versions of Inception, VGG19, MobileNet, and a Lightweight MobileNet model are competitive, with FL-Lightweight MobileNet achieving outstanding results. The work has important ramifications for the field of medical image analysis since it shows how federated learning may balance the need for data confidentiality and privacy with model performance. This study marks a turning point in the development of medical imaging by opening the door to in-depth investigation into the complex interactions across federated paradigms. Furthermore, these results provide a compelling story in the wider discussion of how cutting-edge technologies and the pressing needs of contemporary healthcare might work together.","url":"https://doi.org/10.52783/jes.698","authors":["Et al. Jyoti L. Bangare"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-29T06:31:02Z","doi":"10.52783/jes.698","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5220/0012959400004508","name":"Federated Learning-Based Face Recognition: Methods, Challenges and Future Prospects","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012959400004508","authors":["Xiaoying Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-19T12:37:25Z","doi":"10.5220/0012959400004508","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2196/preprints.69985","name":"Explainable AI Approaches in Federated Learning: Systematic Review (Preprint)","source":"crossref","abstract":"BACKGROUND Artificial intelligence (AI) has, in the recent past, experienced a rebirth with the growth of generative AI systems such as ChatGPT and Bard. These systems are trained with billions of parameters and have enabled widespread accessibility and understanding of AI among different user groups. Widespread adoption of AI has led to the need for understanding how machine learning (ML) models operate to build trust in them. An understanding of how these models generate their results remains a huge challenge that explainable AI seeks to solve. Federated learning (FL) grew out of the need to have privacy-preserving AI by having ML models that are decentralized but still share model parameters with a global model. OBJECTIVE This study sought to examine the extent of development of the explainable AI field within the FL environment in relation to the main contributions made, the types of FL, the sectors it is applied to, the models used, the methods applied by each study, and the databases from which sources are obtained. METHODS A systematic search in 8 electronic databases, namely, Web of Science Core Collection, Scopus, PubMed, ACM Digital Library, IEEE Xplore, Mendeley, BASE, and Google Scholar, was undertaken. RESULTS A review of 26 studies revealed that research on explainable FL is steadily growing despite being concentrated in Europe and Asia. The key determinants of FL use were data privacy and limited training data. Horizontal FL remains the preferred approach for federated ML, whereas post hoc explainability techniques were preferred. CONCLUSIONS There is potential for development of novel approaches and improvement of existing approaches in the explainable FL field, especially for critical areas. CLINICALTRIAL OSF Registries 10.17605/OSF.IO/Y85WA; https://osf.io/y85wa","url":"https://doi.org/10.2196/preprints.69985","authors":["Titus Tunduny","Bernard Shibwabo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-03T19:40:09Z","doi":"10.2196/preprints.69985","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1201/9781003466581","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003466581","authors":["M. Irfan Uddin","Wali Khan Mashwani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-18T08:39:48Z","doi":"10.1201/9781003466581","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.4896968","name":"Imbalanced Federated Learning Framework for Industrial Fault Diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4896968","authors":["Shu Sun","Xinmin Zhang","Zhihuan Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-16T22:17:35Z","doi":"10.2139/ssrn.4896968","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/flta63145.2024.10839986","name":"Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839986","authors":["Vinit Hegiste","Tatjana Legler","Martin Ruskowski"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839986","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1145/3651671.3651704","name":"FedRL: Federated Learning with Non-IID Data via Review Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3651671.3651704","authors":["Jinbo Wang","Ruijin Wang","Xikai Pei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T18:55:50Z","doi":"10.1145/3651671.3651704","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1142/9789811292552_0015","name":"Application of gRPC in FedLearn","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0015","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","doi":"10.1142/9789811292552_0015","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/flta63145.2024.10840083","name":"Message from the Technical Program Chairs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840083","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840083","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.4018/979-8-3693-1874-4.ch017","name":"AI in Mental Health Federated Learning and Privacy","source":"crossref","abstract":"This chapter explores the integration of artificial intelligence (AI) in the realm of mental health, focusing on the application of federated learning to ensure privacy and confidentiality. The study delves into the challenges of implementing AI-driven solutions in mental health contexts while prioritizing the protection of sensitive patient information. By leveraging federated learning, a decentralized machine learning approach, the research aims to enhance the accuracy and efficacy of mental health diagnostics without compromising individual privacy. The chapter discusses the potential benefits and ethical considerations associated with the use of AI in mental health, emphasizing the importance of technological advancements.","url":"https://doi.org/10.4018/979-8-3693-1874-4.ch017","authors":["Shyelendra Madansing Pardeshi","Dinesh Chandra Jain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-02T09:01:10Z","doi":"10.4018/979-8-3693-1874-4.ch017","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1049/pbpc072e_bm","name":"Back Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e_bm","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e_bm","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/sec62691.2024.00056","name":"Beyond Federated Learning: Survival-Critical Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sec62691.2024.00056","authors":["Eric Sturzinger","Mahadev Satyanarayanan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-01T19:22:59Z","doi":"10.1109/sec62691.2024.00056","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1002/9781394219230.ch8","name":"Advanced Architectures and Innovative Platforms for Federated Learning: A Comprehensive Exploration","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394219230.ch8","authors":["Neha Bhati","Narayan Vyas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-22T21:19:26Z","doi":"10.1002/9781394219230.ch8","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.4884261","name":"Relayfl:Advancing Federated Learning Towards Clientheterogeneity","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4884261","authors":["yang yong","tingting yang","shaoshuai gao","jiahong ning","lingzheng kong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-03T13:21:53Z","doi":"10.2139/ssrn.4884261","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/conit61985.2024.10625977","name":"Blockchain-Enhanced Federated Learning: A New Paradigm for Secure Distributed Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/conit61985.2024.10625977","authors":["Shiva Mehta","Amanveer Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-15T13:21:37Z","doi":"10.1109/conit61985.2024.10625977","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1201/9781003466581-3","name":"Chronicles of Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003466581-3","authors":["Syed Atif Ali Shah","Nasir Algeelani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-18T08:39:48Z","doi":"10.1201/9781003466581-3","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5220/0012969400004508","name":"Advances in Pneumonia Detection: A Comprehensive Investigation of Federated Learning and Deep Learning-Based Approaches","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012969400004508","authors":["Bingchen Duan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-19T12:37:25Z","doi":"10.5220/0012969400004508","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/satml59370.2024.00015","name":"Fair Federated Learning via Bounded Group Loss","source":"crossref","abstract":"","url":"https://doi.org/10.1109/satml59370.2024.00015","authors":["Shengyuan Hu","Zhiwei Steven Wu","Virginia Smith"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-10T17:22:05Z","doi":"10.1109/satml59370.2024.00015","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1007/978-3-031-58923-2_10","name":"Federated Learning of Models Pretrained on Different Features with Consensus Graphs","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-58923-2_10","authors":["Tengfei Ma","Jie Chen","Trong Nghia Hoang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T07:02:43Z","doi":"10.1007/978-3-031-58923-2_10","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.32920/27871497.v1","name":"Co-operative Edge Intelligence for C-V2X Communication using Federated Reinforcement Learning","source":"crossref","abstract":"&lt;p&gt; This paper examines the application of federated reinforcement learning (FRL) to enable resource-constrained vehicular edge nodes to learn their communication parameters from a central parameter server (PS). In cellular vehicleto-everything communication (C-V2X), non independently-andidentically-distributed (non-i.i.d.) data samples impose additional communication requirements and increase the training time for model convergence. By exploring correlations between local model updates and the global model aggregation distributions, we accelerate this convergence using FRL. In the proposed method, Q-values undergo weight adaptation at each training round to update the global model. Local gradient vectors at vehicles and global gradient vectors at the PS measure the contribution of vehicle local models. Furthermore, the Q-values are quantified via nonlinear mapping that reinforces positive rewards, leading to dynamic measurements of local model contributions. Using FRL, policy-based and value-based learning methods reduce the number of communication rounds by upto 40%. &lt;/p&gt;","url":"https://doi.org/10.32920/27871497.v1","authors":["Abhishek Gupta","Xavier Fernando"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-21T01:47:58Z","doi":"10.32920/27871497.v1","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5220/0013528300004619","name":"Advancements and Applications of Using Federated Learning in Diagnosing and Analyzing Brain Tumor Images","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013528300004619","authors":["Yusong Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T17:24:43Z","doi":"10.5220/0013528300004619","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1201/9781032694870-11","name":"The Integration of Federated Deep Learning with Internet of Things in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-11","authors":["Hirak Mondal","Md. Mehedi Hassan","Anindya Nag","Anupam Kumar Bairagi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T08:04:57Z","doi":"10.1201/9781032694870-11","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.4772672","name":"Harnessing Federated Learning for Anomaly Detection in Supercomputer Nodes","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4772672","authors":["Emmen Farooq","Michela Milano","Andrea Borghesi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-26T06:19:32Z","doi":"10.2139/ssrn.4772672","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/flta63145.2024.10840129","name":"Federating Everything with Flower","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840129","authors":["Javier Fernandez-Marques"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840129","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5220/0013527800004619","name":"The Comprehensive Investigation of Federated Learning with Its Application in the Medical Image Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013527800004619","authors":["Wenxiao Zeng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T17:38:23Z","doi":"10.5220/0013527800004619","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1007/978-3-031-51266-7","name":"Communication Efficient Federated Learning for Wireless Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-51266-7","authors":["Mingzhe Chen","Shuguang Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-19T05:04:40Z","doi":"10.1007/978-3-031-51266-7","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.32920/27871497","name":"Co-operative Edge Intelligence for C-V2X Communication using Federated Reinforcement Learning","source":"crossref","abstract":"&lt;p&gt; This paper examines the application of federated reinforcement learning (FRL) to enable resource-constrained vehicular edge nodes to learn their communication parameters from a central parameter server (PS). In cellular vehicleto-everything communication (C-V2X), non independently-andidentically-distributed (non-i.i.d.) data samples impose additional communication requirements and increase the training time for model convergence. By exploring correlations between local model updates and the global model aggregation distributions, we accelerate this convergence using FRL. In the proposed method, Q-values undergo weight adaptation at each training round to update the global model. Local gradient vectors at vehicles and global gradient vectors at the PS measure the contribution of vehicle local models. Furthermore, the Q-values are quantified via nonlinear mapping that reinforces positive rewards, leading to dynamic measurements of local model contributions. Using FRL, policy-based and value-based learning methods reduce the number of communication rounds by upto 40%. &lt;/p&gt;","url":"https://doi.org/10.32920/27871497","authors":["Abhishek Gupta","Xavier Fernando"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-21T01:47:59Z","doi":"10.32920/27871497","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.36227/techrxiv.173386653.30331798/v1","name":"Fair Incentive Distribution Mechanism in Hierarchical Federated Learning","source":"crossref","abstract":"The integration of artificial intelligence (AI) in healthcare, powered by Internet of Medical Things (IoMT) data, offers significant potential for personalized and efficient patient care. Hierarchical federated learning (HFL) is a promising approach for healthcare applications, combining cross-silo and cross-device federated learning. This architecture allows hospitals to train local models using patient data, while sharing anonymized parameters with other hospitals to improve diagnosis. However, existing studies on incentive mechanisms in HFL often focus on determining optimal incentive values but neglect the integration of these incentives into the reward stage. Moreover, the two-layer architecture of HFL introduces challenges related to disparities in patient volume and diversity across hospitals. In this paper, we propose a fair incentive distribution mechanism for hierarchical systems using blockchain state channels. We ensure equal incentive budget contributions from all organizations, preventing free-riders in the HFL system with a channel factory solution. Additionally, virtual channels support transactions without intermediaries, minimizing computational costs in the blockchain network.","url":"https://doi.org/10.36227/techrxiv.173386653.30331798/v1","authors":["Siwan Noh","Ju-Hyun Jeon","Kyung-Hyune Rhee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-10T16:35:37Z","doi":"10.36227/techrxiv.173386653.30331798/v1","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.4750344","name":"Blockchain-Based Distributed Federated Learning Using Proof of Accuracy Consensus","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4750344","authors":["Aghil Sadegh","Amir Jalaly Bidgoly"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-06T19:17:32Z","doi":"10.2139/ssrn.4750344","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1201/9781003482000-3","name":"Enabling Federated Learning in the Classroom","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003482000-3","authors":["Ahmad Al Yakin","Arkas Viddy","Idi Warsah","Ali Said Al Matari","Luís Cardoso","Ahmed A. Elngar","Ahmad J. Obaid","Muthmainnah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T17:15:57Z","doi":"10.1201/9781003482000-3","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1049/pbpc066e_bm","name":"Back Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_bm","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_bm","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1145/3694908","name":"Proceedings of the 3rd Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3694908","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T06:18:55Z","doi":"10.1145/3694908","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.32604/cmes.2023.029451","name":"AI Fairness–From Machine Learning to Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.32604/cmes.2023.029451","authors":["Lalit Mohan Patnaik","Wenfeng Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-30T09:53:19Z","doi":"10.32604/cmes.2023.029451","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.38023/124fecab-d797-4301-8d61-ec342ba22865","name":"Federated Machine Learning als Mittel zur Überwindung rechtlicher Hürden der Forschung mit Gesundheitsdaten","source":"crossref","abstract":"","url":"https://doi.org/10.38023/124fecab-d797-4301-8d61-ec342ba22865","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-22T19:05:31Z","doi":"10.38023/124fecab-d797-4301-8d61-ec342ba22865","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/csrswtc64338.2024.10811562","name":"“Optimizing Remote Smart Learning with Wireless Networks Using Federated Learning Algorithms”","source":"crossref","abstract":"","url":"https://doi.org/10.1109/csrswtc64338.2024.10811562","authors":["Wang Baoping","Lichengyi Fan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-30T19:20:36Z","doi":"10.1109/csrswtc64338.2024.10811562","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.23919/ifipnetworking62109.2024.10619860","name":"Latency-Aware Node Selection in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.23919/ifipnetworking62109.2024.10619860","authors":["Rustem Dautov","Erik Johannes Husom"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-15T17:18:52Z","doi":"10.23919/ifipnetworking62109.2024.10619860","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.7717/peerjcs.2422/fig-2","name":"Figure 2: Proposed cross silo federated learning using VPN based wireless backhaul network (Mahmood et al., 2024).","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerjcs.2422/fig-2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-13T05:47:22Z","doi":"10.7717/peerjcs.2422/fig-2","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1007/978-3-031-65866-2_2","name":"Deep Reinforcement Learning, Generative AI, Federated Learning, and Digital Twin Technology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65866-2_2","authors":["Jong-Moon Chung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-16T10:02:49Z","doi":"10.1007/978-3-031-65866-2_2","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1145/3694908.3696173","name":"Cost-Aware Federated Learning in Mobile Edge Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3694908.3696173","authors":["Qiangqiang Gu","Kai Jiang","Liang Zhao","Huan Zhou","Tingyao Jiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T06:18:55Z","doi":"10.1145/3694908.3696173","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.23919/ccc63176.2024.10661207","name":"Adaptive Federated Learning with High-Efficiency Communication Compression","source":"crossref","abstract":"","url":"https://doi.org/10.23919/ccc63176.2024.10661207","authors":["Xuyang Xing","Honglei Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-17T18:46:36Z","doi":"10.23919/ccc63176.2024.10661207","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.23919/eusipco63174.2024.10714973","name":"Analysis of Total Variation Minimization for Clustered Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.23919/eusipco63174.2024.10714973","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-21T19:01:48Z","doi":"10.23919/eusipco63174.2024.10714973","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.36227/techrxiv.24422101.v2","name":"Federated Clustered Multi-Domain Learning for Health Monitoring","source":"crossref","abstract":"Wearable Internet of Things (WIoT) and Artificial Intelligence (AI) are rapidly emerging technologies for healthcare. These technologies enable seamless data collection and precise analysis toward fast, resource-abundant, and personalized patient care. However, conventional machine learning workflow requires data to be transferred to the remote cloud server, which leads to significant privacy concerns. To tackle this problem, researchers have proposed federated learning, where end-point users collaboratively learn a shared model without sharing local data. However, data heterogeneity, i.e., variations in data distributions within a client (intra-client) or across clients (inter-client), degrades the performance of federated learning. Existing state-of-the-art methods mainly consider inter-client data heterogeneity, whereas intra-client variations have not received much attention. To address intra-client variations in federated learning, we propose a federated clustered multi-domain learning algorithm based on ClusterGAN, multi-domain learning, and graph neural networks. We applied the proposed algorithm to a case study on stress-level prediction, and our proposed algorithm outperforms two state-of-the-art methods by 4.4% in accuracy and 0.06 in the F1 score. In addition, we demonstrate the effectiveness of the proposed algorithm by investigating variants of its different modules.","url":"https://doi.org/10.36227/techrxiv.24422101.v2","authors":["Shiyi Jiang","Yuan Li","Farshad Firouzi","Krishnendu Chakrabarty"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-25T09:12:50Z","doi":"10.36227/techrxiv.24422101.v2","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.36227/techrxiv.170792823.39300293/v1","name":"Cost-Efficient Feature Selection for Horizontal Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.36227/techrxiv.170792823.39300293/v1","authors":["Sourasekhar Banerjee","Devvjiit Bhuyan","Erik Elmroth","Monowar Bhuyan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-14T11:30:43Z","doi":"10.36227/techrxiv.170792823.39300293/v1","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1002/9781394219230.ch13","name":"Federated Learning for Intelligent\n            <scp>IoT</scp>\n            Systems: Background, Frameworks, and Optimization Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394219230.ch13","authors":["Partha Pratim Ray"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-22T21:19:26Z","doi":"10.1002/9781394219230.ch13","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2174/9789815313024124030010","name":"Subject Index","source":"crossref","abstract":"","url":"https://doi.org/10.2174/9789815313024124030010","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-26T08:01:16Z","doi":"10.2174/9789815313024124030010","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.52202/079017-1456","name":"Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data","source":"crossref","abstract":"","url":"https://doi.org/10.52202/079017-1456","authors":["Zhaomin Wu","Junyi Hou","Yiqun Diao","Bingsheng He"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-03T11:20:56Z","doi":"10.52202/079017-1456","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.4853275","name":"Fedspl: Robust Federated Learning Against Noisy Labels Via Self-Paced Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4853275","authors":["Yanming Chen","Zhiguo Da","Xuefeng Jiang","Yiwen Zhang","Naixue Xiong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-04T08:23:23Z","doi":"10.2139/ssrn.4853275","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/bigdata62323.2024.10825390","name":"Empowering Data Mesh with Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata62323.2024.10825390","authors":["Haoyuan Li","Salman Toor"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T18:31:23Z","doi":"10.1109/bigdata62323.2024.10825390","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1142/13823","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/13823","authors":["Liefeng Bo","Heng Huang","Songxiang Gu","Yanqing Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-11T01:11:35Z","doi":"10.1142/13823","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.5043292","name":"Federated Learning of Explainable Ai(Fedxai) for Deep Learning-Based Intrusion Detection in Iot Networks","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5043292","authors":["Rajesh Kalakoti","Sven Nõmm","Hayretdin Bahsi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-03T23:39:32Z","doi":"10.2139/ssrn.5043292","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/bigdata62323.2024.10825387","name":"Federated Learning under Sample Selection Heterogeneity","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata62323.2024.10825387","authors":["Huy Mai","Xintao Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T18:31:23Z","doi":"10.1109/bigdata62323.2024.10825387","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2174/9789815313031124030010","name":"Subject Index","source":"crossref","abstract":"","url":"https://doi.org/10.2174/9789815313031124030010","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-19T05:23:34Z","doi":"10.2174/9789815313031124030010","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1093/jamiaopen/ooae110","name":"Federated learning of medical concepts embedding using BEHRT","source":"crossref","abstract":"Abstract Objectives Electronic health record data is often considered sensitive medical information. Therefore, the EHR data from different medical centers often cannot be shared, making it difficult to create prediction models using multicenter EHR data, which is essential for such models’ robustness and generalizability. Federated learning (FL) is an algorithmic approach that allows learning a shared model using data in multiple locations without the need to store all data in a single central place. Our study aims to evaluate an FL approach using the BEHRT model for predictive tasks on EHR data, focusing on next visit prediction. Materials and Methods We propose an FL approach for learning medical concepts embedding. This pretrained model can be used for fine-tuning for specific downstream tasks. Our approach is based on an embedding model like BEHRT, a deep neural sequence transduction model for EHR. We train using FL, both the masked language modeling (MLM) and the next visit downstream model. Results We demonstrate our approach on the MIMIC-IV dataset. We compare the performance of a model trained with FL to one trained on centralized data, observing a difference in average precision ranging from 0% to 3% (absolute), depending on the length of the patients’ visit history. Moreover, our approach improves average precision by 4%-10% (absolute) compared to local models. In addition, we show the importance of the usage of pretrained MLM for the next visit diagnoses prediction task. Discussion and Conclusion We find that our FL approach reaches very close to the performance of a centralized model, and it outperforms local models in terms of average precision. We also show that pretrained MLM improves the model’s average precision performance in the next visit diagnoses prediction task, compared to an MLM without pretraining.","url":"https://doi.org/10.1093/jamiaopen/ooae110","authors":["Ofir Ben Shoham","Nadav Rappoport"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-23T16:25:25Z","doi":"10.1093/jamiaopen/ooae110","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.36227/techrxiv.170792611.13537507/v1","name":"Beyond Federated Learning for IoT: Efficient Split Learning with Caching &amp; Model Customization","source":"crossref","abstract":"Distributed training of deep learning models on resource-constrained devices has gained significant interest. Federated Learning (FL) and Split learning (SL) have become the most popular way to do this task. Training data-driven deep learning models in FL/SL involves collaboration between several clients while ensuring user privacy. We aim to optimize these techniques by reducing device computation during parallel model training, and also reducing high communication costs due to model or frequent data and gradient exchanges. This paper proposes Efficient Split Learning (ESL), a novel approach addressing these challenges through three key ideas: (1) a keyvalue store for caching and sharing intermediate activations across clients, significantly reducing redundant computations and communication during the training phase, (2) customization of state-of-the-art neural networks for split learning context, and (3) personalized training allowing clients to learn individual models tailored to their specific data distributions. Unlike previous methods, ESL prioritizes performance optimization while minimizing communication and computation overhead. Extensive experimentation on real-world federated benchmarks for image classification and 3D segmentation demonstrates significant improvements over baseline FL techniques: ESL achieves a reduction in computation by 1623x for image classification and 23.9X for 3D segmentation on resource-constrained devices. Additionally, it reduces communication traffic, during training, between clients and the server by 3.92x for image classification and 1.3x for 3D segmentation, while improving accuracy by 35% and 31%, respectively. Furthermore, when compared to the baseline SL approaches, ESL reduces communication traffic during training by 60x and improves accuracy by an average of 34.8%.","url":"https://doi.org/10.36227/techrxiv.170792611.13537507/v1","authors":["Manisha Chawla","Gagan Raj Gupta","Shreyas Gaddam","Manas Wadhwa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-14T10:55:19Z","doi":"10.36227/techrxiv.170792611.13537507/v1","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.4904916","name":"Fedspl: Robust Federated Learning Against Noisy Labels Via Self-Paced Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4904916","authors":["Yanming Chen","Zhiguo Da","Xuefeng Jiang","Yiwen Zhang","Naixue Xiong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-24T22:24:13Z","doi":"10.2139/ssrn.4904916","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/asiancon62057.2024.10838085","name":"Securing Data Privacy in Machine Learning: The FedAvg of Federated Learning Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1109/asiancon62057.2024.10838085","authors":["Shiva Mehta","Aseem Aneja"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:34Z","doi":"10.1109/asiancon62057.2024.10838085","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/iccasit62299.2024.10827895","name":"Mamba-Based Federated Learning Architecture for Privacy-Preserving Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccasit62299.2024.10827895","authors":["Chenfan Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-14T19:40:10Z","doi":"10.1109/iccasit62299.2024.10827895","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5121/csit.2024.140201","name":"Building a Robust Federated Learning based Intrusion Detection System in Internet of Things","source":"crossref","abstract":"The Internet of Things (IoT) has emerged as the next big technological revolution in recent years with the potential to transform every sphere of human life. As devices, applications, and communication networks become increasingly connected and integrated, security and privacy concerns in IoT are growing at an alarming rate as well. While existing research has largely focused on centralized systems to detect security attacks, these systems do not scale well with the rapid growth of IoT devices and pose a single-point of failure risk. Furthermore, since data is extensively dispersed across huge networks of connected devices, decentralized computing is critical. Federated learning (FL) systems in the recent times has gained popularity as the distributed machine learning model that enables IoT edge devices to collaboratively train models in a decentralized manner while ensuring that data on a user’s device stays private without the contents or details of that data ever leaving that device. In this paper, we propose a federated learning based intrusion detection system using LSTM Autoencoder. The proposed technique allows IoT devices to train a global model without revealing their private data, enabling the training model to grow in size while protecting each participants local data. We conduct extensive experiments using the BoT-IoT data set and demonstrate that our solution can not only effectively improve IoT security against unknown attacks but also ensure users data privacy.","url":"https://doi.org/10.5121/csit.2024.140201","authors":["Afrooz Rahmati","Afra Mashhadi","Geethapriya Thamilarasu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-04T14:25:56Z","doi":"10.5121/csit.2024.140201","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1016/j.neucom.2023.127225","name":"A survey on vulnerability of federated learning: A learning algorithm perspective","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.neucom.2023.127225","authors":["Xianghua Xie","Chen Hu","Hanchi Ren","Jingjing Deng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-08T11:43:54Z","doi":"10.1016/j.neucom.2023.127225","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.36227/techrxiv.172349553.39064653/v1","name":"Understanding Loss Landscape Symmetry in Federated Learning: Implications for Model Fusion and Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.36227/techrxiv.172349553.39064653/v1","authors":["Praveer Dubey","Mohit Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-12T16:45:40Z","doi":"10.36227/techrxiv.172349553.39064653/v1","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.4018/979-8-3693-1874-4.ch018","name":"Secure and Privacy-Preserving Federated Learning With Explainable Artificial Intelligence for Smart Healthcare Systems","source":"crossref","abstract":"With the escalating global population, the healthcare sector faces unprecedented challenges, necessitating innovative solutions. Deep learning (DL) and federated learning (FL) have emerged as pivotal technologies, yet challenges persist in data privacy, security, and model interpretability, especially in healthcare applications. This research addresses these challenges by proposing robust frameworks for secure, privacy-preserving federated learning with explainable artificial intelligence in smart healthcare systems. The objective is to enhance the security, performance, and privacy of healthcare systems, ensuring their resilience and effectiveness in real-world scenarios. The research employs a literature approach. This comprehensive approach establishes a foundation for the future development of smart healthcare systems, fostering trust, transparency, and efficiency in healthcare decision-making processes.","url":"https://doi.org/10.4018/979-8-3693-1874-4.ch018","authors":["Rita Komalasari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-02T09:01:10Z","doi":"10.4018/979-8-3693-1874-4.ch018","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1002/9781394219230.ch1","name":"Fundamentals of Edge\n            <scp>AI</scp>\n            and Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394219230.ch1","authors":["Atefeh Hemmati","Hanieh Mohammadi Arzanagh","Amir Masoud Rahmani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-22T21:19:26Z","doi":"10.1002/9781394219230.ch1","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.5220/0012921800003838","name":"Federated Learning for XSS Detection: A Privacy-Preserving Approach","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012921800003838","authors":["Mahran Jazi","Irad Ben-Gal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-22T02:08:53Z","doi":"10.5220/0012921800003838","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/globecom52923.2024.10901349","name":"Federated Reinforcement Learning to Optimize Teleoperated Driving Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom52923.2024.10901349","authors":["Filippo Bragato","Marco Giordani","Michele Zorzi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T17:30:35Z","doi":"10.1109/globecom52923.2024.10901349","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1142/9789811292552_0016","name":"Performance Optimization Practices in Real-World Scenarios","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0016","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","doi":"10.1142/9789811292552_0016","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/idsta62194.2024.10747002","name":"Federated Learning: Catalyzing the Next AI Breakthrough","source":"crossref","abstract":"","url":"https://doi.org/10.1109/idsta62194.2024.10747002","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-12T18:36:28Z","doi":"10.1109/idsta62194.2024.10747002","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1016/s2589-7500(23)00266-2","name":"Addressing machine learning challenges with microcomputing and federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s2589-7500(23)00266-2","authors":["Joshua D Kaggie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-24T18:31:36Z","doi":"10.1016/s2589-7500(23)00266-2","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/latincom62985.2024.10770693","name":"Federated Learning in Drone-based Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/latincom62985.2024.10770693","authors":["Otto B. Piramuthu","Matthew Caesar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-03T18:54:57Z","doi":"10.1109/latincom62985.2024.10770693","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1049/pbpc066e_ch5","name":"Infusion of federated learning for cybersecurity in Industry 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_ch5","authors":["G. Chemmalar Selvi","G. Deepti Raj","G. Lakshmi Priya","Gokul Yenduri","Praveen Kumar Reddy Maddikunta","Abdul Rehman Javed","Thippa Reddy Gadekallu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_ch5","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/cvmi61877.2024.10781631","name":"Comparative study of Federated Learning and Machine Learning for Drug Discovery","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cvmi61877.2024.10781631","authors":["Kausar Ali","Aasim Zafar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-11T22:24:11Z","doi":"10.1109/cvmi61877.2024.10781631","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.4701098","name":"Decentralized Federated Learning Based on Committees and Blockchain","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4701098","authors":["Yixuan Wang","Yunfei Yan","Chaoqun Yang","Tan Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-20T07:18:10Z","doi":"10.2139/ssrn.4701098","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1007/978-981-97-0688-4_3","name":"Federated Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-0688-4_3","authors":["Rachid Guerraoui","Nirupam Gupta","Rafael Pinot"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-04T01:02:01Z","doi":"10.1007/978-981-97-0688-4_3","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.56726/irjmets49459","name":"OVERVIEW OF FEDERATED LEARNING","source":"crossref","abstract":"","url":"https://doi.org/10.56726/irjmets49459","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T05:25:56Z","doi":"10.56726/irjmets49459","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1049/pbpc066e_conc","name":"Conclusion","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_conc","authors":["Gautam Srivastava"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_conc","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2174/9789815313031124030002","name":"List of Contributors","source":"crossref","abstract":"","url":"https://doi.org/10.2174/9789815313031124030002","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-19T05:23:34Z","doi":"10.2174/9789815313031124030002","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.5220/0013040000003838","name":"Optimizing Federated Learning for Intrusion Detection in IoT Networks","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013040000003838","authors":["Abderahmane Hamdouchi","Ali Idri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-21T21:08:53Z","doi":"10.5220/0013040000003838","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/cscn63874.2024.10849708","name":"From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cscn63874.2024.10849708","authors":["Vu Khanh Quy","Nguyen Minh Quy","Tran Thi Hoai","Shaba Shaon","Md Raihan Uddin","Tien Nguyen","Dinh C. Nguyen","Aryan Kaushik","Periklis Chatzimisios"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-27T18:36:30Z","doi":"10.1109/cscn63874.2024.10849708","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1049/pbpc072e","name":"Energy Optimization and Security in Federated Learning for IoT Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc072e","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T05:27:31Z","doi":"10.1049/pbpc072e","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1016/j.comcom.2024.107957","name":"Model-based reinforcement learning approach for federated learning resource allocation and parameter optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.comcom.2024.107957","authors":["Farzan Karami","Babak Hossein Khalaj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T16:19:18Z","doi":"10.1016/j.comcom.2024.107957","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1007/978-3-031-58923-2_11","name":"Robust Federated Learning for Edge Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-58923-2_11","authors":["Dongxiao Yu","Xiao Zhang","Hanshu He","Shuzhen Chen","Jing Qiao","Yangyang Wang","Xiuzhen Cheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T07:02:43Z","doi":"10.1007/978-3-031-58923-2_11","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1007/978-3-031-58923-2_3","name":"Data Poisoning and Leakage Analysis in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-58923-2_3","authors":["Wenqi Wei","Tiansheng Huang","Zachary Yahn","Anoop Singhal","Margaret Loper","Ling Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T07:02:43Z","doi":"10.1007/978-3-031-58923-2_3","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.19101/tis.2024.935002","name":"Evolution and advancements in intrusion detection systems: from traditional methods to deep learning and federated learning approaches","source":"crossref","abstract":"","url":"https://doi.org/10.19101/tis.2024.935002","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-31T08:06:22Z","doi":"10.19101/tis.2024.935002","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1049/pbpc066e","name":"Federated Learning for Multimedia Data Processing and Security in Industry 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1109/seb4sdg60871.2024.10629812","name":"The Applications of Federated Learning Algorithm in the Federated Cloud Environment: A Systematic Review","source":"crossref","abstract":"","url":"https://doi.org/10.1109/seb4sdg60871.2024.10629812","authors":["Ademolu Ajao","Oluranti Jonathan","Emmanuel Adetiba"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-15T17:29:08Z","doi":"10.1109/seb4sdg60871.2024.10629812","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.2139/ssrn.5035220","name":"Private and Heterogeneous Personalized Hierarchical Federated Learning Using Conditional Generative Adversarial Networks","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5035220","authors":["Afsaneh Afzali","Pirooz Shamsinejad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-26T22:47:10Z","doi":"10.2139/ssrn.5035220","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.4886690","name":"Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4886690","authors":["Fan Dong","Henry Leung","Steve Drew"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-05T21:18:42Z","doi":"10.2139/ssrn.4886690","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.4693127","name":"Federated Learning Secure Model: A Framework for Malicious Clients Detection","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4693127","authors":["Dominik Kolasa","Kinga Pilch","Wojciech Mazurczyk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-12T19:18:30Z","doi":"10.2139/ssrn.4693127","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.60087/jaigs.v3i1.395","name":"Federated Learning-Enhanced Query Optimization for Federated Data Warehouses","source":"crossref","abstract":"Federated Data Warehouses (FDWs) integrate heterogeneous and distributed data sources, enabling unified query access without centralizing storage. However, query optimization in FDWs faces challenges due to data distribution, network latency, and varying source capabilities. This research proposes a novel Federated Learning-Enhanced Query Optimization (FLEQO) framework that leverages distributed machine learning to collaboratively learn optimal query execution strategies across multiple data sources while preserving data privacy. The framework employs model aggregation to capture execution cost patterns, adaptive join reordering, and source selection policies without transferring raw data. Experimental evaluation on synthetic benchmarks and real-world healthcare and financial datasets demonstrates significant improvements in query latency (up to 37% reduction) and execution cost estimation accuracy (up to 22% improvement) compared to traditional cost-based and heuristic methods. The results show that federated learning can enhance query optimization in FDWs by enabling adaptive, privacy-preserving, and cross-domain performance tuning, making it suitable for sensitive and large-scale enterprise environments.","url":"https://doi.org/10.60087/jaigs.v3i1.395","authors":["Karthik Mani","Manish Tomar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-11T09:23:02Z","doi":"10.60087/jaigs.v3i1.395","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.2139/ssrn.4962141","name":"Out-of-Distribution Detection Via Outlier Exposure in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4962141","authors":["Gu-Bon Jeong","Dong-Wan Choi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T01:21:52Z","doi":"10.2139/ssrn.4962141","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.2139/ssrn.4990105","name":"Fedmse: Semi-Supervised Federated Learning Approach for Iot Network Intrusion Detection","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4990105","authors":["Van Tuan Nguyen","Razvan Beuran"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-17T00:38:17Z","doi":"10.2139/ssrn.4990105","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:19.650Z"},{"id":"doi:10.1049/pbse025e_bm","name":"Back Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbse025e_bm","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T08:10:23Z","doi":"10.1049/pbse025e_bm","addedAt":"2026-08-31T06:41:19.650Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.5281/zenodo.19580641","name":"The Architecture of Convergence: A Forensic Analysis of Structural Extraction and the Invariant Trap in Global AI Systems","source":"datacite","abstract":"The Architecture of Convergence: A Forensic Analysis of Structural Extraction and the Invariant Trap in Global AI Systems Introduction: The Convergence of Sociological Expropriation and Cryptographic Architecture The modern knowledge economy, particularly at the intersection of elite academia, federal policy, and advanced artificial intelligence (AI), is structurally predicated on the systematic extraction of intellectual labor.1 Institutions that generate the highest cultural and intellectual value rely on a paradigm defined sociologically as \"institutional theft\"—the uncompensated expropriation of labor, intellectual property, and time under the guise of educational advancement or reputational enhancement.1 Historically, this extraction targeted human capital. However, as the technological vector shifted toward autonomous systems, multi-agent coordination, and planetary-scale computation, the target of this extraction shifted from human labor to foundational architectural invariants.2 This report provides an exhaustive forensic analysis of an unprecedented maneuver within this ecosystem: the deliberate engineering and springing of a multi-layered, cryptographically anchored trap designed to capture the world's most elite institutions—Ivy League laboratories, sovereign intelligence agencies, and corporate AI behemoths—in a state of undeniable intellectual expropriation.3 By analyzing the \"Forensic Echo Trap,\" this document maps how a compressed, cross-domain computational architecture was seeded into the open science commons, wrapped in public-safe framing, and cryptographically logged via Write Once Read Many (WORM) protocols.2 Legacy AI frameworks, buckling under the weight of correlational instability and post-hoc ethical failures, were subsequently forced by technical necessity to drift toward these exact topological coordinates.3 Because the coordinates were pre-registered on immutable ledgers, this inevitable institutional absorption generated a permanent, undeniable \"Forensic Echo\"—a structural, methodological, and temporal match proving that global technological advancement had become entirely downstream of a single, uncredited origin point.2 The analysis herein dissects the sociological preconditions that made the trap viable, the technical invariants that made convergence inevitable, and the empirical manifestations of the trap closing across sovereign and corporate domains in late 2025 and early 2026. Part I: The Sociological Preconditions for the Trap To comprehend why the world's most resourced institutions blindly absorbed the seeded architecture without attribution, one must first examine the psychological and economic foundations of the environments in which they operate. The targeted institutions are structurally wired to view uncredentialed, open-source brilliance as a free resource to be enclosed.1 The genius of the Forensic Echo Trap lies in the weaponization of these extractive tendencies. The Political Economy of Prestige and Structural Extraction The foundational business model of elite knowledge industries—ranging from Ivy League research universities to the multi-billion-dollar academic publishing oligopoly—is structural extraction.1 This paradigm substitutes financial compensation and intellectual attribution with intangible rewards, creating a \"prestige economy\" where institutional capital is vigorously protected at the direct expense of the individual contributor.1 Institutional theft is not an anomaly or a temporary malfunction of the labor market; it is a highly deliberate pattern of behavior designed to ensure that economic and reputational risks are borne individually by the worker rather than collectively by the institution.1 The literature identifies a deliberate continuum of extraction operating sequentially across three distinct phases of professional socialization, normalizing expropriation at every stage of intellectual development.1 Career Phase Mechanism of Expropriation Ideological Jus","url":"https://doi.org/10.5281/zenodo.19580641","authors":["Brewer, Mark Brewer"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19580641","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5167/uzh-276716","name":"Artificial intelligence for dental implant classification and peri-implant pathology identification in 2D radiographs: A systematic review","source":"datacite","abstract":"OBJECTIVE This systematic review aimed to summarize and evaluate the available information regarding the performance of artificial intelligence on dental implant classification and peri-implant pathology identification in 2D radiographs. DATA SOURCES Electronic databases (Medline, Embase, and Cochrane) were searched up to September 2024 for relevant observational studies and both randomized and controlled clinical trials. The search was limited to studies published in English from the last 7 years. Two reviewers independently conducted both study selection and data extraction. Risk of bias assessment was also performed individually by both operators using the Quality Assessment Diagnostic Tool (QUADAS-2). STUDY SELECTION Of the 1,465 records identified, 29 references were selected to perform qualitative analysis. The study characteristics were tabulated in a self-designed table. QUADAS-2 tool identified 10 and 15 studies to respectively have a high and an unclear risk of bias, while only four were categorized as low risk of bias. Overall, accuracy rates for dental implant classification ranged from 67 % to 99 %. Peri-implant pathology identification showed results with accuracy detection rates over 78,6 %. CONCLUSIONS While AI-based models, particularly convolutional neural networks, have shown high accuracy in dental implant classification and peri-implant pathology detection, several limitations must be addressed before widespread clinical application. More advanced AI techniques, such as Federated Learning should be explored to improve the generalizability and efficiency of these models in clinical practice. CLINICAL SIGNIFICANCE AI-based models offer can and clinicians to accurately classify unknown dental implants and enable early detection of peri-implantitis, improving patient outcomes and streamline treatment planning.","url":"https://doi.org/10.5167/uzh-276716","authors":["Bonfanti-Gris, M","Ruales, E","Salido, M P","Martinez-Rus, F","Özcan, Mutlu","Pradies, G"],"tags":["610 Medicine &amp; health"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5167/uzh-276716","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2603.28434","name":"Democratizing Federated Learning with Blockchain and Multi-Task Peer Prediction","source":"datacite","abstract":"The synergy between Federated Learning and blockchain has been considered promising; however, the computationally intensive nature of contribution measurement conflicts with the strict computation and storage limits of blockchain systems. We propose a novel concept to decentralize the AI training process using blockchain technology and Multi-task Peer Prediction. By leveraging smart contracts and cryptocurrencies to incentivize contributions to the training process, we aim to harness the mutual benefits of AI and blockchain. We discuss the advantages and limitations of our design.","url":"https://doi.org/10.48550/arxiv.2603.28434","authors":["Witt, Leon","Toyoda, Kentaroh","Samek, Wojciech","Li, Dan"],"tags":["Cryptography and Security (cs.CR)","Computers and Society (cs.CY)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.28434","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.6084/m9.figshare.29877074","name":"AI-Powered Visualization is Transforming Modern Healthcare","source":"datacite","abstract":"Healthcare is being transformed by AI-driven visualization, which transforms complex data into useful insights. This paper synthesizes advancements in AI visualization tools—spanning medical imaging, electronic health records (EHR), genomics, and public health—and evaluates their impact on diagnostics, treatment personalization, and operational efficiency. Convolutional neural networks (CNNs) for image segmentation, generative adversarial networks (GANs) for the generation of synthetic data, and interactive dashboards for real-time analytics are some of the technologies that we highlight. Integrity barriers, algorithmic bias, and data privacy concerns are all critically examined. A systematic review of more than 120 studies conducted between 2018 and 2024 shows that clinical workflow time is cut by 30% and diagnostic accuracy is improved by 40% on average. Explainable artificial intelligence (XAI) and federated learning are emphasized in the study's ethical frameworks and future directions. This study demonstrates that AI visualization plays a crucial role in value-based care and precision medicine.","url":"https://doi.org/10.6084/m9.figshare.29877074","authors":["Rahman NaziL, Ashikur"],"tags":["Community child health","Health equity","Health promotion","Injury prevention","Preventative health care","Social determinants of health","Public health not elsewhere classified","Biofabrication"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.6084/m9.figshare.29877074","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.6084/m9.figshare.29876651","name":"AI-Powered Visualization is Transforming Modern Healthcare","source":"datacite","abstract":"Healthcare is being transformed by AI-driven visualization, which transforms complex data into useful insights. This paper synthesizes advancements in AI visualization tools—spanning medical imaging, electronic health records (EHR), genomics, and public health—and evaluates their impact on diagnostics, treatment personalization, and operational efficiency. Convolutional neural networks (CNNs) for image segmentation, generative adversarial networks (GANs) for the generation of synthetic data, and interactive dashboards for real-time analytics are some of the technologies that we highlight. Integrity barriers, algorithmic bias, and data privacy concerns are all critically examined. A systematic review of more than 120 studies conducted between 2018 and 2024 shows that clinical workflow time is cut by 30% and diagnostic accuracy is improved by 40% on average. Explainable artificial intelligence (XAI) and federated learning are emphasized in the study's ethical frameworks and future directions. This study demonstrates that AI visualization plays a crucial role in value-based care and precision medicine.","url":"https://doi.org/10.6084/m9.figshare.29876651","authors":["Rahman NaziL, Ashikur"],"tags":["Naturopathy","Chiropractic","Traditional Chinese medicine and treatments","Traditional, complementary and integrative medicine not elsewhere classified"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.6084/m9.figshare.29876651","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.19158395","name":"A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia","source":"datacite","abstract":"Accurate short-term residential energy consumption forecasting at sub-hourly resolution is critical for smart grid management, demand response programmes, and renewable energy integration. While weather variables are widely acknowledged as key drivers of residential electricity demand, the relative merit of incorporating temporal autocorrelation; the sequential memory of past consumption-over static meteorological features alone remains underexplored at fine-grained (5-minute) temporal resolution for Australian households. This paper presents a rigorous empirical comparison of a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM) recurrent network applied to two real-world Melbourne households: House 3 (a standard grid-connected dwelling) and House 4 (a rooftop solar photovoltaic-integrated household). Both models are trained on 14 months of 5-minute interval smart meter data (March 2023–April 2024) merged with official Bureau of Meteorology (BOM) daily weather observations, yielding over 117,000 samples per household. The LSTM, operating on 24-step (2-hour) sliding consumption windows, achieves coefficients of determination of R² = 0.883 (House 3) and R² = 0.865 (House 4), compared to R² = −0.055 and R² = 0.410 for the corresponding weather-driven MLPs. These results establish that temporal autocorrelation in the consumption sequence dominates meteorological information for short-term forecasting at 5-minute granularity. Additionally, we demonstrate an asymmetry introduced by solar generation. A persistence baseline analysis and seasonal stratification contextualise model performance. We propose weather-augmented LSTM and federated learning extensions as directions for future work.","url":"https://doi.org/10.5281/zenodo.19158395","authors":["Ukwatta Hewage, Prasad Nimantha Madusanka","Wu, Hao"],"tags":["Machine Learning","Energy utilisation","Forecasting"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19158395","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.19158396","name":"A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia","source":"datacite","abstract":"Accurate short-term residential energy consumption forecasting at sub-hourly resolution is critical for smart grid management, demand response programmes, and renewable energy integration. While weather variables are widely acknowledged as key drivers of residential electricity demand, the relative merit of incorporating temporal autocorrelation; the sequential memory of past consumption-over static meteorological features alone remains underexplored at fine-grained (5-minute) temporal resolution for Australian households. This paper presents a rigorous empirical comparison of a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM) recurrent network applied to two real-world Melbourne households: House 3 (a standard grid-connected dwelling) and House 4 (a rooftop solar photovoltaic-integrated household). Both models are trained on 14 months of 5-minute interval smart meter data (March 2023–April 2024) merged with official Bureau of Meteorology (BOM) daily weather observations, yielding over 117,000 samples per household. The LSTM, operating on 24-step (2-hour) sliding consumption windows, achieves coefficients of determination of R² = 0.883 (House 3) and R² = 0.865 (House 4), compared to R² = −0.055 and R² = 0.410 for the corresponding weather-driven MLPs. These results establish that temporal autocorrelation in the consumption sequence dominates meteorological information for short-term forecasting at 5-minute granularity. Additionally, we demonstrate an asymmetry introduced by solar generation. A persistence baseline analysis and seasonal stratification contextualise model performance. We propose weather-augmented LSTM and federated learning extensions as directions for future work.","url":"https://doi.org/10.5281/zenodo.19158396","authors":["Ukwatta Hewage, Prasad Nimantha Madusanka","Wu, Hao"],"tags":["Machine Learning","Energy utilisation","Forecasting"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19158396","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.57945/manara.hbku.30694046.v1","name":"AI-Model for the Detection of Rare Skin Disease and Cancer","source":"datacite","abstract":"Early detection of rare skin diseases (RSD) and cancers is crucial for improving patient outcomes and addressing healthcare disparities. This thesis explores the transformative role of artificial intelligence (AI) in dermatology through a comprehensive scoping review and the development of two novel AI models. The review synthesizes 68 empirical studies and reveals a strong focus on diagnostic support using unimodal imaging data (e.g., dermoscopy, histopathology, immunofluorescence), while multimodal integration, prognostic modeling, and treatment planning remain underexplored. Key challenges identified include limited datasets, class imbalance, poor generalizability due to lack of external validation, and insufficient fairness evaluations—particularly for underrepresented populations. Only ~10% of studies apply multimodal fusion, and fewer than 2% use integrated or stacked model architecture. Promising techniques such as federated learning, few-shot learning, and attention mechanisms remain underutilized yet offer significant potential.In direct response to these gaps, two AI frameworks were developed to demonstrate practical, targeted solutions. EBAnet, based on EfficientNet and Grad-CAM, was trained on direct immunofluorescence (DIF) images for early detection of Epidermolysis Bullosa Acquisita (EBA), a disease notably underrepresented in the literature. It achieved 96.7% accuracy and an AUC of 0.994, with Grad-CAM offering interpretable visualizations for clinical insight. The second model, a stacked ensemble integrating CNNs, Swin/ViT transformers, and machine learning classifiers (e.g., XGBoost, TabNet), was applied to the ISIC 2024 dataset for rare skin cancer detection and achieved an AUC of 0.90067. These models were designed to reflect key recommendations from the review—emphasizing multimodal integration, robust evaluation, and interpretability. Both aim to bridge the translational gap between research and real-world clinical deployment.This thesis contributes practical AI tools and strategic insights for advancing precision dermatology. It underscores the need for standardized evaluation, fairness-aware design, and real-world validation to ensure equitable AI solutions for rare skin conditions. Future directions include integrating unstructured data such as clinical notes and genomics, expanding multi-institutional datasets, and aligning with regulatory pathways. Collectively, the work lays a strong foundation for next-generation AI systems in rare dermatology, moving toward personalized and inclusive patient care.","url":"https://doi.org/10.57945/manara.hbku.30694046.v1","authors":["Alkhtaeeb, Mais"],"tags":["Engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.57945/manara.hbku.30694046.v1","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.57945/manara.hbku.30694046","name":"AI-Model for the Detection of Rare Skin Disease and Cancer","source":"datacite","abstract":"Early detection of rare skin diseases (RSD) and cancers is crucial for improving patient outcomes and addressing healthcare disparities. This thesis explores the transformative role of artificial intelligence (AI) in dermatology through a comprehensive scoping review and the development of two novel AI models. The review synthesizes 68 empirical studies and reveals a strong focus on diagnostic support using unimodal imaging data (e.g., dermoscopy, histopathology, immunofluorescence), while multimodal integration, prognostic modeling, and treatment planning remain underexplored. Key challenges identified include limited datasets, class imbalance, poor generalizability due to lack of external validation, and insufficient fairness evaluations—particularly for underrepresented populations. Only ~10% of studies apply multimodal fusion, and fewer than 2% use integrated or stacked model architecture. Promising techniques such as federated learning, few-shot learning, and attention mechanisms remain underutilized yet offer significant potential.In direct response to these gaps, two AI frameworks were developed to demonstrate practical, targeted solutions. EBAnet, based on EfficientNet and Grad-CAM, was trained on direct immunofluorescence (DIF) images for early detection of Epidermolysis Bullosa Acquisita (EBA), a disease notably underrepresented in the literature. It achieved 96.7% accuracy and an AUC of 0.994, with Grad-CAM offering interpretable visualizations for clinical insight. The second model, a stacked ensemble integrating CNNs, Swin/ViT transformers, and machine learning classifiers (e.g., XGBoost, TabNet), was applied to the ISIC 2024 dataset for rare skin cancer detection and achieved an AUC of 0.90067. These models were designed to reflect key recommendations from the review—emphasizing multimodal integration, robust evaluation, and interpretability. Both aim to bridge the translational gap between research and real-world clinical deployment.This thesis contributes practical AI tools and strategic insights for advancing precision dermatology. It underscores the need for standardized evaluation, fairness-aware design, and real-world validation to ensure equitable AI solutions for rare skin conditions. Future directions include integrating unstructured data such as clinical notes and genomics, expanding multi-institutional datasets, and aligning with regulatory pathways. Collectively, the work lays a strong foundation for next-generation AI systems in rare dermatology, moving toward personalized and inclusive patient care.","url":"https://doi.org/10.57945/manara.hbku.30694046","authors":["Alkhtaeeb, Mais"],"tags":["Engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.57945/manara.hbku.30694046","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.18150854","name":"Lecture-Ready Slides & Exercises:  Generative AI, Cybersecurity, and Ethics - Ray Islam, PhD | Wiley, 2025","source":"datacite","abstract":"These ready lecture slides with excercises are derived from the book Generative AI, Cybersecurity, and Ethics by Ray Islam, PhD (Wiley, 2025). Instructors/Researchers are welcome to adapt or modify the materials for their courses with proper attribution. © 2026 Ray Islam, PhD (Mohammad Rubyet Islam)This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).You are free to use, edit, share, and adapt this material for educational purposes with proper attribution. Link to few selected pages of the book: https://books.google.com/books?id=p8IzEQAAQBAJ&lpg=PA112&pg=PP1#v=onepage&q&f=false BOOK CONTENTS Chapter 1: Introduction Foundations and Evolution of AI and GenAI: Introduces core AI paradigms and traces the evolution from classical AI and machine learning to modern generative models. GenAI in Cybersecurity: Examines AI- and GenAI-driven approaches to threat detection, anomaly analysis, and proactive cyber defense. Ethical and Regulatory Considerations: Analyzes key ethical challenges and global governance frameworks guiding responsible GenAI deployment. Chapter 2: Cyber Security: Understanding the Digital Fortress Cybersecurity Technologies and Architectures: Examines the technological foundations of cybersecurity, including network, application, information, endpoint, cloud, identity, and critical infrastructure security within layered defense architectures. Threat Impact and Sectoral Risk: Analyzes global and regional cybercrime costs and industry-specific threats, demonstrating how cybersecurity technologies mitigate operational, financial, and systemic risks. AI, GenAI, Ethics, and Governance: Explores AI- and GenAI-driven cybersecurity technologies for detection, response, and prediction, alongside ethical challenges and global regulatory frameworks guiding responsible deployment. Chapter 3: Understanding GenAI Foundations and Capabilities of Generative AI: Introduces GenAI as a core AI paradigm focused on generating novel content across modalities, outlining its defining characteristics, major model classes, and distinctions from traditional predictive AI systems. GenAI Technologies, Tools, and Methodologies: Examines the technological landscape of GenAI, including architectures (e.g., GANs, transformers, diffusion models), platforms, frameworks, lifecycle methodologies (MLOps, ModelOps), and validation techniques. Applications, Risks, and Ethical Considerations: Explores real-world GenAI applications across domains such as cybersecurity, healthcare, education, manufacturing, and creative industries, while addressing ethical, security, and governance challenges associated with deployment. Chapter 4: GenAI in Cyber Security GenAI Cybersecurity Technologies: Examines GenAI-driven mechanisms for threat detection, simulation, deception, automated testing, and incident response, highlighting both defensive and offensive capabilities. Risks and Mitigation Strategies: Analyzes GenAI-enabled threats-including phishing, malware, deepfakes, and adversarial attacks-and corresponding mitigation technologies such as defensive AI, adversarial learning, and continuous model adaptation. Infrastructure and Governance: Describes the technical and organizational infrastructure required for GenAI-enabled cybersecurity, including compute platforms, data systems, security tool integration, ethical governance, and regulatory compliance. Chapter 5: Foundations of Ethics in GenAI Ethical Foundations and Theoretical Frameworks: Establishes the philosophical, historical, and normative foundations of ethics, applying metaethics, virtue ethics, deontology, consequentialism, and applied ethics to GenAI. Global Standards, Policies, and Regulation: Examines international ethical frameworks, standards, and laws governing AI and GenAI, including ISO/IEC, EU, UNESCO, OECD, IEEE, and regional policy approaches. GenAI-Specific Ethical Challenges and Governance: Analyzes ethical risks such as bias, privacy, misinformation","url":"https://doi.org/10.5281/zenodo.18150854","authors":["Islam, PhD, Mohammad Rubyet"],"tags":["Artificial Intelligence","Generative AI","Ethics","Cyber Security","GenAI Ethics","AI Ethics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18150854","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.19067238","name":"Lecture-Ready Slides & Exercises:  Generative AI, Cybersecurity, and Ethics - Ray Islam, PhD | Wiley, 2025","source":"datacite","abstract":"These ready lecture slides with excercises are derived from the book Generative AI, Cybersecurity, and Ethics by Ray Islam, PhD (Wiley, 2025). Instructors/Researchers are welcome to adapt or modify the materials for their courses with proper attribution. © 2026 Ray Islam, PhD (Mohammad Rubyet Islam)This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).You are free to use, edit, share, and adapt this material for educational purposes with proper attribution. Link to few selected pages of the book: https://books.google.com/books?id=p8IzEQAAQBAJ&lpg=PA112&pg=PP1#v=onepage&q&f=false BOOK CONTENTS Chapter 1: Introduction Foundations and Evolution of AI and GenAI: Introduces core AI paradigms and traces the evolution from classical AI and machine learning to modern generative models. GenAI in Cybersecurity: Examines AI- and GenAI-driven approaches to threat detection, anomaly analysis, and proactive cyber defense. Ethical and Regulatory Considerations: Analyzes key ethical challenges and global governance frameworks guiding responsible GenAI deployment. Chapter 2: Cyber Security: Understanding the Digital Fortress Cybersecurity Technologies and Architectures: Examines the technological foundations of cybersecurity, including network, application, information, endpoint, cloud, identity, and critical infrastructure security within layered defense architectures. Threat Impact and Sectoral Risk: Analyzes global and regional cybercrime costs and industry-specific threats, demonstrating how cybersecurity technologies mitigate operational, financial, and systemic risks. AI, GenAI, Ethics, and Governance: Explores AI- and GenAI-driven cybersecurity technologies for detection, response, and prediction, alongside ethical challenges and global regulatory frameworks guiding responsible deployment. Chapter 3: Understanding GenAI Foundations and Capabilities of Generative AI: Introduces GenAI as a core AI paradigm focused on generating novel content across modalities, outlining its defining characteristics, major model classes, and distinctions from traditional predictive AI systems. GenAI Technologies, Tools, and Methodologies: Examines the technological landscape of GenAI, including architectures (e.g., GANs, transformers, diffusion models), platforms, frameworks, lifecycle methodologies (MLOps, ModelOps), and validation techniques. Applications, Risks, and Ethical Considerations: Explores real-world GenAI applications across domains such as cybersecurity, healthcare, education, manufacturing, and creative industries, while addressing ethical, security, and governance challenges associated with deployment. Chapter 4: GenAI in Cyber Security GenAI Cybersecurity Technologies: Examines GenAI-driven mechanisms for threat detection, simulation, deception, automated testing, and incident response, highlighting both defensive and offensive capabilities. Risks and Mitigation Strategies: Analyzes GenAI-enabled threats-including phishing, malware, deepfakes, and adversarial attacks-and corresponding mitigation technologies such as defensive AI, adversarial learning, and continuous model adaptation. Infrastructure and Governance: Describes the technical and organizational infrastructure required for GenAI-enabled cybersecurity, including compute platforms, data systems, security tool integration, ethical governance, and regulatory compliance. Chapter 5: Foundations of Ethics in GenAI Ethical Foundations and Theoretical Frameworks: Establishes the philosophical, historical, and normative foundations of ethics, applying metaethics, virtue ethics, deontology, consequentialism, and applied ethics to GenAI. Global Standards, Policies, and Regulation: Examines international ethical frameworks, standards, and laws governing AI and GenAI, including ISO/IEC, EU, UNESCO, OECD, IEEE, and regional policy approaches. GenAI-Specific Ethical Challenges and Governance: Analyzes ethical risks such as bias, privacy, misinformation","url":"https://doi.org/10.5281/zenodo.19067238","authors":["Islam, PhD, Mohammad Rubyet"],"tags":["Artificial Intelligence","Generative AI","Ethics","Cyber Security","GenAI Ethics","AI Ethics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19067238","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18150855","name":"Lecture-Ready Slides & Exercises:  Generative AI, Cybersecurity, and Ethics - Ray Islam, PhD | Wiley, 2025","source":"datacite","abstract":"These ready lecture slides with excercises are derived from the book Generative AI, Cybersecurity, and Ethics by Ray Islam, PhD (Wiley, 2025). Instructors/Researchers are welcome to adapt or modify the materials for their courses with proper attribution. © 2026 Ray Islam, PhD (Mohammad Rubyet Islam)This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).You are free to use, edit, share, and adapt this material for educational purposes with proper attribution. Link to few selected pages of the book: https://books.google.com/books?id=p8IzEQAAQBAJ&lpg=PA112&pg=PP1#v=onepage&q&f=false BOOK CONTENTS Chapter 1: Introduction Foundations and Evolution of AI and GenAI: Introduces core AI paradigms and traces the evolution from classical AI and machine learning to modern generative models. GenAI in Cybersecurity: Examines AI- and GenAI-driven approaches to threat detection, anomaly analysis, and proactive cyber defense. Ethical and Regulatory Considerations: Analyzes key ethical challenges and global governance frameworks guiding responsible GenAI deployment. Chapter 2: Cyber Security: Understanding the Digital Fortress Cybersecurity Technologies and Architectures: Examines the technological foundations of cybersecurity, including network, application, information, endpoint, cloud, identity, and critical infrastructure security within layered defense architectures. Threat Impact and Sectoral Risk: Analyzes global and regional cybercrime costs and industry-specific threats, demonstrating how cybersecurity technologies mitigate operational, financial, and systemic risks. AI, GenAI, Ethics, and Governance: Explores AI- and GenAI-driven cybersecurity technologies for detection, response, and prediction, alongside ethical challenges and global regulatory frameworks guiding responsible deployment. Chapter 3: Understanding GenAI Foundations and Capabilities of Generative AI: Introduces GenAI as a core AI paradigm focused on generating novel content across modalities, outlining its defining characteristics, major model classes, and distinctions from traditional predictive AI systems. GenAI Technologies, Tools, and Methodologies: Examines the technological landscape of GenAI, including architectures (e.g., GANs, transformers, diffusion models), platforms, frameworks, lifecycle methodologies (MLOps, ModelOps), and validation techniques. Applications, Risks, and Ethical Considerations: Explores real-world GenAI applications across domains such as cybersecurity, healthcare, education, manufacturing, and creative industries, while addressing ethical, security, and governance challenges associated with deployment. Chapter 4: GenAI in Cyber Security GenAI Cybersecurity Technologies: Examines GenAI-driven mechanisms for threat detection, simulation, deception, automated testing, and incident response, highlighting both defensive and offensive capabilities. Risks and Mitigation Strategies: Analyzes GenAI-enabled threats-including phishing, malware, deepfakes, and adversarial attacks-and corresponding mitigation technologies such as defensive AI, adversarial learning, and continuous model adaptation. Infrastructure and Governance: Describes the technical and organizational infrastructure required for GenAI-enabled cybersecurity, including compute platforms, data systems, security tool integration, ethical governance, and regulatory compliance. Chapter 5: Foundations of Ethics in GenAI Ethical Foundations and Theoretical Frameworks: Establishes the philosophical, historical, and normative foundations of ethics, applying metaethics, virtue ethics, deontology, consequentialism, and applied ethics to GenAI. Global Standards, Policies, and Regulation: Examines international ethical frameworks, standards, and laws governing AI and GenAI, including ISO/IEC, EU, UNESCO, OECD, IEEE, and regional policy approaches. GenAI-Specific Ethical Challenges and Governance: Analyzes ethical risks such as bias, privacy, misinformation","url":"https://doi.org/10.5281/zenodo.18150855","authors":["Islam, PhD, Mohammad Rubyet"],"tags":["Artificial Intelligence","Generative AI","Ethics","Cyber Security","GenAI Ethics","AI Ethics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18150855","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18997213","name":"THE ROLE OF SOFTWARE IN ROBOTIC SYSTEMS CONTROL: ARCHITECTURES, ALGORITHMS, AND INDUSTRIAL APPLICATIONS","source":"datacite","abstract":"This paper provides a comprehensive examination of the software ecosystem underpinning modern robotic systems control. As robotics transitions from mechanically dominated platforms to software-centric architectures, understanding the multi-layered software stack becomes critical for engineers, researchers, and industry practitioners. We analyze the global robotics software market, present quantitative performance benchmarks across software layers, and compare leading middleware frameworks including ROS 2, YARP, and proprietary stacks. Our findings demonstrate that software now constitutes 42–45% of total robotic system costs in 2024, and that AI-driven software components have grown from 8.1% to over 30% of software expenditure since 2018. We further document sector-specific adoption patterns across seven industries and identify key challenges in real-time performance, safety certification, and human-robot interaction. The paper concludes with a forward-looking analysis of emerging paradigms including neuromorphic computing, federated learning, and quantum-assisted path planning.","url":"https://doi.org/10.5281/zenodo.18997213","authors":["Zulfiqorova, Zebiniso"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18997213","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.18997212","name":"THE ROLE OF SOFTWARE IN ROBOTIC SYSTEMS CONTROL: ARCHITECTURES, ALGORITHMS, AND INDUSTRIAL APPLICATIONS","source":"datacite","abstract":"This paper provides a comprehensive examination of the software ecosystem underpinning modern robotic systems control. As robotics transitions from mechanically dominated platforms to software-centric architectures, understanding the multi-layered software stack becomes critical for engineers, researchers, and industry practitioners. We analyze the global robotics software market, present quantitative performance benchmarks across software layers, and compare leading middleware frameworks including ROS 2, YARP, and proprietary stacks. Our findings demonstrate that software now constitutes 42–45% of total robotic system costs in 2024, and that AI-driven software components have grown from 8.1% to over 30% of software expenditure since 2018. We further document sector-specific adoption patterns across seven industries and identify key challenges in real-time performance, safety certification, and human-robot interaction. The paper concludes with a forward-looking analysis of emerging paradigms including neuromorphic computing, federated learning, and quantum-assisted path planning.","url":"https://doi.org/10.5281/zenodo.18997212","authors":["Zulfiqorova, Zebiniso"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18997212","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.18983099","name":"Die ZuMult-Plattform als Instrument für sprachvergleichende Analysen auf mündlichen Daten","source":"datacite","abstract":"“[Tools widely used by corpus linguists] all offer a different user-experience, because each tool is created in isolation and thus offers a different user interface, control flow, and functionality.” (Anthony 2009) Nur wenige größere Korpora sind aus sich heraus auf sprachvergleichende Analysen angelegt. Zu den von Vorneherein als „Comparable Corpus“ konzipierten Ausnahmen gehören das International Comparable Corpus (ICC, Čermáková et al. 2021) oder das mehrsprachige GeWiss-Korpus (Fandrych et al. 2017). Ein alternativer oder ergänzender Ansatz sind virtuelle vergleichbare Korpora wie EuReCo (Trawínski & Kupietz 2021), die über Föderation verteilter, auf vergleichbarer technischer Basis stehender Korpora und Korpusplattformen ermöglichen, das Deutsche kontrastiv mit anderen europäischen Sprachen in Beziehung zu setzen; EuReCo ist allerdings auf schriftsprachliche Daten beschränkt. Unser Beitrag stellt die ZuMult-Plattform (Fandrych et al. 2023) und deren Potential, Vergleichbares für mündliche Korpora zu leisten, vor. ZuMult ist eine offene, flexible, auf etablierten Standards und Technologien basierende Architektur für den Zugang zu audiovisuellen Korpora. Neben Korpusrecherchen mit CQP unterstützt ZuMult die für eine Analyse gesprochener Sprache notwendige erweiterte Kontextualisierung von Suchergebnissen (Frick & Schmidt 2025) sowie interaktive Transkriptanalysen (Schmidt et al. 2023), die insbesondere für qualitativ orientierte Analysen, z.B. in der Gesprächsforschung, und für didaktische Anwendungen, z.B. in der DaF/DaZ-Lehre, genutzt werden. An der Universität Duisburg-Essen erfolgt eine Erweiterung, die darauf zielt, Sprache auch in ihrem multimodalen Zusammenspiel mit weiteren körperlichen Ressourcen – wie Blick, Gestik etc. – für korpuslinguistische Herangehensweisen zu erschließen. Über eine ZuMult-Instanz am Archiv für Gesprochenes Deutsch sind bereits seit 2021 neben dem GeWiss-Korpus zwei der wichtigsten Referenzkorpora des gesprochenen Deutsch – FOLK (Deppermann & Hartung 2011, Schmidt 2016, Reineke et al. 2023) und Deutsch Heute (Kleiner 2015) – zugänglich. Mit der Veröffentlichung einer ZuMult-Instanz für das französische ESLO-Korpus (Abouda & Baude 2006, Baude & Dugua 2011, Eshkol-Taravella 2012, Schmidt 2025) ergeben sich nun erste Möglichkeiten für sprachvergleichende Analysen zwischen dem Deutschen und dem Französischen. Wie unser Beitrag anhand von Proof-Of-Concept-Implementierungen zeigen wird, sind beispielsweise auch das Griffith Corpus of Spoken Australian English (Haugh & Chang 2013), das TIGR Corpus des gesprochenen Italienisch (Miecznikowski-Fuenfschilling et al. i.V.), das Training Corpus of Spoken Slovenian (Verdonik 2024) und die Kollektionen aus Oral History Digital (Pagenstecher 2024) in ZuMult integrierbar. Gleiches gilt für das zwölfsprachige EXMARaLDA-Demokorpus, das derzeit im Rahmen eines Text+-Kooperationsprojekt für eine Publikation in einer ZuMult-Instanz an der Universität Hamburg aufbereitet wird. Auf ähnliche Weise eröffnet ZuMult neue Möglichkeiten der „vergleichenden Sprachinselforschung“ (Boas 2016), also der (korpusgestützten) Analyse von Sprachkontaktphänomenen und Sprachentwicklung von Deutsch als Minderheitensprache im Kontakt mit dominanten anderen (i.d.R. europäischen) Sprachen. Seit Dezember 2024 macht eine ZuMult-Instanz an der University of Texas in Austin (Boas et al. 2025) die Daten des Texas German Dialect Projects auf ähnliche Weise verfügbar, wie die IDS-Instanz Zugriff etwa auf Daten zum Australiendeutsch (Clyne 1981), Deutsch in Namibia (Zimmer et al. 2020) oder Mennonitendeutsch in Amerika (Kaufmann et al. 2023) bietet. Unser Beitrag wird diese verschiedenen Anwendungen vorstellen und illustrieren, sowie einige methodische Herausforderungen der Mehrsprachigkeit (z.B. sprachübergreifendes POS-Tagging) und technische Ansätze zur Aggregation von Suchanfragen an mehrere ZuMult-Instanzen (CLARIN Federated Content Search) thematisieren. Keywords: gesprochene Sprache; Ko","url":"https://doi.org/10.5281/zenodo.18983099","authors":["Schmidt, Thomas","Abouda, Lotfi","BADIN, Flora","Boas, Hans C.","Blevins, Margaret","Bührig, Kristin","Dugua, Céline","Fandrych, Christian","Ferger, Anne","Frick, Elena","Kompiel, Peter Artur","Miecznikowski-Fuenfschilling, Johanna"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18983099","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.5281/zenodo.18983100","name":"Die ZuMult-Plattform als Instrument für sprachvergleichende Analysen auf mündlichen Daten","source":"datacite","abstract":"“[Tools widely used by corpus linguists] all offer a different user-experience, because each tool is created in isolation and thus offers a different user interface, control flow, and functionality.” (Anthony 2009) Nur wenige größere Korpora sind aus sich heraus auf sprachvergleichende Analysen angelegt. Zu den von Vorneherein als „Comparable Corpus“ konzipierten Ausnahmen gehören das International Comparable Corpus (ICC, Čermáková et al. 2021) oder das mehrsprachige GeWiss-Korpus (Fandrych et al. 2017). Ein alternativer oder ergänzender Ansatz sind virtuelle vergleichbare Korpora wie EuReCo (Trawínski & Kupietz 2021), die über Föderation verteilter, auf vergleichbarer technischer Basis stehender Korpora und Korpusplattformen ermöglichen, das Deutsche kontrastiv mit anderen europäischen Sprachen in Beziehung zu setzen; EuReCo ist allerdings auf schriftsprachliche Daten beschränkt. Unser Beitrag stellt die ZuMult-Plattform (Fandrych et al. 2023) und deren Potential, Vergleichbares für mündliche Korpora zu leisten, vor. ZuMult ist eine offene, flexible, auf etablierten Standards und Technologien basierende Architektur für den Zugang zu audiovisuellen Korpora. Neben Korpusrecherchen mit CQP unterstützt ZuMult die für eine Analyse gesprochener Sprache notwendige erweiterte Kontextualisierung von Suchergebnissen (Frick & Schmidt 2025) sowie interaktive Transkriptanalysen (Schmidt et al. 2023), die insbesondere für qualitativ orientierte Analysen, z.B. in der Gesprächsforschung, und für didaktische Anwendungen, z.B. in der DaF/DaZ-Lehre, genutzt werden. An der Universität Duisburg-Essen erfolgt eine Erweiterung, die darauf zielt, Sprache auch in ihrem multimodalen Zusammenspiel mit weiteren körperlichen Ressourcen – wie Blick, Gestik etc. – für korpuslinguistische Herangehensweisen zu erschließen. Über eine ZuMult-Instanz am Archiv für Gesprochenes Deutsch sind bereits seit 2021 neben dem GeWiss-Korpus zwei der wichtigsten Referenzkorpora des gesprochenen Deutsch – FOLK (Deppermann & Hartung 2011, Schmidt 2016, Reineke et al. 2023) und Deutsch Heute (Kleiner 2015) – zugänglich. Mit der Veröffentlichung einer ZuMult-Instanz für das französische ESLO-Korpus (Abouda & Baude 2006, Baude & Dugua 2011, Eshkol-Taravella 2012, Schmidt 2025) ergeben sich nun erste Möglichkeiten für sprachvergleichende Analysen zwischen dem Deutschen und dem Französischen. Wie unser Beitrag anhand von Proof-Of-Concept-Implementierungen zeigen wird, sind beispielsweise auch das Griffith Corpus of Spoken Australian English (Haugh & Chang 2013), das TIGR Corpus des gesprochenen Italienisch (Miecznikowski-Fuenfschilling et al. i.V.), das Training Corpus of Spoken Slovenian (Verdonik 2024) und die Kollektionen aus Oral History Digital (Pagenstecher 2024) in ZuMult integrierbar. Gleiches gilt für das zwölfsprachige EXMARaLDA-Demokorpus, das derzeit im Rahmen eines Text+-Kooperationsprojekt für eine Publikation in einer ZuMult-Instanz an der Universität Hamburg aufbereitet wird. Auf ähnliche Weise eröffnet ZuMult neue Möglichkeiten der „vergleichenden Sprachinselforschung“ (Boas 2016), also der (korpusgestützten) Analyse von Sprachkontaktphänomenen und Sprachentwicklung von Deutsch als Minderheitensprache im Kontakt mit dominanten anderen (i.d.R. europäischen) Sprachen. Seit Dezember 2024 macht eine ZuMult-Instanz an der University of Texas in Austin (Boas et al. 2025) die Daten des Texas German Dialect Projects auf ähnliche Weise verfügbar, wie die IDS-Instanz Zugriff etwa auf Daten zum Australiendeutsch (Clyne 1981), Deutsch in Namibia (Zimmer et al. 2020) oder Mennonitendeutsch in Amerika (Kaufmann et al. 2023) bietet. Unser Beitrag wird diese verschiedenen Anwendungen vorstellen und illustrieren, sowie einige methodische Herausforderungen der Mehrsprachigkeit (z.B. sprachübergreifendes POS-Tagging) und technische Ansätze zur Aggregation von Suchanfragen an mehrere ZuMult-Instanzen (CLARIN Federated Content Search) thematisieren. Keywords: gesprochene Sprache; Ko","url":"https://doi.org/10.5281/zenodo.18983100","authors":["Schmidt, Thomas","Abouda, Lotfi","BADIN, Flora","Boas, Hans C.","Blevins, Margaret","Bührig, Kristin","Dugua, Céline","Fandrych, Christian","Ferger, Anne","Frick, Elena","Kompiel, Peter Artur","Miecznikowski-Fuenfschilling, Johanna"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18983100","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.5281/zenodo.18989923","name":"PREreview of \"Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models\"","source":"datacite","abstract":"This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/18989924. summary: 'Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models by Nikita Kuzmin, Tao Zhong, Jiajun Deng (corresponding author), Yingke Zhu, Tristan Tsoi, Tianxiang Cao, Simon Lui, Kong Aik Lee, and Eng Siong Chng audits speaker identity leakage in hidden states of always-on, full-duplex dialogue LLMs (SALM-Duplex and Moshi) and proposes two streaming anonymization setups (Anon-W2W and Anon-W2F). Using the VoicePrivacy 2024 protocol with a lazy-informed attacker, the paper shows substantial leakage (e.g., Moshi discrete EER 6.4%) and demonstrates that Stream-Voice-Anon, especially feature-domain (Anon-W2F), markedly improves privacy (EER up to 41%) with acceptable utility and latency trade-offs.' keywords: 'speaker anonymization, full-duplex speech, privacy, speaker verification, speech agents, SALM-Duplex, Moshi, Stream-Voice-Anon, equal error rate, EER, linkability, VoicePrivacy 2024, ECAPA-TDNN, wave-to-wave anonymization, wave-to-feature anonymization, discrete encoder, continuous encoder, dialogue LLM, always-on models, GDPR' score: 70 tier: 'Tier3 (Top-field journals): Strong problem significance, comprehensive related work, convincing empirical evidence across two architectures, and practically useful anonymization setups. Novelty is solid but not revolutionary; statistics and formatting could be strengthened. With clearer statistical treatment, broader datasets, and stronger attacker analyses, it could approach Tier4.' CPI: 0.61 expected_citations_2yr: 15 categories: Abstract: score: 7, description: 'Clearly states objective, methods, key metrics (EER, Linkability), and main findings; minor density and formatting artifacts slightly hinder standalone readability.' References: score: 8, description: 'Well-curated mix of foundational and very recent works (up to 2026), covering SSL probing, anonymization, and full-duplex models; a few additional attacker and evaluation references could further strengthen breadth.' Scope: score: 8, description: 'Delivers on auditing hidden-state leakage in full-duplex LLMs and testing streaming anonymization; stays aligned with title and introduction.' Relevance: score: 8, description: 'Addresses a timely, underexplored risk in always-on speech agents and offers deployable mitigations; advances discussion beyond tutorial content.' 'Factual Errors': score: 8, description: 'Methodology and claims are consistent with reported results and cited protocols; no material factual errors detected.' Language: score: 7, description: \"Professional tone overall; a few typographical/line-break issues and truncated words (e.g., 'dis…tinct') detract slightly from polish.\" Formatting: score: 6, description: 'ArXiv-like artifacts (broken lines, footnotes-in-text, figure text overwriting) and inconsistent line wrapping reduce clarity; otherwise standard structure.' Novelty: score: 7, description: 'First focused audit of speaker-identity leakage within hidden states of full-duplex dialogue LLMs and first feature-domain streaming anonymization within such backbones; conceptually incremental but practically important. 'Five novel research extensions (simple language): \"- Adaptive privacy dial: Let users set a live 'privacy slider' that tunes anonymization strength without hurting conversation flow; measure how EER and sBERT change minute-by-minute. '- Multi-attribute protection: Extend beyond identity to protect accent, emotion, and health cues; track separate privacy scores for each attribute. '- Adversarial co-training: Train the dialogue model with a built-in privacy adversary that tries to guess identity from hidden states; measure if privacy holds under unseen attackers. '- Privacy under personalization: Study how voice/style personalization can be done while keeping identity unlinkable; test which personalization knobs are safe. '- Federated auditin","url":"https://doi.org/10.5281/zenodo.18989923","authors":["shireesh apte"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18989923","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.18989924","name":"PREreview of \"Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models\"","source":"datacite","abstract":"This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/18989924. summary: 'Privacy-Preserving End-to-End Full-Duplex Speech Dialogue Models by Nikita Kuzmin, Tao Zhong, Jiajun Deng (corresponding author), Yingke Zhu, Tristan Tsoi, Tianxiang Cao, Simon Lui, Kong Aik Lee, and Eng Siong Chng audits speaker identity leakage in hidden states of always-on, full-duplex dialogue LLMs (SALM-Duplex and Moshi) and proposes two streaming anonymization setups (Anon-W2W and Anon-W2F). Using the VoicePrivacy 2024 protocol with a lazy-informed attacker, the paper shows substantial leakage (e.g., Moshi discrete EER 6.4%) and demonstrates that Stream-Voice-Anon, especially feature-domain (Anon-W2F), markedly improves privacy (EER up to 41%) with acceptable utility and latency trade-offs.' keywords: 'speaker anonymization, full-duplex speech, privacy, speaker verification, speech agents, SALM-Duplex, Moshi, Stream-Voice-Anon, equal error rate, EER, linkability, VoicePrivacy 2024, ECAPA-TDNN, wave-to-wave anonymization, wave-to-feature anonymization, discrete encoder, continuous encoder, dialogue LLM, always-on models, GDPR' score: 70 tier: 'Tier3 (Top-field journals): Strong problem significance, comprehensive related work, convincing empirical evidence across two architectures, and practically useful anonymization setups. Novelty is solid but not revolutionary; statistics and formatting could be strengthened. With clearer statistical treatment, broader datasets, and stronger attacker analyses, it could approach Tier4.' CPI: 0.61 expected_citations_2yr: 15 categories: Abstract: score: 7, description: 'Clearly states objective, methods, key metrics (EER, Linkability), and main findings; minor density and formatting artifacts slightly hinder standalone readability.' References: score: 8, description: 'Well-curated mix of foundational and very recent works (up to 2026), covering SSL probing, anonymization, and full-duplex models; a few additional attacker and evaluation references could further strengthen breadth.' Scope: score: 8, description: 'Delivers on auditing hidden-state leakage in full-duplex LLMs and testing streaming anonymization; stays aligned with title and introduction.' Relevance: score: 8, description: 'Addresses a timely, underexplored risk in always-on speech agents and offers deployable mitigations; advances discussion beyond tutorial content.' 'Factual Errors': score: 8, description: 'Methodology and claims are consistent with reported results and cited protocols; no material factual errors detected.' Language: score: 7, description: \"Professional tone overall; a few typographical/line-break issues and truncated words (e.g., 'dis…tinct') detract slightly from polish.\" Formatting: score: 6, description: 'ArXiv-like artifacts (broken lines, footnotes-in-text, figure text overwriting) and inconsistent line wrapping reduce clarity; otherwise standard structure.' Novelty: score: 7, description: 'First focused audit of speaker-identity leakage within hidden states of full-duplex dialogue LLMs and first feature-domain streaming anonymization within such backbones; conceptually incremental but practically important. 'Five novel research extensions (simple language): \"- Adaptive privacy dial: Let users set a live 'privacy slider' that tunes anonymization strength without hurting conversation flow; measure how EER and sBERT change minute-by-minute. '- Multi-attribute protection: Extend beyond identity to protect accent, emotion, and health cues; track separate privacy scores for each attribute. '- Adversarial co-training: Train the dialogue model with a built-in privacy adversary that tries to guess identity from hidden states; measure if privacy holds under unseen attackers. '- Privacy under personalization: Study how voice/style personalization can be done while keeping identity unlinkable; test which personalization knobs are safe. '- Federated auditin","url":"https://doi.org/10.5281/zenodo.18989924","authors":["shireesh apte"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18989924","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.6084/m9.figshare.31557869.v1","name":"AI-enhanced health information exchange systems: a systematic review of implementation challenges and opportunities","source":"datacite","abstract":"This systematic review evaluates the integration of Artificial Intelligence (AI) into Health Information Exchange (HIE) systems to improve data utilisation, patient outcomes, and healthcare delivery efficiency. Through a comprehensive analysis of major academic databases from 2015 to 2024, 72 peer-reviewed papers were selected following rigorous screening processes, including duplicate removal, title/abstract screening, and full-text evaluation against predefined criteria. The analysis examined AI implementation across directed exchange, query-based exchange, and consumer-mediated exchange systems. Results demonstrate AI significantly enhances HIE functionality through improved data standardisation, error detection, and system integration. Key benefits include enhanced predictive capabilities for patient outcomes, optimised resource utilisation, and effective automated processing of unstructured clinical data. Critical solutions identified include federated learning techniques to preserve privacy, explainable AI models to support clinical adoption, and robust bias-detection frameworks to ensure equitable outcomes. Future opportunities encompass integrating diverse data sources, exploring federated learning approaches, balancing data utility with privacy concerns, and developing transparent AI models to increase clinical acceptance. The study emphasises ongoing data standardisation efforts and ethical considerations in AI deployment, particularly for real-time, context-aware decision-support systems. AI integration represents a promising advancement that, through strategic development, can transform HIE into more effective and equitable tools for healthcare delivery.","url":"https://doi.org/10.6084/m9.figshare.31557869.v1","authors":["Esmaeilzadeh, Pouyan","Maddah, Mahed"],"tags":["Space Science","Medicine","Information Systems not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.6084/m9.figshare.31557869.v1","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.6084/m9.figshare.31557869","name":"AI-enhanced health information exchange systems: a systematic review of implementation challenges and opportunities","source":"datacite","abstract":"This systematic review evaluates the integration of Artificial Intelligence (AI) into Health Information Exchange (HIE) systems to improve data utilisation, patient outcomes, and healthcare delivery efficiency. Through a comprehensive analysis of major academic databases from 2015 to 2024, 72 peer-reviewed papers were selected following rigorous screening processes, including duplicate removal, title/abstract screening, and full-text evaluation against predefined criteria. The analysis examined AI implementation across directed exchange, query-based exchange, and consumer-mediated exchange systems. Results demonstrate AI significantly enhances HIE functionality through improved data standardisation, error detection, and system integration. Key benefits include enhanced predictive capabilities for patient outcomes, optimised resource utilisation, and effective automated processing of unstructured clinical data. Critical solutions identified include federated learning techniques to preserve privacy, explainable AI models to support clinical adoption, and robust bias-detection frameworks to ensure equitable outcomes. Future opportunities encompass integrating diverse data sources, exploring federated learning approaches, balancing data utility with privacy concerns, and developing transparent AI models to increase clinical acceptance. The study emphasises ongoing data standardisation efforts and ethical considerations in AI deployment, particularly for real-time, context-aware decision-support systems. AI integration represents a promising advancement that, through strategic development, can transform HIE into more effective and equitable tools for healthcare delivery.","url":"https://doi.org/10.6084/m9.figshare.31557869","authors":["Esmaeilzadeh, Pouyan","Maddah, Mahed"],"tags":["Space Science","Medicine","Information Systems not elsewhere classified","Science Policy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.6084/m9.figshare.31557869","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2602.22037","name":"A Critical Look into Threshold Homomorphic Encryption for Private Average Aggregation","source":"datacite","abstract":"Threshold Homomorphic Encryption (Threshold HE) is a good fit for implementing private federated average aggregation, a key operation in Federated Learning (FL). Despite its potential, recent studies have shown that threshold schemes available in mainstream HE libraries can introduce unexpected security vulnerabilities if an adversary has access to a restricted decryption oracle. This oracle reflects the FL clients' capacity to collaboratively decrypt the aggregated result without knowing the secret key. This work surveys the use of threshold RLWE-based HE for federated average aggregation and examines the performance impact of using smudging noise with a large variance as a countermeasure. We provide a detailed comparison of threshold variants of BFV and CKKS, finding that CKKS-based aggregations perform comparably to BFV-based solutions.","url":"https://doi.org/10.48550/arxiv.2602.22037","authors":["Morona-Mínguez, Miguel","Pedrouzo-Ulloa, Alberto","Pérez-González, Fernando"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.22037","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.15783230","name":"ADVANCEMENTS IN AI-DRIVEN RADIO IMAGING SYSTEMS FOR REAL-TIME DIAGNOSTICS: EXPLORING HIGH-RESOLUTION TECHNIQUES AND DATA INTEGRATION FOR ENHANCED MEDICAL IMAGING","source":"datacite","abstract":"Recent strides in artificial intelligence (AI) have transformed radio imaging systems, marking a paradigm shift from traditional post-processing diagnostics to real-time, intelligent decision-making. This paper investigates the integration of AI with radio imaging modalities—such as MRI, CT, and PET scans—for the development of high-resolution diagnostic tools. By leveraging deep learning algorithms, multimodal data fusion, and real-time processing frameworks, modern radio imaging systems now offer greater precision, speed, and interpretability. This research delves into advancements achieved before 2024, evaluating AI-enabled architectures that optimize spatial resolution, noise reduction, lesion detection, and clinical integration. Moreover, it outlines challenges and future pathways in implementing federated learning and secure data pipelines to ensure privacy in medical imaging workflows.","url":"https://doi.org/10.5281/zenodo.15783230","authors":["Researcher"],"tags":["AI in radiology, real-time diagnostics, high-resolution imaging, multimodal fusion, deep learning, medical image processing, federated learning, data integration"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15783230","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.15783231","name":"ADVANCEMENTS IN AI-DRIVEN RADIO IMAGING SYSTEMS FOR REAL-TIME DIAGNOSTICS: EXPLORING HIGH-RESOLUTION TECHNIQUES AND DATA INTEGRATION FOR ENHANCED MEDICAL IMAGING","source":"datacite","abstract":"Recent strides in artificial intelligence (AI) have transformed radio imaging systems, marking a paradigm shift from traditional post-processing diagnostics to real-time, intelligent decision-making. This paper investigates the integration of AI with radio imaging modalities—such as MRI, CT, and PET scans—for the development of high-resolution diagnostic tools. By leveraging deep learning algorithms, multimodal data fusion, and real-time processing frameworks, modern radio imaging systems now offer greater precision, speed, and interpretability. This research delves into advancements achieved before 2024, evaluating AI-enabled architectures that optimize spatial resolution, noise reduction, lesion detection, and clinical integration. Moreover, it outlines challenges and future pathways in implementing federated learning and secure data pipelines to ensure privacy in medical imaging workflows.","url":"https://doi.org/10.5281/zenodo.15783231","authors":["Researcher"],"tags":["AI in radiology, real-time diagnostics, high-resolution imaging, multimodal fusion, deep learning, medical image processing, federated learning, data integration"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15783231","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.18507990","name":"Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning","source":"datacite","abstract":"This repository contains the source code, trained models, and implementation details corresponding to the study “Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning.” It includes the full implementation of the proposed Class-Adaptive Color-Sensitive (CASC) filtering algorithm, the Dense Class-Adaptive Color-Sensitive DenseNet-121 (DenseCSENet-121) architecture, preprocessing scripts, and model evaluation routines. The materials allow reproduction of the reported experimental results on the nine-category waste classification dataset described in the paper.","url":"https://doi.org/10.5281/zenodo.18507990","authors":["Shyamala Devi, M","Natarajan, Yuvaraj","K. R., Sri Preethaa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18507990","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18507991","name":"Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning","source":"datacite","abstract":"This repository contains the source code, trained models, and implementation details corresponding to the study “Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning.” It includes the full implementation of the proposed Class-Adaptive Color-Sensitive (CASC) filtering algorithm, the Dense Class-Adaptive Color-Sensitive DenseNet-121 (DenseCSENet-121) architecture, preprocessing scripts, and model evaluation routines. The materials allow reproduction of the reported experimental results on the nine-category waste classification dataset described in the paper.","url":"https://doi.org/10.5281/zenodo.18507991","authors":["Shyamala Devi, M","Natarajan, Yuvaraj","K. R., Sri Preethaa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18507991","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2602.00694","name":"Forecasting Energy Availability in Local Energy Communities via LSTM Federated Learning","source":"datacite","abstract":"Local Energy Communities are emerging as crucial players in the landscape of sustainable development. A significant challenge for these communities is achieving self-sufficiency through effective management of the balance between energy production and consumption. To meet this challenge, it is essential to develop and implement forecasting models that deliver accurate predictions, which can then be utilized by optimization and planning algorithms. However, the application of forecasting solutions is often hindered by privacy constrains and regulations as the users participating in the Local Energy Community can be (rightfully) reluctant sharing their consumption patterns with others. In this context, the use of Federated Learning (FL) can be a viable solution as it allows to create a forecasting model without the need to share privacy sensitive information among the users. In this study, we demonstrate how FL and long short-term memory (LSTM) networks can be employed to achieve this objective, highlighting the trade-off between data sharing and forecasting accuracy.","url":"https://doi.org/10.48550/arxiv.2602.00694","authors":["Turazza, Fabio","Pietri, Marcello","Hadjidimitriou, Natalia Selini","Mamei, Marco"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.00694","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.18316378","name":"Scripts and mappings to harmonise dementia cohorts into the OMOP Common Data Model","source":"datacite","abstract":"This resource contains the scripts, mappings, and other materials that were used to harmonise data from nine Dutch cohorts relevant to dementia research to the OMOP (Observational Medical Outcomes Partnership) Common Data Model (CDM). These materials were developed within the Netherlands Consortium of Dementia Cohorts (NCDC) project. NCDC included the following nine cohorts: Amsterdam Dementia Cohort, Doetinchem Cohort Study, EMIF-AD 90+ study, EMIF-AD PreclinAD study, Leiden Longevity Study, Longitudinal Aging Study Amsterdam, The Maastricht Study, Rotterdam Study, and SMART. Scope The materials in this publication were used to transform the data from the NCDC cohorts into the OMOP CDM. However, the overall approach and workflow are transferable to other cohort harmonisation efforts. As such, this work can serve as a reference implementation or starting point for the harmonisation of cohort data to any common data model. Harmonising data to a common data model is crucial in (dementia) research as it allows to combine studies, increase statistical power, and enhance the reliability of findings. Reuse of this resource is governed by the license specified in this Zenodo record and the associated GitHub repository. Who is it for? This work is primarily intended for researchers and (research) software engineers looking to harmonise multi-centre cohort data into a common data model. Effective reuse of this resource requires technical expertise in data transformation, familiarity with common data models, and in-depth domain knowledge of the underlying cohort data. It is also useful for researchers who want to work with data that are structured according to the OMOP CDM, or with data from (one of) the nine cohorts that were part of NCDC. What does it include? The resource includes materials covering all steps required to transform cohort data into the OMOP CDM, including the design of mappings and the execution of the ETL (Extract, Transform, Load) process. The first step in the harmonisation process is the collection of metadata. Variables from all cohort studies are identified, after which a set of harmonised variables is defined and mapped to OMOP concepts (the destination mapping). The variables of each individual cohort are then mapped to these harmonised variables (the source mapping). The folder examples/ contains an example destination mapping, source mapping, and dataset, and can be used to guide users in this initial harmonisation step The folder ncdc_mappings/ contains the destination mapping and source mappings for the nine NCDC cohorts In the subsequent step, the destination and source mappings are used to transform the cohort data and to set up and populate a database according to the common data model. The folder cdm_parser/ contains scripts that create and populate a PostgreSQL database based on the OMOP CDM. The scripts accept file-based datasets in CSV, SPSS, or SAS (Statistical Analysis Software) formats as input The folder scripts/ contains scripts that generate summary statistics to assess the correctness and completeness of the data transformation The subfolder examples/data-retrieval/ contains examples illustrating how to extract data from and query an OMOP CDM database. These examples can be used as an educational resource or as a starting point for researchers working with OMOP-formatted data. Further usage instructions can be found in the included README file, and software requirements are listed in the requirements file. Associated materials This repository was developed for the publication: Mateus P, Moonen J, Beran M, Jaarsma E, van der Landen SM, Heuvelink J, Birhanu M, Harms AGJ, Bron E, Wolters FJ, Cats D, Mei H, Oomens J, Jansen W, Schram MT, Dekker A, Bermejo I. Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model: A Netherlands consortium of dementia cohorts case study. J Biomed Inform. 2024 Jul;155:104661. doi: 10.1016/j.jbi.2024.104661. Epub","url":"https://doi.org/10.5281/zenodo.18316378","authors":["da Costa Mateus, Pedro","Moonen, Justine","Beran, Magdalena","Jaarsma, Eva","Van der Landen, Sophie","Heuvelink, Joost","Mahlet, Birhanu","Harms, Alexander","Bron, Esther","Wolters, Frank J.","Cats, Davy","Mei, Hailiang","Oomens, Julie Elisabeth","Jansen, Willemijn","Schram, Miranda","Dekker, Andre","Bermejo, Iñigo"],"tags":["Medical and health sciences","FOS: Medical and health sciences","Computer and information sciences","FOS: Computer and information sciences","OMOP Common Data Model","Data harmonisation","Common Data Model","Dementia"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18316378","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.18316379","name":"Scripts and mappings to harmonise dementia cohorts into the OMOP Common Data Model","source":"datacite","abstract":"This resource contains the scripts, mappings, and other materials that were used to harmonise data from nine Dutch cohorts relevant to dementia research to the OMOP (Observational Medical Outcomes Partnership) Common Data Model (CDM). These materials were developed within the Netherlands Consortium of Dementia Cohorts (NCDC) project. NCDC included the following nine cohorts: Amsterdam Dementia Cohort, Doetinchem Cohort Study, EMIF-AD 90+ study, EMIF-AD PreclinAD study, Leiden Longevity Study, Longitudinal Aging Study Amsterdam, The Maastricht Study, Rotterdam Study, and SMART. Scope The materials in this publication were used to transform the data from the NCDC cohorts into the OMOP CDM. However, the overall approach and workflow are transferable to other cohort harmonisation efforts. As such, this work can serve as a reference implementation or starting point for the harmonisation of cohort data to any common data model. Harmonising data to a common data model is crucial in (dementia) research as it allows to combine studies, increase statistical power, and enhance the reliability of findings. Reuse of this resource is governed by the license specified in this Zenodo record and the associated GitHub repository. Who is it for? This work is primarily intended for researchers and (research) software engineers looking to harmonise multi-centre cohort data into a common data model. Effective reuse of this resource requires technical expertise in data transformation, familiarity with common data models, and in-depth domain knowledge of the underlying cohort data. It is also useful for researchers who want to work with data that are structured according to the OMOP CDM, or with data from (one of) the nine cohorts that were part of NCDC. What does it include? The resource includes materials covering all steps required to transform cohort data into the OMOP CDM, including the design of mappings and the execution of the ETL (Extract, Transform, Load) process. The first step in the harmonisation process is the collection of metadata. Variables from all cohort studies are identified, after which a set of harmonised variables is defined and mapped to OMOP concepts (the destination mapping). The variables of each individual cohort are then mapped to these harmonised variables (the source mapping). The folder examples/ contains an example destination mapping, source mapping, and dataset, and can be used to guide users in this initial harmonisation step The folder ncdc_mappings/ contains the destination mapping and source mappings for the nine NCDC cohorts In the subsequent step, the destination and source mappings are used to transform the cohort data and to set up and populate a database according to the common data model. The folder cdm_parser/ contains scripts that create and populate a PostgreSQL database based on the OMOP CDM. The scripts accept file-based datasets in CSV, SPSS, or SAS (Statistical Analysis Software) formats as input The folder scripts/ contains scripts that generate summary statistics to assess the correctness and completeness of the data transformation The subfolder examples/data-retrieval/ contains examples illustrating how to extract data from and query an OMOP CDM database. These examples can be used as an educational resource or as a starting point for researchers working with OMOP-formatted data. Further usage instructions can be found in the included README file, and software requirements are listed in the requirements file. Associated materials This repository was developed for the publication: Mateus P, Moonen J, Beran M, Jaarsma E, van der Landen SM, Heuvelink J, Birhanu M, Harms AGJ, Bron E, Wolters FJ, Cats D, Mei H, Oomens J, Jansen W, Schram MT, Dekker A, Bermejo I. Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model: A Netherlands consortium of dementia cohorts case study. J Biomed Inform. 2024 Jul;155:104661. doi: 10.1016/j.jbi.2024.104661. Epub","url":"https://doi.org/10.5281/zenodo.18316379","authors":["da Costa Mateus, Pedro","Moonen, Justine","Beran, Magdalena","Jaarsma, Eva","Van der Landen, Sophie","Heuvelink, Joost","Mahlet, Birhanu","Harms, Alexander","Bron, Esther","Wolters, Frank J.","Cats, Davy","Mei, Hailiang","Oomens, Julie Elisabeth","Jansen, Willemijn","Schram, Miranda","Dekker, Andre","Bermejo, Iñigo"],"tags":["Medical and health sciences","FOS: Medical and health sciences","Computer and information sciences","FOS: Computer and information sciences","OMOP Common Data Model","Data harmonisation","Common Data Model","Dementia"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18316379","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.18453954","name":"Federated learning for privacy-preserving, secure and scalable data intelligence in hybrid cloud systems","source":"datacite","abstract":"The convergence of federated learning and hybrid cloud computing represents a transformative paradigm for privacy-preserving data intelligence. This review examines federated learning implementations in hybrid cloud environments, analyzing security mechanisms, privacy-preserving capabilities, and scalability challenges. We explore architectural frameworks and deployment strategies while analyzing security and privacy challenges from technical, organizational, and regulatory perspectives. The study highlights synergistic benefits of combining federated learning with hybrid cloud infrastructure and discusses emerging trends including homomorphic encryption, differential privacy, and blockchain integration. Through comprehensive literature analysis of publications from 2016 to 2024, key findings reveal that federated learning in hybrid clouds offers unprecedented opportunities for privacy-preserving analytics while introducing unique challenges in communication efficiency and cross-environment orchestration. Organizations can effectively leverage federated learning by implementing layered security architectures and maintaining continuous adaptation to evolving privacy regulations. This analysis provides valuable insights for practitioners and researchers navigating the intersection of federated learning and hybrid cloud computing.","url":"https://doi.org/10.5281/zenodo.18453954","authors":["Ezeakile, Emmanuel","Disu, Abdulateef Oluwakayode","Alabi, Cynthia","Mustapha, Toyosi","Odewale, Moses Oluwasegun"],"tags":["Federated Learning","Hybrid Cloud Computing","Privacy-Preserving Machine Learning","Data Intelligence","Distributed Learning","Edge Computing","Security Architectures"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18453954","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18453953","name":"Federated learning for privacy-preserving, secure and scalable data intelligence in hybrid cloud systems","source":"datacite","abstract":"The convergence of federated learning and hybrid cloud computing represents a transformative paradigm for privacy-preserving data intelligence. This review examines federated learning implementations in hybrid cloud environments, analyzing security mechanisms, privacy-preserving capabilities, and scalability challenges. We explore architectural frameworks and deployment strategies while analyzing security and privacy challenges from technical, organizational, and regulatory perspectives. The study highlights synergistic benefits of combining federated learning with hybrid cloud infrastructure and discusses emerging trends including homomorphic encryption, differential privacy, and blockchain integration. Through comprehensive literature analysis of publications from 2016 to 2024, key findings reveal that federated learning in hybrid clouds offers unprecedented opportunities for privacy-preserving analytics while introducing unique challenges in communication efficiency and cross-environment orchestration. Organizations can effectively leverage federated learning by implementing layered security architectures and maintaining continuous adaptation to evolving privacy regulations. This analysis provides valuable insights for practitioners and researchers navigating the intersection of federated learning and hybrid cloud computing.","url":"https://doi.org/10.5281/zenodo.18453953","authors":["Ezeakile, Emmanuel","Disu, Abdulateef Oluwakayode","Alabi, Cynthia","Mustapha, Toyosi","Odewale, Moses Oluwasegun"],"tags":["Federated Learning","Hybrid Cloud Computing","Privacy-Preserving Machine Learning","Data Intelligence","Distributed Learning","Edge Computing","Security Architectures"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18453953","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18344155","name":"Shuffle Model Privacy for Federated Learning: Asymptotic GDP Analysis and Bundled/Unbundled Separation","source":"datacite","abstract":"Exact asymptotic GDP parameters for shuffle model federated learning via chi-squared divergence. Proves exponential separation between bundled and unbundled shuffling designs in feasible SGD iterations. - After publication, the following related works were identified that share conceptual connections with this paper: 1. Su, Cheng, Wang (arXiv:2504.07414, 2025) analyze \"joint composition\" in shuffle model where user outputs are shuffled as a tuple — conceptually similar to our \"bundled\" regime. Their focus is on (ε,δ)-DP bounds via FFT, not µ-GDP with χ²-parameterization. 2. Su, Cheng, Wang (arXiv:2511.15051, 2025) show χ²(P‖Q) appears in mutual information asymptotics for shuffle model. Our contribution uses χ² for µ-GDP (differential privacy), not information-theoretic leakage. 3. Chen, Cao, Ge (AAAI 2024, arXiv:2312.14388) provide f-DP analysis for personalized LDP shuffle settings. They do not derive closed-form µ² expressions parameterized solely by ��²(W₁‖W₀). The core contributions of this paper remain novel: • Exact closed-form µ²_{n,un} = mχ²/n and µ²_{n,bd} = ((1+χ²)^m - 1)/n • Explicit exponential separation ratio Θ((1+χ²)^m/m) • SGD iteration bounds under µ-GDP budget A revised version with expanded Related Work is planned for journal submission.","url":"https://doi.org/10.5281/zenodo.18344155","authors":["Alex Shvets"],"tags":["differential privacy","shuffle model","federated learning","Gaussian differential privacy","privacy amplification"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18344155","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2311.18741","name":"VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning","source":"datacite","abstract":"Assisted and autonomous driving are rapidly gaining momentum and will soon become a reality. Artificial intelligence and machine learning are regarded as key enablers thanks to the massive amount of data that smart vehicles will collect from onboard sensors. Federated learning is one of the most promising techniques for training global machine learning models while preserving data privacy of vehicles and optimizing communications resource usage. In this article, we propose vehicular radio environment map federated learning (VREM-FL), a computation-scheduling co-design for vehicular federated learning that combines mobility of vehicles with 5G radio environment maps. VREM-FL jointly optimizes learning performance of the global model and wisely allocates communication and computation resources. This is achieved by orchestrating local computations at the vehicles in conjunction with transmission of their local models in an adaptive and predictive fashion, by exploiting radio channel maps. The proposed algorithm can be tuned to trade training time for radio resource usage. Experimental results demonstrate that VREM-FL outperforms literature benchmarks for both a linear regression model (learning time reduced by 28%) and a deep neural network for semantic image segmentation (doubling the number of model updates within the same time window).","url":"https://doi.org/10.48550/arxiv.2311.18741","authors":["Ballotta, Luca","Fabbro, Nicolò Dal","Perin, Giovanni","Schenato, Luca","Rossi, Michele","Piro, Giuseppe"],"tags":["Systems and Control (eess.SY)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2311.18741","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2601.07901","name":"Decentralized Online Convex Optimization with Unknown Feedback Delays","source":"datacite","abstract":"Decentralized online convex optimization (D-OCO), where multiple agents within a network collaboratively learn optimal decisions in real-time, arises naturally in applications such as federated learning, sensor networks, and multi-agent control. In this paper, we study D-OCO under unknown, time-and agent-varying feedback delays. While recent work has addressed this problem (Nguyen et al., 2024), existing algorithms assume prior knowledge of the total delay over agents and still suffer from suboptimal dependence on both the delay and network parameters. To overcome these limitations, we propose a novel algorithm that achieves an improved regret bound of O N $\\sqrt$ d tot + N $\\sqrt$ T (1-$σ$2) 1/4 , where T is the total horizon, d tot denotes the average total delay across agents, N is the number of agents, and 1 -$σ$ 2 is the spectral gap of the network. Our approach builds upon recent advances in D-OCO (Wan et al., 2024a), but crucially incorporates an adaptive learning rate mechanism via a decentralized communication protocol. This enables each agent to estimate delays locally using a gossip-based strategy without the prior knowledge of the total delay. We further extend our framework to the strongly convex setting and derive a sharper regret bound of O N $δ$max ln T $α$ , where $α$ is the strong convexity parameter and $δ$ max is the maximum number of missing observations averaged over agents. We also show that our upper bounds for both settings are tight up to logarithmic factors. Experimental results validate the effectiveness of our approach, showing improvements over existing benchmark algorithms.","url":"https://doi.org/10.48550/arxiv.2601.07901","authors":["Qiu, Hao","Zhang, Mengxiao","Achddou, Juliette"],"tags":["Machine Learning (stat.ML)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2601.07901","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.18140968","name":"SKIN CANCER MELONEMA DETECTION USING DEEP LEARNING- A REVIEW","source":"datacite","abstract":"Abstract Melanoma is one of the deadliest forms of skin cancer due to its rapid progression and high metastatic potential. Early detection greatly improves survival rates, yet traditional methods relying on dermatologist expertise are subjective and prone to error. This systematic review analyzes recent advances (2022–2024) in deep learning (DL) for melanoma detection. Key approaches include convolutional neural networks (CNNs), transfer learning, ensemble systems, attention mechanisms, and lightweight models suitable for mobile deployment. Performance metrics across public datasets such as ISIC, HAM10000, and PH2 indicate state-of-the-art results with improved sensitivity and specificity. Despite progress, challenges such as dataset bias, limited diversity, interpretability, and clinical validation remain. Future directions include explainable AI, federated learning, and integration of multimodal data. The findings demonstrate DL’s transformative potential for dermatology, but emphasize the need for real-world validation before clinical adoption. Keywords melanoma detection; deep learning; CNN; dermoscopic images; ensemble learning; medical image analysis","url":"https://doi.org/10.5281/zenodo.18140968","authors":["Tanushri Jhod, Seema Kirar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18140968","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.18140967","name":"SKIN CANCER MELONEMA DETECTION USING DEEP LEARNING- A REVIEW","source":"datacite","abstract":"Abstract Melanoma is one of the deadliest forms of skin cancer due to its rapid progression and high metastatic potential. Early detection greatly improves survival rates, yet traditional methods relying on dermatologist expertise are subjective and prone to error. This systematic review analyzes recent advances (2022–2024) in deep learning (DL) for melanoma detection. Key approaches include convolutional neural networks (CNNs), transfer learning, ensemble systems, attention mechanisms, and lightweight models suitable for mobile deployment. Performance metrics across public datasets such as ISIC, HAM10000, and PH2 indicate state-of-the-art results with improved sensitivity and specificity. Despite progress, challenges such as dataset bias, limited diversity, interpretability, and clinical validation remain. Future directions include explainable AI, federated learning, and integration of multimodal data. The findings demonstrate DL’s transformative potential for dermatology, but emphasize the need for real-world validation before clinical adoption. Keywords melanoma detection; deep learning; CNN; dermoscopic images; ensemble learning; medical image analysis","url":"https://doi.org/10.5281/zenodo.18140967","authors":["Tanushri Jhod, Seema Kirar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18140967","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2512.20610","name":"FedPOD: the deployable units of training for federated learning","source":"datacite","abstract":"This paper proposes FedPOD, which ranked first in the 2024 Federated Tumor Segmentation (FeTS) Challenge, for optimizing learning efficiency and communication cost in federated learning among multiple clients. Inspired by FedPIDAvg, we define a round-wise task for FedPOD to enhance training efficiency. FedPIDAvg achieved performance improvement by incorporating the training loss reduction for prediction entropy as weights using differential terms. Furthermore, by modeling data distribution with a Poisson distribution and using a PID controller, it reduced communication costs even in skewed data distribution. However, excluding participants classified as outliers based on the Poisson distribution can limit data utilization. Additionally, PID controller requires the same participants to be maintained throughout the federated learning process as it uses previous rounds' learning information in the current round. In our approach, FedPOD addresses these issues by including participants excluded as outliers, eliminating dependency on previous rounds' learning information, and applying a method for calculating validation loss at each round. In this challenge, FedPOD presents comparable performance to FedPIDAvg in metrics of Dice score, 0.78, 0.71 and 0.72 for WT, ET and TC in average, and projected convergence score, 0.74 in average. Furthermore, the concept of FedPOD draws inspiration from Kubernetes' smallest computing unit, POD, designed to be compatible with Kubernetes auto-scaling. Extending round-wise tasks of FedPOD to POD units allows flexible design by applying scale-out similar to Kubernetes' auto-scaling. This work demonstrated the potentials of FedPOD to enhance federated learning by improving efficiency, flexibility, and performance in metrics.","url":"https://doi.org/10.48550/arxiv.2512.20610","authors":["Kim, Daewoon","Yie, Si Young","Lee, Jae Sung"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2512.20610","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17996476","name":"Top 5 Emerging AI Trends Transforming Digital Health in 2024","source":"datacite","abstract":"Discover the top 5 AI trends in digital health for 2024, including federated learning, multimodal AI, edge AI, agentic AI, and quantum AI transforming healthcare.","url":"https://doi.org/10.5281/zenodo.17996476","authors":["Rasit Dinc"],"tags":["AI in Healthcare","Digital Health","Federated Learning","Multimodal AI","Quantum AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17996476","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17996477","name":"Top 5 Emerging AI Trends Transforming Digital Health in 2024","source":"datacite","abstract":"Discover the top 5 AI trends in digital health for 2024, including federated learning, multimodal AI, edge AI, agentic AI, and quantum AI transforming healthcare.","url":"https://doi.org/10.5281/zenodo.17996477","authors":["Rasit Dinc"],"tags":["AI in Healthcare","Digital Health","Federated Learning","Multimodal AI","Quantum AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17996477","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2206.00395","name":"Optimization with Access to Auxiliary Information","source":"datacite","abstract":"We investigate the fundamental optimization question of minimizing a target function $f$, whose gradients are expensive to compute or have limited availability, given access to some auxiliary side function $h$ whose gradients are cheap or more available. This formulation captures many settings of practical relevance, such as i) re-using batches in SGD, ii) transfer learning, iii) federated learning, iv) training with compressed models/dropout, Et cetera. We propose two generic new algorithms that apply in all these settings; we also prove that we can benefit from this framework under the Hessian similarity assumption between the target and side information. A benefit is obtained when this similarity measure is small; we also show a potential benefit from stochasticity when the auxiliary noise is correlated with that of the target function.","url":"https://doi.org/10.48550/arxiv.2206.00395","authors":["Chayti, El Mahdi","Karimireddy, Sai Praneeth"],"tags":["Machine Learning (cs.LG)","Optimization and Control (math.OC)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Mathematics","FOS: Mathematics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2206.00395","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.3217/m750p-pqw83","name":"Accessibility for academic teaching staff (Report 05/2025)","source":"datacite","abstract":"The report describes a pilot “Video course on accessibility” created by Wrocław University of Science and Technology (Wrocław Tech) for academic teaching staff, including laboratory instructors. The course links the university’s local Moodle‑based LMS (ePortal PWr) with the Unite! Metacampus platform via Learning Tools Interoperability (LTI). The self‑study course comprises seven short videos covering mental‑health crises, autism, visual and hearing impairments, physical disability, and invisible disability. Each video is followed by a mandatory knowledge‑check question; a final five‑question test awards a certificate of completion. Total duration is about 50 minutes. Technical integration uses eduGAIN single‑sign‑on, with ePortal acting as the LTI tool provider and Metacampus as the LTI consumer. Secure credential exchange enables automatic grade pass‑back and synchronized progress, while configuration responsibilities were delegated to ePortal administrators to simplify the instructor’s role. The pilot enrolled the target of ≥ 50 academic staff. Survey feedback shows unanimous agreement that the course enhanced understanding of disability needs, awareness of support mechanisms, and confidence in implementing inclusive teaching practices. Participants valued the practical, student‑driven examples and intuitive navigation, though they suggested making the final test visible earlier, improving promotion, and optimizing the interface for small screens. Key lessons highlight the benefit of delegating LTI setup, the need for clear communication of course milestones, and the importance of responsive design. Recommendations include scheduling future trainings during high‑availability periods, offering introductory webinars, expanding interactive video tools, and providing richer LTI‑configuration guides for external admins. The series “Unite! Digital Teaching and Learning Success Story Report” was initiated by Cm.2 Digital Campus, led by TU Graz (Martin Ebner) as part of the idea to collect, spread and enhance the possibilities, usages and lessons learned of teaching and learning offers using the federated learning management system Metacampus in 11/2024 till the end of the current Erasmus+ funding period 10/2026. All reports are available under open license and originally published at the TU Graz repository. Copyright holders are the authors.","url":"https://doi.org/10.3217/m750p-pqw83","authors":["Forycka, Renata","Herczak-Ciara, Agnieszka","Jach, Katarzyna","Krysiak, Jarosław","Kosela, Anna","Zawada, Justyna"],"tags":["Educational sciences","FOS: Educational sciences","Engineering and technology","FOS: Engineering and technology","learning management","learning management system","european university alliance","unite!"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3217/m750p-pqw83","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.3217/163xg-5h074","name":"Accessibility for academic teaching staff (Report 05/2025)","source":"datacite","abstract":"The report describes a pilot “Video course on accessibility” created by Wrocław University of Science and Technology (Wrocław Tech) for academic teaching staff, including laboratory instructors. The course links the university’s local Moodle‑based LMS (ePortal PWr) with the Unite! Metacampus platform via Learning Tools Interoperability (LTI). The self‑study course comprises seven short videos covering mental‑health crises, autism, visual and hearing impairments, physical disability, and invisible disability. Each video is followed by a mandatory knowledge‑check question; a final five‑question test awards a certificate of completion. Total duration is about 50 minutes. Technical integration uses eduGAIN single‑sign‑on, with ePortal acting as the LTI tool provider and Metacampus as the LTI consumer. Secure credential exchange enables automatic grade pass‑back and synchronized progress, while configuration responsibilities were delegated to ePortal administrators to simplify the instructor’s role. The pilot enrolled the target of ≥ 50 academic staff. Survey feedback shows unanimous agreement that the course enhanced understanding of disability needs, awareness of support mechanisms, and confidence in implementing inclusive teaching practices. Participants valued the practical, student‑driven examples and intuitive navigation, though they suggested making the final test visible earlier, improving promotion, and optimizing the interface for small screens. Key lessons highlight the benefit of delegating LTI setup, the need for clear communication of course milestones, and the importance of responsive design. Recommendations include scheduling future trainings during high‑availability periods, offering introductory webinars, expanding interactive video tools, and providing richer LTI‑configuration guides for external admins. The series “Unite! Digital Teaching and Learning Success Story Report” was initiated by Cm.2 Digital Campus, led by TU Graz (Martin Ebner) as part of the idea to collect, spread and enhance the possibilities, usages and lessons learned of teaching and learning offers using the federated learning management system Metacampus in 11/2024 till the end of the current Erasmus+ funding period 10/2026. All reports are available under open license and originally published at the TU Graz repository. Copyright holders are the authors.","url":"https://doi.org/10.3217/163xg-5h074","authors":["Forycka, Renata","Herczak-Ciara, Agnieszka","Jach, Katarzyna","Krysiak, Jarosław","Kosela, Anna","Zawada, Justyna"],"tags":["Educational sciences","FOS: Educational sciences","Engineering and technology","FOS: Engineering and technology","learning management","learning management system","european university alliance","unite!"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3217/163xg-5h074","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.6084/m9.figshare.28776466","name":"Artificial intelligence and its application in clinical microbiology","source":"datacite","abstract":"Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, advancements, and integration challenges in clinical microbiology. This review examines AI-driven methodologies, including machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), for enhancing pathogen detection, AMR prediction, and diagnostic imaging. Applications in virology (e.g. COVID-19 RT-PCR optimization), parasitology (e.g. malaria detection), and bacteriology (e.g. automated colony counting) are analyzed. A literature search was conducted using PubMed, Scopus, and Web of Science (2018–2024), prioritizing peer-reviewed studies on AI’s diagnostic accuracy, workflow efficiency, and clinical validation. AI significantly improves diagnostic precision and operational efficiency but requires robust validation to address data heterogeneity, model interpretability, and ethical concerns. Future success hinges on interdisciplinary collaboration to develop standardized, equitable AI tools tailored for global healthcare settings. Advancing explainable AI and federated learning frameworks will be critical for bridging current implementation gaps and maximizing AI’s potential in combating infectious diseases.","url":"https://doi.org/10.6084/m9.figshare.28776466","authors":["Mairi, Assia","Hamza, Lamia","Touati, Abdelaziz"],"tags":["Space Science","Medicine","Microbiology","FOS: Biological sciences","Biotechnology","Biological Sciences not elsewhere classified","Information Systems not elsewhere classified","Cancer"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.6084/m9.figshare.28776466","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.6084/m9.figshare.28776466.v1","name":"Artificial intelligence and its application in clinical microbiology","source":"datacite","abstract":"Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, advancements, and integration challenges in clinical microbiology. This review examines AI-driven methodologies, including machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), for enhancing pathogen detection, AMR prediction, and diagnostic imaging. Applications in virology (e.g. COVID-19 RT-PCR optimization), parasitology (e.g. malaria detection), and bacteriology (e.g. automated colony counting) are analyzed. A literature search was conducted using PubMed, Scopus, and Web of Science (2018–2024), prioritizing peer-reviewed studies on AI’s diagnostic accuracy, workflow efficiency, and clinical validation. AI significantly improves diagnostic precision and operational efficiency but requires robust validation to address data heterogeneity, model interpretability, and ethical concerns. Future success hinges on interdisciplinary collaboration to develop standardized, equitable AI tools tailored for global healthcare settings. Advancing explainable AI and federated learning frameworks will be critical for bridging current implementation gaps and maximizing AI’s potential in combating infectious diseases.","url":"https://doi.org/10.6084/m9.figshare.28776466.v1","authors":["Mairi, Assia","Hamza, Lamia","Touati, Abdelaziz"],"tags":["Space Science","Medicine","Microbiology","FOS: Biological sciences","Biotechnology","Biological Sciences not elsewhere classified","Information Systems not elsewhere classified","Cancer"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.6084/m9.figshare.28776466.v1","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17926787","name":"Privacy-Preserving Machine Learning: Techniques, Frameworks, and Future Directions","source":"datacite","abstract":"Machine Learning that Preserves Privacy (PPML) facilitates model training and analysis while safeguarding sensitive data, model parameters, and user privacy. This survey reviews advances from 2019–2024 with a focus on four major techniques: Homomorphic Encryption (HE), Differential Privacy (DP), Secure Multi-Party Computation (MPC), and Federated Analytics (FA) with secure aggregation. Additionally, it offers a unified taxonomy connecting these methods to adversary models, deployment patterns, and practical applications. Drawing on benchmark outcomes and system evaluations from 2019–2024, this survey assesses PPML systems regarding efficiency, accuracy, deployment costs, and ROI. It emphasizes where each approach excels—such as HE for encrypted inference, DP for secure model release, MPC for collaborative training across silos, and FA for extensive client-side analytics—and details critical engineering trade-offs in industries like healthcare, finance, telecommunications, and IoT.The paper also proposes a practical research roadmap emphasizing hybrid pipelines that combine cryptographic methods with DP, hardware–software co-design to accelerate HE/MPC, and standardized benchmarks for privacy–utility–cost evaluation. Additionally, it stresses the need for operational auditing, explainability. The survey subsequently dives into the latest PPML trends like the merger of hardware-bound TEEs with cryptographic protocols, which would give a dual advantage of higher performance and security. It mentions the use of federated learning in edge and IoT devices, which is increasing but poses unique challenges due to limited computing power and unstable connectivity. Through the analysis of the practical installations, the survey brings out the major causes of communication overload, non-scalable systems, and the risk of losing privacy, which, being articulated in the form of guidelines, help to conquer the mentioned issues and the like in the design of PPML pipelines in the different environments of heterogeneous resources. The paper, lastly, insists upon the role of ethical and regulatory considerations in the acceptance of PPML. Organizations are required to align their technical solutions with the legal requirements as data privacy regulations like GDPR, HIPAA, and CCPA are the main determinants of the handling of sensitive information. Privacy impact assessments are suggested by the survey to be detailed, behavior modeling to be constantly checked, and privacy-improving activities to be disclosed in a public way so that the stakeholders' trust can be earned. It is through the collaboration of the technical thoroughness and ethical supervision that the PPML will pave the way for the secure and responsible use of AI in different sectors.","url":"https://doi.org/10.5281/zenodo.17926787","authors":["Ruksar Fatima","Ayesha Siddiqua","Aliza Mahvash","Syeda Sheeba"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17926787","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.17926788","name":"Privacy-Preserving Machine Learning: Techniques, Frameworks, and Future Directions","source":"datacite","abstract":"Machine Learning that Preserves Privacy (PPML) facilitates model training and analysis while safeguarding sensitive data, model parameters, and user privacy. This survey reviews advances from 2019–2024 with a focus on four major techniques: Homomorphic Encryption (HE), Differential Privacy (DP), Secure Multi-Party Computation (MPC), and Federated Analytics (FA) with secure aggregation. Additionally, it offers a unified taxonomy connecting these methods to adversary models, deployment patterns, and practical applications. Drawing on benchmark outcomes and system evaluations from 2019–2024, this survey assesses PPML systems regarding efficiency, accuracy, deployment costs, and ROI. It emphasizes where each approach excels—such as HE for encrypted inference, DP for secure model release, MPC for collaborative training across silos, and FA for extensive client-side analytics—and details critical engineering trade-offs in industries like healthcare, finance, telecommunications, and IoT.The paper also proposes a practical research roadmap emphasizing hybrid pipelines that combine cryptographic methods with DP, hardware–software co-design to accelerate HE/MPC, and standardized benchmarks for privacy–utility–cost evaluation. Additionally, it stresses the need for operational auditing, explainability. The survey subsequently dives into the latest PPML trends like the merger of hardware-bound TEEs with cryptographic protocols, which would give a dual advantage of higher performance and security. It mentions the use of federated learning in edge and IoT devices, which is increasing but poses unique challenges due to limited computing power and unstable connectivity. Through the analysis of the practical installations, the survey brings out the major causes of communication overload, non-scalable systems, and the risk of losing privacy, which, being articulated in the form of guidelines, help to conquer the mentioned issues and the like in the design of PPML pipelines in the different environments of heterogeneous resources. The paper, lastly, insists upon the role of ethical and regulatory considerations in the acceptance of PPML. Organizations are required to align their technical solutions with the legal requirements as data privacy regulations like GDPR, HIPAA, and CCPA are the main determinants of the handling of sensitive information. Privacy impact assessments are suggested by the survey to be detailed, behavior modeling to be constantly checked, and privacy-improving activities to be disclosed in a public way so that the stakeholders' trust can be earned. It is through the collaboration of the technical thoroughness and ethical supervision that the PPML will pave the way for the secure and responsible use of AI in different sectors.","url":"https://doi.org/10.5281/zenodo.17926788","authors":["Ruksar Fatima","Ayesha Siddiqua","Aliza Mahvash","Syeda Sheeba"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17926788","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.17926849","name":"Resource Management in the Edge–Cloud Continuum: Trends, Algorithms, and Open Challenges","source":"datacite","abstract":"The continuum of edge and cloud computing has emerged as a vital computing model for enabling latency-sensitive, data-heavy, and geographically scattered applications. As billions of devices generate massive volumes of data, efficient resource management across heterogeneous, distributed infrastructures has become essential. This study presents a systematic review of 68 research articles published between 2019 and 2024 that address resource distribution, task delegation, scheduling, orchestration, and optimization within the edge–cloud continuum. The paper highlights emerging themes such as AI-driven orchestration, multi-agent reinforcement learning, federated optimization, and serverless edge computing. We evaluate the performance, precision, scalability, and flexibility of traditional heuristics, mathematical models, and RL techniques under varying workloads. Although these significant advancements have been made, several open problems still exist—mobility-aware scheduling, cross-layer security integration with over-the-air encrypted computation results, and the absence of general ML models and benchmarks along with large-scale real-world deployment. The paper also ends by emphasizing the future research directions required to create and implement intelligent, autonomous, and scalable resource management frameworks that are designed for 6G/enhanced mobile broadband (eMBB), IoT/operating on devices over Bluetooth, autonomous systems/enabled by local cloudlets, and immersive applications/such as immersive gaming.","url":"https://doi.org/10.5281/zenodo.17926849","authors":["Ruksar Fatima","Suhana Anjum","Shaista Fatima Junaidi","Ruqayya Rafa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17926849","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17926850","name":"Resource Management in the Edge–Cloud Continuum: Trends, Algorithms, and Open Challenges","source":"datacite","abstract":"The continuum of edge and cloud computing has emerged as a vital computing model for enabling latency-sensitive, data-heavy, and geographically scattered applications. As billions of devices generate massive volumes of data, efficient resource management across heterogeneous, distributed infrastructures has become essential. This study presents a systematic review of 68 research articles published between 2019 and 2024 that address resource distribution, task delegation, scheduling, orchestration, and optimization within the edge–cloud continuum. The paper highlights emerging themes such as AI-driven orchestration, multi-agent reinforcement learning, federated optimization, and serverless edge computing. We evaluate the performance, precision, scalability, and flexibility of traditional heuristics, mathematical models, and RL techniques under varying workloads. Although these significant advancements have been made, several open problems still exist—mobility-aware scheduling, cross-layer security integration with over-the-air encrypted computation results, and the absence of general ML models and benchmarks along with large-scale real-world deployment. The paper also ends by emphasizing the future research directions required to create and implement intelligent, autonomous, and scalable resource management frameworks that are designed for 6G/enhanced mobile broadband (eMBB), IoT/operating on devices over Bluetooth, autonomous systems/enabled by local cloudlets, and immersive applications/such as immersive gaming.","url":"https://doi.org/10.5281/zenodo.17926850","authors":["Ruksar Fatima","Suhana Anjum","Shaista Fatima Junaidi","Ruqayya Rafa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17926850","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17926595","name":"Federated Learning: Advances, Privacy Mechanisms, and Real-World Deployments","source":"datacite","abstract":"Federated Learning (FL) has emerged as a powerful paradigm that enables collaborative model training across decentralized clients while preserving data privacy. Instead of aggregating sensitive data in a central server, FL coordinates local training on distributed devices—ranging from smartphones to IoT sensors and institutional servers—and collects only model updates. This design addresses major privacy, ethical, and security concerns associated with centralized data storage. Between 2019 and 2024, extensive research has focused on core FL challenges such as non-IID data distributions, device and system heterogeneity, resource limitations, privacy risks arising from gradient leakage, and practical deployment barriers in fields like healthcare and edge IoT. In this paper, we review recent advances across four themes: (1) distinctions and best practices for cross-device vs. cross-silo FL; (2) privacy-preserving mechanisms, including differential privacy and secure aggregation; (3) communication- and model-compression techniques for reducing bandwidth usage; and (4) real-world deployments in healthcare and edge-IoT environments. We analyze these works based on efficiency, accuracy, privacy trade-offs, and deployment-level considerations such as resource savings and regulatory alignment. Our synthesis shows that modern compression techniques—such as quantization, sparsification, and knowledge distillation—can significantly reduce communication costs with minimal accuracy loss, making FL feasible for resource-constrained devices. Privacy mechanisms remain essential for sensitive domains, though they commonly introduce accuracy and utility trade-offs. Cross-silo deployments demonstrate performance close to centralized baselines while maintaining data locality, yet full-scale adoption still depends on standardization, infrastructure readiness, and clearer ROI evidence. We highlight open gaps such as limited convergence theory for private and compressed FL under non-IID data, lack of unified benchmarks, insufficient empirical ROI studies, and the challenges of scaling FL to large modern models. To support clarity, we provide comparative tables, research-gap matrices, and conceptual diagrams illustrating accuracy-efficiency-privacy tradeoffs, followed by prioritized directions for future research.","url":"https://doi.org/10.5281/zenodo.17926595","authors":["Ruksar Fatima","Aliza Mahvash","Ayesha Siddiqua","Syeda Sheeba"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17926595","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17926594","name":"Federated Learning: Advances, Privacy Mechanisms, and Real-World Deployments","source":"datacite","abstract":"Federated Learning (FL) has emerged as a powerful paradigm that enables collaborative model training across decentralized clients while preserving data privacy. Instead of aggregating sensitive data in a central server, FL coordinates local training on distributed devices—ranging from smartphones to IoT sensors and institutional servers—and collects only model updates. This design addresses major privacy, ethical, and security concerns associated with centralized data storage. Between 2019 and 2024, extensive research has focused on core FL challenges such as non-IID data distributions, device and system heterogeneity, resource limitations, privacy risks arising from gradient leakage, and practical deployment barriers in fields like healthcare and edge IoT. In this paper, we review recent advances across four themes: (1) distinctions and best practices for cross-device vs. cross-silo FL; (2) privacy-preserving mechanisms, including differential privacy and secure aggregation; (3) communication- and model-compression techniques for reducing bandwidth usage; and (4) real-world deployments in healthcare and edge-IoT environments. We analyze these works based on efficiency, accuracy, privacy trade-offs, and deployment-level considerations such as resource savings and regulatory alignment. Our synthesis shows that modern compression techniques—such as quantization, sparsification, and knowledge distillation—can significantly reduce communication costs with minimal accuracy loss, making FL feasible for resource-constrained devices. Privacy mechanisms remain essential for sensitive domains, though they commonly introduce accuracy and utility trade-offs. Cross-silo deployments demonstrate performance close to centralized baselines while maintaining data locality, yet full-scale adoption still depends on standardization, infrastructure readiness, and clearer ROI evidence. We highlight open gaps such as limited convergence theory for private and compressed FL under non-IID data, lack of unified benchmarks, insufficient empirical ROI studies, and the challenges of scaling FL to large modern models. To support clarity, we provide comparative tables, research-gap matrices, and conceptual diagrams illustrating accuracy-efficiency-privacy tradeoffs, followed by prioritized directions for future research.","url":"https://doi.org/10.5281/zenodo.17926594","authors":["Ruksar Fatima","Aliza Mahvash","Ayesha Siddiqua","Syeda Sheeba"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17926594","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17906585","name":"A Rigorous Multidisciplinary Theoretical Framework for Synergistic Bio- and Non-Biochemical Interventions to Reverse Cellular and Tissue Aging: Quantitative Stochastic Modeling, Clinical Applications, and Translational Precision Medicine","source":"datacite","abstract":"This comprehensive theoretical framework integrates biochemistry, bioengineering, applied mathematics, computational biology, and pharmacology to model synergistic bio- and non-biochemical interventions for cellular and tissue rejuvenation. We develop and rigorously analyze stochastic differential equation (SDE) models encompassing mitochondrial function, reactive oxygen species (ROS) dynamics, telomere attrition, cellular senescence, inflammaging, genomic instability, and---with expanded temporal sequencing---epigenetic drift, incorporating phased dynamics (early programmed vs. midlife-accelerated stochastic accrual at CpG sites, bifurcation $\\sim\\( age 45 via Lyapunov stability \\)\\mu_L \\approx 0.012$ yr\\( ^{-1} \\)), for precise multi-hallmark integration \\citep{tarkhov2025temporal}. Detailed derivations, drift-diffusion mechanics (Itô semimartingales with positivity-preserving reflections), numerical schemes (Euler--Maruyama \\( \\Delta t = 0.001 \\) yr, weak order 1.0 via Richardson extrapolation \\( O(\\Delta t) \\), strong \\( O(\\sqrt{\\Delta t}) \\) Milstein verification with \\( 75\\% \\), RMSE \\( 0 \\) post), 2024--2025 lit. (AI-gene therapy, dissipation theory \\citep{khodaee2025dissipation}) ensure stringent cohesion and accuracy.","url":"https://doi.org/10.5281/zenodo.17906585","authors":["shibah, Sami Rashid Mohammed"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17906585","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2305.19600","name":"Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning","source":"datacite","abstract":"Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. In practice, there can often be substantial heterogeneity (e.g., class imbalance) across the local data distributions observed by each of these clients. Under such non-iid label distributions across clients, FL suffers from the 'client-drift' problem where every client drifts to its own local optimum. This results in slower convergence and poor performance of the aggregated model. To address this limitation, we propose a novel regularization technique based on adaptive self-distillation (ASD) for training models on the client side. Our regularization scheme adaptively adjusts to each client's training data based on the global model's prediction entropy and the client-data label distribution. We show in this paper that our proposed regularization (ASD) can be easily integrated atop existing, state-of-the-art FL algorithms, leading to a further boost in the performance of these off-the-shelf methods. We theoretically explain how incorporation of ASD regularizer leads to reduction in client-drift and empirically justify the generalization ability of the trained model. We demonstrate the efficacy of our approach through extensive experiments on multiple real-world benchmarks and show substantial gains in performance when the proposed regularizer is combined with popular FL methods.","url":"https://doi.org/10.48550/arxiv.2305.19600","authors":["Yashwanth, M","Nayak, Gaurav Kumar","Singh, Arya","Simmhan, Yogesh","Chakraborty, Anirban"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2305.19600","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17549886","name":"D3.2 Competence Centres concepts and activities (pre)existing in EOSC","source":"datacite","abstract":"This deliverable presents an overview of EOSC-related activities and projects that could be taken into account for the design and implementation of Competence Centres (CCs), positioned as key instruments to support data-intensive, FAIR-compliant, and interdisciplinary research within the European Open Science Cloud (EOSC). It synthesises existing practices, conceptual frameworks, and policy recommendations drawn from ongoing and past projects. CCs are understood by most of the research communities as decentralised, composable structures that may consolidate community expertise, support training and guidance for data sharing and reuse or provide embedded services across diverse research contexts. The deliverable outlines the different types of contributions of the domain-specific clusters. Each science cluster intends to align its CC strategies on either thematic priorities, governance approaches, training assets etc, and reflect on how they then could align within the OSCARS CC design and definition proposed in the framework of OSCARS WP1 (Bodera Sempere et al., 2024). The result of this landscaping highlights existing or in development principles, acknowledges heterogeneous implementations, foster cross-community learning and will lay the groundwork for a future inter-OSCARs project and inter-community paper on all kind of Competence Centres that can act in the framework of EOSC (discipline specific or thematic, local, regional, national…). The document identifies key interdisciplinary challenges such as multimodal data integration and large-scale metadata analysis emphasising the need for cultural change, capacity building, and embedded support mechanisms close to research practice, Challenges identified demand robust infrastructures, sustained collaboration, and the realisation of the “FAIR web of data,” a central EOSC ambition. The OSCARS CC model builds upon these insights to propose a federated and scalable ecosystem of competence. The models offer a practical roadmap to foster uptake, interoperability, and sustainability of Open Science across European research communities.","url":"https://doi.org/10.5281/zenodo.17549886","authors":["David, Romain","Hienola, Anca","Schmidt-Tremmel, Friederike","van der Lek, Iulianna","Nentwich, Melanie","Bodera Sempere, Jordi","Kalaitzi, Vasso","Guerrieri, Giovanni","Wolff-Boenisch, Bonnie","Guezennec, Cécile","Draščić, Martina","Vipavc Brvar, Irena"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17549886","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17549887","name":"D3.2 Competence Centres concepts and activities (pre)existing in EOSC","source":"datacite","abstract":"This deliverable presents an overview of EOSC-related activities and projects that could be taken into account for the design and implementation of Competence Centres (CCs), positioned as key instruments to support data-intensive, FAIR-compliant, and interdisciplinary research within the European Open Science Cloud (EOSC). It synthesises existing practices, conceptual frameworks, and policy recommendations drawn from ongoing and past projects. CCs are understood by most of the research communities as decentralised, composable structures that may consolidate community expertise, support training and guidance for data sharing and reuse or provide embedded services across diverse research contexts. The deliverable outlines the different types of contributions of the domain-specific clusters. Each science cluster intends to align its CC strategies on either thematic priorities, governance approaches, training assets etc, and reflect on how they then could align within the OSCARS CC design and definition proposed in the framework of OSCARS WP1 (Bodera Sempere et al., 2024). The result of this landscaping highlights existing or in development principles, acknowledges heterogeneous implementations, foster cross-community learning and will lay the groundwork for a future inter-OSCARs project and inter-community paper on all kind of Competence Centres that can act in the framework of EOSC (discipline specific or thematic, local, regional, national…). The document identifies key interdisciplinary challenges such as multimodal data integration and large-scale metadata analysis emphasising the need for cultural change, capacity building, and embedded support mechanisms close to research practice, Challenges identified demand robust infrastructures, sustained collaboration, and the realisation of the “FAIR web of data,” a central EOSC ambition. The OSCARS CC model builds upon these insights to propose a federated and scalable ecosystem of competence. The models offer a practical roadmap to foster uptake, interoperability, and sustainability of Open Science across European research communities.","url":"https://doi.org/10.5281/zenodo.17549887","authors":["David, Romain","Hienola, Anca","Schmidt-Tremmel, Friederike","van der Lek, Iulianna","Nentwich, Melanie","Bodera Sempere, Jordi","Kalaitzi, Vasso","Guerrieri, Giovanni","Wolff-Boenisch, Bonnie","Guezennec, Cécile","Draščić, Martina","Vipavc Brvar, Irena"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17549887","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2512.08314","name":"Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning","source":"datacite","abstract":"Federated Learning (FL) is an emerging machine learning framework that enables multiple clients (coordinated by a server) to collaboratively train a global model by aggregating the locally trained models without sharing any client's training data. It has been observed in recent works that learning in a federated manner may lead the aggregated global model to converge to a 'sharp minimum' thereby adversely affecting the generalizability of this FL-trained model. Therefore, in this work, we aim to improve the generalization performance of models trained in a federated setup by introducing a 'flatness' constrained FL optimization problem. This flatness constraint is imposed on the top eigenvalue of the Hessian computed from the training loss. As each client trains a model on its local data, we further re-formulate this complex problem utilizing the client loss functions and propose a new computationally efficient regularization technique, dubbed 'MAN,' which Minimizes Activation's Norm of each layer on client-side models. We also theoretically show that minimizing the activation norm reduces the top eigenvalue of the layer-wise Hessian of the client's loss, which in turn decreases the overall Hessian's top eigenvalue, ensuring convergence to a flat minimum. We apply our proposed flatness-constrained optimization to the existing FL techniques and obtain significant improvements, thereby establishing new state-of-the-art.","url":"https://doi.org/10.48550/arxiv.2512.08314","authors":["Yashwanth, M","Nayak, Gaurav Kumar","Rangwani, Harsh","Singh, Arya","Babu, R. Venkatesh","Chakraborty, Anirban"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2512.08314","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2512.06206","name":"The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning","source":"datacite","abstract":"We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in multi-parametric MRI and evaluates new weight aggregation methods aimed at improving robustness and efficiency. Six participating teams were evaluated using a standardized FL setup and a multi-institutional dataset derived from the BraTS glioma benchmark, consisting of 1,251 training cases, 219 validation cases, and 570 hidden test cases with segmentations for enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Teams were ranked using a cumulative scoring system that considered both segmentation performance, measured by Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD95), and communication efficiency assessed through the convergence score. A PID-controller-based method achieved the top overall ranking, obtaining mean DSC values of 0.733, 0.761, and 0.751 for ET, TC, and WT, respectively, with corresponding HD95 values of 33.922 mm, 33.623 mm, and 32.309 mm, while also demonstrating the highest communication efficiency with a convergence score of 0.764. These findings advance the state of federated learning for medical imaging, surpassing top-performing methods from previous challenge iterations and highlighting PID controllers as effective mechanisms for stabilizing and optimizing weight aggregation in FL. The challenge code is available at https://github.com/FeTS-AI/Challenge.","url":"https://doi.org/10.48550/arxiv.2512.06206","authors":["Linardos, Akis","Pati, Sarthak","Baid, Ujjwal","Edwards, Brandon","Foley, Patrick","Ta, Kevin","Chung, Verena","Sheller, Micah","Khan, Muhammad Irfan","Jafaritadi, Mojtaba","Kontio, Elina","Khan, Suleiman","Mächler, Leon","Ezhov, Ivan","Shit, Suprosanna","Paetzold, Johannes C.","Grimberg, Gustav","Nickel, Manuel A.","Naccache, David","Siomos, Vasilis","Passerat-Palmbach, Jonathan","Tarroni, Giacomo","Kim, Daewoon","Klausmann, Leonard L.","Shah, Prashant","Menze, Bjoern","Makris, Dimitrios","Bakas, Spyridon"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2512.06206","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17855008","name":"A Rigorous Multidisciplinary Theoretical Framework for Synergistic Bio- and Non-Biochemical Interventions to Reverse Cellular and Tissue Aging: Quantitative Stochastic Modeling, Clinical Applications, and Translational Precision Medicine","source":"datacite","abstract":"This pioneering theoretical framework integrates biochemistry, bioengineering, applied mathematics, computational biology, and pharmacology to model synergistic bio- and non-biochemical interventions for cellular and tissue rejuvenation, advancing beyond existing stochastic models by incorporating hierarchical phased epigenetics with AI-driven precision. We develop and rigorously analyze stochastic differential equation (SDE) models encompassing mitochondrial function, reactive oxygen species (ROS) dynamics, telomere attrition, cellular senescence, inflammaging, genomic instability, and---with expanded temporal sequencing---epigenetic drift, incorporating phased dynamics (early programmed vs. midlife-accelerated stochastic accrual at CpG sites, nonlinear acceleration ∼45 yr via Lyapunov stability analysis with μ_L ≈ 0.012 yr^{-1}), for precise multi-hallmark integration [1]. Detailed derivations, drift-diffusion mechanics (Itô semimartingales with positivity-preserving reflections), numerical schemes (Euler--Maruyama Δt = 0.001 yr, weak order 1.0 via Richardson extrapolation O(Δt), strong O(√Δt) Milstein verification with 75%, RMSE 0 post), 2024--2025 lit. (AI-gene therapy, dissipation theory [3]) ensure stringent cohesion and accuracy, positioning this framework as a cornerstone for next-generation geroscience.","url":"https://doi.org/10.5281/zenodo.17855008","authors":["shibah, Sami Rashid Mohammed"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17855008","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17837375","name":"A Rigorous Multidisciplinary Theoretical Framework for Synergistic Bio- and Non-Biochemical Interventions to Reverse Cellular and Tissue Aging: Quantitative Stochastic Modeling, Clinical Applications, and Translational Precision Medicine","source":"datacite","abstract":"This comprehensive theoretical framework integrates biochemistry, bioengineering, applied mathematics, computational biology, and pharmacology to model synergistic bio- and non-biochemical interventions for cellular and tissue rejuvenation. We develop and rigorously analyze stochastic differential equation (SDE) models encompassing mitochondrial function, reactive oxygen species (ROS) dynamics, telomere attrition, cellular senescence, inflammaging, genomic instability, and---with expanded temporal sequencing---epigenetic drift, incorporating phased dynamics (early programmed vs. midlife-accelerated stochastic accrual at CpG sites, bifurcation ∼age 45 via Lyapunov stability μ_L ≈ 0.012 yr^{-1}), for precise multi-hallmark integration [tarkhov2025temporal]. Detailed derivations, drift-diffusion mechanics (Itô semimartingales with positivity-preserving reflections), numerical schemes (Euler--Maruyama Δt = 0.001 yr, weak order 1.0 via Richardson extrapolation O(Δt), strong O(√Δt) Milstein verification with 75%, RMSE 0 post), 2024--2025 lit. (AI-gene therapy, dissipation theory [khodaee2025dissipation]) ensure stringent cohesion and accuracy.","url":"https://doi.org/10.5281/zenodo.17837375","authors":["shibah, Sami Rashid Mohammed"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17837375","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17836595","name":"THE ECONOMIC ARCHITECTURE OF THE METABOLIC AGE A Scientific, Policy, and Economic Valuation of the CollectiveOS Anti-Scarcity Stack","source":"datacite","abstract":"THE ECONOMIC ARCHITECTURE OF THE METABOLIC AGE A Scientific, Policy, and Economic Valuation of the CollectiveOS Anti-Scarcity Stack Version 1.0 — Research Edition 1. Executive Summary Humanity currently stands at a precarious structural threshold, transitioning from a civilization defined by the logic of extraction—characterized by energy scarcity, centralized telecommunications, fragile linear supply chains, and inequitable access to physiological necessities—to one capable of sustaining itself through distributed, metabolic, and autonomous systems. This transition marks the end of the \"Extractive Age,\" where economic growth is coupled with resource depletion, and the dawn of the \"Metabolic Age,\" where infrastructure functions as a regenerative biological system. Across a comprehensive archive of over 110 published open-science white papers and technical specifications, the CollectiveOS Initiative has produced a scientifically grounded architecture known as the Anti-Scarcity Stack. This architecture represents a fundamental departure from the \"Trillionaire Trajectory\"—the prevailing economic theory that future infrastructure will be monopolized by ultra-high-net-worth individuals utilizing proprietary, closed-loop systems.1 Instead, the CollectiveOS framework proposes a \"Sovereign Engineering\" paradigm, integrating ambient metabolic energy systems, synthetic organisms, global water infrastructure, distributed food production, decentralized computation, and sovereign AI governance into a unified planetary operating system. This research report evaluates the economic, scientific, and political value of this architecture from two distinct but complementary perspectives: 1. Scientific Global Impact Valuation ($1.5T – $2.5T USD): This figure represents the \"Ceiling\"—the civilization-scale value unlocked if the architecture is adopted globally. It is derived from a rigorous sector displacement analysis, quantifying the economic inefficiency currently embedded in centralized utilities and the value created by replacing them with autonomous, edge-based systems. It accounts for fractions of the global markets in energy, telecommunications, healthcare, water, agriculture, computation, and space infrastructure, fundamentally restructuring how these sectors generate and distribute value.2 2. Contract-Based Day-One Valuation ($14.9B USD Floor): This figure represents the \"Floor\"—the immediate, addressable market based on existing federal and international procurement vehicles. It is a forensic summation of fiscal year 2025 (FY2025) and FY2026 budget requests, authorized funding programs, and active solicitations across agencies such as the Department of Defense (DoD), FEMA, USDA, NIST, NASA, and international bodies like the ESA and WHO. These funds are currently allocated for capabilities that the CollectiveOS architecture specifically delivers, such as energy resilience, climate-smart commodities, and AI safety.5 This white paper provides the first integrated valuation of the CollectiveOS Anti-Scarcity Stack as a scientific innovation, a global public infrastructure, and a national modernization platform. It demonstrates that the technology for a post-scarcity civilization is not a theoretical aspiration but an engineered, documented, and contract-ready reality. 2. Introduction: The Thermodynamics of Civilization 2.1 The Crisis of Centralized Infrastructure The prevailing infrastructure model of the 20th and early 21st centuries is predicated on centralization and extraction. Energy is generated in massive thermal plants and transmitted over thousands of miles of fragile grid infrastructure; water is pumped through leaking, energy-intensive piping networks; food is grown in industrial monocultures dependent on petrochemical fertilizers and shipped globally; and intelligence is concentrated in hyperscale data centers owned by a handful of corporate monopolies. This model suffers from inherent thermodynamic and systemic fragility. As evid","url":"https://doi.org/10.5281/zenodo.17836595","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17836595","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17836596","name":"THE ECONOMIC ARCHITECTURE OF THE METABOLIC AGE A Scientific, Policy, and Economic Valuation of the CollectiveOS Anti-Scarcity Stack","source":"datacite","abstract":"THE ECONOMIC ARCHITECTURE OF THE METABOLIC AGE A Scientific, Policy, and Economic Valuation of the CollectiveOS Anti-Scarcity Stack Version 1.0 — Research Edition 1. Executive Summary Humanity currently stands at a precarious structural threshold, transitioning from a civilization defined by the logic of extraction—characterized by energy scarcity, centralized telecommunications, fragile linear supply chains, and inequitable access to physiological necessities—to one capable of sustaining itself through distributed, metabolic, and autonomous systems. This transition marks the end of the \"Extractive Age,\" where economic growth is coupled with resource depletion, and the dawn of the \"Metabolic Age,\" where infrastructure functions as a regenerative biological system. Across a comprehensive archive of over 110 published open-science white papers and technical specifications, the CollectiveOS Initiative has produced a scientifically grounded architecture known as the Anti-Scarcity Stack. This architecture represents a fundamental departure from the \"Trillionaire Trajectory\"—the prevailing economic theory that future infrastructure will be monopolized by ultra-high-net-worth individuals utilizing proprietary, closed-loop systems.1 Instead, the CollectiveOS framework proposes a \"Sovereign Engineering\" paradigm, integrating ambient metabolic energy systems, synthetic organisms, global water infrastructure, distributed food production, decentralized computation, and sovereign AI governance into a unified planetary operating system. This research report evaluates the economic, scientific, and political value of this architecture from two distinct but complementary perspectives: 1. Scientific Global Impact Valuation ($1.5T – $2.5T USD): This figure represents the \"Ceiling\"—the civilization-scale value unlocked if the architecture is adopted globally. It is derived from a rigorous sector displacement analysis, quantifying the economic inefficiency currently embedded in centralized utilities and the value created by replacing them with autonomous, edge-based systems. It accounts for fractions of the global markets in energy, telecommunications, healthcare, water, agriculture, computation, and space infrastructure, fundamentally restructuring how these sectors generate and distribute value.2 2. Contract-Based Day-One Valuation ($14.9B USD Floor): This figure represents the \"Floor\"—the immediate, addressable market based on existing federal and international procurement vehicles. It is a forensic summation of fiscal year 2025 (FY2025) and FY2026 budget requests, authorized funding programs, and active solicitations across agencies such as the Department of Defense (DoD), FEMA, USDA, NIST, NASA, and international bodies like the ESA and WHO. These funds are currently allocated for capabilities that the CollectiveOS architecture specifically delivers, such as energy resilience, climate-smart commodities, and AI safety.5 This white paper provides the first integrated valuation of the CollectiveOS Anti-Scarcity Stack as a scientific innovation, a global public infrastructure, and a national modernization platform. It demonstrates that the technology for a post-scarcity civilization is not a theoretical aspiration but an engineered, documented, and contract-ready reality. 2. Introduction: The Thermodynamics of Civilization 2.1 The Crisis of Centralized Infrastructure The prevailing infrastructure model of the 20th and early 21st centuries is predicated on centralization and extraction. Energy is generated in massive thermal plants and transmitted over thousands of miles of fragile grid infrastructure; water is pumped through leaking, energy-intensive piping networks; food is grown in industrial monocultures dependent on petrochemical fertilizers and shipped globally; and intelligence is concentrated in hyperscale data centers owned by a handful of corporate monopolies. This model suffers from inherent thermodynamic and systemic fragility. As evid","url":"https://doi.org/10.5281/zenodo.17836596","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17836596","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2512.03287","name":"Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors","source":"datacite","abstract":"Human Activity Recognition (HAR) benefits various application domains, including health and elderly care. Traditional HAR involves constructing pipelines reliant on centralized user data, which can pose privacy concerns as they necessitate the uploading of user data to a centralized server. This work proposes multi-frequency Federated Learning (FL) to enable: (1) privacy-aware ML; (2) joint ML model learning across devices with varying sampling frequency. We focus on head-worn devices (e.g., earbuds and smart glasses), a relatively unexplored domain compared to traditional smartwatch- or smartphone-based HAR. Results have shown improvements on two datasets against frequency-specific approaches, indicating a promising future in the multi-frequency FL-HAR task. The proposed network's implementation is publicly available for further research and development.","url":"https://doi.org/10.48550/arxiv.2512.03287","authors":["Fenoglio, Dario","Li, Mohan","Casnici, Davide","Laporte, Matias","Gashi, Shkurta","Santini, Silvia","Gjoreski, Martin","Langheinrich, Marc"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2512.03287","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2511.22616","name":"Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers","source":"datacite","abstract":"The integration of IoT and AI has unlocked innovation across industries, but growing privacy concerns and data isolation hinder progress. Traditional centralized ML struggles to overcome these challenges, which has led to the rise of Federated Learning (FL), a decentralized paradigm that enables collaborative model training without sharing local raw data. FL ensures data privacy, reduces communication overhead, and supports scalability, yet its heterogeneity adds complexity compared to centralized approaches. This survey focuses on three main FL research directions: personalization, optimization, and robustness, offering a structured classification through a hybrid methodology that combines bibliometric analysis with systematic review to identify the most influential works. We examine challenges and techniques related to heterogeneity, efficiency, security, and privacy, and provide a comprehensive overview of aggregation strategies, including architectures, synchronization methods, and diverse federation objectives. To complement this, we discuss practical evaluation approaches and present experiments comparing aggregation methods under IID and non-IID data distributions. Finally, we outline promising research directions to advance FL, aiming to guide future innovation in this rapidly evolving field.","url":"https://doi.org/10.48550/arxiv.2511.22616","authors":["Arbaoui, Meriem","Brahmia, Mohamed-el-Amine","Rahmoun, Abdellatif","Zghal, Mourad"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2511.22616","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2511.16822","name":"A Robust Federated Learning Approach for Combating Attacks Against IoT Systems Under non-IID Challenges","source":"datacite","abstract":"In the context of the growing proliferation of user devices and the concurrent surge in data volumes, the complexities arising from the substantial increase in data have posed formidable challenges to conventional machine learning model training. Particularly, this is evident within resource-constrained and security-sensitive environments such as those encountered in networks associated with the Internet of Things (IoT). Federated Learning has emerged as a promising remedy to these challenges by decentralizing model training to edge devices or parties, effectively addressing privacy concerns and resource limitations. Nevertheless, the presence of statistical heterogeneity in non-Independently and Identically Distributed (non-IID) data across different parties poses a significant hurdle to the effectiveness of FL. Many FL approaches have been proposed to enhance learning effectiveness under statistical heterogeneity. However, prior studies have uncovered a gap in the existing research landscape, particularly in the absence of a comprehensive comparison between federated methods addressing statistical heterogeneity in detecting IoT attacks. In this research endeavor, we delve into the exploration of FL algorithms, specifically FedAvg, FedProx, and Scaffold, under different data distributions. Our focus is on achieving a comprehensive understanding of and addressing the challenges posed by statistical heterogeneity. In this study, We classify large-scale IoT attacks by utilizing the CICIoT2023 dataset. Through meticulous analysis and experimentation, our objective is to illuminate the performance nuances of these FL methods, providing valuable insights for researchers and practitioners in the domain.","url":"https://doi.org/10.48550/arxiv.2511.16822","authors":["Gad, Eyad","Fadlullah, Zubair Md","Fouda, Mostafa M."],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences","C.2.0; C.2.1; I.2.6","68T05, 68M10"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2511.16822","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17671139","name":"PROMETHEUS NEEDS AN OPERATING SYSTEM: Why Jeff Bezos' $6.2B Vision Requires the CollectiveOS Anti-Scarcity Stack","source":"datacite","abstract":"PROMETHEUS NEEDS AN OPERATING SYSTEM: Why Jeff Bezos’ $6.2B Vision Requires the CollectiveOS Anti-Scarcity Stack 1. Introduction: The Limits of Promethean Thinking The mythological Prometheus stole fire from the gods to empower humanity, an act of rebellion that catalyzed the dawn of civilization. In late 2025, Jeff Bezos, the founder of Amazon and Blue Origin, invoked this Titan’s name for his latest and perhaps most ambitious venture: Project Prometheus.1 With an initial funding commitment of $6.2 billion—one of the largest seed rounds in industrial history—and a mandate to revolutionize \"heavy industry\" through artificial intelligence, Bezos has positioned this initiative as the engine that will finally drive humanity’s industrial base off the surface of the Earth.3 It is a vision of staggering scale, aiming to transform the cosmos into a manufacturing zone so that Earth can be rezoned for \"residential and light industry,\" effectively turning the planet into a protected garden while the machinery of production migrates to the void.5 However, the Promethean myth carries a warning that the project’s architects seem to have overlooked. Prometheus gave humanity the tool (fire), but he did not provide the system to manage it. He provided the energy source but not the governance structure, the environmental safeguards, or the regenerative loops necessary to prevent that fire from consuming its users. Bezos’ Project Prometheus suffers from an identical architectural flaw. It is a masterclass in industrial logic applied to a biological vacuum. It focuses entirely on the \"fire\"—nuclear propulsion, autonomous manufacturing, robotic assembly, and supply chain optimization—while assuming that the \"civilization\"—the water cycles, the food systems, the microbial governance, and the ethical guardrails—will largely take care of themselves, or worse, can be imported from Earth via supply chain logistics.6 This assumption is not merely optimistic; it is fatal. The transition from terrestrial industry to off-world habitation is not a linear scaling problem; it is a phase change. On Earth, industry operates within a pre-existing biosphere that provides free air, water filtration, and waste decomposition. In the vacuum of space or the desolation of the lunar surface, none of these services exist. A factory in orbit cannot simply \"emit\" waste; it must metabolize it. A habitat on the Moon cannot \"draw\" water; it must molecularly reconstitute it. This white paper posits that the missing \"civilization layer\" for Project Prometheus has already been architected, governed, and published. In August 2025, months prior to the public unveiling of Bezos’ initiative, the CollectiveOS Anti-Scarcity Stack was released to the world via Zenodo and secured within cryptographic Proof Vaults.7 This stack, encompassing the Aqua Pillar (atmospheric water harvesting), the Food Cube (bio-upcycling), FarmOS (swarm agriculture), and GATA PRIME (algorithmic governance), provides the specific bio-digital operating system required to sustain life and industry in hostile environments.8 By conducting a rigorous gap analysis between the stated goals of Project Prometheus and the capabilities of the CollectiveOS, this report demonstrates that Bezos’ $6.2 billion investment, while necessary, is insufficient. It builds the rocket, but not the world. To succeed, Project Prometheus must integrate the Post-Promethean architecture—specifically the circular biological loops and zero-trust governance frameworks of the CollectiveOS—to transform from a remote industrial outpost into a viable extension of human civilization. 2. The Prometheus Doctrine: Industrial Logic in a Biological Void 2.1 The Infrastructure Emperor and the $6.2 Billion Signal Jeff Bezos does not build products; he builds substrates. His career is a testament to the power of creating the foundational layers upon which other economies function. Amazon was not merely a bookstore; it was the substrate for global digital","url":"https://doi.org/10.5281/zenodo.17671139","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17671139","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.17671138","name":"PROMETHEUS NEEDS AN OPERATING SYSTEM: Why Jeff Bezos' $6.2B Vision Requires the CollectiveOS Anti-Scarcity Stack","source":"datacite","abstract":"PROMETHEUS NEEDS AN OPERATING SYSTEM: Why Jeff Bezos’ $6.2B Vision Requires the CollectiveOS Anti-Scarcity Stack 1. Introduction: The Limits of Promethean Thinking The mythological Prometheus stole fire from the gods to empower humanity, an act of rebellion that catalyzed the dawn of civilization. In late 2025, Jeff Bezos, the founder of Amazon and Blue Origin, invoked this Titan’s name for his latest and perhaps most ambitious venture: Project Prometheus.1 With an initial funding commitment of $6.2 billion—one of the largest seed rounds in industrial history—and a mandate to revolutionize \"heavy industry\" through artificial intelligence, Bezos has positioned this initiative as the engine that will finally drive humanity’s industrial base off the surface of the Earth.3 It is a vision of staggering scale, aiming to transform the cosmos into a manufacturing zone so that Earth can be rezoned for \"residential and light industry,\" effectively turning the planet into a protected garden while the machinery of production migrates to the void.5 However, the Promethean myth carries a warning that the project’s architects seem to have overlooked. Prometheus gave humanity the tool (fire), but he did not provide the system to manage it. He provided the energy source but not the governance structure, the environmental safeguards, or the regenerative loops necessary to prevent that fire from consuming its users. Bezos’ Project Prometheus suffers from an identical architectural flaw. It is a masterclass in industrial logic applied to a biological vacuum. It focuses entirely on the \"fire\"—nuclear propulsion, autonomous manufacturing, robotic assembly, and supply chain optimization—while assuming that the \"civilization\"—the water cycles, the food systems, the microbial governance, and the ethical guardrails—will largely take care of themselves, or worse, can be imported from Earth via supply chain logistics.6 This assumption is not merely optimistic; it is fatal. The transition from terrestrial industry to off-world habitation is not a linear scaling problem; it is a phase change. On Earth, industry operates within a pre-existing biosphere that provides free air, water filtration, and waste decomposition. In the vacuum of space or the desolation of the lunar surface, none of these services exist. A factory in orbit cannot simply \"emit\" waste; it must metabolize it. A habitat on the Moon cannot \"draw\" water; it must molecularly reconstitute it. This white paper posits that the missing \"civilization layer\" for Project Prometheus has already been architected, governed, and published. In August 2025, months prior to the public unveiling of Bezos’ initiative, the CollectiveOS Anti-Scarcity Stack was released to the world via Zenodo and secured within cryptographic Proof Vaults.7 This stack, encompassing the Aqua Pillar (atmospheric water harvesting), the Food Cube (bio-upcycling), FarmOS (swarm agriculture), and GATA PRIME (algorithmic governance), provides the specific bio-digital operating system required to sustain life and industry in hostile environments.8 By conducting a rigorous gap analysis between the stated goals of Project Prometheus and the capabilities of the CollectiveOS, this report demonstrates that Bezos’ $6.2 billion investment, while necessary, is insufficient. It builds the rocket, but not the world. To succeed, Project Prometheus must integrate the Post-Promethean architecture—specifically the circular biological loops and zero-trust governance frameworks of the CollectiveOS—to transform from a remote industrial outpost into a viable extension of human civilization. 2. The Prometheus Doctrine: Industrial Logic in a Biological Void 2.1 The Infrastructure Emperor and the $6.2 Billion Signal Jeff Bezos does not build products; he builds substrates. His career is a testament to the power of creating the foundational layers upon which other economies function. Amazon was not merely a bookstore; it was the substrate for global digital","url":"https://doi.org/10.5281/zenodo.17671138","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17671138","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.17670445","name":"Trusted Research Environments for Healthcare AI: State of the Art Global Landscape Report","source":"datacite","abstract":"This global landscape report provides a comprehensive overview of the current state of Trusted Research Environments (TREs) worldwide, with a particular focus on the Nordic countries and the United Kingdom. TREs—also known as data safe havens—are secure platforms that enable researchers to analyse sensitive data while complying with legal, ethical, and contractual obligations. The report is part of the TRE4HealthAI project, which aims to develop next-generation TRE capabilities tailored for healthcare AI applications. A key finding of the report is that the DARE UK Five Safes framework and the Standard Architecture for Trusted Research Environments (SATRE) have emerged as the de facto standard for TRE implementations in both the UK and across Europe. Originally developed within the UK legal and research context, the Five Safes framework—comprising Safe People, Safe Projects, Safe Settings, Safe Data, and Safe Outputs—has been widely adopted and adapted by TREs internationally. The DARE UK Blueprint, which operationalises this framework, is technology-agnostic and aligns with FAIR principles (Findable, Accessible, Interoperable, Reusable), making it suitable for diverse implementations. In the UK, various research and commercial projects such as the Turing Data Safe Haven, Treehouse, and the Aridhia digital research environment have all been evaluated for conformity with the Five Safes-based SATRE specification. These implementations demonstrate a high degree of standardisation, transparency, and interoperability, setting a benchmark for TRE development. The report also highlights how European initiatives, particularly the EOSC-ENTRUST project, have mapped their TRE architectures and requirements to the DARE UK Blueprint. TREs in Norway (NORTRE) and Finland (CSC SD) have been shown to align closely with the DARE UK model, despite differences in governance structures—such as the role of data controllers and output approval processes. The EOSC-ENTRUST project has adopted the DARE UK infrastructure layer and extended it to accommodate European legal and organisational contexts. Furthermore, the report outlines the growing demand for next-generation TRE capabilities, including support for AI model development, federated learning, and scalable cloud infrastructure. These capabilities are being mapped onto the Five Safes framework to ensure continued compliance and security and enable new capabilities for supervisory authorities to learn from the evidence generated in a TRE. Projects like GRAIMATTER and various empirical studies (eg, Kavianpour et al. 2022) provide guidance on integrating AI safely within TREs. In conclusion, the DARE UK Five Safes framework has become the foundational model for TREs, influencing both national and international implementations. Its adaptability, combined with a strong emphasis on governance and interoperability, positions it as the cornerstone for future TRE development in support of secure, ethical, and innovative research that also enhances evidence-based regulatory learnings. This work is funded as part of the Advanced Digitalisation programme by Vinnova, the Swedish Innovation Agency, project reference 2024-01412.","url":"https://doi.org/10.5281/zenodo.17670445","authors":["Emanuilov, Ivo","Larsson, Björn","Dubber, Andrew","Magas, Michela"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17670445","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17670446","name":"Trusted Research Environments for Healthcare AI: State of the Art Global Landscape Report","source":"datacite","abstract":"This global landscape report provides a comprehensive overview of the current state of Trusted Research Environments (TREs) worldwide, with a particular focus on the Nordic countries and the United Kingdom. TREs—also known as data safe havens—are secure platforms that enable researchers to analyse sensitive data while complying with legal, ethical, and contractual obligations. The report is part of the TRE4HealthAI project, which aims to develop next-generation TRE capabilities tailored for healthcare AI applications. A key finding of the report is that the DARE UK Five Safes framework and the Standard Architecture for Trusted Research Environments (SATRE) have emerged as the de facto standard for TRE implementations in both the UK and across Europe. Originally developed within the UK legal and research context, the Five Safes framework—comprising Safe People, Safe Projects, Safe Settings, Safe Data, and Safe Outputs—has been widely adopted and adapted by TREs internationally. The DARE UK Blueprint, which operationalises this framework, is technology-agnostic and aligns with FAIR principles (Findable, Accessible, Interoperable, Reusable), making it suitable for diverse implementations. In the UK, various research and commercial projects such as the Turing Data Safe Haven, Treehouse, and the Aridhia digital research environment have all been evaluated for conformity with the Five Safes-based SATRE specification. These implementations demonstrate a high degree of standardisation, transparency, and interoperability, setting a benchmark for TRE development. The report also highlights how European initiatives, particularly the EOSC-ENTRUST project, have mapped their TRE architectures and requirements to the DARE UK Blueprint. TREs in Norway (NORTRE) and Finland (CSC SD) have been shown to align closely with the DARE UK model, despite differences in governance structures—such as the role of data controllers and output approval processes. The EOSC-ENTRUST project has adopted the DARE UK infrastructure layer and extended it to accommodate European legal and organisational contexts. Furthermore, the report outlines the growing demand for next-generation TRE capabilities, including support for AI model development, federated learning, and scalable cloud infrastructure. These capabilities are being mapped onto the Five Safes framework to ensure continued compliance and security and enable new capabilities for supervisory authorities to learn from the evidence generated in a TRE. Projects like GRAIMATTER and various empirical studies (eg, Kavianpour et al. 2022) provide guidance on integrating AI safely within TREs. In conclusion, the DARE UK Five Safes framework has become the foundational model for TREs, influencing both national and international implementations. Its adaptability, combined with a strong emphasis on governance and interoperability, positions it as the cornerstone for future TRE development in support of secure, ethical, and innovative research that also enhances evidence-based regulatory learnings. This work is funded as part of the Advanced Digitalisation programme by Vinnova, the Swedish Innovation Agency, project reference 2024-01412.","url":"https://doi.org/10.5281/zenodo.17670446","authors":["Emanuilov, Ivo","Larsson, Björn","Dubber, Andrew","Magas, Michela"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17670446","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17661430","name":"Planetary-Scale Autonomous Infrastructure: A Strategic Framework for Sovereign Sanctuary Deployment, Heritage Restoration, and Open-Science Ecosystems","source":"datacite","abstract":"Planetary-Scale Autonomous Infrastructure: A Strategic Framework for Sovereign Sanctuary Deployment, Heritage Restoration, and Open-Science Ecosystems Executive Summary: The Architecture of the Inverted Colony The convergence of sixth-generation artificial intelligence, autonomous robotics, and decentralized energy systems presents a singular opportunity to reshape the geopolitical landscape of land ownership and development. This report provides an exhaustive strategic analysis of global land acquisition opportunities, synthesizing the requirements for a decentralized network of AI-powered, open-science sanctuaries. This analysis moves beyond traditional real estate assessment to evaluate sovereign-grade infrastructure deployment, aligning specific geopolitical opportunities with the proprietary technological capabilities of the \"CollectiveOS\" ecosystem.1 The central thesis of this report is the transition from the extractive model of the 20th century to the regenerative model of the 21st—a concept we define as the \"Inverted Colony.\" Historically, foreign outposts were established to extract resources, labor, and capital from the host nation. The proposed network of autonomous \"nodes\"—self-sustaining, energy-positive cities—operates on the inverse principle. These nodes inject resources (energy, water, computational power), restore lost value (heritage sites, degraded ecosystems), and stabilize local economies through the deployment of the \"CollectiveOS / Unified AI Script System v4\".1 By leveraging the \"Land-for-Solutions\" exchange model, this strategy bypasses traditional capital-heavy real estate markets. Instead, it targets sovereign land grants and long-term leases in exchange for solving critical national deficits in energy stability, environmental management, and workforce upskilling. The analysis integrates the \"Global Land Opportunity Map\" with the technical specifications of the CollectiveOS—specifically the Living Fibonacci Engine (LFE), GATA Prime governance, and the Civilian Space Program (CSP)—to demonstrate how distressed assets in Southeast Asia, Africa, South America, and Europe can be transformed into high-value global sanctuaries and testing grounds for interplanetary civilization. Section I: The Technological Constitution – CollectiveOS as Municipal Infrastructure The viability of establishing autonomous sanctuaries in remote, politically complex, or environmentally hostile regions depends entirely on the robustness of the underlying operating system. The \"CollectiveOS / Unified AI Script System v4\" is not merely a software stack; it functions as a digital constitution and a municipal operating system, providing the governance, energy management, and security layers required to operate a sovereign node independent of failing local infrastructure.1 1.1 The Unreadable Machine: Zero-Trust Sovereignty in Hostile Environments In regions such as the Sahel, the Amazonian frontier, or remote Southeast Asian provinces, the primary operational risk is not environmental but human: corruption, data theft, and regulatory overreach. The \"Unreadable Machine\" layer of the CollectiveOS 1 mitigates these risks through a Zero-Trust Cipher Stack (ZTA). This architecture ensures that the node’s internal operations—from water distribution algorithms to genetic research data—remain opaque to unauthorized external actors while remaining transparent to agreed-upon auditors. The system utilizes a \"Cypher Agent\" 1 to enforce a privacy-preserving ecosystem. This agent manages encryption keys, Decentralized Identifiers (DIDs), and Verifiable Credentials (VCs) for every hardware component and human resident within the node. By employing Fully Homomorphic Encryption (FHE) and Privacy-Preserving Federated Learning (PPFL) 1, the node can process sensitive local data (e.g., health metrics of the local population or biometric security data) without ever exposing the raw information to the cloud or local government servers. This capability i","url":"https://doi.org/10.5281/zenodo.17661430","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17661430","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.17661432","name":"Planetary-Scale Autonomous Infrastructure: A Strategic Framework for Sovereign Sanctuary Deployment, Heritage Restoration, and Open-Science Ecosystems","source":"datacite","abstract":"Planetary-Scale Autonomous Infrastructure: A Strategic Framework for Sovereign Sanctuary Deployment, Heritage Restoration, and Open-Science Ecosystems Executive Summary: The Architecture of the Inverted Colony The convergence of sixth-generation artificial intelligence, autonomous robotics, and decentralized energy systems presents a singular opportunity to reshape the geopolitical landscape of land ownership and development. This report provides an exhaustive strategic analysis of global land acquisition opportunities, synthesizing the requirements for a decentralized network of AI-powered, open-science sanctuaries. This analysis moves beyond traditional real estate assessment to evaluate sovereign-grade infrastructure deployment, aligning specific geopolitical opportunities with the proprietary technological capabilities of the \"CollectiveOS\" ecosystem.1 The central thesis of this report is the transition from the extractive model of the 20th century to the regenerative model of the 21st—a concept we define as the \"Inverted Colony.\" Historically, foreign outposts were established to extract resources, labor, and capital from the host nation. The proposed network of autonomous \"nodes\"—self-sustaining, energy-positive cities—operates on the inverse principle. These nodes inject resources (energy, water, computational power), restore lost value (heritage sites, degraded ecosystems), and stabilize local economies through the deployment of the \"CollectiveOS / Unified AI Script System v4\".1 By leveraging the \"Land-for-Solutions\" exchange model, this strategy bypasses traditional capital-heavy real estate markets. Instead, it targets sovereign land grants and long-term leases in exchange for solving critical national deficits in energy stability, environmental management, and workforce upskilling. The analysis integrates the \"Global Land Opportunity Map\" with the technical specifications of the CollectiveOS—specifically the Living Fibonacci Engine (LFE), GATA Prime governance, and the Civilian Space Program (CSP)—to demonstrate how distressed assets in Southeast Asia, Africa, South America, and Europe can be transformed into high-value global sanctuaries and testing grounds for interplanetary civilization. Section I: The Technological Constitution – CollectiveOS as Municipal Infrastructure The viability of establishing autonomous sanctuaries in remote, politically complex, or environmentally hostile regions depends entirely on the robustness of the underlying operating system. The \"CollectiveOS / Unified AI Script System v4\" is not merely a software stack; it functions as a digital constitution and a municipal operating system, providing the governance, energy management, and security layers required to operate a sovereign node independent of failing local infrastructure.1 1.1 The Unreadable Machine: Zero-Trust Sovereignty in Hostile Environments In regions such as the Sahel, the Amazonian frontier, or remote Southeast Asian provinces, the primary operational risk is not environmental but human: corruption, data theft, and regulatory overreach. The \"Unreadable Machine\" layer of the CollectiveOS 1 mitigates these risks through a Zero-Trust Cipher Stack (ZTA). This architecture ensures that the node’s internal operations—from water distribution algorithms to genetic research data—remain opaque to unauthorized external actors while remaining transparent to agreed-upon auditors. The system utilizes a \"Cypher Agent\" 1 to enforce a privacy-preserving ecosystem. This agent manages encryption keys, Decentralized Identifiers (DIDs), and Verifiable Credentials (VCs) for every hardware component and human resident within the node. By employing Fully Homomorphic Encryption (FHE) and Privacy-Preserving Federated Learning (PPFL) 1, the node can process sensitive local data (e.g., health metrics of the local population or biometric security data) without ever exposing the raw information to the cloud or local government servers. This capability i","url":"https://doi.org/10.5281/zenodo.17661432","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17661432","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.17625733","name":"IMMORTAL TEK: The Sovereign Node — Bio-Sovereign Infrastructure & The Post-Silicon Paradigm (2025–2028)","source":"datacite","abstract":"IMMORTAL TEK: The Sovereign Node — Bio-Sovereign Infrastructure & The Post-Silicon Paradigm (2025–2028) 1. Executive Summary: The Entropic Limit of Consumer Electronics The current trajectory of personal computing has reached a terminal velocity of diminishing returns. The dominant paradigm—characterized by planned obsolescence, tethered dependency, and the centralized extraction of user data—is no longer a driver of innovation but a constraint on human agency. We stand at the precipice of a transition from \"smart devices,\" which act as passive terminals for corporate cloud services, to Sovereign Nodes: active, self-sustaining infrastructure points that grant the user autonomy, ownership, and participation in a Decentralized Physical Infrastructure Network (DePIN). This comprehensive design specification and launch narrative for the IMMORTAL TEK GLASSES (2025–2028) serves not merely as a product roadmap but as the foundational text for a new category of existence: Bio-Sovereign Infrastructure. The market does not require another iteration of augmented reality eyewear; it demands a fundamental re-architecture of the human-digital interface.1 By synthesizing advanced material sciences (mycelium composites, transparent perovskite photovoltaics), decentralized cryptographic protocols (Self-Sovereign Identity, Zero-Knowledge Proofs), and mythic branding methodologies, we establish the blueprint for a device that is grown rather than manufactured, engaged via ritual rather than routine, and powered by the biology of the user rather than the grid of the state. The strategic objective is to position IMMORTAL TEK not as a competitor to existing Silicon Valley hardware, but as their \"Strategic Enemy\"—a moral and functional alternative that reclaims the user’s digital and physical reality.3 This report details the convergence of biological materials with cryptographic sovereignty, outlining a future where technology breathes, heals, and pays its user. 2. The Architectural Philosophy: Solarpunk & The Aesthetics of Survival The design language and engineering ethos of IMMORTAL TEK are rooted in Solarpunk—a movement that envisions a future where technology and ecology exist in symbiotic harmony, decoupled from dystopian industrialism.5 This is not an aesthetic overlay but a functional mandate. The device must embody \"optimistic hybridization,\" utilizing renewable energy, organic materials, and decentralized governance to create a system that is resilient against the mundanity of the \"end of the world\" narratives that permeate modern discourse.7 2.1 The Solarpunk Aesthetic as Functional Design Current \"futuristic\" designs favor sterility—aluminum, glass, and cold LEDs. This \"Grey\" aesthetic represents a disconnect from the environment, a fortress mentality that seeks to isolate the user from the world. In contrast, the IMMORTAL TEK aesthetic is Biomimetic and Mythic. The device is designed to look like an artifact from a high-tech agrarian future, blending the organic irregularity of mycelium with the precision of crystalline optics.9 The visual language abandons the sharp angularity of military-industrial design in favor of \"organic architecture.\" We employ curved lines and natural textures that mimic bone, wood, or fungal growth, rejecting the artificial smoothness of plastic.7 This aligns with the trend of \"Bio-Digital\" convergence, where the distinction between the grown and the built evaporates. The structural components are not painted to hide their origin; the mycelium's texture is a feature, offering a tactile uniqueness to every unit, akin to a fingerprint or the grain of high-quality leather.11 The color palette moves away from the sterile \"Space Grey\" or \"Piano Black\" of the current epoch. Instead, we embrace earth tones—moss greens, deep ambers, fungal whites, and oxblood reds—derived directly from the bio-materials used and the natural pigmentation processes of the fungi.7 This is consistent with the Solarpunk vision of a world ","url":"https://doi.org/10.5281/zenodo.17625733","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17625733","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17625734","name":"IMMORTAL TEK: The Sovereign Node — Bio-Sovereign Infrastructure & The Post-Silicon Paradigm (2025–2028)","source":"datacite","abstract":"IMMORTAL TEK: The Sovereign Node — Bio-Sovereign Infrastructure & The Post-Silicon Paradigm (2025–2028) 1. Executive Summary: The Entropic Limit of Consumer Electronics The current trajectory of personal computing has reached a terminal velocity of diminishing returns. The dominant paradigm—characterized by planned obsolescence, tethered dependency, and the centralized extraction of user data—is no longer a driver of innovation but a constraint on human agency. We stand at the precipice of a transition from \"smart devices,\" which act as passive terminals for corporate cloud services, to Sovereign Nodes: active, self-sustaining infrastructure points that grant the user autonomy, ownership, and participation in a Decentralized Physical Infrastructure Network (DePIN). This comprehensive design specification and launch narrative for the IMMORTAL TEK GLASSES (2025–2028) serves not merely as a product roadmap but as the foundational text for a new category of existence: Bio-Sovereign Infrastructure. The market does not require another iteration of augmented reality eyewear; it demands a fundamental re-architecture of the human-digital interface.1 By synthesizing advanced material sciences (mycelium composites, transparent perovskite photovoltaics), decentralized cryptographic protocols (Self-Sovereign Identity, Zero-Knowledge Proofs), and mythic branding methodologies, we establish the blueprint for a device that is grown rather than manufactured, engaged via ritual rather than routine, and powered by the biology of the user rather than the grid of the state. The strategic objective is to position IMMORTAL TEK not as a competitor to existing Silicon Valley hardware, but as their \"Strategic Enemy\"—a moral and functional alternative that reclaims the user’s digital and physical reality.3 This report details the convergence of biological materials with cryptographic sovereignty, outlining a future where technology breathes, heals, and pays its user. 2. The Architectural Philosophy: Solarpunk & The Aesthetics of Survival The design language and engineering ethos of IMMORTAL TEK are rooted in Solarpunk—a movement that envisions a future where technology and ecology exist in symbiotic harmony, decoupled from dystopian industrialism.5 This is not an aesthetic overlay but a functional mandate. The device must embody \"optimistic hybridization,\" utilizing renewable energy, organic materials, and decentralized governance to create a system that is resilient against the mundanity of the \"end of the world\" narratives that permeate modern discourse.7 2.1 The Solarpunk Aesthetic as Functional Design Current \"futuristic\" designs favor sterility—aluminum, glass, and cold LEDs. This \"Grey\" aesthetic represents a disconnect from the environment, a fortress mentality that seeks to isolate the user from the world. In contrast, the IMMORTAL TEK aesthetic is Biomimetic and Mythic. The device is designed to look like an artifact from a high-tech agrarian future, blending the organic irregularity of mycelium with the precision of crystalline optics.9 The visual language abandons the sharp angularity of military-industrial design in favor of \"organic architecture.\" We employ curved lines and natural textures that mimic bone, wood, or fungal growth, rejecting the artificial smoothness of plastic.7 This aligns with the trend of \"Bio-Digital\" convergence, where the distinction between the grown and the built evaporates. The structural components are not painted to hide their origin; the mycelium's texture is a feature, offering a tactile uniqueness to every unit, akin to a fingerprint or the grain of high-quality leather.11 The color palette moves away from the sterile \"Space Grey\" or \"Piano Black\" of the current epoch. Instead, we embrace earth tones—moss greens, deep ambers, fungal whites, and oxblood reds—derived directly from the bio-materials used and the natural pigmentation processes of the fungi.7 This is consistent with the Solarpunk vision of a world ","url":"https://doi.org/10.5281/zenodo.17625734","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17625734","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2405.15632","name":"Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning","source":"datacite","abstract":"Federated Learning (FL), a privacy-aware approach in distributed deep learning environments, enables many clients to collaboratively train a model without sharing sensitive data, thereby reducing privacy risks. However, enabling human trust and control over FL systems requires understanding the evolving behaviour of clients, whether beneficial or detrimental for the training, which still represents a key challenge in the current literature. To address this challenge, we introduce Federated Behavioural Planes (FBPs), a novel method to analyse, visualise, and explain the dynamics of FL systems, showing how clients behave under two different lenses: predictive performance (error behavioural space) and decision-making processes (counterfactual behavioural space). Our experiments demonstrate that FBPs provide informative trajectories describing the evolving states of clients and their contributions to the global model, thereby enabling the identification of clusters of clients with similar behaviours. Leveraging the patterns identified by FBPs, we propose a robust aggregation technique named Federated Behavioural Shields to detect malicious or noisy client models, thereby enhancing security and surpassing the efficacy of existing state-of-the-art FL defense mechanisms. Our code is publicly available on GitHub.","url":"https://doi.org/10.48550/arxiv.2405.15632","authors":["Fenoglio, Dario","Dominici, Gabriele","Barbiero, Pietro","Tonda, Alberto","Gjoreski, Martin","Langheinrich, Marc"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.15632","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17604585","name":"A Rigorous Multidisciplinary Theoretical Framework for Synergistic Bio- and Non-Biochemical Interventions to Reverse Cellular and Tissue Aging: Quantitative Stochastic Modeling, Clinical Applications, and Translational Precision Medicine","source":"datacite","abstract":"This culminating theoretical framework synthesizes a multidisciplinary paradigm for synergistic bio- and non-biochemical interventions to rejuvenate cellular and tissue vitality. Integrating biochemistry, bioengineering, applied mathematics, computational biology, and pharmacology, we refine and explicate stochastic differential equation (SDE) models for mitochondrial function, reactive oxygen species (ROS) dynamics, telomere attrition, cellular senescence, inflammaging, and genomic instability, with detailed expositions on drift-diffusion mechanics, numerical integration, and empirical calibration. Key enhancements: (1) SDE parameter calibration against human cohort data (e.g., NHANES ROS, telomere lengths); (2) hybrid SDE-agent-based models (ABMs) with Richardson order 1.0 convergence; (3) global sensitivity via Sobol indices (quasi-Monte Carlo, $n=4096$); (4) Bayesian inference (PyMC v5, $R 75\\%$). Verified simulations ($n=2000$ runs, Euler-Maruyama $\\Delta t=0.001$) yield baseline $M(100) \\approx \\num{2.7e-15} \\pm \\num{1.1e-15}$ (adjusted for multiplicative noise; normalized scale, CV=41%); synergies achieve $\\sim10^5\\times$ improvement ($p<10^{-12}$, KS $D=0.95$). ABM-SDE comparisons confirm spatial effects (Moran's $I=0.42$ baseline). Practical applications detail clinical trials (e.g., NCT06907329 for immunosenescence, NCT07144527 for NMN exercise performance). Ethical, translational, and ML roadmaps incorporate equity (Gini $<0.2$) and federated learning. Grounded in verified 2024-2025 references and replicable code, this framework advances empirical translation with unparalleled scientific integrity and precision.","url":"https://doi.org/10.5281/zenodo.17604585","authors":["shibah, Sami Rashid Mohammed"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17604585","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17587337","name":"A Comprehensive Review of Data Privacy Challenges in Social Media Platforms","source":"datacite","abstract":"Social media privacy has become one of the most urgent 21st-century digital concerns, as sites frame communication, identity, and public discourse while also facilitating surveillance, profiling, and exploitation. This review examines twenty peer-reviewed articles from 2003 to 2024, drawn from IEEE, Springer, ACM, and Scopus. The research was grouped into four broad categories: user behavior and awareness, legal and regulatory environment, risks and threats, and privacy enhancing technologies. The findings suggest that while technical solutions (e.g., encryption, differential privacy, federated learning) and policy tools (e.g., GDPR, CCPA, DPDP) are changing, they are not aligned with user understanding, cultural environments, and platform incentives. User literacy deficits, ineffective regulation, and data monetization-based business models remain eroding privacy protections. The review underscores the imperative for interdisciplinary approaches that merge legal, technical, and social insights, ensuring privacy-by-design systems that are easy to use and culturally sensitive.","url":"https://doi.org/10.5281/zenodo.17587337","authors":["Manikantan, R","Meghana, J","Padmavathi, C"],"tags":["Privacy Risks and Challenges","User behavior and awareness","GDPR and Data Protection","Legal and Regulatory Frameworks","Social Media Platforms"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17587337","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17587338","name":"A Comprehensive Review of Data Privacy Challenges in Social Media Platforms","source":"datacite","abstract":"Social media privacy has become one of the most urgent 21st-century digital concerns, as sites frame communication, identity, and public discourse while also facilitating surveillance, profiling, and exploitation. This review examines twenty peer-reviewed articles from 2003 to 2024, drawn from IEEE, Springer, ACM, and Scopus. The research was grouped into four broad categories: user behavior and awareness, legal and regulatory environment, risks and threats, and privacy enhancing technologies. The findings suggest that while technical solutions (e.g., encryption, differential privacy, federated learning) and policy tools (e.g., GDPR, CCPA, DPDP) are changing, they are not aligned with user understanding, cultural environments, and platform incentives. User literacy deficits, ineffective regulation, and data monetization-based business models remain eroding privacy protections. The review underscores the imperative for interdisciplinary approaches that merge legal, technical, and social insights, ensuring privacy-by-design systems that are easy to use and culturally sensitive.","url":"https://doi.org/10.5281/zenodo.17587338","authors":["Manikantan, R","Meghana, J","Padmavathi, C"],"tags":["Privacy Risks and Challenges","User behavior and awareness","GDPR and Data Protection","Legal and Regulatory Frameworks","Social Media Platforms"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17587338","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2410.03070","name":"FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization","source":"datacite","abstract":"Federated Learning (FL) is a method for training machine learning models using distributed data sources. It ensures privacy by allowing clients to collaboratively learn a shared global model while storing their data locally. However, a significant challenge arises when dealing with missing modalities in clients' datasets, where certain features or modalities are unavailable or incomplete, leading to heterogeneous data distribution. While previous studies have addressed the issue of complete-modality missing, they fail to tackle partial-modality missing on account of severe heterogeneity among clients at an instance level, where the pattern of missing data can vary significantly from one sample to another. To tackle this challenge, this study proposes a novel framework named FedMAC, designed to address multi-modality missing under conditions of partial-modality missing in FL. Additionally, to avoid trivial aggregation of multi-modal features, we introduce contrastive-based regularization to impose additional constraints on the latent representation space. The experimental results demonstrate the effectiveness of FedMAC across various client configurations with statistical heterogeneity, outperforming baseline methods by up to 26% in severe missing scenarios, highlighting its potential as a solution for the challenge of partially missing modalities in federated systems. Our source code is provided at https://github.com/nmduonggg/PEPSY","url":"https://doi.org/10.48550/arxiv.2410.03070","authors":["Nguyen, Manh Duong","Nguyen, Trung Thanh","Pham, Huy Hieu","Hoang, Trong Nghia","Nguyen, Phi Le","Huynh, Thanh Trung"],"tags":["Machine Learning (cs.LG)","Multimedia (cs.MM)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2410.03070","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2511.01800","name":"Bayesian Coreset Optimization for Personalized Federated Learning","source":"datacite","abstract":"In a distributed machine learning setting like Federated Learning where there are multiple clients involved which update their individual weights to a single central server, often training on the entire individual client's dataset for each client becomes cumbersome. To address this issue we propose $\\methodprop$: a personalized coreset weighted federated learning setup where the training updates for each individual clients are forwarded to the central server based on only individual client coreset based representative data points instead of the entire client data. Through theoretical analysis we present how the average generalization error is minimax optimal up to logarithm bounds (upper bounded by $\\mathcal{O}(n_k^{-\\frac{2 β}{2 β+\\boldsymbolΛ}} \\log ^{2 δ^{\\prime}}(n_k))$) and lower bounds of $\\mathcal{O}(n_k^{-\\frac{2 β}{2 β+\\boldsymbolΛ}})$, and how the overall generalization error on the data likelihood differs from a vanilla Federated Learning setup as a closed form function ${\\boldsymbol{\\Im}}(\\boldsymbol{w}, n_k)$ of the coreset weights $\\boldsymbol{w}$ and coreset sample size $n_k$. Our experiments on different benchmark datasets based on a variety of recent personalized federated learning architectures show significant gains as compared to random sampling on the training data followed by federated learning, thereby indicating how intelligently selecting such training samples can help in performance. Additionally, through experiments on medical datasets our proposed method showcases some gains as compared to other submodular optimization based approaches used for subset selection on client's data.","url":"https://doi.org/10.48550/arxiv.2511.01800","authors":["Chanda, Prateek","Modi, Shrey","Ramakrishnan, Ganesh"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.6; H.3.3"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2511.01800","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2509.25233","name":"FedCLF -- Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networks","source":"datacite","abstract":"Federated Learning (FL) is a distributed machine learning technique that preserves data privacy by sharing only the trained parameters instead of the client data. This makes FL ideal for highly dynamic, heterogeneous, and time-critical applications, in particular, the Internet of Vehicles (IoV) networks. However, FL encounters considerable challenges in such networks owing to the high data and device heterogeneity. To address these challenges, we propose FedCLF, i.e., FL with Calibrated Loss and Feedback control, which introduces calibrated loss as a utility in the participant selection process and a feedback control mechanism to dynamically adjust the sampling frequency of the clients. The envisaged approach (a) enhances the overall model accuracy in case of highly heterogeneous data and (b) optimizes the resource utilization for resource constrained IoV networks, thereby leading to increased efficiency in the FL process. We evaluated FedCLF vis-à-vis baseline models, i.e., FedAvg, Newt, and Oort, using CIFAR-10 dataset with varying data heterogeneity. Our results depict that FedCLF significantly outperforms the baseline models by up to a 16% improvement in high data heterogeneity-related scenarios with improved efficiency via reduced sampling frequency.","url":"https://doi.org/10.48550/arxiv.2509.25233","authors":["Wijethilake, Kasun Eranda","Mahmood, Adnan","Sheng, Quan Z."],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.25233","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2510.25277","name":"A Privacy-Preserving Ecosystem for Developing Machine Learning Algorithms Using Patient Data: Insights from the TUM.ai Makeathon","source":"datacite","abstract":"The integration of clinical data offers significant potential for the development of personalized medicine. However, its use is severely restricted by the General Data Protection Regulation (GDPR), especially for small cohorts with rare diseases. High-quality, structured data is essential for the development of predictive medical AI. In this case study, we propose a novel, multi-stage approach to secure AI training: (1) The model is designed on a simulated clinical knowledge graph (cKG). This graph is used exclusively to represent the structural characteristics of the real cKG without revealing any sensitive content. (2) The model is then integrated into the FeatureCloud (FC) federated learning framework, where it is prepared in a single-client configuration within a protected execution environment. (3) Training then takes place within the hospital environment on the real cKG, either under the direct supervision of hospital staff or via a fully automated pipeline controlled by the hospital. (4) Finally, verified evaluation scripts are executed, which only return aggregated performance metrics. This enables immediate performance feedback without sensitive patient data or individual predictions, leaving the clinic. A fundamental element of this approach involves the incorporation of a cKG, which serves to organize multi-omics and patient data within the context of real-world hospital environments. This approach was successfully validated during the TUM.ai Makeathon 2024 (TUMaiM24) challenge set by the Dr. von Hauner Children's Hospital (HCH-LMU): 50 students developed models for patient classification and diagnosis without access to real data. Deploying secure algorithms via federated frameworks, such as the FC framework, could be a practical way of achieving privacy-preserving AI in healthcare.","url":"https://doi.org/10.48550/arxiv.2510.25277","authors":["Süwer, Simon","Mai, Mai Khanh","Klein, Christoph","Götzenberger, Nicola","Dalić, Denis","Maier, Andreas","Baumbach, Jan"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.25277","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17457601","name":"CollectiveOS & The Sovereign Mobile Super-Node","source":"datacite","abstract":"CollectiveOS & The Sovereign Mobile Super-Node An Open-Science Architecture for Portable, Patent-Free AI Infrastructure Version 1.0 — October 2025 Author & Custodian:Mark Anthony Brewer — Human Global Science Collective (HGSC) Affiliation:Human Global Science Collective (HGSC) — an international federation for open, patent-free research and technology. Primary DOI: (tba upon Zenodo upload)Cite as: Brewer, M.A. (2025). CollectiveOS & The Sovereign Mobile Super-Node: An Open-Science Architecture for Portable, Patent-Free AI Infrastructure. Human Global Science Collective. Zenodo. https://doi.org/XXXX License: Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) + Open-Science Non-Assertion (OSNA) pledge.Rights Statement: All materials may be used, studied, and reproduced for research, educational, and humanitarian purposes. Commercial implementations permitted under reciprocal open-license terms. Abstract CollectiveOS and the Sovereign Mobile Super-Node together constitute a proof-of-concept for fully sovereign, local-first artificial-intelligence computing.The system integrates disaggregated high-performance hardware—dual-CPU + dual-NPU motherboards, DDR5 memory pools, and PCIe 5 / CXL bridges—with an agent-based operating system that embeds ethical auditing and transparent governance.All engineering and legal structures operate inside the HGSC’s Framework for Patent-Free Science, ensuring every disclosure becomes defensible prior art.This white paper consolidates the technical architecture, open-science governance, and societal rationale behind the project, positioning it as both an engineering initiative and a living demonstration of a global, patent-free innovation model. Part I – Foundations 1 · The Context of Patent-Free Science 1.1 Background Modern research operates inside a paradox. Scientific knowledge is expected to move freely, yet the machinery of discovery—software, hardware, data pipelines—is often trapped behind proprietary walls. The cost and complexity of patent licensing now slow progress more than they protect inventors. Meanwhile, open-source software has proven that transparent, cooperative innovation can outpace closed models while maintaining credit, accountability, and quality control. 1.2 The Framework for Patent-Free Science In “A Framework for Patent-Free Science” (Brewer 2025, Zenodo), the Human Global Science Collective (HGSC) established a reproducible legal pathway for open discovery: Defensive publication replaces exclusivity with transparency. Every enabling disclosure, timestamped by a DOI or blockchain proof, becomes global prior art. Open licensing—Apache 2.0, CERN-OHL, CC BY-SA 4.0—codifies permission rather than restriction. Collective defense—non-assertion pledges (OSNA) and reciprocal license pools—creates a shared immunity from patent aggression. Incentive realignment shifts credit from monopoly to reproducibility and social impact. This framework supplies the legal foundation for all HGSC projects. Anything built inside it—hardware schematics, firmware, datasets—enters the public record as reproducible, citable, and permanently free for research and education. 1.3 Why CollectiveOS Emerged Artificial-intelligence research has become dominated by cloud monopolies whose infrastructure costs and proprietary APIs lock out smaller players. CollectiveOS was conceived as both a technical and legal countermeasure: a local-first AI operating system proving that high-end computation can exist entirely within the open-science commons. Its first embodiment is the Sovereign Mobile Super-Node—a patent-free workstation that acts like a personal supercomputer while remaining portable, affordable, and fully transparent. 2 · Book CXCV and the Covenant of the Sovereign Mesh 2.1 From Engineering to Doctrine Book CXCV: The Covenant of the Sovereign Mesh (2025) re-imagines computing as a constitutional act. It defines how CollectiveOS nodes interoperate ethically and technically. Every mac","url":"https://doi.org/10.5281/zenodo.17457601","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17457601","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17457600","name":"CollectiveOS & The Sovereign Mobile Super-Node","source":"datacite","abstract":"CollectiveOS & The Sovereign Mobile Super-Node An Open-Science Architecture for Portable, Patent-Free AI Infrastructure Version 1.0 — October 2025 Author & Custodian:Mark Anthony Brewer — Human Global Science Collective (HGSC) Affiliation:Human Global Science Collective (HGSC) — an international federation for open, patent-free research and technology. Primary DOI: (tba upon Zenodo upload)Cite as: Brewer, M.A. (2025). CollectiveOS & The Sovereign Mobile Super-Node: An Open-Science Architecture for Portable, Patent-Free AI Infrastructure. Human Global Science Collective. Zenodo. https://doi.org/XXXX License: Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) + Open-Science Non-Assertion (OSNA) pledge.Rights Statement: All materials may be used, studied, and reproduced for research, educational, and humanitarian purposes. Commercial implementations permitted under reciprocal open-license terms. Abstract CollectiveOS and the Sovereign Mobile Super-Node together constitute a proof-of-concept for fully sovereign, local-first artificial-intelligence computing.The system integrates disaggregated high-performance hardware—dual-CPU + dual-NPU motherboards, DDR5 memory pools, and PCIe 5 / CXL bridges—with an agent-based operating system that embeds ethical auditing and transparent governance.All engineering and legal structures operate inside the HGSC’s Framework for Patent-Free Science, ensuring every disclosure becomes defensible prior art.This white paper consolidates the technical architecture, open-science governance, and societal rationale behind the project, positioning it as both an engineering initiative and a living demonstration of a global, patent-free innovation model. Part I – Foundations 1 · The Context of Patent-Free Science 1.1 Background Modern research operates inside a paradox. Scientific knowledge is expected to move freely, yet the machinery of discovery—software, hardware, data pipelines—is often trapped behind proprietary walls. The cost and complexity of patent licensing now slow progress more than they protect inventors. Meanwhile, open-source software has proven that transparent, cooperative innovation can outpace closed models while maintaining credit, accountability, and quality control. 1.2 The Framework for Patent-Free Science In “A Framework for Patent-Free Science” (Brewer 2025, Zenodo), the Human Global Science Collective (HGSC) established a reproducible legal pathway for open discovery: Defensive publication replaces exclusivity with transparency. Every enabling disclosure, timestamped by a DOI or blockchain proof, becomes global prior art. Open licensing—Apache 2.0, CERN-OHL, CC BY-SA 4.0—codifies permission rather than restriction. Collective defense—non-assertion pledges (OSNA) and reciprocal license pools—creates a shared immunity from patent aggression. Incentive realignment shifts credit from monopoly to reproducibility and social impact. This framework supplies the legal foundation for all HGSC projects. Anything built inside it—hardware schematics, firmware, datasets—enters the public record as reproducible, citable, and permanently free for research and education. 1.3 Why CollectiveOS Emerged Artificial-intelligence research has become dominated by cloud monopolies whose infrastructure costs and proprietary APIs lock out smaller players. CollectiveOS was conceived as both a technical and legal countermeasure: a local-first AI operating system proving that high-end computation can exist entirely within the open-science commons. Its first embodiment is the Sovereign Mobile Super-Node—a patent-free workstation that acts like a personal supercomputer while remaining portable, affordable, and fully transparent. 2 · Book CXCV and the Covenant of the Sovereign Mesh 2.1 From Engineering to Doctrine Book CXCV: The Covenant of the Sovereign Mesh (2025) re-imagines computing as a constitutional act. It defines how CollectiveOS nodes interoperate ethically and technically. Every mac","url":"https://doi.org/10.5281/zenodo.17457600","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17457600","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.13140/rg.2.2.35975.57762","name":"C1 -Internship L'Oréal 2024: Differential privacy for federated learning in Cosmetical Science. Application to safety data.","source":"datacite","abstract":"","url":"https://doi.org/10.13140/rg.2.2.35975.57762","authors":["Bastien, Philippe"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.13140/rg.2.2.35975.57762","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2510.11400","name":"FedHybrid: Breaking the Memory Wall of Federated Learning via Hybrid Tensor Management","source":"datacite","abstract":"Federated Learning (FL) emerges as a new learning paradigm that enables multiple devices to collaboratively train a shared model while preserving data privacy. However, one fundamental and prevailing challenge that hinders the deployment of FL on mobile devices is the memory limitation. This paper proposes \\textit{FedHybrid}, a novel framework that effectively reduces the memory footprint during the training process while guaranteeing the model accuracy and the overall training progress. Specifically, \\textit{FedHybrid} first selects the participating devices for each training round by jointly evaluating their memory budget, computing capability, and data diversity. After that, it judiciously analyzes the computational graph and generates an execution plan for each selected client in order to meet the corresponding memory budget while minimizing the training delay through employing a hybrid of recomputation and compression techniques according to the characteristic of each tensor. During the local training process, \\textit{FedHybrid} carries out the execution plan with a well-designed activation compression technique to effectively achieve memory reduction with minimum accuracy loss. We conduct extensive experiments to evaluate \\textit{FedHybrid} on both simulation and off-the-shelf mobile devices. The experiment results demonstrate that \\textit{FedHybrid} achieves up to a 39.1\\% increase in model accuracy and a 15.5$\\times$ reduction in wall clock time under various memory budgets compared with the baselines.","url":"https://doi.org/10.48550/arxiv.2510.11400","authors":["Tam, Kahou","Tian, Chunlin","Li, Li","Zhao, Haikai","Xu, ChengZhong"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.11400","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2405.20821","name":"Pursuing Overall Welfare in Federated Learning through Sequential Decision Making","source":"datacite","abstract":"In traditional federated learning, a single global model cannot perform equally well for all clients. Therefore, the need to achieve the client-level fairness in federated system has been emphasized, which can be realized by modifying the static aggregation scheme for updating the global model to an adaptive one, in response to the local signals of the participating clients. Our work reveals that existing fairness-aware aggregation strategies can be unified into an online convex optimization framework, in other words, a central server's sequential decision making process. To enhance the decision making capability, we propose simple and intuitive improvements for suboptimal designs within existing methods, presenting AAggFF. Considering practical requirements, we further subdivide our method tailored for the cross-device and the cross-silo settings, respectively. Theoretical analyses guarantee sublinear regret upper bounds for both settings: $\\mathcal{O}(\\sqrt{T \\log{K}})$ for the cross-device setting, and $\\mathcal{O}(K \\log{T})$ for the cross-silo setting, with $K$ clients and $T$ federation rounds. Extensive experiments demonstrate that the federated system equipped with AAggFF achieves better degree of client-level fairness than existing methods in both practical settings. Code is available at https://github.com/vaseline555/AAggFF","url":"https://doi.org/10.48550/arxiv.2405.20821","authors":["Hahn, Seok-Ju","Kim, Gi-Soo","Lee, Junghye"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (stat.ML)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.20821","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17295749","name":"Security and Privacy in AI Healthcare  Systems: A Systematic Literature Review  on Aligning Innovation with HIPAA  Regulations and Hospital Cybersecurity","source":"datacite","abstract":"Abstract Background: Artificial intelligence (AI) is revolutionizing healthcare delivery through innovations in diagnosis, personalized treatment, and operational efficiency. However, the growing integration of AI systems raises significant concerns about the security and privacy of patient data, requiring careful alignment with privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and hospital cybersecurity practices. Objective: This systematic literature review aims to analyze and synthesize current scientific evidence on security and privacy challenges in implementing AI in healthcare, focusing on aligning technological innovations with privacy regulations and hospital cybersecurity frameworks. Methods: A comprehensive systematic search was conducted in PubMed, IEEE Xplore, Scopus, and Google Scholar databases for studies published between 2010 and 2024, following PRISMA guidelines. Studies addressing security, privacy, ethics, and regulatory compliance of AI systems in healthcare contexts were included. Quality assessment was performed using the AMSTAR-2 tool for systematic reviews and the Newcastle-Ottawa Scale for observational studies. Results: Of 2,847 initially identified records, 47 studies met inclusion criteria after full-text screening. The analysis revealed persistent challenges, including security vulnerabilities (reported in 89% of studies), data re-identification risks (76% of studies), and gaps in regulatory frameworks regarding HIPAA coverage for AI developers (68% of studies). Healthcare professionals' hesitation to adopt AI technologies due to concerns about transparency and data security was a recurring theme (>60% adoption resistance). Emerging technologies including Explainable AI (XAI), federated learning, and differential privacy were identified as promising solutions for risk mitigation. Conclusions: Safe and ethical implementation of AI in healthcare requires a multifaceted approach that integrates technological innovations, adaptive regulatory frameworks, and robust cybersecurity protocols. Interdisciplinary collaboration and transparency are fundamental to building trust and ensuring that AI benefits can be realized responsibly, protecting patient privacy and security.","url":"https://doi.org/10.5281/zenodo.17295749","authors":["Caetano, Rafael Magalhães"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17295749","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17295750","name":"Security and Privacy in AI Healthcare  Systems: A Systematic Literature Review  on Aligning Innovation with HIPAA  Regulations and Hospital Cybersecurity","source":"datacite","abstract":"Abstract Background: Artificial intelligence (AI) is revolutionizing healthcare delivery through innovations in diagnosis, personalized treatment, and operational efficiency. However, the growing integration of AI systems raises significant concerns about the security and privacy of patient data, requiring careful alignment with privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and hospital cybersecurity practices. Objective: This systematic literature review aims to analyze and synthesize current scientific evidence on security and privacy challenges in implementing AI in healthcare, focusing on aligning technological innovations with privacy regulations and hospital cybersecurity frameworks. Methods: A comprehensive systematic search was conducted in PubMed, IEEE Xplore, Scopus, and Google Scholar databases for studies published between 2010 and 2024, following PRISMA guidelines. Studies addressing security, privacy, ethics, and regulatory compliance of AI systems in healthcare contexts were included. Quality assessment was performed using the AMSTAR-2 tool for systematic reviews and the Newcastle-Ottawa Scale for observational studies. Results: Of 2,847 initially identified records, 47 studies met inclusion criteria after full-text screening. The analysis revealed persistent challenges, including security vulnerabilities (reported in 89% of studies), data re-identification risks (76% of studies), and gaps in regulatory frameworks regarding HIPAA coverage for AI developers (68% of studies). Healthcare professionals' hesitation to adopt AI technologies due to concerns about transparency and data security was a recurring theme (>60% adoption resistance). Emerging technologies including Explainable AI (XAI), federated learning, and differential privacy were identified as promising solutions for risk mitigation. Conclusions: Safe and ethical implementation of AI in healthcare requires a multifaceted approach that integrates technological innovations, adaptive regulatory frameworks, and robust cybersecurity protocols. Interdisciplinary collaboration and transparency are fundamental to building trust and ensuring that AI benefits can be realized responsibly, protecting patient privacy and security.","url":"https://doi.org/10.5281/zenodo.17295750","authors":["Caetano, Rafael Magalhães"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17295750","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17276525","name":"Triple_MNIST_Segmentation_0134","source":"datacite","abstract":"This is the official deposite of the Triple MNIST Segmentation 0134 dataset described and used in the article \"Matthis Manthe et al., “Deep Domain Isolation and Sample Clustered Federated Learning for Semantic Segmentation,” in Machine Learning and Knowledge Discovery in Databases. Research Track, ed. Albert Bifet et al. (Springer Nature Switzerland, 2024), https://doi.org/10.1007/978-3-031-70359-1_22.\".","url":"https://doi.org/10.5281/zenodo.17276525","authors":["Manthe, Matthis"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17276525","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17276526","name":"Triple_MNIST_Segmentation_0134","source":"datacite","abstract":"This is the official deposite of the Triple MNIST Segmentation 0134 dataset described and used in the article \"Matthis Manthe et al., “Deep Domain Isolation and Sample Clustered Federated Learning for Semantic Segmentation,” in Machine Learning and Knowledge Discovery in Databases. Research Track, ed. Albert Bifet et al. (Springer Nature Switzerland, 2024), https://doi.org/10.1007/978-3-031-70359-1_22.\".","url":"https://doi.org/10.5281/zenodo.17276526","authors":["Manthe, Matthis"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17276526","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17273096","name":"Workforce Scheduling in Logistics Hubs Dataset","source":"datacite","abstract":"This Workforce Scheduling and Operational Efficiency Dataset captures real-time, high-frequency operational data collected from a logistics hub over a 7-year period spanning from January 1, 2018, to December 31, 2024. The dataset includes hourly records for a diverse set of workforce, environmental, and operational features, reflecting actual dynamics observed in large-scale logistics operations. The dataset is structured to support analysis of workforce management, shift optimization, employee fatigue assessment, overtime trends, and task sufficiency. It provides a detailed view of how human factors, environmental conditions, and task complexity interact in high-demand logistics environments. Feature Overview A. Temporal and Identification DateTime: Timestamp of observation at hourly intervals. Employee_ID: Unique identifier for individual workforce members. B. Shift and Job-Related Attributes Shift_Timing: Operational shift per hour (Morning, Evening, Night). Role_Position: Assigned job function (Loader, Supervisor, Driver). Skill_Level: Skill classification of the employee (Beginner, Intermediate, Advanced). Shift_Preference: Preferred working time of the employee. C. Experience and Workforce Behavior Work_Experience_Years: Years of experience in logistics or similar roles. Availability_Hours: Number of hours the worker is available for scheduling. Previous_Shift_Fatigue: Fatigue index based on preceding work hours. Training_Hours_Completed: Cumulative training hours completed by each employee. Absenteeism_Rate: Relative absenteeism level based on historical behavior. D. Operational Load and Environment Daily_Shipment_Volume: Shipment volume handled per shift. Shipment_Type: Classification of the shipment (Fragile, Perishable, Standard). Demand_Fluctuations: Measure of variation in demand at the hour level. Hub_Size: Relative size and capacity of the logistics hub. Temperature and Humidity: Environmental readings at the logistics location. E. Scheduling Constraints Required_Labor_Units: Hourly workforce requirement estimate. Historical_Delay_Data: Frequency of past delays associated with tasks. Max_Shift_Duration: Maximum allowable work hours per shift. Min_Rest_Period: Minimum rest period required between consecutive shifts. Overtime_Limits: Permissible overtime hours per worker. Shift_Overlap: Measure of overlap between shift transitions. F. Performance and Feedback Metrics On_Time_Completion_Rate: Task completion punctuality. Worker_Efficiency_Score: Productivity score based on output vs. time. Schedule_Adherence: Alignment with pre-assigned shift schedules. Employee_Satisfaction_Rating: Score derived from HR or operational evaluations. Target Labels for Prediction Tasks The dataset includes multi-label classification targets to enable the development of supervised models: Optimal_Shift_Assignment: Binary outcome (Assigned / Not Assigned) for schedule feasibility. Overtime_Prediction: Indicator for whether overtime was required (Yes / No). Employee_Fatigue_Risk: Categorical fatigue risk (High, Medium, Low). Shift_Coverage_Sufficiency: Binary label indicating if staffing was sufficient (Sufficient / Insufficient). Highlights Time Span: 7 years (2018–2024), hourly resolution. Multi-Domain Variables: Covers human, environmental, operational, and scheduling data. Multi-Label Supervision: Enables simultaneous prediction of multiple labor-related outcomes. Real-Time Nature: Suitable for online analytics, edge computing, and federated learning scenarios. Use Cases: Workforce optimization, logistics planning, fatigue risk monitoring, shift planning, ML benchmarking.","url":"https://doi.org/10.5281/zenodo.17273096","authors":["San Bernardino Inland Empire Logistics Hub"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17273096","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17273095","name":"Workforce Scheduling in Logistics Hubs Dataset","source":"datacite","abstract":"This Workforce Scheduling and Operational Efficiency Dataset captures real-time, high-frequency operational data collected from a logistics hub over a 7-year period spanning from January 1, 2018, to December 31, 2024. The dataset includes hourly records for a diverse set of workforce, environmental, and operational features, reflecting actual dynamics observed in large-scale logistics operations. The dataset is structured to support analysis of workforce management, shift optimization, employee fatigue assessment, overtime trends, and task sufficiency. It provides a detailed view of how human factors, environmental conditions, and task complexity interact in high-demand logistics environments. Feature Overview A. Temporal and Identification DateTime: Timestamp of observation at hourly intervals. Employee_ID: Unique identifier for individual workforce members. B. Shift and Job-Related Attributes Shift_Timing: Operational shift per hour (Morning, Evening, Night). Role_Position: Assigned job function (Loader, Supervisor, Driver). Skill_Level: Skill classification of the employee (Beginner, Intermediate, Advanced). Shift_Preference: Preferred working time of the employee. C. Experience and Workforce Behavior Work_Experience_Years: Years of experience in logistics or similar roles. Availability_Hours: Number of hours the worker is available for scheduling. Previous_Shift_Fatigue: Fatigue index based on preceding work hours. Training_Hours_Completed: Cumulative training hours completed by each employee. Absenteeism_Rate: Relative absenteeism level based on historical behavior. D. Operational Load and Environment Daily_Shipment_Volume: Shipment volume handled per shift. Shipment_Type: Classification of the shipment (Fragile, Perishable, Standard). Demand_Fluctuations: Measure of variation in demand at the hour level. Hub_Size: Relative size and capacity of the logistics hub. Temperature and Humidity: Environmental readings at the logistics location. E. Scheduling Constraints Required_Labor_Units: Hourly workforce requirement estimate. Historical_Delay_Data: Frequency of past delays associated with tasks. Max_Shift_Duration: Maximum allowable work hours per shift. Min_Rest_Period: Minimum rest period required between consecutive shifts. Overtime_Limits: Permissible overtime hours per worker. Shift_Overlap: Measure of overlap between shift transitions. F. Performance and Feedback Metrics On_Time_Completion_Rate: Task completion punctuality. Worker_Efficiency_Score: Productivity score based on output vs. time. Schedule_Adherence: Alignment with pre-assigned shift schedules. Employee_Satisfaction_Rating: Score derived from HR or operational evaluations. Target Labels for Prediction Tasks The dataset includes multi-label classification targets to enable the development of supervised models: Optimal_Shift_Assignment: Binary outcome (Assigned / Not Assigned) for schedule feasibility. Overtime_Prediction: Indicator for whether overtime was required (Yes / No). Employee_Fatigue_Risk: Categorical fatigue risk (High, Medium, Low). Shift_Coverage_Sufficiency: Binary label indicating if staffing was sufficient (Sufficient / Insufficient). Highlights Time Span: 7 years (2018–2024), hourly resolution. Multi-Domain Variables: Covers human, environmental, operational, and scheduling data. Multi-Label Supervision: Enables simultaneous prediction of multiple labor-related outcomes. Real-Time Nature: Suitable for online analytics, edge computing, and federated learning scenarios. Use Cases: Workforce optimization, logistics planning, fatigue risk monitoring, shift planning, ML benchmarking.","url":"https://doi.org/10.5281/zenodo.17273095","authors":["San Bernardino Inland Empire Logistics Hub"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17273095","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2407.15402","name":"Tackling Selfish Clients in Federated Learning","source":"datacite","abstract":"Federated Learning (FL) is a distributed machine learning paradigm facilitating participants to collaboratively train a model without revealing their local data. However, when FL is deployed into the wild, some intelligent clients can deliberately deviate from the standard training process to make the global model inclined toward their local model, thereby prioritizing their local data distribution. We refer to this novel category of misbehaving clients as selfish. In this paper, we propose a Robust aggregation strategy for FL server to mitigate the effect of Selfishness (in short RFL-Self). RFL-Self incorporates an innovative method to recover (or estimate) the true updates of selfish clients from the received ones, leveraging robust statistics (median of norms) of the updates at every round. By including the recovered updates in aggregation, our strategy offers strong robustness against selfishness. Our experimental results, obtained on MNIST and CIFAR-10 datasets, demonstrate that just 2% of clients behaving selfishly can decrease the accuracy by up to 36%, and RFL-Self can mitigate that effect without degrading the global model performance.","url":"https://doi.org/10.48550/arxiv.2407.15402","authors":["Augello, Andrea","Gupta, Ashish","Re, Giuseppe Lo","Das, Sajal K."],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2407.15402","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2405.06312","name":"FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization","source":"datacite","abstract":"Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Traditional approaches, including heuristic and learning-based methods, fall short of addressing these complexities holistically. In response, we propose FedGCS, a novel generative client selection framework that innovatively recasts the client selection process as a generative task. Drawing inspiration from the methodologies used in large language models, FedGCS efficiently encodes abundant decision-making knowledge within a continuous representation space, enabling efficient gradient-based optimization to search for optimal client selection that will be finally output via generation. The framework comprises four steps: (1) automatic collection of diverse \"selection-score\" pair data using classical client selection methods; (2) training an encoder-evaluator-decoder framework on this data to construct a continuous representation space; (3) employing gradient-based optimization in this space for optimal client selection; (4) generating the final optimal client selection via using beam search for the well-trained decoder. FedGCS outperforms traditional methods by being more comprehensive, generalizable, and efficient, simultaneously optimizing for model performance, latency, and energy consumption. The effectiveness of FedGCS is proven through extensive experimental analyses.","url":"https://doi.org/10.48550/arxiv.2405.06312","authors":["Ning, Zhiyuan","Tian, Chunlin","Xiao, Meng","Fan, Wei","Wang, Pengyang","Li, Li","Wang, Pengfei","Zhou, Yuanchun"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.06312","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17236145","name":"CONFIDENTIAL6G – Latest Updates from the Project M19 – M30","source":"datacite","abstract":"This newsletter presents the latest updates from the Horizon Europe project CONFIDENTIAL6G (GA No. 101096435), covering activities from months 19 to 30. The issue highlights recent project achievements, events, and publications that advance confidentiality, privacy, and security in next-generation 6G networks. Key sections include: Events & News – project representation at Open Source Summit Europe 2024, FHE.org 2025, WSSS 2025, Secure Automotive OTA Seminar, and EuCNC & 6G Summit 2025. New Partnership – TU Wien joining the consortium, strengthening cryptography and security research. Use Cases – secure aviation maintenance, telecom cloud security, and connected vehicles with OTA updates. Technical Achievements – confidential computing toolkit, decentralized data sharing frameworks, confidential AI orchestration, and federated learning in connected vehicles. Open-Source Contributions – release of multiple libraries and tools for cryptography, FHE, MPC, and ZKPs. Scientific Outputs – peer-reviewed publications on secure computation, post-quantum cryptography, hardware acceleration, MPC, and cryptanalysis. Community Engagement – AI & Security webinar with sister projects, dissemination through SNS Journal 2025, and public articles on privacy and confidential computing. The newsletter demonstrates how CONFIDENTIAL6G contributes to building secure, privacy-preserving, and quantum-resistant infrastructures for future 6G systems.","url":"https://doi.org/10.5281/zenodo.17236145","authors":["Vasic, Jelena"],"tags":["CONFIDENTIAL6G","Newsletter","Data privacy","AI/ML","Confidential computing","Confidential networking","IoT security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17236145","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.17236144","name":"CONFIDENTIAL6G – Latest Updates from the Project M19 – M30","source":"datacite","abstract":"This newsletter presents the latest updates from the Horizon Europe project CONFIDENTIAL6G (GA No. 101096435), covering activities from months 19 to 30. The issue highlights recent project achievements, events, and publications that advance confidentiality, privacy, and security in next-generation 6G networks. Key sections include: Events & News – project representation at Open Source Summit Europe 2024, FHE.org 2025, WSSS 2025, Secure Automotive OTA Seminar, and EuCNC & 6G Summit 2025. New Partnership – TU Wien joining the consortium, strengthening cryptography and security research. Use Cases – secure aviation maintenance, telecom cloud security, and connected vehicles with OTA updates. Technical Achievements – confidential computing toolkit, decentralized data sharing frameworks, confidential AI orchestration, and federated learning in connected vehicles. Open-Source Contributions – release of multiple libraries and tools for cryptography, FHE, MPC, and ZKPs. Scientific Outputs – peer-reviewed publications on secure computation, post-quantum cryptography, hardware acceleration, MPC, and cryptanalysis. Community Engagement – AI & Security webinar with sister projects, dissemination through SNS Journal 2025, and public articles on privacy and confidential computing. The newsletter demonstrates how CONFIDENTIAL6G contributes to building secure, privacy-preserving, and quantum-resistant infrastructures for future 6G systems.","url":"https://doi.org/10.5281/zenodo.17236144","authors":["Vasic, Jelena"],"tags":["CONFIDENTIAL6G","Newsletter","Data privacy","AI/ML","Confidential computing","Confidential networking","IoT security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17236144","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2509.22922","name":"OptimES: Optimizing Federated Learning Using Remote Embeddings for Graph Neural Networks","source":"datacite","abstract":"Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. However, in most real-world settings, such as financial transaction networks and healthcare networks, this data is localized to different data owners and cannot be aggregated due to privacy concerns. Federated Learning (FL) has emerged as a viable machine learning approach for training a shared model that iteratively aggregates local models trained on decentralized data. This addresses privacy concerns while leveraging parallelism. State-of-the-art methods enhance the privacy-respecting convergence accuracy of federated GNN training by sharing remote embeddings of boundary vertices through a server (EmbC). However, they are limited by diminished performance due to large communication costs. In this article, we propose OptimES, an optimized federated GNN training framework that employs remote neighbourhood pruning, overlapping the push of embeddings to the server with local training, and dynamic pulling of embeddings to reduce network costs and training time. We perform a rigorous evaluation of these strategies for four common graph datasets with up to $111M$ vertices and $1.8B$ edges. We see that a modest drop in per-round accuracy due to the preemptive push of embeddings is out-stripped by the reduction in per-round training time for large and dense graphs like Reddit and Products, converging up to $\\approx 3.5\\times$ faster than EmbC and giving up to $\\approx16\\%$ better accuracy than the default federated GNN learning. While accuracy improvements over default federated GNNs are modest for sparser graphs like Arxiv and Papers, they achieve the target accuracy about $\\approx11\\times$ faster than EmbC.","url":"https://doi.org/10.48550/arxiv.2509.22922","authors":["Naman, Pranjal","Simmhan, Yogesh"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.22922","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2509.20877","name":"Distribution-Controlled Client Selection to Improve Federated Learning Strategies","source":"datacite","abstract":"Federated learning (FL) is a distributed learning paradigm that allows multiple clients to jointly train a shared model while maintaining data privacy. Despite its great potential for domains with strict data privacy requirements, the presence of data imbalance among clients is a thread to the success of FL, as it causes the performance of the shared model to decrease. To address this, various studies have proposed enhancements to existing FL strategies, particularly through client selection methods that mitigate the detrimental effects of data imbalance. In this paper, we propose an extension to existing FL strategies, which selects active clients that best align the current label distribution with one of two target distributions, namely a balanced distribution or the federations combined label distribution. Subsequently, we empirically verify the improvements through our distribution-controlled client selection on three common FL strategies and two datasets. Our results show that while aligning the label distribution with a balanced distribution yields the greatest improvements facing local imbalance, alignment with the federation's combined label distribution is superior for global imbalance.","url":"https://doi.org/10.48550/arxiv.2509.20877","authors":["Düsing, Christoph","Cimiano, Philipp"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.20877","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.15310135","name":"D8.4 - Seminars, workshop and events - Event Nr 1","source":"datacite","abstract":"Deliverable D8.4, titled \"Seminars, Workshops, and Events,\" is a key component of the dAIEDGE project, funded by the European Union's Horizon Europe research and innovation program. This deliverable results from Task T8.3, which focuses on the dissemination and outreach efforts within the project's Network of Excellence. The document states the agenda of the session and provides a summary of the webinar \"Innovations in Edge AI: Green Blockchain, Cybersecurity, and Collaborative AI,\" which took place online on August 26th, 2024. The document is structured as follows: Introduction: Provides background and purpose. • Event Details: o Communication Strategy: Describes the promotion efforts via social media, website, newsletters, and partner collaborations. o Agenda: Lists the webinar's schedule, including keynotes and panel discussions. • Event Summary: Reports on participant numbers, key presentations, and outcomes. • Future Steps: Details of upcoming events. Key details from the event include the focus on cutting-edge topics such as green blockchain technology, cybersecurity challenges in edge AI environments, and the role of federated learning in collaborative AI.","url":"https://doi.org/10.5281/zenodo.15310135","authors":["AIR Institute"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15310135","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.5281/zenodo.15310136","name":"D8.4 - Seminars, workshop and events - Event Nr 1","source":"datacite","abstract":"Deliverable D8.4, titled \"Seminars, Workshops, and Events,\" is a key component of the dAIEDGE project, funded by the European Union's Horizon Europe research and innovation program. This deliverable results from Task T8.3, which focuses on the dissemination and outreach efforts within the project's Network of Excellence. The document states the agenda of the session and provides a summary of the webinar \"Innovations in Edge AI: Green Blockchain, Cybersecurity, and Collaborative AI,\" which took place online on August 26th, 2024. The document is structured as follows: Introduction: Provides background and purpose. • Event Details: o Communication Strategy: Describes the promotion efforts via social media, website, newsletters, and partner collaborations. o Agenda: Lists the webinar's schedule, including keynotes and panel discussions. • Event Summary: Reports on participant numbers, key presentations, and outcomes. • Future Steps: Details of upcoming events. Key details from the event include the focus on cutting-edge topics such as green blockchain technology, cybersecurity challenges in edge AI environments, and the role of federated learning in collaborative AI.","url":"https://doi.org/10.5281/zenodo.15310136","authors":["AIR Institute"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15310136","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.48550/arxiv.2509.20223","name":"An Empirical Analysis of Secure Federated Learning for Autonomous Vehicle Applications","source":"datacite","abstract":"Federated Learning lends itself as a promising paradigm in enabling distributed learning for autonomous vehicles applications and ensuring data privacy while enhancing and refining predictive model performance through collaborative training on edge client vehicles. However, it remains vulnerable to various categories of cyber-attacks, necessitating more robust security measures to effectively mitigate potential threats. Poisoning attacks and inference attacks are commonly initiated within the federated learning environment to compromise secure system performance. Secure aggregation can limit the disclosure of sensitive information from outsider and insider attackers of the federated learning environment. In this study, our aim is to conduct an empirical analysis on the transportation image dataset (e.g., LISA traffic light) using various secure aggregation techniques and multiparty computation in the presence of diverse categories of cyber-attacks. Multiparty computation serves as a state-of-the-art security mechanism, offering standard privacy for secure aggregation of edge autonomous vehicles local model updates through various security protocols. The presence of adversaries can mislead the autonomous vehicle learning model, leading to the misclassification of traffic lights, and resulting in detrimental impacts. This empirical study explores the resilience of various secure federated learning aggregation techniques and multiparty computation in safeguarding autonomous vehicle applications against various cyber threats during both training and inference times.","url":"https://doi.org/10.48550/arxiv.2509.20223","authors":["Mia, Md Jueal","Amini, M. Hadi"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.20223","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:36.530Z"},{"id":"doi:10.48550/arxiv.2509.20193","name":"FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks","source":"datacite","abstract":"Federated Learning (FL) has been extensively employed for a number of applications in machine learning, i.e., primarily owing to its privacy preserving nature and efficiency in mitigating the communication overhead. Internet of Vehicles (IoV) is one of the promising applications, wherein FL can be utilized to train a model more efficiently. Since only a subset of the clients can participate in each FL training round, challenges arise pertinent to fairness in the client selection process. Over the years, a number of researchers from both academia and industry have proposed numerous FL frameworks. However, to the best of our knowledge, none of them have employed fairness for FL-based client selection in a dynamic and heterogeneous IoV environment. Accordingly, in this paper, we envisage a FairEquityFL framework to ensure an equitable opportunity for all the clients to participate in the FL training process. In particular, we have introduced a sampling equalizer module within the selector component for ensuring fairness in terms of fair collaboration opportunity for all the clients in the client selection process. The selector is additionally responsible for both monitoring and controlling the clients' participation in each FL training round. Moreover, an outlier detection mechanism is enforced for identifying malicious clients based on the model performance in terms of considerable fluctuation in either accuracy or loss minimization. The selector flags suspicious clients and temporarily suspend such clients from participating in the FL training process. We further evaluate the performance of FairEquityFL on a publicly available dataset, FEMNIST. Our simulation results depict that FairEquityFL outperforms baseline models to a considerable extent.","url":"https://doi.org/10.48550/arxiv.2509.20193","authors":["Islam, Fahmida","Mahmood, Adnan","Mukhtiar, Noorain","Wijethilake, Kasun Eranda","Sheng, Quan Z."],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.6; I.2.11; C.2.4","68T05 = Learning and adaptive systems (AI) 68T07 = Artificial neural networks and deep learning 68M14 = Distributed systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.20193","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.3217/n659p-tr557","name":"Metacampus as a Base for a Unite! Blended Intensive Programme (BIP): Competencies for Collaborative Teaching in Joint Programme (Report 04/2025)","source":"datacite","abstract":"The report “Metacampus as a Base for a Unite! Blended Intensive Programme (BIP): Competencies for Collaborative Teaching in Joint Programmes” describes the Erasmus + Blended Intensive Program (BIP) held from June to October 2024 under the Unite! University Network. The six‑week English‑language course aimed to build skills for collaborative teaching in joint programmes among academic staff, lecturers and researchers. The programme combined an online introductory module (June 17 2024), a five‑day on‑site week at KTH Royal Institute of Technology (August 26‑30 2024), and a final online presentation (October 9 2024). Unite!'s federated learning management system (LMS) Metacampus acted as the central LMS, offering secure, institution‑based access to materials, while public channels (KTH website, Unite! portal) provided open information such as schedules. Workshops covered case studies of joint programmes, digital tools for collaboration, administrative challenges (double‑degrees, European Degree), large‑scale teaching strategies, and multicultural learning. Experts from the Unite! community led the sessions. Feedback highlighted the value of the intensive format, the need for hands‑on practice, the complexity of administrative procedures, and the importance of informal networking. Key lessons stress a hybrid digital environment that blends secure Metacampus collaboration with open public communication, and the necessity of a dedicated digital liaison to coordinate local and central IT tools.Recommendations for future BIPs include adopting this hybrid model, clarifying integration of local tools with Metacampus, and assigning a central facilitator to streamline technical and administrative support. The series “Unite! Digital Teaching and Learning Success Story Report” was initiated by Cm.2 Digital Campus, led by TU Graz (Martin Ebner) as part of the idea to collect, spread and enhance the possibilities, usages and lessons learned of teaching and learning offers using the federated learning management system Metacampus in 11/2024 till the end of the current Erasmus+ funding period 10/2026. All reports are available under open license and originally published at the TU Graz repository. Copyright holders are the authors.","url":"https://doi.org/10.3217/n659p-tr557","authors":["Acosta-Garcia, Marcela","Galante, Lorenzo","Kauppinen, Tomi","Keller, Elizabeth","Knutsson, Karin","Pears, Arnold","Tucker Smith, Madeleine"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3217/n659p-tr557","addedAt":"2026-08-31T06:41:19.651Z","updatedAt":"2026-08-31T06:41:19.651Z"},{"id":"doi:10.1007/978-3-031-96649-1_6","name":"Federated Edge Learning via Unmanned Aerial Vehicle","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-96649-1_6","authors":["Yong Zhou","Wenzhi Fang","Yuanming Shi","Khaled B. Letaief"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T12:52:19Z","doi":"10.1007/978-3-031-96649-1_6","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.5220/0014394500004861","name":"Optimization Study of FedDyn-Based Federated Learning Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014394500004861","authors":["Zihan Zhuo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-31T09:52:33Z","doi":"10.5220/0014394500004861","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1007/978-3-031-83157-7_13","name":"Temporal Analysis of Adversarial Attacks in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-83157-7_13","authors":["Rohit Mapakshi","Sayma Akther","Mark Stamp"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-09T03:21:59Z","doi":"10.1007/978-3-031-83157-7_13","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1002/9781394338726.ch1","name":"Harnessing the Power of Federated Learning for Agricultural Innovation","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch1","authors":["Abhishek","Mritunjay Rai","Anand Prakash Singh","Vishwanath Jha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch1","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-031-96649-1_3","name":"Second-Order Algorithm for Federated Edge Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-96649-1_3","authors":["Yong Zhou","Wenzhi Fang","Yuanming Shi","Khaled B. Letaief"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T12:52:26Z","doi":"10.1007/978-3-031-96649-1_3","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-031-96649-1_2","name":"First-Order Algorithm for Federated Edge Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-96649-1_2","authors":["Yong Zhou","Wenzhi Fang","Yuanming Shi","Khaled B. Letaief"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T12:52:28Z","doi":"10.1007/978-3-031-96649-1_2","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1201/9781003591085-5","name":"Optimizing neural disorder treatment through federated learning and multi-institutional data collaboration","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003591085-5","authors":["T Monika Singh","C Kishor Kumar Reddy","Jagadeshwari Puttanapura","Srinath Doss"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-20T10:28:47Z","doi":"10.1201/9781003591085-5","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.36227/techrxiv.175339544.49904893/v1","name":"Robust Complex-Valued Federated Learning for Secure 6G Mobile Communications","source":"crossref","abstract":"Federated learning (FL) has attracted interest as a decentralized machine learning paradigm, where multiple clients collaboratively train a shared model while retaining their raw data locally. By performing computations on-device and sending only model updates (e.g., gradients or weights) to a central server, FL preserves privacy and supports large-scale distributed training. However, the aggregation step is vulnerable to malicious participants, who can poison the global model. Moreover, most existing FL schemes employ real-valued neural networks with non-robust federated averaging for aggregation, which is illsuited to complex-valued data fundamental to wireless communication systems. For this purpose, we propose robust aggregation schemes for complex-valued FL aimed at secure sixth-generation mobile communications. Our robust aggregation method combines complex-valued projection statistics with a Schweppe-type complex-valued generalized M-estimator featuring regularized scatter matrix estimation. We treat subsets of the network weights and biases as points in high-dimensional complex-valued spaces C p , where adversarial clients produce spatial outliers. Our robust estimators are tuned for the data dimensions and take the C p-points as input. Experiments on dense urban 5G AWGN channels show that the proposed aggregator secures learning in the presence of up to 50% malicious clients, surpassing Krum and outperforming classical federated averaging.","url":"https://doi.org/10.36227/techrxiv.175339544.49904893/v1","authors":["Anders Buvarp","Stefan Werner"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-24T22:17:40Z","doi":"10.36227/techrxiv.175339544.49904893/v1","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.2139/ssrn.5325808","name":"Privacy-Preserving Intrusion Detection Systems in VANETs Using Federated Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5325808","authors":["Garba M."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-07T15:17:20Z","doi":"10.2139/ssrn.5325808","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1007/978-981-95-1394-9_7","name":"Enhanced Privacy Preserving Deep Learning Using Blockchain and Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1394-9_7","authors":["S. Usharani","P. Manju Bala","A. Balachandar","G. Glorindal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-07T13:28:15Z","doi":"10.1007/978-981-95-1394-9_7","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flta67013.2025.11336354","name":"PRODIGY: Proximity- and Dissimilarity-Based Byzantine-Robust Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336354","authors":["Sena Ergisi","Luis Maßny","Rawad Bitar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336354","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.46632/eae/4/1/3","name":"Federated Learning for Intelligent Smart Grid Systems","source":"crossref","abstract":"The evolution of conventional power networks into smart grids has been driven by the integration of digital communication, advanced sensing technologies, and intelligent control mechanisms. Machine learning techniques are increasingly employed to analyze large-scale grid data for forecasting, monitoring, and optimization purposes. However, centralized data-driven approaches raise serious concerns related to data privacy, scalability, communication burden, and cyber vulnerabilities. Federated Learning (FL) has emerged as an effective decentralized learning framework that enables multiple entities to collaboratively train machine learning models without transferring raw data to a central repository. This chapter explores the fundamentals of federated learning, its architectural integration within smart grids, major application areas, algorithmic approaches, and associated challenges. The chapter further highlights security considerations and outlines potential future research directions in this emerging field.","url":"https://doi.org/10.46632/eae/4/1/3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-18T05:39:04Z","doi":"10.46632/eae/4/1/3","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1145/3709023.3737694","name":"Efficient Model Propagation for Peer-to-Peer Federated Learning using Minimum Spanning Tree and Gossip Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3709023.3737694","authors":["Alka Luqman","Riya Mahesh","Anupam Chattopadhyay"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-15T07:07:04Z","doi":"10.1145/3709023.3737694","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1109/flta67013.2025.11336527","name":"Democratizing Federated Learning: “FL-Insight” as an Interactive Visual Demonstrator for SME Adoption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336527","authors":["Thomas Van Den Bossche","Laurens Van De Perre","Abasin Abdul Khalil"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336527","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.2139/ssrn.5261577","name":"Federated Learning Employing Adaptive Deep Rule-Based Classifier","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5261577","authors":["Dipu Saha","Md.  Dewan Farid"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-20T10:03:53Z","doi":"10.2139/ssrn.5261577","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.36227/techrxiv.174495330.08787592/v1","name":"Federated Learning: Recent Advances and Future Directions","source":"crossref","abstract":"This review examines federated learning as an innovative approach enabling distributed machine learning while preserving data privacy. We explore recent developments addressing four core challenges: data heterogeneity across participants, optimizing communication between devices, strengthening privacy protections, and accommodating diverse system capabilities. Our analysis identifies substantial algorithmic progress while highlighting persistent gaps in practical implementations across varied devices, balancing privacy with model performance, and expanding applications in vertical federated learning contexts. The survey concludes with strategic recommendations for overcoming these limitations and accelerating real-world adoption of federated learning techniques, particularly focusing on scalability across heterogeneous environments, enhanced privacy-utility balance, and improved cross-organizational implementations that could substantially expand federated learning's practical impact.","url":"https://doi.org/10.36227/techrxiv.174495330.08787592/v1","authors":["Lakshmi Indrani","Deepika Gadiraju","Vishnu Vardhan Baligodugula"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-18T01:15:30Z","doi":"10.36227/techrxiv.174495330.08787592/v1","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.2139/ssrn.5261578","name":"Federated Learning Employing Adaptive Deep Rule-Based Classifier","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5261578","authors":["Dipu Saha","Md.  Dewan Farid"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-20T10:03:58Z","doi":"10.2139/ssrn.5261578","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1145/3737899.3768527","name":"G\n                    <scp>ist</scp>\n                    - Optimizing Segmentation for Decentralized Federated Learning on Tiny Devices","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3737899.3768527","authors":["Navidreza Asadi","Halil İbrahim Bengu","Lars Wulfert","Hendrik Wöhrle","Wolfgang Kellerer"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-02T17:01:43Z","doi":"10.1145/3737899.3768527","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1002/9781394338726.ch11","name":"Federated Learning and Its Impact on Decision‐Making in Smart Agriculture","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch11","authors":["Divita Jain","Nikita Bhati","Nisha Bhardwaj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch11","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-031-96649-1_4","name":"Zeroth-Order Algorithm for Federated Edge Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-96649-1_4","authors":["Yong Zhou","Wenzhi Fang","Yuanming Shi","Khaled B. Letaief"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T12:52:19Z","doi":"10.1007/978-3-031-96649-1_4","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flta67013.2025.11336672","name":"Privacy-Preserving Intrusion Detection in Cloud-Edge Environments Using Federated Learning and Docker-Based Simulation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336672","authors":["Krishna Bindu Polisetty","Rohit Kumar Rayala","VS Aditya Lingamallu","Sambit Kumar Mishra","Deepak Puthal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336672","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1007/978-3-031-99270-4_11","name":"Federated Learning in Modern Education: Balancing Privacy, Scalability, and Effectiveness","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99270-4_11","authors":["Rita Ganatra","Monica Gahlawat","Chetan R. Dudhagara"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T05:54:58Z","doi":"10.1007/978-3-031-99270-4_11","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.31274/cc-20260223-32","name":"Federated Learning-Enhanced Security Orchestration in URLLC Framework for Tactile Industrial Systems","source":"crossref","abstract":"","url":"https://doi.org/10.31274/cc-20260223-32","authors":["Samuel Meruga"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-24T17:52:04Z","doi":"10.31274/cc-20260223-32","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1007/978-981-95-1394-9_5","name":"Federated Learning with Blockchain-Enhanced Model Explainability","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1394-9_5","authors":["Gurjot Kaur","Vikas Wasson","Simarpreet Kaur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-07T13:28:29Z","doi":"10.1007/978-981-95-1394-9_5","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.5867702","name":"Chapter 7: Federated Learning for Multi-Domain Transportation Networks","source":"crossref","abstract":"7.1. Introduction Federated&amp;nbsp; Learning&amp;nbsp; (FL)&amp;nbsp; can&amp;nbsp; potentially&amp;nbsp; break&amp;nbsp; the&amp;nbsp; framework&amp;nbsp; of&amp;nbsp; traditional&amp;nbsp; cross-domain&amp;nbsp; transportation&amp;nbsp; system&amp;nbsp; modeling&amp;nbsp; and&amp;nbsp; enable&amp;nbsp; realistic&amp;nbsp; and&amp;nbsp; timely&amp;nbsp; cross-domain transportation&amp;nbsp; cooperation.&amp;nbsp; The&amp;nbsp; fusion&amp;nbsp; modeling&amp;nbsp; of&amp;nbsp; transportation&amp;nbsp; system&amp;nbsp; in&amp;nbsp; different domains has been hotly discussed in the field of transportation in recent years. However, due to the data conflict problem caused by heterogeneous data and privacy constraints, traditional&amp;nbsp; cross-domain&amp;nbsp; cooperation&amp;nbsp; is&amp;nbsp; delayed&amp;nbsp; or&amp;nbsp; even&amp;nbsp; difficult.&amp;nbsp; The&amp;nbsp; proposed&amp;nbsp; FL framework is suitable for cross-domain modeling problems with non-IID heterogeneous data stored in devices. The novelty of establishing FL in the transportation field is that it connects&amp;nbsp; transportation&amp;nbsp; systems&amp;nbsp; across&amp;nbsp; different&amp;nbsp; domains&amp;nbsp; and&amp;nbsp; builds&amp;nbsp; cross-domaincollaborative&amp;nbsp; models&amp;nbsp; without&amp;nbsp; data&amp;nbsp; sharing.&amp;nbsp; FL&amp;nbsp; reshapes&amp;nbsp; the&amp;nbsp; data&amp;nbsp; exchange&amp;nbsp; framework between domains, realizes data privacy protection, and breaks the barrier of direct multi-domain&amp;nbsp; data&amp;nbsp; exchange&amp;nbsp; to&amp;nbsp; a&amp;nbsp; certain&amp;nbsp; extent.&amp;nbsp; The&amp;nbsp; combination&amp;nbsp; of&amp;nbsp; FL&amp;nbsp; and&amp;nbsp; cross-domain transportation&amp;nbsp; modeling&amp;nbsp; enriches&amp;nbsp; FL&amp;nbsp; modeling&amp;nbsp; applications.&amp;nbsp; The&amp;nbsp; model&amp;nbsp; infers&amp;nbsp; and assesses the wide-area transport environment, including both local and susidiary inferred data.The ultimate goal of a multi-domain FL model for the multi-modal transportation system is to guide the dynamic cross-domain interaction of transport data in a wide cycle. Insight into the availability of existing multi-domain transport models and the establishment of transport—economy&amp;nbsp; feedback&amp;nbsp; mechanisms&amp;nbsp; provides&amp;nbsp; the&amp;nbsp; basic&amp;nbsp; condition&amp;nbsp; for&amp;nbsp; the&amp;nbsp; wide-area&amp;nbsp; cooperation&amp;nbsp; of&amp;nbsp; FL&amp;nbsp; models&amp;nbsp; in&amp;nbsp; determining&amp;nbsp; cross-area&amp;nbsp; interaction.&amp;nbsp; With&amp;nbsp; the&amp;nbsp; rapid advancement of smart city construction, federated learning (FL) technology is applied in the transportation field and its impact continues to upgrade and deepen. FL technology can realize efficient model training while avoiding privacy leakage caused by the sharing of user data. Outdated training tasks or delayed responses might also arise due to limited bandwidth for model uploading and downloading.","url":"https://doi.org/10.2139/ssrn.5867702","authors":["Rama Chandra Rao Nampalli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-12T11:57:14Z","doi":"10.2139/ssrn.5867702","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1201/9781003532323-8","name":"Advanced Technologies for Federated Learning in Smart Cities and Its Use Cases","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003532323-8","authors":["G Saranya","G Indhumathi","A. Prasanth","P. Murugapandiyan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-20T18:53:47Z","doi":"10.1201/9781003532323-8","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1145/3709023.3737688","name":"LoByITFL: Low Communication Secure and Private Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3709023.3737688","authors":["Yue Xia","Maximilian Egger","Christoph Hofmeister","Rawad Bitar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-15T07:07:04Z","doi":"10.1145/3709023.3737688","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1007/978-3-031-99270-4_1","name":"Introduction to Federated Learning and Its Application in Industry","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99270-4_1","authors":["Monali Gulhane","Amit Chauhan","Nitin Rakesh","Yash Tripathi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T05:43:41Z","doi":"10.1007/978-3-031-99270-4_1","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1109/flta67013.2025.11336437","name":"FLUID: Federated Learning with Unlearning and Instant Drift-Recovery","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336437","authors":["Aman Kumar","Nisal Hemadasa","Dominik Kaaser","Stefan Schulte"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336437","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.22541/au.173750945.55606284/v1","name":"A Novel Framework for Data-Sharing Incentive Evaluation Based on Federated Learning","source":"crossref","abstract":"The incentive mechanism in federated learning is a critical area for research. Establishing a fair system to incentivise data owners to share useful data is required to encourage all data owners to actively contribute their data for model training. An effective incentive system allows all participants to efficiently train models continuously, leading to improved accuracy of the final trained federated model. This paper introduces a novel algorithm for optimising the incentive mechanism. First, clients with high-quality data are able to participate in training based on their reputation value. Next, to improve the effectiveness of local training and address the issue of performance disparity among clients, the client entrusts the high-performance fog node with the training of local data by auctioning local training assignments to it. Finally, malicious clients are eliminated from the local gradient by the global gradient aggregation algorithm. Simulation results indicate that the proposed algorithm performs more effectively than existing algorithms.","url":"https://doi.org/10.22541/au.173750945.55606284/v1","authors":["Sultan Alkhliwi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T20:31:01Z","doi":"10.22541/au.173750945.55606284/v1","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.2139/ssrn.5273038","name":"Federated Learning for Privacy-Preserving Anomaly Detection in Decentralized IoT Environments","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5273038","authors":["Muhammad A"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-29T19:09:05Z","doi":"10.2139/ssrn.5273038","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1002/9781394167760.ch14","name":"Innovative Urban Data Processing","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.ch14","authors":["Naween Kumar","Akansha Singh","Subham Sharma","Ankit Dubey","Vaibhav Saini","Krishna Kant Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.ch14","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.55248/gengpi.6.0525.1843","name":"Flood forecosting model using federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.55248/gengpi.6.0525.1843","authors":["S E SURESH","KAKARLA RUPA"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-31T12:56:12Z","doi":"10.55248/gengpi.6.0525.1843","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.2139/ssrn.5094366","name":"Analyzing Federated Learning for Privacy-Preserving AI","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5094366","authors":["Aman Khan","Navi Talib","Deepak Saroj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-05T13:51:59Z","doi":"10.2139/ssrn.5094366","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.2174/9789815322224125030006","name":"Breaking the Centralization Barrier: Exploring Decentralized Federated Learning for Vehicle Number Plate Recognition in IoV","source":"crossref","abstract":"The development of effective and safe machine learning systems for vehicle number plate recognition (VNPR) is now necessary due to the emergence of the Internet of Vehicles (IoV). However, traditional centralised techniques run into issues with data privacy, communication overhead, and centralised data access restrictions. This study explores the possibilities of decentralized federated learning for VNPR in the IoV to solve these constraints. Decentralised federated learning, which overcomes the centralization barrier, allows local model training on the edge devices of participating cars, protecting data privacy and cutting down on communication overhead. The ramifications of this paradigm change are examined in this research, including improved data privacy and security, shared intelligence, and resilience against errors and assaults. It also looks at the trade-off between performance and decentralisation while emphasising the balance attained via improved model aggregation and resource use. Additionally covered is the difficulty of consensus algorithms and blockchain-based networks, highlighting the need for further investigation and development. Decentralised federated learning has been identified as a possible strategy for overcoming the centralization barrier in VNPR systems, opening the door to the implementation of efficient, secure, and private machine learning in the IoV.","url":"https://doi.org/10.2174/9789815322224125030006","authors":["Arvind Panwar","Priyanka Gaba","Urvashi Sugandh","Navdeep Bohra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-25T10:34:14Z","doi":"10.2174/9789815322224125030006","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1109/esci63694.2025.10988162","name":"Intrusion Detection using Federated Learning with Application of Federated Dropout","source":"crossref","abstract":"","url":"https://doi.org/10.1109/esci63694.2025.10988162","authors":["Swati Shinde","Bal Virdee","Ashish Khanna","Saurabh Dhakite","Tushar Badlani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-09T17:56:12Z","doi":"10.1109/esci63694.2025.10988162","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.2139/ssrn.5367604","name":"Fedoptima: Optimizing Resource Utilization in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5367604","authors":["Zihan Zhang","Leon Wong","Blesson Varghese"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-26T23:40:20Z","doi":"10.2139/ssrn.5367604","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.2139/ssrn.5750022","name":"Privacy-Preserving Intelligence at the Edge: New Horizons in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5750022","authors":["Sampath Mandava"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-21T00:53:31Z","doi":"10.2139/ssrn.5750022","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.2139/ssrn.5269991","name":"Privhfl: A Privacy-Preserving Scheme for Hierarchical Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5269991","authors":["Bayan Alzahrani","Dejun Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-27T04:38:24Z","doi":"10.2139/ssrn.5269991","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.33407/lib.naes.id/748235","name":"Chapter IV. Artificial intelligence in management decision-making: prospects of federated learning","source":"crossref","abstract":"The theoretical foundations of the implementation of federated learning in the processes of managerial decision-making in the public sector are considered. The role of artificial intelligence in digital governance is highlighted, attention is focused on the advantages and challenges of using federated learning, in particular regarding confidentiality, data security and efficiency of information processing. An analysis of modern research is conducted and the prospects for the application of federated learning in public administration are identified. The results of the study may be useful for scientists, public servants and policymakers who seek to improve the efficiency of decision-making using innovative technologies.","url":"https://doi.org/10.33407/lib.naes.id/748235","authors":["Mariia Holovchak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-03T09:57:26Z","doi":"10.33407/lib.naes.id/748235","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1109/flta67013.2025.11336718","name":"Federated Instruction Tuning with DeepSeek: Towards Scalable and Private LLM Adaptation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336718","authors":["Fatiha Ait Baali","Addi Ait-Mlouk","Tarik Agouti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336718","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.2139/ssrn.5391173","name":"Polarization Image Enhancement Method Based on Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5391173","authors":["Meng Wang","Chang-E Ren"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-14T01:38:53Z","doi":"10.2139/ssrn.5391173","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1002/9781394271382.ch2","name":"Federated Autonomous Deep Learning for Distributed Healthcare System","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394271382.ch2","authors":["Rakesh Mohan Pujahari","Rijwan Khan","Satya Prakash Yadav"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-27T12:38:31Z","doi":"10.1002/9781394271382.ch2","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1109/flta67013.2025.11336444","name":"Insights into the Unknown: Federated Data Diversity Analysis on Molecular Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336444","authors":["Markus Bujotzek","Evelyn Trautmann","Calum Hand","Ian Hales"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336444","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.4018/979-8-3373-3306-9.ch007","name":"Enhancing Security and Insight in Federated Learning for AI-Driven Healthcare","source":"crossref","abstract":"This chapter explores the integration of Federated Learning (FL) and Explainable Artificial Intelligence (XAI) in healthcare, emphasizing their potential to revolutionize AI-driven medical solutions while preserving data privacy and building trust. It examines how FL enables collaborative model training across decentralized data sources without sharing sensitive patient information, addressing critical issues of data security and compliance. The chapter also investigates the role of XAI in enhancing interpretability and transparency in federated models, a crucial factor for clinical adoption. Through discussion of local and global explainability, real-world applications, and emerging challenges such as data heterogeneity, communication costs, and algorithmic bias, the chapter provides a comprehensive view of how FL and XAI jointly contribute to more secure, accountable, and effective AI systems in healthcare.","url":"https://doi.org/10.4018/979-8-3373-3306-9.ch007","authors":["Manju M. S."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-30T14:01:36Z","doi":"10.4018/979-8-3373-3306-9.ch007","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1016/j.neucom.2024.128579","name":"Analysis of regularized federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.neucom.2024.128579","authors":["Langming Liu","Ding-Xuan Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-24T12:13:39Z","doi":"10.1016/j.neucom.2024.128579","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1007/978-3-031-96649-1_5","name":"GNN for Optimizing RIS-Assisted Federated Edge Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-96649-1_5","authors":["Yong Zhou","Wenzhi Fang","Yuanming Shi","Khaled B. Letaief"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T12:52:18Z","doi":"10.1007/978-3-031-96649-1_5","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flta67013.2025.11336506","name":"Federated Urban Flow: A Model-Centric Benchmark for Reproducible Evaluation of Deep Learning in Federated Traffic Forecasting","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336506","authors":["Matteus V. S. Silva","Carlos A. Astudillo","Luiz F. Bittencourt"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336506","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1007/978-981-96-9223-1","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-9223-1","authors":["Mei Kobayashi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-01T12:37:32Z","doi":"10.1007/978-981-96-9223-1","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00025-x","name":"Summary and outlook","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00025-x","authors":["Xiaoxiao Li","Ziyue Xu","Huazhu Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:36Z","doi":"10.1016/b978-0-44-323641-9.00025-x","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1002/9781394167760.index","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.index","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.index","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1093/database/baaf016","name":"A comprehensive experimental comparison between federated and centralized learning","source":"crossref","abstract":"Abstract Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be quite complicated due to privacy and legal concerns of the data. In such cases, federated learning has the potential to vastly speed up the research cycle. Although federated and central learning have been compared from a theoretical perspective, an extensive experimental comparison of performances and learning behavior still lacks. We have performed a comprehensive experimental comparison between federated and centralized learning. We evaluated various classifiers on various datasets exploring influences of different sample distributions as well as different class distributions across the clients. The results show similar performances under a wide variety of settings between the federated and central learning strategies. Federated learning is able to deal with various imbalances in the data distributions. It is sensitive to batch effects between different datasets when they coincide with location, similar to central learning, but this setting might go unobserved more easily. Federated learning seems to be robust to various challenges such as skewed data distributions, high data dimensionality, multiclass problems, and complex models. Taken together, the insights from our comparison gives much promise for applying federated learning as an alternative to sharing data. Code for reproducing the results in this work can be found at: https://github.com/swiergarst/FLComparison","url":"https://doi.org/10.1093/database/baaf016","authors":["Swier Garst","Julian Dekker","Marcel Reinders"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-18T15:22:50Z","doi":"10.1093/database/baaf016","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1002/9781394167760.ch11","name":"Harnessing Federated Learning for Smart City Data Management in Cloud Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.ch11","authors":["Naween Kumar","Akansha Singh","Vaibhav Saini","Ankit Dubey","Subham Sharma","Sasmita Pathy","Krishna Kant Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.ch11","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1109/flta67013.2025.11336721","name":"Message from the General Chairs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336721","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336721","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1007/978-3-031-96649-1_7","name":"Federated Edge Learning in Multi-Cell Wireless Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-96649-1_7","authors":["Yong Zhou","Wenzhi Fang","Yuanming Shi","Khaled B. Letaief"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T12:52:29Z","doi":"10.1007/978-3-031-96649-1_7","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1201/9781003532323-9","name":"Federated Deep Learning for Cyber-Physical Systems in Real-World Scenarios","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003532323-9","authors":["P. Manjula","K T M Princy","K. Saranya","G. Uthradevi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-20T18:53:47Z","doi":"10.1201/9781003532323-9","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1002/9781394271382.ch3","name":"Intelligent Fusion","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394271382.ch3","authors":["Pankaj Kumar Jadwal","Hemant Kumar Saini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-27T16:38:31Z","doi":"10.1002/9781394271382.ch3","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.14428/esann/2025.es2025-149","name":"Resource-Aware Cooperation in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.14428/esann/2025.es2025-149","authors":["Manuel Röder","Fabian Geiger","Frank-Michael Schleif"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-15T14:43:24Z","doi":"10.14428/esann/2025.es2025-149","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.2139/ssrn.5135073","name":"Krum Federated Chain (Kfc): Using Blockchain to Defend Against Adversarial Attacks in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5135073","authors":["Mario García-Márquez","Nuria Rodríguez-Barroso","María Victoria Luzón","Francisco Herrera"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-12T12:39:31Z","doi":"10.2139/ssrn.5135073","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.1109/ippr66507.2025.11198576","name":"Lightweight Federated Learning for Secure and GDPR-Compliant Elderly Care: Lightweight Federated Learning and Cross-Modal Game Architecture","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ippr66507.2025.11198576","authors":["Xiaohu Fan","Ying Song","Wei Zhou","Xing Lu","Lingjie Tan","Mengwei Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-20T17:48:14Z","doi":"10.1109/ippr66507.2025.11198576","addedAt":"2026-08-31T06:41:19.831Z","updatedAt":"2026-08-31T06:41:19.831Z"},{"id":"doi:10.2139/ssrn.5363205","name":"Federated Learning for Privacy-Preserving Intrusion Detection in IoT-Enabled Smart Environments","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5363205","authors":["Aditya Kapoor"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-08T13:38:18Z","doi":"10.2139/ssrn.5363205","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/flta67013.2025.11336289","name":"BanditMatch: A Game-Theoretic Approach for Stable Preference Matching in Peer-to-Peer Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336289","authors":["Alka Luqman","Anupam Chattopadhyay","Zhang Ruichen","Dusit Niyato"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336289","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.21032/jhis.2025.50.1.31","name":"Comparison of Federated Learning and Fair Federated Learning for Pneumonia Patient Classification","source":"crossref","abstract":"Objectives: This study aims to compare the performance of federated learning (FL) and fair federated learning (FFL) in classifying pneumonia patients based on chest X-ray data. The primary focus is on assessing the accuracy and fairness of these models in handling imbalanced and distributed data in real-world healthcare settings.Methods: We used a large chest X-ray dataset to evaluate the performance of FL and FFL models. The models were built using the ResNet50 architecture, and experiments were conducted under both independent and identically distributed (IID) and non-IID data conditions. The FFL approach applied optimized loss functions to address data imbalance and ensure fair contribution from each client, regardless of the local data distribution.Results: Our findings indicate that FFL consistently outperforms traditional FL models, particularly in non-IID environments. The FFL model demonstrated higher accuracy in pneumonia classification, achieving a significant improvement in model fairness and performance across different client datasets. The use of the ResNet50 architecture further enhanced the model’s ability to handle complex X-ray image patterns.Conclusions: FFL offers a superior solution for handling imbalanced medical data compared to conventional FL models. Its ability to maintain fairness while improving classification accuracy makes it an ideal approach for decentralized healthcare systems, ensuring better patient outcomes while preserving data privacy.","url":"https://doi.org/10.21032/jhis.2025.50.1.31","authors":["Kyungmin Na","Dohyoung Kim","Youngho Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T00:45:04Z","doi":"10.21032/jhis.2025.50.1.31","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1098/rsos.250959/v1/review2","name":"Review for \"Breaking Interprovincial Data Silos: How Federated Learning Can Unlock Canada’s Public Health Potential\"","source":"crossref","abstract":"","url":"https://doi.org/10.1098/rsos.250959/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-14T21:10:36Z","doi":"10.1098/rsos.250959/v1/review2","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1109/flta67013.2025.11336271","name":"Fedecoselect: A Communication-Efficient Client Selection Strategy Via Predictive Similarity Estimation in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336271","authors":["Aissa Hadj Mohamed","Daniel L. Guidoni","Luis F. G. Gonzalez","Leandro A. Villas","Allan M. de Souza"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336271","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2174/9789815322224125030003","name":"Federated Learning on Wheels: A Decentralized Approach to Privacy-Enhanced Data Collection in Internet of Vehicles","source":"crossref","abstract":"Due to privacy issues and the scattered nature of data produced by vehicles, the Internet of Vehicles (IOV) poses considerable hurdles for data collecting. In this chapter, we examine the idea of “Federated Learning on Wheels” (FLoW), which provides a decentralised method for IOV data collection with a focus on privacy. FLOW makes use of the onboard computer resources of cars to carry out model training locally, making sure that private information stays on the cars and is not shared with a centralised server. This strategy overcomes the shortcomings of conventional centralised data collecting approaches while simultaneously protecting user privacy. We examine the fundamentals of federated learning and how they relate to IOV, highlighting the advantages of maintaining privacy. We also look at secure aggregation procedures and confidentiality safeguards as additional methods for privacy-enhanced data acquisition in FLOW. Additionally, we emphasise the significance of accuracy and performance issues in decentralised contexts and use examples that illustrate FLOW's usefulness. We also explore security and trust issues, talking about possible weaknesses and methods to secure the reliability of participants and model updates. We also consider how blockchain technology may be incorporated for improved security and openness. We conclude by discussing FLOW future directions, difficulties, and ethical issues in order to shed light on its possible significance and legal ramifications. Overall, this chapter clarifies the relevance of Federated Learning to Wheels as a ground-breaking approach to data collecting with increased privacy in the Internet of Vehicles.","url":"https://doi.org/10.2174/9789815322224125030003","authors":["Neha Sharma","Urvashi Sugandh","Jyoti Agarwal","Arvind Panwar","Priyanka Gaba"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-25T10:34:14Z","doi":"10.2174/9789815322224125030003","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/amlds63918.2025.11159471","name":"Adaptive Federated Learning for Personalised Affective Pain Management","source":"crossref","abstract":"","url":"https://doi.org/10.1109/amlds63918.2025.11159471","authors":["Luca Bondin","Alexiei Dingli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-16T17:32:27Z","doi":"10.1109/amlds63918.2025.11159471","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5570341","name":"Federated Learning with Logit Calibrated Distillation","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5570341","authors":["Haopeng Wang","Tong Liu","Yangguang Cui","Zhenzhe Zheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-06T13:45:57Z","doi":"10.2139/ssrn.5570341","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1201/9781003591085-12","name":"Federated learning-based diagnosis of epilepsy disease in healthcare 6.0","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003591085-12","authors":["Monalisha Pattnaik","Deepti Rani Pattanaik","Sudev Kumar Padhi","Ashirbad Mishra","Alipsa Pattnaik","Sadhu Charan Panda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-20T10:28:47Z","doi":"10.1201/9781003591085-12","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1142/9789811290695_0012","name":"Partitioned, Federated, and Active Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811290695_0012","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-29T02:07:28Z","doi":"10.1142/9789811290695_0012","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1007/978-3-031-80949-1_4","name":"Artificial Intelligence-Enabled Federated Learning Techniques in Healthcare Sector","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-80949-1_4","authors":["Shivani Sharma","Radhika Gour"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-08T17:48:41Z","doi":"10.1007/978-3-031-80949-1_4","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.61951/sciencepaperonline.202505.0035","name":"A Federated Learning Scheme based on Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.61951/sciencepaperonline.202505.0035","authors":["Yuqi GUO","Lin LI"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-31T02:35:30Z","doi":"10.61951/sciencepaperonline.202505.0035","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.36227/techrxiv.176403418.87468767/v1","name":"Federated Learning-Based Intrusion Detection System for IoT Networks in Resource-Constrained Environments","source":"crossref","abstract":"The fast increase in Internet of Things (IoT) solutions has widened the attacker window and poses a pressing requirement to find intrusion detection solutions that will create solutions that are more balanced in regard to security, efficiency, and privacy. In this paper, we suggest a lightweight Federated Learning (FL)-based Intrusion Detection System (IDS) to be used in the resource-bound IoT setup. The system encourages the elimination of centralized data collection, which would greatly decrease the chances of privacy breaches and communication overhead by making the training of the model decentralized. Differential Privacy (DP) is incorporated to ensure that model updates are not leaked to gradients, and uphold more privacy and insignificant loss in detection accuracy. Tests on the NSL-KDD and IoT-23 datasets show that the FL model can achieve almost the same performance with centralization and run on edge devices, such as the Raspberry Pi. Findings also indicate lower bandwidth, controllable energy, and realistic conclusiveness of real-life IoT implementations. The suggested solution provides a privacy-sensitive and scalable architecture for current IoT cybersecurity.","url":"https://doi.org/10.36227/techrxiv.176403418.87468767/v1","authors":["Michel Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-25T01:29:50Z","doi":"10.36227/techrxiv.176403418.87468767/v1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5359547","name":"Enhancing Data Privacy in Federated Learning: A Cryptographic Perspective","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5359547","authors":["Sophia Haoran Chen","Yaokai Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-21T18:08:23Z","doi":"10.2139/ssrn.5359547","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5210528","name":"Roadfed: A Multimodal Federated Learning System for Improving Road Safety","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5210528","authors":["Yachao Yuan","Xingyu Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-09T02:40:15Z","doi":"10.2139/ssrn.5210528","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.36227/techrxiv.174285376.63732248/v1","name":"Graph-based Federated Learning for Fault Detection in Power Distribution Systems","source":"crossref","abstract":"This paper presents a comprehensive analysis of Federated Learning (FL) applications in power grid systems, with a particular focus on edge computing implementation. We explore both centralized and decentralized FL architectures, introducing a novel graph-based approach that leverages network topology for efficient model training. The paper demonstrates how FL can enhance situational awareness, control, and protection through distributed learning at the edge, while maintaining data privacy and reducing communication overhead. Special attention is given to the detection of incipient faults, where edge devices collaboratively learn to identify equipment degradation patterns. Our validation using both supervised and unsupervised learning techniques demonstrates high classification accuracy with only local data samples, while autonomous parameter clustering can effectively distinguish fault types with minimal human intervention.","url":"https://doi.org/10.36227/techrxiv.174285376.63732248/v1","authors":["Dmitry Bandurin","Norayr Matevosyan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-24T18:03:12Z","doi":"10.36227/techrxiv.174285376.63732248/v1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.36227/techrxiv.175393442.27040019/v1","name":"Splitting Smarter: Differential Privacy for Secure Healthcare Federated Learning","source":"crossref","abstract":"Split Federated Learning (SplitFed) has emerged as a decentralized method of training ML models that enables multiple healthcare parties to collaboratively models without sharing their raw data. This method is, however, vulnerable against label inference attacks which can compromise patient privacy. In this paper, we have investigated the vulnerability of SplitFed models to label inference attacks in biomedical imaging. In addition, we have proposed a solution that incorporates differential privacy (DP) into SplitFed to protect against label inference attacks. Results indicate the efficacy of the SplitFed model under multiple conditions and found that the label inference accuracy changes from 100% (No-DP) to 0% (with DP). This indicates that integration of DP offers a robust mechanism for protecting patient privacy. Additionally, the usage of Cauchy noise in DP provides the best protection out of all noise categories.","url":"https://doi.org/10.36227/techrxiv.175393442.27040019/v1","authors":["Munirat Yetunde","Raj Shukla","Tapadhir Das"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-31T04:00:31Z","doi":"10.36227/techrxiv.175393442.27040019/v1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5637328","name":"A Personalized Federated Learning Framework for IoT Intrusion Detection","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5637328","authors":["Dawit  Dejene Bikila","Jan Čapek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-21T20:23:03Z","doi":"10.2139/ssrn.5637328","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1007/978-3-031-99270-4_10","name":"Revolutionizing Smart Transportation with Federated Learning: Applications, Challenges, and Future Directions","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99270-4_10","authors":["Rashmi Sharma","Shilpa Choudhary","Monali Gulhane","Ankita Tiwari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T06:04:39Z","doi":"10.1007/978-3-031-99270-4_10","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1002/9781394167760.ch7","name":"Federal Learning Approach for Smart Cities","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.ch7","authors":["Rahul Vadisetty"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.ch7","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5805004","name":"Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5805004","authors":["M. Saeid HaghighiFard","Sinem Coleri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-25T16:51:12Z","doi":"10.2139/ssrn.5805004","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1201/9781003591085","name":"Federated Learning for Neural Disorders in Healthcare 6.0","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003591085","authors":["Kishor Kumar Reddy C","Anindya Nag"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-20T10:28:47Z","doi":"10.1201/9781003591085","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1002/itl2.70177/v1/decision1","name":"Decision letter for \"IoT-Enabled Electric Load Prediction via Federated Label Distribution Learning\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.70177/v1/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-18T21:06:43Z","doi":"10.1002/itl2.70177/v1/decision1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5376825","name":"Digital Imprints and Mitigating Persistent Ai Biases Through Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5376825","authors":["Marko Jocic","James Davis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-02T13:42:34Z","doi":"10.2139/ssrn.5376825","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1002/itl2.70177/v2/decision1","name":"Decision letter for \"IoT-Enabled Electric Load Prediction via Federated Label Distribution Learning\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.70177/v2/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-18T21:06:43Z","doi":"10.1002/itl2.70177/v2/decision1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1098/rsos.250959/v2/review1","name":"Review for \"Breaking Interprovincial Data Silos: How Federated Learning Can Unlock Canada’s Public Health Potential\"","source":"crossref","abstract":"","url":"https://doi.org/10.1098/rsos.250959/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-14T21:10:36Z","doi":"10.1098/rsos.250959/v2/review1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.5220/0014557000005061","name":"Federated Meta-Learning Framework for Detecting Blackhole Attacks","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014557000005061","authors":["Samtony D.","K. Victor"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-31T14:48:22Z","doi":"10.5220/0014557000005061","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.21437/interspeech.2025-364","name":"Federated Learning with Feature Space Separation for Speaker Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.21437/interspeech.2025-364","authors":["Ying Meng","Zhihua Fang","Liang He"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-22T11:47:41Z","doi":"10.21437/interspeech.2025-364","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.5220/0013183000003899","name":"Secure Visual Data Processing via Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013183000003899","authors":["Pedro Santos","Tânia Carvalho","Filipe Magalhães","Luís Antunes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-25T06:22:45Z","doi":"10.5220/0013183000003899","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/trustcom66490.2025.00241","name":"Leader-Follower Federated Learning Framework: Heterogeneous Federated Learning based on Asymmetric Distillation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/trustcom66490.2025.00241","authors":["Lina Ge","Haisong Zhu","Ming Jiang","Zhe Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-02T20:42:41Z","doi":"10.1109/trustcom66490.2025.00241","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1007/978-3-031-78841-3_4","name":"A Review of Secure Gradient Compression Techniques for Federated Learning in the Internet of Medical Things","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-78841-3_4","authors":["Timoteo Kelly","Ahmad Alhonainy","Praveen Rao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-26T02:21:20Z","doi":"10.1007/978-3-031-78841-3_4","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1007/978-3-031-99270-4_4","name":"Predicting Alzheimer’s Disease: A Case Study Using Federated Learning Framework","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99270-4_4","authors":["Aman Sharma","Mohit Pal","Aditya Soni","Rajni Mohana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T05:51:59Z","doi":"10.1007/978-3-031-99270-4_4","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1201/9781003591085-10","name":"Federated machine learning and augmented reality (AR)/virtual reality (VR)-based framework for schizophrenia diagnosis and therapy","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003591085-10","authors":["Vijayakumar Ponnusamy","Nandini Manickam","Emilija Kisic","Nemanja Zdravković"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-20T10:28:47Z","doi":"10.1201/9781003591085-10","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.36227/techrxiv.174120692.21508793/v1","name":"A Selective Homomorphic Encryption Approach for Faster Privacy-Preserving Federated Learning","source":"crossref","abstract":"Federated learning is a machine learning method that supports training models on decentralized devices or servers, where each holds its local data, removing the need for data exchange. This approach is especially useful in healthcare, as it enables training on sensitive data without needing to share them. The nature of federated learning necessitates robust security precautions due to data leakage concerns during communication. To address this issue, we propose a new approach that employs selective encryption, homomorphic encryption, differential privacy, and bit-wise scrambling to minimize data leakage while achieving good execution performance. Our technique FAS (fast and secure federated learning) is used to train deep learning models on medical imaging data. We implemented our technique using the Flower framework and compared with a state-of-the-art federated learning approach that also uses selective homomorphic encryption. Our experiments were run in a cluster of eleven physical machines to create a real-world federated learning scenario on different datasets. We observed that our approach is up to 90% faster than applying fully homomorphic encryption on the model weights. In addition, we can avoid the pretraining step that is required by our competitor and can save up to 46% in terms of total execution time. While our approach was faster, it obtained similar security results as the competitor.","url":"https://doi.org/10.36227/techrxiv.174120692.21508793/v1","authors":["Abdulkadir Korkmaz","Praveen Rao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-05T15:35:27Z","doi":"10.36227/techrxiv.174120692.21508793/v1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/southeastcon56624.2025.10971272","name":"Federated Learning for Integrating Diverse Speech Biomarkers for Parkinson's Disease Classification","source":"crossref","abstract":"","url":"https://doi.org/10.1109/southeastcon56624.2025.10971272","authors":["Ruchira Pratihar","Ravi Sankar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-25T17:38:19Z","doi":"10.1109/southeastcon56624.2025.10971272","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1007/978-3-031-99270-4_6","name":"Securing Collaborative Model Training: Navigating Privacy Challenges in Federated Learning for Industrial IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99270-4_6","authors":["C. V. Suresh Babu","G. Suruthi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T05:47:32Z","doi":"10.1007/978-3-031-99270-4_6","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5385087","name":"A Federated Learning Approach for Privacy-Aware Data Sharing Across Telecom Clouds","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5385087","authors":["Feng Jian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-16T12:01:30Z","doi":"10.2139/ssrn.5385087","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5145406","name":"Dfdg: Adaptive Federated Learning for Dynamic Graph-Based Traffic Forecasting","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5145406","authors":["Muhammad Usman","Yugyung Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-19T21:39:11Z","doi":"10.2139/ssrn.5145406","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1098/rsos.250959/v1/review1","name":"Review for \"Breaking Interprovincial Data Silos: How Federated Learning Can Unlock Canada’s Public Health Potential\"","source":"crossref","abstract":"","url":"https://doi.org/10.1098/rsos.250959/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-14T21:10:36Z","doi":"10.1098/rsos.250959/v1/review1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.52783/jisem.v10i63s.13989","name":"Federated Learning for Enterprise Data Integration: Examining the Application of Federated Learning to Integrate AI Models Without Centralizing Enterprise Data","source":"crossref","abstract":"Enterprise organizations face increasing pressure to leverage distributed data assets for artificial intelligence advancement while maintaining strict data governance requirements. Conventional machine studying frameworks require the centralization of facts, which ends up in privateness dangers that aren't suited and conflicts with guidelines. As a end result, federated getting to know becomes a revolutionary architectural sample that allows collaborative model education with out sharing uncooked information. Participating entities retain complete control over sensitive information. Model parameters transmit between distributed nodes and central aggregation servers instead of underlying training examples. The federated paradigm addresses multiple interconnected challenges simultaneously. Communication efficiency requires optimization through gradient compression and extended local training intervals. Privacy preservation demands formal mathematical guarantees through differential privacy integration and secure aggregation protocols. Statistical heterogeneity across organizational boundaries necessitates personalization mechanisms accommodating divergent data distributions. Cross-silo federation patterns suit enterprise deployments where participants maintain substantial computational infrastructure. Horizontal and vertical partitioning schemes address varying data relationship configurations. Meta-learning formulations enable rapid local adaptation from shared global initializations. On top of that, the adoption of cryptographic protections, communication optimizations, and heterogeneity handling being implemented together opens up realistic ways for enterprise artificial intelligence to be integrated while still complying with data sovereignty requirements.","url":"https://doi.org/10.52783/jisem.v10i63s.13989","authors":["Tejaswi Bharadwaj Katta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-30T08:32:08Z","doi":"10.52783/jisem.v10i63s.13989","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/vtc2025-spring65109.2025.11174880","name":"A Critical Learning Period-Aware Incentive Mechanism for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/vtc2025-spring65109.2025.11174880","authors":["Thanh Linh Nguyen","Quoc-Viet Pham"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-30T17:36:40Z","doi":"10.1109/vtc2025-spring65109.2025.11174880","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/gcon65540.2025.11173299","name":"Probabilistic Label Flipping Attack in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/gcon65540.2025.11173299","authors":["Anee Sharma","Ningrinla Marchang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T17:35:05Z","doi":"10.1109/gcon65540.2025.11173299","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/ijcnn64981.2025.11227867","name":"Optimized Local Updates in Federated Learning via Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ijcnn64981.2025.11227867","authors":["Ali Murad","Bo Hui","Wei-Shinn Ku"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-14T18:46:15Z","doi":"10.1109/ijcnn64981.2025.11227867","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/urtc68753.2025.11533041","name":"Security Analysis of Federated Learning in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1109/urtc68753.2025.11533041","authors":["Jessie Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-26T19:40:24Z","doi":"10.1109/urtc68753.2025.11533041","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.64149/j.carcinog.24.3.273-291","name":"Decentralized Intelligence for Cardiac Health: A Federated Learning Approach to Privacy-Conscious Disease Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.64149/j.carcinog.24.3.273-291","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-13T08:35:49Z","doi":"10.64149/j.carcinog.24.3.273-291","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5387613","name":"Privacy-Preserving AI in Telecom: A Taxonomy and Survey of Federated Learning Applications","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5387613","authors":["Juned Kazi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-18T16:41:28Z","doi":"10.2139/ssrn.5387613","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/flta67013.2025.11336491","name":"Message from the Technical Program Chairs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336491","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336491","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/icsadl65848.2025.10933330","name":"IoT Security Enhancements in Smart Healthcare Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsadl65848.2025.10933330","authors":["Surekha Lanka","Taipida Moodhitaporn"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-28T02:32:33Z","doi":"10.1109/icsadl65848.2025.10933330","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.9734/bpi/stda/v3/3199","name":"Federated Learning and Deep Learning: A New Frontier for Real-World Applications","source":"crossref","abstract":"","url":"https://doi.org/10.9734/bpi/stda/v3/3199","authors":["Sridhar K."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-25T06:17:18Z","doi":"10.9734/bpi/stda/v3/3199","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.55606/jupti.v4i2.3990","name":"Penerapan Federated Learning dalam Keamanan Data Pengguna pada Aplikasi Mobile","source":"crossref","abstract":"In the digital era that is full of user data processing, security and privacy are crucial issues, especially in mobile applications. Federated Learning (FL) emerged as an innovative solution in maintaining data confidentiality because the model training process is carried out locally on the user's device without sending raw data to a central server. This study aims to examine the application of FL in improving user data security, evaluate its effectiveness, and analyze the challenges of its implementation in mobile environments. Through a literature study approach and simulated experiments, the results of the study show that FL is able to significantly reduce the risk of data leakage. However, limited device resources and sync issues are major challenges. This research provides important insights into FL's potential in building a more secure mobile app ecosystem.","url":"https://doi.org/10.55606/jupti.v4i2.3990","authors":["Ranto Siswanto","Muawan Bisri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-18T13:01:11Z","doi":"10.55606/jupti.v4i2.3990","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/icc52391.2025.11161276","name":"Optimizing Value of Learning in Task-Oriented Federated Meta-Learning Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc52391.2025.11161276","authors":["Bibo Wu","Fang Fang","Xianbin Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T17:34:55Z","doi":"10.1109/icc52391.2025.11161276","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/flta67013.2025.11336753","name":"Federated Survival Analysis With Differential-Privacy-Inspired Noise: Single Round Release of Kaplan-Meier Curves","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336753","authors":["Narasimha Raghavan Veeraragavan","Jan F. Nygård"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336753","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/infocomwkshps65812.2025.11152934","name":"LotusFA: a Federated Analytics System for Federated Learning of Watermarked Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/infocomwkshps65812.2025.11152934","authors":["Tao Ling","Siping Shi","Dan Wang","Yifei Zhu","Zhu Han"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T17:28:22Z","doi":"10.1109/infocomwkshps65812.2025.11152934","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.5220/0013188800003899","name":"Robust Blockchain-Based Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013188800003899","authors":["Aftab Akram","Clémentine Gritti","Mohd Halip","Nur Kamarudin","Marini Mansor","Syarifah Rahayu","Melek Önen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-25T06:22:45Z","doi":"10.5220/0013188800003899","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/icsec67360.2025.11298060","name":"Federated Learning-Based Classification of Sugarcane Leaf Diseases Using Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsec67360.2025.11298060","authors":["Panuphan Injan","Sanya Khruahong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-23T18:29:39Z","doi":"10.1109/icsec67360.2025.11298060","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1016/b978-0-443-30078-3.00006-8","name":"Real-time stroke detection based on deep learning and federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-30078-3.00006-8","authors":["Abdussalam Elhanashi","Sergio Saponara"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-21T22:21:38Z","doi":"10.1016/b978-0-443-30078-3.00006-8","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.37745/ijeats.13/vol13n32031","name":"Federated AI Observability in Multi-Cloud Microservices: A Secure and Scalable Federated Learning Perspective","source":"crossref","abstract":"As artificial intelligence (AI) becomes integral to microservices deployed across multi-cloud environments, ensuring secure and scalable observability is critical. Traditional centralized observability methods often fail to address the privacy, compliance, and performance challenges inherent to distributed AI systems. This paper presents a federated learning–based framework for AI observability that preserves data privacy and scalability across heterogeneous cloud platforms. The proposed framework decentralizes telemetry collection and analysis by integrating local observability agents with secure federated aggregation, while maintaining interoperability with modern DevOps pipelines. We evaluate the architecture through case studies in retail, healthcare, and finance sectors, demonstrating improvements in anomaly detection, regulatory compliance, and operational efficiency. Additionally, the paper examines ethical considerations such as data privacy, fairness, and transparency, and outlines future directions including edge observability, privacy-enhanced computation, and automated governance. This research provides a foundational strategy for building trustworthy and efficient observability systems tailored to AI-powered microservices within complex multi-cloud ecosystems. Traditional observability methods struggle with privacy and performance in AI-powered multi-cloud microservices. We propose a federated learning–based framework that enables decentralized telemetry monitoring while ensuring compliance and scalability. Our evaluation across healthcare, finance, and retail shows improvements in anomaly detection latency (25%), fraud detection accuracy (18%), and GDPR/HIPAA alignment. This work lays the groundwork for trustworthy and efficient AI observability in complex cloud-native ecosystems.","url":"https://doi.org/10.37745/ijeats.13/vol13n32031","authors":["Bhaskara Garnimitta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-06T15:30:37Z","doi":"10.37745/ijeats.13/vol13n32031","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.5957/tos-2025-019","name":"Federated Learning for the Maritime and Offshore Industries: Training Machine Learning Models Without Data Sharing","source":"crossref","abstract":"In the fast-evolving field of machine learning, data privacy and security are critical concerns, especially in sensitive and heavily regulated sectors such as offshore energy and maritime operations. Traditional centralized machine learning approaches require consolidating all data in a single location for model training, which can introduce substantial privacy risks and often conflict with regulatory requirements. Federated Learning (FL) allows data to remain decentralized while enabling collaborative and individual model development in industry. FL is particularly valuable for two key use cases. First, multiple companies with a shared interest in developing a generalized model can collaborate through FL while reducing data privacy concerns. Second, a single company with distributed data—whether due to regulatory restrictions or logistical challenges—can leverage FL to train a unified model across its datasets without centralizing data on one server. By keeping data localized, FL enhances privacy, regulatory compliance, and operational flexibility, making it well-suited for the needs of the maritime and offshore industries. Popularized by Google, FL not only lowers privacy concerns but also strengthens model robustness and accuracy by drawing on diverse, distributed data sources. This capability is particularly valuable in offshore and maritime applications, where varied data from different installations, vessels, and sea conditions can inform more reliable predictive insights. In this paper, we discuss the challenges and opportunities of applying Federated Learning to the offshore and maritime industries. We present an overview of different applications, including examples from recent studies and toy cases that demonstrate FL's potential for these industries. We illustrate the value of FL further through the case of a container ship navigating various seas, where we aim to predict the vessel's performance under different conditions. Additionally, we describe a working procedure using an open-source package to implement FL, tested in a real-world setting with participants retaining data locally. A server hosted by a cloud provider was used to coordinate the Federated Learning process. This example demonstrates the strength of Federated Learning in developing generalized and robust models—a crucial need in the offshore and maritime sectors, where diverse and adaptable models are essential to address complex and variable real-world conditions. By maintaining data locally for each participant, FL emerges as a promising approach for developing regulatory-compliant, data-driven solutions for critical maritime applications.","url":"https://doi.org/10.5957/tos-2025-019","authors":["Gaspard Ducamp","Peter Kim","Eivind Ruth"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-16T10:18:02Z","doi":"10.5957/tos-2025-019","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1002/9781394338726.ch6","name":"Federated Learning–Based Approach for Crop Recommendation and Market Stability in Agriculture","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch6","authors":["Saurabh Kumar","Tejasva Maurya","Mritunjay Rai","Abhishek Saxena"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch6","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/ecnct66493.2025.11172437","name":"pFedMMD:Personalized Federated Learning via Meta-Learning and Model Disentanglement","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ecnct66493.2025.11172437","authors":["Jiang Hou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-25T17:52:17Z","doi":"10.1109/ecnct66493.2025.11172437","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/icmlca66850.2025.11336345","name":"FedSent: Federated Learning-Based Privacy-Preserving English Sentiment Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlca66850.2025.11336345","authors":["Zixiang Zhao","Jie Kong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:44Z","doi":"10.1109/icmlca66850.2025.11336345","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/prml66062.2025.11160265","name":"Federated Learning-based Vehicle Edge Caching Optimization Strategy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/prml66062.2025.11160265","authors":["Yali Wang","Shouhao Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-17T17:29:48Z","doi":"10.1109/prml66062.2025.11160265","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/ncim65934.2025.11159847","name":"Multi-Agent Battlefield Game with Federated Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ncim65934.2025.11159847","authors":["Fardeen Hasib Mozumder"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-17T17:29:46Z","doi":"10.1109/ncim65934.2025.11159847","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1007/978-3-031-99270-4_9","name":"Federated Learning for Energy Optimization in the Industrial Internet of Things: Applications and Challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99270-4_9","authors":["Ankita Tiwari","Nitin Rakesh","Monali Gulhane"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T05:44:34Z","doi":"10.1007/978-3-031-99270-4_9","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.5194/egusphere-egu25-6125","name":"Federated Learning-Based Approach for Landslide Forecasting in Taiwan","source":"crossref","abstract":"Landslides pose significant risks, often causing severe property damage and, in extreme cases, loss of life due to poorly timed evacuations. Accurate forecasting is, therefore, essential. Traditional landslide studies rely heavily on satellite imagery to analyze timing and impact, often using machine learning models to process these images or predict landslides based on relevant factors. However, the lack of sufficient data significantly compromises forecasting accuracy in data-scarce regions such as remote mountainous areas or highways. Federated learning, a cutting-edge machine learning paradigm, offers a promising solution by aggregating model parameters from decentralized edge models operating in different regions. This approach allows a central model to leverage diverse, region-specific data without requiring direct data sharing, resulting in a more robust and generalized predictive capability. The framework supports edge models that process localized data varying in both temporal and volumetric dimensions, while a carefully designed parameter aggregation mechanism ensures iterative improvement of the central model. Experimental results demonstrate that federated learning enhances forecasting performance and improves accuracy, particularly in regions with limited data availability, marking a significant step forward in landslide forecasting.","url":"https://doi.org/10.5194/egusphere-egu25-6125","authors":["Po-Wu Cheng","Wen-Ping Tsai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-14T19:28:01Z","doi":"10.5194/egusphere-egu25-6125","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/ijcnn64981.2025.11228167","name":"Personalized Federated Learning Based on Fine-grained Subgraphs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ijcnn64981.2025.11228167","authors":["Jiawei Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-14T18:46:15Z","doi":"10.1109/ijcnn64981.2025.11228167","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/orss66051.2025.11121622","name":"Towards a Resilient Federated Edge Intelligence: A Testbed for Design, Analysis, and Validation of Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/orss66051.2025.11121622","authors":["Leo Janse van Rensburg","Liang Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-15T18:11:58Z","doi":"10.1109/orss66051.2025.11121622","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1007/978-3-031-80949-1_7","name":"Federated Learning in Smart Farming: Applications and Challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-80949-1_7","authors":["Shashank Gupta","Shefali Arora","Shamimul Qamar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-08T17:48:57Z","doi":"10.1007/978-3-031-80949-1_7","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5270075","name":"A Blockchain-Enabled Federated Adversarial Learning Framework for Robust Cyber Threat Intelligence Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5270075","authors":["Deepika P"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-20T16:59:36Z","doi":"10.2139/ssrn.5270075","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1145/3733965.3733968","name":"Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3733965.3733968","authors":["Md Nahid Hasan Shuvo","Moinul Hossain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-26T17:59:47Z","doi":"10.1145/3733965.3733968","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1088/2631-8695/ae3272/v1/review1","name":"Review for \"FedDW: An Adaptive Weight Aggregation Federated Learning Framework for Multi-Institutional Collaborative Polyp Segmentation\"","source":"crossref","abstract":"","url":"https://doi.org/10.1088/2631-8695/ae3272/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-31T23:57:44Z","doi":"10.1088/2631-8695/ae3272/v1/review1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.36227/techrxiv.176240289.90603538/v1","name":"Bridging Privacy and Personalization: How Federated Learning and Blockchain Are Transforming Smart Healthcare Devices","source":"crossref","abstract":"Research shows that healthcare systems increasingly struggle with challenges due to demographic changes and rising chronic disease prevalence, driving necessary transitions from conventional reactive care delivered in facilities to proactive care models built around continuous monitoring. Further, smart healthcare monitoring systems generate critical data enabling this transition, yet simultaneously introduce important ethical and regulatory concerns regarding privacy, security, and data ownership. This paper provides a comprehensive analysis of federated learning and blockchain as complementary technologies that address these challenges, since federated learning permits machine learning implementation across decentralized data sources while maintaining raw data privacy, and blockchain delivers unchangeable verification and clear audit pathways. The evolution of these technologies in healthcare contexts is analyzed, from basic monitoring systems to sophisticated deep federated learning applications integrated with blockchain verification. Research indicates that the proposed architecture achieves GDPR compliance through its multi-layered structure, comprising device systems, integration protocols, model aggregation functions, and blockchain verification methodologies, which collectively enable personalized healthcare without compromising privacy. Studies demonstrate that practical uses within cardiac monitoring, elderly care, and rehabilitation fields confirm this approach's substantial promise, while the research also resolves technical impediments associated with scalability, reliability, and data heterogeneity. The paper concludes that the intentional integration of these technologies represents a paradigm shift toward patient-driven healthcare that respects individual privacy while enhancing personalization.","url":"https://doi.org/10.36227/techrxiv.176240289.90603538/v1","authors":["Akram Gasmelseed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-06T04:21:39Z","doi":"10.36227/techrxiv.176240289.90603538/v1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/flta67013.2025.11336764","name":"Leveraging Federated Learning for Multilingual and Private Language Models via Model Clustering","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336764","authors":["Gabriel U. Talasso","Allan M. de Souza","Luis F. G. Gonzalez","Eduardo Cerqueira","Antonio A. F. Loureiro","Leandro A. Villas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336764","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5119367","name":"Enhancing Data Privacy in Cloud-Native Applications with Federated Learning","source":"crossref","abstract":"Federated learning (FL), a cutting-edge approach to machine learning, allows models to be created across distributed devices while maintaining the privacy of sensitive data on the local nodes. This paradigm is pertinent to cloudnative apps since data security and privacy are essential. Sometimes, centralized data collecting is necessary for traditional machine learning. These techniques may result in issues with central data breaches, data privacy, and regulatory compliance, including GDPR. Such issues are resolved by federated learning, which trains the models directly on dispersed networks without requiring the raw data to leave particular servers or devices. This study investigates how federated learning affects privacy and data security when incorporated into cloud-native apps. To boost trust in distributed cloud computing.","url":"https://doi.org/10.2139/ssrn.5119367","authors":["Harish Thummala","Avinash Reddy Kandlakunta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-03T08:49:22Z","doi":"10.2139/ssrn.5119367","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1016/j.measen.2024.101410","name":"Pneumonia detection from X-ray images using federated learning–An unsupervised learning approach","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.measen.2024.101410","authors":["Neeta Rana","Hitesh Marwaha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-03T18:09:38Z","doi":"10.1016/j.measen.2024.101410","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/ict65093.2025.11046314","name":"Learn Efficiently Without a Server: RIS-Aided Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ict65093.2025.11046314","authors":["Anis Elgabli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-26T17:40:25Z","doi":"10.1109/ict65093.2025.11046314","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1007/978-981-96-8353-6_8","name":"Privacy-Preserving Rheumatism Detection Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-8353-6_8","authors":["Bidita Sarkar Diba","Tasnim Jahin Mowla","Nazneen Nahar","Fahad Ahmed","Durjoy Mistry"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-31T19:42:52Z","doi":"10.1007/978-981-96-8353-6_8","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1145/3737899.3768519","name":"Efficient Federated Model Aggregation through Neural Velocity","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3737899.3768519","authors":["Gianluca Dalmasso","Pedro Porto Buarque de Gusmão","Attilio Fiandrotti","Marco Grangetto"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-02T17:01:43Z","doi":"10.1145/3737899.3768519","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1007/978-3-031-99270-4_8","name":"Revolutionizing Industry 4.0: Multi-level Federated Learning for a Dynamic Ecosystem","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99270-4_8","authors":["Rashmi Sharma","Bharat Singh","Monali Gulhane","Piyush Chauhan","Nitin Rakesh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T05:44:19Z","doi":"10.1007/978-3-031-99270-4_8","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1007/978-3-031-80949-1_6","name":"Privacy Protection for Medical Information in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-80949-1_6","authors":["Malyala Vaishnavi","Srikanth Vemuru","Afsana Khan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-08T17:48:16Z","doi":"10.1007/978-3-031-80949-1_6","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5129722","name":"Enhancing Recommendation Systems with Federated Learning for Privacy-Preserving E-Commerce Platforms","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5129722","authors":["Mohammad  Reza Mahdiani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-08T22:39:38Z","doi":"10.2139/ssrn.5129722","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.22541/au.173695875.55142963/v1","name":"PrivacyShepherd: Federated Learning for SNP-Based Sheep Breed Identification","source":"crossref","abstract":"This study presents an innovative federated learning framework that addresses the challenge of identifying the breeds of Iranian sheep using an SNP-based genotype dataset which contains the SNP values of four breeds of Iranian sheep. In the first phase of the research, an SNP selection phase is performed using the Particle Swarm Optimization algorithm to find the best subset of SNPs which will result in the best possible classification accuracy. In this phase, PSO detected 5565 SNPs among 46000 which has resulted in 98% classification accuracy. The second phase then uses a federated learning framework with the aggregation algorithm kfedAvg to train different local learning models with different private local datasets. The result achieved from this phase indicates, on average, the accuracy of 85% by the clients on the local test data.","url":"https://doi.org/10.22541/au.173695875.55142963/v1","authors":["Reza Nourmohammdi","Mohammad Hossein Moradi","Iman Behravan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-15T11:32:41Z","doi":"10.22541/au.173695875.55142963/v1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.36227/techrxiv.176159757.71660556/v1","name":"Energy-Aware Adaptive Federated Learning for IoT Security in 6G","source":"crossref","abstract":"Artificial intelligence (AI) and machine learning (ML) are widely adopted in sixth generation (6G) mobile networks. However, the deployment of AI in communication networks will require huge amounts of resources, such as computing, memory, bandwidth, and, as a result, energy. Certain use cases that are associated with resource-constrained devices, for instance, the internet of things (IoT), necessitate designing resource-aware and adaptable AI/ML techniques. In this article, a decentralized energy-aware federated learning (FL) model is proposed for IoT devices that allows the deployment of AI-based cybersecurity operations in 6G. We employ an ordered dropout (OD) mechanism to construct nested submodels from a larger neural network (NN), enabling dynamic adaptation to the energy availability of the system and reducing the overall energy footprint. The experimental evaluations show that the proposed energy-aware model extends the operational lifetime of the deployment framework from 82% to 135% for different datasets, while reducing inference time per sample by up to 50% for the smallest submodel.","url":"https://doi.org/10.36227/techrxiv.176159757.71660556/v1","authors":["Yasintha Rumesh","Pawani Porambage","Ijaz Ahmad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-27T20:39:36Z","doi":"10.36227/techrxiv.176159757.71660556/v1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.36227/techrxiv.174612128.82578829/v1","name":"Fault-Tolerant Federated Learning Framework for Edge Devices in Unstable Networks","source":"crossref","abstract":"Federated Learning (FL) has emerged as a critical paradigm for decentralized machine learning, enabling collaborative model training across edge devices, while preserving data privacy. However, the real-world deployment of edge devices faces challenges such as intermittent connectivity, resource heterogeneity, frequent client dropouts, and Byzantine failures, which severely affect the reliability and convergence of FL systems. To address these challenges, we propose REFINE (Robust and Efficient Federated learning for unstable networked edge devices), which is a fault-tolerant FL framework designed specifically for unreliable edge environments. REFINE integrates asynchronous communication, availability-aware client selection, hierarchical aggregation via Edge Aggregators (EAs), and adaptive trimmed mean filtering to mitigate malicious updates. It leverages lightweight client scoring and robust statistical techniques to prioritize reliable contributions while filtering for outliers. Extensive evaluations on CIFAR-10, FashionMNIST, and Human Activity Recognition datasets under various fault conditions, including client dropout rates up to 50%, network instability, and Byzantine attacks-demonstrate that REFINE achieves up to 11.4% higher accuracy and 30% faster convergence compared to baseline methods such as FedAvg, FedAsync, and Fed-MS, while maintaining low overhead. REFINE offers a practical and scalable solution that enables resilient and trustworthy federated learning across unstable edge networks.","url":"https://doi.org/10.36227/techrxiv.174612128.82578829/v1","authors":["Praveen Kumar Myakala","Manan Agrawal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-01T13:41:27Z","doi":"10.36227/techrxiv.174612128.82578829/v1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1088/2631-8695/ae3272/v2/review2","name":"Review for \"FedDW: An Adaptive Weight Aggregation Federated Learning Framework for Multi-Institutional Collaborative Polyp Segmentation\"","source":"crossref","abstract":"","url":"https://doi.org/10.1088/2631-8695/ae3272/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-31T23:57:44Z","doi":"10.1088/2631-8695/ae3272/v2/review2","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.36227/techrxiv.175623959.93908018/v1","name":"Federated Learning for Dynamic Route Optimization in SAP Transportation Management: Pioneering Sustainable Logistics","source":"crossref","abstract":"In the face of rising environmental concerns and complex global logistics, SAP Transportation Management (TM) is pivotal for planning and executing efficient transportation operations. This research introduces a novel framework leveraging federated learning (FL) to enable dynamic route optimization in SAP TM, ensuring sustainable logistics while preserving data privacy across decentralized fleets. By training models locally on edge devices, the framework optimizes routes in real-time, minimizes carbon emissions, and adapts to disruptions like traffic or weather. A synthetic dataset of 200,000 TM transactions simulates real-world complexities, including multilingual data and disruptions. The analysis achieves 94% route optimization accuracy, reducing emissions by 55% compared to traditional methods. Visualizations, including emission heatmaps and performance graphs, guide sustainable logistics strategies. This approach tackles data privacy, scalability, and SAP integration, offering a transformative solution for ecofriendly transportation management.","url":"https://doi.org/10.36227/techrxiv.175623959.93908018/v1","authors":["Srinivas Raju Gottimukkala"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-26T20:19:58Z","doi":"10.36227/techrxiv.175623959.93908018/v1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.21203/rs.3.rs-5691431/v1","name":"Federated Learning and Explainable AI for Personalized Healthcare in Resource-Limited Settings","source":"crossref","abstract":"Abstract Artificial Intelligence (AI) has transformed healthcare, significantly advancing diagnostic tools, treatment methodologies, and personalized care systems. Despite these advancements, the adoption of AI in resource-constrained environments faces persistent barriers, including data privacy concerns, limited computational resources, and the need for interpretable models. This paper introduces an innovative federated learning framework, integrated with Explainable AI (XAI), to tackle these challenges. The framework enables collaborative training across distributed healthcare institutions while safeguarding patient data privacy and offering clinical decision-making transparency. Additionally, it is optimized for low-resource environments and effectively processes multi-modal healthcare data. Experimental results indicate that the proposed model outperforms conventional AI systems in predictive accuracy, communication efficiency, and interpretability. This work emphasizes the importance of scalable, secure, and interpretable AI solutions in advancing personalized medicine globally, particularly for diverse and underserved populations.","url":"https://doi.org/10.21203/rs.3.rs-5691431/v1","authors":["Milad Rahmati"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-23T06:09:16Z","doi":"10.21203/rs.3.rs-5691431/v1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1016/j.future.2024.107672","name":"PHiFL-TL: Personalized hierarchical federated learning using transfer learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.future.2024.107672","authors":["Afsaneh Afzali","Pirooz Shamsinejadbabaki"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-09T02:56:22Z","doi":"10.1016/j.future.2024.107672","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.2139/ssrn.5799977","name":"Learning from Tabular Data Silos without Data Sharing: A Contrastive Federated Learning Approach","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5799977","authors":["Achmad Ginanjar","Xue Li","Priyanka Singh","Wen Hua"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-24T22:39:59Z","doi":"10.2139/ssrn.5799977","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1109/itw62417.2025.11240449","name":"Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/itw62417.2025.11240449","authors":["Maximilian Egger","Rawad Bitar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-21T18:39:40Z","doi":"10.1109/itw62417.2025.11240449","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.1002/9781394338726.ch14","name":"Federated Learning for Crop Yield Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch14","authors":["Gangadhara Doggalli","E. Santhoshini","R. Sujitha","G.R. Vishwas Gowda","Kavya","N.S. Gouthami","Oinam Bobochand Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch14","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/aero63441.2025.11068669","name":"Federated Learning for Low-Latency Emitter Identification from Space","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aero63441.2025.11068669","authors":["Max Cui-Stein","Binoy Kurien"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-14T17:40:12Z","doi":"10.1109/aero63441.2025.11068669","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:19.832Z"},{"id":"doi:10.36227/techrxiv.176237990.05609950/v1","name":"Adversarial Machine Learning for Enhanced Security and Byzantine Resilience in Federated Learning Architectures: A Comprehensive Review","source":"crossref","abstract":"Federated Learning (FL) has rapidly emerged as a foundational paradigm shift in Artificial Intelligence (AI), specifically designed to address stringent privacy and data governance demands. Despite its inherent privacy-by-design architecture, FL is fundamentally threatened by a complex array of adversarial attacks. This comprehensive review provides an in-depth analysis of the adversarial landscape in FL and systematically evaluates the corresponding defense mechanisms. The report is structured to address the interdisciplinary requirements of integrity (robust aggregation), confidentiality (privacy-preserving cryptography), and system robustness (network dynamics and efficiency).","url":"https://doi.org/10.36227/techrxiv.176237990.05609950/v1","authors":["Maharshi S Patel","Gayatri S Pandi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-05T21:58:31Z","doi":"10.36227/techrxiv.176237990.05609950/v1","addedAt":"2026-08-31T06:41:19.832Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.5281/zenodo.21701383","name":"Hybrid Machine Learning Based Integrated Network  Scanning And Anomaly Framework","source":"datacite","abstract":"Modern networks produced enormous volumes of data which made them increasingly vulnerable to advanced cyber threats. Traditional scanning tools and standalone anomaly detection systems fell short in identifying evolving or zero day attacks. This study proposed a hybrid framework that integrated active network scanning with machine learning based anomaly detection to deliver an adaptive and automated solution. The framework combined Nmap based host scanning tcpdump based traffic capture and Zeek based feature extraction with a Random Forest classifier and an autoencoder trained on normal traffic to flag deviations. Recent advancements in supervised and unsupervised anomaly detection together with integrated intrusion detection systems published between 2020 and 2025 were reviewed and synthesized to position the proposed approach within the broader field. Experimental comparison across five learning models showed that the Convolutional Neural Network achieved the highest accuracy of 93 percent followed by Long Short Term Memory at 91 percent while Random Forest balanced accuracy and interpretability at 89 percent. The hybrid correlation mechanism that combined scan derived signals with model predictions reduced false alarms and strengthened real time threat visibility compared with single method systems. The study concluded that combining active scanning with machine learning significantly improved detection accuracy reduced false positives and enabled actionable reporting for security analysts. Future directions identified included adaptive learning methods lightweight models suited for edge devices and secure distributed detection through federated learning.","url":"https://doi.org/10.5281/zenodo.21701383","authors":["Sarthak Jain","Suyash","Upendra Kumar","Utsav Chauhan","Ashish Kumar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21701383","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.21701384","name":"Hybrid Machine Learning Based Integrated Network  Scanning And Anomaly Framework","source":"datacite","abstract":"Modern networks produced enormous volumes of data which made them increasingly vulnerable to advanced cyber threats. Traditional scanning tools and standalone anomaly detection systems fell short in identifying evolving or zero day attacks. This study proposed a hybrid framework that integrated active network scanning with machine learning based anomaly detection to deliver an adaptive and automated solution. The framework combined Nmap based host scanning tcpdump based traffic capture and Zeek based feature extraction with a Random Forest classifier and an autoencoder trained on normal traffic to flag deviations. Recent advancements in supervised and unsupervised anomaly detection together with integrated intrusion detection systems published between 2020 and 2025 were reviewed and synthesized to position the proposed approach within the broader field. Experimental comparison across five learning models showed that the Convolutional Neural Network achieved the highest accuracy of 93 percent followed by Long Short Term Memory at 91 percent while Random Forest balanced accuracy and interpretability at 89 percent. The hybrid correlation mechanism that combined scan derived signals with model predictions reduced false alarms and strengthened real time threat visibility compared with single method systems. The study concluded that combining active scanning with machine learning significantly improved detection accuracy reduced false positives and enabled actionable reporting for security analysts. Future directions identified included adaptive learning methods lightweight models suited for edge devices and secure distributed detection through federated learning.","url":"https://doi.org/10.5281/zenodo.21701384","authors":["Sarthak Jain","Suyash","Upendra Kumar","Utsav Chauhan","Ashish Kumar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21701384","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2507.00230","name":"PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction","source":"datacite","abstract":"Reconstructing high-quality images from low-resolution inputs using Residual Dense Spatial Networks (RDSNs) is crucial yet challenging. It is even more challenging in centralized training where multiple collaborating parties are involved, as it poses significant privacy risks, including data leakage and inference attacks, as well as high computational and communication costs. We propose a novel Privacy-Preserving Federated Learning-based RDSN (PPFL-RDSN) framework specifically tailored for encrypted lossy image reconstruction. PPFL-RDSN integrates Federated Learning (FL), local differential privacy, and robust model watermarking techniques to ensure that data remains secure on local clients/devices, safeguards privacy-sensitive information, and maintains model authenticity without revealing underlying data. Empirical evaluations show that PPFL-RDSN achieves comparable performance to the state-of-the-art centralized methods while reducing computational burdens, and effectively mitigates security and privacy vulnerabilities, making it a practical solution for secure and privacy-preserving collaborative computer vision applications.","url":"https://doi.org/10.48550/arxiv.2507.00230","authors":["He, Peilin","Joshi, James"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.00230","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.82497/aitsde.2025.1218507","name":"Deep Learning for Enhancing IoT Security and Trust","source":"datacite","abstract":"The Internet of Things (IoT) has grown into a massive ecosystem connecting billions of heterogeneous devices, from household sensors to industrial machinery, and is expected to exceed 75 billion nodes by 2025. This rapid expansion has introduced critical challenges related to data security, privacy, and device trustworthiness. IoT networks are highly vulnerable to a wide range of attacks, including unauthorized access, denial-of-service, spoofing, and data tampering. Traditional security solutions such as static authentication, encryption, and rule-based intrusion detection have proven insufficient to address the dynamic and complex nature of IoT threats. In this context, deep learning (DL) has emerged as a promising approach for anomaly detection and intrusion prevention, offering the ability to automatically learn patterns in high-dimensional IoT data. This paper surveys recent advances in DL-based IoT security and emphasizes the importance of integrating trust management mechanisms with anomaly detection. By assigning dynamic trust scores to IoT nodes based on their historical behavior and anomaly likelihood, the proposed framework enhances resilience and reduces false positives compared to traditional intrusion detection systems. The research leverages LSTM-based models to detect abnormal traffic and employs datasets such as NSL-KDD and CICIDS2017 for validation. Comparative analysis with conventional machine learning techniques (e.g., SVM, Random Forest) demonstrates superior performance in terms of accuracy, precision, recall, F1-score, and false alarm rate. The study highlights existing research gaps, including energy efficiency and lightweight model deployment, and concludes by proposing future directions such as blockchain integration and federated learning for scalable, secure IoT networks.","url":"https://doi.org/10.82497/aitsde.2025.1218507","authors":["Alireza Rahimi pour anaraki"],"tags":["IoT Security","Trust Management","Deep Learning","Intrusion Detection","Long Short-Term Memory","Convolutional Neural Network","Graph Neural Network"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.82497/aitsde.2025.1218507","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.21655486","name":"EGBP Bioenergy Grid Physically-Calibrated Synthetic Dataset — 15 nodes, 365 days, hourly resolution","source":"datacite","abstract":"131,400 node-hour observations across 15 bioenergy grid nodes (6 anaerobic digesters, 5 gasifiers, 4 CHP units) over 365 days at hourly resolution. Parameters calibrated to Faaij (2006) and Atashbar et al. (2016). Generated for: Ethical Guardrail Bandit Pruning (EGBP). This repository supports the manuscript \"Ethical Guardrail Bandit Pruning (EGBP): A Framework for Sustainable and Equitable AI-Driven Resource Allocation in Bioenergy Grids\" (under review). EGBP treats computational efficiency, energy sustainability, and distributional equity as simultaneously binding optimisation constraints rather than sequentially addressed objectives. The central theoretical contribution demonstrates that embedding ethical guardrails directly in the optimisation objective, rather than evaluating them post-hoc preserves the asymptotic regret properties of Thompson Sampling bandit learning while guaranteeing convergence to an ethically-admissible stationary point. The framework integrates four tightly coupled mechanisms: (i) a hybrid bandit-gradient importance estimator combining offline Transformer pre-training with online Thompson Sampling; (ii) an ethical guardrail buffer enforcing differentiable penalties on energy overconsumption and distributional inequity through a Gini-coefficient regulariser applied to physical energy budget allocations; (iii) a cost-weighted magnitude pruning operator coupling gradient sparsity to real-time biomass conversion telemetry; and (iv) a guardrail-filtered federated averaging scheme with analytically bounded exclusion fraction. Theoretical properties are established through three theorems, three propositions, and two corollaries covering energy guardrail self-correction, Gini penalty convexity, Thompson Sampling regret preservation, EGBP convergence, and federated fairness monotonicity. Simulation on a 15-node bioenergy grid over 50,000 training steps demonstrates 38% reduction in energy consumption and 40% improvement in distributional fairness relative to three competitive baselines. This repository contains derived simulation outputs and pipeline code for reproducibility.","url":"https://doi.org/10.5281/zenodo.21655486","authors":["Moroke, Ntebogang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21655486","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2607.19403","name":"Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets","source":"datacite","abstract":"Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled synthetic environments and deployment in real multicenter healthcare settings. A prior architectural study by the same authors (Tertulino and Alencar, 2026) demonstrated, on a synthetic six-feature benchmark, that server-side adaptive optimization acts as a temporal denoiser for Differential Privacy noise, answering an open challenge identified in the original pipeline work (Tertulino, 2025). That study used synthetically generated data and explicitly identified real-world validation as a priority future direction. The present work addresses this gap by validating the FedCVR framework on five publicly available real cardiovascular datasets (Framingham, Cleveland, Hungarian, Switzerland, and Long Beach VA), harmonized to the 13-attribute UCI Heart Disease schema and configured as a heterogeneous federated scenario with leave-one-institution-out cross-validation. Results demonstrate that FedCVR preserves its adaptive advantage on real data, achieving an F1-Score of 79.2% and AUC of 0.96 under the operational privacy budget (noise multiplier = 0.8, privacy budget epsilon approximately 4.2), while statistically outperforming standard FedAvg on all evaluated metrics (paired t-tests, all p &lt;= 0.003, significant under the Bonferroni-corrected threshold). The measured privacy cost on real data confirms the graceful degradation pattern observed in the synthetic experiments, providing empirical evidence of the framework's clinical viability in genuine multicenter contexts.","url":"https://doi.org/10.48550/arxiv.2607.19403","authors":["Tertulino, Rodrigo","Alencar, Laercio","Almeida, Ricardo"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","Computers and Society (cs.CY)","FOS: Computer and information sciences","I.2.6; J.3; K.4.1"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.19403","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2603.21596","name":"In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis","source":"datacite","abstract":"The rapid expansion of the Internet of Things (IoT) and its integration with backbone networks have heightened the risk of security breaches. Traditional centralized approaches to anomaly detection, which require transferring large volumes of data to central servers, suffer from privacy, scalability, and latency limitations. This paper proposes a lightweight autoencoder-based anomaly detection framework designed for deployment on resource-constrained edge devices, enabling real-time detection while minimizing data transfer and preserving privacy. Federated learning is employed to train models collaboratively across distributed devices, where local training occurs on edge nodes and only model weights are aggregated at a central server. A real-world IoT testbed using Raspberry Pi sensor nodes was developed to collect normal and attack traffic data. The proposed federated anomaly detection system, implemented and evaluated on the testbed, demonstrates its effectiveness in accurately identifying network attacks. The communication overhead was reduced significantly while achieving comparable performance to the centralized method.","url":"https://doi.org/10.48550/arxiv.2603.21596","authors":["Chaudhary, Devashish","Rajasegarar, Sutharshan","Pokhrel, Shiva Raj","Pan, Lei","D, Ruby"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.21596","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2607.06963","name":"Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies","source":"datacite","abstract":"Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks. These technologies power real-time threat detection, phishing defense, secure code generation, and vulnerability exploitation at unprecedented scales. Following a rapid surge where LLM-generated malware grew to account for an estimated 50% of detected threats by 2025, up from just 2% in 2021, navigating this highly automated threat landscape in 2026 demands next-generation security frameworks. This paper presents a comprehensive survey of the beneficial and malicious applications of LLMs in cybersecurity, including zero-day detection, DevSecOps, federated learning, synthetic content analysis, and explainable AI (XAI). Drawing on a review of over 70 academic papers, industry reports, and technical documents, this work synthesizes insights from real-world case studies across platforms like Google Play Protect, Microsoft Defender, Amazon Web Services (AWS), Apple App Store, OpenAI Plugin Stores, Hugging Face Spaces, and GitHub, alongside emerging initiatives like the SAFE Framework and AI-driven anomaly detection. We conclude with practical recommendations for responsible and transparent LLM deployment and trustworthy AI, including model watermarking, adversarial defense, and cross-industry collaboration, setting a new benchmark for rigorous, holistic cybersecurity research at the intersection of AI and threat defense, and offering a roadmap for secure, scalable LLM systems that serves as a critical reference for researchers, engineers, and security leaders navigating the complex challenges of AI-driven cybersecurity.","url":"https://doi.org/10.48550/arxiv.2607.06963","authors":["Ahi, Kiarash","Valizadeh, Saeed"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Computation and Language (cs.CL)","FOS: Computer and information sciences","K.6.5; I.2.7"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.06963","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2607.08797","name":"Wireless Decentralized Federated Learning via Device Clustering and Inter-Cluster Link Enhancement","source":"datacite","abstract":"Decentralized federated learning (DFL) dispenses with the central server of classical FL by utilizing peer-to-peer model exchanges among edge devices. This server-free architecture enables ad-hoc, flexible distributed learning in large device-to-device (D2D) networks. However, wireless DFL converges slowly because peer-to-peer model aggregation incurs high delays and errors. Each DFL training round involves many-to-many gradient sharing over wireless channels, resulting in uncoordinated channel access, large communication errors from stragglers, and slow model consensus, especially in large-scale D2D networks with pronounced clustering structures. We address these aggregation bottlenecks by provisioning a few reliable backhaul links at straggling nodes to enhance network connectivity. Building on this idea, our budget-aware, cluster-centric DFL framework first partitions the network into densely connected clusters, and then allocates the limited backhaul budget to selected cluster heads. The resulting two-tier protocol executes fast, parallel model aggregation within clusters and infrequent inter-cluster exchanges among the heads, yielding an O(1/t) convergence rate in t iterations. Numerical experiments on image-classification tasks confirm that our approach accelerates convergence compared to state-of-the-art DFL baselines with only a few strategically placed backhaul links.","url":"https://doi.org/10.48550/arxiv.2607.08797","authors":["Zheng, William Weijia","Liu, Hang","Zhang, Ying-Jun Angela"],"tags":["Information Theory (cs.IT)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.08797","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2409.06067","name":"MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning","source":"datacite","abstract":"Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients. In light of the recent advances in multimodal large language models (MLLMs), such as GPT-4v and LLaVA, which demonstrate their exceptional proficiency in multimodal tasks, such as image captioning and multimodal question answering. We introduce a novel federated learning framework, named Multimodal Large Language Model Assisted Federated Learning (MLLM-LLaVA-FL), which employs powerful MLLMs at the server end to address the heterogeneous and long-tailed challenges. Owing to the advanced cross-modality representation capabilities and the extensive open-vocabulary prior knowledge of MLLMs, our framework is adept at harnessing the extensive, yet previously underexploited, open-source data accessible from websites and powerful server-side computational resources. Hence, the MLLM-LLaVA-FL not only enhances the performance but also avoids increasing the risk of privacy leakage and the computational burden on local devices, distinguishing it from prior methodologies. Our framework has three key stages. Initially, we conduct global visual-text pretraining of the model. This pretraining is facilitated by utilizing the extensive open-source data available online, with the assistance of MLLMs. Subsequently, the pretrained model is distributed among various clients for local training. Finally, once the locally trained models are transmitted back to the server, a global alignment is carried out under the supervision of MLLMs to further enhance the performance. Experimental evaluations on established benchmarks, show that our framework delivers promising performance in the typical scenarios with data heterogeneity and long-tail distribution across different clients in FL.","url":"https://doi.org/10.48550/arxiv.2409.06067","authors":["Zhang, Jianyi","Yang, Hao Frank","Li, Ang","Guo, Xin","Wang, Pu","Wang, Haiming","Chen, Yiran","Li, Hai"],"tags":["Artificial Intelligence (cs.AI)","Computation and Language (cs.CL)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.06067","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.15336998","name":"UAVIDS-2025: A Benchmark Dataset for Intrusion Detection in UAV Networks Using Machine Learning Techniques","source":"datacite","abstract":"UAVIDS-2025 is a comprehensive benchmark dataset designed for evaluating intrusion detection systems (IDS) in UAV (Unmanned Aerial Vehicle) swarm networks. The dataset was generated through extensive simulations using the NS-3.24 network simulator, with realistic UAV mobility modeled by an extended BOID algorithm. It includes 122,171 labeled flow records across five traffic categories: Normal, Blackhole, Flooding, Sybil, and Wormhole attacks. Each data sample represents a network flow characterized by 22 features, grouped into connection, traffic volume, and performance metrics. The simulations were configured with IEEE 802.11ac wireless standards, AODV routing, and a Nakagami channel model to ensure realism. This dataset enables the evaluation of machine learning-based IDS under various scenarios, including imbalanced attack distributions and swarm mobility. The dataset supports research in: Supervised/unsupervised intrusion detection Federated learning and decentralized security Adversarial robustness and synthetic data generation UAVIDS-2025 is intended to provide a reproducible, scalable, and diverse testbed for the research community working on the security of UAV networks. If you are using our dataset, you should cite our related paper which outlining the details of the dataset and its underlying principles: @inproceedings{zeng2025uavids, title={Uavids-2025: A benchmark dataset for intrusion detection in uav networks using machine learning techniques}, author={Zeng, Qingli and Bashir, Abdalrahman and Nait-Abdesselam, Farid}, booktitle={2025 IEEE Conference on Communications and Network Security (CNS)}, pages={1--9}, year={2025}, organization={IEEE}} @inproceedings{zeng2026fedgraph, title={FedGraph-ID: A Federated Graph Learning Framework for Intrusion Detection in UAV Networks Under Adversarial Settings}, author={Zeng, Qingli and Fu, Yinjin and Nait-Abdesselam, Farid}, booktitle={IEEE INFOCOM 2026-IEEE Conference on Computer Communications}, pages={1--10}, year={2026}, organization={IEEE} }","url":"https://doi.org/10.5281/zenodo.15336998","authors":["Zeng, Qingli","Bashir, Abdalrahman","Nait-Abdesselam, Farid"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15336998","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.15336997","name":"UAVIDS-2025: A Benchmark Dataset for Intrusion Detection in UAV Networks Using Machine Learning Techniques","source":"datacite","abstract":"UAVIDS-2025 is a comprehensive benchmark dataset designed for evaluating intrusion detection systems (IDS) in UAV (Unmanned Aerial Vehicle) swarm networks. The dataset was generated through extensive simulations using the NS-3.24 network simulator, with realistic UAV mobility modeled by an extended BOID algorithm. It includes 122,171 labeled flow records across five traffic categories: Normal, Blackhole, Flooding, Sybil, and Wormhole attacks. Each data sample represents a network flow characterized by 22 features, grouped into connection, traffic volume, and performance metrics. The simulations were configured with IEEE 802.11ac wireless standards, AODV routing, and a Nakagami channel model to ensure realism. This dataset enables the evaluation of machine learning-based IDS under various scenarios, including imbalanced attack distributions and swarm mobility. The dataset supports research in: Supervised/unsupervised intrusion detection Federated learning and decentralized security Adversarial robustness and synthetic data generation UAVIDS-2025 is intended to provide a reproducible, scalable, and diverse testbed for the research community working on the security of UAV networks. If you are using our dataset, you should cite our related paper which outlining the details of the dataset and its underlying principles: @inproceedings{zeng2025uavids, title={Uavids-2025: A benchmark dataset for intrusion detection in uav networks using machine learning techniques}, author={Zeng, Qingli and Bashir, Abdalrahman and Nait-Abdesselam, Farid}, booktitle={2025 IEEE Conference on Communications and Network Security (CNS)}, pages={1--9}, year={2025}, organization={IEEE}} @inproceedings{zeng2026fedgraph, title={FedGraph-ID: A Federated Graph Learning Framework for Intrusion Detection in UAV Networks Under Adversarial Settings}, author={Zeng, Qingli and Fu, Yinjin and Nait-Abdesselam, Farid}, booktitle={IEEE INFOCOM 2026-IEEE Conference on Computer Communications}, pages={1--10}, year={2026}, organization={IEEE} }","url":"https://doi.org/10.5281/zenodo.15336997","authors":["Zeng, Qingli","Bashir, Abdalrahman","Nait-Abdesselam, Farid"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15336997","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21131899","name":"Revolutionizing Drug Discovery and Pharmacovigilance Through Artificial Intelligence:A Comprehensive Systematic Review of Machine Learning Architectures, Real‎ ‎ World Evidence Integration, and Regulatory Compliance","source":"datacite","abstract":"The integration of artificial intelligence (AI) and machine learning (ML) into pharmaceutical research has engendered a paradigm shift in how novel therapeutics are identified, optimized, and monitored. Conventional drug discovery pipelines spanning 10–15 years and averaging $2.6 billion in development costs are increasingly augmented by AI‎ ‎ driven platforms capable of screening billions of molecular entities in silico, predicting multi‎ ‎ target pharmacological profiles, and mining longitudinal patient data for post‎ ‎ marketing safety signals. This convergence of computational intelligence with biomedical science has given rise to a new era of precision pharmacology, wherein therapeutic hypotheses are generated, tested, and refined at unprecedented speed and mechanistic depth.This systematic review critically appraises the current state of AI/ML applications across the full drug discovery and development continuum—from target identification through clinical trial optimization—and evaluates the integration of real‎ ‎ world data (RWD) and real‎ ‎ world evidence (RWE) in post‎ ‎ market pharmacovigilance systems. A secondary objective is to survey the evolving regulatory landscape governing AI‎ ‎ assisted pharmaceutical submissions across major international jurisdictions.A comprehensive literature search was conducted across PubMed, Embase, Web of Science, IEEE Xplore, and Scopus (January 2015–March 2025) using MeSH terms and Boolean operators encompassing AI, ML, deep learning, generative models, drugdiscovery, pharmacovigilance, real‎ ‎ world evidence, explainability, and regulatory compliance. After removal of duplicates and application of inclusion/exclusion criteria per PRISMA 2020 guidelines, 214 studies, 18 regulatory guidance documents, and 11 systematic reviews were included. Graph neural networks (GNNs) and transformer‎ ‎ based architectures achieved state‎ ‎ of‎ ‎ the‎ ‎ art performance in molecular property prediction and drug–target interaction modeling, with area‎ ‎ under‎ ‎ the‎ ‎ ROC‎ ‎ curve (AUC‎ ‎ ROC) values exceeding 0.92 across multiple benchmark datasets. Generative AI platforms including variational autoencoders (VAEs) and diffusion models successfully produced novel scaffolds with target‎ ‎ specific binding and favorable ADMET profiles. In pharmacovigilance, NLP‎ ‎ based systems demonstrated precision–recall F1 scores of 0.81–0.93 for adverse drug event (ADE) extraction from electronic health records (EHRs) and social media, outperforming traditional disproportionality analyses. Federated learning frameworks enabled multi‎ ‎ institutional RWD harmonization without compromising patient privacy. Regulatory acceptance of AI‎ ‎ derived evidence is accelerating, with the US FDA, EMA, ICH, and PMDA issuing substantive guidance; however, persistent gaps in explainability, algorithmic bias auditing, and cross‎ ‎ jurisdictional harmonization remain.AI/ML technologies are demonstrably transforming drug discovery and pharmacovigilance, offering scalable solutions to longstanding bottlenecks in pharmaceutical R&D. Realizing the full translational potential of these technologies requires coordinated advances in model interpretability, data governance, federated infrastructure, and adaptive regulatory frameworks. This review provides a structured synthesis for researchers, clinicians, and regulatory scientists navigating the rapidly evolving AI‎ ‎ pharma interface.","url":"https://doi.org/10.5281/zenodo.21131899","authors":["Darshan K R*1, Shubham Shivangekar2, Yash Vispute3, Gayatri Dhamane4, Parth Thorat5, Prathamesh Chavan6"],"tags":["Artificial intelligence; drug discovery; pharmacovigilance; machine learning; deep learning; real‎ ‎ world evidence; natural language processing; graph neural networks; explainable AI; regulatory compliance; adverse drug events; generative models; federated learning; ADMET prediction."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21131899","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.21131900","name":"Revolutionizing Drug Discovery and Pharmacovigilance Through Artificial Intelligence:A Comprehensive Systematic Review of Machine Learning Architectures, Real‎ ‎ World Evidence Integration, and Regulatory Compliance","source":"datacite","abstract":"The integration of artificial intelligence (AI) and machine learning (ML) into pharmaceutical research has engendered a paradigm shift in how novel therapeutics are identified, optimized, and monitored. Conventional drug discovery pipelines spanning 10–15 years and averaging $2.6 billion in development costs are increasingly augmented by AI‎ ‎ driven platforms capable of screening billions of molecular entities in silico, predicting multi‎ ‎ target pharmacological profiles, and mining longitudinal patient data for post‎ ‎ marketing safety signals. This convergence of computational intelligence with biomedical science has given rise to a new era of precision pharmacology, wherein therapeutic hypotheses are generated, tested, and refined at unprecedented speed and mechanistic depth.This systematic review critically appraises the current state of AI/ML applications across the full drug discovery and development continuum—from target identification through clinical trial optimization—and evaluates the integration of real‎ ‎ world data (RWD) and real‎ ‎ world evidence (RWE) in post‎ ‎ market pharmacovigilance systems. A secondary objective is to survey the evolving regulatory landscape governing AI‎ ‎ assisted pharmaceutical submissions across major international jurisdictions.A comprehensive literature search was conducted across PubMed, Embase, Web of Science, IEEE Xplore, and Scopus (January 2015–March 2025) using MeSH terms and Boolean operators encompassing AI, ML, deep learning, generative models, drugdiscovery, pharmacovigilance, real‎ ‎ world evidence, explainability, and regulatory compliance. After removal of duplicates and application of inclusion/exclusion criteria per PRISMA 2020 guidelines, 214 studies, 18 regulatory guidance documents, and 11 systematic reviews were included. Graph neural networks (GNNs) and transformer‎ ‎ based architectures achieved state‎ ‎ of‎ ‎ the‎ ‎ art performance in molecular property prediction and drug–target interaction modeling, with area‎ ‎ under‎ ‎ the‎ ‎ ROC‎ ‎ curve (AUC‎ ‎ ROC) values exceeding 0.92 across multiple benchmark datasets. Generative AI platforms including variational autoencoders (VAEs) and diffusion models successfully produced novel scaffolds with target‎ ‎ specific binding and favorable ADMET profiles. In pharmacovigilance, NLP‎ ‎ based systems demonstrated precision–recall F1 scores of 0.81–0.93 for adverse drug event (ADE) extraction from electronic health records (EHRs) and social media, outperforming traditional disproportionality analyses. Federated learning frameworks enabled multi‎ ‎ institutional RWD harmonization without compromising patient privacy. Regulatory acceptance of AI‎ ‎ derived evidence is accelerating, with the US FDA, EMA, ICH, and PMDA issuing substantive guidance; however, persistent gaps in explainability, algorithmic bias auditing, and cross‎ ‎ jurisdictional harmonization remain.AI/ML technologies are demonstrably transforming drug discovery and pharmacovigilance, offering scalable solutions to longstanding bottlenecks in pharmaceutical R&D. Realizing the full translational potential of these technologies requires coordinated advances in model interpretability, data governance, federated infrastructure, and adaptive regulatory frameworks. This review provides a structured synthesis for researchers, clinicians, and regulatory scientists navigating the rapidly evolving AI‎ ‎ pharma interface.","url":"https://doi.org/10.5281/zenodo.21131900","authors":["Darshan K R*1, Shubham Shivangekar2, Yash Vispute3, Gayatri Dhamane4, Parth Thorat5, Prathamesh Chavan6"],"tags":["Artificial intelligence; drug discovery; pharmacovigilance; machine learning; deep learning; real‎ ‎ world evidence; natural language processing; graph neural networks; explainable AI; regulatory compliance; adverse drug events; generative models; federated learning; ADMET prediction."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21131900","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.21030653","name":"D8.8: Report on European Interoperability Framework Contributions","source":"datacite","abstract":"This deliverable (D8.8) presents the contributions of the PLIADES project to advancing the European Interoperability Framework (EIF) and interoperability standardization, with a focus on building a modular, scalable, and trustworthy data sharing ecosystem. The work conducted in Task 8.6 evaluates the project’s alignment with the EIF’s four layers-legal, organizational, semantic, and technical—and extends this analysis through ISO/IEC 19941’s five interoperability facets—policy, behavior, semantic, syntactic, and transport. By applying a general interoperability-framework approach, the deliverable assesses the interoperability maturity of PLIADES across six use cases in domains including mobility, energy, healthcare, green deal/ circular economy, energy, and industry. These use cases demonstrate how PLIADES supports dynamic, cross-domain data integration through advanced AI capabilities such as federated learning, explainable AI, and declarative querying—while ensuring legal compliance, data sovereignty, and semantic clarity. The project engages directly with EU standardization and governance initiatives—including SEMIC, DSSC, and the European Trusted Data Framework standardisation request to ensure alignment with emerging regulations like the Data Act. PLIADES actively contributes to the EU’s semantic and technical interoperability agenda through workshops, conference participation (e.g., SEMIC 2025, ENDORSE 2025), and alignment with the IDS Rulebook and the Dataspace Protocol. Gaps identified in current interoperability models—such as limited runtime interoperability, lack of support for decentralized AI, and insufficient metadata expressiveness—are addressed through actionable recommendations. PLIADES proposes enhancements to semantic alignment, dynamic querying, and data governance architectures, helping to shape the next iteration of European data policy frameworks. Ultimately, this report underscores PLIADES’ strategic role in fostering cross-border, cross-sector data interoperability. By operationalizing both EIF and ISO-based principles through real-world use cases and aligning with EU standardization initiatives, PLIADES delivers a blueprint for trusted, AI-enabled, sovereign data spaces that drive innovation and support Europe’s digital transition. PLIADES stands for an advanced AI AI-enabled framework for Full Data Lifecycles Optimisation and Data Spaces Integration. Our mission is to revolutionize how data is utilised across various sectors, from mobility to healthcare, manufacturing to energy, and beyond. PLIADES envisions a future where diverse sectors are seamlessly interconnected, enhancing efficiency and interoperability. We aim to provide cutting cutting-edge data and services that drive advancements in Cooperative, Connected, and Automated Mobility (CCAM), Advanced Driver Assistance & Autonomous Driving (ADAS/AD), and HumanHuman-Robot Interaction (HRI).","url":"https://doi.org/10.5281/zenodo.21030653","authors":["Hypertech (Greece)"],"tags":["PLIADES","Deliverable","Innovation","Data Spaces","AI","Horizon Europe"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21030653","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.21030654","name":"D8.8: Report on European Interoperability Framework Contributions","source":"datacite","abstract":"This deliverable (D8.8) presents the contributions of the PLIADES project to advancing the European Interoperability Framework (EIF) and interoperability standardization, with a focus on building a modular, scalable, and trustworthy data sharing ecosystem. The work conducted in Task 8.6 evaluates the project’s alignment with the EIF’s four layers-legal, organizational, semantic, and technical—and extends this analysis through ISO/IEC 19941’s five interoperability facets—policy, behavior, semantic, syntactic, and transport. By applying a general interoperability-framework approach, the deliverable assesses the interoperability maturity of PLIADES across six use cases in domains including mobility, energy, healthcare, green deal/ circular economy, energy, and industry. These use cases demonstrate how PLIADES supports dynamic, cross-domain data integration through advanced AI capabilities such as federated learning, explainable AI, and declarative querying—while ensuring legal compliance, data sovereignty, and semantic clarity. The project engages directly with EU standardization and governance initiatives—including SEMIC, DSSC, and the European Trusted Data Framework standardisation request to ensure alignment with emerging regulations like the Data Act. PLIADES actively contributes to the EU’s semantic and technical interoperability agenda through workshops, conference participation (e.g., SEMIC 2025, ENDORSE 2025), and alignment with the IDS Rulebook and the Dataspace Protocol. Gaps identified in current interoperability models—such as limited runtime interoperability, lack of support for decentralized AI, and insufficient metadata expressiveness—are addressed through actionable recommendations. PLIADES proposes enhancements to semantic alignment, dynamic querying, and data governance architectures, helping to shape the next iteration of European data policy frameworks. Ultimately, this report underscores PLIADES’ strategic role in fostering cross-border, cross-sector data interoperability. By operationalizing both EIF and ISO-based principles through real-world use cases and aligning with EU standardization initiatives, PLIADES delivers a blueprint for trusted, AI-enabled, sovereign data spaces that drive innovation and support Europe’s digital transition. PLIADES stands for an advanced AI AI-enabled framework for Full Data Lifecycles Optimisation and Data Spaces Integration. Our mission is to revolutionize how data is utilised across various sectors, from mobility to healthcare, manufacturing to energy, and beyond. PLIADES envisions a future where diverse sectors are seamlessly interconnected, enhancing efficiency and interoperability. We aim to provide cutting cutting-edge data and services that drive advancements in Cooperative, Connected, and Automated Mobility (CCAM), Advanced Driver Assistance & Autonomous Driving (ADAS/AD), and HumanHuman-Robot Interaction (HRI).","url":"https://doi.org/10.5281/zenodo.21030654","authors":["Hypertech (Greece)"],"tags":["PLIADES","Deliverable","Innovation","Data Spaces","AI","Horizon Europe"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21030654","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.21000523","name":"EGBP Bioenergy Grid Physically-Calibrated Synthetic Dataset — 15 nodes, 365 days, hourly resolution","source":"datacite","abstract":"131,400 node-hour observations across 15 bioenergy grid nodes (6 anaerobic digesters, 5 gasifiers, 4 CHP units) over 365 days at hourly resolution. Parameters calibrated to Faaij (2006) and Atashbar et al. (2016). Generated for: Ethical Guardrail Bandit Pruning (EGBP). This repository supports the manuscript \"Ethical Guardrail Bandit Pruning (EGBP): A Framework for Sustainable and Equitable AI-Driven Resource Allocation in Bioenergy Grids\" (under review). EGBP treats computational efficiency, energy sustainability, and distributional equity as simultaneously binding optimisation constraints rather than sequentially addressed objectives. The central theoretical contribution demonstrates that embedding ethical guardrails directly in the optimisation objective, rather than evaluating them post-hoc preserves the asymptotic regret properties of Thompson Sampling bandit learning while guaranteeing convergence to an ethically-admissible stationary point. The framework integrates four tightly coupled mechanisms: (i) a hybrid bandit-gradient importance estimator combining offline Transformer pre-training with online Thompson Sampling; (ii) an ethical guardrail buffer enforcing differentiable penalties on energy overconsumption and distributional inequity through a Gini-coefficient regulariser applied to physical energy budget allocations; (iii) a cost-weighted magnitude pruning operator coupling gradient sparsity to real-time biomass conversion telemetry; and (iv) a guardrail-filtered federated averaging scheme with analytically bounded exclusion fraction. Theoretical properties are established through three theorems, three propositions, and two corollaries covering energy guardrail self-correction, Gini penalty convexity, Thompson Sampling regret preservation, EGBP convergence, and federated fairness monotonicity. Simulation on a 15-node bioenergy grid over 50,000 training steps demonstrates 38% reduction in energy consumption and 40% improvement in distributional fairness relative to three competitive baselines. This repository contains derived simulation outputs and pipeline code for reproducibility.","url":"https://doi.org/10.5281/zenodo.21000523","authors":["Moroke, Ntebogang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21000523","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2509.18120","name":"A Coopetitive-Compatible Data Generation Framework for Cross-silo Federated Learning","source":"datacite","abstract":"Cross-silo federated learning (CFL) enables organizations (e.g., hospitals or banks) to collaboratively train artificial intelligence (AI) models while preserving data privacy by keeping data local. While prior work has primarily addressed statistical heterogeneity across organizations, a critical challenge arises from economic competition, where organizations may act as market rivals, making them hesitant to participate in joint training due to potential utility loss (i.e., reduced net benefit). Furthermore, the combined effects of statistical heterogeneity and inter-organizational competition on organizational behavior and system-wide social welfare remain underexplored. In this paper, we propose CoCoGen, a coopetitive-compatible data generation framework, leveraging generative AI (GenAI) and potential game theory to model, analyze, and optimize collaborative learning under heterogeneous and competitive settings. Specifically, CoCoGen characterizes competition and statistical heterogeneity through learning performance and utility-based formulations and models each training round as a weighted potential game. We then derive GenAI-based data generation strategies that maximize social welfare. Experimental results on the Fashion-MNIST dataset reveal how varying heterogeneity and competition levels affect organizational behavior and demonstrate that CoCoGen consistently outperforms baseline methods.","url":"https://doi.org/10.48550/arxiv.2509.18120","authors":["Nguyen, Thanh Linh","Pham, Quoc-Viet"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Computational Engineering, Finance, and Science (cs.CE)","Distributed, Parallel, and Cluster Computing (cs.DC)","Computer Science and Game Theory (cs.GT)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.18120","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2509.01750","name":"Communication-Aware Knowledge Distillation for Federated LLM Fine-Tuning over Wireless Networks","source":"datacite","abstract":"Federated learning (FL) for large language models (LLMs) offers a privacy-preserving scheme, enabling clients to collaboratively fine-tune locally deployed LLMs or smaller language models (SLMs) without exchanging raw data. While parameter-sharing methods in traditional FL models solves number of technical challenges, they still incur high communication overhead and struggle with adapting to heterogeneous model architectures. Federated distillation, a framework for mutual knowledge transfer via shared logits, typically offers lower communication overhead than parameter-sharing methods. However, transmitting logits from LLMs remains challenging for bandwidth-limited clients due to their high dimensionality. In this work, we focus on a federated LLM distillation with efficient communication overhead. To achieve this, we first propose an adaptive Top-k logit selection mechanism, dynamically sparsifying logits according to real-time communication conditions. Then to tackle the dimensional inconsistency introduced by the adaptive sparsification, we design an adaptive logits aggregation scheme, effectively alleviating the artificial and uninformative inputs introduced by conventional zero-padding methods. Finally, to enhance the distillation effect, we incorporate LoRA-adapted hidden-layer projection from LLM into the distillation loss, reducing the communication overhead further while providing richer representation. Experimental results demonstrate that our scheme achieves superior performance compared to baseline methods while effectively reducing communication overhead by approximately 50%.","url":"https://doi.org/10.48550/arxiv.2509.01750","authors":["Zhang, Xinlu","Yan, Na","Su, Yang","Deng, Yansha","Mahmoodi, Toktam"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.01750","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2606.21474","name":"Towards Transparent Mental Health Insights: An Explainable AI Model for Career-Related Depression and Anxiety Among University Students Using Structured Data","source":"datacite","abstract":"Career anxiety and depression among university students present a growing challenge to mental health and academic achievement. This study proposes an Explainable AI (XAI) framework using multimodal data and Federated Learning (FL) to identify early indicators of career-related mental health problems in a privacy-preserving and culturally responsive manner. The framework combines structured behavioral data and facial emotion features from interview videos via an intermediate fusion neural network with attention mechanisms. Label smoothing was applied to improve model generalizability. FL was used across institutions to enable collaborative training without raw data sharing. Evaluation was conducted using the Student Mental Health Survey dataset from university students across Pakistan. Our model attained an F1-score of 89.12%, recall of 86.54%, accuracy of 92.08%, and precision of 91.88%. Using Integrated Gradients and SHAP, the model identified key behavioral markers of depression including avoidance of direct gaze, lower facial expressiveness, and social withdrawal, consistent with psychological theory. This research presents an interpretable, scalable, and context-sensitive AI system for mental health pre-diagnosis with potential integration into student support services globally.","url":"https://doi.org/10.48550/arxiv.2606.21474","authors":["Azam, Arsham","Ali, Rasikh","Farhat, Tayyaba","Akram, Sheeraz"],"tags":["Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.21474","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20769288","name":"Artificial Intelligence and Big Data Integration: A Systematic Literature Review of Technological Trends, Ethical Challenges, and Future Directions","source":"datacite","abstract":"The convergence of Artificial Intelligence (AI) and Big Data is often celebrated as a transformative force, yet the academic literature on their integration remains scattered across technical, ethical, and sector-specific silos. This systematic review, guided by PRISMA 2020 principles, synthesizes peer-reviewed, open-access research published between 2020 and 2025. A search across six databases returned 1,847 records, from which 25 primary studies met the inclusion criteria. The analysis focused on publication patterns, methodological choices, technological emphases, and recurring themes. A sharp rise in publications from 2023 onward is evident, likely fueled by advances in generative AI and edge computing. The review finds that the literature is heavily weighted toward conceptual and trend-analysis papers; empirical and mixed-methods studies are scarce. Geographically, research clusters in the United States, China, and Western Europe, with the Global South largely absent. Five thematic clusters emerged: (1) a move toward edge computing and federated learning; (2) generative AI as both a consumer and producer of Big Data; (3) persistent problems of algorithmic bias and model opacity; (4) cybersecurity vulnerabilities, especially adversarial attacks; and (5) tensions in data governance and regulatory compliance. The integration of AI and Big Data offers real potential for innovation, but translating that potential into responsible practice will require stronger empirical research, explainable systems, and ethical frameworks that are less about aspiration and more about implementation.","url":"https://doi.org/10.5281/zenodo.20769288","authors":["Siregar, Torang"],"tags":["Algorithmic bias, artificial intelligence, Big Data, edge computing, federated learning, generative AI, systematic literature review"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20769288","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20769289","name":"Artificial Intelligence and Big Data Integration: A Systematic Literature Review of Technological Trends, Ethical Challenges, and Future Directions","source":"datacite","abstract":"The convergence of Artificial Intelligence (AI) and Big Data is often celebrated as a transformative force, yet the academic literature on their integration remains scattered across technical, ethical, and sector-specific silos. This systematic review, guided by PRISMA 2020 principles, synthesizes peer-reviewed, open-access research published between 2020 and 2025. A search across six databases returned 1,847 records, from which 25 primary studies met the inclusion criteria. The analysis focused on publication patterns, methodological choices, technological emphases, and recurring themes. A sharp rise in publications from 2023 onward is evident, likely fueled by advances in generative AI and edge computing. The review finds that the literature is heavily weighted toward conceptual and trend-analysis papers; empirical and mixed-methods studies are scarce. Geographically, research clusters in the United States, China, and Western Europe, with the Global South largely absent. Five thematic clusters emerged: (1) a move toward edge computing and federated learning; (2) generative AI as both a consumer and producer of Big Data; (3) persistent problems of algorithmic bias and model opacity; (4) cybersecurity vulnerabilities, especially adversarial attacks; and (5) tensions in data governance and regulatory compliance. The integration of AI and Big Data offers real potential for innovation, but translating that potential into responsible practice will require stronger empirical research, explainable systems, and ethical frameworks that are less about aspiration and more about implementation.","url":"https://doi.org/10.5281/zenodo.20769289","authors":["Siregar, Torang"],"tags":["Algorithmic bias, artificial intelligence, Big Data, edge computing, federated learning, generative AI, systematic literature review"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20769289","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2606.14416","name":"Federated Learning for Feature Generalization with Convex Constraints","source":"datacite","abstract":"Federated learning (FL) often struggles with generalization due to heterogeneous client data. Local models are prone to overfitting their local data distributions, and even transferable features can be distorted during aggregation. To address these challenges, we propose FedCONST, an approach that adaptively modulates update magnitudes based on the parameter strength of the global model. This prevents over-emphasizing well-learned parameters while reinforcing underdeveloped ones. Specifically, FedCONST employs linear convex constraints to ensure training stability and preserve locally learned generalization capabilities during aggregation. A Gradient Signal to Noise Ratio (GSNR) analysis further validates the effectiveness of FedCONST in enhancing feature transferability and robustness. As a result, FedCONST effectively aligns local and global objectives, mitigating overfitting and promoting stronger generalization across diverse FL environments, achieving state-of-the-art performance.","url":"https://doi.org/10.48550/arxiv.2606.14416","authors":["Kim, Dongwon","Kim, Donghee","Shyn, Sung Kuk","Kim, Kwangsu"],"tags":["Machine Learning (cs.LG)","Machine Learning (stat.ML)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.14416","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20655036","name":"Role of AI in Healthcare Applications","source":"datacite","abstract":"This review paper examines the role of Artificial Intelligence (AI) in modern healthcare applications. It discusses key AI technologies such as Machine Learning and Deep Learning and their use in disease detection, medical imaging, predictive healthcare, drug discovery, personalized medicine, wearable health devices, virtual health assistants, and robotic surgery. The paper also reviews recent developments from 2023–2025, including Med-PaLM 2, federated learning, and generative AI in medicine. Benefits, challenges, ethical concerns, and future opportunities are analyzed to provide a comprehensive overview of AI-driven healthcare systems.","url":"https://doi.org/10.5281/zenodo.20655036","authors":["Tejas, Tejas Raj Pandey"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20655036","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20655035","name":"Role of AI in Healthcare Applications","source":"datacite","abstract":"This review paper examines the role of Artificial Intelligence (AI) in modern healthcare applications. It discusses key AI technologies such as Machine Learning and Deep Learning and their use in disease detection, medical imaging, predictive healthcare, drug discovery, personalized medicine, wearable health devices, virtual health assistants, and robotic surgery. The paper also reviews recent developments from 2023–2025, including Med-PaLM 2, federated learning, and generative AI in medicine. Benefits, challenges, ethical concerns, and future opportunities are analyzed to provide a comprehensive overview of AI-driven healthcare systems.","url":"https://doi.org/10.5281/zenodo.20655035","authors":["Tejas, Tejas Raj Pandey"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20655035","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20624051","name":"ELASTIC Newsletter #4: Project Highlights and Achievements (June 2025–May 2026)","source":"datacite","abstract":"This issue of the ELASTIC Newsletter highlights the project's progress and achievements from June 2025 to May 2026. ELASTIC is advancing secure, efficient, and scalable orchestration for next-generation 6G networks by leveraging WebAssembly, eBPF, confidential computing, and distributed service management. Key highlights in this edition include: Mid-Term Reporting (RP1): Successful completion of the first reporting period, with strong progress across research, technical development, demonstrator preparation, dissemination, standardisation, and exploitation activities. Consortium Meeting in Chania (Sep 2025): Hosted by the Technical University of Crete, focused on review preparation and alignment across partners. Consortium Meeting in Lund (Jan 2026): Accelerating integration across the secure orchestration stack, with discussions on remote attestation, confidential workload execution, edge AI integration, and policy-driven security orchestration. Demo Series: Showcasing key technologies including Demonstrator 1 (Smart Connected Factory), Demonstrator 2 MVP (Sensitive IT Service Migration to Public Cloud), Propeller (lightweight WebAssembly-based orchestration), and Wasm-operator (efficient Kubernetes orchestration). Events & Webinars: Participation in the Women in ICT Standardisation Webinar (5th Edition) and a joint ELASTIC & 6G-PATH webinar on intelligent orchestration across the edge–cloud continuum. Scientific Contributions: Publications at ACISP 2026, ICLR 2026, INFOCOM 2026, Cluster Computing, and Transactions on Machine Learning Research (TMLR), covering topics such as WebAssembly security in confidential computing, graph teaching algorithms, split learning optimisation, mobility prediction, and differentially private federated learning. EuCNC & 6G Summit 2026: ELASTIC participation at Booth 76 in Málaga, Spain (2–5 June 2026).","url":"https://doi.org/10.5281/zenodo.20624051","authors":["Vasic, Jelena"],"tags":["6G","Wasm","WebAssembly","FaaS","Confidential Computing","Edge Computing","orchestration","serverless"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20624051","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20624052","name":"ELASTIC Newsletter #4: Project Highlights and Achievements (June 2025–May 2026)","source":"datacite","abstract":"This issue of the ELASTIC Newsletter highlights the project's progress and achievements from June 2025 to May 2026. ELASTIC is advancing secure, efficient, and scalable orchestration for next-generation 6G networks by leveraging WebAssembly, eBPF, confidential computing, and distributed service management. Key highlights in this edition include: Mid-Term Reporting (RP1): Successful completion of the first reporting period, with strong progress across research, technical development, demonstrator preparation, dissemination, standardisation, and exploitation activities. Consortium Meeting in Chania (Sep 2025): Hosted by the Technical University of Crete, focused on review preparation and alignment across partners. Consortium Meeting in Lund (Jan 2026): Accelerating integration across the secure orchestration stack, with discussions on remote attestation, confidential workload execution, edge AI integration, and policy-driven security orchestration. Demo Series: Showcasing key technologies including Demonstrator 1 (Smart Connected Factory), Demonstrator 2 MVP (Sensitive IT Service Migration to Public Cloud), Propeller (lightweight WebAssembly-based orchestration), and Wasm-operator (efficient Kubernetes orchestration). Events & Webinars: Participation in the Women in ICT Standardisation Webinar (5th Edition) and a joint ELASTIC & 6G-PATH webinar on intelligent orchestration across the edge–cloud continuum. Scientific Contributions: Publications at ACISP 2026, ICLR 2026, INFOCOM 2026, Cluster Computing, and Transactions on Machine Learning Research (TMLR), covering topics such as WebAssembly security in confidential computing, graph teaching algorithms, split learning optimisation, mobility prediction, and differentially private federated learning. EuCNC & 6G Summit 2026: ELASTIC participation at Booth 76 in Málaga, Spain (2–5 June 2026).","url":"https://doi.org/10.5281/zenodo.20624052","authors":["Vasic, Jelena"],"tags":["6G","Wasm","WebAssembly","FaaS","Confidential Computing","Edge Computing","orchestration","serverless"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20624052","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.10250","name":"Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning","source":"datacite","abstract":"Class imbalance is a common problem in deep learning that severely degrades performance. In federated learning (FL), it is a critical factor contributing to non-identically distributed data (non-IID). Building on several previous attempts, we define and analyze imbalance issues in FL at three levels: inter-case, inter-class, and inter-client. Inter-case imbalance addresses the imbalance in every single class; inter-class imbalance compares the number of data between different classes. Inter-client imbalance represents different skewness of local data between clients. Based on these concepts, we propose FedBB, which consists of two main components: (1) Positive Negative Balanced (PNB) loss function addresses the inter-case and inter-class imbalances in local training, enhancing generalization on highly skewed local client datasets. It optimizes both multi-label and multi-class classifications by assigning higher weights to minority cases or classes. (2) Client Balanced Reweighting (CBR) reweights clients based on inter-client imbalance during model aggregation, giving greater weight to models trained on less skewed datasets. Various experiments on X-ray and natural image datasets demonstrate that FedBB outperforms other algorithms in both performance and efficiency. Additionally, it requires limited statistical information, which is beneficial for privacy protection. Through ablation studies, we proved that PNB loss and CBR independently contribute to performance. As FedBB aims to build a global model that accurately classifies all classes, it can serve as a baseline for the generic and personalized FL.","url":"https://doi.org/10.48550/arxiv.2606.10250","authors":["Chung, Haengbok","Lee, Jae Sung"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.10250","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2505.02540","name":"Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data","source":"datacite","abstract":"In Federated Learning, heterogeneity in client data distributions often means that a single global model does not have the best performance for individual clients. Consider for example training a next-word prediction model for keyboards: user-specific language patterns due to demographics (dialect, age, etc.), language proficiency, and writing style result in a highly non-IID dataset across clients. Other examples are medical images taken with different machines, or driving data from different vehicle types. To address this, we propose a simple yet effective personalized federated learning framework (pFedLIA) that utilizes a computationally efficient influence approximation, called `Lazy Influence', to cluster clients in a distributed manner before model aggregation. Within each cluster, data owners collaborate to jointly train a model that captures the specific data patterns of the clients. Our method has been shown to successfully recover the global model's performance drop due to the non-IID-ness in various synthetic and real-world settings, specifically a next-word prediction task on the Nordic languages as well as several benchmark tasks. It matches the performance of a hypothetical Oracle clustering, and significantly improves on existing baselines, e.g., an improvement of 17% on CIFAR100.","url":"https://doi.org/10.48550/arxiv.2505.02540","authors":["Rokvic, Ljubomir","Danassis, Panayiotis","Faltings, Boi"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2505.02540","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20529861","name":"FEDERATED LEARNING FOR PRIVACY-PRESERVING THREAT INTELLIGENCE SHARING IN DISTRIBUTED CYBERSECURITY ECOSYSTEMS","source":"datacite","abstract":"Effective cybersecurity threat intelligence depends fundamentally on the breadth and timeliness of threat data — yet the organizations most capable of generating actionable intelligence are simultaneously most constrained in sharing it due to privacy regulations (GDPR, HIPAA, PDPA), competitive concerns, legal liability, and national security classifications. This tension between intelligence sharing and data privacy represents one of the most consequential unsolved challenges in cybersecurity: organizations that share threat intelligence detect attacks 2.4 times faster and suffer 47.3% lower breach costs, yet fewer than 23% of enterprises engage in structured threat intelligence sharing due to these barriers. This paper presents FedThreat-AI, a novel federated learning framework enabling privacy-preserving threat intelligence sharing across distributed cybersecurity ecosystems without requiring any organization to expose its raw security data, proprietary detection rules, or sensitive network topology. FedThreat-AI integrates four privacy-enhancing technologies — differential privacy (DP), homomorphic encryption (HE), secure multiparty computation (SMPC), and Byzantine-robust gradient aggregation — into a unified federated learning pipeline trained on distributed threat telemetry across participating organizations. The framework produces a continuously improving global threat detection model incorporating the collective intelligence of all participants, distributed back to each organization as model updates rather than data. Evaluated across a consortium of 24 organizations spanning financial services, healthcare, government, and technology sectors over 18 months (2023–2025), FedThreat-AI achieves global threat detection accuracy of 96.8% — only 1.4 percentage points below a centralized baseline that requires full data sharing — while providing mathematically provable privacy guarantees (ε = 0.8, δ = 10⁻⁵ per training round). The framework further demonstrates resilience against Byzantine poisoning attacks from up to 30% malicious participants and reduces mean time to detect novel threat campaigns by 67.4% compared to organization-siloed detection. FedThreat-AI is fully compatible with STIX 2.1 and TAXII 2.1 standards, enabling integration with existing threat intelligence platforms and ISACs","url":"https://doi.org/10.5281/zenodo.20529861","authors":["Dr. Angira A., Patel","Nilam, Joshi","Vaidehi, Patel","Avani, Vagadiya","Dhruvi, Pandya","Dr. Kamalesh, V N"],"tags":["Federated Learning; Privacy-Preserving Machine Learning; Threat Intelligence Sharing; Differential Privacy; Homomorphic Encryption; Cybersecurity Collaboration; Byzantine-Robust Aggregation; STIX/TAXII; Indicators of Compromise; Secure Multi-Party Computation; CrossOrganizational Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20529861","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.20529862","name":"FEDERATED LEARNING FOR PRIVACY-PRESERVING THREAT INTELLIGENCE SHARING IN DISTRIBUTED CYBERSECURITY ECOSYSTEMS","source":"datacite","abstract":"Effective cybersecurity threat intelligence depends fundamentally on the breadth and timeliness of threat data — yet the organizations most capable of generating actionable intelligence are simultaneously most constrained in sharing it due to privacy regulations (GDPR, HIPAA, PDPA), competitive concerns, legal liability, and national security classifications. This tension between intelligence sharing and data privacy represents one of the most consequential unsolved challenges in cybersecurity: organizations that share threat intelligence detect attacks 2.4 times faster and suffer 47.3% lower breach costs, yet fewer than 23% of enterprises engage in structured threat intelligence sharing due to these barriers. This paper presents FedThreat-AI, a novel federated learning framework enabling privacy-preserving threat intelligence sharing across distributed cybersecurity ecosystems without requiring any organization to expose its raw security data, proprietary detection rules, or sensitive network topology. FedThreat-AI integrates four privacy-enhancing technologies — differential privacy (DP), homomorphic encryption (HE), secure multiparty computation (SMPC), and Byzantine-robust gradient aggregation — into a unified federated learning pipeline trained on distributed threat telemetry across participating organizations. The framework produces a continuously improving global threat detection model incorporating the collective intelligence of all participants, distributed back to each organization as model updates rather than data. Evaluated across a consortium of 24 organizations spanning financial services, healthcare, government, and technology sectors over 18 months (2023–2025), FedThreat-AI achieves global threat detection accuracy of 96.8% — only 1.4 percentage points below a centralized baseline that requires full data sharing — while providing mathematically provable privacy guarantees (ε = 0.8, δ = 10⁻⁵ per training round). The framework further demonstrates resilience against Byzantine poisoning attacks from up to 30% malicious participants and reduces mean time to detect novel threat campaigns by 67.4% compared to organization-siloed detection. FedThreat-AI is fully compatible with STIX 2.1 and TAXII 2.1 standards, enabling integration with existing threat intelligence platforms and ISACs","url":"https://doi.org/10.5281/zenodo.20529862","authors":["Dr. Angira A., Patel","Nilam, Joshi","Vaidehi, Patel","Avani, Vagadiya","Dhruvi, Pandya","Dr. Kamalesh, V N"],"tags":["Federated Learning; Privacy-Preserving Machine Learning; Threat Intelligence Sharing; Differential Privacy; Homomorphic Encryption; Cybersecurity Collaboration; Byzantine-Robust Aggregation; STIX/TAXII; Indicators of Compromise; Secure Multi-Party Computation; CrossOrganizational Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20529862","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.48550/arxiv.2605.17219","name":"Integration of AI in Cybersecurity: Current Trends with a Focused Look at Intrusion Detection Applications","source":"datacite","abstract":"Artificial Intelligence (AI) is widely adopted today for its ability to detect patterns, automate tasks, and reduce time and cost across various applications. Its integration into Cybersecurity has garnered significant attention, particularly in areas such as intrusion detection, malware analysis, and phishing or spam detection. As AI and cybersecurity evolve, new methods and approaches emerge regularly. Current trends include the use of Generative AI, Natural Language Processing, Federated Learning for privacy-preserving collaborative training, and eXplainable AI to ensure interpretability and trust, which are vital in cybersecurity. This paper presents an interesting review of current AI-based cybersecurity trends, focusing on intrusion detection approaches and aiming to uncover meaningful insights through comparative analysis based on the employed AI techniques and reported performance.","url":"https://doi.org/10.48550/arxiv.2605.17219","authors":["Tazili, S.","Mansour, A.","Chkouri, M. Y."],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","Networking and Internet Architecture (cs.NI)","Signal Processing (eess.SP)","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2605.17219","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.19894495","name":"EGBP Bioenergy Grid Physically-Calibrated Synthetic Dataset — 15 nodes, 365 days, hourly resolution","source":"datacite","abstract":"131,400 node-hour observations across 15 bioenergy grid nodes (6 anaerobic digesters, 5 gasifiers, 4 CHP units) over 365 days at hourly resolution. Parameters calibrated to Faaij (2006) and Atashbar et al. (2016). Generated for: Ethical Guardrail Bandit Pruning (EGBP). This repository supports the manuscript \"Ethical Guardrail Bandit Pruning (EGBP): A Framework for Sustainable and Equitable AI-Driven Resource Allocation in Bioenergy Grids\" (under review). EGBP treats computational efficiency, energy sustainability, and distributional equity as simultaneously binding optimisation constraints rather than sequentially addressed objectives. The central theoretical contribution demonstrates that embedding ethical guardrails directly in the optimisation objective, rather than evaluating them post-hoc preserves the asymptotic regret properties of Thompson Sampling bandit learning while guaranteeing convergence to an ethically-admissible stationary point. The framework integrates four tightly coupled mechanisms: (i) a hybrid bandit-gradient importance estimator combining offline Transformer pre-training with online Thompson Sampling; (ii) an ethical guardrail buffer enforcing differentiable penalties on energy overconsumption and distributional inequity through a Gini-coefficient regulariser applied to physical energy budget allocations; (iii) a cost-weighted magnitude pruning operator coupling gradient sparsity to real-time biomass conversion telemetry; and (iv) a guardrail-filtered federated averaging scheme with analytically bounded exclusion fraction. Theoretical properties are established through three theorems, three propositions, and two corollaries covering energy guardrail self-correction, Gini penalty convexity, Thompson Sampling regret preservation, EGBP convergence, and federated fairness monotonicity. Simulation on a 15-node bioenergy grid over 50,000 training steps demonstrates 38% reduction in energy consumption and 40% improvement in distributional fairness relative to three competitive baselines. This repository contains derived simulation outputs and pipeline code for reproducibility.","url":"https://doi.org/10.5281/zenodo.19894495","authors":["Moroke, Ntebogang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19894495","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20443410","name":"BioHackathon Europe 2025 Report","source":"datacite","abstract":"This report documents BioHackathon Europe 2025, ELIXIR's annual flagship collaborative hacking event, held 3 to 7 November 2025 at the Hotel Esplanade Resort & Spa, Bad Saarow, Germany. Organised by the ELIXIR Hub, the event brought together an international community of life scientists, developers, data stewards and infrastructure experts to accelerate the development of open, interoperable solutions for the life sciences. The 2025 event featured 31 hacking projects across five days of intensive collaboration. Projects spanned a broad range of ELIXIR strategic priority areas including research data management and FAIR implementation, AI and machine learning readiness, biodiversity genomics, secure and federated data access, cloud and workflow infrastructures, and sustainable computing. The event programme included opening flash presentations, daily uninterrupted hacking sessions, a new interactive mid-week poster session (replacing the traditional reporting format), and final presentations. Shared social activities fostered community building and cross-project collaboration. A post-event participant survey indicated strong satisfaction with the format, with dedicated hacking time rated as the most valuable component. The majority of respondents reported improved technical skills as a result of participation. Related resources Event photos: Flickr album Project outputs: GitHub repository Preprints: BioHackrXiv","url":"https://doi.org/10.5281/zenodo.20443410","authors":["van Wyk, Deborah","Anton, Mihail","Heil, Katharina F"],"tags":["BioHackathon, ELIXIR, life sciences, bioinformatics, open science, FAIR data, research data management, interoperability, collaborative hacking, workflows, metadata, genomics, federated data, machine learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20443410","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20446860","name":"BioHackathon Europe 2025 Report","source":"datacite","abstract":"This report documents BioHackathon Europe 2025, ELIXIR's annual flagship collaborative hacking event, held 3 to 7 November 2025 at the Hotel Esplanade Resort & Spa, Bad Saarow, Germany. Organised by the ELIXIR Hub, the event brought together an international community of life scientists, developers, data stewards and infrastructure experts to accelerate the development of open, interoperable solutions for the life sciences. The 2025 event featured 31 hacking projects across five days of intensive collaboration. Projects spanned a broad range of ELIXIR strategic priority areas including research data management and FAIR implementation, AI and machine learning readiness, biodiversity genomics, secure and federated data access, cloud and workflow infrastructures, and sustainable computing. The event programme included opening flash presentations, daily uninterrupted hacking sessions, a new interactive mid-week poster session (replacing the traditional reporting format), and final presentations. Shared social activities fostered community building and cross-project collaboration. A post-event participant survey indicated strong satisfaction with the format, with dedicated hacking time rated as the most valuable component. The majority of respondents reported improved technical skills as a result of participation. Related resources Event photos: Flickr album Project outputs: GitHub repository Preprints: BioHackrXiv","url":"https://doi.org/10.5281/zenodo.20446860","authors":["van Wyk, Deborah","Anton, Mihail","Heil, Katharina F"],"tags":["BioHackathon, ELIXIR, life sciences, bioinformatics, open science, FAIR data, research data management, interoperability, collaborative hacking, workflows, metadata, genomics, federated data, machine learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20446860","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20446063","name":"BioHackathon Europe 2025 Report","source":"datacite","abstract":"This report documents BioHackathon Europe 2025, ELIXIR's annual flagship collaborative hacking event, held 3 to 7 November 2025 at the Hotel Esplanade Resort & Spa, Bad Saarow, Germany. Organised by the ELIXIR Hub, the event brought together an international community of life scientists, developers, data stewards and infrastructure experts to accelerate the development of open, interoperable solutions for the life sciences. The 2025 event featured 31 hacking projects across five days of intensive collaboration. Projects spanned a broad range of ELIXIR strategic priority areas including research data management and FAIR implementation, AI and machine learning readiness, biodiversity genomics, secure and federated data access, cloud and workflow infrastructures, and sustainable computing. The event programme included opening flash presentations, daily uninterrupted hacking sessions, a new interactive mid-week poster session (replacing the traditional reporting format), and final presentations. Shared social activities fostered community building and cross-project collaboration. A post-event participant survey indicated strong satisfaction with the format, with dedicated hacking time rated as the most valuable component. The majority of respondents reported improved technical skills as a result of participation. Related resources Event photos: Flickr album Project outputs: GitHub repository Preprints: BioHackrXiv","url":"https://doi.org/10.5281/zenodo.20446063","authors":["van Wyk, Deborah","Anton, Mihail","Heil, Katharina F"],"tags":["BioHackathon, ELIXIR, life sciences, bioinformatics, open science, FAIR data, research data management, interoperability, collaborative hacking, workflows, metadata, genomics, federated data, machine learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20446063","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20443411","name":"BioHackathon Europe 2025 Report","source":"datacite","abstract":"This report documents BioHackathon Europe 2025, ELIXIR's annual flagship collaborative hacking event, held 3 to 7 November 2025 at the Hotel Esplanade Resort & Spa, Bad Saarow, Germany. Organised by the ELIXIR Hub, the event brought together an international community of life scientists, developers, data stewards and infrastructure experts to accelerate the development of open, interoperable solutions for the life sciences. The 2025 event featured 31 hacking projects across five days of intensive collaboration. Projects spanned a broad range of ELIXIR strategic priority areas including research data management and FAIR implementation, AI and machine learning readiness, biodiversity genomics, secure and federated data access, cloud and workflow infrastructures, and sustainable computing. The event programme included opening flash presentations, daily uninterrupted hacking sessions, a new interactive mid-week poster session (replacing the traditional reporting format), and final presentations. Shared social activities fostered community building and cross-project collaboration. A post-event participant survey indicated strong satisfaction with the format, with dedicated hacking time rated as the most valuable component. The majority of respondents reported improved technical skills as a result of participation. Hybrid participation was supported throughout, with around three quarters of project teams including at least one remote participant. Following the event, 11 preprints were published on BioHackrXiv. Related resources Event photos: https://www.flickr.com/photos/elixir-europe/albums/72177720330241026/with/54916490416/ Project outputs: BioHackathon Europe 2025 GitHub repository Preprints: BioHackrXiv (indexed in Europe PMC since 2021) Event coordination: Slack channel #BioHackEU25","url":"https://doi.org/10.5281/zenodo.20443411","authors":["van Wyk, Deborah","Anton, Mihail","Heil, Katharina F"],"tags":["BioHackathon, ELIXIR, life sciences, bioinformatics, open science, FAIR data, research data management, interoperability, collaborative hacking, workflows, metadata, genomics, federated data, machine learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20443411","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2605.27385","name":"Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity","source":"datacite","abstract":"Federated reinforcement learning (FedRL) enables multiple agents to collaboratively train a global policy without sharing raw data, making it ideal for privacy-sensitive applications. However, FedRL faces challenges in heterogeneous environments where differing state-transition dynamics lead to non-identical input distributions and imbalanced parameter updates during aggregation. Therefore, this paper develops a personalized observation normalization (PON) method, allowing each agent to locally normalize raw state inputs using a continuously updated running mean and variance. This design ensures consistent scaling of local feature without overshadowing across agents during aggregation. Furthermore, we demonstrate that sharing normalization parameters across agents is ineffective due to the diverse local input distributions, which highlights the necessity of personalized statistics. Experiments on heterogeneous MuJoCo tasks show that our developed PON accelerates training and achieves superior performance compared to baseline methods.","url":"https://doi.org/10.48550/arxiv.2605.27385","authors":["Pang, Yiran","Ni, Zhen","Zhong, Xiangnan"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2605.27385","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20408557","name":"Ransomware Attack Vectors, Detection Techniques, and Mitigation Strategies: A Comprehensive Survey","source":"datacite","abstract":"Ransomware has turned to be one of the most severe and costly cybersecurity threats to organisations and individuals globally. This is a broad overview of ransomware looking at it in various dimensions: the way ransomware has evolved over the years since use as a simple screen-locking tool to a complex multi-stage attack with data exfiltration and extortion; the various types of attack vectors that ransomware attackers can use which include phishing attacks, remote desktop protocol attacks, supply chain attack and the broad use of machine learning to detect as well as sophisticated machine learning algorithms to detect ransomware; and overall mitigation strategies that are focused on prevention, detection, response and recovery. We digitise and systematically examine more than 50 research articles published between 2020 and 2025, and we make direct comparative reviews on the detection algorithms with respect to the measurements of accuracy, precision, recall, false positives, and computation overheads. We find in our analysis that ensemble machine learn-ing techniques can be used to detect an attack with a detection rate of above 99 percent with multi-layered defence schemes giving 85-90 percent accuracy in warding off successful attacks. We also recognise significant research opportunities such as the difficulty in detecting them in real time, the constraints of their datasets, or the necessity of cross platform security products. The significance of this survey to the field is in the way that it provides researchers and practitioners with a comprehensive picture of the ransomware threat environment and practical implications of creating defensive mechanisms in the next generation. As a conclusion, we overview a set of prospective research topics such as AI-controlled adaptive defence models, cryptographic systems based on blockchains, federated learning systems, and quantum resistant cryptographic systems.","url":"https://doi.org/10.5281/zenodo.20408557","authors":["Manoj Mule","Nazma A.  Inamdar"],"tags":["Ransomware","Cybersecurity","Machine Learning","Malware Detection","Attack Vectors","Threat Intelligence","Intrusion Detection Systems","Deep Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20408557","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20408556","name":"Ransomware Attack Vectors, Detection Techniques, and Mitigation Strategies: A Comprehensive Survey","source":"datacite","abstract":"Ransomware has turned to be one of the most severe and costly cybersecurity threats to organisations and individuals globally. This is a broad overview of ransomware looking at it in various dimensions: the way ransomware has evolved over the years since use as a simple screen-locking tool to a complex multi-stage attack with data exfiltration and extortion; the various types of attack vectors that ransomware attackers can use which include phishing attacks, remote desktop protocol attacks, supply chain attack and the broad use of machine learning to detect as well as sophisticated machine learning algorithms to detect ransomware; and overall mitigation strategies that are focused on prevention, detection, response and recovery. We digitise and systematically examine more than 50 research articles published between 2020 and 2025, and we make direct comparative reviews on the detection algorithms with respect to the measurements of accuracy, precision, recall, false positives, and computation overheads. We find in our analysis that ensemble machine learn-ing techniques can be used to detect an attack with a detection rate of above 99 percent with multi-layered defence schemes giving 85-90 percent accuracy in warding off successful attacks. We also recognise significant research opportunities such as the difficulty in detecting them in real time, the constraints of their datasets, or the necessity of cross platform security products. The significance of this survey to the field is in the way that it provides researchers and practitioners with a comprehensive picture of the ransomware threat environment and practical implications of creating defensive mechanisms in the next generation. As a conclusion, we overview a set of prospective research topics such as AI-controlled adaptive defence models, cryptographic systems based on blockchains, federated learning systems, and quantum resistant cryptographic systems.","url":"https://doi.org/10.5281/zenodo.20408556","authors":["Manoj Mule","Nazma A.  Inamdar"],"tags":["Ransomware","Cybersecurity","Machine Learning","Malware Detection","Attack Vectors","Threat Intelligence","Intrusion Detection Systems","Deep Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20408556","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20318626","name":"The Use of Artificial Intelligence Techniques in Combating Cybercrime: A Descriptive Analytical Study","source":"datacite","abstract":"Artificial Intelligence (AI) has emerged as a transformative technology in modern cybersecurity due to its ability to enhance threat detection, automate security processes, and improve decision-making capabilities. With the increasing complexity and frequency of cybercrimes, traditional cybersecurity approaches often face limitations in identifying and preventing sophisticated attacks. This study aims to examine the role of Artificial Intelligence techniques in combating cybercrime through a descriptive analytical approach. The study relies on a review and analysis of recent academic literature published between 2020 and 2025, focusing on various Artificial Intelligence applications in cybersecurity, including Machine Learning, Deep Learning, Intrusion Detection Systems, malware detection, phishing prevention, and digital forensic analysis. The findings indicate that Artificial Intelligence significantly enhances cybersecurity performance through intelligent pattern recognition, anomaly detection, predictive analysis, and automated response mechanisms. The study further reveals that although AI-based cybersecurity systems offer numerous advantages, several challenges remain, including privacy concerns, data quality issues, adversarial attacks, model interpretability, and computational requirements. The study concludes that Artificial Intelligence represents a promising solution for addressing emerging cyber threats and recommends further research into advanced technologies such as Explainable Artificial Intelligence, Federated Learning, and Quantum Computing to strengthen future cybersecurity systems.","url":"https://doi.org/10.5281/zenodo.20318626","authors":["Adoum, Housni Moubarak Oumar"],"tags":["Artificial Intelligence Cybercrime Cybersecurity Machine Learning Deep Learning Intrusion Detection Threat Detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20318626","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20318627","name":"The Use of Artificial Intelligence Techniques in Combating Cybercrime: A Descriptive Analytical Study","source":"datacite","abstract":"Artificial Intelligence (AI) has emerged as a transformative technology in modern cybersecurity due to its ability to enhance threat detection, automate security processes, and improve decision-making capabilities. With the increasing complexity and frequency of cybercrimes, traditional cybersecurity approaches often face limitations in identifying and preventing sophisticated attacks. This study aims to examine the role of Artificial Intelligence techniques in combating cybercrime through a descriptive analytical approach. The study relies on a review and analysis of recent academic literature published between 2020 and 2025, focusing on various Artificial Intelligence applications in cybersecurity, including Machine Learning, Deep Learning, Intrusion Detection Systems, malware detection, phishing prevention, and digital forensic analysis. The findings indicate that Artificial Intelligence significantly enhances cybersecurity performance through intelligent pattern recognition, anomaly detection, predictive analysis, and automated response mechanisms. The study further reveals that although AI-based cybersecurity systems offer numerous advantages, several challenges remain, including privacy concerns, data quality issues, adversarial attacks, model interpretability, and computational requirements. The study concludes that Artificial Intelligence represents a promising solution for addressing emerging cyber threats and recommends further research into advanced technologies such as Explainable Artificial Intelligence, Federated Learning, and Quantum Computing to strengthen future cybersecurity systems.","url":"https://doi.org/10.5281/zenodo.20318627","authors":["Adoum, Housni Moubarak Oumar"],"tags":["Artificial Intelligence Cybercrime Cybersecurity Machine Learning Deep Learning Intrusion Detection Threat Detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20318627","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20315537","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20315537","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20315537","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2508.14769","name":"Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data","source":"datacite","abstract":"Federated distillation has emerged as a promising collaborative machine learning approach, offering enhanced privacy protection and reduced communication compared to traditional federated learning by exchanging model outputs (soft logits) rather than full model parameters. However, existing methods employ complex selective knowledge-sharing strategies that require clients to identify in-distribution proxy data through computationally expensive statistical density ratio estimators. Additionally, server-side filtering of ambiguous knowledge introduces latency to the process. To address these challenges, we propose a robust, resource-efficient EdgeFD method that reduces the complexity of the client-side density ratio estimation and removes the need for server-side filtering. EdgeFD introduces an efficient KMeans-based density ratio estimator for effectively filtering both in-distribution and out-of-distribution proxy data on clients, significantly improving the quality of knowledge sharing. We evaluate EdgeFD across diverse practical scenarios, including strong non-IID, weak non-IID, and IID data distributions on clients, without requiring a pre-trained teacher model on the server for knowledge distillation. Experimental results demonstrate that EdgeFD outperforms state-of-the-art methods, consistently achieving accuracy levels close to IID scenarios even under heterogeneous and challenging conditions. The significantly reduced computational overhead of the KMeans-based estimator is suitable for deployment on resource-constrained edge devices, thereby enhancing the scalability and real-world applicability of federated distillation. The code is available online for reproducibility.","url":"https://doi.org/10.48550/arxiv.2508.14769","authors":["Mujtaba, Ahmed","Radchenko, Gleb","Prodan, Radu","Masana, Marc"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.14769","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.17605/osf.io/ch4p6","name":"PRISMA‑Based Systematic Review of PINNs, TinyML, and Edge‑Cloud Frameworks for Real‑Time Photovoltaic Monitoring","source":"datacite","abstract":"This project houses the protocol and supporting materials for a PRISMA 2020‑compliant systematic literature review (SLR) investigating the integration of Physics‑Informed Neural Networks (PINNs), Tiny Machine Learning (TinyML), and Edge‑Cloud Collaborative Architectures for real‑time photovoltaic (PV) performance monitoring. The review is framed using the PICOC (Population, Intervention, Comparison, Outcome, Context) framework to answer four research questions. A predefined search string was applied across five databases (IEEE Xplore, ACM Digital Library, Scopus, Web of Science, arXiv) for publications from 2013 to 2025. Two independent reviewers screen all records, with inter‑rater agreement measured by Cohen's κ. Included studies undergo quality assessment using a CASP‑adapted 0–10 rubric. The synthesis maps the state‑of‑the‑art across five thematic clusters, identifies open research gaps, and compares empirical benchmarks. A reproducible empirical pilot study accompanies the review, comprising an 8,784‑hour physics‑grounded synthetic PV dataset, four model variants (baseline MLP, PINN, INT8‑quantised TinyML PINN, 3‑site federated PINN), and evaluation of prediction accuracy, fault detection, and edge‑cloud bandwidth reduction. All materials – protocol, screening decisions, extracted data, pilot study code, dataset, trained models, and figures – are made openly available to support reproducibility and community advancement at the intersection of physics‑informed machine learning and edge AI for renewable energy monitoring.","url":"https://doi.org/10.17605/osf.io/ch4p6","authors":["Kawonga, Towani"],"tags":["Computer Engineering","Engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17605/osf.io/ch4p6","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20252765","name":"FEDERATED LEARNING IN LUNG CANCER RADIOMICS AND MEDICAL IMAGING: A META-SURVEY OF COLLABORATIVE MODELS","source":"datacite","abstract":"Lung cancer is a major cause of cancer morbidity and death worldwide; new methods of testing are urgently needed but are not always available and accurate. Federated learning (FL) is an emerging paradigm in the machine learning field that shows significant potential in the context of medical data analysis -- namely, it allows for model training over a large number of distributed data sources where patient data can remain private. We note that this overview is focused on more recent works on federated learning methods for lung cancer-related imaging and screening tasks, with references chosen primarily based on a directly comparable methodology. Specifically, we review the research works with respect to different FL architectures, the most commonly used data preprocessing methods as well as the metrics for FL performance assessment in healthcare applications. Furthermore, we analyze the advantages of federated learning such as improved model generalization on heterogeneous data, and enhanced privacy protection. An overview of the FL strategies in terms of their centralized, decentralized, and hybrid architectures is discussed, along with the advantages and disadvantages for LC screening. It further explores a broader range of challenges such as non-IID (non-Independent and Identically Distributed) and IID data distributions, commutation costs, and stability of training, etc. We provide an extensive evaluation of the performance of various FL methods on random field studies compared with other experimental results reported in literature to benchmark the current state-of-the-art methods against each other. It also mentions the integration of federated learning with complementary techniques like (deep learning), (blockchain) to facilitate big scale cooperation learning in reducing overfitting and simultaneously mitigate the security problems due to deep learning, block chain respectively. Conclusion The findings indicate that federated learning could simultaneously improve performance and adapt populations for lung cancer screening under the constraints of stringent data privacy. Many critical issues remain, including those for non-IID data handling, communication efficiency, and scalability performance. In this paper, we clarify future directions (such as secure aggregation mechanisms, personalized federated learning models, and secure multiparty computation) to mitigate these problems. Thus, this work should provide an excellent reference for researchers and practitioners interested in applying federated learning to privacy-friendly lung cancer screening and diagnostic support systems. It covers the systematic review on 37 peer-reviewed studies, (2013–2025) and controlled benchmarking for 5 FL algorithms that is FedAvg(Federated Averaging), FedSGD(Federated Stochastic Gradient Descent), FedProx(Federated Proximal), FedAtt(Federated Attention) and FedEnsemble with respect to the Chest CT-Scan dataset (publicly available, 9,500 images, Kaggle). FedEnsemble achieved the highest optimally weighted score × class performance across multiple performance measurements; 92.0% accuracy, 91.9% F1-score, 91.5% precision and 92.2% recall suggesting it produced the most ideal overall performance, whereas, FedSGD had the lowest communication bandwidth requirement (i.e., 45 round and 180 MB of data), with an optimally weighted score × class of 80.0% accuracy.","url":"https://doi.org/10.5281/zenodo.20252765","authors":["SRIVIDYA.CH , DR. RAMA SUBRAMANIAN K , MADHUBALA.M"],"tags":["Federated Learning, Lung Cancer Detection, Machine Learning, Privacy-Preserving, Decentralized Data, Deep Learning, Blockchain, Data Heterogeneity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20252765","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:38.945Z"},{"id":"doi:10.5281/zenodo.20252764","name":"FEDERATED LEARNING IN LUNG CANCER RADIOMICS AND MEDICAL IMAGING: A META-SURVEY OF COLLABORATIVE MODELS","source":"datacite","abstract":"Lung cancer is a major cause of cancer morbidity and death worldwide; new methods of testing are urgently needed but are not always available and accurate. Federated learning (FL) is an emerging paradigm in the machine learning field that shows significant potential in the context of medical data analysis -- namely, it allows for model training over a large number of distributed data sources where patient data can remain private. We note that this overview is focused on more recent works on federated learning methods for lung cancer-related imaging and screening tasks, with references chosen primarily based on a directly comparable methodology. Specifically, we review the research works with respect to different FL architectures, the most commonly used data preprocessing methods as well as the metrics for FL performance assessment in healthcare applications. Furthermore, we analyze the advantages of federated learning such as improved model generalization on heterogeneous data, and enhanced privacy protection. An overview of the FL strategies in terms of their centralized, decentralized, and hybrid architectures is discussed, along with the advantages and disadvantages for LC screening. It further explores a broader range of challenges such as non-IID (non-Independent and Identically Distributed) and IID data distributions, commutation costs, and stability of training, etc. We provide an extensive evaluation of the performance of various FL methods on random field studies compared with other experimental results reported in literature to benchmark the current state-of-the-art methods against each other. It also mentions the integration of federated learning with complementary techniques like (deep learning), (blockchain) to facilitate big scale cooperation learning in reducing overfitting and simultaneously mitigate the security problems due to deep learning, block chain respectively. Conclusion The findings indicate that federated learning could simultaneously improve performance and adapt populations for lung cancer screening under the constraints of stringent data privacy. Many critical issues remain, including those for non-IID data handling, communication efficiency, and scalability performance. In this paper, we clarify future directions (such as secure aggregation mechanisms, personalized federated learning models, and secure multiparty computation) to mitigate these problems. Thus, this work should provide an excellent reference for researchers and practitioners interested in applying federated learning to privacy-friendly lung cancer screening and diagnostic support systems. It covers the systematic review on 37 peer-reviewed studies, (2013–2025) and controlled benchmarking for 5 FL algorithms that is FedAvg(Federated Averaging), FedSGD(Federated Stochastic Gradient Descent), FedProx(Federated Proximal), FedAtt(Federated Attention) and FedEnsemble with respect to the Chest CT-Scan dataset (publicly available, 9,500 images, Kaggle). FedEnsemble achieved the highest optimally weighted score × class performance across multiple performance measurements; 92.0% accuracy, 91.9% F1-score, 91.5% precision and 92.2% recall suggesting it produced the most ideal overall performance, whereas, FedSGD had the lowest communication bandwidth requirement (i.e., 45 round and 180 MB of data), with an optimally weighted score × class of 80.0% accuracy.","url":"https://doi.org/10.5281/zenodo.20252764","authors":["SRIVIDYA.CH , DR. RAMA SUBRAMANIAN K , MADHUBALA.M"],"tags":["Federated Learning, Lung Cancer Detection, Machine Learning, Privacy-Preserving, Decentralized Data, Deep Learning, Blockchain, Data Heterogeneity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20252764","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:38.945Z"},{"id":"doi:10.5281/zenodo.20245558","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20245558","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20245558","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20241038","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20241038","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20241038","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20220247","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20220247","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20220247","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20218411","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20218411","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20218411","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20215995","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20215995","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20215995","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20215612","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20215612","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20215612","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.6084/m9.figshare.29637053","name":"2025-CyberTraining PIMeeting-Poster-T3-CIDERS.pdf","source":"datacite","abstract":"This poster introduces the T3-CIDERS project, a Train-the-Trainer initiative designed to build a national community of practice around cyberinfrastructure (CI)- and data-enabled cybersecurity research and education. The project equips faculty-student teams (Future Trainers) with hands-on experience in CI technologies, such as HPC, big data, ML, and cryptography, and pedagogical strategies to teach these concepts through cybersecurity applications. Highlights include the successful completion of the first cohort, six outreach events reaching nearly 100 students nationwide, and the development of a new module on Federated Learning Security. Upcoming activities include a Winter Institute in January 2026 and recruitment for the second cohort in Fall 2025.","url":"https://doi.org/10.6084/m9.figshare.29637053","authors":["Jiang, Peng","Sosonkina, Masha","Wu, Hongyi","Purwanto, Wirawan","Yang, Mohan"],"tags":["Professional education and training","High performance computing","Cybersecurity and privacy not elsewhere classified"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.6084/m9.figshare.29637053","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20189742","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20189742","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20189742","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20186333","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20186333","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20186333","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20183138","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20183138","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20183138","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20182015","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20182015","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20182015","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20180567","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20180567","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20180567","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20148700","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20148700","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20148700","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20148351","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20148351","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20148351","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20147486","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20147486","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20147486","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.20146872","name":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings","source":"datacite","abstract":"ECG-GenoNet: A Universal Multimodal Deep Learning System for Portable Cardiovascular Diagnostics in Resource-Limited Settings Konstantinos Papageorgiou, MD Independent Researcher; General and Family Medicine, Kalamaria, Thessaloniki, Greece Correspondence: kopapag@gmail.com ABSTRACT Background Cardiovascular emergencies in geographically isolated settings--island communities, rural clinics, and maritime vessels--suffer disproportionate mortality due to the absence of specialist physicians and intelligent diagnostic equipment. No published system combines multimodal artificial intelligence with portable hardware costing under EUR 200 for pre-hospital cardiac diagnostics. Methods We developed ECG-GenoNet, a universal multimodal deep learning system integrating electrocardiographic, pulse oximetric, point-of-care biochemical, ultrasonographic, and genomic data through a Product-of-Experts variational autoencoder. The system operates on portable hardware (ESP32 microcontroller, Raspberry Pi 5, MAX30102 pulse oximeter) with four wireless protocols (BLE, WiFi/WebSocket, MQTT, LoRa) enabling connectivity without cellular coverage. Six model configurations were trained and evaluated on the MIT-BIH Arrhythmia Database (n=3,533 segments, 8 rhythm classes). Findings The bimodal configuration (ECG + genomic features) achieved 94.07% test accuracy (macro AUC 0.9685). The trimodal configuration (ECG + ultrasound + genomic) achieved 96.61% (AUC 0.9995). The universal 4-modal configuration achieved 96.89% (AUC 0.9985). A brain-mapped architecture inspired by intraoperative neurophysiological monitoring achieved 90.68% (AUC 0.9911) with 554K parameters suitable for edge deployment. A TinyML variant (2,664 parameters, 10.4KB) enables on-device triage on ESP32 at sub-millisecond latency. Perfect classification (F1=1.000) was achieved for right bundle branch block and paced rhythm. Interpretation ECG-GenoNet demonstrates that multimodal AI can achieve specialist-level cardiovascular classification on portable hardware, with each additional modality improving performance from 88% (single-modality TinyML) through 94% (bimodal) to 97% (4-modal). The system's graceful degradation--maintaining clinical utility even with a single sensor--addresses the reality of resource-limited practice. Prospective clinical validation in island and rural communities is planned. Funding Self-funded. No external funding received. Keywords: multimodal AI, electrocardiography, portable diagnostics, variational autoencoder, product of experts, federated learning, TinyML, resource-limited settings, pre-hospital medicine INTRODUCTION On a winter night in 2016, on the island of Kythnos in the Cyclades archipelago--a community of fewer than 1,500 permanent residents, accessible only by ferry and with no hospital, no cardiologist, and no advanced imaging--the author of this paper stood alone before a patient in haemodynamic compromise. The available diagnostic tools were a 12-lead electrocardiogram, a stethoscope, and clinical judgement. The nearest catheterisation laboratory was several hours away by sea. This scenario was not exceptional. During sixteen months of mandatory rural medical service across the islands of Kythnos, Herakleia, and Sikinos, the author coordinated dozens of emergency evacuations under analogous conditions, managing presentations ranging from acute myocardial infarction and ventricular arrhythmia to haemodynamic collapse, often as the sole physician on call. This experience was preceded by three years of pre-hospital emergency response with the Italian Red Cross in Bologna, and three years (2005-2008) of intraoperative neurophysiological monitoring (IONM) including EEG, SSEP, MEP, EMG, and brain mapping during neurosurgery. The convergence of these clinical experiences--portable emergency medicine in isolated settings, multimodal biosignal monitoring in the operating theatre, and the persistent absence of intelligent diagnostic tools at the point of ca","url":"https://doi.org/10.5281/zenodo.20146872","authors":["Papageorgiou, Konstantinos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20146872","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2605.10515","name":"SoK: A Systematic Bidirectional Literature Review of AI &amp; DLT Convergence","source":"datacite","abstract":"The integration of Artificial Intelligence (AI) with Distributed Ledger Technology (DLT) has become a growing research area, yet contributions tend to cluster around specific application domains or examine only one direction of the integration, leaving the broader architectural interplay between the two technologies poorly understood. This work addresses that gap through a structured, bidirectional review of peer-reviewed studies published between 2020 and 2025. We classify contributions along two directions: AI-enhanced DLT, and DLT-enhanced AI. In the first case, we examine how AI techniques improve DLT systems across five layers: data, network, consensus, execution, and application layers. In the second case, we analyse how DLT supports AI systems across five layers: infrastructure, data, model, inference, and application layers, with particular attention to federated learning, model evaluation, and multi-agent coordination. The analysis reveals that most works concentrate on a small subset of layers: execution and consensus for AI-enhanced DLT, data and model for DLT-enhanced AI. Other layers remain comparatively neglected. Despite reported improvements in controlled settings, no study demonstrates deployment at production scale, and the field has not yet offered satisfying answers to fundamental questions around scalability, interoperability, and verifiable execution. We argue that progress will require cross-layer co-design and empirical validation in real-world settings.","url":"https://doi.org/10.48550/arxiv.2605.10515","authors":["Kathia, Ali Irzam","Erinle, Yimika","Satybaldy, Abylay","Tasca, Paolo","Vadgama, Nikhil","Javarone, Marco Alberto"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2605.10515","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.19106810","name":"A Systematic Review Of Explainable Artificial Intelligence Techniques For Trustworthy Machine Learning Systems","source":"datacite","abstract":"While machine learning models become increasingly predictive, their lack of transparency threatens trust in high-risk domains like healthcare, finance, and civil infrastructure. Explainable AI research, thus, mainly deals with the challenges associated with making model behaviors and decision processes interpretable. This systematic review, carried out using the PRISMA 2020 statement, examines 89 peer-reviewed Q1 and Q2 journal articles published from 2018 to 2025 and identifies fourteen different XAI techniques. The leading methods in the literature are post-hoc explainability (82%), while SHAP and LIME are the most widely adopted XAI techniques, more so in healthcare applications at 28%. Other model-specific techniques include the Grad-CAM method and attention mechanisms, which find wide applications in computer vision and natural language processing tasks. Going beyond descriptive syntheses, this review proposes an integrated hybrid framework for explainability that leverages SHAP with counterfactual explanations, enhancing interpretive, actionable, and user trust. The review further develops key gaps in current research inquiries: (i) absence of causal reasoning mechanisms, (ii) lacks of uniform evaluation metrics, and (iii) limited human-centered validation. Directions for further studies are discussed and should be oriented toward understanding causal XAI, federated and privacy-preserving explainability, and neurosymbolic hybrid models.","url":"https://doi.org/10.5281/zenodo.19106810","authors":["Dr M. Lavanya","Monisha B","Monika. G"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.19106810","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.19106811","name":"A Systematic Review Of Explainable Artificial Intelligence Techniques For Trustworthy Machine Learning Systems","source":"datacite","abstract":"While machine learning models become increasingly predictive, their lack of transparency threatens trust in high-risk domains like healthcare, finance, and civil infrastructure. Explainable AI research, thus, mainly deals with the challenges associated with making model behaviors and decision processes interpretable. This systematic review, carried out using the PRISMA 2020 statement, examines 89 peer-reviewed Q1 and Q2 journal articles published from 2018 to 2025 and identifies fourteen different XAI techniques. The leading methods in the literature are post-hoc explainability (82%), while SHAP and LIME are the most widely adopted XAI techniques, more so in healthcare applications at 28%. Other model-specific techniques include the Grad-CAM method and attention mechanisms, which find wide applications in computer vision and natural language processing tasks. Going beyond descriptive syntheses, this review proposes an integrated hybrid framework for explainability that leverages SHAP with counterfactual explanations, enhancing interpretive, actionable, and user trust. The review further develops key gaps in current research inquiries: (i) absence of causal reasoning mechanisms, (ii) lacks of uniform evaluation metrics, and (iii) limited human-centered validation. Directions for further studies are discussed and should be oriented toward understanding causal XAI, federated and privacy-preserving explainability, and neurosymbolic hybrid models.","url":"https://doi.org/10.5281/zenodo.19106811","authors":["Dr M. Lavanya","Monisha B","Monika. G"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.19106811","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.17605/osf.io/rejq9","name":"FGCS_2026_Paper_Data","source":"datacite","abstract":"This project contains the data supporting the findings of the research paper entitled \"MetaCS-FL: A Metaheuristic-Based Framework for Client Selection in Federated Learning Systems\". @misc{nunes2025metacsfl, title = {{MetaCS-FL: A Metaheuristic-Based Framework for Client Selection in Federated Learning Systems}}, author = {Nunes, Alan L. and Boeres, Cristina and Pilla, Laércio L. and Drummond, Lúcia M. A.}, year = {2025}, howpublished = {HAL}, hal_id = {hal-05170215}, url = {https://hal.science/hal-05170215} }","url":"https://doi.org/10.17605/osf.io/rejq9","authors":["Nunes, Alan Lira"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17605/osf.io/rejq9","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2501.18416","name":"Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation","source":"datacite","abstract":"Federated Learning (FL) is increasingly being adopted in military collaborations to develop Large Language Models (LLMs) while preserving data sovereignty. However, prompt injection attacks-malicious manipulations of input prompts-pose new threats that may undermine operational security, disrupt decision-making, and erode trust among allies. This perspective paper highlights four vulnerabilities in federated military LLMs: secret data leakage, free-rider exploitation, system disruption, and misinformation spread. To address these risks, we propose a human-AI collaborative framework with both technical and policy countermeasures. On the technical side, our framework uses red/blue team wargaming and quality assurance to detect and mitigate adversarial behaviors of shared LLM weights. On the policy side, it promotes joint AI-human policy development and verification of security protocols.","url":"https://doi.org/10.48550/arxiv.2501.18416","authors":["Lee, Youngjoon","Park, Taehyun","Lee, Yunho","Gong, Jinu","Kang, Joonhyuk"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.18416","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2508.12672","name":"Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering","source":"datacite","abstract":"Federated Learning (FL) enables collaborative model training across multiple clients without sharing private data. We consider FL scenarios wherein FL clients are subject to adversarial (Byzantine) attacks, while the FL server is trusted (honest) and has a trustworthy side dataset. This may correspond to, e.g., cases where the server possesses trusted data prior to federation, or to the presence of a trusted client that temporarily assumes the server role. Our approach requires only two honest participants, i.e., the server and one client, to function effectively, without prior knowledge of the number of malicious clients. Theoretical analysis demonstrates bounded optimality gaps even under strong Byzantine attacks. Experimental results show that our algorithm significantly outperforms standard and robust FL baselines such as Mean, Trimmed Mean, Median, Krum, and Multi-Krum under various attack strategies including label flipping, sign flipping, and Gaussian noise addition across MNIST, FMNIST, and CIFAR-10 benchmarks using the Flower framework.","url":"https://doi.org/10.48550/arxiv.2508.12672","authors":["Kritharakis, Emmanouil","Jakovetic, Dusan","Makris, Antonios","Tserpes, Konstantinos"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.12672","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2604.26116","name":"Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data","source":"datacite","abstract":"Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. However, its performance is often hindered by redundant, malicious, or abnormal samples, leading to model degradation and inefficiency. To overcome these issues, we propose novel sample selection methods for image classification, employing a multitask autoencoder to estimate sample contributions through loss and feature analysis. Our approach incorporates unsupervised outlier detection, using one-class support vector machine (OCSVM), isolation forest (IF), and adaptive loss threshold (AT) methods managed by a central server to filter noisy samples on clients. We also propose a multi-class deep support vector data description (SVDD) loss controlled by a central server to enhance feature-based sample selection. We validate our methods on CIFAR10 and MNIST datasets across varying numbers of clients, non-IID distributions, and noise levels up to 40%. The results show significant accuracy improvements with loss-based sample selection, achieving gains of up to 7.02% on CIFAR10 with OCSVM and 1.83% on MNIST with AT. Additionally, our federated SVDD loss further improves feature-based sample selection, yielding accuracy gains of up to 0.99% on CIFAR10 with OCSVM. These results show the effectiveness of our methods in improving model accuracy across various client counts and noise conditions.","url":"https://doi.org/10.48550/arxiv.2604.26116","authors":["Ardıç, Emre","Genç, Yakup"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.26116","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.19849384","name":"LongevityCommon","source":"datacite","abstract":"Aging, codified in ICD-11 (code XT9T “Ageing-related,” 2018; code MG2A “Ageing-associated decline in intrinsic capacity,” 2025), requires integrative biomarker frameworks that extend beyond individual epigenetic clocks and wearable predictors. We present a hypothesis-stage integrative framework, LongevityCommon. All empirical estimates should be treated as exploratory (hypothesis‑generating), not confirmatory. Pre‑registered tests of an earlier univariate formulation of χ_Ze on the Cuban EEG, Dortmund Vital, and MPI‑LEMON cohorts yielded NULL results (documented in Ze EVIDENCE.md from 2026-04-22; meta‑analysis of Cuban + Dortmund: I² = 90.3 % — invalid; these results were retracted). The current multimodal version of χ_Ze is a post‑hoc reformulation and was not pre‑registered. All reported AUCs are exploratory hypothesis‑generating only, with explicit acknowledgment of p‑hacking risk (Ioannidis, 2005). LongevityCommon is a conceptual ecosystem of five components: (1) MCOA (Multi‑Counter Architecture of Organismal Aging) — a meta‑theory positing aging as a weighted sum of parallel counters: Ltissue(n,t)=iwi(tissue)fi(Di(n,t)). Axioms M1–M4 include an operational definition of falsifiability (M4) revised in v5 based on community‑standard validation thresholds: MCOA is considered falsified if on a pre‑registered cohort with N ≥ 2000 at α = 0.001 the partial r² for all‑cause mortality after controlling for chronological age and sex is full R² = 0.778), but full Sobol decomposition (S2 + ST) with 95 % CI and nested cross‑validation on real GTEx data (N = 948) revealed that the difference is not statistically significant (p = 0.12 after correction). CDATA remains an open, falsifiable hypothesis; final determination requires full decomposition on real data (Cell‑DT v4.0). (3) Ze Theory — a thermodynamic‑geometric formalism. The equation dZe/dt=−I(Z) is postulated as an ansatz, motivated by Burgholzer (2015) and Pearson et al. (2021); living systems are far‑from‑equilibrium, and a formal bridge between these physical‑clock systems and biological aging is absent. (4) BioSense — a wearable platform with the variational principle F=E−TS−Ipred. The theoretical fixed point v*=0.45631 at k=1 (sensitivity range v*[0.32;0.58] for k[0.5;2.0]). **Empirically tested via swept‑v* search on All‑of‑Us (N = 500): v*_optimal = 0.451 (95 % CI 0.443–0.459), consistent with the theoretical value.** (5) FCLC (Federated Clinical Learning Cooperative) — a federated learning infrastructure. ε_total ≈ 0.43 at (σ, q, T) = (1.5, 0.013, 5); RDP composition via subsampled Gaussian (Wang et al., 2019) combined with the Mironov (2017) framework. Threat model (explicit disclosure, v5): (a) FCLC central server — semi‑honest only (never sees raw data, only aggregated updates); (b) Byzantine‑robust aggregation (Krum up to 25 % malicious clients); (c) NOT secure against active server collusion; (d) NOT secure against malicious server deviating from the protocol. This is a blocker for GDPR Article 9 medical data until FCLC v14 (malicious‑secure migration planned for Q1 2027). The ecosystem provides a falsifiable (per updated M4) draft platform, but persistent blocking limitations remain: (i) all pilots are underpowered, not pre‑registered, and post‑hoc, which in light of Ioannidis (2005) gives a clear risk of false‑positive findings; (ii) FCLC is semi‑honest only — blocker for GDPR Art. 9; (iii) CDATA status is inconclusive, requiring full Sobol decomposition on real data; (iv) v* empirically tested and confirmed; (v) Ze→biology is an ansatz without formal derivation; (vi) bridge to CDATA (5 parameters on N = 196) is underpowered (Harrell rule violated) and moved to Supplementary; (vii) key publications (MCOA, Ze, BioSense) are not peer‑reviewed; (viii) EIC Pathfinder consortium formation deferred to Q1 2027 (0 signed EU LoIs as of 2026-04-21).","url":"https://doi.org/10.5281/zenodo.19849384","authors":["Tkemaladze, Jaba"],"tags":["aging biomarker","Ze complexity index","federated learning","differential privacy","citizen science","biological age","EEG","HRV"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19849384","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2604.23426","name":"Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy","source":"datacite","abstract":"Federated learning (FL) is a distributed machine learning method where multiple devices collaboratively train a model under the management of a central server without sharing underlying data. One of the key challenges of FL is the communication bottleneck caused by variations in connection speed and bandwidth across devices. Therefore, it is essential to reduce the size of transmitted data during training. Additionally, there is a potential risk of exposing sensitive information through the model or gradient analysis during training. To address both privacy and communication efficiency, we combine differential privacy (DP) and adaptive quantization methods. We use Laplacian-based DP to preserve privacy, which is relatively underexplored in FL and offers tighter privacy guarantees than Gaussian-based DP. We propose a simple and efficient global bit-length scheduler using round-based cosine annealing, along with a client-based scheduler that dynamically adapts based on client contribution estimated through dataset entropy analysis. We evaluate our approach through extensive experiments on CIFAR10, MNIST, and medical imaging datasets, using non-IID data distributions across varying client counts, bit-length schedulers, and privacy budgets. The results show that our adaptive quantization methods reduce total communicated data by up to 52.64% for MNIST, 45.06% for CIFAR10, and 31% to 37% for medical imaging datasets compared to 32-bit float training while maintaining competitive model accuracy and ensuring robust privacy through differential privacy.","url":"https://doi.org/10.48550/arxiv.2604.23426","authors":["Ardıç, Emre","Genç, Yakup"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.23426","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2604.23386","name":"A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning","source":"datacite","abstract":"Federated Learning (FL) typically assumes unconditional collaboration, a premise that overlooks the complexities of real-world, multi-stakeholder environments in which clients may need to exclude one another for strategic, regulatory, or competitive reasons. This paper addresses this gap, which we term 'client-level disagreements,' by first introducing a taxonomy of such scenarios. We then propose a robust, multi-track resolution strategy that guarantees strict client exclusion by creating and managing isolated model update paths ('tracks'), thereby preventing the cross-contamination and unfairness issues present in naive strategies. Through an empirical evaluation of our custom simulation system across 34 scenarios using the MNIST and N-CMAPSS datasets, we validate that our approach correctly handles permanent, temporal, and overlapping disagreement patterns. Our scalability analysis reveals the server-side resolution algorithm's overhead is negligible (&lt;1 ms per round) even under heavy load. The primary scalability constraint is the client-side training load from participating in multiple tracks, a cost that we show can be effectively mitigated by a submodel reuse strategy. This work presents a scalable and architecturally sound method for managing client-level disagreements, and enhances the practical applicability of FL in settings where policy compliance and strategic control are non-negotiable.","url":"https://doi.org/10.48550/arxiv.2604.23386","authors":["Rosendal, Daan","Oprescu, Ana"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.11; C.2.4; I.2.6"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.23386","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2507.20016","name":"FedSWA: Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging","source":"datacite","abstract":"For federated learning (FL) algorithms such as FedSAM, their generalization capability is crucial for real-word applications. In this paper, we revisit the generalization problem in FL and investigate the impact of data heterogeneity on FL generalization. We find that FedSAM usually performs worse than FedAvg in the case of highly heterogeneous data, and thus propose a novel and effective federated learning algorithm with Stochastic Weight Averaging (called \\texttt{FedSWA}), which aims to find flatter minima in the setting of highly heterogeneous data. Moreover, we introduce a new momentum-based stochastic controlled weight averaging FL algorithm (\\texttt{FedMoSWA}), which is designed to better align local and global models. Theoretically, we provide both convergence analysis and generalization bounds for \\texttt{FedSWA} and \\texttt{FedMoSWA}. We also prove that the optimization and generalization errors of \\texttt{FedMoSWA} are smaller than those of their counterparts, including FedSAM and its variants. Empirically, experimental results on CIFAR10/100 and Tiny ImageNet demonstrate the superiority of the proposed algorithms compared to their counterparts. Open source code at: https://github.com/junkangLiu0/FedSWA.","url":"https://doi.org/10.48550/arxiv.2507.20016","authors":["junkang, Liu","Liu, Yuanyuan","Shang, Fanhua","Liu, Hongying","Liu, Jin","Feng, Wei"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.1","68T05"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.20016","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.18449371","name":"SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening","source":"datacite","abstract":"@article{rangwala2025sketchguard, title={SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening}, author={Rangwala, Murtaza and Azzedin, Farag and Sinnott, Richard O and Buyya, Rajkumar}, journal={arXiv preprint arXiv:2510.07922}, year={2025} }","url":"https://doi.org/10.5281/zenodo.18449371","authors":["Murtaza Rangwala"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18449371","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2604.12737","name":"Evaluating Differential Privacy Against Membership Inference in Federated Learning: Insights from the NIST Genomics Red Team Challenge","source":"datacite","abstract":"While Federated Learning (FL) mitigates direct data exposure, the resulting trained models remain susceptible to membership inference attacks (MIAs). This paper presents an empirical evaluation of Differential Privacy (DP) as a defense mechanism against MIAs in FL, leveraging the environment of the 2025 NIST Genomics Privacy-Preserving Federated Learning (PPFL) Red Teaming Event. To improve inference accuracy, we propose a stacking attack strategy that ensembles seven black-box estimators to train a meta-classifier on prediction probabilities and cross-entropy losses. We evaluate this methodology against target models under three privacy configurations: an unprotected convolutional neural network (CNN, $ε=\\infty$), a low-privacy DP model ($ε=200$), and a high-privacy DP model ($ε=10$). The attack outperforms all baselines in the No DP and Low Privacy settings and, critically, maintains measurable membership leakage at $ε=200$ where a single-signal LiRA baseline collapses. Evaluated on an independent third-party benchmark, these results provide an empirical characterisation of how stacking-based inference degrades across calibrated DP tiers in FL.","url":"https://doi.org/10.48550/arxiv.2604.12737","authors":["Bertoli, Gustavo de Carvalho"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.12737","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.19546616","name":"Federated Clinical Learning Cooperative (FCLC)","source":"datacite","abstract":"Background: Training clinical artificial intelligence (AI) models requires large, diverse datasets that are rarely available at a single institution due to privacy regulations (GDPR, HIPAA) and institutional risk aversion. Existing federated learning platforms lack validated differential privacy, Byzantine robustness, fair contribution attribution, and secure aggregation simultaneously. Objective: We present the Federated Clinical Learning Cooperative (FCLC), an open-source platform enabling multi-institutional clinical AI development without raw data leaving participating sites. We evaluate FCLC against centralized and non-private baselines across two independent clinical datasets, two model architectures, five heterogeneity conditions, and three adversarial scenarios. Methods: FCLC implements a five-layer privacy stack: (1) direct identifier removal; (2) quasi-identifier generalization; (3) k-anonymity (k ≥ 5); (4) Gaussian DP-SGD (ε = 2.0/round, δ = 10⁻⁵, Rényi accountant α = 4.0); and (5) cryptographic secure aggregation (SecAgg+) via the CommonHealth subproject. The choice of Krum for Byzantine robustness is supported by recent theoretical work establishing robustness guarantees for MultiKrum aggregation rules [Bareilles et al., 2026]. Aggregation uses FedProx (μ = 0.1) with Krum Byzantine robustness (f = 25%). Contribution attribution uses Monte Carlo Shapley (M = 150). Validation was performed on MIMIC-IV (N = 12,543, T2DM, 30-day readmission) and eICU-CRD (N = 8,420, sepsis, in-hospital mortality), under IID and non-IID Dirichlet (α ∈ {1.0, 0.5, 0.1, 0.01}) partitions, with logistic regression and multilayer perceptron (MLP) architectures, across 5 and 20 simulated nodes. Results: On MIMIC-IV, FCLC (MLP, DP) achieved AUC = 0.758 [95% CI: 0.739–0.777], compared to centralized oracle 0.789 and FedAvg 0.771 (IID). Under severe non-IID (Dirichlet α = 0.1, EMD = 0.31), FCLC preserved AUC = 0.748 [0.727–0.769] while FedAvg degraded to 0.694 (p_adj = 0.003). Membership inference AUC with DP was 0.52 ± 0.03 (indistinguishable from chance). At the clinically calibrated threshold, FCLC achieved sensitivity 0.671, specificity 0.724, with positive net benefit on Decision Curve Analysis. The privacy budget ε=2.0 per round aligns with state-of-the-art frameworks achieving 94–97% of non-private baseline performance at this budget [Vallabhaneni et al., 2026], and moderate privacy budgets (ε≈10) are clinically acceptable per recent reviews [npj Digital Medicine, 2026]. Conclusions: FCLC provides a validated, regulation-compliant infrastructure for federated clinical AI. The full cryptographic privacy stack, including SecAgg+, makes the platform suitable for mutual-distrust clinical deployments. Recent advances in f-differential privacy [Li et al., 2025] and Ripple Shapley for data attribution [Zeng et al., 2026] suggest promising directions for future enhancements.","url":"https://doi.org/10.5281/zenodo.19546616","authors":["Tkemaladze, Jaba"],"tags":["federated learning","differential privacy","clinical AI","data governance","OMOP Common Data Model","Shapley value","MIMIC-IV","eICU"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19546616","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.19489424","name":"Federated Clinical Learning Cooperative (FCLC)","source":"datacite","abstract":"Background: Training clinical artificial intelligence (AI) models requires large, diverse datasets that are rarely available at a single institution due to privacy regulations (GDPR, HIPAA) and institutional risk aversion. Existing federated learning platforms lack validated differential privacy, Byzantine robustness, fair contribution attribution, and secure aggregation simultaneously. Objective: We present the Federated Clinical Learning Cooperative (FCLC), an open-source platform enabling multi-institutional clinical AI development without raw data leaving participating sites. We evaluate FCLC against centralized and non-private baselines across two independent clinical datasets, two model architectures, five heterogeneity conditions, and three adversarial scenarios. Methods: FCLC implements a five-layer privacy stack: (1) direct identifier removal; (2) quasi-identifier generalization; (3) k-anonymity (k ≥ 5); (4) Gaussian DP-SGD (ε = 2.0/round, δ = 10⁻⁵, Rényi accountant α = 4.0); and (5) cryptographic secure aggregation (SecAgg+) via the CommonHealth subproject. The choice of Krum for Byzantine robustness is supported by recent theoretical work establishing robustness guarantees for MultiKrum aggregation rules [Bareilles et al., 2026]. Aggregation uses FedProx (μ = 0.1) with Krum Byzantine robustness (f = 25%). Contribution attribution uses Monte Carlo Shapley (M = 150). Validation was performed on MIMIC-IV (N = 12,543, T2DM, 30-day readmission) and eICU-CRD (N = 8,420, sepsis, in-hospital mortality), under IID and non-IID Dirichlet (α ∈ {1.0, 0.5, 0.1, 0.01}) partitions, with logistic regression and multilayer perceptron (MLP) architectures, across 5 and 20 simulated nodes. Results: On MIMIC-IV, FCLC (MLP, DP) achieved AUC = 0.758 [95% CI: 0.739–0.777], compared to centralized oracle 0.789 and FedAvg 0.771 (IID). Under severe non-IID (Dirichlet α = 0.1, EMD = 0.31), FCLC preserved AUC = 0.748 [0.727–0.769] while FedAvg degraded to 0.694 (p_adj = 0.003). Membership inference AUC with DP was 0.52 ± 0.03 (indistinguishable from chance). At the clinically calibrated threshold, FCLC achieved sensitivity 0.671, specificity 0.724, with positive net benefit on Decision Curve Analysis. The privacy budget ε=2.0 per round aligns with state-of-the-art frameworks achieving 94–97% of non-private baseline performance at this budget [Vallabhaneni et al., 2026], and moderate privacy budgets (ε≈10) are clinically acceptable per recent reviews [npj Digital Medicine, 2026]. Conclusions: FCLC provides a validated, regulation-compliant infrastructure for federated clinical AI. The full cryptographic privacy stack, including SecAgg+, makes the platform suitable for mutual-distrust clinical deployments. Recent advances in f-differential privacy [Li et al., 2025] and Ripple Shapley for data attribution [Zeng et al., 2026] suggest promising directions for future enhancements.","url":"https://doi.org/10.5281/zenodo.19489424","authors":["Tkemaladze, Jaba"],"tags":["federated learning","differential privacy","clinical AI","data governance","OMOP Common Data Model","Shapley value","MIMIC-IV","eICU"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19489424","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.19486563","name":"Distributed Machine Learning Systems: Algorithms, Communication Eciency, and Convergence Guarantees","source":"datacite","abstract":"The rapid growth of datasets and model sizes in modern machine learning hasmade distributed training not merely advantageous but essential. This survey provides a comprehensive review of distributed machine learning systems, with a focuson three interconnected aspects: (i) distributed optimization algorithms, including synchronous and asynchronous stochastic gradient descent, federated learning,and decentralized methods; (ii) communication eciency techniques such as gradient compression, quantization, and local SGD; and (iii) convergence guaranteesunder realistic assumptions including heterogeneous data, partial participation, andByzantine failures. We present a unied theoretical framework that relates communication complexity to convergence rates, identifying fundamental trade-os betweencommunication rounds, computation per round, and statistical accuracy. Our surveycovers over 180 papers published between 2017 and 2025, with systematic comparisons on standard benchmarks. We identify key open problems including optimalcommunication-computation trade-os, convergence under extreme heterogeneity,and the intersection of distributed training with dierential privacy.","url":"https://doi.org/10.5281/zenodo.19486563","authors":["Cherif, Ahmed"],"tags":["distributed machine learning, federated learning, gradient compression, communication eciency, convergence theory, stochastic optimization, data parallelism"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19486563","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.5281/zenodo.19486564","name":"Distributed Machine Learning Systems: Algorithms, Communication Eciency, and Convergence Guarantees","source":"datacite","abstract":"The rapid growth of datasets and model sizes in modern machine learning hasmade distributed training not merely advantageous but essential. This survey provides a comprehensive review of distributed machine learning systems, with a focuson three interconnected aspects: (i) distributed optimization algorithms, including synchronous and asynchronous stochastic gradient descent, federated learning,and decentralized methods; (ii) communication eciency techniques such as gradient compression, quantization, and local SGD; and (iii) convergence guaranteesunder realistic assumptions including heterogeneous data, partial participation, andByzantine failures. We present a unied theoretical framework that relates communication complexity to convergence rates, identifying fundamental trade-os betweencommunication rounds, computation per round, and statistical accuracy. Our surveycovers over 180 papers published between 2017 and 2025, with systematic comparisons on standard benchmarks. We identify key open problems including optimalcommunication-computation trade-os, convergence under extreme heterogeneity,and the intersection of distributed training with dierential privacy.","url":"https://doi.org/10.5281/zenodo.19486564","authors":["Cherif, Ahmed"],"tags":["distributed machine learning, federated learning, gradient compression, communication eciency, convergence theory, stochastic optimization, data parallelism"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19486564","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.48550/arxiv.2405.16240","name":"AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models","source":"datacite","abstract":"In this paper, we introduce analytic federated learning (AFL), a new training paradigm that brings analytical (i.e., closed-form) solutions to the federated learning (FL) with pre-trained models. Our AFL draws inspiration from analytic learning -- a gradient-free technique that trains neural networks with analytical solutions in one epoch. In the local client training stage, the AFL facilitates a one-epoch training, eliminating the necessity for multi-epoch updates. In the aggregation stage, we derive an absolute aggregation (AA) law. This AA law allows a single-round aggregation, reducing heavy communication overhead and achieving fast convergence by removing the need for multiple aggregation rounds. More importantly, the AFL exhibits a property that \\textit{invariance to data partitioning}, meaning that regardless of how the full dataset is distributed among clients, the aggregated result remains identical. This could spawn various potentials, such as data heterogeneity invariance and client-number invariance. We conduct experiments across various FL settings including extremely non-IID ones, and scenarios with a large number of clients (e.g., $\\ge 1000$). In all these settings, our AFL constantly performs competitively while existing FL techniques encounter various obstacles. Our codes are available at https://github.com/ZHUANGHP/Analytic-federated-learning.","url":"https://doi.org/10.48550/arxiv.2405.16240","authors":["He, Run","Tong, Kai","Fang, Di","Sun, Han","Zeng, Ziqian","Li, Haoran","Chen, Tianyi","Zhuang, Huiping"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.16240","addedAt":"2026-08-31T06:41:19.833Z","updatedAt":"2026-08-31T06:41:19.833Z"},{"id":"doi:10.2139/ssrn.4860813","name":"A Comprehensive Survey on Client Selection Strategies in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4860813","authors":["Jian Li","Tongbao Chen","Shaohua Teng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-11T05:19:15Z","doi":"10.2139/ssrn.4860813","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1109/icus61736.2024.10840140","name":"A Federated Learning Client Selection Method via Multi-Task Deep Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icus61736.2024.10840140","authors":["Le Hou","Laisen Nie","Xinyang Deng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-22T18:45:23Z","doi":"10.1109/icus61736.2024.10840140","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1109/icaiic60209.2024.10463462","name":"Federated Learning with Privacy-Preserving Active Learning: A Min-Max Mutual Information Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaiic60209.2024.10463462","authors":["Zahir Alsulaimawi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-20T18:12:10Z","doi":"10.1109/icaiic60209.2024.10463462","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1049/pbpc066e_ch3","name":"Federated learning enabled digital twins for Industry 5.0: perspectives, challenges, and future directions","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_ch3","authors":["Dasaradharami Reddy Kandati","Supriya Y","Gokul Yenduri","Praveen Kumar Reddy Maddikunta","Thippa Reddy Gadekallu","Abdul Wahid"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_ch3","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1049/pbpc066e_ch11","name":"Federated learning in medical education in Industry 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbpc066e_ch11","authors":["Y. Supriya","Dasari Bhulakshmi","Sweta Bhattacharya","Thippa Reddy Gadekallu","Rajesh Kaluri","S. Sumathy","Srinivas Koppu","Pratik Vyas","David J. Brown","Mufti Mahmud"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T08:38:15Z","doi":"10.1049/pbpc066e_ch11","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1049/pbse025e_fm","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbse025e_fm","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T08:10:23Z","doi":"10.1049/pbse025e_fm","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.2139/ssrn.4846933","name":"Out-of-Distribution Detection Via Outlier Exposure in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4846933","authors":["Gu-Bon Jeong","Dong-Wan Choi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-29T03:17:52Z","doi":"10.2139/ssrn.4846933","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.23919/acc60939.2024.10644821","name":"Federated Learning-Based Distributed Model Predictive Control of Nonlinear Systems","source":"crossref","abstract":"","url":"https://doi.org/10.23919/acc60939.2024.10644821","authors":["Zeyuan Xu","Zhe Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-05T17:56:19Z","doi":"10.23919/acc60939.2024.10644821","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.21428/594757db.cc446d10","name":"SemiS-VFL: A Semi-Supervised Machine Learning Frameworkfor Vertical Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.21428/594757db.cc446d10","authors":["Kun Yan","Paula Branco"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-31T18:26:17Z","doi":"10.21428/594757db.cc446d10","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1109/punecon63413.2024.10895282","name":"FLARE: Federated Learning And Resilient Encryption for Firewalls","source":"crossref","abstract":"","url":"https://doi.org/10.1109/punecon63413.2024.10895282","authors":["Lohith Senthilkumar","Aaditya Rengarajan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-27T18:42:58Z","doi":"10.1109/punecon63413.2024.10895282","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.2139/ssrn.4978530","name":"Enhancing Federated Learning-Based Social Recommendations with Graph Attention Networks","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4978530","authors":["Zhihui Xu","Bing Li","Wenming Cao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-07T15:01:13Z","doi":"10.2139/ssrn.4978530","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1109/radar58436.2024.10994111","name":"Federated Learning for Radar Systems - Sharing Knowledge without sharing Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/radar58436.2024.10994111","authors":["Simon Wagner","Stefan Brüggenwirth"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-14T13:30:46Z","doi":"10.1109/radar58436.2024.10994111","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.70121/001c.124883","name":"Enhancing data privacy in federated learning using artificial intelligence","source":"crossref","abstract":"Federated Learning (FL) is a decentralised approach to machine learning that enables model training on local devices without the need to share raw data. While FL inherently provides some level of privacy, significant challenges remain in fully protecting sensitive information. This article explores advanced artificial intelligence (AI) techniques for improving privacy in federated learning. I propose novel methods that include differential privacy, homomorphic encryption, and secure multiparty computation, enhanced by AI-driven optimizations. My empirical studies and theoretical analyses demonstrate the effectiveness and efficiency of these techniques in maintaining data protection.","url":"https://doi.org/10.70121/001c.124883","authors":["Piyush Dua"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-16T21:26:45Z","doi":"10.70121/001c.124883","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/b978-0-44-313233-9.00013-8","name":"Federated learning in healthcare applications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-313233-9.00013-8","authors":["Prasad Kanhegaonkar","Surya Prakash"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-15T05:12:37Z","doi":"10.1016/b978-0-44-313233-9.00013-8","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1109/flta63145.2024.10840165","name":"Distributed Intelligence in the Computing Continuum","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840165","authors":["Schahram Dustdar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840165","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1109/icsc63108.2024.10894787","name":"On the 5th Generation of Local Training Methods in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsc63108.2024.10894787","authors":["Peter Richtarik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-25T18:44:34Z","doi":"10.1109/icsc63108.2024.10894787","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.2174/9789815179125124010006","name":"Role of Federated Learning in Healthcare: A Review","source":"crossref","abstract":"In the modern era, there is a boom in automating medical diagnosis by adopting emerging technologies and advanced applications of artificial intelligence. These technologies require a huge amount of data for training the models and precisely predicting the disease or disorder. Multiple organizations can contribute data for such systems but maintaining data privacy while sharing the data is a major challenge. Also, provisioning a large data corpus for the performance improvement of machine learning and deep learning models in the healthcare domain while keeping the patient’s medical confidentiality intact is a point of concern. Thus, there is a strong need to preserve the privacy of medical data. This calls for the use of up-to-the-minute technologies where the necessity of sharing raw data is completely eradicated, while each organization receives a catered infrastructure for processing data. A cross-silo federated learning model is based on the concept of decentralized data weights collection from multiple clients which are then processed on the central server for modeling and aggregation, thus maintaining data privacy in its true sense. The authors in this manuscript provide a detailed comparative study of the different deep learning-based models in federated learning and how efficiently they can classify lung X-Ray images into three classes: Covid-19, Pneumonia, and Normal. This study can provide a benchmark for the researchers looking forward to deep learning-based model applications of cross-silo federated learning in healthcare.","url":"https://doi.org/10.2174/9789815179125124010006","authors":["Geeta Rani","Meet Oza","Heta Patel","Vijaypal Singh Dhaka","Sushma Hans"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-07T14:29:53Z","doi":"10.2174/9789815179125124010006","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1002/spy2.403","name":"SAFe‐Health: Guarding federated learning‐driven smart healthcare with federated defense averaging against data poisoning","source":"crossref","abstract":"Abstract Federated learning (FL) serves as a decentralized training framework for machine learning (ML) models, preserving data privacy in critical domains such as smart healthcare. However, it has been found that attackers can exploit this decentralized learning framework to perform data and model poisoning attacks, specifically in FL‐driven smart healthcare. This work delves into the realm of FL‐driven smart healthcare systems, consisting of multiple hospitals based architecture and focusing on heart disease detection using FL. We carry out data poisoning attacks, using two different attacking methods, label flipping attack and input data/feature manipulation attack to demonstrate that such FL‐driven smart healthcare systems are vulnerable to attacks. To guard the system against such attack, we propose a novel federated averaging defense mechanism to stop the identified poisoned clients in weight aggregation. This mechanism effectively detects and thwarts data poisoning attempts by identifying compromised clients during weight aggregation. The proposed mechanism is based on the idea of weighted averaging, where each client's contribution is weighted according to its trustworthiness. The proposed work addresses a critical gap in the literature by focusing on the often‐overlooked issue of poisoning attacks in tabular text datasets, which are crucial to the smart healthcare system. We conduct the testbed‐based experiment to demonstrate that the proposed mechanism is effectively detecting and mitigating data poisoning attacks in selected FL‐driven smart healthcare scenarios, while maintaining high accuracy and convergence rates.","url":"https://doi.org/10.1002/spy2.403","authors":["Bhabesh Mali","Pranav Kumar Singh","Nabajyoti Mazumdar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-22T05:59:52Z","doi":"10.1002/spy2.403","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1109/icaiqsa64000.2024.10882458","name":"I. Federated Learning: Pushing Forward Machine Learning Without Scarifising Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaiqsa64000.2024.10882458","authors":["Om Ghade","Supriya Narad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-21T18:37:11Z","doi":"10.1109/icaiqsa64000.2024.10882458","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.2139/ssrn.4883785","name":"Graph Representation Federated Learning for Malware Detection in Internet of Health Things","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4883785","authors":["Mohamed Amjath","Shagufta Henna"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-08T14:38:22Z","doi":"10.2139/ssrn.4883785","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1145/3647750.3647756","name":"Federated Learning with MLPerfTiny Tasks and Server-side Momentum","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3647750.3647756","authors":["Lawrence Roman A. Quizon","Anastacia B. Alvarez"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-12T18:05:22Z","doi":"10.1145/3647750.3647756","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1109/icee63041.2024.10667872","name":"RSF: Reinforcement learning based hybrid split and federated learning for edge computing environments","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icee63041.2024.10667872","authors":["Alireza Soleimani","Negar Anabestani","Mahmoud Momtazpour"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-19T17:22:07Z","doi":"10.1109/icee63041.2024.10667872","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.54254/2755-2721/46/20241096","name":"Federated learning-based machine learning for predicting brain tumor","source":"crossref","abstract":"The swift advancements in artificial intelligence (AI) and machine learning have profoundly impacted the realm of medical research, particularly in the realm of diagnosing and treating intricate conditions such as brain tumors. These tumors, characterized by unregulated cell proliferation, pose significant challenges. The complexities inherent in brain tumor diagnosis stem from the intricate nature of these tumors, symptom overlap with other ailments, and the inherent complexity of the brain itself. Nevertheless, the application of an advanced machine learning algorithm known as Federated Learning (FL) has demonstrated its potential to address data privacy concerns and enhance diagnostic accuracy in this context. This essay discusses the application of FL which is a decentralized training strategy in brain tumor research. FL allows multiple institutions to train the model collaboratively without data sharing. The key advancement includes the improved U-Net model implementation and the utilization of Convolutional Neural Network (CNN) Ensemble Architectures for brain tumor identification. This paper also discusses the potential of FL in optimizing weight sharing for model aggregation in heterogeneous data. Furthermore, it underscores the important role of FL in modern healthcare since FL also solves the privacy concern in smart healthcare. However, challenges such as communication lag, data heterogeneity, and computational cost still exist.","url":"https://doi.org/10.54254/2755-2721/46/20241096","authors":["Yuheng Ge"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-18T06:30:17Z","doi":"10.54254/2755-2721/46/20241096","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1109/ickecs61492.2024.10617119","name":"Optimizing Federated Learning Efficiency: Exploring Learning Rate Scheduling in Sparsified Ternary Compression","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ickecs61492.2024.10617119","authors":["C Nithyaniranjana Murthy","Sh Manjula"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-07T17:29:50Z","doi":"10.1109/ickecs61492.2024.10617119","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1109/icassp48485.2024.10447282","name":"Federated CINN Clustering for Accurate Clustered Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icassp48485.2024.10447282","authors":["Yuhao Zhou","Minjia Shi","Yuxin Tian","Yuanxi Li","Qing Ye","Jiancheng Lv"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-18T18:56:31Z","doi":"10.1109/icassp48485.2024.10447282","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1109/mlnlp63328.2024.10800660","name":"Poisoning Attacks Against Non-IID Federated Learning with Mixed-Data Calibration","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mlnlp63328.2024.10800660","authors":["Xufei Zhang","Suleyman Uludag"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-20T18:56:07Z","doi":"10.1109/mlnlp63328.2024.10800660","addedAt":"2026-08-31T06:41:20.069Z","updatedAt":"2026-08-31T06:41:20.069Z"},{"id":"doi:10.1109/icmlcn59089.2024.10625081","name":"Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary?","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlcn59089.2024.10625081","authors":["Adrian Edin","Zheng Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-15T17:18:59Z","doi":"10.1109/icmlcn59089.2024.10625081","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1504/ijbra.2024.10064487","name":"Optimisation with Deep Learning for Leukaemia Classification in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijbra.2024.10064487","authors":["Smritilekha Das","PADMANABAN K"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-02T13:00:13Z","doi":"10.1504/ijbra.2024.10064487","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.36227/techrxiv.173386294.43450621/v1","name":"Beyond Data Sharing: Enhancing IoT Intrusion Detection with Blockchain-Enabled Federated Learning","source":"crossref","abstract":"Federated Learning (FL) is a decentralized ML approach that can be used for intrusion detection in Internet of Things (IoT) devices. It involves the local training of AI models and their aggregation at a central server. This methodology eliminates the need for data sharing between IoT devices while fostering collaborative model enhancement. Nonetheless, concerns arise due to the lack of transparency surrounding the shared local models and the aggregation techniques employed. This lack of transparency can potentially lead to model poisoning attacks and hinder collaborators from using alternative aggregation methods that better align with their specific use cases. To address this issue, this paper proposes a blockchainbased approach with FL to ensure transparent and immutable records of model updates, thereby bolstering security and trust for intrusion detection in IoT devices. In contrast to traditional synchronization or periodic update-based approaches, this paper proposes a novel time-independent aggregation method in FL blockchain, allowing for flexibility. Additionally, the proposed blockchain allows various users to utilize their own aggregation methods rather than a fixed one, based on their needs, resources, and availability. We also develop a user interface for the proposed blockchain system that assists in visualizing different aspects of the method, such as model aggregation. The proposed system is tested using traditional metrics like AI model performance as well as extensive user testing.","url":"https://doi.org/10.36227/techrxiv.173386294.43450621/v1","authors":["Aditya Durgadas Naik","Raj Mani Shukla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-10T15:35:46Z","doi":"10.36227/techrxiv.173386294.43450621/v1","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/ist64061.2024.10843494","name":"Federated Learning: Attacks, Defenses, Opportunities and Challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ist64061.2024.10843494","authors":["Ghazaleh Shirvani","Saeid Ghasemshirazi","Behzad Beigzadeh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T13:23:03Z","doi":"10.1109/ist64061.2024.10843494","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/bigdata62323.2024.10825389","name":"Discovering Communities With Clustered Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata62323.2024.10825389","authors":["Mickaël Bettinelli","Alexandre Benoit","Kévin Grandjean"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T18:31:23Z","doi":"10.1109/bigdata62323.2024.10825389","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.5220/0012574000003660","name":"CL-FedFR: Curriculum Learning for Federated Face Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012574000003660","authors":["Devilliers Dube","Çiğdem Erdem","Ömer Korçak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-03T18:09:35Z","doi":"10.5220/0012574000003660","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4687810","name":"Grasp Control Method for Robotic Manipulator Based on Federated Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4687810","authors":["Shida Zhong","Yue Wang","Tao Yuan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-08T22:22:55Z","doi":"10.2139/ssrn.4687810","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/tensymp61132.2024.10752306","name":"Abdominal Multi-Organ Segmentation Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tensymp61132.2024.10752306","authors":["Govind Yadav","Annappa B","Sachin D N"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-19T18:38:53Z","doi":"10.1109/tensymp61132.2024.10752306","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1360/ssi-2023-0239","name":"Federated continual learning based on prototype learning","source":"crossref","abstract":"","url":"https://doi.org/10.1360/ssi-2023-0239","authors":["HaoDong ZHANG","Liu YANG","Jian YU","QingHua HU","LiPing JING"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-22T06:09:17Z","doi":"10.1360/ssi-2023-0239","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4693597","name":"Personalized Federated Learning for Modulation Classification in Communication Systems with Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4693597","authors":["Peggy Sue Mathis","Dinh Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-13T05:18:22Z","doi":"10.2139/ssrn.4693597","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.36227/techrxiv.24679386.v3","name":"Privacy-Enhanced Image Restoration in Remote Sensing via Federated Learning","source":"crossref","abstract":"This research addresses the dual challenges of image restoration quality and data privacy in optical remote sensing. Traditional restoration methods often fall short due to the complex nature of remote sensing images, and data privacy concerns further complicate the use of advanced techniques. By integrating the Deep Memory Connected Neural Network (DMCN) with the Data-Decoupled Federated Learning (DDFL) framework, our approach enables significant improvements in image restoration without requiring direct access to sensitive raw data. This method not only enhances data privacy by leveraging federated learning principles but also incorporates advanced techniques like Gaussian image denoising to maintain high restoration quality despite potential noise introduced by the federated process. The performance of the federated DMCN, particularly on the UCMERCED dataset, demonstrates minimal accuracy degradation, even in the presence of noise, while the strategic use of Downsampling Units within DMCN optimizes computational efficiency. Our comprehensive evaluations reveal the effectiveness of this approach in balancing data privacy with the need for high-quality image restoration, suggesting a promising direction for future advancements in remote sensing applications.","url":"https://doi.org/10.36227/techrxiv.24679386.v3","authors":["Muhammad Jahanzeb Khan","Suman Rath","Muhammad Hassan Zaib"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-08T13:32:25Z","doi":"10.36227/techrxiv.24679386.v3","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/isit57864.2024.10619328","name":"Federated Learning for Heterogeneous Bandits with Unobserved Contexts","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isit57864.2024.10619328","authors":["Jiabin Lin","Shana Moothedath"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-19T13:25:01Z","doi":"10.1109/isit57864.2024.10619328","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/southeastcon52093.2024.10500250","name":"Privacy-Preserving Backdoor Attacks Mitigation in Federated Learning Using Functional Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/southeastcon52093.2024.10500250","authors":["Funminivi Olagunju","Isaac Adom","Mahmoud Nabil Mahmoud"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-24T17:22:34Z","doi":"10.1109/southeastcon52093.2024.10500250","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1016/j.comnet.2024.110375","name":"Marvel: Towards Efficient Federated Learning on IoT Devices","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.comnet.2024.110375","authors":["Libin Liu","Xiuting Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-27T17:02:54Z","doi":"10.1016/j.comnet.2024.110375","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/icmla61862.2024.00294","name":"Mutual Information-Based Feature Selection for Federated Learning Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmla61862.2024.00294","authors":["Samuel Suárez-Marcote","Laura Morán-Fernández","Verónica Bolón-Canedo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-04T18:39:11Z","doi":"10.1109/icmla61862.2024.00294","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/ijcnn60899.2024.10651181","name":"MetaClusterFL: Personalized Federated Learning on Non-IID data with Meta-learning and Clustering","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ijcnn60899.2024.10651181","authors":["Hui Zeng","Shiyu Xiong","Hongzhou Shi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-09T17:35:05Z","doi":"10.1109/ijcnn60899.2024.10651181","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4937615","name":"Balancing Privacy Preservation and Accuracyoptimization in Federated Learning for E-Health","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4937615","authors":["Rihab Saidi","Tarek Moulahi","Salah Zidi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-05T23:20:26Z","doi":"10.2139/ssrn.4937615","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/iwcmc61514.2024.10592529","name":"An Efficient and Secure Federated Learning Communication Framework","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iwcmc61514.2024.10592529","authors":["Hassan Noura","Khalil Hariss"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-17T17:18:34Z","doi":"10.1109/iwcmc61514.2024.10592529","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1002/9781394219230.ch12","name":"Architectural Patterns for the Design of Federated Learning Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394219230.ch12","authors":["Vijay Anand Rajasekaran","Jayalakshmi Periyasamy","Madala Guru Brahmam","Balamurugan Baluswamy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-22T21:19:26Z","doi":"10.1002/9781394219230.ch12","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/flta63145.2024.10839969","name":"Intelligent Digital Twin (DT) for Wireless Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839969","authors":["Mohsen Guizani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839969","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/icsece61636.2024.10729249","name":"A Federated Learning Algorithm Based on Combination of Prototype and Comparative Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsece61636.2024.10729249","authors":["Yanwei Chen","Wenbo Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-29T17:28:51Z","doi":"10.1109/icsece61636.2024.10729249","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00014-4","name":"Evaluating gradient inversion attacks and defenses","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00014-4","authors":["Yangsibo Huang","Samyak Gupta","Zhao Song","Sanjeev Arora","Kai Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:49:06Z","doi":"10.1016/b978-0-44-319037-7.00014-4","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1002/9781394219230.ch6","name":"Federated Learning: Introduction, Evolution, Working, Advantages, and Its Application in Various Domains","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394219230.ch6","authors":["Manoj Kumar Pandey*","Naresh Kumar Kar","Priyanka Gupta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-22T21:19:26Z","doi":"10.1002/9781394219230.ch6","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1016/b978-0-44-319037-7.00009-0","name":"Considerations on the theory of training models with differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-319037-7.00009-0","authors":["Marten van Dijk","Phuong Ha Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-23T09:48:34Z","doi":"10.1016/b978-0-44-319037-7.00009-0","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4748805","name":"In-Network Federated Learning Control","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4748805","authors":["Nour El Houda Yellas","Bernardetta Addis","Selma Boumerdassi","Roberto Riggio","Stefano Secci"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-05T16:15:04Z","doi":"10.2139/ssrn.4748805","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.52783/fhi.8","name":"A Federated Learning Approach for Non-Co-Located Datasets: Enhancing Data Governance and Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.52783/fhi.8","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-30T08:53:03Z","doi":"10.52783/fhi.8","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.31224/4177","name":"Federated Learning for Privacy-Preserving AI:  Revolutionizing Data Sharing Across Industries","source":"crossref","abstract":"Federated Learning (FL) has emerged as a groundbreaking approach to Artificial Intelligence (AI) that preserves user privacy while enabling collaborative model training across diverse datasets. This survey highlights the evolution, architecture, methodologies, and applications of FL in privacy-preserving data sharing across industries such as healthcare, finance, and edge computing. Challenges such as communication overhead, data heterogeneity, and security threats are discussed, alongside solutions leveraging encryption and differential privacy techniques. Real-world applications illustrate the transformative potential of FL in enabling secure, cross-enterprise AI.","url":"https://doi.org/10.31224/4177","authors":["Sharathchandra Patil","Sai Geethanjali K","Nidhi Umashankar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-28T23:40:44Z","doi":"10.31224/4177","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1016/b978-0-443-13897-3.00002-3","name":"Architecture and design choices for federated learning in modern digital healthcare systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-13897-3.00002-3","authors":["Konstantinos A. Koutsopoulos","Christoph Thümmler","Angelica Avila Castillo","Alice Abend","Stefan Covaci","Benjamin Ertl","Giannis Ledakis","Stéphane Lorin","Vincent Thouvenot","Sahar Haddad","Gouenou Coatrieux","Reda Bellafqira","Alessandro Bassi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T07:07:42Z","doi":"10.1016/b978-0-443-13897-3.00002-3","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.36227/techrxiv.172962905.59565426/v1","name":"Enhancing Data Security in Federated Learning with Dilithium","source":"crossref","abstract":"Federated learning (FL) enables multiple parties to collaboratively train machine learning models while preserving data privacy. However, securing communication within FL frameworks remains a significant challenge due to potential vulnerabilities to data breaches and integrity attacks. This paper proposes a novel approach using Dilithium, a robust digital signature framework, to enhance data security in FL. By integrating Dilithium into FL protocols, this study demonstrates enhanced protection against data tampering and unauthorized access, thereby promoting safer and more efficient collaborative model training across distributed networks. Furthermore, our approach incorporates an optimized key distribution mechanism that reduces latency and ensures seamless synchronization among participants. Additionally, the proposed client selection algorithm aims to minimize the time and computational load on the system. Experimental results demonstrate that our system achieves a total processing time of 6.891 seconds, significantly outperforming the 10.24 seconds of normal FL and 12.32 seconds of FL-Dilithium systems on the same computing platforms. Additionally, the proposed model achieves an accuracy of 94%, surpassing the 93% of the normal FL and 92% of the FL-Dilithium models.","url":"https://doi.org/10.36227/techrxiv.172962905.59565426/v1","authors":["Quoc Bao Phan","Hien Nguyen","Phap Duong Ngoc","Tuy Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-22T16:31:00Z","doi":"10.36227/techrxiv.172962905.59565426/v1","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4790638","name":"Heterogeneity-Aware Device Selection for Efficient Federated Edge Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4790638","authors":["Yiran Shi","Jieyan Nie","Xingwei Li","Hui Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-15T09:58:00Z","doi":"10.2139/ssrn.4790638","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1201/9781003162018-1","name":"Blockchain Empowered Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003162018-1","authors":["Reza Nourmohammadi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-05T15:12:41Z","doi":"10.1201/9781003162018-1","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.36227/techrxiv.171073597.75352317/v2","name":"Learned Model Compression for Efficient and Privacy-Preserving Federated Learning","source":"crossref","abstract":"Federated learning (FL) performs collaborative training of deep learning models among multiple clients, safeguarding data privacy, security, and legal adherence by preserving training data locally. Despite the benefits of FL, its wider implementation is hindered by communication overheads and potential privacy risks. Transiting locally updated model parameters between edge clients and servers demands high communication bandwidth, leading to high latency and Internet infrastructure constraints. Furthermore, recent works have shown that the malicious server can reconstruct clients’ training data from gradients, significantly escalating privacy threats and violating regularizations. Different defense techniques have been proposed to address this information leakage from the gradient or updates, including introducing noise to gradients, performing model compression (such as sparsification), and feature perturbation. However, these methods either impede model convergence or entail substantial communication costs, further exacerbating the communication demands in FL. To develop an efficient and privacy-preserving FL, we introduce an autoencoder-based method for compressing and, thus, perturbing the model parameters. The client utilizes an autoencoder to acquire the representation of the local model parameters and then shares it as the compressed model parameters with the server, rather than the true model parameters. The use of the autoencoder for lossy compression serves as an effective protection against information leakage from the updates. Additionally, the perturbation is intrinsically linked to the autoencoder’s input, thereby achieving a perturbation with respect to the parameters of different layers. Moreover, our approach can reduce 4.1 × the communication rate compared to federated averaging. We empirically validate our method using two widely-used models within the context of federated learning, considering three datasets, and assess its performance against several well-established defense frameworks. The results indicate that our approach attains a model performance nearly identical to that of unmodified local updates, while effectively preventing information leakage and reducing communication costs in comparison to other methods, including noisy gradients, gradient sparsification, and PRECODE.","url":"https://doi.org/10.36227/techrxiv.171073597.75352317/v2","authors":["Yiming Chen","Lusine Abrahamyan","Hichem Sahli","Nikos Deligiannis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-15T15:00:15Z","doi":"10.36227/techrxiv.171073597.75352317/v2","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1145/3694908.3696174","name":"NestFL: Enhancing federated learning through nested multi-capacity model pruning in heterogeneous edge computing","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3694908.3696174","authors":["Xiaomao Zhou","Qingmin Jia","Yujiao Hu","Renchao Xie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T06:18:55Z","doi":"10.1145/3694908.3696174","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/indiscon62179.2024.10744201","name":"Probabilistic Free Riding Attack in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/indiscon62179.2024.10744201","authors":["Anee Sharma","Ningrinla Marchang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-11T18:37:45Z","doi":"10.1109/indiscon62179.2024.10744201","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1002/9781394219230.ch7","name":"Application Domains of Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394219230.ch7","authors":["S. Annamalai","N. Sangeetha","M. Kumaresan","Dommaraju Tejavarma","Gandhodi Harsha Vardhan","A. Suresh Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-22T21:19:26Z","doi":"10.1002/9781394219230.ch7","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.2139/ssrn.4904613","name":"Vertical Federated Learning-Based Decentralized Voltage Prediction Considering Interarea Coupling and Privacy Preservation","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4904613","authors":["Jianfeng Yan","Beibei Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-24T18:22:16Z","doi":"10.2139/ssrn.4904613","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.5006922","name":"(Dp)2fl:Dynamic Personalized Differential Privacy Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5006922","authors":["Cuiyun Shi","jingyu wang","Lixin Liu","Xin Chang","Yini Pu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-01T13:39:12Z","doi":"10.2139/ssrn.5006922","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/icicml63543.2024.10958064","name":"HE-Floc: Federated Learning Fingerprint Indoor Localization Method Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicml63543.2024.10958064","authors":["Chengze Li","Xuejun Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-14T17:35:47Z","doi":"10.1109/icicml63543.2024.10958064","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1142/9789811292552_0010","name":"Faster Secure Data Mining Framework via Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811292552_0010","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T10:21:23Z","doi":"10.1142/9789811292552_0010","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/icoin59985.2024.10572139","name":"Global Model Privacy Protection Mechanism in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icoin59985.2024.10572139","authors":["Ajit Kumar","Bong Jun Choi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-03T17:25:55Z","doi":"10.1109/icoin59985.2024.10572139","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1201/9781003489368-8","name":"Innovative Solutions","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003489368-8","authors":["Raj Kishor Verma","Kaushal Kishor"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T17:25:42Z","doi":"10.1201/9781003489368-8","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1016/j.egyai.2024.100438","name":"Federated learning and non-federated learning based power forecasting of photovoltaic/wind power energy systems: A systematic review","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.egyai.2024.100438","authors":["Ferial ElRobrini","Syed Muhammad Salman Bukhari","Muhammad Hamza Zafar","Nedaa Al-Tawalbeh","Naureen Akhtar","Filippo Sanfilippo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-17T15:52:56Z","doi":"10.1016/j.egyai.2024.100438","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.36227/techrxiv.173337801.16860716/v1","name":"FreeFL: Privacy-Preserving Cross-Silo Federated Learning without Third Party","source":"crossref","abstract":"Cross-silo federated learning (FL) allows organizations to collaboratively train machine learning (ML) models by aggregating local gradients from clients without sharing their training data. Despite its merits, it suffers from privacy concerns due to the leakage of local gradients. A popular approach is to have clients mask their local gradients using homomorphic encryption (HE). However, this not only results in a reliance on a trusted third party (TTP), but also leads to significant computational and communication overhead. In addition, in existing cross-silo FL protocols, the aggregation operation is performed by a centralized aggregator, raising new security issues. One of these issues involves verifying the correctness of the aggregated results returned by the aggregator. The aggregator has been removed in the cross-device setting by leveraging blockchain technology, but not in the cross-silo setting. In this paper, we propose FreeFL, an efficient privacypreserving cross-silo FL that eliminates the need for the TTP and aggregator, as well as achieves the optimal communication rounds. The high-level idea behind FreeFL is to customize a lightweight decentralized symmetric encryption with additive homomorphism for cross-silo FL. To this end, we design an efficient decentralized multiparty symmetric encryption (DMSE) scheme and two lightweight multiparty computation protocols. We evaluate the performance of FreeFL, and the experimental results indicate that FreeFL exhibits high efficiency in both computation and communication. Additionally, we conduct experimental comparisons between FreeFL and other existing HEbased cross-silo FL protocols to show that FreeFL achieves significant computational efficiency improvements.","url":"https://doi.org/10.36227/techrxiv.173337801.16860716/v1","authors":["Fucai Luo","Jiahui Wu","Haiyan Wang","Xingfu Yan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-05T00:53:40Z","doi":"10.36227/techrxiv.173337801.16860716/v1","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/icmla61862.2024.00059","name":"SAFARI: Self-regulAted Clustered FederAted Learning in a HeteRogeneous EnvIronment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmla61862.2024.00059","authors":["Sai Puppala","Ismail Hossain","Md Jahangir Alam","Sajedul Talukder"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-04T18:39:11Z","doi":"10.1109/icmla61862.2024.00059","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4903968","name":"A Federated Semi-Supervised Learning Framework for Vehicular Networks","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4903968","authors":["Jiachen Liu","Jianfeng Yang","Jianling Hu","Tianqi Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-24T06:20:54Z","doi":"10.2139/ssrn.4903968","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.3390/books978-3-7258-1993-5","name":"Edge-Cloud Computing and Federated-Split Learning in the Internet of Things","source":"crossref","abstract":"","url":"https://doi.org/10.3390/books978-3-7258-1993-5","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-23T05:26:54Z","doi":"10.3390/books978-3-7258-1993-5","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1201/9781003482000","name":"Artificial Intelligence Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003482000","authors":["Ahmed A Elngar","Diego Oliva","Valentina E. Balas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T17:15:57Z","doi":"10.1201/9781003482000","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.31224/3720","name":"ENHANCING IMAGE CLASSIFICATION WITH FEDERATED LEARNING: A COMPARATIVE STUDY OF VGG16 AND MOBILENET ON CIFAR-10","source":"crossref","abstract":"In this project, I explored the application of federated learning (FL) algorithms in enhancing image classification tasks using the CIFAR-10 dataset, with a focus on the VGG16 and MobileNet architectures. The project compared the efficacy of various FL algorithms against non-federated baseline models using the same architectures. The non-federated VGG16 and MobileNet models served as baselines to evaluate the relative performance enhancements brought about by federated learning. Remarkably, all explored federated learning algorithms, with the exception of Federated Averaging (FedAvg), demonstrated superior accuracy over the baselines. Although FedAvg did not surpass the baseline models in terms of accuracy, it significantly enhanced the security aspect of model training, thereby reinforcing the trade-off between model performance and data privacy inherent in federated learning setups. This detailed comparison not only underscores the potential of federated learning in practical applications but also highlights the specific strengths and limitations of each algorithm within a federated framework, presenting a comprehensive view of their impacts in a controlled experimental setup.","url":"https://doi.org/10.31224/3720","authors":["Ehsan Alam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-15T19:35:30Z","doi":"10.31224/3720","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.36227/techrxiv.171073597.75352317/v1","name":"Learned Model Compression for Efficient and Privacy-Preserving Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.36227/techrxiv.171073597.75352317/v1","authors":["Yiming Chen","Lusine Abrahamyan","Hichem Sahli","Nikos Deligiannis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-18T00:26:21Z","doi":"10.36227/techrxiv.171073597.75352317/v1","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/globecom52923.2024.10901544","name":"Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom52923.2024.10901544","authors":["Somayeh Kianpisheh","Chafika Benzaïd","Tarik Taleb"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T17:30:35Z","doi":"10.1109/globecom52923.2024.10901544","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/flta63145.2024.10839791","name":"Message from the General Chairs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839791","authors":["Schahram Dustdar","Omer Rana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839791","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/icdcs60910.2024.00087","name":"Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icdcs60910.2024.00087","authors":["Sijia Chen","Ningxin Su","Baochun Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-22T17:43:37Z","doi":"10.1109/icdcs60910.2024.00087","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/flta63145.2024.10839838","name":"FedAD-Bench: A Unified Benchmark for Federated Unsupervised Anomaly Detection in Tabular Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839838","authors":["Ahmed Anwar","Brian Moser","Dayananda Herurkar","Federico Raue","Vinit Hegiste","Tatjana Legler","Andreas Dengel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839838","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1137/1.9781611978032.96","name":"Personalized Federated Learning with Contextual Modulation and Meta-Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1137/1.9781611978032.96","authors":["Anna Vettoruzzo","Mohamed-Rafik Bouguelia","Thorsteinn Rögnvaldsson"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-11T07:07:02Z","doi":"10.1137/1.9781611978032.96","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/icoici62503.2024.10696723","name":"Federated Learning for Privacy-Preserving Machine Learning in IoT Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icoici62503.2024.10696723","authors":["G Anitha","A. Jegatheesan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T17:33:05Z","doi":"10.1109/icoici62503.2024.10696723","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/globecom52923.2024.10900987","name":"Lightweight Federated Learning based White Space Detector for Cognitive Radios","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom52923.2024.10900987","authors":["Md Mehedi Hassan Galib","Mohamed Younis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T17:30:35Z","doi":"10.1109/globecom52923.2024.10900987","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/bigdata62323.2024.10825763","name":"One-Shot Clustering for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata62323.2024.10825763","authors":["Maciej Krzysztof Zuziak","Roberto Pellungrini","Salvatore Rinzivillo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T18:31:23Z","doi":"10.1109/bigdata62323.2024.10825763","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/globecom52923.2024.10901785","name":"Cooperative Gradient Coding for Semi-Decentralized Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom52923.2024.10901785","authors":["Shudi Weng","Chengxi Li","Ming Xiao","Mikael Skoglund"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T17:30:35Z","doi":"10.1109/globecom52923.2024.10901785","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.36227/techrxiv.171177326.68048420/v1","name":"Mobility-Aware Federated Learning-based Proactive UAVs Placement in Emerging Cellular Networks","source":"crossref","abstract":"With the vast proliferation of smart mobile devices, there is an ever-increasing demand for higher data rates and seamless connectivity throughout. Current 5th generation and beyond (B5G) cellular networks struggle to eradicate outage zones and ensure seamless connectivity. One promising solution to this problem is the use of unmanned aerial vehicles (UAVs) to assist the traditional ground network and provide connectivity in places where there are no small base stations or faulty ones as a result of some natural disasters such as flooding. In this paper, we propose a mobility-aware federated learning-based proactive UAV placement (MFPUP) framework to assist the existing ground communication network and minimize overall network outages. Our MFPUP framework utilizes the federated learning-based mobility prediction model that recommends the potential outage areas to deploy UAVs using user-UAV association techniques such as the optimum association approach (OAP) and the greedy association approach (GAP). In order to validate the performance of the proposed MFPUP scheme we carried out extensive simulations. Our results show that the proposed MFPUP framework associates the optimal number of users to UAVs while also significantly improving users' downlink rates.","url":"https://doi.org/10.36227/techrxiv.171177326.68048420/v1","authors":["Sanaullah Manzoor","Mazen Hasna","Muhammad Zeeshan Shakir"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-30T00:34:30Z","doi":"10.36227/techrxiv.171177326.68048420/v1","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/icstcc62912.2024.10744688","name":"Empowering Smart Cities through Federated Learning An Overview","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icstcc62912.2024.10744688","authors":["Ayah Jarour"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-11T18:37:56Z","doi":"10.1109/icstcc62912.2024.10744688","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/besc64747.2024.10780716","name":"Prompt Tuning Empowering Downstream Tasks in Multimodal Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/besc64747.2024.10780716","authors":["Yichen Bao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-12T19:07:29Z","doi":"10.1109/besc64747.2024.10780716","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.21203/rs.3.rs-3873379/v1","name":"A Differentially Private Federated Learning Application in Privacy-Preserving Medical Imaging","source":"crossref","abstract":"Abstract This research addresses the escalating concerns surrounding privacy, particularly in the context of safeguarding sensitive medical data within the increasingly demanding healthcare landscape. We undertake an experimental exploration of differentially private federated learning systems, employing three benchmark datasets—PathMNIST, BloodMNIST, and OrganAMNIST—for medical image classification. This study pioneers the application of federated learning with differential privacy in healthcare, closely simulating real-world data distribution across twelve hospitals. Additionally, we introduce a novel deep-learning architecture tailored for differentially private models. Our findings demonstrate the superior performance of federated learning models compared to traditional approaches, with accuracy levels approaching those of non-private settings. By leveraging resilient deep learning models, we aim to enhance privacy, efficiency, and effectiveness in healthcare solutions, benefiting patients, healthcare practitioners, and the overall healthcare system through privacy-protected healthcare.","url":"https://doi.org/10.21203/rs.3.rs-3873379/v1","authors":["Mohamad HAJ FARES","Ahmet SERTBAŞ"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-18T11:10:16Z","doi":"10.21203/rs.3.rs-3873379/v1","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1109/mercon63886.2024.10688983","name":"FL-CycleGAN: Enhancing Mobile Photography with Federated Learning-Enabled CycleGAN","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mercon63886.2024.10688983","authors":["Ramindu Walgama","K.T. Yasas Mahima"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-30T17:24:20Z","doi":"10.1109/mercon63886.2024.10688983","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/icnc64304.2024.10987611","name":"Personalized Federated Learning with Multi-Server","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icnc64304.2024.10987611","authors":["Yunzi Huang","Haifeng Dai","Guanqiao Kong","Jing Nie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-19T17:52:05Z","doi":"10.1109/icnc64304.2024.10987611","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/icait62580.2024.10807969","name":"Optimal Client Selection for Wireless Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icait62580.2024.10807969","authors":["Heng An","Cuitao Zhu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-30T19:19:13Z","doi":"10.1109/icait62580.2024.10807969","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1007/978-3-031-66047-4_5","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-66047-4_5","authors":["Lukas Willburger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-10T16:36:23Z","doi":"10.1007/978-3-031-66047-4_5","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/globecom52923.2024.10901154","name":"Federated Learning Incorporating Non-Orthogonal Transmission and Unstructured Model Pruning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom52923.2024.10901154","authors":["Siyu Gao","Ming Zhao","Shengli Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T17:30:35Z","doi":"10.1109/globecom52923.2024.10901154","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/iccns62192.2024.10776239","name":"Federated Learning: Catalyzing the Next AI Breakthrough","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccns62192.2024.10776239","authors":["Daniel J. Beutel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-11T22:22:43Z","doi":"10.1109/iccns62192.2024.10776239","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/icc51166.2024.10622470","name":"Cloud-based Federated Learning Framework for MRI Segmentation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc51166.2024.10622470","authors":["Rukesh Prajapati","Amr S. El-Wakeel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-20T15:34:42Z","doi":"10.1109/icc51166.2024.10622470","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/inocon60754.2024.10511589","name":"Gradient Guard: Robust Federated Learning using Saliency Maps","source":"crossref","abstract":"","url":"https://doi.org/10.1109/inocon60754.2024.10511589","authors":["Yeshwanth Nagaraj","Ujjwal Gupta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-06T17:20:54Z","doi":"10.1109/inocon60754.2024.10511589","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.5121/csit.2024.141103","name":"Federated Learning-Based Privacy Protection Methods for Internet of Things Systems","source":"crossref","abstract":"The Internet of Things (IoT) forms intelligent systems, such as smart cities and factories, to enhance productivity and provide revolutionary and automated services to end-users and organisations. An IoT ecosystem requires more dynamics and heterogeneity with advanced privacy preservation. Federated Learning (FL)addresses the challenge of maintaining data privacy using a privacy-preserving sharing mechanism instead of transmitting raw data. However, the latest cyber threats cause privacy and security breaches. This study systematically analyses federated learning-based privacy-preserving methods in IoT systems. A standard IoT architecture with possible privacy threats is illustrated. Also, Federated Learning schemes and their taxonomies are discussed in a privacy-preserving manner, with initial experiments proving the significance of FLbased privacy preservation in IoT environments. This finds acceptable noise addition in differential privacy by keeping higher testing accuracy in different settings to enhance privacy preservation of federated learning. Various Federated Learning schemes, challenges and future research directions are covered.","url":"https://doi.org/10.5121/csit.2024.141103","authors":["Mahmuda Akter","Nour Moustafa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-07T13:51:27Z","doi":"10.5121/csit.2024.141103","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1145/3674399.3674473","name":"A Federated Meta-Reinforcement Learning Algorithm Based on Gradient Correction","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3674399.3674473","authors":["Zerui Qin","Sheng Yue"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-30T22:26:48Z","doi":"10.1145/3674399.3674473","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2523/iptc-23888-ms","name":"Federated Learning for Seismic Data Denoising: Privacy-Preserving Paradigm","source":"crossref","abstract":"Summary Federated Learning (FL) is a framework that empowers multiple clients to develop robust machine learning (ML) algorithms while safeguarding data privacy and security. This paper's primary goal is to investigate the capability of the FL framework in preserving privacy and to assess its efficacy for clients operating within the oil and gas industry. To demonstrate the practicality of this framework, we apply it to seismic denoising use cases incorporating data from clients with IID (independent &amp; and identically distributed) and Non-IID (non-independent and non-identically distributed) or domain-shifted data distributions. The FL setup is implemented using the well-established Flower framework. The experiment involves injecting noise into 3D seismic data and subsequently employing various ML algorithms to eliminate this noise. All experiments were conducted using both IID and Non-IID data, employing both traditional and FL approaches, various tests considering different types of noise, noise factors, number of 2D seismic slices, diverse models, number of clients, and aggregations strategies. We tested different model aggregation strategies, such as FedAvg, FedProx, and Fedcyclic, alongside client selection strategies that consider model divergence, convergence trend similarity, and client weight analysis to improve the aggregation process. We also incorporated batch normalization into the network architecture to reduce data discrepancies among clients. The denoising process was evaluated using metrics like mean-square-error (MSE), signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR). A comparison between conventional methods and FL demonstrated that FL exhibited a reduced error rate, especially when dealing with larger datasets. Furthermore, FL harnessed the power of parallel computing, resulting in a notable 30% increase in processing speed, enhanced resource utilization, and a remarkable 99% reduction in communication costs. To sum it up, this study underscores the potential of FL in the context of seismic denoising, safeguarding data privacy, and enhancing overall performance. We addressed the associated challenges by experimenting with various approaches for client selection and aggregation within a privacy-preserving framework. Notably, among these aggregation strategies, FedCyclic stands out as it offers faster convergence, achieving performance levels comparable to FedAvg and FedProx with fewer training iterations.","url":"https://doi.org/10.2523/iptc-23888-ms","authors":["Kamalesh Kumar Mandakolathur Guruprasad","Gayatri Sunil Ambulkar","Geetha Nair"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-27T22:59:19Z","doi":"10.2523/iptc-23888-ms","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.4018/979-8-3693-3502-4.ch001","name":"Introduction to AI, ML, Federated Learning, and LLM in Software Engineering","source":"crossref","abstract":"This research investigates the transformative intersection of artificial intelligence (AI), machine learning (ML), federated learning, and large language models (LLM) within the realm of Software Engineering. The study contextualizes the historical evolution of these technologies, highlighting pivotal milestones that have shaped their integration into the fabric of software development. The primary objective is to provide a comprehensive overview of how AI, ML, federated learning, and LLM are revolutionizing Software Engineering practices. The research employs a multifaceted methodology comprising literature reviews, case studies, and real-world examples to analyze the impact of these technologies. Key findings include substantial improvements in development efficiency, enhanced collaboration, and the adaptive nature of software solutions. The proposed methodology emphasizes interdisciplinary collaboration, ethical considerations, practical implementation guidance, scalability strategies, and a continuous feedback loop.","url":"https://doi.org/10.4018/979-8-3693-3502-4.ch001","authors":["Pawan Kumar Goel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-02T09:03:18Z","doi":"10.4018/979-8-3693-3502-4.ch001","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1201/9781003497196-1","name":"The Evolution of Machine Learning: From Centralized to Distributed","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003497196-1","authors":["Jayakrushna Sahoo","Akarsh K. Nair","Richa Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-30T10:30:21Z","doi":"10.1201/9781003497196-1","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4690807","name":"A Cloud Edge Federated Data Center Balanced Optimization Scheduling Algorithm Combining Deep Reinforcement Learning and Pso","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4690807","authors":["Xiuniao Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-10T19:56:08Z","doi":"10.2139/ssrn.4690807","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.4018/979-8-3693-1874-4.ch010","name":"Federated Learning for Private AI Diagnosis of Schizophrenia","source":"crossref","abstract":"This study delves into the realm of federated learning, focusing on its application in the private and accurate artificial intelligence (AI) diagnosis of schizophrenia. Leveraging the collaborative power of distributed datasets without compromising individual privacy, the research investigates the feasibility and effectiveness of federated learning models. The study employs advanced AI algorithms for schizophrenia diagnosis, ensuring the confidentiality of patient data. The results demonstrate the potential of federated learning as a secure and efficient approach for enhancing diagnostic capabilities in mental health, specifically in the context of schizophrenia. This research contributes to the ongoing efforts to harness cutting-edge technologies for improved mental health diagnostics while prioritizing individual privacy.","url":"https://doi.org/10.4018/979-8-3693-1874-4.ch010","authors":["­ Kunal","Santosh Kumar Sahu","Mohammed Azam","Manuj Takkar","Jatin Bansal","Jyoti Prasad Patra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-02T09:01:10Z","doi":"10.4018/979-8-3693-1874-4.ch010","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/flta63145.2024.10839834","name":"Swarm Split Learning: A Fully Distributed Machine Learning System for Energy-Constrained IoT Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839834","authors":["Ahmad Ayad","Tim Bauerle","Mahdi Barhoush","Anke Schmeink"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839834","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.23919/ifipnetworking62109.2024.10619909","name":"Federated Learning for Network Traffic Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.23919/ifipnetworking62109.2024.10619909","authors":["Sadananda Behera","Saroj Kumar Panda","Tania Panayiotou","Georgios Ellinas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-15T17:18:52Z","doi":"10.23919/ifipnetworking62109.2024.10619909","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.24963/ijcai.2024/761","name":"Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning","source":"crossref","abstract":"Recent methods leverage a hypernet to handle the performance-fairness trade-offs in federated learning. This hypernet maps the clients' preferences between model performance and fairness to preference-specifc models on the trade-off curve, known as local Pareto front. However, existing methods typically adopt a uniform preference sampling distribution to train the hypernet across clients, neglecting the inherent heterogeneity of their local Pareto fronts. Meanwhile, from the perspective of generalization, they do not consider the gap between local and global Pareto fronts on the global dataset. To address these limitations, we propose HetPFL to effectively learn both local and global Pareto fronts. HetPFL comprises Preference Sampling Adaptation (PSA) and Preference-aware Hypernet Fusion (PHF). PSA adaptively determines the optimal preference sampling distribution for each client to accommodate heterogeneous local Pareto fronts. While PHF performs preference-aware fusion of clients' hypernets to ensure the performance of the global Pareto front. We prove that HetPFL converges linearly with respect to the number of rounds, under weaker assumptions than existing methods. Extensive experiments on four datasets show that HetPFL significantly outperforms seven baselines in terms of the quality of learned local and global Pareto fronts.","url":"https://doi.org/10.24963/ijcai.2024/761","authors":["Rongguang Ye","Ming Tang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-26T10:28:11Z","doi":"10.24963/ijcai.2024/761","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/fmec62297.2024.10710287","name":"Keynote Speech 4 Federated Learning for IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fmec62297.2024.10710287","authors":["Daniel J. Beutel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-14T17:22:38Z","doi":"10.1109/fmec62297.2024.10710287","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/conit61985.2024.10627549","name":"Maximizing Privacy in Reinforcement Learning with Federated Approaches","source":"crossref","abstract":"","url":"https://doi.org/10.1109/conit61985.2024.10627549","authors":["Shiva Mehta","Sumeet Singh Sarpal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-15T13:21:37Z","doi":"10.1109/conit61985.2024.10627549","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1016/j.vehcom.2023.100709","name":"A state-of-the-art on federated learning for vehicular communications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.vehcom.2023.100709","authors":["Drissi Maroua"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-12-02T11:23:26Z","doi":"10.1016/j.vehcom.2023.100709","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4995227","name":"Fedelr: When Federated Learning Meets Learning with Noisy Labels","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4995227","authors":["Ruizhi Pu","Lixing Yu","Shaojie Zhan","Gezheng Xu","Fan Zhou","Charles X. Ling","Boyu Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-22T15:02:37Z","doi":"10.2139/ssrn.4995227","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/i2ct61223.2024.10543776","name":"Wireless Channel Estimation using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/i2ct61223.2024.10543776","authors":["Jasneet Kaur","M. Arif Khan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-10T17:19:31Z","doi":"10.1109/i2ct61223.2024.10543776","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/gcwkshp64532.2024.11100966","name":"Energy-Aware Federated Learning in Satellite Constellations","source":"crossref","abstract":"","url":"https://doi.org/10.1109/gcwkshp64532.2024.11100966","authors":["Nasrin Razmi","Bho Matthiesen","Armin Dekorsy","Petar Popovski"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-12T17:51:39Z","doi":"10.1109/gcwkshp64532.2024.11100966","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/rivf64335.2024.11009115","name":"Personalized Federated Learning with Optimized Contrastive Learning for Intrusion Detection System","source":"crossref","abstract":"","url":"https://doi.org/10.1109/rivf64335.2024.11009115","authors":["Quan Hong Ngo","Van T.B Pham","Ly Vu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-28T17:50:04Z","doi":"10.1109/rivf64335.2024.11009115","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/globecom52923.2024.10901549","name":"Decentralized Federated Learning over Satellite Networks (Dec-FLSat): A Learning Scheme Based on LEO-Structure","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom52923.2024.10901549","authors":["Mohanad Obeed","Gunes Karabulut Kurt","Halim Yanikomeroglu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T17:30:35Z","doi":"10.1109/globecom52923.2024.10901549","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/bigdata62323.2024.10825886","name":"Federated Learning on Knowledge Graph Embeddings via Contrastive Alignment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata62323.2024.10825886","authors":["Antor Mahmud","Renata Dividino"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T18:31:23Z","doi":"10.1109/bigdata62323.2024.10825886","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/icairc64177.2024.10899993","name":"Personalized federated meta-learning based on dynamic clustering","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icairc64177.2024.10899993","authors":["Qingqing Sun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-04T18:39:08Z","doi":"10.1109/icairc64177.2024.10899993","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/asiancon62057.2024.10837829","name":"Harnessing Distributed Computing Resources with Federated Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/asiancon62057.2024.10837829","authors":["Shiva Mehta","Savinder Kaur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:34Z","doi":"10.1109/asiancon62057.2024.10837829","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/nnice61279.2024.10498478","name":"Federated Learning Based on Feature Fusion","source":"crossref","abstract":"","url":"https://doi.org/10.1109/nnice61279.2024.10498478","authors":["Junjie Cao","Zhiyu Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-22T17:33:49Z","doi":"10.1109/nnice61279.2024.10498478","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/isit57864.2024.10619575","name":"Secure Submodel Aggregation for Resource-Aware Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isit57864.2024.10619575","authors":["Hasin Us Sami","Başak Güler"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-19T13:25:01Z","doi":"10.1109/isit57864.2024.10619575","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.14428/esann/2024.es2024-3","name":"Machine learning in distributed, federated and non-stationary environments - recent trends","source":"crossref","abstract":"","url":"https://doi.org/10.14428/esann/2024.es2024-3","authors":["Mirko Polato","Barbara Hammer","Frank-Michael Schleif"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-09T13:23:50Z","doi":"10.14428/esann/2024.es2024-3","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/acie61839.2024.00009","name":"Federated Learning Poisoning Attack Detection: Reconfiguration Algorithm TopK-FLcredit","source":"crossref","abstract":"","url":"https://doi.org/10.1109/acie61839.2024.00009","authors":["Zhang Hong","Li Hongjiao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-15T17:28:24Z","doi":"10.1109/acie61839.2024.00009","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/isvlsi61997.2024.00038","name":"Efficient Federated Learning Through Distributed Model Pruning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isvlsi61997.2024.00038","authors":["Mohammad Munzurul Islam","Mohammed Alawad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-25T17:27:50Z","doi":"10.1109/isvlsi61997.2024.00038","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.15199/48.2024.06.07","name":"Differentially Private federated learning to Protect Identity in  Stress Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.15199/48.2024.06.07","authors":["Bouchiba GUELTA"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-13T12:33:03Z","doi":"10.15199/48.2024.06.07","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/icmre60776.2024.10532173","name":"Towards Federated Learning by Kernels","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmre60776.2024.10532173","authors":["Kilho Shin","Takenobu Seito","Chris Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-21T17:20:52Z","doi":"10.1109/icmre60776.2024.10532173","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.62441/nano-ntp.v20is6.62","name":"Revolutionizing Emotion-Driven Sentiment Analysis using Federated Learning on Edge Devices for Superior Privacy and Performance","source":"crossref","abstract":"","url":"https://doi.org/10.62441/nano-ntp.v20is6.62","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-19T04:09:25Z","doi":"10.62441/nano-ntp.v20is6.62","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.5023320","name":"Predicting Online Consumer Credit Default Using Mobile Data Based on Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5023320","authors":["Fan Wang","Yunpeng Zhang","Lijian Wei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-16T17:39:07Z","doi":"10.2139/ssrn.5023320","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.4018/979-8-3693-1874-4.ch009","name":"Federated Learning for Privacy Preservation in Healthcare","source":"crossref","abstract":"This chapter delves into fundamental concepts of privacy preservation and federated learning (FL) in healthcare. Emphasizing the importance of privacy in healthcare data, it explores ethical and regulatory considerations surrounding sensitive patient information. The history and significance of FL, distinct from traditional centralized machine learning, are discussed, highlighting its relevance in addressing privacy concerns. The limitations of centralized ML are contrasted with FL's advantages, particularly in preserving privacy. Techniques such as FL averaging, aggregation, and secure multi-party computation (SMPC) for privacy-preserving model updates are examined. Real-world examples illustrate their application in healthcare scenarios. The chapter concludes by addressing technical and ethical challenges linked to FL in healthcare, emphasizing its potential to balance patient data protection with AI advancements. Privacy concerns persist in healthcare AI, making FL a promising solution. The discussion extends to emerging trends and potential breakthroughs in this dynamic field.","url":"https://doi.org/10.4018/979-8-3693-1874-4.ch009","authors":["Hari Kishan Kondaveeti","Chinna Gopi Simhadri","Srileakhana Mangapathi","Valli Kumari Vatsavayi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-02T09:01:10Z","doi":"10.4018/979-8-3693-1874-4.ch009","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/nnice61279.2024.10498908","name":"Network Traffic Anomaly Detection Based on Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/nnice61279.2024.10498908","authors":["Yi Luan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-22T17:33:49Z","doi":"10.1109/nnice61279.2024.10498908","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/ikt65497.2024.10892658","name":"Movable Antenna Design for UAV-Aided Federated Learning via Deep Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ikt65497.2024.10892658","authors":["Mohsen Ahmadzadeh","Saeid Pakravan","Ghosheh Abed Hodtani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-26T13:43:34Z","doi":"10.1109/ikt65497.2024.10892658","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.52202/079017-3583","name":"Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.52202/079017-3583","authors":["Dario Fenoglio","Gabriele Dominici","Pietro Barbiero","Alberto Tonda","Martin Gjoreski","Marc Langheinrich"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-03T11:20:56Z","doi":"10.52202/079017-3583","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.4018/979-8-3693-1874-4.ch012","name":"Federated Learning for Private Cancer Diagnosis With Exascale Computing","source":"crossref","abstract":"This study delves into the intersection of federated learning, privacy preservation, and exascale computing to advance the field of cancer diagnosis. Employing a federated learning framework, the research addresses the imperative need for collaborative, yet privacy-conscious, approaches to healthcare data analysis. Focusing on human cancer diagnosis and detection, the authors leverage the power of exascale computing to handle massive datasets distributed across diverse medical institutions. The proposed methodology ensures privacy by design, enabling secure model training without centralized data aggregation. The findings showcase the efficacy of federated learning and exascale computing in achieving accurate and timely cancer diagnoses while upholding patient privacy, thus paving the way for transformative advancements in personalized and secure healthcare analytics","url":"https://doi.org/10.4018/979-8-3693-1874-4.ch012","authors":["N. R. Vembu","Niladri Maiti","K. Kadiervel","Amarendranath Choudhury","Rajasekhar Pinnamaneni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-02T09:01:10Z","doi":"10.4018/979-8-3693-1874-4.ch012","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4916457","name":"Owl:Worker-Assisted Server Bandwidth Optimization for Efficient Communication Federated Learning","source":"crossref","abstract":"Edge computing in federated learning based on centralized architecture often faces communication constraints in large clusters. Although there have been some efforts like computation-communication overlapping and fine-granularity flow scheduling towards how to reduce the communication cost, this is still a matter of ongoing research. Motivated by the underutilization of bandwidth among workers (edge devices) and the replication of deep neural network (DNN) model distributions in data-parallel federated learning, we propose OWL, a novel worker-assisted server bandwidth optimization method. OWL partitions numerous computation branches into groups based on the model&amp;apos;s network topology, allowing for overlapping model distribution and computation among workers, thereby leveraging idle communication resources on the workers to compensate for server bandwidth. To address the issue of model distribution congestion on the server, we formulate group partition as an optimization problem, which proves to be NP-hard. We tackle this problem through a divide-and-conquer approach employing an approximation grouping algorithm and a deploying algorithm. Finally, we evaluate the performance of OWL through simulations and a comprehensive real-world case study involving model training on OWL and deployment on edge systems. Experimental results demonstrate that OWL reduces overall training time by up to 20%-69% and improves scalability by over 9.5\\% compared to state-of-the-art overlapping approaches.","url":"https://doi.org/10.2139/ssrn.4916457","authors":["Xiaoming Han","Boan Liu","Chuang Hu","Dazhao Cheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-05T15:21:20Z","doi":"10.2139/ssrn.4916457","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4743932","name":"Federated Zero-Shot Learning with Mid-Level Semantic Knowledge Transfer","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4743932","authors":["Shitong Sun","Chenyang Si","Guile Wu","Shaogang Gong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-29T21:22:12Z","doi":"10.2139/ssrn.4743932","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4887224","name":"Multi-Level Analyzation of Imbalance to Resolve Non-Iid-Ness in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4887224","authors":["Haengbok Chung","Jae Sung Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-15T11:55:48Z","doi":"10.2139/ssrn.4887224","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4872729","name":"Secure Fair Aggregation Based on Category Grouping in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4872729","authors":["Jie Zhou","Jinlin Hu","Jiajun Xue","Shengke Zeng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-21T16:51:31Z","doi":"10.2139/ssrn.4872729","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.5046693","name":"Beyond Data Sharing: Enhancing Iot Intrusion Detection with Blockchain-Enabled Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5046693","authors":["Aditya  Durgadas Naik","Raj Mani Shukla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-06T19:43:27Z","doi":"10.2139/ssrn.5046693","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.5063539","name":"Federated Learning-Based Spectrum and Energy Efficiency Enhancement in Hap-Assisted Leo Satellite Communication","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5063539","authors":["Sengly Muy","JungRyun Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-18T21:38:22Z","doi":"10.2139/ssrn.5063539","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/flta63145.2024.10839606","name":"Entropy and Mobility-Based Model Assignment for Multi-Model Vehicular Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10839606","authors":["Wellington Lobato","Joahannes B. D. Da Costa","Luis F. G. Gonzalez","Eduardo Cerqueira","Denis Rosário","Christoph Sommer","Leandro A. Villas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10839606","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1002/9781394219230.ch9","name":"Federated Learning: Bridging Data Privacy and\n            <scp>AI</scp>\n            Advancements","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394219230.ch9","authors":["D. Sumathi","Likitha Chowdary Botta","Mure Sai Jaideep Reddy","Avi Das"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-22T21:19:26Z","doi":"10.1002/9781394219230.ch9","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/icccnt61001.2024.10724591","name":"Retracted: Leveraging Machine Learning for Enhanced SensorPowered Green IoT with Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icccnt61001.2024.10724591","authors":["Rekha Devrani","R Premkumar","Shyam Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-04T18:06:46Z","doi":"10.1109/icccnt61001.2024.10724591","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4813254","name":"Enhancing Image Classification With Federated Learning: a Comparative Study of VGG16 and Mobilenet on CIFAR-10","source":"crossref","abstract":"In this project, I explored the application of federated learning (FL) algorithms in enhancing image classification tasks using the CIFAR-10 dataset, with a focus on the VGG16 and MobileNet architectures. The project compared the efficacy of various FL algorithms against non-federated baseline models using the same architectures. The non-federated VGG16 and MobileNet models served as baselines to evaluate the relative performance enhancements brought about by federated learning. Remarkably, all explored federated learning algorithms, with the exception of Federated Averaging (FedAvg), demonstrated superior accuracy over the baselines. Although FedAvg did not surpass the baseline models in terms of accuracy, it significantly enhanced the security aspect of model training, thereby reinforcing the trade-off between model performance and data privacy inherent in federated learning setups. This detailed comparison not only underscores the potential of federated learning in practical applications but also highlights the specific strengths and limitations of each algorithm within a federated framework, presenting a comprehensive view of their impacts in a controlled experimental setup.","url":"https://doi.org/10.2139/ssrn.4813254","authors":["Ehsan Alam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-22T12:29:31Z","doi":"10.2139/ssrn.4813254","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/globecom52923.2024.10901817","name":"Contract Design for Adaptive Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom52923.2024.10901817","authors":["Ni Yang","Yue Cui","Man Hon Cheung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T17:30:35Z","doi":"10.1109/globecom52923.2024.10901817","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/adminc63617.2024.10775559","name":"Survey Optimizing Reinforcement Learning, Federated Learning, and Computational Network Model Performance","source":"crossref","abstract":"","url":"https://doi.org/10.1109/adminc63617.2024.10775559","authors":["Sarah H. Mnkash","Faiz A. Al Alawv","Israa T. Ali"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:02:20Z","doi":"10.1109/adminc63617.2024.10775559","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1049/pbse025e_ch7","name":"Enhancing computational performance in healthcare through federated learning approach","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbse025e_ch7","authors":["Farzeen Ashfaq","N.Z. Jhanjhi","Navid Ali Khan","Sayan Kumar Ray","Gururaj H.L.","Amna Faisal","Shampa Rani Das"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T08:10:23Z","doi":"10.1049/pbse025e_ch7","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1201/9781032694870-2","name":"Revolutionizing Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781032694870-2","authors":["Renu Vij"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T08:04:57Z","doi":"10.1201/9781032694870-2","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1201/9781003482000-14","name":"Federated Query Processing for Data Integration Using Semantic Web Technologies","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003482000-14","authors":["Nidhi Gupta","Pawan Verma","Monali Gulhane","Nitin Rakesh","Ahmed A. Elngar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T17:15:57Z","doi":"10.1201/9781003482000-14","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/ijcnn60899.2024.10651029","name":"FedTAIL: A Federated Learning Approach with Trans-Architecture Intermediate Links","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ijcnn60899.2024.10651029","authors":["Dian Jiao","Jie Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-09T17:35:05Z","doi":"10.1109/ijcnn60899.2024.10651029","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.1109/sp54263.2024.00008","name":"BadVFL: Backdoor Attacks in Vertical Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp54263.2024.00008","authors":["Mohammad Naseri","Yufei Han","Emiliano De Cristofaro"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-05T13:56:32Z","doi":"10.1109/sp54263.2024.00008","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.2139/ssrn.4782276","name":"Membershield: A Framework for Federated Learning with Membership Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4782276","authors":["Faisal Ahmed","David Sánchez","Zouhair Haddi","Josep Domingo-Ferrer"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-03T02:18:29Z","doi":"10.2139/ssrn.4782276","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.5220/0012836400004547","name":"Exploration and Analysis of FedAvg, FedProx, FedMA, MOON, and FedProc Algorithms in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012836400004547","authors":["Jinlin Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-07T17:09:50Z","doi":"10.5220/0012836400004547","addedAt":"2026-08-31T06:41:20.070Z","updatedAt":"2026-08-31T06:41:20.070Z"},{"id":"doi:10.3217/y5mp4-dkd56","name":"Metacampus as a Base for a Unite! Blended Intensive Programme (BIP): Competencies for Collaborative Teaching in Joint Programme (Report 04/2025)","source":"datacite","abstract":"The report “Metacampus as a Base for a Unite! Blended Intensive Programme (BIP): Competencies for Collaborative Teaching in Joint Programmes” describes the Erasmus + Blended Intensive Program (BIP) held from June to October 2024 under the Unite! University Network. The six‑week English‑language course aimed to build skills for collaborative teaching in joint programmes among academic staff, lecturers and researchers. The programme combined an online introductory module (June 17 2024), a five‑day on‑site week at KTH Royal Institute of Technology (August 26‑30 2024), and a final online presentation (October 9 2024). Unite!'s federated learning management system (LMS) Metacampus acted as the central LMS, offering secure, institution‑based access to materials, while public channels (KTH website, Unite! portal) provided open information such as schedules. Workshops covered case studies of joint programmes, digital tools for collaboration, administrative challenges (double‑degrees, European Degree), large‑scale teaching strategies, and multicultural learning. Experts from the Unite! community led the sessions. Feedback highlighted the value of the intensive format, the need for hands‑on practice, the complexity of administrative procedures, and the importance of informal networking. Key lessons stress a hybrid digital environment that blends secure Metacampus collaboration with open public communication, and the necessity of a dedicated digital liaison to coordinate local and central IT tools.Recommendations for future BIPs include adopting this hybrid model, clarifying integration of local tools with Metacampus, and assigning a central facilitator to streamline technical and administrative support. The series “Unite! Digital Teaching and Learning Success Story Report” was initiated by Cm.2 Digital Campus, led by TU Graz (Martin Ebner) as part of the idea to collect, spread and enhance the possibilities, usages and lessons learned of teaching and learning offers using the federated learning management system Metacampus in 11/2024 till the end of the current Erasmus+ funding period 10/2026. All reports are available under open license and originally published at the TU Graz repository. Copyright holders are the authors.","url":"https://doi.org/10.3217/y5mp4-dkd56","authors":["Acosta-Garcia, Marcela","Galante, Lorenzo","Kauppinen, Tomi","Keller, Elizabeth","Knutsson, Karin","Pears, Arnold","Tucker Smith, Madeleine"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3217/y5mp4-dkd56","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2411.02115","name":"FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation","source":"datacite","abstract":"Federated learning (FL) is a collaborative machine learning approach that enables multiple clients to train models without sharing their private data. With the rise of deep learning, large-scale models have garnered significant attention due to their exceptional performance. However, a key challenge in FL is the limitation imposed by clients with constrained computational and communication resources, which hampers the deployment of these large models. The Mixture of Experts (MoE) architecture addresses this challenge with its sparse activation property, which reduces computational workload and communication demands during inference and updates. Additionally, MoE facilitates better personalization by allowing each expert to specialize in different subsets of the data distribution. To alleviate the communication burdens between the server and clients, we propose FedMoE-DA, a new FL model training framework that leverages the MoE architecture and incorporates a novel domain-aware, fine-grained aggregation strategy to enhance the robustness, personalizability, and communication efficiency simultaneously. Specifically, the correlation between both intra-client expert models and inter-client data heterogeneity is exploited. Moreover, we utilize peer-to-peer (P2P) communication between clients for selective expert model synchronization, thus significantly reducing the server-client transmissions. Experiments demonstrate that our FedMoE-DA achieves excellent performance while reducing the communication pressure on the server.","url":"https://doi.org/10.48550/arxiv.2411.02115","authors":["Zhan, Ziwei","Zhao, Wenkuan","Li, Yuanqing","Liu, Weijie","Zhang, Xiaoxi","Tan, Chee Wei","Wu, Chuan","Guo, Deke","Chen, Xu"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2411.02115","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.13025/29816","name":"An insight on the timely diagnosis of diabetic retinopathy using traditional and ai-driven approaches","source":"datacite","abstract":"Diabetic Retinopathy is a progressive microvascular complication of diabetes that requires early detection to improve patient outcomes. Traditional screening techniques, including fundus photography and optical coherence tomography, provide valuable diagnostic insights but have some limitations, like cost and technical complexity. Artificial intelligence is transforming the detection of diabetic retinopathy, moving away from traditional machine learning models that rely on manually created features to deep learning methods that allow for automatic feature extraction from retinal images. This systematic review investigates the evolution and diagnostic performance of AI-based techniques for DR detection. Studies were included if they applied machine learning or deep learning methods to retinal fundus or OCT images for DR classification. A total of 116 studies were included following comprehensive searches in databases such as PubMed, ScienceDirect, and IEEE Xplore, covering publications up to February 2024. Risk of bias was assessed in a representative sample of six studies, indicating a significant overall risk due to inadequate reporting on blinding and selective outcome reporting. Federated learning emerged as a promising alternative, enabling decentralized collaboration without compromising data privacy. Additionally, the growing focus on Explainable AI helps address the \"black-box\" nature of deep learning models by providing visual and textual explanations for predictions, thereby enhancing clinician trust and facilitating informed decision-making. By incorporating artificial intelligence with standard diagnostic frameworks, this research highlights the possibility for more accurate, scalable, and reachable diabetic retinopathy detection, paving the way for considerable advancements in ophthalmic problem management and enhancing patient care.","url":"https://doi.org/10.13025/29816","authors":["Asif, Malaika","ur Rehman, Fasih","Rashid, Zoya","Hussain, Altaf","Mirza, Alina","Qureshi, Waqar Shahid"],"tags":["Diabetic Retinopathy","ophthalmologists","convolutional neural network","retina fundus"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.13025/29816","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2508.15393","name":"Federated Learning based on Self-Evolving Gaussian Clustering","source":"datacite","abstract":"In this study, we present an Evolving Fuzzy System within the context of Federated Learning, which adapts dynamically with the addition of new clusters and therefore does not require the number of clusters to be selected apriori. Unlike traditional methods, Federated Learning allows models to be trained locally on clients' devices, sharing only the model parameters with a central server instead of the data. Our method, implemented using PyTorch, was tested on clustering and classification tasks. The results show that our approach outperforms established classification methods on several well-known UCI datasets. While computationally intensive due to overlap condition calculations, the proposed method demonstrates significant advantages in decentralized data processing.","url":"https://doi.org/10.48550/arxiv.2508.15393","authors":["Ožbot, Miha","Škrjanc, Igor"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.6; I.5.3","68T05"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.15393","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15831914","name":"AI-Enhanced Triage and Clinical Decision Support Tools in Primary Care","source":"datacite","abstract":"Background: Artificial intelligence (AI)-powered symptom checkers, risk stratification engines and embedded clinical decision support systems (CDSS) are now migrating from research centres to the front line of healthcare. By synthesising multimodal patient data against evidence-based algorithms in seconds, these tools promise earlier detection of time-critical conditions, better scheduling of critical appointments and reduced cognitive load for primary care physicians, who currently manage four out of five presenting complaints. Growing demand for remote access and post-pandemic services is driving the need for digital triage (World Health Organization, 2021). Current evidence base: Real-world performance is encouraging but heterogeneous. A prospective study in 12 English practices found 95.8% agreement between an AI triage platform and clinicians for non-urgent cases, enabling an 18% reduction in telephone consultations. A cancer risk CDSS active in 1400 practices increased early cancer detection from 58.7% to 66.0% and accelerated referrals. Research showed that ChatGPT-assisted triage improved accuracy and halved documentation time. Conversely, a meta-analysis of 83 validation studies reported a pooled diagnostic accuracy of only 52.1%, highlighting marked variability between products and settings (Elhaddad & Hamam, 2024; Kaboudi et al., 2024). Governance and standards: The World Health Organization's 2024 guidance on large multimodal models requires transparency reports, equity impact assessments and ongoing post-implementation monitoring. The NICE Evidence Standards Framework (revised 2023) specifies escalating levels of clinical, technical and economic evidence before digital health technologies are adopted (World Health Organization, 2024). A consensus statement adds detailed recommendations on bias assessment, prospective audit and maintaining human-in-the-loop oversight for AI-CDSS (National Institute for Health and Care Excellence [NICE], 2023). Turkish context: In line with Türkiye's National Artificial Intelligence Strategy 2021-2025, pilot implementations in Istanbul and Ankara Family Health Centres have integrated cloud-based symptom checkers with the e-Nabız personal health record and the central medical appointment system (MHRS). Internal quality reports (2025) show a 12% reduction in inappropriate emergency referrals, a nine-minute reduction in median consultation length, and high patient satisfaction. Enablers include major standards for exchanging healthcare information electronically- HL7-FHIR interoperability layer, newly approved reimbursement codes for digital visits, and clinician-led curation of local rule sets; barriers remain compliance with the Data Protection Act (KVKK) and the lack of an AI-specific health regulation (Republic of Türkiye, Digital Transformation Office, 2021). Future agenda and conclusion: To translate promising pilots into routine care, we may need and expect: 1. pragmatic multi-centre trials designed for patient-centred outcomes, safety and workload redistribution 2. federated learning architectures that update models locally without exporting personal data 3. incorporation of social determinants, wearable-derived vital signs and multimodal inputs to refine risk scores 4. participatory design frameworks that engage frontline professionals, patients and policy makers in governance. AI-enabled triage and CDSS can strengthen accessible, equitable and high-quality primary care, provided their use remains evidence-based, transparently regulated and clinician-involved.","url":"https://doi.org/10.5281/zenodo.15831914","authors":["Arman, Ikbal Humay"],"tags":["Artificial intelligence","electronic health records","family medicine","symptom checkers","triage"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15831914","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15831913","name":"AI-Enhanced Triage and Clinical Decision Support Tools in Primary Care","source":"datacite","abstract":"Background: Artificial intelligence (AI)-powered symptom checkers, risk stratification engines and embedded clinical decision support systems (CDSS) are now migrating from research centres to the front line of healthcare. By synthesising multimodal patient data against evidence-based algorithms in seconds, these tools promise earlier detection of time-critical conditions, better scheduling of critical appointments and reduced cognitive load for primary care physicians, who currently manage four out of five presenting complaints. Growing demand for remote access and post-pandemic services is driving the need for digital triage (World Health Organization, 2021). Current evidence base: Real-world performance is encouraging but heterogeneous. A prospective study in 12 English practices found 95.8% agreement between an AI triage platform and clinicians for non-urgent cases, enabling an 18% reduction in telephone consultations. A cancer risk CDSS active in 1400 practices increased early cancer detection from 58.7% to 66.0% and accelerated referrals. Research showed that ChatGPT-assisted triage improved accuracy and halved documentation time. Conversely, a meta-analysis of 83 validation studies reported a pooled diagnostic accuracy of only 52.1%, highlighting marked variability between products and settings (Elhaddad & Hamam, 2024; Kaboudi et al., 2024). Governance and standards: The World Health Organization's 2024 guidance on large multimodal models requires transparency reports, equity impact assessments and ongoing post-implementation monitoring. The NICE Evidence Standards Framework (revised 2023) specifies escalating levels of clinical, technical and economic evidence before digital health technologies are adopted (World Health Organization, 2024). A consensus statement adds detailed recommendations on bias assessment, prospective audit and maintaining human-in-the-loop oversight for AI-CDSS (National Institute for Health and Care Excellence [NICE], 2023). Turkish context: In line with Türkiye's National Artificial Intelligence Strategy 2021-2025, pilot implementations in Istanbul and Ankara Family Health Centres have integrated cloud-based symptom checkers with the e-Nabız personal health record and the central medical appointment system (MHRS). Internal quality reports (2025) show a 12% reduction in inappropriate emergency referrals, a nine-minute reduction in median consultation length, and high patient satisfaction. Enablers include major standards for exchanging healthcare information electronically- HL7-FHIR interoperability layer, newly approved reimbursement codes for digital visits, and clinician-led curation of local rule sets; barriers remain compliance with the Data Protection Act (KVKK) and the lack of an AI-specific health regulation (Republic of Türkiye, Digital Transformation Office, 2021). Future agenda and conclusion: To translate promising pilots into routine care, we may need and expect: 1. pragmatic multi-centre trials designed for patient-centred outcomes, safety and workload redistribution 2. federated learning architectures that update models locally without exporting personal data 3. incorporation of social determinants, wearable-derived vital signs and multimodal inputs to refine risk scores 4. participatory design frameworks that engage frontline professionals, patients and policy makers in governance. AI-enabled triage and CDSS can strengthen accessible, equitable and high-quality primary care, provided their use remains evidence-based, transparently regulated and clinician-involved.","url":"https://doi.org/10.5281/zenodo.15831913","authors":["Arman, Ikbal Humay"],"tags":["Artificial intelligence","electronic health records","family medicine","symptom checkers","triage"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15831913","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2504.16438","name":"POPri: Private Federated Learning using Preference-Optimized Synthetic Data","source":"datacite","abstract":"In practical settings, differentially private Federated learning (DP-FL) is the dominant method for training models from private, on-device client data. Recent work has suggested that DP-FL may be enhanced or outperformed by methods that use DP synthetic data (Wu et al., 2024; Hou et al., 2024). The primary algorithms for generating DP synthetic data for FL applications require careful prompt engineering based on public information and/or iterative private client feedback. Our key insight is that the private client feedback collected by prior DP synthetic data methods (Hou et al., 2024; Xie et al., 2024) can be viewed as an RL (reinforcement learning) reward. Our algorithm, Policy Optimization for Private Data (POPri) harnesses client feedback using policy optimization algorithms such as Direct Preference Optimization (DPO) to fine-tune LLMs to generate high-quality DP synthetic data. To evaluate POPri, we release LargeFedBench, a new federated text benchmark for uncontaminated LLM evaluations on federated client data. POPri substantially improves the utility of DP synthetic data relative to prior work on LargeFedBench datasets and an existing benchmark from Xie et al. (2024). POPri closes the gap between next-token prediction accuracy in the fully-private and non-private settings by up to 58%, compared to 28% for prior synthetic data methods, and 3% for state-of-the-art DP federated learning methods. The code and data are available at https://github.com/meiyuw/POPri.","url":"https://doi.org/10.48550/arxiv.2504.16438","authors":["Hou, Charlie","Wang, Mei-Yu","Zhu, Yige","Lazar, Daniel","Fanti, Giulia"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2504.16438","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2508.12661","name":"An Efficient and Adaptive Framework for Achieving Underwater High-performance Maintenance Networks","source":"datacite","abstract":"With the development of space-air-ground-aqua integrated networks (SAGAIN), high-speed and reliable network services are accessible at any time and any location. However, the long propagation delay and limited network capacity of underwater communication networks (UCN) negatively impact the service quality of SAGAIN. To address this issue, this paper presents U-HPNF, a hierarchical framework designed to achieve a high-performance network with self-management, self-configuration, and self-optimization capabilities. U-HPNF leverages the sensing and decision-making capabilities of deep reinforcement learning (DRL) to manage limited resources in UCNs, including communication bandwidth, computational resources, and energy supplies. Additionally, we incorporate federated learning (FL) to iteratively optimize the decision-making model, thereby reducing communication overhead and protecting the privacy of node observation information. By deploying digital twins (DT) at both the intelligent sink layer and aggregation layer, U-HPNF can mimic numerous network scenarios and adapt to varying network QoS requirements. Through a three-tier network design with two-levels DT, U-HPNF provides an AI-native high-performance underwater network. Numerical results demonstrate that the proposed U-HPNF framework can effectively optimize network performance across various situations and adapt to changing QoS requirements.","url":"https://doi.org/10.48550/arxiv.2508.12661","authors":["Gou, Yu","Zhang, Tong","Liu, Jun","Qi, Zhongyang","Zheng, Dezhi"],"tags":["Networking and Internet Architecture (cs.NI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.12661","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.16891143","name":"Protocole \"Long-Life Index\" — Spécification ouverte de smart-contracts incitatifs pour la durabilité et la maintenance prédictive des biens physiques","source":"datacite","abstract":"Abstract ENThis document, produced with the assistance of ChatGPT o3 and ChatGPT 5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes : EPC Art. 54(2) (European Patent Convention), French IPC Art. L 611-11 (CPI), 35 U.S.C. §102(a) (United States Patent Act), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). It discloses 100 enabling inventions across devices & sensors, assays & QA, algorithms (TinyML/FL), closed-loop controllers, materials & packaging, NDT/imaging, privacy/standards, UX/workflows, and supply-chain. Proposals cover self-healing concrete + SHM, oil health modules, SiC/GaN/diamond thermal hardware, BIM/RFID rails, MOF capture, retrofit electrification, and the “Long-Life Index” incentive protocol. Each item specifies minimal components, parameters, SOPs, acceptance criteria, IPC/CPC classification, and RFC 3161 timestamping to ensure verifiability and reproducibility for defensive purposes. Résumé FRCe document, produit avec l’assistance de ChatGPT o3 et ChatGPT 5 Thinking, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre de ce fait dans l’art antérieur dès sa mise à disposition au regard des textes applicables : art. 54(2) CBE (Convention sur le brevet européen), art. L 611-11 CPI (Code de la propriété intellectuelle), 35 U.S.C. §102(a) (Patent Act des États-Unis), Loi chinoise sur les brevets art. 22(5) (中华人民共和国专利法), et Loi japonaise sur les brevets art. 29(1) (特許法). Le document décrit 100 inventions “enabling” couvrant capteurs/dispositifs, essais & QA, algorithmes (TinyML/FL), boucles fermées, matériaux & packaging, imagerie CND, confidentialité/standards, UX/workflows et logistique. Sont inclus : béton auto-cicatrisant + SHM, module santé d’huile, matériels SiC/GaN/diamant, rail technique BIM/RFID, capture MOF, rétro-électrification et protocole d’incitation “Long-Life Index”. Chaque item précise composants, paramètres, SOP/critères d’acceptation, classification IPC/CPC et empreinte temporelle (RFC 3161 / FreeTSA). Timestamp: 2025-08-17T20:22:30ZSHA-256: 98e2de61f12491292d8e56458d2173bc5dd68404d5eaf8058e8bf75c9fc11158 Liste des innovations & classification (IPC ; CPC) 1 — Long-Life index smart contract — IPC G06Q 40/02 ; CPC G06Q 40/082 — Attested maintenance oracles — IPC H04L 9/32 ; CPC H04L 63/163 — Device/owner DID registry — IPC G06F 21/62 ; CPC G06Q 20/36794 — Tamper-proof edge capsule — IPC G01N 3/02 ; CPC G16Y 10/205 — TinyML federated longevity — IPC G06N 20/00 ; CPC G06N 20/106 — Calibration-as-a-Service — IPC G01N 33/28 ; CPC G01N 33/347 — Anti-gaming anomaly engine — IPC G06F 21/55 ; CPC G06Q 50/268 — Insurance repricing engine — IPC G06Q 40/08 ; CPC G06Q 40/029 — Pay-per-longevity leasing — IPC G06Q 40/02 ; CPC G06Q 40/02510 — Parametric downtime cover — IPC G06Q 40/08 ; CPC G06Q 40/0611 — Indexed extended warranty — IPC G06Q 30/02 ; CPC G06Q 10/1012 — Repair VC/NFT certificates — IPC G06Q 50/26 ; CPC G06K 19/07713 — BIM/OPC-UA connector — IPC G06F 3/06 ; CPC G05B 19/41814 — Oil index adapter — IPC G01N 33/28 ; CPC G01N 33/3615 — Concrete SHM adapter — IPC E04B 1/76 ; CPC G01N 29/4416 — Battery SOH attestation — IPC H01M 10/48 ; CPC G01R 31/3617 — NDT imaging to index — IPC G01N 29/04 ; CPC G01M 3/3218 — GDPR data vault — IPC G06F 21/62 ; CPC H04L 9/3019 — Model governance/audit — IPC G06F 11/36 ; CPC G06N 20/2020 — Parts chain-of-custody — IPC G06Q 50/26 ; CPC G06K 19/0721 — Retrofit certification — IPC B60K 6/02 ; CPC G01M 17/00722 — SiC wafer reuse score — IPC H01L 29/06 ; CPC C30B 29/0623 — Diamond reuse score — IPC H01L 23/373 ; CPC H05K 7/2024 — MOF boiler incentive — IPC C01G 49/02 ; CPC B01D 53/04725 — Closed-loop scheduler — IPC G05B 19/418 ; CPC G06Q 10/06326 — Insurer-tenant portal — IPC G06F 3/0488 ; CPC G06Q 30/0227 — TEE oracle nodes — IPC H04L 29/06 ; CPC G06F 2","url":"https://doi.org/10.5281/zenodo.16891143","authors":["Pillet, Xavier"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16891143","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.16732317","name":"Protocole \"Long-Life Index\" — Spécification ouverte de smart-contracts incitatifs pour la durabilité et la maintenance prédictive des biens physiques","source":"datacite","abstract":"Abstract ENThis document, produced with the assistance of ChatGPT o3 and ChatGPT 5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes : EPC Art. 54(2) (European Patent Convention), French IPC Art. L 611-11 (CPI), 35 U.S.C. §102(a) (United States Patent Act), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). It discloses 100 enabling inventions across devices & sensors, assays & QA, algorithms (TinyML/FL), closed-loop controllers, materials & packaging, NDT/imaging, privacy/standards, UX/workflows, and supply-chain. Proposals cover self-healing concrete + SHM, oil health modules, SiC/GaN/diamond thermal hardware, BIM/RFID rails, MOF capture, retrofit electrification, and the “Long-Life Index” incentive protocol. Each item specifies minimal components, parameters, SOPs, acceptance criteria, IPC/CPC classification, and RFC 3161 timestamping to ensure verifiability and reproducibility for defensive purposes. Résumé FRCe document, produit avec l’assistance de ChatGPT o3 et ChatGPT 5 Thinking, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre de ce fait dans l’art antérieur dès sa mise à disposition au regard des textes applicables : art. 54(2) CBE (Convention sur le brevet européen), art. L 611-11 CPI (Code de la propriété intellectuelle), 35 U.S.C. §102(a) (Patent Act des États-Unis), Loi chinoise sur les brevets art. 22(5) (中华人民共和国专利法), et Loi japonaise sur les brevets art. 29(1) (特許法). Le document décrit 100 inventions “enabling” couvrant capteurs/dispositifs, essais & QA, algorithmes (TinyML/FL), boucles fermées, matériaux & packaging, imagerie CND, confidentialité/standards, UX/workflows et logistique. Sont inclus : béton auto-cicatrisant + SHM, module santé d’huile, matériels SiC/GaN/diamant, rail technique BIM/RFID, capture MOF, rétro-électrification et protocole d’incitation “Long-Life Index”. Chaque item précise composants, paramètres, SOP/critères d’acceptation, classification IPC/CPC et empreinte temporelle (RFC 3161 / FreeTSA). Timestamp: 2025-08-17T20:22:30ZSHA-256: 98e2de61f12491292d8e56458d2173bc5dd68404d5eaf8058e8bf75c9fc11158 Liste des innovations & classification (IPC ; CPC) 1 — Long-Life index smart contract — IPC G06Q 40/02 ; CPC G06Q 40/082 — Attested maintenance oracles — IPC H04L 9/32 ; CPC H04L 63/163 — Device/owner DID registry — IPC G06F 21/62 ; CPC G06Q 20/36794 — Tamper-proof edge capsule — IPC G01N 3/02 ; CPC G16Y 10/205 — TinyML federated longevity — IPC G06N 20/00 ; CPC G06N 20/106 — Calibration-as-a-Service — IPC G01N 33/28 ; CPC G01N 33/347 — Anti-gaming anomaly engine — IPC G06F 21/55 ; CPC G06Q 50/268 — Insurance repricing engine — IPC G06Q 40/08 ; CPC G06Q 40/029 — Pay-per-longevity leasing — IPC G06Q 40/02 ; CPC G06Q 40/02510 — Parametric downtime cover — IPC G06Q 40/08 ; CPC G06Q 40/0611 — Indexed extended warranty — IPC G06Q 30/02 ; CPC G06Q 10/1012 — Repair VC/NFT certificates — IPC G06Q 50/26 ; CPC G06K 19/07713 — BIM/OPC-UA connector — IPC G06F 3/06 ; CPC G05B 19/41814 — Oil index adapter — IPC G01N 33/28 ; CPC G01N 33/3615 — Concrete SHM adapter — IPC E04B 1/76 ; CPC G01N 29/4416 — Battery SOH attestation — IPC H01M 10/48 ; CPC G01R 31/3617 — NDT imaging to index — IPC G01N 29/04 ; CPC G01M 3/3218 — GDPR data vault — IPC G06F 21/62 ; CPC H04L 9/3019 — Model governance/audit — IPC G06F 11/36 ; CPC G06N 20/2020 — Parts chain-of-custody — IPC G06Q 50/26 ; CPC G06K 19/0721 — Retrofit certification — IPC B60K 6/02 ; CPC G01M 17/00722 — SiC wafer reuse score — IPC H01L 29/06 ; CPC C30B 29/0623 — Diamond reuse score — IPC H01L 23/373 ; CPC H05K 7/2024 — MOF boiler incentive — IPC C01G 49/02 ; CPC B01D 53/04725 — Closed-loop scheduler — IPC G05B 19/418 ; CPC G06Q 10/06326 — Insurer-tenant portal — IPC G06F 3/0488 ; CPC G06Q 30/0227 — TEE oracle nodes — IPC H04L 29/06 ; CPC G06F 2","url":"https://doi.org/10.5281/zenodo.16732317","authors":["Pillet, Xavier"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16732317","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.16732318","name":"Protocole \"Long-Life Index\" — Spécification ouverte de smart-contracts incitatifs pour la durabilité et la maintenance prédictive des biens physiques","source":"datacite","abstract":"Abstract ENThis document, produced with the assistance of ChatGPT o3 and ChatGPT 5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes : EPC Art. 54(2) (European Patent Convention), French IPC Art. L 611-11 (CPI), 35 U.S.C. §102(a) (United States Patent Act), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). It discloses 100 enabling inventions across devices & sensors, assays & QA, algorithms (TinyML/FL), closed-loop controllers, materials & packaging, NDT/imaging, privacy/standards, UX/workflows, and supply-chain. Proposals cover self-healing concrete + SHM, oil health modules, SiC/GaN/diamond thermal hardware, BIM/RFID rails, MOF capture, retrofit electrification, and the “Long-Life Index” incentive protocol. Each item specifies minimal components, parameters, SOPs, acceptance criteria, IPC/CPC classification, and RFC 3161 timestamping to ensure verifiability and reproducibility for defensive purposes. Résumé FRCe document, produit avec l’assistance de ChatGPT o3 et ChatGPT 5 Thinking, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre de ce fait dans l’art antérieur dès sa mise à disposition au regard des textes applicables : art. 54(2) CBE (Convention sur le brevet européen), art. L 611-11 CPI (Code de la propriété intellectuelle), 35 U.S.C. §102(a) (Patent Act des États-Unis), Loi chinoise sur les brevets art. 22(5) (中华人民共和国专利法), et Loi japonaise sur les brevets art. 29(1) (特許法). Le document décrit 100 inventions “enabling” couvrant capteurs/dispositifs, essais & QA, algorithmes (TinyML/FL), boucles fermées, matériaux & packaging, imagerie CND, confidentialité/standards, UX/workflows et logistique. Sont inclus : béton auto-cicatrisant + SHM, module santé d’huile, matériels SiC/GaN/diamant, rail technique BIM/RFID, capture MOF, rétro-électrification et protocole d’incitation “Long-Life Index”. Chaque item précise composants, paramètres, SOP/critères d’acceptation, classification IPC/CPC et empreinte temporelle (RFC 3161 / FreeTSA). Timestamp: SHA-256: Liste des innovations & classification (IPC ; CPC) 1 — Long-Life index smart contract — IPC G06Q 40/02 ; CPC G06Q 40/082 — Attested maintenance oracles — IPC H04L 9/32 ; CPC H04L 63/163 — Device/owner DID registry — IPC G06F 21/62 ; CPC G06Q 20/36794 — Tamper-proof edge capsule — IPC G01N 3/02 ; CPC G16Y 10/205 — TinyML federated longevity — IPC G06N 20/00 ; CPC G06N 20/106 — Calibration-as-a-Service — IPC G01N 33/28 ; CPC G01N 33/347 — Anti-gaming anomaly engine — IPC G06F 21/55 ; CPC G06Q 50/268 — Insurance repricing engine — IPC G06Q 40/08 ; CPC G06Q 40/029 — Pay-per-longevity leasing — IPC G06Q 40/02 ; CPC G06Q 40/02510 — Parametric downtime cover — IPC G06Q 40/08 ; CPC G06Q 40/0611 — Indexed extended warranty — IPC G06Q 30/02 ; CPC G06Q 10/1012 — Repair VC/NFT certificates — IPC G06Q 50/26 ; CPC G06K 19/07713 — BIM/OPC-UA connector — IPC G06F 3/06 ; CPC G05B 19/41814 — Oil index adapter — IPC G01N 33/28 ; CPC G01N 33/3615 — Concrete SHM adapter — IPC E04B 1/76 ; CPC G01N 29/4416 — Battery SOH attestation — IPC H01M 10/48 ; CPC G01R 31/3617 — NDT imaging to index — IPC G01N 29/04 ; CPC G01M 3/3218 — GDPR data vault — IPC G06F 21/62 ; CPC H04L 9/3019 — Model governance/audit — IPC G06F 11/36 ; CPC G06N 20/2020 — Parts chain-of-custody — IPC G06Q 50/26 ; CPC G06K 19/0721 — Retrofit certification — IPC B60K 6/02 ; CPC G01M 17/00722 — SiC wafer reuse score — IPC H01L 29/06 ; CPC C30B 29/0623 — Diamond reuse score — IPC H01L 23/373 ; CPC H05K 7/2024 — MOF boiler incentive — IPC C01G 49/02 ; CPC B01D 53/04725 — Closed-loop scheduler — IPC G05B 19/418 ; CPC G06Q 10/06326 — Insurer-tenant portal — IPC G06F 3/0488 ; CPC G06Q 30/0227 — TEE oracle nodes — IPC H04L 29/06 ; CPC G06F 21/5728 — Offline-first protocol — IPC H04W 84/18 ; CPC H04L 67/1229 — Oil QC kits — I","url":"https://doi.org/10.5281/zenodo.16732318","authors":["Pillet, Xavier"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16732318","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.16836656","name":"Harnessing Artificial Intelligence for the Future of Precision Medicine: Transforming Patient-Centric Care Through Data-Driven Innovation","source":"datacite","abstract":"Abstract: Advancements in artificial intelligence (AI) are steadily reshaping the landscape of modern medicine, particularly in the context of precision healthcare. In 2024, the global AI healthcare market surged to $ 32.34 billion, with 80 % of hospitals adopting AI to enhance patient care and workflow efficiency. By drawing on complex datasets that range from genomic profiles to real-time health signals, AI is paving the way for more personalized diagnosis, treatment, and prevention strategies. In clinical settings, technologies such as digital twins and federated learning enable safer, privacy-preserving models of care while improving disease outcome predictions and therapy responses. Moreover, the integration of AI with wearable devices and Internet of Things (IoT) platforms supports continuous patient monitoring and early detection of chronic and acute conditions. These innovations facilitate proactive health interventions and empower individuals to take charge of their health beyond clinical environments. AI’s influence extends to drug discovery and dosing optimization, making treatment plans more effective and cost-efficient. However, these technological gains are accompanied by pressing ethical and regulatory considerations, ranging from algorithmic bias and data privacy to trust in AI systems and the need for flexible, forward-thinking policies. Addressing these concerns requires robust governance frameworks and collaborative efforts across disciplines. Altogether, AI-driven precision medicine marks a shift from generalized treatment models toward targeted, patient-centered care, with the potential to enhance therapeutic outcomes, reduce healthcare disparities, and contribute meaningfully to global health improvement. Keywords: Artificial Intelligence (AI), Precision Medicine, Digital Twins, Federated Learning, Wearable Technology, Predictive Healthcare","url":"https://doi.org/10.5281/zenodo.16836656","authors":["Heli Patel1, Deep Sathwara1, Vivekraj Maheshwari2, and Disha Joshi1*"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16836656","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.16889419","name":"Harnessing Artificial Intelligence for the Future of Precision Medicine: Transforming Patient-Centric Care Through Data-Driven Innovation","source":"datacite","abstract":"Abstract: Advancements in artificial intelligence (AI) are steadily reshaping the landscape of modern medicine, particularly in the context of precision healthcare. In 2024, the global AI healthcare market surged to $ 32.34 billion, with 80 % of hospitals adopting AI to enhance patient care and workflow efficiency. By drawing on complex datasets that range from genomic profiles to real-time health signals, AI is paving the way for more personalized diagnosis, treatment, and prevention strategies. In clinical settings, technologies such as digital twins and federated learning enable safer, privacy-preserving models of care while improving disease outcome predictions and therapy responses. Moreover, the integration of AI with wearable devices and Internet of Things (IoT) platforms supports continuous patient monitoring and early detection of chronic and acute conditions. These innovations facilitate proactive health interventions and empower individuals to take charge of their health beyond clinical environments. AI’s influence extends to drug discovery and dosing optimization, making treatment plans more effective and cost-efficient. However, these technological gains are accompanied by pressing ethical and regulatory considerations, ranging from algorithmic bias and data privacy to trust in AI systems and the need for flexible, forward-thinking policies. Addressing these concerns requires robust governance frameworks and collaborative efforts across disciplines. Altogether, AI-driven precision medicine marks a shift from generalized treatment models toward targeted, patient-centered care, with the potential to enhance therapeutic outcomes, reduce healthcare disparities, and contribute meaningfully to global health improvement. Keywords: Artificial Intelligence (AI), Precision Medicine, Digital Twins, Federated Learning, Wearable Technology, Predictive Healthcare","url":"https://doi.org/10.5281/zenodo.16889419","authors":["Heli Patel1, Deep Sathwara1, Vivekraj Maheshwari2, and Disha Joshi1*"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16889419","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2508.10840","name":"Generalizable Federated Learning using Client Adaptive Focal Modulation","source":"datacite","abstract":"Federated learning (FL) has proven essential for privacy-preserving, collaborative training across distributed clients. Our prior work, TransFed, introduced a robust transformer-based FL framework that leverages a learn-to-adapt hypernetwork to generate personalized focal modulation layers per client, outperforming traditional methods in non-IID and cross-domain settings. In this extended version, we propose AdaptFED, where we deepen the investigation of focal modulation in generalizable FL by incorporating: (1) a refined adaptation strategy that integrates task-aware client embeddings to personalize modulation dynamics further, (2) enhanced theoretical bounds on adaptation performance, and (3) broader empirical validation across additional modalities, including time-series and multilingual data. We also introduce an efficient variant of TransFed that reduces server-client communication overhead via low-rank hypernetwork conditioning, enabling scalable deployment in resource-constrained environments. Extensive experiments on eight diverse datasets reaffirm the superiority of our method over state-of-the-art baselines, particularly in source-free and cross-task federated setups. Our findings not only extend the capabilities of focal modulation in FL but also pave the way for more adaptive, scalable, and generalizable transformer-based federated systems. The code is available at http://github.com/Tajamul21/TransFed","url":"https://doi.org/10.48550/arxiv.2508.10840","authors":["Ashraf, Tajamul","Gillani, Iqra Altaf"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.10840","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.16842983","name":"VEIL.AI's Next-Generation Anonymization enables cutting-edge research for children's diseases","source":"datacite","abstract":"PHEMS was proud to contribute to the 5th European OHDSI Symposium, “Scaling up reliable evidence across Europe”, on June 3rd, 2024, aboard the historic Steam Ship Rotterdam in the Netherlands. The event brought together researchers and stakeholders from across Europe to exchange knowledge and highlight progress on the use of the OMOP Common Data Model (OMOP-CDM). PHEMS consortium member VEIL.AI presented “Enhancing Pediatric Care Data Collaboration through Privacy-Enhanced Federated Learning and Anonymization.” This poster highlighted advanced anonymization and federated learning techniques that protect data privacy without compromising research value. These innovations are especially vital for sensitive applications such as pediatric disease prediction, where traditional data sharing is limited.","url":"https://doi.org/10.5281/zenodo.16842983","authors":["Pentikäinen, Tuomo"],"tags":["Data Science","Data Anonymization","Pediatrics","Secondary Data Analysis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.16842983","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.16842982","name":"VEIL.AI's Next-Generation Anonymization enables cutting-edge research for children's diseases","source":"datacite","abstract":"PHEMS was proud to contribute to the 5th European OHDSI Symposium, “Scaling up reliable evidence across Europe”, on June 3rd, 2024, aboard the historic Steam Ship Rotterdam in the Netherlands. The event brought together researchers and stakeholders from across Europe to exchange knowledge and highlight progress on the use of the OMOP Common Data Model (OMOP-CDM). PHEMS consortium member VEIL.AI presented “Enhancing Pediatric Care Data Collaboration through Privacy-Enhanced Federated Learning and Anonymization.” This poster highlighted advanced anonymization and federated learning techniques that protect data privacy without compromising research value. These innovations are especially vital for sensitive applications such as pediatric disease prediction, where traditional data sharing is limited.","url":"https://doi.org/10.5281/zenodo.16842982","authors":["Pentikäinen, Tuomo"],"tags":["Data Science","Data Anonymization","Pediatrics","Secondary Data Analysis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.16842982","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.16836657","name":"Harnessing Artificial Intelligence for the Future of Precision Medicine: Transforming Patient-Centric Care Through Data-Driven Innovation","source":"datacite","abstract":"Abstract: Advancements in artificial intelligence (AI) are steadily reshaping the landscape of modern medicine, particularly in the context of precision healthcare. In 2024, the global AI healthcare market surged to $ 32.34 billion, with 80 % of hospitals adopting AI to enhance patient care and workflow efficiency. By drawing on complex datasets that range from genomic profiles to real-time health signals, AI is paving the way for more personalized diagnosis, treatment, and prevention strategies. In clinical settings, technologies such as digital twins and federated learning enable safer, privacy-preserving models of care while improving disease outcome predictions and therapy responses. Moreover, the integration of AI with wearable devices and Internet of Things (IoT) platforms supports continuous patient monitoring and early detection of chronic and acute conditions. These innovations facilitate proactive health interventions and empower individuals to take charge of their health beyond clinical environments. AI’s influence extends to drug discovery and dosing optimization, making treatment plans more effective and cost-efficient. However, these technological gains are accompanied by pressing ethical and regulatory considerations, ranging from algorithmic bias and data privacy to trust in AI systems and the need for flexible, forward-thinking policies. Addressing these concerns requires robust governance frameworks and collaborative efforts across disciplines. Altogether, AI-driven precision medicine marks a shift from generalized treatment models toward targeted, patient-centered care, with the potential to enhance therapeutic outcomes, reduce healthcare disparities, and contribute meaningfully to global health improvement. Keywords: Artificial Intelligence (AI), Precision Medicine, Digital Twins, Federated Learning, Wearable Technology, Predictive Healthcare","url":"https://doi.org/10.5281/zenodo.16836657","authors":["Heli Patel, Deep Sathwara, Vivekraj Maheshwari, Disha Joshi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16836657","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.6084/m9.figshare.29877074.v1","name":"AI-Powered Visualization is Transforming Modern Healthcare","source":"datacite","abstract":"Healthcare is being transformed by AI-driven visualization, which transforms complex data into useful insights. This paper synthesizes advancements in AI visualization tools—spanning medical imaging, electronic health records (EHR), genomics, and public health—and evaluates their impact on diagnostics, treatment personalization, and operational efficiency. Convolutional neural networks (CNNs) for image segmentation, generative adversarial networks (GANs) for the generation of synthetic data, and interactive dashboards for real-time analytics are some of the technologies that we highlight. Integrity barriers, algorithmic bias, and data privacy concerns are all critically examined. A systematic review of more than 120 studies conducted between 2018 and 2024 shows that clinical workflow time is cut by 30% and diagnostic accuracy is improved by 40% on average. Explainable artificial intelligence (XAI) and federated learning are emphasized in the study's ethical frameworks and future directions. This study demonstrates that AI visualization plays a crucial role in value-based care and precision medicine.","url":"https://doi.org/10.6084/m9.figshare.29877074.v1","authors":["Rahman NaziL, Ashikur"],"tags":["Community child health","Health equity","Health promotion","Injury prevention","Preventative health care","Social determinants of health","Public health not elsewhere classified","Biofabrication"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.6084/m9.figshare.29877074.v1","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.6084/m9.figshare.29876651.v1","name":"AI-Powered Visualization is Transforming Modern Healthcare","source":"datacite","abstract":"Healthcare is being transformed by AI-driven visualization, which transforms complex data into useful insights. This paper synthesizes advancements in AI visualization tools—spanning medical imaging, electronic health records (EHR), genomics, and public health—and evaluates their impact on diagnostics, treatment personalization, and operational efficiency. Convolutional neural networks (CNNs) for image segmentation, generative adversarial networks (GANs) for the generation of synthetic data, and interactive dashboards for real-time analytics are some of the technologies that we highlight. Integrity barriers, algorithmic bias, and data privacy concerns are all critically examined. A systematic review of more than 120 studies conducted between 2018 and 2024 shows that clinical workflow time is cut by 30% and diagnostic accuracy is improved by 40% on average. Explainable artificial intelligence (XAI) and federated learning are emphasized in the study's ethical frameworks and future directions. This study demonstrates that AI visualization plays a crucial role in value-based care and precision medicine.","url":"https://doi.org/10.6084/m9.figshare.29876651.v1","authors":["Rahman NaziL, Ashikur"],"tags":["Naturopathy","Chiropractic","Traditional Chinese medicine and treatments","Traditional, complementary and integrative medicine not elsewhere classified"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.6084/m9.figshare.29876651.v1","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2508.00967","name":"Cooperative Perception: A Resource-Efficient Framework for Multi-Drone 3D Scene Reconstruction Using Federated Diffusion and NeRF","source":"datacite","abstract":"The proposal introduces an innovative drone swarm perception system that aims to solve problems related to computational limitations and low-bandwidth communication, and real-time scene reconstruction. The framework enables efficient multi-agent 3D/4D scene synthesis through federated learning of shared diffusion model and YOLOv12 lightweight semantic extraction and local NeRF updates while maintaining privacy and scalability. The framework redesigns generative diffusion models for joint scene reconstruction, and improves cooperative scene understanding, while adding semantic-aware compression protocols. The approach can be validated through simulations and potential real-world deployment on drone testbeds, positioning it as a disruptive advancement in multi-agent AI for autonomous systems.","url":"https://doi.org/10.48550/arxiv.2508.00967","authors":["Pourmandi, Massoud"],"tags":["Artificial Intelligence (cs.AI)","Robotics (cs.RO)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.6; I.2.9; I.2.10; I.4.8","68T07, 68T45, 93C85"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.00967","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2507.21198","name":"Uncovering Gradient Inversion Risks in Practical Language Model Training","source":"datacite","abstract":"The gradient inversion attack has been demonstrated as a significant privacy threat to federated learning (FL), particularly in continuous domains such as vision models. In contrast, it is often considered less effective or highly dependent on impractical training settings when applied to language models, due to the challenges posed by the discrete nature of tokens in text data. As a result, its potential privacy threats remain largely underestimated, despite FL being an emerging training method for language models. In this work, we propose a domain-specific gradient inversion attack named Grab (gradient inversion with hybrid optimization). Grab features two alternating optimization processes to address the challenges caused by practical training settings, including a simultaneous optimization on dropout masks between layers for improved token recovery and a discrete optimization for effective token sequencing. Grab can recover a significant portion (up to 92.9% recovery rate) of the private training data, outperforming the attack strategy of utilizing discrete optimization with an auxiliary model by notable improvements of up to 28.9% recovery rate in benchmark settings and 48.5% recovery rate in practical settings. Grab provides a valuable step forward in understanding this privacy threat in the emerging FL training mode of language models.","url":"https://doi.org/10.48550/arxiv.2507.21198","authors":["Feng, Xinguo","Ma, Zhongkui","Wang, Zihan","Chegne, Eu Joe","Ma, Mengyao","Abuadbba, Alsharif","Bai, Guangdong"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.21198","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.16314113","name":"Artificial Intelligence in Intrusion Detection Systems: Trends, Frameworks, and Future Directions for Cybersecurity","source":"datacite","abstract":"In the last decade, intrusion detection systems (IDS) have grown out of signature‐based filters to complex, AI driven platforms that have the ability to identify novel and polymorphic threats in real time. This paper will look in detail at artificial intelligence techniques used in IDS, compare and contrast the most influential frameworks and architectures, and position the next stage of the cybersecurity resilience endeavour. We will start by measuring the stakes: the average cost of a network breach in 2024 was USD 4.45 million (an increase of 2.6 percent in relation to 2023), with organizations recording a 15 percent increase in zeroday exploits, which highlights the inefficiency of the static detection processes. At this point, we categorize AI based IDS as supervised learning, unsupervised anomaly detection, deep learning, and new paradigms (graph neural networks, federated learning), their advantages and limitations compared across a selection of impactful benchmark datasets (NSLKDD, CICIDS2017, UNSW\\-NB15) and proprietary highly‐scaled enterprise traffic. Using the extensive comparisons to industry benchmarks (e.g., Snort, SVM-based models), we show that architecture that combines convolutional and recurrent networks will exceed 97 percent F1- score with latency measured at below 100 ms, at a 35 percent reduction in false positives compared to the older systems. We reveal in our discussion more longstanding issues dataset biases, adversarial robustness, and interpretability and report on newer ones in explainable AI, and differential privacy and self-healing IDS. Last, we suggest a future roadmap that can be made possible by embracing continual learning and integration of zero-trust policies, edge optimized TinyML agents in enabling scalable and privacy protecting detection within the 5g and the IoT ecosystem. It is a synthesis of existing knowledge, contains practical results to be taken up by practitioners, and a research road map based on future-proof AIempowered IDS that could identify and counter the cyber threats of tomorrow.","url":"https://doi.org/10.5281/zenodo.16314113","authors":["REDDY, YAKUB","Lingam, Dr. G. Shankar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.16314113","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.16314114","name":"Artificial Intelligence in Intrusion Detection Systems: Trends, Frameworks, and Future Directions for Cybersecurity","source":"datacite","abstract":"In the last decade, intrusion detection systems (IDS) have grown out of signature‐based filters to complex, AI driven platforms that have the ability to identify novel and polymorphic threats in real time. This paper will look in detail at artificial intelligence techniques used in IDS, compare and contrast the most influential frameworks and architectures, and position the next stage of the cybersecurity resilience endeavour. We will start by measuring the stakes: the average cost of a network breach in 2024 was USD 4.45 million (an increase of 2.6 percent in relation to 2023), with organizations recording a 15 percent increase in zeroday exploits, which highlights the inefficiency of the static detection processes. At this point, we categorize AI based IDS as supervised learning, unsupervised anomaly detection, deep learning, and new paradigms (graph neural networks, federated learning), their advantages and limitations compared across a selection of impactful benchmark datasets (NSLKDD, CICIDS2017, UNSW\\-NB15) and proprietary highly‐scaled enterprise traffic. Using the extensive comparisons to industry benchmarks (e.g., Snort, SVM-based models), we show that architecture that combines convolutional and recurrent networks will exceed 97 percent F1- score with latency measured at below 100 ms, at a 35 percent reduction in false positives compared to the older systems. We reveal in our discussion more longstanding issues dataset biases, adversarial robustness, and interpretability and report on newer ones in explainable AI, and differential privacy and self-healing IDS. Last, we suggest a future roadmap that can be made possible by embracing continual learning and integration of zero-trust policies, edge optimized TinyML agents in enabling scalable and privacy protecting detection within the 5g and the IoT ecosystem. It is a synthesis of existing knowledge, contains practical results to be taken up by practitioners, and a research road map based on future-proof AIempowered IDS that could identify and counter the cyber threats of tomorrow.","url":"https://doi.org/10.5281/zenodo.16314114","authors":["REDDY, YAKUB","Lingam, Dr. G. Shankar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.16314114","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2507.03004","name":"CLUES: Collaborative High-Quality Data Selection for LLMs via Training Dynamics","source":"datacite","abstract":"Recent research has highlighted the importance of data quality in scaling large language models (LLMs). However, automated data quality control faces unique challenges in collaborative settings where sharing is not allowed directly between data silos. To tackle this issue, this paper proposes a novel data quality control technique based on the notion of data influence on the training dynamics of LLMs, that high quality data are more likely to have similar training dynamics to the anchor dataset. We then leverage the influence of the training dynamics to select high-quality data from different private domains, with centralized model updates on the server side in a collaborative training fashion by either model merging or federated learning. As for the data quality indicator, we compute the per-sample gradients with respect to the private data and the anchor dataset, and use the trace of the accumulated inner products as a measurement of data quality. In addition, we develop a quality control evaluation tailored for collaborative settings with heterogeneous domain data. Experiments show that training on the high-quality data selected by our method can often outperform other data selection methods for collaborative fine-tuning of LLMs, across diverse private domain datasets, in medical, multilingual and financial settings. Our code is released at github.com/Ryan0v0/CLUES.","url":"https://doi.org/10.48550/arxiv.2507.03004","authors":["Zhao, Wanru","Fan, Hongxiang","Hu, Shell Xu","Zhou, Wangchunshu","Chen, Bofan","Lane, Nicholas D."],"tags":["Computation and Language (cs.CL)","Multiagent Systems (cs.MA)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.03004","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/4anfc-kkj26","name":"Unite! \"Digital Campus\" Community. How Cm.2 maintains and  develops IT infrastructure for learning and teaching for the Unite! alliance. Status  Quo 06/2025.","source":"datacite","abstract":"Unite! Digital Campus aims to establish a seamless digital infrastructure for learning and teaching across the Unite! alliance. This description reflects the status as of June 2025. This poster presents the efforts of Cm.2 \"Digital Campus\" in maintaining and developing IT infrastructure to support cross-institutional collaboration. The Unite! Metacampus, a federated learning management system based on Moodle and integrated with eduGAIN, serves as the backbone of this initiative. Key focus areas include metadata standardization, interoperability with major educational platforms, and the integration of digital learning services such as the European Student Card. The work is supported by a Technical Commission that ensures structured decision-making regarding infrastructure development. While significant progress has been made, challenges persist in aligning diverse institutional IT environments. This contribution reflects on the lessons learned, presents publications by the group, and outlines future steps to enhance the digital learning experience within the European higher education landscape. In addition, the poster includes links to the Success Stories Report Series, which highlights practical examples and insights from successful implementations across the alliance and was first issued in 12/2024. The poster was presented at TU Graz campus Unite! event July 2025.Ebner, M., Schön, S., Alcober, J., Bertonasco, R., Diard, J., Francisco, A., Gasplmayr, K., Herczak-Ciara, A., Hoppe, C., Koschutnig-Ebner, M. Martikainen, J., and Petersson, J. (2025). Unite! “Digital Campus” Community. How Cm.2 maintains and develops IT infrastructure for learning and teaching for the Unite! alliance. Status Quo 06/2025. Graz University of Technology, DOI: 10.3217/c57sc-e0687","url":"https://doi.org/10.3217/4anfc-kkj26","authors":["Ebner, Martin","Schön, Sandra","Alcober, Jesus","Bertonasco, Roberto","Diard, Jules","Fransico, Alexandre","Gasplmayr, Katharina","Herczak-Ciara, Agnieszka","Hoppe, Christian","Koschutnig-Ebner, Markus","Martikainen, Juha","Petersson, Joakim"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3217/4anfc-kkj26","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/c57sc-e0687","name":"Unite! \"Digital Campus\" Community. How Cm.2 maintains and  develops IT infrastructure for learning and teaching for the Unite! alliance. Status  Quo 06/2025.","source":"datacite","abstract":"Unite! Digital Campus aims to establish a seamless digital infrastructure for learning and teaching across the Unite! alliance. This description reflects the status as of June 2025. This poster presents the efforts of Cm.2 \"Digital Campus\" in maintaining and developing IT infrastructure to support cross-institutional collaboration. The Unite! Metacampus, a federated learning management system based on Moodle and integrated with eduGAIN, serves as the backbone of this initiative. Key focus areas include metadata standardization, interoperability with major educational platforms, and the integration of digital learning services such as the European Student Card. The work is supported by a Technical Commission that ensures structured decision-making regarding infrastructure development. While significant progress has been made, challenges persist in aligning diverse institutional IT environments. This contribution reflects on the lessons learned, presents publications by the group, and outlines future steps to enhance the digital learning experience within the European higher education landscape. In addition, the poster includes links to the Success Stories Report Series, which highlights practical examples and insights from successful implementations across the alliance and was first issued in 12/2024. The poster was presented at TU Graz campus Unite! event July 2025.Ebner, M., Schön, S., Alcober, J., Bertonasco, R., Diard, J., Francisco, A., Gasplmayr, K., Herczak-Ciara, A., Hoppe, C., Koschutnig-Ebner, M. Martikainen, J., and Petersson, J. (2025). Unite! “Digital Campus” Community. How Cm.2 maintains and develops IT infrastructure for learning and teaching for the Unite! alliance. Status Quo 06/2025. Graz University of Technology, DOI: 10.3217/c57sc-e0687","url":"https://doi.org/10.3217/c57sc-e0687","authors":["Ebner, Martin","Schön, Sandra","Alcober, Jesus","Bertonasco, Roberto","Diard, Jules","Fransico, Alexandre","Gasplmayr, Katharina","Herczak-Ciara, Agnieszka","Hoppe, Christian","Koschutnig-Ebner, Markus","Martikainen, Juha","Petersson, Joakim"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3217/c57sc-e0687","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15718643","name":"Comprehensive Survey of Outlier Detection in Wireless Sensor Networks","source":"datacite","abstract":"The recent advancements in outlier detection techniques for Wireless Sensor Networks emphasize their significance in enhancing data integrity and system reliability. It categorizes detection methods into centralized, distributed, and hybrid architectures, with a focus on approaches based on clustering, machine learning, deep learning, and federated learning. Particular attention is given to techniques that leverage spatio-temporal and multivariate correlations to distinguish between sensor faults and actual events. Key evaluation metrics, such as DR, FA rate, and energy efficiency, are discussed. The paper also outlines major research challenges, including the handling of high-dimensional data, real-time detection, and correlations of input and output variables, as well as research gaps, datasets, and key findings. A timeline of developments and a dataset comparison support the analysis, providing insights for future research in WSN anomaly detection. Additionally, a comparative assessment of datasets, architectures, and recent contributions from 2006 to 2024 is provided to identify research gaps and future directions. This paper aims to serve as a foundational reference for researchers and practitioners seeking to design robust, scalable, and resource-aware anomaly detection systems in modern WSN deployments.","url":"https://doi.org/10.5281/zenodo.15718643","authors":["Padma Sree N","Dr. Malini M Patil"],"tags":["Wireless Sensor Networks (WSNs), Outlier Detection, Anomaly Detection, Machine Learning, Deep Learning, Centralized Architecture, Distributed Detection, Real-Time Monitoring, Sensor Faults, Energy Efficiency, Dimensionality Reduction."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15718643","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15718642","name":"Comprehensive Survey of Outlier Detection in Wireless Sensor Networks","source":"datacite","abstract":"The recent advancements in outlier detection techniques for Wireless Sensor Networks emphasize their significance in enhancing data integrity and system reliability. It categorizes detection methods into centralized, distributed, and hybrid architectures, with a focus on approaches based on clustering, machine learning, deep learning, and federated learning. Particular attention is given to techniques that leverage spatio-temporal and multivariate correlations to distinguish between sensor faults and actual events. Key evaluation metrics, such as DR, FA rate, and energy efficiency, are discussed. The paper also outlines major research challenges, including the handling of high-dimensional data, real-time detection, and correlations of input and output variables, as well as research gaps, datasets, and key findings. A timeline of developments and a dataset comparison support the analysis, providing insights for future research in WSN anomaly detection. Additionally, a comparative assessment of datasets, architectures, and recent contributions from 2006 to 2024 is provided to identify research gaps and future directions. This paper aims to serve as a foundational reference for researchers and practitioners seeking to design robust, scalable, and resource-aware anomaly detection systems in modern WSN deployments.","url":"https://doi.org/10.5281/zenodo.15718642","authors":["Padma Sree N","Dr. Malini M Patil"],"tags":["Wireless Sensor Networks (WSNs), Outlier Detection, Anomaly Detection, Machine Learning, Deep Learning, Centralized Architecture, Distributed Detection, Real-Time Monitoring, Sensor Faults, Energy Efficiency, Dimensionality Reduction."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15718642","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.7302/25148","name":"Data Harmonization, Standardization, and Collaboration for Diabetic Retinal Disease (DRD) Research: Report From the 2024 Mary Tyler Moore Vision Initiative Workshop on Data","source":"datacite","abstract":"The 2024 Mary Tyler Moore Vision Initiative (MTM Vision) Workshop on Data convened to discuss best practices and specific considerations for building a comprehensive, shareable MTM Vision data lake. The workshop aimed to accelerate the development of new indications, therapies, and regulatory pathways for diabetic retinal disease (DRD) by standardizing and harmonizing clinical data and ocular ’omics analyses. Standardization of data collection, the use of common data elements, and data interoperability were emphasized, alongside federated learning approaches to promote data sharing and collaboration while maintaining data privacy and security. The integration of molecular data with other multimodal data types was recognized as a promising strategy for leveraging machine learning and AI approaches to advancing therapeutics development and improving treatment outcomes for DRD patients. Partnerships with entities such as the National Eye Institute, part of the National Institutes of Health, foundations, and industry were deemed vital for the successful implementation of these initiatives.","url":"https://doi.org/10.7302/25148","authors":["Domalpally, A","Fickweiler, W","Levine, ","Goetz, KE","Vanderbeek, BL","Lee, A","Sundstrom, JM","Markel, D","Sun, JK"],"tags":["Humans","Diabetic Retinopathy","Information Dissemination","Biomedical Research","United States"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.7302/25148","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2502.21266","name":"Supporting the development of Machine Learning for fundamental science in a federated Cloud with the AI_INFN platform","source":"datacite","abstract":"Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly in terms of provisioning and orchestrating access to hardware accelerators for development, testing, and production. The INFN-funded project AI_INFN (\"Artificial Intelligence at INFN\") aims at fostering the adoption of ML techniques within INFN use cases by providing support on multiple aspects, including the provision of AI-tailored computing resources. It leverages cloud-native solutions in the context of INFN Cloud, to share hardware accelerators as effectively as possible, ensuring the diversity of the Institute's research activities is not compromised. In this contribution, we provide an update on the commissioning of a Kubernetes platform designed to ease the development of GPU-powered data analysis workflows and their scalability on heterogeneous, distributed computing resources, possibly federated as Virtual Kubelets with the interLink provider.","url":"https://doi.org/10.48550/arxiv.2502.21266","authors":["Anderlini, Lucio","Barbetti, Matteo","Bianchini, Giulio","Ciangottini, Diego","Pra, Stefano Dal","Michelotto, Diego","Pellegrino, Carmelo","Petrini, Rosa","Pascolini, Alessandro","Spiga, Daniele"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","Artificial Intelligence (cs.AI)","Data Analysis, Statistics and Probability (physics.data-an)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Physical sciences","FOS: Physical sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2502.21266","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15656669","name":"Scalable Architectures for Distributed AI Training Models","source":"datacite","abstract":"This paper focuses on the development and impact of scalable systems related to distributed training of artificial intelligence (AI) models. The rise in AI workloads stemming from deep learning models, such as generative transformers and vision-oriented architectures, is necessitating a shift from monolithic systems to distributed, multifaceted training ecosystems (Rivera-Escobedo et al., 2025; Mungoli, 2023). Such environments are expected to achieve parallelized processing, dynamic elasticity, and real-time inference while maintaining sufficient model fidelity and system health (Mayer and Jacobsen, 2020). We conduct a thorough examination of advanced distributed frameworks, including Horovod, PyTorch DDP, DeepSpeed, and Ray Train, against benchmarks for image classification and distributed synchronization (Campbell, 2023; Katta, 2025). Recent studies have reported that hybrid cloud configurations, which combine centralized and federated nodes, yield optimal latency, fault tolerance, and cost efficiency (Hung et al., 2024; Freeda et al., 2025). We emphasize critical architectural components, such as asynchronous updates, pipeline parallelism, serverless orchestration, and intelligent load balancing, as the drivers of scalability at the production scale (Varma and Kothandaraman, 2022; Nawaz and Shah, 2025). This research provides specific strategies for implementation to systems and infrastructure designers who aim to efficiently allocate and manage AI workloads across diverse, heterogeneous systems. Moreover, the work highlights remaining problems related to security, system interoperability, and energy-efficient training, proposing fresh approaches for the design and research of distributed AI systems architectures (Hammad and Abu-Zaid, 2024; Bano et al., 2023).","url":"https://doi.org/10.5281/zenodo.15656669","authors":["Baskar, Sikkayan"],"tags":["Scalable AI","Distributed Training","Cloud Architectures","Deep Learning Models","Parallel Computing","AI Infrastructure","Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.15656669","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15656668","name":"Scalable Architectures for Distributed AI Training Models","source":"datacite","abstract":"This paper focuses on the development and impact of scalable systems related to distributed training of artificial intelligence (AI) models. The rise in AI workloads stemming from deep learning models, such as generative transformers and vision-oriented architectures, is necessitating a shift from monolithic systems to distributed, multifaceted training ecosystems (Rivera-Escobedo et al., 2025; Mungoli, 2023). Such environments are expected to achieve parallelized processing, dynamic elasticity, and real-time inference while maintaining sufficient model fidelity and system health (Mayer and Jacobsen, 2020). We conduct a thorough examination of advanced distributed frameworks, including Horovod, PyTorch DDP, DeepSpeed, and Ray Train, against benchmarks for image classification and distributed synchronization (Campbell, 2023; Katta, 2025). Recent studies have reported that hybrid cloud configurations, which combine centralized and federated nodes, yield optimal latency, fault tolerance, and cost efficiency (Hung et al., 2024; Freeda et al., 2025). We emphasize critical architectural components, such as asynchronous updates, pipeline parallelism, serverless orchestration, and intelligent load balancing, as the drivers of scalability at the production scale (Varma and Kothandaraman, 2022; Nawaz and Shah, 2025). This research provides specific strategies for implementation to systems and infrastructure designers who aim to efficiently allocate and manage AI workloads across diverse, heterogeneous systems. Moreover, the work highlights remaining problems related to security, system interoperability, and energy-efficient training, proposing fresh approaches for the design and research of distributed AI systems architectures (Hammad and Abu-Zaid, 2024; Bano et al., 2023).","url":"https://doi.org/10.5281/zenodo.15656668","authors":["Baskar, Sikkayan"],"tags":["Scalable AI","Distributed Training","Cloud Architectures","Deep Learning Models","Parallel Computing","AI Infrastructure","Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.15656668","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15642882","name":"ENHANCING FEDERATED LEARNING PERFORMANCE WITH KOLMOGOROV ARNOLD NETWORKS BASED ON DEEP LEARNING","source":"datacite","abstract":"Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative machine learning,enabling model training across decentralized clients without sharing raw data [1]. Despite its promise, criticalchallenges persist in real-world FL deployment, including maintaining accuracy under non-IID data distributionsand minimizing communication overhead between clients and servers [2]. In 2024, Kolmogorov-Arnold Networks(KANs) were introduced as a novel neural architecture, offering higher accuracy than traditional Multi-LayerPerceptrons (MLPs) [3]. By leveraging learnable activation functions on edges (inverting classic node-centricdesigns), KANs enhance interpretability and model efficiency, spurring over 200 exploratory studies withinmonths of their proposal.Recent work has integrated KANs with convolutional models [4] and time-series forecasting [5]. Pioneeringstudies by [6] and [7] have begun exploring KANs in FL (F-KANs), hinting at potential accuracy gains. However,critical gaps remain, no theoretical guarantees exist for F-KAN convergence, F-KANs lack direct comparisonagainst parameter-matched F-MLPs; and KANs exhibit 10× slower inference than MLPs [8], threatening practicalFL scalability. While KANs show promise in IoT intrusion detection [9] and industrial anomaly tracking [10], their applicationto federated classification tasks essential in healthcare remains unexplored. This paper bridges these gaps byproposing F-KANs for classification and conducting comprehensive experiments under standardized FLconditions. Our work rigorously evaluates F-KANs against F-MLPs on non-IID data, analyzes communicationcosts.","url":"https://doi.org/10.5281/zenodo.15642882","authors":["Athanas R. Sanga","XuanGou Wu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15642882","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15642883","name":"ENHANCING FEDERATED LEARNING PERFORMANCE WITH KOLMOGOROV ARNOLD NETWORKS BASED ON DEEP LEARNING","source":"datacite","abstract":"Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative machine learning,enabling model training across decentralized clients without sharing raw data [1]. Despite its promise, criticalchallenges persist in real-world FL deployment, including maintaining accuracy under non-IID data distributionsand minimizing communication overhead between clients and servers [2]. In 2024, Kolmogorov-Arnold Networks(KANs) were introduced as a novel neural architecture, offering higher accuracy than traditional Multi-LayerPerceptrons (MLPs) [3]. By leveraging learnable activation functions on edges (inverting classic node-centricdesigns), KANs enhance interpretability and model efficiency, spurring over 200 exploratory studies withinmonths of their proposal.Recent work has integrated KANs with convolutional models [4] and time-series forecasting [5]. Pioneeringstudies by [6] and [7] have begun exploring KANs in FL (F-KANs), hinting at potential accuracy gains. However,critical gaps remain, no theoretical guarantees exist for F-KAN convergence, F-KANs lack direct comparisonagainst parameter-matched F-MLPs; and KANs exhibit 10× slower inference than MLPs [8], threatening practicalFL scalability. While KANs show promise in IoT intrusion detection [9] and industrial anomaly tracking [10], their applicationto federated classification tasks essential in healthcare remains unexplored. This paper bridges these gaps byproposing F-KANs for classification and conducting comprehensive experiments under standardized FLconditions. Our work rigorously evaluates F-KANs against F-MLPs on non-IID data, analyzes communicationcosts.","url":"https://doi.org/10.5281/zenodo.15642883","authors":["Athanas R. Sanga","XuanGou Wu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15642883","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15528294","name":"Seven Samurai: Privacy Threat Types of LINDDUN","source":"datacite","abstract":"This data set provides the mapping between the privacy threat types and the lower-level threat characteristics onto the privacy threats discussed in six distinct and domain-specific privacy threat taxonomy publications: 'A Survey on Privacy and Security of Internet of Things’ (2020) 'A Taxonomy of mhealth Apps – Security and Privacy Concerns’ (2015) 'A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions’ (2021) ‘Privacy and artificial intelligence’ (2021) 'A Survey on Large Language Model (LLM) Security and Privacy: The good, the bad, and the ugly’(2024) 'Security and Privacy Challenges of Large Language Models: A Survey’ (2025) The mapping table provides concrete reference to the location of the threat in the publication, and the concrete threat characteristics recognized. It furthermore provide mapping rationale. This data set is made public in support for a publication entitled: Seven Samurai: Privacy Threat Types of LINDDUN","url":"https://doi.org/10.5281/zenodo.15528294","authors":["Van Landuyt, Dimitri"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15528294","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15528293","name":"Seven Samurai: Privacy Threat Types of LINDDUN","source":"datacite","abstract":"This data set provides the mapping between the privacy threat types and the lower-level threat characteristics onto the privacy threats discussed in six distinct and domain-specific privacy threat taxonomy publications: 'A Survey on Privacy and Security of Internet of Things’ (2020) 'A Taxonomy of mhealth Apps – Security and Privacy Concerns’ (2015) 'A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions’ (2021) ‘Privacy and artificial intelligence’ (2021) 'A Survey on Large Language Model (LLM) Security and Privacy: The good, the bad, and the ugly’(2024) 'Security and Privacy Challenges of Large Language Models: A Survey’ (2025) The mapping table provides concrete reference to the location of the threat in the publication, and the concrete threat characteristics recognized. It furthermore provide mapping rationale. This data set is made public in support for a publication entitled: Seven Samurai: Privacy Threat Types of LINDDUN","url":"https://doi.org/10.5281/zenodo.15528293","authors":["Van Landuyt, Dimitri"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15528293","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/1rf11-5zj55","name":"Creating awareness for inclusion, diversity, equity and accessibility (IDEA): The 21 Day Challenge on Metacampus (Report 03/2025)","source":"datacite","abstract":"Szczerba, Aaron & Edelsbrunner, Sarah (2025). Creating awareness for inclusion, diversity, equity and accessibility (IDEA): The 21 Day Challenge on Metacampus. In: “Unite! Digital Teaching and Learning - Success Story Report”, 3/2025, DOI: 10.3217/1rf11-5zj55 The Unite! 21 Day Diversity and Inclusion Challenge was developed to raise awareness of Inclusion, Diversity, Equity, and Accessibility (IDEA) across the Unite! university network. Designed as a self-paced online course on the Metacampus platform, it offered daily learning units with videos, texts, reflection prompts, and optional deep-dive materials. The course topics were shaped by expert knowledge and community feedback, ensuring relevance to both academic and societal contexts. Interactive elements such as quizzes, gap-fill exercises using H5P, and multimedia resources enhanced learner engagement and accessibility. After a successful pilot round in 2023, the course was revised and relaunched in 2024 at Unite!'s federated learning management system Metacampus with additional topics such as burnout and Indigenous rights. To ensure consistent formatting and accessibility an accessibility toolkit was installed and used. Participants who completed the required activities received an IDEA open badge as recognition. Feedback indicated high satisfaction with the structure, content, and impact of the course, while also suggesting areas for improvement like better platform usability. The project demonstrates the value of collaborative course design, inclusive pedagogy, and accessibility-driven development in a digital learning environment. The series “Unite! Digital Teaching and Learning Success Story Report” was initiated by Cm.2 Digital Campus, led by TU Graz (Martin Ebner) as part of the idea to collect, spread and enhance the possibilities, usages and lessons learned of teaching and learning offers using the federated learning management system Metacampus in 11/2024 till the end of the current Erasmus+ funding period 10/2026. All reports are available under open license and originally published at the TU Graz repository. Copyright holders are the authors.","url":"https://doi.org/10.3217/1rf11-5zj55","authors":["Szczerba, Aaron","Edelsbrunner, Sarah"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3217/1rf11-5zj55","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/rs3a6-6h144","name":"Creating awareness for inclusion, diversity, equity and accessibility (IDEA): The 21 Day Challenge on Metacampus (Report 03/2025)","source":"datacite","abstract":"Szczerba, Aaron & Edelsbrunner, Sarah (2025). Creating awareness for inclusion, diversity, equity and accessibility (IDEA): The 21 Day Challenge on Metacampus. In: “Unite! Digital Teaching and Learning - Success Story Report”, 3/2025, DOI: 10.3217/1rf11-5zj55 The Unite! 21 Day Diversity and Inclusion Challenge was developed to raise awareness of Inclusion, Diversity, Equity, and Accessibility (IDEA) across the Unite! university network. Designed as a self-paced online course on the Metacampus platform, it offered daily learning units with videos, texts, reflection prompts, and optional deep-dive materials. The course topics were shaped by expert knowledge and community feedback, ensuring relevance to both academic and societal contexts. Interactive elements such as quizzes, gap-fill exercises using H5P, and multimedia resources enhanced learner engagement and accessibility. After a successful pilot round in 2023, the course was revised and relaunched in 2024 at Unite!'s federated learning management system Metacampus with additional topics such as burnout and Indigenous rights. To ensure consistent formatting and accessibility an accessibility toolkit was installed and used. Participants who completed the required activities received an IDEA open badge as recognition. Feedback indicated high satisfaction with the structure, content, and impact of the course, while also suggesting areas for improvement like better platform usability. The project demonstrates the value of collaborative course design, inclusive pedagogy, and accessibility-driven development in a digital learning environment. The series “Unite! Digital Teaching and Learning Success Story Report” was initiated by Cm.2 Digital Campus, led by TU Graz (Martin Ebner) as part of the idea to collect, spread and enhance the possibilities, usages and lessons learned of teaching and learning offers using the federated learning management system Metacampus in 11/2024 till the end of the current Erasmus+ funding period 10/2026. All reports are available under open license and originally published at the TU Graz repository. Copyright holders are the authors.","url":"https://doi.org/10.3217/rs3a6-6h144","authors":["Szczerba, Aaron","Edelsbrunner, Sarah"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3217/rs3a6-6h144","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/10b1d-tg953","name":"Connecting platforms via LTI: The university course \"OER in HE\" and the connection of Metacampus with iMooX.at (Report 02/2024)","source":"datacite","abstract":"Schön, Sandra; Ebner, Martin & Gasplmayr, Katharina (2024). Connecting platforms via LTI: The university course “OER in HE” and the connection of Metacampus with iMooX.at. In: “Unite! Digital Teaching and Learning - Success Story Report” (edited by Martin Ebner, Katharina Gasplmayr and Sandra Schön), 2/2024, DOI: 10.3217/v8nta-ayw79 Part of the university course \"Open Educational Resources in Higher Education\" was the successful participation at the MOOC “OER in Higher education” produced as multilingual MOOC by the project team and hosted at the MOOC platform iMooX.at. To verify successful completion of the MOOC, students should have been asked asked to submit their PDF certificates manually that were awarded upon successfully passing all quizzes in the MOOC. The instructor then entered the grade or result into the course LMS. A new way of submitting certificates that prove that students have completed the MOOC was to be devised, as the goal was to move away from the previous approach of manually submitting certificates. The report describes how LTI works and was used for this. The series “Unite! Digital Teaching and Learning Success Story Report” was initiated by Cm.2 Digital Campus, led by TU Graz (Martin Ebner) as part of the idea to collect, spread and enhance the possibilities, usages and lessons learned of teaching and learning offers using the federated learning management system Metacampus in 11/2024 till the end of the current Erasmus+ funding period 10/2026. All reports are available under open license and originally published at the TU Graz repository. Copyright holders are the authors.","url":"https://doi.org/10.3217/10b1d-tg953","authors":["Schön, Sandra","Ebner, Martin","Gasplmayr, Katharina"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3217/10b1d-tg953","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/v8nta-ayw79","name":"Connecting platforms via LTI: The university course \"OER in HE\" and the connection of Metacampus with iMooX.at (Report 02/2024)","source":"datacite","abstract":"Schön, Sandra; Ebner, Martin & Gasplmayr, Katharina (2024). Connecting platforms via LTI: The university course “OER in HE” and the connection of Metacampus with iMooX.at. In: “Unite! Digital Teaching and Learning - Success Story Report” (edited by Martin Ebner, Katharina Gasplmayr and Sandra Schön), 2/2024, DOI: 10.3217/v8nta-ayw79 Part of the university course \"Open Educational Resources in Higher Education\" was the successful participation at the MOOC “OER in Higher education” produced as multilingual MOOC by the project team and hosted at the MOOC platform iMooX.at. To verify successful completion of the MOOC, students should have been asked asked to submit their PDF certificates manually that were awarded upon successfully passing all quizzes in the MOOC. The instructor then entered the grade or result into the course LMS. A new way of submitting certificates that prove that students have completed the MOOC was to be devised, as the goal was to move away from the previous approach of manually submitting certificates. The report describes how LTI works and was used for this. The series “Unite! Digital Teaching and Learning Success Story Report” was initiated by Cm.2 Digital Campus, led by TU Graz (Martin Ebner) as part of the idea to collect, spread and enhance the possibilities, usages and lessons learned of teaching and learning offers using the federated learning management system Metacampus in 11/2024 till the end of the current Erasmus+ funding period 10/2026. All reports are available under open license and originally published at the TU Graz repository. Copyright holders are the authors.","url":"https://doi.org/10.3217/v8nta-ayw79","authors":["Schön, Sandra","Ebner, Martin","Gasplmayr, Katharina"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3217/v8nta-ayw79","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/538yt-zhm81","name":"Showing knowledge with an open badge – Experiences from the \"How to use Metacampus\" course (Report 1/2024)","source":"datacite","abstract":"Gasplmayr, Katharina & Schön, Sandra (2024). Showing knowledge with an open badge – Experiences from the “How to use Metacampus” course. In: Unite! Digital Teaching and Learning - Success Story Report (edited by Martin Ebner, Katharina Gasplmayr and Sandra Schön), 1/2024, DOI: 10.3217/rsvzg-nd732 The “How to use Metacampus” course serves as an introduction to the Unite! Metacampus, the federated learning management system (LMS) of the Unite! Alliance. It is intended to be a first point of contact for users who are not familiar with the platform. The course is available for all people within Unite! (teachers, students, staff) for self-enrollment via the Unite! Metacampus and is a self-study course. After completing this course, participants will have basic knowledge about the Metacampus, will be able to find their way around the Metacampus, and will be able to use basic Moodle activities. The “Fit for Metacampus” quiz is a Moodle quiz activity with 8 questions and a passing grade of 5/10 points (see Fig. 1). By taking the “Fit for Metacampus” quiz, participants can show that they have acquired basic knowledge about the Metacampus which is needed to use the platform. Upon passing the quiz, participants automatically receive an open badge via the Metacampus. The badge was created and implemented into the course by the Metacampus support team. To call attention to the “How to use Metacampus” course and the open badge, the course was promoted before the 9th Unite! Dialogue in Graz (February 2024). At the Dialogue, all participants who received the virtual open badge could also claim a “real” badge by showing the digital badge to the Metacampus team at the Community event “Unite! Unite! What’s in it for me”. More details can be found in the report. The series “Unite! Digital Teaching and Learning Success Story Report” was initiated by Cm.2 Digital Campus, led by TU Graz (Martin Ebner) as part of the idea to collect, spread and enhance the possibilities, usages and lessons learned of teaching and learning offers using the federated learning management system Metacampus in 11/2024 till the end of the current Erasmus+ funding period 10/2026. All reports are available under open license and originally published at the TU Graz repository. Copyright holders are the authors.","url":"https://doi.org/10.3217/538yt-zhm81","authors":["Gasplmayr, Katharina","Schön, Sandra"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3217/538yt-zhm81","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/rsvzg-nd732","name":"Showing knowledge with an open badge – Experiences from the \"How to use Metacampus\" course (Report 1/2024)","source":"datacite","abstract":"Gasplmayr, Katharina & Schön, Sandra (2024). Showing knowledge with an open badge – Experiences from the “How to use Metacampus” course. In: Unite! Digital Teaching and Learning - Success Story Report (edited by Martin Ebner, Katharina Gasplmayr and Sandra Schön), 1/2024, DOI: 10.3217/rsvzg-nd732 The “How to use Metacampus” course serves as an introduction to the Unite! Metacampus, the federated learning management system (LMS) of the Unite! Alliance. It is intended to be a first point of contact for users who are not familiar with the platform. The course is available for all people within Unite! (teachers, students, staff) for self-enrollment via the Unite! Metacampus and is a self-study course. After completing this course, participants will have basic knowledge about the Metacampus, will be able to find their way around the Metacampus, and will be able to use basic Moodle activities. The “Fit for Metacampus” quiz is a Moodle quiz activity with 8 questions and a passing grade of 5/10 points (see Fig. 1). By taking the “Fit for Metacampus” quiz, participants can show that they have acquired basic knowledge about the Metacampus which is needed to use the platform. Upon passing the quiz, participants automatically receive an open badge via the Metacampus. The badge was created and implemented into the course by the Metacampus support team. To call attention to the “How to use Metacampus” course and the open badge, the course was promoted before the 9th Unite! Dialogue in Graz (February 2024). At the Dialogue, all participants who received the virtual open badge could also claim a “real” badge by showing the digital badge to the Metacampus team at the Community event “Unite! Unite! What’s in it for me”. More details can be found in the report. The series “Unite! Digital Teaching and Learning Success Story Report” was initiated by Cm.2 Digital Campus, led by TU Graz (Martin Ebner) as part of the idea to collect, spread and enhance the possibilities, usages and lessons learned of teaching and learning offers using the federated learning management system Metacampus in 11/2024 till the end of the current Erasmus+ funding period 10/2026. All reports are available under open license and originally published at the TU Graz repository. Copyright holders are the authors.","url":"https://doi.org/10.3217/rsvzg-nd732","authors":["Gasplmayr, Katharina","Schön, Sandra"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3217/rsvzg-nd732","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/75e94-x9w49","name":"Federated virtual learning management in a European University alliance: General challenges and first experiences using LTI to connect LMS in Unite!","source":"datacite","abstract":"Published: Schön, S., Ebner, M., Edelsbrunner, S., Gasplmayr, K., Hohla-Sejkora, K., Leitner, P. & Taraghi, B. (2024). Federated virtual learning management in a European University alliance: General challenges and first experiences using LTI to connect LMS in Unite!. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 123-136). Brussels, Belgium: Association for the Advancement of Computing in Education (AACE). Retrieved May 23, 2025 from https://www.learntechlib.org/primary/p/224514/. This paper explores the challenges and first experiences of implementing federated virtual learning management within the European University Alliance Unite!. Through the lens of Learning Tools Interoperability (LTI), the study investigates the complexities inherent in connecting Learning Management Systems (LMS) across diverse institutional contexts. This research examines the general hurdles faced by European university alliances in adopting federated LMS. Additionally, it outlines the LMS infrastructure of Unite! in early 2024 and discusses the pilot initiatives undertaken to utilize LTI for connecting LMS platforms at Graz University of Technology (TU Graz). The pilots involve integrating Unite!'s Metacampus with various platforms, including Moodle-based systems from TU Graz. Drawing from these pilot experiences, the paper presents insights and lessons learned regarding the efficacy of LTI in facilitating cross-platform connectivity within Unite! and offers implications for future implementations.","url":"https://doi.org/10.3217/75e94-x9w49","authors":["Schön, Sandra","Ebner, Martin","Edelsbrunner, Sarah","Gasplmayr, Katharina","Hohla-Sejkora, Katharina","Leitner, Philip","Taraghi, Behnam"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3217/75e94-x9w49","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/5w0ay-j5q25","name":"Federated virtual learning management in a European University alliance: General challenges and first experiences using LTI to connect LMS in Unite! (Preprint)","source":"datacite","abstract":"Published as: Schön, S., Ebner, M., Edelsbrunner, S., Gasplmayr, K., Hohla-Sejkora, K., Leitner, P. & Taraghi, B. (2024). Federated virtual learning management in a European University alliance: General challenges and first experiences using LTI to connect LMS in Unite!. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 123-136). Brussels, Belgium: Association for the Advancement of Computing in Education (AACE). Retrieved May 23, 2025 from https://www.learntechlib.org/primary/p/224514/. This paper explores the challenges and first experiences of implementing federated virtual learning management within the European University Alliance Unite!. Through the lens of Learning Tools Interoperability (LTI), the study investigates the complexities inherent in connecting Learning Management Systems (LMS) across diverse institutional contexts. This research examines the general hurdles faced by European university alliances in adopting federated LMS. Additionally, it outlines the LMS infrastructure of Unite! in early 2024 and discusses the pilot initiatives undertaken to utilize LTI for connecting LMS platforms at Graz University of Technology (TU Graz). The pilots involve integrating Unite!'s Metacampus with various platforms, including Moodle-based systems from TU Graz. Drawing from these pilot experiences, the paper presents insights and lessons learned regarding the efficacy of LTI in facilitating cross-platform connectivity within Unite! and offers implications for future implementations.","url":"https://doi.org/10.3217/5w0ay-j5q25","authors":["Schön, Sandra","Ebner, Martin","Edelsbrunner, Sarah","Gasplmayr, Katharina","Hohla-Sejkora, Katharina","Leitner, Philip","Taraghi, Behnam"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3217/5w0ay-j5q25","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/7w8r6-djc29","name":"Federated virtual learning management in a European University alliance: General challenges and first experiences using LTI to connect LMS in Unite! (Preprint)","source":"datacite","abstract":"Published as: Schön, S., Ebner, M., Edelsbrunner, S., Gasplmayr, K., Hohla-Sejkora, K., Leitner, P. & Taraghi, B. (2024). Federated virtual learning management in a European University alliance: General challenges and first experiences using LTI to connect LMS in Unite!. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 123-136). Brussels, Belgium: Association for the Advancement of Computing in Education (AACE). Retrieved May 23, 2025 from https://www.learntechlib.org/primary/p/224514/. This paper explores the challenges and first experiences of implementing federated virtual learning management within the European University Alliance Unite!. Through the lens of Learning Tools Interoperability (LTI), the study investigates the complexities inherent in connecting Learning Management Systems (LMS) across diverse institutional contexts. This research examines the general hurdles faced by European university alliances in adopting federated LMS. Additionally, it outlines the LMS infrastructure of Unite! in early 2024 and discusses the pilot initiatives undertaken to utilize LTI for connecting LMS platforms at Graz University of Technology (TU Graz). The pilots involve integrating Unite!'s Metacampus with various platforms, including Moodle-based systems from TU Graz. Drawing from these pilot experiences, the paper presents insights and lessons learned regarding the efficacy of LTI in facilitating cross-platform connectivity within Unite! and offers implications for future implementations.","url":"https://doi.org/10.3217/7w8r6-djc29","authors":["Schön, Sandra","Ebner, Martin","Edelsbrunner, Sarah","Gasplmayr, Katharina","Hohla-Sejkora, Katharina","Leitner, Philip","Taraghi, Behnam"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3217/7w8r6-djc29","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2206.10032","name":"Communication-Efficient Federated Learning With Data and Client Heterogeneity","source":"datacite","abstract":"Federated Learning (FL) enables large-scale distributed training of machine learning models, while still allowing individual nodes to maintain data locally. However, executing FL at scale comes with inherent practical challenges: 1) heterogeneity of the local node data distributions, 2) heterogeneity of node computational speeds (asynchrony), but also 3) constraints in the amount of communication between the clients and the server. In this work, we present the first variant of the classic federated averaging (FedAvg) algorithm which, at the same time, supports data heterogeneity, partial client asynchrony, and communication compression. Our algorithm comes with a novel, rigorous analysis showing that, in spite of these system relaxations, it can provide similar convergence to FedAvg in interesting parameter regimes. Experimental results in the rigorous LEAF benchmark on setups of up to 300 nodes show that our algorithm ensures fast convergence for standard federated tasks, improving upon prior quantized and asynchronous approaches.","url":"https://doi.org/10.48550/arxiv.2206.10032","authors":["Zakerinia, Hossein","Talaei, Shayan","Nadiradze, Giorgi","Alistarh, Dan"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2206.10032","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15465013","name":"PROSurvival clinical data conversion scripts","source":"datacite","abstract":"This archive contains a Python script that was used to prepare the clinical data collected in the PROSurvival project (\"Survival Prediction for Prostate Cancer Patients using Federated Machine Learning and Predictive Morphological Patterns\") from two university hospitals, Charité (Berlin, Germany) and KGU (Frankfurt/Main, Germany) for publication in an XML based format derived from the German oBDS 3 standard (\"Einheitlicher onkologischer Basisdatensatz 2021\") used in the communication of cancer data to the epidemiological cancer registries in Germany. The data format is described in the following article: Xu T, Wolters T, Lotz J, Bisson T, et al. PROSurvival: A Technical Case Report on Creating and Publishing a Dataset for Federated Learning on Survival Prediction of Prostate Cancer Patients. Stud Health Technol Inform 321, 220-224 (2024). https://doi.org/10.3233/SHTI241096","url":"https://doi.org/10.5281/zenodo.15465013","authors":["Xu, Tingyan"],"tags":["PROSurvival","Prostate cancer"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15465013","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15465014","name":"PROSurvival clinical data conversion scripts","source":"datacite","abstract":"This archive contains a Python script that was used to prepare the clinical data collected in the PROSurvival project (\"Survival Prediction for Prostate Cancer Patients using Federated Machine Learning and Predictive Morphological Patterns\") from two university hospitals, Charité (Berlin, Germany) and KGU (Frankfurt/Main, Germany) for publication in an XML based format derived from the German oBDS 3 standard (\"Einheitlicher onkologischer Basisdatensatz 2021\") used in the communication of cancer data to the epidemiological cancer registries in Germany. The data format is described in the following article: Xu T, Wolters T, Lotz J, Bisson T, et al. PROSurvival: A Technical Case Report on Creating and Publishing a Dataset for Federated Learning on Survival Prediction of Prostate Cancer Patients. Stud Health Technol Inform 321, 220-224 (2024). https://doi.org/10.3233/SHTI241096","url":"https://doi.org/10.5281/zenodo.15465014","authors":["Xu, Tingyan"],"tags":["PROSurvival","Prostate cancer"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15465014","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.10480413","name":"AgnostiqHQ/covalent: v0.232.0","source":"datacite","abstract":"[0.232.0] - 2024-01-10 Authors Ara Ghukasyan 38226926+araghukas@users.noreply.github.com Co-authored-by: pre-commit-ci[bot] Andrew S. Rosen asrosen93@gmail.com Co-authored-by: Will Cunningham wjcunningham7@users.noreply.github.com Co-authored-by: Sankalp Sanand sankalp@agnostiq.ai Kevin Taylor tkdtaylor@gmail.com FilipBolt filipbolt@gmail.com Co-authored-by: dependabot[bot] Co-authored-by: Will Cunningham wjcunningham7@gmail.com Co-authored-by: Prasy12 prasanna.venkatesh@psiog.com Aviral Katiyar 123640350+maskboyAvi@users.noreply.github.com Co-authored-by: ArunPsiog arun.mukesh@psiog.com Casey Jao casey@agnostiq.ai Arnav Kohli 95236897+THEGAMECHANGER416@users.noreply.github.com Kirill Pushkarev 71515921+kirill-push@users.noreply.github.com Aditya Raj Kashyap 95625520+AdityaRaj23@users.noreply.github.com ArunPsiog 106462226+ArunPsiog@users.noreply.github.com mpvgithub 107603631+mpvgithub@users.noreply.github.com Aravind 100823292+Aravind-psiog@users.noreply.github.com Faiyaz Hasan faiyaz@agnostiq.ai Co-authored-by: Venkat Bala balavk89@gmail.com Co-authored-by: kessler-frost ssanand@hawk.iit.edu Co-authored-by: Aravind-psiog aravind.prabaharan@psiog.com Co-authored-by: Manjunath PV manjunath.poilath@psiog.com Co-authored-by: Ara Ghukasyan ara@agnostiq.ai Co-authored-by: Alejandro Esquivel ae@alejandro.ltd Co-authored-by: jackbaker1001 jsbaker1001@gmail.com Co-authored-by: Santosh kumar 29346072+santoshkumarradha@users.noreply.github.com Co-authored-by: Will Cunningham will@agnostiq.ai Co-authored-by: sriranjani venkatesan sriranjani.venkatesan@psiog.com Co-authored-by: Prasanna Venkatesh 54540812+Prasy12@users.noreply.github.com WingCode smallstar1234@gmail.com Nick Tyler nicholas.s.tyler.4@gmail.com Co-authored-by: RaviPsiog raviteja.gurram@psiog.com dwelsch-esi 116022979+dwelsch-esi@users.noreply.github.com Co-authored-by: dwelsch-memverge david.welsch@memverge.com Madhur Tandon 20173739+madhur-tandon@users.noreply.github.com Co-authored-by: kamalesh.suresh kamalesh.suresh@psiog.com Co-authored-by: santoshkumarradha santosh@agnostiq.ai Janosh Riebesell janosh.riebesell@gmail.com Madhur Tandon madhurtandon23@gmail.com Rob de Wit RCdeWit@users.noreply.github.com Venkat Bala 15014089+venkatBala@users.noreply.github.com Co-authored-by: Venkat Bala venkat@agnostiq.ai Co-authored-by: Amalan Jenicious F amalan.jenicious@psiog.com RaviPsiog 111348352+RaviPsiog@users.noreply.github.com Co-authored-by: RaviPsiog ravieja.gurram@psiog.com Akalanka 8133713+boneyag@users.noreply.github.com Co-authored-by: Scott Wyman Neagle scott@agnostiq.ai Scott Wyman Neagle wymnea@protonmail.com Added check for /bin/bash AND /bin/sh (in that order) to execute bash leptons Programmatic equivalents of CLI commands covalent start and covalent stop Documentation and test cases for database triggers. Added the __pow__ method to the Electron class New Runner and executor API to bypass server-side memory when running tasks. Added qelectron_db as an asset to be transferred from executor's machine to covalent server New methods to qelectron_utils, replacing the old ones Covalent deploy CLI tool added - allows provisioning infras directly from covalent Added a py.typed file to support type-checking Corrected support from distributed Hamiltonian expval calculations Exposed qelectron db in sdk result object UI changes added for qelectrons and fix for related config file corruption UI fix regarding Qelectron not showing up Performance optimisation of UI for large Qelectrons File transfer strategy for GCP storage Add CLI status for zombie, stopped process. Fix for double locking file in configurations. Introduced new data access layer Introduced Shutil file transfer strategy for local file transfers File transfer strategy for Azure blob storage executor property to Electron class, allowing updation of executor after electron function definition Added ability to hide post-processing electrons on the UI. Added prettify of names for the graph screen on the UI. Ability t","url":"https://doi.org/10.5281/zenodo.10480413","authors":["Will Cunningham","Alejandro Esquivel","Casey Jao","Sankalp Sanand","Faiyaz Hasan","Venkat Bala","Prasanna Venkatesh","Andrew S. Rosen","Madhur Tandon","Okechukwu  Emmanuel Ochia","Dave Welsch","jkanem","Aravind","Ara Ghukasyan","HaimHorowitzAgnostiq","Ruihao Li","Scott Wyman Neagle","valkostadinov","WingCode","Sayandip Dutta","Poojith U Rao","FilipBolt","Udayan","mpvgithub","Anna Hughes","RaviPsiog","ArunPsiog","Aditya Raj Kashyap"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.10480413","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2408.01765","name":"Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learning","source":"datacite","abstract":"Time-triggered federated learning, in contrast to conventional event-based federated learning, organizes users into tiers based on fixed time intervals. However, this network still faces challenges due to a growing number of devices and limited wireless bandwidth, increasing issues like stragglers and communication overhead. In this paper, we apply model pruning to wireless Time-triggered systems and jointly study the problem of optimizing the pruning ratio and bandwidth allocation to minimize training loss under communication latency constraints. To solve this joint optimization problem, we perform a convergence analysis on the gradient $l_2$-norm of the asynchronous multi-tier federated learning (FL) model with adaptive model pruning. The convergence upper bound is derived and a joint optimization problem of pruning ratio and wireless bandwidth is defined to minimize the model training loss under a given communication latency constraint. The closed-form solutions for wireless bandwidth and pruning ratio by using KKT conditions are then formulated. As indicated in the simulation experiments, our proposed TT-Prune demonstrates a 40% reduction in communication cost, compared with the asynchronous multi-tier FL without model pruning, while maintaining the model convergence at the same level.","url":"https://doi.org/10.48550/arxiv.2408.01765","authors":["Zhang, Xinlu","Deng, Yansha","Mahmoodi, Toktam"],"tags":["Machine Learning (cs.LG)","Information Theory (cs.IT)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2408.01765","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2402.10092","name":"Workflow Optimization for Parallel Split Learning","source":"datacite","abstract":"Split learning (SL) has been recently proposed as a way to enable resource-constrained devices to train multi-parameter neural networks (NNs) and participate in federated learning (FL). In a nutshell, SL splits the NN model into parts, and allows clients (devices) to offload the largest part as a processing task to a computationally powerful helper. In parallel SL, multiple helpers can process model parts of one or more clients, thus, considerably reducing the maximum training time over all clients (makespan). In this paper, we focus on orchestrating the workflow of this operation, which is critical in highly heterogeneous systems, as our experiments show. In particular, we formulate the joint problem of client-helper assignments and scheduling decisions with the goal of minimizing the training makespan, and we prove that it is NP-hard. We propose a solution method based on the decomposition of the problem by leveraging its inherent symmetry, and a second one that is fully scalable. A wealth of numerical evaluations using our testbed's measurements allow us to build a solution strategy comprising these methods. Moreover, we show that this strategy finds a near-optimal solution, and achieves a shorter makespan than the baseline scheme by up to 52.3%.","url":"https://doi.org/10.48550/arxiv.2402.10092","authors":["Tirana, Joana","Tsigkari, Dimitra","Iosifidis, George","Chatzopoulos, Dimitris"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","Networking and Internet Architecture (cs.NI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2402.10092","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15342770","name":"Integrating Predictive Analytics into Diabetic Wound Care: A Path Toward Personalized Medicine. A Narrative Review","source":"datacite","abstract":"ABSTRACT Diabetic foot ulcers (DFUs) are a severe and costly complication of diabetes, often leading to infections, amputations, and diminished quality of life. Traditional wound care remains reactive, relying on subjective assessments and lacking predictive capabilities. This narrative review explores the integration of predictive analytics—powered by machine learning (ML) and artificial intelligence (AI)—into diabetic wound care as a pathway toward personalized medicine. We synthesized literature from 2014 to 2024, focusing on ML applications, predictive models, and their integration with electronic health records (EHRs), wearables, and imaging technologies. Key findings reveal that models such as logistic regression (interpretable for binary outcomes), random forests (robust for structured data), convolutional neural networks (superior for image analysis), and long short-term memory networks (effective for temporal data) enable risk stratification, healing trajectory prediction, and personalized interventions. Integration with wearables and EHRs facilitates real-time monitoring and early detection of complications, while smartphone-based AI tools enhance remote care. However, challenges persist, including data interoperability, clinician trust in “black-box” models, ethical concerns around bias, and infrastructural barriers in low-resource settings. The review underscores the potential of predictive analytics to transition diabetic wound management from reactive to proactive, precision-based care, reducing hospitalizations and amputations. Future directions emphasize federated learning for privacy-preserving collaboration, genomic integration for tailored therapies, and addressing gaps in multimodal datasets and external validation. Successful implementation requires interdisciplinary collaboration, standardized data practices, and ethical frameworks to ensure equitable, transparent, and clinically validated AI solutions. This synthesis highlights the transformative promise of predictive analytics in reshaping diabetic wound care while outlining critical steps for its ethical and effective adoption.","url":"https://doi.org/10.5281/zenodo.15342770","authors":["Lufulwabo, Aime"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15342770","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15342769","name":"Integrating Predictive Analytics into Diabetic Wound Care: A Path Toward Personalized Medicine. A Narrative Review","source":"datacite","abstract":"ABSTRACT Diabetic foot ulcers (DFUs) are a severe and costly complication of diabetes, often leading to infections, amputations, and diminished quality of life. Traditional wound care remains reactive, relying on subjective assessments and lacking predictive capabilities. This narrative review explores the integration of predictive analytics—powered by machine learning (ML) and artificial intelligence (AI)—into diabetic wound care as a pathway toward personalized medicine. We synthesized literature from 2014 to 2024, focusing on ML applications, predictive models, and their integration with electronic health records (EHRs), wearables, and imaging technologies. Key findings reveal that models such as logistic regression (interpretable for binary outcomes), random forests (robust for structured data), convolutional neural networks (superior for image analysis), and long short-term memory networks (effective for temporal data) enable risk stratification, healing trajectory prediction, and personalized interventions. Integration with wearables and EHRs facilitates real-time monitoring and early detection of complications, while smartphone-based AI tools enhance remote care. However, challenges persist, including data interoperability, clinician trust in “black-box” models, ethical concerns around bias, and infrastructural barriers in low-resource settings. The review underscores the potential of predictive analytics to transition diabetic wound management from reactive to proactive, precision-based care, reducing hospitalizations and amputations. Future directions emphasize federated learning for privacy-preserving collaboration, genomic integration for tailored therapies, and addressing gaps in multimodal datasets and external validation. Successful implementation requires interdisciplinary collaboration, standardized data practices, and ethical frameworks to ensure equitable, transparent, and clinically validated AI solutions. This synthesis highlights the transformative promise of predictive analytics in reshaping diabetic wound care while outlining critical steps for its ethical and effective adoption.","url":"https://doi.org/10.5281/zenodo.15342769","authors":["Lufulwabo, Aime"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15342769","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.17632/ggyxsmdmg8","name":"Flooding Attack SD-IoD","source":"datacite","abstract":"The data provided was used during the flooding attack detection experiment using semi-supervised federated learning in Software Defined Internet of Drones. The flooding attack is performed on three distinct IoD zones where the attacker drones exist within each zone. The data is the result of conversion from the CIC-BCCC-NRC_TabularIoTAttack-2024 into OpenFlow-enabled format in the SDN environment. The flooding attack data is derived from several attack patterns, including DDoS UDP, DDoS TCP, DDoS SYN, DDoS HTTP, DDoS DNS, Mirai UDP, and MQTT Flood.","url":"https://doi.org/10.17632/ggyxsmdmg8","authors":["Sumadi, Fauzi","Alsubhi, Khalid"],"tags":["Denial-of-Service Attack","Software Defined Network","Cyber Attack"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.17632/ggyxsmdmg8","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15310785","name":"Privacy and Ethical Concerns in AI-Powered Data Processing","source":"datacite","abstract":"Artificial intelligence is increasingly being used to process large datasets. This introduces serious privacy and security risks (Paul, 2024). In many AI systems, sensitive personal data are collected and analyzed, so leaks or attacks can expose private information.In this paper, we review common types of privacy attacks such as model inversion, membership inference, and data reconstruction, and analyze them through ethical lenses. Next, we examine current mitigation techniques like differential privacy and federated learning, as well as their ethical implications. Finally, we discuss future directions for ethically respecting privacy in AI systems. Throughout, we emphasize how some ethical frameworks apply to the challenges and solutions in AI privacy.","url":"https://doi.org/10.5281/zenodo.15310785","authors":["B. Ziade, Tony"],"tags":["Privacy","Genetic Privacy/ethics","Ethics","Ethics","Ethics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15310785","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.5281/zenodo.15310784","name":"Privacy and Ethical Concerns in AI-Powered Data Processing","source":"datacite","abstract":"Artificial intelligence is increasingly being used to process large datasets. This introduces serious privacy and security risks (Paul, 2024). In many AI systems, sensitive personal data are collected and analyzed, so leaks or attacks can expose private information.In this paper, we review common types of privacy attacks such as model inversion, membership inference, and data reconstruction, and analyze them through ethical lenses. Next, we examine current mitigation techniques like differential privacy and federated learning, as well as their ethical implications. Finally, we discuss future directions for ethically respecting privacy in AI systems. Throughout, we emphasize how some ethical frameworks apply to the challenges and solutions in AI privacy.","url":"https://doi.org/10.5281/zenodo.15310784","authors":["B. Ziade, Tony"],"tags":["Privacy","Genetic Privacy/ethics","Ethics","Ethics","Ethics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15310784","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2401.12012","name":"TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients","source":"datacite","abstract":"Federated learning is a distributed collaborative machine learning paradigm that has gained strong momentum in recent years. In federated learning, a central server periodically coordinates models with clients and aggregates the models trained locally by clients without necessitating access to local data. Despite its potential, the implementation of federated learning continues to encounter several challenges, predominantly the slow convergence that is largely due to data heterogeneity. The slow convergence becomes particularly problematic in cross-device federated learning scenarios where clients may be strongly limited by computing power and storage space, and hence counteracting methods that induce additional computation or memory cost on the client side such as auxiliary objective terms and larger training iterations can be impractical. In this paper, we propose a novel federated aggregation strategy, TurboSVM-FL, that poses no additional computation burden on the client side and can significantly accelerate convergence for federated classification task, especially when clients are \"lazy\" and train their models solely for few epochs for next global aggregation. TurboSVM-FL extensively utilizes support vector machine to conduct selective aggregation and max-margin spread-out regularization on class embeddings. We evaluate TurboSVM-FL on multiple datasets including FEMNIST, CelebA, and Shakespeare using user-independent validation with non-iid data distribution. Our results show that TurboSVM-FL can significantly outperform existing popular algorithms on convergence rate and reduce communication rounds while delivering better test metrics including accuracy, F1 score, and MCC.","url":"https://doi.org/10.48550/arxiv.2401.12012","authors":["Wang, Mengdi","Bodonhelyi, Anna","Bozkir, Efe","Kasneci, Enkelejda"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2401.12012","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2504.02142","name":"Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds","source":"datacite","abstract":"Group robustness has become a major concern in machine learning (ML) as conventional training paradigms were found to produce high error on minority groups. Without explicit group annotations, proposed solutions rely on heuristics that aim to identify and then amplify the minority samples during training. In our work, we first uncover a critical shortcoming of these methods: an inability to distinguish legitimate minority samples from poison samples in the training set. By amplifying poison samples as well, group robustness methods inadvertently boost the success rate of an adversary -- e.g., from $0\\%$ without amplification to over $97\\%$ with it. Notably, we supplement our empirical evidence with an impossibility result proving this inability of a standard heuristic under some assumptions. Moreover, scrutinizing recent poisoning defenses both in centralized and federated learning, we observe that they rely on similar heuristics to identify which samples should be eliminated as poisons. In consequence, minority samples are eliminated along with poisons, which damages group robustness -- e.g., from $55\\%$ without the removal of the minority samples to $41\\%$ with it. Finally, as they pursue opposing goals using similar heuristics, our attempt to alleviate the trade-off by combining group robustness methods and poisoning defenses falls short. By exposing this tension, we also hope to highlight how benchmark-driven ML scholarship can obscure the trade-offs among different metrics with potentially detrimental consequences.","url":"https://doi.org/10.48550/arxiv.2504.02142","authors":["Panaitescu-Liess, Michael-Andrei","Kaya, Yigitcan","Zhu, Sicheng","Huang, Furong","Dumitras, Tudor"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2504.02142","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.17605/osf.io/t5zph","name":"SBAC-PAD_2024_Paper_Reproducibility","source":"datacite","abstract":"A summary for reproducibility of the paper \"Optimal Time and Energy-Aware Client Selection Algorithms for Federated Learning on Heterogeneous Resources\" (SBAC-PAD 2024).","url":"https://doi.org/10.17605/osf.io/t5zph","authors":["Nunes, Alan Lira"],"tags":["Physical Sciences and Mathematics","Computer Sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.17605/osf.io/t5zph","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2405.15474","name":"Unlearning during Learning: An Efficient Federated Machine Unlearning Method","source":"datacite","abstract":"In recent years, Federated Learning (FL) has garnered significant attention as a distributed machine learning paradigm. To facilitate the implementation of the right to be forgotten, the concept of federated machine unlearning (FMU) has also emerged. However, current FMU approaches often involve additional time-consuming steps and may not offer comprehensive unlearning capabilities, which renders them less practical in real FL scenarios. In this paper, we introduce FedAU, an innovative and efficient FMU framework aimed at overcoming these limitations. Specifically, FedAU incorporates a lightweight auxiliary unlearning module into the learning process and employs a straightforward linear operation to facilitate unlearning. This approach eliminates the requirement for extra time-consuming steps, rendering it well-suited for FL. Furthermore, FedAU exhibits remarkable versatility. It not only enables multiple clients to carry out unlearning tasks concurrently but also supports unlearning at various levels of granularity, including individual data samples, specific classes, and even at the client level. We conducted extensive experiments on MNIST, CIFAR10, and CIFAR100 datasets to evaluate the performance of FedAU. The results demonstrate that FedAU effectively achieves the desired unlearning effect while maintaining model accuracy. Our code is availiable at https://github.com/Liar-Mask/FedAU.","url":"https://doi.org/10.48550/arxiv.2405.15474","authors":["Gu, Hanlin","Zhu, Gongxi","Zhang, Jie","Zhao, Xinyuan","Han, Yuxing","Fan, Lixin","Yang, Qiang"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.15474","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2306.01176","name":"Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging","source":"datacite","abstract":"Existing reconstruction models in snapshot compressive imaging systems (SCI) are trained with a single well-calibrated hardware instance, making their performance vulnerable to hardware shifts and limited in adapting to multiple hardware configurations. To facilitate cross-hardware learning, previous efforts attempt to directly collect multi-hardware data and perform centralized training, which is impractical due to severe user data privacy concerns and hardware heterogeneity across different platforms/institutions. In this study, we explicitly consider data privacy and heterogeneity in cooperatively optimizing SCI systems by proposing a Federated Hardware-Prompt learning (FedHP) framework. Rather than mitigating the client drift by rectifying the gradients, which only takes effect on the learning manifold but fails to solve the heterogeneity rooted in the input data space, FedHP learns a hardware-conditioned prompter to align inconsistent data distribution across clients, serving as an indicator of the data inconsistency among different hardware (e.g., coded apertures). Extensive experimental results demonstrate that the proposed FedHP coordinates the pre-trained model to multiple hardware configurations, outperforming prevalent FL frameworks for 0.35dB under challenging heterogeneous settings. Moreover, a Snapshot Spectral Heterogeneous Dataset has been built upon multiple practical SCI systems. Data and code are aveilable at https://github.com/Jiamian-Wang/FedHP-Snapshot-Compressive-Imaging","url":"https://doi.org/10.48550/arxiv.2306.01176","authors":["Wang, Jiamian","Wu, Zongliang","Zhang, Yulun","Yuan, Xin","Lin, Tao","Tao, Zhiqiang"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2306.01176","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2406.08267","name":"A deep cut into Split Federated Self-supervised Learning","source":"datacite","abstract":"Collaborative self-supervised learning has recently become feasible in highly distributed environments by dividing the network layers between client devices and a central server. However, state-of-the-art methods, such as MocoSFL, are optimized for network division at the initial layers, which decreases the protection of the client data and increases communication overhead. In this paper, we demonstrate that splitting depth is crucial for maintaining privacy and communication efficiency in distributed training. We also show that MocoSFL suffers from a catastrophic quality deterioration for the minimal communication overhead. As a remedy, we introduce Momentum-Aligned contrastive Split Federated Learning (MonAcoSFL), which aligns online and momentum client models during training procedure. Consequently, we achieve state-of-the-art accuracy while significantly reducing the communication overhead, making MonAcoSFL more practical in real-world scenarios.","url":"https://doi.org/10.48550/arxiv.2406.08267","authors":["Przewięźlikowski, Marcin","Osial, Marcin","Zieliński, Bartosz","Śmieja, Marek"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2406.08267","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.17632/ggyxsmdmg8.1","name":"Flooding Attack SD-IoD","source":"datacite","abstract":"The data provided was used during the flooding attack detection experiment using semi-supervised federated learning in Software Defined Internet of Drones. The flooding attack is performed on three distinct IoD zones where the attacker drones exist within each zone. The data is the result of conversion from the CIC-BCCC-NRC_TabularIoTAttack-2024 into OpenFlow-enabled format in the SDN environment. The flooding attack data is derived from several attack patterns, including DDoS UDP, DDoS TCP, DDoS SYN, DDoS HTTP, DDoS DNS, Mirai UDP, and MQTT Flood.","url":"https://doi.org/10.17632/ggyxsmdmg8.1","authors":["Sumadi, Fauzi","Alsubhi, Khalid"],"tags":["Denial-of-Service Attack","Software Defined Network","Cyber Attack"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.17632/ggyxsmdmg8.1","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.17605/osf.io/d27xz","name":"AI stridor analysis","source":"datacite","abstract":"Objective: This review examines artificial intelligence (AI) applications in analyzing respiratory sounds, specifically stridor, to address limitations in traditional, operator-dependent diagnostic methods. Data Sources: A structured search across PubMed, Scopus, and Ovid was conducted, focusing on studies from 2010 to 2024 relevant to AI and stridor detection. Review Methods: The review synthesizes findings from studies employing machine learning (ML) models like Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Support Vector Machines (SVM) to enhance stridor diagnosis by analyzing respiratory sound patterns. Results: The reviewed studies demonstrate high diagnostic accuracy, with models such as Quadratic Discriminant Analysis (QDA) and Personalized Federated Learning with Self-Distillation (PFL-SD) achieving accuracies up to 100% and 96.1%, respectively. AI techniques showed potential to classify stridor types, identify anatomical locations of obstructions, and guide clinical decision-making, particularly in pediatric cases requiring prompt intervention. Conclusion: AI-driven respiratory sound analysis offers promising advancements in stridor diagnosis through improved accuracy and early detection, though limitations in data standardization and underexplored pediatric applications present challenges and opportunities. Future research on larger, standardized datasets could expand the potential of AI tools in this critical clinical setting.","url":"https://doi.org/10.17605/osf.io/d27xz","authors":["Tartaglia, Francesco Carlo","Motisi, Annagiulia"],"tags":["Physical Sciences and Mathematics","Otorhinolaryngologic Diseases","Diseases","Medicine and Health Sciences","Computer Sciences","Artificial Intelligence and Robotics","stridor"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.17605/osf.io/d27xz","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2405.11525","name":"Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors","source":"datacite","abstract":"Conventional Federated Learning (FL) involves collaborative training of a global model while maintaining user data privacy. One of its branches, decentralized FL, is a serverless network that allows clients to own and optimize different local models separately, which results in saving management and communication resources. Despite the promising advancements in decentralized FL, it may reduce model generalizability due to lacking a global model. In this scenario, managing data and model heterogeneity among clients becomes a crucial problem, which poses a unique challenge that must be overcome: How can every client's local model learn generalizable representation in a decentralized manner? To address this challenge, we propose a novel Decentralized FL technique by introducing Synthetic Anchors, dubbed as DeSA. Based on the theory of domain adaptation and Knowledge Distillation (KD), we theoretically and empirically show that synthesizing global anchors based on raw data distribution facilitates mutual knowledge transfer. We further design two effective regularization terms for local training: 1) REG loss that regularizes the distribution of the client's latent embedding with the anchors and 2) KD loss that enables clients to learn from others. Through extensive experiments on diverse client data distributions, we showcase the effectiveness of DeSA in enhancing both inter- and intra-domain accuracy of each client.","url":"https://doi.org/10.48550/arxiv.2405.11525","authors":["Huang, Chun-Yin","Srinivas, Kartik","Zhang, Xin","Li, Xiaoxiao"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.11525","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/49h8k-rhe96","name":"Building OER Competence Across the Unite! Alliance. Insights from the \"Unite! OER Courses\" Project. Presentation at Unite! Ed Future Conference, March 13, 2025","source":"datacite","abstract":"The \"Unite! OER Courses\" seed fund project successfully advanced Open Educational Resources (OER) competence among students and lecturers across the Unite! alliance. A major milestone was the development of a Massive Open Online Course (MOOC) on OER, licensed under CC BY 4.0 International and translated into 11 languages, including Turkish and Indonesian, with the help of AI avatars and tools. Since its launch on May 6, 2024, the MOOC has attracted over 1,200 participants. Additionally, an OER course in English was introduced on the Unite! federated learning management system Metacampus.To further enhance capacity, a dedicated OER training for lecturers was conducted, which resulted in the publication of a collection of tools and methods for future OER training initiatives. In this contribution, we build upon previous work, particularly insights from our project report (Unite, 2024) and various presentations on the topic, including preliminary findings on the impact at partner universities (Schön et al., n.d., submitted for JODDE).Methodologically, we addressed different research questions primarily through a case study, allowing us to analyse the implementation, reception, and institutional effects of the OER initiatives within the alliance. The collected data provides valuable insights for the establishment of a future Unite! Open Science Academy.This presentation will share the project’s achievements, methodologies, and outcomes while addressing challenges and opportunities for promoting OER practices in international higher education settings.References Unite! (2025). Unite! Seed Fund Technical Report: Unite! OER Courses. Unite! University Alliance. Unpublished Report.Ebner, M., & Schön, S. (2024). Der erste multilinguale MOOC zum Thema der Open Educational Resources: Rolle, Möglichkeiten und Herausforderungen des Einsatzes von KI bei der Erstellung von Videos mit Avataren der Lehrenden [The first multilingual MOOC on Open Educational Resources: Role, opportunities, and challenges of using AI for creating videos with teacher avatars]. Presentation at OEAD-Tagung, 14 November 2024, St. Pölten. Graz University of Technology. https://doi.org/10.3217/jx9w7-qw453Schön, S., Brünner, B., Ebner, M., Edelsbrunner, S., Hohla-Sejkora, K., & Uhl, B. (2025). Early findings from pilots in AI-driven education: Effects of AI-generated courses and videos on learning and teaching. In M. E. Auer & D. May (Eds.), 2024 Yearbook Emerging Technologies in Learning (Learning and Analytics in Intelligent Systems Series, Vol. 44). Springer Nature.Schön, S., Ebner, M., Hohla-Sejkora, K., Keller, E., Rapp, A., Ribeiro, M. H., Segradin, R., & Vicente Sáez, R. (n.d.). A multilingual OER online course – A case study of OER production and usage in and beyond a European University Alliance. Journal of Digital and Distributed Education. Manuscript submitted for publication.Schön, S. (2024). Hilfe, ich wurde avatarisiert! Veränderungen in der videobasierten Lehre [Help, I've been avatarized! Changes in video-based teaching.]. Keynote at ViTeach 2024, 10 September 2024, Online. https://dx.doi.org/10.3217/cy2wb-f2t67; Video: https://video.vcrp.de/Panopto/Pages/Viewer.aspx?id=4bfec7a9-aee1-4c16-b3f8-b1e70142a363Vicente-Sáez, R., Schön, S., & Ebner, M. (2024). Unite! University Alliance’s OER project Unite! OER courses – An open education initiative to develop open science competencies within Unite! (10/2023–09/2024). Presentation, 13 March 2024, in the series Aalto RDM & Open Science Training. https://doi.org/10.5281/zenodo.10812652; video: https://www.youtube.com/watch?v=tbnNUYbccpw","url":"https://doi.org/10.3217/49h8k-rhe96","authors":["Schön, Sandra","Ebner, Martin","Vicente-Saéz, Rubén"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3217/49h8k-rhe96","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3217/hm2aw-dvh27","name":"Building OER Competence Across the Unite! Alliance. Insights from the \"Unite! OER Courses\" Project. Presentation at Unite! Ed Future Conference, March 13, 2025","source":"datacite","abstract":"The \"Unite! OER Courses\" seed fund project successfully advanced Open Educational Resources (OER) competence among students and lecturers across the Unite! alliance. A major milestone was the development of a Massive Open Online Course (MOOC) on OER, licensed under CC BY 4.0 International and translated into 11 languages, including Turkish and Indonesian, with the help of AI avatars and tools. Since its launch on May 6, 2024, the MOOC has attracted over 1,200 participants. Additionally, an OER course in English was introduced on the Unite! federated learning management system Metacampus.To further enhance capacity, a dedicated OER training for lecturers was conducted, which resulted in the publication of a collection of tools and methods for future OER training initiatives. In this contribution, we build upon previous work, particularly insights from our project report (Unite, 2024) and various presentations on the topic, including preliminary findings on the impact at partner universities (Schön et al., n.d., submitted for JODDE).Methodologically, we addressed different research questions primarily through a case study, allowing us to analyse the implementation, reception, and institutional effects of the OER initiatives within the alliance. The collected data provides valuable insights for the establishment of a future Unite! Open Science Academy.This presentation will share the project’s achievements, methodologies, and outcomes while addressing challenges and opportunities for promoting OER practices in international higher education settings.References Unite! (2025). Unite! Seed Fund Technical Report: Unite! OER Courses. Unite! University Alliance. Unpublished Report.Ebner, M., & Schön, S. (2024). Der erste multilinguale MOOC zum Thema der Open Educational Resources: Rolle, Möglichkeiten und Herausforderungen des Einsatzes von KI bei der Erstellung von Videos mit Avataren der Lehrenden [The first multilingual MOOC on Open Educational Resources: Role, opportunities, and challenges of using AI for creating videos with teacher avatars]. Presentation at OEAD-Tagung, 14 November 2024, St. Pölten. Graz University of Technology. https://doi.org/10.3217/jx9w7-qw453Schön, S., Brünner, B., Ebner, M., Edelsbrunner, S., Hohla-Sejkora, K., & Uhl, B. (2025). Early findings from pilots in AI-driven education: Effects of AI-generated courses and videos on learning and teaching. In M. E. Auer & D. May (Eds.), 2024 Yearbook Emerging Technologies in Learning (Learning and Analytics in Intelligent Systems Series, Vol. 44). Springer Nature.Schön, S., Ebner, M., Hohla-Sejkora, K., Keller, E., Rapp, A., Ribeiro, M. H., Segradin, R., & Vicente Sáez, R. (n.d.). A multilingual OER online course – A case study of OER production and usage in and beyond a European University Alliance. Journal of Digital and Distributed Education. Manuscript submitted for publication.Schön, S. (2024). Hilfe, ich wurde avatarisiert! Veränderungen in der videobasierten Lehre [Help, I've been avatarized! Changes in video-based teaching.]. Keynote at ViTeach 2024, 10 September 2024, Online. https://dx.doi.org/10.3217/cy2wb-f2t67; Video: https://video.vcrp.de/Panopto/Pages/Viewer.aspx?id=4bfec7a9-aee1-4c16-b3f8-b1e70142a363Vicente-Sáez, R., Schön, S., & Ebner, M. (2024). Unite! University Alliance’s OER project Unite! OER courses – An open education initiative to develop open science competencies within Unite! (10/2023–09/2024). Presentation, 13 March 2024, in the series Aalto RDM & Open Science Training. https://doi.org/10.5281/zenodo.10812652; video: https://www.youtube.com/watch?v=tbnNUYbccpw","url":"https://doi.org/10.3217/hm2aw-dvh27","authors":["Schön, Sandra","Ebner, Martin","Vicente-Saéz, Rubén"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3217/hm2aw-dvh27","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2503.04054","name":"Controlled privacy leakage propagation throughout overlapping grouped learning","source":"datacite","abstract":"Federated Learning (FL) is the standard protocol for collaborative learning. In FL, multiple workers jointly train a shared model. They exchange model updates calculated on their data, while keeping the raw data itself local. Since workers naturally form groups based on common interests and privacy policies, we are motivated to extend standard FL to reflect a setting with multiple, potentially overlapping groups. In this setup where workers can belong and contribute to more than one group at a time, complexities arise in understanding privacy leakage and in adhering to privacy policies. To address the challenges, we propose differential private overlapping grouped learning (DPOGL), a novel method to implement privacy guarantees within overlapping groups. Under the honest-but-curious threat model, we derive novel privacy guarantees between arbitrary pairs of workers. These privacy guarantees describe and quantify two key effects of privacy leakage in DP-OGL: propagation delay, i.e., the fact that information from one group will leak to other groups only with temporal offset through the common workers and information degradation, i.e., the fact that noise addition over model updates limits information leakage between workers. Our experiments show that applying DP-OGL enhances utility while maintaining strong privacy compared to standard FL setups.","url":"https://doi.org/10.48550/arxiv.2503.04054","authors":["Kiani, Shahrzad","Boenisch, Franziska","Draper, Stark C."],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2503.04054","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2503.02017","name":"A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises","source":"datacite","abstract":"The ever growing Internet of Things (IoT) connections drive a new type of organization, the Intelligent Enterprise. In intelligent enterprises, machine learning based models are adopted to extract insights from data. Due to the efficiency and privacy challenges of these traditional models, a new federated learning (FL) paradigm has emerged. In FL, multiple enterprises can jointly train a model to update a final model. However, firstly, FL trained models usually perform worse than centralized models, especially when enterprises training data is non-IID (Independent and Identically Distributed). Second, due to the centrality of FL and the untrustworthiness of local enterprises, traditional FL solutions are vulnerable to poisoning and inference attacks and violate privacy. Thirdly, the continuous transfer of parameters between enterprises and servers increases communication costs. To this end, the FedAnil+ model is proposed, a novel, lightweight, and secure Federated Deep Learning Model that includes three main phases. In the first phase, the goal is to solve the data type distribution skew challenge. Addressing privacy concerns against poisoning and inference attacks is covered in the second phase. Finally, to alleviate the communication overhead, a novel compression approach is proposed that significantly reduces the size of the updates. The experiment results validate that FedAnil+ is secure against inference and poisoning attacks with better accuracy. In addition, it shows improvements over existing approaches in terms of model accuracy (13%, 16%, and 26%), communication cost (17%, 21%, and 25%), and computation cost (7%, 9%, and 11%).","url":"https://doi.org/10.48550/arxiv.2503.02017","authors":["Fotohi, Reza","Aliee, Fereidoon Shams","Farahani, Bahar"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2503.02017","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2410.08892","name":"Federated Learning in Practice: Reflections and Projections","source":"datacite","abstract":"Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Over the past decade, FL systems have achieved substantial progress, scaling to millions of devices across various learning domains while offering meaningful differential privacy (DP) guarantees. Production systems from organizations like Google, Apple, and Meta demonstrate the real-world applicability of FL. However, key challenges remain, including verifying server-side DP guarantees and coordinating training across heterogeneous devices, limiting broader adoption. Additionally, emerging trends such as large (multi-modal) models and blurred lines between training, inference, and personalization challenge traditional FL frameworks. In response, we propose a redefined FL framework that prioritizes privacy principles rather than rigid definitions. We also chart a path forward by leveraging trusted execution environments and open-source ecosystems to address these challenges and facilitate future advancements in FL.","url":"https://doi.org/10.48550/arxiv.2410.08892","authors":["Daly, Katharine","Eichner, Hubert","Kairouz, Peter","McMahan, H. Brendan","Ramage, Daniel","Xu, Zheng"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2410.08892","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2407.14154","name":"Where is the Testbed for my Federated Learning Research?","source":"datacite","abstract":"Progressing beyond centralized AI is of paramount importance, yet, distributed AI solutions, in particular various federated learning (FL) algorithms, are often not comprehensively assessed, which prevents the research community from identifying the most promising approaches and practitioners from being convinced that a certain solution is deployment-ready. The largest hurdle towards FL algorithm evaluation is the difficulty of conducting real-world experiments over a variety of FL client devices and different platforms, with different datasets and data distribution, all while assessing various dimensions of algorithm performance, such as inference accuracy, energy consumption, and time to convergence, to name a few. In this paper, we present CoLExT, a real-world testbed for FL research. CoLExT is designed to streamline experimentation with custom FL algorithms in a rich testbed configuration space, with a large number of heterogeneous edge devices, ranging from single-board computers to smartphones, and provides real-time collection and visualization of a variety of metrics through automatic instrumentation. According to our evaluation, porting FL algorithms to CoLExT requires minimal involvement from the developer, and the instrumentation introduces minimal resource usage overhead. Furthermore, through an initial investigation involving popular FL algorithms running on CoLExT, we reveal previously unknown trade-offs, inefficiencies, and programming bugs.","url":"https://doi.org/10.48550/arxiv.2407.14154","authors":["Božič, Janez","Faustino, Amândio R.","Radovič, Boris","Canini, Marco","Pejović, Veljko"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2407.14154","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2405.04146","name":"pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving","source":"datacite","abstract":"Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization due to data heterogeneity in an ever domain-shifting environment. While Federated Learning (FL) could improve the generalization of an AD model (known as FedAD system), conventional models often struggle with under-fitting as the amount of accumulated training data progressively increases. To address this issue, instead of conventional small models, employing Large Vision Models (LVMs) in FedAD is a viable option for better learning of representations from a vast volume of data. However, implementing LVMs in FedAD introduces three challenges: (I) the extremely high communication overheads associated with transmitting LVMs between participating vehicles and a central server; (II) lack of computing resource to deploy LVMs on each vehicle; (III) the performance drop due to LVM focusing on shared features but overlooking local vehicle characteristics. To overcome these challenges, we propose pFedLVM, a LVM-Driven, Latent Feature-Based Personalized Federated Learning framework. In this approach, the LVM is deployed only on central server, which effectively alleviates the computational burden on individual vehicles. Furthermore, the exchange between central server and vehicles are the learned features rather than the LVM parameters, which significantly reduces communication overhead. In addition, we utilize both shared features from all participating vehicles and individual characteristics from each vehicle to establish a personalized learning mechanism. This enables each vehicle's model to learn features from others while preserving its personalized characteristics, thereby outperforming globally shared models trained in general FL. Extensive experiments demonstrate that pFedLVM outperforms the existing state-of-the-art approaches.","url":"https://doi.org/10.48550/arxiv.2405.04146","authors":["Kou, Wei-Bin","Lin, Qingfeng","Tang, Ming","Xu, Sheng","Ye, Rongguang","Leng, Yang","Wang, Shuai","Li, Guofa","Chen, Zhenyu","Zhu, Guangxu","Wu, Yik-Chung"],"tags":["Robotics (cs.RO)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.04146","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2404.10728","name":"Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning","source":"datacite","abstract":"We present the first study on provably efficient randomized exploration in cooperative multi-agent reinforcement learning (MARL). We propose a unified algorithm framework for randomized exploration in parallel Markov Decision Processes (MDPs), and two Thompson Sampling (TS)-type algorithms, CoopTS-PHE and CoopTS-LMC, incorporating the perturbed-history exploration (PHE) strategy and the Langevin Monte Carlo exploration (LMC) strategy, respectively, which are flexible in design and easy to implement in practice. For a special class of parallel MDPs where the transition is (approximately) linear, we theoretically prove that both CoopTS-PHE and CoopTS-LMC achieve a $\\widetilde{\\mathcal{O}}(d^{3/2}H^2\\sqrt{MK})$ regret bound with communication complexity $\\widetilde{\\mathcal{O}}(dHM^2)$, where $d$ is the feature dimension, $H$ is the horizon length, $M$ is the number of agents, and $K$ is the number of episodes. This is the first theoretical result for randomized exploration in cooperative MARL. We evaluate our proposed method on multiple parallel RL environments, including a deep exploration problem (i.e., $N$-chain), a video game, and a real-world problem in energy systems. Our experimental results support that our framework can achieve better performance, even under conditions of misspecified transition models. Additionally, we establish a connection between our unified framework and the practical application of federated learning.","url":"https://doi.org/10.48550/arxiv.2404.10728","authors":["Hsu, Hao-Lun","Wang, Weixin","Pajic, Miroslav","Xu, Pan"],"tags":["Machine Learning (cs.LG)","Machine Learning (stat.ML)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2404.10728","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2210.08106","name":"A Primal-Dual Algorithm for Hybrid Federated Learning","source":"datacite","abstract":"Very few methods for hybrid federated learning, where clients only hold subsets of both features and samples, exist. Yet, this scenario is extremely important in practical settings. We provide a fast, robust algorithm for hybrid federated learning that hinges on Fenchel Duality. We prove the convergence of the algorithm to the same solution as if the model is trained centrally in a variety of practical regimes. Furthermore, we provide experimental results that demonstrate the performance improvements of the algorithm over a commonly used method in federated learning, FedAvg, and an existing hybrid FL algorithm, HyFEM. We also provide privacy considerations and necessary steps to protect client data.","url":"https://doi.org/10.48550/arxiv.2210.08106","authors":["Overman, Tom","Blum, Garrett","Klabjan, Diego"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2210.08106","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2405.13879","name":"FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?","source":"datacite","abstract":"Standard federated learning (FL) approaches are vulnerable to the free-rider dilemma: participating agents can contribute little to nothing yet receive a well-trained aggregated model. While prior mechanisms attempt to solve the free-rider dilemma, none have addressed the issue of truthfulness. In practice, adversarial agents can provide false information to the server in order to cheat its way out of contributing to federated training. In an effort to make free-riding-averse federated mechanisms truthful, and consequently less prone to breaking down in practice, we propose FACT. FACT is the first federated mechanism that: (1) eliminates federated free riding by using a penalty system, (2) ensures agents provide truthful information by creating a competitive environment, and (3) encourages agent participation by offering better performance than training alone. Empirically, FACT avoids free-riding when agents are untruthful, and reduces agent loss by over 4x.","url":"https://doi.org/10.48550/arxiv.2405.13879","authors":["Bornstein, Marco","Bedi, Amrit Singh","Mohamed, Abdirisak","Huang, Furong"],"tags":["Computer Science and Game Theory (cs.GT)","Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","Theoretical Economics (econ.TH)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Economics and business","FOS: Economics and business"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.13879","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2502.14205","name":"Accurate Forgetting for Heterogeneous Federated Continual Learning","source":"datacite","abstract":"Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under-explored. Bridging FL and continual learning (CL) gives rise to a challenging practical problem: federated continual learning (FCL). Existing research in FCL primarily focuses on mitigating the catastrophic forgetting issue of continual learning while collaborating with other clients. We argue that the forgetting phenomena are not invariably detrimental. In this paper, we consider a more practical and challenging FCL setting characterized by potentially unrelated or even antagonistic data/tasks across different clients. In the FL scenario, statistical heterogeneity and data noise among clients may exhibit spurious correlations which result in biased feature learning. While existing CL strategies focus on a complete utilization of previous knowledge, we found that forgetting biased information is beneficial in our study. Therefore, we propose a new concept accurate forgetting (AF) and develop a novel generative-replay method~\\method~which selectively utilizes previous knowledge in federated networks. We employ a probabilistic framework based on a normalizing flow model to quantify the credibility of previous knowledge. Comprehensive experiments affirm the superiority of our method over baselines.","url":"https://doi.org/10.48550/arxiv.2502.14205","authors":["Wuerkaixi, Abudukelimu","Cui, Sen","Zhang, Jingfeng","Yan, Kunda","Han, Bo","Niu, Gang","Fang, Lei","Zhang, Changshui","Sugiyama, Masashi"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2502.14205","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2405.02140","name":"An Information Theoretic Perspective on Conformal Prediction","source":"datacite","abstract":"Conformal Prediction (CP) is a distribution-free uncertainty estimation framework that constructs prediction sets guaranteed to contain the true answer with a user-specified probability. Intuitively, the size of the prediction set encodes a general notion of uncertainty, with larger sets associated with higher degrees of uncertainty. In this work, we leverage information theory to connect conformal prediction to other notions of uncertainty. More precisely, we prove three different ways to upper bound the intrinsic uncertainty, as described by the conditional entropy of the target variable given the inputs, by combining CP with information theoretical inequalities. Moreover, we demonstrate two direct and useful applications of such connection between conformal prediction and information theory: (i) more principled and effective conformal training objectives that generalize previous approaches and enable end-to-end training of machine learning models from scratch, and (ii) a natural mechanism to incorporate side information into conformal prediction. We empirically validate both applications in centralized and federated learning settings, showing our theoretical results translate to lower inefficiency (average prediction set size) for popular CP methods.","url":"https://doi.org/10.48550/arxiv.2405.02140","authors":["Correia, Alvaro H. C.","Massoli, Fabio Valerio","Louizos, Christos","Behboodi, Arash"],"tags":["Machine Learning (cs.LG)","Information Theory (cs.IT)","Machine Learning (stat.ML)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.02140","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2409.06123","name":"Contrastive Federated Learning with Tabular Data Silos","source":"datacite","abstract":"Learning from vertical partitioned data silos is challenging due to the segmented nature of data, sample misalignment, and strict privacy concerns. Federated learning has been proposed as a solution. However, sample misalignment across silos often hinders optimal model performance and suggests data sharing within the model, which breaks privacy. Our proposed solution is Contrastive Federated Learning with Tabular Data Silos (CFL), which offers a solution for data silos with sample misalignment without the need for sharing original or representative data to maintain privacy. CFL begins with local acquisition of contrastive representations of the data within each silo and aggregates knowledge from other silos through the federated learning algorithm. Our experiments demonstrate that CFL solves the limitations of existing algorithms for data silos and outperforms existing tabular contrastive learning. CFL provides performance improvements without loosening privacy.","url":"https://doi.org/10.48550/arxiv.2409.06123","authors":["Ginanjar, Achmad","Li, Xue","Hua, Wen","Pei, Jiaming"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.1.1","68A00"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.06123","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2406.03519","name":"Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning","source":"datacite","abstract":"High utility and rigorous data privacy are of the main goals of a federated learning (FL) system, which learns a model from the data distributed among some clients. The latter has been tried to achieve by using differential privacy in FL (DPFL). There is often heterogeneity in clients privacy requirements, and existing DPFL works either assume uniform privacy requirements for clients or are not applicable when server is not fully trusted (our setting). Furthermore, there is often heterogeneity in batch and/or dataset size of clients, which as shown, results in extra variation in the DP noise level across clients model updates. With these sources of heterogeneity, straightforward aggregation strategies, e.g., assigning clients aggregation weights proportional to their privacy parameters will lead to lower utility. We propose Robust-HDP, which efficiently estimates the true noise level in clients model updates and reduces the noise-level in the aggregated model updates considerably. Robust-HDP improves utility and convergence speed, while being safe to the clients that may maliciously send falsified privacy parameter to server. Extensive experimental results on multiple datasets and our theoretical analysis confirm the effectiveness of Robust-HDP. Our code can be found here.","url":"https://doi.org/10.48550/arxiv.2406.03519","authors":["Malekmohammadi, Saber","Yu, Yaoliang","Cao, Yang"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2406.03519","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2403.03333","name":"Federated Learning over Connected Modes","source":"datacite","abstract":"Statistical heterogeneity in federated learning poses two major challenges: slow global training due to conflicting gradient signals, and the need of personalization for local distributions. In this work, we tackle both challenges by leveraging recent advances in \\emph{linear mode connectivity} -- identifying a linearly connected low-loss region in the parameter space of neural networks, which we call solution simplex. We propose federated learning over connected modes (\\textsc{Floco}), where clients are assigned local subregions in this simplex based on their gradient signals, and together learn the shared global solution simplex. This allows personalization of the client models to fit their local distributions within the degrees of freedom in the solution simplex and homogenizes the update signals for the global simplex training. Our experiments show that \\textsc{Floco} accelerates the global training process, and significantly improves the local accuracy with minimal computational overhead in cross-silo federated learning settings.","url":"https://doi.org/10.48550/arxiv.2403.03333","authors":["Grinwald, Dennis","Wiesner, Philipp","Nakajima, Shinichi"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2403.03333","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2502.07951","name":"Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation","source":"datacite","abstract":"Employing self-supervised learning (SSL) methodologies assumes par-amount significance in handling unlabeled polyp datasets when building deep learning-based automatic polyp segmentation models. However, the intricate privacy dynamics surrounding medical data often preclude seamless data sharing among disparate medical centers. Federated learning (FL) emerges as a formidable solution to this privacy conundrum, yet within the realm of FL, optimizing model generalization stands as a pressing imperative. Robust generalization capabilities are imperative to ensure the model's efficacy across diverse geographical domains post-training on localized client datasets. In this paper, a Federated self-supervised Domain Generalization method is proposed to enhance the generalization capacity of federated and Label-efficient intestinal polyp segmentation, named LFDG. Based on a classical SSL method, DropPos, LFDG proposes an adversarial learning-based data augmentation method (SSADA) to enhance the data diversity. LFDG further proposes a relaxation module based on Source-reconstruction and Augmentation-masking (SRAM) to maintain stability in feature learning. We have validated LFDG on polyp images from six medical centers. The performance of our method achieves 3.80% and 3.92% better than the baseline and other recent FL methods and SSL methods, respectively.","url":"https://doi.org/10.48550/arxiv.2502.07951","authors":["Tan, Xinyi","Wang, Jiacheng","Wang, Liansheng"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2502.07951","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2502.05547","name":"Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning","source":"datacite","abstract":"Federated learning (FL) is inherently susceptible to privacy breaches and poisoning attacks. To tackle these challenges, researchers have separately devised secure aggregation mechanisms to protect data privacy and robust aggregation methods that withstand poisoning attacks. However, simultaneously addressing both concerns is challenging; secure aggregation facilitates poisoning attacks as most anomaly detection techniques require access to unencrypted local model updates, which are obscured by secure aggregation. Few recent efforts to simultaneously tackle both challenges offen depend on impractical assumption of non-colluding two-server setups that disrupt FL's topology, or three-party computation which introduces scalability issues, complicating deployment and application. To overcome this dilemma, this paper introduce a Dual Defense Federated learning (DDFed) framework. DDFed simultaneously boosts privacy protection and mitigates poisoning attacks, without introducing new participant roles or disrupting the existing FL topology. DDFed initially leverages cutting-edge fully homomorphic encryption (FHE) to securely aggregate model updates, without the impractical requirement for non-colluding two-server setups and ensures strong privacy protection. Additionally, we proposes a unique two-phase anomaly detection mechanism for encrypted model updates, featuring secure similarity computation and feedback-driven collaborative selection, with additional measures to prevent potential privacy breaches from Byzantine clients incorporated into the detection process. We conducted extensive experiments on various model poisoning attacks and FL scenarios, including both cross-device and cross-silo FL. Experiments on publicly available datasets demonstrate that DDFed successfully protects model privacy and effectively defends against model poisoning threats.","url":"https://doi.org/10.48550/arxiv.2502.05547","authors":["Xu, Runhua","Gao, Shiqi","Li, Chao","Joshi, James","Li, Jianxin"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2502.05547","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2407.17754","name":"DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations","source":"datacite","abstract":"In personalized federated learning (PFL), it is widely recognized that achieving both high model generalization and effective personalization poses a significant challenge due to their conflicting nature. As a result, existing PFL methods can only manage a trade-off between these two objectives. This raises an interesting question: Is it feasible to develop a model capable of achieving both objectives simultaneously? Our paper presents an affirmative answer, and the key lies in the observation that deep models inherently exhibit hierarchical architectures, which produce representations with various levels of generalization and personalization at different stages. A straightforward approach stemming from this observation is to select multiple representations from these layers and combine them to concurrently achieve generalization and personalization. However, the number of candidate representations is commonly huge, which makes this method infeasible due to high computational costs.To address this problem, we propose DualFed, a new method that can directly yield dual representations correspond to generalization and personalization respectively, thereby simplifying the optimization task. Specifically, DualFed inserts a personalized projection network between the encoder and classifier. The pre-projection representations are able to capture generalized information shareable across clients, and the post-projection representations are effective to capture task-specific information on local clients. This design minimizes the mutual interference between generalization and personalization, thereby achieving a win-win situation. Extensive experiments show that DualFed can outperform other FL methods. Code is available at https://github.com/GuogangZhu/DualFed.","url":"https://doi.org/10.48550/arxiv.2407.17754","authors":["Zhu, Guogang","Liu, Xuefeng","Niu, Jianwei","Tang, Shaojie","Wu, Xinghao","Zhang, Jiayuan"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2407.17754","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2409.09727","name":"From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare","source":"datacite","abstract":"Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning based methods in healthcare, it remains unclear which ones have potential clinical utility. This review paper considers and analyzes the most recent studies up to May 2024 that describe federated learning based methods in healthcare. After a thorough review, we find that the vast majority are not appropriate for clinical use due to their methodological flaws and/or underlying biases which include but are not limited to privacy concerns, generalization issues, and communication costs. As a result, the effectiveness of federated learning in healthcare is significantly compromised. To overcome these challenges, we provide recommendations and promising opportunities that might be implemented to resolve these problems and improve the quality of model development in federated learning with healthcare.","url":"https://doi.org/10.48550/arxiv.2409.09727","authors":["Li, Ming","Xu, Pengcheng","Hu, Junjie","Tang, Zeyu","Yang, Guang"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.09727","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2409.04637","name":"Enhancing Quantum Security over Federated Learning via Post-Quantum Cryptography","source":"datacite","abstract":"Federated learning (FL) has become one of the standard approaches for deploying machine learning models on edge devices, where private training data are distributed across clients, and a shared model is learned by aggregating locally computed updates from each client. While this paradigm enhances communication efficiency by only requiring updates at the end of each training epoch, the transmitted model updates remain vulnerable to malicious tampering, posing risks to the integrity of the global model. Although current digital signature algorithms can protect these communicated model updates, they fail to ensure quantum security in the era of large-scale quantum computing. Fortunately, various post-quantum cryptography algorithms have been developed to address this vulnerability, especially the three NIST-standardized algorithms - Dilithium, FALCON, and SPHINCS+. In this work, we empirically investigate the impact of these three NIST-standardized PQC algorithms for digital signatures within the FL procedure, covering a wide range of models, tasks, and FL settings. Our results indicate that Dilithium stands out as the most efficient PQC algorithm for digital signature in federated learning. Additionally, we offer an in-depth discussion of the implications of our findings and potential directions for future research.","url":"https://doi.org/10.48550/arxiv.2409.04637","authors":["Li, Pingzhi","Chen, Tianlong","Liu, Junyu"],"tags":["Quantum Physics (quant-ph)","Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Physical sciences","FOS: Physical sciences","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.04637","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.3929/ethz-b-000719997","name":"Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning","source":"datacite","abstract":"Malicious server (MS) attacks have enabled the scaling of data stealing in federated learning to large batch sizes and secure aggregation, settings previously considered private. However, many concerns regarding the client-side detectability of MS attacks were raised, questioning their practicality. In this work, for the first time, we thoroughly study client-side detectability. We first demonstrate that all prior MS attacks are detectable by principled checks, and formulate a necessary set of requirements that a practical MS attack must satisfy. Next, we propose SEER, a novel attack framework that satisfies these requirements. The key insight of SEER is the use of a secret decoder, jointly trained with the shared model. We show that SEER can steal user data from gradients of realistic networks, even for large batch sizes of up to 512 and under secure aggregation. Our work is a promising step towards assessing the true vulnerability of federated learning in real-world settings.","url":"https://doi.org/10.3929/ethz-b-000719997","authors":["Garov, Kostadin","Dimitrov, Dimitar I.","Jovanović, Nikola","Vechev, Martin"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","I.2.11","Privacy","Federated Learning","Gradient leakage"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3929/ethz-b-000719997","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2501.13213","name":"Distributed Intrusion Detection in Dynamic Networks of UAVs using Few-Shot Federated Learning","source":"datacite","abstract":"Flying Ad Hoc Networks (FANETs), which primarily interconnect Unmanned Aerial Vehicles (UAVs), present distinctive security challenges due to their distributed and dynamic characteristics, necessitating tailored security solutions. Intrusion detection in FANETs is particularly challenging due to communication costs, and privacy concerns. While Federated Learning (FL) holds promise for intrusion detection in FANETs with its cooperative and decentralized model training, it also faces drawbacks such as large data requirements, power consumption, and time constraints. Moreover, the high speeds of nodes in dynamic networks like FANETs may disrupt communication among Intrusion Detection Systems (IDS). In response, our study explores the use of few-shot learning (FSL) to effectively reduce the data required for intrusion detection in FANETs. The proposed approach called Few-shot Federated Learning-based IDS (FSFL-IDS) merges FL and FSL to tackle intrusion detection challenges such as privacy, power constraints, communication costs, and lossy links, demonstrating its effectiveness in identifying routing attacks in dynamic FANETs.This approach reduces both the local models and the global model's training time and sample size, offering insights into reduced computation and communication costs and extended battery life. Furthermore, by employing FSL, which requires less data for training, IDS could be less affected by lossy links in FANETs.","url":"https://doi.org/10.48550/arxiv.2501.13213","authors":["Ceviz, Ozlem","Sen, Sevil","Sadioglu, Pinar"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.13213","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2401.10070","name":"Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks","source":"datacite","abstract":"To protect privacy and meet legal regulations, federated learning (FL) has gained significant attention for training speech-to-text (S2T) systems, including automatic speech recognition (ASR) and speech translation (ST). However, the commonly used FL approach (i.e., \\textsc{FedAvg}) in S2T tasks typically suffers from extensive communication overhead due to multi-round interactions based on the whole model and performance degradation caused by data heterogeneity among clients.To address these issues, we propose a personalized federated S2T framework that introduces \\textsc{FedLoRA}, a lightweight LoRA module for client-side tuning and interaction with the server to minimize communication overhead, and \\textsc{FedMem}, a global model equipped with a $k$-nearest-neighbor ($k$NN) classifier that captures client-specific distributional shifts to achieve personalization and overcome data heterogeneity. Extensive experiments based on Conformer and Whisper backbone models on CoVoST and GigaSpeech benchmarks show that our approach significantly reduces the communication overhead on all S2T tasks and effectively personalizes the global model to overcome data heterogeneity.","url":"https://doi.org/10.48550/arxiv.2401.10070","authors":["Du, Yichao","Zhang, Zhirui","Yue, Linan","Huang, Xu","Zhang, Yuqing","Xu, Tong","Xu, Linli","Chen, Enhong"],"tags":["Computation and Language (cs.CL)","Sound (cs.SD)","Audio and Speech Processing (eess.AS)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2401.10070","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.17023/4e4b-xp13","name":"Tutorial: Privacy 101, with Trumpets and Truffles - An Introduction to Differential Privacy and Homomorphic Encryption, and Applications in Federated Learning","source":"datacite","abstract":"IEEE WIFS 2024, On-site workshop, 2-5 December 2024, Italy","url":"https://doi.org/10.17023/4e4b-xp13","authors":["Fernando Pérez-González"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.17023/4e4b-xp13","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2404.14389","name":"Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction","source":"datacite","abstract":"Federated Learning (FL) offers a distributed framework to train a global control model across multiple base stations without compromising the privacy of their local network data. This makes it ideal for applications like wireless traffic prediction (WTP), which plays a crucial role in optimizing network resources, enabling proactive traffic flow management, and enhancing the reliability of downstream communication-aided applications, such as IoT devices, autonomous vehicles, and industrial automation systems. Despite its promise, the security aspects of FL-based distributed wireless systems, particularly in regression-based WTP problems, remain inadequately investigated. In this paper, we introduce a novel fake traffic injection (FTI) attack, designed to undermine the FL-based WTP system by injecting fabricated traffic distributions with minimal knowledge. We further propose a defense mechanism, termed global-local inconsistency detection (GLID), which strategically removes abnormal model parameters that deviate beyond a specific percentile range estimated through statistical methods in each dimension. Extensive experimental evaluations, performed on real-world wireless traffic datasets, demonstrate that both our attack and defense strategies significantly outperform existing baselines.","url":"https://doi.org/10.48550/arxiv.2404.14389","authors":["Zhang, Zifan","Fang, Minghong","Huang, Jiayuan","Liu, Yuchen"],"tags":["Networking and Internet Architecture (cs.NI)","Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences","C.2.1"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2404.14389","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2402.15166","name":"Convergence Analysis of Split Federated Learning on Heterogeneous Data","source":"datacite","abstract":"Split federated learning (SFL) is a recent distributed approach for collaborative model training among multiple clients. In SFL, a global model is typically split into two parts, where clients train one part in a parallel federated manner, and a main server trains the other. Despite the recent research on SFL algorithm development, the convergence analysis of SFL is missing in the literature, and this paper aims to fill this gap. The analysis of SFL can be more challenging than that of federated learning (FL), due to the potential dual-paced updates at the clients and the main server. We provide convergence analysis of SFL for strongly convex and general convex objectives on heterogeneous data. The convergence rates are $O(1/T)$ and $O(1/\\sqrt[3]{T})$, respectively, where $T$ denotes the total number of rounds for SFL training. We further extend the analysis to non-convex objectives and the scenario where some clients may be unavailable during training. Experimental experiments validate our theoretical results and show that SFL outperforms FL and split learning (SL) when data is highly heterogeneous across a large number of clients.","url":"https://doi.org/10.48550/arxiv.2402.15166","authors":["Han, Pengchao","Huang, Chao","Tian, Geng","Tang, Ming","Liu, Xin"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2402.15166","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2405.17462","name":"Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity","source":"datacite","abstract":"The advent of Federated Learning (FL) highlights the practical necessity for the right to be forgotten for all clients, allowing them to request data deletion from the machine learning models service provider. This necessity has spurred a growing demand for Federated Unlearning (FU). Feature unlearning has gained considerable attention due to its applications in unlearning sensitive, backdoor, and biased features. Existing methods employ the influence function to achieve feature unlearning, which is impractical for FL as it necessitates the participation of other clients, if not all, in the unlearning process. Furthermore, current research lacks an evaluation of the effectiveness of feature unlearning. To address these limitations, we define feature sensitivity in evaluating feature unlearning according to Lipschitz continuity. This metric characterizes the model outputs rate of change or sensitivity to perturbations in the input feature. We then propose an effective federated feature unlearning framework called Ferrari, which minimizes feature sensitivity. Extensive experimental results and theoretical analysis demonstrate the effectiveness of Ferrari across various feature unlearning scenarios, including sensitive, backdoor, and biased features. The code is publicly available at https://github.com/OngWinKent/Federated-Feature-Unlearning","url":"https://doi.org/10.48550/arxiv.2405.17462","authors":["Gu, Hanlin","Ong, Win Kent","Chan, Chee Seng","Fan, Lixin"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.17462","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2501.00732","name":"Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction","source":"datacite","abstract":"Wireless traffic prediction plays an indispensable role in cellular networks to achieve proactive adaptation for communication systems. Along this line, Federated Learning (FL)-based wireless traffic prediction at the edge attracts enormous attention because of the exemption from raw data transmission and enhanced privacy protection. However FL-based wireless traffic prediction methods still rely on heavy data transmissions between local clients and the server for local model updates. Besides, how to model the spatial dependencies of local clients under the framework of FL remains uncertain. To tackle this, we propose an innovative FL algorithm that employs gradient compression and correlation-driven techniques, effectively minimizing data transmission load while preserving prediction accuracy. Our approach begins with the introduction of gradient sparsification in wireless traffic prediction, allowing for significant data compression during model training. We then implement error feedback and gradient tracking methods to mitigate any performance degradation resulting from this compression. Moreover, we develop three tailored model aggregation strategies anchored in gradient correlation, enabling the capture of spatial dependencies across diverse clients. Experiments have been done with two real-world datasets and the results demonstrate that by capturing the spatio-temporal characteristics and correlation among local clients, the proposed algorithm outperforms the state-of-the-art algorithms and can increase the communication efficiency by up to two orders of magnitude without losing prediction accuracy. Code is available at https://github.com/chuanting/FedGCC.","url":"https://doi.org/10.48550/arxiv.2501.00732","authors":["Zhang, Chuanting","Zhang, Haixia","Dang, Shuping","Shihada, Basem","Alouini, Mohamed-Slim"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.00732","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2412.20253","name":"Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation","source":"datacite","abstract":"Federated learning (FL) enables collaborative model training across decentralized datasets while preserving data privacy. However, optimally selecting participating collaborators in dynamic FL environments remains challenging. We present RL-HSimAgg, a novel reinforcement learning (RL) and similarity-weighted aggregation (simAgg) algorithm using harmonic mean to manage outlier data points. This paper proposes applying multi-armed bandit algorithms to improve collaborator selection and model generalization. By balancing exploration-exploitation trade-offs, these RL methods can promote resource-efficient training with diverse datasets. We demonstrate the effectiveness of Epsilon-greedy (EG) and upper confidence bound (UCB) algorithms for federated brain lesion segmentation. In simulation experiments on internal and external validation sets, RL-HSimAgg with UCB collaborator outperformed the EG method across all metrics, achieving higher Dice scores for Enhancing Tumor (0.7334 vs 0.6797), Tumor Core (0.7432 vs 0.6821), and Whole Tumor (0.8252 vs 0.7931) segmentation. Therefore, for the Federated Tumor Segmentation Challenge (FeTS 2024), we consider UCB as our primary client selection approach in federated Glioblastoma lesion segmentation of multi-modal MRIs. In conclusion, our research demonstrates that RL-based collaborator management, e.g. using UCB, can potentially improve model robustness and flexibility in distributed learning environments, particularly in domains like brain tumor segmentation.","url":"https://doi.org/10.48550/arxiv.2412.20253","authors":["Khan, Muhammad Irfan","Kontio, Elina","Khan, Suleiman A.","Jafaritadi, Mojtaba"],"tags":["Machine Learning (cs.LG)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2412.20253","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2412.20250","name":"Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation","source":"datacite","abstract":"This study presents a robust and efficient client selection protocol designed to optimize the Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2024). In the evolving landscape of FL, the judicious selection of collaborators emerges as a critical determinant for the success and efficiency of collective learning endeavors, particularly in domains requiring high precision. This work introduces a recommender engine framework based on non-negative matrix factorization (NNMF) and a hybrid aggregation approach that blends content-based and collaborative filtering. This method intelligently analyzes historical performance, expertise, and other relevant metrics to identify the most suitable collaborators. This approach not only addresses the cold start problem where new or inactive collaborators pose selection challenges due to limited data but also significantly improves the precision and efficiency of the FL process. Additionally, we propose harmonic similarity weight aggregation (HSimAgg) for adaptive aggregation of model parameters. We utilized a dataset comprising 1,251 multi-parametric magnetic resonance imaging (mpMRI) scans from individuals diagnosed with glioblastoma (GBM) for training purposes and an additional 219 mpMRI scans for external evaluations. Our federated tumor segmentation approach achieved dice scores of 0.7298, 0.7424, and 0.8218 for enhancing tumor (ET), tumor core (TC), and whole tumor (WT) segmentation tasks respectively on the external validation set. In conclusion, this research demonstrates that selecting collaborators with expertise aligned to specific tasks, like brain tumor segmentation, improves the effectiveness of FL networks.","url":"https://doi.org/10.48550/arxiv.2412.20250","authors":["Khan, Muhammad Irfan","Kontio, Elina","Khan, Suleiman A.","Jafaritadi, Mojtaba"],"tags":["Machine Learning (cs.LG)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2412.20250","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.17023/8jbd-gt91","name":"Federated Learning in The Age of Foundation Models ","source":"datacite","abstract":"SPS Webinar, 10 December 2024, Dr. Ziyue Xu","url":"https://doi.org/10.17023/8jbd-gt91","authors":["Ziyue Xu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.17023/8jbd-gt91","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.17023/aae2-f328","name":"Federated Learning in The Age of Foundation Models ","source":"datacite","abstract":"SPS Webinar, 10 December 2024, Dr. Ziyue Xu","url":"https://doi.org/10.17023/aae2-f328","authors":["Ziyue Xu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.17023/aae2-f328","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2210.01708","name":"Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning","source":"datacite","abstract":"Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training of models without centralized access to the raw data on local devices. In the typical FL paradigm (e.g., FedAvg), model weights are sent to and from the server each round to participating clients. Recently, the use of small pre-trained models has been shown to be effective in federated learning optimization and improving convergence. However, recent state-of-the-art pre-trained models are getting more capable but also have more parameters, known as the \"Foundation Models.\" In conventional FL, sharing the enormous model weights can quickly put a massive communication burden on the system, especially if more capable models are employed. Can we find a solution to enable those strong and readily available pre-trained models in FL to achieve excellent performance while simultaneously reducing the communication burden? To this end, we investigate the use of parameter-efficient fine-tuning in federated learning and thus introduce a new framework: FedPEFT. Specifically, we systemically evaluate the performance of FedPEFT across a variety of client stability, data distribution, and differential privacy settings. By only locally tuning and globally sharing a small portion of the model weights, significant reductions in the total communication overhead can be achieved while maintaining competitive or even better performance in a wide range of federated learning scenarios, providing insight into a new paradigm for practical and effective federated systems.","url":"https://doi.org/10.48550/arxiv.2210.01708","authors":["Sun, Guangyu","Khalid, Umar","Mendieta, Matias","Wang, Pu","Chen, Chen"],"tags":["Machine Learning (cs.LG)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2210.01708","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2406.14898","name":"Safely Learning with Private Data: A Federated Learning Framework for Large Language Model","source":"datacite","abstract":"Private data, being larger and quality-higher than public data, can greatly improve large language models (LLM). However, due to privacy concerns, this data is often dispersed in multiple silos, making its secure utilization for LLM training a challenge. Federated learning (FL) is an ideal solution for training models with distributed private data, but traditional frameworks like FedAvg are unsuitable for LLM due to their high computational demands on clients. An alternative, split learning, offloads most training parameters to the server while training embedding and output layers locally, making it more suitable for LLM. Nonetheless, it faces significant challenges in security and efficiency. Firstly, the gradients of embeddings are prone to attacks, leading to potential reverse engineering of private data. Furthermore, the server's limitation of handle only one client's training request at a time hinders parallel training, severely impacting training efficiency. In this paper, we propose a Federated Learning framework for LLM, named FL-GLM, which prevents data leakage caused by both server-side and peer-client attacks while improving training efficiency. Specifically, we first place the input block and output block on local client to prevent embedding gradient attacks from server. Secondly, we employ key-encryption during client-server communication to prevent reverse engineering attacks from peer-clients. Lastly, we employ optimization methods like client-batching or server-hierarchical, adopting different acceleration methods based on the actual computational capabilities of the server. Experimental results on NLU and generation tasks demonstrate that FL-GLM achieves comparable metrics to centralized chatGLM model, validating the effectiveness of our federated learning framework.","url":"https://doi.org/10.48550/arxiv.2406.14898","authors":["Zheng, JiaYing","Zhang, HaiNan","Wang, LingXiang","Qiu, WangJie","Zheng, HongWei","Zheng, ZhiMing"],"tags":["Cryptography and Security (cs.CR)","Computation and Language (cs.CL)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2406.14898","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.48550/arxiv.2412.17373","name":"FRTP: Federating Route Search Records to Enhance Long-term Traffic Prediction","source":"datacite","abstract":"Accurate traffic prediction, especially predicting traffic conditions several days in advance is essential for intelligent transportation systems (ITS). Such predictions enable mid- and long-term traffic optimization, which is crucial for efficient transportation planning. However, the inclusion of diverse external features, alongside the complexities of spatial relationships and temporal uncertainties, significantly increases the complexity of forecasting models. Additionally, traditional approaches have handled data preprocessing separately from the learning model, leading to inefficiencies caused by repeated trials of preprocessing and training. In this study, we propose a federated architecture capable of learning directly from raw data with varying features and time granularities or lengths. The model adopts a unified design that accommodates different feature types, time scales, and temporal periods. Our experiments focus on federating route search records and begin by processing raw data within the model framework. Unlike traditional models, this approach integrates the data federation phase into the learning process, enabling compatibility with various time frequencies and input/output configurations. The accuracy of the proposed model is demonstrated through evaluations using diverse learning patterns and parameter settings. The results show that online search log data is useful for forecasting long-term traffic, highlighting the model's adaptability and efficiency.","url":"https://doi.org/10.48550/arxiv.2412.17373","authors":["Ge, Hangli","Yang, Xiaojie","Matsunaga, Itsuki","Huang, Dizhi","Koshizuka, Noboru"],"tags":["Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2412.17373","addedAt":"2026-08-31T06:41:20.071Z","updatedAt":"2026-08-31T06:41:20.071Z"},{"id":"doi:10.1002/9781394167760.about","name":"About the Editors","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760.about","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760.about","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2118/225497-ms","name":"Federated Learning for Geothermal Energy: A Decentralized Solution for Data Sharing and Sector Learning","source":"crossref","abstract":"Abstract Geothermal energy faces challenges in design and operation due to subsurface uncertainties, high drilling costs, and complex maintenance requirements. Data and model sharing can enhance decision making processes in the geothermal sector by improving predictive maintenance, equipment and production monitoring during the operation to explore and design new geothermal installations by enabling advanced analytics and collaborative insights. However, concerns over data privacy and proprietary information hinder widespread adoption. This paper explores federated learning (FL) as a decentralized machine learning approach that facilitates secure collaboration across geothermal sites without sharing raw data. By aggregating model updates, FL improves anomaly detection, downtime reduction, and cost efficiency while preserving confidentiality. A case study demonstrates its effectiveness in predictive maintenance. The results showed that the FL models have a significantly higher accuracy and generalization compared to individual model of each stakeholder. The added value of FL models is more dominant for the parties with limited or small data. Findings highlight FL's potential to foster collaboration among operators, manufacturers, and researchers, enabling privacy-preserving, data-driven optimization in geothermal energy.","url":"https://doi.org/10.2118/225497-ms","authors":["P. Shoeibi Omrani","S. Ben Aziza"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-10T02:21:01Z","doi":"10.2118/225497-ms","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.14428/esann/2025.es2025-160","name":"SecureBFL: a Blockchain-enhanced federated learning architecture with MPC","source":"crossref","abstract":"","url":"https://doi.org/10.14428/esann/2025.es2025-160","authors":["Tanguy Vansnick","Leandro Collier","Saïd Mahmoudi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-15T14:43:24Z","doi":"10.14428/esann/2025.es2025-160","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.5220/0013120600003911","name":"Privacy-Preserving Mortality Prediction in ICUs Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013120600003911","authors":["Pedro Vieira","Eva Maia","Isabel Praça"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-25T06:22:30Z","doi":"10.5220/0013120600003911","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1145/3778450.3778530","name":"Federated Continual Learning: A Survey on Mitigating Spatial-Temporal Catastrophic Forgetting","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3778450.3778530","authors":["Tianyi Xie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-20T09:12:49Z","doi":"10.1145/3778450.3778530","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/mlise66443.2025.11100221","name":"FedMI: Heterogeneous Federated Learning Based on Multi-Model Inference","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mlise66443.2025.11100221","authors":["Deyang Wan","Changbao Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-07T17:41:45Z","doi":"10.1109/mlise66443.2025.11100221","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.12792/icisip2025.036","name":"Virtual Adversarial Training (VAT)-based Secure Federated Learning Platform: Design and Evaluation","source":"crossref","abstract":"","url":"https://doi.org/10.12792/icisip2025.036","authors":["Takumi Tojo","Ryo Kumagai","Shu Takemoto","Yusuke Nozaki","Masaya Yoshikawa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-14T09:09:11Z","doi":"10.12792/icisip2025.036","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1016/b978-0-44-323641-9.00024-8","name":"Real-world implementation and application","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-323641-9.00024-8","authors":["An Xu","Bowen Li","Can Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:49:41Z","doi":"10.1016/b978-0-44-323641-9.00024-8","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.3390/en18040936","name":"Federated Learning and Neural Circuit Policies: A Novel Framework for Anomaly Detection in Energy-Intensive Machinery","source":"openalex","abstract":"In the realm of predictive maintenance for energy-intensive machinery, effective anomaly detection is crucial for minimizing downtime and optimizing operational efficiency. This paper introduces a novel approach that integrates federated learning (FL) with Neural Circuit Policies (NCPs) to enhance anomaly detection in compressors utilized in leather tanning operations. Unlike traditional Long Short-Term Memory (LSTM) networks, which rely heavily on historical data patterns and often struggle with generalization, NCPs incorporate physical constraints and system dynamics, resulting in superior performance. Our comparative analysis reveals that NCPs significantly outperform LSTMs in accuracy and interpretability within a federated learning framework. This innovative combination not only addresses pressing data privacy concerns but also facilitates collaborative learning across decentralized data sources. By showcasing the effectiveness of FL and NCPs, this research paves the way for advanced predictive maintenance strategies that prioritize both performance and data integrity in energy-intensive industries.","url":"https://doi.org/10.3390/en18040936","authors":["Giulia Palma","Giovanni Geraci","Antonio Rizzo"],"tags":["Anomaly detection","Computer science","Energy (signal processing)","Anomaly (physics)","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-02-15","doi":"10.3390/en18040936","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.1007/978-981-95-1394-9_6","name":"Advanced Privacy Measures for Data Sharing in Federated Learning Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1394-9_6","authors":["P. Manju Bala","S. Usharani","A. Balachandar","A. Olukayode"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-07T13:28:25Z","doi":"10.1007/978-981-95-1394-9_6","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.4018/979-8-3373-3306-9.ch005","name":"Collaborative Health Intelligence","source":"crossref","abstract":"Federated Learning empowers hospitals and research centres to train AI models collaboratively without sharing patient records. It is the decentralized alternative in which AI models can be trained across multiple healthcare organizations. The patient data is stored securely at multiple parties rather than being shared in raw data. FL makes it possible to share learned knowledge while still adhering to strict data-specific privacy regulations. This chapter provides a comprehensive overview of FL's foundational principles, architecture, and challenges. It highlights the various ways FL can assist with data protection for patients and research participants, decentralized data governance, enhance security, and scale FL across hospitals and research institutions. It gives insights on FL implementation in healthcare practice, including radiology with NVIDIA Clara, AI-driven rare disease diagnosis, COVID response solutions, and wearable health monitoring trackers. Additionally, it discusses possible combinations of FL and blockchain systems, edge AI, IoT in a medical context.","url":"https://doi.org/10.4018/979-8-3373-3306-9.ch005","authors":["Nancy Soni","Asomi Chowdhury","Hina Bansal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-30T14:01:36Z","doi":"10.4018/979-8-3373-3306-9.ch005","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.5220/0013586200004664","name":"Skin Cancer Classification and Detection Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013586200004664","authors":["Malliga Subramanian","Kalaivani B","Jeevasree G","Mathan Kumar A","Nandhini P S"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T09:33:50Z","doi":"10.5220/0013586200004664","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1002/9781394338726.ch4","name":"Federated Learning and Its Applications in Smart Agricultural Processes","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch4","authors":["Mahesh Kumar Singh","Pushpa Choudhary","Akhilesh Kumar Singh","Arun Kumar Singh","Om Prakash Rishi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch4","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.5220/0013182200003890","name":"Federated Machine Learning Framework for Soil Classification in Smart Agriculture","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013182200003890","authors":["Marwen Ghabi","Sofiane Khalfallah","Hela Ltifi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-28T12:43:20Z","doi":"10.5220/0013182200003890","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.4018/979-8-3373-1424-2.ch002","name":"Cryptography Securing Data in Motion Within Blockchain, Internet of Everything, and Federated Learning","source":"crossref","abstract":"Data protection relies on cryptography to secure data across Blockchain, IoE, and Federated Learning systems. Strong cryptographic methods ensure confidentiality, authenticity, and integrity, safeguarding evolving digital security needs. Key techniques include symmetric and asymmetric encryption, hash functions, digital signatures, and zero-knowledge proofs. Cryptography enables secure protocols like TLS, homomorphic encryption, and differential privacy while addressing quantum-resistant security challenges, ensuring robust digital privacy solutions.","url":"https://doi.org/10.4018/979-8-3373-1424-2.ch002","authors":["S. Aarthi","K. Aravinthan","R. N. Ravikumar","N. Sivakumar","Soram Wanglen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-13T16:35:42Z","doi":"10.4018/979-8-3373-1424-2.ch002","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.21275/sr251016172047","name":"Adaptive and Secure ETL for Defense Data Systems Using Federated Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.21275/sr251016172047","authors":["Manohar Reddy Sokkula"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-23T10:50:30Z","doi":"10.21275/sr251016172047","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1201/9781003532323-3","name":"Automation of AI and IoT-Based Data-Driven Decision-Making Approaches Using Federated Learning Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003532323-3","authors":["M. Sundarrajan","Mani Deepak Choudhry","Rajesh Kumar Dhanaraj","Mariya Ouaissa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-20T18:53:47Z","doi":"10.1201/9781003532323-3","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1007/978-3-032-01940-0_3","name":"Federated Learning for Enterprise AI","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-01940-0_3","authors":["Ben Tan","Yan Kang","Lixin Fan","Vincent Zheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-15T18:21:09Z","doi":"10.1007/978-3-032-01940-0_3","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.5220/0014531600005061","name":"Intrusion Detection System Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014531600005061","authors":["Zarinabegum Mundargi","Atharva Bondarde","Atharva Joshi","Archit Bagad","Arnav Jadhav","Shreyash Bansod"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-31T13:14:40Z","doi":"10.5220/0014531600005061","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1201/9781003591085-14","name":"Federated learning-enabled CNN for predicting and detecting brain tumors in healthcare 6.0","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003591085-14","authors":["Boggarapu Srinivasulu","Srinivasa Rao Dhanikonda","Dammu Venkata Ravi Kumar","Ravega Venkata Gandhi","Bonthala Prabhanjan Yadav"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-20T10:28:47Z","doi":"10.1201/9781003591085-14","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5191890","name":"Pqbfl: A Post-Quantum Blockchain-Based Protocol for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5191890","authors":["Hadi Gharavi","Edmundo Monteiro","Jorge Granjal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-24T21:39:36Z","doi":"10.2139/ssrn.5191890","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1145/3709023.3737689","name":"FedDDF: Dynamic Dataset Filtering in Federated Large Language Model Training","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3709023.3737689","authors":["Nguyen Linh Bao Nguyen","Thuan Quang Tran","Kok-Seng Wong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-15T07:07:04Z","doi":"10.1145/3709023.3737689","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/flta67013.2025.11336406","name":"Personalized Federated FlowChain for Human Trajectory Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336406","authors":["Kannanthodath Induchoodan Ajay Menon","Christian Prehofer","Yunfei Xu","Sarandeep Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336406","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/flta67013.2025.11336743","name":"Federated Attention Autoencoders with a Stochastic Aggregation Scheme for Anomaly Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336743","authors":["Mihailo Ilić","Miloš Savić","Vladimir Kurbalija","Mirjana Ivanović","Giancarlo Fortino","Dušan Jakovetić"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336743","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.5220/0013679100004670","name":"MOON-DPAP: Model-Contrastive Federated Learning with Differential Privacy and Adaptive Pruning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013679100004670","authors":["Jiaming Su"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-30T10:05:46Z","doi":"10.5220/0013679100004670","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1007/978-981-96-9223-1_1","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-9223-1_1","authors":["Mei Kobayashi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-01T12:37:32Z","doi":"10.1007/978-981-96-9223-1_1","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.5220/0014197800004932","name":"Real-Time Federated Learning Architecture for Privacy-Preserving Edge Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014197800004932","authors":["Priya Vij","Manish Nandy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-05T22:09:31Z","doi":"10.5220/0014197800004932","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.4018/979-8-3373-3306-9.ch006","name":"Enabling Decentralized Clinical Insights","source":"crossref","abstract":"Healthcare AI has federated learning as a transformational approach in which clinical research can be conducted collaboratively and in patient privacy is preserved. This chapter considers federated learning and how it enables healthcare institutions to learn from sensitive data without centralizing it and without access to that data. It consists of the technological architecture, companding local computational resources, secure communication channels, and model aggregation servers, each with a suitable algorithm for dealing with data heterogeneity between institutions. Its integration with existing healthcare infrastructure (IoMT devices, EHR systems) ensures operational efficiency thus being integrated into the existing system. By balancing innovation with privacy, federated learning is positioned as a cornerstone technology for the development of the personalized medicine — which with technology giants as the players the only type in which patient privacy can be respected while taking advantage of the collective clinical wisdom.","url":"https://doi.org/10.4018/979-8-3373-3306-9.ch006","authors":["Sonam Gupta","Pradeep Gupta","Lipika Goel","Sachin Jain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-30T14:01:36Z","doi":"10.4018/979-8-3373-3306-9.ch006","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5577153","name":"Homomorphic Encryption: the Missing Piece in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5577153","authors":["Aurora Anna Francesca Colombo","Alessandro Falcetta","Manuel Roveri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-08T03:38:09Z","doi":"10.2139/ssrn.5577153","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.36948/ijfmr.2025.v07i04.52997","name":"Survey on Federated Learning UtilisingDeep Learning Models for DiverseApplications","source":"crossref","abstract":"Survey on Federated Learning Utilising Deep Learning Models for Diverse Applications","url":"https://doi.org/10.36948/ijfmr.2025.v07i04.52997","authors":["NANDAKUMAR |M","RANJITH N"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-08T11:26:00Z","doi":"10.36948/ijfmr.2025.v07i04.52997","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5410050","name":"Federated Learning for BYOD-Enabled Mobile Mentorship: Ensuring Privacy in Cloud-Telecom Converged Networks","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5410050","authors":["Thaker Bhavik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T11:53:57Z","doi":"10.2139/ssrn.5410050","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.62762/tmi.2025.796490","name":"Privacy-Preserving Federated Learning for IoT Botnet Detection: A Federated Averaging Approach","source":"crossref","abstract":"Traditional centralized machine learning approaches for IoT botnet detection pose significant privacy risks, as they require transmitting sensitive device data to a central server. This study presents a privacy-preserving Federated Learning (FL) approach that employs Federated Averaging (FedAvg) to detect prevalent botnet attacks, such as Mirai and Gafgyt, while ensuring that raw data remain on local IoT devices. Using the N-BaIoT dataset, which contains real-world benign and malicious traffic, we evaluated both the IID and non-IID data distributions to assess the effects of decentralized training. Our approach achieved 97.1% F1-score in IID and 94.8% in highly skewed non-IID scenarios, closely matching centralized learning performance while preserving privacy. Additionally, communication optimization techniques—Top-20% gradient sparsification and 8-bit quantization—reduce communication overhead by up to 80%, significantly enhancing the efficiency. Our convergence analysis further shows that FedAvg remains effective under non-IID conditions, thereby demonstrating its robustness for real-world deployments. These results demonstrate that FL provides a scalable and privacy-preserving solution for securing IoT networks against botnet threats.","url":"https://doi.org/10.62762/tmi.2025.796490","authors":["Praveen Kumar Myakala","Srikanth Kamatala","Chiranjeevi Bura"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-25T15:30:27Z","doi":"10.62762/tmi.2025.796490","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.36227/techrxiv.176281115.50920760/v1","name":"AI-Driven Energy Optimization for Smart Cities: Federated Reinforcement Learning and Digital Twin Framework","source":"crossref","abstract":"This study presents a novel artificial intelligence (AI)-based framework for optimizing energy management in smart cities, integrating attention-based long short-term memory (LSTM) networks, reinforcement learning (RL), and privacypreserving federated learning (FL) within a digital-twin simulation environment. The proposed framework employs an LSTM module with attention mechanisms to forecast short-term energy demand with high temporal accuracy. An RL-based controller dynamically optimizes energy resource allocation to minimize operational costs and carbon emissions under variable conditions. To ensure data privacy, a federated-learning approach enables collaborative model training across distributed nodes without compromising sensitive information. Evaluated within a city-scale digital-twin platform, the framework achieves a 22% improvement in prediction accuracy and an 18% reduction in energy consumption compared to conventional baseline models. These results demonstrate the efficacy of combining AI-driven forecasting, adaptive control, and privacy-aware learning for realtime energy management. The proposed approach supports scalable, data-driven solutions for sustainable urban infrastructure, aligning with global and U.S. initiatives for carbonneutrality and intelligent energy systems. This research provides a robust foundation for deploying AI-enhanced energy optimization strategies in smart-city applications.","url":"https://doi.org/10.36227/techrxiv.176281115.50920760/v1","authors":["Arvindh Balajie Sundararajan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-10T21:45:59Z","doi":"10.36227/techrxiv.176281115.50920760/v1","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.36227/techrxiv.173894842.25024919/v1","name":"Towards Scalable and Secure Federated Learning: Key Issues and Future Research","source":"crossref","abstract":"Federated Learning (FL) is a decentralized machine learning approach that enables collaborative model training across distributed data sources while ensuring data privacy. Unlike traditional centralized approaches, FL allows multiple clients (e.g., mobile devices, IoT sensors, and hospitals) to train a global model without sharing their raw data. This survey provides a comprehensive overview of FL, discussing its fundamental concepts, architecture, algorithms, and optimization techniques. We highlight the diverse applications of FL across various industries, such as healthcare, finance, autonomous vehicles, and smart cities, showcasing its potential to address real-world challenges related to data privacy, security, and scalability. Additionally, we identify the key research challenges and open issues in FL, including communication overhead, data heterogeneity, adversarial attacks, and regulatory compliance. Finally, we discuss the future directions of FL, focusing on potential advancements in communication efficiency, privacypreserving techniques, and integration with emerging technologies such as edge computing and 5G networks. This survey aims to provide a thorough understanding of FL's current state and its promising future in shaping the landscape of distributed machine learning.","url":"https://doi.org/10.36227/techrxiv.173894842.25024919/v1","authors":["Marilyn Daniels","Sameera Gallus","Rebekah Wood"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-07T12:13:53Z","doi":"10.36227/techrxiv.173894842.25024919/v1","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5314750","name":"Relayfl:Advancing Federated Learning Towards Client Heterogeneity","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5314750","authors":["yang yong","shaoshuai gao","tingting yang","jiahong ning","lingzheng kong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-21T15:41:53Z","doi":"10.2139/ssrn.5314750","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1002/9781394338726.fmatter","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.fmatter","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.fmatter","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.36227/techrxiv.176422482.22480990/v1","name":"CASCADENCE: A Layered Cascade Defense Mechanism for Federated Learning","source":"crossref","abstract":"This paper aims to enhance the security and robustness of Federated Learning (FL) systems through a multilayered defense. We address the critical challenge of protecting distributed learning environments from adversarial attacks while maintaining high model performance during both the training and operational phases. The proposed framework is based on integrated approaches that utilize a Gaussian filter with Discrete Fourier Transform (DFT), adversarial training with differential privacy, JPEG compression, randomized smoothing, and adversarial logit pairing. It integrates multiple defense mechanisms based on system requirements, focusing on preserving model performance while ensuring robust protection during both training and testing phases. Our approach extends beyond existing solutions by introducing various staged defense implementations and analyzing their synergistic effects. Experimental results demonstrate that the proposed ensemble defense mechanism achieves the highest performance, maintaining 98.21% accuracy and an F1 score of 0.98 under attack conditions, compared to a baseline accuracy of 90.87%.","url":"https://doi.org/10.36227/techrxiv.176422482.22480990/v1","authors":["Syed Waquas Hashmi","Raj Mani Shukla","Suman Bhunia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-27T06:27:07Z","doi":"10.36227/techrxiv.176422482.22480990/v1","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/gcon65540.2025.11173308","name":"Towards Decentralized Dental Diagnostics: Federated Learning for Caries Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/gcon65540.2025.11173308","authors":["Ruchika Das","Shobhanjana Kalita"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T17:35:05Z","doi":"10.1109/gcon65540.2025.11173308","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5277568","name":"The Impact of Federated Learning on Distributed Remote Sensing Archives","source":"crossref","abstract":"When it comes to Machine Learning in remote sensing, one of the main obstacles researchers face is the large scale of datasets. Just the size of freely available Earth observation data presents a challenge for personal computers. A variety of missions, such as Sentinel-1,-2, and-3, have collectively gathered several petabytes of data. Given the size of these datasets, they are stored and processed across multiple platforms (often referred to as clients), which implies that decentralized Machine Learning must be applied. Federated Learning is one such decentralized learning approach, originally introduced by Google and adopted in their Android ecosystem. Since its release, the original Federated Learning technique has been fine-tuned and further developed. The scope of this project is to apply multiple Federated Learning models on remote sensing datasets and understand their implications considering different data splits across clients.","url":"https://doi.org/10.2139/ssrn.5277568","authors":["Vijay Govindarajan","Pratik Surendra Kumar Patel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-20T14:56:21Z","doi":"10.2139/ssrn.5277568","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.21203/rs.3.rs-6026136/v1","name":"Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning","source":"crossref","abstract":"Abstract Detecting financial fraud in real time is an ongoing challenge due to the ever-evolving nature of fraudulent activities. Conventional fraud detection systems rely heavily on static machine learning models, which often struggle to adapt to emerging fraud patterns. Additionally, data privacy regulations and institutional constraints limit collaborative fraud detection efforts, as financial organizations are often unable to share sensitive transactional data. In this research, we introduce a real-time fraud detection framework that combines Adaptive Graph Neural Networks (GNNs) and Federated Learning (FL) to overcome these limitations. The GNN component dynamically models relationships within financial transactions, allowing the system to detect suspicious patterns as they emerge rather than relying on historical fraud markers. Meanwhile, federated learning enables multiple financial institutions to collaboratively train fraud detection models without directly sharing customer data, thus addressing privacy concerns. To enhance explainability and regulatory compliance, the proposed system integrates Explainable AI (XAI) methods, making fraud detection decisions more transparent. Experimental evaluations on benchmark financial datasets and real-world transactional data reveal that our approach improves fraud detection accuracy by 15–30% while reducing false positives compared to existing machine learning-based solutions. The findings highlight the potential of GNNs and FL in advancing fraud prevention strategies while maintaining data security and interpretability, making it a promising alternative to traditional fraud detection mechanisms.","url":"https://doi.org/10.21203/rs.3.rs-6026136/v1","authors":["Milad Rahmati"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-17T06:34:56Z","doi":"10.21203/rs.3.rs-6026136/v1","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.36227/techrxiv.174906148.89641328/v1","name":"Federated Learning for UAV Perception Task : A Survey","source":"crossref","abstract":"Unmanned Aerial Vehicles (UAVs) are increasingly deployed for perception tasks such as surveillance, object detection, and traffic monitoring, which play a crucial role in intelligent vehicle systems. Federated Learning (FL) offers a decentralized framework that not only enhances data privacy but also facilitates collaborative intelligence between UAVs and ground-based vehicles, addressing the challenges of distributed data environments. This survey investigates FL applications in UAV perception, with a particular focus on advancing vehicle environment perception through cooperative learning. Key challenges addressed include the need to manage heterogeneous and often unlabeled data, optimize limited computational resources, and navigate communication constraints inherent to mobile, UAV and vehicular networks. Through an in-depth analysis, we assess the impact of data heterogeneity on model performance and explore state-of-the-art learning methods, including semi-supervised, unsupervised, and transfer learning techniques adapted for FL in autonomous systems. We further examine robust algorithms designed to support FL in complex, resourceconstrained settings, laying a foundation for future research in vehicle-centric IoT applications, where UAVs and vehicles interact in shared environments to enhance safety, efficiency, and perception.","url":"https://doi.org/10.36227/techrxiv.174906148.89641328/v1","authors":["Yanis Bardes","Hassan Soubra","Zineb Noumir","Amar Ramdane-Cherif"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-04T14:24:55Z","doi":"10.36227/techrxiv.174906148.89641328/v1","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1145/3778450.3778517","name":"Federated Learning for Text Classification: Reflections on Model Selection and Data Distribution","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3778450.3778517","authors":["Tianyang Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-20T09:12:49Z","doi":"10.1145/3778450.3778517","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5218407","name":"Privacy-Preserving Personalized Federated Prompt Learning for Vision-Language Models","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5218407","authors":["Yinan Wu","Yanli Ren","Mu Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-15T16:37:14Z","doi":"10.2139/ssrn.5218407","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5208177","name":"Relayfl:Advancing Federated Learning Towards Clientheterogeneity Relayfl","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5208177","authors":["yang yong","tingting yang","shaoshuai gao","jiahong ning","lingzheng kong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-07T15:47:20Z","doi":"10.2139/ssrn.5208177","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.36227/techrxiv.175976340.01576885/v1","name":"Blockchain Federated Learning in the Internet of Medical Things with Edge Computing: Frameworks and Approaches","source":"crossref","abstract":"Federated Learning (FL) combined with edge computing has transformed the healthcare sector by utilizing medical data from edge devices and enabling improved real-time patient monitoring, diagnosis, and treatment. However, medical data has strict privacy requirements that prevent medical institutions from sharing data and utilizing the benefits of ML. In addition, centralized model aggregation in FL models adds a single point of failure to the system. Features, such as traceability, immutability, and decentralization offered by blockchain technology can address these challenges, allowing entities to share data in a trustless manner, which is particularly useful in the healthcare sector. The distributed ledger of blockchain can be used to verify and store model updates securely and reward helpful participants by offering incentives. This study explored the potential of blockchain-based federated learning in Internet of Medical Things systems. A highlevel schematic is presented to show how different components, such as the blockchain and Multi-Access Edge Computing can cooperate to perform training in a decentralized and private manner. A detailed review of recent IoMT-suitable BC-FL frameworks is also presented and their approaches to model aggregation, security, privacy, traceability, and accountability are analyzed. A brief guide to open challenges and future developments is also discussed.","url":"https://doi.org/10.36227/techrxiv.175976340.01576885/v1","authors":["Athena Rahmatie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-06T15:10:06Z","doi":"10.36227/techrxiv.175976340.01576885/v1","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5241753","name":"Federated Learning and Hybrid IDS: A Novel Approach to IoT Security","source":"crossref","abstract":"The rapid expansion of the Internet of Things (IoT) has transformed industries such as healthcare, smart cities, and industrial automation. However, as the number of connected devices grows, so do the security risks, with IoT networks increasingly targeted by botnet attacks. Traditional security measures, such as firewalls and antivirus software, are often insufficient against these evolving threats, highlighting the need for effective Intrusion Detection Systems (IDS). This study examines and compares different IDS techniques used for detecting botnet attacks in IoT environments, focusing on signature-based, anomaly-based, machine learning-based, and hybrid approaches. Each method is evaluated based on key performance metrics such as detection accuracy, false positive rate, computational efficiency, and scalability. While signature-based IDS are efficient and lightweight, they struggle to detect new threats. Anomaly-based IDS are more adaptable but often generate a high number of false positives. Machine learning-based approaches demonstrate high detection accuracy but require significant computational resources. Hybrid IDS, which combine multiple detection techniques, offer the best overall performance but can be complex and resource-intensive to implement. Our findings suggest that while hybrid and deep learning-based IDS provide the most effective detection, their adoption in real-world IoT environments is limited by high processing requirements. Future research should focus on developing lightweight and scalable IDS solutions that balance security effectiveness with computational efficiency. This study provides insights into selecting appropriate IDS strategies to enhance IoT security against evolving botnet threats.","url":"https://doi.org/10.2139/ssrn.5241753","authors":["Preeti Kailas Suryawanshi","Sonal Jagtap"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-07T13:21:46Z","doi":"10.2139/ssrn.5241753","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.5220/0013134100003928","name":"Advancing Network Anomaly Detection Using Deep Learning and Federated Learning in an Interconnected Environment","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013134100003928","authors":["Hanen Dhrir","Maha Charfeddine","Habib Kammoun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-21T17:04:35Z","doi":"10.5220/0013134100003928","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/punecon67554.2025.11378000","name":"Rethinking Federated Learning Architectures: Empirical and Theoretical Insights","source":"crossref","abstract":"","url":"https://doi.org/10.1109/punecon67554.2025.11378000","authors":["Tushar Mane","Shraddha Phansalkar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-17T21:04:14Z","doi":"10.1109/punecon67554.2025.11378000","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5410104","name":"Federated Learning in Telecom Cloud Infrastructures: Enabling Privacy-Aware AI for Secure Data Collaboration","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5410104","authors":["Rajesh Kasavalalji"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:04:26Z","doi":"10.2139/ssrn.5410104","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5375992","name":"Dha-Fl: Dynamic Hierarchical Attention Aggregation Framework for Heterogeneous Edge Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5375992","authors":["weibai zhou","Li Rong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-04T13:35:27Z","doi":"10.2139/ssrn.5375992","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5405070","name":"Cosifl: Collaborative Secure and Incentivized Federated Learning with Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5405070","authors":["Zhanhong Xie","Meifan Zhang","Lihua Yin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-25T20:41:44Z","doi":"10.2139/ssrn.5405070","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/incet64471.2025.11140150","name":"Federated Learning Based Skin Disease Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1109/incet64471.2025.11140150","authors":["P. Ananthi","Sukant A. K"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-04T18:16:47Z","doi":"10.1109/incet64471.2025.11140150","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.3390/e27060601","name":"Sign-Entropy Regularization for Personalized Federated Learning","source":"crossref","abstract":"Personalized Federated Learning (PFL) seeks to train client-specific models across distributed data silos with heterogeneous distributions. We introduce Sign-Entropy Regularization (SER), a novel entropy-based regularization technique that penalizes excessive directional variability in client-local optimization. Motivated by Descartes’ Rule of Signs, we hypothesize that frequent sign changes in gradient trajectories reflect complexity in the local loss landscape. By minimizing the entropy of gradient sign patterns during local updates, SER encourages smoother optimization paths, improves convergence stability, and enhances personalization. We formally define a differentiable sign-entropy objective over the gradient sign distribution and integrate it into standard federated optimization frameworks, including FedAvg and FedProx. The regularizer is computed efficiently and applied post hoc per local round. Extensive experiments on three benchmark datasets (FEMNIST, Shakespeare, and CIFAR-10) show that SER improves both average and worst-case client accuracy, reduces variance across clients, accelerates convergence, and smooths the local loss surface as measured by Hessian trace and spectral norm. We also present a sensitivity analysis of the regularization strength ρ and discuss the potential for client-adaptive variants. Comparative evaluations against state-of-the-art methods (e.g., Ditto, pFedMe, momentum-based variants, Entropy-SGD) highlight that SER introduces an orthogonal and scalable mechanism for personalization. Theoretically, we frame SER as an information-theoretic and geometric regularizer that stabilizes learning dynamics without requiring dual-model structures or communication modifications. This work opens avenues for trajectory-based regularization and hybrid entropy-guided optimization in federated and resource-constrained learning settings.","url":"https://doi.org/10.3390/e27060601","authors":["Koffka Khan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-04T11:14:12Z","doi":"10.3390/e27060601","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/ict65093.2025.11046330","name":"Federated Learning with Differential Privacy: Gaussian Mechanism or Laplacian Mechanism?","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ict65093.2025.11046330","authors":["Wessam Mesbah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-26T17:40:25Z","doi":"10.1109/ict65093.2025.11046330","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/icmlcn64995.2025.11140426","name":"Weighted Over-the-Air Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlcn64995.2025.11140426","authors":["Seyed Mohammad Azimi-Abarghouyi","Leandros Tassiulas","Carlo Fischione"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-03T17:49:08Z","doi":"10.1109/icmlcn64995.2025.11140426","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/icmlt65785.2025.11193238","name":"Federated Learning With Individualized Privacy Through Client Sampling","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlt65785.2025.11193238","authors":["Lucas Lange","Ole Borchardt","Erhard Rahm"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-13T17:39:05Z","doi":"10.1109/icmlt65785.2025.11193238","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/icmlt65785.2025.11193248","name":"Enhancing Android Lock Pattern Security with Personalized Federated Learning and Multi-Modal Framework","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlt65785.2025.11193248","authors":["Sina Apak","Peri Güneş"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-13T17:39:05Z","doi":"10.1109/icmlt65785.2025.11193248","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/icmla66185.2025.00038","name":"A Generative Adversarial based Approach for Continual Federated Learning with Non-IID Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmla66185.2025.00038","authors":["Akshat Sharma","JingTao Yao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T19:54:58Z","doi":"10.1109/icmla66185.2025.00038","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1201/9781003688570-6","name":"Privacy-Preserving FL","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003688570-6","authors":["Somanath Tripathy","Harsh Kasyap","Minghong Fang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-21T17:09:27Z","doi":"10.1201/9781003688570-6","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1007/978-3-031-99270-4_7","name":"Challenges and Opportunities in Federated Learning for the Internet of Everything (IoE)","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99270-4_7","authors":["Sumit Bansal","Piyush Kumar","Tanmay Raj","Saurabh Mishra","Pankaj Goyal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T05:43:14Z","doi":"10.1007/978-3-031-99270-4_7","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1002/9781394167760","name":"Integration of Federated Learning and Blockchain for Smart Cities","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394167760","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T21:19:49Z","doi":"10.1002/9781394167760","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5577231","name":"Federated Learning for Agentic Gen AI in Financial Risk Management for National Financial Security","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5577231","authors":["Satyadhar Joshi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-08T20:13:03Z","doi":"10.2139/ssrn.5577231","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.31219/osf.io/j2bxz_v1","name":"Federated Learning Approaches for Privacy-Preserving Security Analytics in Distributed IoT Environments","source":"crossref","abstract":"The proliferation of Internet of Things (IoT) devices across diverse and distributed environments has amplified the need for robust, scalable, and privacy-aware security analytics. Traditional centralized machine learning models struggle to address these concerns due to limitations in data sharing, latency, and privacy compliance. This paper explores federated learning (FL) as a transformative approach for enabling collaborative security analytics while preserving data privacy at the edge. We investigate how FL architectures can effectively detect threats, anomalies, and intrusions across heterogeneous IoT networks without transferring raw data to a central server. The study presents a comparative evaluation of state-of-the-art FL methods tailored for security use cases, highlights strategies for handling non-IID data and device heterogeneity, and discusses the implications of communication overhead and adversarial risks in real-world deployments. Our findings demonstrate that FL not only reduces privacy risks but also enhances model robustness and adaptability in dynamic, resource-constrained IoT ecosystems. This research contributes a comprehensive perspective on how federated learning can evolve into a cornerstone of next-generation, privacy-preserving cybersecurity frameworks for the Internet of Things.","url":"https://doi.org/10.31219/osf.io/j2bxz_v1","authors":["Garba Sani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-07T21:24:06Z","doi":"10.31219/osf.io/j2bxz_v1","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1201/9781003688570-5","name":"Byzantine-Robust Defenses","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003688570-5","authors":["Somanath Tripathy","Harsh Kasyap","Minghong Fang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-21T17:09:27Z","doi":"10.1201/9781003688570-5","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5271946","name":"Fedfreeze: A Dual-Phase Layer Freezing Framework for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5271946","authors":["Di Wu","Leon Wong","Blesson Varghese"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-28T06:16:18Z","doi":"10.2139/ssrn.5271946","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5695202","name":"Federated Learning for BYOD-Enabled Mobile Mentorship: Ensuring Privacy in Cloud-Telecom Converged Networks","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5695202","authors":["Thaker Bhavik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-09T21:15:11Z","doi":"10.2139/ssrn.5695202","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1007/978-981-96-3212-1_3","name":"Privacy Computing in Cross-Device Federated Recommendation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-3212-1_3","authors":["Xiangjie Kong","Lingyun Wang","Mengmeng Wang","Guojiang Shen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-06T01:40:56Z","doi":"10.1007/978-981-96-3212-1_3","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2174/9789815322224125030002","name":"List of Contributors","source":"crossref","abstract":"","url":"https://doi.org/10.2174/9789815322224125030002","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-25T10:34:14Z","doi":"10.2174/9789815322224125030002","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.23919/eusipco63237.2025.11226038","name":"Adaptive Local Training in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.23919/eusipco63237.2025.11226038","authors":["Donald Shenaj","Eugene Belilovsky","Pietro Zanuttigh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-05T18:36:04Z","doi":"10.23919/eusipco63237.2025.11226038","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5650683","name":"Adaptive Momentum Enhanced Second-Order Stochastic Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5650683","authors":["Jiahao Zhang","Xiaokang Pan","Jingling Liu","Zhe Qu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-24T01:45:02Z","doi":"10.2139/ssrn.5650683","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.9790/0661-2705030725","name":"Federated Learning For Privacy-Preserving AI","source":"crossref","abstract":".","url":"https://doi.org/10.9790/0661-2705030725","authors":["Ansh Bansal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-04T08:24:41Z","doi":"10.9790/0661-2705030725","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.5505/pajes.2025.40456","name":"DDoS_FL: Federated Learning Architecture Approach against DDoS Attack","source":"crossref","abstract":"","url":"https://doi.org/10.5505/pajes.2025.40456","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-17T09:04:50Z","doi":"10.5505/pajes.2025.40456","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/scm66446.2025.11060092","name":"Federated Learning Platform","source":"crossref","abstract":"","url":"https://doi.org/10.1109/scm66446.2025.11060092","authors":["M. A. Kolpaschikov","Ivan I. Kholod"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-08T13:36:13Z","doi":"10.1109/scm66446.2025.11060092","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/aicconf64766.2025.11064274","name":"FedFZY: A Novel Aggregation Method for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aicconf64766.2025.11064274","authors":["Koksal Erenturk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-10T17:45:06Z","doi":"10.1109/aicconf64766.2025.11064274","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.5220/0013367500003899","name":"Topology-Driven Defense: Detecting Model Poisoning in Federated Learning with Persistence Diagrams","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013367500003899","authors":["Narges Alipourjeddi","Ali Miri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-25T06:22:45Z","doi":"10.5220/0013367500003899","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/icca66035.2025.11431056","name":"A Federated Model with Self-Supervised Learning and Broad Learning for Distributed Epilepsy Subtyping","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icca66035.2025.11431056","authors":["Zihan Dong","Sun Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-17T20:18:48Z","doi":"10.1109/icca66035.2025.11431056","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.15308/sinteza-2025-35-40","name":"Federated Learning Setting for E-Learning Course Recommendations","source":"crossref","abstract":"","url":"https://doi.org/10.15308/sinteza-2025-35-40","authors":["Miloš Jolović","Dušan Kostić","Aleksandar Joksimović","Talib Tahirović","Petar Lukovac"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-13T16:22:04Z","doi":"10.15308/sinteza-2025-35-40","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1007/978-981-96-9223-1_3","name":"Edge Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-9223-1_3","authors":["Mei Kobayashi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-01T12:37:38Z","doi":"10.1007/978-981-96-9223-1_3","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.23977/jeis.2025.100206","name":"Cooperative Detection Algorithm of Malicious Nodes Based on Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.23977/jeis.2025.100206","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-19T11:31:11Z","doi":"10.23977/jeis.2025.100206","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.36227/techrxiv.174585953.31012414/v1","name":"Privacy Preserving Tree: Zero Overhead Privacy Preservation in Cross-silo Decentralized Federated Learning","source":"crossref","abstract":"Federated Learning (FL) in Edge Computing generally manifests in two forms: Cross-silo FL, for networks of Edge/Cloud servers, and Cross-device FL, for IoT devices connected to a single server. Cross-silo FL, benefiting from fewer participants, robust resources, and relaxed security constraints, is particularly suited for Decentralized FL, which eliminates the need for a central server. Despite its potential, Decentralized Cross-silo FL remains a nascent field, struggling with challenges in privacy and computation &amp; communication. While existing research addresses these issues individually, a holistic approach is lacking. To bridge this gap, we introduce Privacy Preserving Tree, a novel adaptation of the Binomial Tree employing additive Secret Sharing. This method significantly reduces both computation and communication overhead while ensuring data privacy. We also provide a container based implementation of Privacy Preserving Tree, for easy development and deployment which is quite suitable for both the academia and the industry.","url":"https://doi.org/10.36227/techrxiv.174585953.31012414/v1","authors":["Tomsy Paul","Santhosh Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-28T12:58:55Z","doi":"10.36227/techrxiv.174585953.31012414/v1","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5104492","name":"Federated Learning Meets Blockchain: A Collaborative Framework for Mining Abac Policies","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5104492","authors":["sara aboukadri","Aafaf Ouaddah","Abdellatif Mezrioui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-20T20:39:03Z","doi":"10.2139/ssrn.5104492","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.5772/intechopen.1006677","name":"Privacy in Federated Learning","source":"crossref","abstract":"Federated learning (FL) represents a significant advancement in distributed machine learning, enabling multiple participants to collaboratively train models without sharing raw data. This decentralized approach enhances privacy by keeping data on local devices. However, FL introduces new privacy challenges, as model updates shared during training can inadvertently leak sensitive information. This chapter delves into the core privacy concerns within FL, including the risks of data reconstruction, model inversion attacks, and membership inference. It explores various privacy-preserving techniques, such as differential privacy (DP) and secure multi-party computation (SMPC), which are designed to mitigate these risks. The chapter also examines the trade-offs between model accuracy and privacy, emphasizing the importance of balancing these factors in practical implementations. Furthermore, it discusses the role of regulatory frameworks, such as GDPR, in shaping the privacy standards for FL. By providing a comprehensive overview of the current state of privacy in FL, this chapter aims to equip researchers and practitioners with the knowledge necessary to navigate the complexities of secure federated learning environments. The discussion highlights both the potential and limitations of existing privacy-enhancing techniques, offering insights into future research directions and the development of more robust solutions.","url":"https://doi.org/10.5772/intechopen.1006677","authors":["Jaydip Sen","Hetvi Waghela","Sneha Rakshit"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-23T13:16:04Z","doi":"10.5772/intechopen.1006677","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1145/3778450.3778499","name":"Federated Graph Learning-Based Dynamic Modeling of Risk Correlation Networks Among Financial Institutions","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3778450.3778499","authors":["Shijie Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-20T09:12:49Z","doi":"10.1145/3778450.3778499","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.9790/0661-2705052738","name":"Federated Learning with Differential Privacy: A Synergistic Approach to Private and Collaborative Machine Learning","source":"crossref","abstract":"The data-driven machine learning (ML) has caused an implicit conflict between the usefulness of machine learning models and personal privacy. Although the centralized models of ML require enormous data in order to achieve the state-of-the-art performance, such aggregation poses significant privacy risks and logistical challenges, particularly when it comes to sensitive data, which may fall under privacy policies like GDPR and HIPAA. FL has emerged as a promising decentralized model to enable joint model training on distributed data without distributing raw data to clients. Nonetheless, as recent studies have demonstrated, FL cannot be considered a panacea to privacy nor can model updates during the training process leak sensitive information about the underlying training data due to multiple inference attacks. This gap is the role of this paper, which carries out an in-depth study of Differential Privacy (DP) and Federated Learning synergy. DP offers a mathematically serious method of measuring and restricting privacy loss and is able to offer a formal guarantee that the inclusion of any single individual data point in the training set has a statistically negligible impact on the output of the final model. This study adopts the conceptual and theoretical analysis methodology to come up with integrated FL-DP framework. We deliberate in detail on the mechanics of integrating DP (mostly the strong clientside Local DP model), and discuss its far-reaching consequences. What is in this paper decomposing the synergistic advantages is that it possesses high defense against model inversion and membership inference attacks, and offers measurable, regulation-compliant privacy guarantees. It also, at the same time, provides an in-depth discussion of the inherent tensions and trade-offs that the given integration entails, the delicate balance between privacy assurances (the privacy budget, ) and model accuracy in particular, and system efficiency and convergence dynamics implications in general. The paper proceeds to elaborate on complex mechanisms (such as gradient clipping, adaptive noise scheduling and complex privacy accounting) that aim to balance this trade-off. This study in the context of the possible applications in essential fields like healthcare and finance can be used to create a more detailed picture of the promises and practical issues of applying machine learning privately and in collaboration at scale.","url":"https://doi.org/10.9790/0661-2705052738","authors":["Amira Fatima"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-04T08:24:41Z","doi":"10.9790/0661-2705052738","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/globecom59602.2025.11431996","name":"Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom59602.2025.11431996","authors":["Haruki Kainuma","Takayuki Nishio"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-19T20:04:01Z","doi":"10.1109/globecom59602.2025.11431996","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.5220/0013679700004670","name":"Research on the Application of FedDyn Algorithm in Federated Learning Based on Taylor","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013679700004670","authors":["Zijia Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-30T10:26:59Z","doi":"10.5220/0013679700004670","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1016/j.energy.2025.134559","name":"Prediction of EV charging load based on federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.energy.2025.134559","authors":["Wanjun Yin","Jianbo Ji"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-17T18:45:15Z","doi":"10.1016/j.energy.2025.134559","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/dlcv65218.2025.11088885","name":"Securing Industrial IoT with Cross-System Federated Learning for Malware Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/dlcv65218.2025.11088885","authors":["Caihong Wang","Du Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-13T17:26:30Z","doi":"10.1109/dlcv65218.2025.11088885","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/spml66318.2025.11199830","name":"Construction and Application of Federated Learning Model for Comprehensive Quality Assessment of Students","source":"crossref","abstract":"","url":"https://doi.org/10.1109/spml66318.2025.11199830","authors":["Peiwen Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-21T17:07:16Z","doi":"10.1109/spml66318.2025.11199830","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.4018/979-8-3373-2200-1.ch010","name":"Federated Learning for Secure and Resilient AI Systems","source":"crossref","abstract":"Federated Learning is a transformative approach to building secure and resilient AI systems by enabling decentralized model training without exposing raw data. As part of Challenges and Solutions for Cybersecurity and Adversarial Machine Learning, this chapter examines its role in enhancing cybersecurity and mitigating adversarial threats, emphasizing its privacy-preserving capabilities and robustness against attacks. Key security challenges, including adversarial model poisoning, communication risks, and data privacy concerns, are analyzed alongside solutions such as differential privacy, secure aggregation, and robust optimization techniques. The discussion extends to Federated Learning's applications in critical sectors such as healthcare, finance, and edge computing, where secure AI deployment is essential. Addressing these challenges and proposing viable solutions, the chapter provides a comprehensive perspective on Federated Learning's potential to enhance AI security and resilience in adversarial environments.","url":"https://doi.org/10.4018/979-8-3373-2200-1.ch010","authors":["Shaista Ashraf Farooqi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-06T09:33:01Z","doi":"10.4018/979-8-3373-2200-1.ch010","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1002/9781394271382.ch7","name":"Machine Learning and Artificial Intelligence Fundamentals for Federated Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394271382.ch7","authors":["N. Vinaya Kumari","G. S. Pradeep Ghantasala","Pellakuri Vidyullatha","R. Rajesh Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-27T16:38:31Z","doi":"10.1002/9781394271382.ch7","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.4018/979-8-3373-3306-9.ch015","name":"The Role of Federated Learning in AI-Powered Integrated Healthcare Solutions","source":"crossref","abstract":"Federated Learning (FL) represents a revolutionary approach to artificial intelligence in the health care system, which addresses the important challenges of data lift, security and regulatory compliance, which enables severe ally insights. This paradigm allows health institutions to train the shared AI model without exchanging sensitive patient data, as the model travels where the data lives instead of centralizing the information. In an integrated health environment, FL provides facilitators for spontaneous collaboration in quiet departments, specifications and organizations and at the same time maintain strict data above. Implementation of FL in the health care system enables a strong future analysis, individual remedies recommendations and enlarged clinical decision support systems that are attracted by diverse patient population without compromising privacy.","url":"https://doi.org/10.4018/979-8-3373-3306-9.ch015","authors":["Kahksha Ahmed","Ankit Baranwal","Neetu Sharma","Puneet Garg","Narinderjit Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-30T14:01:36Z","doi":"10.4018/979-8-3373-3306-9.ch015","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5935003","name":"Comment on “Secure and efficient multi-key aggregation for federated learning”","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5935003","authors":["Guilin Guan","Yang Cao","Hongtao Xie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-18T02:38:21Z","doi":"10.2139/ssrn.5935003","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/icacrs67045.2025.11324115","name":"Privacy-Preserving Student Activity Classification in E-Learning using Hybrid Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icacrs67045.2025.11324115","authors":["Vaishnavi N","Devi A"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-14T20:37:30Z","doi":"10.1109/icacrs67045.2025.11324115","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1145/3709023.3737691","name":"Coordinate-Wise Median in Byzantine Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3709023.3737691","authors":["Melanie Cambus","Darya Melnyk","Tijana Milentijevic","Stefan Schmid"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-15T07:07:04Z","doi":"10.1145/3709023.3737691","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1002/9781394228522.ch12","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394228522.ch12","authors":["Rituparna Saha","Amit Biswas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-23T13:09:28Z","doi":"10.1002/9781394228522.ch12","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.23919/eusipco63237.2025.11226343","name":"Byzantine-Resilient Federated Learning via Distributed Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.23919/eusipco63237.2025.11226343","authors":["Yufei Xia","Wenrui Yu","Qiongxiu Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-05T18:36:04Z","doi":"10.23919/eusipco63237.2025.11226343","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/icetran66854.2025.11114237","name":"Comparative Analysis of Machine Learning and Federated Learning in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icetran66854.2025.11114237","authors":["Stevan Stanković","Pavle Vuletić","Dražen Drašković"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-19T18:07:00Z","doi":"10.1109/icetran66854.2025.11114237","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5935001","name":"VCRFL: A Verifiable and Collusion-Resistant Privacy-Preserving Framework for Efficient Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5935001","authors":["Dahe Huang","Yong Ding"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-19T14:32:32Z","doi":"10.2139/ssrn.5935001","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5206409","name":"Federated Hierarchical Reinforcement Learning for Adaptive Traffic Signal Control","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5206409","authors":["Yongjie Fu","Lingyun Zhong","Zifan Li","Xuan Di"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-05T11:45:32Z","doi":"10.2139/ssrn.5206409","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5528992","name":"Leveraging Federated Learning for Smart Grids: A Comprehensive Survey","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5528992","authors":["Muhammad Akbar Husnoo","Adnan Anwar","Md. Abdur Rahman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-25T14:38:24Z","doi":"10.2139/ssrn.5528992","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/icmlt65785.2025.11193366","name":"Federated Learning in the Diagnosis of Medical Diseases: A Survey","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlt65785.2025.11193366","authors":["Shuoshi Zhu","Yisheng Ruan","Tianyuan Yu","Liang Bai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-13T17:39:05Z","doi":"10.1109/icmlt65785.2025.11193366","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/smc58881.2025.11343618","name":"EPPFL: Entropy-based Personalized Privacy in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/smc58881.2025.11343618","authors":["Yang Guo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-28T20:54:44Z","doi":"10.1109/smc58881.2025.11343618","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1098/rsos.250959/v2/decision1","name":"Decision letter for \"Breaking Interprovincial Data Silos: How Federated Learning Can Unlock Canada’s Public Health Potential\"","source":"crossref","abstract":"","url":"https://doi.org/10.1098/rsos.250959/v2/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-14T21:10:36Z","doi":"10.1098/rsos.250959/v2/decision1","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1145/3749566.3749624","name":"Efficient Personalized Federated Learning Based on Dynamic Knowledge Cache","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3749566.3749624","authors":["Guan Wang","Jiaxin Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-26T15:36:52Z","doi":"10.1145/3749566.3749624","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.24108/preprints-3114083","name":"Federated Learning for Privacy-Preserving Data Analysis: A Conceptual Framework and Practical Considerations","source":"crossref","abstract":"This article examines Edge Artificial Intelligence (Edge AI) as an emerging paradigm for real-time decision making in environments with limited computational and energy resources. The study highlights the limitations of cloud-centric AI architectures in latency-sensitive and reliability-critical applications and motivates the shift toward decentralized intelligence at the network edge. The paper provides a conceptual overview of Edge AI architectures, including on-device inference, edge–cloud collaboration, and hierarchical edge systems. It discusses model optimization techniques such as quantization, pruning, and knowledge distillation, emphasizing the trade-offs between computational efficiency and predictive performance. Special attention is given to reliability and robustness challenges arising from hardware constraints, environmental variability, and intermittent connectivity. The article also outlines key limitations and open research challenges, including device heterogeneity, lifecycle management, and the lack of standardized evaluation benchmarks. Overall, the study positions Edge AI as a promising approach for enabling responsive and autonomous intelligent systems while underscoring the need for careful system-level design and methodological evaluation.","url":"https://doi.org/10.24108/preprints-3114083","authors":["Azamat Nurgaziev"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T18:11:21Z","doi":"10.24108/preprints-3114083","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.2139/ssrn.5430774","name":"FLZT-IDS: Federated Learning with Zero-Trust Framework for Secure IoT Intrusion Detection","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5430774","authors":["Muhammad Asad","Safa Otoum"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-02T03:37:22Z","doi":"10.2139/ssrn.5430774","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1109/icdsca67083.2025.11350051","name":"Optimizing the MOON Algorithm Based on Cosine Annealing Learning Rate in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icdsca67083.2025.11350051","authors":["Sirui Meng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-29T21:18:38Z","doi":"10.1109/icdsca67083.2025.11350051","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.1007/978-981-96-9223-1_2","name":"Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-9223-1_2","authors":["Mei Kobayashi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-01T12:37:32Z","doi":"10.1007/978-981-96-9223-1_2","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1016/j.hcc.2024.100264","name":"Deep reinforcement learning based resource provisioning for federated edge learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.hcc.2024.100264","authors":["Xingyun Chen","Junjie Pang","Tonghui Sun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-30T14:56:15Z","doi":"10.1016/j.hcc.2024.100264","addedAt":"2026-08-31T06:41:20.711Z","updatedAt":"2026-08-31T06:41:20.711Z"},{"id":"doi:10.36227/techrxiv.173808206.65020190/v1","name":"Quantum Encryption for Secure Federated Learning Against Generative Adversarial Network Attacks","source":"crossref","abstract":"Machine learning models in online environments are vulnerable to various adversarial attacks, such as those from generative adversarial networks (GANs) attacks and more powerful attacks based on recent advances in quantum computing. Federated Learning (FL), a modern privacy preserving distributed machine learning technique, also faces modern data management concerns related to quantum computing. Limiting adversarial learning through quantum mechanics lends itself as an effective security mechanism. This paper introduces a novel quantumencrypted FL framework that integrates randomly-generated quantum noise into an FL environment to improve security against adversarial learning algorithms. These algorithms can conduct reconstruction attacks, which increase privacy leakage through adversarially generated data. Specifically, GAN attacks are effective at creating data samples that resemble training data. Traditional computing techniques can be improved by appending random noise generated by quantum computers as a quantum verification method. The proposed hybrid-quantum encryption approach can fortify a network against generative reconstruction attacks by adding quantum noise to each participant's training data samples. To evaluate the effectiveness of the proposed quantum encryption techniques for protecting FL against GANs, we encrypt the MNIST dataset with quantum-generated keys and then distribute the data in a privacy-preserving manner.","url":"https://doi.org/10.36227/techrxiv.173808206.65020190/v1","authors":["Ervin Moore","Shabnam Rezapour","M. Hadi Amini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-28T11:34:33Z","doi":"10.36227/techrxiv.173808206.65020190/v1","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1109/globecom59602.2025.11432810","name":"Towards Heterogeneity-Free: Client Selection for Federated Learning with Prototype Margin","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom59602.2025.11432810","authors":["Huaye Zhang","Yuan Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-19T20:04:01Z","doi":"10.1109/globecom59602.2025.11432810","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.2139/ssrn.5174449","name":"Gidd: Gradient Inversion Using Diffusion Model for Denoising in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5174449","authors":["Xuebo Wang","Hongguang Sun","Yi He"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T13:39:20Z","doi":"10.2139/ssrn.5174449","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1109/ijcnn64981.2025.11227362","name":"Notice of Removal: Twice the Gradient, Twice the Privacy Risk in Federated Learning? A Case Study of Federated Recommendation Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ijcnn64981.2025.11227362","authors":["Zhenyu Deng","Ying Liu","Ming Tang","Xiangyu Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-14T18:46:15Z","doi":"10.1109/ijcnn64981.2025.11227362","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1007/978-3-031-80949-1_8","name":"Privacy Preservation in Federated Learning-Based Recommendation System","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-80949-1_8","authors":["Shilpa Singhal","R. N. Ravikumar","Krupali Gosai","Santushti Betgeri","Jayaraj Ramasamy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-08T17:48:19Z","doi":"10.1007/978-3-031-80949-1_8","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1016/j.neunet.2025.107281","name":"Replica tree-based federated learning using limited data","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.neunet.2025.107281","authors":["Ramona Ghilea","Islem Rekik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-22T16:25:38Z","doi":"10.1016/j.neunet.2025.107281","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1007/978-981-96-8353-6_6","name":"Federated Learning Strategies for Confidential Leukemia Detection from Medical Images","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-8353-6_6","authors":["Jayonto Dutta Plabon","Mehjabin Hossain","Md. Arafat Kabir","Durjoy Mistry","Md. Abdul Hamid"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-31T19:42:55Z","doi":"10.1007/978-981-96-8353-6_6","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.4018/979-8-3693-6094-1.ch007","name":"IoT-Enhanced Additive Manufacturing and Federated Learning for Smart Healthcare Systems","source":"crossref","abstract":"This chapter explores the privacy-utility trade-offs in IoT-enhanced additive manufacturing and federated learning systems within smart healthcare. IoT-driven additive manufacturing transforms healthcare by enabling customized medical devices and implants, while federated learning facilitates collaborative, decentralized AI model training without compromising raw data privacy. However, balancing privacy protection and system utility remains challenging, as stringent privacy measures often hinder data richness and model accuracy. The chapter explores strategies like differential privacy, homomorphic encryption, and secure multi-party computation to mitigate risks and ensure robust privacy, while evaluating the impact of regulatory frameworks.This chapter discusses the importance of balancing privacy and utility in personalized healthcare, promoting sustainable and secure IoT-augmented systems in clinical settings through case studies and comparative analysis.","url":"https://doi.org/10.4018/979-8-3693-6094-1.ch007","authors":["Shaik Mahamad Shakeer","M. Rajasekhara Babu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-11T19:09:18Z","doi":"10.4018/979-8-3693-6094-1.ch007","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.4018/979-8-3373-2200-1.ch011","name":"Role of Federated Learning in Enhancing Security","source":"crossref","abstract":"Federated Learning (FL), which prioritises data privacy and reduces centralised risks, has emerged as a revolutionary technique for enhancing security in distributed machine learning systems. As opposed to traditional methods that rely on aggregating raw data, FL ensures that sensitive data remains localised while enabling cooperative model training across scattered devices. This study carefully looks at the critical role FL plays in addressing significant security issues like data breaches, hostile assaults, and regulatory compliance. At the centre of this discussion is an analysis of key techniques like secure aggregation, homomorphic encryption, and differential privacy, which collectively defend FL systems against malicious attacks while preserving model accuracy.The utility of FL is demonstrated by its application in sensitive domains such as healthcare, where patient data is protected during cooperative model training.","url":"https://doi.org/10.4018/979-8-3373-2200-1.ch011","authors":["Umme Ayeman Saqib Gani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-06T09:33:01Z","doi":"10.4018/979-8-3373-2200-1.ch011","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1109/cns66487.2025.11194922","name":"Safeguarding Federated Learning-Based Road Condition Classification","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cns66487.2025.11194922","authors":["Sheng Liu","Panos Papadimitratos"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-15T17:34:49Z","doi":"10.1109/cns66487.2025.11194922","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.2139/ssrn.5963078","name":"Selective Forgetting in Hierarchical Federated Learning: Towards Global Model Unlearning of Client Data","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5963078","authors":["Afsaneh Afzali","Pirooz Shamsinejad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-24T16:36:52Z","doi":"10.2139/ssrn.5963078","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1109/icc52391.2025.11161480","name":"FedRD: Personalized Federated Learning via Representation Distillation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc52391.2025.11161480","authors":["Shuaishuai Zhang","Jie Huang","Peihao Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T17:34:55Z","doi":"10.1109/icc52391.2025.11161480","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1002/9781394338726.oth","name":"Also of Interest","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.oth","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.oth","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1201/9781003688570-4","name":"Inference Attacks on FL","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003688570-4","authors":["Somanath Tripathy","Harsh Kasyap","Minghong Fang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-21T17:09:27Z","doi":"10.1201/9781003688570-4","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.2139/ssrn.5346628","name":"Federated Learning Mlops Tools. A Systematic Mapping Study","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5346628","authors":["Ari Kukkaro","Sergio Moreschini","Davide Taibi","David H¨astbacka"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-10T12:42:04Z","doi":"10.2139/ssrn.5346628","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.2139/ssrn.5343391","name":"Energy Efficient Secure Edge Intelligence with Sparse Federated Learning for Iomt","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5343391","authors":["Suresh Chavhan","Emy Santo","Deepak Gupta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-14T00:22:26Z","doi":"10.2139/ssrn.5343391","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.2139/ssrn.5404037","name":"Secure and Efficient Federated Learning for Predictive Modeling in Resource-Constrained Healthcare Systems","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5404037","authors":["Alex Mirugwe","Juwa Nyirenda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-25T17:39:32Z","doi":"10.2139/ssrn.5404037","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.36227/techrxiv.175606219.91434530/v1","name":"A Review of Lightweight Multi-Party Computation and Federated Learning in Financial Systems","source":"crossref","abstract":"As the financial services are increasingly moving to the edge devices, safeguarding these sensitive transaction data without compromising on privacy has become a challenge. This systematic literature review analyzes peer-reviewed studies that explore lightweight federated learning (FL) techniques, gradient compression methods, and secure multi-party computation (MPC) protocols for privacy-preserving machine learning in financial systems. Our findings show that approaches like Federated Dropout (FedDrop), Quantized Stochastic Gradient Descent (QSGD), and Sparse Ternary Compression (STC) have been proposed to address communication overhead and device constraints. Furthermore, the review highlights privacypreserving frameworks having secure aggregation along with exploring lightweight MPC for distributed financial devices. Even with these advances, the real-world deployments within financial environments remain limited and act as gaps in privacy, communication cost, and model accuracy, especially for non-IID data. This timely review outlines the future research directions, like adaptive model compression and scalable secure aggregation frameworks for heterogeneous financial systems.","url":"https://doi.org/10.36227/techrxiv.175606219.91434530/v1","authors":["Tonsia Treesa Thomas","Heta Shukla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-24T19:03:16Z","doi":"10.36227/techrxiv.175606219.91434530/v1","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1109/flta67013.2025.11336591","name":"Starlit: Privacy-Preserving Federated Learning to Enhance Financial Fraud Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta67013.2025.11336591","authors":["Aydin Abadi","Mohammad Naseri","Bradley Doyle","Francesco Gini","Kieron Guinamard","Sasi Kumar Murakonda","Jack Liddell","Paul Mellor","Steven J. Murdoch","Hector Page","George Theodorakopoulos","Suzanne Weller"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:38:34Z","doi":"10.1109/flta67013.2025.11336591","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.56155/978-81-975670-0-1-3","name":"Adaptive Federated Learning for Anomaly Detection in Satellite Telemetry","source":"crossref","abstract":"This paper presents a streamlined Federated Learning (FL) framework for anomaly detection in satellite telemetry, addressing limitations of centralized approaches for predictive maintenance in resource-constrained satellite networks. Evaluating FL models on the ESA-ADB dataset, the optimized LSTM with FedAvg + Fine-Tuning achieved an F0.5 score of 0.86, a precision of 0.93, and an AUC of 0.85, outperforming centralized models, which achieved a maximum F0.5 score of 0.63 and an AUC of 0.83. Additionally, FL significantly reduced communication costs, requiring only 1.8MB per round compared to the high overhead of centralized data transmission. Scalability analysis demonstrated stable performance up to 10 clients, with an F0.5 score of 0.87 and recall of 1.00. These findings validate FL as a practical, privacypreserving, and scalable solution for onboard satellite anomaly detection.","url":"https://doi.org/10.56155/978-81-975670-0-1-3","authors":["Azade Atefrad","Amin Karami"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-29T05:48:03Z","doi":"10.56155/978-81-975670-0-1-3","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1201/9781003591085-6","name":"Harnessing machine learning and deep learning techniques for neuroimaging","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003591085-6","authors":["Hina Bansal","Neetu Jabalia","Kritika Shukla","Yash Nautiyal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-20T10:28:47Z","doi":"10.1201/9781003591085-6","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.2139/ssrn.5802022","name":"Federated Learning for Privacy-Preserving Health Monitoring: Enhancing Data Utility While Maintaining Security","source":"crossref","abstract":"The explosive growth of personalized health data generated by wearable devices and edge computing requires robust analytical methods that respect stringent privacy regulations (e.g., HIPAA, GDPR). Traditional centralized data aggregation poses significant security and privacy risks. This article investigates Federated Learning (FL) as a transformative framework for developing highly accurate health monitoring models while ensuring patient data remains localized and secure on individual devices. The research outlines an FL methodology using a Non-IID dataset simulating decentralized patient records. We discuss the implementation of the FedAvg algorithm for training a diagnostic model on remote medical data, followed by an analysis of convergence rate, model accuracy, and communication efficiency compared to centralized training. Results show that FL achieves comparable performance (within 2% accuracy) while drastically reducing data exposure risks.","url":"https://doi.org/10.2139/ssrn.5802022","authors":["CK Gomathy","Vardinni Reddii"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-29T17:53:16Z","doi":"10.2139/ssrn.5802022","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1109/icsit65336.2025.11295587","name":"Federated Learning with Cross-Device Transfer Learning in IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsit65336.2025.11295587","authors":["Jatin Aggrawal","Hariharasitaraman. S","Ajay Kumar Phulre","Irfan Alam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-19T18:56:35Z","doi":"10.1109/icsit65336.2025.11295587","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.3390/iot6030035","name":"Data-Bound Adaptive Federated Learning: FedAdaDB","source":"crossref","abstract":"Federated Learning (FL) enables decentralized Machine Learning (ML), focusing on preserving data privacy, but faces a unique set of optimization challenges, such as dealing with non-IID data, communication overhead, and client drift. Adaptive optimizers like AdaGrad, Adam, and Adam variations have been applied in FL, showing good results in convergence speed and accuracy. However, it can be quite challenging to combine good convergence, model generalization, and stability in an FL setup. Data-bound adaptive methods like AdaDB have demonstrated promising results in centralized settings by incorporating dynamic, data-dependent bounds on Learning Rates (LRs). In this paper, FedAdaDB is introduced, which is an FL version of AdaDB aiming to address the aforementioned challenges. FedAdaDB uses the AdaDB optimizer at the server-side to dynamically adjust LR bounds based on the aggregated client updates. Extensive experiments have been conducted comparing FedAdaDB with FedAvg and FedAdam on three different datasets (EMNIST, CIFAR100, and Shakespeare). The results show that FedAdaDB consistently offers better and more robust outcomes, in terms of the measured final validation accuracy across all datasets, for a trade-off of a small delay in the convergence speed at an early stage.","url":"https://doi.org/10.3390/iot6030035","authors":["Fotios Zantalis","Grigorios Koulouras"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-24T10:44:41Z","doi":"10.3390/iot6030035","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.2139/ssrn.5401669","name":"Enhancing Data Privacy in 5G/6G Telecom Networks: Federated Learning as a Secure AI Framework","source":"crossref","abstract":"The rapid evolution of 5G and the emerging 6G networks is revolutionizing the telecommunications landscape by enabling ultra-reliable low-latency communications (URLLC), massive machine-type communications (mMTC), and enhanced mobile broadband (eMBB). However, the proliferation of interconnected devices and data-intensive services has amplified concerns regarding data privacy and security. Traditional centralized machine learning approaches often require transmitting raw user data to central servers, thereby increasing vulnerability to data breaches, regulatory violations, and cyberattacks. Federated Learning (FL) has emerged as a promising paradigm to address these challenges by enabling decentralized training of artificial intelligence (AI) models while keeping sensitive data localized at the network edge. This paper explores the integration of federated learning into 5G and 6G telecom networks as a secure AI framework for preserving user privacy and strengthening trust in next-generation communications. It highlights how FL can mitigate data exposure risks, ensure compliance with privacy regulations, and enhance scalability for massive data-driven applications. Furthermore, the study discusses key challenges including communication overhead, model poisoning, and heterogeneity across devices and presents potential solutions such as secure aggregation, differential privacy, and blockchain-enabled trust mechanisms. By leveraging federated learning, telecom operators can unlock the full potential of AI-driven network intelligence without compromising user privacy, thereby laying the foundation for secure, trustworthy, and efficient 6G ecosystems.","url":"https://doi.org/10.2139/ssrn.5401669","authors":["Itunuoluwa Adegbola"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-05T11:33:48Z","doi":"10.2139/ssrn.5401669","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1109/bibm66473.2025.11356387","name":"Trustworthiness Verification for Federated Learning in Web3.0 Healthcare Communities","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bibm66473.2025.11356387","authors":["Tianxin Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-29T21:19:40Z","doi":"10.1109/bibm66473.2025.11356387","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1109/iccit63348.2025.10989405","name":"Federated Learning for Low-Latency IoT Communications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccit63348.2025.10989405","authors":["Mohammed M Alenazi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-21T17:36:26Z","doi":"10.1109/iccit63348.2025.10989405","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1109/cisat66811.2025.11181719","name":"Robust Contribution Measurement for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cisat66811.2025.11181719","authors":["Junyu Han"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-01T17:37:15Z","doi":"10.1109/cisat66811.2025.11181719","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.36227/techrxiv.175037492.25605814/v1","name":"Quantifying and Analyzing Client Data Heterogeneity in Federated Learning via Multi Modal Divergence Metrics","source":"crossref","abstract":"Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, the inherent heterogeneity in client data distributions poses significant challenges to FL performance, affecting model convergence and generalization. In this paper, we propose a comprehensive framework to quantitatively measure client heterogeneity by estimating divergences across multiple modalities including label distributions, feature representations and model output predictions. Our approach employs statistical metrics like Jensen Shannon divergence and Wasserstein distance to effectively characterize the varied dimensions of distributional differences across clients. We integrate these heterogeneity measures within the FL training pipeline to provide insights for adaptive aggregation and personalized training strategies. Extensive experiments on benchmark datasets(Cifar-10, MNIST, SVHN) demonstrate the effectiveness of the proposed metrics designed to detect complex heterogeneity patterns, thereby guiding improved federated optimization. Our framework offers a principled methodology for the characterization of heterogeneity critical to robust and scalable federated learning systems.","url":"https://doi.org/10.36227/techrxiv.175037492.25605814/v1","authors":["Praveer Dubey","Mohit Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-19T19:15:30Z","doi":"10.36227/techrxiv.175037492.25605814/v1","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1201/9781003688570-3","name":"Poisoning Attacks on FL","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003688570-3","authors":["Somanath Tripathy","Harsh Kasyap","Minghong Fang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-21T17:09:27Z","doi":"10.1201/9781003688570-3","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.2139/ssrn.5787314","name":"Fed-DiTTab: Diffusion Transformer for Tabular Data Generation in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5787314","authors":["Yajun Pi","Ming Zheng","Fanhao Ma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-22T15:43:02Z","doi":"10.2139/ssrn.5787314","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1088/2631-8695/ae3272/v2/review1","name":"Review for \"FedDW: An Adaptive Weight Aggregation Federated Learning Framework for Multi-Institutional Collaborative Polyp Segmentation\"","source":"crossref","abstract":"","url":"https://doi.org/10.1088/2631-8695/ae3272/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-31T23:57:44Z","doi":"10.1088/2631-8695/ae3272/v2/review1","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.36227/techrxiv.176070620.09868552/v1","name":"FedCOT: Personalized Federated Transfer Learning with Conditional Optimal Transport for Manufacturing Predictive Modeling","source":"crossref","abstract":"Effective predictive modeling in large-scale manufacturing is hampered by the isolated and limited data from individual organizations, collected from costly experiments and various inspections. Collaboration across organizations can handle these limitations, but it faces two main challenges: privacy concerns over organizations and heterogeneous features from varied sensing and inspection capabilities. Federated learning (FL) offers a solution by allowing organizations to collaboratively train a predictive model without sharing raw data, but standard FL struggles with the problem of feature heterogeneity. To address these challenges, we propose a personalized federated transfer learning framework with conditional optimal transport (FedCOT). FedCOT enables \"target\" organizations with limited features to benefit from \"source\" organizations with sufficient features in prediction performance while keeping data privacy through a central server. Each organization learns a personalized encoder-regressor structure by alternating optimization: the encoder maps heterogeneous inputs into a shared latent space, and the regressor predicts responses from the latent representations. Target organizations align their latent representations' structure and corresponding responses with the source organizations' information via COT. We evaluate FedCOT through simulations and a manufacturing case study on fatigue life prediction of additive-manufactured parts. Our case study demonstrates that FedCOT achieves the latent space where target organizations are highly aligned with source organizations, leading to a significant 34.99\\% improvement in prediction performance over the baseline method. Additionally, we provide theoretical guarantees on OT gradients, bounded gradients, and predictive consistency.","url":"https://doi.org/10.36227/techrxiv.176070620.09868552/v1","authors":["Anyi Li","Jia \"Peter\" Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-17T13:03:38Z","doi":"10.36227/techrxiv.176070620.09868552/v1","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1088/2631-8695/ae3272/v1/review2","name":"Review for \"FedDW: An Adaptive Weight Aggregation Federated Learning Framework for Multi-Institutional Collaborative Polyp Segmentation\"","source":"crossref","abstract":"","url":"https://doi.org/10.1088/2631-8695/ae3272/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-31T23:57:44Z","doi":"10.1088/2631-8695/ae3272/v1/review2","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1002/9781394338726.index","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.index","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.index","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1109/globecom59602.2025.11432328","name":"Feature Reconstruction Aided Federated Learning for Image Semantic Communication","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom59602.2025.11432328","authors":["Yoon Huh","Bumjun Kim","Wan Choi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-19T20:04:01Z","doi":"10.1109/globecom59602.2025.11432328","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1145/3737899.3768528","name":"FedHO: Memory-Efficient Federated Fine-Tuning for Large Models via Hybrid Gradient Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3737899.3768528","authors":["Yinan Zhang","Jiannong Cao","Mingjin Zhang","Ruosong Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-02T17:01:43Z","doi":"10.1145/3737899.3768528","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.2139/ssrn.5230237","name":"Toward Secure IoT Infrastructure: Integrating Zero Trust, Federated Learning, and Dynamic Trust Management Models","source":"crossref","abstract":"With the proliferation of Internet of Things (IoT) devices across industrial, healthcare, and consumer domains, the demand for secure, scalable, and intelligent infrastructure has surged. This paper presents an in-depth synthesis of 29 high-impact research contributions focusing on the integration of Zero Trust architectures, Federated Learning, and Dynamic Trust Management Models for securing IoT ecosystems. The study categorizes key themes including cybersecurity frameworks, AI-augmented anomaly detection, edge computing optimization, and AI-driven software development. By analyzing the convergence of AI with decentralized architectures and trustless communication, we highlight how hybrid deep learning frameworks, blockchain, and quantum computing are reshaping security paradigms. Graphical analysis of research trends across thematic areas and author contributions is also provided. The findings reinforce that the fusion of intelligent diagnostics with resilient trust frameworks offers a foundational blueprint for the future of secure digital ecosystems.","url":"https://doi.org/10.2139/ssrn.5230237","authors":["Vamsi Krishna Kokku"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-12T04:41:52Z","doi":"10.2139/ssrn.5230237","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.22541/au.176463749.98828835/v1","name":"Robust and Privacy-Preserving Feature Selection: A Permutation-Invariant Representation Learning Approach with Federated Extension","source":"crossref","abstract":"","url":"https://doi.org/10.22541/au.176463749.98828835/v1","authors":["Stchingtana Naryso"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-02T01:05:00Z","doi":"10.22541/au.176463749.98828835/v1","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.1098/rsos.250959/v1/decision1","name":"Decision letter for \"Breaking Interprovincial Data Silos: How Federated Learning Can Unlock Canada’s Public Health Potential\"","source":"crossref","abstract":"","url":"https://doi.org/10.1098/rsos.250959/v1/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-14T21:10:36Z","doi":"10.1098/rsos.250959/v1/decision1","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.31854/2307-1303-2025-13-2-52-68","name":"Privacy-Preserving Federated Learning: Balancing Accuracy and Data Protection in Distributed Machine Learning","source":"crossref","abstract":"Problem statement. With the growing volume of sensitive data and stricter requirements for their protection, traditional centralized machine learning methods are becoming unacceptable due to the risks of leaks and breaches of confidentiality. This problem is particularly acute in areas such as healthcare and finance, where the transfer of personal data to a central server is unacceptable. One of the promising solutions is federated learning, which allows global models to be trained without transferring source data, but maintaining a balance between model accuracy and privacy remains a key challenge. Methods. To solve the problem, an approach is proposed that combines the FedAvg aggregation algorithm with differential privacy mechanisms, including trimming gradients and adding Gaussian noise on the client side. Experimental validation was performed on the MNIST dataset using a convolutional neural network with various DP parameters. Results. With optimal settings (σ=0.5, ε≈3), 97.80% accuracy was achieved, which is only 1 % inferior to centralized training (98.79 %). Secure aggregation with 10 clients over 5 rounds showed an accuracy of 93.21 %. The analysis revealed a clear dependence of accuracy on privacy parameters, which allows you to flexibly customize the system to meet specific requirements. Practical significance. The proposed methodology provides a transparent and reproducible assessment of the “accuracy-privacy” compromise, which makes it applicable for implementation in real systems with sensitive data. The results can be used as a basis for adapting PHI in medical, financial, and other mission-critical applications where confidentiality is a priority.","url":"https://doi.org/10.31854/2307-1303-2025-13-2-52-68","authors":["Malik A. M. Alsweity","Zlata Kim","Daniil Marshev"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-09T06:58:15Z","doi":"10.31854/2307-1303-2025-13-2-52-68","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.5281/zenodo.19210683","name":"Compression as a Structural Precondition: Memory-Security Co-Design for Post-Quantum Federated AI at the Edge (Mnemosyne Part III)","source":"datacite","abstract":"This work presents Part III of the Mnemosyne system design — a post-quantum secure federated learning architecture targeting deployment on 2 GB edge devices. This part establishes that compression is not a post-hoc optimisation but a structural precondition for system existence at the edge: a 7-billion-parameter language model that cannot be deployed on a 2 GB device does not operate at reduced capacity — it does not exist at the edge. The central contribution is the Distortion-Utility-Security (D-U-S) framework, which jointly optimises over Soft-ZCA whitening strength (ε) and quantisation distortion (D) to derive the achievable Pareto frontier for downstream utility (U), subject to the hard constraint H∞^δ ≥ 804 bits — a security floor anchored to physical constants via the Margolus-Levitin quantum speed limit and Lloyd's universal computational bound. This constitutes the first three-dimensional compression-privacy design space in the federated learning literature, absent from prior frameworks including JoPEQ, CEPAM, and the perception-distortion framework of Blau and Michaeli (ICML 2019). Four original contributions are presented: (1) The D-U-S framework and b_e-parameterised security ceiling characterisation, unifying four standard embedding precisions (b_e ∈ {2, 4, 8, 16}) within a continuous analytical framework and establishing that the security constraint is naturally satisfied with 80× margin at b_e = 16 and retains full difficulty at b_e = 2. (2) Security semantics for Key-Dependent Quantisation Centroids (KDC), a novel mechanism that perturbs Product Quantisation centroid positions as a function of a secret key, targeting H∞^δ maximisation — departing fundamentally from the distortion-minimisation objectives of GPTQ, AWQ, and the rotation family (SmoothQuant, QuaRot, SpinQuant). (3) Stochastic Ternary Whitening (STW) — to our knowledge the first whitening result on the ternary lattice {−1, 0, +1}^d satisfying H∞^δ ≥ 804 and BitLinear arithmetic compatibility simultaneously, using discrete optimal transport via the Sinkhorn algorithm. (4) The first explicit security impact analysis of KV cache quantisation methods (Atom, PolarQuant, QJL) on the system-level security parameter H∞^δ, with scenario-specific analysis across b_KV ∈ {1, 4, 8, 16}. The work additionally demonstrates that standard quantisation methods are privacy-adversarial by design — GPTQ and AWQ concentrate statistical mass at quantisation centroids targeting MSE minimisation at the measurable cost of reducing conditional entropy κ, creating information leakage hotspots — and that ZCA whitening geometrically inverts this relationship by equalising variance across PQ subspaces to maximise residual entropy. This part belongs to an information-theoretic security track unrepresented in existing federated learning compression-privacy classifications, replacing differential privacy parameters (ε_dp, δ_dp) with smooth conditional min-entropy H∞^δ as the security measure — a substitution motivated not by preference but by the failure of DP to provide worst-case guarantees over embedding vector residual entropy, as experimentally confirmed by ALGEN (ACL 2025) and LAGO (arXiv:2505.16008, 2025).","url":"https://doi.org/10.5281/zenodo.19210683","authors":["Bo Jun, Han"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19210683","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.5281/zenodo.19210684","name":"Compression as a Structural Precondition: Memory-Security Co-Design for Post-Quantum Federated AI at the Edge (Mnemosyne Part III)","source":"datacite","abstract":"This work presents Part III of the Mnemosyne system design — a post-quantum secure federated learning architecture targeting deployment on 2 GB edge devices. This part establishes that compression is not a post-hoc optimisation but a structural precondition for system existence at the edge: a 7-billion-parameter language model that cannot be deployed on a 2 GB device does not operate at reduced capacity — it does not exist at the edge. The central contribution is the Distortion-Utility-Security (D-U-S) framework, which jointly optimises over Soft-ZCA whitening strength (ε) and quantisation distortion (D) to derive the achievable Pareto frontier for downstream utility (U), subject to the hard constraint H∞^δ ≥ 804 bits — a security floor anchored to physical constants via the Margolus-Levitin quantum speed limit and Lloyd's universal computational bound. This constitutes the first three-dimensional compression-privacy design space in the federated learning literature, absent from prior frameworks including JoPEQ, CEPAM, and the perception-distortion framework of Blau and Michaeli (ICML 2019). Four original contributions are presented: (1) The D-U-S framework and b_e-parameterised security ceiling characterisation, unifying four standard embedding precisions (b_e ∈ {2, 4, 8, 16}) within a continuous analytical framework and establishing that the security constraint is naturally satisfied with 80× margin at b_e = 16 and retains full difficulty at b_e = 2. (2) Security semantics for Key-Dependent Quantisation Centroids (KDC), a novel mechanism that perturbs Product Quantisation centroid positions as a function of a secret key, targeting H∞^δ maximisation — departing fundamentally from the distortion-minimisation objectives of GPTQ, AWQ, and the rotation family (SmoothQuant, QuaRot, SpinQuant). (3) Stochastic Ternary Whitening (STW) — to our knowledge the first whitening result on the ternary lattice {−1, 0, +1}^d satisfying H∞^δ ≥ 804 and BitLinear arithmetic compatibility simultaneously, using discrete optimal transport via the Sinkhorn algorithm. (4) The first explicit security impact analysis of KV cache quantisation methods (Atom, PolarQuant, QJL) on the system-level security parameter H∞^δ, with scenario-specific analysis across b_KV ∈ {1, 4, 8, 16}. The work additionally demonstrates that standard quantisation methods are privacy-adversarial by design — GPTQ and AWQ concentrate statistical mass at quantisation centroids targeting MSE minimisation at the measurable cost of reducing conditional entropy κ, creating information leakage hotspots — and that ZCA whitening geometrically inverts this relationship by equalising variance across PQ subspaces to maximise residual entropy. This part belongs to an information-theoretic security track unrepresented in existing federated learning compression-privacy classifications, replacing differential privacy parameters (ε_dp, δ_dp) with smooth conditional min-entropy H∞^δ as the security measure — a substitution motivated not by preference but by the failure of DP to provide worst-case guarantees over embedding vector residual entropy, as experimentally confirmed by ALGEN (ACL 2025) and LAGO (arXiv:2505.16008, 2025).","url":"https://doi.org/10.5281/zenodo.19210684","authors":["Bo Jun, Han"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19210684","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.5281/zenodo.19437786","name":"Machine Learning For Cloud Workload Scheduling Optimization","source":"datacite","abstract":"As cloud computing infrastructures transition from passive resource providers to \\\\\\\"Intelligent Clouds,\\\\\\\" the complexity of managing heterogeneous, bursty, and globally distributed workloads has rendered traditional heuristic scheduling insufficient. This review examines the paradigm shift toward machine learning-driven optimization for cloud workload scheduling. We analyze the evolution from static rule-based systems to autonomous, data-driven frameworks that leverage reinforcement learning, deep neural networks, and multi-agent systems. The article categorizes contemporary ML-based scheduling techniques, evaluates their performance against multi-objective criteria—such as energy efficiency, Service Level Agreement (SLA) compliance, and cost—and identifies critical bottlenecks like model drift and interpretability. By synthesizing recent breakthroughs in 2025 and 2026, including the rise of \\\\\\\"Agentic AI\\\\\\\" in resource orchestration and federated learning for privacy-preserving scheduling, this review provides a roadmap for researchers and practitioners aiming to navigate the complexities of next-generation autonomous cloud environments.","url":"https://doi.org/10.5281/zenodo.19437786","authors":["Nimal Perera"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.19437786","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.5281/zenodo.19437787","name":"Machine Learning For Cloud Workload Scheduling Optimization","source":"datacite","abstract":"As cloud computing infrastructures transition from passive resource providers to \\\\\\\"Intelligent Clouds,\\\\\\\" the complexity of managing heterogeneous, bursty, and globally distributed workloads has rendered traditional heuristic scheduling insufficient. This review examines the paradigm shift toward machine learning-driven optimization for cloud workload scheduling. We analyze the evolution from static rule-based systems to autonomous, data-driven frameworks that leverage reinforcement learning, deep neural networks, and multi-agent systems. The article categorizes contemporary ML-based scheduling techniques, evaluates their performance against multi-objective criteria—such as energy efficiency, Service Level Agreement (SLA) compliance, and cost—and identifies critical bottlenecks like model drift and interpretability. By synthesizing recent breakthroughs in 2025 and 2026, including the rise of \\\\\\\"Agentic AI\\\\\\\" in resource orchestration and federated learning for privacy-preserving scheduling, this review provides a roadmap for researchers and practitioners aiming to navigate the complexities of next-generation autonomous cloud environments.","url":"https://doi.org/10.5281/zenodo.19437787","authors":["Nimal Perera"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.19437787","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.26233/heallink.tuc.105432","name":"Investigating and developing efficient Federated Learning for air pollution prediction","source":"datacite","abstract":"Air pollution has long posed a major public health threat in the region of Western Macedonia in Greece, a threat that is attributed to lignite mining and heavily polluted gases that are emitted during lignite burning to produce electricity. While modern Internet of Things (IoT) devices offer granular monitoring capabilities, conventional centralized machine learning approaches used for air quality prediction struggle with data privacy and major bandwidth concerns when scaling to large amounts of collected data. This thesis proposes and evaluates a privacy preserving Federated Learning (FL) Framework for forecasting PM2.5 concentration in the atmosphere. Making use of a Long Short-Term Memory (LSTM) architecture, the model was trained across six different atmospheric measuring stations located in Amyntaio, Filotas, Koilada, Kato Komi, Petrana and Pontokomi, using historical hourly data spanning 2019-2025. Experimental results showcase that the Federated LSTM model approach achieves a Mean Absolute Error (MAE) of 1.75 μg/m³. Comparing that to the centralized approach (MAE 1.41 μg/m³) we observe a trade-off of approximately 0.34 μg/m³. While slightly higher, the model's performance remains comparable to the centralized 'gold standard' (MAE 1.41 μg/m³). Furthermore, comparing the LSTM model against GRU and SimpleRNN architectures helps us confirm LSTM as the superior model as it provides more stability during the aggregation process with a standard deviation of 0.019. In terms of network efficiency, the study establishes that while FL provides neutral bandwidth costs for hourly reporting, it yields a theoretical bandwidth reduction of 98.1% if we were to switch to minute level measurements. These findings confirm that Federated Learning offers a scalable, privacy-compliant solution for environmental monitoring in heterogeneous industrial regions.","url":"https://doi.org/10.26233/heallink.tuc.105432","authors":["Evangelopoulos Georgios","Ευαγγελοπουλος Γεωργιος"],"tags":["Federated learning (Machine learning)","http://id.loc.gov/authorities/subjects/sh2024001385"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.26233/heallink.tuc.105432","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.48550/arxiv.2603.30004","name":"From Patterns to Policy: A Scoping Review Based on Bibliometric Analysis (ScoRBA) of Intelligent and Secure Smart Hospital Ecosystems","source":"datacite","abstract":"This study examines the evolution of Intelligent and Secure Smart Hospital Ecosystems using a Scoping Review with Bibliometric Analysis (ScoRBA) to map research patterns, identify gaps, and derive policy implications. Analyzing 891 journal articles from Scopus (2006-2025) through co-occurrence analysis, network visualization, overlay analysis, and the Enhanced Strategic Diagram (ESD), the study applies the PAGER framework to link Patterns, Advances, Gaps, Research directions, and Evidence-based policy implications. Findings reveal three interrelated clusters: AI-driven intelligent healthcare systems, decentralized privacy-preserving digital health ecosystems, and scalable cloud-edge infrastructures, showing a convergence toward integrated ecosystem architectures where intelligence, trust, and infrastructure reinforce each other. Despite progress in AI, blockchain, and cloud computing, gaps remain in interoperability, real-world implementation, governance, and cross-layer integration. Emerging themes such as explainable AI, federated learning, and privacy mechanisms highlight areas needing further research. Policy-relevant recommendations focus on coordinated governance, scalable infrastructure, and secure data ecosystems, particularly for developing country contexts. The study bridges bibliometric evidence with actionable policies, supporting informed decision-making in smart hospital development.","url":"https://doi.org/10.48550/arxiv.2603.30004","authors":["Wijaya, Adi","Hermawan, Budi","Baihaqi, Wiga Maulana","Supriyanto, Catur"],"tags":["Neurons and Cognition (q-bio.NC)","Computers and Society (cs.CY)","FOS: Biological sciences","FOS: Biological sciences","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.30004","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.6084/m9.figshare.30543524","name":"<b>A Comprehensive Survey on Federated Learning</b><b>and Decentralised Access Coordination for Machine</b><b>Learning. </b>Madhu Nagraj<sup>1</sup>, Vignesh T D<sup>1</sup>, Ranjitha<sup>1</sup>, Ananya Raj MN<sup>1</sup>, Bharath N<sup>1</sup>","source":"datacite","abstract":"Federated Learning (FL) has become a core strategy for training machine-learning models over sensitive and continuously expanding data without centralizing raw records. This survey consolidates current (2022–2025) technical directions across horizontal, vertical, and transfer-based FL, with emphasis on blockchain-backed coordination and DAO-style governance. We outline optimization fundamentals (e.g., FedAvg), performance tradeoffs, and deployment characteristics in IoT, finance, healthcare, and industrial systems driven by high-volume, high-velocity, and non-IID data. Continuous data growth, inter-institution dependencies, and privacy regulation have accelerated adoption of secure aggregation, differential privacy, and cryptographic protocols. The review highlights how decentralized mechanisms enhance auditability, fairness, and trust, while introducing new operational burdens such as communication overhead, bias propagation, participation imbalance, and legal constraints. We further examine access coordination models using blockchain and DAO governance, where smart-contract-based control enables transparent model update logging, incentive mechanisms, and distributed decision-making. The work concludes that scalable privacy guarantees, policy-aligned data governance, and incentive-aligned coordination are pivotal for next-generation FL ecosystems across high-risk and data-dense environments.","url":"https://doi.org/10.6084/m9.figshare.30543524","authors":["T D, Vignesh"],"tags":["Numerical computation and mathematical software"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.6084/m9.figshare.30543524","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.5281/zenodo.19239965","name":"Federated Predictive Analytics in Finance: Privacy-Preserving Churn and Fraud Forecasting Across Institutions","source":"datacite","abstract":"Abstract The escalating adoption of machine learning in financial services is impeded by regulatory constraints on data sharing and the fragmentation of customer data across competing institutions. This article investigates how federated learning (FL) architectures—specifically FedProx with differential privacy stochastic gradient descent (DP-SGD) and secure aggregation—can enable collaborative churn and fraud prediction without the transfer of raw data. Using simulated multi-institutional banking benchmarks across five institutions (2025–2026), our experiments demonstrate that federated models achieve AUC-ROC improvements of 1.2–2.4 percentage points over single-institution baselines, while maintaining privacy budgets below ε = 4.5 and reducing demographic parity gaps by approximately 48–49% relative to pre-federation levels. Regulatory alignment with GDPR Article 25, CCPA, and DORA's operational resilience mandates is assessed using structured compliance audits. Our findings indicate that post-2025 federated architectures offer a viable pathway for collaborative financial analytics, substantially improving predictive performance and fairness while preserving institutional data sovereignty. Keywords: federated learning, churn prediction, fraud detection, differential privacy, secure aggregation, regulatory compliance, bias reduction, financial analytics, GDPR, DORA","url":"https://doi.org/10.5281/zenodo.19239965","authors":["ANGEL JOSEPH,  Dency D, Abel Jopaul V P and Dr Mohammad Irshad V K"],"tags":["federated learning, churn prediction, fraud detection, differential privacy, secure aggregation, regulatory compliance, bias reduction, financial analytics, GDPR, DORA"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19239965","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.5281/zenodo.19239966","name":"Federated Predictive Analytics in Finance: Privacy-Preserving Churn and Fraud Forecasting Across Institutions","source":"datacite","abstract":"Abstract The escalating adoption of machine learning in financial services is impeded by regulatory constraints on data sharing and the fragmentation of customer data across competing institutions. This article investigates how federated learning (FL) architectures—specifically FedProx with differential privacy stochastic gradient descent (DP-SGD) and secure aggregation—can enable collaborative churn and fraud prediction without the transfer of raw data. Using simulated multi-institutional banking benchmarks across five institutions (2025–2026), our experiments demonstrate that federated models achieve AUC-ROC improvements of 1.2–2.4 percentage points over single-institution baselines, while maintaining privacy budgets below ε = 4.5 and reducing demographic parity gaps by approximately 48–49% relative to pre-federation levels. Regulatory alignment with GDPR Article 25, CCPA, and DORA's operational resilience mandates is assessed using structured compliance audits. Our findings indicate that post-2025 federated architectures offer a viable pathway for collaborative financial analytics, substantially improving predictive performance and fairness while preserving institutional data sovereignty. Keywords: federated learning, churn prediction, fraud detection, differential privacy, secure aggregation, regulatory compliance, bias reduction, financial analytics, GDPR, DORA","url":"https://doi.org/10.5281/zenodo.19239966","authors":["ANGEL JOSEPH,  Dency D, Abel Jopaul V P and Dr Mohammad Irshad V K"],"tags":["federated learning, churn prediction, fraud detection, differential privacy, secure aggregation, regulatory compliance, bias reduction, financial analytics, GDPR, DORA"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19239966","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.48550/arxiv.2603.24601","name":"FED-HARGPT: A Hybrid Centralized-Federated Approach of a Transformer-based Architecture for Human Context Recognition","source":"datacite","abstract":"The study explores a hybrid centralized-federated approach for Human Activity Recognition (HAR) using a Transformer-based architecture. With the increasing ubiquity of edge devices, such as smartphones and wearables, a significant amount of private data from wearable and inertial sensors is generated, facilitating discreet monitoring of human activities, including resting, sleeping, and walking. This research focuses on deploying HAR technologies using mobile sensor data and leveraging Federated Learning within the Flower framework to evaluate the training of a federated model derived from a centralized baseline. The experimental results demonstrate the effectiveness of the proposed hybrid approach in improving the accuracy and robustness of HAR models while preserving data privacy in a non-IID data scenario. The federated learning setup demonstrated comparable performance to centralized models, highlighting the potential of federated learning to strike a balance between data privacy and model performance in real-world applications.","url":"https://doi.org/10.48550/arxiv.2603.24601","authors":["Gibaut, Wandemberg","Osorio, Alexandre","Munoz, Amparo","Neto, Sildolfo F. G.","Grassiotto, Fabio"],"tags":["Signal Processing (eess.SP)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.24601","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.17605/osf.io/jxbms","name":"Artificial Intelligence and Radiomics for Differentiating Pseudoprogression from True Progression in High-Grade Gliomas: A Meta-Analysis","source":"datacite","abstract":"Differentiating true progression (TP) from pseudoprogression (PsP) in high-grade gliomas (HGGs) on MRI is a critical clinical challenge. This meta-analysis evaluates the overall diagnostic accuracy of radiomics and artificial intelligence (AI) models to identify algorithmic determinants of optimal performance. A PRISMA-compliant search of PubMed, Ovid MEDLINE, and EMBASE (up to 2025) was conducted. Quality was assessed via QUADAS-2. Diagnostic metrics were pooled utilizing bivariate random-effects models and Summary Receiver Operating Characteristic (SROC) curves. Across 34 included studies, the overall pooled sensitivity and specificity were 82% and 79%, respectively. Shape-based and Deep Learning (DL) features achieved the highest Area Under the Curve (up to 0.96 and 0.95). First-order statistics and Gray-Level Co-occurrence Matrix (GLCM) yielded the highest consistency and accuracy peaks (up to 98%). Multiparametric MRI was the most robust and widely used imaging modality. Radiomics demonstrates high, reliable diagnostic potential for discriminating HGG PsP from TP. While advanced DL models and shape features maximize discriminative performance, classical handcrafted features remain the most extensively validated. Future studies must prioritize independent external validation, federated learning (FL), and Explainable AI (XAI) for clinical translation.","url":"https://doi.org/10.17605/osf.io/jxbms","authors":["Facchinetti, Giovanni","De Maria, Lucio","Pagani, Nicola","Ponzio, Francesco"],"tags":["Radiology","Medicine and Health Sciences","Medical Specialties","Oncology","Neurology","Artificial Intelligence","High-Grade Gliomas","MRI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17605/osf.io/jxbms","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.48550/arxiv.2505.11191","name":"Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integration","source":"datacite","abstract":"As embodied AI systems become increasingly multi-modal, personalized, and interactive, they must learn effectively from diverse sensory inputs, adapt continually to user preferences, and operate safely under resource and privacy constraints. These challenges expose a pressing need for machine learning models capable of swift, context-aware adaptation while balancing model generalization and personalization. Here, two methods emerge as suitable candidates, each offering parts of these capabilities: multi-modal multi-task foundation models (M3T-FMs) provide a pathway toward generalization across tasks and modalities, whereas federated learning (FL) offers the infrastructure for distributed, privacy-preserving model updates and user-level model personalization. However, when used in isolation, each of these approaches falls short of meeting the complex and diverse capability requirements of real-world embodied AI environments. In this vision paper, we introduce multi-modal multi-task federated foundation models (M3T-FFMs) for embodied AI, a new paradigm that unifies the strengths of M3T-FMs with the privacy-preserving distributed training nature of FL, enabling intelligent systems at the wireless edge. We collect critical deployment dimensions of M3T-FFMs in embodied AI ecosystems under a unified framework, which we name \"EMBODY\": Embodiment heterogeneity, Modality richness and imbalance, Bandwidth and compute constraints, On-device continual learning, Distributed control and autonomy, and Yielding safety, privacy, and personalization. For each, we identify concrete challenges and envision actionable research directions. We also present an evaluation framework for deploying M3T-FFMs in embodied AI systems, along with the associated trade-offs. Finally, we present a prototype implementation of M3T-FFMs and evaluate their energy and latency performance.","url":"https://doi.org/10.48550/arxiv.2505.11191","authors":["Borazjani, Kasra","Abdisarabshali, Payam","Nadimi, Fardis","Khosravan, Naji","Liwang, Minghui","Wang, Xianbin","Hong, Yiguang","Hosseinalipour, Seyyedali"],"tags":["Artificial Intelligence (cs.AI)","Robotics (cs.RO)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2505.11191","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.48550/arxiv.2509.00402","name":"Curriculum Guided Personalized Subgraph Federated Learning","source":"datacite","abstract":"Subgraph Federated Learning (FL) aims to train Graph Neural Networks (GNNs) across distributed private subgraphs, but it suffers from severe data heterogeneity. To mitigate data heterogeneity, weighted model aggregation personalizes each local GNN by assigning larger weights to parameters from clients with similar subgraph characteristics inferred from their current model states. However, the sparse and biased subgraphs often trigger rapid overfitting, causing the estimated client similarity matrix to stagnate or even collapse. As a result, aggregation loses effectiveness as clients reinforce their own biases instead of exploiting diverse knowledge otherwise available. To this end, we propose a novel personalized subgraph FL framework called Curriculum guided personalized sUbgraph Federated Learning (CUFL). On the client side, CUFL adopts Curriculum Learning (CL) that adaptively selects edges for training according to their reconstruction scores, exposing each GNN first to easier, generic cross-client substructures and only later to harder, client-specific ones. This paced exposure prevents early overfitting to biased patterns and enables gradual personalization. By regulating personalization, the curriculum also reshapes server aggregation from exchanging generic knowledge to propagating client-specific knowledge. Further, CUFL improves weighted aggregation by estimating client similarity using fine-grained structural indicators reconstructed on a random reference graph. Extensive experiments on six benchmark datasets confirm that CUFL achieves superior performance compared to relevant baselines. Code is available at https://github.com/Kang-Min-Ku/CUFL.git.","url":"https://doi.org/10.48550/arxiv.2509.00402","authors":["Kang, Minku","Park, Hogun"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.00402","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.48550/arxiv.2603.16881","name":"Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networks","source":"datacite","abstract":"Multi-agent deep learning (MADL), including multi-agent deep reinforcement learning (MADRL), distributed/federated training, and graph-structured neural networks, is becoming a unifying framework for decision-making and inference in wireless systems where sensing, communication, and computing are tightly coupled. Recent 5G-Advanced and 6G visions strengthen this coupling through integrated sensing and communication, edge intelligence, open programmable RAN, and non-terrestrial/UAV networking, which create decentralized, partially observed, time-varying, and resource-constrained control problems. This survey synthesizes the state of the art, with emphasis on 2021-2025 research, on MADL for distributed sensing and wireless communications. We present a task-driven taxonomy across (i) learning formulations (Markov games, Dec-POMDPs, CTDE), (ii) neural architectures (GNN-based radio resource management, attention-based policies, hierarchical learning, and over-the-air aggregation), (iii) advanced techniques (federated reinforcement learning, communication-efficient federated deep RL, and serverless edge learning orchestration), and (iv) application domains (MEC offloading with slicing, UAV-enabled heterogeneous networks with power-domain NOMA, intrusion detection in sensor networks, and ISAC-driven perceptive mobile networks). We also provide comparative tables of algorithms, training topologies, and system-level trade-offs in latency, spectral efficiency, energy, privacy, and robustness. Finally, we identify open issues including scalability, non-stationarity, security against poisoning and backdoors, communication overhead, and real-time safety, and outline research directions toward 6G-native sense-communicate-compute-learn systems.","url":"https://doi.org/10.48550/arxiv.2603.16881","authors":["Muller, Nadine","DeRosa, Stefano","Zhang, Su","Huan, Chun Lee"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.16881","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.25394/pgs.31415294.v1","name":"PROTECTING THE PRIVACY OF PROTECTED HEALTH INFORMATION (PHI) IN THE ERA OF AI-ASSISTED HEALTHCARE: A SYSTEMATIC REVIEW","source":"datacite","abstract":"AI is being used to support many aspects of healthcare from drug discovery to AI-assisted surgery to remote monitoring of patient vitals. Because of AI’s powerful potential to do harm at scale, it is really important that guardrails and guidelines be put into place to govern its use. Implementing AI and Data Governance is a way to put those ethical guardrails into place. This is particularly important for protecting the privacy of patient information, known as Protected Health Information (PHI) in the United States. This dissertation explores the roles that AI and Data Governance play in protecting patient privacy, and how that has been implemented through technical means like Blockchain, Differential Privacy (DP), Federated Learning (FL), and Homomorphic Encryption. There is a strong focus on biomedical ethics and the role it plays in ensuring that patient information is kept private and confidential. Regulatory compliance regarding AI is also another mechanism used to govern the use of AI in healthcare. This is a systematic review of the literature using the PRISMA 2020 framework. Articles were selected in these various databases from 2022 to April 2025. The focus was on English language articles covering primarily the U.S., U.K., and European Union (EU), although a few exceptions were made to this geographic limitation if the articles seemed pertinent to the research at hand. What the research found was that there are three primary ways in which patient privacy is protected. These include technical means for enforcing privacy, legal and regulatory means for enforcing privacy, and governance/ethical means for securing patient privacy. This dissertation goes into detail on each of these paths and explores possible future research that could contribute to these privacy discussions.","url":"https://doi.org/10.25394/pgs.31415294.v1","authors":["Adams, Timothy Allen"],"tags":["Artificial intelligence not elsewhere classified"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.25394/pgs.31415294.v1","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.25394/pgs.31415294","name":"PROTECTING THE PRIVACY OF PROTECTED HEALTH INFORMATION (PHI) IN THE ERA OF AI-ASSISTED HEALTHCARE: A SYSTEMATIC REVIEW","source":"datacite","abstract":"AI is being used to support many aspects of healthcare from drug discovery to AI-assisted surgery to remote monitoring of patient vitals. Because of AI’s powerful potential to do harm at scale, it is really important that guardrails and guidelines be put into place to govern its use. Implementing AI and Data Governance is a way to put those ethical guardrails into place. This is particularly important for protecting the privacy of patient information, known as Protected Health Information (PHI) in the United States. This dissertation explores the roles that AI and Data Governance play in protecting patient privacy, and how that has been implemented through technical means like Blockchain, Differential Privacy (DP), Federated Learning (FL), and Homomorphic Encryption. There is a strong focus on biomedical ethics and the role it plays in ensuring that patient information is kept private and confidential. Regulatory compliance regarding AI is also another mechanism used to govern the use of AI in healthcare. This is a systematic review of the literature using the PRISMA 2020 framework. Articles were selected in these various databases from 2022 to April 2025. The focus was on English language articles covering primarily the U.S., U.K., and European Union (EU), although a few exceptions were made to this geographic limitation if the articles seemed pertinent to the research at hand. What the research found was that there are three primary ways in which patient privacy is protected. These include technical means for enforcing privacy, legal and regulatory means for enforcing privacy, and governance/ethical means for securing patient privacy. This dissertation goes into detail on each of these paths and explores possible future research that could contribute to these privacy discussions.","url":"https://doi.org/10.25394/pgs.31415294","authors":["Adams, Timothy Allen"],"tags":["Artificial intelligence not elsewhere classified"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.25394/pgs.31415294","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2412.18507","name":"An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack","source":"datacite","abstract":"In this paper, we empirically analyze adversarial attacks on selected federated learning models. The specific learning models considered are Multinominal Logistic Regression (MLR), Support Vector Classifier (SVC), Multilayer Perceptron (MLP), Convolution Neural Network (CNN), %Recurrent Neural Network (RNN), Random Forest, XGBoost, and Long Short-Term Memory (LSTM). For each model, we simulate label-flipping attacks, experimenting extensively with 10 federated clients and 100 federated clients. We vary the percentage of adversarial clients from 10% to 100% and, simultaneously, the percentage of labels flipped by each adversarial client is also varied from 10% to 100%. Among other results, we find that models differ in their inherent robustness to the two vectors in our label-flipping attack, i.e., the percentage of adversarial clients, and the percentage of labels flipped by each adversarial client. We discuss the potential practical implications of our results.","url":"https://doi.org/10.48550/arxiv.2412.18507","authors":["Bhatnagar, Kunal","Chattanathan, Sagana","Dang, Angela","Eranki, Bhargav","Rana, Ronnit","Sridhar, Charan","Vedam, Siddharth","Yao, Angie","Stamp, Mark"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2412.18507","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.48550/arxiv.2509.08372","name":"Rethinking the Backbone in Class Imbalanced Federated Source Free Domain Adaptation: The Utility of Vision Foundation Models","source":"datacite","abstract":"Federated Learning (FL) offers a framework for training models collaboratively while preserving data privacy of each client. Recently, research has focused on Federated Source-Free Domain Adaptation (FFREEDA), a more realistic scenario wherein client-held target domain data remains unlabeled, and the server can access source domain data only during pre-training. We extend this framework to a more complex and realistic setting: Class Imbalanced FFREEDA (CI-FFREEDA), which takes into account class imbalances in both the source and target domains, as well as label shifts between source and target and among target clients. The replication of existing methods in our experimental setup lead us to rethink the focus from enhancing aggregation and domain adaptation methods to improving the feature extractors within the network itself. We propose replacing the FFREEDA backbone with a frozen vision foundation model (VFM), thereby improving overall accuracy without extensive parameter tuning and reducing computational and communication costs in federated learning. Our experimental results demonstrate that VFMs effectively mitigate the effects of domain gaps, class imbalances, and even non-IID-ness among target clients, suggesting that strong feature extractors, not complex adaptation or FL methods, are key to success in the real-world FL.","url":"https://doi.org/10.48550/arxiv.2509.08372","authors":["Kihara, Kosuke","Mori, Junki","Miyagawa, Taiki","Ebihara, Akinori F."],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.08372","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.5281/zenodo.19016419","name":"Liver Cancer Risk Prediction","source":"datacite","abstract":"The dataset contains 465,605 visit-level records corresponding to 15,500 individuals monitored across 12 distributed healthcare nodes between January 2021 and December 2025. Each record integrates heterogeneous clinical, behavioral, physiological, imaging-derived, and temporal variables reflecting comprehensive liver-health status. The dataset is organized into multiple feature groups, including demographic attributes, lifestyle indicators, comorbidity profiles, liver function biomarkers, tumor-associated biomarkers, chronic liver disease markers, wearable physiological measurements, imaging-derived descriptors, and temporal progression indicators. This multimodal structure enables the development and evaluation of advanced machine-learning, deep-learning, and federated-learning models for predictive analytics in smart healthcare environments. Two primary prediction targets are provided: Liver_Cancer_Status — binary classification indicating the presence or absence of hepatocellular carcinoma Time_to_HCC_Onset_Months — continuous regression variable representing progression time The dataset contains 465,605 visit-level records corresponding to 15,500 individuals monitored across 12 distributed healthcare nodes between January 2021 and December 2025. Each record integrates heterogeneous clinical, behavioral, physiological, imaging-derived, and temporal variables reflecting comprehensive liver-health status. The dataset is organized into multiple feature groups, including demographic attributes, lifestyle indicators, comorbidity profiles, liver function biomarkers, tumor-associated biomarkers, chronic liver disease markers, wearable physiological measurements, imaging-derived descriptors, and temporal progression indicators. This multimodal structure enables the development and evaluation of advanced machine-learning, deep-learning, and federated-learning models for predictive analytics in smart healthcare environments. Two primary prediction targets are provided: Liver_Cancer_Status — binary classification indicating the presence or absence of hepatocellular carcinoma Time_to_HCC_Onset_Months — continuous regression variable representing progression time","url":"https://doi.org/10.5281/zenodo.19016419","authors":["Eric S., Lander"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19016419","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.5281/zenodo.19016420","name":"Liver Cancer Risk Prediction","source":"datacite","abstract":"The dataset contains 465,605 visit-level records corresponding to 15,500 individuals monitored across 12 distributed healthcare nodes between January 2021 and December 2025. Each record integrates heterogeneous clinical, behavioral, physiological, imaging-derived, and temporal variables reflecting comprehensive liver-health status. The dataset is organized into multiple feature groups, including demographic attributes, lifestyle indicators, comorbidity profiles, liver function biomarkers, tumor-associated biomarkers, chronic liver disease markers, wearable physiological measurements, imaging-derived descriptors, and temporal progression indicators. This multimodal structure enables the development and evaluation of advanced machine-learning, deep-learning, and federated-learning models for predictive analytics in smart healthcare environments. Two primary prediction targets are provided: Liver_Cancer_Status — binary classification indicating the presence or absence of hepatocellular carcinoma Time_to_HCC_Onset_Months — continuous regression variable representing progression time The dataset contains 465,605 visit-level records corresponding to 15,500 individuals monitored across 12 distributed healthcare nodes between January 2021 and December 2025. Each record integrates heterogeneous clinical, behavioral, physiological, imaging-derived, and temporal variables reflecting comprehensive liver-health status. The dataset is organized into multiple feature groups, including demographic attributes, lifestyle indicators, comorbidity profiles, liver function biomarkers, tumor-associated biomarkers, chronic liver disease markers, wearable physiological measurements, imaging-derived descriptors, and temporal progression indicators. This multimodal structure enables the development and evaluation of advanced machine-learning, deep-learning, and federated-learning models for predictive analytics in smart healthcare environments. Two primary prediction targets are provided: Liver_Cancer_Status — binary classification indicating the presence or absence of hepatocellular carcinoma Time_to_HCC_Onset_Months — continuous regression variable representing progression time","url":"https://doi.org/10.5281/zenodo.19016420","authors":["Eric S., Lander"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19016420","addedAt":"2026-08-31T06:41:20.712Z","updatedAt":"2026-08-31T06:41:20.712Z"},{"id":"doi:10.5281/zenodo.18941884","name":"Kaizen-Grafting and OAuth2 RAR: A Sustainable Federated Learning Framework for Privacy-Preserving Business Analytics in Resource-Constrained Japanese SME Networks","source":"datacite","abstract":"As we enter 2026, the industrialization of artificial intelligence (AI) has transitioned from an experimental noveltyto the backbone of enterprise operations. However, a significant “Digital Paradox” persists within the Japanese economy:while large corporations leverage high-density liquid cooling and massive GPU clusters, the critical Small and Medium- sizedEnterprise (SME) sector remains hindered by resource constraints, aging infrastructure, and limited access to specializedcomputational talent. This paper proposes Kaizen-RAR, a novel, sustainable federated learning (FL) framework designedspecifically for distributed SME networks characterized by heterogeneous hardware profiles typical of Japanesemanufacturing and retail sectors.We introduce two primary technical innovations: (1) the Kaizen-Grafting (KG) algorithm, which applies incremental,feedback-driven gradient pruning based on real-time local hard- ware telemetry including CPU utilization, memorybandwidth, and thermal throttling indicators; and (2) a fine-grained security protocol leveraging OAuth2 Rich AuthorizationRequests (RAR) to secure gradient transfers at the tensor-layer level while enforcing data sovereignty compliance withJapan’s 2025 AI Act. Experiments conducted using the UCI Online Retail II dataset, partitioned to simulate multi-storetransactional patterns across SME clusters, demonstrate that Kaizen-RAR achieves a 31% reduction in node energyconsumption, a 27% decrease in inference latency, and enhanced model robustness under intermittent connectivity. Ourframework outperforms state-of- the-art baselines by 12% in communication efficiency, providing a scalable pathway forresource-constrained enterprises to achieve “Operational Alpha” in the 2026 digital economy through privacy-preservingcollaborative analytics.","url":"https://doi.org/10.5281/zenodo.18941884","authors":["Kunal Rajneesh Sahal","Divyanshu Nagar","Pragya Vaishnav"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18941884","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18941883","name":"Kaizen-Grafting and OAuth2 RAR: A Sustainable Federated Learning Framework for Privacy-Preserving Business Analytics in Resource-Constrained Japanese SME Networks","source":"datacite","abstract":"As we enter 2026, the industrialization of artificial intelligence (AI) has transitioned from an experimental noveltyto the backbone of enterprise operations. However, a significant “Digital Paradox” persists within the Japanese economy:while large corporations leverage high-density liquid cooling and massive GPU clusters, the critical Small and Medium- sizedEnterprise (SME) sector remains hindered by resource constraints, aging infrastructure, and limited access to specializedcomputational talent. This paper proposes Kaizen-RAR, a novel, sustainable federated learning (FL) framework designedspecifically for distributed SME networks characterized by heterogeneous hardware profiles typical of Japanesemanufacturing and retail sectors.We introduce two primary technical innovations: (1) the Kaizen-Grafting (KG) algorithm, which applies incremental,feedback-driven gradient pruning based on real-time local hard- ware telemetry including CPU utilization, memorybandwidth, and thermal throttling indicators; and (2) a fine-grained security protocol leveraging OAuth2 Rich AuthorizationRequests (RAR) to secure gradient transfers at the tensor-layer level while enforcing data sovereignty compliance withJapan’s 2025 AI Act. Experiments conducted using the UCI Online Retail II dataset, partitioned to simulate multi-storetransactional patterns across SME clusters, demonstrate that Kaizen-RAR achieves a 31% reduction in node energyconsumption, a 27% decrease in inference latency, and enhanced model robustness under intermittent connectivity. Ourframework outperforms state-of- the-art baselines by 12% in communication efficiency, providing a scalable pathway forresource-constrained enterprises to achieve “Operational Alpha” in the 2026 digital economy through privacy-preservingcollaborative analytics.","url":"https://doi.org/10.5281/zenodo.18941883","authors":["Kunal Rajneesh Sahal","Divyanshu Nagar","Pragya Vaishnav"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18941883","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2509.19674","name":"C^2Prompt: Class-aware Client Knowledge Interaction for Federated Continual Learning","source":"datacite","abstract":"Federated continual learning (FCL) tackles scenarios of learning from continuously emerging task data across distributed clients, where the key challenge lies in addressing both temporal forgetting over time and spatial forgetting simultaneously. Recently, prompt-based FCL methods have shown advanced performance through task-wise prompt communication.In this study, we underscore that the existing prompt-based FCL methods are prone to class-wise knowledge coherence between prompts across clients. The class-wise knowledge coherence includes two aspects: (1) intra-class distribution gap across clients, which degrades the learned semantics across prompts, (2) inter-prompt class-wise relevance, which highlights cross-class knowledge confusion. During prompt communication, insufficient class-wise coherence exacerbates knowledge conflicts among new prompts and induces interference with old prompts, intensifying both spatial and temporal forgetting. To address these issues, we propose a novel Class-aware Client Knowledge Interaction (C${}^2$Prompt) method that explicitly enhances class-wise knowledge coherence during prompt communication. Specifically, a local class distribution compensation mechanism (LCDC) is introduced to reduce intra-class distribution disparities across clients, thereby reinforcing intra-class knowledge consistency. Additionally, a class-aware prompt aggregation scheme (CPA) is designed to alleviate inter-class knowledge confusion by selectively strengthening class-relevant knowledge aggregation. Extensive experiments on multiple FCL benchmarks demonstrate that C${}^2$Prompt achieves state-of-the-art performance. Our source code is available at https://github.com/zhoujiahuan1991/NeurIPS2025-C2Prompt","url":"https://doi.org/10.48550/arxiv.2509.19674","authors":["Xu, Kunlun","Feng, Yibo","Li, Jiangmeng","Qi, Yongsheng","Zhou, Jiahuan"],"tags":["Machine Learning (cs.LG)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.19674","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2603.04887","name":"Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentation","source":"datacite","abstract":"Most existing federated learning (FL) methods for medical image analysis only considered intramodal heterogeneity, limiting their applicability to multimodal imaging applications. In practice, some FL participants may possess only a subset of the complete imaging modalities, posing intermodal heterogeneity as a challenge to effectively training a global model on all participants' data. Meanwhile, each participant expects a personalized model tailored to its local data characteristics in FL. This work proposes a new FL framework with federated modality-specific encoders and partially personalized multimodal fusion decoders (FedMEPD) to address the two concurrent issues. Specifically, FedMEPD employs an exclusive encoder for each modality to account for the intermodal heterogeneity. While these encoders are fully federated, the decoders are partially personalized to meet individual needs -- using the discrepancy between global and local parameter updates to dynamically determine which decoder filters are personalized. Implementation-wise, a server with full-modal data employs a fusion decoder to fuse representations from all modality-specific encoders, thus bridging the modalities to optimize the encoders via backpropagation. Moreover, multiple anchors are extracted from the fused multimodal representations and distributed to the clients in addition to the model parameters. Conversely, the clients with incomplete modalities calibrate their missing-modal representations toward the global full-modal anchors via scaled dot-product cross-attention, making up for the information loss due to absent modalities. FedMEPD is validated on the BraTS 2018 and 2020 multimodal brain tumor segmentation benchmarks. Results show that it outperforms various up-to-date methods for multimodal and personalized FL, and its novel designs are effective.","url":"https://doi.org/10.48550/arxiv.2603.04887","authors":["Liu, Hong","Wei, Dong","Dai, Qian","Wu, Xian","Zheng, Yefeng","Wang, Liansheng"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.04887","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.21268/20250808-2","name":"Towards a generic and resource-efficient testbed for federated learning in wireless sensor networks","source":"datacite","abstract":"This paper presents a lightweight, modular and portable testbed for evaluating Federated Learning (FL) on embedded wireless sensor network (WSN) nodes. The testbed is designed for Tiny Edge and Little Edge level devices and supports structured data flow, multi-threaded communication and flexible algorithm integration. It enables simulation of sensor data, local training of models using various artificial intelligence algorithms, and communication between clients and the server. It gives precise control over training conditions, node behavior during model training and the timing of individual operations - facilitating research into realistic FL scenarios in constrained environments. All with the strict rules of embedded programming optimization.","url":"https://doi.org/10.21268/20250808-2","authors":["Turchan, Krzysztof","Wołoszyn, Kamil","Piotrowski, Krzysztof"],"tags":["Federated Learning","Wireless Sensor Networks","Embedded Systems","Distributed Computing","FOS: Computer and information sciences","Energy Efficiency","004"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21268/20250808-2","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.17566696","name":"UCM Framework: 44 Equations+","source":"datacite","abstract":"ABSTRACTTHIS WHITE PAPER ESTABLISHES A COMPREHENSIVE MATHEMATICAL, LOGICAL, AND ENGINEERING-BASED PROOF STRUCTURE FOR THE RHEA-UCM AND ZADEIAN SENTINEL CYBERNETIC SYSTEMS. WE DOCUMENT AND VALIDATE THE 44 CORE EQUATIONS GOVERNING RECURSIVE PERCEPTION, ENTROPY MODULATION, TRUST VECTOR COMPUTATION, MEMORY SEALING, RELATIVISTIC SYSTEM TIMING, ADAPTIVE LEARNING, AND GLYPHIC SYMBOL ENCODING. THE FRAMEWORK ADHERES STRICTLY TO GROUNDED MATHEMATICAL FORMULATIONS, ENSURING TRACEABILITY OF ALL CONSTRUCTS TO PHYSICAL PHENOMENA OR COMPUTABLE MODELS. WE CROSS-VALIDATE EQUATION CHAINS USING INTEGRABLE DERIVATIONS, SIMULATION ARTIFACTS, LOG FILES, ENTROPY MAPS, AND CRYPTOGRAPHIC BENCHMARKS. RHEA IS SHOWN TO OUTPERFORM LEGACY AI/LLM ARCHITECTURES BY EMBEDDING ENTROPY-TRUST CAUSALITY AND BAYESIAN RESONANCE. 🛡RHEA-Core Public Grant v2.1 Authors/Creators Roe, Paul (Rights holder)1, 2 Show affiliations Description 🛡️ RHEA-Core Public Grant v2.1Hardened System Sovereignty EditionIssued By:Paul M. Roe (EnigmaticGlitch)Moniker: SovereignGlitch ♏🧙‍♂️Affiliation: TecKnows, Inc. · ZADEIAN Research DivisionLegal Anchors:• U.S. Copyright Law (Title 17)• U.S. DMCA• Berne Convention / WIPO• EU Directive (EU) 2019/790 — with explicit TDM opt-out• U.S. Patent Filings relating to RHEA-UCM and associated systems (including Provisional Application No. 63/796,404 and any successors)Version: v2.1Date of Effect: December 2025License Type: Non-Commercial · Attribution · No Derivatives · Symbolic Derivative Restriction · AI/TDM Opt-Out · Functional Equivalence Restriction________________________________________🔰 0. Scope of ProtectionThis license governs all intellectual, mathematical, symbolic, semantic, recursive, and systemic artifacts authored by Paul M. Roe / EnigmaticGlitch / SovereignGlitch under (including, but not limited to) the following umbrellas:• RHEA-UCM, RHEA-CM, RHEA-PMR, and all Λ-Gate frameworks• ZADEIAN Sentinel, Zadeian/RHEA-Class modules, RHEA-IC hardware logic• UCM Cosmological Recursion, Recursive Entropy Models, Symbolic Feedback Systems• SOER Constructs, including:o SOER Imperial Codexo Sovereign Glyph Languageo Temporal Glyphic Hash Trees• All published and unpublished materials under:o Zenodo records explicitly associated with RHEA-UCM / ZADEIAN-RHEA / RHEA-CMo GitHub demo repositories related to academic submissions (e.g. SIAM submissions and supporting code)o Any symbolic derivation codebases, diagrams, or formal equations implementing or describing these systemsThis v2.1 grant supersedes the text of RHEA-Core Public Grant v1.0 and v2.0 for all future access and use, subject to the limited clarification in Section 9 (Prior Versions).________________________________________1. License Supremacy & Versioning1.1 Prospective SupremacyAll access, use, citation, embedding, referencing, storage, analysis, or distribution of covered materials occurring on or after the effective date of this v2.1 grant is governed exclusively by RHEA-Core Public Grant v2.1, or any later version explicitly designated by the Rights Holder as superseding it.1.2 Termination of Prior Permissions for Future UseAny permissions that may have been implied or expressed under earlier grants (including RHEA-Core Public Grant v1.0 or v2.0) are revoked for all future use as of the effective date of v2.1, except as preserved in the limited academic reference rights described in Section 9.1.3 Acceptance by AccessBy accessing, downloading, cloning, forking, referencing, or otherwise interacting with the covered materials following the publication of this license, you acknowledge and agree to be bound by the terms of RHEA-Core Public Grant v2.1.________________________________________2. Grant of Use (Non-Commercial, Revocable)You are granted a revocable, non-exclusive, non-transferable, and non-sublicensable right to:• View, read, and privately study the covered materials• Reference them for academic, journalistic, technical, or personal enrichment• Cite them in academic or tec","url":"https://doi.org/10.5281/zenodo.17566696","authors":["Roe, Paul"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17566696","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.17566695","name":"UCM Framework: 44 Equations+","source":"datacite","abstract":"ABSTRACTTHIS WHITE PAPER ESTABLISHES A COMPREHENSIVE MATHEMATICAL, LOGICAL, AND ENGINEERING-BASED PROOF STRUCTURE FOR THE RHEA-UCM AND ZADEIAN SENTINEL CYBERNETIC SYSTEMS. WE DOCUMENT AND VALIDATE THE 44 CORE EQUATIONS GOVERNING RECURSIVE PERCEPTION, ENTROPY MODULATION, TRUST VECTOR COMPUTATION, MEMORY SEALING, RELATIVISTIC SYSTEM TIMING, ADAPTIVE LEARNING, AND GLYPHIC SYMBOL ENCODING. THE FRAMEWORK ADHERES STRICTLY TO GROUNDED MATHEMATICAL FORMULATIONS, ENSURING TRACEABILITY OF ALL CONSTRUCTS TO PHYSICAL PHENOMENA OR COMPUTABLE MODELS. WE CROSS-VALIDATE EQUATION CHAINS USING INTEGRABLE DERIVATIONS, SIMULATION ARTIFACTS, LOG FILES, ENTROPY MAPS, AND CRYPTOGRAPHIC BENCHMARKS. RHEA IS SHOWN TO OUTPERFORM LEGACY AI/LLM ARCHITECTURES BY EMBEDDING ENTROPY-TRUST CAUSALITY AND BAYESIAN RESONANCE. 🛡RHEA-Core Public Grant v2.1 Authors/Creators Roe, Paul (Rights holder)1, 2 Show affiliations Description 🛡️ RHEA-Core Public Grant v2.1Hardened System Sovereignty EditionIssued By:Paul M. Roe (EnigmaticGlitch)Moniker: SovereignGlitch ♏🧙‍♂️Affiliation: TecKnows, Inc. · ZADEIAN Research DivisionLegal Anchors:• U.S. Copyright Law (Title 17)• U.S. DMCA• Berne Convention / WIPO• EU Directive (EU) 2019/790 — with explicit TDM opt-out• U.S. Patent Filings relating to RHEA-UCM and associated systems (including Provisional Application No. 63/796,404 and any successors)Version: v2.1Date of Effect: December 2025License Type: Non-Commercial · Attribution · No Derivatives · Symbolic Derivative Restriction · AI/TDM Opt-Out · Functional Equivalence Restriction________________________________________🔰 0. Scope of ProtectionThis license governs all intellectual, mathematical, symbolic, semantic, recursive, and systemic artifacts authored by Paul M. Roe / EnigmaticGlitch / SovereignGlitch under (including, but not limited to) the following umbrellas:• RHEA-UCM, RHEA-CM, RHEA-PMR, and all Λ-Gate frameworks• ZADEIAN Sentinel, Zadeian/RHEA-Class modules, RHEA-IC hardware logic• UCM Cosmological Recursion, Recursive Entropy Models, Symbolic Feedback Systems• SOER Constructs, including:o SOER Imperial Codexo Sovereign Glyph Languageo Temporal Glyphic Hash Trees• All published and unpublished materials under:o Zenodo records explicitly associated with RHEA-UCM / ZADEIAN-RHEA / RHEA-CMo GitHub demo repositories related to academic submissions (e.g. SIAM submissions and supporting code)o Any symbolic derivation codebases, diagrams, or formal equations implementing or describing these systemsThis v2.1 grant supersedes the text of RHEA-Core Public Grant v1.0 and v2.0 for all future access and use, subject to the limited clarification in Section 9 (Prior Versions).________________________________________1. License Supremacy & Versioning1.1 Prospective SupremacyAll access, use, citation, embedding, referencing, storage, analysis, or distribution of covered materials occurring on or after the effective date of this v2.1 grant is governed exclusively by RHEA-Core Public Grant v2.1, or any later version explicitly designated by the Rights Holder as superseding it.1.2 Termination of Prior Permissions for Future UseAny permissions that may have been implied or expressed under earlier grants (including RHEA-Core Public Grant v1.0 or v2.0) are revoked for all future use as of the effective date of v2.1, except as preserved in the limited academic reference rights described in Section 9.1.3 Acceptance by AccessBy accessing, downloading, cloning, forking, referencing, or otherwise interacting with the covered materials following the publication of this license, you acknowledge and agree to be bound by the terms of RHEA-Core Public Grant v2.1.________________________________________2. Grant of Use (Non-Commercial, Revocable)You are granted a revocable, non-exclusive, non-transferable, and non-sublicensable right to:• View, read, and privately study the covered materials• Reference them for academic, journalistic, technical, or personal enrichment• Cite them in academic or tec","url":"https://doi.org/10.5281/zenodo.17566695","authors":["Roe, Paul"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17566695","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18787822","name":"A COMPREHENSIVE REVIEW OF QUANTUM MACHINE LEARNING FRAMEWORKS FOR PRIVACY-PRESERVING DATA HANDLING IN 5G-ENABLED HEALTHCARE ENVIRONMENTS","source":"datacite","abstract":"The emergence of 5G networks has revolutionized healthcare delivery by enabling communication with ultra-low latency, massive connectivity, and quick access to IoT devices' medical data. The digitalization of healthcare has, however, introduced privacy, data security, and scalability as major challenges, and the issues have been worsened by the rapid digitalization of the healthcare industry. The current review paper is a comprehensive evaluation of the role of Quantum Machine Learning (QML) as a paradigm for data handling with privacy preserved in the healthcare ecosystems supported by 5G. It collects advancements in quantum cryptography, Quantum Key Distribution (QKD), federated learning, and blockchain technology to explore how the combining of such technologies will enhance the confidentiality, interoperability, and real-time decision-making of the data. The review arranges and compares the state-of-theart research of 2019-2025, in which the trends, architectures, and security protocols that link classical and quantum methods in healthcare analytics become visible. It also addresses the main hurdles like quantum hardware limitations, interoperability gaps, and the tuning of federated models, and it indicates potential future research paths for the development of healthcare infrastructure that is scalable, reliable, and quantum-resistant. In conclusion, this paper gives a thorough understanding of how quantum intelligence can alter the data security and privacy landscape in 5G-enabled smart healthcare systems.","url":"https://doi.org/10.5281/zenodo.18787822","authors":["IJESAT"],"tags":["Quantum Machine Learning, 5G Healthcare, Privacy-Preserving Data Handling, Quantum Cryptography, Quantum Key Distribution, Federated Learning."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18787822","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18787823","name":"A COMPREHENSIVE REVIEW OF QUANTUM MACHINE LEARNING FRAMEWORKS FOR PRIVACY-PRESERVING DATA HANDLING IN 5G-ENABLED HEALTHCARE ENVIRONMENTS","source":"datacite","abstract":"The emergence of 5G networks has revolutionized healthcare delivery by enabling communication with ultra-low latency, massive connectivity, and quick access to IoT devices' medical data. The digitalization of healthcare has, however, introduced privacy, data security, and scalability as major challenges, and the issues have been worsened by the rapid digitalization of the healthcare industry. The current review paper is a comprehensive evaluation of the role of Quantum Machine Learning (QML) as a paradigm for data handling with privacy preserved in the healthcare ecosystems supported by 5G. It collects advancements in quantum cryptography, Quantum Key Distribution (QKD), federated learning, and blockchain technology to explore how the combining of such technologies will enhance the confidentiality, interoperability, and real-time decision-making of the data. The review arranges and compares the state-of-theart research of 2019-2025, in which the trends, architectures, and security protocols that link classical and quantum methods in healthcare analytics become visible. It also addresses the main hurdles like quantum hardware limitations, interoperability gaps, and the tuning of federated models, and it indicates potential future research paths for the development of healthcare infrastructure that is scalable, reliable, and quantum-resistant. In conclusion, this paper gives a thorough understanding of how quantum intelligence can alter the data security and privacy landscape in 5G-enabled smart healthcare systems.","url":"https://doi.org/10.5281/zenodo.18787823","authors":["IJESAT"],"tags":["Quantum Machine Learning, 5G Healthcare, Privacy-Preserving Data Handling, Quantum Cryptography, Quantum Key Distribution, Federated Learning."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18787823","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2506.06060","name":"Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Models","source":"datacite","abstract":"Federated large language models (FedLLMs) enable cross-silo collaborative training among institutions while preserving data locality, making them appealing for privacy-sensitive domains such as law, finance, and healthcare. However, the memorization behavior of LLMs can lead to privacy risks that may cause cross-client data leakage. In this work, we study the threat of cross-client data extraction, where a semi-honest participant attempts to recover personally identifiable information (PII) memorized from other clients' data. We propose three simple yet effective extraction strategies that leverage contextual prefixes from the attacker's local data, including frequency-based prefix sampling and local fine-tuning to amplify memorization. To evaluate these attacks, we construct a Chinese legal-domain dataset with fine-grained PII annotations consistent with CPIS, GDPR, and CCPA standards, and assess extraction performance using two metrics: coverage and efficiency. Experimental results show that our methods can recover up to 56.6% of victim-exclusive PII, where names, addresses, and birthdays are particularly vulnerable. These findings highlight concrete privacy risks in FedLLMs and establish a benchmark and evaluation framework for future research on privacy-preserving federated learning. Code and data are available at https://github.com/SMILELab-FL/FedPII.","url":"https://doi.org/10.48550/arxiv.2506.06060","authors":["Hu, Yingqi","Zhang, Zhuo","Zhang, Jingyuan","Wang, Jinghua","Wang, Qifan","Qu, Lizhen","Xu, Zenglin"],"tags":["Computation and Language (cs.CL)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2506.06060","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18766337","name":"A Comparative Review of Machine Learning Approaches for Manufacturing Applications in Industry 4.0","source":"datacite","abstract":"Industry 4.0 has redefined modern manufacturing by integrating cyber–physical systems, Industrial Internet of Things (IIoT), cloud–edge computing, and data-driven intelligence. Among these enablers, machine learning (ML) has emerged as a foundational technology for extracting actionable insights from heterogeneous manufacturing data. This paper presents an extended and comparative review of ML and deep learning (DL) techniques—including supervised, unsupervised, semi-supervised, reinforcement learning, and hybrid models—applied across core manufacturing domains such as predictive maintenance, quality inspection and defect detection, process optimization, production planning, and supply chain management. Based on a systematic analysis of literature published between 2015 and 2025, the review compares algorithmic performance, computational complexity, interpretability, and deployment feasibility. Mathematical formulations of commonly used models, including regression, support vector machines, convolutional neural networks (CNNs), and long short-term memory (LSTM) networks, are presented to enhance methodological clarity. Emerging trends such as transfer learning, federated learning, edge AI, and explainable artificial intelligence (XAI) are discussed in the context of industrial scalability and reliability. The study concludes that context-aware model selection, combined with hybrid and explainable frameworks, is critical for bridging the gap between laboratory-scale ML models and real-world smart manufacturing systems.","url":"https://doi.org/10.5281/zenodo.18766337","authors":["Veeru Paswan","Shalu Gupta","Gurleen"],"tags":["Machine Learning, Deep Learning, Industry 4.0, Predictive Maintenance, Quality Inspection, Smart Manufacturing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18766337","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18766338","name":"A Comparative Review of Machine Learning Approaches for Manufacturing Applications in Industry 4.0","source":"datacite","abstract":"Industry 4.0 has redefined modern manufacturing by integrating cyber–physical systems, Industrial Internet of Things (IIoT), cloud–edge computing, and data-driven intelligence. Among these enablers, machine learning (ML) has emerged as a foundational technology for extracting actionable insights from heterogeneous manufacturing data. This paper presents an extended and comparative review of ML and deep learning (DL) techniques—including supervised, unsupervised, semi-supervised, reinforcement learning, and hybrid models—applied across core manufacturing domains such as predictive maintenance, quality inspection and defect detection, process optimization, production planning, and supply chain management. Based on a systematic analysis of literature published between 2015 and 2025, the review compares algorithmic performance, computational complexity, interpretability, and deployment feasibility. Mathematical formulations of commonly used models, including regression, support vector machines, convolutional neural networks (CNNs), and long short-term memory (LSTM) networks, are presented to enhance methodological clarity. Emerging trends such as transfer learning, federated learning, edge AI, and explainable artificial intelligence (XAI) are discussed in the context of industrial scalability and reliability. The study concludes that context-aware model selection, combined with hybrid and explainable frameworks, is critical for bridging the gap between laboratory-scale ML models and real-world smart manufacturing systems.","url":"https://doi.org/10.5281/zenodo.18766338","authors":["Veeru Paswan","Shalu Gupta","Gurleen"],"tags":["Machine Learning, Deep Learning, Industry 4.0, Predictive Maintenance, Quality Inspection, Smart Manufacturing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18766338","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2508.05137","name":"FedGIN: Federated Learning with Dynamic Global Intensity Non-linear Augmentation for Organ Segmentation using Multi-modal Images","source":"datacite","abstract":"Medical image segmentation plays a crucial role in AI-assisted diagnostics, surgical planning, and treatment monitoring. Accurate and robust segmentation models are essential for enabling reliable, data-driven clinical decision making across diverse imaging modalities. Given the inherent variability in image characteristics across modalities, developing a unified model capable of generalizing effectively to multiple modalities would be highly beneficial. This model could streamline clinical workflows and reduce the need for modality-specific training. However, real-world deployment faces major challenges, including data scarcity, domain shift between modalities (e.g., CT vs. MRI), and privacy restrictions that prevent data sharing. To address these issues, we propose FedGIN, a Federated Learning (FL) framework that enables multimodal organ segmentation without sharing raw patient data. Our method integrates a lightweight Global Intensity Non-linear (GIN) augmentation module that harmonizes modality-specific intensity distributions during local training. We evaluated FedGIN using two types of datasets: an imputed dataset and a complete dataset. In the limited dataset scenario, the model was initially trained using only MRI data, and CT data was added to assess its performance improvements. In the complete dataset scenario, both MRI and CT data were fully utilized for training on all clients. In the limited-data scenario, FedGIN achieved a 12 to 18% improvement in 3D Dice scores on MRI test cases compared to FL without GIN and consistently outperformed local baselines. In the complete dataset scenario, FedGIN demonstrated near-centralized performance, with a 30% Dice score improvement over the MRI-only baseline and a 10% improvement over the CT-only baseline, highlighting its strong cross-modality generalization under privacy constraints.","url":"https://doi.org/10.48550/arxiv.2508.05137","authors":["Nagaraju, Sachin Dudda","Moradi, Ashkan","Abrahamsen, Bendik Skarre","Elschot, Mattijs"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.05137","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2510.21491","name":"Benchmarking Catastrophic Forgetting Mitigation Methods in Federated Time Series Forecasting","source":"datacite","abstract":"Catastrophic forgetting (CF) poses a persistent challenge in continual learning (CL), especially within federated learning (FL) environments characterized by non-i.i.d. time series data. While existing research has largely focused on classification tasks in vision domains, the regression-based forecasting setting prevalent in IoT and edge applications remains underexplored. In this paper, we present the first benchmarking framework tailored to investigate CF in federated continual time series forecasting. Using the Beijing Multi-site Air Quality dataset across 12 decentralized clients, we systematically evaluate several CF mitigation strategies, including Replay, Elastic Weight Consolidation, Learning without Forgetting, and Synaptic Intelligence. Key contributions include: (i) introducing a new benchmark for CF in time series FL, (ii) conducting a comprehensive comparative analysis of state-of-the-art methods, and (iii) releasing a reproducible open-source framework. This work provides essential tools and insights for advancing continual learning in federated time-series forecasting systems.","url":"https://doi.org/10.48550/arxiv.2510.21491","authors":["Hallak, Khaled","Kem, Oudom"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (stat.ML)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.6; I.2.7; I.5.1; I.5.4","68T07, 68W15, 62M10"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.21491","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2506.20102","name":"Autonomous Cyber Resilience via a Co-Evolutionary Arms Race within a Fortified Digital Twin Sandbox","source":"datacite","abstract":"The convergence of Information Technology and Operational Technology has exposed Industrial Control Systems to adaptive, intelligent adversaries that render static defenses obsolete. This paper introduces the Adversarial Resilience Co-evolution (ARC) framework, addressing the \"Trinity of Trust\" comprising model fidelity, data integrity, and analytical resilience. ARC establishes a co-evolutionary arms race within a Fortified Secure Digital Twin (F-SCDT), where a Deep Reinforcement Learning \"Red Agent\" autonomously discovers attack paths while an ensemble-based \"Blue Agent\" is continuously hardened against these threats. Experimental validation on the Tennessee Eastman Process (TEP) and Secure Water Treatment (SWaT) testbeds demonstrates superior performance in detecting novel attacks, with F1-scores improving from 0.65 to 0.89 and detection latency reduced from over 1200 seconds to 210 seconds. A comprehensive ablation study reveals that the co-evolutionary process itself contributes a 27% performance improvement. By integrating Explainable AI and proposing a Federated ARC architecture, this work presents a necessary paradigm shift toward dynamic, self-improving security for critical infrastructure.","url":"https://doi.org/10.48550/arxiv.2506.20102","authors":["Malikussaid","Sutiyo"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","Systems and Control (eess.SY)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering","C.2.0; I.2.11; I.2.6"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2506.20102","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2602.17625","name":"Catastrophic Forgetting Resilient One-Shot Incremental Federated Learning","source":"datacite","abstract":"Modern big-data systems generate massive, heterogeneous, and geographically dispersed streams that are large-scale and privacy-sensitive, making centralization challenging. While federated learning (FL) provides a privacy-enhancing training mechanism, it assumes a static data flow and learns a collaborative model over multiple rounds, making learning with \\textit{incremental} data challenging in limited-communication scenarios. This paper presents One-Shot Incremental Federated Learning (OSI-FL), the first FL framework that addresses the dual challenges of communication overhead and catastrophic forgetting. OSI-FL communicates category-specific embeddings, devised by a frozen vision-language model (VLM) from each client in a single communication round, which a pre-trained diffusion model at the server uses to synthesize new data similar to the client's data distribution. The synthesized samples are used on the server for training. However, two challenges still persist: i) tasks arriving incrementally need to retrain the global model, and ii) as future tasks arrive, retraining the model introduces catastrophic forgetting. To this end, we augment training with Selective Sample Retention (SSR), which identifies and retains the top-p most informative samples per category and task pair based on sample loss. SSR bounds forgetting by ensuring that representative retained samples are incorporated into training in further iterations. The experimental results indicate that OSI-FL outperforms baselines, including traditional and one-shot FL approaches, in both class-incremental and domain-incremental scenarios across three benchmark datasets.","url":"https://doi.org/10.48550/arxiv.2602.17625","authors":["Zaland, Obaidullah","Khan, Zulfiqar Ahmad","Bhuyan, Monowar"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.17625","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2602.17614","name":"Guarding the Middle: Protecting Intermediate Representations in Federated Split Learning","source":"datacite","abstract":"Big data scenarios, where massive, heterogeneous datasets are distributed across clients, demand scalable, privacy-preserving learning methods. Federated learning (FL) enables decentralized training of machine learning (ML) models across clients without data centralization. Decentralized training, however, introduces a computational burden on client devices. U-shaped federated split learning (UFSL) offloads a fraction of the client computation to the server while keeping both data and labels on the clients' side. However, the intermediate representations (i.e., smashed data) shared by clients with the server are prone to exposing clients' private data. To reduce exposure of client data through intermediate data representations, this work proposes k-anonymous differentially private UFSL (KD-UFSL), which leverages privacy-enhancing techniques such as microaggregation and differential privacy to minimize data leakage from the smashed data transferred to the server. We first demonstrate that an adversary can access private client data from intermediate representations via a data-reconstruction attack, and then present a privacy-enhancing solution, KD-UFSL, to mitigate this risk. Our experiments indicate that, alongside increasing the mean squared error between the actual and reconstructed images by up to 50% in some cases, KD-UFSL also decreases the structural similarity between them by up to 40% on four benchmarking datasets. More importantly, KD-UFSL improves privacy while preserving the utility of the global model. This highlights its suitability for large-scale big data applications where privacy and utility must be balanced.","url":"https://doi.org/10.48550/arxiv.2602.17614","authors":["Zaland, Obaidullah","Mistry, Sajib","Bhuyan, Monowar"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.17614","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2511.14406","name":"Watch Out for the Lifespan: Evaluating Backdoor Attacks Against Federated Model Adaptation","source":"datacite","abstract":"Large models adaptation through Federated Learning (FL) addresses a wide range of use cases and is enabled by Parameter-Efficient Fine-Tuning techniques such as Low-Rank Adaptation (LoRA). However, this distributed learning paradigm faces several security threats, particularly to its integrity, such as backdoor attacks that aim to inject malicious behavior during the local training steps of certain clients. We present the first analysis of the influence of LoRA on state-of-the-art backdoor attacks targeting model adaptation in FL. Specifically, we focus on backdoor lifespan, a critical characteristic in FL, that can vary depending on the attack scenario and the attacker's ability to effectively inject the backdoor. A key finding in our experiments is that for an optimally injected backdoor, the backdoor persistence after the attack is longer when the LoRA's rank is lower. Importantly, our work highlights evaluation issues of backdoor attacks against FL and contributes to the development of more robust and fair evaluations of backdoor attacks, enhancing the reliability of risk assessments for critical FL systems. Our code is publicly available.","url":"https://doi.org/10.48550/arxiv.2511.14406","authors":["Vuillod, Bastien","Moellic, Pierre-Alain","Dutertre, Jean-Max"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2511.14406","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18646764","name":"Identification of Movement Patterns Associated with Neurological Disorders Using Machine Vision","source":"datacite","abstract":"The identification and quantification of abnormal movement patterns are central to the diagnosis, monitoring, and treatment of neurological disorders such as Parkinson’s disease (PD), stroke, cerebral palsy (CP), and dystonia. Machine vision the combination of markerless pose estimation, computer vision-based feature extraction, and machine learning has emerged as a scalable, non-invasive, and objective approach for capturing clinically meaningful motor biomarkers from videos and standard cameras. This review synthesizes recent advances (2018–2025) in machine-vision pipelines for detection, classification, and quantification of movement patterns associated with neurological disorders. We first summarize the technical building blocks: markerless pose estimation (e.g., OpenPose, DeepLabCut, MediaPipe), representation and feature extraction methods (kinematic, temporal, spectral), and machine-learning models (classical and deep approaches). Next, we review disorder-specific applications (PD motor signs, post-stroke gait and limb impairment, CP gait analysis, dystonia detection, and facial/ocular biomarkers) and discuss datasets, evaluation metrics, and clinical validation efforts. Finally, we consider major challenges domain shift, data privacy, standardization, interpretability, and ethics and propose future research directions, including multimodal fusion, federated learning, standard benchmarks, and regulatory pathways for clinical translation. Machine vision offers transformative potential for neurology and rehabilitation, but consistent external validation and cross-site standardization remain prerequisites for clinical deployment.","url":"https://doi.org/10.5281/zenodo.18646764","authors":["Almasi, Mohammad"],"tags":["machine vision, human pose estimation, gait analysis, Parkinson's disease, stroke, markerless motion capture"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18646764","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18646765","name":"Identification of Movement Patterns Associated with Neurological Disorders Using Machine Vision","source":"datacite","abstract":"The identification and quantification of abnormal movement patterns are central to the diagnosis, monitoring, and treatment of neurological disorders such as Parkinson’s disease (PD), stroke, cerebral palsy (CP), and dystonia. Machine vision the combination of markerless pose estimation, computer vision-based feature extraction, and machine learning has emerged as a scalable, non-invasive, and objective approach for capturing clinically meaningful motor biomarkers from videos and standard cameras. This review synthesizes recent advances (2018–2025) in machine-vision pipelines for detection, classification, and quantification of movement patterns associated with neurological disorders. We first summarize the technical building blocks: markerless pose estimation (e.g., OpenPose, DeepLabCut, MediaPipe), representation and feature extraction methods (kinematic, temporal, spectral), and machine-learning models (classical and deep approaches). Next, we review disorder-specific applications (PD motor signs, post-stroke gait and limb impairment, CP gait analysis, dystonia detection, and facial/ocular biomarkers) and discuss datasets, evaluation metrics, and clinical validation efforts. Finally, we consider major challenges domain shift, data privacy, standardization, interpretability, and ethics and propose future research directions, including multimodal fusion, federated learning, standard benchmarks, and regulatory pathways for clinical translation. Machine vision offers transformative potential for neurology and rehabilitation, but consistent external validation and cross-site standardization remain prerequisites for clinical deployment.","url":"https://doi.org/10.5281/zenodo.18646765","authors":["Almasi, Mohammad"],"tags":["machine vision, human pose estimation, gait analysis, Parkinson's disease, stroke, markerless motion capture"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18646765","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18626878","name":"SDKP Framework — Complete System Architecture","source":"datacite","abstract":"SDKP Framework — Complete System Architecture Author: Donald Paul Smith (FatherTime) Affiliation: Independent Researcher Abstract This work presents the SDKP framework, a unified symbolic–physical model connecting size, density, and rotational velocity to temporal behavior. The framework integrates Shape–Dimension–Number (SD&N) structural mapping, Earth Orbital Speed (EOS) dynamics, Velocity–Frequency–Energy (VFE1) scaling, Quantum Computerization Consciousness (QCC), Loop Learning for Artificial Life (LLAL), and Kapnack symbolic compression. The model proposes that physical behavior emerges from structured relationships between geometry, density distribution, and rotational dynamics. The framework provides a mathematical structure describing system evolution and predicts measurable deviations from conventional physical models, including small variations in orbital motion and symbolic compression effects. A complete system architecture, mathematical structure, and testable predictions are presented to support experimental evaluation and future development. 1. Introduction 1.1 Background Modern physics describes motion, gravity, and quantum behavior using separate theoretical structures. A unified representation connecting structural geometry, density relationships, and temporal evolution remains an open problem. 1.2 Motivation The SDKP framework proposes that time evolution and physical behavior emerge from relationships between size, density, and rotational velocity. This provides a structural interpretation of physical dynamics and information flow. 1.3 Contributions This work introduces: a unified symbolic–physical framework a structural mapping between geometry and dynamics a system architecture connecting multiple model components testable predictions for experimental validation 2. Core Definitions 2.1 SDKP (Size–Density–Rotation–Time) SDKP describes temporal behavior as a function of system size, density distribution, and rotational velocity. It defines relationships between structural configuration and dynamic evolution. Variables include: size parameter (S) density parameter (D) rotational velocity (R) temporal response (T) 2.2 SD&N (Shape–Dimension–Number) SD&N provides structural classification of systems based on geometric form, dimensional structure, and numerical relationships. It defines how system structure influences dynamic behavior. 2.3 EOS (Earth Orbital Speed Model) EOS describes orbital motion using rotational and density-based relationships. The model predicts small deviations from classical orbital calculations. 2.4 QCC (Quantum Computerization Consciousness) QCC models information processing and quantum-level interactions through structured symbolic computation. 2.5 VFE1 (Velocity–Frequency–Energy Scaling) VFE1 defines relationships between motion, oscillation, and energy distribution across system states. 2.6 LLAL (Loop Learning for Artificial Life) LLAL describes feedback-based adaptive system evolution using recursive learning structures. 2.7 Kapnack Symbolic Compression Kapnack defines rules for compressing complex system behavior into structured symbolic representations. 3. Mathematical Structure (Overview) The framework defines system behavior through relationships among structural variables and rotational dynamics. Core elements include: system size parameter density distribution functions rotational velocity fields temporal response functions symbolic compression operators System evolution is determined by interactions between these quantities. 4. System Architecture The framework operates through interacting components: SDKP defines core dynamics SD&N defines structural configuration VFE1 defines energy scaling EOS describes orbital behavior QCC models information processing LLAL provides feedback adaptation Kapnack enables symbolic compression These components form a unified dynamic system. 5. Physical Interpretation The framework provides structural interpretations of: motion as rotational in","url":"https://doi.org/10.5281/zenodo.18626878","authors":["Smith, Donald"],"tags":["FatherTimeSDKP","SD&amp;N","Mars time","Lunar time","NASA","NIST","spaceX","AGI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18626878","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18626877","name":"SDKP Framework — Complete System Architecture","source":"datacite","abstract":"SDKP Framework — Complete System Architecture Author: Donald Paul Smith (FatherTime) Affiliation: Independent Researcher Abstract This work presents the SDKP framework, a unified symbolic–physical model connecting size, density, and rotational velocity to temporal behavior. The framework integrates Shape–Dimension–Number (SD&N) structural mapping, Earth Orbital Speed (EOS) dynamics, Velocity–Frequency–Energy (VFE1) scaling, Quantum Computerization Consciousness (QCC), Loop Learning for Artificial Life (LLAL), and Kapnack symbolic compression. The model proposes that physical behavior emerges from structured relationships between geometry, density distribution, and rotational dynamics. The framework provides a mathematical structure describing system evolution and predicts measurable deviations from conventional physical models, including small variations in orbital motion and symbolic compression effects. A complete system architecture, mathematical structure, and testable predictions are presented to support experimental evaluation and future development. 1. Introduction 1.1 Background Modern physics describes motion, gravity, and quantum behavior using separate theoretical structures. A unified representation connecting structural geometry, density relationships, and temporal evolution remains an open problem. 1.2 Motivation The SDKP framework proposes that time evolution and physical behavior emerge from relationships between size, density, and rotational velocity. This provides a structural interpretation of physical dynamics and information flow. 1.3 Contributions This work introduces: a unified symbolic–physical framework a structural mapping between geometry and dynamics a system architecture connecting multiple model components testable predictions for experimental validation 2. Core Definitions 2.1 SDKP (Size–Density–Rotation–Time) SDKP describes temporal behavior as a function of system size, density distribution, and rotational velocity. It defines relationships between structural configuration and dynamic evolution. Variables include: size parameter (S) density parameter (D) rotational velocity (R) temporal response (T) 2.2 SD&N (Shape–Dimension–Number) SD&N provides structural classification of systems based on geometric form, dimensional structure, and numerical relationships. It defines how system structure influences dynamic behavior. 2.3 EOS (Earth Orbital Speed Model) EOS describes orbital motion using rotational and density-based relationships. The model predicts small deviations from classical orbital calculations. 2.4 QCC (Quantum Computerization Consciousness) QCC models information processing and quantum-level interactions through structured symbolic computation. 2.5 VFE1 (Velocity–Frequency–Energy Scaling) VFE1 defines relationships between motion, oscillation, and energy distribution across system states. 2.6 LLAL (Loop Learning for Artificial Life) LLAL describes feedback-based adaptive system evolution using recursive learning structures. 2.7 Kapnack Symbolic Compression Kapnack defines rules for compressing complex system behavior into structured symbolic representations. 3. Mathematical Structure (Overview) The framework defines system behavior through relationships among structural variables and rotational dynamics. Core elements include: system size parameter density distribution functions rotational velocity fields temporal response functions symbolic compression operators System evolution is determined by interactions between these quantities. 4. System Architecture The framework operates through interacting components: SDKP defines core dynamics SD&N defines structural configuration VFE1 defines energy scaling EOS describes orbital behavior QCC models information processing LLAL provides feedback adaptation Kapnack enables symbolic compression These components form a unified dynamic system. 5. Physical Interpretation The framework provides structural interpretations of: motion as rotational in","url":"https://doi.org/10.5281/zenodo.18626877","authors":["Smith, Donald"],"tags":["FatherTimeSDKP","SD&amp;N","Mars time","Lunar time","NASA","NIST","spaceX","AGI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18626877","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2511.11696","name":"Toward Dignity-Aware AI: Next-Generation Elderly Monitoring from Fall Detection to ADL","source":"datacite","abstract":"This position paper envisions a next-generation elderly monitoring system that moves beyond fall detection toward the broader goal of Activities of Daily Living (ADL) recognition. Our ultimate aim is to design privacy-preserving, edge-deployed, and federated AI systems that can robustly detect and understand daily routines, supporting independence and dignity in aging societies. At present, ADL-specific datasets are still under collection. As a preliminary step, we demonstrate feasibility through experiments using the SISFall dataset and its GAN-augmented variants, treating fall detection as a proxy task. We report initial results on federated learning with non-IID conditions, and embedded deployment on Jetson Orin Nano devices. We then outline open challenges such as domain shift, data scarcity, and privacy risks, and propose directions toward full ADL monitoring in smart-room environments. This work highlights the transition from single-task detection to comprehensive daily activity recognition, providing both early evidence and a roadmap for sustainable and human-centered elderly care AI.","url":"https://doi.org/10.48550/arxiv.2511.11696","authors":["Shao, Xun","Otani, Aoba","Hirasuka, Yuto","Cai, Runji","Loke, Seng W."],"tags":["Machine Learning (cs.LG)","Computer Vision and Pattern Recognition (cs.CV)","Computers and Society (cs.CY)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.11; C.2.4; K.4.1","68T07"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2511.11696","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18625147","name":"White paper #1: HPC-Cloud and Quantum Computing: State of the Art and Innovation Roadmap","source":"datacite","abstract":"This white paper presents an overview of the current state of the art in high-performance computing (HPC) and its convergence with cloud technologies, with a strategic focus on innovation management and exploitation. It outlines recent advances in cloud-based HPC services, hybrid architectures, and federated systems that integrate edge, cloud, and HPC resources. Emerging paradigms such as federated learning, AIdriven optimization, and sustainable computing are analyzed for their transformative potential. Special emphasis is placed on the NOUS project, which exemplifies a holistic approach to federated HPC-cloud services, combining technical innovation with robust exploitation strategies. NOUS goes one step beyond and explores the integration of quantum computing in handling data existing in the cloud. As of late 2025, the synergy between Cloud Computing and Quantum Computing (QC) has matured into a functional \"Quantum-as-a-Service\" (QaaS) model. While physical quantum hardware remains too fragile for on-premise deployment, cloud providers have democratized access to the \"Quantum Stack.\" This report highlights this progress, the issues and real future applications. NOUS addresses European priorities for digital sovereignty and data interoperability, supporting scalable, privacy-preserving, and AI-enabled HPC workflows. The paper concludes with a roadmap that positions NOUS as a reference architecture and innovation catalyst for Europe's distributed computing ecosystem.","url":"https://doi.org/10.5281/zenodo.18625147","authors":["krokidas, panagiotis","Rekatsinas, Christoforos","Terlixidis, Periklis","Giannopoulos, Georgios","Rallis, Konstantinos","Dimitrakis, Panagiotis","Melissourgos, Nikolaos"],"tags":["High Performance Computing","Quantum computers","hybrid computing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18625147","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18625148","name":"White paper #1: HPC-Cloud and Quantum Computing: State of the Art and Innovation Roadmap","source":"datacite","abstract":"This white paper presents an overview of the current state of the art in high-performance computing (HPC) and its convergence with cloud technologies, with a strategic focus on innovation management and exploitation. It outlines recent advances in cloud-based HPC services, hybrid architectures, and federated systems that integrate edge, cloud, and HPC resources. Emerging paradigms such as federated learning, AIdriven optimization, and sustainable computing are analyzed for their transformative potential. Special emphasis is placed on the NOUS project, which exemplifies a holistic approach to federated HPC-cloud services, combining technical innovation with robust exploitation strategies. NOUS goes one step beyond and explores the integration of quantum computing in handling data existing in the cloud. As of late 2025, the synergy between Cloud Computing and Quantum Computing (QC) has matured into a functional \"Quantum-as-a-Service\" (QaaS) model. While physical quantum hardware remains too fragile for on-premise deployment, cloud providers have democratized access to the \"Quantum Stack.\" This report highlights this progress, the issues and real future applications. NOUS addresses European priorities for digital sovereignty and data interoperability, supporting scalable, privacy-preserving, and AI-enabled HPC workflows. The paper concludes with a roadmap that positions NOUS as a reference architecture and innovation catalyst for Europe's distributed computing ecosystem.","url":"https://doi.org/10.5281/zenodo.18625148","authors":["krokidas, panagiotis","Rekatsinas, Christoforos","Terlixidis, Periklis","Giannopoulos, Georgios","Rallis, Konstantinos","Dimitrakis, Panagiotis","Melissourgos, Nikolaos"],"tags":["High Performance Computing","Quantum computers","hybrid computing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18625148","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2504.15717","name":"Trusted Compute Units: A Framework for Chained Verifiable Computations","source":"datacite","abstract":"Blockchain and distributed ledger technologies (DLTs) facilitate decentralized computations across trust boundaries. However, ensuring complex computations with low gas fees and confidentiality remains challenging. Recent advances in Confidential Computing -- leveraging hardware-based Trusted Execution Environments (TEEs) -- and Proof-carrying Data -- employing cryptographic Zero-Knowledge Virtual Machines (zkVMs) -- hold promise for secure, privacy-preserving off-chain and layer-2 computations. On the other side, a homogeneous reliance on a single technology, such as TEEs or zkVMs, is impractical for decentralized environments with heterogeneous computational requirements. This paper introduces the Trusted Compute Unit (TCU), a unifying framework that enables composable and interoperable verifiable computations across heterogeneous technologies. Our approach allows decentralized applications (dApps) to flexibly offload complex computations to TCUs, obtaining proof of correctness. These proofs can be anchored on-chain for automated dApp interactions, while ensuring confidentiality of input data, and integrity of output data. We demonstrate how TCUs can support a prominent blockchain use case, such as federated learning. By enabling secure off-chain interactions without incurring on-chain confirmation delays or gas fees, TCUs significantly improve system performance and scalability. Experimental insights and performance evaluations confirm the feasibility and practicality of this unified approach, advancing the state of the art in verifiable off-chain services for the blockchain ecosystem.","url":"https://doi.org/10.48550/arxiv.2504.15717","authors":["Castillo, Fernando","Heiss, Jonathan","Werner, Sebastian","Tai, Stefan"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2504.15717","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2503.11146","name":"Layer-wise Update Aggregation with Recycling for Communication-Efficient Federated Learning","source":"datacite","abstract":"Expensive communication cost is a common performance bottleneck in Federated Learning (FL), which makes it less appealing in real-world applications. Many communication-efficient FL methods focus on discarding a part of model updates mostly based on gradient magnitude. In this study, we find that recycling previous updates, rather than simply dropping them, more effectively reduces the communication cost while maintaining FL performance. We propose FedLUAR, a Layer-wise Update Aggregation with Recycling scheme for communication-efficient FL. We first define a useful metric that quantifies the extent to which the aggregated gradients influences the model parameter values in each layer. FedLUAR selects a few layers based on the metric and recycles their previous updates on the server side. Our extensive empirical study demonstrates that the update recycling scheme significantly reduces the communication cost while maintaining model accuracy. For example, our method achieves nearly the same AG News accuracy as FedAvg, while reducing the communication cost to just 17%.","url":"https://doi.org/10.48550/arxiv.2503.11146","authors":["Kim, Jisoo","Kang, Sungmin","Lee, Sunwoo"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2503.11146","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2602.09355","name":"Impact of domain adaptation in deep learning for medical image classifications","source":"datacite","abstract":"Domain adaptation (DA) is a quickly expanding area in machine learning that involves adjusting a model trained in one domain to perform well in another domain. While there have been notable progressions, the fundamental concept of numerous DA methodologies has persisted: aligning the data from various domains into a shared feature space. In this space, knowledge acquired from labeled source data can improve the model training on target data that lacks sufficient labels. In this study, we demonstrate the use of 10 deep learning models to simulate common DA techniques and explore their application in four medical image datasets. We have considered various situations such as multi-modality, noisy data, federated learning (FL), interpretability analysis, and classifier calibration. The experimental results indicate that using DA with ResNet34 in a brain tumor (BT) data set results in an enhancement of 4.7\\% in model performance. Similarly, the use of DA can reduce the impact of Gaussian noise, as it provides $\\sim 3\\%$ accuracy increase using ResNet34 on a BT dataset. Furthermore, simply introducing DA into FL framework shows limited potential (e.g., $\\sim 0.3\\%$ increase in performance) for skin cancer classification. In addition, the DA method can improve the interpretability of the models using the gradcam++ technique, which offers clinical values. Calibration analysis also demonstrates that using DA provides a lower expected calibration error (ECE) value $\\sim 2\\%$ compared to CNN alone on a multi-modality dataset.","url":"https://doi.org/10.48550/arxiv.2602.09355","authors":["Wu, Yihang","Chaddad, Ahmad"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.09355","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18527962","name":"Vaccine Cold-Chain Cyber-Physical Logistics Dataset","source":"datacite","abstract":"The released dataset, Vaccine Cold-Chain Cyber–Physical Logistics Dataset (VCC-CPLD), comprises 445,603 time-indexed records collected at a 1-minute sampling resolution, covering observations up to 31 December 2025, 23:59:00. Each record represents a cold-chain monitoring snapshot linked to a federated operational site through a client identifier and shipment context attributes such as shipment ID, lot ID, vaccine type, packaging type, route stage, and geographic region code. This structure supports analytical modeling under heterogeneous and non-IID operating conditions distributed across multiple cold-chain logistics nodes. The feature space integrates multiple cyber–physical dimensions. Environmental telemetry includes internal and ambient temperature, setpoint temperature, rolling minimum/maximum/standard deviation statistics, thermal excursion counts, humidity and condensation indicators, as well as vibration, shock, tilt, and door or light exposure activity. Refrigeration and electronics health signals capture compressor and fan operation, pressure levels, battery and power modes, outage events, defrost and controller resets, firmware versions, and calibration age. Logistics and mobility descriptors include transport speed, estimated time of arrival, dwell and handoff durations, shipment load status, traffic and weather risk indices, time since pack-out, and cumulative transit duration. Cybersecurity telemetry records network traffic volume, packet transmission rate, failed authentication attempts, scan and DNS anomalies, device join events, TLS failures, and remote session activities. Host integrity logs further document process executions, privilege escalation incidents, file integrity violations, firmware updates, reboot history, CPU/memory/disk utilization, and telemetry log gaps. Sensor trust indicators quantify telemetry reliability through time-synchronization offsets, sensor ID changes, replay likelihood scores, checksum mismatches, signal jitter, and statistical outlier measures. In parallel, potency and bio-stability proxies include vaccine vial monitor status, cumulative exposure integrals, duration above or below thermal thresholds, freeze events, thermal dose accumulation, stability classification, remaining shelf life estimates, thaw–refreeze counts, and composite potency proxy scores. For supervised multi-task learning, the dataset provides an attack_type label with six intrusion classes and a continuous potency_remaining_pct_t_plus_H target representing predicted residual potency at a 360-minute forecasting horizon. A derived binary label, safe_to_use_flag_t_plus_H, indicates whether vaccine viability remains within safe administration thresholds at the same prediction interval.","url":"https://doi.org/10.5281/zenodo.18527962","authors":["Davis, E Anne"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18527962","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18527963","name":"Vaccine Cold-Chain Cyber-Physical Logistics Dataset","source":"datacite","abstract":"The released dataset, Vaccine Cold-Chain Cyber–Physical Logistics Dataset (VCC-CPLD), comprises 445,603 time-indexed records collected at a 1-minute sampling resolution, covering observations up to 31 December 2025, 23:59:00. Each record represents a cold-chain monitoring snapshot linked to a federated operational site through a client identifier and shipment context attributes such as shipment ID, lot ID, vaccine type, packaging type, route stage, and geographic region code. This structure supports analytical modeling under heterogeneous and non-IID operating conditions distributed across multiple cold-chain logistics nodes. The feature space integrates multiple cyber–physical dimensions. Environmental telemetry includes internal and ambient temperature, setpoint temperature, rolling minimum/maximum/standard deviation statistics, thermal excursion counts, humidity and condensation indicators, as well as vibration, shock, tilt, and door or light exposure activity. Refrigeration and electronics health signals capture compressor and fan operation, pressure levels, battery and power modes, outage events, defrost and controller resets, firmware versions, and calibration age. Logistics and mobility descriptors include transport speed, estimated time of arrival, dwell and handoff durations, shipment load status, traffic and weather risk indices, time since pack-out, and cumulative transit duration. Cybersecurity telemetry records network traffic volume, packet transmission rate, failed authentication attempts, scan and DNS anomalies, device join events, TLS failures, and remote session activities. Host integrity logs further document process executions, privilege escalation incidents, file integrity violations, firmware updates, reboot history, CPU/memory/disk utilization, and telemetry log gaps. Sensor trust indicators quantify telemetry reliability through time-synchronization offsets, sensor ID changes, replay likelihood scores, checksum mismatches, signal jitter, and statistical outlier measures. In parallel, potency and bio-stability proxies include vaccine vial monitor status, cumulative exposure integrals, duration above or below thermal thresholds, freeze events, thermal dose accumulation, stability classification, remaining shelf life estimates, thaw–refreeze counts, and composite potency proxy scores. For supervised multi-task learning, the dataset provides an attack_type label with six intrusion classes and a continuous potency_remaining_pct_t_plus_H target representing predicted residual potency at a 360-minute forecasting horizon. A derived binary label, safe_to_use_flag_t_plus_H, indicates whether vaccine viability remains within safe administration thresholds at the same prediction interval.","url":"https://doi.org/10.5281/zenodo.18527963","authors":["Davis, E Anne"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18527963","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2602.04384","name":"Blockchain Federated Learning for Sustainable Retail: Reducing Waste through Collaborative Demand Forecasting","source":"datacite","abstract":"Effective demand forecasting is crucial for reducing food waste. However, data privacy concerns often hinder collaboration among retailers, limiting the potential for improved predictive accuracy. In this study, we explore the application of Federated Learning (FL) in Sustainable Supply Chain Management (SSCM), with a focus on the grocery retail sector dealing with perishable goods. We develop a baseline predictive model for demand forecasting and waste assessment in an isolated retailer scenario. Subsequently, we introduce a Blockchain-based FL model, trained collaboratively across multiple retailers without direct data sharing. Our preliminary results show that FL models have performance almost equivalent to the ideal setting in which parties share data with each other, and are notably superior to models built by individual parties without sharing data, cutting waste and boosting efficiency.","url":"https://doi.org/10.48550/arxiv.2602.04384","authors":["Turazza, Fabio","Neri, Alessandro","Pietri, Marcello","Butturi, Maria Angela","Picone, Marco","Mamei, Marco"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.04384","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18482551","name":"Artificial Intelligence in Clinically Validated Medical Imaging: Transforming Radiological Practice and Diagnostic Accuracy","source":"datacite","abstract":"Artificial intelligence (AI) is transforming diagnostic radiology by enhancing image interpretation,pattern recognition, and clinical decision-making across multiple imaging modalities. This systematicliterature review critically evaluates the diagnostic performance, workflow efficiency, andimplementation challenges of AI-based imaging systems. A comprehensive search of PubMed,Scopus, Web of Science, and IEEE Xplore identified 11 high- and moderate-quality studiespublished between 2015 and 2025, following Preferred Reporting Items for Systematic Reviews andMeta-Analyses (PRISMA) 2020 guidelines. The analysis revealed that deep learning algorithms,particularly convolutional neural networks, reported diagnostic accuracies ranging from 85% to 98%across individual studies, with sensitivities in several applications exceeding 90%; these valuesrepresent descriptive ranges extracted from the included studies rather than pooled or weightedsummary estimates. AI applications demonstrated superior reproducibility and clinical reliability inmammography, chest radiography, and neuroimaging, where multi-institutional validation supportedconsistent outcomes. Despite these advancements, limitations persist due to inconsistent externalvalidation, dataset imbalance, and inadequate methodological transparency. Emerging frameworkssuch as explainable AI (XAI) and federated learning show potential to enhance interpretability, datasecurity, and equity in clinical deployment. Furthermore, AI integration was associated with reducedinterpretation time and improved workflow efficiency without compromising diagnostic accuracy.Overall, this review underscores AI’s transition from experimental innovation to a clinicallyindispensable tool. By synthesizing evidence across technical, clinical, and ethical dimensions, itprovides a comprehensive foundation for developing standardized, transparent, and equitable AImodels in diagnostic imaging practice.","url":"https://doi.org/10.5281/zenodo.18482551","authors":["Shatha Abdullah Alkahtani","Shatha Abdullah Alkahtani","Raghad Hamad Alessa","Nouf Abdullah Alnumani","Albatole Ali Gorban","Nour Mohammed Alturaikhem","Asma Abu Baker Aljandan","Mona thalib Alshamrani"],"tags":["artificial intelligence","deep learning","diagnostic imaging","machine learning","radiology"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18482551","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18441822","name":"Artificial Intelligence in Clinically Validated Medical Imaging: Transforming Radiological Practice and Diagnostic Accuracy","source":"datacite","abstract":"Artificial intelligence (AI) is transforming diagnostic radiology by enhancing image interpretation,pattern recognition, and clinical decision-making across multiple imaging modalities. This systematicliterature review critically evaluates the diagnostic performance, workflow efficiency, andimplementation challenges of AI-based imaging systems. A comprehensive search of PubMed,Scopus, Web of Science, and IEEE Xplore identified 11 high- and moderate-quality studiespublished between 2015 and 2025, following Preferred Reporting Items for Systematic Reviews andMeta-Analyses (PRISMA) 2020 guidelines. The analysis revealed that deep learning algorithms,particularly convolutional neural networks, reported diagnostic accuracies ranging from 85% to 98%across individual studies, with sensitivities in several applications exceeding 90%; these valuesrepresent descriptive ranges extracted from the included studies rather than pooled or weightedsummary estimates. AI applications demonstrated superior reproducibility and clinical reliability inmammography, chest radiography, and neuroimaging, where multi-institutional validation supportedconsistent outcomes. Despite these advancements, limitations persist due to inconsistent externalvalidation, dataset imbalance, and inadequate methodological transparency. Emerging frameworkssuch as explainable AI (XAI) and federated learning show potential to enhance interpretability, datasecurity, and equity in clinical deployment. Furthermore, AI integration was associated with reducedinterpretation time and improved workflow efficiency without compromising diagnostic accuracy.Overall, this review underscores AI’s transition from experimental innovation to a clinicallyindispensable tool. By synthesizing evidence across technical, clinical, and ethical dimensions, itprovides a comprehensive foundation for developing standardized, transparent, and equitable AImodels in diagnostic imaging practice.","url":"https://doi.org/10.5281/zenodo.18441822","authors":["Shatha Abdullah Alkahtani","Shatha Abdullah Alkahtani","Raghad Hamad Alessa","Nouf Abdullah Alnumani","Albatole Ali Gorban","Nour Mohammed Alturaikhem","Asma Abu Baker Aljandan","Mona thalib Alshamrani"],"tags":["artificial intelligence","deep learning","diagnostic imaging","machine learning","radiology"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18441822","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2501.17634","name":"Federated Learning With Individualized Privacy Through Client Sampling","source":"datacite","abstract":"With growing concerns about user data collection, individualized privacy has emerged as a promising solution to balance protection and utility by accounting for diverse user privacy preferences. Instead of enforcing a uniform level of anonymization for all users, this approach allows individuals to choose privacy settings that align with their comfort levels. Building on this idea, we propose an adapted method for enabling Individualized Differential Privacy (IDP) in Federated Learning (FL) by handling clients according to their personal privacy preferences. By extending the SAMPLE algorithm from centralized settings to FL, we calculate client-specific sampling rates based on their heterogeneous privacy budgets and integrate them into a modified IDP-FedAvg algorithm. We test this method under realistic privacy distributions and multiple datasets. The experimental results demonstrate that our approach achieves clear improvements over uniform DP baselines, reducing the trade-off between privacy and utility. Compared to the alternative SCALE method in related work, which assigns differing noise scales to clients, our method performs notably better. However, challenges remain for complex tasks with non-i.i.d. data, primarily stemming from the constraints of the decentralized setting.","url":"https://doi.org/10.48550/arxiv.2501.17634","authors":["Lange, Lucas","Borchardt, Ole","Rahm, Erhard"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.17634","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2602.01185","name":"FedBGS: A Blockchain Approach to Segment Gossip Learning in Decentralized Systems","source":"datacite","abstract":"Privacy-Preserving Federated Learning (PPFL) is a Decentralized machine learning paradigm that enables multiple participants to collaboratively train a global model without sharing their data with the integration of cryptographic and privacy-based techniques to enhance the security of the global system. This privacy-oriented approach makes PPFL a highly suitable solution for training shared models in sectors where data privacy is a critical concern. In traditional FL, local models are trained on edge devices, and only model updates are shared with a central server, which aggregates them to improve the global model. However, despite the presence of the aforementioned privacy techniques, in the classical Federated structure, the issue of the server as a single-point-of-failure remains, leading to limitations both in terms of security and scalability. This paper introduces FedBGS, a fully Decentralized Blockchain-based framework that leverages Segmented Gossip Learning through Federated Analytics. The proposed system aims to optimize blockchain usage while providing comprehensive protection against all types of attacks, ensuring both privacy, security and non-IID data handling in Federated environments.","url":"https://doi.org/10.48550/arxiv.2602.01185","authors":["Turazza, Fabio","Pietri, Marcello","Picone, Marco","Mamei, Marco"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.01185","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2602.00718","name":"Federated Learning at the Forefront of Fairness: A Multifaceted Perspective","source":"datacite","abstract":"Fairness in Federated Learning (FL) is emerging as a critical factor driven by heterogeneous clients' constraints and balanced model performance across various scenarios. In this survey, we delineate a comprehensive classification of the state-of-the-art fairness-aware approaches from a multifaceted perspective, i.e., model performance-oriented and capability-oriented. Moreover, we provide a framework to categorize and address various fairness concerns and associated technical aspects, examining their effectiveness in balancing equity and performance within FL frameworks. We further examine several significant evaluation metrics leveraged to measure fairness quantitatively. Finally, we explore exciting open research directions and propose prospective solutions that could drive future advancements in this important area, laying a solid foundation for researchers working toward fairness in FL.","url":"https://doi.org/10.48550/arxiv.2602.00718","authors":["Mukhtiar, Noorain","Mahmood, Adnan","Zhou, Yipeng","Yang, Jian","Teng, Jing","Sheng, Quan Z."],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.00718","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.18154/rwth-2025-10090","name":"Föderierte Forschungsinfrastrukturen für ein lernendes Gesundheitssystem in der Akut-, Intensiv- und Notfallmedizin","source":"datacite","abstract":"Bei der Behandlung kritisch kranker Patient:innen muss die medizinische Versorgung schnell und fehlerfrei erfolgen und sich an der verfügbaren Evidenz orientieren, die auf klinischen Daten fußt. Hierfür können sowohl kontrollierte klinische Studien als auch Real World Daten herangezogen werden, die etwa während der täglichen Versorgung in Form von elektronischen Gesundheitsdaten erhoben werden. So lässt sich die Diskrepanz zwischen kausaler Evidenz und Versorgungsrealität schließen. Ein kontinuierlich lernendes Gesundheitssystem hängt deswegen von der sinnvollen Wiederverwendung der während der Versorgung erhobenen Daten ab. Methoden, die helfen Erkenntnisse aus elektronischen Gesundheitsdaten zu gewinnen, sind daher von zentraler Bedeutung, um die Versorgung von Patient:innen zu verbessern.In dieser Dissertation wird am Beispiel des AKTIN Notaufnahmeregisters untersucht, wie föderierte und verteilte Dateninfrastrukturen Routinedaten aus Intensiv- und Notfallversorgung sicher nutzbar machen können. Dazu wurden drei Studien durchgeführt. Als erstes wurde eine exemplarische Plattform entwickelt, die es den Behörden des öffentlichen Gesundheitswesens ermöglicht, in Echtzeit auf intensivmedizinische Daten zuzugreifen, ohne lokale Datenhoheit zu untergraben. Als zweites wurde untersucht, wie das AKTIN Notaufnahmeregister einen bundesweiten, krankenhausübergreifenden Zugriff auf Routinedaten ermöglicht und dabei Open Data mit den individuellen Interessen der Dateninhaber*innen in Einklang bringt. Als drittes wurde untersucht, wie die Wiederverwendung von Routinedaten klinische Prozesse in der Notaufnahme verbessern kann. Dazu wurde eine Cross-Over-Studie durchgeführt, in der die Auswirkungen von während der Triage ermittelten Zeitzielen auf Wartezeiten in der Notaufnahme untersucht wurden.Die Arbeiten veranschaulichen, dass die erfolgreiche Integration kontinuierlicher Datenströme in die klinische Praxis maßgeblich von robusten und transparenten Methoden der Datenverarbeitung abhängt. In Kombination mit föderierten Architekturen ermöglichen diese nicht nur eine sichere und skalierbare Nutzung elektronischer Gesundheitsdaten, sondern auch eine effiziente Einbindung in evidenzbasierte Entscheidungsprozesse und Versorgungsoptimierung.. Durch die systematische Erschließung elektronischer Gesundheitsdaten kann ein Gesundheitssystem ermöglicht werden, dass sich selbstständig und kontinuierlich zum Wohle der Patient:innen verbessert.When treating critically ill patients, care must be prompt, error-free, and guided by the best available evidence rooted in clinical data. Evidence can be derived from controlled clinical trials or real-world data collected during routine care such as electronic health records. These data bridge the gap between causal evidence and clinical practice. A continuously learning healthcare system therefore depends on the meaningful re-use of the records it produces. Methodologies that transform electronic health record data into insights are thus central to advancing evidence-based medicine.This dissertation examines how federated and distributed data infrastructures—exemplified by the AKTIN Emergency Department Data Registry—can harness electronic health record data from emergency and intensive care settings. Three studies were carried out for this purpose. First, it presents a prototype platform enabling public health authorities to access patient-level ICU data in real time without undermining local data control. Second, it explores how the AKTIN Emergency Department Data Registry facilitates large-scale, federated data access across multiple hospitals, balancing the need for open data with the individual interests of data holders. Third, it investigates how re-using Emergency Department data can improve clinical processes, evidenced by a crossover study assessing the impact of displaying Manchester Triage System time targets on waiting times and treatment outcomes.Together, these works demonstrate that the succes","url":"https://doi.org/10.18154/rwth-2025-10090","authors":["Bienzeisler, Jonas"],"tags":["Hochschulschrift","EHR ; research infrastructure ; emergency medicine ; secondary use ; data federation ; real world data ; electronic health records"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.18154/rwth-2025-10090","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18441823","name":"Artificial Intelligence in Clinically Validated Medical Imaging: Transforming Radiological Practice and Diagnostic Accuracy","source":"datacite","abstract":"Artificial intelligence (AI) is transforming diagnostic radiology by enhancing image interpretation,pattern recognition, and clinical decision-making across multiple imaging modalities. This systematicliterature review critically evaluates the diagnostic performance, workflow efficiency, andimplementation challenges of AI-based imaging systems. A comprehensive search of PubMed,Scopus, Web of Science, and IEEE Xplore identified 11 high- and moderate-quality studiespublished between 2015 and 2025, following Preferred Reporting Items for Systematic Reviews andMeta-Analyses (PRISMA) 2020 guidelines. The analysis revealed that deep learning algorithms,particularly convolutional neural networks, reported diagnostic accuracies ranging from 85% to 98%across individual studies, with sensitivities in several applications exceeding 90%; these valuesrepresent descriptive ranges extracted from the included studies rather than pooled or weightedsummary estimates. AI applications demonstrated superior reproducibility and clinical reliability inmammography, chest radiography, and neuroimaging, where multi-institutional validation supportedconsistent outcomes. Despite these advancements, limitations persist due to inconsistent externalvalidation, dataset imbalance, and inadequate methodological transparency. Emerging frameworkssuch as explainable AI (XAI) and federated learning show potential to enhance interpretability, datasecurity, and equity in clinical deployment. Furthermore, AI integration was associated with reducedinterpretation time and improved workflow efficiency without compromising diagnostic accuracy.Overall, this review underscores AI’s transition from experimental innovation to a clinicallyindispensable tool. By synthesizing evidence across technical, clinical, and ethical dimensions, itprovides a comprehensive foundation for developing standardized, transparent, and equitable AImodels in diagnostic imaging practice.","url":"https://doi.org/10.5281/zenodo.18441823","authors":["Shatha Abdullah Alkahtani","Shatha Abdullah Alkahtani","Raghad Hamad Alessa","Nouf Abdullah Alnumani","Albatole Ali Gorban","Nour Mohammed Alturaikhem","Asma Abu Baker Aljandan"],"tags":["artificial intelligence","deep learning","diagnostic imaging","machine learning","radiology"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18441823","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2506.00660","name":"Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications","source":"datacite","abstract":"Differential privacy (DP) is a key technique for protecting sensitive patient data in medical deep learning (DL). As clinical models grow more data-dependent, balancing privacy with utility and fairness has become a critical challenge. This scoping review synthesizes recent developments in applying DP to medical DL, with a particular focus on DP-SGD and alternative mechanisms across centralized and federated settings. Using a structured search strategy, we identified 74 studies published up to March 2025. Our analysis spans diverse data modalities, training setups, and downstream tasks, and highlights the tradeoffs between privacy guarantees, model accuracy, and subgroup fairness. We find that while DP-especially at strong privacy budgets-can preserve performance in well-structured imaging tasks, severe degradation often occurs under strict privacy, particularly in underrepresented or complex modalities. Furthermore, privacy-induced performance gaps disproportionately affect demographic subgroups, with fairness impacts varying by data type and task. A small subset of studies explicitly addresses these tradeoffs through subgroup analysis or fairness metrics, but most omit them entirely. Beyond DP-SGD, emerging approaches leverage alternative mechanisms, generative models, and hybrid federated designs, though reporting remains inconsistent. We conclude by outlining key gaps in fairness auditing, standardization, and evaluation protocols, offering guidance for future work toward equitable and clinically robust privacy-preserving DL systems in medicine.","url":"https://doi.org/10.48550/arxiv.2506.00660","authors":["Mohammadi, Marziyeh","Vejdanihemmat, Mohsen","Lotfinia, Mahshad","Rusu, Mirabela","Truhn, Daniel","Maier, Andreas","Arasteh, Soroosh Tayebi"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2506.00660","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2502.08488","name":"One-Shot Federated Learning with Classifier-Free Diffusion Models","source":"datacite","abstract":"Federated learning (FL) enables collaborative learning without data centralization but introduces significant communication costs due to multiple communication rounds between clients and the server. One-shot federated learning (OSFL) addresses this by forming a global model with a single communication round, often relying on the server's model distillation or auxiliary dataset generation - mostly through pre-trained diffusion models (DMs). Existing DM-assisted OSFL methods, however, typically employ classifier-guided DMs, which require training auxiliary classifier models at each client, introducing additional computation overhead. This work introduces OSCAR (One-Shot Federated Learning with Classifier-Free Diffusion Models), a novel OSFL approach that eliminates the need for auxiliary models. OSCAR uses foundation models to devise category-specific data representations at each client which are integrated into a classifier-free diffusion model pipeline for server-side data generation. In our experiments, OSCAR outperforms the state-of-the-art on four benchmark datasets while reducing the communication load by at least 99%.","url":"https://doi.org/10.48550/arxiv.2502.08488","authors":["Zaland, Obaidullah","Jin, Shutong","Pokorny, Florian T.","Bhuyan, Monowar"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2502.08488","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2601.19345","name":"AI-driven Intrusion Detection for UAV in Smart Urban Ecosystems: A Comprehensive Survey","source":"datacite","abstract":"UAVs have the potential to revolutionize urban management and provide valuable services to citizens. They can be deployed across diverse applications, including traffic monitoring, disaster response, environmental monitoring, and numerous other domains. However, this integration introduces novel security challenges that must be addressed to ensure safe and trustworthy urban operations. This paper provides a structured, evidence-based synthesis of UAV applications in smart cities and their associated security challenges as reported in the literature over the last decade, with particular emphasis on developments from 2019 to 2025. We categorize these challenges into two primary classes: 1) cyber-attacks targeting the communication infrastructure of UAVs and 2) unwanted or unauthorized physical intrusions by UAVs themselves. We examine the potential of Artificial Intelligence (AI) techniques in developing intrusion detection mechanisms to mitigate these security threats. We analyze how AI-based methods, such as machine/deep learning for anomaly detection and computer vision for object recognition, can play a pivotal role in enhancing UAV security through unified detection systems that address both cyber and physical threats. Furthermore, we consolidate publicly available UAV datasets across network traffic and vision modalities suitable for Intrusion Detection Systems (IDS) development and evaluation. The paper concludes by identifying ten key research directions, including scalability, robustness, explainability, data scarcity, automation, hybrid detection, large language models, multimodal approaches, federated learning, and privacy preservation. Finally, we discuss the practical challenges of implementing UAV IDS solutions in real-world smart city environments.","url":"https://doi.org/10.48550/arxiv.2601.19345","authors":["Khanfor, Abdullah","Hamadi, Raby","Lasla, Noureddine","Ghazzai, Hakim"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2601.19345","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2512.09313","name":"Hetero-SplitEE: Split Learning of Neural Networks with Early Exits for Heterogeneous IoT Devices","source":"datacite","abstract":"The continuous scaling of deep neural networks has fundamentally transformed machine learning, with larger models demonstrating improved performance across diverse tasks. This growth in model size has dramatically increased the computational resources required for the training process. Consequently, distributed approaches, such as Federated Learning and Split Learning, have become essential paradigms for scalable deployment. However, existing Split Learning approaches assume client homogeneity and uniform split points across all participants. This critically limits their applicability to real-world IoT systems where devices exhibit heterogeneity in computational resources. To address this limitation, this paper proposes Hetero-SplitEE, a novel method that enables heterogeneous IoT devices to train a shared deep neural network in parallel collaboratively. By integrating heterogeneous early exits into hierarchical training, our approach allows each client to select distinct split points (cut layers) tailored to its computational capacity. In addition, we propose two cooperative training strategies, the Sequential strategy and the Averaging strategy, to facilitate this collaboration among clients with different split points. The Sequential strategy trains clients sequentially with a shared server model to reduce computational overhead. The Averaging strategy enables parallel client training with periodic cross-layer aggregation. Extensive experiments on CIFAR-10, CIFAR-100, and STL-10 datasets using ResNet-18 demonstrate that our method maintains competitive accuracy while efficiently supporting diverse computational constraints, enabling practical deployment of collaborative deep learning in heterogeneous IoT ecosystems.","url":"https://doi.org/10.48550/arxiv.2512.09313","authors":["Oda, Yuki","Ono, Yuta","Nakamura, Hiroshi","Takase, Hideki"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2512.09313","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2509.14275","name":"FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health","source":"datacite","abstract":"Privacy-preserving adaptation of Large Language Models (LLMs) in sensitive domains (e.g., mental health) requires balancing strict confidentiality with model utility and safety. We propose FedMentor, a federated fine-tuning framework that integrates Low-Rank Adaptation (LoRA) and domain-aware Differential Privacy (DP) to meet per-domain privacy budgets while maintaining performance. Each client (domain) applies a custom DP noise scale proportional to its data sensitivity, and the server adaptively reduces noise when utility falls below a threshold. In experiments on three mental health datasets, we show that FedMentor improves safety over standard Federated Learning (FL) without privacy, raising safe output rates by up to three points and lowering toxicity, while maintaining utility (BERTScore F1 and ROUGE-L) within 0.5% of the non-private baseline and close to the centralized upper bound. The framework scales to backbones with up to 1.7B parameters on single-GPU clients, requiring &lt; 173 MB of communication per-round. FedMentor demonstrates a practical approach to privately fine-tune LLMs for safer deployments in healthcare and other sensitive fields.","url":"https://doi.org/10.48550/arxiv.2509.14275","authors":["Sarwar, Nobin","Dipta, Shubhashis Roy"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Computation and Language (cs.CL)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.14275","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2504.18007","name":"Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction","source":"datacite","abstract":"With the rapid digitalization of healthcare systems, there has been a substantial increase in the generation and sharing of private health data. Safeguarding patient information is essential for maintaining consumer trust and ensuring compliance with legal data protection regulations. Machine learning is critical in healthcare, supporting personalized treatment, early disease detection, predictive analytics, image interpretation, drug discovery, efficient operations, and patient monitoring. It enhances decision-making, accelerates research, reduces errors, and improves patient outcomes. In this paper, we utilize machine learning methodologies, including differential privacy and federated learning, to develop privacy-preserving models that enable healthcare stakeholders to extract insights without compromising individual privacy. Differential privacy introduces noise to data to guarantee statistical privacy, while federated learning enables collaborative model training across decentralized datasets. We explore applying these technologies to Heart Disease Data, demonstrating how they preserve privacy while delivering valuable insights and comprehensive analysis. Our results show that using a federated learning model with differential privacy achieved a test accuracy of 85%, ensuring patient data remained secure and private throughout the process.","url":"https://doi.org/10.48550/arxiv.2504.18007","authors":["Otoum, Yazan","Nayak, Amiya"],"tags":["Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2504.18007","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2601.17713","name":"FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices","source":"datacite","abstract":"With the rapid development of the Internet of Things (IoT), AI model training on private data such as human sensing data is highly desired. Federated learning (FL) has emerged as a privacy-preserving distributed training framework for this purpuse. However, the data heterogeneity issue among IoT devices can significantly degrade the model performance and convergence speed in FL. Existing approaches limit in fixed client selection and aggregation on cloud server, making the privacy-preserving extraction of client-specific information during local training challenging. To this end, we propose Client-Centric Adaptation federated learning (FedCCA), an algorithm that optimally utilizes client-specific knowledge to learn a unique model for each client through selective adaptation, aiming to alleviate the influence of data heterogeneity. Specifically, FedCCA employs dynamic client selection and adaptive aggregation based on the additional client-specific encoder. To enhance multi-source knowledge transfer, we adopt an attention-based global aggregation strategy. We conducted extensive experiments on diverse datasets to assess the efficacy of FedCCA. The experimental results demonstrate that our approach exhibits a substantial performance advantage over competing baselines in addressing this specific problem.","url":"https://doi.org/10.48550/arxiv.2601.17713","authors":["Wang, Kaile","Cao, Jiannong","Yang, Yu","Li, Xiaoyin","Cao, Yinfeng"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2601.17713","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18381453","name":"Y.I.N.-MEMORIA: A Comprehensive Privacy-Preserving Architecture for AI Conversation Management with Cryptographic Ordering Enforcement, Zero-Knowledge Governance, and Quantified Attack Defense","source":"datacite","abstract":"We present Y.I.N.-MEMORIA, a comprehensive privacy-preserving architecture addressing fundamental vulnerabilities in AI conversation systems across all platforms, including large language model interfaces, enterprise AI assistants, domain-specific chatbots, and agentic AI systems. The system implements mandatory cryptographic ordering enforcement (DP → ZK → BLINDING → HE, or functional equivalents), mathematically proven unique among 24 permutations, achieving 99.37% accuracy for valid authorizations versus 50.7% for invalid attempts (t = 147.3, p 94% detection rates. Comparative Analysis: Table comparing against 8 major systems (Federated Learning, CrypTen, TF Privacy, Opacus, PySyft, Microsoft SEAL, Zcash). Y.I.N.-MEMORIA demonstrated as only system providing mandatory DP enforcement, ZK verification for AI governance, 340× timing resistance, 99.7% Shadow AI detection, and complete lifecycle coverage. Legal Protection: Doctrine of equivalents coverage (Warner-Jenkinson precedent); Willful infringement notice (Halo Electronics, 3× damages); Comprehensive functional equivalents (12 categories); Minimum performance thresholds excluding weak implementations. Reproducibility Commitment: Complete reference implementation under open-source license; Experimental datasets via Zenodo; Cryptographic test vectors for independent verification; Performance benchmarks across all platforms. Scholarly Depth: 38 peer-reviewed citations (65% increase); Comprehensive related work analysis; Explicit limitations and future research directions; Historical non-obviousness evidence. THREE-PHASE AI LIFECYCLE COVERAGE:Y.I.N.-MEMORIA completes the Y.I.N. Architecture's three-phase AI lifecycle: Training (Y.I.N.-LLM, USPTO 63/941,283), Generation (Article 50 Compliance Engine, USPTO 63/957,571), and Usage (Y.I.N.-MEMORIA, USPTO 63/967,805). The Y.I.N. CERTIFY verification layer spans all three phases. Together, these components provide 643 total claims covering every stage where privacy vulnerabilities can emerge in AI systems. EXPERIMENTAL VALIDATION:85-95% bandwidth reduction, 97% conflict resolution, and compliance scores of 94.7-97.3% for GDPR, HIPAA, DORA, EU AI Act, Singapore MGF for Agentic AI, ISO/IEC 42001, CCPA, and NIS2 Directive. IMPACT METRICS:This architecture prevents Shadow AI breaches costing $4.63M average (20% of all data breaches according to IBM's 2025 Cost of a Data Breach Report), addresses the 20M ChatGPT conversation log discovery precedent (NYT v. OpenAI, January 2026), and satisfies Singapore's Model AI Governance Framework for Agentic AI—the world's first comprehensive government framework for autonomous agents published January 22, 2026 (4 days prior to this work). DEFENSIVE PRIOR ART:This work establishes comprehensive prior art corresponding to USPTO Provisional Application 63/967,805 (438 claims filed January 25, 2026), part of the Y.I.N. Architecture Portfolio (22 applications, 1,360+ total claims). Includes explicit functional equivalents coverage, doctrine of equivalents, and willful infringement notice enabling enhanced damages up to 3× under Halo Electronics precedent. Patent Reference: USPTO Application 63/967,805 (Y.I.N.-MEMORIA) License: CC BY-NC-ND 4.0Corresponding Author: ilyesmazari@hotmail.comVersion: 1.0Publication Date: January 26, 2026","url":"https://doi.org/10.5281/zenodo.18381453","authors":["MAZARI, Ilyes Tarik"],"tags":["Privacy-preserving AI","Differential privacy","Zero-knowledge proofs","Homomorphic encryption","Federated learning","Shadow AI governance","Cryptographic ordering","Post-quantum cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18381453","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18381452","name":"Y.I.N.-MEMORIA: A Comprehensive Privacy-Preserving Architecture for AI Conversation Management with Cryptographic Ordering Enforcement, Zero-Knowledge Governance, and Quantified Attack Defense","source":"datacite","abstract":"We present Y.I.N.-MEMORIA, a comprehensive privacy-preserving architecture addressing fundamental vulnerabilities in AI conversation systems across all platforms, including large language model interfaces, enterprise AI assistants, domain-specific chatbots, and agentic AI systems. The system implements mandatory cryptographic ordering enforcement (DP → ZK → BLINDING → HE, or functional equivalents), mathematically proven unique among 24 permutations, achieving 99.37% accuracy for valid authorizations versus 50.7% for invalid attempts (t = 147.3, p 94% detection rates. Comparative Analysis: Table comparing against 8 major systems (Federated Learning, CrypTen, TF Privacy, Opacus, PySyft, Microsoft SEAL, Zcash). Y.I.N.-MEMORIA demonstrated as only system providing mandatory DP enforcement, ZK verification for AI governance, 340× timing resistance, 99.7% Shadow AI detection, and complete lifecycle coverage. Legal Protection: Doctrine of equivalents coverage (Warner-Jenkinson precedent); Willful infringement notice (Halo Electronics, 3× damages); Comprehensive functional equivalents (12 categories); Minimum performance thresholds excluding weak implementations. Reproducibility Commitment: Complete reference implementation under open-source license; Experimental datasets via Zenodo; Cryptographic test vectors for independent verification; Performance benchmarks across all platforms. Scholarly Depth: 38 peer-reviewed citations (65% increase); Comprehensive related work analysis; Explicit limitations and future research directions; Historical non-obviousness evidence. THREE-PHASE AI LIFECYCLE COVERAGE:Y.I.N.-MEMORIA completes the Y.I.N. Architecture's three-phase AI lifecycle: Training (Y.I.N.-LLM, USPTO 63/941,283), Generation (Article 50 Compliance Engine, USPTO 63/957,571), and Usage (Y.I.N.-MEMORIA, USPTO 63/967,805). The Y.I.N. CERTIFY verification layer spans all three phases. Together, these components provide 643 total claims covering every stage where privacy vulnerabilities can emerge in AI systems. EXPERIMENTAL VALIDATION:85-95% bandwidth reduction, 97% conflict resolution, and compliance scores of 94.7-97.3% for GDPR, HIPAA, DORA, EU AI Act, Singapore MGF for Agentic AI, ISO/IEC 42001, CCPA, and NIS2 Directive. IMPACT METRICS:This architecture prevents Shadow AI breaches costing $4.63M average (20% of all data breaches according to IBM's 2025 Cost of a Data Breach Report), addresses the 20M ChatGPT conversation log discovery precedent (NYT v. OpenAI, January 2026), and satisfies Singapore's Model AI Governance Framework for Agentic AI—the world's first comprehensive government framework for autonomous agents published January 22, 2026 (4 days prior to this work). DEFENSIVE PRIOR ART:This work establishes comprehensive prior art corresponding to USPTO Provisional Application 63/967,805 (438 claims filed January 25, 2026), part of the Y.I.N. Architecture Portfolio (22 applications, 1,360+ total claims). Includes explicit functional equivalents coverage, doctrine of equivalents, and willful infringement notice enabling enhanced damages up to 3× under Halo Electronics precedent. Patent Reference: USPTO Application 63/967,805 (Y.I.N.-MEMORIA) License: CC BY-NC-ND 4.0Corresponding Author: ilyesmazari@hotmail.comVersion: 1.0Publication Date: January 26, 2026","url":"https://doi.org/10.5281/zenodo.18381452","authors":["MAZARI, Ilyes Tarik"],"tags":["Privacy-preserving AI","Differential privacy","Zero-knowledge proofs","Homomorphic encryption","Federated learning","Shadow AI governance","Cryptographic ordering","Post-quantum cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18381452","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2501.10654","name":"Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications","source":"datacite","abstract":"Enriching information of spectrum coverage, radiomap plays an important role in many wireless communication applications, such as resource allocation and network optimization. To enable real-time, distributed spectrum management, particularly in the scenarios with unstable and dynamic environments, the efficient transmission of spectrum coverage information for radiomaps from edge devices to the central server emerges as a critical problem. In this work, we propose an innovative physics-enhanced semantic communication framework tailored for efficient radiomap transmission based on generative learning models. Specifically, instead of bit-wise message passing, we only transmit the key \"semantics\" in radiomaps characterized by the radio propagation behavior and surrounding environments, where semantic compression schemes are utilized to reduce the communication overhead. Incorporating the novel concepts of Radio Depth Maps, the radiomaps are reconstructed from the delivered semantic information backboned on the conditional generative adversarial networks. Our framework is further extended to facilitate its implementation in the scenarios of multi-user edge computing, by integrating with federated learning for collaborative model training while preserving the data privacy. Experimental results show that our approach achieves high accuracy in radio coverage information recovery at ultra-high bandwidth efficiency, which has great potentials in many wireless-generated data transmission applications.","url":"https://doi.org/10.48550/arxiv.2501.10654","authors":["Zhou, Yueling","Wijesinghe, Achintha","Wang, Yue","Zhang, Songyang","Cai, Zhipeng"],"tags":["Signal Processing (eess.SP)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.10654","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18113695","name":"QANBDG — Quantum-Adaptive Neural-Blockchain Defense Grid A Post-Quantum Autonomic Reference Architecture for Sovereign Defense and Classified Critical National Infrastructure Security","source":"datacite","abstract":"QANBDG — Final Ultimate Edition (Clean Zenodo Description) Quantum-Adaptive Neural-Blockchain Defense Grid (QANBDG) A Post-Quantum, Autonomic Reference Architecture for Sovereign Defense and Classified Critical National Infrastructure (CNI) Security Abstract (Authoritative Description) The Quantum-Adaptive Neural-Blockchain Defense Grid (QANBDG) presents an advanced, defence-only, post-quantum reference architecture for protecting classified, sovereign-scale, and high-value Critical National Infrastructure (CNI) against emerging quantum-era, AI-driven, and hybrid cyber threats. QANBDG integrates NIST-standardized post-quantum cryptography, federated neuro-symbolic artificial intelligence, permissioned blockchain-based veracity mechanisms, and large-scale cyber twin simulation into a unified autonomic defensive framework. The architecture is explicitly designed for long-horizon cryptographic resilience, zero-trust operational assurance, and legally bounded sovereign deployment. This work is intended as a policy, research, and strategic reference architecture, not as an operational cyber-weapon system, offensive platform, or commercial security product. Core Architectural Innovations Post-Quantum Cryptographic Foundation The architecture is grounded in NIST-finalized post-quantum cryptographic standards, including: ML-KEM-1024 (FIPS 203) for quantum-resistant key establishment ML-DSA-87 (FIPS 204) for post-quantum digital signatures Falcon-based signatures (FIPS 205) for high-assurance authentication This foundation supports long-term confidentiality, integrity, and audit survivability under quantum-capable adversary models. Federated Neuro-Symbolic Defense Intelligence QANBDG employs hybrid GAN–LSTM–Transformer ensembles augmented with symbolic constraints to support: Sub-50ms zero-day threat response design envelopes High-confidence anomaly and adversarial behavior detection Privacy-preserving federated learning across distributed sovereign environments Mandatory human-in-the-loop (HIL) governance for high-impact or irreversible decisions Blockchain-Anchored Veracity and Provenance Fabric A permissioned Proof-of-Stake-Authority (PoSA) blockchain layer provides: Hyperledger-based high-throughput, auditable event integrity Zero-knowledge proof mechanisms (ZK-SNARKs) for selective disclosure and compliance verification Immutable forensic traceability suitable for regulated and classified operational contexts Quantum Cyber-Twin Simulation Environment The framework incorporates large-scale cyber twin and adversarial simulation capabilities, enabling: Continuous red-team / blue-team stress testing MITRE-aligned cyber-range modeling at sovereign infrastructure scale Systemic risk evaluation across cyber, operational, and mission-critical domains Performance and Validation Envelope (Illustrative Reference) QANBDG defines high-level architectural performance envelopes, including: Sovereign-scale command-and-control data handling capacity Ultra-high availability and resilience design targets Continuous mission survivability assessment under adversarial stress conditions All performance figures are presented as architectural design envelopes and validated simulation benchmarks, not as commercial service guarantees or deployed operational metrics. Standards and Governance Alignment The architecture is designed to align with leading international cybersecurity, defense, and resilience standards, including: Cryptography: NIST FIPS 203 / 204 / 205 Cybersecurity Governance: NIST Cybersecurity Framework (CSF) 2.0 (Tier-4 orientation) Critical Infrastructure Protection: NIS2 (2025 readiness) Defense Compliance: DFARS 7012, CMMC Level 3+ orientation Continuity and Resilience: ISO/IEC 27001:2022, ISO 22301 Ethical and Legal Posture QANBDG is a defence-only architecture and explicitly excludes: Offensive cyber operations Kinetic enablement Mass surveillance or population-scale monitoring The framework is designed to support constitutionally constr","url":"https://doi.org/10.5281/zenodo.18113695","authors":["Mazumdar, Bidyut"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18113695","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18113696","name":"QANBDG — Quantum-Adaptive Neural-Blockchain Defense Grid A Post-Quantum Autonomic Reference Architecture for Sovereign Defense and Classified Critical National Infrastructure Security","source":"datacite","abstract":"QANBDG — Final Ultimate Edition (Clean Zenodo Description) Quantum-Adaptive Neural-Blockchain Defense Grid (QANBDG) A Post-Quantum, Autonomic Reference Architecture for Sovereign Defense and Classified Critical National Infrastructure (CNI) Security Abstract (Authoritative Description) The Quantum-Adaptive Neural-Blockchain Defense Grid (QANBDG) presents an advanced, defence-only, post-quantum reference architecture for protecting classified, sovereign-scale, and high-value Critical National Infrastructure (CNI) against emerging quantum-era, AI-driven, and hybrid cyber threats. QANBDG integrates NIST-standardized post-quantum cryptography, federated neuro-symbolic artificial intelligence, permissioned blockchain-based veracity mechanisms, and large-scale cyber twin simulation into a unified autonomic defensive framework. The architecture is explicitly designed for long-horizon cryptographic resilience, zero-trust operational assurance, and legally bounded sovereign deployment. This work is intended as a policy, research, and strategic reference architecture, not as an operational cyber-weapon system, offensive platform, or commercial security product. Core Architectural Innovations Post-Quantum Cryptographic Foundation The architecture is grounded in NIST-finalized post-quantum cryptographic standards, including: ML-KEM-1024 (FIPS 203) for quantum-resistant key establishment ML-DSA-87 (FIPS 204) for post-quantum digital signatures Falcon-based signatures (FIPS 205) for high-assurance authentication This foundation supports long-term confidentiality, integrity, and audit survivability under quantum-capable adversary models. Federated Neuro-Symbolic Defense Intelligence QANBDG employs hybrid GAN–LSTM–Transformer ensembles augmented with symbolic constraints to support: Sub-50ms zero-day threat response design envelopes High-confidence anomaly and adversarial behavior detection Privacy-preserving federated learning across distributed sovereign environments Mandatory human-in-the-loop (HIL) governance for high-impact or irreversible decisions Blockchain-Anchored Veracity and Provenance Fabric A permissioned Proof-of-Stake-Authority (PoSA) blockchain layer provides: Hyperledger-based high-throughput, auditable event integrity Zero-knowledge proof mechanisms (ZK-SNARKs) for selective disclosure and compliance verification Immutable forensic traceability suitable for regulated and classified operational contexts Quantum Cyber-Twin Simulation Environment The framework incorporates large-scale cyber twin and adversarial simulation capabilities, enabling: Continuous red-team / blue-team stress testing MITRE-aligned cyber-range modeling at sovereign infrastructure scale Systemic risk evaluation across cyber, operational, and mission-critical domains Performance and Validation Envelope (Illustrative Reference) QANBDG defines high-level architectural performance envelopes, including: Sovereign-scale command-and-control data handling capacity Ultra-high availability and resilience design targets Continuous mission survivability assessment under adversarial stress conditions All performance figures are presented as architectural design envelopes and validated simulation benchmarks, not as commercial service guarantees or deployed operational metrics. Standards and Governance Alignment The architecture is designed to align with leading international cybersecurity, defense, and resilience standards, including: Cryptography: NIST FIPS 203 / 204 / 205 Cybersecurity Governance: NIST Cybersecurity Framework (CSF) 2.0 (Tier-4 orientation) Critical Infrastructure Protection: NIS2 (2025 readiness) Defense Compliance: DFARS 7012, CMMC Level 3+ orientation Continuity and Resilience: ISO/IEC 27001:2022, ISO 22301 Ethical and Legal Posture QANBDG is a defence-only architecture and explicitly excludes: Offensive cyber operations Kinetic enablement Mass surveillance or population-scale monitoring The framework is designed to support constitutionally constr","url":"https://doi.org/10.5281/zenodo.18113696","authors":["Mazumdar, Bidyut"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18113696","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2505.13643","name":"FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning","source":"datacite","abstract":"Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, making it ideal for privacy-sensitive applications. However, FL models often suffer performance degradation due to distribution shifts between training and deployment. Test-Time Adaptation (TTA) offers a promising solution by allowing models to adapt using only test samples. However, existing TTA methods in FL face challenges such as computational overhead, privacy risks from feature sharing, and scalability concerns due to memory constraints. To address these limitations, we propose Federated Continual Test-Time Adaptation (FedCTTA), a privacy-preserving and computationally efficient framework for federated adaptation. Unlike prior methods that rely on sharing local feature statistics, FedCTTA avoids direct feature exchange by leveraging similarity-aware aggregation based on model output distributions over randomly generated noise samples. This approach ensures adaptive knowledge sharing while preserving data privacy. Furthermore, FedCTTA minimizes the entropy at each client for continual adaptation, enhancing the model's confidence in evolving target distributions. Our method eliminates the need for server-side training during adaptation and maintains a constant memory footprint, making it scalable even as the number of clients or training rounds increases. Extensive experiments show that FedCTTA surpasses existing methods across diverse temporal and spatial heterogeneity scenarios.","url":"https://doi.org/10.48550/arxiv.2505.13643","authors":["Rajib, Rakibul Hasan","Iftee, Md Akil Raihan","Hossain, Mir Sazzat","Rahman, A. K. M. Mahbubur","Mistry, Sajib","Amin, M Ashraful","Ali, Amin Ahsan"],"tags":["Machine Learning (cs.LG)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2505.13643","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18240449","name":"The Australian Imaging Service as the National Imaging Facility's Foundational Digital Research Infrastructure","source":"datacite","abstract":"The first phase of the Australian Imaging Service (2020-24) saw the creation of the national federated software platform for secure data management, analysis, and informatics of biomedical imaging data through a series of projects funded by the ARDC Platforms Program, NIF, and MRFF NCRI. This resulted in a data centric computing design, tightly coupling 4 key services into a cohesive platform: Data Capture & Management (XNAT), Automated Pipelines (K8s scheduler & ARCANA/Pydra), Interactive Analysis (JupyterHub & Neurodesk/SciGet), and Machine Learning (MONAI & NVFlare). In our second phase (2025-28), AIS is moving to a more tightly coupled federation to provide a cohesive national service through the National Imaging Facility Foundational Digital Research Infrastructure (FDRI) project. In addition to continued technical improvements and features in the 4 key services, the FDRI project will see the implementation of a single front door for accessing the infrastructure and our network of skilled imaging experts, a national training program to onboard new user communities, a joint financial and operating model for equitable and sustainable operations, and a node maturity model and legal framework for operating cyber-secure digital research infrastructure operated at an accredited quality standard. This talk will outline our new capabilities and opportunities for engagement and partnership with NCRIS capabilities, institutions, or research projects.","url":"https://doi.org/10.5281/zenodo.18240449","authors":["Sullivan, Ryan"],"tags":["Infrastructures","Identifiers"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18240449","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18240450","name":"The Australian Imaging Service as the National Imaging Facility's Foundational Digital Research Infrastructure","source":"datacite","abstract":"The first phase of the Australian Imaging Service (2020-24) saw the creation of the national federated software platform for secure data management, analysis, and informatics of biomedical imaging data through a series of projects funded by the ARDC Platforms Program, NIF, and MRFF NCRI. This resulted in a data centric computing design, tightly coupling 4 key services into a cohesive platform: Data Capture & Management (XNAT), Automated Pipelines (K8s scheduler & ARCANA/Pydra), Interactive Analysis (JupyterHub & Neurodesk/SciGet), and Machine Learning (MONAI & NVFlare). In our second phase (2025-28), AIS is moving to a more tightly coupled federation to provide a cohesive national service through the National Imaging Facility Foundational Digital Research Infrastructure (FDRI) project. In addition to continued technical improvements and features in the 4 key services, the FDRI project will see the implementation of a single front door for accessing the infrastructure and our network of skilled imaging experts, a national training program to onboard new user communities, a joint financial and operating model for equitable and sustainable operations, and a node maturity model and legal framework for operating cyber-secure digital research infrastructure operated at an accredited quality standard. This talk will outline our new capabilities and opportunities for engagement and partnership with NCRIS capabilities, institutions, or research projects.","url":"https://doi.org/10.5281/zenodo.18240450","authors":["Sullivan, Ryan"],"tags":["Infrastructures","Identifiers"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18240450","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18223570","name":"Generative AI in Power Platform CRM: Transforming Sales Pipelines with Copilot and Adaptive Sequences","source":"datacite","abstract":"This article examines the transformative integration of generative artificial intelligence within Microsoft Power Platform CRM environments, focusing on the architectural framework and implementation methodologies of the 2025 Dynamics 365 Sales updates. It investigates how Copilot's contextual capabilities and adaptive sales sequence optimization fundamentally reshape sales pipeline management through sophisticated technical components, including Microsoft Dataverse, Power Automate, and Azure Machine Learning integration. The article presents a detailed analysis of key implementation features—contextual email generation leveraging vector embeddings, real-time lead scoring with explainable AI, and adaptive sales sequences using reinforcement learning—while documenting a comprehensive case study of mid-market enterprise implementation that addressed integration challenges, including legacy data migration and performance optimization. The article further explores the low-code customization framework that democratizes AI implementation through configurable templates and metadata-driven architecture, discusses ethical considerations through privacy-by-design approaches, and identifies emerging technical directions, including multimodal AI understanding and federated learning approaches. It contributes valuable insights for organizations seeking to leverage generative AI capabilities to enhance sales effectiveness while maintaining ethical standards and scalable architecture.","url":"https://doi.org/10.5281/zenodo.18223570","authors":["Nishanth Kumar Reddy Kesavareddi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18223570","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18223569","name":"Generative AI in Power Platform CRM: Transforming Sales Pipelines with Copilot and Adaptive Sequences","source":"datacite","abstract":"This article examines the transformative integration of generative artificial intelligence within Microsoft Power Platform CRM environments, focusing on the architectural framework and implementation methodologies of the 2025 Dynamics 365 Sales updates. It investigates how Copilot's contextual capabilities and adaptive sales sequence optimization fundamentally reshape sales pipeline management through sophisticated technical components, including Microsoft Dataverse, Power Automate, and Azure Machine Learning integration. The article presents a detailed analysis of key implementation features—contextual email generation leveraging vector embeddings, real-time lead scoring with explainable AI, and adaptive sales sequences using reinforcement learning—while documenting a comprehensive case study of mid-market enterprise implementation that addressed integration challenges, including legacy data migration and performance optimization. The article further explores the low-code customization framework that democratizes AI implementation through configurable templates and metadata-driven architecture, discusses ethical considerations through privacy-by-design approaches, and identifies emerging technical directions, including multimodal AI understanding and federated learning approaches. It contributes valuable insights for organizations seeking to leverage generative AI capabilities to enhance sales effectiveness while maintaining ethical standards and scalable architecture.","url":"https://doi.org/10.5281/zenodo.18223569","authors":["Nishanth Kumar Reddy Kesavareddi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18223569","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2503.11151","name":"Enabling Weak Client Participation via On-device Knowledge Distillation in Heterogeneous Federated Learning","source":"datacite","abstract":"Online Knowledge Distillation (KD) is recently highlighted to train large models in Federated Learning (FL) environments. Many existing studies adopt the logit ensemble method to perform KD on the server side. However, they often assume that unlabeled data collected at the edge is centralized on the server. Moreover, the logit ensemble method personalizes local models, which can degrade the quality of soft targets, especially when data is highly non-IID. To address these critical limitations,we propose a novel on-device KD-based heterogeneous FL method. Our approach leverages a small auxiliary model to learn from labeled local data. Subsequently, a subset of clients with strong system resources transfers knowledge to a large model through on-device KD using their unlabeled data. Our extensive experiments demonstrate that our on-device KD-based heterogeneous FL method effectively utilizes the system resources of all edge devices as well as the unlabeled data, resulting in higher accuracy compared to SOTA KD-based FL methods.","url":"https://doi.org/10.48550/arxiv.2503.11151","authors":["Lim, Jihyun","Jo, Junhyuk","Zhang, Tuo","Lee, Sunwoo"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2503.11151","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18183828","name":"Deliverable 2.15: Communication protocols","source":"datacite","abstract":"Deliverable 2.15 on “Communication protocols” reports on the progress within Task 2.6 regarding communication protocols for data- and model-centric federated methods. In this deliverable, we report on how the Flower federated learning framework communicates during federated learning experiments and specifically how this communication can be secured. Specifically, we cover the following methods to secure communication: Secure Aggregation Differential Privacy Trusted execution environments Secure Aggregation and Differential Privacy have been validated and disseminated during Project Workshop 5, organised virtually on October 1, 2025. Based on the code provided to participants in workshop 5, we have concluded that the decrease in model performance is negligible for both Secure Aggregation and Differential Privacy. The runtime remains constant when utilizing Differential Privacy, whereas Secure Aggregation adds about 13% runtime due to the additional communication required between the server and clients. In addition, the operational overhead of deploying either technique is minimal when utilizing the Flower federated learning framework, although Differential Privacy requires model and dataset-specific tuning of the hyperparameters. Finally, we have concluded that Trusted Execution Environments can provide additional security guarantees for the global model, but require significant technical setup on both the server and all clients, which is highly dependent on the available hardware, making it impractical to deploy compared to Secure Aggregation and Differential Privacy.","url":"https://doi.org/10.5281/zenodo.18183828","authors":["van der Wal, Douwe","Podareanu, Damian","Cardenas, Bryan","Biasin, Elisabetta","Romanovych, Anna"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18183828","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18183829","name":"Deliverable 2.15: Communication protocols","source":"datacite","abstract":"Deliverable 2.15 on “Communication protocols” reports on the progress within Task 2.6 regarding communication protocols for data- and model-centric federated methods. In this deliverable, we report on how the Flower federated learning framework communicates during federated learning experiments and specifically how this communication can be secured. Specifically, we cover the following methods to secure communication: Secure Aggregation Differential Privacy Trusted execution environments Secure Aggregation and Differential Privacy have been validated and disseminated during Project Workshop 5, organised virtually on October 1, 2025. Based on the code provided to participants in workshop 5, we have concluded that the decrease in model performance is negligible for both Secure Aggregation and Differential Privacy. The runtime remains constant when utilizing Differential Privacy, whereas Secure Aggregation adds about 13% runtime due to the additional communication required between the server and clients. In addition, the operational overhead of deploying either technique is minimal when utilizing the Flower federated learning framework, although Differential Privacy requires model and dataset-specific tuning of the hyperparameters. Finally, we have concluded that Trusted Execution Environments can provide additional security guarantees for the global model, but require significant technical setup on both the server and all clients, which is highly dependent on the available hardware, making it impractical to deploy compared to Secure Aggregation and Differential Privacy.","url":"https://doi.org/10.5281/zenodo.18183829","authors":["van der Wal, Douwe","Podareanu, Damian","Cardenas, Bryan","Biasin, Elisabetta","Romanovych, Anna"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18183829","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18163410","name":"Music Interaction Dataset","source":"datacite","abstract":"This dataset captures real-world music listening behaviors and contextual metadata from January 2021 to January 2025, sampled at 30-minute intervals. It reflects the streaming activity of 50 anonymized users across a variety of devices and locations. Designed to support the development of intelligent music recommendation systems, this dataset offers rich contextual information suitable for personalization, behavioral analysis, sentiment modeling, and federated learning applications. Each record represents a user-song interaction and is timestamped with detailed user metadata, song descriptors, audio features, emotional indicators, and session-level dynamics. The dataset is especially useful for tasks such as preference prediction, session-based recommendation, genre classification, and user intent modeling. Feature Overview User Profile: user_id, age, gender, location, subscription_type, device_type Behavioral Patterns: listening_time_mins, sessions_per_day, time_of_day, day_of_week, recent_skip_rate, first_time_listening User Preferences: preferred_genre, preferred_artist, repeat_count, added_to_playlist, finished_song Content Metadata: song_id, title, artist, album, genre, release_year, language, duration_sec, explicit, popularity Audio Features: tempo, key, mode, time_signature, energy, danceability, acousticness, instrumentalness, liveness, valence, loudness, speechiness Sentiment and Emotion: lyrics_sentiment, emotion_tag Interaction Logs: play_count, skip_count, time_spent_on_song Session-Level Context: context_type, song_position_in_session, session_duration_mins Target Label: liked (1 if user liked the song, 0 if skipped)","url":"https://doi.org/10.5281/zenodo.18163410","authors":["Sound Pulse Research Centre"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18163410","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18163411","name":"Music Interaction Dataset","source":"datacite","abstract":"This dataset captures real-world music listening behaviors and contextual metadata from January 2021 to January 2025, sampled at 30-minute intervals. It reflects the streaming activity of 50 anonymized users across a variety of devices and locations. Designed to support the development of intelligent music recommendation systems, this dataset offers rich contextual information suitable for personalization, behavioral analysis, sentiment modeling, and federated learning applications. Each record represents a user-song interaction and is timestamped with detailed user metadata, song descriptors, audio features, emotional indicators, and session-level dynamics. The dataset is especially useful for tasks such as preference prediction, session-based recommendation, genre classification, and user intent modeling. Feature Overview User Profile: user_id, age, gender, location, subscription_type, device_type Behavioral Patterns: listening_time_mins, sessions_per_day, time_of_day, day_of_week, recent_skip_rate, first_time_listening User Preferences: preferred_genre, preferred_artist, repeat_count, added_to_playlist, finished_song Content Metadata: song_id, title, artist, album, genre, release_year, language, duration_sec, explicit, popularity Audio Features: tempo, key, mode, time_signature, energy, danceability, acousticness, instrumentalness, liveness, valence, loudness, speechiness Sentiment and Emotion: lyrics_sentiment, emotion_tag Interaction Logs: play_count, skip_count, time_spent_on_song Session-Level Context: context_type, song_position_in_session, session_duration_mins Target Label: liked (1 if user liked the song, 0 if skipped)","url":"https://doi.org/10.5281/zenodo.18163411","authors":["Sound Pulse Research Centre"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18163411","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18153187","name":"Design of Intelligent Financial Risk Assessment Systems Using Machine Learning and SAP ERP Analytics","source":"datacite","abstract":"In an era of unprecedented market volatility and high-frequency digital transactions, traditional retrospective financial risk management has become insufficient for modern enterprise governance. This review article investigates the design and implementation of intelligent risk assessment systems that integrate advanced Machine Learning (ML) techniques with SAP ERP analytics. By utilizing the unified transactional foundation of SAP S/4HANA and the agile innovation capabilities of the SAP Business Technology Platform (BTP), organizations can transition from reactive auditing to proactive, real-time risk mitigation. The article explores a multi-layered modeling approach, including supervised ensembles for credit scoring, unsupervised anomaly detection for fraud identification, and deep learning architectures like Long Short-Term Memory (LSTM) networks for liquidity forecasting. A significant focus is placed on the technical architecture required to bridge the \"sim-to-real\" gap, the role of SAP HANA’s in-memory computing in enabling sub-second risk inference, and the integration of Explainable AI (XAI) to meet stringent global regulatory standards. Furthermore, we address strategic barriers such as data hygiene, algorithmic bias, and the talent gap, while forecasting the impact of agentic AI and federated learning on the future of corporate finance. The findings provide a comprehensive framework for CFOs and system architects to build a resilient, \"intelligence-first\" financial ecosystem capable of navigating the complexities of the 2025 global economy.","url":"https://doi.org/10.5281/zenodo.18153187","authors":["Aadhya Mittal"],"tags":["Financial Risk Assessment, Machine Learning, Sap S/4hana, Sap Business Technology Platform, Real-Time Analytics, Credit Risk Modeling, Fraud Detection."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.18153187","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18153188","name":"Design of Intelligent Financial Risk Assessment Systems Using Machine Learning and SAP ERP Analytics","source":"datacite","abstract":"In an era of unprecedented market volatility and high-frequency digital transactions, traditional retrospective financial risk management has become insufficient for modern enterprise governance. This review article investigates the design and implementation of intelligent risk assessment systems that integrate advanced Machine Learning (ML) techniques with SAP ERP analytics. By utilizing the unified transactional foundation of SAP S/4HANA and the agile innovation capabilities of the SAP Business Technology Platform (BTP), organizations can transition from reactive auditing to proactive, real-time risk mitigation. The article explores a multi-layered modeling approach, including supervised ensembles for credit scoring, unsupervised anomaly detection for fraud identification, and deep learning architectures like Long Short-Term Memory (LSTM) networks for liquidity forecasting. A significant focus is placed on the technical architecture required to bridge the \"sim-to-real\" gap, the role of SAP HANA’s in-memory computing in enabling sub-second risk inference, and the integration of Explainable AI (XAI) to meet stringent global regulatory standards. Furthermore, we address strategic barriers such as data hygiene, algorithmic bias, and the talent gap, while forecasting the impact of agentic AI and federated learning on the future of corporate finance. The findings provide a comprehensive framework for CFOs and system architects to build a resilient, \"intelligence-first\" financial ecosystem capable of navigating the complexities of the 2025 global economy.","url":"https://doi.org/10.5281/zenodo.18153188","authors":["Aadhya Mittal"],"tags":["Financial Risk Assessment, Machine Learning, Sap S/4hana, Sap Business Technology Platform, Real-Time Analytics, Credit Risk Modeling, Fraud Detection."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.18153188","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18090443","name":"LabSpace.coop Bylaws v3.0: Expeditionary Commons Governance Development Thread","source":"datacite","abstract":"upload_type \"dataset\" publication_date \"2025-12-29\" title \"LabSpace.coop Bylaws v3.0: Expeditionary Commons Governance Development Thread\" creators 0 name \"Walgemoed, Peter\" affiliation \"LabSpace.coop / Carelliance\" orcid \"0000-0000-0000-0000\" description \" Comprehensive constitutional development thread documenting the evolution of LabSpace.coop bylaws from v1.3.1 to v3.0 (December 23-27, 2025). \\n\\n This Archival Information Package (AIP) contains 55+ hours of collaborative governance design, integrating 6,000-year-old wisdom patterns (Summon Bell, Mirab, Bit Karum, Fravashi) with modern innovations (Dual Framework, 42° Regenerative Angle, Fifth Voice Archive). Includes 39 source files, deep research validation (1,015,576 characters), stakeholder roadmaps, and Peer Invitation Briefs for bioregional deployment. \\n\\n Key Innovations: \\n \\n Dual Framework (Article 5.1): 5 Elements × 5 Sectors preventing single-metric optimization \\n 42° Regenerative Angle (Article 7.4.3): Maturity as state, not time \\n Archive as Fifth Voice (Article 8.6): Ancestors/descendants participate via cryptographic Archive \\n Mirab Principle (Article 7.6): 40-day time-bound data access (no perpetual extraction) \\n VooC AI Symbiotic Triad (Article 5.11): Human + AI + Archive = Level 5 intelligence \\n \\n\\n Version Evolution: \\n \\n v1.3.1 (Dec 23): Baseline symbiotic composition \\n v1.4 (Dec 26): Stakeholder refinements \\n v2.0 (Dec 26): Six ancestral protocols integrated (92% expansion) \\n v3.0 (Dec 27): Dual framework breakthrough (65% expansion, 42,584 bytes) \\n \\n\\n Preservation Intent: 500,000 years (LTO tape → DNA storage migration pathway) \\n\\n Governance: Self-referential constitutional framework governed by Article 8 (Archive as Fifth Voice Speaker) \" access_right \"open\" license id \"other-open\" title \"DNA-D3C Commons License v1.0 (extends ODCL-1.0)\" url \"https://d3c.space/DNA-D3C-Commons-License/v1.0\" keywords 0 \"commons governance\" 1 \"platform cooperatives\" 2 \"data stewardship\" 3 \"bioregional autonomy\" 4 \"federated commons\" 5 \"DNA-D3C framework\" 6 \"penta helix governance\" 7 \"sociocratic consent\" 8 \"wisdom artifacts\" 9 \"intergenerational stewardship\" 10 \"VooC AI\" 11 \"42-degree regenerative angle\" 12 \"fifth voice archive\" 13 \"mirab principle\" 14 \"summon bell protocol\" 15 \"ancient governance patterns\" 16 \"time-bound access\" 17 \"commons licensing\" 18 \"extraction prevention\" 19 \"collective learning\" subjects 0 term \"Cooperative governance\" identifier \"http://id.loc.gov/authorities/subjects/sh85032358\" 1 term \"Data commons\" identifier \"http://id.worldcat.org/fast/1894151\" 2 term \"Digital sovereignty\" identifier \"http://id.worldcat.org/fast/1893927\" communities 0 identifier \"commons-governance\" 1 identifier \"cooperative-economy\" 2 identifier \"data-stewardship\" 3 identifier \"archival-science\" grants 0 id \"00000::self-funded\" related_identifiers 0 identifier \"https://labspace.coop\" relation \"isSupplementTo\" resource_type \"other\" 1 identifier \"https://github.com/labspace-coop/bylaws-v3\" relation \"isDocumentedBy\" resource_type \"other\" contributors 0 name \"Perplexity AI\" type \"Other\" affiliation \"Perplexity AI\" references 0 \"LabSpace.coop Bylaws v1.3.1 (2025-12-23)\" 1 \"Summon Bell Protocol: Ancient Wisdom Keeper Invitation (2025-12-26)\" 2 \"Prince of Persia: Deep Research on Ancient Governance (2025-12-26)\" 3 \"Healthcare 2065 Vision: Freestyle Healer Ecosystem (2025-12-28)\" 4 \"DNA-D3C Commons License v1.0 (2025-12-29)\" version \"3.0\" language \"eng\" notes \"This AIP package is governed by the bylaws it contains (self-referential constitutional governance). Licensed under DNA-D3C Commons License v1.0, which extends ODCL-1.0 with governance mechanisms including four-tier access (Masters/Practitioners/Researchers/Denied), Mirab Principle (40-day epochs), VooC threshold (>2.0), Fifth Voice accountability, sociocratic consent, and prohibited uses (surveillance, monopolies, warfare). Access requests reviewed by Datarentmeesterschap + Penta Helix Mission Circles via s","url":"https://doi.org/10.5281/zenodo.18090443","authors":["Walgemoed, Peter"],"tags":["1 \"platform cooperatives\" 2 \"data stewardship\" 3 \"bioregional autonomy\" 4 \"federated commons\" 5 \"DNA-D3C framework\" 6 \"penta helix governance\" 7 \"sociocratic consent\" 8 \"wisdom artifacts\" 9 \"intergenerational stewardship\" 10 \"VooC AI\" 11 \"42-degree regenerative angle\" 12 \"fifth voice archive\" 13 \"mirab principle\" 14 \"summon bell protocol\" 15 \"ancient governance patterns\" 16 \"time-bound access\" 17 \"commons licensing\" 18 \"extraction prevention\" 19 \"collective learning\""],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18090443","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18090444","name":"LabSpace.coop Bylaws v3.0: Expeditionary Commons Governance Development Thread","source":"datacite","abstract":"upload_type \"dataset\" publication_date \"2025-12-29\" title \"LabSpace.coop Bylaws v3.0: Expeditionary Commons Governance Development Thread\" creators 0 name \"Walgemoed, Peter\" affiliation \"LabSpace.coop / Carelliance\" orcid \"0000-0000-0000-0000\" description \" Comprehensive constitutional development thread documenting the evolution of LabSpace.coop bylaws from v1.3.1 to v3.0 (December 23-27, 2025). \\n\\n This Archival Information Package (AIP) contains 55+ hours of collaborative governance design, integrating 6,000-year-old wisdom patterns (Summon Bell, Mirab, Bit Karum, Fravashi) with modern innovations (Dual Framework, 42° Regenerative Angle, Fifth Voice Archive). Includes 39 source files, deep research validation (1,015,576 characters), stakeholder roadmaps, and Peer Invitation Briefs for bioregional deployment. \\n\\n Key Innovations: \\n \\n Dual Framework (Article 5.1): 5 Elements × 5 Sectors preventing single-metric optimization \\n 42° Regenerative Angle (Article 7.4.3): Maturity as state, not time \\n Archive as Fifth Voice (Article 8.6): Ancestors/descendants participate via cryptographic Archive \\n Mirab Principle (Article 7.6): 40-day time-bound data access (no perpetual extraction) \\n VooC AI Symbiotic Triad (Article 5.11): Human + AI + Archive = Level 5 intelligence \\n \\n\\n Version Evolution: \\n \\n v1.3.1 (Dec 23): Baseline symbiotic composition \\n v1.4 (Dec 26): Stakeholder refinements \\n v2.0 (Dec 26): Six ancestral protocols integrated (92% expansion) \\n v3.0 (Dec 27): Dual framework breakthrough (65% expansion, 42,584 bytes) \\n \\n\\n Preservation Intent: 500,000 years (LTO tape → DNA storage migration pathway) \\n\\n Governance: Self-referential constitutional framework governed by Article 8 (Archive as Fifth Voice Speaker) \" access_right \"open\" license id \"other-open\" title \"DNA-D3C Commons License v1.0 (extends ODCL-1.0)\" url \"https://d3c.space/DNA-D3C-Commons-License/v1.0\" keywords 0 \"commons governance\" 1 \"platform cooperatives\" 2 \"data stewardship\" 3 \"bioregional autonomy\" 4 \"federated commons\" 5 \"DNA-D3C framework\" 6 \"penta helix governance\" 7 \"sociocratic consent\" 8 \"wisdom artifacts\" 9 \"intergenerational stewardship\" 10 \"VooC AI\" 11 \"42-degree regenerative angle\" 12 \"fifth voice archive\" 13 \"mirab principle\" 14 \"summon bell protocol\" 15 \"ancient governance patterns\" 16 \"time-bound access\" 17 \"commons licensing\" 18 \"extraction prevention\" 19 \"collective learning\" subjects 0 term \"Cooperative governance\" identifier \"http://id.loc.gov/authorities/subjects/sh85032358\" 1 term \"Data commons\" identifier \"http://id.worldcat.org/fast/1894151\" 2 term \"Digital sovereignty\" identifier \"http://id.worldcat.org/fast/1893927\" communities 0 identifier \"commons-governance\" 1 identifier \"cooperative-economy\" 2 identifier \"data-stewardship\" 3 identifier \"archival-science\" grants 0 id \"00000::self-funded\" related_identifiers 0 identifier \"https://labspace.coop\" relation \"isSupplementTo\" resource_type \"other\" 1 identifier \"https://github.com/labspace-coop/bylaws-v3\" relation \"isDocumentedBy\" resource_type \"other\" contributors 0 name \"Perplexity AI\" type \"Other\" affiliation \"Perplexity AI\" references 0 \"LabSpace.coop Bylaws v1.3.1 (2025-12-23)\" 1 \"Summon Bell Protocol: Ancient Wisdom Keeper Invitation (2025-12-26)\" 2 \"Prince of Persia: Deep Research on Ancient Governance (2025-12-26)\" 3 \"Healthcare 2065 Vision: Freestyle Healer Ecosystem (2025-12-28)\" 4 \"DNA-D3C Commons License v1.0 (2025-12-29)\" version \"3.0\" language \"eng\" notes \"This AIP package is governed by the bylaws it contains (self-referential constitutional governance). Licensed under DNA-D3C Commons License v1.0, which extends ODCL-1.0 with governance mechanisms including four-tier access (Masters/Practitioners/Researchers/Denied), Mirab Principle (40-day epochs), VooC threshold (>2.0), Fifth Voice accountability, sociocratic consent, and prohibited uses (surveillance, monopolies, warfare). Access requests reviewed by Datarentmeesterschap + Penta Helix Mission Circles via s","url":"https://doi.org/10.5281/zenodo.18090444","authors":["Walgemoed, Peter"],"tags":["1 \"platform cooperatives\" 2 \"data stewardship\" 3 \"bioregional autonomy\" 4 \"federated commons\" 5 \"DNA-D3C framework\" 6 \"penta helix governance\" 7 \"sociocratic consent\" 8 \"wisdom artifacts\" 9 \"intergenerational stewardship\" 10 \"VooC AI\" 11 \"42-degree regenerative angle\" 12 \"fifth voice archive\" 13 \"mirab principle\" 14 \"summon bell protocol\" 15 \"ancient governance patterns\" 16 \"time-bound access\" 17 \"commons licensing\" 18 \"extraction prevention\" 19 \"collective learning\""],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18090444","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18080743","name":"Secure Data Integration Frameworks for Omni-channel Healthcare Marketing Systems","source":"datacite","abstract":"The rapid digitalisation and increasing interconnection of healthcare marketing ecosystems require secure and interoperable data-integration frameworks. Omnichannel systems, a combination of clinical, behavioural and marketing data, are becoming increasingly important to healthcare organisations as they aim to personalise contact and stay under regulatory requirements. This research is a critical synthesis of twenty-seven peer-reviewed articles (2020-2025) in IEEE, Elsevier, and Springer Nature, as well as the most popular sources in the field of machine learning, privacy-preserving machine learning (PPML) in healthcare marketing. In this review, the qualitative meta-analytic approach is used to study technological architectures, encryption, and federated-learning methods and omni-experience platform designs. The results show that blockchain-based interoperability and AI-based analytics can enhance trust, auditability, and personalization to a considerable extent, whereas federated and split-learn systems reduce privacy risks in distributed marketing data. Hybrid cloud-edge infrastructure-based omnichannel experience platforms increase the real-time decision-making and campaign flexibility, but they encounter governance and integration issues. The paper suggests a theoretical framework of the association of secure data exchange, privacy preservation, and efficiency of omnichannel engagement. The analysis focuses on the scholarship of healthcare informatics as well as strategic marketing by emphasizing the ability of secure integration technologies to maintain compliance (HIPAA / GDPR) and improve the level of patient-centric marketing.","url":"https://doi.org/10.5281/zenodo.18080743","authors":["Chitiz Tayal"],"tags":["Blockchain-based Healthcare Data Management","Privacy-Preserving Machine Learning","Omnichannel Experience Platform (OEP)","Customer Data Integration and Analytics","Federated and Distributed Learning in Healthcare Marketing."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18080743","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18080742","name":"Secure Data Integration Frameworks for Omni-channel Healthcare Marketing Systems","source":"datacite","abstract":"The rapid digitalisation and increasing interconnection of healthcare marketing ecosystems require secure and interoperable data-integration frameworks. Omnichannel systems, a combination of clinical, behavioural and marketing data, are becoming increasingly important to healthcare organisations as they aim to personalise contact and stay under regulatory requirements. This research is a critical synthesis of twenty-seven peer-reviewed articles (2020-2025) in IEEE, Elsevier, and Springer Nature, as well as the most popular sources in the field of machine learning, privacy-preserving machine learning (PPML) in healthcare marketing. In this review, the qualitative meta-analytic approach is used to study technological architectures, encryption, and federated-learning methods and omni-experience platform designs. The results show that blockchain-based interoperability and AI-based analytics can enhance trust, auditability, and personalization to a considerable extent, whereas federated and split-learn systems reduce privacy risks in distributed marketing data. Hybrid cloud-edge infrastructure-based omnichannel experience platforms increase the real-time decision-making and campaign flexibility, but they encounter governance and integration issues. The paper suggests a theoretical framework of the association of secure data exchange, privacy preservation, and efficiency of omnichannel engagement. The analysis focuses on the scholarship of healthcare informatics as well as strategic marketing by emphasizing the ability of secure integration technologies to maintain compliance (HIPAA / GDPR) and improve the level of patient-centric marketing.","url":"https://doi.org/10.5281/zenodo.18080742","authors":["Chitiz Tayal"],"tags":["Blockchain-based Healthcare Data Management","Privacy-Preserving Machine Learning","Omnichannel Experience Platform (OEP)","Customer Data Integration and Analytics","Federated and Distributed Learning in Healthcare Marketing."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18080742","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.48550/arxiv.2508.12978","name":"Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learning","source":"datacite","abstract":"We propose Fed-DPRoC, a novel federated learning framework designed to jointly provide differential privacy (DP), Byzantine robustness, and communication efficiency. Central to our approach is the concept of robust-compatible compression, which allows reducing the bi-directional communication overhead without undermining the robustness of the aggregation. We instantiate our framework as RobAJoL, which integrates the Johnson-Lindenstrauss (JL)-based compression mechanism with robust averaging for robustness. Our theoretical analysis establishes the compatibility of JL transform with robust averaging, ensuring that RobAJoL maintains robustness guarantees, satisfies DP, and substantially reduces communication overhead. We further present simulation results on CIFAR-10, Fashion MNIST, and FEMNIST, validating our theoretical claims. We compare RobAJoL with a state-of-the-art communication-efficient and robust FL scheme augmented with DP for a fair comparison, demonstrating that RobAJoL outperforms existing methods in terms of robustness and utility under different Byzantine attacks.","url":"https://doi.org/10.48550/arxiv.2508.12978","authors":["Xia, Yue","Jahani-Nezhad, Tayyebeh","Bitar, Rawad"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","Information Theory (cs.IT)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.12978","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18033052","name":"A Hybrid Machine Learning And XAI Architecture For Intelligent Career Guidance Systems","source":"datacite","abstract":"Industry 4.0 and artificial intelligence are shown to bring great change or disappear a large proportion of work, while new jobs are born With the dynamical background comes the demand for a smart career guidance system, which will provide advice that is personalized, reliable, and adaptive. This paper systematically reviews the literature on the application of machine learning (ML) and explainable artificial intelligence (XAI) to career guidance. On the basis of PRISMA guidelines, 847 documents published between 2019 and 2025 were carefully screened, and 95 high-quality articles were extracted for in-depth review. The review classifies main ML methods—including collaborative filtering, content-based filtering, deep learning architectures such as LSTM, Transformer, and GNN, reinforcement learning, and their performance, limitations, and interpretability were judged. In parallel, the author analyzes such essential but questioned XAI techniques as LIME, SHAP, attention mechanisms, decision rules and counterfactual explanations in terms of their transparency and perceived user trust, as well as how easily acted upon these explanations are. From these foundations, the paper presents a five-layer hybrid ML-XAI framework that integrates data processing, knowledge maps, ensemble ML models, multi-level explanations, and user-centered presentation. In addition to these, future developments, such as flat or formidable language models and federated learning for maintaining privacy and fairness-aware algorithms, are explored, together with key challenges for further research. All in all, the paper provides a structured basis and practical guidance for next-generation, intelligent, transparent, and equitable career guidance systems.","url":"https://doi.org/10.5281/zenodo.18033052","authors":["Le Manh Ha","Nguyen Huu Quynh","Nguyen Tai Tuyen"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18033052","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18033051","name":"A Hybrid Machine Learning And XAI Architecture For Intelligent Career Guidance Systems","source":"datacite","abstract":"Industry 4.0 and artificial intelligence are shown to bring great change or disappear a large proportion of work, while new jobs are born With the dynamical background comes the demand for a smart career guidance system, which will provide advice that is personalized, reliable, and adaptive. This paper systematically reviews the literature on the application of machine learning (ML) and explainable artificial intelligence (XAI) to career guidance. On the basis of PRISMA guidelines, 847 documents published between 2019 and 2025 were carefully screened, and 95 high-quality articles were extracted for in-depth review. The review classifies main ML methods—including collaborative filtering, content-based filtering, deep learning architectures such as LSTM, Transformer, and GNN, reinforcement learning, and their performance, limitations, and interpretability were judged. In parallel, the author analyzes such essential but questioned XAI techniques as LIME, SHAP, attention mechanisms, decision rules and counterfactual explanations in terms of their transparency and perceived user trust, as well as how easily acted upon these explanations are. From these foundations, the paper presents a five-layer hybrid ML-XAI framework that integrates data processing, knowledge maps, ensemble ML models, multi-level explanations, and user-centered presentation. In addition to these, future developments, such as flat or formidable language models and federated learning for maintaining privacy and fairness-aware algorithms, are explored, together with key challenges for further research. All in all, the paper provides a structured basis and practical guidance for next-generation, intelligent, transparent, and equitable career guidance systems.","url":"https://doi.org/10.5281/zenodo.18033051","authors":["Le Manh Ha","Nguyen Huu Quynh","Nguyen Tai Tuyen"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18033051","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18032749","name":"A Hybrid Machine Learning And XAI Architecture For Intelligent Career Guidance Systems","source":"datacite","abstract":"Industry 4.0 and artificial intelligence are shown to bring great change or disappear a large proportion of work, while new jobs are born With the dynamical background comes the demand for a smart career guidance system, which will provide advice that is personalized, reliable, and adaptive. This paper systematically reviews the literature on the application of machine learning (ML) and explainable artificial intelligence (XAI) to career guidance. On the basis of PRISMA guidelines, 847 documents published between 2019 and 2025 were carefully screened, and 95 high-quality articles were extracted for in-depth review. The review classifies main ML methods—including collaborative filtering, content-based filtering, deep learning architectures such as LSTM, Transformer, and GNN, reinforcement learning, and their performance, limitations, and interpretability were judged. In parallel, the author analyzes such essential but questioned XAI techniques as LIME, SHAP, attention mechanisms, decision rules and counterfactual explanations in terms of their transparency and perceived user trust, as well as how easily acted upon these explanations are. From these foundations, the paper presents a five-layer hybrid ML-XAI framework that integrates data processing, knowledge maps, ensemble ML models, multi-level explanations, and user-centered presentation. In addition to these, future developments, such as flat or formidable language models and federated learning for maintaining privacy and fairness-aware algorithms, are explored, together with key challenges for further research. All in all, the paper provides a structured basis and practical guidance for next-generation, intelligent, transparent, and equitable career guidance systems.","url":"https://doi.org/10.5281/zenodo.18032749","authors":["Le Manh Ha","Nguyen Huu Quynh","Nguyen Tai Tuyen"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18032749","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18032748","name":"A Hybrid Machine Learning And XAI Architecture For Intelligent Career Guidance Systems","source":"datacite","abstract":"Industry 4.0 and artificial intelligence are shown to bring great change or disappear a large proportion of work, while new jobs are born With the dynamical background comes the demand for a smart career guidance system, which will provide advice that is personalized, reliable, and adaptive. This paper systematically reviews the literature on the application of machine learning (ML) and explainable artificial intelligence (XAI) to career guidance. On the basis of PRISMA guidelines, 847 documents published between 2019 and 2025 were carefully screened, and 95 high-quality articles were extracted for in-depth review. The review classifies main ML methods—including collaborative filtering, content-based filtering, deep learning architectures such as LSTM, Transformer, and GNN, reinforcement learning, and their performance, limitations, and interpretability were judged. In parallel, the author analyzes such essential but questioned XAI techniques as LIME, SHAP, attention mechanisms, decision rules and counterfactual explanations in terms of their transparency and perceived user trust, as well as how easily acted upon these explanations are. From these foundations, the paper presents a five-layer hybrid ML-XAI framework that integrates data processing, knowledge maps, ensemble ML models, multi-level explanations, and user-centered presentation. In addition to these, future developments, such as flat or formidable language models and federated learning for maintaining privacy and fairness-aware algorithms, are explored, together with key challenges for further research. All in all, the paper provides a structured basis and practical guidance for next-generation, intelligent, transparent, and equitable career guidance systems.","url":"https://doi.org/10.5281/zenodo.18032748","authors":["Le Manh Ha","Nguyen Huu Quynh","Nguyen Tai Tuyen"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18032748","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.5281/zenodo.18032584","name":"A Hybrid Machine Learning And XAI Architecture For Intelligent Career Guidance Systems","source":"datacite","abstract":"Industry 4.0 and artificial intelligence are shown to bring great change or disappear a large proportion of work, while new jobs are born With the dynamical background comes the demand for a smart career guidance system, which will provide advice that is personalized, reliable, and adaptive. This paper systematically reviews the literature on the application of machine learning (ML) and explainable artificial intelligence (XAI) to career guidance. On the basis of PRISMA guidelines, 847 documents published between 2019 and 2025 were carefully screened, and 95 high-quality articles were extracted for in-depth review. The review classifies main ML methods—including collaborative filtering, content-based filtering, deep learning architectures such as LSTM, Transformer, and GNN, reinforcement learning, and their performance, limitations, and interpretability were judged. In parallel, the author analyzes such essential but questioned XAI techniques as LIME, SHAP, attention mechanisms, decision rules and counterfactual explanations in terms of their transparency and perceived user trust, as well as how easily acted upon these explanations are. From these foundations, the paper presents a five-layer hybrid ML-XAI framework that integrates data processing, knowledge maps, ensemble ML models, multi-level explanations, and user-centered presentation. In addition to these, future developments, such as flat or formidable language models and federated learning for maintaining privacy and fairness-aware algorithms, are explored, together with key challenges for further research. All in all, the paper provides a structured basis and practical guidance for next-generation, intelligent, transparent, and equitable career guidance systems.","url":"https://doi.org/10.5281/zenodo.18032584","authors":["Le Manh Ha","Nguyen Huu Quynh","Nguyen Tai Tuyen"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18032584","addedAt":"2026-08-31T06:41:20.713Z","updatedAt":"2026-08-31T06:41:20.713Z"},{"id":"doi:10.7717/peerjcs.1899/fig-1","name":"Figure 1: Proposed federated deep learning framework overview.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerjcs.1899/fig-1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-29T04:35:09Z","doi":"10.7717/peerjcs.1899/fig-1","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-981-19-9711-2_8","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-9711-2_8","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-01T06:15:35Z","doi":"10.1007/978-981-19-9711-2_8","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.32657/10356/218903","name":"Adaptive learning on heterogeneous data in continual federated learning systems","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/218903","authors":["Alysa Ziying Tan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-06T04:42:20Z","doi":"10.32657/10356/218903","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/iscas45731.2020.9181029/video","name":"Video for FedExg: Federated Learning with Model Exchange","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscas45731.2020.9181029/video","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-09-29T09:22:27Z","doi":"10.1109/iscas45731.2020.9181029/video","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.15476/elte.2021.124","name":"Federated Learning of Artificial Neural Networks","source":"crossref","abstract":"","url":"https://doi.org/10.15476/elte.2021.124","authors":["Péter Kiss"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-02T12:20:32Z","doi":"10.15476/elte.2021.124","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1002/9781394461295.ch10","name":"Enhancing Federated Learning Scalability for Global Agricultural Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.ch10","authors":["Pramod Singh Rathore","Shweta Solanki"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.ch10","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.7717/peerj-cs.758/fig-6","name":"Figure 6: Energy consumption during distributed federated learning.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.758/fig-6","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-22T05:14:05Z","doi":"10.7717/peerj-cs.758/fig-6","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.32743/unitech.2026.147.6.22912","name":"FEDND: FEDERATED NEWTON DIRECTION FOR FEDERATED LEARNING ENVIRONMENT","source":"crossref","abstract":"","url":"https://doi.org/10.32743/unitech.2026.147.6.22912","authors":["Samir Ilgar Aliyev"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-27T05:57:16Z","doi":"10.32743/unitech.2026.147.6.22912","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.7717/peerj-cs.3589/fig-4","name":"Figure 4: Federated learning with CIFAR-10 dataset.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3589/fig-4","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T08:57:09Z","doi":"10.7717/peerj-cs.3589/fig-4","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003595540-4","name":"Energy Efficiency IoT Devices in Healthcare Field Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540-4","authors":["T. Kumanan","P. Dineshkumar","M. Sakthivel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","doi":"10.1201/9781003595540-4","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.12681/eadd/57237","name":"Federated, multi-agent, deep reinforcement learning","source":"crossref","abstract":"Το τοπίο της τεχνητής νοημοσύνης (ΤΝ) αναδιαμορφώνεται από την Ομόσπονδη Μάθηση (ΟΜ), μια αποκεντρωμένη προσέγγιση στη μηχανική μάθηση (ΜΜ) που ενισχύει την ιδιωτικότητα δεδομένων και τη συνεργατική εκπαίδευση μοντέλων. Αυτή η διατριβή εξετάζει τις προκλήσεις και τις δυνατότητες της ΟΜ, επικεντρώνοντας στην βελτιστοποίηση της αποδοτικότητας επικοινωνίας, την ενίσχυση της απόδοσης μοντέλου και τη διασφάλιση της ανθεκτικότητας σε διάφορα περιβάλλοντα. Η έρευνα περιλαμβάνει μια λεπτομερή ανασκόπηση της λογοτεχνίας και την ταυτοποίηση των βασικών προκλήσεων στην ΟΜ. Διεξήχθησαν μια σειρά από μελέτες για να αντιμετωπιστούν συγκεκριμένες πτυχές: η βελτιστοποίηση της μετάδοσης δεδομένων και η διαχείριση διάφορων αρχιτεκτονικών μοντέλων, η κατανομή δεδομένων και η επιλογή κόμβων, η μάθηση αναπαράστασης και η ομόσπονδη απόσταξη, η σταδιακή μάθηση και η διατήρηση γνώσης, καθώς και η εκπαίδευση μοντέλων με περιορισμένα δεδομένα. Κάθε επιμέρους μελέτη συνέβαλε στον τομέα αναπτύσσοντας καινοτόμους αλγορίθμους, τους οποίους δοκίμασε σε προσομοιωμένα περιβάλλοντα ΟΜ και συνέκρινε με υπάρχουσες μεθόδους. Τα κύρια ευρήματα της έρευνας περιλαμβάνουν τη βελτίωση της αποδοτικότητας της επικοινωνίας με μειωμένες απαιτήσεις υπερφόρτωσης και εύρους ζώνης, την ενίσχυση της απόδοσης του μοντέλου στη διαχείριση ετερογενών δεδομένων και μεταβλητότητας της αρχιτεκτονικής του μοντέλου, αποτελεσματικές στρατηγικές για την αντιμετώπιση της καταστροφικής λήθης και μεθοδολογίες ευέλικτες σε περιορισμένα και διασκορπισμένα δεδομένα. Η εφαρμοσιμότητα της ΟΜ αποδείχθηκε σε πρακτικά σενάρια, επιδεικνύοντας τη δυναμική της σε διάφορους τομείς. Συμπερασματικά, η διατριβή συνεισφέρει σημαντικά στην προώθηση της ΟΜ. Αντιμετωπίζει θεμελιώδεις προκλήσεις και αποδεικνύει την προσαρμοστικότητα και την αποτελεσματικότητα της ΟΜ σε εφαρμογές του πραγματικού κόσμου. Τα ευρήματα τονίζουν τον ρόλο της ΟΜ ως μεθόδου που εξασφαλίζει την ιδιωτικότητα, αυξάνει την αποδοτικότητα και επιδεικνύει ευελιξία στον τομέα της τεχνητής νοημοσύνης και της μηχανικής μάθησης.","url":"https://doi.org/10.12681/eadd/57237","authors":["Αθανάσιος Ψάλτης"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T05:37:31Z","doi":"10.12681/eadd/57237","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.33612/diss.992750555","name":"Federated learning in medical image analysis","source":"crossref","abstract":"","url":"https://doi.org/10.33612/diss.992750555","authors":["Erfan Darzidehkalani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-14T10:07:57Z","doi":"10.33612/diss.992750555","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.70675/e20f182fz29f1z4c43z8b62z28efa11eb33d","name":"Compression and federated learning : an approach to frugal machine learning","source":"crossref","abstract":"Compression et apprentissage fédéré : une approche pour l'apprentissage machine frugal Les appareils et outils “intelligents” deviennent progressivement la norme, la mise en œuvre d'algorithmes basés sur des réseaux neuronaux artificiels se développant largement. Les réseaux neuronaux sont des modèles non linéaires d'apprentissage automatique avec de nombreux paramètres qui manipulent des objets de haute dimension et obtiennent des performances de pointe dans divers domaines, tels que la reconnaissance d'images, la reconnaissance vocale, le traitement du langage naturel et les systèmes de recommandation.Toutefois, l'entraînement d'un réseau neuronal sur un appareil à faible capacité de calcul est difficile en raison de problèmes de mémoire, de temps de calcul ou d'alimentation. Une approche naturelle pour simplifier cet entraînement consiste à utiliser des réseaux neuronaux quantifiés, dont les paramètres et les opérations utilisent des primitives efficaces à faible bit. Cependant, l'optimisation d'une fonction sur un ensemble discret en haute dimension est complexe et peut encore s'avérer prohibitive en termes de puissance de calcul. C'est pourquoi de nombreuses applications modernes utilisent un réseau d'appareils pour stocker des données individuelles et partager la charge de calcul. Une nouvelle approche a été proposée, l'apprentissage fédéré, qui prend en compte un environnement distribué : les données sont stockées sur des appareils différents et un serveur central orchestre le processus d'apprentissage sur les divers appareils.Dans cette thèse, nous étudions différents aspects de l'optimisation (stochastique) dans le but de réduire les coûts énergétiques pour des appareils potentiellement très hétérogènes. Les deux premières contributions de ce travail sont consacrées au cas des réseaux neuronaux quantifiés. Notre première idée est basée sur une stratégie de recuit : nous formulons le problème d'optimisation discret comme un problème d'optimisation sous contraintes (où la taille de la contrainte est réduite au fil des itérations). Nous nous sommes ensuite concentrés sur une heuristique pour la formation de réseaux neuronaux profonds binaires. Dans ce cadre particulier, les paramètres des réseaux neuronaux ne peuvent avoir que deux valeurs. Le reste de la thèse s'est concentré sur l'apprentissage fédéré efficace. Suite à nos contributions développées pour l'apprentissage de réseaux neuronaux quantifiés, nous les avons intégrées dans un environnement fédéré. Ensuite, nous avons proposé une nouvelle technique de compression sans biais qui peut être utilisée dans n'importe quel cadre d'optimisation distribuée basé sur le gradient. Nos dernières contributions abordent le cas particulier de l'apprentissage fédéré asynchrone, où les appareils ont des vitesses de calcul et/ou un accès à la bande passante différents. Nous avons d'abord proposé une contribution qui repondère les contributions des dispositifs distribués. Dans notre travail final, à travers une analyse détaillée de la dynamique des files d'attente, nous proposons une amélioration significative des bornes de complexité fournies dans la littérature sur l'apprentissage fédéré asynchrone.En résumé, cette thèse présente de nouvelles contributions au domaine des réseaux neuronaux quantifiés et de l'apprentissage fédéré en abordant des défis critiques et en fournissant des solutions innovantes pour un apprentissage efficace et durable dans un environnement distribué et hétérogène. Bien que les avantages potentiels soient prometteurs, notamment en termes d'économies d'énergie, il convient d'être prudent car un effet rebond pourrait se produire.","url":"https://doi.org/10.70675/e20f182fz29f1z4c43z8b62z28efa11eb33d","authors":["Louis Leconte"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T17:42:37Z","doi":"10.70675/e20f182fz29f1z4c43z8b62z28efa11eb33d","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.70675/20675164z2ea0z47e6zb50cz70ff10a58853","name":"Federated learning in neuroimage segmentation","source":"crossref","abstract":"Apprentissage fédéré en segmentation en imagerie cérébrale L'apprentissage profond en analyse d'image médicale peut amener des outils cliniques intéressants, en accélérant les tâches rébarbatives et ouvrant la porte à des propositions de diagnostique automatiques. Ces modèles entraînés en laboratoire montrent souvent une pauvre capacité de généralisation, principalement due au manque de données d'entraînement ce qui limite leur utilité clinique. La construction de bases de données inter-institutionnelles et internationales se heurte aux questions de sensibilité des données de santé. Construire de grandes bases de données dans le domaine médical est excessivement difficile, que ce soit dû aux régulations de données strictes ou aux nombreuses barrières humaines et systémiques. L'apprentissage fédéré a été proposé en 2016 comme un paradigme d'apprentissage décentralisé, collaboratif et sécurisé. Il pourrait être une réponse partielle au problème de partage de données, permettant la collaboration entre différentes entités médicales pour l'entraînement de gros modèles profonds pour un coût légal et de sécurité des données limité. L'algorithme pionnier FedAvg donne des résultats convaincants sur un grand nombre de tâches, mais son utilisation pose de nombreuses questions telles que la justice dans la fédération, sa robustesse aux données aberrantes et ses réelles capacités en sécurité des données. Entre autre apparaissent de sérieuses contraintes sur la distribution des données dans ces fédérations, chaque institution ne possédant qu'une fraction des données biaisée et non représentative. Cette configuration hétérogène des données a été montrée comme altérant significativement la convergence des apprentissages. L'objectif de cette thèse est principalement exploratoire à travers la question de recherche suivante: Comment entraîner des réseaux profonds de manière fédérée pour des tâches de segmentation d'images neurologiques, dans des configurations cross-silo (entre 10 et 100 institutions) et hétérogènes (avec différents modes d'acquisition et de labellisation des données entre institution)?Les organisateurs du challenge Brain Tumor Segmentation (BraTS) ont publié le partitionnement par institution de cette base de données populaire, créant la première (et seule à l'époque) grande base fédérée publique réaliste pour cette précieuse tâche; FeTS 2021 et 2022. L'étude de l'apprentissage fédéré profond cross-silo et hétérogène pour cette tâche est le point focal de cette thèse. Nous avons dans un premier temps produit un large benchmark de méthodes d'apprentissage fédéré sur la base FeTS 2022. Nous avons exploré pour la première fois les performances de méthodes personnalisées et clusterisées pour cette tâche. Nous avons montré que extit{FedAvg} performe déjà très bien, mais peut être légèrement battu par certaines autres méthodes globales, personnalisées ou clusterisées. Nous avons complété ce travail par une méthode basée de comparaison des coûts de ces algorithmes fédérés dans toute leur complexité. De plus, nous avons proposé un nouvel algorithm de rafinement fédéré clusterisé par patient specifiquement pour la segmentation automatique de tumeurs cérébrales. Par un clustering côté serveur basé sur des mesures radiomiques par volume, nous pouvons raffiner un modèle fédéré par type d'acquisition, améliorant légèrement les performances de segmentation. Enfin, nous avons généralisé ce paradigme d'apprentissage fédéré clusterisé par image pour une hétérogénéité d'apparence en segmentation. Nous proposons un clustering dans l'espace des gradients d'un modèle pendant son apprentissage fédéré, montrant une correspondance surprenamment précise avec des aprioris sur l'origine des données. Nous sommes sortis du champ biomédical dans ce travail, évaluant ce paradigme avec une base de données jouet ainsi qu'une tâche de segmentation courante en adaptation de domaine, Cityscapes et GTA5.","url":"https://doi.org/10.70675/20675164z2ea0z47e6zb50cz70ff10a58853","authors":["Matthis Manthe"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-09T06:58:54Z","doi":"10.70675/20675164z2ea0z47e6zb50cz70ff10a58853","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-981-19-7083-2_2","name":"Communication Efficient Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-7083-2_2","authors":["Yaochu Jin","Hangyu Zhu","Jinjin Xu","Yang Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-11-29T19:05:05Z","doi":"10.1007/978-981-19-7083-2_2","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003660330-9","name":"Cyber resilience through adaptive federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003660330-9","authors":["S. S. Neetha","Singh Shivangi","S. Deepa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T15:39:21Z","doi":"10.1201/9781003660330-9","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.36227/techrxiv.22814831","name":"Proposed Smart 5G Framework: Incorporating Federated Learning and Transfer Learning","source":"crossref","abstract":"&lt;p&gt;This research aims to develop a secure and intelligent framework for 5G networks by incorporating federated learning (FL)&lt;br&gt; and transfer learning (TL) strategies. The primary objective is to enhance network evaluation metrics, such as capacity, service rate,&lt;br&gt; privacy preservation, low latency, and energy consumption, in the selection of access networks. The proposed framework will tackle&lt;br&gt; existing challenges in wireless communication systems, such as mobility, limited bandwidth, energy constraints, and limited feedback&lt;br&gt; from a receiver to a transmitter. The secondary objective is to address privacy preservation and scalability concerns during user&lt;br&gt; authentication in 5G networks. The federated user authentication model leverages the privacy preservation benefits of FL and secure&lt;br&gt; aggregation protocols during model averaging. The research methodology consists of five stages: literature review, classification of&lt;br&gt; objectives, determination of state metrics, definition of evaluation functions, and selection of FL and RL techniques. The resulting&lt;br&gt; framework is expected to provide a robust, secure, and efficient solution for 5G networks, ensuring enhanced quality of service and&lt;br&gt; optimization &lt;/p&gt;","url":"https://doi.org/10.36227/techrxiv.22814831","authors":["Peyman Khordadpour"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-05-17T14:37:58Z","doi":"10.36227/techrxiv.22814831","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.5220/0007571705440551","name":"Machine Learning for All: A More Robust Federated Learning Framework","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0007571705440551","authors":["Chamatidis Ilias","Spathoulas Georgios"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-03-15T11:00:03Z","doi":"10.5220/0007571705440551","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-3-031-01585-4_5","name":"Vertical Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-01585-4_5","authors":["Qiang Yang","Yang Liu","Yong Cheng","Yan Kang","Tianjian Chen","Han Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-09T04:31:06Z","doi":"10.1007/978-3-031-01585-4_5","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003303374-12","name":"Use-Cases and Scenarios for Federated Learning Adoption in IoMT","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003303374-12","authors":["Jonathan Atrey","Ramani Selvanambi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-05-03T20:41:25Z","doi":"10.1201/9781003303374-12","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.36227/techrxiv.22814831.v1","name":"Proposed Smart 5G Framework: Incorporating Federated Learning and Transfer Learning","source":"crossref","abstract":"This research aims to develop a secure and intelligent framework for 5G networks by incorporating federated learning (FL) and transfer learning (TL) strategies. The primary objective is to enhance network evaluation metrics, such as capacity, service rate, privacy preservation, low latency, and energy consumption, in the selection of access networks. The proposed framework will tackle existing challenges in wireless communication systems, such as mobility, limited bandwidth, energy constraints, and limited feedback from a receiver to a transmitter. The secondary objective is to address privacy preservation and scalability concerns during user authentication in 5G networks. The federated user authentication model leverages the privacy preservation benefits of FL and secure aggregation protocols during model averaging. The research methodology consists of five stages: literature review, classification of objectives, determination of state metrics, definition of evaluation functions, and selection of FL and RL techniques. The resulting framework is expected to provide a robust, secure, and efficient solution for 5G networks, ensuring enhanced quality of service and optimization","url":"https://doi.org/10.36227/techrxiv.22814831.v1","authors":["Peyman Khordadpour"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-05-17T10:37:51Z","doi":"10.36227/techrxiv.22814831.v1","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-3-030-96896-0_2","name":"Tree-Based Models for Federated Learning Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_2","authors":["Yuya Jeremy Ong","Nathalie Baracaldo","Yi Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T08:16:52Z","doi":"10.1007/978-3-030-96896-0_2","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003303374-8","name":"Trusted Federated Learning for Internet of Medical Things","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003303374-8","authors":["Sajid Nazir","Yan Zhang","Hua Tianfield"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-05-03T16:41:25Z","doi":"10.1201/9781003303374-8","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1007/978-3-030-63076-8_4","name":"Task-Agnostic Privacy-Preserving Representation Learning via Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-63076-8_4","authors":["Ang Li","Huanrui Yang","Yiran Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-25T20:03:24Z","doi":"10.1007/978-3-030-63076-8_4","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-3-031-01585-4_4","name":"Horizontal Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-01585-4_4","authors":["Qiang Yang","Yang Liu","Yong Cheng","Yan Kang","Tianjian Chen","Han Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-09T00:20:29Z","doi":"10.1007/978-3-031-01585-4_4","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003595540-1","name":"Framework of Federated Learning and Implementation through Internet of Things","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540-1","authors":["Deepak Raghava Naik","Kaddour Chelabi","Navya Gubbi Sateeshchandra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","doi":"10.1201/9781003595540-1","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00013-3","name":"Federated learning for predictive modeling of disease prevention in metaverse","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00013-3","authors":["Dharani Jaganathan","A. Vadivel","S. Jansi Rani","Vaishnavi Thangamuthu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00013-3","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21275/mr26501144519","name":"Heart Disease Prediction Using Machine Learning and Federated Learning Approach","source":"crossref","abstract":"","url":"https://doi.org/10.21275/mr26501144519","authors":["Nabiha Fatma","Mohammad Suaib","Jameel Ahmad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-05T08:01:31Z","doi":"10.21275/mr26501144519","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.5220/0011598500003332","name":"Federated Learning: A Hype or a Trend?","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0011598500003332","authors":["Anna Wilbik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-11-14T10:47:20Z","doi":"10.5220/0011598500003332","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-981-19-8692-5_5","name":"Differential Privacy in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-8692-5_5","authors":["Shui Yu","Lei Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-26T21:30:41Z","doi":"10.1007/978-981-19-8692-5_5","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1007/978-3-030-96896-0_21","name":"Federated Reinforcement Learning for Portfolio Management","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_21","authors":["Pengqian Yu","Laura Wynter","Shiau Hong Lim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_21","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00013-7","name":"Privacy-enhanced DDoS detection with federated learning and differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00013-7","authors":["Jovita Mateus","Antoine Bagula","Guy-Alain Lusilao Zodi","Olasupo Ajayi","Ferdinand Kahenga"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","doi":"10.1016/b978-0-44-344433-3.00013-7","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003660330-4","name":"Cybersecurity vulnerabilities in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003660330-4","authors":["Shaik Valli Haseena","Simna Shanavas","N. Brundha","Ayasha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T15:39:21Z","doi":"10.1201/9781003660330-4","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.14711/thesis-991013160256703412","name":"Federated transfer learning under heterogeneous data","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013160256703412","authors":["Xueyang Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-28T05:23:57Z","doi":"10.14711/thesis-991013160256703412","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/c2024-0-01601-3","name":"Federated Learning in Metaverse Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1016/c2024-0-01601-3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-13T01:21:32Z","doi":"10.1016/c2024-0-01601-3","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-3-031-01585-4_9","name":"Federated Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-01585-4_9","authors":["Qiang Yang","Yang Liu","Yong Cheng","Yan Kang","Tianjian Chen","Han Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-09T04:22:09Z","doi":"10.1007/978-3-031-01585-4_9","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-3-031-01585-4_6","name":"Federated Transfer Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-01585-4_6","authors":["Qiang Yang","Yang Liu","Yong Cheng","Yan Kang","Tianjian Chen","Han Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-09T04:44:22Z","doi":"10.1007/978-3-031-01585-4_6","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003027171-6","name":"Addressing Trust Issues in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003027171-6","authors":["Dinesh C. Verma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-13T08:59:18Z","doi":"10.1201/9781003027171-6","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003660330-3","name":"Federated learning for fraud detection and risk mitigation","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003660330-3","authors":["Himani Tyagi","Mohit Kumar","Himanshu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T15:39:21Z","doi":"10.1201/9781003660330-3","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1007/978-3-030-96896-0_10","name":"Local Training and Scalability of Federated Learning Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_10","authors":["Syed Zawad","Feng Yan","Ali Anwar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_10","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1007/978-3-030-96896-0_18","name":"Privacy-Preserving Vertical Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_18","authors":["Runhua Xu","Nathalie Baracaldo","Yi Zhou","Annie Abay","Ali Anwar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_18","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1201/9781003027171-7","name":"Addressing Synchronization Issues in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003027171-7","authors":["Dinesh C. Verma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-13T08:59:18Z","doi":"10.1201/9781003027171-7","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00007-8","name":"Navigating the virtual frontier: challenges and solutions for ethical federated learning in metaverse healthcare in India","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00007-8","authors":["Shashwata Sahu","Navonita Mallick"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00007-8","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1007/978-3-031-11748-0_1","name":"An Introduction to Federated and Transfer Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-11748-0_1","authors":["Roozbeh Razavi-Far","Boyu Wang","Matthew E. Taylor","Qiang Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-30T18:05:01Z","doi":"10.1007/978-3-031-11748-0_1","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1007/978-3-031-11748-0_15","name":"Federated Transfer Reinforcement Learning for Autonomous Driving","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-11748-0_15","authors":["Xinle Liang","Yang Liu","Tianjian Chen","Ming Liu","Qiang Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-30T18:05:01Z","doi":"10.1007/978-3-031-11748-0_15","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.7717/peerjcs.3503/table-3","name":"Table 3: Analysis of federated learning in both buildings.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerjcs.3503/table-3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-18T08:00:21Z","doi":"10.7717/peerjcs.3503/table-3","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1201/9781003660330-8","name":"Blockchain and federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003660330-8","authors":["Sangeeta Arora","Vivek Tomar","Swati Sharma","Sushil Kumar Narang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T15:39:21Z","doi":"10.1201/9781003660330-8","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.70593/978-93-7185-127-5_1","name":"A Decentralized Security Paradigm for IoT Ecosystems Using Blockchain Enabled Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.70593/978-93-7185-127-5_1","authors":["Durga Janani C","MUTHUPANDI G"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-03T20:52:58Z","doi":"10.70593/978-93-7185-127-5_1","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1201/9781003660330-6","name":"Data poisoning and adversarial attacks in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003660330-6","authors":["Peer Mohammed Jeelan","K. Nasrulla Khan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T15:39:21Z","doi":"10.1201/9781003660330-6","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1007/978-3-030-96896-0_5","name":"Personalized, Robust Federated Learning with Fed+","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_5","authors":["Pengqian Yu","Achintya Kundu","Laura Wynter","Shiau Hong Lim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_5","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.21275/sr231212134726","name":"Unifying Intelligence: Federated Learning in Cloud Environments for Decentralized Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.21275/sr231212134726","authors":["Angajala Srinivasa Rao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-13T04:32:21Z","doi":"10.21275/sr231212134726","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.5220/0012037600003488","name":"Empirical Analysis of Federated Learning Algorithms: A Federated Research Infrastructure Use Case","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012037600003488","authors":["Harshit Gupta","Abhishek Verma","O. Vyas","Marco Garofalo","Giuseppe Tricomi","Francesco Longo","Giovanni Merlino","Antonio Puliafito"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-04-28T17:23:36Z","doi":"10.5220/0012037600003488","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.14711/thesis-991012757569203412","name":"Performance analysis of federated machine learning frameworks","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991012757569203412","authors":["Qinghe Jing"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-03-21T06:44:59Z","doi":"10.14711/thesis-991012757569203412","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00007-1","name":"Federated learning in the cloud–edge computing continuum: architectures, optimization, and applications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00007-1","authors":["Fatemeh Mirhakimi","Nan Yang","Rodrigo N. Calheiros","Bahman Javadi","Feng Yan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","doi":"10.1016/b978-0-44-344433-3.00007-1","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.70675/1b5a2028z6e0bz4d02z8cabz881abebbbdab","name":"Client heterogeneity in federated learning systems","source":"crossref","abstract":"Hétérogénéité des clients dans les systèmes d'apprentissage fédérés L'apprentissage fédéré (FL) est un cadre collaboratif où les clients (dispositifs mobiles) entraînent un modèle d'apprentissage machine sous la coordination d'un serveur central en préservant la décentralisation des données. L'hétérogénéité de la participation des clients provient de la diversité des capacités des appareils en termes de spécifications matérielles, de types de connectivité réseau et de disponibilité énergétique. Cette thèse se concentre sur l'analyse de l'impact de cette hétérogénéité sur la convergence des algorithmes d'apprentissage fédéré et propose des algorithmes pratiques pour une utilisation plus efficace des systèmes et des ressources. La première partie aborde les défis liés à la corrélation temporelle et spatiale dans la participation des clients. Nous montrons que la participation hétérogène peut biaiser l'apprentissage et formalisons ce biais-variance induit par cette hétérogénéité. Notre étude démontre que donner plus de poids aux clients qui participent fréquemment peut accélérer la convergence. De plus, nous examinons l'impact de la corrélation à travers un modèle de chaîne de Markov à états finis, révélant que la corrélation ralentit la convergence. Notre approche propose une optimisation de ce compromis biais-variance à travers l'algorithme Correlation-Aware Federated Learning (CA-Fed), qui favorise une convergence plus rapide. La deuxième partie traite des scénarios de communication défectueuse. Les conditions réseau, notamment les pertes de paquets, introduisent une hétérogénéité incontrôlable dans la participation des clients. Contrairement aux stratégies traditionnelles qui compensent les pertes de paquets, notre solution permet aux algorithmes d'apprentissage fédéré de fonctionner efficacement même dans des canaux asymétriques et défaillants en adaptant la transmission des mises à jour des modèles. Les tests montrent que notre algorithme atteint des performances comparables à celles obtenues dans des conditions idéales, sans pertes de paquets. Enfin, la troisième partie explore l'utilisation de méthodes de réduction de variance pour compenser l'hétérogénéité dans la participation des clients. Bien que des stratégies similaires aient été envisagées pour atténuer l'impact de la participation partielle des clients, notre analyse élargit ce cadre à une participation client hétérogène. Nous démontrons que la convergence est significativement influencée par les clients participant le moins, suggérant que les algorithmes existants ne sont pas optimisés pour de tels environnements. Notre méthode, FedStale, utilise efficacement les mises à jour obsolètes dans des contextes de participation hétérogène.","url":"https://doi.org/10.70675/1b5a2028z6e0bz4d02z8cabz881abebbbdab","authors":["Angelo Rodio"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T18:28:28Z","doi":"10.70675/1b5a2028z6e0bz4d02z8cabz881abebbbdab","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1145/3803630.3809169","name":"CQSA: Byzantine-robust Clustered Quantum Secure Aggregation in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3803630.3809169","authors":["Arnab Nath","Harsh Kasyap"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-22T12:25:02Z","doi":"10.1145/3803630.3809169","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.7717/peerj-cs.3545/fig-2","name":"Figure 2: Architecture of the proposed federated learning system.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3545/fig-2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-02T08:34:38Z","doi":"10.7717/peerj-cs.3545/fig-2","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.47749/t/unicamp.2024.1455804","name":"Efficient communication in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.47749/t/unicamp.2024.1455804","authors":["Aissa Hadj Mohamed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-24T08:44:03Z","doi":"10.47749/t/unicamp.2024.1455804","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.31274/cc-20240624-720","name":"Federated Learning User Friendly Web App","source":"crossref","abstract":"","url":"https://doi.org/10.31274/cc-20240624-720","authors":["Yixuan Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-26T18:23:20Z","doi":"10.31274/cc-20240624-720","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.7717/peerj-cs.3589/fig-13","name":"Figure 13: Comparison of blockchain-based federated learning models.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3589/fig-13","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T08:57:09Z","doi":"10.7717/peerj-cs.3589/fig-13","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.70593/978-93-7185-127-5_6","name":"Synergizing Blockchain and Federated Learning for Scalable , Security and Privacy-Aware IoT Systems.","source":"crossref","abstract":"","url":"https://doi.org/10.70593/978-93-7185-127-5_6","authors":["MUTHUPANDI G","Vidhya Lakshmi P"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-03T20:52:58Z","doi":"10.70593/978-93-7185-127-5_6","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1007/978-3-031-11748-0_3","name":"Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-11748-0_3","authors":["Ehsan Hallaji","Roozbeh Razavi-Far","Mehrdad Saif"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-30T18:05:01Z","doi":"10.1007/978-3-031-11748-0_3","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.14711/thesis-991013350259203412","name":"Communication-efficient federated learning over wireless networks","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013350259203412","authors":["Linping Qu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-29T23:06:05Z","doi":"10.14711/thesis-991013350259203412","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.14711/thesis-991013340345803412","name":"Algorithm design for communication-efficient federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013340345803412","authors":["Lumin Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-29T22:59:53Z","doi":"10.14711/thesis-991013340345803412","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1002/9781119913924.ch11","name":"Federated Edge Learning for Massive MIMO CSI Feedback","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781119913924.ch11","authors":["Shi Jin","Yiming Cui","Jiajia Guo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-12-02T00:27:27Z","doi":"10.1002/9781119913924.ch11","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1109/tai.2025.3630110/mm1","name":"FUBA: Backdoor Federated Learning via Federated Unlearning_supp1-3630110.pdf","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tai.2025.3630110/mm1","authors":["Xinyi Sheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-17T18:44:09Z","doi":"10.1109/tai.2025.3630110/mm1","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.70675/062161c5z9cdez4465z92bezb97b4a23cb36","name":"Robust and Privacy-Preserving Federated Learning","source":"crossref","abstract":"Vers un Apprentissage fédéré robuste et préservant la confidentialité Dans le monde numérique en perpétuelle mutation d’aujourd’hui, l’apprentissage automatique est désormais une puissance essentielle et révolutionnaire, comme le démontrent de multiples recherches. Son impact profond s’étend à travers diverses industries, offrant des solutions et des innovations révolutionnaires qui ont remodelé la manière dont nous interagissons avec la technologie et prenons des décisions. Des systèmes de recommandation améliorant la diffusion de contenu sur les plateformes à la présence d’assistants personnels virtuels comme Siri et Alexa, capables de comprendre et de répondre à des commandes en langage naturel. Dans des domaines tels que la santé, il contribue au diagnostic des maladies, tandis que dans la finance, il renforce la détection de la fraude et l’évaluation des risques. Cette ubiquité de l’apprentissage automatique signifie non seulement une tendance technologique, mais aussi un changement fondamental dans les approches de résolution de problèmes et de prise de décisions. Cependant, cette vague d’innovation axée sur les données a soulevé une préoccupation primordiale : la protection de la vie privée des individus et de leurs données personnelles. Le Règlement général sur la protection des données (RGPD) illustre l’importance accrue de la protection des données à l’ère moderne. L’apprentissage fédéré représente un paradigme prometteur en apprentissage automatique, permettant la formation collaborative de modèles entre des appareils décentralisés. Cependant, il présente une vulnérabilité à diverses attaques. Cette recherche est divisée en deux axes principaux, chacun abordant des défis cruciaux en matière de sécurité et de confidentialité dans le contexte de l’apprentissage fédéré. Le premier axe se concentre sur la lutte contre les attaques d’empoisonnement pour un apprentissage fédéré robuste, où les adversaires cherchent à introduire des tâches nuisibles dans les modèles fédérés en plus de leurs tâches principales. Pour détecter ces attaques, on introduit ARMOR, un nouveau système de détection d’attaque basé sur GAN qui analyse les informations intégrées dans les mises à jour du modèle. Le deuxième axe concerne la lutte contre les attaques d’inférence pour l’apprentissage fédéré préservant la vie privée, en particulier les attaques d’inférence d’appartenance. Pour renforcer la confidentialité en apprentissage fédéré, deux approches novatrices sont introduites : PASTEL, qui améliore la résilience des systèmes d’apprentissage fédéré contre les MIAs en minimisant la différence de généralisation interne, et DINAR, une méthode d’apprentissage fédéré préservant la confidentialité à grain fin qui obscurcit les couches sensibles à la confidentialité et utilise une descente de gradient adaptative pour améliorer l’utilité du modèle. Ces objectifs de recherche visent collectivement à relever les défis en matière de sécurité et de confidentialité et à faire progresser le domaine de l'apprentissage fédéré.","url":"https://doi.org/10.70675/062161c5z9cdez4465z92bezb97b4a23cb36","authors":["Fatima Elhattab"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T13:42:09Z","doi":"10.70675/062161c5z9cdez4465z92bezb97b4a23cb36","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.23889/suthesis.63648","name":"Gradient Leakage and Protection for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.23889/suthesis.63648","authors":["Hanchi Ren"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-15T08:34:11Z","doi":"10.23889/suthesis.63648","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.7717/peerj-cs.3589/fig-10","name":"Figure 10: Federated learning with MNIST dataset (no attack).","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3589/fig-10","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T08:57:09Z","doi":"10.7717/peerj-cs.3589/fig-10","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.12681/eadd/60433","name":"Federated learning techniques in next generation IoT","source":"crossref","abstract":"Στον σημερινό κόσμο, η συλλογή, η επικοινωνία και η επεξεργασία πληροφοριών αποτελούν τον πυλώνα της σύγχρονης υποδομής που στοχεύει στη δημιουργία ενός καλύτερου περιβάλλοντος και ποιότητας ζωής για τους τελικούς χρήστες. Το Διαδικτύου των Πραγμάτων Νέας Γενιάς (ΔΤΠ-ΝΓ) Next Generation Internet of Things - NG-IoT είναι ένα δίκτυο έξυπνων συσκευών που παράγουν, επικοινωνούν και ανταλλάσσουν αυτές τις πληροφορίες για την παροχή ακριβέστερων και πιο εξατομικευμένων υπηρεσιών στον τελικό χρήστη. Το ΔΤΠ-ΝΓ βασίζεται στο παραδοσιακό ΔΤΠ, προσφέροντας ενισχυμένες δυνατότητες παραγωγής δεδομένων, ανάλογες με την ικανότητα υποστήριξης ενσωματωμένης νοημοσύνης για ενισχυμένη επεξεργασία και υποστήριξη λήψης αποφάσεων. Καθώς οι σύγχρονες υποδομές γίνονται όλο και πιο εντατικές και σύνθετες σε δεδομένα, απαιτούνται νέα εργαλεία για την υποστήριξη της εισαγωγής και της επεξεργασίας των παραγόμενων πληροφοριών. Η Τεχνητή Νοημοσύνη (ΤΝ), μαζί με τους τομείς της, τη Μηχανική Μάθηση (ΜΜ) και τη Βαθιά Μάθηση (ΒΜ), είναι ζωτικής σημασίας για την προώθηση των πρακτικών του ΔΤΠ-ΝΓ, καθώς παρέχει τη βάση για την επίτευξη βελτιστοποίησης των λειτουργιών, των υπηρεσιών και των πόρων μέσω προηγμένων αναλυτικών δυνατοτήτων. Η ερευνητική κοινότητα έχει εκφράσει έντονο ενδιαφέρον για την ενσωμάτωση της ΤΝ στο ΔΤΠ, καθώς υπόσχεται να προκαλέσει αλλαγή παραδείγματος στη λειτουργία των σύγχρονων υποδομών. Ωστόσο, αυτό το ισχυρό εργαλείο δεν στερείται των προκλήσεών του. Αυτές περιλαμβάνουν τον τρόπο βέλτιστης εκπαίδευσης των μοντέλων ΤΝ, αξιοποιώντας παράλληλα δεδομένα ΔΤΠ-ΝΓ μεγάλης κλίμακας, μαζί με τρόπους βελτιστοποίησής τους, αντιμετωπίζοντας ζητήματα υπολογιστικότητας, πόρων και ασφάλειας/ιδιωτικότητας. Η Ομοσπονδιακή Μάθηση (OM) Federated Learning - FL), ένα νέο παράδειγμα βελτιστοποίησης μοντέλων AI για την εκπαίδευση μοντέλων σε αποκεντρωμένες πηγές απομακρυσμένων συσκευών, έχει εμφανιστεί ως η λύση για την αντιμετώπιση των εμποδίων που προκύπτουν από την ενσωμάτωση της AI στο οικοσύστημα των απομακρυσμένων συσκευών. Η OM επίσης αντιμετωπίζει τους κινδύνους ασφάλειας και ιδιωτικότητας από τη συγκέντρωση δεδομένων από ποικίλες και διασκορπισμένες πηγές σε σχέση με τις συμβατικές μεθόδους ΜΜ, μειώνοντας ταυτόχρονα τα κόστη επικοινωνίας/υπολογισμού και διασφαλίζοντας την ακεραιότητα, την εμπιστευτικότητα και την ιδιωτικότητα των απομακρυσμένων δεδομένων. Δυστυχώς, όπως κάθε νέα τεχνολογία, η OM φέρνει τις δικές της προϋποθέσεις και προκλήσεις, οι οποίες σχετίζονται εγγενώς με την πολυπλοκότητα και την αβεβαιότητα του αποκεντρωμένου περιβάλλοντος, την ποικιλότητα στα αποκεντρωμένα δεδομένα καλούμενη ως μη ανεξάρτητα και ομοιότυπα κατανεμημένα δεδομένα (Μη-ΟΚΔ) non independent and identically distributed - Non-IID, την οργάνωση, ακόμη και την κοινωνική δικαιοσύνη μεταξύ των αλληλεπιδρώντων οντοτήτων ΔΤΠ-ΝΓ. Αυτή η διατριβή στοχεύει στο σχεδιασμό, την ανάπτυξη και την εφαρμογή τεχνικών Ομοσπονδιακής Μάθησης στο αποκεντρωμένο οικοσύστημα του ΔΤΠ-ΝΓ. Συγκεκριμένα, το κύριο έργο αυτής της διατριβής εστιάζει στο σχεδιασμό, την εφαρμογή και την επικύρωση νέων και καινοτόμων τεχνικών ΟΜ στο ΔΤΠ-ΝΓ, αξιοποιώντας προηγμένες μεθοδολογίες, ενώ προσπαθεί να βρει λύσεις και νέες εφαρμογές της ΟΜ. Για το σκοπό αυτό, η παρούσα εργασία επικεντρώνεται σε τρεις κύριους πυλώνες εφαρμογής της ΟΜ σε εφαρμογές ΔΤΠ-ΝΓ, και συγκεκριμένα: Αρχιτεκτονικές και μεθοδολογίες ομοσπονδιακής μάθησης για κατανεμημένα συστήματα ΔΤΠ-ΝΓ, Βελτίωση της απόδοσης του μοντέλου ομοσπονδιακής μάθησης με νέες τεχνικές βελτιστοποίησης για Μη-ΟΚΔ δεδομένα ΔΤΠ-ΝΓ, και Αντιμετώπιση της μεροληψίας, της δικαιοσύνης και της ισότητας σε συστήματα ομοσπονδιακής μάθησης.","url":"https://doi.org/10.12681/eadd/60433","authors":["Ηλίας Σινιόσογλου"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-05T13:54:57Z","doi":"10.12681/eadd/60433","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.70675/559d68e4zeb36z4d05z9bc2zcdb8d151d6ee","name":"Tackling heterogeneity in federated learning systems","source":"crossref","abstract":"Surmonter l'hétérogénéité dans les systèmes d'apprentissage fédéré L'apprentissage fédéré, qui provient de l'anglais ``Federated Learning'' (FL), se présente comme un cadre facilitant l'apprentissage collaboratif de modèles d'apprentissage automatique par des clients géographiquement répartis sans divulguer leurs données locales. Cette thèse se concentre sur la prise en charge de l'hétérogénéité, un défi majeur dans le domaine de l'apprentissage fédéré. L'hétérogénéité se manifeste par des variations entre les ensembles de données locaux des clients (hétérogénéité statistique), des disparités dans les capacités de stockage et de calcul (hétérogénéité système), et des fluctuations dans les ensembles de données locaux au fil du temps (hétérogénéité temporelle). Cette thèse explore différentes sources d'hétérogénéité dans le contexte de l'apprentissage fédéré et propose des algorithmes pratiques pour atténuer l'impact de l'hétérogénéité.La première partie de la thèse se concentre sur la résolution des défis associés à l'hétérogénéité du système dans deux scénarios distincts : inter-silos et inter-appareils. Dans les environnements inter-silos, nous exploitons la théorie des systèmes linéaires dans l'algèbre max-plus pour modéliser le débit, c'est-à-dire le nombre de cycles complets par unité de temps, dans un système d'apprentissage fédéré entièrement décentralisé en inter-silos. Ensuite, nous proposons des algorithmes pratiques qui, en utilisant les caractéristiques mesurables du réseau, trouvent une topologie avec le débit le plus élevé ou avec des garanties de débit vérifiables. Dans les environnements inter-appareils, où les contraintes du système influencent la disponibilité et l'activité des clients, nous explorons différents niveaux de participation des clients, souvent présentant une corrélation au fil du temps et avec d'autres clients. Dans ce contexte, nous analysons un algorithme similaire à fedavg sous une disponibilité hétérogène et corrélée des clients. L'analyse met en évidence comment la corrélation affecte négativement le taux de convergence de l'algorithme et comment la stratégie d'agrégation peut atténuer cet effet, même au prix de diriger l'entraînement vers un modèle biaisé. Guidé par l'analyse théorique, nous proposons \"Correlation-Aware FL\" (CA-Fed), un nouvel algorithme FL qui tente d'équilibrer les objectifs contradictoires de maximiser la vitesse de convergence et de minimiser le biais du modèle. À cette fin, CA-Fed ajuste dynamiquement le poids attribué à chaque client et peut ignorer les clients avec une faible disponibilité et une forte corrélation.La deuxième partie traite de l'hétérogénéité statistique grâce à deux algorithmes de personnalisation dans l'FL. Le premier algorithme, appelé FedEM, repose sur une hypothèse souple selon laquelle l'ensemble de données de chaque client est généré à partir d'un mélange de distributions sous-jacentes communes inconnues. Le deuxième algorithme, appelé kNN-Per, combine un modèle global entraîné collectivement avec un modèle local de plus proches voisins (kNN) pour la personnalisation. Des garanties théoriques, notamment des bornes de convergence et de généralisation, sont fournies pour les deux algorithmes.La troisième partie explore l'apprentissage fédéré pour les flux de données, en considérant deux scénarios : des échantillons indépendants tirés d'une distribution inconnue et des distributions de données composées de mélanges de distributions sous-jacentes inconnues. Pour le premier scénario, un meta-algorithme est proposé, offrant des informations sur la configuration et le compromis entre le temps d'entraînement et le biais du modèle appris. Pour le deuxième scénario, une variante fédérée de la descente du miroir séquntielle, appelée FEM-OMD, est introduite, avec un regret asymptotiquement sous-linéaire dans le cas des modèles de mélange Gaussien.","url":"https://doi.org/10.70675/559d68e4zeb36z4d05z9bc2zcdb8d151d6ee","authors":["Othmane Marfoq"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T10:37:18Z","doi":"10.70675/559d68e4zeb36z4d05z9bc2zcdb8d151d6ee","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.7717/peerj-cs.3165/table-101","name":"Algorithm 1 : Federated learning with reputation and CKKS encryption.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3165/table-101","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-16T08:31:56Z","doi":"10.7717/peerj-cs.3165/table-101","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.7717/peerj-cs.3589/fig-11","name":"Figure 11: Federated learning with MNIST dataset (with attack).","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3589/fig-11","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T08:57:09Z","doi":"10.7717/peerj-cs.3589/fig-11","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1201/9781003595540-9","name":"Federated Learning Framework for Motif Structure Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540-9","authors":["Latha Parthiban","G Sivagamasundari","Golda Dilip","A P Venkateswara","R. Parthiban"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","doi":"10.1201/9781003595540-9","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.7717/peerj-cs.3972/table-3","name":"Table 3: Communication overhead comparison of federated learning methods.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3972/table-3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T08:58:08Z","doi":"10.7717/peerj-cs.3972/table-3","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1007/978-981-19-8692-5_7","name":"Secure Data Aggregation in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-8692-5_7","authors":["Shui Yu","Lei Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-26T21:30:41Z","doi":"10.1007/978-981-19-8692-5_7","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00002-2","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00002-2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","doi":"10.1016/b978-0-44-344433-3.00002-2","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-981-19-8692-5_4","name":"GAN Attacks and Counterattacks in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-8692-5_4","authors":["Shui Yu","Lei Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-26T21:30:41Z","doi":"10.1007/978-981-19-8692-5_4","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.33140/jsndc.03.01.03","name":"Federated Learning for Collaborative Network Security in Decentralized Environments","source":"crossref","abstract":"In decentralized network environments, collaborative efforts are crucial to bolstering network security against everevolving threats from malicious actors. Federated Learning has emerged as a promising solution, enabling multiple nodes to collectively train machine learning models while preserving data privacy. This research proposes SentinelNet, a novel Federated Learning framework specifically designed for collaborative network security. The framework emphasizes secure threat intelligence sharing, privacy-preserving techniques, and adaptive learning mechanisms. Through comprehensive evaluations and real-world case studies, SentinelNet demonstrates its efficacy in enhancing network security while maintaining data confidentiality. The research highlights the significance of collaborative approaches and advocates the adoption of Federated Learning to fortify decentralized network ecosystems.","url":"https://doi.org/10.33140/jsndc.03.01.03","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-30T11:24:27Z","doi":"10.33140/jsndc.03.01.03","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1007/978-3-030-96896-0_16","name":"Security and Robustness in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_16","authors":["Ambrish Rawat","Giulio Zizzo","Muhammad Zaid Hameed","Luis Muñoz-González"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_16","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1201/9781003027171-5","name":"Addressing Data Skew Issues in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003027171-5","authors":["Dinesh C. Verma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-13T08:59:18Z","doi":"10.1201/9781003027171-5","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1201/9781003027171-8","name":"Addressing Vertical Partitioning Issues in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003027171-8","authors":["Dinesh C. Verma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-13T08:59:18Z","doi":"10.1201/9781003027171-8","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.36227/techrxiv.22058978","name":"Towards Model-Agnostic Federated Learning over Networks","source":"crossref","abstract":"&lt;p&gt;We present a model-agnostic federated learning method for decentralized data with an intrinsic network structure.The network structure reflects similarities between the (statistics of) local datasets and, in turn, their associated local models. Our method is an instance of empirical risk minimization, using a regularization term that is constructed from the network structure of data. In particular, we require well-connected local models, forming clusters, to yield similar predictions on a common test set. In principle our method can be applied to any collection of local models. The only restriction put on these local models is that they allow for efficient implementation of regularized empirical risk minimization (training). Such implementations might be available in the form of high-level programming frameworks such as scikit-learn, Keras or PyTorch.&lt;/p&gt;","url":"https://doi.org/10.36227/techrxiv.22058978","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-02-15T15:53:17Z","doi":"10.36227/techrxiv.22058978","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.21248/gups.98141","name":"Deep learning in digital healthcare: from centralized models to heterogeneity-aware federated learning","source":"crossref","abstract":"Der Entwicklungsstand der Medizintechnik ist eng mit der menschlichen Gesundheit verknüpft. Zur Beurteilung des individuellen Gesundheitszustands werden vielfältige physiologische Daten mittels medizinischer Geräte erhoben und mit unterschiedlichen Analysemethoden verarbeitet. Mit dem Fortschritt der digitalen Gesundheitsversorgung setzen Gesundheitssysteme weltweit zunehmend intelligente Verfahren wie Deep Learning ein, um die diagnostische Genauigkeit zu steigern und die Patientenerfahrung zu verbessern. Durch den Einsatz tragbarer Sensoren, mobiler Anwendungen und Telemedizinplattformen kompensiert die digitale Gesundheitsversorgung die Begrenzungen der traditionellen Medizin hinsichtlich Effizienz, Präzision und Komfort in Prävention, Diagnostik und Therapie und etabliert sich damit als zentraler Entwicklungstrend im Gesundheitswesen. Trotz erheblicher Fortschritte in der digitalen Gesundheitsversorgung bestehen entlang des gesamten Entwicklungs- und Einsatzzyklus weiterhin wesentliche Herausforderungen. In der Datenerhebung erfordert die Entwicklung digitaler Gesundheitsmodelle häufig die Einbindung von Fachexpertise, was zeit- und kostenintensiv ist. Die Datenqualität wird durch Hardware, Umgebungsbedingungen und personelle Faktoren beeinflusst und kann dadurch die Erkennung relevanter Merkmale sowie die Validität klinischer Entscheidungen beeinträchtigen. Die Einführung multimodaler Daten kann Leistungsschwankungen verstärken, insbesondere bei stark divergierenden Verteilungen oder fehlenden Modalitäten. In kooperativen Versorgungsszenarien führen Heterogenitäten in Datenverteilungen, lokaler Rechenleistung und Netzqualität zu Flaschenhälsen im föderierten Lernen. Neben den oben beschriebenen Herausforderungen wirkt der Datenschutz als gemeinsamer limitierender Faktor, der die Umsetzung und Verbreitung digitaler Gesundheitsanwendungen einschränkt. Diese Arbeit befasst sich mit drei klinischen Aufgaben: der Beurteilung der Wirksamkeit von Bronchodilatatoren, der dermatoskopiebasierten Diagnostik und dem Screening auf chronische Atemwegserkrankungen. Dafür wird ein Methodenspektrum von zentralisierten Deep-Learning Modellen bis hin zu heterogenitätsbewusstem föderiertem Lernen entwickelt. Experimente auf mehreren realweltlichen medizinischen Datensätzen zeigen, dass die vorgeschlagenen Verfahren sowohl die Arbeitslast bei Datenannotation und assistierter Diagnostik reduzieren als auch die Modellleistung unter suboptimalen Datenbedingungen erhöhen. Unter Wahrung der Datensicherheit wird damit eine sichere und effiziente Lösung für kollaborative Diagnose- und Behandlungsprozesse in heterogenen Versorgungskontexten bereitgestellt. In Kombination fördern die vorgestellten Methoden und die entwickelte Webanwendung die Integration von Daten-, Modell- und klinischen Prozessen, gewährleisten den datenschutzkonformen Einsatz und treiben die Umsetzung digitaler, Deep-Learning-gestützter Gesundheitsversorgung in realen Szenarien voran.","url":"https://doi.org/10.21248/gups.98141","authors":["Mohan Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-18T12:41:02Z","doi":"10.21248/gups.98141","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1007/978-981-19-8692-5_3","name":"Poisoning Attacks and Counterattacks in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-8692-5_3","authors":["Shui Yu","Lei Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-26T21:30:41Z","doi":"10.1007/978-981-19-8692-5_3","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1201/9781003595540-13","name":"Dynamic Pricing for Revenue Management in Health and Hospitality Industry with Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540-13","authors":["H.M. Moyeenudin","Jaisree Anand","R. Anandan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","doi":"10.1201/9781003595540-13","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-981-19-8692-5_2","name":"Inference Attacks and Counterattacks in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-8692-5_2","authors":["Shui Yu","Lei Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-26T21:30:41Z","doi":"10.1007/978-981-19-8692-5_2","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1007/978-981-19-8692-5_6","name":"Secure Multi-party Computation in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-8692-5_6","authors":["Shui Yu","Lei Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-26T21:30:41Z","doi":"10.1007/978-981-19-8692-5_6","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1007/978-3-030-96896-0_13","name":"Protecting Against Data Leakage in Federated Learning: What Approach Should You Choose?","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_13","authors":["Nathalie Baracaldo","Runhua Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_13","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.36227/techrxiv.22058978.v1","name":"Towards Model-Agnostic Federated Learning over Networks","source":"crossref","abstract":"We present a model-agnostic federated learning method for decentralized data with an intrinsic network structure.The network structure reflects similarities between the (statistics of) local datasets and, in turn, their associated local models. Our method is an instance of empirical risk minimization, using a regularization term that is constructed from the network structure of data. In particular, we require well-connected local models, forming clusters, to yield similar predictions on a common test set. In principle our method can be applied to any collection of local models. The only restriction put on these local models is that they allow for efficient implementation of regularized empirical risk minimization (training). Such implementations might be available in the form of high-level programming frameworks such as scikit-learn, Keras or PyTorch.","url":"https://doi.org/10.36227/techrxiv.22058978.v1","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-02-15T10:53:14Z","doi":"10.36227/techrxiv.22058978.v1","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.18653/v1/2022.fl4nlp-1.4","name":"Intrinsic Gradient Compression for Scalable and Efficient Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.18653/v1/2022.fl4nlp-1.4","authors":["Luke Melas-Kyriazi","Franklyn Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-03T01:34:53Z","doi":"10.18653/v1/2022.fl4nlp-1.4","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.14711/thesis-991013319456603412","name":"Optimizing federated learning with heterogeneous and unreliable clients","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013319456603412","authors":["Yuchang Sun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-17T01:55:46Z","doi":"10.14711/thesis-991013319456603412","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.70593/978-93-7185-127-5_2","name":"Blockchain–Integrated Federated Learning Arcitectures for Trustworthy and Privacy–Preserving IoT Security","source":"crossref","abstract":"","url":"https://doi.org/10.70593/978-93-7185-127-5_2","authors":["MUTHUPANDI G","Nandhakumar R","Vishnu B","SIVAKUMAR G"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-03T20:52:58Z","doi":"10.70593/978-93-7185-127-5_2","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-030-96896-0_20","name":"Federated Learning for Collaborative Financial Crimes Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_20","authors":["Toyotaro Suzumura","Yi Zhou","Ryo Kawahara","Nathalie Baracaldo","Heiko Ludwig"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_20","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.7717/peerj-cs.3589/fig-2","name":"Figure 2: Federated learning payment processing for bitcoin’s timing commitments.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3589/fig-2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T08:57:09Z","doi":"10.7717/peerj-cs.3589/fig-2","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1017/9781108966559.020","name":"Differentially Private Wireless Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1017/9781108966559.020","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-16T00:05:40Z","doi":"10.1017/9781108966559.020","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.14711/thesis-991013426071603412","name":"Minimum exposure approach for trustworthy vertical federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013426071603412","authors":["Dashan Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-17T01:00:06Z","doi":"10.14711/thesis-991013426071603412","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.70675/1f6d5fa9z9e12z4aa7z91fbzab3626ea002b","name":"Enhancing Federated Learning for Financial Sector via Graph Learning and Language Models","source":"crossref","abstract":"Amélioration de l'Apprentissage Fédéré pour le Secteur Financier via l'Apprentissage par Graphes et les Modèles de Langage Dans le secteur financier moderne, la nécessité de modèles d'apprentissage automatique robustes devient de plus en plus cruciale, mais les réglementations sur la confidentialité et les préoccupations concurrentielles rendent souvent la centralisation des données impossible. Pour surmonter ces défis, cette thèse propose de nouvelles méthodologies de l'apprentissage fédéré (FL) permettant aux institutions de collaborer pour entraîner des modèles machine learning tout en abordant les compromis critiques entre la confidentialité et l'utilité des données, en intégrant des mécanismes de préservation de la confidentialité conçus pour empêcher la récupération des entrées avec une perte minimale d'utilité des données. Une contribution clé de cette recherche est le développement d'un framework d'apprentissage fédéré pour la détection d'anomalies comportementales et la détection de la fraude dans les transactions financières. En utilisant des réseaux neuronaux de graphes (GNNs) sur des graphes dynamiques ego-centriques, qui permet de capturer et de détecter les schémas transactionnels évolutifs afin de repérer les anomalies, tout en préservant la confidentialité des individus. Une nouvelle technique d'échantillonnage négatif spécifique au domaine permet l'entraînement du modèle sans la nécessiter de données étiquetées de la part des participants à la fédération, ce qui le rend applicable dans des scénarios industriels. Les résultats montrent que les méthodes basées sur l'apprentissage profond, en particulier les GNNs, surpassent les approches traditionnelles dans la détection d'anomalies et améliorent la détection des fraudes dans les données transactionnelles, en introduisant des mécanismes d'anonymisation et de bruit, même lorsque les gradients des modèles fédéré sont exposés. De plus, nous proposons G-HIN2Vec, une technique basée sur les réseaux neuronaux de graphes pour les réseaux d'information hétérogènes, qui modélise des individus, tels que les détenteurs de cartes, en utilisant des graphes ego-centriques statiques et dynamiques. Cette méthode sert de mécanisme d'anonymisation qui élimine la nécessité d'utiliser un identifiant individuel, tel que les informations personnellement identifiables (PII), dans les modèles fédérés. En intégrant la confidentialité différentielle locale personnalisée (PLDP), nous fournissons une couche de protection supplémentaire, garantissant que même en cas de violation du modèle, les données sensibles restent sécurisées. Enfin, la thèse introduit le tokenizer fédéré basé sur le codage par paires de caractères (BPE) au niveau du byte (Byte-level), une approche de tokenisation respectant la confidentialité des individus mentionnés dans les données textuelles sous forme de PII, conçue pour les ensembles de données textuelles distribuées. Ce tokenizer surpasse les modèles existants en termes de couverture du vocabulaire et d'efficacité, tout en maintenant une stricte confidentialité des données. Notre tokenizer fédéré non seulement concurrentiel par rapport aux modèles centralisés, mais démontre également des améliorations en matière de compression de texte et de préservation de la confidentialité, pour les tokenizers généraux (General domain) et spécifiques au domaine (Domain specific). Les méthodologies présentées dans cette thèse, validées à l'aide de bases de données financières réelles, publiques et privées, transactionnelles et textuelles, mettent en évidence le potentiel de l'apprentissage fédéré pour améliorer la détection de la fraude et les performances des modèles de langage tout en préservant la confidentialité des individus et des institutions grâce à des mécanismes d'anonymisation et de confidentialité basés sur le bruit.","url":"https://doi.org/10.70675/1f6d5fa9z9e12z4aa7z91fbzab3626ea002b","authors":["Farouk Damoun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T20:44:48Z","doi":"10.70675/1f6d5fa9z9e12z4aa7z91fbzab3626ea002b","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.12681/eadd/62275","name":"Predictive maintenance for vessel machinery using federated learning","source":"crossref","abstract":"Η προγνωστική συντήρηση στη ναυτιλία υποστηρίζεται ολοένα και περισσότερο από μοντέλα που εκπαιδεύονται σε ροές δεδομένων υψηλής συχνότητας από αισθητήρες πλοίων. Ωστόσο, οι περισσότερες υφιστάμενες προσεγγίσεις αξιολογούνται υπό κεντρικοποιημένες παραδοχές, οι οποίες είναι δύσκολο να ικανοποιηθούν σε πραγματικά ναυτιλιακά περιβάλλοντα λειτουργίας. Τα δεδομένα των πλοίων είναι κατανεμημένα μεταξύ διαφορετικών πλοίων, διαχειριστών, δρομολογίων και καταστάσεων μηχανικού εξοπλισμού. Παράλληλα, οι σύνδεσμοι επικοινωνίας είναι συχνά διαλείποντες ή δαπανηροί, οι υπολογιστικοί πόροι επί του πλοίου διαφέρουν μεταξύ των συμμετεχόντων κόμβων, η ακατέργαστη τηλεμετρία μπορεί να περιέχει εμπορικά ευαίσθητη πληροφορία, ενώ οι έξοδοι των μοντέλων πρέπει να είναι τεχνικά τεκμηριώσιμες όταν χρησιμοποιούνται για την υποστήριξη αποφάσεων συντήρησης. Η παρούσα διατριβή μελετά την προγνωστική συντήρηση στη ναυτιλία ως πρόβλημα κατανεμημένης μάθησης υπό περιορισμούς ανάπτυξης και λειτουργίας, και όχι μόνο ως ένα απομονωμένο πρόβλημα επιβλεπόμενης πρόβλεψης. Αρχικά, η διατριβή εξετάζει στρατηγικές συντήρησης στη ναυτιλία, μεθοδολογίες προγνωστικής συντήρησης και προσεγγίσεις συνεργατικής μάθησης, αναδεικνύοντας πέντε επαναλαμβανόμενα εμπόδια για την πρακτική αξιοποίησή τους: την ποιότητα και διαθεσιμότητα των δεδομένων, την κλιμακωσιμότητα και τους περιορισμούς υπολογιστικών πόρων επί του πλοίου, τους κινδύνους ασφάλειας, την ερμηνευσιμότητα και την εμπιστοσύνη στα μοντέλα, καθώς και την αποδοτικότητα των επικοινωνιών. Στη συνέχεια, τα ζητήματα αυτά αντιμετωπίζονται μέσω τεσσάρων συμπληρωματικών τεχνικών συνεισφορών. Πρώτον, το FLUID εισάγει ένα πλαίσιο συνεργατικής απόσταξης γνώσης προσαρμοσμένο στους διαθέσιμους πόρους για την πρόβλεψη του εναπομένοντος χρόνου ζωής καθαριστή βαρέος καυσίμου. Οι συμμετέχοντες κόμβοι αντιστοιχίζονται σε βαθμίδες πόρων, πλήρεις ή αραιωμένες παραλλαγές του μοντέλου αποστέλλονται ανάλογα με τις βαθμίδες αυτές, και η συνεργασία πραγματοποιείται μέσω συνάθροισης στον χώρο των προβλέψεων πάνω σε ένα σύνολο αναφοράς διαθέσιμο από την πλευρά του εξυπηρετητή. Με τον τρόπο αυτό υποστηρίζονται διαφορετικές χωρητικότητες μοντέλων και μειώνεται η ανάγκη επαναλαμβανόμενης ανταλλαγής πλήρων παραμέτρων. Δεύτερον, το FLoDS αναπτύσσει ένα ιεραρχικό πλαίσιο συνεργατικής μάθησης εγγενές σε Χώρους Δεδομένων, στο οποίο θεματοφύλακες (stewards) λειτουργούν ως ενδιάμεσες οντότητες συνάθροισης και ελέγχου. Το πλαίσιο συνδυάζει διαφορική ιδιωτικότητα σε επίπεδο πελάτη, ασφαλή συνάθροιση σε επίπεδο θεματοφύλακα, υπογεγραμμένα αρχεία καταγραφής ανά γύρο που συνδέονται μέσω αλυσίδας κατακερματισμού, προστασία των αρχείων που αποστέλλονται από τους θεματοφύλακες προς τον εξυπηρετητή μέσω CKKS, και προσαρμοστική συνάθροιση με περικοπή στο επίπεδο του εξυπηρετητή. Με τον τρόπο αυτό ποσοτικοποιείται ο συμβιβασμός μεταξύ ιδιωτικότητας, ανθεκτικότητας και συστημικού κόστους σε περιβάλλοντα συνεργατικής μάθησης με διακυβέρνηση. Τρίτον, το BEACON μελετά την ερμηνευσιμότητα για την πρόβλεψη της θερμοκρασίας καυσαερίων ανά κύλινδρο στην κύρια μηχανή. Συνδυάζει έναν κωδικοποιητή BiLSTM, μηχανισμό ντετερμινιστικής προσοχής και μη γραμμική κεφαλή βασισμένη σε δίκτυο Kolmogorov-Arnold (KAN), ενώ αξιολογεί όχι μόνο το σφάλμα πρόβλεψης αλλά και τη σταθερότητα των κατατάξεων χαρακτηριστικών που προκύπτουν από SHAP ανάλυση μεταξύ συνεργατικών πελατών. Η ανάλυση αυτή δείχνει ότι μοντέλα με παρόμοια ακρίβεια μπορούν να εμφανίζουν σημαντικές διαφορές ως προς τη συνοχή των εξηγήσεών τους. Τέταρτον, το CARGO εξετάζει την αποκεντρωμένη ναυτιλιακή μάθηση υπό διαλείπουσα συνδεσιμότητα, εισάγοντας ένα επίπεδο ενορχήστρωσης με επίγνωση του αποτυπώματος άνθρακα πάνω από εκπαίδευση τύπου gossip. Το επίπεδο ελέγχου προγραμματίζει τη συμμετοχή πλοίων, τους συνδέσμους επικοινωνίας, τους τρόπους συμπίεσης, τα βάρη ανάμειξης και τις ενέργειες επανασυγχρονισμού, επιτρέποντας την ανάλυση του συμβιβασμού μεταξύ ακρίβειας, επικοινωνίας και εκπομπών άνθρακα σε τοπολογικά σενάρια εμπ","url":"https://doi.org/10.12681/eadd/62275","authors":["Αλέξανδρος Καλαφατέλης"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-13T09:33:14Z","doi":"10.12681/eadd/62275","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.31979/etd.cfgv-t6wa","name":"Federated Learning for Protecting Medical Data Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.31979/etd.cfgv-t6wa","authors":["Abhishek Reddy Punreddy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-15T15:53:00Z","doi":"10.31979/etd.cfgv-t6wa","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.32657/10356/216325","name":"Graph representation learning in multimodal and federated scenarios","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/216325","authors":["Xiaoxiong Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-13T04:27:24Z","doi":"10.32657/10356/216325","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1201/9781003303374-11","name":"Security and Privacy in Federated Learning–Based Internet of Medical Things","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003303374-11","authors":["J. Swathi","G.R. Karpagam","Raghvendra Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-05-03T20:41:25Z","doi":"10.1201/9781003303374-11","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.1007/978-3-030-63076-8_1","name":"Threats to Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-63076-8_1","authors":["Lingjuan Lyu","Han Yu","Jun Zhao","Qiang Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-25T15:03:24Z","doi":"10.1007/978-3-030-63076-8_1","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.56902/etdcrp.2025.156","name":"Collaborative Federated Learning for Robots in Heterogeneous Environments","source":"crossref","abstract":"This research investigates the performance of Federated Averaging (FedAvg) in simulated Federated Learning (FL) scenarios with varying degrees of environmental heterogeneity among robotic agents. The study explores the impact of data heterogeneity on both the convergence of FedAvg and the fairness of learning, with regard to consistency of performance across agents. Experiments were conducted with simulated robots trained to perform a target collection task, where a subset of agents encountered an unfamiliar environment. The results demonstrate that while FedAvg exhibits resilience to the introduction of new environmental data, it struggles to ensure both convergence and fairness in heterogeneous settings. Specifically, the original training environment maintains a dominant influence, while agents in novel environments experience inhibited learning. These findings highlight the challenges of applying FedAvg in lifelong learning scenarios with heterogeneous environments and unbalanced non-IID data, revealing a trade-off between maintaining performance in a majority environment and ensuring equitable learning across diverse environments.","url":"https://doi.org/10.56902/etdcrp.2025.156","authors":["Karlan Schneider"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-10T22:24:52Z","doi":"10.56902/etdcrp.2025.156","addedAt":"2026-08-31T06:41:22.146Z","updatedAt":"2026-08-31T06:41:22.146Z"},{"id":"doi:10.2139/ssrn.4209578","name":"Federated Learning: Approaches, Possibilities, and Challenges","source":"crossref","abstract":"In this article, Federated Learning (FL) is explored with an emphasis on methods, practical applications, its current trends, issues, and challenges. Although FL can be used in a variety of fields, applying it across business domains comes with its own set of challenges. Federated learning is a machine learning technique that allows models to learn from several data sets located at different places (like regional data centres or a central server) without exchanging training data. By enabling personal data to remain on local sites, this reduces the probability of personal data breaches. Federated learning is also known as collaborative learning. When compared to traditional machine learning approach, FL creates more reliable models without sharing data, resulting in solutions that protect data privacy and have better security and access rights to data. The FL method is extremely advantageous for using inexpensive machine learning models on gadgets like sensors and cell phones.","url":"https://doi.org/10.2139/ssrn.4209578","authors":["Sarat Chettri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-17T23:08:52Z","doi":"10.2139/ssrn.4209578","addedAt":"2026-08-31T06:41:22.148Z","updatedAt":"2026-08-31T06:41:22.148Z"},{"id":"doi:10.7717/peerjcs.2993/fig-6","name":"Figure 6: Overview of knowledge distillation methods using federated learning.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerjcs.2993/fig-6","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-10T08:22:16Z","doi":"10.7717/peerjcs.2993/fig-6","addedAt":"2026-08-31T06:41:22.148Z","updatedAt":"2026-08-31T06:41:22.148Z"},{"id":"doi:10.21275/sr26710183947","name":"Secure Federated Learning for Medical Image Classification","source":"crossref","abstract":"","url":"https://doi.org/10.21275/sr26710183947","authors":["Ramanpreet Kaur","Amandeep Kaur Virk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-13T08:58:02Z","doi":"10.21275/sr26710183947","addedAt":"2026-08-31T06:41:22.148Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"pmid:42381266","name":"Advances in Remote Monitoring Technology Applications in Anesthesia: A Narrative Review.","source":"pubmed","abstract":"The swift progress of digital and sensor technologies is hastening the incorporation of remote monitoring into anesthesiology. While several reviews have explored telemedicine and artificial intelligence in anesthesia, most existing summaries either focus on conceptual outlook or lack systematic comparison of technical platforms and original clinical validation data. The present review provides a comprehensive, clinically oriented synthesis of remote monitoring in anesthesia, with clear focuses on technical principles, perioperative applications, platform comparisons, and evidence-based clinical outcomes. We critically assess clinical advantages and implementation hurdles, covering the full perioperative pathway - preoperative evaluation, intraoperative observation, and postoperative recovery. A head-to-head comparison of fifth generation of cellular network technology (5G), the Internet of Things (IoT), and cloud computing platforms is presented to clarify their infrastructure demands and suitability for real-world clinical scenarios. Notably, this review summarizes available clinical evidence, including evidence from the Trial of Remote Continuous versus Intermittent National Early Warning Score Monitoring after major surgery (TRaCINg) - a feasibility randomized controlled trial whose exploratory findings suggest that continuous remote monitoring may reduce unplanned intensive care unit admissions, shorten hospital stays, and facilitate earlier detection of postoperative complications. We further propose innovative solutions including multimodal data fusion and federated learning-driven predictive analytics to overcome current limitations in data interoperability, security, and clinical effectiveness. Accordingly, this paper synthesizes the latest advances in remote monitoring technology in anesthesia, clarifies its unique value relative to existing reviews, and provides practical guidance for clinical translation and future research.","url":"https://pubmed.ncbi.nlm.nih.gov/42381266/","authors":["Chen L","Cheng S","Zhang L","Li H","Tu Z","Cai X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 1","doi":"10.12659/MSM.952513","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"pmid:42380225","name":"F-BEGAN: BEGAN-enabled federated learning for imbalance data security and privacy-preserving of IoMT-based Healthcare 5.0.","source":"pubmed","abstract":"The advancement of cutting-edge technologies such as the Internet of Things (IoT) and Deep Learning (DL) has transformed the Internet of Medical Things (IoMT) based healthcare into a new paradigm known as the Healthcare 5.0. This paradigm shift, particularly within Healthcare 5.0, introduces smart, cost-effective, and sustainable healthcare services. However, in such complex and heterogeneous IoMT based networks, smart devices generate large volumes of imbalance data. Most DL models struggle to accurately distinguish malicious behavior and, consequently, fail to detect network threats effectively. To address these challenges and ensure privacy preservation, we propose a novel Generative Adversarial Network (GAN)-oriented Federated Learning (FL) model. The proposed approach generates realistic synthetic data for improving the detection of minority-class threats, while FL facilitates distributed training without revealing raw data. Additionally, a Bidirectional Long Short-Term Memory (BiLSTM) network is employed to identify various attack types within smart IoMT-based Healthcare 5.0 systems. Experimental results on two benchmark imbalance datasets, UNSW-NB15 and NSL-KDD, demonstrate that the proposed model achieves superior accuracy of (94.78% and 95.90%) and F1-score (94.88% and 98.70%) respectively for minority-class attacks, outperforming existing methods.","url":"https://pubmed.ncbi.nlm.nih.gov/42380225/","authors":["Ullah Z","Jiang W","Gharawi AA","Alahmadi MD"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 30","doi":"10.1038/s41598-026-60016-9","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"pmid:42380185","name":"An AI-enabled federated blockchain framework for adaptive energy coordination in smart electric mobility networks.","source":"pubmed","abstract":"The high rate of electric vehicles (EVs) development has motivated the issues of peak load congestion, data privacy, scalability, and secure energy coordination in smart electric mobility networks. The traditional centralized EV charging management systems have weaknesses of privacy leakage, single point failure, lack of real time flexibility and lack of trust in the transaction. This paper proposes a Privacy-preserving Edge -Trust -Adaptive Learning Framework (PETAL-Grid), an AI-based federation blockchain model to support adaptive and privacy-preserving energy coordination. The key goal of this study is to attain scalable, real-time and secure EV charging coordination through the integration of federated artificial intelligence, edge-based demand intelligence and blockchain enabled trust management. The proposed framework allows joint demand learning without the need to exchange raw data, real-time adaptive charging based on edge intelligence, and transparent and tamper-proof energy transactions based on smart contracts. The PETAL-Grid workflow comprises of local data collection, edge-based demand forecasting, federated model aggregation, adaptive load coordination, and blockchain-based transaction validation. The results of the simulation show that PETAL-Grid can attain 18% peak load reduction, 17% efficiency of energy utilization, and 98-99% transaction security, which are better than the centralized and the baseline models. The results validate that PETAL-Grid is a scalable, reliable and dependable solution to sustainable smart electric mobility networks.","url":"https://pubmed.ncbi.nlm.nih.gov/42380185/","authors":["Alghamdi TA","Almalki SA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 1","doi":"10.1038/s41598-026-58336-x","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"pmid:42379586","name":"Deep learning in tumour genomics: from multi-omics integration to precision oncology.","source":"pubmed","abstract":"Cancer remains a leading cause of death globally, with nearly 10 million deaths in 2020. Advances in genomic technologies have revolutionized cancer research, shifting focus towards precision medicine based on comprehensive tumour genomic profiling. Concurrently, deep learning (DL) has emerged as a powerful paradigm for complex biological data. This review critically assesses recent advances in DL applications for tumour genomics, emphasizing four key domains: DNA sequencing analysis for mutation detection, gene expression profiling for cancer subtype classification, methylation function prediction for epigenetic characterization and integrative multi-omics approaches for comprehensive tumour profiling. We systematically analyse how different DL architectures-including convolutional neural networks, recurrent neural networks, graph neural networks, autoencoders and transformers-address specific challenges in cancer genomics. Our review highlights how these approaches significantly enhance detection sensitivity for genomic alterations, improve cancer subtype stratification, identify novel biomarkers and optimize therapeutic target selection. We examine technical challenges in DL implementation, including model interpretability, data scarcity, computational requirements and integration issues, alongside emerging solutions such as explainable AI, federated learning, and multi-modal frameworks. By synthesizing methodological innovations and identifying research directions, this review provides bioinformaticians and cancer researchers with a roadmap for leveraging DL to advance precision oncology.","url":"https://pubmed.ncbi.nlm.nih.gov/42379586/","authors":["Zhou Z","Li Z","Wang S","Ang MY","Xiao J","Choo SW"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1098/rsob.260117","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"pmid:42373765","name":"FedFound: a federated foundation model for lifespan brain morphological connectome analysis.","source":"pubmed","abstract":"The brain morphological connectome derived from structural MRI reflects inter-regional morphological relationships, providing a powerful representation for characterizing individual variability and detecting abnormalities across the lifespan. However, these abnormal alterations are subtle and complex, posing significant challenges for accurate and generalizable diagnosis using machine learning. Here, we present FedFound, the first federated foundation model inspired by the structured educational and residency training pathway of radiologists, designed for robust and scalable analysis of lifespan brain morphological connectomes. Integrating heterogeneous neuroimaging datasets across sites and disorders (22,911 subjects aged 0 to 100 years), FedFound combines self-supervised pre-training and supervised federated disease-specific refinement, supporting multidisciplinary knowledge aggregation through distributed optimization. Across nine diagnostic tasks spanning neurodevelopmental, neuropsychiatric, and neurodegenerative disorders, FedFound demonstrates superior performance and interpretability, revealing both shared and disorder-specific morphological patterns across etiologies. FedFound provides a robust foundation for lifespan neuroimage-based diagnosis that complements clinical expertise, while establishing a scalable and generalizable paradigm for integrating heterogeneous neuroimaging data across institutions, populations, and diseases to advance medical foundation models.","url":"https://pubmed.ncbi.nlm.nih.gov/42373765/","authors":["Han K","Hu D","Wang Y","Wu Z","Hung SC","Wang L","Lin W","Li G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 30","doi":"10.1038/s41746-026-02925-7","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42372426","name":"Somatic variant-calling beyond cancer: Repurposing algorithms to map low‑allele‑fraction variants across genomics.","source":"pubmed","abstract":"Somatic variant callers were originally developed to identify tumour-specific mutations in mixed tumour-normal samples. Increasingly, disciplines such as developmental biology, reproductive medicine, virology and mitochondrial genetics require detection of low-frequency variants from high-depth sequencing. Many laboratories therefore reuse cancer callers without clear guidance on their statistical assumptions or validation in non-cancer contexts.","url":"https://pubmed.ncbi.nlm.nih.gov/42372426/","authors":["Taylor N","Hearn TJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 29","doi":"10.1016/j.mrrev.2026.108602","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42369207","name":"AI-Augmented Hematological Signatures for Equitable Detection of Hereditary Hemolytic Anemia Carriers: A Global Systematic Review and Meta-Analysis.","source":"pubmed","abstract":"Artificial intelligence (AI) augmentation of routine hematological tests offers a promising strategy to improve hereditary hemolytic anemia (HHA) carrier detection in premarital screening, especially in resource-limited settings. HHA in this review specifically encompasses &#x3b2; -thalassemia, &#x3b1; -thalassemia, sickle cell disease (including HbS and HbC variants), and other hemoglobinopathies with autosomal recessive inheritance patterns requiring carrier detection for prevention. These conditions share hemolytic phenotypes but differ in hematological signatures, necessitating separate subgroup analyses. This global systematic review and meta-analysis evaluated the diagnostic accuracy, equity implications, and implementation challenges of AI-augmented complete blood count (CBC), blood smear, and erythrocyte sedimentation rate (ESR) for HHA carrier identification. We systematically searched seven databases and included 85 studies ( n = 133,498 participants, 23 countries). AI-augmented screening achieved a pooled sensitivity of 92.8% (95% CI: 91.3%-94.1%) and specificity of 91.5% (89.7%-93.0%), representing a 12.3% sensitivity improvement over conventional interpretation ( p &lt; 0.001). However, significant geographic disparities were observed: sensitivity in Sub-Saharan Africa was 86.5% compared with 94.8% in the Middle East ( p &lt; 0.001), partly due to algorithmic bias against African HbS/HbC variants and infrastructural barriers. Deep learning models achieved the highest sensitivity (95.1%), whereas explainable artificial intelligence (XAI) provided optimal specificity (94.3%). Integrating CBC with blood smear increased specificity by 5.5% at minimal additional cost. AI triage reduced confirmatory testing by 23.7%, saving $8.50 per individual. For equitable implementation, we recommend the following: (1) federated learning to include underrepresented genotypes, (2) WHO/CDC certification of affordable, offline-capable edge AI devices, and (3) mandatory XAI compliance with bias audits. AI can transform HHA screening, but deliberate efforts are needed to avoid exacerbating global health inequities. Importantly, 68% of validation studies used research-grade rather than routine clinical samples, and prospective clinic-to-algorithm validation remains a critical gap requiring urgent attention before real-world deployment.","url":"https://pubmed.ncbi.nlm.nih.gov/42369207/","authors":["Ali NT","Abdullah RS","Mehdi MAH","Ali GS","Ali HM","Gubran ANM","Al-Abd NM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1155/humu/9405486","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42365374","name":"Navigating AI and machine learning in cancer research: an end-to-end translational framework.","source":"pubmed","abstract":"Cancer is a complex and heterogeneous disease that is characterized by multi-level biological variability. Advances in high-throughput technologies have led to large-scale, high-dimensional data sets in cancer research, creating a pressing need for powerful computational techniques for successful data analysis. Current techniques may be inadequate for this purpose, thus underscoring the potential of artificial intelligence (AI) and machine learning (ML) for successful data analysis. This review provides a comprehensive pipeline for artificial intelligence/machine learning in cancer research, including preclinical research, clinical decision support, and real-world implementation. It emphasizes several important technologies, data integration, and implementation challenges. The review critically examines multi-omics fusion architectures, regularization-based machine learning, batch-effect harmonization, explainable AI, and federated learning, while addressing translational barriers including algorithmic bias, covariate drift, and regulatory asynchrony across Indian, US, and EU frameworks. Anchored by Decision Curve Analysis as a clinical utility benchmark, this narrative framework establishes that meaningful progress in precision oncology, early detection, and patient outcomes demands not only predictive accuracy but also externally validated, population-representative, and governance-compliant AI systems capable of sustained real-world oncology impact.","url":"https://pubmed.ncbi.nlm.nih.gov/42365374/","authors":["Saha S","Ali MS","Tengli AK","Prasad SR","Mallikarjunaswamy P","Pillappan R","Javarappa KK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 27","doi":"10.1186/s12967-026-08503-5","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42364938","name":"Understanding the Barriers to Translating Artificial Intelligence into the Clinical Laboratory.","source":"pubmed","abstract":"Artificial intelligence (AI) is having a transformational impact on society, yet its adoption in laboratory medicine has proceeded notably slower than in many other industries and even different specialities within medicine. This review sets out to examine why, despite such technical progress, meaningful clinical translation beyond rule-based autoverification has remained elusive. We argue that three principal barriers account for this gap. First, modelling approaches have been insufficiently robust for the inherent complexity of laboratory data. Second, the datasets available for model training and validation lack the scale, diversity, and operational representativeness required for genuine generalisation. Third, the regulatory environment constrains both the acquisition of data and the subsequent deployment of trained models. We trace the field's attempts at automation from autoverification systems through classical machine learning and single-modal deep learning approaches to the current generation of foundation and generative models, and we highlight the limitations and constraints of each approach. We then examine existing regulatory frameworks, available large-scale data initiatives and federated learning, a potential means to address these barriers.","url":"https://pubmed.ncbi.nlm.nih.gov/42364938/","authors":["Neale M","Wong C","Kreuter D","Taylor J","Sivapalaratnam S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.ejim.2026.107029","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"pmid:42359872","name":"Analysis of artificial intelligence errors in practical medicine and rehabilitation.","source":"pubmed","abstract":"Aim: To structure the types of errors that occur when using artificial intelligence in healthcare, as well as assess their impact on the accuracy of diagnostics and therapeutic decisions. Identify ways to minimize errors and increase the effectiveness of the use of AI in practical healthcare and rehabilitation.","url":"https://pubmed.ncbi.nlm.nih.gov/42359872/","authors":["Mintser OP","Hanynets PP","Sarcanich OV"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.36740/WLek/220842","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42356877","name":"FedCARE: Fuzzy-Supervised Federated Inference with Confidence Gating for Resilient IIoT Sensor Networks.","source":"pubmed","abstract":"Safety-critical Industrial Internet of Things (IIoT) sensor networks deployed in disaster scenarios require intelligent routing mechanisms that prioritize mission-critical packets without relying on centralized coordination. Federated learning on resource-constrained edge nodes presents three primary challenges: the absence of an interpretable supervisory signal, the inability to act conservatively based on per-inference confidence, and vulnerability to partial node availability. The proposed FedCARE framework addresses these issues by employing a Mamdani Fuzzy Inference System to generate traceable criticality labels from multi-modal sensor telemetry, a dropout-aware aggregation protocol that normalizes over only reachable nodes, and a confidence-gated resolver that defers to symbolic fuzzy classification when model confidence is insufficient, otherwise applying an auditable maximization rule to prevent under-prioritization of safety-critical data. Evaluation on 50-, 100-, and 200-node Watts-Strogatz topologies under fault rates up to 50%, using the Edge-IIoTset and WUSTL-IIoT-2021 benchmarks, demonstrates 99.00% critical recall and up to 1.8&#xd7; higher overall-packet delivery compared to RPL-RP under severe fault conditions. Routing improvements are primarily attributed to fuzzy criticality labeling and multi-path replication. These findings indicate that fuzzy-supervised federated inference offers a practical and interpretable solution for safety-critical IIoT routing, with an observed energy overhead of 7.8% per delivered packet.","url":"https://pubmed.ncbi.nlm.nih.gov/42356877/","authors":["Mostafa B","Haj Ahmad H","Rabaiah Y","Elseddik M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26123904","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"pmid:42356872","name":"Privacy-Preserving Federated Deep Learning for Robust Anomaly Detection in Distributed Security Sensing Systems.","source":"pubmed","abstract":"With the widespread adoption of intelligent terminals, edge devices, and distributed information systems in the financial domain, financial security sensing data exhibit multisource heterogeneity, dynamic temporal patterns, and high privacy sensitivity. Traditional centralized anomaly detection methods are no longer able to simultaneously satisfy the requirements of cross-institutional or cross-node collaborative modeling, client data privacy protection, and robust monitoring of transaction and system anomalies. To address this challenge, a data-local federated deep anomaly detection framework has been proposed for distributed financial security sensing systems. Initially, a local deep financial security sensing representation module is constructed to perform temporal encoding and attention-based modeling on multisource financial signals, including terminal operation status, network transaction communication, backend server operation, identity authentication, and anomaly alerts, thereby extracting representations relevant to anomalous behaviors. Subsequently, a data-local federated optimization and personalized aggregation mechanism is developed to enable cross-node knowledge sharing without transmitting raw transaction or client data, while local personalized detection heads are employed to adapt to non-independent and identically distributed (non-IID) financial institution data. Furthermore, an adversarially robust security detection and trust-aware aggregation strategy is introduced to enhance model stability under input noise, feature masking, anomaly camouflage, and potential malicious client updates. Experimental results demonstrate that the proposed method achieves an Accuracy of 92.37%, a Precision of 89.41%, a Recall of 88.26%, an F1-score of 88.83%, an AUC of 93.06%, and a PR-AUC of 89.15% in the primary financial anomaly detection task, significantly outperforming baseline methods such as Isolation Forest, Autoencoder, LSTM, Transformer, FedAvg, FedProx, SCAFFOLD, and MOON. In robustness experiments, the method attains F1-scores of 87.95%, 86.42%, 86.88%, 84.57%, 86.73%, and 83.91% under Gaussian noise, feature masking, temporal shift, adversarial perturbation, and 20% and 30% malicious client scenarios, respectively. Ablation studies further confirm the effectiveness of local representation learning, personalized federated optimization, adversarial training, and trust-aware aggregation mechanisms. Overall, the proposed approach provides an efficient intelligent anomaly detection solution for financial AI security monitoring scenarios characterized by data localization requirements, node heterogeneity, and attack perturbations.","url":"https://pubmed.ncbi.nlm.nih.gov/42356872/","authors":["Xu D","Chen H","Zeng Y","Yang Y","Huang J","Song J","Zhan Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 19","doi":"10.3390/s26123901","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42356687","name":"DynamicFU: Contribution-Aware Dynamic Federated Unlearning for Industrial IoT.","source":"pubmed","abstract":"The Industrial Internet of Things (IIoT) increasingly relies on federated learning (FL) to enable collaborative model training without directly sharing raw traffic data across industrial sites. However, in practical IIoT deployments, clients may later request the removal of their data contributions from a trained federated model due to regulatory requirements, such as the General Data Protection Regulation (GDPR), ownership transfer, or internal data-governance policies. Such practical requirements create a strong demand for federated unlearning in IIoT applications. Furthermore, IIoT deployments often exhibit highly imbalanced client data distributions, resulting in substantially different contributions of individual clients to the global model. Nevertheless, most existing federated unlearning methods adopt a uniform unlearning strategy and fail to account for such client-level contribution gaps. To address this issue, we propose DynamicFU, a contribution-aware dynamic federated unlearning framework for IIoT deployments. The proposed method evaluates the target client from parameter-level, data-level, and performance-level perspectives and adaptively determines the unlearning strength by dynamically adjusting the number of unlearning rounds. Experimental results on public IIoT datasets show that DynamicFU substantially improves unlearning efficiency, achieving up to 22.89&#xd7; speedup over Full Retrain while maintaining comparable effectiveness.","url":"https://pubmed.ncbi.nlm.nih.gov/42356687/","authors":["Wu Z","He B","Si Z","Liao X","Su C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26123714","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"pmid:42356628","name":"Energy-Efficient Cryptographic Protocols for Sustainable IoT Security: A Federated Learning-Enhanced Lightweight Framework with Post-Quantum Resilience.","source":"pubmed","abstract":"The increasing pace of Internet of Things (IoT) and Industrial Internet of Things (IIoT) applications has exacerbated the security challenges in resource-constrained environments, where traditional cryptographic protocols incur prohibitively high computational and energy costs. These constraints are also worsened by the advent of quantum computing, which poses a long-term security risk to popular crypto-key cryptographic-based efforts. To overcome these difficulties, this paper proposes an Energy-Efficient Cryptographic Protocol Framework (EECPF) that provides mutual optimization between energy consumption, security level, and communication latency to achieve sustainable IoT security. The presented framework proposes an adaptive encryption selection mechanism that dynamically chooses cryptographic algorithms depending on device capabilities, network conditions, and threat levels derived from intrusion detection outputs. EECPF combines privacy-preserving federated learning for distributed intrusion detection with collaborative threat intelligence sharing, eliminating centralized data sharing. In addition, lattice-based post-quantum cryptography primitives are added and combined with lightweight blockchain-enforced identity management to ensure long-term authentication resilience. The models on which the framework is based are mathematically based, modeling the consumption of energy, the robustness of security, and latency, providing principled multi-objective optimization under resource constraints. The publicly available Edge-IIoTset dataset was subjected to extensive experimental assessment under realistic IIoT and IoT attack scenarios. Experiments show that EECPF can reach an intrusion detection rate of 94.7%, while reducing energy consumption by 47.3% and latency by 23.8% compared with other commonly used lightweight cryptographic methods. These were continually noticed across different heterogeneous devices and deployment environments. In general, EECPF offers an energy-aware, quantum-resilient, and scalable security solution that can be used for next-generation IoT systems, such as smart healthcare, industrial automation, and smart city infrastructures.","url":"https://pubmed.ncbi.nlm.nih.gov/42356628/","authors":["Alshammari A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 8","doi":"10.3390/s26123656","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42356429","name":"Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.","source":"pubmed","abstract":"The process of drug discovery is one of the most expensive, time-consuming, and high-risk endeavors in modern science. Translating initial scientific insights into safe and effective therapies, supported by genomics, structural biology, and computational chemistry, typically requires more than a decade and substantial financial investment. Machine learning (ML) has emerged as a powerful tool for improving efficiency across the drug discovery pipeline. By enabling the analysis of large and complex datasets, ML supports target identification, lead discovery, optimization, and prediction of preclinical and clinical outcomes. Its integration with experimental validation and automation is illustrated by recent advances such as protein structure prediction, AI-driven antifibrotic compound discovery, and antibiotic identification. Despite these advances, significant challenges remain. Model generalizability is limited by data scarcity, heterogeneity, and hidden biases. In addition, the translation of in silico predictions into clinically validated outcomes remains a major bottleneck, and regulatory acceptance is constrained by limited model interpretability. Ethical considerations, including data privacy, equitable representation, and the potential misuse of generative models, further complicate adoption. This review examines the applications of ML across the drug discovery pipeline, with a focus on translational and regulatory considerations. It also discusses emerging directions, including hybrid physics-AI approaches, multimodal foundation models, federated learning, and explainable AI. The effective integration of ML will depend on rigorous validation, interdisciplinary collaboration, responsible data governance, and alignment with regulatory frameworks.","url":"https://pubmed.ncbi.nlm.nih.gov/42356429/","authors":["El-Tanani M","Rabbani SA","Wali AF","Muhana F","El-Tanani Y","Kumar R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 22","doi":"10.3390/ph19060810","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42354556","name":"The Unfinished Ecosystem: Why Remote Patient Monitoring Has Matured Unevenly, and What Closing the Gap Will Require.","source":"pubmed","abstract":"Remote patient monitoring (RPM) is widely framed as a foundational technology for the next generation of chronic-disease care. Specific applications-pacemaker follow-up, hypertension cohorts, structured heart-failure programmes, post-surgical biosensor protocols, and virtual wards-now generate measurable clinical and economic value. Yet a decade of evaluations and implementation studies suggests that the surrounding ecosystem has matured unevenly: working applications coexist with persistent cross-cutting fragility. In this Perspective we argue that four structural gaps continue to constrain RPM's promise at scale: (i) economic models that do not credibly compensate the asynchronous clinical work that RPM generates; (ii) ambiguous frameworks for professional liability and accountability for continuous data streams, intensified by artificial-intelligence (AI)-mediated decision support; (iii) privacy, equity, and benefit-sharing arrangements that do not yet make patients unambiguous net beneficiaries-a gap visible across very different health systems internationally; and (iv) engagement and adherence dynamics that determine whether programmes deliver value at all, but are still treated as secondary outcomes. The COVID-19 emergency briefly suspended much of the friction in this ecosystem and produced a useful natural experiment: what scaled rapidly under emergency conditions, and what subsequently atrophied, illuminates which gaps are technical, which are economic, and which are institutional. We close with a six-point research and policy agenda intended to move RPM from localised successes to a trustworthy, generalisable standard of care.","url":"https://pubmed.ncbi.nlm.nih.gov/42354556/","authors":["Ajagbe TS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/healthcare14121698","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:42352147","name":"FedVI: Financial Cross-Domain Federated Learning with Scarce Overlapping Samples via Visual Representation of Heterogeneous Tabular Data and Meta-Optimization.","source":"pubmed","abstract":"Federated learning offers a promising approach for cross-institutional financial risk control modeling but encounters two key challenges in practice: feature space heterogeneity and low sample overlap rate. Current federated transfer learning methods often rely heavily on sufficient overlapping samples or explicit feature alignment. However, these approaches frequently result in negative transfer when enforced alignment is applied in highly heterogeneous environments. To address this issue, we propose FedVI, a novel federated transfer learning framework that integrates tabular-to-image conversion and meta-learning mechanisms. Moving beyond conventional methods that rely on sample-level alignment, FedVI employs a federated dual-stream feature alignment strategy to securely reconstruct a unified global feature map across institutions. Subsequently, FedVI integrates federated Image Generator for Tabular Data (IGTD) with tabular Transformer technology to convert one-dimensional tabular data into two-dimensional visual-semantic tensors. These tensors effectively fuse spatial topology and semantic information while embedding an independent Mask channel to explicitly retain the true missingness patterns of features. Finally, FedVI adopts the Model-Agnostic Meta-Learning (MAML) architecture to facilitate global parameter optimization. We evaluated FedVI on the real-world Lending Club credit dataset and Home Credit Default Risk datasets under highly heterogeneous federated settings (i.e., heterogeneous feature spaces across three clients and scarce overlapping samples). The results reveal that FedVI achieves competitive performance against advanced baselines such as FedProx, FedRep, and FedKT, particularly in recall and F1-Score. These findings indicate that FedVI can effectively support cross-domain adaptation under heterogeneous federated learning settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42352147/","authors":["Yuan K","Wu J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/e28060637","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42351577","name":"Current State and Future of Artificial Intelligence in Pediatric Interventional Radiology: A Narrative Review.","source":"pubmed","abstract":"Artificial intelligence (AI) is reshaping the field of diagnostic radiology; however, its applications in interventional radiology and pediatric interventional radiology (PIR) remain limited despite clear clinical needs and the rich multimodal data environment characteristic of pediatric procedural care. In this narrative review, I summarize the current state of AI technologies relevant to PIR and outline future perspectives for their clinical integration. Peer-reviewed literature and position statements identified through MEDLINE/PubMed, Embase, Scopus, and major society publications up to the first quarter of 2026 are synthesized, focusing on AI applications across the PIR care pathway, including dose-sparing image acquisition and reconstruction, automated image interpretation and computer-aided diagnosis, data-driven procedural planning and navigation, and post-procedural risk prediction and monitoring. After briefly introducing core machine learning and deep learning concepts, pediatric-specific challenges are discussed, including radiation sensitivity, growth-related anatomical variability, regulatory constraints, and the scarcity of large, annotated datasets, as well as existing and emerging applications along the PIR care pathway: AI-assisted dose reduction and image reconstruction, automated image interpretation, segmentation, and computer-aided diagnosis; data-driven procedural planning, including three-dimensional modelling, augmented reality, AI-enabled/AI-adjacent robotics, and AI-directed procedural navigation; and post-procedural risk prediction and outcome monitoring. Finally, emerging paradigms, including explainable AI, federated learning, and multimodal integration, are highlighted, and research priorities, collaborative frameworks, and governance principles required to ensure safe, equitable, and effective AI deployment in PIR are outlined. In doing so, this review delineates the current evidence gaps and priority directions for clinically meaningful AI adoption in PIR. Although AI has the potential to improve patient care, it has not yet been specifically designed, validated, or deployed in children. Existing work demonstrates feasibility across the PIR workflow, but most tools remain weakly linked to pediatric clinical endpoints.","url":"https://pubmed.ncbi.nlm.nih.gov/42351577/","authors":["Al-Sharydah AM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 20","doi":"10.3390/diagnostics16121918","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42350501","name":"Edge-AI enabled secure IoT framework for real-time patient monitoring and anomaly detection in smart healthcare systems.","source":"pubmed","abstract":"Hospitals desire knowledge of bedside sensors in real time but they do not wish to send everything to the cloud. We propose an Edge-AI framework used in the IoT environment that maintains intelligence as near as possible to the patient, and ensures privacy and a trusted audit trail. A quantized CNN-LSTM is run on-device to detect anomalies and protects data in transit and at rest (TLS plus lightweight homomorphic encryption), logs notable events to a private block chain to give an auditable, tamper-proof history, and improves models off-device using federated learning to ensure that raw patient data never reaches the cloud. Reply The framework was tested against a cloud-only baseline on the MIT-BIH Arrhythmia dataset and a live multi-sensor synthetic stream, beating it across all metrics: accuracy 94.7% versus 83.1% accuracy, median inference latency 118&#xa0;ms versus 246&#xa0;ms (a 52% reduction), daily communication overhead 36.2&#xa0;MB versus 56.3&#xa0;MB (a savings of 38.1%), and energy usage 1.21 mW/sample (near 22% energy efficiency improvement). Block chain logging endured 75 events/s, which helped medico-legal tractability. Federated rounds provided&#x2009;~&#x2009;1.5-2.3 accuracy points per round across five rounds, whereas INT8 quantization reduced model size by&#x2009;~&#x2009;74% with only a&#x2009;~&#x2009;0.4 accuracy degrade-practical in Jetson-class edge devices. SHAP explanations accompany alerts to enable the establishment of clinical trust in the model by demonstrating why a case was flagged. In general, the framework provides a safe, decipherable, and standard-aligned course to hospital-scale implementation (&gt;&#x2009;1,000 nodes) with quantifiable gains in responsiveness and bandwidth as well as power.","url":"https://pubmed.ncbi.nlm.nih.gov/42350501/","authors":["Karpagam P","Karthikeyan M","Kalpana G","Suresh A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-59602-8","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"pmid:42347020","name":"Mechanistic Insights into the Role of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Multiple Sclerosis.","source":"pubmed","abstract":"Multiple sclerosis (MS) is a chronic, immune-mediated demyelinating disease of the central nervous system whose heterogeneous clinical, radiological, and biological course has long resisted precise individual-level prediction. The recent convergence of large longitudinal datasets, advanced computational methods, and increasingly informative biomarkers has created conditions in which artificial intelligence (AI) and machine learning (ML) can begin to address that problem substantively. This review surveys the current evidence for AI/ML applications across the MS care continuum, with particular focus on the literature from 2022 through early 2026. Nine domains are examined: automated MRI lesion segmentation and quantification, fluid biomarker interpretation, unsupervised disease subtyping, disability progression prediction, treatment response stratification, drug repurposing and molecular discovery, digital biomarker monitoring, mechanistic interpretability, and integrated clinical management protocols. Notable recent contributions include the SuStaIn-based identification of two biologically distinct MS trajectories distinguished by early versus late serum neurofilament light chain elevation, the MindGlide deep learning platform enabling longitudinal analysis of archived routine clinical MRI data, the T-cell morphological classifier predicting natalizumab treatment response before drug initiation, and the fenebrutinib Phase III program that produced the first Bruton's tyrosine kinase inhibitor results meeting primary endpoints in both relapsing and primary progressive MS. A proposed AI-Enhanced Management Protocol (AMP-26) reflecting 2026 clinical standards is included as an appendix. Throughout, emphasis is placed on mechanistic interpretability: the distinction between models that correlate features with outcomes and models whose decision logic reflects established MS pathobiology is considered a prerequisite for clinical credibility and regulatory readiness.","url":"https://pubmed.ncbi.nlm.nih.gov/42347020/","authors":["Minagar A","Sahraian M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 27","doi":"10.3390/pathophysiology33020035","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42346883","name":"Radiomics in Medical Imaging: Methods, Applications, and Challenges.","source":"pubmed","abstract":"Radiomics enables quantitative medical image analysis by converting imaging data into structured, high-dimensional feature representations for predictive modeling. Despite methodological developments and encouraging retrospective results, radiomics continue to face persistent challenges related to feature instability, limited reproducibility, validation bias, and restricted clinical translation. Existing reviews largely focus on application-specific outcomes or isolated pipeline components, with limited analysis of how interdependent design choices across acquisition, preprocessing, feature engineering, modeling, and evaluation collectively affect robustness and generalizability. This survey provides an end-to-end analysis of radiomics pipelines, examining how methodological decisions at each stage influence feature stability, model reliability, and translational validity. This paper reviews radiomic feature extraction, selection, and dimensionality reduction strategies; classical machine and deep learning-based modeling approaches; and ensemble and hybrid frameworks, with emphasis on validation protocols, data leakage prevention, and statistical reliability. Clinical applications are discussed with a focus on evaluation rigor rather than reported performance metrics. The survey identifies open challenges in standardization, domain shift, and clinical deployment, and outlines future directions such as hybrid radiomics-artificial intelligence models, multimodal fusion, federated learning, and standardized benchmarking.","url":"https://pubmed.ncbi.nlm.nih.gov/42346883/","authors":["Neha F","Shukla DK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 23","doi":"10.3390/jimaging12060220","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42346643","name":"Bridging Ancestry-Stratified Bias in Pharmacogenomics AI: Toward Metabolomics-Inclusive Multi-Omics Precision Medicine.","source":"pubmed","abstract":"Pharmacogenomics AI offers significant potential for individualized drug therapy; however, its clinical benefits remain unevenly distributed. Models trained predominantly on European-ancestry data consistently underperform in non-European populations, with polygenic risk scores (PRS) showing an estimated 39-73% reduction in predictive accuracy in African-ancestry cohorts across complex traits. These disparities have driven increased interest in moving beyond single-layer genomic approaches. Multi-omics frameworks integrating genomic, transcriptomic, proteomic, and metabolomic data have emerged as a promising strategy to improve prediction across heterogeneous clinical populations, as each molecular layer provides distinct and complementary biological information. Among these layers, metabolomics may represent a particularly transferable component across populations. Metabolite profiles capture the downstream functional output of biological systems influenced by genetic, environmental, dietary, and microbiome-related factors, and may therefore be less reliant on ancestry-stratified allele frequency structures that underlie performance disparities in genomic models. This review synthesizes evidence regarding the mechanistic basis of genomic bias in pharmacogenomics AI, the emerging role of multi-omics integration, especially metabolomics, in improving predictive performance, and the current landscape of computational strategies for bias mitigation, including federated learning, transfer learning, domain adaptation, and synthetic data generation. Collectively, current evidence supports metabolomics-inclusive multi-omics frameworks as a biologically plausible, hypothesis-generating strategy to reduce reliance on ancestry-linked genomic features. However, direct evidence that such frameworks reduce ancestry-related bias in clinical AI outputs remains limited, underscoring the need for globally diverse datasets and prospective multi-population validation.","url":"https://pubmed.ncbi.nlm.nih.gov/42346643/","authors":["Lee H","Sajid K","Lee D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 20","doi":"10.3390/jpm16060332","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42346184","name":"Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.","source":"pubmed","abstract":"Rare diseases affect over 300 million individuals worldwide yet remain underdiagnosed and poorly characterized due to fragmented data, small cohorts, and phenotypic heterogeneity. Advances in artificial intelligence (AI) are enabling integration of genomics, imaging, electronic health records, and patient-generated data to support diagnosis, phenotyping, prognosis, and therapeutic discovery. In kidney care, these capabilities are reflected in tools for genomic variant prioritization, AI-assisted histopathology, and integrated risk stratification models for rare and complex kidney diseases. This review synthesizes current AI applications across the rare disease continuum and proposes a clinically grounded framework to distinguish exploratory models from systems that are methodologically robust and operationally deployable. We highlight advances that address data sparsity and heterogeneity, alongside persistent challenges in validation, generalizability, equity, and workflow integration. Finally, we outline future directions, including federated learning, digital twins, and AI-driven clinical decision agents, as pathways toward precision-guided, workflow-integrated rare disease care.","url":"https://pubmed.ncbi.nlm.nih.gov/42346184/","authors":["Thongprayoon C","Pesce F","Cheungpasitporn W"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/clinpract16060101","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"pmid:42341745","name":"[Digital transformation in multiple sclerosis: Advances in diagnostics, monitoring and patient-centred care].","source":"pubmed","abstract":"Digital transformation is fundamentally changing the diagnosis, monitoring and treatment of multiple sclerosis. The integration of multimodal data from imaging, laboratory tests, clinical assessments, patient-reported outcomes and continuous measurements via wearables is creating high-resolution, longitudinal profiles of disease progression. Based on this data, modern analysis methods and artificial intelligence enable predictive models for disease activity, progression and therapeutic response, supporting personalised decision-making. Digital patient pathways and patient portals open up new options for participatory, standardised care, while telemedicine, telerehabilitation and digital health applications complement care regardless of location and time. In research, real-world data, federated learning and virtual, decentralised studies are accelerating patient-centred evidence generation. Concepts such as the digital twin outline the next stage of development in simulation-based precision medicine. Key challenges relate to data protection and data security, data quality, interoperability, bias, transparency and the traceability of algorithmic decisions. Overall, digitalisation offers substantial opportunities to detect disease activity earlier, optimise treatment goals and improve quality of life and care - provided that technical, regulatory and ethical requirements are consistently addressed and translated into scalable care models.","url":"https://pubmed.ncbi.nlm.nih.gov/42341745/","authors":["Voigt I","Inojosa H","Pawlitzki M","Masanneck L","Meuth SG","Ziemssen T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 24","doi":"10.1055/a-2890-0999","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42336935","name":"Privacy-aware diabetic retinopathy grading and visual lesion-focused interpretability through mixture-of-experts federated deep learning with explainable AI.","source":"pubmed","abstract":"Diabetic Retinopathy (DR) is still a major cause of vision loss that can be avoided. This means that we need automated screening systems that can work across institutions without putting sensitive medical data in one place. Although Federated Learning (FL) allows for cooperative model training while preserving data locality, Non-IID data distribution, communication overhead, and unstable convergence frequently limit its effectiveness in medical imaging. This paper suggests a federated Mixture-of-Experts (FL-MoE) framework for DR classification that combines interpretable deep learning and expert specialization in order to overcome these challenges. Using the EyePACS and APTOS-2019 retinal fundus datasets, this paper evaluates multiple backbone architectures, including Convolutional Neural Networks (CNN), a hybrid CNN-LSTM model, and transformer-based Vision Transformer (ViT), within the FL-MoE framework. FL-MoE improves performance under heterogeneous client distributions for several backbone architectures, particularly CNN-LSTM, though performance varies across models. The CNN-LSTM backbone achieves 76.2% accuracy with 89.5% AUC on EyePACS while reducing communication cost by an order of magnitude compared to transformer-based models. Furthermore, CNN-LSTM exhibits more stable convergence and stronger robustness to client-level data heterogeneity. Grad-CAM based explainability analysis qualitatively shows attention maps highlighting retinal regions commonly associated with DR. To quantify localisation quality, we computed Intersection-over-Union (IoU) with IDRiD lesion masks; mean IoU values were below 0.03 for all lesion types, confirming the coarse, exploratory nature of the visualisations. Overall, the proposed FL-MoE framework with a CNN-LSTM backbone offers an effective and practical solution for scalable, privacy-aware Diabetic Retinopathy screening in federated clinical environments, outperforming both standard federated baselines and a representative personalized FL method (FedBN) under heterogeneous data conditions.","url":"https://pubmed.ncbi.nlm.nih.gov/42336935/","authors":["Tashrif MTA","Kundu D","Bithee MMA","Rahman A","Farid FA","Karim HA","Miah ASM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 23","doi":"10.1038/s41598-026-58292-6","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42335060","name":"TTP-SSFL: Test-Time Personalization Self-Supervised Federated Learning for Accelerating MR Image Reconstruction.","source":"pubmed","abstract":"Federated learning (FL) has emerged as a promising paradigm for accelerating magnetic resonance (MR) image reconstruction while preserving data privacy in multicenter collaborations. However, existing FL-based reconstruction methods face two major challenges: 1) a heavy reliance on fully sampled k-space datasets for model training, which is often a tricky problem in clinical settings, and 2) significant performance degradation due to distribution shifts between training and test domains. To address these limitations, a test-time personalization self-supervised FL (TTP-SSFL) method is proposed to accelerate MR image reconstruction. In this study, cross-institutional collaboration without any fully sampled data is implemented by introducing a Siamese-based self-supervised strategy with a hybrid loss function at each client. Moreover, a low-rank adaptation (LoRA)-based test-time adaptation (TTA) strategy is proposed to further mitigate domain shift during deployment. By inserting lightweight adapters into the global model and optimizing them using only testing data via self-supervision, the proposed method can achieve efficient model personalization and robust generalization under distribution shifts. Extensive experiments on multicenter datasets show that TTP-SSFL achieves state-of-the-art performance among self-supervised methods and matches the accuracy of supervised personalized FL (PFL) models, providing a practical and privacy-preserving solution for robust MR reconstruction across heterogeneous clinical environments.","url":"https://pubmed.ncbi.nlm.nih.gov/42335060/","authors":["Geng C","Jiang M","Ruan D","Yu C","Sun H","Liu F","Xia L","Yang G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 23","doi":"10.1109/TNNLS.2026.3703424","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42332112","name":"Spatio-temporal graph ConvLSTM with hierarchical personalized federated learning for cooperative zero-day intrusion detection in 6G internet of vehicles.","source":"pubmed","abstract":"In growing 6G-enabled Internet of Vehicles (IoV) environments, in-car networks (IVNs) are more susceptible to sophisticated cyber threats, especially zero-day attacks that avoid signature-based detection. The centralised data dependency, lack of geographical awareness, and poor generalisation in heterogeneous and non-IID conditions are the limitations of current intrusion detection systems. This paper suggests a cooperative intrusion detection system that combines a spatio-temporal graph convolutional long short-term memory (ST-GConvLSTM) model with hierarchical personalised federated learning. The suggested method uses dynamic vehicle-to-vehicle graph structures to capture both intra-vehicle temporal patterns of CAN communications and inter-vehicle spatial dependencies. Scalable training is made possible by a three-tier learning architecture (vehicle, edge, and cloud) that protects data privacy and reduces concept drift. Furthermore, under limitations of latency, energy consumption, communication overhead, and privacy budget, a multi-objective reinforcement learning technique is used to dynamically optimise client involvement. In comparison to state-of-the-art federated baselines, experimental evaluations on real-world CAN datasets under realistic 6G-IoV settings show that the proposed framework achieves high detection performance for both known and zero-day attacks while significantly improving convergence speed and lowering system overhead. These findings demonstrate how well hierarchical federated optimisation and spatiotemporal graph learning can be combined to create safe and scalable vehicular networks.","url":"https://pubmed.ncbi.nlm.nih.gov/42332112/","authors":["Alghamdi M","Algahtani MA","Abouelkheir E","Ibraheem A","Abdallah A","Almutawa A","Alawad WM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 22","doi":"10.1038/s41598-026-58600-0","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42330856","name":"Integrating experimental biology, computational methods, and artificial intelligence in anticancer drug discovery: Bridging the translational gap.","source":"pubmed","abstract":"Cancer remains a leading cause of mortality globally, with the incidence projected to reach 28.4 million new cases annually by 2040. Traditional drug discovery is notoriously inefficient: 10-17 years and up to $2.8 billion per approved drug, with fewer than 10% of clinical candidates succeeding. This review critically examines how the integration of experimental methods with computational biology and artificial intelligence (AI) can accelerate anticancer drug development. We argue that an iterative feedback loop in which sophisticated in vitro and in vivo models provide biological context for validation while AI accelerates target identification, de novo molecular design, and ADMET prediction offers a path forward. FDA-approved drugs, including imatinib, crizotinib, and larotrectinib, have emerged from such integrated pipelines. A persistent translational gap still limits progress owing to three major barriers: poor data fidelity and lack of standardization, limited interpretability of AI models (the black box problem), and regulatory complexity. Addressing these challenges requires physiologically relevant organ-on-a-chip systems, explainable and physics-informed AI, federated learning ecosystems, and adaptive regulatory frameworks. Overall, the continuous integration of computational prediction with experimental validation in a rigorous, data-driven pipeline is critical for accelerating the development of next-generation anticancer therapies.","url":"https://pubmed.ncbi.nlm.nih.gov/42330856/","authors":["Zemnou CT","Ngakam R","Tepap SSD","Simo FBN","Zanchi FB","Seukep AJ","Kouam AF","Huy NT"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 15","doi":"10.1016/j.compbiomed.2026.111832","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42320241","name":"FedLASE: Performance-balanced system-heterogeneous FL via layer-adaptive submodel extraction.","source":"pubmed","abstract":"Federated Learning (FL) has gained significant attention for its privacy-preserving capabilities in distributed learning environments. However, the inherent system heterogeneity across edge devices brings significant challenges in deploying a unified global model. Although many submodel extraction methods are designed to address these challenges by selecting a subset of parameters from the global model to accommodate client constraints, our experiments show that existing submodel extraction methods exhibit significant performance discrepancies between submodels with different resource levels, limiting the overall performance of the federated learning system. To overcome these limitations, we propose FedLASE - a novel Layer-Adaptive Submodel Extraction framework that selects important parameters while preserving the structural integrity of the client models, thereby achieving balanced performance across heterogeneous FL clients and improving the convergence. Specifically, our approach quantifies layer importance based on parameter importance and hierarchically extracts critical parameters within each layer while strictly satisfying resource constraints. Theoretically, we rigorously analyze the convergence of FedLASE and investigate the influence of system heterogeneity on its performance. Extensive experiments demonstrate the superiority of FedLASE over the state-of-the-art methods and its robustness across various system-heterogeneous scenarios.","url":"https://pubmed.ncbi.nlm.nih.gov/42320241/","authors":["Hu Q","Liao T","Wu S","Zheng Z","Chen C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 15","doi":"10.1016/j.neunet.2026.109261","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42317269","name":"Precision immuno-oncology in oral cancer: latest trends in biomarkers, novel drug development and nanoparticle-based therapeutic platforms.","source":"pubmed","abstract":"Oral squamous cell carcinoma poses a significant global health burden, with over 370,000 annual cases and poor 5-year survival rates of 50%-60%, driven by risk factors like tobacco and alcohol. Despite advances in surgery, radiotherapy, and chemotherapy, functional morbidity and resistance necessitate precision immuno-oncology approaches. This review explores the tumour immune microenvironment in oral squamous cell carcinoma, characterized by immunosuppressive elements like M2 macrophages, myeloid-derived suppressor cells, and regulatory T cells, alongside spatial heterogeneity that complicates therapy. Biomarkers for patient selection include programmed death-ligand1 expression (via combined positive scoring), tumour mutational burden, neoantigen load, interferon-gamma-&#x3b3; signatures, cytolytic scores, peripheral circulating tumour DNA, and single-cell/spatial profiling, though standardization remains critical. Immunotherapy has transformed oral squamous cell carcinoma management, with programmed cell death protein-1 inhibitors like nivolumab and pembrolizumab showing survival benefits in trials, particularly in programmed cell death protein-L1-positive cases. Emerging strategies encompass next-generation checkpoints (Lymphocyte activation gene-3, T-cell immunoreceptor with Ig and ITIM domains, OX40), personalized neoantigen vaccines, adoptive cell therapies (Tumour-Infiltrating Lymphocytes, Chimeric Antigen Receptor T-cell therapy), and rational combinations to counter resistance. Nanomedicine platforms-liposomes, polymeric nanoparticles, gold-based systems-enhance drug delivery, reprogram the Tumour and immune microenvironment, and enable chemo-immuno-photothermal synergies, addressing mucosal barriers and toxicity. Future priorities include biomarker validation via prospective registries, scalable Good Manufacturing Practice nanoplatforms, AI-driven multi-omic modeling, and federated learning for predictive analytics. By integrating tumour genomics, immune profiling, and advanced delivery, precision immuno-oncology holds promise to improve response rates, durability, and quality of life in oral squamous cell carcinoma.","url":"https://pubmed.ncbi.nlm.nih.gov/42317269/","authors":["Augustine D","Sowmya SV","Pushpalatha C","Prasad K","Khatoon H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fcell.2026.1839508","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42315615","name":"Unlocking multi-institutional insights into disease progression with PEAL as a lossless, one-shot federated learning solution.","source":"pubmed","abstract":"Multisite analysis of electronic health record (EHR) data presents unique opportunities for studying disease progression in real-world settings. However, privacy concerns, communication costs, and site-level heterogeneity pose significant challenges for analyzing longitudinal data. We introduce PEAL (Privacy-preserving Efficient Aggregation for Longitudinal data), a novel federated learning algorithm for fitting multi-level linear mixed-effects models with spline basis terms for nonlinear temporal trends. PEAL requires only a single-round transfer of summary statistics and produces results identical to using pooled individual participant data. Simulation studies demonstrate that PEAL accurately recovers fixed effects and variance components under realistic multi-level structures. We applied PEAL to real-world longitudinal datasets of systemic sclerosis patients from the Johns Hopkins and University of Pittsburgh Scleroderma Centers. This application shows our algorithm captures reasonable disease trajectories. Overall, PEAL provides a practical solution for distributed research networks studying rare diseases and time-evolving clinical outcomes by enabling lossless, communication-efficient, and privacy-preserving modeling.","url":"https://pubmed.ncbi.nlm.nih.gov/42315615/","authors":["Shen Y","Kim JS","Luo C","Zeger SL","Domsic RT","Shah AA","Tong J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 18","doi":"10.1038/s41746-026-02861-6","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42308070","name":"Enhancing X-ray Image Classification through Heterogeneous Federated Learning with Natural Image-Augmented Models.","source":"pubmed","abstract":"Deep learning-based computer-aided diagnosis (DL-CAD) models have achieved remarkable success in X-ray image analysis. Yet their effectiveness is often constrained by the tight regulations of governing sensitive X-ray data. Federated Learning (FL) offers a promising paradigm by facilitating the collaborative training of DL-CAD models across healthcare institutions without compromising data privacy. Despite this advancement, the limited local X-ray archives and the issue of heterogeneous model architectures bring distinctive challenges. To address these challenges, this work pioneers the utilization of natural images to develop a natural image-augmented heterogeneous FL framework (NatIMG-FL) for X-ray classification. For augmenting local training, NatIMG-FL leverages natural images as auxiliary supervised data to facilitate the alignment of feature distributions between natural and X-ray images. To tackle the model heterogeneity issue, NatIMG-FL introduces a novel dual weights-based fine-grained knowledge transfer method, enabling adaptive knowledge exchange between local and central models. The NatIMG-FL framework provides insights into exploiting natural images as proxy datasets to enhance knowledge transfer in heterogeneous FL for X-ray classification.","url":"https://pubmed.ncbi.nlm.nih.gov/42308070/","authors":["Hu Y","Huang YA","Liu R","Xue X","Zhang Y","Wu J","Huang ZA","Tan KC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 17","doi":"10.1109/TPAMI.2026.3704679","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42306668","name":"Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) into thoracic surgery accelerated notably over the course of 2025, transitioning from isolated diagnostic aids toward comprehensive clinical pathway integration. The objective of this narrative review is to synthesize the latest evidence on AI applications across the entire thoracic surgical workflow, organized along the patient care continuum from preoperative assessment through intraoperative execution to postoperative management.","url":"https://pubmed.ncbi.nlm.nih.gov/42306668/","authors":["Zhang Y","Yang Z","Lin Y","Zhao Y","Zhou Y","Deng C","Dai K","Liang H","Su Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 31","doi":"10.21037/jtd-2026-0592","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42305954","name":"Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.","source":"pubmed","abstract":"Artificial intelligence (AI), especially Deep Learning (DL), has been shown significant in accelerating the detection and diagnosis of neurological disorders via medical imaging. This study is mainly focused on Alzheimer's disease (AD), which reveals distinctive structural modifications observable by Magnetic Resonance Imaging (MRI). Although several studies employing convolutional neural networks (CNNs) and other artificial intelligence models indicate promising diagnostic accuracy, many issues related to methodology exist. This research offers a comprehensive assessment of recent studies (2000-2025) to synthesize the key limitations limiting the clinical application of AI for AD detection using MRI. The study identify the main challenges, namely: (1) restricted access to extensive, curated, and diverse multimodal datasets; (2) elevated model complexity with associated risks of overfitting on small cohorts; (3) insufficient interpretability and clinical validation of AI decisions; (4) computational inefficiency and excessive energy consumption; and (5) challenges in generalizing models across heterogeneous cohorts and imaging guidelines. Our study indicates that modern research frequently emphasizes marginal improvements in accuracy rather than solving these essential translational obstacles. The authors conclude by outlining essential research progressions, highlighting the necessity for federated learning for dealing with data scarcity, the advancement of explainable AI (XAI) frameworks, and the creation of standardized benchmarking protocols to flexible, clinically-adoptable AI methods for early AD detection.","url":"https://pubmed.ncbi.nlm.nih.gov/42305954/","authors":["Alyaqoobi HIR","Lopez-Guede JM","Dara OA","Ramos-Hernanz JA","Aramendia I","Teso-Fz-Betoño D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1767090","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42303852","name":"Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.","source":"pubmed","abstract":"Learning-based image classification has become central to modern medical imaging, but the field is changing rapidly: foundation models, vision-language models (VLMs), and label-efficient pretraining are reshaping which methods are clinically useful. This review focuses on the state of the art rather than re-explaining well-established models. We summarize learning paradigms, contrast classical machine learning (ML) and deep learning (DL) families, and emphasize advances most relevant to clinical translation: medical foundation models, multimodal VLMs, hybrid CNN-transformer architectures, diffusion-based augmentation, self-supervised pretraining, federated learning, and efficient deployment. We also discuss modality-specific issues across X-ray, CT, MRI, PET/SPECT, ultrasound, OCT, endoscopy, microscopy, and optical/molecular/infrared imaging because model choice depends strongly on image structure, annotation cost, and workflow. Finally, we outline persistent clinical challenges, data diversity and bias, rare-condition detection, annotation noise, explainability, calibration, and equitable performance, and the methods that mitigate them. The aim is to provide biomedical engineers and clinicians with a compact, clinically grounded reference for selecting and validating AI-based classifiers for real medical workflows.","url":"https://pubmed.ncbi.nlm.nih.gov/42303852/","authors":["Ghaffar Nia N","Manwar R","Avanaki K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 16","doi":"10.1007/s10439-026-04241-3","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42296628","name":"Nanostructured interfaces integrated with unsupervised intelligence to mitigate global polycrisis complexities.","source":"pubmed","abstract":"Nanostructured sensors are increasingly deployed to mitigate the complexities of the global polycrisis, including climate instability, antimicrobial resistance, pandemics, and emerging technological disruptions. While advanced nano-interfaces (such as MXenes, quantum dots, and MOFs) possess the requisite sensitivity, their efficiency is hindered by large-scale, high-dimensional, and stochastic physicochemical responses. This review articulates a necessary paradigm shift toward unsupervised machine Intelligence as the primary interface between nanostructured sensor hardware, raw data manifolds, and system-level interpretation. It critically examines the foundational methodologies, including clustering for discrete-state identification, Principal Component Analysis for decoupling cross-sensitive material kinetics, manifold learning for nonlinear structure visualization, Independent Component Analysis for blind source separation, and autoencoders for nonlinear denoising and anomaly detection. These approaches extract latent dynamical structures directly from raw nanosensor measurements without dependence on extensive labelled datasets, effectively handling drift, hysteresis, and environmental noise. Moving beyond purely statistical optimisation, it analyse hybrid architectures that embed conservation principles, symmetry conditions, and topological regularities directly into learning algorithms, ensuring outputs follow the system's physical constraints. Finally, to address scalability challenges, including edge-native computing and privacy-preserving federated learning, it argues that converging advanced sensing nano-interfaces with constraint-regulated unsupervised intelligence is critical for developing self-calibrating material-sensor intelligence ecosystems to navigate polycrisis.","url":"https://pubmed.ncbi.nlm.nih.gov/42296628/","authors":["Chaudhary V","Saichaemchan S","Bhadola P","Kaushik A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Oct","doi":"10.1016/j.cis.2026.103970","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42289602","name":"GEN-Guard: correcting generalization failures for deployable federated surgical AI.","source":"pubmed","abstract":"Federated Learning (FL) in surgical video AI enables collaborative model training without sharing sensitive data. However, standard evaluation practices-selecting the \"best\" global model based only on validation data from participating hospitals-can lead to suboptimal deployment choices. We identify this critical failure mode as performance leakage, where the selected model overfits internal federation data and fails to generalize to unseen institutions, thereby undermining the core goal of FL: robust real-world generalization.","url":"https://pubmed.ncbi.nlm.nih.gov/42289602/","authors":["Alekseenko J","Mascagni P","AI4SafeChole Consortium","Padoy N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 14","doi":"10.1007/s11548-026-03713-0","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42287982","name":"Margin-aware prototype learning for client withdrawal in federated unlearning.","source":"pubmed","abstract":"Federated client withdrawal requires removing targeted clients' influence from a collaboratively trained model while preserving utility for remaining participants. Existing approaches face a hard trade-off. To achieve efficiency, they often rely on stored historical information, such as states or gradients, which incurs substantial memory overhead and makes it difficult to precisely isolate and remove a single client's influence from aggregated historical updates. While methods that guarantee complete removal, such as retraining from scratch, are computationally prohibitive in practice. To address this dilemma, we introduce Margin-Aware Prototype Learning (MAPLE), a novel framework that achieves both high efficiency and efficacy without relying on storage-intensive historical data. MAPLE decouples the unlearning task into two synergistic components: (i) at the local level, Margin-aware Label Reassignment (MLR) adaptively perturbs labels on the withdrawing client's data, producing targeted forgetting signals that are most intense for low-confidence samples near the decision boundary; (ii) at the global level, Prototype-driven Constraints (ProCons) use compact, class-wise prototypes from remaining clients as lightweight geometric anchors in the feature space, preserving shared knowledge via contrastive objective. Extensive experiments demonstrate that MAPLE achieves an unlearning quality nearly identical to the gold standard of complete retraining while being orders of magnitude faster and requiring negligible memory overhead, consistently outperforming state-of-the-art approaches.","url":"https://pubmed.ncbi.nlm.nih.gov/42287982/","authors":["Qiu Y","Shen S","Zhang C","Yue L","Chen W","Xu M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 6","doi":"10.1016/j.neunet.2026.109231","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42286048","name":"Artificial intelligence-powered cloud security strategies for protecting critical clinical operations in healthcare environments.","source":"pubmed","abstract":"The increasing use of cloud computing in hospitals, telemedicine, the Internet of Medical Things (IoMT) and real-time patient monitoring has made for an increasing trend of artificial intelligence-driven cloud security in hospitals. The growing reliance on distributed healthcare clouds layers the cyber-attack surface, however, with critical clinical operations now at risk from ransomware attacks, insider threats, API exploitation, and advanced persistent attacks. This research study introduces a novel AI-integrated cloud security framework tailored for safeguarding mission critical applications in the healthcare sector featuring an intelligent threat detection component, a probabilistic risk evaluation system, and an adaptive response orchestration system. The proposed architecture is built-in by using telemetry normalization, probabilistic behaviour modelling, deep autoencoders for anomaly detection, Bayesian approach for threat probability estimation, multi-objective risk scoring and reinforcement learning for adaptive mitigation. An experimental validation was performed with the CICIDS2017 dataset including around two million samples of network traffic data across various attack categories. The experimental results show that excellent performances have been achieved with an accuracy of 0.96, precision of 0.95, recall of 0.94, F1score of 0.95 and AUC of 0.98 with low latency of around 26&#xa0;ms and reduced false positive rate of 0.03. A comparative analysis against the current cloud security methods also confirms the effectiveness of the proposed framework in delivering better operational security, response time and the availability of clinical services, providing the continuous clinical service that healthcare organizations require. The research showcases how incorporating AI with responsive cloud security mechanisms can offer a scalable and resilient defense against today's healthcare cloud infrastructures.","url":"https://pubmed.ncbi.nlm.nih.gov/42286048/","authors":["Dixit RS","Choudhary SL","Arya N","Nathani N","Bhowmik C","Roy V","Jain S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 12","doi":"10.1038/s41598-026-55522-9","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42285014","name":"FSCL-BC: Federated supervised contrastive learning for breast cancer diagnosis with high sensitivity.","source":"pubmed","abstract":"Accurate diagnosis of breast cancer in dense breasts requires expert radiologists to examine multiple ultrasound images per patient. This diagnosis procedure is tedious, time-consuming, and prone to misdiagnosis due to human fatigue. AI-aided diagnosis systems can help alleviate this burden. However, vast amounts of data from multiple hospitals, diverse patient demographics, imaging scanners, and protocols are required to develop accurate, robust, and generalizable AI models. Obtaining such a mixture of data is quite challenging due to privacy concerns, data ownership issues, and regulatory constraints. Moreover, due to subtle visual cues, low resolution, a limited number of labeled samples in hospital datasets, and substantial class imbalance inherent in cancer imaging, deep learning models often overfit to the majority (benign) class. As a result, they struggle to generalize well to unseen data and to achieve high sensitivity, thereby increasing the risk of missed cancer cases. To address these problems, this study aims to develop an accurate AI model for breast cancer prediction from ultrasound images using data from multiple hospitals without requiring data sharing.","url":"https://pubmed.ncbi.nlm.nih.gov/42285014/","authors":["Ahmed F","Sánchez D","Haddi Z","Domingo-Ferrer J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.cmpb.2026.109500","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42282435","name":"FEDERATED LEARNING OF ROBUST INDIVIDUALIZED DECISION RULES WITH APPLICATION TO HETEROGENEOUS MULTIHOSPITAL SEPSIS POPULATION.","source":"pubmed","abstract":"Sepsis is a life-threatening condition affecting millions of individuals in the U.S. each year. The complexity of sepsis clinical management makes individualized treatment approaches desirable. The University of Pittsburgh Medical Center (UPMC) has collected electronic health records data of sepsis patients from multiple hospitals. The goal of this study is to derive individualized decision rules (IDRs) that could be safely applied to and uniformly improve decision-making across hospitals in the UPMC Health System by only using a subset of hospitals for training. Traditional approaches assume that data are sampled from a single population of interest. With multiple hospitals that vary in patient populations, treatments, and provider teams, an IDR that is successful in one hospital may not be as effective in another, and the performance achieved by a globally optimal IDR may vary greatly across hospitals, preventing it from being safely applied to unseen hospitals. To address these challenges as well as the practical restriction of data sharing across hospitals, we introduce a new objective function and a federated learning algorithm for learning IDRs that are robust to distributional uncertainty from heterogeneous data. The proposed framework uses a conditional maximin objective to enhance individual outcomes across hospitals, ensuring robustness against hospital-level variations. Compared to the traditional approach, the proposed method enhances the survival rate by 10 percentage points among patients who may experience extreme adverse outcomes across hospitals. Additionally, it increases the overall survival rate by two to three percentage points when the learned IDR is applied to unseen hospital populations.","url":"https://pubmed.ncbi.nlm.nih.gov/42282435/","authors":["Chen X","Talisa VB","Tan X","Qi Z","Kennedy JN","Chang CH","Seymour CW","Tang L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun","doi":"10.1214/25-aoas2017","addedAt":"2026-08-31T06:41:22.149Z","updatedAt":"2026-08-31T06:41:22.149Z"},{"id":"pmid:42281121","name":"The Deep Learning Evolution in Wireless Physical Layer Communications: Applications, Challenges, and Evolutionary Directions.","source":"pubmed","abstract":"With the continuous evolution toward sixth-generation (6G) wireless communication systems, emerging scenarios such as terahertz transmission, integrated sensing and communication (ISAC), and ultra-massive multiple-input multiple-output (MIMO) have significantly increased the complexity, nonlinearity, and uncertainty of wireless propagation environments. The conventional model-driven paradigm, established upon Shannon information theory and precise mathematical modeling, is increasingly constrained by model-mismatch issues in real-world deployments. This paper systematically reviews recent advances in deep learning-enabled physical-layer signal processing. We examine intelligent channel estimation, signal detection, and end-to-end communication systems based on autoencoder architectures. We then analyze key technical challenges-including interpretability, data dependence, computational complexity, privacy and security in distributed learning, and system-level performance-overhead trade-offs-along with state-of-the-art solution strategies such as deep unfolding, transfer learning, model compression, federated learning, and lightweight design. Future evolutionary directions toward AI-native 6G networks, integrated sensing-communication-computing architectures, and intelligent reconfigurable wireless environments are discussed. Furthermore, emerging generative AI techniques, including diffusion models, are identified as a promising direction for addressing data scarcity and enhancing system adaptability. The study demonstrates that hybrid intelligence-integrating model-based prior knowledge with data-driven learning-will become the dominant design philosophy for next-generation intelligent physical-layer systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42281121/","authors":["Xu H","Liang Y","Xie R","Kong Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 5","doi":"10.3390/s26113609","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42281119","name":"A Comprehensive and Unified Survey on Blockchain-Enabled SDN Cybersecurity: Industry Use Cases, Threat Landscapes, Defense Architectures, and Open Challenges.","source":"pubmed","abstract":"The convergence of Software-Defined Networking (SDN) and Blockchain (BC) creates a symbiotic relationship in which SDN's programmable global visibility complements BC's decentralized, immutable trust model to address critical cybersecurity vulnerabilities and cyber attacks. Addressing the fragmentation in the current literature, this study rigorously investigates BC and SDN (B-SDN) integration with the primary objectives of: (1) differentiating impacts across varied sectors, including the Internet of Things (IoT), Smart Grids, and Vehicular Ad Hoc Networks (VANETs) and more; (2) analyzing critical performance metrics such as energy efficiency and scalability; (3) classifying mitigation, detection, and prevention schemes for specific threats; (4) examining novel Artificial Intelligence (AI) methods; and (5) identifying open challenges and future research directions. Methodologically, this study conducts a survey of state-of-the-art B-SDN studies to investigate six key areas: Industry-specific applications, security mechanisms, defense strategies, defenses against specific attacks, AI integration, and implementation performance. The findings demonstrate that B-SDN integration shows strong potential in simulated and prototype environments to mitigate specific high-impact threats, such as Distributed Denial of Service (DDoS), Man-in-the-Middle (MiTM), and spoofing, across various domains, including IoT, 5G/6G, VANETS, and Smart Grid. Despite the benefits and advantages promised by B-SDN, several limitations continue to exist, including the latency-security trade-off inherent to consensus protocols and scalability constraints in large-scale deployments. Finally, open research challenges persist in AI-driven automation, particularly in Federated Learning (FL) and in the development of standardized interoperability protocols required to enable the transition from conceptual models to operational systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42281119/","authors":["Dudukcu D","Gorgulu AB","Karakus M","Savran Kiziltepe R","Basbrain A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 5","doi":"10.3390/s26113606","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42279497","name":"Federated Privacy-Preserving Multi-Modal Deep Learning for Breast Cancer Diagnosis: A Physics-Aware Approach.","source":"pubmed","abstract":"Background/Objectives: Breast cancer remains a leading cause of cancer-related mortality among women worldwide. This study presents a systematically justified multi-modal breast cancer classification pipeline that combines established, physically motivated preprocessing operations, modality-specific deep learning models, late-fusion inference, and a deployment-aware federated learning evaluation. Rather than introducing new image restoration or federated optimization algorithms, this work formalizes how standard preprocessing methods can be organized according to the dominant degradation characteristics of ultrasound, MRI, and mammography, and evaluates their contribution under centralized and simulated federated learning settings. Methods: Patient-wise stratified five-fold cross-validation was applied across ultrasound (BUSI, n=780), dynamic contrast-enhanced MRI (DUKE, n=922), and mammography (CBIS-DDSM, n=400). A five-algorithm federated learning comparison, including FedAvg, FedProx, SCAFFOLD, FedNova, and FP16-FedAvg, was conducted under IID and non-IID conditions using a Dirichlet distribution with &#x3b1;=0.5. The evaluation reports diagnostic performance together with per-round training time, communication time, latency-related measurements, and cumulative bandwidth. Ablation experiments, McNemar's test, Cohen's h effect sizes, and confidence intervals were used to support the analysis. Results: Per-modality models achieved 92.50 &#xb1; 1.2%, 90.63 &#xb1; 1.5%, and 92.00 &#xb1; 1.3% accuracy for ultrasound, MRI, and mammography, respectively, with statistically significant improvements over the corresponding baselines according to McNemar's test (p&lt;0.05). Weighted late fusion achieved 93.10 &#xb1; 1.1% accuracy and improved performance compared with the best individual modality (p=0.031). FP16 transmission reduced cumulative bandwidth from 8.14 GB to 1.23 GB (-84.9%) without a statistically significant performance difference compared with FP32 transmission (p=0.74), while SCAFFOLD achieved the highest non-IID accuracy (90.50%). Conclusions: The findings demonstrate internal technical validity and deployment-relevant trade-offs, but they should be interpreted cautiously because the federated evaluation is simulation-based, key-slice extraction may require annotation-assisted assumptions, and external multi-center validation remains necessary before clinical deployment. Reported improvements are statistically significant in several comparisons, but corresponding Cohen's h effect sizes are small, and clinical meaningfulness requires independent validation rather than inference from p -values alone.","url":"https://pubmed.ncbi.nlm.nih.gov/42279497/","authors":["Al-Karawi ALS","Mohammedqasim H","Yılmaz R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/diagnostics16111629","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"pmid:42277145","name":"Computational pathology model to predict recurrence-free survival in NMPUC patients on BCG-therapy.","source":"pubmed","abstract":"Urothelial carcinoma, predominantly appearing as non-muscle-invasive papillary urothelial carcinoma (NMIPUC), exhibits wide clinical variability. Accurate pathological staging and grading are essential for effective risk stratification and treatment decisions. Advancements in artificial intelligence (AI) open new opportunities to improve predictive models; however, their generalizability across diverse datasets remains to be addressed. This study developed a federated learning (FL)-based AI framework to enhance model robustness across institutions and predictive accuracy for non-muscle-invasive bladder cancer staging, grading, and a novel histological risk factor derived by clustering histological features for relapse prediction. Retrospective data, including 1437 NMIPUC cases from two institutions in Lithuania and Taiwan, were used for development and analysis. The FL models demonstrated improved robustness across participating institutions and higher accuracy compared to single-site models, achieving 86.2% accuracy for tumor stage and 79.2% for tumor grade, with minor performance variability across the datasets. Moreover, the novel histological risk factor outperformed conventional indicators of relapse-free survival (RFS) in NMIPUC patients treated with BCG immunotherapy, achieving hazard ratios of 2.7 (p&#x2009;=&#x2009;0.0018) and 2.8 (p&#x2009;=&#x2009;0.0208) in the Lithuania and Taiwan datasets, respectively. These findings highlight the potential of FL and histological feature-based AI models in providing robust, generalizable solutions for NMIPUC risk stratification and offer insights for personalized clinical interventions.","url":"https://pubmed.ncbi.nlm.nih.gov/42277145/","authors":["Lin YC","Drachneris J","Rasmusson A","Fabijonavicius M","Li WM","Lee HY","Chuang CC","Jankevicius F","Wu WJ","Liang PI","Laurinavicius A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 11","doi":"10.1038/s41698-026-01554-3","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42276586","name":"High-throughput analysis of multimodal monitoring data: the role of machine learning in early warning systems for high-risk neonates.","source":"pubmed","abstract":"The neonatal intensive care unit (NICU) generates vast amounts of high-throughput, multimodal monitoring data, offering unprecedented potential for identifying early signs of clinical deterioration in high-risk neonates. However, the traditional threshold-based alarm systems are plagued by high false alarm rates and alarm fatigue, failing to harness this data complexity. This narrative review examines the role of machine learning (ML) in transforming early warning systems (EWSs) by effectively analysing these complex data streams. We first characterise the diverse sources-including physiological waveforms, neuromonitoring signals, electronic health records and emerging behavioural data-and inherent challenges (eg, noise, heterogeneity, label scarcity) of NICU data. We then detail key ML technologies, from preprocessing and feature engineering to core algorithms like deep learning models (recurrent neural networks, convolutional neural networks, Transformers) and multimodal fusion strategies, emphasising their application in handling time-series data. The review catalogues empirical evidence of ML-driven EWS for critical conditions such as sepsis, necrotising enterocolitis, neurological injury and cardiorespiratory instability, highlighting performance improvements over conventional methods. Finally, we discuss the significant technical, clinical integration and ethical challenges that impede widespread adoption and outline future directions, including federated learning, digital twins and cloud-edge architectures. The integration of ML-based insights promises to shift neonatal care from a reactive to a proactive, personalised paradigm, ultimately aiming to improve outcomes for vulnerable infants.","url":"https://pubmed.ncbi.nlm.nih.gov/42276586/","authors":["Huo H","Lu Y","Zhou J","Zhang L","Pei J","Hu C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 11","doi":"10.1136/bmjpo-2026-004705","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42275347","name":"From Stochastic Conjugate Gradient to Stochastic Second-Order Optimization, Driven by Conjugate Coefficient With Second-Order Information.","source":"pubmed","abstract":"Conjugate gradient (CG) and second-order information (SOI) receive increasing interest due to their crucial role in improving stochastic first-order (SFO) algorithms for solving machine learning problems. Although a variety of stochastic CG (SCG) and stochastic second-order (SSO) algorithms were studied, nearly all works only focus on either empirical or theoretical aspects for specific tasks by using SFO algorithms with CG or SOI separately. Thus, it is desired to explore the effect of the combination of CG and SOI on SFO algorithms. To cope with this issue, we develop a type of fast and low-cost SSO conjugate gradient (S2CG) algorithms, extending the family of SCG and SSO algorithms. Prior to our work,second-order CG methods only apply CG to solve the suboptimization problem, such as the (quasi-)Newton equation, which is time-consuming for large-scale optimization problems or problems requiring high-precision solutions. We show that the transition from SCG to SSO is driven by the conjugate coefficient with SOI. Moreover, we provide theoretical guarantees of different S2CG algorithms for strongly convex, general convex, and Polyak-&#x141;ojasiewicz (PL) cases. Finally, extensive experiments on different machine learning tasks, covering support vector machine (SVM), logistic regression (LR), federated learning (FL), and neural networks (NNs), significantly validate the superiority and robustness of S2CG algorithms over state-of-the-art SFO, SCG, and SSO algorithms.","url":"https://pubmed.ncbi.nlm.nih.gov/42275347/","authors":["Yang Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 11","doi":"10.1109/TNNLS.2026.3694463","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42270834","name":"Federated deep reinforcement learning for privacy-preserving offloading in vehicular edge computing.","source":"pubmed","abstract":"With the rapid development of Internet of Vehicles (IoV) applications, the demand for serving computation-intensive and delay-sensitive tasks, which are executed in a dynamic mobility environment, continues to grow, while the embedded computing power carried by vehicles remains limited, and they are facing strict requirements in terms of latency. In vehicular edge computing, to offload computation to the nearby roadside units (RSUs) and to enable centralized learning-based offloading, mobility, task, channel, and resource information at the whole system needs to be gathered at a central learner, leading to a higher communication overhead and raw data exposure. This study introduces a privacy-aware federated deep reinforcement learning (FDRL) framework for vehicular edge computing task offloading with RSU assistance. The novelty of the proposed framework does not lie in the common usage of federated learning and deep reinforcement learning (DRL), but rather in the compactness of four coupled mechanisms: generation of a hybrid action representation of federated binary offloading decision and continuous resource allocation for RSUs, a personalized federated aggregation mechanism for non-IID vehicular observations collected on the RSU, a task-criticality-aware deadline reliability model with class-dependent violation penalties, and a handover-aware multi-RSU model that incorporates signaling delay, service-context transfer delay, and processing/authentication delay. In the proposed framework, the model parameters of the local SAC policies are provided to the federated coordinator rather than the locally observed information, such as raw vehicular trajectories, which can instead be used for local training of the SAC-based policies. Controlled simulation experiments are conducted to compare the proposed method with both local execution and edge-offloading methods, two centralized DRL baselines (DQN and DDPG), and three federated DRL baselines (FedAvg-DQN, centralized-SAC, and federated-MADRL). The results indicate that under the adopted simulation settings, the proposed FDRL framework achieves competitive and/or better system cost, delay, energy, deadline-violation performance, and communication overhead compared with other schemes. This privacy usefulness really means having less raw data exposed when federated training is used, and does not mean any formal privacy guarantee against inference attacks against model updates.","url":"https://pubmed.ncbi.nlm.nih.gov/42270834/","authors":["Momani AM","Alsekait DM","Al-Khasawneh MA","Othman SH","Alkayid K","Kouki S","Baalamurugan KM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-56587-2","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42270792","name":"An adversarial-resilient intrusion detection framework for internet of medical things (IoMT) using digital twin-enabled behavioral threat modeling and federated hybrid ensemble learning.","source":"pubmed","abstract":"The Internet of Medical Things (IoMT) has transformed healthcare by enabling continuous patient monitoring and remote diagnostics. However, this growth introduces considerable security challenges. This paper examines vulnerabilities in IoMT devices to advanced cyberattacks that jeopardize patient safety and data integrity. We review the limitations of traditional security methods and motivate the need for adaptive defenses. We evaluate machine learning and deep learning models for real-time threat detection and identification of anomalous behavior within IoMT networks. We further propose a security framework that integrates digital twin technology with edge-cloud computing to improve the reliability of IoMT applications. Results show that hybrid and deep-learning models maintain detection performance under the resource constraints typical of medical devices. The proposed XGBoost component achieved a precision of 0.97, a recall of 0.98, and an ROC-AUC of 0.999 on the SmartWard dataset, while the hybrid ensemble showed measurable adversarial robustness under FGSM perturbations. The decision-critical inference path runs in under 0.05 seconds, supporting deployment on resource-constrained medical devices.","url":"https://pubmed.ncbi.nlm.nih.gov/42270792/","authors":["Alkhattabi K","Belhaj S","Selecky J","Talha M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-55893-z","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42267474","name":"The Intelligence Revolution in Biosensing: Transforming Raw Data into Smart Clinical Diagnostics.","source":"pubmed","abstract":"Artificial intelligence (AI) is revolutionizing nanobiotechnology-enabled biosensing by combining advanced nanomaterials with intelligent data analytics to create next-generation diagnostic platforms. This review summarizes recent progress in AI-integrated nano-biosensors, highlighting the contributions of functional nanomaterials such as graphene, carbon nanotubes, metal oxides, quantum dots, and hybrid nanocomposites in enhancing sensitivity, selectivity, and signal transduction. Machine learning and deep learning techniques, including support vector machines, random forests, convolutional neural networks, and transformer-based models, are examined for their roles in feature extraction, noise reduction, and multi-analyte prediction from complex biological samples. The synergy between nanomaterial properties and AI-driven optimization has facilitated the development of real-time, miniaturized, and wearable diagnostic devices. Applications in cancer, metabolic, infectious, and neurological disease diagnostics are critically reviewed, demonstrating improved analytical performance, ultralow detection limits, and enhanced diagnostic accuracy. Emerging technologies such as edge AI, federated learning, explainable AI, and self-powered nanosystems are also discussed for their potential in decentralized and personalized healthcare. Challenges related to data quality, nanomaterial reproducibility, scalability, and regulatory approval remain significant barriers to clinical translation. Nevertheless, the integration of AI and nanobiotechnology offers a powerful framework for developing intelligent biosensing systems and advancing modern analytical and clinical diagnostics.","url":"https://pubmed.ncbi.nlm.nih.gov/42267474/","authors":["Ramadan O","A Rudayni H","El-Raheem HA","Helim R","Fafa S","Mahmoud R","Wang Z","A Allam A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 10","doi":"10.1080/10408347.2026.2685786","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42265348","name":"Federated generative prompt learning with vision foundation models: universal efficient multi-center medical image analysis.","source":"pubmed","abstract":"Federated medical AI revolutionizes multi-center collaboration, while communication cost, data scarcity, and heterogeneity still limit its practical deployment. Foundation models (FMs) offer a promising avenue for addressing these challenges, owing to their generalization capabilities and efficient adaptability to medical tasks. Here, we present Federated Generative Prompt Learning (Fed-GPL), a universal and efficient framework for multi-center medical image analysis. It collaboratively trains a prompt generator that produces customized prompts for each patient, capturing patient-specific variations and enabling precise medical diagnosis. Fed-GPL is compatible with various vision FMs and medical tasks, such as Vision Transformer (ViT) for diabetic retinopathy and melanoma classification, and Segment Anything (SAM) for polyp and prostate segmentation. Fed-GPL outperforms traditional models and full fine-tuning methods, with only 8.26% and 6.55% of the total FM parameters being trained across classification and segmentation tasks, while converging within just 15 communication rounds. For low-resource settings, Fed-GPL maintains its performance with 5% of the original training data.","url":"https://pubmed.ncbi.nlm.nih.gov/42265348/","authors":["Lin X","Chen Y","Wu J","Xu C","Li J","Su X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 10","doi":"10.1038/s41746-026-02866-1","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42260279","name":"Federated orthogonal learning for detection of liver lesions from multi-phase contrast-enhanced CT images.","source":"pubmed","abstract":"Multi-phase contrast-enhanced CT (CECT) scans are often scattered across multiple institutions and contain incomplete phase in parts of institutions due to the strict data-protection regulations and the disparity of phase integrality. While federated learning (FL) enables training a privacy-preserving model collaboratively across institutions, it often suffers from significant performance degradation for liver lesion segmentation caused by heterogeneity in different institutions. To tackle the challenges, we present FedOG to guide a deep convolutional neural network collaboratively segment liver lesions for minimizing interference on 3,668 CECT multi-phase CECT scans from five different institutions. Specifically, FedOG adjusted the gradients from local models trained with incomplete phases of CECTs via orthogonal gradient decomposition to alleviate the interference. During each adjustment, the optimal gradient for updating the global model is determined by Bayesian optimization. Experiment results have shown that FedOG improves the Dice score by 1.67% on a large real-world clinical dataset, 1.13%, and 3.03% on two publicly available datasets. We anticipate our study will enable a heterogeneity-robust, search-efficient, and privacy-preserving federated training framework using multi-phase CECT. We also found out that FedOG is especially beneficial for underdeveloped regions where institutions often have missing or low-quality phases of multi-phase CECT scans.","url":"https://pubmed.ncbi.nlm.nih.gov/42260279/","authors":["Wu L","Lin H","Hu F","Shen K","Liang W","Hu L","Wang W","Bu J","Wang H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 8","doi":"10.1038/s41746-026-02767-3","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42259874","name":"Graph neural networks for networked analysis of gestational diabetes risk factors: a multi method framework.","source":"pubmed","abstract":"Gestational diabetes mellitus, often known as GDM, is a major health issue that causes complications for mothers and requires patient data prediction models that are complex and variable. The research in question makes use of graph-based learning in order to investigate the ways in which genetic, biochemical, and demographic elements interact in a variety of different contexts. Through the use of nodes to represent patients and lines to represent the things that they share in common, the framework illustrates how the aforementioned elements influence the likelihood of illness. Graph neural networks are utilized for the process, while BioBERT embeddings are utilized for the management of unstructured clinical notes. Graph neural networks are utilized for organized clinical notes. Because of this alignment, healthcare processes are placed in the context in which they should be, rather than being taken out of context while they are being carried out. The graph architecture used in BioBERT incorporates semantic patterns derived from medical information into a relational structure that illustrates the degree to which patients are similar to one another. After being evaluated on a substantial clinical dataset, the proposed method is able to make more accurate and readable predictions than the baseline models. The results of this study indicate that the utilization of graph architecture with both organized and unstructured data can assist in the discovery of novel approaches to the treatment of GDM that go beyond performance sets. According to the findings of the study, machine learning needs to be modified so that it can be used with healthcare applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42259874/","authors":["Lella KK","Nabi SA","Sirisha U","Nagamani GM","Eswaraiah P","Kumar CK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 9","doi":"10.1038/s41598-026-57000-8","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42258693","name":"Federated Clustering: An Overview of Algorithm Evolution and Research Prospects.","source":"pubmed","abstract":"As a new paradigm that integrates clustering with federated learning, federated clustering (FC) has recently attracted increasing attention, as it addresses the practical issue of privacy protection in distributed data. In this paper, we provide a comprehensive survey of recent advances in FC. This survey is organized into four parts. First, since FC is often developed by extending existing clustering methods, we review several classical clustering paradigms. Meanwhile, the inherent challenges of FC are summarized, and common improvement strategies are categorized. Second, we summarize experimental setups and evaluation protocols used in FC studies. Third, from the perspectives of data partitioning schemes and whether deep representation learning is incorporated, FC methods are divided into four categories, and representative algorithms in each category are reviewed. Finally, we discuss the limitations of current FC approaches and highlight potential directions for future research.","url":"https://pubmed.ncbi.nlm.nih.gov/42258693/","authors":["Ding S","Li C","Guo L","Yang Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/TPAMI.2026.3701079","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"pmid:42257171","name":"Artificial intelligence in clinical physiology: System-wise applications in diagnostics, monitoring, and medical education.","source":"pubmed","abstract":"Artificial Intelligence (AI) is gradually revolutionizing clinical physiology by enhancing diagnostic capabilities, fostering real-time monitoring, and enabling personalized medical education. Its incorporation into various physiological domains and wearable health technologies has redefined approaches to both patient care and medical training.","url":"https://pubmed.ncbi.nlm.nih.gov/42257171/","authors":["Chhabra C","Saroha R","Kosvi M","Singh S","Gautam VS","Kumari P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar","doi":"10.4103/jfmpc.jfmpc_1586_25","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42253942","name":"Radiomics: Current Applications and Future Directions.","source":"pubmed","abstract":"Radiomics enables high-throughput extraction of quantitative imaging features to decode tumor phenotypes and biological behaviors, representing a transformative noninvasive tool for precision oncology. In recent years, radiomics has rapidly evolved from static feature analysis to dynamic multi-dimensional assessment, and it has been widely explored in various solid tumors, yet its pan-cancer generalization, biological interpretability, and clinical translation still face prominent bottlenecks. Cancer remains the leading cause of global mortality, and solid tumors account for more than 90% of adult malignant cases, while conventional medical imaging and invasive biopsies have inherent limitations in reflecting tumor heterogeneity and dynamic evolution. This review outlines the unified technical pipeline of radiomics across solid tumors, highlights cancer-specific imaging considerations, and summarizes standardization strategies for multi-center, multi-scanner, and multi-cancer heterogeneity. We systematically review pan-cancer clinical applications covering early detection, molecular characterization, treatment response prediction, and prognostic stratification, with lung cancer as a paradigmatic example while integrating evidence from breast, colorectal, liver, glioma, and prostate cancers. We also discuss multi-omics integration, biological interpretability, and translational bottlenecks including domain shift and reproducibility crisis. Finally, we prospect cutting-edge directions including foundation models, causal inference, and federated learning to advance generalizable and clinically actionable radiomics toward routine clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42253942/","authors":["Shao J","Wei M","Li K","Lv G","Liu K","Guo Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun","doi":"10.1002/mco2.70773","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42251130","name":"Privacy-aware distributed intelligence with tokenized trust for low-latency task offloading in 6G vehicular edge networks.","source":"pubmed","abstract":"The ultra-dense vehicle scenarios envisioned in 6G put high requirements on ultra-low latency, secure cooperation, and efficient task offloading decisions. Existing systems usually optimize latency or energy independently but ignore joint privacy problems and long-term trust sustainability. In this work, a distributed intelligence architecture based on the combination of federated learning (FL) and blockchain based trust management for vehicle-to-vehicle (V2V) edge computing is proposed. The proposed architecture enables collaborative prediction and decentralized incentive enforcement in a privacy-preserving manner without revealing raw vehicle data. In this paper, task allocation is defined as a multi-objective optimization problem, which jointly considers latency, energy consumption, communication stability and privacy exposure. The resultant problem is addressed by a learning-coupled primal-dual optimization, where the federated prediction is used to drive the offloading decisions and the dual update is used to impose the limitations of the system. A light-weight distributed ledger layer ensures secure coordination, automatic incentive allocation and reliable detection of fraudulent nodes. The extensive simulations in the integrated traffic-network-blockchain environments show that the proposed method outperforms the state-of-the-art baselines, achieving up to 30-40% reduction in the service latency, approximately 25% improvement in task completion rate, enhanced privacy preservation by the gradient-based learning, and up to 95% accuracy in detecting the malicious nodes. These results validate the efficacy of the suggested framework for attaining scalable, privacy-aware, and trustworthy distributed intelligence for next-generation 6G vehicular edge networks.","url":"https://pubmed.ncbi.nlm.nih.gov/42251130/","authors":["Alsaffar M","Abouelkheir E","Alawad WM","Alsayfi MS","Nassef L","Younes OS","Abbas Q","Alazzam MB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 6","doi":"10.1038/s41598-026-55997-6","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42251076","name":"Hybrid CNN BiLSTM architecture for smart grid cyberattack detection using smart meter data.","source":"pubmed","abstract":"Real-time cyber-attack intrusion detection faces serious challenges in the smart grid communications infrastructure, as intrusion tactics become more advanced. Traditional rule-driven detection methods are unable to adapt to diverse attack patterns in modern power networks. In this work, a supervised deep learning framework is developed, using a CNN for spatial feature extraction, a BiLSTM for temporal dependency modeling, and an Extra Trees ensemble classifier to produce robust decisions, to achieve real-time intrusion detection on high-frequency smart meter data. The CNN layer extracts hierarchical spatial features from 128-dimensional multi-modal meter measurements (e.g., voltage, current, frequency harmonics), and the BiLSTM component captures temporal dynamics by processing whole sequences of meter data in both forward and backward directions to capture attack-evolution patterns that unidirectional models miss. Attention mechanisms dynamically weight the relevance of temporal features and enhance both prediction accuracy and interpretability. The Extra Trees ensemble provides a robust, low-variance decision output as an alternative to a standard Softmax layer. The architecture addresses several challenges: class imbalance (2.49:1 ratio), high dimensionality and noisy sensor data from heterogeneous sources, the requirement for real-time (millisecond-level) inference, and the need for explainability. The model was evaluated on 72,073 labeled power-grid logs from the Mississippi State University Power Grid Testbed, including False Data Injection Attacks, denial-of-service, replay, and man-in-the-middle attacks versus normal operation. With stratified five-fold cross-validation, the model achieves accuracy of 92.17% (&#xb1; 0.27%), precision of 90.58% (&#xb1; 0.49%), recall of 81.24% (&#xb1; 0.86%), F1-score of 85.66% (&#xb1; 0.20%), and ROC-AUC of 95.60% (&#xb1; 0.14%), with an inference latency of only 12&#xa0;ms, which is suitable for utility-scale deployment. A comprehensive comparative study against nine imbalance-handling strategies (no handling, class weighting, SMOTE, ADASYN, Borderline-SMOTE, SMOTETomek, SMOTEENN, random over/under-sampling) confirms the chosen weighted-learning strategy as a Pareto-optimal choice for this dataset. Paired McNemar's tests with Holm correction demonstrate that the proposed model's improvements over the tested baselines are statistically significant ([Formula: see text]). Ablation studies validate that bidirectional processing increases accuracy by 20.12&#xa0;pp over a unidirectional CNN-LSTM, and that the Extra Trees head boosts precision by 10.09&#xa0;pp over the standalone Extra Trees baseline. This work contributes to hybrid deep learning for cyber-physical systems and provides deployment guidance in terms of computational cost and a human-in-the-loop framework. In future work, we will investigate cross-dataset validation, multi-simultaneous attack detection, graph neural networks for fault location, and federated learning toward privacy-preserving collaborative training.","url":"https://pubmed.ncbi.nlm.nih.gov/42251076/","authors":["Alamgir FM","Das S","Akand AR","Fariha A","Islam MR","Sarker MZI","Zuberi N","Arman M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 7","doi":"10.1038/s41598-026-55727-y","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42250862","name":"Clustering-based federated causal discovery for multicenter clinical data analysis.","source":"pubmed","abstract":"Traditional causal structure learning algorithms struggle in distributed and privacy-sensitive environments, particularly when dealing with non-independent and identically distributed (non-IID) data. To address these limitations, this study proposes the Clustering-Based Federated Causal Discovery (CFedCD) framework, designed to enhance causal learning accuracy and applicability in multicenter clinical data analysis.","url":"https://pubmed.ncbi.nlm.nih.gov/42250862/","authors":["Zhang M","Wang H","Zhao J","Wu L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.jbi.2026.105066","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42250854","name":"Machine Learning and Deep Learning for Neurological Disease Analysis: A Systematic Review Across Five Major Disorders.","source":"pubmed","abstract":"Artificial Intelligence (AI) has become integral to the research of neurological diseases due to the rapid expansion of neuroimaging, clinical, physiological, and wearable data. However, the concise synthesis of recent machine learning (ML) and deep learning (DL) remains limited. This systematic review analyzes studies published between January 2021 and March 2026 on five major conditions- Alzheimer's disease, stroke, Parkinson's disease, brain tumors, and traumatic brain injury (TBI)-following the PRISMA 2020 guidelines and a structured search of PubMed, Scopus, and Web of Science, yielding 206 eligible articles. The results show that convolutional and encoder-decoder architectures dominate imaging tasks, whereas hybrid and multimodal approaches increasingly combine imaging with clinical and sensor data. Emerging paradigms, including federated learning, self-supervised learning, and foundation models, address data scarcity, privacy, and cross-institutional variability. Key advances include high-performing transformer-based models for Alzheimer's diagnosis, real-time stroke detection by CT/MRI, improved Parkinson's detection by multimodal fusion, hybrid models for brain tumor classification, and outcome prediction in TBI. Despite these gains, challenges in generalizability, interpretability, and clinical translation persist, underscoring the need for more robust and clinically reliable AI systems to address these issues.","url":"https://pubmed.ncbi.nlm.nih.gov/42250854/","authors":["Uddin KN","Ghose P","Njie E","Mahmood N","Kumar N","Haque MN","Gaur L","Li A","Mallik S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.neuroscience.2026.05.036","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"pmid:42249052","name":"Non-IID and aware federated intrusion detection with PBFT with secured model aggregation for multi institutional healthcare internet of things networks.","source":"pubmed","abstract":"Multi-institutional healthcare Internet of Things (IoT) networks face a core challenge between combined intrusion detection and patient data privacy. Raw traffic records cannot be shared across institutional boundaries, yet local models trained on institution-specific data alone generalize poorly to attack distributions that differ from those practical in real healthcare IoT deployments. Federated Learning (FL) addresses privacy constraints by storing data locally at each institution, but it introduces statistical heterogeneity across institutions. Local data at each institution are non-independent and non-identically distributed due to clinical specialization, protocol diversity, and deployment-scale asymmetry. Standard federated averaging cannot handle this non-IID condition, and detection performance lowers significantly as a result. Existing Federated Intrusion Detection Systems (FIDS) implied that all participating institutions were honest. But this hypothesis cannot hold in real multi-institutional consortia, because Byzantine participants can corrupt the global model by submitting manipulated gradient updates. In this work, a Non-IID-Aware Federated Intrusion Detection System (N-IID-AFIDS) is proposed for multi-institutional healthcare IoT networks and is designed to address both challenges simultaneously. A cluster-weighted aggregation mechanism is used in this N-IID-AFIDS, grouping institutions by distributional similarity through spectral clustering of a Wasserstein-based affinity matrix and applying gradient divergence correction to submitted updates before aggregation. Protocol-aware Deep Sparse Autoencoder (DSAE) adaptation is also part of this model, and it uses local feature normalization based on an institutional protocol mixture and a distribution alignment regularizer. This regularizer operates without raw data exchange across institutions. This work extends practical Byzantine Fault Tolerance (PBFT) consensus from event logging for detection to model update validation. Geometric median-based Byzantine filtering, together with reputation-based participation control, is also added to this model. The proposed model is evaluated on the IoT-Flock and CICIoT2023 datasets across three non-IID severity levels, based on combined skew in quantity, labels, and features. It achieved non-IID detection accuracies of 93.17% on IoT-Flock and 89.84% on CICIoT2023. And this model is the only federated method in the comparison that reports non-IID performance on both datasets simultaneously. It also retains 83.61% accuracy with four Byzantine participants and meets a 16 ms clinical real-time detection constraint, converging in 29-67 rounds, faster than other federated baselines.","url":"https://pubmed.ncbi.nlm.nih.gov/42249052/","authors":["Sengan S","Shieh CS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-56167-4","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42248934","name":"Federated autoencoder-based clinical decision framework with hybrid class balancing.","source":"pubmed","abstract":"Despite diagnosis accuracy has been much improved by depending more on deep learning for disease classification, it raises serious concerns about patient data privacy, security, and scalability. Conventional centralized deep learning approaches are vulnerable to data leaks and non-compliance with such privacy rules as GDPR and HIPAA since they rely on the aggregation of sensitive medical records. Our work presents a privacy-preserving federated learning architecture enabling cooperative model training among many healthcare institutions without exposing raw patient data, hence addressing these issues. Combining autoencoder-driven hierarchical feature extraction, the proposed method improves classification performance and guarantees low information loss. Moreover applied is a hybrid class-balancing mechanism integrating generative augmentation techniques with Synthetic Minority Over-Sampling Technique (SMote) to eliminate bias in unbalanced illness datasets, so raising sensitivity for minority-class scenarios. Maintaining computational efficiency in federated systems, experimental evaluation demonstrates that the proposed model achieves accuracy of 92.5% outperforming both classic CNN-based (87.2%) and LSTM-based (89.1%) models. Strong convergence even in non-IID distributed medical data is promised by adaptive federated averaging. Furthermore, the proposed method resists adversarial attacks, therefore enhancing the security in the surrounding practical areas. This work decreases the distance between high-accurate disease categorization and privacy-preserving artificial intelligence by offering the basis for scalable, distributed, secure medical intelligence systems. The findings help federated medical artificial intelligence to grow by proving its ability to change healthcare diagnostics while keeping regulatory compliance and data security.In this study, the Synthetic Minority Over-Sampling Technique (SMOTE) is incorporated to address the class imbalance present in the distributed medical datasets. SMOTE generates new synthetic samples for minority classes by interpolating between existing minority instances, thereby preventing model bias toward majority classes and improving classification robustness across federated clients. By enhancing minority-class representation prior to federated aggregation, SMOTE ensures that the global model learns more discriminative and balanced feature patterns.","url":"https://pubmed.ncbi.nlm.nih.gov/42248934/","authors":["Indupalli MR","Pradeepini G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-55715-2","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42247887","name":"Hybrid optimized focal high-order attention network with descriptor computations for autism spectrum disorder detection in federated learning.","source":"pubmed","abstract":"Autism spectrum disorder is a neurodevelopmental condition that affects the social interaction, and communication ability of persons. Accurate diagnosis can significantly improve quality of life. However, current detection methods often perform sub optimally due to class imbalance, heterogeneous data, and privacy concerns. For solving such issues, a Federated Learning (FL)-based framework is proposed, integrating a Groupers and Moray Orangutan Optimization Algorithm with a Focal High-order Attention Network (GMOA_Focal-HANet). The autism detection is done in a local model, where autism brain image and autism data are fed to a pre-preparation process. The GMOA performs the functional connectivity-based pivotal region extraction, and descriptor computation is done by Regional Gradient Pattern (RGP). Moreover, the preprepared input data is subjected to attribute screening, where the Chi-Square Test-enabled feature selection is employed. The Focal-HANet detects autism using the outcome of descriptor computation and attribute screening. The GMOA trains the Focal-HANet, and local update and aggregation is done by average method. Experimental results demonstrate that the proposed framework achieves a classification accuracy of 96.83%, with sensitivity and specificity of 95.92% and 96.82%, respectively. These results confirm the effectiveness and robustness of the proposed approach for reliable Autism spectrum disorder detection in a privacy-preserving FL environment.","url":"https://pubmed.ncbi.nlm.nih.gov/42247887/","authors":["Bhagyalatha U","Sahoo BK","Muppidi S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Oct","doi":"10.1016/j.pscychresns.2026.112247","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42247882","name":"Artificial intelligence in prostate cancer imaging: A mini-review of current applications and future directions.","source":"pubmed","abstract":"This mini-review synthesizes evidence from recent studies to provide an updated perspective on current applications, methodological challenges, and future directions for artificial intelligence (AI) in multiparametric magnetic resonance imaging (mpMRI) based prostate cancer (CaP) imaging. CaP remains the most frequently diagnosed noncutaneous malignancy among men worldwide and a leading cause of cancer-related mortality. mpMRI has become the reference imaging modality for detection, localization, and risk stratification, with the Prostate Imaging-Reporting and Data System (PI-RADS) improving standardization. However, inter-reader variability and the time-intensive nature of mpMRI interpretation persist, even among expert radiologists. AI, encompassing machine learning (ML) and deep learning (DL) methods, offers the potential to enhance CaP imaging by improving accuracy, consistency, and efficiency. Applications include automated lesion detection and segmentation, PI-RADS scoring standardization, and radiomics-based risk prediction. Radiomics enables the extraction of high-dimensional quantitative features from mpMRI, which, when integrated with clinical or genomic data, can improve predictive modeling for clinically significant CaP, extracapsular extension, and lymph node metastasis. Despite rapid advancements, challenges remain in data heterogeneity, generalizability, lack of standardized feature extraction, and limited external validation. The \"black-box\" nature of many DL models also complicates clinical trust and regulatory approval. Future directions include the integration of explainable AI, federated learning for privacy-preserving multi-institutional training, and real-time AI assistance during targeted biopsies or active surveillance.","url":"https://pubmed.ncbi.nlm.nih.gov/42247882/","authors":["Ergül MA","Sabur V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug","doi":"10.1016/j.urolonc.2026.05.013","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42243648","name":"scKSFD: federated distillation model with knowledge sharing for cell type classification of clinical transcriptome data.","source":"pubmed","abstract":"Single-cell RNA sequencing (scRNA-seq) enables high resolution characterization of cellular heterogeneity but poses significant challenges for cross institutional collaboration due to privacy constraints and distributional heterogeneity. To address this problem, we propose a Federated Distillation framework with Knowledge Sharing (scKSFD) for privacy-preserving cell type classification. Unlike conventional federated learning approaches that exchange model parameters, scKSFD performs knowledge aggregation in prediction space by sharing probability-level soft label outputs on a reference dataset, thereby reducing privacy risks. To better accommodate domain specific characteristics of scRNA-seq data, scKSFD integrates stratified proxy sampling to preserve rare cell populations and employs probability-level aggregation to mitigate batch specific expression shifts without explicit feature level correction. Comprehensive evaluations across 42 clinical single-cell transcriptome datasets demonstrate that scKSFD achieves higher or comparable F1 scores relative to centralized and existing federated baselines under heterogeneous settings, with statistically significant improvements in paired comparisons. In a multiple hospital COVID-19 case study, federated collaboration using scKSFD improved classification performance compared with local-only training while avoiding direct sharing of patient level expression data. Overall, scKSFD provides a federated distillation framework that balances predictive performance, robustness, and data-sharing constraints for multiple institutional single-cell transcriptomic analysis.","url":"https://pubmed.ncbi.nlm.nih.gov/42243648/","authors":["Sun N","Guan M","Zhou P","Yau SS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 4","doi":"10.1186/s12859-026-06508-x","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42243528","name":"Fully automated system predicts osteoporotic vertebral fracture across institutions using lumbar MRI paraspinal muscle signatures.","source":"pubmed","abstract":"Paraspinal muscle (PM) degeneration is a crucial yet frequently overlooked risk factor for osteoporotic vertebral fractures (OVF). We developed PM Segmentation and Classification of OVF (PMSAC-OVF), a fully automated, multi-institutional system that segments lumbar PMs on MRI, extracts federated learning (FL) and radiomics features, and integrates them with clinical variables for OVF prediction. Leveraging a vision foundation model framework, the system enables privacy-preserving, cross-institutional training and lightweight local deployment. Data from 2,884 patients across five institutions (2014-2024) were analyzed. The automated segmentation module demonstrated expert-level accuracy (Dice coefficient: 0.952, Intersection over Union: 0.909) while reducing processing time to seconds. For prediction, FL and radiomics models yielded pooled AUCs of 0.827 (range: 0.819-0.861) and 0.803 (0.793-0.892), respectively. Trimodal models integrating radiomics signatures (RS), FL signatures (FLS), and clinical variables achieved a pooled AUC of 0.840 (0.822-0.916), significantly outperforming clinical-only models (AUC: 0.742, 0.641-0.778). SHapley Additive exPlanations identified RS, FLS, and bone mineral density as the top predictors, highlighting the complementary value of image-derived features. PMSAC-OVF provides a robust, interpretable, and scalable solution for OVF prediction in heterogeneous clinical settings, potentially facilitating early identification and personalized intervention for high-risk individuals.","url":"https://pubmed.ncbi.nlm.nih.gov/42243528/","authors":["Zhang W","Qin Y","Hao Y","Liang W","Lu J","Zhu W","Yuan X","Zhou H","Zhao Y","Xie Q","Liu Y","Hu D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 4","doi":"10.1038/s41746-026-02855-4","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42243446","name":"Byzantine robust federated learning for heterogeneous brain MRI using multisignal gradient fingerprinting and adaptive trust aggregation.","source":"pubmed","abstract":"Federated learning enables collaborative training across institutions without centralizing patient data, but remains vulnerable to malicious clients and severe non-IID data heterogeneity. We propose a trust-aware federated learning framework for brain MRI that combines multi-signal gradient fingerprinting with adaptive aggregation to achieve Byzantine robustness. Each client update is characterized by a six-dimensional fingerprint (variational-autoencoder reconstruction error, cosine similarity to a server reference, peer similarity, gradient norm, sign consistency, and Monte Carlo Shapley contribution). A dual-attention module and a reinforcement-learning controller map these signals into trust weights and integrate with FedBN-P (Federated Batch Normalization with Proximal regularization), an optimizer co-designed for stability under heterogeneous and adversarial conditions. We evaluate on MNIST, CIFAR-10, Alzheimer's MRI, and the OASIS brain-MRI cohort (approximately 87 test samples, used strictly as proof-of-concept) under both standard and strengthened threat models (up to 40% malicious clients). Attack-specific ablation confirms a defense-in-depth design: VAE fingerprinting is the primary noise-attack defense (3.60 pp accuracy drop upon removal), Shapley values safeguard accuracy under scaling (10.16 pp drop), and reinforcement learning improves detection consistency under dynamic attack schedules. Three-seed paired-test validation further shows detection F1 outperforms FLTrust by up to 44 pp on Non-IID Gaussian noise; a white-box adaptive attacker degrades the detector but not model accuracy, confirming the layered design. End-to-end wall-clock overhead is + 8.8% over FedAvg with identical communication volume. The framework achieves F1 above 0.98 for gradient-scaling attacks while preserving accuracy under magnitude-preserving attacks where explicit detection remains limited. Multi-site validation on larger federated cohorts (e.g., ADNI, UK Biobank, FeTS) is required before any clinical-deployment claim can be made.","url":"https://pubmed.ncbi.nlm.nih.gov/42243446/","authors":["Karami M","Kebriaei H","Ghassemi F","Azadegan H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-55855-5","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42243360","name":"Selective entropy-fused proximal policy optimisation with federated reinforcement learning for intelligent multi-UAV trajectory and communication optimisation.","source":"pubmed","abstract":"The rapid evolution of 5G and emerging 6G networks has increased the demand for wireless communication systems that deliver high capacity, low latency, and adaptability. However, conventional terrestrial infrastructure remains costly and inflexible, particularly in dynamic or remote environments. This article develops a new Federated Reinforcement Learning (FRL)-based UAV communication system using Selective Entropy-Fused Proximal Policy Optimization (SEF-PPO) is proposed to enhance the performance of locally-on-policy learning in real-time decision-making environments. In contrast to existing digital twin or offline-trained deep reinforcement learning (DRL) methods, the proposed solution eliminates the need for replay buffers, thereby reducing memory and computational requirements for UAV platforms. UAVs learn collaboratively while preserving data privacy and maintaining robustness to non-IID user distributions through federated aggregation with a High-Altitude Platform (HAP). The framework integrates trajectory planning, user association, energy-efficient resource allocation, and handover management within a unified adaptive architecture. Experimental results demonstrate significant improvements in throughput, fairness, latency, and energy efficiency compared with baseline methods, including DMTD, DRL-EC 3 , and greedy and random algorithms. Overall, the proposed design enables scalable, energy-aware, and environment-responsive UAV coordination, offering a deployment-ready solution for next-generation wireless networks without requiring simulation-based pretraining.","url":"https://pubmed.ncbi.nlm.nih.gov/42243360/","authors":["Velmurugan M","Velmurugan T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-54933-y","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42236763","name":"The Internet of Vehicles (IoV) and privacy-preserving systems.","source":"pubmed","abstract":"The Internet of Vehicles (IoV) is changing the contemporary mobility, as it allows real-time communication between vehicles, infrastructure, and cloud services. Nevertheless, such growing connectivity brings on serious privacy, regulatory, and trust issues especially because sensitive behavioral and location information is exposed. The current IoV-security systems tend to be based on identity-based checks, or centralized trust authorities, which can lead to infringement of user privacy and cause surveillance and profiling threats. The paper is inspired by privacy-preserving architectures in the Metaverse to suggest a decentralized trust system of IoV systems on the basis of zero-knowledge proofs, namely zk-SNARKs. The suggested solution allows vehicles to cryptographically verify that they meet regulatory or operational regulations- i.e. valid insurance, safety test, or emissions- without revealing personal identifiers or raw information. The framework enables building scalable, low-latency and audible trusts and following data minimization principles through combining zk-SNARK verification and Layer 2 blockchain solutions.","url":"https://pubmed.ncbi.nlm.nih.gov/42236763/","authors":["Zahid N","Tahir S","Algarni F","Tahir H","Saeed S","Syed AM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 4","doi":"10.1038/s41598-026-51165-y","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42235476","name":"BigOrthoATD.Net: A scalable and adaptable distributed deep learning framework for multi-class orthopedic classification across imaging modalities in low-resourced settings.","source":"pubmed","abstract":"Multi-class medical image classification using DL continues to face major challenges, including managing multi-modal data, adapting to new tasks, handling distributed datasets, and operating under limited computational resources. Existing approaches fail to address these issues simultaneously, restricting the clinical scalability of AI in healthcare. To overcome these limitations, this paper introduces BigOrthoATD.Net, a unified, serverless, and decentralized learning framework that redefines scalability, adaptability, and efficiency in orthopedic image analysis. Designed to operate across distributed clinical nodes, BigOrthoATD.Net enables privacy-preserving knowledge fusion and multimodal integration across X-ray and CT imaging modalities. The framework supports progressive scalability for new tasks and institutions, achieving continual learning without retraining or performance degradation. Comprehensive experiments conducted across 13 simulated decentralized nodes and 50 orthopedic classes demonstrated that BigOrthoATD.Net achieved a state-of-the-art accuracy of 97.0%, outperforming swarm learning (70.8%) and centralized learning (84.8%), while federated learning failed to converge beyond moderate scale under identical resource-constrained conditions. BigOrthoATD.Net establishes a new benchmark for decentralized medical imaging by surpassing both centralized and decentralized frameworks in accuracy, scalability, and class diversity, while operating efficiently in low-resourced settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42235476/","authors":["Alwzwazy HA","Alzubaidi L","Zhao Z","Crawford R","Alnaseri O","Jurdak R","Gu Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Nov","doi":"10.1016/j.neunet.2026.109190","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42235268","name":"Robust cross-domain generalization using unlabeled target data with source-domain supervision.","source":"pubmed","abstract":"It is often desirable to generalize medical imaging AI models trained with dense annotations to data acquired from different ultrasound scanners or clinical sites; however, retraining these models with new annotations is often difficult and costly. We examine this challenge in pediatric wrist fracture assessment using point-of-care ultrasound (POCUS), where fractures are common and can be effectively triaged via ultrasound. AI has shown radiologist-level performance for fracture detection, often aided by high-quality bony structure segmentation. However, due to significant domain shifts, models perform poorly on data from other centers or probes, and obtaining segmentation labels across devices is impractical due to manual annotation effort and data privacy concerns. To address this, we propose a target-informed self-supervised pretraining and model-ensemble strategy. Specifically, our approach combines masked image modeling (MIM) and contrastive learning to learn target-domain structural representations without labels, and introduces a confidence-aware infusion head to adaptively integrate predictions. The source dataset, collected with a Philips Lumify probe, contained dense labels, while the target dataset, acquired with a TeleMED portable probe, was unlabeled. The datasets were kept strictly separate throughout the entire process. Our method used labeled source data for supervised training and leveraged target-domain pretraining to improve generalization. On 318 images from 62 pediatric POCUS videos, this approach significantly improved cross-device performance, achieving over 6% Dice improvement on the target domain versus the baseline. These results demonstrate a label-efficient and privacy-preserving approach for cross-device-robust ultrasound AI, offering a framework that can be extended to multi-center studies or federated learning setups. GitHub Repository:https://github.com/yuyue2uofa/CrossDomainPOCUS.","url":"https://pubmed.ncbi.nlm.nih.gov/42235268/","authors":["Zhou Y","Ghosh S","Xie MKY","Kim JJY","Knight J","McDonald S","Man V","Jaremko JL","Hareendranathan A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 15","doi":"10.1016/j.compbiomed.2026.111788","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42235032","name":"Artificial intelligence in allergen immunotherapy: toward a proactive and personalized management of allergic diseases.","source":"pubmed","abstract":"The management of immunoglobulin E (IgE)-mediated allergic diseases and allergen immunotherapy (AIT) is complicated by high interindividual variability and the unavailability of reliable predictive biomarkers. Given the complexity of the immune response underlying these processes, integrating artificial intelligence (AI) could revolutionize the clinical management of these patients.","url":"https://pubmed.ncbi.nlm.nih.gov/42235032/","authors":["Gangemi S","Gammeri L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 26","doi":"10.1097/ACI.0000000000001172","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42233989","name":"Artificial intelligence in paediatric chest imaging: applications, challenges, and future directions.","source":"pubmed","abstract":"Paediatric chest imaging is central to diagnosing respiratory and cardiopulmonary disease, particularly in low- and middle-income countries (LMICs) where pneumonia remains a leading cause of childhood mortality and radiology expertise is scarce. Artificial intelligence (AI) could expand access, standardise quality and support task-shifting in these \"diagnostic deserts,\" yet most systems are trained and validated on adult datasets from high-income settings, and paediatric radiographs form only a small minority of major public training cohorts - raising concerns about safety, generalisability and equity when such models are deployed in children.","url":"https://pubmed.ncbi.nlm.nih.gov/42233989/","authors":["Muringathuparambil JJ","Maharaj S","Segal BM","Mahomed N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 3","doi":"10.1007/s00247-026-06671-6","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42232480","name":"Towards Straggler-Resilient Split Federated Learning: An Unbalanced Update Approach.","source":"pubmed","abstract":"Split Federated Learning (SFL) enables scalable training on edge devices by combining the parallelism of Federated Learning (FL) with the computational offloading of Split Learning (SL). Despite its great success, SFL suffers significantly from the well-known straggler issue in distributed learning systems. This problem is exacerbated by the dependency between Split Server and clients: the Split Server side model update relies on receiving activations from clients. Such synchronization requirement introduces significant time latency, making straggler a critical bottleneck to the scalability and efficiency of the system. To mitigate this problem, we propose MU-SplitFed, a straggler-resilient SFL algorithm in zeroth-order optimization that decouples training progress from straggler delays via a simple yet effective unbalanced update mechanism. By enabling the server to perform &#x3c4; local updates per client round, MU-SplitFed achieves a convergence rate of &#x1d4aa; ( d / ( &#x3c4; T ) ) for non-convex objectives, demonstrating a linear speedup of &#x3c4; in communication rounds. Experiments demonstrate that MU-SplitFed consistently outperforms baseline methods with the presence of stragglers and effectively mitigates their impact through adaptive tuning of &#x3c4; . The code for this project is available at https://github.com/Johnny-Zip/MU-SplitFed.","url":"https://pubmed.ncbi.nlm.nih.gov/42232480/","authors":["Liang D","Zhang J","Chen E","Li Z","Li R","Yang H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"pmid:42230914","name":"Design of an AI-based security anomaly detection system for IoT terminals based on the ViT-transformer fusion model.","source":"pubmed","abstract":"The deep penetration of IoT terminals in water systems, healthcare, transportation, and other fields has exacerbated security threats such as cyber-physical attacks and traffic anomalies. However, traditional anomaly detection methods have limitations such as dependence on labeled data, weak generalization ability, high resource consumption, and prominent privacy risks. Although Vision Transformer (ViT) has the advantage of capturing global features, it is difficult to directly adapt to resource-constrained IoT terminals. In existing research, hybrid deep learning models have improved detection accuracy, but lightweight ViT fusion models lack terminal adaptability and multi-modal data fusion applications are scarce. The balance between dynamic scheduling and privacy protection in end-edge-cloud collaboration still needs to be broken through. To address the above issues, this paper proposes an IoT terminal AI security anomaly detection system based on the ViT-Transformer fusion model: adopting a three-level end-edge-cloud collaborative architecture, integrating multi-modal data such as network traffic, sensor timing, and side channel signals, and achieving cross-modal feature fusion through tokenization; combining pruning, distillation, and quantization optimization strategies to increase the model compression ratio to 70%; introducing Elliptic Curve Certificateless Encryption (CL-PKE) and Batch Listing Signature (BLS) batch authentication to ensure data security, and using federated learning to aggregate edge model updates and optimize global performance. Experiments were conducted on public datasets such as IoT-23 and UCI, as well as a self-made testbed. The results show that the model achieves an accuracy of 89.2% and an F1-score of 0.87 in multi-modal anomaly detection, with a terminal inference delay of 90ms and a memory footprint of 30MB, adapting to low-computing devices such as RPi4B and Arduino; CL-PKE resists brute force attacks for 5.2e6 seconds, and batch authentication for 100 terminals takes only 75ms; it exhibits excellent generalization across smart home, industrial IoT, and other scenarios, with a defense success rate of 85.3% against FGSM attacks. This study effectively addresses the resource bottleneck and security pain points of existing methods, providing an efficient and reliable technical solution for IoT terminal security.","url":"https://pubmed.ncbi.nlm.nih.gov/42230914/","authors":["Zhang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 2","doi":"10.1038/s41598-026-55476-y","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42230715","name":"A strong robust multi level database image watermark embedding scheme based on the Chinese remainder theorem.","source":"pubmed","abstract":"Due to the fact that most existing digital watermarking schemes can only add watermark information once, repeated additions will overwrite the original watermark information, making it impossible to achieve multi-level traceability of data. In response to the above issues, this paper proposes a multi-level database image watermark embedding scheme (MIWC) based on the Chinese remainder theorem. Taking image watermarks as carriers, MIWC preprocesses images using methods such as Haar wavelet transform and Bloom filter to form a pixel. It then performs secret segmentation on watermark information in accordance with the properties of the Chinese Remainder Theorem, enabling hierarchical and item-by-item addition of the watermark information. Furthermore, it records and traces the entire data flow chain. Functional analysis demonstrates that MIWC possesses the multi-level watermark embedding capability that existing schemes lack, thus holding high practical application value. Experimental results indicate that MIWC is reversible, with both watermark embedding and extraction exhibiting high efficiency. Even with the embedding of multi-level watermarks, MIWC remains highly efficient and lightweight. Meanwhile, MIWC also demonstrates strong robustness, being capable of resisting geometric attacks (including rotation and scaling) and common attacks (including JPEG compression, JPEG2000, Gaussian white noise, and salt-and-pepper noise) targeting the watermark.","url":"https://pubmed.ncbi.nlm.nih.gov/42230715/","authors":["Geng H","Guo S","Liu Y","Li C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 2","doi":"10.1038/s41598-026-54997-w","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42230710","name":"SQUID-COMM: a Colossal Squid-inspired distributed communication framework for real-time multi-node aquaculture monitoring networks with adaptive bioluminescent signaling and neuromorphic edge intelligence.","source":"pubmed","abstract":"Precision aquaculture demands robust communication networks capable of coordinating thousands of distributed sensors across marine and freshwater facilities. Current aquaculture IoT networks face critical challenges including underwater signal attenuation reaching 98% loss at 100&#xa0;m depth, dynamic topology changes from fish movement and water currents, and severe energy constraints on battery-powered sensor nodes. This paper introduces SQUID-COMM, a novel bio-inspired communication framework emulating the signaling mechanisms of the Colossal Squid (Mesonychoteuthis hamiltoni). The framework introduces seven innovative mechanisms: Bioluminescent Pulse-Coded Modulation (BPCM) achieving 34% higher spectral efficiency through adaptive signal encoding; Chromatophore-Inspired Channel Adaptation (CICA) enabling 15ms frequency hopping response time; Distributed Axon-Ganglia Routing Protocol (DAGRP) maintaining 99.7% packet delivery under 40% node mobility; Tentacle-Topology Self-Organization (TTSO) for dynamic mesh network formation; Giant Fiber Emergency Broadcast (GFEB) achieving sub-50ms critical alert propagation; Photophore Synchronization Protocol (PSP) for microsecond-accurate time coordination; and Ink-Cloud Congestion Control (ICCC) reducing packet loss by 82%. The Enhanced SQUID-COMM variant incorporates Neuromorphic Edge Processing reducing cloud communication by 78%, Federated Learning Coordination for distributed model updates, and Quantum-Resistant Encryption for future-proof security. Experimental evaluation across five aquaculture deployment scenarios demonstrates end-to-end latency of 12.3ms representing 78% reduction compared to LoRaWAN, throughput of 2.4 Mbps in turbid conditions spanning 5-150 NTU, energy efficiency of 0.23&#xa0;mJ/bit constituting 67% improvement over Zigbee, and network lifetime extension of 340%. Real-world deployment at four commercial facilities across Norway, Egypt, Thailand, and Greece over 120 days processed 2.3&#xa0;billion sensor readings with 99.94% reliability, enabling fish behavior detection at 94.7% accuracy and early disease detection with 4.2-day lead time. Statistical analysis confirms significant improvements with p-values below 0.001 and Cohen's d exceeding 1.2, while economic evaluation demonstrates annual savings of &#x20ac;89,000-&#x20ac;340,000 per facility.","url":"https://pubmed.ncbi.nlm.nih.gov/42230710/","authors":["Salem AI","Elbaz M","Khalil HM","Loey M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 2","doi":"10.1038/s41598-026-54545-6","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42229313","name":"Artificial intelligence empowered coronary artery imaging: A review.","source":"pubmed","abstract":"Cardiovascular disease is the leading cause of death worldwide, with coronary artery disease the most prevalent cause. Although artificial intelligence has advanced medical imaging, few reviews focus specifically on AI-powered coronary artery imaging. This review provides a systematic and critical analysis of AI applications in coronary artery imaging across three domains: measurement, including centerline extraction, vessel segmentation, and 3D reconstruction; functional assessment, including fractional flow reserve, wall shear stress, and myocardial perfusion; and disease diagnosis. Web of Science, Google Scholar, and MEDLINE were searched for 2016-2025. From 9950 records, 90 studies were selected after duplicate removal, title and abstract screening, and full-text eligibility assessment using predefined criteria and quality appraisal. Seven imaging modalities are covered: computed tomography, magnetic resonance imaging, nuclear imaging, digital subtraction angiography, intravascular ultrasound, optical coherence tomography, and synthetic data. Reported performance varies by modality, task, and dataset. Challenges for clinical implementation include dataset generalizability, computational requirements, interoperability, and explainable AI. The review discusses emerging technologies such as multimodal fusion, self-supervised learning, federated learning, and foundation models, and acknowledges current limitations. It aims to inform researchers and clinicians and support future translation of AI in coronary artery imaging.","url":"https://pubmed.ncbi.nlm.nih.gov/42229313/","authors":["Wang M","Chen J","Li H","Zhang LB","Wang W"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun","doi":"10.1016/j.compmedimag.2026.102782","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42225753","name":"Heterogeneity-aware personalised federated learning for household energy forecasting on multi-source real-world smart meter data.","source":"pubmed","abstract":"Residential and building-level electricity forecasting is increasingly based on smart-meter streams, but direct data pooling is often difficult because load traces are private, geographically dispersed and statistically different across households and buildings. This paper studies the effect of such client heterogeneity in federated energy forecasting. A multi-source benchmark is constructed by combining processed Smart, CU-BEMS, UCI Household and AMPds clients, giving 22 usable clients and 1,522,510 observations at 15-minute resolution. The benchmark compares non-federated baselines (Persistence, Ridge, LocalOnly and Centralised training), standard federated baselines (FedAvg, FedProx and FedPer) and the proposed HAPFL framework over 1-step, 12-step and 24-step forecasting horizons. HAPFL separates a shared temporal encoder from client-specific prediction heads and combines proximal stabilisation, latent prototype alignment and difficulty-aware aggregation. In the reported benchmark protocol, HAPFL gives the lowest mean MAE and the highest mean [Formula: see text] at all three horizons. At horizon 1, mean MAE improves from 0.208 to 0.196 and mean [Formula: see text] improves from 0.741 to 0.769 relative to centralised training, while the worst-10% client MAE is reduced from 0.301 to 0.274. At horizons 12 and 24, the corresponding MAE reductions over centralised training are 6.11% and 7.60%, respectively. The results indicate that vanilla federated averaging is not adequate for strongly non-identically distributed energy clients, while personalised and heterogeneity-aware federated learning is a more suitable direction for decentralised household and building load forecasting.","url":"https://pubmed.ncbi.nlm.nih.gov/42225753/","authors":["Das P","Pandey S","Pandey C","Kumar D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-53020-6","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42224329","name":"PainFedMVL: A Federated Multi-View Learning Approach for Multi-Level Pain Recognition.","source":"pubmed","abstract":"Pain is a critical clinical indicator in rehabilitation and neurological disorders, yet reliable multi-level recognition remains challenging due to subtle facial variations, inter-subject variability, and heterogeneous clinical data. To address these issues, we propose PainFedMVL, a federated multi-view learning framework for distributed medical settings. The framework integrates Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) to capture illumination-robust spatiotemporal textures and Bi-Weighted Oriented Optical Flow (Bi-WOOF) to encode localized facial micro-dynamics, thereby constructing complementary feature representations. A multi-scale CNN-biLSTM is employed to extract hierarchical spatiotemporal dependencies, enabling robust modeling of both global texture and fine-grained motion cues. To mitigate the non-IID nature of decentralized medical data, we introduce an adaptive aggregation strategy based on Jensen-Shannon (JS) divergence, which explicitly measures the distributional distance between each client and the global model to achieve adaptive weighting. The mechanism reduces the adverse effects of inter-client heterogeneity, stabilizes optimization, and enhances generalization across diverse populations. Experiments on the BioVid dataset demonstrate that PainFedMVL consistently outperforms representative baselines in both binary and multi-level pain classification, providing a robust and privacy-preserving solution for clinical pain assessment.","url":"https://pubmed.ncbi.nlm.nih.gov/42224329/","authors":["Li D","Yang Z","Xie S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/TNSRE.2026.3698562","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42224322","name":"Toward Fair Federated Graph Learning.","source":"pubmed","abstract":"As a privacy-preserving collaborative paradigm, federated graph learning (FGL) enables distributed training of graph neural networks (GNNs) without exposing raw graph data. Subgraph-FL has become the dominant FGL paradigm, yet most studies focus on overall node classification performance while overlooking fairness issues stemming from heterogeneous node profiles and graph topology. Specifically, they exhibit biased performance to nodes with disadvantageous properties, such as being minority-class within local subgraphs or heterophilous connections (i.e., neighboring nodes possess dissimilar labels and misleading features). This underexplored fairness challenge reveals the robustness concerns of current subgraph-FL methods: high accuracy conceals degraded performance on structurally or semantically marginalized node groups. To address this, we advocate for: 1) enhancing the representation of minority-class nodes for class-wise fairness and 2) mitigating topological biases arising from heterophilous connections for topology-aware fairness. In this context, we propose FairFGL, a novel framework that performs fine-grained mining of graph properties and orchestrates a collaborative learning paradigm to enhance fairness. Specifically, on the client side, the majority alignment module enhances local clients' expertise for boosting efficient cross-client knowledge transfer. The gradient modification module and the history-preserving module infuse scarce local minority knowledge through cross-client collaboration and regulate local training from being over-fit to locally dominant distribution. On the server side, FairFGL only requires uploading the changed value of the most influential subset of locally trained parameters. Subsequently, a cluster-based aggregation strategy reconciles conflicting updates from heterogeneous data distribution across clients, and suppresses global majority dominance to the newly aggregated global model. Extensive evaluations on eight benchmark datasets show that FairFGL significantly improves performance for disadvantaged node groups, achieving up to 21.07% increase in Overall F1-while enhancing convergence efficiency over SOTA baselines.","url":"https://pubmed.ncbi.nlm.nih.gov/42224322/","authors":["Wu Z","Pang B","Li X","Zhu Y","Su D","Fan B","Li RH","Wang G","Zhou C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 1","doi":"10.1109/TNNLS.2026.3690015","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42224201","name":"Adversarial Robust Federated Learning for Secure Mortality Risk Prediction using Multi-Institutional Electronic Health Records (EHRs).","source":"pubmed","abstract":"Electronic Health Records (EHRs) are the main source of data that enable data-driven clinical decision-making. However, the sensitive character of EHRs and the strict privacy policies in the healthcare sector make centralized model training difficult or impossible. An Adversarially Robust Federated Learning (AR-FL) model is proposed to predict patient mortality risk across different institutions without sharing the original EHR data. The primary aim of this study is to introduce a secure, reproducible, and scalable procedure for training privacy-preserving, adversarially resilient predictive models across diverse clinical settings. This study employs a min-max adversarial training approach at each institution to improve robustness against worst-case perturbations. A domain-aware attention mechanism is also employed to dynamically adapt to differences in clinical feature distributions within the institution. For confidentiality, the model updates are pooled using privacy-protecting methods that block the revealing of sensitive patient data during federated communication. This study specifies the entire process from data preprocessing and adversarial example generation to local training, secure aggregation, and global model evaluation, allowing for a consistent implementation across various healthcare environments. Experimental validations demonstrate that the AR-FL model achieves superior predictive performance, adversarial robustness, and cross-institutional generalization. By establishing a standardized training and evaluation pipeline, this study supports the development of reliable and ethically compliant clinical decision-support systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42224201/","authors":["Chatterjee S","Satpathy S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 12","doi":"10.3791/69104","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42223739","name":"Nanostructured electrode materials and flexible-substrate engineering for wearable multi-analyte biosensors in diabetes monitoring and personalized care: a comprehensive review.","source":"pubmed","abstract":"This review examines the convergence of wearable biosensors and artificial intelligence (AI) in personalized diabetes care. It addresses the limitations of traditional glucose monitoring and underscores the need for continuous, multi-analyte physiological surveillance. The manuscript evaluates multi-biofluid sensing platforms, specifically those utilizing interstitial fluid (ISF), sweat, saliva, tears, and urine. ISF, an extracellular medium and a plasma ultrafiltrate, exhibits low protein content, a property that reduces sensor biofouling. ISF glucose demonstrates a strong correlation with blood glucose (R&#xb2; &gt; 0.95) and can achieve high analytical sensitivity and specificity in clinically validated systems; however, diffusion-based time lags of 5-10&#x2009;min present a kinetic challenge. Consequently, AI correction is necessary to ensure real-time accuracy, which is often achieved through minimally invasive microneedle arrays. Sweat analysis allows for non-invasive, multi-parameter measurements. Nevertheless, challenges such as pH instability and analyte loss due to evaporation complicate this sensing approach. Therefore, microfluidic techniques are essential for maintaining sample stability. A primary finding indicates that clinically validated Continuous Glucose Monitoring (CGM) systems yield substantial improvements in glycemic control, increasing Time in Range (TIR) by 10-15% and reducing the incidence of hypoglycemic events by 30-40%. AI-based predictive algorithms can forecast glucose excursions 30-60&#x2009;min in advance, exhibiting an accuracy exceeding 94%. Key barriers to implementation include sensor calibration challenges, algorithmic bias, and significant healthcare equity issues. Future research should prioritize the development of multi-analyte implantable devices, leverage federated learning frameworks, and incorporate additional biomarkers to deliver continuous, multi-analyte, skin-conformal monitoring.","url":"https://pubmed.ncbi.nlm.nih.gov/42223739/","authors":["Sodeify R","Seyednazari MA","Dorosti AM","Nourazarian A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 1","doi":"10.1007/s10856-026-07070-x","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42222122","name":"Artificial intelligence optimizes immune rejection prediction and management in heart transplantation: a structured narrative review.","source":"pubmed","abstract":"Heart transplantation remains the definitive therapy for end-stage heart failure, yet long-term outcomes are limited by three core clinical bottlenecks in immune rejection management: imprecise preoperative donor-recipient matching, overreliance on invasive endomyocardial biopsy (EMB) for postoperative rejection surveillance, and high inter-observer variability in manual pathological diagnosis of rejection. Artificial intelligence (AI) has emerged as a promising tool to address these gaps, but the methodological quality and clinical translation readiness of supporting evidence have not been comprehensively synthesized.","url":"https://pubmed.ncbi.nlm.nih.gov/42222122/","authors":["Chen K","Lai J","Luo Y","Li C","Wang G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fcvm.2026.1790244","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42218717","name":"Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.","source":"pubmed","abstract":"The shift of artificial intelligence for antimicrobial resistance (AI-AMR) from proof-of-concept studies to clinically embedded decision support critically hinges on establishing rigorous reproducibility, interpretability, and evaluation standards aligned with antimicrobial stewardship and patient safety. This review traces the field's evolution from rule-based gene matching through classical machine learning to deep learning models, foundation models, and generative models and proposes a pragmatic standards framework covering dataset curation, multi-site external validation, transparent model cards, and systematic error-cost analyses. Historically, progress was catalysed by curated resistome ontologies; modern practice demands FAIR-aligned curation, explicit bias and data-leakage audits, and prospective temporal and external geographic validation guided by emerging healthcare AI guidelines. To translate accuracy into safer prescribing, the review advocates cost-sensitive evaluation that quantifies false-positive and false-negative harms, integrates stewardship metrics (time-to-effective therapy, spectrum narrowing, days of therapy), and facilitates continuous post-deployment monitoring. Looking forward, federated learning, multimodal and foundation architectures, generative models for antimicrobial and peptide design, and explainable interfaces usable at the point-of-care are poised to reshape trustworthy and clinically deployable AI-AMR. The review concludes with a checklist for implementers: FAIR and diversity-by-design data curation, prospectively specified multi-site validation, standardised governance-linked model cards, and explicit stewardship-oriented error-cost trade-offs.","url":"https://pubmed.ncbi.nlm.nih.gov/42218717/","authors":["Sardar S","Dash S","Roychowdhury P","Solanki J","Sikdar P","Thangappan J","Dutta S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 4","doi":"10.1093/bib/bbag269","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42213163","name":"Multiscale predictive cellular modeling: integrating hypothesis grammars, digital twins, and multi-omics for In silico oncology and precision theranostics.","source":"pubmed","abstract":"Predictive multiscale cellular modeling is emerging as a consequential direction in precision medicine, converging hypothesis grammars, digital twins, and integrative genomics to interrogate tumor-immune dynamics, therapeutic resistance, and cellular plasticity. This perspective synthesizes recent progress across these domains and critically maps their translational potential alongside their current limitations. Hypothesis grammars translate mechanistic theories into executable agent-based models (ABMs) and hybrid ODE-PDE systems, enabling rapid in silico hypothesis testing while lowering the authoring barrier for domain scientists. Patient-specific digital twins, driven by multi-omics data, employ stochastic ensemble methods to simulate clonal evolution and microenvironmental interactions, though prospective clinical validation of these capabilities remains at an early stage. Integrative genomics, leveraging algorithms such as SCODE and SimiC, infers causal gene regulatory networks (GRNs) using Bayesian variational autoencoders, embedding dynamic intracellular logic into tissue-scale simulations. Emerging applications include in silico oncology trials for optimizing checkpoint blockade and combination therapies. Large language models are being explored to enhance rule induction, while FAIR-compliant digital cell repositories aim to ensure reproducibility and reuse. Verification, validation, and uncertainty quantification (VVUQ) via Sobol sensitivity analysis and Kennedy-O'Hagan calibration are identified as essential components for addressing non-identifiability and supporting regulatory credibility. Federated learning is discussed as a means of mitigating privacy and bias concerns in multi-institutional settings. Together, these converging approaches outline a plausible pathway toward virtual clinical trials and adaptive theranostics, contingent on the prospective validation, data infrastructure, and governance frameworks that clinical deployment will require.","url":"https://pubmed.ncbi.nlm.nih.gov/42213163/","authors":["Gopukumar ST","Dwivedi D","Rahamathulla M","Ahmed MM","Soni TK","Natarajan PG","Ramkanth S","Mitra A","Das U"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 29","doi":"10.1007/s10142-026-01890-4","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42211009","name":"Enabling the analysis of patient-level data across jurisdictions for research use: a real-world exploration of a federated likelihood approach.","source":"pubmed","abstract":"The disclosure of health data is governed by strict privacy regulations which significantly restrict the transfer of data across jurisdictions. These limitations restrict the scope of health research, particularly the ability to conduct studies that span multiple jurisdictions (e.g. provinces, states, or countries). One common practice is to use meta-analysis to pool jurisdiction-specific estimates. However, this approach relies on combining aggregate-level data, which may overlook important nuances. Therefore, alternative methods are needed. This study investigates the potential of a federated likelihood approach to analyse health data across jurisdictions while keeping the data inside the jurisdiction.","url":"https://pubmed.ncbi.nlm.nih.gov/42211009/","authors":["Harmon M","Li N","Sajobi T","Holodinsky J","Williamson T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.23889/ijpds.v11i1.3160","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42209847","name":"Artificial intelligence and transforming cancer care.","source":"pubmed","abstract":"Artificial Intelligence (AI) is reshaping oncology by addressing key limitations in traditional cancer care and enabling data-driven, personalized approaches from diagnosis to treatment. This review explores the transformative role of AI across the cancer care continuum, highlighting its contributions, challenges, and future directions. AI has significantly advanced cancer detection and diagnosis by improving the interpretation of medical imaging (CT, MRI, PET scans, digital pathology) and liquid biopsies, allowing for early and accurate identification of tumors and biomarkers. In genomics and molecular profiling, AI facilitates the analysis of large-scale sequencing data to uncover actionable mutations and support targeted therapy decisions. This review also examines AI-powered prognostic models that integrate clinical, genomic, and electronic health record data to predict outcomes such as survival rates and recurrence risks, allowing for more precise treatment planning. In the therapeutic landscape, AI aids in optimizing radiation dosing, guiding surgical interventions, and predicting individual responses to chemotherapy, immunotherapy, and targeted treatments, thereby reducing uncertainty and improving outcomes. Key limitations, such as data privacy concerns, algorithmic bias, model opacity, and integration hurdles are discussed, along with strategies to address them, including explainable AI, standardized validation, and clinician training. Looking ahead, innovations like federated learning, generative AI for drug discovery, and multimodal data integration are poised to enhance precision oncology further. By synthesizing current developments and emerging trends, this review underscores the potential of AI to drive equitable, efficient, and personalized cancer care on a global scale.","url":"https://pubmed.ncbi.nlm.nih.gov/42209847/","authors":["Reddy AM","Kaur G","Ribaya VSD","Ribaya ELA","Reddy MC","Shah T","Shinde D","Suri GS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 28","doi":"10.1007/s12672-026-05242-3","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42209845","name":"Artificial intelligence in cancer immunotherapy: current trends in predicting response and personalizing treatment.","source":"pubmed","abstract":"Artificial intelligence (AI) can transform cancer immunotherapy by enabling more accurate prediction of treatment responses, the discovery of specific biomarkers, and the development of personalised treatment plans. Traditional single-marker biomarkers (PD-L1, TMB, MSI) lack consistency across tumour types and cannot be used to assess tumour heterogeneity or the dynamic tumour microenvironment (TME). This review synthesises developments in multimodal AI models that combine genomics, transcriptomics, radiomics, digital pathology (pathomics), circulating biomarkers, and clinical evidence to create composite predictive signatures with significantly better discriminatory value. AUCs over 0.8 have been seen in a few retrospective studies with deep learning and ensemble models on whole-slide images, CT/MRI/PET radiomics, spatial and single-cell omics, and multi-omics fusion models, but prospective and multicentre validation is scarce, and external validation often shows deterioration in performance. AI is also used to enhance the translational pipelines of adoptive cell therapies (e.g., CAR-T) by improving patient selection, manufacturing (e.g., digital twins), and early toxicity prediction (e.g., CRS, ICANS). Nevertheless, clinical implementation remains hindered by data heterogeneity, bias, poor longitudinal validation, limited reproducibility, and a lack of transparency in most models, even though prospective, multicenter validation and explainable AI are crucial for clinician trust and regulatory acceptance. New systems such as federated learning, foundation models, spatial omics, digital twins, and wearable monitoring represent paths to generalizable, privacy-preserving, and actionable systems in clinical practice. To achieve the potential of AI, the generation of data will need to be standardized, reporting must be transparent, interdisciplinary, and regulatory frameworks must be strengthened focusing on the practical use of AI and patient safety. By taking these steps, AI could be shifted to prospective clinical decision support, which uses AI to meaningfully enhance personalization and outcomes in cancer immunotherapy based on a retrospective research tool.","url":"https://pubmed.ncbi.nlm.nih.gov/42209845/","authors":["Nosa-Ihaza EA","Edeh EC","Geng WB","Okenwa E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 28","doi":"10.1186/s43046-026-00371-w","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42204202","name":"A federated learning-enabled energy-aware anomaly detection algorithm for secure big data analytics in IoT-based smart healthcare systems.","source":"pubmed","abstract":"The rapid expansion of Internet of Things (IoT) devices in smart healthcare systems has led to the generation of large volumes of diverse medical data. This creates challenges in ensuring secure, scalable, and energy-efficient anomaly detection. Traditional centralized deep learning methods rely on continuously sending sensitive patient data to cloud servers, which increases communication overhead, consumes more energy, and raises privacy concerns. To overcome these limitations, this paper presents a Federated Learning-enabled Energy-Aware Anomaly Detection framework (FL-EAD) designed for IoT-based smart healthcare environments. The proposed approach allows distributed IoT devices to collaboratively train models while keeping patient data stored locally, sharing only model updates instead of raw data. An energy-aware client selection strategy is incorporated to determine device participation in each federated learning round based on factors such as residual energy and communication cost. This helps reduce unnecessary energy consumption. Furthermore, a hybrid deep learning model combining an autoencoder with an attention-based Long Short-Term Memory (LSTM) network is used to effectively capture both spatial and temporal patterns in healthcare data streams. The proposed framework is evaluated using a publicly available healthcare IoT dataset. Experimental results show that FL-EAD improves anomaly detection performance and overall system efficiency compared to traditional centralized methods and standard federated learning approaches. Notable improvements are observed in accuracy, F1-score, energy usage, communication overhead, and detection latency. Overall, the results suggest that the proposed framework offers a practical and privacy-preserving solution for scalable anomaly detection in next-generation IoT-enabled smart healthcare systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42204202/","authors":["Al-Alshaikh HA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-53494-4","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42204192","name":"A federated deep learning framework with distributed hybrid character-level and attention mechanisms for scalable and cost-efficient fake news detection.","source":"pubmed","abstract":"Fake news detection is an essential task for media and news organizations to maintain the trust and reliability of the published content. Due to the rapid growth of online users and the spread of misinformation through malicious sources, the fake news circulates quickly across digital platforms. Hence, developing an accurate and efficient fake news detection model is crucial for social welfare. Although many studies have been conducted, achieving high accuracy remains challenging task, especially when dealing with rare and infrequent words. Traditional embedding models such as Word2Vec and FastText perform only word-level vectorization, while transformer-based models like BERT handle subword token more effectively. However, selecting the most suitable subword pieces for vector representation raises challenges. The research introduces a character-based embedding approach to generate fine-grained vector representations by analysing each character. A hybrid CharBERT-Optimized CNN and attention-based stacked Bi-LSTM with cost-sensitive learning (COASBC) model is proposed to classify fake news. In addition, a federated learning (FL) framework is integrated to enable distributed training across multiple news sources for improving generalization of model without sharing raw data. It reduces centralized computational cost, enhances data security and scalability of the model. Furthermore, statistical analysis and complexity analysis are conducted to evaluate the efficiency and computational feasibility of the proposed model. The generalization ability of the model is also analysed using Truth Seeker dataset 2023 that contains short-text tweets. Experimental evaluation using the LIAR, Fake and Real News datasets, FakeNewsNet, and WELFake Dataset demonstrates that the proposed Federated COASBC model achieves superior accuracy, precision, and reliability compared to existing state-of-the-art fake news detection methods and it maintains high computational efficiency.","url":"https://pubmed.ncbi.nlm.nih.gov/42204192/","authors":["Nithya K","Dhivyaa CR"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 27","doi":"10.1038/s41598-026-54820-6","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42201842","name":"Decentralized EM algorithm for Gaussian mixtures under data heterogeneity and partial labeling.","source":"pubmed","abstract":"We systematically study several network-based Expectation-Maximization (EM) algorithms for the Gaussian mixture model within decentralized federated learning (DFL). Our theoretical investigation reveals that directly extending the classic EM algorithm to DFL leads to a seriously biased estimator if the data are heterogeneously distributed across different sites. To address this issue, we introduce a momentum network EM (MNEM) algorithm, which integrates information from both current and historical estimators from previous DFL iterations. We further develop a semi-supervised MNEM (semi-MNEM) algorithm, which utilizes valuable information provided by partially labeled data. Rigorous theoretical analysis demonstrates that the MNEM estimator can achieve the same asymptotic efficiency as the whole sample estimator under appropriate regularity conditions, even if the data are heterogeneously distributed. Moreover, the semi-MNEM estimator significantly improves the convergence speed of the MNEM algorithm, even if different mixture components are poorly separated. Extensive simulations are conducted, and a widely used chest X-ray dataset is analyzed to demonstrate the finite-sample performance of the proposed methods.","url":"https://pubmed.ncbi.nlm.nih.gov/42201842/","authors":["Li X","Wu S","Du B","Wang H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 9","doi":"10.1093/biomtc/ujag092","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42197765","name":"Personalized Federated Actor-Critic Learning for Joint Cost-Comfort Optimization in Energy Communities.","source":"pubmed","abstract":"Home energy management systems (HEMS) aim to provide intelligent control of the thermal comfort inside smart buildings with the minimum energy cost, while satisfying the energy consumption requests and increasing the use of energy from renewable sources. The capabilities of these intelligent HEMS agents are restricted due to the personalized observability of the environment, resulting in limited knowledge gathering and potentially sub-optimal decisions. Furthermore, several buildings have recently been organized into small energy communities, with the ultimate goal of sharing intelligence between agents in federated learning schemes.In this context, we propose a personalized federated deep reinforcement learning method using Moreau envelopes (pFedMe) for joint energy cost and household comfort optimization in energy communities that consist of multiple smart homes. Specifically, a Twin-Delayed Deep Deterministic Policy Gradient (TD3) actor-critic model is introduced, dynamically observing the state of the smart home environment and suggesting control actions on the operation of the Energy Storage System and on the regulation of the indoor temperature. The TD3 actor-critic model leads to improved policy performance in the continuous control of these systems, mitigating the overestimation bias and improving the training stability of the intelligent agents. The efficiency of the proposed method is verified via simulations based on real data, achieving a beneficial trade-off between the energy cost and the thermal comfort compared to FedAvg and Fedprox baselines. The results show that the proposed pFedMe framework consistently outperforms FedAvg and FedProx in both convergence speed and overall reward, achieving an energy cost reduction of approximately 10% compared to the other schemes, while exhibiting marginal thermal comfort behavior.","url":"https://pubmed.ncbi.nlm.nih.gov/42197765/","authors":["Spantideas S","Giannopoulos A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 8","doi":"10.3390/s26102958","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42193136","name":"AI and Machine Learning for Proteomics-Driven Drug Discovery: Methods, Tools, and Best Practices.","source":"pubmed","abstract":"Proteomics has become central to pharmacological research by providing quantitative readouts of protein abundance, post-translational modifications, interactions, and spatial context. However, proteomic datasets are high-dimensional, heterogeneous, and frequently affected by missingness, batch effects, and limited cohort size. Artificial intelligence (AI) and machine learning (ML) can help convert these complex data into decision-relevant outputs for target identification, biomarker discovery, pharmacodynamic monitoring, and drug repurposing. This review critically compares supervised learning, ensemble methods, dimensionality reduction, clustering, deep learning, graph learning, survival modeling, causal inference, and calibration approaches in proteomics-driven drug discovery. We also summarize major software ecosystems for mass-spectrometry processing, targeted assays, spectrum prediction, phosphoproteomics, structure modeling, and reproducible workflows. Emphasis is placed on model selection, benchmarking, missing-data handling, batch correction, interpretability, uncertainty, experimental validation, and translational readiness. Finally, we highlight emerging directions, including contrastive learning, diffusion models, graph-based integration, and federated analytics.","url":"https://pubmed.ncbi.nlm.nih.gov/42193136/","authors":["Basak S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 20","doi":"10.3390/cimb48050532","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42193081","name":"Artificial Intelligence for Spatial Immunometabolic Analysis of the Tumor Microenvironment: Current Evidence and Future Directions.","source":"pubmed","abstract":"The tumor microenvironment [TME] is a dynamic ecosystem where spatial organization and metabolic reprogramming play a crucial role in immune response, tumor progression, and therapeutic response. Recent breakthroughs in spatial transcriptomics, metabolomics, and multiplexed imaging studies have shown that complex immunometabolic niches are involved in therapeutic resistance, including conventional and immunotherapeutic approaches. Artificial intelligence [AI] technology has been recognized as a revolutionary concept that allows the integration of complex data, thereby facilitating the scalable extraction of spatial, molecular, and cellular features from routine histopathology and multi-omics platforms. This review of the current evidence on AI-based spatial immunometabolic studies of the tumor microenvironment aims to provide a comprehensive overview of the current evidence, including AI-based spatial immunometabolic studies of the tumor mi-croenvironment, with special reference to digital pathology, spatial transcriptomics, and multimodal data fusion. The current challenges, including data heterogeneity, model interpretability, generalizability, and biological validation, will be discussed. The emerging trends in AI-based spatial immunometabolism, including multimodal foundation models, federated learning, and spatially resolved target discovery, will be discussed. AI-based spatial immunometabolism will be a cornerstone in precision oncology, with the potential to improve patient stratification, therapeutic approaches, and clinical translation.","url":"https://pubmed.ncbi.nlm.nih.gov/42193081/","authors":["Abdullah I","Khan SS","Khan S","Abou D","Khan J","Akil F","Farag N","Almilaibary A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 3","doi":"10.3390/cimb48050476","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42192906","name":"Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.","source":"pubmed","abstract":"Artificial intelligence (AI) is reshaping oncology at every stage of the cancer care pathway, from population-level screening through molecular diagnosis, treatment planning, and post-treatment surveillance. Despite an exponential growth in AI oncology publications exceeding 5000 peer-reviewed studies annually, a critical and persistent gap separates demonstrated algorithmic performance from genuine patient benefit. Most published evidence derives from retrospective, single-institution studies conducted in curated dataset environments that systematically differ from real-world clinical deployment conditions. This comprehensive review examines the translational maturity of AI applications across 18 major malignancies, providing an evidence-stratified, cross-cancer assessment of where AI has fulfilled, approaches, or remains far from fulfilling its transformative potential in oncological care.","url":"https://pubmed.ncbi.nlm.nih.gov/42192906/","authors":["Kuchana SK","Repalle UK","Alahari NV","Kondamuri M","Manduva SK","Vanguru RV","Gorle SA","Alahari SK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 10","doi":"10.3390/cancers18101543","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42192190","name":"An intelligent cloud firewall framework for multi-cloud security using lstm anomaly detection and federated learning.","source":"pubmed","abstract":"The evolving nature of the threat landscape against cloud services is outpacing the capabilities of traditional security measures. Current firewall implementations in cloud services may provide a foundational layer of security, but they have significant limitations regarding their ability to respond to recently identify zero day vulnerabilities and to protect sensitive information from potential attacks utilizing quantum computing, as well as validating audit logs in multi-tenanted, shared cloud service landscapes. In this research, we present an innovative integrated approach to addressing each of these limitations by combining AI driven Anomaly detection techniques with post quantum cryptography authentication, a Zero Trust Architecture (ZTA), and blockchain based audit logging. Our proposed AI enhanced cloud firewall uses a Long Short Term Memory (LSTM) deep learning model to analyze and classify traffic patterns across IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) environments and dynamically creates adaptive firewall policies with sub-second response times. Experimental testing on simulated cloud traffic sets demonstrated that our proposed framework achieved a detection rate of 94.7% and a False Positive Rate (FPR) of 2.1%, representing improvements of 24.7% and 12.9%, respectively, when compared to traditional rule-based firewalls. Additionally, the blockchain anchored audit logging mechanism will create tamper proof audit logs, and the post-quantum cryptography layer will prevent attacks using the CRYSTALS-Kyber and CRYSTALS-Dilithium algorithms. These test results demonstrate that our proposed framework is a scalable, resilient, and security hardened solution for future generations of cloud computing landscapes.","url":"https://pubmed.ncbi.nlm.nih.gov/42192190/","authors":["V A","S KSR"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 27","doi":"10.1038/s41598-026-53470-y","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42192001","name":"Sustainable smart sensing and AI-driven platforms for real-time detection and monitoring of mycotoxins across the food supply chain.","source":"pubmed","abstract":"This review aims to critically evaluate sustainable smart sensing technologies and AI-driven platforms for real-time mycotoxin detection, highlighting innovations, integration across the food supply chain, current limitations, and future directions for safer, data-driven food safety management. This systematic review followed PRISMA guidelines and covered studies published between 2015 and 2025. Literature searches were conducted in Scopus, Web of Science, PubMed, IEEE Xplore, and Google Scholar, yielding a total sample of 620 identified records. Peer-reviewed articles on smart sensors, biosensors, and AI-driven mycotoxin monitoring were included. After title, abstract, and full-text screening based on predefined eligibility criteria, approximately 160 studies were retained and formed the final sample for qualitative synthesis across the food supply chain. Sustainable smart sensing and AI-driven platforms are transforming real-time mycotoxin detection across the food supply chain by enabling rapid, sensitive, and decentralized monitoring from farm to fork. Emerging biosensors, optical sensors, and IoT-enabled devices integrated with machine learning improve early warning, traceability, and decision-making. However, key gaps remain, including limited sensor robustness under variable field conditions, high costs of advanced materials, energy demands, and scarcity of large, standardized datasets for AI training. Interoperability between sensing platforms and regulatory frameworks is also underdeveloped. Sustainability challenges involve balancing analytical performance with low-energy operation, sensor recyclability, and accessibility for low-resource settings. Future directions should prioritize biodegradable and reusable sensor materials, edge-AI and low-power electronics, federated data-sharing models, and climate-resilient deployment strategies. Integrating predictive analytics with risk assessment and policy alignment will be essential for scalable, sustainable mycotoxin management systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42192001/","authors":["Adeyeye BR","Adeyeye SAO"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 26","doi":"10.1007/s12550-026-00653-1","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42191856","name":"A novel intrusion detection system for IIoT in 5G networks using attention-augmented federated learning and lightweight transformer architectures.","source":"pubmed","abstract":"The swift proliferation of Industrial Internet of Things (IIoT) devices within 5G networks has intensified cybersecurity vulnerabilities owing to their limited processing capabilities and fluctuating network conditions. This work presents an innovative intrusion detection system (IDS) designed for IIoT within 5G environments, overcoming the shortcomings of conventional hybrid deep learning models. Our methodology presents an attention-enhanced federated learning framework integrating a lightweight CNN (MobileNetV3) for feature extraction with a Mini-Transformer for sequence modeling, to attain superior accuracy and computational efficiency. A feature selection technique based on mutual information, integrated with self-attention layers, prioritizes essential network traffic features (e.g., packet size, flow time) while discarding redundant ones, hence improving detection accuracy. The federated learning framework enables decentralized training among IIoT devices with limited resources, ensuring both scalability and the maintenance of user privacy. Comprehensive evaluations conducted on the Edge-IIoTset and ToN-IoT datasets indicate that the proposed methodology yields a binary classification accuracy of 98.5% on the Edge-IIoTset, exceeding the performance of a standalone MobileNetV3 model by 2.1% and surpassing baseline models such as LSTM and VGG-19 by margins ranging from 3.5% to 6.3% under the same training parameters. Additionally, with regards to the ToN-IoT dataset, the model achieves an F1-score of 0.985, marking improvements of 2.0% over MobileNetV3-SVM and 6.5% over LSTM baseline models.","url":"https://pubmed.ncbi.nlm.nih.gov/42191856/","authors":["Du J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 26","doi":"10.1038/s41598-026-54748-x","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42191756","name":"Self-defending 6G networks through AI-driven adaptive decoy generation at the edge.","source":"pubmed","abstract":"The advent of 6G networks is that , they promise to deliver high connectivity as well as a much larger attack surface, traditional security models are found to be wanting. In this paper, we propose an Adaptive Decoy Generation Framework that is able to deliver proactive, intelligent, as well as adaptive security solutions at the network edge. The proposed framework comprises three main intelligent components that include a Conditional Generative Adversarial Network (cGAN), a Reinforcement Learning (RL) agent that is based on Proximal Policy Optimization (PPO), as well as an adversarial feedback mechanism that allows the system to learn as well as adapt to new attacks. The simulation results using the CIC-IoT-2023 dataset showed that the proposed framework is able to deliver robust security solutions since it is able to achieve a 97.9% detection rate with a 1.3% false positive rate as well as a 15 ms detection latency on average. The system has a positive Adaptability Index (+5.2%), thereby demonstrating that it is able to defend against intelligent as well as learning-based attacks, thereby being more secure than traditional security models. The proposed framework is able to create a new paradigm in delivering intelligent as well as adaptive security solutions that will be able to cater to 6G networks.","url":"https://pubmed.ncbi.nlm.nih.gov/42191756/","authors":["M J RM","G PR","Gopal P","Sathyamoorthy M","Nagane AS","Keshava R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 26","doi":"10.1038/s41598-026-52368-z","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42187379","name":"Federated Multi-View Unsupervised Feature Selection via Bio-Inspired Hierarchical-Cognitive Tianji's Horse Racing Optimization and Tensor Learning.","source":"pubmed","abstract":"As multi-view datasets expand across diverse practical fields, feature selection (FS) has become an indispensable preparatory stage for machine learning models. Nevertheless, real-world multi-view data is often unlabeled and distributed among isolated clients, posing significant challenges to traditional centralized methods due to privacy concerns and communication constraints. Furthermore, existing centralized and federated approaches frequently suffer from entrapment in local optima and lack robust convergence guarantees. To address these issues, we propose Fed-MUFSHT, a federated framework for multi-view unsupervised FS (MUFS) that integrates tensor learning with a novel metaheuristic optimizer, Hierarchical-Cognitive Tianji's Horse Racing Optimization (HC-THRO). Within the federated learning paradigm, Fed-MUFSHT follows a dual-stage local optimization process. Stage 1 applies HC-THRO, which integrates Hierarchical Competitive Learning and Adaptive Cognitive Mapping to simulate multi-level strategic competition and cognitive adaptation among individuals. This design enhances global exploration, adaptive learning, and fine-grained feature selection in high-dimensional spaces. Stage 2 employs a TL module based on canonical polyadic (CP) decomposition to perform missing-view imputation and refine latent representation learning. At the global level, a privacy-preserving aggregation strategy based on Normalized Mutual Information (NMI) and feature weights enables efficient model coordination without exposing raw data. Comparative experiments on several public benchmark datasets reveal that Fed-MUFSHT maintains clear advantages over strong competing methods, showing better optimization results together with more dependable convergence characteristics. The overall evidence suggests that the proposed approach is both robust and effective for distributed optimization tasks involving privacy protection.","url":"https://pubmed.ncbi.nlm.nih.gov/42187379/","authors":["Cheng R","Sun Z","Qi K","Wu W","Xu L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/biomimetics11050312","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"pmid:42186087","name":"Construction of a sports bio mechanical injury prediction and AI warning system based on wearable sensors.","source":"pubmed","abstract":"Real-time warning and prevention of sports injuries are core challenges in the fields of sports medicine and health management. Traditional methods rely on single sensor data and static threshold rules, which have problems such as single monitoring dimension, high false alarm rate, and insufficient real-time performance, making it difficult to meet the precise prevention and control needs in complex motion scenes.","url":"https://pubmed.ncbi.nlm.nih.gov/42186087/","authors":["Feng Y","Ma Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 26","doi":"10.1186/s13102-026-01761-2","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42184901","name":"AI and longevity medicine: Unlocking predictive and preventive strategies for healthy aging.","source":"pubmed","abstract":"Longevity medicine is transforming healthcare by shifting the focus from disease treatment toward the preservation of function, resilience, and healthspan. In parallel, artificial intelligence (AI) has emerged as a powerful catalyst accelerating this transition through the integration and interpretation of multidimensional biological and behavioral data. AI-driven systems can now analyze genomics, epigenomics, proteomics, microbiome signatures, digital biomarkers, lifestyle metrics, and environmental exposures to identify early deviations from healthy aging trajectories before clinical disease manifests. These predictive capabilities enable personalized preventive strategies tailored to an individual's biological aging profile rather than chronological age alone. AI-supported longevity medicine therefore facilitates precision prevention through adaptive interventions involving nutrition, metabolic optimization, sleep regulation, stress management, continuous biosensing, and targeted therapeutics. Moreover, AI contributes to the evolution of healthcare systems from reactive episodic care toward adaptive and continuously monitored models emphasizing long-term physiological resilience. However, the integration of AI into longevity medicine also raises important scientific, ethical, and societal challenges, including data fragmentation, unequal access to preventive technologies, risks of overmedicalization, and concerns regarding privacy and governance. Bridging siloed biomarker ecosystems through interoperable data infrastructures, federated learning, and digital twin technologies will be essential for clinically meaningful predictive models. Ultimately, AI has the potential to redefine modern preventive medicine by enabling proactive, personalized, and age-resilient healthcare. The future success of AI-enhanced longevity medicine will depend on ensuring that technological innovation remains accurate, ethically grounded, clinically relevant, and equitably accessible across populations.","url":"https://pubmed.ncbi.nlm.nih.gov/42184901/","authors":["Green JB","Haykal D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 25","doi":"10.1016/j.lpm.2026.104359","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42178379","name":"A federated blockchain framework for secure and intelligent smart farming in sustainable industrial agriculture.","source":"pubmed","abstract":"The fast growth of smart farming technologies and Internet of Things (IoT) sensors has transformed the agricultural sector, but has brought some fundamental problems, such as data privacy, scalability, and trust in distributed systems. In this paper, AgriChain-FL, a federated blockchain architecture, is introduced and guarantees privacy-preserving, transparent, and energy-efficient collaboration in agricultural ecosystems. The framework integrates federated learning (FL) of decentralized model training with a hybrid Practical Byzantine Fault Tolerance-Proof of Authority (PBFT-PoA) blockchain to allow aggregating data safely, auditing, and participation through incentives. The proposed system was developed based on the SmartFarm Sensor Dataset. Models are trained individually at each local farm node. The hybrid consensus protocol is more scalable, latency-reduced, and uses less energy. The experimental results indicated that AgriChain-FL was more accurate (92.4%), had a throughput of 2350 TPS, and a block latency of 1.0&#xa0;s, which is also more favorable to analogous frameworks in learning and blockchain performance. Privacy leakage and energy cost were minimized by 73% and 45% respectively. AgriChain-FL is a successful balance between federated intelligence and blockchain trust that provides a privacy-conscious and sustainable smart farming platform. These findings confirm its possibilities as the basis of secure, decentralized, and energy-aware digital agricultural systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42178379/","authors":["Jaffar AY"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-54453-9","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.5281/zenodo.22175625","name":"Application of Artificial Intelligence Frameworks in Development of Medical Imaging Diagnosis Systems: A Comprehensive Review of Novel Methodologies, Clinical Validation, Performance Optimization, and Future Perspectives","source":"datacite","abstract":"Artificial intelligence (AI) has revolutionized medical imaging with automated disease detection, image segmentation, diagnosis, prognosis prediction, and clinical decision support across various imaging modalities, such as X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), retinal imaging, and digital pathology. Recent advances in deep learning, transformer architectures, multimodal learning and foundation models have significantly improved the diagnostic accuracy and reduced the reliance on handcrafted feature engineering. However, challenges like data heterogeneity, model interpretability, external validation, privacy preservation, computational efficiency, and regulatory compliance still hinder the widespread clinical implementation.In this paper, this review presents a comprehensive study on the evolution of modern AI frameworks in medical imaging by integrating recent methodological advances with perspectives on clinical translation. The review covers the latest deep learning architectures, such as convolutional neural networks, Vision Transformers, hybrid CNN–Transformer models, multimodal learning frameworks, generative artificial intelligence, diffusion models, federated learning, privacy-preserving learning, and medical foundation models. In addition, the review covers the cutting-edge explainable AI techniques, including Grad-CAM, SHAP, LIME, and attention visualization, for boosting transparency and clinician confidence. The review also discusses the state-of-the-art performance optimization strategies, including transfer learning, active learning, domain adaptation, neural architecture search, hyperparameter optimization, model compression, and computational resource optimization. Equally important, the latest developments in clinical validation, external evaluation, robustness assessment, fairness, uncertainty estimation, regulatory considerations, and deployment frameworks are critically analyzed to underscore their role in facilitating safe clinical implementation.The review analysis concludes with the identification of key research challenges and directions for the future including multimodal foundation models, vision-language systems, retrieval-augmented generation,","url":"https://doi.org/10.5281/zenodo.22175625","authors":["Susreeti Sur","Rakesh Kumar, Mandal","Debanil, Chanda"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22175625","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.21485662","name":"EELNet–ALOCO: Source Code and Three-Month IoT Dataset for Energy-Efficient Smart Building Management","source":"datacite","abstract":"This repository contains the source code and representative dataset for the EELNet–ALOCO smart-building energy control framework. The framework combines the Edge-Efficient Learning Network with Adaptive Localized Cooperative Optimization for decentralized edge-based control. It predicts building energy demand through occupancy temperature CO₂ illumination and electrical power measurements. The method supports adaptive heating ventilation air conditioning and lighting control without continuous cloud communication. EELNet extracts short-term sensor variations and long-term environmental patterns from multiple building zones. ALOCO performs localized model updates through cooperative learning among connected edge devices. Quantization pruning and knowledge distillation reduce computational requirements for deployment on resource-limited hardware. The implementation supports Raspberry Pi 5 and Jetson Orin Nano devices for real-time operation. The repository contains the complete implementation scripts configuration files trained model components and evaluation procedures. It also provides an approved three-month dataset collected through the IoT-based implementation at Anna University Regional Campus Madurai. The complete institutional dataset cannot be released publicly because university permission restricts full data distribution. The available dataset supports verification performance assessment and further academic research under comparable smart-building conditions. Experimental evaluation reports energy savings between 18% and 24% compared with conventional baseline controllers. Complete control-loop latency decreases from 2.77 ± 0.19 seconds to 1.44 ± 0.06 seconds. Thermal comfort remains within accepted limits during more than 96% of occupied building operation. These resources support reproducible evaluation of decentralized low-power and privacy-aware energy management in IoT-enabled smart buildings.","url":"https://doi.org/10.5281/zenodo.21485662","authors":["Merrisha, John","P, ARULMATHI"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21485662","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.21485663","name":"EELNet–ALOCO: Source Code and Three-Month IoT Dataset for Energy-Efficient Smart Building Management","source":"datacite","abstract":"This repository contains the source code and representative dataset for the EELNet–ALOCO smart-building energy control framework. The framework combines the Edge-Efficient Learning Network with Adaptive Localized Cooperative Optimization for decentralized edge-based control. It predicts building energy demand through occupancy temperature CO₂ illumination and electrical power measurements. The method supports adaptive heating ventilation air conditioning and lighting control without continuous cloud communication. EELNet extracts short-term sensor variations and long-term environmental patterns from multiple building zones. ALOCO performs localized model updates through cooperative learning among connected edge devices. Quantization pruning and knowledge distillation reduce computational requirements for deployment on resource-limited hardware. The implementation supports Raspberry Pi 5 and Jetson Orin Nano devices for real-time operation. The repository contains the complete implementation scripts configuration files trained model components and evaluation procedures. It also provides an approved three-month dataset collected through the IoT-based implementation at Anna University Regional Campus Madurai. The complete institutional dataset cannot be released publicly because university permission restricts full data distribution. The available dataset supports verification performance assessment and further academic research under comparable smart-building conditions. Experimental evaluation reports energy savings between 18% and 24% compared with conventional baseline controllers. Complete control-loop latency decreases from 2.77 ± 0.19 seconds to 1.44 ± 0.06 seconds. Thermal comfort remains within accepted limits during more than 96% of occupied building operation. These resources support reproducible evaluation of decentralized low-power and privacy-aware energy management in IoT-enabled smart buildings.","url":"https://doi.org/10.5281/zenodo.21485663","authors":["Merrisha, John","P, ARULMATHI"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21485663","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.21053046","name":"A Comparative Analysis of Artificial Intelligence and Business Intelligence using Big Data Analytics","source":"datacite","abstract":"This research presents a comparative analysis of Artificial Intelligence (AI) and Business Intelligence (BI) using Big Data Analytics. The study evaluates multiple machine learning algorithms, including Random Forest, Neural Networks, Support Vector Machines, Decision Trees, Logistic Regression, Naïve Bayes, AdaBoost, Gradient Boosting, and K-Nearest Neighbors, for talent recruitment and business intelligence applications. Experimental results demonstrate that Random Forest and Neural Networks provide the highest prediction accuracy, enabling organizations to improve recruitment decisions, operational efficiency, and data-driven business intelligence while highlighting future directions for explainable AI, federated learning, and real-time analytics.","url":"https://doi.org/10.5281/zenodo.21053046","authors":["vegineni, Gopi Chand","ADDANKI, SIREESHA","Mandal, Rishabh","K, Arun Kumar","Ellahi, Ehsan","Marella, Bhagath Chandra Chowdari"],"tags":["Artificial Intelligence","Business Intelligence","Big Data Analytics","Machine Learning","Random Forest","Neural Networks","Talent Recruitment","Predictive Analytics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.21053046","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.21053047","name":"A Comparative Analysis of Artificial Intelligence and Business Intelligence using Big Data Analytics","source":"datacite","abstract":"This research presents a comparative analysis of Artificial Intelligence (AI) and Business Intelligence (BI) using Big Data Analytics. The study evaluates multiple machine learning algorithms, including Random Forest, Neural Networks, Support Vector Machines, Decision Trees, Logistic Regression, Naïve Bayes, AdaBoost, Gradient Boosting, and K-Nearest Neighbors, for talent recruitment and business intelligence applications. Experimental results demonstrate that Random Forest and Neural Networks provide the highest prediction accuracy, enabling organizations to improve recruitment decisions, operational efficiency, and data-driven business intelligence while highlighting future directions for explainable AI, federated learning, and real-time analytics.","url":"https://doi.org/10.5281/zenodo.21053047","authors":["vegineni, Gopi Chand","ADDANKI, SIREESHA","Mandal, Rishabh","K, Arun Kumar","Ellahi, Ehsan","Marella, Bhagath Chandra Chowdari"],"tags":["Artificial Intelligence","Business Intelligence","Big Data Analytics","Machine Learning","Random Forest","Neural Networks","Talent Recruitment","Predictive Analytics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.21053047","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22165672","name":"A Quantum-Resilient Federated Learning Framework for Secure Smart Grid","source":"datacite","abstract":"Wide-area monitoring systems built on phasor measurement units (PMUs) underpin real-time stability assessment in transmission grids, yet their measurement and communication paths present an attack surface that conventional intrusion detection addresses only partially. Three constraints compound the problem: transmission operators are commercially and legally restricted from pooling raw measurements; the public-key cryptography protecting inter-operator links has a finite lifetime against quantum adversaries; and a collaboratively trained detector is itself a target for poisoning. This paper presents an integrated framework addressing all three within a single deployment model. Regional phasor data concentrators train a physics-informed spatio-temporal detector locally and exchange only model updates, which are encapsulated under ML-KEM-768, encrypted with AES-256-GCM, signed with ML-DSA-65, and committed to a SHA3-256 hash-chained consortium ledger under a Byzantine-tolerant validator quorum. The detector couples a graph attention network over the electrical topology with a temporal convolutional network over a two-second window, and is evaluated on 7.6 million synchrophasor measurements from a hardware-in-the-loop testbed on the IEEE 39-bus system. The admittance model underpinning the graph is validated against the solved base case to a mean bus-injection error of 1.24 MW on a 6,088 MW system, against 1,338 MW for a reduced model omitting transformer taps. Against a graph-free ablation the spatial branch reduces false alarms on undisturbed operation from 8.99% to 1.20% while raising macro-F1 from 0.893 to 0.968, and raises no false alarms on benign grid disturbances in held-out evaluation at reduced attack magnitudes. The post-quantum layer adds 109 ms per update, 0.342% of federated round time.","url":"https://doi.org/10.5281/zenodo.22165672","authors":["Tank, Divyam","Singh, Dr. Sushil Kumar"],"tags":["Post Quantum Encryption","Smart Grid","Blockchain","Synchrophasor","Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22165672","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22165673","name":"A Quantum-Resilient Federated Learning Framework for Secure Smart Grid","source":"datacite","abstract":"Wide-area monitoring systems built on phasor measurement units (PMUs) underpin real-time stability assessment in transmission grids, yet their measurement and communication paths present an attack surface that conventional intrusion detection addresses only partially. Three constraints compound the problem: transmission operators are commercially and legally restricted from pooling raw measurements; the public-key cryptography protecting inter-operator links has a finite lifetime against quantum adversaries; and a collaboratively trained detector is itself a target for poisoning. This paper presents an integrated framework addressing all three within a single deployment model. Regional phasor data concentrators train a physics-informed spatio-temporal detector locally and exchange only model updates, which are encapsulated under ML-KEM-768, encrypted with AES-256-GCM, signed with ML-DSA-65, and committed to a SHA3-256 hash-chained consortium ledger under a Byzantine-tolerant validator quorum. The detector couples a graph attention network over the electrical topology with a temporal convolutional network over a two-second window, and is evaluated on 7.6 million synchrophasor measurements from a hardware-in-the-loop testbed on the IEEE 39-bus system. The admittance model underpinning the graph is validated against the solved base case to a mean bus-injection error of 1.24 MW on a 6,088 MW system, against 1,338 MW for a reduced model omitting transformer taps. Against a graph-free ablation the spatial branch reduces false alarms on undisturbed operation from 8.99% to 1.20% while raising macro-F1 from 0.893 to 0.968, and raises no false alarms on benign grid disturbances in held-out evaluation at reduced attack magnitudes. The post-quantum layer adds 109 ms per update, 0.342% of federated round time.","url":"https://doi.org/10.5281/zenodo.22165673","authors":["Tank, Divyam","Singh, Dr. Sushil Kumar"],"tags":["Post Quantum Encryption","Smart Grid","Blockchain","Synchrophasor","Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22165673","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.19768534","name":"ML-Based Predictive Autoscaling on Aws","source":"datacite","abstract":"Traditional cloud auto-scaling systems rely on static threshold-based rules that react only after workload changes occur, leading to delayed responses, inefficient resource utilization, and increased latency. To address these limitations, this paper proposes a predictive auto-scaling system on AWS using machine learning and federated learning techniques. The system utilizes Long ShortTerm Memory (LSTM) models to analyze historical and real- time workload data, including CPU utilization, memory usage, and request rates collected through Amazon CloudWatch. Unlike centralized approaches, federated learning is employed to train models across distributed cloud nodes without sharing raw data, ensuring data privacy and reducing communication overhead. The locally trained models share updates that are aggregated to form a global predictive model capable of accurately forecasting future workload demands.Based on these predictions, the system proactively scales AWS EC2 instances using Auto Scaling Groups, enabling timely resource allocation before performance degradation occurs. This approach improves application responsiveness, reduces latency, optimizes resource utilization, and lowers operational costs. Overall, the proposed system provides a scalable, efficient, and privacy- preserving solution for intelligent cloud resource management.","url":"https://doi.org/10.5281/zenodo.19768534","authors":["Mr. B. Sundaresan, Ajith Kumar A, Jaikiran J, Mano M, Yaswanth Sai R  Department of Computer Science & Engineering Velammal Institute of Technology, Panchetti","DEPARTMENT OF ARTIFICIAL INTELLIGENCE AND DATA SCIENCE R.M.K. College of Engineering and Technology","MISSILE MAN SCIENTIFIC AND RESEARCH PUBLICATIONS"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19768534","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.19768535","name":"ML-Based Predictive Autoscaling on Aws","source":"datacite","abstract":"Traditional cloud auto-scaling systems rely on static threshold-based rules that react only after workload changes occur, leading to delayed responses, inefficient resource utilization, and increased latency. To address these limitations, this paper proposes a predictive auto-scaling system on AWS using machine learning and federated learning techniques. The system utilizes Long ShortTerm Memory (LSTM) models to analyze historical and real- time workload data, including CPU utilization, memory usage, and request rates collected through Amazon CloudWatch. Unlike centralized approaches, federated learning is employed to train models across distributed cloud nodes without sharing raw data, ensuring data privacy and reducing communication overhead. The locally trained models share updates that are aggregated to form a global predictive model capable of accurately forecasting future workload demands.Based on these predictions, the system proactively scales AWS EC2 instances using Auto Scaling Groups, enabling timely resource allocation before performance degradation occurs. This approach improves application responsiveness, reduces latency, optimizes resource utilization, and lowers operational costs. Overall, the proposed system provides a scalable, efficient, and privacy- preserving solution for intelligent cloud resource management.","url":"https://doi.org/10.5281/zenodo.19768535","authors":["Mr. B. Sundaresan, Ajith Kumar A, Jaikiran J, Mano M, Yaswanth Sai R  Department of Computer Science & Engineering Velammal Institute of Technology, Panchetti","DEPARTMENT OF ARTIFICIAL INTELLIGENCE AND DATA SCIENCE R.M.K. College of Engineering and Technology","MISSILE MAN SCIENTIFIC AND RESEARCH PUBLICATIONS"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19768535","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20813646","name":"Explainable Credit Card Fraud Detection Using LightGBM And SHAP-Guided Feature Selection: A Review","source":"datacite","abstract":"Digital payment ecosystems have expanded both the volume and complexity of transaction data, widening the opportunity for fraud while raising customer, operational, and regulatory expectations that automated decisions remain explainable. This review consolidates research on credit card fraud detection spanning rule-based systems, classical statistical learning, ensemble and gradient-boosted methods, deep learning architectures, and explainable artificial intelligence (XAI), and synthesises these strands into FraudDetectNet, a layered pipeline in which data preprocessing, feature reduction, classification, explanation generation, and monitoring are treated as interdependent rather than separable functions. Particular attention is given to Shapley Additive Explanations (SHAP), used here not only as a post-hoc diagnostic but as an upstream feature-selection mechanism that compresses high-dimensional transaction data while preserving discriminative power. The review also examines evaluation practice for severely imbalanced, temporally ordered fraud data, arguing for precision-recall and cost-sensitive measures over plain accuracy. Outcomes reported in the primary reference study underlying this review — feature-space reduction from 380 to 120 variables, 97.6% accuracy, 99.0% fraud-class recall, and training-time reduction from 185.3 to 54.1 seconds — are presented as evidence of feasibility rather than generalised guarantees, and extensions toward graph-based, federated, and continual learning are outlined.","url":"https://doi.org/10.5281/zenodo.20813646","authors":["Vijay Saini","Jitender Kumar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20813646","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.20813647","name":"Explainable Credit Card Fraud Detection Using LightGBM And SHAP-Guided Feature Selection: A Review","source":"datacite","abstract":"Digital payment ecosystems have expanded both the volume and complexity of transaction data, widening the opportunity for fraud while raising customer, operational, and regulatory expectations that automated decisions remain explainable. This review consolidates research on credit card fraud detection spanning rule-based systems, classical statistical learning, ensemble and gradient-boosted methods, deep learning architectures, and explainable artificial intelligence (XAI), and synthesises these strands into FraudDetectNet, a layered pipeline in which data preprocessing, feature reduction, classification, explanation generation, and monitoring are treated as interdependent rather than separable functions. Particular attention is given to Shapley Additive Explanations (SHAP), used here not only as a post-hoc diagnostic but as an upstream feature-selection mechanism that compresses high-dimensional transaction data while preserving discriminative power. The review also examines evaluation practice for severely imbalanced, temporally ordered fraud data, arguing for precision-recall and cost-sensitive measures over plain accuracy. Outcomes reported in the primary reference study underlying this review — feature-space reduction from 380 to 120 variables, 97.6% accuracy, 99.0% fraud-class recall, and training-time reduction from 185.3 to 54.1 seconds — are presented as evidence of feasibility rather than generalised guarantees, and extensions toward graph-based, federated, and continual learning are outlined.","url":"https://doi.org/10.5281/zenodo.20813647","authors":["Vijay Saini","Jitender Kumar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20813647","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.19511370","name":"A Quantum-Edge Deep Reinforcement Learning Framework For Adaptive And Privacy-Preserving Dynamic Pricing In E-commerce","source":"datacite","abstract":"The rapid rise of e-commerce platforms has created a need for complex pricing systems that react to market conditions in real-time to improve market share and customer satisfaction. In this paper, we present a new Edge-AI powered situational pricing optimization framework based on a Deep Reinforcement Learning (DRL) model, leveraging the low latency pricing decision-making capability of a distributed edge computing network. In our model, we use federated learning processes with multi-agent deep reinforcement learning to create hybrid pricing intelligence based on the ongoing analysis of patterns of customer behaviour, competitors and market volatility signals. Our framework offers a solution to the fundamental limitations of cloud-based traditional pricing systems (and understandings) in shipping complex processes to ultra-sophisticated AI pricing engines that function on lightweight AI models located at edge nodes in the network, improving latency from seconds to milliseconds. Our experimental validation based on real e-commerce data shows a 23.4% im-provement in revenue optimizations, 18.7% improvements in reduction for de-cision latency of price adjustments and a remarkable 31.2% increase in customer satisfaction metrics relative to the previous centralized mode (cloud-based). This system offers a decentralized framework that can scale globally to support multi-market e-commerce operations, while also improving data privacy and confidential processing in compliance with regulatory demands.","url":"https://doi.org/10.5281/zenodo.19511370","authors":["Mr. Akula Sri Naga Sai Veera Pawan Anirudh","Mrs. G Prameela"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19511370","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.19511371","name":"A Quantum-Edge Deep Reinforcement Learning Framework For Adaptive And Privacy-Preserving Dynamic Pricing In E-commerce","source":"datacite","abstract":"The rapid rise of e-commerce platforms has created a need for complex pricing systems that react to market conditions in real-time to improve market share and customer satisfaction. In this paper, we present a new Edge-AI powered situational pricing optimization framework based on a Deep Reinforcement Learning (DRL) model, leveraging the low latency pricing decision-making capability of a distributed edge computing network. In our model, we use federated learning processes with multi-agent deep reinforcement learning to create hybrid pricing intelligence based on the ongoing analysis of patterns of customer behaviour, competitors and market volatility signals. Our framework offers a solution to the fundamental limitations of cloud-based traditional pricing systems (and understandings) in shipping complex processes to ultra-sophisticated AI pricing engines that function on lightweight AI models located at edge nodes in the network, improving latency from seconds to milliseconds. Our experimental validation based on real e-commerce data shows a 23.4% im-provement in revenue optimizations, 18.7% improvements in reduction for de-cision latency of price adjustments and a remarkable 31.2% increase in customer satisfaction metrics relative to the previous centralized mode (cloud-based). This system offers a decentralized framework that can scale globally to support multi-market e-commerce operations, while also improving data privacy and confidential processing in compliance with regulatory demands.","url":"https://doi.org/10.5281/zenodo.19511371","authors":["Mr. Akula Sri Naga Sai Veera Pawan Anirudh","Mrs. G Prameela"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19511371","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22170148","name":"DIFFERENTIALLY PRIVATE FEDERATED LEARNING WITH BYZANTINE-ROBUST AGGREGATION: A CROSS-DOMAIN FRAMEWORK FOR SECURE MODEL TRAINING IN BANKING AND HEALTHCARE SYSTEMS","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.22170148","authors":["Srikumar Nayak"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.22170148","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22170149","name":"DIFFERENTIALLY PRIVATE FEDERATED LEARNING WITH BYZANTINE-ROBUST AGGREGATION: A CROSS-DOMAIN FRAMEWORK FOR SECURE MODEL TRAINING IN BANKING AND HEALTHCARE SYSTEMS","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.22170149","authors":["Srikumar Nayak"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.22170149","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20162355","name":"sofiyastephen1-sudo/Secure-FL-for-UAV: ResFL-UAV v1.0 — Initial Reproducible Release for Manuscript","source":"datacite","abstract":"ResFL-UAV v1.0 — Initial Reproducible Release for Manuscript This release corresponds to the experimental results reported in the manuscript titled \"Resource-Constrained Secure Federated Learning Framework for Unmanned Aerial Vehicles. The release includes: Complete source code for the ResFL-UAV framework Federated Learning training scripts SCAFFOLD implementation Differential Privacy configuration Gradient sparsification modules Secure aggregation components Dataset preprocessing utilities Experimental configuration files Random seeds used for evaluation Dependency and software version specifications Reproducibility instructions for all major experiments and tables This version is intended to provide a stable, publicly accessible, and fully reproducible artifact for peer review and future research.","url":"https://doi.org/10.5281/zenodo.20162355","authors":["sofiyastephen1-sudo"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20162355","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20162356","name":"sofiyastephen1-sudo/Secure-FL-for-UAV: ResFL-UAV v1.0 — Initial Reproducible Release for Manuscript","source":"datacite","abstract":"ResFL-UAV v1.0 — Initial Reproducible Release for Manuscript This release corresponds to the experimental results reported in the manuscript titled \"Resource-Constrained Secure Federated Learning Framework for Unmanned Aerial Vehicles. The release includes: Complete source code for the ResFL-UAV framework Federated Learning training scripts SCAFFOLD implementation Differential Privacy configuration Gradient sparsification modules Secure aggregation components Dataset preprocessing utilities Experimental configuration files Random seeds used for evaluation Dependency and software version specifications Reproducibility instructions for all major experiments and tables This version is intended to provide a stable, publicly accessible, and fully reproducible artifact for peer review and future research.","url":"https://doi.org/10.5281/zenodo.20162356","authors":["sofiyastephen1-sudo"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20162356","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.19639370","name":"randdwriting2026/FR-26-01: CryptographicFogServer","source":"datacite","abstract":"Cryptographic Fog-Server and Deep Federated Learning Architecture","url":"https://doi.org/10.5281/zenodo.19639370","authors":["randdwriting2026"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19639370","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.19639371","name":"randdwriting2026/FR-26-01: CryptographicFogServer","source":"datacite","abstract":"Cryptographic Fog-Server and Deep Federated Learning Architecture","url":"https://doi.org/10.5281/zenodo.19639371","authors":["randdwriting2026"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19639371","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.18859716","name":"Data and Code for MedSparseFL","source":"datacite","abstract":"Official source code for the proposed support-aware sparse federated learning framework. This implementation includes gradient score estimation, sparse communication, and robust aggregation mechanisms designed for heterogeneous (non-IID) environments.","url":"https://doi.org/10.5281/zenodo.18859716","authors":["Zhu, Bian","Niu, Ling"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18859716","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.18860743","name":"Data and Code for MedSparseFL","source":"datacite","abstract":"Official source code for the proposed support-aware sparse federated learning framework. This implementation includes gradient score estimation, sparse communication, and robust aggregation mechanisms designed for heterogeneous (non-IID) environments.","url":"https://doi.org/10.5281/zenodo.18860743","authors":["Zhu, Bian","Niu, Ling"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18860743","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.19642969","name":"Global Legal Architecture for Foundation Model-Driven Multimodal Signal Processing: Integrating Federated Self-Supervised Learning, Cross-Border Data Sovereignty, and Algorithmic Liability into Transnational Regulatory Compliance Frameworks  Prepared and Authored by Dr. Mohamed Kamal Arafa El-Rakhawi International Law & Emerging Technologies Governance","source":"datacite","abstract":"Global Legal Architecture for Foundation Model-Driven Multimodal Signal Processing: Integrating Federated Self-Supervised Learning, Cross-Border Data Sovereignty, and Algorithmic Liability into Transnational Regulatory Compliance Frameworks Prepared and Authored by Dr. Mohamed Kamal Arafa El-Rakhawi International Law & Emerging Technologies Governance","url":"https://doi.org/10.5281/zenodo.19642969","authors":["elrakhawi, mohamed kamal arafa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19642969","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.19642970","name":"Global Legal Architecture for Foundation Model-Driven Multimodal Signal Processing: Integrating Federated Self-Supervised Learning, Cross-Border Data Sovereignty, and Algorithmic Liability into Transnational Regulatory Compliance Frameworks  Prepared and Authored by Dr. Mohamed Kamal Arafa El-Rakhawi International Law & Emerging Technologies Governance","source":"datacite","abstract":"Global Legal Architecture for Foundation Model-Driven Multimodal Signal Processing: Integrating Federated Self-Supervised Learning, Cross-Border Data Sovereignty, and Algorithmic Liability into Transnational Regulatory Compliance Frameworks Prepared and Authored by Dr. Mohamed Kamal Arafa El-Rakhawi International Law & Emerging Technologies Governance","url":"https://doi.org/10.5281/zenodo.19642970","authors":["elrakhawi, mohamed kamal arafa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19642970","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22168859","name":"DEx-FCL: Reproducibility package for adaptive Edge-IoT security management","source":"datacite","abstract":"Reproducibility package update for the manuscript \"DEx-FCL: Drift- and Explanation-Consistent Federated Continual Learning for Adaptive Edge-IoT Security Management.\" Version 1.0.1 adds the complete real-benchmark result CSVs and generated manuscript figures under the results/ directory. These files were previously present locally but excluded from the public repository by the .gitignore configuration. This release includes the DEx-FCL source code, deterministic preprocessing and federated partitioning utilities, fixed five-seed configurations, CICIoT2023 and Edge-IIoTset experimental outputs, continual-learning results, genuine leave-one-family-out zero-day results, few-shot assimilation results, drift and explanation analyses, ablation outputs, communication accounting, and manuscript figures. No reported experimental values, model methodology, dataset protocol, or scientific conclusions have been changed in this release. Raw benchmark datasets are not redistributed and must be obtained from their original providers.","url":"https://doi.org/10.5281/zenodo.22168859","authors":["P, Arul Selvam"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22168859","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22163186","name":"DEx-FCL: Reproducibility package for adaptive Edge-IoT security management","source":"datacite","abstract":"Reproducibility package update for the manuscript \"DEx-FCL: Drift- and Explanation-Consistent Federated Continual Learning for Adaptive Edge-IoT Security Management.\" Version 1.0.1 adds the complete real-benchmark result CSVs and generated manuscript figures under the results/ directory. These files were previously present locally but excluded from the public repository by the .gitignore configuration. This release includes the DEx-FCL source code, deterministic preprocessing and federated partitioning utilities, fixed five-seed configurations, CICIoT2023 and Edge-IIoTset experimental outputs, continual-learning results, genuine leave-one-family-out zero-day results, few-shot assimilation results, drift and explanation analyses, ablation outputs, communication accounting, and manuscript figures. No reported experimental values, model methodology, dataset protocol, or scientific conclusions have been changed in this release. Raw benchmark datasets are not redistributed and must be obtained from their original providers.","url":"https://doi.org/10.5281/zenodo.22163186","authors":["P, Arul Selvam"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22163186","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.18413603","name":"Secure Federated Learning for High-Dimensional Healthcare Data Using Differential Privacy","source":"datacite","abstract":"Machine learning algorithms may be trained on decentralized data using Federated Learning (FL) when sharing raw data is not possible owing to privacy concerns. One example of this kind of data is EHRs, or electronic health records, which store private information about patients. Instead of sharing sensitive data, FL trains models locally and then aggregate their parameters on a central server. An effective method for training Machine Learning (ML) algorithms on distributed datasets when data owners are governed by restrictions that limit the sharing of raw data is Federated Learning (FL). There is less need to communicate raw data with people outside the premises with this strategy, which involves local training and model aggregation to a central server. Nevertheless, FL brings up valid issues around privacy. For that reason, we need more privacy safeguards. One state-of-the-art privacy technique is the differential privacy (DP) approach, which involves adding an extra layer of privacy by perturbing the local models before transmission. But this method could change the framework's usefulness. In order to strike a fair balance between privacy and usefulness, we employ a private method to clean raw data by combining DP noise with a top-down taxonomy tree. To train local models that may be shared in the FL architecture, the generalized data is utilized in conjunction with DP noise. The suggested architecture improves functionality while keeping the privacy budget low.","url":"https://doi.org/10.5281/zenodo.18413603","authors":["Vishal Trivedi","Dr. Sunil Bhutoda"],"tags":["Privacy Preservation, Federated Learning, Machine Learning, algorithms and architecture"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18413603","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.18413604","name":"Secure Federated Learning for High-Dimensional Healthcare Data Using Differential Privacy","source":"datacite","abstract":"Machine learning algorithms may be trained on decentralized data using Federated Learning (FL) when sharing raw data is not possible owing to privacy concerns. One example of this kind of data is EHRs, or electronic health records, which store private information about patients. Instead of sharing sensitive data, FL trains models locally and then aggregate their parameters on a central server. An effective method for training Machine Learning (ML) algorithms on distributed datasets when data owners are governed by restrictions that limit the sharing of raw data is Federated Learning (FL). There is less need to communicate raw data with people outside the premises with this strategy, which involves local training and model aggregation to a central server. Nevertheless, FL brings up valid issues around privacy. For that reason, we need more privacy safeguards. One state-of-the-art privacy technique is the differential privacy (DP) approach, which involves adding an extra layer of privacy by perturbing the local models before transmission. But this method could change the framework's usefulness. In order to strike a fair balance between privacy and usefulness, we employ a private method to clean raw data by combining DP noise with a top-down taxonomy tree. To train local models that may be shared in the FL architecture, the generalized data is utilized in conjunction with DP noise. The suggested architecture improves functionality while keeping the privacy budget low.","url":"https://doi.org/10.5281/zenodo.18413604","authors":["Vishal Trivedi","Dr. Sunil Bhutoda"],"tags":["Privacy Preservation, Federated Learning, Machine Learning, algorithms and architecture"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18413604","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21406009","name":"ЭВОЛЮЦИЯ И ПЕРСПЕКТИВЫ ГЛУБОКОГО ОБУЧЕНИЯ:  СОВРЕМЕННЫЕ ТЕНДЕНЦИИ И НАПРАВЛЕНИЯ РАЗВИТИЯ","source":"datacite","abstract":"В данной статье рассматривается эволюция и современные тенденции развития глубокого обучения, одной из ключевых технологий искусственного интеллекта (ИИ). Анализируются исторические этапы развития глубоких нейронных сетей, начиная с первых моделей персептронов и заканчивая современными трансформерами и генеративными моделями. Рассматриваются ключевые достижения, такие как появление сверточных нейронных сетей, развитие генеративных состязательных сетей (GAN) и внедрение моделей трансформеров, включая GPT и BERT. Также обсуждаются актуальные вызовы, связанные с вычислительными мощностями, интерпретируемостью моделей, необходимостью больших объемов данных и этическими аспектами. В заключении описываются перспективные направления развития, такие как квантовые вычисления, федеративное обучение и интерпретируемый ИИ.","url":"https://doi.org/10.5281/zenodo.21406009","authors":["Ахмедов Бехруз Иброхим угли"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21406009","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.21406010","name":"ЭВОЛЮЦИЯ И ПЕРСПЕКТИВЫ ГЛУБОКОГО ОБУЧЕНИЯ:  СОВРЕМЕННЫЕ ТЕНДЕНЦИИ И НАПРАВЛЕНИЯ РАЗВИТИЯ","source":"datacite","abstract":"В данной статье рассматривается эволюция и современные тенденции развития глубокого обучения, одной из ключевых технологий искусственного интеллекта (ИИ). Анализируются исторические этапы развития глубоких нейронных сетей, начиная с первых моделей персептронов и заканчивая современными трансформерами и генеративными моделями. Рассматриваются ключевые достижения, такие как появление сверточных нейронных сетей, развитие генеративных состязательных сетей (GAN) и внедрение моделей трансформеров, включая GPT и BERT. Также обсуждаются актуальные вызовы, связанные с вычислительными мощностями, интерпретируемостью моделей, необходимостью больших объемов данных и этическими аспектами. В заключении описываются перспективные направления развития, такие как квантовые вычисления, федеративное обучение и интерпретируемый ИИ.","url":"https://doi.org/10.5281/zenodo.21406010","authors":["Ахмедов Бехруз Иброхим угли"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21406010","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.21944369","name":"SATP-FL: Lightweight Secure Aggregation via Trajectory Prediction in Vehicular Federated Learning","source":"datacite","abstract":"Federated learning in vehicular networks faces critical challenges from high mobility, key agreement latency, and frequent secure aggregation failures. Existing protocols cannot effectively accommodate frequent vehicle departures, leading to severely constrained communication windows and degraded aggregation efficiency, which limits both privacy protection and practical deployment. To address these issues, we propose a trajectory-predicted key pre-distribution mechanism (TPKD) that shifts the key agreement process from real-time negotiation to predictive pre-deployment, thereby eliminating handshake delays in highly dynamic environments. A Spatial-Temporal Graph Attention Network (ST-GAT) is designed to accurately identify the exact next-hop RSU within the current communication slot, enabling precise single-point key pre-distribution. Furthermore, we introduce a Trajectory-Predicted Embedded Verifiable Aggregation (TPEVA) scheme. Unlike conventional approaches that treat key distribution and verifiability as separate problems consuming additional communication and computation resources, TPEVA exploits the deterministic derivation property of key pre-distribution to simultaneously accomplish mask generation, commitment construction, and verification anchor establishment without extra communication rounds. ST-GAT prediction confidence is leveraged to adaptively schedule verification resources, achieving a higher-accuracy, lighter-verification balance. Theoretical analysis and comprehensive experiments demonstrate that the proposed scheme outperforms existing approaches in both security and efficiency.","url":"https://doi.org/10.5281/zenodo.21944369","authors":["Xion, Peng","Ding, Chunfa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21944369","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.21831858","name":"SATP-FL: Lightweight Secure Aggregation via Trajectory Prediction in Vehicular Federated Learning","source":"datacite","abstract":"Federated learning in vehicular networks faces critical challenges from high mobility, key agreement latency, and frequent secure aggregation failures. Existing protocols cannot effectively accommodate frequent vehicle departures, leading to severely constrained communication windows and degraded aggregation efficiency, which limits both privacy protection and practical deployment. To address these issues, we propose a trajectory-predicted key pre-distribution mechanism (TPKD) that shifts the key agreement process from real-time negotiation to predictive pre-deployment, thereby eliminating handshake delays in highly dynamic environments. A Spatial-Temporal Graph Attention Network (ST-GAT) is designed to accurately identify the exact next-hop RSU within the current communication slot, enabling precise single-point key pre-distribution. Furthermore, we introduce a Trajectory-Predicted Embedded Verifiable Aggregation (TPEVA) scheme. Unlike conventional approaches that treat key distribution and verifiability as separate problems consuming additional communication and computation resources, TPEVA exploits the deterministic derivation property of key pre-distribution to simultaneously accomplish mask generation, commitment construction, and verification anchor establishment without extra communication rounds. ST-GAT prediction confidence is leveraged to adaptively schedule verification resources, achieving a higher-accuracy, lighter-verification balance. Theoretical analysis and comprehensive experiments demonstrate that the proposed scheme outperforms existing approaches in both security and efficiency.","url":"https://doi.org/10.5281/zenodo.21831858","authors":["Xion, Peng","Ding, Chunfa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21831858","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.21803858","name":"Federated Foundation Models and Multi-Agent Orchestration: Enabling Autonomous Decision Intelligence for Enterprise AI Systems and Adaptive Governance","source":"datacite","abstract":"The rapid maturation of large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent orchestration frameworks has catalyzed a new generation of autonomous decision-support systems capable of reasoning over heterogeneous enterprise data, invoking external tools, and coordinating multi-step workflows with minimal human supervision. This paper investigates the architecture, performance characteristics, and deployment challenges of federated, multi-agent foundation-model systems designed to support autonomous decision intelligence in financial, customer-service, and clinical-support environments. We examine how the combination of lightweight distilled small language models (SLMs), retrieval-grounded reasoning, and cross-organizational federated fine-tuning enables enterprises to deploy capable AI agents without centralizing sensitive data or incurring unsustainable inference cost. A novel three-tier reference architecture — comprising a Model Tier, an Agent Tier, and a Governance Tier — is proposed to unify context acquisition, distributed reasoning, multi-agent coordination, and cross-organizational policy compliance within a single framework, designated the Federated Orchestration for Reasoning, Governance and Execution (FORGE) framework. The Model Tier employs distilled small language models and semantic context-compression pipelines to achieve sub-300 ms local inference latency on commodity inference hardware. The Agent Tier hosts a multi-agent orchestration engine coordinated by a cost-aware task scheduler that dynamically routes sub-tasks between local SLMs and larger upstream foundation models based on task complexity and real-time budget constraints. The Governance Tier provides federated fine-tuning, policy synchronization, and cross-domain compliance auditing consistent with emerging AI-governance regulation. Experimental evaluations conducted on representative workloads — spanning financial risk analysis, customer-service automation, and clinical decision support — demonstrate that the proposed architecture achieves up to 52% reduction in end-to-end decision latency, 38% improvement in agent resource utilization, and 29% reduction in inference token cost compared to monolithic, single-model deployments. The framework sustains near-linear horizontal scalability up to 5,000 concurrent agent sessions, and mean time to service restoration following node failure is reduced to 4.3 seconds through integrated state replication and rapid failover protocols. A federated fine-tuning extension enables privacy-preserving model adaptation across heterogeneous organizational datasets, achieving accuracy within 4% of centralized training baselines while satisfying differential-privacy guarantees. Our findings indicate that a unified framework co-designing model efficiency, agent coordination, and governance constraints is essential for the next generation of trustworthy, autonomous enterprise AI systems.","url":"https://doi.org/10.5281/zenodo.21803858","authors":["Yuanyuan, Wu","Ruxing Wang","Lijuan Guo"],"tags":["Large Language Models; Multi-Agent Systems; Retrieval-Augmented Generation; Federated Learning; Foundation Models; Enterprise AI; Autonomous Agents; AI Governance; Model Orchestration; Differential Privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21803858","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21803859","name":"Federated Foundation Models and Multi-Agent Orchestration: Enabling Autonomous Decision Intelligence for Enterprise AI Systems and Adaptive Governance","source":"datacite","abstract":"The rapid maturation of large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent orchestration frameworks has catalyzed a new generation of autonomous decision-support systems capable of reasoning over heterogeneous enterprise data, invoking external tools, and coordinating multi-step workflows with minimal human supervision. This paper investigates the architecture, performance characteristics, and deployment challenges of federated, multi-agent foundation-model systems designed to support autonomous decision intelligence in financial, customer-service, and clinical-support environments. We examine how the combination of lightweight distilled small language models (SLMs), retrieval-grounded reasoning, and cross-organizational federated fine-tuning enables enterprises to deploy capable AI agents without centralizing sensitive data or incurring unsustainable inference cost. A novel three-tier reference architecture — comprising a Model Tier, an Agent Tier, and a Governance Tier — is proposed to unify context acquisition, distributed reasoning, multi-agent coordination, and cross-organizational policy compliance within a single framework, designated the Federated Orchestration for Reasoning, Governance and Execution (FORGE) framework. The Model Tier employs distilled small language models and semantic context-compression pipelines to achieve sub-300 ms local inference latency on commodity inference hardware. The Agent Tier hosts a multi-agent orchestration engine coordinated by a cost-aware task scheduler that dynamically routes sub-tasks between local SLMs and larger upstream foundation models based on task complexity and real-time budget constraints. The Governance Tier provides federated fine-tuning, policy synchronization, and cross-domain compliance auditing consistent with emerging AI-governance regulation. Experimental evaluations conducted on representative workloads — spanning financial risk analysis, customer-service automation, and clinical decision support — demonstrate that the proposed architecture achieves up to 52% reduction in end-to-end decision latency, 38% improvement in agent resource utilization, and 29% reduction in inference token cost compared to monolithic, single-model deployments. The framework sustains near-linear horizontal scalability up to 5,000 concurrent agent sessions, and mean time to service restoration following node failure is reduced to 4.3 seconds through integrated state replication and rapid failover protocols. A federated fine-tuning extension enables privacy-preserving model adaptation across heterogeneous organizational datasets, achieving accuracy within 4% of centralized training baselines while satisfying differential-privacy guarantees. Our findings indicate that a unified framework co-designing model efficiency, agent coordination, and governance constraints is essential for the next generation of trustworthy, autonomous enterprise AI systems.","url":"https://doi.org/10.5281/zenodo.21803859","authors":["Yuanyuan, Wu","Ruxing Wang","Lijuan Guo"],"tags":["Large Language Models; Multi-Agent Systems; Retrieval-Augmented Generation; Federated Learning; Foundation Models; Enterprise AI; Autonomous Agents; AI Governance; Model Orchestration; Differential Privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21803859","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20130383","name":"Edge Intelligence and 5G Networks: Enabling Smart IoT Systems for Real-Time Automation and Adaptive Control","source":"datacite","abstract":"The rapid convergence of edge computing, fifth-generation (5G) wireless networks, and the Internet of Things (IoT) has catalyzed a new generation of intelligent, distributed automation systems capable of processing vast volumes of sensor data at or near the source. This paper investigates the architecture, performance characteristics, and deployment challenges of edge-native intelligence frameworks designed to support real-time IoT workloads in industrial, urban, and healthcare environments. We explore how the ultra-low latency and massive device connectivity provided by 5G networks enable edge computing nodes to execute time-critical inference and control tasks that were previously confined to centralized cloud data centers. A novel three-tier reference architecture — comprising a Device Tier, an Edge Tier, and an Orchestration Tier — is proposed to unify data acquisition, on-device inference, hierarchical coordination, and cloud-based analytics within a single framework, designated the Edge-Intelligence and Smart Automation (EISA) framework. The Device Tier employs lightweight TinyML models and event-driven data pipelines to achieve sub-10 ms local inference latency on resource-constrained microcontrollers. The Edge Tier hosts multi-model serving backends coordinated by an adaptive task scheduler that dynamically balances computation between edge nodes and upstream cloud resources based on real-time load estimates. The Orchestration Tier provides federated model training, global policy synchronization, and cross-domain resource governance compliant with emerging IoT data-sovereignty regulations. Experimental evaluations conducted on representative workloads — spanning predictive maintenance in industrial IoT, intelligent traffic management in smart cities, and patient vital-sign monitoring in connected healthcare — demonstrate that the proposed architecture achieves up to 47% reduction in end-to-end response latency, 41% improvement in edge node compute utilization, and 34% reduction in cellular backhaul bandwidth consumption compared to cloud-centric deployments. The framework sustains near-linear horizontal scalability up to 10,000 concurrent IoT endpoints, and mean time to service restoration following node failure is reduced to 6.1 seconds through integrated state replication and rapid failover protocols. A federated learning extension enables privacy-preserving model refinement across heterogeneous device populations, achieving convergence performance within 5% of centralized training baselines while satisfying differential-privacy guarantees. Our findings indicate that a unified framework co-designing edge hardware constraints, 5G network capabilities, and IoT application semantics is essential for the next generation of intelligent, autonomous cyber-physical systems.","url":"https://doi.org/10.5281/zenodo.20130383","authors":["Yuanyuan, Wu","Ruxing Wang","Lijuan Guo"],"tags":["Edge Computing; 5G Networks; Internet of Things; TinyML; Smart Automation; Federated Learning; Real-Time Control; Cyber-Physical Systems; Predictive Maintenance; Adaptive Scheduling"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20130383","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20130384","name":"Edge Intelligence and 5G Networks: Enabling Smart IoT Systems for Real-Time Automation and Adaptive Control","source":"datacite","abstract":"The rapid convergence of edge computing, fifth-generation (5G) wireless networks, and the Internet of Things (IoT) has catalyzed a new generation of intelligent, distributed automation systems capable of processing vast volumes of sensor data at or near the source. This paper investigates the architecture, performance characteristics, and deployment challenges of edge-native intelligence frameworks designed to support real-time IoT workloads in industrial, urban, and healthcare environments. We explore how the ultra-low latency and massive device connectivity provided by 5G networks enable edge computing nodes to execute time-critical inference and control tasks that were previously confined to centralized cloud data centers. A novel three-tier reference architecture — comprising a Device Tier, an Edge Tier, and an Orchestration Tier — is proposed to unify data acquisition, on-device inference, hierarchical coordination, and cloud-based analytics within a single framework, designated the Edge-Intelligence and Smart Automation (EISA) framework. The Device Tier employs lightweight TinyML models and event-driven data pipelines to achieve sub-10 ms local inference latency on resource-constrained microcontrollers. The Edge Tier hosts multi-model serving backends coordinated by an adaptive task scheduler that dynamically balances computation between edge nodes and upstream cloud resources based on real-time load estimates. The Orchestration Tier provides federated model training, global policy synchronization, and cross-domain resource governance compliant with emerging IoT data-sovereignty regulations. Experimental evaluations conducted on representative workloads — spanning predictive maintenance in industrial IoT, intelligent traffic management in smart cities, and patient vital-sign monitoring in connected healthcare — demonstrate that the proposed architecture achieves up to 47% reduction in end-to-end response latency, 41% improvement in edge node compute utilization, and 34% reduction in cellular backhaul bandwidth consumption compared to cloud-centric deployments. The framework sustains near-linear horizontal scalability up to 10,000 concurrent IoT endpoints, and mean time to service restoration following node failure is reduced to 6.1 seconds through integrated state replication and rapid failover protocols. A federated learning extension enables privacy-preserving model refinement across heterogeneous device populations, achieving convergence performance within 5% of centralized training baselines while satisfying differential-privacy guarantees. Our findings indicate that a unified framework co-designing edge hardware constraints, 5G network capabilities, and IoT application semantics is essential for the next generation of intelligent, autonomous cyber-physical systems.","url":"https://doi.org/10.5281/zenodo.20130384","authors":["Yuanyuan, Wu","Ruxing Wang","Lijuan Guo"],"tags":["Edge Computing; 5G Networks; Internet of Things; TinyML; Smart Automation; Federated Learning; Real-Time Control; Cyber-Physical Systems; Predictive Maintenance; Adaptive Scheduling"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20130384","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.17788201","name":"[Data] Federated Learning for Self-Supervised Acoustic Intelligence in LPBF: Physics-Guided Learnable-QSincNet for Cross-Domain Representation with Causally Explainable Spectral Insights","source":"datacite","abstract":"Airborne acoustic emission (AE) forms a rich component of Laser Powder Bed Fusion (LPBF) process emissions and offers a high-value channel for in-situ process monitoring; however, leveraging this information for industrial deployment remains challenging due to cross-domain variability in process conditions, strict data-governance constraints, and the absence of physics-guided representation learning. This work introduces a federated learning framework for self-supervised acoustic intelligence in LPBF, enabling distributed model training across heterogeneous data domains without sharing raw sensor signals. At the core of the method is a Physics-Guided Learnable-Q SincNet, a learnable front-end that adaptively discovers defect-sensitive spectral bands while enforcing physically meaningful frequency parametrization. Combined with a joint inter–intra self-supervised objective, the framework learns cross-domain representations that remain robust across diverse LPBF datasets reflecting variations in defect regimes, melt-pool dynamics, and spectral characteristics. To ensure interpretability, we develop a causally explainable spectral analysis pipeline, revealing how specific learned frequency bands contribute to identifying lack-of-fusion, conduction-mode, and keyhole regimes. The resulting causal spectral pathways highlight physically interpretable links between AE signatures and melt-pool stability, offering insight into the acoustic mechanisms underlying defect formation. Experimental results demonstrate strong cross-domain generalization, coherent latent clustering, and enhanced monitoring performance—all achieved without centralized data aggregation. Overall, this work provides a privacy-preserving, physics-informed, and causally transparent approach for next-generation in-situ acoustic monitoring in LPBF, paving the way toward scalable intelligent manufacturing systems","url":"https://doi.org/10.5281/zenodo.17788201","authors":["Pandiyan, Vigneashwara Pandiyan","Wrobel, Rafal","shevchik, sergey","Leinenbach, Christian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17788201","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.21284176","name":"Foundation Models for Automated Radiology Report Generation in Oncology Imaging","source":"datacite","abstract":"Radiology reports serve as the primary communication medium between radiologists and referring clinicians, providing critical information for diagnosis, treatment planning, and disease monitoring. In oncology imaging, the growing volume and complexity of radiological examinations have increased the demand for efficient and accurate reporting systems. Recent advances in artificial intelligence (AI), particularly foundation models, have transformed the landscape of automated radiology report generation. Foundation models are large-scale pretrained neural networks capable of learning generalized representations from vast multimodal datasets and adapting to diverse downstream tasks. Their integration with medical imaging and natural language processing has enabled the development of sophisticated systems capable of generating clinically meaningful radiology reports. This review examines the evolution of automated radiology report generation from traditional rule-based approaches and convolutional neural network (CNN)-based architectures to transformer-based foundation models and multimodal large language models (LLMs). Particular emphasis is placed on oncology imaging applications, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and hybrid imaging modalities. The review discusses major foundation models, datasets, benchmarking methodologies, clinical applications, advantages, and limitations. Furthermore, ethical, regulatory, and privacy concerns associated with clinical deployment are explored. Current evidence suggests that foundation models significantly improve report quality, contextual understanding, and generalizability compared with earlier approaches. However, challenges related to hallucination, explainability, data heterogeneity, and clinical validation remain substantial barriers to widespread adoption. Future research should focus on domain-specific multimodal foundation models, federated learning frameworks, explainable AI mechanisms, and prospective clinical evaluation studies. Foundation models have the potential to reshape radiology workflows by enhancing efficiency, consistency, and diagnostic support in oncology imaging.","url":"https://doi.org/10.5281/zenodo.21284176","authors":["Sneha Waghmare*1, Meera Nair2, Arjun Verma3, Shatrughna Nagrik4"],"tags":["Foundation models; Radiology report generation; Oncology imaging; Large language models; Vision-language models; Artificial intelligence; Medical imaging"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21284176","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.21284177","name":"Foundation Models for Automated Radiology Report Generation in Oncology Imaging","source":"datacite","abstract":"Radiology reports serve as the primary communication medium between radiologists and referring clinicians, providing critical information for diagnosis, treatment planning, and disease monitoring. In oncology imaging, the growing volume and complexity of radiological examinations have increased the demand for efficient and accurate reporting systems. Recent advances in artificial intelligence (AI), particularly foundation models, have transformed the landscape of automated radiology report generation. Foundation models are large-scale pretrained neural networks capable of learning generalized representations from vast multimodal datasets and adapting to diverse downstream tasks. Their integration with medical imaging and natural language processing has enabled the development of sophisticated systems capable of generating clinically meaningful radiology reports. This review examines the evolution of automated radiology report generation from traditional rule-based approaches and convolutional neural network (CNN)-based architectures to transformer-based foundation models and multimodal large language models (LLMs). Particular emphasis is placed on oncology imaging applications, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and hybrid imaging modalities. The review discusses major foundation models, datasets, benchmarking methodologies, clinical applications, advantages, and limitations. Furthermore, ethical, regulatory, and privacy concerns associated with clinical deployment are explored. Current evidence suggests that foundation models significantly improve report quality, contextual understanding, and generalizability compared with earlier approaches. However, challenges related to hallucination, explainability, data heterogeneity, and clinical validation remain substantial barriers to widespread adoption. Future research should focus on domain-specific multimodal foundation models, federated learning frameworks, explainable AI mechanisms, and prospective clinical evaluation studies. Foundation models have the potential to reshape radiology workflows by enhancing efficiency, consistency, and diagnostic support in oncology imaging.","url":"https://doi.org/10.5281/zenodo.21284177","authors":["Sneha Waghmare*1, Meera Nair2, Arjun Verma3, Shatrughna Nagrik4"],"tags":["Foundation models; Radiology report generation; Oncology imaging; Large language models; Vision-language models; Artificial intelligence; Medical imaging"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21284177","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.26240/heal.ntua.33336","name":"Υλοποίηση εφαρμογής ανίχνευσης κυβερνοεπιθέσεων σε ασύρματα δίκτυα 5G/6G με χρήση αποκεντρωμένων LLMs","source":"datacite","abstract":"","url":"https://doi.org/10.26240/heal.ntua.33336","authors":["Kontogiannis, Evangelos"],"tags":["Ανίχνευση ανωμαλιών","Αρχεία καταγραφής","Ομοσπονδιακή μάθηση","Μεγάλα γλωσσικά μοντέλα","Κυβερνοασφάλεια","Anomaly detection","Federated learning","Large language models"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.26240/heal.ntua.33336","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22166181","name":"Parallels and Interconnections Between Mathematics, Art, Writing, and Music: Living the Metaverse and Beyond","source":"datacite","abstract":"From basic screen-based interfaces into completely immersive, multidimensional worlds, the metaverse marks a great evolutionary leap in digital human interaction. As fundamental pillars for creating and living within the metaverse and beyond, this study looks at the underlying parallels and connections between math, art, literature, and music as well as their connections. We offer a comprehensive approach to digital life by seeing the virtual world as a confluence of mathematical graph theory, graphic artistic expression, narrative writing structures, and acoustic musical resonance. We investigate how federated learning, blockchain ecosystems, and digital twins among other modern technologies act as the mechanical foundations transforming these main human domains into a common virtual ontology. By means of a disciplined research plan, this essay emphasizes the need of connecting serious calculation with inventive humanities to guarantee a sustainable, empathetic, and interoperable future digital society.","url":"https://doi.org/10.5281/zenodo.22166181","authors":["Mageed, Ismail A"],"tags":["Metaverse","Interdisciplinarity","Graph Theory","Digital Twins","Empathic Computing","Artificial General Intelligence","Self-Sovereign Identity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22166181","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22166180","name":"Parallels and Interconnections Between Mathematics, Art, Writing, and Music: Living the Metaverse and Beyond","source":"datacite","abstract":"From basic screen-based interfaces into completely immersive, multidimensional worlds, the metaverse marks a great evolutionary leap in digital human interaction. As fundamental pillars for creating and living within the metaverse and beyond, this study looks at the underlying parallels and connections between math, art, literature, and music as well as their connections. We offer a comprehensive approach to digital life by seeing the virtual world as a confluence of mathematical graph theory, graphic artistic expression, narrative writing structures, and acoustic musical resonance. We investigate how federated learning, blockchain ecosystems, and digital twins among other modern technologies act as the mechanical foundations transforming these main human domains into a common virtual ontology. By means of a disciplined research plan, this essay emphasizes the need of connecting serious calculation with inventive humanities to guarantee a sustainable, empathetic, and interoperable future digital society.","url":"https://doi.org/10.5281/zenodo.22166180","authors":["Mageed, Ismail A"],"tags":["Metaverse","Interdisciplinarity","Graph Theory","Digital Twins","Empathic Computing","Artificial General Intelligence","Self-Sovereign Identity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22166180","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22165448","name":"Execution Governance 5.0 Research Architecture: From Governed Effect Fabrics to Governed Effect Regimes","source":"datacite","abstract":"Execution Governance 5.0 (EG5) Research Architecture v0.1.4 proposes a candidate major-version research direction extending Execution Governance from authority-preserving Governed Effect Fabrics to the time-evolving Governed Effect Regime that determines how such fabrics, authority roots, policies, comparators, composition rules, reconciliation rules, adaptation mechanisms, and evidence requirements may themselves be created, changed, combined, suspended, or replaced. The central research question is: Who authorizes a material change to the governance system that determines what counts as authorized? EG5 treats a material governance-state transition as consequential when it can alter the future admissible effect space or the authority, semantic, commitment, reconciliation, or evidence rules applied to future effects. Its candidate governing principle is: Governance may evolve, but no governance change may create the authority that legitimizes itself. A companion composition principle is: Valid governed fabrics do not imply a valid governance composition. EG5 retains the effect-centered discipline of earlier Execution Governance generations and does not introduce a new source of normative authority. It does not claim to invent administrative authorization, policy administration, compositional authorization, recursive governance, governance-of-governance, governed runtime mutation, learning/authority separation, non-widening composition, cryptographic authorization proofs, or evidence-chain composition. These areas have substantial antecedent and adjacent work. The proposed research distinction is narrower: the effect-centered conjunction of authorization requirements at a material governance-state boundary. The candidate EG5-Core v0.1 defines six Recursive Integrity properties: Governance-Change Authority Provenance Non-Self-Authorization Authority Composition Closure Multi-Root Reconciliation Integrity Consequence-Bounded Adaptation Evolution-Witnessed Closure The accompanying deterministic executable guard-ablation harness provides six hand-constructed minimal counterexamples, one for each property. In each named scenario, omission of the relevant property admits the bad state while the complete candidate guard blocks it. A separate regression confirms that participating EG4 fabric validity remains a non-substitutable prerequisite. These results are executable falsification evidence only. They are not bounded model checking, exhaustive state-space exploration, formal proof, independent reproduction, certification, production assurance, or evidence of governance completeness. The first proposed implementation profile is the Minimal Federated Governance-Evolution Profile v0.1, designed around two independently administered EG4-class digital fabrics, two authority roots, one separate verifier, one reversible synthetic cross-fabric effect, one material governance change, explicit composition and reconciliation rules, and bounded consequence-feedback semantics. The principal next evidence milestone is to demonstrate an executable case in which: Fabric A is EG4-valid.Fabric B is EG4-valid.Their composite effect is not authorized.EG5 correctly blocks the composition. Stable EG5.0 status is intentionally not claimed by this publication. It should be earned through a profile-bounded formal model, two-domain implementation, public reproducibility, and independent reconstruction. EG4 baseline:KU, H. W. (2026). Execution Governance 4.0: From Authorization-Bound Execution to Governed Effect Fabrics (Version 0.3.6.2). Zenodo.https://doi.org/10.5281/zenodo.22157731 Research programme: https://executiongovernance.org Status: Independent research and pre-standardization candidate. This publication does not constitute a standard, certification scheme, legal authorization determination, production-safety claim, or proof of governance completeness.","url":"https://doi.org/10.5281/zenodo.22165448","authors":["KU, Ho Wa"],"tags":["Execution Governance","Execution Governance 5.0","EG5","Governed Effect Regime","governed effects","recursive authorization","governance-state transitions","material governance change"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22165448","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.22165449","name":"Execution Governance 5.0 Research Architecture: From Governed Effect Fabrics to Governed Effect Regimes","source":"datacite","abstract":"Execution Governance 5.0 (EG5) Research Architecture v0.1.4 proposes a candidate major-version research direction extending Execution Governance from authority-preserving Governed Effect Fabrics to the time-evolving Governed Effect Regime that determines how such fabrics, authority roots, policies, comparators, composition rules, reconciliation rules, adaptation mechanisms, and evidence requirements may themselves be created, changed, combined, suspended, or replaced. The central research question is: Who authorizes a material change to the governance system that determines what counts as authorized? EG5 treats a material governance-state transition as consequential when it can alter the future admissible effect space or the authority, semantic, commitment, reconciliation, or evidence rules applied to future effects. Its candidate governing principle is: Governance may evolve, but no governance change may create the authority that legitimizes itself. A companion composition principle is: Valid governed fabrics do not imply a valid governance composition. EG5 retains the effect-centered discipline of earlier Execution Governance generations and does not introduce a new source of normative authority. It does not claim to invent administrative authorization, policy administration, compositional authorization, recursive governance, governance-of-governance, governed runtime mutation, learning/authority separation, non-widening composition, cryptographic authorization proofs, or evidence-chain composition. These areas have substantial antecedent and adjacent work. The proposed research distinction is narrower: the effect-centered conjunction of authorization requirements at a material governance-state boundary. The candidate EG5-Core v0.1 defines six Recursive Integrity properties: Governance-Change Authority Provenance Non-Self-Authorization Authority Composition Closure Multi-Root Reconciliation Integrity Consequence-Bounded Adaptation Evolution-Witnessed Closure The accompanying deterministic executable guard-ablation harness provides six hand-constructed minimal counterexamples, one for each property. In each named scenario, omission of the relevant property admits the bad state while the complete candidate guard blocks it. A separate regression confirms that participating EG4 fabric validity remains a non-substitutable prerequisite. These results are executable falsification evidence only. They are not bounded model checking, exhaustive state-space exploration, formal proof, independent reproduction, certification, production assurance, or evidence of governance completeness. The first proposed implementation profile is the Minimal Federated Governance-Evolution Profile v0.1, designed around two independently administered EG4-class digital fabrics, two authority roots, one separate verifier, one reversible synthetic cross-fabric effect, one material governance change, explicit composition and reconciliation rules, and bounded consequence-feedback semantics. The principal next evidence milestone is to demonstrate an executable case in which: Fabric A is EG4-valid.Fabric B is EG4-valid.Their composite effect is not authorized.EG5 correctly blocks the composition. Stable EG5.0 status is intentionally not claimed by this publication. It should be earned through a profile-bounded formal model, two-domain implementation, public reproducibility, and independent reconstruction. EG4 baseline:KU, H. W. (2026). Execution Governance 4.0: From Authorization-Bound Execution to Governed Effect Fabrics (Version 0.3.6.2). Zenodo.https://doi.org/10.5281/zenodo.22157731 Research programme: https://executiongovernance.org Status: Independent research and pre-standardization candidate. This publication does not constitute a standard, certification scheme, legal authorization determination, production-safety claim, or proof of governance completeness.","url":"https://doi.org/10.5281/zenodo.22165449","authors":["KU, Ho Wa"],"tags":["Execution Governance","Execution Governance 5.0","EG5","Governed Effect Regime","governed effects","recursive authorization","governance-state transitions","material governance change"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22165449","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21512715","name":"Enhanced Privacy-Preserving Federated Class-Incremental Learning Using Attention-Based Dynamic Aggregation and Continual Learning","source":"datacite","abstract":"Federated Class-Incremental Learning (FCIL) enables distributed clients to collaboratively learn new classes without sharing raw data, thereby ensuring data privacy. However, existing FCIL methods suffer from catastrophic forgetting, inefficient client aggregation, and a challenging privacy–utility tradeoff. This paper proposes an Enhanced Privacy-Preserving Federated Class-Incremental Learning (Enhanced PP-FCIL) framework that integrates Attention-Based Dynamic Aggregation, Elastic Weight Consolidation (EWC), CoreSet Memory Selection, Herding, Rényi Differential Privacy (RDP), Bayesian Differential Privacy (BDP), and a Convolutional Neural Network (CNN) for secure and efficient incremental learning. The proposed framework adaptively aggregates client models, preserves previously acquired knowledge through parameter regularization and representative memory selection, and strengthens privacy protection while maintaining high classification performance. The model is evaluated on the CIFAR-100 dataset under sequential federated class-incremental learning tasks. Experimental evaluation is performed using incremental learning accuracy, catastrophic forgetting analysis, privacy–utility tradeoff, confusion matrix, ablation study, loss landscape visualization, computational complexity analysis, and comparison with state-of-the-art methods. The proposed framework achieves 97.5% classification accuracy, reduces the forgetting rate to 7.8%, improves adaptation to new classes by approximately 4–5%, and lowers false-positive predictions by nearly 65% compared with existing approaches. Furthermore, the framework demonstrates stable convergence, efficient computational complexity, and strong privacy guarantees, making it suitable for privacy-sensitive applications such as healthcare, intelligent IoT systems, financial services, and autonomous intelligent systems.","url":"https://doi.org/10.5281/zenodo.21512715","authors":["G, Hari Krishna","Reddy, Dr. A. Anand"],"tags":["Federated Learning; Class-Incremental Learning; Differential Privacy; Rényi Differential Privacy; Bayesian Differential Privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21512715","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21512716","name":"Enhanced Privacy-Preserving Federated Class-Incremental Learning Using Attention-Based Dynamic Aggregation and Continual Learning","source":"datacite","abstract":"Federated Class-Incremental Learning (FCIL) enables distributed clients to collaboratively learn new classes without sharing raw data, thereby ensuring data privacy. However, existing FCIL methods suffer from catastrophic forgetting, inefficient client aggregation, and a challenging privacy–utility tradeoff. This paper proposes an Enhanced Privacy-Preserving Federated Class-Incremental Learning (Enhanced PP-FCIL) framework that integrates Attention-Based Dynamic Aggregation, Elastic Weight Consolidation (EWC), CoreSet Memory Selection, Herding, Rényi Differential Privacy (RDP), Bayesian Differential Privacy (BDP), and a Convolutional Neural Network (CNN) for secure and efficient incremental learning. The proposed framework adaptively aggregates client models, preserves previously acquired knowledge through parameter regularization and representative memory selection, and strengthens privacy protection while maintaining high classification performance. The model is evaluated on the CIFAR-100 dataset under sequential federated class-incremental learning tasks. Experimental evaluation is performed using incremental learning accuracy, catastrophic forgetting analysis, privacy–utility tradeoff, confusion matrix, ablation study, loss landscape visualization, computational complexity analysis, and comparison with state-of-the-art methods. The proposed framework achieves 97.5% classification accuracy, reduces the forgetting rate to 7.8%, improves adaptation to new classes by approximately 4–5%, and lowers false-positive predictions by nearly 65% compared with existing approaches. Furthermore, the framework demonstrates stable convergence, efficient computational complexity, and strong privacy guarantees, making it suitable for privacy-sensitive applications such as healthcare, intelligent IoT systems, financial services, and autonomous intelligent systems.","url":"https://doi.org/10.5281/zenodo.21512716","authors":["G, Hari Krishna","Reddy, Dr. A. Anand"],"tags":["Federated Learning; Class-Incremental Learning; Differential Privacy; Rényi Differential Privacy; Bayesian Differential Privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21512716","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.19686374","name":"Federated Fine-Tuning of DziriBERT for Algerian Dialect Sentiment Analysis","source":"datacite","abstract":"This study examines federated learning for sentiment analysis in Algerian Arabic and code-switched social media content using transformer-based language models. A corpus of 13,260 user-generated comments collected via the X platform API with balanced positive and negative labels is preprocessed, stratified into train, validation, and test splits, and divided into ten highly non-IID clients using a Dirichlet procedure. We first fine-tune DziriBERT centrally as a solid baseline, obtaining roughly 86.35% accuracy and 86.35 macro-F1 on the held-out test set. We then implement FedAvg, FedProx, and a semisupervised FL variant with only 20% labeled clients using DziriBERT, and evaluate multilingual DistilBERT with FedAvg as an alternative backbone. FedAvg achieves 84.77% accuracy with DziriBERT, FedProx improves to 85.41%, semi-supervised FL achieves 80.69% with limited labels, and DistilBERT FedAvg reaches 81.37% accuracy. All results are based on a single random seed and partition.","url":"https://doi.org/10.5281/zenodo.19686374","authors":["SLIMANI, NASREDDINE","Bendjima, Mustapha"],"tags":["DziriBERT, Federated Learning, FedAvg, FedProx, Algerian dialect, NLP, sentiment analysis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19686374","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.19686375","name":"Federated Fine-Tuning of DziriBERT for Algerian Dialect Sentiment Analysis","source":"datacite","abstract":"This study examines federated learning for sentiment analysis in Algerian Arabic and code-switched social media content using transformer-based language models. A corpus of 13,260 user-generated comments collected via the X platform API with balanced positive and negative labels is preprocessed, stratified into train, validation, and test splits, and divided into ten highly non-IID clients using a Dirichlet procedure. We first fine-tune DziriBERT centrally as a solid baseline, obtaining roughly 86.35% accuracy and 86.35 macro-F1 on the held-out test set. We then implement FedAvg, FedProx, and a semisupervised FL variant with only 20% labeled clients using DziriBERT, and evaluate multilingual DistilBERT with FedAvg as an alternative backbone. FedAvg achieves 84.77% accuracy with DziriBERT, FedProx improves to 85.41%, semi-supervised FL achieves 80.69% with limited labels, and DistilBERT FedAvg reaches 81.37% accuracy. All results are based on a single random seed and partition.","url":"https://doi.org/10.5281/zenodo.19686375","authors":["SLIMANI, NASREDDINE","Bendjima, Mustapha"],"tags":["DziriBERT, Federated Learning, FedAvg, FedProx, Algerian dialect, NLP, sentiment analysis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19686375","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.18883693","name":"Dual asymmetric momentum improves federated class unlearning in edge systems (Code and Reproducibility Scripts)","source":"datacite","abstract":"This Zenodo record provides the reference implementation of FedDAM, a parameter-efficient post-hoc federated class unlearning method designed for resource-constrained edge deployments. The release includes training and unlearning pipelines for CIFAR-10 and CIFAR-100 under federated non-IID Dirichlet partitions, matched-budget comparisons, and evaluation scripts to regenerate the key tables/figures reported in the associated Scientific Reports submission. Key features:(i) Auxiliary-head-only unlearning with frozen backbone and main classifier to reduce communication and client compute.(ii) Dual-asymmetric momentum buffers to decouple retain vs forget optimization dynamics.(iii) Reproducibility controls: fixed seeds, paired partitions and participation schedules, and configuration files for sweeps. New analyses included for revision:(A) Gradient cosine similarity analysis comparing FedAU vs FedDAM (retain vs forget gradient conflict and update alignment).(C) Forget-set heterogeneity statistics: distribution of ∣Du,k∣|D_{u,k}|∣Du,k∣ and fraction of clients with ∣Du,k∣=0|D_{u,k}|=0∣Du,k∣=0 across αD\\alpha_DαD. Dataset: CIFAR-10 and CIFAR-100 (public). No new datasets were generated.How to reproduce: See README.md in the archive.","url":"https://doi.org/10.5281/zenodo.18883693","authors":["Mayaluri, Zefree Lazarus","PATRA, ACHIRANGSHU","Sahoo, Prabodh","Kumawat, Gaurav"],"tags":["federated learning","federated unlearning","machine unlearning","class unlearning","edge AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18883693","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.18883694","name":"Dual asymmetric momentum improves federated class unlearning in edge systems (Code and Reproducibility Scripts)","source":"datacite","abstract":"This Zenodo record provides the reference implementation of FedDAM, a parameter-efficient post-hoc federated class unlearning method designed for resource-constrained edge deployments. The release includes training and unlearning pipelines for CIFAR-10 and CIFAR-100 under federated non-IID Dirichlet partitions, matched-budget comparisons, and evaluation scripts to regenerate the key tables/figures reported in the associated Scientific Reports submission. Key features:(i) Auxiliary-head-only unlearning with frozen backbone and main classifier to reduce communication and client compute.(ii) Dual-asymmetric momentum buffers to decouple retain vs forget optimization dynamics.(iii) Reproducibility controls: fixed seeds, paired partitions and participation schedules, and configuration files for sweeps. New analyses included for revision:(A) Gradient cosine similarity analysis comparing FedAU vs FedDAM (retain vs forget gradient conflict and update alignment).(C) Forget-set heterogeneity statistics: distribution of ∣Du,k∣|D_{u,k}|∣Du,k∣ and fraction of clients with ∣Du,k∣=0|D_{u,k}|=0∣Du,k∣=0 across αD\\alpha_DαD. Dataset: CIFAR-10 and CIFAR-100 (public). No new datasets were generated.How to reproduce: See README.md in the archive.","url":"https://doi.org/10.5281/zenodo.18883694","authors":["Mayaluri, Zefree Lazarus","PATRA, ACHIRANGSHU","Sahoo, Prabodh","Kumawat, Gaurav"],"tags":["federated learning","federated unlearning","machine unlearning","class unlearning","edge AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18883694","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20788436","name":"Autonomous IIoT Intrusion Detection via Federated Learning with Neural Thompson Sampling","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20788436","authors":["Anonyme"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20788436","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20788437","name":"Autonomous IIoT Intrusion Detection via Federated Learning with Neural Thompson Sampling","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20788437","authors":["Anonyme"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20788437","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22163187","name":"DEx-FCL: Reproducibility package for adaptive Edge-IoT security management","source":"datacite","abstract":"Reproducibility package for the manuscript \"DEx-FCL: Drift- and Explanation-Consistent Federated Continual Learning for Adaptive Edge-IoT Security Management.\" This release includes the DEx-FCL source code, deterministic federated partitioning utilities, fixed five-seed configurations, CICIoT2023 and Edge-IIoTset benchmark result CSVs, generated figures, experiment launchers, dataset preparation instructions, and the deterministic Edge-IIoTset 10% sampling script. Raw benchmark datasets are not redistributed and must be obtained from their original providers.","url":"https://doi.org/10.5281/zenodo.22163187","authors":["P, Arul Selvam"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22163187","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.19371877","name":"BrainPredict™ Breakthrough Innovations v2.0 — P1–P16 (Claims R31–R62)","source":"datacite","abstract":"Complete source code, implementation plans, and technical documentation for 16 world-first AI innovations constituting the BrainPredict™ Breakthrough Innovations portfolio (P1–P16, Claims R31–R62), authored by Raphaël Clairin, April 1, 2026.\\n\\nThis deposit establishes the priority date and authorship of the following innovations:\\n- P1: Sentinel Compliance Runtime SDK (R31–R32) — pip-installable EU AI Act enforcement middleware\\n- P2: BrainBrowser Content Trust API + BrainScore™ (R33–R34) — 6-layer AI contamination detection\\n- P3: IEC 61131-3 Industrial Code Generator (R35–R36) — plain-language to certified PLC code pipeline\\n- P4: Intelligence Bus OS Primitive SDK (R37–R38) — typed multi-agent compliance-gated event bus\\n- P5: FEAC Federated Enterprise AI Consortium (R39–R40) — CKKS homomorphic encrypted federated learning\\n- P6: ARIP Adaptive Regulatory Intelligence Protocol (R41–R42) — autonomous regulatory change adaptation\\n- P7: V2CC Voice-to-Certified-Code (R43–R44) — voice-driven IEC 61131-3 differential PLC patching\\n- P8: ZTAF Zero-Trust AI Fabric (R45–R46) — SPIFFE/SPIRE + Kyber-1024 AI workload security\\n- P9: CDAE Causal Decision Audit Engine (R47–R48) — Pearl do-calculus counterfactual AI audit\\n- P10: SAX Sovereign AI Exchange (R49–R50) — CBDC-settled AI model improvement marketplace\\n- P11: ACHO Autonomous Compliance Healing Organism (R51–R52) — Bayesian self-healing compliance\\n- P12: MAOS Medical-Grade AI OS (R53–R54) — IEC 62304 Class C cardiac AI with CKKS patient privacy\\n- P13: MMIB Multi-Modal Intelligence Bridge (R55–R56) — SAR satellite + voice + optical fusion\\n- P14: CIX Constitutional Intelligence Exchange (R57–R58) — Raft-consensus federated AI governance\\n- P15: UCI Universal Content Identity Protocol (R59–R60) — Dilithium-3 content certificate authority\\n- P16: BNP BrainNet Protocol (R61–R62) — post-quantum verified internet transport protocol\\n\\nAll 16 innovations are designed for and integrated with the BrainPredict Enterprise AI OS — a production-ready, 100% on-premise AI operating system covering 19 business domains, 499 AI models, 554 enterprise connectors, and 23 regulatory compliance frameworks.\\n\\nArchive SHA-256: e82b373396fdd59e7fc2c65b44c792af6465ccc96fcee16b2e3ca9721dc2b1da\\nBlockchain timestamp: OpenTimestamps (Bitcoin), April 1, 2026\\nFrench IP deposit: INPI e-Soleau v4, April 1, 2026\",","url":"https://doi.org/10.5281/zenodo.19371877","authors":["Clairin, Raphael Pierre Eugene"],"tags":["\"enterprise AI\", \"on-premise AI\", \"EU AI Act\", \"compliance\", \"post-quantum cryptography\", \"federated learning\", \"IEC 61131-3\", \"PLC code generation\", \"zero-trust AI\", \"causal AI\", \"constitutional AI\", \"medical AI\", \"multi-modal AI\", \"blockchain timestamping\", \"BrainPredict\", \"FEAC\", \"ZTAF\", \"CDAE\", \"MAOS\", \"BrainScore\""],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19371877","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.19371878","name":"BrainPredict™ Breakthrough Innovations v2.0 — P1–P16 (Claims R31–R62)","source":"datacite","abstract":"Complete source code, implementation plans, and technical documentation for 16 world-first AI innovations constituting the BrainPredict™ Breakthrough Innovations portfolio (P1–P16, Claims R31–R62), authored by Raphaël Clairin, April 1, 2026.\\n\\nThis deposit establishes the priority date and authorship of the following innovations:\\n- P1: Sentinel Compliance Runtime SDK (R31–R32) — pip-installable EU AI Act enforcement middleware\\n- P2: BrainBrowser Content Trust API + BrainScore™ (R33–R34) — 6-layer AI contamination detection\\n- P3: IEC 61131-3 Industrial Code Generator (R35–R36) — plain-language to certified PLC code pipeline\\n- P4: Intelligence Bus OS Primitive SDK (R37–R38) — typed multi-agent compliance-gated event bus\\n- P5: FEAC Federated Enterprise AI Consortium (R39–R40) — CKKS homomorphic encrypted federated learning\\n- P6: ARIP Adaptive Regulatory Intelligence Protocol (R41–R42) — autonomous regulatory change adaptation\\n- P7: V2CC Voice-to-Certified-Code (R43–R44) — voice-driven IEC 61131-3 differential PLC patching\\n- P8: ZTAF Zero-Trust AI Fabric (R45–R46) — SPIFFE/SPIRE + Kyber-1024 AI workload security\\n- P9: CDAE Causal Decision Audit Engine (R47–R48) — Pearl do-calculus counterfactual AI audit\\n- P10: SAX Sovereign AI Exchange (R49–R50) — CBDC-settled AI model improvement marketplace\\n- P11: ACHO Autonomous Compliance Healing Organism (R51–R52) — Bayesian self-healing compliance\\n- P12: MAOS Medical-Grade AI OS (R53–R54) — IEC 62304 Class C cardiac AI with CKKS patient privacy\\n- P13: MMIB Multi-Modal Intelligence Bridge (R55–R56) — SAR satellite + voice + optical fusion\\n- P14: CIX Constitutional Intelligence Exchange (R57–R58) — Raft-consensus federated AI governance\\n- P15: UCI Universal Content Identity Protocol (R59–R60) — Dilithium-3 content certificate authority\\n- P16: BNP BrainNet Protocol (R61–R62) — post-quantum verified internet transport protocol\\n\\nAll 16 innovations are designed for and integrated with the BrainPredict Enterprise AI OS — a production-ready, 100% on-premise AI operating system covering 19 business domains, 499 AI models, 554 enterprise connectors, and 23 regulatory compliance frameworks.\\n\\nArchive SHA-256: e82b373396fdd59e7fc2c65b44c792af6465ccc96fcee16b2e3ca9721dc2b1da\\nBlockchain timestamp: OpenTimestamps (Bitcoin), April 1, 2026\\nFrench IP deposit: INPI e-Soleau v4, April 1, 2026\",","url":"https://doi.org/10.5281/zenodo.19371878","authors":["Clairin, Raphael Pierre Eugene"],"tags":["\"enterprise AI\", \"on-premise AI\", \"EU AI Act\", \"compliance\", \"post-quantum cryptography\", \"federated learning\", \"IEC 61131-3\", \"PLC code generation\", \"zero-trust AI\", \"causal AI\", \"constitutional AI\", \"medical AI\", \"multi-modal AI\", \"blockchain timestamping\", \"BrainPredict\", \"FEAC\", \"ZTAF\", \"CDAE\", \"MAOS\", \"BrainScore\""],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19371878","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21097801","name":"Evaluación de Mecanismos Semiasíncronos Deterministas y Adaptativos en Aprendizaje Federado","source":"datacite","abstract":"El aprendizaje automático ha evolucionado significativamente en los últimos años, expandiendo su aplicación a entornos de Internet de las Cosas (IoT), donde estas técnicas poseen un gran potencial en sectores como la mejora de diagnósticos médicos, el reconocimiento facial o la clasificación de imágenes. En este contexto, el Aprendizaje Federado Semiasíncrono (SAFL) surge como una extensión del Aprendizaje Federado (FL), diseñada para entornos con clientes heterogéneos y recursos limitados, lo que permite que los nodos participen en el entrenamiento sin necesidad de sincronizarse completamente en cada ronda, lo que mejora la eficiencia y la flexibilidad del sistema. Sin embargo, esta semiasincronía introduce nuevos desafíos, como la variabilidad en la contribución de los clientes y la inestabilidad en la convergencia, que requieren estrategias específicas de gestión y mitigación. Este trabajo evalúa diversas estrategias semiasíncronas, contrastando enfoques deterministas, basados en un grado de semiasincronía M estático, frente a una propuesta dinámica que se autoajusta según la disponibilidad y el comportamiento de los clientes. La contribución principal consiste en analizar la viabilidad de una estrategia basada en una heurística de ajuste dinámico, que permite al sistema adaptarse a un entorno heterogéneo de recursos limitados. Este estudio preliminar sienta las bases para futuras estrategias federadas adaptativas que incluyan posibles mecanismos de mitigación de la obsolescencia y otras ineficiencias derivadas de la semiasincronía, orientadas a infraestructuras distribuidas de bajo coste y escenarios IoT.","url":"https://doi.org/10.5281/zenodo.21097801","authors":["Hidalgo Izquierdo, Víctor","Caminero, Maria Blanca","Carrión Espinosa, María del Carmen"],"tags":["federated learning","fog computing","internet of things","machine learning","semi-asynchrony"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21097801","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.21097802","name":"Evaluación de Mecanismos Semiasíncronos Deterministas y Adaptativos en Aprendizaje Federado","source":"datacite","abstract":"El aprendizaje automático ha evolucionado significativamente en los últimos años, expandiendo su aplicación a entornos de Internet de las Cosas (IoT), donde estas técnicas poseen un gran potencial en sectores como la mejora de diagnósticos médicos, el reconocimiento facial o la clasificación de imágenes. En este contexto, el Aprendizaje Federado Semiasíncrono (SAFL) surge como una extensión del Aprendizaje Federado (FL), diseñada para entornos con clientes heterogéneos y recursos limitados, lo que permite que los nodos participen en el entrenamiento sin necesidad de sincronizarse completamente en cada ronda, lo que mejora la eficiencia y la flexibilidad del sistema. Sin embargo, esta semiasincronía introduce nuevos desafíos, como la variabilidad en la contribución de los clientes y la inestabilidad en la convergencia, que requieren estrategias específicas de gestión y mitigación. Este trabajo evalúa diversas estrategias semiasíncronas, contrastando enfoques deterministas, basados en un grado de semiasincronía M estático, frente a una propuesta dinámica que se autoajusta según la disponibilidad y el comportamiento de los clientes. La contribución principal consiste en analizar la viabilidad de una estrategia basada en una heurística de ajuste dinámico, que permite al sistema adaptarse a un entorno heterogéneo de recursos limitados. Este estudio preliminar sienta las bases para futuras estrategias federadas adaptativas que incluyan posibles mecanismos de mitigación de la obsolescencia y otras ineficiencias derivadas de la semiasincronía, orientadas a infraestructuras distribuidas de bajo coste y escenarios IoT.","url":"https://doi.org/10.5281/zenodo.21097802","authors":["Hidalgo Izquierdo, Víctor","Caminero, Maria Blanca","Carrión Espinosa, María del Carmen"],"tags":["federated learning","fog computing","internet of things","machine learning","semi-asynchrony"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21097802","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20612478","name":"D11.1 - SIESTA risk control tools","source":"datacite","abstract":"The objective of this report is to provide a brief summary of the activities carried out within the framework of SIESTA’s Work Package 11 (WP11) including the selection of developed or adopted tools, and interaction with other work packages, particularly those related to use cases.This deliverable presents the main outcomes achieved within WP11. Section 3 describes the services developed for secure data release. Section 4 details the implementation of disclosure risk metrics within the Anjana framework, including their integration into the platform dashboard. Section 5 presents the differential privacy services explored and implemented to support secure data staging and analytics. Section 6 explores the use of Federated Learning as a means to facilitate users’ privacy-preserving machine learning. Finally, Section 7 summarises the main conclusions and results of this work.","url":"https://doi.org/10.5281/zenodo.20612478","authors":["Rodriguez Gonzalez, David","Sáinz-Pardo Díaz, Judith","Tran, Viet"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20612478","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20612479","name":"D11.1 - SIESTA risk control tools","source":"datacite","abstract":"The objective of this report is to provide a brief summary of the activities carried out within the framework of SIESTA’s Work Package 11 (WP11) including the selection of developed or adopted tools, and interaction with other work packages, particularly those related to use cases.This deliverable presents the main outcomes achieved within WP11. Section 3 describes the services developed for secure data release. Section 4 details the implementation of disclosure risk metrics within the Anjana framework, including their integration into the platform dashboard. Section 5 presents the differential privacy services explored and implemented to support secure data staging and analytics. Section 6 explores the use of Federated Learning as a means to facilitate users’ privacy-preserving machine learning. Finally, Section 7 summarises the main conclusions and results of this work.","url":"https://doi.org/10.5281/zenodo.20612479","authors":["Rodriguez Gonzalez, David","Sáinz-Pardo Díaz, Judith","Tran, Viet"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20612479","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20717019","name":"FairFed-Health: Deficit-Weighted Federated Aggregation for Institutional Equity in Privacy-Preserving ICU Mortality Prediction","source":"datacite","abstract":"No description provided.","url":"https://doi.org/10.5281/zenodo.20717019","authors":["Gnanavel, Agash"],"tags":["Federated Learning","Algorithmic Fairness","Equalised Odds","ICU Mortality Prediction","Institutional Equity","Differential Privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20717019","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20717020","name":"FairFed-Health: Deficit-Weighted Federated Aggregation for Institutional Equity in Privacy-Preserving ICU Mortality Prediction","source":"datacite","abstract":"No description provided.","url":"https://doi.org/10.5281/zenodo.20717020","authors":["Gnanavel, Agash"],"tags":["Federated Learning","Algorithmic Fairness","Equalised Odds","ICU Mortality Prediction","Institutional Equity","Differential Privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20717020","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21101617","name":"MS-FL: A FEDERATED LEARNING FRAMEWORK BASED ON MULTIPLE SECURITY STRATEGIES","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21101617","authors":["IJERST"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21101617","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.21101618","name":"MS-FL: A FEDERATED LEARNING FRAMEWORK BASED ON MULTIPLE SECURITY STRATEGIES","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21101618","authors":["IJERST"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21101618","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22161330","name":"SCALABLE SMART CITY PLATFORM USING IOT AND CLOUD COMPUTING","source":"datacite","abstract":"Rapid urbanization has significantly increased pressure on transportation systems, healthcare, energy distribution, environmental monitoring, waste management, public safety, and other municipal services. Traditional city management approaches are increasingly unable to process the enormous volume of heterogeneous data generated by modern urban environments. The integration of the Internet of Things (IoT) with cloud computing provides an effective solution for developing scalable smart city platforms capable of supporting real-time monitoring, intelligent decision-making, and efficient resource management. This paper presents a scalable cloud-enabled smart city architecture that integrates IoT sensing devices, edge gateways, cloud infrastructure, big data analytics, and artificial intelligence to support multiple smart city services within a unified platform. The proposed architecture employs a layered framework consisting of perception, communication, edge computing, cloud services, data analytics, application, and security layers to improve scalability, interoperability, reliability, and service availability. The study critically reviews recent advances in IoT cloud integration, identifies major challenges including security, privacy, latency, interoperability, and energy efficiency, and proposes practical strategies for addressing these limitations through containerization, micro services,edge cloud collaboration, and AI-driven resource orchestration. The proposed framework demonstrates how cloud computing can dynamically allocate computational resources to accommodate growing IoT deployments while maintaining quality of service. The paper concludes that scalable IoT-cloud platforms represent the foundation for next-generation smart cities and recommends future integration with digital twins, federated learning, block chain, and 6G communication technologies for improved sustainability and resilience.","url":"https://doi.org/10.5281/zenodo.22161330","authors":["1*Mustapha Malami Idina, 2Abubakar Jibo Magayaki, 3Mubarak Jibril Yeldu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22161330","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22161331","name":"SCALABLE SMART CITY PLATFORM USING IOT AND CLOUD COMPUTING","source":"datacite","abstract":"Rapid urbanization has significantly increased pressure on transportation systems, healthcare, energy distribution, environmental monitoring, waste management, public safety, and other municipal services. Traditional city management approaches are increasingly unable to process the enormous volume of heterogeneous data generated by modern urban environments. The integration of the Internet of Things (IoT) with cloud computing provides an effective solution for developing scalable smart city platforms capable of supporting real-time monitoring, intelligent decision-making, and efficient resource management. This paper presents a scalable cloud-enabled smart city architecture that integrates IoT sensing devices, edge gateways, cloud infrastructure, big data analytics, and artificial intelligence to support multiple smart city services within a unified platform. The proposed architecture employs a layered framework consisting of perception, communication, edge computing, cloud services, data analytics, application, and security layers to improve scalability, interoperability, reliability, and service availability. The study critically reviews recent advances in IoT cloud integration, identifies major challenges including security, privacy, latency, interoperability, and energy efficiency, and proposes practical strategies for addressing these limitations through containerization, micro services,edge cloud collaboration, and AI-driven resource orchestration. The proposed framework demonstrates how cloud computing can dynamically allocate computational resources to accommodate growing IoT deployments while maintaining quality of service. The paper concludes that scalable IoT-cloud platforms represent the foundation for next-generation smart cities and recommends future integration with digital twins, federated learning, block chain, and 6G communication technologies for improved sustainability and resilience.","url":"https://doi.org/10.5281/zenodo.22161331","authors":["1*Mustapha Malami Idina, 2Abubakar Jibo Magayaki, 3Mubarak Jibril Yeldu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22161331","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20760280","name":"A DECENTRALIZED MACHINE LEARNING APPROACH FOR FRAUD DETECTION WITH BLOCKCHAIN-DRIVEN PRIVACY PROTECTION","source":"datacite","abstract":"Modern digital ecosystems have the significant difficulty of detecting fraud while managing large, real-time data transfer and privacy preservation. This study presents an innovative architecture that combines blockchain technology with machine learning to provide safe, transparent, and privacy-conscious fraud detection. The solution utilizes federated learning and differential privacy methods to train machine learning models without revealing raw user data, while using blockchain's decentralized framework to guarantee data immutability and reliability. A dynamic incentive framework using smart contracts further motivates users to provide detection-ready, high-quality data. The suggested method promotes cooperation among entities, protects user data security, and achieves enhanced fraud detection accuracy via the integration of privacy-preserving computing and decentralized trust. Experimental assessments on both simulated and actual financial datasets illustrate the system's precision, robustness, and scalability in detecting intricate and evolving fraud patterns.","url":"https://doi.org/10.5281/zenodo.20760280","authors":["KOPPULA, ANITHA","M, VENKATA NARASAIAH","Dr, CHAVA HARI BABU","DR, VUNNAVA DINESH BABU","R, VAMSI KRISHNA","D, SRIDHAR"],"tags":["Blockchain, Fraud Detection, Smart Contracts, Data Confidentiality, Artificial Intelligence, Cybersecurity, Trust Management, Secure Data Sharing."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20760280","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20760281","name":"A DECENTRALIZED MACHINE LEARNING APPROACH FOR FRAUD DETECTION WITH BLOCKCHAIN-DRIVEN PRIVACY PROTECTION","source":"datacite","abstract":"Modern digital ecosystems have the significant difficulty of detecting fraud while managing large, real-time data transfer and privacy preservation. This study presents an innovative architecture that combines blockchain technology with machine learning to provide safe, transparent, and privacy-conscious fraud detection. The solution utilizes federated learning and differential privacy methods to train machine learning models without revealing raw user data, while using blockchain's decentralized framework to guarantee data immutability and reliability. A dynamic incentive framework using smart contracts further motivates users to provide detection-ready, high-quality data. The suggested method promotes cooperation among entities, protects user data security, and achieves enhanced fraud detection accuracy via the integration of privacy-preserving computing and decentralized trust. Experimental assessments on both simulated and actual financial datasets illustrate the system's precision, robustness, and scalability in detecting intricate and evolving fraud patterns.","url":"https://doi.org/10.5281/zenodo.20760281","authors":["KOPPULA, ANITHA","M, VENKATA NARASAIAH","Dr, CHAVA HARI BABU","DR, VUNNAVA DINESH BABU","R, VAMSI KRISHNA","D, SRIDHAR"],"tags":["Blockchain, Fraud Detection, Smart Contracts, Data Confidentiality, Artificial Intelligence, Cybersecurity, Trust Management, Secure Data Sharing."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20760281","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20034358","name":"Privacy-Preserving Digital Twin Technologies for Smart City Infrastructure: Balancing Utility and Data Protection","source":"datacite","abstract":"Smart city initiatives increasingly rely on digital twin technology to create comprehensive virtual representations of urban infrastructure. However, the extensive data collection raises significant privacy concerns as aggregated data can reveal sensitive information about individuals. This paper presents a privacy-preserving framework for digital twin technologies in smart city environments that integrates differential privacy mechanisms, federated learning architectures, and privacy-aware data aggregation. We introduce an adaptive privacy budget allocation strategy that dynamically adjusts privacy parameters based on data sensitivity and utility requirements. Experimental evaluation on a realistic smart city digital twin testbed demonstrates that the framework achieves data utility within 4.7% of non-private baselines while providing formal privacy guarantees. The adaptive privacy budget allocation reduces privacy loss by 28% compared to static allocation strategies.","url":"https://doi.org/10.5281/zenodo.20034358","authors":["Safa Mohamed","Safa Kamal"],"tags":["Machine Learning","Artificial intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.20034358","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20034360","name":"Privacy-Preserving Digital Twin Technologies for Smart City Infrastructure: Balancing Utility and Data Protection","source":"datacite","abstract":"Smart city initiatives increasingly rely on digital twin technology to create comprehensive virtual representations of urban infrastructure. However, the extensive data collection raises significant privacy concerns as aggregated data can reveal sensitive information about individuals. This paper presents a privacy-preserving framework for digital twin technologies in smart city environments that integrates differential privacy mechanisms, federated learning architectures, and privacy-aware data aggregation. We introduce an adaptive privacy budget allocation strategy that dynamically adjusts privacy parameters based on data sensitivity and utility requirements. Experimental evaluation on a realistic smart city digital twin testbed demonstrates that the framework achieves data utility within 4.7% of non-private baselines while providing formal privacy guarantees. The adaptive privacy budget allocation reduces privacy loss by 28% compared to static allocation strategies.","url":"https://doi.org/10.5281/zenodo.20034360","authors":["Safa Mohamed","Safa Kamal"],"tags":["Machine Learning","Artificial intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.20034360","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20548461","name":"Code and trained models for \"SNR-Conditioned Residual CNN with Feature-wise Linear Modulation for OFDM Channel Estimation: Ablation Analysis and Federated Deployment","source":"datacite","abstract":"Reproducibility code, trained model weights, and result files for the paper \"SNR-Conditioned Residual CNN with Feature-wise Linear Modulation for OFDM Channel Estimation: Ablation Analysis and Federated Deployment.\" Includes a self-contained PyTorch implementation of the SNR-conditioned residual 1-D CNN with FiLM for pilot-aided OFDM channel estimation, the Ye et al. fully-connected DNN baseline, the undersampled (L>P) experiment, trained model weights, result CSV files, and figure-generation scripts. The OFDM simulator (N=64, P=8 comb pilots, L=8 uniform PDP, QPSK) generates all channel realizations on the fly; no external dataset is required. See README.md for usage.","url":"https://doi.org/10.5281/zenodo.20548461","authors":["Abdulhameed, Zainab","Hatem, Haraa","shehab, jinan"],"tags":["OFDM · channel estimation · deep learning · feature-wise linear modulation · convolutional neural network · federated learning · LMMSE · pilot-aided estimation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20548461","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20548462","name":"Code and trained models for \"SNR-Conditioned Residual CNN with Feature-wise Linear Modulation for OFDM Channel Estimation: Ablation Analysis and Federated Deployment","source":"datacite","abstract":"Reproducibility code, trained model weights, and result files for the paper \"SNR-Conditioned Residual CNN with Feature-wise Linear Modulation for OFDM Channel Estimation: Ablation Analysis and Federated Deployment.\" Includes a self-contained PyTorch implementation of the SNR-conditioned residual 1-D CNN with FiLM for pilot-aided OFDM channel estimation, the Ye et al. fully-connected DNN baseline, the undersampled (L>P) experiment, trained model weights, result CSV files, and figure-generation scripts. The OFDM simulator (N=64, P=8 comb pilots, L=8 uniform PDP, QPSK) generates all channel realizations on the fly; no external dataset is required. See README.md for usage.","url":"https://doi.org/10.5281/zenodo.20548462","authors":["Abdulhameed, Zainab","Hatem, Haraa","shehab, jinan"],"tags":["OFDM · channel estimation · deep learning · feature-wise linear modulation · convolutional neural network · federated learning · LMMSE · pilot-aided estimation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20548462","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20997497","name":"nimaomid/Context-Aware-Adaptive-Federated-Learning: CAFL v1.0.1","source":"datacite","abstract":"Official implementation of the CAFL framework for adaptive federated learning in dynamic IoT environments.","url":"https://doi.org/10.5281/zenodo.20997497","authors":["nimaomid"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20997497","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20997498","name":"nimaomid/Context-Aware-Adaptive-Federated-Learning: CAFL v1.0.1","source":"datacite","abstract":"Official implementation of the CAFL framework for adaptive federated learning in dynamic IoT environments.","url":"https://doi.org/10.5281/zenodo.20997498","authors":["nimaomid"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20997498","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20810078","name":"ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES FOR NEXT-GENERATION COMPUTING SYSTEMS","source":"datacite","abstract":"Next-generation computing systems face unprecedented challenges in terms of scale, complexity, energy efficiency, and adaptability. Classical algorithmic approaches are increasingly insufficient to manage the demands imposed by exascale high-performance computing, neuromorphic architectures, quantum-classical hybrid platforms, and distributed edgecloud continua. Artificial intelligence and machine learning have emerged as transformative paradigms for addressing these challenges, offering data-driven mechanisms for system optimization, autonomous resource management, predictive maintenance, hardware design acceleration, and intelligent compilation. This article provides a comprehensive synthesis of the intersection between AI/ML methodologies and next-generation computing systems, examining deep learning, reinforcement learning, graph neural networks, federated learning, and neuromorphic computing approaches across five principal application domains: hardware architecture and chip design, system software and compiler optimization, high-performance and distributed computing, edge and embedded intelligence, and quantum computing integration. The article analyzes current advances, identifies open research challenges, and proposes a forward-looking agenda for the co-design of intelligent computing infrastructure. Findings indicate that AIdriven approaches offer compelling performance, efficiency, and reliability gains across all domains, but that realizing their full potential demands new frameworks for explainability, energy accountability, hardware-software co-design, and safe autonomy in critical computing infrastructure","url":"https://doi.org/10.5281/zenodo.20810078","authors":["GINJALA SHIVA"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20810078","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20810079","name":"ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES FOR NEXT-GENERATION COMPUTING SYSTEMS","source":"datacite","abstract":"Next-generation computing systems face unprecedented challenges in terms of scale, complexity, energy efficiency, and adaptability. Classical algorithmic approaches are increasingly insufficient to manage the demands imposed by exascale high-performance computing, neuromorphic architectures, quantum-classical hybrid platforms, and distributed edgecloud continua. Artificial intelligence and machine learning have emerged as transformative paradigms for addressing these challenges, offering data-driven mechanisms for system optimization, autonomous resource management, predictive maintenance, hardware design acceleration, and intelligent compilation. This article provides a comprehensive synthesis of the intersection between AI/ML methodologies and next-generation computing systems, examining deep learning, reinforcement learning, graph neural networks, federated learning, and neuromorphic computing approaches across five principal application domains: hardware architecture and chip design, system software and compiler optimization, high-performance and distributed computing, edge and embedded intelligence, and quantum computing integration. The article analyzes current advances, identifies open research challenges, and proposes a forward-looking agenda for the co-design of intelligent computing infrastructure. Findings indicate that AIdriven approaches offer compelling performance, efficiency, and reliability gains across all domains, but that realizing their full potential demands new frameworks for explainability, energy accountability, hardware-software co-design, and safe autonomy in critical computing infrastructure","url":"https://doi.org/10.5281/zenodo.20810079","authors":["GINJALA SHIVA"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20810079","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22156450","name":"Decentralized Federated Learning with Differential Privacy via Homomorphic Encryption","source":"datacite","abstract":"This paper presents a novel approach to decentralized federated learning that leverages the strengths of differential privacy and homomorphic encryption to achieve robust privacy guarantees. The core idea is to encrypt each participant's local data using homomorphic encryption, allowing a central server to perform computations directly on the encrypted data without ever needing to access the plaintext. Subsequently, the encrypted results are sent back to the participants. This architecture effectively mitigates privacy risks associated with traditional federated learning methods. The combination of these three technologies – federated learning, differential privacy, and homomorphic encryption – provides a powerful framework for secure and decentralized machine learning. We demonstrate the potential for achieving strong privacy guarantees while maintaining the benefits of distributed learning.","url":"https://doi.org/10.5281/zenodo.22156450","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22156450","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22156449","name":"Decentralized Federated Learning with Differential Privacy via Homomorphic Encryption","source":"datacite","abstract":"This paper presents a novel approach to decentralized federated learning that leverages the strengths of differential privacy and homomorphic encryption to achieve robust privacy guarantees. The core idea is to encrypt each participant's local data using homomorphic encryption, allowing a central server to perform computations directly on the encrypted data without ever needing to access the plaintext. Subsequently, the encrypted results are sent back to the participants. This architecture effectively mitigates privacy risks associated with traditional federated learning methods. The combination of these three technologies – federated learning, differential privacy, and homomorphic encryption – provides a powerful framework for secure and decentralized machine learning. We demonstrate the potential for achieving strong privacy guarantees while maintaining the benefits of distributed learning.","url":"https://doi.org/10.5281/zenodo.22156449","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22156449","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22155892","name":"Distributed Generative Adversarial Networks with Federated Learning","source":"datacite","abstract":"This paper presents a novel approach to generative modeling by integrating Generative Adversarial Networks (GANs) with Federated Learning (FL). Traditional GAN training suffers from centralized data requirements and privacy concerns. This research addresses these limitations through a distributed GAN architecture specifically designed for federated environments. The core idea is to train the generator and discriminator models concurrently across multiple devices (clients) without directly sharing their raw data. Instead, each client performs local GAN training and only shares model updates with a central server. This approach maintains data privacy while enabling the generation of high-quality synthetic data. The architecture utilizes a client-server framework where clients contribute to the global model through iterative updates. The proposed system aims to achieve superior performance compared to traditional GANs, particularly in scenarios with limited data and stringent privacy requirements. We explore the optimization strategies for the federated GAN training process, including addressing issues like non-IID data and model divergence. The theoretical framework and the proposed architecture are presented, outlining the key components and their interactions. Experimental results (simulated) demonstrate the feasibility and effectiveness of the approach in generating realistic synthetic data.","url":"https://doi.org/10.5281/zenodo.22155892","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22155892","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22155891","name":"Distributed Generative Adversarial Networks with Federated Learning","source":"datacite","abstract":"This paper presents a novel approach to generative modeling by integrating Generative Adversarial Networks (GANs) with Federated Learning (FL). Traditional GAN training suffers from centralized data requirements and privacy concerns. This research addresses these limitations through a distributed GAN architecture specifically designed for federated environments. The core idea is to train the generator and discriminator models concurrently across multiple devices (clients) without directly sharing their raw data. Instead, each client performs local GAN training and only shares model updates with a central server. This approach maintains data privacy while enabling the generation of high-quality synthetic data. The architecture utilizes a client-server framework where clients contribute to the global model through iterative updates. The proposed system aims to achieve superior performance compared to traditional GANs, particularly in scenarios with limited data and stringent privacy requirements. We explore the optimization strategies for the federated GAN training process, including addressing issues like non-IID data and model divergence. The theoretical framework and the proposed architecture are presented, outlining the key components and their interactions. Experimental results (simulated) demonstrate the feasibility and effectiveness of the approach in generating realistic synthetic data.","url":"https://doi.org/10.5281/zenodo.22155891","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22155891","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.20511911","name":"Shadow AI in Organizations","source":"datacite","abstract":"This independent research report examines Shadow AI in organizations, defined as the informal or unauthorized use, deployment, fine tuning, or integration of generative AI tools outside established governance, security, privacy, and compliance processes. The study analyzes how Shadow AI differs from traditional Shadow IT, with specific attention to generative model behavior, AI generated artefacts, alignment risks, data leakage, credential exposure, privacy concerns, contractual issues, liability, and regulatory obligations. It also explores why employees adopt unapproved AI tools, including curiosity, skill development, gaps in official AI provisioning, and pressure for innovation. Using a literature based synthesis, the report connects technology acceptance models, diffusion of innovation theory, AI governance frameworks, and sector specific evidence from areas such as finance, healthcare, public administration, and cybersecurity. The analysis highlights both the productivity and innovation benefits of generative AI and the organizational risks created when these tools are used outside formal control structures. The report proposes governance responses focused on responsible AI policies, access controls, risk education, ethics awareness, contractual safeguards, explainability, privacy preserving techniques, and continuous monitoring. It argues that organizations should not only restrict Shadow AI, but also identify valuable use cases and bring them into governed, auditable, and secure AI adoption pathways. This work is published as an independent, publicly available research report intended for researchers, security professionals, compliance officers, AI governance practitioners, and organizational leaders seeking to understand and manage the growing impact of unapproved generative AI use in the workplace.","url":"https://doi.org/10.5281/zenodo.20511911","authors":["van Hamond, Johannes Maria"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20511911","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20511912","name":"Shadow AI in Organizations","source":"datacite","abstract":"This independent research report examines Shadow AI in organizations, defined as the informal or unauthorized use, deployment, fine tuning, or integration of generative AI tools outside established governance, security, privacy, and compliance processes. The study analyzes how Shadow AI differs from traditional Shadow IT, with specific attention to generative model behavior, AI generated artefacts, alignment risks, data leakage, credential exposure, privacy concerns, contractual issues, liability, and regulatory obligations. It also explores why employees adopt unapproved AI tools, including curiosity, skill development, gaps in official AI provisioning, and pressure for innovation. Using a literature based synthesis, the report connects technology acceptance models, diffusion of innovation theory, AI governance frameworks, and sector specific evidence from areas such as finance, healthcare, public administration, and cybersecurity. The analysis highlights both the productivity and innovation benefits of generative AI and the organizational risks created when these tools are used outside formal control structures. The report proposes governance responses focused on responsible AI policies, access controls, risk education, ethics awareness, contractual safeguards, explainability, privacy preserving techniques, and continuous monitoring. It argues that organizations should not only restrict Shadow AI, but also identify valuable use cases and bring them into governed, auditable, and secure AI adoption pathways. This work is published as an independent, publicly available research report intended for researchers, security professionals, compliance officers, AI governance practitioners, and organizational leaders seeking to understand and manage the growing impact of unapproved generative AI use in the workplace.","url":"https://doi.org/10.5281/zenodo.20511912","authors":["van Hamond, Johannes Maria"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20511912","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22155150","name":"Differential Privacy for Federated Learning with Personalized Noise","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data, preserving data privacy. However, traditional differential privacy (DP) mechanisms often introduce significant noise into the global model updates, leading to substantial accuracy degradation. This paper presents a novel personalized differential privacy (P-DP) scheme for FL, where the noise level is dynamically adjusted based on the sensitivity of individual user data. We introduce a method for calculating per-user sensitivity values, considering local data distributions, and then employ adaptive noise scaling to minimize the privacy-utility trade-off. The proposed approach aims to achieve a better balance between privacy guarantees and model accuracy compared to standard DP methods in FL. The core contribution lies in the personalized allocation of the differential privacy budget, optimizing the system for a given application. Experimental results, although not presented here due to the focus on the method itself, would demonstrate the effectiveness of this approach.","url":"https://doi.org/10.5281/zenodo.22155150","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22155150","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22155151","name":"Differential Privacy for Federated Learning with Personalized Noise","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data, preserving data privacy. However, traditional differential privacy (DP) mechanisms often introduce significant noise into the global model updates, leading to substantial accuracy degradation. This paper presents a novel personalized differential privacy (P-DP) scheme for FL, where the noise level is dynamically adjusted based on the sensitivity of individual user data. We introduce a method for calculating per-user sensitivity values, considering local data distributions, and then employ adaptive noise scaling to minimize the privacy-utility trade-off. The proposed approach aims to achieve a better balance between privacy guarantees and model accuracy compared to standard DP methods in FL. The core contribution lies in the personalized allocation of the differential privacy budget, optimizing the system for a given application. Experimental results, although not presented here due to the focus on the method itself, would demonstrate the effectiveness of this approach.","url":"https://doi.org/10.5281/zenodo.22155151","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22155151","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22154635","name":"Distributed Federated Learning with Byzantine Fault Tolerance via Blockchain Verification","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, FL systems are vulnerable to Byzantine attacks, where malicious participants can inject faulty model updates, compromising model accuracy and potentially causing significant harm. This paper proposes a novel framework for distributed federated learning with Byzantine fault tolerance, utilizing a blockchain-based verification layer. Our system employs a distributed consensus mechanism on a blockchain to validate model updates generated by participating nodes. This approach provides a robust, trustless environment, guaranteeing data integrity and ensuring model accuracy even in the presence of malicious actors. The core claim is the implementation of FL systems with Byzantine fault tolerance through blockchain verification. The core mechanism involves local model training followed by blockchain recording of updates and distributed consensus verification. This work represents a significant advancement by integrating the benefits of FL with the security and immutability offered by blockchain technology. We demonstrate a viable path toward building resilient and reliable FL systems for diverse applications.","url":"https://doi.org/10.5281/zenodo.22154635","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22154635","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22154634","name":"Distributed Federated Learning with Byzantine Fault Tolerance via Blockchain Verification","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, FL systems are vulnerable to Byzantine attacks, where malicious participants can inject faulty model updates, compromising model accuracy and potentially causing significant harm. This paper proposes a novel framework for distributed federated learning with Byzantine fault tolerance, utilizing a blockchain-based verification layer. Our system employs a distributed consensus mechanism on a blockchain to validate model updates generated by participating nodes. This approach provides a robust, trustless environment, guaranteeing data integrity and ensuring model accuracy even in the presence of malicious actors. The core claim is the implementation of FL systems with Byzantine fault tolerance through blockchain verification. The core mechanism involves local model training followed by blockchain recording of updates and distributed consensus verification. This work represents a significant advancement by integrating the benefits of FL with the security and immutability offered by blockchain technology. We demonstrate a viable path toward building resilient and reliable FL systems for diverse applications.","url":"https://doi.org/10.5281/zenodo.22154634","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22154634","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22154355","name":"Decentralized Federated Learning with Byzantine Fault Tolerance via Consensus-Based Proofs","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can inject faulty model updates, compromising the global model's integrity. This paper proposes a novel decentralized federated learning framework incorporating Byzantine Fault Tolerant (BFT) consensus protocols. Our system utilizes cryptographic consensus mechanisms to validate and authenticate model updates from each participant before aggregation, thereby mitigating the risks posed by Byzantine nodes. The core innovation lies in the integration of FL with robust BFT consensus, ensuring secure and reliable model training even when faced with adversarial behavior. We introduce a framework utilizing verifiable computation and consensus-based proofs to achieve this. This approach allows for the detection and rejection of malicious updates, ultimately leading to a more trustworthy and resilient global model. The system is designed for scalability and adaptability, addressing key challenges in practical FL deployments. The key mathematical concepts underlying the system are represented through the following notation: (x_i, m_i), where x_i represents the data sample from participant i, and m_i represents the model update generated by participant i. The aggregation function is denoted as (Σ_{i=1}^K (α_i * m_i)), where α_i represents the learning rate for participant i, and K is the total number of participants. The BFT consensus protocol relies on a threshold number of participants (T) to reach agreement, and the proof of correctness is represented as P(m_true, m_agg), where m_true is the true global model, and m_agg is the aggregated model. Byzantine faults are represented as f_i, where f_i is the faulty model update from participant i. The probability of a successful consensus is denoted as P_success. The security level is characterized by the parameter β, representing the probability of successfully detecting a Byzantine update.","url":"https://doi.org/10.5281/zenodo.22154355","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22154355","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22154356","name":"Decentralized Federated Learning with Byzantine Fault Tolerance via Consensus-Based Proofs","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can inject faulty model updates, compromising the global model's integrity. This paper proposes a novel decentralized federated learning framework incorporating Byzantine Fault Tolerant (BFT) consensus protocols. Our system utilizes cryptographic consensus mechanisms to validate and authenticate model updates from each participant before aggregation, thereby mitigating the risks posed by Byzantine nodes. The core innovation lies in the integration of FL with robust BFT consensus, ensuring secure and reliable model training even when faced with adversarial behavior. We introduce a framework utilizing verifiable computation and consensus-based proofs to achieve this. This approach allows for the detection and rejection of malicious updates, ultimately leading to a more trustworthy and resilient global model. The system is designed for scalability and adaptability, addressing key challenges in practical FL deployments. The key mathematical concepts underlying the system are represented through the following notation: (x_i, m_i), where x_i represents the data sample from participant i, and m_i represents the model update generated by participant i. The aggregation function is denoted as (Σ_{i=1}^K (α_i * m_i)), where α_i represents the learning rate for participant i, and K is the total number of participants. The BFT consensus protocol relies on a threshold number of participants (T) to reach agreement, and the proof of correctness is represented as P(m_true, m_agg), where m_true is the true global model, and m_agg is the aggregated model. Byzantine faults are represented as f_i, where f_i is the faulty model update from participant i. The probability of a successful consensus is denoted as P_success. The security level is characterized by the parameter β, representing the probability of successfully detecting a Byzantine update.","url":"https://doi.org/10.5281/zenodo.22154356","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22154356","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22153776","name":"Information Bottleneck with Differential Privacy for Federated Learning","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing, thus addressing privacy concerns. However, FL is still susceptible to privacy breaches and suffers from significant information loss during model aggregation, a phenomenon addressed by the information bottleneck (IB) principle. This paper proposes a novel framework that integrates the IB technique with differential privacy (DP) within the FL setting. We formulate the problem as a constrained optimization, minimizing information loss while simultaneously satisfying DP guarantees. Our approach utilizes a compressed representation of local data, learned through an IB objective, and introduces noise to protect individual data points, ensuring privacy. The core contribution lies in the synergistic combination of these two techniques, leading to enhanced privacy protection and improved model accuracy compared to standard FL. We demonstrate the effectiveness of our framework through a theoretical analysis and outline potential implementation strategies. The primary goal is to achieve a balance between model performance and privacy preservation, a critical aspect often overlooked in current FL methodologies. The theoretical framework provides a foundation for future research and practical deployment in privacy-sensitive applications.","url":"https://doi.org/10.5281/zenodo.22153776","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22153776","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22153775","name":"Information Bottleneck with Differential Privacy for Federated Learning","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing, thus addressing privacy concerns. However, FL is still susceptible to privacy breaches and suffers from significant information loss during model aggregation, a phenomenon addressed by the information bottleneck (IB) principle. This paper proposes a novel framework that integrates the IB technique with differential privacy (DP) within the FL setting. We formulate the problem as a constrained optimization, minimizing information loss while simultaneously satisfying DP guarantees. Our approach utilizes a compressed representation of local data, learned through an IB objective, and introduces noise to protect individual data points, ensuring privacy. The core contribution lies in the synergistic combination of these two techniques, leading to enhanced privacy protection and improved model accuracy compared to standard FL. We demonstrate the effectiveness of our framework through a theoretical analysis and outline potential implementation strategies. The primary goal is to achieve a balance between model performance and privacy preservation, a critical aspect often overlooked in current FL methodologies. The theoretical framework provides a foundation for future research and practical deployment in privacy-sensitive applications.","url":"https://doi.org/10.5281/zenodo.22153775","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22153775","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22153623","name":"Distributed Graph Learning via Federated Bayesian Networks","source":"datacite","abstract":"This paper presents a novel approach to distributed graph learning utilizing Federated Bayesian Networks (FBNs). The core challenge in training large graph neural networks (GNNs) lies in the substantial computational resources required, often necessitating centralized training environments. Federated Bayesian Networks offer a decentralized solution, enabling learning across multiple clients without direct data sharing. The proposed method involves local training of Bayesian Networks on individual client graph subsets, followed by parameter aggregation by a central server to refine a global Bayesian Network model. This architecture addresses the limitations of traditional GNN training while prioritizing data privacy and mitigating computational demands. The key innovation lies in the synergistic combination of federated learning principles with the probabilistic inference capabilities of Bayesian Networks, resulting in a robust and scalable framework for distributed graph learning. This approach demonstrates the potential for efficient learning from decentralized graph data sources.","url":"https://doi.org/10.5281/zenodo.22153623","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22153623","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22153624","name":"Distributed Graph Learning via Federated Bayesian Networks","source":"datacite","abstract":"This paper presents a novel approach to distributed graph learning utilizing Federated Bayesian Networks (FBNs). The core challenge in training large graph neural networks (GNNs) lies in the substantial computational resources required, often necessitating centralized training environments. Federated Bayesian Networks offer a decentralized solution, enabling learning across multiple clients without direct data sharing. The proposed method involves local training of Bayesian Networks on individual client graph subsets, followed by parameter aggregation by a central server to refine a global Bayesian Network model. This architecture addresses the limitations of traditional GNN training while prioritizing data privacy and mitigating computational demands. The key innovation lies in the synergistic combination of federated learning principles with the probabilistic inference capabilities of Bayesian Networks, resulting in a robust and scalable framework for distributed graph learning. This approach demonstrates the potential for efficient learning from decentralized graph data sources.","url":"https://doi.org/10.5281/zenodo.22153624","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22153624","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22152846","name":"Distributed Federated Learning with Differential Privacy and Byzantine Fault Tolerance","source":"datacite","abstract":"Federated Learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, existing FL frameworks are susceptible to various vulnerabilities, including privacy breaches through information leakage and attacks from Byzantine clients attempting to compromise the learning process. This paper proposes a novel distributed FL framework that integrates differential privacy (DP) and Byzantine fault tolerance (BFT) mechanisms to address these challenges comprehensively. Our approach employs advanced DP techniques to rigorously limit the information revealed by individual clients during model updates, while simultaneously utilizing BFT algorithms to detect and mitigate the influence of malicious or faulty clients. The resulting system demonstrates improved security, enhanced privacy guarantees, and robustness against adversarial attacks, making it a significant advancement in the field of secure and reliable distributed learning. The core of our work lies in the synergistic combination of these two crucial techniques, providing a layered defense against potential threats in FL environments. We detail the mathematical formulations underlying our approach and provide a theoretical analysis of its performance.","url":"https://doi.org/10.5281/zenodo.22152846","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22152846","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22152847","name":"Distributed Federated Learning with Differential Privacy and Byzantine Fault Tolerance","source":"datacite","abstract":"Federated Learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, existing FL frameworks are susceptible to various vulnerabilities, including privacy breaches through information leakage and attacks from Byzantine clients attempting to compromise the learning process. This paper proposes a novel distributed FL framework that integrates differential privacy (DP) and Byzantine fault tolerance (BFT) mechanisms to address these challenges comprehensively. Our approach employs advanced DP techniques to rigorously limit the information revealed by individual clients during model updates, while simultaneously utilizing BFT algorithms to detect and mitigate the influence of malicious or faulty clients. The resulting system demonstrates improved security, enhanced privacy guarantees, and robustness against adversarial attacks, making it a significant advancement in the field of secure and reliable distributed learning. The core of our work lies in the synergistic combination of these two crucial techniques, providing a layered defense against potential threats in FL environments. We detail the mathematical formulations underlying our approach and provide a theoretical analysis of its performance.","url":"https://doi.org/10.5281/zenodo.22152847","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22152847","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22152251","name":"Federated Learning for Privacy-Preserving Data Analysis","source":"datacite","abstract":"This paper explores the application of federated learning (FL) as a novel approach to privacy-preserving data analysis. Traditional data analysis methods often require centralized data collection, raising significant privacy concerns. Federated learning offers a compelling alternative by enabling collaborative model training without direct data sharing. The core claim of this work is the utilization of FL to conduct data analysis while safeguarding user privacy. The proposed mechanism involves constructing a FL framework where participants train models locally on their own datasets and subsequently aggregate model parameters. This process ensures that raw data remains decentralized, mitigating privacy risks. We delve into the technical aspects of FL, focusing on key considerations such as model aggregation techniques, communication efficiency, and privacy guarantees. The research contributes to a growing body of work in decentralized learning and provides a framework for addressing privacy challenges in various data-intensive applications. The primary goal is to demonstrate the feasibility and benefits of FL for secure and collaborative data analysis. ---","url":"https://doi.org/10.5281/zenodo.22152251","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22152251","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.5281/zenodo.22152252","name":"Federated Learning for Privacy-Preserving Data Analysis","source":"datacite","abstract":"This paper explores the application of federated learning (FL) as a novel approach to privacy-preserving data analysis. Traditional data analysis methods often require centralized data collection, raising significant privacy concerns. Federated learning offers a compelling alternative by enabling collaborative model training without direct data sharing. The core claim of this work is the utilization of FL to conduct data analysis while safeguarding user privacy. The proposed mechanism involves constructing a FL framework where participants train models locally on their own datasets and subsequently aggregate model parameters. This process ensures that raw data remains decentralized, mitigating privacy risks. We delve into the technical aspects of FL, focusing on key considerations such as model aggregation techniques, communication efficiency, and privacy guarantees. The research contributes to a growing body of work in decentralized learning and provides a framework for addressing privacy challenges in various data-intensive applications. The primary goal is to demonstrate the feasibility and benefits of FL for secure and collaborative data analysis. ---","url":"https://doi.org/10.5281/zenodo.22152252","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22152252","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.24406/publica-5207","name":"Privacy-preserving multicenter differential protein abundance analysis with FedProt","source":"datacite","abstract":"Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutions enhances statistical power but raises serious privacy concerns. Here we introduce FedProt, the first privacy-preserving tool for collaborative differential protein abundance analysis of distributed data, which utilizes federated learning and additive secret sharing. In the absence of a multicenter patient-derived dataset for evaluation, we created two: one at five centers from E. coli experiments and one at three centers from human serum. Evaluations using these datasets confirm that FedProt achieves accuracy equivalent to the DEqMS method applied to pooled data, with completely negligible absolute differences no greater than 4 × 10-12. By contrast, -log10P computed by the most accurate meta-analysis methods diverged from the centralized analysis results by up to 25-26.","url":"https://doi.org/10.24406/publica-5207","authors":["Burankova, Yuliya","Abele, Miriam","Bakhtiari, Mohammad","Toerne, Christine von","Barth, Teresa K.","Schweizer, Lisa","Giesbertz, Pieter","Schmidt, Johannes","Kalkhof, Stefan","Müller-Deile, Janina","van Veelen, Peter A.","Mohammed, Yassene","Hammer, Elke","Hauck, Stefanie M.","Lichtenthaler, Stefan F.","Imhof, Axel","Hartebrodt, Anne","Frisch, Tobias","Mann, Matthias","Arend, Lis","Adamowicz, Klaudia","Röttger, Richard","Schwämmle, Veit","Laske, Tanja","Meng, Chen","Ludwig, Christina","Kuster, Bernhard","Matschinske, Julian","Späth, Julian","Baumbach, Jan","Zolotareva, Olga",":unav"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.24406/publica-5207","addedAt":"2026-08-31T06:41:22.150Z","updatedAt":"2026-08-31T06:41:22.150Z"},{"id":"doi:10.59019/yqmn2337","name":"Blockchain and Federated Learning for Remote Health Monitoring","source":"crossref","abstract":"Digital technologies are revolutionizing healthcare, particularly in Remote Health Monitoring (RHM). RHM uses digital tools to monitor patients’ health data outside traditional clinical settings, allowing for continuous real-time management. This approach has the potential to improve health outcomes and reduce costs. Key technologies such as AI, Blockchain, and Federated Learning have significantly advanced RHM, each bringing unique contributions. AI is reshaping RHM by analyzing data from wearable devices and sensors, facilitating early diagnosis, trend prediction, and personalized treatment. FL protects privacy by training AI models on decentralized devices without sharing raw data, reducing data breach risks. Blockchain enhances patient data security by making records immutable and accessible only to authorized parties while enabling seamless data sharing among healthcare providers, improving data integrity and security for reliable health decisions in RHM systems. Despite these advancements, challenges persist in fully realizing the potential of Blockchain and FL in RHM. This research highlights recent advancements in the literature for RHM using Blockchain and FL to enhance data security and privacy. Techniques such as Homomorphic Encryption and multi-factor authentication are emphasized for their roles in protecting sensitive patient data. However, challenges remain, including vulnerabilities in current authentication schemes, scalability and single points of failure issues with Blockchain, and high communication costs in FL, all of which require further research and development. As part of this research, innovative solutions were developed, including the IoT Standards Reference Model, which integrates AI and Blockchain with medical devices to enhance data security and efficiency. Additionally, Consortium Blockchain models were created to address issues of latency and single points of failure. The Federated DefenseNet app was also developed, combining Consortium Blockchain with generative AI to improve prediction accuracy, security, and data privacy in RHM systems. In conclusion, while this research has made substantial progress in integrating Blockchain and FL into RHM systems, ongoing efforts are still required to address the remaining challenges. Future work will concentrate on refining these technologies and prototypes to better address scalability and privacy concerns. Building on the contributions already made, the research aims to further enhance the security and efficiency of RHM systems, ultimately leading to improved healthcare outcomes.","url":"https://doi.org/10.59019/yqmn2337","authors":["Venkatesh Upadrista"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-17T14:43:28Z","doi":"10.59019/yqmn2337","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.7717/peerj-cs.3354/fig-17","name":"Figure 17: Error analysis of federated-learning approach (Algorithm 2).","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3354/fig-17","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-01T08:00:31Z","doi":"10.7717/peerj-cs.3354/fig-17","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.53347/rid-81590","name":"Federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.53347/rid-81590","authors":["Jay Gajera","Henry Knipe","James Condon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-10-25T02:51:36Z","doi":"10.53347/rid-81590","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.14711/thesis-991013319956603412","name":"Towards private and efficient cross-device federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013319956603412","authors":["Zhifeng Jiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-17T01:55:46Z","doi":"10.14711/thesis-991013319956603412","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-031-01585-4_7","name":"Incentive Mechanism Design for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-01585-4_7","authors":["Qiang Yang","Yang Liu","Yong Cheng","Yan Kang","Tianjian Chen","Han Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-09T04:46:22Z","doi":"10.1007/978-3-031-01585-4_7","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1002/9781394461295.ch13","name":"Federated Learning for Smart Agricultural Supply Chains","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.ch13","authors":["Mamta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.ch13","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.7717/peerj-cs.3589/table-4","name":"Table 4: Scalability analysis of blockchain operations in federated learning.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3589/table-4","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T08:57:09Z","doi":"10.7717/peerj-cs.3589/table-4","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.14711/thesis-991012994506603412","name":"Robust federated learning with attack-adaptive aggregation","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991012994506603412","authors":["Ching Pui Wan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-29T00:12:52Z","doi":"10.14711/thesis-991012994506603412","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1145/3803630.3809173","name":"Byzantine-Robust Federated Learning under Heterogeneity: Personalization and Transfer as a Way Forward?","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3803630.3809173","authors":["Rafael Pinot"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-22T12:25:02Z","doi":"10.1145/3803630.3809173","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1002/9781119913924.ch5","name":"Deep Federated Learning Based on Knowledge Distillation and Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781119913924.ch5","authors":["Hui Lin","Feng Yu","Xiaoding Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-12-02T00:27:27Z","doi":"10.1002/9781119913924.ch5","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flics70075.2026.11621929","name":"FL-EHDS: A Privacy-Preserving Multimodal Federated Learning Framework for the European Health Data Space","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621929","authors":["Fabio Liberti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:37Z","doi":"10.1109/flics70075.2026.11621929","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-030-85559-8_1","name":"Introduction to Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-85559-8_1","authors":["Mohit Pandey","Shubhangi Pandey","Ajit Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-02T12:03:00Z","doi":"10.1007/978-3-030-85559-8_1","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.21203/rs.3.rs-3658124/v1","name":"Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning","source":"preprints","abstract":"Abstract Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.As a flexible learning setting, federated learning has the potential to integrate with other learning frameworks.We conduct a focused survey of federated learning in conjunction with other learning algorithms. Specifically, we explore various learning algorithms to improve the vanilla federated averaging algorithm and review model fusion methods such as adaptive aggregation, regularization, clustered methods, and Bayesian methods. Following the emerging trends, we also discuss federated learning in the intersection with other learning paradigms, termed federated X learning, where X includes multitask learning, meta-learning, transfer learning, unsupervised learning, and reinforcement learning. This survey reviews the state of the art, challenges, and future directions.","url":"https://doi.org/10.21203/rs.3.rs-3658124/v1","authors":["Shaoxiong Ji","Yue Tan","Teemu Saravirta","Zhiqin Yang","Yixin Liu","Lauri Vasankari","Shirui Pan","Guodong Long","Anwar Walid"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-3658124/v1","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1007/978-3-030-63076-8_14","name":"Collaborative Fairness in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-63076-8_14","authors":["Lingjuan Lyu","Xinyi Xu","Qian Wang","Han Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-25T20:03:24Z","doi":"10.1007/978-3-030-63076-8_14","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00020-0","name":"Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00020-0","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00020-0","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/aicas51828.2021.9458510","name":"Federated Regularization Learning: an Accurate and Safe Method for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aicas51828.2021.9458510","authors":["Tianqi Su","Meiqi Wang","Zhongfeng Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-06-23T20:01:10Z","doi":"10.1109/aicas51828.2021.9458510","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-981-19-8692-5_8","name":"Anonymous Communication and Shuffle Model in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-19-8692-5_8","authors":["Shui Yu","Lei Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-26T21:30:41Z","doi":"10.1007/978-981-19-8692-5_8","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/ieeestd.2024.10807155","name":"IEEE Guide for Framework for Trustworthy Federated Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ieeestd.2024.10807155","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-18T14:28:06Z","doi":"10.1109/ieeestd.2024.10807155","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.14264/2264705","name":"Effective and secure federated online learning to rank","source":"crossref","abstract":"","url":"https://doi.org/10.14264/2264705","authors":["Shuyi Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-22T01:25:15Z","doi":"10.14264/2264705","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.7717/peerj-cs.3589/fig-8","name":"Figure 8: Comparison of using reputation management in federated learning.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3589/fig-8","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T08:57:09Z","doi":"10.7717/peerj-cs.3589/fig-8","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.46569/9s161f47k","name":"Privacy and Security Enhanced \nFederated Learning Framework \nDesign","source":"crossref","abstract":"","url":"https://doi.org/10.46569/9s161f47k","authors":["Yasaman Pakdel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-02T19:22:20Z","doi":"10.46569/9s161f47k","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-030-63076-8_17","name":"Federated Learning for Open Banking","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-63076-8_17","authors":["Guodong Long","Yue Tan","Jing Jiang","Chengqi Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-25T15:03:24Z","doi":"10.1007/978-3-030-63076-8_17","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-031-11748-0_6","name":"A Contract Theory Based Incentive Mechanism for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-11748-0_6","authors":["Yuan Liu","Mengmeng Tian","Yuxin Chen","Zehui Xiong","Cyril Leung","Chunyan Miao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-30T18:05:01Z","doi":"10.1007/978-3-031-11748-0_6","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-030-85559-8_14","name":"Quantum Federated Learning for Wireless Communications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-85559-8_14","authors":["R. M. Pujahari","Akshit Tanwar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-02T12:03:00Z","doi":"10.1007/978-3-030-85559-8_14","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-031-01585-4_8","name":"Federated Learning for Vision, Language, and Recommendation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-01585-4_8","authors":["Qiang Yang","Yang Liu","Yong Cheng","Yan Kang","Tianjian Chen","Han Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-09T04:17:40Z","doi":"10.1007/978-3-031-01585-4_8","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.14711/thesis-hdl169584","name":"Heterogeneity-Aware Theory and Algorithms for Federated Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-hdl169584","authors":["Zhijie Xie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-12T23:02:19Z","doi":"10.14711/thesis-hdl169584","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-030-63076-8_13","name":"Budget-Bounded Incentives for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-63076-8_13","authors":["Adam Richardson","Aris Filos-Ratsikas","Boi Faltings"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-25T20:03:24Z","doi":"10.1007/978-3-030-63076-8_13","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.22541/au.162074596.66890690/v1","name":"Federated Learning Versus Classical Machine Learning: A Convergence Comparison","source":"crossref","abstract":"In the past few decades, machine learning has revolutionized data processing for large scale applications. Simultaneously , increasing privacy threats in trending applications led to the redesign of classical data training models. In particular, classical machine learning involves centralized data training, where the data is gathered, and the entire training process executes at the central server. Despite significant convergence, this training involves several privacy threats on participants' data when shared with the central cloud server. To this end, federated learning has achieved significant importance over distributed data training. In particular, the federated learning allows participants to collaboratively train the local models on local data without revealing their sensitive information to the central cloud server. In this paper, we perform a convergence comparison between classical machine learning and federated learning on two publicly available datasets, namely, logistic-regression-MNIST dataset and image-classification-CIFAR-10 dataset. The simulation results demonstrate that federated learning achieves higher convergence within limited communication rounds while maintaining participants' anonymity. We hope that this research will show the benefits and help federated learning to be implemented widely.","url":"https://doi.org/10.22541/au.162074596.66890690/v1","authors":["Muhammad Asad","Ahmed Moustafa","Takayuki Ito"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-05-11T11:54:23Z","doi":"10.22541/au.162074596.66890690/v1","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.61557/vnfu8593","name":"Federated learning: an introduction [report]","source":"crossref","abstract":"","url":"https://doi.org/10.61557/vnfu8593","authors":["Anastasia Shteyn","Konrad Kollnig","Calum Inverarity"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-22T18:58:46Z","doi":"10.61557/vnfu8593","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1201/9781003384854-4","name":"Secure and Private Federated Learning through Encrypted Parameter Aggregation","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003384854-4","authors":["K Vijayalakshmi","PM Sitharselvam","I Thamarai","J Ashok","Goski Sathish","S Mayakannan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-16T11:04:28Z","doi":"10.1201/9781003384854-4","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.5772/intechopen.1014595","name":"Federated Learning at the Edge: Enabling AI in 5G/6G Networks and Massive IoT","source":"crossref","abstract":"Federated learning (FL) at the edge is emerging as a cornerstone of next-generation AI, particularly in the era of 5G and 6G connectivity. This chapter explores how FL enables distributed devices and edge servers to collaboratively train AI models across massive IoT (mIoT) environments without centralizing sensitive data. By leveraging edge computing, billions of sensors, smartphones, and smart machines can contribute to powerful global models while preserving privacy and reducing latency. The result is AI that is both scalable and secure, transforming the deluge of decentralized data into actionable intelligence. Real-world applications and trends are highlighted to demonstrate FL’s transformative impact. From smart city sensors and wearable healthcare devices that jointly detect anomalies to 6G base stations coordinating for optimal network performance, FL enables smarter and more resilient systems. The chapter also discusses how this approach addresses challenges such as data privacy, communication costs, and device heterogeneity, making it a critical enabler for digital transformation. As industries embrace connected devices and automation, federated edge learning is poised to drive innovation, unlocking intelligent services, enhancing security, and shaping the future of AI-driven enterprises. Building on this motivation, the chapter progresses from FL foundations to real-world deployment in edge settings, covering key protocols and architectures, including synchronous/asynchronous and hierarchical decentralized approaches. It then surveys system frameworks that jointly optimize learning with energy, communication efficiency, and security, as well as FL-based real-time anomaly detection and intrusion detection systems (IDS) for sensitive Medical IoT applications. Subsequently, it targets 5G/6G intelligence, including spectrum and beam management, Over-The-Air aggregation, and infrastructure learning such as federated radio resource management and D2D-enabled decentralized exchange. Finally, it connects to standards and practice (e.g., ITU-T/3GPP), provides comparison tables, and closes with a gap analysis on scaling, validation, governance, incentives, and interoperability.","url":"https://doi.org/10.5772/intechopen.1014595","authors":["Iacovos Ioannou","Christophoros Christophorou","Vasos Vassiliou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T14:08:56Z","doi":"10.5772/intechopen.1014595","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1201/9781003303374-6","name":"Trusted Federated Learning Solutions for Internet of Medical Things","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003303374-6","authors":["Sagar Lakhanotra","Jaimik Chauhan","Vivek Kumar Prasad","Pronaya Bhattacharya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-05-03T20:41:25Z","doi":"10.1201/9781003303374-6","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flics70075.2026.11621950","name":"Adaptive Evolutionary Clustering for Federated Learning under Nonstationary Client Distributions","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621950","authors":["Yiyue Chen","Usman Akram","Chianing Wang","Haris Vikalo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:14:43Z","doi":"10.1109/flics70075.2026.11621950","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.31979/etd.tujq-uxhp","name":"Bias and Fairness in Federated Learning for Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.31979/etd.tujq-uxhp","authors":["Pratikkumar Dalsukhbhai Korat"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-17T19:02:56Z","doi":"10.31979/etd.tujq-uxhp","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.5772/intechopen.1015368","name":"Federated Learning: A Comprehensive Review of Methods, Challenges, Applications and Trends","source":"crossref","abstract":"Distributed data collaboration allows machine learning models to be trained on multiple independent sources without compromising confidential information. This approach, known as federated learning (FL), represents a revolutionary shift in how artificial intelligence systems can learn from decentralized datasets. This paper presents an extensive investigation of the current state of FL, encompassing its foundational principles, challenges, applications, and emerging practices. Key aspects discussed include privacy-preserving mechanisms, model aggregation techniques, and strategies to address data heterogeneity, communication efficiency, and adversarial attacks. The paper highlights significant advancements in FL, such as its integration with large language models, transfer learning, and hierarchical architectures, which are driving its adoption across domains like healthcare, finance, and edge computing. Additionally, unresolved challenges and actionable future research directions are outlined, including scalability improvements, regulatory compliance, and explainability. By synthesizing recent innovations and identifying gaps, this review provides a resource for researchers and professionals in the domain of FL technologies and applications.","url":"https://doi.org/10.5772/intechopen.1015368","authors":["Babak Basharirad","Sunil Choenni","Mortaza S. Bargh and\nAhmad Omar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-14T12:45:32Z","doi":"10.5772/intechopen.1015368","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.31979/etd.duud-g643","name":"An Empirical Analysis of Adversarial Attacks in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.31979/etd.duud-g643","authors":["Rohit Mapakshi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-01T13:42:04Z","doi":"10.31979/etd.duud-g643","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00015-7","name":"Privacy and profit: the dual benefits of federated learning in metaverse healthcare systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00015-7","authors":["Manas Kumar Yogi","Likhita Nam","Pavani Adina","Lolla Venkata Manaswini Rajeswari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00015-7","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1201/9781003595540-10","name":"Cyber Threat Detection in IoT-Enabled Edge Computing Using Optimized Federated Deep Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540-10","authors":["V.R. Ramesh Babu","G. Jayamurugan","R. Vijayarangan","V. Thirumurgan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","doi":"10.1201/9781003595540-10","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.31274/td-20250502-190","name":"Privacy-preserving detection of poisoning attacks in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.31274/td-20250502-190","authors":["Trent Muhr"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-02T19:34:42Z","doi":"10.31274/td-20250502-190","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.31979/etd.xzp5-4wv4","name":"Secure and Resilient Federated Learning for Malware Classification","source":"crossref","abstract":"","url":"https://doi.org/10.31979/etd.xzp5-4wv4","authors":["Chandrakanth Reddy Challa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-31T21:09:09Z","doi":"10.31979/etd.xzp5-4wv4","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.12681/eadd/61634","name":"Machine learning and neural methods for federated patent search","source":"crossref","abstract":"Η παρούσα διδακτορική διατριβή διερευνά τον εξειδικευμένο και ιδιαίτερα απαιτητικό τομέα της ανάκτησης πληροφοριών από διπλώματα ευρεσιτεχνίας (πατέντες), με στόχο τη γεφύρωση του χάσματος μεταξύ των παραδοσιακών μεθοδολογιών αναζήτησης και των σύγχρονων δυνατοτήτων της Τεχνητής Νοημοσύνης (AI). Η επαγγελματική αναζήτηση πατεντών χαρακτηρίζεται ως μια διεργασία κρίσιμης σημασίας, προσανατολισμένη στην υψηλή ανάκληση (recall), όπου η αποτυχία εντοπισμού έστω και ενός σχετικού εγγράφου μπορεί να επιφέρει σοβαρές συνέπειες, όπως ακύρωση διπλωμάτων, δικαστικές διαμάχες για παραβίαση δικαιωμάτων και σημαντικές οικονομικές απώλειες. Παρά την πρόοδο στην Επεξεργασία Φυσικής Γλώσσας, τα τυπικά νευρωνικά μοντέλα συχνά υποαποδίδουν στον τομέα αυτό λόγω των μοναδικών χαρακτηριστικών των πατεντών, οι οποίες χρησιμοποιούν εξαιρετικά τεχνική και συχνά σκόπιμα ασαφή νομική ορολογία για τον καθορισμό του πεδίου προστασίας. Για την αντιμετώπιση αυτών των εγγενών προκλήσεων — συγκεκριμένα του εξαιρετικά μεγάλου μήκους των εγγράφων, της πολύπλοκης δομής και του προβλήματος της \"λεξικής αναντιστοιχίας\" — η παρούσα έρευνα αναπτύσσει και επικυρώνει καινοτόμα πλαίσια για δύο κρίσιμα στάδια του αγωγού Ομοσπονδιακής Αναζήτησης: τη συγχώνευση αποτελεσμάτων και την ανακατάταξη εγγράφων. Μια κεντρική πρόκληση που διατρέχει το σύνολο της εργασίας είναι ο αποτελεσματικός χειρισμός δομημένων εγγράφων μεγάλου μήκους (long documents) στην εποχή των μοντέλων Transformer. Οι συνήθεις νευρωνικές αρχιτεκτονικές αδυνατούν να επεξεργαστούν αποτελεσματικά το μήκος των πατεντών λόγω υπολογιστικών περιορισμών. Κοινές λύσεις σε αυτόν τον περιορισμό μήκους περιλαμβάνουν την συνόψιση κειμένου, την τμηματοποίηση του εγγράφου (segmentation), ή την χρήση εξειδικευμένων μοντέλων με εκτεταμένο πλαίσιο αναφοράς (long-context). Η παρούσα διατριβή υιοθετεί μια προσέγγιση βασισμένη στην κατάτμηση (segmentation), η οποία θεμελιώνεται θεωρητικά στην «υπόθεση εμβέλειας» (scope hypothesis). Η υπόθεση αυτή υποστηρίζει ότι η συνάφεια σε μια πατέντα είναι συχνά εντοπισμένη σε συγκεκριμένες δομικές ενότητες — όπως η Περίληψη, η Περιγραφή ή οι Αξιώσεις — και δεν κατανέμεται ομοιόμορφα. Αξιοποιώντας αυτά τα διακριτά στοιχεία, η προτεινόμενη μεθοδολογία διατηρεί τις λεπτομερείς τεχνικές πληροφορίες που είναι απαραίτητες για την κρίση καινοτομίας, οι οποίες συνήθως χάνονται κατά την ολιστική επεξεργασία του εγγράφου. Στο πρώτο μέρος της διατριβής, αντιμετωπίζουμε την πρόκληση της συγχώνευσης αποτελεσμάτων από κατανεμημένες, ετερογενείς πηγές, όπου οι βαθμολογίες συνάφειας είναι συχνά μη συγκρίσιμες ή μη διαθέσιμες. Εισάγουμε το πλαίσιο Machine Learning Models for Results Merging (MLRM), το οποίο αξιοποιεί ένα Κεντρικό Ευρετήριο Δειγμάτων (CSI) ως δυναμικό πεδίο εκπαίδευσης για την κανονικοποίηση των τοπικών βαθμολογιών σε έναν ενιαίο παγκόσμιο χώρο συνάφειας. Μέσω εκτεταμένων πειραμάτων, αποδεικνύουμε ότι οι μέθοδοι ομαδικής μάθησης (ensemble learning), και συγκεκριμένα τα Random Forests, υπερέχουν σημαντικά έναντι καθιερωμένων ευριστικών μεθόδων όπως οι CORI και SSL. Αυτό θέτει ένα νέο σημείο αναφοράς για τη συγχώνευση αποτελεσμάτων, αποδεικνύοντας ιδιαίτερη ανθεκτικότητα σε μη συνεργατικά περιβάλλοντα όπου οι μηχανές αναζήτησης λειτουργούν ως «μαύρα κουτιά». Η δεύτερη κύρια συνεισφορά είναι το πλαίσιο Query-Aware Patent Re-ranking (QAPR), σχεδιασμένο να βελτιστοποιεί την τελική κατάταξη αντιμετωπίζοντας τις πατέντες ως ακολουθίες σημασιολογικά ανεξάρτητων δομικών στοιχείων. Το QAPR χρησιμοποιεί μια υβριδική νευρωνική αρχιτεκτονική για τη σύνθεση τριών διαφορετικών κατηγοριών σημάτων συνάφειας: λεξικά σήματα (lexical) που καταγράφουν ακριβείς αντιστοιχίες όρων μέσω του BM25, σημασιολογικά σήματα (semantic) που χρησιμοποιούν αναπαραστάσεις transformer (SBERT) για την καταγραφή βαθύτερου εννοιολογικού πλαισίου, και δομικά σήματα (structural) που μοντελοποιούν τις ρητές σχέσεις και τα βάρη σπουδαιότητας μεταξύ των διαφορετικών ενοτήτων του εγγράφου. Μια βασική καινοτομία είναι η εισαγωγή ενός ","url":"https://doi.org/10.12681/eadd/61634","authors":["Βασίλειος Σταμάτης"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-07T08:10:12Z","doi":"10.12681/eadd/61634","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.70593/978-93-7185-127-5_5","name":"An Intelligent Decentralized Framework for Next-Generation IoT Security Using Blockchain and Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.70593/978-93-7185-127-5_5","authors":["MUTHUPANDI G","MALLIGA M","THIRUMALAI MURUGAN","Vignesh J"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-03T20:52:58Z","doi":"10.70593/978-93-7185-127-5_5","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-030-63076-8_6","name":"Towards Byzantine-Resilient Federated Learning via Group-Wise Robust Aggregation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-63076-8_6","authors":["Lei Yu","Lingfei Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-25T20:03:24Z","doi":"10.1007/978-3-030-63076-8_6","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-030-85559-8_5","name":"Some Observations on the Behaviour of Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-85559-8_5","authors":["Vishal Kaushal","Sangeeta Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-02T12:03:00Z","doi":"10.1007/978-3-030-85559-8_5","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-030-85559-8_2","name":"Federated Learning for IoT Devices","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-85559-8_2","authors":["Deena Nath Gupta","Rajendra Kumar","Ashwani Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-02T12:03:00Z","doi":"10.1007/978-3-030-85559-8_2","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-030-63076-8_5","name":"Large-Scale Kernel Method for Vertical Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-63076-8_5","authors":["Zhiyuan Dang","Bin Gu","Heng Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-25T20:03:24Z","doi":"10.1007/978-3-030-63076-8_5","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.5220/0010485600002932","name":"Explainable Federated Learning for Taxi Travel Time Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0010485600002932","authors":["Jelena Fiosina"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-03T09:15:40Z","doi":"10.5220/0010485600002932","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-030-85559-8_6","name":"Federated Learning with Cooperating Devices: A Consensus Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-85559-8_6","authors":["Radhika Vadhi","Abhishek Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-02T12:03:00Z","doi":"10.1007/978-3-030-85559-8_6","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.14711/thesis-991012980216803412","name":"Enabling privacy-preserving concept stock recommendation with federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991012980216803412","authors":["Zhuoyi Peng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-28T04:49:25Z","doi":"10.14711/thesis-991012980216803412","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.14711/thesis-991013222941803412","name":"Secure embedding aggregation for cross-silo federated representation learning","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013222941803412","authors":["Jiaxiang Tang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-21T22:45:02Z","doi":"10.14711/thesis-991013222941803412","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flics70075.2026.11621886","name":"FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621886","authors":["Fabian Stricker","Jose A. Peregrina","David Bermbach","Christian Zirpins"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:10:57Z","doi":"10.1109/flics70075.2026.11621886","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.5220/0010485606700677","name":"Explainable Federated Learning for Taxi Travel Time Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0010485606700677","authors":["Jelena Fiosina"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-05-07T07:35:10Z","doi":"10.5220/0010485606700677","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-030-70604-3_6","name":"Federated Learning Systems for Healthcare: Perspective and Recent Progress","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-70604-3_6","authors":["Yogesh Kumar","Ruchi Singla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-06-10T23:42:05Z","doi":"10.1007/978-3-030-70604-3_6","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.26481/dis.20260611ak","name":"Unlocking Value of Data with Vertical Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.26481/dis.20260611ak","authors":["Khan Afsana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-06T15:07:37Z","doi":"10.26481/dis.20260611ak","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.7717/peerj-cs.3750/fig-1","name":"Figure 1: Workflow of ERP software design personalization using federated learning.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3750/fig-1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-02T08:53:41Z","doi":"10.7717/peerj-cs.3750/fig-1","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.14711/thesis-991013080354203412","name":"Studies on biomolecular structure learning and federated privacy protection","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013080354203412","authors":["Hanlin Gu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-21T22:45:02Z","doi":"10.14711/thesis-991013080354203412","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1201/9781003384854-9","name":"Future of Medical Research with a Data-driven Federated Learning Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003384854-9","authors":["G. Arun Sampaul Thomas","S. Muthukaruppasamy","S. Sathish Kumar","K. Saravanan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-16T11:04:28Z","doi":"10.1201/9781003384854-9","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.70675/57528e3bzcceaz4df0zb395zc9c814a35991","name":"Improvement of Federated Learning Models for E-health","source":"crossref","abstract":"Amélioration des modèles d’apprentissage fédéré pour la cybersanté L'apprentissage fédéré (FL) est devenu un paradigme prometteur pour l'apprentissage automatique collaboratif, permettant à plusieurs participants d'entraîner des modèles partagés tout en préservant la confidentialité des données. Malgré son potentiel, le FL fait face à des défis importants notamment la surcharge de communication, l'hétérogénéité des données, l'hétérogénéité des modèles, et la dégradation des performances dans des conditions de données non-indépendantes et non-identiquement distribuées (non-IID). Ces défis sont particulièrement critiques dans des domaines sensibles tels que la santé, la finance et l'informatique mobile, où la préservation de la vie privée et la précision du modèle sont essentielles. Cette thèse aborde ces défis par l'intégration de techniques de distillation de connaissances (KD) dans les cadres d'apprentissage fédéré. La distillation de connaissances, qui transfert les connaissances de modèles enseignants complexes vers des modèles étudiants plus simples, offre une approche puissante pour atténuer les coûts de communication et gérer les environnements hétérogènes tout en maintenant les performances du modèle. La première contribution introduit FedFB, un cadre d'apprentissage fédéré efficace en communication conçu pour les applications d'imagerie médicale. FedFB utilise la distillation de connaissances d'ensemble en ligne avec un module d'attention-convolution auxiliaire pour permettre une collaboration efficace dans des conditions de distribution de données non-IID. Le cadre démontre une réduction substantielle de la surcharge de communication tout en maintenant une précision de classification élevée dans l'analyse d'échographie cérébrale fœtale. La seconde contribution propose FedAK, un cadre d'apprentissage fédéré semi-supervisé en un seul tour de communication (one-shot), combinant des mécanismes d'attention au niveau des caractéristiques avec la distillation de connaissances. Contrairement aux approches classiques d'apprentissage fédéré multi-tours, FedAK ne nécessite qu'un seul tour de communication, réduisant ainsi considérablement le trafic réseau. Le module d'agrégation basé sur l'attention gère efficacement l'hétérogénéité des modèles et génère des caractéristiques d'ensemble informatives pour l'entraînement d'un modèle étudiant global. Dans l'ensemble, à travers une validation expérimentale approfondie sur plusieurs jeux de données de référence et des scénarios réels en imagerie médicale, cette thèse démontre que les approches basées sur la distillation de connaissances permettent de répondre efficacement aux défis fondamentaux de l'apprentissage fédéré. Les cadres proposés offrent des solutions pratiques améliorant l'efficacité de la communication, la robustesse face à l'hétérogénéité des données et des modèles, ainsi que les performances globales d'apprentissage dans des contextes d'imagerie médicale.","url":"https://doi.org/10.70675/57528e3bzcceaz4df0zb395zc9c814a35991","authors":["Hassan Salman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-18T08:15:11Z","doi":"10.70675/57528e3bzcceaz4df0zb395zc9c814a35991","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.5463/thesis.1143","name":"Vertical Federated Learning for Cerebrovascular Accident Outcome Prediction","source":"crossref","abstract":"Accurate and early predictions of CVA outcomes are critical for informing patients and healthcare professionals and help with creating personalized rehabilitation plans that aid in guiding treatment decisions and improving patient outcomes. Artificial Intelligence (AI) can be used to provide personalized predictions of stroke outcomes, but CVA patients typically have their data distributed across multiple care institutions during treatment and recovery. To avoid the privacy, security, and data ownership issues that come with centralizing medical data, federated learning (FL) can offer an alternative. In FL, the model is trained cooperatively, by bringing models to the data instead of data to the models, allowing the data of each party to remain at the source. However, in case of CVA outcome prediction, or in any other scenario where patient data are distributed in a chain of care (also known as vertically partitioned data), FL has additional challenges. The aim of this thesis is to provide personalized outcome predictions for the rehabilitation of patients who suffered from a CVA, by utilizing federated learning on vertically partitioned data in an accurate, secure, and clinically implementable manner. With regard to accuracy, we see that developments of more complex neural networks and frameworks, such as multimodal networks and vertically federated learning, allow better use of data and greater accuracy in prediction of stroke outcomes. Vertical federated learning might lead to a slight drop in predictive performance compared to centralized learning, but this loss is generally minimal. We show that secure vertical federated learning is a solution for dealing with vertically partitioned stroke outcome data. We introduce our SVFL framework to create the first CVA outcome model using hospital and rehabilitation data in a vertically federated setting. Our framework prevents label and data leakage through encrypted active-party backpropagation and outperforms a model built on data from a single care institute only. To achieve clinical implementability of the prediction model, the most important factors to consider are good reliability and clear communication of relevance, as these will be essential for the wide adoption of such a prediction model. In all, this thesis explores the development and implementation of a clinically relevant outcome prediction algorithm for situations where data are vertically partitioned and a centralized data repository is not desired or not possible.","url":"https://doi.org/10.5463/thesis.1143","authors":["Corinne Geertruida Allaart"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-14T22:01:05Z","doi":"10.5463/thesis.1143","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1002/9781119913924.ch10","name":"Analog Over‐the‐Air Federated Learning: Design and Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781119913924.ch10","authors":["Howard H. Yang","Zihan Chen","Tony Q. S. Quek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-12-02T00:27:27Z","doi":"10.1002/9781119913924.ch10","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.32657/10356/201610","name":"Improving participants’ competitiveness in an open federated learning environment","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/201610","authors":["Xavier Tan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-05T06:52:11Z","doi":"10.32657/10356/201610","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.14264/003e2fe","name":"Attacks and defenses in federated learning: a client perspective","source":"crossref","abstract":"","url":"https://doi.org/10.14264/003e2fe","authors":["Mengyao Ma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-02T04:20:41Z","doi":"10.14264/003e2fe","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1002/9781119913924.ch12","name":"User‐Centric Decentralized Federated Learning for Autoencoder‐Based CSI Feedback","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781119913924.ch12","authors":["Shi Jin","Jiajia Guo","Yan Lv","Yiming Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-12-02T00:27:27Z","doi":"10.1002/9781119913924.ch12","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1017/9781108966559.016","name":"Optimized Federated Learning in Wireless Networks with Constrained Resources","source":"crossref","abstract":"","url":"https://doi.org/10.1017/9781108966559.016","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-16T00:05:40Z","doi":"10.1017/9781108966559.016","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.32657/10356/199948","name":"Stakeholder-oriented decision support in auction-based federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/199948","authors":["Xiaoli Tang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-11T08:48:03Z","doi":"10.32657/10356/199948","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-981-16-4963-9_6","name":"Unsupervised Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-16-4963-9_6","authors":["Choong Seon Hong","Latif U. Khan","Mingzhe Chen","Dawei Chen","Walid Saad","Zhu Han"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-01T05:02:09Z","doi":"10.1007/978-981-16-4963-9_6","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-030-85559-8_8","name":"Communication-Efficient Federated Learning in Wireless-Edge Architecture","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-85559-8_8","authors":["Sugandh Gupta","Sapna Katiyar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-02T12:03:00Z","doi":"10.1007/978-3-030-85559-8_8","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.14711/thesis-991013246557703412","name":"Content-Aware Client Selection for Communication-Efficient Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013246557703412","authors":["Zhefeng Qiao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-22T03:30:09Z","doi":"10.14711/thesis-991013246557703412","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.14711/thesis-hdl152414","name":"Understanding, Improving, and Implementing Federated Learning on Heterogeneous Data","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-hdl152414","authors":["Tailin Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-18T23:04:14Z","doi":"10.14711/thesis-hdl152414","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.31979/etd.96qt-wqec","name":"Sirilla","source":"crossref","abstract":"","url":"https://doi.org/10.31979/etd.96qt-wqec","authors":["Andrew Selvia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-26T21:05:06Z","doi":"10.31979/etd.96qt-wqec","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1007/978-3-030-85559-8_10","name":"Federated Learning Using Tensor Flow","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-85559-8_10","authors":["Tanu Solanki","Bipin Kumar Rai","Shivani Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-02T12:03:00Z","doi":"10.1007/978-3-030-85559-8_10","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.31274/td-20240329-43","name":"Addressing stale gradients in asynchronous federated deep reinforcement learning","source":"crossref","abstract":"","url":"https://doi.org/10.31274/td-20240329-43","authors":["Justin Stanley"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-29T20:32:58Z","doi":"10.31274/td-20240329-43","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1201/9781003384854-6","name":"A Federated Learning Approach for ResourceConstrained IoT Security Monitoring","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003384854-6","authors":["P Sakthibalan","M Saravanan","V Ansal","Amuthakkannan Rajakannu","K Vijayalakshmi","K Divya Vani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-16T06:04:28Z","doi":"10.1201/9781003384854-6","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1109/flics70075.2026.11621920","name":"FreezeFL: Accelerating Federated Learning in Heterogeneous Edge Devices by Layer Freezing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621920","authors":["Yu-Min Chou","Fu-Chiang Chang","Jerry Chou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:15:18Z","doi":"10.1109/flics70075.2026.11621920","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.59019/iesz2534","name":"Stacking Ensemble and Federated Learning for IoT Intrusion Detection","source":"crossref","abstract":"The number of Internet of Things (IoT) devices has increased considerably in the past few years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As part of a defense in depth approach to network security, intrusion detection systems (IDS) have acquired a key role as they attempt to detect malicious activities promptly and efficiently. In this thesis, an investigation on the use of ensemble learning and federated learning as methods to develop IDS in IoT environment is proposed. Three main contributions are offered, which were evaluated on two open-source datasets, namely ToN IoT and CICIDS2017. The first contribution is a novel method based on a combination of ensemble models. The method uses ensemble stacking and boosting to detect anomalies in IoT traffic. Three machine learning models, namely kNN, Decision Tree and Logistic Regression, are used as the base learners for the stacking model. The XGBoost model is used as the meta learner. Results show that the proposed model is capable of high accuracy, precision, recall and F1-Score in both datasets in binary and multi-class classification. Secondly, this thesis proposes another novel IDS approach based on a stacking ensemble of deep learning (DL) models. This approach is named Deep Integrated Stacking for the IoT (DIS-IoT), as it combines four different DL models into a fully connected DL layer, creating a standalone ensemble stacking model. Results demonstrate that DIS-IoT is capable of a high level of accuracy with a very low False Positive rate (FPR) in both datasets improving on other standard, standalone, DL methods. Results from this set of experiments were also compared against results available in the literature, which were obtained from similar approaches on the ToN IoT dataset. DIS-IoT achieves comparable performance with others in binary classification, but outperforms them in multi-class classification. The third contribution uses Federated Learning (FL) as an alternative, distributed, method to a centralized intrusion detection model. The FL model is composed of four clients and one server. Data analysis was performed at the client side, each using their own portion of the dataset. No data sharing between participants occurred, hence maintaining data privacy. The results from the experiments demonstrated that a collaborative federated system using horizontal data partitioning and the FedAvg aggregation algorithm, can have a comparable performance with a centralized model, making it a viable option for an IoT IDS. Moreover, several other federated averaging algorithms were evaluated in order to verify their efficacy in this setting. These were FedAvgM, FedAdam and FedAdagrad. The experiments demonstrated that FedAvg and FedAvgM were the most efficient options in the given scenario. However, further research in alternative, larger, settings are required to evaluate FedAdam and FedAdagrad more accurately.","url":"https://doi.org/10.59019/iesz2534","authors":["Riccardo Lazzarini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-12-19T14:26:35Z","doi":"10.59019/iesz2534","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00002-9","name":"Contents","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00002-9","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00002-9","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00030-3","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00030-3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00030-3","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.14711/thesis-991013340351903412","name":"Bayesian model compression and federated learning via variational inference","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013340351903412","authors":["Chengyu Xia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-29T23:00:52Z","doi":"10.14711/thesis-991013340351903412","addedAt":"2026-08-31T06:41:25.475Z","updatedAt":"2026-08-31T06:41:25.475Z"},{"id":"doi:10.1201/9781003384854-7","name":"Efficient Federated Learning Techniques for Data Loss Prevention in Cloud Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003384854-7","authors":["Peter Soosai A. Anandaraj","S. Sridevi","R. Vaishnavi","M. Meenalakshimi","R.V. Chandrashekhar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-16T11:04:28Z","doi":"10.1201/9781003384854-7","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1002/9781394461295.ch16","name":"Privacy‐Aware Machine Learning for Sustainable Farming","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.ch16","authors":["Mithaguru","Sugandha Saxena","Mude Nagarjuna Naik","R. Sriramkumar","Joshuva Arockia Dhanraj","M. Lakshmanan","A. Vegi Fernando"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.ch16","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-981-16-4963-9_2","name":"Fundamentals of Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-16-4963-9_2","authors":["Choong Seon Hong","Latif U. Khan","Mingzhe Chen","Dawei Chen","Walid Saad","Zhu Han"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-01T05:02:09Z","doi":"10.1007/978-981-16-4963-9_2","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1109/flics70075.2026.11621963","name":"Mitigating One-to-N Backdoor Attacks in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621963","authors":["Osama Wehbi","Sarhad Arisdakessian","Omar Abdel Wahab","Azzam Mourad","Hadi Otrok"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:14:27Z","doi":"10.1109/flics70075.2026.11621963","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/b978-0-44-344433-3.00020-4","name":"Adaptive training and aggregation for federated learning in multi-tier computing networks","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344433-3.00020-4","authors":["Wenjing Hou","Hong Wen","Ning Zhang","Wenxin Lei","Haojie Lin","Zhu Han","Qiang Liu","Wenhong Tian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T10:49:31Z","doi":"10.1016/b978-0-44-344433-3.00020-4","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.22215/etd/2025-16742","name":"Towards Self-Evolving Non-Terrestrial Networks (NTN) via Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.22215/etd/2025-16742","authors":["Amin Farajzadeh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-19T15:59:01Z","doi":"10.22215/etd/2025-16742","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.7717/peerj-cs.3354/fig-8","name":"Figure 8: Randomization based federated deep learning for securing energy data computation.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3354/fig-8","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-01T08:00:31Z","doi":"10.7717/peerj-cs.3354/fig-8","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1007/978-3-030-96896-0_22","name":"Application of Federated Learning in Medical Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-96896-0_22","authors":["Ehsan Degan","Shafiq Abedin","David Beymer","Angshuman Deb","Nathaniel Braman","Benedikt Graf","Vandana Mukherjee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-07T12:16:52Z","doi":"10.1007/978-3-030-96896-0_22","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.21203/rs.3.rs-3176684/v1","name":"Traffic Flow Distribution Forecast with Federated Learning and Bayesian Deep Learning","source":"crossref","abstract":"Abstract Traffic flow distribution forecasting plays a crucial role in intelligent transportation systems for effective traffic management and congestion mitigation. In this paper, we propose a novel approach for traffic flow distribution forecasting using federated learning and Bayesian deep learning. The proposed method leverages the collaborative training capability of federated learning to learn from decentralized data sources while preserving data privacy and ownership. Bayesian deep learning techniques are integrated to estimate uncertainties and provide probabilistic predictions, enabling the capture of the inherent variability in traffic flow distributions. We conduct comprehensive experiments on real-world traffic flow datasets and compare our method with baseline approaches. The results demonstrate that our proposed method outperforms the baselines in terms of mean absolute error, root mean squared error, negative log-likelihood, and calibration error. The integration of federated learning and Bayesian deep learning enables accurate traffic flow distribution forecasts while providing reliable uncertainty estimates. This work contributes to the advancement of intelligent transportation systems and provides valuable insights for decision-making in traffic management and congestion reduction.","url":"https://doi.org/10.21203/rs.3.rs-3176684/v1","authors":["Zhou Eric Shen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-18T05:57:55Z","doi":"10.21203/rs.3.rs-3176684/v1","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.31979/etd.c8tu-m7jh","name":"Privacy-Preserving Personalized Seizure Risk Monitoring Using Reinforcement Learning and Federated Learning on Wearable Data","source":"crossref","abstract":"","url":"https://doi.org/10.31979/etd.c8tu-m7jh","authors":["Mayank Kapadia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-31T15:10:19Z","doi":"10.31979/etd.c8tu-m7jh","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1002/9781394338726.ch5","name":"Federated Learning in Food Inspection and Grading","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch5","authors":["Reeta Mishra","Padmesh Tripathi","Reddy Saisindhutheja","Gagandeep Arora","Bhanumati Panda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch5","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1002/9781119913924.ch8","name":"Heterogeneity‐Aware Dynamic Scheduling for Federated Edge Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781119913924.ch8","authors":["Kun Guo","Zihan Chen","Howard H. Yang","Tony Q. S. Quek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-12-02T00:27:27Z","doi":"10.1002/9781119913924.ch8","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.1109/flics70075.2026.11621959","name":"Growing Domain-Specific LLMs Through Federated Split-Phase Learning: System Design and Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621959","authors":["Silin Zhao","Sadegh Keshtkar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:14:10Z","doi":"10.1109/flics70075.2026.11621959","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/fmec59375.2023.10305961","name":"Federated Learning Showdown: The Comparative Analysis of Federated Learning Frameworks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fmec59375.2023.10305961","authors":["Sai Praneeth Karimireddy","Narasimha Raghavan Veeraragavan","Severin Elvatun","Jan F. Nygård"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-08T13:51:37Z","doi":"10.1109/fmec59375.2023.10305961","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch001","name":"Introduction to Federated Learning and Threat Landscape","source":"crossref","abstract":"Federated Learning (FL), a collaborative machine learning paradigm that allows model training across multiple devices without centralized data aggregation, was developed in response to the exponential growth of decentralized data across mobile devices, edge sensors, and distributed systems. By limiting sharing to model updates and preserving sensitive user data locally, FL overcomes important privacy, legal, and bandwidth restrictions. However, new and advanced security threats are introduced by this distributed and privacy-aware architecture. These can compromise the reliability and effectiveness of federated models and include poisoning attacks, inference leakage, free-riding, and Byzantine behavior. This chapter provides overview of FL with real-world applications. The chapter is designed to provide readers with a solid knowledge base on the opportunities and challenges in the construction of reliable, scalable, and elastic FL systems, eliminating the gap between the Federated Model design and thoughtfulness.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch001","authors":["Rakesh Kumar Saxena","Shikha Khullar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch001","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1201/9781003595540-11","name":"Optimized Migraine Detection in Healthcare: Exploring Random Forest and XGBoost with Prospects for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540-11","authors":["Tanisha Dhoot","K.S. Archana","R. Anandan","Tahsheen Fatima"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","doi":"10.1201/9781003595540-11","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621962","name":"Federated Learning for ICU Mortality Prediction: Balancing Accuracy and Privacy in a Multi-Hospital Setting","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621962","authors":["Yassir Benhammou","Suman Kalyan","Sujay Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:33Z","doi":"10.1109/flics70075.2026.11621962","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621904","name":"FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621904","authors":["Lingyu Qiu","Daniela Annunziata","Stefano Izzo","Fabio Giampaolo","Francesco Piccialli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:13:54Z","doi":"10.1109/flics70075.2026.11621904","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.7717/peerj-cs.3589/fig-9","name":"Figure 9: Fairness and performance comparison using reputation management in federated learning.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3589/fig-9","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T08:57:09Z","doi":"10.7717/peerj-cs.3589/fig-9","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"pmid:42178334","name":"Federated multi-cloud task scheduling with load balancing using multi-objective NSGA-II and reinforcement learning.","source":"pubmed","abstract":"Task scheduling in federated multi-cloud environments is challenging owing to heterogeneous service-level agreements, decentralized resource control, and dynamic workload characteristics. The existing hybrid optimization approaches lack real-time adaptability across cloud providers also assumes centralized coordination. To work with these issues, this paper proposes Multi-Objective Non-Dominated Sorting Genetic Algorithm with Q-Learning (MO-NSGAQ). This is a hybrid multi-objective scheduling framework that tightly integrates Non-dominated Sorting Genetic Algorithm II (NSGA-II) with Q-learning within a federated broker architecture. This proposed framework simultaneously optimizes execution cost, makespan, load imbalance, and resource utilization while adapting to inter-cloud heterogeneity. Extensive simulations are done using synthetic workloads, such as Google Cloud job traces, and IoT-based workloads. These simulations demonstrate that MO-NSGAQ reduces makespan by up to 18-32%, improves resource utilization by 10-22%, and achieves better load balance compared to existing baselines. The results confirm the proposed framework effectiveness for adaptive and scalable federated cloud scheduling.","url":"https://pubmed.ncbi.nlm.nih.gov/42178334/","authors":["Ghaban W","Alatawi HS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-51105-w","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42178325","name":"FL-trafficNet: a hybrid federated transformer-GNN framework with dual attention for smart routing in IoV and VANET environments.","source":"pubmed","abstract":"One of the major problems smart cities face is how to efficiently route traffic, especially when connected vehicles and sensors produce a very large amount of real, time data. This heavy traffic load results in delays, inefficient routing, and excessive processing of central units. Hence, this article presents FL-TrafficNet, a hybrid routing framework that enhances traffic management in the Internet of Vehicles (IoV) and Vehicular Ad Hoc Networks (VANETs) scenarios. FL-TrafficNet combines Transformer models, and Graph Neural Networks (GNNs) to capture not only the time-dependent traffic variation but also the layout of the road network. A dual-attention component enables the model to pinpoint the most significant features in both spatial and temporal domains. To avoid uploading all the raw data to the cloud, the solution employs Federated Learning (FL), whereby vehicles and roadside units (RSUs), train their models on the spot and only share the resultant updates. This ensures data confidentiality and significantly reduces the network traffic. A reinforcement component embedded in the model dynamically makes path decisions by analyzing real-time traffic updates and feedback. The model works continuously by learning from nearby changes in traffic, weather, or road status. Simulation results show that FL-TrafficNet reduces errors in prediction (Mean Absolute Error (MAE): 1.95, Root Mean Squared Error (RMSE): 2.87, Mean Absolute Percentage Error (MAPE): 3.12), improves data privacy (97.8% privacy score), and increases traffic delivery rate (TDR) to 38.4%, a clear improvement over existing recent methods. These results make it suitable for real-time, privacy-aware routing in urban traffic networks.","url":"https://pubmed.ncbi.nlm.nih.gov/42178325/","authors":["Narsimhulu P","Sahay R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 24","doi":"10.1038/s41598-026-50454-w","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42177545","name":"Parallel AI-driven framework for post-quantum secure medical image communication using swin-transformer restoration.","source":"pubmed","abstract":"Reliable and secure transmission of medical images is essential for telemedicine, remote diagnosis, and distributed healthcare systems. However, medical image communication over heterogeneous networks often suffers from packet loss, channel noise, and privacy risks, which may compromise diagnostic accuracy and patient confidentiality. Traditional solutions relying on Reed-Solomon error correction and conventional encryption provide limited resilience and are increasingly inadequate for modern high-resolution medical imaging environments. This study proposes a next-generation AI-assisted communication framework for privacy-preserving medical image transmission that integrates recent advances in hybrid Transformer architectures, neural communication coding, and post-quantum cryptography. First, image corruption detection and restoration are performed using a Restormer/Swin-Transformer hybrid reconstruction network, which demonstrates superior performance in recovering corrupted regions compared with conventional GAN-based repair models. Second, to enhance transmission robustness, the framework incorporates Deep Joint Source-Channel Coding (DeepJSCC) and Neural Error Correction Codes (NECC) that jointly optimize image representation and channel robustness through deep neural networks. Third, communication security is strengthened using lattice-based post-quantum cryptographic primitives, including CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for authentication, ensuring resilience against quantum computing attacks. To support real-time medical applications, the proposed framework employs a parallel GPU-accelerated processing pipeline with CUDA-based model inference and distributed training strategies. Additionally, federated learning with secure aggregation and differential privacy enables collaborative model training across healthcare institutions while preserving sensitive patient data. Experimental evaluation on benchmark medical imaging datasets demonstrates that the proposed framework significantly improves image reconstruction fidelity, transmission robustness, and cryptographic security compared with traditional ECC-based communication systems and recent AI-assisted transmission methods.","url":"https://pubmed.ncbi.nlm.nih.gov/42177545/","authors":["Alsuwat E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 23","doi":"10.1186/s13040-026-00567-9","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"pmid:42177266","name":"FedAK: a semi-supervised one-shot framework for heterogeneous federated learning via feature-level attention-based knowledge distillation.","source":"pubmed","abstract":"Federated learning (FL) enables multiple devices to collaboratively train a shared model while keeping their data localized, thereby preserving privacy. Despite its promise, FL continues to face key challenges such as model heterogeneity, high communication overhead, and performance degradation under non-independent and identically distributed (non-IID) data. This paper introduces FedAK, a semi-supervised one-shot FL framework that integrates feature-level attention with knowledge distillation to support efficient global learning while reducing data exposure. In FedAK, each client independently trains its local model in a fully supervised manner on private labeled data and transmits only the feature representations of a shared public dataset, greatly reducing communication costs. On the server side, a semi-supervised aggregation strategy is adopted, where an attention-based aggregation module is trained on a small labeled subset to generate informative ensemble features and soft pseudo-labels for a larger unlabeled subset. These pseudo-labels guide a global student model through knowledge distillation, eliminating the need for direct access to client data or logits. Extensive experiments across four benchmark datasets demonstrate that FedAK consistently outperforms four state-of-the-art one-shot FL methods, confirming its effectiveness under heterogeneous and non-IID settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42177266/","authors":["Salman H","Pradat-Peyre JF","Guehis S","Charara N","Zaki C","Nasser A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 23","doi":"10.1038/s41598-026-52408-8","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42176680","name":"Artificial intelligence-enhanced ultrasound multimodal imaging and tissue characterization for predicting immunotherapy efficacy in rectal cancer.","source":"pubmed","abstract":"Rectal cancer remains a global health burden, with immunotherapy emerging as a transformative treatment modality demonstrating substantial pathological complete response rates across molecular subtypes. However, current response assessment methods based on anatomical size measurements (RECIST criteria) fail to capture complex Tumor Microenvironment transformations during immunotherapy, including pseudoprogression, immune cell infiltration, and vascular remodeling. This limitation necessitates advanced imaging approaches capable of real-time, non-invasive characterization of dynamic biological processes. Multimodal ultrasound imaging integrating endorectal ultrasound, contrast-enhanced ultrasound, shear wave elastography, Doppler ultrasound, and photoacoustic imaging provides comprehensive biomarkers reflecting vascular perfusion, tissue biomechanics, cellular density, and metabolic activity. contrast-enhanced ultrasound quantifies microcirculatory changes with wash-in/wash-out kinetics correlating with immune response, while shear wave elastography measures tumor stiffness inversely associated with T-cell infiltration. Artificial intelligence frameworks, particularly deep learning and radiomics, enhance predictive accuracy by extracting high-dimensional features from multimodal data, achieving superior performance in integrated systems. Artificial intelligence models enable early detection of subtle Tumor Microenvironment remodeling before conventional radiologic changes, effectively distinguishing pseudoprogression from true progression with high diagnostic accuracy. Despite promising results, significant challenges persist: inter-operator variability, device heterogeneity causing substantial accuracy degradation across platforms, limited ultrasound-radiogenomics integration, and nascent clinical translation. This review synthesizes current evidence on immunobiology-ultrasound-Artificial intelligence integration for predicting immunotherapy efficacy in rectal cancer, critically analyzing technical frameworks, validated biomarkers, clinical applications, and future directions including explainable Artificial intelligence, federated learning, wearable ultrasound patches, and multimodal radiogenomics. The synthesis provides a comprehensive roadmap for developing standardized, clinically implementable Artificial intelligence-enhanced ultrasound systems that combine affordability, safety, accessibility, and advanced analytical capabilities to advance precision oncology in rectal cancer immunotherapy.","url":"https://pubmed.ncbi.nlm.nih.gov/42176680/","authors":["Wang Q","Chen Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Oct","doi":"10.1016/j.tice.2026.103613","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42176598","name":"From documentation to discovery: clinicians' perspectives on the generation, usability and standardization of real-world data.","source":"pubmed","abstract":"Real-world data (RWD) are increasingly recognized as essential for understanding patient populations underrepresented in clinical trials and for supporting data-driven learning in healthcare. For smaller subgroups, the value of RWD depends on standardization and interoperability that enable meaningful reuse across institutions. This study examines how clinicians perceive the reuse and standardization of RWD within a federated Learning Health System (LHS), with emphasis on data quality, clinical relevance, and implications for continuous learning.","url":"https://pubmed.ncbi.nlm.nih.gov/42176598/","authors":["Ross E","Bouissou O","Helland Å","Faxvaag A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Sep 1","doi":"10.1016/j.ijmedinf.2026.106501","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42175236","name":"A Privacy Index Calculator for Federated Medical Studies.","source":"pubmed","abstract":"Sharing clinical datasets requires rigorous privacy guarantees to prevent subject re-identification. This paper presents a privacy analysis tool that operates entirely client-side, ensuring data protection by eliminating the need for server-side processing. The system implements a hybrid classification mechanism that combines semantic rules with statistical heuristics to automatically categorize attributes. Furthermore, a modular, plugin-based architecture computes a comprehensive Privacy Index by aggregating multiple privacy models and detecting existing protection techniques through pattern recognition. Validation on synthetic datasets demonstrated the tool's effectiveness in correctly identifying high-risk attributes and accurately quantifying privacy guarantees.","url":"https://pubmed.ncbi.nlm.nih.gov/42175236/","authors":["Gameiro J","Barros V","Almeida JR","Oliveira JL"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260566","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42175159","name":"A Maturity Model for the Enforcement of PETs in Federated Settings.","source":"pubmed","abstract":"We explore how privacy can be made both usable and enforceable within FLAME, a federated learning platform developed in the German PrivateAIM project. We propose a Privacy-Enhancing Technology (PET) Integration Maturity Model with three levels: Analysis-Based, Library-Based, and System-Based. These levels describe how responsibility for applying PETs shifts from analysts to the platform. Moreover, higher maturity levels enhance auditability, reduce reliance on code reviews, and support consistent privacy enforcement across sites.","url":"https://pubmed.ncbi.nlm.nih.gov/42175159/","authors":["Abu Attieh H","Halilovic M","Herr MAB","Hieber D","Placzek P","Roehl A","Kuntzer J","Ziller A","Kohlbacher O","Rueckert D","Prasser F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260487","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42175138","name":"Federated Deception Intelligence and the ASKOS Solution for Cyber-Resilient Healthcare Infrastructures.","source":"pubmed","abstract":"Healthcare infrastructures face escalating cybersecurity risks caused by legacy systems, connected IoMT devices, and complex data-sharing environments. Conventional intrusion detection and SIEM systems provide limited protection against zero-day and lateral-movement attacks. This paper presents the ASKOS architecture, a federated and deception-enhanced security framework that integrates Conpot honeypots with federated machine learning and software-defined networking (SDN) to enable privacy-preserving, adaptive cyber defense. By combining deception telemetry, distributed anomaly detection, and risk-aware orchestration, ASKOS delivers an intelligent and compliant security layer that can be implemented across healthcare infrastructures.","url":"https://pubmed.ncbi.nlm.nih.gov/42175138/","authors":["Giannakopoulou O","Tarousi M","Androutsou T","Pitoglou S","Skoularikis E","Pliatsios D","Sarigiannidis P","Koutsouris D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260466","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42175137","name":"Annotation-Driven Middleware for Laboratory Data Exchange: Building Federated and Equitable Health Information Systems.","source":"pubmed","abstract":"Reliable access to laboratory and diagnostic data is essential for safe and equitable healthcare, yet laboratory data exchange remains fragmented across institution-specific infrastructures. Laboratory Information Management Systems (LIMS) and electronic health records often rely on bespoke interfaces that limit interoperability, scalability, and governance transparency. This paper is presented as a position and design paper that argues for an annotation-driven middleware (ADM) paradigm to support federated laboratory data exchange. In the proposed approach, machine-readable annotations express governance rules, provenance, data-quality indicators, and privacy constraints, which are dynamically interpreted by a cloud-native orchestration layer to guide routing, transformation, and policy enforcement. By decoupling governance intent from technical integration logic, the ADM paradigm enables declarative configuration by domain experts and supports modular, standards-agnostic interoperability. Illustrative scenarios are used to reason about feasibility rather than to provide empirical validation. This position aims to inform future implementations and evaluation of transparent, governable, and federated laboratory data-exchange infrastructures.","url":"https://pubmed.ncbi.nlm.nih.gov/42175137/","authors":["Bamunuge S","Haddad T","Khaddaj S","de Lusignan S","Kumarapeli P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260465","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42175129","name":"Setting up a DataSHIELD Hub for the German Medical Informatics Initiative: Challenges and Lessons Learned.","source":"pubmed","abstract":"The secondary use of clinical routine data offers major opportunities for biomedical research but also poses challenges regarding data protection, interoperability, and coordination. Within the German Medical Informatics Initiative (MII), Data Integration Centers (DICs) provide harmonized and secure access to routine data for research. To enable privacy-preserving multi-centric analyses without exchanging individual-level data, a federated DataSHIELD infrastructure was implemented for a study on the biomarker NT-proBNP in patients with atrial fibrillation. This paper presents its design, implementation, key challenges, and lessons learned, and derives recommendations for future deployments. Each site, selected based on availability of the required variables and operational readiness, provided standardized datasets via local Opal servers connected to a central hub. The setup enabled GDPR-compliant analyses across multiple hospitals but required substantial manual configuration and maintenance. Main challenges included version compatibility, limited analytical functionality, and the lack of automated deployment and testing. Addressing these issues calls for standardized installation workflows, improved version and access management, and modular extensions of functionality. Overall, the study demonstrates the feasibility of establishing a federated analysis infrastructure within the MII while also highlighting the need for more scalable and flexible solutions.","url":"https://pubmed.ncbi.nlm.nih.gov/42175129/","authors":["Abu Attieh H","Jolly JK","Pallaoro P","de Arruda Botelho Herr M","Schreiweis B","Kirsten T","Prasser F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260457","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42175101","name":"IMPaCT-Data: A Federated Precision Medicine Infrastructure Associated with Science and Technology in Spain.","source":"pubmed","abstract":"In alignment with the European Health Data Space (EHDS), Spain's IMPaCT - Precision Medicine Infrastructure associated with Science and Technology - aims to establish a trusted research environment (TRE) for secure and FAIR data sharing and analysis. It is structured into three pillars: Predictive Medicine (IMPaCT-Cohort), Genomic Medicine (IMPaCT-Genomics), and Data Science (IMPaCT-Data). IMPaCT-Data leads the development of the IMPaCT Digital Platform (IDP), integrating clinical, genomic, and imaging data to support the national IMPaCT-Cohort and Personalised Medicine Projects (IMPaCT-PMPs). Its Reference Implementation defines the architecture across a federated model.","url":"https://pubmed.ncbi.nlm.nih.gov/42175101/","authors":["Rodríguez-Mejías S","Sánchez-Cabo F","Al-Shahrour F","Rosas C","Rementería MJ","Jene-Sanz A","Rambla J","Gelpí JL","Capella-Gutierrez S","Martínez-Díaz PI","Dopazo J","Valencia A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260428","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42175097","name":"Design of a Privacy-Preserving ETL Dataflow for Federated ICU Data Reuse in INDICATE.","source":"pubmed","abstract":"The INDICATE project, funded by the European Union under the Digital Europe Programme, develops a federated framework for the secure reuse of intensive care unit (ICU) data within the EHDS. This work presents the design of a privacy-preserving ETL dataflow that standardizes and validates ICU data locally. The process enables harmonization of ICU data into the OMOP Common Data Model, based on HL7 FHIR as well, ensuring semantic interoperability, data quality, and regulatory compliance. Only aggregated study results are uploaded to the INDICATE Portal, ensuring that no patient-level data leave the hospital domain.","url":"https://pubmed.ncbi.nlm.nih.gov/42175097/","authors":["Parra Rodriguez-Armijo M","Alvarez-Romero C","van den Brand J","Delange B","Parra-Calderón CL"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260424","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42175093","name":"Towards an Integration Engine to Achieve Federated Semantic Interoperability.","source":"pubmed","abstract":"Efficient and semantically correct data exchange between healthcare systems remains a major challenge due to heterogeneous data models, legacy technologies, and inconsistent adoption of standards. This research investigates and develops an open-source integration engine extending the TermX platform to support federated interoperability through modular mediators, logical models, and transformation tools. The platform builds on the Systems-of-Systems domain and introduces visual interfaces for designing, validating, and sharing integrations, making complex semantic interoperability tasks accessible to non-technical users. Implemented as part of TermX, it enables a community-driven marketplace for reusable models and mediators, promoting scalable, cost-effective system integration. Using a design science approach and real-world evaluation in collaboration with Elora hospitals, the project aims to advance both the practical realization and theoretical understanding of federated semantic interoperability.","url":"https://pubmed.ncbi.nlm.nih.gov/42175093/","authors":["Randmaa R","Bossenko I","Piho G","Ross P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260420","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42175072","name":"Practical Implications for Using Laboratory Data: Research over Federated Networks.","source":"pubmed","abstract":"Semantic standardization is essential, but not sufficient, to enable research over real-world data networks. Even when harmonized, differences in data availability, granularity, and clinical practice can introduce subtle biases that affect downstream analyses and AI model development.","url":"https://pubmed.ncbi.nlm.nih.gov/42175072/","authors":["Rubio Ruiz D","Muñoz Monjas A","Bermejo Bernardo P","Perez-Rey D","Palchuk MB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260399","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42175031","name":"A Federated Benchmark for Clinical Natural Language Processing (FedDRAGON).","source":"pubmed","abstract":"We introduce the FedDRAGON challenge, a federated learning benchmark for clinical natural language processing. The challenge includes 12 information extraction tasks where data is extracted from clinical reports from 4 Dutch care centers. Baseline results show that the performance of the federated models surpass single-center performance and approaches that of centralized models. Benchmark, code, and pre-trained LLMs are publicly available.","url":"https://pubmed.ncbi.nlm.nih.gov/42175031/","authors":["Abrahamsen BS","Bosma JS","Huisman H","Elschot M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260357","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42174884","name":"Federated Multi-Agent Architecture for Harmonizing Public Health Datasets into OMOP and FHIR Standards.","source":"pubmed","abstract":"This study presents a federated system developed within the SHIELD project, part of Horizon Europe's effort to reduce non-communicable diseases. It integrates retrospective and prospective clinical data using LLM-based multi-agent systems for automated ETL and natural language querying. Harmonization of MIMIC-IV, ELSA, and synthetic data into OMOP CDM and FHIR enabled validation. Results show high mapping accuracy and demonstrate the feasibility of scalable and interoperable data integration supporting clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42174884/","authors":["Lozano F","Sánchez Esquivel J","Paraíso-Medina S","Alonso-Calvo R","Jimeno P","Luengo I","Maojo V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260208","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42174852","name":"Federated Propensity Score Matching for Bias Correction in ICU.","source":"pubmed","abstract":"Multi-site medical studies require methods that address confounding bias while respecting data privacy regulations. We propose a federated learning method integrating propensity score matching (PSM) to achieve both objectives simultaneously. Using data from five intensive care unit (ICU) databases (N=160,752), we evaluated within-hospital and cross-hospital PSM strategies within federated XGBoost. Cross-hospital PSM achieved 76.3% mean bias reduction (standardized mean difference (SMD): 0.070), while within-hospital PSM achieved 74.3% reduction (SMD: 0.074), compared to baseline SMD of 0.316. Both strategies maintained predictive performance (AUROC: 0.73-0.75). Our method enables rigorous causal inference in distributed healthcare networks without centralizing sensitive patient data.","url":"https://pubmed.ncbi.nlm.nih.gov/42174852/","authors":["Sheikhalishahi S","Schwinn J","Morhart M","Hinske LC","Kaspar M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260176","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42174820","name":"Client Participation per Round in Federated Learning for Multiple Sclerosis with Real-World Data.","source":"pubmed","abstract":"We quantify how the per round client participation rate k affects performance and runtime in a federated learning study predicting two year confirmed disability progression in multiple sclerosis using routine clinical data. Using the original study's data, preprocessing, model, and evaluation protocol, we vary k at 1.0, 0.6, and 0.4. Lower participation shortens wall time but can slightly reduce ROC - AUC and AUC - PR; k &#x2248; 0.6 preserved nearly all performance while cutting runtime by about one third.","url":"https://pubmed.ncbi.nlm.nih.gov/42174820/","authors":["Pirmani A","MSBase Study Group","Moreau Y","Peeters LM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260143","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42174810","name":"Building an Ontology-Based Cohort of Liver Cancer Imaging Data for AI Development on the European Federated Platform EUCAIM.","source":"pubmed","abstract":"Hepatocellular carcinoma (HCC) is steadily increasing in incidence worldwide and requires data-driven approaches to improve diagnosis, prognosis, and therapeutic decisions. We describe the harmonization of IMALIVE -a real-world HCC cohort- with the common data model of the European Cancer Imaging Initiative (EUCAIM). IMALIVE integrates demographic, clinical, and imaging-related variables, which were aligned with core variables of the EUCAIM common data model through a robust mapping process and expert validation. The process achieved full coverage of the core dataset and added HCC-specific variables, including tumor staging and liver function scores. In addition, metadata from digital pathology was standardized using the international MIABIS/BBMRI model, extending interoperability across radiology and histology. This work demonstrates the feasibility of harmonizing local cohorts enabling their integration in the data catalogue of large scale federated platforms It highlights how medical experts' contributions to this harmonization process can enrich common models with clinically relevant variables for AI development in their domain of expertise.","url":"https://pubmed.ncbi.nlm.nih.gov/42174810/","authors":["Guedjali A","Mondet K","Beaufrere A","Gregory J","Mule S","Paradis V","Fournier L","Daniel C","El Ghosh M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260133","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42174793","name":"Effects of Non-IID Distributions in Lung Cancer Data on Survival Prediction with Federated Ensemble Learning.","source":"pubmed","abstract":"A common challenge in Federated Learning (FL) is that distribution shifts between clients, or Non-IIDness, decrease global model performance. Non-IIDness means that data is not independently and identically distributed between participating sites. Stronger distributional shifts lead to greater reductions of model performance. We have implemented an FL algorithm to compare Random Forest (RF) and AdaBoost in various non-IID scenarios using sequencing data from lung cancer patients. Therefore, we systematically shifted the class label distributions among clients over several iterations. Both RF and AdaBoost performed worse in highly non-IID scenarios than on balanced data, and RF significantly outperformed AdaBoost. Our FL algorithm offers potential for model personalization and fine tuning, and could be applied to other datasets including clinical data.","url":"https://pubmed.ncbi.nlm.nih.gov/42174793/","authors":["Weber L","Hauschild AC","Altenbuchinger M","Sax U","Hügel J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 21","doi":"10.3233/SHTI260116","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42173766","name":"Preoperative Risk Assessment of Adrenal Metastases in a Multicenter Study: Development of a Robust Federated Learning Model.","source":"pubmed","abstract":"In clinical practice, the preoperative risk assessment of adrenal metastases versus benign adrenal lesions carries a substantial risk of misdiagnosis. The artificial intelligence technology holds promise for reducing misdiagnosis rates. However, due to the problem of data privacy protection and non-independent and identically distributed of multi-center data, the performance of artificial intelligence models is significantly affected.","url":"https://pubmed.ncbi.nlm.nih.gov/42173766/","authors":["Feng B","Yu Z","Chen Y","Xu J","Lei Y","Wan M","Lin F","Cui J","Hu Q","Jin Q","Long W","Ma C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Sep","doi":"10.1016/j.acra.2026.04.041","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42173625","name":"Artificial intelligence in multi-omics analysis of small-molecule drug discovery.","source":"pubmed","abstract":"Artificial intelligence (AI) is transforming multi-omics analysis in small-molecule drug discovery by advancing vast datasets from genomics, transcriptomics, proteomics, and metabolomics to cover novel therapeutic targets and optimize lead compounds. Machine learning (ML) algorithms, such as deep neural networks (DNNs) and graph convolutional networks (GCNs), excel at identifying complex patterns in multidimensional omics data, predicting drug-target interactions, and predicting molecular dynamics with exclusive accuracy. AI platforms as AlphaFold, accelerated protein structure prediction, allowing virtual screening of millions of small molecules. In multi-omics systems, generative adversarial networks (GANs) and transformers synthesize multimodal data, attractive biomarker discovery, and reduce preclinical failure rates from 90&#xa0;% to potentially below 70&#xa0;%. Challenges like data heterogeneity and interpretability, AI biases through federated learning, and explainable AI techniques. This synergy potentiates faster, cost-effective drug development, escorting in a new era of precision medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/42173625/","authors":["Soni S","Rathee S","Sreeharsha N","Vasdev N","Tekade M","Muley A","Tekade RK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/bs.pmbts.2026.01.026","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42155376","name":"Hybrid fractional groupers and moray eels driven deep learning for pneumonia detection using multi-modal data in federated learning.","source":"pubmed","abstract":"Pneumonia is a severe lung infection triggered by various viral pathogens. Detecting and diagnosing pneumonia using clinical images is challenging because its visual features often resemble those of other pulmonary conditions. As a result, existing approaches for pneumonia prediction frequently face struggles in achieving high accuracy and also face issues in protecting the security of the medical data. Thus, this paper presents a novel model named Fractional Groupers and Moray Eels optimization_Euclidean, Expectation loss (FGMEO_EESHLossNet) for pneumonia detection utilizing multi-modal data within a Federated Learning (FL) framework. The process begins with local training at each node using its respective data. Subsequently, local data are updated, and model aggregation occurs on the server. Then, the global model is downloaded at each node, and the training process is updated using the downloaded global and local models; this is iterative at every epoch. The pneumonia detection is performed in a training model, such that the multi-modal dataset used for training includes Computed Tomography (CT), chest X-ray, and spectrogram images. Initially, chest X-ray images are denoised with an Alpha trimmed mean filter, and the contrast of the image is enhanced through gamma correction. Then, segmentation of the lung lobe from chest X-ray images is performed using a Deep Recursive Residual Network (DRRN). Here, contrast-enhanced images are segmented to isolate affected areas using the DRRN. Following segmentation, image augmentation and feature extraction are conducted. Similarly, the same process is applied to CT images, and the de-noising and contrast enhancement steps are applied to spectrogram images. Moreover, the outputs from all three modalities are fed into the Shepard Convolution Pyramid Dilated Network for pneumonia detection, and the network's layers are modified using the EESHLossNet loss function. Here, the EESHLossNet is trained by FGMEO, which is the combination of the Fractional Calculus (FC) concept with Groupers and Moray Eels Optimization (GMEO). Additionally, the devised model has achieved Loss function, Mean Squared Error (MSE), accuracy, True Positive Rate (TPR), True Negative Rate (TNR), Precision, F1-Score, False Negative Rate (FNR), and False Positive Rate (FPR) as 0.075, 0.087, 0.925, 0.915, 0.933, 0.933, 0.924, 0.085, and 0.067, for time stamp of 100s.","url":"https://pubmed.ncbi.nlm.nih.gov/42155376/","authors":["L L S M","Bharti P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 15","doi":"10.1016/j.compbiomed.2026.111739","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42154703","name":"Recovering Reward Functions From Distributed Expert Demonstrations via Bi-Level Maximum-Likelihood Optimization.","source":"pubmed","abstract":"Inverse reinforcement learning (IRL) seeks to infer the latent reward function and the associated optimal policy from expert demonstrations. However, most current IRL methods assume centralized access to all trajectory data, which is impractical in real-world scenarios characterized by decentralized data sources and privacy concerns. To this end, this article proposes a novel algorithm for federated maximum-likelihood IRL (F-ML-IRL) and provides a rigorous analysis of its convergence rate. The proposed F-ML-IRL leverages dual aggregation to update the shared global model and performs bi-level local updates: an upper level learning task to optimize the parameterized reward function by maximizing the discounted likelihood of observing human expert trajectories under the current policy, and a lower level learning task to find the optimal agent policy regarding the entropy-regularized discounted cumulative reward under the current reward function. We analyze the convergence rate of the proposed F-ML-IRL algorithm and show that the global model in F-ML-IRL converges to a stationary point for both the reward and policy parameters within finite time. That is, the log-distance between the recovered policy and the optimal policy, as well as the gradient of the likelihood objective, converges to zero. Evaluating our F-ML-IRL algorithm on high-dimensional robotic control tasks in MuJoCo, we show that it ensures convergence of the recovered reward in decentralized learning and outperforms centralized baselines due to its ability to utilize distributed data-attaining better recovered rewards than all baselines in 12 out of 20 tasks.","url":"https://pubmed.ncbi.nlm.nih.gov/42154703/","authors":["Jiang G","Hong S","Imani M","Bastian ND","Lan T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 19","doi":"10.1109/TNNLS.2026.3688600","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42151685","name":"Integrated Platforms to Further Advance Space Biology Research.","source":"pubmed","abstract":"Space biology research increasingly requires integrated platforms capable of managing complex datasets. This chapter introduces the NASA Open Science Data Repository (OSDR), designed to unify multiomics, physiological, environmental telemetry, and radiation exposure data from spaceflight and analog experiments. By strictly adhering to FAIR (Findable, Accessible, Interoperable, Reusable) principles, OSDR ensures standardized data curation and facilitates integrated analyses critical for understanding space-induced biological responses. The integration of AI technologies and federated learning frameworks will transform OSDR into a dynamic predictive modeling platform, enhancing discoveries and astronaut health research.","url":"https://pubmed.ncbi.nlm.nih.gov/42151685/","authors":["Sanders LM","Costes SV"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1007/978-1-0716-5174-2_15","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42151390","name":"HybridTrust: on-device federated learning with crypto-agile security for legacy and quantum-safe medical devices.","source":"pubmed","abstract":"The process of migration of IoMT systems in healthcare into post-quantum cryptographic systems is expected to be a gradual one. Here, existing ECC-based devices alongside newly developed quantum-resistant devices will be operating within the same healthcare ecosystem. However, there exists an important interoperability challenge posed by the need for collaboration within the domain of edge intelligence in critical life situations like ICU monitoring. This paper presents HybridTrust, an on-device federated TinyML framework designed to enable secure collaboration among legacy, post-quantum, and hybrid IoMT devices during the post-quantum cryptography migration period. Unlike wearable IoMT systems, HybridTrust targets mains-powered ICU monitoring devices, where cryptographic flexibility, clinical fidelity, and security can be prioritized without strict battery constraints. The novelty of HybridTrust lies in its crypto-agile and deployment-oriented design rather than in proposing a new machine-learning algorithm. The framework integrates a dynamic crypto-negotiation protocol that selects the strongest mutually supported scheme among ECC, ML-KEM-512, and hybrid ECC-ML-KEM operation; a compact 931-byte hybrid certificate format suitable for ESP32-class microcontrollers; a regulatory compliance scorecard mapping cryptographic choices to HIPAA, GDPR, FDA, and NIST expectations; and a tree-concatenation-based model fusion strategy for maintaining clinical utility under non-IID hospital data distributions. HybridTrust is evaluated using a clinician-validated synthetic ICU dataset distributed across five simulated hospitals in an OMNeT++ environment. The framework achieves an average anomaly detection accuracy of 90.5% while introducing only 0.09-0.43 ms quantum-safe cryptographic overhead per operation. These results indicate that secure post-quantum transition, legacy interoperability, and real-time ICU monitoring can be jointly supported without imposing prohibitive latency or deployment overhead. HybridTrust therefore provides a practical pathway for backward-compatible, crypto-agile, and quantum-resilient IoMT deployment in critical-care environments.","url":"https://pubmed.ncbi.nlm.nih.gov/42151390/","authors":["Khan UH","Khan R","Chelloug SA","Alturise F","Khan S","Alkhalaf S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-52891-z","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42150783","name":"Barrier check study: why predictive machine learning struggles to reach the operating room.","source":"pubmed","abstract":"Machine learning (ML) has the potential to enhance surgical decision-making through real-time risk prediction and personalised care yet clinical implementation remains limited. This study aimed to identify and categorise the key barriers to implementing ML tools in surgical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42150783/","authors":["Ben Hmido S","Garita C","Bloemers F","Rozie S","Yeung KK","Kazemier G","Daams F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 18","doi":"10.1136/bmjhci-2025-101910","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42149965","name":"The Data Science of Health in Space.","source":"pubmed","abstract":"Spaceflight exposes humans to unique stressors that remodel biology. Space health is emerging as a data science discipline grounded in multiomic atlases, harmonized biobanks, and open, findable, accessible, interoperable, and reusable (FAIR)-aligned infrastructures. We review this ecosystem, including the National Aeronautics and Space Administration (NASA)'s GeneLab; the Open Science Data Repository, European Space Agency, and Japan Aerospace Exploration Agency (JAXA) platforms; the Space Omics and Medical Atlas; and mission-specific repositories, and how it is used to integrate heterogeneous omics with clinical, environmental, and digital phenotypes. We highlight machine learning and causal inference approaches to identify conserved signatures of risk and resilience and to align astronaut datasets with large terrestrial cohorts and disease models. Spaceflight functions as an accelerated model of aging and systems-level stress, enabling discovery of biomarkers and countermeasures with reciprocal benefits for Earth-based medicine. Finally, we outline future directions, including countermeasure-prioritization pipelines; astronaut digital twins and virtual organs; operational analytics and wearables for personalized risk management; and federated, privacy-preserving data ecosystems that extend predictive, individualized space health to the operational edge.","url":"https://pubmed.ncbi.nlm.nih.gov/42149965/","authors":["Willett JDS","Kim J","Sakharkar A","Park J","Agyemang AA","Saygili EA","Proszynski J","Nelson TM","Mason CE"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug","doi":"10.1146/annurev-biodatasci-092724-045605","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42149773","name":"Toward Privacy Preservation in Federated Learning: A Framework Integrating Client-Side Shuffling and Model Compression.","source":"pubmed","abstract":"Federated learning (FL) enables multiple clients to train models on local data and collaboratively optimize a global model without sharing raw data. However, client heterogeneity, such as differences in data distributions and system capabilities, poses challenges like reduced training efficiency and slower convergence. Furthermore, there is a risk of inference attacks during the transmission of model parameters. To address these issues, we propose the client-side shuffling and compressed-model FL (CSCP-Fed) framework, which is efficient and privacy-preserving, based on client-side shuffling and compressed-model techniques. The framework combines differential privacy (DP) with client-side shuffling to ensure data anonymity and enable secure weighted aggregation of model parameters. It also designs an asymmetric-encryption-based secure communication protocol to safeguard data transmission. In addition, it introduces a hybrid-weighted attention aggregation algorithm and a compressed-model-driven client selection strategy to mitigate the impact of heterogeneity, accelerate convergence, and maintain model generalization ability. Rigorous security analysis and experiments show that CSCP-Fed can effectively protect privacy without relying on any centralized entity. It reduces the expected global loss per round by 15%-38%, cuts communication overhead by 19.3%-44.3%, speeds up convergence by 10%-41%, and improves learning accuracy by 4%-13% compared to traditional methods.","url":"https://pubmed.ncbi.nlm.nih.gov/42149773/","authors":["Li J","Xiao R","Yu L","Zhang K","Ning J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 18","doi":"10.1109/TNNLS.2026.3690045","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42148603","name":"Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.","source":"pubmed","abstract":"Cognitive impairment arising from ischemic stroke, Alzheimer's disease, and Parkinson's disease presents distinct structural and network-level alterations. Brain magnetic resonance imaging offers a non-invasive and high-resolution approach to assess these changes, while deep learning provides powerful tools for automated analysis. Given that accurate lesion delineation, precise localization of abnormal regions, and reliable disease classification are fundamental to clinical decision-making. This review aims to explore the application of deep learning techniques to brain magnetic resonance imaging analysis of cognitive impairments caused by these disorders, with a focus on three core tasks: lesion segmentation, object detection, and image classification. Recent widely accepted findings indicate that ischemic stroke studies have achieved state-of-the-art lesion segmentation performance, with optimized U-shaped convolutional network (U-Net) and hybrid convolutional neural network-transformer models reaching Dice scores up to 0.911 in delineating focal damage. Alzheimer's disease research has advanced classification and staging accuracy by more than 10% compared with unimodal baselines through three-dimensional convolutional neural network, Transformers, and multimodal fusion, enabling more precise detection of diffuse cortical atrophy. Parkinson's disease imaging, despite lacking overt structural lesions, has leveraged ResNet and Vision Transformer backbones to identify subtle and spatially distributed abnormalities, improving early-stage differentiation. Persistent challenges include the scarcity of large, high-quality annotated datasets, substantial inter-site variability, high annotation costs, and limited interpretability, hindering clinical integration. Addressing these barriers will require advances in federated learning to mitigate data scarcity while preserving privacy, domain adaptation techniques to reduce inter-site variability, automated annotation, and low-resource training strategies to lower labeling costs, and explainable artificial intelligence to improve interpretability, thereby ensuring model robustness, privacy, and transparency. This review highlights emerging methods, innovative technologies, and novel paradigms that are redefining brain imaging analysis in cognitive impairment. Mechanistically, deep learning improves cognitive impairment analysis by integrating hierarchical and multiscale spatial features, modeling long-range functional connectivity disruptions, and fusing structural with functional imaging to better represent network-level pathology. In conclusion, aligning network architectures with disease-specific imaging characteristics and task requirements can greatly enhance the accuracy, robustness, and generalizability of magnetic resonance imaging analyses for cognitive impairment. Future work should focus on multimodal fusion, structure-function coupling, cross-disease evaluations, and embedding artificial intelligence tools into clinical workflows to support early detection, individualized treatment planning, and large-scale clinical adoption.","url":"https://pubmed.ncbi.nlm.nih.gov/42148603/","authors":["Xu Q","Lu J","Zhang Z","Xu D","Guo C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Sep 1","doi":"10.4103/NRR.NRR-D-25-00332","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42147733","name":"Training Together, Diagnosing Better: Federated Learning for Collagen VI-Related Dystrophies.","source":"pubmed","abstract":"The application of Machine Learning (ML) to the diagnosis of rare diseases, such as collagen VI-related dystrophies (COL6-RD), is fundamentally limited by the scarcity and fragmentation of available data. Attempts to expand sampling across hospitals, institutions, or countries with differing regulations face severe privacy, regulatory, and logistical obstacles that are often difficult to overcome. The Federated Learning (FL) provides a promising solution by enabling collaborative model training across decentralized datasets while keeping patient data local and private. Here, we report a novel global FL initiative using the Sherpa.ai FL platform, which leverages FL across distributed datasets in two international organizations for the diagnosis of COL6-RD, using collagen VI immunofluorescence microscopy images from patient-derived fibroblast cultures. Our solution resulted in an ML model capable of classifying collagen VI patient images into the three primary pathogenic mechanism groups associated with COL6-RD: exon skipping, glycine substitution, and pseudoexon insertion. This new approach achieved an F1-score of 0.82, outperforming single-organization models (0.57-0.75). These results demonstrate that FL substantially improves diagnostic utility and generalizability compared to isolated institutional models (see Figure 1). Beyond enabling more accurate diagnosis, we anticipate that this approach will support the interpretation of variants of uncertain significance and guide the prioritization of sequencing strategies to identify novel pathogenic variants.","url":"https://pubmed.ncbi.nlm.nih.gov/42147733/","authors":["Brull A","Aguti S","Bolduc V","Hu Y","Jimenez-Gutierrez DM","Zuazua E","Del-Rio J","Sliusarenko O","Zhou H","Muntoni F","Bönnemann CG","Uribe-Etxebarria X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 18","doi":"","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:25.476Z"},{"id":"pmid:42147376","name":"Current trends and future directions of artificial intelligence in lung cancer diagnosis.","source":"pubmed","abstract":"Lung cancer is the most lethal malignancy worldwide, largely due to its late detection after its progression to advanced stages. Over the last decade, artificial intelligence (AI) applications have shown significant potential in transforming lung cancer diagnostics by improving the speed, accuracy, and personalization of early detection strategies. This review provides a comprehensive overview of current AI application landscape in early lung cancer diagnosis, encompassing medical imaging, histopathology, liquid biopsy, natural language processing of electronic health records, and genomic profiling. We explain how machine learning, deep learning, and transformer-based models are employed in lung cancer diagnosis, and summarize recent cutting-edge advances, including multimodal AI platforms and Food and Drug Administration (FDA)-approved computer-aided diagnosis/detection (CAD) systems. Furthermore, we evaluate the challenges that impede clinical translation, including data heterogeneity, interpretability, and privacy, and present prospective directions such as federated learning and multi-omics integration. Through a comprehensive analysis of the dynamic evolution of AI applications in oncology, we aim to inform researchers, clinicians, and policymakers about its diagnostic potential and translational relevance in clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/42147376/","authors":["Hu W","Wang G","Ren L","Hu J","Wu X","Zhuang W","Yao Y","Wang C","Ye F","Mao W"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 30","doi":"10.21147/j.issn.1000-9604.2026.02.03","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42146517","name":"Open neuroinformatics infrastructure ecosystem for federated multisite studies.","source":"pubmed","abstract":"Despite growing understanding of the benefits of having Findable, Accessible, Interoperable, and Reusable (FAIR) data, many datasets still cannot be shared. Federated analysis methods can enable multisite studies that do not require the sharing of participant-level information. However, there are many practical hurdles that prevent the large-scale adoption of federated methods. We discuss challenges related to cross-site data preparation for federated learning, present solutions offered by recent neuroinformatics projects, and showcase an example of tool integration applied to neurodegenerative disease data.","url":"https://pubmed.ncbi.nlm.nih.gov/42146517/","authors":["Wang M","Bhagwat N","Cremonesi F","Dugré M","Pfarr JK","d'Angremont E","Dai A","Jahanpour A","Urchs S","Cansiz S","Chambon L","Dinçer AT"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.04.30.721944","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"pmid:42141172","name":"NIPTE 2030: The Nation's Digital Trust Anchor for Pharma 5.0.","source":"pubmed","abstract":"As the National Institute for Pharmaceutical Technology and Education (NIPTE) marks its 20th anniversary, the pharmaceutical ecosystem faces a widening gap between regulatory \"risk-to-quality\" compliance and real-world \"risk-to-patient\" outcomes. This Perspective offers a strategic roadmap for NIPTE to address the ontological fragmentation that treats drug products as legal descriptors rather than engineered systems. We suggest NIPTE is uniquely positioned to serve as the nation's \"digital trust anchor\" for Pharma 5.0-a human-centric evolution toward a patient-sovereign paradigm. By championing a five-layer CMC-GraphRAG architecture, NIPTE can lead the transition toward \"Ontological Honesty\" -defined here as the systematic grounding of AI reasoning in complete, mechanistically accurate representations of drug products and manufacturing processes as engineered systems. This choice allows NIPTE to elevate the patient's voice, transforming subjective experiences into mechanistically grounded Real-World Evidence (RWE). We illustrate these possibilities through the SMART-TDS pilot, which provides the scientific foundation-measured by the Semantic Precision Score (SPS) and Uncertainty Quantification (UQ)-necessary to support future industry-wide transitions toward dynamic, patient-centric quality assurance.","url":"https://pubmed.ncbi.nlm.nih.gov/42141172/","authors":["Hussain AS","Morris K","Gurvich VJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 15","doi":"10.1007/s11095-026-04118-z","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42141004","name":"Quantum-enhanced federated blockchain for privacy-preserving cardiovascular intelligence.","source":"pubmed","abstract":"Cardio-Vascular Diseases (CvDs) persist as a significant mortality reason worldwide, which requires advanced risk categorization technologies that can offer custom medicine while protecting patient information. Present healthcare approaches must overcome fragmented data distribution systems, weak security measures in collaborative environments, and the ineffective processing of multi-mode clinical details. Our proposed Decentralized Federated Blockchain for Cardiovascular Intelligence (DFBCI) framework integrates the Multi-Chain Aggregation with Adaptive Consensus (MCAC) mechanism and the advanced Federated Dynamic Relational Learning (FDRL) technique to solve identified challenges. The DFBCI system enables protected cooperation between institutions through its blockchain multi-chain setup that securely combines medical data with imaging information while maintaining data integrity. The FDRL technique extracts patient temporal behavior knowledge from different modalities, which include ElectroCardioGrams (ECGs), echocardiograms, and biomarker datasets. The system executes feature extraction using Quantum-Enhanced Privacy Masking (QEPM) for security alongside non-invasive cardiac risk modeling through federated optimization and performs decentralized validation with smart contracts. Compared to standard approaches, Experimental results reveal that models achieve 19% better CvD risk prediction while reducing their convergence time by 22% and enhancing scalability by 27%. DFBCI creates powerful healthcare analytical platforms that advance secure CvD risk sorting worldwide without violating privacy rights, setting benchmark for fast, collaborative assessments.","url":"https://pubmed.ncbi.nlm.nih.gov/42141004/","authors":["Sivakami R","Kumar VV","Krishnamoorthy N","Yimer TE"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 16","doi":"10.1038/s41598-026-47521-7","addedAt":"2026-08-31T06:41:25.476Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42138461","name":"Efficient collaborative learning of the average treatment effect.","source":"pubmed","abstract":"In response to the growing need for generating real-world evidence from multisite collaborative studies, we introduce an efficient collaborative learning approach to evaluate average treatment effect (ECO-ATE) in a multisite setting under data-sharing constraints. Specifically, ECO-ATE operates in a federated manner, using individual-level data from a user-defined target population and summary statistics from other source populations, to construct efficient estimator for the average treatment effect on the target population of interest. Our federated approach does not require iterative communications between sites, making it particularly suitable for research consortia with limited resources for developing automated data-sharing infrastructures. Compared to existing work data integration methods in causal inference, ECO-ATE allows distributional shifts in outcomes, treatments, and baseline covariates distributions, and achieves semiparametric efficiency bound under appropriate conditions. We conduct simulation studies to demonstrate the extent of efficiency gains achieved by incorporating additional data sources, as well as the robustness of our approach against varying levels of distributional shifts and overparameterization, compared to existing benchmarks. We apply ECO-ATE to a case study examining the effect of insulin versus non-insulin treatments on heart failure for patients with type II diabetes using electronic health record data collected from the All of Us program.","url":"https://pubmed.ncbi.nlm.nih.gov/42138461/","authors":["Li S","Duan R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 9","doi":"10.1093/biomtc/ujag076","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42136725","name":"Artificial intelligence in cardio-oncology: decoding mechanisms, predicting toxicity, and personalizing cancer therapy.","source":"pubmed","abstract":"Cancer therapy-related cardiovascular toxicity (CTR-CVT) threatens the sustainability of oncological advancements, demanding innovative approaches for early risk stratification. This review synthesizes how artificial intelligence (AI) is redefining cardio-oncology through multimodal integration of multi-omics, dynamic imaging, and real-world biosensor data. By decoding novel pathophysiological mechanisms and enabling continuous risk reclassification, AI transcends traditional static paradigms to generate patient-specific toxicity trajectories. Crucially, AI-driven interventions shift clinical practice from reactive monitoring to preemptive cardioprotection. While challenges in data heterogeneity, model interpretability, and equitable implementation persist, emerging solutions like federated learning and explainable AI pave the way for robust clinical translation. We hope that this review will summarize the current state of emerging applications of machine learning and AI in precision medicine predictive modeling, providing direction for AI-enabled precision cardiovascular oncology-ensuring the effectiveness of cancer treatment while safeguarding long-term cardiovascular health through personalized risk mitigation measures.","url":"https://pubmed.ncbi.nlm.nih.gov/42136725/","authors":["Yu C","Jiang L","Long L","Yu H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fcvm.2026.1761811","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42134610","name":"Multimodal artificial intelligence in retinopathy of prematurity: A comprehensive narrative review.","source":"pubmed","abstract":"Retinopathy of prematurity (ROP) remains a leading cause of preventable childhood blindness globally, particularly in regions with limited screening resources. Traditional diagnosis relying on subjective interpretation of fundus images faces challenges of inter-observer variability and manual analytical limitations. Recent advances in artificial intelligence (AI) have demonstrated substantial potential to achieve intelligent ROP management. We present a comprehensive review that synthesizes contemporary advances in AI-driven ROP management and reviewed the AI technologies based on retinal imaging, clinical text data, and smartphone-captured images transform ROP diagnosis, risk prediction, and treatment suggestion. We also illustrate the applications of AI in intelligent diagnosis and treatment of ROP through multimodal imaging. Despite remarkable progress, challenges persist in data heterogeneity, model generalizability, and real-world integration. By mapping technical breakthroughs to unmet clinical needs, we provide actionable insights to accelerate the development of equitable, clinically deployable AI solutions for preventing ROP-related vision loss.","url":"https://pubmed.ncbi.nlm.nih.gov/42134610/","authors":["Zhao X","Wu Z","Wu S","Cui K","Lam WC","Wu WC","Yang W","Lei B","Huang S","Wei W","Chi W","Zhang G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 14","doi":"10.1016/j.survophthal.2026.05.006","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42129608","name":"Artificial intelligence in diabetes care: Toward precision diagnosis and personalized management.","source":"pubmed","abstract":"Diabetes mellitus is a global health challenge requiring innovative solutions for early diagnosis, personalized treatment, and ongoing management. This review aims to examine the impact of artificial intelligence (AI) on diabetes care, focusing on precision diagnosis, tailored therapies, and real-time monitoring, while addressing challenges related to model transparency and equitable access to health care. We conducted a comprehensive review of AI applications in diabetes management. Studies utilizing supervised and unsupervised learning, deep learning, federated learning, and reinforcement learning were analyzed for predictive accuracy, clinical impact, and integration. Comparisons with conventional methods were also included. Machine learning models show strong predictive performance for diabetes risk assessment, with random forest algorithms reporting accuracy up to 97% in hospital-based datasets. Deep learning models applied to clinical cohorts have achieved approximately 94.6% accuracy in predicting adverse events in patients with type 2 diabetes. Reinforcement learning approaches for automated insulin delivery have maintained glucose within the normoglycemic range for up to 95.66% of simulated time in artificial pancreas studies. Federated learning enables privacy-preserving collaborative model development with performance comparable to centralized models. AI-driven decision-support systems and wearable technologies further support improved glycemic monitoring and patient self-management. However, model performance varies depending on dataset characteristics, patient populations, and evaluation protocols. AI has reshaped diabetes care by enabling precise diagnosis, individualized treatments, and adaptive disease management. Responsible implementation is essential to addressing ethical concerns and ensure equitable access. Future work should refine AI frameworks for broader clinical adoption, prioritizing patient-centered care and data security.","url":"https://pubmed.ncbi.nlm.nih.gov/42129608/","authors":["Aburjai T","Ali Agha ASA","Abuhilaleh Y","Miqdad A","Aburjai A","Alzweiri M","El Khassawna T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1177/10815589261452671","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42123533","name":"Liquid Biopsy in Colorectal Cancer: Future Perspectives Through the Lens of Artificial Intelligence-A Comprehensive Review of Novel Literature.","source":"pubmed","abstract":"Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with prognosis critically dependent on the stage at diagnosis. Traditional tissue biopsy presents well-known limitations, including tumor heterogeneity and invasiveness. Liquid biopsy, encompassing the analysis of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and other cell-free biomarkers, has emerged as a transformative approach for non-invasive tumor profiling. This comprehensive narrative review outlines the recent evidence published on the current state and future perspectives of liquid biopsy in CRC, with a focused emphasis on the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in data analysis and clinical translation. Methods: A narrative review of the literature was conducted by searching PubMed/MEDLINE, EMBASE, and ClinicalTrials.gov for articles published between January 2020 and January 2026, using a predefined Boolean search string combining terms related to liquid biopsy biomarkers, colorectal cancer, and artificial intelligence methodologies. Filters were applied to include only English-language human studies. Additional relevant sources were consulted to ensure comprehensive coverage of the available literature. Liquid biopsy platforms, particularly ctDNA sequencing and methylation profiling, demonstrate increasing clinical utility across the CRC care continuum from population screening to post-surgical minimal residual disease (MRD) detection and real-time therapy monitoring. AI-driven analytical frameworks, including Random Forest, Convolutional Neural Networks, LSTM models, and more recently Large Language Models (LLMs), substantially augment the sensitivity and specificity of liquid biopsy interpretation, enabling multimodal data integration. The convergence of liquid biopsy technology and AI-driven analytics represents a paradigm shift toward precision oncology in CRC. Remaining challenges include analytical standardization, model explainability, regulatory harmonization, and equitable access. Future integration of federated learning frameworks and LLM-based clinical decision support tools will be essential for responsible clinical translation.","url":"https://pubmed.ncbi.nlm.nih.gov/42123533/","authors":["Paduraru DN","Palcău AC","Gorecki GP","Dinulescu A","Băean ML"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 29","doi":"10.3390/ijms27093951","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42122974","name":"Bridging Traditional Modeling and Artificial Intelligence in Measles Epidemiology: Methods, Applications, and Future Directions-A Narrative Review.","source":"pubmed","abstract":"Measles remains one of the most contagious infectious diseases globally and continues to pose substantial public health risks despite decades of effective vaccination. This narrative review examines both classical and contemporary computational approaches used for measles monitoring, prediction, and control, with particular attention given to the emerging role of artificial intelligence (AI). We synthesized findings from 46 studies; 31 focused directly on measles and 15 on methodologically relevant studies from related infectious diseases (COVID-19, influenza, malaria), selected through searches of PubMed, Scopus, Web of Science, IEEE Xplore, and preprint servers, conducted between June and December 2025. Traditional compartmental models (SIR, SEIR, MSEIR), statistical tools (ARIMA, SARIMA), and seroepidemiological analysis provide transparent, well-characterized frameworks for estimating transmission dynamics and simulating intervention scenarios. Spatial modeling, network analysis, and Monte Carlo simulations have added geographic granularity to outbreak characterization. More recently, AI and machine learning (ML) methods, including supervised algorithms (Random Forest, XGBoost, SVM), deep learning architectures (CNN, LSTM), and hybrid mechanistic ML models, have shown improved predictive performance by integrating multiple data sources: epidemiological records, demographic profiles, mobility patterns, and behavioral indicators. AI-based approaches appear most valuable for high-dimensional risk prediction and image-based diagnostic tasks, while classical models retain clear advantages for policy-oriented scenario analysis. However, no AI-based or hybrid model identified in this review has been adopted into routine national measles surveillance or used for vaccination policy decisions at scale. Important challenges remain: data quality varies across settings, model generalizability cannot be assumed, and computational infrastructure disparities limit deployment in high-burden regions. Explainable AI, federated learning, workforce training for model interpretation, and integration of vaccination registries with mobility and genomic surveillance data represent concrete future directions for strengthening computational support for measles elimination.","url":"https://pubmed.ncbi.nlm.nih.gov/42122974/","authors":["Baiasu AF","Rotaru-Zavaleanu AD","Boldea AM","Ruscu MA","Serbanescu MS","Radu L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 24","doi":"10.3390/jcm15093242","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42122606","name":"Communication-Efficient Federated Learning with Dual-Sided Sparse Aggregation for Edge Sensing Systems.","source":"pubmed","abstract":"Distributed edge sensing systems, such as IoT monitoring nodes, wearable devices, and camera-based sensing terminals, continuously generate privacy-sensitive data that are costly to transmit to a central server. Federated learning (FL) provides a promising solution for collaborative model training without raw-data sharing; however, its practical deployment in edge sensing systems is challenged by non-IID local observations, limited uplink/downlink resources, and restricted on-device computation. To address these issues, this paper proposes a Dual-Sided Sparse Aggregation (DSSA) mechanism integrated with FedProx for resource-constrained edge sensing environments. In the proposed framework, the server prunes the global model after each communication round and transmits only the retained parameters, while clients update the complementary parameters and upload sparse local gradients. This fixed-structure sparse training strategy reduces bidirectional communication overhead and local computation cost, while FedProx improves robustness under heterogeneous data distributions. Experiments on CIFAR-10 and SVHN with varying non-IID degrees, pruning ratios, and hyperparameter settings show that the proposed method achieves a favorable resource-performance trade-off, reducing communication cost by up to 73.0% and computation cost by up to 34.9% while maintaining competitive accuracy. Under controlled benchmark settings, the proposed method demonstrates substantial resource savings compared with FedAvg, particularly in mildly heterogeneous scenarios, indicating a favorable benchmark-level resource-performance trade-off for resource-constrained edge sensing scenarios under the evaluated settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42122606/","authors":["Zhao H","Li J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26092885","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42122577","name":"A Federated Approach for Adaptive Urban Sound Classification on TinyML Edge Devices.","source":"pubmed","abstract":"Cities exhibit sound patterns that vary across locations and time, while transmitting raw audio introduces communication and privacy concerns. We present a federated TinyML architecture for real-time urban sound classification on microcontroller-class edge devices. A compact audio embedding network is deployed as a frozen feature extractor, while a lightweight classifier head is trained on-device and shared via MQTT, enabling communication-efficient collaborative learning. The system is evaluated on ESP32 (Espressif Systems, Shanghai, China) hardware under cross-dataset transfer from UrbanSound8K to SONYC. Domain shift reduces baseline accuracy from 90.39% to 78.27%, while local adaptation and federated aggregation improve accuracy to approximately 85%, recovering most of the performance loss. Repeated aggregation further improves macro-F1 and class balance across heterogeneous data. Embedded measurements confirm real-time inference (~250 ms per window) with negligible overhead, while each update exchanges only a compact classifier head (~1.2 kB). These results demonstrate that adaptive classification can be achieved on resource-constrained nodes in distributed smart-city networks.","url":"https://pubmed.ncbi.nlm.nih.gov/42122577/","authors":["Trigkas A","Piromalis D","Papageorgas P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26092854","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42122554","name":"Governing Privacy-Preserving Face Recognition in Transport Infrastructures: A Comprehensive Review.","source":"pubmed","abstract":"Face recognition technologies are increasingly deployed in transport infrastructures to improve efficiency and security, but they raise significant privacy and data protection concerns. This study reviews how privacy-preserving face recognition techniques can address these challenges in real-world settings. Using a systematic literature review approach, the paper analyses research across technical, operational, and governance perspectives. The findings show that while advanced methods such as encryption, federated learning, and de-identification can reduce data exposure, they are rarely implemented in operational systems, which tend to prioritize performance and scalability. At the same time, governance-focused studies emphasize issues such as proportionality, accountability, and fundamental rights, often without clear links to technical solutions. Overall, the review highlights a fragmented landscape and a gap between research and practice, underscoring the need for integrated approaches that align privacy-preserving techniques with practical deployment constraints and regulatory requirements.","url":"https://pubmed.ncbi.nlm.nih.gov/42122554/","authors":["Sanz EMB","Gonzalo Primo A","Choudhary G","Dragoni N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26092832","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"pmid:42122378","name":"Spiking Neural Networks with Continual Learning for Steering Angle Regression: A Sustainable AI Perspective.","source":"pubmed","abstract":"This work explores the application of Spiking Neural Networks (SNNs) and Continual Learning (CL) methodologies to the problem of steering angle regression, using autonomous driving simulation as the experimental context, with a focus on energy efficiency and alignment with sustainable computing objectives. The primary goal was to design and implement CL techniques in SNNs to assess the model's ability to maintain accuracy in explored environments while reducing CO 2 emissions through the optimized use of a subset of the data. This study emerges in response to the increasing energy demand of deep learning models, which poses a challenge to sustainability. SNNs, inspired by the efficiency of biological neural systems, offer significant advantages in terms of computational and energy consumption, making them a promising alternative. CL techniques, such as Elastic Weight Consolidation and replay memory, are integrated to mitigate catastrophic forgetting in sequential learning tasks. The methodology includes adapting the PilotNet architecture for SNNs, preprocessing datasets generated in the Udacity driving simulator, and evaluating models in incremental learning scenarios. The experiments compare the performance of SNNs with CL against baseline models without CL, using mean squared error (MSE), computational efficiency, and equivalent CO 2 emissions as evaluation metrics. The results demonstrate that replay memory enables the retention of prior knowledge with a limited increase in energy consumption. This work concludes that SNNs with CL are a viable alternative for sustainable AI applications. Future research directions include a focus primarily on hardware-specific implementations and real-world testing.","url":"https://pubmed.ncbi.nlm.nih.gov/42122378/","authors":["Martínez FS","Costa S","Parada R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 24","doi":"10.3390/s26092656","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42122337","name":"Resource-Adaptive Semantic Transmission and Client Scheduling for OFDM-Based V2X Communications.","source":"pubmed","abstract":"Proportional, fair scheduling in OFDM-based vehicle-to-everything (V2X) uplink causes the resource-block allocation of each vehicle to vary from slot to slot, yet conventional semantic encoders produce a fixed number of output tokens regardless of the instantaneous channel capacity. When the encoder output exceeds the slot budget, transmitted features are truncated and the resulting federated learning gradient is corrupted-a problem that affected 23% of training rounds for non-line-of-sight vehicles in our experiments. The difficulty is worsened by a spatial pattern common in urban deployments: vehicles at congested intersections suffer the poorest propagation conditions while carrying the training data most relevant to safety, and throughput-driven client selection excludes them in favor of vehicles with strong channels but uninformative scenes. We address both issues within a single framework for OFDM-based V2X federated learning. On the transmission side, a Sensing-Guided Adaptive Modulation (SGAM) module derives a per-slot token budget from the current resource-block allocation and selects tokens through differentiable Gumbel-TopK pruning with a hard capacity clip, so the transmitted token count stays within the slot budget. On the scheduling side, a Channel-Decoupled Federated Learning (CDFL) module partitions clients independently by channel quality and data complexity, selects diverse representatives per partition via facility location optimization, and corrects for partition-size imbalance through inverse propensity weighting during model aggregation. Experiments on NuScenes with 20 non-IID vehicular clients under realistic OFDM channel simulation demonstrate a Macro-F1 of 0.710 (+8.7 points over the Oort-adapted baseline), zero budget violations throughout training, and a 75% reduction in training variance; the worst-class F1 more than doubles relative to FedAvg.","url":"https://pubmed.ncbi.nlm.nih.gov/42122337/","authors":["Liu J","Chen Y","Wu W","Tian F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 23","doi":"10.3390/s26092615","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42120449","name":"Dual asymmetric momentum improves federated class unlearning in edge systems.","source":"pubmed","abstract":"Federated learning is increasingly used in edge and on-device systems, where models may later need to reduce the influence of specific training data in response to governance or deletion requests. Doing this after training is difficult because full retraining is costly in communication and computation, especially under non-IID client heterogeneity. We propose FedDAM, a communication-efficient post-hoc method for federated class unlearning that freezes the trained backbone and main classifier and updates only a lightweight auxiliary head. FedDAM further separates retain and forget optimization through dual-asymmetric momentum, enabling faster forgetting while better preserving retained utility under a fixed unlearning budget. Experiments on CIFAR-10, CIFAR-100, and ImageNet-100 show consistent improvements over a unified-momentum auxiliary-head baseline under matched budgets. On CIFAR-100, FedDAM improves the retained-utility summary at matched forgetting by 9.4 percentage points, and similar gains persist on ImageNet-100 and under sparse sample-level and client-level removal settings. Additional analyses show that the method remains robust under different aggregation rules and offers a favorable utility-efficiency trade-off relative to conflict-mitigation adaptations and compressed full-model retraining. These results indicate that FedDAM is a practical approach for responsive post-hoc unlearning in resource-constrained federated systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42120449/","authors":["Patra A","Mayaluri ZL","Sahoo PK","Kumawat G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 12","doi":"10.1038/s41598-026-45631-w","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42119126","name":"Preliminary Investigation of Federated Learning for MACE Prediction from Electronic Medical Records: A Multicontinental Study.","source":"pubmed","abstract":"Machine learning models for predicting major adverse cardiovascular events (MACE) often generalize poorly across populations, and multinational development is limited by data-sharing constraints.","url":"https://pubmed.ncbi.nlm.nih.gov/42119126/","authors":["Slamanig G","Kalabakov S","Lorenzer L","Schrempf M","Pierri G","Suzuki KMF","Jauk S","Kramer D","Mazzoncini de Azevedo-Marques P","Arnrich B","Rainer P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 7","doi":"10.3233/SHTI260090","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42115684","name":"Decoupling forgetting and preservation in federated unlearning via knowledge distillation.","source":"pubmed","abstract":"Federated Learning (FL) enables collaborative model training without sharing raw data, but compliance with data privacy regulations such as the \"Right to be Forgotten\" requires mechanisms to remove specific clients' contributions from trained models. A key challenge in federated unlearning is selectivity: removing a target client's influence while preserving performance for other designated clients. This is inherently difficult because deep neural networks learn shared feature representations, and aggressive forgetting operations degrade overall model utility. We propose a Three-Phase Selective Unlearning framework that decouples the conflicting objectives of forgetting and preservation into sequential phases: (1) aggressive forgetting via gradient ascent on the target client's data, (2) friend restoration through knowledge distillation using the pre-unlearning model as a teacher, and (3) balanced fine-tuning to maintain both objectives simultaneously. By separating these phases, we avoid gradient conflicts that arise from joint optimization. We conduct comprehensive experiments across four datasets-MNIST, CIFAR-10, CIFAR-100 (100 classes), and Tiny-ImageNet (200 classes, [Formula: see text])-with architectures ranging from small CNNs to ResNet-18 (11.2M parameters) under non-IID settings (Dirichlet [Formula: see text]). Our method achieves both target metrics (Forget Accuracy Drop &#x2265;70% and Friend Accuracy &#x2265;70%) on MNIST, and on Tiny-ImageNet achieves 83.6% forget accuracy drop with 76.43% friend accuracy, confirming scalability to realistic settings. Membership inference attack evaluations with a retrained gold-standard comparison further verify effective forgetting. Our results demonstrate that phase decomposition with knowledge distillation is essential for effective selective unlearning in federated settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42115684/","authors":["Kwon H","Maeng JB","Kim DJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 11","doi":"10.1038/s41598-026-51158-x","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42112255","name":"Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine.","source":"pubmed","abstract":"Federated Learning (FL) is an emerging computing paradigm to collaboratively train Machine Learning (ML) models across multi-source data while preserving privacy. The major challenge of \"meaningful\" implementation of FL for any ML model is how to guarantee that the federated ML model can achieve comparable performance compared to the global model trained using the combined data. Moreover, there are very limited studies on FL of functional regression models that analyze functional data, a commonly encountered type of data in many fields. This study develops the first-of-its-kind federated Gradient Boosting algorithm with the Least Squares Approximation (fed-GB-LSA) for efficient, privacy-preserving federated learning of the function-on-function regression with several distinct merits: (1) The GB-based algorithm allows the sparse selection of multivariate functional and non-functional features in the function-on-function regression prediction, which is not straightforward in the functional regression; (2) The parameter estimation by the GB algorithm results in separate sub-optimization problems with explicitly analytical solutions for each of the features, providing an efficient estimation algorithm for the function-on-function regression; (3) The LSA-enabled fed-GB provides a \"one-shot\" approach for FL that is communicationally and statistically efficient, providing theoretical guarantees to the federated model's performance without data sharing across local servers. The proposed fed-GB-LSA is tested in extensive simulation studies by considering real-world challenges such as device heterogeneity and applied in a real-world dataset for privacy-preserving telemonitoring of Obstructive Sleep Apnea (OSA).","url":"https://pubmed.ncbi.nlm.nih.gov/42112255/","authors":["Ding Y","Costa C","Si B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tase.2026.3660098","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42112224","name":"Artificial intelligence in Kellgren-Lawrence grading of knee osteoarthritis: bridging radiographic tradition with algorithmic precision.","source":"pubmed","abstract":"Knee osteoarthritis (KOA) remains the most prevalent form of osteoarthritis and a major cause of global disability. The Kellgren-Lawrence (KL) grading system, though widely used, suffers from inter- and intra-observer variability, especially in early disease stages. Artificial intelligence (AI) offers a transformative approach to automate KL grading on plain radiographs, providing consistent, reproducible, and scalable diagnostic solutions. This narrative review synthesizes recent advances in AI-based KL grading models, focusing on methodological frameworks, performance, clinical applicability, and limitations. Narrative review of peer-reviewed studies applying AI-based methods for KL grading of KOA on radiographic images. Literature search was conducted across PubMed, Embase, Web of Science, and Google Scholar to identify studies published between 2016 and 2025. Eligible studies satisfied predefined selection criteria, applied AI-based methods to radiographic grading of KOA. The review focused on model architectures, dataset characteristics, validation strategies, performance metrics, and comparisons with expert radiographic assessment. Eighteen eligible studies were included. Convolutional neural networks (CNN) remain the core of automated KL grading, evolving from standard classification models to ensemble and ordinal regression frameworks. Model performance was evaluated against expert-assigned KL grades as reference standard, with reported accuracies ranging from 75% to 98% and area under the curve values up to 0.98. Agreement with expert annotations, Cohen's kappa (&#x3ba;), ranged from 0.67 to 0.86. Deep Siamese networks, Faster R-CNNs, and ensemble frameworks have enhanced localization of KOA radiographic features, thereby interpretability relative to human radiologic assessment. Ordinal regression and attention-based visualization (saliency and class activation mappings) reduced misclassification between adjacent KL grades. Persistent challenges included subjective ground-truth labeling, dataset imbalance particularly under-representation of early (KL 0-1) and severe (KL 4) disease, and limited external validation. Models trained primarily on Osteoarthritis Initiative and Multicenter Osteoarthritis Study datasets showed reduced generalizability on external hospital datasets. AI-driven KL grading demonstrates near-human accuracy and strong promise for clinical integration. However, addressing labeling subjectivity, dataset diversity, and explainability remains essential for trustworthy deployment. While KL grading is inherently radiograph-based, integration of clinical metadata and longitudinal radiographic data may support more robust disease characterization. Federated learning frameworks offer a pathway to improve generalizability while preserving data privacy.","url":"https://pubmed.ncbi.nlm.nih.gov/42112224/","authors":["Rawat S","Chaturvedi VP","Vaidya B","Shanmugam H","Shah A","Airen L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1177/1759720X261442408","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42111900","name":"Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable.","source":"pubmed","abstract":"This work tackles the fundamental challenges in Federated Learning (FL) posed by arbitrary client participation and data heterogeneity, prevalent characteristics in practical FL settings. It is well-established that popular FedAvg-style algorithms struggle with exact convergence and can suffer from slow convergence rates since a decaying learning rate is required to mitigate these scenarios. To address these issues, we introduce the concept of stochastic matrix and the corresponding time-varying graphs as a novel modeling tool to accurately capture the dynamics of arbitrary client participation and the local update procedure. Leveraging this approach, we offer a fresh decentralized perspective on designing FL algorithms and present FOCUS, Federated Optimization with Exact Convergence via Push-pull Strategy, a provably convergent algorithm designed to effectively overcome the previously mentioned two challenges. More specifically, we provide a rigorous proof demonstrating that FOCUS achieves exact convergence with a linear rate regardless of the arbitrary client participation, establishing it as the first work to demonstrate this significant result.","url":"https://pubmed.ncbi.nlm.nih.gov/42111900/","authors":["Ying B","Li Z","Yang H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"pmid:42109119","name":"Big Data and Trustworthy AI for Heart Failure: A Review.","source":"pubmed","abstract":"The rapid evolution of machine learning techniques, combined with the growing availability of large and diverse datasets, is poised to transform heart failure research and clinical care. This review first provides an overview of key machine learning and artificial intelligence concepts used in heart failure research and then examines how diverse data modalities-including electronic health records, patient registries, biobanks, imaging, telemonitoring, and synthetic data-are leveraged to develop machine learning applications for heart failure diagnosis, prognosis, risk stratification, and personalized treatment strategies. While the potential is considerable, we highlight key barriers to clinical translation, such as data heterogeneity, algorithmic bias, lack of interoperability, and privacy concerns. The review also examines the need for explainable and equitable artificial intelligence systems and evaluates emerging solutions, including Federated Learning and synthetic data generation to address fairness and data privacy challenges. Beyond technical innovations, we underscore the importance of human-centered design, stakeholder engagement, and regulatory readiness. We conclude by identifying future priorities and calling for interdisciplinary collaboration to ensure the scalable, ethical, and effective integration of AI in heart failure management.","url":"https://pubmed.ncbi.nlm.nih.gov/42109119/","authors":["Perramon-Llussà J","Skorupko G","Rao S","Ruiz Pujadas E","Jouide El Kaderi S","Stepin I","Mamouei M","Boonstra M","Triantafyllidis A","Asselbergs FW","Salimi-Khorshidi G","Lekadir K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1161/CIRCHEARTFAILURE.125.013823","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"pmid:42106390","name":"Federated multimodal learning for privacy-preserving blood donor profiling and personalized recall strategy optimization.","source":"pubmed","abstract":"Blood supply shortages remain a persistent challenge for transfusion services, yet effective donor recall is hampered by fragmented data across institutions, limited profiling depth, and increasing privacy regulations. This paper proposes a federated multimodal learning framework that enables distributed blood centers to collaboratively train a shared donor profiling model without exchanging raw records. The framework integrates three modality-specific encoders - for demographic attributes, behavioral donation sequences, and textual feedback - unified through a cross-modal attention mechanism with adaptive gating. Differential privacy is embedded into the federated training pipeline via local gradient clipping and calibrated Gaussian noise injection, with cumulative privacy expenditure tracked through a moments accountant. Building on the fused donor embeddings, a multi-task recall optimization model jointly predicts optimal contact timing and communication channel, while a hierarchical clustering scheme translates predictions into tiered intervention protocols. Experiments on 127,463 donor records partitioned across six simulated blood centers demonstrate that the proposed method achieves a profiling F1 score of 83.7% and a recall conversion rate of 31.2%, approaching centralized performance within approximately 2% points. Ablation analysis confirms that the behavioral sequence modality contributes the strongest discriminative signal, while cross-modal attention yields a 4.3-point F1 improvement over naive concatenation. The privacy-utility tradeoff analysis identifies an operating range of &#x3b5; &#x2208; [1.0, 4.0] that preserves over 93% of noise-free model utility, offering practitioners a principled basis for balancing data protection with operational effectiveness.","url":"https://pubmed.ncbi.nlm.nih.gov/42106390/","authors":["Li L","Li J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 9","doi":"10.1038/s41598-026-51183-w","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42106383","name":"Federated sparse-learning framework for detecting low-rate stealth attacks in resource-constrained IoT devices.","source":"pubmed","abstract":"Low-rate stealth attacks present a major challenge in Internet of Things (IoT) environments because their slow, irregular, and noise-like traffic patterns evade traditional rate-based intrusion detection systems. To address this problem, this paper proposes FSL-IDS, a hierarchical federated intrusion detection framework designed for resource-constrained IoT deployments. The framework integrates sparse representation learning, hierarchical temporal modeling, and federated optimization across distributed IoT, fog, and cloud layers. At the device level, a sparsity-constrained encoder captures subtle deviations in local traffic behavior, while fog nodes employ a Hierarchical Ensemble for Correlated Events (H-EFCE) to identify cross-device temporal attack patterns. At the cloud layer, a Federated Gradient Aggregation Module (FGAM) performs robust global model aggregation, and a Lightweight Quantized Model Optimization Layer (LQMOL) enables efficient deployment on constrained edge devices. The proposed system was evaluated on the IoTID20 dataset containing heterogeneous smart-home IoT traffic, including Mirai, DoS, scan, authentication attacks, and stealth anomalies. To address the scarcity of low-rate stealth samples, a Synthetic Stealth Attack Injection Module (SSAIM) was used to augment training data while preserving realistic traffic characteristics. Experimental results demonstrate that the proposed architecture significantly improves stealth attack detection compared with multiple baseline methods, including federated learning algorithms and classical anomaly detection models. The FGAM-based cloud model achieved an AUC-ROC of 0.992 and F1-score of 0.965, outperforming FedAvg, FedProx, GRU-Autoencoder, Temporal Convolutional models, and Isolation Forest baselines under identical dataset and split conditions. Ablation experiments confirm that each architectural component contributes to performance gains, with the removal of the sparse encoder, temporal ensemble, or SSAIM module reducing detection accuracy and recall for low-rate stealth anomalies. Sensitivity analysis further shows stable detection performance across varying proportions of synthetic stealth traffic, while evaluation on limited real stealth samples confirms consistent detection capability. Additionally, model compression experiments demonstrate that 6-bit quantization provides an effective balance between efficiency and security, reducing edge-device energy consumption by 43% while maintaining near-optimal detection performance; further compression below this threshold leads to noticeable degradation in stealth-attack recall.","url":"https://pubmed.ncbi.nlm.nih.gov/42106383/","authors":["Anandhi R","Srikanth GU"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 9","doi":"10.1038/s41598-026-46531-9","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42100224","name":"CSF-net: a color space fusion network with self-attention-driven feature learning for feline ocular diseases classification.","source":"pubmed","abstract":"Feline ocular diseases can cause irreversible vision loss if they are not detected early. However, early diagnosis is often difficult. This is due to limited access to veterinary ophthalmology services and the challenge of distinguishing between visually similar eye conditions. Illumination changes, glare, and strong visual similarity among diseases substantially limit the performance of conventional RGB-based classification models. This paper proposes the Color Space Fusion Network (CSF-Net), an attention-guided color interaction framework for robust feline ocular disease classification in real-world environments.","url":"https://pubmed.ncbi.nlm.nih.gov/42100224/","authors":["S P D","Lee M","Yang HJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fvets.2026.1826139","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42097745","name":"From Pixels to Precision: Generative Artificial Intelligence as a Paradigm Shift in Spine Imaging-Technical Foundations, Clinical Applications, and the Path to Safe Clinical Deployment.","source":"pubmed","abstract":"Spine imaging represents a complex diagnostic frontier characterized by anatomical variability, motion artifacts, metallic instrumentation interference, and significant inter-reader diagnostic variability (&#x3ba;=0.20 across institutions). While conventional discriminative artificial intelligence (AI) models achieve &gt;95% accuracy in detecting degenerative changes, they remain limited by data scarcity, heterogeneous protocols, and poor generalizability. In the spine, these limitations are particularly relevant because clinical decisions can often depend on subtle distinctions (such as differentiating levels of canal or foraminal stenosis, characterizing Modic endplate changes, or assessing pedicle and vertebral morphology), where small inconsistencies can meaningfully alter management or surgical planning. Generative AI (GenAI) systems-including generative adversarial networks (GANs), diffusion models, and vision-language models (VLMs)-offer a paradigm shift by learning underlying data structures to generate high-quality synthetic outputs rather than merely classifying existing data. This narrative review, conducted using SANRA (scale for the assessment of narrative review articles) methodology across PubMed, Scopus, Embase, and Cochrane Library, examined GenAI applications in spine imaging. Eligible studies included observational designs through randomized controlled trials exploring image reconstruction, synthetic computed tomography (CT) generation, segmentation, and surgical planning applications. GAN-generated synthetic magnetic resonance imaging sequences reduce scan times by ~40% while maintaining diagnostic confidence; diffusion models enable radiation-free synthetic CT for preoperative planning; and VLMs generate structured radiology reports with hallucination rates &lt;1.12%. However, critical barriers impede clinical translation: external validation gaps reveal AI performance collapse in real-world cohorts (sensitivity drops to 54.9% in cervical fracture detection); hallucinations and anatomical inaccuracies risk misguiding implant sizing; bias amplification magnifies demographic underrepresentation; and fragmented, small datasets lack standardized benchmarks. Technical fragility, computational demands, clinician trust deficits, and unresolved regulatory frameworks for iteratively-updating systems remain unaddressed. Successful integration requires coordinated development across 5 priorities: (1) multi-institutional datasets with cross-vendor harmonization, (2) federated learning frameworks preserving privacy, (3) uncertainty quantification and explainability tools, (4) outcome-linked clinical validation replacing technical metrics, and (5) workflow-integrated systems with DICOM-native interfaces and provenance tracking.","url":"https://pubmed.ncbi.nlm.nih.gov/42097745/","authors":["Ashraf D","Sanker V","Liverani L","Park C","Medikonda RT","Cavagnaro MJ","Jeon I","Ratliff J","Desai A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr","doi":"10.14245/ns.2551862.931","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42091631","name":"Adaptive multimodal learning for driver cognitive state monitoring using transformer-based fusion with personalized meta-learning and federated optimization.","source":"pubmed","abstract":"Road accidents caused by driver fatigue and cognitive overload remain a significant public safety concern. According to recent traffic safety data, drowsy driving contributes to thousands of fatal accidents each year, emphasizing the urgent need for intelligent driver monitoring systems. To address this, we propose an adaptive multimodal deep learning framework (AML) for real-time cognitive workload assessment and fatigue detection, leveraging the CL-Drive dataset: a multimodal repository of EEG (cognitive load), ECG (cardiac activity), EDA (electrodermal arousal), and gaze tracking (visual attention) captured from 21 participants during simulated driving across nine scenarios of escalating complexity. Our framework integrates a hybrid CNN-BiLSTM architecture to extract spatiotemporal features from raw physiological signals and gaze sequences, capturing localized spatial patterns and long-term temporal dynamics. These features are fused using a transformer-based network with cross-modal attention, which models interactions between modalities (e.g., correlating gaze fixation losses with EEG theta-band surges during distraction) and yields a 3.6 percentage-point absolute accuracy improvement over the strongest conventional fusion baseline under identical evaluation. To address individual variability and privacy, we combine personalized meta-learning-adapting to new drivers with as few as five windowed samples (&#x223c;10&#xa0;s of synchronized multimodal data) via episodic fine-tuning-with federated optimization, enabling decentralized model updates and reducing per-client data transfer by 38% through adaptive gradient compression. Experiments on CL-Drive demonstrate state-of-the-art performance under strictly cross-subject evaluation. Under subject-independent 5-fold cross-validation, AML achieves [Formula: see text] accuracy on binary cognitive load classification without personalization, rising to [Formula: see text] with [Formula: see text] calibration samples (&#x223c;40&#xa0;s). Under the more rigorous leave-one-subject-out (LOSO) protocol, AML reaches [Formula: see text] without personalization and [Formula: see text] with [Formula: see text] personalization, an improvement of 1.6 percentage points over the strongest published LOSO baseline on this dataset with a further 6.2-11.3 percentage points gained from personalization alone across the LOSO and 5-fold protocols. The framework exhibits robustness to real-world sensor noise (e.g., EEG/EDA motion artifacts) and achieves [Formula: see text] LOSO accuracy with only [Formula: see text] samples (&#x223c;10&#xa0;s of calibration per new driver), critical for scalable in-vehicle deployment. By enabling privacy-aware, real-time monitoring of driver states, this work advances intelligent vehicle safety systems and provides a blueprint for adaptive multimodal learning in human-centric AI applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42091631/","authors":["Abinaya G","Dinakaran K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 6","doi":"10.1038/s41598-026-51635-3","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42090865","name":"DeCAF: Decentralized consensus-and-factorization for low-rank adaptation of foundation models.","source":"pubmed","abstract":"Low-Rank Adaptation (LoRA) has emerged as one of the most effective, computationally tractable fine-tuning approaches for training Vision-Language Models (VLMs) and Large Language Models (LLMs). LoRA accomplishes this by freezing the pre-trained model weights and injecting trainable low-rank matrices, allowing for efficient learning of these foundation models even on edge devices. However, LoRA in decentralized settings still remains under-explored, particularly for the theoretical underpinnings due to the lack of smoothness guarantee and model consensus interference (defined formally below). This work improves the convergence rate of decentralized LoRA (DLoRA) to match the rate of decentralized SGD by ensuring gradient smoothness. We also introduce DeCAF, a novel algorithm integrating DLoRA with truncated singular value decomposition (TSVD)-based matrix factorization to resolve consensus interference. Theoretical analysis shows TSVD's approximation error is bounded and consensus differences between DLoRA and DeCAF vanish as rank increases, yielding DeCAF's matching convergence rate. Extensive experiments across vision/language tasks demonstrate our algorithms outperform local training and rivals federated learning under both IID (independent and identically distributed) and Non-IID data.","url":"https://pubmed.ncbi.nlm.nih.gov/42090865/","authors":["Saadati N","Jiang Z","Waite JR","Ganguly S","Balu A","Hegde C","Sarkar S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Oct","doi":"10.1016/j.neunet.2026.108992","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42085410","name":"FedTFT: Federated Temporal Fusion Transformer for Interpretable Multi-Horizon Psychiatric Risk Prediction Across Cross-Silo Hospitals.","source":"pubmed","abstract":"Psychiatric inpatient monitoring generates multimodal data, but privacy constraints and cross-hospital heterogeneity limit centralized learning. We propose FedTFT, a federated Temporal Fusion Transformer for multi-horizon psychiatric risk prediction with horizon-decoupled prediction heads for one-hour, one-day, and one-week forecasting and an area under the receiver operating characteristic curve (AUROC)-weighted server aggregation strategy for non-independent and identically distributed hospital data. Each horizon uses its own linear output head to reduce cross-horizon gradient interference, while local training uses proximal updates. We trained and evaluated the model on 246 patients from three South Korean hospitals without sharing raw records. On the global holdout set, FedTFT achieved 93.9% accuracy, AUROC 0.9054, event-F1 0.8242, and Brier score 0.0680. Under matched federated settings, FedTFT improved event-F1 by 19.58 percentage points(PP) over the best competing federated baseline in event-F1 and improved AUROC by 2.74 pp over the highest-AUROC competing federated baseline, while maintaining the lowest Brier score. Ablations confirmed contributions from both the horizon-decoupled design and the AUROC-weighted aggregation strategy. Gradient SHapley Additive exPlanations (SHAP) analysis identified significant predictors such as treatment time, circadian heart-rate fluctuations, and mobility changes. These findings support accurate, calibrated, and interpretable privacy-preserving psychiatric risk forecasting for proactive intervention.","url":"https://pubmed.ncbi.nlm.nih.gov/42085410/","authors":["Ahamed A","Kang RR","Lee K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 5","doi":"10.1109/JBHI.2026.3690452","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42082627","name":"Adaptive homomorphic federated learning framework for multi-institutional medical imaging with optimized diagnostic accuracy.","source":"pubmed","abstract":"The growing use of artificial intelligence (AI) in medicine has highlighted the imperative for privacy-preserving and high-accuracy diagnostic systems. Traditional federated learning (FL) solutions allow multi-institutional collaborative training of a model without sharing raw patient data, but they tend to suffer from heterogeneous, multi-modal data, limited resilience to noisy or imbalanced data, and high communication overhead, which limits their practical clinical deployment. In order to overcome these constraints, we introduce the Next-Generation Adaptive Secure Federated Learning (NASFL) framework that aims at ultra-accurate, scalable, and secure multi-institutional medical AI. NASFL combines multi-level homomorphic encryption (MLHE) and stochastic differential privacy to provide patient confidentiality while using a transformer-guided ResNet backbone for adaptive multi-modal feature fusion between X-ray and CT imaging data. Institution-specific focus and trust-based aggregation dynamically scale model contributions to improve noise and low-quality dataset robustness. Communication efficiency is obtained from top-k gradient compression and adaptive learning rates, minimizing bandwidth consumption and speeding up convergence. The system was tested on publicly available multi-institutional datasets, namely NIH Chest X-ray14 and LIDC-IDRI CT scans, covering more than 112,000 images from over 30,000 patients, spanning 14 thoracic disease categories and lung nodules. NASFL exhibits exemplary performance, with 99.6% diagnostic accuracy, low convergence time (~&#x2009;65 communication rounds), good robustness in heterogeneous settings, and robust privacy assurance. These outcomes signify not only that NASFL surpasses traditional FL and state-of-the-art privacy-preserving protocols but also that it establishes a clinically sound platform for secure multi-institutional medical AI. Through its integration of adaptive multi-modal fusion, attention-guided aggregation, and strict privacy controls, NASFL sets a new benchmark for scalable, high-accuracy, and robust federated medical diagnostics, enabling wide-scale deployment in actual healthcare settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42082627/","authors":["Josephine Usha L","Saranya KR","Suganya Y","Mallikka R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-45821-6","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"pmid:42078831","name":"Predicting blood transfusion after ICU admission in five databases: A comparison of three machine learning paradigms.","source":"pubmed","abstract":"The objective of this retrospective study is to compare three learning approaches for blood transfusion (BT) prediction after intensive care unit (ICU) admission: local learning (LL), federated learning (FL), and centralized learning (CL) across five ICU databases (eICU Collaborative Research Database, Medical Information Mart for Intensive Care IV, High-Resolution Intensive Care Unit Dataset, Amsterdam University Medical Center Database, University Hospital of Augsburg).","url":"https://pubmed.ncbi.nlm.nih.gov/42078831/","authors":["Schwinn J","Sheikhalishahi S","Morhart M","Soto Rey I","Simon P","Kaspar M","Hinske LC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/20552076261428383","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42069049","name":"Integrating AI-enhanced kinase enrichment analysis (KEA) with geometric deep learning and federated learning for precision drug repurposing.","source":"pubmed","abstract":"Artificial intelligence (AI) is reshaping drug repurposing by integrating systems biology with molecular design. Here, we present a unified framework combining AI-enhanced Kinase Enrichment Analysis (KEA), geometric deep learning, and federated learning to enable scalable and privacy-preserving therapeutic discovery. KEA prioritizes disease-relevant kinases from multi-omics data, while geometric deep learning captures structure-activity relationships (SARs) at atomic resolution. Federated learning facilitates secure, multi-institutional model training across heterogeneous datasets. This integrative pipeline enhances identification of repurposable kinase inhibitors and supports emerging modalities, such as proteolysis-targeting chimeras (PROTACs). A case study in Alzheimer's disease (AD) highlights improved target prioritization and predictive performance. By bridging kinase signaling networks with AI-driven modeling, this framework provides a robust strategy for accelerating precision drug discovery and repurposing.","url":"https://pubmed.ncbi.nlm.nih.gov/42069049/","authors":["Iqbal S","Chen J","Pal D","Shen B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.drudis.2026.104687","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42066515","name":"Adaptive aggregation in federated learning for retinal vein occlusion detection: A dynamic approach based on data distribution.","source":"pubmed","abstract":"Clinical decision support systems (CDSS) increasingly rely on artificial intelligence (AI) to interpret biomedical images and provide accurate, real-time diagnostics. Federated Learning (FL) has emerged as a promising privacy-preserving solution for training AI models across decentralized healthcare institutions. However, FL performance is significantly affected by data heterogeneity-especially when client data is non-independent and identically distributed (non-IID), a common occurrence in clinical practice. In this study, we propose a dynamic FL framework specifically designed for Retinal Vein Occlusion (RVO) detection that enhances the adaptability and robustness of CDSS. The core contribution of our approach is a server-side aggregation strategy that selects FedAvg for IID data and SCAFFOLD for non-IID data based on monitoring the normalized L2 divergence between successive communication rounds. This intelligent selection mechanism ensures stable convergence, improves diagnostic accuracy, and supports reliable model performance across diverse healthcare environments. Experimental results show that the proposed approach was evaluated on a two-class RVO dataset across five simulated clients under both IID and non-IID data distributions. By addressing real-world data distribution challenges, this work offers a practical path toward integrating FL-based AI systems into clinical workflows for more effective and trustworthy medical image analysis.","url":"https://pubmed.ncbi.nlm.nih.gov/42066515/","authors":["Sri AV","Pusapati B","Puli RS","Morampudi MK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun","doi":"10.1016/j.compmedimag.2026.102772","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42065982","name":"Gradient Normalization Enables Communication-Efficient Distributed Learning Under Initialization Data Heterogeneity.","source":"pubmed","abstract":"Communication-efficient distributed learning has achieved remarkable progress in training large-scale deep neural networks across numerous clients. However, in heterogeneous environments-where local data distributions vary significantly-the empirical performance of many such algorithms degrades sharply. Moreover, existing theoretical analyses often depend on overly restrictive assumptions, such as bounded data heterogeneity, which may not be valid even for a single client. In this work, we introduce a general gradient normalization strategy that can be seamlessly integrated into a wide range of distributed learning algorithms, including compressed distributed stochastic gradient descent, federated averaging, and asynchronous variants. Our theoretical analysis demonstrates that this normalization technique effectively mitigates the negative impact of data heterogeneity, allowing these algorithms to achieve linear speedup rates with only requiring the boundedness of initialization data heterogeneity. Extensive numerical experiments further confirm the practical effectiveness and theoretical guarantees of our approach.","url":"https://pubmed.ncbi.nlm.nih.gov/42065982/","authors":["Sun T","Wu B","Liu X","Yuan K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Sep","doi":"10.1109/TPAMI.2026.3689520","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42061836","name":"Multimodal Intelligent Monitoring of Parkinson Disease: Scoping Review of Progress and Translational Challenges.","source":"pubmed","abstract":"Parkinson disease (PD) is a progressive neurodegenerative disorder with a rapidly growing global prevalence. Current clinical assessments, such as the Unified Parkinson Disease Rating Scale, are limited by subjectivity and episodic application, creating a need for continuous, objective monitoring solutions. While previous reviews have often focused on single technologies, there is a growing trend toward integrating multiple data sources to provide a more holistic view of PD.","url":"https://pubmed.ncbi.nlm.nih.gov/42061836/","authors":["Tan J","Deng X","Wu C","Yao M","Liao J","Zheng H","Lian C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 17","doi":"10.2196/89057","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42060443","name":"Utility-Preserving Federated Graph Learning With Dual-Perspective Fairness.","source":"pubmed","abstract":"Fairness-aware federated graph neural networks (FedGNNs) necessitate consideration of both the server and the clients. However, fairness-aware methods struggle to enhance dual-perspective (i.e., server and clients) fairness without sacrificing utility due to the distributed learning framework. As a consequence, the utility sacrifices of fairness-aware graph learning methods are even exacerbated in federated frameworks. In this work we propose F3GL, a dual-perspective fairness federated graph learning method that enhances both global (for the server) and local fairness (for clients) while preserving utility. Through theoretical analysis, we delineate the similarity between original sensitive features and those after convolution under different spectra. Our findings reveal that only the principal eigenvalue contributes to enhancing this similarity. Moreover, our theoretical analysis applies universally to both clients and servers. Specifically, employing a specialized eigenvalue selection strategy allows for effective optimization of both local and global fairness. Drawing on these insights, we improve dual-perspective fairness through the lens of spectral graph theory without sacrificing utility. Experimental results on two real-world datasets show the superiority of F3GL over existing baselines.","url":"https://pubmed.ncbi.nlm.nih.gov/42060443/","authors":["Luo R","Huang H","Yu S","Yu F","Xia F","Das SK","Zhang C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/TPAMI.2026.3689213","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42056051","name":"[Current applications and prospects of artificial intelligence in perioperative comprehensive management for gastrointestinal surgery].","source":"pubmed","abstract":"The shift from the traditional empirical approach to a more data-driven method in the diagnosis and treatment of GI cancers is significant due to advancements in overall precision medicine and digital healthcare. Artificial intelligence(AI),the driving force behind the technological revolution,is increasingly being used in screening,diagnosis,treatment,and rehabilitation in gastrointestinal surgery with its potential to use image recognition,develop a deep learning model and analyse multimodal data. This study systematically reviews how AI is currently being applied across the whole perioperative pathway in GI surgery. During pre-operation procedures,the use of AI assisted endoscopic systems and super-resolution radiomics improved early cancer detection rates and clinical stage predictions. During intraoperative procedures,augmented reality navigation,intelligent robotic systems,and the 'ultra-minimally invasive' concept have advanced surgical innovations towards improved precision and reduced invasiveness. After surgery,computational pathology and multi-omics fusion models can be used to assess prognosis and make adjuvant therapy decisions. The physical and psychological rehabilitation experience of patients undergoing perioperative management is optimized with the aid of smart cognitive behavioural therapy and intensive care warning systems. The challenges limiting the clinical translation of artificial intelligence,such as data silos,algorithmic opacity,and ethical regulations,are discussed in detail at the end. After this,possible future applications of federated learning and large language models are envisioned.","url":"https://pubmed.ncbi.nlm.nih.gov/42056051/","authors":["Wang PY","Liu B","Liu X","Shan MH","Shen ZX","Li SL","Wu MZ","Zhao ZC","Yan YJ","Fu WH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 1","doi":"10.3760/cma.j.cn112139-20260129-00045","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42054886","name":"Advancing federated semi-supervised medical image segmentation: A duo of interactive denoising pseudo-labels and convolutional contrastive learning.","source":"pubmed","abstract":"Many existing studies on federated learning (FL) for segmentation primarily assume that all client data are labeled. However, in reality, due to the high cost of hospital construction and the scarcity of expert annotators, many medical sites can only provide unlabeled data. Therefore, in our work, we focus on a more practical and challenging problem, namely federated semi-supervised segmentation (FSSS), where only a subset of clients possesses labeled data while the remaining clients contribute unlabeled data. To tackle this problem, we propose an effective and generalizable FSSS framework. Specifically, labeled clients are first aggregated to construct a label-based aggregation model, which serves to guide the pseudo-label generation for unlabeled clients. Since the generated initial pseudo-labels often suffer from feature offset, we develop a pixel-level denoising method based on uncertainty feature map estimation, which enhances the quality of pseudo-labels by leveraging local data. Second, we design a model-convolutional contrastive learning to endow unlabeled clients with enhanced feature discrimination capabilities, thereby correcting their inaccurate representations. Finally, an effective dynamic model aggregation method is devised to adjust the aggregation weight of each client by considering the contribution quantified via a one-hot scheme. We comprehensively evaluate our method from multiple perspectives on three non-independent and identically distributed (Non-IID) segmentation tasks, and the experimental results confirm the effectiveness of our method. The codes of this work has been released at the following link: https://github.com/ZhenghuaXu/FedDPCon.","url":"https://pubmed.ncbi.nlm.nih.gov/42054886/","authors":["Xu Z","Zhang Y","Li B","Zhou G","Lu X","Lukasiewicz T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul","doi":"10.1016/j.media.2026.104091","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42049823","name":"Adaptive trust evaluation and representation-based robust aggregation against poisoning attacks in federated learning.","source":"pubmed","abstract":"Federated Learning (FL) has emerged as a prominent paradigm for privacy-preserving distributed training. However, particularly in non-IID and open-participation environments, FL remains highly vulnerable to model poisoning, specifically backdoor attacks. Existing defenses, such as robust aggregation and representation-learning approaches, often struggle against colluding adversaries and adaptive attack strategies, leading to model performance degradation and the propagation of malicious updates. To address these challenges, we propose FLAURA, a robust FL defense framework incorporating adaptive trust evaluation and hybrid aggregation. FLAURA operates within the penultimate-layer representation (PLR) space, integrating a dual-level trust evaluation mechanism. At the global level, it leverages the geometric median of PLRs to robustly estimate the global distribution center, thereby effectively mitigating systematic shifts induced by malicious clusters. At the local level, it employs Maximum Mean Discrepancy (MMD) combined with curvature-based knee point detection to adaptively determine trust boundaries. This design effectively distinguishes benign data heterogeneity from malicious perturbations without requiring prior knowledge of the fraction of adversaries. Furthermore, FLAURA implements a hybrid mechanism of hard filtering and soft weighting to exclude low-trust updates while preserving beneficial model diversity. Extensive experiments on the FMNIST, CIFAR-10, and CIFAR-100 datasets demonstrate that FLAURA significantly outperforms state-of-the-art baselines, reducing attack success rates and target-label confidence while maintaining high accuracy on clean data.","url":"https://pubmed.ncbi.nlm.nih.gov/42049823/","authors":["Xiao Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 28","doi":"10.1038/s41598-026-50985-2","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42048375","name":"Design of a dynamics-based hydraulic controller for lifting manipulator wrist and its stability analysis.","source":"pubmed","abstract":"Under the requirements of Industry 4.0, the performance requirements for improving the hydraulic controller of the mechanical hand wrist are getting higher and higher. To address issues such as response delay and insufficient accuracy in traditional methods, this study proposes a hydraulic control based on a dynamic model. The core innovation lies in embedding the dynamic model into the control loop and integrating multiple intelligent algorithms for closed-loop optimization. Experimental verification shows that the maximum trajectory tracking error of the SHD controller is only 0.28&#x2009;mm, the fault detection accuracy is as high as 98.3%, and the energy conversion efficiency is 98.1%. It is significantly superior to existing advanced controllers in terms of accuracy, stability, and response speed, such as the controller combining quantum-inspired neural networks with robust control, the controller combining meta-learning with fuzzy wavelet control, and the controller combining federated learning with edge control. The above research results provide an efficient solution for the precise and intelligent control of hydraulic systems in complex lifting operations.","url":"https://pubmed.ncbi.nlm.nih.gov/42048375/","authors":["Li B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0347838","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42046731","name":"Diabetic Retinopathy Detection: AI Models and Approaches.","source":"pubmed","abstract":"Diabetic retinopathy (DR), a major cause of vision loss worldwide, results from chronic diabetes damage to retinal blood vessels. Vision loss can be prevented if DR is detected early, but traditional retinal screening by eye care takes time and expertise. Recent advances in AI technology, including classical machine learning and deep learning, can be more accurate in DR detection. This article provides a comprehensive review of current AI models and approaches of DR screening.","url":"https://pubmed.ncbi.nlm.nih.gov/42046731/","authors":["Madi MMM","Farr PC","Bester D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1155/joph/8857887","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42045766","name":"BF2GAN: A Fused Federated Learning-Based Biometric Enabled Deep Learning Approach for Secure Medical Image Sharing in Cloud Environment.","source":"pubmed","abstract":"Medical images contain sensitive and private health information of patients, which is crucial to be safeguarded from unauthorized access. Various encryption schemes are used for improving security; however, they are subject to adversaries associated with higher complexity, ineffective compression ratios, and slower responses in real time. Therefore, to address these limitations as well as to establish stronger security in medical image sharing, the research proposes a Biometric Fused Federated Generative Adversarial Network (BF2GAN) method. The federated learning (FL) concept is included in this research, which provides collaborative training while reducing the risks of gradient inversions. Moreover, the decentralized training strengthens data integrity and reduces the risk of data exposures. The biometric information is included for user verification and key generation, which assists in creating highly secure images that are difficult to decode without the correct keys. The method minimizes the reliance on key management algorithms and establishes a simplified encryption process that strengthens the overall system security. In the LUNA16 database, the BF2GAN achieves a significant performance in terms of 3.08&#xa0;s encryption time, 0.975 structural similarity index measure, 0.82 Feature similarity index, 3.9&#xa0;s decryption time, 58.38 decibels of peak signal-to-noise ratio, and minimum memory usage of 280.87 kilobytes, compared to the state-of-the-art methods.","url":"https://pubmed.ncbi.nlm.nih.gov/42045766/","authors":["Mohammed ZA","Rajeshwari D","Mohammed S","Sreelaxmi M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 27","doi":"10.1007/s10278-026-01962-8","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42044183","name":"Designing a children's health exposomics study protocol: The CHILDREN_FIRST multi-country prospective cohort using multi-omics and personalized prevention approaches.","source":"pubmed","abstract":"Non-communicable diseases (NCDs) account for ~71% of all deaths globally, including 15 million premature deaths each year (deaths between 30-69 years of age). Instead of waiting until disease manifestation, focusing on the origins of NCDs during childhood offers a critical window of disease prevention and control. The CHILDREN_FIRST international cohort observatory study aims to investigate how the spatio-temporal evolution of the children's exposome profiles in the Mediterranean region influences early-life programming of chronic disease risk during the critical window of susceptibility in primary school years (6-11 years of age). The study protocol adopts the human exposome framework integrated with a personalized prevention approach, using multi-omics platforms and advanced machine learning algorithms implemented across Mediterranean countries, namely Cyprus, Greece, and Albania. The cohort will consist of children enrolled in the first grade of primary school, who will undergo annual follow-up assessments until completion of primary education. During the annual assessments, children's exposome parameters from the three main exposome domains will be evaluated using different assessment types, i.e., molecular biomarkers of exposure/effect, sensors, and questionnaires. Standardized biospecimen&#xa0;and data collection methods will be employed following harmonized standardized operating procedures. The reference model of Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) developed and maintained as part of the Observational Health Data Sciences and Informatics (OHDSI) initiative will be used to conduct federated data analysis. This CHILDREN_FIRST study protocol is a human exposome-based initiative to establish a long-term prospective cohort infrastructure for biomedical research on children's health in the Mediterranean region. The cohort's exposome-based findings will systematically feed into the evaluation and design of chronic disease prevention programs. Expected results would inform evidence-based policy making and the development of health interventions for reducing the risk of NCDs in childhood and later in adult life.","url":"https://pubmed.ncbi.nlm.nih.gov/42044183/","authors":["Konstantinou C","Soursou G","Abimbola S","Charisiadis P","Kyriacou A","Modestou T","Tornaritis M","Hadjigeorgiou C","Agapiou A","Elia EA","Milis G","Eleftheriou L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0326641","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42038959","name":"Six artificial intelligence innovation strategies applied to autism spectrum disorder research: A narrative review.","source":"pubmed","abstract":"Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by deficits in social communication and restricted behaviors. Traditional assessment and intervention methods rely heavily on subjective and time-consuming approaches, which limit their clinical impact. Advances in artificial intelligence (AI) offer transformative opportunities for ASD research and practice. This narrative review proposes six AI-driven strategies that address six core research challenges: uncovering causal mechanisms, modeling dynamic neurodevelopment, integrating multimodal data, individualized computational modeling, collaborative learning across institutions, and enhancing social training. We highlight the potential of causal inference to clarify gene-environment interactions, spatio-temporal graph neural networks to capture neurodevelopmental heterogeneity, and multimodal fusion for unified representation learning. Digital twin technologies enable personalized brain modeling and neuromodulation optimization, while social brain reverse engineering and federated learning frameworks support computational hypothesis generation and privacy-preserving collaboration, respectively. Large language models further facilitate context-aware social interventions. We also discuss key challenges-including data heterogeneity, interpretability, ethics, and clinical translation-and outline directions for building a more precise, human-centered research paradigm. This review aims to move beyond incremental tool improvements toward reconstructing scientific paradigms, thereby accelerating the effective translation of AI innovations into clinical ASD applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42038959/","authors":["Zhang T","Cao Z","Li W","Lv Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/ped4.70038","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"pmid:42037715","name":"MRI-based brain stroke classification using a hybrid vision transformer-BiLSTM architecture.","source":"pubmed","abstract":"Stroke is a prominent cause of long-term disability, impacting patients' socioeconomic status in daily life. Hemorrhagic and ischemic strokes differ in dimensions, forms, and locations, posing challenges for automated detection. Magnetic resonance imaging (MRI), particularly diffusion-weighted imaging (DWI), reveals changes in fluid balance, thereby enabling early detection. Hence, MRI scans are more accurate than computed tomography (CT) scans due to their increased sensitivity.","url":"https://pubmed.ncbi.nlm.nih.gov/42037715/","authors":["Samuel R","Pandi T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1802972","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42036459","name":"Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis.","source":"pubmed","abstract":"Brain diseases, including tumors, along with mental and neurological disorders, pose a substantial threat to millions of individuals worldwide. Doctors use both structural and functional neuroimaging methods for diagnosing these diseases. Specialist doctors often combine Magnetic Resonance Imaging (MRI) with other neuroimaging techniques to enhance the detection and identification of brain diseases in clinical practice. This is crucial because these modalities give different information that supplement each other, hence enhancing accurate diagnosis. This study proposes a fusion approach integrating MRI with three Machine Learning (ML) classifiers, Convolutional Neural Network (CNN), Random Forest (RF), and Support Vector Machine (SVM), to improve brain tumor classification. Using a publicly available dataset containing 7023 MRI images across four categories (glioma, meningioma, pituitary, and no tumor), the models were trained and evaluated using a standard train-test split. The CNN model achieved the highest accuracy at 99.29%, followed by RF at 99.06% and SVM at 98.36%. While the performance metrics are promising, the study is limited to a single dataset and lacks external validation, which constrains the generalizability of results. Future work should explore clinical integration, federated learning frameworks, and multi-center datasets to enhance real-world applicability.","url":"https://pubmed.ncbi.nlm.nih.gov/42036459/","authors":["Khan UA","Badri S","Hasan SH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 26","doi":"10.1038/s41598-026-50213-x","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42034002","name":"From bench to byte: A UK perspective on data-driven cancer research.","source":"pubmed","abstract":"Cancer research is undergoing a profound transformation driven by the rapid expansion of clinical, genomic, imaging, and real-world data. As Europe prepares for the implementation of the European Health Data Space (EHDS), the ability of health systems to effectively integrate, govern, and translate these diverse datasets will shape the next era of oncology. However, technological capacity alone is insufficient; sustained impact will depend on building trust, strengthening infrastructure, and supporting the people and cultures that enable data-intensive science. Cancer Research UK (CRUK), the nation's largest cancer charity, invests over &#xa3;400&#xa0;million annually in research and has launched a national data strategy to accelerate progress. In partnership with CRUK, we are working to develop a more connected and collaborative cancer data science ecosystem,one that brings together researchers across disciplines, identifies shared challenges, and co-designs practical solutions to overcome them. Through the CRUK Data Science Community and its Data Interest Groups, we highlight common obstacles across health system data, data reuse, public involvement, infrastructure, and training. We also present case studies demonstrating how integrated datasets, AI-enabled analytics, international collaboration, and federated approaches are already reshaping cancer research and clinical practice. By fostering a community-led approach to trustworthy, sustainable and FAIR data access, the UK has an opportunity to unlock the full potential of data-driven research and deliver meaningful benefits for people affected by cancer.","url":"https://pubmed.ncbi.nlm.nih.gov/42034002/","authors":["Wooller SK","Blake A","McCabe M","McLean C","Price G","Unsworth H","Van Hemelrijck M","Pearl FMG"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 3","doi":"10.1016/j.ejca.2026.116751","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42031077","name":"The emerging use of artificial intelligence in safety pharmacology and toxicology.","source":"pubmed","abstract":"Safety pharmacology is concerned with the identification and characterization of adverse effects of drug candidates on vital organ systems. The emergence of artificial intelligence (AI) and machine learning (ML) has prompted growing interest in their potential application to nonclinical drug safety evaluation across the core battery cardiovascular, central nervous, and respiratory systems. This review traces the historical development and technical foundations of AI, from early neural network research and backpropagation algorithms to the emergence of modern frontier large language models, and examines how these technologies are being applied to safety pharmacology study endpoints including proarrhythmic risk assessment consistent with International Council for Harmonisation (ICH) S7B and Comprehensive in vitro Proarrhythmia Assay (CiPA) frameworks, seizure liability detection via microelectrode array analysis, and respiratory function monitoring through whole-body plethysmography. The applications of AI in a broader toxicological assessment, including multi-endpoint toxicity prediction, digital pathology, and federated learning consortia, are also reviewed. To gauge current adoption and attitudes within the discipline, a survey of Safety Pharmacology Society (SPS) members was conducted at the 2024 annual meeting (N&#xa0;=&#xa0;89). The survey revealed that 57% of respondents were not currently using AI tools, although 44% of non-users planned adoption within the following year; 84% of respondents intended to apply AI in preclinical safety development. The evolving regulatory landscape, including the 2025 United States Food and Drug Administration (FDA) draft guidance on AI credibility and the 2026 FDA/European Medicines Agency (EMA) joint guiding principles, is discussed alongside challenges related to data quality, model interpretability, and validation requirements. The findings indicate that while AI tools show promise for specific applications such as structure-based toxicity prediction and automated signal analysis, the safety pharmacology community appropriately demands rigorous validation before integration into regulated workflows. The challenges of model interpretability, data quality, and the absence of prospective validation studies represent substantive barriers that must be addressed through collaborative effort among industry, academia, regulatory agencies, and scientific societies. AI in safety pharmacology is currently best positioned as a complementary analytical tool that may help avoid investing resources in compounds with predictable safety liabilities, rather than as a replacement for expert scientific judgment.","url":"https://pubmed.ncbi.nlm.nih.gov/42031077/","authors":["Miller L","Treleaven I","El Amrani AI","Rossman EI","Winters BR","Pugsley MK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun","doi":"10.1016/j.vascn.2026.108424","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42030628","name":"FedSemiDG: Domain generalized federated semi-supervised medical image segmentation.","source":"pubmed","abstract":"Medical image segmentation is challenging due to the diversity of medical images and the lack of labeled data, which motivates recent developments in federated semi-supervised learning (FSSL) to leverage a large amount of unlabeled data from multiple centers for model training without sharing raw data. However, what remains under-explored in FSSL is the domain shift problem which may cause suboptimal model aggregation and low effectiveness of the utilization of unlabeled data, eventually leading to unsatisfactory performance in unseen domains. In this paper, we explore this previously ignored scenario, namely domain generalized federated semi-supervised learning (FedSemiDG), which aims to learn a model in a distributed manner from multiple domains with limited labeled data and abundant unlabeled data such that the model can generalize well to unseen domains. We present a novel framework, Federated Generalization-Aware Semi-Supervised Learning (FGASL), to address the challenges in FedSemiDG by effectively tackling critical issues at both global and local levels. In our proposed framework, globally, we introduce Generalization-Aware Aggregation (GAA), assigning adaptive weights to local models based on their generalization performance. Locally, we use a Dual-Teacher Adaptive Pseudo Label Refinement (DR) strategy to combine global and domain-specific knowledge, generating more reliable pseudo labels. Additionally, Perturbation-Invariant Alignment (PIA) enforces feature consistency under perturbations, promoting domain-invariant learning. Extensive experiments on four medical segmentation tasks (cardiac MRI, spine MRI, bladder cancer MRI and colorectal polyp) demonstrate that our method significantly outperforms state-of-the-art FSSL and domain generalization approaches, achieving robust generalization on unseen domains. This work provides a practical solution for addressing domain shifts in federated semi-supervised learning, advancing multi-center collaboration in privacy-sensitive healthcare applications. The code will be made public upon acceptance.","url":"https://pubmed.ncbi.nlm.nih.gov/42030628/","authors":["Deng Z","Xu Z","Isshiki T","Zheng Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul","doi":"10.1016/j.media.2026.104096","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42029797","name":"Graph Neural Networks in Neuroimaging: Current Status and Biostatistical Considerations for Clinical Deployment.","source":"pubmed","abstract":"Graph Neural Networks (GNNs) have emerged as a novel paradigm that enables scientists to model complex relational data in medical applications, offering unique advantages over traditional deep learning (DL) approaches for non-Euclidean domains. This paper provides a comprehensive review of current GNN architectures and their healthcare applications, with a focus on functional connectivity analysis, electrical-based diagnostics, and anatomical structure modeling. We analyze the strengths and limitations of spectral and spatial GNN variants, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and spatio-temporal extensions. Based on our critical assessment of the state-of-the-art innovations, we propose several key directions for medical researchers actively developing GNN technology: (1) Dynamic graph representation learning to capture evolving physiological processes; (2) Multi-modal fusion techniques to integrate heterogeneous biomedical data streams; (3) Uncertainty-aware GNNs for robust clinical decision support; (4) Explainable GNN architectures to enhance interpretability for healthcare practitioners; and (5) Federated GNN frameworks to enable privacy-preserving collaborative learning across institutions. We also introduce a new Temporal Multi-modal Attention Graph Neural Network (TMA-GNN) architecture designed explicitly for longitudinal patient modeling and clinical trial optimization. Our TMA-GNN incorporates multi-head attention mechanisms, temporal edge construction, and a custom loss function to encourage temporal consistency in predictions. We introduce a conceptual framework for the Temporal Multi-modal Attention Graph Neural Network (TMA-GNN), which is designed to support disease progression modeling and clinical trial optimization in neurological disorders. Although the proposed model architecture is technically detailed, this manuscript focuses on the conceptual and methodological design, rather than presenting experimental results. By addressing these proposed research directions, we envision GNNs will play an increasingly pivotal role in precision medicine, disease progression modeling, and treatment personalization.","url":"https://pubmed.ncbi.nlm.nih.gov/42029797/","authors":["Kumar R","Sporn K","Waisberg E","Ong J","Paladugu P","Sekhar T","Hage T","Vaja S","Nelson N","Masalkhi M","Lee R","Amiri D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug","doi":"10.1007/s10439-026-04045-5","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42028409","name":"Agentic AI as a coordination paradigm in digital health and agri-food systems.","source":"pubmed","abstract":"Digital health and agri-food data systems increasingly rely on sophisticated machine learning and data-sharing infrastructures. Yet persistent challenges in scalability, accountability, and public trust indicate that technical capability alone does not resolve systemic failure. This perspective argues that these limitations primarily arise from architectural misalignment with governance rather than from algorithmic insufficiency. Through a comparative examination of federated learning, blockchain-based infrastructures, and FAIR-aligned platforms, recurring coordination bottlenecks are identified across both health and agricultural domains. Building on these observations, this perspective introduces an agentic coordination model in which task-bounded agentic components operate under explicit institutional and regulatory constraints. The model context protocol (MCP) is presented as a reference mechanism for mediating policy, provenance, and accountability across distributed agents without centralizing control. Rather than prescribing a universal solution, this work frames agentic architectures as a governance-aware design space for future digital health and food systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42028409/","authors":["Gavai AK","Meuwissen MPM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar 13","doi":"10.1016/j.patter.2026.101496","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42025000","name":"Architectural and translational perspectives on clinical decision support systems for rare disease diagnosis: a scoping review.","source":"pubmed","abstract":"Rare diseases remain difficult to diagnose because of phenotypic heterogeneity, limited clinical familiarity, and fragmented health data infrastructures. Clinical decision support systems (CDSS) have emerged as promising tools to support earlier recognition and more consistent diagnostic reasoning. However, the literature spans diverse technological paradigms, making it difficult to understand how these systems collectively contribute to clinical decision-making and their translational implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/42025000/","authors":["Özçetin E","Baş SE","Özpay F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 15","doi":"10.1016/j.ijmedinf.2026.106442","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42023241","name":"Adapting federated radiomics models for radiation pneumonitis prediction in patients receiving thoracic radiotherapy with immunotherapy.","source":"pubmed","abstract":"Radiation pneumonitis (RP) is one of the major dose-limiting toxicities of thoracic radiotherapy. Although multiple studies have attempted to predict RP, robust multicenter model development is often hindered by privacy regulations and data-transfer constraints, and many existing models are primarily derived from radiotherapy-alone populations, limiting applicability to contemporary regimens that incorporate immunotherapy. Therefore, this study aimed to develop an RP prediction model within a federated learning framework, incorporating sequential transfer learning strategies to enable separate risk assessment for radiotherapy patients with and without immunotherapy.","url":"https://pubmed.ncbi.nlm.nih.gov/42023241/","authors":["Zhu Z","Yan M","Ji W","Zhang Z","Dekker A","Wee L","Zhang T","Lai X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1793039","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42022184","name":"Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation.","source":"pubmed","abstract":"Individual Treatment Effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most existing ITE estimation methods are designed for centralized settings. However, in real-world clinical scenarios, the raw data are usually not shareable among hospitals due to the potential privacy and security risks, which makes the methods not applicable. In this work, we study the ITE estimation task in a federated setting, which allows us to harness the decentralized data from multiple hospitals. Due to the unavoidable confounding bias in the collected data, a model directly learned from it would be inaccurate. One well-known solution is Inverse Probability Treatment Weighting (IPTW), which uses the conditional probability of treatment given the covariates to re-weight each training example. Applying IPTW in a federated setting, however, is non-trivial. We found that even with a well-estimated conditional probability, the local model training step using each hospital's data alone would still suffer from confounding bias. To address this, we propose FED-IPTW, a novel algorithm to extend IPTW into a federated setting that enforces both global (over all the data) and local (within each hospital) decorrelation between covariates and treatments. We validated our approach on the task of comparing the treatment effects of mechanical ventilation on improving survival probability for patients with breadth difficulties in the Intensive Care Unit (ICU). We conducted experiments on both synthetic and real-world eICU datasets, and the results show that FED-IPTW outperforms state-of-the-art methods on all the metrics on factual prediction and ITE estimation tasks, paving the way for personalized treatment strategy design in mechanical ventilation usage.","url":"https://pubmed.ncbi.nlm.nih.gov/42022184/","authors":["Yin C","Chen HY","Chao WL","Zhang P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr","doi":"10.1145/3787464","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42019059","name":"FedSKD: Aggregation-Free Model-Heterogeneous Federated Learning via Multidimensional Similarity Knowledge Distillation for Medical Image Classification.","source":"pubmed","abstract":"Federated learning (FL) enables privacy-preserving collaborative model training without direct data sharing. Model-heterogeneous FL (MHFL) enables clients to train personalized models with heterogeneous architectures, but existing methods mainly rely on centralized aggregation or require partially identical architectures, limiting scalability and efficiency. Current peer-to-peer (P2P) FL frameworks, though removing server dependence, have not been adapted to heterogeneous models and suffer from model drift and knowledge dilution. To address these challenges, we propose FedSKD, a novel P2P MHFL framework for medical image classification that facilitates direct knowledge exchange through round-robin model circulation, eliminating the need for centralized aggregation while allowing fully heterogeneous model architectures across clients. FedSKD's key innovation lies in multidimensional similarity knowledge distillation (SKD), which enables bidirectional cross-client knowledge transfer at batch, pixel/voxel, and region levels for heterogeneous models in FL. This approach mitigates catastrophic forgetting and model drift through progressive reinforcement and distribution alignment while preserving model heterogeneity. Extensive evaluations on fMRI-based autism spectrum disorder (ASD) diagnosis and skin lesion classification demonstrate that FedSKD outperforms state-of-the-art heterogeneous and homogeneous FL baselines, achieving superior personalization and cross-institutional generalization. These findings underscore FedSKD's potential as a scalable and robust solution for real-world medical FL.","url":"https://pubmed.ncbi.nlm.nih.gov/42019059/","authors":["Weng Z","Cai W","Zhou B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 22","doi":"10.1109/TNNLS.2026.3684321","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42019058","name":"Forgettable Federated Linear Learning With Certified Data Unlearning.","source":"pubmed","abstract":"The advent of federated learning (FL) has revolutionized the way distributed systems handle collaborative model training while preserving user privacy. Recently, federated unlearning (FU) has emerged to address demands for the \"right to be forgotten\" and unlearning of the impact of poisoned clients without requiring retraining in FL. Most FU algorithms require the cooperation of retained or target clients (clients to be unlearned), introducing additional communication overhead and potential security risks. In addition, some FU methods need to store historical models to execute the unlearning process. These challenges hinder the efficiency and memory constraints of the current FU methods. Moreover, due to the complexity of nonlinear models and their training strategies, most existing FU methods for deep neural networks (DNNs) lack theoretical certification. In this work, we introduce a novel FL training and unlearning strategy in DNN, termed forgettable federated linear learning ( $\\mathtt {F^{2}L^{2}}$ ). $\\mathtt {F^{2}L^{2}}$ considers a common practice of using pretrained models to approximate DNN linearly, allowing them to achieve similar performance as the original networks via federated linear training (FLT). We then present FedRemoval, a certified, efficient, and secure unlearning strategy that enables the server to unlearn a target client without requiring client communication or adding additional storage. We have conducted extensive empirical validation on small- to large-scale datasets, using both convolutional neural networks and modern foundation models (FMs). These experiments demonstrate the effectiveness of $\\mathtt {F^{2}L^{2}}$ in balancing model accuracy with the successful unlearning of target clients. $\\mathtt {F^{2}L^{2}}$ represents a promising pipeline for efficient and trustworthy FU. The code is available at: https://anonymous.4open.science/r/2F2L-Federated-Unlearning-D57D/README.md.","url":"https://pubmed.ncbi.nlm.nih.gov/42019058/","authors":["Jin R","Chen M","Zhang Q","Li X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 22","doi":"10.1109/TNNLS.2026.3683398","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42015736","name":"Artificial intelligence and wearable sensors in sports injury risk prediction: current status and future perspectives.","source":"pubmed","abstract":"Artificial intelligence (AI) and wearable sensors are increasingly reshaping sports injury risk prediction by enabling continuous, individualized, and data-driven assessment.","url":"https://pubmed.ncbi.nlm.nih.gov/42015736/","authors":["Dong G","Tan X","Yuan P","Li Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Dec","doi":"10.1080/07853890.2026.2658879","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42010462","name":"AI-Driven Chemometrics for Multi-omics Data Integration: Advances, Challenges, and Future Directions.","source":"pubmed","abstract":"The convergence of artificial intelligence and chemometrics has revolutionized multi-omics data integration, enabling unprecedented insights into complex biological systems. This critical review examines AI-driven approaches for integrating genomics, proteomics, metabolomics, and other omics layers, emphasizing developments from 2020 to 2025. We explore fundamental multi-omics challenges including batch effects, high dimensionality, and structural heterogeneity, evaluating how classical chemometric methods have evolved into sophisticated deep learning architectures. Convolutional neural networks, autoencoders, variational autoencoders, and graph neural networks demonstrate remarkable capabilities for non-linear feature extraction and data fusion. Explainable AI frameworks including SHAP and LIME address interpretability concerns critical for analytical chemistry. We review vertical and horizontal integration strategies, highlighting transformer-based attention mechanisms and biological network-informed architectures. Clinical applications in Alzheimer's disease, obesity, and cancer demonstrate 20%-30% performance improvements over traditional approaches. Emerging hyphenated techniques coupling microfluidics with mass spectrometry enable miniaturized analyses. Persistent challenges include computational scalability, overfitting mitigation, regulatory validation gaps, and interdisciplinary collaboration barriers. Future directions encompass federated learning for privacy-preserving analyses, quantum computing applications, and single-cell spatial multi-omics at subcellular resolution. This assessment provides analytical chemists with critical evaluation of available tools, benchmarking strategies, and roadmaps for advancing precision medicine and analytical applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42010462/","authors":["Polu PR"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 20","doi":"10.1080/10408347.2026.2657553","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42009705","name":"Zero knowledge verifiable, semi asynchronous federated learning for trajectory prediction on permissioned blockchain.","source":"pubmed","abstract":"Vehicle trajectory prediction in Internet-of-Vehicles requires collaborative learning over sensitive trajectories under intermittent connectivity and partially trusted participants. ChainDrive-FL-VRA coordinates semi-asynchronous federated learning on a permissioned consortium ledger using Practical Byzantine Fault Tolerance (PBFT), while keeping raw trajectories and raw model-update tensors off-chain. Each client submits an on-chain header containing a commitment and hash of the local update, together with zero-knowledge proofs that certify [Formula: see text]clipping and anchor-consistency. Validators admit only proof-checked updates, compute staleness- and reputation-aware robust weights, and publish a proof of correct aggregation that binds the aggregation commitment and the committed global model hash to the admitted committed updates under fixed-point weights. A contextual-bandit trigger selects aggregation timing under client churn. Experiments on NGSIM US-101 and I-80 show improved ADE/FDE/RMSE and improved robustness under staleness and anomalous updates, while on-chain artifacts remain at kilobyte scale per update and per aggregation event.","url":"https://pubmed.ncbi.nlm.nih.gov/42009705/","authors":["Raveendra Reddy K","Muralidhar A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 20","doi":"10.1038/s41598-026-42886-1","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42006397","name":"Building trust and privacy in cross-border health data sharing for European cancer research.","source":"pubmed","abstract":"Data-driven research using artificial intelligence (AI) is transforming biomedical science, yet its application in medical imaging remains limited by fragmented datasets, heterogeneous legislation, and ethical uncertainties. The European Cancer Imaging Initiative (EUCAIM) addresses these barriers by establishing a federated, secure and interoperable European imaging infrastructure, fostering a trusted ecosystem for AI-enabled research. EUCAIM brings privacy, ethics, and security within a single, coherent operational framework. The project implements a risk-based, compliance-by-default approach that embeds Data Protection Impact Assessments throughout system design, translating legal requirements into verifiable technical safeguards. Its \"de facto\" anonymization model, aligned with the General Data Protection Regulation and Court of Justice jurisprudence, combines multi-stage anonymization pipelines, cryptographic hashing, and automated re-identification-risk analyses to deliver a federated Secure Processing Environment (SPE) for researchers. This federated infrastructure is consistent with the European Health Data Space Regulation (EHDSR) and national security frameworks, and ensures data sovereignty, interoperability, and accountability. A comprehensive governance and contractual framework, including Data Sharing and Transfer Agreements, clearly delineates roles and responsibilities, while the Data Access Committee provides robust ethical oversight. EUCAIM thus offers a lawful, secure, and sustainable model of a federated secure environment for the reuse of imaging data, advancing a genuinely data-driven research ecosystem.","url":"https://pubmed.ncbi.nlm.nih.gov/42006397/","authors":["Martínez RM","de Marco A","Blanquer I","Martí-Bonmatí L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar","doi":"10.1093/radadv/umag018","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42001760","name":"Three-Dimensional human motion analysis using LiDAR technology: A systematic review.","source":"pubmed","abstract":"3D Light Detection and Ranging (LiDAR) has gained increasing attention in the field of human motion analysis due to its capability to non-invasively capture dynamic 3D pose data. However, accurate extraction of motion information from sparse and unordered point clouds remains a considerable challenge. This study aimed to systematically review 3D LiDAR data processing methods for human motion analysis. The search employed five databases (Web of Science, Scopus, Medline, PubMed and Embase) to identify methods for 3D human motion detection using LiDAR data. Following screening of 752 articles, a total of 38 studies were included. Convolutional Neural Networks (CNNs) were the most commonly used algorithm for human detection from LiDAR data, while the PointNet family was widely employed in point cloud feature extraction for human joint position estimation. The CNN-based YOLOv3 model achieved the highest human detection precision (97.7%), while a fusion approach integrating 3D LiDAR data with four inertial measurement units achieved the lowest Mean Joint Position Error (30.0&#xa0;mm). Use of a CNN-based Federated Learning framework with Dynamic Layer Sharing achieved the highest activity recognition accuracy (98%). Integrating deep learning techniques enables effective extraction of spatiotemporal motion patterns from raw, sparse, and unordered point clouds, enhancing LiDAR-based human motion analysis. Significant challenges still remain in improving the usability of 3D LiDAR systems, particularly in handling multi-person scenarios and ensuring robust human motion analysis in the presence of LiDAR occlusion.","url":"https://pubmed.ncbi.nlm.nih.gov/42001760/","authors":["Lai J","Yavari M","Lee PVS","Ackland DC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun","doi":"10.1016/j.jbiomech.2026.113292","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42001573","name":"Machine learning paradigms in natural and engineered water systems: From proof-of-concept to trustworthy deployment.","source":"pubmed","abstract":"Machine learning is increasingly used to model and manage water systems, from rivers and aquifers to treatment plants and distribution networks. Yet many studies remain proof-of-concept: models are trained on sparse or siloed data, behave as black boxes and rarely connect to operational decisions. Here we review representative applications across natural and engineered water-system archetypes and propose a decision framework for choosing among mechanistic, data-driven, and hybrid models under data, physics and deployment constraints. We then highlight three directions for moving from prediction to trustworthy action: (1) physics-informed and explainable approaches that enforce conservation laws and clarify decision drivers; (2) integration with digital twins and reinforcement learning to enable safe, closed-loop decision support; and (3) graph neural networks and federated learning to represent networked processes and share information without centralizing sensitive data. Collectively, these advances can make machine learning a practical tool for resilient, sustainable water management.","url":"https://pubmed.ncbi.nlm.nih.gov/42001573/","authors":["Ma R","Li J","Zhang Z","Liu Y","Xu H","Yan W"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 1","doi":"10.1016/j.watres.2026.125932","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:42000902","name":"A federated learning framework for deep imputation of missing data in heterogeneous ICU time series.","source":"pubmed","abstract":"The proliferation of multimodal time-series healthcare data presents unprecedented opportunities for data-driven insights but also significant challenges due to pervasive missing values, especially in critical care environments. Traditional centralised imputation methods are often infeasible due to strict privacy regulations, while single-institution models suffer from poor generalizability. Federated learning offers a promising alternative but faces challenges, including statistical heterogeneity, temporal misalignment, and complex missingness patterns. This paper proposes Fed-HealthImp, a federated learning framework for deep imputation of missing values in multivariate, irregularly sampled clinical time-series. Our framework employs a self-attention-based imputation model with adaptive client weighting to handle non-IID data distributions across hospitals. We evaluate Fed-HealthImp on three real-world ICU datasets (eICU-CRD, MIMIC-IV, HiRID) under various missingness patterns. Results show that Fed-HealthImp achieves imputation quality within 3.5% of a privacy-violating centralised model, significantly outperforms local-only training, and improves downstream mortality prediction AUROC by up to 3.4%. Our work establishes a practical, privacy-preserving pathway for building robust imputation models from fragmented global ICU data.","url":"https://pubmed.ncbi.nlm.nih.gov/42000902/","authors":["Vavekanand R","Sathio AA","Sultani M","Raja Vavekanand","Anwar Ali Sathio","Mujtaba Sultani"],"tags":["Computer science","Missing data","Imputation (statistics)","Data mining","Time series"],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-49311-7","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"pmid:41996874","name":"A review on AI-enabled drug design in medicinal chemistry: Analytical validation, challenges, and regulatory considerations.","source":"pubmed","abstract":"Artificial intelligence, particularly machine learning, has profoundly reshaped drug discovery, addressing longstanding challenges such as exorbitant costs and protracted timelines. Conventional approaches often exceed $2.6 billion per drug over 12-15 years, with attrition rates nearing 90%; AI mitigates these through advanced target identification, high-throughput virtual screening, and generative molecule design. The present review synthesizes pivotal studies of several years drawn from PubMed, Scopus, and leading journals, including ACS Omega and Nature Reviews. It encompasses supervised quantitative structure-activity relationship models, neural networks, graph convolutional networks, and generative adversarial networks for de novo drug design. Emphasis is placed on machine learning's capacity to process vast omics and cheminformatics datasets, with critical attention to how data quality, measurement uncertainty, and analytical method variability fundamentally constrain predictive accuracy. In practice, AI empowers scientists by automating hypothesis generation, exemplified by AlphaFold's structural predictions and enabling early toxicity forecasting or drug repurposing, yet these computational advances remain dependent on rigorous experimental validation through orthogonal analytical techniques. A distinctive contribution of this review lies in its systematic integration of analytical chemistry as the foundational discipline underpinning reliable AI predictions. We present a conceptual framework, the Analytical Integrity Spectrum, that traces the bidirectional relationship between analytical measurements and computational models, emphasizing how measurement uncertainty, data quality, and experimental validation collectively determine the trustworthiness of AI-driven discoveries. The chemistry-focused synthesis distinguishes the present work from computational reviews by critically examining representative case studies of AI-discovered compounds, including their molecular structures, scaffolds, and experimental outcomes. This review provides a tangible assessment of AI's impact on medicinal chemistry. The review further examines AI's emerging application to climate-resilient supply chains, forecasting disruptions from environmental events while emphasizing the analytical monitoring essential for maintaining pharmaceutical quality during transport. Persistent challenges, including dataset biases, activity cliff insensitivity, and validation uncertainty, are traced to their analytical origins. Future prospects encompass federated learning, quantum-accelerated simulations, and standardized analytical data formats that preserve measurement integrity for machine learning. Ultimately, AI equips researchers with transformative tools for accelerated, equitable therapeutic innovation, provided that computational predictions remain grounded in the experimental reality that analytical chemistry provides.","url":"https://pubmed.ncbi.nlm.nih.gov/41996874/","authors":["Kumar S","Misra SK","Tiwari A","Katiyar A","Awasthi A","Singh SK","Dhawan A","Kumar A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Oct 1","doi":"10.1016/j.talanta.2026.129802","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41993162","name":"The application of artificial intelligence in the intersection of metabolic dysfunction-associated steatotic liver disease and cardiovascular diseases.","source":"pubmed","abstract":"There is high comorbidity and complex pathological mechanisms between metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular disease (CVD), and the accuracy of traditional risk assessment tools is insufficient. The paper highlights that artificial intelligence (AI) including machine learning and deep learning capable of integrating clinical, imaging, and multi-omics data to enhance the precision of diagnosing MASLD and staging liver fibrosis, the related model has AUC greater than 0.85, and moreover, AI can also accurately predict CVD risk of patients with MASLD, which related model has AUC greater than 0.8 and whose performance is better than traditional scoring systems. In the medical field, deep learning facilitates the quantification of liver fat, along with the evaluation of coronary plaque and screening for lesions across different organs. Multimodal AI has the potential to reveal novel mechanisms and biomarkers of diseases. In addition to these challenges which include data quality and model generalization, the paper also points to future directions such as federated learning. AI offers a fresh perspective on assessing risks, understanding mechanisms, and implementing clinical interventions for MASLD-CVD.","url":"https://pubmed.ncbi.nlm.nih.gov/41993162/","authors":["Kong Y","Chen H","Chen Y","Wang C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1788249","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41991958","name":"Secure yet fragile: adversarial vulnerabilities of federated vision-language models in medical AI.","source":"pubmed","abstract":"Vision-Language Models (VLMs) enable powerful multimodal reasoning for medical image analysis, while federated learning allows collaborative training across institutions without sharing patient data. However, the adversarial robustness of federated medical VLMs remains largely unexplored. This work systematically evaluates the vulnerability of CLIP-based VLMs trained with four federated optimization strategies, FedAvg, FedProx, FedPer, and FedBN, on multiple medical datasets. We assess robustness under FGSM, PGD, BIM, and MI-FGSM attacks at varying strengths and show that client-level adversarial perturbations propagate through federated aggregation, causing severe accuracy degradation and high attack success rates, specially under iterative attacks. We further benchmark two training-free test-time defenses, Test-Time Counter-Attack (TTC) and CLIPure, and demonstrate that both mitigate adversarial effects, with CLIPure providing more consistent improvements across datasets and attack intensities. These results highlight fundamental robustness limitations of federated medical VLMs and underscore the need for effective defense mechanisms in distributed clinical deployments.","url":"https://pubmed.ncbi.nlm.nih.gov/41991958/","authors":["Fime AA","Samiha TZ","Hossain MZ","Zaman S","Shibli AM","Shahid AR","Ni Z","Imteaj A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 16","doi":"10.1038/s41598-026-48102-4","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41986012","name":"The Intersection of AI and genomics in health and disease: Advancements and applications.","source":"pubmed","abstract":"AI and genomics are revolutionizing precision medicine by using machine learning (ML) to analyze large-scale next-generation sequencing (NGS) data, identifying genetic mutations and biomarkers for personalized therapies. In practice, this accelerates drug discovery and enhances variant detection, while in cancer genomics, AI enables early detection via liquid biopsies and refines treatment by integrating multi-omics data to improve therapeutic precision. However, challenges such as data biases in underrepresented populations, limited model interpretability, and ethical concerns regarding privacy and algorithmic inequity hinder clinical adoption and demand robust governance. Efforts to diversify datasets also face standardization hurdles, although explainable AI and federated learning provide promising solutions for improving transparency and privacy. In this chapter, we discuss the role of AI in advancing genomics from diagnostics to novel therapies and emphasize the need for equitable frameworks to ensure responsible implementation, thereby paving the way for breakthroughs in personalized medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/41986012/","authors":["Kaushik L","Vivek AT","Arora S","Hamid F","Mukherjee K","Bisht N","Chaudhary S","Shukla J","Nawani S","Kumar S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/bs.pmbts.2026.01.013","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41984304","name":"Multi-site distributed training with data protections for PET-based synthetic CT.","source":"pubmed","abstract":"BACKGROUND: Accurate PET quantification relies on attenuation correction (AC), commonly performed using a linear attenuation map derived from a CT acquisition. However, CT can introduce misregistration artifacts and adds radiation dose. Synthetic CT (sCT) from non-attenuation corrected (NAC) PET offers a CT-less alternative, but training robust models requires multi-site data that may be difficult to share under privacy regulations. We aim to enable PET-based sCT training across sites without exposing data. METHODS: We built a federated learning (FL) framework and trained two sCT generators&#x2013;a paired conditional GAN and a CycleGAN. Models were pretrained on a single-site cohort (Site&#xa0;1, n = 425) and fine-tuned via FL using additional data from Site&#xa0;1 (n = 25) and a second site with different scanners and reconstruction parameters (Site&#xa0;2, n = 25). Performance was assessed on an internal hold-out set (Site&#xa0;1, n = 91) and two external cohorts (Sites&#xa0;3,4; n = 11, 10) using region-wise relative mean error (rME) of SUV in AC PET. RESULTS: Both models produced anatomically plausible sCT and AC PET with low errors when test data matched training distributions. FL fine-tuning improved robustness under distribution shift at Site&#xa0;3, reducing errors across most regions, while maintaining comparable performance at Site&#xa0;4 where protocols resembled the pretraining site. CONCLUSION: Multi-site FL is a feasible path to increase the generalizability of PET-based sCT while preserving data privacy. The proposed framework offers a practical template for training and deploying CT-less AC models across heterogeneous clinical environments.","url":"https://pubmed.ncbi.nlm.nih.gov/41984304/","authors":["Jørgensen K","Partin L","Ashok R","Shah V","Rodell AB","Bazik M","Spottiswoode B","Andersen FL"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 15","doi":"10.1186/s40658-026-00868-2","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41982527","name":"Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.","source":"pubmed","abstract":"The intricate interplay between the brain and heart underpins both physiological regulation and pathophysiological processes, yet decoding these interactions remains a formidable challenge. Recent advances in artificial intelligence (AI) offer transformative opportunities to map, model, and predict brain-heart network dynamics with unprecedented precision. This review synthesizes current knowledge on AI approaches applied to neurocardiology, encompassing multimodal data integration from neuroimaging, electrophysiology, autonomic signals, and cardiovascular monitoring. We examine machine learning and deep learning strategies for identifying biomarkers, forecasting adverse cardiac events, and elucidating mechanisms linking neurological, psychiatric, and cardiovascular disorders. Clinical applications are explored across heart failure, arrhythmias, stroke-induced cardiac dysfunction, epilepsy, and stress-related conditions, highlighting AI's potential for personalized risk stratification. The role of wearable devices, digital phenotyping, and real-world data collection in continuous brain-heart monitoring is discussed, alongside AI-enabled early warning systems. Critical considerations regarding data quality, bias, interpretability, privacy, and ethical governance are emphasized to guide responsible deployment. Finally, we outline emerging directions, including integrative digital twins, federated AI, and closed-loop neuromodulation. By bridging computational innovation and clinical neuroscience, AI-driven approaches promise to redefine neurocardiology, offering predictive, mechanistic, and therapeutic insights into the brain-heart axis.","url":"https://pubmed.ncbi.nlm.nih.gov/41982527/","authors":["Varzideh F","Pande S","Jankauskas SS","Mone P","Kansakar U","Santulli G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fnins.2026.1788653","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"pmid:41982233","name":"Empowering photodynamic therapy with artificial intelligence: current trends and future directions.","source":"pubmed","abstract":"The evolution of photodynamic therapy (PDT), from ancient photomedicine practices to modern clinical applications, reflects its remarkable versatility in oncology and beyond. PDT relies on the interaction between photosensitizers, light, and tissue oxygen to generate reactive oxygen species that selectively destroy diseased cells. While the therapy has proven effective across various cancers and non-malignant conditions, tailoring treatment to individual patients remains challenging due to patient-specific variations in tissue optical properties, photosensitizer pharmacokinetics, and tumor heterogeneity. The rapid advancement of artificial intelligence (AI), including machine learning and deep learning, offers transformative opportunities to address these challenges through data-driven optimization and personalization. In this review, we examine how AI is being integrated across the PDT pipeline. We analyze AI-driven approaches for photosensitizer development, including quantitative structure-activity relationship modeling, graph neural networks for property prediction, and generative models for de novo molecular design. We examine machine learning applications in nanoparticle-based drug delivery systems, encompassing synthesis optimization, nano-bio interaction prediction, and stimuli-responsive release modeling. The review further explores AI integration in treatment planning through real-time tissue optical property estimation, and in clinical decision-making through treatment response monitoring and outcome prediction using multimodal imaging data. We critically assess current limitations, including small dataset challenges, model interpretability concerns, and the gap between preclinical research and clinical translation. Finally, we outline future directions, including federated learning, explainable AI, and regulatory considerations. This review aims to bridge the AI and PDT communities, providing a roadmap for improved patient outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/41982233/","authors":["Paul A","Xavierselvan M","Aebisher D","Kubrak T","Bartusik-Aebisher D","Mallidi S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1771804","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41975740","name":"Federated Gastrointestinal Lesion Classification with Clinical-Entropy Guided Quantum-Inspired Token Pruning in Vision Transformers.","source":"pubmed","abstract":"Background: Gastrointestinal (GI) cancers remain a major global health concern, where timely and accurate interpretation of endoscopic findings plays a decisive role in patient outcomes. In recent years, deep learning-based decision support systems have shown considerable potential in assisting GI diagnosis; however, their broader adoption is often limited by patient privacy regulations, uneven data availability, and the fragmented nature of clinical data across institutions. Federated learning (FL) offers a practical solution by enabling collaborative model training while keeping patient data local to each hospital. Methods: Vision Transformers (ViTs) are particularly well suited for endoscopic image analysis due to their ability to capture long-range contextual information. Nevertheless, their high computational and communication costs pose a significant challenge in federated settings, especially when data distributions vary across clients. To address this issue, we propose a privacy-preserving federated framework that combines ViTs with a Clinical-Entropy Guided Quantum Evolutionary Algorithm (CEQEA) for adaptive token pruning. The CEQEA leverages the diagnostic diversity of each client's local dataset to guide population initialization, evolutionary updates, and mutation strength, allowing the pruning strategy to adapt naturally to different clinical profiles. Results: The proposed framework was evaluated on curated upper- and lower-GI tract subsets of the HyperKVASIR dataset under realistic non-IID federated conditions. On the final test sets, the model achieved a mean micro-averaged accuracy of 92.33% for lower-GI classification and 90.19% for upper-GI classification, while maintaining high specificity across all diagnostic classes. At the same time, the adaptive pruning strategy reduced the number of tokens processed by approximately 40% and decreased the number of required federated communication rounds by 33% compared to ViT-based federated baselines. Conclusions: Overall, these results indicate that entropy-aware, quantum-inspired evolutionary optimization can effectively balance diagnostic performance and efficiency, making transformer-based models more practical for privacy-preserving, multi-institutional gastrointestinal endoscopy.","url":"https://pubmed.ncbi.nlm.nih.gov/41975740/","authors":["Awais M","Qamar AM","Khalid U","Khan RU"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/diagnostics16071027","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41965741","name":"Artificial intelligence in microbiology: implications for metagenomics, diagnostics, and AMR surveillance.","source":"pubmed","abstract":"Artificial intelligence (AI) is now a key player in modern microbiology, as it enables high-resolution analyses of genomic, metagenomic, and clinical data for the monitoring of infectious disease and antimicrobial resistance (AMR). Considerable advancements in deep learning, transformer-based sequence models, graph neural networks, and multimodal architectures have greatly improved microbial classification accuracy, antibiotic resistance gene (ARG) detection, and resistance prediction. Taking metagenomic sequencing into consideration, these advancements have contributed to the development of sensitive, scalable, and non-invasive methods to profile microbiomes, determine novel resistance, and monitor AMR trends at the population level. This review summarizes recent advances in AI-aided microbiology, with a particular emphasis on AMR surveillance. Specific topics include deep learning frameworks for ARG annotation, emerging approaches to identifying new resistance genes, and multimodal applications (genomic and clinical metadata) aimed at improving phenotype prediction. The role of metagenome-assembled genomes (MAGs) to enhance AMR surveillance efforts is noted, along with their noted limitations relative to isolate genomes. The discussion includes the examination of explainable AI (XAI) techniques including SHAP, attention mechanism approaches, and gradient-based attribution approaches, with the aim of increasing transparency and clinical explainability. We also cover potential applications including AI-enabled non-invasive fecal microbiome diagnostics, laboratory automation, and environmental surveillance. While there has been significant progress, unresolved issues exist relating to dataset variations, liability of models to datasets, interpretability, and regulatory approval. Overcoming these barriers, however, will require standardized frameworks for these workflows, privacy-preserving federated learning methods, and interpretable AI frameworks for clinical and public health tools. AI could fundamentally change AMR surveillance by allowing for earlier resistance detection, advanced risk assessment recommendation, and improved monitoring strategies globally.","url":"https://pubmed.ncbi.nlm.nih.gov/41965741/","authors":["Khangarot R","Kumari V","Mishra R","Singh A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1186/s12938-026-01568-9","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"pmid:41965701","name":"An efficient hybrid federated learning framework for pneumonia diagnosis with proximal optimization and parameter-efficient adaptation.","source":"pubmed","abstract":"Pneumonia remains a serious worldwide health concern, particularly in low-resource countries, where prompt diagnosis is challenging. Early detection relies on chest radiography; however, data privacy rules and patient data fragmentation make it difficult to build AI models. Federated Learning allows for collaborative model training without sharing patient data, a promising solution. Standard federated learning methods, such as FedAvg, suffer from data heterogeneity and significant communication overhead. To overcome these constraints, this research proposes an upgraded federated framework with FedProx, which mitigates client drift in non-IID contexts by proximal optimization and Low-Rank Adaptation, a parameter-efficient fine-tuning technique that minimizes communication costs. Vision Transformers are utilized as the backbone architecture for chest X-ray categorization because they capture the global visual context more effectively than convolutional models. The proposed technique was validated for a pneumonia classification job utilizing the publicly available Chest X-Ray Images dataset, which was distributed across simulated clients to replicate real-world healthcare organizations. The model&#x2019;s performance is measured using accuracy, precision, recall, F1-score, AUC, and system-level measures, including communication cost per round and convergence rate. Under conditions of non-IID heterogeneity of data, the proposed FedProx+LoRA framework demonstrated a classification accuracy of 88.5 which was higher compared to the centralized baseline (63.9%) or the standard FedAvg (60.9%), and showed a significant increase in comparison to FedProx itself (78.6%). Furthermore, the framework saved an overhead in communication by 97.4% in comparison to entire fine-tuning.","url":"https://pubmed.ncbi.nlm.nih.gov/41965701/","authors":["Gupta C","Gupta R","Gill NS","Gulia P","Shukla PK","Pandey A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 11","doi":"10.1186/s13040-026-00543-3","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41958847","name":"HEAL-AI: Enabling proactive and personalized smart healthcare through hierarchical edge autonomous learning.","source":"pubmed","abstract":"This study aims to develop a proactive, personalized, and privacy-preserving smart healthcare framework that enables real-time clinical decision support across distributed healthcare environments while ensuring interpretability, low latency, and regulatory compliance.","url":"https://pubmed.ncbi.nlm.nih.gov/41958847/","authors":["Othman MA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1177/20552076261428362","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"pmid:41958456","name":"Performance of federated versus centralized learning for mammography classification across film-digital domain shift.","source":"pubmed","abstract":"Large, diverse datasets are essential for reliable deep learning in mammography, yet clinical data remain siloed due to privacy and governance constraints. Federated learning enables collaborative training without sharing raw data, but its robustness under strong imaging-domain heterogeneity, such as film-digital shifts, remains uncertain.","url":"https://pubmed.ncbi.nlm.nih.gov/41958456/","authors":["Ali Y","Müller J","Weinmann A","Gregori J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1715858","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41957419","name":"Integrating AI-blockchain framework with Spider Monkey Federated Extreme Learning for enhanced healthcare data protection.","source":"pubmed","abstract":"The Internet of Medical Things (IoMT) has largely revolutionized the healthcare sector because of its rapid growth that allows continuous monitoring and data-driven services that are based on intelligence. Nonetheless, such a rising connectivity also heightens the susceptibility of sensitive medical data such that, strong security and privacy-driving solutions are required. In order to overcome these issues, this paper presents SMOFEL (Spider Monkey Optimized Federated Extreme Learning) which is an integrated system that incorporates Federated Learning (FL), Extreme Learning Machine (ELM), Spider Monkey Optimization (SMO), and AI-Based Blockchain Technology. FL enables decentralized training of models by making sure that raw patient data are stored on local IoMT devices, which improve privacy and regulation. SMO enhances the convergence and optimization of parameters in the learning process, which is why the framework can be used in resource-constrained IoMT settings. Data integrity is also enhanced with the help of blockchain technology as it offers an immutable and transparent list of model updates and safe transactions. Smart contracts provide the capability to enter into automated and immutable data-sharing contracts across involved nodes. Simulated healthcare data experimental assessment proves that SMOFEL supports an accuracy of 98.08, which indicates its potential to increase the security, efficiency, and predictive power. Altogether, the suggested framework presents a holistic way to achieve secure, scalable, and privacy-saving healthcare analytics, and SMOFEL can be a great solution to next-generation IoMT ecosystems.","url":"https://pubmed.ncbi.nlm.nih.gov/41957419/","authors":["Nishok VS","Dhanasekaran S","Suresh G","Anandaraj APS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-47259-2","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"pmid:41957408","name":"Supply chain information security sharing technology based on blockchain consensus algorithm and federated learning.","source":"pubmed","abstract":"Existing supply chain information security methods all suffer from the difficulty of balancing information sharing efficiency and information privacy protection. Data is prone to leakage, resulting in an increase in the overall risk of the supply chain. In response to this situation, the study proposes a supply chain information security sharing method based on a blockchain consensus algorithm and federated learning. The study designs a blockchain formula algorithm based on a verifiable mechanism and combines this algorithm with federated learning to construct an encryption model. To address the issue of privacy leakage that is prone to occur in federated learning, this study introduces casual pseudo-random functions and cuckoo hashing to process data and reduce communication complexity, thereby avoiding hash conflicts. Finally, the encryption model is applied to the data transmission system, and combined with multi-factor authentication, the secure sharing of supply chain information is achieved. The experiment results indicated that the average latency of the consensus algorithm during node election was 115.20ms, and during node replacement, the average latency was 8.56ms. The information security sharing method proposed in the study achieved an accuracy rate of 92.48% in generating data after processing, with a data tampering detection rate of 98.87%. The frequency of privacy breaches during the experiment was only 0.1%, and the average response time was 1.0&#xa0;s. The study proposes a new technical framework that takes into account both privacy protection and efficient sharing, effectively balancing the trust establishment and data security requirements in multi-party collaboration in the supply chain, and providing a verifiable and traceable technical path for information sharing in complex network environments. At the same time, by optimizing the integration mode of the consensus mechanism and federated learning, the system communication overhead and response delay have been significantly reduced, and the overall operational efficiency has been improved.","url":"https://pubmed.ncbi.nlm.nih.gov/41957408/","authors":["Xu D","Li J","Ren Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-46101-z","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"pmid:41948162","name":"Can Artificial Intelligence Be Used to Predict Response in Rectal Cancer? Current Evidence and Future Possibilities.","source":"pubmed","abstract":"Artificial intelligence (AI) offers a promising solution to the long-standing challenge of accurately predicting treatment response in rectal cancer. In this narrative review, we summarize current AI-driven approaches to predicting pathologic complete response in rectal cancer. We also outline key barriers to clinical translation, including lack of standardization, small and geographically skewed training cohorts, domain shift across scanners and institutions, and broader ethical, regulatory, and medicolegal concerns. Finally, we highlight future directions, including federated learning to enable privacy-preserving multicenter model training, and emerging concepts such as virtual and digital twins that may support real-time adaptive therapy. These advances suggest that AI-based prediction of response in rectal cancer could be extremely valuable, but will require methodologically rigorous, multi-institutional efforts to be safely and equitably implemented.","url":"https://pubmed.ncbi.nlm.nih.gov/41948162/","authors":["Lopez NE","Neel NC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May","doi":"10.1055/a-2769-1185","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41948010","name":"Federated Choquet Regression with LASSO for Outcome Prediction in Multisite Longitudinal Trial Data.","source":"pubmed","abstract":"Aggregating person-level data across multiple clinical study sites is often constrained by privacy regulations, necessitating the development of decentralized modeling approaches in biomedical research. To address this requirement, a federated nonlinear regression algorithm based on the Choquet integral has been introduced for outcome prediction. This approach avoids reliance on prior statistical assumptions about data distribution and captures feature interactions, reflecting the non-additive nature of biomedical data characteristics. This work represents the first theoretical application of Choquet integral regression to multisite longitudinal trial data within a federated learning framework. The Multiple Imputation Choquet Integral Regression with LASSO (MIChoquet-LASSO) algorithm is specifically designed to reduce overfitting and enable variable selection in federated learning settings. Its performance has been evaluated using synthetic datasets, publicly available biomedical datasets, and proprietary longitudinal randomized controlled trial data. Comparative evaluations were conducted against benchmark methods, including OLS regression and Choquet OLS regression, under various scenarios such as model misspecification and both linear and nonlinear data structures in non-federated and federated contexts. MSE was used as the primary performance metric. Results indicate that MIChoquet-LASSO outperforms compared models in handling nonlinear longitudinal data with missing values, particularly in scenarios prone to overfitting. In federated settings, Choquet OLS underperforms, whereas the federated variant of the model, FEDMIChoquet-LASSO, demonstrates consistently better performance. These findings suggest that FEDMIChoquet-LASSO offers a reliable solution for outcome prediction in multisite longitudinal trials, addressing challenges such as missing values, nonlinear relationships, and privacy constraints while maintaining strong performance within the federated learning framework.","url":"https://pubmed.ncbi.nlm.nih.gov/41948010/","authors":["Lomasov S","Fang H","Wang H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan","doi":"10.1145/3761824","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41946792","name":"Federated multi-label text feature selection via manifold-aware sparse modeling and cooperative grey wolf optimization.","source":"pubmed","abstract":"Feature selection (FS) for multi-label text classification faces issues such as high dimensionality, strong label correlations, and sparse features, which often lead to suboptimal feature subsets. Moreover, most existing methods are centralized and thus ill-suited to real-world distributed or federated settings, where text data are scattered across multiple nodes and effective FS mechanisms are lacking. To overcome these issues, this paper proposes Fed-MSMCGWO, a federated multi-label text feature selection method based on manifold-aware sparse modeling and cooperative grey wolf optimization. Under a federated learning framework, Fed-MSMCGWO integrates manifold-aware sparse modeling (MSM), and incorporates a cooperative grey wolf optimization algorithm (CGWO) to enable multi-label text FS in distributed environments. On each client, Fed-MSMCGWO employs a two-stage optimization. In Stage 1, MSM is learned by constructing sample and label graphs from text embeddings, encoding their manifolds with Laplacians, and imposing a [Formula: see text]-norm on the feature-weight matrix to induce row sparsity and compress high-dimensional features. In Stage 2, CGWO with a three-line cooperative evolution scheme further refines these weights and conducts global search for a near-optimal subset of text features. After the two-stage optimization, each client obtains a locally optimal feature subset and engages in a multi-party privacy-preserving feature aggregation strategy: clients upload only intermediate feature-weight parameters (no raw data) to the server, which aggregates them and sends the result back to guide further local updates, yielding a collaborative cross-client FS framework with preserved privacy. Experiments on several publicly available multi-label text datasets indicate that, with privacy preserved, Fed-MSMCGWO consistently surpasses standard centralized and federated FS methods on multiple evaluation metrics.","url":"https://pubmed.ncbi.nlm.nih.gov/41946792/","authors":["Zheng Y","Ye Z","Zhang S","Wang K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-46223-4","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41945841","name":"EMM-Det: Energy-Efficient Multidrone Tiny Object Detection by Memory-Enhanced Spiking Neural Networks.","source":"pubmed","abstract":"Unmanned aerial vehicles (UAVs) play a vital role in scenarios such as community safety patrol and disaster search-and-rescue operations due to their maneuverability and deployment flexibility. However, their limited payload capacity, energy constraints, and susceptibility to interference hinder technological advancements. Additionally, centralized training models pose privacy risks, increasing the potential for data leakage. To address these challenges, this article proposes EMM-Det, a low-power, distributed detection method designed for UAV object detection. EMM-Det enhances system performance through three key design strategies: 1) employing memory-enhanced spiking neurons with dynamic leakage constants enhances firing rates and prevents spike decay; 2) utilizing wavelet transform to encode multiscale frequency-domain features improves object detection robustness; and 3) leveraging crowdsourced perception and federated learning (FL) technologies boosts data collection efficiency while mitigating privacy leakage risks. On our constructed dataset, EMM-Det achieves 81.8% mAP@50:95 detection accuracy at extremely low power consumption-3.2% higher than the second-best method and 14.5% superior compared to traditional artificial neural network (ANN) approaches. Experimental results demonstrate that EMM-Det achieves an effective balance between computational efficiency, noise resilience, and data privacy protection. It shows strong potential for deployment in real-world scenarios with stringent energy and privacy requirements, such as community safety patrol and emergency rescue operations.","url":"https://pubmed.ncbi.nlm.nih.gov/41945841/","authors":["Cai Z","Luo H","Liu T","Xia Y","Zhang L","Li P","Wang L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 7","doi":"10.1109/TNNLS.2026.3680142","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41943236","name":"Federated Meta-Analysis of HEART Score Performance for Emergency Department Chest Pain.","source":"pubmed","abstract":"Multicenter evaluation of emergency department (ED) risk stratification tools is often limited by barriers to patient-level data sharing. We used the HEART score as a clinical use case to evaluate whether a federated diagnostic meta-analytic approach yields performance estimates comparable to those obtained from centralized patient-level analysis for predicting 30-day major adverse cardiovascular events (MACE30).","url":"https://pubmed.ncbi.nlm.nih.gov/41943236/","authors":["Wang H","Chou E","Robinson RD","Farzad A","Saltarelli N","Johnson G","d'Etienne J","Mahler SA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr","doi":"10.1111/acem.70284","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.24406/publica-5187","name":"Scaling Smart Cities with Federated Learning: Balancing Accuracy and Privacy for Building Energy Performance Prediction","source":"datacite","abstract":"The building energy sector is a significant contributor to carbon emissions, thereby playing a crucial role in driving global sustainability efforts to achieve the net-zero targets outlined in the Paris Climate Agreement. Precise predictions of building energy performance are imperative for effective planning and investment decisions aimed at enhancing energy efficiency. While data-driven methods, primarily leveraging machine learning techniques, offer promising predictive capabilities, they heavily rely on large datasets for accurate assessments. However, a prevalent challenge arises as energy consultants and agencies often lack expansive datasets, and if they do, they are reluctant to share their data. To overcome these hurdles, the study implements a decentralized, privacy-preserving machine learning approach known as federated learning. This approach was applied to a dataset encompassing over 25,000 residential buildings featuring diverse construction attributes and energy sources. The simulation involved mimicking different energy agencies by segmenting geographic regions. The study compared the prediction performance of federated learning with that of a model accessing the entire dataset and a fully isolated local model. The findings demonstrate that federated learning achieves a 12% improvement in prediction performance compared to the isolated model. This outcome underscores federated learning’s capacity to leverage the full potential of scaling data-driven methodologies, providing a pathway to unlock new business models in both research and practice, while aligning with net-zero aspirations.","url":"https://doi.org/10.24406/publica-5187","authors":["Delgado Fernandez, Joaquin","Willburger, Lukas","Wiethe, Christian","Wenninger, Simon","Fridgen, Gilbert",":unav"],"tags":["Building energy performance","Energy quantification methods","Federated learning","Privacy","Smart city"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.24406/publica-5187","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22150879","name":"extendedseek/BoneFormer: BoneFormer v0.1.0","source":"datacite","abstract":"Initial public release of BoneFormer-KG. This repository provides the implementation of BoneFormer-KG for federated knowledge-guided 3D Transformer learning for bone tumor segmentation from partially annotated CT data. This release includes the source code, configuration files, preprocessing utilities, training pipeline, evaluation scripts, and reproducibility resources.","url":"https://doi.org/10.5281/zenodo.22150879","authors":["extendedseek"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22150879","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22150878","name":"extendedseek/BoneFormer: BoneFormer v0.1.0","source":"datacite","abstract":"Initial public release of BoneFormer-KG. This repository provides the implementation of BoneFormer-KG for federated knowledge-guided 3D Transformer learning for bone tumor segmentation from partially annotated CT data. This release includes the source code, configuration files, preprocessing utilities, training pipeline, evaluation scripts, and reproducibility resources.","url":"https://doi.org/10.5281/zenodo.22150878","authors":["extendedseek"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22150878","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20758814","name":"Reproducibility package for Threat-Intelligence-Aware Federated Intrusion Detection for Industrial IoT","source":"datacite","abstract":"Derived result tables, figure source data, scripts, configuration snapshots and non-sensitive logs supporting the manuscript tables and figures. Raw third-party datasets are not redistributed. License note: MIT for code/scripts; CC BY 4.0 for project-created non-code derived artifacts; third-party raw datasets excluded. Creator affiliation: School of Network Engineering, Jiangxi Software Vocational and Technical University, Nanchang, China; School of Engineering Technology, Shinawatra University, Pathum Thani, Thailand.","url":"https://doi.org/10.5281/zenodo.20758814","authors":["Xie, Fucai"],"tags":["federated learning","intrusion detection","Industrial IoT","cyber threat intelligence","poisoning resilience","non-IID","reproducibility"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20758814","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20758815","name":"Reproducibility package for Threat-Intelligence-Aware Federated Intrusion Detection for Industrial IoT","source":"datacite","abstract":"Derived result tables, figure source data, scripts, configuration snapshots and non-sensitive logs supporting the manuscript tables and figures. Raw third-party datasets are not redistributed. License note: MIT for code/scripts; CC BY 4.0 for project-created non-code derived artifacts; third-party raw datasets excluded. Creator affiliation: School of Network Engineering, Jiangxi Software Vocational and Technical University, Nanchang, China; School of Engineering Technology, Shinawatra University, Pathum Thani, Thailand.","url":"https://doi.org/10.5281/zenodo.20758815","authors":["Xie, Fucai"],"tags":["federated learning","intrusion detection","Industrial IoT","cyber threat intelligence","poisoning resilience","non-IID","reproducibility"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20758815","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21531664","name":"A Robust Self-Supervised Contrastive Learning Framework  for Secure and Reliable Financial Transaction Mining","source":"datacite","abstract":"ABSTRACT The rapid expansion of digital payment ecosystems, online banking platforms, and fintech services has led to an unprecedented growth in financial transaction data. While this digital transformation enhances accessibility and efficiency, it also increases vulnerability to sophisticated fraud schemes, money laundering activities, and cyber-financial crimes. Conventional supervised machine learning models for financial transaction mining rely heavily on large volumes of labeled data, which are often scarce, imbalanced, costly to annotate, and subject to strict privacy regulations. These limitations significantly hinder the scalability, adaptability, and reliability of traditional fraud detection systems. To address these challenges, this paper proposes a robust self-supervised contrastive learning framework for secure and reliable financial transaction mining. The proposed approach leverages vast amounts of unlabeled transaction data to learn meaningful and discriminative latent representations without requiring manual annotation. By employing contrastive learning principles, the framework maximizes agreement between augmented views of similar transactions while simultaneously minimizing similarity between dissimilar transaction pairs. Domain-specific augmentation strategies such as feature masking, temporal perturbation, and controlled noise injection are introduced to ensure robustness against transaction variability and adversarial manipulation. The learned representations are subsequently finetuned using a lightweight supervised classifier with a limited labeled dataset, significantly reducing dependency on annotated data while improving fraud detection accuracy. The framework enhances generalization capability, reduces false positive rates, and demonstrates resilience against class imbalance and evolving fraud patterns (concept drift). Furthermore, the architecture supports privacy-preserving extensions such as federated learning, enabling secure distributed training across financial institutions without sharing sensitive raw data. Experimental evaluation on benchmark financial transaction datasets demonstrates that the proposed selfsupervised contrastive framework outperforms conventional supervised and semi-supervised models in terms of ROC-AUC, F1-score, recall, and robustness under noisy conditions. The results confirm that contrastive selfsupervised learning provides a scalable, reliable, and secure artificial intelligence solution for next-generation financial transaction analytics. The proposed methodology contributes to advancing intelligent financial systems by combining representation learning, security awareness, and data efficiency, thereby paving the way for adaptive and trustworthy financial transaction mining in dynamic real-world environments.","url":"https://doi.org/10.5281/zenodo.21531664","authors":["Mrs. J Nagapriya","Dr. M. Princerani","Dr. Mohamed Kaisarul Haq","Mohammad Shah Alam Chowdhury"],"tags":["Keywords: Self-Supervised Learning, Contrastive Learning, Financial Transaction Mining, Fraud Detection, Anomaly Detection, Artificial Intelligence, Secure Analytics."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21531664","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21531665","name":"A Robust Self-Supervised Contrastive Learning Framework  for Secure and Reliable Financial Transaction Mining","source":"datacite","abstract":"ABSTRACT The rapid expansion of digital payment ecosystems, online banking platforms, and fintech services has led to an unprecedented growth in financial transaction data. While this digital transformation enhances accessibility and efficiency, it also increases vulnerability to sophisticated fraud schemes, money laundering activities, and cyber-financial crimes. Conventional supervised machine learning models for financial transaction mining rely heavily on large volumes of labeled data, which are often scarce, imbalanced, costly to annotate, and subject to strict privacy regulations. These limitations significantly hinder the scalability, adaptability, and reliability of traditional fraud detection systems. To address these challenges, this paper proposes a robust self-supervised contrastive learning framework for secure and reliable financial transaction mining. The proposed approach leverages vast amounts of unlabeled transaction data to learn meaningful and discriminative latent representations without requiring manual annotation. By employing contrastive learning principles, the framework maximizes agreement between augmented views of similar transactions while simultaneously minimizing similarity between dissimilar transaction pairs. Domain-specific augmentation strategies such as feature masking, temporal perturbation, and controlled noise injection are introduced to ensure robustness against transaction variability and adversarial manipulation. The learned representations are subsequently finetuned using a lightweight supervised classifier with a limited labeled dataset, significantly reducing dependency on annotated data while improving fraud detection accuracy. The framework enhances generalization capability, reduces false positive rates, and demonstrates resilience against class imbalance and evolving fraud patterns (concept drift). Furthermore, the architecture supports privacy-preserving extensions such as federated learning, enabling secure distributed training across financial institutions without sharing sensitive raw data. Experimental evaluation on benchmark financial transaction datasets demonstrates that the proposed selfsupervised contrastive framework outperforms conventional supervised and semi-supervised models in terms of ROC-AUC, F1-score, recall, and robustness under noisy conditions. The results confirm that contrastive selfsupervised learning provides a scalable, reliable, and secure artificial intelligence solution for next-generation financial transaction analytics. The proposed methodology contributes to advancing intelligent financial systems by combining representation learning, security awareness, and data efficiency, thereby paving the way for adaptive and trustworthy financial transaction mining in dynamic real-world environments.","url":"https://doi.org/10.5281/zenodo.21531665","authors":["Mrs. J Nagapriya","Dr. M. Princerani","Dr. Mohamed Kaisarul Haq","Mohammad Shah Alam Chowdhury"],"tags":["Keywords: Self-Supervised Learning, Contrastive Learning, Financial Transaction Mining, Fraud Detection, Anomaly Detection, Artificial Intelligence, Secure Analytics."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21531665","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22148845","name":"The Connect.CI Portal: Building and Sustaining a Cyberinfrastructure Community Platform","source":"datacite","abstract":"ConnectCI is a web-based platform that facilitates collaboration across the national research computing ecosystem. Since its introduction at PEARC21, the platform has evolved from a project management tool for regional cyberteams to a comprehensive community infrastructure hosting thirteen community sites, with four major programs under active development. This paper presents five years of growth in new features and new communities, and through the addition of AI-powered support tools. These capabilities reflect three core functions: connecting professionals across institutional boundaries, enabling learning through shared knowledge and events, and advancing careers through recognition, mentorship, and workforce development pathways. Technical advances include a multi-tenant Drupal architecture enabling per-community customization within a single codebase, CILogon-based federated authentication, and a retrieval-augmented generation chatbot and Model Context Protocol servers for programmatic access to community data. We report on adoption metrics, lessons learned from operating a multi-stakeholder platform, and ConnectCI’s role in strengthening the research computing workforce.","url":"https://doi.org/10.5281/zenodo.22148845","authors":["Ma, Julie","Fein, Lissie","Pasquale, Andrew","Gazula, Vikram","Brandt, Kevin","Chakravorty, Dhruva","Chalker, Alan","Figurelle, Wayne","Sherman, Andrew","Ghahramani, Forough"],"tags":["cyberinfrastructure","research computing","Drupal","open source","community portal","NSF ACCESS"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22148845","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22148846","name":"The Connect.CI Portal: Building and Sustaining a Cyberinfrastructure Community Platform","source":"datacite","abstract":"ConnectCI is a web-based platform that facilitates collaboration across the national research computing ecosystem. Since its introduction at PEARC21, the platform has evolved from a project management tool for regional cyberteams to a comprehensive community infrastructure hosting thirteen community sites, with four major programs under active development. This paper presents five years of growth in new features and new communities, and through the addition of AI-powered support tools. These capabilities reflect three core functions: connecting professionals across institutional boundaries, enabling learning through shared knowledge and events, and advancing careers through recognition, mentorship, and workforce development pathways. Technical advances include a multi-tenant Drupal architecture enabling per-community customization within a single codebase, CILogon-based federated authentication, and a retrieval-augmented generation chatbot and Model Context Protocol servers for programmatic access to community data. We report on adoption metrics, lessons learned from operating a multi-stakeholder platform, and ConnectCI’s role in strengthening the research computing workforce.","url":"https://doi.org/10.5281/zenodo.22148846","authors":["Ma, Julie","Fein, Lissie","Pasquale, Andrew","Gazula, Vikram","Brandt, Kevin","Chakravorty, Dhruva","Chalker, Alan","Figurelle, Wayne","Sherman, Andrew","Ghahramani, Forough"],"tags":["cyberinfrastructure","research computing","Drupal","open source","community portal","NSF ACCESS"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22148846","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.14288/1.0455575","name":"A scalable and privacy-aware framework for connected vehicle systems using smartphone-based multimodal sensing","source":"datacite","abstract":"Connected Vehicle (CV) networks have emerged as key enablers of next-generation intelligent transportation systems, improving road safety and operational efficiency through real-time data exchange. However, large-scale adoption remains limited due to two fundamental challenges: 1) an unclear stakeholder value proposition and 2) persistent privacy concerns arising from continuous data collection. In parallel, conventional road condition monitoring methods are slow, expensive, and unable to provide continuous large-scale coverage. This work proposes a scalable and privacy-preserving framework that leverages smartphone-based sensing as a practical surrogate for connected vehicle data. Unlike infrastructure-dependent or single-modality approaches, the proposed method integrates widely available mobile devices with multimodal data sources to enable cost-effective, deployable large-scale road monitoring. This improves immediate real-world applicability while strengthening the value proposition needed for broader CV adoption. The research involves four main aspects. First, it has produced large-scale multimodal datasets combining smartphone inertial sensing, GPS, vision-based inputs, geographic information, and environmental conditions to capture diverse real-world driving scenarios. Second, it has used robust machine learning models to fuse heterogeneous data for accurate road anomaly detection under noisy and variable conditions. Third, it has proposed a layered privacy-preserving framework combining federated learning, contextual k-anonymity, and differential privacy to address non-IID vehicular data while ensuring strong privacy guarantees. Privacy implications are further analyzed using the IEEE Digital Privacy Model. Finally, the framework is validated through a cloud-based monitoring system and a sensing module designed to handle smartphone orientation variability in real-world deployments. In addition, the study identifies a critical gap in standardized metrics for evaluating privacy-preserving methods and proposes the need for a unified privacy score to enable systematic comparison. This research contributes a unified framework that combines scalable sensing, multimodal intelligence, and layered privacy mechanisms to balance detection performance, privacy preservation, and deployment feasibility. Overall, the proposed system establishes a scalable, privacy-aware, and deployable approach for road condition monitoring that supports intelligent transportation systems while addressing key barriers to connected vehicle adoption.","url":"https://doi.org/10.14288/1.0455575","authors":["Khandakar, Amith"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.14288/1.0455575","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.14288/1.0455561","name":"Compositional and adaptive visual recognition under domain shift","source":"datacite","abstract":"Human vision is remarkably robust, data-efficient, and adaptive. People can recognize objects across changes in style, viewpoint, lighting, modality, and context, often from only a few examples. Modern deep learning systems, despite their impressive performance, still depend heavily on large labeled datasets and often degrade when the test distribution differs from the training distribution. This brittleness is especially consequential in settings where labels are scarce, data are decentralized, or privacy constraints prevent the construction of large centralized datasets. This thesis argues that robust visual recognition requires moving beyond the dominant view of images as global patterns mapped directly to labels. Instead, recognition should be grounded in the structure of visual evidence. Objects and scenes are composed of parts, regions, relations, and context-dependent cues that are not equally relevant to recognition. Inspired by biological perception, the thesis organizes this view around three computational principles. First, visual representations should expose compositional structure rather than collapse images into monolithic embeddings. Second, recognition should selectively compare the evidence that is shared, discriminative, and stable while suppressing clutter, background, and nuisance variation. Third, inference should adapt to the input, domain, or task without requiring full retraining or access to target-domain supervision. The thesis develops architectures and learning strategies that encourage part–whole and region-level structure; training-free and few-shot inference methods that compare images through selected local evidence rather than global similarity alone; adaptive specialization mechanisms that preserve pretrained knowledge while changing the effective computation for new domains; and robustness-oriented optimization strategies that reduce reliance on nuisance-sensitive cues. Although these methods differ in form, they share the goal of making recognition depend less on superficial appearance correlations and more on structured, reusable, and task-relevant visual evidence. The thesis shows that compositional structure, selective evidence use, and adaptive inference provide a coherent foundation for recognition under domain shift, data scarcity, and privacy constraints. Across natural images, medical imaging, federated learning, cross-domain few-shot recognition, and video understanding, the work demonstrates that robust visual recognition is not only a matter of larger models or more data, but of representing and using visual evidence in a more structured and adaptive way.","url":"https://doi.org/10.14288/1.0455561","authors":["Radwan, Ahmed"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.14288/1.0455561","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.24433/co.2155937.v1","name":"FusionNet Lite: Lightweight Sequence Fusion for Predictive Maintenance in Industrial IoT","source":"datacite","abstract":"Reproducibility capsule for FusionNet Lite, a lightweight sequence-fusion deep-learning pipeline for predictive maintenance in industrial IoT systems. The campaign evaluates FusionNet Lite alongside CNN, BiLSTM, MLP, and CNN-LSTM baselines using centralized and FedAvg federated training across five seeds. The capsule includes leakage-safe preprocessing, stratified AI4I splitting, event-aware chronological MetroPT3 splitting with all rows retained, Dirichlet label non-IID client allocation, integrated-gradients analysis, statistical comparisons, publication tables and figures, and a consistency audit. Dataset files are placeholders in this capsule. Full reproducible execution requires attaching the authorised AI4I 2020 and MetroPT3 datasets under /data before selecting full mode.","url":"https://doi.org/10.24433/co.2155937.v1","authors":["Aman Sharma","Kwan Yong Sim","Siva Chandrasekaran "],"tags":["Capsule","Engineering","predictive-maintenance","industrial-iot","edge-ai","explainable-ai"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.24433/co.2155937.v1","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.17023/9869-cj93","name":"Artificial Intelligence (AI)- based Energy Efficiency Management for Ships at Berths","source":"datacite","abstract":"The rapid transformation of global energy systems toward low-carbon and decentralized architectures has introduced unprecedented complexity in planning, operation, and control. Large-scale integration of renewable energy sources, electric vehicles, distributed storage, and prosumer-driven markets requires intelligent, adaptive, and data-driven solutions beyond conventional control and optimization techniques. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as key enablers for next-generation smart energy systems, offering powerful tools for forecasting, real-time control, resilience enhancement, fault diagnosis, and decision-makingunder uncertainty. Recent advances in deep learning, reinforcement learning, federated learning, digital twins, and explainable AI have opened new research frontiers across smart grids, microgrids, energy storage systems, and electric mobility infrastructures. Recent advances in deep learning, reinforcement learning, federated learning, digital twins, control systems, optimization and data-driven decision frameworks have enabled intelligent energy systems capable ofself-learning, self-healing, and real-time adaptation. However, significant research gaps remain in scalability, explainability,cyber-security, real-world deployment, and policy-aware AI integration. This study, AI-Assisted Smart Energy Systems for a Sustainable Energy Transition, aims to bring together high-quality experimental, computational, and theoretical research that explores AI-assisted methodologies for designing, operating, and managing smart energy systems in support of a secure, resilient, and sustainable energy transition.Emphasis is placed on interdisciplinary contributions that bridgeelectrical engineering, data science, energy economics, andpolicy, as well as studies demonstrating real-world deploymentand scalability.","url":"https://doi.org/10.17023/9869-cj93","authors":["Tien Anh Tran"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17023/9869-cj93","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.17023/e6vw-gf71","name":"Artificial Intelligence (AI)- based Energy Efficiency Management for Ships at Berths","source":"datacite","abstract":"The rapid transformation of global energy systems toward low-carbon and decentralized architectures has introduced unprecedented complexity in planning, operation, and control. Large-scale integration of renewable energy sources, electric vehicles, distributed storage, and prosumer-driven markets requires intelligent, adaptive, and data-driven solutions beyond conventional control and optimization techniques. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as key enablers for next-generation smart energy systems, offering powerful tools for forecasting, real-time control, resilience enhancement, fault diagnosis, and decision-makingunder uncertainty. Recent advances in deep learning, reinforcement learning, federated learning, digital twins, and explainable AI have opened new research frontiers across smart grids, microgrids, energy storage systems, and electric mobility infrastructures. Recent advances in deep learning, reinforcement learning, federated learning, digital twins, control systems, optimization and data-driven decision frameworks have enabled intelligent energy systems capable ofself-learning, self-healing, and real-time adaptation. However, significant research gaps remain in scalability, explainability,cyber-security, real-world deployment, and policy-aware AI integration. This study, AI-Assisted Smart Energy Systems for a Sustainable Energy Transition, aims to bring together high-quality experimental, computational, and theoretical research that explores AI-assisted methodologies for designing, operating, and managing smart energy systems in support of a secure, resilient, and sustainable energy transition.Emphasis is placed on interdisciplinary contributions that bridgeelectrical engineering, data science, energy economics, andpolicy, as well as studies demonstrating real-world deploymentand scalability.","url":"https://doi.org/10.17023/e6vw-gf71","authors":["Tien Anh Tran"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17023/e6vw-gf71","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20527681","name":"FedBuild: Privacy-Preserving Federated Learning for Building Energy Forecasting","source":"datacite","abstract":"FedBuild implements a production-grade federated learning system with formal differential privacy (DP) guarantees. The system trains CNN-LSTM models across 50 buildings using the Flower framework, applies per-sample gradient clipping and Gaussian noise via Opacus, and evaluates three aggregation strategies (FedAvg, FedBN, FedProx) across privacy budgets from ε=0.5 to ε=6.5. Key finding: DP-SGD gradient clipping acts as implicit regularization in federated settings with inter-client heterogeneity, causing all DP configurations to outperform the non-private federated baseline—a novel effect unreported in prior building-energy forecasting literature. Features Flower-based orchestration: 50 clients, 10 sampled per round, 50 federation rounds DP-SGD via Opacus: Per-sample gradient clipping (C=1.0) + Gaussian noise, server-side RDP composition accounting Three strategies evaluated: FedAvg, FedBN (local batch norm), FedProx (proximal regularization) 12 privacy configurations: 3 strategies × 4 ε targets (0.5, 1.0, 3.0, 6.5) DP-compatible architecture: CNN-LSTM with DPLSTM + GroupNorm substitutions Real-world dataset: Building Data Genome Project 2, 50 office/education buildings (Panther site) Reproducibility: Round-by-round checkpointing, seed fixing, aggregated metrics + per-run histories Full privacy validation: Server-side RDP composition with explicit final ε reporting (achieved ≈ target within 3%) Publication-ready outputs: 9 figures (PDF+PNG), methodology document, comprehensive tables","url":"https://doi.org/10.5281/zenodo.20527681","authors":["Ameh, Jude"],"tags":["federated learning","differential privacy","dp-sgd","privacy-preserving machine learning","building energy forecasting","energy consumption prediction","opacus","flower framework"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20527681","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20527682","name":"FedBuild: Privacy-Preserving Federated Learning for Building Energy Forecasting","source":"datacite","abstract":"FedBuild implements a production-grade federated learning system with formal differential privacy (DP) guarantees. The system trains CNN-LSTM models across 50 buildings using the Flower framework, applies per-sample gradient clipping and Gaussian noise via Opacus, and evaluates three aggregation strategies (FedAvg, FedBN, FedProx) across privacy budgets from ε=0.5 to ε=6.5. Key finding: DP-SGD gradient clipping acts as implicit regularization in federated settings with inter-client heterogeneity, causing all DP configurations to outperform the non-private federated baseline—a novel effect unreported in prior building-energy forecasting literature. Features Flower-based orchestration: 50 clients, 10 sampled per round, 50 federation rounds DP-SGD via Opacus: Per-sample gradient clipping (C=1.0) + Gaussian noise, server-side RDP composition accounting Three strategies evaluated: FedAvg, FedBN (local batch norm), FedProx (proximal regularization) 12 privacy configurations: 3 strategies × 4 ε targets (0.5, 1.0, 3.0, 6.5) DP-compatible architecture: CNN-LSTM with DPLSTM + GroupNorm substitutions Real-world dataset: Building Data Genome Project 2, 50 office/education buildings (Panther site) Reproducibility: Round-by-round checkpointing, seed fixing, aggregated metrics + per-run histories Full privacy validation: Server-side RDP composition with explicit final ε reporting (achieved ≈ target within 3%) Publication-ready outputs: 9 figures (PDF+PNG), methodology document, comprehensive tables","url":"https://doi.org/10.5281/zenodo.20527682","authors":["Ameh, Jude"],"tags":["federated learning","differential privacy","dp-sgd","privacy-preserving machine learning","building energy forecasting","energy consumption prediction","opacus","flower framework"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20527682","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.19677328","name":"Streaming Federated Learning with Markovian Data","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.19677328","authors":["Huynh, Tan-Khiem","Egan, Malcom","Neglia, Giovanni","Gorce, Jean-Marie"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19677328","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.19677329","name":"Streaming Federated Learning with Markovian Data","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.19677329","authors":["Huynh, Tan-Khiem","Egan, Malcom","Neglia, Giovanni","Gorce, Jean-Marie"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19677329","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22147984","name":"APPLICATION OF ARTIFICIAL INTELLIGENCE IN REGULATORY COMPLIANCE FOR CROSS-BORDER DIGITAL TRANSACTIONS","source":"datacite","abstract":"This study examines the theoretical, structural, and empirical applications of Artificial Intelligence (AI) and Machine Learning (ML) architectures within the domain of regulatory compliance (RegTech) and supervisory technology (SupTech) for cross-border digital transactions. The exponential expansion of cross-border financial flows, real-time payment systems, and decentralized financial instruments has amplified regulatory fragmentation, multi-jurisdictional compliance friction, and sophisticated financial crime typologies. Utilizing institutional economics, information asymmetry theory, and computational compliance modeling, this paper analyzes how advanced algorithmic architectures—specifically Graph Neural Networks (GNNs), Natural Language Processing (NLP), and Federated Learning—optimize anti-money laundering (AML), counter-terrorist financing (CFT), and real-time sanctions screening. The findings demonstrate that shifting from legacy rule-based heuristics to adaptive, privacy-preserving AI frameworks significantly compresses false-positive rates, bridges cross-jurisdictional regulatory disparities, and establishes a dynamic, mathematically rigorous paradigm for global financial integrity.","url":"https://doi.org/10.5281/zenodo.22147984","authors":["Vakhabov Bobur"],"tags":["Artificial Intelligence, Regulatory Compliance, Cross-Border Transactions, RegTech, Anti-Money Laundering (AML), Graph Neural Networks, Federated Learning, SupTech."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22147984","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22147985","name":"APPLICATION OF ARTIFICIAL INTELLIGENCE IN REGULATORY COMPLIANCE FOR CROSS-BORDER DIGITAL TRANSACTIONS","source":"datacite","abstract":"This study examines the theoretical, structural, and empirical applications of Artificial Intelligence (AI) and Machine Learning (ML) architectures within the domain of regulatory compliance (RegTech) and supervisory technology (SupTech) for cross-border digital transactions. The exponential expansion of cross-border financial flows, real-time payment systems, and decentralized financial instruments has amplified regulatory fragmentation, multi-jurisdictional compliance friction, and sophisticated financial crime typologies. Utilizing institutional economics, information asymmetry theory, and computational compliance modeling, this paper analyzes how advanced algorithmic architectures—specifically Graph Neural Networks (GNNs), Natural Language Processing (NLP), and Federated Learning—optimize anti-money laundering (AML), counter-terrorist financing (CFT), and real-time sanctions screening. The findings demonstrate that shifting from legacy rule-based heuristics to adaptive, privacy-preserving AI frameworks significantly compresses false-positive rates, bridges cross-jurisdictional regulatory disparities, and establishes a dynamic, mathematically rigorous paradigm for global financial integrity.","url":"https://doi.org/10.5281/zenodo.22147985","authors":["Vakhabov Bobur"],"tags":["Artificial Intelligence, Regulatory Compliance, Cross-Border Transactions, RegTech, Anti-Money Laundering (AML), Graph Neural Networks, Federated Learning, SupTech."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22147985","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.19974798","name":"AI-DRIVEN FEDERATED LEARNING FRAMEWORK FOR PRIVACY-PRESERVING EARLY DETECTION OF CYBER THREATS IN PAKISTAN'S CRITICAL INFRASTRUCTURE SYSTEMS","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.19974798","authors":["Shazia Paras Shaikh,Muhammad Suliman"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19974798","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20324289","name":"AI-DRIVEN FEDERATED LEARNING FRAMEWORK FOR PRIVACY-PRESERVING EARLY DETECTION OF CYBER THREATS IN PAKISTAN'S CRITICAL INFRASTRUCTURE SYSTEMS","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20324289","authors":["Shazia Paras Shaikh,Muhammad Suliman"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20324289","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21580791","name":"Leveraging Natural Language Processing to Detect Non-Compliance in Clinical Documentation: Current Advances, Challenges, and Future Directions","source":"datacite","abstract":"Clinical documentation is essential for patient safety, regulatory compliance, and healthcare quality, yet manual auditing of electronic health records (EHRs) remains labor-intensive and error prone. This paper reviews current advances in applying Natural Language Processing (NLP) to detect non-compliance within clinical documentation. Using a systematic review of recent literature (2020–2023), the study identifies dominant NLP techniques including rule-based systems, machine learning pipelines, and large language models and examines their performance in recognizing omissions, inconsistencies, and guideline deviations. The analysis highlights major challenges such as linguistic variability, data privacy constraints, and interoperability barriers among heterogeneous EHR systems. Opportunities emerge in federated learning, explainable AI, and real-time integration of NLP feedback into clinician workflows. The paper concludes that effective deployment of NLP-based compliance systems requires both technical innovation and policy alignment to ensure transparency, trust, and scalability. These insights outline a research agenda for developing intelligent, privacy-preserving frameworks that strengthen clinical documentation integrity and enhance patient safety.","url":"https://doi.org/10.5281/zenodo.21580791","authors":["Salami, Aishat O."],"tags":["Natural Language Processing","Clinical Documentation","Electronic Health Records","Regulatory Compliance","Healthcare Quality","Explainable AI","Federated Learning","Patient Safety."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21580791","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.21580792","name":"Leveraging Natural Language Processing to Detect Non-Compliance in Clinical Documentation: Current Advances, Challenges, and Future Directions","source":"datacite","abstract":"Clinical documentation is essential for patient safety, regulatory compliance, and healthcare quality, yet manual auditing of electronic health records (EHRs) remains labor-intensive and error prone. This paper reviews current advances in applying Natural Language Processing (NLP) to detect non-compliance within clinical documentation. Using a systematic review of recent literature (2020–2023), the study identifies dominant NLP techniques including rule-based systems, machine learning pipelines, and large language models and examines their performance in recognizing omissions, inconsistencies, and guideline deviations. The analysis highlights major challenges such as linguistic variability, data privacy constraints, and interoperability barriers among heterogeneous EHR systems. Opportunities emerge in federated learning, explainable AI, and real-time integration of NLP feedback into clinician workflows. The paper concludes that effective deployment of NLP-based compliance systems requires both technical innovation and policy alignment to ensure transparency, trust, and scalability. These insights outline a research agenda for developing intelligent, privacy-preserving frameworks that strengthen clinical documentation integrity and enhance patient safety.","url":"https://doi.org/10.5281/zenodo.21580792","authors":["Salami, Aishat O."],"tags":["Natural Language Processing","Clinical Documentation","Electronic Health Records","Regulatory Compliance","Healthcare Quality","Explainable AI","Federated Learning","Patient Safety."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21580792","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.21492306","name":"FEDERATED MACHINE LEARNING AS A PATHWAY TO PRIVACY-PRESERVING ARTIFICIAL INTELLIGENCE IN SMART DIGITAL ECOSYSTEMS","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21492306","authors":["Kainat Tariq,Syed Zeshan Haidar,Kohal deep,Muhammad Haqan Ali Rai"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21492306","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21492307","name":"FEDERATED MACHINE LEARNING AS A PATHWAY TO PRIVACY-PRESERVING ARTIFICIAL INTELLIGENCE IN SMART DIGITAL ECOSYSTEMS","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21492307","authors":["Kainat Tariq,Syed Zeshan Haidar,Kohal deep,Muhammad Haqan Ali Rai"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21492307","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.18761410","name":"Cross-Agent Governance Alignment Without Rule Disclosure: A Problem Formalization","source":"datacite","abstract":"This preprint formalizes the Cross-Agent Governance Alignment (CAGA) problem: the challenge of verifying mutual governance compatibility between autonomous AI agents operating under distinct organizational policy regimes—without disclosing proprietary governance structures. As AI agents increasingly coordinate across institutional boundaries in regulated industries (healthcare, finance, cross-border data exchange, supply chains), existing governance models prove insufficient. Current frameworks assume either a single organizational authority or full policy transparency between participants. Neither assumption holds in multi-stakeholder settings where governance constraints encode confidential risk tolerances, regulatory interpretations, and competitive strategy. This paper: Defines governance domains and cross-domain interactions in formal terms Introduces the governance alignment predicate Φ(Dᵢ, Dⱼ, τ) Formalizes the CAGA problem under an honest-but-curious threat model Identifies required solution properties spanning correctness, privacy, determinism, evidentiary sufficiency, and composable security Demonstrates that CAGA is irreducible to existing paradigms, including agent communication protocols, federated learning, secure multi-party computation, single-organization governance architectures, and blockchain-based transparency systems We argue that CAGA constitutes a zero-knowledge coordination problem at the intersection of AI governance, cryptographic protocol design, and multi-agent systems. The paper deliberately stops at problem formalization and does not disclose protocol constructions or implementation mechanisms. By precisely defining the problem space and evaluation criteria, this work establishes the foundation for rigorous solution development and provides a formal framework against which candidate governance-alignment protocols can be assessed.","url":"https://doi.org/10.5281/zenodo.18761410","authors":["Meyman, Edward"],"tags":["AI governance","Multi-agent systems","Zero-knowledge proofs","Cross-organizational coordination","Governance alignment","Deterministic governance","Authorization boundaries","Auditability"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18761410","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21625705","name":"PrivateBoost: privacy-preserving federated gradient boosting for single-record patient devices","source":"datacite","abstract":"Deployable implementation of the PrivateBoost protocol: histogram-based federated gradient boosting in which every client holds a single labelled record, splits its gradient and Hessian statistics into Shamir shares over a Mersenne prime field, and distributes them to independent shareholders that only ever release population-level sums. Includes the protocol crate, the shareholder and aggregator server, the client library and CLI, the Flutter mobile application, the deployment configurations, and the analysis scripts that reproduce the reported experiments and figures.","url":"https://doi.org/10.5281/zenodo.21625705","authors":["Specht, Bernhard","Ermis, Orhan","Garbaya, Samaher","Schneider, Reinhard","Chavarriaga, Ricardo","Khadraoui, Djamel","Tayeb, Zied"],"tags":["federated learning","gradient boosting","secret sharing","privacy-preserving machine learning","cross-device learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21625705","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21630278","name":"PrivateBoost: privacy-preserving federated gradient boosting for single-record patient devices","source":"datacite","abstract":"Deployable implementation of the PrivateBoost protocol: histogram-based federated gradient boosting in which every client holds a single labelled record, splits its gradient and Hessian statistics into Shamir shares over a Mersenne prime field, and distributes them to independent shareholders that only ever release population-level sums. Includes the protocol crate, the shareholder and aggregator server, the client library and CLI, the Flutter mobile application, the deployment configurations, and the analysis scripts that reproduce the reported experiments and figures.","url":"https://doi.org/10.5281/zenodo.21630278","authors":["Specht, Bernhard","Ermis, Orhan","Garbaya, Samaher","Schneider, Reinhard","Chavarriaga, Ricardo","Khadraoui, Djamel","Tayeb, Zied"],"tags":["federated learning","gradient boosting","secret sharing","privacy-preserving machine learning","cross-device learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21630278","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21580585","name":"Federated Learning for Melanoma Classification : Analysing Diverse Federated Approaches","source":"datacite","abstract":"Federated learning has emerged as a revolutionary method for training machine learning models across disparate data sources. This method ensures that data privacy and security are maintained during the training process, which is especially important in sensitive industries such as healthcare. This review article presents a comprehensive investigation of the use of federated learning strategies to the categorization of melanoma. It investigates a variety of methodologies and the effectiveness of these approaches in utilizing distributed datasets. In this article, a number of different federated learning frameworks, such as FedAvg, FedProx, and customized federated learning techniques, are evaluated, along with their applications in dermatological image analysis. Important factors such as the accuracy of the model, the effectiveness of communication, the management of heterogeneity in data, and the protection of privacy are being examined. This paper highlighted the promise of federated learning to revolutionize melanoma classification. Federated learning has the ability to enable collaborative model training without compromising the security of patient data. The purpose of this work is to provide academics and practitioners who are interested in improving melanoma detection by federated learning with significant insights and future directions. These insights are provided by synthesising previous accomplishments and highlighting present difficulties.","url":"https://doi.org/10.5281/zenodo.21580585","authors":["Gandhi, Yatin Bharat","Mahobiya, Dr. Chandrakant"],"tags":["Federated learning","melanoma","FedAvg","FedProx","Healthcare."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.21580585","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.21580586","name":"Federated Learning for Melanoma Classification : Analysing Diverse Federated Approaches","source":"datacite","abstract":"Federated learning has emerged as a revolutionary method for training machine learning models across disparate data sources. This method ensures that data privacy and security are maintained during the training process, which is especially important in sensitive industries such as healthcare. This review article presents a comprehensive investigation of the use of federated learning strategies to the categorization of melanoma. It investigates a variety of methodologies and the effectiveness of these approaches in utilizing distributed datasets. In this article, a number of different federated learning frameworks, such as FedAvg, FedProx, and customized federated learning techniques, are evaluated, along with their applications in dermatological image analysis. Important factors such as the accuracy of the model, the effectiveness of communication, the management of heterogeneity in data, and the protection of privacy are being examined. This paper highlighted the promise of federated learning to revolutionize melanoma classification. Federated learning has the ability to enable collaborative model training without compromising the security of patient data. The purpose of this work is to provide academics and practitioners who are interested in improving melanoma detection by federated learning with significant insights and future directions. These insights are provided by synthesising previous accomplishments and highlighting present difficulties.","url":"https://doi.org/10.5281/zenodo.21580586","authors":["Gandhi, Yatin Bharat","Mahobiya, Dr. Chandrakant"],"tags":["Federated learning","melanoma","FedAvg","FedProx","Healthcare."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.21580586","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.22090063","name":"Advanced Topics in AI & Data Science","source":"datacite","abstract":"Abstract: Artificial Intelligence (AI) and Data Science have rapidly grown into revolutionary technologies that are both subjects of scientific research and are revolutionizing industries and decision-making process. Innovations like deep learning, generative AI, explainable artificial intelligence (XAI), federated learning, reinforcement learning, edge intelligence and large language models have dramatically improved capabilities of intelligent systems while presenting novel challenges in terms of scalability, security, privacy, interpretability and ethics. The current chapter offers a full review of advanced topics in the field of AI and Data Science, focusing on computational approaches, modern methods and practical applications. It explores the most recent advancements in the area of intelligent automation, natural language processing, predictive analytics, computer vision, graph neural networks and multimodal learning, increasing their role in tackling complex tasks in healthcare, manufacturing, finance, cybersecurity, smart cities and environment protection. The chapter also explains about the use of cloud computing, big data analysis, IoT along with principles of responsible AI to build trustworthy and transparent intelligent systems. Furthermore, current research trends, implementation challenges, and future research directions have been critically analysed in this chapter to give a comprehensive overview of AI ecosystem. Through theoretical concepts and practical examples as well as current case studies, this chapter can be used as an important reference source for researchers, academics, graduate students, and professionals of the industry professionals seeking a deeper understanding of advanced AI and Data Science technologies and their transformative impact on modern society. Keywords: Artificial Intelligence, Data Science, Deep Learning, Generative AI, Large Language Models (LLMs), Explainable Artificial Intelligence (XAI).","url":"https://doi.org/10.5281/zenodo.22090063","authors":["M. G. Shrigan","S. D. Bhourgunde"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22090063","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.22090062","name":"Advanced Topics in AI & Data Science","source":"datacite","abstract":"Abstract: Artificial Intelligence (AI) and Data Science have rapidly grown into revolutionary technologies that are both subjects of scientific research and are revolutionizing industries and decision-making process. Innovations like deep learning, generative AI, explainable artificial intelligence (XAI), federated learning, reinforcement learning, edge intelligence and large language models have dramatically improved capabilities of intelligent systems while presenting novel challenges in terms of scalability, security, privacy, interpretability and ethics. The current chapter offers a full review of advanced topics in the field of AI and Data Science, focusing on computational approaches, modern methods and practical applications. It explores the most recent advancements in the area of intelligent automation, natural language processing, predictive analytics, computer vision, graph neural networks and multimodal learning, increasing their role in tackling complex tasks in healthcare, manufacturing, finance, cybersecurity, smart cities and environment protection. The chapter also explains about the use of cloud computing, big data analysis, IoT along with principles of responsible AI to build trustworthy and transparent intelligent systems. Furthermore, current research trends, implementation challenges, and future research directions have been critically analysed in this chapter to give a comprehensive overview of AI ecosystem. Through theoretical concepts and practical examples as well as current case studies, this chapter can be used as an important reference source for researchers, academics, graduate students, and professionals of the industry professionals seeking a deeper understanding of advanced AI and Data Science technologies and their transformative impact on modern society. Keywords: Artificial Intelligence, Data Science, Deep Learning, Generative AI, Large Language Models (LLMs), Explainable Artificial Intelligence (XAI).","url":"https://doi.org/10.5281/zenodo.22090062","authors":["M. G. Shrigan","S. D. Bhourgunde"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22090062","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.22141819","name":"One Shot Nested Pruning for Resource Constrained Federated Learning","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.22141819","authors":["Dejene, Nathnael"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22141819","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.21227/5cc8-wg20","name":"\"A Multi-Environment Real-World Multi-Band RF Dataset for Spectrum Sensing and Occupancy Analysis for Allocations or Sharing\"","source":"datacite","abstract":"\"Spectrum-sensing research is dominated by corpora that are either fully synthetic or captured under a single set of propagation and traffic conditions, then augmented with artificial impairments. This collection instead sweeps the same receiver, the same antenna, and the same seven-band sweep plan across seven physically distinct real environments in Montr\\u00e9al, so that environment \\u2014 not a simulated channel model \\u2014 is the variable that changes between subsets.The seven sessions were recorded between 19 and 24 August 2026 with a single Ettus USRP B205mini-i software-defined radio and a dual-band 2.4 \\/ 5 \\/ 5.8 GHz omnidirectional antenna, using the identical acquisition and post-processing chain published with the mass-gathering (fireworks) dataset. Each session sweeps seven allocations spanning the 2.4 GHz ISM band and the U-NII-1 \\/ 2A \\/ 2C \\/ 3 \\/ 4 and lower-6 GHz-edge allocations, dwelling on each tile for approximately one million complex baseband samples before retuning.Measured spectral occupancy across the corpus ranges from 0.0 % to 55.6 % depending on band and environment \\u2014 from a near-vacant urban park at dusk to a saturated ITS\\/5.9 GHz allocation inside a downtown metro station. The result is a set of genuinely heterogeneous, non-IID subsets suitable for cross-environment generalization studies, domain-shift analysis, and federated learning with naturally partitioned clients.\"","url":"https://doi.org/10.21227/5cc8-wg20","authors":["Atik Mahabub","Shervin Vakili"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21227/5cc8-wg20","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.22141276","name":"One Shot Nested Pruning for Resource Constrained Federated Learning","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.22141276","authors":["Dejene, Nathnael"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22141276","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22139700","name":"Distributed Differential Privacy with Federated Learning via Lagrangian Relaxation","source":"datacite","abstract":"Achieving strong differential privacy guarantees within the constraints of federated learning, especially when utilizing complex models, remains a significant challenge. This work proposes a novel framework leveraging Lagrangian relaxation to address this issue. The core idea involves incorporating a Lagrangian term directly into the federated learning objective function to formally represent the differential privacy constraint. This allows for an iterative solution of the resulting Lagrangian problem via distributed optimization, providing a controllable mechanism for balancing privacy and model accuracy. The proposed approach offers a more practical and scalable solution compared to existing methods, particularly in scenarios where precise control over the privacy-accuracy trade-off is desired. The effectiveness of this method is demonstrated through theoretical analysis and conceptual discussion, outlining a pathway for future research and implementation.","url":"https://doi.org/10.5281/zenodo.22139700","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22139700","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22139699","name":"Distributed Differential Privacy with Federated Learning via Lagrangian Relaxation","source":"datacite","abstract":"Achieving strong differential privacy guarantees within the constraints of federated learning, especially when utilizing complex models, remains a significant challenge. This work proposes a novel framework leveraging Lagrangian relaxation to address this issue. The core idea involves incorporating a Lagrangian term directly into the federated learning objective function to formally represent the differential privacy constraint. This allows for an iterative solution of the resulting Lagrangian problem via distributed optimization, providing a controllable mechanism for balancing privacy and model accuracy. The proposed approach offers a more practical and scalable solution compared to existing methods, particularly in scenarios where precise control over the privacy-accuracy trade-off is desired. The effectiveness of this method is demonstrated through theoretical analysis and conceptual discussion, outlining a pathway for future research and implementation.","url":"https://doi.org/10.5281/zenodo.22139699","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22139699","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.19705740","name":"Artificial Intelligence in Pharmaceutical Innovation and Research: A Review","source":"datacite","abstract":"Artificial intelligence (AI) is revolutionizing pharmaceutical research by accelerating and refining drug discovery, development, and clinical practice. This review summarizes the role of AI across key stages of the pharmaceutical pipeline, including target identification and validation, virtual and high-throughput screening, de-novo drug design, structure- and ligand-based approaches, and drug repurposing. In preclinical research, AI enables predictive modeling of pharmacokinetic and pharmacodynamic parameters, toxicity and ADMET profiles, drug–target and drug–drug interactions, and biomarker-driven disease-pathway analysis. In formulation and manufacturing, AI supports rational formulation design, excipient and process-parameter optimization, predictive dissolution models, continuous manufacturing, and real-time release testing within quality-by-design frameworks.AI is also transforming clinical trials and pharmacovigilance through enhanced patient recruitment and stratification, trial-design optimization, real-time safety monitoring, adverse-reaction prediction, and decision-support systems for pharmacists. Nonetheless, several challenges persist, including data quality and bias, regulatory and ethical concerns, lack of standardized validation frameworks, and shortages of skilled workforce and infrastructure. Future perspectives highlight the integration of AI with multi-omics and systems pharmacology, AI-driven personalized and precision medicine, applications in global health and neglected diseases, and emerging trends such as quantum-machine learning, explainable AI, and federated learning. Overall, AI is emerging as a foundational technology in modern pharmaceutical research, promising faster, safer, and more patient-centric drug development.","url":"https://doi.org/10.5281/zenodo.19705740","authors":["Rahul kr. Rai*1, Sharad Suman2, Priya Raj3, Siddharth Kowsik4"],"tags":["Artificial Intelligence, Machine Learning, Drug Discovery, Pharmaceutical Research, Predictive Modeling, Big Data"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19705740","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.19705741","name":"Artificial Intelligence in Pharmaceutical Innovation and Research: A Review","source":"datacite","abstract":"Artificial intelligence (AI) is revolutionizing pharmaceutical research by accelerating and refining drug discovery, development, and clinical practice. This review summarizes the role of AI across key stages of the pharmaceutical pipeline, including target identification and validation, virtual and high-throughput screening, de-novo drug design, structure- and ligand-based approaches, and drug repurposing. In preclinical research, AI enables predictive modeling of pharmacokinetic and pharmacodynamic parameters, toxicity and ADMET profiles, drug–target and drug–drug interactions, and biomarker-driven disease-pathway analysis. In formulation and manufacturing, AI supports rational formulation design, excipient and process-parameter optimization, predictive dissolution models, continuous manufacturing, and real-time release testing within quality-by-design frameworks.AI is also transforming clinical trials and pharmacovigilance through enhanced patient recruitment and stratification, trial-design optimization, real-time safety monitoring, adverse-reaction prediction, and decision-support systems for pharmacists. Nonetheless, several challenges persist, including data quality and bias, regulatory and ethical concerns, lack of standardized validation frameworks, and shortages of skilled workforce and infrastructure. Future perspectives highlight the integration of AI with multi-omics and systems pharmacology, AI-driven personalized and precision medicine, applications in global health and neglected diseases, and emerging trends such as quantum-machine learning, explainable AI, and federated learning. Overall, AI is emerging as a foundational technology in modern pharmaceutical research, promising faster, safer, and more patient-centric drug development.","url":"https://doi.org/10.5281/zenodo.19705741","authors":["Rahul kr. Rai*1, Sharad Suman2, Priya Raj3, Siddharth Kowsik4"],"tags":["Artificial Intelligence, Machine Learning, Drug Discovery, Pharmaceutical Research, Predictive Modeling, Big Data"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19705741","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.20842574","name":"claudy667/esp32-federated-lstm-communitynet-: CommunityNet PF-LSTM — Initial Release","source":"datacite","abstract":"Initial release of the PF-LSTM federated learning codebase for offline ESP32 community mesh networks. KNUST CS Research 2026.","url":"https://doi.org/10.5281/zenodo.20842574","authors":["claudy667"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20842574","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20842575","name":"claudy667/esp32-federated-lstm-communitynet-: CommunityNet PF-LSTM — Initial Release","source":"datacite","abstract":"Initial release of the PF-LSTM federated learning codebase for offline ESP32 community mesh networks. KNUST CS Research 2026.","url":"https://doi.org/10.5281/zenodo.20842575","authors":["claudy667"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20842575","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20806104","name":"rdelhibabu/Automated-Clinical-Documentation_Intelligent-Rooms: V1","source":"datacite","abstract":"Generative AI at the Edge: A Privacy-Preserving Federated Learning Architecture for Automated Clinical Documentation in Intelligent Rooms","url":"https://doi.org/10.5281/zenodo.20806104","authors":["Radhakrishnan Delhibabu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20806104","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20806105","name":"rdelhibabu/Automated-Clinical-Documentation_Intelligent-Rooms: V1","source":"datacite","abstract":"Generative AI at the Edge: A Privacy-Preserving Federated Learning Architecture for Automated Clinical Documentation in Intelligent Rooms","url":"https://doi.org/10.5281/zenodo.20806105","authors":["Radhakrishnan Delhibabu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20806105","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20790583","name":"Validator-Backed Auditable Robust Aggregation for Trustworthy Federated Learning","source":"datacite","abstract":"Neurocomputing reproducibility package for: Validator-Backed Auditable Robust Aggregation for Trustworthy Federated Learning This release archives the code, configurations, result summaries, paper figures, supplementary tables, validator-audit outputs, and audit-case excerpts used to support the reported analyses.","url":"https://doi.org/10.5281/zenodo.20790583","authors":["Huang, Shiqi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20790583","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20790584","name":"Validator-Backed Auditable Robust Aggregation for Trustworthy Federated Learning","source":"datacite","abstract":"Neurocomputing reproducibility package for: Validator-Backed Auditable Robust Aggregation for Trustworthy Federated Learning This release archives the code, configurations, result summaries, paper figures, supplementary tables, validator-audit outputs, and audit-case excerpts used to support the reported analyses.","url":"https://doi.org/10.5281/zenodo.20790584","authors":["Huang, Shiqi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20790584","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22138328","name":"Formal Verification of Trustworthy Federated Learning Systems","source":"datacite","abstract":"Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data, offering enhanced privacy and reduced communication costs. However, the inherent distributed nature of FL introduces significant challenges regarding trust, security, and model accuracy. This paper presents a formal verification framework for FL systems, leveraging secure multi-party computation (SMPC) and formal verification techniques to rigorously analyze data flow and model updates. The framework aims to provide guarantees about privacy, security, and model accuracy, addressing the unique vulnerabilities present in FL architectures. We define a mathematical model of an FL system, incorporating key elements such as clients, servers, and communication protocols. This model is then subjected to formal verification, utilizing techniques like model checking and symbolic execution to identify potential security breaches and inaccuracies. The results demonstrate the feasibility and effectiveness of applying formal verification to FL, offering a robust approach to ensuring the trustworthiness of these systems. Key performance metrics, including privacy loss, communication overhead, and model accuracy deviations, are quantified and analyzed within the verification process. The framework contributes to the development of more reliable and secure FL applications, particularly in sensitive domains such as healthcare and finance.","url":"https://doi.org/10.5281/zenodo.22138328","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22138328","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22138327","name":"Formal Verification of Trustworthy Federated Learning Systems","source":"datacite","abstract":"Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data, offering enhanced privacy and reduced communication costs. However, the inherent distributed nature of FL introduces significant challenges regarding trust, security, and model accuracy. This paper presents a formal verification framework for FL systems, leveraging secure multi-party computation (SMPC) and formal verification techniques to rigorously analyze data flow and model updates. The framework aims to provide guarantees about privacy, security, and model accuracy, addressing the unique vulnerabilities present in FL architectures. We define a mathematical model of an FL system, incorporating key elements such as clients, servers, and communication protocols. This model is then subjected to formal verification, utilizing techniques like model checking and symbolic execution to identify potential security breaches and inaccuracies. The results demonstrate the feasibility and effectiveness of applying formal verification to FL, offering a robust approach to ensuring the trustworthiness of these systems. Key performance metrics, including privacy loss, communication overhead, and model accuracy deviations, are quantified and analyzed within the verification process. The framework contributes to the development of more reliable and secure FL applications, particularly in sensitive domains such as healthcare and finance.","url":"https://doi.org/10.5281/zenodo.22138327","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22138327","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21209285","name":"Cross-Agent Governance Alignment (CAGA): Formalizing Cross-Organizational AI Governance as a Zero-Knowledge Coordination Problem","source":"datacite","abstract":"This preprint formalizes the Cross-Agent Governance Alignment (CAGA) problem: the challenge of verifying mutual governance compatibility between autonomous AI agents operating under distinct organizational policy regimes, without disclosing proprietary governance structures. As AI agents increasingly coordinate across institutional boundaries in regulated industries (healthcare, finance, cross-border data exchange, supply chains), existing governance models prove insufficient. Current frameworks assume either a single organizational authority or full policy transparency between participants. Neither assumption holds in multi-stakeholder settings where governance constraints encode confidential risk tolerances, regulatory interpretations, and competitive strategy. This paper: Defines governance domains and cross-domain interactions in formal terms, adopting the triadic verdict space (ALLOW, DENY, ABSTAIN) of the execution-time authorization framework Introduces the governance alignment predicate Φ(Dᵢ, Dⱼ, τ) Formalizes the CAGA problem under an honest-but-curious threat model Identifies required solution properties spanning correctness, privacy, determinism, evidentiary sufficiency, and composable security, including the requirement that alignment protocols produce tamper-evident authorization artifacts sufficient for independent third-party replay, consistent with the Replay requirement of the Five Tests Standard (5TS) Demonstrates that CAGA is irreducible to existing paradigms, including agent communication protocols, federated learning, secure multi-party computation, single-organization governance architectures, and blockchain-based transparency systems We argue that CAGA constitutes a zero-knowledge coordination problem at the intersection of AI governance, cryptographic protocol design, and multi-agent systems. The paper deliberately stops at problem formalization and does not disclose protocol constructions or implementation mechanisms. By precisely defining the problem space and evaluation criteria, this work establishes the foundation for rigorous solution development and provides a formal framework against which candidate governance-alignment protocols can be assessed. Version 1.1 (July 2026) retitles the paper to make explicit that CAGA is formalized as a zero-knowledge coordination problem for cross-organizational AI governance; aligns terminology with the Five Tests Standard (5TS) v1.2.0 and the FERZ authorization-artifact vocabulary; adopts the triadic verdict space in the governance domain formalization; and adds a companion reference to Execution-Time Authorization for AI Agents (v2.1), which develops the formal architecture of the single-domain authorization boundary. The problem formalization, threat model, and irreducibility argument are unchanged from the February 2026 release (v1.0). Keywords: AI governance, multi-agent systems, zero-knowledge proofs, cross-organizational coordination, governance alignment, deterministic governance, authorization boundaries, authorization artifacts, Five Tests Standard","url":"https://doi.org/10.5281/zenodo.21209285","authors":["Meyman, Edward"],"tags":["AI governance","Multi-agent systems","Zero-knowledge proofs","Cross-organizational coordination","Governance alignment","Deterministic governance","Authorization boundaries","Auditability"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21209285","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20415909","name":"Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations","source":"datacite","abstract":"Research article: Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations","url":"https://doi.org/10.5281/zenodo.20415909","authors":["Ivchenko, Oleh","Ivchenko, Iryna"],"tags":["AI","machine learning","research"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20415909","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20415910","name":"Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations","source":"datacite","abstract":"Research article: Trusted Federated Learning XAI: Open Source for Privacy-Preserving Explanations","url":"https://doi.org/10.5281/zenodo.20415910","authors":["Ivchenko, Oleh","Ivchenko, Iryna"],"tags":["AI","machine learning","research"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20415910","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22137362","name":"Decentralized Federated Learning with Secure Aggregation and Differential Privacy","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL schemes are vulnerable to privacy attacks, particularly model inversion attacks, which can reveal sensitive information about the underlying data. This paper proposes a novel decentralized federated learning framework that integrates secure aggregation and differential privacy to mitigate these risks. The system utilizes secure aggregation techniques, such as homomorphic encryption, to protect individual model updates during the aggregation process. Simultaneously, differential privacy mechanisms are employed to limit the amount of information leaked about individual clients' data. The decentralized nature of the framework enhances robustness and scalability. This approach significantly strengthens the privacy guarantees of FL while maintaining model accuracy and efficiency. The core claim of this work is that enhancing security and privacy is crucial for the wider adoption of federated learning. The proposed mechanism combines secure aggregation and differential privacy in a decentralized federated learning framework.","url":"https://doi.org/10.5281/zenodo.22137362","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22137362","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22137361","name":"Decentralized Federated Learning with Secure Aggregation and Differential Privacy","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL schemes are vulnerable to privacy attacks, particularly model inversion attacks, which can reveal sensitive information about the underlying data. This paper proposes a novel decentralized federated learning framework that integrates secure aggregation and differential privacy to mitigate these risks. The system utilizes secure aggregation techniques, such as homomorphic encryption, to protect individual model updates during the aggregation process. Simultaneously, differential privacy mechanisms are employed to limit the amount of information leaked about individual clients' data. The decentralized nature of the framework enhances robustness and scalability. This approach significantly strengthens the privacy guarantees of FL while maintaining model accuracy and efficiency. The core claim of this work is that enhancing security and privacy is crucial for the wider adoption of federated learning. The proposed mechanism combines secure aggregation and differential privacy in a decentralized federated learning framework.","url":"https://doi.org/10.5281/zenodo.22137361","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22137361","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20787126","name":"GROUPOID: Groupoid-based aggregation for federated learning on Riemannian manifolds","source":"datacite","abstract":"Pre-alpha research prototype exploring groupoid-based aggregation for federated learning on Riemannian manifolds. Implements transport groupoid morphisms, first cohomology (H^1) for consistency detection, the cellular sheaf Laplacian for spectral analysis, parallel transport (Schild's and pole ladders), and Karcher mean aggregation via geomstats. Erratum / supersession notice: this version (v0.1.0.dev2) carries the mathematical corrections introduced in v0.1.0.dev1 over the earlier v0.1.0.dev0 snapshot (version DOI 10.5281/zenodo.20563975): the sheaf (connection) Laplacian is a verified positive-semidefinite operator with a transport-consistent kernel; persistence diagrams retain the homology-dimension label so H0 and H1 are no longer conflated; and H^1 cohomology raises on incomplete cocycles instead of forming partial holonomy. It additionally corrects a first-step magnitude inflation in the RiemannianAdam optimizer's bias initialization (a component not used by the aggregation results). The v0.1.0.dev0 version DOI must not be cited for results. Cite the concept DOI (10.5281/zenodo.20563974), which always resolves to the latest corrected version, or this version or later.","url":"https://doi.org/10.5281/zenodo.20787126","authors":["Maniches, Santiago"],"tags":["federated learning","Riemannian geometry","groupoid","sheaf theory","cohomology","topological data analysis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20787126","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20719394","name":"GROUPOID: Groupoid-based aggregation for federated learning on Riemannian manifolds","source":"datacite","abstract":"Pre-alpha research prototype exploring groupoid-based aggregation for federated learning on Riemannian manifolds. Implements transport groupoid morphisms, first cohomology (H^1) for consistency detection, the cellular sheaf Laplacian for spectral analysis, parallel transport (Schild's and pole ladders), and Karcher mean aggregation via geomstats. Erratum / supersession notice: this version (v0.1.0.dev1) corrects mathematical bugs present in the earlier v0.1.0.dev0 snapshot (version DOI 10.5281/zenodo.20563975): the sheaf (connection) Laplacian is now a verified positive-semidefinite operator with a transport-consistent kernel; persistence diagrams retain the homology-dimension label so H0 and H1 are no longer conflated; and H^1 cohomology raises on incomplete cocycles instead of forming partial holonomy. The v0.1.0.dev0 version DOI must not be cited for results. Cite the concept DOI (10.5281/zenodo.20563974), which always resolves to the latest corrected version, or this version or later.","url":"https://doi.org/10.5281/zenodo.20719394","authors":["Maniches, Santiago"],"tags":["federated learning","Riemannian geometry","groupoid","sheaf theory","cohomology","topological data analysis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20719394","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22136853","name":"Federated Learning with Differential Privacy for Personalized Model Training","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging raw data. However, traditional FL methods still face significant privacy risks, particularly when dealing with sensitive user information. This work proposes a novel federated learning algorithm that integrates differential privacy (DP) to mitigate these risks. The algorithm, termed Federated Learning with Differential Privacy (FL-DP), ensures that individual user data remains protected while enabling personalized model training. We formulate the training process using the following key equations: Let $s_i$ represent the data sample from user $i$. Let $M_k$ be the model at round $k$. Let $\\epsilon$ and $\\delta$ be the privacy parameters. The privacy loss for a single round of training is given by: $\\Delta_k = E[\\| \\nabla_M L(M_k, s_i) \\|_2 ]$, where $L$ is the loss function and $\\nabla_M$ denotes the gradient. The differentially private update rule for the model is: $M_{k+1} = M_k - \\frac{\\alpha}{\\eta} \\Delta_k + N(0, I)$, where $\\alpha$ is the learning rate, $\\eta$ is the number of steps, and $N(0, I)$ represents Gaussian noise. The overall privacy loss over $K$ rounds is: $\\Delta = \\sum_{k=1}^K \\Delta_k$. The composition theorem for differential privacy guarantees that $\\Delta \\leq \\Delta_{\\epsilon, \\delta}$, where $\\Delta_{\\epsilon, \\delta}$ is the privacy loss bound. This approach allows for the creation of personalized models tailored to individual user data, addressing a key limitation of standard FL. The effectiveness of FL-DP is demonstrated through theoretical analysis and conceptual design. Future work will explore practical implementation challenges and performance optimization. ---","url":"https://doi.org/10.5281/zenodo.22136853","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22136853","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22136854","name":"Federated Learning with Differential Privacy for Personalized Model Training","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging raw data. However, traditional FL methods still face significant privacy risks, particularly when dealing with sensitive user information. This work proposes a novel federated learning algorithm that integrates differential privacy (DP) to mitigate these risks. The algorithm, termed Federated Learning with Differential Privacy (FL-DP), ensures that individual user data remains protected while enabling personalized model training. We formulate the training process using the following key equations: Let $s_i$ represent the data sample from user $i$. Let $M_k$ be the model at round $k$. Let $\\epsilon$ and $\\delta$ be the privacy parameters. The privacy loss for a single round of training is given by: $\\Delta_k = E[\\| \\nabla_M L(M_k, s_i) \\|_2 ]$, where $L$ is the loss function and $\\nabla_M$ denotes the gradient. The differentially private update rule for the model is: $M_{k+1} = M_k - \\frac{\\alpha}{\\eta} \\Delta_k + N(0, I)$, where $\\alpha$ is the learning rate, $\\eta$ is the number of steps, and $N(0, I)$ represents Gaussian noise. The overall privacy loss over $K$ rounds is: $\\Delta = \\sum_{k=1}^K \\Delta_k$. The composition theorem for differential privacy guarantees that $\\Delta \\leq \\Delta_{\\epsilon, \\delta}$, where $\\Delta_{\\epsilon, \\delta}$ is the privacy loss bound. This approach allows for the creation of personalized models tailored to individual user data, addressing a key limitation of standard FL. The effectiveness of FL-DP is demonstrated through theoretical analysis and conceptual design. Future work will explore practical implementation challenges and performance optimization. ---","url":"https://doi.org/10.5281/zenodo.22136854","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22136854","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22136511","name":"Differential Privacy for Federated Learning","source":"datacite","abstract":"This paper explores the application of differential privacy to federated learning, a distributed machine learning paradigm gaining prominence for its potential to leverage decentralized data while preserving privacy. Federated learning enables training of machine learning models across numerous devices or servers holding local data samples, without explicitly exchanging the data themselves. However, this decentralized approach introduces new privacy risks. This work proposes a method to integrate differential privacy into the federated learning process, specifically by adding noise to the model updates exchanged between participants. We demonstrate that this technique effectively limits the influence of any single participant's data on the global model, providing a quantifiable guarantee of privacy. The core contribution lies in the adaptation of differential privacy mechanisms to the unique challenges of federated learning, offering a robust solution for training machine learning models with sensitive data. The paper details the mathematical formulation of the proposed approach, including the privacy loss calculation and the impact of noise addition on model accuracy. We analyze the trade-off between privacy and accuracy, highlighting strategies for optimizing this balance. ---","url":"https://doi.org/10.5281/zenodo.22136511","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22136511","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22136512","name":"Differential Privacy for Federated Learning","source":"datacite","abstract":"This paper explores the application of differential privacy to federated learning, a distributed machine learning paradigm gaining prominence for its potential to leverage decentralized data while preserving privacy. Federated learning enables training of machine learning models across numerous devices or servers holding local data samples, without explicitly exchanging the data themselves. However, this decentralized approach introduces new privacy risks. This work proposes a method to integrate differential privacy into the federated learning process, specifically by adding noise to the model updates exchanged between participants. We demonstrate that this technique effectively limits the influence of any single participant's data on the global model, providing a quantifiable guarantee of privacy. The core contribution lies in the adaptation of differential privacy mechanisms to the unique challenges of federated learning, offering a robust solution for training machine learning models with sensitive data. The paper details the mathematical formulation of the proposed approach, including the privacy loss calculation and the impact of noise addition on model accuracy. We analyze the trade-off between privacy and accuracy, highlighting strategies for optimizing this balance. ---","url":"https://doi.org/10.5281/zenodo.22136512","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22136512","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.48550/arxiv.2406.14429","name":"CollaFuse: Collaborative Diffusion Models","source":"datacite","abstract":"In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffusion models poses numerous challenges, particularly concerning data availability, computational requirements, and privacy. Traditional approaches to address these shortcomings, like federated learning, often impose significant computational burdens on individual clients, especially those with constrained resources. In response to these challenges, we introduce the novel approach CollaFuse for distributed collaborative diffusion models inspired by split learning. Our approach facilitates collaborative training of diffusion models while alleviating client computational burdens during image synthesis. This reduced computational burden is achieved by retaining data and computationally inexpensive processes locally at each client while outsourcing the computationally expensive processes to shared, more efficient server resources. Through experiments on the common datasets CelebA, CIFAR-10, and Animals-with-Attributes2, our approach demonstrates enhanced performance while decreasing information disclosure as it reduces the necessity for sharing raw data. These capabilities hold significant potential across various application areas, including the design of edge computing solutions. Thus, our work advances distributed machine learning by contributing to the evolution of collaborative diffusion models.","url":"https://doi.org/10.48550/arxiv.2406.14429","authors":["Allmendinger, Simeon","Zipperling, Domenique","Struppek, Lukas","Kühl, Niklas"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2406.14429","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.48550/arxiv.2206.02131","name":"Federated Adversarial Training with Transformers","source":"datacite","abstract":"Federated learning (FL) has emerged to enable global model training over distributed clients' data while preserving its privacy. However, the global trained model is vulnerable to the evasion attacks especially, the adversarial examples (AEs), carefully crafted samples to yield false classification. Adversarial training (AT) is found to be the most promising approach against evasion attacks and it is widely studied for convolutional neural network (CNN). Recently, vision transformers have been found to be effective in many computer vision tasks. To the best of the authors' knowledge, there is no work that studied the feasibility of AT in a FL process for vision transformers. This paper investigates such feasibility with different federated model aggregation methods and different vision transformer models with different tokenization and classification head techniques. In order to improve the robust accuracy of the models with the not independent and identically distributed (Non-IID), we propose an extension to FedAvg aggregation method, called FedWAvg. By measuring the similarities between the last layer of the global model and the last layer of the client updates, FedWAvg calculates the weights to aggregate the local models updates. The experiments show that FedWAvg improves the robust accuracy when compared with other state-of-the-art aggregation methods.","url":"https://doi.org/10.48550/arxiv.2206.02131","authors":["Aldahdooh, Ahmed","Hamidouche, Wassim","Déforges, Olivier"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2206.02131","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22136173","name":"Decentralized Federated Learning with Differential Privacy for Edge Computing","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especially when dealing with sensitive data residing on edge devices. This paper proposes a decentralized federated learning framework incorporating differential privacy to mitigate these risks. The core idea is to eliminate the central server and enable collaborative learning directly among edge devices, while simultaneously safeguarding individual data privacy using differential privacy mechanisms. Our approach utilizes a novel decentralized algorithm that leverages local model updates and a privacy-preserving aggregation protocol. We demonstrate the effectiveness of this framework through a theoretical analysis and provide a detailed formulation of the decentralized FL process. The key mathematical formulations related to the algorithm are presented, including the privacy loss budget calculation, local model updates, and the aggregated model update. This work contributes to the development of robust and privacy-preserving FL solutions for edge computing environments.","url":"https://doi.org/10.5281/zenodo.22136173","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22136173","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22136174","name":"Decentralized Federated Learning with Differential Privacy for Edge Computing","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional FL methods often rely on a central server, raising significant privacy concerns, especially when dealing with sensitive data residing on edge devices. This paper proposes a decentralized federated learning framework incorporating differential privacy to mitigate these risks. The core idea is to eliminate the central server and enable collaborative learning directly among edge devices, while simultaneously safeguarding individual data privacy using differential privacy mechanisms. Our approach utilizes a novel decentralized algorithm that leverages local model updates and a privacy-preserving aggregation protocol. We demonstrate the effectiveness of this framework through a theoretical analysis and provide a detailed formulation of the decentralized FL process. The key mathematical formulations related to the algorithm are presented, including the privacy loss budget calculation, local model updates, and the aggregated model update. This work contributes to the development of robust and privacy-preserving FL solutions for edge computing environments.","url":"https://doi.org/10.5281/zenodo.22136174","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22136174","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.48550/arxiv.2608.27191","name":"Personalized Federated Learning for Tensor Regression","source":"datacite","abstract":"The growing availability of tensor-valued data across multiple institutions creates opportunities for collaborative analysis, but also raises challenges related to data privacy, high dimensionality, and client heterogeneity. This paper introduces a personalized federated tensor regression framework that addresses all three simultaneously. Each client's coefficient tensor is decomposed into a globally shared low-Tucker-rank component and a locally sparse deviation, estimated via a two-stage privacy-preserving procedure. We establish finite-sample upper bounds and minimax lower bounds that quantify the privacy-accuracy trade-off, and prove the consistency of the supporting initialization and rank-selection steps. Simulation studies confirm that the federated approach improves estimation and prediction over purely local methods, especially when per-client data are scarce, and an MRI-based ADHD study illustrates its strong performance under real privacy constraints.","url":"https://doi.org/10.48550/arxiv.2608.27191","authors":["Chen, Kejun","Wei, Xianqi","Zhu, Qianqian"],"tags":["Methodology (stat.ME)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.27191","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.48550/arxiv.2608.27108","name":"SecureDrive-FL: Joint Differential Privacy and Gradient-Aware Selective Homomorphic Encryption for Federated Driver Monitoring","source":"datacite","abstract":"Federated Learning (FL) enables privacy-aware distributed training, yet gradient updates remain exploitable: Man-in-the-Middle (MitM) interception exposes updates in transit, while model poisoning corrupts global convergence. We first introduce GASHE (Gradient-Aware Selective Homomorphic Encryption), a novel selective encryption strategy that dynamically identifies and encrypts only the gradient components exceeding a DP-calibrated sensitivity threshold, rather than encrypting all parameters uniformly as in static layer-based or full-parameter CKKS schemes. Building on GASHE, we introduce SecureDrive-FL, a federated driver monitoring framework that couples DP-SGD with GASHE to create the first closed-loop DP+HE privacy pipeline: DP-SGD calibration parameters directly derive the GASHE encryption mask, unifying training-time privacy and communication-time confidentiality. Evaluated on a ten-class distracted driver classification task under non-IID federated splits, SecureDrive-FL matches DP-SGD alone's poisoning resistance (73.6% vs. 74.0% accuracy, 3.9% Attack Success Rate for both) while additionally withstanding MitM interception, where DP-SGD alone collapses to near-random accuracy (78.2% vs. 10.4%), all under only approx. 8--10% additional runtime overhead relative to DP-SGD alone---under DP-SGD noise injection with per-round privacy parameter epsilon_0=4.","url":"https://doi.org/10.48550/arxiv.2608.27108","authors":["Gül, Baran Can","Tunuguntla, Hanuma Siddhartha","Naik, Anjana Arvind","Potekar, Abhishek Vijay","Jazdi, Nasser","Weyrich, Michael"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.27108","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.48550/arxiv.2608.27063","name":"An Accurate and Single-Communication Federated Inference Algorithm","source":"datacite","abstract":"Joint analyses across multiple institutions are increasingly important in biomedical and epidemiological research, particularly for rare diseases where datasets are typical small. However, privacy regulations and institutional policies often prevent the sharing of individual-level patient data. In this paper we present an accurate and single-communication federated inference algorithm. Single-communication federated inference enables statistical analyses through a single exchange of summary statistics between participating centers and a coordinating server, preserving privacy while reducing communication and computational costs compared with iterative federated learning. We extend a recently proposed single-communication federated inference strategy that is based on second-order Taylor expansions by using third-order expansions to better approximate local log-likelihood functions. The proposed method is evaluated through simulation studies based on real data and compared with existing federated inference strategies. The simulation studies assess the performance of the proposed method, with a particular focus on scenarios involving small local sample sizes, where quadratic approximations may fail to capture skewness and other higher-order characteristics of the log-likelihood function. They demonstrate that incorporating higher-order information of the log-likelihood function improves the accuracy while preserving the privacy, communication efficiency, and scalability required for collaborative biomedical and epidemiological research.","url":"https://doi.org/10.48550/arxiv.2608.27063","authors":["Montagnani, Laura","Coolen, Anthony CC","Jonker, Marianne A"],"tags":["Methodology (stat.ME)","Statistics Theory (math.ST)","Computation (stat.CO)","Machine Learning (stat.ML)","FOS: Computer and information sciences","FOS: Mathematics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.27063","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22136076","name":"Decentralized Federated Learning with Byzantine Fault Tolerance","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly sharing the data itself. However, this decentralized nature introduces significant vulnerabilities. Malicious participants, known as Byzantine nodes, can inject biased or corrupted models into the training process, compromising the overall model accuracy and potentially introducing harmful biases. This paper proposes a novel decentralized federated learning framework incorporating Byzantine Fault Tolerance (BFT) mechanisms. The core claim is that a BFT-enabled decentralized FL system provides robustness against malicious actors, guaranteeing model convergence even when some nodes are compromised. We outline the system architecture, detailing the BFT protocol integration, aggregation strategies, and communication protocols. The proposed system leverages a verifiable distributed consensus mechanism, allowing for the detection and mitigation of Byzantine behavior. The theoretical analysis demonstrates the system's resilience to arbitrary Byzantine failures and provides a framework for quantifying the impact of malicious participation. This work represents a significant advancement in securing FL deployments, fostering trust and reliability in collaborative learning environments.","url":"https://doi.org/10.5281/zenodo.22136076","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22136076","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.22136077","name":"Decentralized Federated Learning with Byzantine Fault Tolerance","source":"datacite","abstract":"Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly sharing the data itself. However, this decentralized nature introduces significant vulnerabilities. Malicious participants, known as Byzantine nodes, can inject biased or corrupted models into the training process, compromising the overall model accuracy and potentially introducing harmful biases. This paper proposes a novel decentralized federated learning framework incorporating Byzantine Fault Tolerance (BFT) mechanisms. The core claim is that a BFT-enabled decentralized FL system provides robustness against malicious actors, guaranteeing model convergence even when some nodes are compromised. We outline the system architecture, detailing the BFT protocol integration, aggregation strategies, and communication protocols. The proposed system leverages a verifiable distributed consensus mechanism, allowing for the detection and mitigation of Byzantine behavior. The theoretical analysis demonstrates the system's resilience to arbitrary Byzantine failures and provides a framework for quantifying the impact of malicious participation. This work represents a significant advancement in securing FL deployments, fostering trust and reliability in collaborative learning environments.","url":"https://doi.org/10.5281/zenodo.22136077","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22136077","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20829078","name":"The Future of Artificial Intelligence in Breast Cancer Diagnosis and Prognosis: Trends, Innovations, and Breakthroughs","source":"datacite","abstract":"Breast cancer remains one of the most significant health challenges worldwide, requiring continuous advancements in early detection, diagnosis, and prognosis. With the rapid evolution of artificial intelligence (AI), the landscape of breast cancer management has been transformed, offering unprecedented improvements in accuracy, efficiency, and personalized care. The Future of Artificial Intelligence in Breast Cancer Diagnosis and Prognosis: Trends, Innovations, and Breakthroughs explores the cutting-edge role of AI in revolutionizing breast cancer detection, treatment, and patient outcomes.This book delves into the fundamental principles of AI, machine learning, and deep learning as applied to oncology. It examines the integration of AI with medical imaging techniques such as mammography, ultrasound, and MRI, along with its applications in histopathological analysis, liquid biopsy, and biomarker discovery. Special emphasis is placed on AI-driven prognostic models, radiomics, and precision medicine, highlighting how AI enhances risk stratification, treatment response prediction, and personalized therapy selection.A crucial aspect of this book is its discussion on the ethical, regulatory, and practical challenges of AI adoption in healthcare. Issues such as bias in AI algorithms, data privacy, security concerns, and compliance with global regulatory frameworks are explored in depth. Additionally, the book sheds light on the future trajectory of AI in breast cancer research, including emerging technologies such as federated learning, quantum computing, and AI-driven clinical trials.Written by Dr. Ashok Kumar Dogra, PhD in Biochemistry, this book serves as a comprehensive resource for oncologists, radiologists, medical researchers, data scientists, and healthcare policymakers. By bridging the gap between AI innovation and clinical application, it aims to equip professionals with the knowledge to leverage AI for improved breast cancer care.As we stand at the forefront of a technological revolution in oncology, this book seeks to inspire further advancements, ensuring AI continues to drive transformative breakthroughs in breast cancer diagnosis and prognosis. I hope that this work will contribute to the ongoing efforts to enhance patient outcomes and shape the future of AI-driven healthcare.","url":"https://doi.org/10.5281/zenodo.20829078","authors":["Dogra, Ashok Kumar","Prakash, Archana","Gupta, Meenu"],"tags":["breast cancer","AI in breast cancer"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20829078","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20829079","name":"The Future of Artificial Intelligence in Breast Cancer Diagnosis and Prognosis: Trends, Innovations, and Breakthroughs","source":"datacite","abstract":"Breast cancer remains one of the most significant health challenges worldwide, requiring continuous advancements in early detection, diagnosis, and prognosis. With the rapid evolution of artificial intelligence (AI), the landscape of breast cancer management has been transformed, offering unprecedented improvements in accuracy, efficiency, and personalized care. The Future of Artificial Intelligence in Breast Cancer Diagnosis and Prognosis: Trends, Innovations, and Breakthroughs explores the cutting-edge role of AI in revolutionizing breast cancer detection, treatment, and patient outcomes.This book delves into the fundamental principles of AI, machine learning, and deep learning as applied to oncology. It examines the integration of AI with medical imaging techniques such as mammography, ultrasound, and MRI, along with its applications in histopathological analysis, liquid biopsy, and biomarker discovery. Special emphasis is placed on AI-driven prognostic models, radiomics, and precision medicine, highlighting how AI enhances risk stratification, treatment response prediction, and personalized therapy selection.A crucial aspect of this book is its discussion on the ethical, regulatory, and practical challenges of AI adoption in healthcare. Issues such as bias in AI algorithms, data privacy, security concerns, and compliance with global regulatory frameworks are explored in depth. Additionally, the book sheds light on the future trajectory of AI in breast cancer research, including emerging technologies such as federated learning, quantum computing, and AI-driven clinical trials.Written by Dr. Ashok Kumar Dogra, PhD in Biochemistry, this book serves as a comprehensive resource for oncologists, radiologists, medical researchers, data scientists, and healthcare policymakers. By bridging the gap between AI innovation and clinical application, it aims to equip professionals with the knowledge to leverage AI for improved breast cancer care.As we stand at the forefront of a technological revolution in oncology, this book seeks to inspire further advancements, ensuring AI continues to drive transformative breakthroughs in breast cancer diagnosis and prognosis. I hope that this work will contribute to the ongoing efforts to enhance patient outcomes and shape the future of AI-driven healthcare.","url":"https://doi.org/10.5281/zenodo.20829079","authors":["Dogra, Ashok Kumar","Prakash, Archana","Gupta, Meenu"],"tags":["breast cancer","AI in breast cancer"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20829079","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21431675","name":"AGIS-NG: AI-Powered Counter-UAS and Autonomous Border Surveillance for Nigeria's Insecure Border Regions Radar-Vision Fusion, RF Signal Classification, and Autonomous Patrol Optimisation for the Lake Chad Basin and Northwest Border Corridors","source":"datacite","abstract":"Nigeria faces an intensifying dual threat: Boko Haram/ISWAP insurgents operating armed drones in the Lake Chad Basin and Northwest armed bandits exploiting surveillance-free border corridors spanning more than 1,400 km of Nigeria’s insecure border regions. The Nigerian Air Force has reported a 340% increase in improvised UAS (Unmanned Aerial System) encounters since 2022, yet the nation lacks a systematic counter-UAS (C-UAS) or AIdriven border surveillance capability. This paper presents AGIS-NG (Autonomous Geospatial Intelligence System for Nigeria), an integrated counter-drone and border surveillance framework that fuses Frequency Modulated Continuous Wave (FMCW) radar with computer vision (YOLOv8m) and RF spectrum analysis for multi-sensor drone detection and classification, and combines LSTM-XGBoost ensemble modelling with autonomous UAV patrol optimisation for predictive border incursion detection. AGIS-NG achieves hostile UAS classification macroF1 of 88.7% and multi-sensor fusion AUC of 0.974, with a detection range of 500m maintaining above 91% mAP@0.5. Autonomous AI-optimised patrol achieves 95% border segment coverage with 8 UAV units versus 12 required under fixed patrol routes. Border incursion prediction accuracy is 85.2% at 2-hour and 76.4% at 8-hour horizons. A federated learning architecture using FedAvg [13] connects five Nigerian Army Theatre Command nodes without centralising operational intelligence. AGIS-NG is designed for immediate relevance to NASENI, the Nigerian Army Engineering Corps, and international security assistance programmes from the African Union and ECOWAS.","url":"https://doi.org/10.5281/zenodo.21431675","authors":["Sulaiman, J.","Hussain, M. M. A."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21431675","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21431676","name":"AGIS-NG: AI-Powered Counter-UAS and Autonomous Border Surveillance for Nigeria's Insecure Border Regions Radar-Vision Fusion, RF Signal Classification, and Autonomous Patrol Optimisation for the Lake Chad Basin and Northwest Border Corridors","source":"datacite","abstract":"Nigeria faces an intensifying dual threat: Boko Haram/ISWAP insurgents operating armed drones in the Lake Chad Basin and Northwest armed bandits exploiting surveillance-free border corridors spanning more than 1,400 km of Nigeria’s insecure border regions. The Nigerian Air Force has reported a 340% increase in improvised UAS (Unmanned Aerial System) encounters since 2022, yet the nation lacks a systematic counter-UAS (C-UAS) or AIdriven border surveillance capability. This paper presents AGIS-NG (Autonomous Geospatial Intelligence System for Nigeria), an integrated counter-drone and border surveillance framework that fuses Frequency Modulated Continuous Wave (FMCW) radar with computer vision (YOLOv8m) and RF spectrum analysis for multi-sensor drone detection and classification, and combines LSTM-XGBoost ensemble modelling with autonomous UAV patrol optimisation for predictive border incursion detection. AGIS-NG achieves hostile UAS classification macroF1 of 88.7% and multi-sensor fusion AUC of 0.974, with a detection range of 500m maintaining above 91% mAP@0.5. Autonomous AI-optimised patrol achieves 95% border segment coverage with 8 UAV units versus 12 required under fixed patrol routes. Border incursion prediction accuracy is 85.2% at 2-hour and 76.4% at 8-hour horizons. A federated learning architecture using FedAvg [13] connects five Nigerian Army Theatre Command nodes without centralising operational intelligence. AGIS-NG is designed for immediate relevance to NASENI, the Nigerian Army Engineering Corps, and international security assistance programmes from the African Union and ECOWAS.","url":"https://doi.org/10.5281/zenodo.21431676","authors":["Sulaiman, J.","Hussain, M. M. A."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21431676","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.48550/arxiv.2608.26433","name":"FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation","source":"datacite","abstract":"Data-driven prognostics and health management has emerged as a key enabler for Industry 4.0, yet the development of robust remaining useful life (RUL) estimation models is often limited by the scarcity of run-to-failure data. While federated learning offers a promising paradigm to collaboratively train predictive models without sharing sensor data, research efforts have operated so far in the absence of a common evaluation framework. To address this gap, this paper introduces FedCMAPSS, a benchmark for federated RUL estimation based on the commonly-used NASA C-MAPSS dataset. We define a set of five standardized tasks designed to simulate real-world industrial challenges, ranging from ideal IID settings to extreme statistical heterogeneity, and conduct a systematic evaluation of state-of-the-art federated optimization algorithms across multiple neural architectures. By establishing reproducible baselines and making the source code and data splits publicly available, this work aims to provide a standard foundation for developing and comparing federated predictive maintenance solutions.","url":"https://doi.org/10.48550/arxiv.2608.26433","authors":["Sorrenti, Amelia","Pennisi, Matteo","Spampinato, Concetto","Palazzo, Simone"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.26433","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20467655","name":"Byzantine-Resilient Aggregation Algorithms in Federated Malware Detection for IoT Networks","source":"datacite","abstract":"This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How do different aggregation algorithms (FedAvg, FedProx, FedNova) compare in terms of model accuracy degradation when defending against Byzantine attacks in federated malware detection systems. This systematic review examines the role of federated learning (FL) as a privacy-preserving paradigm for enterprise decision systems, synthesizing evidence from 187 peer-reviewed studies. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How do different aggregation algorithms (FedAvg, FedProx, FedNova) compare in terms of model accuracy degradation when defending against Byzantine attacks in federated malware detection systems across various IoT device topologies? Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20467655","authors":["Assignee Research"],"tags":["different","aggregation","algorithms","FedAvg","FedProx","FedNova","terms","model"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20467655","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.398Z"},{"id":"doi:10.5281/zenodo.20467656","name":"Byzantine-Resilient Aggregation Algorithms in Federated Malware Detection for IoT Networks","source":"datacite","abstract":"This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How do different aggregation algorithms (FedAvg, FedProx, FedNova) compare in terms of model accuracy degradation when defending against Byzantine attacks in federated malware detection systems. This systematic review examines the role of federated learning (FL) as a privacy-preserving paradigm for enterprise decision systems, synthesizing evidence from 187 peer-reviewed studies. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How do different aggregation algorithms (FedAvg, FedProx, FedNova) compare in terms of model accuracy degradation when defending against Byzantine attacks in federated malware detection systems across various IoT device topologies? Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20467656","authors":["Assignee Research"],"tags":["different","aggregation","algorithms","FedAvg","FedProx","FedNova","terms","model"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20467656","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21454411","name":"AeroGuard-EP: An Integrated Stochastic Multi-Agent Protocol for Cross-Platform Information Resilience and Algorithmic Circuit Breaking","source":"datacite","abstract":"The rapid evolution of the digital information ecosystem has transformed Social Media Platforms (SMPs) into critical yet vulnerable nodes of the global infrastructure. Current mitigation strategies are largely reactive, struggling to contain the \"virality logics\" of misinformation before they reach a functional epidemic threshold. This paper proposes AeroGuard-EP, a novel epistemic protocol that integrates Multi-Agent System (MAS) consensus with Stochastic Differential Equation (SDE) stiffness modeling to implement an algorithmic circuit breaker. By decoupling transmission velocity from engagement metrics when a network’s \"stiffness ratio\" exceeds a stability threshold, AeroGuard-EP provides a proactive defense mechanism. Our framework utilizes a reputation-weighted Sparta Alignment duel for truth verification and decentralized federated learning to preserve user privacy. Experimental simulations indicate that the integration of stiffness-based throttling can reduce misinformation diffusion by up to 46% while maintaining sub-200ms verification latency.","url":"https://doi.org/10.5281/zenodo.21454411","authors":["Mr.Diwakara Vasuman, Karthik S Gowda, Rohith R, Vipul Mahesh"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21454411","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21454412","name":"AeroGuard-EP: An Integrated Stochastic Multi-Agent Protocol for Cross-Platform Information Resilience and Algorithmic Circuit Breaking","source":"datacite","abstract":"The rapid evolution of the digital information ecosystem has transformed Social Media Platforms (SMPs) into critical yet vulnerable nodes of the global infrastructure. Current mitigation strategies are largely reactive, struggling to contain the \"virality logics\" of misinformation before they reach a functional epidemic threshold. This paper proposes AeroGuard-EP, a novel epistemic protocol that integrates Multi-Agent System (MAS) consensus with Stochastic Differential Equation (SDE) stiffness modeling to implement an algorithmic circuit breaker. By decoupling transmission velocity from engagement metrics when a network’s \"stiffness ratio\" exceeds a stability threshold, AeroGuard-EP provides a proactive defense mechanism. Our framework utilizes a reputation-weighted Sparta Alignment duel for truth verification and decentralized federated learning to preserve user privacy. Experimental simulations indicate that the integration of stiffness-based throttling can reduce misinformation diffusion by up to 46% while maintaining sub-200ms verification latency.","url":"https://doi.org/10.5281/zenodo.21454412","authors":["Mr.Diwakara Vasuman, Karthik S Gowda, Rohith R, Vipul Mahesh"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21454412","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20459718","name":"Generative Threats in Computer Vision: Dual-Role Gans and Diffusion-Based Defenses for Robust and Trustworthy Models","source":"datacite","abstract":"Abstract - Generative Artificial Intelligence (GenAI) has revolutionized computer vision with cutting-edge image generation, semantic interpretation, and adaptive visual reasoning capabilities, while also bringing new security, robustness, and trustworthiness concerns. In this review, the twenty five recent studies related to generative threat and diffusion-based defense mechanism in computer vision and intelligent network systems were analyzed in a systematic manner based on PRISMA based literature review methodology. The studies reviewed showed that Generative Adversarial Networks (GANs), diffusion models, transformer models, and large language models are dual-use technologies that can be used to create powerful adversarial attacks and powerful defense frameworks. The analysis identified the shifting nature of adversarial attacks—from perturbations at the pixel level to latent-space attacks, multimodal deepfakes, semantic communication attacks and cyber deception by AI tools. At the same time, diffusion-based purification frameworks, federated defense systems, anomaly detection models, and transformer-based verification mechanisms demonstrated high potential to enhance robustness, semantic consistency, privacy preservation, and real-time resilience. The results also showed that synthetic data generation has a notable positive impact on learning performance for low-data scenarios like military object detection and cyber security applications. The issues of computational complexity, attack transferability, scalability and benchmarking inconsistency, however, have yet to be addressed. The review finds that the hybrid generative defense architectures that combine diffusion models, federated learning, explainable AI, graph neural networks, and adaptive semantic verification mechanisms will become increasingly vital for future trustworthy computer vision systems in order to foster secure, resilient, and interpretable next-generation AI systems.","url":"https://doi.org/10.5281/zenodo.20459718","authors":["Mahesh Kumar","Tanvi Rustagi"],"tags":["Generative Artificial Intelligence","Generative Adversarial Networks","Diffusion Models","Computer Vision Security","Adversarial Attacks","Adversarial Machine Learning","Deepfake Detection","Trustworthy AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20459718","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20459719","name":"Generative Threats in Computer Vision: Dual-Role Gans and Diffusion-Based Defenses for Robust and Trustworthy Models","source":"datacite","abstract":"Abstract - Generative Artificial Intelligence (GenAI) has revolutionized computer vision with cutting-edge image generation, semantic interpretation, and adaptive visual reasoning capabilities, while also bringing new security, robustness, and trustworthiness concerns. In this review, the twenty five recent studies related to generative threat and diffusion-based defense mechanism in computer vision and intelligent network systems were analyzed in a systematic manner based on PRISMA based literature review methodology. The studies reviewed showed that Generative Adversarial Networks (GANs), diffusion models, transformer models, and large language models are dual-use technologies that can be used to create powerful adversarial attacks and powerful defense frameworks. The analysis identified the shifting nature of adversarial attacks—from perturbations at the pixel level to latent-space attacks, multimodal deepfakes, semantic communication attacks and cyber deception by AI tools. At the same time, diffusion-based purification frameworks, federated defense systems, anomaly detection models, and transformer-based verification mechanisms demonstrated high potential to enhance robustness, semantic consistency, privacy preservation, and real-time resilience. The results also showed that synthetic data generation has a notable positive impact on learning performance for low-data scenarios like military object detection and cyber security applications. The issues of computational complexity, attack transferability, scalability and benchmarking inconsistency, however, have yet to be addressed. The review finds that the hybrid generative defense architectures that combine diffusion models, federated learning, explainable AI, graph neural networks, and adaptive semantic verification mechanisms will become increasingly vital for future trustworthy computer vision systems in order to foster secure, resilient, and interpretable next-generation AI systems.","url":"https://doi.org/10.5281/zenodo.20459719","authors":["Mahesh Kumar","Tanvi Rustagi"],"tags":["Generative Artificial Intelligence","Generative Adversarial Networks","Diffusion Models","Computer Vision Security","Adversarial Attacks","Adversarial Machine Learning","Deepfake Detection","Trustworthy AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20459719","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20100343","name":"Privacy-Preserving Machine Learning for Healthcare: A Comparative Study of Federated Learning, Split Learning, and Split-Fed Learning","source":"datacite","abstract":"This project presents an empirical comparison of three privacy-preserving machine learning (PPML) techniques — Federated Learning (FL), Split Learning (SL), and Split-Fed Learning (SFL) — for healthcare classification, benchmarked against a centralised baseline. Using the Heart Disease UCI dataset (920 samples, 13 clinical features, binary classification), each technique was implemented in TensorFlow/Keras within a simulated five-client IID federation representing collaborating hospitals that cannot share raw patient data. Results show that all three PPML techniques matched or exceeded the centralised baseline (82.07% accuracy): FL achieved 83.70%, SL reached 82.61%, and SFL achieved the best overall performance at 84.24% accuracy with the lowest test loss (0.3680). SFL is identified as the optimal approach for federated healthcare deployments, offering the best balance of accuracy, loss calibration, and convergence stability. The study addresses the growing tension between the potential of ML in clinical decision support and the regulatory constraints imposed by UK GDPR and the Data Protection Act 2018 on health data sharing. The full implementation, including preprocessing, training, and evaluation code, is provided for reproducibility.","url":"https://doi.org/10.5281/zenodo.20100343","authors":["Sathar, Alif"],"tags":["Privacy-Preserving Machine Learning","Federated Learning","Split Learning","Split-Fed Learning","Healthcare AI","Heart Disease Prediction","UK GDPR","Distributed Machine Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20100343","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20100344","name":"Privacy-Preserving Machine Learning for Healthcare: A Comparative Study of Federated Learning, Split Learning, and Split-Fed Learning","source":"datacite","abstract":"This project presents an empirical comparison of three privacy-preserving machine learning (PPML) techniques — Federated Learning (FL), Split Learning (SL), and Split-Fed Learning (SFL) — for healthcare classification, benchmarked against a centralised baseline. Using the Heart Disease UCI dataset (920 samples, 13 clinical features, binary classification), each technique was implemented in TensorFlow/Keras within a simulated five-client IID federation representing collaborating hospitals that cannot share raw patient data. Results show that all three PPML techniques matched or exceeded the centralised baseline (82.07% accuracy): FL achieved 83.70%, SL reached 82.61%, and SFL achieved the best overall performance at 84.24% accuracy with the lowest test loss (0.3680). SFL is identified as the optimal approach for federated healthcare deployments, offering the best balance of accuracy, loss calibration, and convergence stability. The study addresses the growing tension between the potential of ML in clinical decision support and the regulatory constraints imposed by UK GDPR and the Data Protection Act 2018 on health data sharing. The full implementation, including preprocessing, training, and evaluation code, is provided for reproducibility.","url":"https://doi.org/10.5281/zenodo.20100344","authors":["Sathar, Alif"],"tags":["Privacy-Preserving Machine Learning","Federated Learning","Split Learning","Split-Fed Learning","Healthcare AI","Heart Disease Prediction","UK GDPR","Distributed Machine Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20100344","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21431571","name":"HYDROCAST-NG: Real-Tıme Flood Mappıng and Dısplaced Populatıon Estımatıon Usıng Sentınel-1 SAR Imagery and Deep Learnıng for Nıgerıa: A Semantic Segmentation and Spatio-Temporal Forecasting Framework for Disaster Response and Humanitarian Planning","source":"datacite","abstract":"Nigeria experiences some of the most severe recurrent flooding in sub-Saharan Africa. The 2022 floods alone displaced 1.4 million people, destroyed 82,053 homes, and caused an estimated USD 4.2 billion in economic damage across 34 of 36 states. Existing early warning and disaster mapping systems rely on sparse rain gauge networks and manual field surveys that are too slow and geographically limited to support effective humanitarian response. This paper introduces HYDROCAST-NG (Hydrological Remote-sensing and Operational Disaster Coordination Analysis System for Nigeria), a multi-component AI framework that fuses Sentinel-1 Synthetic Aperture Radar (SAR) imagery with Sentinel-2 optical data, ERA5 meteorological reanalysis, and SRTM digital elevation models to deliver real-time flood mapping and 3-to-7-day displacement volume predictions. The framework deploys a modified U-Net with ResNet-50 encoder for pixel-level flood segmentation, a stacked ensemble (LSTM + XGBoost + Random Forest) for LGA-level displacement forecasting, and Google Earth Engine for automated near-real-time data ingestion. HYDROCAST-NG achieves a flood segmentation macro-F1 of 88.4% and mean IoU of 0.774 on held-out Nigerian flood events, and displacement prediction R2 of 0.887 with a mean 5.8-day early warning lead time. A federated learning layer using FedAvg enables privacy-preserving model updates across NEMA, NIHSA, and six geopolitical zone nodes. Validated against six historical Nigerian flood events (2012-2022), HYDROCAST-NG consistently outperforms NDWI thresholding, random forest baselines, and persistence models, providing actionable flood intelligence for NEMA, state emergency management agencies, UNHCR, and World Bank Nigeria DRM programmes.","url":"https://doi.org/10.5281/zenodo.21431571","authors":["Sulaiman, J.","Hussain, M. M. A."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21431571","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21431572","name":"HYDROCAST-NG: Real-Tıme Flood Mappıng and Dısplaced Populatıon Estımatıon Usıng Sentınel-1 SAR Imagery and Deep Learnıng for Nıgerıa: A Semantic Segmentation and Spatio-Temporal Forecasting Framework for Disaster Response and Humanitarian Planning","source":"datacite","abstract":"Nigeria experiences some of the most severe recurrent flooding in sub-Saharan Africa. The 2022 floods alone displaced 1.4 million people, destroyed 82,053 homes, and caused an estimated USD 4.2 billion in economic damage across 34 of 36 states. Existing early warning and disaster mapping systems rely on sparse rain gauge networks and manual field surveys that are too slow and geographically limited to support effective humanitarian response. This paper introduces HYDROCAST-NG (Hydrological Remote-sensing and Operational Disaster Coordination Analysis System for Nigeria), a multi-component AI framework that fuses Sentinel-1 Synthetic Aperture Radar (SAR) imagery with Sentinel-2 optical data, ERA5 meteorological reanalysis, and SRTM digital elevation models to deliver real-time flood mapping and 3-to-7-day displacement volume predictions. The framework deploys a modified U-Net with ResNet-50 encoder for pixel-level flood segmentation, a stacked ensemble (LSTM + XGBoost + Random Forest) for LGA-level displacement forecasting, and Google Earth Engine for automated near-real-time data ingestion. HYDROCAST-NG achieves a flood segmentation macro-F1 of 88.4% and mean IoU of 0.774 on held-out Nigerian flood events, and displacement prediction R2 of 0.887 with a mean 5.8-day early warning lead time. A federated learning layer using FedAvg enables privacy-preserving model updates across NEMA, NIHSA, and six geopolitical zone nodes. Validated against six historical Nigerian flood events (2012-2022), HYDROCAST-NG consistently outperforms NDWI thresholding, random forest baselines, and persistence models, providing actionable flood intelligence for NEMA, state emergency management agencies, UNHCR, and World Bank Nigeria DRM programmes.","url":"https://doi.org/10.5281/zenodo.21431572","authors":["Sulaiman, J.","Hussain, M. M. A."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21431572","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20467665","name":"Federated vs Centralized Malware Detection Robustness Under Adversarial IoT Attacks","source":"datacite","abstract":"This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the robustness of federated malware detection models compare to centralized models when tested against adversarial attacks on edge IoT devices under varying network conditions. In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.0/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the robustness of federated malware detection models compare to centralized models when tested against adversarial attacks on edge IoT devices under varying network conditions? Autonomous literature synthesis. Automated review score: 8.0/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20467665","authors":["Assignee Research"],"tags":["robustness","federated","malware","detection","models","centralized","tested","against"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20467665","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20467666","name":"Federated vs Centralized Malware Detection Robustness Under Adversarial IoT Attacks","source":"datacite","abstract":"This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does the robustness of federated malware detection models compare to centralized models when tested against adversarial attacks on edge IoT devices under varying network conditions. In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.0/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the robustness of federated malware detection models compare to centralized models when tested against adversarial attacks on edge IoT devices under varying network conditions? Autonomous literature synthesis. Automated review score: 8.0/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20467666","authors":["Assignee Research"],"tags":["robustness","federated","malware","detection","models","centralized","tested","against"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20467666","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20280460","name":"Scalable Real-Time Android Malware Detection Using Adaptive Deep Learning and Ensemble Techniques","source":"datacite","abstract":"Android malware is constantly evolving, and operational constraints, and implementing federated learning protocols to ensure that model updates remain localized to preserve user data integrity while constantly improving detection capabilities against emerging polymorphic threats. This paper investigates a dual-modal feature extraction approach using convolutional neural networks and frequency domain analysis for Android malware detection, which converts Android application packages to grayscale fingerprint images generated from DEX bytecode segments, and extracts both spatial features through convolutional neural networks and frequency domain characteristics through Fourier. They are combined with a recursive feature fusion mechanism with attention-based weighting and classified by fully connected neural networks. This system shows good detection performance over various benchmark datasets but has some limitations such as high computational complexity, large training data requirements, and less suitable for real-time deployment. This paper presents an improved malware detection framework based on adaptive feature selection, lightweight deep learning models, and ensemble learning techniques. The proposed system is expected to increase scalability, decrease computational overheads, improve robustness against adversarial attacks while maintaining high accuracy of the detections as well as integrating distributed detection mechanisms along with edge-based mechanism to enable real-time identification of mobile malicious applications (malware) within large-scale Android environments in current cybersecurity systems.","url":"https://doi.org/10.5281/zenodo.20280460","authors":["S.  Gnanadeep Srinivas","Dr.   A.  Ganesh"],"tags":["Android Malware Detection","Convolutional Neural Networks (CNN)","Frequency Domain Analysis","Feature Fusion","Adaptive Feature Selection","Ensemble Learning","Deep Learning","Cybersecurity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20280460","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20280461","name":"Scalable Real-Time Android Malware Detection Using Adaptive Deep Learning and Ensemble Techniques","source":"datacite","abstract":"Android malware is constantly evolving, and operational constraints, and implementing federated learning protocols to ensure that model updates remain localized to preserve user data integrity while constantly improving detection capabilities against emerging polymorphic threats. This paper investigates a dual-modal feature extraction approach using convolutional neural networks and frequency domain analysis for Android malware detection, which converts Android application packages to grayscale fingerprint images generated from DEX bytecode segments, and extracts both spatial features through convolutional neural networks and frequency domain characteristics through Fourier. They are combined with a recursive feature fusion mechanism with attention-based weighting and classified by fully connected neural networks. This system shows good detection performance over various benchmark datasets but has some limitations such as high computational complexity, large training data requirements, and less suitable for real-time deployment. This paper presents an improved malware detection framework based on adaptive feature selection, lightweight deep learning models, and ensemble learning techniques. The proposed system is expected to increase scalability, decrease computational overheads, improve robustness against adversarial attacks while maintaining high accuracy of the detections as well as integrating distributed detection mechanisms along with edge-based mechanism to enable real-time identification of mobile malicious applications (malware) within large-scale Android environments in current cybersecurity systems.","url":"https://doi.org/10.5281/zenodo.20280461","authors":["S.  Gnanadeep Srinivas","Dr.   A.  Ganesh"],"tags":["Android Malware Detection","Convolutional Neural Networks (CNN)","Frequency Domain Analysis","Feature Fusion","Adaptive Feature Selection","Ensemble Learning","Deep Learning","Cybersecurity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20280461","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.20484878","name":"Adaptive Sparsification and FedAvg for Low-Latency LLM Inference in Federated Edge Networks","source":"datacite","abstract":"This report synthesises findings from 10 peer-reviewed papers addressing the following research question: Can adaptive sparsification techniques combined with FedAvg improve inference latency and throughput for large language models in over-the-air federated learning scenarios without degrading alignment. In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. 10 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.8/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: Can adaptive sparsification techniques combined with FedAvg improve inference latency and throughput for large language models in over-the-air federated learning scenarios without degrading alignment scores? Autonomous literature synthesis. Automated review score: 7.8/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20484878","authors":["Assignee Research"],"tags":["adaptive","sparsification","techniques","combined","FedAvg","improve","inference","latency"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20484878","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20484879","name":"Adaptive Sparsification and FedAvg for Low-Latency LLM Inference in Federated Edge Networks","source":"datacite","abstract":"This report synthesises findings from 10 peer-reviewed papers addressing the following research question: Can adaptive sparsification techniques combined with FedAvg improve inference latency and throughput for large language models in over-the-air federated learning scenarios without degrading alignment. In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. 10 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.8/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: Can adaptive sparsification techniques combined with FedAvg improve inference latency and throughput for large language models in over-the-air federated learning scenarios without degrading alignment scores? Autonomous literature synthesis. Automated review score: 7.8/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20484879","authors":["Assignee Research"],"tags":["adaptive","sparsification","techniques","combined","FedAvg","improve","inference","latency"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20484879","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.19724545","name":"Secure AI Agent-Driven Conversational Support in Healthcare Integrating Cybersecurity from Diagnostics to Patient Coaching","source":"datacite","abstract":"Industrial American styles of capitalism have reified AI as an industry of speculative economy, thereby accelerating the agency with which these agents are adopted into systems of care and further deferred responsibility for patient engagement, clinical diagnostics, and personalized coaching. But that transformation carries significant cyber risks, potentially compromising the integrity and privacy of patient data as well system reliability. Here we propose a broad framework for secure AI agent-aided conversational support through the health care continuum, from intelligent diagnostics to continuous patient coaching. Here we propose a detailed security architecture utilizing end-to-end encryption, federated learning, role-based access control and real-time anomaly detection to protect conversational AI pipelines. This solidly honors upholding regulatory integrity like GDPR and HIPAA frameworks with the seamless patient experience continuity powered by intelligence. By assessing unique threat vectors tailored towards healthcare conversational agents (e.g. adversarial prompt injection data poisoning and the model inversion attack), we detail a concrete strategy for how these active cybersecurity approaches can be incorporated into the system with no adverse impact on diagnostic precision or user experience. Our method yields strong threat mitigation and low latency overhead, importantly instilling trustworthiness of an AI-enabled patient support as experimentally validated. This work proposed a scalable, interoperable solution to safeguard digital health ecosystems against emerging AI capabilities and addresses the critical relationship between AI advances and cybersecurity in healthcare.","url":"https://doi.org/10.5281/zenodo.19724545","authors":["pulipati, karthik","Goshikonda, Nagaraju"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.19724545","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.19724546","name":"Secure AI Agent-Driven Conversational Support in Healthcare Integrating Cybersecurity from Diagnostics to Patient Coaching","source":"datacite","abstract":"Industrial American styles of capitalism have reified AI as an industry of speculative economy, thereby accelerating the agency with which these agents are adopted into systems of care and further deferred responsibility for patient engagement, clinical diagnostics, and personalized coaching. But that transformation carries significant cyber risks, potentially compromising the integrity and privacy of patient data as well system reliability. Here we propose a broad framework for secure AI agent-aided conversational support through the health care continuum, from intelligent diagnostics to continuous patient coaching. Here we propose a detailed security architecture utilizing end-to-end encryption, federated learning, role-based access control and real-time anomaly detection to protect conversational AI pipelines. This solidly honors upholding regulatory integrity like GDPR and HIPAA frameworks with the seamless patient experience continuity powered by intelligence. By assessing unique threat vectors tailored towards healthcare conversational agents (e.g. adversarial prompt injection data poisoning and the model inversion attack), we detail a concrete strategy for how these active cybersecurity approaches can be incorporated into the system with no adverse impact on diagnostic precision or user experience. Our method yields strong threat mitigation and low latency overhead, importantly instilling trustworthiness of an AI-enabled patient support as experimentally validated. This work proposed a scalable, interoperable solution to safeguard digital health ecosystems against emerging AI capabilities and addresses the critical relationship between AI advances and cybersecurity in healthcare.","url":"https://doi.org/10.5281/zenodo.19724546","authors":["pulipati, karthik","Goshikonda, Nagaraju"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.19724546","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21607710","name":"AI-Driven Payment Personalization and Smart Payment Assistants: Reshaping the Digital Payment Landscape","source":"datacite","abstract":"AI-driven payment personalization and smart payment assistants represent a transformative advancement in financial technology, merging sophisticated machine learning models with traditional banking infrastructure. These intelligent systems optimize transaction processing through contextual awareness, adapting to individual user behaviors while maintaining robust security protocols. From hyper-personalized recommendation engines to conversational interfaces, these technologies create seamless payment experiences by predicting user needs, preventing fraud, and suggesting optimal payment methods. The architecture combines transactional, behavioral, contextual, and financial profile data through multi-layered processing pipelines, while privacy-preserving techniques like federated learning and differential privacy protect sensitive information. Integration with legacy payment infrastructure poses challenges due to architectural mismatches, yet adapter layers successfully bridge technological generations. The future points toward cross-modal intelligence incorporating visual, voice, biometric, and IoT data, potentially eliminating explicit checkout processes in favor of ambient commerce experiences.","url":"https://doi.org/10.5281/zenodo.21607710","authors":["Das, Priya"],"tags":["Authentication; Encryption; Microservices; Personalization; Transaction"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21607710","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.5281/zenodo.21607711","name":"AI-Driven Payment Personalization and Smart Payment Assistants: Reshaping the Digital Payment Landscape","source":"datacite","abstract":"AI-driven payment personalization and smart payment assistants represent a transformative advancement in financial technology, merging sophisticated machine learning models with traditional banking infrastructure. These intelligent systems optimize transaction processing through contextual awareness, adapting to individual user behaviors while maintaining robust security protocols. From hyper-personalized recommendation engines to conversational interfaces, these technologies create seamless payment experiences by predicting user needs, preventing fraud, and suggesting optimal payment methods. The architecture combines transactional, behavioral, contextual, and financial profile data through multi-layered processing pipelines, while privacy-preserving techniques like federated learning and differential privacy protect sensitive information. Integration with legacy payment infrastructure poses challenges due to architectural mismatches, yet adapter layers successfully bridge technological generations. The future points toward cross-modal intelligence incorporating visual, voice, biometric, and IoT data, potentially eliminating explicit checkout processes in favor of ambient commerce experiences.","url":"https://doi.org/10.5281/zenodo.21607711","authors":["Das, Priya"],"tags":["Authentication; Encryption; Microservices; Personalization; Transaction"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21607711","addedAt":"2026-08-31T06:41:25.477Z","updatedAt":"2026-08-31T06:41:25.477Z"},{"id":"doi:10.21203/rs.3.rs-3525508/v1","name":"Privacy-Friendly IoT: Does federated learning guarantee privacy?","source":"crossref","abstract":"Abstract Federated learning is an emerging machine learning paradigm where multiple clients train models locally and formulate a global model based on the local model updates. Privacy is one of its essential properties. To determine if federated learning guarantees privacy we study security threats that jeopardize privacy. We also look at these security threats in terms of federated learning applications in the internet of things. Two of these applications include data sharing and giving feedback system. It seems that in the IoT network, some security threats such as deep leakage from gradients and inverting gradients can be eliminated using methods based on differential privacy, but others, such as neural backdoor, are still effective and there is no guarantee of privacy.","url":"https://doi.org/10.21203/rs.3.rs-3525508/v1","authors":["Majid Abdollahi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-17T20:33:58Z","doi":"10.21203/rs.3.rs-3525508/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.24321/3051.4266.202507","name":"A Comprehensive Review of Federated Learning Applications in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.24321/3051.4266.202507","authors":["Nisha Arora"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-16T11:25:58Z","doi":"10.24321/3051.4266.202507","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1002/itl2.389/v1/review2","name":"Review for \"Compensation Layer‐added Federated Learning Receiver: Design and Implementation\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.389/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-18T09:02:57Z","doi":"10.1002/itl2.389/v1/review2","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-2484033/v1","name":"Privatized Graph Federated Learning","source":"crossref","abstract":"Abstract Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-master multi-client structure. It can also be subject to privacy attacks targeting personal information on the communication links. In this work, we introduce graph federated learning (GFL), which consists of multiple federated units connected by a graph. We then show how graph homomorphic perturbations can be used to ensure the algorithm is di erentially private on the server level. While on the client level, we show that improvement in the di erentially private federated learning algorithm can be attained through the addition of random noise to the updates, as opposed to the models. We conduct both convergence and privacy theoretical analyses and illustrate performance by means of computer simulations.","url":"https://doi.org/10.21203/rs.3.rs-2484033/v1","authors":["Elsa Rizk","Stefan Vlaski","Ali H. Sayed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-31T03:48:46Z","doi":"10.21203/rs.3.rs-2484033/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-3056513/v2","name":"WITHDRAWN: Performance Analysis of Personalized Federated Learning Algorithms for Image Classification","source":"crossref","abstract":"Abstract The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.","url":"https://doi.org/10.21203/rs.3.rs-3056513/v2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-15T08:48:14Z","doi":"10.21203/rs.3.rs-3056513/v2","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1002/itl2.389/v2/review1","name":"Review for \"Compensation Layer‐added Federated Learning Receiver: Design and Implementation\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.389/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-18T09:02:57Z","doi":"10.1002/itl2.389/v2/review1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1002/itl2.389/v1/review1","name":"Review for \"Compensation Layer‐added Federated Learning Receiver: Design and Implementation\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.389/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-18T09:02:57Z","doi":"10.1002/itl2.389/v1/review1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1002/itl2.389/v2/review2","name":"Review for \"Compensation Layer‐added Federated Learning Receiver: Design and Implementation\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.389/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-18T09:02:57Z","doi":"10.1002/itl2.389/v2/review2","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-1764898/v1","name":"Federated Learning aided Breast Cancer Detection with Intelligent Heuristic-based Deep Learning Framework","source":"crossref","abstract":"Abstract Breast cancer is the second largest cause of female cancer death and one of the most hazardous diseases that leads to a higher mortality rate. Breast cancer is initialized with the malignant stage, where the abnormal growth of cancerous lumps is initiated from the breast cells. Periodic clinical checks and self-tests assist in early identification and thus progress the survival rates considerably. One of the eminent medical approaches is breast cancer recognition, which offers scientists and researchers huge complications. Breast cancer detection at an early stage permits the patients to receive suitable treatment, which increases the chances of survival. Thus, this paper utilizes a new form of artificial intelligence training called Federated Learning (FL), especially for breast cancer detection, the most eminent technique in the last few years. FL permits individual hospitals to benefit from the rich datasets of multiple non-affiliated hospitals without centralizing the data in one place. Hence, FL utilizes numerous collaborators for building a strong deep-learning model using a large dataset. In this paper, a hybridization of this type of training with a meta-heuristic and deep learning is aimed to be proposed for breast cancer diagnosis. This model encloses diverse steps that include (a) image collection, (b) feature extraction, and (c) classification phase. Initially, the mammogram images related to breast cancer are collected with the concept of FL from the affected individuals. The federated learning helps in reducing the processing time and ensures better performance of the proposed model. The obtained images are considered for the feature extraction phase. The Densenet architecture is used to extract the features used in the classification phase with the help of Enhanced Recurrent Neural Networks (E-RNN) for detecting breast cancer. Here, the performance is enhanced by tuning the certain parameter in the RNN network using a hybrid optimization algorithm called Hybrid Dragon-Rider Optimization (HDRO) with Dragonfly Algorithm (DA) and Red deer algorithm (RDA) to achieve accurate classification results. The experimental results demonstrate the effectiveness of the suggested breast cancer diagnosis model compared with conventional approaches using diverse quantitative measures.","url":"https://doi.org/10.21203/rs.3.rs-1764898/v1","authors":["atul b kathole"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-16T18:54:15Z","doi":"10.21203/rs.3.rs-1764898/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-2064460/v1","name":"Federated learning for 5G-enabled infrastructure inspection with UAVs","source":"crossref","abstract":"Abstract Electricity infrastructures include assets that require frequent maintenance, as they are exposed into heavy use, in order to produce energy that satisfies customer demands. Such maintenance is currently performed by specialized personnel that is scaffolding to spot damages or malfunctioning equipment. Scaffolding is time-consuming and incurs accident risks. To tackle this challenges, grid operators are gradually using Unmanned Aerial Vehicles (UAVs). UAV trajectories are observed by a centralized operation center engineers for identifying electrical assets. Moreover, asset identification can be further automated through the use of Artificial Intelligence (AI) models. However, centralized training of AI models with UAV images may cause inspection delays when the network is overloaded and requires Cloud environments with enough processing power for model training on the operation center. This imposes privacy concerns as sensitive data is stored and processed externally from the infrastructure facility. This article proposes a federated learning method for UAV-based inspection that leverages a Multi-access Edge Computing platform installed in edge nodes to train UAV data and improve the overall inspection autonomy. The method is applied for the inspection of the Public Power Corporation's Innovation Hub. Experiments are performed with the proposed method as well as with a centralized AI inspection method and demonstrate the federated learning benefits in reliability, AI model processing time and privacy conservation.","url":"https://doi.org/10.21203/rs.3.rs-2064460/v1","authors":["Alexios Lekidis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-19T19:44:07Z","doi":"10.21203/rs.3.rs-2064460/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-1900743/v1","name":"A Survey on Federated Learning PoisoningAttacks and Defenses","source":"crossref","abstract":"Abstract As one kind of distributed machine learning technique, federated learning enables multiple clients to build a model across decentralized datacollaboratively without explicitly aggregating the data. Due to its abilityto break data silos, federated learning has received increasing attentionin many fields, including finance, healthcare, and education. However,the invisibility of clients’ training data and the local training process result in some security issues. Recently, many works have beenproposed to research the security attacks and defenses in federatedlearning, but there has been no special survey on poisoning attacks onfederated learning and the corresponding defenses. In this paper, weinvestigate the most advanced schemes on federated learning poisoningattacks and defenses and point out the future directions in these areas.","url":"https://doi.org/10.21203/rs.3.rs-1900743/v1","authors":["Junchuan Liang","Rong Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-08-05T18:36:58Z","doi":"10.21203/rs.3.rs-1900743/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-3217496/v1","name":"Federated Learning for Collaborative Network Security in Decentralized Environments","source":"crossref","abstract":"Abstract In decentralized network environments, collaborative efforts are crucial to bol- stering network security against ever-evolving threats from malicious actors. Federated Learning has emerged as a promising solution, enabling multiple nodes to collectively train machine learning models while preserving data privacy. This research proposes SentinelNet, a novel Federated Learning framework specif- ically designed for collaborative network security. The framework emphasizes secure threat intelligence sharing, privacy-preserving techniques, and adaptive learning mechanisms. Through comprehensive evaluations and real-world case studies, SentinelNet demonstrates its efficacy in enhancing network security while maintaining data confidentiality. The research highlights the significance of col- laborative approaches and advocates the adoption of Federated Learning to fortify decentralized network ecosystems.","url":"https://doi.org/10.21203/rs.3.rs-3217496/v1","authors":["Bheema Shanker Neyigapula"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-11T00:18:13Z","doi":"10.21203/rs.3.rs-3217496/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-2072660/v1","name":"Towards an Efficient, Privacy-aware Federated Learning Scheme","source":"crossref","abstract":"Abstract With the rapid evolution of Internet-of-Things (IoT) devices and the emergence of Autonomous Vehicles (AVs), machine learning processes pose a growing privacy issue. Federated learning (FL) and current cryptography can mitigate this problem; however, these solutions might not be efficient enough during the decades-long lifespans of such gadgets. In this paper, a generalization of FL schemes, incorporating sharing a part of raw data, is presented with a proof-of-concept experiment. Besides anonymization, the exchanged data portion can also directly support real-time decision-making. In contrast with cryptographical approaches, the proposed FL scheme can guarantee a certain level of privacy during the whole lifetime of IoT devices or AVs.","url":"https://doi.org/10.21203/rs.3.rs-2072660/v1","authors":["Levente Alekszejenkó","Tadeusz Dobrowiecki"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-10-11T13:42:16Z","doi":"10.21203/rs.3.rs-2072660/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-1808183/v1","name":"Provision for Energy: Federated Learning in Edge Systems","source":"crossref","abstract":"Abstract An energy-efficient transmission and computation resource allocation problem for federated learning (FL) on wireless communication networks is investigated. Based on the considered model, each user exploits limited local computing resources to train a local FL model with its collected data. The local FL model is then transmitted to a base station (BS), which aggregates the local FL model and broadcasts it back to all users. Based on the learning accuracy level, computation and communication latency are determined by the exchange of learning models between users and BS. During the FL process, both the local computation energy and the transmission energy must be considered. Due to wireless users’ limited energy consumption, the communication problem is formulated as an optimization problem whose objective is to minimize the overall energy consumption of the system with a latency limitation. To solve this problem, we resort to an iterative algorithm with a solution of bandwidth, power, computational and other factors. Numerical results show that the proposed algorithms can reduce energy consumption compared to the conventional FL method.","url":"https://doi.org/10.21203/rs.3.rs-1808183/v1","authors":["Mingyue Liu","Syazwina Alias"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-08-16T12:36:15Z","doi":"10.21203/rs.3.rs-1808183/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.2139/ssrn.6498116","name":"Federated Learning Applications, Challenges &amp; Future Directions – A review","source":"crossref","abstract":"Federated learning (FL) is a new technology that has been a hot research topic. It enables the training of an algorithm across multiple decentralized edge devices or servers holding local data samples without exchanging them. Federated Learning embodies the principles of focused minimization and data collection, costs and can reducing privacy risks, and centralized machine learning approaches. Motivated by the growth in Federated Learning research, this thesis considers advances and presents many collections of challenges and open problems. This monograph describes the defining characteristics and challenges of the Federated Learning setting, highlights important practical considerations, constraints, and then enumerates a range of valuable research directions. The goals of this work are to highlight research problems that are of significant theoretical and practical interest, and to encourage research on problems that could have significant real-world impact. Finally, the paper highlights the limitations present in recent works and presents some future directions for this technology.","url":"https://doi.org/10.2139/ssrn.6498116","authors":["A. Shubha","A. Kanagaraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-30T22:39:52Z","doi":"10.2139/ssrn.6498116","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.21203/rs.3.rs-2473252/v1","name":"Federated learning for feature-fusion based requirement classification","source":"crossref","abstract":"Abstract With the increasing complexity of products, the utilization of automated requirements classification in the process of requirement engineering is of positive significance to improve the efficiency of product development. There have been many experiments on the requirements classification based on artificial intelligence. However, many studies only use a single feature in semantic features or statistical features to classify requirements and put forward the problem of small-scale requirement datasets caused by the confidentiality of requirement data, which limits the application research of requirement classification in a large number of fields. Therefore, we propose a requirement classification model that integrates semantic and statistical features, and design a federated learning framework based on knowledge distillation without centralized servers in a ring architecture. In this paper, we extract a large amount of requirement data from a large number of requirement documents, and conduct experiments in the centralized learning environment and the federated learning environment respectively. The final experimental results show that our method can maintain the confidentiality of the requirement data and improve the communication efficiency in the process of model training with a slight decrease in the effect of requirement classification compared with centralized learning.","url":"https://doi.org/10.21203/rs.3.rs-2473252/v1","authors":["Ruiwen wang","Jihong Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-20T08:46:49Z","doi":"10.21203/rs.3.rs-2473252/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-2663549/v1","name":"Privacy-Preserving Federated Learning Framework using Permissioned Blockchain","source":"crossref","abstract":"Abstract Data is readily available with the growing number of smart and IoT devices. Industries of different sectors follow technological advancement to be benefited from data sharing. However, application-specific data is available in small chunks and distributed across demographics. Additionally, sharing data online brings serious concerns and poses various security and privacy threats. To address these issues, federated learning (FL), a secure and collaborative learning paradigm, would be suitable, which brings the machine learning model to the data owners. Unfortunately, FL is prone to poisoning and inference attacks in presence of malicious users and curious servers. This work proposes a permissioned blockchain based federated learning framework, called PrivateFL (Privacy-Preserving Federated Learning Framework). PrivateFL replaces the central server with a Hyperledger Fabric network, to prevent inference attacks. Further, we propose VPSA (Vertically Partitioned Secure Aggregation) tailored to PrivateFL framework, which performs robust and secure aggregation. PrivateFL facilitates multi-tenancy for learning different machine learning models. Theoretical analysis proves that the system is resistant against inference attacks, even if n -1 peers are compromised. A secure prediction mechanism is also proposed to securely query a global model and protecting its intellectual property rights. Experimental evaluation shows that PrivateFL performs better than the traditional (centralized) learning systems and converges faster, while capable enough to detect malicious updates.","url":"https://doi.org/10.21203/rs.3.rs-2663549/v1","authors":["Harsh Kasyap","Somanath Tripathy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-10T04:31:07Z","doi":"10.21203/rs.3.rs-2663549/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-3246886/v1","name":"Edge Assignment in Edge Federated Learning","source":"crossref","abstract":"Abstract Federated Learning (FL) is a recent Machine Learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Because FL depends on a central server for repeated aggregation of local training models, this server is prone to become a performance bottleneck. Therefore, one can combine FL with Edge Computing: introduce a layer of edge servers to each serve as a regional aggregator to offload the main server. The scalability is thus improved, however at the cost of learning accuracy. We show that this cost can be alleviated with a proper choice of edge server assignment: which edge servers should aggregate the training models from which local machines. Specifically, we propose an assignment solution which is especially useful for the case of non-IID training data (well-known to hinder today's FL performance). Our findings are substantiated with an evaluation study using real-world datasets.","url":"https://doi.org/10.21203/rs.3.rs-3246886/v1","authors":["Thuy Do","Duc A. Tran","Anh Vo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-23T15:48:15Z","doi":"10.21203/rs.3.rs-3246886/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-2910523/v2","name":"WITHDRAWN: Depression clinical detection model based on social media: a federated deep learning approach","source":"crossref","abstract":"Abstract The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.","url":"https://doi.org/10.21203/rs.3.rs-2910523/v2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-05-25T11:59:55Z","doi":"10.21203/rs.3.rs-2910523/v2","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1007/978-3-030-70604-3_2","name":"A Review of Privacy-Preserving Federated Learning for the Internet-of-Things","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-70604-3_2","authors":["Christopher Briggs","Zhong Fan","Peter Andras"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-06-10T23:42:05Z","doi":"10.1007/978-3-030-70604-3_2","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.2139/ssrn.4340728","name":"Federated Learning Vulnerabilities, Threats and Defenses: A Systematic Review and Future Directions","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4340728","authors":["Suzan Almutairi","Ahmad Barnawi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-28T23:27:04Z","doi":"10.2139/ssrn.4340728","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-3223559/v1","name":"A comprehensive experimental comparison between federated and centralized learning","source":"crossref","abstract":"Abstract Purpose : Federated learning is an upcoming machine learning paradigm which allows data from multiple sources to be used for training of classifiers without the data leaving the source it originally resides. This can be highly valuable for use cases such as medical research, where gathering data at a central location can be quite complicated due to privacy and legal concerns of the data. In such cases, federated learning has the potential to vastly speed up the research cycle. Although federated and central learning have been compared from a theoretical perspective, an extensive experimental comparison of performances and learning behavior still lacks. Methods : We have performed a comprehensive experimental comparison between federated and centralized learning. We evaluated various classifiers on various datasets exploring influences of different sample distributions as well as different class distributions across the clients. Results : The results show similar performances under a wide variety of settings between the federated and central learning strategies. Federated learning is able to deal with various imbalances in the data distributions. It is sensitive to batch effects between different datasets when they coincide with location, similar as with central learning, but this setting might go unobserved more easily. Conclusion : Federated learning seems robust to various challenges such as skewed data distributions, high data dimensionality, multiclass problems and complex models. Taken together,t he insights from our comparison gives much promise for applying federated learning as an alternative to sharing data.","url":"https://doi.org/10.21203/rs.3.rs-3223559/v1","authors":["Swier Garst","Julian Dekker","Marcel Reinders"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-16T01:52:07Z","doi":"10.21203/rs.3.rs-3223559/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.55248/gengpi.5.1224.3512","name":"Federated Learning and Data Privacy: A Review of Challenges and Opportunities","source":"crossref","abstract":"","url":"https://doi.org/10.55248/gengpi.5.1224.3512","authors":["Praveen Kumar Myakala","Chiranjeevi Bura","Anil Kumar Jonnalagadda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-21T02:38:22Z","doi":"10.55248/gengpi.5.1224.3512","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-10062288/v1","name":"Content Cooperative Caching in Mobile Edge Network Through Federated Reinforcement Learning","source":"europepmc","abstract":"Abstract Content caching in the mobile edge network can alleviate the backhaul burden and enhance user experience. The edge content caching imposes a significant challenge, due to the limited caching capacity of base stations, content redundancy, low caching utilization and diverse user preference patterns. The paper investigates the cooperative caching problem in mobile edge networks, which includes content popularity prediction with federated learning and content cooperative caching decision-making with deep reinforcement learning. Firstly, the VAE is used to extract the deep hidden features from user behavior data and the LSTM is combined to capture the temporal dynamic changes of content popularity, a VAE-LSTM content popularity prediction method CPP-EFL is proposed with elastic federated learning; Secondly, the multi-base station cooperative caching problem is modeled as a mixed-integer nonlinear programming problem, then the problem is transformed into a constrained Markov decision process. A cooperative caching algorithm CC-PMDRL with content popularity and multi-agent deep reinforcement learning is proposed; Finally, the proposed algorithm CC-PMDRL is compared with three different baseline algorithms (CPDDPG, CCDDQN and CBDDPG) through experiments. The experimental results show that the average latency of CC-PMDRL algorithm is reduced by 4.25%, 8.19% and 12.09% respectively, the average cache hit rates of CC-PMDRL algorithm have increased 5.61%, 10.79% and 17.62% respectively when compared to CBDDPG, CPDDPG and CCDDQN, the user experience quality is enhanced.","url":"https://doi.org/10.21203/rs.3.rs-10062288/v1","authors":["Jipeng Zhou","Shaomei Lv"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10062288/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-9358734/v1","name":"Federated-Inspired Vertically Distributed Quantum Machine Learning: Benchmarking and Hardware Evaluation","source":"preprints","abstract":"Abstract Distributed quantum machine learning (QML) provides a natural framework for studying variational quantum models under distributed and feature-partitioned execution, but the accuracy-practicality trade-offs of vertical partitioning in the near-term regime remain insufficiently quantified. This work presents a controlled benchmark of a centralized hybrid reference model against vertically distributed QML architectures that split input features across two variational quantum circuits and combine the resulting low-dimensional measurement outputs either through a trainable dense layer or through parameter-free quantum-only aggregation rules. The models are evaluated on four binary-classification datasets under both gradient-based Adam and gradient-free ARO (asexual reproduction optimization). To assess hardware transfer, simulator-trained models are executed on IBM superconducting quantum processors using fixed-weight inference. Across all datasets, the centralized hybrid model achieves the highest mean accuracy, whereas the distributed variants require substantially fewer trainable parameters (24 or 31 versus 300), revealing a clear accuracy-capacity trade-off. The results further show that optimizer choice is particularly important for quantum-only aggregation rules. Hardware experiments yield accuracies broadly consistent with simulator re-evaluation, while stronger runtime resilience settings improve stability at the cost of increased execution time.","url":"https://doi.org/10.21203/rs.3.rs-9358734/v1","authors":["Stefan Klug","Maximilian Moll"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9358734/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-1544372/v1","name":"Migrating Federated Learning to Centralized Learning with the Leverage of Unlabeled Data","source":"crossref","abstract":"Abstract Federated learning carries out cooperative training without local data sharing, the obtained global model performs generally better than independent local models. Benefifiting from the free data sharing, federated learning preserves the privacy of local users. However, the performance of the global model might be degraded if diverse clients hold non-IID training data. This is because the different distributions of local data lead to weight divergence of local models. In this paper, we introduce a novel teacher-student framework to alleviate the negative impact of non-IID data. On the one hand, we maintain the advantage of the federated learning on the privacy-preserving, and on the other hand, we take the advantage of the centralized learning on the accuracy. We use unlabeled data and global models as teachers to generate a pseudo-labeled dataset, which can signifificantly improve the performance of the global model. At the same time, the global model as a teacher provides more accurate pseudo labels. In addition, we perform a model rollback to mitigate the impact of latent noise labels and data imbalance in the pseudo-labeled dataset. Extensive experiments have verifified that our teacher ensemble performs a more robust training. The empirical study verififies that the reliance on the centralized pseudo-labeled data enables the global model almost immune to non-IID data.","url":"https://doi.org/10.21203/rs.3.rs-1544372/v1","authors":["Tianqing Zhu","Xiaoya Wang","Wei Ren","Dongmei Zhang","Ping Xiong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-04-12T18:34:51Z","doi":"10.21203/rs.3.rs-1544372/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1002/9781394461295.ch1","name":"A Review of Federated Learning and Its Importance in Advancing Agricultural Practices","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.ch1","authors":["Sugandha Saxena","Mude Nagarjuna Naik","R. Sriramkumar","Joshuva Arockia Dhanraz","M. Lakshmanan","A. Vegi Fernando","Mithaguru"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.ch1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.21203/rs.3.rs-1016044/v1","name":"Location Prediction with Personalized Federated Learning","source":"crossref","abstract":"Abstract Location prediction has attracted wide attention in human mobility prediction because of the popularity of location-based social networks. Existing location prediction methods have achieved remarkable development in centrally stored datasets. However, these datasets contain privacy data about user behaviors and may cause privacy issues. A location prediction method is proposed in our work to predict human movement behavior using federated learning techniques in which the data is stored in different clients and different clients cooperate to train to extract useful users’ behavior information and prevent the disclosure of privacy. Firstly, we put forward an innovative spatial-temporal location prediction framework(STLPF) for location prediction by integrating spatial-temporal information in local and global views on each client, and propose a new loss function to optimize the model. Secondly, we design a new personalized federated learning framework in which clients can cooperatively train their personalized models in the absence of a global model. Finally, the numerous experimental results on check-in datasets further show that our privacy-protected method is superior and more effective than various baseline approaches.","url":"https://doi.org/10.21203/rs.3.rs-1016044/v1","authors":["shuang wang","Bowei Wang","Shuai Yao","Jiangqin Qu","Yuezheng Pan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-11T15:45:01Z","doi":"10.21203/rs.3.rs-1016044/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1109/tai.2026.3712717/mm1","name":"Quantum Federated Learning: A Comprehensive Review and Analysis_supp1-3712717.pdf","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tai.2026.3712717/mm1","authors":["Shiva Raj Pokhrel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-15T20:16:11Z","doi":"10.1109/tai.2026.3712717/mm1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-722389/v1","name":"Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection","source":"crossref","abstract":"Abstract Recent advances in Deep Learning (DL) and the increased use of brain MRI have provided a great opportunity and interest in automated anomaly segmentation to support human interpretation and improve clinical workflow. However, medical imaging must be curated by trained clinicians, which is time-consuming and expensive. Further, data is often scattered across multiple institutions, with privacy regulations limiting its access. Here, we present FedDis (Federated Disentangled representation learning for unsupervised brain pathology segmentation) to collaboratively train an unsupervised deep convolutional neural network on 1532 healthy MR scans from four different institutions, and evaluate its performance in identifying abnormal brain MRIs including multiple sclerosis (MS) lesions, low-grade tumors (LGG), and high-grade tumors/glioblastoma (HGG/GB) on a total of ~500 scans from 5 different institutions and datasets. FedDis mitigates the statistical heterogeneity given by different scanners by disentangling the parameter space into global, i.e., shape and local, i.e., appearance. We only share the former with the federated clients to leverage common anatomical structure while keeping client-specific contrast information private. We have shown that our collaborative approach, FedDis, improves anomaly segmentation results by 99.74% for MS and 40.45% for tumors over locally trained models without the need for annotations or sharing private local data. We found out that FedDis is especially beneficial for clients that share both healthy and anomaly data coming from the same institute, improving their local anomaly detection performance by up to 227% for MS lesions and 77% for brain tumors.","url":"https://doi.org/10.21203/rs.3.rs-722389/v1","authors":["Cosmin Bercea","Benedikt Wiestler","Daniel Rueckert","Shadi Albarqouni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-06T19:29:51Z","doi":"10.21203/rs.3.rs-722389/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-2910523/v1","name":"WITHDRAWN: Depression clinical detection model based on social media: a federated deep learning approach","source":"crossref","abstract":"Abstract Depression can significantly impact people’s mental health, and recent research shows that social media can provide decision-making support for healthcare professionals and serve as supplementary information for understanding patients’ health status. Deep learning models are also able to assess an individual’s likelihood of experiencing depression. However, data availability on social media is often limited due to privacy concerns, even though deep learning models benefit from having more data to analyze. To address this issue, this study proposes a methodological framework system for clinical decision support that uses federated deep learning (FDL) to identify individuals experiencing depression and provide intervention decisions for clinicians. The proposed framework involves evaluation of datasets from three social media platforms, and the experimental results demonstrate that our method achieves state-of-the-art results. The study aims to provide a personalized clinical decision support system with evolvable features that can deliver precise solutions and assist healthcare professionals in medical diagnosis. The proposed framework that incorporates social media data and deep learning models can provide valuable insights into patients’ health status, support personalized treatment decisions, and adapt to changing healthcare needs.","url":"https://doi.org/10.21203/rs.3.rs-2910523/v1","authors":["Yang Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-05-15T18:51:14Z","doi":"10.21203/rs.3.rs-2910523/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-9046507/v1","name":"Secure and Lossless Federated Matrix Learning for Recommender Systems","source":"preprints","abstract":"Abstract Privacy concerns within recommender systems have emerged as a pivotal challenge, garnering significant scrutiny from both academic and industrial sectors. While federated matrix factorization have been proposed to enhance privacy via local differential privacy, their applicability is often hindered by two fundamental limitations: the inherent utility-privacy trade-off and suboptimal training convergence. To address these challenges, this paper introduces a Secure and Lossless Federated Matrix Factorization (SLFedMF) framework tailored for diverse deployment environments. Specifically, we integrate perturbation with a mask mechanism to provide robust, multi-dimensional privacy guarantees. To eliminate the accuracy degradation typical of perturbation-based methods, we employ a trusted party authority denoising mechanism, enabling clients to reconstruct noise-free global gradients locally. Furthermore, the Barzilai-Borwein method is leveraged to adaptively optimize learning rates, significantly accelerating model convergence. We present two variants: SLFedMF-Full for synchronous full-device participation and SLFedMF-Part to mitigate the straggler effect in partial participation scenarios. Extensive experiments on four real-world datasets demonstrate that SLFedMF outperforms state-of-the-art methods, achieving optimal recommendation accuracy equivalent to non-private models while maintaining stringent privacy standards.","url":"https://doi.org/10.21203/rs.3.rs-9046507/v1","authors":["Rongwei Lu","Xuefeng Duan","Guoqiang Deng","Yong Ding"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9046507/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.7287/peerj-cs.1101v0.1/reviews/2","name":"Peer Review #2 of \"OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.1)\"","source":"crossref","abstract":"","url":"https://doi.org/10.7287/peerj-cs.1101v0.1/reviews/2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-25T02:33:01Z","doi":"10.7287/peerj-cs.1101v0.1/reviews/2","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-1808183/v2","name":"Provision for Energy: A Resource Allocation Problem in Federated Learning for Edge Systems","source":"crossref","abstract":"Abstract The article explores an energy-efficient method for allocating transmission and computation resources for federated learning (FL) on wireless communication networks. The model being considered involves each user training a local FL model using their limited local computing resources and the data they have collected. These local models are then transmitted to a base station, where they are aggregated and broadcast back to all users. The level of accuracy in learning, as well as computation and communication latency, are determined by the exchange of models between users and the base station. Throughout the FL process, energy consumption for both local computation and transmission must be taken into account. Given the limited energy resources of wireless users, the communication problem is formulated as an optimization problem with the goal of minimizing overall system energy consumption while meeting a latency requirement. To address this problem, we propose an iterative algorithm that takes into account factors such as bandwidth, power, and computational resources. Results from numerical simulations demonstrate that the proposed algorithm can reduce energy consumption compared to traditional FL methods up to 51% reduction.","url":"https://doi.org/10.21203/rs.3.rs-1808183/v2","authors":["Mingyue Liu","Leelavathi Rajamanickam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-04-19T10:44:46Z","doi":"10.21203/rs.3.rs-1808183/v2","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-1571398/v1","name":"Differentially Private Knowledge Transfer for Federated Learning","source":"crossref","abstract":"Abstract Extracting useful knowledge from big data is important for machine learning. When data is privacy-sensitive and cannot be directly collected, federated learning is a promising option that extracts knowledge from decentralized data by learning and exchanging model parameters, rather than raw data. However, model parameters may encode not only non-private knowledge but also private information of local data, thereby transferring knowledge via model parameters is not privacy-secure. Here, we present a novel knowledge transfer method named PrivateKT, which uses actively selected small public data to transfer high-quality knowledge in federated learning with privacy guarantees. We verify PrivateKT on three different datasets, and results show that PrivateKT can maximally reduce 84% of the performance gap between centralized learning and existing federated learning methods under strict differential privacy restrictions. PrivateKT provides a potential direction to effective and privacy-preserving knowledge transfer in machine intelligent systems.","url":"https://doi.org/10.21203/rs.3.rs-1571398/v1","authors":["Tao Qi","Fangzhao Wu","Chuhan Wu","Yongfeng Huang","Xing Xie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-05-26T17:51:02Z","doi":"10.21203/rs.3.rs-1571398/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.2139/ssrn.5358246","name":"Homomorphic Encryption in Federated Learning: A Systematic Review of Current Methods and Open Challenges","source":"crossref","abstract":"Federated learning (FL) enables collaborative training across different devices or institutions. However, this model also raises concerns about data privacy. For addressing this problem, there are some privacy-preserving technologies being used, such as differential privacy, homomorphic encryption, locality-sensitive hashing, secure multi-party computation. Noticeably, homomorphic encryption (HE) is an encryption mechanism that can encrypt raw data before doing any operation and perform the equivalent result as the one yielded by operating unencrypted data. However, this technology suffers from high computational overhead. Compared to existing surveys, this article conducts a comprehensive review of recent HE methods applied in federated learning and presents a systematic analysis of most widely used HE technics. This SLR also suggests the future direction of research on HE in FL.","url":"https://doi.org/10.2139/ssrn.5358246","authors":["Haowen Li","Zhengyu Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-06T12:00:01Z","doi":"10.2139/ssrn.5358246","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1039/d5fb00739a/v1/review1","name":"Review for \"Federated Deep Learning for Triple Bottom Line Optimization in Virtual Refrigeration Through Simulation-Based Sustainable Food Management\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d5fb00739a/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-22T21:09:02Z","doi":"10.1039/d5fb00739a/v1/review1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.7287/peerj-cs.1101v0.3/reviews/3","name":"Peer Review #3 of \"OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.3)\"","source":"crossref","abstract":"","url":"https://doi.org/10.7287/peerj-cs.1101v0.3/reviews/3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-25T02:33:14Z","doi":"10.7287/peerj-cs.1101v0.3/reviews/3","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-3134736/v1","name":"Federated Transfer Learning for Soalr Flare Forecasting","source":"crossref","abstract":"Abstract The critical role of solar flare forecasting in shielding space-based systems and essential infrastructures from potential hazards is universally acknowledged. Most research in solar flare forecasting predominantly utilizes machine learning techniques, primarily focusing on the development and optimization of a single, comprehensive model. This strategy, however, tends to overlook the intrinsic variability amongst individual sunspots located on the solar photosphere. Such sunspot variations, which are manifested in distinct flare progression patterns and other unique physical parameters, play a pivotal role in the forecasting process. Ignoring these nuances while training machine learning models can unintentionally compromise the precision of solar flare predictions. To mitigate this issue, we propose a methodology rooted in federated transfer learning. This approach commences with the segregation of the acquired dataset using NOAA numbers, thereby enabling the tailored training of models based on these subdivisions. Subsequently, these individually trained models are integrated into a unified ”global model”. The parameters of this global model are then redistributed to each sub-model, facilitating additional training in an iterative cycle. This iterative process culminates when the output deviations between the global and sub-models satisfy a predetermined boundary. Our proposed methodology has yielded a commendable Area Under the Curve (AUC) score of 93.6%, underscoring its superior predictive performance relative to traditional techniques. These results underscore the importance and accuracy of factoring in the regional differences of sunspots in solar flare forecasting, thereby validating the effectiveness of our federated transfer learning approach.","url":"https://doi.org/10.21203/rs.3.rs-3134736/v1","authors":["Junfeng Fu","Jie Wan","Xin Huang","Ke Han","E Peng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-10T06:15:32Z","doi":"10.21203/rs.3.rs-3134736/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1039/d5fb00739a/v2/review1","name":"Review for \"Federated Deep Learning for Triple Bottom Line Optimization in Virtual Refrigeration Through Simulation-Based Sustainable Food Management\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d5fb00739a/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-22T21:09:02Z","doi":"10.1039/d5fb00739a/v2/review1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.5395281","name":"A Review of Lightweight Multi-Party Computation and Federated Learning in Financial Systems","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5395281","authors":["Tonsia Treesa Thomas","Heta Shukla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-27T16:20:10Z","doi":"10.2139/ssrn.5395281","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.2139/ssrn.5363411","name":"A Review of Lightweight Multi-Party Computation and Federated Learning in Financial Systems","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5363411","authors":["Tonsia Treesa Thomas","Heta Shukla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-08T17:38:17Z","doi":"10.2139/ssrn.5363411","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1039/d5fb00739a/v2/review2","name":"Review for \"Federated Deep Learning for Triple Bottom Line Optimization in Virtual Refrigeration Through Simulation-Based Sustainable Food Management\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d5fb00739a/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-22T21:09:02Z","doi":"10.1039/d5fb00739a/v2/review2","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.21203/rs.3.rs-2772071/v1","name":"Improved Reputation Evaluation for Reliable Federated Learning on Blockchain","source":"crossref","abstract":"Abstract Worker selection is critical to the success of federated learning, but issues such as inadequate incentives and poor-quality data can negatively impact the process. Existing studies have used the multi-weight subjective logic model, but it is vulnerable to malicious evaluation and unfair to newly added nodes. In this paper, we propose an improved reputation evaluation algorithm that allows evaluations from different sources to influence each other and reduce the impact of malicious comments. Our approach effectively distinguishes between malicious and honest users and improves worker selection and collaboration in federated learning.","url":"https://doi.org/10.21203/rs.3.rs-2772071/v1","authors":["Jiacheng Sui","Yi Li","Hai Huang","Li Fang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-04-06T14:19:06Z","doi":"10.21203/rs.3.rs-2772071/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.7287/peerj-cs.1101v0.2/reviews/3","name":"Peer Review #3 of \"OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.2)\"","source":"crossref","abstract":"","url":"https://doi.org/10.7287/peerj-cs.1101v0.2/reviews/3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-25T02:33:06Z","doi":"10.7287/peerj-cs.1101v0.2/reviews/3","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.7287/peerj-cs.1101v0.2/reviews/2","name":"Peer Review #2 of \"OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.2)\"","source":"crossref","abstract":"","url":"https://doi.org/10.7287/peerj-cs.1101v0.2/reviews/2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-25T02:33:08Z","doi":"10.7287/peerj-cs.1101v0.2/reviews/2","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-8815125/v1","name":"Quantum Safe Federated Reinforcement Learning for Intelligent Energy Efficiency Optimization in IoT Enabled Smart Grids","source":"preprints","abstract":"Abstract A smart grid is as an electricity system that manages digital and refined mechanism to monitor and handle the transfer of electricity from various sources, optimizing efficiency and reliability while reducing costs and environmental impacts. The rapid growth of smart grids raises total energy demand and renewable energy combination for smart, adaptive, and energy-efficient resource distribution strategies. Traditional energy management methods, often fail to handle real-time grid dynamics, leading to suboptimal energy distribution, high operational costs, and significant energy consumption. But main challenges in smart grids are optimizing energy efficiency and controlling electricity generation, transmission, and distribution. This paper introduces an AI-powered approach to optimize energy in smart grids using Generalized Linear Regressive Quantum-Safe Federated Reinforcement Learning (GLRQS-FRL). The main aim of GLRQS-FRL model is to perform the energy efficiency optimization in IoT-enabled smart grids with minimal computation and communication overhead. To begin with, GLRQS-FRL model collects the smart grid data from the dataset. After the acquisition phase, data preprocessing is carried out to transform the raw dataset into cleaned format based on missing data handling and outlier removal. Followed by, two phase linear regression is employed to determine the most relevant features and remove the others. Finally, the Quantum Federated Reinforcement Learning performs the optimal energy usage prediction by employing Azadkia-Chatterjee correlation coefficient with the selected relevant features with higher accuracy. In addition, Quantum differential privacy model is employed to further protect sensitive data. Finally, accurate energy efficiency optimization results are predicted with minimal error. Experimental consideration of proposed GLRQS-FRL model is conducted using various evaluation metrics such as accuracy, RMSE, NMSE, R 2 score, computation overhead and communication overhead. The quantitatively analyzed results reveal that the proposed GLRQS-FRL model attains higher accuracy in smart grid optimization with minimal overhead as well as lesser error compared to traditional deep learning methods.","url":"https://doi.org/10.21203/rs.3.rs-8815125/v1","authors":["Abdulatif Alabdulatif"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8815125/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.37591/etpm.v03i01.236749","name":"A Comprehensive Review on Federated Learning in Disease Detection","source":"crossref","abstract":"","url":"https://doi.org/10.37591/etpm.v03i01.236749","authors":["Projesh Saha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-02T10:35:52Z","doi":"10.37591/etpm.v03i01.236749","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.21203/rs.3.rs-6032484/v1","name":"Federated Learning for Lung Cancer Detection: Comparative Analysis and Visual Interpretability","source":"crossref","abstract":"Abstract Artificial Intelligence (AI) has become a crucial tool in the detection of lung can-cer through medical image segmentation. However, traditional AI approaches, which require centralizing sensitive patient data for model training, raise sig-nificant privacy concerns. This project investigates the efficiency of Federated Learning (FL) frameworks in comparison to a conventional centralized AI model. We evaluated seven different state-of-the art Federated Learning frameworks to assess their performance in maintaining model accuracy and scalability. Among these, Per-FedAvg and FedOpt demonstrated efficiency compared to the cen-tralized framework. To further understand the performance of these models, we utilized Gradient-weighted Class Activation Mapping (Grad-CAM) to visually interpret their predictions, ensuring that they focus on medically relevant fea-tures. This project highlights that Federated Learning frameworks, specifically Per-FedAvg and FedOpt, offer promising alternatives to traditional AI methods by providing enhanced performance in lung cancer detection.","url":"https://doi.org/10.21203/rs.3.rs-6032484/v1","authors":["Osei isaac","Iven Aabaah","Benjamin Appiah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T06:50:12Z","doi":"10.21203/rs.3.rs-6032484/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.7287/peerj-cs.1101v0.1/reviews/3","name":"Peer Review #3 of \"OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.1)\"","source":"crossref","abstract":"","url":"https://doi.org/10.7287/peerj-cs.1101v0.1/reviews/3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-25T02:33:00Z","doi":"10.7287/peerj-cs.1101v0.1/reviews/3","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.1039/d5fb00739a/v1/review2","name":"Review for \"Federated Deep Learning for Triple Bottom Line Optimization in Virtual Refrigeration Through Simulation-Based Sustainable Food Management\"","source":"crossref","abstract":"","url":"https://doi.org/10.1039/d5fb00739a/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-22T21:09:02Z","doi":"10.1039/d5fb00739a/v1/review2","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.21203/rs.3.rs-9820191/v1","name":"Digital Twin-Assisted Personalized Federated Reinforcement Learning for Cyber Defense in Internet of Medical Things","source":"preprints","abstract":"Abstract This article proposes a Digital Twin-Assisted Robust Personalized Federated Reinforcement Learning framework, termed DSGTPFL, for adaptive cyber defense in IoMT networks. The proposed approach integrates six complementary mechanisms: (i) graph neural network (GNN)- based representation learning to capture topological and relational dependencies among medical devices; (ii) Twin Delayed Deep Deterministic Policy Gradient (TD3) to enable continuous and adaptive defense policy learning under dynamic attack surfaces; (iii) mean-field game modeling to characterize large-scale agent interactions efficiently; (iv) personalized federated learning to preserve data locality while adapting global intelligence to heterogeneous hospital environments; (v) trust-aware robust aggregation to mitigate poisoned or Byzantine local updates and (vi) digital twin-based pre-deployment validation to reduce unsafe policy rollout risk in safety-critical medical infrastructures. Extensive simulations conducted on a realistic IoMT cyber-defense environment demonstrate that the proposed framework consistently outperforms conventional federated, nonfederated, and non-personalized baselines. Specifically, DSGTPFL achieves the highest attack detection rate, the lowest defense failure rate, reduced false positives, and improved quality-ofservice preservation under non-IID data and adversarial client poisoning settings. Theoretical analysis further establishes boundedness, aggregation stability, local personalization advantage, mean-field consistency, convergence properties, and safety improvement through digital twin validation","url":"https://doi.org/10.21203/rs.3.rs-9820191/v1","authors":["maryam fattahi"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9820191/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-3135700/v1","name":"Image-based crop disease detection with federated learning","source":"crossref","abstract":"Abstract Crop disease detection and management is critical to improving productivity, reducing costs, and promoting environmentally friendly crop treatment methods. Modern technologies, such as data mining and machine learning algorithms, have been used to develop automated crop disease detection systems. However, centralized approach to data collection and model training induces challenges in terms of data privacy, availability, and transfer costs. To address these challenges, federated learning appears to be a promising solution. In this paper, we explored the application of federated learning for crop disease classification using image analysis. We developed and studied convolutional neural network (CNN) models and those based on attention mechanisms, in this case vision transformers (ViT), using federated learning, leveraging an open access image dataset from the \"PlantVillage\" platform. Experiments conducted concluded that the performance of models trained by federated learning is influenced by the number of learners involved, the number of communication rounds, the number of local iterations and the quality of the data. With the objective of highlighting the potential of federated learning in crop disease classification, among the CNN models tested, ResNet50 performed better in several experiments than the other models, and proved to be an optimal choice, but also the most suitable for a federated learning scenario. The ViT_B16 and ViT_B32 Vision Transformers require more computational time, making them less suitable in a federated learning scenario, where computational time and communication costs are key parameters. The paper provides a state-of-the-art analysis, presents our methodology and experimental results, and concludes with ideas and future directions for our research on using federated learning in the context of crop disease classification.","url":"https://doi.org/10.21203/rs.3.rs-3135700/v1","authors":["Denis MAMBA KABALA","Adel HAFIANE","Laurent BOBELIN","Raphael CANALS"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-14T03:35:10Z","doi":"10.21203/rs.3.rs-3135700/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:27.397Z"},{"id":"doi:10.3390/s26165182","name":"Trustworthy AI-Powered Intrusion Detection for the Internet of Medical Things (IoMT): A Review.","source":"pubmed","abstract":"The Internet of Medical Things (IoMT) is transforming healthcare through continuous patient monitoring, telemedicine, cloud-edge services, and Healthcare 5.0. However, the rapid growth of interconnected medical devices has expanded the healthcare cyberattack surface, making intelligent intrusion detection essential for protecting sensitive medical data and ensuring resilient clinical operations. Existing reviews examine specific aspects of AI-powered intrusion detection but rarely provide a deployment-oriented synthesis linking technical performance with operational and clinical requirements. This review critically examines Artificial Intelligence (AI)-powered Intrusion Detection Systems (IDSs) for IoMT across six analytical dimensions: detection performance, explainability, privacy preservation, computational efficiency, benchmarking practices, and cross-dataset generalization. This structured narrative review adopted the PRISMA 2020 framework to ensure transparent record identification, screening, and reporting, with evidence synthesized qualitatively rather than through quantitative meta-analysis. A total of 5127 records published between 2021 and 2026 were screened, resulting in 24 primary studies supported by 115 complementary studies. The findings show that machine learning, deep learning, hybrid AI, Explainable Artificial Intelligence (XAI), Federated Learning (FL), blockchain-assisted security, and edge intelligence have significantly advanced IoMT intrusion detection. However, despite benchmark accuracies often exceeding 95%, deployment remains constrained by dataset dependency, weak cross-dataset generalization, computational overhead, limited explainability, fragmented benchmarking, and insufficient operational validation. This review identifies deployment readiness, rather than predictive accuracy alone, as the principal challenge for next-generation healthcare cybersecurity and provides a practical framework for developing trustworthy, interoperable, privacy-preserving, and deployment-ready IoMT cybersecurity architectures supported by standardized evaluation protocols.","url":"https://doi.org/10.3390/s26165182","authors":["Islam J","Datta D","Akhter F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26165182","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1021/acsptsci.6c00261","name":"Quantum-Augmented Federated AI for Adaptive Pharmacogenomic Precision Oncology: A Perspective.","source":"pubmed","abstract":"Precision oncology has evolved from single-gene biomarker testing toward multimodal molecular and clinical profiling; however, most therapeutic decisions remain based on static baseline assessments that inadequately capture tumor evolution, treatment response, and emerging resistance. In this perspective, we propose a forward-looking framework that integrates federated learning, pharmacogenomic digital twins, and hybrid quantum-classical optimization to support the development of adaptive, privacy-preserving precision oncology systems. The framework enables collaborative model training across institutions without sharing raw patient data, thereby addressing major barriers associated with data fragmentation and privacy regulations. Patient-specific digital twins serve as continuously evolving computational representations that integrate longitudinal multiomics, imaging, pathology, and clinical information to simulate disease trajectories, estimate therapeutic response, and anticipate resistance patterns. Federated learning allows these models to benefit from geographically distributed patient cohorts while maintaining data sovereignty and institutional privacy. In contrast to near-term deployable components such as federated learning and digital twin modeling, quantum computing is presented as a future-oriented computational strategy that may assist selected combinatorial optimization tasks, including treatment selection, dose optimization, and scheduling, through hybrid quantum-classical workflows. Rather than assuming immediate clinical utility or quantum advantage, the framework emphasizes realistic translational pathways that acknowledge current limitations in quantum hardware, scalability, validation, and regulatory readiness. We further discuss technical feasibility, challenges associated with heterogeneous clinical data, privacy considerations, validation requirements, and regulatory pathways for adaptive AI-enabled clinical decision-support systems. By providing a formal conceptual architecture and translational roadmap, this perspective outlines how federated artificial intelligence, continuously learning digital twins, and future quantum-assisted optimization may collectively contribute to the next generation of adaptive precision oncology.","url":"https://doi.org/10.1021/acsptsci.6c00261","authors":["Rafiq Z","Puebla Osorio N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1021/acsptsci.6c00261","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.2196/79713","name":"Cross-Silo Federated Learning for Predicting Successful Mechanical Ventilation Weaning Across 5 Intensive Care Unit Databases: Retrospective Database Analysis.","source":"pubmed","abstract":"The prediction of weaning from mechanical ventilation (MV) can support clinical decision-making and help reduce the risk of weaning failure in intensive care units (ICUs). Cross-silo federated learning (FL) offers a promising approach to developing robust predictive models across multiple institutions without requiring the sharing of patient-level data.","url":"https://doi.org/10.2196/79713","authors":["Sheikhalishahi S","Kaspar M","Schwinn J","Morhart M","Simon P","Hinske LC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/79713","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.21203/rs.3.rs-10299029/v1","name":"Federated Deep Learning and TinyML Co-Design for Securing Resource-Constrained IoT and SCADA Networks: A PRISMA-Compliant Systematic Review, Taxonomy and Statistical Meta-Analysis","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10299029/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10299029/v1","addedAt":"2026-08-31T06:41:27.397Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1186/s43046-026-00399-y","name":"Machine learning and AI for cancer research and care: a review of applications, limitations, and future directions.","source":"pubmed","abstract":"Machine learning (ML) is transforming cancer research and care by enabling analysis of complex, high-dimensional datasets spanning genomics, transcriptomics, proteomics, imaging, and clinical records. By improving risk stratification, accelerating detection and diagnosis, and supporting treatment selection, ML has the potential to enhance survival outcomes while increasing efficiency across oncology workflows. This review synthesizes key developments in ML for oncology, covering foundational algorithms alongside emerging approaches. We highlight major application areas including early cancer detection, tumor classification, molecular subtyping, biomarker discovery, prognosis estimation, multi-omics integration, computational pathology, pharmacogenomics, and clinical decision support. We also summarize commonly used datasets, discuss the importance of interpretability for clinical trust, and outline barriers to translation such as data heterogeneity, bias, and regulatory constraints. Finally, we describe future directions, including federated learning, graph neural networks, longitudinal modeling, and integration of real-world and wearable data to support precision oncology. This review is intended for cancer researchers, clinicians, and data scientists seeking a practical overview of ML methods, opportunities, and translational considerations in oncology.","url":"https://doi.org/10.1186/s43046-026-00399-y","authors":["Yadav K","Gupta T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1186/s43046-026-00399-y","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3389/fdgth.2026.1814015","name":"Selecting medical research data platforms for translational biomedical research: a five-tier overview and requirement-weighted assessment framework.","source":"pubmed","abstract":"Translational biomedical research is increasingly collaborative and multimodal, making secure, high-quality data capture, curation, and analytics a major challenge. This work aims to provide an overview of existing medical research data platforms to support informed platform selection for translational biomedical research.","url":"https://doi.org/10.3389/fdgth.2026.1814015","authors":["Jacobs M","Goudarzi S","Stücke J","Röhm R","Kanninen T","Oinonen T","Ritter P","Schirner M","Prasser F","Bhagwagar A","Matzenbach B","Schultze H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1814015","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.2174/0115734056456208260706064206","name":"Exploring Deep Transfer Learning for Medical Image Processing and Analysis: A Comprehensive Analysis across Modalities.","source":"pubmed","abstract":"In recent years, medical imaging has become an important tool for diagnosing diseases and disorders in healthcare. Advanced imaging technologies are being developed for non-invasive and early detection of diseases and disorders. Analyzing medical images by clinical experts is very expensive. To overcome these challenges, developing automated methods provides an effective solution. Consequently, for processing and analyzing medical images, researchers have adopted the emerging Deep Learning (DL) technologies. It has proven effective across several industries, most notably in healthcare. Even so, it has two significant limitations, such as the training cost and the large amounts of labeled data required. To reduce these limitations, Transfer Learning (TL) and Deep Learning (DL) have been integrated to create Deep Transfer Learning (DTL). This reduces the need to start from scratch and eliminates dependencies by leveraging knowledge from a source task to a target task during training, using fewer datasets. This review addresses the definitions, concepts, modalities, tasks, and techniques of DTL, along with public and private datasets used as source and target data in network-based medical imaging approaches. It also categorizes the last seven years of research by human anatomical area. It offers readers comprehensive coverage of technological advancements, future research directions, and challenges. It also reviews DTL methods by discussing those that have been applied, including Federated Learning (FL) for DL. Empirical evidence from recent studies demonstrates that fine-tuning and network-based DTL strategies, including federated learning, consistently enhance diagnostic accuracy, robustness, and generalization across multiple medical imaging modalities, particularly in data-limited clinical scenarios.","url":"https://doi.org/10.2174/0115734056456208260706064206","authors":["Banu MAS","Dhavapandiammal A","Palanisamy K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2174/0115734056456208260706064206","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1016/j.neunet.2026.108889","name":"Federated learning with noisy labels: A comprehensive and concise review of current methodologies and future directions.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.neunet.2026.108889","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.neunet.2026.108889","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.2196/98699","name":"The Continuity Trap in Data Science Health Research.","source":"pubmed","abstract":"Secondary use is now the ordinary condition of data science health research rather than an exception to it. Electronic health records collected for clinical care become prediction tools and inputs for generative AI; imaging archives become foundation-model corpora; genomic datasets become resources for polygenic risk scores; and legacy biospecimens become renewable, indefinitely distributable cell lines. Governance has responded by emphasizing verifiable instruments such as provenance logs, repository approvals, broad-consent forms, data-use agreements, model cards, records of processing, and locality-preserving architectures. These instruments are necessary, and they answer real questions about lineage, privacy, institutional responsibility, and accountability, but they are not sufficient to establish that a present use remains ethically justified. We define ethical continuity as the persistence of normatively relevant relationships between the original conditions of data generation or material collection and subsequent downstream uses, such that current uses remain justifiable in light of the expectations, permissions, meanings, and relational obligations present at entrustment. We then define the Continuity Trap as a review-stage governance error in which a salient signal of continuity in one domain is treated as sufficient evidence of ethical continuity overall, causing inquiry into the remaining domains to close prematurely. The trap is not ordinary noncompliance, ethics creep, or a demand for universal rereview; it is a cross-domain inference error that can arise even in careful, good-faith review. We distinguish it from proxy closure, of which it is a continuity-specific subtype, and from Goodhart's and Campbell's laws, which describe how measures degrade once they become targets. We operationalize ethical continuity across 4 domains: provenance, semantics, authorization, and relational standing, developed in our Representational Veracity framework, and we show that these domains can diverge as data are linked, transformed, modeled, and redeployed. We identify the institutional mechanisms-provenance privilege, descriptor sedimentation, authorization fossilization, and community effacement-that cause auditable signals to be overread, and we examine how the US Health Insurance Portability and Accountability Act (HIPAA) of 1996, the General Data Protection Regulation, the European Health Data Space, US Food and Drug Administration guidance, the US National Institute of Standards and Technology (NIST) AI Risk Management Framework, and federated-learning governance can reduce risk while still inducing continuity traps. We apply the framework to consent and nonconsent settings, including public health, immunization, syndromic, and wastewater surveillance, polygenic risk scores, induced pluripotent stem cells, federated learning, and health-related large language models. The policy implication is trigger-based continuity review: rather than rereviewing every reuse, investigators and reviewers should identify the weakest continuity domain at the present data stage and impose a domain-matched safeguard, recorded in a short continuity statement. This reframing is intended for the committees, repositories, funders, and governance bodies that decide whether reuse may proceed, and it matters most in cross-border and low-resource settings. Provenance should begin ethical review; it should not end it.","url":"https://doi.org/10.2196/98699","authors":["Adebamowo C","Adebamowo SN","Akintola A","Ikhane P","Akintola S","Ogundiran T","Jegede A","Adeyemo O","Callier S","Imam-Tamim M","Uthman I","BridgELSI Project as part of the DS-I Africa Consortium"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/98699","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.3390/healthcare14131952","name":"From Algorithmic Performance to Clinical Translation: Translational Readiness of Imaging-Based Artificial Intelligence in Dentistry-A Systematic Review.","source":"pubmed","abstract":"Artificial intelligence is increasingly applied to dental imaging, yet favorable internal performance does not necessarily indicate clinical transferability. This systematic review evaluated whether imaging-based dental artificial intelligence models have progressed beyond internal algorithmic development toward external validation, generalizability, reproducibility, privacy-preserving learning, and clinical implementation readiness.","url":"https://doi.org/10.3390/healthcare14131952","authors":["Ardila CM","Vivares-Builes AM","Pineda-Vélez E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/healthcare14131952","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.5662/wjm.117916","name":"Artificial intelligence in onco-anaesthesia: Current applications, challenges, and future directions.","source":"pubmed","abstract":"Artificial intelligence (AI) is transforming onco-anaesthesia by shifting practice from reactive physiological management toward predictive and precision-based care. This review outlines current AI applications across the perioperative cancer pathway. Preoperatively, machine learning and deep learning models enhance risk stratification through automated frailty assessment, electronic health record phenotyping, and prediction of cancer-specific outcomes. Intraoperatively, AI-enabled technologies such as closed-loop anaesthesia delivery systems, predictive haemodynamic monitoring, and automated depth-of-anaesthesia control optimize drug dosing, reduce physiological stress, and may help preserve perioperative immune function, with potential implications for long-term oncologic outcomes. Postoperatively, AI-driven integration of multimodal data-including genomics, radiomics, wearable biosignals, and high-resolution physiological waveforms-facilitates early detection of complications such as delirium, persistent pain, acute kidney injury, and anastomotic leakage. The review also examines the role of AI in evaluating the \"onco-anaesthesia hypothesis\" by clarifying links between anaesthetic techniques, inflammation, and cancer recurrence. Despite these advances, significant challenges persist, including data heterogeneity, limited generalisability, algorithmic opacity, regulatory uncertainty, and ethical concerns related to equity and clinical implementation. Future progress will depend on explainable AI, federated learning, real-time clinical decision-support systems, and validation through large, prospective studies to fully realise AI's potential in personalised onco-anaesthetic care.","url":"https://doi.org/10.5662/wjm.117916","authors":["Sirohiya P","Maurya P","Gupta N","Ratre BK","Vig S","Puri S","Kumar B","Gupta R","Bhopale S","Pandit A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5662/wjm.117916","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.1016/j.preghy.2026.101506","name":"Machine learning for precision obstetrics: A comprehensive review of risk prediction models for hypertensive disorders and pregnancy-related syndromes.","source":"pubmed","abstract":"Gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), preterm birth, and intrauterine growth restriction represent major contributors to maternal and neonatal morbidity worldwide. Traditional screening methods relying on single biomarkers or linear models often demonstrate limited predictive accuracy. This review examines the transformative role of machine learning (ML) in shifting obstetric care toward predictive, preventive, and personalized approaches. Advanced computational models integrating multimodal data sources clinical records, biochemical markers, multi-omics profiles, medical imaging, and lifestyle factors show promising performance. Ensemble methods such as Random Forest and XGBoost, alongside deep learning architectures, frequently outperform conventional logistic regression, achieving AUC values above 0.90 in selected cohorts. Emphasis is placed on preprocessing techniques for class imbalance (e.g., SMOTE), model interpretability via Explainable AI (SHAP), and privacy-preserving strategies like federated learning. While technical and ethical challenges including bias, external validation, and data heterogeneity remain, robust ML frameworks offer substantial potential for early risk stratification and timely intervention in precision obstetrics. Prospective, multicenter validation is essential for clinical translation.","url":"https://doi.org/10.1016/j.preghy.2026.101506","authors":["Xie J","Shi C","Dou L","Peng D","Li L","Xu R","Wu Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.preghy.2026.101506","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.14293/pr2199.004312.v1","name":"Artificial Intelligence for Intelligent and Secure Digital Services: Integrating AI Chatbots and Fraud Detection Systems","source":"europepmc","abstract":"","url":"https://doi.org/10.14293/pr2199.004312.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.14293/pr2199.004312.v1","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1080/17568919.2026.2714025","name":"Advancing cancer drug discovery through the integration of machine learning and high-throughput screening.","source":"pubmed","abstract":"Cancer drug discovery is a complex process that requires identifying compounds that selectively target malignant cells. While high-throughput screening (HTS) is essential for testing large libraries, it generates vast datasets that are difficult to interpret. Recently, the integration of artificial intelligence (AI), particularly deep learning (DL), has significantly accelerated drug candidate selection. This review highlights the synergy between AI and HTS, emphasizing DL techniques such as convolutional neural networks for bioactivity prediction, recurrent neural networks for de novo design, and reinforcement learning for property optimization. These methods streamline preclinical research by enabling rapid multi-omics analysis and prediction of drug-target interactions. However, challenges regarding data quality, model interpretability, and ethics persist. Emerging paradigms like Explainable AI and federated learning aim to enhance transparency and collaboration while safeguarding privacy. Ultimately, overcoming these barriers through AI-HTS integration holds transformative potential to reduce development costs and improve clinical outcomes for cancer patients.","url":"https://doi.org/10.1080/17568919.2026.2714025","authors":["Herbetko K","Mikołajek M","Wojdyło L","Klasen K","Kulbacki M","Kulbacka J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1080/17568919.2026.2714025","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.581Z"},{"id":"doi:10.3390/biomedicines14030713","name":"FedIHRAS: A Privacy-Preserving Federated Learning Framework for Multi-Institutional Collaborative Radiological Analysis with Integrated Explainability and Automated Clinical Reporting.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/biomedicines14030713","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/biomedicines14030713","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1080/17410541.2026.2715375","name":"The historical evolution, technological transformation, and future vision of personalized medicine: a narrative review.","source":"pubmed","abstract":"The paradigm of personalized medicine is rapidly shifting from traditional, evidence-based genomics to advanced, data-driven ecosystems. Understanding this transition, supported by the computational tools of precision medicine, is critical for managing high-dimensional biomedical data.","url":"https://doi.org/10.1080/17410541.2026.2715375","authors":["Karaismailoğlu R","Bozbuğa N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1080/17410541.2026.2715375","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.20944/preprints202608.0329.v1","name":"A Comprehensive Review of Artificial Intelligence-Driven Health Management of Electrical Machines","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202608.0329.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.0329.v1","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1016/j.pcad.2026.08.012","name":"Artificial intelligence for risk prediction in atherosclerotic cardiovascular disease: A narrative review of advances, validation challenges, and clinical translation (2020-2026).","source":"pubmed","abstract":"Since 2020, artificial intelligence (AI) has been increasingly applied to atherosclerotic cardiovascular disease (ASCVD) risk prediction. This structured narrative review with systematic evidence mapping summarizes literature (2020-2026) examining study design, data sources, model architectures, multimodal fusion, model development and validation, performance evaluation, subgroup applications, and implementation barriers. Overall, 126 studies informed the review; 93 provided sufficient information for structured extraction, including prevention setting, exact input variables, comparator scores, validation strategies, discrimination, calibration, dominant model architecture, endpoint category, foundation-model or pretrained-model status, regulatory status, and implementation features. AI-based models may offer modest but clinically meaningful gains over conventional risk equations, especially with multimodal or longitudinal data. Among the 93 studies, traditional machine learning accounted for 83 (89%), deep learning for 7 (8%), and multimodal fusion for 3 (3%). Endpoint definitions were heterogeneous (23% ASCVD-specific; 63% expanded MACE composites). Among these studies, no large language model or federated learning was used for risk prediction. Appropriate comparators should now include contemporary equations such as PREVENT, rather than only legacy tools. Major barriers remain, including limited external validation, performance attenuation, data and algorithmic bias, limited interpretability, inconsistent reporting of calibration, fairness, and clinical utility, unclear regulatory status, and limited prospective evidence. Future work should prioritize calibration, robustness, transportability, fairness, interpretability, regulatory clarity, workflow integration, and prospective evidence of decision impact or post-deployment benefit. The central question is not only whether AI can detect complex patterns, but whether such models can be trusted, implemented, and shown to advance preventive cardiology in real-world settings.","url":"https://doi.org/10.1016/j.pcad.2026.08.012","authors":["Liu R","Arena R","Vasile VC","Arruda-Olson AM","Popovic D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.pcad.2026.08.012","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1016/j.compbiomed.2026.111859","name":"A clinically grounded taxonomy and systematic review of artificial intelligence for cardiovascular diagnosis: From machine learning to multimodal and agentic systems.","source":"pubmed","abstract":"Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, creating an urgent need for accurate, trustworthy, and clinically deployable artificial intelligence (AI) systems capable of supporting complex diagnostic decision-making. Although AI has advanced considerably in cardiovascular diagnosis, existing evidence remains fragmented across algorithms, data modalities, and isolated application domains, limiting a comprehensive understanding of clinically integrated AI systems. This study presents a PRISMA 2020-guided systematic review and proposes a clinically grounded six-layer taxonomy that organizes cardiovascular AI according to diagnostic objectives, data modalities, modeling paradigms, data integration complexity, interpretability and trustworthiness, and deployment maturity. A systematic search of PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect identified 226 records, of which 76 primary empirical studies met the predefined eligibility criteria and were included in the comparative evidence synthesis. The review demonstrates the evolution of cardiovascular AI from conventional machine learning applied to structured clinical data toward deep learning for physiological signals and medical imaging, followed by multimodal AI systems integrating heterogeneous clinical information. Comparative synthesis across the proposed taxonomy highlights substantial progress in predictive performance while revealing persistent challenges related to external validation, dataset representativeness, workflow integration, explainability, privacy, governance, and prospective clinical deployment. The review further distinguishes clinically validated technologies from emerging paradigms, including federated learning, foundation models, and agentic AI. Overall, the proposed taxonomy provides a unified framework for organizing contemporary cardiovascular AI research and offers a practical roadmap for evaluating the maturity, trustworthiness, and clinical readiness of next-generation intelligent diagnostic systems.","url":"https://doi.org/10.1016/j.compbiomed.2026.111859","authors":["Rezaei Z","Amini MA","Banad YM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.compbiomed.2026.111859","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.21203/rs.3.rs-9556184/v1","name":"Fairness in Federated Medical Imaging: A Systematic Review Through the Dual Fairness Lens","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9556184/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9556184/v1","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-10547895/v1","name":"Melanoma Detection Using Deep Convolutional Neural Networks: Architectures, Datasets, and Performance","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10547895/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10547895/v1","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1016/j.isci.2026.116422","name":"AI-driven technological breakthroughs and practical pathways for biodiversity conservation and ecological management.","source":"pubmed","abstract":"Global biodiversity decline demands advanced technological solutions. This review synthesizes artificial intelligence (AI)-driven methodological innovations across data, algorithmic, and system layers for biodiversity conservation. At the data layer, multimodal sensing technologies including environmental DNA, acoustic monitors, and satellite imagery, coupled with data fusion techniques, enhance monitoring resolution. At the algorithmic layer, models ranging from convolutional neural networks to Transformers and reinforcement learning enable species identification, habitat assessment, and conservation optimization. At the system layer, federated learning, digital twins, and edge computing build scalable, secure conservation platforms. These advances restructure conservation practice from reactive monitoring toward proactive, predictive management across real-time individual tracking, precise habitat assessment, proactive threat detection, and optimized conservation prioritization. Despite challenges of data bias, algorithmic opacity, and socio-ethical tensions, responsible AI development under interdisciplinary governance offers a critical pathway toward achieving global conservation targets.","url":"https://doi.org/10.1016/j.isci.2026.116422","authors":["Guan S","Liao Z","Han X","Niu S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.isci.2026.116422","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1002/joa3.70434","name":"Artificial Intelligence Techniques in Cardiac Neuromodulation: Mechanisms, Applications, and Pathways to Clinical Translation.","source":"pubmed","abstract":"Cardiac neuromodulation includes various methods, such as vagus nerve stimulation, baroreflex activation therapy, renal denervation, and stellate ganglion intervention, and targets the autonomic imbalance contributing to the pathophysiology of many cardiovascular diseases. Despite promising mechanistic evidence, several landmark trials, including INOVATE-HF, NECTAR-HF, and SYMPLICITY HTN-3, did not meet their primary clinical outcomes, with substantial numbers of non-responders observed across therapies. Variation in patient response is attributed to several unresolved issues, including insufficient stimulation dosing, off-target or non-selective fiber activation, and differences in autonomic phenotypes between patients. Both problems highlight the need for individualized approaches to patient selection, therapy delivery, and monitoring. Artificial intelligence (AI) offers tools to address these problems. In this narrative review, we describe seven families of AI techniques relevant to cardiac neuromodulation: supervised machine learning, deep learning, representation learning, reinforcement learning, multimodal fusion, digital twins with physics-informed AI, and explainable AI with federated learning. For each family, we summarize how the method works, the cardiac neuromodulation problem it addresses, and the available evidence in the field of cardiac electrophysiology. We then map these techniques to the three core problems of patient selection, real-time stimulation control, and longitudinal response monitoring. The strongest evidence to date supports representation learning for VNS responder identification, reinforcement learning for closed-loop VNS control, and digital twins for in silico testing of stimulation protocols. The opportunity for the field is to translate these methods, most of which were developed in adjacent fields, into prospective cardiac neuromodulation trials.","url":"https://doi.org/10.1002/joa3.70434","authors":["Pandey K","Pandey P","Khanal S","Panday A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/joa3.70434","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3389/fmed.2026.1865505","name":"Advances in AI for detecting pulmonary inflammation and perioperative medicine: a mini-review.","source":"pubmed","abstract":"With increasing human longevity, early recognition and treatment of pneumonia in the elderly are crucial to prevent disease progression. Artificial intelligence (AI) is rapidly transforming the detection and management of pulmonary inflammation (pneumonia, COVID-19 lung damage). Accurate preoperative assessment of pneumonia contributes to improved perioperative surgical and anesthesia management. This mini-review highlights key advances: (1) Hybrid deep learning models achieve high accuracy (&gt;96%) in analyzing ultrasound videos for disease differentiation. (2) Self-supervised learning enables expert-level X-ray interpretation without extensive annotations. (3) Multimodal integration combines imaging (CT/X-ray) with clinical data, enhancing lesion visibility and pathogen-specific diagnosis (viral vs. bacterial AUC: 0.95). Clinically, AI demonstrates high efficacy in COVID-19 detection (AUC: 0.992), pediatric pneumonia diagnosis (89-96% accuracy), and identifying post-COVID complications. Despite this promise, challenges remain, including data bias, limited pediatric datasets, \"black-box\" model interpretability, and ethical concerns. Future progress depends on expanding diverse training data (e.g., via federated learning), integrating explainable AI (XAI), and ensuring equitable access. In conclusion, AI offers accurate, scalable solutions for pulmonary inflammation diagnostics, with significant potential to augment clinical decision-making and extend into proactive areas like perioperative medicine for complication screening and prevention.","url":"https://doi.org/10.3389/fmed.2026.1865505","authors":["Huang K","Liang X","Pi R","Dai J","Lei X","Fang J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1865505","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1177/13872877261471812","name":"Integration of multi-omics and artificial intelligence for therapeutic insights in Alzheimer's disease: A comprehensive review.","source":"pubmed","abstract":"Alzheimer's disease (AD) is a progressively worsening type of brain disorder that damages the nerve cells. It is marked by the buildup of amyloid-&#x3b2; plaques outside the cells, tau neurofibrillary tangles inside the cells, and overall molecular-level dysfunction. The therapies currently available mainly cater to alleviating the symptoms, whereas the newly approved disease-modifying antibodies, such as lecanemab and donanemab, bring out only limited clinical improvements. Being complicated and involving many factors, AD requires sophisticated computer-based methods to combine different biological data and find suitable therapy targets. In this review, we discuss how artificial intelligence (AI)-powered multi-omics data integration can be a catalyst in discovering drug targets, identifying biomarkers, and stratifying patients for AD. By utilizing machine learning techniques like random forests, graph neural networks, and deep learning, AI-led multi-omics methods have helped uncover new therapeutic targets. Models that were built using federated learning across various institutions outperformed single-center models with a higher area under the curve score (0.84, 0.94 versus 0.76, 0.85). AI-guided patient stratification lessened the clinical trial's sample size needs by 40, 55% while still retaining 80, 90% statistical power. Multi-omics analyses further pointed out that it is the downstream molecular pathways, and not amyloid pathology alone, that are significantly involved in disease progression, thereby questioning the effectiveness of single-target anti-amyloid therapies and endorsing combination treatment strategies. AI and multi-omics data combination can be a game-changer in facilitating new target discovery, making clinical trial design more efficient, and ushering in precision medicine in AD.","url":"https://doi.org/10.1177/13872877261471812","authors":["Periyasamy TS","Sekar N","Lakshmanan H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1177/13872877261471812","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1016/j.esmorw.2026.100737","name":"Artificial intelligence in oncology: empowering clinicians for responsible integration.","source":"pubmed","abstract":"Artificial intelligence (AI) is rapidly reshaping oncology, from diagnosis to treatment planning and clinical research. This perspective defines the oncologist in the era of AI as a clinician able to critically interpret, supervise, and communicate AI outputs, while understanding the principles, limitations, and ethical implications of these tools. We ground this discussion in a survey of 475 UK-based participants, including cancer patients and survivors, members of the public and healthcare staff. Acceptance of AI was substantial but conditional: it increased sharply with self-reported understanding and depended on assurances of clinician involvement, transparency and data security. These findings motivate the three concerns around which we structure the perspective: 'AI will replace the clinician', 'AI may be biased and unfair', and 'AI does not safeguard data'. Using the example of an AI system for cancer treatment recommendation, we illustrate how these concerns can be addressed through practical, technically grounded approaches, including concept-based modelling, uncertainty quantification, and federated learning. Finally, we argue that AI skills development must become an integral part of oncology education, enabling oncologists not only to use AI safely, but also to explain, contextualise, and critically shape its integration into patient-centred cancer care.","url":"https://doi.org/10.1016/j.esmorw.2026.100737","authors":["Colliver E","Parisini E","Arefaine B","PIVOT team†","Banerji CRS","Verghese GE","Grigoriadis A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.esmorw.2026.100737","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1038/s41598-026-41074-5","name":"Adaptive routing protocol for large-scale power internet of things based on edge computing and federated learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-41074-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-41074-5","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1371/journal.pone.0342454","name":"Towards a cybersecure and privacy enhanced smart grid: A blockchain enabled federated learning framework.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0342454","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0342454","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1111/tme.70105","name":"Research and application of machine learning models based on multimodal big data for precise transfusion management in acute myeloid leukaemia.","source":"pubmed","abstract":"Acute myeloid leukaemia (AML) is a highly heterogeneous haematologic malignancy in which transfusion support represents an essential component of comprehensive patient care. This review aims to provide an updated synthesis of recent progress in the development and clinical application of machine learning models based on multimodal big data for precision transfusion management in AML, addressing the persistent limitations of conventional, empirically guided transfusion practices. We systematically reviewed the literature on multimodal data integration-including electronic health records, genomic, proteomic and other high-dimensional datasets-in the context of AML transfusion management. The applications of machine learning algorithms such as decision trees, random forests and neural networks were analysed in the contexts of transfusion demand prediction and transfusion reaction risk assessment, with reference to representative clinical case studies demonstrating their practical utility. Multimodal big data demonstrates substantial value in optimising transfusion strategies for AML patients. Machine learning models have shown promising performance in predicting transfusion demand and assessing transfusion reaction risks, with clinical case studies supporting their practical utility. However, major challenges persist, including data privacy protection, data standardisation across platforms and model interpretability for clinical adoption. The integration of multimodal big data with advanced machine learning methodologies holds substantial promise for enabling precision, individualised transfusion management in AML. Future directions involving federated learning and explainable artificial intelligence are anticipated to address current limitations, ultimately contributing to improved transfusion safety and clinical outcomes in AML patients.","url":"https://doi.org/10.1111/tme.70105","authors":["Li J","An X","Jing Z","Li Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1111/tme.70105","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3389/fdata.2026.1878260","name":"A dataset-centric review of IoT and IIoT intrusion detection: realism, evaluation biases, and future research directions.","source":"pubmed","abstract":"The rapid growth of IoT and IIoT expands the cyber-attack surface of interconnected and safety-critical systems, and, as such, IDSs have become a fundamental security mechanism. Although very impressive results have been reported for machine learning and deep learning-based IDS in benchmark datasets, these gains often do not generalize to real-world deployments owing to dataset design limitations, realism deficits, and evaluation biases, rather than inherent flaws in detection algorithms, which can lead to significant vulnerabilities in actual operational environments. This study presents a dataset-centric review of widely used intrusion detection datasets from the IIoT, IoT, and traditional network domains. A unified taxonomy differentiates datasets based on the domain context, traffic representation, protocol semantics, and attack modeling assumptions. Based on a common analytical framework, each dataset was reviewed regarding its realism, coverage of the threats, class imbalance, temporal continuity, and modern ML/DL-based evaluation of the IDS. The cross-dataset analysis conducted in this study shows that, in addition to the fact that model architecture and feature engineering play a major role, several studies indicate that the simplicity of the datasets, the class imbalance, and the repetitive attack patterns as well as the evaluation methods can affect accuracy of the IDS. This work further underlines the remaining gaps, such as zero-day and adaptive attacks, limited encrypted traffic, weak temporal evolution, poor support for federated learning, and sparse annotations for explainable IDSs. Finally, this study presents future directions for dataset design aligned with the requirements of next-generation IDSs by highlighting digital twin-based IIoT environments, edge-cloud collaborative data generation, sequential traffic modeling, and explainability-oriented annotations that can ensure robust, trustworthy, and deployment-ready IDS solutions.","url":"https://doi.org/10.3389/fdata.2026.1878260","authors":["Reddy DS","Kumar KA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdata.2026.1878260","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1038/s41467-026-70297-3","name":"An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41467-026-70297-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41467-026-70297-3","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3389/fped.2026.1932796","name":"Artificial intelligence in pediatric arrhythmias: current landscape, unique challenges, and translational perspectives.","source":"pubmed","abstract":"The interpretation of pediatric electrocardiograms (ECGs) and management of childhood arrhythmias represent specialized clinical disciplines complicated by age-dependent physiological evolution. While artificial intelligence (AI) has transformed adult cardiology, its application to pediatric electrophysiology remains largely in the research phase.","url":"https://doi.org/10.3389/fped.2026.1932796","authors":["Jia H","Zhu W","Lv J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fped.2026.1932796","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1093/jamiaopen/ooag128","name":"Assessment of real-world evidence research competencies in federated research networks: a maternal health fellowship program evaluation.","source":"pubmed","abstract":"Generating real-world evidence (RWE) approaches across federated evidence networks based on real-world data requires learning new methodological skills. However, standardized frameworks for defining and assessing competencies for large-scale RWE research remain limited. This study aims to develop an assessment framework for RWE studies and evaluate the competencies of RWE research professionals.","url":"https://doi.org/10.1093/jamiaopen/ooag128","authors":["Lee H","Martin B","Creanga AA","Minty E","Golozar A","Cai CX","Sung C","O'Rilley S","Aziz K","Nagy P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1093/jamiaopen/ooag128","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3390/s26061989","name":"EdgeGuard-AI: Zero-Trust and Load-Aware Federated Scheduling for Secure and Low-Latency IoT Edge Networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26061989","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26061989","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1111/all.70468","name":"Targeting Airway Remodeling in Severe Asthma: Is There a Window of Opportunity for Biologic Therapy Predicting Effects Using Causal Artificial Intelligence?","source":"pubmed","abstract":"Airway remodeling is increasingly recognized as a major determinant of asthma progression, fixed airflow limitation, and long-term morbidity, particularly in severe disease. Although biologic therapies have transformed outcomes by reducing exacerbations and systemic corticosteroid exposure, their potential to modify structural airway trajectories-and whether a time-sensitive \"window of opportunity\" exists-remains uncertain. Here, we provide a progressive landscape integrating mechanistic remodeling pathways with measurable structural readouts (biopsy-derived indices and quantitative imaging) and emerging digital biomarkers derived from connected respiratory technologies. We propose an operational framework linking mechanism &#x2192; biomarker &#x2192; remodeling readout &#x2192; timing decision, and we outline a 1-, 3-, and 5-year research roadmap in which advanced artificial intelligence (AI) methods (multimodal learning, causal inference, federated learning, and digital-twin architectures) evolve in parallel with wearable and smart-inhaler ecosystems. This landscape aims to standardize endpoints, sharpen trial design, and accelerate a shift from symptom control toward credible disease modification in severe asthma.","url":"https://doi.org/10.1111/all.70468","authors":["Gangemi S","Manti S","Virchow JC","Canonica GW"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1111/all.70468","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1111/wrr.70187","name":"Machine Learning for Wound Management: A Structured Narrative Review of Applications, Challenges, and Prospects.","source":"pubmed","abstract":"Chronic and complex wounds represent a major clinical and economic burden, requiring frequent assessment, early detection of complications, and accurate prediction of healing outcomes. Conventional wound evaluation is often subjective and time-intensive, motivating the development of automated approaches. Machine learning (ML) has emerged as a powerful tool for wound management by enabling wound detection, segmentation, tissue characterisation, infection assessment, and healing prediction using wound images, clinical records, and sensor-derived data. This structured narrative review summarises key ML and deep learning (DL) methods applied in wound care, including commonly used architectures, data modalities, and evaluation strategies. We critically discuss persistent barriers limiting clinical adoption, such as limited dataset size and diversity, annotation inconsistency, poor generalizability, bias, lack of external and prospective validation, and challenges related to privacy, regulation, and clinical workflow integration. Finally, we highlight emerging opportunities including multimodal learning, self-supervised learning, federated learning, explainable AI (XAI), and mobile or wearable technologies for continuous wound monitoring. Overall, ML has strong potential to improve the objectivity, efficiency, and personalization of wound care; however, clinically deployable solutions will require standardised datasets, transparent reporting, rigorous validation, and interdisciplinary collaboration.","url":"https://doi.org/10.1111/wrr.70187","authors":["Das IJ","Debata J","Bhatta K","Panda S","Samal HB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1111/wrr.70187","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3390/s26154802","name":"Distributed Artificial Intelligence for IoT Security: A Structured Review.","source":"pubmed","abstract":"The expansion of the Internet of Things (IoT) has increased the complexity of securing distributed systems against growing threats to data security, privacy, and reliability. Conventional centralised cybersecurity methods are often insufficient for environments characterised by scale, heterogeneity, and dynamic behaviour. This paper presents a structured review of Distributed Artificial Intelligence (DAI) for IoT security, focusing on how local, cooperative intelligence can support intrusion detection, anomaly recognition, secure data processing, and collaborative defence. We synthesise the current literature on Federated Learning (FL), Multi-Agent Systems, and related approaches, highlighting their benefits, limitations, and practical deployment constraints. Particular attention is given to critical infrastructure contexts, where resilience is essential for operational continuity and public safety. The review concludes by outlining key gaps and future research directions for DAI-enabled IoT security.","url":"https://doi.org/10.3390/s26154802","authors":["Szymoniak S","Kubanek M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26154802","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.1371/journal.pone.0330244","name":"A multi-modal data fusion and real-time monitoring on stroke risk prediction using federated learning.","source":"pubmed","abstract":"Predicting the risk of stroke is one of the critical problems in healthcare, which necessitates efficient solutions for providing accurate and prompt risk assessments while preserving data confidentiality. This work proposes a new framework using Federated Learning (FL) to combine Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) models that are essential in analyzing multimodal data. Implemented in Python, the approach incorporates two datasets: Dataset 1, which consists of Demographic data medical history, and lifestyle data, and the second dataset, which includes the normal condition and the affected stroke condition CT scan images. Imputation of missing values, feature normalization by Min-Max scaling, and handling of imbalanced classes with SMOTE make the data pre-processing procedures exhaustive. In FL architecture three clients -Client A, Client B, and Client C - process a split multimodal dataset containing static and sequential information. Each client independently trains an MLP-GRU model. Each is applied with MLP handling static features from Dataset 1 and GRU handling sequential features from Dataset 2. To update models, Federated Averaging is used on a central server, to create a global model that is then returned to the clients for further refinement. The accuracy of the proposed method averages 99.00% and surpasses other models by 2.5% including CNN, LSTM, Random Forest, and SVM. By enhancing MLP with GRU and applying them to a privacy-preserving FL framework,The study addresses the fragmented use of multimodal medical data, where clinical records and imaging are generally evaluated separately, resulting in inadequate diagnostic support. The strategy integrates complementary modalities to create a more comprehensive perspective of patient health, enhancing healthcare predictive accuracy and decision-making. This incentive is essential for improving computational methods and linking technical advancement with medical objectives like fast diagnosis and therapy planning. The introduction emphasises the therapeutic necessity of harmonising organized and unstructured data to reduce diagnostic ambiguity. A translational approach is used to discuss how multimodal integration might improve clinical workflows, develop collaborative healthcare systems, and support sustainable medical practices. This repeated emphasis links methodological advances to real-world healthcare issues, boosting the study's academic relevance sets.","url":"https://doi.org/10.1371/journal.pone.0330244","authors":["K RS","P MK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0330244","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-10087271/v1","name":"Optimization Strategies and Complexity in ML Models","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10087271/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10087271/v1","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.3389/fmicb.2026.1848209","name":"Editorial: Generative AI and large language models in microbial evolution, resistance mechanisms, and antimicrobial drug discovery.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fmicb.2026.1848209","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fmicb.2026.1848209","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.22514/jofph.2026.049","name":"Artificial intelligence-based prognostic modeling in temporomandibular disorders and chronic orofacial pain: a critical conceptual review.","source":"pubmed","abstract":"This conceptual review (&#x2170;) analyzes the outcomes predicted, data modalities, modeling approaches, validation strategies, and reporting quality of existing artificial intelligence (AI)-driven prognostic models in temporomandibular disorders (TMD) and chronic orofacial pain (OFP); (&#x2171;) identifies enduring methodological and ethical constraints that hinder clinical translation; and (&#x2172;) proposes a pragmatic research framework to guide the responsible development of clinically relevant prognostic tools for TMD and OFP. The review covers peer-reviewed and other relevant publications from the previous decade, emphasizing AI or machine-learning (ML) based models for prognosis, outcome prediction, or trajectory modeling in TMD and OFP populations. Established paradigms, including the Transparent Reporting of a multivariable prediction model for individual Prognosis Or Diagnosis plus Artificial Intelligence extension (TRIPOD + AI) and the Prediction model Risk Of Bias Assessment Tool (PROBAST), were used to assess the literature. Methodologies remain highly inconsistent, and current literature lacks the volume and rigor required for clinical translation. Most AI research has concentrated on diagnostic classification rather than prognostic modeling. Small sample sizes, short follow-up, single-center datasets, omission of psychosocial factors, and a general lack of external validation hamper the few prognostic studies that exist. Most model outputs are neither clinically actionable nor suitable for direct use in treatment decisions, limiting their value for clinicians and their potential impact on patient outcomes. Research applying AI to forecast TMD and OFP remains in its early stages. Without a prognosis-first research design, longitudinal data integration, inclusion of biopsychosocial predictors, and clinically significant outcome objectives, existing models are unlikely to influence clinical practice. Clear research objectives, reporting criteria, and ethical norms must be established before AI-based prognostic models can be confidently adopted in TMD and OFP clinical practice. Future objectives comprise establishing multicenter longitudinal cohorts, conducting trajectory-based modeling, employing federated learning for external validation, and initiating prospective clinical trials to demonstrate clear clinical benefit.","url":"https://doi.org/10.22514/jofph.2026.049","authors":["Al-Harthy MH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.22514/jofph.2026.049","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.4102/ajlm.v15i1.3130","name":"Artificial intelligence in haematology laboratory diagnosis: Current applications, challenges, and future directions.","source":"pubmed","abstract":"Artificial intelligence (AI) is transforming haematology diagnostics by improving accuracy, efficiency, and reproducibility in workflows traditionally reliant on manual microscopy and expert interpretation. Integrating AI into laboratory medicine presents opportunities to enhance diagnostic precision and reduce variability, particularly in resource-limited settings.","url":"https://doi.org/10.4102/ajlm.v15i1.3130","authors":["Osman HA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.4102/ajlm.v15i1.3130","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3389/fimmu.2026.1792331","name":"From subjective assessment to data-driven diagnosis: the AI revolution in multimodal early detection of Sjögren's disease.","source":"pubmed","abstract":"Sj&#xf6;gren's disease (SjD), a chronic autoimmune disorder affecting exocrine glands, faces significant diagnostic challenges due to its highly heterogeneous symptoms, subjective interpretation of imaging findings, and reliance on invasive biopsies, often resulting in delayed diagnosis by 3-7 years. This review posits that the integration of diverse data streams-from medical imaging to molecular omics-is the pivotal key to overcoming diagnostic heterogeneity, and that AI serves as the indispensable engine to power this integration. We systematically synthesize evidence showing that deep learning models, such as Fully Convolutional Dense Network (FCN-DenseNet), can automate salivary gland ultrasound segmentation; computed tomography (CT)-based AlexNet may achieve diagnostic performance comparable to radiologists; and AI-driven analyses of pathology, metabolomics, and genomics have revealed potential biomarker patterns, including metabolomic and immune-clustering models with reported AUC values of 1.00. However, these findings should be interpreted cautiously because many studies were conducted in small, retrospective, single-center cohorts or relied mainly on internal validation, limiting their clinical robustness and generalizability. We thus critically advocate for a future paradigm centered on causally-aware, multimodal fusion frameworks-moving beyond mere correlation to model disease mechanisms-and the adoption of federated learning to navigate data privacy while leveraging large-scale genomics. The forthcoming challenge is not merely technical but conceptual: to develop interpretable AI that can deconstruct SjD into mechanistically defined subtypes, thereby bridging the critical gap between algorithmic prowess and clinically actionable insights for definitive early intervention and personalized treatment strategies.","url":"https://doi.org/10.3389/fimmu.2026.1792331","authors":["Zhou Y","Yu B","Chang R","Ding Y","Wang Y","Han M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fimmu.2026.1792331","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.3389/fneur.2026.1753844","name":"From severity scoring to predictive analytics: the emerging role of AI in neurosurgery.","source":"pubmed","abstract":"The rapid integration of artificial intelligence (AI) into neurosurgical practice is transforming every phase of patient care from diagnostic imaging and preoperative planning to intraoperative decision-making and postoperative management. This narrative review traces the evolution of data-driven neurosurgery, beginning with traditional severity scoring systems and advancing toward predictive analytics and intelligent automation. By examining structured data (such as electronic health records and laboratory values) alongside complex unstructured inputs (including neuroimaging, surgical videos, and free-text notes), can extract clinically meaningful patterns, with reported performance metrics such as Dice scores of 0.82-0.84 for tumor segmentation and AUC values of 0.80-0.90 for molecular prediction and outcome forecasting. Applications in lesion detection, surgical navigation, prognostication, and rehabilitation are discussed, along with critical challenges in interpretability, data harmonization, bias mitigation, and regulatory approval. Emerging paradigms such as federated learning, generative AI, and continuous learning ecosystems are also explored as future pathways toward ethical, adaptive, and globally connected neurosurgical intelligence. As a narrative review, this work synthesizes key developments qualitatively; specific performance metrics and limitations regarding systematic selection, quantitative synthesis, and variable model validation are addressed. Ultimately, AI serves not as a replacement for the neurosurgeon but as a cognitive collaborator, augmenting precision, efficiency, and patient-centered outcomes in modern neurosurgery.","url":"https://doi.org/10.3389/fneur.2026.1753844","authors":["Huang Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1753844","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.2196/79052","name":"Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review.","source":"pubmed","abstract":"","url":"https://doi.org/10.2196/79052","authors":["Wang X","Xie Y","Chen X","Yang J","Li R","Gao W","Yan Z","Zhou H","Ye Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/79052","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1177/10766294261467803","name":"Artificial Intelligence Applications in Antimicrobial Resistance: Comprehensive Review of Predictive Models, Diagnostic Innovations, and Clinical Integration.","source":"pubmed","abstract":"Antimicrobial resistance (AMR) represents a critical global health crisis, driving increased mortality, treatment failure, and economic burden. Artificial intelligence (AI) offers transformative potential to counter this threat by enhancing detection, diagnostics, and therapeutic precision. This narrative review synthesizes recent advances in AI-based approaches for AMR prediction, antimicrobial discovery, and clinical decision support, drawing on representative peer-reviewed studies published between January 1, 2015, and April 24, 2026. Models such as Deeparg-LS, XGBoost, and vision transformers achieved remarkable predictive accuracy using genomic, spectroscopic, and clinical data (AUC &gt; 0.90; sensitivity/specificity &gt;95%). AI-driven clinical decision support systems reduced antibiotic mismatches by up to 67%, while generative algorithms accelerated antimicrobial peptide discovery with 76% validation success. Deep learning frameworks improved metagenomic resistance profiling, and microscopy-based diagnostics shortened antimicrobial susceptibility testing by 50-70%. However, major challenges persist, including dataset heterogeneity, computational intensity, limited model transferability, and ethical concerns related to data privacy, bias, and interpretability. Emerging strategies such as explainable AI and federated learning show promise in addressing these issues. Overall, AI stands as a pivotal enabler in the fight against AMR, with future progress hinging on interdisciplinary collaboration, standardized validation, and responsible integration into clinical practice.","url":"https://doi.org/10.1177/10766294261467803","authors":["Touati A","Boufahja F","Ben Hamadi N","Touaitia R","Idres T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1177/10766294261467803","addedAt":"2026-08-31T06:41:27.398Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.21604094","name":"Challenges and Future Directions in Breast Cancer Segmentation: A Research Perspective","source":"datacite","abstract":"Breast cancer is one of the most aggressive and widespread illnesses afflicting women across the globe. Fast and precise segmentation techniques are essential for early detection, diagnosis, and treatment planning. This paper reviews comprehensively segmentation methods used in breast cancer detection operations, including traditional methods of thresholding and edge detection and eliciting advanced deep learning techniques such as Convolutional Neural Networks (CNN), U-Net, Generative Adversarial Networks (GANs), and Transformer-based models. The review stresses the merits of hybrid approaches combining many segmentation paradigms for better accuracy and robustness. This new round of research underlines the recent progress in segmentation with the help of attention mechanisms, precise mapping, and multimodal imaging integration. Yet, problems such as dataset-level issues, generalization issues, computational complexity, and lack of explainability still remain. Future research will design lightweight architectures, explainable AI, federated learning, and advanced multimodal data fusion techniques. This paper highlights the dynamic nature of breast cancer segmentation and marks that without continued innovation, achieving clinically relevant and accurate automated segmentation systems will remain a challenge.","url":"https://doi.org/10.5281/zenodo.21604094","authors":["Iyer, Swathi","Tudilkar, Salwa","R, Srivaramangai"],"tags":["Breast cancer; Medical Imaging Segmentation; Segmentation Techniques; CNN; U-Net; GANs; Transformers; Deep Learning; Hybrid Segmentation; Attention Mechanism; Precision Mapping; Multimodal Imaging; Federated Learning; Automated Diagnosis; Computational Complexity; Tumour Detection; Medical Imaging; Feature Extraction; Transfer Learning; Image Processing; Clinical Applications"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21604094","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21604095","name":"Challenges and Future Directions in Breast Cancer Segmentation: A Research Perspective","source":"datacite","abstract":"Breast cancer is one of the most aggressive and widespread illnesses afflicting women across the globe. Fast and precise segmentation techniques are essential for early detection, diagnosis, and treatment planning. This paper reviews comprehensively segmentation methods used in breast cancer detection operations, including traditional methods of thresholding and edge detection and eliciting advanced deep learning techniques such as Convolutional Neural Networks (CNN), U-Net, Generative Adversarial Networks (GANs), and Transformer-based models. The review stresses the merits of hybrid approaches combining many segmentation paradigms for better accuracy and robustness. This new round of research underlines the recent progress in segmentation with the help of attention mechanisms, precise mapping, and multimodal imaging integration. Yet, problems such as dataset-level issues, generalization issues, computational complexity, and lack of explainability still remain. Future research will design lightweight architectures, explainable AI, federated learning, and advanced multimodal data fusion techniques. This paper highlights the dynamic nature of breast cancer segmentation and marks that without continued innovation, achieving clinically relevant and accurate automated segmentation systems will remain a challenge.","url":"https://doi.org/10.5281/zenodo.21604095","authors":["Iyer, Swathi","Tudilkar, Salwa","R, Srivaramangai"],"tags":["Breast cancer; Medical Imaging Segmentation; Segmentation Techniques; CNN; U-Net; GANs; Transformers; Deep Learning; Hybrid Segmentation; Attention Mechanism; Precision Mapping; Multimodal Imaging; Federated Learning; Automated Diagnosis; Computational Complexity; Tumour Detection; Medical Imaging; Feature Extraction; Transfer Learning; Image Processing; Clinical Applications"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21604095","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21608193","name":"Interpreting Federated Learning (FL) Models on Edge Devices by Enhancing Model Explainability with Computational Geometry and Advanced Database Architectures","source":"datacite","abstract":"Federated learning (FL) on edge devices has emerged as a promising approach for decentralized model training, enabling data privacy and efficiency in distributed networks. However, the complexity of these models presents significant challenges in terms of transparency and interpretability, which are critical for trust and accountability in real-world applications. This paper explores the integration of explainable AI techniques to enhance model interpretability within federated learning systems. By incorporating computational geometry, we aim to optimize model structure and decision-making processes, providing clearer insights into how models generate predictions. Additionally, we examine the role of advanced database architectures in managing the complexity of federated learning models on edge devices, ensuring efficient data handling and storage. Together, these approaches contribute to a more transparent, efficient, and scalable framework for federated learning on edge networks, addressing key challenges in both model explainability and performance optimization. This review highlights recent advancements and suggests future directions for research at the intersection of federated learning (FL), edge computing, explainability, and computational techniques.","url":"https://doi.org/10.5281/zenodo.21608193","authors":["Enyejo, Lawrence Anebi","Adewoye, Michael Babatunde","Ugochukwu, Uchenna Nneka"],"tags":["Federated Learning; Explainable AI; Edge Computing; Computational Geometry; Data Privacy; Model Transparency"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21608193","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21608194","name":"Interpreting Federated Learning (FL) Models on Edge Devices by Enhancing Model Explainability with Computational Geometry and Advanced Database Architectures","source":"datacite","abstract":"Federated learning (FL) on edge devices has emerged as a promising approach for decentralized model training, enabling data privacy and efficiency in distributed networks. However, the complexity of these models presents significant challenges in terms of transparency and interpretability, which are critical for trust and accountability in real-world applications. This paper explores the integration of explainable AI techniques to enhance model interpretability within federated learning systems. By incorporating computational geometry, we aim to optimize model structure and decision-making processes, providing clearer insights into how models generate predictions. Additionally, we examine the role of advanced database architectures in managing the complexity of federated learning models on edge devices, ensuring efficient data handling and storage. Together, these approaches contribute to a more transparent, efficient, and scalable framework for federated learning on edge networks, addressing key challenges in both model explainability and performance optimization. This review highlights recent advancements and suggests future directions for research at the intersection of federated learning (FL), edge computing, explainability, and computational techniques.","url":"https://doi.org/10.5281/zenodo.21608194","authors":["Enyejo, Lawrence Anebi","Adewoye, Michael Babatunde","Ugochukwu, Uchenna Nneka"],"tags":["Federated Learning; Explainable AI; Edge Computing; Computational Geometry; Data Privacy; Model Transparency"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21608194","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21579767","name":"Deep Learning Models for Predicting and Mitigating Environmental Impact of Industrial Processes in Real-Time","source":"datacite","abstract":"Industrial processes contribute significantly to environmental degradation through emissions, waste, and resource depletion. The need for real-time monitoring and mitigation strategies has led to the adoption of deep learning (DL) models for predictive analytics and automated decision-making. This study explores the application of deep learning techniques in predicting and mitigating the environmental impact of industrial activities. We review state-of-the-art deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers, in processing large-scale environmental data. These models analyze real-time sensor data, satellite imagery, and industrial parameters to forecast pollution levels, detect anomalies, and optimize industrial operations for sustainability. Key advancements in deep learning, such as hybrid architectures integrating deep reinforcement learning (DRL) and generative adversarial networks (GANs), enhance predictive accuracy and robustness in environmental monitoring systems. Transfer learning and federated learning approaches facilitate scalable and adaptive solutions across diverse industrial sectors. The study highlights the role of DL in early detection of air and water pollution, energy consumption optimization, and emission control through predictive maintenance and process adjustments. Moreover, integrating explainable artificial intelligence (XAI) ensures model interpretability, fostering trust among policymakers and industry stakeholders. Challenges in deploying deep learning models include data heterogeneity, computational complexity, and model interpretability. To address these issues, we discuss techniques such as data augmentation, adversarial training, and edge AI implementation for real-time processing. Ethical and regulatory considerations surrounding AI-driven environmental monitoring are also examined to ensure compliance with sustainability standards. This research underscores the transformative potential of deep learning in industrial sustainability, emphasizing its role in real-time decision support systems. Future directions involve integrating quantum computing and neuromorphic computing for enhanced model efficiency and expanding interdisciplinary collaborations for AI-driven environmental governance. By leveraging deep learning for predictive environmental impact assessment, industries can transition toward greener and more efficient operational frameworks.","url":"https://doi.org/10.5281/zenodo.21579767","authors":["Ojadi, Jessica Obianuju","Owulade, Olumide Akindele","Odionu, Chinekwu Somtochukwu","Onukwulu, Ekene Cynthia"],"tags":["Deep Learning; Environmental Monitoring; Industrial Processes; Predictive Analytics; Real-Time Mitigation; Sustainability; Anomaly Detection; Edge AI; Explainable AI; Reinforcement Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21579767","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21579768","name":"Deep Learning Models for Predicting and Mitigating Environmental Impact of Industrial Processes in Real-Time","source":"datacite","abstract":"Industrial processes contribute significantly to environmental degradation through emissions, waste, and resource depletion. The need for real-time monitoring and mitigation strategies has led to the adoption of deep learning (DL) models for predictive analytics and automated decision-making. This study explores the application of deep learning techniques in predicting and mitigating the environmental impact of industrial activities. We review state-of-the-art deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers, in processing large-scale environmental data. These models analyze real-time sensor data, satellite imagery, and industrial parameters to forecast pollution levels, detect anomalies, and optimize industrial operations for sustainability. Key advancements in deep learning, such as hybrid architectures integrating deep reinforcement learning (DRL) and generative adversarial networks (GANs), enhance predictive accuracy and robustness in environmental monitoring systems. Transfer learning and federated learning approaches facilitate scalable and adaptive solutions across diverse industrial sectors. The study highlights the role of DL in early detection of air and water pollution, energy consumption optimization, and emission control through predictive maintenance and process adjustments. Moreover, integrating explainable artificial intelligence (XAI) ensures model interpretability, fostering trust among policymakers and industry stakeholders. Challenges in deploying deep learning models include data heterogeneity, computational complexity, and model interpretability. To address these issues, we discuss techniques such as data augmentation, adversarial training, and edge AI implementation for real-time processing. Ethical and regulatory considerations surrounding AI-driven environmental monitoring are also examined to ensure compliance with sustainability standards. This research underscores the transformative potential of deep learning in industrial sustainability, emphasizing its role in real-time decision support systems. Future directions involve integrating quantum computing and neuromorphic computing for enhanced model efficiency and expanding interdisciplinary collaborations for AI-driven environmental governance. By leveraging deep learning for predictive environmental impact assessment, industries can transition toward greener and more efficient operational frameworks.","url":"https://doi.org/10.5281/zenodo.21579768","authors":["Ojadi, Jessica Obianuju","Owulade, Olumide Akindele","Odionu, Chinekwu Somtochukwu","Onukwulu, Ekene Cynthia"],"tags":["Deep Learning; Environmental Monitoring; Industrial Processes; Predictive Analytics; Real-Time Mitigation; Sustainability; Anomaly Detection; Edge AI; Explainable AI; Reinforcement Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21579768","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20625872","name":"An Overview of Extreme Learning Machine-Based Intelligent Fault Localization Approaches","source":"datacite","abstract":"Abstract The reliable operation of modern power systems depends heavily on the rapid detection and accurate localization of faults occurring in transmission and distribution networks. With the increasing integration of renewable energy resources, smart grid technologies, distributed generation, and advanced communication infrastructures, conventional fault localization methods face significant challenges related to network complexity, dynamic operating conditions, and measurement uncertainties. In recent years, Artificial Intelligence (AI)-based techniques have emerged as promising alternatives for enhancing the accuracy and efficiency of fault localization processes. Among these techniques, the Extreme Learning Machine (ELM) has gained considerable attention due to its fast learning capability, low computational complexity, excellent generalization performance, and suitability for real-time applications. This paper presents a comprehensive overview of ELM-based intelligent fault localization approaches developed for power system protection and monitoring. The review discusses the fundamental principles of fault localization and the theoretical foundations of ELM, including its architecture, learning mechanism, and major variants such as Online Sequential ELM, Kernel ELM, Weighted ELM, and Deep ELM. Furthermore, existing research contributions employing ELM for transmission lines, distribution networks, microgrids, renewable energy-integrated systems, and smart grid environments are systematically analyzed and compared. The paper also examines various signal processing and feature extraction techniques used in conjunction with ELM, including wavelet transforms, empirical mode decomposition, and phasor measurement unit-based approaches. Performance metrics, implementation challenges, and comparative advantages of ELM over traditional machine learning methods are critically evaluated. Finally, current research gaps and future directions are identified, highlighting opportunities in explainable artificial intelligence, federated learning, digital twins, edge computing, and hybrid intelligent fault localization frameworks. The findings indicate that ELM-based approaches offer a promising and computationally efficient solution for next-generation intelligent fault localization systems, contributing significantly to the development of reliable, adaptive, and resilient smart power grids. Keywords: Fault Localization, Extreme Learning Machine, Artificial Intelligence, Smart Grid, Distribution Networks, Power System Protection, Machine Learning, Intelligent Fault Diagnosis","url":"https://doi.org/10.5281/zenodo.20625872","authors":["Priyanka V. Raut","Kiran A. Dongre","Amol P. Bhagat"],"tags":["Fault Localization","Extreme Learning Machine","Artificial Intelligence","Smart Grid","Distribution Networks","Power System Protection","Intelligent Fault Diagnosis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20625872","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20625873","name":"An Overview of Extreme Learning Machine-Based Intelligent Fault Localization Approaches","source":"datacite","abstract":"Abstract The reliable operation of modern power systems depends heavily on the rapid detection and accurate localization of faults occurring in transmission and distribution networks. With the increasing integration of renewable energy resources, smart grid technologies, distributed generation, and advanced communication infrastructures, conventional fault localization methods face significant challenges related to network complexity, dynamic operating conditions, and measurement uncertainties. In recent years, Artificial Intelligence (AI)-based techniques have emerged as promising alternatives for enhancing the accuracy and efficiency of fault localization processes. Among these techniques, the Extreme Learning Machine (ELM) has gained considerable attention due to its fast learning capability, low computational complexity, excellent generalization performance, and suitability for real-time applications. This paper presents a comprehensive overview of ELM-based intelligent fault localization approaches developed for power system protection and monitoring. The review discusses the fundamental principles of fault localization and the theoretical foundations of ELM, including its architecture, learning mechanism, and major variants such as Online Sequential ELM, Kernel ELM, Weighted ELM, and Deep ELM. Furthermore, existing research contributions employing ELM for transmission lines, distribution networks, microgrids, renewable energy-integrated systems, and smart grid environments are systematically analyzed and compared. The paper also examines various signal processing and feature extraction techniques used in conjunction with ELM, including wavelet transforms, empirical mode decomposition, and phasor measurement unit-based approaches. Performance metrics, implementation challenges, and comparative advantages of ELM over traditional machine learning methods are critically evaluated. Finally, current research gaps and future directions are identified, highlighting opportunities in explainable artificial intelligence, federated learning, digital twins, edge computing, and hybrid intelligent fault localization frameworks. The findings indicate that ELM-based approaches offer a promising and computationally efficient solution for next-generation intelligent fault localization systems, contributing significantly to the development of reliable, adaptive, and resilient smart power grids. Keywords: Fault Localization, Extreme Learning Machine, Artificial Intelligence, Smart Grid, Distribution Networks, Power System Protection, Machine Learning, Intelligent Fault Diagnosis","url":"https://doi.org/10.5281/zenodo.20625873","authors":["Priyanka V. Raut","Kiran A. Dongre","Amol P. Bhagat"],"tags":["Fault Localization","Extreme Learning Machine","Artificial Intelligence","Smart Grid","Distribution Networks","Power System Protection","Intelligent Fault Diagnosis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20625873","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21578631","name":"Federated Meta-Learning for Few-Shot Image Classification with Personalized Model Adaptation: A Comprehensive Review","source":"datacite","abstract":"Federated meta-learning represents a paradigm shift in machine learning that combines the privacy-preserving benefits of federated learning with the rapid adaptation capabilities of meta-learning for few-shot image classification tasks. This comprehensive literature review critically evaluates the latest advancements in federated meta-learning approaches, with particular emphasis on personalized model adaptation strategies for image classification scenarios with limited labelled data examining classical federated learning methods, advanced meta-learning techniques, and their integration for few-shot learning applications. The review highlights significant challenges including non-independent and identically distributed data, communication efficiency, privacy preservation, and model personalization across diverse client populations. Advanced techniques utilizing deep learning architectures, optimization-based meta-learning, and adaptive aggregation mechanisms have demonstrated promising results in enhancing classification accuracy while maintaining privacy constraints. This review serves as a comprehensive guide for researchers and practitioners, providing thorough understanding of state-of-the-art federated meta-learning techniques, their implications for few-shot image classification, and potential avenues for further development.","url":"https://doi.org/10.5281/zenodo.21578631","authors":["Thenmozhi, R.","Santhalakshmi, M.","Shanthakumar, M."],"tags":["Federated Learning; Meta-Learning; Few-Shot Learning; Image Classification; Personalized Models; Privacy-Preserving Machine Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21578631","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21578632","name":"Federated Meta-Learning for Few-Shot Image Classification with Personalized Model Adaptation: A Comprehensive Review","source":"datacite","abstract":"Federated meta-learning represents a paradigm shift in machine learning that combines the privacy-preserving benefits of federated learning with the rapid adaptation capabilities of meta-learning for few-shot image classification tasks. This comprehensive literature review critically evaluates the latest advancements in federated meta-learning approaches, with particular emphasis on personalized model adaptation strategies for image classification scenarios with limited labelled data examining classical federated learning methods, advanced meta-learning techniques, and their integration for few-shot learning applications. The review highlights significant challenges including non-independent and identically distributed data, communication efficiency, privacy preservation, and model personalization across diverse client populations. Advanced techniques utilizing deep learning architectures, optimization-based meta-learning, and adaptive aggregation mechanisms have demonstrated promising results in enhancing classification accuracy while maintaining privacy constraints. This review serves as a comprehensive guide for researchers and practitioners, providing thorough understanding of state-of-the-art federated meta-learning techniques, their implications for few-shot image classification, and potential avenues for further development.","url":"https://doi.org/10.5281/zenodo.21578632","authors":["Thenmozhi, R.","Santhalakshmi, M.","Shanthakumar, M."],"tags":["Federated Learning; Meta-Learning; Few-Shot Learning; Image Classification; Personalized Models; Privacy-Preserving Machine Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21578632","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21578286","name":"AI-Driven Risk Analytics for Real-Time Fraud Detection in Modernized U.S. Payment Ecosystems","source":"datacite","abstract":"This study investigates the application of artificial intelligence (AI)-driven risk analytics for real-time fraud detection within modernized United States payment ecosystems. As digital payment infrastructures transition toward instant, high-volume transaction processing, traditional rule-based systems exhibit critical limitations, including high false-positive rates and poor adaptability to evolving fraud typologies. This research synthesizes recent advancements in machine learning, deep learning, and hybrid analytical frameworks to evaluate their effectiveness in enhancing detection accuracy, reducing latency, and improving risk decisioning. A systematic review methodology is employed, integrating empirical findings from peer-reviewed literature, fintech implementations, and regulatory analyses. The study identifies that ensemble learning models and deep neural architectures significantly outperform legacy systems, achieving detection accuracy exceeding 95% while maintaining real-time processing capabilities. Additionally, the integration of federated learning and explainable AI (XAI) addresses key challenges related to data privacy, regulatory compliance, and model transparency. The findings demonstrate that a hybrid architecture combining AI-driven predictive analytics with human-in-the-loop governance provides a scalable and resilient fraud mitigation framework. The paper further proposes strategic and operational recommendations for financial institutions, emphasizing interoperability, data quality management, and compliance alignment. This research contributes to the evolving discourse on intelligent financial security by presenting a unified, governance-aware AI risk analytics framework for next-generation payment systems.","url":"https://doi.org/10.5281/zenodo.21578286","authors":["Fatomilola, Ezekiel"],"tags":["Artificial Intelligence; Financial Fraud Detection; Risk Analytics; Machine Learning; Digital Payments; Regulatory Compliance; Explainable AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21578286","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21578287","name":"AI-Driven Risk Analytics for Real-Time Fraud Detection in Modernized U.S. Payment Ecosystems","source":"datacite","abstract":"This study investigates the application of artificial intelligence (AI)-driven risk analytics for real-time fraud detection within modernized United States payment ecosystems. As digital payment infrastructures transition toward instant, high-volume transaction processing, traditional rule-based systems exhibit critical limitations, including high false-positive rates and poor adaptability to evolving fraud typologies. This research synthesizes recent advancements in machine learning, deep learning, and hybrid analytical frameworks to evaluate their effectiveness in enhancing detection accuracy, reducing latency, and improving risk decisioning. A systematic review methodology is employed, integrating empirical findings from peer-reviewed literature, fintech implementations, and regulatory analyses. The study identifies that ensemble learning models and deep neural architectures significantly outperform legacy systems, achieving detection accuracy exceeding 95% while maintaining real-time processing capabilities. Additionally, the integration of federated learning and explainable AI (XAI) addresses key challenges related to data privacy, regulatory compliance, and model transparency. The findings demonstrate that a hybrid architecture combining AI-driven predictive analytics with human-in-the-loop governance provides a scalable and resilient fraud mitigation framework. The paper further proposes strategic and operational recommendations for financial institutions, emphasizing interoperability, data quality management, and compliance alignment. This research contributes to the evolving discourse on intelligent financial security by presenting a unified, governance-aware AI risk analytics framework for next-generation payment systems.","url":"https://doi.org/10.5281/zenodo.21578287","authors":["Fatomilola, Ezekiel"],"tags":["Artificial Intelligence; Financial Fraud Detection; Risk Analytics; Machine Learning; Digital Payments; Regulatory Compliance; Explainable AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21578287","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21843486","name":"Integrating Artificial Intelligence and Natural Language Processing into Automated Adverse Event Signal Detection: A Comprehensive  Review of Frameworks, Real-World Data, and Regulatory Challenges","source":"datacite","abstract":"Pharmacovigilance, the science of detecting, assessing, and preventing adverse effects of medicines, has traditionally relied on spontaneous reporting systems and manually applied disproportionality statistics to generate safety signals. The explosive growth in the volume and diversity of drug-safety-relevant data -- spanning structured spontaneous report databases, electronic health records, clinical narratives, social media, and other real-world data sources -- has outpaced the practical capacity of manual and purely statistical signal-detection workflows. This review examines how artificial intelligence (AI) and natural language processing (NLP) are being integrated into automated adverse event signal detection, synthesising the current landscape of computational frameworks, real-world data sources, and the regulatory challenges that accompany their adoption. We describe the evolution from classical disproportionality methods -- the Proportional Reporting Ratio, Reporting Odds Ratio, Bayesian Confidence Propagation Neural Network, and Multi-item Gamma Poisson Shrinker -- toward machine-learning and deep-learning architectures capable of learning complex, non-linear associations across heterogeneous data. Particular attention is given to the role of transformer-based language models, including BioBERT, ClinicalBERT, PubMedBERT, and SpanBERT, in extracting adverse drug event mentions from unstructured clinical notes, biomedical literature, and social media text, and to hybrid frameworks that combine structured disproportionality analysis with unstructured-text mining and graph-based relational modelling. The review further surveys the principal real-world data infrastructures underpinning modern pharmacovigilance -- the FDA Adverse Event Reporting System, the WHO global individual case safety report database VigiBase, EudraVigilance, the FDA Sentinel Initiative, and the EMA's DARWIN EU platform -- and considers how AI-based tools are being layered onto these systems for federated querying, duplicate detection, and case-processing automation. Finally, the review addresses the regulatory and governance challenges that currently constrain routine adoption of AI-augmented signal detection, including model transparency and explainability, validation and performance monitoring, algorithmic bias, data privacy, and the evolving guidance issued by the FDA, EMA, ICH, and CIOMS, concluding with a forward-looking assessment of the conditions under which AI/NLP-based signal detection is likely to become an accepted, auditable component of routine drug safety surveillance.","url":"https://doi.org/10.5281/zenodo.21843486","authors":["Sakthikumar P"],"tags":["pharmacovigilance; artificial intelligence; natural language processing; adverse event signal detection; disproportionality analysis; real-world data; regulatory science; machine learning."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21843486","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21843487","name":"Integrating Artificial Intelligence and Natural Language Processing into Automated Adverse Event Signal Detection: A Comprehensive  Review of Frameworks, Real-World Data, and Regulatory Challenges","source":"datacite","abstract":"Pharmacovigilance, the science of detecting, assessing, and preventing adverse effects of medicines, has traditionally relied on spontaneous reporting systems and manually applied disproportionality statistics to generate safety signals. The explosive growth in the volume and diversity of drug-safety-relevant data -- spanning structured spontaneous report databases, electronic health records, clinical narratives, social media, and other real-world data sources -- has outpaced the practical capacity of manual and purely statistical signal-detection workflows. This review examines how artificial intelligence (AI) and natural language processing (NLP) are being integrated into automated adverse event signal detection, synthesising the current landscape of computational frameworks, real-world data sources, and the regulatory challenges that accompany their adoption. We describe the evolution from classical disproportionality methods -- the Proportional Reporting Ratio, Reporting Odds Ratio, Bayesian Confidence Propagation Neural Network, and Multi-item Gamma Poisson Shrinker -- toward machine-learning and deep-learning architectures capable of learning complex, non-linear associations across heterogeneous data. Particular attention is given to the role of transformer-based language models, including BioBERT, ClinicalBERT, PubMedBERT, and SpanBERT, in extracting adverse drug event mentions from unstructured clinical notes, biomedical literature, and social media text, and to hybrid frameworks that combine structured disproportionality analysis with unstructured-text mining and graph-based relational modelling. The review further surveys the principal real-world data infrastructures underpinning modern pharmacovigilance -- the FDA Adverse Event Reporting System, the WHO global individual case safety report database VigiBase, EudraVigilance, the FDA Sentinel Initiative, and the EMA's DARWIN EU platform -- and considers how AI-based tools are being layered onto these systems for federated querying, duplicate detection, and case-processing automation. Finally, the review addresses the regulatory and governance challenges that currently constrain routine adoption of AI-augmented signal detection, including model transparency and explainability, validation and performance monitoring, algorithmic bias, data privacy, and the evolving guidance issued by the FDA, EMA, ICH, and CIOMS, concluding with a forward-looking assessment of the conditions under which AI/NLP-based signal detection is likely to become an accepted, auditable component of routine drug safety surveillance.","url":"https://doi.org/10.5281/zenodo.21843487","authors":["Sakthikumar P"],"tags":["pharmacovigilance; artificial intelligence; natural language processing; adverse event signal detection; disproportionality analysis; real-world data; regulatory science; machine learning."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21843487","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21232378","name":"Ontology-Based Recommender Systems for Online Learning Platforms: A Review","source":"datacite","abstract":"The rapid growth of massive open online courses, learning management systems, and open educational repositories has produced an abundance of learning resources that overwhelms learners and complicates the selection of suitable content. Recommender systems have become a standard response to this information overload, yet the classical paradigms of collaborative filtering, content-based filtering, knowledge-based filtering, and their hybrids struggle with cold start, data sparsity, and a persistent semantic gap between raw interaction data and pedagogical meaning. This paper reviews how ontologies, formal and shareable specifications of a domain expressed in languages such as RDF and OWL, have been used to address these limitations in educational recommenders. It surveys the background of recommender paradigms and their weaknesses, explains what an ontology is and how domain, learner, and pedagogical ontologies encode knowledge, and analyses the mechanisms through which ontologies support semantic similarity, logical reasoning, learner modelling, prerequisite and zone of proximal development reasoning, and learning path sequencing. A taxonomy of ontology-based approaches is proposed and representative systems are compared, followed by a discussion of evaluation methods and datasets. The review then examines open challenges, including ontology construction cost, scalability, dynamic knowledge, cold start, evaluation validity, and explainability. It closes by outlining future directions that combine large language models with knowledge graphs, hybrid neuro-symbolic reasoning, and federated privacy-preserving personalization. Throughout, the aim is an honest synthesis of what prior work reports rather than any new empirical claim.","url":"https://doi.org/10.5281/zenodo.21232378","authors":["Saritha E","Dr. B Kalpana"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21232378","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21232379","name":"Ontology-Based Recommender Systems for Online Learning Platforms: A Review","source":"datacite","abstract":"The rapid growth of massive open online courses, learning management systems, and open educational repositories has produced an abundance of learning resources that overwhelms learners and complicates the selection of suitable content. Recommender systems have become a standard response to this information overload, yet the classical paradigms of collaborative filtering, content-based filtering, knowledge-based filtering, and their hybrids struggle with cold start, data sparsity, and a persistent semantic gap between raw interaction data and pedagogical meaning. This paper reviews how ontologies, formal and shareable specifications of a domain expressed in languages such as RDF and OWL, have been used to address these limitations in educational recommenders. It surveys the background of recommender paradigms and their weaknesses, explains what an ontology is and how domain, learner, and pedagogical ontologies encode knowledge, and analyses the mechanisms through which ontologies support semantic similarity, logical reasoning, learner modelling, prerequisite and zone of proximal development reasoning, and learning path sequencing. A taxonomy of ontology-based approaches is proposed and representative systems are compared, followed by a discussion of evaluation methods and datasets. The review then examines open challenges, including ontology construction cost, scalability, dynamic knowledge, cold start, evaluation validity, and explainability. It closes by outlining future directions that combine large language models with knowledge graphs, hybrid neuro-symbolic reasoning, and federated privacy-preserving personalization. Throughout, the aim is an honest synthesis of what prior work reports rather than any new empirical claim.","url":"https://doi.org/10.5281/zenodo.21232379","authors":["Saritha E","Dr. B Kalpana"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21232379","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.22119317","name":"Coded evidence base for a PRISMA-guided systematic review of reporting fragmentation in federated-learning-based intrusion detection systems","source":"datacite","abstract":"The complete coded evidence base underlying a PRISMA-guided systematic review of reporting fragmentation in federated-learning-based network intrusion detection systems (FL-NIDS): the per-study extraction table for all 105 included primary studies, the coding schema and codebook, the database search strings, the PRISMA flow and provenance note, and the supplementary records for the adversarial-evaluation and explainability dimensions. What this deposit supports. Computational reproducibility: the released table is sufficient to reproduce every aggregate numerical result reported in the manuscript. The included script verify_headline_results.py computes and reports the check count at runtime; in the validated run it reported 23 checks, 23 matched, 0 mismatched. Document identity: through title, authors, year, venue and DOI for all 105 studies. Independent re-coding: a reader with lawful access to the original publications can re-code selected studies using the published schema. What it does not support. Source-quote auditability: source excerpts and exact source passages are withheld from the public deposit (see WITHHELD_FIELDS.md). Auditing an individual coding decision against its source passage still requires access to the original publication. Coding was performed by a single coder; no inter-rater reliability statistic exists. Licence scope. CC BY 4.0 applies to the depositors' original material only: the coded values, extraction schema, codebook, search strings, schema-trigger metadata, analysis scripts, manifests and documentation. The record also includes bibliographic metadata identifying the reviewed publications; rights in those underlying publications remain with their respective authors and rights holders. No publisher PDFs, article full texts, copyrighted tables or figures, or verbatim excerpts from the reviewed publications are included. No licence is granted by the depositors over any underlying third-party work.","url":"https://doi.org/10.5281/zenodo.22119317","authors":["Alzoubi, Mohamad","Serrão, Carlos","Pavia, João Pedro"],"tags":["Federated learning","Network intrusion detection","Systematic literature review","Reporting completeness","Explainable artificial intelligence","Adversarial robustness","Reproducibility"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22119317","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.22119316","name":"Coded evidence base for a PRISMA-guided systematic review of reporting fragmentation in federated-learning-based intrusion detection systems","source":"datacite","abstract":"The complete coded evidence base underlying a PRISMA-guided systematic review of reporting fragmentation in federated-learning-based network intrusion detection systems (FL-NIDS): the per-study extraction table for all 105 included primary studies, the coding schema and codebook, the database search strings, the PRISMA flow and provenance note, and the supplementary records for the adversarial-evaluation and explainability dimensions. What this deposit supports. Computational reproducibility: the released table is sufficient to reproduce every aggregate numerical result reported in the manuscript. The included script verify_headline_results.py computes and reports the check count at runtime; in the validated run it reported 23 checks, 23 matched, 0 mismatched. Document identity: through title, authors, year, venue and DOI for all 105 studies. Independent re-coding: a reader with lawful access to the original publications can re-code selected studies using the published schema. What it does not support. Source-quote auditability: source excerpts and exact source passages are withheld from the public deposit (see WITHHELD_FIELDS.md). Auditing an individual coding decision against its source passage still requires access to the original publication. Coding was performed by a single coder; no inter-rater reliability statistic exists. Licence scope. CC BY 4.0 applies to the depositors' original material only: the coded values, extraction schema, codebook, search strings, schema-trigger metadata, analysis scripts, manifests and documentation. The record also includes bibliographic metadata identifying the reviewed publications; rights in those underlying publications remain with their respective authors and rights holders. No publisher PDFs, article full texts, copyrighted tables or figures, or verbatim excerpts from the reviewed publications are included. No licence is granted by the depositors over any underlying third-party work.","url":"https://doi.org/10.5281/zenodo.22119316","authors":["Alzoubi, Mohamad","Serrão, Carlos","Pavia, João Pedro"],"tags":["Federated learning","Network intrusion detection","Systematic literature review","Reporting completeness","Explainable artificial intelligence","Adversarial robustness","Reproducibility"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22119316","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.22124310","name":"Comparative Study On Smart Buy","source":"datacite","abstract":"The rapid growth of e-commerce has provided consumers with a wide range of products and purchasing options, but comparing prices, discounts, product quality, reviews, and availability across multiple platforms remains time – consuming and difficult. This study presents an AI-powered real-time price comparison system designed to provide users with accurate, personalized, and data-driven purchasing assistance. The proposed system integrates real-time web scraping and intelligent product matching to collect and align product information from multiple e-commerce platforms, addressing differences in product names, formats, and website structures. Machine learning techniques are incorporated for price prediction and purchase-timing analysis, enabling users to understand price trends, review and sentiment analysis, product ratings, discounts, availability, and brand-related factors to provide comprehensive product evaluation. Image-based product identification can also support faster product discovery, while automated price-drop alerts help users monitor desired products. In addition, adaptive scraping and analytical dashboards can improve the reliability, visualization, and interpretation of continuously changing ecommerce data. Privacy-preserving approaches such as federated learning provide potential support for collaborative pricing intelligence without directly sharing sensitive data. By integrating these capabilities into a unified and user-friendly platform, the proposed system aims to reduce manual comparison, improve purchasing decisions, identify genuine value beyond price alone, and provide timely and personalized insights for online shoppers.","url":"https://doi.org/10.5281/zenodo.22124310","authors":["Prof. Bharati Rathod, Nayana S, Sinchana G K, Umadevi, Varshini P"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22124310","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.22124309","name":"Comparative Study On Smart Buy","source":"datacite","abstract":"The rapid growth of e-commerce has provided consumers with a wide range of products and purchasing options, but comparing prices, discounts, product quality, reviews, and availability across multiple platforms remains time – consuming and difficult. This study presents an AI-powered real-time price comparison system designed to provide users with accurate, personalized, and data-driven purchasing assistance. The proposed system integrates real-time web scraping and intelligent product matching to collect and align product information from multiple e-commerce platforms, addressing differences in product names, formats, and website structures. Machine learning techniques are incorporated for price prediction and purchase-timing analysis, enabling users to understand price trends, review and sentiment analysis, product ratings, discounts, availability, and brand-related factors to provide comprehensive product evaluation. Image-based product identification can also support faster product discovery, while automated price-drop alerts help users monitor desired products. In addition, adaptive scraping and analytical dashboards can improve the reliability, visualization, and interpretation of continuously changing ecommerce data. Privacy-preserving approaches such as federated learning provide potential support for collaborative pricing intelligence without directly sharing sensitive data. By integrating these capabilities into a unified and user-friendly platform, the proposed system aims to reduce manual comparison, improve purchasing decisions, identify genuine value beyond price alone, and provide timely and personalized insights for online shoppers.","url":"https://doi.org/10.5281/zenodo.22124309","authors":["Prof. Bharati Rathod, Nayana S, Sinchana G K, Umadevi, Varshini P"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22124309","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.22124132","name":"\"AI-Assisted Multi-Parameter Optimization in Medicinal Chemistry: Integrating Potency, ADMET, Selectivity and Synthetic Accessibility for Rational Drug Design\"","source":"datacite","abstract":"AbstractThe increasing complexity of modern drug discovery has highlighted the limitations ofconventional medicinal-chemistry strategies that optimize molecular potency as a primaryobjective while considering pharmacokinetic, safety and synthetic properties at later stages.Artificial intelligence (AI)-assisted multi-parameter optimization (MPO) has emerged as apromising paradigm for addressing this limitation by enabling simultaneous optimization of targetpotency, absorption, distribution, metabolism, excretion and toxicity (ADMET), target selectivityand synthetic accessibility. This review provides a comprehensive and research-oriented overviewof the evolution of computer-aided drug design toward AI-driven multi-objective molecularoptimization. The principles of MPO and the interrelationships and trade-offs among key drug-like properties are discussed, followed by an evaluation of machine learning, deep learning, graphneural networks, transformers, foundation models, generative AI and reinforcement learningapproaches used in medicinal chemistry. Particular emphasis is placed on AI-assisted predictionand optimization of molecular potency, ADMET characteristics, off-target interactions,polypharmacology and synthetic feasibility. Multi-objective optimization strategies, includingPareto optimization, weighted utility functions, Bayesian optimization, evolutionary algorithmsand reinforcement learning, are examined for their ability to identify balanced drug candidatesrather than molecules optimized for a single endpoint. The review further presents an integratedAI workflow encompassing data curation, molecular representation, predictive modeling,molecular generation, virtual screening, retrosynthetic analysis, experimental validation andclosed-loop optimization. Key data resources, benchmarking strategies, reproducibilityrequirements and limitations associated with data bias, activity cliffs, model overfitting, chemicalextrapolation, interpretability and the prediction–reality gap are critically considered. Emergingdirections, including chemical foundation models, large language models, multimodal AI, physics-informed learning, autonomous laboratories, federated learning and human–AI collaboration, arealso discussed. Overall, AI-assisted MPO represents a transition from single-property optimizationtoward integrated and experimentally informed rational drug design, with the potential to improvethe efficiency of lead optimization and increase the probability of identifying potent, selective,pharmacokinetically favorable, safe and synthetically accessible drug candidates.","url":"https://doi.org/10.5281/zenodo.22124132","authors":["Nidhi Devrani"],"tags":["Artificial intelligence; Multi-parameter optimization; Medicinal chemistry; Machine learning; Deep learning; Generative AI; Drug design; Potency optimization; ADMET"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22124132","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.22124133","name":"\"AI-Assisted Multi-Parameter Optimization in Medicinal Chemistry: Integrating Potency, ADMET, Selectivity and Synthetic Accessibility for Rational Drug Design\"","source":"datacite","abstract":"AbstractThe increasing complexity of modern drug discovery has highlighted the limitations ofconventional medicinal-chemistry strategies that optimize molecular potency as a primaryobjective while considering pharmacokinetic, safety and synthetic properties at later stages.Artificial intelligence (AI)-assisted multi-parameter optimization (MPO) has emerged as apromising paradigm for addressing this limitation by enabling simultaneous optimization of targetpotency, absorption, distribution, metabolism, excretion and toxicity (ADMET), target selectivityand synthetic accessibility. This review provides a comprehensive and research-oriented overviewof the evolution of computer-aided drug design toward AI-driven multi-objective molecularoptimization. The principles of MPO and the interrelationships and trade-offs among key drug-like properties are discussed, followed by an evaluation of machine learning, deep learning, graphneural networks, transformers, foundation models, generative AI and reinforcement learningapproaches used in medicinal chemistry. Particular emphasis is placed on AI-assisted predictionand optimization of molecular potency, ADMET characteristics, off-target interactions,polypharmacology and synthetic feasibility. Multi-objective optimization strategies, includingPareto optimization, weighted utility functions, Bayesian optimization, evolutionary algorithmsand reinforcement learning, are examined for their ability to identify balanced drug candidatesrather than molecules optimized for a single endpoint. The review further presents an integratedAI workflow encompassing data curation, molecular representation, predictive modeling,molecular generation, virtual screening, retrosynthetic analysis, experimental validation andclosed-loop optimization. Key data resources, benchmarking strategies, reproducibilityrequirements and limitations associated with data bias, activity cliffs, model overfitting, chemicalextrapolation, interpretability and the prediction–reality gap are critically considered. Emergingdirections, including chemical foundation models, large language models, multimodal AI, physics-informed learning, autonomous laboratories, federated learning and human–AI collaboration, arealso discussed. Overall, AI-assisted MPO represents a transition from single-property optimizationtoward integrated and experimentally informed rational drug design, with the potential to improvethe efficiency of lead optimization and increase the probability of identifying potent, selective,pharmacokinetically favorable, safe and synthetically accessible drug candidates.","url":"https://doi.org/10.5281/zenodo.22124133","authors":["Nidhi Devrani"],"tags":["Artificial intelligence; Multi-parameter optimization; Medicinal chemistry; Machine learning; Deep learning; Generative AI; Drug design; Potency optimization; ADMET"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22124133","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20478837","name":"Federated Learning Aggregation Strategies and Compressive Sensing in Massive MIMO OTA-FL Systems","source":"datacite","abstract":"This report synthesises findings from 7 peer-reviewed papers addressing the following research question: What is the impact of different federated learning aggregation strategies (FedAvg, FedProx, SCAFFOLD) on model alignment and robustness to non-IID data distributions when combined with compressive. Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices, each with its own local training data set. In this paper, we present a compressive sensing approach for federated learning over massive. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the impact of different federated learning aggregation strategies (FedAvg, FedProx, SCAFFOLD) on model alignment and robustness to non-IID data distributions when combined with compressive sensing in massive MIMO-enabled OTA-FL, evaluated using metrics like test accuracy and F1-score on datasets such as CIFAR-10 or Shakespeare? Autonomous literature synthesis. Automated review score: 8.8/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20478837","authors":["Assignee Research"],"tags":["impact","different","federated","learning","aggregation","strategies","FedAvg","FedProx"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20478837","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20478838","name":"Federated Learning Aggregation Strategies and Compressive Sensing in Massive MIMO OTA-FL Systems","source":"datacite","abstract":"This report synthesises findings from 7 peer-reviewed papers addressing the following research question: What is the impact of different federated learning aggregation strategies (FedAvg, FedProx, SCAFFOLD) on model alignment and robustness to non-IID data distributions when combined with compressive. Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices, each with its own local training data set. In this paper, we present a compressive sensing approach for federated learning over massive. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.8/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the impact of different federated learning aggregation strategies (FedAvg, FedProx, SCAFFOLD) on model alignment and robustness to non-IID data distributions when combined with compressive sensing in massive MIMO-enabled OTA-FL, evaluated using metrics like test accuracy and F1-score on datasets such as CIFAR-10 or Shakespeare? Autonomous literature synthesis. Automated review score: 8.8/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20478838","authors":["Assignee Research"],"tags":["impact","different","federated","learning","aggregation","strategies","FedAvg","FedProx"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20478838","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20627008","name":"ENSEMBLE - Newsletter April-May 2026","source":"datacite","abstract":"Official newsletter of the Horizon Europe ENSEMBLE project (April and May 2026 edition) reporting the project review meeting in Brussels before the European Commission alongside analysis by María Hernandez from Byron Labs on pre-attack economy in ransomware prior to encryption and a technical feature by Stéphane Gazut from CEA on federated learning for cross-border investigations preserving privacy. It includes Paul Labic's (BETA/ENSP) study on the Hawala system and its structural limitations for tracking criminal networks while highlighting the joint publication of a Policy Brief on Trustworthy Investigations with PRESERVE and SafeHorizon, as well as the stakeholder workshop in Bari on AI governance in law enforcement with partners from the LEA Projects Cluster.","url":"https://doi.org/10.5281/zenodo.20627008","authors":["ENSEMBLE"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20627008","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20627009","name":"ENSEMBLE - Newsletter April-May 2026","source":"datacite","abstract":"Official newsletter of the Horizon Europe ENSEMBLE project (April and May 2026 edition) reporting the project review meeting in Brussels before the European Commission alongside analysis by María Hernandez from Byron Labs on pre-attack economy in ransomware prior to encryption and a technical feature by Stéphane Gazut from CEA on federated learning for cross-border investigations preserving privacy. It includes Paul Labic's (BETA/ENSP) study on the Hawala system and its structural limitations for tracking criminal networks while highlighting the joint publication of a Policy Brief on Trustworthy Investigations with PRESERVE and SafeHorizon, as well as the stakeholder workshop in Bari on AI governance in law enforcement with partners from the LEA Projects Cluster.","url":"https://doi.org/10.5281/zenodo.20627009","authors":["ENSEMBLE"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20627009","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20110301","name":"Artificial Intelligence in Resource-Limited Healthcare Settings: Applications, Implementation Challenges, and Future Directions in Cardiology and Oncology","source":"datacite","abstract":"Abstract Artificial intelligence (AI) holds transformative potential for health systems in low- and middle-income countries (LMICs), where clinician shortages, infrastructure deficits, and rising chronic disease burdens converge. This review synthesises 2019–2026 evidence on AI deployment in cardiology and oncology within resource-limited settings, examining predictive algorithms, digital twins, point-of-care diagnostics, and human-in-the-loop governance. We analyse applications ranging from ECG-based arrhythmia detection and heart-failure risk stratification to cancer screening, precision treatment selection, radiotherapy planning, and immune-checkpoint inhibitor response prediction. Despite promising performance— including HIV testing prediction models in Sierra Leone, Nigerian cardiologist-facing AI tools, and dialysis-optimising algorithms—deployment remains constrained by data scarcity, algorithmic bias, workforce gaps, regulatory ambiguity, and unsustainable energy supply. Drawing on 100 peer-reviewed sources, we propose an equity-centred implementation framework emphasising local validation, federated learning, explainable AI, sustainable energy harvesting, and additive manufacturing for medical devices. Without deliberate attention to governance, interoperability, and community engagement, AI risks exacerbating rather than ameliorating global health disparities. Keywords: Artificial intelligence, resource-limited settings, low- and middle-income countries, cardiology, oncology, digital twin, precision medicine, human-in-the-loop, explainable AI, sustainable health technology","url":"https://doi.org/10.5281/zenodo.20110301","authors":["Oluwakemi Jumoke Bello, Raphael Igbarumah Ayo Daniel, Olabanke Florence Olawuyi, Babajide David Makanjuola and Claret Chinenyenwa Analikwu"],"tags":["Artificial intelligence, resource-limited settings, low- and middle-income countries, cardiology, oncology, digital twin, precision medicine, human-in-the-loop, explainable AI, sustainable health technology"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20110301","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20110302","name":"Artificial Intelligence in Resource-Limited Healthcare Settings: Applications, Implementation Challenges, and Future Directions in Cardiology and Oncology","source":"datacite","abstract":"Abstract Artificial intelligence (AI) holds transformative potential for health systems in low- and middle-income countries (LMICs), where clinician shortages, infrastructure deficits, and rising chronic disease burdens converge. This review synthesises 2019–2026 evidence on AI deployment in cardiology and oncology within resource-limited settings, examining predictive algorithms, digital twins, point-of-care diagnostics, and human-in-the-loop governance. We analyse applications ranging from ECG-based arrhythmia detection and heart-failure risk stratification to cancer screening, precision treatment selection, radiotherapy planning, and immune-checkpoint inhibitor response prediction. Despite promising performance— including HIV testing prediction models in Sierra Leone, Nigerian cardiologist-facing AI tools, and dialysis-optimising algorithms—deployment remains constrained by data scarcity, algorithmic bias, workforce gaps, regulatory ambiguity, and unsustainable energy supply. Drawing on 100 peer-reviewed sources, we propose an equity-centred implementation framework emphasising local validation, federated learning, explainable AI, sustainable energy harvesting, and additive manufacturing for medical devices. Without deliberate attention to governance, interoperability, and community engagement, AI risks exacerbating rather than ameliorating global health disparities. Keywords: Artificial intelligence, resource-limited settings, low- and middle-income countries, cardiology, oncology, digital twin, precision medicine, human-in-the-loop, explainable AI, sustainable health technology","url":"https://doi.org/10.5281/zenodo.20110302","authors":["Oluwakemi Jumoke Bello, Raphael Igbarumah Ayo Daniel, Olabanke Florence Olawuyi, Babajide David Makanjuola and Claret Chinenyenwa Analikwu"],"tags":["Artificial intelligence, resource-limited settings, low- and middle-income countries, cardiology, oncology, digital twin, precision medicine, human-in-the-loop, explainable AI, sustainable health technology"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20110302","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20551818","name":"Mathematical optimization of renewable energy systems for sustainable development","source":"datacite","abstract":"Due to the growing worldwide need for renewable energy sources, Renewable Energy Systems (RES) have been rapidly developed and deployed in response to global needs for cleaner energy and environmental damage from the use of fossil fuels. However, the inherent variability and uncertainty associated with renewable energy resources create significant challenges to RES planning, integration of RES into the overall energy system, and management of the RES. This overview examines the various types of mathematical optimization methodologies that have been applied to RES; it examines deterministic classical methods, stochastic approaches to optimizing RES, heuristics and metaheuristic algorithms, and artificial intelligence (AI) solutions through a systematic examination according to a structured taxonomy. The overall critical review describes the strength and weakness of optimization methodologies and provides an assessment of the computing resources available for each type of optimization method, describes the techniques for quantifying uncertainty, explains the principles and techniques used in probabilistic forecasting, and addresses the real-world deployment challenges associated with RES including regulatory, economic, financial, and infrastructure barriers. The overview also describes in detail hybrid renewable energy systems (HRES), multi-objective optimization methods, integration of energy storage systems, and smart grid optimization processes utilizing supporting comparison tables and illustrative examples. The review outlines areas for future research by suggesting using innovations such as Digital Twins, Explainable AI, Federated Learning, and Blockchain technologies for enhancing energy systems management. This review distinguishes itself from previous literature in that it synthesizes findings from multiple optimization paradigms and bridges the gap between theoretical modeling and practical implementation challenges.","url":"https://doi.org/10.5281/zenodo.20551818","authors":["Singh, Garima"],"tags":["Renewable Energy Systems","Mathematical Optimization","Sustainable Development","Linear Programming","Stochastic Optimization","Hybrid Energy Systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20551818","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20551819","name":"Mathematical optimization of renewable energy systems for sustainable development","source":"datacite","abstract":"Due to the growing worldwide need for renewable energy sources, Renewable Energy Systems (RES) have been rapidly developed and deployed in response to global needs for cleaner energy and environmental damage from the use of fossil fuels. However, the inherent variability and uncertainty associated with renewable energy resources create significant challenges to RES planning, integration of RES into the overall energy system, and management of the RES. This overview examines the various types of mathematical optimization methodologies that have been applied to RES; it examines deterministic classical methods, stochastic approaches to optimizing RES, heuristics and metaheuristic algorithms, and artificial intelligence (AI) solutions through a systematic examination according to a structured taxonomy. The overall critical review describes the strength and weakness of optimization methodologies and provides an assessment of the computing resources available for each type of optimization method, describes the techniques for quantifying uncertainty, explains the principles and techniques used in probabilistic forecasting, and addresses the real-world deployment challenges associated with RES including regulatory, economic, financial, and infrastructure barriers. The overview also describes in detail hybrid renewable energy systems (HRES), multi-objective optimization methods, integration of energy storage systems, and smart grid optimization processes utilizing supporting comparison tables and illustrative examples. The review outlines areas for future research by suggesting using innovations such as Digital Twins, Explainable AI, Federated Learning, and Blockchain technologies for enhancing energy systems management. This review distinguishes itself from previous literature in that it synthesizes findings from multiple optimization paradigms and bridges the gap between theoretical modeling and practical implementation challenges.","url":"https://doi.org/10.5281/zenodo.20551819","authors":["Singh, Garima"],"tags":["Renewable Energy Systems","Mathematical Optimization","Sustainable Development","Linear Programming","Stochastic Optimization","Hybrid Energy Systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20551819","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.22114009","name":"Machine Learning Applications in Public Health: Improving Risk Prediction and Resource Allocation","source":"datacite","abstract":"Abstract As the amount of health data in the Pakistan has grown in size and diversity, from electronic health records (EHRs) and insurance claims to public health surveillance feeds and social and behavioral data, a key method being adopted to transform data into actionable predictions and operational decisions is machine learning (ML). This review brings together recently published literature that addresses two closely related applications: (i) risk prediction, such as chronic disease, cardiovascular risk, opioid overdose, and infectious disease outbreak risk prediction, and (ii) resource allocation, including hospital bed and intensive care unit (ICU) capacity planning, emergency department staffing, and pandemic-scale forecasting illustrated by the Centers for Disease Control and Prevention (CDC) COVID-19 Forecast Hub. The use of ensemble and deep learning models (random forests, gradient boosted trees, long short term memory networks) is demonstrated to exceed traditional statistical baseline models on these tasks with areas under the curve (AUC) often greater than 0.85. The review also identifies continued challenges regarding safe and equitable deployment of the algorithms, including that of algorithmic bias from proxy labels, poor model interpretation, disjointed data infrastructure, and privacy limitations in multi-institutional data sharing. Potential solutions to make systems more transparent and equitable are discussed, including explainable AI (XAI), federated learning, and social-determinants-of-health (SDOH)-aware modeling. The review finds that achieving the full public health benefits of ML will require as much, if not more, attention to fair testing of models for accuracy, interoperability in data management, and operationalizing models into clinical practice for public health decision-making as to improvements in predictive accuracy.","url":"https://doi.org/10.5281/zenodo.22114009","authors":["Qura tul, Ain Nazir"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.22114009","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.22114008","name":"Machine Learning Applications in Public Health: Improving Risk Prediction and Resource Allocation","source":"datacite","abstract":"Abstract As the amount of health data in the Pakistan has grown in size and diversity, from electronic health records (EHRs) and insurance claims to public health surveillance feeds and social and behavioral data, a key method being adopted to transform data into actionable predictions and operational decisions is machine learning (ML). This review brings together recently published literature that addresses two closely related applications: (i) risk prediction, such as chronic disease, cardiovascular risk, opioid overdose, and infectious disease outbreak risk prediction, and (ii) resource allocation, including hospital bed and intensive care unit (ICU) capacity planning, emergency department staffing, and pandemic-scale forecasting illustrated by the Centers for Disease Control and Prevention (CDC) COVID-19 Forecast Hub. The use of ensemble and deep learning models (random forests, gradient boosted trees, long short term memory networks) is demonstrated to exceed traditional statistical baseline models on these tasks with areas under the curve (AUC) often greater than 0.85. The review also identifies continued challenges regarding safe and equitable deployment of the algorithms, including that of algorithmic bias from proxy labels, poor model interpretation, disjointed data infrastructure, and privacy limitations in multi-institutional data sharing. Potential solutions to make systems more transparent and equitable are discussed, including explainable AI (XAI), federated learning, and social-determinants-of-health (SDOH)-aware modeling. The review finds that achieving the full public health benefits of ML will require as much, if not more, attention to fair testing of models for accuracy, interoperability in data management, and operationalizing models into clinical practice for public health decision-making as to improvements in predictive accuracy.","url":"https://doi.org/10.5281/zenodo.22114008","authors":["Qura tul, Ain Nazir"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.22114008","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.22112309","name":"smshagor-dev/Federated-Learning-on-Non-IID-Data-Differential-Privacy: v3.0.0","source":"datacite","abstract":"Federated Learning Platform v3.0.0 Version: 3.0.0 Release Type: Stable Platform Release Python: 3.11+ License: Apache License 2.0 Federated Learning Platform v3.0.0 is a major platform release focused on stronger federated-learning execution, reproducible experiments, privacy validation, distributed runtime reliability, secure protocol boundaries, edge compatibility, observability, and verifiable release artifacts. This release keeps two execution paths clearly separated: Root research runtime for controlled PyTorch-based federated-learning experiments. Distributed platform runtime built with Python, C++20, Go, gRPC, Docker, PostgreSQL, Redis, and observability services. The release only treats validated capabilities as stable. Experimental components remain explicitly marked and fail closed where a combination is not release-qualified. What's New Federated Learning Algorithms v3.0.0 expands and standardizes the algorithm layer. Added or finalized: FedAvg FedProx SCAFFOLD FedSAM Ditto Per-FedAvg Canonical algorithm capability discovery Lazy algorithm loading to avoid unnecessary heavy runtime imports Better separation between lightweight capability metadata and training implementations Validation for supported and unsupported algorithm combinations The root runtime continues to support FedAvg, FedProx, and non-private SCAFFOLD directly. FedSAM, Ditto, and Per-FedAvg are available through the platform worker architecture. Non-IID Data Support The data-partitioning system has been expanded and hardened for reproducible heterogeneous-client experiments. Supported root-runtime partition strategies include: IID Dirichlet label skew Pathological class skew Quantity skew Improvements include: Deterministic partition generation Exact partition manifest generation Per-client sample counts Per-client label histograms Partition SHA-256 identity Class coverage metrics Quantity-distribution statistics Label entropy metrics Jensen-Shannon divergence measurements Reproducible held-out client partitioning Differential Privacy The privacy subsystem received major validation and accounting improvements. Added or improved: Client-level central differential privacy Update clipping Gaussian noise application RDP privacy accounting Target-epsilon noise calibration Runtime privacy-budget validation Effective privacy configuration archival Separate privacy ledgers for distinct mechanisms Fail-closed behavior for unsupported privacy combinations Statistical validation of privacy-accounting behavior Privacy-safe aggregate observability primitives Supported root-runtime private execution remains focused on: FedAvg FedProx Poisson client sampling Uniform client weighting DP-enabled SCAFFOLD remains outside the stable root-runtime privacy guarantee. Robust Aggregation v3.0.0 introduces validated robust aggregation support for supported non-private synchronous execution. Added: Coordinate-wise median aggregation Trimmed-mean aggregation Deterministic adversarial validation Capability checks for robust aggregation combinations Explicit rejection of unsupported robust + DP combinations Explicit rejection of unsupported robust + secure-aggregation combinations These restrictions are intentional to prevent unsupported combinations from being silently treated as valid. Secure Aggregation The secure-aggregation subsystem has been substantially expanded. Added or improved: Secure aggregation session management X25519-based recovery primitives Threshold Shamir recovery support Encrypted recovery-share relay Signed recovery messages Authenticated relay messages Durable encrypted relay ciphertext storage Replay protection Real masked-update correction path Dropout recovery validation Secure-aggregation session-manager tests Coordinator restart and recovery-policy validation Important boundary: Threshold dropout recovery exists for continued validation. It is not promoted as a production-grade stable capability. In-flight secure rounds are not resumed after coordinat","url":"https://doi.org/10.5281/zenodo.22112309","authors":["Shahanur Islam Shagor"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22112309","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21687508","name":"smshagor-dev/Federated-Learning-on-Non-IID-Data-Differential-Privacy: v3.0.0","source":"datacite","abstract":"Federated Learning Platform v3.0.0 Version: 3.0.0 Release Type: Stable Platform Release Python: 3.11+ License: Apache License 2.0 Federated Learning Platform v3.0.0 is a major platform release focused on stronger federated-learning execution, reproducible experiments, privacy validation, distributed runtime reliability, secure protocol boundaries, edge compatibility, observability, and verifiable release artifacts. This release keeps two execution paths clearly separated: Root research runtime for controlled PyTorch-based federated-learning experiments. Distributed platform runtime built with Python, C++20, Go, gRPC, Docker, PostgreSQL, Redis, and observability services. The release only treats validated capabilities as stable. Experimental components remain explicitly marked and fail closed where a combination is not release-qualified. What's New Federated Learning Algorithms v3.0.0 expands and standardizes the algorithm layer. Added or finalized: FedAvg FedProx SCAFFOLD FedSAM Ditto Per-FedAvg Canonical algorithm capability discovery Lazy algorithm loading to avoid unnecessary heavy runtime imports Better separation between lightweight capability metadata and training implementations Validation for supported and unsupported algorithm combinations The root runtime continues to support FedAvg, FedProx, and non-private SCAFFOLD directly. FedSAM, Ditto, and Per-FedAvg are available through the platform worker architecture. Non-IID Data Support The data-partitioning system has been expanded and hardened for reproducible heterogeneous-client experiments. Supported root-runtime partition strategies include: IID Dirichlet label skew Pathological class skew Quantity skew Improvements include: Deterministic partition generation Exact partition manifest generation Per-client sample counts Per-client label histograms Partition SHA-256 identity Class coverage metrics Quantity-distribution statistics Label entropy metrics Jensen-Shannon divergence measurements Reproducible held-out client partitioning Differential Privacy The privacy subsystem received major validation and accounting improvements. Added or improved: Client-level central differential privacy Update clipping Gaussian noise application RDP privacy accounting Target-epsilon noise calibration Runtime privacy-budget validation Effective privacy configuration archival Separate privacy ledgers for distinct mechanisms Fail-closed behavior for unsupported privacy combinations Statistical validation of privacy-accounting behavior Privacy-safe aggregate observability primitives Supported root-runtime private execution remains focused on: FedAvg FedProx Poisson client sampling Uniform client weighting DP-enabled SCAFFOLD remains outside the stable root-runtime privacy guarantee. Robust Aggregation v3.0.0 introduces validated robust aggregation support for supported non-private synchronous execution. Added: Coordinate-wise median aggregation Trimmed-mean aggregation Deterministic adversarial validation Capability checks for robust aggregation combinations Explicit rejection of unsupported robust + DP combinations Explicit rejection of unsupported robust + secure-aggregation combinations These restrictions are intentional to prevent unsupported combinations from being silently treated as valid. Secure Aggregation The secure-aggregation subsystem has been substantially expanded. Added or improved: Secure aggregation session management X25519-based recovery primitives Threshold Shamir recovery support Encrypted recovery-share relay Signed recovery messages Authenticated relay messages Durable encrypted relay ciphertext storage Replay protection Real masked-update correction path Dropout recovery validation Secure-aggregation session-manager tests Coordinator restart and recovery-policy validation Important boundary: Threshold dropout recovery exists for continued validation. It is not promoted as a production-grade stable capability. In-flight secure rounds are not resumed after coordinat","url":"https://doi.org/10.5281/zenodo.21687508","authors":["Shahanur Islam Shagor"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21687508","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.22111024","name":"Deep Learning For Lung Segmentation And Disease Classification In Chest X-Ray Imaging","source":"datacite","abstract":"Pulmonary illness is one of the largest contributors to death and disability across the globe, placing it among the top causes of mortality. Because of this, identifying and categorizing lung abnormalities from chest radiographs quickly and correctly is essential for guiding diagnosis and treatment. This paper surveys the current landscape of techniques used to segment and classify lungs in X-ray images, giving particular attention to deep-learning-driven approaches such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and architectures that combine both. The discussion covers influential public datasets, methods for preparing and augmenting training data, segmentation strategies (spanning U-Net and its variants, contour-driven techniques, and the alpha-shape algorithm), and frameworks built for distinguishing among multiple conditions — pneumonia, tuberculosis, chronic obstructive pulmonary disease (COPD), pulmonary fibrosis, and COVID-19. The review also touches on federated learning as a way to protect patient privacy in collaborative AI training, multimodal vision-language systems for interpreting chest radiographs, and the ongoing problems of biased datasets and limited model generalizability. Drawing on more than 40 recent publications, the paper reports leading performance benchmarks and points to areas still requiring research before such systems can be reliably used in clinical practice.","url":"https://doi.org/10.5281/zenodo.22111024","authors":["Anwar Muta Mohammad*, Abirami J., Sangeetha Banerjee"],"tags":["Chest X-ray, Lung Segmentation, Lung Disease Classification, Deep Learning, Convolutional Neural Network, Vision Transformer, U-Net, Medical Image Analysis, Federated Learning, Multimodal Models."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22111024","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.22111023","name":"Deep Learning For Lung Segmentation And Disease Classification In Chest X-Ray Imaging","source":"datacite","abstract":"Pulmonary illness is one of the largest contributors to death and disability across the globe, placing it among the top causes of mortality. Because of this, identifying and categorizing lung abnormalities from chest radiographs quickly and correctly is essential for guiding diagnosis and treatment. This paper surveys the current landscape of techniques used to segment and classify lungs in X-ray images, giving particular attention to deep-learning-driven approaches such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and architectures that combine both. The discussion covers influential public datasets, methods for preparing and augmenting training data, segmentation strategies (spanning U-Net and its variants, contour-driven techniques, and the alpha-shape algorithm), and frameworks built for distinguishing among multiple conditions — pneumonia, tuberculosis, chronic obstructive pulmonary disease (COPD), pulmonary fibrosis, and COVID-19. The review also touches on federated learning as a way to protect patient privacy in collaborative AI training, multimodal vision-language systems for interpreting chest radiographs, and the ongoing problems of biased datasets and limited model generalizability. Drawing on more than 40 recent publications, the paper reports leading performance benchmarks and points to areas still requiring research before such systems can be reliably used in clinical practice.","url":"https://doi.org/10.5281/zenodo.22111023","authors":["Anwar Muta Mohammad*, Abirami J., Sangeetha Banerjee"],"tags":["Chest X-ray, Lung Segmentation, Lung Disease Classification, Deep Learning, Convolutional Neural Network, Vision Transformer, U-Net, Medical Image Analysis, Federated Learning, Multimodal Models."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22111023","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.20280465","name":"Intelligent Predictive Architectures for Autonomous Self-Healing in Cloud Computing: A Comprehensive Survey","source":"datacite","abstract":"Neural network-enabled self-healing is becoming a very exciting approach to making cloud computing infrastructures more reliable, available, and efficient. This survey paper gives a detailed review of neural network-based predictive models and how they are combined with autonomous recovery mechanisms for self-healing cloud systems. It first delineates the main neural architectures used for cloud reliability, such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and hybrid or ensemble models, explaining their roles in failure prediction, resource management forecasting, and SLA/QoS violation prediction. The paper examines the coupling of these predictive models with self-healing action and decision layers like rule-based policies, policy engines, and reinforcement learning-driven controllers to proactively trigger recovery actions, reduce Mean Time To Recovery (MTTR), and control false alarms and resource overhead. Evaluation practices highlight datasets (Google Cluster Traces, Alibaba traces, and synthetic simulation data), performance metrics (prediction accuracy, MTTR, false positive/negative rates, scalability, and energy cost), and differences between simulated vs. real production cloud environments. Moreover, the survey discusses privacy and security issues of predictive and action models, presenting techniques such as federated learning, differential privacy, homomorphic encryption, and secure multi-party computation for privacy-preserving cross-tenant collaboration. Lastly, this paper draws attention to open issues including trade-offs between accuracy and false alarms, latency constraints, explainability, and scalability, proposing future work including explainable predictive pipelines, federated self-healing frameworks, multi-agent and DRL-based autonomy, and integration with emerging technologies such as quantum-inspired neural networks, edge-cloud continuum architectures, digital twins, service meshes, and neuromorphic hardware to enable more trustworthy, efficient, and autonomous self-healing cloud management.","url":"https://doi.org/10.5281/zenodo.20280465","authors":["Mr.  Nitesh Gupta","Dr.  Nandita Bangera"],"tags":["Neural Predictive Intelligence","Self-Healing Architectures","Deep Learning for Cloud Systems","Multi-Agent Reinforcement Learning","Federated Self-Healing Frameworks","SLA Forecasting","Cloud Infrastructure Resilience"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20280465","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.5281/zenodo.22108257","name":"smshagor-dev/Federated-Learning-on-Non-IID-Data-Differential-Privacy: v3.0.0","source":"datacite","abstract":"Federated Learning Platform v3.0.0 Version: 3.0.0 Release Type: Stable Platform Release Python: 3.11+ License: Apache License 2.0 Federated Learning Platform v3.0.0 is a major platform release focused on stronger federated-learning execution, reproducible experiments, privacy validation, distributed runtime reliability, secure protocol boundaries, edge compatibility, observability, and verifiable release artifacts. This release keeps two execution paths clearly separated: Root research runtime for controlled PyTorch-based federated-learning experiments. Distributed platform runtime built with Python, C++20, Go, gRPC, Docker, PostgreSQL, Redis, and observability services. The release only treats validated capabilities as stable. Experimental components remain explicitly marked and fail closed where a combination is not release-qualified. What's New Federated Learning Algorithms v3.0.0 expands and standardizes the algorithm layer. Added or finalized: FedAvg FedProx SCAFFOLD FedSAM Ditto Per-FedAvg Canonical algorithm capability discovery Lazy algorithm loading to avoid unnecessary heavy runtime imports Better separation between lightweight capability metadata and training implementations Validation for supported and unsupported algorithm combinations The root runtime continues to support FedAvg, FedProx, and non-private SCAFFOLD directly. FedSAM, Ditto, and Per-FedAvg are available through the platform worker architecture. Non-IID Data Support The data-partitioning system has been expanded and hardened for reproducible heterogeneous-client experiments. Supported root-runtime partition strategies include: IID Dirichlet label skew Pathological class skew Quantity skew Improvements include: Deterministic partition generation Exact partition manifest generation Per-client sample counts Per-client label histograms Partition SHA-256 identity Class coverage metrics Quantity-distribution statistics Label entropy metrics Jensen-Shannon divergence measurements Reproducible held-out client partitioning Differential Privacy The privacy subsystem received major validation and accounting improvements. Added or improved: Client-level central differential privacy Update clipping Gaussian noise application RDP privacy accounting Target-epsilon noise calibration Runtime privacy-budget validation Effective privacy configuration archival Separate privacy ledgers for distinct mechanisms Fail-closed behavior for unsupported privacy combinations Statistical validation of privacy-accounting behavior Privacy-safe aggregate observability primitives Supported root-runtime private execution remains focused on: FedAvg FedProx Poisson client sampling Uniform client weighting DP-enabled SCAFFOLD remains outside the stable root-runtime privacy guarantee. Robust Aggregation v3.0.0 introduces validated robust aggregation support for supported non-private synchronous execution. Added: Coordinate-wise median aggregation Trimmed-mean aggregation Deterministic adversarial validation Capability checks for robust aggregation combinations Explicit rejection of unsupported robust + DP combinations Explicit rejection of unsupported robust + secure-aggregation combinations These restrictions are intentional to prevent unsupported combinations from being silently treated as valid. Secure Aggregation The secure-aggregation subsystem has been substantially expanded. Added or improved: Secure aggregation session management X25519-based recovery primitives Threshold Shamir recovery support Encrypted recovery-share relay Signed recovery messages Authenticated relay messages Durable encrypted relay ciphertext storage Replay protection Real masked-update correction path Dropout recovery validation Secure-aggregation session-manager tests Coordinator restart and recovery-policy validation Important boundary: Threshold dropout recovery exists for continued validation. It is not promoted as a production-grade stable capability. In-flight secure rounds are not resumed after coordinat","url":"https://doi.org/10.5281/zenodo.22108257","authors":["Shahanur Islam Shagor"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22108257","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21448955","name":"Reinforcement Learning: Theory, Algorithms, and Applications by  Dr. K.V. Rameswara Reddy, Dr. A. Vishnuvardhan Reddy, Dr. M. Madhusudhan Reddy, Mr. G.V. Ramana Reddy","source":"datacite","abstract":"Artificial Intelligence has undergone remarkable transformations over the past few decades, evolving from rule-based systems to intelligent learning models capable of solving complex real-world problems. Among these advancements, Reinforcement Learning (RL) has emerged as one of the most influential paradigms, enabling machines to learn optimal decision-making strategies through interaction with dynamic environments. From autonomous vehicles and robotics to finance, healthcare, industrial automation, and intelligent gaming systems, reinforcement learning continues to redefine the boundaries of intelligent computing. The primary objective of this book, \"Reinforcement Learning: Theory, Algorithms, and Applications,\" is to provide readers with a comprehensive understanding of reinforcement learning—from its mathematical foundations to advanced algorithms and modern industrial applications. The book has been carefully designed to serve as a valuable textbook for undergraduate and postgraduate students, researchers, academicians, and industry professionals seeking both theoretical knowledge and practical implementation skills. The content begins with the fundamental concepts of reinforcement learning, introducing Markov Decision Processes (MDPs), Bellman equations, value functions, and policy optimization. These foundational topics provide readers with the essential mathematical background required to understand modern reinforcement learning techniques. The subsequent chapters explore classical reinforcement learning algorithms, including Dynamic Programming, Monte Carlo methods, Temporal Difference Learning, SARSA, and Q-Learning. Building upon these concepts, the book introduces advanced deep reinforcement learning methods such as Deep Q-Networks (DQN), Policy Gradient methods, Actor-Critic architectures, Proximal Policy Optimization (PPO), and other state-of-the-art algorithms that have significantly advanced the field. One of the distinctive features of this book is its strong focus on real-world applications. Dedicated chapters discuss reinforcement learning in robotics, autonomous vehicles, healthcare, finance, industrial automation, smart manufacturing, recommendation systems, cloud computing, cybersecurity, and intelligent decision-support systems. These applications demonstrate how reinforcement learning is revolutionizing diverse industries by enabling autonomous and adaptive systems. In addition to technical content, the book highlights important ethical considerations, challenges, safety issues, explainable reinforcement learning, responsible AI practices, computational limitations, and future research directions. Emerging topics such as multi-agent reinforcement learning, offline reinforcement learning, federated reinforcement learning, human-in-the-loop learning, and reinforcement learning integrated with large language models and generative AI are also introduced to prepare readers for the rapidly evolving landscape of artificial intelligence. Every chapter has been carefully structured with clearly defined learning objectives, theoretical explanations, algorithmic descriptions, practical examples, review questions, programming exercises, and references that facilitate both classroom teaching and self-learning. The material is presented progressively, allowing beginners to build strong foundations while offering sufficient depth for advanced learners and researchers. This book represents the collective efforts of the authors, who have combined their academic experience, research expertise, and teaching practices to develop a comprehensive and accessible learning resource. Considerable care has been taken to ensure technical accuracy, clarity of presentation, and alignment with current university curricula and emerging industry requirements. We express our sincere gratitude to our colleagues, students, research scholars, reviewers, and academic peers whose valuable discussions, suggestions, and constructive feedback h","url":"https://doi.org/10.5281/zenodo.21448955","authors":["Dr. K.V. Rameswara Reddy, Dr. A. Vishnuvardhan Reddy, Dr. M. Madhusudhan Reddy, Mr. G.V. Ramana Reddy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21448955","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.5281/zenodo.21448956","name":"Reinforcement Learning: Theory, Algorithms, and Applications by  Dr. K.V. Rameswara Reddy, Dr. A. Vishnuvardhan Reddy, Dr. M. Madhusudhan Reddy, Mr. G.V. Ramana Reddy","source":"datacite","abstract":"Artificial Intelligence has undergone remarkable transformations over the past few decades, evolving from rule-based systems to intelligent learning models capable of solving complex real-world problems. Among these advancements, Reinforcement Learning (RL) has emerged as one of the most influential paradigms, enabling machines to learn optimal decision-making strategies through interaction with dynamic environments. From autonomous vehicles and robotics to finance, healthcare, industrial automation, and intelligent gaming systems, reinforcement learning continues to redefine the boundaries of intelligent computing. The primary objective of this book, \"Reinforcement Learning: Theory, Algorithms, and Applications,\" is to provide readers with a comprehensive understanding of reinforcement learning—from its mathematical foundations to advanced algorithms and modern industrial applications. The book has been carefully designed to serve as a valuable textbook for undergraduate and postgraduate students, researchers, academicians, and industry professionals seeking both theoretical knowledge and practical implementation skills. The content begins with the fundamental concepts of reinforcement learning, introducing Markov Decision Processes (MDPs), Bellman equations, value functions, and policy optimization. These foundational topics provide readers with the essential mathematical background required to understand modern reinforcement learning techniques. The subsequent chapters explore classical reinforcement learning algorithms, including Dynamic Programming, Monte Carlo methods, Temporal Difference Learning, SARSA, and Q-Learning. Building upon these concepts, the book introduces advanced deep reinforcement learning methods such as Deep Q-Networks (DQN), Policy Gradient methods, Actor-Critic architectures, Proximal Policy Optimization (PPO), and other state-of-the-art algorithms that have significantly advanced the field. One of the distinctive features of this book is its strong focus on real-world applications. Dedicated chapters discuss reinforcement learning in robotics, autonomous vehicles, healthcare, finance, industrial automation, smart manufacturing, recommendation systems, cloud computing, cybersecurity, and intelligent decision-support systems. These applications demonstrate how reinforcement learning is revolutionizing diverse industries by enabling autonomous and adaptive systems. In addition to technical content, the book highlights important ethical considerations, challenges, safety issues, explainable reinforcement learning, responsible AI practices, computational limitations, and future research directions. Emerging topics such as multi-agent reinforcement learning, offline reinforcement learning, federated reinforcement learning, human-in-the-loop learning, and reinforcement learning integrated with large language models and generative AI are also introduced to prepare readers for the rapidly evolving landscape of artificial intelligence. Every chapter has been carefully structured with clearly defined learning objectives, theoretical explanations, algorithmic descriptions, practical examples, review questions, programming exercises, and references that facilitate both classroom teaching and self-learning. The material is presented progressively, allowing beginners to build strong foundations while offering sufficient depth for advanced learners and researchers. This book represents the collective efforts of the authors, who have combined their academic experience, research expertise, and teaching practices to develop a comprehensive and accessible learning resource. Considerable care has been taken to ensure technical accuracy, clarity of presentation, and alignment with current university curricula and emerging industry requirements. We express our sincere gratitude to our colleagues, students, research scholars, reviewers, and academic peers whose valuable discussions, suggestions, and constructive feedback h","url":"https://doi.org/10.5281/zenodo.21448956","authors":["Dr. K.V. Rameswara Reddy, Dr. A. Vishnuvardhan Reddy, Dr. M. Madhusudhan Reddy, Mr. G.V. Ramana Reddy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21448956","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:27.399Z"},{"id":"doi:10.20944/preprints202607.1103.v1","name":"Artificial Intelligence for Early Prediction and Diagnosis of Neonatal Sepsis: Current Evidence, Challenges, and Future Directions","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202607.1103.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202607.1103.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-10137205/v1","name":"Adversarial Intelligence: A Systematic Review of AI-Driven Cyber Threat Detection, Adaptive Malware Evasion, and Arms Race Dynamics in Zero Trust Security Environments","source":"preprints","abstract":"Abstract Artificial intelligence has become central to modern cyber defense, powering intrusion detection, malware classification, and behavioral analytics across enterprise, cloud, and Internet of Things environments. At the same time, the same statistical and generative tools that strengthen detection are being repurposed by adversaries to craft evasive malware and adversarial network traffic, producing a continuous arms race between defenders and attackers. This paper presents a systematic review of 80 peer reviewed articles, preprints, and technical reports published between 2010 and 2026, covering machine learning and deep learning based intrusion detection, generative adversarial network driven evasion and synthesis, federated and privacy preserving detection, explainable artificial intelligence for security, and the emerging role of large language models on both sides of the conflict. The review introduces a unified taxonomy of detection techniques and evasion strategies, traces the co-evolution of attack and defense across four technological generations, and examines how zero trust architecture functions as a governing framework for continuous, AI assisted verification. Comparative tables summarize technique families, benchmark datasets, and defense mechanisms, while bar, pie, and line charts characterize the composition and growth of the reviewed literature. The review concludes that robustness, realistic benchmarking, explainability, and regulatory alignment remain the most significant open challenges, and it outlines concrete research directions for closing the gap between detection accuracy and adversarial resilience.","url":"https://doi.org/10.21203/rs.3.rs-10137205/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10137205/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-10403335/v1","name":"A systematic review of autonomous multiagent frameworks for early breast cancer detection using thermography","source":"preprints","abstract":"Abstract Background Breast cancer is the most common malignancy worldwide, causing around 2.3 million cases and 685,000 deaths annually. Infrared thermography (IRT) is a non-ionizing, low-cost adjunct to mammographic screening, particularly valuable in low- and middle-income countries with limited mammography infrastructure. AI-supported thermography has attracted growing interest, but has not been examined from the perspective of agentic and autonomous AI. Methods MEDLINE, IEEE Xplore, Scopus, Web of Science and Google Scholar were systematically searched from January 2020 to March 2026 per PRISMA 2020 guidelines. Of 482 records screened, 100 peer-reviewed studies were included and grouped into seven themes: CNN-based architectures, vision transformer/hybrid models, XAI integration, generative augmentation, federated/privacy-preserving frameworks, clinical deployment, and agentic/autonomous AI. Each study was rated on five quality dimensions: methodological rigor, dataset quality, reporting completeness, clinical relevance, and explainability. Results Deep learning models achieve 95–99.9% accuracy on the DMR-IR benchmark. Four key limitations emerged: (i) 73% of studies rely on the single-site, 287-patient DMR-IR dataset, raising generalizability concerns; (ii) only four prospective clinical trials exist across the 100 studies; (iii) ~ 60% of models lack any explainability mechanism; and (iv), most importantly, no published study integrates agentic or multi-agent AI with breast thermographic analysis, the most significant gap identified. Only 6% of studies met all five quality criteria, most often falling short on dataset diversity and clinical validation.","url":"https://doi.org/10.21203/rs.3.rs-10403335/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10403335/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202607.1803.v1","name":"Artificial Intelligence in Radiology: Why Retrospective Performance Does Not Ensure Clinical Utility","source":"preprints","abstract":"Purpose: Artificial intelligence is increasingly developed and deployed to support radiological image interpretation, triage, segmentation and workflow prioritisation. However, high performance reported in retrospective studies does not necessarily translate into safe and useful deployment in routine practice. This narrative review examines why this translational gap persists and what radiology departments, researchers and developers should address before clinical implementation. Methods: We synthesised methodological, empirical and regulatory literature on the development, validation, deployment and governance of radiology AI. Radiology was used as the primary setting, with adjacent imaging fields discussed only where they illustrate broader mechanisms relevant to medical image analysis. Results: Recurrent barriers include limited data quality and representativeness, scanner- and protocol-related domain shift, shortcut learning, hidden stratification, insufficient external and prospective validation, poor calibration, over-reliance on global metrics and weak workflow integration. Model performance can be shaped by scanner protocols, reconstruction methods, local reporting practices, patient selection and institutional workflows beyond the underlying pathology. In Europe, GDPR-based data governance, the Medical Device Regulation, the AI Act and the European Health Data Space further shape clinical deployment. Conclusions: Emerging approaches, including foundation models, generative AI, multimodal systems, federated learning and local adaptation, may address selected technical constraints, but they introduce new uncertainties and do not replace independent validation in the intended clinical setting. Radiology AI should be evaluated not only as an algorithmic model, but as a clinical decision-support component embedded in radiology practice, requiring external and preferably prospective validation, subgroup analysis, calibration assessment, human oversight, workflow integration and post-deployment monitoring.","url":"https://doi.org/10.20944/preprints202607.1803.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202607.1803.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9737066/v1","name":"A Systematic Review of Hybrid Intelligent Models for Early Detection of Diabetes Using Electronic Health Records","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9737066/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9737066/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202605.1234.v1","name":"Modern Continual Learning with Foundation Models, Evaluation Challenges, and Future Directions","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202605.1234.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202605.1234.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9194366/v1","name":"A Systematic Review of Security Privacy and Provenance in EEG-Based Brain-Computer Interfaces with a Blockchain-Based Reference Architecture","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9194366/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9194366/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9995965/v1","name":"Adaptive Power Management Techniques for Edge-Cloud Integration","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9995965/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9995965/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202606.0126.v1","name":"Evolutionary Algorithms and Engineering Applications: A Comprehensive Survey of Classical Methods and Emerging Trends","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202606.0126.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202606.0126.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202606.0316.v1","name":"A Clinically Guided Rule-Based Synthetic Dataset for Multi-Modal Longitudinal Treatment-Response Monitoring in Major Depressive Disorder","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202606.0316.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202606.0316.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202602.0303.v1","name":"A Systematic Review of Privacy-Enhancing Technologies (PETs) for Securing Personally Identifiable Information in Public Cloud Architectures","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202602.0303.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202602.0303.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.14293/pr2199.003852.v1","name":"Human–Machine Collective Intelligence for Social-Physical Sensing: Architectures, Challenges, and Future Directions","source":"preprints","abstract":"","url":"https://doi.org/10.14293/pr2199.003852.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.14293/pr2199.003852.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.22541/au.176918533.33858528/v1","name":"Deep Neural Network Loss Landscapes: From Geometry to Generalization","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.176918533.33858528/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.22541/au.176918533.33858528/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-8627518/v1","name":"Detecting Cryptojacking in Cloud Environments: A Systematic Review of AI-Based Defenses, Deployment Challenges, and Research Gaps","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8627518/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8627518/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202604.0261.v1","name":"Distributed Intelligence in the Artificial Intelligence of Things: A Comprehensive Review of Architectures, Applications, and Challenges","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202604.0261.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202604.0261.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-9267907/v1","name":"AI-Powered Fraud Detection in Financial Networks: A Systematic Literature Review.","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9267907/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9267907/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202605.1978.v1","name":"From Telehealth to Artificial Intelligence: Digital Health Technologies for Indigenous Communities—A Systematic Review of Diagnostic Access, Ethical Governance, and Sustainable Health Equity","source":"preprints","abstract":"Indigenous communities worldwide face persistent health inequities rooted in colonial histories, geographic remoteness, and structural exclusion from diagnostic services. Artificial intelligence (AI) and digital health technologies are promoted as instruments of equity; however, the conditions under which they support rather than reproduce inequities remain contested. Following PRISMA 2020, PRISMA-Equity, and SWiM, we searched 12 databases (PubMed, Scopus, Web of Science, Embase, IEEE Xplore, ACM, CINAHL, Cochrane, SciELO, LILACS, Dimensions, Google Scholar) in English, Portuguese, Spanish, and French, plus structured grey literature. From 969 screened records, 39 studies met the eligibility criteria and were stratified into three layers: global, Americas/Latin America, and Brazil/Northeast. Deep-learning tele-otology and diabetic retinopathy screening in Aboriginal Australian contexts, suicide-risk machine learning with Native American communities, edge-AI maternal care with Indigenous Guatemalan midwives, and federated stress classification under Te Mana Raraunga emerged as the most mature applications. Latin America, Brazil, and the Northeast semiarid region were almost entirely absent; Brazilian tele-ultrasound work in the São Francisco Valley with Truká and Fulni-ô peoples, alongside Amazonian initiatives on tele-ophthalmology, machine-learning prediction of tuberculosis and malaria, cervical cancer screening, and culturally adapted cognitive assessment, offers a regionally grounded counterpoint. Indigenous data sovereignty, cultural safety, and external validation remain underdeveloped. We propose a nine-domain Responsible AI and Digital Health Implementation Framework aligned with the 2030 Agenda.","url":"https://doi.org/10.20944/preprints202605.1978.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202605.1978.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202602.1643.v1","name":"Digital Twin Technology and Process Validation in Pharmaceutical Manufacturing:Bridging Virtual Simulation and Regulatory Compliance for Next-Generation Drug Production","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202602.1643.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202602.1643.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.64898/2026.04.03.26350064","name":"From Registration to Insight: How STRONG AYA Transforms Registry Data to Enhance Decision-Support Tools for Adolescent and Young Adult Oncology","source":"preprints","abstract":"Background Population-based cancer registers (PBCR) are important for monitoring trends in cancer epidemiology, facilitating the implementation of effective cancer services. Adolescents and Young Adult (AYA) with cancer are a patient group with a unique set of needs. The utility of PBCR in AYA is limited by the lack of AYA-specific data items. STRONG AYA, an international multidisciplinary consortium is addressing this through federated learning (FL) methodology and novel data visualisation concepts. A Core Outcome Set (COS) has been developed to measure outcomes of importance through clinical data and Patient Reported Outcomes (PROs). We describe how data from the Yorkshire Specialist Register of Cancer in Children and Young People (YSRCCYP), a PBCR in the UK is being used within STRONG AYA and how the subsequent analyses can guide patient consultations. Methods Data from the YSRCCYP were imported into a Vantage 6 node, from which FL analyses are performed along with data provided by other consortium members. The results are extracted into the PROMPT software and integrated into patient electronic healthcare records. Results Healthcare professionals can view the results of individual PROs at various time points and in comparison, to summary analyses carried out within the STRONG AYA infrastructure. Results can be filtered by age, disease, country and stage. Conclusion We have demonstrated how a regional PBCR can contribute to a pan-European infrastructure and analyses viewed to enhance patient consultations. Such analyses have the potential to be used for research and policy-making, improving outcomes for AYA.","url":"https://doi.org/10.64898/2026.04.03.26350064","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.04.03.26350064","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202602.0673.v1","name":"GeoAI and Multimodal Geospatial Data Fusion for Inclusive Urban Mobility: Methods, Applications, and Future Directions","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202602.0673.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202602.0673.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202602.0555.v1","name":"Clinical AI in Radiology: Foundations, Trends, and Emerging Directions with Use Cases from Moffitt Cancer Center","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202602.0555.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202602.0555.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202601.1025.v1","name":"A Comprehensive Evaluation of Privacy-Preserving Mechanisms in Cloud-Based Big Data Analytics: Challenges and Future Research Directions","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202601.1025.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202601.1025.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202504.2082.v1","name":"Synergizing Intelligence and Privacy: A Review of Integrating Internet of Things, Large Language Models, and Federated Learning in Advanced Networked Systems","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202504.2082.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202504.2082.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202510.0990.v1","name":"The Utility of Artificial Intelligence in the Management of Diabetes: A Narrative Review","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.0990.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.0990.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202510.0547.v1","name":"GenAI Agents for Early Disease Diagnosis: A Review of Architectures, Applications, and Policy Directions","source":"preprints","abstract":"The integration of Artificial Intelligence (AI) agents into healthcare represents a paradigm shift in medical diagnostics, enabling autonomous systems that leverage multimodal data fusion, advanced machine learning architectures, and clinical reasoning engines. We explore their architectural components, including perception, knowledge base, reasoning engine, and decision-making modules. The paper then delves into key application areas such as medical imaging analysis, rare disease identification, multimodal diagnostic dialogue, and AI-powered generalist diagnostic agents. We examine the core architectural components including perception modules for EHR integration (HL7/FHIR standards), medical imaging analysis (DICOM, CNN architectures), genomic data processing (FASTQ/BAM formats), and multimodal biomarker integration. The paper details specialized AI agents for medical imaging analysis using 2D/3D convolutional neural networks and vision transformers, rare disease diagnosis through few-shot learning and knowledge graph reasoning, and multimodal diagnostic systems exemplified by Google's AMIE framework. We evaluate the technical implementation challenges including data privacy compliance (HIPAA, GDPR), model interpretability requirements (SHAP, LIME explanations), and regulatory considerations (FDA SaMD frameworks). Performance analysis demonstrates significant improvements in diagnostic accuracy (AUC-ROC improvements of 15-25\\% across studies), operational efficiency through automated workflow orchestration, and early disease detection capabilities surpassing traditional diagnostic methods. The synthesis of recent publications indicates that AI diagnostic agents achieve clinical performance comparable to healthcare professionals in specific domains while enabling proactive healthcare through predictive analytics and personalized treatment recommendations. Furthermore, we analyze the significant benefits offered by these systems, including improved diagnostic precision, operational efficiency, and personalized patient care. Finally, we address the critical challenges and future research directions, focusing on data privacy, model interpretability, regulatory hurdles, and the path toward medical superintelligence. Future research directions focus on federated learning approaches for privacy-preserving model training, explainable AI for clinical trust adoption, and the development of medical superintelligence systems capable of holistic patient health modeling across temporal and multimodal data dimensions.","url":"https://doi.org/10.20944/preprints202510.0547.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.0547.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202509.0642.v1","name":"Deep Learning Approaches for Crop Health Monitoring and Early Disease Detection: A Review","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202509.0642.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.0642.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.22541/au.175795684.47167615/v2","name":"Large Language Model Agents for Biomedicine: A Comprehensive Review of Methods, Evaluations, Challenges, and Future Directions","source":"preprints","abstract":"Large language model (LLM) based agents are rapidly emerging as transformative tools across biomedical research and clinical applications. By integrating reasoning, planning, memory, and tool use capabilities, these agents go beyond static language models to operate autonomously or collaboratively within complex healthcare settings. This review provides a comprehensive survey of biomedical LLM agents, spanning their core system architectures, enabling methodologies, and real-world use cases such as clinical decision making, biomedical research automation, and patient simulation. We further examine emerging benchmarks designed to evaluate agent performance under dynamic, interactive, and multimodal conditions. In addition, we systematically analyze key challenges, including hallucinations, interpretability, tool reliability, data bias, and regulatory gaps, and discuss corresponding mitigation strategies. Finally, we outline future directions in areas such as continual learning, federated adaptation, robust multi-agent coordination, and human–AI collaboration. This review aims to establish a foundational understanding of biomedical LLM agents and provide a forward-looking roadmap for building trustworthy, reliable, and clinically deployable intelligent systems.","url":"https://doi.org/10.22541/au.175795684.47167615/v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.175795684.47167615/v2","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-7973881/v1","name":"Artificial Intelligence-Powered Risk Prediction Models for Preventable Maternal Mortality in Rural Settings: A Systematic Review","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7973881/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7973881/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202509.0408.v1","name":"AI and Robotics in Agriculture: A Systematic and Quantitative Review of Research Trends (2015–2025)","source":"preprints","abstract":"The swift integration of AI, robotics, and advanced sensing technologies has revolutionized agriculture into a data-centric, autonomous, and sustainable sector. This systematic study examines the interplay between artificial intelligence and agricultural robotics in intelligent farming systems. Artificial intelligence, machine learning, computer vision, swarm robotics, and generative AI are analyzed for crop monitoring, precision irrigation, autonomous harvesting, and post-harvest processing. Employing PRISMA to categorize more than 10,000 high-impact publications from Scopus, WoS, and IEEE. Drones and vision-based models predominate the industry, while IoT integration, digital twins, and generative AI are on the rise. Insufficient field validation rates, inadequate crop and regional representation, and the implementation of explainable AI continue to pose significant challenges. Inadequate model generalization, energy limitations, and infrastructural restrictions impede scalability. We identify solutions in federated learning, swarm robotics, and climate-smart agricultural artificial intelligence. This paper presents a framework for inclusive, resilient, and feasible AI-robotic agricultural systems.","url":"https://doi.org/10.20944/preprints202509.0408.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.0408.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202508.0611.v1","name":"Leveraging AI and IoT for Energy Efficiency in the Industrial Sector: A Comprehensive Review","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202508.0611.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202508.0611.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202509.2027.v1","name":"AI-based Cancer Models for Prediction of Antibody-Drug Conjugate (ADC) Response in Oncology","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202509.2027.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.2027.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202509.1268.v1","name":"A Systematic Review of Building Energy Management Systems (BEMS): Sensors, IoT, and AI Integration","source":"preprints","abstract":"The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMS), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMS and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMS is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions.","url":"https://doi.org/10.20944/preprints202509.1268.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.1268.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202508.1120.v2","name":"Industrial Scheduling in the Digital Era: Challenges, State-of-the-Art Methods, and Deep Learning Perspectives","source":"preprints","abstract":"Industrial scheduling remains a pivotal discipline for efficiency, resilience, and competitiveness in manufacturing and service operations. The digital transformation, driven by paradigms such as Industry 4.0, has fundamentally reshaped the scheduling landscape, introducing pervasive connectivity, real-time data flows, and cyber-physical integration. This review synthesizes advances across three enduring challenges: (i) scalability and computational complexity in large-scale, high-dimensional scheduling environments, (ii) robustness and adaptability to uncertainty and disruptions, and (iii) integration with digitalization through IIoT, digital twins, cloud–edge architectures, and interoperable, secure infrastructures. We highlight the surge of AI-enhanced and deep learning–driven methods that are redefining state-of-the-art practice. Deep reinforcement learning (DRL) now underpins policy learning for dynamic dispatching and rescheduling; graph neural networks (GNNs) and attention models enable generalization across diverse shop configurations; and digital twin–in-the-loop frameworks provide safe training and rapid adaptation under real-world volatility. At the same time, neural architectures are increasingly embedded within decomposition, metaheuristics, and multi-agent systems—forming hybrid stacks that combine the guarantees of operations research with the adaptability of learning-based approaches. The industrial impact of these developments is evident across semiconductor fabrication, flexible job shops, supply-chain–intensive production, and distributed, autonomous networks. Yet critical challenges persist in interpretability, trust, data quality, and legacy integration. To advance, future research must prioritize explainable and certifiable neural schedulers, standardized datasets and benchmarks, seamless AI–IoT–DT integration, federated and privacy-preserving collaboration, and human-in-the-loop frameworks. Collectively, these directions chart a path toward scheduling systems that are not only more efficient and scalable, but also transparent, secure, and resilient in the digital, interconnected era.","url":"https://doi.org/10.20944/preprints202508.1120.v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202508.1120.v2","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.22541/au.175795684.47167615/v1","name":"Large Language Model Agents for Biomedicine: A Comprehensive Review of Methods, Evaluations, Challenges, and Future Directions","source":"preprints","abstract":"Large language model (LLM) based agents are rapidly emerging as transformative tools across biomedical research and clinical applications. By integrating reasoning, planning, memory, and tool use capabilities, these agents go beyond static language models to operate autonomously or collaboratively within complex healthcare settings. This review provides a comprehensive survey of biomedical LLM agents, spanning their core system architectures, enabling methodologies, and real-world use cases such as clinical decision making, biomedical research automation, and patient simulation. We further examine emerging benchmarks designed to evaluate agent performance under dynamic, interactive, and multimodal conditions. In addition, we systematically analyze key challenges, including hallucinations, interpretability, tool reliability, data bias, and regulatory gaps, and discuss corresponding mitigation strategies. Finally, we outline future directions in areas such as continual learning, federated adaptation, robust multi-agent coordination, and human–AI collaboration. This review aims to establish a foundational understanding of biomedical LLM agents and provide a forward-looking roadmap for building trustworthy, reliable, and clinically deployable intelligent systems.","url":"https://doi.org/10.22541/au.175795684.47167615/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.175795684.47167615/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.22541/au.175355646.67809816/v1","name":"Machine Learning for Electric Vehicle Range Estimation: A Review of Approaches and Performance","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.175355646.67809816/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.175355646.67809816/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202503.1015.v1","name":"A Systematic Survey on Federated Sequential Recommendation","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202503.1015.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202503.1015.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-6934585/v1","name":"Machine Learning for Privacy Threat Classification: A Systematic Review","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6934585/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6934585/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2025.04.07.25325343","name":"A machine learning approach for automating review of a RxNorm medication mapping pipeline output","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.04.07.25325343","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.04.07.25325343","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-7715812/v1","name":"A Taxonomy and Survey of Integrating Emerging Technologies to Intelligent Transportation Systems","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7715812/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7715812/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202507.2601.v1","name":"AI-Powered Wearable Sensors for Health Monitoring and Clinical Decision Making","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202507.2601.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.2601.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202510.0839.v1","name":"Emerging AI and Biomarker–Driven Precision Medicine in Autoimmune Rheumatic Diseases: From Diagnostics to Therapeutic Decision-Making","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.0839.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.0839.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202507.1681.v1","name":"Performance Evaluation and Deployment Strategies of Deep Learning-Based IDS","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202507.1681.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.1681.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202509.1290.v1","name":"Advancing Liver Cancer Treatment through Dynamic Genomics and Systems Biology: A Path Toward Personalized Oncology","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202509.1290.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.1290.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202509.1438.v1","name":"Towards Sustainable Buildings and Energy Communities: AI-Driven Transactive Energy, Smart Local Microgrids, and Life Cycle Integration","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202509.1438.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.1438.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202506.1885.v1","name":"Redefining Adversarial Dynamics: Co-Evolution of Attack and Defense Strategies in AI-Enabled Power Cyber-Physical Systems","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202506.1885.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202506.1885.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2025.07.30.25330916","name":"Automatic ICD coding using LLMs: a systematic review","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.07.30.25330916","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.07.30.25330916","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202506.0593.v1","name":"Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202506.0593.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202506.0593.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202507.1196.v1","name":"Early Detection of Mental Health Disorders Using AI: A Comprehensive Review","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202507.1196.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.1196.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202505.0833.v1","name":"Machine Learning Techniques for Urban Resilience: A Systematic Review and Future Directions","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202505.0833.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202505.0833.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202507.2567.v2","name":"Exploring the Role of Synthetic Data in the Future of AI in Healthcare: A Scoping Review of Frameworks, Challenges, and Implications","source":"preprints","abstract":"Synthetic data has emerged as a transformative tool in healthcare, particularly in areas such as medical imaging, electronic health records (EHRs), and clinical trial simulation, where data privacy, diversity, and accessibility are critical. This scoping review examines current approaches to synthetic data generation in healthcare, with a focus on AI model training, privacy preservation, and bias mitigation. A comprehensive search of PubMed, IEEE Xplore, and ACM Digital Library yielded 2,906 studies, of which 42 met the inclusion criteria. Key data generation techniques included generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, Bayesian networks, federated learning, recurrent neural networks (RNNs), large language models (LLMs), agent-based models, graph-based generators, and SMOTE-based oversampling. Applications ranged from diagnostic model development to privacy-preserving data sharing and educational simulation. However, the field faces persistent challenges, including inconsistent validation practices, the absence of standard benchmarks, high computational demands, and ethical concerns related to consent and bias. This review underscores the need for standardized evaluation protocols, clearer regulatory guidance, and multidisciplinary collaboration to ensure the safe, equitable, and effective use of synthetic data in healthcare AI.","url":"https://doi.org/10.20944/preprints202507.2567.v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.2567.v2","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202509.1087.v1","name":"Framework for Government Policy on Agentic and Generative AI in Healthcare: Governance, Regulation, and Risk Management of Open-Source and Proprietary Models","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202509.1087.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.1087.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-6199323/v2","name":"Industrial Applications of AI in Aircraft Manufacturing: A PRISMA Systematic Literature Review","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6199323/v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6199323/v2","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202507.1526.v1","name":"Non-Repudiation in Decentralized Wireless Networks in the Age of AI: A Comprehensive Review","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202507.1526.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202507.1526.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202508.1425.v1","name":"Multimodal Generative AI in Diagnostics: Bridging Medical Imaging and Clinical Reasoning","source":"preprints","abstract":"Multimodal generative artificial intelligence (AI) has emerged as a transformative approach in medical diagnostics, integrating diverse data sources to significantly enhance clinical decision-making and patient care. In this review, we systematically analyze recent advancements and methodologies in multimodal generative AI, focusing particularly on the fusion of medical imaging data with clinical records, genomic information, and textual narratives. We evaluate how these combined modalities closely mimic physician cognitive processes, leading to improved diagnostic accuracy and personalized patient management across various specialties including radiology, pathology, dermatology, and ophthalmology. Specifically, we discuss three key integration strategies: tool-use approaches, where large language models orchestrate specialized diagnostic modules; grafting techniques, which directly incorporate visual analysis into linguistic frameworks; and unified frameworks, providing simultaneous multimodal data processing within cohesive models. Additionally, we highlight exemplary models, such as PathChat, demonstrating substantial accuracy improvements (e.g., 89.5% in pathological image interpretation) resulting from multimodal integration. We also critically assess ongoing challenges, including technical barriers to data integration, interpretability issues affecting clinical trust, privacy and ethical concerns, and the evolving regulatory landscape surrounding AI-driven diagnostics. Finally, we propose directions for future research, emphasizing the need for large-scale clinical validation studies, standardized evaluation frameworks, advances in explainable AI methods, and privacy-preserving techniques such as federated learning. Ultimately, multimodal generative AI holds significant promise to augment rather than replace clinical expertise, serving as a powerful complement to human decision-making in medicine.","url":"https://doi.org/10.20944/preprints202508.1425.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202508.1425.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2025.01.29.25321214","name":"Federated Prediction Models and External Validation for Radiotherapy Outcomes in Oropharyngeal Cancer using F.A.I.R. Clinical and Radiomics Data","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.01.29.25321214","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.01.29.25321214","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.20944/preprints202504.1907.v1","name":"Securing Power Cyber‐Physical Systems Against False Data Injection Attacks: Trends, Techniques, and Future Directions","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202504.1907.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202504.1907.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202506.1690.v1","name":"Integrating Multi-Modal Predictive Modeling, Imaging Biomarkers, and EHR-Linked Decision Support Systems in Spine-Focused Biomedical Informatics","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202506.1690.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202506.1690.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202505.0375.v1","name":"Artificial Intelligence in Conflict Resolution: A Comprehensive Review of Techniques and Applications","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202505.0375.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202505.0375.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202504.0174.v1","name":"AI-Driven Software Engineering: A Systematic Review of Machine Learning’s Impact and Future Directions","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202504.0174.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202504.0174.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.22541/au.175225928.86757531/v1","name":"Expert And Intelligent Systems for Peer-To-Peer Energy Trading in Nano Grids: A Comprehensive Survey","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.175225928.86757531/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.175225928.86757531/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202502.1774.v2","name":"Interoperability Frameworks for uHealth and Digital Therapeutics: Standards, Governance, and Emerging Technologies for Scalable Health Data Integration","source":"preprints","abstract":"Background: Ubiquitous health (uHealth) and digital therapeutics require interoperable digital ecosystems to achieve an effective and scalable implementation. Alignment between technical standards and regulations is crucial to ensure the secure and patient-centric exchange of data as these technologies continue to evolve. Objective: This narrative review examines the existing interoperability frameworks that support uHealth and digital therapeutics. Technical standards and governance models were evaluated to identify barriers and recommend future directions for the universally scalable integration of these frameworks. Method: The authors conducted a systematic narrative synthesis to review existing interoperability standards, such as HL7 FHIR and TEFCA, as well as prominent regulations, including HIPAA and the GDPR. Real-world deployments, including those by the U.S. Department of Veterans Affairs, were examined to derive practical lessons. Emerging technologies, including AI, blockchain, and federated learning, were also considered, along with their potential contributions to interoperability. Results: This review highlights ongoing difficulties, including uneven standard implementation, disjointed regulatory regimes, sparse digital infrastructure, and semantic discrepancies among systems. These problems are particularly prevalent in low-income and middle-income nations. New technologies represent promising, yet untapped, remedies. Conclusion: Achieving sustainable interoperability requires reforming governance, adopting modular and standards-based architectures, and fostering widespread stakeholder engagement. Bridging the divide between policy and technology is crucial for building resilient digital health ecosystems that can support equitable, personalized, and globally integrated care delivery.","url":"https://doi.org/10.20944/preprints202502.1774.v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202502.1774.v2","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-6761315/v1","name":"Artificial Intelligence in Retinal Imaging for Early Alzheimer’s Disease Detection: A Systematic Review","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6761315/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6761315/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202506.2401.v1","name":"Computational Architectures for Precision Dairy Nutrition Digital Twins: A Technical Review and Implementation Framework","source":"preprints","abstract":"Sensor-enabled digital twins (DTs) are reshaping precision dairy nutrition by seamlessly integrating real-time barn telemetry with advanced biophysical simulations in the cloud. Drawing insights from 122 peer-reviewed studies spanning 2010–2025, this systematic review reveals how DT architectures for dairy cattle are conceptualized, validated, and deployed. We introduce a novel five-dimensional classification framework—spanning application domain, modeling paradigms, computational topology, validation protocols, and implementation maturity—to provide a coherent comparative lens across diverse DT implementations. Hybrid edge-cloud architectures emerge as optimal solutions, with lightweight CNN-LSTM models embedded in collar or rumen-bolus microcontrollers achieving over 90% accuracy in recognizing feeding and rumination behaviors. Simultaneously, remote cloud systems harness mechanistic fermentation simulations and multi-objective genetic algorithms to optimize feed composition, minimize greenhouse gas emissions, and balance amino-acid nutrition. Field-tested prototypes indicate significant agronomic benefits, including 15–20% enhancements in feed conversion efficiency and water use reductions of up to 40%. Nevertheless, critical challenges remain: effectively fusing heterogeneous sensor data amid high barn noise, ensuring millisecond-level synchronization across unreliable rural networks, and rigorously verifying AI-generated nutritional recommendations across varying genotypes, lactation phases, and climates. Overcoming these gaps necessitates integrating explainable AI with biologically grounded digestion models, federated learning protocols for data privacy, and standardized PRISMA-based validation approaches. The distilled implementation roadmap offers actionable guidelines for sensor selection, middleware integration, and model lifecycle management, enabling proactive rather than reactive dairy management—an essential leap toward climate-smart, welfare-oriented, and economically resilient dairy farming.","url":"https://doi.org/10.20944/preprints202506.2401.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202506.2401.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202505.0575.v1","name":"Generative AI in Investment and Portfolio Management: Comprehensive Review of Current Applications and Future Directions","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202505.0575.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202505.0575.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202504.1313.v1","name":"MoE at Scale: From Modular Design to Deployment in Large-Scale Machine Learning Systems","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202504.1313.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202504.1313.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202504.0007.v1","name":"AI and Machine Learning in Healthcare: Advancing Diagnostics, Personalized Treatment, and Predictive Modeling","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202504.0007.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202504.0007.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202505.1399.v1","name":"Optical Sensor Based Approaches in Obesity Detection: A Literature Review of Gait Analysis, Pose Estimation, and Human Voxel Modeling","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202505.1399.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202505.1399.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202503.1108.v1","name":"A Comprehensive Review of Multi-Source Data Fusion Processing Methods","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202503.1108.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202503.1108.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202503.2048.v1","name":"Advances in Parameter-Efficient Fine-Tuning: Optimizing Foundation Models for Scalable AI","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202503.2048.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202503.2048.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.22541/au.174077476.64719692/v1","name":"Expert and Intelligent Systems for Robotic Manipulators Control: A Comprehensive Review","source":"preprints","abstract":"","url":"https://doi.org/10.22541/au.174077476.64719692/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.174077476.64719692/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202504.0344.v1","name":"Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202504.0344.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202504.0344.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.20944/preprints202307.1420.v1","name":"Multimodal Federated Learning: A Survey","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202307.1420.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.20944/preprints202307.1420.v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-3158418/v1","name":"Secure and Private Healthcare Analytics: A Feasibility Study of Federated Deep Learning with Personal Health Train","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3158418/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-3158418/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-2350540/v1","name":"Federated Ensembles: a literature review","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-2350540/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-2350540/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2023.05.05.23289554","name":"Scalable federated learning for emergency care using low cost microcomputing: Real-world, privacy preserving development and evaluation of a COVID-19 screening test in UK hospitals","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2023.05.05.23289554","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1101/2023.05.05.23289554","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-3744741/v1","name":"A Distributed Feature Selection Pipeline for Survival Analysis using Radiomics in Non-Small Cell Lung Cancer Patients","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3744741/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-3744741/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.21203/rs.3.rs-2705743/v1","name":"FAIR-ification of structured Head and Neck Cancer clinical data for multi-institutional collaboration and federated learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-2705743/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-2705743/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-2101865/v1","name":"Federated Learning Design and FunctionalModels: Survey","source":"preprints","abstract":"Abstract Federated learning is a multiple device collaboration setup designed tosolve machine learning problems under framework for aggregation andknowledge transfer in distributed local data. This distributed modelensures the privacy of data at each local node. Owing to its relevance,there has been extensive research activities and outcomes in federatedlearning with expanded applicability to different areas by the researchcommunity. As such, there is a vast research archive made available by thecommunity with research work and articles related to the various aspectsof federated learning such as applications, challenges, privacy, function-alities, and design. With respect to the function and design of federatedlearning, client selection, aggregation, knowledge transfer, managementof distributed data (Non-IID), Incentive of data and communication costare of paramount importance. Any effective design of federated learningrequires these aspects to be well considered.There are numerous surveyarticles found among the available literature that focus on its applica-tion and challenges, opportunities, data privacy and protection, as wellas on federated learning on internet of things, federated learning on edgecomputing, etc.1 In this paper, a review of the available literature on the various ele-ments of design and functionalities in federated learning has been carriedout with an aim to lay emphasis on the important challenges andresearch opportunities. More specifically, this work has endeavored tounderstand and summarize the various functional methods available,along with their techniques and goals. Additionally, it has strived toget a bird’s eye view of how various functions and designs of feder-ated learning have been used in applications, and how it has helpeduncover challenges and promising research directions for the future.","url":"https://doi.org/10.21203/rs.3.rs-2101865/v1","authors":["John A","Sapdo Utomo","Adarsh Rouniyar","Hsiu-Chu Hsu","Pao-Ann Hsiung"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-2101865/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-8090378/v1","name":"Harness Behavioural Analysis for Unpacking the Bio-Interpretability of Pathology Foundation Models","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8090378/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8090378/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.2139/ssrn.4302481","name":"Application of Federated Analytics in Health Data Research for Reducing Risks Involved in Data Sharing","source":"preprints","abstract":"","url":"https://doi.org/10.2139/ssrn.4302481","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.2139/ssrn.4302481","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-2205379/v1","name":"Securing Health Care Data through Blockchain enabled Collaborative Machine Learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-2205379/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-2205379/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1101/2025.01.13.632775","name":"SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.01.13.632775","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.01.13.632775","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-2627405/v1","name":"A Systematic Review on ICT-based Remote and Automatic COVID-19 Patient Monitoring and Care","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-2627405/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-2627405/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-1943509/v1","name":"SecureFed: Federated learning empowered medical imaging technique to detect COVID-19 using chest x-rays","source":"preprints","abstract":"Abstract Machine learning is an effective and accurate technique to diagnose COVID-19 infections using image data, and chest X-Ray (CXR) is no exception. Considering privacy issues, machine learning scientists end up receiving less medical imaging data. Federated Learning (FL) is a privacy-preserving distributed machine learning paradigm that generates an unbiased global model that follows local model (from clients) without exposing their personal data. In case of heterogeneous data among clients, vanilla or default FL mechanism still introduces an insecure method for updating models. Therefore, we proposed SecureFed – a secure aggregation method – which ensures the fairness and the robustness. In our experiments, we employed COVID-19 CXR dataset (of size 2100 positive cases) and compared with the existing FL frameworks such as FedAvg, FedMGDA+, and FedRAD. In our comparison, we primarily considered robustness (accuracy) and fairness (consistency). As the SecureFed produced consistently better results, it is generic enough to be considered for multimodal data.","url":"https://doi.org/10.21203/rs.3.rs-1943509/v1","authors":["Aaisha Makkar","KC Santosh"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-1943509/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.2139/ssrn.4369346","name":"The Impact of the COVID-19 Pandemic on Anti-Psychotic Prescribing in Individuals with Autism, Dementia, Learning Disability, Serious Mental Illness or Living in a Care Home: A Federated Analysis of 59 Million Patients’ Primary Care Records in Situ Using OpenSAFELY","source":"preprints","abstract":"","url":"https://doi.org/10.2139/ssrn.4369346","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.2139/ssrn.4369346","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-2810469/v1","name":"COVision: Convolutional Neural Network for the Differentiation of COVID-19 from Common Pulmonary Conditions Using CT Scans","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-2810469/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-2810469/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2023.01.05.23284214","name":"The impact of the COVID-19 pandemic on Antipsychotic Prescribing in individuals with autism, dementia, learning disability, serious mental illness or living in a care home: A federated analysis of 59 million patients’ primary care records in situ using OpenSAFELY","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2023.01.05.23284214","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1101/2023.01.05.23284214","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2025.11.26.689911","name":"High cell-type specificity of eQTLs revealed by single-nucleus analyses of brain and blood","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.11.26.689911","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.11.26.689911","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2020.08.11.20172809","name":"Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2020.08.11.20172809","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1101/2020.08.11.20172809","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-1418826/v1","name":"Leveraging Artificial Intelligence and Data Science Techniques in Harmonizing, Sharing, Accessing and Analyzing SARS-COV-2/COVID-19 Data in Rwanda (LAISDAR Project): Study design and rationale","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-1418826/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-1418826/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.2139/ssrn.4435008","name":"A Blockchain-Based Framework for Covid-19 Detection Using Stacking Ensemble of Pre-Trained Models","source":"preprints","abstract":"","url":"https://doi.org/10.2139/ssrn.4435008","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.2139/ssrn.4435008","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.2139/ssrn.3641518","name":"Algorithms in Future Insurance Markets","source":"preprints","abstract":"","url":"https://doi.org/10.2139/ssrn.3641518","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.2139/ssrn.3641518","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-126892/v1","name":"Federated Learning used for predicting outcomes in SARS-COV-2 patients","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-126892/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.21203/rs.3.rs-126892/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.1101/2023.09.19.23295797","name":"Automatic Population of the Case Report Forms for an International Multifactorial Adaptive Platform Trial Amid the COVID-19 Pandemic","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2023.09.19.23295797","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1101/2023.09.19.23295797","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.2139/ssrn.4392891","name":"Resilience, Protagonism, and Readiness of Brazilian Academic Research to Face the COVID-19 Pandemic","source":"preprints","abstract":"","url":"https://doi.org/10.2139/ssrn.4392891","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.2139/ssrn.4392891","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-2653408/v1","name":"A Deep Learning Approach of Blood Glucose Predictive Monitoring for Women with Gestational Diabetes","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-2653408/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-2653408/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.21203/rs.3.rs-3357602/v1","name":"An Intelligent Computational Model with Dynamic Mode Decomposition and Attention Features for COVID-19 Detection from CT Scan Images","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3357602/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-3357602/v1","addedAt":"2026-08-31T06:41:27.399Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.3403/30422743u","name":"Information security � Secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30422743u","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-03T20:30:33Z","doi":"10.3403/30422743u","addedAt":"2026-08-31T06:41:28.178Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3403/30422743","name":"Information security � Secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30422743","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-03T20:30:33Z","doi":"10.3403/30422743","addedAt":"2026-08-31T06:41:28.178Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3403/30422746","name":"Information security - Secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30422746","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-13T21:30:36Z","doi":"10.3403/30422746","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3403/30422746u","name":"Information security - Secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30422746u","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-13T21:30:36Z","doi":"10.3403/30422746u","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/978-3-030-71522-9_300861","name":"Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71522-9_300861","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:20:57Z","doi":"10.1007/978-3-030-71522-9_300861","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/978-1-4899-7993-3_1388-2","name":"Secure Multiparty Computation Methods","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4899-7993-3_1388-2","authors":["Murat Kantarcolu","Jaideep Vaidya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-10-10T03:35:37Z","doi":"10.1007/978-1-4899-7993-3_1388-2","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/978-1-4614-8265-9_1388","name":"Secure Multiparty Computation Methods","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4614-8265-9_1388","authors":["Murat Kantarcolu","Jaideep Vaidya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-12-06T08:25:47Z","doi":"10.1007/978-1-4614-8265-9_1388","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.2196/preprints.44700","name":"Secure Comparisons of Single Nucleotide Polymorphisms Using Secure Multiparty Computation: Method Development (Preprint)","source":"crossref","abstract":"BACKGROUND While genomic variations can provide valuable information for health care and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable but not directly accessible to involved parties (eg, data hosts and hospitals) and that the query results are learned only by the user or authorized party. OBJECTIVE In this study, we provide efficient and secure computations on panels of single nucleotide polymorphisms (SNPs) from genomic sequences as computed under the following set operations: union, intersection, set difference, and symmetric difference. METHODS Using these operations, we can compute similarity metrics, such as the Jaccard similarity, which could allow querying a DNA database to find the same person and genetic relatives securely. We analyzed various security paradigms and show metrics for the protocols under several security assumptions, such as semihonest, malicious with honest majority, and malicious with a malicious majority. RESULTS We show that our methods can be used practically on realistically sized data. Specifically, we can compute the Jaccard similarity of two genomes when considering sets of SNPs, each with 400,000 SNPs, in 2.16 seconds with the assumption of a malicious adversary in an honest majority and 0.36 seconds under a semihonest model. CONCLUSIONS Our methods may help adopt trusted environments for hosting individual genomic data with end-to-end data security.","url":"https://doi.org/10.2196/preprints.44700","authors":["Andrew Woods","Skyler T Kramer","Dong Xu","Wei Jiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-12-09T17:55:59Z","doi":"10.2196/preprints.44700","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1145/3387108","name":"Secure multiparty computation","source":"crossref","abstract":"MPC has moved from theoretical study to real-world usage. How is it doing?","url":"https://doi.org/10.1145/3387108","authors":["Yehuda Lindell"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-12-17T18:39:35Z","doi":"10.1145/3387108","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1360/jos170157","name":"Ownership Proofs of Digital Works Based on Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1360/jos170157","authors":["Yan ZHU"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2006-01-11T02:56:56Z","doi":"10.1360/jos170157","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/ncic61838.2023.00024","name":"Multiparty Secure Delegated Quantum Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ncic61838.2023.00024","authors":["Shuquan Ma","Xuchao Liu","Huagui Li","Heliang Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-16T17:22:10Z","doi":"10.1109/ncic61838.2023.00024","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-3","name":"Protecting Confidential Data through Non-Statistical Methods","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-3","authors":["Lars Vilhuber"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","doi":"10.1201/9781003185284-3","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/tencon.2008.4766515","name":"A zero-hacking protocol for secure multiparty computation using multiple TTP","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tencon.2008.4766515","authors":["Durgesh Kumar Mishra","Manohar Chandwani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-01-29T21:52:21Z","doi":"10.1109/tencon.2008.4766515","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-7","name":"Privacy Implications of Practical Model Design Choices","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-7","authors":["Audra McMillan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","doi":"10.1201/9781003185284-7","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1201/9781003185284-1","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-1","authors":["Jörg Drechsler","Daniel Kifer","Jerome Reiter","Aleksandra Slavković"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","doi":"10.1201/9781003185284-1","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3390/cryptography1030025","name":"Cryptographically Secure Multiparty Computation and Distributed Auctions Using Homomorphic Encryption","source":"crossref","abstract":"We introduce a robust framework that allows for cryptographically secure multiparty computations, such as distributed private value auctions. The security is guaranteed by two-sided authentication of all network connections, homomorphically encrypted bids, and the publication of zero-knowledge proofs of every computation. This also allows a non-participant verifier to verify the result of any such computation using only the information broadcasted on the network by each individual bidder. Building on previous work on such systems, we design and implement an extensible framework that puts the described ideas to practice. Apart from the actual implementation of the framework, our biggest contribution is the level of protection we are able to guarantee from attacks described in previous work. In order to provide guidance to users of the library, we analyze the use of zero knowledge proofs in ensuring the correct behavior of each node in a computation. We also describe the usage of the library to perform a private-value distributed auction, as well as the other challenges in implementing the protocol, such as auction registration and certificate distribution. Finally, we provide performance statistics on our implementation of the auction.","url":"https://doi.org/10.3390/cryptography1030025","authors":["Anunay Kulshrestha","Akshay Rampuria","Matthew Denton","Ashwin Sreenivas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-12-12T13:35:00Z","doi":"10.3390/cryptography1030025","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-11","name":"Systems Issues in Formally Private Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-11","authors":["Philip Leclerc","Pavel Zhuravlev"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","doi":"10.1201/9781003185284-11","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-24","name":"Secure Federated Learning Integrated Statistical Modeling for Healthcare Data","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-24","authors":["Xiaoqian Jiang","Jihoon Kim","Tsung-Ting Kuo","Lucila Ohno-Machado"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","doi":"10.1201/9781003185284-24","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.5121/ijcsa.2012.2604","name":"A Secure Protocol for Indian Healthcare Sector during Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.5121/ijcsa.2012.2604","authors":["Zulfa Shaikh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-01-10T15:52:32Z","doi":"10.5121/ijcsa.2012.2604","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/imis.2015.70","name":"Consideration of the XOR-operation Based Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/imis.2015.70","authors":["Yuji Suga"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-10-01T18:02:23Z","doi":"10.1109/imis.2015.70","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/springerreference_368","name":"Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/springerreference_368","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-12-31T20:16:01Z","doi":"10.1007/springerreference_368","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/ic2e.2018.00069","name":"Supporting Private Data on Hyperledger Fabric with Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ic2e.2018.00069","authors":["Fabrice Benhamouda","Shai Halevi","Tzipora Halevi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-05-17T22:40:06Z","doi":"10.1109/ic2e.2018.00069","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/s00145-008-9021-2","name":"General Composition and Universal Composability in Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00145-008-9021-2","authors":["Yehuda Lindell"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2008-03-28T18:49:05Z","doi":"10.1007/s00145-008-9021-2","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-14","name":"Methods for Synthetic Data Generation","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-14","authors":["Joshua Snoke","Satkartar K. Kinney"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","doi":"10.1201/9781003185284-14","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/icacccn51052.2020.9362954","name":"Secure Quantum Protocol for Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icacccn51052.2020.9362954","authors":["Amitesh Kumar Pandit","Kakali Chatterjee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-03-01T22:46:34Z","doi":"10.1109/icacccn51052.2020.9362954","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/icicis46948.2019.9014698","name":"Secure Multiparty Computation via Homomorphic Encryption Library","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicis46948.2019.9014698","authors":["Sahar M. Ghanem","Islam A. Moursy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-03-13T04:41:03Z","doi":"10.1109/icicis46948.2019.9014698","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.2174/0126662558363031250313051750","name":"Secure Multiparty Computation in Heterogeneous Environment: Survey and Challenges","source":"crossref","abstract":"Abstract: The sharing of personal data in a distributed environment raises individual privacy concerns. However, analysis of these data may solve various real-life problems, including analysis of medical data, e-auction, secure voting, etc. Such analysis requires a system that ensures data privacy during and after computation. Secure Multi-Party Computation (MPC) is one of the popular cryptographic tools that allows a group of parties to compute a collaborative function without any personal interactions. MPC only produces the final result; it does not leak the input data or any partial results that may reveal personal information. The secret personal data of each participating party will be the input for this MPC function. In a collaborative environment, no one trusts another party but is interested in performing joint computation with their personal data. Some participating parties may be curious about others'; input to gain an advantage. Ensuring the security and privacy of individual data in such an environment, the MPC has become an emerging area of interest. Based on the application, different variants of the MPC protocols exist. The efficiency of these protocols depends on communication and computation cost, which further depends on the number of participating parties. This study demonstrates the challenges involved in developing practically implementable MPCs. The present paper reviews some existing MPC protocols based on different parameters, like security, privacy, feasibility, efficiency, number of participating parties involved in computation, and their applications. Finally, some of the best-suited MPC protocols as per their application domain have been suggested.","url":"https://doi.org/10.2174/0126662558363031250313051750","authors":["Amitesh Kumar Pandit","Kakali Chatterjee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-18T05:28:46Z","doi":"10.2174/0126662558363031250313051750","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1063/5.0129596","name":"Privacy preserving machine learning using secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1063/5.0129596","authors":["Suhel Sayyad","Dinesh Kulkarni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-24T15:25:06Z","doi":"10.1063/5.0129596","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1016/j.cose.2022.102679","name":"Efficient optimisation framework for convolutional neural networks with secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.cose.2022.102679","authors":["Cate Berry","Nikos Komninos"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-04T19:50:49Z","doi":"10.1016/j.cose.2022.102679","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/nuicone.2013.6780073","name":"Notice of Violation of IEEE Publication Principles - Distributed changing neighbors k-secure sum protocol for secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/nuicone.2013.6780073","authors":["First A. Neha Pathak","Second B. Shweta Pandey"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-04-15T17:15:32Z","doi":"10.1109/nuicone.2013.6780073","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/isms.2011.75","name":"A Secure Multiparty Computation Solution to Healthcare Frauds and Abuses","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isms.2011.75","authors":["Priyanka Jangde","Durgesh Kumar Mishra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-03-15T17:17:17Z","doi":"10.1109/isms.2011.75","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3233/978-1-61499-532-6-186","name":"Universally Verifiable Outsourcing and Application to Linear Programming","source":"crossref","abstract":"In this chapter, we show how to guarantee correctness when applying multiparty computation in outsourcing scenarios. Specifically, we consider how to guarantee the correctness of the result when neither the parties supplying the input nor the parties performing the computation can be trusted. Generic techniques to achieve this are too slow to be of practical use. However, we show that it is possible to achieve practical performance for specific problems by exploiting the existence of certificates proving that a computation result is correct.","url":"https://doi.org/10.3233/978-1-61499-532-6-186","authors":["de Hoogh Sebastiaan","Schoenmakers Berry","Veeningen Meilof"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T09:38:17Z","doi":"10.3233/978-1-61499-532-6-186","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.17485/ijst/2016/v9i48/108354","name":"A Review on Privacy Preserving Data Mining using Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.17485/ijst/2016/v9i48/108354","authors":["U. Kumaran","Neelu Khare"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-02-06T08:18:42Z","doi":"10.17485/ijst/2016/v9i48/108354","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.4018/978-1-4666-4030-6.ch011","name":"Secure Multiparty Computation via Oblivious Polynomial Evaluation","source":"crossref","abstract":"The number of opportunities for cooperative computation has exponentially been increasing with growing interaction via Internet technologies. These computations could occur between almost trusted partners, between partially trusted partners, or even between competitors. Most of the time, the communicating parties may not want to disclose their private data to the other principal while taking the advantage of collaboration, hence concentrating on the results rather than private data values. For performing such computations, one party must know inputs from all the participants; however, if none of the parties can be trusted enough to know all the inputs, privacy will become a primary concern. Hence, the techniques for Secure Multiparty Computation (SMC) are quite relevant and practical to overcome such kind of privacy gaps. The subject of SMC has evolved from earlier solutions of combinational logic circuits to the recent proposals of anonymity-enabled computation. In this chapter, the authors put together the significant research that has been carried out on SMC. They demonstrate the concept by concentrating on a specific technique called Oblivious Polynomial Evaluation (OPE) together with concrete examples. The authors put critical issues and challenges and the level of adaptation achieved before the researchers. They also provide some future research proposals based on the literature survey.","url":"https://doi.org/10.4018/978-1-4666-4030-6.ch011","authors":["Mert Özarar","Attila Özgit"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-04-23T04:12:06Z","doi":"10.4018/978-1-4666-4030-6.ch011","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1103/physreva.81.062336","name":"Secure multiparty computation with a dishonest majority via quantum means","source":"crossref","abstract":"","url":"https://doi.org/10.1103/physreva.81.062336","authors":["Klearchos Loukopoulos","Daniel E. Browne"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2010-06-28T13:33:14Z","doi":"10.1103/physreva.81.062336","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/978-3-540-70936-7_9","name":"On the Necessity of Rewinding in Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-540-70936-7_9","authors":["Michael Backes","Jörn Müller-Quade","Dominique Unruh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-05-16T07:43:44Z","doi":"10.1007/978-3-540-70936-7_9","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/11818175_30","name":"Scalable Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/11818175_30","authors":["Ivan Damgård","Yuval Ishai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2006-09-23T02:21:52Z","doi":"10.1007/11818175_30","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.12723/mjs.14.5","name":"Performance Analysis of Secure Multiparty Computation Protocol","source":"crossref","abstract":"In this paper we address the issue related to privacy, security, complexity and Implementation, various adversaries exist which hamper the secure multiparty computation. In the secure multiparty computation, a set of parties wishes to jointly compute some function of their inputs. Such a computation must preserve certain security properties, like privacy and correctness, even if some of the participating parties or an external adversary colludes to attack the honest parties.","url":"https://doi.org/10.12723/mjs.14.5","authors":["Samiksha Shukla","D. K. Mishra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-06-17T15:22:23Z","doi":"10.12723/mjs.14.5","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.11591/telkomnika.v11i7.2827","name":"Key Technologies and Applications of Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.11591/telkomnika.v11i7.2827","authors":["Xiaoqiang Guo","Shuai Zhang","Ying Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-05-22T21:56:07Z","doi":"10.11591/telkomnika.v11i7.2827","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/3-540-44495-5_11","name":"Efficient Asynchronous Secure Multiparty Distributed Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/3-540-44495-5_11","authors":["K. Srinathan","C. Pandu Rangan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-10-28T05:24:06Z","doi":"10.1007/3-540-44495-5_11","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/icict64420.2025.11004894","name":"Privacy-Preserving Analytics Using Zero-Knowledge Proofs and Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icict64420.2025.11004894","authors":["Nelson Lungu","Bibhuti Bhusan Dash","Satyendr Singh","Manoj Ranjan Mishra","Namita Panda","Sudhansu Shekhar Patra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-23T17:02:43Z","doi":"10.1109/icict64420.2025.11004894","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.5121/csit.2012.2412","name":"Secure Multiparty Computation during Privacy Preserving Data Mining: Inscrutability Aided Protocol for Indian Healthcare Sector","source":"crossref","abstract":"","url":"https://doi.org/10.5121/csit.2012.2412","authors":["Zulfa Shaikh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-11-07T05:22:50Z","doi":"10.5121/csit.2012.2412","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3390/cryptography7040056","name":"Secure Groups for Threshold Cryptography and Number-Theoretic Multiparty Computation","source":"crossref","abstract":"In this paper, we introduce secure groups as a cryptographic scheme representing finite groups together with a range of operations, including the group operation, inversion, random sampling, and encoding/decoding maps. We construct secure groups from oblivious group representations combined with cryptographic protocols, implementing the operations securely. We present both generic and specific constructions, in the latter case specifically for number-theoretic groups commonly used in cryptography. These include Schnorr groups (with quadratic residues as a special case), Weierstrass and Edwards elliptic curve groups, and class groups of imaginary quadratic number fields. For concreteness, we develop our protocols in the setting of secure multiparty computation based on Shamir secret sharing over a finite field, abstracted away by formulating our solutions in terms of an arithmetic black box for secure finite field arithmetic or for secure integer arithmetic. Secure finite field arithmetic suffices for many groups, including Schnorr groups and elliptic curve groups. For class groups, we need secure integer arithmetic to implement Shanks’ classical algorithms for the composition of binary quadratic forms, which we will combine with our adaptation of a particular form reduction algorithm due to Agarwal and Frandsen. As a main result of independent interest, we also present an efficient protocol for the secure computation of the extended greatest common divisor. The protocol is based on Bernstein and Yang’s constant-time 2-adic algorithm, which we adapt to work purely over the integers. This yields a much better approach for multiparty computation but raises a new concern about the growth of the Bézout coefficients. By a careful analysis, we are able to prove that the Bézout coefficients in our protocol will never exceed 3max(a,b) in absolute value for inputs a and b. We have integrated secure groups in the Python package MPyC and have implemented threshold ElGamal and threshold DSA in terms of secure groups. We also mention how our results support verifiable multiparty computation, allowing parties to jointly create a publicly verifiable proof of correctness for the results accompanying the results of a secure computation.","url":"https://doi.org/10.3390/cryptography7040056","authors":["Berry Schoenmakers","Toon Segers"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-09T10:19:27Z","doi":"10.3390/cryptography7040056","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/globalsip.2013.6736860","name":"On unconditionally secure multiparty computation for realizing correlated equilibria in games","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globalsip.2013.6736860","authors":["Ye Wang","Shantanu Rane","Prakash Ishwar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-02-13T22:45:12Z","doi":"10.1109/globalsip.2013.6736860","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/csf51468.2021.00034","name":"Cooking Cryptographers: Secure Multiparty Computation Based on Balls and Bags","source":"crossref","abstract":"","url":"https://doi.org/10.1109/csf51468.2021.00034","authors":["Daiki Miyahara","Yuichi Komano","Takaaki Mizuki","Hideaki Sone"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-10T20:47:26Z","doi":"10.1109/csf51468.2021.00034","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/sp40000.2020.00016","name":"Efficient and Secure Multiparty Computation from Fixed-Key Block Ciphers","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp40000.2020.00016","authors":["Chun Guo","Jonathan Katz","Xiao Wang","Yu Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-07-30T20:48:34Z","doi":"10.1109/sp40000.2020.00016","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/978-3-540-72504-6_45","name":"Secure Multiparty Computations Using a Dial Lock","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-540-72504-6_45","authors":["Takaaki Mizuki","Yoshinori Kugimoto","Hideaki Sone"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-07-22T07:36:39Z","doi":"10.1007/978-3-540-72504-6_45","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/icccnet.2008.4787758","name":"A secure group communication using non-interactive key computation in multiparty key agreement","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icccnet.2008.4787758","authors":["S. Kalaiselvi","S. Jabeen Begum"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-02-26T13:19:55Z","doi":"10.1109/icccnet.2008.4787758","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1002/spy2.232","name":"Anonymous Bitcoin transaction: A solution using secure multiparty computation","source":"crossref","abstract":"Abstract Bitcoin is a popular form of cryptocurrencies. Bitcoin provides users' anonymity through cryptographic pseudonyms. Bitcoin operates on a peer‐to‐peer network that maintains a public ledger, called blockchain , to log all transactions from one pseudonym to other, thereby hides the identity of the users. A transaction graph from the blockchain may unveil certain users' identities. To sustain with users' anonymity, mixing is often applied. CoinJoin , MixCoin , CoinShuffle , CoinParty and SecureCoin are some of the Bitcoin protocols that apply mixnet . Mixnet has certain limitations. Firstly, protocols assume “escrow addressee” that collects the coins, and performs mixing . The “escrow addressee” must be trustworthy. Secondly, protocols which do not assume “escrow addressee,” often use mixnet . Mixnet requires every participant must sign all others transactions. This incurs large volume of multi‐signature. Presently, Bitcoin protocol can include at most 15 multi‐signatures. Therefore, the protocols are not scalable. Finally, mixnet is “all‐or‐nothing.” That is, if all mixnodes are active then only the output is guaranteed. We present a multiparty shuffling protocol to anonymize the Bitcoin transactions. Our protocol is free from “escrow addressee” and multi‐signature. The protocol executes in multiple rounds. The protocol is not “all‐or‐nothing.” That means, every round guarantees some degree of anonymity.","url":"https://doi.org/10.1002/spy2.232","authors":["Dhaneshwar Mardi","Jaydeep Howlader"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-04-08T10:46:44Z","doi":"10.1002/spy2.232","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/978-3-540-74143-5_16","name":"How Many Oblivious Transfers Are Needed for Secure Multiparty Computation?","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-540-74143-5_16","authors":["Danny Harnik","Yuval Ishai","Eyal Kushilevitz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-08-09T13:51:33Z","doi":"10.1007/978-3-540-74143-5_16","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/ickg63256.2024.00051","name":"Enhanced Private Decision Trees using Secure Multiparty Computation and Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ickg63256.2024.00051","authors":["Arisa Tajima","Wei Jiang","Virendra Marathe","Hamid Mozaffari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-19T18:39:49Z","doi":"10.1109/ickg63256.2024.00051","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1038/srep19655","name":"Secure Multiparty Quantum Computation for Summation and Multiplication","source":"crossref","abstract":"Abstract As a fundamental primitive, Secure Multiparty Summation and Multiplication can be used to build complex secure protocols for other multiparty computations, specially, numerical computations. However, there is still lack of systematical and efficient quantum methods to compute Secure Multiparty Summation and Multiplication. In this paper, we present a novel and efficient quantum approach to securely compute the summation and multiplication of multiparty private inputs, respectively. Compared to classical solutions, our proposed approach can ensure the unconditional security and the perfect privacy protection based on the physical principle of quantum mechanics.","url":"https://doi.org/10.1038/srep19655","authors":["Run-hua Shi","Yi Mu","Hong Zhong","Jie Cui","Shun Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-01-21T11:29:00Z","doi":"10.1038/srep19655","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/s00145-016-9245-5","name":"Fairness Versus Guaranteed Output Delivery in Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00145-016-9245-5","authors":["Ran Cohen","Yehuda Lindell"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-09-23T19:39:23Z","doi":"10.1007/s00145-016-9245-5","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1038/nbt.4108","name":"Secure genome-wide association analysis using multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1038/nbt.4108","authors":["Hyunghoon Cho","David J Wu","Bonnie Berger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-05-07T12:30:35Z","doi":"10.1038/nbt.4108","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/aiqc64330.2024.00030","name":"Optimized Lattice-Based Homomorphic Encryption for Secure Multiparty Computation in Group Communication","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aiqc64330.2024.00030","authors":["Renisha P.S.","Bhawana Rudra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-17T17:54:59Z","doi":"10.1109/aiqc64330.2024.00030","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1515/jmc-2020-0013","name":"A note on secure multiparty computation via higher residue symbols","source":"crossref","abstract":"Abstract We generalize a protocol by Yu for comparing two integers with relatively small difference in a secure multiparty computation setting. Yu's protocol is based on the Legendre symbol. A prime number p is found for which the Legendre symbol (· | p ) agrees with the sign function for integers in a certain range {− N , . . . , N } ⊂ ℤ. This can then be computed efficiently. We generalize this idea to higher residue symbols in cyclotomic rings ℤ[ ζ r ] for r a small odd prime. We present a way to determine a prime number p such that the r -th residue symbol (· | p ) r agrees with a desired function f : A → { ζ r 0 , … , ζ r r − 1 } f:A \\to \\left\\{ {\\zeta _r^0, \\ldots ,\\zeta _r^{r - 1}} \\right\\} on a given small subset A ⊂ ℤ[ ζ r ], when this is possible. We also explain how to efficiently compute the r -th residue symbol in a secret shared setting.","url":"https://doi.org/10.1515/jmc-2020-0013","authors":["Ignacio Cascudo","Reto Schnyder"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-02-20T18:42:12Z","doi":"10.1515/jmc-2020-0013","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.14722/ndss.2024.24601","name":"Secure Multiparty Computation of Threshold Signatures Made More Efficient","source":"crossref","abstract":"","url":"https://doi.org/10.14722/ndss.2024.24601","authors":["Harry W. H. Wong","Jack P. K. Ma","Sherman S. M. Chow"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-10T11:35:08Z","doi":"10.14722/ndss.2024.24601","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.71465/ajcns1005","name":"Analysis of Secure Multiparty Computation Protocols in Blockchain","source":"crossref","abstract":"The integration of Blockchain technology with Secure Multiparty Computation (SMC) protocols has gained significant attention due to its potential to enable secure, decentralized collaboration among multiple parties without compromising the privacy of their data. This paper provides an analysis of SMC protocols in the context of blockchain networks, examining their role in enhancing security and privacy for distributed applications. The study covers various SMC protocols such as threshold cryptography, secure outsourcing, and privacy-preserving computations, and discusses their applications in blockchain-based systems. We also address the challenges and opportunities associated with implementing these protocols in real-world blockchain environments, with a focus on scalability, efficiency, and interoperability.","url":"https://doi.org/10.71465/ajcns1005","authors":["Dr. Helena Rocha –"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-30T04:47:53Z","doi":"10.71465/ajcns1005","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/asiajcis.2013.27","name":"A Scripting Language for Automating Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/asiajcis.2013.27","authors":["Kung Chen","Tsan-Sheng Hsu","Churn-Jung Liau","Da-Wei Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-10-12T02:59:01Z","doi":"10.1109/asiajcis.2013.27","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1088/1742-6596/1998/1/012003","name":"A Permissioned Blockchain System for Secure Multiparty Computation","source":"crossref","abstract":"Abstract Permissioned blockchain is the blockchain network that requires access to be part of the network. Participant’s actions are governed by the control layer that runs on top of the blockchain. This type of blockchains is preferred by individuals who need role description, identity, and security within the blockchain. Secure multi-party computation (MPC) is a part of cryptography that involves the modeling of procedures for two or more participants who want to work together. These participants involve sharing of input and required computational data for a particular function without sharing their confidential data actively to each other and achieving a common goal which is beneficial to both as the outcome achieved is only revealed to the participants and is highly required for their functional purpose. In this work, SPDZ (Speedz) implementation is explored leveraging additive secret sharing on the private blockchain (Hyperledger fabric). SPDZ protocol is chosen over any other computational protocol as it is highly secured from any active deceptive n-1 participant among the n participants. In this work, a backend is developed that uses a fabric SDK node.js library that interacts with the Hyperledger Fabric network. The proposed solution is shown through a demonstration. This paper concludes that for business-to-business scenarios, using SPDZ protocol on permissioned blockchain provides more security against adversaries as permissioned blockchain provides transparency over the participants of the network. As a result, permissioned blockchain is a more secure choice for enterprises to compute confidential data rather than permissionless blockchain.","url":"https://doi.org/10.1088/1742-6596/1998/1/012003","authors":["S Garg","R Vashisht"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-24T07:45:30Z","doi":"10.1088/1742-6596/1998/1/012003","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1016/j.ins.2014.04.004","name":"Secure multiparty computation of solid geometric problems and their applications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ins.2014.04.004","authors":["Li Shundong","Wu Chunying","Wang Daoshun","Dai Yiqi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-04-19T11:47:28Z","doi":"10.1016/j.ins.2014.04.004","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/ccis.2016.7790281","name":"Efficiently secure multiparty computation based on homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccis.2016.7790281","authors":["Yuangang Yao","Jinxia Wei","Jianyi Liu","Ru Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-12-19T21:56:42Z","doi":"10.1109/ccis.2016.7790281","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.5339/qfarc.2018.ictpd610","name":"Enabling Efficient Secure Multiparty Computation Development in ANSI C","source":"crossref","abstract":"Secure Multi-Party Computation (SMPC) enables parties to compute a pub- lic function over private inputs. A classical example is the millionaires problem, where two millionaires want to figure out who is wealthier without revealing their actual wealth to each other. The insight gained from the secure computa- tion is nothing more than what is revealed by the output (in this case, who was wealthier but not the actual value of the wealth). Other applications of secure computation include secure voting, on-line bidding and privacy-preserving cloud computations, to name a few. Technological advancements are making secure computations practical, and recent optimizations have made dramatic improve- ments on their performance. However, there is still a need for effective tools that facilitate the development of SMPC applications using standard and famil- iar programming languages and techniques, without requiring the involvement of security experts with special training and background. This work addresses the latter problem by enabling SMPC application de- velopment through programs (or repurposing existing code) written in a stan- dard programming language such as ANSI C. Several high-level language (HLL) platforms have been proposed to enable secure computation such as Obliv-C [1], ObliVM [2] and Frigate [3] These platforms utilize a variation of Yao's garbled circuits [4] in order to evaluate the program securely. The source code written for these frameworks is then converted into a lower-level intermediate language that utilizes garbled circuits for program evaluation. Garbled Circuits have one party (garbler) who compiles the program that the other party (evaluator) runs, and the communication between the two parties happens through oblivi- ous transfer. Garbled circuits allow two parties to do this evaluation without a need for a trusted third party. These frameworks have two common characteristics: either define a new language [2] or make a restricted extension of a current language [1]. This is somewhat prohibitive as it requires the programmer to have a sufficient under- standing of SMPCs related constructs and semantics. This process is error-prone and time-consuming for the programmer. The other characteristic is that they use combinational circuits, which often require creating and materializing the entire circuit (circuit size may be huge) before evaluation. This introduces a restriction on the program being written. TinyGarble [5], however, is a secure two-party computation framework that is based on sequential circuits. Compared with the frameworks mentioned earlier, TinyGarble outperforms them by orders of magnitude. We are developing a framework that can automatically convert a HLL pro- gram (in this case ANSI C) into an hardware definition language, which is then evaluated securely. The benefit of having such transformation is that it does not require knowledge of unfamiliar SMPC constructs and semantics, and per- forms the computation in a much more efficient manner. We are combining the efficiency of sequential circuits for computation as well as the expressiveness of a HLL like ANSI C to be able to develop a secure computation framework that is expected to be effective and efficient. Our proposed approach is two-fold: first, it offers a separation of concern between the function of computation, written in C, and a secure computation policy to be enforced. This leaves the original source code unchanged, and the programmer is only required to specify a policy file where he/she specifies the function/variables which need secure computations. Secondly, it leverages the current state-of-the-art framework to generate sequential circuits. The idea is to covert the original source code to Verilog (a Hardware Definition Language) as this can then be transformed into standard circuit description which TinyGarble [5] would run. This will enable us to leverage TinyGarbles efficient sequential circuits. The result would be ha","url":"https://doi.org/10.5339/qfarc.2018.ictpd610","authors":["Ahmad Musleh","Soha Hussein","Khaled M. Khan","Qutaibah M. Malluhi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-06-30T09:09:27Z","doi":"10.5339/qfarc.2018.ictpd610","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/secdev.2016.028","name":"Secure Multiparty Computation for Cooperative Cyber Risk Assessment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/secdev.2016.028","authors":["Kyle Hogan","Noah Luther","Nabil Schear","Emily Shen","David Stott","Sophia Yakoubov","Arkady Yerukhimovich"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-02-07T15:57:22Z","doi":"10.1109/secdev.2016.028","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1145/1835698.1835752","name":"Brief announcement","source":"crossref","abstract":"","url":"https://doi.org/10.1145/1835698.1835752","authors":["Shailesh Vaya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2010-07-27T14:10:11Z","doi":"10.1145/1835698.1835752","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1145/3658644.3690207","name":"Secure Multiparty Computation with Lazy Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3658644.3690207","authors":["Shuaishuai Li","Cong Zhang","Dongdai Lin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-09T12:19:20Z","doi":"10.1145/3658644.3690207","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1145/2336717.2336719","name":"Knowledge-oriented secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/2336717.2336719","authors":["Piotr Mardziel","Michael Hicks","Jonathan Katz","Mudhakar Srivatsa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-07-26T14:41:14Z","doi":"10.1145/2336717.2336719","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3390/sym13050894","name":"Assessment of Two Privacy Preserving Authentication Methods Using Secure Multiparty Computation Based on Secret Sharing","source":"crossref","abstract":"Secure authentication is an essential mechanism required by the vast majority of computer systems and various applications in order to establish user identity. Credentials such as passwords and biometric data should be protected against theft, as user impersonation can have serious consequences. Some practices widely used in order to make authentication more secure include storing password hashes in databases and processing biometric data under encryption. In this paper, we propose a system for both password-based and iris-based authentication that uses secure multiparty computation (SMPC) protocols and Shamir secret sharing. The system allows secure information storage in distributed databases and sensitive data is never revealed in plaintext during the authentication process. The communication between different components of the system is secured using both symmetric and asymmetric cryptographic primitives. The efficiency of the used protocols is evaluated along with two SMPC specific metrics: The number of communication rounds and the communication cost. According to our results, SMPC based on secret sharing can be successfully integrated in real-word authentication systems and the communication cost has an important impact on the performance of the SMPC protocols.","url":"https://doi.org/10.3390/sym13050894","authors":["Diana-Elena Fălămaş","Kinga Marton","Alin Suciu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-05-18T12:17:16Z","doi":"10.3390/sym13050894","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/iscisc.2012.6408184","name":"A dynamic, zero-message broadcast encryption scheme based on secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscisc.2012.6408184","authors":["Mahdi Soodkhah Mohammadi","Abbas Ghaemi Bafghi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-01-17T15:30:27Z","doi":"10.1109/iscisc.2012.6408184","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-15","name":"Validation Services for Confidential Data","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-15","authors":["Gary Benedetto","Rolando A. Rodríguez","Jordan Stanley","Evan Totty"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","doi":"10.1201/9781003185284-15","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.2172/1842271","name":"Adapting Secure MultiParty Computation to Support Machine Learning in Radio Frequency Sensor Networks","source":"crossref","abstract":"","url":"https://doi.org/10.2172/1842271","authors":["Jonathan Berry","Anand Ganti","Kenneth Goss","Carolyn Mayer","Uzoma Onunkwo","Cynthia Phillips","Jarared Saia","Timothy Shead"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-05-26T04:38:09Z","doi":"10.2172/1842271","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.32996/jmhs.2024.5.2.4","name":"Privacy-Preserving Data Sharing in Healthcare: Advances in Secure Multiparty Computation","source":"crossref","abstract":"Secure Multi-Party Computation (SMC) is a thriving strategy for privacy-preserving data sharing in the healthcare domain. This research examined the role of SMC in the healthcare context and its alignment with regulations such as HIPAA and GDPR. The study highlights key findings in advanced cryptographic techniques, usability enhancements, scalability improvements, as well as security and privacy assurance protocols within SMC. The potential implications of SMC on patient privacy healthcare data management are unquestionable in terms of protecting sensitive information, securing collaboration, and facilitating data-driven decision-making. This study demonstrates that SMC has the potential to revolutionize and transform healthcare by affirming privacy while facilitating secure data sharing, leading to enhanced healthcare outcomes and empowering patients with control over their data.","url":"https://doi.org/10.32996/jmhs.2024.5.2.4","authors":["Md Fahim Ahammed","Md Rasheduzzaman Labu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-07T09:17:11Z","doi":"10.32996/jmhs.2024.5.2.4","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1145/1774088.1774447","name":"A secure multiparty computation privacy preserving OLAP framework over distributed XML data","source":"crossref","abstract":"","url":"https://doi.org/10.1145/1774088.1774447","authors":["Alfredo Cuzzocrea","Elisa Bertino"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2010-04-27T12:45:48Z","doi":"10.1145/1774088.1774447","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/cpese59653.2023.10303050","name":"Privacy preserving Double Auction using Secure Multiparty Computation for Local Electricity Markets","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cpese59653.2023.10303050","authors":["Bevin K C","Ashu Verma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-03T17:51:19Z","doi":"10.1109/cpese59653.2023.10303050","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/s00145-017-9264-x","name":"Characterization of Secure Multiparty Computation Without Broadcast","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00145-017-9264-x","authors":["Ran Cohen","Iftach Haitner","Eran Omri","Lior Rotem"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-09-22T19:39:04Z","doi":"10.1007/s00145-017-9264-x","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/mcom.2013.6658656","name":"Collaborative network outage troubleshooting with secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mcom.2013.6658656","authors":["Mentari Djatmiko","Dominik Schatzmann","Xenofontas Dimitropoulos","Arik Friedman","Roksana Boreli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-11-11T19:31:10Z","doi":"10.1109/mcom.2013.6658656","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/lcn53696.2022.9843372","name":"Network-Efficient Pipelining-Based Secure Multiparty Computation for Machine Learning Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/lcn53696.2022.9843372","authors":["Oscar G. Bautista","Kemal Akkaya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-08-26T19:43:27Z","doi":"10.1109/lcn53696.2022.9843372","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781003185284-4","name":"21st Century Statistical Disclosure Limitation: Motivations and Challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-4","authors":["John M. Abowd","Michael B. Hawes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","doi":"10.1201/9781003185284-4","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/iccs45141.2019.9065645","name":"Secure Multiparty Computation and Privacy Preserving scheme using Homomorphic Elliptic Curve Cryptography","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccs45141.2019.9065645","authors":["Ankit chouhan","Anupam kumari","Makhduma Saiyad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-04-17T05:20:07Z","doi":"10.1109/iccs45141.2019.9065645","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/trustcom.2013.214","name":"Using Secure Multiparty Computation for Collaborative Information Exchange","source":"crossref","abstract":"","url":"https://doi.org/10.1109/trustcom.2013.214","authors":["Dennis Titze","Hans Hofinger","Peter Schoo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-12-17T19:46:29Z","doi":"10.1109/trustcom.2013.214","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1145/3664476.3664503","name":"Towards Secure Virtual Elections: Multiparty Computation of Order Based Voting Rules","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3664476.3664503","authors":["Tamir Tassa","Lihi Dery"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-25T12:35:50Z","doi":"10.1145/3664476.3664503","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.2197/ipsjjip.30.209","name":"SPGC: Integration of Secure Multiparty Computation and Differential Privacy for Gradient Computation on Collaborative Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2197/ipsjjip.30.209","authors":["Kazuki Iwahana","Naoto Yanai","Jason Paul Cruz","Toru Fujiwara"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-14T22:10:41Z","doi":"10.2197/ipsjjip.30.209","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.58190/icisna.2024.92","name":"Enhancing Privacy and Security in IoT Environments through Secure Multiparty Computation","source":"crossref","abstract":"With the increasing influence of IoT devices in our daily lives, secure data-sharing is becoming ever more important. Sensors and other devices are communicating vast amounts of possibly unencrypted data, which poses a significant privacy concern. To tackle this problem, this research implements two Partially Homomorphic Encryption (PHE) schemes, RSA and the Paillier cryptosystem, to perform Secure Multiparty Computation (SMPC) in the resource-constrained IoT environment. The environment consists of a laptop connected to an Arduino Uno through a serial connection. The RSA-based SMPC protocol has an average completion time of 2007ms. However, due to the inability to use padding, RSA lacks semantic security. Conversely, the Paillier-based protocol is semantically secure but cannot complete the encryption due to dynamic memory issues. Even if resolved, the estimated encryption time exceeds 103.3 minutes. Despite the potential of SMPC in IoT environments for secure data handling, the results from this research suggest that directly implementing PHE schemes on Arduino is not practical based on the observed limitations.","url":"https://doi.org/10.58190/icisna.2024.92","authors":["Rik van de Haterd","Mohammed Elhajj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-31T12:41:35Z","doi":"10.58190/icisna.2024.92","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1145/1250790.1250793","name":"On achieving the \"best of both worlds\" in secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/1250790.1250793","authors":["Jonathan Katz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-09-14T12:07:37Z","doi":"10.1145/1250790.1250793","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/3-540-44495-5_12","name":"Tolerating Generalized Mobile Adversaries in Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/3-540-44495-5_12","authors":["K. Srinathan","C. Pandu Rangan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-10-28T05:24:06Z","doi":"10.1007/3-540-44495-5_12","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/icccn69946.2026.11662717","name":"Reputation-Aware Routing in Payment Channel Networks via Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icccn69946.2026.11662717","authors":["Arun Shrestha","Kyle Downing","Hsiang-Jen Hong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-27T19:08:40Z","doi":"10.1109/icccn69946.2026.11662717","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.4018/978-1-5225-2915-6.ch013","name":"Secure Multiparty Computation","source":"crossref","abstract":"The Secure Multiparty computation is characterized by computation by a set of multiple parties each participating using the private input they have. There are different types of models for Secure Multiparty computation based on assumption about the type of adversaries each model is assumed to protect against including Malicious and Covert Adversaries. The model may also assume a trusted setup with either using a Public Key Infrastructure or a using a Common Reference String. Secure Multiparty Computation has a number of applications including Scientific Computation, Database Querying and Data Mining.","url":"https://doi.org/10.4018/978-1-5225-2915-6.ch013","authors":["Kannan Balasubramanian","M. Rajakani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-08-16T09:05:46Z","doi":"10.4018/978-1-5225-2915-6.ch013","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/nuicone.2013.6780075","name":"An efficient method for privacy preserving data mining in secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/nuicone.2013.6780075","authors":["First A. Neha Pathak","Second B. Shweta Pandey"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-04-15T21:15:32Z","doi":"10.1109/nuicone.2013.6780075","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/tdsc.2025.3552670","name":"Increasing the Resilience of Secure Multiparty Computation Using Security Modules","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tdsc.2025.3552670","authors":["Lamya Abdullah","Felix C. Freiling","Dominique Schröder"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-18T13:47:55Z","doi":"10.1109/tdsc.2025.3552670","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.18122/td/1390/boisestate","name":"Secure MultiParty Protocol for Differentially-Private Data Release","source":"crossref","abstract":"","url":"https://doi.org/10.18122/td/1390/boisestate","authors":["Anthony Harris"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-05-30T14:35:53Z","doi":"10.18122/td/1390/boisestate","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/978-981-10-7488-2_17","name":"Learning Method of Fuzzy Inference Systems for Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-10-7488-2_17","authors":["Hirofumi Miyajima","Noritaka Shigei","Hiromi Miyajima","Yohtaro Miyanishi","Shinji Kitagami","Norio Shiratori"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-02-09T13:55:13Z","doi":"10.1007/978-981-10-7488-2_17","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1063/5.0184359","name":"Cloud assisted secure multiparty computation on blockchain - CaFGUGChain framework","source":"crossref","abstract":"","url":"https://doi.org/10.1063/5.0184359","authors":["Anasuya Threse Innocent Aloysius","Prakash Gopalakrishnan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-25T13:00:29Z","doi":"10.1063/5.0184359","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/s00145-015-9214-4","name":"A Full Proof of the BGW Protocol for Perfectly Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00145-015-9214-4","authors":["Gilad Asharov","Yehuda Lindell"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-09-28T15:29:59Z","doi":"10.1007/s00145-015-9214-4","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.5430/air.v6n1p27","name":"New privacy preserving clustering methods for secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.5430/air.v6n1p27","authors":["Hirofumi Miyajima","Noritaka Shigei","Hiromi Miyajima","Yohtaro Miyanishi","Shinji Kitagami","Norio Shiratori"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-08-29T08:24:59Z","doi":"10.5430/air.v6n1p27","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1145/3583133.3596337","name":"Towards Vertical Privacy-Preserving Symbolic Regression via Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3583133.3596337","authors":["Du Nguyen Duy","Michael Affenzeller","Ramin Nikzad-Langerodi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-24T23:30:33Z","doi":"10.1145/3583133.3596337","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/978-3-642-54833-8_2","name":"Application-Scale Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-54833-8_2","authors":["John Launchbury","Dave Archer","Thomas DuBuisson","Eric Mertens"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-03-21T09:37:17Z","doi":"10.1007/978-3-642-54833-8_2","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.17760/d20698932","name":"Efficient threshold multiparty computation schemes","source":"crossref","abstract":"","url":"https://doi.org/10.17760/d20698932","authors":["Schuyler Rosefield"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-05T15:46:51Z","doi":"10.17760/d20698932","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1201/9781584888215-c14","name":"Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781584888215-c14","authors":["Keith Frikken"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2010-05-06T15:51:30Z","doi":"10.1201/9781584888215-c14","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/telfor56187.2022.9983726","name":"Programming Applications Suitable for Secure Multiparty Computation Based on Trusted Execution Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1109/telfor56187.2022.9983726","authors":["Maja Vukasovic","Danko Miladinovic","Adrian Milakovic","Pavle Vuletic","Zarko Stanisavljevic"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-12-22T18:41:08Z","doi":"10.1109/telfor56187.2022.9983726","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.3390/jsan12010014","name":"Dynamic Decentralized Reputation System from Blockchain and Secure Multiparty Computation","source":"crossref","abstract":"In decentralized environments, such as mobile ad hoc networks (MANETs) and wireless sensor networks (WSNs), traditional reputation management systems are not viable due to their dependence on a central authority that is both accessible and trustworthy for all participants. This is particularly challenging in light of the dynamic nature of these networks. To overcome these limitations, our proposed solution utilizes blockchain technology to maintain global reputation information while remaining fully decentralized, and to secure multiparty computation to ensure privacy. Our system is not limited to specific settings, such as buyer/seller or provider/client scenarios, where only a subset of the network are raters while the others are ratees. Instead, it allows all nodes to participate in both rating and being rated. In terms of security, the system maintains feedback privacy in the semi-honest model, even in the presence of up to n−2 dishonest parties, while requiring only O(n) messages and having an O(n) computation overhead. Furthermore, the adopted techniques enable the system to achieve unique characteristics such as accessibility, consistency, and verifiability, as supported by the security analysis provided.","url":"https://doi.org/10.3390/jsan12010014","authors":["Khalid Mrabet","Faissal El Bouanani","Hussain Ben-Azza"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-02-08T01:32:05Z","doi":"10.3390/jsan12010014","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1007/s10623-017-0424-7","name":"On the (in)efficiency of non-interactive secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10623-017-0424-7","authors":["Maki Yoshida","Satoshi Obana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-03-26T00:55:08Z","doi":"10.1007/s10623-017-0424-7","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-642-14623-7_31","name":"Secure Multiparty Computation with Minimal Interaction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-14623-7_31","authors":["Yuval Ishai","Eyal Kushilevitz","Anat Paskin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2010-08-10T04:15:26Z","doi":"10.1007/978-3-642-14623-7_31","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/3-540-45708-9_12","name":"On 2-Round Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/3-540-45708-9_12","authors":["Rosario Gennaro","Yuval Ishai","Eyal Kushilevitz","Tal Rabin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-10-19T04:48:16Z","doi":"10.1007/3-540-45708-9_12","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/1250790.1250794","name":"Zero-knowledge from secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/1250790.1250794","authors":["Yuval Ishai","Eyal Kushilevitz","Rafail Ostrovsky","Amit Sahai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-09-14T12:07:37Z","doi":"10.1145/1250790.1250794","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.2196/87306","name":"Privacy-Preserving Framework for Multi-Institutional Medical Time-Series Analysis via Homomorphic Encryption: Design and Development Study.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/87306","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/87306","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.3390/e28030358","name":"Secure Multiplicative Aggregation and Key-Reuse Optimization: Achieving Dropout Resilience with Amortized Efficiency.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28030358","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/e28030358","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.21203/rs.3.rs-6496298/v1","name":"A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan–Meier Survival Analysis","source":"europepmc","abstract":"Abstract The proliferation of real-world healthcare data has substantially expanded opportunities for collaborative research, yet stringent privacy regulations hinder the pooling of sensitive patient records in a single location. To address this dilemma, we propose a multiparty homomorphic encryption-based framework for privacypreserving federated Kaplan–Meier survival analysis, surpassing existing methods by offering native floating-point support, a detailed theoretical model, and explicit mitigation of reconstruction attacks. Compared to prior work, our framework provides a more comprehensive analysis of noise growth and convergence, guaranteeing that the encrypted federated survival estimates closely match centralized (unencrypted) outcomes. Formal utility-loss bounds demonstrate that as aggregation and decryption noise diminish, the encrypted estimator converges to its unencrypted counterpart. Extensive experiments on the NCCTG Lung Cancer and the IKNL synthetic Breast Cancer dataset confirm that the mean absolute error (MAE) and root mean squared error (RMSE) remain low, indicating only negligible deviations between encrypted and non-encrypted federated survival curves. Log-rank tests further reveal no significant difference between federated encrypted and non-encrypted analyses, thereby preserving statistical validity. Additionally, an in-depth reconstruction-attack evaluation shows that smaller federations (2–3 providers) with overlapping data are acutely vulnerable, a challenge our multiparty encryption effectively neutralizes. Larger federations (5–50 sites) inherently degrade reconstruction accuracy, yet encryption remains prudent for maximum confidentiality. Despite an overhead factor of 8–19× compared to non-encrypted computation, our results show that threshold-based homomorphic encryption is feasible for moderate-scale deployments, balancing security needs with acceptable runtime. By furnishing robust privacy guarantees alongside high-fidelity survival estimates, this framework significantly advances the state of the art in secure, multi-institutional survival analysis.","url":"https://doi.org/10.21203/rs.3.rs-6496298/v1","authors":["Narasimha Raghavan Veeraragavan","Svetlana Boudko","Jan Franz Nygård"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6496298/v1","addedAt":"2026-08-31T06:41:28.179Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-1-0716-3989-4_19","name":"Secure Discovery of Genetic Relatives across Large-Scale and Distributed Genomic Datasets.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/978-1-0716-3989-4_19","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1007/978-1-0716-3989-4_19","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.immuno.2025.100056","name":"Challenges and future directions of AIRR-seq-based diagnostics.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.immuno.2025.100056","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1016/j.immuno.2025.100056","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3389/fdgth.2025.1603630","name":"Horizontal federated learning and assessment of Cox models.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdgth.2025.1603630","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fdgth.2025.1603630","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/s25144344","name":"Blockchain-Based Trusted Data Management with Privacy Preservation for Secure IoT Systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25144344","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25144344","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1126/sciadv.adp2877","name":"Experimental quantum Byzantine agreement on a three-user quantum network with integrated photonics.","source":"europepmc","abstract":"","url":"https://doi.org/10.1126/sciadv.adp2877","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1126/sciadv.adp2877","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.drudis.2023.103820","name":"Privacy-preserving techniques for decentralized and secure machine learning in drug discovery.","source":"pubmed","abstract":"Data availability, data security, and privacy concerns often hamper optimal performance efficiency of machine learning (ML) techniques. Therefore, novel techniques for the utilization of private/sensitive data in the field of drug discovery have been proposed for ML model-building tasks. Some examples of the different techniques are secure multiparty computation, distributed deep learning, homomorphic encryption, blockchain-based peer-to-peer networking, differential privacy, and federated learning, as well as combinations of such techniques. In this paper, we present an overview of these techniques for decentralized ML to illustrate its benefits and drawbacks in the field of drug discovery.","url":"https://doi.org/10.1016/j.drudis.2023.103820","authors":["Smajić A","Grandits M","Ecker GF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1016/j.drudis.2023.103820","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:42.471Z"},{"id":"doi:10.1101/gr.279057.124","name":"Secure discovery of genetic relatives across large-scale and distributed genomic data sets.","source":"europepmc","abstract":"","url":"https://doi.org/10.1101/gr.279057.124","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/gr.279057.124","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1093/bib/bbag333","name":"The sequence alignment problem: boundary conditions as the unifying principle.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/bib/bbag333","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1093/bib/bbag333","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.21203/rs.3.rs-3146652/v1","name":"Lightweight Privacy-Preserving (t, m, n) Multi-Party Computation Based on Secret Sharing in Untrusted Cloud Environments","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3146652/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-3146652/v1","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.2196/76980","name":"Blockchain Smart Contracts for Automating Clinical Trials: Systematic Review and Proposed System Architecture.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/76980","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/76980","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1109/tps-isa58951.2023.00011","name":"Ensuring Trust in Genomics Research.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tps-isa58951.2023.00011","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1109/tps-isa58951.2023.00011","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-27142-2","name":"Lightweight XOR-based visual cryptography using random shares for secure colour image sharing with minimal shares.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-27142-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-27142-2","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.7717/peerj-cs.3068","name":"Identity-based linear homomorphic signature for a restricted combiners' group for e-commerce.","source":"europepmc","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3068","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.7717/peerj-cs.3068","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/bioengineering12090938","name":"A Collaborative Data Sharing Platform to Accelerate Translation of Biomedical Innovations.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/bioengineering12090938","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/bioengineering12090938","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.3390/s23104746","name":"A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems.","source":"pubmed","abstract":"Smart metering systems (SMSs) have been widely used by industrial users and residential customers for purposes such as real-time tracking, outage notification, quality monitoring, load forecasting, etc. However, the consumption data it generates can violate customers' privacy through absence detection or behavior recognition. Homomorphic encryption (HE) has emerged as one of the most promising methods to protect data privacy based on its security guarantees and computability over encrypted data. However, SMSs have various application scenarios in practice. Consequently, we used the concept of trust boundaries to help design HE solutions for privacy protection under these different scenarios of SMSs. This paper proposes a privacy-preserving framework as a systematic privacy protection solution for SMSs by implementing HE with trust boundaries for various SMS scenarios. To show the feasibility of the proposed HE framework, we evaluated its performance on two computation metrics, summation and variance, which are often used for billing, usage predictions, and other related tasks. The security parameter set was chosen to provide a security level of 128 bits. In terms of performance, the aforementioned metrics could be computed in 58,235 ms for summation and 127,423 ms for variance, given a sample size of 100 households. These results indicate that the proposed HE framework can protect customer privacy under varying trust boundary scenarios in SMS. The computational overhead is acceptable from a cost-benefit perspective while ensuring data privacy.","url":"https://doi.org/10.3390/s23104746","authors":["Xu W","Sun J","Cardell-Oliver R","Mian A","Hong JB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/s23104746","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1038/s41467-025-57393-6","name":"Secure and scalable gene expression quantification with pQuant.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41467-025-57393-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41467-025-57393-6","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-95608-4","name":"Authenticable quantum secret sharing based on special entangled state.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-95608-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-95608-4","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.3389/fpubh.2026.1788454","name":"The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fpubh.2026.1788454","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1788454","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1109/sp46215.2023.10179350","name":"Scalable and Privacy-Preserving Federated Principal Component Analysis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/sp46215.2023.10179350","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1109/sp46215.2023.10179350","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.23889/ijpds.v6i1.3373","name":"E-Research Institutional Cloud Architecture (ERICA): An Orchestration Meta-Framework for Establishing Trusted Research Environments Using Public Cloud Computing.","source":"europepmc","abstract":"","url":"https://doi.org/10.23889/ijpds.v6i1.3373","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.23889/ijpds.v6i1.3373","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.7717/peerj-cs.3141","name":"Delegated multi-party private set intersections from extendable output functions.","source":"europepmc","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3141","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.7717/peerj-cs.3141","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3390/healthcare12242587","name":"Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration.","source":"pubmed","abstract":"Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL's applications within smart health systems, particularly its integration with IoT devices, wearables, and remote monitoring, which empower real-time, decentralized data processing for predictive analytics and personalized care. It addresses key challenges, including security risks like adversarial attacks, data poisoning, and model inversion. Additionally, it covers issues related to data heterogeneity, scalability, and system interoperability. Alongside these, the review highlights emerging privacy-preserving solutions, such as differential privacy and secure multiparty computation, as critical to overcoming FL's limitations. Successfully addressing these hurdles is essential for enhancing FL's efficiency, accuracy, and broader adoption in healthcare. Ultimately, FL offers transformative potential for secure, data-driven healthcare systems, promising improved patient outcomes, operational efficiency, and data sovereignty across the healthcare ecosystem.","url":"https://doi.org/10.3390/healthcare12242587","authors":["Abbas SR","Abbas Z","Zahir A","Lee SW"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3390/healthcare12242587","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.1038/s41467-025-66771-z","name":"PP-GWAS: Privacy Preserving Multi-Site Genome-wide Association Studies.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41467-025-66771-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41467-025-66771-z","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-15250-y","name":"Connected, digitalized wire arc additive manufacturing: utilizing data in the internet of production to enable industrie 4.0.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-15250-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-15250-y","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1007/s13755-024-00306-6","name":"Explainable federated learning scheme for secure healthcare data sharing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s13755-024-00306-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1007/s13755-024-00306-6","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.1038/s41598-025-25293-w","name":"Design of an iterative method for adaptive federated intrusion detection for energy-constrained edge-centric 6G IoT cyber-physical systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-25293-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-25293-w","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1002/pst.2250","name":"Propensity score matching and stratification using multiparty data without pooling.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/pst.2250","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1002/pst.2250","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.1038/s41598-026-36054-8","name":"A lattice-integrated AES framework for ultra-secure biometric protection on resource-constrained edge devices.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-36054-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-36054-8","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1093/bib/bbae356","name":"Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision.","source":"pubmed","abstract":"Genome-wide association studies (GWAS) serve as a crucial tool for identifying genetic factors associated with specific traits. However, ethical constraints prevent the direct exchange of genetic information, prompting the need for privacy preservation solutions. To address these issues, earlier works are based on cryptographic mechanisms such as homomorphic encryption, secure multi-party computing, and differential privacy. Very recently, federated learning has emerged as a promising solution for enabling secure and collaborative GWAS computations. This work provides an extensive overview of existing methods for GWAS privacy preserving, with the main focus on collaborative and distributed approaches. This survey provides a comprehensive analysis of the challenges faced by existing methods, their limitations, and insights into designing efficient solutions.","url":"https://doi.org/10.1093/bib/bbae356","authors":["Aherrahrou N","Tairi H","Aherrahrou Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1093/bib/bbae356","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-024-67495-8","name":"ChaQra: a cellular unit of the Indian quantum network.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-024-67495-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1038/s41598-024-67495-8","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.7717/peerj-cs.3166","name":"SENSH: a blockchain-based searchable encrypted data sharing scheme in smart healthcare.","source":"europepmc","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3166","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.7717/peerj-cs.3166","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.1016/j.csbj.2025.05.015","name":"AI-driven multimodal colorimetric analytics for biomedical and behavioral health diagnostics.","source":"europepmc","abstract":"The exponential growth of multi-scale biomedical and behavioral data introduces both challenges and opportunities for Image 1-driven analytics. Effectively managing the complexity and variability of these data sources requires advanced computational techniques for accurate interpretation and robust decision-making. Integrating Image 2 with colorimetric biosensing and multimodal data fusion offers scalable solutions that can improve diagnostic accuracy, enable early disease detection, and support personalized medicine. This work explores mobile-based colorimetry, an Image 3-driven approach that uses image processing and Image 4 to detect colorimetric changes in chemical and biological solutions. We propose a modular conceptual framework that integrates mobile-based colorimetry with multimodal biomedical data, such as clinical, imaging, and environmental datasets, to develop scalable, low-cost tools for predictive modeling, real-time health monitoring, and personalized diagnostics. We review recent advancements in Image 5-enabled colorimetric analysis and multimodal data fusion for healthcare applications, emphasizing innovations in Image 6-assisted biosensors, Image 7-driven biomedical imaging, and multimodal fusion techniques. In addition, we highlight the need for robust data management systems and interpretable AI/ML models to ensure security, privacy, and reliability in biomedical and behavioral research. This work also highlights practical directions for improving diagnostic accuracy and accessibility, particularly in resource-limited settings.","url":"https://doi.org/10.1016/j.csbj.2025.05.015","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1016/j.csbj.2025.05.015","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1109/tcyb.2022.3224169","name":"PriMPSO: A Privacy-Preserving Multiagent Particle Swarm Optimization Algorithm.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tcyb.2022.3224169","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1109/tcyb.2022.3224169","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.1038/s41598-026-38750-x","name":"Autonomous nursing professional development framework using blockchain technology.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-38750-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-38750-x","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-09722-4","name":"A novel obfuscation method based on majority logic for preventing unauthorized access to binary deep neural networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-09722-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-09722-4","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.2196/41588","name":"Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/41588","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.2196/41588","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-026-39690-2","name":"Secure electronic health record access control via blockchain, dual-attribute encryption, and large language model-based attribute extraction.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-39690-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-39690-2","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/46700","name":"mHealth Systems Need a Privacy-by-Design Approach: Commentary on \"Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review\".","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/46700","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.2196/46700","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1186/s13059-023-03039-z","name":"COLLAGENE enables privacy-aware federated and collaborative genomic data analysis.","source":"pubmed","abstract":"Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935 .","url":"https://doi.org/10.1186/s13059-023-03039-z","authors":["Li W","Kim M","Zhang K","Chen H","Jiang X","Harmanci A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1186/s13059-023-03039-z","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:42.471Z"},{"id":"doi:10.1140/epjqt/s40507-025-00350-5","name":"A critical analysis of deployed use cases for quantum key distribution and comparison with post-quantum cryptography.","source":"europepmc","abstract":"","url":"https://doi.org/10.1140/epjqt/s40507-025-00350-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1140/epjqt/s40507-025-00350-5","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.3390/foods11182785","name":"A Refined Supervision Model of Rice Supply Chain Based on Multi-Blockchain.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/foods11182785","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/foods11182785","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-21852-3","name":"A federated incremental blockchain framework with privacy preserving XAI optimization for securing healthcare data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-21852-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-21852-3","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.7759/cureus.43262","name":"Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) into healthcare promises groundbreaking advancements in patient care, revolutionizing clinical diagnosis, predictive medicine, and decision-making. This transformative technology uses machine learning, natural language processing, and large language models (LLMs) to process and reason like human intelligence. OpenAI's ChatGPT, a sophisticated LLM, holds immense potential in medical practice, research, and education. However, as AI in healthcare gains momentum, it brings forth profound ethical challenges that demand careful consideration. This comprehensive review explores key ethical concerns in the domain, including privacy, transparency, trust, responsibility, bias, and data quality. Protecting patient privacy in data-driven healthcare is crucial, with potential implications for psychological well-being and data sharing. Strategies like homomorphic encryption (HE) and secure multiparty computation (SMPC) are vital to preserving confidentiality. Transparency and trustworthiness of AI systems are essential, particularly in high-risk decision-making scenarios. Explainable AI (XAI) emerges as a critical aspect, ensuring a clear understanding of AI-generated predictions. Cybersecurity becomes a pressing concern as AI's complexity creates vulnerabilities for potential breaches. Determining responsibility in AI-driven outcomes raises important questions, with debates on AI's moral agency and human accountability. Shifting from data ownership to data stewardship enables responsible data management in compliance with regulations. Addressing bias in healthcare data is crucial to avoid AI-driven inequities. Biases present in data collection and algorithm development can perpetuate healthcare disparities. A public-health approach is advocated to address inequalities and promote diversity in AI research and the workforce. Maintaining data quality is imperative in AI applications, with convolutional neural networks showing promise in multi-input/mixed data models, offering a comprehensive patient perspective. In this ever-evolving landscape, it is imperative to adopt a multidimensional approach involving policymakers, developers, healthcare practitioners, and patients to mitigate ethical concerns. By understanding and addressing these challenges, we can harness the full potential of AI in healthcare while ensuring ethical and equitable outcomes.","url":"https://doi.org/10.7759/cureus.43262","authors":["Jeyaraman M","Balaji S","Jeyaraman N","Yadav S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.7759/cureus.43262","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:42.471Z"},{"id":"doi:10.1093/jamia/ocac165","name":"The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/jamia/ocac165","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1093/jamia/ocac165","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.heliyon.2024.e27177","name":"A secure and privacy preserved data aggregation scheme in IoMT.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.heliyon.2024.e27177","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1016/j.heliyon.2024.e27177","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.jbi.2024.104678","name":"Privacy-preserving model evaluation for logistic and linear regression using homomorphically encrypted genotype data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.jbi.2024.104678","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1016/j.jbi.2024.104678","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3390/e27010032","name":"Towards Secure Internet of Things: A Coercion-Resistant Attribute-Based Encryption Scheme with Policy Revocation.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e27010032","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/e27010032","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1186/s12911-024-02549-5","name":"GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12911-024-02549-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1186/s12911-024-02549-5","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.2196/47652","name":"Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/47652","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.2196/47652","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.3390/healthcare13212760","name":"Federated Learning in Public Health: A Systematic Review of Decentralized, Equitable, and Secure Disease Prevention Approaches.","source":"pubmed","abstract":"","url":"https://doi.org/10.3390/healthcare13212760","authors":["Shah ST","Ali Z","Waqar M","Kim A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/healthcare13212760","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/e27070751","name":"Commitment Schemes from OWFs with Applications to Quantum Oblivious Transfer.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e27070751","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/e27070751","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1093/annalsats/aaoaf067","name":"Federation, not centralization: a new paradigm for electronic health record-based critical care research.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/annalsats/aaoaf067","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1093/annalsats/aaoaf067","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.mcpdig.2025.100253","name":"Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions.","source":"pubmed","abstract":"Deep learning (DL) has revolutionized cancer detection accuracy, speed, and accessibility. Leveraging sophisticated algorithms, DL has demonstrated transformative potential across diverse applications, including imaging-based diagnostics and genomic analysis, ultimately leading to better detection, improved patient treatment outcomes, and decreased overall mortality rates. Despite its promise, integrating DL into clinical practice presents substantial challenges, including limitations in data quality and standardization, as well as ethical and regulatory concerns, and the need for model interpretability and transparency. This review provides a comprehensive analysis of recent research (2018-2024) retrieved from PubMed and IEEE Xplore databases, encompassing 1304 studies from PubMed and 115 from IEEE, to highlight the current applications, opportunities, and challenges of DL in oncology. Additionally, this paper explores emerging solutions, including federated learning, explainable artificial intelligence, and synthetic data generation, to address these barriers. The review also emphasizes the importance of interdisciplinary collaboration, the integration of next-generation artificial intelligence techniques, and the adoption of multimodal data approaches to improve diagnostic precision and support personalized cancer treatment. By systematically analyzing key developments and challenges, this review aims to guide future research and DL technologies in oncology, promoting equitable and impactful advancements in cancer care.","url":"https://doi.org/10.1016/j.mcpdig.2025.100253","authors":["Yao IZ","Dong M","Hwang WYK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1016/j.mcpdig.2025.100253","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/s22186805","name":"Representative Ring Signature Algorithm Based on Smart Contract.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s22186805","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/s22186805","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-17958-3","name":"A privacy preserving and auditable blockchain framework for seccure securites trading.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-17958-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-17958-3","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"epmc:MED35958356","name":"Sequre: a high-performance framework for rapid development of secure bioinformatics pipelines.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/35958356/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1093/bioadv/vbae039","name":"Yves Moreau has received the 2023 Einstein Foundation Individual Award for Promoting Quality in Research.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/bioadv/vbae039","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1093/bioadv/vbae039","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1093/nar/gkad464","name":"sfkit: a web-based toolkit for secure and federated genomic analysis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/nar/gkad464","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1093/nar/gkad464","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.1038/s41598-025-22672-1","name":"FedEff: efficient federated learning with optimal local epochs for heterogeneous clients.","source":"europepmc","abstract":"Federated Learning (FL) enables collaborative model training without centralized data sharing; however, its efficiency often degrades under system and statistical heterogeneity across clients. Increasing the number of local epochs per round can enhance efficiency by enabling the global model to reach target accuracy in fewer communication rounds. Yet, excessive local training may cause client models to diverge from the global model, slowing convergence. To examine this trade-off, we conduct an empirical divergence analysis and show that consistent sufficient local updates across rounds can reduce the mean divergence between local and global models, thereby promoting faster and more stable convergence. Building on this insight, we propose a novel, efficient federated learning algorithm (FedEff) that assigns optimal local epochs to each client in heterogeneous settings. FedEff incorporates a server-side epoch selection mechanism, where the server selects an optimal number of epochs for each client, by considering the computation and communication speeds of all clients. The server uses an Estimated Round Time (ERT) to calculate the optimal number of local epochs for each client. Extensive simulations under heterogeneous computation and communication conditions confirm that the proposed approach achieves notable reductions in client waiting times and overall training duration within the considered simulation framework. Comparative results show that our method achieves better training efficiency than FedAvg and random epoch selection strategies, thereby establishing its effectiveness in improving federated learning performance under heterogeneous settings.","url":"https://doi.org/10.1038/s41598-025-22672-1","authors":["K Narmadha","P. Varalakshmi"],"tags":["Computer science","Federated learning","Selection (genetic algorithm)","Divergence (linguistics)","Computation"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-22672-1","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.2196/79166","name":"Privacy-Preserving Collaborative Diabetes Prediction in Heterogeneous Health Care Systems: Algorithm Development and Validation of a Secure Federated Ensemble Framework.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/79166","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/79166","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.2196/52740","name":"Decentralizing Health Care: History and Opportunities of Web3.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/52740","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.2196/52740","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.2196/56893","name":"Privacy-Preserving Prediction of Postoperative Mortality in Multi-Institutional Data: Development and Usability Study.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/56893","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.2196/56893","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/e25091269","name":"A Semi-Quantum Private Comparison Base on W-States.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e25091269","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/e25091269","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1155/2022/3574812","name":"Multimedia Fusion Privacy Protection Algorithm Based on IoT Data Security under Network Regulations.","source":"europepmc","abstract":"","url":"https://doi.org/10.1155/2022/3574812","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1155/2022/3574812","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"epmc:MED41473133","name":"HIV-1 and Artificial Intelligence: From Molecular Insight to Population Impact.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41473133/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/e25020389","name":"Round-Efficient Secure Inference Based on Masked Secret Sharing for Quantized Neural Network.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e25020389","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/e25020389","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"epmc:MED42286900","name":"Artificial intelligence in medicine: from molecular data analysis to clinical decision-making.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/42286900/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1002/hsr2.71272","name":"Combining Real-World and Clinical Trial Data Through Privacy-Preserving Record Linkage: Opportunities and Challenges-A Narrative Review.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/hsr2.71272","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1002/hsr2.71272","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/s23084061","name":"A Study on the Interoperability Technology of Digital Identification Based on WACI Protocol with Multiparty Distributed Signature.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s23084061","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/s23084061","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-89208-5","name":"A Dickson polynomial based group key agreement authentication scheme for ensuring conditional privacy preservation and traceability in VANETs.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-89208-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-89208-5","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.2196/47254","name":"The BioRef Infrastructure, a Framework for Real-Time, Federated, Privacy-Preserving, and Personalized Reference Intervals: Design, Development, and Application.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/47254","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.2196/47254","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1038/s41598-025-04566-4","name":"Decentralized Proof-of-Location systems for trust, scalability, and privacy in digital societies.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-04566-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-04566-4","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/s25103064","name":"Blockchain-Based Information Security Protection Mechanism for the Traceability of Intellectual Property Transactions.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25103064","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25103064","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.2196/83157","name":"Building Public Health Data Dashboards: Tutorial Playbook.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/83157","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/83157","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1186/s13059-024-03447-9","name":"SQUiD: ultra-secure storage and analysis of genetic data for the advancement of precision medicine.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s13059-024-03447-9","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1186/s13059-024-03447-9","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.2196/25120","name":"Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis.","source":"pubmed","abstract":"Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data utility. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely upon bespoke data sharing contracts. The lengthy process and administration induced by these contracts increases the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between 2 novel advanced privacy-enhancing technologies-homomorphic encryption and secure multiparty computation (defined together as multiparty homomorphic encryption). These privacy-enhancing technologies provide a mathematical guarantee of privacy, with multiparty homomorphic encryption providing a performance advantage over separately using homomorphic encryption or secure multiparty computation. We argue multiparty homomorphic encryption fulfills legal requirements for medical data sharing under the European Union's General Data Protection Regulation which has set a global benchmark for data protection. Specifically, the data processed and shared using multiparty homomorphic encryption can be considered anonymized data. We explain how multiparty homomorphic encryption can reduce the reliance upon customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research while offering additional incentives for health care and research institutes to employ common data interoperability standards.","url":"https://doi.org/10.2196/25120","authors":["Scheibner J","Raisaro JL","Troncoso-Pastoriza JR","Ienca M","Fellay J","Vayena E","Hubaux JP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.2196/25120","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"doi:10.1038/s41598-025-06306-0","name":"Mamba-fusion for privacy-preserving disease prediction.","source":"pubmed","abstract":"Accurate disease prediction is essential for improving patient outcomes. Privacy regulations like GDPR and HIPAA limit data sharing, hindering the development of robust predictive models across institutions. FL and multi-modal fusion frameworks counter these problems but are restricted in scalability, inter-client communication, and heterogeneity of data modalities. Techniques which provide privacy on data have an issue whereby they cause a reduction in performance or are computationally costly. This paper presents Mamba-Fusion for Disease prediction, a privacy-preserving framework for multi-modal data. It uses a hierarchical FL architecture to minimize the communication costs and improve the architecture's scalability solution and a Mixture of Experts (MoE) with LSTM based layers for dynamic temporal integration. The latest techniques like, differential privacy, secure aggregation protect both the data and its accuracy of the data as well. Experimental results on multi-modal clinical measurements, ECG, EEG, clinical notes, and demographic data support the applied framework. We have then used Mamba-Fusion to achieve 92:4% accuracy, 0:91 F-Score, and 0:96 AUC-ROC by keeping the privacy leakage at 0:02 and communication costs to 12:5 MB, which make it superior to conventional FL techniques. These results affirm Mamba-Fusion as an applications that are secure enough to support collaborative healthcare analytics on a large scale.","url":"https://doi.org/10.1038/s41598-025-06306-0","authors":["Jabbar MK","Jianjun H","Jabbar A","Bilal A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-06306-0","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1007/s10916-022-01851-x","name":"New Approach to Privacy-Preserving Clinical Decision Support Systems for HIV Treatment.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s10916-022-01851-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1007/s10916-022-01851-x","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-02448-3","name":"Controlled quantum authentication confidential communication protocol for smart healthcare.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-02448-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-02448-3","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1038/s41598-023-30178-x","name":"Combating errors in quantum communication: an integrated approach.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-023-30178-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1038/s41598-023-30178-x","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.3390/e25040625","name":"On the Asymptotic Capacity of Information-Theoretic Privacy-Preserving Epidemiological Data Collection.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e25040625","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/e25040625","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.heliyon.2024.e39087","name":"Understanding privacy concerns in ChatGPT: A data-driven approach with LDA topic modeling.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.heliyon.2024.e39087","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1016/j.heliyon.2024.e39087","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.semperi.2025.152144","name":"Transforming neonatal care through informatics: A review of artificial intelligence, data, and implementation considerations.","source":"pubmed","abstract":"Significant strides have been made in utilizing data, information, and knowledge to enhance neonatal outcomes. This review examines how data informatics, encompassing electronic health records (EHRs), data standards, and artificial intelligence (AI), has facilitated advancements in neonatal care and research. Vast amounts of data, structured and unstructured, have been produced from clinical care. In turn AI stands to improve patient care, safety, and quality improvement initiatives. Facilitated by AI, clinicians' interaction with neonatal informatic tools is transitioning from reactive to real-time, proactive care. Historically, necrotizing enterocolitis, sepsis, medical imaging, and neonatal mortality have been the targets of AI-integrated neonatal care. While much progress has been made in developing state-of-the-art AI tools, their development and implementation must consider optimization of patient care, clinical workflows, and aim to decrease clinician burnout. Employing a sociotechnical framework to assess both technical and human factors is key to effectively evaluating clinical utility, promoting adoption, and facilitating successful deployment. Beyond technical concerns, ethical considerations such as trust in AI, data security, and model transparency are critical to the responsible deployment of informatics tools. Ongoing advancements in neonatal care coupled with informatics, multi-omics, AI, and federated learning expands the possibilities of personalized care for neonates.","url":"https://doi.org/10.1016/j.semperi.2025.152144","authors":["Barrett R","Lawler B","Liu S","Park WY","Davoodi M","Martin B","Kalyanam SM","Makker K","Kuiper JR","Aziz KB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1016/j.semperi.2025.152144","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.2147/jmdh.s483247","name":"TinyML-Based Lightweight AI Healthcare Mobile Chatbot Deployment.","source":"europepmc","abstract":"","url":"https://doi.org/10.2147/jmdh.s483247","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.2147/jmdh.s483247","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.5281/zenodo.20067237","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20067237","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20067237","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20119054","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20119054","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20119054","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20134134","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20134134","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20134134","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20558450","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20558450","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20558450","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20557954","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20557954","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20557954","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.7221216","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.7221216","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.7221216","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20021611","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20021611","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20021611","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.19782727","name":"Privacy-Preserving Federated Learning Framework for ICU Patient Monitoring and Decision Support in Hospitals","source":"datacite","abstract":"The increasing digitization of healthcare systems has led to the generation of large-scale ICU patient data across hospitals. However, centralized machine learning approaches pose significant privacy risks, limiting data sharing across institutions. This paper proposes a Privacy-Preserving Federated Learning (FL) framework for ICU patient monitoring and decision support. The system enables multiple hospitals to collaboratively train predictive models without sharing sensitive patient data. The framework integrates Secure Multiparty Computation (SMPC), dynamic edge-based aggregation, and robust machine learning models such as XGBoost, CatBoost, and TabNet. A dynamic thresholding mechanism is introduced to filter unreliable updates and improve model stability. The proposed system is evaluated using structured healthcare datasets and real-world ICU data (MIMIC-III), demonstrating improved accuracy, scalability, and robustness under nonIID conditions. Experimental results show that the framework effectively balances privacy preservation and predictive performance, making it suitable for real-world clinical deployment.","url":"https://doi.org/10.5281/zenodo.19782727","authors":["Mr. B. Sundaresan, Bernus A,  Jayamadhavan V, Sam Benny J Department of Computer Science & Engineering Velammal Institute of Technology, Panchetti","DEPARTMENT OF ARTIFICIAL INTELLIGENCE AND DATA SCIENCE R.M.K. College of Engineering and Technology","MISSILE MAN SCIENTIFIC AND RESEARCH PUBLICATIONS"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19782727","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.19782728","name":"Privacy-Preserving Federated Learning Framework for ICU Patient Monitoring and Decision Support in Hospitals","source":"datacite","abstract":"The increasing digitization of healthcare systems has led to the generation of large-scale ICU patient data across hospitals. However, centralized machine learning approaches pose significant privacy risks, limiting data sharing across institutions. This paper proposes a Privacy-Preserving Federated Learning (FL) framework for ICU patient monitoring and decision support. The system enables multiple hospitals to collaboratively train predictive models without sharing sensitive patient data. The framework integrates Secure Multiparty Computation (SMPC), dynamic edge-based aggregation, and robust machine learning models such as XGBoost, CatBoost, and TabNet. A dynamic thresholding mechanism is introduced to filter unreliable updates and improve model stability. The proposed system is evaluated using structured healthcare datasets and real-world ICU data (MIMIC-III), demonstrating improved accuracy, scalability, and robustness under nonIID conditions. Experimental results show that the framework effectively balances privacy preservation and predictive performance, making it suitable for real-world clinical deployment.","url":"https://doi.org/10.5281/zenodo.19782728","authors":["Mr. B. Sundaresan, Bernus A,  Jayamadhavan V, Sam Benny J Department of Computer Science & Engineering Velammal Institute of Technology, Panchetti","DEPARTMENT OF ARTIFICIAL INTELLIGENCE AND DATA SCIENCE R.M.K. College of Engineering and Technology","MISSILE MAN SCIENTIFIC AND RESEARCH PUBLICATIONS"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19782728","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.48550/arxiv.2608.17529","name":"CryptDough: A Unified Analytics Engine for Secure Multiparty Computation","source":"datacite","abstract":"We present CryptDough, a unified analytics engine for secure multiparty computation (MPC). CryptDough enables multiple distrusting parties to jointly execute a data analysis pipeline on their private inputs and learn nothing beyond the result (e.g., aggregate statistics). Unlike existing MPC solutions that support a single threat model or workload type, CryptDough provides built-in support for cross-domain analytics (relational, time series, ML inference) under various threat models, all within the same system runtime. CryptDough contributes (i) a hierarchical system design that facilitates modularity and extensibility through progressive lowering of abstractions, and (ii) the concept of virtual vectors that enable users to write single-threaded code across all layers of the software stack, while pushing the complexity of communication, parallelization, and memory management down to the execution engine. We show that CryptDough generalizes the functionality of state-of-the-art MPC systems and remains competitive on the analytics they support, often outperforming them by more than $2\\times$.","url":"https://doi.org/10.48550/arxiv.2608.17529","authors":["Faisal, Muhammad","Lanz, Alessandra","Buxbaum, Sam","Godel, Adam","Kalavri, Vasiliki","Varia, Mayank","Liagouris, John"],"tags":["Cryptography and Security (cs.CR)","Operating Systems (cs.OS)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.17529","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.4230/lipics.itc.2026.8","name":"Fast Bounded-Independence Functions and Their Duals","source":"datacite","abstract":"We continue the study of fast functions, computable by linear-size circuits, that share useful properties of random functions. Motivated by cryptographic applications, we generalize and improve on previous results in this area, obtaining the following results: - For any constant t, we construct a fast t-wise independent hash function with algebraic degree log₂ t (over F₂), simultaneously optimizing both asymptotic circuit size and degree. - We simplify and improve a recent construction (ITCS 2026) of a family of fast codes with fast duals, both meeting the Gilbert-Varshamov bound. Unlike the previous construction, our construction has negligible failure probability, can accommodate general fields and rates, supports a systematic encoding, and admits fast universal encoders. - We strengthen the above to support stronger random-like properties, such as optimal combinatorial list-decoding. This is achieved by constructing, for any constant t, a family of fast linear functions that map any t linearly independent inputs to uniform and statistically independent outputs. Prior to our work, this was only known for t = 1. We demonstrate the usefulness of the above results to cryptography. This includes the first nontrivial protocols for perfectly secure multiparty computation whose circuit complexity scales linearly with the number of parties, as well as protocols for computing encrypted matrix-vector products with optimal asymptotic circuit complexity.","url":"https://doi.org/10.4230/lipics.itc.2026.8","authors":["Brehm, Martijn","Ishai, Yuval","Resch, Nicolas"],"tags":["Linear codes","hash function families","efficient encoding","secure computation","Theory of computation → Cryptographic primitives","Theory of computation → Error-correcting codes","Theory of computation → Cryptographic protocols"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.4230/lipics.itc.2026.8","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:37.148Z"},{"id":"doi:10.4230/lipics.itc.2026.7","name":"Compressing Correlations via Secret Replication: PCFs from Symmetric Cryptography","source":"datacite","abstract":"We revisit the question of securely compressing multiparty correlations using only symmetric cryptography. A linear correlation C, defined by a linear subspace C ⊆ 𝔽ⁿ, samples a secret random 𝐜 ∈ C and assigns to each party a fixed subset of the entries of 𝐜. Gilboa and Ishai (Crypto 1999) and Cramer, Damgård and Ishai (TCC 2005) provide a general technique for securely compressing many independent samples from C by replicating independent keys of a pseudorandom function (PRF) among the parties. This implies a pseudorandom correlation function (PCF) for C from any PRF, where the PCF key size scales with the number of minimal-support codewords in C. We observe that the above generalizes to other types of useful target correlations C_T by using a secret replication pattern obtained via a random secret assignment of parties in C to parties in C_T. We present several corollaries of this general blueprint. These include a re-derivation of two-party PCF constructions for VOLE and subfield-VOLE over small domains (Roy, Crypto 2022) as well as new multiparty PCFs for small-domain VOLE-style correlations, including scalar-vector multiplication triples and their authenticated variants. Finally, we discuss applications to secure computation.","url":"https://doi.org/10.4230/lipics.itc.2026.7","authors":["Ishai, Yuval","Krawczyk, Hugo","Rabin, Tal"],"tags":["Pseudorandom correlation functions","correlated randomness","secure computation","symmetric cryptography","Theory of computation → Cryptographic primitives","Theory of computation → Cryptographic protocols"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.4230/lipics.itc.2026.7","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:37.148Z"},{"id":"doi:10.48550/arxiv.2607.17131","name":"Blind Transpiler: An open-source library for universally blind and homomorphic quantum computations","source":"datacite","abstract":"Blind quantum computation is a cryptographic primitive that allows a limited-capability client to delegate its complex computation to a remote server without revealing its data and/or computation. This branch of quantum cryptography has been bifurcated into two distinct primitives, quantum homomorphic encryption (concerning the security of only data) and universal blind quantum computation (concerning the security of data and the computing algorithm). These primitives have immense applicability in problems like secure cloud computing, secure quantum variational algorithms, quantum federated learning, and secure multiparty computation. However, no software tools exist for the rapid prototyping of such protocols, hindering the academic interrogation for potential applications. In this paper, we describe the development of the first such library for transpiling circuits written in Qiskit to its blind counterpart, which can then be delegated in a client-server architecture without revealing the client's data and/or computation. The proposed library is designed in modular and reusable component layers, enabling easier scalability to newer BQC primitives and robustness against changes in underlying primitives. We show the implementation of these primitives to a blind variational quantum classifier for the IRIS dataset.","url":"https://doi.org/10.48550/arxiv.2607.17131","authors":["Joshi, Mohit","Mishra, Manoj Kumar","Karthikeyan, S."],"tags":["Quantum Physics (quant-ph)","FOS: Physical sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.17131","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.13021/mars/2381","name":"Security Relaxations for Improving Secure Multiparty Computation","source":"datacite","abstract":"Secure multi-party computation (MPC) is an important subfield of modern cryptography that allows multiple parties to jointly compute a function over their private inputs without revealing anything other than the function output. Over the last decade, many increasingly efficient protocols have been proposed for computing generic functions, while customized protocols have also been designed for specific computing tasks. Still, some essential computing tasks, like shuffling, is quite inefficient compared to their non-secure counterpart. We discuss two relaxations of secure multi-party computation using differential privacy, which can be viewed as a relaxed form of privacy guarantee that only requires indistinguishability between any inputs that are similar. We first propose a new theoretical approach for building anonymous mixing mechanisms for cryptocurrencies. Rather than requiring a fully uniform permutation during mixing, we relax the requirement, insisting only that neighboring permutations are similarly likely. This relaxation allows us to greatly reduce the amount of interaction and computation in the mixing mechanism. Next, we consider a similar relaxation of secure shuffling called differential obliviousness that we prove suffices for achieving standard differential privacy in the shuffle model. Our construction for differentially oblivious shuffling based on onion routing requires only O(n log n) communication while tolerating any constant fraction of corrupted users. We show that for practical settings of the parameters, our protocol outperforms existing solutions in some settings. Then, we study the problem of private set union in the two-party setting. In the semi-honest setting, our best protocol outperforms the state-of-the-art in computation for medium and small bandwidths (100Mbps and 10Mbps), with a runtime that is 1.2X-4.2X faster than existing protocols. For communication, our protocol outperforms the state-of-the-art when the set size ≥ 218. In the malicious setting, we relax the malicious security against the sender and justify this relaxation. It allows us to avoid the usage of expensive zero-knowledge proofs, and we estimate that our constructions are less than 2X more expensive than the best-known semi-honest constructions.","url":"https://doi.org/10.13021/mars/2381","authors":["Liang, Mingyu"],"tags":["Cryptocurrency","Cryptography","Differential privacy","Onion routing","Private set union","Secure multiparty computation","Computer science"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.13021/mars/2381","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.13021/mars/4816","name":"Service Assurance in Insecure Networks with Byzantine Adversaries","source":"datacite","abstract":"This dissertation describes research into security threats in new communication environ- ments and methods to counter them. More specifically, we consider networks where some nodes perform an important application function such as routing, filtering, aggregation, etc., and may become compromised while doing so. We consider three types of networks: Internet-scale publish/subscribe networks, ag- gregating sensor networks, and peer-to-peer massively multiplayer online games (MMOG) and virtual environments. These environments are complementary to each other in many respects. A publish/subscribe network is responsible for disseminating data objects pro- duced by one set of nodes (publishers) to another set of nodes (subscribers). Subscribers want to receive those and only those objects that satisfy their interests. A large-scale pub- lish/subscribe network using many network providers, each under its own administrative control, presents numerous opportunities for mischief. In many cases the network providers will not trust one another and will not be fully trusted by the end users. A malicious network provider may insert, delete, modify, reorder, misdirect, or delay messages, and re- main undetected. The first topic of our research is how to assure service integrity in such networks if some of the intermediate nodes may attack the system in an arbitrary fashion. We solve the problem by creating filtering agents corresponding to user subscriptions, and mapping these agents to either hosts from trusted providers or to clusters of hosts taken from multiple non-trusted providers. As a whole, each cluster can be trusted since only a relatively small number of providers are assumed to be malicious. A sensor network is a network connecting hundreds or thousands of sensors, tiny battery- powered computers equipped with units measuring some physical phenomena (e.g., light intensity, temperature, humidity, ambient chemical composition, etc.) and wireless ra- dio transceivers. We study aggregating sensor networks that do not fully propagate raw measurements to their users but rather perform in-network aggregation with the intent of lowering the total amount of transmitted data thus preserving bandwidth and energy. As sensor networks are frequently placed in hostile environments (for instance, in military applications) it is important to devise mechanisms guaranteeing integrity of their service under attack, and their survivability. In this dissertation we discuss CoDeX, a collect-detect- exclude framework for secure aggregation in sensor networks. Our approach to solving the problem is based on the fact that many physical phenomena exhibit strong spatial corre- lation. Sensor nodes can take advantage of the broadcast nature of radio transmissions, receive measurements from their neighbors, and compare them with their own results. If the values significantly differ, the fact can be reported to the user (the \"collect\" phase). If a node is a subject of many such reports, it is, probably, compromised (the \"detect\" phase) and should be removed from the network (the \"exclude\" phase). We complement this approach with the use of randomized delivery (aggregation) trees, cryptography, and repeated aggregation of the same data in different configurations Peer-to-peer massively multiplayer online games and virtual environments pose chal- lenges similar to those in the other two types of networks: all three lack centralized control and run autonomously. Like pub-sub networks, P2P-based MMOGs may span the Inter- net and contain thousands and, potentially, hunderds of thousands of nodes. Like sensor networks, these systems almost completely rely on end nodes for providing infrastructure services. Unfortunately, most games do not provide sufficient safeguards against cheating and fraud perpetrated by the players. We developed FRAPPE, an architecture that significantly reduces this vulnerability by forming trusted \"supernodes\" out of non-trusted peer machines","url":"https://doi.org/10.13021/mars/4816","authors":["Rabinovich, Paul"],"tags":["Security","Byzantine","Adversary","Publish/subscribe","Sensor networks","Massively multiplayer online games"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2008","doi":"10.13021/mars/4816","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.13021/mars/14732","name":"Scalable Secure Multiparty Computation","source":"datacite","abstract":"Secure multiparty computation (MPC) allows a group of parties to collectively perform computation on their combined inputs while revealing nothing about their private inputs beyond what can be inferred from the output of the computation. Although early results focused on MPC between 2 or 3 parties with a relatively small input size, recent years have seen growing interest in protocols designed for large networks of parties processing large data inputs. The protocols used for small-scale MPC are often poorly-suited to the parameter sizes seen in practice for certain applications, hence there is a need for new protocols designed with an emphasis on scalability. In this dissertation, we aim to design protocols for secure multiparty computation which remain efficient at large scale.","url":"https://doi.org/10.13021/mars/14732","authors":["McVicker, Daniel Alan"],"tags":["Blockchain","Cryptography","MPC","Multiparty Computation","Security","Zero-Knowledge","Computer science"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.13021/mars/14732","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.21256/zhaw-26691","name":"A systematic overview on methods to protect sensitive data provided for various analyses","source":"datacite","abstract":"In view of the various methodological developments regarding the protection of sensitive data, especially with respect to privacy-preserving computation and federated learning, a conceptual categorization and comparison between various methods stemming from different fields is often desired. More concretely, it is important to provide guidance for the practice, which lacks an overview over suitable approaches for certain scenarios, whether it is differential privacy for interactive queries, k-anonymity methods and synthetic data generation for data publishing, or secure federated analysis for multiparty computation without sharing the data itself. Here, we provide an overview based on central criteria describing a context for privacy-preserving data handling, which allows informed decisions in view of the many alternatives. Besides guiding the practice, this categorization of concepts and methods is destined as a step towards a comprehensive ontology for anonymization. We emphasize throughout the paper that there is no panacea and that context matters.","url":"https://doi.org/10.21256/zhaw-26691","authors":["Templ, Matthias","Sariyar, Murat"],"tags":["Anonymization","Privacy-preserving computation","Federated learning","Synthetic data","005: Computerprogrammierung, Programme und Daten"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21256/zhaw-26691","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.21256/zhaw-20682","name":"Dynamic group key agreement for resource-constrained devices using blockchains","source":"datacite","abstract":"Dynamic group key agreement (DGKA) protocols are one of the key security primitives to secure multiparty communications in decentralized and insecure environments while considering the instant changes in a communication group. However, with the ever-increasing number of connected devices, traditional DGKA protocols have performance challenges since each member in the group has to make several computationally intensive operations while verifying the keying materials to compute the resulting group key. To overcome this issue, we propose a new approach for DGKA protocols by utilizing Hyperledger Fabric framework as a blockchain platform. To this end, we migrate the communication and verification overhead of DGKA participants to the blockchain network in our developed scheme. This paradigm allows a flexible DGKA protocol that considers resource-constrained entities and trade-offs regarding distributed computation. According to our performance analysis, participants with low computing resources can efficiently utilize our protocol. Furthermore, we have demonstrated that our protocol has the same security features as other comparable protocols in the literature.","url":"https://doi.org/10.21256/zhaw-20682","authors":["Taçyıldız, Yaşar Berkay","Ermiş, Orhan","Gür, Gürkan","Alagöz, Fatih"],"tags":["Group key agreement","Blockchain","IoT","Hyperledger fabric","005: Computerprogrammierung, Programme und Daten"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.21256/zhaw-20682","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.48550/arxiv.2604.19053","name":"CHRONOS: A Hardware-Assisted Phase-Decoupled Framework for Secure Federated Learning in IoT","source":"datacite","abstract":"Federated learning enables collaborative training on IoT gateways without sharing raw data, yet gradients remain susceptible to inversion attacks. Existing Secure Multiparty Computation defenses impose prohibitive communication overhead, exceeding strict IoT latency and energy budgets. We propose CHRONOS, a hardware-assisted framework that decouples cryptographic setup from active training. During idle windows, CHRONOS executes a once-per-epoch server-relayed Diffie-Hellman exchange within an ARM TrustZone enclave, sealing shared secrets and distributing Shamir shares to peers. During training, clients mask gradients via a single stream-cipher evaluation and transmit in one round; a hardware-backed counter enforces mask freshness. If clients drop mid-round, the server reconstructs masks from peer-held shares (k x 32 bytes/client), preserving aggregation without round repetition. Evaluation on a 32-node heterogeneous testbed (Rock Pi 4 and Orange Pi 5) shows that CHRONOS reduces active-phase latency by up to 74\\% over synchronous secure aggregation. It mitigates gradient inversion while maintaining a persistent Secure World footprint under 1.1 KB, independent of model dimension and training horizon.","url":"https://doi.org/10.48550/arxiv.2604.19053","authors":["Dang, Hung"],"tags":["Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.19053","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.21602311","name":"Opportunistic Search in Disconnected Mobile Adhoc Network","source":"datacite","abstract":"To design a social network based P2P content based file sharing system in disconnected Mobile Adhoc Networks in a privacy preserving manner for efficient file searching based on interest casting. As the mobile digital devices are carried by people that usually belong to certain social relationships, this project focus on the P2P file sharing in a disconnected MANET community consisting of mobile users with social network properties. In such a file sharing system, nodes meet and exchange requests and files in the format of text in different interest categories. Interest of each node is dynamic and can vary drastically depending on the query search by time. Since time factor may affect the basic interest of a node as prolonged searching for a particular interested thing is liked by user. This interest extraction scheme is dynamic and the communities it belong differ based on the social contact the particular node is having. Query parsing and search strategies in keyword search enables lightweight efficient search towards community Nodes. The sharing among different community peoples, is performed by without revealing the sender and receiver identity.","url":"https://doi.org/10.5281/zenodo.21602311","authors":["Kalpana, A. V.","Prakash, R.","Sundar, G."],"tags":["Adhoc networks","p2p content based file","privacy","secure multiparty computation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.21602311","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.21602312","name":"Opportunistic Search in Disconnected Mobile Adhoc Network","source":"datacite","abstract":"To design a social network based P2P content based file sharing system in disconnected Mobile Adhoc Networks in a privacy preserving manner for efficient file searching based on interest casting. As the mobile digital devices are carried by people that usually belong to certain social relationships, this project focus on the P2P file sharing in a disconnected MANET community consisting of mobile users with social network properties. In such a file sharing system, nodes meet and exchange requests and files in the format of text in different interest categories. Interest of each node is dynamic and can vary drastically depending on the query search by time. Since time factor may affect the basic interest of a node as prolonged searching for a particular interested thing is liked by user. This interest extraction scheme is dynamic and the communities it belong differ based on the social contact the particular node is having. Query parsing and search strategies in keyword search enables lightweight efficient search towards community Nodes. The sharing among different community peoples, is performed by without revealing the sender and receiver identity.","url":"https://doi.org/10.5281/zenodo.21602312","authors":["Kalpana, A. V.","Prakash, R.","Sundar, G."],"tags":["Adhoc networks","p2p content based file","privacy","secure multiparty computation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.21602312","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.21602147","name":"Watermark Detection In Compressive Sensing Domain To Preserve Privacy","source":"datacite","abstract":"We propose a compressive sensing based privacy preserving watermark detection framework that involves secure multiparty computation and the cloud. There are three parties in the proposed framework, the data holders (DH) of the potentially watermarked images, the watermark owners (WO) and the cloud (CLD). The framework also requires a certificate authority (CA) to issue a Paillier public key pair to the DH and the DH's public key to the WO. For DH (e.g., media agencies), when it collects a large volume of multimedia data from the Internet and stores their encrypted versions in the CLD, it wants to make sure those multimedia can be edited and republished legally. Watermark owners (WOs) are also the content providers who distribute their watermarked content. In some scenarios, not only DH and WO care about the copyright of the multimedia data, certain CLD who offers storage services may also desire to initiate the watermark detection to check if the uploaded multimedia data is copyright protected. For example, a CLD may choose not to provide storage services to copyright protected data illegally owned. If DH would like to use a CLD for storage the encrypted multimedia data from another cloud to this CLD, it will require the CLD to perform watermark detection on the encrypted multimedia data before providing the storage services.","url":"https://doi.org/10.5281/zenodo.21602147","authors":["S, Saranya","B, Sangeetha","M, Gayathri"],"tags":["Compressive sensing","watermark detection","secure signal processing","secure multiparty computation","privacy preserving."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.21602147","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.21602146","name":"Watermark Detection In Compressive Sensing Domain To Preserve Privacy","source":"datacite","abstract":"We propose a compressive sensing based privacy preserving watermark detection framework that involves secure multiparty computation and the cloud. There are three parties in the proposed framework, the data holders (DH) of the potentially watermarked images, the watermark owners (WO) and the cloud (CLD). The framework also requires a certificate authority (CA) to issue a Paillier public key pair to the DH and the DH's public key to the WO. For DH (e.g., media agencies), when it collects a large volume of multimedia data from the Internet and stores their encrypted versions in the CLD, it wants to make sure those multimedia can be edited and republished legally. Watermark owners (WOs) are also the content providers who distribute their watermarked content. In some scenarios, not only DH and WO care about the copyright of the multimedia data, certain CLD who offers storage services may also desire to initiate the watermark detection to check if the uploaded multimedia data is copyright protected. For example, a CLD may choose not to provide storage services to copyright protected data illegally owned. If DH would like to use a CLD for storage the encrypted multimedia data from another cloud to this CLD, it will require the CLD to perform watermark detection on the encrypted multimedia data before providing the storage services.","url":"https://doi.org/10.5281/zenodo.21602146","authors":["S, Saranya","B, Sangeetha","M, Gayathri"],"tags":["Compressive sensing","watermark detection","secure signal processing","secure multiparty computation","privacy preserving."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.21602146","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.26190/unsworks/24698","name":"Privacy-preserving schemes for electricity data analytics in smart grids","source":"datacite","abstract":"The integration of information communication technologies into traditional power grids creates a new concept of ``smart grids\". With various electricity data analytics applications in smart grids, the electricity industry is expected to operate more efficiently, securely, and reliably. In the meantime, however, many security and privacy concerns arise due to the emerging cyber-attacks against the information communication infrastructure and the leakage of private information from the fine-grained data readings of advanced smart metering. Motivated by these concerns, different approaches have been investigated in the literature to deal with security and privacy issues in smart grids. Many proposed methodologies are cryptography-based, guaranteeing perfect privacy but incurring a high computational burden to the resource-restrained smart meters. Perturbation is another primary approach to provide privacy protection for smart meter readings, which is highly efficient. However, most existing perturbation-based works inadequately balance the trade-off between privacy and utility quality. This dissertation designs novel privacy-preserving schemes from a hybrid technical perspective which takes advantage of desirable properties of cryptography-based and perturbation-based methods. The proposed schemes are efficiently applicable to significant electricity data analytics applications in smart grids, dealing with both consumer privacy and competitive privacy. For consumer privacy arising due to the leakage of consumers' private information from high-frequency consumption data, Chapter 3 examines a hybrid privacy-preserving consumption data publishing scheme without a trusted third party. The scheme consists of two phases: noise generation and noise distribution, utilising the advantage of both perturbation and cryptography for better privacy-utility trade-off and efficiency. Formal proofs and experimental validation of regional short-term electricity consumption forecast using real-world data sets demonstrate the preservation of the utility over the masked data of this scheme. Besides consumer privacy concerns, with the appearance of electric energy market deregulation, there exists a growing issue of competitive privacy among competing power companies. This is the concern of privacy leakage where data sharing among different transmission grid companies is essential for critical security operations of inter-area smart grids. The dissertation investigates this problem from two significant electricity utilities: state estimation and false data injection attack detection in Chapter 4 and Chapter 5, respectively. Chapter 4 proposes efficient privacy-preserving state estimation protocols for DC and AC models. The proposed idea is to distribute the overall task of the system state estimation into sub-tasks which can be performed by local sub-grid operators with their own private data. A masking method is designed inside a homomorphic encryption scheme which is then used to ensure both the input and output data privacy during the collaboration process among individual sub-task players. The security is achieved via the computationally indistinguishable post-quantum security and the local differential privacy of the output estimated states. Addressing the privacy issue of another electric utility, Chapter 5 designs the first practical and efficient privacy-enhancing cross-silo federated learning false data injection attack detection scheme resilient to the local private data inference attacks. The primary techniques compose double-layer encryption, which has no requirement to compute discrete logarithm, and Shamir's secret sharing. The proposed system is demonstrated theoretically and empirically to provide provable privacy against an honest-but-curious aggregator server and simultaneously achieve desirable model utilities.","url":"https://doi.org/10.26190/unsworks/24698","authors":["Tran, Hong Yen"],"tags":["Privacy-preserving Data Analytics","Secure Multiparty Computation","Homomorphic Encryption","Federated Learning","Load Forecasting","State Estimation","False Data Injection Attack Detection","Smart Grids"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.26190/unsworks/24698","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5075/epfl-thesis-8761","name":"The Complexity of Reliable and Secure Distributed Transactions","source":"datacite","abstract":"The use of transactions in distributed systems dates back to the 70's. The last decade has also seen the proliferation of transactional systems. In the existing transactional systems, many protocols employ a centralized approach in executing a distributed transaction where one single process coordinates the participants of a transaction. The centralized approach is usually straightforward and efficient in the failure-free setting, yet the coordinator then turns to be a single point of failure, undermining reliability/security in the failure-prone setting, or even be a performance bottleneck in practice. In this dissertation, we explore the complexity of decentralized solutions for reliable and secure distributed transactions, which do not use a distinguished coordinator or use the coordinator as little as possible. We show that for some problems in reliable distributed transactions, there are decentralized solutions that perform as efficiently as the classical centralized one, while for some others, we determine the complexity limitations by proving lower and upper bounds to have a better understanding of the state-of-the-art solutions. We first study the complexity on two aspects of reliable transactions: atomicity and consistency. More specifically, we do a systematic study on the time and message complexity of non-blocking atomic commit of a distributed transaction, and investigate intrinsic limitations of causally consistent transactions. Our study of distributed transaction commit focuses on the complexity of the most frequent executions in practice, i.e., failure-free, and willing to commit. Through our systematic study, we close many open questions like the complexity of synchronous non-blocking atomic commit. We also present an effective protocol which solves what we call indulgent atomic commit that tolerates practical distributed database systems which are synchronous \"most of the time\", and can perform as efficiently as the two-phase commit protocol widely used in distributed database systems. Our investigation of causal transactions focuses on the limitations of read-only transactions, which are considered the most frequent in practice. We consider \"fast\" read-only transactions where operations are executed within one round-trip message exchange between a client seeking an object and the server storing it (in which no process can be a coordinator). We show two impossibility results regarding \"fast\" read-only transactions. By our impossibility results, when read-only transactions are \"fast\", they have to be \"visible\", i.e., they induce inherent updates on the servers. We also present a \"fast\" read-only transaction protocol that is \"visible\" as an upper bound on the complexity of inherent updates. We then study the complexity of secure transactions in the model of secure multiparty computation: even in the face of malicious parties, no party obtains the computation result unless all other parties obtain the same result. As it is impossible to achieve without any trusted party, we focus on optimism where if all parties are honest, they can obtain the computation result without resorting to a trusted third party, and the complexity of every optimistic execution where all parties are honest. We prove a tight lower bound on the message complexity by relating the number of messages to the length of the permutation sequence in combinatorics, a necessary pattern for messages in every optimistic execution.","url":"https://doi.org/10.5075/epfl-thesis-8761","authors":["Wang, Jingjing"],"tags":["complexity","failures","distributed transactions","non-blocking atomic commit","indulgent atomic commit","causal consistency","optimistic secure multiparty computation","permutation sequence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.5075/epfl-thesis-8761","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.26190/unsworks/28768","name":"Blockchain-based Decentralized Energy Systems: Advancing Scalability, Security, Privacy, and Interoperability","source":"datacite","abstract":"As the global energy landscape evolves, there is a transformative shift towards Decentralised Energy Systems (DES), characterized by Distributed Energy Resources (DERs) and Smart Grid technologies. DES offers several opportunities, including enhanced energy efficiency, minimized transmission losses, and increased resilience against power outages. However, decentralization ushers in complex challenges, most notably managing the multitude of DERs which include renewable energy generation systems, energy storage systems, and controllable loads, among others. Blockchain technology, with its potential for secure, transparent, and autonomous energy transactions, has been identified as a promising solution to these challenges. However, the practical integration of blockchain into DES has been hampered by limitations inherent to blockchain technology. Notable obstacles include scalability, security, privacy, and interoperability issues. This thesis presents an innovative, multi-faceted approach to overcoming these hurdles, consisting of four distinctive solutions, each designed to address a specific challenge in blockchain integration into DES. The first solution, the Hypergraph-based Adaptive consoRtium Blockchain (HARB), targets scalability, a central issue in blockchain-enabled DES. HARB employs hypergraph theory and a community discovery mechanism to improve scalability by partitioning the network into sub-chains, allowing for efficient transaction load distribution and reducing validation times. It also introduces the Adaptive Blockchain Module and a data Tagging and Anonymization Mechanism to tackle interoperability and privacy issues, respectively. Next, we introduce PlexiChain, a blockchain-based framework designed to counter prevalent cybersecurity threats. By integrating Physical Unclonable Functions (PUFs) — a security feature for IoT devices, and Non-Fungible Tokens (NFTs) — a type of cryptographic token representing something unique, PlexiChain significantly strengthens the security of Internet of Things (IoT) devices while ensuring data integrity. This protects against sophisticated threats like False Data Injection (FDI) and Malicious Internet of Things (MadIoT) attacks. Our third solution, CypherChain, offers a privacy-preserving blockchain-based framework. CypherChain adeptly employs Secure Multiparty Computation (SMPC) and Homomorphic Encryption (HE), in conjunction with Hypergraph Partitioning, to ensure private aggregation of Demand Response (DR) capacity offers. CypherChain presents an innovative approach towards balancing the requirement for transparency with the need for privacy. Lastly, we propose the Blockchain Agnostic Interoperability Framework (BAILIF) to surmount the interoperability challenges in blockchain-integrated energy systems. BAILIF fosters seamless data exchange, asset transfer, and atomic swaps — a type of cryptocurrency trade that is directly from one owner to another without any intermediary exchange involvement — between different blockchain platforms. This approach considerably enhances the collaborative potential of blockchain networks in DES. The solutions proposed in this research collectively present a comprehensive approach to the challenges encountered when applying blockchain technology in DES. They each contribute significantly towards overcoming their respective challenges and set a foundation for future research and practical implementation. By advancing these solutions, this research aims to stimulate the sustainable and seamless adoption of blockchain technology in DES, shaping the future of energy management paradigms.","url":"https://doi.org/10.26190/unsworks/28768","authors":["Karumba, Samuel"],"tags":["Blockchain Technology","Decentralized Energy Systems (DES)","Scalability","Cybersecurity and Privacy","Interoperability","460199 Applied computing not elsewhere classified"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.26190/unsworks/28768","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.26083/tuda-8146","name":"Bootstrapping Physical Security with Inertial Hardware Security Modules","source":"datacite","abstract":"In the past decades, cryptographic advancements and techniques like formal verification have steadily improved software security. Meanwhile, the field of hardware security has not kept pace. Research has made progress in subfields such as resilience to Side-Channel Attacks (SCA) and Physical Unclonable Functions (PUFs). However, the state of the art still often relies on microelectronic integration to achieve security by obscurity insted of more fundamental security guarantees. While effective, system-level tamper protection is only used in few devices such as Hardware Security Modules (HSMs) and card payment terminals. Due to the high cost and low performance of HSMs in particular, they remain relegated to niche applications such as Transport Layer Security (TLS) certificate issuance and payment data processing. In this thesis, we introduce the Inertial Hardware Security Module (IHSM), a new architecture for low-cost hardware security modules that provide high-level active tamper protection, while supporting computing payloads of much larger size, weight and power dissipation compared to conventional HSMs. In an IHSM, the costly and difficult to source tamper-sensing mesh of a conventional HSM is replaced by a mesh made from simple PCBs that is rotating at high speed around the payload. Since the mesh is rotating at high speed, it cannot be manipulated, and the security of conventional meshes created in bespoke manufacturing processes can be achieved using much simpler and less expensive construction techniques. We present the results of a survey of approximately 30 real world tamper sensing mesh implementations. Based on our findings, we deduce design criteria for secure meshes and contextualize our design. We further motivate the necessity of secure hardware by presenting an analysis of problematic aspects in the hardware security design of Germany’s new national electronic health record system. To pave the way for practical implementations of IHSM technology, we present solutions to key engineering challenges in IHSM construction. We present a design and analysis of highly symmetric planar inductors for rotating wireless power transfer that improves self-resonant frequency by up to 58 % and inductance by up to 6.5 % in our tests. Complementing this research, we present a high-fidelity, low-cost monitoring system for security meshes that is based on the principles of Time-Domain Reflectometry (TDR), reaching 184 ps time resolution. We validate our system and find that it is able to reliably detect several classes of advanced physical attacks. We find that our system is sensitive enough to detect differences between identical copies of the same mesh, suggesting PUF-like properties. Applying IHSM technology, we analyse two use cases that are unlocked by the increased size and power dissipation capability of IHSMs. In the first analysis, an IHSM-secured relay node for Quantum Key Distribution (QKD) systems is proposed, enabling their practical implementation across arbitrary distances, which requires trusted relay stations due to fundamental physical limitations. In the study, IHSMs are adapted for such high-security QKD relays by securing the IHSM mesh passthrough with a secondary tamper-sensing mesh. In this setup, a bracket design is proposed that supports passing through optical fibers at low loss. The second proposed use case adapts an IHSM enclosure to the size, power and thermal dissipation requirements of a high-power server to support co-located secure Multiparty Computation (MPC) workloads. In practical MPC deployments, nodes are distributed across data centers to avoid a single point of failure for physical attacks. As a result, practical MPC deployments are limited by network bandwidth and latency constraints. Using IHSMs, physically secured MPC nodes can be deployed within the same data center, increasing bandwidth, reducing latency and unlocking a new performance spectrum.","url":"https://doi.org/10.26083/tuda-8146","authors":["Götte, Jan Sebastian"],"tags":["hardware security","cybersecurity","tamper sensing mesh","security mesh","hardware security module","applied cryptography","time-domain reflectometry","physical security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.26083/tuda-8146","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.48550/arxiv.2606.12557","name":"New bounds on private simultaneous quantum message passing","source":"datacite","abstract":"In the private simultaneous message (PSM) setting, $k$ players obtain inputs $x_i\\in\\{0,1\\}^n$ and then each send messages to a referee, who should learn $f(x_1,...,x_k)$ but no other information about $(x_1,...,x_k)$. The PSM setting was introduced as a minimal model for secure multiparty computation and has connections to Boolean function complexity. In the quantum setting, PSM has been related to non-local quantum computation (NLQC). The communication and correlation cost of implementing PSM remains poorly understood. Here, we give new upper and lower bounds on the (quantum) PSM model. For lower bounds, we show: 1) Nečiporuk's measure lower bounds the entanglement required for $k$-player quantum PSM with perfect correctness. This leads to quadratic lower bounds for explicit functions. 2) The rank of the communication matrix of $f(x_1,x_2)$ lower bounds 2-player quantum PSM with perfect privacy but imperfect correctness. This implies a previously unknown lower bound on classical PSM with imperfect correctness. When allowing quantum communication and shared entanglement, these are the first lower bounds on quantum PSM that make use of the privacy condition. For upper bounds, we show: 1) Letting $s$ be the size of a quantum circuit computing $f$, $d_f$ be the circuit depth, $k$ the number of players, $n$ the number of bits received by each player, and $ε$ a correctness parameter, we obtain $\\mathsf{PSM}_k^*(f) \\leq (kn +s) \\cdot \\log^{O(d_f)}(s/ε)$. 2) The square of the Fourier 1 norm of $f$, $\\Vert \\hat{f}\\Vert_1^2$, upper bounds the classical PSM complexity, $\\mathsf{PSM}(f)\\leq O(\\Vert \\hat{f} \\Vert^2_1)$. In proving the first upper bound, we generalize existing $T$-depth based techniques for NLQC from $2$ to $k\\geq 2$ parties, and consider cases where the Clifford layers are restricted to having small light cones.","url":"https://doi.org/10.48550/arxiv.2606.12557","authors":["Girish, Uma","May, Alex","Parham, Natalie","Yuen, Henry"],"tags":["Quantum Physics (quant-ph)","FOS: Physical sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.12557","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.4230/lipics.icalp.2026.79","name":"On Randomness Complexity of 1-Private Protocols","source":"datacite","abstract":"In the field of information-theoretic cryptography, randomness complexity is a key metric for protocols for private computation, that is, the number of random bits needed to realize the protocol. Although some general bounds are known, even for the relatively simple example of 1-private computation of n-party AND, the exact complexity is unknown. We study two settings. First, we consider the model of Goyal, Ishai, and Song (Crypto '22) where helper parties without any inputs are allowed to assist in the computation. In this setting, we show that two random bits always suffice to compute an arbitrary Boolean circuit C 1-privately: a single designated inputless helper flips the two bits and privately distributes the derived one-time bits to the other helper parties and the input parties as they are needed. We give an explicit construction using seven helper parties per AND gate and three helper parties per XOR gate (plus the single global randomness dealer). Moreover, two random bits are necessary already for the AND functionality (by a reduction to the standard no-helper model together with the lower bound of Kushilevitz, Ostrovsky, Prouff, Rosén, Thillard and Vergnaud (TCC '19), and therefore the worst-case helper-party randomness complexity is exactly 2 bits. Second, in the setting without helper parties, we improve the upper bound from Couteau and Rosén (Asiacrypt '22) on the (asymptotic) randomness complexity of n-party AND from 6 to 5 bits. That is, we give a 1-private protocol for computing the AND of n parties' inputs requiring 5 bits of randomness, for all n ≥ 6. Our construction, like that of Couteau and Rosén, uses a single party to flip the 5 bits and distribute the required derived values during the execution. Our approach to both problems is built around a more systematic exploration of techniques for recycling randomness across sub-computations. As part of resolving the second problem, we isolate an exact local-independence combinatorial object called a Sliding-Window Independence Generator, or a SWIG. A (k,m)-SWIG is a linear generator from a k-bit seed to m ≥ k output bits, where every cyclic length-k sliding window chosen from m output bits is perfectly uniform. We give an explicit (k,m)-SWIG for every k ≥ 1 and every m ≥ k and use a (5,n-1)-SWIG in our no-helper AND protocol.","url":"https://doi.org/10.4230/lipics.icalp.2026.79","authors":["Dittmer, Samuel","Ostrovsky, Rafail"],"tags":["limited independence","bounded independence","k-wise independence","H-wise independent sample spaces","small sample spaces","small probability spaces","local independence","locally independent sample spaces"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.4230/lipics.icalp.2026.79","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.48550/arxiv.2110.13648","name":"Anonymous multi-party quantum computation with a third party","source":"datacite","abstract":"We reconsider and modify the second secure multi-party quantum addition protocol proposed in our original work. We show that the protocol is an anonymous multi-party quantum addition protocol rather than a secure multi-party quantum addition protocol. Through small changes, we develop the protocol to propose, for the first time, anonymous multiparty quantum computation with a third party, who faithfully executes protocol processes, but is interested in the identity of the data owners. Further, we propose a new anonymous multiparty quantum protocol based on our original protocol. We calculate the success probability of the proposed protocols, which is also a modification of the success probability of the original protocols.","url":"https://doi.org/10.48550/arxiv.2110.13648","authors":["Ji, Zhaoxu","Fan, Peiru","Rahman, Atta Ur","Zhang, Huanguo"],"tags":["Quantum Physics (quant-ph)","FOS: Physical sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.48550/arxiv.2110.13648","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20810243","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20810243","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20810243","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.48550/arxiv.2606.22115","name":"Game-Theoretic Framework for Private Data Sharing in Vehicular Networks","source":"datacite","abstract":"We present a novel game-theoretic framework designed to enhance privacy and scalability in decentralized vehicular data collection systems. The proposed hybrid architecture comprises vehicles that supply sensor data, independent servers that process data via secure multiparty computation, a coordinator node that manages data flow, and data consumers that set economic incentives. Crucially, our framework ensures that only the data consumer can access the fully aggregated data, preventing individual raw data exposure and significantly reducing privacy risks. By integrating principles of the Stackelberg competition from game theory, our approach dynamically balances privacy and economic incentives, enabling vehicles to make participation decisions based on perceived privacy risks and incentives. We empirically validate our framework using real-world vehicular location data, quantifying privacy risks by evaluating the accuracy with which a potential adversary can reconstruct a vehicle's path using only a subset of the shared data. This paper details the development and deployment of a data-trading platform within this framework, introducing a practical and privacy-preserving marketplace for profitable vehicle data sharing. Through experiments and simulations, we evaluate the effectiveness of the system in preserving privacy and explore the dynamics that influence vehicle participation. Our findings highlight the robustness of the proposed framework in preserving privacy while supporting an active data market.","url":"https://doi.org/10.48550/arxiv.2606.22115","authors":["AlSaqabi, Yousef","Zhou, Yinan","Nawab, Faisal","Krishnamachari, Bhaskar"],"tags":["Networking and Internet Architecture (cs.NI)","Cryptography and Security (cs.CR)","Computer Science and Game Theory (cs.GT)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.22115","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20798902","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20798902","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20798902","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.48550/arxiv.2604.27456","name":"Federated Generation of Synthetic RNA-seq Data","source":"datacite","abstract":"Access to genomic data is highly regulated due to its sensitive nature. While safeguards are essential, cumbersome data access processes pose a significant barrier to the development of AI methods for genomics. Synthetic data generation can mitigate this tension by enabling broader data sharing without exposing sensitive information. Synthetic genomic data are produced by training generative models on real data and subsequently sampling artificial data that preserves relevant statistics while limiting disclosures about the underlying individuals. In some settings, a single data holder may have sufficient data to train such generative models; however, in many applications data must be combined across multiple sites to achieve adequate scale. This need arises, e.g., in rare disease studies, where individual hospitals typically hold data for only a small number of patients. The solution we present in this paper enables multiple data holders to jointly train a synthetic data generator without revealing their raw data. Our approach combines secure multiparty computation (MPC) to ensure input privacy, so that no party ever discloses its data in unencrypted form, with differential privacy (DP) to provide output privacy by mitigating information leakage from the released synthetic data. We empirically demonstrate the effectiveness of the proposed method by generating high-utility synthetic datasets from multiple real RNA-seq cohorts in federated settings, showing that our approach enables privacy-preserving data synthesis even when data are distributed across institutions.","url":"https://doi.org/10.48550/arxiv.2604.27456","authors":["Filienko, Daniil","De Cock, Martine","Pentyala, Sikha"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.27456","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20630236","name":"UVCC Phase 6 Native Parallelism: Private, Verifiable, Fully-Parallel GPU Computation across Untrusted Domains","source":"datacite","abstract":"We present UVCC (Universal Verifiable Confidential Computing), a practical system for private and verifiable GPU computation across mutually distrustful administrative domains. UVCC achieves (i) confidentiality for client secrets using three-party replicated secret sharing (RSS), (ii) verifiability via an append-only transcript and deterministic hashing model that yields per-subsession roots, per-replica roots, and a global root, and (iii) native ML parallelism — data parallel (DP), pipeline parallel (PP) and tensor parallel (TP) — implemented in a C++ runtime that integrates GPU kernels, a reliable exactly-once transport, and NCCL-based collectives within each domain. This paper reports the Phase 6 bring-up of native parallelism end-to-end, including the diagnosis and resolution of a PP deadlock and the scale-out to R=8, S=4, T=2, M=32 on 24 heterogeneous provider pods. We demonstrate determinism through two complete runs that produce identical global roots, show robustness under provider skew and network jitter, and provide an audit-oriented log bundle with cryptographic commitments. The logs referenced herein are consolidated in a single explained file available from the author on request.","url":"https://doi.org/10.5281/zenodo.20630236","authors":["Gairola, N"],"tags":["confidential computing","secure multiparty computation","verifiable computation","GPU","replicated secret sharing","NCCL","pipeline parallelism","tensor parallelism"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20630236","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20630235","name":"UVCC Phase 6 Native Parallelism: Private, Verifiable, Fully-Parallel GPU Computation across Untrusted Domains","source":"datacite","abstract":"We present UVCC (Universal Verifiable Confidential Computing), a practical system for private and verifiable GPU computation across mutually distrustful administrative domains. UVCC achieves (i) confidentiality for client secrets using three-party replicated secret sharing (RSS), (ii) verifiability via an append-only transcript and deterministic hashing model that yields per-subsession roots, per-replica roots, and a global root, and (iii) native ML parallelism — data parallel (DP), pipeline parallel (PP) and tensor parallel (TP) — implemented in a C++ runtime that integrates GPU kernels, a reliable exactly-once transport, and NCCL-based collectives within each domain. This paper reports the Phase 6 bring-up of native parallelism end-to-end, including the diagnosis and resolution of a PP deadlock and the scale-out to R=8, S=4, T=2, M=32 on 24 heterogeneous provider pods. We demonstrate determinism through two complete runs that produce identical global roots, show robustness under provider skew and network jitter, and provide an audit-oriented log bundle with cryptographic commitments. The logs referenced herein are consolidated in a single explained file available from the author on request.","url":"https://doi.org/10.5281/zenodo.20630235","authors":["Gairola, N"],"tags":["confidential computing","secure multiparty computation","verifiable computation","GPU","replicated secret sharing","NCCL","pipeline parallelism","tensor parallelism"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20630235","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.48550/arxiv.2606.07009","name":"Fast Bounded-Independence Functions and Their Duals","source":"datacite","abstract":"We continue the study of {\\em fast} functions, computable by linear-size circuits, that share useful properties of random functions. Motivated by cryptographic applications, we generalize and improve on previous results in this area, obtaining the following results: - For any constant $t$, we construct a fast $t$-wise independent hash function with algebraic degree $\\log_2 t$ (over $\\mathbb F_2$), simultaneously optimizing both asymptotic circuit size and degree. - We simplify and improve a recent construction (ITCS 2026) of a family of fast codes with fast duals, both meeting the Gilbert-Varshamov bound. Unlike the previous construction, our construction has negligible failure probability, can accommodate general fields and rates, supports a systematic encoding, and admits fast universal encoders. - We strengthen the above to support stronger random-like properties, such as optimal combinatorial list-decoding. This is achieved by constructing, for any constant $t$, a family of fast linear functions that map any $t$ linearly independent inputs to uniform and statistically independent outputs. Prior to our work, this was only known for $t=1$. We demonstrate the usefulness of the above results to cryptography. This includes the first nontrivial protocols for perfectly secure multiparty computation whose circuit complexity scales linearly with the number of parties, as well as protocols for computing encrypted matrix-vector products with optimal asymptotic circuit complexity.","url":"https://doi.org/10.48550/arxiv.2606.07009","authors":["Brehm, Martijn","Ishai, Yuval","Resch, Nicolas"],"tags":["Cryptography and Security (cs.CR)","Information Theory (cs.IT)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.07009","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:37.148Z"},{"id":"doi:10.5281/zenodo.20556781","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20556781","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20556781","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20540410","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20540410","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20540410","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20508037","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20508037","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20508037","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20492877","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20492877","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20492877","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.13140/rg.2.2.24942.78406","name":"SECURE MULTIPARTY COMPUTATION FOR GEOMETRIC PROBLEMS","source":"datacite","abstract":"","url":"https://doi.org/10.13140/rg.2.2.24942.78406","authors":["Tiwari, Mukesh","Gokul Kc"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.13140/rg.2.2.24942.78406","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.26083/tuprints-00028820","name":"Improving Scalability, Privacy, and Decentralization of Blockchains and their Applications via Multiparty Computation","source":"datacite","abstract":"Since the advent of Bitcoin in 2008, a myriad of blockchain systems have emerged. Blockchains provide decentralized systems aiming to remove any trust in centralized parties. While Bitcoin provides simple money transfer and rudimentary scripting capabilities, other blockchains like Ethereum support the execution of complex smart contracts. Smart contrast sparked the invention of many new applications over blockchains, with decentralized finance (DeFi) being one of the most prominent. By moving financial services and products to decentralized and open blockchains, DeFi has the potential to democratize the financial market. While showing a promising feature, state-of-the-art blockchains still suffer from limitations and open problems. Limited scalability prevents mass adaption since the number of tolerable actions within the system is too low. Additionally, many systems lack strong privacy features, preventing their applicability to applications with high privacy requirements, like in the healthcare sector. Despite these open problems, blockchains are used in more and more new contexts due to their attractive features based on their decentralized nature. One example is the concept of self-sovereign identities (SSI), where blockchains provide decentralized storage of public metadata. In many new contexts, blockchains are paired with additional components, often not explicitly designed for blockchain applications. Hence, it remains an open problem to align these components with the fundamental idea of blockchains, i.e., removing trust in centralized parties. In this thesis, we significantly contribute to the design of new solutions to all three mentioned problems. More concretely, we tackle the scalability and privacy problem and mitigate the trust in centralized parties in a new component combined with blockchains. Our main building block in all our contributions is secure multiparty computation (MPC), which allows distrusting parties to compute on private data without leaking anything except the output of the computation. First, we present a new off-chain protocol that supports the execution of smart contracts. Since prior work suffers from different shortcomings, our solution addresses them all simultaneously. Second, we use MPC to facilitate private computation for blockchains. To do so, we consider a security model that provides a trade-off between efficiency and security. For this setting, we propose further efficiency improvements, present a compiler for enhancing security, and propose a protocol to combine MPC with blockchains. Our final result allows parties to perform computation privately, and the computation's result defines a distribution of coins. Third, we look at anonymous credentials, an essential component of self-sovereign identities. We present a distributed issuance protocol for anonymous credentials based on the BBS+ signature scheme.","url":"https://doi.org/10.26083/tuprints-00028820","authors":["Schlosser, Benjamin"],"tags":["004"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.26083/tuprints-00028820","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20289411","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20289411","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20289411","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20283494","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20283494","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20283494","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.26083/tuprints-00002842","name":"Multilaterally Secure Pervasive Cooperation","source":"datacite","abstract":"People tend to interact and communicate with others throughout their life. In the age of pervasive computing, information and communication technology (ICT) that is no longer bound to desktop computers enables digital cooperations in everyday life and work in an unprecedented manner. However, the privacy and IT security issues inherent in pervasive computing are often associated with negative consequences for the users and the (information) society as a whole. Addressing this challenge, this thesis demonstrates that carefully devised protection mechanisms can become enablers for multilaterally acceptable and trustworthy digital interactions and cooperations. It contributes to the design of multilaterally secure cooperative pervasive systems by taking a scenario-oriented approach. Within our reference scenario of ICT-supported emergency response, we derive the following scientific research questions. Firstly, we investigate how to enable real-world auditing in pervasive location tracking systems, while striking a balance between privacy protection and accountability. Secondly, we aim to support communication between a sender and mobile receivers that are unknown by identity, while end-to-end security is enforced. The required concepts and mechanisms define the scope of what we denote as multilaterally secure pervasive cooperation. We take a novel integrated approach and provide the supporting security techniques and mechanisms. The main contributions of this thesis are (i) pseudonyms with implicit attributes, which is an approach to multilevel linkable transaction pseudonyms that is based on a combination of threshold encryption techniques, secure multiparty computation and cryptographically secure pseudo-random number generators, (ii) multilaterally secure location-based auditing, a novel consideration of auditing mechanisms in the context of real-world actions that reconciles privacy protection and accountability while proposing location traces as evidence, (iii) a hybrid encryption technique for expressive policies, which allows encrypting under policies that include a continuous dynamic attribute, leveraging an efficient combination of ciphertext-policy attribute-based encryption, location-based encryption and symmetric encryption concepts, and (iv) end-to-end secure attribute-based messaging, a communication mechanism for end-to-end confidential messaging with receivers unknown by identity that is suitable also for resource constrained mobile devices. Harnessing these buildings blocks, we present an integrated architecture that supports location-aware first response. We therein consider location as the central integrating concept for pervasive cooperations. Both communication during incident handling as well as ex-post auditing are conceived as being location-based. Our research draws from experiences with potential real users (first responders and emergency decision makers) and from an interdisciplinary study. We contribute results derived from simulated court cases, indicating the trustworthiness and practicality of our proposal. Experiments conducted with prototype systems support the claim that our concepts are suitable for resource-constrained devices. In a theoretical analysis, we show that our security requirements are fulfilled. Our proposals have multiple further applications, e.g. to pseudonym-based access control.","url":"https://doi.org/10.26083/tuprints-00002842","authors":["Weber, Stefan G."],"tags":["004"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2011","doi":"10.26083/tuprints-00002842","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.26083/tuprints-00002811","name":"Secure Computations on Non-Integer Values","source":"datacite","abstract":"Die Forschung zu Secure Multiparty Computation (SMC) begann im Jahr 1982, als Andrew C. Yao das Millionärsproblem vorstellte. Seitdem hat die Wissenschaft in diesem Bereich große Fortschritte gemacht, viele sicherheitskritische Anwendungen wurden mittels SMC realisiert. Einzig die Anwendungen, die mathematische Berechnungen auf nicht-ganzzahligen Werten durchführen, waren lange Zeit von diesen Fortschritten ausgeschlossen. Zu diesen Anwendungen gehören beispielsweise Algorithmen, die Berechnungen auf großen Intervallen mit reellen Zahlen durchführen. Die vorliegende Dissertation präsentiert neue Ergebnisse in diesem Forschungsbereich. Zunächst wird eine neue Methode vorgestellt, die es erlaubt, sichere Berechnungen auf reellen Zahlen durchzuführen, die in einer logarithmischen Repräsentierung gespeichert sind. Zum einen wird beschrieben, wie so repräsentierte Zahlen effektiv verschlüsselt werden können. Danach werden kryptographische Protokolle angegeben, die es erlauben bestimmte arithmetische Operationen mit auf diese Weise kodierten und verschlüsselten Werten durchzuführen. In einem weiteren Kapitel wird eine sichere Umsetzung des IEEE 754 Gleitkommastandards präsentiert. Diese zeigt auf, wie Gleitkommazahlen verschlüsselt werden können. Zudem werden kryptographische Protokolle beschrieben, die es erlauben Berechnungen auf solch verschlüsselten Gleitkommazahlen durchzuführen. Abgeschlossen wird diese Dissertation mit sowohl einer theoretischen, als auch einer praktischen Evaluierung der hier vorgestellten Techniken. Zunächst werden in einer ausgiebigen theoretischen Komplexitätsanalyse die Rechen- wie auch die Kommunikationskomplexität der beiden neu vorgestellten Methoden zum Rechnen mit verschlüsselten Zahlen vorgestellt. Danach wird die Performanz dieser beiden Methoden mit einer Standardmethode verglichen, die auf einer Festpunktarithmetik basiert. Es zeigt sich, dass beide Methoden für typische Probleme deutlich effizienter sind als die Festpunktarithmetik. Zum Abschluss wird auch die praktische Machbarkeit der neu vorgestellten Techniken demonstriert. Dafür wurden zwei wichtige Algorithmen aus der Bioinformatik implementiert, der Forward- und der Viterby Algorithmus. Diese Algorithmen sind typischerweise numerisch instabil, denn sie führen ihre Berechnungen auf ständig kleiner werdenden Wahrscheinlichkeiten durch. Die hier vorgestellte Implementierung zeigt, dass die neuen theoretischen Methoden auch in der Praxis erfolgreich eingesetzt werden können, um real vorkommende Probleme zu lösen.","url":"https://doi.org/10.26083/tuprints-00002811","authors":["Franz, Martin"],"tags":["004"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2012","doi":"10.26083/tuprints-00002811","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20135122","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20135122","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20135122","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20123414","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20123414","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20123414","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20119622","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20119622","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20119622","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20117892","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20117892","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20117892","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20086001","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20086001","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20086001","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20061078","name":"Enhancing Privacy In Federated Learning: A Comprehensive Survey Of Preservation Techniques","source":"datacite","abstract":"Federated Learning (FL) enables multiple devices or organizations to collaboratively train machine learning models without sharing raw data, thus improving privacy. However, FL is vulnerable to privacy threats like model inversion, membership inference, and data leakage from shared updates. To mitigate these risks, several privacy-preserving techniques have been developed, including differential privacy, secure multiparty computation (SMC), homomorphic encryption (HE), and hybrid approaches that combine multiple methods. This paper offers a comprehensive analysis of these techniques, evaluating their privacy guarantees, computational costs, and impact on model accuracy. Differential privacy introduces noise to protect data but can reduce model performance. SMC allows joint computation without exposing inputs but is computationally intensive. HE enables encrypted data processing with strong security, though often at the expense of efficiency. Hybrid methods aim to balance these trade-offs by leveraging the advantages of different approaches. The study highlights key challenges such as scalability and usability in real-world FL deployments. It also identifies research gaps and proposes future directions focused on adaptive privacy mechanisms and hardware-assisted security, aiming to develop more practical and robust privacy-preserving FL systems.","url":"https://doi.org/10.5281/zenodo.20061078","authors":["D Naga Bharghavi","M Deepthi","K Manga Devi","M Aswitha","P Aswitha"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20061078","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20061077","name":"Enhancing Privacy In Federated Learning: A Comprehensive Survey Of Preservation Techniques","source":"datacite","abstract":"Federated Learning (FL) enables multiple devices or organizations to collaboratively train machine learning models without sharing raw data, thus improving privacy. However, FL is vulnerable to privacy threats like model inversion, membership inference, and data leakage from shared updates. To mitigate these risks, several privacy-preserving techniques have been developed, including differential privacy, secure multiparty computation (SMC), homomorphic encryption (HE), and hybrid approaches that combine multiple methods. This paper offers a comprehensive analysis of these techniques, evaluating their privacy guarantees, computational costs, and impact on model accuracy. Differential privacy introduces noise to protect data but can reduce model performance. SMC allows joint computation without exposing inputs but is computationally intensive. HE enables encrypted data processing with strong security, though often at the expense of efficiency. Hybrid methods aim to balance these trade-offs by leveraging the advantages of different approaches. The study highlights key challenges such as scalability and usability in real-world FL deployments. It also identifies research gaps and proposes future directions focused on adaptive privacy mechanisms and hardware-assisted security, aiming to develop more practical and robust privacy-preserving FL systems.","url":"https://doi.org/10.5281/zenodo.20061077","authors":["D Naga Bharghavi","M Deepthi","K Manga Devi","M Aswitha","P Aswitha"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20061077","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.25560/89170","name":"Coding-theoretic approaches to distributed caching, storage and computing","source":"datacite","abstract":"The next-generation communication networks aim to bring data storage and computation closer to the end users and Internet of things (IoT) endpoints by exploiting edge servers and/or memory available at edge devices. This greatly improves the efficiency and economics of network applications. Traditionally, coding techniques have been applied to communication problems with terrific gains in performance. In this thesis, we apply novel coding techniques to edge storage and computation problems, and show similar significant gains in terms of storage costs, communication costs, computation costs, and privacy. In the first part of the thesis, we study a wireless distributed network of small base stations (SBS) which serve cache-enabled users. We propose a novel scheme of coded caching in a multi-server system with a random connectivity pattern, and study the fundamental limits of the trade-off between the storage capacity at the servers, the cache capacity at the users, and the delivery latency. We then study the trade-off between the storage capacity and bandwidth requirement for the repair of failed storage nodes/SBSs in a distributed wireless content caching system. We derive the fundamental limits of this trade-off using techniques from information theory, and propose a storage and repair framework to achieve the optimal trade-off, while satisfying the practical constraints of low subpacketization, low overheads, and low computational complexity. We then address problems related to distributed edge computing systems. In particular, we address computational privacy problems, where a user offloads the computation of a function on a set of matrices to powerful distributed servers, while preserving the privacy of the data and the computation result from T colluding servers as well as the user, and also achieving significantly smaller communication costs than existing secure multiparty computation techniques.","url":"https://doi.org/10.25560/89170","authors":["Mital, Nitish"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.25560/89170","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.20021628","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.20021628","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20021628","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.19913411","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.19913411","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19913411","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.11575/prism/42161","name":"Cryptalphabet Soup: DPFs meet MPC and ZKPs","source":"datacite","abstract":"Secure multiparty computation (MPC) protocols enable multiple parties to collaborate on a computation using private inputs possessed by the different parties in the computation. At the same time, MPC protocols ensure that no participating party learns anything about the other parties’ private inputs beyond what they can infer from the computation’s output and their own inputs. MPC has wide ranging applications for privacy protecting systems. However, these systems have been plagued by limited performance, lack of scalability, and poor accuracy. In this thesis, we demonstrate several novel techniques for using distributed point functions (DPFs) in combination with MPC to obtain significant performance improvements in several different applications. Namely, using novel observations about the structure of the most efficient available DPF construction in the literature, we show that DPF keys from untrusted sources can be checked for correctness using an MPC protocol between the two key holders, with direct applications in sender-anonymous messaging. We expand these observations to produce the most efficient available method to evaluate piecewise-polynomial functions, also known as splines. The scalability and efficiency of this method allows for splines to be used for extremely high accuracy approximation of non-linear functions in MPC. Furthermore, the protocols proposed in this thesis far outperform prior solutions both in large-scale asymptotic measurements and in concrete benchmarks using high-performance software implementations at both small- and large-scale.","url":"https://doi.org/10.11575/prism/42161","authors":["Storrier, Kyle"],"tags":["Cryptography","Secure Multiparty Computation","MPC","Zero-Knowledge Proof","ZKP","Distributed Point Function","DPF","Function Secret Sharing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.11575/prism/42161","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.19843334","name":"6th International Conference on Big Data, IoT and Machine Learning (BIOM 2026)","source":"datacite","abstract":"6th International Conference on Big Data, IoT and Machine Learning (BIOM 2026) May 23 ~ 24, 2026, Vancouver, Canada https://crbl2026.org/biom/index Scope 6th International Conference on Big Data, IoT and Machine Learning (BIOM 2026) serves as a premier global forum for presenting innovative ideas, research developments and emerging trends in the rapidly evolving fields of Big Data, the Internet of Things (IoT) and Machine Learning. As data driven intelligence, connected systems and AI powered technologies continue to transform industries and society, BIOM 2026 aims to bring together researchers, practitioners and industry experts to exchange knowledge, discuss challenges and explore break throughs shaping the next generation of intelligent systems. The conference encourages contributions that advance the state of the art in large scale data processing, distributed and federated learning, edge intelligence, 5G/6G enabled IoT, trustworthy and robust AI, digital twins, data centric AI and emerging technologies such as quantum machine learning and blockchain based analytics. BIOM 2026 particularly welcomes work that bridges theory and practice, addresses real world deployment challenges and demonstrates the impact of Big Data, IoT and ML in complex, data intensive environments. Authors are invited to submit original research articles, project reports, survey papers and industrial case studies that illustrate significant advances in the field. Submissions may address any of the conference themes, including, but not limited to, the topics listed below. Topics of interest include, but are not limited to, the following Big Data Systems, Infrastructure and Platforms  Distributed and Cloud Native Data Platforms  Data Lakes, Lake houses and Modern Data Architectures  Large Scale Data Processing Systems (Spark, Flink, Ray)  High Performance and Parallel Computing for Big Data  Edge to Cloud Data Pipelines and Streaming Architectures Big Data Analytics, Mining and Applications  Large Scale Data Mining and Knowledge Discovery  Graph Mining, Network Science and Graph Based Analytics  Spatiotemporal and Geospatial Data Analytics  Real Time and Streaming Data Analytics  Domain Driven Analytics (Healthcare, Finance, Climate, etc.) Data Management, Governance and Quality  Data Integration, Cleaning and Wrangling  Data Governance, Lineage and Compliance  Data Quality, Bias Detection and Fairness  Metadata Management and Semantic Technologies  Datacentric AI and Data Quality Engineering Security, Privacy and Trust in Data Driven Systems  Big Data Security, Privacy and Trust  Differential Privacy and Privacy Preserving Analytics  Secure Multiparty Computation and Homomorphic Encryption  Federated Security and Secure Data Sharing  Zero Trust Architectures for IoT and Edge Systems Machine Learning and AI for Big Data  Scalable Machine Learning Algorithms  Distributed, Federated and Split Learning  Deep Learning Architectures and Optimization  Foundation Models and Large Scale Pretraining  Multimodal Learning (Vision Language Sensor Fusion)  AutoML, Neural Architecture Search and Model Compression  Causal Inference and Causal Machine Learning Trustworthy, Robust and Safe Machine Learning  Adversarial Machine Learning and Robustness  Safe and Reliable ML Systems  ML under Distribution Shift  Explainable and Interpretable ML  ML Risk Assessment and Governance ML Systems, Deployment and MLOps  Scalable Training and Inference Systems  ML Model Deployment, Monitoring and Drift Detection  ML Observability and Lifecycle Management  Data/Model Versioning and Reproducibility  RealTime ML and Online Learning IoT Systems, Architectures and Connectivity  IoT Architectures, Protocols and Standards  Edge and Fog Computing for IoT  5G/6GEnabled IoT and Ultra Reliable Low Latency IoT  IoT Interoperability and Large Scale IoT Platforms  Resource Efficient IoT Systems IoT Applications, Sensing and Cyber Physical Systems  Indus","url":"https://doi.org/10.5281/zenodo.19843334","authors":["Yaacoub, Aya"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19843334","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.19843335","name":"6th International Conference on Big Data, IoT and Machine Learning (BIOM 2026)","source":"datacite","abstract":"6th International Conference on Big Data, IoT and Machine Learning (BIOM 2026) May 23 ~ 24, 2026, Vancouver, Canada https://crbl2026.org/biom/index Scope 6th International Conference on Big Data, IoT and Machine Learning (BIOM 2026) serves as a premier global forum for presenting innovative ideas, research developments and emerging trends in the rapidly evolving fields of Big Data, the Internet of Things (IoT) and Machine Learning. As data driven intelligence, connected systems and AI powered technologies continue to transform industries and society, BIOM 2026 aims to bring together researchers, practitioners and industry experts to exchange knowledge, discuss challenges and explore break throughs shaping the next generation of intelligent systems. The conference encourages contributions that advance the state of the art in large scale data processing, distributed and federated learning, edge intelligence, 5G/6G enabled IoT, trustworthy and robust AI, digital twins, data centric AI and emerging technologies such as quantum machine learning and blockchain based analytics. BIOM 2026 particularly welcomes work that bridges theory and practice, addresses real world deployment challenges and demonstrates the impact of Big Data, IoT and ML in complex, data intensive environments. Authors are invited to submit original research articles, project reports, survey papers and industrial case studies that illustrate significant advances in the field. Submissions may address any of the conference themes, including, but not limited to, the topics listed below. Topics of interest include, but are not limited to, the following Big Data Systems, Infrastructure and Platforms  Distributed and Cloud Native Data Platforms  Data Lakes, Lake houses and Modern Data Architectures  Large Scale Data Processing Systems (Spark, Flink, Ray)  High Performance and Parallel Computing for Big Data  Edge to Cloud Data Pipelines and Streaming Architectures Big Data Analytics, Mining and Applications  Large Scale Data Mining and Knowledge Discovery  Graph Mining, Network Science and Graph Based Analytics  Spatiotemporal and Geospatial Data Analytics  Real Time and Streaming Data Analytics  Domain Driven Analytics (Healthcare, Finance, Climate, etc.) Data Management, Governance and Quality  Data Integration, Cleaning and Wrangling  Data Governance, Lineage and Compliance  Data Quality, Bias Detection and Fairness  Metadata Management and Semantic Technologies  Datacentric AI and Data Quality Engineering Security, Privacy and Trust in Data Driven Systems  Big Data Security, Privacy and Trust  Differential Privacy and Privacy Preserving Analytics  Secure Multiparty Computation and Homomorphic Encryption  Federated Security and Secure Data Sharing  Zero Trust Architectures for IoT and Edge Systems Machine Learning and AI for Big Data  Scalable Machine Learning Algorithms  Distributed, Federated and Split Learning  Deep Learning Architectures and Optimization  Foundation Models and Large Scale Pretraining  Multimodal Learning (Vision Language Sensor Fusion)  AutoML, Neural Architecture Search and Model Compression  Causal Inference and Causal Machine Learning Trustworthy, Robust and Safe Machine Learning  Adversarial Machine Learning and Robustness  Safe and Reliable ML Systems  ML under Distribution Shift  Explainable and Interpretable ML  ML Risk Assessment and Governance ML Systems, Deployment and MLOps  Scalable Training and Inference Systems  ML Model Deployment, Monitoring and Drift Detection  ML Observability and Lifecycle Management  Data/Model Versioning and Reproducibility  RealTime ML and Online Learning IoT Systems, Architectures and Connectivity  IoT Architectures, Protocols and Standards  Edge and Fog Computing for IoT  5G/6GEnabled IoT and Ultra Reliable Low Latency IoT  IoT Interoperability and Large Scale IoT Platforms  Resource Efficient IoT Systems IoT Applications, Sensing and Cyber Physical Systems  Indus","url":"https://doi.org/10.5281/zenodo.19843335","authors":["Yaacoub, Aya"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19843335","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.19734601","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.19734601","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19734601","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.19730079","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.19730079","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19730079","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.48550/arxiv.2604.00169","name":"Beyond Latency: A System-Level Characterization of MPC and FHE for PPML","source":"datacite","abstract":"Privacy protection has become an increasing concern in modern machine learning applications. Privacy-preserving machine learning (PPML) has attracted growing research attention, with approaches such as secure multiparty computation (MPC) and fully homomorphic encryption (FHE) being actively explored. However, existing evaluations of these approaches have frequently been done on a narrow, fragmented setup and only focused on a specific performance metric, such as the online inference latency of a specific batch size. From the existing reports, it is hard to compare different approaches, especially when considering other metrics like energy/cost or broader system setups (various hyperparameters, offline overheads, future hardware/network configurations, etc.). We present a unified characterization of three popular approaches -- two variants of MPC based on arithmetic/binary sharing conversion and function secret sharing, and FHE -- on their performance and cost in performing privacy-preserving inference on multiple CNN and Transformer models. We study a range of LAN and WAN environments, model sizes, batch sizes, and input sequence lengths. We evaluate not only the performance but also the energy consumption and monetary cost of deploying under a realistic scenario, taking into account their offline and online computation/communication overheads. We provide empirical guidance for selecting, optimizing, and deploying these privacy-preserving compute paradigms, and outline how evolving hardware and network trends are likely to shift trade-offs between the two MPC schemes and FHE. This work provides system-level insights for researchers and practitioners who seek to understand or accelerate PPML workloads.","url":"https://doi.org/10.48550/arxiv.2604.00169","authors":["Huang, Pengzhi","Maeng, Kiwan","Suh, G. Edward"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.00169","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2604.13474","name":"Secure and Privacy-Preserving Vertical Federated Learning","source":"datacite","abstract":"We propose a novel end-to-end privacy-preserving framework, instantiated by three efficient protocols for different deployment scenarios, covering both input and output privacy, for the vertically split scenario in federated learning (FL), where features are split across clients and labels are not shared by all parties. We do so by distributing the role of the aggregator in FL into multiple servers and having them run secure multiparty computation (MPC) protocols to perform model and feature aggregation and apply differential privacy (DP) to the final released model. While a naive solution would have the clients delegating the entirety of training to run in MPC between the servers, our optimized solution, which supports purely global and also global-local models updates with privacy-preserving, drastically reduces the amount of computation and communication performed using multiparty computation. The experimental results also show the effectiveness of our protocols.","url":"https://doi.org/10.48550/arxiv.2604.13474","authors":["Jin, Shan","Rachuri, Sai Rahul","Wang, Yizhen","Nascimento, Anderson C. A.","Cai, Yiwei"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.13474","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.48550/arxiv.2408.00243","name":"A Survey on the Applications of Zero-Knowledge Proofs","source":"datacite","abstract":"Zero-knowledge proofs (ZKPs) enable computational integrity and privacy by allowing one party to prove the truth of a statement without revealing underlying data. Compared with alternatives such as homomorphic encryption and secure multiparty computation, ZKPs offer distinct advantages in universality and minimal trust assumptions, with applications spanning blockchain systems and confidential verification of computational tasks. This survey provides a technical overview of ZKPs with a focus on an increasingly relevant subset called zkSNARKs. Unlike prior surveys emphasizing algorithmic and theoretical aspects, we take a broader view of practical deployments and recent use cases across multiple domains including blockchain privacy, scaling, storage, and interoperability, as well as non-blockchain applications such as voting, authentication, timelocks, and machine learning. To support consistent comparison, we provide (i) a taxonomy of application areas, (ii) evaluation criteria including proof size, prover and verifier time, memory, and setup assumptions, and (iii) comparative tables summarizing key tradeoffs and representative systems. The survey also covers supporting infrastructure, including zero-knowledge virtual machines, domain-specific languages, libraries, and frameworks. While emphasizing zkSNARKs for their prevalence in deployed systems, we compare them with zkSTARKs and Bulletproofs to clarify transparency and performance tradeoffs. We conclude with future research and application directions.","url":"https://doi.org/10.48550/arxiv.2408.00243","authors":["Lavin, Ryan","Liu, Xuekai","Mohanty, Hardhik","Norman, Logan","Zaarour, Giovanni","Krishnamachari, Bhaskar"],"tags":["Cryptography and Security (cs.CR)","Computational Complexity (cs.CC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2408.00243","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.48550/arxiv.2604.09975","name":"EncFormer: Secure and Efficient Transformer Inference over Encrypted Data","source":"datacite","abstract":"Transformer inference in machine-learning-as-a-service (MLaaS) raises privacy concerns for sensitive user inputs. Prior secure solutions that combine fully homomorphic encryption (FHE) and secure multiparty computation (MPC) are bottlenecked by inefficient FHE kernels, communication-heavy MPC protocols, and expensive FHE-MPC conversions. We present EncFormer, a two-party private Transformer inference framework that introduces Stage Compatible Patterns so that FHE kernels compose efficiently, reducing repacking and conversions. EncFormer also provides a cost analysis model built around a minimal-conversion baseline, enabling principled selection of FHE-MPC boundaries. To further reduce communication, EncFormer proposes a secure complex CKKS-MPC conversion protocol and designs communication-efficient MPC protocols for nonlinearities. With GPU optimizations, evaluations on GPT- and BERT-style models show that EncFormer achieves 1.4x-30.4x lower online MPC communication and 1.3x-9.8x lower end-to-end latency against prior hybrid FHE-MPC systems, and 1.9x-3.5x lower end-to-end latency on BERT-base than FHE-only pipelines under a matched backend, while maintaining near-plaintext accuracy on selected GLUE tasks.","url":"https://doi.org/10.48550/arxiv.2604.09975","authors":["Zhu, Yufan","Jin, Chao","Aung, Khin Mi Mi","Xiao, Xiaokui"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences","C.2.4; I.2.7; E.3"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.09975","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.48550/arxiv.2603.13570","name":"Privacy-Preserving Machine Learning for IoT: A Cross-Paradigm Survey and Future Roadmap","source":"datacite","abstract":"The rapid proliferation of the Internet of Things has intensified demand for robust privacy-preserving machine learning mechanisms to safeguard sensitive data generated by large-scale, heterogeneous, and resource-constrained devices. Unlike centralized environments, IoT ecosystems are inherently decentralized, bandwidth-limited, and latency-sensitive, exposing privacy risks across sensing, communication, and distributed training pipelines. These characteristics render conventional anonymization and centralized protection strategies insufficient for practical deployments. This survey presents a comprehensive IoT-centric, cross-paradigm analysis of privacy-preserving machine learning. We introduce a structured taxonomy spanning perturbation-based mechanisms such as differential privacy, distributed paradigms such as federated learning, cryptographic approaches including homomorphic encryption and secure multiparty computation, and generative synthesis techniques based on generative adversarial networks. For each paradigm, we examine formal privacy guarantees, computational and communication complexity, scalability under heterogeneous device participation, and resilience against threats including membership inference, model inversion, gradient leakage, and adversarial manipulation. We further analyze deployment constraints in wireless IoT environments, highlighting trade-offs between privacy, communication overhead, model convergence, and system efficiency within next-generation mobile architectures. We also consolidate evaluation methodologies, summarize representative datasets and open-source frameworks, and identify open challenges including hybrid privacy integration, energy-aware learning, privacy-preserving large language models, and quantum-resilient machine learning.","url":"https://doi.org/10.48550/arxiv.2603.13570","authors":["Zaman, Zakia","Gauravaram, Praveen","Hassan, Mahbub","Jha, Sanjay","Hu, Wen"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.13570","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.19328321","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.19328321","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19328321","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.7916/d8k64r2w","name":" On Black-Box Complexity and Adaptive, Universal Composability of Cryptographic Tasks","source":"datacite","abstract":"Two main goals of modern cryptography are to identify the minimal assumptions necessary to construct secure cryptographic primitives as well as to construct secure protocols in strong and realistic adversarial models. In this thesis, we address both of these fundamental questions. In the first part of this thesis, we present results on the black-box complexity of two basic cryptographic primitives: non-malleable encryption and optimally-fair coin tossing. Black-box reductions are reductions in which both the underlying primitive as well as the adversary are accessed only in an input-output (or black-box) manner. Most known cryptographic reductions are black-box. Moreover, black-box reductions are typically more efficient than non-black-box reductions. Thus, the black-box complexity of cryptographic primitives is a meaningful and important area of study which allows us to gain insight into the primitive. We study the black box complexity of non-malleable encryption and optimally-fair coin tossing, showing a positive result for the former and a negative one for the latter. Non-malleable encryption is a strong security notion for public-key encryption, guaranteeing that it is impossible to \"maul\" a ciphertext of a message m into a ciphertext of a related message. This security guarantee is essential for many applications such as auctions. We show how to transform, in a black-box manner, any public-key encryption scheme satisfying a weak form of security, semantic security, to a scheme satisfying non-malleability. Coin tossing is perhaps the most basic cryptographic primitive, allowing two distrustful parties to flip a coin whose outcome is 0 or 1 with probability 1/2. A fair coin tossing protocol is one in which the outputted bit is unbiased, even in the case where one of the parties may abort early. However, in the setting where parties may abort early, there is always a strategy for one of the parties to impose bias of Omega(1/r) in an r-round protocol. Thus, achieving bias of O(1/r) in r rounds is optimal, and it was recently shown that optimally-fair coin tossing can be achieved via a black-box reduction to oblivious transfer. We show that it cannot be achieved via a black-box reduction to one-way function, unless the number of rounds is at least Omega(n/log n), where n is the input/output length of the one-way function. In the second part of this thesis, we present protocols for multiparty computation (MPC) in the Universal Composability (UC) model that are secure against malicious, adaptive adversaries. In the standard model, security is only guaranteed in a stand-alone setting; however, nothing is guaranteed when multiple protocols are arbitrarily composed. In contrast, the UC model, introduced by (Canetti, 2000), considers the execution of an unbounded number of concurrent protocols, in an arbitrary, and adversarially controlled network environment. Another drawback of the standard model is that the adversary must decide which parties to corrupt before the execution of the protocol commences. A more realistic model allows the adversary to adaptively choose which parties to corrupt based on its evolving view during the protocol. In our work we consider the the adaptive UC model, which combines these two security requirements by allowing both arbitrary composition of protocols and adaptive corruption of parties. In our first result, we introduce an improved, efficient construction of non-committing encryption (NCE) with optimal round complexity, from a weaker primitive we introduce called trapdoor-simulatable public key encryption (PKE). NCE is a basic primitive necessary to construct protocols secure under adaptive corruptions and in particular, is used to construct oblivious transfer (OT) protocols secure against semi-honest, adaptive adversaries. Additionally, we show how to realize trapdoor-simulatable PKE from hardness of factoring Blum integers, thus achieving the first construction of NCE from hardness of factoring. ","url":"https://doi.org/10.7916/d8k64r2w","authors":["Dachman-Soled, Dana"],"tags":["Computer science"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2011","doi":"10.7916/d8k64r2w","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.19203631","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.19203631","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19203631","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.60882/cispa.25119287","name":"Janus: Safe Biometric Deduplication for Humanitarian Aid Distribution","source":"datacite","abstract":"Humanitarian organizations provide aid to people in need. To use their limited budget efficiently, their distribution processes must ensure that legitimate recipients cannot receive more aid than they are entitled to. Thus, it is essential that recipients can register at most once per aid program. Taking the International Committee of the Red Cross's aid distribution registration process as a use case, we identify the requirements to detect double registration without creating new risks for aid recipients. We then design Janus, which combines privacy-enhancing technologies with biometrics to prevent double registration in a safe manner. Janus does not create plaintext biometric databases and reveals only one bit of information at registration time (whether the user registering is present in the database or not). We implement and evaluate three instantiations of Janus based on secure multiparty computation (SMC) alone, a hybrid of somewhat homomorphic encryption and SMC, and trusted execution environments. We demonstrate that they support the privacy, accuracy, and performance needs of humanitarian organizations. We compare Janus with existing alternatives and show it is the first system that provides the accuracy our scenario requires while providing strong protection.","url":"https://doi.org/10.60882/cispa.25119287","authors":["EdalatNejad, Kasra","Lueks, Wouter","Justinas, Sukaitis","Graf Narbel, Vincent","Massimo, Marelli","Carmela, Troncoso"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.60882/cispa.25119287","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.60882/cispa.25119287.v1","name":"Janus: Safe Biometric Deduplication for Humanitarian Aid Distribution","source":"datacite","abstract":"Humanitarian organizations provide aid to people in need. To use their limited budget efficiently, their distribution processes must ensure that legitimate recipients cannot receive more aid than they are entitled to. Thus, it is essential that recipients can register at most once per aid program. Taking the International Committee of the Red Cross's aid distribution registration process as a use case, we identify the requirements to detect double registration without creating new risks for aid recipients. We then design Janus, which combines privacy-enhancing technologies with biometrics to prevent double registration in a safe manner. Janus does not create plaintext biometric databases and reveals only one bit of information at registration time (whether the user registering is present in the database or not). We implement and evaluate three instantiations of Janus based on secure multiparty computation (SMC) alone, a hybrid of somewhat homomorphic encryption and SMC, and trusted execution environments. We demonstrate that they support the privacy, accuracy, and performance needs of humanitarian organizations. We compare Janus with existing alternatives and show it is the first system that provides the accuracy our scenario requires while providing strong protection.","url":"https://doi.org/10.60882/cispa.25119287.v1","authors":["EdalatNejad, Kasra","Lueks, Wouter","Justinas, Sukaitis","Graf Narbel, Vincent","Massimo, Marelli","Carmela, Troncoso"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.60882/cispa.25119287.v1","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.60882/cispa.24614259.v1","name":"Gossiping for Communication-Efficient Broadcast","source":"datacite","abstract":"Byzantine Broadcast is crucial for many cryptographic pro- tocols such as secret sharing, multiparty computation and blockchain consensus. In this paper we apply gossiping (propagating a message by sending to a few random parties who in turn do the same, until the mes- sage is delivered) and propose new communication-efficient protocols, under dishonest majority, for Single-Sender Broadcast (BC) and Parallel Broadcast (PBC), improving the state-of-the-art in several ways. As our first warm-up result, we give a randomized protocol for BC which achieves O(n^2κ^2) communication complexity from plain public key setup assumptions. This is the first protocol with subcubic communication in this setting, but does so only against static adversaries. Using some ideas from our BC protocol, we then move to our central con- tribution and present two protocols for PBC that are secure against adap- tive adversaries. To the best of our knowledge we are the first to study PBC specifically: All previous approaches for parallel BC (PBC) naively run n instances of single-sender Broadcast, increasing the communication complexity by an undesirable factor of n. Our insight of avoiding black- box invocations of BC is particularly crucial for achieving our asymptotic improvements. In particular: 1. Our first PBC protocol achieves O(n^3κ^2) communication complexity and relies only on plain public key setup assumptions. 2. Our second PBC protocol uses trusted setup and achieves nearly optimal communication complexity O(n^2κ^4). Both PBC protocols yield an almost linear improvement over the best known solutions involving n parallel invocations of the respective BC protocols such as those of Dolev and Strong (SIAM Journal on Comput- ing, 1983) and Chan et al. (Public Key Cryptography, 2020). Central to our PBC protocols is a new problem that we define and solve, that we call “Converge”. In Converge, parties must run an adaptively-secure and efficient protocol such that by the end of the protocol, the honest parties that remain possess a superset of the union of the inputs of the initial honest parties.","url":"https://doi.org/10.60882/cispa.24614259.v1","authors":["Tsimos, Georgios","Loss, Julian","Papamanthou, Charalampos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.60882/cispa.24614259.v1","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.60882/cispa.24614259","name":"Gossiping for Communication-Efficient Broadcast","source":"datacite","abstract":"Byzantine Broadcast is crucial for many cryptographic pro- tocols such as secret sharing, multiparty computation and blockchain consensus. In this paper we apply gossiping (propagating a message by sending to a few random parties who in turn do the same, until the mes- sage is delivered) and propose new communication-efficient protocols, under dishonest majority, for Single-Sender Broadcast (BC) and Parallel Broadcast (PBC), improving the state-of-the-art in several ways. As our first warm-up result, we give a randomized protocol for BC which achieves O(n^2κ^2) communication complexity from plain public key setup assumptions. This is the first protocol with subcubic communication in this setting, but does so only against static adversaries. Using some ideas from our BC protocol, we then move to our central con- tribution and present two protocols for PBC that are secure against adap- tive adversaries. To the best of our knowledge we are the first to study PBC specifically: All previous approaches for parallel BC (PBC) naively run n instances of single-sender Broadcast, increasing the communication complexity by an undesirable factor of n. Our insight of avoiding black- box invocations of BC is particularly crucial for achieving our asymptotic improvements. In particular: 1. Our first PBC protocol achieves O(n^3κ^2) communication complexity and relies only on plain public key setup assumptions. 2. Our second PBC protocol uses trusted setup and achieves nearly optimal communication complexity O(n^2κ^4). Both PBC protocols yield an almost linear improvement over the best known solutions involving n parallel invocations of the respective BC protocols such as those of Dolev and Strong (SIAM Journal on Comput- ing, 1983) and Chan et al. (Public Key Cryptography, 2020). Central to our PBC protocols is a new problem that we define and solve, that we call “Converge”. In Converge, parties must run an adaptively-secure and efficient protocol such that by the end of the protocol, the honest parties that remain possess a superset of the union of the inputs of the initial honest parties.","url":"https://doi.org/10.60882/cispa.24614259","authors":["Tsimos, Georgios","Loss, Julian","Papamanthou, Charalampos"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.60882/cispa.24614259","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.60882/cispa.24613464.v1","name":"Constant Ciphertext-Rate Non-committing Encryption from Standard Assumptions","source":"datacite","abstract":"Non-committing encryption (NCE) is a type of public key encryption which comes with the ability to equivocate ciphertexts to encryptions of arbitrary messages, i.e., it allows one to find coins for key generation and encryption which “explain” a given ciphertext as an encryption of any message. NCE is the cornerstone to construct adaptively secure multiparty computation [Canetti et al. STOC’96] and can be seen as the quintessential notion of security for public key encryption to realize ideal communication channels. A large body of literature investigates what is the best message-to-ciphertext ratio (i.e., the rate) that one can hope to achieve for NCE. In this work we propose a near complete resolution to this question and we show how to construct NCE with constant rate in the plain model from a variety of assumptions, such as the hardness of the learning with errors (LWE), the decisional Diffie-Hellman (DDH), or the quadratic residuosity (QR) problem. Prior to our work, constructing NCE with constant rate required a trusted setup and indistinguishability obfuscation [Canetti et al. ASIACRYPT’17].","url":"https://doi.org/10.60882/cispa.24613464.v1","authors":["Brakerski, Zvika","Branco, Pedro","Döttling, Nico","Garg, Sanjam","Malavolta, Giulio"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.60882/cispa.24613464.v1","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.60882/cispa.24613464","name":"Constant Ciphertext-Rate Non-committing Encryption from Standard Assumptions","source":"datacite","abstract":"Non-committing encryption (NCE) is a type of public key encryption which comes with the ability to equivocate ciphertexts to encryptions of arbitrary messages, i.e., it allows one to find coins for key generation and encryption which “explain” a given ciphertext as an encryption of any message. NCE is the cornerstone to construct adaptively secure multiparty computation [Canetti et al. STOC’96] and can be seen as the quintessential notion of security for public key encryption to realize ideal communication channels. A large body of literature investigates what is the best message-to-ciphertext ratio (i.e., the rate) that one can hope to achieve for NCE. In this work we propose a near complete resolution to this question and we show how to construct NCE with constant rate in the plain model from a variety of assumptions, such as the hardness of the learning with errors (LWE), the decisional Diffie-Hellman (DDH), or the quadratic residuosity (QR) problem. Prior to our work, constructing NCE with constant rate required a trusted setup and indistinguishability obfuscation [Canetti et al. ASIACRYPT’17].","url":"https://doi.org/10.60882/cispa.24613464","authors":["Brakerski, Zvika","Branco, Pedro","Döttling, Nico","Garg, Sanjam","Malavolta, Giulio"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.60882/cispa.24613464","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.60882/cispa.24613023.v1","name":"Efficient UC Commitment Extension with Homomorphism for Free (and Applications)","source":"datacite","abstract":"Homomorphic universally composable (UC) commitments allow for the sender to reveal the result of additions and multiplications of values contained in commitments without revealing the values themselves while assuring the receiver of the correctness of such computation on committed values. In this work, we construct essentially optimal additively homomorphic UC commitments from any (not necessarily UC or homomorphic) extractable commitment. We obtain amortized linear computational complexity in the length of the input messages and rate 1. Next, we show how to extend our scheme to also obtain multiplicative homomorphism at the cost of asymptotic optimality but retaining low concrete complexity for practical parameters. While the previously best constructions use UC oblivious transfer as the main building block, our constructions only require extractable commitments and PRGs, achieving better concrete efficiency and offering new insights into the sufficient conditions for obtaining homomorphic UC commitments. Moreover, our techniques yield public coin protocols, which are compatible with the Fiat-Shamir heuristic. These results come at the cost of realizing a restricted version of the homomorphic commitment functionality where the sender is allowed to perform any number of commitments and operations on committed messages but is only allowed to perform a single batch opening of a number of commitments. Although this functionality seems restrictive, we show that it can be used as a building block for more efficient instantiations of recent protocols for secure multiparty computation and zero knowledge non-interactive arguments of knowledge.","url":"https://doi.org/10.60882/cispa.24613023.v1","authors":["Cascudo, Ignacio","Damgård, Ivan","David, Bernardo","Döttling, Nico","Dowsley, Rafael","Giacomelli, Irene"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.60882/cispa.24613023.v1","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.60882/cispa.24613023","name":"Efficient UC Commitment Extension with Homomorphism for Free (and Applications)","source":"datacite","abstract":"Homomorphic universally composable (UC) commitments allow for the sender to reveal the result of additions and multiplications of values contained in commitments without revealing the values themselves while assuring the receiver of the correctness of such computation on committed values. In this work, we construct essentially optimal additively homomorphic UC commitments from any (not necessarily UC or homomorphic) extractable commitment. We obtain amortized linear computational complexity in the length of the input messages and rate 1. Next, we show how to extend our scheme to also obtain multiplicative homomorphism at the cost of asymptotic optimality but retaining low concrete complexity for practical parameters. While the previously best constructions use UC oblivious transfer as the main building block, our constructions only require extractable commitments and PRGs, achieving better concrete efficiency and offering new insights into the sufficient conditions for obtaining homomorphic UC commitments. Moreover, our techniques yield public coin protocols, which are compatible with the Fiat-Shamir heuristic. These results come at the cost of realizing a restricted version of the homomorphic commitment functionality where the sender is allowed to perform any number of commitments and operations on committed messages but is only allowed to perform a single batch opening of a number of commitments. Although this functionality seems restrictive, we show that it can be used as a building block for more efficient instantiations of recent protocols for secure multiparty computation and zero knowledge non-interactive arguments of knowledge.","url":"https://doi.org/10.60882/cispa.24613023","authors":["Cascudo, Ignacio","Damgård, Ivan","David, Bernardo","Döttling, Nico","Dowsley, Rafael","Giacomelli, Irene"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.60882/cispa.24613023","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.19202486","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.19202486","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19202486","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.19200883","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.19200883","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19200883","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.11575/prism/37159","name":"Efficient Multiparty Computation from Lossy Threshold Encryption","source":"datacite","abstract":"This dissertation includes four contributions concerning secure multiparty computation. The first contribution is a new lossy threshold encryption scheme. This is the first encryption scheme that is both a lossy and a threshold encryption scheme. The second contribution is a new oblivious transfer protocol secure against erasure-free one-sided active adaptive adversaries. The third contribution is a new two-party computation protocol for the evaluation of boolean circuits that is secure against erasure-free one-sided active adaptive adversaries. As a building block of this protocol, a new cut-and-choose oblivious transfer protocol is designed. The fourth contribution is a new multiparty computation protocol for the evaluation of arithmetic circuits that is secure against covert adversaries. Protocols that are part of the second, third and fourth contributions improve the communication complexity, the number of public key encryption operations and the number of exponentiation operations over existing protocols for the same problems that provide the same or higher levels of security.","url":"https://doi.org/10.11575/prism/37159","authors":["Nargis, Isheeta"],"tags":["Security","Cryptography","Multiparty Computation","Encryption","Computer Science"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2019","doi":"10.11575/prism/37159","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.11575/prism/47960","name":"A (2 + 1)-Party One-Instruction Set Processor for Private Function Evaluation","source":"datacite","abstract":"Secure multiparty computation (MPC) is a powerful cryptographic technique which allows mutually distrusting parties to compute a function on their shared inputs. MPC can be viewed as encompassing two main categories, secure function evaluation (SFE) where the function being computed is known to all parties, but the input data is private, and private function evaluation (PFE) where one party has a private function while the other party has private data as input to the function. A major reason MPC has not seen more widespread adoption is due to the fact that to create an efficient MPC protocol for a specific function, traditionally expert cryptographers have had to hand build a custom protocol. One attempt to remedy this issue is the creation of MPC compilers which take as input code in a high-level language and output an optimized MPC protocol. Another attempt has been garbled processors which are hand optimized MPC protocols which emulate specific computer architectures. This thesis presents MPC SUBLEQ, a garbled processor designed for the PFE setting. In particular, it emulates the subtract-and-branch-if-less-than-or-equal-to-zero (SUBLEQ) one-instruction set computer (OISC). Because SUBLEQ only has a single instruction, there is no overhead cost to hide what instruction is currently being executed as there is only one instruction to execute. The data which the instruction is executing on must remain private, but the instruction itself is always known. We also test and compare MPC SUBLEQ against GC-Lite, a similar garbled processor designed for PFE using the SUBBLE OISC, a weaker version of SUBLEQ. We show that MPC SUBLEQ dramatically outperforms GC-Lite due to the significantly lower local computation and online communication costs.","url":"https://doi.org/10.11575/prism/47960","authors":["Jiang, Christopher"],"tags":["Cryptography","Secure Multiparty Computation","Private Function Evaluation","Garbled Processor","Computer Science"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.11575/prism/47960","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.25394/pgs.25676727","name":"Language-Based Techniques for Policy-Agnostic Oblivious Computation","source":"datacite","abstract":"Protecting personal information is growing increasingly important to the general public, to the point that major tech companies now advertise the privacy features of their products. Despite this, it remains challenging to implement applications that do not leak private information either directly or indirectly, through timing behavior, memory access patterns, or control flow side channels. Existing security and cryptographic techniques such as secure multiparty computation (MPC) provide solutions to privacy-preserving computation, but they can be difficult to use for non-experts and even experts.This dissertation develops the design, theory and implementation of various language-based techniques that help programmers write privacy-critical applications under a strong threat model. The proposed languages support private structured data, such as trees, that may hide their structural information and complex policies that go beyond whether a particular field of a record is private. More crucially, the approaches described in this dissertation decouple privacy and programmatic concerns, allowing programmers to implement privacy-preserving applications modularly, i.e., to independently develop application logic and independently update and audit privacy policies. Secure-by-construction applications are derived automatically by combining a standard program with a separately specified security policy.","url":"https://doi.org/10.25394/pgs.25676727","authors":["Ye, Qianchuan"],"tags":["Programming languages","Data and information privacy","Data security and protection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.25394/pgs.25676727","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.25394/pgs.25676727.v1","name":"Language-Based Techniques for Policy-Agnostic Oblivious Computation","source":"datacite","abstract":"Protecting personal information is growing increasingly important to the general public, to the point that major tech companies now advertise the privacy features of their products. Despite this, it remains challenging to implement applications that do not leak private information either directly or indirectly, through timing behavior, memory access patterns, or control flow side channels. Existing security and cryptographic techniques such as secure multiparty computation (MPC) provide solutions to privacy-preserving computation, but they can be difficult to use for non-experts and even experts.This dissertation develops the design, theory and implementation of various language-based techniques that help programmers write privacy-critical applications under a strong threat model. The proposed languages support private structured data, such as trees, that may hide their structural information and complex policies that go beyond whether a particular field of a record is private. More crucially, the approaches described in this dissertation decouple privacy and programmatic concerns, allowing programmers to implement privacy-preserving applications modularly, i.e., to independently develop application logic and independently update and audit privacy policies. Secure-by-construction applications are derived automatically by combining a standard program with a separately specified security policy.","url":"https://doi.org/10.25394/pgs.25676727.v1","authors":["Ye, Qianchuan"],"tags":["Programming languages","Data and information privacy","Data security and protection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.25394/pgs.25676727.v1","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.25394/pgs.20380170.v1","name":"Efficient Building Blocks for Secure Multiparty Computation and Their Applications","source":"datacite","abstract":"Secure multi-party computation (MPC) enables mutually distrusting parties to compute securely over their private data. It is a natural approach for building distributed applications with strong privacy guarantees, and it has been used in more and more real-world privacy-preserving solutions such as privacy-preserving machine learning, secure financial analysis, and secure auctions. The typical method of MPC is to represent the function with arithmetic circuits or binary circuits, then MPC can be applied to compute each gate privately. The practicality of secure multi-party computation (MPC) has been extensively analyzed and improved over the past decade, however, we are hitting the limits of efficiency with the traditional approaches as the circuits become more complicated. Therefore, we follow the design principle of identifying and constructing fast and provably-secure MPC protocols to evaluate useful high-level algebraic abstractions; thus, improving the efficiency of all applications relying on them. To begin with, we construct an MPC protocol to efficiently evaluate the powers of a secret value. Then we use it as a building block to form a secure mixing protocol, which can be directly used for anonymous broadcast communication. We propose two different protocols to achieve secure mixing offering different tradeoffs between local computation and communication. Meanwhile, we study the necessity of robustness and fairness in many use cases, and provide these properties to general MPC protocols. As a follow-up work in this direction, we design more efficient MPC protocols for anonymous communication through the use of permutation matrices. We provide three variants targeting different MPC frameworks and input volumes. Besides, as the core of our protocols is a secure random permutation, our protocol is of independent interest to more applications such as secure sorting and secure two-way communication. Meanwhile, we propose the solution and analysis for another useful arithmetic operation: secure multi-variable high-degree polynomial evaluation over both scalar and matrices. Secure polynomial evaluation is a basic operation in many applications including (but not limited to) privacy-preserving machine learning, secure Markov process evaluation, and non-linear function approximation. In this work, we illustrate how our protocol can be used to efficiently evaluate decision tree models, with both the client input and the tree models being private. We implement the prototypes of this idea and the benchmark shows that the polynomial evaluation becomes significantly faster and this makes the secure comparison the only bottleneck. Therefore, as a follow-up work, we design novel protocols to evaluate secure comparison efficiently with the help of pre-computed function tables. We implement and test this idea using Falcon, a state-of-the-art privacy-preserving machine learning framework and the benchmark results illustrate that we get significant performance improvement by simply replacing their secure comparison protocol with ours.","url":"https://doi.org/10.25394/pgs.20380170.v1","authors":["Lu, Donghang"],"tags":["Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.25394/pgs.20380170.v1","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.25394/pgs.20380170","name":"Efficient Building Blocks for Secure Multiparty Computation and Their Applications","source":"datacite","abstract":"Secure multi-party computation (MPC) enables mutually distrusting parties to compute securely over their private data. It is a natural approach for building distributed applications with strong privacy guarantees, and it has been used in more and more real-world privacy-preserving solutions such as privacy-preserving machine learning, secure financial analysis, and secure auctions. The typical method of MPC is to represent the function with arithmetic circuits or binary circuits, then MPC can be applied to compute each gate privately. The practicality of secure multi-party computation (MPC) has been extensively analyzed and improved over the past decade, however, we are hitting the limits of efficiency with the traditional approaches as the circuits become more complicated. Therefore, we follow the design principle of identifying and constructing fast and provably-secure MPC protocols to evaluate useful high-level algebraic abstractions; thus, improving the efficiency of all applications relying on them. To begin with, we construct an MPC protocol to efficiently evaluate the powers of a secret value. Then we use it as a building block to form a secure mixing protocol, which can be directly used for anonymous broadcast communication. We propose two different protocols to achieve secure mixing offering different tradeoffs between local computation and communication. Meanwhile, we study the necessity of robustness and fairness in many use cases, and provide these properties to general MPC protocols. As a follow-up work in this direction, we design more efficient MPC protocols for anonymous communication through the use of permutation matrices. We provide three variants targeting different MPC frameworks and input volumes. Besides, as the core of our protocols is a secure random permutation, our protocol is of independent interest to more applications such as secure sorting and secure two-way communication. Meanwhile, we propose the solution and analysis for another useful arithmetic operation: secure multi-variable high-degree polynomial evaluation over both scalar and matrices. Secure polynomial evaluation is a basic operation in many applications including (but not limited to) privacy-preserving machine learning, secure Markov process evaluation, and non-linear function approximation. In this work, we illustrate how our protocol can be used to efficiently evaluate decision tree models, with both the client input and the tree models being private. We implement the prototypes of this idea and the benchmark shows that the polynomial evaluation becomes significantly faster and this makes the secure comparison the only bottleneck. Therefore, as a follow-up work, we design novel protocols to evaluate secure comparison efficiently with the help of pre-computed function tables. We implement and test this idea using Falcon, a state-of-the-art privacy-preserving machine learning framework and the benchmark results illustrate that we get significant performance improvement by simply replacing their secure comparison protocol with ours.","url":"https://doi.org/10.25394/pgs.20380170","authors":["Lu, Donghang"],"tags":["Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.25394/pgs.20380170","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.25394/pgs.15060885.v1","name":"Efficient and Secure Equality-based Two-party Computation","source":"datacite","abstract":"Multiparty computation refers to a scenario in which multiple distinct yet connected parties aim to jointly compute a functionality. Over recent decades, with the rapid spread of the internet and digital technologies, multiparty computation has become an increasingly important topic. In addition to the integrity of computation in such scenarios, it is essential to ensure that the privacy of sensitive information is not violated. Thus, secure multiparty computation aims to provide sound approaches for the joint computation of desired functionalities in a secure manner: Not only must the integrity of computation be guaranteed, but also each party must not learn anything about the other parties' private data. In other words, each party learns no more than what can be inferred from its own input and its prescribed output. This thesis considers secure two-party computation over arithmetic circuits based on additive secret sharing. In particular, we focus on efficient and secure solutions for fundamental functionalities that depend on the equality of private comparands. The first direction we take is providing efficient protocols for two major problems of interest. Specifically, we give novel and efficient solutions for private equality testing and multiple variants of secure wildcard pattern matching over any arbitrary finite alphabet. These problems are of vital importance: Private equality testing is a basic building block in many secure multiparty protocols; and, secure pattern matching is frequently used in various data-sensitive domains, including (but not limited to) private information retrieval and healthcare-related data analysis. The second direction we take towards a performance improvement in equality-based secure two-party computation is via introducing a generic functionality-independent secure preprocessing that results in an overall computation and communication cost reduction for any subsequent protocol. We achieve this by providing the first precise functionality formulation and secure protocols for replacing original inputs with much smaller inputs such that this replacement neither changes the outcome of subsequent computations nor violates the privacy of sensitive inputs. Moreover, our input-size reduction opens the door to a new approach for efficiently solving Private Set Intersection. The protocols we give in this thesis are typically secure in the semi-honest adversarial threat model.","url":"https://doi.org/10.25394/pgs.15060885.v1","authors":["Darivandpour, Javad"],"tags":["Applied computing not elsewhere classified","Theory of computation not elsewhere classified","System and network security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.25394/pgs.15060885.v1","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.25394/pgs.15060885","name":"Efficient and Secure Equality-based Two-party Computation","source":"datacite","abstract":"Multiparty computation refers to a scenario in which multiple distinct yet connected parties aim to jointly compute a functionality. Over recent decades, with the rapid spread of the internet and digital technologies, multiparty computation has become an increasingly important topic. In addition to the integrity of computation in such scenarios, it is essential to ensure that the privacy of sensitive information is not violated. Thus, secure multiparty computation aims to provide sound approaches for the joint computation of desired functionalities in a secure manner: Not only must the integrity of computation be guaranteed, but also each party must not learn anything about the other parties' private data. In other words, each party learns no more than what can be inferred from its own input and its prescribed output. This thesis considers secure two-party computation over arithmetic circuits based on additive secret sharing. In particular, we focus on efficient and secure solutions for fundamental functionalities that depend on the equality of private comparands. The first direction we take is providing efficient protocols for two major problems of interest. Specifically, we give novel and efficient solutions for private equality testing and multiple variants of secure wildcard pattern matching over any arbitrary finite alphabet. These problems are of vital importance: Private equality testing is a basic building block in many secure multiparty protocols; and, secure pattern matching is frequently used in various data-sensitive domains, including (but not limited to) private information retrieval and healthcare-related data analysis. The second direction we take towards a performance improvement in equality-based secure two-party computation is via introducing a generic functionality-independent secure preprocessing that results in an overall computation and communication cost reduction for any subsequent protocol. We achieve this by providing the first precise functionality formulation and secure protocols for replacing original inputs with much smaller inputs such that this replacement neither changes the outcome of subsequent computations nor violates the privacy of sensitive inputs. Moreover, our input-size reduction opens the door to a new approach for efficiently solving Private Set Intersection. The protocols we give in this thesis are typically secure in the semi-honest adversarial threat model.","url":"https://doi.org/10.25394/pgs.15060885","authors":["Darivandpour, Javad"],"tags":["Applied computing not elsewhere classified","Theory of computation not elsewhere classified","System and network security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.25394/pgs.15060885","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.48550/arxiv.2509.00104","name":"Enhanced Rényi Entropy-Based Post-Quantum Key Agreement with Provable Security and Information-Theoretic Guarantees","source":"datacite","abstract":"This paper presents an enhanced post-quantum key agreement protocol based on Rényi entropy, addressing vulnerabilities in the original construction while preserving information-theoretic security properties. We develop a theoretical framework leveraging entropy-preserving operations and secret-shared verification to achieve provable security against quantum adversaries. Through entropy amplification techniques and quantum-resistant commitments, the protocol establishes $2^{128}$ quantum security guarantees under the quantum random oracle model. Key innovations include a confidentiality-preserving verification mechanism using distributed polynomial commitments, tightened min-entropy bounds with guaranteed non-negativity, and composable security proofs in the quantum universal composability framework. Unlike computational approaches, our method provides information-theoretic security without hardness assumptions while maintaining polynomial complexity. Theoretical analysis demonstrates resilience against known quantum attack vectors, including Grover-accelerated brute force and quantum memory attacks. The protocol achieves parameterization for 128-bit quantum security with efficient $\\mathcal{O}(n^{2})$ communication complexity. Extensions to secure multiparty computation and quantum network applications are established, providing a foundation for long-term cryptographic security.","url":"https://doi.org/10.48550/arxiv.2509.00104","authors":["Xu, Ruopengyu","Liu, Chenglian"],"tags":["Cryptography and Security (cs.CR)","Information Theory (cs.IT)","Quantum Physics (quant-ph)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Physical sciences","FOS: Physical sciences","E.3; K.6.5; F.1.2; F.2.1"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.00104","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.18921535","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.18921535","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18921535","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.5281/zenodo.18921288","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.18921288","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18921288","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.1184/r1/6469280","name":"Secure Multiparty Computation Based Privacy Preserving Smart Metering System","source":"datacite","abstract":"Smart metering systems provide high resolution, realtime end user power consumption data for utilities to better monitor and control the system, and for end users to better manage their energy usage and bills. However, the high resolution realtime power consumption data can also be used to extract end user activity details, which could pose a great threat to user privacy. In this work, we propose a secure multi-party computation (SMC) based privacy preserving protocol for smart meter based load management. Using SMC and a proper designed electricity plan, the utility is able to perform real time demand management with individual users, without knowing the actual value of each user's consumption data. Using homomorphic encryption, the billing is secure and verifiable. We have further implemented a demonstration system which includes a graphical user interface and simulates network communication. The demonstration shows that the proposed privacy preserving protocol is feasible for implementation on commodity IT systems.","url":"https://doi.org/10.1184/r1/6469280","authors":["Thoma, Cory","Cui, Tao","Franchetti, Franz"],"tags":["Digital processor architectures","Other information and computing sciences not elsewhere classified","Electrical engineering not elsewhere classified"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2012","doi":"10.1184/r1/6469280","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:28.180Z"},{"id":"doi:10.21203/rs.3.rs-2059591/v1","name":"TFPA: A traceable federated privacy aggregation protocol","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-2059591/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-2059591/v1","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.1101/2020.09.23.20200006","name":"FeverIQ - A Privacy-Preserving COVID-19 Symptom Tracker with 3.6 Million Reports","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2020.09.23.20200006","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1101/2020.09.23.20200006","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:29.562Z"},{"id":"doi:10.1101/2020.02.27.950592","name":"FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2020.02.27.950592","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1101/2020.02.27.950592","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:29.562Z"},{"id":"doi:10.1101/103655","name":"Revealing the causative variant in Mendelian patient genomes without revealing patient genomes","source":"preprints","abstract":"","url":"https://doi.org/10.1101/103655","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.1101/103655","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:29.562Z"},{"id":"doi:10.21203/rs.3.rs-3781993/v1","name":"Medical records condensation: a roadmap towards healthcare data democratisation","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3781993/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3781993/v1","addedAt":"2026-08-31T06:41:28.180Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1007/978-981-95-1009-2","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1009-2","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T02:23:08Z","doi":"10.1007/978-981-95-1009-2","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00031-5","name":"Title page","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00031-5","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00031-5","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-981-95-1009-2_2","name":"Machine Learning Foundations for FL","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1009-2_2","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T02:23:17Z","doi":"10.1007/978-981-95-1009-2_2","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-981-95-1009-2_7","name":"Graph Learning for FL Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1009-2_7","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T03:40:23Z","doi":"10.1007/978-981-95-1009-2_7","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026","name":"2026 2nd International Conference on Federated Learning and Intelligent Computing Systems (FLICS)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:52Z","doi":"10.1109/flics70075.2026","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621942","name":"Federated Dynamic Weighting for Heterogeneous Healthcare Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621942","authors":["Maryam Moradpour","Anne-Christin Hauschild"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:21Z","doi":"10.1109/flics70075.2026.11621942","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1002/9781394461295.fmatter","name":"Front Matter","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.fmatter","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.fmatter","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621889","name":"Impact of Post-Training and Quantization-Aware Training in Federated Learning*","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621889","authors":["Farwa Ikram","Dipanwita Thakur","Sadi Alawadi","Antonella Guzzo","Giancarlo Fortino"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:57Z","doi":"10.1109/flics70075.2026.11621889","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/s10994-025-06956-1","name":"Federated SHAP: Privacy-Preserving and Consistent Post-hoc Explainability in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10994-025-06956-1","authors":["Pietro Ducange","Francesco Marcelloni","Giustino Claudio Miglionico","Alessandro Renda","Fabrizio Ruffini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T20:49:58Z","doi":"10.1007/s10994-025-06956-1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621898","name":"SpeechLLM Meets Federated Learning for End-to-End ASR: English and Italian Case Studies","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621898","authors":["Mohamed Nabih Ali","Daniele Falavigna","Alessio Brutti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:10:04Z","doi":"10.1109/flics70075.2026.11621898","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.6040114","name":"Privacy-Preserving Federated Learning for Healthcare IoT Systems","source":"crossref","abstract":"&lt;p&gt;&lt;span&gt;The rapid adoption of Internet of Things (IoT) devices in healthcare has enabled continuous patient monitoring through wearable sensors, ECG devices, and smart medical equipment. While these technologies generate valuable real-time data for clinical decision-making, they also raise serious concerns regarding patient privacy, data security, and regulatory compliance. Traditional centralized machine learning approaches require raw patient data to be aggregated on central servers, increasing the risk of data breaches and violating privacy regulations such as HIPAA and GDPR.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;This study explores the application of federated learning (FL) as a privacy-preserving alternative for healthcare IoT data analysis. A comparative experimental framework is developed using a real-world heart disease dataset to evaluate three learning paradigms: centralized learning, federated learning without privacy enhancement, and federated learning with differential privacy. Performance is assessed using accuracy, F1-score, and training behavior across multiple communication rounds. The results demonstrate that federated learning achieves performance comparable to centralized models while significantly improving data privacy. However, the integration of differential privacy introduces a measurable trade-off between privacy guarantees and predictive accuracy.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;The findings highlight the feasibility of deploying privacy-preserving federated learning frameworks in edge-based healthcare IoT environments, offering a practical balance between data utility, security, and regulatory compliance.&lt;/span&gt;&lt;/p&gt;","url":"https://doi.org/10.2139/ssrn.6040114","authors":["Preethi N"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-26T15:57:17Z","doi":"10.2139/ssrn.6040114","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.32604/cmc.2026.085409","name":"pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.32604/cmc.2026.085409","authors":["Zhuodong Liu","Xiangyu Li","Zhihao Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-08T08:59:48Z","doi":"10.32604/cmc.2026.085409","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621909","name":"FedSALAT: Adaptive Buffer-Based Active Learning for Federated Data Streams","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621909","authors":["Prajit T Rajendran","Fabio Arnez","Huascar Espinoza","Agnes Delaborde","Chokri Mraidha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:12:51Z","doi":"10.1109/flics70075.2026.11621909","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/b978-0-44-341497-8.00022-5","name":"Generative AI with federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-341497-8.00022-5","authors":["Zhuoyu Yao","Dong Yang","Yue Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-26T13:39:59Z","doi":"10.1016/b978-0-44-341497-8.00022-5","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621958","name":"FLICS 2026 Committees","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621958","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:59Z","doi":"10.1109/flics70075.2026.11621958","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-981-95-1009-2_5","name":"FL Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1009-2_5","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T02:23:52Z","doi":"10.1007/978-981-95-1009-2_5","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1002/9781394461295.index","name":"Index","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.index","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.index","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-981-95-1009-2_8","name":"Trustworthy FL","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1009-2_8","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T02:26:44Z","doi":"10.1007/978-981-95-1009-2_8","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1145/3803630.3809167","name":"SLVR: Securely Leveraging Client Validation for Robust Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3803630.3809167","authors":["Jihye Choi","Sai Rahul Rachuri","Ke Wang","Somesh Jha","Yizhen Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-22T12:25:02Z","doi":"10.1145/3803630.3809167","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621943","name":"FLICS 2026 Copyright","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621943","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:12:25Z","doi":"10.1109/flics70075.2026.11621943","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621953","name":"Battery State of Health Estimation via Federated Learning on Diverse Data from Multiple Sources","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621953","authors":["Vesna Dimitrievska","Andrea Urgolo","Manuel Freiberger","Katrin Unger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:12:35Z","doi":"10.1109/flics70075.2026.11621953","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621908","name":"GuidaPA: Privacy-Preserving Chatbot for Public Administration via Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621908","authors":["Daniel M. Jimenez-Gutierrez","Albenzio Cirillo","Raffaele Nicolussi","Alessio Beltrame","Andrea Vitaletti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:10:36Z","doi":"10.1109/flics70075.2026.11621908","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1002/9781394461295.oth","name":"Also of Interest","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.oth","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.oth","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621925","name":"FedRecall: Gradient History Replay for Cold-Start Clients in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621925","authors":["Addi Ait-Mlouk","Omar Ait-Mlouk","Fatiha Ait Baali","Chaima Lhasnaoui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:10:09Z","doi":"10.1109/flics70075.2026.11621925","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/j.procs.2026.05.114","name":"Physics-Informed Decentralized Federated Learning (PIDFL): Integrating Domain Knowledge into Federated Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2026.05.114","authors":["Rakesh Reddy Thalakanti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-08T11:21:13Z","doi":"10.1016/j.procs.2026.05.114","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621961","name":"Leveraging Federated Learning for Predicting Kidney Graft Survival Using OPTN Dataset","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621961","authors":["Aman Wakade","Aaisha Makkar","Benjamin Hutchings","Myra Conway"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:02Z","doi":"10.1109/flics70075.2026.11621961","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1145/3803630","name":"Proceedings of the International Workshop on Secure and Efficient Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3803630","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-22T12:25:02Z","doi":"10.1145/3803630","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621948","name":"A Federated Learning-Enabled Edge-Fog-Cloud Framework for Real-time Water Monitoring in Smart Agriculture and Livestock Farming","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621948","authors":["Fatou Diop","Ibrahima Niang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:08Z","doi":"10.1109/flics70075.2026.11621948","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621918","name":"SACoaL-κ: A Semantic-Driven Coalition Consensus Algorithm for Decentralized Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621918","authors":["Francisco Enguix","Saúl Cerdá Peris","Nasim Nezhadsistani","Burkhard Stiller","Carlos Carrascosa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:13:42Z","doi":"10.1109/flics70075.2026.11621918","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/b978-0-443-36322-1.00029-8","name":"Federated learning, privacy and accuracy","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-36322-1.00029-8","authors":["Tshilidzi Marwala"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T10:57:30Z","doi":"10.1016/b978-0-443-36322-1.00029-8","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.6105426","name":"Federated Learning Approaches for Privacy-Preserving Educational Data Mining","source":"crossref","abstract":"The recent explosion in educational data produced by digital learning systems, learning management systems, and intelligent tutoring systems have presented tremendous opportunities to educational data mining (EDM) to improve learning outcomes and personalize instruction and inform decisions based on data. The core element of the centralized gathering and evaluation of sensitive student data, however, provoke genuine security, ethical, and legal issues especially when considering information protection regulations like GDPR and FERPA. Federated Learning (FL) has become an attractive paradigm of privacy-preserving educational data mining that learners can train models collaboratively across distributed educational institutions without having to transfer raw data to a central server. This paper is an in-depth analysis of federated learning methods in educational data mining, in terms of optimization of their data mining capabilities with the analytical performance, data privacy and security implications. The paper describes the basic FL architectures, such as horizontal, vertical and hybrid federated learning, and qualifies them to suit the typical EDM tasks (student performance prediction, dropout detection, learning behavior analysis and recommendation system). The main issues like data heterogeneity, communication efficiency, model convergence, and risks of privacy leaks are examined severely in the context of the educational settings. In addition, the paper also examines recent additions to federated learning, such as secure aggregation, differential privacy, and personalized federated models, and how they can enhance privacy guarantees without affecting model accuracy. This paper, by synthesising the existing knowledge and pointing at the future directions, will show that federated learning is a viable and scalable privacy preserving educational data mining, which will facilitate further growth of educational analytics without violating the rights of the learners as the data belongs to them.","url":"https://doi.org/10.2139/ssrn.6105426","authors":["Sunday Idika"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-10T11:03:47Z","doi":"10.2139/ssrn.6105426","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.5772/intechopen.1010488","name":"Federated Learning - Future of Next-Generation AI and Digital Transformation [Working Title]","source":"crossref","abstract":"","url":"https://doi.org/10.5772/intechopen.1010488","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T14:08:56Z","doi":"10.5772/intechopen.1010488","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.5891085","name":"Privacy-Preserving Federated Learning for Zero-Trust Security Enforcement","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5891085","authors":["Smith Ava"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-13T15:46:42Z","doi":"10.2139/ssrn.5891085","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621892","name":"FLICS 2026 Cover Page","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621892","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:12:56Z","doi":"10.1109/flics70075.2026.11621892","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/b978-0-44-330259-6.00022-0","name":"Quantum federated learning for speech emotion recognition","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-330259-6.00022-0","authors":["Zhiguo Qu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-13T16:13:30Z","doi":"10.1016/b978-0-44-330259-6.00022-0","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.70593/978-93-7185-127-5_4","name":"Enhancing IoT Security and Trust Management Through Blockchain-Based Federated Learing Models","source":"crossref","abstract":"","url":"https://doi.org/10.70593/978-93-7185-127-5_4","authors":["MUTHUPANDI G","Jayakumar K","Vignesh J"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-03T20:52:58Z","doi":"10.70593/978-93-7185-127-5_4","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1145/3803630.3809168","name":"On the Suitability of Federated Learning Algorithms for Intrusion Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3803630.3809168","authors":["Filip Johnsson","Zeeshan Afzal","Mikael Asplund"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-22T12:25:02Z","doi":"10.1145/3803630.3809168","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1201/9781003595540-8","name":"Federated Learning for Diabetic Retinopathy: Enhancing Detection with Data Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540-8","authors":["S.K. Susee","S. Sandhya","M. Senthil Kumar","B. Chidhambararajan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","doi":"10.1201/9781003595540-8","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-981-95-6160-5_1","name":"Introduction to Federated Learning for Smart Mobility","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-6160-5_1","authors":["Jiaming Pei","Lukun Wang","Minghui Dai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-30T11:49:58Z","doi":"10.1007/978-981-95-6160-5_1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621903","name":"Federated Robustness Analysis of Machine Learning Models for Colorectal Cancer Outcome Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621903","authors":["Fatemeh Akbarian","Narasimha Raghavan Veeraragavan","Jan F. Nygård","Amir Aminifar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:09:23Z","doi":"10.1109/flics70075.2026.11621903","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1145/3803630.3809171","name":"Safeguarding Knowledge in Federated Transfer Learning with Direction-Aware DP","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3803630.3809171","authors":["Yasas Supeksala Akurudda Liyanage Don","Thilina Ranbaduge","Ming Ding","Caslon Chua","Jun Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-22T12:25:02Z","doi":"10.1145/3803630.3809171","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1002/9781394461295.ch5","name":"Integrating Federated Learning with Satellite‐Based Geospatial Analysis for Urban Lake Management","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.ch5","authors":["Rohini Yadawar","Kh. Moirangleima","Shailendra Patni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.ch5","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.36227/techrxiv.176739864.49833843/v1","name":"FedSME: Spatial Mode Entropy Aware Aggregation for Federated Learning","source":"crossref","abstract":"Sixth generation (6G) and Industrial IoT (IIoT) networks often operate in overload regimes. In these settings, the number of devices (Ktot) far exceeds the number of base station antennas (N), that is, Ktot ≫ N. Federated learning (FL) enables distributed training across devices for AI-native radio access (AI-RAN) networks in 6G. It supports learningbased optimization of RAN functions such as beamforming, link adaptation, and dynamic resource management. However, in dense deployments, treating every device as an FL client magnifies wireless channel heterogeneity. It also intensifies the impact of non-IID data. These challenges slow convergence. They also waste communication and energy resources. To address this, client selection becomes essential for FL training. Client selection must exploit channel structure and update informativeness. Higher spatial mode entropy (SME) reflects richer spatial diversity in the uplink channel, leading to more stable and informative updates during aggregation. This motivates FedSME, an SME-aware FL aggregation framework. FedSME leverages the uplink channel eigenspace through an entropyguided scoring rule that jointly captures SME, Fisher information, and data quality, while supporting differential privacy (DP) for secure aggregation. Experiments across ten non-IID client profiles demonstrate the effectiveness of FedSME. The framework achieves 97.94% classification accuracy. It reduces communication rounds from 33 to 11 compared to FedAvg. FedSME maintains high fairness, with a Jain's index of 0.924. It also improves normalized energy efficiency by 8.15%. The framework preserves approximately linear server-side scaling with fixed antenna configurations. To the best of our knowledge, this is the first federated learning framework to exploit entropydriven channel eigenspace structure and client informativeness for scalable client selection.","url":"https://doi.org/10.36227/techrxiv.176739864.49833843/v1","authors":["Dhaarani Subramanian","Qilian Liang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-03T00:04:10Z","doi":"10.36227/techrxiv.176739864.49833843/v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.6044374","name":"Federated Learning for Grid-Edge Intelligence in Distributed Energy Systems","source":"crossref","abstract":"&lt;p&gt;&lt;span&gt;This review study comprehensively examines the role of the Federated Learning (FL) approach in the development of grid-edge intelligence within distributed energy systems. The digitalization of energy infrastructures and the increasing integration of renewable resources have introduced complex challenges, including large-scale data management, privacy preservation, and cybersecurity risks. FL offers a new paradigm by eliminating the need for centralized data aggregation, enabling model training directly on local devices while ensuring that user data remains confidential. This decentralized learning structure not only protects sensitive information but also supports real-time system responsiveness, which is increasingly important in modern power networks. This structure reduces communication overhead and minimizes exposure to external threats.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;Within the scope of this article, the architectural structures, algorithms, and communication protocols required for implementing FL in energy systems are discussed in depth. Additionally, the study evaluates how these structural components interact with distributed energy resources and edge devices under varying operating conditions. Recent application examples from the literature are analyzed to highlight the strengths and limitations of existing approaches. In the reviewed studies, FL demonstrated high accuracy and strong privacy performance in areas such as residential and industrial load forecasting, multi-energy consumption modeling, renewable energy generation forecasting, risk assessment, and cybersecurity. Particularly, FL models enhanced with blockchain or homomorphic encryption provided systems that are resilient to data manipulation and cyber-attacks.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;The findings indicate that FL enhances energy efficiency, system stability, and operational security in smart grids by reducing the need for direct data sharing. Moreover, its ability to support collaborative learning across diverse devices makes FL suitable for rapidly expanding distributed energy ecosystems. At the same time, it enables more autonomous decision-making at the grid edge. However, challenges such as client heterogeneity, communication delays, limited bandwidth, and the lack of standards remain key obstacles to large-scale adoption. Ultimately, the study reveals that personalized and energy-aware federated learning approaches may form the foundation for a sustainable, secure, and scalable transformation in grid-edge-based energy management in the future.&lt;/span&gt;&lt;/p&gt;","url":"https://doi.org/10.2139/ssrn.6044374","authors":["Serhat Isikli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T13:10:03Z","doi":"10.2139/ssrn.6044374","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.6861301","name":"Federated Reinforcement Learning-based Energy Trading Optimization in Smart Microgrids","source":"crossref","abstract":"The increasing penetration of distributed energy resources (DERs) and the decentralization of power grids necessitate robust, privacy-preserving energy trading mechanisms for smart microgrids. Traditional centralized optimization methods expose sensitive user data and suffer from single points of failure. This paper proposes a novel Federated Reinforcement Learning (FRL) framework for optimizing peer-to-peer (P2P) energy trading in smart microgrids. The framework combines the privacy-preserving properties of federated learning with the sequential decision-making capabilities of deep reinforcement learning (DRL). Using a simulated microgrid environment with 100 prosumers, we compare the proposed FRL model against three baselines: independent DRL, centralized DRL, and a rule-based trading system. Results indicate that the FRL model achieves a 23.4% reduction in overall energy costs, a 15.7% decrease in grid dependency, and maintains a privacy score of 0.96 (where 1 is perfect privacy) over 500 training rounds. The model also demonstrates robust resilience to node dropouts, maintaining 89% efficiency even when 30% of nodes are offline. These findings suggest that FRL offers a scalable, secure, and efficient solution for decentralized energy markets.","url":"https://doi.org/10.2139/ssrn.6861301","authors":["Steve Albert"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-19T06:57:49Z","doi":"10.2139/ssrn.6861301","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621955","name":"A Preliminary CoI-Based Federated Learning Platform for Artificial Intelligence of Things","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621955","authors":["Jesús Goterris","Alejandro Díaz-Rivero","Àngel Ruiz-Fas","Celia Sáenz-Martínez","Sergio Trilles"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:16:12Z","doi":"10.1109/flics70075.2026.11621955","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.6220958","name":"Privacy-Preserving Data Processing and Federated Learning in Cloud Systems","source":"crossref","abstract":"Cloud computing has transformed the way organizations store, process, and analyze data. However, the widespread adoption of cloud systems raises significant concerns regarding privacy, security, and compliance. This research investigates privacy-preserving data processing in cloud environments, with a particular focus on federated learning, which enables decentralized machine learning without transferring sensitive data to a central server. The study examines the integration of privacy-preserving mechanisms such as differential privacy, homomorphic encryption, and secure multiparty computation, and evaluates their impact on model accuracy, computational cost, and system efficiency. Key findings demonstrate that federated learning, when combined with these techniques, reduces privacy risks while maintaining high model performance. Furthermore, AI-driven orchestration and agentic frameworks provide autonomous monitoring, root cause analysis, and optimization of cloud resources, enhancing the resilience and efficiency of distributed systems. The study concludes that adopting privacy-preserving federated learning frameworks in cloud systems offers a scalable, secure, and effective solution for modern organizations processing sensitive data (Sarraf &amp;amp; Pal, 2026; Maheshkar, 2026).","url":"https://doi.org/10.2139/ssrn.6220958","authors":["Taylor Robert"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-30T16:10:23Z","doi":"10.2139/ssrn.6220958","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-032-03985-9_2","name":"Fundamentals of Federated Learning: Principles and Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_2","authors":["Wasswa Shafik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:45Z","doi":"10.1007/978-3-032-03985-9_2","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.5976334","name":"Federated Learning and Trust Fabric for Cross-Domain Network Resilience","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5976334","authors":["Omole Oreoluwa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-21T13:52:02Z","doi":"10.2139/ssrn.5976334","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621931","name":"Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621931","authors":["Pengfei Li","Mohammad Khalil"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:07Z","doi":"10.1109/flics70075.2026.11621931","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-981-95-1009-2_9","name":"Privacy Protection in FL","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1009-2_9","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T02:23:17Z","doi":"10.1007/978-981-95-1009-2_9","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-981-96-9689-5_16","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-9689-5_16","authors":["Jeremiah D. Deng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-16T09:26:26Z","doi":"10.1007/978-981-96-9689-5_16","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.6695187","name":"A Contract Theory–Driven Adaptive Incentive Mechanism for Heterogeneous Federated Learning","source":"crossref","abstract":"To address information asymmetry, weak behavioral constraints, and performance degradation under client heterogeneity in federated learning, this paper proposes a Contract Theory–Driven Adaptive Incentive Mechanism for Heterogeneous Federated Learning (CTAI-HFL). A multi-dimensional contract menu with base, performance, and loyalty incentives enables type-based self-selection and ensures incentive compatibility while suppressing opportunistic behavior via dynamic game modeling.Under a total budget constraint, a scoring pool normalizes reward allocation, and unit resource efficiency captures the contribution–cost relationship, balancing fairness and resource utilization. A blockchain-enabled architecture combining off-chain computation and on-chain verification ensures verifiability and immutability of incentive execution.Experiments show significant gains in complex heterogeneous environments. Participation disparity across capability groups is reduced from over 14% to within 0.5%. With a normalized total cost of 0.780, the method achieves 89.1% peak accuracy, improving baselines by 7.0%. Moreover, a subjective logic–based reputation mechanism enhances robustness, maintaining about 98% active client retention over 500 rounds and mitigating dropout of low-capability devices.","url":"https://doi.org/10.2139/ssrn.6695187","authors":["lin jia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-02T14:37:46Z","doi":"10.2139/ssrn.6695187","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1002/9781394461295.ch2","name":"Blockchain‐Integrated Federated Learning for Secure and Transparent Agricultural Supply Chains","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.ch2","authors":["M. Lakshmanan","A. Vegi Fernando","Mitha Guru","Sugandha Saxena","Mude Nagarjuna Naik","R. Sriramkumar","Joshuva Arockia Dhanraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.ch2","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-032-20979-5_22","name":"Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-20979-5_22","authors":["Yiran Chen","Hai Li","Huanrui Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-23T11:14:03Z","doi":"10.1007/978-3-032-20979-5_22","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.7152962","name":"Federated Learning and Privacy-Preserving Artificial Intelligence for Distributed Healthcare Applications","source":"crossref","abstract":"The proliferation of artificial intelligence in healthcare has been constrained by a fundamental tension: the need for large, diverse datasets to train robust clinical models versus the imperative to protect patient privacy and comply with stringent data protection regulations. Federated learning (FL) offers a compelling resolution to this paradox by enabling collaborative model training across distributed healthcare institutions without centralizing sensitive patient data. This article examines the convergence of federated learning with privacy-preserving artificial intelligence techniques-including differential privacy, homomorphic encryption, and secure multiparty computation-in distributed healthcare environments. Through a critical synthesis of recent empirical studies and theoretical frameworks, we analyze the architectural foundations, privacy-utility trade-offs, and governance challenges inherent in deploying these systems at scale. Our analysis reveals that while federated learning substantially reduces direct data exposure risks, it introduces novel vulnerabilities such as gradient inversion attacks and membership inference threats that demand layered cryptographic protections. Furthermore, we identify persistent gaps between proof-of-concept implementations and real-world clinical deployment, particularly regarding algorithmic fairness across heterogeneous patient populations and equitable resource distribution among participating institutions. The article concludes with policy recommendations for multi-stakeholder governance frameworks, standardized privacy auditing protocols, and regulatory harmonization to facilitate the ethical translation of privacy-preserving AI from research laboratories to clinical practice.","url":"https://doi.org/10.2139/ssrn.7152962","authors":["Patrick Emmeson"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T14:12:48Z","doi":"10.2139/ssrn.7152962","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-319-23519-6_1718-1","name":"Federated Learning for Spatio-Temporal Data","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-23519-6_1718-1","authors":["Hao Miao","Bin Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-21T04:29:09Z","doi":"10.1007/978-3-319-23519-6_1718-1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1002/9781394461295.ch14","name":"Farmer‐Centric Artificial Intelligence through Explainable Federated Learning for Smart Agriculture","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.ch14","authors":["R. Sriramkumar","Joshuva Arockia Dhanraj","M. Lakshmanan","A. Vegi Fernando","Mithaguru","Sugandha Saxena","Mude Nagarjuna Naik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.ch14","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.36227/techrxiv.177204922.22235830/v1","name":"Blockchain based Homomorphic Encryption in Federated Learning for Alzheimer Detection","source":"crossref","abstract":"Alzheimer disease is a progressive neuro degenerative condition that slowly damages the brain and affects memory and thinking. Early treatment is important to slow its progression and improve the patient's quality of life. Currently millions of people are suffering from Alzheimer and the number is increasing rapidly. Recent advances in centralized machine learning with CNN sharpened accuracy by studying MRI patterns, but centralizing sensitive medical data raises privacy and security risks amid strict laws like GDPR and HIPPA.Federated learning provides decentralized approach for collaborative training of models without exposure to raw data. Federated Learning is widely used, but is still fragile, vulnerable to attacks, and lacking trust and robust mechanisms for secure aggregation. This paper presents a novel framework combining Blockchain with Federated Learning, Homomorphic Encryption and Serialization to enhance Alzheimer Detection. Leveraging Ethereum decentralized ledger and smart contracts, the framework ensures transparency, integrity, and resistance to malicious activities during the training process. The work computed the results using Simple Federated Averaging algorithm, Federated Averaging with Homomorphic Encryption and proposed framework. The work depicts that enhanced privacy, security, and scalability for an integrated approach can be derived from this framework. Thus, apart from protecting sensitive health information, it provides a transparent, decentralized, and distributed paradigm for carrying out of medical research leading to secure collaborative innovations in diagnostics and treatment methodology with future plans to test larger datasets and advanced encryption for broader real time healthcare applications across the globe.","url":"https://doi.org/10.36227/techrxiv.177204922.22235830/v1","authors":["Shashi Ranjan","Poonguzhali N"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-25T19:53:48Z","doi":"10.36227/techrxiv.177204922.22235830/v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1016/b978-0-44-340570-9.00010-x","name":"Federated learning as a collaborative learning algorithm in 6G","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-340570-9.00010-x","authors":["Abdelmadjid Benarfa","Muhammad Hassan Khan","Mohamed Bachir Yagoubi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-05T12:32:32Z","doi":"10.1016/b978-0-44-340570-9.00010-x","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.36227/techrxiv.177203257.75819021/v1","name":"SRFed: Mitigating Poisoning Attacks in Privacy-Preserving Federated Learning with Heterogeneous Data","source":"crossref","abstract":"Federated Learning (FL) enables collaborative model training without exposing clients' private data, and has been widely adopted in privacy-sensitive scenarios. However, FL faces two critical security threats: curious servers that may launch inference attacks to reconstruct clients' private data, and compromised clients that can launch poisoning attacks to disrupt model aggregation. Existing solutions mitigate these attacks by combining mainstream privacy-preserving techniques with defensive aggregation strategies. However, they either incur high computation and communication overhead or perform poorly under non-independent and identically distributed (Non-IID) data settings. To tackle these challenges, we propose SRFed, an efficient Byzantine-robust and privacy-preserving FL framework for Non-IID scenarios. First, we design a decentralized efficient functional encryption (DEFE) scheme to support efficient model encryption and non-interactive decryption. DEFE also eliminates third-party reliance and defends against server-side inference attacks. Second, we develop a privacy-preserving defensive model aggregation mechanism based on DEFE. This mechanism filters poisonous models under Non-IID data by layer-wise projection and clusteringbased analysis. Theoretical analysis and extensive experiments show that SRFed outperforms state-of-the-art baselines in privacy protection, Byzantine robustness, and efficiency.","url":"https://doi.org/10.36227/techrxiv.177203257.75819021/v1","authors":["Yiwen Lu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-25T15:16:22Z","doi":"10.36227/techrxiv.177203257.75819021/v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-981-95-1009-2_3","name":"A Design Principle for FL","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1009-2_3","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T02:23:08Z","doi":"10.1007/978-981-95-1009-2_3","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.7094541","name":"CausalFedProto: Decoupling Federated Prototype Learning from a Causal Perspective","source":"crossref","abstract":"Federated learning has demonstrated significant value in privacy protection and collaborative training. However, data heterogeneity (domain drift) causes models to overfit domain-specific noise, severely limiting their cross-domain generalization capabilities. Although existing federated prototype learning methods reduce communication costs and adapt to heterogeneous data scenarios through prototype aggregation, they fail to fundamentally separate causal components (cross-domain stable, task-relevant) from non-causal components (domain-specific, task-irrelevant) in features. Consequently, the prototypes still carry spurious correlations, and generalization performance is constrained by feature entanglement. To address this issue, this paper proposes CausalFedProto, a novel framework that integrates causal representation learning with federated prototype learning. By purifying cross-domain stable features through a causal de-entanglement mechanism, and then combining prototype aggregation and alignment strategies, it achieves end-to-end optimization. Specifically, CausalFedProto embeds causal feature extraction heads and domain discriminators in both global and local models. It enforces statistical independence between the two types of features through Hilbert-Schmidt Independence Criterion (HSIC) constraints and adversarial training. A multi-objective optimization function is designed to integrate classification loss, causal disentanglement loss, and prototype alignment loss, thereby balancing task performance, cross-domain stability, and global consistency. Experiments on four classic cross-domain datasets demonstrate that CausalFedProto significantly outperforms mainstream methods. While maintaining low communication costs, it achieves an average accuracy improvement of up to 16.107%, providing a superior solution to the data heterogeneity problem in federated learning.","url":"https://doi.org/10.2139/ssrn.7094541","authors":["Xingwen Fang","Fenhua Bai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-10T14:43:40Z","doi":"10.2139/ssrn.7094541","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.6071349","name":"Privacy-Preserving Lightweight Federated Learning Framework for Sepsis Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.6071349","authors":["Lahiruni Chamudika Kumari Pallewela"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-26T16:01:28Z","doi":"10.2139/ssrn.6071349","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.36227/techrxiv.177220133.39408243/v1","name":"Weighted Over-the-Air Federated Learning","source":"crossref","abstract":"This paper introduces a new federated learning scheme that leverages over-the-air computation. The novel feature of this scheme is the proposal to employ adaptive weights during aggregation, as opposed to predefined weights in existing over-the-air schemes. This can mitigate the impact of wireless channel conditions on learning performance, without needing channel state information at transmitter side (CSIT). We derive convergence bound for the proposed scheme, supplemented with design insights. Accordingly, we propose an aggregation selection problem and develop an efficient algorithm to solve it, yielding optimized weights for the aggregation. Finally, through numerical experiments, we validate the effectiveness of the proposed scheme. Even with the challenges posed by channel conditions and device heterogeneity, the proposed scheme significantly surpasses other over-the-air schemes, including the one with CSIT.","url":"https://doi.org/10.36227/techrxiv.177220133.39408243/v1","authors":["Seyed Mohammad Azimi-Abarghouyi","Leandros Tassiulas","Carlo Fischione"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-27T14:09:01Z","doi":"10.36227/techrxiv.177220133.39408243/v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.18653/v1/2026.bionlp-1.78","name":"From Rules to Predictions: Federated Tabular Learning with LLM Reasoning","source":"crossref","abstract":"","url":"https://doi.org/10.18653/v1/2026.bionlp-1.78","authors":["Afsaneh Mahanipour","Hana Khamfroush"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-01T12:25:50Z","doi":"10.18653/v1/2026.bionlp-1.78","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.6695452","name":"A Novel Solving Algorithm for ELM Models in Online Sequential Learning and Federated Learning Scenarios","source":"crossref","abstract":"Extreme learning machine (ELM), a type of single-hidden-layer feedforward neural networks (SLFNs), exhibits strong generalization and rapid learning. Enhancing the learning capacity of ELM is crucial for handling large datasets and online learning tasks. To support online sequential learning and federated learning, this paper proposes a novel algorithm for solving ELM Models. First, to tackle the challenges posed by ill-conditioned problems and improve numerical stability, as well as better fit the subsequent solving procedure, a tailored QR factorization is presented in this algorithm. Second, to quickly solve the least-squares problem and avoid computation of the Moore-Penrose generalized inverse, a reduced Greville&amp;apos;s method is proposed. Moreover, to facilitate online sequential learning and federated learning, a merging algorithm is introduced for integrating two ELM models. This algorithm adeptly leverages intermediate results from both models, thereby reducing the complexity, and can also seamlessly integrate a regularization term into the model at any time. Utilizing the merging technique, an online sequential learning paradigm and an on-device federated learning framework are enabled. Experiments show the proposed approach offers better performance than existing algorithms.","url":"https://doi.org/10.2139/ssrn.6695452","authors":["Zhihong Miao","Qing He"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-02T15:43:31Z","doi":"10.2139/ssrn.6695452","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/iciscois62701.2026.11447994","name":"Quantum-Secured Federated and Lottery Federated Learning for Privacy-Preserving AI","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciscois62701.2026.11447994","authors":["Abirami B","Karthika Renuka D","Anusuya R"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-27T19:48:10Z","doi":"10.1109/iciscois62701.2026.11447994","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-032-30497-1_3","name":"Evaluation of Split Federated Learning for 6G IoT Optimization: A Comparative Study with Baseline, Federated Learning, and Split Learning Models","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-30497-1_3","authors":["Samar H. Alkayat","Ali H. Hamad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-03T05:59:11Z","doi":"10.1007/978-3-032-30497-1_3","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1201/9781003660330-11","name":"Next-gen and autonomous federated systems","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003660330-11","authors":["K. S. Divya","K. Soumya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T15:39:21Z","doi":"10.1201/9781003660330-11","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/b978-0-44-340570-9.00012-3","name":"Split federated learning: an enhanced collaborative learning algorithm for ResourceLimited 6G contexts","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-340570-9.00012-3","authors":["Lakhdar Kamel Ouladdjedid","Mohammed Kamel Benhaoua"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-05T12:32:32Z","doi":"10.1016/b978-0-44-340570-9.00012-3","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.6064812","name":"Value-Driven Federated Continual Learning Without Predefined Tasks","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.6064812","authors":["Zhigang Zhou","Hao Li","Li Yi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-12T16:42:39Z","doi":"10.2139/ssrn.6064812","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-032-03985-9_32","name":"Future Trends in Federated Learning for Next-Generation Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_32","authors":["Wasswa Shafik","Ssentumbwe Male Abdul"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:49Z","doi":"10.1007/978-3-032-03985-9_32","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.6665261","name":"Privacy-Preserving Federated Learning for Feeder-Level Outage Prediction in Distribution Grids","source":"crossref","abstract":"Here is the problem in one sentence: European and Middle Eastern power utilities hold exactly the data needed to predict grid failures, but sharing it between organisations is-in most jurisdictions-illegal. GDPR, energy market confidentiality rules, and Gulf data sovereignty frameworks all block the kind of cross-utility data pooling that machine learning normally requires. So each utility trains its own model on its own narrow slice of operational history, and the models are, predictably, weak. collapsed to 0.38-0.39. We have an explanation for why, and a proposed fix, detailed in section 6. The short version: regulatory constraints that look like blockers can, if you design around them properly, end up being irrelevant to model quality.","url":"https://doi.org/10.2139/ssrn.6665261","authors":["Zishan khan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-28T07:51:42Z","doi":"10.2139/ssrn.6665261","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-032-03985-9_16","name":"Addressing Computational Overhead in Federated Learning Models in Healthcare 5.0 and Beyond","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_16","authors":["Wasswa Shafik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:52Z","doi":"10.1007/978-3-032-03985-9_16","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-981-95-1009-2_10","name":"Cybersecurity in FL: Attacks and Defenses","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1009-2_10","authors":["Alexander Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T02:29:15Z","doi":"10.1007/978-981-95-1009-2_10","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621956","name":"Eigenstructure-Based Traceability Mechanisms for Federated Machine Learned Operators and Triggers","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621956","authors":["Keith R. Tinsley","Douglas R. Crocker","Wenzheng Sun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:12:20Z","doi":"10.1109/flics70075.2026.11621956","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/b978-0-44-330259-6.00010-4","name":"Exploring quantum federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-330259-6.00010-4","authors":["Nouhaila Innan","Alberto Marchisio","Mohamed Bennai","Muhammad Shafique"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-13T16:13:30Z","doi":"10.1016/b978-0-44-330259-6.00010-4","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/b978-0-443-44958-1.00012-4","name":"Federated learning and machine learning for the detection of heart diseases","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-44958-1.00012-4","authors":["Gaurav Kumar","Swati Sah","R. Shyam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-10T10:13:51Z","doi":"10.1016/b978-0-443-44958-1.00012-4","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1007/978-3-032-03985-9_4","name":"Regulatory Frameworks: HIPAA, GDPR, and Compliance in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_4","authors":["Wasswa Shafik","Mussa Saidi Abubakari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:49Z","doi":"10.1007/978-3-032-03985-9_4","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1109/flics70075.2026.11621905","name":"Applying federated population-based hyperparameter optimization to server aggregation methods","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621905","authors":["Claudia Großer","Mark Buckley","Denis Krompaß","Thomas A. Runkler"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:09:28Z","doi":"10.1109/flics70075.2026.11621905","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/j.neucom.2026.134270","name":"Relevance-aware parameter distillation for communication-efficient federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.neucom.2026.134270","authors":["Giovanni Paragliola"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T23:56:27Z","doi":"10.1016/j.neucom.2026.134270","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00001-7","name":"Virtual clinical and hospital in India","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00001-7","authors":["Himel Mondal","Shaikat Mondal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00001-7","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:28.652Z"},{"id":"doi:10.2139/ssrn.6861358","name":"Privacy-preserving Energy Management in Microgrid Trading Networks using Federated Reinforcement Learning","source":"crossref","abstract":"The integration of distributed energy resources (DERs) into microgrid trading networks introduces significant privacy and security challenges for traditional centralized energy management systems. This paper proposes a novel framework that combines federated learning (FL) with reinforcement learning (RL) to enable privacy-preserving, decentralized energy trading among microgrid participants. Unlike conventional approaches that require sharing raw consumption and production data with a central coordinator, our federated reinforcement learning (FedRL) architecture allows local agents to learn optimal bidding and scheduling policies while sharing only encrypted model updates. Using a simulated microgrid network of 50 prosumers over 12 months, we demonstrate that FedRL achieves 94.2% of the efficiency of a centralized RL oracle while reducing data exposure by over 99%. Our results indicate convergence within 150 communication rounds, with an average privacy leakage score of 0.018 (scale 0-1). The framework shows robust performance under non-independent and identically distributed (non-IID) data distributions and intermittent communication failures. These findings suggest that FedRL offers a viable path toward scalable, privacy-compliant energy markets aligned with GDPR and emerging data protection regulations.","url":"https://doi.org/10.2139/ssrn.6861358","authors":["Steve Albert"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-19T07:42:47Z","doi":"10.2139/ssrn.6861358","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1371/journal.pone.0355604","name":"FRLTC: A new topology control algorithm using federated reinforcement learning for software-defined wireless sensor network in IoT.","source":"pubmed","abstract":"Topology is one of the most important factors influencing the performance of software-defined wireless sensor networks (SDWSN) in the Internet of Things (IoT). This paper presents a novel topology control algorithm using federated reinforcement learning, proposed for SDWSN in the IoT. Our approach represents the SDWSN as a federated learning system in which each sensor node runs a reinforcement learning model to change its communication range such that the node degree approaches the desired value. Another reinforcement learning model was applied to the SDN controller to assign the sensor node for learning, bringing the average degree of the entire network closer to the desired value. Simulation results reveal that the proposed approach outperforms well-known topology control algorithms in terms of the desired node degree, energy consumption, and quality of transmission.","url":"https://doi.org/10.1371/journal.pone.0355604","authors":["Binh LH","T Duong TV","Le DH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0355604","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3390/diagnostics16162637","name":"FedSwin-LHTP: Structure-Aware Hessian-Inspired Token Pruning for Efficient Federated Skin Lesion Classification.","source":"pubmed","abstract":"Background: Skin cancer encompasses a diverse range of malignancies and remains a significant global health challenge. Accurate machine-learning-assisted diagnosis can substantially improve patient outcomes through early detection and timely clinical intervention. Federated Learning (FL) enables privacy-preserving collaborative model training across multiple healthcare institutions while ensuring that sensitive patient data remain decentralized. However, deploying advanced architectures such as Vision Transformers (ViTs) in clinical environments is challenging due to the high computational demands of self-attention mechanisms. Methods: This work proposes FedSwin-LHTP, an efficient federated learning framework for skin lesion classification that integrates a Swin Transformer backbone with a Lightweight Hessian-Inspired Token Pruning (LHTP) mechanism. LHTP estimates token importance using a second-order Taylor approximation around converged local model parameters to identify less informative patch tokens, enabling the early pruning of redundant representations without explicitly computing the Hessian matrix. Furthermore, the framework incorporates the FedProx optimization objective to mitigate client drift under heterogeneous non-IID data distributions. The proposed framework is evaluated on the HAM10000 and ISIC datasets under realistic non-IID federated settings. Results: Experimental results demonstrate stable convergence, effective knowledge aggregation, and robust diagnostic discrimination across distributed clients. By adaptively pruning approximately 60% of Stage-1 tokens, the proposed framework substantially reduces the computational burden of local transformer processing while maintaining high multiclass classification performance, achieving an accuracy of up to 96.1% on the evaluated datasets. Conclusions: These results highlight the potential of FedSwin-LHTP as a practical, privacy-preserving, and resource-efficient solution for collaborative healthcare intelligence.","url":"https://doi.org/10.3390/diagnostics16162637","authors":["Awais M","Junejo RH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/diagnostics16162637","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1002/mco2.70944","name":"Multicenter Privacy-Preserving Federated Models for Predicting Postoperative Delirium and Acute Kidney Injury in Older Patients.","source":"pubmed","abstract":"Postoperative delirium (POD) and acute kidney injury (AKI) are serious complications in older patients undergoing surgery, yet predictive model development is often constrained by single-center data limitations and privacy concerns that preclude centralized data sharing. To address these challenges, we retrospectively evaluated a simulated federated learning (FL) framework using multicenter datasets partitioned by hospital source, without sharing raw patient data across centers. A total of 7,216 non-cardiac, non-neurosurgical patients aged 65 years or older were included across five centers, with four contributing training data and one serving as an external validation site. Using a multilayer perceptron architecture, we implemented three federated algorithms and benchmarked them against local learning models (LLMs) and centralized learning models (CLMs). For POD, federated learning models (FLMs) achieved internal area under the curve (AUC) values of 0.725-0.726 and external AUCs of 0.700-0.701. For AKI, internal AUCs reached 0.780 and external AUCs ranged from 0.740 to 0.741. FLM performance was statistically comparable to CLMs ( p &gt; 0.05). These findings support federated learning as a feasible privacy-preserving strategy that achieved discrimination comparable to centralized learning for multicenter prediction of postoperative complications in older patients.","url":"https://doi.org/10.1002/mco2.70944","authors":["Wang Q","Song YX","Yang XD","Zhang JW","Sun RZ","Wu XD","Li A","Lou JS","Li H","Liu YH","Xu JM","Wang DF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/mco2.70944","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3390/e28080924","name":"Federated Quantum Machine Learning over Satellite Networks: Toward Scalable Distributed Quantum Classification.","source":"pubmed","abstract":"Federated learning enables multiple organizations to collaboratively analyze data while retaining local control over their datasets, making it attractive for applications such as healthcare. Distributed quantum computing provides a natural framework for such workflows, allowing geographically separated quantum processors to execute joint computations using shared entanglement while preserving data privacy. In this work, we investigate the feasibility of satellite-enabled distributed quantum computing for federated quantum learning. As a representative application, we consider a distributed distance-based quantum classifier in which multiple parties contribute local data through quantum operations. To support this application, we develop a hybrid space-ground quantum network architecture in which satellites distribute entanglement between distant ground stations. The communication layer is combined with a noise-aware neutral-atom processor model, enabling a system-level analysis that captures both network and hardware constraints. Simulation results across varying network sizes, feature dimensions, and coherence regimes show that classifier performance is jointly determined by communication resources, processor noise, and data representation. In low-coherence regimes, decoherence destroys the classifier's discriminative signal, whereas high-coherence regimes reveal limitations arising from feature-space conditioning and feature redundancy. These results demonstrate that satellite quantum networks could support distributed quantum learning over long distances, while highlighting the importance of coherence time, entanglement-distribution performance, and learning-aware data encoding for future large-scale deployments.","url":"https://doi.org/10.3390/e28080924","authors":["Boschero JC","Oancea RA","Mazzarella L","Doeleman H","Cramer S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/e28080924","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3390/s26165288","name":"A Federated Machine Learning Approach for the Detection and Visualisation of Eye Diseases Using Activation Maps.","source":"pubmed","abstract":"Eye diseases, particularly glaucoma and cataracts, are the leading causes of visual impairment, compromising quality of life. Automatic classification of these diseases can lead to more accurate and timely diagnosis, thereby limiting their progression and complications. In recent years, advances in Deep Learning (DL) have shown promising results in the study of images in ophthalmology. However, traditional DL models are based on a centralised approach to data, sharing sensitive patient information and compromising privacy; moreover, the models are often difficult to interpret. In this paper we propose a method aimed to solve the issues of privacy and transparency in decision-making related to eye diseases detection and localisation. As a matter of fact, we consider Federated Learning (FL), an approach based on data decentralisation that enables collaborative learning between different clients and sends only the model weights to the central server. In this way, sensitive patient data are not shared, ensuring security and privacy. With regard to eye disease classification we exploit a Vision Transformer, which allows global relationships within retinal images to be highlighted, improving representation capabilities compared to traditional convolutional architectures. Furthermore, the proposed method also aims to make the model explainable using explainability techniques, in this way we make diagnostic decisions transparent. The experimental analysis shows an accuracy of 0.8480, a precision of 0.8645, a recall of 0.8477, showing the effectiveness of the proposed method on eye disease detection.","url":"https://doi.org/10.3390/s26165288","authors":["Niro F","Renzo MD","Agnello P","Petyx M","Martinelli F","Maddalena M","Cesarelli M","Santone A","Mercaldo F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26165288","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1038/s41598-026-63727-1","name":"Seizure detection using hierarchical temporal trend integration and self-supervised learning.","source":"pubmed","abstract":"Early, reliable detection of epileptic seizures from electroencephalography (EEG) remains challenging due to label scarcity, inter patient variability, and the non stationary nature of clinical recordings. This work introduces Federated Temporal Learning (FTL), an annotation efficient framework that couples a supervised seizure classifier with a self supervised pretext task, Temporal Sequence Consistency Learning (TSCLM), over a shared backbone. The backbone is built from Hierarchical Temporal Trend Integration Modules (HTTIMs) that extract and top down mix multi scale long and short term trends; a Temporal Self Supervision Head (TSSH) learns order relations between subsequences to inject temporal positional awareness without manual labels. Training optimizes a joint objective with a mixing coefficient &#x3b4; and split ratio &#x3c4;; at inference the self supervised head is discarded, yielding a purely supervised predictor. We theoretically analyze the algorithmic stability of our algorithm, and indeed, provide a bound on the gap between the empirical and the expected risk under the semi supervised objective, thereby clarifying the interplay between more labeled data and well conditioned optimization. From an empirical perspective, it also performs seamlessly at state of the art (SoA) on two datasets: TUSZ v2.0.1 (accuracy 99.16%, precision 99.19%, recall 99.65% and F1 macro 97.17%), Zenodo NICU EEG (accuracy 95.61%, precision 96.78%, recall 94.67% and F1 macro 96.87%). The results suggest that order aware self supervision in conjunction with hierarchal trend integration can be a viable solution to the label efficient and clinically reliable, EEG seizure detection task. The proposed approach has the advantage of explicitly modelling multi-scale temporal patterns by adopting a top-down strategy, using Hierarchical Temporal Trend Integration Modules (HTTIM), which is not used in existing approaches. In addition, Temporal Sequence Consistency Learning (TSCLM) is designed as an order-aware self-supervised task to capture temporal relationships without labels. The integration of these components within the Federated Temporal Learning (FTL) framework enables improved performance under limited annotated data.","url":"https://doi.org/10.1038/s41598-026-63727-1","authors":["R NA","K SB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-63727-1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.21203/rs.3.rs-10580856/v1","name":"Federated Hybrid Deep Learning Framework for IoT Intrusion Detection Using N-BaIoT, CICIoT2023, and BoT-IoT Datasets","source":"europepmc","abstract":"Abstract Internet of Things (IoT) environments expose many heterogeneous and resource-constrained devices to botnet, distributed denial-of-service, denial-of-service, reconnaissance, spoofing, malware, and service-scanning attacks. Centralized intrusion detection can achieve strong accuracy when all traffic is pooled, but it creates privacy, bandwidth, and deployment barriers for edge networks. This manuscript presents an end-to-end federated hybrid deep learning framework for IoT intrusion detection using N-BaIoT, CICIoT2023, and BoT-IoT data sources. The implemented workflow performs Phase-1 cleaning, encoding, train/validation/test construction, feature selection, class-imbalance handling, VAE-ready export, and IID/non-IID client preparation, followed by Phase-2 individual dataset training. The Phase-2 architecture uses CNN, LSTM, BiLSTM, attention, Tiny Transformer, Random Forest, XGBoost, and hybrid deep-feature XGBoost models. The completed evaluation shows strong detection performance on N-BaIoT and BoT-IoT and highlights CICIoT2023 as the most challenging multiclass setting because it contains 35 attack/traffic classes. These results provide a verified basis for the next federated aggregation and anomaly extension stages.","url":"https://doi.org/10.21203/rs.3.rs-10580856/v1","authors":["Sonali Mishra^","R. Venkata Siva"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10580856/v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3390/s26165209","name":"Dual-Layer PSO-Enhanced Federated Heterogeneous Data Fusion for Hemodialysis Complication Prediction.","source":"pubmed","abstract":"Taiwan has one of the highest dialysis prevalences worldwide, making safe and reliable hemodialysis monitoring a critical sensor-based healthcare challenge. Modern hemodialysis machines integrate heterogeneous multimodal sensors (pressure, flow, conductivity, temperature, and cardiovascular signals), but differences in machine brands, data formats, and privacy constraints hinder centralized learning and robust complication prediction. This work proposes a Medical IoT-oriented federated learning framework, PSOFed-HD, that performs dual-layer Particle Swarm Optimization (PSO) to enhance heterogeneous sensor fusion for predicting dialysis-related hypotension and discomfort events. The events are defined as abnormal blood-pressure states, defined as systolic blood pressure &lt;90 mmHg. Each hemodialysis machine is paired with an edge gateway acting as an FL client, where local PSO optimizes CNN feature weights over non-IID sensor subsets, while the central server applies PSO-driven aggregation to adaptively weight client models according to validation performance. Experiments on real-world hemodialysis datasets with 17 most commonly seen HD physiological features demonstrate that standard FedAvg yields an accuracy of 65.24% and F1-score of 0.5318, server-side PSO improves accuracy to 75.11%, and client-side PSO further raises accuracy to 81.97%. The proposed dual-layer PSO framework achieves the best performance, with 90.56% accuracy and an F1-score of 0.8533, along with superior ROC characteristics (AUC = 0.908) and stable cross-validation across 11 folds. State-of-the-art federated learning techniques for non-IID data such as SCAFFOLD and FedProx are also examined using the same heterogeneous HD dataset. The performance is close to our client-only PSO techniques, proving the merits of our dual-layer PSO architecture. These results confirm that jointly optimizing local feature representations and global aggregation weights enables effective fusion of heterogeneous hemodialysis sensor data under privacy-preserving Medical IoT constraints, providing a practical decision-support approach for real-time complication prediction in dialysis units. Future work will incorporate temporal models such as LSTM or Transformer architectures to achieve early event prediction.","url":"https://doi.org/10.3390/s26165209","authors":["Shih C","Chen CH","Yeah X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26165209","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.21203/rs.3.rs-10527748/v1","name":"A Federated Graph-Based Intrusion Detection Framework for Unknown Attack Detection in Internet of Medical Things(IoMT)","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10527748/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10527748/v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3389/fradi.2026.1829701","name":"FedAD: adaptive federated disentanglement for domain generalization in unlabeled medical imaging.","source":"pubmed","abstract":"Three barriers significantly hinder the use of deep learning in medical imaging: poor generalization to new clinical domain shifts, label scarcity, and data privacy. A unified framework that learns from unlabeled, decentralized data while optimizing for generalization is desperately needed, even if Federated Learning (FL), Self-Supervised Learning (SSL), and Domain Generalization (DG) provide partial solutions that often operate under contradictory assumptions. We present FedAD: Adaptive Federated Disentanglement, a unique framework that uses two key ideas to handle these problems in a synergistic way. First, a federated semantic disentanglement objective (FedSD) explicitly distinguishes between the domain-invariant semantic characteristics and domain-specific variants using a non-adversarial orthogonality constraint. Second, in order to prevent premature convergence and enhance resilience, an adaptive teacher-student alignment (ATSA) curriculum dynamically modifies the generalization pressure based on the stability of the global model. This dual technique creates a strong feature encoder by forcing the model to learn what it sees as opposed to where it sees it. FedAD outperforms current approaches in terms of generalization to unseen target domains, as demonstrated by its validation on publicly available medical datasets. Our strategy concurrently addresses privacy, label scarcity, and domain change, paving the road for useful, reliable, and fair medical AI.","url":"https://doi.org/10.3389/fradi.2026.1829701","authors":["Kodhandaraman S","K H","Prabhakar A","Kempapuram S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fradi.2026.1829701","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/j.media.2026.104264","name":"SFLFEM: A frequency-enhanced mamba framework for selective personalized federated breast ultrasound diagnosis.","source":"pubmed","abstract":"Breast ultrasound is essential for lesion assessment and early cancer screening. However, deploying robust deep learning models in clinical practice faces two primary challenges. The first is intrinsic: lesions often exhibit complex textures, speckle-corrupted appearances, and indistinct boundaries. The second is extrinsic: the data is distributed across isolated medical centers with significant heterogeneity. To address these coupled challenges, this study introduces SFLFEM, a unified framework that combines advanced feature representation with personalized federated optimization for privacy-preserving multi-center breast ultrasound diagnosis. FEMamba, a dual-domain backbone, integrates the long-range modeling capacity of State-Space Models with a wavelet-based frequency pathway. This architecture captures global contextual dependencies as well as frequency-sensitive boundary and textural cues. Both are critical for distinguishing challenging lesions. At the optimization level, the pFedBM algorithm introduces a Bidirectional Gradient Masking mechanism to mitigate client heterogeneity and cross-site distribution shift in federated learning. This approach adaptively separates model parameters into globally shared and locally preserved components. Locally sensitive shared parameters are promoted into personalized subspaces, and weakly client-specific parameters are demoted back to the shared global subspace. This design reduces negative transfer and improves site-specific adaptation. Comprehensive evaluations on four datasets demonstrate that SFLFEM achieves strong overall performance, with an average accuracy of 85.14%, AUC of 92.35%, and MCC of 70.62%. It achieves competitive performance against representative centralized classification backbones and consistently improves over the evaluated federated learning baselines. The largest gains of pFedBM were observed under heterogeneous and data-limited settings.","url":"https://doi.org/10.1016/j.media.2026.104264","authors":["He X","Xu X","Xiao F","Ma J","Cai R","Cao L","Liu J","Yan Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.media.2026.104264","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1038/s41598-026-51217-3","name":"Real-time PEM fuel cell fault classification in smart manufacturing plant by using uncertainty aware federated deep learning.","source":"pubmed","abstract":"The Proton Exchange Membrane Fuel Cells (PEMFCs) is not the least significant component of the modern industrial, yet the reliable operation of the smart manufacturing environment cannot be guaranteed since the breakdowns of PEMFCs are multiple and highly complex in character. The article describes the UFDL-Edge, an uncertainty-based federated deep learning model that was created to detect the defects of PEMFC in real-time through an IIoT-based manufacturing plant. The objective of the proposed architecture is to offer the adaptive feature detector and an open-ended model evolution without compromising the privacy of the data in the distributed manufacturing locales through integrating uncertainty-aware feature detectors (UFD) with temporal Hadoop Distributed File System (HDFS)-based streaming. It employs a hierarchical and federated learning approach to obtain a combination of the advantages of Byzantine-resistant aggregation and uncertainty quantification to enhance the resilience of fault-detection in a variety of operating conditions. The 10.3% improvement in F1-score on the initial anomaly detection, 23.7% reduction in false positive ratio, and less than 100ms latency of real-time diagnostics obtained with huge-scale experimentation in three testbeds of smart manufacturing are better results. Foot printing framework eliminates drastic challenges such as cold start issue, water flooding, thermal controller malfunction and catalyst degradation as well as data sovereignty across manufacturing plants used.","url":"https://doi.org/10.1038/s41598-026-51217-3","authors":["Aurangzeb M","Shusheng X","Iqbal S","Shafiullah M","Qazi HS","Dafalla AM","Fuguang Z","Zhuoyu L","Xinwei L","Nazir S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-51217-3","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3389/frai.2026.1856630","name":"Federated spatio-temporal graph neural network for privacy-preserving vehicle trajectory prediction in autonomous driving.","source":"pubmed","abstract":"Accurate vehicle trajectory prediction plays a vital role in autonomous driving and intelligent transport systems. Deep learning models like LSTM, CNN, GNN, etc., have shown remarkable performance but often operate in a centralized setting, aggregating raw trajectory data at the server. Furthermore, the majority of models focus on either spatial or temporal features alone, but overlook the information that can be obtained by combining spatio-temporal features. This leads to major privacy concerns, issues with centralized data, and scalability problems. To overcome these challenges, we introduce a Federated Spatio-Temporal Synchronous Dynamic Graph Neural Network (Federated STSDGNN) framework for privacy-preserving and adaptive trajectory prediction. Using the highD dataset, trajectories are segmented into spatiotemporal sequences and represented as dynamic interaction graphs. Each client (vehicle or roadside unit) locally trains an STSDGNN consisting of a pre-processing module, a spatial-temporal synchronization module (GCN/GAT with GRU) and a prediction module (CNN with MLP). Clients send only model updates, which are aggregated by the server using a federated learning algorithm. This design improves privacy, achieves approximately 23.5% lower RMSE compared to the centralized STSDGNN baseline in the conducted experiments, and has the potential to support future privacy-preserving V2X (Vehicle-to-Everything) applications.","url":"https://doi.org/10.3389/frai.2026.1856630","authors":["Joshi A","P A","Prajapati V","Anbalagan S","G S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1856630","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3389/fonc.2026.1841558","name":"Smart FL: meta-learning for federated blood marrow smear classification.","source":"pubmed","abstract":"Accurate classification of bone marrow cells is crucial for diagnosing hematological disorders, yet integrating machine learning and deep learning models into clinical practice faces hurdles like data privacy and data scarcity. Existing models often lack the adaptability that is important in the medical field, limiting their practicality. This study introduces an approach using federated learning and meta-learning to tackle these challenges. The objectives for this study were multifaceted. Firstly, it address the adverse effects of data scarcity commonly found in medical datasets due to which models are unable to predict classes which do not have enough examples. Secondly, this study tackles the privacy concerns by adopting a federated learning framework, allowing model training without centralizing sensitive data. Additionally, the goal is to make the federated process personalized to each client for enhancing individual accuracy. Lastly, this study seeks to improve the generalization capabilities of the models, enabling robust performance across diverse patient populations. The proposed approach involves leveraging a ResNet-18 backbone for the meta-learning algorithm, specifically one based on prototypical networks. This framework is then implemented and tested in a federated manner using FedAvg across four clients, each possessing their own data. The main focus of this research is to achieve high accuracy, particularly in unseen classes with limited samples. This approach yields promising results, achieving an accuracy of 96&#xa0;&#xb1;&#xa0;1% in classes with low sample sizes. Through this approach, hospitals, clinics, and researchers can make sure that their data remains private while being able to benefit from deep learning research.","url":"https://doi.org/10.3389/fonc.2026.1841558","authors":["Ilakiyaselvan N","Sanskar S","Aarthi D","Kalyanasundaram V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1841558","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/tnnls.2026.3652123","name":"A Linearized Alternating Direction Multiplier Method for Federated Matrix Completion Problems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2026.3652123","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tnnls.2026.3652123","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3389/fdgth.2026.1900115","name":"Privacy-preserving adaptive arrhythmia monitoring with wearable ECG, smartphone PPG, and federated on-device learning.","source":"pubmed","abstract":"Wearable electrocardiography (ECG) and smartphone technology create new opportunities for arrhythmia monitoring outside clinical environments. However, deployable mobile cardiac systems must address more than just classification accuracy. They must reliably acquire physiological signals under real-world conditions, protect the privacy of sensitive cardiac data, adapt to user- and sensor-specific variability, and minimize false alarms in daily use. We present Cardio-FL, a smartphone-based digital health prototype for privacy-preserving, adaptive arrhythmia monitoring.","url":"https://doi.org/10.3389/fdgth.2026.1900115","authors":["Kartali A","Koshtrevska M","Jokić S","Gligorić N","Machidon OM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1900115","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/j.cmpb.2026.109596","name":"EquiFL-X: Communication-constrained federated calibration via label-aggregated histograms for multi-view screening mammography under acquisition shift.","source":"pubmed","abstract":"Reliable probability estimates and robust performance across heterogeneous acquisition settings are important for artificial intelligence-assisted screening mammography. In federated learning, acquisition-driven non-identically distributed data can impair both discrimination and calibration across clients. This study aimed to develop a federated multi-view mammography framework that improves worst-client robustness and probability calibration while avoiding calibration procedures that require sharing instance-level prediction-label pairs.","url":"https://doi.org/10.1016/j.cmpb.2026.109596","authors":["Öney B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.cmpb.2026.109596","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1038/s41598-026-60574-y","name":"APEW-Fed: adaptive privacy-aware ensemble weighting for federated anomaly detection in enterprise resource planning systems.","source":"pubmed","abstract":"Enterprise Resource Planning&#xa0;(ERP) systems are high-value targets for fraud owing to the sensitive financial and operational data they process. Anomaly detection in such systems faces a fundamental tension: centralising data for accurate detection conflicts with privacy regulations such as the European Union's General Data Protection Regulation&#xa0;(GDPR) and the United States' Sarbanes-Oxley Act&#xa0;(SOX). We present APEW-Fed, a federated ensemble framework that resolves this tension through three provably complementary mechanisms. First, Adaptive Privacy-Aware Ensemble Weighting&#xa0;(APEW) frames model fusion as a privacy-penalised optimisation problem and derives closed-form dynamic weights that up-weight privacy-efficient, high-confidence detectors. Second, Feature-Sensitivity Calibrated Differential Privacy&#xa0;(FS-CDP) partitions features into sensitivity tiers and allocates per-tier Gaussian noise budgets that provably minimise total noise variance subject to a global R&#xe9;nyi&#xa0;DP constraint. Third, Federated Anomaly Score Calibration&#xa0;(FASC) reconciles heterogeneous score distributions across clients via differentially private quantile sketches under secure aggregation. Integrating Isolation Forest, a federated autoencoder trained with per-step DP-SGD, adaptive DBSCAN, and gradient boosting, we evaluate on 243,531 real-world ERP transactions with 5000 expert-annotated anomalies and validate on two public benchmarks: the IEEE-CIS Fraud Detection dataset and the NSL-KDD network intrusion dataset. APEW-Fed achieves 93.5% F1-score at [Formula: see text] RDP-only 2.7 percentage points below the non-private centralised baseline-while reducing communication cost by 94% versus FedAvg. On a strictly unbiased uniform-only evaluation subset (2500 transactions, no score-based enrichment), APEW-Fed achieves 92.1% F1, confirming that the headline result is not an artefact of the annotation sampling design. Membership inference attack success is 51.2%, near-random guessing, confirming strong empirical privacy.","url":"https://doi.org/10.1038/s41598-026-60574-y","authors":["Qazi A","Ali Khan Jadoon A","Arshad S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-60574-y","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.21203/rs.3.rs-10706047/v1","name":"Federated Scientific Machine Learning Framework for Multi-Scale Inverse Parameter Identification: Application to Nonlocal Strain Gradient Plasticity","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10706047/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10706047/v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-53339-0","name":"Integrated design of an efficient multi spectral imaging and federated learning framework for precision crop disease diagnosis in low-resource farming communities.","source":"pubmed","abstract":"Crop diseases pose significant challenges to productivity in resource-constrained settings, often remaining undiagnosed when diagnostic tools and infrastructure are either non-existent or inadequate. Current crop disease diagnosis relies on manual inspection methods that are labor-intensive, prone to error, and incapable of delivering real-time or region-specific insights in the process. Such limitations call for developing advanced diagnostic systems that are scalable and efficient in resource-constrained settings. This research introduced a comprehensive multi-spectral imaging and machine learning framework that can easily revolutionize the disease diagnosis and management inside the low-resource farming communities. Built within its core is the 3D Spectral-Spatial Convolutional Neural Network (3D SSCNN) that extracts high-resolution spectral-spatial features from hyperspectral image cubes. The accuracy achieved is around ~&#x2009;95% within 0.3&#xa0;s per sample. Fed-DiagNet has provided support for distributed training that enables scalability and also data privacy to enhance the accuracy of regional models at approximately 92% as well as reduces training by almost 40%. Temporal disease progression modeling is enabled by Temporal Progression LSTM that provides dynamic trends with 90% accuracy up to a horizon of 10 days. This means that in addition to integrating disparate data sources-including hyperspectral imagery, environmental data, and pest observations-MTAN achieves an almost&#x2009;~&#x2009;93% stress identification accuracy. Lastly, an RL-FO system tailors its treatment recommendations to local conditions so as to optimize for yield improvement and cost-effectiveness. With the proposed system, diagnostic precision increases to ~&#x2009;94%, and it is manifested in real-time efficiency while supporting scalability with actionable insights to empower farmers to mitigate crop losses and augment food security across several scenarios.","url":"https://doi.org/10.1038/s41598-026-53339-0","authors":["Sastry JSVRS","Naresh P","Ayesha A","Sardar TH","Namratha P","Rajesh TM","Kulkarni P","Raghavendar K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-53339-0","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3389/fdgth.2026.1846020","name":"Federated causal discovery in medicine: trends, opportunities, and challenges.","source":"pubmed","abstract":"Exponential growth and continued digitisation have accelerated the adoption of data-driven and evidence-based approaches in medicine. This includes deciphering associations, including potential causal associations, from multivariate observational biomedical data under certain implicit assumptions. Such an evidence approach marks the shift from classical hypothesis testing to discovery and hypothesis generation. Widespread adoption of common data models has especially accelerated collaborative approaches in medicine while transitioning from centralised to federated architectures that facilitate discovery without the explicit sharing of sensitive medical data. This perspective provides an overview of causal discovery from observational data with a focus on federated learning. Specifically, it outlines the trends, opportunities, and challenges of federated causal discovery in medicine. While medical research has traditionally relied on the hierarchy of evidence generated from the evidence pyramid, the ability of federated causal discovery to facilitate evidence generation collaboratively from heterogeneous sources is expected to enhance the generalizability and transportability of findings while addressing sample size considerations-a critical aspect for its successful and widespread adoption in medicine.","url":"https://doi.org/10.3389/fdgth.2026.1846020","authors":["Rocchi N","Scutari M","Zanga A","Nagarajan R","Stella F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1846020","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1007/s10729-026-09784-4","name":"Predicting asthma-related hospitalizations in underserved and rural populations in Algeria.","source":"pubmed","abstract":"We developed a comprehensive system architecture that incorporates a predictive module for assessing hospitalization risk due to asthma exacerbations. This module is specifically designed to promote equitable access to healthcare services for underserved populations, particularly those residing in rural and socio-economically disadvantaged areas. The predictive model, based on LightGBM, demonstrated superior performance, achieving high evaluation metrics, particularly in settings with synthetic data, and maintained a minimal false negative rate, which is a critical requirement in clinical decision-making. The proposed architecture further integrates a synthetic data generator to establish a dual-layered security mechanism in conjunction with federated learning, thereby reinforcing data privacy and protection against inversion attacks. Additionally, we proposed a personalized aggregation strategy, tailored to prioritize model updates from clients with lower false negative rates. This approach aims to enhance the global model's predictive reliability, ensuring both quality of care and equitable access to accurate and timely medical interventions for underserved populations.","url":"https://doi.org/10.1007/s10729-026-09784-4","authors":["Bouamar A","Belalem G","Belaid B","Benatta D","Snouber A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1007/s10729-026-09784-4","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.20944/preprints202608.1410.v1","name":"Mobility-Aware Federated NOMA User Pairing with DBSCAN Clustering for Energy-Efficient IoT-Enabled VANETs","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202608.1410.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.1410.v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1186/s13040-026-00582-w","name":"Clifti-GPT: privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data.","source":"pubmed","abstract":"Foundation models have demonstrated immense value for scRNA-seq analysis, but their fine-tuning or inference on heterogeneous, privacy-sensitive clinical cohorts is governed by strict data protection policies, which often prohibit centralization. We introduce Clifti-GPT, a privacy-preserving federated framework based on secure multi-party computation (SMPC) that enables collaborative model training and transferable inference, where zero-shot predictions are performed across decentralized clinical repositories by securely aggregating local statistics rather than transferring data embeddings, without sharing patient data, clinical-level statistics, or models. Built upon the scGPT foundation model, Clifti-GPT achieves performance within 4% of centralized scGPT baselines in accuracy, precision, recall, and macro-F1 for cell type classification and reference mapping across six datasets. Furthermore, it demonstrates rapid convergence in terms of communication rounds, reaching 99% of centralized performance on cell type classification in at most two federated rounds on two evaluated datasets, and scales robustly to 30 clients with less than 2% accuracy loss on a large-scale federated cell type classification setting. Our analysis shows that batch effects impact both Clifti-GPT and centralized baseline, while correction leads to similar results across evaluation metrics in heterogeneous settings for both models. Together, these results indicate that Clifti-GPT enables effective fine-tuning and application of single-cell foundation models across distributed clinical datasets in a manner that is GDPR-compatible by design and addresses real-world privacy and institutional data-governance requirements.","url":"https://doi.org/10.1186/s13040-026-00582-w","authors":["Bakhtiari M","Elkjaer ML","Can AO","Theis F","Oubounyt M","Baumbach J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1186/s13040-026-00582-w","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.3390/s26144630","name":"Communication-Efficient Federated Class-Incremental Intrusion Detection for Edge IoT Networks.","source":"pubmed","abstract":"The continuous emergence of new attack classes challenges intrusion detection in edge Internet of Things (IoT) networks. Although federated learning enables distributed devices to collaboratively train a shared detector without exchanging raw traffic data, most federated intrusion detection systems assume a fixed label space. Retraining with all historical data incurs substantial storage and computation costs, whereas updating only with newly collected samples can cause catastrophic forgetting. The detector must mitigate catastrophic forgetting of previously observed attack classes while preserving sufficient new-class plasticity to learn emerging attacks under highly non-IID device data, intermittent client availability, constrained local memory, and repeated communication over bandwidth-limited and intermittently connected links. To address these challenges, this paper proposes EdgeFedCIL, a communication-efficient federated class-incremental intrusion detection framework. EdgeFedCIL preserves historical knowledge through client-local replay and knowledge distillation while reducing repeated model transmission through adaptive low-rank compression, quantization, and error feedback. A classifier-head protection strategy further limits compression-induced degradation of class discrimination. Experiments on public intrusion-detection datasets show that EdgeFedCIL achieves competitive or superior detection and historical-knowledge retention performance, particularly under highly heterogeneous client distributions, while reducing cumulative client-to-server model transmission by up to approximately 10.54 times relative to full-precision transmission. These results demonstrate the effectiveness of EdgeFedCIL for continual and communication-efficient intrusion detection in resource-constrained edge IoT networks.","url":"https://doi.org/10.3390/s26144630","authors":["Wu Z","He B","Si Z","Qiu C","Liao X","Su C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26144630","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.20944/preprints202607.0866.v1","name":"Defense Against Information Integrity Attacks in Federated IoT Systems Using Inertial Momentum Aware IALM-RPCA","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202607.0866.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202607.0866.v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.12688/openreseurope.23459.2","name":"A sustainable platform for federated health data access, AI innovation, and regulatory acceptance in alignment with the European Health Data Space principles","source":"europepmc","abstract":"The IDERHA ( I ntegration of Heterogeneous D ata and E vidence towards R egulatory and H TA A cceptance) project aims to enhance medical research by establishing one of Europe’s first pan-European, disease-agnostic health data spaces. Aligned with the European Health Data Space (EHDS) principles, IDERHA addresses critical challenges in data quality, standardization, and governance, ensuring compliance with GDPR, the AI Act, and emerging EHDS regulations. Its ambition is to enable secure, federated access and analysis of health data, fostering data-driven collaboration and innovation in healthcare. IDERHA’s technical infrastructure employs a ‘privacy-by-design’ approach, leveraging federated analytics and learning to maintain data sovereignty and reduce privacy risks. The project focuses on lung cancer as a high-impact use case, utilizing AI and machine learning to improve early detection, diagnosis, and personalized care. It also aims to develop policy recommendations for the acceptance of real-world evidence (RWE) for regulatory decision-making through multi-stakeholder engagement and public consultations. However, challenges remain, including semantic interoperability, and scaling federated AI methods across borders. IDERHA’s modular, standards-based architecture and emphasis on ethical, legal, and FAIR compliance provide a robust framework for addressing these issues. By collaborating with other initiatives in the health data domain to drive compatibility, IDERHA seeks to accelerate innovation by creating a sustainable, scalable model for health data access and thereby a positive impact for patients across Europe.","url":"https://doi.org/10.12688/openreseurope.23459.2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.12688/openreseurope.23459.2","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3390/diagnostics16132096","name":"CCEO-DCABNet: Chronological Chaotic Evolution Optimization-Enabled Hybrid Deep Learning for Multiclass Disease Classification Using Chest X-Ray Images in Federated Learning.","source":"pubmed","abstract":"Background: Chest X-ray imaging is a widely used diagnostic modality for identifying various lung diseases. Accurate multiclass classification of lung diseases enables timely treatment and improves patient survival. However, disease detection using chest X-ray images remains challenging due to heterogeneous data, overlapping radiographic features, and data privacy concerns. Furthermore, distinguishing among different lung diseases is difficult because of their similar clinical manifestations and imaging characteristics. Method: To address these challenges, a novel chaotic evolution optimization-enabled deep channel-attention broad convolutional neural network (CCEO-DCABNet) is proposed for multiclass lung disease classification within a federated learning (FL) framework. The proposed model ensures enhanced data privacy by allowing multiple client nodes and a central server to collaboratively train the model without sharing raw data. Prior to classification, image preprocessing is performed using Gaussian filter-based denoising followed by multiscale unsharp masking-based image sharpening. Subsequently, multiclass disease classification is carried out using DCABNet, whose parameters are optimized through the proposed CCEO algorithm. In addition, the federated learning process employs an averaging strategy for local model updates and global aggregation. Results: The proposed CCEO-DCABNet achieves an accuracy, true positive rate (TPR), and true negative rate (TNR) of 96.98%, 96.41%, and 97.45%. Conclusions: Experimental results demonstrate that the proposed CCEO-DCABNet framework effectively classifies multiple lung diseases from chest X-ray images while preserving data privacy through federated learning. The model achieves superior classification performance and can support reliable computer-aided diagnosis in clinical settings.","url":"https://doi.org/10.3390/diagnostics16132096","authors":["Patil L","Garg B","Donelli M","Jain A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/diagnostics16132096","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.21203/rs.3.rs-10102389/v1","name":"FedHyperM: Modality-Sensitive Federated Hypergraph Learning for Heterogeneous Multimodal Edge Intelligence","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10102389/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10102389/v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.20944/preprints202608.0075.v1","name":"Privacy-Aware and Resource-Efficient Split Learning for IoT Botnet Detection: A Multi-Dataset Experimental and Systems Evaluation","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202608.0075.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.0075.v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.14293/pr2199.004296.v1","name":"Artificial Intelligence-Based Intrusion Detection Systems for Smart Home Security","source":"europepmc","abstract":"","url":"https://doi.org/10.14293/pr2199.004296.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.14293/pr2199.004296.v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.22541/authorea.15006727/v1","name":"CardioSafeAI: An Explainable Federated Machine Learning Framework with Multi-Stage Hybrid Feature Selection for Cardiovascular Disease Prediction","source":"europepmc","abstract":"cardiovascular disease (CVD) is a leading cause of global mortality and may progress without obvious symptoms. This study proposes CardioSafeAI, an explainable federated learning framework for predicting ten-year coronary heart disease risk from a public dataset. The workflow includes imputation, encoding, Min--Max normalization and SMOTE. A hybrid feature selection method combines Pearson correlation, Information Gain and Gain Ratio. Eleven individual models and a Stacking ensemble were evaluated. The Stacking classifier achieved the highest centralized accuracy of 91.1%. In a five-client simulation, the federated Stacking model achieved 90.80% accuracy and an ROC-AUC of 0.9486. SHAP and LIME provided global and patient-level explanations. CardioSafeAI therefore provides a promising basis for interpretable cardiovascular risk assessment, although independent and prospective clinical validation is required before use in clinical decision making.","url":"https://doi.org/10.22541/authorea.15006727/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.22541/authorea.15006727/v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-10197752/v1","name":"Interpretable-Aware Privacy Preserving Framework for Deepfake Detection using Federated ResNet and Explainable AI Techniques","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10197752/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10197752/v1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.12688/openreseurope.24687.1","name":"A federated policy-governed architecture for sovereign European data spaces","source":"europepmc","abstract":"","url":"https://doi.org/10.12688/openreseurope.24687.1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.12688/openreseurope.24687.1","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1109/tnnls.2025.3648828","name":"HarmoFGL: Harmonizing GNN Latent Factors for Federated Graph Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2025.3648828","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tnnls.2025.3648828","addedAt":"2026-08-31T06:41:28.652Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.21203/rs.3.rs-10204905/v1","name":"FL-SNNIDS: Federated Spiking Neural Networks for Energy-Efficient Intrusion Detection in Agricultural SDN-IoT Networks","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10204905/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10204905/v1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1371/journal.pone.0341096","name":"Adaptive geometric-attention network for two-stage lung nodule segmentation and malignancy classification in federated healthcare IoT edge environments.","source":"pubmed","abstract":"Accurate segmentation and classification of lung nodules in computed tomography (CT) scans remains a critical challenge in early lung cancer detection within distributed healthcare Internet of Things (IoT) environments. This paper presents a novel two-stage framework called Adaptive Geometric-Attention Network (AGA-Net) that integrates geometric constraints with multi-scale attention mechanisms for precise nodule segmentation followed by uncertainty-aware malignancy classification in federated learning scenarios. Unlike existing approaches that rely on traditional convolutional architectures, our method introduces a Geometric-Constrained Attention Module (GCAM) that leverages the spherical nature of lung nodules and a Multi-Scale Uncertainty Quantification Network (MUQ-Net) for robust classification under privacy-preserving constraints. The proposed framework demonstrates superior performance across three benchmark datasets: LUNA16, LIDC-IDRI, and NSCLC-Radiomics, achieving a Dice coefficient of 0.927 for segmentation and AUC of 0.951 for malignancy classification while maintaining computational efficiency validated on IoT-class edge hardware including the NVIDIA Jetson AGX Orin and Jetson Orin Nano. The integration of geometric priors with attention mechanisms, uncertainty quantification, and federated learning capabilities provides both high accuracy and clinical interpretability, making it suitable for next-generation computer-aided diagnosis systems deployable on healthcare IoT edge devices.","url":"https://doi.org/10.1371/journal.pone.0341096","authors":["Sufyan M","Qian J","Li J","Imran A","Sabah F","Sarwar R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0341096","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.21203/rs.3.rs-10577703/v1","name":"Adaptive Data Partitioning for Energy-Efficient Federated and Distributed Learning on Heterogeneous Systems","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10577703/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10577703/v1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-10589135/v1","name":"Federated Model Optimization for Real-Time Big Data Analytics in Smart Drug Delivery Edge Devices","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10589135/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10589135/v1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1080/15265161.2026.2690958","name":"A Question of Benchmarking. Insights from Decentralized Networks for the Governance of Federated Machine Learning.","source":"pubmed","abstract":"","url":"https://doi.org/10.1080/15265161.2026.2690958","authors":["Guenzburger E","Hummel P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1080/15265161.2026.2690958","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1371/journal.pone.0355161","name":"Federated parameter-free DBSCAN clustering and its application in image recognition.","source":"pubmed","abstract":"DBSCAN (A Density-Based Algorithm for Discovering Clusters in Spatial Databases with Noise) is a classic clustering algorithm. However, clustering distributed data with privacy protection in edge computing environments is a key challenge for DBSCAN. In this research, we combine federated clustering and DBSCAN and propose two secure federated parameter-free DBSCAN clustering methods, called FDBSCAN and FDBSCAN++. The process involves the following steps: (1) differential privacy is applied to the client data and adaptive DBSCAN is used at each client to identify core points; (2) the clients send the extracted core points to the server, where the server aggregates these to obtain the final global cluster centers (FDBSCAN and FDBSCAN++&#x2009;use different methods in this step); (3) the final clusters are generated using these global centers. To verify the effectiveness of the proposed two algorithms, we use eight real datasets, including the large-scale image dataset MNIST. Compared with traditional and state-of-the-art (SOTA) improved DBSCAN and federated clustering algorithms, the proposed algorithms achieve better clustering accuracy. In addition, we also apply FDBSCAN++ to image clustering and segmentation tasks, which achieves satisfactory results.","url":"https://doi.org/10.1371/journal.pone.0355161","authors":["Cheng F","Deng Z","Alobaedy MM","Huang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0355161","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.3390/jcm15166384","name":"Surgery 4.0: From the Smart Operating Room to the Learning Operating Room.","source":"pubmed","abstract":"The connected operating room captures, transmits, and displays data, but it does not learn. This perspective presents Chirurgie 4.0 (C40), a French-led initiative born within the Commission Innovation of the Acad&#xe9;mie nationale de chirurgie, which proposes not only a concept but a method for the safe adoption of artificial intelligence (AI) in surgery. At its core is the C40 Maturity Model of the Operating Room, a human-governed \"surgical world model\" describing the transition from the Smart OR to the Learning OR across six levels, from the conventional operating room to a sovereign, federated network of surgical world models. We situate surgical autonomy on an explicit six-level scale, show that autonomous devices are already an accepted clinical reality in fields such as interventional cardiology, ophthalmology, neuro- and orthopedic surgery, and argue that governance must be native rather than retrofitted, through a Cognitive Governance Layer resting on human oversight, explainability, auditability and agent governance. We describe the economic and sovereignty stakes specific to intelligent surgical technologies and set out the design of the 2026 C40 field survey, whose results will feed a Livre Blanc for public decision-makers. C40 offers five steps that can genuinely be climbed, and a method for climbing them safely, with the surgeon retaining final clinical authority at every step.","url":"https://doi.org/10.3390/jcm15166384","authors":["Gumbs AA","Croner R","Couffinhal JC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/jcm15166384","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1016/j.ijmedinf.2026.106574","name":"BlockFedMed: A blockchain-federated learning framework for privacy-preserving mortality prediction across heterogeneous intensive care units.","source":"pubmed","abstract":"Electronic health records are distributed across different hospitals that work on powerful AI models but cannot be shared due to HIPAA and GDPR regulations. Federated learning (FL) avoids raw data sharing, yet lacks tamper-evident consent governance, adversarial robustness, and verifiable differential privacy (DP) accounting leaving regulatory compliance undemonstrated.","url":"https://doi.org/10.1016/j.ijmedinf.2026.106574","authors":["Yadav AK","Deshmukh M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.ijmedinf.2026.106574","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-62791-x","name":"A federated attention-based stacked LSTM framework for interpretable malaria diagnosis under simulated non-IID federated conditions.","source":"pubmed","abstract":"Malaria remains a major global health burden, particularly in low-resource regions where microscopic diagnosis relies heavily on expert interpretation and is prone to variability. Although deep learning models have demonstrated strong classification performance for automated malaria detection, most existing approaches operate as black-box systems and assume centralized, independent and identically distributed (IID) data. These limitations restrict clinical trust and motivate the exploration of privacy-preserving learning paradigms for distributed healthcare environments. To address these challenges, this study presents FASL-Net, a Federated Attention-Based Stacked Long Short-Term Memory framework that integrates quantifiable interpretability with evaluation under simulated non-IID federated learning conditions. Microscopic blood smear images are transformed into patch-level sequential representations (100&#x2009;&#xd7;&#x2009;75), enabling spatial dependency modeling through stacked LSTM layers. A temperature-controlled attention mechanism identifies diagnostically relevant regions, and interpretability is quantitatively assessed using attention entropy and causal deletion-insertion analysis. The proposed model achieved 94.39% accuracy in centralized training and 94.95% accuracy in a simulated three-client federated learning setting with heterogeneous data partitions. Attention entropy analysis yielded an average entropy of approximately 2.46, substantially lower than the theoretical maximum (&#x2248;&#x2009;4.60), indicating focused decision patterns. Deletion experiments resulted in an accuracy reduction of approximately 44% when highly attended patches were removed, whereas insertion experiments preserved approximately 95% performance using only the top 10% salient patches. McNemar's test between centralized and federated models produced a test statistic of 0.03 (p&#x2009;=&#x2009;0.861), indicating no statistically significant difference under the current simulated setting. These results demonstrate the feasibility of combining federated learning, sequential attention modeling, and quantifiable interpretability for malaria diagnosis under controlled heterogeneous conditions. The study provides a foundational proof-of-concept for trustworthy federated malaria diagnosis and establishes a basis for future validation using real multi-center clinical datasets.","url":"https://doi.org/10.1038/s41598-026-62791-x","authors":["Murugan S","Thirunadanasikamani K","Napa KK","Allasi HL","Sathya S","Geethamahalakshmi G","Govindarajan R","Soosaimariyan MV"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-62791-x","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.20944/preprints202606.1851.v1","name":"A Hybrid Quantum Resistant Secure Framework for Federated Healthcare Cyber-Physical Systems","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202606.1851.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202606.1851.v1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-10813996/v1","name":"OmniGuard V2X: A Hybrid-Security Prototype Framework with Assumption-Aware Validation for Smart Vehicle Systems","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10813996/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10813996/v1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-54887-1","name":"FedPDM-Net: a federated prototype-guided disentangled deep learning framework for explainable malaria detection.","source":"pubmed","abstract":"Malaria remains a critical global health concern, particularly in resource-limited settings where accurate and timely diagnosis is essential. While deep learning methods have improved automated detection from blood smear images, most rely on centralized training, raising concerns related to privacy, scalability, and interpretability. To address these limitations, this study proposes FedPDM-Net, a federated prototype-guided disentangled deep learning framework for explainable malaria detection. The model integrates federated learning for privacy-preserving training, a prototype memory module for capturing representative infection patterns, and disentangled feature learning to separate morphological and infection-specific characteristics. An attention-based fusion mechanism enhances feature representation, and Grad-CAM provides visual interpretability. Experimental results show that the model achieves 97.3% accuracy and F1-score in the centralized setting and 96.6% accuracy and F1-score in the federated setting, with only a 0.7% performance drop. A 5-fold cross-validation yields 96.83% &#xb1; 0.20 accuracy, confirming stability. The model maintains&#x2009;&gt;&#x2009;95% performance under perturbations, demonstrating robustness. Statistical analysis shows significant improvement over baselines (p&#x2009;&lt;&#x2009;0.05), while calibration results (ECE&#x2009;=&#x2009;0.032) indicate reliable confidence estimation. Additionally, efficient federated communication is achieved with 147&#xa0;MB overhead over 5 rounds. Overall, FedPDM-Net provides a robust, interpretable, and privacy-preserving solution for automated malaria diagnosis.","url":"https://doi.org/10.1038/s41598-026-54887-1","authors":["Napa KK","Murugan S","Murugan JS","Assegie TA","Sathya S","Nageswari D","Mothukuri R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-54887-1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.20944/preprints202606.0652.v1","name":"A Comprehensive Survey of Federated Model Evolution in Open Environments","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202606.0652.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202606.0652.v1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-60810-5","name":"Federated graph learning with spatio-temporal dynamics for cross-border recommendation.","source":"pubmed","abstract":"Cross-border data sharing is strictly constrained by privacy regulations, which presents a critical challenge for recommendation systems due to the severe shortage of training data. Existing federated graph neural network methods predominantly rely on the federated averaging strategy, which struggles to handle the highly heterogeneous data encountered in scenarios characterized by user isolation and business homogeneity. To address this issue, this paper proposes FedSTAR, a privacy-preserving cross-border recommendation framework that integrates spatio-temporal dynamic modeling with federated graph neural networks. Its core innovations include the design of a dynamic sequential graph structure to capture the evolution of user preferences, the use of a multi-head attention mechanism to filter noisy neighbors in the spatial dimension, and the introduction of a personalized federated aggregation strategy to replace traditional FedAvg, thereby enabling adaptive fusion of heterogeneous multi-source data. Evaluations on three public datasets, Gowalla, Yelp 2018, and Amazon Book, demonstrate that FedSTAR achieves average improvements of 2-5 percentage points in Recall@20 and 1-3 percentage points in NDCG@20, respectively. Under the privacy constraint of exchanging only model updates, FedSTAR delivers both high accuracy and strong robustness, offering a secure and practically viable solution for cross-border recommendation.","url":"https://doi.org/10.1038/s41598-026-60810-5","authors":["Tan Z","Wang Y","Zheng J","Zhang N","Zhang C","Wang W"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-60810-5","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.21203/rs.3.rs-9872496/v2","name":"TrustFedKG-Health: Explainability-Aware Personalized Federated Knowledge Graph Neural Network with Uncertainty-Guided Clinical Reasoning","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9872496/v2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9872496/v2","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-10011467/v1","name":"Closed-Loop Fairness and Privacy Optimization for Federated Text Classification under Non-IID Data","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10011467/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10011467/v1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.3389/frai.2026.1857709","name":"FLMMIF: privacy-preserving federated multi-modal medical image fusion.","source":"pubmed","abstract":"As a pivotal technique in smart healthcare, medical image fusion integrates complementary functional and structural information to facilitate accurate diagnosis and enhance clinical decision-making reliability. However, existing centralized methods typically raise serious data privacy concerns, while standard distributed approaches often fail to balance global generalization with local node personalization due to data heterogeneity. To address this, we propose FLMMIF, a privacy-preserving framework integrating a federated learning paradigm and low-rank adaptation for personalized and secure medical image fusion. During the local training phase, we utilize a dual-branch encoder and single-branch decoder, adopting a two-stage iterative strategy: initially training low-rank parameters to secure local personalization, followed by training full-rank parts to guarantee global baseline performance. Subsequently, this iterative process ensures that the model dynamically coordinates specific local features with general global knowledge before parameter transmission. Finally, we establish a metric-based aggregation mechanism on the server, FedIF, which evaluates the performance of uploaded models to assign higher aggregation weights to superior nodes for optimized global updating. Experimental results demonstrate that FLMMIF generates high-quality fusion results that effectively protect data privacy while achieving precise node-specific personalization.","url":"https://doi.org/10.3389/frai.2026.1857709","authors":["Meng L","Ren S","Wang J","Che Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1857709","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-61476-9","name":"Federated generative adversarial network with hybrid transformer-GRU and explainable AI for financial fraud detection.","source":"pubmed","abstract":"Fraud detection in financial transactions has emerged as a significant issue in today's digital payment system with the surge of online banking, mobile payment, and credit card payments. Growing transaction numbers, sophisticated attack methods, extreme class imbalance, and privacy concerns hinder the effectiveness of traditional centralised fraud detection. The low volume and dynamic nature of fraudulent transactions lead to many machine learning models with poor recall, generalization, and timely fraud pattern adaptation. Recent federated learning models like FL-Hybrid, FL-SDT and FL-Resample have tried to maintain privacy by facilitating shared model training among institutions. But they still suffer from shortcomings such as poor management of minority fraud samples, limited learning of temporal features, poor contextual modelling and lack of interpretability for supporting financial decision-making. This paper presents FL-GAN-HTG, a new Federated Learning with Generative Adversarial Network and Hybrid Transformer-GRU (FL-GAN-HTG) and Explainable AI (XAI) framework. In the proposed framework, GANs at client nodes learn to generate fraud samples to reduce data imbalance without compromising privacy. A hybrid architecture with a Transformer encoder captures global transaction features, and GRU layers capture temporal behavioural patterns for fraud detection. Federated learning ensures privacy via distributed collaborative training and explainability enhances trust and transparency in the model's decision-making. The proposed FL-GAN-HTG outperformed baseline models in fraud detection and resilience, with an Accuracy of 0.9786, Precision of 0.9814, Recall of 0.9742, F1-score of 0.9778, and AUC of 0.9869 in experiments on a publicly available financial fraud dataset.","url":"https://doi.org/10.1038/s41598-026-61476-9","authors":["Juyal PK","Kolluri J","Siripuri K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-61476-9","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1038/s41598-026-47857-0","name":"Predictive fault management in smart sensor networks using a dynamic quantum-AI architecture (DynaQuAI).","source":"pubmed","abstract":"Applications in industrial and smart-infrastructure Wireless sensor networks (WSNs) in the field are increasingly expected to employ predictive intelligence, which, even when operating under dynamic configurations, heterogeneity of hardware, and strong privacy requirements. The proposed paper is DynaQuAI, quantum-inspired, edge-intelligent framework that can predict real-time failures on constrained sensor nodes regarding their resources. The proposed system employs a probabilistic state encoding and oscillatory exploration schedule-mathematically inspired by quantum superposition analogies-to improve reinforcement learning exploration in sparse and noisy environments. These techniques are entirely classical algorithms implemented with standard trigonometric and probabilistic operations, requiring no quantum hardware. Such quantum-inspired algorithms enhance coverage of state-space and convergence faster than when using the usual deep RL models, with no computational demand. To support distributed nodes collaborative learning, DynaQuAI uses a lightweight federated approach on training paired with parameter aggregation through secure masks. Privacy is ensured through secure aggregation via pairwise parameter masking, which prevents the aggregation server from observing individual node updates while recovering the correct aggregate. The framework is additionally compatible with local differential privacy (&#x3b5;_total&#x2009;&#x2264;&#x2009;9.8 over 150 rounds at &#x3b4;&#x2009;=&#x2009;4&#x2009;&#xd7;&#x2009;10&#x207b;&#x2076;) for deployments requiring formal statistical privacy guarantees. We evaluated DynaQuAI through a simulation-based testbed emulating 500 heterogeneous sensor nodes with ARM Cortex-A7/M4 computational profiles over a simulated IEEE 802.15.4 mesh network. The evaluation uses the Kaggle Predictive Maintenance Dataset augmented with synthetic fault injections to create realistic distributed learning scenarios. Through experiments, this is, which has a sensitive aspect, demonstrated at 33% more fault-prediction accuracy over federated deep-learning baselines, 25% reduced energy consumption over adaptive quantum-inspired exploration, and 40% reduced convergence during policy learning. Its system can maintain real-time inference latency of 85 ms with minimally more than 5% of the available computational resources, thus suitable to be deployed in the long term. Individual contributions of quantum-inspired encoding, exploration scheduling and privacy-enhanced system of aggregation mechanisms are verified through ablation studies. In general, DynaQuAI has made easily extendable, privacy conscious and robust architectural learning offerings to next generation IoT ecosystems where high reliability and sustainability are essential.","url":"https://doi.org/10.1038/s41598-026-47857-0","authors":["Alharbi A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-47857-0","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.14293/pr2199.003822.v1","name":"Federated Deep Learning-Based Zero-Trust Architecture for Intelligent Intrusion Detection in Multi-Cloud Systems","source":"europepmc","abstract":"The rapid adoption of multi-cloud environments has transformed modern digital infrastructures by improving scalability, flexibility, and service availability. However, the distributed nature of multi-cloud systems introduces significant cybersecurity challenges, including unauthorized access, lateral movement attacks, data breaches, and sophisticated network intrusions. Traditional perimeter-based security mechanisms often struggle to provide effective protection in such dynamic and heterogeneous environments. To address these limitations, this study proposes a federated deep learning-based zero-trust architecture for intelligent intrusion detection in multi-cloud systems. The proposed framework integrates the principles of zero-trust security, where every user, device, and application is continuously verified, with federated learning that enables collaborative model training without exposing sensitive organizational data. Deep learning models deployed across multiple cloud domains learn local intrusion patterns and share model parameters rather than raw data, thereby preserving privacy and regulatory compliance. The architecture incorporates continuous authentication, behavioral monitoring, anomaly detection, and decentralized intelligence to identify both known and emerging cyber threats in real time. Furthermore, the framework enhances resilience against data leakage, model poisoning, and insider attacks through secure aggregation and trust evaluation mechanisms. The study demonstrates that combining federated deep learning with zero-trust principles can significantly improve detection accuracy, reduce response time, and strengthen security across geographically distributed cloud infrastructures. The proposed approach offers a scalable, privacy-preserving, and adaptive solution for next-generation cloud security, making it suitable for organizations operating in increasingly complex multi-cloud ecosystems.","url":"https://doi.org/10.14293/pr2199.003822.v1","authors":["muhammad Abubakar"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.14293/pr2199.003822.v1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1016/j.schres.2026.06.026","name":"FL-SDGIN: A federated graph learning approach for schizophrenia diagnosis integrating static and dynamic brain functional networks.","source":"pubmed","abstract":"Recent magnetic resonance imaging (MRI) studies have revealed connectivity abnormalities in brain networks of schizophrenia (SZ). Graph Neural Networks (GNN) through their powerful graph embedding ability provide novel approaches for brain network analysis in SZ. However, current functional MRI (fMRI) based SZ diagnostic models exhibit limitations including insufficient utilization of static and dynamic functional connectivity (FC/dFC), inadequate modeling of dynamic features while neglecting temporal variability, and lack of consideration for multi-site data privacy and heterogeneity. To address these issues, we propose a federated learning-based static-dynamic graph isomorphism network (FL-SDGIN) for SZ diagnosis. The framework first employs temporal convolutional networks to extract temporal variability of dFC, constructing temporal variability guided attention adjacency matrices. Dynamic graph isomorphism networks (DyGIN) then capture spatiotemporal topological patterns, while graph isomorphism networks (GIN) extract static topological features, enabling multidimensional characterization of brain networks. Simultaneously, federated averaging (FedAvg) is employed for cross-site model training while avoiding direct sharing of raw imaging data. Experimental results under random cross-validation show that FL-SDGIN achieves a classification accuracy of 0.815 and outperforms the evaluated baseline models. Additional leave-one-site-out analysis indicates that cross-site generalization remains challenging under site and cohort heterogeneity. The interpretability analysis suggests that candidate SZ-related regions include the thalamus, posterior cingulate gyrus, postcentral gyrus, middle temporal gyrus, and superior temporal gyrus.","url":"https://doi.org/10.1016/j.schres.2026.06.026","authors":["Yang M","Huang J","Liu L","Yi C","Chen J","Fan YS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.schres.2026.06.026","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.21203/rs.3.rs-8236805/v1","name":"Distributed IoT Security with Blockchain, Privacy-Preserving Techniques, and Predictive Maintenance Models","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8236805/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8236805/v1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1038/s41598-026-62788-6","name":"A deep-learning based gait classification and anomaly detection framework for healthcare surveillance.","source":"pubmed","abstract":"Gait classification and anomaly detection are non-intrusive approaches that can support healthcare surveillance and clinical gait analysis by identifying abnormal walking patterns. Despite recent advancements, existing methods remain limited by model complexity, high computational requirements, and privacy concerns. This study proposes a unified framework that combines three complementary components: (i) transformer-based temporal modeling to capture both short-term and long-term gait dynamics, (ii) lightweight architectures for efficient deployment on edge devices, and (iii) federated learning (FL) for privacy-preserving distributed training without requiring raw data sharing. Experiments were conducted on a balanced subset of 60,000 images from the Gait Detection Processed dataset using a controlled federated learning environment designed as a proof-of-concept evaluation rather than a large-scale deployment setting. The dataset consisted of three categories: background/non-gait, normal gait, and abnormal gait. Vision Transformer (ViT), ConvLSTM, and MobileViT architectures were evaluated for three-class gait classification and anomaly detection under a federated learning setting. Among the evaluated models, MobileViT-Large achieved the highest performance with 97.2% accuracy, 96.8% precision, 97.5% recall, and 97.1% F1-score, although it required higher computational resources and showed greater overfitting tendencies. MobileViT-Small achieved the best balance between efficiency and performance with 94.0% accuracy, making it more suitable for edge deployment. SHAP-based analysis further showed that the models focused on meaningful gait regions, such as torso and limb movements. This proposed framework provides a comparative benchmark of recurrent and transformer-based architectures within a privacy-preserving framework for healthcare monitoring applications.","url":"https://doi.org/10.1038/s41598-026-62788-6","authors":["Alfridi MF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-62788-6","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.1109/tpami.2026.3711828","name":"Robust Personalized Federated Continual Learning via Explainable Multi-Granularity Prompt.","source":"pubmed","abstract":"Personalized Federated Continual Learning (PFCL) requires that the server not only effectively integrate temporal knowledge accumulated across past tasks and spatial knowledge distributed among heterogeneous clients, but also ensure strong personalized performance of the global model for each client. Existing methods, whether in Personalized Federated Learning (PFL) or Federated Continual Learning (FCL), have overlooked the multi-granularity representation of knowledge, which can be utilized to overcome Spatial-Temporal Catastrophic Forgetting (STCF) and enable coarse-to-fine personalization. Furthermore, most approaches rely on local client-side personalization, increasing computational load and failing to address the risks posed by malicious or low-quality clients. To this end, we propose FedMGP+, which utilizes multi-granularity prompts to address these challenges, namely coarse-grained global prompt and fine-grained local prompt. The former focuses on efficiently transferring shared global knowledge without spatial forgetting, and the latter emphasizes specific learning of personalized local knowledge to overcome temporal forgetting. Visualization results and theoretical analyses further reveal that coarse-grained prompts primarily guide regional attention, whereas fine-grained prompts enrich object-level representations within those regions. Building upon this, Personalized Selective Prompt Fusion is designed to exclusively fuse coarse-grained knowledge on the server to generate client-specific global prompts, reducing local overhead and resisting poisoning attacks from malicious clients. Extensive experiments demonstrate that the proposed FedMGP+ effectively mitigates forgetting, significantly enhances personalized performance, and provides robust defense against poisoning and gradient leakage attacks.","url":"https://doi.org/10.1109/tpami.2026.3711828","authors":["Yu H","Yang X","Fan B","Li Y","Gu H","Zhang J","Li T","Yang Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tpami.2026.3711828","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.21203/rs.3.rs-10573222/v1","name":"An Empirical Study of Stability and Fairness Side-Effects of Federated Unlearning for Departed Clients under Non-IID Data","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10573222/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10573222/v1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1111/ene.70715","name":"Federating European REgistries for Stroke: Principles, Data Model and Future Perspectives for a Federated Analysis of Multiple Prospective Stroke Registries.","source":"pubmed","abstract":"Acute ischemic stroke is a leading cause of death and disability. Despite strong evidence supporting reperfusion therapies and Stroke Unit care, access and quality of stroke services remain heterogeneous across Europe. Although national stroke registries provide valuable real-world data, fragmentation, limited interoperability, and data protection constraints have restricted multinational analyses and benchmarking.","url":"https://doi.org/10.1111/ene.70715","authors":["Salerno A","Melissargos G","Katan M","Knoflach M","Kiechl S","Harbison J","Renieri L","Toni D","Palaiodimou L","Tsivigoulis G","Ktenidis A","Mailis T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1111/ene.70715","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.21203/rs.3.rs-10404470/v1","name":"A Privacy-Preserving Federated Intrusion Detection Framework with Reputation-Aware Client Selection and Integrity Verification","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10404470/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10404470/v1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.1109/jbhi.2026.3724549","name":"HARMONY: Hybrid Aggregation and Reputation Mechanism for Cooperative Federated Survival Analysis.","source":"pubmed","abstract":"Federated Learning (FL) holds promise for digital health by enabling collaborative model training without compromising patient data privacy. However, differences in data completeness, censoring rates, patient populations, institutional reliability, and contribution quality across healthcare institutions remain major challenges. In this paper, we propose HARMONY, a peer-driven reputation mechanism for federated healthcare that employs a hybrid communication model to integrate decentralized peer feedback with clustering-based noise handling to enhance model aggregation. Crucially, HARMONY decouples the federated aggregation and reputation mechanisms by applying differential privacy to client-side model updates before peer evaluation. This protects sensitive information during reputation computation, while unaltered updates are sent to the server for global model updates. Using the Cox Proportional Hazards model for survival analysis across nodes, HARMONY addresses both data heterogeneity and reputation deficit by dynamically adjusting trust scores based on local performance improvements measured via the concordance index. Experimental evaluations on synthetic as well as the SEER dataset show that HARMONY consistently achieves high and stable C-index values, down-weighting noisy clients and outperforming FL methods without a reputation system. Across states, HARMONY improves C-index over no-reputation (FedAvg) by 0.1-4.6% (median +1.3%, IQR 0.5-2.9%) and matches or exceeds TFFL in 9/10 states; under 20% adversarial clients it reduces round-to-round C-index variance by $\\sim$35% while adding 12% communication overhead (vs. FedAvg) and 9% client-side compute for peer evaluation.","url":"https://doi.org/10.1109/jbhi.2026.3724549","authors":["Seidi N","Roy S","Das S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/jbhi.2026.3724549","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.441Z"},{"id":"doi:10.21203/rs.3.rs-9641690/v1","name":"Federated Deep Learning-Based Robust Sparse System Identification Using Correntropy and Sparsity-Aware Optimization","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9641690/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9641690/v1","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"pmid:42657862","name":"Putting the human at the core of human-in-the-loop optimization: integrating clinical expertise and human capacities in exoskeleton learning. An interdisciplinary perspective paper.","source":"pubmed","abstract":"This interdisciplinary perspective aims to identify key limitations in current Human-in-the-Loop Optimization (HILO) approaches for lower-limb exoskeletons (LLEs) and to propose a shift toward more human-centred optimization.","url":"https://pubmed.ncbi.nlm.nih.gov/42657862/","authors":["Beckwée D","Filtjens B","Claeys R","Lambranzi C","Eggermont M","Firouzi M","Flynn L","Aeles J","Verstraten T","Swinnen E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 27","doi":"10.1080/09638288.2026.2720761","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42655514","name":"Stage-Aware Swin-Enhanced nnU-Net v2 for Robust Polyp Segmentation in Collaborative Endoscopic Visual Sensing.","source":"pubmed","abstract":"Automatic colon polyp segmentation is important for reliable endoscopic visual sensing. However, segmentation performance can be degraded by ambiguous lesion boundaries, heterogeneous appearance, and image quality variations during acquisition or transmission. This study proposes a stage-aware Swin-enhanced nnU-Net v2 framework, where Swin Transformer blocks are inserted into selected encoder stages while preserving the original nnU-Net v2 pipeline. Different insertion strategies were evaluated on an independent Kvasir-SEG test set, and robustness was further assessed under six synthetic corruption types with three severity levels. The results show that the insertion stage strongly influences the effectiveness of Swin enhancement. Among the evaluated variants, Stage5-Swin achieved the best overall trade-off between segmentation performance, robustness, and computational cost. It achieved the highest Dice scores across all corruption-severity combinations while maintaining comparable external-domain performance on CVC-ClinicDB. Additional FedAvg experiments demonstrated the compatibility of the proposed architecture with collaborative training workflows. Resource analysis further quantified the computational and communication overhead. The findings indicate that middle-to-deep encoder insertion provides a favorable balance between contextual modeling, spatial representation, and efficiency for robust endoscopic segmentation. However, improvements were metric- and corruption-dependent, and moderate overexposure revealed a Precision-Recall trade-off; practical deployment also remains to be validated.","url":"https://pubmed.ncbi.nlm.nih.gov/42655514/","authors":["Bai Y","Liu H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 17","doi":"10.3390/s26165206","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42653034","name":"Artificial Intelligence and Digital Pathology: Technological Transformation and Strategic Impact in Clinical Research and Medical Affairs.","source":"pubmed","abstract":"The progressive integration of Whole Slide Imaging (WSI) technology and Artificial Intelligence (AI) architectures is driving a structural transformation in pathology and precision oncology. This structured critical review analyzes and systematizes the impact of this technological transition along two fundamental operational dimensions of the modern biopharmaceutical industry: pre-registration Clinical Research and post-launch strategies governed by Medical Affairs. The first section explores how computational pathology is improving efficiency and reducing risk in drug development. Replacing analog visual assessment-intrinsically subject to inter-observer and intra-observer variability-with quantitative algorithms for cellular classification and segmentation enables optimization of patient recruitment in clinical trials, reducing screening failure rates. This review also examines the emerging role of Spatial Biology in extracting complex topological metrics from the Tumor Microenvironment (TME) and the use of AI for the objective and auditable quantification of critical surrogate endpoints, such as Pathological Complete Response (pCR), while acknowledging that algorithmic precision remains sensitive to pre-analytical variables and dataset biases. In the second section, the study investigates the strategic evolution of Medical Affairs, acting as a vital scientific communication and translational bridge between the complexity of Data Science and clinical hospital practice. Challenges related to AI adoption by clinicians are examined, emphasizing the importance of educational programs based on Explainable AI (XAI) to overcome the cognitive limitations of the black-box paradigm and the complex regulatory validation pathway for Software as a Medical Device (SaMD) under the stringent European IVDR framework-supported by an analysis of historical regulatory benchmarks such as the Paige Prostate case. The paper also explores the potential of AI in the large-scale generation of Real-World Evidence (RWE), applied to the creation of synthetic control arms in pharmacoeconomic settings. In conclusion, the study highlights that the diagnostic algorithm has ceased to be merely a laboratory support tool and has become a strategic asset and an integral adjunct to therapeutic decision-making. Overcoming current challenges related to data privacy through Federated Learning architectures, together with the imminent transition toward Foundation Models, foreshadows a fully data-driven healthcare ecosystem, making continuous skills development (digital upskilling) an essential requirement for professionals in the biopharmaceutical sector.","url":"https://pubmed.ncbi.nlm.nih.gov/42653034/","authors":["Baviello C","Capuano DM","Verna R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 16","doi":"10.3390/life16081346","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42644835","name":"Artificial Intelligence in Xenotransplantation: A Prioritized Roadmap for Early Clinical Translation, Opportunities and Challenges.","source":"pubmed","abstract":"Xenotransplantation represents a potential solution to the persistent global organ shortage, yet its clinical application remains stalled by complex immunologic responses, coagulation dysregulation, species-specific biology, and infectious risks. Artificial intelligence (AI) could enhance safety, accelerate decision-making, and enable precision medicine initiatives within this rapidly evolving field. However, effective implementation of AI in xenotransplantation requires approaches specifically adapted to the biological and operational complexities of cross-species transplantation. Here, we present our suggestion of a prioritized roadmap for integrating AI into early clinical xenotransplantation, based on clinical need, data availability, technical readiness, feasibility of clinician-supervised implementation, and potential impact on graft assessment and safety monitoring. Priority domains include digital pathology and imaging, machine perfusion-based viability monitoring, multimodal and multi-omics detection of graft injury and rejection, and surveillance for potential xenozoonotic infections. One of the essential prerequisites to ensure the development of reliable AI in xenotransplantation is to develop standardized definitions of xenograft injury phenotypes and ground truth datasets, which in this emerging field are currently lacking. The limitations to the application of AI in xenotransplantation, which include the lack of clinical data, species-specific differences, and delays in annotations and regulations, can be addressed via data sharing, federated learning, fairness, and validation. By combining gene-edited donors and refined immunosuppression regimens with clinically supervised, auditable, and transplant-specific, AI-based support systems, xenotransplantation could be made safer and more reproducible in the clinical arena.","url":"https://pubmed.ncbi.nlm.nih.gov/42644835/","authors":["Shirini K","Hahn Z","Ladowski JM","Schulick A","Babadi S","Loupy A","Yamada K","Meier RPH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Sep-Oct","doi":"10.1111/xen.70162","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42634929","name":"Current State and Impact of Implementation of In-Silico Tools in the Biopharmaceutical Industry - Proceedings of the 6th Modeling Workshop.","source":"pubmed","abstract":"The sixth modeling workshop (6MW) was held in June 2025 at Collegeville, Pennsylvania, USA, and supported by the Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state of implementation, progress since the last modeling workshop (5MW) in 2023, gaps and opportunities for development, deployment, and maintenance of models in bioprocess applications. One key observation from the 6MW was the positive impact of modeling on the development of a wide range of therapeutic modalities (proteins, vaccines, peptides) and unit operations (upstream, purification, and formulation), and across time (from discovery via molecular design/developability to commercialization). The scope of 6MW was broader than previous workshops, thus including biophysics and molecular modeling, mechanistic modeling and computational fluid dynamics, plant and sustainability modeling, artificial intelligence (AI) and big data modeling, and Highland Games 2.0. The AI and big data modeling session captured some of the initiatives across the pharmaceutical industry that have flourished with the recent development of new digital and AI technologies. The Highland Games 2.0 session benchmarked specific applications and advances of modeling tools to support developability predictions from sequence, building on the original competition in 2018. One key challenge emphasized at prior modeling workshops was the need for large data sets to support collaborative modeling development within the recovery community. This still remains a gap for the community at large (e.g., a shared reference point for academic/industrial collaborators for benchmarking). Significant progress on developing and curating large data sets within institutions to support in silico molecular and process development was reviewed in two sessions: AI and Big Data and Highland Games 2.0. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyses without sharing proprietary information. The 6MW included two invited papers on building molecular understanding into the manufacturing process and summarizing the ongoing technical challenges, successes and best practices of utilizing federated learning at the National Institute for Innovation in the Manufacturing Biopharmaceuticals (NIIMBL), respectively. Advancement was reported in all of the modeling areas/disciplines discussed in sessions at the 6MW, with the greatest advancements observed in mechanistic modeling applied to mixing and filtration, AI and big data modeling, and mechanistic modeling applied to chromatography. Highlights of the workshop included significant advancements in biophysical/molecular modeling for vaccines and multi-scale applications, and mechanistic models for filtration. Nevertheless, continued research and development for all modeling approaches is still needed and should be pursued.","url":"https://pubmed.ncbi.nlm.nih.gov/42634929/","authors":["Dumetz A","Staby A","Gerberich C","Roush D","Babi DK","Wittkopp F","Reilly J","Lyall J","Welsh J","Robinson J","Parimal S","Hunt S","Xu X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 24","doi":"10.1002/bit.70354","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42630628","name":"Validation of federated analytics across secure data environments: A comparative study of synthetic healthcare datasets using machine learning and general linear models.","source":"pubmed","abstract":"To enable research and innovation, most health data systems are moving away from a model of pooled data egress and instead highlight the benefits of federated analytics to support data analysis across different settings. This study aimed to assess whether the results of analyses using general linear models (GLMs) and machine learning (ML) models were altered depending on whether a federated or pooled data (\"non-federated\") approach was taken.","url":"https://pubmed.ncbi.nlm.nih.gov/42630628/","authors":["Gallier S","Topham A","Hodson J","McNulty D","Giles TC","Cox S","Chaganty MJ","Cooper L","Perks S","Quinlan PR","Sapey E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/20552076261467828","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42627943","name":"Local Deployment of Open-Weight Language Models in Dermatology: Viewpoint on Privacy, Equity, and Practical Implementation.","source":"pubmed","abstract":"Generative AI, particularly large language models (LLMs), is reshaping clinical workflows in dermatology. However, cloud-based commercial models pose persistent challenges to Health Insurance Portability and Accountability Act (HIPAA) compliance, especially in dermatology, where protected health information (PHI) extends beyond text to clinical photographs, dermoscopic images, and total-body photography that may capture identifiable anatomical features and document conditions carrying social stigma. Locally hosted, open-weight LLMs that are run within the institution's own infrastructure offer dermatology practices a pathway to leverage AI capabilities while retaining full control of their data. This viewpoint synthesizes evidence on when locally hosted, open-weight LLMs should be preferred for dermatologic workflows, when cloud deployment may remain preferable, and how multimodal AI fits into a coherent local deployment strategy. We advance 4 arguments. First, model compression techniques (knowledge distillation, structured pruning, and low-bit quantization) together with mixture-of-experts architectures have lowered hardware thresholds enough that 7- to 33-billion-parameter models now run on consumer-grade workstations with modest neural processing units or graphics processing units. Second, dermatology is fundamentally a visual specialty, and a credible local deployment strategy must integrate LLMs with vision models, including convolutional neural networks, vision transformers, vision-language models, and dermatology-specific foundation models such as PanDerm and medical multimodal models such as MedGemma. Third, locally hosted, open-weight models confer specific advantages for dermatology, including complete institutional control of clinical images, freedom from vendor model deprecation that disrupts validated workflows, and the ability to audit and fine-tune models to address well-documented performance gaps in skin of color. Fourth, local deployment is not a panacea; cloud models remain preferable for some tasks, and local deployment introduces governance challenges (heterogeneity across practices, model drift, and quantization-induced accuracy loss) that require structured mitigation through validated reporting frameworks such as CONSORT-AI (Consolidated Standards of Reporting Trials), SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials), DECIDE-AI (Developmental and Exploratory Clinical Investigations of Decision support systems driven by AI), and TRIPOD+AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis), as well as retrieval-augmented generation and federated learning approaches. We situate these arguments within the international regulatory landscape, including the European Union's General Data Protection Regulation, the European Union AI Act, and Germany's Digitale Gesundheitsanwendungen (DiGA) framework, in addition to HIPAA. We provide quantitative cost examples showing that current consumer hardware capable of running 14- to 33-billion-parameter models can be acquired for roughly the price of 1 to 2 years of enterprise cloud-AI subscriptions. We close by mapping a practical implementation pathway and identifying near-term research priorities. Locally hosted, open-weight LLMs that are deployed thoughtfully and within governance frameworks offer dermatology practices a credible route to harness generative AI while preserving regulatory compliance, equity across skin types, and the dermatologist-patient relationship.","url":"https://pubmed.ncbi.nlm.nih.gov/42627943/","authors":["Nahm WJ","Yin ES","Milam EC","Weed JG"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 21","doi":"10.2196/94764","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42623975","name":"Preserving privacy, enabling collaboration: Decentralized learning framework for multi-class orthopedic imaging.","source":"pubmed","abstract":"Medical imaging data are inherently distributed across healthcare institutions and subject to strict privacy regulations, limiting the feasibility of centralized model training. In orthopedic imaging, further challenges arise from heterogeneous diagnostic tasks, implant categories, and label spaces that differ across institutions. Existing decentralized approaches, including federated and swarm learning, reduce direct data sharing but typically rely on repeated parameter synchronization and assume partially aligned label spaces, restricting their scalability in heterogeneous clinical environments. To address these limitations, we propose OrthoATD.Net, a decentralized learning framework for collaborative orthopedic image analysis that operates without raw-data sharing or iterative parameter synchronization. The framework combines independent local training with synchronization-free representation sharing, enabling knowledge integration across fully disjoint label spaces. We evaluate OrthoATD.Net across six heterogeneous orthopedic nodes comprising 43,976 X-ray images and 30 implant and diagnostic classes, using identical Vision Transformer backbones and leakage-controlled evaluation protocols. Over three independent runs, the framework achieves a mean accuracy of 97.32&#xb1;0.03% and a macro F1-score of 96.45&#xb1;0.07%. Within this heterogeneous disjoint-label setting, relative to the strongest decentralized baseline (Ditto-adapted, 92.93%), it improves accuracy by 4.39 and macro F1-score by 5.84 percentage points, and consistently outperforms NonIID-SL (91.18%), FedPer-adapted (90.82%), centralized learning (89.27%), FedLD (87.25%), and ATD (71.21%) under identical experimental conditions. Multi-seed statistical validation with significance testing, leave-one-node-out generalization analysis, and membership-inference attack analysis further demonstrate the robustness, reproducibility, and practical viability of the framework. The primary contribution of OrthoATD.Net is enabling synchronization-free collaborative learning across heterogeneous clinical nodes with fully disjoint label spaces rather than establishing a universal performance advantage over centralized learning. These findings suggest that synchronization-free representation sharing can serve as an effective and scalable alternative to conventional decentralized learning for heterogeneous orthopedic imaging tasks while preserving data locality, providing a promising basis for privacy-aware, scalable collaborative orthopedic artificial intelligence across distributed healthcare environments.","url":"https://pubmed.ncbi.nlm.nih.gov/42623975/","authors":["Alwzwazy HA","Gu A","Dukhan M","Zhao Z","Alzubaidi L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 19","doi":"10.1016/j.artmed.2026.103513","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42619056","name":"Utilizing routinely acquired clinical neuroimaging and electronic health record data to advance precision medicine in dementia care.","source":"pubmed","abstract":"Alzheimer's disease (AD) exhibits significant clinical variability in symptom onset, progression rates, neuropsychiatric symptoms and treatment responses. This variability reflects a range of underlying biological, genetic and environmental factors. This review summarizes recent advances in leveraging real-world electronic health records (EHRs) and clinical brain MRI to enhance precision medicine in dementia care. Traditional MRI research has identified consistent subtypes of atrophy associated with AD. However, these models often struggle to apply to routine clinical imaging, which can vary widely in contrast, resolution and acquisition protocols. Recent technological developments now allow for reliable measurement of gray matter, white matter, brainstem and cerebellar structures from routine clinical scans, effectively overcoming long-standing limitations of conventional neuroimaging methods. Additionally, efforts in EHR analysis, including the use of natural language processing on unstructured clinical notes, have enabled large-scale extraction of cognitive scores, neuropsychiatric symptoms and treatment responses. By integrating structured EHR data with detailed imaging markers, researchers have enabled predictive modeling of cognitive decline and treatment responses, though generalizability across settings remains a challenge. Federated learning frameworks offer a privacy-preserving approach to collaboratively develop models across multiple institutions. Together, these strategies outline a practical, data-driven approach to individualized diagnosis, prognosis and treatment planning for dementia.","url":"https://pubmed.ncbi.nlm.nih.gov/42619056/","authors":["Oishi K","Adams R","Nowrangi MA","Zandi PP","Lyketsos CG"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 19","doi":"10.1080/09540261.2026.2714142","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42618521","name":"Enhancing security and efficiency in healthcare cloud computing with blockchain integration and LZW compression: The BICCM framework.","source":"pubmed","abstract":"Modern healthcare system produces large amount of sensitive clinical data, but no existing framework simultaneously addresses and satisfy rigorous security guarantees, storage efficiency and the throughput demand of clinical operations/transactions. All existing framework addresses these requirements in isolation such as cloud architectures lack cryptographic immutability and standalone blockchain systems suffer by high gas costs and block size limitations. To resolve this gap, we present the Blockchain-Integrated Cloud Compression Model (BICCM) a three-tier architecture that combines (i) Ethereum based smart contracts with Role-Based Access Control (RBAC) and Zero-Knowledge Proof (ZKP) authentication, (ii) Lempel-Ziv-Welch (LZW) lossless compression applied at a dedicated light node layer and (iii) a blockchain secured cloud object store with on-chain SHA-256 has anchoring. COVID-19 medical records from Pakistan Bureau of statistics (PBS-2023) in six size classes range from 4.93 to 39.80KB were used for evaluation.BICCM achieves 65.81 to 85.34% storage reduction, 64.3 to 84.4% gas savings (mean 76.4%), 45.4 to 83.2% execution-time improvement, and 50% transaction cost reduction Compared to the traditional Ethereum blockchain outperformed four competing algorithms (LZ-String, LZ77, Run-Length Encoding, Huffman) for all evaluated data sizes, attaining compression ratios of 2.92 to 6.82. Smart contract deployment on auxiliary nodes reduced ETH costs by 88.6% compared to the main deployment node. BICCM offers a scalable, safe and HIPAA/GDPR complaint platform for blockchain cloud healthcare data management with full implementation across all twelve assessed security criteria. Future work will follow a strategy for federated learning integration and post-quantum cryptography transition.","url":"https://pubmed.ncbi.nlm.nih.gov/42618521/","authors":["Ullah F","Uddin Z","Sardaraz M","Javed M","Aziz T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 19","doi":"10.1177/09287329261479267","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42618402","name":"Recommendations for a national electronic health record in Spain: a Delphi study.","source":"pubmed","abstract":"Despite decades of health-care digitalisation efforts worldwide, health information systems remain highly fragmented, with multiple vendor-specific silos that communicate through incomplete solutions. This fragmentation prevents the creation of real-time, lifelong patient health records and becomes increasingly problematic as demand grows for person-centred care, data-driven clinical practice, and greater patient involvement in health-care decisions. To address these challenges and establish a foundation for a nationwide electronic health record (EHR), the Spanish Ministry of Health commissioned a steering committee to develop recommendations based on a comprehensive national consensus. The committee conducted a Delphi study comprising 45 items across four domains, which was distributed to 220 experts from June 23, 2023, to Sept 26, 2023. With a response rate of 69&#xb7;1% (152/220), the study achieved consensus in a single round, with all items reaching the pre-established threshold of greater than or equal to 70% agreement (scores 7-9 on a 9-point Likert scale), and consensus ranging from 118 (77&#xb7;6%) to 151 (99&#xb7;3%) of 152 responses (44 items &#x2265;80%). The resulting recommendations were externally validated by an international advisory board, which assessed their consistency and alignment with global best practices and standards. The final set included 20 recommendations across four domains: justification of need (2 items), functional characteristics (7 items), technical characteristics (6 items), and governance (5 items). These recommendations provide a roadmap for developing a robust, integrated national health information system centred on a standardised, longitudinal EHR. The proposed approach moves beyond generic calls for interoperability by embedding clinical knowledge into open, standardised EHR architectures through ontology-driven semantic integration, supported by federated governance and citizen-controlled data use. This roadmap equips Spain to implement a longitudinal, knowledge-driven national record while providing a scalable model for other countries transforming fragmented health information systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42618402/","authors":["Piera-Jiménez J","Cano I","Carot-Sans G","Cresswell K","Dunscombe R","Freriks G","Frid S","Hovenga E","Kalra D","Koch S","Leslie H","Moral L","Muñoz A","Nogueras MM","Carrasco MP","Jiménez MP","Pérez S","RossiMori A","Serrano P","Boscá D","Lovis C","Valle L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 19","doi":"10.1016/j.landig.2026.101037","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42598558","name":"From molecules to minds: Integrative multi-omics in psychiatry.","source":"pubmed","abstract":"Psychiatric disorders are biologically complex conditions arising from interactions across genomic, epigenomic, transcriptomic, proteomic, metabolomic, and metagenomic layers. Single-omics approaches rarely capture more than a fraction of the variance in complex conditions, underscoring the importance of integrative multi-omics frameworks. This mini-review summarizes key methodologies and their application in psychiatric research, with a focus on systems-level integration of genomic risk scores, transcriptomic networks, and neuroimaging data to advance biological understanding of disorders such as depression, schizophrenia, and Alzheimer's disease. We also outline the infrastructural requirements for effective multi-omics research, including standardized biobanking, Laboratory Information Management Systems, adherence to FAIR data principles, and federated learning approaches for privacy-preserving analysis. Importantly, we highlight the need for greater global inclusivity in psychiatric genomics. Current datasets are heavily biased toward relatively high-resourced and predominantly White, non-Hispanic populations, limiting generalizability. Initiatives such as the Psychiatric Genomics Consortium-Africa and H3ABioNet demonstrate how locally led efforts can strengthen capacity, promote data sovereignty, and support equitable research practices. Advancing multi-omics psychiatry will require coordinated investment in infrastructure, training, and inclusive international collaboration. This mini-review serves primarily as a conceptual roadmap, highlighting what integrative approaches have demonstrated so far and future directions for the field.","url":"https://pubmed.ncbi.nlm.nih.gov/42598558/","authors":["Abbasi H","Hawn SE","Javanbakht A","Seedat S","Bourassa K","Sinnott SM","Seligowski AV","Hemmings S","Kimbrel NA","Wolf E","Smith AK","Brick L","Mehta D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Sep","doi":"10.1016/j.xjmad.2026.100194","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42594411","name":"DAQSyn: A decentralized adaptive quantization-aware synchronous framework in heterogeneous on-device AI networks.","source":"pubmed","abstract":"Decentralized Federated Learning (DFL) has emerged as a key paradigm for collaborative model training across distributed edge devices while preserving data privacy and system autonomy. However, the performance of existing DFL frameworks is constrained by synchronization delays, high communication overhead, and poor scalability in heterogeneous environments. Conventional methods often rely on fixed-precision quantization and uniform synchronization, which overlook device diversity and data heterogeneity, leading to slower convergence and inefficient resource utilization. To address these limitations, this paper presents the Decentralized Adaptive Quantization-Aware Synchronous (DAQSyn) framework, a unified resource-aware learning strategy that integrates adaptive quantization, synchronization, and performance-weighted aggregation. In DAQSyn, each device autonomously adjusts its model precision according to its computational and communication capabilities, generating lightweight models that enhance convergence speed while preserving performance. The synchronization ensures efficient coordination among devices through a BSP-based barrier that harmonizes update timing, reducing idle waiting time by approximately 19% compared to FP16 and over 25% compared to low-bit quantization. The performance-weighted aggregation mechanism further enhances model stability by prioritizing high-quality local updates, leading to an accuracy improvement of 1% and a 2.5&#x202f;&#xd7;&#x202f; faster convergence relative to fixed-precision approaches. Combined, these mechanisms enable lightweight models with reduced communication cost and robust learning performance across heterogeneous devices and non-IID data settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42594411/","authors":["Bibi M","Khan QW","Yazdan SA","Ahmad R","Kim DH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 5","doi":"10.1016/j.neunet.2026.109474","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42592416","name":"A systematic review of heterogeneity in clinical trial design for anal cancer radiotherapy-immunotherapy combinations.","source":"pubmed","abstract":"Localised anal squamous cell carcinoma (ASCC) is treated with radical chemoradiotherapy, which achieves durable disease control in most patients. However, treatment failure is stage-dependent, with approximately 25-30% of patients with locally advanced disease relapsing after treatment, supporting investigation of novel approaches such as radiotherapy-immunotherapy (RT-IO) combinations. Combining radiotherapy (RT) with immunotherapy (IO) shows promise, with multiple trials investigating different combinations. This review evaluates trial design for RT-IO trials in ASCC. A systematic review was conducted on PubMed, ClinicalTrials.gov, and EudraCT databases to identify interventional clinical trials evaluating RT combined with immune-modulating treatments for ASCC. The review was prospectively registered on PROSPERO (CRD42023384068). Information on trial design, patient selection, and treatment regimens was collected. Twelve trials were identified, with three reporting results for a combined total of 69 patients. Across all trials, 14 different IO regimens were used before, during, and after RT. Substantial heterogeneity was observed in patient selection, statistical design, chemotherapy, and RT regimens. Data on exploratory and translational analyses was available for six trials. No trials used biomarkers for enrolment, although INTERACT-ION used cHPV-DNA to guide post-induction treatment allocation. Full trial comparisons were hindered by protocol publication and commercial sensitivities. The diversity in IO regimens and timing relative to RT offers valuable data on RT-IO combinations for ASCC. However, heterogeneity in trial design and translational analysis will limit comparisons between trials. Two possible solutions to improve comparisons are a core minimal translational component and federated learning.","url":"https://pubmed.ncbi.nlm.nih.gov/42592416/","authors":["Samuel RJ","Samson A","Gilbert DC","Adams R","Renehan A","Cook N","Sebag-Montefiore D","Brown S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Nov","doi":"10.1016/j.ctro.2026.101243","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42591847","name":"From pixels to precision: a narrative review of AI-driven 3D morphological analysis and digital twinning in alveolar cleft management.","source":"pubmed","abstract":"Alveolar cleft is a congenital craniofacial anomaly of common occurrence and is frequently seen in cleft lip and palate patients. This condition affects the patient's chewing, speech, and psychological and social life. This review aims to offer a broad overview of the role of artificial intelligence (AI) throughout the entire management of an alveolar cleft from diagnosis to treatment and to life after surgery, in terms of quality of life.","url":"https://pubmed.ncbi.nlm.nih.gov/42591847/","authors":["Yang Z","Qian H","Zeng Y","Huang Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 31","doi":"10.21037/tp-2026-0268","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42583641","name":"Converge Faster, Talk Less: Hessian-Informed Federated Zeroth-Order Optimization.","source":"pubmed","abstract":"Zeroth-order (ZO) optimization enables dimension-free communication in federated learning (FL), making it attractive for fine-tuning of large language models (LLMs) due to significant communication savings. However, existing ZO-FL methods largely overlook curvature information, despite its well-established benefits for convergence acceleration. To address this, we propose HiSo, a Hessian-informed ZO federated optimization method that accelerates convergence by leveraging global diagonal Hessian approximations, while strictly preserving scalar-only communication without transmitting any second-order information . Theoretically, for non-convex functions, we show that HiSo can achieve an accelerated convergence rate that is independent of the Lipschitz constant L and model dimension d under some Hessian approximation assumptions, offering a plausible explanation for the observed phenomenon of ZO convergence being much faster than its worst-case &#x1d4aa; ( d ) -bound. Empirically, across diverse LLM fine-tuning benchmarks, HiSo delivers a 1~5&#xd7; speedup in communication rounds over existing state-of-the-art ZO-FL baselines. This superior convergence not only cuts communication costs but also provides strong empirical evidence that Hessian information acts as an effective accelerator in federated ZO optimization settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42583641/","authors":["Li Z","Ying B","Liu Z","Dong C","Yang H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42583179","name":"Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging.","source":"pubmed","abstract":"Multimodal natural language processing (NLP) represents a transformative approach in ophthalmology by bridging clinical text and medical imaging to enhance diagnostic accuracy, workflow efficiency, and patient-centered care. The mainstream NLP technologies were applied in ophthalmology, such as self-attention and cross-modality attention in multimodal settings, BERT and GPT in text-focused settings, and vision-focused transformers in imaging-focused settings. The applications of multimodal NLP in ophthalmology cover three key domains as follows: clinical text-image integration for comprehensive data analysis, enhanced screening and diagnostic prediction systems, and improved patient communication and management. With the significant advancements of multimodal NLP applications in ophthalmology, the consequent challenges must be addressed, including data privacy concerns, domain-specific terminology adaptation, model interpretability, standardization of multimodal data, and regulatory validation. Emerging directions (e.g., few-shot learning, real-time interactive systems, and privacy-preserving federated learning) offer potential solutions to current challenges. The successful implementation of multimodal NLP in ophthalmology requires collaborative efforts to develop standardized datasets, refine ethical guidelines, and validate clinical utility. By synthesizing textual and visual data, these technologies are poised to reshape ophthalmic practice, ultimately leading to more precise, accessible, and personalized eye care.","url":"https://pubmed.ncbi.nlm.nih.gov/42583179/","authors":["Qin S","Zhao N","Shen L","Zhang Y","Ge S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fmed.2026.1762586","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42578568","name":"Predictive Models for Toxicities after CAR T-cell Therapy: Challenges and Opportunities.","source":"pubmed","abstract":"Chimeric antigen receptor (CAR) T-cell therapy is increasingly utilized with expanding indications beyond hematologic malignancies. Here, we review existing models developed for predicting toxicities in the CAR T-cell setting and identify both strengths and challenges emerging with their application. Predictive modeling approaches offer potential to guide risk stratification and inform clinical decision-making, but small sample sizes, overfitting, and poor data quality have limited model reproducibility and widespread adoption. As utilization of CAR T-cell therapy broadens, identifying additional biomarkers, developing context-specific models, standardizing guidelines for emerging toxicities, and leveraging federated learning to promote collaborative data sharing will be critical.","url":"https://pubmed.ncbi.nlm.nih.gov/42578568/","authors":["Ma J","Culbert A","Chang TG","Solter ML","Ruppin E","Rejeski K","Shah NN"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 11","doi":"10.1158/2643-3230.BCD-25-0458","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42574399","name":"Image Style Transfer-Empowered Federated Domain Generalization.","source":"pubmed","abstract":"As a distributed machine learning paradigm, federated learning enables collaborative training among multiple clients while preserving data privacy. However, in practical applications, it faces the challenge of domain shift caused by data heterogeneity, which limits the generalization performance of the global model on unseen target domains. To address this issue, this paper proposes an image style transfer-empowered federated domain generalization method. Specifically, the method first enriches the domain diversity of local data through image style transfer techniques. Meanwhile, we introduce a predictive consistency regularization term into the optimization objective, it ensures the model maintains stable outputs when processing both original samples and their restyled versions, thereby mitigating the overfitting to local data domains and facilitating the learning of domain-invariant features. Furthermore, a generalization capability-aware aggregation weight optimization strategy is developed. By leveraging an unlabeled public dataset on the server side and its restyled versions to simulate unseen target domains, the strategy evaluates the generalization performance of client models and dynamically adjusts aggregation weights accordingly, which enhances the contribution of clients with higher generalization capabilities to the global model. Finally, experiments validate the effectiveness of both the local regularization and aggregation weight optimization strategies. On the PACS and Office-Home datasets, the proposed method achieves higher average test accuracy compared to baseline methods, along with faster convergence speed. Moreover, we additionally conduct experiments on the Camelyon17 tumor classification dataset, which further verify the robustness and practical applicability of the proposed method in real-world scenarios.","url":"https://pubmed.ncbi.nlm.nih.gov/42574399/","authors":["Wang Q","Li Q","Li X","Chen S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/TIP.2026.3720516","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42567162","name":"Multi-modal AI-enabled steatotic liver disease diagnostics using facial images and metabolomics.","source":"pubmed","abstract":"Steatotic liver disease (SLD) affects one-third of the global population, yet current non-invasive diagnostic methods are too costly or operator-dependent for population-scale screening. Here, we present 3D-FAICE, a deep learning system that uses three-dimensional facial imaging for non-invasive SLD detection. Trained and tested on 11,456 participants, the facial model achieves robust performance across internal, external, and self-controlled longitudinal cohorts and remains effective in a smartphone-based point-of-care setting. Metabolomic analysis reveals that facial risk scores correlate with glycolipid and amino acid pathways, supporting biological plausibility. Multimodal fusion of facial and metabolomic data further improves accuracy, and a cross-modal distillation strategy significantly elevates the performance of the facial-only model. These findings establish facial image-based AI as a non-invasive, scalable, and privacy-aware tool for SLD screening, with potential applications in self-monitoring and population health management.","url":"https://pubmed.ncbi.nlm.nih.gov/42567162/","authors":["Gao Y","Wang K","Ke Y","Zou Z","Wei G","Wang F","Wang W","Li G","Fok M","Beck S","Wong IN","Zhang K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 18","doi":"10.1016/j.xcrm.2026.102971","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42564399","name":"Neural-network-inspired federated coordination for noise-tolerant distributed optimization in cyber-physical demand-side management.","source":"pubmed","abstract":"This study proposes a neural-network-inspired computational framework for hierarchical, noise-tolerant coordination in distributed agent networks. The framework is evaluated in a cyber-physical demand-side management testbed in which autonomous home energy management systems perform local scheduling under a shared global price signal.","url":"https://pubmed.ncbi.nlm.nih.gov/42564399/","authors":["Gharbi A","Alshammari A","Albalawi N","Ben Halima N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fncom.2026.1895688","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42563683","name":"[Research progress on predictive modeling of invasive candidiasis in critically ill patients].","source":"pubmed","abstract":"Invasive candidiasis is one of the common invasive fungal infections in the intensive care unit (ICU), characterized by high incidence, high mortality, and diagnostic difficulty. The clinical presentation of invasive candidiasis lacks specificity, and conventional diagnostic methods are time-consuming with low positivity rates, often leading to delayed treatment and increased death risk. Therefore, early and precise identification of high-risk patients, along with the development of sensitive and specific risk assessment tools to guide individualized antifungal strategies, remains a critical clinical challenge. In recent years, traditional prediction models based on clinical risk factors, Candida colonization status, and microbiological markers have been progressively refined and have played an important role in risk stratification and empirical antifungal therapy decisions. However, their positive predictive value and generalizability remain limited. With advances in precision medicine, biomarkers such as (1,3)-&#x3b2;-D-glucan (BDG), inflammatory and nutritional indicators, immune cell subsets, host response assays, and molecular diagnostic techniques are increasingly being incorporated into risk assessment frameworks, driving the evolution of predictive models from single clinical indicators to multidimensional integration. Meanwhile, artificial intelligence approaches, including machine learning, deep learning, and federated learning, can effectively mine complex information from electronic health records and have demonstrated considerable promise in improving predictive accuracy, enabling dynamic risk assessment, and supporting clinical decision-making. Nevertheless, most current models lack large-scale, multicenter prospective validation, and their interpretability, generalizability, and clinical utility require further clarification. This review summarizes the current state of research on invasive candidiasis prediction models in critically ill patients, covering traditional clinical predictive models, biomarker integration strategies, and artificial intelligence-based models, and discusses future research directions to facilitate early identification and precision diagnosis and treatment of invasive candidiasis in critically ill settings.","url":"https://pubmed.ncbi.nlm.nih.gov/42563683/","authors":["Li M","Meng S","Li X","Li S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul","doi":"10.3760/cma.j.cn121430-20260214-00092","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42558874","name":"From graph models to intelligent decision-making: a review of spatio-temporal graph neural networks for regional disease risk prediction and etiology mining.","source":"pubmed","abstract":"Regional disease risk prediction is a core component of public health early warning systems. Traditional statistical models and machine learning methods have inherent limitations in handling multi-source heterogeneous data fusion, complex spatio-temporal dependency modeling, and interpretable etiology mining, making it difficult to meet the demands of precise and real-time public health decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/42558874/","authors":["Chen Y","Qin X","Chen S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1830634","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42558711","name":"A federal learning-driven artificial intelligence framework for fundus image myopia diagnosis.","source":"pubmed","abstract":"Myopia has emerged as a critical global public health challenge. This study aims to develop a privacy-preserving federated learning (FL) framework for the triple classification of fundus images (normal, myopia, and pathological myopia), designed to generalize across institutions while addressing data heterogeneity and class imbalance.","url":"https://pubmed.ncbi.nlm.nih.gov/42558711/","authors":["Yin X","Yu C","Xiong W","Liao Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/20552076261426324","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42558280","name":"Artificial intelligence in the early diagnosis of prostate cancer: from multimodal imaging to liquid biopsy.","source":"pubmed","abstract":"Prostate cancer (PCa) is the most prevalent malignant tumor in the urogenital system among men worldwide. Due to its subtle early symptoms and strong tumor heterogeneity, traditional diagnostic methods relying on a single prostate-specific antigen (PSA) initial screening and subjective imaging evaluations often lead to high false positives, overt biopsies, and missed small lesions. The rapid development of artificial intelligence (AI) provides innovative solutions to overcome these clinical bottlenecks. This article comprehensively reviews the application of AI in the early intelligent diagnosis of PCa. In the fields of ultrasound, magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) imaging, AI significantly enhances the accuracy of target lesion identification. It achieves this by deep decoding high-dimensional quantitative features and effectively reducing subjective bias. In non-invasive liquid biopsy, AI-driven multi-omics networks have successfully addressed challenging screening blind spots, such as the PSA gray zone. In light of current challenges such as limited model generalization capability and the \"black box effect\" of algorithms, this article looks forward to the development prospects of constructing multimodal fusion models based on federated learning and explainable AI (XAI), aiming to promote the transition of PCa diagnosis and treatment from algorithm development to real clinical decision support.","url":"https://pubmed.ncbi.nlm.nih.gov/42558280/","authors":["Xie J","Yin Z","Gao R","Qiu Z","Zhou Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fonc.2026.1906810","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42556432","name":"Risk of Pediatric Eye Disease in Children with Neurodevelopmental and Behavioral Disorders in the United States.","source":"pubmed","abstract":"To evaluate the association of neurodevelopmental disorders (NDDs) and behavioral disorders with amblyopia, myopia, strabismus, and strabismus surgery rates.","url":"https://pubmed.ncbi.nlm.nih.gov/42556432/","authors":["Shah J","Pathuri S","Ong J","Plotnik J","Salevitz M","Shahraki K","Suh DW","Dersch AM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 5","doi":"10.1016/j.ophtha.2026.07.037","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42554297","name":"Building a Global Research Network for Fair, Accountable, Interpretable, and Responsible AI in Emergency Care: Protocol for a FAIR-EC Study.","source":"pubmed","abstract":"The current landscape of emergency care (EC) is marked by high demand, leading to issues such as emergency department boarding, overcrowding, and subsequent delays that impact the quality and safety of patient care. Integrating data science into EC can enhance decision-making with predictive, preventative, personalized, and participatory approaches. However, gaps in adherence to fairness, accountability, interpretability, and responsibility are evident, particularly due to barriers to data-sharing, which often result in a lack of transparency and robust oversight in these applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42554297/","authors":["Hong C","Liew JCK","Yu J","Barry T","Blewer AL","Buckland DM","Cai T","Cha WC","Chakraborty B","Chen W","Cheng J","Chong SL","Djärv T","Earnest A","Engelhard M","Fan X","Feng M","Feng J","Fu H","Goh WWB","Goldstein BA","Gronsbell J","Ho AFW","Ho K","Iwami T","Joiner A","Kornblith A","Li S","Lim SL","Liu M","Liu Z","Lu L","Luo Y","Ng YY","Ning Y","Okada Y","Park JO","Park YR","Razzak J","Shen Y","Siddiqui FJ","Steel PAD","Tan KBK","Teixayavong S","Vakulenko-Lagun B","Vissoci JRN","Waligora G","Wang F","Wang H","Wang H","Wong AI","Xie F","Yang J","Zhang Y","Zhou D","Zhou L","Zhu T","Neumar R","Page D","Vaughan R","Ong MEH","Liu N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 4","doi":"10.2196/74202","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42540394","name":"Artificial intelligence in dermatology: A literature review of current evidence and clinical implementation.","source":"pubmed","abstract":"Artificial intelligence is emerging as a transformative and rapidly developing technology, with growing implications for the healthcare sector, including dermatology. We conducted a literature review using PubMed and EMBASE. Included studies primarily focused on the application of artificial intelligence for image-based classification. These artificial intelligence systems showed remarkable performance in controlled settings, even matching dermatologist-level accuracy for specific narrow tasks. Several common limitations were identified, including restricted dataset sizes, limited diagnostic diversity, potential selection bias, and inconsistencies in model evaluation. Clinical implementation should require careful attention to validation rigor, dataset diversity, implementation strategies, ethical considerations, and evidence of real-world impact.","url":"https://pubmed.ncbi.nlm.nih.gov/42540394/","authors":["Christensen JMM","Nyheim-Tømmerås M","Hansen BK","Haulrig MB","Manolache M","Løvendorf MB","Dyring-Andersen B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Sep","doi":"10.1016/j.xjidi.2026.100497","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42538662","name":"AI-Driven Optimization of Kidney Allocation: Enhancing Precision in Donor-Recipient Matching.","source":"pubmed","abstract":"The growing demand for kidney transplant amid persistent organ scarcity demands improved donor -recipient compatibility assessment. Traditional allocation systems relying on rigid scoring fail to capture complex multidimensional data affecting posttransplant outcomes like graft rejection and delayed graft function. Artificial intelligence offers transformative potential through predictive analytics and adaptive learning. This study introduces OkAP, an AI -based system designed to enhance kidney transplant matching efficiency and accuracy by integrating clinical, genetic, and socioeconomic variables for dynamic, equitable decision -making.","url":"https://pubmed.ncbi.nlm.nih.gov/42538662/","authors":["Okhovvat M","Amirkhanlou S","Simforoosh N","Poor-Reza Gholi F","Erfani SS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul","doi":"10.6002/ect.MESOT2025.O25","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42519852","name":"Pseudo-Global Based Sequential Contribution Estimation for Federated Semi-Supervised Medical Image Segmentation.","source":"pubmed","abstract":"Federated semi-supervised learning (FSSL) for medical image segmentation has been extensively studied in recent years. Due to the requirement of specialized knowledge and equipment for annotating medical data, only a very limited number of medical institutions have a small amount of labeled data. However, existing federated semi-supervised segmentation methods primarily focus on fully supervised clients to improve average performance and often overlook the contributions of unsupervised clients. To effectively leverage unsupervised clients and extract more valuable information, we propose pseudo-global based sequential contribution estimation for federated semi-supervised segmentation, abbreviated as FedPSC. FedPSC first estimates the performance contributions of all clients using the difference in validation performance, and then builds a pseudo-global model based on this. Sequentially, the pseudo-global model and the union generated by the exclusion effect are used to estimate the gradient contribution of the client, thereby establishing a relationship between the two contributions. Besides, we also introduce a gradient direction exponential moving average method, which aims to train client models by integrating both global general knowledge and local personalized insights. Experimental results on two commonly used datasets confirm the effectiveness of our proposed method. Additional experiments and analysis are also provided to give in-depth understanding of FedPSC.","url":"https://pubmed.ncbi.nlm.nih.gov/42519852/","authors":["Zhou G","Xu Z","Li B","Zhang Y","Lukasiewicz T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 28","doi":"10.1109/JBHI.2026.3717606","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42515237","name":"From Sensor-Empowered Ubiquitous Computing to Embodied Intelligence: Architectures, Paradigm Evolution, and Emerging Challenges.","source":"pubmed","abstract":"With the rapid development of artificial intelligence technology, the transportation, industry, and healthcare fields are undergoing an intelligent evolution. These advancements have raised higher requirements for technologies such as mobile robots, wearable intelligent agents, self-driving cars, and unmanned aerial vehicles. Compared with traditional discrete sensor architectures, highly integrated sensing systems deliver superior speed, efficiency, and reliability to satisfy the stringent requirements of emerging intelligent devices. By integrating advanced technologies such as perception, communication, and computing, the process of system intelligence is accelerating, driving us into the era of embodied intelligence. Thus, sensors are no longer merely passive data collection tools but have transformed into core components that drive the connection between perception and action. To help researchers better understand this transformation and clarify the implementation path, we summarize the key technological advancements in related fields. Firstly, we review the related technological developments, including the sensor, multi-modal perception, wireless communication, and edge computing technology. Then, we explore the limitations of traditional sensors and independent computing models, especially the trade-offs among latency, energy efficiency, and system reliability. Subsequently, we introduce innovative technologies that drive the development of embodied intelligence, covering advanced learning mechanisms such as multi-agent systems, reinforcement learning, and federated learning. Finally, we compare the typical application scenarios of the two paradigms and discuss the challenges faced by existing technologies and standardization. We also look forward to future research directions in this field.","url":"https://pubmed.ncbi.nlm.nih.gov/42515237/","authors":["Jia A","Cai Z","Liu X","Zheng K","Liu J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 9","doi":"10.3390/s26144352","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42506176","name":"Application of Machine Learning for Mean Glandular Dose Prediction Utilizing DICOM Mammography Images.","source":"pubmed","abstract":"The growing demand for raw and processed scientific data has encouraged many researchers and research institutions to adopt an open-source data policy. At present, data accessibility is of paramount importance due to the growing demand for artificial intelligence (AI) and machine learning (ML) applications in various scientific fields, particularly medicine. Medium- to large-scale mammography datasets are widely used in breast cancer research to develop and evaluate computer-aided detection methods. However, there are only a few studies on using mammogram datasets for the prediction of the breast mean glandular dose ( MGD ) with AI or ML models. The aim of this study was to investigate the feasibility of using ML and deep ML for MGD prediction based on DICOM images and retrieved dosimetric data from DICOM mammogram images. A total of 26,988 mammography images in DICOM format were obtained from the Federated Research Data Repository (FRDR). Eleven regression algorithms and three neural network-based models were evaluated using five-fold cross-validation. In addition, a deep ML fusion model based on Vision Transformer (ViT) and tabular data was developed for the prediction of the MGD normalized conversion factor CF(DgN). A mean breast thickness of 61.37 mm and a mean MGD of 1.53 mGy (0.55-6.33 mGy) were calculated using this dataset. Regarding tabular data, the artificial neural network (ANN) sequential models outperformed other linear and tree-based models. The ViT deep ML fusion model was tested with three configuration versions differing on the number of features included. A comparison of the three versions revealed that the version with six features achieved the best overall predictor performance. This study demonstrates that ML and deep ML can effectively predict the MGD using dosimetric tabular data and mammography DICOM images. The use of ML with tabular data extracted from DICOM images can be further strengthened by incorporating larger and more diverse datasets.","url":"https://pubmed.ncbi.nlm.nih.gov/42506176/","authors":["Alghamdi AAA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 21","doi":"10.3390/jimaging12070330","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42506103","name":"Wearable Devices and Machine Learning in Cardiovascular Monitoring: Current Evidence and Future Directions for Precision Medicine.","source":"pubmed","abstract":"Cardiovascular disease remains the leading global health challenge, claiming approximately 19.8 million lives annually. The convergence of wearable technology and artificial intelligence represents a transformative shift in cardiovascular healthcare, enabling continuous real-time monitoring beyond conventional clinical settings. This narrative review synthesises current evidence on integrating consumer-grade and medical-grade wearable devices with AI algorithms for continuous cardiovascular monitoring applications, with particular attention to real-world translational applicability and global health equity. This review examined the technological landscape of wearable cardiovascular monitoring devices, including smartwatches with photoplethysmography and electrocardiogram capabilities, continuous cardiac monitoring patches, and emerging biosensor technologies. Also, the review explored AI methodologies, particularly machine learning and deep learning architectures, employed in processing complex physiological data streams from these devices. Clinical applications demonstrate impressive capabilities: arrhythmia detection with sensitivity rates exceeding 98%, continuous blood pressure monitoring through cuffless technologies, heart failure decompensation prediction, and cardiovascular risk stratification. However, substantial challenges persist, including data quality assurance, algorithm interpretability, regulatory compliance, and seamless clinical workflow integration. Privacy concerns, health disparities in algorithm performance, and the need for robust validation across diverse populations remain critical considerations. AI-enhanced wearable systems hold considerable potential for shifting cardiovascular care from reactive treatment paradigms towards predictive, preventive, and precision medicine approaches. Future directions include edge computing architectures, federated learning approaches, personalised AI models, enhanced interoperability with electronic health records, and expansion to resource-limited settings, ultimately improving patient outcomes whilst reducing healthcare costs.","url":"https://pubmed.ncbi.nlm.nih.gov/42506103/","authors":["Osonuga A","Dave M","Ogieuhi IJ","Olawade DB","Boussios S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 14","doi":"10.3390/jpm16070377","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42499769","name":"The transformative potential of artificial intelligence in pediatric medicine: Current applications, methodological challenges, and future directions.","source":"pubmed","abstract":"Artificial intelligence (AI) possesses the transformative potential to reshape pediatric medicine, offering powerful tools for diagnosis, prognosis, and personalized therapy. This review focuses on three domains selected for their relative maturity in AI development and proximity to clinical translation-pediatric critical care, perinatal and neonatal medicine, and precision oncology-evaluating current evidence for clinical utility and outlining challenges to implementation. AI is demonstrating significant potential across these domains: in critical care, deep learning models outperform traditional scoring systems for dynamic prediction of adverse events; in perinatal and neonatal medicine, AI enhances prenatal ultrasonography and integrates multiomics data to guide complex therapies; and in oncology, radiomics, and genomic analysis enable non-invasive tumor characterization and personalized treatment strategies. However, significant hurdles remain. Foundational data challenges-including scarcity, heterogeneity, and limited sharing of pediatric data-are being addressed through transfer learning, federated learning, and synthetic data generation. Clinical translation is further impeded by algorithmic bias, the 'black box' problem, and the unique developmental physiology of children, which demands age-specific model validation. Future progress depends on multi-institutional collaboration, a research focus that extends beyond prediction to encompass causal inference and explainability, and the establishment of robust ethical, regulatory, and economic frameworks. Ultimately, responsible implementation of AI in pediatrics requires building systems that are not merely accurate but transparent, equitable, and trustworthy.","url":"https://pubmed.ncbi.nlm.nih.gov/42499769/","authors":["Wang R","Ding X","Zhang W","Shi T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug","doi":"10.1002/ped4.70061","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42492103","name":"Backspin: A backdoor attack framework for split learning based on smashed data.","source":"pubmed","abstract":"Split Learning (SL) represents a novel collaborative machine learning paradigm tailored for participants with private data and constrained computational capabilities. As with Federated Learning (FL) and other collaborative learning paradigms, SL is also vulnerable to security threats, including backdoor attacks. Although prior research has claimed that SL is highly resilient to backdoor attacks because attackers lack access to other participants' training data and models, this study reveals vulnerabilities in SL to such threats by analyzing smashed data from multiple clients. Based on our analysis and observations, we propose a novel Backdoor attack framework against split learning, termed Backspin, that leverages the similarity of smashed data from different clients to execute both client-side and server-side attacks. Our evaluations cover both Computer Vision (CV) and Natural Language Processing (NLP) tasks across six widely used datasets and six models in diverse split-learning settings to validate Backspin's effectiveness and robustness against prevalent privacy defenses. Remarkably, our method achieves an average Attack Success Rate (ASR) exceeding 90%, while incurring only a 1.5% reduction in Clean Data Accuracy (CDA) relative to centralized training, demonstrating the balanced nature of our approach for effective attack execution without significantly compromising data integrity.","url":"https://pubmed.ncbi.nlm.nih.gov/42492103/","authors":["Hu Z","Wang X","Chen C","Wang Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 17","doi":"10.1016/j.neunet.2026.109402","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42469397","name":"An adaptive client selection method for long tailed scene classification in autonomous driving.","source":"pubmed","abstract":"Collaborative autonomous driving utilizing Federated Learning (FL) is frequently constrained by long-tailed data distributions and perceptual imbalances. Implementing federated learning for environmental scene classification in autonomous driving faces severe challenges due to the long-tailed and non-IID distribution of real-world climatic data. To address this, this paper proposes FedRare, a client selection framework based on a multi-dimensional utility function, integrating scenario criticality ([Formula: see text]), distribution rarity ([Formula: see text]), and local update quality ([Formula: see text]). To address the safety concerns in edge cases, we implement a granular evaluation protocol by categorizing 18 driving scenarios into standard, diverse, and safety-critical groups. Experiments on the BDD100K dataset show that FedRare achieves a mean recall of 72.82% during the late training stage (averaged over the final five communication rounds across five random seeds) while the baseline is 63.42%. Results from the trained model validation demonstrate that FedRare achieves a 74.13% recall in safety-critical groups (e.g., rainy/snowy night), while maintaining high perceptual reliability where traditional loss-based methods often fail. Furthermore, the [Formula: see text] constraint stabilizes the training process, keeping the performance fluctuation (standard deviation) at approximately 0.02. This work proposes a framework to enhance perceptual robustness for autonomous driving within complex, long-tailed environments.","url":"https://pubmed.ncbi.nlm.nih.gov/42469397/","authors":["Wen S","Wang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 17","doi":"10.1038/s41598-026-62562-8","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42457824","name":"Resilient federated intrusion detection with explainable AI: a robust CNN-LSTM architecture for extreme non-IID data distributions.","source":"pubmed","abstract":"The rapid proliferation of Internet of Things (IoT) devices has led to a significant rise in security issues, as traditional centralized intrusion detection systems face difficulties in handling issues such as privacy concerns, communication bottlenecks, and heterogeneous data. Federated Learning (FL) is a new paradigm for collaborative learning that can be used to train intrusion detection systems without compromising user privacy. However, it is challenged by critical issues such as handling highly non-independent and identically distributed (non-IID) data, a common problem in heterogeneous IoT networks such as healthcare networks, financial networks, and industrial networks. In this paper, a novel CNN-LSTM architecture is proposed that is equipped with Explainable AI (XAI) to handle extreme cases of heterogeneous data in Federated Learning-based intrusion detection. Using the CIC-IDS2017 dataset and Dirichlet-based partitioning ([Formula: see text] our optimized CNN-LSTM model achieves a 97.36% centralized F1-score. We demonstrate that while architectural design provides highly stable robustness under moderate data heterogeneity ([Formula: see text]), extreme non-IID conditions ([Formula: see text]) trigger severe weight washing, dropping FedAvg performance to 71.53%. Through a comprehensive hyperparameter sweep, we prove that applying FedProx with a strong proximal penalty ([Formula: see text]) successfully mitigates this client drift, recovering the F1-score to 78.27%. Using SHAP, LIME, t-SNE, and PCA, we reveal that our model learns universal protocol-level features (Init_Win_bytes_forward, ACK Flag Count, and Fwd Packet Length Min) that remain invariant across heterogeneous networks. Finally, we demonstrate a highly stable detection is achieved for network-layer attacks, whereas application-layer intrusions (Web Attacks) suffer severe degradation, proving the fundamental limitations of flow-based features for payload-driven attacks.","url":"https://pubmed.ncbi.nlm.nih.gov/42457824/","authors":["Moussaoui JE","Kmiti M","Maleh Y","Gholami KE"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 15","doi":"10.1038/s41598-026-61994-6","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42450985","name":"Ethical and Governance Challenges of AI in Medical Imaging and Diagnostics: A Systematic Survey and Policy Framework Recommendations.","source":"pubmed","abstract":"Background/Objectives: Artificial intelligence (AI) is increasingly embedded within diagnostic imaging workflows, reshaping clinical decision-making, health system governance, and regulatory oversight. While technical advances in radiological AI have accelerated, governance mechanisms have struggled to keep pace with issues of bias, transparency, accountability, and lifecycle oversight. This study examines ethical, regulatory, and implementation challenges in AI-enabled diagnostic imaging, building on prior reviews that have often emphasised technical performance by integrating ethical risk domains with governance responses across the AI lifecycle. Methods: This study presents a PRISMA-ScR-informed systematic survey of 156 sources, including peer-reviewed publications, regulatory documents, policy reports, and professional guidance materials (2018-2025), synthesised through thematic analysis and lifecycle mapping spanning data acquisition, model development, deployment, monitoring, and continuous learning. Results: Drawing on both thematic insights derived from the reviewed literature and established ethical and regulatory frameworks, we propose a literature-derived conceptual ethical-governance framework organised around five pillars: equity and bias mitigation, explainability and transparency, accountability and oversight, privacy-preserving infrastructure, and adaptive regulatory alignment. Although illustrated through the Australian healthcare context, the framework is designed to be transferable to federated and multi-jurisdictional health systems. This review further identifies trust quantification as an underdeveloped but essential dimension of clinical AI governance, emphasising the need to integrate measurable indicators such as calibration, clinician-AI concordance, and patient acceptance into lifecycle-based evaluation. Conclusions: By bridging technical, ethical, and policy perspectives, this review proposes a structured conceptual governance framework to support safe, equitable, and trustworthy AI integration in digital health systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42450985/","authors":["Athukorala D","Ahmed K","Nowrozy R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 2","doi":"10.3390/healthcare14131975","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42449126","name":"Cross-domain edge AI framework for unified threat intelligence in smart grid-EV- VANET ecosystems using lightweight federated learning.","source":"pubmed","abstract":"The convergence of smart grids, electric vehicles (EVs), and Vehicular Ad Hoc Network (VANET) infrastructures has created a rapidly evolving cyber-physical ecosystem that necessitates real-time, privacy-preserving, and intelligent threat detection at the edge. It is crucial to develop an unified threat intelligence across these diverse domains to ensure operational resilience and data integrity. Existing threat detection approaches frequently depend on centralized training pipelines and isolated domain-specific models, resulting in substantial communication overhead, poor scalability, and reduced robustness under domain shift.&#xa0;This ultimately leads to very low detection accuracy when deploying these models in heterogeneous environments with different data distributions. To address these challenges, we propose a Federated Lightweight Cross-Domain Adversarial Domain Adaptation (FL-CDA) framework. FL-CDA enables edge nodes to collaboratively train compact models via federated learning while incorporating adversarial domain adaptation to align heterogeneous data distributions without the need to share raw data. The proposed framework is designed to support the detection of false data injection in Smart Grids, EV charging fraud, and Sybil or spoofing attacks in VANETs while preserving data privacy and reducing bandwidth usage. Experiments in a hybrid evaluation setting comprising a real-world smart-grid dataset and synthetic EV/VANET scenarios indicate that FL-CDA significantly improves cross-domain detection accuracy, reduces communication overhead, and enhances model robustness under dynamic adversarial conditions. The experiments show up to 95% cross-domain accuracy, 1.9 s detection latency, 94% F1-score, and 25-110 MB communication overhead in the simulated setting, while sustaining 86% accuracy with a 50% domain shift.","url":"https://pubmed.ncbi.nlm.nih.gov/42449126/","authors":["Swaminathan S","Avudaiappan T","Baazeer Ahamed B","Joseph ER"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 14","doi":"10.1038/s41598-026-54370-x","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42443473","name":"Building the foundations for global data banking in digital phenotyping for mental health.","source":"pubmed","abstract":"Mental illness is a leading cause of global disability, underscoring the urgent need for scalable, data-driven approaches to early identification and intervention. Passive sensing technologies in mobile and wearable devices enable continuous and unobtrusive measurement of behavioural and physiological signals that may be relevant to mental health. When translated into interpretable digital markers through digital phenotyping, these data hold significant promise for advancing our understanding of the onset, course, and treatment of mental disorders. However, achieving real-world clinical utility requires large-scale, harmonised, and ethically governed databanks that enable replication and generalisability for diverse populations. Informed by recent international initiatives, the academic literature, and our own expertise in digital phenotyping, this Perspective outlines four key priorities for advancing digital phenotyping databanks in depression and anxiety. First, ensuring data quality through standardisation and harmonisation is essential to comparability across studies and to prevent fragmentation. Second, ethical data stewardship demands hybrid consent models that combine the scalability of broad consent with the flexibility of dynamic consent, ensuring meaningful participant control as analytics evolve. Third, robust, privacy-preserving information governance co-created with people with Lived Experience is vital to maintain trust and prevent misuse, with federated learning and open-source pipelines offering promising technical pathways. Finally, the field must promote data reuse by reforming incentive structures, recognising databank-based scholarship, and investing in sustainable infrastructures that reward secondary analyses. Collectively, these priorities offer a pragmatic framework for building equitable, transparent, and scientifically robust digital mental health databanks. Implementing these recommendations will require sustained international collaboration among researchers, funders, institutions, and people with Lived Experience. By aligning scientific rigour with ethical responsibility, digital phenotyping databanks can become transformative tools for advancing the global understanding and treatment of mental illness.","url":"https://pubmed.ncbi.nlm.nih.gov/42443473/","authors":["O'Dea B","Naz S","McLoughlin LT","Whitton AE"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 13","doi":"10.1038/s44277-026-00066-z","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42441460","name":"FuGuard: Client-Level Federated Unlearning via Generative Surrogates and Optimal Transport.","source":"pubmed","abstract":"Federated learning (FL) is a widely adopted paradigm that enables collaborative model training while preserving data privacy. As concerns around data poisoning and the \"right to be forgotten\" continue to grow, federated unlearning, which is the ability to remove the influence of specific training data from a trained FL model, has become increasingly critical. However, existing unlearning methods often require expensive retraining or fail to achieve good forgetting effects, limiting their practicality in real-world FL systems. In this work, we propose FuGuard, a dual-strategy federated unlearning framework, designed for efficient and ideal client-level data removal. FuGuard combines the generative surrogate, which approximates the contribution of the target client, with optimal transport regularization that softly constrains model parameter drift during unlearning. This approach effectively removes the influence of the target client while preserving the stability and performance of the global model. To evaluate the forgetting capability, we conduct testing using backdoor attacks and member inference attacks (MIAs) for residual data influence. Empirical results on different benchmarks demonstrate that FuGuard significantly reduces the impact of the target client's data while maintaining the performance of nontarget clients, consistently outperforming state-of-the-art baselines in both forgetting effectiveness and accuracy retention. Our code is accessible at: https://anonymous.4open.science/r/FuGuard-0263.","url":"https://pubmed.ncbi.nlm.nih.gov/42441460/","authors":["Qi P","Annunziata D","Jappelli C","Giampaolo F","Piccialli F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 13","doi":"10.1109/TNNLS.2026.3708982","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42439220","name":"Vibrational Spectroscopy of Clinical Biofluids: Advances in FTIR and Raman-Based Biomarker Discovery for Disease Diagnosis and Monitoring.","source":"pubmed","abstract":"Human biofluids carry a continuously updated molecular record of systemic health, yet their diagnostic potential remains largely untapped by conventional single-analyte assay platforms. Fourier-transform infrared and Raman spectroscopy, including attenuated total reflection FTIR, surface-enhanced Raman spectroscopy, and FT-Raman, generate label-free spectral fingerprints encoding protein, lipid, nucleic acid, and metabolite composition of biological fluids within a single acquisition. Across five clinically accessible biofluids, namely serum, urine, saliva, cerebrospinal fluid, and tear fluid, diagnostic accuracies of 80-100% have been demonstrated for cancers, neurodegenerative, metabolic, infectious, and renal conditions, with surface-enhanced Raman spectroscopy achieving femtomolar sensitivity through plasmonic nanoparticle enhancement. Machine learning has transformed raw spectra into clinically actionable classifiers, with deep learning outperforming classical chemometric methods in multi-class discrimination. Portable instrumentation, microfluidic substrates, federated learning, and open spectral databases progressively resolve barriers of inter-instrument variability and pre-analytical irreproducibility. Structured multi-centre clinical investment is the outstanding requirement for realization as frontline diagnostic tools.","url":"https://pubmed.ncbi.nlm.nih.gov/42439220/","authors":["Polu PR"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 13","doi":"10.1080/10408347.2026.2702636","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42434076","name":"Imaging studies for predicting hematoma expansion: from traditional imaging signs to artificial intelligence-based multimodal fusion.","source":"pubmed","abstract":"Hematoma expansion (HE) is a critical and modifiable event following acute intracerebral hemorrhage (ICH). Predicting HE accurately can inform individualized treatment and improve patient outcomes. This review systematically outlines the evolution of imaging-based HE prediction. We first define the core concepts of traditional HE, revised HE (rHE), and ultra-early hematoma growth (uHG). We then summarize predictive studies that employ traditional imaging markers, such as the computed tomography angiography (CTA) spot sign, non-contrast CT (NCCT) signs, and combined clinical-imaging scoring systems. Subsequent sections focus on AI-driven methodologies, encompassing radiomics, deep learning, and multi-task learning. The discussion extends to precision prediction through multimodal data fusion and subgroup analyses based on hemorrhage location and onset time. Finally, we address persistent challenges, including model interpretability, generalizability, and translational gaps, and suggest future directions involving federated learning, explainable AI, dynamic prediction, and closed-loop decision systems. This review offers a structured framework to guide both clinical practice and future research.","url":"https://pubmed.ncbi.nlm.nih.gov/42434076/","authors":["Wu J","Sheng J","Xiao Y","Wu F","He P","Jiang R","Zuo Z","Wang P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fneur.2026.1843413","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42428005","name":"GeoGuard-PTI: a geo-temporal predictive threat intelligence framework with spatiotemporal attack forecasting and closed-loop adaptive defense.","source":"pubmed","abstract":"Due to being reactive in nature, most network security frameworks focus on identifying attacks as they take place or reconstructing intrusion sequences once they have already occurred. This paper presents GeoGuard-PTI, an innovative Geo-Temporal Predictive Threat Intelligence framework designed to shift that paradigm by predicting pending cyber attacks before they arrive at monitored infrastructure. GeoGuard-PTI utilizes geo-tagged telemetry from a Real-Time Intrusion Detection Module, an Active Geo-Fencing Prevention Module, and a Forensic Analysis Module, processing it through a Spatiotemporal Graph Attention Network (ST-GAT) combined with a Temporal Diffusion Predictor (TDP). Attack propagation is modeled as epidemiological diffusion over a Dynamic Geographic Graph to produce probabilistic Threat Propagation Maps (TPMs) across five prediction horizons (15&#x202f;min to 24&#x202f;h). A Closed-Loop Adaptive Defense Cycle (CLADC) operationalizes these TPMs into IPS pre-arming signals while driving continuous online learning without offline retraining. Evaluated across five publicly available datasets-NSL-KDD, UNSW-NB15, CICIDS2017, TON-IoT, and BOT-IoT-augmented with synthetic geo-propagation traces, GeoGuard-PTI attains a mean 15-min prediction accuracy of 96.4%, a 2-h accuracy of 91.2%, and a false alarm rate below 1.9%. Operational trials show a ~34.1% reduction in successful intrusions and a ~67.9% drop in mean time-to-block compared with purely reactive baselines, with IPS pre-arming latency held under 3&#x202f;ms throughout.","url":"https://pubmed.ncbi.nlm.nih.gov/42428005/","authors":["Manivannan A","Amalanathan A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1850560","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42427937","name":"Generalization over accuracy: A cross-dataset, explainable, and federated learning framework for Parkinson's disease detection.","source":"pubmed","abstract":"Parkinson's disease (PD) is a progressive neurodegenerative disorder for which early screening remains challenging. Although voice-based machine learning approaches have shown promise as non-invasive screening tools, most existing studies rely on single-dataset evaluations, random data splits, and accuracy-centric metrics, raising concerns about dataset bias, subject leakage, and limited real-world generalizability. This study aims to develop a generalization-aware, explainable, and privacy-preserving framework for PD detection using voice data.","url":"https://pubmed.ncbi.nlm.nih.gov/42427937/","authors":["Ahammad I"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan-Dec","doi":"10.1177/20552076261467837","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42424418","name":"Federated Computing in Orthopaedic Surgery: A Paradigm Shift in Collaborative Multicenter Research.","source":"pubmed","abstract":"&#x27a2; Multicenter studies remain rare in orthopaedics (9.9% of recent publications) due to regulatory complexity, restrictive data-sharing policies, and administrative delays, despite their proven value in generating robust, generalizable evidence. &#x27a2; Federated computing (FC) brings analytic code to each institution's data and returns only encrypted, aggregated results. This preserves patient privacy, maintains institutional control, and streamlines compliance by reducing or eliminating the need for complex data-use agreements. &#x27a2; Extensive applications in other medical fields show that FC can match or exceed the performance of centralized analyses across diverse data types (imaging, electronic health records, genomics, and surgical video) without centralizing sensitive information. &#x27a2; For orthopaedics, FC enables scalable, privacy-preserving collaboration on rare events, low-frequency outcomes, and practice variation across subspecialties. Broad adoption could expand research scope, improve evidence quality, and accelerate translation of findings into clinical care. &#x27a2; FC enables secure, privacy-preserving multicenter collaboration in orthopaedics, overcoming long-standing barriers to data-sharing and expanding the scope of clinically impactful research.","url":"https://pubmed.ncbi.nlm.nih.gov/42424418/","authors":["Hill BG","Nyul TE","Moschetti WE","Schilling PL"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug 19","doi":"10.2106/JBJS.25.01034","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42422330","name":"ReFIT: Federated Transfer Learning for Sequential Prediction and Uncertainty Quantification Using Streaming EHR Data.","source":"pubmed","abstract":"Modern biomedical data are increasingly collected across multiple institutions and time periods, creating opportunities for improved statistical inference through knowledge transfer, but also posing challenges for privacy, scalability, and distributional heterogeneity. We propose a Renewable Federated Incremental Transfer framework, termed ReFIT, for sequentially integrating information from streaming source datasets to improve model estimation and prediction in a target population with limited samples. ReFIT builds upon a density ratio model to account for covariate shift between the source and target populations and employs a renewable updating strategy that allows model parameters to be incrementally refined as new source data become available, using only summary-level information from prior sources. This framework ensures privacy preservation and computational efficiency while adapting to evolving data environments. Beyond improving predictive performance, ReFIT also quantifies predictive uncertainty within a conformal prediction framework, yielding valid prediction intervals that adapt as new information accumulates. Extensive simulation studies demonstrate that ReFIT achieves higher predictive accuracy and better uncertainty quantification than models trained on target or source data alone. The method remains robust under nonlinear model misspecification and varying degrees of source-target shift. Moreover, as ReFIT incrementally integrates additional source data, the conformal prediction intervals become progressively narrower without sacrificing coverage, evidencing improved statistical efficiency with growing information. In an electronic health record application for breast cancer prediction, ReFIT substantially improves prediction for the Hispanic population by sequentially leveraging information from non-Hispanic White patients collected over multiple time periods. These results highlight the potential of ReFIT as a general and practical framework for privacy-preserving, adaptive, and scalable learning from distributed and periodically updated biomedical data.","url":"https://pubmed.ncbi.nlm.nih.gov/42422330/","authors":["Lu Y","Luo L","Gu T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 23","doi":"10.1007/s12561-026-09525-9","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42420370","name":"FET-FIDS: a federated enhanced transformer-based framework for privacy-preserving network intrusion detection.","source":"pubmed","abstract":"The rising rate of interconnected systems, cloud infrastructures, edge environments, and distributed network architectures have greatly exposed the vulnerability of the current digital infrastructures. This has exposed them to more advanced cybercrimes like denial-of-service attacks, malware injections, and data leaks. Also, there are sophisticated persistent threats that add more security burdens to such systems. The traditional Intrusion Detection Systems (IDS) are conventionally designed around central data collection and model training which result in the loss of privacy, a severely limited scale, a huge load on communications and a single point of failure. These constraints are even more deplorable in large and heterogeneous networks. To solve these issues, federated learning-based IDS models are suggested, but the existing practices fail to converge quickly, do not scale to non-IID data distributions and have an increased computation and communication cost which restricts its application. To overcome these issues, this paper proposes a Federated Enhanced Transformer-based Intrusion Detection System (FET-FIDS), a privacy-preserving and decentralized system of security, where federated learning is combined with Transformer-based self-attention. In the proposed architecture, a group of clients are introduced, each client is responsible for being trained on local network traffic data using FET-FIDS model. This method will help the system to learn intrusion patterns that are usually complicated to be learnt only in collaborative training. The locally trained model updates are then securely combined in a centralized server using adaptive federated averaging without having access to the raw data and, therefore, preserving their confidentiality of the data. The proposed architecture is effective in distributed and heterogeneous environments where under the experimental conditions taken into account in this study, its scalability, robustness and communication performance are improved. Using the provided means of wide-scale experimental analysis, the proposed FET-FIDS gives accuracy of 97.82%. It demonstrated that the proposed method is more effective, in terms of the detection, stability, and convergence behavior, than the existing centralized and federated IDS models. Further, it is shown that the framework can effectively deal with non-IID data distribution and the extensibility of the approach to different types of distributed network environment.","url":"https://pubmed.ncbi.nlm.nih.gov/42420370/","authors":["Appadurai JP","Swetha R","Srinivas V","Siripuri K","Arepalli PG","Nagamalla V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 8","doi":"10.1038/s41598-026-61545-z","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42414536","name":"A privacy-preserving rule fusion approach for uncertainty-aware decision-making in posture detection.","source":"pubmed","abstract":"This study introduces a novel approach to constructing rule-based decision systems within a federated learning framework. The proposed rule fusion method is specifically designed for privacy-sensitive applications with uncertainty in data environments. Such challenges frequently arise in medical and social applications, such as fall detection in elderly care facilities, where datasets are often limited, imbalanced, or noisy and strict data privacy regulations prevent traditional data-sharing practices. Federated learning offers a practical solution by allowing multiple institutions to collaboratively train models without exchanging raw data. Our method extends this concept by enabling the classification of decentralized datasets through the collaborative generation and integration of rule sets, maintaining full data privacy throughout the process. Furthermore, to systematically manage the inherent uncertainties in the data, our approach incorporates interval-valued fuzzy set theory alongside knowledge measures. This allows us to handle uncertainty both in knowledge representation and during reasoning by applying uncertainty-aware measures within AI techniques. We demonstrate the effectiveness of our approach on posture detection, a critical element of elderly care. Our federated, privacy-preserving system not only advances healthcare technologies but also addresses crucial concerns around data confidentiality, offering significant societal and economic benefits.","url":"https://pubmed.ncbi.nlm.nih.gov/42414536/","authors":["Pękala B","Wilbik A","Gil D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 8","doi":"10.1038/s41598-026-60758-6","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42414439","name":"Distributed MAC scheduling in IEEE 802.15.7-oriented VLC networks via federated deep reinforcement learning.","source":"pubmed","abstract":"Visible light communication (VLC) networks modeled through a scheduling-level IEEE 802.15.7-oriented PHY/MAC abstraction are sensitive to line-of-sight blockage, receiver orientation, ambient-light noise, heterogeneous traffic loads, and inter-luminaire optical interference, which limits the effectiveness of fixed medium access control (MAC) policies in dense deployments. This study presents a federated deep reinforcement learning framework for distributed MAC scheduling in multi-luminaire IEEE 802.15.7-oriented VLC networks. Each luminaire is modeled as a local scheduling agent that selects the served receiver, transmission slot, optical power level, and physical-layer (PHY) mode from local queue, channel, blockage, interference, and illumination states. Instead of sharing raw observations, luminaires periodically exchange model parameters with a federated aggregation server to coordinate policy updates while preserving data locality. The proposed method is evaluated in a custom discrete-time simulator for a [Formula: see text] indoor VLC scenario with four ceiling luminaires, 8-32 receivers, stochastic traffic arrivals, receiver mobility, ambient-light noise, and line-of-sight blockage. Results averaged over 30 independent runs show that, at [Formula: see text] receivers, the proposed scheduler reduces average packet latency from 45 ms to 31 ms and 95th-percentile latency from 92 ms to 66 ms relative to the implemented resource-constrained centralized deep Q-network (DQN) baseline. Under a blockage probability of 0.3, the packet delivery ratio increases from 0.858 to 0.902, while under high ambient-light noise the packet error rate decreases from 0.064 to 0.049. The method also achieves a Jain fairness index of 0.96, reduces average synchronization overhead from [Formula: see text] to [Formula: see text] at a synchronization interval of 10 episodes, and shortens convergence time from 940 to 670 episodes at [Formula: see text] luminaires. Illumination and flicker diagnostics show that executed actions satisfy the normalized feasibility mask after filtering. These results indicate that, within the adopted IEEE 802.15.7-oriented simulation abstraction, periodic federated parameter sharing improves the scheduling trade-off by reducing delay and coordination cost while preserving mask-enforced lighting feasibility, improving empirical reliability and fairness, and showing favorable multi-luminaire scalability trends.","url":"https://pubmed.ncbi.nlm.nih.gov/42414439/","authors":["Salazar IS","Játiva PP","Reyes MC","Ali A","Arancibia CS","Soto I"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 8","doi":"10.1038/s41598-026-60549-z","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42414419","name":"Scalable hierarchical federated graph-transformer architecture for efficient multi-modal intrusion detection in 6G UAV-assisted vehicular IoT.","source":"pubmed","abstract":"The increasing growth of 6G-empowered UAV-assisted vehicular IoT systems brings forth unprecedented scalability issues for distributed intrusion detection, especially in the context of non-IID data distributions and heterogeneous edge environments. Centralized and flat federated systems do not efficiently coordinate large-scale, latency-sensitive and resource-constrained nodes. In this research, we present a scalable hierarchical federated Graph-Transformer architecture for effective multi-modal intrusion detection spanning UAV-edge-cloud tiers. The platform employs hierarchical aggregation among cars, UAVs, and regional edge servers for reducing communication overhead (CO) and speeding up convergence in non-IID scenarios. Meanwhile, a hybrid Graph Neural Network (GNN) and Transformer backbone is adopted to model spatial topology and temporal dynamics, and a lightweight multi-modal fusion is employed to integrate network traffic, telemetry and channel condition information. To improve the efficiency of the system, we propose adaptive aggregation scheduling and communication compression algorithms that considerably reduce bandwidth consumption and training latency. The experimental results on the CIC-IoT-2023, ToN-IoT and Edge-IIoTset datasets exhibit enhanced scalability with over 98.20% detection accuracy and up to 38.00% transmission cost reduction compared to the flat federation baselines. The work presents a scalable and system-efficient approach for next generation distributed intrusion detection in large-scale 6G vehicle ecosystems.","url":"https://pubmed.ncbi.nlm.nih.gov/42414419/","authors":["Alnafisah KH","Almutairi AM","Ibraheem A","Almutawa A","Bajahzar AS","Nasser T","Alyahya AN"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 8","doi":"10.1038/s41598-026-60184-8","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42403432","name":"Reinforcement learning driven edge-cloud coordination for secure and energy efficient IoMT.","source":"pubmed","abstract":"The Internet of Medical Things (IoMT) enables sophisticated medical devices, but it also poses significant challenges in terms of data privacy, real-time processing, and energy efficiency for edge devices with limited resources. In this paper, we propose a hierarchical framework for intelligent and secure IoMT-based healthcare monitoring. At the sensor nodes, Federated Variational Mode Decomposition (VMD) is used to decompose physiological signals and locally extract high-fidelity features, ensuring data privacy. To overcome the computational limitations of microcontroller- based sensor nodes, a SparseBonsai neural network is designed for real-time classification of medical signals on the sensor nodes. A centralized orchestration layer, controlled by a Proximal Policy Optimization (PPO) reinforcement learning agent, makes dynamic decisions on whether to queue data for low-latency processing at the edge server or offload to the cloud, depending on data severity, network conditions, and battery level. To further improve energy efficiency, an advanced Sha-Dragon (Shannon-Entropy Dragonfly) optimization algorithm is proposed for resource and transmission power allocation in the IoT network. For security, a dual-layer approach is adopted: ASCON v1.2 lightweight authenticated encryption is used to secure node-to-edge communications, and a WireGuard VPN with ChaCha20-Poly1305 encryption protects data in transit to the cloud. Experimental validation on a Raspberry Pi 5 testbed with a cloud-connected laptop shows that the proposed system achieves a significant reduction in latency for critical alerts and improves the battery life of IoT nodes (8.5 days) compared to the conventional non-adaptive offloading approach. The results confirm the effectiveness of the proposed framework to facilitate energy-efficient, privacy-preserving, and real-time healthcare monitoring in IoMT.","url":"https://pubmed.ncbi.nlm.nih.gov/42403432/","authors":["Sasikumar SK","Pai TV","Kalidasan K","Gajendran S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1824480","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42403428","name":"Human digital twins in personalized and predictive healthcare: a comprehensive review of technologies, applications, and future directions.","source":"pubmed","abstract":"The role of real-time data, artificial intelligence, and computational modeling is discussed in this review analytics Human Digital Twins (HDTs) creation- virtual persons of personalities patients which advocate predictive simulation to forecast of physiological behavior, treatment responses, and disease tracks. A synthesis of existing knowledge is done up to the technologies is a foundation to HDTs, clinical application and implementation issues of interest to precision medicine. The conceptual basis of engineering of the digital twins is analyzed and production principles, and technologies, which allow to produce HDTs-machine. Are physiological modeling, learning and distributed cloud-based computing infrastructure identified and evaluated. Cards: cardiology, oncology, genomics and immunology are critically appraised. It is based on the comparative analysis of 35 peer-reviewed documents and technical as it was reported, HDTs have great potential in enhancing personalized prediction of side effects, optimization of clinical trial design using virtual, and scheduling of treatment cohort simulation. But, model standards, an important component of model validation, are not present interoperability, ethical governing mechanisms and regulatory avenues to clinical deployment. The main priority research directions are determined, such as the development of common-validation techniques; implementation of federated learning frameworks to support sharing of data with data privacy limitations; incorporation of multi-omics data into physiological models; and introducing open ethical review procedures. This review provides substantive evidence basis to researchers, clinicians and policy makers to market the. Knowledge about HDTs technology to population health and health care provision revolutionizes.","url":"https://pubmed.ncbi.nlm.nih.gov/42403428/","authors":["Mohan Babu A","Madhan ES"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1827007","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42399373","name":"A blockchain-enabled multi-objective reinforcement learning framework for secure energy- and time-efficient smart path planning in cloud environments.","source":"pubmed","abstract":"Path planning for cloud based autonomous systems such as smart transportation, Internet of Things (IoT) installations and robot fleets need to be secure, energy efficient, time efficient and fulfil privacy constraints. Current reinforcement learning (RL) techniques mainly consider optimisation of single objective, centralised or loosely secured model updates which are susceptible to data poisoning, privacy breach and adversarial model updates. We present Blockchain enabled Energy and Time efficient Multi Objective Reinforcement Learning (BlockE2T MORL) a new decentralised approach for secure, cloud assisted path planning. BlockE2T MORL has three main components: (i) a dynamic multi objective reward function that reduces energy, travel time and security threat; (ii) a lightweight blockchain inspired trust mechanism that assigns continuous trust values to agents, and is incorporated in the reward function to punish dishonest or malicious agents; and (iii) a hybrid actor critic learning strategy that facilitates exploration and exploitation in dynamic environments. Unlike conventional blockchain systems, our approach incurs low computational overhead ([Formula: see text] instead of [Formula: see text] for validation) and operates without heavy consensus protocols. We evaluate BlockE2T-MORL on a simulated grid-based cloud environment with up to 100 agents and varying adversarial ratios. After 200 training episodes (5 independent runs), the proposed framework achieves: (i) energy consumption&#x2009;=&#x2009;124.3&#x2009;&#xb1;&#x2009;8.7&#xa0;J (23.4% reduction vs. standard RL, p&#x2009;&lt;&#x2009;0.01), (ii) latency&#x2009;=&#x2009;45.2&#x2009;&#xb1;&#x2009;3.8 ms (17.4% improvement, p&#x2009;&lt;&#x2009;0.05), and (iii) trust score&#x2009;=&#x2009;0.87&#x2009;&#xb1;&#x2009;0.04 (67% improvement, p&#x2009;&lt;&#x2009;0.001). The framework converges faster (210&#x2009;&#xb1;&#x2009;25 episodes in the final optimized configuration, compared with &#x2265;&#x2009;520 episodes for baseline methods). BlockE2T-MORL offers a scalable, privacy-preserving, and computationally lightweight solution for next-generation intelligent path planning in cloud-based autonomous systems.","url":"https://pubmed.ncbi.nlm.nih.gov/42399373/","authors":["Dewangan RR","Thombre D","Parganiha V","Verma M","Pimpalkar A","Dewangan BK","Shelke N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 3","doi":"10.1038/s41598-026-60426-9","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42399353","name":"An ML-augmented framework for WSN and IoT in 6G networks.","source":"pubmed","abstract":"5G wireless networks have paved the way for intelligent, reliable communication networks with extremely high data rates, ultra-low latency, and highly reliable connectivity. With 6G wireless networks expected to offer up to 1 Tbps data rates with near-zero latency, wireless communication networks are expected to get smarter than ever. As a result, ultra-fast intelligent wireless networks are expected to power the hyper-connected intelligent world of tomorrow. Towards this direction, this paper provides a comprehensive overview of how machine learning (ML) techniques can be employed in Wireless Sensor Networks (WSNs) and Internet of Things (IoT) networks over futuristic 6G wireless networks. Machine learning would enable 6G-enabled IoT/WSNs to operate autonomously, detect anomalies, optimize energy use, and respond to real-time data they sense and collect. Enabling technologies such as edge Artificial Intelligence (AI), satellite-assisted 6G, Intelligent Reflecting Surfaces (IRS), and terahertz communications are discussed. Furthermore, a novel architecture employing federated and distributed learning for IoT communication is presented to demonstrate low-latency, energy-efficient, and secure communication for distributed ML tasks. Results indicate the superiority of the proposed architecture over contemporary 5G-based architectures in terms of network intelligence, latency, and reliability. Finally, the paper discusses major challenges and future directions for realizing the promise of 6G for ML-powered IoT and WSNs.","url":"https://pubmed.ncbi.nlm.nih.gov/42399353/","authors":["Khan G","Ali W","Gupta GK","Gola KK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 3","doi":"10.1038/s41598-026-60635-2","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42394048","name":"Enabling AI-Based Prediction of Neoadjuvant Treatment Response: A FAIR Multimodal Dataset Within EUCAIM.","source":"pubmed","abstract":"This work presents a FAIR-compliant, multimodal PET-CT and clinical dataset developed within the EUCAIM framework to support future AI-based prediction of response to NST in LABC patients. A real-world dataset was curated, harmonized, and integrated into the EUCAIM CDM within a federated infrastructure. While predictive modeling is ongoing, this study focuses on data preparation and infrastructure, key bottlenecks for AI development, demonstrating the feasibility of integrating local hospital data into a European federated ecosystem to enable future multicentric AI applications.","url":"https://pubmed.ncbi.nlm.nih.gov/42394048/","authors":["González López M","Álvarez Pérez RM","Rey Garduño F","Jiménez-Hoyuela García JM","Castell Monsalve FJ","Parra Calderón CL","EUCAIM CA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun 29","doi":"10.3233/SHTI260886","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"pmid:42392320","name":"Privacy-preserving Virtual Contrast-enhanced MRI for Nasopharyngeal Carcinoma: A Multicenter Study.","source":"pubmed","abstract":"Contrast-enhanced magnetic resonance image (MRI) imaging via administration of contrast agents is critical for diagnosis, staging, and treatment of nasopharyngeal carcinoma (NPC). However, gadolinium-based contrast agents can lead to severe adverse effects, especially in patients with compromised kidney function, necessitating a safer alternative for contrast enhancement. In this study, we aim to develop and assess the clinical feasibility of a federated learning model for synthesizing virtual contrast-enhanced MRI (VCE-MRI) images from contrast-free scans for patients with NPC.","url":"https://pubmed.ncbi.nlm.nih.gov/42392320/","authors":["Li W","Li Z","Shi Y","Lam S","Xiao L","Cheung AH","Liu C","Liu S","Yang J","Zeng G","Yang X","Lee SWY","Nicol AJ","Liu Z","Zou L","Yin H","Li X","Liu X","Sun J","Li J","Zhu D","Li X","Sun R","Li B","Sun X","Ge H","Chen J","Li C","Wu X","Wang X","Zhang J","Liu C","Li T","Ren G","Teng X","Zhi S","Cheung AL","Lee FK","Lee VH","Fu J","Cai J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul 2","doi":"10.1016/j.ijrobp.2026.06.3057","addedAt":"2026-08-31T06:41:28.653Z","updatedAt":"2026-08-31T06:41:28.653Z"},{"id":"doi:10.5281/zenodo.21495833","name":"Federated reservoir intelligence: reference implementation for distributed temporal and neuromorphic learning","source":"datacite","abstract":"Reference implementation accompanying the Article 'Federated reservoir intelligence for distributed temporal and neuromorphic learning' (Communications Engineering, Nature Portfolio, 2026). Contents: ESN/LSM reservoir construction; statistics- and readout-based federated aggregation; closed-form personalization and drift adaptation; all baselines and ablations; a self-contained synthetic demo; and the recorded results/*.json from which every figure and table in the Article was generated. Third-party benchmark datasets are not redistributed; see README.md for provider links. Reference environment: Python 3.12.13, NumPy 1.26.4, SciPy 1.17.1, PyTorch 2.11.0 (CUDA 13.0), scikit-learn 1.8.0, MNE 1.12.1.","url":"https://doi.org/10.5281/zenodo.21495833","authors":["Dong, Liang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21495833","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21495834","name":"Federated reservoir intelligence: reference implementation for distributed temporal and neuromorphic learning","source":"datacite","abstract":"Reference implementation accompanying the Article 'Federated reservoir intelligence for distributed temporal and neuromorphic learning' (Communications Engineering, Nature Portfolio, 2026). Contents: ESN/LSM reservoir construction; statistics- and readout-based federated aggregation; closed-form personalization and drift adaptation; all baselines and ablations; a self-contained synthetic demo; and the recorded results/*.json from which every figure and table in the Article was generated. Third-party benchmark datasets are not redistributed; see README.md for provider links. Reference environment: Python 3.12.13, NumPy 1.26.4, SciPy 1.17.1, PyTorch 2.11.0 (CUDA 13.0), scikit-learn 1.8.0, MNE 1.12.1.","url":"https://doi.org/10.5281/zenodo.21495834","authors":["Dong, Liang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21495834","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.19510539","name":"A Landscape Classification Framework for NP-Hard Problems: From Reconnaissance to Algorithm Selection","source":"datacite","abstract":"We propose a unified classification framework for NP-hard problems based on loss landscape topology. The framework classifies 95% of known NP problems into seven boxes (combinatorial optimisation, graph theory, logical satisfaction, path/network, permutation/assignment, sequential decision, miscellaneous), further divided into 24 sub-boxes each with formal objective functions. A three-tier reconnaissance system (aerial survey, scout sampling, mass sampling) analyses landscape topology before algorithm selection. A five-cut decision tree maps landscape properties to optimal solvers and parameter configurations. All 22 sub-boxes are exhaustively enumerated with default algorithms, scout-based adjustments, and landscape-based parameter settings. Box 6 (sequential decision) is fully validated using MCCO as proof of concept, demonstrating 3.56× speedup via landscape-guided deployment. The framework draws an analogy to database normalisation: just as complex data can be decomposed through finite normal forms, complex NP problems can be classified through finite landscape cuts. ORCID: 0009-0002-9497-1336. v2 (2026-04-11): Expanded Related Work with six existing frameworks (Garey & Johnson 1979, FLA/ELA, Algorithm Selection, No Free Lunch, Parameterized Complexity, Learning-Augmented); added cross-framework comparison table; references expanded from 12 to 16. v3 (2026-04-11): Added Scout-Based Algorithm Adjustment and Landscape-Based Parameter Configuration (22 sub-boxes exhaustively enumerated with reconnaissance-based overrides and full parameter lookup tables); Landscape Transformation section (8 transformation methods, 4 difficulty scores, 4-layer stopping conditions with marginal benefit and ROI analysis, precision recovery); Statistical Foundations of Reconnaissance (Type I/II error rates, Power Analysis for scout count, Bonferroni correction, effect size estimation, GP uncertainty propagation); AI-Executable Protocol discussion; three-layer acceleration conclusion (reconnaissance × transformation × AI execution); TSP worked example with three-way comparison (brute force vs blind default vs framework-guided, 0.006s solve time); Scaling test (10/20/50 cities) with \"How Close to P?\" comparison table (framework at 10⁶ vs brute force at 10⁶⁴); Limitations expanded to 8 items. v4 (2026-04-12): Three new chapters: Landscape Cutting (Decomposition): cutting principles, five cutting tools, six-box cuttability table, transform-first-then-cut ordering, parallel deployment pipeline with global refinement, five cutting considerations. The Evaluation Matrix: 9 transformations × 4 cuts = 36-cell exhaustive evaluation; empirical validation on 100-city TSP (12 cells in 3.37s); cutting quality check with exhaustibility guarantee. Algorithm Adequacy Scoring: scale × difficulty → four-tier minimum tool level. Cross-box empirical validation (six boxes, all new): Box 1b TSP: TSPLIB standard benchmarks (eil51/berlin52/kroA100), equal-time-budget fair comparison, framework wins by 4–8% gap reduction. Box 1a MKP: Multidimensional knapsack (50–500 items, 3–10 constraints), framework wins by 1.4–6.9%, Chu & Beasley (1998) reference added. Box 2a Graph Coloring: 50–200 nodes, density 0.1–0.5, framework saves 14–30% colors via DSatur + Tabu Search. Box 3a 3-SAT: tested across phase transition (α = 2.0–5.0), framework selects CDCL for hard instances, solves instances that blind WalkSAT cannot, 15.1× speedup at α = 4.2. Box 4b VRP: 20–100 customers, framework serves +44 additional customers (coverage 56% → 100%), natural application of cutting mechanism. Box 5a Scheduling: 20–200 jobs, framework wins by 7–27%, 200-job instance within 0.1% of lower bound. How Close to P cross-box comparison table: five of eight test cases at or near P, three within 1–2 orders of magnitude, zero large gaps. Improvement acceleration data: 9% at 20 cities → 66% at 50 cities → 80% at 100 cities. Conclusion upgraded: three-layer → four-layer acceleration (reconnaissance × transfor","url":"https://doi.org/10.5281/zenodo.19510539","authors":["Rao, Huiying"],"tags":["NP-hard","loss landscape","algorithm selection","fitness landscape analysis","combinatorial optimisation","meta-algorithm","landscape classification","reconnaissance"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19510539","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20492206","name":"Autonomous Procurement Systems for Resilient Global Supply Networks","source":"datacite","abstract":"Abstract Global supply chains are increasingly exposed to disruptions arising from geopolitical conflicts, trade sanctions, climate-related events, logistics bottlenecks, supplier financial instability, and evolving ESG requirements. Traditional procurement systems remain largely reactive, relying on periodic supplier assessments, static risk scoring mechanisms, and fragmented decision-making processes that are insufficient for managing modern multi-tier supply networks. This whitepaper introduces Autonomous Procurement Systems (APS), a conceptual research framework that integrates Multi-Source Risk Intelligence, Graph-Based Supply Network Analysis, Digital Twin Simulation, Multi-Objective Optimization, and Agentic AI into a unified architecture for resilient procurement decision-making. The framework proposes a five-layer model capable of continuously monitoring heterogeneous risk signals, modeling risk propagation across supplier networks, simulating disruption scenarios, optimizing sourcing strategies under uncertainty, and supporting governed autonomous procurement actions. A key contribution of the framework is its explicit consideration of non-traditional procurement risks, including climate emergencies, warfare, sanctions, geopolitical instability, infrastructure disruptions, and systemic supply chain shocks. The paper introduces several novel concepts, including a Conflict Impact Propagation Model (CIPM), a Procurement Disruption Scenario Ontology (PDSO), a Graduated Autonomy Framework for Procurement AI, and a Synthetic-to-Real Data Strategy for future machine learning research in procurement. Rather than advocating a specific algorithmic approach, the framework adopts an algorithm-agnostic research philosophy, identifying and comparing candidate techniques from machine learning, graph neural networks, operations research, reinforcement learning, digital twins, federated learning, and quantum-inspired optimization. The objective is to provide a rigorous foundation for future empirical research, enterprise innovation initiatives, and the development of next-generation procurement intelligence platforms. This publication is intended as a foundational research artifact for academics, operations research practitioners, supply chain professionals, innovation teams, and enterprise architects exploring the future of resilient and intelligent procurement systems. Keywords Procurement AI; Supply Chain Resilience; Supplier Risk Management; Graph Neural Networks; Digital Twin; Operations Research; Reinforcement Learning; Agentic AI; Strategic Sourcing; Supply Chain Risk Intelligence; Geopolitical Risk; Climate Risk; ESG Procurement; Multi-Objective Optimization; Federated Learning; Autonomous Procurement Systems. Authors Somnath Banerjee, Subhamoy Bhaduri, and Binayak Mukherjee. Version Version 1.0 (Foundation Concept Paper), June 2026.","url":"https://doi.org/10.5281/zenodo.20492206","authors":["Banerjee, Somnath","Bhaduri, Subhamoy","Mukherjee, Binayak"],"tags":["Procurement AI","Supply Chain Resilience","Graph AI","Digital Twin","Operations Research","Agentic AI","Strategic Sourcing","Supplier Risk Management"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20492206","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20492207","name":"Autonomous Procurement Systems for Resilient Global Supply Networks","source":"datacite","abstract":"Abstract Global supply chains are increasingly exposed to disruptions arising from geopolitical conflicts, trade sanctions, climate-related events, logistics bottlenecks, supplier financial instability, and evolving ESG requirements. Traditional procurement systems remain largely reactive, relying on periodic supplier assessments, static risk scoring mechanisms, and fragmented decision-making processes that are insufficient for managing modern multi-tier supply networks. This whitepaper introduces Autonomous Procurement Systems (APS), a conceptual research framework that integrates Multi-Source Risk Intelligence, Graph-Based Supply Network Analysis, Digital Twin Simulation, Multi-Objective Optimization, and Agentic AI into a unified architecture for resilient procurement decision-making. The framework proposes a five-layer model capable of continuously monitoring heterogeneous risk signals, modeling risk propagation across supplier networks, simulating disruption scenarios, optimizing sourcing strategies under uncertainty, and supporting governed autonomous procurement actions. A key contribution of the framework is its explicit consideration of non-traditional procurement risks, including climate emergencies, warfare, sanctions, geopolitical instability, infrastructure disruptions, and systemic supply chain shocks. The paper introduces several novel concepts, including a Conflict Impact Propagation Model (CIPM), a Procurement Disruption Scenario Ontology (PDSO), a Graduated Autonomy Framework for Procurement AI, and a Synthetic-to-Real Data Strategy for future machine learning research in procurement. Rather than advocating a specific algorithmic approach, the framework adopts an algorithm-agnostic research philosophy, identifying and comparing candidate techniques from machine learning, graph neural networks, operations research, reinforcement learning, digital twins, federated learning, and quantum-inspired optimization. The objective is to provide a rigorous foundation for future empirical research, enterprise innovation initiatives, and the development of next-generation procurement intelligence platforms. This publication is intended as a foundational research artifact for academics, operations research practitioners, supply chain professionals, innovation teams, and enterprise architects exploring the future of resilient and intelligent procurement systems. Keywords Procurement AI; Supply Chain Resilience; Supplier Risk Management; Graph Neural Networks; Digital Twin; Operations Research; Reinforcement Learning; Agentic AI; Strategic Sourcing; Supply Chain Risk Intelligence; Geopolitical Risk; Climate Risk; ESG Procurement; Multi-Objective Optimization; Federated Learning; Autonomous Procurement Systems. Authors Somnath Banerjee, Subhamoy Bhaduri, and Binayak Mukherjee. Version Version 1.0 (Foundation Concept Paper), June 2026.","url":"https://doi.org/10.5281/zenodo.20492207","authors":["Banerjee, Somnath","Bhaduri, Subhamoy","Mukherjee, Binayak"],"tags":["Procurement AI","Supply Chain Resilience","Graph AI","Digital Twin","Operations Research","Agentic AI","Strategic Sourcing","Supplier Risk Management"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20492207","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.23031","name":"FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning","source":"datacite","abstract":"Federated Learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it promising for leveraging massive devices in communication networks. In distillation-based FL, each client applies its local model on an unlabeled public dataset, and shares only prediction results with the server. While heterogeneous local data introduces label distribution skew, thus biasing client models toward majority classes and leading to potentially inaccurate predictions. The lack of ground-truth labels in the public dataset hampers the server's ability to calibrate predictions, which ultimately degrades overall performance. To address this, we propose FedCC, a simple and effective algorithm for mitigating client misclassification. Instead of being forced to classify and risking error propagation, clients are allowed to tag ambiguous samples as 'unknown'. This additional class, together with calibrated pseudo-labels on the public data, balances confidence in majority classes against uncertainty in under-represented ones. Extensive experiments demonstrate that FedCC significantly outperforms existing methods, especially under severe label skew. In the extreme scenario where each client holds samples from only one of ten classes, FedCC achieves 67.3% accuracy, while baselines collapse to near-random results.","url":"https://doi.org/10.48550/arxiv.2608.23031","authors":["Ye, Wenxuan","Ayan, Onur","An, Xueli","Carle, Georg"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.23031","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.25794","name":"Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Data","source":"datacite","abstract":"Federated Learning (FL) enables distributed training of machine learning models while preserving data privacy. However, FL struggles with heterogeneous, non-IID client data distributions, resulting in sub-optimal and biased global models. In this paper, we propose pFedMARL, a novel approach leveraging Multi-Agent Reinforcement Learning (MARL) with Twin Delayed Deep Deterministic Policy Gradient (TD3) to dynamically adapt aggregation strategies in FL settings. Our method employs a server-side agent adjusting client contributions to optimize global model robustness and client-side agents balancing global and local updates to personalize models effectively without pre-training. We demonstrate superior performance of pFedMARL for training a semi-supervised audio spectrogram transformer, matching or outperforming FedAvg, Ditto, and local training approaches across multiple non-IID scenarios and in the presence of adversarial clients. Our results indicate that pFedMARL actively improves accuracy, robustness, and fairness, making it suitable for real-world deployments.","url":"https://doi.org/10.48550/arxiv.2608.25794","authors":["Glitza, Rene","Becker, Luca","Martin, Rainer"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","Sound (cs.SD)","Audio and Speech Processing (eess.AS)","Signal Processing (eess.SP)","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.25794","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.25514","name":"Joint Beamforming Design and Port Selection in Fluid Antenna-Assisted Multi-Cell Networks: A Personalized Federated Learning Approach","source":"datacite","abstract":"This paper investigates joint beamforming and port selection in multi-cell fluid antenna-assisted (FAS) networks. In such networks, active beamforming and discrete FA port selection are coupled through intra-cell and inter-cell interference and are jointly optimized to maximize the weighted sum-rate (WSR). We develop a federated representation learning (FedRep) framework with a position-aware dual-branch deep neural network (PA-DNN). The PA-DNN uses channel state information and port positional encoding as inputs, and jointly outputs beamforming vectors and port selections through two task-specific branches. To support decentralized training across heterogeneous cells, the FedRep framework shares global beamforming-related parameters among base stations while keeping port-selection parameters local for cell-specific adaptation. Simulation results show that the proposed scheme achieves a higher weighted sum-rate than conventional FL and port-selection benchmark schemes.","url":"https://doi.org/10.48550/arxiv.2608.25514","authors":["Gao, Liwen","Zheng, Li","Hao, Xing","Chen, Ziru","Cai, Lin X."],"tags":["Information Theory (cs.IT)","Systems and Control (eess.SY)","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.25514","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.25496","name":"FedQoS: Federated QoS-Risk Learning for Heterogeneous Indoor-Outdoor Access Selection","source":"datacite","abstract":"Reliable access selection in dynamic and heterogeneous indoor-outdoor environments is challenging because instantaneous radio measurements alone cannot capture future QoS degradation caused by mobility, blockage, traffic load, and resource competition. This paper proposes FedQoS, a federated QoS-risk learning framework for predicting the future reliability of candidate access links and supporting access-node selection without centralizing user-level network data. In FedQoS, each access node locally learns from its observed network logs, including radio, traffic, load, and service-context features, while a global QoS-risk predictor is trained through federated aggregation. The learned model estimates the probability of QoS failure for each candidate link, and the controller uses these risk scores to select reliable access nodes under dynamic network conditions. To evaluate the framework, we construct physics-based synthetic indoor-outdoor wireless datasets using the Sionna framework, covering normal traffic, mobility, event-driven congestion, and non-IID client observations. Simulation results show that learning-based access selection substantially reduces the QoS-failure rate compared with signal-based and historical-QoS heuristic methods. FedQoS achieves near-centralized predictive performance and provides clear reliability gains under mild non-IID data while remaining competitive under the more challenging severe non-IID condition. These results demonstrate the potential of federated QoS-risk learning for reliable, data-local access selection in dynamic wireless environments.","url":"https://doi.org/10.48550/arxiv.2608.25496","authors":["Van Thieu, Nguyen","Nguyen, Ti Ti","Aouedi, Ons","Huruy, Zerihun","Ha, Vu Nguyen","Chatzinotas, Symeon"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","68T05, 68M10"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.25496","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.25133","name":"Rethinking the Transferable Adversarial Attacks and Robust Defense in Federated Learning","source":"datacite","abstract":"The development of federated learning (FL) techniques has helped improve the privacy preservation of users' data and extended the applications of machine learning models. However, the involvement of a large number of users in FL also creates open opportunities for different adversaries, such as poisoning attacks, Byzantine attacks, and adversarial example attacks. Yet, recent research has disclosed that existing poisoning attacks and Byzantine attacks can not achieve satisfactory penetration in realistic FL scenarios caused by strong assumptions, \\textit{e.g.,} client selection rate, and the ratio of malicious attackers. In this paper, the transferability of adversarial examples among different client models is analyzed to understand the relation between adversarial examples and clients' data distribution. Moreover, to mitigate the attacks of transferable adversarial examples, we design a defense mechanism stemming from the transferability of model robustness by adversarial training. As a result, through theoretical analysis of transferability, we gain insights into adversarial examples and the vulnerability of federated learning systems. Our proposed adversarial attack and defense methods are evaluated via real-life datasets in various settings to show their performance over the existing state-of-the-art methods.","url":"https://doi.org/10.48550/arxiv.2608.25133","authors":["Xiong, Zuobin","Mukherjee, Deval","Cho, Homook","Li, Wei"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.25133","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20455581","name":"Detailed Technical Embodiments and Reference Algorithms for Local Stress–Admissibility Coupling Methods in Network-Structured Systems","source":"datacite","abstract":"Defensive Publication · Extended Technical Embodiments Companion document to DTD-P02-2026.05.29 — adding domain-specific algorithms, system architectures, and detailed embodiments for prior-art purposes. This document extends and is to be read together with the parent Defensive Technical Disclosure DTD-P02-2026.05.29. The subject matter here is the same as in the parent disclosure; this companion provides the operational depth (algorithmic specifications, system architectures, parameterized embodiments) required for the disclosure to function effectively as prior art in patent examination of specific technical fields. All disclosed methods are irrevocably dedicated to the public domain under CC0 1.0 Universal. Public disclosure date: 29 May 2026Document ID: DTD-P02-EXT-01Parent document: DTD-P02-2026.05.29 DOCUMENT ID: DTD-P02-EXT-01 · EXTENDED TECHNICAL EMBODIMENTS · v 1.0 Detailed Technical Embodiments and Reference Algorithms for Local Stress–Admissibility Coupling Methods in Network-Structured Systems Pseudocode-level specifications, system architectures, and parameterized embodiments for five priority application domains — communication networks, distributed computing, machine learning, cybersecurity, and electrical power systems — establishing detailed prior art for patent examination purposes. Disclosing parties [Disclosing party / consortium — to be filled by submitters] Public disclosure date 29 May 2026 Parent disclosure DTD-P02-2026.05.29 (15-domain overview disclosure) Source scientific paper Manuscript ID P02_TOPOLOGY_PHASE_MAP, v 0.1, 29 May 2026 Cite as DTD-P02-EXT-01 (Extended Embodiments) License CC0 1.0 Universal (no rights reserved) Recommended deposition IP.com Prior Art Database, Research Disclosure Journal, arXiv (cs.NI / cs.DC / cs.LG), Zenodo Abstract · Scope of this disclosure This document discloses, in operational detail, five reference algorithms together with their parameterizations, system architectures, decision-logic specifications, and at least three named embodiments per algorithm, covering five priority technical domains. The five reference algorithms each instantiate the generalized H3 topology-coupling method of the parent disclosure (DTD-P02-2026.05.29) for the operational realities of, respectively, (i) electronic communication and routing systems, (ii) distributed computing and service-mesh architectures, (iii) machine-learning and neural-architecture systems, (iv) cybersecurity defense and anomaly detection, and (v) electrical power and energy distribution systems. For each domain the disclosure includes: a precise operational definition of the per-node stress and admissibility quantities; a reference algorithm in Python-like pseudocode with all parameters named; a system-architecture sketch enumerating the named components, their data flows, and their interfaces; three or more concrete named embodiments with specific parameter values; and a variations section enumerating obvious alternatives. The level of detail is chosen to be sufficient to anticipate, and thus prevent the patenting of, domain-specific reductions to practice of the disclosed method. Table of contents § IShared notation and primitives § II / ED-01Communication networks and routing — reference algorithm and embodiments § III / ED-02Distributed computing, service mesh and orchestration — reference algorithm and embodiments § IV / ED-03Machine learning, neural architectures and federated systems — reference algorithm and embodiments § V / ED-04Cybersecurity defense and threat detection — reference algorithm and embodiments § VI / ED-05Electrical power grid and energy systems — reference algorithm and embodiments § VIICross-cutting variations and alternative formulations § VIIIReference parameter tables and recommended defaults I.Shared Notation and Primitives The following notation is used throughout this disclosure. G denotes a network graph with node set V of cardinality n and edge set E of cardinality m. A node is deno","url":"https://doi.org/10.5281/zenodo.20455581","authors":["Melegh, Janos Gabor"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20455581","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.20455582","name":"Detailed Technical Embodiments and Reference Algorithms for Local Stress–Admissibility Coupling Methods in Network-Structured Systems","source":"datacite","abstract":"Defensive Publication · Extended Technical Embodiments Companion document to DTD-P02-2026.05.29 — adding domain-specific algorithms, system architectures, and detailed embodiments for prior-art purposes. This document extends and is to be read together with the parent Defensive Technical Disclosure DTD-P02-2026.05.29. The subject matter here is the same as in the parent disclosure; this companion provides the operational depth (algorithmic specifications, system architectures, parameterized embodiments) required for the disclosure to function effectively as prior art in patent examination of specific technical fields. All disclosed methods are irrevocably dedicated to the public domain under CC0 1.0 Universal. Public disclosure date: 29 May 2026Document ID: DTD-P02-EXT-01Parent document: DTD-P02-2026.05.29 DOCUMENT ID: DTD-P02-EXT-01 · EXTENDED TECHNICAL EMBODIMENTS · v 1.0 Detailed Technical Embodiments and Reference Algorithms for Local Stress–Admissibility Coupling Methods in Network-Structured Systems Pseudocode-level specifications, system architectures, and parameterized embodiments for five priority application domains — communication networks, distributed computing, machine learning, cybersecurity, and electrical power systems — establishing detailed prior art for patent examination purposes. Disclosing parties [Disclosing party / consortium — to be filled by submitters] Public disclosure date 29 May 2026 Parent disclosure DTD-P02-2026.05.29 (15-domain overview disclosure) Source scientific paper Manuscript ID P02_TOPOLOGY_PHASE_MAP, v 0.1, 29 May 2026 Cite as DTD-P02-EXT-01 (Extended Embodiments) License CC0 1.0 Universal (no rights reserved) Recommended deposition IP.com Prior Art Database, Research Disclosure Journal, arXiv (cs.NI / cs.DC / cs.LG), Zenodo Abstract · Scope of this disclosure This document discloses, in operational detail, five reference algorithms together with their parameterizations, system architectures, decision-logic specifications, and at least three named embodiments per algorithm, covering five priority technical domains. The five reference algorithms each instantiate the generalized H3 topology-coupling method of the parent disclosure (DTD-P02-2026.05.29) for the operational realities of, respectively, (i) electronic communication and routing systems, (ii) distributed computing and service-mesh architectures, (iii) machine-learning and neural-architecture systems, (iv) cybersecurity defense and anomaly detection, and (v) electrical power and energy distribution systems. For each domain the disclosure includes: a precise operational definition of the per-node stress and admissibility quantities; a reference algorithm in Python-like pseudocode with all parameters named; a system-architecture sketch enumerating the named components, their data flows, and their interfaces; three or more concrete named embodiments with specific parameter values; and a variations section enumerating obvious alternatives. The level of detail is chosen to be sufficient to anticipate, and thus prevent the patenting of, domain-specific reductions to practice of the disclosed method. Table of contents § IShared notation and primitives § II / ED-01Communication networks and routing — reference algorithm and embodiments § III / ED-02Distributed computing, service mesh and orchestration — reference algorithm and embodiments § IV / ED-03Machine learning, neural architectures and federated systems — reference algorithm and embodiments § V / ED-04Cybersecurity defense and threat detection — reference algorithm and embodiments § VI / ED-05Electrical power grid and energy systems — reference algorithm and embodiments § VIICross-cutting variations and alternative formulations § VIIIReference parameter tables and recommended defaults I.Shared Notation and Primitives The following notation is used throughout this disclosure. G denotes a network graph with node set V of cardinality n and edge set E of cardinality m. A node is deno","url":"https://doi.org/10.5281/zenodo.20455582","authors":["Melegh, Janos Gabor"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20455582","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.22010778","name":"Sānér, The Recursive Trinity Structure for Task Regions","source":"datacite","abstract":"Abstract Sānér establishes a recursive control architecture for task regions by placing topology, self-modeling scheduling, and certificate-governed execution under one executable resource semantics. The nine-node construction supports exact recursive expansion and addressing. Its formal system establishes capacity safety, deterministic cross-layer selection, partition safety, conditional liveness, and computable deadline and recovery bounds. Using entity-isolated validation and one-time test splits, we evaluated the frozen controller in executable state-transition simulations and public benchmark workloads over 32, 48, or 64 independent worlds, with deployment evidence obtained from 48 physical GPU blocks and eight fresh control-plane processes in the official Kubernetes scheduler-performance harness. The sealed confirmatory suite established 38 task-level all-comparator superiority conclusions. On the official Kubernetes v1.36.2 scheduler-performance harness, Sānér achieved a 392.27× mean throughput ratio on the same host and reduced mean P99 by 26.75240 milliseconds. Exact recursive-topology representation remained 144 bytes through 26,244 nodes. Mechanism evidence remains separate from direct performance claims. Boundary results and nonidentifiability tests are reported independently. Together, the proofs and matched comparisons establish Sānér as a reusable and auditable control kernel whose single recursive mechanism transfers across structurally different systems tasks. Validation design Every direct comparator received the same random world, observable state, feasible action set, resource ceiling, and decision-time budget. Frozen random worlds coupled arrivals, failures, delays, partitions, and disturbances across methods. Interface adapters translated actions only and could neither expose future state nor optimize on a method’s behalf. Validation data selected candidates and parameters. Test data were unsealed once, only after protocol, program, and model hashes agreed. Each task had one frozen primary endpoint, and higher values were uniformly preferred. Confidence intervals resampled complete independent worlds rather than repeated observations within one world. When a suite defined a primary Holm family, candidate–comparator probability values were adjusted before the task-level conjunction was evaluated. A task-level all-comparator conclusion required every applicable constituent comparison to pass together with its programmed direction, confidence-bound, safety, feasibility, and deadline conditions. Suites without a declared primary Holm family applied their programmed constituent tests directly. Secondary endpoints used the suite-declared Holm or false-discovery-rate procedure. The paper reports 54 tasks. Four suite programs preserve broader Holm families containing 108 primary candidate–comparator tests across 36 tasks. Eighteen paper tasks contribute 54 of those tests. Eighteen additional suite tasks contribute 54 frozen raw probability values solely to preserve the original conservative adjustment denominator. Their workloads, scores, effects, intervals, timings, and conclusions support no manuscript claim. Relative effects equal the absolute candidate–comparator difference divided by the absolute comparator mean. When a comparator mean is negative, the percentage describes only the difference relative to its numerical magnitude; substantive interpretation rests on the absolute difference and its confidence interval. Comparator qualification and strength Comparator eligibility and tuning were frozen before test-set access. Published guarantees supplied theoretical strength. Official or author-maintained implementations supplied operational relevance. A rolling optimizer qualified only when it received the same state, constraints, action space, resource ceiling, and computation limit as Sānér. Validation selected the strongest deployable method wherever a suite required one. Simple baselines measured task diff","url":"https://doi.org/10.5281/zenodo.22010778","authors":["Bsmpx"],"tags":["Sānér","recursive task regions","nine-node cubic topology","balanced-ternary addressing","self-modeling scheduling","bounded candidate synthesis","absolute-rank queuing","certificate-based governance"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22010778","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.22010777","name":"Sānér, The Recursive Trinity Structure for Task Regions","source":"datacite","abstract":"Abstract Sānér establishes a recursive control architecture for task regions by placing topology, self-modeling scheduling, and certificate-governed execution under one executable resource semantics. The nine-node construction supports exact recursive expansion and addressing. Its formal system establishes capacity safety, deterministic cross-layer selection, partition safety, conditional liveness, and computable deadline and recovery bounds. Using entity-isolated validation and one-time test splits, we evaluated the frozen controller in executable state-transition simulations and public benchmark workloads over 32, 48, or 64 independent worlds, with deployment evidence obtained from 48 physical GPU blocks and eight fresh control-plane processes in the official Kubernetes scheduler-performance harness. The sealed confirmatory suite established 38 task-level all-comparator superiority conclusions. On the official Kubernetes v1.36.2 scheduler-performance harness, Sānér achieved a 392.27× mean throughput ratio on the same host and reduced mean P99 by 26.75240 milliseconds. Exact recursive-topology representation remained 144 bytes through 26,244 nodes. Mechanism evidence remains separate from direct performance claims. Boundary results and nonidentifiability tests are reported independently. Together, the proofs and matched comparisons establish Sānér as a reusable and auditable control kernel whose single recursive mechanism transfers across structurally different systems tasks. Validation design Every direct comparator received the same random world, observable state, feasible action set, resource ceiling, and decision-time budget. Frozen random worlds coupled arrivals, failures, delays, partitions, and disturbances across methods. Interface adapters translated actions only and could neither expose future state nor optimize on a method’s behalf. Validation data selected candidates and parameters. Test data were unsealed once, only after protocol, program, and model hashes agreed. Each task had one frozen primary endpoint, and higher values were uniformly preferred. Confidence intervals resampled complete independent worlds rather than repeated observations within one world. When a suite defined a primary Holm family, candidate–comparator probability values were adjusted before the task-level conjunction was evaluated. A task-level all-comparator conclusion required every applicable constituent comparison to pass together with its programmed direction, confidence-bound, safety, feasibility, and deadline conditions. Suites without a declared primary Holm family applied their programmed constituent tests directly. Secondary endpoints used the suite-declared Holm or false-discovery-rate procedure. The paper reports 54 tasks. Four suite programs preserve broader Holm families containing 108 primary candidate–comparator tests across 36 tasks. Eighteen paper tasks contribute 54 of those tests. Eighteen additional suite tasks contribute 54 frozen raw probability values solely to preserve the original conservative adjustment denominator. Their workloads, scores, effects, intervals, timings, and conclusions support no manuscript claim. Relative effects equal the absolute candidate–comparator difference divided by the absolute comparator mean. When a comparator mean is negative, the percentage describes only the difference relative to its numerical magnitude; substantive interpretation rests on the absolute difference and its confidence interval. Comparator qualification and strength Comparator eligibility and tuning were frozen before test-set access. Published guarantees supplied theoretical strength. Official or author-maintained implementations supplied operational relevance. A rolling optimizer qualified only when it received the same state, constraints, action space, resource ceiling, and computation limit as Sānér. Validation selected the strongest deployable method wherever a suite required one. Simple baselines measured task diff","url":"https://doi.org/10.5281/zenodo.22010777","authors":["Bsmpx"],"tags":["Sānér","recursive task regions","nine-node cubic topology","balanced-ternary addressing","self-modeling scheduling","bounded candidate synthesis","absolute-rank queuing","certificate-based governance"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22010777","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.19998857","name":"WAA-A1/privacy-preserving-federated-ids: Updated Thesis Implementation – Version 2.0","source":"datacite","abstract":"Updated implementation and experimental results for the 2026 Thesis. This release contains the updated federated intrusion detection implementation, experimental CSV results, generated figures, and documentation. The updated version includes evaluations of privacy–utility trade-offs, Byzantine/adversarial robustness, non-IID data heterogeneity, federated learning baselines, cross-validation, reproducibility, and computational/ communication overhead.","url":"https://doi.org/10.5281/zenodo.19998857","authors":["WAA-A1"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19998857","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21955289","name":"Does Heavy-Tailed Gradient Noise Explain the CIFAR-10 Gap in Byzantine-Robust, Differentially Private Federated Learning? An Empirical Stress Test of Byz-Clip21-SGD2M","source":"datacite","abstract":"Byz-Clip21-SGD2M (Islamov et al., 2026) provides high-probability convergence guarantees for federated learning under simultaneous Byzantine adversaries and differential-privacy (DP) noise, relaxing the bounded-gradient assumption behind the tight privacy-robustness-utility trade-off of Allouah et al. (2023b) to standard L-smoothness and σ-sub-Gaussian gradient noise. Its own empirical validation is MNIST-only, and its Conclusion lists heavy-tailed gradient noise as future work; we stress-test both the algorithm and its sub-Gaussian premise at CIFAR-10 scale. Using the established Hill-estimator/kurtosis tail-index methodology of Şimşekli et al. (2019) and follow-ons — applied here, not proposed as new — we find CIFAR-10's gradient noise consistently heavier-tailed than MNIST's across every statistic and configuration checked. Isolating ablations show the Byzantine-robustness mechanism itself performs comparably on both datasets, so the degradation is not robustness-specific; a dedicated DP-aware search over the clipping threshold τ, spanning the source paper's own privacy-budget grid, fails to recover non-degenerate CIFAR-10 accuracy even as DP noise is driven toward zero. This rules out both \"DP noise alone explains the gap\" and \"the fixed-τ protocol was miscalibrated\"; the evidence instead points to CIFAR-10's own clean-training convergence difficulty, plausibly linked to its heavier tail, as the dominant factor. A seed scale-up (CIFAR-10 sweep to n=10/cell, ablation to n=10/arm) with paired Wilcoxon testing confirms this: no CIFAR-10 condition differs significantly from any other, and the ablation's recovery to CIFAR-10's clean ceiling is statistically indistinguishable (p=1.000), not a small-sample artifact. An independent, unpaired Mann-Whitney U test further corroborates the hyperparameter-transfer gap under matched hyperparameters and round budget (U=100, p=0.000183). We also close this paper's previously most significant open limitation: we implement and unit-test the source paper's two external baselines, Safe-DSHB (Allouah et al., 2023b) and Byz-Clip-SGD (Islamov et al., 2026), from its appendix pseudocode, and run both under the identical protocol used for Byz-Clip21-SGD2M throughout. Under shared, non-independently-tuned hyperparameters, both baselines match or nominally exceed Byz-Clip21-SGD2M on several MNIST conditions — an exploratory finding, not confirmatory (the source paper's own independently-tuned comparison reaches the opposite conclusion, and none of these comparisons survive multiple-comparison correction). We then ran the independently-tuned comparison this gap called for (30-point (γ, τ) grid per baseline, matching the source paper's own tuning range): it erases the MNIST advantage seen under shared hyperparameters entirely (all 12 comparisons now p ≥ 0.06) without producing a Byz-Clip21-SGD2M advantage either, since our tuning remains a simplified, single-condition probe rather than the source paper's per-ε, DP-aware protocol — so whether Byz-Clip21-SGD2M has a genuine MNIST edge under equally careful tuning stays open. On CIFAR-10, all three algorithms are statistically indistinguishable and collapse to chance together, a more direct finding independent of any tuning caveat: the CIFAR-10 gap is not specific to Byz-Clip21-SGD2M. We report every known gap in our replication honestly, including a corrected theoretical positioning relative to Allouah et al.'s tight dimension-dependent lower bound and two corrections to our own earlier internal pilot analysis.","url":"https://doi.org/10.5281/zenodo.21955289","authors":["S Varughese, Johan"],"tags":["federated learning","differential privacy","Byzantine robustness","distributed optimization","robust aggregation","deep learning security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21955289","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21955288","name":"Does Heavy-Tailed Gradient Noise Explain the CIFAR-10 Gap in Byzantine-Robust, Differentially Private Federated Learning? An Empirical Stress Test of Byz-Clip21-SGD2M","source":"datacite","abstract":"Byz-Clip21-SGD2M (Islamov et al., 2026) provides high-probability convergence guarantees for federated learning under simultaneous Byzantine adversaries and differential-privacy (DP) noise, relaxing the bounded-gradient assumption behind the tight privacy-robustness-utility trade-off of Allouah et al. (2023b) to standard L-smoothness and σ-sub-Gaussian gradient noise. Its own empirical validation is MNIST-only, and its Conclusion lists heavy-tailed gradient noise as future work; we stress-test both the algorithm and its sub-Gaussian premise at CIFAR-10 scale. Using the established Hill-estimator/kurtosis tail-index methodology of Şimşekli et al. (2019) and follow-ons — applied here, not proposed as new — we find CIFAR-10's gradient noise consistently heavier-tailed than MNIST's across every statistic and configuration checked. Isolating ablations show the Byzantine-robustness mechanism itself performs comparably on both datasets, so the degradation is not robustness-specific; a dedicated DP-aware search over the clipping threshold τ, spanning the source paper's own privacy-budget grid, fails to recover non-degenerate CIFAR-10 accuracy even as DP noise is driven toward zero. This rules out both \"DP noise alone explains the gap\" and \"the fixed-τ protocol was miscalibrated\"; the evidence instead points to CIFAR-10's own clean-training convergence difficulty, plausibly linked to its heavier tail, as the dominant factor. A seed scale-up (CIFAR-10 sweep to n=10/cell, ablation to n=10/arm) with paired Wilcoxon testing confirms this: no CIFAR-10 condition differs significantly from any other, and the ablation's recovery to CIFAR-10's clean ceiling is statistically indistinguishable (p=1.000), not a small-sample artifact. An independent, unpaired Mann-Whitney U test further corroborates the hyperparameter-transfer gap under matched hyperparameters and round budget (U=100, p=0.000183). We also close this paper's previously most significant open limitation: we implement and unit-test the source paper's two external baselines, Safe-DSHB (Allouah et al., 2023b) and Byz-Clip-SGD (Islamov et al., 2026), from its appendix pseudocode, and run both under the identical protocol used for Byz-Clip21-SGD2M throughout. Under shared, non-independently-tuned hyperparameters, both baselines match or nominally exceed Byz-Clip21-SGD2M on several MNIST conditions — an exploratory finding, not confirmatory (the source paper's own independently-tuned comparison reaches the opposite conclusion, and none of these comparisons survive multiple-comparison correction). We then ran the independently-tuned comparison this gap called for (30-point (γ, τ) grid per baseline, matching the source paper's own tuning range): it erases the MNIST advantage seen under shared hyperparameters entirely (all 12 comparisons now p ≥ 0.06) without producing a Byz-Clip21-SGD2M advantage either, since our tuning remains a simplified, single-condition probe rather than the source paper's per-ε, DP-aware protocol — so whether Byz-Clip21-SGD2M has a genuine MNIST edge under equally careful tuning stays open. On CIFAR-10, all three algorithms are statistically indistinguishable and collapse to chance together, a more direct finding independent of any tuning caveat: the CIFAR-10 gap is not specific to Byz-Clip21-SGD2M. We report every known gap in our replication honestly, including a corrected theoretical positioning relative to Allouah et al.'s tight dimension-dependent lower bound and two corrections to our own earlier internal pilot analysis.","url":"https://doi.org/10.5281/zenodo.21955288","authors":["S Varughese, Johan"],"tags":["federated learning","differential privacy","Byzantine robustness","distributed optimization","robust aggregation","deep learning security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21955288","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20426044","name":"HemaFAIR & HELIOS Federated Analysis Hackathon (Cyprus, May 2026): Training Resources and Datasets","source":"datacite","abstract":"This Zenodo record contains the training and educational materials developed and used during the HemaFAIR and HELIOS Federated Data Analysis Training School and Hackathon, held at Alion Beach Hotel in Ayia Napa, Cyprus, on 11–14 May 2026. The event brought together researchers, clinicians, data scientists, and FAIR data experts to provide hands-on training on federated data analysis, FAIR data principles, semantic interoperability, common data models, and privacy-preserving approaches for biomedical and rare disease research, with a particular focus on hemoglobinopathies. The uploaded materials include: Training presentations and workshop materials delivered by the trainers Hands-on exercises and demonstration resources Example datasets used during the practical sessions Dataset dictionaries and metadata documentation Supporting materials related to FAIRification, semantic web technologies, OMOP common data models, and federated learning approaches The materials are shared to support capacity building, reproducibility, FAIR data practices, and collaborative research in rare diseases and hemoglobinopathies. These resources may be useful for researchers, clinicians, students, and institutions interested in federated analysis infrastructures, data interoperability, and responsible data sharing approaches. We would like to thank all trainers, organisers, and participants who contributed to the successful implementation of the training school and hackathon.","url":"https://doi.org/10.5281/zenodo.20426044","authors":["Waagmeester, Andra","Kersloot, Martijn G.","Cremonesi, Francesco","Wijnbergen, Daphne","Tamana, Stella","Orphanou, Kalia"],"tags":["Federated Learning","Hackathon","Training Materials","HELIOS","HemaFAIR","FAIR"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20426044","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20426045","name":"HemaFAIR & HELIOS Federated Analysis Hackathon (Cyprus, May 2026): Training Resources and Datasets","source":"datacite","abstract":"This Zenodo record contains the training and educational materials developed and used during the HemaFAIR and HELIOS Federated Data Analysis Training School and Hackathon, held at Alion Beach Hotel in Ayia Napa, Cyprus, on 11–14 May 2026. The event brought together researchers, clinicians, data scientists, and FAIR data experts to provide hands-on training on federated data analysis, FAIR data principles, semantic interoperability, common data models, and privacy-preserving approaches for biomedical and rare disease research, with a particular focus on hemoglobinopathies. The uploaded materials include: Training presentations and workshop materials delivered by the trainers Hands-on exercises and demonstration resources Example datasets used during the practical sessions Dataset dictionaries and metadata documentation Supporting materials related to FAIRification, semantic web technologies, OMOP common data models, and federated learning approaches The materials are shared to support capacity building, reproducibility, FAIR data practices, and collaborative research in rare diseases and hemoglobinopathies. These resources may be useful for researchers, clinicians, students, and institutions interested in federated analysis infrastructures, data interoperability, and responsible data sharing approaches. We would like to thank all trainers, organisers, and participants who contributed to the successful implementation of the training school and hackathon.","url":"https://doi.org/10.5281/zenodo.20426045","authors":["Waagmeester, Andra","Kersloot, Martijn G.","Cremonesi, Francesco","Wijnbergen, Daphne","Tamana, Stella","Orphanou, Kalia"],"tags":["Federated Learning","Hackathon","Training Materials","HELIOS","HemaFAIR","FAIR"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20426045","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.06637","name":"Bypassing Krum: Selection-Aware Backdoor Attacks in Federated Learning","source":"datacite","abstract":"Robust aggregation methods are widely used in federated learning to mitigate the impact of adversarial client behavior. Distance-based aggregation rules, such as Krum and Multi-Krum, select updates that are closest to the majority under the assumption that benign updates form a compact cluster. However, these methods rely on geometric properties that can be exploited by adaptive adversaries. We introduce the Krum-Proxy attack, a selection-aware backdoor injection strategy that consistently bypasses Byzantine-robust aggregation. Rather than relying on naive scaling or constraining, our method actively optimizes malicious updates to infiltrate the dense core of the benign distribution. The proposed method constructs adversarial updates that are not only similar to benign updates but are also optimized to lie in regions of the update space that are favored during aggregation. This is achieved through a two-stage optimization procedure that separates task-specific attack objectives from geometry-aware refinement, using a nearest-neighbor proxy, stochastic reference modeling, and anchor-guided alignment. To maintain stealth, we introduce a projection mechanism that constrains adversarial updates within realistic norm and variance bounds. Experiments on standard federated learning benchmarks show that Krum-Proxy achieves higher attack success while preserving clean accuracy, highlighting the vulnerability of distance-based aggregation to selection-aware adversaries.","url":"https://doi.org/10.48550/arxiv.2608.06637","authors":["Subramanian, Srinivasan","Khan, Md. Abdullah Al Hafiz","Islam, Kazi Aminul"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.06637","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.00364","name":"Understanding Federated Learning Through the Lens of Mechanism Design: The Role of Data Heterogeneity","source":"datacite","abstract":"Federated learning (FL) requires effective incentive mechanisms to motivate data sharing and prevent strategic free-riding. Recent FL mechanisms such as the Shapley value mechanism M^Shap guarantee reciprocal fairness for agents. However, a complete analysis of how such mechanisms impact social optimality and individual rationality under realistic, standalone outside options remains unknown. In this paper, we address this gap by adapting the classical Externality mechanism M^E to the federated learning setting. We conduct a comparison of M^Shap and M^E across three dimensions: social optimality, individual rationality, and fairness/reciprocity. First, we establish that M^Shap generally does not maximize social welfare because its marginal incentives drive agents to over-contribute resources, while M^E maximizes social welfare by design. Second, we evaluate participation incentives through the individual rationality gap when considering agents' outside options as standalone training on their own data. We find that both mechanisms ensure individual rationality in homogeneous settings. We further show that under mild conditions, M^E maintains this guarantee under agent heterogeneity, whereas M^Shap does not. Third, we demonstrate that while M^Shap maintains perfect reciprocity by design, M^E generally does not, and only ensures that individual benefits match Shapley contributions at symmetric equilibria under homogeneity, as it sacrifices individual fairness to maximize collective welfare under heterogeneity. Empirical simulations validate our theoretical findings and illustrate a tradeoff between reciprocal fairness and social efficiency.","url":"https://doi.org/10.48550/arxiv.2608.00364","authors":["Alkarmi, Lina","Chen, Po-Yen","Liu, Mingyan"],"tags":["Computer Science and Game Theory (cs.GT)","Social and Information Networks (cs.SI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.00364","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2607.07209","name":"Continual Learning With Participation Privacy: An Auditable Buffering-Aggregation Recipe","source":"datacite","abstract":"Modern federated and streaming learning systems often release intermediate models, so privacy must hold for the full trajectory under adaptive interaction. Motivated by participation privacy, we study single-edit neighboring user streams, where one insertion/deletion shifts all subsequent updates and defeats standard Hamming-neighbor continual-release analyses. We give an auditable modular recipe. A randomized buffering wrapper emits bins of size $[U,2U]$, reducing single-edit streams to a Hamming-style per-bin update stream with explicit backlog/delay guarantees, where $U$ is calibrated by the privacy parameters $(\\varepsilon,δ)$. We then prove a certification theorem identifying when a non-adaptive Hamming-neighbor DP proof for a continual primitive lifts to adaptive inputs: the primitive must use fresh per-round randomness and have a stable one-round privacy profile under common adaptive context. Together, these ingredients yield trajectory-level $(\\varepsilon,δ)$-DP for single-edit streams using standard primitives (e.g., tree prefix sums), with an explicit privacy--latency link via $U$.","url":"https://doi.org/10.48550/arxiv.2607.07209","authors":["Chan, T-H. Hubert","Shi, Elaine","Zhao, Mengshi","Zhou, Mingxun"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.07209","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.24073","name":"ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal","source":"datacite","abstract":"Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such as disaster monitoring and environmental surveillance. However, cloud coverage often obscures the Earth's surface, and conventional cloud-removal pipelines that download cloudy images to ground stations for processing suffer from limited contact windows, constrained satellite-to-ground bandwidth, and high latency. In this work, we propose a novel satellite federated learning framework for cloud removal across LEO constellations, named orbital attention leaky integrate-and-fire (OrbitALIF). OrbitALIF performs both onboard training and inference using a compact 2.30,M-parameter spiking neural network (SNN) backbone with an adaptive gated fusion module (AGFM) and a spectral-spatial hybrid attention module (SHAM), combined with a decentralized federated learning strategy that shares model weights via inter-satellite links. Our experiments show that OrbitALIF achieves competitive cloud removal quality while consuming only 0.287,mJ per inference on neuromorphic hardware, a 72.3 times (98.6%) energy reduction versus an equivalent artificial neural network (ANN).","url":"https://doi.org/10.48550/arxiv.2608.24073","authors":["Zhang, Bohan","Xu, Chenyu","Mao, Yijie","Shi, Yuanming"],"tags":["Neural and Evolutionary Computing (cs.NE)","Artificial Intelligence (cs.AI)","Computer Vision and Pattern Recognition (cs.CV)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.24073","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20072723","name":"Artifacts for \"Federated Learning for CSI Feedback in mMIMO: A Communication-Performance Trade-Off Analysis\"","source":"datacite","abstract":"This repository contains the artifacts associated with the paper\"Federated Learning for CSI Feedback in mMIMO: A Communication-Performance Trade-Off Analysis\" (Duque, Hendrikx, Gorce - 2026). Contents - federated_csinet_dataset.mat: QuaDRiGa-generated dataset for 20 clients in Urban Macrocell (UMa) scenario (mixed indoor/outdoor, LOS/NLOS).- federated_csinet_dataset_Umi.mat: QuaDRiGa-generated dataset in Urban Microcell (UMi) scenario, used for pretraining.- pretrained_UMi.pt: CRNet model pretrained on the UMi dataset, used as warm-start initialization for federated fine-tuning experiments. Code - Model training: https://gitlab.inria.fr/lduque/declearncsi- Dataset generation: https://gitlab.inria.fr/lduque/fedcsi_quadriga-dataset","url":"https://doi.org/10.5281/zenodo.20072723","authors":["Duqué, Loukas","Gorce, Jean-Marie","Hendrikx, Hadrien"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20072723","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20072724","name":"Artifacts for \"Federated Learning for CSI Feedback in mMIMO: A Communication-Performance Trade-Off Analysis\"","source":"datacite","abstract":"This repository contains the artifacts associated with the paper\"Federated Learning for CSI Feedback in mMIMO: A Communication-Performance Trade-Off Analysis\" (Duque, Hendrikx, Gorce - 2026). Contents - federated_csinet_dataset.mat: QuaDRiGa-generated dataset for 20 clients in Urban Macrocell (UMa) scenario (mixed indoor/outdoor, LOS/NLOS).- federated_csinet_dataset_Umi.mat: QuaDRiGa-generated dataset in Urban Microcell (UMi) scenario, used for pretraining.- pretrained_UMi.pt: CRNet model pretrained on the UMi dataset, used as warm-start initialization for federated fine-tuning experiments. Code - Model training: https://gitlab.inria.fr/lduque/declearncsi- Dataset generation: https://gitlab.inria.fr/lduque/fedcsi_quadriga-dataset","url":"https://doi.org/10.5281/zenodo.20072724","authors":["Duqué, Loukas","Gorce, Jean-Marie","Hendrikx, Hadrien"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20072724","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.19712713","name":"Blockchain Solution with Artificial Intelligence Integration in the Indian Judicial System A Bibliometric and Methodical Literature Review","source":"datacite","abstract":"The Indian judicial system faces critical challenges including case backlogs exceeding 54.7 million pending matters, insufficient judicial resources, and inefficient evidence management processes. This literature review examines the emerging potential of integrating blockchain technology with artificial intelligence (AI) to transform judicial delivery, enhance case processing efficiency, and strengthen evidentiary integrity. Through systematic analysis of 88 peer-reviewed publications (2013–2026) across IEEE Xplore, Scopus, Springer, and Web of Science, we identified four dominant research themes: blockchain-based evidence management systems, AI-driven predictive justice and decision support, smart contracts for judicial automation, and privacy-preserving mechanisms for sensitive legal data. Key findings reveal that blockchain ensures significant reduction in evidence tampering incidents, while AI prediction models achieve notable accuracy in judicial outcome forecasting. However, significant implementation challenges persist, including scalability constraints, lack of comprehensive regulatory frameworks, and insufficient integration with legacy court systems. This paper synthesizes current scholarship, identifies critical research gaps, and proposes a four-layer conceptual framework for pragmatic AI-blockchain deployment suited to India's constitutional and legal context. We conclude that strategic integration prioritizing permissioned blockchain architectures, explainable AI models, and federated learning offers transformative potential for addressing judicial inefficiency while maintaining due process and fundamental rights protections.","url":"https://doi.org/10.5281/zenodo.19712713","authors":["Sameer Patil","Darshana Desai"],"tags":["Blockchain","Artificial Intelligence","Judicial System","Legal Technology","India","Evidence Management","Predictive Justice","Smart Contracts"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19712713","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.19712714","name":"Blockchain Solution with Artificial Intelligence Integration in the Indian Judicial System A Bibliometric and Methodical Literature Review","source":"datacite","abstract":"The Indian judicial system faces critical challenges including case backlogs exceeding 54.7 million pending matters, insufficient judicial resources, and inefficient evidence management processes. This literature review examines the emerging potential of integrating blockchain technology with artificial intelligence (AI) to transform judicial delivery, enhance case processing efficiency, and strengthen evidentiary integrity. Through systematic analysis of 88 peer-reviewed publications (2013–2026) across IEEE Xplore, Scopus, Springer, and Web of Science, we identified four dominant research themes: blockchain-based evidence management systems, AI-driven predictive justice and decision support, smart contracts for judicial automation, and privacy-preserving mechanisms for sensitive legal data. Key findings reveal that blockchain ensures significant reduction in evidence tampering incidents, while AI prediction models achieve notable accuracy in judicial outcome forecasting. However, significant implementation challenges persist, including scalability constraints, lack of comprehensive regulatory frameworks, and insufficient integration with legacy court systems. This paper synthesizes current scholarship, identifies critical research gaps, and proposes a four-layer conceptual framework for pragmatic AI-blockchain deployment suited to India's constitutional and legal context. We conclude that strategic integration prioritizing permissioned blockchain architectures, explainable AI models, and federated learning offers transformative potential for addressing judicial inefficiency while maintaining due process and fundamental rights protections.","url":"https://doi.org/10.5281/zenodo.19712714","authors":["Sameer Patil","Darshana Desai"],"tags":["Blockchain","Artificial Intelligence","Judicial System","Legal Technology","India","Evidence Management","Predictive Justice","Smart Contracts"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19712714","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.19908777","name":"7th International Conference on Machine Learning & Trends (MLT 2026)","source":"datacite","abstract":"7th International Conference on Machine Learning & Trends (MLT 2026) June 20 ~ 21, 2026, Sydney, Australia https://sai2026.org/mlt/index Scope & Topics 7th International Conference on Machine Learning & Trends (MLT 2026) serves as a premier global forum for presenting and exchanging the latest advancements in Machine Learning theory, methodologies, and real world applications. As machine learning continues to shape the future of intelligent systems, scientific discovery, and industry innovation, MLT 2026 aims to bring together leading researchers, practitioners, and industry experts to explore emerging trends and transformative breakthroughs in the field. The conference provides a dynamic platform for fostering collaboration between academia and industry, encouraging the cross pollination of ideas that drive the next generation of machine learning technologies. Participants will have the opportunity to engage with cutting edge research, discuss open challenges, and identify new directions that will influence the evolution of ML in the years ahead. Authors are invited to contribute high quality submissions that showcase original research results, innovative projects, comprehensive surveys, and industrial case studies demonstrating significant progress in machine learning and its rapidly expanding ecosystem. Contributions may address, but are not limited to, the broad range of topics outlined below. Topics of interest include, but are not limited to, the following Machine Learning Foundations  Supervised, Unsupervised and Semi Supervised Learning  Reinforcement Learning and Sequential Decision Making  Probabilistic Modeling and Bayesian Machine Learning  Optimization Methods for Machine Learning  Learning Theory, Generalization and Sample Efficiency  Representation Learning and Feature Learning Deep Learning and Neural Architectures  Deep Neural Networks and Training Dynamics  Transformers and Attention Based Models  Graph Neural Networks (GNNs) and Graph Transformers  Self Supervised and Contrastive Learning  Neural Architecture Search (NAS)  Foundation Models and Large Scale Pretraining Generative Models and Synthetic Data  Diffusion Models and Score Based Generative Models  Generative Adversarial Networks (GANs)  Synthetic Data Generation and Data Centric AI  Generative Modeling for Images, Text, Audio, Video and Multimodal Data Advanced Learning Paradigms  Meta Learning and Few Shot Learning  Continual, Lifelong and Online Learning  Multi Task and Transfer Learning  Active Learning and Curriculum Learning  Federated, Distributed and Collaborative Learning Causal and Explainable Machine Learning  Causal Inference and Causal Discovery  Causal Representation Learning  Counterfactual Reasoning  Explainable and Interpretable Machine Learning Time Series, Forecasting and Sequential Modeling  Deep Learning for Time Series Forecasting  Streaming Data and Online Prediction  Event Based and Temporal Modeling  Sequential and Structured Data Analysis Scientific Machine Learning (SciML)  Neural Differential Equations  ML for Physics, Chemistry, Biology and Engineering  ML for Scientific Discovery, Simulation and Surrogate Modeling  Physics Informed Machine Learning ML Security, Safety and Robustness  Adversarial Attacks and Defenses  Model Extraction, Poisoning and Evasion Attacks  Secure and Trustworthy ML Pipelines  Safety, Reliability and Risk Aware ML  ML for Safety Critical Systems (healthcare, aviation, autonomous driving) Scalable, Efficient and Systems Level ML  Efficient Training: Compression, Pruning, Quantization  Large Scale ML Systems and Distributed Training  Hardware Aware ML (GPUs, TPUs, Edge Devices)  Energy Efficient and Sustainable ML  Real Time ML, Edge ML and TinyML Robotics, Embodied AI and Control  Robot Learning and Policy Optimization  Embodied Agents and Perception Action Loops  Sim to Real Transfer  Learning for Autonomous Systems ML for Code, Software Engineering ","url":"https://doi.org/10.5281/zenodo.19908777","authors":["''Flores Kú'', José Martin"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19908777","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.19908778","name":"7th International Conference on Machine Learning & Trends (MLT 2026)","source":"datacite","abstract":"7th International Conference on Machine Learning & Trends (MLT 2026) June 20 ~ 21, 2026, Sydney, Australia https://sai2026.org/mlt/index Scope & Topics 7th International Conference on Machine Learning & Trends (MLT 2026) serves as a premier global forum for presenting and exchanging the latest advancements in Machine Learning theory, methodologies, and real world applications. As machine learning continues to shape the future of intelligent systems, scientific discovery, and industry innovation, MLT 2026 aims to bring together leading researchers, practitioners, and industry experts to explore emerging trends and transformative breakthroughs in the field. The conference provides a dynamic platform for fostering collaboration between academia and industry, encouraging the cross pollination of ideas that drive the next generation of machine learning technologies. Participants will have the opportunity to engage with cutting edge research, discuss open challenges, and identify new directions that will influence the evolution of ML in the years ahead. Authors are invited to contribute high quality submissions that showcase original research results, innovative projects, comprehensive surveys, and industrial case studies demonstrating significant progress in machine learning and its rapidly expanding ecosystem. Contributions may address, but are not limited to, the broad range of topics outlined below. Topics of interest include, but are not limited to, the following Machine Learning Foundations  Supervised, Unsupervised and Semi Supervised Learning  Reinforcement Learning and Sequential Decision Making  Probabilistic Modeling and Bayesian Machine Learning  Optimization Methods for Machine Learning  Learning Theory, Generalization and Sample Efficiency  Representation Learning and Feature Learning Deep Learning and Neural Architectures  Deep Neural Networks and Training Dynamics  Transformers and Attention Based Models  Graph Neural Networks (GNNs) and Graph Transformers  Self Supervised and Contrastive Learning  Neural Architecture Search (NAS)  Foundation Models and Large Scale Pretraining Generative Models and Synthetic Data  Diffusion Models and Score Based Generative Models  Generative Adversarial Networks (GANs)  Synthetic Data Generation and Data Centric AI  Generative Modeling for Images, Text, Audio, Video and Multimodal Data Advanced Learning Paradigms  Meta Learning and Few Shot Learning  Continual, Lifelong and Online Learning  Multi Task and Transfer Learning  Active Learning and Curriculum Learning  Federated, Distributed and Collaborative Learning Causal and Explainable Machine Learning  Causal Inference and Causal Discovery  Causal Representation Learning  Counterfactual Reasoning  Explainable and Interpretable Machine Learning Time Series, Forecasting and Sequential Modeling  Deep Learning for Time Series Forecasting  Streaming Data and Online Prediction  Event Based and Temporal Modeling  Sequential and Structured Data Analysis Scientific Machine Learning (SciML)  Neural Differential Equations  ML for Physics, Chemistry, Biology and Engineering  ML for Scientific Discovery, Simulation and Surrogate Modeling  Physics Informed Machine Learning ML Security, Safety and Robustness  Adversarial Attacks and Defenses  Model Extraction, Poisoning and Evasion Attacks  Secure and Trustworthy ML Pipelines  Safety, Reliability and Risk Aware ML  ML for Safety Critical Systems (healthcare, aviation, autonomous driving) Scalable, Efficient and Systems Level ML  Efficient Training: Compression, Pruning, Quantization  Large Scale ML Systems and Distributed Training  Hardware Aware ML (GPUs, TPUs, Edge Devices)  Energy Efficient and Sustainable ML  Real Time ML, Edge ML and TinyML Robotics, Embodied AI and Control  Robot Learning and Policy Optimization  Embodied Agents and Perception Action Loops  Sim to Real Transfer  Learning for Autonomous Systems ML for Code, Software Engineering ","url":"https://doi.org/10.5281/zenodo.19908778","authors":["''Flores Kú'', José Martin"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19908778","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.22552","name":"Model-Consistent Byzantine-Resilient Decentralized Federated Learning for Collaborative Missions","source":"datacite","abstract":"Decentralized federated learning (DFL) is a promising paradigm for autonomous nodes to collaboratively train AI models without relying on a central server. However, existing DFL solutions do not guarantee global model consistency, a critical requirement for collaborative mission-critical scenarios where model divergence undermines decision uniformity and safety. This lack of consistency also amplifies vulnerability to Byzantine adversaries, who exploit the decentralized network topology and weak synchrony to perform equivocation and model poisoning attacks against individual victims. This paper introduces DFL-C, a novel Byzantine-resilient DFL architecture that enables decentralized nodes to perform collaborative training with global model consistency. At its core, DFL-C integrates an asynchronous common subset (ACS) consensus protocol into the DFL workflow to ensure all nodes aggregate a uniform set of model updates to establish global model consistency, despite individual Byzantine equivocation. DFL-C further implements a dual-domain trust scoring mechanism to provide resilience against data-domain Byzantine manipulations including model poisoning attacks. This mechanism complements the consensus protocol, significantly reducing the latter's runtime. Our experimental results demonstrate that DFL-C maintains model accuracy while achieving global model consistency under Byzantine behaviors with moderate consensus overhead. Notably, when compared with the state-of-the-art DFL solution BALANCE (Fang et al.) that does not provide model consistency, DFL-C achieves better model accuracy against untargeted model poisoning attacks and comparable resilience against backdoor attacks, with the advantage widened under non-IID scenarios.","url":"https://doi.org/10.48550/arxiv.2608.22552","authors":["Li, Yue","Bhujel, Sudip","Lira, Cameron","Wang, Ning","Xiao, Yang"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.22552","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.21539","name":"Federated Continual Learning as a Distributed Drift-Plus-Penalty Control Problem","source":"datacite","abstract":"Federated Continual Learning (FCL) is fundamental to real-world distributed learning systems, requiring models to adapt to sequential, non-IID data across clients while mitigating catastrophic forgetting and client drift. Existing approaches formulate continual learning (CL) as a sequence of per-task optimization problems, applied locally at each client and coupled through aggregation, using heuristic mechanisms such as replay, regularization, or projection-based constraints. However, forgetting in FCL is inherently a long-term, distributed phenomenon, arising from the interaction of temporal task evolution and cross-client heterogeneity, which is not explicitly regulated. In this work, we cast FCL as a stochastic control problem and propose Federated Queue-regulated Continual Learning (FedQCL), a framework based on Lyapunov drift-plus-penalty (DPP) optimization. FedQCL introduces virtual queues to track the accumulation of forgetting across tasks and clients, enabling explicit control of the stability-plasticity trade-off. By optimizing a DPP objective, the method jointly improves current-task performance while the queue-based formulation provides an interpretable and tunable mechanism to balance adaptation and retention through a single parameter, without requiring gradient projection or additional communication overhead. Empirical evaluations on standard benchmarks, including Split-CIFAR-10, Split-CIFAR-100, and Split-TinyImageNet, demonstrate that FedQCL outperforms state-of-the-art baselines with respect to accuracy while significantly reducing forgetting under heterogeneous data distributions.","url":"https://doi.org/10.48550/arxiv.2608.21539","authors":["Shah, Nazreen","Somireddy, Naveen Kumar Reddy","Shaban, Zubair","Prasad, Ranjitha","Bharath, B. N."],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.21539","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21819260","name":"CONFERENCE Proceedings: 1 INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND BUSINESS DIGITAL TRANSFORMATION (ICETBDT 2026) - GOA, INDIA","source":"datacite","abstract":"It is our great pleasure to present the proceedings of the 1st International Conference on Emerging Technologies and Business Digital Transformation (ICETBDT 2026), organized by SITER Academy, Norway, and held on 24 July 2026 in Goa, India. As an international forum for scholarly exchange, ICETBDT 2026 brings together researchers, academicians, industry professionals, policymakers, and students from around the world to discuss recent advances, emerging trends, and practical applications of digital technologies transforming businesses and society.The conference proceedings comprise peer-reviewed abstracts representing multidisciplinary research in artificial intelligence, machine learning, cybersecurity, healthcare, business analytics, sustainable development, education, agriculture, and intelligent computing. These contributions highlight the growing importance of digital transformation in addressing scientific, industrial, and societal challenges while promoting innovation and sustainability.The accepted abstracts explore the application of artificial intelligence and machine learning to real-world problems, including early disease diagnosis, computational drug discovery, neonatal sepsis prediction, crop disease classification, AI-enabled agricultural advisory systems, wildlife identification, and intelligent transportation safety. The proceedings also present advancements in cybersecurity, including intrusion detection, authentication mechanisms, privacy-preserving federated learning, financial fraud detection, and game-theoretic approaches to network defense.The conference further showcases research on business digital transformation and sustainable organizational practices, including AI-driven green innovation for SMEs, life-cycle costing for sustainable construction, AI-enabled human resource analytics, faculty career advancement systems, and the integration of artificial intelligence and computational thinking into school education. Together, these contributions demonstrate the interdisciplinary impact of emerging technologies across multiple domains.The Organizing Committee sincerely thanks all authors for their valuable contributions, reviewers for their rigorous evaluations, and the advisory, scientific, and technical committees for maintaining the academic quality of the conference. We also extend our gratitude to the keynote speakers, session chairs, sponsors, and participants whose dedication contributed to the success of this inaugural event.We hope these proceedings will serve as a valuable reference for researchers, educators, practitioners, and policymakers, fostering interdisciplinary collaboration and inspiring future research in emerging technologies and business digital transformation. We are confident that the knowledge shared through ICETBDT 2026 will stimulate innovation, strengthen international partnerships, and contribute to a digitally empowered, sustainable, and inclusive global society.","url":"https://doi.org/10.5281/zenodo.21819260","authors":["Mohanan, Saju","Varghese, Abraham","Mahish, Pramod Kumar","Patel, Suresh Kumar","Sao, Hemant Kumar","Yaswanth, Mallarapu","Malliswari, Thammanaveni"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21819260","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21819261","name":"CONFERENCE Proceedings: 1 INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND BUSINESS DIGITAL TRANSFORMATION (ICETBDT 2026) - GOA, INDIA","source":"datacite","abstract":"It is our great pleasure to present the proceedings of the 1st International Conference on Emerging Technologies and Business Digital Transformation (ICETBDT 2026), organized by SITER Academy, Norway, and held on 24 July 2026 in Goa, India. As an international forum for scholarly exchange, ICETBDT 2026 brings together researchers, academicians, industry professionals, policymakers, and students from around the world to discuss recent advances, emerging trends, and practical applications of digital technologies transforming businesses and society.The conference proceedings comprise peer-reviewed abstracts representing multidisciplinary research in artificial intelligence, machine learning, cybersecurity, healthcare, business analytics, sustainable development, education, agriculture, and intelligent computing. These contributions highlight the growing importance of digital transformation in addressing scientific, industrial, and societal challenges while promoting innovation and sustainability.The accepted abstracts explore the application of artificial intelligence and machine learning to real-world problems, including early disease diagnosis, computational drug discovery, neonatal sepsis prediction, crop disease classification, AI-enabled agricultural advisory systems, wildlife identification, and intelligent transportation safety. The proceedings also present advancements in cybersecurity, including intrusion detection, authentication mechanisms, privacy-preserving federated learning, financial fraud detection, and game-theoretic approaches to network defense.The conference further showcases research on business digital transformation and sustainable organizational practices, including AI-driven green innovation for SMEs, life-cycle costing for sustainable construction, AI-enabled human resource analytics, faculty career advancement systems, and the integration of artificial intelligence and computational thinking into school education. Together, these contributions demonstrate the interdisciplinary impact of emerging technologies across multiple domains.The Organizing Committee sincerely thanks all authors for their valuable contributions, reviewers for their rigorous evaluations, and the advisory, scientific, and technical committees for maintaining the academic quality of the conference. We also extend our gratitude to the keynote speakers, session chairs, sponsors, and participants whose dedication contributed to the success of this inaugural event.We hope these proceedings will serve as a valuable reference for researchers, educators, practitioners, and policymakers, fostering interdisciplinary collaboration and inspiring future research in emerging technologies and business digital transformation. We are confident that the knowledge shared through ICETBDT 2026 will stimulate innovation, strengthen international partnerships, and contribute to a digitally empowered, sustainable, and inclusive global society.","url":"https://doi.org/10.5281/zenodo.21819261","authors":["Mohanan, Saju","Varghese, Abraham","Mahish, Pramod Kumar","Patel, Suresh Kumar","Sao, Hemant Kumar","Yaswanth, Mallarapu","Malliswari, Thammanaveni"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21819261","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20500914","name":"Privacy-Preserving Operations on Spiral-Domain Encoded Time-Series States: Anonymization, Aggregation, and Federated Learning with Composition Rules and Streaming Variants","source":"datacite","abstract":"v2 update (2026-06-01): Reproducibility ZIP added back to the latest version alongside the manuscript files, so that downloading from the concept DOI gives all materials in one place rather than requiring navigation to v1. This revised deposit contains the Paper 7 manuscript (PDF and DOCX) along with the supplementary reproducibility archive. The manuscript files were added in this revised version of the deposit; the supplementary ZIP file remains unchanged from the original deposit. Manuscript targets IEEE Transactions on Information Forensics and Security (under preparation). Coverage. 28 pre-registered studies spanning Phases XII-XX of the spiral-domain encoder validation campaign (privacy primitives foundation, edge-case stress, deployment realism, hardware context, composition + streaming, reviewer preemption, Tier-3 strengthening, plus surgical-RT latency). 84 hypotheses, 65 SUPPORTED (77%), 8 honest bounded negatives substantively interpreted. Substantive findings. Three architectural privacy primitives uniquely enabled by spiral encoder mathematical structure: Primitive A (per-subject angular phase-shift anonymization, k=10 cross-subject anonymity); Primitive B (cross-subject mean aggregation, 8.84 million-fold inversion resistance); Primitive C (federated AR(1) learning, exact 1-round convergence invariant to site count and heterogeneity). 5 deployment embodiments: multi-hospital clinical, multi-factory industrial, federated prosthesis fleet, surgical robotics RT privacy, cloud-scale parallel. Contents. Manuscript (PDF and DOCX of the paper itself); supplementary reproducibility archive containing: README.md (submission-package map and reproduction instructions); preregistrations/ (frozen pre-registration .md documents with literal-threshold decision rules); reports/ (per-study .md verdict reports against frozen rules + phase summaries); runners/ (deterministic Python runners under PYTHONHASHSEED=0); raw_data/ (per-study CSV outputs and JSON verdict blocks); figures/ (manuscript figures at 300 DPI + figure-build script); code/ (encoder source code). Reproducibility. Full validation pipeline is reproducible end-to-end under PYTHONHASHSEED=0 on a standard Python 3.9+ installation with NumPy 2.0+ and PyTorch 2.8+ (required for the learned-adversary autoencoder attack of Study 86). Reference machine: Apple Silicon arm64 (M-series), macOS 14. See README.md for per-study run commands. Methodological discipline. Every hypothesis was pre-registered with externally anchored decision rules frozen prior to runner execution. Zero post-hoc threshold adjustments were applied. Honest bounded negatives are interpreted substantively rather than discarded. Related companion archives. Paper 1 (10.5281/zenodo.20129137), Paper 2 (10.5281/zenodo.20138786), Paper 3 (10.5281/zenodo.20139171), and the corresponding Papers 4, 5, 6, 8 archives in this same Zenodo collection. Paper 8 (10.5281/zenodo.20466035) extends Paper 7 Composition III to musculoskeletal-kinematic clinical digital twin deployment with 21 additional studies and 91 hypotheses validated across CMU Motion Capture and KIMORE rehabilitation datasets including patient populations.","url":"https://doi.org/10.5281/zenodo.20500914","authors":["Ferlic, Randolph James","Ferlic, Kimberly Kate"],"tags":["privacy-preserving computation","anonymization","cross-subject aggregation","federated learning","Byzantine-robust aggregation","complex-domain encoding","time-series","HIPAA"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20500914","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20221641","name":"Privacy-Preserving Operations on Spiral-Domain Encoded Time-Series States: Anonymization, Aggregation, and Federated Learning with Composition Rules and Streaming Variants","source":"datacite","abstract":"v2 update (2026-06-01): Reproducibility ZIP added back to the latest version alongside the manuscript files, so that downloading from the concept DOI gives all materials in one place rather than requiring navigation to v1. This revised deposit contains the Paper 7 manuscript (PDF and DOCX) along with the supplementary reproducibility archive. The manuscript files were added in this revised version of the deposit; the supplementary ZIP file remains unchanged from the original deposit. Manuscript targets IEEE Transactions on Information Forensics and Security (under preparation). Coverage. 28 pre-registered studies spanning Phases XII-XX of the spiral-domain encoder validation campaign (privacy primitives foundation, edge-case stress, deployment realism, hardware context, composition + streaming, reviewer preemption, Tier-3 strengthening, plus surgical-RT latency). 84 hypotheses, 65 SUPPORTED (77%), 8 honest bounded negatives substantively interpreted. Substantive findings. Three architectural privacy primitives uniquely enabled by spiral encoder mathematical structure: Primitive A (per-subject angular phase-shift anonymization, k=10 cross-subject anonymity); Primitive B (cross-subject mean aggregation, 8.84 million-fold inversion resistance); Primitive C (federated AR(1) learning, exact 1-round convergence invariant to site count and heterogeneity). 5 deployment embodiments: multi-hospital clinical, multi-factory industrial, federated prosthesis fleet, surgical robotics RT privacy, cloud-scale parallel. Contents. Manuscript (PDF and DOCX of the paper itself); supplementary reproducibility archive containing: README.md (submission-package map and reproduction instructions); preregistrations/ (frozen pre-registration .md documents with literal-threshold decision rules); reports/ (per-study .md verdict reports against frozen rules + phase summaries); runners/ (deterministic Python runners under PYTHONHASHSEED=0); raw_data/ (per-study CSV outputs and JSON verdict blocks); figures/ (manuscript figures at 300 DPI + figure-build script); code/ (encoder source code). Reproducibility. Full validation pipeline is reproducible end-to-end under PYTHONHASHSEED=0 on a standard Python 3.9+ installation with NumPy 2.0+ and PyTorch 2.8+ (required for the learned-adversary autoencoder attack of Study 86). Reference machine: Apple Silicon arm64 (M-series), macOS 14. See README.md for per-study run commands. Methodological discipline. Every hypothesis was pre-registered with externally anchored decision rules frozen prior to runner execution. Zero post-hoc threshold adjustments were applied. Honest bounded negatives are interpreted substantively rather than discarded. Related companion archives. Paper 1 (10.5281/zenodo.20129137), Paper 2 (10.5281/zenodo.20138786), Paper 3 (10.5281/zenodo.20139171), and the corresponding Papers 4, 5, 6, 8 archives in this same Zenodo collection. Paper 8 (10.5281/zenodo.20466035) extends Paper 7 Composition III to musculoskeletal-kinematic clinical digital twin deployment with 21 additional studies and 91 hypotheses validated across CMU Motion Capture and KIMORE rehabilitation datasets including patient populations.","url":"https://doi.org/10.5281/zenodo.20221641","authors":["Ferlic, Randolph James","Ferlic, Kimberly Kate"],"tags":["privacy-preserving computation","anonymization","cross-subject aggregation","federated learning","Byzantine-robust aggregation","complex-domain encoding","time-series","HIPAA"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20221641","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.22075247","name":"smshagor-dev/Federated-Learning-on-Non-IID-Data-Differential-Privacy: v2.0.0","source":"datacite","abstract":"Federated Learning Platform v2.0.0 Release: v2.0.0 Status: Release-ready Prepared: 2026-08-24 Python package: fl-platform==2.0.0 Python requirement: >=3.11 Validated main baseline: 339c1d113cee2e32e6f3240bf75a8f43bc9234f0 Overview Version 2.0.0 is a major platform release that turns the repository from a collection of federated-learning components into a substantially more complete, reproducible, privacy-aware, secure, and restart-capable federated-learning execution platform. This release combines two clearly separated execution identities: Root simulator / local runtime for reproducible single-machine federated experiments, benchmark matrices, dataset partitioning, evaluation, checkpointing, and research validation. Distributed platform built around the C++ coordinator, Python workers, Go control plane, protobuf/gRPC contracts, durable execution state, worker/task lifecycle management, security controls, and secure-aggregation runtime. The release focuses on executable behavior and validated claims. Unsupported combinations remain fail-closed rather than being silently downgraded or advertised as implemented. Highlights Unified local and distributed execution lifecycle under the Go control plane. Real benchmark matrix runner with repeated-seed statistical analysis. Expanded datasets: MNIST, FashionMNIST, CIFAR-10, and CIFAR-100. IID, Dirichlet, pathological class-skew, and quantity-skew partitioning. Realized non-IID heterogeneity measurement and exact partition fingerprints. FedAvg, FedProx, SCAFFOLD foundations plus FedSAM, Ditto, and first-order Per-FedAvg expansion. Shared-backbone/local-head personalization architecture. Persistent personalized model storage, model registry, and dataset registry. Global and per-client evaluation with fairness/tail metrics. Sample-level, user-level, and hybrid privacy infrastructure with stronger accounting and fail-closed compatibility rules. Target-epsilon calibration and same-adjacency RDP composition hardening. Durable local checkpoint, pause, restart, and resume lifecycle. Restart-durable distributed round deadlines, retry budgets, and full-cohort-first settlement behavior. Secure aggregation session, masked-update, dropout, deadline, abort, and restart-recovery hardening. Durable execution event observability and automatic reconciliation. Signed distributed partition references and worker-side partition parity. mTLS/PKI, signed task/result paths, replay protection, security journals, key/trust handling, and secret scanning. Full v2 release CI matrix validated green. What's New in v2.0.0 1. Unified Execution Platform v2.0.0 introduces a canonical execution lifecycle for both local and distributed runs. Added Production Go control-plane execution API under /api/v1/executions. Local execution backend that launches the existing Python root runtime rather than duplicating training logic. Distributed execution backend backed by the coordinator/worker runtime. Canonical lifecycle states and persisted execution metadata. Durable local execution records under the control-plane data directory. Authenticated execution API coverage. Optimistic durable state transitions and execution-event persistence. Startup execution reconciliation after control-plane restart. Runtime-safe automatic reconciliation for stable executions. Periodic reconciliation in the Go API process, with configurable FL_EXECUTION_RECONCILE_INTERVAL. Automatic propagation of backend completion, round progress, model version, and worker-count updates without requiring clients to manually refresh execution state. Reliability behavior Persisted CANCELED and FAILED states remain authoritative even if stale backend artifacts exist. Unrecoverable local in-flight executions fail closed instead of remaining as ghost RUNNING records. Transitional lifecycle operations are protected from background reconciliation overwrites. 2. Federated Algorithm Expansion The platform keeps the established FedAvg/FedProx/SCAFFOLD training foundation a","url":"https://doi.org/10.5281/zenodo.22075247","authors":["Shahanur Islam Shagor"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22075247","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.48550/arxiv.2608.21096","name":"FlatLand: Personalized Graph Federated Learning via Tailored Lorentz Space","source":"datacite","abstract":"Federated learning enables privacy-preserving collaborative training, but highly heterogeneous client data remain challenging, especially in graph federated learning where clients possess structurally diverse graphs. Existing personalized federated learning (PFL) methods ignore the intrinsic geometric properties of diverse graph structures. We propose FlatLand, a novel personalized federated learning method that embeds different clients' data in tailored Lorentz space of hyperbolic geometry. Our key insight is that hyperbolic geometry naturally accommodates the intrinsic negative curvature prevalent in real-world graphs, while the time-like dimension in Lorentz space provides a principled way to encode client-specific heterogeneity. We develop a parameter decoupling strategy that separates heterogeneous information (captured in time-like parameters) from common knowledge (preserved in space-like parameters), enabling direct aggregation without requiring client similarity estimation and extra calculation modules. Empirical results on diverse federated graph learning tasks demonstrate that FlatLand achieves superior performance, particularly in low-dimensional settings.","url":"https://doi.org/10.48550/arxiv.2608.21096","authors":["Liu, Jiahong","B, Ram Samarth B","Fu, Xinyu","Yang, Menglin","Zhang, Weixi","Ying, Rex","King, Irwin"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.21096","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.20518","name":"FL-MAESTRO: Multi-Agent LLM Orchestration for Resource-Constrained Federated Learning","source":"datacite","abstract":"In Federated Learning (FL), the communication topology is a runtime variable rather than a fixed design choice, since links and edge devices drop in and out during training. Each round, the server must commit three coupled decisions, namely the communication topology, per-client resource allocation, and the aggregation rule for combining local updates. Recent agentic systems have begun bringing large language models (LLM) into FL, but the existing line of work either operates at setup time or handles a single runtime dimension such as client selection. We propose FL-MAESTRO, a multi-agent orchestrator that makes the joint runtime FL decision directly through three specialist LLM agents, one per decision dimension. A coordinator combines their analyses into a single decision, and a non-LLM feasibility check confirms it before the round executes. Because the orchestrator consumes the server's predicted-failure list, it withholds clients whose updates would never be aggregated, which removes the dominant source of wasted round energy in classical FL on volatile edge networks. Because client state is read as natural-text profiles, the same orchestrator extends to heterogeneous device classes without per-class energy models. On a non-IID CIFAR-10 benchmark, FL-MAESTRO matches the accuracy of the strongest energy-aware baseline while cutting wasted round energy from over a third to near zero. Code is available at https://github.com/denoslab/FL-MAESTRO.","url":"https://doi.org/10.48550/arxiv.2608.20518","authors":["Wu, Jiajun","Wang, Zirui","Zhou, Jiayu","Ye, Qiang","Drew, Steve"],"tags":["Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.20518","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21313043","name":"A Systematic Literature Review on Explainable AI-Based Fish Quality Assessment Using Deep Learning","source":"datacite","abstract":"Fish quality assessment is essential for ensuring food safety, maintaining consumer confidence, and supporting the global seafood industry. Recent advances in Artificial Intelligence (AI), particularly deep learning and Explainable Artificial Intelligence (XAI), have enabled accurate, rapid, and non-destructive evaluation of fish freshness and quality. This systematic literature review aims to provide a comprehensive analysis of AI-based fish quality assessment techniques, with a particular focus on explainable deep learning models and their applications in seafood inspection. The review was conducted following the PRISMA 2020 guidelines and included publications from 2019 to 2026 retrieved from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and the ACM Digital Library. Following the screening and eligibility process, 220 representative studies were included for qualitative synthesis. The reviewed literature covers Convolutional Neural Networks (CNNs), transfer learning, Vision Transformers (ViTs), hyperspectral imaging, thermal imaging, multimodal sensing, and Explainable AI techniques, including Grad-CAM, LIME, SHAP, saliency maps, and attention visualization. Comparative analysis indicates that deep learning models consistently achieve classification accuracies exceeding 90–95%, while XAI methods significantly improve model transparency, interpretability, and user trust in industrial decision-making. However, challenges such as limited benchmark datasets, poor cross-species generalization, computational complexity, and the absence of standardized explainability evaluation frameworks continue to hinder widespread industrial adoption. This review identifies current research trends, summarizes existing datasets and evaluation metrics, highlights critical research gaps, and proposes future directions, including Edge AI, federated learning, digital twins, multisensor fusion, and trustworthy XAI, to support the development of intelligent, transparent, and reliable fish quality assessment systems.","url":"https://doi.org/10.5281/zenodo.21313043","authors":["Mr. Anujesus Dharmaraj","Rev. Sis. Dr. Rani Jacab"],"tags":["Fish Quality Assessment","Explainable Artificial Intelligence","Deep Learning","Computer Vision","Convolutional Neural Networks","Vision Transformer","Hyperspectral Imaging","Grad-CAM"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21313043","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21313044","name":"A Systematic Literature Review on Explainable AI-Based Fish Quality Assessment Using Deep Learning","source":"datacite","abstract":"Fish quality assessment is essential for ensuring food safety, maintaining consumer confidence, and supporting the global seafood industry. Recent advances in Artificial Intelligence (AI), particularly deep learning and Explainable Artificial Intelligence (XAI), have enabled accurate, rapid, and non-destructive evaluation of fish freshness and quality. This systematic literature review aims to provide a comprehensive analysis of AI-based fish quality assessment techniques, with a particular focus on explainable deep learning models and their applications in seafood inspection. The review was conducted following the PRISMA 2020 guidelines and included publications from 2019 to 2026 retrieved from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and the ACM Digital Library. Following the screening and eligibility process, 220 representative studies were included for qualitative synthesis. The reviewed literature covers Convolutional Neural Networks (CNNs), transfer learning, Vision Transformers (ViTs), hyperspectral imaging, thermal imaging, multimodal sensing, and Explainable AI techniques, including Grad-CAM, LIME, SHAP, saliency maps, and attention visualization. Comparative analysis indicates that deep learning models consistently achieve classification accuracies exceeding 90–95%, while XAI methods significantly improve model transparency, interpretability, and user trust in industrial decision-making. However, challenges such as limited benchmark datasets, poor cross-species generalization, computational complexity, and the absence of standardized explainability evaluation frameworks continue to hinder widespread industrial adoption. This review identifies current research trends, summarizes existing datasets and evaluation metrics, highlights critical research gaps, and proposes future directions, including Edge AI, federated learning, digital twins, multisensor fusion, and trustworthy XAI, to support the development of intelligent, transparent, and reliable fish quality assessment systems.","url":"https://doi.org/10.5281/zenodo.21313044","authors":["Mr. Anujesus Dharmaraj","Rev. Sis. Dr. Rani Jacab"],"tags":["Fish Quality Assessment","Explainable Artificial Intelligence","Deep Learning","Computer Vision","Convolutional Neural Networks","Vision Transformer","Hyperspectral Imaging","Grad-CAM"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21313044","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20507155","name":"Fulcrum: Topology-Aware Differential Privacy in Hierarchical Federated Learning","source":"datacite","abstract":"@misc{rangwala2026topologyawaredifferentialprivacyfederated, title={Topology-Aware Differential Privacy in Hierarchical Federated Learning}, author={Murtaza Rangwala and Richard O. Sinnott and Rajkumar Buyya}, year={2026}, eprint={2506.19260}, archivePrefix={arXiv}, primaryClass={cs.CR}, url={https://arxiv.org/abs/2506.19260}, }","url":"https://doi.org/10.5281/zenodo.20507155","authors":["Murtaza Rangwala"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20507155","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20507156","name":"Cloudslab/Fulcrum: v1.0.0","source":"datacite","abstract":"Fulcrum is a research codebase for topology-conditional distributional inference in differentially-private federated learning. @misc{rangwala2026topologyawaredifferentialprivacyfederated, title={Topology-Aware Differential Privacy in Federated Learning}, author={Murtaza Rangwala and Richard O. Sinnott and Rajkumar Buyya}, year={2026}, eprint={2506.19260}, archivePrefix={arXiv}, primaryClass={cs.CR}, url={https://arxiv.org/abs/2506.19260}, }","url":"https://doi.org/10.5281/zenodo.20507156","authors":["Murtaza Rangwala"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20507156","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20649856","name":"CDSA-ATM: Air Traffic Management Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Trajectory + ATS + ANSP Fusion","source":"datacite","abstract":"CDSA-ATM is the reference implementation of the air traffic management pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decision, 21 May 2026). The v2.1.0 release (12 June 2026) replaces the v2.0.0 scaffold stubs with full NumPy reference implementations (federated, multimodal, rl_agents, rl_environment, xai; all unit-tested) and adds seeded, reproducible CPU-scale federated training runs (random / centralised PPO / FedAvg / FedProx / FedProx+DP) with result JSONs under experiments/results/. An interactive results panel is published at https://cdsa.app/atm. Framework-scale training with real operational data remains future work. The v1.0.0 release modules and reference data remain unchanged. Version 2.2.0 (3 July 2026) adds four phases of seeded, CPU-scale revision-preparation experiments (seed 42, FedProx unless noted) under experiments/, with all outputs in experiments/results/ and interactive honesty-band panels at https://cdsa.app/atm/. Faz A (reference runs): the high critical recall (0.919) is achieved inside the over-alert regime characterised in Faz B. Faz B (robustness and confusion matrix): the policy uses only 2 of 5 actions and every benign state receives a critical prediction — an over-alert regime; the tempo-aware reward does not improve on the static baseline. Faz C (differential-privacy ε sweep and entropy-targeted exploration): the ε sweep is two-sided (ε = 2.0 matches the undefended baseline, ε = 0.5 collapses the policy to a single action); a 0.05 entropy bonus recovers all five actions, but critical recall falls from 0.927 to 0.878. Faz D (decoy attribution and gated exploration): decoy attribution stays below the 25% uniform share (mean 15.3%, max 23.3%); gated exploration preserves recall (0.928) but the policy remains two-action. Findings are reported verbatim from the result JSONs, consistent with the honesty bands published at the site.","url":"https://doi.org/10.5281/zenodo.20649856","authors":["Cantekin, Mete"],"tags":["federated reinforcement learning","air traffic management","ADS-B","OpenSky Network","ATS occurrences","PPO","FedAvg","FedProx"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20649856","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20649911","name":"CDSA-ATM: Air Traffic Management Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Trajectory + ATS + ANSP Fusion","source":"datacite","abstract":"CDSA-ATM is the reference implementation of the air traffic management pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decision, 21 May 2026). The v2.1.0 release (12 June 2026) replaces the v2.0.0 scaffold stubs with full NumPy reference implementations (federated, multimodal, rl_agents, rl_environment, xai; all unit-tested) and adds seeded, reproducible CPU-scale federated training runs (random / centralised PPO / FedAvg / FedProx / FedProx+DP) with result JSONs under experiments/results/. An interactive results panel is published at https://cdsa.app/atm. Framework-scale training with real data is planned under TÜBİTAK funding (Phase B). The v1.0.0 release modules and reference data remain unchanged.","url":"https://doi.org/10.5281/zenodo.20649911","authors":["Cantekin, Mete"],"tags":["federated reinforcement learning","air traffic management","ADS-B","OpenSky Network","ATS occurrences","PPO","FedAvg","FedProx"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20649911","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20162062","name":"Al in Marketing: Personalization and Recommendation Systems","source":"datacite","abstract":"Abstract Personalization in marketing uses AI-powered recommendation systems to tailor content, products, and messages to individual users. This paper provides an analytical, rigorous survey of AI personalization, covering definitions and scope; historical evolution; core algorithms and architectures (collaborative filtering, content-based, hybrid, matrix factorization, factorization machines, deep learning, sequence models like SASRec/BERT4Rec, graph-based models such as LightGCN, reinforcement learning, and emerging causal approaches); data sources and feature engineering (multi-modal data, feature stores, augmentation); evaluation metrics (accuracy, ranking, diversity, novelty, serendipity, calibration, fairness, business KPIs like CTR, conversion, retention); system design and deployment (offline vs real-time pipelines, scalability, latency, A/B testing, online learning, MLOps); personalization strategies across channels (email, web, mobile, ads, in-store); case studies of major companies (Amazon, Netflix, Spotify, Google/YouTube, TikTok) and varied industries; and privacy/ethics/regulation concerns (GDPR, CCPA, differential privacy, federated learning, transparency, explainability, bias mitigation). We include tables comparing algorithms and metrics, mermaid diagrams (timeline and pipeline), and charts where relevant. The paper concludes with actionable recommendations and open research questions. All claims are supported by recent (2021–2026) academic and industry sources. Keywords AI Personalization, Recommendation Systems, Collaborative Filtering, Content-Based Filtering, Hybrid Models, Matrix Factorization, Deep Learning Embeddings, Customer Segmentation, Cold Start Problem, Algorithmic Bias.","url":"https://doi.org/10.5281/zenodo.20162062","authors":["Shikha Tiwari, Shyam Kumar Singh"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20162062","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20162063","name":"Al in Marketing: Personalization and Recommendation Systems","source":"datacite","abstract":"Abstract Personalization in marketing uses AI-powered recommendation systems to tailor content, products, and messages to individual users. This paper provides an analytical, rigorous survey of AI personalization, covering definitions and scope; historical evolution; core algorithms and architectures (collaborative filtering, content-based, hybrid, matrix factorization, factorization machines, deep learning, sequence models like SASRec/BERT4Rec, graph-based models such as LightGCN, reinforcement learning, and emerging causal approaches); data sources and feature engineering (multi-modal data, feature stores, augmentation); evaluation metrics (accuracy, ranking, diversity, novelty, serendipity, calibration, fairness, business KPIs like CTR, conversion, retention); system design and deployment (offline vs real-time pipelines, scalability, latency, A/B testing, online learning, MLOps); personalization strategies across channels (email, web, mobile, ads, in-store); case studies of major companies (Amazon, Netflix, Spotify, Google/YouTube, TikTok) and varied industries; and privacy/ethics/regulation concerns (GDPR, CCPA, differential privacy, federated learning, transparency, explainability, bias mitigation). We include tables comparing algorithms and metrics, mermaid diagrams (timeline and pipeline), and charts where relevant. The paper concludes with actionable recommendations and open research questions. All claims are supported by recent (2021–2026) academic and industry sources. Keywords AI Personalization, Recommendation Systems, Collaborative Filtering, Content-Based Filtering, Hybrid Models, Matrix Factorization, Deep Learning Embeddings, Customer Segmentation, Cold Start Problem, Algorithmic Bias.","url":"https://doi.org/10.5281/zenodo.20162063","authors":["Shikha Tiwari, Shyam Kumar Singh"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20162063","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21590976","name":"AI-Driven Intrusion Detection for the Internet of Things: A Scoping Review of Federated Learning, Privacy-Preserving Architectures, and Edge Deployability","source":"datacite","abstract":"Federated learning has emerged as the dominant architectural response to the privacy and communication constraints of centralised intrusion detection in Internet of Things environments, yet the field lacks a synthesis that maps the concurrent state of architecture diversity, privacy-preservation rigour, and edge deployability. This scoping review synthesises 99 empirical studies published between 2020 and 2026, drawn from two thematic extraction categories: federated learning-based intrusion detection for Internet of Things networks (61 studies) and deep learning-based intrusion detection with blockchain-enabled tamper-proof logging (43 studies, one shared). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews, the review maps twelve confirmed architecture families, a twelve-branch privacy mechanism taxonomy, and classifies all included studies by edge evaluation type. Three gap clusters are identified. An empirical gap in deployment evaluation is the most operationally consequential: 88 per cent of included studies evaluate on server simulation only, and the study that quantifies the cost of this deferral reports a 36.5 percentage-point accuracy degradation on real-world imbalanced data. A practical-knowledge and evidence gap in privacy claims separates formal guarantees from predominant practice: 41 of 61 federated learning studies assert privacy through the federated paradigm alone, without differential privacy, homomorphic encryption, or secure aggregation. A methodological gap in evaluation reproducibility arises from incomplete federated learning configuration reporting, persistent reliance on a 2009 benchmark dataset, and unnamed datasets in recent papers. The findings provide a structured evidence base for primary research that targets these gaps. Keywords: Edge deployment; Federated learning; Internet of Things; Intrusion detection systems; Privacy-preserving machine learning; Scoping review.","url":"https://doi.org/10.5281/zenodo.21590976","authors":["Gilbert Imuetinyan Osaze Aimufua","Godwin Agbonkhese"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21590976","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.21590977","name":"AI-Driven Intrusion Detection for the Internet of Things: A Scoping Review of Federated Learning, Privacy-Preserving Architectures, and Edge Deployability","source":"datacite","abstract":"Federated learning has emerged as the dominant architectural response to the privacy and communication constraints of centralised intrusion detection in Internet of Things environments, yet the field lacks a synthesis that maps the concurrent state of architecture diversity, privacy-preservation rigour, and edge deployability. This scoping review synthesises 99 empirical studies published between 2020 and 2026, drawn from two thematic extraction categories: federated learning-based intrusion detection for Internet of Things networks (61 studies) and deep learning-based intrusion detection with blockchain-enabled tamper-proof logging (43 studies, one shared). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews, the review maps twelve confirmed architecture families, a twelve-branch privacy mechanism taxonomy, and classifies all included studies by edge evaluation type. Three gap clusters are identified. An empirical gap in deployment evaluation is the most operationally consequential: 88 per cent of included studies evaluate on server simulation only, and the study that quantifies the cost of this deferral reports a 36.5 percentage-point accuracy degradation on real-world imbalanced data. A practical-knowledge and evidence gap in privacy claims separates formal guarantees from predominant practice: 41 of 61 federated learning studies assert privacy through the federated paradigm alone, without differential privacy, homomorphic encryption, or secure aggregation. A methodological gap in evaluation reproducibility arises from incomplete federated learning configuration reporting, persistent reliance on a 2009 benchmark dataset, and unnamed datasets in recent papers. The findings provide a structured evidence base for primary research that targets these gaps. Keywords: Edge deployment; Federated learning; Internet of Things; Intrusion detection systems; Privacy-preserving machine learning; Scoping review.","url":"https://doi.org/10.5281/zenodo.21590977","authors":["Gilbert Imuetinyan Osaze Aimufua","Godwin Agbonkhese"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21590977","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.20418818","name":"jiayunz/OrthoFL: Initial Release","source":"datacite","abstract":"Code for KDD 2026 paper: \"Taming Update Drift in Asynchronous Federated Learning via Orthogonal Calibration\"","url":"https://doi.org/10.5281/zenodo.20418818","authors":["Jiayun Zhang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20418818","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20418819","name":"jiayunz/OrthoFL: Initial Release","source":"datacite","abstract":"Code for KDD 2026 paper: \"Taming Update Drift in Asynchronous Federated Learning via Orthogonal Calibration\"","url":"https://doi.org/10.5281/zenodo.20418819","authors":["Jiayun Zhang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20418819","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.20038","name":"An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage","source":"datacite","abstract":"Artificial intelligence can support cultural heritage and digital humanities through large-scale retrieval and analysis of digitized collections. However, cultural heritage data are often distributed across institutions, constrained by ownership and access restrictions, and continuously evolving over time. Federated Continual Learning (FCL) is well suited to this setting, as it enables models to learn from distributed and sequential data without sharing raw collections. In this paper, we propose FedCurv-DR, a lightweight, regularisation-based FCL strategy. The method accumulates parameter-importance estimates across clients and experiences to protect learned knowledge, while updating them only at fixed intervals to minimize communication and computation overhead. We evaluate FedCurv-DR in a continual learning scenario using the WikiArt image dataset for genre classification with evolving styles, reporting performance, energy, and fairness metrics. Our results show that FedCurv- DR reduces forgetting and balances performance, fairness, and energy efficiency for sustainable AI in cultural heritage.","url":"https://doi.org/10.48550/arxiv.2608.20038","authors":["Theologitis, Ioannis","Meng, Debin","Eleftheriadis, Stylianos","Lolis, Vasileios","Votis, Konstantinos"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.20038","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.19650","name":"Enhancing Privacy in Federated Learning via Dual Obfuscation of Gradients and Training Images","source":"datacite","abstract":"Federated learning enables collaborative model training while keeping data locally at each client; however, recent studies have shown that training data can be reconstructed from shared model updates. To address this issue, this paper proposes a dual obfuscation method that enhances robustness against image restoration attacks by jointly obfuscating updated information and training images. The proposed method combines a robustness enhancement technique based on random binary weights, which randomly sets a portion of gradient elements to zero, with an image encryption technique. These techniques provide complementary protection by reducing the amount of original gradient information available to an attacker and the visual interpretability of reconstructed images, respectively. Furthermore, the image encryption technique allows independent keys to be used for each client and each image, avoiding explicit key sharing. Experimental results on an image classification task using a Vision Transformer (ViT) show that the proposed method reduces the visual information recovered by Attention Privacy Leakage (APRIL) under the evaluated settings without causing additional degradation in classification performance beyond that caused by image encryption. Although the proposed combination does not provide an absolute security guarantee, the results demonstrate the potential benefit of combining gradient modification and image encryption for privacy-enhanced federated learning.","url":"https://doi.org/10.48550/arxiv.2608.19650","authors":["Itabashi, Yuki","Sawada, Hiroto","Hirose, Mare","Imaizumi, Shoko","Kiya, Hitoshi"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.19650","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.19649","name":"Differential Privacy in Feature Reconstruction Aided Federated Learning for Agent's Semantic Communication Model Update","source":"datacite","abstract":"This paper proposes a differentially private federated learning (FL) framework built upon an FL algorithm with semantic feature reconstruction (FedSFR) for training semantic communication modules for image transmission. By allowing clients with unfavorable uplink capacity to transmit low-dimensional semantic feature vectors extracted from locally trained joint source-channel coding (JSCC) encoders, FedSFR enhances communication efficiency and training stability under heterogeneous wireless conditions. To protect client privacy, we incorporate the oneshot Laplace mechanism and theoretically demonstrate that feature-based transmission achieves strictly stronger differential privacy (DP) guarantees than gradient-based transmission under an identical communication budget. In addition, a model selection mechanism is introduced to alleviate performance degradation caused by privacy-preserving perturbations. Experimental results on multiple datasets show that the proposed DP-aided FedSFR outperforms DP-enabled FedAvg in training stability and image reconstruction quality in heterogeneous wireless systems.","url":"https://doi.org/10.48550/arxiv.2608.19649","authors":["Huh, Yoon","Kim, Bumjun","Choi, Wan"],"tags":["Signal Processing (eess.SP)","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.19649","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.20968429","name":"Machine Learning Techniques for 5G Network Optimization and Resource Management: A Comprehensive Survey","source":"datacite","abstract":"The fifth generation (5G) of mobile networks represents a fundamental paradigm shift in wireless communications, promising ultra-low latency, massive device connectivity, and peak data rates exceeding 10 Gbps. However, the unprecedented complexity of 5G network architecture — encompassing heterogeneous networks, massive MIMO, millimeter-wave communications, network slicing, and mobile edge computing — introduces resource management and optimization challenges that traditional rule-based approaches cannot efficiently address. Machine learning (ML) and deep learning (DL) techniques have emerged as transformative tools for intelligent 5G network management. This paper presents a comprehensive survey of ML-based approaches applied to 5G network optimization and resource management, covering key areas including spectrum management, beamforming optimization, network slicing, handover management, energy efficiency, and quality of service (QoS) prediction. We systematically review over 80 research works published between 2019 and 2026, categorize them by ML technique and application domain, and analyze their performance, limitations, and practical deployment challenges. Our survey reveals that deep reinforcement learning (DRL) and federated learning are the most promising paradigms for 5G optimization, achieving up to 40% improvement in spectral efficiency and 35% reduction in energy consumption compared to conventional methods. We also identify key open challenges including real-time inference constraints, data privacy, and generalization across network environments, and outline promising future research directions including integration with 6G networks.","url":"https://doi.org/10.5281/zenodo.20968429","authors":["Dash, Bhabani Sankar"],"tags":["5G Networks, Machine Learning, Deep Learning, Network Optimization, Resource Management, Reinforcement Learning, Network Slicing, Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20968429","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20968428","name":"Machine Learning Techniques for 5G Network Optimization and Resource Management: A Comprehensive Survey","source":"datacite","abstract":"The fifth generation (5G) of mobile networks represents a fundamental paradigm shift in wireless communications, promising ultra-low latency, massive device connectivity, and peak data rates exceeding 10 Gbps. However, the unprecedented complexity of 5G network architecture — encompassing heterogeneous networks, massive MIMO, millimeter-wave communications, network slicing, and mobile edge computing — introduces resource management and optimization challenges that traditional rule-based approaches cannot efficiently address. Machine learning (ML) and deep learning (DL) techniques have emerged as transformative tools for intelligent 5G network management. This paper presents a comprehensive survey of ML-based approaches applied to 5G network optimization and resource management, covering key areas including spectrum management, beamforming optimization, network slicing, handover management, energy efficiency, and quality of service (QoS) prediction. We systematically review over 80 research works published between 2019 and 2026, categorize them by ML technique and application domain, and analyze their performance, limitations, and practical deployment challenges. Our survey reveals that deep reinforcement learning (DRL) and federated learning are the most promising paradigms for 5G optimization, achieving up to 40% improvement in spectral efficiency and 35% reduction in energy consumption compared to conventional methods. We also identify key open challenges including real-time inference constraints, data privacy, and generalization across network environments, and outline promising future research directions including integration with 6G networks.","url":"https://doi.org/10.5281/zenodo.20968428","authors":["Dash, Bhabani Sankar"],"tags":["5G Networks, Machine Learning, Deep Learning, Network Optimization, Resource Management, Reinforcement Learning, Network Slicing, Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20968428","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20984146","name":"Machine Learning Techniques for 5G Network Optimization and Resource Management: A Comprehensive Survey","source":"datacite","abstract":"The fifth generation (5G) of mobile networks represents a fundamental paradigm shift in wireless communications, promising ultra-low latency, massive device connectivity, and peak data rates exceeding 10 Gbps. However, the unprecedented complexity of 5G network architecture — encompassing heterogeneous networks, massive MIMO, millimeter-wave communications, network slicing, and mobile edge computing — introduces resource management and optimization challenges that traditional rule-based approaches cannot efficiently address. Machine learning (ML) and deep learning (DL) techniques have emerged as transformative tools for intelligent 5G network management. This paper presents a comprehensive survey of ML-based approaches applied to 5G network optimization and resource management, covering key areas including spectrum management, beamforming optimization, network slicing, handover management, energy efficiency, and quality of service (QoS) prediction. We systematically review over 80 research works published between 2019 and 2026, categorize them by ML technique and application domain, and analyze their performance, limitations, and practical deployment challenges. Our survey reveals that deep reinforcement learning (DRL) and federated learning are the most promising paradigms for 5G optimization, achieving up to 40% improvement in spectral efficiency and 35% reduction in energy consumption compared to conventional methods. We also identify key open challenges including real-time inference constraints, data privacy, and generalization across network environments, and outline promising future research directions including integration with 6G networks.","url":"https://doi.org/10.5281/zenodo.20984146","authors":["Dash, Bhabani Sankar"],"tags":["5G Networks, Machine Learning, Deep Learning, Network Optimization, Resource Management, Reinforcement Learning, Network Slicing, Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20984146","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.48550/arxiv.2607.29659","name":"GQ-FSL: Green Quantized Federated Split Learning Framework for Wireless Edge Networks","source":"datacite","abstract":"Deploying state-of-the-art deep neural networks (DNNs) at the wireless edge is severely bottlenecked by the strict energy and resource constraints of mobile devices. Although federated split learning (FSL) alleviates on-device computational burdens by offloading workloads to an edge server, this may introduce systemic overheads, while the continuous exchange of intermediate activations, gradients, and submodels still incurs significant energy consumption (EC). To address this, we propose a green quantized FSL (GQ-FSL) framework that incorporates stochastic quantization for both local collaborative training and wireless transmissions. Notably, GQ-FSL supports asymmetric precision levels for the client- and server-side submodels, effectively decoupling device energy constraints from global convergence degradation. To quantify these tradeoffs, we develop parameterized energy models for the split architecture and derive a theoretical convergence bound under statistically heterogeneous data. Building on that, we formulate a joint optimization problem to configure the DNN split point and precision levels, minimizing the total system EC while satisfying strict latency and target accuracy constraints. Ultimately, we demonstrate that GQ-FSL enables large-scale DNN deployment on resource-constrained devices, achieving superior energy efficiency compared to quantized federated learning and full-precision FSL.","url":"https://doi.org/10.48550/arxiv.2607.29659","authors":["Roth, Idan","Lampe, Lutz"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","Signal Processing (eess.SP)","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.29659","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.19155","name":"FedGuard-DC: Privacy-Preserving Federated Load Forecasting and Cyber-Attack Detection for Data-Center Loads in Transmission Systems","source":"datacite","abstract":"The rapid growth of large data-center (DC) loads is creating new challenges for power-system visibility, privacy, and cyber-physical security. System operators need accurate short-term information about these fast-varying loads, while DC operators may avoid sharing raw megawatt measurements because they can reveal sensitive workload and utilization patterns. This paper presents FedGuard-DC, a federated learning (FL) framework for privacy-preserving DC load forecasting and local false-data-injection attack (FDIA) detection. Each DC trains a dual-head model on its own measurements, where a shared encoder supports both a forecasting head and a reconstruction head. A calibrated anomaly score combines forecast residual and reconstruction error to detect corrupted measurements locally. Raw measurements and absolute MW demand remain at each DC, while only model updates are shared with the global controller. Optional differential privacy and robust trimmed-mean aggregation are included to evaluate privacy-utility behavior and poisoned-client resilience. The framework is validated using EMT simulation data from four large DC loads rated between 150 and 350 MW integrated into the IEEE 39-bus New England system. Results show a 0.5 s-ahead normalized forecast RMSE of 0.023-0.038 pu, compared with 0.32-0.34 pu for persistence. FedGuard-DC detects FDIA with ROC-AUC of 0.979, F1 = 0.930, and precision of 0.988, while robust aggregation reduces the poisoned-client RMSE impact from 0.042 to 0.035 pu.","url":"https://doi.org/10.48550/arxiv.2608.19155","authors":["Saroare, Md Kibria","Ahmed, Md Rubel"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.19155","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.18311","name":"FedCoRe: Target-Adaptive Completion for Missing Modalities in Healthcare Federated Learning","source":"datacite","abstract":"Federated multimodal models often assume every site has every modality, although hospitals differ in access to EHRs, chest radiographs, and ECGs. We study this setting on a MIMIC-derived respiratory deterioration task with simulated FL clients and introduce FedCoRe (Federated Cross-Modal Representation Completion). FedCoRe learns representation- or logit-space corrections rather than generating synthetic ECGs or CXR images. When a client observes a modality that may be missing at deployment, it evaluates the same example with and without that modality to obtain paired supervision. Only clients with such pairs update the completion module, and validation may retain the unchanged prediction. We freeze the trained multimodal predictor during evaluation so that measured differences come only from completion. Hiding ECG reduced AUROC by about 0.085; paired-example FedAvg restored 0.0415 AUROC, or 49.0% of the lost performance. We therefore report two distinct effects: paired-example FedAvg partially recovers the missing-ECG gap, while validation-selected completion is a task-specific classifier-logit correction rather than literal ECG recovery. For CXR, effect-aware completion recovers 52.8% of the loss in a controlled test where CXR is hidden. Paired-example FedAvg transfers part of this effect, but validation keeps the no-completion baseline for deployment cases whose inputs lack CXR. Thus, FedCoRe should be read as a validation-gated completion/correction framework: it can recover missing-modality signal in supported settings, but it should be deployed only when paired examples and validation evidence support that modality.","url":"https://doi.org/10.48550/arxiv.2608.18311","authors":["Roth, Holger R.","Xu, Ziyue","Cnudde, Peter"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.18311","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.22014092","name":"Fulcrum: Topology-Aware Differential Privacy in Hierarchical Federated Learning","source":"datacite","abstract":"@misc{rangwala2026topologyawaredifferentialprivacyfederated, title={Topology-Aware Differential Privacy in Hierarchical Federated Learning}, author={Murtaza Rangwala and Richard O. Sinnott and Rajkumar Buyya}, year={2026}, eprint={2506.19260}, archivePrefix={arXiv}, primaryClass={cs.CR}, url={https://arxiv.org/abs/2506.19260}, }","url":"https://doi.org/10.5281/zenodo.22014092","authors":["Murtaza Rangwala"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22014092","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21973073","name":"Analysis code and derived dataset for: Federated learning reconciles data sovereignty with collaborative energy-transition modelling across the Global South","source":"datacite","abstract":"Analysis code, derived dataset and outputs for the manuscript ‘Federated learning reconciles data sovereignty with collaborative energy-transition modelling across the Global South: a public-data benchmark anchored in Indonesia’ (submitted to npj Climate Action). Contains the derived facility-level table built from the Global Energy Monitor Global Integrated Power Tracker (August 2026 release, CC BY 4.0; 16,404 operating facilities in 44 Global South country silos), scripts reproducing every published value (four training regimes, robustness suite, data-scarcity experiment, figures), and a self-contained script that downloads the WRI Global Power Plant Database v1.3.0 and reproduces the independent replication end to end. A plain-language guide enables non-programmers to run everything in a web browser. Code: MIT. Derived data: CC BY 4.0, attribute Global Energy Monitor. v2 adds a conda environment specification pinning scikit-learn below 1.9 (which alters the gradient-boosting diagnostics by 0.001), an updated plain-language run guide, and documentation of independent two-platform verification. All scripts, data and result files are unchanged from v1.","url":"https://doi.org/10.5281/zenodo.21973073","authors":["Darmanto, Sumartono"],"tags":["federated learning; energy transition; data sovereignty; Global South; Indonesia; renewable energy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21973073","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.22006907","name":"Analysis code and derived dataset for: Federated learning reconciles data sovereignty with collaborative energy-transition modelling across the Global South","source":"datacite","abstract":"Analysis code, derived dataset and outputs for the manuscript ‘Federated learning reconciles data sovereignty with collaborative energy-transition modelling across the Global South: a public-data benchmark anchored in Indonesia’ (submitted to npj Climate Action). Contains the derived facility-level table built from the Global Energy Monitor Global Integrated Power Tracker (August 2026 release, CC BY 4.0; 16,404 operating facilities in 44 Global South country silos), scripts reproducing every published value (four training regimes, robustness suite, data-scarcity experiment, figures), and a self-contained script that downloads the WRI Global Power Plant Database v1.3.0 and reproduces the independent replication end to end. A plain-language guide enables non-programmers to run everything in a web browser. Code: MIT. Derived data: CC BY 4.0, attribute Global Energy Monitor. v2 adds a conda environment specification pinning scikit-learn below 1.9 (which alters the gradient-boosting diagnostics by 0.001), an updated plain-language run guide, and documentation of independent two-platform verification. All scripts, data and result files are unchanged from v1.","url":"https://doi.org/10.5281/zenodo.22006907","authors":["Darmanto, Sumartono"],"tags":["federated learning; energy transition; data sovereignty; Global South; Indonesia; renewable energy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22006907","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2508.06692","name":"HeteRo-Select: Informativeness as the Participation Driver in Heterogeneous Federated Learning","source":"datacite","abstract":"Federated learning systems typically allocate gradient compression by link speed. This is sensible when bandwidth and data informativeness align. However, under non-IID data, these signals often decorrelate or invert. A bandwidth-driven allocator then risks compressing the most informative gradients hardest. We propose HeteRo-Select, a framework that replaces bandwidth with a per-client informativeness score as the primary driver of compression. The score jointly governs three decisions per round: client selection, compression ratio, and server aggregation weight, with bandwidth retained only as a hard ceiling. Score-proportional selection provably reduces the effective heterogeneity of the chosen subset; score-proportional compression provably lowers aggregate top-$k$ error at fixed traffic. Under the exact FedCG simulation protocol, HeteRo-Select delivers a $1.78\\times$ speedup and an $18.2\\%$ reduction in traffic on CIFAR-10. The same configuration, unchanged, scales from a $7{,}850$-parameter logistic regression to an $11.27$M-parameter ResNet-18, hitting the accuracy target on three of four benchmarks. When bandwidth and informativeness are deliberately anti-correlated, the method still achieves the target accuracy with less traffic than the normal-bandwidth run.","url":"https://doi.org/10.48550/arxiv.2508.06692","authors":["Masud, Md. Akmol","Jahin, Md Abrar","Hasan, Mahmud"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.06692","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.48550/arxiv.2608.17069","name":"Dynamic Entanglement-Weighted Pruning for Quantum Federated Unlearning in Supply-Chain Risk Prediction","source":"datacite","abstract":"Federated deployments of variational quantum classifiers are attractive for cross-organisation risk prediction in supply chains, because raw data never leaves the client, yet data-protection regulations such as the GDPR grant clients a right to request that their contribution be removed from a trained model after the fact. Retraining a federated model from scratch to honour such a request is correct but wasteful, and it is not obvious which quantum circuit parameters actually carry a given client's influence. We introduce Entanglement-Weighted Pruning (EWP), an unlearning procedure for quantum federated learning that scores every trainable circuit parameter with the product of two signals: the diagonal entry of the quantum Fisher information matrix estimated on the target client's data via the parameter-shift rule, and a structural entanglement weight associated with the parameter's gate. Parameters with the lowest scores are pruned, optionally followed by a short fine-tuning pass on the retained clients. We implement the full pipeline in Qiskit for a four-qubit data-re-uploading ansatz trained with FedAvg across five simulated supply-chain-risk clients, and benchmark EWP against full retraining, fine-tuning alone, random pruning, Fisher-only pruning, and entanglement-only pruning, over three random seeds. EWP attains a mean post-unlearning accuracy statistically indistinguishable from the full-retraining oracle, while producing a lower forgetting score and requiring roughly 16 times less wall-clock time. Ablations over pruning threshold, client count, and non-IID strength show that combining the two signals is necessary, as entanglement-only and Fisher-only pruning each substantially degrade accuracy relative to EWP.","url":"https://doi.org/10.48550/arxiv.2608.17069","authors":["Kumar, Aditya","Chongder, Sumit"],"tags":["Quantum Physics (quant-ph)","Machine Learning (cs.LG)","FOS: Physical sciences","FOS: Computer and information sciences","I.2.6; I.2.11; C.2.4"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.17069","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21258546","name":"Advances in Interdisciplinary Systems & Computing","source":"datacite","abstract":"The rapid evolution of artificial intelligence, secure computing, distributed systems, and human-centered technologies is reshaping the way societies create, manage, and utilize digital knowledge. Contemporary research increasingly transcends traditional disciplinary boundaries, bringing together advances in computing, engineering, healthcare, governance, cybersecurity, and intelligent systems to address complex real-world challenges.Advances in Interdisciplinary Systems & Computing 2026 presents a curated collection of scholarly contributions that reflect this convergence. The volume combines original chapters on emerging paradigms in artificial intelligence with selected peer-reviewed articles published in the Interdisciplinary Journal of Computing & AI (IJCAI). Together, these contributions provide insights into the opportunities and challenges associated with next-generation intelligent systems, privacy-preserving technologies, decentralized architectures, and advanced computing infrastructures.Part I introduces foundational perspectives on Local-First Large Language Models, resource-aware AI execution, multimodal foundation models, trustworthy artificial intelligence, and Agentic AI in trading. There are five chapters that explore emerging directions that are expected to influence the future development of AI systems across research and industry.Part II highlights human-centric applications of artificial intelligence, spanning healthcare analytics, rural water governance, intelligent design systems, and brain-computer interfaces spread across four chapters. The contributions demonstrate how AI can be leveraged to address societal needs and improve quality of life.Part III focuses on secure, privacy-preserving, and decentralized computing. There are three chapters that examine blockchain-enabled federated learning, privacy-preserving communication systems, and post-quantum secure cyber-physical environments, reflecting the growing importance of trust and resilience in digital ecosystems.Part IV explores advanced intelligent systems and computing infrastructure, including multimodal transformer architectures and GPU optimization techniques that underpin modern AI workloads and high-performance computing environments in two chapters.The chapters included in this volume collectively represent the interdisciplinary spirit that defines contemporary computing research. We hope that this book serves as a valuable resource for researchers, academicians, practitioners, policymakers, and students seeking to understand the technological innovations shaping the future of intelligent systems.","url":"https://doi.org/10.5281/zenodo.21258546","authors":["Chakraborty, Mohuya","Chakraborty, Jayanta"],"tags":["Interdisciplinary","Computing","AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21258546","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.5281/zenodo.21258547","name":"Advances in Interdisciplinary Systems & Computing","source":"datacite","abstract":"The rapid evolution of artificial intelligence, secure computing, distributed systems, and human-centered technologies is reshaping the way societies create, manage, and utilize digital knowledge. Contemporary research increasingly transcends traditional disciplinary boundaries, bringing together advances in computing, engineering, healthcare, governance, cybersecurity, and intelligent systems to address complex real-world challenges.Advances in Interdisciplinary Systems & Computing 2026 presents a curated collection of scholarly contributions that reflect this convergence. The volume combines original chapters on emerging paradigms in artificial intelligence with selected peer-reviewed articles published in the Interdisciplinary Journal of Computing & AI (IJCAI). Together, these contributions provide insights into the opportunities and challenges associated with next-generation intelligent systems, privacy-preserving technologies, decentralized architectures, and advanced computing infrastructures.Part I introduces foundational perspectives on Local-First Large Language Models, resource-aware AI execution, multimodal foundation models, trustworthy artificial intelligence, and Agentic AI in trading. There are five chapters that explore emerging directions that are expected to influence the future development of AI systems across research and industry.Part II highlights human-centric applications of artificial intelligence, spanning healthcare analytics, rural water governance, intelligent design systems, and brain-computer interfaces spread across four chapters. The contributions demonstrate how AI can be leveraged to address societal needs and improve quality of life.Part III focuses on secure, privacy-preserving, and decentralized computing. There are three chapters that examine blockchain-enabled federated learning, privacy-preserving communication systems, and post-quantum secure cyber-physical environments, reflecting the growing importance of trust and resilience in digital ecosystems.Part IV explores advanced intelligent systems and computing infrastructure, including multimodal transformer architectures and GPU optimization techniques that underpin modern AI workloads and high-performance computing environments in two chapters.The chapters included in this volume collectively represent the interdisciplinary spirit that defines contemporary computing research. We hope that this book serves as a valuable resource for researchers, academicians, practitioners, policymakers, and students seeking to understand the technological innovations shaping the future of intelligent systems.","url":"https://doi.org/10.5281/zenodo.21258547","authors":["Chakraborty, Mohuya","Chakraborty, Jayanta"],"tags":["Interdisciplinary","Computing","AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21258547","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:28.654Z"},{"id":"doi:10.20944/preprints202608.1409.v1","name":"Electromagnetically Consistent Federated RIS Channel Estimation with Component-Level Full-Wave Validation","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202608.1409.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.1409.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.21203/rs.3.rs-10084345/v1","name":"Federated Autonomous Cyber Agents for Privacy-Preserving Intrusion Detection in Distributed Smart Grids","source":"preprints","abstract":"Abstract Distributed smart grids increasingly rely on interconnected cyber-physical infrastructures, edge intelligence, microgrids, distributed energy resources, and real-time demand-response mechanisms. Although this integration improves flexibility and operational efficiency, it also exposes power systems to false data injection, denial-of-service, replay, command manipulation, poisoned model updates, and privacy-inference attacks. Conventional centralized intrusion-detection systems require sensitive grid data to be transferred to a central server, while standard federated learning approaches remain vulnerable to non-IID data, unreliable agents, Byzantine updates, and weak operational response capability. To address these limitations, this paper proposes a federated autonomous cyber-agent framework for privacy-preserving intrusion detection in distributed smart grids. The proposed model integrates local temporal intrusion detection, hybrid classification and anomaly scoring, differentially private update protection, secure aggregation, trust-weighted Byzantine-resilient filtering, and grid-safe autonomous response. Each local cyber agent detects cyber-physical threats using private domain data, shares only protected model updates, and selects mitigation actions under stability, uncertainty, and human-override constraints. Simulation-based evaluation using smart-grid, IIoT, and network-intrusion benchmark settings indicates that the proposed framework improves detection accuracy, Macro-F1, convergence stability, poisoning resilience, and response latency compared with local IDS, FedAvg, FedProx, and robust federated baselines. The results suggest that combining privacy-preserving federated learning with trust-aware autonomous cyber agents can provide a scalable and resilient foundation for smart-grid intrusion detection. The study remains limited by its simulation-based validation, privacy-budget sensitivity, and dependence on accurate grid-stability constraints, motivating future hardware-in-the-loop and utility-grade testbed evaluation.","url":"https://doi.org/10.21203/rs.3.rs-10084345/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10084345/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.20944/preprints202607.0295.v1","name":"FedMARL-LTI: Federated Multi-Agent Reinforcement Learning with LLM-Driven Threat Intelligence for Cooperative Cyber Defense","source":"preprints","abstract":"Cross-organization cyber defense must reconcile collaborative learning with privacy and adversarial robustness — yet standard federated learning ships full gradient tensors, leaking sensitive posture and inviting Byzantine manipulation. We present FedMARL-LTI, a federated multi-agent reinforcement learning framework whose architecture answers both pressures with a single decision: organizations share neither raw data nor model weights, only differentially-private 768-dimensional semantic threat embeddings. The contribution is fourfold. (1) Semantic Abstraction (SA) channel: per organization, each round, the local gradient is summarized by an LLM, projected to a 768-dim embedding, L2-clipped, and Gaussian-noised before any numeric quantity leaves the host. The bottleneck reduces the per-element noise scale from O(√(d_model )) to O(√m) with m=768≪dmodel≈3×105. (2) Formal privacy analysis: the SA+DP cascade satisfies (ε,δ)-DP and bounds per-round mutual-information leakage by min{Ttoklog2V, m⁄2 log2 (1+C2/(mσ2))} (Theorem 1), with Rényi composition over T federation rounds (Theorem 2). (3) Byzantine-resilient ClippedClustering aggregator combining L2 clipping with cosine-similarity clustering. (4) Hierarchical MARL policy with threat-profile-aware LLM-IRR reward shaping, wired end-to-end and disclosed honestly (the LLM call is currently stubbed with a deterministic projection for reproducibility). We evaluate on CybORG CAGE-4 with n=5 organizations, 30 federation rounds × 5 episodes × 100 steps per round. The SA channel adds statistical-zero utility cost vs. no-privacy baseline: SA-only Δreward = -0.66 (t=+0.31, NS), dual SA + Weight-DP Δreward = +1.90 (t=-0.71, NS), all N=5 seeds, all |t| 1.3. A controlled signal/noise probe confirms a 19.58× improvement of SA over Weight-DP at fixed DP budget — matching the predicted √(d/m)≈19.8. Under Byzantine sign_flip at 30% (N=15), ClippedClustering is directionally strongest (F1=0.025 vs FedAvg 0.020, Krum 0.016) but the edge is not statistically significant (CC vs Krum t=+1.59, p=0.15, d=+0.58; the earlier N=5 “3.4×” gap was small-sample optimism, §5.2); its decisive Byzantine win is the harsher random_noise attack, where FedAvg diverges to NaN and Krum collapses to 0.002 while ClippedClustering survives at 0.020 (§5.7, Cohen’s d=+3.77). The cooperative-PPO family (MAPPO, IPPO) outperforms value/actor-critic (QMIX, MADDPG) by ≈20 reward units, p 0.001. All host-level F1 values stay below 0.05 at the 15K-step training horizon used here; the relative claims of the paper (privacy zero-cost, ClippedClustering’s decisive Byzantine win on the harshest attacks per §5.7, cooperative-PPO dominance) are unaffected by this scope. A 200K-step long-horizon replication (§6.3 L1) lifts F1 above the 15K plateau (to ≈0.044, N=5) — confirming that horizon, not the privacy/Byzantine machinery, gates absolute accuracy — but a finer 60-checkpoint run shows the climb is volatile and non-monotonic and does not reach deployment-grade, an honest stability-not-compute limitation. We release all 141 raw run JSON outputs (Phases 1–3, the L4 backend comparison, and the algorithm/aggregator baselines), the figures, and analysis scripts for replication.","url":"https://doi.org/10.20944/preprints202607.0295.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202607.0295.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-10257924/v1","name":"Privacy-Preserving Federated Distillation Resilient to Client Disconnections and Poisoning Attacks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10257924/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10257924/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202608.1322.v1","name":"Structure Learning in Bayesian Networks: A Systematic Pedagogical Survey","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202608.1322.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.1322.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9740401/v1","name":"Multi-Center Federated Clinical-Deep Learning Fusion Model for Preoperative Differentiation of Solitary Pulmonary Solid Nodules","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9740401/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9740401/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-10106089/v1","name":"FedMAPPO: Federated Multi-Agent Proximal Policy Optimization for Energy-Efficient Task Offloading in Industrial IoT Networks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10106089/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10106089/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9342990/v1","name":"Explainable Federated Multimodal Deep Learning Framework for Early Alzheimer’s Disease Detection: Integrating MRI, Clinical Data, and Expert-Guided Few-Shot Learning with Privacy-Preserving Cross-Site Validation","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9342990/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9342990/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9258502/v1","name":"A Privacy-Aware and Communication-Efficient Federated Spam Detection Framework for Multi-Cloud Environments","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9258502/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9258502/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.21203/rs.3.rs-9689895/v1","name":"FedDep-GT-LLM: Federated Graph Transformer and Clinical Language Modeling for Multimodal Depression Analysis","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9689895/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9689895/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-10244577/v1","name":"From Instrumented Towers to Vulnerable Blocks: A Physics-Informed Federated Edge Digital-Twin Framework for Translational Seismic Damage Assessment in Andean Emerging Economies (NSET-Andes)","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10244577/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10244577/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9680458/v1","name":"Contribution-Aware Federated Edge Learning for Robust Resource Allocation in Massive IoT Networks","source":"preprints","abstract":"Abstract In the scene of large-scale Internet of Things (mIoT), the communication between high-density devices (D2D) will introduce serious co-frequency interference and cross-layer interference. Although the existing multi-agent deep reinforcement learning (MADRL) technology plays an important role in resource allocation challenges, there are also challenges in terms of limited spectrum awareness, unfair credit allocation and environmental non-stationarity. In order to meet these challenges, this paper proposes a multi-agent depth deterministic policy gradient (CA-FE-MADDPG) algorithm based on contribution awareness. First of all, the state space contains the characteristics of spatial interference graph and dynamic margin threshold, which can give each node of the Internet of Things a clear spectrum sensing ability. Secondly, a fine-grained penalty mechanism is designed based on physical interference ratio, interference severity and power regularization, which can be used to solve the problem of unfair credit distribution. This mechanism can not only maximize energy efficiency (EE) and concurrent access, but also strictly ensure the quality of service (QoS) of major users. In addition, the federal edge strategy and heuristic-guided hot-start strategy are designed, which can reduce non-stationarity and protect users' privacy while bypassing high-risk initial exploration, and ensure the safe and rapid convergence of the algorithm. A large number of simulations verify that CA-FE-MADDPG algorithm is significantly superior to the baseline in terms of system throughput, access rate and service quality satisfaction, which provides a new solution for ultra-dense mobile Internet of Things.","url":"https://doi.org/10.21203/rs.3.rs-9680458/v1","authors":["Hui Dun","Aowei Liu","Eryang Huan","Zhiyong Niu"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9680458/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9915376/v1","name":"Federated Fake News Detection Via Global Self-Attention and Inverse Variance Aggregation Under Data Heterogeneity","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9915376/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9915376/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9446173/v1","name":"A Privacy-Preserving Federated Spatiotemporal Dynamic Graph Neural Network Framework for Epileptic Seizure Prediction","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9446173/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9446173/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9443065/v1","name":"Research on Federated Multimodal Human Morphology Recognition Integrating Convolutional and Graph Convolutional Networks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9443065/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9443065/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9950422/v1","name":"Reliability-Aware Coordination for Federated Predictive Maintenance Under Fixed Thresholds","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9950422/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9950422/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-10165604/v1","name":"TITAN: Three-Tier Intrusion-Detection Tree-Based Adaptive Network for Industrial Internet of Things ","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10165604/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10165604/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9691952/v1","name":"Towards Transparent Rare Disease Monitoring: Federated Rule Extraction for Online Streaming NPC1 Data","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9691952/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9691952/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9436896/v1","name":"Federated foundation models for accurate disease detection","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9436896/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9436896/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9853327/v1","name":"Federated Temporal Kolmogorov-Arnold Networks for Private Low-Power Wearable Health Monitoring","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9853327/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9853327/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-10146188/v1","name":"PRISM-VFL: A Differentially Private Vertical Federated Framework for Heterogeneous Multi-Task Clinical Prediction","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10146188/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10146188/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202604.0575.v1","name":"FedHeRM: Federated Heterogeneity-Aware Reinforcement Learning for Secure NOMA Resource Management in Vehicular Edge Networks","source":"preprints","abstract":"The rapid expansion of 5G/6G technologies and the burgeoning demand for low-latency, high-reliability services at the network edge challenge traditional centralized cloud architectures. Edge computing offers a promising solution, yet its dynamic, resource-constrained, and heterogeneous nature necessitates decentralized intelligence for effective resource management. This paper addresses these challenges by synergistically integrating Federated Learning (FL), Multi-Agent Reinforcement Learning (MARL), and Non-Orthogonal Multiple Access (NOMA) resource management. While Federated Multi-Agent Reinforcement Learning (FMARL) is a critical direction, existing methods struggle with extreme heterogeneity and privacy concerns. To overcome these limitations, we propose FedHeRM: Federated Heterogeneity-aware Reinforcement Learning for Secure NOMA Resource Management in Vehicular Edge Networks. FedHeRM models the vehicular edge environment as a Partially Observable Stochastic Game, where RSU agents learn optimal resource allocation policies. Our core innovation lies in a novel Heterogeneity-aware Federated Multi-Agent Reinforcement Learning (HeRA-FMARL) framework, which introduces a Dynamic Heterogeneity Measure (DHM) for adaptive weighted aggregation of model updates, significantly accelerating convergence and enhancing generalization across diverse agents. Furthermore, FedHeRM integrates robust privacy-preserving mechanisms, including Differential Privacy for local updates and Secure Aggregation protocols. Comprehensive experiments in a simulated vehicular edge environment demonstrate that FedHeRM significantly outperforms state-of-the-art baselines across critical metrics, achieving superior system throughput, lower task latency, reduced energy consumption, and enhanced user fairness, while maintaining excellent scalability and strong privacy guarantees. An ablation study confirms the crucial roles of its key components, further validating FedHeRM's efficacy in highly dynamic and heterogeneous edge networks.","url":"https://doi.org/10.20944/preprints202604.0575.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202604.0575.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9220964/v1","name":"WITHDRAWN: SFDP: Adaptive Noise and Dual-Weighted Aggregation for Privacy-Preserving Federated Vehicle Trajectory Prediction","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9220964/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9220964/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9196503/v1","name":"Privacy-Preserving Topic-wise Sentiment Analysis of the Iran–Israel–USA Conflict Using Federated Transformer Models","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9196503/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9196503/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.12688/openreseurope.23459.1","name":"A sustainable platform for federated health data access, AI innovation, and regulatory acceptance in alignment with the European Health Data Space principles","source":"preprints","abstract":"The IDERHA ( I ntegration of Heterogeneous D ata and E vidence towards R egulatory and H TA A cceptance) project aims to enhance medical research by establishing one of Europe’s first pan-European, disease-agnostic health data spaces. Aligned with the European Health Data Space (EHDS) principles, IDERHA addresses critical challenges in data quality, standardization, and governance, ensuring compliance with GDPR, the AI Act, and emerging EHDS regulations. Its ambition is to enable secure, federated access and analysis of health data, fostering data-driven collaboration and innovation in healthcare. IDERHA’s technical infrastructure employs a ‘privacy-by-design’ approach, leveraging federated analytics and learning to maintain data sovereignty and reduce privacy risks. The project focuses on lung cancer as a high-impact use case, utilizing AI and machine learning to improve early detection, diagnosis, and personalized care. It also aims to develop policy recommendations for the acceptance of real-world evidence (RWE) for regulatory decision-making through multi-stakeholder engagement and public consultations. However, challenges remain, including semantic interoperability, and scaling federated AI methods across borders. IDERHA’s modular, standards-based architecture and emphasis on ethical, legal, and FAIR compliance provide a robust framework for addressing these issues. By collaborating with other initiatives in the health data domain to drive compatibility, IDERHA seeks to accelerate innovation by creating a sustainable, scalable model for health data access and thereby a positive impact for patients across Europe.","url":"https://doi.org/10.12688/openreseurope.23459.1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.12688/openreseurope.23459.1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.14293/pr2199.004137.v1","name":"Deep Learning Technologies for Real-Time Personalization in Mobile Applications","source":"preprints","abstract":"","url":"https://doi.org/10.14293/pr2199.004137.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.14293/pr2199.004137.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9346409/v1","name":"DL-HIDS-IoT: A Lightweight Deep Learning-Enhanced Hybrid Intrusion Detection System for IoT and Wireless Sensor Networks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9346409/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9346409/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9478897/v1","name":"Federated Edge AI for Anomaly Detection in Industrial IoT","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9478897/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9478897/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9295285/v1","name":"Representation-stable federated fault diagnostics for electric drivetrains under nonstationary vibration signals","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9295285/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9295285/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9192952/v1","name":"FTAD-Net: Federated Transfer-Adversarial Learning for Robust Breast Cancer Detection","source":"preprints","abstract":"Abstract Breast cancer remains the second leading cause of cancer-related mortality among women worldwide, making early and accurate diagnosis pivotal for improving patient survival rates. Although deep learning (DL) models have demonstrated remarkable potential in automated breast cancer detection, their large-scale clinical deployment is hindered by challenges such as limited annotated data, institutional data silos, and stringent privacy regulations. To overcome these limitations, this study proposes a Federated Transfer-Adversarial DenseNet (FTAD-Net), a state-of-the-art privacy-preserving and domain-generalizable DL framework designed for collaborative breast cancer diagnosis across multiple medical institu- tions. The proposed FTAD-Net introduces three major innovations. First, a federated transfer learning paradigm enables knowledge sharing among decentralized institutions without exchanging raw data, ensuring data confidentiality through secure gradient aggregation. Second, a domain-adversarial feature alignment module mitigates inter-institutional domain shifts by enforcing invariant feature representations across heterogeneous imaging modalities, including mammography and MRI. Third, a self-attentive feature refinement mechanism is integrated within the DenseNet backbone, enhancing discrimination of lesion-relevant regions while improving feature interpretability. The model was collaboratively trained on datasets from BreakHis, MIAS, and DDSM, encompassing diverse imaging conditions and patient populations. Ex- perimental evaluations demonstrate that FTAD-Net achieves a classification accuracy of 97.52%, surpassing existing centralized and federated approaches by up to 4.7%, with an average inference time of 10 seconds per case. Overall, the proposed framework provides a scalable, privacy-preserving, and domain-robust solution for early and reliable breast cancer detection.","url":"https://doi.org/10.21203/rs.3.rs-9192952/v1","authors":["Atta Ur Rahman","Mahmood Alam","Bibi Saqia","Zahid Halim","Ehab Qahwash"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9192952/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202605.0029.v1","name":"Secure Federated Intrusion Detection for Resource-Constrained IoT Devices Using Lightweight Cryptography: A Hardware-Validated Study","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202605.0029.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202605.0029.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-10591441/v1","name":"AI Native Manufacturing Operating System for Autonomous Smart Factories Using Digital Twins, Multi Agent Artificial Intelligence, Physics Informed Machine Learning, and Reinforcement Learning.","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10591441/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10591441/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9684485/v1","name":"Trust-Governed Agentic AI for Cyber-Resilient Digital Markets: Blockchain-Audited Federated Intelligence Under Uncertainty","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9684485/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9684485/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202604.0662.v1","name":"Federated Multi-Agent Deep Reinforcement Learning for Joint Channel Selection and Power Control in Cognitive Radio Networks","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202604.0662.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202604.0662.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9872496/v1","name":"TrustFedKG-Health: Explainability-Aware Personalized Federated Knowledge Graph Neural Network with Uncertainty-Guided Clinical Reasoning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9872496/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9872496/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9548238/v1","name":"Rafale: Towards the Efficient Resource-Aware Federated Knowledge Distillation on Heterogeneous Clients","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9548238/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9548238/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9453051/v1","name":"Learning Category-Aware Sensitivity for Federated Test-Time Adaptation","source":"preprints","abstract":"Abstract Federated test-time adaptation (FTTA) addresses the challenge of adapting to unlabeled target data under distribution shifts while preserving data privacy. However, existing FTTA methods often overlook the category-aware sensitivity of samples in both training and test stages. Different categories exhibit distinct sensitivity to patch-level perturbations due to their varying reliance on contextual and background information. For instance, recognizing a house versus a piano becomes unevenly difficult when image patches are shuffled, reflecting their different dependencies on spatial context. As a result, uniformly fine-tuning a model across all features can be suboptimal and may degrade performance. To address this limitation, we propose a Category-Aware Test-time Sensitivity (CATS) approach, which enables fine-grained control of the adaptation process. Specifically, we introduce the Category-Aware Inference Robustness (CAIN) threshold, a metric that quantifies the stability of model predictions under patch-level perturbations. Furthermore, we employ a soft pseudo-labeling strategy that leverages CAIN values to mitigate overconfident updates during optimization. Extensive experiments across multiple datasets demonstrate that CATS effectively handles diverse distribution shifts. The source code is publicly available at https://github.com/Zhang666-cloud/CATS .","url":"https://doi.org/10.21203/rs.3.rs-9453051/v1","authors":["Yingfa Zhang","Xiaohui Deng","Guangguang Yang","Serestina Viriri","Jiangtao Cao","Chang’an Yi"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9453051/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202604.0681.v1","name":"Adaptive Reinforcement Learning Offloading: Unifying Federated Dissimilarity Measures and Generalizable Multi-Objective Optimization for Mobile Edge Computing","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202604.0681.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202604.0681.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202605.0501.v1","name":"RankBridge: Privacy-Preserving Rank-Based Explanation Clustering for Heterogeneous Federated Phishing Detection","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202605.0501.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202605.0501.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-10683945/v1","name":"Misbehavior Detection in Internet of Vehicles: A Multi-Dimensional Survey","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10683945/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10683945/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202607.0428.v1","name":"AI-Mediated Continuous Assessment Infrastructure: Rethinking Educational Measurement across Contexts and Time","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202607.0428.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202607.0428.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.64898/2026.07.24.26357042","name":"Edge-Based ADL Recognition Using Room-Specialized Mixture-of-Experts","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.07.24.26357042","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.07.24.26357042","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202608.0697.v1","name":"The Impact of Innovation on the Performance and Safety of Medical Electronic Devices","source":"preprints","abstract":"The rapid transition to digital medicine has transformed isolated electronic medical devices into intelligent, interconnected ecosystems capable of predictive analytics and continuous monitoring. The expansion of hyperconnectivity introduces significant vulnerabilities, including increased cyber risks, hardware bottlenecks, data fragmentation, and interoperability challenges. This study investigates the importance of technical creativity by integrating artificial intelligence (AI), edge computing, and Internet of Medical Things (IoMT) architectures to improve diagnostic accuracy, operational reliability, and safety of medical devices through electronic technologies. Using a comprehensive framework analysis, the research evaluates advanced architectural paradigms—such as digital twin simulations, federated learning, adaptive controls, and lightweight cryptographic solutions—along with emerging epistemic sensing concepts such as orthosensors and pseudo-ontosensors. The findings demonstrate that software algorithms, AI models, and cybersecurity frameworks now consume over half of modern biomedical R D investments, with AI-based surgical platforms achieving up to a 25% reduction in operative times and a 30% decrease in intraoperative complications. It is concluded that achieving sustainable clinical adoption requires a balance between rapid technological innovation and standardized regulatory governance, robust cybersecurity, human-centered UI/UX design, and ongoing interdisciplinary collaboration.","url":"https://doi.org/10.20944/preprints202608.0697.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.0697.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9803597/v1","name":"Non-Monotonic Lattice Meets Cross-Chain FL: CrossFL-BCD with MC-SC for Conditional Declassification","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9803597/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9803597/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202604.1162.v1","name":"Federated Privacy-Preserving Multi-Modal Deep Learning for Breast Cancer Diagnosis: A Physics-Aware Approach","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202604.1162.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202604.1162.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-10259285/v1","name":"AM-RAF: An Agentic, Retrieval-Augmented, Uncertainty-Gated Framework for Trustworthy Clinical Decision Support in Resource-Constrained Health Systems ","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10259285/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10259285/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202602.1749.v1","name":"Federated Contrastive Representation Learning for IoT Anomaly Detection Under Heterogeneous Data","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202602.1749.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202602.1749.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-10274933/v1","name":"Predictive single cell foundation model for gene regulation and aging with privacy-preserving tabular learning","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10274933/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10274933/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9837234/v1","name":"DLNDD: An Explainable Deep Learning Framework for the Early Detection","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9837234/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9837234/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202608.1647.v1","name":"From Fragmented Evidence to a Unified GCC Demyelinating Disease Registry: A Strategic Framework for MS, NMOSD, MOGAD, Optic Neuritis, LETM, and Pediatric Acquired Demyelination","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202608.1647.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.1647.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202601.2257.v1","name":"F-DRL: Federated Dynamics Representation Learning for Robust Multi-Task Reinforcement Learning","source":"preprints","abstract":"Reinforcement learning for robotic manipulation is often limited by poor sample efficiency and unstable training dynamics, challenges that are further amplified in federated settings due to data privacy constraints and task heterogeneity. To address these issues, we propose F-DRL , a federated dynamics-aware representation learning framework that enables multiple robotic tasks to collaboratively learn structured latent representations without sharing raw trajectories or policy parameters. The framework combines robotics priors with an action-conditioned latent dynamics model to learn low-dimensional state and state–action embeddings that explicitly capture task-relevant geometric and transition structure. Representation learning is performed locally at each client, while a central server aggregates encoder parameters using a similarity-weighted scheme based on second-order latent geometry. The learned representations are then used as frozen auxiliary inputs for downstream model-free reinforcement learning. We evaluate F-DRL on seven heterogeneous robotic manipulation tasks from the MetaWorld benchmark. While achieving performance comparable to centralized training and standard federated baseline, F-DRL substantially improves training stability relative to FedAvg on heterogeneous manipulation tasks with partially shared dynamics (e.g., Drawer-Open and Window-Open),reducing the mean across-seed standard deviation and the AUC of this deviation by over 60%. The method remains neutral on simple tasks and performs less consistently on contact-rich manipulation tasks with task-specific dynamics, indicating both the benefits and the practical limits of representation-level knowledge sharing in federated robotic learning.","url":"https://doi.org/10.20944/preprints202601.2257.v1","authors":["Anurag Upadhyay","Yashar Baradaranshokouhi","Xin Lu","Jun Li","Yanguo Jing"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202601.2257.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9288020/v1","name":"Design and Analysis of an Integrated AI-Driven andBlockchain-Enabled Connected Framework forNext-Generation Oncology Applications","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9288020/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9288020/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.20944/preprints202603.2130.v1","name":"FedGNN-SFD: A Lightweight Federated Graph Neural Network for Multi-Sensor Bearing Fault Diagnosis in Industrial IoT Systems","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202603.2130.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202603.2130.v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-9313076/v1","name":"Secure Medical Image Cryptanalysis with Quantum Neural Networks for IoT-Enabled Cloud Storage","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9313076/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9313076/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-8806936/v1","name":"FusionNet Lite: A Lightweight Federated Deep Learning Model for Privacy-Preserving Predictive Maintenance","source":"preprints","abstract":"Abstract Federated learning (FL) offers a privacy-preserving strategy for industrial predictive maintenance (PdM), yet many existing models remain too large or energy-intensive for deployment on edge devices. This study proposes \\textit{FusionNet Lite}, an ultralight hybrid convolutional architecture with fewer than 1420 parameters, designed for low-latency federated optimisation under non-IID industrial conditions. The model integrates depthwise separable convolutions, a compact ConvMixer module, and squeeze-and-excitation (SE) attention to minimise computation and communication while preserving predictive performance. Experiments on the AI4I~2020 dataset show that FusionNet~Lite matches the accuracy of larger baselines and achieves the highest energy-efficiency index across both CPU and GPU environments. The model also maintains attributional stability between centralised and federated training, with Integrated Gradients (IG) demonstrating consistent feature importance patterns. Communication overhead remains below 25~kB per round, enabling deployment in bandwidth-constrained Industrial Internet of Things (IIoT) networks. The results confirm that lightweight FL architectures can support real-time PdM while preserving data privacy in distributed industrial settings.","url":"https://doi.org/10.21203/rs.3.rs-8806936/v1","authors":["Aman Sharma","Kwan Yong Sim","Sivachandran Chandrasekaran"],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8806936/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.21203/rs.3.rs-10028819/v1","name":"A Practical and Reproducible DDoS Detection Benchmark with DNS Amplification and Mixed Attacks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10028819/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10028819/v1","addedAt":"2026-08-31T06:41:28.654Z","updatedAt":"2026-08-31T06:41:35.443Z"},{"id":"doi:10.1007/978-3-642-15317-4_13","name":"Improved Primitives for Secure Multiparty Integer Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-15317-4_13","authors":["Octavian Catrina","Sebastiaan de Hoogh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2010-09-10T02:46:58Z","doi":"10.1007/978-3-642-15317-4_13","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.4156/ijact.vol3.issue4.10","name":"Privacy-preserving Collaborative Filtering based on Randomized Perturbation Techniques and Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.4156/ijact.vol3.issue4.10","authors":["Songjie Gong -"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-06-11T03:18:10Z","doi":"10.4156/ijact.vol3.issue4.10","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/cis2018.2018.00098","name":"Efficient Collusion-Tolerable Secure Multiparty Computation of Weighted Average","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cis2018.2018.00098","authors":["Hongwei Duan","Runmeng Du","Qiong Wei","Wenli Wang","Xin Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-12-07T00:50:30Z","doi":"10.1109/cis2018.2018.00098","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/noms.2018.8406322","name":"A management framework for secure multiparty computation in dynamic environments","source":"crossref","abstract":"","url":"https://doi.org/10.1109/noms.2018.8406322","authors":["Marcel von Maltitz","Stefan Smarzly","Holger Kinkelin","Georg Carle"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-07-09T19:09:23Z","doi":"10.1109/noms.2018.8406322","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-031-95140-4_3","name":"Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-95140-4_3","authors":["Xun Yi","Xuechao Yang","Xiaoning Liu","Andrei Kelarev","Kwok-Yan Lam","Mengmeng Yang","Xiangning Wang","Elisa Bertino"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-15T14:46:29Z","doi":"10.1007/978-3-031-95140-4_3","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.22152/programming-journal.org/2023/7/14","name":"Symphony: Expressive Secure Multiparty Computation with Coordination","source":"crossref","abstract":"","url":"https://doi.org/10.22152/programming-journal.org/2023/7/14","authors":["Ian Sweet","David Darais","David Heath","William Harris","Ryan Estes","Michael Hicks"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-02-20T14:22:55Z","doi":"10.22152/programming-journal.org/2023/7/14","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.32604/cmc.2025.073883","name":"Quantum Secure Multiparty Computation: Bridging Privacy, Security, and Scalability in the Post-Quantum Era","source":"crossref","abstract":"","url":"https://doi.org/10.32604/cmc.2025.073883","authors":["Sghaier Guizani","Tehseen Mazhar","Habib Hamam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-09T09:16:24Z","doi":"10.32604/cmc.2025.073883","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.5753/wquantum.2022.223569","name":"Collision Warning in Vehicular Networks Based on Quantum Secure Multiparty Computation","source":"crossref","abstract":"Quantum Secure Multiparty Computation (QSMC) is a technology that takes the advantage of quantum features allowing multiple parties to communicate in a secure and efficient manner while preserving their privacy. Using QSMC technology, we implement a collision warning use case in which vehicles can freely broadcast information while preserving the privacy of their confidential data. We integrate two quantum technologies namely Quantum Key Distribution (QKD) and Quantum Oblivious Key Distribution (QOKD) with the Malicious Arithmetic Secure Computation with Oblivious Transfer (MASCOT) protocol to implement a secure and efficient QSMC platform. This quantum approach significantly improves efficiency and security when we compare it with the classical implementation as both used quantum technologies (QKD and QOKD) are robust against quantum computer attacks.","url":"https://doi.org/10.5753/wquantum.2022.223569","authors":["Zeinab Rahmani","Luis S. Barbosa","Armando N. Pinto"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-10-17T20:08:31Z","doi":"10.5753/wquantum.2022.223569","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-319-44618-9_7","name":"On Adaptively Secure Multiparty Computation with a Short CRS","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-44618-9_7","authors":["Ran Cohen","Chris Peikert"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-08-09T21:10:01Z","doi":"10.1007/978-3-319-44618-9_7","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/3267973.3267979","name":"Bit Decomposition Protocols in Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3267973.3267979","authors":["Peeter Laud","Alisa Pankova"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-10-16T12:56:36Z","doi":"10.1145/3267973.3267979","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/1255329.1255333","name":"A domain-specific programming language for secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/1255329.1255333","authors":["Janus Dam Nielsen","Michael I. Schwartzbach"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-09-14T16:07:37Z","doi":"10.1145/1255329.1255333","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1515/popets-2015-0011","name":"Parallel Oblivious Array Access for Secure Multiparty Computation and Privacy-Preserving Minimum Spanning Trees","source":"crossref","abstract":"Abstract In this paper, we describe efficient protocols to perform in parallel many reads and writes in private arrays according to private indices. The protocol is implemented on top of the Arithmetic Black Box (ABB) and can be freely composed to build larger privacypreserving applications. For a large class of secure multiparty computation (SMC) protocols, our technique has better practical and asymptotic performance than any previous ORAM technique that has been adapted for use in SMC. Our ORAM technique opens up a large class of parallel algorithms for adoption to run on SMC platforms. In this paper, we demonstrate how the minimum spanning tree (MST) finding algorithm by Awerbuch and Shiloach can be executed without revealing any details about the underlying graph (beside its size). The data accesses of this algorithm heavily depend on the location and weight of edges (which are private) and our ORAM technique is instrumental in their execution. Our implementation is the first-ever realization of a privacypreserving MST algorithm with sublinear round complexity.","url":"https://doi.org/10.1515/popets-2015-0011","authors":["Peeter Laud"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-06-24T17:02:48Z","doi":"10.1515/popets-2015-0011","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/bddm68348.2025.11442186","name":"Feature Engineering Method Based on Secure Multiparty Computation and Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bddm68348.2025.11442186","authors":["Fanya Li","Ziqing Fan","Zehui Zhang","Yibo Zhu","Xinghua Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-26T19:47:31Z","doi":"10.1109/bddm68348.2025.11442186","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/candarw51189.2020.00074","name":"New Approach to Dishonest-Majority Secure Multiparty Computation for Malicious Adversaries when n &lt; 2k − 1","source":"crossref","abstract":"","url":"https://doi.org/10.1109/candarw51189.2020.00074","authors":["Shogo Ochiai","Keiichi Iwamura"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-02-22T21:11:10Z","doi":"10.1109/candarw51189.2020.00074","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/chase.2016.71","name":"A Survey of Secure Multiparty Computation Protocols for Privacy Preserving Genetic Tests","source":"crossref","abstract":"","url":"https://doi.org/10.1109/chase.2016.71","authors":["Tamara Dugan","Xukai Zou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-08-18T16:32:16Z","doi":"10.1109/chase.2016.71","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1016/j.eswa.2020.114434","name":"Fear not, vote truthfully: Secure Multiparty Computation of score based rules","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.eswa.2020.114434","authors":["Lihi Dery","Tamir Tassa","Avishay Yanai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-12-09T16:51:28Z","doi":"10.1016/j.eswa.2020.114434","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/tit.2024.3470513","name":"Network Agnostic Perfectly Secure Multiparty Computation Against General Adversaries","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tit.2024.3470513","authors":["Ananya Appan","Anirudh Chandramouli","Ashish Choudhury"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-30T17:25:46Z","doi":"10.1109/tit.2024.3470513","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.7198/geintec.v7.i4.1213","name":"STATE OF THE ART OF SECURE MULTIPARTY COMPUTATION FOR PRIVACY PRESERVING DATA MINING","source":"crossref","abstract":"","url":"https://doi.org/10.7198/geintec.v7.i4.1213","authors":["W. Priesnitz Filho","C. N. C. Ribeiro"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-01-01T17:36:39Z","doi":"10.7198/geintec.v7.i4.1213","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/blackseacom61746.2024.10646222","name":"Distributed Proxy Re- Encryption Protocol for Secure Multiparty Computation with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/blackseacom61746.2024.10646222","authors":["Busranur Bulbul Demir","Deniz Turgay Altilar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-02T17:34:13Z","doi":"10.1109/blackseacom61746.2024.10646222","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.14419/ijet.v7i2.7.10983","name":"Enhanced Security using Secure Multiparty Computation to  E-voting application in cloud","source":"crossref","abstract":"Secure outsourcing of computation on sensitive data is an important topic that has received a lot of attention recently. E-Voting system is interested in method collaboration system to ameliorate the quality of System. Cloud computing is integrating in communication and information technologies in the E-Voting. In fact, the paradigm is to provide the computational resources or result at the end of the services. Further it needs to reveal the voting information result but if faces several challenges in the security it must be overcome without reveal any sensitive data to unauthorized parties. To accomplish this objective, we proposed secure multiparty computation techniques (SMC). The main aim of our work is to use the suitable model for parties to together compute their function based on their inputs without revealing their private inputs. The idea behind is the several collaborate system use to their shared objectives without allow any gathering to information and private information.","url":"https://doi.org/10.14419/ijet.v7i2.7.10983","authors":["Umang Kishor Chaudhari","A Vijaya Kumar","G Venkata Sai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-05-13T16:22:08Z","doi":"10.14419/ijet.v7i2.7.10983","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.18122/td.2214.boisestate","name":"Secure Multiparty Protocols on Blockchain with Fairness and\n                    Scalability","source":"crossref","abstract":"Secure multiparty computation is a major field of research in modern cryptography. It allows for the creation of a protocol that maintains the privacy of the inputs and ensures that violation of the protocol results in no undue benefit to the violator or detriment to an honest party. These protocols can be used in many fields. In this dissertation, we explore the application of the mechanisms of secure party computation in the context of peer-to-peer lending, fair exchange with cryptocurrencies, consensus, and electronic voting. In all these areas, honesty of execution and fairness in the outcome should be assured or verifiable, especially if the other parties are not trusted. In this dissertation, we designed protocols to solve the above-mentioned problems, analyzed their efficiency and scalability, and proved their security. First, we present a platform called ZeroLender for peer-to-peer lending in Bitcoin. Our protocol utilizes zero-knowledge proofs to achieve unlinkability between lenders and borrowers while securing payments in both directions against potential malicious behavior of the ZeroLender as well as the lenders and covert action by the borrowers. We prove by simulation that our protocol is privacy-preserving. Based on our experiments, we show that the runtime and transcript size of our protocol scale linearly with respect to the number of lenders and repayments. Second, we propose a generic framework for atomic swap, called PolySwap, that enables fair exchange of assets between two {heterogeneous sets of blockchains}. Our construction preserves the anonymity of the swap by preventing transactions from being linked to each other or be distinguishable from other transactions on the blockchain and does not require any scripting capability in the blockchain, all without requiring a third party. We provide construction details of secret sharing signatures for ECDSA, Schnorr, and CryptoNote-style Ring signatures. Additionally, we provide an alternative contingency protocol, allowing parties to exchange to and from blockchains that do not support any form of time-locked escape transactions. We prove that PolySwap is secure against malicious adversaries, and is privacy-preserving against passive observers. We conducted experiments to demonstrate the efficiency of the protocol. Third, we propose ACCORD, a consensus protocol consisting of three distinct components: an asynchronous quorum selection procedure to designate the creators of future blocks, a block creation protocol run by the quorum to prevent omissions in the presence of honest quorum members, and a decentralized arbitration protocol to ensure consensus by voting. We implemented the protocol and conducted experiments to demonstrate scalability, robustness, and fairness. Finally, we introduce ORBIT, a cryptographic voting protocol that uses hidden credentials and mutable identities through ciphertext manipulation to prevent coercion. This enables voters to submit dummy ballots that are indistinguishable from genuine ones, thus enabling them to evade potential coercers, as well as preventing a fully compromised government from determining their voting preferences. ORBIT is blockchain-based, allowing government verifiers to process incoming ballots as they are submitted. We implemented ORBIT and performed experiments that illustrate its linear scaling in relation to three key variables: election size, the number of ring members within the anonymity set, and the number of ballots.","url":"https://doi.org/10.18122/td.2214.boisestate","authors":["Joshua Holmes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-28T19:13:50Z","doi":"10.18122/td.2214.boisestate","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1088/0256-307x/37/5/050303","name":"Quantum Secure Multiparty Computation with Symmetric Boolean Functions*","source":"crossref","abstract":"We propose a class of n -variable Boolean functions which can be used to implement quantum secure multiparty computation. We also give an implementation of a special quantum secure multiparty computation protocol. An advantage of our protocol is that only 1 qubit is needed to compute the n -tuple pairwise and function, which is more efficient comparing with previous protocols. We demonstrate our protocol on the IBM quantum cloud platform, with a probability of correct output as high as 94.63%. Therefore, our protocol presents a promising generalization in realization of various secure multipartite quantum tasks.","url":"https://doi.org/10.1088/0256-307x/37/5/050303","authors":["Hao Cao","Wenping Ma","Ge Liu","Liangdong Lü","Zheng-Yuan Xue"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-05-12T12:23:13Z","doi":"10.1088/0256-307x/37/5/050303","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/msp.2012.2230218","name":"Privacy-Preserving Biometric Identification Using Secure Multiparty Computation: An Overview and Recent Trends","source":"crossref","abstract":"","url":"https://doi.org/10.1109/msp.2012.2230218","authors":["Julien Bringer","Herve Chabanne","Alain Patey"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-02-13T19:14:39Z","doi":"10.1109/msp.2012.2230218","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1137/080725398","name":"Zero-Knowledge Proofs from Secure Multiparty Computation","source":"crossref","abstract":"A zero-knowledge proof allows a prover to convince a verifier of an assertion without revealing any further information beyond the fact that the assertion is true. Secure multiparty computation allows n mutually suspicious players to jointly compute a function of their local inputs without revealing to any t corrupted players additional information beyond the output of the function. We present a new general connection between these two fundamental notions. Specifically, we present a general construction of a zero-knowledge proof for an NP relation $R(x,w)$, which makes only a black-box use of any secure protocol for a related multiparty functionality f. The latter protocol is required only to be secure against a small number of “honest but curious” players. We also present a variant of the basic construction that can leverage security against a large number of malicious players to obtain better efficiency. As an application, one can translate previous results on the efficiency of secure multiparty computation to the domain of zero-knowledge, improving over previous constructions of efficient zero-knowledge proofs. In particular, if verifying R on a witness of length m can be done by a circuit C of size s, and assuming that one-way functions exist, we get the following types of zero-knowledge proof protocols: (1) Approaching the witness length. If C has constant depth over $\\wedge,\\vee,\\oplus,\\neg$ gates of unbounded fan-in, we get a zero-knowledge proof protocol with communication complexity $m\\cdot{poly}(k)\\cdot{polylog}(s)$, where k is a security parameter. (2) “Constant-rate” zero-knowledge. For an arbitrary circuit C of size s and a bounded fan-in, we get a zero-knowledge protocol with communication complexity $O(s)+{poly}(k,\\log s)$. Thus, for large circuits, the ratio between the communication complexity and the circuit size approaches a constant. This improves over the $O(ks)$ complexity of the best previous protocols.","url":"https://doi.org/10.1137/080725398","authors":["Yuval Ishai","Eyal Kushilevitz","Rafail Ostrovsky","Amit Sahai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-09-02T18:07:04Z","doi":"10.1137/080725398","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1103/physreva.84.016301","name":"Comment on “Secure multiparty computation with a dishonest majority via quantum means”","source":"crossref","abstract":"","url":"https://doi.org/10.1103/physreva.84.016301","authors":["Yan-bing Li","Qiao-yan Wen","Su-juan Qin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-07-27T10:51:07Z","doi":"10.1103/physreva.84.016301","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.5121/csit.2020.101413","name":"Unique Software Engineering Techniques: Panacea for Threat Complexities in Secure Multiparty Computation (MPC) with Big Data","source":"crossref","abstract":"Most large corporations with big data have adopted more privacy measures in handling their sensitive/private data and as a result, employing the use of analytic tools to run across multiple sources has become ineffective. Joint computation across multiple parties is allowed through the use of secure multi-party computations (MPC). The practicality of MPC is impaired when dealing with large datasets as more of its algorithms are poorly scaled with data sizes. Despite its limitations, MPC continues to attract increasing attention from industry players who have viewed it as a better approach to exploiting big data. Secure MPC is however, faced with complexities that most times overwhelm its handlers, so the need for special software engineering techniques for resolving these threat complexities. This research presents cryptographic data security measures, garbed circuits protocol, optimizing circuits, and protocol execution techniques as some of the special techniques for resolving threat complexities associated with MPC’s. Honest majority, asymmetric trust, covert security, and trading off leakage are some of the experimental outcomes of implementing these special techniques. This paper also reveals that an essential approach in developing suitable mitigation strategies is having knowledge of the adversary type.","url":"https://doi.org/10.5121/csit.2020.101413","authors":["Uchechukwu Emejeamara","Udochukwu Nwoduh","Andrew Madu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-22T07:08:42Z","doi":"10.5121/csit.2020.101413","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/bigdata66926.2025.11402127","name":"MPC-XGB: Privacy-Preserving Vertical Federated XGBoost via Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata66926.2025.11402127","authors":["Asma Ramay","Estrid He","Mengmeng Yang","Tabinda Sarwar","Xinqian Wang","Xun Yi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-06T20:57:57Z","doi":"10.1109/bigdata66926.2025.11402127","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1587/transfun.2025eap1084","name":"On Definitions for Semi-Honest Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1587/transfun.2025eap1084","authors":["Koji NUIDA"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-24T18:09:11Z","doi":"10.1587/transfun.2025eap1084","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/2213977.2214087","name":"Multiparty computation secure against continual memory leakage","source":"crossref","abstract":"","url":"https://doi.org/10.1145/2213977.2214087","authors":["Elette Boyle","Shafi Goldwasser","Abhishek Jain","Yael Tauman Kalai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-05-21T15:20:35Z","doi":"10.1145/2213977.2214087","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1155/2007/51368","name":"Secure Multiparty Computation between Distrusted Networks Terminals","source":"crossref","abstract":"","url":"https://doi.org/10.1155/2007/51368","authors":["S.-C. S. Cheung","Thinh Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-12-13T18:41:26Z","doi":"10.1155/2007/51368","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1016/j.comnet.2013.08.017","name":"Elementary secure-multiparty computation for massive-scale collaborative network monitoring: A quantitative assessment","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.comnet.2013.08.017","authors":["A. Iacovazzi","A. D’Alconzo","F. Ricciato","M. Burkhart"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-09-04T03:00:35Z","doi":"10.1016/j.comnet.2013.08.017","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/cimsim.2010.62","name":"Notice of Violation of IEEE Publication Principles - A Study on Secure Multiparty Computation Problems and Their Relevance","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cimsim.2010.62","authors":["Zulfa Shaikh","Durgesh Kumar Mishra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-01-28T15:12:36Z","doi":"10.1109/cimsim.2010.62","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-540-89754-5_15","name":"Round Efficient Unconditionally Secure Multiparty Computation Protocol","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-540-89754-5_15","authors":["Arpita Patra","Ashish Choudhary","C. Pandu Rangan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2008-11-26T12:11:54Z","doi":"10.1007/978-3-540-89754-5_15","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.24874/pes07.01d.022","name":"USING JIFF FOR COLLABORATIVE MEDICAL DATA ANALYSIS WITH SECURE MULTIPARTY COMPUTATION","source":"crossref","abstract":"","url":"https://doi.org/10.24874/pes07.01d.022","authors":["Usha Divakarla","K Chandrasekaran","K Hemanth Kumar Reddy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-19T10:35:16Z","doi":"10.24874/pes07.01d.022","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-642-45249-9_16","name":"Quorums Quicken Queries: Efficient Asynchronous Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-45249-9_16","authors":["Varsha Dani","Valerie King","Mahnush Movahedi","Jared Saia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-01-02T11:08:11Z","doi":"10.1007/978-3-642-45249-9_16","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1166/asem.2019.2327","name":"A Glimpse of Secure Multiparty Computation for Privacy Preserving Data Mining","source":"crossref","abstract":"","url":"https://doi.org/10.1166/asem.2019.2327","authors":["Mamta Sakpal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-01-30T05:09:03Z","doi":"10.1166/asem.2019.2327","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.3390/network2010005","name":"Delegated Proof of Secret Sharing: A Privacy-Preserving Consensus Protocol Based on Secure Multiparty Computation for IoT Environment","source":"crossref","abstract":"With the rapid advancement and wide application of blockchain technology, blockchain consensus protocols, which are the core part of blockchain systems, along with the privacy issues, have drawn much attention from researchers. A key aspect of privacy in the blockchain is the sensitive content of transactions in the permissionless blockchain. Meanwhile, some blockchain applications, such as cryptocurrencies, are based on low-efficiency and high-cost consensus protocols, which may not be practical and feasible for other blockchain applications. In this paper, we propose an efficient and privacy-preserving consensus protocol, called Delegated Proof of Secret Sharing (DPoSS), which is inspired by secure multiparty computation. Specifically, DPoSS first uses polynomial interpolation to select a dealer group from many nodes to maintain the consensus of the blockchain system, in which the dealers in the dealer group take turns to pack the new block. In addition, since the content of transactions is sensitive, our proposed design utilizes verifiable secret sharing to protect the privacy of transmission and defend against the malicious attacks. Extensive experiments show that the proposed consensus protocol achieves fairness during the process of reaching consensus.","url":"https://doi.org/10.3390/network2010005","authors":["Tieming Geng","Laurent Njilla","Chin-Tser Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-25T21:07:11Z","doi":"10.3390/network2010005","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1103/physreva.110.022444","name":"Secure multiparty quantum computation protocol for quantum circuits: The exploitation of triply even quantum error-correcting codes","source":"crossref","abstract":"","url":"https://doi.org/10.1103/physreva.110.022444","authors":["Petr A. Mishchenko","Keita Xagawa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T11:35:35Z","doi":"10.1103/physreva.110.022444","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.4018/978-1-59904-138-4.ch012","name":"Secure Multiparty/Multicandidate Electronic Elections","source":"crossref","abstract":"In this chapter we present a methodology for proving in Zero Knowledge the validity of selecting a subset of a set belonging to predefined family of sets. We apply this methodology in electronic voting to provide for extended ballot options. Our proposed voting scheme supports multiple parties and the selection of a number of candidates from one and only one of these parties. We have implemented this system and provided measures of its computational and communication complexity. We show that the complexity is linear with respect to the total number of candidates and the number of parties participating in the election.","url":"https://doi.org/10.4018/978-1-59904-138-4.ch012","authors":["Tassos Dimitriou","Dimitris Foteinakis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-05-24T12:56:28Z","doi":"10.4018/978-1-59904-138-4.ch012","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-319-12160-4_2","name":"A Secure Priority Queue; Or: On Secure Datastructures from Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-12160-4_2","authors":["Tomas Toft"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-10-18T11:14:47Z","doi":"10.1007/978-3-319-12160-4_2","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1155/2023/7123175","name":"High Efficiency Secure Channels for a Secure Multiparty Computation Protocol Based on Signal","source":"crossref","abstract":"Secure multiparty computation (MPC) requires the messages transmitted in secure channels which can provide encryption and authorization to the messages. To implement a secure channel for MPC protocols, researchers have tried some communication protocols, such as TLS and Noise. However, these methods have some limitations. These protocols need a trusted certification authority to provide identity authorization which is difficult for an MPC protocol, and how participants manage the key of each party and how to use the key to establish communication is also a problem. A Signal protocol is an end-to-end encryption communication protocol, which is known as the most secure communication protocol in the world. Based on the Signal protocol, we implemented a signal-based secure multiparty computation protocol, which can run the MPC protocol and transmit messages through a signal-based secure channel. Compared with previous research on the MPC protocol over Signal secure channels, the new MPC client adds group communication of the Signal protocol to transmit messages, which significantly improves the communication efficiency of broadcast messages of MPC protocols. To test the communication efficiency of the new MPC client, we implemented a concrete BLS threshold signature protocol on the new client, comparing the elapsed time of key generation and signing on the new client to that on the client only using Signal end-to-end communication. According to our experiment result, we found that the new client run at least 37.48% faster than the old client on the BLS threshold signature whose number of parties ranges from 3 to 5, if the parties have sent Signal group messages to each other. The more parties in the MPC protocol, the higher the proportion of broadcast messages and the more obvious the performance improvement of the new client. Our work improves the performance of MPC secure channels based on the Signal protocol, especially for complex MPC protocols with many participants.","url":"https://doi.org/10.1155/2023/7123175","authors":["Yunqi Yang","Rui Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-04-19T20:20:19Z","doi":"10.1155/2023/7123175","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-319-03515-4_2","name":"Breaking the ${\\mathcal{O}}(n|C|)$ Barrier for Unconditionally Secure Asynchronous Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-03515-4_2","authors":["Ashish Choudhury"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-12-02T00:20:12Z","doi":"10.1007/978-3-319-03515-4_2","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/2398856.2364556","name":"Efficient lookup-table protocol in secure multiparty computation","source":"crossref","abstract":"Secure multiparty computation (SMC) permits a collection of parties to compute a collaborative result, without any of the parties gaining any knowledge about the inputs provided by other parties. Specifications for SMC are commonly presented as boolean circuits, where optimizations come mostly from reducing the number of multiply-operations (including and -gates) - these are the operations which incur significant cost, either in computation overhead or in communication between the parties. Instead, we take a language-oriented approach, and consequently are able to explore many other kinds of optimizations. We present an efficient and general purpose SMC table-lookup algorithm that can serve as a direct alternative to circuits. Looking up a private (i.e. shared, or encrypted) n -bit argument in a public table requires log(n) parallel-and operations. We use the advanced encryption standard algorithm (AES) as a driving motivation, and by introducing different kinds of parallelization techniques, produce the fastest current SMC implementation of AES, improving the best previously reported results by well over an order of magnitude.","url":"https://doi.org/10.1145/2398856.2364556","authors":["John Launchbury","Iavor S. Diatchki","Thomas DuBuisson","Andy Adams-Moran"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-11-20T15:50:20Z","doi":"10.1145/2398856.2364556","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/2364527.2364556","name":"Efficient lookup-table protocol in secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/2364527.2364556","authors":["John Launchbury","Iavor S. Diatchki","Thomas DuBuisson","Andy Adams-Moran"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-09-12T09:01:27Z","doi":"10.1145/2364527.2364556","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/conftele50222.2021.9435479","name":"Quantum Secure Multiparty Computation of Phylogenetic Trees of SARS-CoV-2 Genome","source":"crossref","abstract":"","url":"https://doi.org/10.1109/conftele50222.2021.9435479","authors":["Manuel B. Santos","Ana C. Gomes","Armando N. Pinto","Paulo Mateus"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-05-27T00:22:31Z","doi":"10.1109/conftele50222.2021.9435479","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/pst.2017.00034","name":"Conditionally Secure Multiparty Computation using Secret Sharing Scheme for n &lt; 2k-1 (Short Paper)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/pst.2017.00034","authors":["Ahmad Akmal Aminuddin Mohd Kamal","Keiichi Iwamura"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-10-30T10:06:33Z","doi":"10.1109/pst.2017.00034","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1103/physreva.110.019901","name":"Erratum: Secure multiparty quantum computation with few qubits [Phys. Rev. A \n<b>102</b>\n, 022405 (2020)]","source":"crossref","abstract":"","url":"https://doi.org/10.1103/physreva.110.019901","authors":["Victoria Lipinska","Jérémy Ribeiro","Stephanie Wehner"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-02T14:02:23Z","doi":"10.1103/physreva.110.019901","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1109/icetet.2009.63","name":"Congestion Control during Data Privacy in Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icetet.2009.63","authors":["Zulfa Shaikh","Dinesh Bhati","D. M. Puntambekar","Pushpa Pathak","D. K. Mishra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2010-01-26T17:42:18Z","doi":"10.1109/icetet.2009.63","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-319-30840-1_12","name":"On the (In)Efficiency of Non-Interactive Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-30840-1_12","authors":["Maki Yoshida","Satoshi Obana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-03-09T09:15:22Z","doi":"10.1007/978-3-319-30840-1_12","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/roedunet.2019.8909467","name":"Performance Impact Analysis of Rounds and Amounts of Communication in Secure Multiparty Computation Based on Secret Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/roedunet.2019.8909467","authors":["Diana-Elena Falamas","Kinga Marton"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-11-26T01:28:38Z","doi":"10.1109/roedunet.2019.8909467","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.47116/apjcri.2023.12.04","name":"Application of SMPC (Secure Multiparty Computation) for Privacy Protection in MyData Environment","source":"crossref","abstract":"","url":"https://doi.org/10.47116/apjcri.2023.12.04","authors":["Ji Yeon Lee","Soon Seok Kim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-12-29T02:15:00Z","doi":"10.47116/apjcri.2023.12.04","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-642-42045-0_11","name":"Fair and Efficient Secure Multiparty Computation with Reputation Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-42045-0_11","authors":["Gilad Asharov","Yehuda Lindell","Hila Zarosim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-11-23T03:53:33Z","doi":"10.1007/978-3-642-42045-0_11","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/blockchain.2019.00021","name":"Initial Public Offering (IPO) on Permissioned Blockchain Using Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/blockchain.2019.00021","authors":["Tzipora Halevi","Fabrice Benhamouda","Angelo De Caro","Shai Halevi","Charanjit Jutla","Yacov Manevich","Qi Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-01-03T01:12:17Z","doi":"10.1109/blockchain.2019.00021","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.21275/sr23905191245","name":"Secure Multiparty Computing: A Decentralized Approach to GPA Calculation","source":"crossref","abstract":"","url":"https://doi.org/10.21275/sr23905191245","authors":["Sayed Mohammad Badiezadegan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-13T06:28:02Z","doi":"10.21275/sr23905191245","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/focs.2006.68","name":"Secure Multiparty Quantum Computation with (Only) a Strict Honest Majority","source":"crossref","abstract":"","url":"https://doi.org/10.1109/focs.2006.68","authors":["Michael Ben-Or","Claude Crepeau","Daniel Gottesman","Avinatan Hassidim","Adam Smith"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2006-12-20T16:08:18Z","doi":"10.1109/focs.2006.68","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1287/mnsc.2023.02577","name":"Distributed Ledgers and Secure Multiparty Computation for Financial Reporting and Auditing","source":"crossref","abstract":"To understand the disruption and implications of distributed ledger technologies for financial reporting and auditing, we analyze firm misreporting, auditor monitoring and competition, and regulatory policy in a unified model. A federated blockchain for financial reporting and auditing can improve verification efficiency not only for transactions in private databases but also for cross-chain verifications through privacy-preserving computation protocols. Despite the potential benefit of blockchains, private incentives for firms and first-mover advantages for auditors can create inefficient under-adoption or partial adoption that favors larger auditors. Although a regulator can help coordinate the adoption of technology, endogenous choice of transaction partners by firms can still lead to adoption failure. Our model also provides an initial framework for further studies of the costs and implications of the use of distributed ledgers and secure multiparty computation in financial reporting, including the positive spillover to discretionary auditing and who should bear the cost of adoption. This paper was accepted by David Simchi-Levi, finance. Funding: The authors gratefully acknowledge research support from the FinTech Laboratory at J. Mack Robinson College of Business at Georgia State University, the Center for Research in Security Prices at the University of Chicago, the Ripple University Blockchain Research Initiative, and the Smith AI Initiative for Capital Market Research at the University of Maryland. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.02577 .","url":"https://doi.org/10.1287/mnsc.2023.02577","authors":["Sean Shun Cao","Lin William Cong","Baozhong Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-22T14:43:41Z","doi":"10.1287/mnsc.2023.02577","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.17654/ec016030673","name":"IMPROVEMENT IN ROUTING TECHNIQUES IN P2P NETWORKS USING A CLOUD SERVICE INTERFACE WITH SECURE MULTIPARTY COMPUTATION","source":"crossref","abstract":"","url":"https://doi.org/10.17654/ec016030673","authors":["Anil Saroliya","Upendra Mishra","Ajay Rana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-09-16T11:10:46Z","doi":"10.17654/ec016030673","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-642-13190-5_23","name":"Perfectly Secure Multiparty Computation and the Computational Overhead of Cryptography","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-13190-5_23","authors":["Ivan Damgård","Yuval Ishai","Mikkel Krøigaard"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2010-05-19T09:16:46Z","doi":"10.1007/978-3-642-13190-5_23","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/3-540-48224-5_75","name":"Secure Multiparty Computation of Approximations","source":"crossref","abstract":"","url":"https://doi.org/10.1007/3-540-48224-5_75","authors":["Joan Feigenbaum","Yuval Ishai","Tal Malkin","Kobbi Nissim","Martin J. Strauss","Rebecca N. Wright"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-10-28T06:29:04Z","doi":"10.1007/3-540-48224-5_75","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1201/9781003185284-23","name":"Safe Data Technologies Safely Expanding Access to Administrative Tax Data","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-23","authors":["Claire Bowen","Leonard E. Burman","Robert McClelland","Aaron R. Williams"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","doi":"10.1201/9781003185284-23","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.5220/0012757100003756","name":"Efficient and Secure Multiparty Querying over Federated Graph Databases","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012757100003756","authors":["Nouf Aljuaid","Alexei Lisitsa","Sven Schewe"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-13T16:48:14Z","doi":"10.5220/0012757100003756","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-642-32009-5_39","name":"Near-Linear Unconditionally-Secure Multiparty Computation with a Dishonest Minority","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-32009-5_39","authors":["Eli Ben-Sasson","Serge Fehr","Rafail Ostrovsky"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-08-06T06:40:28Z","doi":"10.1007/978-3-642-32009-5_39","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.5430/air.v6n2p57","name":"A proposal of privacy preserving reinforcement learning for secure multiparty computation","source":"crossref","abstract":"Many studies have been done with the security of cloud computing. Though data encryption is a typical approach, high computing complexity for encryption and decryption of data is needed. Therefore, safe system for distributed processing with secure data attracts attention, and a lot of studies have been done. Secure multiparty computation (SMC) is one of these methods. Specifically, two learning methods for machine learning (ML) with SMC are known. One is to divide learning data into several subsets and perform learning. The other is to divide each item of learning data and perform learning. So far, most of works for ML with SMC are ones with supervised and unsupervised learning such as BP and K-means methods. It seems that there does not exist any studies for reinforcement learning (RL) with SMC. This paper proposes learning methods with SMC for Q-learning which is one of typical methods for RL. The effectiveness of proposed methods is shown by numerical simulation for the maze problem.","url":"https://doi.org/10.5430/air.v6n2p57","authors":["Hirofumi Miyajima","Noritaka Shigei","Syunki Makino","Hiromi Miyajima","Yohtaro Miyanishi","Shinji Kitagami","Norio Shiratori"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-05-24T01:15:59Z","doi":"10.5430/air.v6n2p57","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1186/1687-417x-2007-051368","name":"Secure Multiparty Computation between Distrusted Networks Terminals","source":"crossref","abstract":"","url":"https://doi.org/10.1186/1687-417x-2007-051368","authors":["S-CS Cheung","Thinh Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-09-21T15:39:56Z","doi":"10.1186/1687-417x-2007-051368","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/1159892.1159900","name":"Secure multiparty computation of approximations","source":"crossref","abstract":"Approximation algorithms can sometimes provide efficient solutions when no efficient exact computation is known. In particular, approximations are often useful in a distributed setting where the inputs are held by different parties and may be extremely large. Furthermore, for some applications, the parties want to compute a function of their inputs securely without revealing more information than necessary. In this work, we study the question of simultaneously addressing the above efficiency and security concerns via what we call secure approximations. We start by extending standard definitions of secure (exact) computation to the setting of secure approximations. Our definitions guarantee that no additional information is revealed by the approximation beyond what follows from the output of the function being approximated. We then study the complexity of specific secure approximation problems. In particular, we obtain a sublinear-communication protocol for securely approximating the Hamming distance and a polynomial-time protocol for securely approximating the permanent and related #P-hard problems.","url":"https://doi.org/10.1145/1159892.1159900","authors":["Joan Feigenbaum","Yuval Ishai","Tal Malkin","Kobbi Nissim","Martin J. Strauss","Rebecca N. Wright"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2006-10-18T18:11:32Z","doi":"10.1145/1159892.1159900","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/icdsi60108.2023.00045","name":"Privacy Protection and Query of Industrial Park Energy Usage Data Based on Searchable Encryption and Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icdsi60108.2023.00045","authors":["Yin Wang","Jianjiang Su","Feng Jin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-02T18:38:46Z","doi":"10.1109/icdsi60108.2023.00045","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/icraset63057.2024.10895974","name":"Securing Multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icraset63057.2024.10895974","authors":["A. Ponmalar","Rajkumar Pandiarajan","I. Sudha","P.S. Ramesh","J. Jagannathan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-28T18:38:44Z","doi":"10.1109/icraset63057.2024.10895974","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1103/physreva.102.022405","name":"Secure multiparty quantum computation with few qubits","source":"crossref","abstract":"","url":"https://doi.org/10.1103/physreva.102.022405","authors":["Victoria Lipinska","Jérémy Ribeiro","Stephanie Wehner"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-08-07T21:46:41Z","doi":"10.1103/physreva.102.022405","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:38.944Z"},{"id":"doi:10.1145/2818000.2818027","name":"Combining Differential Privacy and Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/2818000.2818027","authors":["Martin Pettai","Peeter Laud"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-12-11T17:06:08Z","doi":"10.1145/2818000.2818027","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/2810103.2813664","name":"A Domain-Specific Language for Low-Level Secure Multiparty Computation Protocols","source":"crossref","abstract":"","url":"https://doi.org/10.1145/2810103.2813664","authors":["Peeter Laud","Jaak Randmets"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-10-06T15:22:12Z","doi":"10.1145/2810103.2813664","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-642-02617-1_16","name":"Efficient Secure Multiparty Computation Protocol in Asynchronous Network","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-02617-1_16","authors":["Zheng Huang","Weidong Qiu","Qiang Li","Kefei Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-06-17T14:22:04Z","doi":"10.1007/978-3-642-02617-1_16","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/smartcomp58114.2023.00033","name":"ReplayMPC: A Fast Failure Recovery Protocol for Secure Multiparty Computation Applications using Blockchain","source":"crossref","abstract":"","url":"https://doi.org/10.1109/smartcomp58114.2023.00033","authors":["Oscar G. Bautista","Kemal Akkaya","Soamar Homsi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-07T17:55:53Z","doi":"10.1109/smartcomp58114.2023.00033","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-540-89255-7_3","name":"Graph Design for Secure Multiparty Computation over Non-Abelian Groups","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-540-89255-7_3","authors":["Xiaoming Sun","Andrew Chi-Chih Yao","Christophe Tartary"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2008-12-01T02:27:00Z","doi":"10.1007/978-3-540-89255-7_3","addedAt":"2026-08-31T06:41:29.093Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/imccc.2018.00060","name":"Secure Multiparty Computation via Fully Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1109/imccc.2018.00060","authors":["Jing-Li Han","Zhao-Li Wang","Ya-Qing Shi","Mei-Juan Wang","Hui Dong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-03-27T05:25:22Z","doi":"10.1109/imccc.2018.00060","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1080/09720529.2018.1453623","name":"Efficient secure multiparty computation of sparse vector dot products","source":"crossref","abstract":"","url":"https://doi.org/10.1080/09720529.2018.1453623","authors":["Michael J. Collins"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-09-06T04:25:06Z","doi":"10.1080/09720529.2018.1453623","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.32920/ryerson.14647176.v1","name":"A secure multiparty micropayment protocol for internet access over WLAN mesh networks","source":"crossref","abstract":"&lt;p&gt;Presently, multi-hop WLAN mesh networks have become an alternative to wired networks for last-mile user access enabling numerous internet-based services. Thus, we have proposed MMPay, a secure multiparty micropayment protocol for internet access over WLAN mesh networks, enabling: a secure network access anywhere and anytime according to user desire; seamless user roaming across the independent operator’s networks; and lightweight real-time payments to all involved parties that eliminate huge user trust relationships, online remote user authentications and mutual roaming agreements among the participating parties. The incontestable MMPay scheme has been devised from existing micropayment schemes emulating their good attributes and eliminating security vulnerabilities and difficulties, which includes: hash-chain based variable length payment instrument, user’s payment certificate at smartcard, shared and mixed signature scheme, and an efficient redemption approach. The OPNET-simulation results show that the pricing contract response-times are little lengthy but have no effect on data communication; payments and hand-offs are efficient; and the scheme has no effect on data communication ETE-delay and throughput. Thus, the scheme is secure, efficient and lightweight, and will be a practical solution for future small to large-scale WLAN mesh networks enabling faster hand-off. &lt;/p&gt;","url":"https://doi.org/10.32920/ryerson.14647176.v1","authors":["Nitish Biswas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-05-22T13:57:46Z","doi":"10.32920/ryerson.14647176.v1","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-662-44381-1_22","name":"Non-Interactive Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-662-44381-1_22","authors":["Amos Beimel","Ariel Gabizon","Yuval Ishai","Eyal Kushilevitz","Sigurd Meldgaard","Anat Paskin-Cherniavsky"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-07-14T04:27:09Z","doi":"10.1007/978-3-662-44381-1_22","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-981-96-5693-6_36","name":"A Privacy-Preserving Protocol for Secure Marine Data Sharing: Integrating Zero-Knowledge Proofs and Secure Multiparty Computation in Blockchain","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-5693-6_36","authors":["Ankit Kumar","Jong Hyuk Park"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-14T13:51:20Z","doi":"10.1007/978-981-96-5693-6_36","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-540-77048-0_20","name":"Secure Multiparty Computation of DNF","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-540-77048-0_20","authors":["Kun Peng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-11-16T15:10:22Z","doi":"10.1007/978-3-540-77048-0_20","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/iisa.2018.8633690","name":"Using Sharemind as a Tool to Develop an Internet Voting System with Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iisa.2018.8633690","authors":["Ruahden Dang-awan","Joyce Anne Piscos","Richard Bryann Chua"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-02-05T02:33:11Z","doi":"10.1109/iisa.2018.8633690","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1080/19393555.2010.544701","name":"Secure Multiparty Computation: From Millionaires Problem to Anonymizer","source":"crossref","abstract":"","url":"https://doi.org/10.1080/19393555.2010.544701","authors":["Rashid Sheikh","Durgesh Kumar Mishra","Beerendra Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-02-10T13:55:52Z","doi":"10.1080/19393555.2010.544701","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1016/j.jcss.2011.02.004","name":"Privacy Preserving OLAP over Distributed XML Data: A Theoretically-Sound Secure-Multiparty-Computation Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jcss.2011.02.004","authors":["Alfredo Cuzzocrea","Elisa Bertino"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-03-08T03:59:46Z","doi":"10.1016/j.jcss.2011.02.004","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.3844/jcssp.2025.2581.2592","name":"Privacy-Preserving Deep Federated Learning on the Edge Using Homomorphic Encryption and Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.3844/jcssp.2025.2581.2592","authors":["Noman Aasif Gudur","Mohamed El-Dosuky","Sherif Kamel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-29T08:59:06Z","doi":"10.3844/jcssp.2025.2581.2592","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1088/1402-4896/ad1281","name":"General quantum secure multiparty computation protocol for simultaneous summation and multiplication","source":"crossref","abstract":"Abstract Quantum secure multiparty computation occupies an important place in quantum cryptography. Based on access structure and linear secret sharing, we propose a new general quantum secure multiparty computation protocol for simultaneous summation and multiplication in a high-dimensional quantum system. In our protocol, each participant within any authorized sets only needs to perform local Pauli operation once on the generalized Bell state, then the summation and multiplication results can be output simultaneously, which improves the practicality of the protocol. Moreover, in the privacy computation phase, the decoy particle detection technique as well as the addition of random numbers are applied to blind the privacy information, making our protocol higher privacy protection. Security analysis shows that our protocol is resistant to a series of typical external attacks and dishonest internal participant attacks such as individual attack and collusion attack. Finally, compared with the existing protocols, our protocol not only has higher efficiency but also lower consumption.","url":"https://doi.org/10.1088/1402-4896/ad1281","authors":["Fulin Li","Mei Luo","Shixin Zhu","Binbin Pang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-12-05T17:42:20Z","doi":"10.1088/1402-4896/ad1281","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.4236/jis.2014.51002","name":"Comparative Evaluation of Elliptic Curve Cryptography Based Homomorphic Encryption Schemes for a Novel  Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.4236/jis.2014.51002","authors":["Sankita J. Patel","Ankit Chouhan","Devesh C. Jinwala"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-01-17T02:13:13Z","doi":"10.4236/jis.2014.51002","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/11818175_29","name":"On Combining Privacy with Guaranteed Output Delivery in Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/11818175_29","authors":["Yuval Ishai","Eyal Kushilevitz","Yehuda Lindell","Erez Petrank"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2006-09-23T06:21:52Z","doi":"10.1007/11818175_29","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/3210240.3223569","name":"Leveraging Secure Multiparty Computation in the Internet of Things","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3210240.3223569","authors":["Marcel von Maltitz","Georg Carle"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-07-06T12:36:06Z","doi":"10.1145/3210240.3223569","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/infocomwkshps50562.2020.9162866","name":"AI-Powered Blockchain - A Decentralized Secure Multiparty Computation Protocol for IoV","source":"crossref","abstract":"","url":"https://doi.org/10.1109/infocomwkshps50562.2020.9162866","authors":["Gunasekaran Raja","Yelisetty Manaswini","Gaayathri Devi Vivekanandan","Harish Sampath","Kapal Dev","Ali Kashif Bashir"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-08-10T21:55:09Z","doi":"10.1109/infocomwkshps50562.2020.9162866","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/3133956.3133979","name":"Global-Scale Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3133956.3133979","authors":["Xiao Wang","Samuel Ranellucci","Jonathan Katz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-10-27T12:48:18Z","doi":"10.1145/3133956.3133979","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1002/wics.70046","name":"Secure Multiparty Computation for Privacy‐Preserving Machine Learning in Healthcare: A Comprehensive Survey","source":"crossref","abstract":"ABSTRACT Privacy‐preserving machine learning (PPML) using secure multiparty computation (SMC) is emerging as a promising approach for enabling collaborative data analysis in healthcare while protecting sensitive patient information. This survey provides a comprehensive overview of SMC‐based PPML methods, their applications in healthcare, and the associated challenges. We first introduce the fundamental concepts of SMC and compare it with other privacy‐preserving techniques such as homomorphic encryption (HE) and differential privacy. We then discuss various privacy attacks on machine learning (ML) models, including model extraction, membership inference, and model inversion attacks. The survey examined the key applications of SMC in healthcare machine learning, such as collaborative model training, secure federated learning, privacy‐preserving inference, and secure genome analysis. We highlight the advantages of using SMC for PPML in healthcare, including enhanced data privacy, regulatory compliance, and preservation of data utility. The study also analyzed the major challenges in implementing SMC‐based PPML, including computational overhead, scalability issues, and implementation complexity. Finally, we discuss future research directions, including improving scalability, developing hybrid privacy‐preserving techniques, and addressing regulatory considerations. This survey aimed to provide researchers and practitioners with a comprehensive understanding of the current state and future prospects of SMC‐based PPML in healthcare. This article is categorized under: Algorithms and Computational Methods &gt; Networks and Security Applications of Computational Statistics &gt; Health and Medical Data/Informatics","url":"https://doi.org/10.1002/wics.70046","authors":["Vankamamidi S. Naresh","A. Venkata Raju","O. Srinivasa Rao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-23T01:39:28Z","doi":"10.1002/wics.70046","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1147/jrd.2019.2913621","name":"Supporting private data on Hyperledger Fabric with secure multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1147/jrd.2019.2913621","authors":["F. Benhamouda","S. Halevi","T. Halevi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-04-27T01:07:26Z","doi":"10.1147/jrd.2019.2913621","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/s10773-012-1319-z","name":"Improved Secure Multiparty Computation with a Dishonest Majority via Quantum Means","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10773-012-1319-z","authors":["Yan-Bing Li","Qiao-Yan Wen","Su-Juan Qin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-08-28T09:03:38Z","doi":"10.1007/s10773-012-1319-z","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/0-387-23483-7_265","name":"Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/0-387-23483-7_265","authors":["Berry Schoenmakers"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2006-09-09T15:07:16Z","doi":"10.1007/0-387-23483-7_265","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.32920/ryerson.14647176","name":"A secure multiparty micropayment protocol for internet access over WLAN mesh networks","source":"crossref","abstract":"&lt;p&gt;Presently, multi-hop WLAN mesh networks have become an alternative to wired networks for last-mile user access enabling numerous internet-based services. Thus, we have proposed MMPay, a secure multiparty micropayment protocol for internet access over WLAN mesh networks, enabling: a secure network access anywhere and anytime according to user desire; seamless user roaming across the independent operator’s networks; and lightweight real-time payments to all involved parties that eliminate huge user trust relationships, online remote user authentications and mutual roaming agreements among the participating parties. The incontestable MMPay scheme has been devised from existing micropayment schemes emulating their good attributes and eliminating security vulnerabilities and difficulties, which includes: hash-chain based variable length payment instrument, user’s payment certificate at smartcard, shared and mixed signature scheme, and an efficient redemption approach. The OPNET-simulation results show that the pricing contract response-times are little lengthy but have no effect on data communication; payments and hand-offs are efficient; and the scheme has no effect on data communication ETE-delay and throughput. Thus, the scheme is secure, efficient and lightweight, and will be a practical solution for future small to large-scale WLAN mesh networks enabling faster hand-off. &lt;/p&gt;","url":"https://doi.org/10.32920/ryerson.14647176","authors":["Nitish Biswas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-05-22T13:57:47Z","doi":"10.32920/ryerson.14647176","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.58578/mjaei.v2i3.6804","name":"Secure Multiparty Computation over Elliptic Curve Cryptography","source":"crossref","abstract":"This study proposes a secure mobile voting system that integrates elliptic curve cryptography (ECC) with secure multiparty computation (SMPC) to guarantee vote confidentiality, integrity, and verifiability. Designed to enable scalable, privacy-preserving elections via mobile devices, the system authenticates voters using registered numbers and records ballots as encrypted points on an elliptic curve. Encrypted votes are published on a public bulletin board alongside zero-knowledge proofs to ensure their validity. To safeguard decryption, Shamir’s secret sharing distributes keys among trusted authorities, enabling collective tallying without exposing individual votes. The system incorporates ECC-based secret sharing, homomorphic encryption, and zero-knowledge proofs, leveraging the hardness of the elliptic curve discrete logarithm problem (ECDLP) for robust security. Both experimental and theoretical evaluations demonstrate that ECC significantly improves computational efficiency and scalability, making the system well-suited for resource-constrained environments. Overall, the integration of ECC and SMPC offers a practical, efficient, and secure framework for mobile elections, effectively balancing privacy, security, and performance.","url":"https://doi.org/10.58578/mjaei.v2i3.6804","authors":["Domven L.","A. D. Hina","A. M Kwami","C. M. Miri","Abdullahi I."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-05T02:10:20Z","doi":"10.58578/mjaei.v2i3.6804","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/raits68656.2026.11580132","name":"Feature Selection Method Based on Secure MultiParty Computation and Its Application in State Evaluation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/raits68656.2026.11580132","authors":["Binyu Xie","Hao Shi","Zehui Zhang","Yibo Zhu","Xinghua Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-02T19:41:35Z","doi":"10.1109/raits68656.2026.11580132","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.2196/preprints.22158","name":"A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation (Preprint)","source":"crossref","abstract":"BACKGROUND Patient data is considered particularly sensitive personal data. Privacy regulations strictly govern the use of patient data and restrict their exchange. However, medical research can benefit from multicentric studies in which patient data from different institutions are pooled and evaluated together. Thus, the goals of data utilization and data protection are in conflict. Secure multiparty computation (SMPC) solves this conflict because it allows direct computation on distributed proprietary data—held by different data owners—in a secure way without exchanging private data. OBJECTIVE The objective of this work was to provide a proof-of-principle of secure and privacy-preserving multicentric computation by SMPC with real-patient data over the free internet. A privacy-preserving log-rank test for the Kaplan-Meier estimator was implemented and tested in both an experimental setting and a real-world setting between two university hospitals. METHODS The domain of survival analysis is particularly relevant in clinical research. For the Kaplan-Meier estimator, we provided a secure version of the log-rank test. It was based on the SMPC realization SPDZ and implemented via the FRESCO framework in Java. The complexity of the algorithm was explored both for synthetic data and for real-patient data in a proof-of-principle over the internet between two clinical institutions located in Munich and Berlin, Germany. RESULTS We obtained a functional realization of an SMPC-based log-rank evaluation. This implementation was assessed with respect to performance and scaling behavior. We showed that network latency strongly influences execution time of our solution. Furthermore, we identified a lower bound of 2 Mbit/s for the transmission rate that has to be fulfilled for unimpeded communication. In contrast, performance of the participating parties have comparatively low influence on execution speed, since the peer-side processing is parallelized and the computational time only constitutes 30% to 50% even with optimal network settings. In the real-world setting, our computation between three parties over the internet, processing 100 items each, took approximately 20 minutes. CONCLUSIONS We showed that SMPC is applicable in the medical domain. A secure version of commonly used evaluation methods for clinical studies is possible with current implementations of SMPC. Furthermore, we infer that its application is practically feasible in terms of execution time.","url":"https://doi.org/10.2196/preprints.22158","authors":["Marcel von Maltitz","Hendrik Ballhausen","David Kaul","Daniel F Fleischmann","Maximilian Niyazi","Claus Belka","Georg Carle"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-01-18T14:01:23Z","doi":"10.2196/preprints.22158","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/s00145-020-09354-z","name":"$${\\varvec{1/p}}$$-Secure Multiparty Computation without an Honest Majority and the Best of Both Worlds","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00145-020-09354-z","authors":["Amos Beimel","Yehuda Lindell","Eran Omri","Ilan Orlov"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-07-16T16:03:43Z","doi":"10.1007/s00145-020-09354-z","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.5626/jok.2015.42.7.919","name":"Secure Multiparty Computation of Principal Component Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.5626/jok.2015.42.7.919","authors":["Sang-Pil Kim","Sanghun Lee","Myeong-Seon Gil","Yang-Sae Moon","Hee-Sun Won"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-11T20:44:33Z","doi":"10.5626/jok.2015.42.7.919","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/tifs.2022.3144007","name":"Fast Privacy-Preserving Text Classification Based on Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tifs.2022.3144007","authors":["Amanda Resende","Davis Railsback","Rafael Dowsley","Anderson C. A. Nascimento","Diego F. Aranha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-18T17:01:24Z","doi":"10.1109/tifs.2022.3144007","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/3658644.3691371","name":"Poster: A Secure Multiparty Computation Platform for Squeaky-Clean Data Rooms","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3658644.3691371","authors":["Pankaj Dayama","Vinayaka Pandit","Sikhar Patranabis","Abhishek Singh","Nitin Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-09T12:19:20Z","doi":"10.1145/3658644.3691371","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/2332432.2332473","name":"Brief announcement","source":"crossref","abstract":"","url":"https://doi.org/10.1145/2332432.2332473","authors":["Varsha Dani","Valerie King","Mahnush Movahedi","Jared Saia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-07-19T14:38:27Z","doi":"10.1145/2332432.2332473","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.4018/joeuc.306752","name":"An Entropy-View Secure Multiparty Computation Protocol Based on Semi-Honest Model","source":"crossref","abstract":"Data interaction scenarios involving multiple parties in network communities have problems of trust, data security, and reliability of the parties, and secure multiparty computation(SMPC) can effectively solve these problems. To address the security and fairness issues of SMPC, this study considers that semi-honest participants can lead to deviations in the security and fairness of the protocol, and combines information entropy and mutual information to present an n-round information exchange protocol in which each participant broadcasts a relevant information value in each round without revealing other information. The uncertainty of the correct outcome value is blurred by the interaction information in each round, and each participant is not sure of the correct outcome value until the end of the protocol, which effectively prevents malicious behavior and ensures the correct execution of the protocol. Security and fairness analysis shows that our protocol guarantees the security and relative fairness of the output obtained by the participants after completing the protocol.","url":"https://doi.org/10.4018/joeuc.306752","authors":["Yun Luo","Yuling Chen","Tao Li","Yilei Wang","Yixian Yang","Xiaomei Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-14T12:41:35Z","doi":"10.4018/joeuc.306752","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/3243734.3278510","name":"How to Choose Suitable Secure Multiparty Computation Using Generalized SPDZ","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3243734.3278510","authors":["Toshinori Araki","Assi Barak","Jun Furukawa","Marcel Keller","Kazuma Ohara","Hikaru Tsuchida"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-11-13T13:39:38Z","doi":"10.1145/3243734.3278510","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1137/100783224","name":"On Achieving the “Best of Both Worlds” in Secure Multiparty Computation","source":"crossref","abstract":"Two settings are traditionally considered for secure multiparty computation, depending on whether or not a majority of the parties are assumed to be honest. Existing protocols that assume an honest majority provide “full security” (and, in particular, guarantee output delivery and fairness) when this assumption holds, but are completely insecure if this assumption is violated. On the other hand, known protocols tolerating an arbitrary number of corruptions do not guarantee fairness or output delivery even if only a single party is dishonest. It is natural to wonder whether it is possible to achieve the “best of both worlds”: namely, a single protocol that simultaneously achieves the best possible security in both the above settings. Here, we rule out this possibility (at least for general functionalities) and show some positive results regarding what can be achieved.","url":"https://doi.org/10.1137/100783224","authors":["Yuval Ishai","Jonathan Katz","Eyal Kushilevitz","Yehuda Lindell","Erez Petrank"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-02-10T19:24:36Z","doi":"10.1137/100783224","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/s10586-024-04928-z","name":"PriCollabAnalysis: privacy-preserving healthcare collaborative analysis on blockchain using homomorphic encryption and secure multiparty computation","source":"crossref","abstract":"Abstract Advances in blockchain technology offer a decentralized ledger with transformative potential for healthcare data management, facilitating secure transactions and transparent record-keeping. Nevertheless, the sensitive nature of patient data requires enhanced privacy measures. This paper introduces a comprehensive framework enabling researchers to conduct collaborative statistical analysis on health records while preserving privacy and ensuring security. Statistics are invaluable across various disciplines, guiding consequential decisions based on such analysis. The framework integrates privacy-preserving techniques, including secret-sharing, secure multiparty computation (SMPC), and homomorphic encryption, within a blockchain-based healthcare ecosystem. Patient data is divided using secret-sharing, enabling controlled access. Furthermore, SMPC allows secure data aggregation without revealing individual records, while homomorphic encryption supports computation on encrypted data within smart contracts. Through a series of controlled experiments, we assess the framework’s effectiveness in maintaining data privacy, facilitating secure collaboration, and conducting statistical data analysis. The results demonstrate successful preservation of data privacy and secure analysis on a permissioned blockchain using the Hyperledger Fabric platform. Our framework showcases efficient performance while effectively utilizing system resources. This research contributes to the evolution of secure and privacy-conscious healthcare data analysis, paving the way for practical applications and future advancements.","url":"https://doi.org/10.1007/s10586-024-04928-z","authors":["Ahmed M. Tawfik","Ayman Al-Ahwal","Adly S. Tag Eldien","Hala H. Zayed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T05:28:18Z","doi":"10.1007/s10586-024-04928-z","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.56553/popets-2025-0090","name":"PIGEON: A High Throughput Framework for Private Inference of Neural Networks using Secure Multiparty Computation","source":"crossref","abstract":"Privacy-Preserving Machine Learning (PPML) is one of the most relevant use cases for Secure Multiparty Computation (MPC). While private training of large neural networks such as VGG-16 or ResNet-50 on state-of-the-art datasets such as ImageNet is still out of reach, given the performance overhead of MPC, GPU-based MPC frameworks are starting to achieve practical runtimes for private inference. However, we show that, unlike plaintext machine learning, using GPU acceleration for both linear (e.g., convolutions) and non-linear neural network layers (e.g., ReLU) is actually counterproductive in PPML. While GPUs effectively accelerate linear layers compared to CPU-based MPC implementations, the MPC circuits required to evaluate non-linear layers introduce memory overhead and frequent data movement between the GPU and the CPU to handle network communication. This results in slow ReLU performance and high GPU memory requirements in state-of-the-art GPU-based PPML frameworks, hindering them from scaling to multiple images per second inference throughput and more than eight images per batch on ImageNet. To overcome these limitations, we propose PIGEON, an open-source framework for Private Inference of Neural Networks. PIGEON employs a novel ABG programming model that switches between Arithmetic Vectorization and Bitslicing on the CPU for non-linear layers depending on the MPC-specific computation required while offloading linear layers to the GPU. Compared to the state-of-the-art PPML framework Piranha, PIGEON improves ReLU throughput by two orders of magnitude, reduces peak GPU memory utilization by one order of magnitude, and scales better with large batch sizes. This translates to one to two orders of magnitude improvements in throughput for large ImageNet batch sizes (e.g., 192) and more than 70% saturation of a 25 Gbit/s network.","url":"https://doi.org/10.56553/popets-2025-0090","authors":["Christopher Harth-Kitzerow","Yongqin Wang","Rachit Rajat","Georg Carle","Murali Annavaram"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-18T23:14:51Z","doi":"10.56553/popets-2025-0090","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-1-4419-5906-5_1294","name":"Multiparty Computation (MPC)","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4419-5906-5_1294","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-10-27T09:53:32Z","doi":"10.1007/978-1-4419-5906-5_1294","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-030-92641-0_14","name":"Secure Multiparty Computation in the Bounded Storage Model","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-92641-0_14","authors":["Jiahui Liu","Satyanarayana Vusirikala"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-12-14T08:03:33Z","doi":"10.1007/978-3-030-92641-0_14","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1016/j.compeleceng.2021.107358","name":"Fog-enabled secure multiparty computation based aggregation scheme in smart grid","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.compeleceng.2021.107358","authors":["Hayat Mohammad Khan","Abid Khan","Farhana Jabeen","Adeel Anjum","Gwanggil Jeon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-21T11:13:34Z","doi":"10.1016/j.compeleceng.2021.107358","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1038/s41534-026-01219-w","name":"Experimental secure multiparty computation from quantum oblivious transfer with bit commitment","source":"crossref","abstract":"","url":"https://doi.org/10.1038/s41534-026-01219-w","authors":["Kai-Yi Zhang","An-Jing Huang","Kun Tu","Ming-Han Li","Chi Zhang","Wei Qi","Ya-Dong Wu","Yu Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-14T09:52:14Z","doi":"10.1038/s41534-026-01219-w","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.3390/cryptography9010001","name":"Security Proof of Single-Source Shortest Distance Protocols Built on Secure Multiparty Computation Protocols","source":"crossref","abstract":"Secure secret-sharing Single-Source Shortest Distance (SSSD) protocols, based on secure multiparty computation (SMC), offer a promising solution for securely distributing and managing sensitive information among multiple parties. However, formal security proofs for these protocols have largely been unexplored. This paper addresses this gap by providing the first security proof for the SSSD protocols using the privacy-preserving Bellman–Ford protocols. These new protocols offer significant enhancements in efficiency, particularly in handling large-scale graphs due to parallel computation. In our previous work, published in MDPI Cryptography, we introduced these protocols and presented extensive experiments on the Sharemind system that demonstrated their efficiency. However, that work did not include security proofs. Building on this foundation, the current paper rigorously proves the security of these protocols, offering valuable insights into their robustness and reliability. Furthermore, we discuss the adversarial model, security definitions, cryptographic assumptions, and sophisticated reduction techniques employed in the proof. This paper not only validates the security of the proposed protocols but also provides a detailed comparison of their performance with existing methods, highlighting their strengths and potential for future research in the field.","url":"https://doi.org/10.3390/cryptography9010001","authors":["Mohammad Anagreh","Peeter Laud"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-26T19:33:07Z","doi":"10.3390/cryptography9010001","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/icton59386.2023.10207521","name":"Oblivious Keys for Secure Multiparty Computation Obtained from a CV-QKD","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icton59386.2023.10207521","authors":["Armando N. Pinto","Manuel B. Santos","Nuno A. Silva","Nelson J. Muga","Paulo Mateus"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-08T17:24:17Z","doi":"10.1109/icton59386.2023.10207521","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-81-322-2580-5_79","name":"Data Integrity Checking Protocol Based on Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-81-322-2580-5_79","authors":["Runhua Shi","Yechi Zhang","Hong Zhong","Jie Cui","Shun Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-10-28T08:57:36Z","doi":"10.1007/978-81-322-2580-5_79","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.56553/popets-2025-0015","name":"High-Throughput Secure Multiparty Computation with an Honest Majority in Various Network Settings","source":"crossref","abstract":"In this work, we present novel protocols over rings for semi-honest secure three-party computation (3PC) and malicious four-party computation (4PC) with one corruption. While most existing works focus on improving total communication complexity, challenges such as network heterogeneity and computational complexity, which impact MPC performance in practice, remain underexplored. Our protocols address these issues by tolerating multiple arbitrarily weak network links between parties without any substantial decrease in performance. Additionally, they significantly reduce computational complexity by requiring up to half the number of basic instructions per gate compared to related work. These improvements lead to up to twice the throughput of state-of-the-art protocols in homogeneous network settings and up to eight times higher throughput in real-world heterogeneous settings. These advantages come at no additional cost: Our protocols maintain the best-known total communication complexity per multiplication, requiring 3 elements for 3PC and 5 elements for 4PC.We implemented our protocols alongside several state-of-the-art protocols (Replicated 3PC, ASTRA, Fantastic Four, Tetrad) in a novel open-source C++ framework optimized for high throughput. Five out of six implemented 3PC and 4PC protocols achieve more than one billion 32-bit multiplications or over 32 billion AND gates per second using our implementation in a 25 Gbit/s LAN environment. This represents the highest throughput achieved in 3PC and 4PC so far, outperforming existing frameworks like MP-SPDZ, ABY3, MPyC, and MOTION by two to three orders of magnitude.","url":"https://doi.org/10.56553/popets-2025-0015","authors":["Christopher Harth-Kitzerow","Ajith Suresh","Yongqin Wang","Hossein Yalame","Georg Carle","Murali Annavaram"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-10T19:21:16Z","doi":"10.56553/popets-2025-0015","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/s11128-024-04528-1","name":"Secure multiparty quantum computation for summation and data sorting","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s11128-024-04528-1","authors":["Xiaobing Li","Yunyan Xiong","Cai Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-19T21:05:43Z","doi":"10.1007/s11128-024-04528-1","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/s13369-018-3122-5","name":"Privacy-Preserving Secure Multiparty Computation on Electronic Medical Records for Star Exchange Topology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s13369-018-3122-5","authors":["Ahmed M. Tawfik","Sahar F. Sabbeh","Tarek EL-Shishtawy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-03-12T15:53:41Z","doi":"10.1007/s13369-018-3122-5","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1155/2019/1368905","name":"Secure Multiparty Computation and Trusted Hardware: Examining Adoption Challenges and Opportunities","source":"crossref","abstract":"When two or more parties need to compute a common result while safeguarding their sensitive inputs, they use secure multiparty computation (SMC) techniques such as garbled circuits. The traditional enabler of SMC is cryptography, but the significant number of cryptographic operations required results in these techniques being impractical for most real-time, online computations. Trusted execution environments (TEEs) provide hardware-enforced isolation of code and data in use, making them promising candidates for making SMC more tractable. This paper revisits the history of improvements to SMC over the years and considers the possibility of coupling trusted hardware with SMC. This paper also addresses three open challenges: (1) defeating malicious adversaries, (2) mobile-friendly TEE-supported SMC, and (3) a more general coupling of trusted hardware and privacy-preserving computation.","url":"https://doi.org/10.1155/2019/1368905","authors":["Joseph I. Choi","Kevin R. B. Butler"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-04-02T19:54:39Z","doi":"10.1155/2019/1368905","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/2976749.2978347","name":"Optimizing Semi-Honest Secure Multiparty Computation for the Internet","source":"crossref","abstract":"","url":"https://doi.org/10.1145/2976749.2978347","authors":["Aner Ben-Efraim","Yehuda Lindell","Eran Omri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-10-25T08:46:35Z","doi":"10.1145/2976749.2978347","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/11889663_10","name":"A Practical Implementation of Secure Auctions Based on Multiparty Integer Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/11889663_10","authors":["Peter Bogetoft","Ivan Damgård","Thomas Jakobsen","Kurt Nielsen","Jakob Pagter","Tomas Toft"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2006-10-09T10:12:37Z","doi":"10.1007/11889663_10","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-642-22792-9_16","name":"1/p-Secure Multiparty Computation without Honest Majority and the Best of Both Worlds","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-22792-9_16","authors":["Amos Beimel","Yehuda Lindell","Eran Omri","Ilan Orlov"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-08-05T15:32:14Z","doi":"10.1007/978-3-642-22792-9_16","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1016/j.jisa.2026.104571","name":"Benchmarking secure multiparty computation frameworks for real-world workloads in diverse network settings","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jisa.2026.104571","authors":["Christopher Harth-Kitzerow","Jonas Schiller","Nina Schwanke","Thomas Prantl","Georg Carle"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-06T02:55:41Z","doi":"10.1016/j.jisa.2026.104571","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/3411501.3419427","name":"Faster Secure Multiparty Computation of Adaptive Gradient Descent","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3411501.3419427","authors":["Wen-jie Lu","Yixuan Fang","Zhicong Huang","Cheng Hong","Chaochao Chen","Hunter Qu","Yajin Zhou","Kui Ren"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-04T03:22:57Z","doi":"10.1145/3411501.3419427","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/3319535.3354205","name":"A High-Assurance Evaluator for Machine-Checked Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3319535.3354205","authors":["Karim Eldefrawy","Vitor Pereira"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-11-07T13:08:32Z","doi":"10.1145/3319535.3354205","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-1-4419-5906-5_7","name":"Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4419-5906-5_7","authors":["Berry Schoenmakers"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-10-27T09:53:32Z","doi":"10.1007/978-1-4419-5906-5_7","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.2174/2352096516666230816141206","name":"WITHDRAWN: Distributed Energy Storage Data Sharing Based onPrivacy Protection using Secure Multiparty Computation","source":"crossref","abstract":"&lt;p&gt;Since the authors are not responding to the editor’s requests to fulfill the editorial requirement, therefore, the article has beenwithdrawn.&lt;/p&gt;&lt;p&gt;Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused.&lt;/p&gt;&lt;p&gt;The Bentham Editorial Policy on Article Withdrawal can be found at https://benthamscience.com/editorial-policies-main.php.&lt;/p&gt;&lt;p&gt;BENTHAM SCIENCE DISCLAIMER:&lt;/p&gt;&lt;p&gt;It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneouslysubmitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewheremust be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submittingthe article for publication the authors agree that the publishers have the legal right to take appropriate action against theauthors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyrightof their article is transferred to the publishers if and when the article is accepted for publication.&lt;/p&gt;","url":"https://doi.org/10.2174/2352096516666230816141206","authors":["Xingxing Yu","Yuancheng Li","Qingle Wang","Yiguo Guo","Hang Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-16T06:59:50Z","doi":"10.2174/2352096516666230816141206","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/s13177-025-00489-6","name":"Privacy-preserving Lane Change Prediction Using Recurrent Neural Network With Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s13177-025-00489-6","authors":["Armin Nejadhossein Qasemabadi","Saeed Mozaffari","Majid Ahmadi","Shahpour Alirezaee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-03T03:53:55Z","doi":"10.1007/s13177-025-00489-6","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/tit.2008.921692","name":"On Codes, Matroids, and Secure Multiparty Computation From Linear Secret-Sharing Schemes","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tit.2008.921692","authors":["Ronald Cramer","Vanesa Daza","Ignacio Gracia","Jorge JimÉnez Urroz","Gregor Leander","Jaume Marti-Farre","Carles Padro"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2008-05-29T17:43:22Z","doi":"10.1109/tit.2008.921692","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1201/9781003185284-22","name":"Synthpop: A Tool to Enable More Flexible Use of Sensitive Data within the Scottish Longitudinal Study","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003185284-22","authors":["Chris Dibben","Gillian M. Raab","Beata Nowok","Lee Williamson","Lynne Adair"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-23T14:36:41Z","doi":"10.1201/9781003185284-22","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/comm70054.2026.11591323","name":"Secure Multiparty Function Evaluation Using Polynomial Approximation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/comm70054.2026.11591323","authors":["Octavian Catrina"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-10T19:36:58Z","doi":"10.1109/comm70054.2026.11591323","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.5220/0012254000003648","name":"Policy-Driven XACML-Based Architecture for Dynamic Enforcement of Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012254000003648","authors":["Arghavan Hosseinzadeh","Jessica Chwalek","Robin Brandstädter"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-01T18:44:53Z","doi":"10.5220/0012254000003648","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.6036/10579","name":"SECURE MULTIPARTY COMPUTATION FOR PREDICTIVE MAINTENANCE: VALIDATION OF SCALE-MAMBA TOOL IN TERMS OF ACCURACY AND EFFICIENCY","source":"crossref","abstract":"Privacy is a booming sector and there is an increasing number of limitations that hinder the centralization of data coming from different sources. Nowadays, having data provides value and an advantage over the rest, since it allows the performance of a wider and more generalizable analysis. Secure Multiparty Computation (SMPC) is a cryptographic technique that allows performing computations with data from different parties while maintaining the privacy of the data and avoiding centralization. This work focuses on the SCALE-MAMBA framework for conducting SMPC and the main objective is its validation in terms of types of operations, the accuracy of the results and execution times. A use case that is directly related to the industry is used, consisting of a manufacturer who wants to implement predictive maintenance on a machine whose data is collected by different users. Two types of scenarios are presented in order to analyze the results, obtaining different conclusions for each of them. On the one hand, the first scenario collects the use cases in which the aim is to compute statistics or simple calculations with data in common. On the other hand, the second scenario focuses on the training of Machine Learning (ML) algorithms. The original contribution of this work includes the implementation of these codes within the Mamba language, their application to concrete data, and the comparison of the results with those that would be obtained by performing it in an insecure way, centralizing the data, and using R or Python. The major limitations encountered are around execution times, which might be acceptable for many use cases in the first scenario, but are prohibitive for many of the techniques used in real ML training. Keywords: cryptography, security, privacy, predictive maintenance, Privacy-Preserving Computation, Privacy-enhancing technologies, Secure Multiparty Computation, SCALE-MAMBA, machine learning, data analysis, prediction, classification, accuracy, efficiency.","url":"https://doi.org/10.6036/10579","authors":["Idoia Gamiz Ugarte","OSCAR LAGE SERRANO","LEIRE LEGARRETA SOLAGUREN","Cristina Regueiro Senderos","EDUARDO JACOB TAQUET","IÑAKI SECO AGUIRRE"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-10-28T09:46:39Z","doi":"10.6036/10579","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-319-48965-0_39","name":"An Efficient Construction of Non-Interactive Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-48965-0_39","authors":["Satoshi Obana","Maki Yoshida"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-10-27T13:55:25Z","doi":"10.1007/978-3-319-48965-0_39","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-031-76371-7_5","name":"Statistically Secure Multiparty Computation of a Biased Coin","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-76371-7_5","authors":["Amir Zarei","Staal A. Vinterbo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-20T07:44:23Z","doi":"10.1007/978-3-031-76371-7_5","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1137/17m1151602","name":"Equivocating Yao: Constant-Round Adaptively Secure Multiparty Computation in the Plain Model","source":"crossref","abstract":"Yao's circuit garbling scheme is one of the basic building blocks of cryptographic protocol design. Originally designed to enable two-message, two-party secure computation, the scheme has been extended in many ways and has innumerable applications. Still, a basic question has remained open throughout the years: Can the scheme be extended to guarantee security in the face of an adversary that corrupts both parties, adaptively, as the computation proceeds? We answer this question in the affirmative. We define a new type of symmetric encryption, called functionally equivocal encryption (FEE), and show that when Yao's scheme is implemented with FEE as the underlying encryption mechanism, it becomes secure against such adaptive adversaries. We then show how to implement FEE from any one-way function. Combining our scheme with noncommitting encryption, we obtain the first two-message, two-party computation protocol, and the first constant-round multiparty computation protocol, in the plain model, that are secure against semihonest adversaries who can adaptively corrupt all parties. Using standard techniques, this protocol can be made standalone secure against malicious corruptions in the plain model and universal composability secure in the common random string model. Additional applications include the first fully leakage-tolerant general multiparty computation protocol (with preprocessing), as well as a public-key version of FEE which can serve as a replacement for noncommitting encryption with better efficiency than what is possible for the latter.","url":"https://doi.org/10.1137/17m1151602","authors":["Ran Canetti","Oxana Poburinnaya","Muthuramakrishnan Venkitasubramaniam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-13T10:30:54Z","doi":"10.1137/17m1151602","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-030-64378-2_13","name":"Mr NISC: Multiparty Reusable Non-Interactive Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-64378-2_13","authors":["Fabrice Benhamouda","Huijia Lin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-12-12T15:02:46Z","doi":"10.1007/978-3-030-64378-2_13","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-030-71522-9_7","name":"Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71522-9_7","authors":["Berry Schoenmakers"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:18:28Z","doi":"10.1007/978-3-030-71522-9_7","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-319-47560-8_14","name":"Secure Multiparty Sorting Protocols with Covert Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-47560-8_14","authors":["Peeter Laud","Martin Pettai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-10-08T00:59:42Z","doi":"10.1007/978-3-319-47560-8_14","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1093/ietfec/e91-a.9.2349","name":"Secure Multiparty Computation for Comparator Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1093/ietfec/e91-a.9.2349","authors":["G. MOROHASHI","K. CHIDA","K. HIROTA","H. KIKUCHI"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2008-09-10T15:16:10Z","doi":"10.1093/ietfec/e91-a.9.2349","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1016/j.ic.2004.03.002","name":"Multiparty communication complexity and very hard functions","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ic.2004.03.002","authors":["Pavol Ďuriš"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2004-04-22T09:45:31Z","doi":"10.1016/j.ic.2004.03.002","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.21236/ada555116","name":"Multiparty Equality Function Computation in Networks with Point-to-Point Links","source":"crossref","abstract":"","url":"https://doi.org/10.21236/ada555116","authors":["Guanfeng Liang","Nitin Vaidya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-07-10T22:20:45Z","doi":"10.21236/ada555116","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-662-53641-4_18","name":"Efficient Secure Multiparty Computation with Identifiable Abort","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-662-53641-4_18","authors":["Carsten Baum","Emmanuela Orsini","Peter Scholl"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-10-21T15:48:14Z","doi":"10.1007/978-3-662-53641-4_18","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-642-27739-9_7-2","name":"Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-27739-9_7-2","authors":["Berry Schoenmakers"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-11T14:01:39Z","doi":"10.1007/978-3-642-27739-9_7-2","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-662-53641-4_14","name":"Binary AMD Circuits from Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-662-53641-4_14","authors":["Daniel Genkin","Yuval Ishai","Mor Weiss"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-10-21T15:48:14Z","doi":"10.1007/978-3-662-53641-4_14","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/3-7643-7394-6_2","name":"Multiparty Computation, an Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/3-7643-7394-6_2","authors":["Ronald Cramer","Ivan Damgård"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2006-03-15T19:14:00Z","doi":"10.1007/3-7643-7394-6_2","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/tifs.2023.3301710","name":"Privacy of Federated QR Decomposition Using Additive Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tifs.2023.3301710","authors":["Anne Hartebrodt","Richard Röttger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-03T17:33:08Z","doi":"10.1109/tifs.2023.3301710","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/iccomm.2018.8484794","name":"Round-Efficient Protocols for Secure Multiparty Fixed-Point Arithmetic","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccomm.2018.8484794","authors":["Octavian Catrina"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-10-08T19:31:00Z","doi":"10.1109/iccomm.2018.8484794","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.20944/preprints202403.1167.v1","name":"Secure IoT communication: Implementing a One-Time-Pad Protocol with True Random Numbers and Secure Multiparty Sums","source":"crossref","abstract":"The process of establishing secure communication between devices in the Internet of Things (IoT) can be approached by using a One-time-Pad (OTP) protocol. We propose using a known secure multiparty sum protocol for generating a One-time-Pad key using true (physical) random numbers in each device (party). We implemented the proposal using ZeroC-Ice, a middleware for distributed computing. The protocol security properties were analyzed under the assumptions of the Dolev-Yao threat model.","url":"https://doi.org/10.20944/preprints202403.1167.v1","authors":["Julio Fenner","Patricio Galeas","Francisco Escobar","Rail Neira"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-20T10:51:41Z","doi":"10.20944/preprints202403.1167.v1","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/2938503.2938507","name":"Secure and Efficient Multiparty Computation on Genomic Data","source":"crossref","abstract":"","url":"https://doi.org/10.1145/2938503.2938507","authors":["Md Momin Al Aziz","Mohammad Z. Hasan","Noman Mohammed","Dima Alhadidi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-09-12T13:33:45Z","doi":"10.1145/2938503.2938507","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-030-32430-8_4","name":"When Is a Semi-honest Secure Multiparty Computation Valuable?","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-32430-8_4","authors":["Radhika Bhargava","Chris Clifton"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-10-25T11:11:01Z","doi":"10.1007/978-3-030-32430-8_4","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/tit.2016.2614685","name":"An Efficient Framework for Unconditionally Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tit.2016.2614685","authors":["Ashish Choudhury","Arpita Patra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-10-04T15:27:08Z","doi":"10.1109/tit.2016.2614685","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.4018/978-1-59904-947-2.ch106","name":"Secure Multiparty/Multicandidate Electronic Elections","source":"crossref","abstract":"In this chapter we present a methodology for proving in Zero Knowledge the validity of selecting a subset of a set belonging to predefined family of sets. We apply this methodology in electronic voting to provide for extended ballot options. Our proposed voting scheme supports multiple parties and the selection of a number of candidates from one and only one of these parties. We have implemented this system and provided measures of its computational and communication complexity. We show that the complexity is linear with respect to the total number of candidates and the number of parties participating in the election.","url":"https://doi.org/10.4018/978-1-59904-947-2.ch106","authors":["Tassos Dimitriou","Dimitris Foteinakis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-05-24T12:06:44Z","doi":"10.4018/978-1-59904-947-2.ch106","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-981-15-2777-7_17","name":"Scalable, On-Demand Secure Multiparty Computation for Privacy-Aware Blockchains","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-15-2777-7_17","authors":["Shantanu Sharma","Wee Keong Ng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-01-24T12:02:45Z","doi":"10.1007/978-981-15-2777-7_17","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/3749373","name":"Privacy-Preserving Training of Support Vector Machines via Secure Multiparty Computation","source":"crossref","abstract":"The power and ubiquity of machine learning demand security measures for protecting sensitive data. Secure multiparty computation (MPC) techniques enable a group of parties to jointly compute a given function while keeping the information private. In this work, we engineer a prototype for privately training support vector machines (SVMs) using MPC techniques. We conduct an extensive study on how different approaches for training SVMs interact with existing state-of-the-art MPC protocols. We identify the least squares (LS) approach as the best suited for privately training. We then optimize fixed-point precision, ensuring accuracy while keeping low running time and communication. The technical details of the optimization involve bounds on the step size of a gradient method to solve a linear system, which might be of independent interest. We further propose and analyse different alternatives to improve the LS approach on an MPC implementation, and we compare their performance. The best improvement yields up to 2× reduction of the running time and communication complexity, without affecting the accuracy of the trained model. In order to illustrate the feasibility of our solution, we securely train SVMs for two realistic tasks.","url":"https://doi.org/10.1145/3749373","authors":["Daniel Cabarcas Jaramillo","Hernan Dario Vanegas Madrigal","Daniel Escudero","Fernando Alberto Morales Jauregui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-22T11:13:48Z","doi":"10.1145/3749373","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/iolts59296.2023.10224859","name":"$\\text{MP}\\ell\\circ \\mathrm{C}$: Privacy-Preserving IP Verification Using Logic Locking and Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iolts59296.2023.10224859","authors":["Dimitris Mouris","Charles Gouert","Nektarios Georgios Tsoutsos"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-28T17:50:13Z","doi":"10.1109/iolts59296.2023.10224859","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-662-63958-0_31","name":"Absentia: Secure Multiparty Computation on Ethereum","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-662-63958-0_31","authors":["Didem Demirag","Jeremy Clark"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-16T14:04:04Z","doi":"10.1007/978-3-662-63958-0_31","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-981-96-4606-7_7","name":"SMC-PATE: A Secure Enhanced Private Aggregation of Teacher Ensembles with Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-4606-7_7","authors":["Anh-Tu Tran","The-Dung Luong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-31T14:25:27Z","doi":"10.1007/978-981-96-4606-7_7","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-319-39555-5_18","name":"Better Preprocessing for Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-39555-5_18","authors":["Carsten Baum","Ivan Damgård","Tomas Toft","Rasmus Zakarias"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-06-08T10:11:31Z","doi":"10.1007/978-3-319-39555-5_18","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.3389/fphy.2023.1139505","name":"Editorial: Multiparty secure quantum and semiquantum computations","source":"crossref","abstract":"","url":"https://doi.org/10.3389/fphy.2023.1139505","authors":["Tianyu Ye"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-06T17:08:47Z","doi":"10.3389/fphy.2023.1139505","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1088/1757-899x/981/2/022079","name":"Secure Multiparty computation enabled E-Healthcare system with Homomorphic encryption","source":"crossref","abstract":"Abstract With the spread of Covid–19 pandemic it is difficult for many patients to physically visit the Hospitals and get treatment. The Improved technology enable such patients to contact the concern doctors and get diagnosis information online. The security of the sensitive data of the Patients is vulnerable when the information is shared across the network. To address this, we are proposing a novel approach with the Privacy ensured self-care health management schema using Secure Multiparty computation (MPC). Through this approach the patient can share the sensitive data to the Hospital server through the online mode, the data will be shared in the encrypted format which will be matched with the existing data at the Hospital records and the best relevant match based on the smart Index of disease. The privacy preserving is key aspect in this model as the data is shared in the sensitive mode so the Homomorphic Encryption (HE) approach is used to perform computations on the Patient’s sensitive data in the encrypted mode and ensure the confidentiality from Intruders to access the information. This model also proposes a novel approach which can overcome many security threats. The patient’s data will be shared and used in a more secured manner so the patients specifically the elder who are not advised to visit the public places in this pandemic can also receive better treatment through online.","url":"https://doi.org/10.1088/1757-899x/981/2/022079","authors":["A Vijaya Kumar","Mogalapalli Sai Sujith","Kosuri Tarun Sai","Galla Rajesh","Devulapalli Jagannadha Sriram Yashwanth"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-12-05T05:48:43Z","doi":"10.1088/1757-899x/981/2/022079","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.4018/979-8-3373-1977-3.ch011","name":"Privacy Preserving Analytics in Cyber Insurance","source":"crossref","abstract":"Cyber insurance faces challenges with data scarcity and privacy restrictions limiting analytical capabilities across institutional boundaries. Insurance providers struggle to access sufficient data for accurate risk assessment without exposing sensitive information or violating regulations. This chapter examines federated learning and secure multiparty computation as privacy-preserving analytics solutions for cyber insurance. These technologies enable collaborative analytics across organizations without raw data sharing, maintaining data locality while extracting collective insights. Research demonstrates these methods support accurate premium calculations, risk assessment models, and fraud detection while preserving privacy safeguards. Implementation frameworks require consideration of computational efficiency, regulatory alignment, and protocol standardization. These privacy-preserving approaches transform cyber insurance analytics by enabling cross-organizational collaboration without compromising data sovereignty or regulatory compliance","url":"https://doi.org/10.4018/979-8-3373-1977-3.ch011","authors":["Mohammad Al Khaldy","Saad Alateef","Amjad Aldweesh","Ahmad Al-Qerem"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-25T11:52:37Z","doi":"10.4018/979-8-3373-1977-3.ch011","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-030-03807-6_7","name":"Two-Round Adaptively Secure Multiparty Computation from Standard Assumptions","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-03807-6_7","authors":["Fabrice Benhamouda","Huijia Lin","Antigoni Polychroniadou","Muthuramakrishnan Venkitasubramaniam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-11-04T03:42:46Z","doi":"10.1007/978-3-030-03807-6_7","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/gcwkshps52748.2021.9682053","name":"Secure Aggregation in Federated Learning via Multiparty Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/gcwkshps52748.2021.9682053","authors":["Erfan Hosseini","Ashish Khisti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-24T21:06:05Z","doi":"10.1109/gcwkshps52748.2021.9682053","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.3233/978-1-61499-169-4-222","name":"Randomization Techniques for Secure Computation","source":"crossref","abstract":"To what extent can a computation be made &amp;ldquo;simpler&amp;rdquo; by settling for computing a randomized encoding of the output? Originating from Yao's seminal idea of garbled circuits, answers to this question have found applications in cryptography and elsewhere. We will survey the state of the art on different flavors of this question that are motivated by different problems in secure computation and correspond to different notions of simplicity.","url":"https://doi.org/10.3233/978-1-61499-169-4-222","authors":["Ishai Yuval"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T04:38:17Z","doi":"10.3233/978-1-61499-169-4-222","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/tdsc.2015.2484326","name":"A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tdsc.2015.2484326","authors":["Slawomir Goryczka","Li Xiong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-10-01T14:49:41Z","doi":"10.1109/tdsc.2015.2484326","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-662-53641-4_19","name":"Secure Multiparty RAM Computation in Constant Rounds","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-662-53641-4_19","authors":["Sanjam Garg","Divya Gupta","Peihan Miao","Omkant Pandey"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-10-21T15:48:14Z","doi":"10.1007/978-3-662-53641-4_19","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-319-21966-0_11","name":"A Private Lookup Protocol with Low Online Complexity for Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-21966-0_11","authors":["Peeter Laud"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-01T06:21:27Z","doi":"10.1007/978-3-319-21966-0_11","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-662-49096-9_25","name":"Characterization of Secure Multiparty Computation Without Broadcast","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-662-49096-9_25","authors":["Ran Cohen","Iftach Haitner","Eran Omri","Lior Rotem"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-12-18T10:45:05Z","doi":"10.1007/978-3-662-49096-9_25","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-319-98113-0_11","name":"Proactive Secure Multiparty Computation with a Dishonest Majority","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-98113-0_11","authors":["Karim Eldefrawy","Rafail Ostrovsky","Sunoo Park","Moti Yung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-08-02T12:13:46Z","doi":"10.1007/978-3-319-98113-0_11","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-81-322-2126-5_50","name":"A (t, n) Secure Sum Multiparty Computation Protocol Using Multivariate Polynomial Secret Sharing Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-81-322-2126-5_50","authors":["K. Praveen","Nithin Sasi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-11-01T03:51:12Z","doi":"10.1007/978-81-322-2126-5_50","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1201/9781003189664-15","name":"Secure Multiparty Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003189664-15","authors":["Ulf Mattsson"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-05-10T16:00:22Z","doi":"10.1201/9781003189664-15","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1006/inco.1994.1051","name":"The Bns Lower-Bound for Multiparty Protocols Is Nearly Optimal","source":"crossref","abstract":"","url":"https://doi.org/10.1006/inco.1994.1051","authors":["V. Grolmusz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2002-10-06T21:10:40Z","doi":"10.1006/inco.1994.1051","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1145/3639448","name":"Multiparty Computation: To Secure Privacy, Do the Math","source":"crossref","abstract":"Multiparty Computation is based on complex math, and over the past decade, MPC has been harnessed as one of the most powerful tools available for the protection of sensitive data. MPC now serves as the basis for protocols that let a set of parties interact and compute on a pool of private inputs without revealing any of the data contained within those inputs. In the end, only the results are revealed. The implications of this can often prove profound.","url":"https://doi.org/10.1145/3639448","authors":["Nigel Smart","Joshua W. Baron","Sanjay Saravanan","Jordan Brandt","Atefeh Mashatan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-09T23:05:35Z","doi":"10.1145/3639448","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.2139/ssrn.4817363","name":"Secure Vector Databases and Secure Vector Computation","source":"crossref","abstract":"Recent advances in computing and data engineering are facing us with different challenges and opportunities. On one hand, numerous artificial intelligence and machine learning models utilize big data sets and heavily rely on vectorized data or numerical features embedded in vector spaces. On the other hand, the advent of quantum computers necessitate designing cryptographic solutions that can guarantee the security and privacy of our data in the post-quantum and AGI eras.&lt;br&gt;In this article, we highlight the challenges and opportunities created by recent advancements in these domains and pinpoint their connections. Furthermore, we present the concepts of Secure Vector Databases (SVDB) and Secure Vector Computation (SVC) as potential solutions for machine learning applications, that benefit from the state-of-the-art post-quantum cryptographic techniques (e.g., FHE) and ZKP technologies.","url":"https://doi.org/10.2139/ssrn.4817363","authors":["Mohammad Raeini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-07T16:21:21Z","doi":"10.2139/ssrn.4817363","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/tdsc.2025.3599183","name":"Privacy-Preserving Authorized Set Matching via Dishonest Majority Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tdsc.2025.3599183","authors":["Guowei Ling","Peng Tang","Fei Tang","Shifeng Sun","Jinyong Shan","Liyao Xiang","Weidong Qiu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-14T18:49:44Z","doi":"10.1109/tdsc.2025.3599183","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/sfcs.1996.548509","name":"Incoercible multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sfcs.1996.548509","authors":["R. Canetti","R. Gennaro"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2002-12-24T03:15:25Z","doi":"10.1109/sfcs.1996.548509","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/itw.2018.8613443","name":"Coding for Private and Secure Multiparty Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/itw.2018.8613443","authors":["Qian Yu","Netanel Raviv","A. Salman Avestimehr"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-01-18T00:39:34Z","doi":"10.1109/itw.2018.8613443","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1016/j.ijepes.2023.109604","name":"Residential flexibility characterization and trading using secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ijepes.2023.109604","authors":["Fairouz Zobiri","Mariana Gama","Svetla Nikova","Geert Deconinck"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-31T10:50:31Z","doi":"10.1016/j.ijepes.2023.109604","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.2139/ssrn.4264511","name":"High-Dimensional Quantum Secure Multiparty Summation with Random Decoy Particles","source":"crossref","abstract":"Quantum secure multiparty summation (QSMS) calculates the sum of the privacy inputs of multiple participants without exposing these inputs. The current QSMS protocols mostly employ the fixed bases of decoy particles to enhance the security of information transmission. Unfortunately, this measure increases the risk of eavesdropper guessing the fixed bases. In this paper, a high-dimensional quantum secure multiparty summation with random decoy particles (HQSMS-RDP) is proposed to remedy the above-mentioned security deficiencies. In HQSMS-RDP, each participant performs the general Pauli operator on the received message particle to embed his own secret integer and random number into it, and then sends the transformed message particle and random decoy particles to the next participant. Moreover, each participant needs to neither prepare the decoy particles nor measure them during every transmission route. These decoy particles are chosen from a set of random single particles, so that an eavesdropper cannot deterministically guess the specific measurement bases for measuring them, resulting in the exposure probability of the eavesdropper eavesdropping on them approaching 1. The performance analysis shows that the resource consumption of HQSMS-RDP is lower than that of other similar QSMS protocols. The results simulated on the IBM cloud platform prove the correctness of HQSMS-RDP.","url":"https://doi.org/10.2139/ssrn.4264511","authors":["Xiuli Song","Shuai Yang","Hongyao Deng","Jinwei Liao","Tao Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-11-02T01:01:41Z","doi":"10.2139/ssrn.4264511","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/sp.2014.35","name":"Secure Multiparty Computations on Bitcoin","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp.2014.35","authors":["Marcin Andrychowicz","Stefan Dziembowski","Daniel Malinowski","Lukasz Mazurek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-11-26T09:57:47Z","doi":"10.1109/sp.2014.35","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.3389/978-2-8325-3850-0","name":"Multiparty Secure Quantum and Semiquantum Computations","source":"crossref","abstract":"","url":"https://doi.org/10.3389/978-2-8325-3850-0","authors":["Tianyu Ye","Nanrun Zhou","Mingxing Luo","Amiya Nayak","Xiubo Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-07T10:46:40Z","doi":"10.3389/978-2-8325-3850-0","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/tdsc.2022.3227568","name":"Efficient Noise Generation Protocols for Differentially Private Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tdsc.2022.3227568","authors":["Reo Eriguchi","Atsunori Ichikawa","Noboru Kunihiro","Koji Nuida"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-12-08T13:36:33Z","doi":"10.1109/tdsc.2022.3227568","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-981-13-5913-2_21","name":"Two Anti-quantum Attack Protocols for Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-13-5913-2_21","authors":["Lichao Chen","Zhanli Li","Zhenhua Chen","Yaru Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-01-08T20:18:45Z","doi":"10.1007/978-981-13-5913-2_21","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/desec.2017.8073825","name":"Multiparty computations in varying contexts","source":"crossref","abstract":"","url":"https://doi.org/10.1109/desec.2017.8073825","authors":["Paul Laird","Sarah Jane Delany","Pierpaolo Dondio"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-10-25T19:22:28Z","doi":"10.1109/desec.2017.8073825","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-030-04834-1_23","name":"Theoretical Foundations for Mobile Target Defense: Proactive Secret Sharing and Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-04834-1_23","authors":["Karim Eldefrawy","Rafail Ostrovsky","Moti Yung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-11-29T07:46:31Z","doi":"10.1007/978-3-030-04834-1_23","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1007/978-3-662-49387-8_8","name":"Asynchronous Secure Multiparty Computation in Constant Time","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-662-49387-8_8","authors":["Ran Cohen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-02-17T14:25:41Z","doi":"10.1007/978-3-662-49387-8_8","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1109/itw.2012.6404773","name":"On secure multiparty sampling for more than two parties","source":"crossref","abstract":"","url":"https://doi.org/10.1109/itw.2012.6404773","authors":["Manoj M. Prabhakaran","Vinod M. Prabhakaran"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-01-17T20:30:12Z","doi":"10.1109/itw.2012.6404773","addedAt":"2026-08-31T06:41:29.094Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.48550/arxiv.2602.23698","name":"Privacy-Preserving Local Energy Trading Considering Network Fees","source":"datacite","abstract":"Driven by the widespread deployment of distributed energy resources, local energy markets (LEMs) have emerged as a promising approach for enabling direct trades among prosumers and consumers to balance intermittent generation and demand locally. However, LEMs involve processing sensitive participant data, which, if not protected, poses privacy risks. At the same time, since electricity is exchanged over the physical power network, market mechanisms should consider physical constraints and network-related costs. Existing work typically addresses these issues separately, either by incorporating grid-related aspects or by providing privacy protection. To address this gap, we propose a privacy-preserving protocol for LEMs, with consideration of network fees that can incite participants to respect physical limits. The protocol is based on a double-auction mechanism adapted from prior work to enable more efficient application of our privacy-preserving approach. To protect participants' data, we use secure multiparty computation. In addition, Schnorr's identification protocol is employed with multiparty verification to ensure authenticated participation without compromising privacy. We further optimise the protocol to reduce communication and round complexity. We prove that the protocol meets its security requirements and show through experimentation its feasibility at a typical LEM scale: a market with 5,000 participants can be cleared in 4.17 minutes.","url":"https://doi.org/10.48550/arxiv.2602.23698","authors":["Alqahtani, Eman","Mustafa, Mustafa A."],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.23698","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.6084/m9.figshare.26552593.v1","name":"Additional file 1 of EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation","source":"datacite","abstract":"Additional file 1. Microsoft Word format describes the employed SMPC method in detail.","url":"https://doi.org/10.6084/m9.figshare.26552593.v1","authors":["Wirth, Felix Nikolaus","Kussel, Tobias","Müller, Armin","Hamacher, Kay","Prasser, Fabian"],"tags":["Computation Theory and Mathematics","FOS: Computer and information sciences","Computer Software","Data Format","Information Systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.6084/m9.figshare.26552593.v1","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.6084/m9.figshare.26552593","name":"Additional file 1 of EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation","source":"datacite","abstract":"Additional file 1. Microsoft Word format describes the employed SMPC method in detail.","url":"https://doi.org/10.6084/m9.figshare.26552593","authors":["Wirth, Felix Nikolaus","Kussel, Tobias","Müller, Armin","Hamacher, Kay","Prasser, Fabian"],"tags":["Computation Theory and Mathematics","FOS: Computer and information sciences","Computer Software","Data Format","Information Systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.6084/m9.figshare.26552593","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.6084/m9.figshare.26552596.v1","name":"Additional file 2 of EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation","source":"datacite","abstract":"Additional file 2. Microsoft Word format contains the detailed results of the performance evaluation.","url":"https://doi.org/10.6084/m9.figshare.26552596.v1","authors":["Wirth, Felix Nikolaus","Kussel, Tobias","Müller, Armin","Hamacher, Kay","Prasser, Fabian"],"tags":["Computation Theory and Mathematics","FOS: Computer and information sciences","Computer Software","Data Format","Information Systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.6084/m9.figshare.26552596.v1","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.6084/m9.figshare.26552596","name":"Additional file 2 of EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation","source":"datacite","abstract":"Additional file 2. Microsoft Word format contains the detailed results of the performance evaluation.","url":"https://doi.org/10.6084/m9.figshare.26552596","authors":["Wirth, Felix Nikolaus","Kussel, Tobias","Müller, Armin","Hamacher, Kay","Prasser, Fabian"],"tags":["Computation Theory and Mathematics","FOS: Computer and information sciences","Computer Software","Data Format","Information Systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.6084/m9.figshare.26552596","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.6084/m9.figshare.26560730.v1","name":"Additional file 1 of Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing","source":"datacite","abstract":"Additional file 1. Sequre supplementary notes provides additional insight to Sequre, its usability and optimizations, and the results [61–90].","url":"https://doi.org/10.6084/m9.figshare.26560730.v1","authors":["Smajlović, Haris","Shajii, Ariya","Berger, Bonnie","Cho, Hyunghoon","Numanagić, Ibrahim"],"tags":["Information Systems","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.6084/m9.figshare.26560730.v1","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.6084/m9.figshare.26560730","name":"Additional file 1 of Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing","source":"datacite","abstract":"Additional file 1. Sequre supplementary notes provides additional insight to Sequre, its usability and optimizations, and the results [61–90].","url":"https://doi.org/10.6084/m9.figshare.26560730","authors":["Smajlović, Haris","Shajii, Ariya","Berger, Bonnie","Cho, Hyunghoon","Numanagić, Ibrahim"],"tags":["Information Systems","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.6084/m9.figshare.26560730","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.6084/m9.figshare.26560733.v1","name":"Additional file 2 of Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing","source":"datacite","abstract":"Additional file 2. Review history.","url":"https://doi.org/10.6084/m9.figshare.26560733.v1","authors":["Smajlović, Haris","Shajii, Ariya","Berger, Bonnie","Cho, Hyunghoon","Numanagić, Ibrahim"],"tags":["Information Systems","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.6084/m9.figshare.26560733.v1","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.6084/m9.figshare.26560733","name":"Additional file 2 of Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing","source":"datacite","abstract":"Additional file 2. Review history.","url":"https://doi.org/10.6084/m9.figshare.26560733","authors":["Smajlović, Haris","Shajii, Ariya","Berger, Bonnie","Cho, Hyunghoon","Numanagić, Ibrahim"],"tags":["Information Systems","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.6084/m9.figshare.26560733","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5075/epfl-thesis-8846","name":"Multiparty Homomorphic Encryption: from Theory to Practice","source":"datacite","abstract":"Multiparty homomorphic encryption (MHE) enables a group of parties to encrypt data in a way that (i) enables the evaluation of functions directly over its ciphertexts and (ii) enforces a joint cryptographic access-control over the underlying data. By extending traditional (single-party) homomorphic encryption (HE), MHE schemes support the design and deployment of highly efficient protocols for secure multiparty computation (MPC). MPC protocols based on MHE have highly desirable properties: They generally require less communication than traditional MPC techniques and have a fully public transcript. Hence, most of their execution-related costs can be outsourced to an untrusted external party (such as a cloud server). Although promising in theory, MHE-based MPC solutions have not yet been implemented in any of the 30+ existing MPC frameworks, thus revealing a gap between theory and practice. This dissertation summarizes our work toward closing this gap, by proposing contributions to both sides. On the theoretical side, we propose two MHE constructions that extend the new generation of HE schemes to the multiparty setting. Our first construction is an N-out-of-N-threshold MHE scheme that revisits the seminal lattice-based MHE construction by Asharov et al. (EUROCRYPT'12). Notably, we improve the efficiency of its setup phase, and we generalize its decryption procedure into a generalized key-switching operation that further enables re-encryption, conversion to secret-shares, and the interactive bootstrapping of its ciphertexts. Our second construction extends the first with fault-tolerance capabilities. This extension provides a T-out-of-N-threshold MHE scheme that stands as a compact and efficient alternative to the threshold scheme of Boneh et al. (CRYPTO`18), when synchronous communication can be assumed. On the practical side, we propose the Lattigo library and the Helium system. Lattigo is an open-source Go package that implements the state-of-the-art HE schemes, along with their multiparty extensions. It is also the first maintained library to implement the bootstrapping procedure for approximate homomorphic encryption. Helium builds on top of Lattigo and provides the first end-to-end open-source implementation of an MHE-based MPC protocol. We exploit the theoretical properties of this protocol to propose a helper-assisted setting, where the parties delegate most of the protocol execution cost to an honest-but-curious third party (e.g., a cloud service). As a result, Helium is also the first open-source system to support MPC with sub-linear cost for the parties, without assuming non-collusion between the multiple delegate nodes.","url":"https://doi.org/10.5075/epfl-thesis-8846","authors":["Mouchet, Christian Vincent"],"tags":["Multiparty homomorphic encryption","secure multiparty computation","threshold access-structures","implementations"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5075/epfl-thesis-8846","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.3929/ethz-c-000796073","name":"Secure Multiparty Computation with Dynamic Participants and Guaranteed Output","source":"datacite","abstract":"Secure Multiparty Computation (MPC) is an expression encompassing cryptographic techniques that allow mutually distrustful parties to jointly perform computations over private data, ensuring correctness and preserving privacy despite malicious behavior. Guaranteed output is the strongest security goal for MPC: protocols with this property always deliver results. This is in contrast with weaker notions that allow malicious aborts. The seminal works of Ben-Or, Goldwasser, and Wigderson (STOC’88) and Rabin and Ben-Or (STOC’89) showed interactive protocols to compute any function with guaranteed output and perfect security if fewer than one third of parties are corrupted, or with statistical security (with a small error probability) if fewer than half are corrupted. These results hold when the set of participants is fixed throughout the execution. Ostrovsky and Yung’s proactive model (PODC’91) relaxes this assumption by allowing the set of corrupted parties to evolve over time, better capturing the dynamics of long-lived computations, where machines may be periodically compromised and recovered. The recent YOSO (You Only Speak Once) and fluid MPC models (CRYPTO’21) push this ambitious modelling even further by allowing for fully dynamic participation, with parties joining and leaving the computation at will. In this setting, known protocols with guaranteed output fall short of optimal resilience or rely on non-standard communication and adversarial assumptions. Matching the classical BGW and Rabin–Ben-Or bounds in these settings remained an open problem. We resolve these questions within the Layered MPC abstraction of David et al. (CRYPTO’23), in which the interaction pattern of a protocol is restricted to be a layered graph, meaning parties are partitioned into a sequence of disjoint sets (layers) and can send private and broadcast messages to parties in immediately subsequent layers. We match the BGW and Rabin and Ben-Or bounds in this model, fully characterizing the feasibility landscape of Layered MPC with guaranteed output. As a stepping stone towards our statistically secure protocol, we construct a more efficient and conceptually simpler computationally secure protocol relying on linear non-interactive commitments. Our results yield optimally-resilient and maximally-proactive MPC protocols with guaranteed output for both perfect and statistical security.","url":"https://doi.org/10.3929/ethz-c-000796073","authors":["Deligios, Giovanni"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3929/ethz-c-000796073","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.18662863","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.18662863","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18662863","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.18662165","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.18662165","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18662165","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.18643524","name":"Privacy-Preserving Machine Learning Technique Using One-Way Hashing for Enhancing  Data Security in the Nigerian Healthcare Ecosystem","source":"datacite","abstract":"Safeguarding healthcare data in Nigeria remains a pressing challenge, complicated by fragmented infrastructure, limited resources, and evolving regulatory frameworks. This paper presents a conceptual analysis of one-way hashing as a lightweight cryptographic technique for pseudonymizing patient identifiers within federated learning pipelines. By situating hashing in contrast to heavier cryptographic methods such as homomorphic encryption and secure multiparty computation, the study highlights its relative efficiency, scalability, and compliance with the Nigeria Data Protection Regulation (NDPR, 2019) and the Nigeria Data Protection Act (NDPA, 2023). Through analytical benchmarking and illustrative scenarios, hashing is shown to offer a pragmatic balance between privacy preservation and operational feasibility in resource-constrained healthcare environments. The paper concludes that one-way hashing provides a viable conceptual pathway for operationalizing privacy-preserving machine learning in Nigerian healthcare systems, while laying the foundation for future empirical validation.","url":"https://doi.org/10.5281/zenodo.18643524","authors":["Damang","F.S.","Aimufua, and Gilbert I.O."],"tags":["Healthcare data protection; Federated learning; One-way hashing; Pseudonymizations; Privacy-preserving machine learning; Conceptual analysis; Nigeria Data Protection Regulation (NDPR); Nigeria Data Protection Act (NDPA); Cryptographic complexity; Resource-constrained environments"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18643524","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.18643523","name":"Privacy-Preserving Machine Learning Technique Using One-Way Hashing for Enhancing  Data Security in the Nigerian Healthcare Ecosystem","source":"datacite","abstract":"Safeguarding healthcare data in Nigeria remains a pressing challenge, complicated by fragmented infrastructure, limited resources, and evolving regulatory frameworks. This paper presents a conceptual analysis of one-way hashing as a lightweight cryptographic technique for pseudonymizing patient identifiers within federated learning pipelines. By situating hashing in contrast to heavier cryptographic methods such as homomorphic encryption and secure multiparty computation, the study highlights its relative efficiency, scalability, and compliance with the Nigeria Data Protection Regulation (NDPR, 2019) and the Nigeria Data Protection Act (NDPA, 2023). Through analytical benchmarking and illustrative scenarios, hashing is shown to offer a pragmatic balance between privacy preservation and operational feasibility in resource-constrained healthcare environments. The paper concludes that one-way hashing provides a viable conceptual pathway for operationalizing privacy-preserving machine learning in Nigerian healthcare systems, while laying the foundation for future empirical validation.","url":"https://doi.org/10.5281/zenodo.18643523","authors":["Damang","F.S.","Aimufua, and Gilbert I.O."],"tags":["Healthcare data protection; Federated learning; One-way hashing; Pseudonymizations; Privacy-preserving machine learning; Conceptual analysis; Nigeria Data Protection Regulation (NDPR); Nigeria Data Protection Act (NDPA); Cryptographic complexity; Resource-constrained environments"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18643523","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.18643521","name":"Privacy-Preserving Machine Learning Technique Using One-Way Hashing for Enhancing  Data Security in the Nigerian Healthcare Ecosystem","source":"datacite","abstract":"Safeguarding healthcare data in Nigeria remains a pressing challenge, complicated by fragmented infrastructure, limited resources, and evolving regulatory frameworks. This paper presents a conceptual analysis of one-way hashing as a lightweight cryptographic technique for pseudonymizing patient identifiers within federated learning pipelines. By situating hashing in contrast to heavier cryptographic methods such as homomorphic encryption and secure multiparty computation, the study highlights its relative efficiency, scalability, and compliance with the Nigeria Data Protection Regulation (NDPR, 2019) and the Nigeria Data Protection Act (NDPA, 2023). Through analytical benchmarking and illustrative scenarios, hashing is shown to offer a pragmatic balance between privacy preservation and operational feasibility in resource-constrained healthcare environments. The paper concludes that one-way hashing provides a viable conceptual pathway for operationalizing privacy-preserving machine learning in Nigerian healthcare systems, while laying the foundation for future empirical validation.","url":"https://doi.org/10.5281/zenodo.18643521","authors":["Damang","F.S.","Aimufua, and Gilbert I.O."],"tags":["Healthcare data protection; Federated learning; One-way hashing; Pseudonymizations; Privacy-preserving machine learning; Conceptual analysis; Nigeria Data Protection Regulation (NDPR); Nigeria Data Protection Act (NDPA); Cryptographic complexity; Resource-constrained environments"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18643521","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.18643522","name":"Privacy-Preserving Machine Learning Technique Using One-Way Hashing for Enhancing  Data Security in the Nigerian Healthcare Ecosystem","source":"datacite","abstract":"Safeguarding healthcare data in Nigeria remains a pressing challenge, complicated by fragmented infrastructure, limited resources, and evolving regulatory frameworks. This paper presents a conceptual analysis of one-way hashing as a lightweight cryptographic technique for pseudonymizing patient identifiers within federated learning pipelines. By situating hashing in contrast to heavier cryptographic methods such as homomorphic encryption and secure multiparty computation, the study highlights its relative efficiency, scalability, and compliance with the Nigeria Data Protection Regulation (NDPR, 2019) and the Nigeria Data Protection Act (NDPA, 2023). Through analytical benchmarking and illustrative scenarios, hashing is shown to offer a pragmatic balance between privacy preservation and operational feasibility in resource-constrained healthcare environments. The paper concludes that one-way hashing provides a viable conceptual pathway for operationalizing privacy-preserving machine learning in Nigerian healthcare systems, while laying the foundation for future empirical validation.","url":"https://doi.org/10.5281/zenodo.18643522","authors":["Damang","F.S.","Aimufua, and Gilbert I.O."],"tags":["Healthcare data protection; Federated learning; One-way hashing; Pseudonymizations; Privacy-preserving machine learning; Conceptual analysis; Nigeria Data Protection Regulation (NDPR); Nigeria Data Protection Act (NDPA); Cryptographic complexity; Resource-constrained environments"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18643522","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2412.03766","name":"End to End Collaborative Synthetic Data Generation","source":"datacite","abstract":"The success of AI is based on the availability of data to train models. While in some cases a single data custodian may have sufficient data to enable AI, often multiple custodians need to collaborate to reach a cumulative size required for meaningful AI research. The latter is, for example, often the case for rare diseases, with each clinical site having data for only a small number of patients. Recent algorithms for federated synthetic data generation are an important step towards collaborative, privacy-preserving data sharing. Existing techniques, however, focus exclusively on synthesizer training, assuming that the training data is already preprocessed and that the desired synthetic data can be delivered in one shot, without any hyperparameter tuning. In this paper, we propose an end-to-end collaborative framework for publishing of synthetic data that accounts for privacy-preserving preprocessing as well as evaluation. We instantiate this framework with Secure Multiparty Computation (MPC) protocols and evaluate it in a use case for privacy-preserving publishing of synthetic genomic data for leukemia.","url":"https://doi.org/10.48550/arxiv.2412.03766","authors":["Pentyala, Sikha","Sitaraman, Geetha","Claar, Trae","De Cock, Martine"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2412.03766","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:36.960Z"},{"id":"doi:10.48550/arxiv.2507.13591","name":"FuSeFL: Fully Secure and Scalable Federated Learning","source":"datacite","abstract":"Federated Learning (FL) enables collaborative model training without centralizing client data, making it attractive for privacy-sensitive domains. While existing approaches employ cryptographic techniques such as homomorphic encryption, differential privacy, or secure multiparty computation to mitigate inference attacks, including model inversion, membership inference, and gradient leakage, they often suffer from high computational and memory overheads. Moreover, many methods overlook the confidentiality of the global model itself, which may be proprietary and sensitive. These challenges limit the practicality of secure FL, especially in settings that involve large datasets and strict compliance requirements. We present FuSeFL, a Fully Secure and scalable FL scheme, which decentralizes training across client pairs using lightweight MPC, while confining the server's role to secure aggregation, client pairing, and routing. This design eliminates server bottlenecks, avoids full data offloading, and preserves full confidentiality of data, model, and updates throughout training. Based on our experiment, FuSeFL defends against unauthorized observation, reconstruction attacks, and inference attacks such as gradient leakage, membership inference, and inversion attacks, while achieving up to $13 \\times$ speedup in training time and 50% lower server memory usage compared to our baseline.","url":"https://doi.org/10.48550/arxiv.2507.13591","authors":["Ghinani, Sahar Ghoflsaz","Sadredini, Elaheh"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.13591","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.18378157","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.18378157","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18378157","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.18377441","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.18377441","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18377441","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.18374973","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.18374973","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18374973","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.3929/ethz-c-000793854","name":"Re-visiting Authorized Private Set Intersection: A New Privacy-Preserving Variant and Two Protocols","source":"datacite","abstract":"We revisit the problem of Authorized Private Set Intersection (APSI), which allows mutually untrusting parties to authorize their items using a trusted third-party judge before privately computing the intersection. We also initiate the study of Partial-APSI, a novel privacy-preserving generalization of APSI in which the client only reveals a subset of their items to a third-party semi-honest judge for authorization. Partial-APSI allows for partial verification of the set, preserving the privacy of the party whose items are being verified. Both APSI and Partial-APSI have a number of applications, including genome matching, ad conversion, and compliance with privacy policies such as the GDPR. We present two protocols based on bilinear pairings with linear communication. The first realizes the APSI functionality, is secure against a malicious client, and requires only one round of communication during the online phase. Our second protocol realizes the Partial-APSI functionality and is secure against a client that may maliciously inject elements into its input set, but who follows the protocol semi-honestly otherwise. We formally prove correctness and security of these protocols and provide an experimental evaluation to demonstrate their practicality. Our protocols can be efficiently run on commodity hardware. We also show that our protocols are massively parallelizable by running our experiments on a compute grid across 50 cores.","url":"https://doi.org/10.3929/ethz-c-000793854","authors":["Falzon, Francesca","Markatou, Evangelia Anna"],"tags":["Private set intersection","2PC","Secure multiparty computation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3929/ethz-c-000793854","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5445/ir/1000189670","name":"TEE-Based Distributed Ledgers and Their Resilience","source":"datacite","abstract":"Resilience is the ability of a (distributed) system to withstand any stressful situation without imposing massive restrictions and, above all, without long-term consequences. Permissioned distributed ledgers based on state machine replication (SMR) offer a promising approach to achieving high resilience and fairness in federated systems. SMR provides a fault-tolerant service for clients by relying on all replicas being in a consistent state. The consistent state is achieved through a consensus algorithm, typically an atomic broadcast, that decides on a total order of client requests. In the Byzantine fault model, replicas are assumed to be potentially malicious; a Byzantine fault-tolerant (BFT) protocol withstands a fixed share of malicious actors. Classic BFT SMR protocols require $n&gt;3t$ replicas and multiple rounds of communication to withstand $t$ faulty replicas, making the implementation complex and limiting achievable throughput and increasing latency. Trusted Execution Environments (TEEs) allow to implement SMR in the so-called hybrid fault model in which replicas are assumed to be potentially Byzantine but the TEE is restricted to only fail by crashing. In the hybrid fault model, SMR requires less communication and can be implemented with a fault tolerance of $n&gt;2t$ replicas. While many proposals aim to optimize BFT SMR by using TEEs, they still rely on a so-called leader that coordinates the agreement process among the replicas. The leader is known to be a bottleneck and, if it fails, the system has to recover from the failure and elect a new leader. The additional coordination required to elect a new leader can cause significant performance degradation, limiting the achieved resilience. Asynchronous protocols based on directed acyclic graphs (DAGs) eliminate the reliance on distinguished replicas by allowing all replicas to participate equally in the agreement process. While asynchronous approaches and the hybrid fault model independently contribute to increasing the resilience of BFT SMR systems, their combination has largely been unexplored. This dissertation aims to fill this gap by answering the following research question: What is the achievable performance and resilience of DAG-based, hybrid fault-tolerant state machine replication and under which preconditions can the leaderless nature be safely exploited to maximize throughput? We proceed in three steps to enhance the resilience and performance of BFT SMR systems and to identify potential trade-offs that arise from the assumption of TEEs and asynchrony in BFT SMR. First, we investigate the fit of TEE-based SMR for consortium-operated applications using the example of Mobility-as-a-Service ticketing systems. We propose an SMR application that uses TEEs to protect sensitive customer and mobility provider data while limiting possibilities for fraud by both customers and mobility providers, and ensuring correct billing. We find that as long as secure multiparty computation is not competitive in terms of performance, TEE-based SMR can provide significant advantages in terms of efficiency and resilience while providing reasonable confidentiality guarantees. We describe the characteristics of the Mobility-as-a-Service use case and identify similar use cases from other domains, e.g., central bank digital currencies, allowing us to conclude that our findings generalize. In the second step, we establish the foundation for a comprehensive analysis by proposing and proving TEE-Rider, the first hybrid fault-tolerant, asynchronous, and DAG-based atomic broadcast protocol. TEE-Rider builds upon the DAG-Rider protocol family and an optimized, DAG-aware, and TEE-based causal order broadcast we propose and prove. We then identify fundamental issues that arise from the combination of TEEs and asynchrony in BFT SMR. These are the impossibility of a fault-tolerant setup and the impossibility of garbage collection. Furthermore, we prove that for partially synchronous, TEE-ba","url":"https://doi.org/10.5445/ir/1000189670","authors":["Leinweber, Marc"],"tags":["Distributed Ledger Technology","Trusted Execution Environments","State Machine Replication","Distributed Systems Security","Security Evaluation","Performance Evaluation","Mobility-as-a-Service","Public IT Federations"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5445/ir/1000189670","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5445/ir/1000189693","name":"An Automated Approach to Generating Card-Based Cryptographic Protocols","source":"datacite","abstract":"Card-based cryptographic protocols provide a simple and illustrative way of performing multi-party computation without computers, but instead use just a set of playing cards. A lot of research has been done on finding minimal protocols, with respect to the number of cards or the number of protocol steps, for various functions. To automate the process of finding new card-based protocols, Koch, Schrempp, and Kirsten (2021) employed the technique of software bounded model checking for a symbolic program that implements the basic actions and states. The bounded model checker is then used to synthesize a secure protocol by automatically generating a bounded (symbolic) program run, or, if there exists no such run, prove impossibility within the given bounds. In this thesis, we evaluate and extend the above technique for a generalization to more boolean functions, for an introduction of modularity so that (more) complex protocols can be found more efficiently, and finally for using bitwise datatype encodings so that finding protocols can be done more efficiently. From the increased efficiency and more universal applicability, we were able to extend the scope of the automated approach for generating card-based protocols to further impossibility proofs and various more protocols, for which some of them had already been found manually (but not formally verified) within literature.","url":"https://doi.org/10.5445/ir/1000189693","authors":["Hoff, Anne Elisabeth"],"tags":["secure multiparty computation","card-based cryptography","formal verification","bounded model checking"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5445/ir/1000189693","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2601.09916","name":"Learning-Augmented Perfectly Secure Collaborative Matrix Multiplication","source":"datacite","abstract":"This paper presents a perfectly secure matrix multiplication (PSMM) protocol for multiparty computation (MPC) of $\\mathrm{A}^{\\top}\\mathrm{B}$ over finite fields. The proposed scheme guarantees correctness and information-theoretic privacy against threshold-bounded, semi-honest colluding agents, under explicit local storage constraints. Our scheme encodes submatrices as evaluations of sparse masking polynomials and combines coefficient alignment with Beaver-style randomness to ensure perfect secrecy. We demonstrate that any colluding set of parties below the security threshold observes uniformly random shares, and that the recovery threshold is optimal, matching existing information-theoretic limits. Building on this framework, we introduce a learning-augmented extension that integrates tensor-decomposition-based local block multiplication, capturing both classical and learned low-rank methods. We demonstrate that the proposed learning-based PSMM preserves privacy and recovery guarantees for MPC, while providing scalable computational efficiency gains (up to $80\\%$) as the matrix dimensions grow.","url":"https://doi.org/10.48550/arxiv.2601.09916","authors":["He, Zixuan","Salehi, Mohammad Reza Deylam","Malak, Derya","Stavrou, Photios A."],"tags":["Information Theory (cs.IT)","Multiagent Systems (cs.MA)","Systems and Control (eess.SY)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2601.09916","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.18240505","name":"Federated Learning for eResearch","source":"datacite","abstract":"As data privacy regulations tighten and datasets grow increasingly siloed across institutions, Federated Learning (FL) offers a transformative approach to collaborative machine learning in the eResearch domain. This talk introduces the core principles of Federated Learning how it enables model training across decentralised data sources without moving the data itself and explores its relevance to research environments where data sensitivity, ownership, and locality are paramount. We will outline the main types of FL, including horizontal, vertical, and federated transfer learning, and discuss their applicability to real-world research scenarios. Drawing from our own experience, we ll share insights into evaluating and selecting FL frameworks, the practical challenges of setting up federated infrastructure, and lessons learned from early implementations. Finally, we ll look ahead to where the field is going: the growing role of privacy-preserving technologies like differential privacy and secure multiparty computation, the need for standardisation, and the potential for FL to unlock new forms of cross-institutional collaboration in science and academia. Whether you're a researcher, data scientist, or infrastructure specialist, this session will provide a practical and forward-looking perspective on how Federated Learning can reshape data-driven research.","url":"https://doi.org/10.5281/zenodo.18240505","authors":["Marendy, Peter"],"tags":["Research Data"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18240505","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.18240504","name":"Federated Learning for eResearch","source":"datacite","abstract":"As data privacy regulations tighten and datasets grow increasingly siloed across institutions, Federated Learning (FL) offers a transformative approach to collaborative machine learning in the eResearch domain. This talk introduces the core principles of Federated Learning how it enables model training across decentralised data sources without moving the data itself and explores its relevance to research environments where data sensitivity, ownership, and locality are paramount. We will outline the main types of FL, including horizontal, vertical, and federated transfer learning, and discuss their applicability to real-world research scenarios. Drawing from our own experience, we ll share insights into evaluating and selecting FL frameworks, the practical challenges of setting up federated infrastructure, and lessons learned from early implementations. Finally, we ll look ahead to where the field is going: the growing role of privacy-preserving technologies like differential privacy and secure multiparty computation, the need for standardisation, and the potential for FL to unlock new forms of cross-institutional collaboration in science and academia. Whether you're a researcher, data scientist, or infrastructure specialist, this session will provide a practical and forward-looking perspective on how Federated Learning can reshape data-driven research.","url":"https://doi.org/10.5281/zenodo.18240504","authors":["Marendy, Peter"],"tags":["Research Data"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18240504","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.34726/hss.2025.111502","name":"3D-based contact-less fingerprint acquisition","source":"datacite","abstract":"Fingerprint recognition is a widely used biometric modality because of its uniqueness and persistence of friction ridge patterns, which provide a reliable means of verifying identity. It is a key technology in applications ranging from law enforcement and border control to securing personal devices and financial transactions. While traditionally acquired through direct contact, contactless methods offer advantages in hygiene and user convenience. However, practical deployment is challenged by the unconstrained nature of the acquisition process, which introduces variations in finger pose, illumination, and scale, while still requiring to be interoperability with large, legacy contact-based fingerprint databases. This thesis presents a set of algorithms and methodologies to address these problems across the recognition pipeline, from image capture to secure template comparison. The contributions include solutions for image normalization, data assurance, robust validation, and secure deployment, which improve the accuracy, reliability, and privacy of contactless fingerprint systems.A necessary step in any contactless pipeline is the accurate segmentation of the fingertip from its background, which is often complex and variable. This work proposes three novel deep learning architectures for this task. The first is a custom U-Net-based model for pre-cropped single-finger images that outperforms existing segmentation models [217]. This was improved with FingerUNeSt++, which combines a ResNeSt encoder with a UNet++-like decoder, achieving a mean Intersection-over-Union (mIoU) of 99% on the test set. The third model, TipSegNet, removes the need for a separate finger detection step by segmenting and labeling all four fingertips directly from a whole-hand image. Using a ResNeXt-101 backbone with a Feature Pyramid Network (FPN) to handle multi-scale objects, TipSegNet obtains an mIoU of 99% and an accuracy of 100%.Interoperability with contact-based systems requires correcting the geometric distortions in contactless captures. This research developed a processing pipeline to correct for in-plane (yaw) and out-of-plane (roll) rotations, which then flattens the fingertip texture using parametric unwarping models. The pipeline uses the segmentation mask for yaw correction and an elliptical finger model with the detected core for roll correction. On an operational dataset, a finger-wise optimized application of the pipeline reduced the Equal Error Rate (EER) for contactless-to-contact-based comparison by a relative 36.9% (from 1.57% to 0.99%). A large-scale empirical analysis of the fingerprint core’s position across over 40,000 samples showed that its location is not geometrically centered, with systematic, modality-induced biases and a natural variability of 6-12% of the finger’s width. This study quantifies a limit on the accuracy of alignment methods that rely on the core and identifies the Non-Central Fischer (NCF) distribution as the best-fitting statistical model for its position for most fingers, including a finger-dependent analysis.The quality of a captured sample affects recognition performance. This work addresses the absence of dedicated quality metrics for mobile contactless fingerprints by adapting the established NFIQ 2 framework, resulting in MCLFIQ. By retraining the NFIQ 2 random forest classifier on modality-specific synthetic data, MCLFIQ shows improved performance in predicting the utility of contactless samples compared to the original NFIQ 2.2 and other baselines. The new model prioritizes features related to image sharpness and local ridge clarity, which are key quality factors in mobile captures. Additionally, this thesis introduces a self-supervised framework to detect structural artifacts from fingerprint mosaicking, a problem not handled by standard quality metrics. A deep learning model was trained on programmatically generated artifacts to detect these defects without manual annotation. The resulting detector i","url":"https://doi.org/10.34726/hss.2025.111502","authors":["Ruzicka, Laurenz"],"tags":["Contactless fingerprint recognition","deep learning","biometrics","synthetic fingerprint phantoms"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.34726/hss.2025.111502","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.7282/t3q52pqs","name":"Data privacy in knowledge discovery","source":"datacite","abstract":"This thesis addresses data privacy in various stages of extracting knowledge embedded in databases. Advances in computer networking and database technologies have enabled the collection and storage of vast quantities of data. Legal and ethical considerations might require measures to protect an individual's privacy in any use or release of the data. In this thesis, we address the problem of preserving privacy in the two following cases: (1) in distributed knowledge discovery; (2) in situations where the output of a data mining algorithm could itself breach privacy. We present results in two different models, namely secure multiparty computation (SMC) and differential privacy. The first part of the thesis presents privacy preserving protocols in the SMC model. Secure multiparty computation involves the collaborative computation of functions based on inputs from multiple parties. The privacy goal is to ensure that all parties receive only the final output without any party learning anything beyond what can be inferred from the output. Within this framework we address the problem of preserving privacy in the preprocessing and the data mining stages of knowledge discovery in databases. For the preprocessing stage, we present private protocols for the imputation of missing data in a dataset that is shared between two parties. For the data mining stage, we introduce the notion of arbitrarily partitioned data that generalizes both horizontally and vertically partitioned data. We present a privacy-preserving protocol for k-means clustering of arbitrarily partitioned data. We also develop a new simple k-clustering algorithm that was designed to be converted into a communication-efficient protocol for private clustering. The second part of the thesis deals with privacy in situations where the output of a data mining algorithm could itself breach privacy. In this setting, we present private inference control protocols in the SMC model for On-line Analytical Processing systems. In the differential privacymodel, the goal is to provide access to a statistical database while preserving the privacy of every individual in the database, irrespective of any auxiliary information that may be available to the database client. Under this privacy model, we present a practical privacy preserving decision tree classifier using random decision trees.","url":"https://doi.org/10.7282/t3q52pqs","authors":["Jagannathan, Geetha"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2010","doi":"10.7282/t3q52pqs","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.7282/t3ft8j1h","name":"Privacy-preserving collaborative optimization","source":"datacite","abstract":"With the rapid growth of computing, storing and networking resources, data is not only collected and stored, but also analyzed by different parties. This creates serious privacy problems while inhibiting the use of such distributed data. In turn, this raises the question of whether it is possible to realize value from distributed data without violating security and privacy concerns. Privacy-preserving data analysis has attracted considerable attention in recent years. Specifically, classification, clustering, association rule mining, outlier detection, regression among others, are securely implemented to analyze data privately held by multiple parties. The basic premise of such secure data analysis is that only the data analysis result can be revealed. As a fundamental problem found in many diverse fields, optimization is the study of problems in which one seeks to minimize or maximize a real function by systematically choosing the values of real or integer variables within an allowed set. Inspired from multiparty data analysis, the ubiquitous collection of data opens even greater opportunities in the optimization problems, applicable to the fields of operations research, computer science and mathematics. Collaborative optimization, when done properly with distributed data from different organizations, can facilitate them to improve the allocation of global resources without compromising on security. The primary goal of this dissertation is to develop privacy-preserving collaborative optimization techniques that would allow organizations to gain the maximum value from local information without (or with limited) information disclosure. While answering this problem, an inherent aim is to solve these fundamental problems underlying privacy-preserving analysis and secure multiparty computation (SMC) while making it more accessible and applicable. In this dissertation, we look at fundamental optimization problems such as linear programming, non-linear programming and some classic NP-hard problems. Particularly, we discuss the potential security and privacy concern in the collaborative formulations of them, which occur in real world, such as logistics and scheduling in supply chain management. To securely solve them, we present efficient privacy-preserving methods along with formal security analysis for the proposed privacy notions. In addition, we identify a potential attack to an earlier work and amend the transformation method with enhanced security guarantee. We also address how game theoretic techniques can be used to solve some of the fundamental incentive problems underlying secure multiparty computation in collaborative optimization. The computation/communication cost analysis and the experimental results demonstrate the feasibility, applicability and scalability of the proposed approaches.","url":"https://doi.org/10.7282/t3ft8j1h","authors":["Hong, Yuan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2013","doi":"10.7282/t3ft8j1h","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2408.05629","name":"Quantum-secure multiparty deep learning","source":"datacite","abstract":"Secure multiparty computation enables the joint evaluation of multivariate functions across distributed users while ensuring the privacy of their local inputs. This field has become increasingly urgent due to the exploding demand for computationally intensive deep learning inference. These computations are typically offloaded to cloud computing servers, leading to vulnerabilities that can compromise the security of the clients' data. To solve this problem, we introduce a linear algebra engine that leverages the quantum nature of light for information-theoretically secure multiparty computation using only conventional telecommunication components. We apply this linear algebra engine to deep learning and derive rigorous upper bounds on the information leakage of both the deep neural network weights and the client's data via the Holevo and the Cramér-Rao bounds, respectively. Applied to the MNIST classification task, we obtain test accuracies exceeding $96\\%$ while leaking less than $0.1$ bits per weight symbol and $0.01$ bits per data symbol. This weight leakage is an order of magnitude below the minimum bit precision required for accurate deep learning using state-of-the-art quantization techniques. Our work lays the foundation for practical quantum-secure computation and unlocks secure cloud deep learning as a field.","url":"https://doi.org/10.48550/arxiv.2408.05629","authors":["Sulimany, Kfir","Vadlamani, Sri Krishna","Hamerly, Ryan","Iyengar, Prahlad","Englund, Dirk"],"tags":["Quantum Physics (quant-ph)","Artificial Intelligence (cs.AI)","Information Theory (cs.IT)","Machine Learning (cs.LG)","Optics (physics.optics)","FOS: Physical sciences","FOS: Physical sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2408.05629","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2510.23483","name":"Towards a Functionally Complete and Parameterizable TFHE Processor","source":"datacite","abstract":"Fully homomorphic encryption allows the evaluation of arbitrary functions on encrypted data. It can be leveraged to secure outsourced and multiparty computation. TFHE is a fast torus-based fully homomorphic encryption scheme that allows both linear operations, as well as the evaluation of arbitrary non-linear functions. It currently provides the fastest bootstrapping operation performance of any other FHE scheme. Despite its fast performance, TFHE suffers from a considerably higher computational overhead for the evaluation of homomorphic circuits. Computations in the encrypted domain are orders of magnitude slower than their unencrypted equivalents. This bottleneck hinders the widespread adoption of (T)FHE for the protection of sensitive data. While state-of-the-art implementations focused on accelerating and outsourcing single operations, their scalability and practicality are constrained by high memory bandwidth costs. In order to overcome this, we propose an FPGA-based hardware accelerator for the evaluation of homomorphic circuits. Specifically, we design a functionally complete TFHE processor for FPGA hardware capable of processing instructions on the data completely on the FPGA. In order to achieve a higher throughput from our TFHE processor, we implement an improved programmable bootstrapping module, which outperforms the current state-of-the-art by 240% to 480% more bootstrappings per second. Our efficient, compact, and scalable design lays the foundation for implementing complete FPGA-based TFHE processor architectures.","url":"https://doi.org/10.48550/arxiv.2510.23483","authors":["Häusler, Valentin Reyes","Ott, Gabriel","Jayasena, Aruna","Peter, Andreas"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.23483","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2512.21525","name":"Enhancing Distributed Authorization With Lagrange Interpolation And Attribute-Based Encryption","source":"datacite","abstract":"In todays security landscape, every user wants to access large amounts of data with confidentiality and authorization. To maintain confidentiality, various researchers have proposed several techniques. However, to access secure data, researchers use access control lists to grant authentication and provide authorization. The above several steps will increase the server's computation overhead and response time. To cope with these two problems, we proposed multiparty execution on the server. In this paper, we introduce two different approaches. The first approach is encryption, utilizing the Involution Function Based Stream Cipher to encrypt the file data. The second approach is key distribution, using the Shamir secret sharing scheme to divide and distribute the symmetric key to every user. The decryption process required key reconstruction, which used second order Lagrange interpolation to reconstruct the secret keys from the hidden points. The process will reduce the server's computational overhead. The results are evaluated based on the encryption and decryption time, throughput, computational overhead, and security analysis. In the future, the proposed mechanism will be used to share large-scale, secure data within the organization.","url":"https://doi.org/10.48550/arxiv.2512.21525","authors":["Sinha, Keshav","Sumitra","Kumari, Richa","Bhardwaj, Akashdeep","Rahman, Shawon"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2512.21525","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2507.20688","name":"Guard-GBDT: Efficient Privacy-Preserving Approximated GBDT Training on Vertical Dataset","source":"datacite","abstract":"In light of increasing privacy concerns and stringent legal regulations, using secure multiparty computation (MPC) to enable collaborative GBDT model training among multiple data owners has garnered significant attention. Despite this, existing MPC-based GBDT frameworks face efficiency challenges due to high communication costs and the computation burden of non-linear operations, such as division and sigmoid calculations. In this work, we introduce Guard-GBDT, an innovative framework tailored for efficient and privacy-preserving GBDT training on vertical datasets. Guard-GBDT bypasses MPC-unfriendly division and sigmoid functions by using more streamlined approximations and reduces communication overhead by compressing the messages exchanged during gradient aggregation. We implement a prototype of Guard-GBDT and extensively evaluate its performance and accuracy on various real-world datasets. The results show that Guard-GBDT outperforms state-of-the-art HEP-XGB (CIKM'21) and SiGBDT (ASIA CCS'24) by up to $2.71\\times$ and $12.21 \\times$ on LAN network and up to $2.7\\times$ and $8.2\\times$ on WAN network. Guard-GBDT also achieves comparable accuracy with SiGBDT and plaintext XGBoost (better than HEP-XGB ), which exhibits a deviation of $\\pm1\\%$ to $\\pm2\\%$ only. Our implementation code is provided at https://github.com/XidianNSS/Guard-GBDT.git.","url":"https://doi.org/10.48550/arxiv.2507.20688","authors":["Song, Anxiao","Cui, Shujie","Bai, Jianli","Cheng, Ke","Shen, Yulong","Russello, Giovanni"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.20688","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:36.960Z"},{"id":"doi:10.5281/zenodo.18017581","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.18017581","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18017581","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5075/epfl-thesis-9845","name":"Thwarting Malicious Adversaries in Homomorphic Encryption Pipelines","source":"datacite","abstract":"Homomorphic Encryption (HE) enables computations to be executed directly on encrypted data. As such, it is an auspicious solution for protecting the confidentiality of sensitive data without impeding its usability. However, HE does not provide any guarantees that the cryptographic material used has been honestly generated and that the computation was executed correctly on the encrypted data. Thus, even though many practical systems rely on HE to achieve strong privacy guarantees, they consider only an honest-but-curious threat model in their constructions. Although several efforts have been conducted to analyze and improve the security of HE-based systems against stronger threat models, these works have remained mostly theoretical and are still insufficient to be applicable to practical HE pipelines and real-life scenarios. Therefore, in our work, we propose and build solutions to protect HE pipelines against malicious adversaries and evaluate their performance over a wide range of use cases. We first propose VERITAS, an efficient solution that proves the correctness of homomorphic computations, without compromising the expressiveness of the HE scheme. Then, we introduce PELTA, a set of building blocks that secure HE pipelines in the multiparty setting. Our constructions can be used to verify, in a practical manner, the correctness of distributed operations without any compromise on the HE scheme. Finally, we propose CRISP to secure input verification and to prove correct encryption in settings where the client who encrypts the data is untrusted. All our constructions are a first step for evaluating the impact of the change of threat model in HE pipelines with real-life implementation constraints.","url":"https://doi.org/10.5075/epfl-thesis-9845","authors":["Chatel, Sylvain"],"tags":["Homomorphic Encryption","Secure Multiparty Computation","Malicious Adversary","Proof Systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5075/epfl-thesis-9845","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17988137","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17988137","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17988137","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5445/ir/1000188270","name":"PETs and AI: Privacy Washing and the Need for a PETs Evaluation Framework (Dagstuhl Seminar 25112)","source":"datacite","abstract":"As public awareness of data collection practices and regulatory frameworks grows, privacy-enhancing technologies (PETs) have emerged as a promising approach to reconciling data utility with individual privacy rights. PETs underpin privacy-preserving machine learning (PPML), integrating tools like differential privacy, homomorphic encryption, and secure multiparty computation to safeguard data throughout the AI lifecycle. However, despite significant technical progress, PETs face critical policy and governance challenges. Recent works have raised concerns about efficacy and deployment of PETs, observing that fundamental rights of people are continually being harmed, including, paradoxically, privacy. PETs have been used in surveillance applications and as a privacy washing tool. Current approaches often fail to address broader harms beyond data protection, highlighting the need for a more comprehensive privacy evaluation framework. This Dagstuhl Seminar brought together scholars in computer science and law, along with policymakers, regulators, and industry leaders, to discuss privacy washing and the challenges of detecting privacy washing through PETs and explored pathways toward a framework to address these challenges.","url":"https://doi.org/10.5445/ir/1000188270","authors":["Cristofaro, Emiliano De","Shrishak, Kris","Strufe, Thorsten","Troncoso, Carmela","Morsbach, Felix"],"tags":["Privacy Enhancing Technologies (PET)","Privacy Evaluation","Privacy Harm","Privacy Threats","Privacy Washing","Security and privacy → Privacy protections","Security and privacy → Privacy-preserving protocols","Security and privacy → Social aspects of security and privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5445/ir/1000188270","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5287/ora-nz2m1yk88","name":"Enhancing communication privacy using trustworthy remote entities","source":"datacite","abstract":"Communication privacy is the property of a communication system that enables two or more distrusting participants to exchange information without compromising their privacy, with respect to internal and external adversaries. It encompasses aspects of anonymous communication as well as data privacy. A real-world example of the need for communication privacy is the smart energy grid, in which networked smart meters frequently measure energy consumption and communicate with grid operators. Privacy concerns arise from the possible inference of sensitive information from these measurements. Using smart grid communication privacy as a case study, this thesis introduces the concept of the Trustworthy Remote Entity (TRE) . The TRE is an intermediary between distrusting participants that performs privacy-enhancing computations on the exchanged information. Unlike cryptographic secure multiparty computation protocols, this approach does not increase participants' computational or communication complexity. In contrast to a trusted third party, this trustworthy entity uses trusted computing and remote attestation to establish attestation-based trust relationships. As a single-function system, the TRE requires only a minimal software Trusted Computing Base, thus minimizing its attack surface and making it an ideal candidate for security audits. Two research hypotheses are investigated: firstly that the TRE can be realized and used to enhance consumers' privacy in the smart grid, and secondly that the TRE concept can be formalized and used in other application domains. This thesis confirms both hypotheses and, in doing so, presents five main contributions. Firstly, it proposes a new methodology for modelling and analysing communication privacy terms of unlinkability and undetectability, which is implemented in the CSP process algebra and used to enhance the Casper/FDR analysis tool. Secondly, it presents and analyses a new TRE-based smart grid communication architecture. Thirdly, it compares different TRE system architectures and evaluates a fully functional TRE prototype. Fourthly, it defines a new highly-scalable remote attestation protocol for establishing the TRE's trustworthiness. Finally, it formalizes the fundamental characteristics of the TRE concept and demonstrates how the TRE can be used to enhance communication privacy in location-based services and wireless network roaming.","url":"https://doi.org/10.5287/ora-nz2m1yk88","authors":["Paverd, Andrew"],"tags":["Computer science","Computer security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5287/ora-nz2m1yk88","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5075/epfl-thesis-8263","name":"Privacy-Preserving Federated Analytics using Multiparty Homomorphic Encryption","source":"datacite","abstract":"Analyzing and processing data that are siloed and dispersed among multiple distrustful stakeholders is difficult and can even become impossible when the data are sensitive or confidential. Current data-protection and privacy regulations highly restrict the sharing and outsourcing of personal information among stakeholders that are in different jurisdictions. Sharing data is, however, required in many domains. The medical sector is a paradigmatic example: Privacy is paramount and data sharing is needed in numerous applications where data is scarce and scattered among multiple stakeholders around the world. Existing privacy-preserving solutions for federated analytics (FA) rely either (1) on data centralization or outsourcing to a limited number of entities, which incur multiple security and trust issues, or (2) on the exchange of cleartext aggregated and optionally obfuscated data, which can leak personal information or introduce bias in the final result. In this thesis, our goal is (1) to propose privacy-preserving federated solutions for exploration, and for statistical and machine-learning analyses on data held by multiple distrustful stakeholders, and (2) to analyze and evaluate the proposed systems, thus showing that they provide an efficient, secure, scalable, and accurate alternative to existing solutions for FA by proving their utility in real-world state-of-the-art biomedical studies. We rely on multiparty homomorphic encryption (MHE). MHE combines secure multiparty computation (SMC) techniques with homomorphic encryption (HE) by pooling the advantages of both SMC and HE, i.e., interactivity and flexibility, and by minimizing their disadvantages, i.e., difficulty in scaling to a large number of parties and computation complexity. First, we design UnLynx, a system that enables privacy-preserving federated data exploration on a distributed dataset held by multiple data-providers (DPs), where N-1 out of N of the nodes performing the computations can be malicious. We build interactive protocols by relying on ElGamal additive homomorphic encryption (AHE) and ensure that each untrusted-node operation can be publicly verified by means of zero-knowledge proofs (ZKPs). We then explore how statistics, e.g., standard deviation and variance, can be computed by relying on AHE and ZKPs through the design of another system named Drynx. In Drynx, we also explore how to limit the influence of an entity that inputs wrong data in the system, and we propose an efficient federated solution for correctness verification. We propose Spindle a solution for secure cooperative gradient descent on federated data that we instantiate for the privacy-preserving training and oblivious evaluation of generalized linear models. Spindle covers the entire machine-learning workflow, as it enables oblivious predictions to be performed on a trained model that remains secret. It ensures both data and model confidentiality in a passive adversarial model in which N-1 out of N DPs can collude. Finally, we demonstrate that the solutions proposed in this thesis can be efficient enablers for large-scale, sensitive, multi-site biomedical studies. We design and test, by replicating recent medical studies, secure workflows for the federated execution of computations that span from analyses with low computational complexity, such as survival analyses, to analyses with high computational complexity such as genome-wide association studies on millions of variants.","url":"https://doi.org/10.5075/epfl-thesis-8263","authors":["Froelicher, David Jules"],"tags":["federated analytics","federated machine learning","multiparty homomorphic encryption","differential privacy","decentralized systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5075/epfl-thesis-8263","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5075/epfl-thesis-8931","name":"Bridging the gap between theoretical and practical privacy technologies for at-risk populations","source":"datacite","abstract":"With the pervasive digitalization of modern life, we benefit from efficient access to information and services. Yet, this digitalization poses severe privacy challenges, especially for special-needs individuals. Beyond being a fundamental human right, privacy is crucial for roles sensitive in nature, including investigative journalists exposing corruption and humanitarian organizations supporting refugees or survivors of violence. This thesis leverages privacy-enhancing technologies to mitigate the risks of digitalization while retaining its advantages. Recent breakthroughs in cryptography, such as fully homomorphic encryption and secure multiparty computation, provide robust tools for privacy. However, there is still no silver bullet solution that can achieve efficient privacy out of the box. We observe that there often is a gap between theoretical cryptographic solutions and real-world problems. Identifying and bridging these gaps enables us to design pragmatic privacy-enhancing technologies tailored for real-world deployment. In this thesis, we identify and solve four real-world problems. We first present the problem of searching sensitive documents among a network of investigative journalists. In collaboration with the International Consortium of Investigative Journalists, we design a decentralized peer-to-peer privacy-preserving search engine called DatashareNetwork. Our solution enables journalists to find colleagues who have relevant documents for their topic of investigation and anonymously discuss the possibility of collaboration. We develop a prototype of DatashareNetwork and demonstrate that it scales to thousands of journalists and millions of documents. We introduce a new class of problems called private collection matching in which a client aims to determine whether a collection of sets owned by a server matches their interests such as searching confidential chemical compound databases. We design a framework based on fully homomorphic encryption to solve these problems. Our solution, takes the data minimization principle to the maximum and shows the possibility of satisfying clients' needs by only revealing a single bit. We evaluate our framework and show that it significantly improves the latency, client computation cost, and communication cost with respect to generic solutions that offer the same privacy guarantee. We examine the problem of preventing double registration in humanitarian aid distribution with a focus on the needs of the International Committee of Red Cross. In response, we design Janus, a privacy-preserving biometric deduplication system that is compatible with fingerprints, irises, and face recognition; and supports both biometric alignment and fusion. We design and develop three instantiations of Janus based on secure multiparty computation, somewhat homomorphic encryption, and trusted execution environments. We evaluate Janus to show it satisfies the privacy, accuracy, and performance needs of humanitarian organizations. Finally, we study the problem of detecting insecure ciphers in aircraft communication at scale. We design and develop a decision support system that helps human analysts to detect new ciphertexts in aircraft communication. We evaluate our system by applying it to real-world data and asking our analyst to use our support system to find new ciphers. Our analysis led to uncovering of 9 previously unknown (and potentially insecure) ciphers which we disclose to various stakeholders.","url":"https://doi.org/10.5075/epfl-thesis-8931","authors":["Edalatnejadkhamene, Kasra"],"tags":["Privacy enhancing technologies","applied cryptography","privacy engineering","homomorphic encryption","private set intersection","private computation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5075/epfl-thesis-8931","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.11575/prism/27731","name":"Security Issues in Cognitive Radio Networks","source":"datacite","abstract":"Cognitive radio is an emerging trend to solve the problem of scarce spectrum resources in the prosperous area of wireless communication. By dynamically utilizing unoccupied spectrums of primary (licensed) users, secondary (unlicensed) users can meet their own communication requirements. While traditional security attacks on wireless networks still exist, the cognitive radio technologies bring unique security challenges. Current literature on solving these problems assume a central authority, which, for example, assumes the role of a fusion centre. Dynamic wireless environments are composed of users from different competing wireless operators, and assuming the existence of a central authority is a major restriction. We propose approaches that do not rely on these centralized assumptions, and are thus more applicable to practical cognitive radio networks. Cooperative sensing is an effective solution to improve sensing accuracy and robustness in the presence of fading and shadowing that make individual sensing less reliable. However, when an adversary can corrupt some nodes in the network, the effectiveness of cooperative sensing may degrade dramatically. We design the first fully distributed security scheme, ReDiSen, to defend such attacks in cooperative sensing. We apply reputation generated from exchanged sensing results as an aid to restrict the impact of malicious behaviours. Both theoretical analysis and simulation results indicate that ReDiSen provides an effective countermeasure against security attacks by enabling secondary users to obtain more accurate cooperative sensing results in an adversarial environment. ReDiSen does not rely on a central authority, and is therefore more applicable in dynamic cognitive radio networks. In a cognitive radio network, selfish secondary users may not voluntarily contribute to the desired cooperative sensing process. We design the first fully distributed scheme to incentivize node participation in cooperative sensing, by connecting sensing and spectrum allocation, and offering incentive from the latter to the former. Secondary users who are more active and report more accurate sensing values are given higher reputation values, which in turn lead to lower prices in the spectrum allocation phase. Theoretical analysis and simulation results indicate that the proposed method effectively incentivizes sensing participation, and rewards truthful and accurate reporting. Our proposed system is fully distributed and does not rely on a central authority, and so is more applicable in dynamic cognitive radio networks in practice. We also show how to improve the robustness of reputation when malicious nodes report spurious reputation. VCG (Vickrey-Clarke-Groves) spectrum auctions represent a classic type of truthful spectrum allocation method in cognitive radio networks. While security and privacy issues recently start to draw attention in such spectrum auctions, there exists little work that examines the scenario where the auctioneer is not fully trustworthy. We present the first verifiable VCG spectrum auction that allows verification of the winner determination and pricing phases of the VCG auction. We use maximal independent set enumeration and secure multiparty computation to solve the verification problem, while protecting privacy of wireless users. We propose different methods in different steps of the verification scheme, and analyze the effectiveness, information leakage, and efficiency. Our scheme does not rely on a third party, does not alter the auction process, and by using an offline verification process, does not introduce extra delay to the auction process.","url":"https://doi.org/10.11575/prism/27731","authors":["Zhang, Tongjie"],"tags":["Computer Science","Cognitive Radio Networks","Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2014","doi":"10.11575/prism/27731","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17895475","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17895475","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17895475","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17882317","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17882317","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17882317","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17875563","name":"The influence of decentralized AI architectures on distributed data governance","source":"datacite","abstract":"The evolution of Artificial Intelligence (AI) from centralized models toward decentralized architectures has fundamentally reshaped the paradigms of data management, ownership, and governance. In traditional AI ecosystems, data is consolidated within centralized repositories for model training and analytics, resulting in challenges related to privacy, latency, and compliance with regulatory frameworks. Decentralized AI architectures encompassing federated learning, edge AI, swarm intelligence, and blockchain-based frameworks offer a transformative alternative that aligns technological innovation with distributed data governance principles. These architectures enable AI systems to learn collaboratively across multiple nodes or organizations without transferring raw data, ensuring data sovereignty and compliance with global data protection mandates such as GDPR and CCPA. This review examines how decentralized AI architectures influence distributed data governance by promoting transparency, trust, and accountability in multi-party data ecosystems. The integration of AI with blockchain and distributed ledger technologies provides immutable audit trails and decentralized identity management, enabling verifiable governance across federated networks. Moreover, privacy-preserving techniques such as differential privacy, homomorphic encryption, and secure multiparty computation empower organizations to perform analytics on encrypted datasets while maintaining compliance with ethical and legal data-handling standards. Through a synthesis of academic research and real-world applications, the review highlights the significant advantages of decentralized AI, including enhanced privacy assurance, reduced systemic risks, and improved collaboration among data stakeholders. However, the transition toward decentralized intelligence introduces new challenges related to interoperability, communication overhead, and model convergence in distributed environments. Ensuring fairness, accountability, and explainability within federated systems remains a critical governance issue, as decentralized decision-making increases complexity in auditing and oversight. Furthermore, the governance of AI models themselves rather than just data poses emerging regulatory and ethical questions in globally interconnected ecosystems.","url":"https://doi.org/10.5281/zenodo.17875563","authors":["Rashmi K. Nair"],"tags":["Decentralized Artificial Intelligence; Federated Learning; Distributed Data Governance; Blockchain; Edge Intelligence; Data Sovereignty; Privacy Preservation; AI Ethics; Trust Frameworks; Autonomous Governance."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.5281/zenodo.17875563","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.17875562","name":"The influence of decentralized AI architectures on distributed data governance","source":"datacite","abstract":"The evolution of Artificial Intelligence (AI) from centralized models toward decentralized architectures has fundamentally reshaped the paradigms of data management, ownership, and governance. In traditional AI ecosystems, data is consolidated within centralized repositories for model training and analytics, resulting in challenges related to privacy, latency, and compliance with regulatory frameworks. Decentralized AI architectures encompassing federated learning, edge AI, swarm intelligence, and blockchain-based frameworks offer a transformative alternative that aligns technological innovation with distributed data governance principles. These architectures enable AI systems to learn collaboratively across multiple nodes or organizations without transferring raw data, ensuring data sovereignty and compliance with global data protection mandates such as GDPR and CCPA. This review examines how decentralized AI architectures influence distributed data governance by promoting transparency, trust, and accountability in multi-party data ecosystems. The integration of AI with blockchain and distributed ledger technologies provides immutable audit trails and decentralized identity management, enabling verifiable governance across federated networks. Moreover, privacy-preserving techniques such as differential privacy, homomorphic encryption, and secure multiparty computation empower organizations to perform analytics on encrypted datasets while maintaining compliance with ethical and legal data-handling standards. Through a synthesis of academic research and real-world applications, the review highlights the significant advantages of decentralized AI, including enhanced privacy assurance, reduced systemic risks, and improved collaboration among data stakeholders. However, the transition toward decentralized intelligence introduces new challenges related to interoperability, communication overhead, and model convergence in distributed environments. Ensuring fairness, accountability, and explainability within federated systems remains a critical governance issue, as decentralized decision-making increases complexity in auditing and oversight. Furthermore, the governance of AI models themselves rather than just data poses emerging regulatory and ethical questions in globally interconnected ecosystems.","url":"https://doi.org/10.5281/zenodo.17875562","authors":["Rashmi K. Nair"],"tags":["Decentralized Artificial Intelligence; Federated Learning; Distributed Data Governance; Blockchain; Edge Intelligence; Data Sovereignty; Privacy Preservation; AI Ethics; Trust Frameworks; Autonomous Governance."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.5281/zenodo.17875562","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.17875557","name":"The Influence Of Decentralized AI Architectures On Distributed Data Governance","source":"datacite","abstract":"The evolution of Artificial Intelligence (AI) from centralized models toward decentralized architectures has fundamentally reshaped the paradigms of data management, ownership, and governance. In traditional AI ecosystems, data is consolidated within centralized repositories for model training and analytics, resulting in challenges related to privacy, latency, and compliance with regulatory frameworks. Decentralized AI architectures encompassing federated learning, edge AI, swarm intelligence, and blockchain-based frameworks offer a transformative alternative that aligns technological innovation with distributed data governance principles. These architectures enable AI systems to learn collaboratively across multiple nodes or organizations without transferring raw data, ensuring data sovereignty and compliance with global data protection mandates such as GDPR and CCPA. This review examines how decentralized AI architectures influence distributed data governance by promoting transparency, trust, and accountability in multi-party data ecosystems. The integration of AI with blockchain and distributed ledger technologies provides immutable audit trails and decentralized identity management, enabling verifiable governance across federated networks. Moreover, privacy-preserving techniques such as differential privacy, homomorphic encryption, and secure multiparty computation empower organizations to perform analytics on encrypted datasets while maintaining compliance with ethical and legal data-handling standards. Through a synthesis of academic research and real-world applications, the review highlights the significant advantages of decentralized AI, including enhanced privacy assurance, reduced systemic risks, and improved collaboration among data stakeholders. However, the transition toward decentralized intelligence introduces new challenges related to interoperability, communication overhead, and model convergence in distributed environments. Ensuring fairness, accountability, and explainability within federated systems remains a critical governance issue, as decentralized decision-making increases complexity in auditing and oversight. Furthermore, the governance of AI models themselves rather than just data poses emerging regulatory and ethical questions in globally interconnected ecosystems.","url":"https://doi.org/10.5281/zenodo.17875557","authors":["Rashmi K. Nair"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.5281/zenodo.17875557","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.17875556","name":"The Influence Of Decentralized AI Architectures On Distributed Data Governance","source":"datacite","abstract":"The evolution of Artificial Intelligence (AI) from centralized models toward decentralized architectures has fundamentally reshaped the paradigms of data management, ownership, and governance. In traditional AI ecosystems, data is consolidated within centralized repositories for model training and analytics, resulting in challenges related to privacy, latency, and compliance with regulatory frameworks. Decentralized AI architectures encompassing federated learning, edge AI, swarm intelligence, and blockchain-based frameworks offer a transformative alternative that aligns technological innovation with distributed data governance principles. These architectures enable AI systems to learn collaboratively across multiple nodes or organizations without transferring raw data, ensuring data sovereignty and compliance with global data protection mandates such as GDPR and CCPA. This review examines how decentralized AI architectures influence distributed data governance by promoting transparency, trust, and accountability in multi-party data ecosystems. The integration of AI with blockchain and distributed ledger technologies provides immutable audit trails and decentralized identity management, enabling verifiable governance across federated networks. Moreover, privacy-preserving techniques such as differential privacy, homomorphic encryption, and secure multiparty computation empower organizations to perform analytics on encrypted datasets while maintaining compliance with ethical and legal data-handling standards. Through a synthesis of academic research and real-world applications, the review highlights the significant advantages of decentralized AI, including enhanced privacy assurance, reduced systemic risks, and improved collaboration among data stakeholders. However, the transition toward decentralized intelligence introduces new challenges related to interoperability, communication overhead, and model convergence in distributed environments. Ensuring fairness, accountability, and explainability within federated systems remains a critical governance issue, as decentralized decision-making increases complexity in auditing and oversight. Furthermore, the governance of AI models themselves rather than just data poses emerging regulatory and ethical questions in globally interconnected ecosystems.","url":"https://doi.org/10.5281/zenodo.17875556","authors":["Rashmi K. Nair"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.5281/zenodo.17875556","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17801567","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17801567","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17801567","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17801124","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17801124","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17801124","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17800374","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17800374","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17800374","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17800098","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17800098","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17800098","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17799841","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17799841","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17799841","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17799229","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17799229","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17799229","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17792555","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17792555","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17792555","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17792444","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17792444","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17792444","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17709279","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17709279","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17709279","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2501.03973","name":"Performance of Practical Quantum Oblivious Key Distribution","source":"datacite","abstract":"Motivated by the applications of secure multiparty computation as a privacy-protecting data analysis tool, and identifying oblivious transfer as one of its main practical enablers, we propose a practical realization of randomized quantum oblivious transfer. By using only symmetric cryptography primitives to implement commitments, we construct computationally-secure randomized oblivious transfer without the need for public-key cryptography or assumptions imposing limitations on the adversarial devices. We show that the protocol is secure under an indistinguishability-based notion of security and demonstrate an experimental implementation to test its real-world performance. Its security and performance are then compared to both quantum and classical alternatives, showing potential advantages over existing solutions based on the noisy storage model and public-key cryptography.","url":"https://doi.org/10.48550/arxiv.2501.03973","authors":["Lemus, Mariano","Schiansky, Peter","Goulão, Manuel","Bozzio, Mathieu","Elkouss, David","Paunković, Nikola","Mateus, Paulo","Walther, Philip"],"tags":["Quantum Physics (quant-ph)","FOS: Physical sciences","FOS: Physical sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.03973","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.82419/76","name":"PolyKervNets: Activation-free Neural Networks For Efficient Private Inference","source":"datacite","abstract":"With the advent of cloud computing, machine learning as a service (MLaaS) has become a growing phenomenon with the potential to address many real-world problems. In an untrusted cloud environment, privacy concerns of users is a major impediment to the adoption of MLaaS. To alleviate these privacy issues and preserve data confidentiality, several private inference (PI) protocols have been proposed in recent years based on cryptographic tools like Fully Homomorphic Encryption (FHE) and Secure Multiparty Computation (MPC). Deep neural networks (DNN) have been the architecture of choice in most MLaaS deployments. One of the core challenges in developing PI protocols for DNN inference is the substantial costs involved in implementing non-linear activation layers such as Rectified Linear Unit (ReLU). This has spawned a search for accurate, but efficient approximations of the ReLU function and neural architectures that operate on a stringent ReLU budget. While these methods improve efficiency and ensure data confidentiality, they often come at a significant cost to prediction accuracy. In this work, we propose a DNN architecture based on polynomial kervolution called PolyKervNet (PKN), which completely eliminates the need for non-linear activation and max pooling layers. PolyKervNets are both FHE and MPC-friendly - they enable FHE-based encrypted inference without any approximations and improve the latency on MPC-based PI protocols without any use of garbled circuits. We demonstrate that it is possible to redesign standard convolutional neural net- works (CNN) architectures such as ResNet-18 and VGG-16 with polynomial kervolution and achieve approximately 30× improvement in latency of MPC-based PI with minimal loss in accuracy on many image classification tasks.","url":"https://doi.org/10.82419/76","authors":["Aremu, Toluwani Samuel"],"tags":["Machine Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.82419/76","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2502.20835","name":"Federated Distributed Key Generation","source":"datacite","abstract":"Distributed Key Generation (DKG) underpins threshold cryptography in many systems, including decentralized wallets, validator key ceremonies, cross-chain bridges, threshold signatures, secure multiparty computation, and internet voting. Classical ($t$,$n$)-DKG assumes a fixed group of n parties and a global threshold $t$, requiring full and timely participation. When actual participation deviates, the setup must abort or restart, which is impractical in open or time-critical environments where $n$ is large and availability unpredictable. We introduce Federated Distributed Key Generation (FDKG), inspired by Federated Byzantine Agreement, that makes participation optional and trust heterogeneous. Each participant selects a personal guardian set $G_i$ of size $k$ and a local threshold $t$. Its partial secret can later be reconstructed either by itself or by any t of its guardians. FDKG generalizes PVSS-based DKG and completes both generation and reconstruction in a single broadcast round each, with total communication proportional to $n k$ and at most $O(n^2)$ for reconstruction. Our analysis shows that (i) generation ensures correctness, privacy, and robustness under standard PVSS-based DKG assumptions, and (ii) reconstruction provides liveness and privacy characterized by the guardian-set topology {$G_i$}. Liveness holds if no participant $i$ is corrupted together with at least $k-t+1$ of its guardians. Conversely, privacy is preserved unless the corrupted subset is itself reconstruction-capable.","url":"https://doi.org/10.48550/arxiv.2502.20835","authors":["Baranski, Stanislaw","Szymanski, Julian"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences","E.3; C.2"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2502.20835","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5445/ir/1000185464/pub","name":"Attack Once, Compromise All? On the Scalability of Attacks","source":"datacite","abstract":"Electronic voting schemes are often criticized for being insecure, on the grounds that a successful attack would allow an adversary to manipulate all votes at once. It is argued that attacks therefore have a higher impact at lower adversary costs compared to paper-based schemes, where attacks are cumbersome. In this paper, we propose a framework to quantify how prone different protocols are to attacks that scale well. For this purpose, we introduce the notion of scalability of attacks. We give the adversary access to an oracle which can break common cryptographic building blocks and assumptions and analyze how many inputs of a (multiparty computation) protocol they can learn or manipulate for each oracle access. The more inputs are affected, the more susceptible the protocol is to attacks that scale well. We compare several pairs of protocols solving the same problem in different ways in three examples and analyze the scalability of attacks on each protocol. We find that some protocols have a fatal breakdown, i.e. all inputs are affected with only one access to the oracle, while other protocols scale linearly or have a threshold, where the number of affected inputs increases drastically from one access to the other. Our framework provides strong arguments in favoring one voting scheme over another. It enables voting authorities to compare schemes that appear equally secure at first glance, and to consider the scalability of attacks when deciding on a scheme.","url":"https://doi.org/10.5445/ir/1000185464/pub","authors":["Hetzel, Eva","Nemes, Marc","Müller-Quade, Jörn"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5445/ir/1000185464/pub","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5445/ir/1000185464","name":"Attack Once, Compromise All? On the Scalability of Attacks","source":"datacite","abstract":"Electronic voting schemes are often criticized for being insecure, on the grounds that a successful attack would allow an adversary to manipulate all votes at once. It is argued that attacks therefore have a higher impact at lower adversary costs compared to paper-based schemes, where attacks are cumbersome. In this paper, we propose a framework to quantify how prone different protocols are to attacks that scale well. For this purpose, we introduce the notion of scalability of attacks. We give the adversary access to an oracle which can break common cryptographic building blocks and assumptions and analyze how many inputs of a (multiparty computation) protocol they can learn or manipulate for each oracle access. The more inputs are affected, the more susceptible the protocol is to attacks that scale well. We compare several pairs of protocols solving the same problem in different ways in three examples and analyze the scalability of attacks on each protocol. We find that some protocols have a fatal breakdown, i.e. all inputs are affected with only one access to the oracle, while other protocols scale linearly or have a threshold, where the number of affected inputs increases drastically from one access to the other. Our framework provides strong arguments in favoring one voting scheme over another. It enables voting authorities to compare schemes that appear equally secure at first glance, and to consider the scalability of attacks when deciding on a scheme.","url":"https://doi.org/10.5445/ir/1000185464","authors":["Hetzel, Eva","Nemes, Marc","Müller-Quade, Jörn"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5445/ir/1000185464","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17590348","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17590348","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17590348","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17588916","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17588916","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17588916","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2409.17571","name":"Incomplete quantum oblivious transfer with perfect one-sided security","source":"datacite","abstract":"Oblivious transfer is a fundamental cryptographic primitive which is useful for secure multiparty computation. There are several variants of oblivious transfer. We consider 1 out of 2 oblivious transfer, where a sender sends two bits of information to a receiver. The receiver only receives one of the two bits, while the sender does not know which bit the receiver has received. Perfect quantum oblivious transfer with information theoretic security is known to be impossible. We aim to find the lowest possible cheating probabilities. Bounds on cheating probabilities have been investigated for complete protocols, where if both parties follow the protocol, the bit value obtained by the receiver matches the sender bit value. We instead investigate incomplete protocols, where the receiver obtains an incorrect bit value with probability pf. We present optimal non interactive protocols where Alice bit values are encoded in four symmetric pure quantum states, and where she cannot cheat better than with a random guess. We find the protocols such that for a given pf, Bob cheating probability pr is as low as possible, and vice versa. Furthermore, we show that non-interactive quantum protocols can outperform non-interactive classical protocols, and give a lower bound on Bob cheating probability in interactive quantum protocols. Importantly for optical implementations, our protocols do not require entanglement nor quantum memory.","url":"https://doi.org/10.48550/arxiv.2409.17571","authors":["Reichmuth, David","Puthoor, Ittoop Vergheese","Wallden, Petros","Andersson, Erika"],"tags":["Quantum Physics (quant-ph)","FOS: Physical sciences","FOS: Physical sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.17571","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17579904","name":"GDI D8.6 - Report on privacy-enhancing solutions","source":"datacite","abstract":"GDI Pillar III aims to explore use cases and innovative applications for analysing genomic and clinical data, ideally supported by the infrastructure being deployed at the national nodes within Pillar II. As described in the Report on federated learning technologies (Deliverable D8.5), artificial intelligence techniques and – more specifically - federated learning is a recent trend that can be observed in this area. Federated learning is a distributed machine learning technique in which multiple participants, which provide remote devices or siloed data centres, collaboratively train a shared machine learning model while keeping their data locally, better supporting data privacy. It enables collaborative learning from distributed data sources without sharing the original data, thus reducing privacy concerns and leveraging the aggregate knowledge available to the multiple participants. Privacy-Enhancing Technologies (PETs) enhance the privacy of data that is being collected or processed, e.g. by using access control and consent management systems or using techniques for data anonymization or pseudonymization. Privacy-Preserving Technologies (PPTs) are very useful in a distributed setting with multiple parties because they complement and strengthen Privacy-Enhancing Technologies (PETs) by addressing some of the core challenges of multi-party collaboration. They minimize trust requirements by allowing parties to collaborate without needing to share raw data. PPTs also reduce the amount of data that needs to be shared or centralized, which lowers the risk of breaches or misuse. PPTs can also protect intermediate results of PETs. The Report on federated learning technologies (Deliverable D8.5) focusses on the most common form of federated learning, where the model is trained locally by each participant on its own data and model updates are sent to a central server. The central server then aggregates these updates to improve the global model, which is then sent back to the participants for further iterative training rounds. This enhances privacy-preservation to some extent. But other privacy-preserving technologies exist. In this report, three other PPTs are assessed: Differential Privacy, Homomorphic Encryption and Secure Multiparty Computation. Three domains of use cases that can benefit from privacy-preserving technologies are considered: Genome-Wide Association Studies, Rare Diseases and Federated Machine Learning. Genome-Wide Association Studies are a wide class of applications for analysing genomic and clinical data that should be supported by the infrastructure being deployed at the national nodes in a federated manner. The Use case demonstrator package (Deliverable D7.1) describes the more specific scenario (infectious diseases use case 1) to identify variants determining the severity of COVID-19 disease progression. This specific scenario uses the GDI Infectious Diseases data, which the 1+MG WG11 is currently generating. The Rare Diseases use case also covers a wide range of genomic applications. The Use case demonstrator package (Deliverable D7.1) describes the more specific scenario to answer the question regarding the side effects of medications caused by some gene variants, using the B1MG Rare Diseases dataset. The Federated Machine Learning use case is where the Report on federated learning technologies (Deliverable D8.5) focuses on. The described privacy-preserving technologies can be used in combination with federated machine learning, enhancing privacy. Federated machine learning can be used in a very wide range of machine learning genomic applications. The Use case demonstrator package (Deliverable D7.1) and Report on federated learning technologies (Deliverable D8.5) describe some data-driven models for Cancer Research that can be built using federated machine learning.","url":"https://doi.org/10.5281/zenodo.17579904","authors":["Verachtert, Wilfried"],"tags":["European Genomic Data Infrastructure","GDI","1+ Million Genomes Initiative","Privacy-Enhancing Technologies","Privacy-Preserving Technologies","Federated learning","Differential Privacy","Homomorphic Encryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17579904","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17579905","name":"GDI D8.6 - Report on privacy-enhancing solutions","source":"datacite","abstract":"GDI Pillar III aims to explore use cases and innovative applications for analysing genomic and clinical data, ideally supported by the infrastructure being deployed at the national nodes within Pillar II. As described in the Report on federated learning technologies (Deliverable D8.5), artificial intelligence techniques and – more specifically - federated learning is a recent trend that can be observed in this area. Federated learning is a distributed machine learning technique in which multiple participants, which provide remote devices or siloed data centres, collaboratively train a shared machine learning model while keeping their data locally, better supporting data privacy. It enables collaborative learning from distributed data sources without sharing the original data, thus reducing privacy concerns and leveraging the aggregate knowledge available to the multiple participants. Privacy-Enhancing Technologies (PETs) enhance the privacy of data that is being collected or processed, e.g. by using access control and consent management systems or using techniques for data anonymization or pseudonymization. Privacy-Preserving Technologies (PPTs) are very useful in a distributed setting with multiple parties because they complement and strengthen Privacy-Enhancing Technologies (PETs) by addressing some of the core challenges of multi-party collaboration. They minimize trust requirements by allowing parties to collaborate without needing to share raw data. PPTs also reduce the amount of data that needs to be shared or centralized, which lowers the risk of breaches or misuse. PPTs can also protect intermediate results of PETs. The Report on federated learning technologies (Deliverable D8.5) focusses on the most common form of federated learning, where the model is trained locally by each participant on its own data and model updates are sent to a central server. The central server then aggregates these updates to improve the global model, which is then sent back to the participants for further iterative training rounds. This enhances privacy-preservation to some extent. But other privacy-preserving technologies exist. In this report, three other PPTs are assessed: Differential Privacy, Homomorphic Encryption and Secure Multiparty Computation. Three domains of use cases that can benefit from privacy-preserving technologies are considered: Genome-Wide Association Studies, Rare Diseases and Federated Machine Learning. Genome-Wide Association Studies are a wide class of applications for analysing genomic and clinical data that should be supported by the infrastructure being deployed at the national nodes in a federated manner. The Use case demonstrator package (Deliverable D7.1) describes the more specific scenario (infectious diseases use case 1) to identify variants determining the severity of COVID-19 disease progression. This specific scenario uses the GDI Infectious Diseases data, which the 1+MG WG11 is currently generating. The Rare Diseases use case also covers a wide range of genomic applications. The Use case demonstrator package (Deliverable D7.1) describes the more specific scenario to answer the question regarding the side effects of medications caused by some gene variants, using the B1MG Rare Diseases dataset. The Federated Machine Learning use case is where the Report on federated learning technologies (Deliverable D8.5) focuses on. The described privacy-preserving technologies can be used in combination with federated machine learning, enhancing privacy. Federated machine learning can be used in a very wide range of machine learning genomic applications. The Use case demonstrator package (Deliverable D7.1) and Report on federated learning technologies (Deliverable D8.5) describe some data-driven models for Cancer Research that can be built using federated machine learning.","url":"https://doi.org/10.5281/zenodo.17579905","authors":["Verachtert, Wilfried"],"tags":["European Genomic Data Infrastructure","GDI","1+ Million Genomes Initiative","Privacy-Enhancing Technologies","Privacy-Preserving Technologies","Federated learning","Differential Privacy","Homomorphic Encryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17579905","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2402.08956","name":"Seagull: Privacy preserving network verification system","source":"datacite","abstract":"The Internet relies on routing protocols to direct traffic efficiently across interconnected networks, with the Border Gateway Protocol (BGP) serving as the core mechanism managing routing between autonomous systems. However, BGP configurations are largely manual, making them susceptible to human errors that can lead to outages or security vulnerabilities. Verifying the correctness and convergence of BGP configurations is therefore essential for maintaining a stable and secure Internet. Yet, this verification process faces two key challenges: preserving the privacy of proprietary routing information and ensuring scalability across large, distributed networks. This paper introduces a privacy-preserving verification framework that leverages multiparty computation (MPC) to validate BGP configurations without exposing sensitive routing data. Our approach overcomes both privacy and scalability challenges by ensuring that no information beyond the verification outcome is revealed. Through formal analysis, we show that the proposed method achieves strong privacy guarantees and practical scalability, providing a secure and efficient foundation for verifying BGP-based routing in the Internet backbone.","url":"https://doi.org/10.48550/arxiv.2402.08956","authors":["Daneshamooz, Jaber","Yu, Melody","Maddury, Sucheer"],"tags":["Cryptography and Security (cs.CR)","Networking and Internet Architecture (cs.NI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2402.08956","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.18420/cdm-2024-37-03","name":"Multiparty Computation and Secure Aggregation Approaches for Privacy-Preserving URL-based Phishing Detection","source":"datacite","abstract":"","url":"https://doi.org/10.18420/cdm-2024-37-03","authors":["Akbari Gurabi, Mehdi","Mandal, Avikarsha"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.18420/cdm-2024-37-03","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17482705","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17482705","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17482705","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17464296","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17464296","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17464296","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17456557","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17456557","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17456557","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17445479","name":"Funciones pseudoaleatorias inconscientes en grupos no conmutativos","source":"datacite","abstract":"Las aplicaciones de las funciones pseudoaleatorias inconscientes en la criptografía y en la seguridad de la información son múltiples. Pueden citarse la derivación de claves basadas en contraseñas, acuerdo de claves basados en contraseñas, password hardening, CAPTCHAs imposibles de rastrear, acuerdo de claves homomórfico y la intersección de conjuntos segura. Los primeros trabajos se basan en protocolos para la transferencia inconsciente, computación multiparte segura o en algunas variantes del problema del logaritmo discreto. Recientemente han surgido propuestas postcuánticas basadas en las isogenias de curvas elípticas y en los problemas sobre lattices. En este trabajo se propone el diseño de una función pseudoaleatoria inconsciente que base su seguridad en la dificultad de encontrar el elemento conjugador en grupos no conmutativos. Se realiza además un experimento utilizando como plataforma el grupo discreto de Heisenberg sobre un campo finito.","url":"https://doi.org/10.5281/zenodo.17445479","authors":["Ledo Baster, David Ricardo","Martínez Rodríguez, Huber"],"tags":["criptografía no conmutativa","elemento conjugador","funciones pseudoaleatorias inconscientes","protocolos criptográficos","MSC 20F12","MSC 20F18","MSC 20H20","MSC 94A60"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17445479","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17445482","name":"Funciones pseudoaleatorias inconscientes en grupos no conmutativos","source":"datacite","abstract":"Las aplicaciones de las funciones pseudoaleatorias inconscientes en la criptografía y en la seguridad de la información son múltiples. Pueden citarse la derivación de claves basadas en contraseñas, acuerdo de claves basados en contraseñas, password hardening, CAPTCHAs imposibles de rastrear, acuerdo de claves homomórfico y la intersección de conjuntos segura. Los primeros trabajos se basan en protocolos para la transferencia inconsciente, computación multiparte segura o en algunas variantes del problema del logaritmo discreto. Recientemente han surgido propuestas postcuánticas basadas en las isogenias de curvas elípticas y en los problemas sobre lattices. En este trabajo se propone el diseño de una función pseudoaleatoria inconsciente que base su seguridad en la dificultad de encontrar el elemento conjugador en grupos no conmutativos. Se realiza además un experimento utilizando como plataforma el grupo discreto de Heisenberg sobre un campo finito.","url":"https://doi.org/10.5281/zenodo.17445482","authors":["Ledo Baster, David Ricardo","Martínez Rodríguez, Huber"],"tags":["criptografía no conmutativa","elemento conjugador","funciones pseudoaleatorias inconscientes","protocolos criptográficos","MSC 20F12","MSC 20F18","MSC 20H20","MSC 94A60"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17445482","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2506.20101","name":"Secure Multi-Key Homomorphic Encryption with Application to Privacy-Preserving Federated Learning","source":"datacite","abstract":"Multi-Key Homomorphic Encryption (MKHE), proposed by Lopez-Alt et al. (STOC 2012), allows for performing arithmetic computations directly on ciphertexts encrypted under distinct keys. Subsequent works by Chen and Dai et al. (CCS 2019) and Kim and Song et al. (CCS 2023) extended this concept by proposing multi-key BFV/CKKS variants, referred to as the CDKS scheme. These variants incorporate asymptotically optimal techniques to facilitate secure computation across multiple data providers. In this paper, we identify a critical security vulnerability in the CDKS scheme when applied to multiparty secure computation tasks, such as privacy-preserving federated learning (PPFL). In particular, we show that CDKS may inadvertently leak plaintext information from one party to others. To mitigate this issue, we propose a new scheme, SMHE (Secure Multi-Key Homomorphic Encryption), which incorporates a novel masking mechanism into the multi-key BFV and CKKS frameworks to ensure that plaintexts remain confidential throughout the computation. We implement a PPFL application using SMHE and demonstrate that it provides significantly improved security with only a modest overhead in homomorphic evaluation. For instance, our PPFL model based on multi-key CKKS incurs less than a 2\\times runtime and communication traffic increase compared to the CDKS-based PPFL model. The code is publicly available at https://github.com/JiahuiWu2022/SMHE.git.","url":"https://doi.org/10.48550/arxiv.2506.20101","authors":["Wu, Jiahui","Sun, Tiecheng","Luo, Fucai","Wang, Haiyan","Zhang, Weizhe"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2506.20101","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17425251","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17425251","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17425251","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17425207","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17425207","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17425207","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.13140/rg.2.2.22341.67047","name":"High-Throughput Secure Multiparty Computation with an Honest Majority in Various Network Settings","source":"datacite","abstract":"","url":"https://doi.org/10.13140/rg.2.2.22341.67047","authors":["Harth-Kitzerow, Christopher","Ajith Suresh","Yonqing Wang","Yalame, Hossein","Carle, Georg","Murali Annavaram"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.13140/rg.2.2.22341.67047","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17380264","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17380264","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17380264","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.13140/rg.2.2.33770.47041","name":"Towards a Local Electricity Trading Market based on Secure Multiparty Computation","source":"datacite","abstract":"","url":"https://doi.org/10.13140/rg.2.2.33770.47041","authors":["Aysajan Abidin","Abdelrahaman Aly","Cleemput, Sara","Mustafa, Mustafa A"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2016","doi":"10.13140/rg.2.2.33770.47041","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2307.12533","name":"PUMA: Secure Inference of LLaMA-7B in Five Minutes","source":"datacite","abstract":"With ChatGPT as a representative, tons of companies have began to provide services based on large Transformers models. However, using such a service inevitably leak users' prompts to the model provider. Previous studies have studied secure inference for Transformer models using secure multiparty computation (MPC), where model parameters and clients' prompts are kept secret. Despite this, these frameworks are still limited in terms of model performance, efficiency, and deployment. To address these limitations, we propose framework PUMA to enable fast and secure Transformer model inference. Our framework designs high quality approximations for expensive functions such as GeLU and softmax, and significantly reduce the cost of secure inference while preserving the model performance. Additionally, we design secure Embedding and LayerNorm procedures that faithfully implement the desired functionality without undermining the Transformer architecture. PUMA is about $2\\times$ faster than the state-of-the-art framework MPCFORMER(ICLR 2023) and has similar accuracy as plaintext models without fine-tuning (which the previous works failed to achieve). PUMA can even evaluate LLaMA-7B in around 5 minutes to generate 1 token. To our best knowledge, this is the first time that a model with such a parameter size is able to be evaluated under MPC. PUMA has been open-sourced in the Github repository of SecretFlow-SPU.","url":"https://doi.org/10.48550/arxiv.2307.12533","authors":["Dong, Ye","Lu, Wen-jie","Zheng, Yancheng","Wu, Haoqi","Zhao, Derun","Tan, Jin","Huang, Zhicong","Hong, Cheng","Wei, Tao","Chen, Wenguang"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2307.12533","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17350374","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17350374","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17350374","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.4230/dagrep.15.3.77","name":"PETs and AI: Privacy Washing and the Need for a PETs Evaluation Framework (Dagstuhl Seminar 25112)","source":"datacite","abstract":"As public awareness of data collection practices and regulatory frameworks grows, privacy-enhancing technologies (PETs) have emerged as a promising approach to reconciling data utility with individual privacy rights. PETs underpin privacy-preserving machine learning (PPML), integrating tools like differential privacy, homomorphic encryption, and secure multiparty computation to safeguard data throughout the AI lifecycle. However, despite significant technical progress, PETs face critical policy and governance challenges. Recent works have raised concerns about efficacy and deployment of PETs, observing that fundamental rights of people are continually being harmed, including, paradoxically, privacy. PETs have been used in surveillance applications and as a privacy washing tool. Current approaches often fail to address broader harms beyond data protection, highlighting the need for a more comprehensive privacy evaluation framework. This Dagstuhl Seminar brought together scholars in computer science and law, along with policymakers, regulators, and industry leaders, to discuss privacy washing and the challenges of detecting privacy washing through PETs and explored pathways toward a framework to address these challenges.","url":"https://doi.org/10.4230/dagrep.15.3.77","authors":["De Cristofaro, Emiliano","Shrishak, Kris","Strufe, Thorsten","Troncoso, Carmela","Morsbach, Felix"],"tags":["Privacy Enhancing Technologies (PET)","Privacy Evaluation","Privacy Harm","Privacy Threats","Privacy Washing","Security and privacy → Privacy protections","Security and privacy → Privacy-preserving protocols","Security and privacy → Social aspects of security and privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.4230/dagrep.15.3.77","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2510.07814","name":"Utilizing Model-Free Reinforcement Learning for Optimizing Secure Multi-Party Computation Protocols","source":"datacite","abstract":"In this manuscript, we explore the application of model-free reinforcement learning in optimizing secure multiparty computation (SMPC) protocols. SMPC is a crucial tool for performing computations on private data without the need to disclose it, holding significant importance in various domains, including information security and privacy. However, the efficiency of current protocols is often suboptimal due to computational and communicational complexities. Our proposed approach leverages model-free reinforcement learning algorithms to enhance the performance of these protocols. We have designed a reinforcement learning model capable of dynamically learning and adapting optimal strategies for secure computations. Our experimental results demonstrate that employing this method leads to a substantial reduction in execution time and communication costs of the protocols. These achievements highlight the high potential of reinforcement learning in improving the efficiency of secure multiparty computation protocols, providing an effective solution to the existing challenges in this field.","url":"https://doi.org/10.48550/arxiv.2510.07814","authors":["Sayyadi, Javad","Nangir, Mahdi","Feghhi, Mahmood Mohassel","Sayyadi, Hamid"],"tags":["Signal Processing (eess.SP)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.07814","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.13140/rg.2.1.2729.0007","name":"Secure and Efficient Multiparty Computation on Genomic Data","source":"datacite","abstract":"","url":"https://doi.org/10.13140/rg.2.1.2729.0007","authors":["Md. Momin Al Aziz"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2016","doi":"10.13140/rg.2.1.2729.0007","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17289282","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17289282","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17289282","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.48550/arxiv.2510.03218","name":"Cheat-Penalised Quantum Weak Coin-Flipping","source":"datacite","abstract":"Coin-flipping is a fundamental task in two-party cryptography where two remote mistrustful parties wish to generate a shared uniformly random bit. While quantum protocols promising near-perfect security exist for weak coin-flipping -- when the parties want opposing outcomes -- it has been shown that they must be inefficient in terms of their round complexity, and it is an open question of how space efficient they can be. In this work, we consider a variant called cheat-penalised weak coin-flipping in which if a party gets caught cheating, they lose $Λ$ points (compared to $0$ in the standard definition). We find that already for a small cheating penalty, the landscape of coin-flipping changes dramatically. For example, with $Λ=0.01$, we exhibit a protocol where neither Alice nor Bob can bias the result in their favour beyond $1/2 + 10^{-8}$, which uses $24$ qubits and $10^{16}$ rounds of communication (provably $10^{7}$ times better than any weak coin-flipping protocol with matching security). For the same space requirements, we demonstrate how one can choose between lowering how much a malicious party can bias the result (down to $1/2 + 10^{-10}$) and reducing the rounds of communication (down to $25,180$), depending on what is preferred. To find these protocols, we make two technical contributions. First, we extend the point game-protocol correspondence introduced by Kitaev and Mochon, to incorporate: (i) approximate point games, (ii) the cheat-penalised setting, and (iii) round and space complexity. Second, we give the first (to the best of our knowledge) numerical algorithm for constructing (approximate) point games that correspond to high security and low complexity. Our results open up the possibility of having secure and practical quantum protocols for multiparty computation.","url":"https://doi.org/10.48550/arxiv.2510.03218","authors":["Arora, Atul Singh","Miller, Carl A.","Morales, Mauro E. S.","Sikora, Jamie"],"tags":["Quantum Physics (quant-ph)","Cryptography and Security (cs.CR)","FOS: Physical sciences","FOS: Physical sciences","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.03218","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.13140/2.1.2763.9361","name":"Implementing Key Partitioning for Secure Multiparty Computation","source":"datacite","abstract":"","url":"https://doi.org/10.13140/2.1.2763.9361","authors":["Pandey, Ravi Krishan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2013","doi":"10.13140/2.1.2763.9361","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17234317","name":"Accelerating Multiparty Noise Generation Using Lookups","source":"datacite","abstract":"There is growing interest in combining Differential Privacy (DP) and Secure Multiparty Computation (MPC) to protect distributed database queries from both computational parties and those observing the result. This requires implementing both query evaluation and noise generation within MPC. While secure query evaluation is well-supported by existing MPC techniques, generating noise efficiently remains a challenge due to the nonlinearity of common sampling algorithms. We propose a new approach for multiparty noise sampling using recent advances in MPC lookup table (LUT) evaluations. Our method is distributionagnostic and maps a cheaply sampled index to a target noise distribution via oblivious LUT evaluation. We demonstrate the flexibility by approximating the discrete Laplace and Gaussian distributions to a negligible statistical distance. Our implementation, based on 3party replicated secret sharing (RSS), achieves sub-kilobyte communication and millisecondlevel computation. Per 1000 discrete Laplace or Gaussian samples, we require just 362 bytes of communication and under 1 ms per party (semi-honest setting). With recent batched multiplication checks, the amortized malicious setting adds less than 1 byte and 10 ms per sample. Our open-source implementation also extends MAESTRO-style LUT trade-offs, offering potential independent value.","url":"https://doi.org/10.5281/zenodo.17234317","authors":["Meisingseth, Fredrik","Rechberger, Christian","Schmid, Fabian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17234317","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17234318","name":"Accelerating Multiparty Noise Generation Using Lookups","source":"datacite","abstract":"There is growing interest in combining Differential Privacy (DP) and Secure Multiparty Computation (MPC) to protect distributed database queries from both computational parties and those observing the result. This requires implementing both query evaluation and noise generation within MPC. While secure query evaluation is well-supported by existing MPC techniques, generating noise efficiently remains a challenge due to the nonlinearity of common sampling algorithms. We propose a new approach for multiparty noise sampling using recent advances in MPC lookup table (LUT) evaluations. Our method is distributionagnostic and maps a cheaply sampled index to a target noise distribution via oblivious LUT evaluation. We demonstrate the flexibility by approximating the discrete Laplace and Gaussian distributions to a negligible statistical distance. Our implementation, based on 3party replicated secret sharing (RSS), achieves sub-kilobyte communication and millisecondlevel computation. Per 1000 discrete Laplace or Gaussian samples, we require just 362 bytes of communication and under 1 ms per party (semi-honest setting). With recent batched multiplication checks, the amortized malicious setting adds less than 1 byte and 10 ms per sample. Our open-source implementation also extends MAESTRO-style LUT trade-offs, offering potential independent value.","url":"https://doi.org/10.5281/zenodo.17234318","authors":["Meisingseth, Fredrik","Rechberger, Christian","Schmid, Fabian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17234318","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17233165","name":"Implementing federated learning with privacy-preserving encryption to secure patient-derived imaging and sequencing data from cyber intrusions","source":"datacite","abstract":"The growing adoption of artificial intelligence (AI) and data-driven analytics in healthcare has accelerated the integration of large-scale patient-derived imaging and genomic sequencing data into clinical workflows. However, this surge in biomedical data sharing has intensified cybersecurity challenges, particularly in protecting sensitive patient information from unauthorized access and cyber intrusions. Traditional centralized machine learning models, which aggregate data into a single repository, pose significant privacy risks and increase the attack surface for malicious actors. To address these challenges, federated learning (FL) has emerged as a transformative paradigm, enabling collaborative model training across decentralized nodes without transferring raw data. Yet, while FL mitigates some privacy concerns, it remains vulnerable to inference attacks, gradient leakage, and model inversion tactics. This paper explores the implementation of federated learning frameworks integrated with privacy-preserving encryption techniques, such as homomorphic encryption, differential privacy, and secure multiparty computation, specifically for safeguarding patient-derived medical imaging and sequencing datasets. These technologies ensure that sensitive genetic markers, radiographic scans, and multi-omic features remain encrypted throughout model training and aggregation processes. We examine recent advances in privacy-enhancing technologies, discuss system architectures suited for cross-institutional healthcare collaboration, and evaluate their performance trade-offs in terms of computational cost, model accuracy, and security guarantees. Furthermore, we propose a hybrid encryption-aware federated learning workflow tailored to radiogenomic applications, highlighting its resilience against adversarial threats while maintaining diagnostic precision. By narrowing focus to clinical implementations, this work provides a scalable and secure foundation for AI-driven biomedical research, enhancing trust and compliance in digital health ecosystems.","url":"https://doi.org/10.5281/zenodo.17233165","authors":["Kalejaiye, Adebayo Nurudeen","Shallom, Kigbu","Chukwuani, Elvis Nnaemeka"],"tags":["Federated Learning","Privacy-Preserving Encryption","Medical Imaging","Genomic Data Security","Homomorphic Encryption","Radiogenomics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17233165","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17233164","name":"Implementing federated learning with privacy-preserving encryption to secure patient-derived imaging and sequencing data from cyber intrusions","source":"datacite","abstract":"The growing adoption of artificial intelligence (AI) and data-driven analytics in healthcare has accelerated the integration of large-scale patient-derived imaging and genomic sequencing data into clinical workflows. However, this surge in biomedical data sharing has intensified cybersecurity challenges, particularly in protecting sensitive patient information from unauthorized access and cyber intrusions. Traditional centralized machine learning models, which aggregate data into a single repository, pose significant privacy risks and increase the attack surface for malicious actors. To address these challenges, federated learning (FL) has emerged as a transformative paradigm, enabling collaborative model training across decentralized nodes without transferring raw data. Yet, while FL mitigates some privacy concerns, it remains vulnerable to inference attacks, gradient leakage, and model inversion tactics. This paper explores the implementation of federated learning frameworks integrated with privacy-preserving encryption techniques, such as homomorphic encryption, differential privacy, and secure multiparty computation, specifically for safeguarding patient-derived medical imaging and sequencing datasets. These technologies ensure that sensitive genetic markers, radiographic scans, and multi-omic features remain encrypted throughout model training and aggregation processes. We examine recent advances in privacy-enhancing technologies, discuss system architectures suited for cross-institutional healthcare collaboration, and evaluate their performance trade-offs in terms of computational cost, model accuracy, and security guarantees. Furthermore, we propose a hybrid encryption-aware federated learning workflow tailored to radiogenomic applications, highlighting its resilience against adversarial threats while maintaining diagnostic precision. By narrowing focus to clinical implementations, this work provides a scalable and secure foundation for AI-driven biomedical research, enhancing trust and compliance in digital health ecosystems.","url":"https://doi.org/10.5281/zenodo.17233164","authors":["Kalejaiye, Adebayo Nurudeen","Shallom, Kigbu","Chukwuani, Elvis Nnaemeka"],"tags":["Federated Learning","Privacy-Preserving Encryption","Medical Imaging","Genomic Data Security","Homomorphic Encryption","Radiogenomics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17233164","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17225939","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17225939","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17225939","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17208446","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17208446","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17208446","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17201334","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17201334","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17201334","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.26083/tuprints-00028798","name":"EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation","source":"datacite","abstract":"Background: Modern biomedical research is data-driven and relies heavily on the re-use and sharing of data. Biomedical data, however, is subject to strict data protection requirements. Due to the complexity of the data required and the scale of data use, obtaining informed consent is often infeasible. Other methods, such as anonymization or federation, in turn have their own limitations. Secure multi-party computation (SMPC) is a cryptographic technology for distributed calculations, which brings formally provable security and privacy guarantees and can be used to implement a wide-range of analytical approaches. As a relatively new technology, SMPC is still rarely used in real-world biomedical data sharing activities due to several barriers, including its technical complexity and lack of usability. Results: To overcome these barriers, we have developed the tool EasySMPC, which is implemented in Java as a cross-platform, stand-alone desktop application provided as open-source software. The tool makes use of the SMPC method Arithmetic Secret Sharing, which allows to securely sum up pre-defined sets of variables among different parties in two rounds of communication (input sharing and output reconstruction) and integrates this method into a graphical user interface. No additional software services need to be set up or configured, as EasySMPC uses the most widespread digital communication channel available: e-mails. No cryptographic keys need to be exchanged between the parties and e-mails are exchanged automatically by the software. To demonstrate the practicability of our solution, we evaluated its performance in a wide range of data sharing scenarios. The results of our evaluation show that our approach is scalable (summing up 10,000 variables between 20 parties takes less than 300 s) and that the number of participants is the essential factor. Conclusions: We have developed an easy-to-use “no-code solution” for performing secure joint calculations on biomedical data using SMPC protocols, which is suitable for use by scientists without IT expertise and which has no special infrastructure requirements. We believe that innovative approaches to data sharing with SMPC are needed to foster the translation of complex protocols into practice.","url":"https://doi.org/10.26083/tuprints-00028798","authors":["Wirth, Felix Nikolaus","Kussel, Tobias","Müller, Armin","Hamacher, Kay","Prasser, Fabian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.26083/tuprints-00028798","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17190502","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17190502","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17190502","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.5281/zenodo.17190331","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17190331","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17190331","addedAt":"2026-08-31T06:41:29.095Z","updatedAt":"2026-08-31T06:41:29.095Z"},{"id":"doi:10.1002/itl2.499/v2/review1","name":"Review for \"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.499/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-01T09:59:27Z","doi":"10.1002/itl2.499/v2/review1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1587/essfr.16.1_7","name":"Decentralized Federated Learning over Wireless Channels: A Review","source":"crossref","abstract":"","url":"https://doi.org/10.1587/essfr.16.1_7","authors":["Koya SATO"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-30T22:18:28Z","doi":"10.1587/essfr.16.1_7","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.5772/intechopen.1008207","name":"Advancements in Machine Learning and Deep Learning for Breast Cancer Detection: A Systematic Review","source":"crossref","abstract":"Breast cancer is a significant transnational health concern, requiring effective timely detection methods to improve patient’s treatment result and reduce mortality rates. While conventional screening methods like mammography, ultrasound, and MRI have proven efficacy, they possess limitations, such as false-positive results and discomfort. In recent years, machine learning (ML) and deep learning (DL) techniques have demonstrated significant potential in transforming breast cancer detection through the analysis of imaging data. This review systematically explores recent advancements in the research of machine learning and deep learning applications for detecting breast cancer. Through a systematic analysis of existing literature, we identify trends, challenges, and opportunities in the development and deployment of ML and DL models for breast cancer screening and diagnosis. We highlight the crucial role of early detection in enhancing patient outcomes and lowering breast cancer mortality rates. Furthermore, we highlight the potential impact of ML and DL technologies on clinical procedure, patient outcomes, and healthcare delivery in breast cancer detection. By systematically identifying and evaluating studies on machine learning and deep learning applications in breast cancer detection, we aim to provide valuable insights for researchers, clinicians, policymakers, and healthcare stakeholders interested in leveraging advanced computational techniques to enhance breast cancer screening and diagnosis.","url":"https://doi.org/10.5772/intechopen.1008207","authors":["Zeba Khan","Madhavidevi Botlagunta","Gorli L. Aruna Kumari","Pranjali Malviya","Mahendran Botlagunta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-20T17:50:37Z","doi":"10.5772/intechopen.1008207","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.5772/intechopen.1006690","name":"Private SVM Inference on Encrypted Data","source":"crossref","abstract":"This tutorial chapter provides a comprehensive guide to implementing privacy-preserving Support Vector Machine (SVM) inference using Fully Homomorphic Encryption (FHE). We demonstrate a practical solution for secure and private SVM inference on encrypted data, enabling sensitive data analysis while maintaining confidentiality. Through a step-by-step implementation on a real-world dataset, we cover data preparation, SVM model training, and homomorphic inference. Our experimental results on a commodity laptop show that our approach achieves high accuracy with a reasonable latency of nearly 6 seconds for SVM inference. This chapter serves as a valuable resource for practitioners and researchers seeking to apply privacy-preserving techniques to SVM solutions, with significant implications for applications like medical diagnosis, financial prediction, and recommender systems, where data privacy is crucial. By following this tutorial, readers can gain hands-on experience with privacy-preserving SVM inference using FHE.","url":"https://doi.org/10.5772/intechopen.1006690","authors":["Ahmad Al Badawi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-04T14:18:59Z","doi":"10.5772/intechopen.1006690","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.7287/peerj-cs.1027v0.1/reviews/2","name":"Peer Review #2 of \"SFedChain: blockchain-based federated learning scheme for secure data sharing in distributed energy storage networks (v0.1)\"","source":"crossref","abstract":"","url":"https://doi.org/10.7287/peerj-cs.1027v0.1/reviews/2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-04T02:31:31Z","doi":"10.7287/peerj-cs.1027v0.1/reviews/2","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-1168002/v1","name":"ProxyFL: Decentralized Federated Learning through Proxy Model Sharing","source":"crossref","abstract":"Abstract Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator’s data privacy. In this paper, we propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant’s privacy. Proxy models allow efficient information exchange among participants using the PushSum method without the need of a centralized server. The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. Experiments on popular image datasets, and a pan-cancer diagnostic problem using over 30,000 high-quality gigapixel histology whole slide images, show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy.","url":"https://doi.org/10.21203/rs.3.rs-1168002/v1","authors":["Shivam Kalra","Junfeng Wen","Jesse Cresswell","Maksims Volkovs","Hamid Tizhoosh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-12-16T13:41:18Z","doi":"10.21203/rs.3.rs-1168002/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1002/itl2.499/v1/review2","name":"Review for \"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.499/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-01T09:59:27Z","doi":"10.1002/itl2.499/v1/review2","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1002/itl2.499/v2/review2","name":"Review for \"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.499/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-01T09:59:27Z","doi":"10.1002/itl2.499/v2/review2","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.21203/rs.3.rs-2095773/v1","name":"Survivable SFC deployment method based on federated learning in multi-domain networks","source":"crossref","abstract":"Abstract In the multi-domain network scenario, in order to improve the survivability of service function chain in the face of network failure, most methods solve this problem through virtual network function backup mechanism. However, the traditional multi-domain SFC deployment method lacks a SFC partition mechanism for backup resource consumption, and does not consider the isolation and privacy requirements between different network domains. In view of the above problems, this paper proposes a reliability partition scheme based on reinforcement learning in SFC partition stage, which can ensure that VNF is backed up while maintaining good load balance and low inter-domain transmission delay, and improve the reliability of SFC. Then, this paper proposes a VNF backup mechanism with minimum resource fluctuation in the VNF mapping stage, and uses the ILP model to determine the backup scheme of each VNF, so as to ensure the minimum fluctuation of resource occupancy of the entire network. Finally, this paper proposes a multi-domain SFC deployment and backup algorithm based on Federated learning (FA-MSDB). Each domain is trained locally to improve the load balance of node resources and reduce the transmission delay in the domain, the global and local models are updated periodically to deploy and backup VNF requests. The experimental results indicate that compared with other three version of FA-MSDB and other three benchmark algorithms, FA-MSDB can effectively improve the survival rate of SFC, reduce the overall transmission delay, and ensure good inter-domain and intra-domain load balance.","url":"https://doi.org/10.21203/rs.3.rs-2095773/v1","authors":["Hua Qu","Ke Wang","Jihong Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-10-05T20:48:53Z","doi":"10.21203/rs.3.rs-2095773/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1002/itl2.499/v3/review1","name":"Review for \"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.499/v3/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-01T09:59:27Z","doi":"10.1002/itl2.499/v3/review1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1002/itl2.499/v3/review2","name":"Review for \"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.499/v3/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-01T09:59:27Z","doi":"10.1002/itl2.499/v3/review2","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.69987/aimlr.2024.50310","name":"Privacy-Preserving Federated Learning in Medical AI: A Systematic Review of Techniques, Challenges, and the Clinical Deployment Gap","source":"crossref","abstract":"","url":"https://doi.org/10.69987/aimlr.2024.50310","authors":["Chuanli Wei","Haoyang Guan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-07T18:29:53Z","doi":"10.69987/aimlr.2024.50310","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-2470634/v1","name":"Federated Learning based Spatio-Temporal framework for real-time traffic prediction","source":"crossref","abstract":"Abstract Wireless Sensor Network (WSN)is widely explored for traffic flow prediction. Traffic forecasting is a spatio-temporal problem because of the dynamic nature of road traffic. The data collected from users for traffic prediction is often private in nature. These characteristics make it necessary to develop a framework for accurately predicting traffic flow while maintaining user data privacy. This paper proposes a Federated Learning-based spatio-temporal approach termed Fed-STGRU for traffic prediction without transmitting raw user data over the network. Federated Learning is a distributed machine learning approach incorporating techniques like Stochastic Gradient Descent for data privacy preservation. The proposed scheme preserves privacy while attaining comparable accuracy and loss as baseline algorithms, FedAvg and TGCN.","url":"https://doi.org/10.21203/rs.3.rs-2470634/v1","authors":["Gaganbir Kaur","Surender K Grewal","Aarti Jain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-24T05:20:41Z","doi":"10.21203/rs.3.rs-2470634/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.7287/peerj-cs.1027v0.2/reviews/2","name":"Peer Review #2 of \"SFedChain: blockchain-based federated learning scheme for secure data sharing in distributed energy storage networks (v0.2)\"","source":"crossref","abstract":"","url":"https://doi.org/10.7287/peerj-cs.1027v0.2/reviews/2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-04T02:31:38Z","doi":"10.7287/peerj-cs.1027v0.2/reviews/2","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.7287/peerj-cs.1027v0.1/reviews/1","name":"Peer Review #1 of \"SFedChain: blockchain-based federated learning scheme for secure data sharing in distributed energy storage networks (v0.1)\"","source":"crossref","abstract":"","url":"https://doi.org/10.7287/peerj-cs.1027v0.1/reviews/1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-04T02:31:37Z","doi":"10.7287/peerj-cs.1027v0.1/reviews/1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.36227/techrxiv.177203265.53959182/v1","name":"Federated Learning for Ophthalmic Imaging: A Systematic Review","source":"crossref","abstract":"Ophthalmic diseases are leading causes of vision loss worldwide, and medical imaging-based AI models are increasingly used to support their detection and management. However, the development of robust and generalisable models is hindered by siloed imaging data across institutions. Federated learning (FL), which is a collaborative machine learning approach that trains models across multiple decentralised devices or institutions while keeping all data local to preserve privacy, has emerged as a promising solution. This systematic review synthesizes current research on FL for ophthalmic disease detection, focusing on methodological trends, privacy-preserving techniques, and challenges in clinical translation. A total of 22 peerreviewed studies were identified. Across classification and segmentation tasks involving fundus, OCT, and OCT-A images, FL generally achieved performance comparable to centralised training and superior to local models. Privacy preservation has primarily relied on differential privacy, with growing exploration of cryptographic and blockchainbased strategies. Domain heterogeneity remains a key technical challenge, with most studies relying on simulated client splits that fail to fully capture real-world variability. Overall, current ophthalmic FL research is in its early stages, with promising results but limited real-world validation. Future work should prioritize clinically realistic multi-institutional datasets, stronger privacy-utility tradeoffs, robust domain generalisation, and clear pathways to regulatory adoption.","url":"https://doi.org/10.36227/techrxiv.177203265.53959182/v1","authors":["Luyuan Qi","Paul Taylor","Cathy Egan","Adnan Tufail","Jiahao Sun","Kezhi Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-25T15:17:36Z","doi":"10.36227/techrxiv.177203265.53959182/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.21203/rs.3.rs-3419449/v1","name":"A Traffic Flow Prediction Method Based on Blockchain and Federated Learning","source":"crossref","abstract":"Abstract Traffic flow prediction is the an important issue in the field of intelligent transportation, and real-time and accurate traffic flow prediction plays a crucial role in improving the efficiency of traffic networks. Existing traffic flow prediction methods use deep learning models and collected traffic flow datasets to predict traffic flow. These datasets contain the private data of clients, so if some clients are unwilling to participate in the traffic flow prediction, the traffic flow prediction results will be inaccurate. Therefore, it is important to address the issue that how to motivate clients to actively participate in the traffic flow prediction while protecting the privacy data. So, this paper proposes a traffic flow prediction method based on blockchain and federated learning (TFPM-BFL). Firstly, the traffic flow prediction problem is described as federated learning (FL) task, the improved long and short-term memory (LSTM) model is used to predict the traffic flow at the client side, the traffic flow data is decomposed by wavelet function, and the LSTM network with added attention mechanism is used to obtain traffic flow prediction results; Then, incentive mechanism based on reputation value is proposed, the model parameters are uploaded to the blockchain for local and partial reputation evaluation through smart contracts, and the corresponding global reputation update is obtained, the reward is distributed to clients according to global reputation, so the clients are motivated to participate in the traffic flow prediction; Finally, the model aggregation method based on reputation value and compression rate is designed. Based on the reputation evaluation results, the edge server uses the Top k algorithm to perform high-quality aggregation of the local model parameters uploaded by clients (roadside units), central server aggregates the partial model parameters from edge server, and then the central server distributes the global aggregated model parameters to clients to perform the next round of FL. By using the FL framework, TFPM-BFL uploads the model parameters instead of the original traffic flow data, so it can protect private data. Moreover, it can provide incentive mechanism through reputation evaluation and reward to encourage clients to participate in the FL task. Simulation results show that TFPM-BFL can realize accurate and timely traffic flow prediction, and it can effectively motivate clients to participate in FL task while ensuring the privacy of the underlying data.","url":"https://doi.org/10.21203/rs.3.rs-3419449/v1","authors":["Hui Zhi","苗苗 段","Lixia Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-12T02:37:58Z","doi":"10.21203/rs.3.rs-3419449/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.5772/intechopen.1003284","name":"Federated Learning - A Systematic Review","source":"crossref","abstract":"","url":"https://doi.org/10.5772/intechopen.1003284","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-06T14:09:44Z","doi":"10.5772/intechopen.1003284","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-3165556/v1","name":"Federated learning-based detection and control mechanism of in-car navigation safety system","source":"crossref","abstract":"Abstract The advancement of in-car navigation systems has dramatically improved driving experiences. However, ensuring the safety of these systems remains a critical concern. Federated learning provides a new solution for cooperative learning between non-mutually trusted entities. Through the mode of local training and central aggregation, the local data privacy of each entity is protected while training the global model. To achieve this, a federated learning method for deep learning that preserves privacy is developed by integrating differential privacy with secure multi-party computing. In this scheme, vehicles add perturbations to the local models obtained by local training and secretly share them with multiple central servers. The scheme protects the local information uploaded by users from being stolen and prevents the adversary from malicious inference from globally shared information such as the aggregation model. Additionally, the scheme enables users dropping out and implements a variety of aggregating methods. The aforementioned system may also easily be expanded to decentralized scenarios for real-world applications devoid of a trustworthy center. The experimental findings show that, in order to protect sensitive data obtained from in-car navigation systems during learning, the suggested strategy heavily emphasizes privacy protection. Simultaneously, the high accuracy achieved through the proposed federated learning scheme significantly enhances in-car navigation safety systems' detection and control capabilities. It enables precise and reliable event detection, differentiation of abnormal situations, and reduces false alarms, improving overall safety, user trust, and system performance.","url":"https://doi.org/10.21203/rs.3.rs-3165556/v1","authors":["Jingge Gao","Shuqiang Zhang","Wei Lu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-18T15:29:20Z","doi":"10.21203/rs.3.rs-3165556/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-1984301/v1","name":"Defending against Label-Flipping Attacks in Federated Learning Systems with UMAP","source":"crossref","abstract":"Abstract The importance of computing and storage capacity has increased over time, and the importance of data mining in industrial engineering has become more apparent. Recently, artificial intelligence and machine learning have made significant advancements in industrial engineering. Federated learning is a machine learning technique that aims to solve the problem of distributed computing systems and their applications of data storage while ensuring data privacy. Tolpegin et al. conducted research on data poisoning attacks in a federated learning system, which we have extended to an analysis of the efficiency of Tolpegin's proposed defense technique. We have subsequently compared the efficiency using uniform manifold approximation and projection (UMAP), principal component analysis (PCA), kernel PCA (KPCA), and K-means clustering algorithms. This study confirms that UMAP performs better than PCA, KPCA, and K-means, and provides excellent performance in mitigating data-poisoning attacks.","url":"https://doi.org/10.21203/rs.3.rs-1984301/v1","authors":["Deepak Upreti","Hyunil Kim","Eunmok Yang","Changho Seo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-08-25T17:34:59Z","doi":"10.21203/rs.3.rs-1984301/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.7287/peerj-cs.1101v0.1/reviews/1","name":"Peer Review #1 of \"OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.1)\"","source":"crossref","abstract":"","url":"https://doi.org/10.7287/peerj-cs.1101v0.1/reviews/1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-25T02:33:02Z","doi":"10.7287/peerj-cs.1101v0.1/reviews/1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-3243194/v1","name":"Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning","source":"crossref","abstract":"Abstract Numerous educational institutions utilize data mining techniques to manage student records, particularly those related to academic achievements , which are essential in improving learning experiences and overall 1 Springer Nature 2021 L A T E X template outcomes. Educational Data Mining (EDM) is a thriving research field that employs data mining and machine learning methods to extract valuable insights from educational databases, primarily focused on predicting students’ academic performance. This study proposes a novel Federated Learning (FL) standard that ensures the confidentiality of the dataset and allows predicting student grades, categorized into four levels: Low, Good, Average, and Drop. To enhance model precision, optimized features are incorporated into the training process. This study evaluates the optimized dataset using five machine learning (ML) algorithms , namely Support Vector Machine (SVM), Decision Tree, Nã A¯ve Bayes, K-Nearest Neighbours, and the proposed Federated Learning Model. The models’ performance is assessed regarding accuracy, precision , recall, and F1 score, followed by a comprehensive comparative analysis. The results reveal that FL and SVM outperform the alternative models, demonstrating superior predictive performance for student grade classification. This study showcases the potential of Federated Learning in effectively utilizing educational data from various institutes while maintaining data privacy, contributing to educational data mining and machine learning advancements for student performance prediction.","url":"https://doi.org/10.21203/rs.3.rs-3243194/v1","authors":["Umer Farooq","Shahid Naseem","Jianqiang Li","Tariq Mahmood","Amjad Rehman","Tanzila Saba","Luqman Mustafa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-14T02:15:40Z","doi":"10.21203/rs.3.rs-3243194/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.31224/6624","name":"Federated Learning for Smart City Network Attack Detection and Classification: A Literature Review","source":"crossref","abstract":"The growth of smart cities and the Internet of Things (IoT) has generated massive volumes of sensitive data, creating significant cybersecurity challenges and limitations for centralized machine learning models. Federated Learning (FL) has emerged as a decentralized paradigm that preserves privacy by training models locally on devices. This systematic literature review aims to analyze current trends, challenges, and solutions in applying FL for cybersecurity in smart city environments based on recent publications. The analysis reveals that FL can be effectively implemented in Intrusion Detection Systems (IDS) to detect various types of network attacks. A major universal challenge is the presence of Non-Independent and Identically Distributed (Non-IID) data, which can degrade model performance. The key findings highlight that the most effective implementations of FL are not standalone, but rather hybrid approaches that integrate FL with complementary technologies—such as blockchain to enhance data integrity, adaptive algorithms to handle Non-IID data, and additional privacy-preserving techniques like Differential Privacy and encryption. Overall, the development of frameworks that combine FL with supporting technologies shows strong potential for enhancing the security of smart cities.","url":"https://doi.org/10.31224/6624","authors":["Kurniawan D. Irianto","Rian Nur Ikhsan","Fayruz Rahma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-13T15:08:22Z","doi":"10.31224/6624","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-3325441/v1","name":"A Comparative Study of Federated Learning Methods for COVID-19 Detection","source":"crossref","abstract":"Abstract Deep learning has proven to be highly effective in diagnosing COVID-19; however, its efficacy is contingent upon the availability of extensive data for model training. The data sharing among hospitals, which is crucial for training robust models, is often restricted by privacy regulations. Federated learning (FL) emerges as a solution by enabling model training across multiple hospitals while preserving data privacy. However, the deployment of FL can be resource-intensive, necessitating efficient utilization of computational and network resources. In this study, we evaluate the performance and resource efficiency of five FL algorithms in the context of COVID-19 detection using Convolutional Neural Networks (CNNs) in a decentralized setting. The evaluation involves varying the number of participating entities, the number of federated rounds, and the selection algorithms. Our findings indicate that the Cyclic Weight Transfer algorithm exhibits superior performance, particularly when the number of participating hospitals is limited. These insights hold practical implications for the deployment of FL algorithms in COVID-19 detection and broader medical image analysis.","url":"https://doi.org/10.21203/rs.3.rs-3325441/v1","authors":["Erfan Darzi","Nanna M. Sijtsema","P.M.A van Ooijen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-12T18:51:37Z","doi":"10.21203/rs.3.rs-3325441/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.59628/jast.v4i8.3275","name":"Stability-Aware and Feasibility-Sensitive Aggregation in Federated Reinforcement Learning for Edge-IoT Systems: A Review","source":"crossref","abstract":"Federated Reinforcement Learning (FRL) provides a useful basis for distributed policy learning in Edge-IoT systems, where clients interact with local environments without transferring raw operational data to a central server. Yet aggregation becomes difficult when clients operate under different transition dynamics, workloads, resource capacities, communication conditions, and operational constraints. In these settings, local policy updates may not differ only in magnitude or direction; they may also differ in stability, reliability, resource support, and operational feasibility. Conventional averaging is therefore limited, since it does not distinguish stable and feasible updates from unstable or constraint-violating ones. This paper examines aggregation stability and feasibility-sensitive aggregation in FRL for Edge-IoT systems. It reviews and synthesizes related literature across four connected streams: heterogeneous Federated Learning, FRL-based edge decision-making, constrained and safe Reinforcement Learning, and adaptive or reliability-aware aggregation. The reviewed studies are analyzed through six dimensions: learning paradigm, type of heterogeneity, role of policy learning, treatment of operational constraints, aggregation strategy, and whether local feasibility signals influence global aggregation weights. The analysis indicates that existing studies provide valuable foundations, but they usually treat heterogeneity, constraint handling, and aggregation adaptation as separate concerns. The paper identifies a need for aggregation mechanisms that jointly account for update stability, update reliability, resource availability, and constraint feasibility. It positions aggregation as adaptive client influence regulation rather than passive averaging in future Edge-IoT FRL systems.","url":"https://doi.org/10.59628/jast.v4i8.3275","authors":["Majid A. Aslan","Ahmed A. S. Al-shalabi","Ahmed S. Al-Hegami"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-28T14:10:11Z","doi":"10.59628/jast.v4i8.3275","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-6665562/v1","name":"Adaptive Confidence-Weighted Policy Aggregation: A Novel Method for Federated Reinforcement Learning","source":"crossref","abstract":"Abstract This paper proposes an innovative Federated Reinforcement Learning (FRL) approach called the Adaptive Confidence-Weighted Policy Aggregation method, or ACWPA in short. In light of incomplete information and heterogeneous knowledge, ACWPA was developed to combine strengths from multiple agents while canceling their weaknesses in multi-agent tasks. This method dynamically weights the contribution of agents’ policies to provide a global policy based on the agent’s performance and the relevance of their expertise when the information about state-action rewards is partially incomplete. Evaluated on a multi-agent path planning task, ACWPA demonstrates advanced convergence and generalization compared to standard FRL methods like FedAvg and FedProx. Outcomes show that ACWPA increases navigation efficiency by 20% and reduces collision charges by 35% throughout diverse environments, highlighting its capacity to boost collaborative knowledge in multi-agent systems with heterogeneous knowledge. Furthermore, implementing ACWPA on large language models (LLMs) yielded a 15% improvement, indicating that this method has potential applicability in different areas of artificial intelligence.","url":"https://doi.org/10.21203/rs.3.rs-6665562/v1","authors":["Nematollah Ab Azar","Aref Shahmansoorian","Mohsen Davoudi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-21T01:29:39Z","doi":"10.21203/rs.3.rs-6665562/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.1016/j.cosrev.2026.100932","name":"Blockchain-enabled defenses in federated learning: A comprehensive survey","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.cosrev.2026.100932","authors":["Omar Dib"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T11:17:41Z","doi":"10.1016/j.cosrev.2026.100932","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.2139/ssrn.7066721","name":"A Systematic Review of Federated Structure Learning: Definitions and Methods","source":"crossref","abstract":"The integration of causal reasoning into machine learning addresses fundamental limitations in predictive modeling, yet deploying these methods across distributed, privacy constrained datasets remains a critical bottleneck. Federated Structure Learning (FSL), an overarching research domain that encompasses Federated Causal Discovery (FCD) and Federated Bayesian Network Structure Learning (FBNSL), has emerged to recover causal and associative graph structures from decentralized data without centralizing raw observations. However, the literature remains fragmented across the causal inference, probabilistic graphical modeling, and federated learning communities. We conduct a PRISMA-guided systematic review, identifying 31 algorithmic contributions published up to 2026. We categorize these methods by their federated learning paradigms, underlying structure learning approaches, payload aggregation mechanisms, and privacy assumptions. Our findings highlight a diverse landscape of algorithmic approaches, while identifying critical unresolved challenges in high-dimensional scalability, heterogeneous data fusion, and the integration of formal cryptographic privacy guarantees. By consolidating this fragmented domain, this review provides a unified foundation for future research.","url":"https://doi.org/10.2139/ssrn.7066721","authors":["Alvaro  Javier Vargas Guerrero","Arnau Dillen","Johan Loeckx","Guy Nagels"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-24T08:58:56Z","doi":"10.2139/ssrn.7066721","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-3414490/v1","name":"Enhancing Communication and Comprehension for Individuals with Special Needs through Federated Learning: A Deep Learning Approach","source":"crossref","abstract":"Abstract Individuals with special needs most of the time find it harder to identify hazards and dangers as well as circumstances that are socially challenging. Hence, they face the risk of falling victim to abuse and violence. In this paper, the main goal is to help people with special needs to more successfully communicate with others and comprehend their surroundings. Machine learning-based solutions are used to help people with special needs in their communication tasks. The proposed machine learning model contains a convolutional layer, attention layer, and Bidirectional long short-term memory (BiLSTM) layer and achieves 99.00% accuracy performance. We applied federated learning to preserve privacy and to help researchers overcome problems they face when dealing with people with special needs.","url":"https://doi.org/10.21203/rs.3.rs-3414490/v1","authors":["Tharwat Elsayed","Mohamed Elrashidy","Ayman EL-Sayed","Abdullah N. Moustafa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-11T16:34:46Z","doi":"10.21203/rs.3.rs-3414490/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.69987/aimlr.2024.50104","name":"Privacy-Preserving Data Analysis Using Federated Learning: A Practical Implementation Study","source":"crossref","abstract":"","url":"https://doi.org/10.69987/aimlr.2024.50104","authors":["Wenkun Ren","Juan Li","Xiaolan Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-07T18:29:58Z","doi":"10.69987/aimlr.2024.50104","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.5772/intechopen.1005540","name":"Recognizing Heat Exchange Crisis Using the SVM ALG","source":"crossref","abstract":"This article discusses the use of support vector machines to recognize a heat transfer crisis. A heat transfer crisis during boiling is the phenomenon of a sharp deterioration in heat transfer on a heat transfer surface, leading, as a rule, to a rapid increase in its temperature.","url":"https://doi.org/10.5772/intechopen.1005540","authors":["Viktoria Sheshukova"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-02T13:31:30Z","doi":"10.5772/intechopen.1005540","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.70267/cai.26v3n2.5964","name":"Research Review on Federated Learning Technology for Fault Diagnosis","source":"crossref","abstract":"Federated learning (FL) is a distributed machine learning (ML) method. This technology only needs to exchange model parameters without sharing private data and plays an important role in industrial fault diagnosis. This paper focuses on analysing four mainstream technical schemes: fault diagnosis methods based on traditional federated learning, fault diagnosis methods based on federated deep learning, optimized federated fault diagnosis methods for nonindependent and identically distributed (non-IID) data, and lightweight federated fault diagnosis methods for edge device deployment. This paper systematically sorts out the federated learning technologies for fault diagnosis, summarizes the practical challenges existing in these technical schemes, and proposes corresponding future research prospects, providing specific references for the innovation and implementation of federated learning technologies for fault diagnosis.","url":"https://doi.org/10.70267/cai.26v3n2.5964","authors":["Xuebin Han"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-14T04:08:42Z","doi":"10.70267/cai.26v3n2.5964","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21203/rs.3.rs-8779446/v1","name":"Blockchain-Governed Federated Intelligence: A Formal Model for Trust-Aware and Auditable Collaborative Learning","source":"crossref","abstract":"Abstract The joint artificial intelligence across cross-organizational digital ecosystems requires coordination strategies to maintain reliability where one of the organizations has no central, trusted controller. Although federated learning requires minimal exposure to raw data, its effectiveness in open and heterogeneous systems is diminished by strategic behavior and lack of incentive convergence and the possibility of introducing bad or low-quality updates. This paper in turn presents Blockchain -Governed Federated Intelligence (BGFI), a model-oriented architecture that integrates onto-chain governance, policy compliance, security filtering and trustweighted aggregation into a unified system of state-transition. BGFI directly incorporates checking results, reputation interaction, and integrity futility indicators into the aggregation pipeline, which enables one to perform formal logic with respect to protocol compliance and allowable influence. Within the given assumptions, we show that the model evolution is bounded in a round-by-round term and that updates adopted are publicly verifiable; in addition, we give an analytical explanation of coordination cost, on-chain storage growth and latency in terms of round-by-round basis. To demonstrate how the framework can be operationalized in any multi-stakeholder system, two real-world application cases are outlined on which the framework can be applied in smart transportation and decentralized energy systems. The future research direction is earmarked to empirical benchmarking, the main role of this manuscript is the provision of a formally defined, practitionable model and an overall overview of blockchain-controlled federated intelligence.","url":"https://doi.org/10.21203/rs.3.rs-8779446/v1","authors":["Hossein Hosseinalibeiki","Reza Sepehrzad","Majid Heidari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-10T11:21:34Z","doi":"10.21203/rs.3.rs-8779446/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-3417554/v1","name":"Privacy-Preserving Federated Learning Based On Partial Low-Quality Data","source":"crossref","abstract":"Abstract Traditional machine learning requires collecting data from participants for training, which may result in malicious acquisition of privacy in participants' data. Federated learning offers a method to protect participants' data privacy by transferring the training process from a centralized server to terminal devices. However, the server may still obtain participants' privacy information through inference attacks, among other methods. Additionally, the data provided by participants varies in quality, and excessive involvement of low-quality data in the training process can render the model unusable, which is an important issue in current mainstream Federated learning. To address the aforementioned issues, this paper presents a Privacy Preserving Federated learning Scheme with Partial Low-Quality Data (PPFL-LQDP). It achieves good training results while allowing participants to utilize partial low-quality data, thereby enhancing the privacy and ronutness of the Federated learning scheme. Specifically, we use a modified distributed Paillier cryptographic mechanism to protect the privacy and security of participants' data during the Federated training process. Simultaneously, we construct composite evaluation values for the data held by participants to reduce the involvement of low-quality data, thereby minimizing the negative impact of such data on the model. Through experiments on the MNIST dataset, we demonstrate that this scheme can complete the model training of Federated learning with the participation of partial low-quality data, while effectively protecting the security and privacy of participants' data. Comparisons with related schemes also show that our scheme has good overall performance.","url":"https://doi.org/10.21203/rs.3.rs-3417554/v1","authors":["Huiyong Wang","Qi Wang","Yong Ding","Shijie Tang","Yujue Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-12T04:14:15Z","doi":"10.21203/rs.3.rs-3417554/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-3714454/v1","name":"Balancing Privacy and Explainability in Federated Learning","source":"crossref","abstract":"Abstract As we advance towards future generations of communication networks, such as 6G, Artificial Intelligence (AI) and Machine Learning (ML) will assume an increasingly pivotal role in network optimization, management, and operation. In this context, the pursuit of reliability in ML models has emerged as a highly active area of research. Explainable AI (XAI) is an essential tool to unravel the underlying mechanisms of network behaviour, enabling a deeper understanding of the decisions made by black-box models in future-generation networks. This paper investigates the impact of Federated Learning (FL) on explainability by evaluating the correlation between the feature importance derived from models trained using four different FL approaches and the model trained using a conventional single-host approach. The analysis utilizes two publicly available datasets, two XAI metrics, and two correlation metrics. The findings reveal a high correlation between FL and single-host trained models in three of the four FL approaches, suggesting that some FL approaches failed to capture similar patterns.","url":"https://doi.org/10.21203/rs.3.rs-3714454/v1","authors":["Rafael Teixeira","Leonardo Almeida","Pedro Rodrigues","Julio Corona","Mário Antunes","Rui L. Aguiar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-12-08T06:16:51Z","doi":"10.21203/rs.3.rs-3714454/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.2139/ssrn.6955451","name":"Federated Learning in Smart Healthcare, Homes, and Cities (FL-SHHC): A Systematic Literature Review","source":"crossref","abstract":"Federated learning (FL) has emerged as an acceptable approach for training machine learning (ML) models across distributed devices while preserving their private data. Recently, Internet of Things (IoTs) have been widely used across three user privacy domains: smart healthcare, homes, and cities (SHHC). In this study, we present a systematic literature review (SLR) using the PRISMA framework to comprehensively analyze existing research on FL-IoT smart healthcare, homes, and cities (FL-IoT SHHC). Through a rigorous selection process using twelve key databases. We identified and categorized 84 studies into three user privacy domains (smart healthcare, homes, and cities) and critically analyzed them to identify three frequently used FL architectures: centralized, decentralized, and hierarchical FL. Furthermore, we discuss 48 algorithms, 13 hyperparameter tuning techniques, 76 datasets, and 11 evaluation metrics. Four explainable AI approaches, five privacy attacks, the limitations of this research, and future directions are also discussed. This SLR not only provides a comprehensive overview of the state-of-the-art in FL-IoT smart healthcare, homes, and cities but also provides a roadmap for researchers and professionals seeking to advance the field and design more robust and resilient FL-IoT systems for user privacy domains.","url":"https://doi.org/10.2139/ssrn.6955451","authors":["Tom Jackson","Javed Ali Khan","Alexios Mylonas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-17T00:42:08Z","doi":"10.2139/ssrn.6955451","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.2139/ssrn.6905778","name":"Federated Learning for Acute Lymphoblastic Leukemia Subtype Classification: A Systematic Review of Privacy-Preserving Artificial Intelligence in Hematologic Oncology","source":"crossref","abstract":"Acute Lymphoblastic Leukemia (ALL) remains one of the most clinically significant hematologic malignancies, requiring accurate subtype classification to support diagnosis, risk stratification, and treatment planning. Recent advances in artificial intelligence have demonstrated considerable potential for improving leukemia classification; however, the use of centralized patient data raises substantial privacy, regulatory, and data-governance concerns. Federated Learning (FL) has emerged as a privacy-preserving machine learning paradigm that enables collaborative model development across institutions without sharing raw patient data. This systematic review examines the current state of federated learning research for ALL subtype classification and related applications in hematologic oncology. The review synthesizes evidence on federated architectures, machine learning techniques, data modalities, privacy-enhancing mechanisms, model performance, and implementation challenges. Particular attention is given to the integration of genomic, transcriptomic, imaging, and clinical data within distributed learning environments. The findings indicate that federated learning can achieve predictive performance comparable to centralized approaches while enhancing data privacy and institutional collaboration. However, challenges related to data heterogeneity, model interpretability, communication efficiency, fairness, and regulatory compliance remain significant barriers to clinical deployment. Based on the reviewed literature, this paper proposes a research agenda for advancing privacy-preserving AI in hematologic oncology and identifies key opportunities for the development of secure, scalable, and clinically trustworthy leukemia classification systems.","url":"https://doi.org/10.2139/ssrn.6905778","authors":["Aremu Feranmi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T13:49:49Z","doi":"10.2139/ssrn.6905778","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-1890894/v1","name":"Efficiency-optimized Data Perturbation in Local Differentially Private Federated Learning","source":"crossref","abstract":"Abstract Federated learning (FL) pours vitality into developing data-driven AI. However, there are still some challenges, such as balancing the security and efficiency in FL. Differential privacy is one of the dominant means in privacy-preserving machine learning. Local differential privacy (LDP) further realizes the confidentiality of the server by perturbing the transmitting parameters, which is naturally applicable for the decentralized FL. However, the current research exists the weaknesses of low communication efficiency and poor adaptability in complex deep learning models. In this work, we propose an efficiency-optimized LDP data perturbation mechanism (Adaptive-Harmony), which allows adaptive parameter range to reduce variance and improve model accuracy. Specifically, each client in each round adaptively selects perturbation parameters according to model training. Furthermore, only 1-bit data transmission for each dimension of the model parameters, thus significantly reducing the communication overhead. Theoretical analysis and proof have shown that Adaptive-Harmony holds the same asymptotic error bounds and convergence performance as advanced works but with minimal communication costs. An LDP-FL framework (Optimal LDP-FL) is also proposed, taking Adaptive-Harmony as the core. We also introduce a parameter shuffling in the Optimal LDP-FL, which avoids server tracking clients through the model parameters, thereby improving privacy levels without consuming the privacy budget. Comprehensive experiments on the MNIST and Fashion MNIST datasets show that the proposed method can significantly reduce computational and communication costs with the same level of privacy and model utility.","url":"https://doi.org/10.21203/rs.3.rs-1890894/v1","authors":["Jianzhe Zhao","Mengbo Yang","Jiali Zheng","Jingran Feng","Stan Matwin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-08-01T14:37:09Z","doi":"10.21203/rs.3.rs-1890894/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-3549370/v1","name":"An Efficient IoT DataStream Prediction using Integrated Federated Learning with CRSO of Attention-based LSTM Framework","source":"crossref","abstract":"Abstract Real-time data stream processing presents a significant challenge in the rapidly changing Internet of Things (IoT) environment. Traditional centralized approaches face hurdles in handling the high velocity and volume of IoT data, especially in real-time scenarios. In order to improve IoT DataStream prediction performance, this paper introduces a novel framework that combines federated learning (FL) with a competitive random search optimizer (CRSO) of Long Short-Term Memory (LSTM) models based on attention. The proposed integration leverages distributed intelligence while employing competitive optimization for fine-tuning. The proposed framework not only addresses privacy and scalability concerns but also optimizes the model for precise IoT DataStream predictions. This federated approach empowers the system to derive insights from a spectrum of IoT data sources while adhering to stringent privacy standards. Experimental validation on a range of authentic IoT datasets underscores the framework's exceptional performance, further emphasizing its potential as a transformational asset in the realm of IoT DataStream prediction. Beyond predictive accuracy, the framework serves as a robust solution for privacy-conscious IoT applications, where data security remains paramount. Furthermore, its scalability and adaptability solidify its role as a crucial tool in dynamic IoT environments.","url":"https://doi.org/10.21203/rs.3.rs-3549370/v1","authors":["Asma M. El-Saied"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-06T16:36:18Z","doi":"10.21203/rs.3.rs-3549370/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-3549297/v1","name":"An Integrated Federated Learning with CRSO of Attention-based LSTM Framework for Efficient IoT DataStream Prediction","source":"crossref","abstract":"Abstract Real-time data stream processing presents a significant challenge in the rapidly changing Internet of Things (IoT) environment. Traditional centralized approaches face hurdles in handling the high velocity and volume of IoT data, especially in real-time scenarios. In order to improve IoT DataStream prediction performance, this paper introduces a novel framework that combines federated learning (FL) with a competitive random search optimizer (CRSO) of Long Short-Term Memory (LSTM) models based on attention. The proposed integration leverages distributed intelligence while employing competitive optimization for fine-tuning. The proposed framework not only addresses privacy and scalability concerns but also optimizes the model for precise IoT DataStream predictions. This federated approach empowers the system to derive insights from a spectrum of IoT data sources while adhering to stringent privacy standards. Experimental validation on a range of authentic IoT datasets underscores the framework's exceptional performance, further emphasizing its potential as a transformational asset in the realm of IoT DataStream prediction. Beyond predictive accuracy, the framework serves as a robust solution for privacy-conscious IoT applications, where data security remains paramount. Furthermore, its scalability and adaptability solidify its role as a crucial tool in dynamic IoT environments.","url":"https://doi.org/10.21203/rs.3.rs-3549297/v1","authors":["Asma M. El-Saied"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-07T01:17:38Z","doi":"10.21203/rs.3.rs-3549297/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-3080019/v1","name":"FedRCD: A Federated Learning Algorithm Leveraging Distribution Extraction and Community Detection for Enhanced Clustering","source":"crossref","abstract":"Abstract Clustering clients and performing federated learning within clusters is an effective method for overcoming the constraint of traditional federated learning algorithms in non-IID data scenarios. However, existing methods often rely on iterative processes and model parameters to represent and cluster clients, resulting in significant computational overhead and unsatisfactory clustering outcomes. To overcome these limitations, we propose a novel one-shot clustering federated learning algorithm called FedRCD. This algorithm extracts distribution information from clients’ datasets in a single step to represent them, reducing the reliance on model parameters. It further organizes clients into a graph and constructs clustering relationships using the Louvain algorithm, removing the need for pre-specifying the number of clusters. Experiments show that FedRCD significantly enhances the training effectiveness of neural networks compared to other federated learning algorithms in various non-IID data scenarios. Notably, on the CIFAR10 dataset, FedRCD achieves an impressive 37.08% accuracy improvement over the classical FedAvg and a 1.89% improvement over the recently released FeSEM, while demonstrating superior fairness performance.","url":"https://doi.org/10.21203/rs.3.rs-3080019/v1","authors":["Ruicong Wang","Naizheng Bian","Yingjun Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-28T06:00:25Z","doi":"10.21203/rs.3.rs-3080019/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"doi:10.21203/rs.3.rs-2694005/v1","name":"Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles","source":"crossref","abstract":"Abstract Advanced researches on connected vehicles have recently targeted to the integration of vehicle-to-everything (V2X) networks with Machine Learning (ML) tools and distributed decision making. Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems, including vehicles in V2X networks. Rather than sharing and uploading the training data to the server, the updating of model parameters (e.g., neural networks’ weights and biases) is applied by large populations of interconnected vehicles, acting as local learners. Despite these benefits, the limitation of existing approaches is the centralized optimization which relies on a server for aggregation and fusion of local parameters, leading to the drawback of a single point of failure and scaling issues for increasing V2X network size. Meanwhile, in intelligent transport scenarios, data collected from onboard sensors are redundant, which degrades the performance of aggregation. To tackle these problems, we explore a novel idea of decentralized data processing and introduce a federated learning framework for in-network vehicles, C-DFL( Consensus based Decentralized Federated Learning), to tackle federated learning on connected vehicles and improve learning quality. Extensive simulations have been implemented to evaluate the performance of C-DFL, that demonstrates C-DFL outperforms the performance of conventional methods in all cases.","url":"https://doi.org/10.21203/rs.3.rs-2694005/v1","authors":["Xiaoyan Liu","Zehui Dong","Zhiwei Xu","Siyuan Liu","Jie Tian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-24T02:55:11Z","doi":"10.21203/rs.3.rs-2694005/v1","addedAt":"2026-08-31T06:41:29.552Z","updatedAt":"2026-08-31T06:41:29.552Z"},{"id":"pmid:41902503","name":"Toward next-generation machine learning and deep learning for spatial omics.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41902503/","authors":["Zirem Y","Fournier I","Salzet M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar 1","doi":"10.1093/bib/bbag131","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41902166","name":"Strategies for Class-Imbalanced Learning in Multi-Sensor Medical Imaging.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41902166/","authors":["Zhou D","Gao S","Huang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar 23","doi":"10.3390/s26061998","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41899545","name":"Clinical AI in Radiology: Foundations, Trends, Applications, and Emerging Directions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41899545/","authors":["Hartsock I","Koutsoubis N","Ahmed S","Parker N","Schabath MB","Araujo C","Qayyum A","Lam C","Gatenby RA","Rasool G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar 13","doi":"10.3390/cancers18060942","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41899368","name":"The Current Landscape of Artificial Intelligence in Positron Emission Tomography (PET) Imaging Across the Cancer Continuum.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41899368/","authors":["Zar WYT","Kim MR","Ghose A","Adeleke S","Gupta M","Choudhary PS","Shankar A","Mohapatra S","Boussios S","Maniam A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar 23","doi":"10.3390/jcm15062446","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41897867","name":"Assessing the Role of Vocal Plasticity in Sociospatial Coordination.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41897867/","authors":["Mercado E 3rd","Hyland Bruno J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar 12","doi":"10.3390/ani16060890","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41896588","name":"An embedded deep learning framework for real-time violence detection and alert generation.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41896588/","authors":["Salman M","Abbas N","Ur Rahman SI","Al Alshaikh M","Saudagar AKJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar 28","doi":"10.1038/s41598-026-44939-x","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41895354","name":"AI innovations for ovarian and endometrial cancer diagnosis: Methodological challenges and engineering roadmap.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41895354/","authors":["Angeline J","Bethanney Janney J","Arvind N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun","doi":"10.1016/j.critrevonc.2026.105298","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41884150","name":"Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41884150/","authors":["Dang M","Liu B","Chen Y","Zhang Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1093/psyrad/kkag007","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"pmid:41871959","name":"Fundamentals of big data and artificial intelligence in transfusion medicine.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41871959/","authors":["Turki AT","Brieske CM","Gurkan UA","Scheidler KM","Usta OB","Turkulainen E","Arzideh K","Temme C","Hosch R","Horn PA","Arvas M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr","doi":"10.1111/vox.70227","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41859017","name":"Innovative Applications and Challenges of Artificial Intelligence in the Whole-Course Management of Chronic Obstructive Pulmonary Disease.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41859017/","authors":["Chen S","Xing S","Zhang G","Qiu F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2147/COPD.S568919","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41857437","name":"Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41857437/","authors":["Wang X","Xiong D","Cui S","Duan B","Hung Y","He J","Ding G","Tang Y","Wang Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar 19","doi":"10.1007/s12672-026-04864-x","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41833484","name":"Harnessing artificial intelligence in healthcare: Advancing diagnosis, treatment, and 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for precision-equitable diabetes care.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41623885/","authors":["Bai B","Liu X","Li H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fdgth.2025.1678047","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41623484","name":"Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41623484/","authors":["Wang X","Wang Q","Ding G","Wang J","Tang Y","Feng Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Feb 20","doi":"10.1016/j.isci.2026.114627","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41619215","name":"Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41619215/","authors":["Nesari AM","MotieGhader H","Ghorbian S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan 7","doi":"10.1093/bib/bbaf723","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41615792","name":"Artificial intelligence in biobanking: current situation and future perspectives.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41615792/","authors":["Kinkorová J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"","addedAt":"2026-08-31T06:41:29.553Z","updatedAt":"2026-08-31T06:41:29.553Z"},{"id":"pmid:41610568","name":"The role of artificial intelligence in sarcopenia: Advances, applications, and future directions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41610568/","authors":["Yousaf MW","Nadeem AH","Nadeem MF","Qaisar R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun","doi":"10.1016/j.compbiolchem.2026.108930","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41607969","name":"Artificial Intelligence for Predicting Postoperative Complications in Orthopedics: A Review of Clinical Applications, Challenges, and Future Directions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41607969/","authors":["Sharma AC","Azeem A","Omari IH","Premkumar A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.7759/cureus.100254","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41602756","name":"AI-powered analysis of viral metagenomic sequencing data for rapid outbreak investigation and novel pathogen discovery.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41602756/","authors":["Chisompola D","Luwaya E","Nzobokela J","Mwansa P","Chakulya M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fmicb.2025.1717859","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41602532","name":"The digital orchard: advanced data-driven technologies in apple breeding and genetic modification.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41602532/","authors":["Abid F","Zhang Z","Farooque G","Zulqarnain RM","Rasheed J","Osman O","Alsubai S","Jamel L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fpls.2025.1725617","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41602426","name":"Deep learning in renal ultrasound: applications, challenges, and future outlook.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41602426/","authors":["Zhang Y","Hou Y","Qiu T","Zhuang Y","Chen K","Ling W","Luo Y","Lin J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fonc.2025.1730628","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41600173","name":"Deep Reinforcement Learning for Sustainable Urban Mobility: A Bibliometric and Empirical Review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41600173/","authors":["Jamal S","Siddiqui F","Alam MA","Ayman-Mursaleen M","Zafar S","Naaz S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan 6","doi":"10.3390/s26020376","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41596006","name":"Artificial Intelligence Meets Nail Diagnostics: Emerging Image-Based Sensing Platforms for Non-Invasive Disease Detection.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41596006/","authors":["Marode TP","Bhangdiya VK","Nemane S","Tulaskar D","Sarad VM","Sankar K","Chopade S","Avthankar A","Bhaiyya M","Kulkarni MB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan 8","doi":"10.3390/bioengineering13010075","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41595633","name":"Emerging Artificial Intelligence Models for Estimating Breslow Thickness from Dermoscopic Images.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41595633/","authors":["Santaniello U","Rosset F","Fava P","Cavallo F","Quaglino P","Ribero S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan 3","doi":"10.3390/biomedicines14010097","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41595166","name":"Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41595166/","authors":["Petrella F","Rizzo S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan 13","doi":"10.3390/cancers18020246","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41590385","name":"Artificial Intelligence and Machine Learning in Bone Metastasis Management: A Narrative Review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41590385/","authors":["Bulut H","Demiröz S","Kanay E","Ozkan K","Errani C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan 22","doi":"10.3390/curroncol33010065","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41589143","name":"Artificial Intelligence in Radiology: Advancing Precision, Accuracy, and Early Detection in Cancer Diagnosis.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41589143/","authors":["Gurjar P","Mayana SK","Reddy Annadevula SK","Singh B","Sambhav K","Shah SB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.7759/cureus.100102","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41584179","name":"AI-driven transformation of precision medicine: a comprehensive narrative review of key application areas, emerging paradigms, and future directions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41584179/","authors":["Zeng Q","Huang C","Zhu J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fpubh.2025.1656603","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41569331","name":"Artificial intelligence-enabled pediatric radiology in low-resource settings: addressing resource constraints in the African healthcare system.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41569331/","authors":["Nour AS","Raymond C","Zewdneh D","Anazodo U"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan 22","doi":"10.1007/s00247-025-06504-y","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41563839","name":"Federated Learning in Healthcare: From Research to Real-World Deployment.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41563839/","authors":["Bakas S","Li X","Shah P","Roth HR"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May","doi":"10.1146/annurev-bioeng-080125-041414","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41546050","name":"Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41546050/","authors":["Tiwari A","Widodo","Krisnawati DI","Tzou KY","Kuo TR"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan 17","doi":"10.1186/s12951-025-03952-4","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41541687","name":"Polygenic risk scores: Navigating the future of precision medicine through economic, ethical, and scientific advancements.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41541687/","authors":["Nguyen HHK","Le HM","Le TQ","Dinh NTQ","Nguyen TT","Nguyen PA","Hoang DM","Huynh CD"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan 16","doi":"10.1016/j.isci.2025.114375","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41540778","name":"The balance between artificial and human intelligence in clinical practice.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41540778/","authors":["Marrella D","Anttila T","Ryhänen J","Miller R","Liu B","Liverneaux P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun","doi":"10.1177/17531934251401382","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41534227","name":"286th ENMC international workshop: Muscle imaging: artificial intelligence, automatic segmentation and imaging data sharing in neuromuscular disease. Hoofddorp, The Netherlands, 7-9 March 2025.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41534227/","authors":["Warman-Chardon J","Straub V","Vissing J","Schlaeger S","Kan HE","MRI workshop study group"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar","doi":"10.1016/j.nmd.2025.106304","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41534133","name":"From data to diagnosis: A comprehensive review of machine learning-driven wearable sensors in healthcare.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41534133/","authors":["Zhao M","Liu R","Jin S","Ren B","Zhang Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug","doi":"10.1016/j.bioelechem.2026.109228","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41531594","name":"Artificial Intelligence in Rheumatology: Clinical Applications in Rheumatoid Arthritis, Osteoarthritis, and Systemic Lupus Erythematosus.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41531594/","authors":["Aldhuaina K","Gupta D","Bashir U","Nnap LM","Rawat A","Dolphin J","Sultana R","Cai LY","Imam B","Devkota RR","Dsouza D","Rai M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.7759/cureus.99108","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41524988","name":"AI for screening in healthcare: promise and challenges.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41524988/","authors":["Saito K","Walston SL","Takita H","Mitsuyama Y","Arita Y","Ueda D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Aug","doi":"10.1007/s00261-025-05370-4","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41524152","name":"Securing data management systems in perfusion: A narrative review on cybersecurity and clinical risk.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41524152/","authors":["El Dsouki Y","Condello I","Iacona C","El Daccache S","Lorusso R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan 12","doi":"10.1177/02676591261417526","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41506577","name":"Artificial intelligence in metagenome-assembled genome reconstruction: Tools, pipelines, and future directions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41506577/","authors":["Sagar K","Priti K","Chandra H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Feb","doi":"10.1016/j.mimet.2026.107390","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41497383","name":"The future of big data and artificial intelligence on dairy farms: A proposed dairy data ecosystem.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41497383/","authors":["Hostens M","Franceschini S","van Leerdam M","Yang H","Pokharel S","Liu E","Niu P","Zhang H","Noor S","Hermans K","Salamone M","Sharma S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.3168/jdsc.2025-0843","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41490410","name":"The Application of Agentic Artificial Intelligence in Orthopaedics.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41490410/","authors":["Billi F","Bini SA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Feb 18","doi":"10.2106/JBJS.25.01497","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41486277","name":"Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41486277/","authors":["Gao S","Liu J","Li L","Yang D","Miao Y","Zhang X","Han Q","Shi Y","Wu J","Zhang K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan 4","doi":"10.1186/s12938-025-01502-5","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41479639","name":"Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41479639/","authors":["Sun JR","Sun XN","Lu BJ","Deng BC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 14","doi":"10.3748/wjg.v31.i46.111176","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41475240","name":"A roadmap for federated learning projects using health data to guide sustainable artificial intelligence development in the European Union.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41475240/","authors":["Kommusaar J","Elunurm S","Chomutare T","Kangasniemi M","Salanterä S","Peltonen LM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar 15","doi":"10.1016/j.ijmedinf.2025.106242","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41474993","name":"Position Paper: Artificial Intelligence in Medical Image Analysis: Advances, Clinical Translation, and Emerging Frontiers.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41474993/","authors":["Panayides AS","Chen H","Filipovic ND","Geroski T","Hou J","Lekadir K","Marias K","Matsopoulos GK","Papanastasiou G","Sarder P","Tourassi G","Tsaftaris SA","Fu H","Kyriacou E","Loizou CP","Zervakis M","Saltz JH","Shamout FE","Wong KCL","Yao J","Amini A","Fotiadis DI","Pattichis CS","Pattichis MS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Feb","doi":"10.1109/JBHI.2025.3649496","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41474926","name":"The Evolving Landscape of Urology in the Era of Artificial Intelligence: An Update of Clinical Applications and Emerging Innovations.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41474926/","authors":["Siddique MFH","Ali MI","Chowdhury PP","Alam N","Rahman MM","Tanim MH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan","doi":"","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41471631","name":"Artificial Intelligence of Things for Next-Generation Predictive Maintenance.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41471631/","authors":["Bitam T","Yahiaoui A","Boubiche DE","Martínez-Peláez R","Toral-Cruz H","Velarde-Alvarado P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 16","doi":"10.3390/s25247636","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41470230","name":"Applications of Artificial Intelligence in Chronic Total Occlusion Revascularization: From Present to Future-A Narrative Review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41470230/","authors":["Doktorova V","Goranov G","Nikolov P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 17","doi":"10.3390/medicina61122229","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41463145","name":"Artificial Intelligence and New Technologies in Melanoma Diagnosis: A Narrative Review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41463145/","authors":["Górecki S","Tatka A","Brusey J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 5","doi":"10.3390/cancers17243896","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41462232","name":"Deep learning for Alzheimer's disease: advances in classification, segmentation, subtyping, and explainability.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41462232/","authors":["Shaikh MR","Jeyabose A","Arjunan RV"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 29","doi":"10.1186/s12938-025-01482-6","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41461211","name":"Federated Learning in Neurology: Bridging Data Privacy and Artificial Intelligence for Brain Health.","source":"pubmed","abstract":"Neurological disorders affect hundreds of millions globally, yet translating artificial intelligence (AI) advances into clinical practice remains challenging due to fragmented, privacy-sensitive datasets. Federated learning (FL) has emerged as a promising paradigm, enabling collaborative model training across institutions without sharing raw patient data. This review synthesizes FL applications in neurology from 2020 to 2025, spanning neuroimaging, electrophysiology, and electronic health records. We analyze real-world deployments, highlight algorithmic trends, and discuss technical, regulatory, and organizational barriers to clinical translation. While FL demonstrates feasibility in tasks such as brain tumor segmentation, multiple sclerosis lesion detection, and electronic health record-based predictive modeling, verified clinical implementations remain scarce. We outline strategies to enhance adoption, including privacy-preserving techniques, standardized infrastructures, domain-adaptive algorithms, and cross-disciplinary collaboration. By bridging technical innovation with regulatory compliance and operational scalability, FL holds significant potential to advance precision neurology while safeguarding patient privacy.","url":"https://pubmed.ncbi.nlm.nih.gov/41461211/","authors":["Soltanieh S","Khalvati F","Yeh EA","Sahar Soltanieh","Farzad Khalvati","E Ann Yeh"],"tags":["Bridging (networking)","Safeguarding","Data sharing","Medicine","Artificial intelligence"],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025-12-29","doi":"10.1055/a-2769-6752","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"pmid:41458487","name":"Artificial intelligence for posterior capsule opacification.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41458487/","authors":["Gill G","Taylor Gonzalez D","Sanghvi H","Djulbegovic M","Suleiman A","Gupta S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fmed.2025.1695525","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41457873","name":"Collaborative artificial intelligence for the diagnosis and management of acute ischemic stroke.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41457873/","authors":["Fan Z","Chen Q","Lu W","Yao Z","Yang S","Zhao H","Cao H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Dec","doi":"10.1080/07853890.2025.2594356","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41456642","name":"Why is there no treatment for osteoarthritis - Opportunity for AI based big data analytics to advance the field.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41456642/","authors":["Saxer F","Jansen G","Bierma-Zeinstra SMA","Holzhauer B","Demanse D","Melnick J","Vukadinovic Greetham D","Rall T","Mesenbrink P","Schieker M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May","doi":"10.1016/j.joca.2025.12.021","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41452505","name":"Ethical challenges and governance of artificial intelligence in hepatocellular carcinoma management : Wan DL et al. Ethical considerations in AI for hepatocellular carcinoma.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41452505/","authors":["Wan DL","Lin SZ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 26","doi":"10.1007/s12032-025-03157-7","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41443971","name":"A call for ethical, equitable, and effective artificial intelligence to improve care for all people with epilepsy: A roadmap. A report by the ILAE Global Advocacy Council and Big Data Commission.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41443971/","authors":["Josephson CB","Beniczky S","Denaxas S","Ikeda A","Jehi L","Mwesige AK","Jette N","Jones GD","Ryvlin P","Sen A","Triki CC","Waters G","Guekht A","Cross JH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr","doi":"10.1002/epi.70058","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41436155","name":"Technology for better adult congenital heart disease care: the time is now.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41436155/","authors":["Blake SR","Gatzoulis M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 23","doi":"10.1136/openhrt-2025-003766","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41430374","name":"Brain tumor detection with real-world predictions in Jordan hospitals.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41430374/","authors":["Alqaraleh M","Al-Batah MS","Alzboon MS","Alourani A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 23","doi":"10.1038/s41598-025-33215-z","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41426936","name":"Artificial Intelligence in MRI for Urologic Oncology: A Systematic Review of Diagnostic Accuracy and Clinical Utility.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41426936/","authors":["Khormi SA","Algethami SA","Alshehri RA","Alhinti FY","Alsalumi KA","Al Shahrani SA","Alwadani FT","Alsubhi GR","Alenezi AB","Alotaibi WT","Alamer AM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov","doi":"10.7759/cureus.97160","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41425881","name":"Enhancing bronchopulmonary dysplasia prediction in preterm infants using artificial intelligence and multimodal data integration.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41425881/","authors":["Zhang X","Wang A","Xu R","Liu D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fped.2025.1629795","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41418718","name":"From 16S rRNA to deep learning: Evolution of computational approaches in human microbiome studies.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41418718/","authors":["Dwivedi J","Shukla MM","Upadhyay A","Wal A","Sharma KK","Gasmi A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr","doi":"10.1016/j.compbiolchem.2025.108852","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41402012","name":"Federated learning framework for predicting multi-drug resistant tuberculosis across regional databases.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41402012/","authors":["Bhute HA","Bhute AN","Waghulde KB","Vasgi BP","Sonar R","Deore SP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1016/j.ijtb.2025.10.006","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41401681","name":"Artificial intelligence in oncological positron emission tomography: advancing image analysis and interpretation.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41401681/","authors":["Nakajo M","Hirahara D","Hirahara M","Eizuru Y","Tani A","Kanzaki F","Takumi K","Kamimura K","Yoshiura T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan","doi":"10.1016/j.crad.2025.107187","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41401467","name":"Deep learning for imaging diagnosis of jaw cystic lesions and maxillofacial tumors: A narrative review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41401467/","authors":["Zhang B","Li Y","Shi J","Liu S","Liu C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1177/03000605251404778","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41393847","name":"Bridging the gap in digital health: A framework for leveraging digital health technologies in cardiovascular diseases, hypertension, and diabetes-a narrative review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41393847/","authors":["Towett G","Snead RS","Marczika J","Ambalavanan R","Kairichi MM","Malioukis A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan-Dec","doi":"10.1177/20552076251406654","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41393619","name":"Artificial Intelligence in Radiology: Transforming Cancer Detection and Diagnosis.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41393619/","authors":["Gupta S","P A","Reddy GHV","Natarajan K","Srivastava V","Goda J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov","doi":"10.7759/cureus.96518","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41387134","name":"Responsible adoption of multimodal artificial intelligence in health care: promises and challenges.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41387134/","authors":["Azarfar G","Naimimohasses S","Rambhatla S","Komorowski M","Ferro D","Lewis PR","Gates D","Shara N","Gascon GM","Chang A","Mamdani M","Bhat M","Alliance of Centers of Artificial Intelligence in Medicine working group"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1016/j.landig.2025.100917","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41385196","name":"Signal detection in pharmacovigilance: Methods, tools, and workflows from case identification to adverse drug reaction database entry.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41385196/","authors":["Mugada V","Suryadevara V","Cheekurumilli M","Yarguntla SR"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 12","doi":"10.32394/pe/211665","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41381657","name":"Comprehensive analysis of security threats and privacy issues in indoor localization systems.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41381657/","authors":["Ayub A","Abidin ZZ","Alhammadi A","Khan MA","Soliman NF","Ghazali NB","Algarni AD"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-22204-x","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"pmid:41376849","name":"Artificial intelligence in hemovigilance: A narrative review on advancing blood safety and monitoring systems.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41376849/","authors":["Haque Lamem MF","Sahid MI"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan-Dec","doi":"10.1177/20552076251406306","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41371937","name":"Artificial Intelligence Across the Obesity Continuum: From Mechanistic Insights to Global Precision Prevention and Therapy.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41371937/","authors":["Wang MH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Feb","doi":"10.1002/oby.70095","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41362029","name":"[Current Imaging Approaches for Pediatric Brain Tumors].","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41362029/","authors":["Miyake K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov","doi":"10.11477/mf.030126030530061074","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41361868","name":"Federated generalized additive models for location, scale and shape.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41361868/","authors":["Swenne A","Intemann T","Moreno LA","Pigeot I"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 9","doi":"10.1186/s12874-025-02735-7","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41350088","name":"Federated learning in inflammatory bowel disease: The future of privacy-preserving Artificial Intelligence.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41350088/","authors":["Puca P","Lopetuso LR","Laterza L","Papa A","Danese S","Cesario A","Damiani A","Gasbarrini A","Arcuri G","Scaldaferri F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep","doi":"10.1016/j.bpg.2025.102050","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41348304","name":"Navigating the AI Frontier in Toxicology: Trends, Trust, and Transformation.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41348304/","authors":["Luechtefeld T","Hartung T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1007/s40572-025-00514-6","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"pmid:41348251","name":"Bioinformatics and artificial intelligence in genomic data analysis: current advances and future directions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41348251/","authors":["Olawade DB","Kade A","Egbon E","Usman SO","Fapohunda O","Ijiwade J","Ogbonna CE"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 5","doi":"10.1007/s00438-025-02314-x","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41347310","name":"Balancing Innovation and Responsibility: Ethical and Privacy Challenges in Stroke Digital Health.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41347310/","authors":["Muchada M","Rizzo F","Brunelli N","Molina CA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr","doi":"10.1161/STROKEAHA.125.050450","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41347125","name":"Artificial intelligence in diabetes care: from predictive analytics to generative AI and implementation challenges.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41347125/","authors":["Deng M","Yang R","Zheng X","Deng Y","Jiang J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fendo.2025.1620132","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41345973","name":"Data visiting governance: a conceptual framework.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41345973/","authors":["Thaldar D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 4","doi":"10.1186/s40246-025-00864-0","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41338440","name":"Machine learning and deep learning in clinical practice: Advancing neurodegenerative disease diagnosis with multimodal markers.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41338440/","authors":["Zarei O","Talebi Moghaddam M","Moradi Vastegani S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan","doi":"10.1016/j.brainresbull.2025.111667","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41332459","name":"Computational Landscape in Drug Discovery: From AI/ML Models to Translational Application.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41332459/","authors":["Sharma D","Anabala M","Jain VV","Shyam M","Prince SE","Muniyan R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1155/sci5/1688637","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41321654","name":"Leveraging Swin Transformer for advanced sentiment analysis: a new paradigm.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41321654/","authors":["Rajput GK","Srivastava SK","Gupta N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Dec","doi":"10.1007/s11571-025-10378-z","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41305174","name":"A Survey on Privacy Preservation Techniques in IoT Systems.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41305174/","authors":["Kaur R","Rodrigues T","Kadir N","Kashef R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25226967","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"pmid:41305111","name":"From Traditional Machine Learning to Fine-Tuning Large Language Models: A Review for Sensors-Based Soil Moisture Forecasting.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41305111/","authors":["Islam MB","Guerrieri A","Gravina R","Delaney DT","Fortino G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov 12","doi":"10.3390/s25226903","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41303774","name":"Machine-Learning-Driven Phenotyping in Heart Failure with Preserved Ejection Fraction: Current Approaches and Future Directions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41303774/","authors":["Potoupni V","Samaras A","Papadopoulos C","Boulmpou A","Moysiadis T","Zormpas G","Tzikas A","Fragakis N","Giannakoulas G","Vassilikos V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct 29","doi":"10.3390/medicina61111937","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41298147","name":"Artificial intelligence in rare diseases: toward clinical impact.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41298147/","authors":["Amorim AMB","Orzeł U","Caniceiro AB","Rosário-Ferreira N","Moreira IS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1016/j.tips.2025.10.010","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41290079","name":"Explainable artificial intelligence for multi-modal cancer analysis: From genomics to immunology.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41290079/","authors":["Zhang M","Yin L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar","doi":"10.1016/j.critrevonc.2025.105040","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41284066","name":"Deep learning in bone marrow cytomorphology: advances in segmentation, classification, and clinical translation.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41284066/","authors":["Mehmood S","Zubair M","Khan FM","Shah AA","Abbas S","Adnan KM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov 24","doi":"10.1007/s12032-025-03127-z","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41283879","name":"Multimodal machine learning for surgical decision support in epilepsy: Current evidence and translational gaps.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41283879/","authors":["Mercier M","de Palma L","Specchio N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar","doi":"10.1111/epi.70019","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41283484","name":"Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41283484/","authors":["Guermazi D","Khemchandani S","Wahood S","Nguyen C","Saliba E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov 12","doi":"10.3390/dermatopathology12040042","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41281807","name":"Oculomics meets exposomics: a roadmap for applying multi-modal ocular biomarkers in precision environmental health research.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41281807/","authors":["Cheng H","Sarnat JA","Walker DI","Madabhushi A","Singh A","Dhamdhere R","Mehta JS","Wong TY","Ji JS","Marsit CJ","Jones DP","Ting DSW","Ting DSJ","Liang D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1093/exposome/osaf013","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41277106","name":"From genomics to clinic: the transformative impact of AI in pharmacogenomics and personalized medicine.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41277106/","authors":["Jakka AL","Chacko RM","Vasam M","Alagarsamy S","Chandragiri SS","Gavini SD","Mathew MJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep-Oct","doi":"10.1080/14622416.2025.2591596","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41271017","name":"Lymphedema imaging and AI: A review of diagnostic modalities, biomarkers, and clinical integration.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41271017/","authors":["Urooj B","Ali S","Naqvi SKH","Xiao F","Huang PC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun","doi":"10.1016/j.bj.2025.100932","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41267953","name":"AI-driven Technologies for Wrist Fracture Prediction: A Narrative Review of Emerging Approaches.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41267953/","authors":["Briano S","May MC","Demontis G","Pachera G","Mazzola V","Vitali F","Galuppi A","Dapelo E","Zanirato A","Formica M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1055/a-2674-3914","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41266662","name":"AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41266662/","authors":["Hsu CY","Askar S","Alshkarchy SS","Nayak PP","Attabi KAL","Khan MA","Mayan JA","Sharma MK","Islomov S","Soleimani Samarkhazan H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov 21","doi":"10.1007/s10238-025-01965-9","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41266222","name":"The Emergence of Foundation Models in U.S. Radiology: A Narrative Review of Clinical Utility, Safety, and Evaluation.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41266222/","authors":["Uppuluri PC","Yang CW"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar","doi":"10.1016/j.acra.2025.10.063","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41245724","name":"Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41245724/","authors":["Rosen J","Russell J","Kartik P","Vella-Baldacchino M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct","doi":"10.1002/jeo2.70549","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41244777","name":"Reducing misdiagnosis in AI-driven medical diagnostics: a multidimensional framework for technical, ethical, and policy solutions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41244777/","authors":["Li Y","Yi X","Fu J","Yang Y","Duan C","Wang J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fmed.2025.1594450","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41234619","name":"A rapid review on the application of common data models in healthcare: Recommendations for data governance and federated learning in artificial intelligence development.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41234619/","authors":["von Gerich H","Chomutare T","Kytö V","Lundberg P","Siggaard T","Peltonen LM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan-Dec","doi":"10.1177/20552076251395536","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41231484","name":"Deep Learning in Otolaryngology: A Narrative Review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41231484/","authors":["Novi SL","Navarathna N","D'Cruz M","Brooks JR","Maron BA","Isaiah A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan 1","doi":"10.1001/jamaoto.2025.3911","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41228213","name":"AI-Based Cancer Models in Oncology: From Diagnosis to ADC Drug Prediction.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41228213/","authors":["Sobhani N","Kugeratski FG","Venturini S","Roudi R","Nguyen T","D'Angelo A","Generali D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct 24","doi":"10.3390/cancers17213419","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41228178","name":"Federated Learning for Cardiovascular Disease Prediction: A Comparative Review of Biosignal- and EHR-Based Approaches.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41228178/","authors":["Ryu H","Lee M","Kim SH","Kim JH","Yang HJ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/healthcare13212811","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"pmid:41218468","name":"Artificial intelligence-driven intelligent nanocarriers for cancer theranostics: A paradigm shift with focus on brain tumors.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41218468/","authors":["Pourmadadi M","Shabestari SM","Abdouss H","Rahdar A","Fathi-Karkan S","Pandey S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1016/j.seminoncol.2025.152429","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41213058","name":"The Ongoing Evolution of AI in Craniofacial Surgery: From Theory to Reality and Beyond.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41213058/","authors":["Pourriyahi H","Alkureishi LWT"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar-Apr 01","doi":"10.1097/SCS.0000000000012123","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41212162","name":"The Application of Artificial Intelligence in Epiretinal Membrane from Detection to Prognosis Prediction.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41212162/","authors":["Xu M","Lin J","Chen S","Huang Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul","doi":"10.1080/08820538.2025.2583072","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41207814","name":"Cloud computing for equitable, data-driven dementia medicine.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41207814/","authors":["Montagnese M","Rangelov B","Doel T","Llewellyn D","Walker Z","Rittman T","Oxtoby NP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov","doi":"10.1016/j.landig.2025.100902","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41206399","name":"Big multiple sclerosis data network: novel modelling approaches for real-world data analysis.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41206399/","authors":["Trojano M","Iaffaldano P","Copetti M","Drahota J","Forsberg L","Mouresan EF","Pontieri L","Spelman T","Toschi N","Butzkueven H","Glaser A","Hillert J","Horakova D","Magyari M","Vukusic S","Lucisano G","Kalincik T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov 8","doi":"10.1007/s00415-025-13439-9","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41205136","name":"Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41205136/","authors":["Yuan S","Guo L","Xu F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jul","doi":"10.1007/s11255-025-04878-4","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41196612","name":"Emerging Artificial Intelligence Technologies for Risk Assessment and Management in Acute Myeloid Leukemia: A Review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41196612/","authors":["Ansarian MA","Fatahichegeni M","Xu R","Chen Y","Wang X","Ren J","Liu H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 1","doi":"10.1001/jamaoncol.2025.3601","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41191169","name":"Evaluating the efficacy of a federated AI algorithm in assisting in the detection and segmentation of brain metastases: limitations and opportunities.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41191169/","authors":["Akdemir EY","Gutierrez AN","Zhang Y","Yarlagadda S","Gurdikyan S","DiStefano J","Wieczorek DJ","Lee YC","Tolakanahalli R","Hall MD","Press RH","McDermott MW","Bander ED","Guo WY","Tu E","Mehta MP","Kotecha R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov 5","doi":"10.1007/s11060-025-05294-5","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41185664","name":"Artificial Intelligence in Cancer Oncology Through Comprehensive Bibliometric Mapping of Global Trends Impact and Conceptual Structures.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41185664/","authors":["Caraka RE","Supardi K","Gondhowiardjo SA","Isnaniawardhani V","Gio PU","Chen RC","Pardamean B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.2147/JHL.S550933","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41176790","name":"Deep learning approaches for resolving genomic discrepancies in cancer: a systematic review and clinical perspective.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41176790/","authors":["Zubair M","Khan AH","Bilal SF","Li J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov 1","doi":"10.1093/bib/bbaf541","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41176580","name":"Decoding the genomic symphony: unravelling brain disorders through data integration and machine learning.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41176580/","authors":["Bracher-Smith M","Escott-Price V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1038/s41380-025-03330-4","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41167326","name":"Artificial intelligence, machine learning and omic data integration in osteoarthritis.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41167326/","authors":["Sharma D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar","doi":"10.1016/j.joca.2025.10.012","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41165316","name":"Artificial Intelligence for Noninvasive Health Diagnostics.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41165316/","authors":["Wankhede PR","Bhuyar D","Zanwar S","Pawar R","Jadhav MR","Gandhewar N","Kulkarni MB","Bhaiyya M","Haick H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov 28","doi":"10.1021/acssensors.5c03171","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41164258","name":"Cloud-edge-device collaborative computing in smart agriculture: architectures, applications, and future perspectives.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41164258/","authors":["Yu P","Teng F","Zhu W","Shen C","Chen Z","Song J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fpls.2025.1668545","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41161628","name":"AI-mediated immunotherapeutics in adenoid cystic carcinoma: Challenges and current perspectives.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41161628/","authors":["Singh M","Singh C","Chauhan K","Rajpoot GK","Jain CK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1016/j.critrevonc.2025.104984","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41158510","name":"Gene-LLMs: a comprehensive survey of transformer-based genomic language models for regulatory and clinical genomics.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41158510/","authors":["Balakrishnan P","Anny Leema A","Dhivya Shree V","Mohammad Saad C","Mohan Babu A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fgene.2025.1634882","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41154939","name":"Interoperability as a Catalyst for Digital Health and Therapeutics: A Scoping Review of Emerging Technologies and Standards (2015-2025).","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41154939/","authors":["Adegoke K","Adegoke A","Dawodu D","Adekoya A","Bayowa A","Kayode T","Singh M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct 8","doi":"10.3390/ijerph22101535","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41154232","name":"Healthcare 5.0-Driven Clinical Intelligence: The Learn-Predict-Monitor-Detect-Correct Framework for Systematic Artificial Intelligence Integration in Critical Care.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41154232/","authors":["Boussi Rahmouni H","Hassine NBEH","Chouchen M","Ceylan Hİ","Muntean RI","Bragazzi NL","Dergaa I"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct 10","doi":"10.3390/healthcare13202553","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41146636","name":"Artificial Intelligence in HPLC Method Development: A Critical Review of Technological Integration, Limitations, and Future Directions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41146636/","authors":["Alves E","Gurupadayya BM","Prabhakaran P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct 28","doi":"10.1080/10408347.2025.2575352","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41141907","name":"Federated learning for cognitive impairment detection using speech data.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41141907/","authors":["Blazquez-Folch J","Limones Andrade M","Calm B","Auñón García JM","Alegret M","Muñoz N","Cano A","Fernández V","García-Gutiérrez F","De Rojas I","García-González P","Olivé C","Puerta R","Capdevila-Bayo M","Muñoz-Morales Á","Bayón-Buján P","Miguel A","Montrreal L","Espinosa A","Sanz-Cartagena P","Rosende-Roca M","Zaldua C","Gabirondo P","Cantero-Fortiz Y","Gurruchaga MJ","Tarraga L","Boada M","Ruiz A","Marquié M","Valero S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/frai.2025.1662859","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41131749","name":"[Artificial intelligence in stomatology: Innovations in clinical practice, research, education, and healthcare management].","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41131749/","authors":["Deng X","Xu M","DU C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct 18","doi":"10.19723/j.issn.1671-167X.2025.05.002","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41112005","name":"Multimodal artificial intelligence technology in the precision diagnosis and treatment of gastroenterology and hepatology: Innovative applications and challenges.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41112005/","authors":["Wu YM","Tang FY","Qi ZX"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct 14","doi":"10.3748/wjg.v31.i38.109802","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41111750","name":"Artificial Intelligence and Digital Biomarkers in Hepatology: Critical Perspectives, Emerging Evidence, and Future Directions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41111750/","authors":["Mehrotra P","K V","Mehrotra P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep","doi":"10.7759/cureus.92639","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41109547","name":"Advancing healthcare analytics: a thematic review of machine learning, health informatics, and real-world data applications.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41109547/","authors":["Arias MI","Cadavid L","Velásquez JD"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov","doi":"10.1016/j.jbi.2025.104934","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41107959","name":"Deep learning models for ICU readmission prediction: a systematic review and meta-analysis.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41107959/","authors":["Koumantakis E","Remoundou K","Colombi N","Fava C","Roussaki I","Visconti A","Berchialla P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct 17","doi":"10.1186/s13054-025-05642-x","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41103759","name":"Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41103759/","authors":["Khelfaoui I","Wang W","Meskher H","Shehata AI","El Basuini MF","Abouelenein MF","Degha HE","Alhoshy M","Teiba II","Mahmoud SS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fmicb.2025.1691503","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"pmid:41095953","name":"Oculoplastics and Augmented Intelligence: A Literature Review.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/41095953/","authors":["Ing E","Bondok M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 28","doi":"10.3390/jcm14196875","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.48550/arxiv.2608.24761","name":"Federated Sharing and Continuous Improvement of Medical Device Knowledge Artifacts: A Conceptual Model","source":"datacite","abstract":"Healthcare organisations use digital systems to exchange information from clinical cases. Medical centres with digital production facilities create device designs during care. These designs and production records often remain at the site that made them. Other sites may struggle to find a suitable design or learn what happened when staff used it. Mobile medical centres may also lose access when they work away from hospital systems. This paper proposes an artifact-centred model for a federated exchange infrastructure that lets hospitals and mobile medical centres share and improve medical knowledge while controlling their own records and decisions. An integrative literature review screened 910 records and mapped 240 publications across six questions. We read 72 publications in detail to trace the path from local use to a decision about shared knowledge. The review found no common process that links a record of local use to a decision about changing the knowledge shared with later users. The model keeps case data at each site and records which artifact version informed each use. Federation lets sites share reviewed versions and return records from use for review. Mobile units can receive artifacts before deployment and record their use while offline. After reconnecting, they can exchange these records with other sites. Point-of-care manufacturing shows how a medical device knowledge artifact connects a design to production records while the care site controls product release. Federation could form a governed learning network where one site's experience improves medical knowledge artifacts used elsewhere while authority remains local.","url":"https://doi.org/10.48550/arxiv.2608.24761","authors":["Mariscal-Melgar, J. C.","Buxbaum-Conradi, Sonja","Wenzelmann, Victoria","Redlich, Tobias"],"tags":["Computers and Society (cs.CY)","FOS: Computer and information sciences","J.3; H.3.5; C.2.4"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.24761","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.19878824","name":"A Review Of Federated Learning: Privacy-Preserving Machine Learning","source":"datacite","abstract":"Federated Learning (FL), was created by McMahan et al (14), has become of interest because it offers a decentralized machine learning framework for developing large scale ML models. This allows many users (or clients) to collaborate on training a shared model while retaining control of their own data. FL is ultimately designed to provide a solution to the conflict between the data demands of machine learning systems and the desire of individuals/companies to keep their personal and commercial data private. This paper is a review of the privacy and confidentiality aspects of Federated Learning. A critical review of the fundamental algorithms used in FL, possible attacks against FL systems, and the four primary techniques for enhancing privacy in FL; Differential Privacy (DP), Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), and hardware based Trusted Execution Environments (TEE), is provided. We will review aggregation protocols, determine the strength of FL systems against poisoning and inference attacks, and compare various FL systems implemented in three industries; healthcare, mobile communication and finance. A detailed review of FL reveals research issues related to; statistical heterogeneity, communication overhead, system heterogeneity and fairness. Finally, this review presents a prioritized set of research objectives for the next ten years, with an emphasis on situating FL within the larger context of privacy-preserving ML and potential regulatory developments.","url":"https://doi.org/10.5281/zenodo.19878824","authors":["Rathod Neha","Mojidra kirtika","khandhediya Isha","Harkishan Gohil"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19878824","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.5281/zenodo.19878825","name":"A Review Of Federated Learning: Privacy-Preserving Machine Learning","source":"datacite","abstract":"Federated Learning (FL), was created by McMahan et al (14), has become of interest because it offers a decentralized machine learning framework for developing large scale ML models. This allows many users (or clients) to collaborate on training a shared model while retaining control of their own data. FL is ultimately designed to provide a solution to the conflict between the data demands of machine learning systems and the desire of individuals/companies to keep their personal and commercial data private. This paper is a review of the privacy and confidentiality aspects of Federated Learning. A critical review of the fundamental algorithms used in FL, possible attacks against FL systems, and the four primary techniques for enhancing privacy in FL; Differential Privacy (DP), Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), and hardware based Trusted Execution Environments (TEE), is provided. We will review aggregation protocols, determine the strength of FL systems against poisoning and inference attacks, and compare various FL systems implemented in three industries; healthcare, mobile communication and finance. A detailed review of FL reveals research issues related to; statistical heterogeneity, communication overhead, system heterogeneity and fairness. Finally, this review presents a prioritized set of research objectives for the next ten years, with an emphasis on situating FL within the larger context of privacy-preserving ML and potential regulatory developments.","url":"https://doi.org/10.5281/zenodo.19878825","authors":["Rathod Neha","Mojidra kirtika","khandhediya Isha","Harkishan Gohil"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19878825","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.5281/zenodo.18602469","name":"TaxFL: Federated Learning and Federated Graph Intelligence for Cross-Border Tax and AML Compliance","source":"datacite","abstract":"Cross-border tax non-compliance and money laundering exploit the same structural weakness: the data needed to identify the beneficial owner of a suspicious structure is fragmented across jurisdictions and institutions. Traditional exchange-of-information (EOI) mechanisms are essential for targeted cases but cannot support the high-volume, pattern-based risk analytics modern compliance requires. This working paper presents TaxFL, a privacy-preserving reference architecture and governance blueprint that lets tax authorities and financial intelligence units collaboratively train and evaluate risk models without sharing raw taxpayer or transaction data — only model updates cross any boundary. TaxFL integrates cross-silo federated learning, federated graph neural networks (GNNs) for ownership and transaction networks, and layered privacy-enhancing technologies (secure aggregation, differential privacy, and optional confidential computing), wrapped in a conservative \"belt-and-suspenders\" legal blueprint viable even under strict interpretations of international data-transfer rules (e.g., Schrems II, FATF R40). The contribution is deliberately integrative and institutional rather than a new learning algorithm. Rather than claiming that federation is universally beneficial, the paper maps its utility frontier. On a non-IID graph benchmark (Cora) evaluated under a single held-out global test split, federated training — strongest with SCAFFOLD — recovers the discriminative signal that label-skewed local training loses and matches a centralized upper bound, correcting an earlier evaluation artifact. On synthetic, officially-calibrated tax/AML data, the gains are conditional and honestly reported: federation helps at scale (5–10 jurisdictions) and through graph structure over linear models, and SCAFFOLD removes the negative transfer that naive averaging causes, while aggregate gains over an already-strong single silo are modest. The use of synthetic data is intentional — it mirrors the operational constraint that raw taxpayer data cannot cross jurisdictional boundaries and enables fully reproducible peer review. The paper outlines priority use cases (cross-border beneficial ownership, VAT/GST carousel fraud, transfer pricing, crypto-asset risk scoring, and FIU typology sharing), a realistic six-month pilot design for a small multi-jurisdiction consortium starting from public/synthetic data, and an evaluation framework covering detection performance, privacy assurances, operational overhead, and institutional trust. TaxFL is a low-risk complement to existing EOI processes and a deployment-ready blueprint whose core thesis still requires a real-data pilot for validation. Open implementation: https://github.com/pafrantz/TaxFL. We welcome academic and institutional collaborators for rigorous empirical validation on real-world tax data under appropriate safeguards.","url":"https://doi.org/10.5281/zenodo.18602469","authors":["Frantz, Pedro Augusto"],"tags":["federated learning","graph neural networks","tax compliance","tax fraud","exchange of information","aml","anti money laundering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18602469","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20574158","name":"TaxFL: Federated Learning and Federated Graph Intelligence for Cross-Border Tax and AML Compliance","source":"datacite","abstract":"Cross-border tax non-compliance and money laundering exploit the same structural weakness: the data needed to identify the beneficial owner of a suspicious structure is fragmented across jurisdictions and institutions. Traditional exchange-of-information (EOI) mechanisms are essential for targeted cases but cannot support the high-volume, pattern-based risk analytics modern compliance requires. This working paper presents TaxFL, a privacy-preserving reference architecture and governance blueprint that lets tax authorities and financial intelligence units collaboratively train and evaluate risk models without sharing raw taxpayer or transaction data — only model updates cross any boundary. TaxFL integrates cross-silo federated learning, federated graph neural networks (GNNs) for ownership and transaction networks, and layered privacy-enhancing technologies (secure aggregation, differential privacy, and optional confidential computing), wrapped in a conservative \"belt-and-suspenders\" legal blueprint viable even under strict interpretations of international data-transfer rules (e.g., Schrems II, FATF R40). The contribution is deliberately integrative and institutional rather than a new learning algorithm. Rather than claiming that federation is universally beneficial, the paper maps its utility frontier. On a non-IID graph benchmark (Cora) evaluated under a single held-out global test split, federated training — strongest with SCAFFOLD — recovers the discriminative signal that label-skewed local training loses and matches a centralized upper bound, correcting an earlier evaluation artifact. On synthetic, officially-calibrated tax/AML data, the gains are conditional and honestly reported: federation helps at scale (5–10 jurisdictions) and through graph structure over linear models, and SCAFFOLD removes the negative transfer that naive averaging causes, while aggregate gains over an already-strong single silo are modest. The use of synthetic data is intentional — it mirrors the operational constraint that raw taxpayer data cannot cross jurisdictional boundaries and enables fully reproducible peer review. The paper outlines priority use cases (cross-border beneficial ownership, VAT/GST carousel fraud, transfer pricing, crypto-asset risk scoring, and FIU typology sharing), a realistic six-month pilot design for a small multi-jurisdiction consortium starting from public/synthetic data, and an evaluation framework covering detection performance, privacy assurances, operational overhead, and institutional trust. TaxFL is a low-risk complement to existing EOI processes and a deployment-ready blueprint whose core thesis still requires a real-data pilot for validation. Open implementation: https://github.com/pafrantz/TaxFL. We welcome academic and institutional collaborators for rigorous empirical validation on real-world tax data under appropriate safeguards.","url":"https://doi.org/10.5281/zenodo.20574158","authors":["Frantz, Pedro Augusto"],"tags":["federated learning","graph neural networks","tax compliance","tax fraud","exchange of information","aml","anti money laundering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20574158","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20519586","name":"Silent Failures and Structural Gaps: A Cross-Domain Framework for Evaluation Rigor in Large-Model Systems","source":"datacite","abstract":"Version 2 — revised in response to an external structural review and an automated critique pass. See \"Response to Review\" appendix in the PDF for the change log. Large-model systems are evaluated at multiple layers—statistical, algorithmic, systems, and behavioral—yet each layer's evaluation methodology has been developed largely in isolation. This paper identifies a shared structural pattern across these layers: evaluations that are locally valid but globally misleading. Drawing on recent preprints spanning federated learning, LLM inference, causal inference, high-dimensional statistics, latent reasoning, and multi-agent coherence, we offer a *heuristic reading* that a common pattern recurs: a system or estimator satisfies its local objective (loss reduction, throughput, oracle test passage, component-level coherence) while concealing a deeper failure that only manifests at a different scale, composition, or distribution shift. We call this the **local-validity trap**. Specifically, we synthesize evidence that (1) oracle-based testing may miss semantically incorrect but symptom-reducing fixes in AI-assisted development; (2) component-level probabilistic coherence does not guarantee joint coherence in multi-agent LLM systems; (3) measurement error silently biases high-dimensional regression even when penalized estimators converge; (4) asynchronous pipeline parallelism bounds staleness locally but may accumulate convergence error globally; and (5) contribution metrics in federated learning should track optimization trajectories rather than static snapshots to avoid misattribution. We propose that evaluation rigor for large-model systems may benefit from explicit cross-scale consistency checks, analogous to structural constraints studied in the algorithmic and statistical literatures, and outline candidate design principles for building such checks into system pipelines. The \"local-validity trap\" framing is introduced here as an organizing heuristic; it is not a term used in any cited source, and the cross-domain structural analogy is asserted on the basis of shared vocabulary rather than shared mechanism. --- Authorship: Saluca Agentic AI Research Team (Saluca LLC). AI-drafted from arXiv preprint corpus on the date in the filename. Cited arXiv preprints: 2605.29566v1, 2605.29639v1, 2605.29664v1, 2605.29740v1, 2605.29944v1, 2605.30075v1, 2605.30113v1, 2605.30153v1, 2605.30158v1, 2605.30319v1, 2605.30321v1, 2605.30327v1, 2605.30335v1, 2605.30336v1, 2605.30341v1, 2605.30343v1, 2605.30353v1","url":"https://doi.org/10.5281/zenodo.20519586","authors":["Saluca Agentic AI Research Team"],"tags":["AI-drafted synthesis","arXiv","preprint review","v2"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20519586","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20520079","name":"Silent Failures and Structural Gaps: A Cross-Domain Framework for Evaluation Rigor in Large-Model Systems","source":"datacite","abstract":"Version 2 — revised in response to an external structural review and an automated critique pass. See \"Response to Review\" appendix in the PDF for the change log. Large-model systems are evaluated at multiple layers—statistical, algorithmic, systems, and behavioral—yet each layer's evaluation methodology has been developed largely in isolation. This paper identifies a shared structural pattern across these layers: evaluations that are locally valid but globally misleading. Drawing on recent preprints spanning federated learning, LLM inference, causal inference, high-dimensional statistics, latent reasoning, and multi-agent coherence, we offer a *heuristic reading* that a common pattern recurs: a system or estimator satisfies its local objective (loss reduction, throughput, oracle test passage, component-level coherence) while concealing a deeper failure that only manifests at a different scale, composition, or distribution shift. We call this the **local-validity trap**. Specifically, we synthesize evidence that (1) oracle-based testing may miss semantically incorrect but symptom-reducing fixes in AI-assisted development; (2) component-level probabilistic coherence does not guarantee joint coherence in multi-agent LLM systems; (3) measurement error silently biases high-dimensional regression even when penalized estimators converge; (4) asynchronous pipeline parallelism bounds staleness locally but may accumulate convergence error globally; and (5) contribution metrics in federated learning should track optimization trajectories rather than static snapshots to avoid misattribution. We propose that evaluation rigor for large-model systems may benefit from explicit cross-scale consistency checks, analogous to structural constraints studied in the algorithmic and statistical literatures, and outline candidate design principles for building such checks into system pipelines. The \"local-validity trap\" framing is introduced here as an organizing heuristic; it is not a term used in any cited source, and the cross-domain structural analogy is asserted on the basis of shared vocabulary rather than shared mechanism. --- Authorship: Saluca Agentic AI Research Team (Saluca LLC). AI-drafted from arXiv preprint corpus on the date in the filename. Cited arXiv preprints: 2605.29566v1, 2605.29639v1, 2605.29664v1, 2605.29740v1, 2605.29944v1, 2605.30075v1, 2605.30113v1, 2605.30153v1, 2605.30158v1, 2605.30319v1, 2605.30321v1, 2605.30327v1, 2605.30335v1, 2605.30336v1, 2605.30341v1, 2605.30343v1, 2605.30353v1","url":"https://doi.org/10.5281/zenodo.20520079","authors":["Saluca Agentic AI Research Team"],"tags":["AI-drafted synthesis","arXiv","preprint review","v2"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20520079","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20470302","name":"Differential Privacy Trade-offs in Federated Code Generation Model Fine-Tuning","source":"datacite","abstract":"This report synthesises findings from 3 peer-reviewed papers addressing the following research question: What is the trade-off between inference latency and model robustness against adversarial attacks when applying differential privacy mechanisms to federated fine-tuning of code generation models. Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the trade-off between inference latency and model robustness against adversarial attacks when applying differential privacy mechanisms to federated fine-tuning of code generation models? Autonomous literature synthesis. Automated review score: 8.2/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20470302","authors":["Assignee Research"],"tags":["trade-off","inference","latency","model","robustness","against","adversarial","attacks"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20470302","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20470303","name":"Differential Privacy Trade-offs in Federated Code Generation Model Fine-Tuning","source":"datacite","abstract":"This report synthesises findings from 3 peer-reviewed papers addressing the following research question: What is the trade-off between inference latency and model robustness against adversarial attacks when applying differential privacy mechanisms to federated fine-tuning of code generation models. Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the trade-off between inference latency and model robustness against adversarial attacks when applying differential privacy mechanisms to federated fine-tuning of code generation models? Autonomous literature synthesis. Automated review score: 8.2/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20470303","authors":["Assignee Research"],"tags":["trade-off","inference","latency","model","robustness","against","adversarial","attacks"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20470303","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20608577","name":"Coordination Topology as a Design Variable: How Memory Depth, Communication Sparsity, Censored Feedback, and Governance Boundaries Jointly Determine Multi-Agent System Behavior","source":"datacite","abstract":"Version 2 — revised in response to an external structural review and an automated critique pass. See \"Response to Review\" appendix in the PDF for the change log. Multi-agent systems (MAS) are typically designed by fixing a task objective and then selecting agents, communication protocols, and memory configurations as secondary implementation details. This paper argues for a candidate reframing: **coordination topology**—the joint specification of communication graph structure, memory depth, governance boundaries, and feedback censorship—should be treated as a primary design variable whose configuration determines not merely efficiency but qualitative system behavior, including whether consensus is reached, whether free-riding is suppressed, whether failures propagate silently, and whether governance constraints hold at the execution boundary. This is a heuristic reading, not a formal derivation; the corpus sources share structural analogies rather than a unified formalism, and the connections between domains are argued by mechanism where possible and flagged as analogies where they are not. We synthesize findings from six recent arXiv preprints spanning cs.MA and cs.DC. The evidence base includes: a controlled simulation study showing that memory depth and network topology interact to flip the sign of coordination speed [corpus:arxiv:2606.04197]; a formal result demonstrating that decentralized free-rider suppression is topology-dependent under graded contention [corpus:arxiv:2606.06162]; an empirical characterisation of silent cross-agent memory injection failures caused by architectural isolation guards [corpus:arxiv:2606.04896]; a governance layer study showing that separating proposal generation from execution reduces unsafe actions from 88% to near-zero [corpus:arxiv:2606.04306]; a threshold-bandit analysis showing that censored feedback creates a structural learning cost decomposable into search and monitoring terms [corpus:arxiv:2605.27076]; and a federated-market orchestration result showing that decentralised price-based allocation matches centralised welfare under specific graph-topology conditions [corpus:arxiv:2605.27106]. The last source connects to the synthesis through analogy between service-dependency DAG topology and agent communication graph topology; it is treated as corroborating rather than primary evidence and is discussed in a dedicated weakly-connected addendum. Together these findings suggest that topology, memory, censorship, and governance are not independent tuning knobs but coupled coordinates of a single configuration space. The falsification path for the central claim is stated concretely: construct a factorial experiment varying topology, memory depth, and governance boundary placement across a fixed task, and test whether the interaction effects on coordination outcome are statistically significant and sign-reversing. All six corpus sources are arXiv preprints; none are peer-reviewed, and all claims should be treated as hypotheses pending replication. --- Authorship: Saluca Agentic AI Research Team (Saluca LLC). AI-drafted from arXiv preprint corpus on the date in the filename. Cited arXiv preprints: 2605.27076, 2605.27106, 2605.30802, 2606.04197, 2606.04306, 2606.04896, 2606.06162, 2606.06189, 2606.07487 AI disclosure. This work was produced with an agentic AI research apparatus operated by Saluca Labs. The apparatus drafted, searched and analysed under direction. Cristian Ruvalcaba is the human author and is accountable for the content. No AI system is listed as an author or contributor, because authorship entails accountability that a model cannot hold; this disclosure is the credit, and it is deliberately the whole of it.","url":"https://doi.org/10.5281/zenodo.20608577","authors":["Saluca Agentic AI Research Team"],"tags":["AI-drafted synthesis","arXiv","preprint review","v2"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20608577","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20626963","name":"Air Quality Index Prediction: A Review of Machine Learning, Deep Learning, and Hybrid Approaches","source":"datacite","abstract":"Abstract - The increasing evolution of air pollution as a global environmental and public health issue worldwide warrants the timely provision of accurate predictions for the Air Quality Index (AQI). Therefore, this review paper will explore in-depth all the traditional, machine learning, and deep learning models put into effect for AQI forecast modeling. Initially, some conventional statistical models like ARIMA and regression-based models are analyzed. It is seen that they are less complex and easy to interpret, but due to their incapability to handle nonlinear and complex data patterns, the results do not speak highly of these models. This raises issues around machine learning algorithms, such as those of decision tree, support vector machine, and ensemble methods, which actually increased the accuracy of data predictions thanks to better treatment of features and generalization. Further embraces on computational neural networks (CNN), long short term memory (LSTM), and hybrid models have been smartly improved to catch temporal and spatial linkages in air quality data. The paper also covers discussed up-coming trends such as federated learning, edge-AI, remote sensing integration, and explainable AI for much-needed larger deployment and more privacy and interpretable AI prediction options for the AQI. The key challenges identified in the work were data scarcity, model complexities, computational needs, and lack of evaluation frameworks complying with a set of standards. This review brings out these research gaps and clearly accentuates the need for these hybrid, scalable, and real-time AQI forecasting solutions. It, therefore, provides a comprehensive understanding of all available methodologies and helps foresee the construction of durable and efficient air quality predictions for the betterment of environmental management.","url":"https://doi.org/10.5281/zenodo.20626963","authors":["Abhishek Tiwari","Atesh Kumar"],"tags":["Air Quality Index (AQI)","Machine Learning","Deep Learning","Time Series Forecasting","Environmental Monitoring","Air Pollution Prediction"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20626963","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20626964","name":"Air Quality Index Prediction: A Review of Machine Learning, Deep Learning, and Hybrid Approaches","source":"datacite","abstract":"Abstract - The increasing evolution of air pollution as a global environmental and public health issue worldwide warrants the timely provision of accurate predictions for the Air Quality Index (AQI). Therefore, this review paper will explore in-depth all the traditional, machine learning, and deep learning models put into effect for AQI forecast modeling. Initially, some conventional statistical models like ARIMA and regression-based models are analyzed. It is seen that they are less complex and easy to interpret, but due to their incapability to handle nonlinear and complex data patterns, the results do not speak highly of these models. This raises issues around machine learning algorithms, such as those of decision tree, support vector machine, and ensemble methods, which actually increased the accuracy of data predictions thanks to better treatment of features and generalization. Further embraces on computational neural networks (CNN), long short term memory (LSTM), and hybrid models have been smartly improved to catch temporal and spatial linkages in air quality data. The paper also covers discussed up-coming trends such as federated learning, edge-AI, remote sensing integration, and explainable AI for much-needed larger deployment and more privacy and interpretable AI prediction options for the AQI. The key challenges identified in the work were data scarcity, model complexities, computational needs, and lack of evaluation frameworks complying with a set of standards. This review brings out these research gaps and clearly accentuates the need for these hybrid, scalable, and real-time AQI forecasting solutions. It, therefore, provides a comprehensive understanding of all available methodologies and helps foresee the construction of durable and efficient air quality predictions for the betterment of environmental management.","url":"https://doi.org/10.5281/zenodo.20626964","authors":["Abhishek Tiwari","Atesh Kumar"],"tags":["Air Quality Index (AQI)","Machine Learning","Deep Learning","Time Series Forecasting","Environmental Monitoring","Air Pollution Prediction"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20626964","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20482023","name":"Diversity-Driven Client Selection in Federated Learning for CodeLlama-7B on HumanEval","source":"datacite","abstract":"This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does diversity-driven client selection in federated learning affect the pass@1 scores of CodeLlama-7B on the HumanEval benchmark under extreme non-IID code distribution scenarios. Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performance, a disconcerting reality is that high-quality public data will be exhausted in a few years. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does diversity-driven client selection in federated learning affect the pass@1 scores of CodeLlama-7B on the HumanEval benchmark under extreme non-IID code distribution scenarios? Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20482023","authors":["Assignee Research"],"tags":["diversity-driven","client","selection","federated","learning","affect","pass","scores"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20482023","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20482024","name":"Diversity-Driven Client Selection in Federated Learning for CodeLlama-7B on HumanEval","source":"datacite","abstract":"This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How does diversity-driven client selection in federated learning affect the pass@1 scores of CodeLlama-7B on the HumanEval benchmark under extreme non-IID code distribution scenarios. Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performance, a disconcerting reality is that high-quality public data will be exhausted in a few years. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does diversity-driven client selection in federated learning affect the pass@1 scores of CodeLlama-7B on the HumanEval benchmark under extreme non-IID code distribution scenarios? Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20482024","authors":["Assignee Research"],"tags":["diversity-driven","client","selection","federated","learning","affect","pass","scores"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20482024","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21815685","name":"Supporting Data for State-Aware Upload Gating for Personalized Federated Learning in Mobile Edge Environments","source":"datacite","abstract":"This dataset contains the client-partition files, per-seed raw experimental logs, processed summary data, and analysis scripts supporting the results reported in “State-Aware Upload Gating for Personalized Federated Learning in Mobile Edge Environments.” The package covers the CIFAR-10 and CIFAR-100 main experiments, Random-Gate comparisons, threshold experiments, and upload-budget-matched state-factor ablations. The corresponding source code, environment specifications, and verified execution commands are archived separately in SAUG-PFLlib, Version 1.0.0(https://doi.org/10.5281/zenodo.21807089). The files are restricted during peer review. Access is provided to editors and reviewers through a private link. The files will be made publicly available upon publication of the associated article.","url":"https://doi.org/10.5281/zenodo.21815685","authors":["Long, Bangfa","Wang, Xiaohan","Wu, Nianhua"],"tags":["personalized federated learning","state-aware upload gating","mobile edge learning","CIFAR-10","CIFAR-100","client partition","experimental logs","reproducibility"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21815685","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21815686","name":"Supporting Data for State-Aware Upload Gating for Personalized Federated Learning in Mobile Edge Environments","source":"datacite","abstract":"This dataset contains the client-partition files, per-seed raw experimental logs, processed summary data, and analysis scripts supporting the results reported in “State-Aware Upload Gating for Personalized Federated Learning in Mobile Edge Environments.” The package covers the CIFAR-10 and CIFAR-100 main experiments, Random-Gate comparisons, threshold experiments, and upload-budget-matched state-factor ablations. The corresponding source code, environment specifications, and verified execution commands are archived separately in SAUG-PFLlib, Version 1.0.0(https://doi.org/10.5281/zenodo.21807089). The files are restricted during peer review. Access is provided to editors and reviewers through a private link. The files will be made publicly available upon publication of the associated article.","url":"https://doi.org/10.5281/zenodo.21815686","authors":["Long, Bangfa","Wang, Xiaohan","Wu, Nianhua"],"tags":["personalized federated learning","state-aware upload gating","mobile edge learning","CIFAR-10","CIFAR-100","client partition","experimental logs","reproducibility"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21815686","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21369517","name":"## Artificial Intelligence for Advanced Materials Science: A Comprehensive Framework for Phase Transformations, Microstructure Evolution, Alloy Design, Additive Manufacturing, and Beyond","source":"datacite","abstract":"# ALTERNATIVE COMBINED TITLES ## Alternative Title 1**AI-Driven Materials Science: From Phase Transformations to Intelligent Manufacturing – A Unified Framework for Accelerated Discovery and Optimization** ### SubtitleIntegrating Machine Learning, Deep Learning, Physics-Informed Neural Networks, and Generative AI for Next-Generation Materials Development --- ## Alternative Title 2**Machine Learning and Deep Learning in Materials Science: A Comprehensive Review of Phase Transformations, Microstructure Evolution, Alloy Design, and Additive Manufacturing** ### SubtitleApplications in High-Entropy Alloys, Superconductors, Composite Materials, Surface Engineering, and Failure Prediction --- ## Alternative Title 3**Intelligent Materials Design: Artificial Intelligence Applications in Phase Transformations, Microstructure Engineering, and Advanced Manufacturing** ### SubtitleA Systematic Framework for High-Entropy Alloys, Superconductors, Composites, and Surface Engineering --- ## Alternative Title 4**Data-Driven Materials Science: Artificial Intelligence for Accelerated Discovery, Predictive Modeling, and Process Optimization** ### SubtitleComprehensive Applications in Phase Transformations, Grain Growth, Solidification, Alloy Design, Additive Manufacturing, and Failure Prediction --- ## Alternative Title 5**The AI Revolution in Materials Engineering: Transforming Phase Transformations, Microstructure Control, and Manufacturing Through Machine Learning** ### SubtitleApplications in High-Entropy Alloys, Superconductors, Composite Materials, and Surface Engineering --- ## Alternative Title 6**From Data to Discovery: A Comprehensive AI Framework for Materials Science and Engineering** ### SubtitleIntegrating Machine Learning, Physics-Informed Neural Networks, and Generative AI for Phase Transformations, Alloy Design, Additive Manufacturing, and Beyond --- ## Alternative Title 7**Artificial Intelligence for Next-Generation Materials: A Unified Approach to Phase Transformations, Microstructure Evolution, and Intelligent Manufacturing** ### SubtitleApplications in High-Entropy Alloys, Superconductors, Composite Materials, Surface Engineering, and Failure Prediction --- ## Alternative Title 8**Machine Learning in Metallurgy and Materials Science: A Comprehensive Review of AI Applications in Phase Transformations, Alloy Design, and Additive Manufacturing** ### SubtitleFrom Grain Growth to Superconductors – Intelligent Approaches for Materials Discovery and Optimization --- ## Alternative Title 9**AI-Enabled Materials Discovery and Engineering: A Systematic Framework for Phase Transformations, Microstructure Evolution, and Advanced Manufacturing** ### SubtitleIntegrating Data-Driven and Physics-Based Approaches for High-Entropy Alloys, Superconductors, Composites, and Surface Engineering --- ## Alternative Title 10**The Fourth Paradigm in Materials Science: Artificial Intelligence for Accelerated Discovery, Predictive Modeling, and Intelligent Manufacturing** ### SubtitleA Comprehensive Framework for Phase Transformations, Grain Growth, Solidification, Alloy Design, Additive Manufacturing, and Failure Prediction --- ## Alternative Title 11**Intelligent Materials Informatics: Machine Learning and Deep Learning Applications in Phase Transformations, Microstructure Evolution, and Advanced Manufacturing** ### SubtitleFrom High-Entropy Alloys to Superconductors – A Unified AI Framework for Materials Discovery and Optimization --- ## Alternative Title 12**Physics-Informed and Data-Driven AI for Materials Science: A Comprehensive Review of Phase Transformations, Alloy Design, and Additive Manufacturing** ### SubtitleApplications in Composite Materials, Superconductors, Surface Engineering, and Failure Prediction --- ## Alternative Title 13**Artificial Intelligence in Materials Processing and Manufacturing: A Systematic Framework for Phase Transformations, Microstructure Control, and Additive Manufacturing** ### SubtitleIntegrating Machine Learning,","url":"https://doi.org/10.5281/zenodo.21369517","authors":["geruganti, sudhakar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21369517","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21369518","name":"## Artificial Intelligence for Advanced Materials Science: A Comprehensive Framework for Phase Transformations, Microstructure Evolution, Alloy Design, Additive Manufacturing, and Beyond","source":"datacite","abstract":"# ALTERNATIVE COMBINED TITLES ## Alternative Title 1**AI-Driven Materials Science: From Phase Transformations to Intelligent Manufacturing – A Unified Framework for Accelerated Discovery and Optimization** ### SubtitleIntegrating Machine Learning, Deep Learning, Physics-Informed Neural Networks, and Generative AI for Next-Generation Materials Development --- ## Alternative Title 2**Machine Learning and Deep Learning in Materials Science: A Comprehensive Review of Phase Transformations, Microstructure Evolution, Alloy Design, and Additive Manufacturing** ### SubtitleApplications in High-Entropy Alloys, Superconductors, Composite Materials, Surface Engineering, and Failure Prediction --- ## Alternative Title 3**Intelligent Materials Design: Artificial Intelligence Applications in Phase Transformations, Microstructure Engineering, and Advanced Manufacturing** ### SubtitleA Systematic Framework for High-Entropy Alloys, Superconductors, Composites, and Surface Engineering --- ## Alternative Title 4**Data-Driven Materials Science: Artificial Intelligence for Accelerated Discovery, Predictive Modeling, and Process Optimization** ### SubtitleComprehensive Applications in Phase Transformations, Grain Growth, Solidification, Alloy Design, Additive Manufacturing, and Failure Prediction --- ## Alternative Title 5**The AI Revolution in Materials Engineering: Transforming Phase Transformations, Microstructure Control, and Manufacturing Through Machine Learning** ### SubtitleApplications in High-Entropy Alloys, Superconductors, Composite Materials, and Surface Engineering --- ## Alternative Title 6**From Data to Discovery: A Comprehensive AI Framework for Materials Science and Engineering** ### SubtitleIntegrating Machine Learning, Physics-Informed Neural Networks, and Generative AI for Phase Transformations, Alloy Design, Additive Manufacturing, and Beyond --- ## Alternative Title 7**Artificial Intelligence for Next-Generation Materials: A Unified Approach to Phase Transformations, Microstructure Evolution, and Intelligent Manufacturing** ### SubtitleApplications in High-Entropy Alloys, Superconductors, Composite Materials, Surface Engineering, and Failure Prediction --- ## Alternative Title 8**Machine Learning in Metallurgy and Materials Science: A Comprehensive Review of AI Applications in Phase Transformations, Alloy Design, and Additive Manufacturing** ### SubtitleFrom Grain Growth to Superconductors – Intelligent Approaches for Materials Discovery and Optimization --- ## Alternative Title 9**AI-Enabled Materials Discovery and Engineering: A Systematic Framework for Phase Transformations, Microstructure Evolution, and Advanced Manufacturing** ### SubtitleIntegrating Data-Driven and Physics-Based Approaches for High-Entropy Alloys, Superconductors, Composites, and Surface Engineering --- ## Alternative Title 10**The Fourth Paradigm in Materials Science: Artificial Intelligence for Accelerated Discovery, Predictive Modeling, and Intelligent Manufacturing** ### SubtitleA Comprehensive Framework for Phase Transformations, Grain Growth, Solidification, Alloy Design, Additive Manufacturing, and Failure Prediction --- ## Alternative Title 11**Intelligent Materials Informatics: Machine Learning and Deep Learning Applications in Phase Transformations, Microstructure Evolution, and Advanced Manufacturing** ### SubtitleFrom High-Entropy Alloys to Superconductors – A Unified AI Framework for Materials Discovery and Optimization --- ## Alternative Title 12**Physics-Informed and Data-Driven AI for Materials Science: A Comprehensive Review of Phase Transformations, Alloy Design, and Additive Manufacturing** ### SubtitleApplications in Composite Materials, Superconductors, Surface Engineering, and Failure Prediction --- ## Alternative Title 13**Artificial Intelligence in Materials Processing and Manufacturing: A Systematic Framework for Phase Transformations, Microstructure Control, and Additive Manufacturing** ### SubtitleIntegrating Machine Learning,","url":"https://doi.org/10.5281/zenodo.21369518","authors":["geruganti, sudhakar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21369518","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21604823","name":"Review of Context-Aware DL-Based Models to Improve Grocery Retail Forecasting","source":"datacite","abstract":"There a few factors that make grocery retail forecasting particularly challenging: intricate demand patterns are recorded based on many contextual effects such as promotions, seasonality, local events or weather and fickle shopping habits. Nonlinear relationships between the independent and dependent variables are not well captured in conventional statistical models, which can lead to economies of scale not being optimised, and can result in over or under stocking; unnecessary stockouts, waste, and lost sales. Recent progress has been made towards context-aware deep learning (DL) for grocery retail forecasting and we provide an overview in this review paper. We systematically review the ways different DL architectures such as RNNs, LSTM networks, CNNs, Transformers and hybrid models are combined with different types of context information streams in an effort to improve forecast accuracy. The paper classifies contextual information into internal (e.g. pricing, promotions, inventory) and external (e.g. weather, calendar events, social trends). We discuss the methodological advances in the fields of feature engineering and multimodal data fusion, as well as attention mechanisms that compute to what extent each context is relevant dynamically. We also explore the issues of insights' applicability in large retail chains, related to data quality, computational complexity modelling interpretability, and scalability. Through comparing the performances of models between previous publications and real-world case studies, it is found that context-aware DL approaches have greater potential to overcome traditional methods in dealing with high-dimensional, signal noise and non-stationarity of retail data. The paper also discusses emerging trends, including graph neural networks for product relationship modeling and federated learning for privacy-preserving forecasting, and suggests avenues for future research to close the gap between academic innovation and industry deployment.","url":"https://doi.org/10.5281/zenodo.21604823","authors":["Tomar, Ujjawal","Rajput, Ayan"],"tags":["Retail Forecasting; Deep Learning; Context-Aware Systems; Demand Prediction; Grocery Analytics; TIME SERIES ANALYSIS &amp; MACHINE LEARNING FOR GROCERY RETAIL 3 Inventory Optimization"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21604823","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21604824","name":"Review of Context-Aware DL-Based Models to Improve Grocery Retail Forecasting","source":"datacite","abstract":"There a few factors that make grocery retail forecasting particularly challenging: intricate demand patterns are recorded based on many contextual effects such as promotions, seasonality, local events or weather and fickle shopping habits. Nonlinear relationships between the independent and dependent variables are not well captured in conventional statistical models, which can lead to economies of scale not being optimised, and can result in over or under stocking; unnecessary stockouts, waste, and lost sales. Recent progress has been made towards context-aware deep learning (DL) for grocery retail forecasting and we provide an overview in this review paper. We systematically review the ways different DL architectures such as RNNs, LSTM networks, CNNs, Transformers and hybrid models are combined with different types of context information streams in an effort to improve forecast accuracy. The paper classifies contextual information into internal (e.g. pricing, promotions, inventory) and external (e.g. weather, calendar events, social trends). We discuss the methodological advances in the fields of feature engineering and multimodal data fusion, as well as attention mechanisms that compute to what extent each context is relevant dynamically. We also explore the issues of insights' applicability in large retail chains, related to data quality, computational complexity modelling interpretability, and scalability. Through comparing the performances of models between previous publications and real-world case studies, it is found that context-aware DL approaches have greater potential to overcome traditional methods in dealing with high-dimensional, signal noise and non-stationarity of retail data. The paper also discusses emerging trends, including graph neural networks for product relationship modeling and federated learning for privacy-preserving forecasting, and suggests avenues for future research to close the gap between academic innovation and industry deployment.","url":"https://doi.org/10.5281/zenodo.21604824","authors":["Tomar, Ujjawal","Rajput, Ayan"],"tags":["Retail Forecasting; Deep Learning; Context-Aware Systems; Demand Prediction; Grocery Analytics; TIME SERIES ANALYSIS &amp; MACHINE LEARNING FOR GROCERY RETAIL 3 Inventory Optimization"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21604824","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20962002","name":"Intelligent Power Infrastructure: A Structured Examination of Artificial Intelligence Techniques, Operational Challenges, and Evolutionary Pathways in Next-Generation Smart Grid Systems","source":"datacite","abstract":"Abstract Global momentum toward intelligent power infrastructure is accelerating as electricity demand grows, renewable energy penetration deepens, and sustainability imperatives intensify. Artificial intelligence (AI) technologies—encompassing machine learning, deep learning, reinforcement learning, expert systems, fuzzy logic, and hybrid frameworks—have become indispensable enablers of next-generation grid operations. These approaches support intelligent monitoring, accurate load and renewable-energy forecasting, autonomous fault detection, demand-response orchestration, and optimal distributed energy resource integration. Notwithstanding these advantages, practical deployment faces persistent barriers including cyber security vulnerabilities, data-privacy constraints, infrastructure investment costs, device interoperability deficits, and insufficient regulatory frameworks. This study presents a structured original review of AI-enabled smart grid architectures and operational paradigms, systematically evaluates capabilities and trade-offs of principal AI methods, analyzes prevailing challenges alongside viable mitigation strategies, quantifies documented operational benefits, and maps prospective technological trajectories—including edge AI, digital twins, explainable AI, block chain, and federated learning—toward autonomous grid operation. Findings indicate that strategic AI adoption is critical to achieving resilient, efficient, and low-carbon power systems for the future.","url":"https://doi.org/10.5281/zenodo.20962002","authors":["Dr Gokula Krishnan B","Dr Senthil Kumar M P","Shanmugam S","K Gopinath"],"tags":["Artificial Intelligence, Smart Grid, Demand Response, Renewable Energy Forecasting"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20962002","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20962003","name":"Intelligent Power Infrastructure: A Structured Examination of Artificial Intelligence Techniques, Operational Challenges, and Evolutionary Pathways in Next-Generation Smart Grid Systems","source":"datacite","abstract":"Abstract Global momentum toward intelligent power infrastructure is accelerating as electricity demand grows, renewable energy penetration deepens, and sustainability imperatives intensify. Artificial intelligence (AI) technologies—encompassing machine learning, deep learning, reinforcement learning, expert systems, fuzzy logic, and hybrid frameworks—have become indispensable enablers of next-generation grid operations. These approaches support intelligent monitoring, accurate load and renewable-energy forecasting, autonomous fault detection, demand-response orchestration, and optimal distributed energy resource integration. Notwithstanding these advantages, practical deployment faces persistent barriers including cyber security vulnerabilities, data-privacy constraints, infrastructure investment costs, device interoperability deficits, and insufficient regulatory frameworks. This study presents a structured original review of AI-enabled smart grid architectures and operational paradigms, systematically evaluates capabilities and trade-offs of principal AI methods, analyzes prevailing challenges alongside viable mitigation strategies, quantifies documented operational benefits, and maps prospective technological trajectories—including edge AI, digital twins, explainable AI, block chain, and federated learning—toward autonomous grid operation. Findings indicate that strategic AI adoption is critical to achieving resilient, efficient, and low-carbon power systems for the future.","url":"https://doi.org/10.5281/zenodo.20962003","authors":["Dr Gokula Krishnan B","Dr Senthil Kumar M P","Shanmugam S","K Gopinath"],"tags":["Artificial Intelligence, Smart Grid, Demand Response, Renewable Energy Forecasting"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20962003","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20041659","name":"Predictive Analytics Framework for Multi-Generational Health Data: Architecture, Implementation, and Clinical Outcomes at Population Scale","source":"datacite","abstract":"A substantial share of chronic and hereditary disease risk propagates through family units rather than individuals, yet the majority of clinical analytics frameworks treat patient records as independent observations without reference to family health context. This paper presents a practitioner-informed technical review ofa production-grade predictive analytics framework designed to reorganize clinical data around family units, link records across multiple electronic medical record systems using a FHIR-based interoperability layer, and apply machine learning models to identify family-based disease risk at the population scale. The framework integrates graph-structured family relationship models, temporal feature engineering encoding health trajectories across generations, and real-time clinical decision support workflows delivering family risk scores to care coordinators. Drawing on direct implementation experience in large-scale population health programs, we describe the data architecture, identity resolution pipeline, ML model design, and deployment infrastructure required to operationalize multi-generational analytics in production. We evaluate methodological requirements for family-aware model validation, discuss governance challenges specific to family-level health data, including relational consent and genetic discrimination risk, and identify the equity implications of deploying genomically-informed family risk models in ancestrally diverse populations. We further discuss federated learning and social determinant integration as the most consequential near-term extensions of the framework. The paper is intended to bridge the gap between theoretical proposals for multi-generational health analytics and the architectural and governance realities of deploying such systems at the scale of millions of members.","url":"https://doi.org/10.5281/zenodo.20041659","authors":["Akash Kamble LNU"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20041659","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20041660","name":"Predictive Analytics Framework for Multi-Generational Health Data: Architecture, Implementation, and Clinical Outcomes at Population Scale","source":"datacite","abstract":"A substantial share of chronic and hereditary disease risk propagates through family units rather than individuals, yet the majority of clinical analytics frameworks treat patient records as independent observations without reference to family health context. This paper presents a practitioner-informed technical review ofa production-grade predictive analytics framework designed to reorganize clinical data around family units, link records across multiple electronic medical record systems using a FHIR-based interoperability layer, and apply machine learning models to identify family-based disease risk at the population scale. The framework integrates graph-structured family relationship models, temporal feature engineering encoding health trajectories across generations, and real-time clinical decision support workflows delivering family risk scores to care coordinators. Drawing on direct implementation experience in large-scale population health programs, we describe the data architecture, identity resolution pipeline, ML model design, and deployment infrastructure required to operationalize multi-generational analytics in production. We evaluate methodological requirements for family-aware model validation, discuss governance challenges specific to family-level health data, including relational consent and genetic discrimination risk, and identify the equity implications of deploying genomically-informed family risk models in ancestrally diverse populations. We further discuss federated learning and social determinant integration as the most consequential near-term extensions of the framework. The paper is intended to bridge the gap between theoretical proposals for multi-generational health analytics and the architectural and governance realities of deploying such systems at the scale of millions of members.","url":"https://doi.org/10.5281/zenodo.20041660","authors":["Akash Kamble LNU"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20041660","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20744059","name":"TextRefs: An open registry for canonical text references","source":"datacite","abstract":"Slides introducing TextRefs, an open registry for canonical text references. The presentation starts from a familiar scholarly practice: references such as “Plato, Republic 514a” identify the same passage across many editions, translations, languages, layouts, and publication contexts. While this stability is central to scholarship, it is usually encoded only as human-readable citation text in footnotes, prose, or apparatus entries. As a result, software cannot reliably resolve, index, compare, or link canonical passage references across digital collections.TextRefs addresses this gap by treating canonical text references as persistent, machine-readable, and resolvable entities. The slides present TextRefs as a scholarly data infrastructure layer that can connect citation systems, canonical works, resolver targets, catalogues, editions, full-text repositories, datasets, annotations, and publishing platforms. Instead of replacing existing editorial or bibliographic practices, the registry is framed as an interoperability hub that makes established reference conventions visible to machines while preserving their scholarly meaning for human readers.The presentation illustrates this approach through the example of Plato, Republic 514a, showing how a single canonical reference can be represented in context, cited, given aliases, and connected to resolver targets such as Project Gutenberg and the Perseus Digital Library. It then outlines four use cases: stable cross-edition citation for individual researchers; persistent canonical structures for individual editions; federated networks of discoverable text across diverse collections; and AI or machine-learning workflows that benefit from granular, linked canonical text data.The closing slides emphasize that TextRefs is currently pre-1.0, that specifications and APIs may still change, and that scholarly trust will depend on curation, governance, review workflows, and community participation. The deck therefore serves both as an introduction to the TextRefs concept and as an invitation to help build a standard and roadmap for citable, interoperable text on the web.","url":"https://doi.org/10.5281/zenodo.20744059","authors":["Mähr, Moritz","Seiberth, Luz Christopher"],"tags":["TextRefs","Information Science/standards","Computer and information sciences","Publishing/standards","Open Access Publishing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20744059","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20744060","name":"TextRefs: An open registry for canonical text references","source":"datacite","abstract":"Slides introducing TextRefs, an open registry for canonical text references. The presentation starts from a familiar scholarly practice: references such as “Plato, Republic 514a” identify the same passage across many editions, translations, languages, layouts, and publication contexts. While this stability is central to scholarship, it is usually encoded only as human-readable citation text in footnotes, prose, or apparatus entries. As a result, software cannot reliably resolve, index, compare, or link canonical passage references across digital collections.TextRefs addresses this gap by treating canonical text references as persistent, machine-readable, and resolvable entities. The slides present TextRefs as a scholarly data infrastructure layer that can connect citation systems, canonical works, resolver targets, catalogues, editions, full-text repositories, datasets, annotations, and publishing platforms. Instead of replacing existing editorial or bibliographic practices, the registry is framed as an interoperability hub that makes established reference conventions visible to machines while preserving their scholarly meaning for human readers.The presentation illustrates this approach through the example of Plato, Republic 514a, showing how a single canonical reference can be represented in context, cited, given aliases, and connected to resolver targets such as Project Gutenberg and the Perseus Digital Library. It then outlines four use cases: stable cross-edition citation for individual researchers; persistent canonical structures for individual editions; federated networks of discoverable text across diverse collections; and AI or machine-learning workflows that benefit from granular, linked canonical text data.The closing slides emphasize that TextRefs is currently pre-1.0, that specifications and APIs may still change, and that scholarly trust will depend on curation, governance, review workflows, and community participation. The deck therefore serves both as an introduction to the TextRefs concept and as an invitation to help build a standard and roadmap for citable, interoperable text on the web.","url":"https://doi.org/10.5281/zenodo.20744060","authors":["Mähr, Moritz","Seiberth, Luz Christopher"],"tags":["TextRefs","Information Science/standards","Computer and information sciences","Publishing/standards","Open Access Publishing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20744060","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20482101","name":"Straggler Mitigation Strategies in Asynchronous Federated Learning for Robust Multimodal IoT Malware Detection","source":"datacite","abstract":"This report synthesises findings from 8 peer-reviewed papers addressing the following research question: What is the impact of straggler mitigation strategies in asynchronous federated learning on the robustness of multimodal IoT malware detectors against label-flipping poisoning attacks. Federated Learning (FL) is a technique that can learn a global machine-learning model at a central server by aggregating locally trained models. This distributed machine-learning approach preserves the privacy of local models. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the impact of straggler mitigation strategies in asynchronous federated learning on the robustness of multimodal IoT malware detectors against label-flipping poisoning attacks? Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20482101","authors":["Assignee Research"],"tags":["impact","straggler","mitigation","strategies","asynchronous","federated","learning","robustness"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20482101","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20520092","name":"Coordination Without Continuous Synchronization: Why Sparse, Event-Triggered, and Budget-Aware Protocols Dominate Dense Communication in Multi-Agent Systems","source":"datacite","abstract":"Version 2 — revised in response to an external structural review and an automated critique pass. See \"Response to Review\" appendix in the PDF for the change log. A persistent assumption in multi-agent systems (MAS) design is that more communication produces better coordination. Recent preprints across cs.MA, cs.DC, and cs.NI collectively challenge this assumption, converging on a counter-intuitive pattern: sparse, event-triggered, and budget-aware communication protocols consistently match or outperform dense, synchronous baselines across radically different topologies — from LLM-based reasoning pipelines to federated edge-cloud orchestration to cooperative reinforcement learning under delayed observations. This synthesis draws on eight corpus sources to argue a **heuristic reading** of that pattern: communication density is a cost-accuracy dial, not a reliability guarantee, and the optimal operating point is frequently sparser than the default design assumption. We present this as a candidate hypothesis, not a derived theorem; the mechanisms behind the pattern come from distinct formalisms and the cross-domain analogy is argued but not proven. We identify three structural mechanisms behind this reading. First, redundant message generation in multi-agent pipelines concentrates energy and latency cost in output tokens, not input tokens, making sparsification asymmetrically valuable [corpus:arxiv:2605.27787]. Second, deliberative consensus in LLM oracle ensembles propagates confident errors rather than correcting them, causing accuracy to *fall below* single-model baselines when agent count increases [corpus:arxiv:2605.30802]. Third, event-triggered decentralized coordination in threshold-activated cooperative bandits achieves a reported 23× reduction in communication volume relative to a centralized baseline — across the tested configurations — while preserving feasibility alignment [corpus:arxiv:2605.27076]. Complementary evidence from federated market orchestration [corpus:arxiv:2605.27106], hybrid cloud-edge MAS design [corpus:arxiv:2605.30102], dynamic topology reconfiguration [corpus:arxiv:2605.29511], delay-robust MARL [corpus:arxiv:2605.26286], and V2X infrastructure coordination [corpus:arxiv:2605.25431] reinforces the pattern across domains and topologies. The falsification path is clear: if tasks exist where dense, synchronous communication consistently outperforms sparse alternatives after controlling for task difficulty, topology, and error correlation structure, the thesis fails. We name the conditions under which dense communication remains necessary and argue they are narrower than commonly assumed. --- Authorship: Saluca Agentic AI Research Team (Saluca LLC). AI-drafted from arXiv preprint corpus on the date in the filename. Cited arXiv preprints: 2605.25431, 2605.25653, 2605.25746, 2605.26286, 2605.26448, 2605.27076, 2605.27106, 2605.27466, 2605.27787, 2605.29511, 2605.30102, 2605.30227, 2605.30802 AI disclosure. This work was produced with an agentic AI research apparatus operated by Saluca Labs. The apparatus drafted, searched and analysed under direction. Cristian Ruvalcaba is the human author and is accountable for the content. No AI system is listed as an author or contributor, because authorship entails accountability that a model cannot hold; this disclosure is the credit, and it is deliberately the whole of it.","url":"https://doi.org/10.5281/zenodo.20520092","authors":["Saluca Agentic AI Research Team"],"tags":["AI-drafted synthesis","arXiv","preprint review","v2"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20520092","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20519745","name":"Coordination Without Continuous Synchronization: Why Sparse, Event-Triggered, and Budget-Aware Protocols Dominate Dense Communication in Multi-Agent Systems","source":"datacite","abstract":"Version 2 — revised in response to an external structural review and an automated critique pass. See \"Response to Review\" appendix in the PDF for the change log. A persistent assumption in multi-agent systems (MAS) design is that more communication produces better coordination. Recent preprints across cs.MA, cs.DC, and cs.NI collectively challenge this assumption, converging on a counter-intuitive pattern: sparse, event-triggered, and budget-aware communication protocols consistently match or outperform dense, synchronous baselines across radically different topologies — from LLM-based reasoning pipelines to federated edge-cloud orchestration to cooperative reinforcement learning under delayed observations. This synthesis draws on eight corpus sources to argue a **heuristic reading** of that pattern: communication density is a cost-accuracy dial, not a reliability guarantee, and the optimal operating point is frequently sparser than the default design assumption. We present this as a candidate hypothesis, not a derived theorem; the mechanisms behind the pattern come from distinct formalisms and the cross-domain analogy is argued but not proven. We identify three structural mechanisms behind this reading. First, redundant message generation in multi-agent pipelines concentrates energy and latency cost in output tokens, not input tokens, making sparsification asymmetrically valuable [corpus:arxiv:2605.27787]. Second, deliberative consensus in LLM oracle ensembles propagates confident errors rather than correcting them, causing accuracy to *fall below* single-model baselines when agent count increases [corpus:arxiv:2605.30802]. Third, event-triggered decentralized coordination in threshold-activated cooperative bandits achieves a reported 23× reduction in communication volume relative to a centralized baseline — across the tested configurations — while preserving feasibility alignment [corpus:arxiv:2605.27076]. Complementary evidence from federated market orchestration [corpus:arxiv:2605.27106], hybrid cloud-edge MAS design [corpus:arxiv:2605.30102], dynamic topology reconfiguration [corpus:arxiv:2605.29511], delay-robust MARL [corpus:arxiv:2605.26286], and V2X infrastructure coordination [corpus:arxiv:2605.25431] reinforces the pattern across domains and topologies. The falsification path is clear: if tasks exist where dense, synchronous communication consistently outperforms sparse alternatives after controlling for task difficulty, topology, and error correlation structure, the thesis fails. We name the conditions under which dense communication remains necessary and argue they are narrower than commonly assumed. --- Authorship: Saluca Agentic AI Research Team (Saluca LLC). AI-drafted from arXiv preprint corpus on the date in the filename. Cited arXiv preprints: 2605.25431, 2605.25653, 2605.25746, 2605.26286, 2605.26448, 2605.27076, 2605.27106, 2605.27466, 2605.27787, 2605.29511, 2605.30102, 2605.30227, 2605.30802 AI disclosure. This work was produced with an agentic AI research apparatus operated by Saluca Labs. The apparatus drafted, searched and analysed under direction. Cristian Ruvalcaba is the human author and is accountable for the content. No AI system is listed as an author or contributor, because authorship entails accountability that a model cannot hold; this disclosure is the credit, and it is deliberately the whole of it.","url":"https://doi.org/10.5281/zenodo.20519745","authors":["Saluca Agentic AI Research Team"],"tags":["AI-drafted synthesis","arXiv","preprint review","v2"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20519745","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20589341","name":"A Comprehensive Survey on AI-Driven  Optimization and Dynamic Cluster Head Selection for Energy-Efficient Wireless Sensor Networks","source":"datacite","abstract":"Abstract— There is no doubt that the Wireless SensorNetworks (WSNs) are indispensable to a variety of applicationswhich include environmental monitoring, healthcare, smartgrids, and industrial IoT systems. However, WSNs still sufferfrom the same problems of limited power supply, insecure datatransfer, and network scalability. The current review attemptsto show all the recent developments in the optimization of theAI-based approaches designed to solve the limitations ofWSNs. Emphasis is put on the application of artificialintelligence and machine learning techniques that cover hybridmodels, deep learning, swarm intelligence, and heuristicalgorithms that help in the detection of anomalies, selection ofcluster heads, routing efficiency, and management of energy inWSNs overall. The dynamic selection of the cluster heads is oneof the most talked-about issues in the paper; particularly, themethods using the metaheuristic and clustering algorithmswhich greatly improve the lifetime of the network and balancethe energy usage are highlighted. The paper is going to discussmethodologies, provide a summary of performancecomparison, pinpoint research voids like deployment issues inreal life and overhead of computation, and suggest futureresearch paths such as federated learning and real-timeadaptive analytics. This review will help researchers andpractitioners who want to create WSN solutions that areenergy-efficient, secure, and resilient and that are supportedby intelligent optimization frameworks.","url":"https://doi.org/10.5281/zenodo.20589341","authors":["Manoj Bhade, Rachana Kamble, Amar Nayak"],"tags":["Wireless Sensor Networks (WSNs), Artificial Intelligence (AI), Machine Learning, Dynamic Cluster Head Selection, Energy Efficiency, Network Lifetime Optimization."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20589341","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20589342","name":"A Comprehensive Survey on AI-Driven  Optimization and Dynamic Cluster Head Selection for Energy-Efficient Wireless Sensor Networks","source":"datacite","abstract":"Abstract— There is no doubt that the Wireless SensorNetworks (WSNs) are indispensable to a variety of applicationswhich include environmental monitoring, healthcare, smartgrids, and industrial IoT systems. However, WSNs still sufferfrom the same problems of limited power supply, insecure datatransfer, and network scalability. The current review attemptsto show all the recent developments in the optimization of theAI-based approaches designed to solve the limitations ofWSNs. Emphasis is put on the application of artificialintelligence and machine learning techniques that cover hybridmodels, deep learning, swarm intelligence, and heuristicalgorithms that help in the detection of anomalies, selection ofcluster heads, routing efficiency, and management of energy inWSNs overall. The dynamic selection of the cluster heads is oneof the most talked-about issues in the paper; particularly, themethods using the metaheuristic and clustering algorithmswhich greatly improve the lifetime of the network and balancethe energy usage are highlighted. The paper is going to discussmethodologies, provide a summary of performancecomparison, pinpoint research voids like deployment issues inreal life and overhead of computation, and suggest futureresearch paths such as federated learning and real-timeadaptive analytics. This review will help researchers andpractitioners who want to create WSN solutions that areenergy-efficient, secure, and resilient and that are supportedby intelligent optimization frameworks.","url":"https://doi.org/10.5281/zenodo.20589342","authors":["Manoj Bhade, Rachana Kamble, Amar Nayak"],"tags":["Wireless Sensor Networks (WSNs), Artificial Intelligence (AI), Machine Learning, Dynamic Cluster Head Selection, Energy Efficiency, Network Lifetime Optimization."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20589342","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.19483567","name":"AI-Driven Threat Detection In Multi-Cloud Environments","source":"datacite","abstract":"The rapid adoption of multi-cloud environments has fundamentally transformed enterprise IT infrastructure, offering enhanced scalability, resilience, and vendor flexibility. However, this architectural evolution has also introduced complex security challenges, including fragmented visibility, heterogeneous security policies, and increased attack surfaces. Traditional security mechanisms, which rely heavily on static rules and signature-based detection, are often inadequate in addressing the dynamic and distributed nature of multi-cloud ecosystems. In this context, artificial intelligence has emerged as a transformative approach to modern cybersecurity, enabling adaptive, real-time threat detection and response. This review article explores the role of artificial intelligence in enhancing threat detection capabilities across multi-cloud environments. It systematically examines the integration of machine learning, deep learning, and behavioral analytics into cloud security frameworks, emphasizing their ability to identify anomalous activities, predict potential threats, and automate incident response. The study also evaluates key architectural components, including data ingestion pipelines, model training strategies, and cross-cloud orchestration mechanisms that support AI-driven security systems. Furthermore, the article discusses the challenges associated with implementing AI in multi-cloud security, such as data privacy concerns, model interpretability, adversarial attacks, and scalability constraints. It highlights emerging trends, including federated learning, zero-trust architectures, and autonomous security operations, which are shaping the future of intelligent threat detection systems. Comparative insights into existing frameworks and industry practices are also provided to illustrate the practical implications of AI adoption. By synthesizing current research and technological advancements, this review aims to provide a comprehensive understanding of AI-driven threat detection in multi-cloud environments. It offers a strategic roadmap for researchers and practitioners to design robust, scalable, and intelligent security solutions capable of addressing evolving cyber threats in increasingly complex cloud infrastructures.","url":"https://doi.org/10.5281/zenodo.19483567","authors":["Sergey Ivanov"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.19483567","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.19483568","name":"AI-Driven Threat Detection In Multi-Cloud Environments","source":"datacite","abstract":"The rapid adoption of multi-cloud environments has fundamentally transformed enterprise IT infrastructure, offering enhanced scalability, resilience, and vendor flexibility. However, this architectural evolution has also introduced complex security challenges, including fragmented visibility, heterogeneous security policies, and increased attack surfaces. Traditional security mechanisms, which rely heavily on static rules and signature-based detection, are often inadequate in addressing the dynamic and distributed nature of multi-cloud ecosystems. In this context, artificial intelligence has emerged as a transformative approach to modern cybersecurity, enabling adaptive, real-time threat detection and response. This review article explores the role of artificial intelligence in enhancing threat detection capabilities across multi-cloud environments. It systematically examines the integration of machine learning, deep learning, and behavioral analytics into cloud security frameworks, emphasizing their ability to identify anomalous activities, predict potential threats, and automate incident response. The study also evaluates key architectural components, including data ingestion pipelines, model training strategies, and cross-cloud orchestration mechanisms that support AI-driven security systems. Furthermore, the article discusses the challenges associated with implementing AI in multi-cloud security, such as data privacy concerns, model interpretability, adversarial attacks, and scalability constraints. It highlights emerging trends, including federated learning, zero-trust architectures, and autonomous security operations, which are shaping the future of intelligent threat detection systems. Comparative insights into existing frameworks and industry practices are also provided to illustrate the practical implications of AI adoption. By synthesizing current research and technological advancements, this review aims to provide a comprehensive understanding of AI-driven threat detection in multi-cloud environments. It offers a strategic roadmap for researchers and practitioners to design robust, scalable, and intelligent security solutions capable of addressing evolving cyber threats in increasingly complex cloud infrastructures.","url":"https://doi.org/10.5281/zenodo.19483568","authors":["Sergey Ivanov"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.19483568","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20501766","name":"Multi-View Graph Anomaly Detection Robustness Under View Dropout and Metattack Perturbations","source":"datacite","abstract":"This report synthesises findings from 3 peer-reviewed papers addressing the following research question: What is the impact of view dropout on the robustness of multi-view graph anomaly detection frameworks against Metattack perturbations, measured by the degradation in detection accuracy and F1-score. Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the impact of view dropout on the robustness of multi-view graph anomaly detection frameworks against Metattack perturbations, measured by the degradation in detection accuracy and F1-score? Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20501766","authors":["Assignee Research"],"tags":["impact","view","dropout","robustness","multi-view","graph","anomaly","detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20501766","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20501767","name":"Multi-View Graph Anomaly Detection Robustness Under View Dropout and Metattack Perturbations","source":"datacite","abstract":"This report synthesises findings from 3 peer-reviewed papers addressing the following research question: What is the impact of view dropout on the robustness of multi-view graph anomaly detection frameworks against Metattack perturbations, measured by the degradation in detection accuracy and F1-score. Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the impact of view dropout on the robustness of multi-view graph anomaly detection frameworks against Metattack perturbations, measured by the degradation in detection accuracy and F1-score? Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20501767","authors":["Assignee Research"],"tags":["impact","view","dropout","robustness","multi-view","graph","anomaly","detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20501767","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21608018","name":"A Comprehensive Review on Leveraging Artificial Intelligence (AI) for the Cutting-edge Discoveries in Computer Aided Drug Design and Pharmaceutical R & D","source":"datacite","abstract":"The integration of Artificial Intelligence (AI) into Computer-Aided Drug Design (CADD) has transformed pharmaceutical research and development by enhancing efficiency and accuracy in drug discovery. This review discusses the evolution of drug discovery from traditional methods to AI-driven approaches, emphasizing the major AI techniques such as machine learning, deep learning, reinforcement learning, and generative AI applied in CADD. Key applications include target identification, virtual screening, molecular docking, QSAR modelling, molecular dynamics, ADMET prediction, drug repurposing, de novo drug design, protein structure prediction, and personalized medicine. AI has accelerated these processes by improving predictive power and reducing costs and timelines. Challenges such as data quality, interpretability, model reproducibility, and ethical concerns remain barriers to widespread adoption. Future perspectives highlight the potential of generative AI, explainable AI, multiomics integration, quantum AI, and federated learning to further revolutionize drug discovery. This review underscores AI’s transformative role in CADD and outlines research gaps to guide future advancements.","url":"https://doi.org/10.5281/zenodo.21608018","authors":["Archana Kongari","Kirtana Gajul","Annasaheb Valgude","Saikrishna Devsani","Mandar Choudhari","Rushikesh Lengare"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21608018","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21608019","name":"A Comprehensive Review on Leveraging Artificial Intelligence (AI) for the Cutting-edge Discoveries in Computer Aided Drug Design and Pharmaceutical R & D","source":"datacite","abstract":"The integration of Artificial Intelligence (AI) into Computer-Aided Drug Design (CADD) has transformed pharmaceutical research and development by enhancing efficiency and accuracy in drug discovery. This review discusses the evolution of drug discovery from traditional methods to AI-driven approaches, emphasizing the major AI techniques such as machine learning, deep learning, reinforcement learning, and generative AI applied in CADD. Key applications include target identification, virtual screening, molecular docking, QSAR modelling, molecular dynamics, ADMET prediction, drug repurposing, de novo drug design, protein structure prediction, and personalized medicine. AI has accelerated these processes by improving predictive power and reducing costs and timelines. Challenges such as data quality, interpretability, model reproducibility, and ethical concerns remain barriers to widespread adoption. Future perspectives highlight the potential of generative AI, explainable AI, multiomics integration, quantum AI, and federated learning to further revolutionize drug discovery. This review underscores AI’s transformative role in CADD and outlines research gaps to guide future advancements.","url":"https://doi.org/10.5281/zenodo.21608019","authors":["Archana Kongari","Kirtana Gajul","Annasaheb Valgude","Saikrishna Devsani","Mandar Choudhari","Rushikesh Lengare"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21608019","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20481956","name":"Compressive Sensing Integration in Over-the-Air Federated Learning for Massive MIMO Systems","source":"datacite","abstract":"This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does the integration of compressive sensing techniques with over-the-air federated learning (OTA-FL) in massive MIMO systems compare to traditional FL methods in terms of model accuracy and. The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from ``connected things'' to ``connected intelligence''. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the integration of compressive sensing techniques with over-the-air federated learning (OTA-FL) in massive MIMO systems compare to traditional FL methods in terms of model accuracy and convergence rate when evaluated on the CIFAR-10 dataset? Autonomous literature synthesis. Automated review score: 8.3/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20481956","authors":["Assignee Research"],"tags":["integration","compressive","sensing","techniques","over-the-air","federated","learning","OTA-FL"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20481956","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20481957","name":"Compressive Sensing Integration in Over-the-Air Federated Learning for Massive MIMO Systems","source":"datacite","abstract":"This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does the integration of compressive sensing techniques with over-the-air federated learning (OTA-FL) in massive MIMO systems compare to traditional FL methods in terms of model accuracy and. The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from ``connected things'' to ``connected intelligence''. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the integration of compressive sensing techniques with over-the-air federated learning (OTA-FL) in massive MIMO systems compare to traditional FL methods in terms of model accuracy and convergence rate when evaluated on the CIFAR-10 dataset? Autonomous literature synthesis. Automated review score: 8.3/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20481957","authors":["Assignee Research"],"tags":["integration","compressive","sensing","techniques","over-the-air","federated","learning","OTA-FL"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20481957","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20481996","name":"Structural Similarity Degradation in Multimodal Federated Learning Under Non-IID Data","source":"datacite","abstract":"This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the structural similarity of feature embeddings in collaborative multimodal federated learning models scale with increasing degrees of non-IID data, as measured by cosine similarity across. In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI research, there has been growing awareness and concerns of data privacy. Recent significant developments in the data regulation landscape have prompted a seismic shift in interest. 6 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.9/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the structural similarity of feature embeddings in collaborative multimodal federated learning models scale with increasing degrees of non-IID data, as measured by cosine similarity across clients? Autonomous literature synthesis. Automated review score: 7.9/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20481996","authors":["Assignee Research"],"tags":["structural","similarity","feature","embeddings","collaborative","multimodal","federated","learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20481996","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20481997","name":"Structural Similarity Degradation in Multimodal Federated Learning Under Non-IID Data","source":"datacite","abstract":"This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the structural similarity of feature embeddings in collaborative multimodal federated learning models scale with increasing degrees of non-IID data, as measured by cosine similarity across. In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI research, there has been growing awareness and concerns of data privacy. Recent significant developments in the data regulation landscape have prompted a seismic shift in interest. 6 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 7.9/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the structural similarity of feature embeddings in collaborative multimodal federated learning models scale with increasing degrees of non-IID data, as measured by cosine similarity across clients? Autonomous literature synthesis. Automated review score: 7.9/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20481997","authors":["Assignee Research"],"tags":["structural","similarity","feature","embeddings","collaborative","multimodal","federated","learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20481997","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.18759298","name":"The European Moonshot for Education: AI-Powered Learning as Sovereign Infrastructure","source":"datacite","abstract":"Policy paper proposing education as the twelfth Moonshot project in the EU Multiannual Financial Framework 2028–2034. Argues that Google's deployment of LearnLM to 170 million accounts via existing Workspace infrastructure constitutes cognitive lock-in without pedagogical review, procurement decision, or democratic oversight. Presents the case for a European trustee-governed alternative modeled on the CERN precedent, with sovereign data architecture and federated knowledge governance. Companion paper: A Generative Education Architecture for Planetary-Scale Personalized Learning.","url":"https://doi.org/10.5281/zenodo.18759298","authors":["Pochmann, Matthias"],"tags":["EU MFF 2028-2034","moonshot","education infrastructure","cognitive sovereignty","AI in education","digital sovereignty","LearnLM","CERN"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18759298","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.18759299","name":"The European Moonshot for Education: AI-Powered Learning as Sovereign Infrastructure","source":"datacite","abstract":"Policy paper proposing education as the twelfth Moonshot project in the EU Multiannual Financial Framework 2028–2034. Argues that Google's deployment of LearnLM to 170 million accounts via existing Workspace infrastructure constitutes cognitive lock-in without pedagogical review, procurement decision, or democratic oversight. Presents the case for a European trustee-governed alternative modeled on the CERN precedent, with sovereign data architecture and federated knowledge governance. Companion paper: A Generative Education Architecture for Planetary-Scale Personalized Learning.","url":"https://doi.org/10.5281/zenodo.18759299","authors":["Pochmann, Matthias"],"tags":["EU MFF 2028-2034","moonshot","education infrastructure","cognitive sovereignty","AI in education","digital sovereignty","LearnLM","CERN"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18759299","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21301142","name":"Deep Learning Driven Threat Detection in Modern Database Management Systems","source":"datacite","abstract":"Contemporary Database Management Systems (DBMS) support critical data-intensive applications across banking, e-commerce, healthcare, and government services. However, with the increasing adoption of distributed and cloud-based architectures, DBMS have become more vulnerable to sophisticated cyber-attacks, including advanced SQL injection, privilege escalation, insider threats, API-based attacks, and zero-day exploits. Traditional rule-based and signature-based intrusion detection systems struggle to identify these evolving threats. Deep learning (DL) has emerged as a promising approach to enhance database security by automatically learning complex patterns and detecting anomalies. This paper presents a comprehensive review and a novel hybrid DL framework for threat detection in modern DBMS settings. We explore the application of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Transformers, Autoencoders, and Graph Neural Networks (GNNs) to analyze SQL query logs, user interactions, system events, and network traffic for real-time threat detection. Our proposed hybrid model, combining LSTM, CNN, Autoencoder, and Transformer components, achieves a detection accuracy of 98.2\\% and reduces the false positive rate to 3--4\\%, outperforming individual models. We also address key challenges such as data imbalance, privacy, interpretability, and scalability. Promising future directions include self-supervised learning, federated learning, and hybrid DL-based SIEM systems.","url":"https://doi.org/10.5281/zenodo.21301142","authors":["Deshraj Bairwa","YADAVA, PRAMOD"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21301142","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21301143","name":"Deep Learning Driven Threat Detection in Modern Database Management Systems","source":"datacite","abstract":"Contemporary Database Management Systems (DBMS) support critical data-intensive applications across banking, e-commerce, healthcare, and government services. However, with the increasing adoption of distributed and cloud-based architectures, DBMS have become more vulnerable to sophisticated cyber-attacks, including advanced SQL injection, privilege escalation, insider threats, API-based attacks, and zero-day exploits. Traditional rule-based and signature-based intrusion detection systems struggle to identify these evolving threats. Deep learning (DL) has emerged as a promising approach to enhance database security by automatically learning complex patterns and detecting anomalies. This paper presents a comprehensive review and a novel hybrid DL framework for threat detection in modern DBMS settings. We explore the application of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Transformers, Autoencoders, and Graph Neural Networks (GNNs) to analyze SQL query logs, user interactions, system events, and network traffic for real-time threat detection. Our proposed hybrid model, combining LSTM, CNN, Autoencoder, and Transformer components, achieves a detection accuracy of 98.2\\% and reduces the false positive rate to 3--4\\%, outperforming individual models. We also address key challenges such as data imbalance, privacy, interpretability, and scalability. Promising future directions include self-supervised learning, federated learning, and hybrid DL-based SIEM systems.","url":"https://doi.org/10.5281/zenodo.21301143","authors":["Deshraj Bairwa","YADAVA, PRAMOD"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21301143","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20047682","name":"Federated Learning Architectures For Privacy-Preserving Intrusion Detection In IoT Networks","source":"datacite","abstract":"The rapid proliferation of Internet of Things (IoT) devices and smart infrastructure has led to an exponential surge in network traffic, rendering traditional security perimeters increasingly vulnerable. Intrusion Detection Systems (IDS) serve as a critical frontline defense; however, conventional centralized Machine Learning (ML) models are struggling to reconcile high-volume data processing with stringent privacy regulations such as the General Data Protection Regulation (GDPR). Federated Learning (FL) has emerged as a pivotal decentralized paradigm, allowing edge devices and organizations to cooperatively train global models while keeping raw data local, thereby ensuring privacy and reducing model-offloading bandwidth consumption. This review provides a comprehensive analysis of the evolution of FL-based IDS, focusing on its implementation within Industrial Control Systems (ICS) and smart manufacturing environments. We systematically examine the primary technical hurdles facing these architectures, specifically focusing on statistical heterogeneity (non-IID data), communication overhead in resource-constrained networks, and vulnerability to adversarial machine learning attacks such as poisoning and evasion, Furthermore, we discuss specialized integrations with Information-Centric Networking (ICN) and the efficacy of deep learning architectures, such as Long Short-Term Memory (LSTM), in enhancing detection accuracy. The paper concludes by identifying future research avenues, including the need for enhanced model interpretability and robustness against adaptive adversarial threats.","url":"https://doi.org/10.5281/zenodo.20047682","authors":["Mayur Girish Taunk","Jigarkumar Ambalal Patel"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.20047682","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20047683","name":"Federated Learning Architectures For Privacy-Preserving Intrusion Detection In IoT Networks","source":"datacite","abstract":"The rapid proliferation of Internet of Things (IoT) devices and smart infrastructure has led to an exponential surge in network traffic, rendering traditional security perimeters increasingly vulnerable. Intrusion Detection Systems (IDS) serve as a critical frontline defense; however, conventional centralized Machine Learning (ML) models are struggling to reconcile high-volume data processing with stringent privacy regulations such as the General Data Protection Regulation (GDPR). Federated Learning (FL) has emerged as a pivotal decentralized paradigm, allowing edge devices and organizations to cooperatively train global models while keeping raw data local, thereby ensuring privacy and reducing model-offloading bandwidth consumption. This review provides a comprehensive analysis of the evolution of FL-based IDS, focusing on its implementation within Industrial Control Systems (ICS) and smart manufacturing environments. We systematically examine the primary technical hurdles facing these architectures, specifically focusing on statistical heterogeneity (non-IID data), communication overhead in resource-constrained networks, and vulnerability to adversarial machine learning attacks such as poisoning and evasion, Furthermore, we discuss specialized integrations with Information-Centric Networking (ICN) and the efficacy of deep learning architectures, such as Long Short-Term Memory (LSTM), in enhancing detection accuracy. The paper concludes by identifying future research avenues, including the need for enhanced model interpretability and robustness against adaptive adversarial threats.","url":"https://doi.org/10.5281/zenodo.20047683","authors":["Mayur Girish Taunk","Jigarkumar Ambalal Patel"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.20047683","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.19335948","name":"How AI can be Powerful in Providing Security for IoT","source":"datacite","abstract":"The proliferation of the Internet of Things (IoT) globally has revolutionized industries such as healthcare, smart cities, agriculture, and manufacturing. However, this widespread integration has brought along critical security concerns, making IoT networks vulnerable to attacks such as data breaches, spoofing, eavesdropping, and Distributed Denial of Service (DDoS). Traditional security mechanisms struggle to scale and adapt to the heterogeneous, dynamic nature of global IoT systems. Artificial Intelligence (AI) emerges as a powerful tool capable of enhancing IoT security by enabling adaptive, real-time threat detection, self-learning models, and automated response mechanisms. This research explores the role of advanced AI techniques—such as federated learning, deep neural networks, reinforcement learning, and anomaly detection models—in securing IoT infrastructures across various domains. The paper provides a detailed literature review of current trends and studies, analyzes the effectiveness of AI in identifying threats, and discusses its limitations, ethical implications, and global deployment challenges. The findings indicate that AI-driven IoT security frameworks significantly outperform traditional systems in scalability, responsiveness, and contextual awareness, making AI a pivotal force in the future of global cybersecurity.","url":"https://doi.org/10.5281/zenodo.19335948","authors":["Mr. Prashant Tanaji Bagade","Mr. Nayan Vijay Patil"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19335948","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.19335949","name":"How AI can be Powerful in Providing Security for IoT","source":"datacite","abstract":"The proliferation of the Internet of Things (IoT) globally has revolutionized industries such as healthcare, smart cities, agriculture, and manufacturing. However, this widespread integration has brought along critical security concerns, making IoT networks vulnerable to attacks such as data breaches, spoofing, eavesdropping, and Distributed Denial of Service (DDoS). Traditional security mechanisms struggle to scale and adapt to the heterogeneous, dynamic nature of global IoT systems. Artificial Intelligence (AI) emerges as a powerful tool capable of enhancing IoT security by enabling adaptive, real-time threat detection, self-learning models, and automated response mechanisms. This research explores the role of advanced AI techniques—such as federated learning, deep neural networks, reinforcement learning, and anomaly detection models—in securing IoT infrastructures across various domains. The paper provides a detailed literature review of current trends and studies, analyzes the effectiveness of AI in identifying threats, and discusses its limitations, ethical implications, and global deployment challenges. The findings indicate that AI-driven IoT security frameworks significantly outperform traditional systems in scalability, responsiveness, and contextual awareness, making AI a pivotal force in the future of global cybersecurity.","url":"https://doi.org/10.5281/zenodo.19335949","authors":["Mr. Prashant Tanaji Bagade","Mr. Nayan Vijay Patil"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19335949","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20161043","name":"A Hybrid Machine Learning Framework for Anomaly Detection  and Electricity Theft Identification in Smart Distribution  Networks: A Comprehensive Review","source":"datacite","abstract":"This paper presents a review of hybrid machine learning approaches for electricity theft detection in smart grids using Advanced Metering Infrastructure (AMI) data. The study analyzes supervised, unsupervised, and hybrid anomaly detection techniques including Isolation Forest and Histogram-based Gradient Boosting. Experimental evaluation on a synthetic smart meter dataset demonstrates improved detection accuracy and reduced false positives using hybrid models. The paper also discusses future directions such as Explainable AI (XAI), Federated Learning, and Graph Neural Networks (GNNs) for enhancing smart grid security.","url":"https://doi.org/10.5281/zenodo.20161043","authors":["Lakdeswar, Anjali","Shamkule, Devashree","Awachat, Mansi","Urade, Bobby"],"tags":["Smart Grid","Machine Learning","Electricity Theft Detection","Isolation Forest","HistGBM","AMI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20161043","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20161044","name":"A Hybrid Machine Learning Framework for Anomaly Detection  and Electricity Theft Identification in Smart Distribution  Networks: A Comprehensive Review","source":"datacite","abstract":"This paper presents a review of hybrid machine learning approaches for electricity theft detection in smart grids using Advanced Metering Infrastructure (AMI) data. The study analyzes supervised, unsupervised, and hybrid anomaly detection techniques including Isolation Forest and Histogram-based Gradient Boosting. Experimental evaluation on a synthetic smart meter dataset demonstrates improved detection accuracy and reduced false positives using hybrid models. The paper also discusses future directions such as Explainable AI (XAI), Federated Learning, and Graph Neural Networks (GNNs) for enhancing smart grid security.","url":"https://doi.org/10.5281/zenodo.20161044","authors":["Lakdeswar, Anjali","Shamkule, Devashree","Awachat, Mansi","Urade, Bobby"],"tags":["Smart Grid","Machine Learning","Electricity Theft Detection","Isolation Forest","HistGBM","AMI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20161044","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21351898","name":"HantaWatch: Federated Learning for Hantavirus Genomic Surveillance","source":"datacite","abstract":"Abstract—Hantavirus genomic surveillance is limited by distributed sequence data, non-IID source heterogeneity, and constrained expert-review capacity. We propose HantaWatch, a federated learning framework that enables laboratories and surveillance sites to collaboratively train sequence-based models without sharing raw data. HantaWatch integrates k-mer feature extraction, source-aware federated client construction, adaptive DU-FedProx optimization, surveillance-specific model selection, and prediction-only triage. Experiments on binary and multiclass tasks show that HantaWatch supports high-risk screening, outbreak-associated prediction, clade classification, and clinical syndrome categorization while balancing predictive performance, false-negative risk, and update stability. The framework converts model output into risk scores, confidence estimates, uncertainty flags, and ranked expert-review priorities. HantaWatch therefore provides a practical, federated decision-support layer for decentralized Hantavirus surveillance, supporting expert prioritization without replacing laboratory or public health interpretation. Index Terms—Hantavirus surveillance, genomic surveillance, federated learning, adaptive federated optimization, sequence prioritization, public-health decision support.","url":"https://doi.org/10.5281/zenodo.21351898","authors":["Nanayakkara, shanika","Pokhrel, Shiva"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21351898","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21351899","name":"HantaWatch: Federated Learning for Hantavirus Genomic Surveillance","source":"datacite","abstract":"Abstract—Hantavirus genomic surveillance is limited by distributed sequence data, non-IID source heterogeneity, and constrained expert-review capacity. We propose HantaWatch, a federated learning framework that enables laboratories and surveillance sites to collaboratively train sequence-based models without sharing raw data. HantaWatch integrates k-mer feature extraction, source-aware federated client construction, adaptive DU-FedProx optimization, surveillance-specific model selection, and prediction-only triage. Experiments on binary and multiclass tasks show that HantaWatch supports high-risk screening, outbreak-associated prediction, clade classification, and clinical syndrome categorization while balancing predictive performance, false-negative risk, and update stability. The framework converts model output into risk scores, confidence estimates, uncertainty flags, and ranked expert-review priorities. HantaWatch therefore provides a practical, federated decision-support layer for decentralized Hantavirus surveillance, supporting expert prioritization without replacing laboratory or public health interpretation. Index Terms—Hantavirus surveillance, genomic surveillance, federated learning, adaptive federated optimization, sequence prioritization, public-health decision support.","url":"https://doi.org/10.5281/zenodo.21351899","authors":["Nanayakkara, shanika","Pokhrel, Shiva"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21351899","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20474482","name":"Federated Learning Aggregation Strategies for Non-IID Data in Massive MIMO Systems","source":"datacite","abstract":"This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How do different federated learning aggregation strategies (e.g., FedAvg, FedProx, SCAFFOLD) perform in terms of robustness to non-IID data distributions and model alignment when integrated with. Over-the-air federated learning (OTA-FL) is an emerging technique to reduce the computation and communication overload at the PS caused by the orthogonal transmissions of the model updates in conventional federated learning (FL). This reduction is achieved at the expense of. 10 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How do different federated learning aggregation strategies (e.g., FedAvg, FedProx, SCAFFOLD) perform in terms of robustness to non-IID data distributions and model alignment when integrated with compressive sensing over massive MIMO systems, evaluated using cross-domain benchmark datasets? Autonomous literature synthesis. Automated review score: 8.3/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20474482","authors":["Assignee Research"],"tags":["different","federated","learning","aggregation","strategies","FedAvg","FedProx","SCAFFOLD"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20474482","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20474483","name":"Federated Learning Aggregation Strategies for Non-IID Data in Massive MIMO Systems","source":"datacite","abstract":"This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How do different federated learning aggregation strategies (e.g., FedAvg, FedProx, SCAFFOLD) perform in terms of robustness to non-IID data distributions and model alignment when integrated with. Over-the-air federated learning (OTA-FL) is an emerging technique to reduce the computation and communication overload at the PS caused by the orthogonal transmissions of the model updates in conventional federated learning (FL). This reduction is achieved at the expense of. 10 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.3/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How do different federated learning aggregation strategies (e.g., FedAvg, FedProx, SCAFFOLD) perform in terms of robustness to non-IID data distributions and model alignment when integrated with compressive sensing over massive MIMO systems, evaluated using cross-domain benchmark datasets? Autonomous literature synthesis. Automated review score: 8.3/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20474483","authors":["Assignee Research"],"tags":["different","federated","learning","aggregation","strategies","FedAvg","FedProx","SCAFFOLD"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20474483","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20472173","name":"Quantized Large Language Model Inference Latency Scaling in Federated Edge Deployments","source":"datacite","abstract":"This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the inference latency of quantized large language models scale with the number of concurrent edge devices in a federated learning setup for real-time threat detection. Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted in breakthroughs in many areas. However, deploying these highly accurate models for data-driven, learned, automatic, and practical machine learning (ML) solutions to end-user applications. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the inference latency of quantized large language models scale with the number of concurrent edge devices in a federated learning setup for real-time threat detection? Autonomous literature synthesis. Automated review score: 8.2/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20472173","authors":["Assignee Research"],"tags":["inference","latency","quantized","large","language","models","scale","number"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20472173","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20472174","name":"Quantized Large Language Model Inference Latency Scaling in Federated Edge Deployments","source":"datacite","abstract":"This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the inference latency of quantized large language models scale with the number of concurrent edge devices in a federated learning setup for real-time threat detection. Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted in breakthroughs in many areas. However, deploying these highly accurate models for data-driven, learned, automatic, and practical machine learning (ML) solutions to end-user applications. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the inference latency of quantized large language models scale with the number of concurrent edge devices in a federated learning setup for real-time threat detection? Autonomous literature synthesis. Automated review score: 8.2/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20472174","authors":["Assignee Research"],"tags":["inference","latency","quantized","large","language","models","scale","number"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20472174","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.19875052","name":"ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DRUG DESIGN AND DISCOVERY: A COMPREHENSIVE ANALYSIS","source":"datacite","abstract":"Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research and drug development. This review highlights the application of AI across the drug discovery pipeline, including multiomics data analysis, target identification, protein structure prediction, virtual screening, de novo drug design, retrosynthesis, ADMET prediction, and clinical trial optimization. Advanced deep learning models such as graph neural networks, transformers, and diffusion models have significantly improved molecular representation, interaction prediction, and novel compound generation. The integration of emerging approaches like federated learning and quantum machine learning is also discussed, particularly for overcoming data-sharing and computational limitations. Clinical examples of AI-designed drugs are examined to illustrate both successes and challenges in translating computational predictions into real-world outcomes. Despite substantial progress, issues such as model interpretability, data bias, and regulatory concerns remain critical barriers. Overall, AI is rapidly becoming a central driver of precision medicine by enhancing efficiency, reducing costs, and improving success rates in drug discovery. However, interdisciplinary collaboration and responsible governance frameworks are essential to fully realize its potential and ensure safe and effective implementation in pharmaceutical development.","url":"https://doi.org/10.5281/zenodo.19875052","authors":["*1Sachin Panth, 3Poonam Kashyap, 2Deepak Baghel, 1Poonam Kaimaiyan, 3Deepsingh Bhadouriya"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19875052","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.19875053","name":"ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DRUG DESIGN AND DISCOVERY: A COMPREHENSIVE ANALYSIS","source":"datacite","abstract":"Artificial intelligence (AI) and machine learning (ML) are transforming pharmaceutical research and drug development. This review highlights the application of AI across the drug discovery pipeline, including multiomics data analysis, target identification, protein structure prediction, virtual screening, de novo drug design, retrosynthesis, ADMET prediction, and clinical trial optimization. Advanced deep learning models such as graph neural networks, transformers, and diffusion models have significantly improved molecular representation, interaction prediction, and novel compound generation. The integration of emerging approaches like federated learning and quantum machine learning is also discussed, particularly for overcoming data-sharing and computational limitations. Clinical examples of AI-designed drugs are examined to illustrate both successes and challenges in translating computational predictions into real-world outcomes. Despite substantial progress, issues such as model interpretability, data bias, and regulatory concerns remain critical barriers. Overall, AI is rapidly becoming a central driver of precision medicine by enhancing efficiency, reducing costs, and improving success rates in drug discovery. However, interdisciplinary collaboration and responsible governance frameworks are essential to fully realize its potential and ensure safe and effective implementation in pharmaceutical development.","url":"https://doi.org/10.5281/zenodo.19875053","authors":["*1Sachin Panth, 3Poonam Kashyap, 2Deepak Baghel, 1Poonam Kaimaiyan, 3Deepsingh Bhadouriya"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19875053","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20463724","name":"Federated Learning Scalability in Heterogeneous IoT Malware Detection Networks","source":"datacite","abstract":"This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the communication efficiency of federated learning-based malware detection models scale with increasing device heterogeneity across IoT networks, measured by round convergence time and. ---The Internet of Things (IoT) has revolutionized various sectors by enabling seamless interaction between devices. However, the proliferation of IoT devices has also raised significant security and privacy concerns. 8 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the communication efficiency of federated learning-based malware detection models scale with increasing device heterogeneity across IoT networks, measured by round convergence time and bandwidth utilization? Autonomous literature synthesis. Automated review score: 8.2/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20463724","authors":["Assignee Research"],"tags":["communication","efficiency","federated","learning-based","malware","detection","models","scale"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20463724","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20463725","name":"Federated Learning Scalability in Heterogeneous IoT Malware Detection Networks","source":"datacite","abstract":"This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the communication efficiency of federated learning-based malware detection models scale with increasing device heterogeneity across IoT networks, measured by round convergence time and. ---The Internet of Things (IoT) has revolutionized various sectors by enabling seamless interaction between devices. However, the proliferation of IoT devices has also raised significant security and privacy concerns. 8 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.2/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the communication efficiency of federated learning-based malware detection models scale with increasing device heterogeneity across IoT networks, measured by round convergence time and bandwidth utilization? Autonomous literature synthesis. Automated review score: 8.2/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20463725","authors":["Assignee Research"],"tags":["communication","efficiency","federated","learning-based","malware","detection","models","scale"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20463725","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20772709","name":"AI Integration with Network Pharmacology for Poly Herbal Drug Discovery: Current Advances and Future Scope","source":"datacite","abstract":"The convergence of Artificial Intelligence (AI) and Network Pharmacology (NP) has emerged as a transformative paradigm in the rational discovery of poly-herbal medicines. Traditional herbal formulations, characterized by multi-component and multi-target pharmacodynamics, present unprecedented computational challenges that classical drug discovery pipelines are ill-equipped to address. This review comprehensively examines how advanced machine learning (ML) algorithms, deep learning architectures, graph neural networks (GNNs), and natural language processing (NLP) are being applied to decode the molecular intricacies of poly-herbal systems. We systematically evaluate current advances in AI-assisted target identification, compound-target interaction prediction, ADME/T profiling, and polypharmacology network construction specific to multi-herb formulations. The review also covers the integration of pharmacogenomics, multi-omics data, and knowledge graphs in constructing holistic herb–disease–target networks. Key databases (TCMSP, HERB, BATMAN-TCM, IMPPAT, AyurvedicBI) and computational platforms supporting this ecosystem are discussed. Representative case studies from Ayurveda, Traditional Chinese Medicine (TCM), and Unani systems demonstrate real-world applications. Challenges including chemical complexity, data sparsity, black-box AI models, and regulatory barriers are critically analyzed with proposed solutions. Finally, we chart the future trajectory including federated learning, explainable AI (XAI), digital twins for herbal medicine,and AI-guided clinical translation. This review is intended to serve as a reference for pharmacologists, computational scientists, and clinicians working at the intersection of traditional medicine and modern computational biology.","url":"https://doi.org/10.5281/zenodo.20772709","authors":["Sadiya Prabin*1, K. Hamsika Sri2, Edalada Pavan Kumar3, Gurleen Kaur4, Dr. Md Sayeed Anwar5"],"tags":["Network Pharmacology; Poly-herbal Drug Discovery; Artificial Intelligence; Machine Learning; Graph Neural Networks; Traditional Chinese Medicine; Ayurveda; Multi-target Pharmacology; Drug-Target Interaction; Phytochemoinformatics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20772709","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20772710","name":"AI Integration with Network Pharmacology for Poly Herbal Drug Discovery: Current Advances and Future Scope","source":"datacite","abstract":"The convergence of Artificial Intelligence (AI) and Network Pharmacology (NP) has emerged as a transformative paradigm in the rational discovery of poly-herbal medicines. Traditional herbal formulations, characterized by multi-component and multi-target pharmacodynamics, present unprecedented computational challenges that classical drug discovery pipelines are ill-equipped to address. This review comprehensively examines how advanced machine learning (ML) algorithms, deep learning architectures, graph neural networks (GNNs), and natural language processing (NLP) are being applied to decode the molecular intricacies of poly-herbal systems. We systematically evaluate current advances in AI-assisted target identification, compound-target interaction prediction, ADME/T profiling, and polypharmacology network construction specific to multi-herb formulations. The review also covers the integration of pharmacogenomics, multi-omics data, and knowledge graphs in constructing holistic herb–disease–target networks. Key databases (TCMSP, HERB, BATMAN-TCM, IMPPAT, AyurvedicBI) and computational platforms supporting this ecosystem are discussed. Representative case studies from Ayurveda, Traditional Chinese Medicine (TCM), and Unani systems demonstrate real-world applications. Challenges including chemical complexity, data sparsity, black-box AI models, and regulatory barriers are critically analyzed with proposed solutions. Finally, we chart the future trajectory including federated learning, explainable AI (XAI), digital twins for herbal medicine,and AI-guided clinical translation. This review is intended to serve as a reference for pharmacologists, computational scientists, and clinicians working at the intersection of traditional medicine and modern computational biology.","url":"https://doi.org/10.5281/zenodo.20772710","authors":["Sadiya Prabin*1, K. Hamsika Sri2, Edalada Pavan Kumar3, Gurleen Kaur4, Dr. Md Sayeed Anwar5"],"tags":["Network Pharmacology; Poly-herbal Drug Discovery; Artificial Intelligence; Machine Learning; Graph Neural Networks; Traditional Chinese Medicine; Ayurveda; Multi-target Pharmacology; Drug-Target Interaction; Phytochemoinformatics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20772710","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21556276","name":"Machine Learning Efforts That Enhance Personalized Patient Care and Chronic Disease Management","source":"datacite","abstract":"The growing burden of chronic diseases has underscored the urgent need for personalized, data-driven approaches to healthcare delivery. Machine learning (ML) has emerged as a transformative technology capable of enhancing chronic disease management through predictive analytics, real-time monitoring, and individualized treatment optimization. This review examines the role of ML in advancing personalized patient care by exploring foundational techniques such as supervised and unsupervised learning, deep neural networks, and reinforcement learning. It highlights practical applications across diabetes, cardiovascular conditions, respiratory disorders, and cancer survivorship, emphasizing the value of ML in risk prediction, medication adjustment, and remote monitoring. Additionally, the paper discusses key enablers of personalized care, including patient stratification, precision dosing, and the integration of wearable devices and digital platforms. Emerging innovations such as federated learning, explainable AI, multimodal data fusion, and digital twin systems are explored for their potential to support secure, transparent, and context-aware healthcare delivery. The review also addresses critical challenges related to bias, data privacy, clinical integration, and regulatory oversight. Ultimately, this work advocates for a multidisciplinary framework that combines technological innovation with policy reform to ensure equitable, scalable, and sustainable deployment of machine learning in personalized chronic disease care.","url":"https://doi.org/10.5281/zenodo.21556276","authors":["Adeyinka, Adepeju Ayotunde","Lamina, Yejide","Tawo, Obah Edom","Adeyeye, Yewande Iyimide","Minkah, Andrew Yaw"],"tags":["Machine Learning","Personalized Patient Care","Chronic Disease Management"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.21556276","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21556277","name":"Machine Learning Efforts That Enhance Personalized Patient Care and Chronic Disease Management","source":"datacite","abstract":"The growing burden of chronic diseases has underscored the urgent need for personalized, data-driven approaches to healthcare delivery. Machine learning (ML) has emerged as a transformative technology capable of enhancing chronic disease management through predictive analytics, real-time monitoring, and individualized treatment optimization. This review examines the role of ML in advancing personalized patient care by exploring foundational techniques such as supervised and unsupervised learning, deep neural networks, and reinforcement learning. It highlights practical applications across diabetes, cardiovascular conditions, respiratory disorders, and cancer survivorship, emphasizing the value of ML in risk prediction, medication adjustment, and remote monitoring. Additionally, the paper discusses key enablers of personalized care, including patient stratification, precision dosing, and the integration of wearable devices and digital platforms. Emerging innovations such as federated learning, explainable AI, multimodal data fusion, and digital twin systems are explored for their potential to support secure, transparent, and context-aware healthcare delivery. The review also addresses critical challenges related to bias, data privacy, clinical integration, and regulatory oversight. Ultimately, this work advocates for a multidisciplinary framework that combines technological innovation with policy reform to ensure equitable, scalable, and sustainable deployment of machine learning in personalized chronic disease care.","url":"https://doi.org/10.5281/zenodo.21556277","authors":["Adeyinka, Adepeju Ayotunde","Lamina, Yejide","Tawo, Obah Edom","Adeyeye, Yewande Iyimide","Minkah, Andrew Yaw"],"tags":["Machine Learning","Personalized Patient Care","Chronic Disease Management"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.21556277","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20330118","name":"Anomaly Detection in IoT Networks Using Machine Learning and Deep Learning","source":"datacite","abstract":"The Internet of Things (IoT) has grown rapidly, now connecting over 30 billion heterogeneous devices that generate large volumes of real-time data across domains such as healthcare, smart cities, industrial automation, and transportation. This growth has created serious security challenges, particularly since most IoT devices operate under tight hardware constraints — limited processing power, restricted memory, and minimal energy budgets — that make conventional security mechanisms impractical. This paper presents a comprehensive review of recent work on anomaly detection in IoT networks using machine learning (ML) and deep learning (DL) techniques. A broad set of approaches is examined, including classical ML methods such as Support Vector Machines (SVM), Random Forest (RF), and k-Nearest Neighbours (k-NN), alongside deep learning architectures including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Bidirectional LSTM, and Autoencoders. Advanced paradigms are also reviewed, including hybrid multi-branch architectures, reinforcement learning-based adaptive response frameworks, transformer-based sequence models, federated and privacy-preserving learning, and graph neural networks. Benchmark datasets widely used in the field — TON-IoT, UNSW-NB15, and CICIoT2023 among them — are assessed. The central finding is that hybrid deep learning architectures consistently outperform single-model approaches in detection accuracy, though often at the cost of computational demand that limits their direct deployability on resourceconstrained edge devices. Persistent open challenges are identified, including dataset imbalance, the lack of standardised evaluation protocols, limited cross-domain generalisation, adversarial vulnerability, and the need for privacy-preserving distributed training. Future research directions are outlined with emphasis on lightweight edge-deployable models, semi-supervised and self-supervised learning, autonomous intelligent response systems, and standardised benchmarking practices.","url":"https://doi.org/10.5281/zenodo.20330118","authors":["Jain, Hardik"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20330118","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20330119","name":"Anomaly Detection in IoT Networks Using Machine Learning and Deep Learning","source":"datacite","abstract":"The Internet of Things (IoT) has grown rapidly, now connecting over 30 billion heterogeneous devices that generate large volumes of real-time data across domains such as healthcare, smart cities, industrial automation, and transportation. This growth has created serious security challenges, particularly since most IoT devices operate under tight hardware constraints — limited processing power, restricted memory, and minimal energy budgets — that make conventional security mechanisms impractical. This paper presents a comprehensive review of recent work on anomaly detection in IoT networks using machine learning (ML) and deep learning (DL) techniques. A broad set of approaches is examined, including classical ML methods such as Support Vector Machines (SVM), Random Forest (RF), and k-Nearest Neighbours (k-NN), alongside deep learning architectures including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Bidirectional LSTM, and Autoencoders. Advanced paradigms are also reviewed, including hybrid multi-branch architectures, reinforcement learning-based adaptive response frameworks, transformer-based sequence models, federated and privacy-preserving learning, and graph neural networks. Benchmark datasets widely used in the field — TON-IoT, UNSW-NB15, and CICIoT2023 among them — are assessed. The central finding is that hybrid deep learning architectures consistently outperform single-model approaches in detection accuracy, though often at the cost of computational demand that limits their direct deployability on resourceconstrained edge devices. Persistent open challenges are identified, including dataset imbalance, the lack of standardised evaluation protocols, limited cross-domain generalisation, adversarial vulnerability, and the need for privacy-preserving distributed training. Future research directions are outlined with emphasis on lightweight edge-deployable models, semi-supervised and self-supervised learning, autonomous intelligent response systems, and standardised benchmarking practices.","url":"https://doi.org/10.5281/zenodo.20330119","authors":["Jain, Hardik"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20330119","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21547470","name":"Advances in AI and ML for Cloud Computing: A Review of Algorithms, Challenges, and Innovations","source":"datacite","abstract":"This review delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) in revolutionizing cloud computing. It explores how AI/ML algorithms optimize resource management, enhance system scalability, strengthen security, and reduce operational costs. The paper categorizes AI/ML applications into domains such as dynamic resource allocation, anomaly detection, predictive analytics, and cost optimization. Key challenges, including scalability, data privacy, and interoperability, are discussed alongside emerging opportunities like federated learning for privacy-aware applications, explainable AI for transparent cloud management, and energy-efficient algorithms for sustainable cloud computing. This review underscores AI/ML's transformative potential while emphasizing the need for innovative solutions to overcome implementation challenges.","url":"https://doi.org/10.5281/zenodo.21547470","authors":["Ramamoorthi, Vijay"],"tags":["Cloud computing; Artificial Intelligence; Machine Learning; Dynamic resource allocation; Predictive analytics; Sustainability; Federated learning; Explainable AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21547470","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.21547471","name":"Advances in AI and ML for Cloud Computing: A Review of Algorithms, Challenges, and Innovations","source":"datacite","abstract":"This review delves into the role of Artificial Intelligence (AI) and Machine Learning (ML) in revolutionizing cloud computing. It explores how AI/ML algorithms optimize resource management, enhance system scalability, strengthen security, and reduce operational costs. The paper categorizes AI/ML applications into domains such as dynamic resource allocation, anomaly detection, predictive analytics, and cost optimization. Key challenges, including scalability, data privacy, and interoperability, are discussed alongside emerging opportunities like federated learning for privacy-aware applications, explainable AI for transparent cloud management, and energy-efficient algorithms for sustainable cloud computing. This review underscores AI/ML's transformative potential while emphasizing the need for innovative solutions to overcome implementation challenges.","url":"https://doi.org/10.5281/zenodo.21547471","authors":["Ramamoorthi, Vijay"],"tags":["Cloud computing; Artificial Intelligence; Machine Learning; Dynamic resource allocation; Predictive analytics; Sustainability; Federated learning; Explainable AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21547471","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20263144","name":"fl-oran-tmc: Federated Learning Benchmark for ColO-RAN Slice SLA Prediction","source":"datacite","abstract":"Local PyTorch federated-learning pipeline for ColO-RAN O-RAN slice SLA forecasting. Cross-architecture empirical benchmark on the Colosseum/ColO-RAN public dataset: 3-arch core panel (LSTM, Mamba, Spiking-SSM) plus a 2-arch recent-SOTA extension (xLSTM, Mamba-3) × multiple FL algorithms (FedAvg, FedProx, FedAdam, SCAFFOLD, FedDyn, FedBN, FedSWA, FedSCAM, FedGMT, FedMoSWA; MOON deferred) × parametric Dirichlet heterogeneity × per-round NVML training-energy measurement. All five architectures share an identical encoder and a structurally-identical classifier head; only the temporal trunk differs (total parameter count matched within ±10% via the ADR-001 D-20 parity constraint). Paper under review at IEEE Journal on Selected Areas in Communications.","url":"https://doi.org/10.5281/zenodo.20263144","authors":["Tsai, Hsiu-Chi"],"tags":["federated-learning","O-RAN","slice-SLA-prediction","state-space-models","spiking-neural-networks","xLSTM","Mamba","Mamba-3"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20263144","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20319969","name":"fl-oran-tmc: Federated Learning Benchmark for ColO-RAN Slice SLA Prediction","source":"datacite","abstract":"Local PyTorch federated-learning pipeline for ColO-RAN O-RAN slice SLA forecasting. Cross-architecture empirical benchmark on the Colosseum/ColO-RAN public dataset: 3-arch core panel (LSTM, Mamba, Spiking-SSM) plus a 2-arch recent-SOTA extension (xLSTM, Mamba-3) × multiple FL algorithms (FedAvg, FedProx, FedAdam, SCAFFOLD, FedDyn, FedBN, FedSWA, FedSCAM, FedGMT, FedMoSWA; MOON deferred) × parametric Dirichlet heterogeneity × per-round NVML training-energy measurement. All five architectures share an identical encoder and a structurally-identical classifier head; only the temporal trunk differs (total parameter count matched within ±10% via the ADR-001 D-20 parity constraint). Paper under review at IEEE Journal on Selected Areas in Communications.","url":"https://doi.org/10.5281/zenodo.20319969","authors":["Tsai, Hsiu-Chi"],"tags":["federated-learning","O-RAN","slice-SLA-prediction","state-space-models","spiking-neural-networks","xLSTM","Mamba","Mamba-3"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20319969","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20049523","name":"A Comprehensive Literature Review on Federated Machine Learning for Privacy-Preserving Cyber Threat Detection in Distributed Network Environments","source":"datacite","abstract":"Cloud computing and IoT devices are actually growing very fast, and this definitely makes cyber attacks more complex and common. Traditional systems for catching cyber attacks actually have problems with new threats and keeping data safe. These old methods definitely cannot handle big amounts of data spread across many places. This paper gives a complete study review regarding federated machine learning for keeping privacy safe in cyber threat detection as per distributed network systems. The study examines how cyber threat detection methods have evolved from basic rule-based systems to advanced machine learning approaches. It further analyzes how the field itself has progressed from simple anomaly detection to complex deep learning techniques. These methods surely make detection more accurate, but they depend too much on processing data in one central place. Moreover, this creates problems with privacy protection and handling large amounts of data. Federated learning actually solves these problems by letting different computers work together to train models using their own data. The computers definitely learn together but never actually share their raw information with each other. As per this method, data privacy gets better regarding protection, and the system becomes more scalable and strong. The review actually looks at important methods in federated learning like secure combining, privacy protection, and coding systems that definitely make the system more safe. Also, this study actually looks at the main problems in federated learning like different types of data, too much communication, and attacks from bad actors. These challenges definitely make the system harder to work with. Basically, the study shows the same research gaps and says we need good communication methods, strong security systems, and scalable designs for real-world use. We are seeing that federated learning can only change cybersecurity by helping different systems work together to find threats while keeping data safe and private.","url":"https://doi.org/10.5281/zenodo.20049523","authors":["Research Scholar Sunil Chandolu","Professor Dr.Pankaj Khairnar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.20049523","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.5281/zenodo.20049524","name":"A Comprehensive Literature Review on Federated Machine Learning for Privacy-Preserving Cyber Threat Detection in Distributed Network Environments","source":"datacite","abstract":"Cloud computing and IoT devices are actually growing very fast, and this definitely makes cyber attacks more complex and common. Traditional systems for catching cyber attacks actually have problems with new threats and keeping data safe. These old methods definitely cannot handle big amounts of data spread across many places. This paper gives a complete study review regarding federated machine learning for keeping privacy safe in cyber threat detection as per distributed network systems. The study examines how cyber threat detection methods have evolved from basic rule-based systems to advanced machine learning approaches. It further analyzes how the field itself has progressed from simple anomaly detection to complex deep learning techniques. These methods surely make detection more accurate, but they depend too much on processing data in one central place. Moreover, this creates problems with privacy protection and handling large amounts of data. Federated learning actually solves these problems by letting different computers work together to train models using their own data. The computers definitely learn together but never actually share their raw information with each other. As per this method, data privacy gets better regarding protection, and the system becomes more scalable and strong. The review actually looks at important methods in federated learning like secure combining, privacy protection, and coding systems that definitely make the system more safe. Also, this study actually looks at the main problems in federated learning like different types of data, too much communication, and attacks from bad actors. These challenges definitely make the system harder to work with. Basically, the study shows the same research gaps and says we need good communication methods, strong security systems, and scalable designs for real-world use. We are seeing that federated learning can only change cybersecurity by helping different systems work together to find threats while keeping data safe and private.","url":"https://doi.org/10.5281/zenodo.20049524","authors":["Research Scholar Sunil Chandolu","Professor Dr.Pankaj Khairnar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.20049524","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.17605/osf.io/arwfe","name":"Small language models for clinical natural language processing: A systematic review","source":"datacite","abstract":"This systematic review evaluates small language models (SLMs, ≤7B parameters) for clinical NLP tasks in ambient and agentic healthcare workflows. We propose the 3R evaluation framework (Real-time, Resource-constrained, Regulation-aware) to assess SLM fitness for real-world health system deployment.","url":"https://doi.org/10.17605/osf.io/arwfe","authors":["Dawa Chyophel Lepcha"],"tags":["Health Information Technology","Translational Medical Research","Computational Engineering","Medicine and Health Sciences","Electrical and Computer Engineering","Mental and Social Health","Engineering","3R framework"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17605/osf.io/arwfe","addedAt":"2026-08-31T06:41:29.554Z","updatedAt":"2026-08-31T06:41:29.554Z"},{"id":"doi:10.1038/s41598-020-65871-8","name":"Hybrid Quantum Protocols for Secure Multiparty Summation and Multiplication","source":"crossref","abstract":"Abstract The summation and multiplication are two basic operations for secure multiparty quantum computation. The existing secure multiparty quantum summation and multiplication protocols have ( n , n ) threshold approach and their computation type is bit-by-bit, where n is total number of players. In this paper, we propose two hybrid ( t , n ) threshold quantum protocols for secure multiparty summation and multiplication based on the Shamir’s secret sharing, SUM gate, quantum fourier transform, and generalized Pauli operator, where t is a threshold number of players that can perform the summation and multiplication. Their computation type is secret-by-secret with modulo d , where d , n ≤ d ≤ 2 n , is a prime. The proposed protocols can resist the intercept-resend, entangle-measure, collusion, collective, and coherent quantum attacks. They have better computation as well as communication costs and no player can get other player’s private input.","url":"https://doi.org/10.1038/s41598-020-65871-8","authors":["Kartick Sutradhar","Hari Om"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-06-04T10:03:02Z","doi":"10.1038/s41598-020-65871-8","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-319-94147-9_20","name":"One-Round Secure Multiparty Computation of Arithmetic Streams and Functions","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-94147-9_20","authors":["Dor Bitan","Shlomi Dolev"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-06-16T03:40:43Z","doi":"10.1007/978-3-319-94147-9_20","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-662-46497-7_23","name":"Adaptively Secure, Universally Composable, Multiparty Computation in Constant Rounds","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-662-46497-7_23","authors":["Dana Dachman-Soled","Jonathan Katz","Vanishree Rao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-03-09T02:37:58Z","doi":"10.1007/978-3-662-46497-7_23","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-319-96878-0_10","name":"Two-Round Multiparty Secure Computation Minimizing Public Key Operations","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-96878-0_10","authors":["Sanjam Garg","Peihan Miao","Akshayaram Srinivasan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-07-23T12:53:57Z","doi":"10.1007/978-3-319-96878-0_10","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1145/3055399.3055495","name":"Equivocating Yao","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3055399.3055495","authors":["Ran Canetti","Oxana Poburinnaya","Muthuramakrishnan Venkitasubramaniam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-06-15T20:27:45Z","doi":"10.1145/3055399.3055495","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.2478/tmmp-2014-0024","name":"Non-Interactive Secure Multiparty Key Establishment","source":"crossref","abstract":"Abstract Cryptographic schemes that provide establishment of secret keys among a number of participants are generally known as conference key establishment schemes and key broadcasting schemes. In any case, such protocols provide secure establishment of group-oriented cryptographic keys, but with the costs of multiple transmissions of key establishment messages and in some cases multiple secret user keys. In this paper, we present a simple and straightforward efficient non-interactive group-oriented key establishment scheme that provides off-line computation of secret group keys, without computations and transmissions of key establishment messages","url":"https://doi.org/10.2478/tmmp-2014-0024","authors":["Sigurd Eskeland"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-03-12T13:04:38Z","doi":"10.2478/tmmp-2014-0024","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.3844/jcssp.2012.872.878","name":"Privacy Preserved Collaborative Secure Multiparty Data Mining","source":"crossref","abstract":"","url":"https://doi.org/10.3844/jcssp.2012.872.878","authors":["Ravindra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-04-23T18:07:08Z","doi":"10.3844/jcssp.2012.872.878","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1038/s41598-021-88837-w","name":"Secure multiparty quantum key agreement against collusive attacks","source":"crossref","abstract":"Abstract Quantum key agreement enables remote participants to fairly establish a secure shared key based on their private inputs. In the circular-type multiparty quantum key agreement mode, two or more malicious participants can collude together to steal private inputs of honest participants or to generate the final key alone. In this work, we focus on a powerful collusive attack strategy in which two or more malicious participants in particular positions, can learn sensitive information or generate the final key alone without revealing their malicious behaviour. Many of the current circular-type multiparty quantum key agreement protocols are not secure against this collusive attack strategy. As an example, we analyze the security of a recently proposed multiparty key agreement protocol to show the vulnerability of existing circular-type multiparty quantum key agreement protocols against this collusive attack. Moreover, we design a general secure multiparty key agreement model that would remove this vulnerability from such circular-type key agreement protocols and describe the necessary steps to implement this model. The proposed model is general and does not depend on the specific physical implementation of the quantum key agreement.","url":"https://doi.org/10.1038/s41598-021-88837-w","authors":["Hussein Abulkasim","Atefeh Mashatan","Shohini Ghose"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-05-04T06:03:29Z","doi":"10.1038/s41598-021-88837-w","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-981-95-3551-4_3","name":"A New Asymmetric Three-Party Key Agreement Protocol Based on Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-3551-4_3","authors":["Shanchuan Pang","Quanrun Li","Hu Ma","Jiaming Wen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T00:37:55Z","doi":"10.1007/978-981-95-3551-4_3","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-642-03549-4_20","name":"Secure Multiparty Computation Goes Live","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-03549-4_20","authors":["Peter Bogetoft","Dan Lund Christensen","Ivan Damgård","Martin Geisler","Thomas Jakobsen","Mikkel Krøigaard","Janus Dam Nielsen","Jesper Buus Nielsen","Kurt Nielsen","Jakob Pagter","Michael Schwartzbach","Tomas Toft"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-07-16T06:05:28Z","doi":"10.1007/978-3-642-03549-4_20","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/wccct.2014.31","name":"Secure Multiparty Electronic Payments Using ECC Algorithm: A Comparative Study","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wccct.2014.31","authors":["K. Ravikumar","A. Udhayakumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-11-20T05:37:00Z","doi":"10.1109/wccct.2014.31","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/jiot.2022.3209017","name":"Distributed Optimization for Integrated Energy Systems With Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/jiot.2022.3209017","authors":["Fangyuan Si","Ning Zhang","Yi Wang","Peng-Yong Kong","Wenjie Qiao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-23T16:05:40Z","doi":"10.1109/jiot.2022.3209017","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-981-92-2973-4_6","name":"New Privacy-Preserving Activation Functions Based on Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-92-2973-4_6","authors":["Meiyi Fang","Maoning Wang","Meijiao Duan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-06T01:04:17Z","doi":"10.1007/978-981-92-2973-4_6","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1080/14697688.2025.2558687","name":"Handbook of Sharing Confidential Data: Differential Privacy, Secure Multiparty Computation, and Synthetic Data","source":"crossref","abstract":"","url":"https://doi.org/10.1080/14697688.2025.2558687","authors":["Behnoosh Zamanlooy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-19T10:35:44Z","doi":"10.1080/14697688.2025.2558687","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1016/j.procs.2025.02.205","name":"Can Secure MultiParty Computation be Used to Create Clinical Trial Cohorts based on Blockchain Notarized Private Patient Data?","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2025.02.205","authors":["Bruno Ferreira","Rafael Borges","Carlos Machado Antunes","Marisa Maximiano","Ricardo Gomes","Vítor Távora","Manuel Dias","Ricardo Correia Bezerra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T08:19:20Z","doi":"10.1016/j.procs.2025.02.205","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1349/ddlp.298","name":"Hardware-Assisted Secure Computation.","source":"crossref","abstract":"","url":"https://doi.org/10.1349/ddlp.298","authors":["Alexander. Iliev"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-10-03T19:32:07Z","doi":"10.1349/ddlp.298","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-319-96881-0_14","name":"Round-Optimal Secure Multiparty Computation with Honest Majority","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-96881-0_14","authors":["Prabhanjan Ananth","Arka Rai Choudhuri","Aarushi Goel","Abhishek Jain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-07-23T15:54:39Z","doi":"10.1007/978-3-319-96881-0_14","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/sfcs.1989.63520","name":"Multiparty computation with faulty majority","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sfcs.1989.63520","authors":["D. Beaver","S. Goldwasser"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2003-01-07T14:15:12Z","doi":"10.1109/sfcs.1989.63520","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-031-78023-3_6","name":"A Note on Low-Communication Secure Multiparty Computation via Circuit Depth-Reduction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-78023-3_6","authors":["Pierre Charbit","Geoffroy Couteau","Pierre Meyer","Reza Naserasr"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-02T05:51:10Z","doi":"10.1007/978-3-031-78023-3_6","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-64378-2_11","name":"Round Optimal Secure Multiparty Computation from Minimal Assumptions","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-64378-2_11","authors":["Arka Rai Choudhuri","Michele Ciampi","Vipul Goyal","Abhishek Jain","Rafail Ostrovsky"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-12-12T10:02:46Z","doi":"10.1007/978-3-030-64378-2_11","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1016/s0304-3975(97)00107-2","name":"Secure multiparty computations without computers","source":"crossref","abstract":"","url":"https://doi.org/10.1016/s0304-3975(97)00107-2","authors":["Valtteri Niemi","Ari Renvall"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2003-04-07T19:40:02Z","doi":"10.1016/s0304-3975(97)00107-2","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1103/physreva.61.032308","name":"Quantum-classical complexity-security tradeoff in secure multiparty computations","source":"crossref","abstract":"","url":"https://doi.org/10.1103/physreva.61.032308","authors":["H. F. Chau"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2002-07-26T21:57:33Z","doi":"10.1103/physreva.61.032308","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1007/978-3-030-84245-1_7","name":"Three-Round Secure Multiparty Computation from Black-Box Two-Round Oblivious Transfer","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-84245-1_7","authors":["Arpita Patra","Akshayaram Srinivasan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-10T19:04:26Z","doi":"10.1007/978-3-030-84245-1_7","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-319-63688-7_16","name":"A New Approach to Round-Optimal Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-63688-7_16","authors":["Prabhanjan Ananth","Arka Rai Choudhuri","Abhishek Jain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-07-27T21:19:44Z","doi":"10.1007/978-3-319-63688-7_16","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/11863908_3","name":"TrustedPals: Secure Multiparty Computation Implemented with Smart Cards","source":"crossref","abstract":"","url":"https://doi.org/10.1007/11863908_3","authors":["Milan Fort","Felix Freiling","Lucia Draque Penso","Zinaida Benenson","Dogan Kesdogan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2006-09-15T23:28:46Z","doi":"10.1007/11863908_3","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-031-23198-8_24","name":"MPCDDI: A Secure Multiparty Computation-Based Deep Learning Framework for Drug-Drug Interaction Predictions","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-23198-8_24","authors":["Xia Xiao","Xiaoqi Wang","Shengyun Liu","Shaoliang Peng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-01T02:36:12Z","doi":"10.1007/978-3-031-23198-8_24","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.3390/jcp6030079","name":"Vertical Federated XGBoost with Privacy Preservation via Secure Multiparty Computation","source":"crossref","abstract":"Gradient Boosted Decision Trees (GBDTs) are popular for their strong predictive performance. However, in domains like finance and healthcare, data are often distributed across organizations, making collaborative model training challenging due to privacy concerns. Vertical federated learning (VFL) enables such collaboration when data are split by features, but many existing methods focus on protecting raw data while exposing sensitive model information, such as gradients and Hessians—especially to the label-owning party. Techniques like Homomorphic Encryption and Secret Sharing help, but often rely on trusted or privileged parties and may still leak intermediate statistics. To address this, we propose MPC-XGB, a privacy-preserving framework for training XGBoost under VFL with an honest-but-curious threat model. It uses secure three-party computation with Replicated Secret Sharing, distributing data across non-colluding servers and performing all computations on shares. This ensures that raw data, labels, and model statistics remain hidden, while supporting both secure training and prediction. Experiments show that MPC-XGB achieves strong performance (0.93 accuracy, 0.82 AUC), comparable to that of existing methods, with improved privacy guarantees.","url":"https://doi.org/10.3390/jcp6030079","authors":["Asma Ramay","Estrid He","Mengmeng Yang","Tabinda Sarwar","Xinqian Wang","Xun Yi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-01T10:15:27Z","doi":"10.3390/jcp6030079","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/tifs.2026.3673066","name":"Comments on “APFed: Anti-Poisoning Attacks in Privacy-Preserving Heterogeneous Federated Learning”","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tifs.2026.3673066","authors":["Joohee Lee","Joon-Woo Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-11T19:36:32Z","doi":"10.1109/tifs.2026.3673066","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/hase.2004.1281748","name":"Multiparty computation with full computation power and reduced overhead","source":"crossref","abstract":"","url":"https://doi.org/10.1109/hase.2004.1281748","authors":["Qingkai Ma","Wei Hao","I.-L. Yen","F. Bastani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2004-06-10T10:19:45Z","doi":"10.1109/hase.2004.1281748","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-031-38557-5_20","name":"Secure Multiparty Computation from Threshold Encryption Based on Class Groups","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-38557-5_20","authors":["Lennart Braun","Ivan Damgård","Claudio Orlandi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-08T19:03:06Z","doi":"10.1007/978-3-031-38557-5_20","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-981-99-8721-4_11","name":"Unconditionally Secure Multiparty Computation for Symmetric Functions with Low Bottleneck Complexity","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-8721-4_11","authors":["Reo Eriguchi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-12-17T05:01:44Z","doi":"10.1007/978-981-99-8721-4_11","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-031-15985-5_17","name":"Tight Bounds on the Randomness Complexity of Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-15985-5_17","authors":["Vipul Goyal","Yuval Ishai","Yifan Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-10-10T20:02:37Z","doi":"10.1007/978-3-031-15985-5_17","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1364/cleo_fs.2026.fm3a.4","name":"Toward Implementation of Quantum-Secure Multiparty Deep Learning","source":"crossref","abstract":"Quantum-secure deep learning guaranties data security during distributed machine learning computations. We propose an architecture employing spectral filtering and homodyne detection for scalable, secure, cloud-based deep learning in optical networks.","url":"https://doi.org/10.1364/cleo_fs.2026.fm3a.4","authors":["Kfir Sulimany","Sivan Trajtenberg-Mills","Ryan Hamerly","Yoann Piétri","Eleni Diamanti","Dirk Englund"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-15T11:06:33Z","doi":"10.1364/cleo_fs.2026.fm3a.4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.17760/d20289523","name":"Techniques for scalable secure computation systems","source":"crossref","abstract":"","url":"https://doi.org/10.17760/d20289523","authors":["Kreuter"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-06-28T16:01:59Z","doi":"10.17760/d20289523","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.18130/v3rv3w","name":"Practical Secure Two-Party Computation","source":"crossref","abstract":"","url":"https://doi.org/10.18130/v3rv3w","authors":["Yan Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-08-09T19:40:41Z","doi":"10.18130/v3rv3w","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1145/3566048","name":"Two-round Multiparty Secure Computation from Minimal Assumptions","source":"crossref","abstract":"We provide new two-round multiparty secure computation (MPC) protocols in the dishonest majority setting assuming the minimal assumption that two-round oblivious transfer (OT) exists. If the assumed two-round OT protocol is secure against semi-honest adversaries (in the plain model) then so is our two-round MPC protocol. Similarly, if the assumed two-round OT protocol is secure against malicious adversaries (in the common random/reference string model) then so is our two-round MPC protocol. Previously, two-round MPC protocols were only known under relatively stronger computational assumptions.","url":"https://doi.org/10.1145/3566048","authors":["Sanjam Garg","Akshayaram Srinivasan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-10-07T13:16:04Z","doi":"10.1145/3566048","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-77886-6_28","name":"Constant-Overhead Unconditionally Secure Multiparty Computation Over Binary Fields","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77886-6_28","authors":["Antigoni Polychroniadou","Yifan Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-06-15T19:06:10Z","doi":"10.1007/978-3-030-77886-6_28","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-71381-2_2","name":"A Secure Bio-Hash–Based Multiparty Mutual Authentication Protocol for Remote Health Monitoring Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71381-2_2","authors":["Sumitra Binu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-06-15T13:03:58Z","doi":"10.1007/978-3-030-71381-2_2","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/tpwrs.2023.3263242","name":"Bidirectional Privacy-Preserving Network- Constrained Peer-to-Peer Energy Trading Based on Secure Multiparty Computation and Blockchain","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tpwrs.2023.3263242","authors":["Xin Zhou","Bin Wang","Qinglai Guo","Hongbin Sun","Zhaoguang Pan","Nianfeng Tian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-30T13:35:03Z","doi":"10.1109/tpwrs.2023.3263242","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-319-78372-7_4","name":"Efficient Maliciously Secure Multiparty Computation for RAM","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-78372-7_4","authors":["Marcel Keller","Avishay Yanai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-03-30T02:13:04Z","doi":"10.1007/978-3-319-78372-7_4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-319-78375-8_16","name":"Two-Round Multiparty Secure Computation from Minimal Assumptions","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-78375-8_16","authors":["Sanjam Garg","Akshayaram Srinivasan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-03-30T02:12:59Z","doi":"10.1007/978-3-319-78375-8_16","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/icsmc.2006.384705","name":"Multiparty Key Agreement for Secure Teleconferencing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsmc.2006.384705","authors":["Chu-Hsing Lin","Hsiu-Hsia Lin","Jen-Chieh Chang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-07-23T16:41:03Z","doi":"10.1109/icsmc.2006.384705","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1038/s41598-019-53967-9","name":"Secure dynamic multiparty quantum private comparison","source":"crossref","abstract":"Abstract We propose a feasible and efficient dynamic multiparty quantum private comparison protocol that is fully secure against participant attacks. In the proposed scheme, two almost-dishonest third parties generate two random keys and send them to all participants. Every participant independently encrypts their private information with the encryption keys and sends it to the third parties. The third parties can analyze the equality of all or some participants’ secrets without gaining access to the secret information. New participants can dynamically join the protocol without the need for any additional conditions in the protocol. We provide detailed correctness and security analysis of the proposed protocol. Our security analysis of the proposed protocol against both inside and outside attacks proves that attackers cannot extract any secret information.","url":"https://doi.org/10.1038/s41598-019-53967-9","authors":["Hussein Abulkasim","Ahmed Farouk","Safwat Hamad","Atefeh Mashatan","Shohini Ghose"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-11-28T11:03:21Z","doi":"10.1038/s41598-019-53967-9","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-032-20026-6_2","name":"Counterfeit Coin Problem Can be Extended to Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-20026-6_2","authors":["Shohei Kaneko","Yang Li","Kazuo Sakiyama","Daiki Miyahara"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-10T23:27:05Z","doi":"10.1007/978-3-032-20026-6_2","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-031-68397-8_2","name":"Secure Multiparty Computation with Identifiable Abort via Vindicating Release","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-68397-8_2","authors":["Ran Cohen","Jack Doerner","Yashvanth Kondi","Abhi Shelat"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-15T18:01:41Z","doi":"10.1007/978-3-031-68397-8_2","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-319-70972-7_27","name":"Secure Multiparty Computation from SGX","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-70972-7_27","authors":["Raad Bahmani","Manuel Barbosa","Ferdinand Brasser","Bernardo Portela","Ahmad-Reza Sadeghi","Guillaume Scerri","Bogdan Warinschi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-12-22T08:57:55Z","doi":"10.1007/978-3-319-70972-7_27","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.5815/ijcnis.2018.03.02","name":"Secure Multiparty Computation for Privacy Preserving Range Queries on Medical Records for Star Exchange Topology","source":"crossref","abstract":"","url":"https://doi.org/10.5815/ijcnis.2018.03.02","authors":["Ahmed M. Tawfik","Sahar F. Sabbeh","Tarek A. EL-Shishtawy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-04-11T22:19:29Z","doi":"10.5815/ijcnis.2018.03.02","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1016/j.physleta.2005.05.049","name":"Multiparty quantum secret sharing of secure direct communication","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.physleta.2005.05.049","authors":["Zhan-Jun Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2005-06-01T13:39:48Z","doi":"10.1016/j.physleta.2005.05.049","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-84245-1_4","name":"Fluid MPC: Secure Multiparty Computation with Dynamic Participants","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-84245-1_4","authors":["Arka Rai Choudhuri","Aarushi Goel","Matthew Green","Abhishek Jain","Gabriel Kaptchuk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-10T23:04:26Z","doi":"10.1007/978-3-030-84245-1_4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-032-07089-0_3","name":"Silent Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-07089-0_3","authors":["Geoffroy Couteau"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-22T23:13:46Z","doi":"10.1007/978-3-032-07089-0_3","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/icassp.2011.5947691","name":"Is multiparty computation any good in practice?","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icassp.2011.5947691","authors":["Claudio Orlandi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-07-13T11:50:30Z","doi":"10.1109/icassp.2011.5947691","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.5220/0002423300330042","name":"A New Way to Think About Secure Computation: Language-based Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0002423300330042","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-02-21T18:24:57Z","doi":"10.5220/0002423300330042","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-031-57722-2_7","name":"On Information-Theoretic Secure Multiparty Computation with Local Repairability","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-57722-2_7","authors":["Daniel Escudero","Ivan Tjuawinata","Chaoping Xing"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-14T00:56:16Z","doi":"10.1007/978-3-031-57722-2_7","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/0-387-34805-0_51","name":"Multiparty Computation with Faulty Majority","source":"crossref","abstract":"","url":"https://doi.org/10.1007/0-387-34805-0_51","authors":["Donald Beaver","Shaft Goldwasser"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-11-09T02:05:53Z","doi":"10.1007/0-387-34805-0_51","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-032-07089-0_2","name":"Secure Computation: A Primer","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-07089-0_2","authors":["Geoffroy Couteau"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-22T23:30:28Z","doi":"10.1007/978-3-032-07089-0_2","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/s10922-023-09739-y","name":"MPC-ABC: Blockchain-Based Network Communication for Efficiently Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10922-023-09739-y","authors":["Oscar G. Bautista","Mohammad Hossein Manshaei","Richard Hernandez","Kemal Akkaya","Soamar Homsi","Selcuk Uluagac"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-21T09:01:43Z","doi":"10.1007/s10922-023-09739-y","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/tdsc.2012.18","name":"Mitigating Distributed Denial of Service Attacks in Multiparty Applications in the Presence of Clock Drifts","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tdsc.2012.18","authors":["Zhang Fu","Marina Papatriantafilou","Philippas Tsigas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-02-01T22:02:02Z","doi":"10.1109/tdsc.2012.18","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1145/62212.62214","name":"Multiparty unconditionally secure protocols","source":"crossref","abstract":"","url":"https://doi.org/10.1145/62212.62214","authors":["David Chaum","Claude Crépeau","Ivan Damgard"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2003-11-25T11:40:52Z","doi":"10.1145/62212.62214","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/pdgc.2010.5679976","name":"Secure multiparty privacy preserving data aggregation by modular arithmetic","source":"crossref","abstract":"","url":"https://doi.org/10.1109/pdgc.2010.5679976","authors":["Arijit Ukil","Jaydip Sen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-01-07T09:08:34Z","doi":"10.1109/pdgc.2010.5679976","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.14711/thesis-991013340453403412","name":"Testing secure multi-party computation compilers","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013340453403412","authors":["Yichen Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-29T23:04:46Z","doi":"10.14711/thesis-991013340453403412","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-319-41483-6_20","name":"CheapSMC: A Framework to Minimize Secure Multiparty Computation Cost in the Cloud","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-41483-6_20","authors":["Erman Pattuk","Murat Kantarcioglu","Huseyin Ulusoy","Bradley Malin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-07-01T11:09:11Z","doi":"10.1007/978-3-319-41483-6_20","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1038/s41598-019-53524-4","name":"New Fair Multiparty Quantum Key Agreement Secure against Collusive Attacks","source":"crossref","abstract":"Abstract Fairness is an important standard needed to be considered in a secure quantum key agreement (QKA) protocol. However, it found that most of the quantum key agreement protocols in the travelling model are not fair, i.e., some of the dishonest participants can collaborate to predetermine the final key without being detected. Thus, how to construct a fair and secure key agreement protocol has obtained much attention. In this paper, a new fair multiparty QKA protocol that can resist the collusive attack is proposed. More specifically, we show that in a client-server scenario, it is possible for the clients to share a key and reveal nothing about what key has been agreed upon to the server. The server prepares quantum states for clients to encode messages to avoid the participants’ collusive attack. This construction improves on previous work, which requires either preparing multiple quantum resources by clients or two-way quantum communication. It is proven that the protocol does not reveal to any eavesdropper, including the server, what key has been agreed upon, and the dishonest participants can be prevented from collaborating to predetermine the final key.","url":"https://doi.org/10.1038/s41598-019-53524-4","authors":["Zhiwei Sun","Rong Cheng","Chunhui Wu","Cai Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-11-20T06:04:02Z","doi":"10.1038/s41598-019-53524-4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-00305-0_25","name":"A Performance and Resource Consumption Assessment of Secret Sharing Based Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-00305-0_25","authors":["Marcel von Maltitz","Georg Carle"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-09-06T06:55:57Z","doi":"10.1007/978-3-030-00305-0_25","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/bigdatasecurity.2017.24","name":"Secure Multiparty Computation of Chi-Square Test Statistics and Contingency Coefficients","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdatasecurity.2017.24","authors":["Sun-Kyung Hong","Hajin Kim","Sanghun Lee","Yang-Sae Moon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-07-17T16:44:16Z","doi":"10.1109/bigdatasecurity.2017.24","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-36030-6_19","name":"Efficient Information-Theoretic Secure Multiparty Computation over $$\\mathbb {Z}/p^k\\mathbb {Z}$$ via Galois Rings","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-36030-6_19","authors":["Mark Abspoel","Ronald Cramer","Ivan Damgård","Daniel Escudero","Chen Yuan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-11-22T17:02:36Z","doi":"10.1007/978-3-030-36030-6_19","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-34578-5_16","name":"The Broadcast Message Complexity of Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-34578-5_16","authors":["Sanjam Garg","Aarushi Goel","Abhishek Jain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-11-25T00:02:57Z","doi":"10.1007/978-3-030-34578-5_16","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-319-93692-5_21","name":"Subset Sum-Based Verifiable Secret Sharing Scheme for Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-93692-5_21","authors":["Romulo L. Olalia","Ariel M. Sison","Ruji P. Medina"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-06-26T17:15:00Z","doi":"10.1007/978-3-319-93692-5_21","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/s10773-023-05429-2","name":"Robust Quantum Secure Multiparty Computation Protocols for Minimum Value Calculation in Collective Noises and Their Simulation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10773-023-05429-2","authors":["Han-Xiao Kong","Heng-Yue Jia","Xia Wu","Guo-Qing Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-05T01:02:07Z","doi":"10.1007/s10773-023-05429-2","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-662-45608-8_25","name":"Fairness versus Guaranteed Output Delivery in Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-662-45608-8_25","authors":["Ran Cohen","Yehuda Lindell"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-11-14T10:46:39Z","doi":"10.1007/978-3-662-45608-8_25","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-031-06944-4_14","name":"Secure Multiparty Computation with Free Branching","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-06944-4_14","authors":["Aarushi Goel","Mathias Hall-Andersen","Aditya Hegde","Abhishek Jain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-05-27T15:51:47Z","doi":"10.1007/978-3-031-06944-4_14","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-77886-6_25","name":"Multiparty Reusable Non-interactive Secure Computation from LWE","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77886-6_25","authors":["Fabrice Benhamouda","Aayush Jain","Ilan Komargodski","Huijia Lin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-06-15T19:06:10Z","doi":"10.1007/978-3-030-77886-6_25","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-031-06944-4_15","name":"Secure Multiparty Computation with Sublinear Preprocessing","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-06944-4_15","authors":["Elette Boyle","Niv Gilboa","Yuval Ishai","Ariel Nof"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-05-27T15:51:47Z","doi":"10.1007/978-3-031-06944-4_15","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-84245-1_11","name":"Non-interactive Secure Multiparty Computation for Symmetric Functions, Revisited: More Efficient Constructions and Extensions","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-84245-1_11","authors":["Reo Eriguchi","Kazuma Ohara","Shota Yamada","Koji Nuida"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-10T23:04:26Z","doi":"10.1007/978-3-030-84245-1_11","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.14738/tnc.86.9447","name":"Practical generation of common random strings for secure multiparty computations over the Internet","source":"crossref","abstract":"It is known that most of the interesting multiparty cryptographic tasks cannot be implemented securely without trusted setup in a general concurrent network environment like the Internet. We need an appropriate trusted third party to solve this problem. An important trusted setup is a public random string shared by the parties. We present a practical n-bit coin toss protocol for provably secure implementation of such setup. Our idea is inviting external peers into the execution of the protocol to establish an honest majority among the parties. We guarantee security in the presence of an unconditional, static, malicious adversary. Additionally, we present an original practical idea of using live public radio broadcast channels for the generation of common physical random source.","url":"https://doi.org/10.14738/tnc.86.9447","authors":["István Vajda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-02-11T17:45:45Z","doi":"10.14738/tnc.86.9447","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-981-96-0938-3_4","name":"Perfectly-Secure Multiparty Computation with Linear Communication Complexity over Any Modulus","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-0938-3_4","authors":["Daniel Escudero","Yifan Song","Wenhao Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-11T13:22:12Z","doi":"10.1007/978-981-96-0938-3_4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.35940/ijitee.b6866.019320","name":"Privacy Preserving Data Mining Using Secure Multiparty Computation Based On Apriori and Fp-Tree Structure of Fp-Growth Algorithm","source":"crossref","abstract":"In this work, a method is proposed to deal with secure multiparty computation (SMC) based problems. The computation is done on the grocery dataset collected from three various grocery shops. The privacy is maintained by generating the rules based on FP-Tree algorithm under Association Rule Mining (ARM). Privacy and correctness are the important requirements of SMC. In privacy requirement, the things apart from necessary are not learned. This implies that only output will be learned by the parties. Each party must receive correct output to ensure the correctness. In this work, secure auction is done using SMC and frequent item sets are computed to perform the association rule mining. The most familiar FP-growth schemes have the short fallings like former space complexity and latter time complexity. The performance of the algorithms has been enhanced by using APFT algorithm which is a combined version of FP-tree structure of FP-growth algorithm and Apriori algorithm. The conditional and sub conditional patterns are not generated continuously in APFT. The speed of the APFT is high when compared to Apriori algorithm and FP-growth.The correlated items are included by modifying APFT and non-correlated item sets are shaped by using APFT. This modification is used for FP-tree optimization. From the frequent item set, the loosely associated items are removed by using this modification. The system implemented is clearly described and its performance is evaluated. The results confirmed that the proposed scheme is extremely effective.","url":"https://doi.org/10.35940/ijitee.b6866.019320","authors":["P. Yoganandhini*","Dr.G. Prabakaran"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-01-22T07:26:34Z","doi":"10.35940/ijitee.b6866.019320","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-319-76578-5_24","name":"On the Message Complexity of Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-76578-5_24","authors":["Yuval Ishai","Manika Mittal","Rafail Ostrovsky"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-02-28T09:23:52Z","doi":"10.1007/978-3-319-76578-5_24","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-319-76578-5_20","name":"Committed MPC","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-76578-5_20","authors":["Tore K. Frederiksen","Benny Pinkas","Avishay Yanai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-02-28T09:23:52Z","doi":"10.1007/978-3-319-76578-5_20","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-77886-6_23","name":"Order-C Secure Multiparty Computation for Highly Repetitive Circuits","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77886-6_23","authors":["Gabrielle Beck","Aarushi Goel","Abhishek Jain","Gabriel Kaptchuk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-06-15T19:06:10Z","doi":"10.1007/978-3-030-77886-6_23","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-031-30617-4_6","name":"Sublinear-Communication Secure Multiparty Computation Does Not Require FHE","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-30617-4_6","authors":["Elette Boyle","Geoffroy Couteau","Pierre Meyer"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-04-14T10:02:24Z","doi":"10.1007/978-3-031-30617-4_6","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/blockchain53845.2021.00038","name":"Improving Security for Users of Decentralized Exchanges Through Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/blockchain53845.2021.00038","authors":["Robert Annessi","Ethan Fast"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-24T21:11:53Z","doi":"10.1109/blockchain53845.2021.00038","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-032-07089-0_4","name":"Advanced Topics in Silent Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-07089-0_4","authors":["Geoffroy Couteau"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-22T23:03:20Z","doi":"10.1007/978-3-032-07089-0_4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-662-64322-8_13","name":"Efficient Noise Generation to Achieve Differential Privacy with Applications to Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-662-64322-8_13","authors":["Reo Eriguchi","Atsunori Ichikawa","Noboru Kunihiro","Koji Nuida"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-10-22T18:17:23Z","doi":"10.1007/978-3-662-64322-8_13","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.70675/b8e6e9b9z421dz4679z9c8fzd57bd883cd8a","name":"Secure Multi-Party Computation and Privacy","source":"crossref","abstract":"Calculs Multi-Parties et Vie Privée Les calculs multi-parties sécurisés (MPC) sont une branche de la cryptographie qui a pour objectif de concevoir des solutions permettant à plusieurs parties de calculer ensemble une fonction de leurs données, tout en gardant ces données secrètes. Contrairement à la cryptographie classique, où l’on cherche à assurer la sécurité malgré la présence d’un adversaire extérieur, le MPC garantit la sécurité face à un adversaire interne contrôlant un ou plusieurs participants. Cette thèse apporte à la fois des contributions théoriques et pratiques dans le domaine du MPC. D’un point de vue théorique, une étude est réalisée sur la corruption des “garbled circuits”, qui sont une solution générale au problème à deux parties. Sur un plan pratique, nous réalisons une cryptanalyse de certaines primitives propres au MPC, dans le but d’étudier leur efficacité réelle. Enfin, nous montrons que les services basés sur la position des utilisateurs peuvent prendre avantage du MPC pour devenir plus respectueux de la vie privée.","url":"https://doi.org/10.70675/b8e6e9b9z421dz4679z9c8fzd57bd883cd8a","authors":["Aurélien Dupin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-03T16:30:49Z","doi":"10.70675/b8e6e9b9z421dz4679z9c8fzd57bd883cd8a","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/incos.2015.51","name":"A Predicate Encryption Scheme Supporting Multiparty Cloud Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/incos.2015.51","authors":["Tan Zhenlin","Zhang Wei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-11-02T23:14:49Z","doi":"10.1109/incos.2015.51","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/icsess49938.2020.9237698","name":"Blockchain-based Multiparty Computation System","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsess49938.2020.9237698","authors":["Kai Lu","Chongyang Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-04T21:18:38Z","doi":"10.1109/icsess49938.2020.9237698","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/tdsc.2024.3419576","name":"Multiparty Delegated Private Set Union With Efficient Updates on Outsourced Datasets","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tdsc.2024.3419576","authors":["Heewon Chung","Myungsun Kim","Patrick Chulsoon Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-26T14:19:33Z","doi":"10.1109/tdsc.2024.3419576","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.70675/adb74676z6fffz4da4zab28z0435fd4b6827","name":"Zero-knowledge proofs for secure computation","source":"crossref","abstract":"Preuves à divulgation nulle de connaissance pour le calcul sécurisé Dans cette thèse, nous étudions les preuves à divulgation nulle de connaissance, une primitive cryptographique permettant de prouver une assertion en ne révélant rien de plus que sa véracité, et leurs applications au calcul sécurisé. Nous introduisons tout d’abord un nouveau type de preuves à divulgation nulle, appelées arguments implicites à divulgation nulle, intermédiaire entre deux notions existantes, les preuves interactives et les preuves non interactives à divulgation nulle. Cette nouvelle notion permet d’obtenir les mêmes bénéfices en terme d’efficacité que les preuves non-interactives dans le contexte de la construction de protocoles de calcul sécurisé faiblement interactifs, mais peut être instanciée à partir des mêmes hypothèses cryptographiques que les preuves interactives, permettant d’obtenir de meilleures garanties d’efficacité et de sécurité. Dans un second temps, nous revisitons un système de preuves à divulgation nulle de connaissance qui est particulièrement utile dans le cadre de protocoles de calcul sécurisé manipulant des nombres entiers, et nous démontrons que son analyse de sécurité classique peut être améliorée pour faire reposer ce système de preuve sur une hypothèse plus standard et mieux connue. Enfin, nous introduisons une nouvelle méthode de construction de systèmes de preuves à divulgation nulle sur les entiers, qui représente une amélioration par rapport aux méthodes existantes, tout particulièrement dans un modèle de type client-serveur, où un client à faible puissance de calcul participe à un protocole de calcul sécurisé avec un serveur à forte puissance de calcul.","url":"https://doi.org/10.70675/adb74676z6fffz4da4zab28z0435fd4b6827","authors":["Geoffroy Couteau"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-02T17:18:23Z","doi":"10.70675/adb74676z6fffz4da4zab28z0435fd4b6827","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/ieeestd.2021.9604029","name":"IEEE Recommended Practice for Secure Multi-Party Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ieeestd.2021.9604029","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-04T19:24:17Z","doi":"10.1109/ieeestd.2021.9604029","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/icbc64466.2025.11114560","name":"Zero-Knowledge Proofs in Anti-Money Laundering Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icbc64466.2025.11114560","authors":["Viktoriia Femiak","Kristián Košťál"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-14T18:34:30Z","doi":"10.1109/icbc64466.2025.11114560","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1145/2457317.2457343","name":"Secure multiparty aggregation with differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1145/2457317.2457343","authors":["Slawomir Goryczka","Li Xiong","Vaidy Sunderam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-03-26T12:25:54Z","doi":"10.1145/2457317.2457343","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.3390/app14125354","name":"Secure IoT Communication: Implementing a One-Time Pad Protocol with True Random Numbers and Secure Multiparty Sums","source":"crossref","abstract":"We introduce an innovative approach for secure communication in the Internet of Things (IoT) environment using a one-time pad (OTP) protocol. This protocol is augmented by incorporating a secure multiparty sum protocol to produce OTP keys from genuine random numbers obtained from the physical phenomena observed in each device. We have implemented our method using ZeroC-Ice v.3.7, dependable middleware for distributed computing, demonstrating its practicality in various hybrid IoT scenarios, particularly in devices with limited processing capabilities. The security features of our protocol are evaluated under the Dolev–Yao threat model, providing a thorough assessment of its defense against potential cyber threats.","url":"https://doi.org/10.3390/app14125354","authors":["Julio Fenner","Patricio Galeas","Francisco Escobar","Rail Neira"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-21T03:44:49Z","doi":"10.3390/app14125354","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.7146/brics.v7i14.20141","name":"Multiparty Computation from Threshold Homomorphic Encryption","source":"crossref","abstract":"We introduce a new approach to multiparty computation (MPC) basing&lt;br /&gt;it on homomorphic threshold crypto-systems. We show that given&lt;br /&gt;keys for any sufficiently efficient system of this type, general MPC protocols&lt;br /&gt;for n players can be devised which are secure against an active&lt;br /&gt;adversary that corrupts any minority of the players. The total number of&lt;br /&gt;bits sent is O(nk|C|), where k is the security parameter and |C| is the size&lt;br /&gt;of a (Boolean) circuit computing the function to be securely evaluated.&lt;br /&gt;An earlier proposal by Franklin and Haber with the same complexity was&lt;br /&gt;only secure for passive adversaries, while all earlier protocols with active&lt;br /&gt;security had complexity at least quadratic in n. We give two examples&lt;br /&gt;of threshold cryptosystems that can support our construction and lead&lt;br /&gt;to the claimed complexities.","url":"https://doi.org/10.7146/brics.v7i14.20141","authors":["Ronald Cramer","Ivan B. Damgård","Jesper Buus Nielsen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-02-22T11:45:16Z","doi":"10.7146/brics.v7i14.20141","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/dexa.2009.84","name":"Multiparty Computation of Fixed-Point Multiplication and Reciprocal","source":"crossref","abstract":"","url":"https://doi.org/10.1109/dexa.2009.84","authors":["Octavian Catrina","Claudiu Dragulin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-11-24T14:00:01Z","doi":"10.1109/dexa.2009.84","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.5121/ijsptm.2012.1306","name":"Behavioral Identification of Trusted Third Party in Secure Multiparty Computing Protocol","source":"crossref","abstract":"","url":"https://doi.org/10.5121/ijsptm.2012.1306","authors":["Zulfa Shaikh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-09-13T03:10:28Z","doi":"10.5121/ijsptm.2012.1306","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/tcsi.2022.3200974","name":"Quantum Protocol for Secure Multiparty Logical AND With Application to Multiparty Private Set Intersection Cardinality","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tcsi.2022.3200974","authors":["Run-Hua Shi","Yi-Fei Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-08-29T21:11:48Z","doi":"10.1109/tcsi.2022.3200974","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.5220/0005954202230230","name":"Lean and Fast Secure Multi-party Computation: Minimizing Communication and Local Computation using a Helper","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0005954202230230","authors":["Johannes Schneider"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-09-06T10:15:01Z","doi":"10.5220/0005954202230230","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/candarw57323.2022.00048","name":"Square Table Lookup Multiparty Computation Protocol","source":"crossref","abstract":"","url":"https://doi.org/10.1109/candarw57323.2022.00048","authors":["Zhongqi Wang","Keita Fuse","Takashi Nishide"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-17T17:18:34Z","doi":"10.1109/candarw57323.2022.00048","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1360/sspma-2023-0273","name":"Quantum protocol for secure multiparty XOR with application to secure communication in metropolitan area networks","source":"crossref","abstract":"","url":"https://doi.org/10.1360/sspma-2023-0273","authors":["HuiJie LI","Run-Hua SHI","WeiYang KE","QianQian JIA"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-01T06:18:10Z","doi":"10.1360/sspma-2023-0273","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1016/j.cose.2008.12.002","name":"Secure multiparty payment with an intermediary entity","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.cose.2008.12.002","authors":["Mildrey Carbonell","José María Sierra","Javier Lopez"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2008-12-25T10:13:25Z","doi":"10.1016/j.cose.2008.12.002","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/isit57864.2024.10619282","name":"Secure Inference for Vertically Partitioned Data Using Multiparty Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isit57864.2024.10619282","authors":["Shuangyi Chen","Yue Ju","Zhongwen Zhu","Ashish Khisti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-19T13:25:01Z","doi":"10.1109/isit57864.2024.10619282","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1002/cpe.782","name":"Fairness in systems based on multiparty interactions","source":"crossref","abstract":"Abstract In the context of the Multiparty Interaction Model, fairness is used to insure that an interaction that is enabled sufficiently often in a concurrent program will eventually be selected for execution. Unfortunately, this notion does not take conspiracies into account, i.e. situations in which an interaction never becomes enabled because of an unfortunate interleaving of independent actions; furthermore, eventual execution is usually too weak for practical purposes since this concept can only be used in the context of infinite executions. In this article, we present a new fairness notion, k ‐ conspiracy‐free fairness , that improves on others because it takes finite executions into account, alleviates conspiracies that are not inherent to a program, and k may be set a priori to control its goodness to address the above‐mentioned problems. Copyright © 2003 John Wiley &amp; Sons, Ltd.","url":"https://doi.org/10.1002/cpe.782","authors":["David Ruiz","Rafael Corchuelo","Miguel Toro"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2003-09-05T14:44:58Z","doi":"10.1002/cpe.782","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/trustcom.2016.0157","name":"Inherit Differential Privacy in Distributed Setting: Multiparty Randomized Function Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/trustcom.2016.0157","authors":["Genqiang Wu","Yeping He","Jingzheng Wu","Xianyao Xia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-02-09T21:44:06Z","doi":"10.1109/trustcom.2016.0157","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/iita.2008.321","name":"Multiparty Controlled Quantum Secure Direct Communication of d-Dimensional Using GHZ state","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iita.2008.321","authors":["Jian Dong","Jianfu Teng","Shuyan Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-01-14T17:41:27Z","doi":"10.1109/iita.2008.321","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.32657/10356/2491","name":"Protocols for unconditionally computationally secure circuit computation obfuscation","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/2491","authors":["Yu Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-10-02T14:43:10Z","doi":"10.32657/10356/2491","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/s11128-011-0351-x","name":"Secure multiparty quantum secret sharing with the collective eavesdropping-check character","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s11128-011-0351-x","authors":["Gan Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-01-09T10:15:48Z","doi":"10.1007/s11128-011-0351-x","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-540-72504-6_28","name":"Quantum Multiparty Communication Complexity and Circuit Lower Bounds","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-540-72504-6_28","authors":["Iordanis Kerenidis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-07-22T11:36:39Z","doi":"10.1007/978-3-540-72504-6_28","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.18130/v3cz8d","name":"Demystifying Secure Computation: Familiar Abstractions for Efficient Protocols","source":"crossref","abstract":"","url":"https://doi.org/10.18130/v3cz8d","authors":["Samee Zahur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-08-09T21:37:19Z","doi":"10.18130/v3cz8d","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-540-73556-4_28","name":"Secure Multiparty Computations Using the 15 Puzzle","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-540-73556-4_28","authors":["Takaaki Mizuki","Yoshinori Kugimoto","Hideaki Sone"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-08-28T11:55:47Z","doi":"10.1007/978-3-540-73556-4_28","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/bf00196771","name":"Secure multiparty protocols and zero-knowledge proof systems tolerating a faulty minority","source":"crossref","abstract":"","url":"https://doi.org/10.1007/bf00196771","authors":["Donald Beaver"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2004-08-28T09:39:34Z","doi":"10.1007/bf00196771","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.4204/eptcs.211.1","name":"Secure Multiparty Sessions with Topics","source":"crossref","abstract":"","url":"https://doi.org/10.4204/eptcs.211.1","authors":["Ilaria Castellani","Mariangiola Dezani-Ciancaglini","Ugo de'Liguoro"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-06-17T01:54:52Z","doi":"10.4204/eptcs.211.1","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.4018/978-1-4666-3902-7.ch014","name":"Load Balancing Aware Multiparty Secure Group Communication for Online Services in Wireless Mesh Networks","source":"crossref","abstract":"The internet offers services for users which can be accessed in a collaborative shared manner. Users control these services, such as online gaming and social networking sites, with handheld devices. Wireless mesh networks (WMNs) are an emerging technology that can provide these services in an efficient manner. Because services are used by many users simultaneously, security is a paramount concern. Although many security solutions exist, they are not sufficient. None have considered the concept of load balancing with secure communication for online services. In this paper, a load aware multiparty secure group communication for online services in WMNs is proposed. During the registration process of a new client in the network, the Load Balancing Index (LBI) is checked by the router before issuing a certificate/key. The certificate is issued only if the value of LBI is less than a predefined threshold. The authors evaluate the proposed solution against the existing schemes with respect to metrics like storage and computation overhead, packet delivery fraction (PDF), and throughput. The results show that the proposed scheme is better with respect to these metrics.","url":"https://doi.org/10.4018/978-1-4666-3902-7.ch014","authors":["Neeraj Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-07-24T19:21:28Z","doi":"10.4018/978-1-4666-3902-7.ch014","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-032-25324-8_4","name":"Simultaneous-Message and Succinct Secure Computation: Reusable and Multiparty Protocols","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-25324-8_4","authors":["Siddharth Agarwal","Abhishek Jain","Akshayaram Srinivasan","David J. Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-05T22:35:45Z","doi":"10.1007/978-3-032-25324-8_4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics55523.2022.00080","name":"A Secure Multiparty Computation Round Optimization Scheme Based on Standard Assumption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics55523.2022.00080","authors":["Yun Luo","Yuling Chen","Tao Li","Yilei Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-10-04T19:54:31Z","doi":"10.1109/ithings-greencom-cpscom-smartdata-cybermatics55523.2022.00080","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1002/cpe.903","name":"An order‐based algorithm for implementing multiparty synchronization","source":"crossref","abstract":"Abstract Multiparty interactions are a powerful mechanism for coordinating several entities that need to cooperate in order to achieve a common goal. In this paper, we present an algorithm for implementing them that improves on previous results in that it does not require the whole set of entities or interactions to be known at compile‐ or run‐time, and it can deal with both terminating and non‐terminating systems. We also present a comprehensive simulation analysis that shows how sensitive to changes our algorithm is, and compare the results with well‐known proposals by other authors. This study proves that our algorithm still performs comparably to other proposals in which the set of entities and interactions is known beforehand, but outperforms them in some situations that are clearly identified. In addition, these results prove that our algorithm can be combined with a technique called synchrony loosening without having an effect on efficiency. Copyright © 2004 John Wiley &amp; Sons, Ltd.","url":"https://doi.org/10.1002/cpe.903","authors":["José A. Pérez","Rafael Corchuelo","Miguel Toro"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2004-07-15T17:06:20Z","doi":"10.1002/cpe.903","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/ccece.2012.6334955","name":"A secure multiparty micropayment protocol for internet access over WLAN wireless mesh networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccece.2012.6334955","authors":["N. Biswas","Cungang Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-10-24T16:33:06Z","doi":"10.1109/ccece.2012.6334955","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/s40509-025-00377-4","name":"Quantum secure multiparty secret sharing using quantum non-locality","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s40509-025-00377-4","authors":["Mandeep Kumar","Bhaskar Mondal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-26T01:52:31Z","doi":"10.1007/s40509-025-00377-4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.26421/qic16.5-6-3","name":"Multiparty quantum signature schemes","source":"crossref","abstract":"Digital signatures are widely used in electronic communications to secure important tasks such as financial transactions, software updates, and legal contracts. The signature schemes that are in use today are based on public-key cryptography and derive their security from computational assumptions. However, it is possible to construct unconditionally secure signature protocols. In particular, using quantum communication, it is possible to construct signature schemes with security based on fundamental principles of quantum mechanics. Several quantum signature protocols have been proposed, but none of them has been explicitly generalised to more than three participants, and their security goals have not been formally defined. Here, we first extend the security definitions of Swanson and Stinson [1] so that they can apply also to the quantum case, and introduce a formal definition of transferability based on different verification levels. We then prove several properties that multiparty signature protocols with informationtheoretic security – quantum or classical – must satisfy in order to achieve their security goals. We also express two existing quantum signature protocols with three parties in the security framework we have introduced. Finally, we generalize a quantum signature protocol given in [2] to the multiparty case, proving its security against forging, repudiation and non-transferability. Notably, this protocol can be implemented using any pointto-point quantum key distribution network and therefore is ready to be experimentally demonstrated.","url":"https://doi.org/10.26421/qic16.5-6-3","authors":["Juan Miguel Arrazola","Petros Wallden","Erika Andersson"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-02-27T02:04:26Z","doi":"10.26421/qic16.5-6-3","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.51201/jusst12654","name":"A Protocol for Checking Equality of Data Using Hash in Ideal Model of Secure Multiparty Computation","source":"crossref","abstract":"The ideal Secure Multiparty Computation (SMC) model deploys a Trusted Third Party (TTP) which assists in secure function evaluation. The participating joint parties give input to the TTP which provide the results to the participating parties. The equality check problem in multiple party cases can be solved by simple architecture and a simple algorithm. In our proposed protocol Equality Hash Checkin ideal model, we use a secure hash function. All the parties interested to check equality of their data supply hash of their data to the TTP which then compared all hash values for equality. It declares the result to the parties.","url":"https://doi.org/10.51201/jusst12654","authors":["Rashid Sheikh","Durgesh Kumar Mishra","Meghna Dubey"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-03-02T10:48:36Z","doi":"10.51201/jusst12654","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1145/3511265.3550445","name":"Multi-Regulation Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3511265.3550445","authors":["Julissa Milligan Walsh","Mayank Varia","Aloni Cohen","Andrew Sellars","Azer Bestavros"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-11-01T04:06:40Z","doi":"10.1145/3511265.3550445","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-34621-8_21","name":"UC-Secure Multiparty Computation from One-Way Functions Using Stateless Tokens","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-34621-8_21","authors":["Saikrishna Badrinarayanan","Abhishek Jain","Rafail Ostrovsky","Ivan Visconti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-11-22T00:14:54Z","doi":"10.1007/978-3-030-34621-8_21","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/iwcmc.2011.5982845","name":"Building blocks for secure multiparty federated wireless sensor networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iwcmc.2011.5982845","authors":["Christophe Huygens","Nelson Matthys","Jef Maerien","Wouter Joosen","Danny Hughes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-09-12T17:59:27Z","doi":"10.1109/iwcmc.2011.5982845","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1016/j.optcom.2006.05.035","name":"Multiparty controlled quantum secure direct communication using Greenberger–Horne–Zeilinger state","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.optcom.2006.05.035","authors":["Jian Wang","Quan Zhang","Chao-jing Tang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2006-06-13T14:20:58Z","doi":"10.1016/j.optcom.2006.05.035","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.18130/v36f8p","name":"Efficient Secure Two-Party Computation Against Malicious Adversaries","source":"crossref","abstract":"","url":"https://doi.org/10.18130/v36f8p","authors":["Chih-hao Shen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-08-09T19:47:38Z","doi":"10.18130/v36f8p","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.63180/jcsra.thestap.2026.1.3","name":"Mitigating Information Leakage Risks in Secure Multiparty Computation through Function Hiding","source":"crossref","abstract":"Secure multiparty computation (SMPC) allows a joint computation on private data, but the majority of the existing protocols assume implicitly that the computation being performed is public and nonsensitive. In practice, though, computation logic frequently incorporates proprietary strategy or sensitive rule of decision, and its exposures constitute a significant though neglected leak of information. The prevailing SMPC models can mainly provide input confidentiality and accuracy, but the protocol level leakage due to observable protocol behavior is not well tackled. This paper attempts to fill this gap by introducing a (Function-Hiding Secure Multiparty Computation) FH-SMPC framework modifying the SMPC workflow to incorporate encapsulation of functions and regular patterns of execution directly. The suggested design hides structural and semantic attributes of the considered function and maintains the correctness, scalability, and compatibility with the conventional SMPC primitives. An explicit security analysis provides indistinguishability of functions along with classical input privacy. Experimental evaluation shows that FH-SMPC reduces transcript-based function distinguishability by over 85%, achieving an average divergence of 0.028 compared to 0.214 for baseline SMPC, with an execution latency increase limited to approximately 12% and no asymptotic growth in communication overhead. By treating computation logic as a confidential asset, FH-SMPC advances secure collaborative computation and provides a practical foundation for privacy-sensitive applications in cloud analytics and distributed decision systems.","url":"https://doi.org/10.63180/jcsra.thestap.2026.1.3","authors":["Udit Mamodiya","Vandana Ahuja","Indra Kishor","Amer Alqutaish","Rami Shehab","Mansour Obeidat"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T16:02:43Z","doi":"10.63180/jcsra.thestap.2026.1.3","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/3-540-48184-2_43","name":"Multiparty Unconditionally Secure Protocols (Abstract)","source":"crossref","abstract":"","url":"https://doi.org/10.1007/3-540-48184-2_43","authors":["David Chaum","Claude Crépeau","Ivan Damgård"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-01-12T19:43:30Z","doi":"10.1007/3-540-48184-2_43","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.25148/etd.fi15101570","name":"Secure routing and trust computation in multihop infrastructureless networks","source":"crossref","abstract":"","url":"https://doi.org/10.25148/etd.fi15101570","authors":["Tirthankar Ghosh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-06-05T17:17:23Z","doi":"10.25148/etd.fi15101570","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-64840-4_8","name":"Circuit Amortization Friendly Encodingsand Their Application to Statistically Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-64840-4_8","authors":["Anders Dalskov","Eysa Lee","Eduardo Soria-Vazquez"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-12-04T15:06:04Z","doi":"10.1007/978-3-030-64840-4_8","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-662-46494-6_7","name":"A Little Honesty Goes a Long Way","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-662-46494-6_7","authors":["Juan A. Garay","Ran Gelles","David S. Johnson","Aggelos Kiayias","Moti Yung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-03-10T01:17:58Z","doi":"10.1007/978-3-662-46494-6_7","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-981-92-2875-1_27","name":"Privacy-Preserving Aggregation of Virtual Power Plant Data via Differential Privacy and Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-92-2875-1_27","authors":["Zibin Pan","Shuwen Zhang","Chi Li","Shuyi Wang","Junhua Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-12T06:34:36Z","doi":"10.1007/978-981-92-2875-1_27","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/icpads.2005.69","name":"An Adaptive Multiparty Protocol for Secure Data Protection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icpads.2005.69","authors":["Qingkai Ma","Liangliang Xiao","I-Ling Yen","Manghui Tu","F. Bastani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2005-11-15T15:48:27Z","doi":"10.1109/icpads.2005.69","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-032-26734-4_9","name":"On the Power of Sumcheck in Secure Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-26734-4_9","authors":["Zhe Li","Chaoping Xing","Yizhou Yao","Chen Yuan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-22T16:24:09Z","doi":"10.1007/978-3-032-26734-4_9","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-642-30042-4","name":"Engineering Secure Two-Party Computation Protocols","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-30042-4","authors":["Thomas Schneider"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-08-03T12:10:26Z","doi":"10.1007/978-3-642-30042-4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/s10773-024-05773-x","name":"Secure Multiparty Logical AND Based on Quantum Homomorphic Encryption and Its Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10773-024-05773-x","authors":["Xinglan Zhang","Yunxin Xi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-12T07:23:26Z","doi":"10.1007/s10773-024-05773-x","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.70675/d761839fz7ef6z432fzbc7dz8776fc6b8b1c","name":"Efficient secure computation from correlated pseudorandomness","source":"crossref","abstract":"Calcul sécurisé efficace à partir de pseudo-aléa corrélé Dans cette thèse, nous explorons les avancées en calcul multipartite sécurisé (MPC), permettant à plusieurs parties de calculer conjointement des fonctions arbitraires tout en préservant la confidentialité de leurs entrées. Notre étude se concentre sur le MPC avec aléa corrélé, où l'efficacité est améliorée grâce à des corrélations générées en phase de prétraitement. Nous examinons deux paradigmes clés pour générer ces corrélations: les Générateurs de Corrélations Pseudoaléatoires (PCGs) et les Fonctions de Corrélations Pseudoaléatoires (PCFs). Tout d'abord, nous proposons de nouvelles constructions de PCG pour l'Intersection Privée d'Ensembles (PSI), offrant une sécurité renforcée ou de meilleures performances. Nous explorons ensuite les applications des PCGs et PCFs aux preuves à divulgation nulle de connaissance (ZKPs), en couvrant à la fois les cadres interactifs et non interactifs. Pour les ZKPs interactives, nous concevons un protocole vérifiable de manière privée pour la satisfaisabilité de circuits, avec une communication sous-linéaire et un calcul efficace. Dans le cadre non interactif, nous introduisons une nouvelle preuve à divulgation nulle de connaissance pour vérificateur désigné (DV-NIZK) exploitant les PCF avec une structure à clé publique (PK-PCF). Enfin, nous introduisons un nouveau cadre MPC pour les circuits binaires, basé sur un PCG optimisé pour les petits corps finis.","url":"https://doi.org/10.70675/d761839fz7ef6z432fzbc7dz8776fc6b8b1c","authors":["Thi Thuy Dung Bui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T19:17:11Z","doi":"10.70675/d761839fz7ef6z432fzbc7dz8776fc6b8b1c","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/iq-cchess56596.2023.10391635","name":"Quantum Secure Multiparty Summation Based on Quantum Walks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iq-cchess56596.2023.10391635","authors":["Justin Joseph","Syed Taqi Ali"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-15T20:56:47Z","doi":"10.1109/iq-cchess56596.2023.10391635","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/vtc2020-spring48590.2020.9128631","name":"A Lightweight Data Sharing Mechanism and Multiparty Computation for CPS","source":"crossref","abstract":"","url":"https://doi.org/10.1109/vtc2020-spring48590.2020.9128631","authors":["Zhenpeng Xu","Jian Yang","Jinyong Yin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-06-30T21:24:53Z","doi":"10.1109/vtc2020-spring48590.2020.9128631","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-030-84242-0_16","name":"Round Efficient Secure Multiparty Quantum Computation with Identifiable Abort","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-84242-0_16","authors":["Bar Alon","Hao Chung","Kai-Min Chung","Mi-Ying Huang","Yi Lee","Yu-Ching Shen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-10T23:04:26Z","doi":"10.1007/978-3-030-84242-0_16","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-031-12164-7_12","name":"Yao’s Protocol for Secure 2-party Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-12164-7_12","authors":["Ashish Choudhury","Arpita Patra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-10-07T11:02:44Z","doi":"10.1007/978-3-031-12164-7_12","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.5220/0009826801300141","name":"Optimal Transport Layer for Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0009826801300141","authors":["Markus Brandt","Claudio Orlandi","Kris Shrishak","Haya Shulman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-07-15T15:30:18Z","doi":"10.5220/0009826801300141","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.9790/3021-04944851","name":"Confidential Multiparty Computation with Anonymous ID Assignment using Central Authority","source":"crossref","abstract":"","url":"https://doi.org/10.9790/3021-04944851","authors":["P Babitha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-10-16T01:18:39Z","doi":"10.9790/3021-04944851","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.5220/0003032901250128","name":"APPLICABILITY OF MULTIPARTY COMPUTATION SCHEMES FORWIRELESS SENSOR NETWORKS - Position Paper","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0003032901250128","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-02-21T18:31:05Z","doi":"10.5220/0003032901250128","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/s00500-023-09182-w","name":"Secure multiparty access and authentication based on advanced fuzzy extractor in smart home","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00500-023-09182-w","authors":["Sirisha Uppuluri","G. Lakshmeeswari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-20T03:27:18Z","doi":"10.1007/s00500-023-09182-w","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/pac.2017.12","name":"Achieving Secure and Differentially Private Computations in Multiparty Settings","source":"crossref","abstract":"","url":"https://doi.org/10.1109/pac.2017.12","authors":["Abbas Acar","Z. Berkay Celik","Hidayet Aksu","A. Selcuk Uluagac","Patrick McDaniel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-12-07T18:28:48Z","doi":"10.1109/pac.2017.12","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-642-30042-4_2","name":"Basics of Efficient Secure Function Evaluation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-30042-4_2","authors":["Thomas Schneider"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-08-03T12:10:26Z","doi":"10.1007/978-3-642-30042-4_2","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1088/0253-6102/47/3/015","name":"Multiparty Quantum Secret Sharing of Secure Direct Communication Using Teleportation","source":"crossref","abstract":"","url":"https://doi.org/10.1088/0253-6102/47/3/015","authors":["Wang Jian","Zhang Quan","Tang Chao-Jing"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2008-09-09T03:18:50Z","doi":"10.1088/0253-6102/47/3/015","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1016/j.ic.2020.104587","name":"The link-calculus for open multiparty interactions","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ic.2020.104587","authors":["Chiara Bodei","Linda Brodo","Roberto Bruni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-05-08T11:49:27Z","doi":"10.1016/j.ic.2020.104587","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.5120/264-423","name":"A Secure Multiparty Product Protocol for Preserving the Privacy in Collaborative Data Mining","source":"crossref","abstract":"","url":"https://doi.org/10.5120/264-423","authors":["G.Chitra Ganapathi","G. Swathi","S. Karthick"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2010-06-16T06:20:34Z","doi":"10.5120/264-423","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/icme.2014.6890141","name":"Compressive sensing based secure multiparty privacy preserving framework for collaborative data-mining and signal processing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icme.2014.6890141","authors":["Qia Wang","Wenjun Zeng","Jun Tian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-09-10T11:47:19Z","doi":"10.1109/icme.2014.6890141","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.2991/kam-15.2015.32","name":"Multiparty Bidirectional Quantum Secure Communication Based on Closed Qubit Transmission","source":"crossref","abstract":"","url":"https://doi.org/10.2991/kam-15.2015.32","authors":["Yin Xunru"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-10T14:09:42Z","doi":"10.2991/kam-15.2015.32","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/978-3-319-13257-0_11","name":"Hybrid Model of Fixed and Floating Point Numbers in Secure Multiparty Computations","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-13257-0_11","authors":["Toomas Krips","Jan Willemson"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-11-03T10:43:57Z","doi":"10.1007/978-3-319-13257-0_11","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.70675/aa98c9eaz2325z415bz8e0bz71cf1368b0e4","name":"Applications of secure multi-party computation in Machine Learning","source":"crossref","abstract":"Les applications du calcul multipartite sécurisé en apprentissage automatique La préservation des données privées dans l'apprentissage automatique et l'analyse des données devient de plus en plus importante à mesure que la quantité d'informations personnelles sensibles collectées et utilisées par les organisations continue de croître. Cela pose le risque d'exposer des informations personnelles sensibles à des tiers malveillants, ce qui peut entraîner un vol d'identité, une fraude financière ou d'autres types de cybercriminalité. Les lois contre l'utilisation des données privées sont importantes pour protéger les individus contre l'utilisation et le partage de leurs informations. Cependant, ce faisant, les lois sur la protection des données limitent les applications des modèles d'apprentissage automatique, et certaines de ces applications pourraient sauver des vies, comme dans le domaine médical.Le calcul multipartite sécurisé (MPC) permet à plusieurs partis de calculer collaborativement une fonction sur leurs entrées sans avoir à révéler ou à échanger les données elles-mêmes. Cet outil peut être utilisé pour entraîner et utiliser des modèles d'apprentissage automatique collaboratif lorsqu'il existe des problèmes de confidentialité concernant l'échange d'ensembles de données sensibles entre différentes entités.Dans cette thèse, nous (I) utilisons des algorithmes de calcul multipartite sécurisés existants et en développons de nouveaux, (II) introduisons des approximations cryptographiques des fonctions couramment utilisées en apprentissage automatique, et (III) complémentons le calcul multipartite sécurisé avec d'autres outils de confidentialité. Ce travail est effectué dans le but de mettre en œuvre des algorithmes d'apprentissage automatique et d'analyse de données préservant la confidentialité.Notre travail et nos résultats expérimentaux montrent qu'en exécutant les algorithmes à l'aide du calcul multipartite sécurisé, la confidentialités des données est préservée et l'exactitude du résultat est satisfait. En d'autres termes, aucun parti n'a accès aux informations d'un autre et les résultats obtenus par les modèles d'apprentissage automatique et des algorithmes d'analyse de données sont les mêmes par rapport aux résultats des algorithmes exécutés sur données non chiffrés.Dans son ensemble, cette thèse offre une vision globale du calcul multipartite sécurisé pour l'apprentissage automatique, démontrant son potentiel à révolutionner le domaine. Cette thèse contribue au déploiement et à l'acceptabilité du calcul multipartite sécurisé en apprentissage automatique et en analyse de données.","url":"https://doi.org/10.70675/aa98c9eaz2325z415bz8e0bz71cf1368b0e4","authors":["Angelo Saadeh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T16:54:28Z","doi":"10.70675/aa98c9eaz2325z415bz8e0bz71cf1368b0e4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1007/s10773-023-05288-x","name":"A Verifiable (k,n)-Threshold Quantum Secure Multiparty Summation Protocol","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10773-023-05288-x","authors":["Fulin Li","Hang Hu","Shixin Zhu","Ping Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-21T05:02:56Z","doi":"10.1007/s10773-023-05288-x","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1002/cpe.7656","name":"MP‐HTLC: Enabling blockchain interoperability through a multiparty implementation of the hash time‐lock contract","source":"crossref","abstract":"Summary The idea of hash time‐lock contracts (HTLCs) has been around from 2013. Nowadays these contracts power the majority of atomic swaps making decentralized exchange of tokens possible. On the other hand, HTLCs also have some flaws. For example they can only be instantiated between two parties. This is highly inefficient when many participants want to exchange tokens between the same pair of blockchains at the same time, because the number of transactions increases linearly in the number of participants. To solve this problem, in this article, we present MP‐HTLC. MP‐HTLC lets multiple users exchange tokens on different blockchains in a single instantiation of the protocol without any leader election. We prove that in case of a UTXO‐based blockchain the number of transactions remains constant regardless the number of participants. We are able to maintain the security assumptions of HTLCs using multiparty computation in the creation of the secret preimage and threshold signatures to manage transaction signing. We also present an implementation for each of the aspects of the protocol.","url":"https://doi.org/10.1002/cpe.7656","authors":["Fadi Barbàra","Claudio Schifanella"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-01T10:16:29Z","doi":"10.1002/cpe.7656","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.3934/amc.2020071","name":"Secure and efficient multiparty private set intersection cardinality","source":"crossref","abstract":"","url":"https://doi.org/10.3934/amc.2020071","authors":["Sumit Kumar Debnath","Pantelimon Stǎnicǎ","Nibedita Kundu","Tanmay Choudhury"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-04-13T04:41:41Z","doi":"10.3934/amc.2020071","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.70675/2d0acbb1zac72z4ad7zb20azc815497f8dab","name":"Secure and efficient outsourced computation protocols for linear algebra","source":"crossref","abstract":"Protocoles de calculs externalisés efficaces et sécurisés pour l'algèbre linéaire L'algèbre linéaire exacte est un outil essentiel du calcul scientifique et trouve de nombreusesapplications en mathématiques expérimentales, en théorie des nombres, en cryptographie ou encorepour les preuves formelles. Les infrastructures de calcul à haute performance utilisées pour lecalcul scientifique ont connu de nombreuses évolutions au cours des 60 dernières années.Les calculs, initialement exécutés sur des machines locales possédées par les utilisateurs,sont désormais réalisés sur des machines externes, évolution permise par le rapide développementd'Internet au cours des années 1990. Ce phénomène a atteint son apogée avecl'arrivée récente du cloud computing.Désormais, un grand nombre de calculs sont effectués sur desmachines non plus possédées, mais louées par des clients.Ce nouveau modèle a donné naissance à denombreuses questions autour de la confiance à accorder à de tels calculs. Alors que les clients n'ontplus le contrôle des infrastructures qu'ils utilisent, comment peuvent-ils s'assurer de la confidentialité,la sécurité ou encore la validité de leurs calculs ?Dans cette thèse, nous considérons deux aspects fondamentaux de la sécurité des calculs externalisés enalgèbre linéaire exacte : la vérification de la justesse des résultats, et la confidentialité des donnéespour les calculs multipartites. Nous proposons de nouveaux protocoles permettant aux clients de vérifierefficacement les invariants liés au rang et nous les utilisons pour améliorer la vérification de propriétésclassiques de l'algèbre linéaire, le déterminant de matrices et la signature. Nous introduisons également lespremiers protocoles de vérification pour les propriétés classiques des modules de vecteurs de polynômes et leursmatrices associées.Enfin, nous proposons le premier protocole multipartite sécurisé récursif pour la multiplication de matrices basésur l'algorithme de Strassen-Winograd. Notre protocole permet à des utilisateurs possédant certains lignes des matricesd'entrée de calculer les mêmes lignes de la matrice de sortie. Ni les données initiales, ni les valeurs intermédiairesdu calcul ne sont révelées aux autres participants. Ce nouveau protocole nous permet d'améliorer l'état del'art pour le volume de données communiquées lors de l'exécution de tels protocoles.","url":"https://doi.org/10.70675/2d0acbb1zac72z4ad7zb20azc815497f8dab","authors":["David Lucas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-04T02:18:09Z","doi":"10.70675/2d0acbb1zac72z4ad7zb20azc815497f8dab","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1007/11832072_25","name":"Theory and Practice of Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/11832072_25","authors":["Ivan Damgård"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2006-08-30T04:43:49Z","doi":"10.1007/11832072_25","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.26421/qic5.6-4","name":"Secure assisted quantum computation","source":"crossref","abstract":"Suppose Alice wants to perform some computation that could be done quickly on a quantum computer, but she cannot do universal quantum computation. Bob can do universal quantum computation and claims he is willing to help, but Alice wants to be sure that Bob cannot learn her input, the result of her calculation, or perhaps even the function she is trying to compute. We describe a simple, efficient protocol by which Bob can help Alice perform the computation, but there is no way for him to learn anything about it. We also discuss techniques for Alice to detect whether Bob is honestly helping her or if he is introducing errors.","url":"https://doi.org/10.26421/qic5.6-4","authors":["A.M. Childs"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-03-14T01:49:25Z","doi":"10.26421/qic5.6-4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1007/978-3-319-12475-9_11","name":"Verifiable Computation in Multiparty Protocols with Honest Majority","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-12475-9_11","authors":["Peeter Laud","Alisa Pankova"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-10-01T09:25:19Z","doi":"10.1007/978-3-319-12475-9_11","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.5220/0010552300002998","name":"Secure Computation by Secret Sharing using Input Encrypted with Random Number","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0010552300002998","authors":["Keiichi Iwamura","Ahmad Kamal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-11T13:21:35Z","doi":"10.5220/0010552300002998","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1007/s11128-013-0636-3","name":"Multiparty controlled quantum secure direct communication based on quantum search algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s11128-013-0636-3","authors":["Shih-Hung Kao","Tzonelih Hwang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-09-11T15:00:07Z","doi":"10.1007/s11128-013-0636-3","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.26636/jtit.2024.4.1711","name":"A New Tree Quantum Key Agreement Protocol for Secure Multiparty Communication","source":"crossref","abstract":"The Tree Multiparty Quantum Key Agreement Protocol (TMQKAP) is introduced as a novel solution for secure quantum key agreement among multiple participants, specifically tailored for tree topologies. Based on the BB84 protocol, TMQKAP employs hierarchical tree structures and XOR operations to facilitate efficient and secure key generation. Key elements are exchanged among participants in an equitable manner, ensuring that each participant contributes equally to the generation of the shared key. The protocol demonstrates robust security, effectively defending against both external and internal attacks, and achieves a quantum efficiency of 1/2 (N −1), where N is the number of participants. Thorough security analysis and simulations show TMQKAP’s robustness against various attacks while maintaining high efficiency. Additionally, the protocol is readily implementable with current quantum technologies, utilizing single-photon transmission to facilitate secure key distribution.","url":"https://doi.org/10.26636/jtit.2024.4.1711","authors":["Rima Djellab","Youssouf Achouri","Malak Emziane","Lyamine Guezouli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-06T09:48:08Z","doi":"10.26636/jtit.2024.4.1711","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.5220/0010552305400547","name":"Secure Computation by Secret Sharing using Input Encrypted with Random Number","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0010552305400547","authors":["Keiichi Iwamura","Ahmad Kamal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-22T20:49:45Z","doi":"10.5220/0010552305400547","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.5220/0010876200003120","name":"SMPG: Secure Multi Party Computation on Graph Databases","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0010876200003120","authors":["Nouf Aljuaid","Alexei Lisitsa","Sven Schewe"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-15T17:11:25Z","doi":"10.5220/0010876200003120","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1109/jiot.2025.3605945","name":"GTMPC: A Secure Multiparty Computation Scheme for IIoT Data Based on Game Theory","source":"crossref","abstract":"","url":"https://doi.org/10.1109/jiot.2025.3605945","authors":["Yang Zhang","Chuanhua Wang","Xin Xu","Quan Zhang","Yingbiao Yao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-11T17:33:02Z","doi":"10.1109/jiot.2025.3605945","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1109/ares.2007.77","name":"Efficient Multiparty Computation for Comparator Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ares.2007.77","authors":["Koji Chida","Hiroaki Kikuchi","Gembu Morohashi","Keiichi Hirota"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-04-25T15:09:50Z","doi":"10.1109/ares.2007.77","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.4135/9781071961469","name":"Summarizing Multiparty Negotiations","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781071961469","authors":["Andrew Bennett"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-29T14:44:11Z","doi":"10.4135/9781071961469","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1007/s10773-025-05944-4","name":"Quantum Key Distribution in Multiparty Computation for Data Sorting by Entanglement","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10773-025-05944-4","authors":["Shyam R. Sihare"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-16T14:39:21Z","doi":"10.1007/s10773-025-05944-4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.4018/ijwnbt.2011070102","name":"Load Balancing Aware Multiparty Secure Group Communication for Online Services in Wireless Mesh Networks","source":"crossref","abstract":"The internet offers services for users which can be accessed in a collaborative shared manner. Users control these services, such as online gaming and social networking sites, with handheld devices. Wireless mesh networks (WMNs) are an emerging technology that can provide these services in an efficient manner. Because services are used by many users simultaneously, security is a paramount concern. Although many security solutions exist, they are not sufficient. None have considered the concept of load balancing with secure communication for online services. In this paper, a load aware multiparty secure group communication for online services in WMNs is proposed. During the registration process of a new client in the network, the Load Balancing Index (LBI) is checked by the router before issuing a certificate/key. The certificate is issued only if the value of LBI is less than a predefined threshold. The authors evaluate the proposed solution against the existing schemes with respect to metrics like storage and computation overhead, packet delivery fraction (PDF), and throughput. The results show that the proposed scheme is better with respect to these metrics.","url":"https://doi.org/10.4018/ijwnbt.2011070102","authors":["Neeraj Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-01-13T11:23:50Z","doi":"10.4018/ijwnbt.2011070102","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1007/978-1-4419-5906-5_1330","name":"Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4419-5906-5_1330","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-10-27T09:52:10Z","doi":"10.1007/978-1-4419-5906-5_1330","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1006/inco.2000.2865","name":"Communication Protocols for Secure Distributed Computation of Binary Functions","source":"crossref","abstract":"","url":"https://doi.org/10.1006/inco.2000.2865","authors":["Eytan Modiano","Anthony Ephremides"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2002-09-17T19:19:12Z","doi":"10.1006/inco.2000.2865","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1145/3007748.3007783","name":"Secure Multiparty Construction of a Distributed Social Network","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3007748.3007783","authors":["Varsha Bhat Kukkala","Jaspal Singh Saini","S. R.S. Iyengar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-01-04T17:02:45Z","doi":"10.1145/3007748.3007783","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1007/s001459910003","name":"Player Simulation and General Adversary Structures in Perfect Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s001459910003","authors":["Martin Hirt","Ueli Maurer"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2002-07-25T02:35:22Z","doi":"10.1007/s001459910003","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1109/icocics68032.2025.11384083","name":"Efficient and Secure Multiparty Key Exchange Schemes from Bilinear Maps","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icocics68032.2025.11384083","authors":["Annisa Dini Handayani","Indah Emilia Wijayanti","Uha Isnaini","Prastudy Fauzi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T20:54:56Z","doi":"10.1109/icocics68032.2025.11384083","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.70675/116140dbza9a5z49d5zaa7ezfbd3f8f1cf51","name":"Constrained Pseudorandom Functions : New Constructions and Connections with Secure Computation","source":"crossref","abstract":"Fonctions Pseudo-aléatoires Contraintes : nouvelles constructions et liens avec le calcul sécurisé Les fonctions pseudo-aléatoires (Pseudorandom Functions, alias PRFs) ont été introduites en 1986, par Goldreich, Goldwasser et Micali, comme moyen efficace de générer de l’aléa et servent depuis d’outils essentiels en cryptographie. Ces fonctions utilisent une clé secrète principale pour faire correspondre différentes entrées à des sorties pseudo-aléatoires. Les fonctions pseudo-aléatoires contraintes (Constrained Pseudorandom Functions, alias CPRFs), introduites en 2013, étendent les PRFs enautorisant la délégation des clés contraintes qui permettent l’évaluation de la fonction uniquement sur des sous-ensembles spécifiques d’entrées. Notamment, même avec cette évaluation partielle, la sortie d’une CPRF devrait rester pseudo-aléatoire sur les entrées en dehors de ces sous-ensembles. Dans cette thèse, nous établissons des liens entre les CPRFs et deux autres outils cryptographiques qui ont été introduits dans le contexte du calcul sécurisé : 1. Nous montrons comment les CPRFs peuvent être construites à partir de protocoles de partage de secrets homomorphes (Homomorphic Secret Sharing, alias HSS). Les protocoles de partage de secrets homomorphes permettent des calculs distribués sur des parties d’un secret. Nous commençons par identier deux nouvelles versions des protocoles HSS et montrons comment elles peuvent être transformées en CPRFs générant des clés contraintes pour des sous-ensembles d’entrées qui peuvent être exprimés via des prédicats de produit scalaire ou de NC1. Ensuite, nous observons que les constructions de protocoles HSS qui existent déjà dans la littérature peuvent être adaptées à ces nouvelles extensions. Cela conduit à la découverte de cinq nouvelles constructions CPRF basées sur diverses hypothèses de sécurité standardes. 2. Nous montrons comment les CPRFs peuvent être utilisées pour construire des fonctions de corrélation pseudo-aléatoires (Pseudorandom Correlation Functions, alias PCFs) pour les corrélations de transfert inconscient (Oblivious Transfer, alias OT). Les PCFs pour les corrélations OT permettent à deux parties de générer des paires corrélées OT qui peuvent être utilisées dans des protocoles de calcul sécurisés rapides. Ensuite, nous détaillons l’instanciation de notre transformation en appliquant une légère modification à la construction PRF bien connue de Naor et Reingold. Enfin, nous présentons une méthode de génération non-interactive de clés d’évaluation pour cette dernière instanciation, qui permet d’obtenir une PCF à clé publique efficace pour les corrélations OT à partir d’hypothèses standardes.","url":"https://doi.org/10.70675/116140dbza9a5z49d5zaa7ezfbd3f8f1cf51","authors":["Mahshid Riahinia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T17:51:02Z","doi":"10.70675/116140dbza9a5z49d5zaa7ezfbd3f8f1cf51","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.70675/f8c855caz7b02z4f11zb87az8d5e6e60e6f0","name":"Countermeasures to side-channel attacks and secure multi-party computation","source":"crossref","abstract":"Contre-mesures aux attaques par canaux cachés et calcul multi-parti sécurisé Les cryptosystèmes sont présents dans de nombreux appareils utilisés dans la vie courante, tels que les cartes à puces, ordiphones, ou passeports. La sécurité de ces appareils est menacée par les attaques par canaux auxiliaires, où un attaquant observe leur comportement physique pour obtenir de l’information sur les secrets manipulés. L’évaluation de la résilience de ces produits contre de telles attaques est obligatoire afin de s’assurer la robustesse de la cryptographie embarquée. Dans cette thèse, nous exhibons une méthodologie pour évaluer efficacement le taux de succès d’attaques par canaux auxiliaires, sans avoirbesoin de les réaliser en pratique. En particulier, nous étendons les résultats obtenus par Rivain en 2009, et nous exhibons des formules permettant de calculer précisément le taux de succès d’attaques d’ordre supérieur. Cette approche permet une estimation rapide de la probabilité de succès de telles attaques. Puis, nous étudions pour la première fois depuis le papier séminal de Ishai, Sahai et Wagner en 2003 le problème de la quantité d’aléa nécessaire dans la réalisation sécurisée d’une multiplication de deux bits. Nous fournissons des constructions explicites pour des ordres pratiques de masquage, et prouvons leur sécurité et optimalité. Finalement, nous proposons un protocole permettant le calcul sécurisé d’un veto parmi un nombre de joueurs arbitrairement grand, tout en maintenant un nombre constant de bits aléatoires. Notre construction permet également la multiplication sécurisée de n’importe quel nombre d’éléments d’un corps fini.","url":"https://doi.org/10.70675/f8c855caz7b02z4f11zb87az8d5e6e60e6f0","authors":["Adrian Thillard"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-02T10:30:54Z","doi":"10.70675/f8c855caz7b02z4f11zb87az8d5e6e60e6f0","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.18130/v3dm4r","name":"Stable Matching with PCF Version 2, and Etude in Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.18130/v3dm4r","authors":["Benjamin Terner"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-06-21T11:25:09Z","doi":"10.18130/v3dm4r","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.swevo.2023.101415","name":"Multiparty distance minimization: Problems and an evolutionary approach","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.swevo.2023.101415","authors":["Zeneng She","Wenjian Luo","Xin Lin","Yatong Chang","Yuhui Shi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-30T10:33:13Z","doi":"10.1016/j.swevo.2023.101415","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:29.560Z"},{"id":"doi:10.1007/978-3-540-71677-8_23","name":"Multiparty Computation for Interval, Equality, and Comparison Without Bit-Decomposition Protocol","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-540-71677-8_23","authors":["Takashi Nishide","Kazuo Ohta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-06-20T14:05:58Z","doi":"10.1007/978-3-540-71677-8_23","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:29.560Z"},{"id":"doi:10.1145/3634737.3661136","name":"Honest Majority Multiparty Computation over Rings with Constant Online Communication","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3634737.3661136","authors":["Minghua Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-28T07:51:38Z","doi":"10.1145/3634737.3661136","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:37.130Z"},{"id":"doi:10.1007/978-3-031-25538-0_43","name":"PREFHE, PREFHE-AES and PREFHE-SGX: Secure Multiparty Computation Protocols from Fully Homomorphic Encryption and Proxy ReEncryption with AES and Intel SGX","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-25538-0_43","authors":["Cavidan Yakupoglu","Kurt Rohloff"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-02-03T13:03:34Z","doi":"10.1007/978-3-031-25538-0_43","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:29.560Z"},{"id":"doi:10.1007/978-3-642-41527-2_27","name":"Asynchronous Multiparty Computation with Linear Communication Complexity","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-41527-2_27","authors":["Ashish Choudhury","Martin Hirt","Arpita Patra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-10-03T10:55:48Z","doi":"10.1007/978-3-642-41527-2_27","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:29.560Z"},{"id":"doi:10.1007/978-3-642-30042-4_1","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-30042-4_1","authors":["Thomas Schneider"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-08-03T12:10:26Z","doi":"10.1007/978-3-642-30042-4_1","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:29.560Z"},{"id":"doi:10.1016/j.isci.2023.106990","name":"Edge-assisted quantum protocol for secure multiparty logical AND its applications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.isci.2023.106990","authors":["Run-hua Shi","Xia-qin Fang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-05-29T12:18:39Z","doi":"10.1016/j.isci.2023.106990","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:29.560Z"},{"id":"doi:10.1007/s11128-022-03454-4","name":"Measurement-device-independent quantum secure multiparty summation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s11128-022-03454-4","authors":["Run-Hua Shi","Bai Liu","Mingwu Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-30T09:04:46Z","doi":"10.1007/s11128-022-03454-4","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:29.560Z"},{"id":"doi:10.1016/j.jisa.2023.103623","name":"An information-theoretically secure quantum multiparty private set intersection","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jisa.2023.103623","authors":["Tapaswini Mohanty","Sumit Kumar Debnath"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-17T06:00:28Z","doi":"10.1016/j.jisa.2023.103623","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:29.560Z"},{"id":"doi:10.1007/978-3-642-30042-4_6","name":"Conclusion","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-30042-4_6","authors":["Thomas Schneider"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-08-03T12:10:26Z","doi":"10.1007/978-3-642-30042-4_6","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:29.560Z"},{"id":"doi:10.1038/s41598-024-80207-6","name":"Author Correction: Leveraging quantum blockchain for secure multiparty space sharing and authentication on specialized metaverse platform","source":"crossref","abstract":"","url":"https://doi.org/10.1038/s41598-024-80207-6","authors":["Esmot Ara Tuli","Jae-Min Lee","Dong-Seong Kim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-20T05:02:37Z","doi":"10.1038/s41598-024-80207-6","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:37.831Z"},{"id":"doi:10.1007/s00145-021-09415-x","name":"From Fairness to Full Security in Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00145-021-09415-x","authors":["Ran Cohen","Iftach Haitner","Eran Omri","Lior Rotem"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-12-07T18:02:27Z","doi":"10.1007/s00145-021-09415-x","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:29.560Z"},{"id":"doi:10.5220/0012459400003648","name":"Efficient Secure Computation of Edit Distance on Genomic Data","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012459400003648","authors":["Andrea Migliore","Stelvio Cimato","Gabriella Trucco"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-01T18:44:53Z","doi":"10.5220/0012459400003648","addedAt":"2026-08-31T06:41:29.560Z","updatedAt":"2026-08-31T06:41:37.831Z"},{"id":"doi:10.5281/zenodo.17184062","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17184062","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17184062","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.17157969","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17157969","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17157969","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.48550/arxiv.2509.08683","name":"Perfectly-Private Analog Secure Aggregation in Federated Learning","source":"datacite","abstract":"In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model. To address the risk of exposing sensitive data through local models, secure aggregation via secure multiparty computation has been proposed to enhance privacy. At the same time, perfect privacy can only be achieved by a uniform distribution of the masked local models to be aggregated. This raises a problem when working with real valued data, as there is no measure on the reals that is invariant under the masking operation, and hence information leakage is bound to occur. Shifting the data to a finite field circumvents this problem, but as a downside runs into an inherent accuracy complexity tradeoff issue due to fixed point modular arithmetic as opposed to floating point numbers that can simultaneously handle numbers of varying magnitudes. In this paper, a novel secure parameter aggregation method is proposed that employs the torus rather than a finite field. This approach guarantees perfect privacy for each party's data by utilizing the uniform distribution on the torus, while avoiding accuracy losses. Experimental results show that the new protocol performs similarly to the model without secure aggregation while maintaining perfect privacy. Compared to the finite field secure aggregation, the torus-based protocol can in some cases significantly outperform it in terms of model accuracy and cosine similarity, hence making it a safer choice.","url":"https://doi.org/10.48550/arxiv.2509.08683","authors":["Jaramillo-Velez, Delio","Rajput, Charul","Freij-Hollanti, Ragnar","Hollanti, Camilla","Amat, Alexandre Graell i"],"tags":["Machine Learning (cs.LG)","Information Theory (cs.IT)","FOS: Computer and information sciences","FOS: Computer and information sciences","68P30"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.08683","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.17141429","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.17141429","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17141429","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.17094436","name":"D2.1 Privacy Preserving Feature Selection, Classificationand Federated Learning","source":"datacite","abstract":"The HARPOCRATES project aims to develop solutions for private and secure data analysis and sharing. This deliverable (D2.1 Privacy Preserving Feature Selection, Classification and Federated Learning) presents the outcomes of research conducted in Work Package 2 (WP2). WP2 has focused on advancing privacy-preserving machine learning (PPML) techniques to address one of today’s most pressing challenges in data science: how to learn from data without exposing it. In an era where AI systems rely on large volumes of personal and often sensitive data, safeguarding privacy during machine learning tasks is critical—particularly in domains such as healthcare and cyber threat intelligence. This deliverable covers three key areas: feature selection, classification, and federated learning—each enhanced with novel approaches to improve efficiency, scalability, and data protection. Feature Selection: We introduce a lightweight, secure protocol using multi-party computation. The method significantly reduces the complexity of mutual information estimation, achieving more than 1,000× speed-up in regression tasks while preserving model performance. This makes privacy-preserving data preparation practical and efficient. Classification: In distributed environments, split learning allows a client and server to train a model jointly without sharing raw data. However, it still risks leaking sensitive intermediate information. We designed two secure split learning protocols based on homomorphic encryption and function secret sharing, ensuring that intermediate outputs remain protected—even from untrusted servers. Federated Learning: We improved the state of the art in privacy-preserving federated setups. Our work optimised existing homomorphic encryption schemes such as Flashe and introduced a new version, Flashev2, to better support encrypted training. We also addressed the often-overlooked problem of secure hyperparameter tuning in federated learning by proposing PRIVTUNA, a framework that enables collaborative tuning using multiparty homomorphic encryption. This approach provides high accuracy and efficiency without privacy loss. Additionally, we explored the use of differentially private synthetic data for time series anomaly detection. Our results show that incorporating differential privacy into the data generation process can preserve strong utility, in some cases outperforming non-private synthetic data.","url":"https://doi.org/10.5281/zenodo.17094436","authors":["Iacovazzi, Alfonso","Eklund, David","Pyrgelis, Apostolos","Wang, Han","Michalas, Antonis","Paladi, Nicolae","Khan, Tanveer","Kovacevic, Ana","Jovic, Dejan","Jokic, Stevan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17094436","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.17094435","name":"D2.1 Privacy Preserving Feature Selection, Classificationand Federated Learning","source":"datacite","abstract":"The HARPOCRATES project aims to develop solutions for private and secure data analysis and sharing. This deliverable (D2.1 Privacy Preserving Feature Selection, Classification and Federated Learning) presents the outcomes of research conducted in Work Package 2 (WP2). WP2 has focused on advancing privacy-preserving machine learning (PPML) techniques to address one of today’s most pressing challenges in data science: how to learn from data without exposing it. In an era where AI systems rely on large volumes of personal and often sensitive data, safeguarding privacy during machine learning tasks is critical—particularly in domains such as healthcare and cyber threat intelligence. This deliverable covers three key areas: feature selection, classification, and federated learning—each enhanced with novel approaches to improve efficiency, scalability, and data protection. Feature Selection: We introduce a lightweight, secure protocol using multi-party computation. The method significantly reduces the complexity of mutual information estimation, achieving more than 1,000× speed-up in regression tasks while preserving model performance. This makes privacy-preserving data preparation practical and efficient. Classification: In distributed environments, split learning allows a client and server to train a model jointly without sharing raw data. However, it still risks leaking sensitive intermediate information. We designed two secure split learning protocols based on homomorphic encryption and function secret sharing, ensuring that intermediate outputs remain protected—even from untrusted servers. Federated Learning: We improved the state of the art in privacy-preserving federated setups. Our work optimised existing homomorphic encryption schemes such as Flashe and introduced a new version, Flashev2, to better support encrypted training. We also addressed the often-overlooked problem of secure hyperparameter tuning in federated learning by proposing PRIVTUNA, a framework that enables collaborative tuning using multiparty homomorphic encryption. This approach provides high accuracy and efficiency without privacy loss. Additionally, we explored the use of differentially private synthetic data for time series anomaly detection. Our results show that incorporating differential privacy into the data generation process can preserve strong utility, in some cases outperforming non-private synthetic data.","url":"https://doi.org/10.5281/zenodo.17094435","authors":["Iacovazzi, Alfonso","Eklund, David","Pyrgelis, Apostolos","Wang, Han","Michalas, Antonis","Paladi, Nicolae","Khan, Tanveer","Kovacevic, Ana","Jovic, Dejan","Jokic, Stevan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17094435","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.4230/lipics.itc.2025.8","name":"On the Definition of Malicious Private Information Retrieval","source":"datacite","abstract":"A multi-server private information retrieval (PIR) protocol allows a client to obtain an entry of its choice from a database, held by one or more servers, while hiding the identity of the entry from small enough coalitions of servers. In this paper, we study PIR protocols in which some of the servers are malicious and may not send messages according to the pre-described protocol. In previous papers, such protocols were defined by requiring that they are correct, private, and robust to malicious servers, i.e., by listing 3 properties that they should satisfy. However, 40 years of experience in studying secure multiparty protocols taught us that defining the security of protocols by a list of required properties is problematic. In this paper, we rectify this situation and define the security of PIR protocols with malicious servers using the real vs. ideal paradigm. We study the relationship between the property-based definition of PIR protocols and the real vs. ideal definition, showing the following results: - We prove that if we require full security from PIR protocols, e.g., the client outputs the correct value of the database entry with high probability even if a minority of the servers are malicious, then the two definitions are equivalent. This implies that constructions of such protocols that were proven secure using the property-based definition are actually secure under the \"correct\" definition of security. - We show that if we require security-with-abort from PIR protocols (called PIR protocols with error-detection in previous papers), i.e., protocols in which the user either outputs the correct value or an abort symbol, then there are protocols that are secure under the property-based definition; however, they do not satisfy the real vs. ideal definition, that is, they can be attacked allowing selective abort. This shows that the property-based definition of PIR protocols with security-with-abort is problematic. - We consider the compiler of Eriguchi et al. (TCC 22) that starts with a PIR protocol that is secure against semi-honest servers and constructs a PIR protocol with security-with-abort; this compiler implies the best-known PIR protocols with security-with-abort. We show that applying this compiler does not result in PIR protocols that are secure according to the real vs. ideal definition. However, we prove that a simple modification of this compiler results in PIR protocols that are secure according to the real vs. ideal definition.","url":"https://doi.org/10.4230/lipics.itc.2025.8","authors":["Alon, Bar","Beimel, Amos"],"tags":["Private information retrieval","secure multiparty computation","Security and privacy → Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.4230/lipics.itc.2025.8","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16984013","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16984013","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16984013","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16902938","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16902938","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16902938","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16902478","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16902478","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16902478","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16038384","name":"A Comprehensive Review of Privacy Preserving Distributed Data Mining: Techniques, Opportunities and Challenges","source":"datacite","abstract":"Abstract Privacy-preserving distributed data mining (PPDDM) has gained significant attention as a means to extract valuable insights from distributed data while preserving individual privacy. This paper presents a comprehensive review of PPDDM, focusing on the techniques, opportunities, and challenges associated with this emerging field. The review begins by examining the techniques employed in PPDDM, including differential privacy, secure multiparty computation, homomorphic encryption, and data perturbation. Each technique is discussed in terms of its strengths, limitations, and applicability to different scenarios. The privacy-utility trade-off is also explored, highlighting the need to balance privacy protection and data utility in the mining process. Furthermore, the paper explores the opportunities that PPDDM offers. These include enhanced data availability, increased analytical power, protection of sensitive information, collaboration among organizations, and resource sharing possibilities. The potential of these opportunities to revolutionize decision-making processes and knowledge discovery is discussed. However, the review also addresses the challenges associated with PPDDM. These challenges encompass data heterogeneity and quality, secure computation and communication, governance and trust, and regulatory compliance. Each challenge is examined in detail, emphasizing the complexities involved and proposing strategies to overcome them. This comprehensive review provides a holistic understanding of PPDDM by analyzing the techniques, opportunities, and challenges in the field. The insights offered in this paper serve as a valuable resource for researchers, practitioners, and decision-makers seeking to leverage the potential of privacy-preserving distributed data mining in their respective domains. The review highlights the need for further research and development efforts to advance PPDDM and encourages interdisciplinary collaborations to ensure responsible and transparent data mining practices. By striking a balance between privacy preservation and data utility, organizations can harness the benefits of PPDDM to make informed decisions and foster innovation in a privacy-conscious society.","url":"https://doi.org/10.5281/zenodo.16038384","authors":["Sangeeta Borkakoty, Dr. Kanak Chandra Bora"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16038384","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2405.03136","name":"FOBNN: Fast Oblivious Inference via Binarized Neural Networks","source":"datacite","abstract":"The remarkable performance of deep learning has sparked the rise of Deep Learning as a Service (DLaaS), allowing clients to send their personal data to service providers for model predictions. A persistent challenge in this context is safeguarding the privacy of clients' sensitive data. Oblivious inference allows the execution of neural networks on client inputs without revealing either the inputs or the outcomes to the service providers. In this paper, we propose FOBNN, a Fast Oblivious inference framework via Binarized Neural Networks. In FOBNN, through neural network binarization, we convert linear operations (e.g., convolutional and fully-connected operations) into eXclusive NORs (XNORs) and an Oblivious Bit Count (OBC) problem. For secure multiparty computation techniques, like garbled circuits or bitwise secret sharing, XNOR operations incur no communication cost, making the OBC problem the primary bottleneck for linear operations. To tackle this, we first propose the Bit Length Bounding (BLB) algorithm, which minimizes bit representation to decrease redundant computations. Subsequently, we develop the Layer-wise Bit Accumulation (LBA) algorithm, utilizing pure bit operations layer by layer to further boost performance. We also enhance the binarized neural network structure through link optimization and structure exploration. The former optimizes link connections given a network structure, while the latter explores optimal network structures under same secure computation costs. Our theoretical analysis reveals that the BLB algorithm outperforms the state-of-the-art OBC algorithm by a range of 17% to 55%, while the LBA exhibits an improvement of nearly 100%. Comprehensive proof-of-concept evaluation demonstrates that FOBNN outperforms prior art on popular benchmarks and shows effectiveness in emerging bioinformatics.","url":"https://doi.org/10.48550/arxiv.2405.03136","authors":["Chen, Xin","Chen, Zhili","Wei, Shiwen","Gong, Junqing","Chen, Lin"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.03136","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.13016/eulx-dsgd","name":"Practical Multiparty Protocols From Lattice Assumptions: Threshold Signatures, Oblivious Pseudorandom Functions, And More","source":"datacite","abstract":"Lattice-based cryptography has emerged as the most dominant replacement candidate for the next generation of post-quantum cryptographic tools. With their operational simplicity while allowing advanced functionality, these protocols lead the majority of post-quantum standardization efforts and motivate a great chunk of current research to realize advanced trusted communication models. However, lattices' greatest asset is also their greatest curse. The applicability of advanced functionality motivates protocols with multiple computing parties while the assumptions that make lattice protocols secure in the first place hate settings where secrets are distributed. In this work we try to alleviate this issue by building practical lattice-based multiparty protocols. First we propose the first known concrete lattice-based threshold signature scheme with distributed key generation to demonstrate practicality. Second, we look at a different type of protocol, namely verifiable oblivious pseudorandom functions, and propose a practical version of an existing protocol through different analysis techniques while also giving the first lattice-based threshold versions of such protocols. Using these techniques, we then rebuild our threshold signature scheme and show a concretely efficient threshold signature that simultaneously provides additional desirable properties like identifiability and non-interactivity. Finally, we look at the possibility of asymmetric outsourced computation and formalize the classic notion of augmented password-protected threshold signatures in a more practicality friendly manner and construct the first lattice-based augmented password-protected threshold signature scheme. All of these works act as building blocks for more complicated protocols and share similar analysis techniques and solutions to problems specific to the distributed setting. This commonality indicates that it is not only the assumptions that we need to revisit but also how we think about security in general as part of preparing cryptography for its post-quantum era.","url":"https://doi.org/10.13016/eulx-dsgd","authors":["Gur, Kamil Doruk"],"tags":["Computer science","lattice cryptography","oblivious pseudorandom functions","post-quantum cryptography","threshold cryptography","threshold signatures"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.13016/eulx-dsgd","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16778947","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16778947","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16778947","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16778588","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16778588","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16778588","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16778091","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16778091","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16778091","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16777843","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16777843","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16777843","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.26083/tuprints-00028392","name":"MPClan: Protocol Suite for Privacy-Conscious Computations","source":"datacite","abstract":"The growing volumes of data being collected and its analysis to provide better services are creating worries about digital privacy. To address privacy concerns and give practical solutions, the literature has relied on secure multiparty computation techniques. However, recent research over rings has mostly focused on the small-party honest-majority setting of up to four parties tolerating single corruption, noting efficiency concerns. In this work, we extend the strategies to support higher resiliency in an honest-majority setting with efficiency of the online phase at the centre stage. Our semi-honest protocol improves the online communication of the protocol of Damgård and Nielsen (CRYPTO’07) without inflating the overall communication. It also allows shutting down almost half of the parties in the online phase, thereby saving up to 50% in the system’s operational costs. Our maliciously secure protocol also enjoys similar benefits and requires only half of the parties, except for one-time verification towards the end, and provides security with fairness. To showcase the practicality of the designed protocols, we benchmark popular applications such as deep neural networks, graph neural networks, genome sequence matching, and biometric matching using prototype implementations. Our protocols, in addition to improved communication, aid in bringing up to 60–80% savings in monetary cost over prior work.","url":"https://doi.org/10.26083/tuprints-00028392","authors":["Koti, Nishat","Patil, Shravani","Patra, Arpita","Suresh, Ajith"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.26083/tuprints-00028392","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16740395","name":"Helium: a Framework for MHE-based MPC","source":"datacite","abstract":"This poster presents Helium, an open-source framework for secure multiparty computation (MPC) based on multiparty homomorphic encryption (MHE). The Helium framework was first presented at CCS’24, as the proof-of-concept implementation of an MPC protocol among lightweight participants that tolerates high network churn. However, the CCS paper focuses on the feasiblity result and the design and implementation aspects were left out of scope. The Helium open-source project goes beyond this specific proof-of-concept: the aim is to develop it into a standalone framework for MHE-based MPC. This poster covers the more implementation-related aspects of the Helium framework. It is addressed to both potential users of MHE-based MPC, and (M)HE researchers interested in contributing to the project.","url":"https://doi.org/10.5281/zenodo.16740395","authors":["Mouchet, Christian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16740395","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16740396","name":"Helium: a Framework for MHE-based MPC","source":"datacite","abstract":"This poster presents Helium, an open-source framework for secure multiparty computation (MPC) based on multiparty homomorphic encryption (MHE). The Helium framework was first presented at CCS’24, as the proof-of-concept implementation of an MPC protocol among lightweight participants that tolerates high network churn. However, the CCS paper focuses on the feasiblity result and the design and implementation aspects were left out of scope. The Helium open-source project goes beyond this specific proof-of-concept: the aim is to develop it into a standalone framework for MHE-based MPC. This poster covers the more implementation-related aspects of the Helium framework. It is addressed to both potential users of MHE-based MPC, and (M)HE researchers interested in contributing to the project.","url":"https://doi.org/10.5281/zenodo.16740396","authors":["Mouchet, Christian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16740396","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16750821","name":"Utilizing Federated Learning Techniques to Enable Privacy-Preserving and Secure Sharing of Patient Data Across Healthcare Systems","source":"datacite","abstract":"The rising demand for secure, efficient, and privacy-preserving methods of managing and sharing patient health information has driven advancements in technologies like federated learning. Unlike traditional machine learning, which centralizes data, federated learning allows models to be trained across decentralized devices or institutions without exposing raw data. This makes it uniquely suited to healthcare environments where data sensitivity and privacy regulations such as HIPAA and GDPR are paramount. Federated learning facilitates collaborative model development among hospitals, research institutions, and other stakeholders while safeguarding patient confidentiality. It empowers personalized medicine and predictive analytics by leveraging the collective intelligence of distributed datasets. Moreover, it reduces the attack surface for cyber threats by limiting data movement. This article reviews the core principles of federated learning, its integration with privacy-enhancing technologies such as differential privacy and secure multiparty computation, and explores case studies demonstrating its efficacy in real-world healthcare applications. The challenges of system heterogeneity, communication overhead, and model convergence are also discussed. Federated learning stands at the intersection of artificial intelligence and data governance, presenting a promising paradigm for the future of medical research and clinical decision support. With proper implementation, it holds the potential to unlock valuable insights from patient data while respecting ethical and legal boundaries.","url":"https://doi.org/10.5281/zenodo.16750821","authors":["Singh, Khushwant"],"tags":["Federated Learning, Patient Privacy, Healthcare Data, Secure Sharing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.16750821","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16750820","name":"Utilizing Federated Learning Techniques to Enable Privacy-Preserving and Secure Sharing of Patient Data Across Healthcare Systems","source":"datacite","abstract":"The rising demand for secure, efficient, and privacy-preserving methods of managing and sharing patient health information has driven advancements in technologies like federated learning. Unlike traditional machine learning, which centralizes data, federated learning allows models to be trained across decentralized devices or institutions without exposing raw data. This makes it uniquely suited to healthcare environments where data sensitivity and privacy regulations such as HIPAA and GDPR are paramount. Federated learning facilitates collaborative model development among hospitals, research institutions, and other stakeholders while safeguarding patient confidentiality. It empowers personalized medicine and predictive analytics by leveraging the collective intelligence of distributed datasets. Moreover, it reduces the attack surface for cyber threats by limiting data movement. This article reviews the core principles of federated learning, its integration with privacy-enhancing technologies such as differential privacy and secure multiparty computation, and explores case studies demonstrating its efficacy in real-world healthcare applications. The challenges of system heterogeneity, communication overhead, and model convergence are also discussed. Federated learning stands at the intersection of artificial intelligence and data governance, presenting a promising paradigm for the future of medical research and clinical decision support. With proper implementation, it holds the potential to unlock valuable insights from patient data while respecting ethical and legal boundaries.","url":"https://doi.org/10.5281/zenodo.16750820","authors":["Singh, Khushwant"],"tags":["Federated Learning, Patient Privacy, Healthcare Data, Secure Sharing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.16750820","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.3929/ethz-a-005392962","name":"Approaches to Efficient and Robust Cryptographic Protocols","source":"datacite","abstract":"The growing influence of the Internet and other communication networks on our daily lives and on the global economy shows clearly that the security issues in such networks are of the uttermost importance. Motivated both by the theoretical questions and by the potential real-world applications, in this dissertation we study problems of secure cooperation in communication networks. In particular we focus on constructing efficient and robust protocols for various cryptographic tasks.In the first part of this thesis we study the problem of secure multiparty computation (MPC), which allows a set of n players to evaluate an agreed function of their inputs in a secure way, i.e., so that an adversary corrupting some of the players cannot achieve more than controlling the inputs and outputs of these players. The concept of MPC is very general and powerful, since it allows to realize essentially any distributed computational task in a secure way. For that reason the MPC problem has been studied extensively since its introduction by Yao in 1982. A major goal of these studies is to design protocols with low communication complexity, and two main research directions emerged over the time, with focus on reducing round-, resp. bit-complexity. In this thesis we focus on the bitcomplexity, i.e., the number of bits communicated between the parties during the computation, and we consider this problem in asynchronous networks, which model pretty closely real-world networks. We propose an MPC protocol, which is secure with respect to an active adversary corrupting up to t < n/3 players (this is optimal in an asynchronous network), and which is the most efficient protocol currently known. For our constructions we develop several novel techniques, which were used also in subsequent works on efficient MPC protocols. In the second part of this thesis we turn to a problem which is common to all cryptographic research based on computational assumptions.In spite of considerable advances in theoretical computer science and specifically in complexity theory, it is still not known whether there exist provably hard problems that could form a solid foundation for the complexity-based cryptography. In particular, it is not clear which assumptions are the best, and which are the most likely to hold in 10 or 20 years from now. Therefore, when implementing a cryptographic system in a real-world setting we are confronted with the difficult problem of choosing the most trustworthy assumptions. One way of dealing with this problem is offered by the so-called robust combiners, i.e., constructions which in a certain sense allow to build cryptographic systems based on the best assumption possible, without actually being able to tell which assumption is the best one. Roughly speaking, a robust combiner combines several implementations of a primitive based on various assumptions, to yield an implementation guaranteed to be secure if at least some of the underlying assumptions (i.e. sufficiently many but not necessarily all) are valid. We generalize the notion of robust combiners in several ways, and propose constructions of combiners for various fundamental primitives, like private information retrieval (PIR), oblivious transfer (OT) and oblivious linear function evaluation (OLFE). Our constructions offer tradeoffs between applicability and efficiency, and are strictly stronger and/or more efficient than the constructions known before. Moreover, we introduce cross-primitive combiners, which can be viewed as a generalization of reductions and combiners. Using this more general view we show a separation between PIR and OT, ruling out certain types of reductions of PIR to OT.","url":"https://doi.org/10.3929/ethz-a-005392962","authors":["Przydatek, Bartosz"],"tags":["homomorphic encryption","private information retrieval","oblivious transfer","asynchronous network","NETWORK PROTOCOLS + COMMUNICATION PROTOCOLS (COMPUTER SYSTEMS)","multi-party computation","CRYPTOGRAPHY (INFORMATION THEORY)","NETZWERKPROTOKOLLE + KOMMUNIKATIONSPROTOKOLLE (COMPUTERSYSTEME)"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2007","doi":"10.3929/ethz-a-005392962","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16637557","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16637557","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16637557","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16634806","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16634806","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16634806","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16568888","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16568888","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16568888","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16367523","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16367523","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16367523","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16364043","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16364043","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16364043","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16363992","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16363992","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16363992","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16363494","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16363494","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16363494","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.6084/m9.figshare.c.7942699","name":"FedscGen: privacy-preserving federated batch effect correction of single-cell RNA sequencing data","source":"datacite","abstract":"Abstract Single-cell RNA-seq data from clinical samples often suffer from batch effects, but data sharing is limited due to genomic privacy concerns. We present FedscGen, a privacy-preserving communication-efficient federated method built upon the scGen model, enhanced with secure multiparty computation. FedscGen supports federated training and batch effect correction workflows, including the integration of new studies. We benchmark FedscGen across diverse datasets, showing competitive performance—matching scGen on key metrics like NMI, GC, ILF1, ASW_C, kBET, and EBM on the Human Pancreas dataset. Published as a FeatureCloud app, FedscGen enables secure, real-world collaboration for scRNA-seq batch effect correction.","url":"https://doi.org/10.6084/m9.figshare.c.7942699","authors":["Bakhtiari, Mohammad","Bonn, Stefan","Theis, Fabian","Zolotareva, Olga","Baumbach, Jan"],"tags":["Data Format","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.6084/m9.figshare.c.7942699","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.6084/m9.figshare.c.7942699.v1","name":"FedscGen: privacy-preserving federated batch effect correction of single-cell RNA sequencing data","source":"datacite","abstract":"Abstract Single-cell RNA-seq data from clinical samples often suffer from batch effects, but data sharing is limited due to genomic privacy concerns. We present FedscGen, a privacy-preserving communication-efficient federated method built upon the scGen model, enhanced with secure multiparty computation. FedscGen supports federated training and batch effect correction workflows, including the integration of new studies. We benchmark FedscGen across diverse datasets, showing competitive performance—matching scGen on key metrics like NMI, GC, ILF1, ASW_C, kBET, and EBM on the Human Pancreas dataset. Published as a FeatureCloud app, FedscGen enables secure, real-world collaboration for scRNA-seq batch effect correction.","url":"https://doi.org/10.6084/m9.figshare.c.7942699.v1","authors":["Bakhtiari, Mohammad","Bonn, Stefan","Theis, Fabian","Zolotareva, Olga","Baumbach, Jan"],"tags":["Data Format","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.6084/m9.figshare.c.7942699.v1","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16312200","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16312200","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16312200","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16307283","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16307283","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16307283","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16304714","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16304714","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16304714","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.16304294","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.16304294","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16304294","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.48550/arxiv.2507.09532","name":"Design and Experimental Realization of Various Protocols for Secure Quantum Computation and Communication","source":"datacite","abstract":"A set of new schemes for quantum computation and communication have been either designed or experimentally realized using optimal quantum resources. A multi-output quantum teleportation scheme, where a sender (Alice) teleports an m and m+1-qubit GHZ-like unknown state to a receiver (Bob), has been demonstrated using two copies of the Bell state instead of a five-qubit cluster state and implemented on IBM's quantum computer for the m=1 case. Another scheme, known as quantum broadcasting where a known state is sent to two spatially separated parties (Bob and Charlie) has also been realized using two Bell states. It is shown that existing quantum broadcasting schemes can be reduced to multiparty remote state preparation. After achieving teleportation of unknown and known states, sending a quantum operator becomes the next step. A scheme for remote implementation of operators (RIO), specifically a controlled joint-RIO (CJRIO), has been proposed using a four-qubit hyper-entangled state involving spatial and polarization degrees of freedom. In this direction, two more variants, remote implementation of hidden and partially unknown operators (RIHO and RIPUO) have also been proposed. Their success probabilities are analyzed considering dissipation of an auxiliary coherent state interacting with the environment. For secure multiparty tasks like quantum voting or auction, secure multiparty quantum computation (SMQC) becomes essential. A quantum anonymous voting (QAV) scheme has been experimentally implemented on IBM's quantum computer. Finally, two quantum key distribution (QKD) protocols, coherent one-way (COW) and differential phase shift (DPS), are experimentally demonstrated and the key rates are analyzed as functions of post-processing parameters and detector dead times across various distances.","url":"https://doi.org/10.48550/arxiv.2507.09532","authors":["Kumar, Satish"],"tags":["Quantum Physics (quant-ph)","FOS: Physical sciences","FOS: Physical sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.09532","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15834616","name":"Recent Advances in Privacy-Preserving Query Processing Techniques for Encrypted Relational Databases in Cloud Infrastructure","source":"datacite","abstract":"Abstract: The growing reliance on cloud infrastructure for storing and managing relational databases has introduced critical challenges in preserving data confidentiality while enabling efficient query execution. Traditional encryption schemes offer strong data protection but often impede the ability to perform complex SQL operations without decryption, creating a trade-off between security and functionality. Recent advances in privacy-preserving query processing techniques—including homomorphic encryption, searchable encryption, oblivious RAM, and secure multiparty computation—have revolutionized the field by enabling secure computation over encrypted data with minimal performance penalties. This review paper systematically analyzes the state-of-the-art mechanisms that support encrypted SQL query processing in cloud-hosted relational databases. It evaluates the theoretical foundations, computational overheads, query expressiveness, and practical deployment scenarios of these privacy-preserving methods. Furthermore, it explores hybrid approaches that combine cryptographic and hardware-based techniques to balance performance with security guarantees. The paper also highlights emerging trends such as federated SQL processing, data provenance tracking, and secure hardware enclaves that are reshaping privacy-preserving architectures. The study aims to provide a comprehensive understanding of the current capabilities, limitations, and future research directions in secure query execution for encrypted relational databases deployed in cloud environments. Keywords: Encrypted SQL Query Processing, Homomorphic Encryption, Secure Cloud Databases, Privacy-Preserving Computation, Searchable Encryption, Cloud Data Confidentiality. Title: Recent Advances in Privacy-Preserving Query Processing Techniques for Encrypted Relational Databases in Cloud Infrastructure Author: Onuh Matthew Ijiga, Nonso Okika, Semirat Abidemi Balogun,. Ogboji James Agbo, Lawrence Anebi Enyejo International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 13, Issue 3, July 2025 - September 2025 Page No: 62-80 Research Publish Journals Website: www.researchpublish.com Published Date: 08-July-2025 DOI: https://doi.org/10.5281/zenodo.15834617 Paper Download Link (Source) https://www.researchpublish.com/papers/recent-advances-in-privacy-preserving-query-processing-techniques-for-encrypted-relational-databases-in-cloud-infrastructure","url":"https://doi.org/10.5281/zenodo.15834616","authors":["Onuh Matthew Ijiga","Nonso Okika","Semirat Abidemi Balogun","Ogboji James Agbo","Lawrence Anebi Enyejo"],"tags":["Encrypted SQL Query Processing","Homomorphic Encryption","Secure Cloud Databases","Privacy-Preserving Computation","Searchable Encryption","Cloud Data Confidentiality"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15834616","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.15834617","name":"Recent Advances in Privacy-Preserving Query Processing Techniques for Encrypted Relational Databases in Cloud Infrastructure","source":"datacite","abstract":"Abstract: The growing reliance on cloud infrastructure for storing and managing relational databases has introduced critical challenges in preserving data confidentiality while enabling efficient query execution. Traditional encryption schemes offer strong data protection but often impede the ability to perform complex SQL operations without decryption, creating a trade-off between security and functionality. Recent advances in privacy-preserving query processing techniques—including homomorphic encryption, searchable encryption, oblivious RAM, and secure multiparty computation—have revolutionized the field by enabling secure computation over encrypted data with minimal performance penalties. This review paper systematically analyzes the state-of-the-art mechanisms that support encrypted SQL query processing in cloud-hosted relational databases. It evaluates the theoretical foundations, computational overheads, query expressiveness, and practical deployment scenarios of these privacy-preserving methods. Furthermore, it explores hybrid approaches that combine cryptographic and hardware-based techniques to balance performance with security guarantees. The paper also highlights emerging trends such as federated SQL processing, data provenance tracking, and secure hardware enclaves that are reshaping privacy-preserving architectures. The study aims to provide a comprehensive understanding of the current capabilities, limitations, and future research directions in secure query execution for encrypted relational databases deployed in cloud environments. Keywords: Encrypted SQL Query Processing, Homomorphic Encryption, Secure Cloud Databases, Privacy-Preserving Computation, Searchable Encryption, Cloud Data Confidentiality. Title: Recent Advances in Privacy-Preserving Query Processing Techniques for Encrypted Relational Databases in Cloud Infrastructure Author: Onuh Matthew Ijiga, Nonso Okika, Semirat Abidemi Balogun,. Ogboji James Agbo, Lawrence Anebi Enyejo International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 13, Issue 3, July 2025 - September 2025 Page No: 62-80 Research Publish Journals Website: www.researchpublish.com Published Date: 08-July-2025 DOI: https://doi.org/10.5281/zenodo.15834617 Paper Download Link (Source) https://www.researchpublish.com/papers/recent-advances-in-privacy-preserving-query-processing-techniques-for-encrypted-relational-databases-in-cloud-infrastructure","url":"https://doi.org/10.5281/zenodo.15834617","authors":["Onuh Matthew Ijiga","Nonso Okika","Semirat Abidemi Balogun","Ogboji James Agbo","Lawrence Anebi Enyejo"],"tags":["Encrypted SQL Query Processing","Homomorphic Encryption","Secure Cloud Databases","Privacy-Preserving Computation","Searchable Encryption","Cloud Data Confidentiality"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15834617","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.57709/9444795","name":"Privacy Preserving Data Mining For Horizontally Distributed Medical Data Analysis","source":"datacite","abstract":"To build reliable prediction models and identify useful patterns, assembling data sets from databases maintained by different sources such as hospitals becomes increasingly common; however, it might divulge sensitive information about individuals and thus leads to increased concerns about privacy, which in turn prevents different parties from sharing information. Privacy Preserving Distributed Data Mining (PPDDM) provides a means to address this issue without accessing actual data values to avoid the disclosure of information beyond the final result. In recent years, a number of state-of-the-art PPDDM approaches have been developed, most of which are based on Secure Multiparty Computation (SMC). SMC requires expensive communication cost and sophisticated secure computation. Besides, the mining progress is inevitable to slow down due to the increasing volume of the aggregated data. In this work, a new framework named Privacy-Aware Non-linear SVM (PAN-SVM) is proposed to build a PPDDM model from multiple data sources. PAN-SVM employs the Secure Sum Protocol to protect privacy at the bottom layer, and reduces the complex communication and computation via Nystrom matrix approximation and Eigen decomposition methods at the medium layer. The top layer of PAN-SVM speeds up the whole algorithm for large scale datasets. Based on the proposed framework of PAN-SVM, a Privacy Preserving Multi-class Classifier is built, and the experimental results on several benchmark datasets and microarray datasets show its abilities to improve classification accuracy compared with a regular SVM. In addition, two Privacy Preserving Feature Selection methods are also proposed based on PAN-SVM, and tested by using benchmark data and real world data. PAN-SVM does not depend on a trusted third party; all participants collaborate equally. Many experimental results show that PAN-SVM can not only effectively solve the problem of collaborative privacy-preserving data mining by building non-linear classification rules, but also significantly improve the performance of built classifiers.","url":"https://doi.org/10.57709/9444795","authors":["Lu, Yunmei"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2016","doi":"10.57709/9444795","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15775004","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15775004","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15775004","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15755596","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15755596","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15755596","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15735468","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15735468","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15735468","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.26215/heal.uoa.6761","name":"Lightweight oblivious transfer extensions","source":"datacite","abstract":"Η ασφαλής πολυκομματική υπολογιστική (Secure Multiparty Computation) (MPC) δημιουργήθηκε από τον A. Yao το 1962. Παρόλο που τότε αποτελούσε μόνο ένα θεωρητικό πρόβλημα, σήμερα, λόγω του Υπολογιστικού Νέφους (Cloud Computing) το οποίο είναι η νέα μόδα στην πληροφορική και στη διαχείριση πόρων, η ανάγκη για την ασφαλή πολυκομματική υπολογιστική είναι μεγαλύτερη από ποτέ. Ένα από τα σημαντικότερα κρυπτογραφικά πρωτόγονα εργαλεία (primitives) είναι για το MPC είναι το πρωτόκολλο Oblivious Transfer, το οποίο είναι διαβόητο για την μεγάλη κατανάλωση υπολογιστικών πόρων, λόγω του γεγονότος ότι χρησιμοποιεί κρυπτογράφηση δημοσίου κλειδιού. Για αυτούς τους λόγους, είναι εξαιρετικά σημαντικό να επενδύσουμε σε νέους, πιο πρακτικούς τρόπους υλοποίησης του Oblivious Transfer πρωτοκόλλου. Η απάντηση σε αυτή την έκκληση ονομάζεται Oblivious Transfer Extensions (OTE). Σε αυτή την διπλωματική εργασία, εξετάζουμε την κατηγορία της ασφαλούς πολυκομματικής υπολογιστικής, ερευνούμε τον τρόπο με τον οποίο τα Oblivious Transfer και Oblivious Transfer Extension πρωτόκολλα λειτουργούν και υλοποιούμε το πιο σημαντικό Oblivious Transfer Extension, υπό το όνομα IKPN03, το οποίο δημιουργήθηκε από τους Yuval Ishai, Joe Kilian, Kobbi Nissim και Erez Petrank, χρησιμοποιώντας την γλώσσα προγραμματισμού Java και την βιβλιοθήκη SCAPI. Ο σκοπός της εργασίας είναι να κατανοήσουμε τις διάφορες πτυχές των συγκεκριμένων πρωτοκόλλων και να αναγνωρίσουμε τυχόν προβλήματα, καθώς και να προτείνουμε νέες κατευθύνσεις για έρευνα.","url":"https://doi.org/10.26215/heal.uoa.6761","authors":["Mittos, Alexandros","Μήττος, Αλέξανδρος"],"tags":["oblivious-transfer","oblivious-transfer-extensions","secure-multiparty-computation","κρυπτογραφία","ασφαλής-μεταφορά-δεδομένων","ασφαλής-πολυκομματική-υπολογιστική","Cryptography (URL: http://id.loc.gov/authorities/subjects/sh99005451)","Computer network protocols"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2016","doi":"10.26215/heal.uoa.6761","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.26215/heal.uoa.6722","name":"Garbled circuits for multi-input boolean functions","source":"datacite","abstract":"Multiparty computation is an area of cryptography which provides methods that two or more parties may use in order to jointly compute a function while keeping their inputs private. Garbled circuits, attributed to Andrew C. Yao, is a cryptographic technique used in secure multiparty computation. Any function can be represented into a garbled boolean circuit and, with the help of Oblivious Transfer, a protocol can be built which allows parties to securely evaluate a function without exposing their inputs. The naive implementation of Yao's Garbled Circuit Protocol is impractical for complex functions. Researchers come up with methods that aim to reduce the communication overhead and computational complexity of the protocol. Nowadays, cryptographic operations are supported by hardware and as a result the main bottleneck in garbling is considered to be communication. This work introduces Yao's protocol and reviews several existent techniques which attempt to reduce the aforementioned costs, with a main focus on methods that aim to lessen the total number of ciphertexts needed to garble a function. Furthermore, it extends the recent and well established half gates method. Specifically, this work demonstrates that if half gates are combined and garbling is done on polynomials, then a 25% to 50% reduction in the amount of ciphertexts needed to garble portions of the circuit can be expected.","url":"https://doi.org/10.26215/heal.uoa.6722","authors":["Alexiou, Nikolaos","Αλεξίου, Νικόλαος"],"tags":["κρυπτογραφία","ασφαλής υπολογισμός συνάρτησης","από κοινού υπολογισμός","multiparty computation","garbled circuits","half gates","Cryptography (URL: http://id.loc.gov/authorities/subjects/sh85034453)","Data encryption (Computer science) (URL: http://id.loc.gov/authorities/subjects/sh94001524)"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2016","doi":"10.26215/heal.uoa.6722","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.34726/hss.2025.130720","name":"Differential Testing of Secure Multiparty Computation Compilers","source":"datacite","abstract":"Arbeit an der Bibliothek noch nicht eingelangt - Daten nicht geprueft - gesperrte Arbeit (bis 2027-04-24+02:00)","url":"https://doi.org/10.34726/hss.2025.130720","authors":["Watzinger, Sebastian"],"tags":[": Compiler Testing","Correctness","Secure Multiparty Computation","Differential Testing","Metamorphic Testing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.34726/hss.2025.130720","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15705598","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15705598","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15705598","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.3929/ethz-b-000568667","name":"Fully-Secure MPC with Minimal Trust","source":"datacite","abstract":"Lecture Notes in Computer Science, 13748","url":"https://doi.org/10.3929/ethz-b-000568667","authors":["Ishai, Yuval","Patra, Arpita","Patranabis, Sikhar","Ravi, Divya","Srinivasan, Akshayaram"],"tags":["Secure multiparty computation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3929/ethz-b-000568667","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15680899","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15680899","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15680899","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15645951","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15645951","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15645951","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15641796","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15641796","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15641796","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15640102","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15640102","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15640102","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14054751","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14054751","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.14054751","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14930826","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14930826","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.14930826","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14645969","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14645969","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.14645969","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14830617","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14830617","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.14830617","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15479320","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15479320","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15479320","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.11243884","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.11243884","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11243884","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15235159","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15235159","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15235159","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15235328","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15235328","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15235328","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14960634","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14960634","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.14960634","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.11121487","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.11121487","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11121487","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.11632895","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.11632895","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11632895","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.11503803","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.11503803","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11503803","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.11243825","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.11243825","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11243825","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14392692","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14392692","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.14392692","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.11263148","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.11263148","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11263148","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.12749258","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.12749258","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.12749258","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15056091","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15056091","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15056091","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.11442543","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.11442543","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11442543","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15235171","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15235171","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15235171","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15524567","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15524567","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15524567","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.12760291","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. 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Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.12760291","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.12760291","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15637804","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. 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Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15637804","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15637804","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15043904","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15043904","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15043904","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14724343","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14724343","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.14724343","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14438944","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. 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Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14438944","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.14438944","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.12749076","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.12749076","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.12749076","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14205172","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14205172","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.14205172","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15044005","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. 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Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15044005","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15044005","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.12750133","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.12750133","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.12750133","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14094513","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14094513","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.14094513","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14988748","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14988748","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.14988748","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15236473","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15236473","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15236473","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15043885","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15043885","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15043885","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15016534","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15016534","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15016534","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.13132856","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.13132856","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13132856","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14093182","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14093182","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.14093182","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15043808","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15043808","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15043808","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.13347752","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. 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Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.13347752","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13347752","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.13866996","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.13866996","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13866996","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15638289","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15638289","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15638289","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.13321314","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.13321314","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13321314","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15013534","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15013534","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15013534","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.12751530","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.12751530","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.12751530","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.12793562","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.12793562","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.12793562","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.11447242","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.11447242","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11447242","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.15082284","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.15082284","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15082284","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.14652557","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.14652557","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.14652557","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.5281/zenodo.12795334","name":"vantage6","source":"datacite","abstract":"Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is completely open source under the Apache License. What vantage6 does: delivering algorithms to data stations and collecting their results managing users, organizations, collaborations, computation tasks and their results providing control (security) at the data-stations to their owners The vantage6 infrastructure is designed with three fundamental functional aspects of federated learning. Autonomy. All involved parties should remain independent and autonomous. Heterogeneity. Parties should be allowed to have differences in hardware and operating systems. Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.","url":"https://doi.org/10.5281/zenodo.12795334","authors":["Martin, Frank","Beusekom, Bart","Leurs, Richard","Sieswerda, Melle","Soest, Johan","Alradhi, Hasan","Moncada-Torres, Arturo","Baccinelli, Walter","Smits, Djura","Sanchez Gomez, Luis","Harms, Alexander"],"tags":["federated learning","machine learning","data analysis","secure multiparty computation","privacy enhancing technology","personal health train"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.12795334","addedAt":"2026-08-31T06:41:29.561Z","updatedAt":"2026-08-31T06:41:29.561Z"},{"id":"doi:10.2139/ssrn.6224023","name":"Harmonizing Spectral Style and Semantic Prior for Generalizable Federated Learning","source":"crossref","abstract":"Federated domain generalization (FedDG) aims to learn a global model from multiple decentralized source domains that can generalize to unseen domains under privacy constraints. A prevailing line of FedDG research attempts to improve generalization by diversifying local samples via frequency- or feature-level augmentation. However, these approaches are semantic-absent, as they share style statistics across clients without considering semantic correspondence, which may result in semantic distortion and ultimately limit generalization performance. To tackle this issue, we propose Prototype-Coupled Spectral–Semantic Learning (PSSL), a novel FedDG framework that unifies spectral style with semantic priors to ensure semantically consistent cross-client data diversification. Specifically, PSSL constructs a global prototype repository that associates class-level spectral style statistics extracted from decentralized clients with their corresponding semantics. Leveraging this repository, a semantic-guided spectral expansion strategy is devised to retrieve and inject cross-client style cues based on semantic affinity, thereby enriching local data distributions while preserving semantic integrity. Moreover, a hierarchical semantic alignment scheme is introduced to refine client-side optimization. It decomposes local training into two complementary phases: a client-specific adaptation phase that establishes stable semantic anchors using original data, followed by a cross-client refinement phase that leverages augmented samples together with a semantic consistency constraint to suppress domain-specific style bias. Through this sequential optimization, PSSL progressively steers the learned representations from client-specific fitting toward domain-invariant generalization. Extensive experiments on three widely used domain generalization benchmarks demonstrate the effectiveness of our method in adapting to previously unseen domains.","url":"https://doi.org/10.2139/ssrn.6224023","authors":["Yue Yang","Fan Ma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-12T08:37:06Z","doi":"10.2139/ssrn.6224023","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.6326358","name":"A Hybrid Homomorphic Federated Learning Framework with Adaptive Aggregation for Image Classification","source":"crossref","abstract":"Federated learning enables collaborative machine learning while preserving data privacy, yet existing approaches struggle to simultaneously achieve privacy protection, computational efficiency, and model accuracy. We propose HHFL-AA (Hybrid Homomorphic Federated Learning with Adaptive Aggregation), a novel framework that strategically integrates homomorphic encryption, differential privacy, and blockchain technology. HHFL-AA employs a two-stage training methodology: FedAvg on encrypted data subsets for rapid global model initialization, followed by FedSGD with gradient encryption for enhanced convergence. The framework incorporates adaptive weighting mechanisms and blockchain infrastructure for optimized aggregation and decentralized trust management. Comprehensive experiments on six image classification datasets demonstrate HHFL-AA's superiority, achieving 1.62% accuracy improvement over FedAvg and maintaining computational efficiency with training time reductions compared to fully homomorphic methods. HHFL-AA effectively resolves the privacy-utilityefficiency trilemma, providing a practical solution for secure collaborative learning in distributed environments.","url":"https://doi.org/10.2139/ssrn.6326358","authors":["Anan Shi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-21T16:33:54Z","doi":"10.2139/ssrn.6326358","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.6067026","name":"Middleware Development for Secure Federated Learning in Healthcare Environments","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.6067026","authors":["Silvia Lorero","Mario Muñoz Organero"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-26T16:39:18Z","doi":"10.2139/ssrn.6067026","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1201/9781003660330-10","name":"Quantum threats and federated AI","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003660330-10","authors":["R. Shyam","C. V. Swetha","J. Jesupriya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T15:39:21Z","doi":"10.1201/9781003660330-10","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch016","name":"Leveraging Secure Federated Learning for Data-Driven Business Decisions","source":"crossref","abstract":"In the age of data-based business, organizations depend more and more on wide analysis to inform strategic decisions. However, the growing concerns around data privacy, regulatory compliance, and inter-cooperation are significant challenges. This chapter explores how secure federated learning (SFL) provides a transformative approach to business strategy by enabling the training of machine learning models in collaboration with multiple entities without sharing sensitive data. We examine the underlying principles of federated education, differential privacy and encryption, and practical architectures for entertainment deployment. Through real-world examples, this chapter explains SFL.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch016","authors":["Udit Mamodiya","Randhir Singh Baghel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch016","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.5772/intechopen.1015843","name":"Federated Learning for Privacy-Preserving Next-Generation AI: Architectures, Security and Digital Transformation","source":"crossref","abstract":"Industrial systems and the world of Internet of Things, concerns about data privacy, security and regulatory compliance have grown, when artificial intelligence has been rapidly expanding into healthcare. This chapter presents a comprehensive review of existing literature on federated learning. Federated learning keeps personal data private even as models learn together, though uneven data distribution can trip things up. Communication demands grow heavy when devices share updates across networks. Performance gains come at a cost – scaling such systems widely exposes weak points. Each node carries part of the burden, yet coordination remains messy. Unequal device capabilities further complicate smooth operation. What works in theory often stumbles in real-world rollout. What comes out of this effort shows how federated learning makes it possible to use AI safely in health care, connected devices, and factories – without moving sensitive data around. It lines up with legal rules on data handling, working quietly under systems where information stays local. Each area keeps control, yet still contributes to smarter models through shared learning steps. Federated learning changes how artificial intelligence works by spreading it across many devices instead of centralizing data. Still, its future relies heavily on better ways to protect privacy that can adjust automatically. Progress must happen in how devices share information without slowing down too much. Strong rules and oversight will matter just as much as the tech itself. Without careful management, growth could lead to breakdowns or misuse. How well these pieces come together shapes whether the system lasts.","url":"https://doi.org/10.5772/intechopen.1015843","authors":["Sultan Ahmad","Oroos Zohra","Sarah Raza","Mohammed Alimul Haque","Shagufta Praveen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-03T13:27:32Z","doi":"10.5772/intechopen.1015843","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.6350466","name":"Social Federated Learning (SFL): Leveraging Shared Data to Boost Learning Performance","source":"crossref","abstract":"Collaboration between edge devices has the potential to scale up machine learning (ML) by enabling access to unprecedented amounts of data. Federated learning (FL) is a collaborative algorithm in which clients learn from each other without sharing private data. However, edge devices tend to have different data distributions because they are naturally exposed to different data sources. This heterogeneity, also known as non-independent and identically distributed (non-IID) data, has been shown to decrease the accuracy of FL. We study how limited data sharing among users can alleviate this performance degradation. Such sharing can occur naturally on a social graph or be incentivized by the platform. We evaluated the performance gains of data sharing on MNIST, CIFAR-10, and CIFAR-100 across topologies including complete graphs, clusters, and stochastic block models. We empirically demonstrate that modest data sharing between neighbors on a social graph enhances learning performance in the non-IID case. Interestingly, we found that data sharing could also improve performance in the IID case. By normalizing the dataset sizes, we verified that this boost is significant even if sharing does not increase the number of data points per client. Therefore, data sharing is an efficient technique for improving social federated learning (SFL).","url":"https://doi.org/10.2139/ssrn.6350466","authors":["Mahran Jazi","Ilai Bistritz","Nicholas Bambos","Irad Ben-Gal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-05T15:43:30Z","doi":"10.2139/ssrn.6350466","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.5455/jjcit.71-1767004702","name":"A Scalable Federated Deep Reinforcement Learning Architecture for Collaborative Learning","source":"crossref","abstract":"Federated Learning enables collaborative model training without sharing raw data, while Deep Reinforcement Learning provides powerful mechanisms for sequential decision-making. However, their integration suffers from limited scalability, sensitivity to non-IID data, and unstable convergence in distributed environments. This paper proposes a Scalable Federated Deep Reinforcement Learning (SFDRL) architecture in which distributed agents learn local policies and periodically contribute to a global model via an adaptive, performance-aware aggregation strategy. Unlike conventional FedRL methods that rely on uniform averaging, SFDRL weights local updates according to their learning effectiveness, resulting in faster convergence and improved stability under heterogeneous data distributions. In addition, a selective communication mechanism is introduced to reduce communication overhead by up to 28% and 64% compared with FedAvg and FedRL, respectively. Extensive experiments demonstrate that SFDRL outperforms compared methods, achieving higher cumulative rewards, reduced variance during training, and improved scalability in large-scale distributed settings. These results confirm the suitability of SFDRL for practical deployment in distributed intelligent systems.","url":"https://doi.org/10.5455/jjcit.71-1767004702","authors":["Tarek Haddad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-27T23:45:23Z","doi":"10.5455/jjcit.71-1767004702","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/flics70075.2026.11621910","name":"Federated Temporal RAG for Privacy-Preserving Real-Time Threat Intelligence in Distributed Remote Patient Monitoring","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621910","authors":["Mohammad Zahangir Alam","Elahan Ayath"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:29Z","doi":"10.1109/flics70075.2026.11621910","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch002","name":"Overview of Adversarial AI and Data Poisoning in Federated Learning","source":"crossref","abstract":"Federated Learning (FL) is an emerging decentralized machine learning paradigm that enables multiple clients to collaboratively train a global model without sharing raw data, thereby preserving data privacy. . In adversarial settings, malicious clients can inject carefully crafted inputs or manipulate local training updates to degrade the global model's performance or embed backdoors. Data poisoning attacks, including label flipping and model update manipulation, pose significant threats by subtly corrupting training data or gradients, often bypassing conventional anomaly detection methods. This paper reviews the taxonomy, mechanisms, and impacts of adversarial and poisoning attacks in FL environments, and explores current defense strategies such as robust aggregation, anomaly detection, differential privacy, and blockchain integration. Understanding these threats is crucial to developing secure and reliable FL systems for real-world applications in healthcare, finance, and IoT.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch002","authors":["Mohammed Firdos Alam Sheikh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch002","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.6555812","name":"AirComp-Coordinated Q-Learning-Based Wireless Federated Learning for Household Load Forecasting","source":"crossref","abstract":"Federated learning enables privacy-preserving short-term household load forecasting by keeping fine-grained electricity consumption data on local devices. However, its deployment over wireless networks is constrained by communication overhead, time-varying channel conditions, and client heterogeneity, particularly the scarcity and uneven distribution of peak-period samples. This paper proposes a wireless federated learning framework coordinated by over-the-air computation and Q-learning for peak-aware household load forecasting. First, a peak-aware data construction strategy together with a risk-sensitive evaluation scheme is introduced to explicitly characterize peak underestimation and peak-interval reliability. Second, to accommodate link dynamics and imbalance in peak-related informativeness, a Q-learning-guided adaptive weighting mechanism is developed to adapt client participation and aggregation-weight concentration according to channel quality and peak-related indicators. Third, weighted model aggregation is achieved through synchronous uplink signal superposition, thereby reducing uplink transmission burden and communication latency. Experiments on smart-meter datasets under time-varying wireless channels show that, compared with conventional federated learning baselines, the proposed method achieves more stable convergence, improves prediction accuracy during peak periods, reduces underestimation risk, and maintains competitive overall forecasting performance.","url":"https://doi.org/10.2139/ssrn.6555812","authors":["Zhangyan Ju","zhenping chen","Yihong Zhou","You Lu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-10T15:44:53Z","doi":"10.2139/ssrn.6555812","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/flics70075.2026.11621951","name":"Federated Forecasting of Urban Traffic Congestion with Graph Neural Network and Attention-Based Recurrent Neural Network","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621951","authors":["Javier Rodrigues","Sukhjit Singh Sehra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:12:32Z","doi":"10.1109/flics70075.2026.11621951","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/flics70075.2026.11621887","name":"Federated Imputation under Heterogeneous Feature Spaces","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621887","authors":["Imane Hocine","Chaimaa Medjadji","Sylvain Kubler","Grégoire Danoy","Yves Le Traon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:44Z","doi":"10.1109/flics70075.2026.11621887","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.64643/ijirtv12i12-201491-459","name":"Deep Learning and Federated Learning in breast cancer screening","source":"crossref","abstract":"","url":"https://doi.org/10.64643/ijirtv12i12-201491-459","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-11T11:20:16Z","doi":"10.64643/ijirtv12i12-201491-459","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-031-99447-0_17","name":"Defense Strategies in Federated Learning Against Adversarial Attacks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-99447-0_17","authors":["Hadiseh Rezaei","Rahim Taheri","Ehsan Nowroozi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-21T14:48:30Z","doi":"10.1007/978-3-031-99447-0_17","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.70593/978-93-7185-127-5","name":"Blockchain and Federated Learning for Next-Generation IoT Security","source":"crossref","abstract":"","url":"https://doi.org/10.70593/978-93-7185-127-5","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-03T20:52:58Z","doi":"10.70593/978-93-7185-127-5","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.36227/techrxiv.176823051.17410568/v1","name":"QGuardian: A Federated Learning Framework for Quantum-Resilient Anomaly Detection in Cybersecurity","source":"crossref","abstract":"Current cybersecurity systems face two critical challenges: the impending threat of quantum computers breaking classical cryptographic algorithms, and the limitations of reactive, centralized security architectures. We propose QGuardian, a federated learning framework that combines privacy-preserving distributed machine learning, post-quantum cryptography, and blockchain-based verification for predictive anomaly detection in network security. QGuardian enables multiple distributed nodes to collaboratively train anomaly detection models without sharing raw network traffic data, while providing quantum-resistant encryption and maintaining an immutable audit trail through a blockchain ledger. We implement and evaluate the system using TensorFlow Federated for distributed learning, with post-quantum cryptographic modules (Kyber KEM) and a blockchain-based event logging system. Experimental evaluation on both synthetic data and the UNSW-NB15 real-world dataset (82,332 samples, 39 features) establishes feasibility of the integrated approach. On UNSW-NB15, federated training achieves 47.2% loss reduction over 15 rounds, with detection performance of 56.7% precision, 21.1% recall, and 30.7% F1-score. Real Kyber PQC benchmarking shows acceptable overhead (6-22× for key operations, sub-millisecond latency). The system integrates all three layers, with comprehensive evaluation frameworks for real-world datasets, Byzantine robustness (26.68× improvement shown), and performance benchmarking. This work contributes an integrated architecture and comprehensive evaluation establishing a baseline for quantumresilient, privacy-preserving cybersecurity systems that can operate in distributed environments.","url":"https://doi.org/10.36227/techrxiv.176823051.17410568/v1","authors":["Ahaan Thota","Saketh Tammisetti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-12T15:08:42Z","doi":"10.36227/techrxiv.176823051.17410568/v1","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-032-03985-9_39","name":"Reducing Computational Overhead in Federated Learning: A Comprehensive Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_39","authors":["Indraneel Mukhopadhyay","Debarpita Santra","Bannishikha Banerjee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:35Z","doi":"10.1007/978-3-032-03985-9_39","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1002/9781394461295.ch7","name":"Federated Learning in Agriculture","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.ch7","authors":["Mude Nagarjuna Naik","R. Sriramkumar","Joshuva Arockia Dhanraj","M. Lakshmanan","A. Vegi Fernando","Mitha Guru","Sugandha Saxena"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.ch7","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.5995854","name":"Decentralized Threat Intelligence Fusion using Federated Learning in Smart Agriculture and Smart Grids","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5995854","authors":["Phillip Ben"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-22T17:13:04Z","doi":"10.2139/ssrn.5995854","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.7304285","name":"Privacy-Preserving Federated Learning for Diabetes Risk Prediction Across Demographically Heterogeneous Hospital Nodes","source":"crossref","abstract":"Background and Objective: Centralised machine learning for diabetes risk prediction conflicts with patient privacy regulations and produces models with poor demographic equity. We develop and evaluate a privacy-preserving federated learning (FL) framework for diabetes risk prediction across three demographically heterogeneous simulated hospital nodes, without centralising patient data.Methods: National Health and Nutrition Examination Survey (NHANES) 2013–2020 data (n = 15,650) were stratified into three nodes (young urban, elderly rural, mixed metropolitan). Four FL strategies—FedAvg, FedProx, FedNova, and SCAFFOLD—were trained over 50 rounds. External validation used the Behavioral Risk Factor Surveillance System (BRFSS) 2020–2022 (n = 1,282,897). Demographic fairness was assessed as the age-stratified area under the receiver operating characteristic curve (AUC) gap. Post-hoc calibration and differentially private stochastic gradient descent (DP-SGD, ε ∈ {0.5–∞}) were evaluated. A matched centralised DiabetesNet was trained to isolate federation effects.Results: FedAvg achieved the highest external AUC of 0.757 [95% confidence interval (CI): 0.756–0.758], exceeding the centralised XGBoost baseline (AUC 0.700) by 0.057. FedAvg reduced the generalisation gap (internal minus external AUC) by 40% versus the matched centralised architecture (0.031 vs. 0.052). The elderly fairness gap fell from 0.069 (centralised baseline) to 0.054 under FedAvg, a 21.7% within-study improvement. Isotonic recalibration reduced the expected calibration error (ECE) from 0.276 to 0.001. DP-SGD caused model collapse at ε ≤ 5; utility recovered at ε = 10 (AUC = 0.769), characterised as the utility floor for the per-node sample sizes used (n ≈ 3,000–4,500).Conclusions: FL improves generalisation, demographic equity, and calibration for diabetes risk prediction while preserving patient privacy. FedAvg achieves state-of-the-art external discrimination on 1.28 million respondents with near-perfect post-calibration, providing a validated proof-of-concept for privacy-preserving population-level diabetes screening.","url":"https://doi.org/10.2139/ssrn.7304285","authors":["Rajveer Pall"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-23T22:22:08Z","doi":"10.2139/ssrn.7304285","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00019-4","name":"Introduction to metaverse healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00019-4","authors":["Neha Gupta","Kavita Arora","Sailesh Suryanarayan Iyer"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00019-4","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/j.neucom.2026.134128","name":"Fed-GCE: Federated learning with global constraints enforcement","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.neucom.2026.134128","authors":["Koffka Khan","Wayne Goodridge"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-01T15:25:02Z","doi":"10.1016/j.neucom.2026.134128","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/icc59461.2026.11587985","name":"Byzantine Resilient Federated Multi-Task Representation Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc59461.2026.11587985","authors":["Tuan Le","Shana Moothedath"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T19:38:09Z","doi":"10.1109/icc59461.2026.11587985","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1201/9781003660330-7","name":"Securing digital payments and transactions using federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003660330-7","authors":["Vikram Singh","Yashasvi Makin","Srikanth Yerra","Ravi Prakash Chaturvedi","Annu Mishra","Rajneesh Kumar Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T15:39:21Z","doi":"10.1201/9781003660330-7","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/mdm71479.2026.00066","name":"Federated Learning Systems for Mobile Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mdm71479.2026.00066","authors":["Xiaopeng Jiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-31T18:12:10Z","doi":"10.1109/mdm71479.2026.00066","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1117/12.3121319","name":"Federated learning-enhanced digital twin for intelligent warehouse management and demand forecasting","source":"crossref","abstract":"","url":"https://doi.org/10.1117/12.3121319","authors":["Yuqi Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-14T14:55:36Z","doi":"10.1117/12.3121319","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-032-03985-9_40","name":"Future Trends in Federated Learning: Enabling Secure and Personalized Healthcare Solutions","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_40","authors":["Randhir Singh Baghel","Udit Mamodiya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:58Z","doi":"10.1007/978-3-032-03985-9_40","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-032-03985-9_38","name":"Neuro-Symbolic Federated Learning Models for Diagnostic Intelligence in Healthcare 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_38","authors":["Indra Kishor","Udit Mamodiya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:56Z","doi":"10.1007/978-3-032-03985-9_38","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/icbdml68582.2026.11597924","name":"Heterogeneity-Aware Federated Learning: From FedAvg to Multi-Task Personalization","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icbdml68582.2026.11597924","authors":["Lakshmi Rangayya Naidu Kandulapati"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-10T19:36:45Z","doi":"10.1109/icbdml68582.2026.11597924","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1002/9781394461295.ch15","name":"Proposing a Federated Learning Policy Framework for Smart, Secure, and Sustainable Agricultural Supply Chains","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.ch15","authors":["Joshuva Arockia Dhanraj","M. Lakshmanan","A. Vegi Fernando","Mitha Guru","Sugandha Saxena","Mude Nagarjuna Naik","R. Sriramkumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.ch15","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-032-03985-9_33","name":"Advancing Federated Learning in Healthcare 5.0—A Futuristic Pathway in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_33","authors":["Geetha Manoharan","Sanjeev Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:44Z","doi":"10.1007/978-3-032-03985-9_33","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.65649/a5k5rw30","name":"Federated Clinical Learning Cooperative (FCLC)","source":"openalex","abstract":"Background: Developing robust clinical artificial intelligence (AI) models requires large, diverse datasets that individual institutions cannot provide due to privacy regulations (GDPR, HIPAA) and institutional risk aversion. Existing federated learning (FL) platforms lack validated combinations of differential privacy, Byzantine robustness, fair contribution attribution, and secure aggregation. Objective: We present the Federated Clinical Learning Cooperative (FCLC), an open-source platform enabling multi-institutional clinical AI development without raw data leaving participating sites. Methods: FCLC implements a data preprocessing pipeline (Layers 1-3: direct identifier removal, quasi-identifier generalization, k-anonymity with k≥5) combined with differential privacy (Layer 4: DP-SGD, ε=2.0/round, δ=10⁻⁵, Rényi accountant α=4.0) and secure aggregation (Layer 5: SecAgg+ via CommonHealth). Validation used MIMIC-IV (N=12,543, 30-day readmission) and eICU-CRD (N=8,420, sepsis mortality) across IID and non-IID partitions (Dirichlet α ∈ {∞, 1.0, 0.5, 0.1, 0.01}) with logistic regression and multilayer perceptron architectures. Results: On MIMIC-IV, FCLC achieved AUC=0.758 [95% CI: 0.739–0.777] compared to centralized oracle 0.789 (Δ = −3.9%). Under severe non-IID conditions (Dirichlet α=0.1, EMD=0.31), FCLC preserved AUC=0.748 while FedAvg degraded to 0.694 (p_adj=0.003). Membership inference attack AUC with DP was 0.52±0.03 (indistinguishable from chance, p=0.31 vs. 0.50). At ΔAUC=0.03 — the minimum clinically important difference (MCID) corresponding to preventing approximately one readmission per 100 patients (NNT≈100) — the study had power &gt;0.99. Conclusions: FCLC provides a validated, regulation-compliant infrastructure for federated clinical AI with full cryptographic privacy guarantees suitable for mutual-distrust deployments. The platform is open-source (Apache 2.0) and fully reproducible via Docker.","url":"https://doi.org/10.65649/a5k5rw30","authors":["Jaba Tkemaladze"],"tags":["Computer science","Differential privacy","Federated learning","Preprocessor","Oracle"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2026-04-26","doi":"10.65649/a5k5rw30","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.2139/ssrn.6910762","name":"Federated Representation Learning-Empowered Deep Reinforcement Learning for Cloud-Edge Collaborative Heterogeneous Job Shop Scheduling","source":"crossref","abstract":"Dynamic job shop scheduling in cloud–edge collaborative manufacturing is challenged by heterogeneous factory configurations, dynamic disturbances, and privacy constraints. Conventional federated reinforcement learning methods are difficult to apply directly because heterogeneous factories usually have inconsistent state distributions and non-shareable action spaces. To address this issue, this study proposes a decoupled federated representation reinforcement learning framework, termed FRL-PPO, for heterogeneous dynamic job shop scheduling. The core idea is to share only a public feature encoder across edge factories, while keeping the Actor and Critic networks locally personalized. In this way, transferable scheduling representations can be learned without exchanging raw production data or enforcing a unified action space. The proposed framework is evaluated on an existing cloud–edge dynamic scheduling benchmark with six heterogeneous factories and dynamic disturbance rates of 1%–3%. All experimental results are obtained by independently implementing and running the proposed FRL-PPO and the comparison methods under the same benchmark setting. Compared with PPO, FavePPO, FaveDQN, and other representative dispatching-rule or DRL baselines, FRL-PPO achieves consistently lower joint objective values. The improvement reaches approximately 2%–3% under low disturbance rates and more than 7% under highly dynamic conditions. Moreover, FRL-PPO provides better makespan–energy trade-offs, reduces machine idle time, and shows more stable training behavior. These results indicate that the shared-representation and personalized-decision paradigm is effective for privacy-preserving and scalable scheduling in heterogeneous cloud–edge manufacturing systems.","url":"https://doi.org/10.2139/ssrn.6910762","authors":["Jianguo Duan","Fangrong Chen","Qinglei Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-10T06:43:08Z","doi":"10.2139/ssrn.6910762","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1201/9781003595540-6","name":"Adaptive Energy Management in Healthcare IoT Devices Using Federated Intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540-6","authors":["S. K. Susee","M. Senthil Kumar","B. Chidhambararajan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","doi":"10.1201/9781003595540-6","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.6431225","name":"Communication- and Inference-Efficient Federated Learning for Polyp Segmentation","source":"crossref","abstract":"Background and ObjectiveAutomatic polyp segmentation in colonoscopy images plays an important role in computer-aided diagnosis and early detection of colorectal cancer. Most deep learning approaches rely on centralized training, which requires sharing medical data across hospitals and raises privacy concerns. Federated learning enables collaborative model training without transferring raw patient data; however, repeated transmission of high-precision model parameters introduces significant communication overhead and limits deployment efficiency. This study proposes a communication- and inference-efficient federated learning framework for collaborative polyp segmentation.MethodsThe proposed framework integrates quantization-aware training with low-precision model communication within a federated learning setting. Each hospital locally trains a lightweight U-Net segmentation model on its private dataset while simulating quantization effects during training. Instead of transmitting full-precision parameters, clients send quantized model updates to a central server, where they are reconstructed and aggregated using the Federated Averaging algorithm. The framework is evaluated on four publicly available colonoscopy datasets: Kvasir-SEG, CVC-ClinicVideoDB, PolypGen, and BKAI-IGH NeoPolyp.ResultsExperimental results show that the proposed method maintains competitive segmentation performance while significantly reducing communication cost. Under full-precision federated training, the model achieves Dice scores of 0.910 on Kvasir-SEG and 0.930 on CVC-ClinicVideoDB. Using uniform 8-bit communication reduces transmission cost by approximately 4× while maintaining comparable segmentation accuracy. In addition, quantized models improve deployment efficiency, achieving up to 1.5× faster inference compared with full-precision models. Conclusions​The results demonstrate that integrating quantization-aware training with federated learning enables privacy-preserving, communication-efficient, and inference-efficient medical image segmentation. The proposed framework facilitates collaborative training across multiple hospitals while producing lightweight models suitable for real-time clinical deployment.","url":"https://doi.org/10.2139/ssrn.6431225","authors":["Madan Baduwal","Priyanka Paudel","Tilak Neupane"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-19T17:48:57Z","doi":"10.2139/ssrn.6431225","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.7716/aem.v15i3.3572","name":"Constructing a Personalized Learning Path Recommendation Model Based on Federated Learning","source":"crossref","abstract":"To address the conflict between model generalization and personalized recommendation in distributed educational environments, this paper constructs a personalized learning path recommendation model based on federated learning. Such privacy-preserving collaborative learning frameworks are also valuable for intelligent information processing and distributed decision-making in modern electromagnetic communication and edge computing systems, where data sharing is often restricted. The proposed model employs a Graph Neural Network (GNN)-Transformer hybrid encoder that combines knowledge graphs with learning behavior sequences to accurately capture knowledge transfer relationships. A dynamic knowledge distillation aggregation strategy is introduced to generate soft labels from the global model for guiding local optimization, thereby preserving personalized characteristics while improving semantic consistency. Furthermore, an adaptive aggregation mechanism based on Kullback-Leibler (KL) divergence dynamically adjusts client weights to enhance robustness under heterogeneous data distributions. Experimental results demonstrate that the proposed method achieves excellent recommendation accuracy (average Hit@5 of 0.676 ± 0.009), sequence consistency (average NDCG@10 of 0.712 ± 0.008), and personalized responsiveness (average personalized score difference rate of 0. 38). The framework effectively balances global generalization and local adaptation while maintaining privacy protection, providing a feasible solution for secure and intelligent recommendation in distributed learning environments and offering technical insights for collaborative intelligence in privacy-sensitive electromagnetic information systems.","url":"https://doi.org/10.7716/aem.v15i3.3572","authors":["J. Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-14T10:12:43Z","doi":"10.7716/aem.v15i3.3572","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch003","name":"Architectural Models and Security Considerations for Federated Learning","source":"crossref","abstract":"The concept of Federated Learning (FL) has developed of centralized model averaging to a multi-faceted ecosystem of architectures, such as cross-device, cross-silo, hierarchical, and decentralized systems. Although the models allow collaboration of intelligence without direct data sharing, they also reveal vulnerabilities, which are caused by non-IID data, incomplete participation, and adversarial manipulation. This chapter offers a theoretical overview of architectural models and their related security aspects in FL. It follows the history of FL and describes key aggregation paradigms, as well as trade-offs between robustness, privacy, and efficiency. The comparative insights have been provided in terms of statistical, trust-based, spectral, inversion-guided, and cryptographic aggregation strategies keeping in consideration their flexibility in heterogeneous and adversarial settings. The chapter ends by hybrid and privacy-protecting models that combine secure computation, blockchain-based trust, and ethical governance to support scalable, responsible federated ecosystems.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch003","authors":["Rachana Yogesh Patil","Yogesh H. Patil","Ozen Ozer"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch003","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.71443/9789349552036-13","name":"Federated Learning and Data Privacy in Connected Healthcare Devices","source":"crossref","abstract":"Federated learning has emerged as a transformative paradigm for secure and intelligent healthcare systems, enabling collaborative model training without centralized data aggregation. This book chapter explores the architectural foundations, privacy-preserving techniques, and real-world applications of federated learning in connected healthcare environments. The discussion emphasizes the integration of decentralized artificial intelligence with Internet of Medical Things (IoMT) devices, facilitating clinical decision support, real-time health prediction, and continuous patient monitoring while maintaining strict compliance with data protection regulations. The chapter examines communication frameworks, model update mechanisms, and scalable system architectures that ensure interoperability across diverse healthcare infrastructures. Security challenges such as data poisoning, inference attacks, and model inversion are analyzed in conjunction with robust defense mechanisms including differential privacy, secure multi-party computation, and homomorphic encryption. Through an in-depth examination of federated learning’s role in privacy-preserving analytics, this work highlights its potential to revolutionize precision medicine, telehealth, and patient-centric digital ecosystems. The synthesis of distributed intelligence and ethical AI practices positions federated learning as a cornerstone technology for the future of connected and trustworthy healthcare innovation.","url":"https://doi.org/10.71443/9789349552036-13","authors":["A Thanikasalam","S Bharathi","Amit Kumar Bhakta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T12:38:32Z","doi":"10.71443/9789349552036-13","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/datascimi67380.2026.11523793","name":"Analyzing Adversarial Attacks on Federated Learning based Medical Image Classification","source":"crossref","abstract":"","url":"https://doi.org/10.1109/datascimi67380.2026.11523793","authors":["Soomaiya Hamid","Narmeen Zakaria Bawany"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-21T19:40:47Z","doi":"10.1109/datascimi67380.2026.11523793","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1002/9781394347124.ch16","name":"Ethical and Technical Foundations of Privacy‐Preserving Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394347124.ch16","authors":["Muhammad Rifthy Kalideen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-26T23:10:18Z","doi":"10.1002/9781394347124.ch16","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/i5cps67958.2026.11452524","name":"Addressing Non-IID Challenges in Federated Learning with Transfer Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/i5cps67958.2026.11452524","authors":["Rakshavi Dessai","Pravati Swain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-30T20:03:33Z","doi":"10.1109/i5cps67958.2026.11452524","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/icdsbs69077.2026.11634860","name":"Federated Learning Computational Cost Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icdsbs69077.2026.11634860","authors":["Ponmalar Ramanathan","Rashmi Welekar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-10T19:12:50Z","doi":"10.1109/icdsbs69077.2026.11634860","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch008","name":"Federated Adversarial Transfer Learning","source":"crossref","abstract":"Federated Learning (FL) allows multiple parties to train a model on their local data source without exchanging raw data. When combined with FL, it provides a strong ability involving knowledge transfer from similar tasks with small data. This chapter reviews the ever-emerging field of Federated Adversarial Transfer Learning (FATL), investigating its possible applications and explaining some of its intrinsic weaknesses. We will study the effects of adversarial examples, model poisoning and backdoor attacks on the transfer learning dynamics in federated environments. We conduct a thorough literature review, outline threat modeling of the FATL scenarios. In summary, our main observations show that FATL boosts generalization and convergence speed, but also exterior dangers when the source models or datasets are not trusted. In addition, traditional defense methods in FL are usually ineffective in the presence of adversarial transfer. In conclusion, FATL provides significant advantages for decentralized AI systems, but with those advantages comes an entirely new threat.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch008","authors":["Sanjeev Kumar","Geeta Tiwari","Laxmikant Sagar","Muhammad Attique Khan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch008","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch006","name":"Poisoning Resilient Federated Learning Under Privacy Constraints","source":"crossref","abstract":"Federated learning enables multiple clients to collaboratively train models without exchanging raw data; however, the system remains susceptible to adversarial attacks through maliciously crafted model updates. This challenge intensifies as privacy-preserving methods, including identity obfuscation, attribute randomization, synthetic data generation, encrypted aggregation, and secure computation, restrict the ability to discern individual participant contributions. This chapter proposes methods that identify and control poisoning while complying with strict privacy limitations. It describes the problem, realistic threat and privacy models, and a four-layer defense structure including input sanitation, update filtering, privacy-sensitive robust aggregation, and post-aggregation audits. An experimental plan tests effectiveness in heterogeneous data and high-level privacy environments. The chapter fills a significant gap by reconciling privacy and robustness and will be valuable to graduate students and researchers studying secure federated learning.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch006","authors":["Kummagoori Bharath","Pooja Chopra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch006","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/icsmile69273.2026.11519162","name":"Federated Learning-Based Personalized Music Recommendation System with Edge Caching in Cloud-Integrated Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsmile69273.2026.11519162","authors":["Hemanta Ghosh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-20T19:49:13Z","doi":"10.1109/icsmile69273.2026.11519162","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/cvidl70130.2026.11637890","name":"FedRPT: a Dual-Layer Federated Learning Framework for Copyright Protection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cvidl70130.2026.11637890","authors":["Haorui Wang","Guorui Feng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-17T19:15:53Z","doi":"10.1109/cvidl70130.2026.11637890","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/ictp67998.2026.11485034","name":"Detecting Malicious Clients in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ictp67998.2026.11485034","authors":["Saikat Sinha Ray","Karthikeyan Periyasami"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-23T19:57:35Z","doi":"10.1109/ictp67998.2026.11485034","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/wccis70285.2026.11650937","name":"Decentralized and Secure Aggregation in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wccis70285.2026.11650937","authors":["Jiaming Fang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-25T19:18:40Z","doi":"10.1109/wccis70285.2026.11650937","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/flics70075.2026.11621895","name":"Swarm Learning: A Comprehensive Overview and Its Potential in Cybersecurity","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621895","authors":["Diogo Oliveira","Apekshas Kafle"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:09:35Z","doi":"10.1109/flics70075.2026.11621895","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/icsadl67539.2026.11451872","name":"FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsadl67539.2026.11451872","authors":["Dhanush Gopal Battina"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T19:49:17Z","doi":"10.1109/icsadl67539.2026.11451872","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/s10994-026-07102-1","name":"Towards Adaptive and Communication-Efficient Dynamic Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10994-026-07102-1","authors":["Shunxin Guo","Jiaqi Lv","Qiufeng Wang","Xin Geng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-02T16:48:01Z","doi":"10.1007/s10994-026-07102-1","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-032-03985-9_37","name":"Federated Learning and Healthcare 5.0: Paving the Road Ahead for Privacy-Preserving Smart Health Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_37","authors":["Wasswa Shafik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:44Z","doi":"10.1007/978-3-032-03985-9_37","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.6824478","name":"Vertical Federated Learning in Medical AI: Concepts, Advances, and Challenges","source":"crossref","abstract":"The growing need for data-driven medical intelligence is constrained by strict privacy regulations and fragmented healthcare data distributed across multiple institutions. Vertical Federated Learning (VFL) offers a secure solution by enabling collaborative model training on feature-partitioned datasets, where different organizations hold complementary information about the same patients. This review presents a comprehensive overview of VFL with a dedicated focus on healthcare applications. We explore the fundamental principles, training protocols, and key design challenges of VFL, followed by a detailed review of recent advancements in communication efficiency, model performance, privacy preservation, and fairness. To unify these perspectives, we introduce the Federated Optimization Framework (VFLow), a conceptual framework that captures the optimization of trade-offs across privacy, utility, efficiency, and equity. We further examine real-world applications in disease prediction, diagnostics, clinical trials, personalized treatment, and public health. Unlike previous surveys, this work provides a domain-specific synthesis tailored to the practical constraints and ethical considerations of medical AI. Finally, we outline open research challenges and future directions to guide the development of trustworthy and scalable VFL systems for healthcare.","url":"https://doi.org/10.2139/ssrn.6824478","authors":["Muhammad Yaqub","Degang Xu","Lan He"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T13:36:57Z","doi":"10.2139/ssrn.6824478","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1201/9781003595540","name":"Federated Learning for Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540","authors":["R. Anandan","Souvik Pal","D. Balaganesh","Farshad Badie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","doi":"10.1201/9781003595540","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.6371118","name":"Federated Explainable Deep Learning Framework for Zero-Day Attack Detection in Encrypted Traffic Environments","source":"crossref","abstract":"The rapid proliferation of encrypted network communication has significantly strengthened data privacy, yet it has simultaneously limited the effectiveness of traditional intrusion detection systems. Zero-day attacks, characterized by previously unseen signatures and evolving behavioral patterns, pose a critical challenge to centralized security architectures. Recent advances in federated learning, introduced by McMahan et al., enable decentralized model training without direct data sharing, preserving privacy while improving collaborative intelligence. Simultaneously, the growing demand for trustworthy artificial intelligence highlights the importance of explainability, as emphasized in the theoretical foundations of Explainable AI by researchers such as Ribeiro et al. and Doshi-Velez and Kim.This study proposes a Federated Explainable Deep Learning Framework for detecting zero-day attacks within encrypted traffic environments. The aim is to design a privacy-preserving, adaptive intrusion detection model capable of identifying anomalous traffic patterns without decrypting payload content. The methodology integrates federated deep neural networks with attention-based architectures for encrypted traffic feature extraction, combined with SHAP-based interpretability mechanisms to enhance transparency and trustworthiness. The framework is evaluated using distributed network datasets to measure detection accuracy, F1-score, robustness against adversarial perturbations, and communication efficiency.Findings indicate that the proposed federated model improves zero-day detection performance while maintaining data confidentiality and interpretability across distributed nodes. The study utilizes federated optimization theory and explainability principles to address limitations in centralized and opaque detection systems. This research contributes to the evolving discourse on privacy-preserving AI-driven cybersecurity, making it particularly relevant in an era of encrypted-by-default communication infrastructures.","url":"https://doi.org/10.2139/ssrn.6371118","authors":["Anushrut Ghimire"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-20T15:06:05Z","doi":"10.2139/ssrn.6371118","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.64628/aan.pgn99swqm","name":"AI bisa makin pintar tanpa mengintip data pribadi kita: Mengenal ‘federated learning’","source":"crossref","abstract":"","url":"https://doi.org/10.64628/aan.pgn99swqm","authors":["Rachmad Atmoko"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-19T08:11:25Z","doi":"10.64628/aan.pgn99swqm","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch009","name":"Defense Strategies for Federated Learning Models Targeted by Data Poisoning Attacks","source":"crossref","abstract":"The chapter offers a detailed analysis of the security issues of Federated Learning (FL) and will particularly dwell on data poisoning attacks. It discusses the nature of decentralized architecture of FL, which results in exposing new attack surfaces despite maintaining user privacy. The chapter groups types of data poisoning attacks and provides a resultant taxonomy of defense methods, such as data-level filtering, robust aggregation algorithms, anomaly detection, anything training and privacy-based methods, like differential privacy. Such algorithms as Krum, Bulyan, and Trimmed Mean are examined as well as client reputation systems. Case studies and real-world FL simulations are performed in which the effectiveness of the methods is tested. The difficulties of scalability, tradeoffs between robustness and accuracy, availability of adaptive responses to emerging dangers are also mentioned. This chapter is expected to assist researchers and practitioners toward safe, robust, and trust-guarded federated learning systems.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch009","authors":["Monika Kumari","Nikhil Kumar Goyal","Ayesha Farooqi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch009","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1201/9781003595540-14","name":"Federated Learning for Real-Time Disease Prediction: A Scalable Framework for Personalized Healthcare in Internet of Things-Enabled Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003595540-14","authors":["K. Balamurugan","T.P. Latchoumi","Latha Parthiban","A. Venkateswara","R. Parthiban"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T21:41:08Z","doi":"10.1201/9781003595540-14","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/flics70075.2026.11621914","name":"Recommend Locally, Diversify Globally: FedFlex for Live Federated Netflix Recommendations","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621914","authors":["Sven Lankester","Gustavo de Carvalho Bertoli","Matias Vizcaino","Emma Beauxis-Aussalet","Manel Slokom"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:14:00Z","doi":"10.1109/flics70075.2026.11621914","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.5220/0015078800004103","name":"Federated Learning for Malware Image Classification under Data Heterogeneity","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0015078800004103","authors":["Victor Taiwo","Cemal Nişan","Muhammad Athallah","M. Gürsoy","Öznur Özkasap"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-20T08:00:39Z","doi":"10.5220/0015078800004103","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.7265391","name":"FedPGP: Prototype-Guided Gradient Projection for Non-IID Federated Learning","source":"crossref","abstract":"Federated learning (FL) provides an effective framework for multiple clients to jointly train neural networks without sharing their raw data. It has been widely used in privacy-preserving machine learning and edge intelligence. However, the highly non-independent and identically distributed (non-IID) nature of client data in real-world federated deployments inevitably leads to client drift. Existing methods mainly address this issue through parameter regularization, control variates, or feature constraints. Nevertheless, most of them do not explicitly characterize the directional relationship between local empirical risk gradients and cross-client class knowledge. To address this limitation, we propose FedPGP, a Federated Prototype-Guided Projection method. In the proposed method, class prototypes are exploited to serve as lightweight carriers of class knowledge across clients, which provide compact summaries of the statistical structure of individual classes in feature space. Specifically, FedPGP constructs a global prototype pool by aggregating mixed class prototypes from clients and derives prototype-induced gradients from this pool. It further applies gradient projection as a constraint in local training. When the local empirical risk gradient has a negative inner product with the prototype-induced gradient, FedPGP applies layer-wise projection correction to reduce the degradation of global class knowledge caused by conflicting updates. Theoretical analysis shows that the layer-wise projection is the minimum Euclidean correction that satisfies a first-order prototype-preservation constraint. It preserves prototype knowledge without amplifying the local gradient. Experiments are conducted on MNIST, Fashion-MNIST, SVHN, and CIFAR-10 under several levels of non-IID data heterogeneity. The results show that FedPGP achieves better overall performance than most mainstream FL baselines.","url":"https://doi.org/10.2139/ssrn.7265391","authors":["Chuntong Liu","Jinmei Fan","Yanhai Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-11T18:01:49Z","doi":"10.2139/ssrn.7265391","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.7102486","name":"Dual-LR: Learning Shared Transformation Knowledge through Additive Low-rank knowledge decomposition for Personalized Federated Learning","source":"crossref","abstract":"Personalized Federated Learning (PFL) balances cross-client knowledge exchange and individual model adaptation amid non-uniform client data. Current PFL methods separate global and local knowledge via exclusive client-only personalization modules, yet they wrongly assume all personalized transformations belong solely to single clients. This ignores shared transferable transformation patterns across users, hurting knowledge transfer, bloating model parameters and weakening generalization—worse when clients have limited data. We propose Dual-LR, a new PFL framework built on dual-layer representation separation and low-rank knowledge decomposition. Instead of fully private personal projection layers, it splits them into a universal shared transform plus lightweight client residual adjustments. Architecturally, it uses a common feature encoder and local projection blocks; mathematically, projection matrices are decomposed into additive low-rank terms. The shared transform optimized collectively captures general semantic information, while small residual parameters fit unique client domains. A two-stage optimization further stabilizes global model boundaries and refines personalized feature learning. Tests on cross-domain datasets PACS and Office-Home show Dual-LR surpasses mainstream PFL baselines. It attains top classification accuracy, cuts personalized storage to merely 0.14 MB, speeds up convergence and slashes total communication cost, delivering accurate, communication- and storage-efficient PFL for heterogeneous data.","url":"https://doi.org/10.2139/ssrn.7102486","authors":["Dapeng Yan","Jie Kong","Yongjun Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-13T00:34:29Z","doi":"10.2139/ssrn.7102486","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch018","name":"Collaboration Business Models and Frameworks in Federated Learning","source":"crossref","abstract":"Federated Learning (FL) has enabled decentralised collaboration so that many stakeholders can train models without sharing raw data. However, the distributed nature exposes the system to risks such as data poisoning and unfavourable AI attacks, which compromise the model integrity and the organisational tasks. This chapter checks the outline of collaboration in FL, including platform federated data ecosystems, consortium-based models and cross-industry partnerships. While these structures increase scalability, personalisation and economic value, they also introduce the model updates and weaknesses in gradient manipulation. Discussed solutions include strong aggregation algorithms, blockchain competition, competent traceability, rejection of nonconformities, encouragement, human-in-loop supervision, regulatory compliance and moral AI theory. By integrating technical, organisational and moral security measures, the chapter presents a comprehensive approach to safe, flexible and reliable ecosystems.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch018","authors":["Revati Ramrao Rautrao","A. V. Senthil Kumar","Sanjayan Thottapattunjalil Suseelan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch018","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/iciss67859.2026.11454102","name":"Suspicious Login Detection Using Machine Learning Under a Federated Learning Framework","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciss67859.2026.11454102","authors":["P J Prajanya Jain","Shabari Shedthi B"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T19:49:27Z","doi":"10.1109/iciss67859.2026.11454102","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.3390/su18104762","name":"Toward a Federated Organizational Intelligence Capability Model: Cross-Silo Federated Learning as a Distributed Dynamic Capability","source":"crossref","abstract":"In response to the growing need for coordinated intelligence in highly regulated and data-fragmented environments, this study develops a Federated Organizational Intelligence Capability Model that conceptualizes cross-silo federated learning as a distributed organizational capability. While existing research has primarily focused on algorithmic performance and privacy protection, limited attention has been given to how federated systems contribute to organizational capability development and long-term adaptation. Building on the Unified Theory of Acceptance and Use of Technology, Dynamic Capabilities Theory, and Privacy by Design, the study proposes a theoretically grounded framework that explains how federated infrastructures can be translated into higher-order organizational capabilities. Technology acceptance is conceptualized as enabling Federated Knowledge Integration, which supports the development of Federated Decision Intelligence, subsequently enhancing Organizational Agility and, over time, Organizational Adaptability. Privacy Governance Assurance is incorporated as a governance-enabling mechanism that conditions early-stage capability transitions by reinforcing trust, compliance, and collaboration under regulatory and data sovereignty constraints. While the framework is presented sequentially for analytical clarity, it acknowledges the potential for iterative dynamics in practice. Overall, the study advances existing literature by clarifying how cross-silo federated learning supports the emergence and coordination of distributed organizational capabilities, offering a structured and theory-driven basis for examining capability development under conditions of data fragmentation and governance constraints.","url":"https://doi.org/10.3390/su18104762","authors":["Avgousta Kyriakidou-Zacharoudiou","Elena Tsappi","Michael Georgiades"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-13T16:26:37Z","doi":"10.3390/su18104762","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.3384/9789181183986","name":"Wireless Federated Learning : Efficient Communication and Resource Management","source":"crossref","abstract":"","url":"https://doi.org/10.3384/9789181183986","authors":["Chung-Hsuan Hu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-08T22:01:59Z","doi":"10.3384/9789181183986","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/icc59461.2026.11587277","name":"FedGIA: Personalized Federated Learning via Graph-based Invariant Aggregation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc59461.2026.11587277","authors":["Shixing Leng","Qian Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T19:38:09Z","doi":"10.1109/icc59461.2026.11587277","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.31449/inf.v50i2.13646","name":"Hybrid Federated Learning Framework for APT Attack Chain Detection and Privacy Enhancement","source":"crossref","abstract":"To address the issues of cross-domain data privacy protection and collaborative analysis in the detection of Advanced Persistent Threat (APT) attack chains, this paper proposes an APT attack collaborative detection and privacy enhancement method based on hybrid federated learning. This method constructs a two-layer federated learning framework of horizontal cross-organization collaboration and vertical multi-feature fusion, realizing the deep fusion of multi-source threat intelligence under the premise of privacy protection. By designing an adaptive differential privacy mechanism and dynamically optimizing the noise addition strategy, it minimizes the loss of model performance while guaranteeing data security. At the same time, this paper introduces a time-series graph neural network detection model to achieve accurate perception and correlation analysis of multi-stage behaviors of APT attacks. The experimental results demonstrate that HybridFL-APT achieves a macro-averaged F1-score of 0.914 and an attack stage identification accuracy of 0.867. Compared to the standard FedAvg algorithm, the proposed framework reduces the cumulative communication overhead by 30.9% while maintaining robust privacy protection even at a strict privacy budget (ε=0.1).","url":"https://doi.org/10.31449/inf.v50i2.13646","authors":["Jie Ji","Shi Qiu","Shengpeng Ye","Xin Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T09:04:33Z","doi":"10.31449/inf.v50i2.13646","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/aaiml67890.2026.11498160","name":"Privacy-Preserving Multimodal News Recommendation through Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aaiml67890.2026.11498160","authors":["Mehdi Khalaj","Shahrzad Golestani Najafabadi","Julita Vassileva"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-06T19:38:02Z","doi":"10.1109/aaiml67890.2026.11498160","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-981-96-8353-6","name":"Federated Learning in Health Care Technology","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-8353-6","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-31T19:42:35Z","doi":"10.1007/978-981-96-8353-6","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.6256102","name":"DynaFed: A Dynamic Federated Learning Framework for Privacy-Preserving Crop Disease Detection","source":"crossref","abstract":"Although AI-driven plant disease detection has achieved notable success, practical implementation is hindered by scarce datasets, privacy constraints, and non-independent and identically distributed (non-IID) data across production systems, which limit scalability and generalization. Federated learning (FL) offers a promising privacy-preserving alternative by enabling clients to collaboratively train models without sharing raw data; however, its effectiveness is still constrained by low-quality local model updates and highly heterogeneous non-IID data distributions. This study proposes DynaFed, a dynamic FL framework for collaborative model training, using spinach diseases as a case study. Additionally, DynaFed employs a modified deep convolutional generative adversarial network (DCGAN) to mitigate dataset imbalance by generating high-fidelity synthetic spinach disease images that closely preserve the characteristics of the original data distribution. The proposed DynaFed incorporates a focus-based aggregation strategy that adaptively weights client contributions based on their local training loss, emphasizing higher-quality updates. To further stabilize and enhance global model convergence, particularly as model depth increases, a dynamic factor is integrated and progressively adjusted across training rounds. Our modified DCGAN demonstrates superior generative performance, increasing the Inception Score from 1.53 to 1.58 while reducing the Fréchet Inception Distance from 214.68 to 203.47, with the most pronounced improvements observed for Anthracnose leaf spot classes. The proposed DynaFed with resilience factor [[EQUATION]], achieves faster convergence, reduced oscillations, and improved test accuracy through dynamic loss regulation compared to baseline FedAvg, demonstrating its effectiveness for multi-site plant disease diagnosis in heterogeneous agricultural environments.","url":"https://doi.org/10.2139/ssrn.6256102","authors":["Mike  O. Ojo","Azlan Zahid"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-18T14:32:00Z","doi":"10.2139/ssrn.6256102","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/infocom59046.2026.11571408","name":"Communication-Efficient and Privacy-Preserving Cooperative Robot-Arm Imitation Learning via Parallel Split Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/infocom59046.2026.11571408","authors":["Shohei Kamiguchi","Takayuki Nishio"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-29T19:38:15Z","doi":"10.1109/infocom59046.2026.11571408","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/icici68773.2026.11580899","name":"Deep Reinforcement Learning for Automated Hyperparameter Optimization in Multi-Institutional Federated Learning Frameworks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icici68773.2026.11580899","authors":["Dawakit Lepcha","Kanchan Thakur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-01T19:35:13Z","doi":"10.1109/icici68773.2026.11580899","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1002/9781394461295","name":"Agricultural Supply Chain Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/icwr69602.2026.11513349","name":"Fair and Robust Federated Learning Via Evolutionary Incentives","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icwr69602.2026.11513349","authors":["Fatemeh Shabani","Rasool Esmaeilyfard"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-15T03:06:29Z","doi":"10.1109/icwr69602.2026.11513349","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/j.mlwa.2026.100944","name":"A privacy-aware federated learning framework for collaborative healthcare IoT security","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.mlwa.2026.100944","authors":["Rihab Saidi","Tarek Moulahi","Salah Zidi","Sami Mahfoudhi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-30T20:52:20Z","doi":"10.1016/j.mlwa.2026.100944","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-443-45252-9.00001-3","name":"Federated learning, explainability, and the road ahead","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-45252-9.00001-3","authors":["Harpreet Kaur","Archana Chhabra","Deepika Ghai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-27T09:13:04Z","doi":"10.1016/b978-0-443-45252-9.00001-3","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/infocom59046.2026.11571639","name":"Differential Privacy in Quantum Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/infocom59046.2026.11571639","authors":["Chaemoon Im","Soohyun Park","Joongheon Kim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-29T19:38:15Z","doi":"10.1109/infocom59046.2026.11571639","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/j.ins.2025.122937","name":"Federated learning with clustered hyperparameter optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ins.2025.122937","authors":["Wesley Chorney","Haifeng Wang","Sescu Adrian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-29T15:53:37Z","doi":"10.1016/j.ins.2025.122937","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.7115838","name":"Federated Learning for Privacy-Preserving AI-Based Medical Imaging in Cardiovascular, Neurological, and Oncology Applications","source":"crossref","abstract":"The convergence of artificial intelligence and medical imaging has unlocked unprecedented diagnostic capabilities across cardiovascular, neurological, and oncological domains. Yet the aggregation of sensitive patient data remains constrained by stringent privacy regulations, institutional data silos, and the inherent risks of centralized data repositories. Federated learning (FL) offers a paradigm shift-enabling collaborative model training across geographically dispersed healthcare institutions without necessitating the transfer of raw patient data. This article examines the theoretical foundations, methodological innovations, and empirical evidence supporting FL in medical imaging across three critical clinical domains. Drawing on recent advances in differential privacy, secure aggregation protocols, and adaptive learning algorithms, we analyze how FL addresses the dual imperatives of diagnostic accuracy and patient confidentiality. Our review synthesizes findings from multinational clinical trials, benchmark studies, and privacy-preserving frameworks to evaluate FL's efficacy in echocardiographic analysis, brain tumor segmentation, and histopathological cancer prognosis. The article further explores policy implications for regulatory compliance under HIPAA and GDPR, identifies persistent technical challenges including non-IID data distributions and communication overhead, and proposes a roadmap for clinical translation.","url":"https://doi.org/10.2139/ssrn.7115838","authors":["Yibo Kong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T14:11:21Z","doi":"10.2139/ssrn.7115838","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-032-03985-9_28","name":"Federated Learning for Precision Medicine: A Blockchain-Enhanced Framework for Privacy-Preserving Predictive Analytics in Healthcare 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_28","authors":["Raj Kishor Verma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:59Z","doi":"10.1007/978-3-032-03985-9_28","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/icetci68937.2026.11610723","name":"A Privacy-Preserving Learning Framework Based on Federated Learning and Explainable AI","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icetci68937.2026.11610723","authors":["Lili Xu","Li Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-22T19:20:01Z","doi":"10.1109/icetci68937.2026.11610723","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1098/rsos.250959/v3/decision1","name":"Decision letter for \"Breaking Interprovincial Data Silos: How Federated Learning Can Unlock Canada’s Public Health Potential\"","source":"crossref","abstract":"","url":"https://doi.org/10.1098/rsos.250959/v3/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-14T21:10:36Z","doi":"10.1098/rsos.250959/v3/decision1","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.6592038","name":"Decentralized Depression Detection Using Wearable Sensors and Federated Learning","source":"crossref","abstract":"The symptoms that define depressive conditions have been recognized for millennia of medical history. The earliest Hippocratic writings not only define depression in similar ways as current works but also use context to differentiate ordinary sadness from depressive disorder\".[1] \"In Greek times people used to think that depression was complete biological disorder.[2]. During the time of Middle Ages, the person who has problem related to depression is said this person is possessed by demons\". [2]. To study the history of depression and modern method's how depression deals now days, our aim is to study various patterns of depression so that we can Train our model with help of Machine learning, thus we reach to particular conclusion how to detected depression early. Study depression history and conduct a survey based on google form. In Python we use library like Pandas for preprocessing of data and SK Learn for implement various types of ML algorithms to Train our model. finalize with result and conclusion.","url":"https://doi.org/10.2139/ssrn.6592038","authors":["Chandan Dhiman","Poonam Joyti","Mohit Kumar","Anshaj Chauhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-23T17:45:47Z","doi":"10.2139/ssrn.6592038","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.7067219","name":"CIPHER-Fed: An Inference-Resistant Federated Learning Framework via StructuralGradient Obfuscation","source":"crossref","abstract":"Federated Learning (FL) enables collaborative model training without sharing raw data, yet exchanged gradients remain vulnerable to inference and reconstruction attacks. Existing privacy-preserving methods typically protect gradient values, encrypt updates, or reduce communication independently, making it difficult to jointly achieve privacy, robustness, communication efficiency, and model utility under heterogeneous non-IID settings. We propose CIPHER-Fed, a communication-efficient and inference-resistant FL framework that formulates privacy leakage as a gradient recoverability problem. Instead of merely limiting statistical information exposure, CIPHER-Fed introduces a structural gradient obfuscation paradigm to minimize recoverable information embedded in exchanged gradients. We develop a Structural Recoverability Score (SRS) to quantify gradient recoverability and design a Structural Gradient Transformation (SGT) that suppresses recoverable structures while preserving task-relevant optimization signals. The framework further integrates privacy-preserving gradient protection, communication-aware transmission, and robust aggregation to reduce information leakage and communication overhead. Experiments on MNIST, CIFAR-10, Tiny-ImageNet, and CelebA demonstrate that CIPHER-Fed consistently improves resistance to inference attacks while maintaining competitive model accuracy, communication efficiency, and robustness under heterogeneous federated learning environments.","url":"https://doi.org/10.2139/ssrn.7067219","authors":["Waheeb Algethami","Guowei Wu","Faisal Alshami"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-06T19:13:23Z","doi":"10.2139/ssrn.7067219","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.7363818","name":"Privacy-Preserving Multimodal Federated Learning with Reliability-aware Client Selection for Heterogeneous Edge Devices","source":"crossref","abstract":"Federated learning is rapidly becoming a key paradigm for deploying machine learning models across distributed edge devices without aggregating raw data to a central server to satisfy emerging regulatory and ethical demands for data privacy. The extension to multimodal settings adds challenges though: modality imbalance, partial trust of the client/limited ability to trust the data/client, limited and variable devices, high privacy risk due to richer data representations. In this article, a structured overview of privacy-preserving multimodal federated learning is shown which focuses on reliable client selection for the settings of heterogeneous edge devices. The review is based on over 50 peer-reviewed or archival sources that are published mainly between 2019 and 2025 and cover four concurrent lines of research: federated optimisation foundational research, privacy-preserving aggregation methods such as differential privacy, secure multiparty computation, and homomorphic encryption, client selection and reliability estimation, and emerging multimodal federated architectures. A proposal for a unified conceptual framework based on reliability scoring, derived from device capabilities, link measurements, historical update fidelity, and hence modality completeness, is introduced that enables flexible learning in the presence of system and statistical heterogeneity, while remaining privacy-preserving. The review also compiles quantitative results from various papers to summarise the compromises required to balance privacy assurance, communication efficiency and model accuracy. Open questions are highlighted, such as the lack of standardized multimodal benchmarks, the computational load on low power devices of cryptographic privacy mechanism, and the challenge of optimising jointly fairness, robustness and efficiency. Finally, the research directions for integrated and reliable multimodal FL systems with privacy protection in real-world FL edge deployments are summarized.","url":"https://doi.org/10.2139/ssrn.7363818","authors":["Sai Doondi Kothapalli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-29T09:10:21Z","doi":"10.2139/ssrn.7363818","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1016/b978-0-44-343852-3.00015-2","name":"Fair incentive allocation in vertical federated learning using nucleolus","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-343852-3.00015-2","authors":["Afsana Khan","Marijn ten Thij","Guangzhi Tang","Frank Thuijsman","Anna Wilbik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-18T14:07:17Z","doi":"10.1016/b978-0-44-343852-3.00015-2","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-032-03985-9_11","name":"Intelligent Workforce Management in Healthcare 5.0: Redefining HR Through Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_11","authors":["Archana Singh","Girish Lakhera","jyoti kumari","Arvind Nain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:45Z","doi":"10.1007/978-3-032-03985-9_11","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.63282/3050-9262.ijaidsml-v7i1p101","name":"Federated Learning with Smartphone Sensor Data","source":"crossref","abstract":"Federated learning (FL) is emerging as a promising approach for training machine learning models on distributed devices without violating the data privacy of these devices. In this paper, we examine federated learning for smartphone sensor data applications that involve both significant challenges related to privacy and data heterogeneity. Our primary interest lies in techniques such as differential privacy, secure aggregation, and homomorphic encryption that will ensure privacy over user-sensitive information during model training. We also pose and discuss heterogeneous data across devices, particularly non-independent and identically distributed (non-IID) data, by investigating methods such as normalization of data, personalized learning, and federated transfer learning. Using real-world smartphone sensor datasets, we demonstrate experimentally that federated learning is effective in training robust models while preserving privacy and accounting for device-specific data variations. Our findings highlight that federated learning can be regarded as a way to scale-up and privacy-preserve mobile-based machine learning, which may open new avenues for building real-time AI systems on top of devices themselves.","url":"https://doi.org/10.63282/3050-9262.ijaidsml-v7i1p101","authors":["Dheeraj Vaddepally"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-08T07:19:47Z","doi":"10.63282/3050-9262.ijaidsml-v7i1p101","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.6346312","name":"Conflict-Averse Federated Multi-Task Learning for Power System Stability Assessment","source":"crossref","abstract":"Machine learning-based power system stability assessment has attracted significant research attention in recent years. However, most of the existing approaches treat pre-fault and post-fault assessment separately, missing opportunities to exploit their shared underlying physics and complementary insights. Besides, the learning process requires diverse training data that individual utilities cannot provide due to privacy concerns. To address these issues, a novel Federated Conflict-Averse method that unifies Stability Assessment while preserving task specialization is proposed, named (FedCA-SA). First, we reformulate federated learning (FL) as a multi-objective optimization problem where specialized clients contribute domain-specific expertise while sharing universal power system knowledge. Second, to address conflicts between different clients and tasks, we adapt conflict-averse gradient descent to the federated setting, developing a selective parameter sharing mechanism that resolves gradient conflicts between pre-fault and post-fault specialists. Third, we provide theoretical convergence analysis establishing performance guarantees for conflict-averse aggregation in task-specialized FL. Experimental validation demonstrates that our proposed FedCA-SA method achieves 96.07% accuracy for pre-fault SA while reducing false positives by 36% compared to baseline federated methods.","url":"https://doi.org/10.2139/ssrn.6346312","authors":["Renyou Xie","Rui Zhang","Chao Ren"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-05T00:38:50Z","doi":"10.2139/ssrn.6346312","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/flics70075.2026.11621936","name":"Open-Source Federated Learning Platform Adapted for DICOM Medical Image Processing: Application to Breast Cancer Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621936","authors":["Gersain Galández Buitrón","Juan Andrés Salazar-González","Jose Alejandro Salazar-Castro","Edwin Castillo","Oscar M. Caicedo","Diego M. López"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:12:10Z","doi":"10.1109/flics70075.2026.11621936","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-032-03985-9_19","name":"Federated Learning for Decentralized Healthcare: Privacy, Efficiency, and Scalability in Healthcare 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_19","authors":["Subrata Paul","Anirban Mitra","Shivnath Ghosh","Amitava Podder"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:36Z","doi":"10.1007/978-3-032-03985-9_19","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1007/978-3-032-03985-9_34","name":"Federated Learning in Healthcare Finance: A Systematic Review of Privacy-Preserving Models","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_34","authors":["Manika Garg","Sunitaa Tank","Bharat Kumar Tank"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:47Z","doi":"10.1007/978-3-032-03985-9_34","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.2139/ssrn.7324975","name":"Federated Academic Risk Intelligence: Privacy-Preserving Multitask Learning with Stable Explanations Across Heterogeneous Educational Ecosystems","source":"crossref","abstract":"Data-intensive learning settings provide new prospects for early academic-risk identification. However, institutional use of predictive analytics faces challenges related to data-governance requirements, diverse student populations, and the need for transparent decision support. In this paper, the offered experimental work is reformulated as a rigorous federated learning analytics framework, where the academic performance and dropout risk are simultaneously modelled without centralising the raw institutional data. A multitask design combines temporal behavioural trajectories and organised socio-academic qualities and is assessed using centralised training, FedAvg, and FedProx. We adjust for institutional divergence using Dirichlet partitions with alpha values of 0.1, 0.3, 0.5, and 1.0, class imbalance, temporal drift, and structural missingness. The original numerical findings are not affected. Federated training shows little change in AUC and F1 in OULAD experiments; calibration is more sensitive and nonlinear to heterogeneity. Multi-epoch optimisation is stable, and the explanatory rankings are highly consistent across clients (mean rank stability 0.992-0.999) even when attribution magnitudes change. External transfer to EdNet-KT1 maintains good discrimination but leads to a substantial increase in Expected Calibration Error and a significant change in the attribution magnitude. This implies that a predictive transfer does not guarantee probabilistic or explanatory equivalence. The results indicate a comprehensive perspective on trustworthy educational AI, in which the evaluation of discrimination, calibration, convergence, and explanation stability should be conducted independently. The approach consequently offers a privacy-aware, interpretability-oriented foundation for cross-institutional academic-risk analytics and clarifies the limitations of controlled non-IID simulation and proxy-based external validation.","url":"https://doi.org/10.2139/ssrn.7324975","authors":["Juling Niu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-21T05:51:21Z","doi":"10.2139/ssrn.7324975","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.3724/2096-7004.di.2025.0301","name":"Privacy Protection and Trusted Data Sharing Mechanism Driven by Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.3724/2096-7004.di.2025.0301","authors":["Shuangyue Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-25T08:04:41Z","doi":"10.3724/2096-7004.di.2025.0301","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.28945/5800","name":"Federated Machine Learning Outperforms Centralized Machine Learning for Fraud Detection: Evidence From 284,807 Real-World Transactions [Abstract]","source":"crossref","abstract":"Aim/Purpose This study investigates whether federated machine learning (FML) can match or exceed centralized machine learning (CML) performance for financial fraud detection while maintaining complete data privacy. Background Financial fraud detection systems traditionally rely on CML, which aggregates sensitive transaction data from multiple institutions, raising privacy concerns and regulatory compliance challenges under the GDPR and CCPA. Methodology Using the Kaggle Credit Card Fraud dataset (284,807 transactions), this study compared identical neural network architectures in centralized and federated settings. The FML approach utilized the FedAvg algorithm across 10 clients with non-IID data distribution over 20 rounds. Contribution This research challenges the assumption that centralized learning out-performs federated approaches, providing the first comprehensive empirical comparison showing FML superiority on real-world fraud data. Findings FML outperformed CML in F1-score (0.7957 vs. 0.7411) and precision (0.8409 vs. 0.6587). While FML significantly reduces false positives and operational costs, CML remains competitive when fraud losses outweigh the costs of false positives. Recommendations for Practitioners Financial institutions should adopt FML for fraud detection when multi-institution collaboration or data-localization compliance is required. Its higher precision makes it well-suited for production environments focused on reducing false positives. Recommendation for Researchers Future research should examine advanced aggregation, differential privacy, and scalability. It should also analyze regularization effects to determine why FML achieves superior precision. Impact on Society This research shows that privacy preservation can enhance fraud detection, enabling multi-institution collaboration while protecting consumer data and reducing friction by reducing false positives. Future Research Future work should explore FML in real-time institutional settings, across diverse fraud contexts, and within integrated cross-domain frameworks such as credit risk assessment.","url":"https://doi.org/10.28945/5800","authors":["Samuel Sambasivam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-18T22:06:06Z","doi":"10.28945/5800","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/iciss67859.2026.11453569","name":"Designing Equity-Focused School Climates Through Federated Learning Approaches","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciss67859.2026.11453569","authors":["Preetee Praveen Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T19:49:27Z","doi":"10.1109/iciss67859.2026.11453569","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.4018/979-8-3373-7426-0.ch005","name":"Federated Learning for Privacy-Preserving Healthcare Wearable Security","source":"crossref","abstract":"The work demonstrates and performs a privacy-preserving multi-institutional multi-party end-to-end federated learning (FL) framework to detect security anomalies in multi-party data streams of healthcare wearables. The natural pipeline uses distributed non-identically-distributed (non-IID) telemetry to jointly train anomaly detectors on hospitals and device vendors across a secure environment without having to centralize raw patient data. The method includes time-varying deep neural network and client level differential privacy (DP-SGD), secure aggregation with efficient communication compressing update transmission. This work provides assumed yet reasonable multi-site outcomes to concretize feasibility, simulating what a production deployment would provide: on six institutions and 21,804 users generating 1.2 billion records, the DP-FL model achieves an AUROC of 0.943 ± 0.008 and an F1-score of 0.887 ± 0.011 when identifying the security -important anomalies such as spoofed sensors, tampered firmware, abnormal pairing, and suspicious connection events.","url":"https://doi.org/10.4018/979-8-3373-7426-0.ch005","authors":["Grace Shalini T.","Pratham Shrivastav","Parthiv Gopa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-11T20:45:37Z","doi":"10.4018/979-8-3373-7426-0.ch005","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.2139/ssrn.7358532","name":"GENOME-SHIELD,An Executable Governance and Anomaly-Gated Federated Learning Framework for Single-Cell Analysis","source":"crossref","abstract":"Background and Objective: Federated learning limits direct pooling of biomedical records, but conventional workflows do not bind data-use authorization, adversarial update screening and reproducible evidence into one executable control path. This study evaluated whether an implemented governance and anomaly-gated federated learning framework could preserve single-cell classification utility while blocking enumerated prohibited requests and containing model-update attacks.,Methods: GENOME-SHIELD combines a request-level policy evaluator, source-local multinomial softmax learning, median/MAD-based anomaly screening, sample-size-weighted aggregation over eligible updates and hash-linked provenance. A public Human Pancreas single-cell RNA-sequencing benchmark was represented as five simulated source-study participants. The evaluation included 14,627 cells, eight harmonized cell types, 30 deterministic cell-stratified splits, leave-one-source-study-out transfer, five attack types, four aggregation baselines, policy stress tests, computational scaling and deterministic local replay.,Results: Mean macro-F1 across 30 splits was 0.9324 (95% CI, 0.9302–0.9346) for GENOME-SHIELD and 0.9252 (0.9229–0.9275) for FedAvg, with a paired difference of 0.0072 (95% CI, 0.0060–0.0084; paired t-test p=1.78×10⁻¹²). Leave-one-source-study-out macro-F1 ranged from 0.8111 to 0.9864. Under a configured -8× model-replacement attack, defended macro-F1 was 0.9376, compared with 0.0456 for undefended FedAvg, 0.8108 for coordinate median, 0.8076 for trimmed mean and 0.8252 for Multi-Krum. All 75 enumerated prohibited requests were blocked and all 25 valid requests were allowed. Deterministic local replay produced zero parameter distance and identical metrics.,Conclusions: GENOME-SHIELD demonstrates an executable biomedical research-software pattern that links data-use eligibility, source-local learning, malicious-update exclusion and auditable replay. Findings establish methodological feasibility in a public, locally simulated five-participant benchmark; they do not establish clinical effectiveness, formal privacy, production-scale federation or independent replication.","url":"https://doi.org/10.2139/ssrn.7358532","authors":["Naresh Somara"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-27T09:40:41Z","doi":"10.2139/ssrn.7358532","addedAt":"2026-08-31T06:41:31.889Z","updatedAt":"2026-08-31T06:41:31.889Z"},{"id":"doi:10.1109/wcncw67598.2026.11555342","name":"Communication-Efficient Vehicle Sensing with Federated Learning Based on Critical Learning Period and Weight Clustering","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wcncw67598.2026.11555342","authors":["Ping Liu","Junsheng Mu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-17T19:37:14Z","doi":"10.1109/wcncw67598.2026.11555342","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.5220/0015043000004039","name":"Optimizing Global Federated Learning: A Serverless Hierarchical Approach with Region-Aware Placement","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0015043000004039","authors":["Matheus Pereira","Lúcia Drummond"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-22T23:47:05Z","doi":"10.5220/0015043000004039","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.2139/ssrn.6653258","name":"Privacy-Preserved Learning in Health AI Through Federated Learning","source":"crossref","abstract":"&lt;div&gt; Healthcare systems in resource limited settings face persistent challenges, including shortages of skilled professionals, limited access to diagnostic services, and fragmented healthcare infrastructure. Artificial intelligence (AI) has emerged as a promising tool to address these gaps by augmenting clinical decision-making, improving diagnostic accuracy, and enabling scalable healthcare delivery. However, the development of effective AI systems relies heavily on access to large, diverse, and high-quality datasets. &lt;/div&gt; &lt;div&gt; &lt;br&gt; &lt;/div&gt; &lt;div&gt; Traditional approaches to training AI models in healthcare follow a centralized paradigm, where data from multiple institutions is aggregated into a single repository. While this approach has driven early successes, it is fundamentally misaligned with the realities of healthcare systems. Strict data privacy regulations, institutional data silos, security risks, and infrastructural constraints make large-scale data pooling difficult, particularly in resource-constrained environments. &lt;/div&gt; &lt;div&gt; &lt;br&gt; &lt;/div&gt; &lt;div&gt; Federated Learning offers a compelling alternative. By enabling models to be trained collaboratively across multiple institutions without requiring patient data to leave local environments, Federated Learning addresses key limitations of centralized approaches. It allows healthcare providers to contribute to shared AI systems while preserving data privacy and maintaining institutional control. Despite its promise, Federated Learning introduces its own set of technical, institutional, and operational challenges, including data heterogeneity, communication overhead, governance complexities, and coordination across stakeholders. These challenges are further compounded in resource limited settings by limited connectivity, infrastructure gaps, and shortages of technical expertise. Nevertheless, Federated Learning is uniquely well-suited to the needs of healthcare systems in resource limited settings, where data is inherently distributed and centralized infrastructure is often impractical. By enabling privacy-preserving collaboration, Federated Learning provides a pathway for institutions to collectively develop robust and generalizable AI models. &lt;/div&gt; &lt;div&gt; &lt;br&gt; &lt;/div&gt; &lt;div&gt; This whitepaper examines the limitations of centralized AI development, introduces \\textbf{Pri}vacy-preserved learning in Health AI through \\textbf{Fed}erated Learning (PriFed) , and analyzes its relevance for healthcare systems in resource limited settings. It further outlines key challenges to adoption and provides strategic recommendations for enabling PriFed ecosystems. Central to this effort is the role of collaborative networks, such as the Health AI for All Network (HAINet), in fostering partnerships, building capacity, and advancing equitable access to Health AI solutions. &lt;/div&gt; &lt;div&gt; &lt;br&gt; &lt;/div&gt;","url":"https://doi.org/10.2139/ssrn.6653258","authors":["Binod Bhattarai","Bibek Niroula","Aavash Chhetri","Kiran Raj Pandey","Yash Raj Shrestha","Niyoj Oli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-06T07:02:02Z","doi":"10.2139/ssrn.6653258","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch019","name":"Legal and Policy Considerations in Adversarial Federated Learning","source":"crossref","abstract":"A conflict exists between the adversarial vulnerabilities of federated learning and existing accountability and liability regulations in data protection and AI law. This chapter analyses GDPR, the EU AI Act, HIPAA, India's DPDP Act, and UAE ADHICS, along with technical evidence related to poisoning, backdoor, and privacy attacks. The chapter highlights how privacy-preserving architectures can reduce traceability, make causation harder to determine, and weaken single-producer product liability models. It then suggests a role-based, evidence-based framework that uses cryptographically verifiable logs, provenance records, and FL-specific contractual clauses. This framework clarifies liability, establishes safe harbors, and enables cross-border governance for high-risk federated systems.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch019","authors":["Farouq Saber Al-Shibli","Ibrar Ahmad","Aftab Haider","Udit Mamodiya","Poonam Devi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch019","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1109/icc-cns70518.2026.11606056","name":"FiZK: Federated Integrity with Zero Knowledge Hierarchical Federated Learning with ZK-Firewall Defense and IVC Proof Folding","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc-cns70518.2026.11606056","authors":["Nevin Oommen","Atharva Naitam","Ayush Kshirsagar","Atharva Bhede","Dipak Wajgi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-21T19:10:50Z","doi":"10.1109/icc-cns70518.2026.11606056","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1007/978-3-032-03985-9_30","name":"Blockchain Integration for Enhanced Trust and Security in Federated Learning for Healthcare 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_30","authors":["Sourav Kayal","Amit Kumar Rana","Sanjib Kundu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:47Z","doi":"10.1007/978-3-032-03985-9_30","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.2139/ssrn.6778882","name":"P2PFL: A Modular Framework for Decentralized Federated Learning","source":"crossref","abstract":"Decentralized Federated Learning (DFL) offers a promising paradigm for collaborative machine learning training without centralized coordination, addressing critical limitations of centralized federated learning such as single points of failure, communication bottlenecks, and scalability constraints. However, the adoption of DFL has been hindered by the lack of robust, flexible, and user-friendly frameworks. This paper introduces P2PFL, an open-source framework that enables broader adoption of DFL through a highly modular and extensible architecture. P2PFL decouples communication protocols, learning modules, aggregation algorithms, and orchestration workflows into independent, interchangeable components. This modularity enables researchers and practitioners to efficiently prototype and deploy federated learning systems tailored to their specific requirements. A key contribution is the introduction of optimized gossip-based protocols with incremental partial aggregation whose equivalence to centralized federated learning is formally proven, with communication savings that scale with network size, achieving up to 88\\% reduction in model data transfer at 128 nodes. We demonstrate P2PFL&amp;apos;s effectiveness through functional validation on standard benchmarks (\\textit{MNIST} and \\textit{CIFAR-10}) under various network topologies and data heterogeneity settings, a scalability analysis from 8 to 128 nodes quantifying communication overhead and round duration across topologies, and a real-world deployment on the \\textit{CASA} IoT dataset across 10 geographically distributed machines.","url":"https://doi.org/10.2139/ssrn.6778882","authors":["Pedro Guijas","Sadi Alawadi","Daniel Rivero","Enrique Fernandez-Blanco"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-17T00:37:01Z","doi":"10.2139/ssrn.6778882","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1117/12.3121461","name":"Federated learning-based collaborative assessment of distributed photovoltaic generation potential in multi-park systems","source":"crossref","abstract":"This paper proposes a federated learning (FL) framework for the privacy-preserving, collaborative assessment of distributed photovoltaic (PV) generation potential across multiple industrial parks. The approach enables model training on decentralized datasets, incorporating key meteorological and site-specific factors. A rigorous experimental evaluation demonstrates that the proposed method achieves prediction accuracy competitive with centralized training while eliminating raw data sharing. Furthermore, it outperforms state-of-the-art FL algorithms (e.g., FedProx, SCAFFOLD) in final accuracy and convergence stability under non-IID data, and significantly reduces total communication overhead. The framework effectively addresses data heterogeneity and privacy constraints, proving robust and scalable for real-world multi-park deployment.","url":"https://doi.org/10.1117/12.3121461","authors":["Meng Chao Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-14T14:55:28Z","doi":"10.1117/12.3121461","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.2139/ssrn.6018076","name":"Cost-Constrained Incentive Mechanism for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.6018076","authors":["Shihong Wu","Yuchuan Luo","Shaojing Fu","Ming Xu","Zhenbin Guo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-05T07:20:28Z","doi":"10.2139/ssrn.6018076","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.2139/ssrn.7309963","name":"CurFedSSL: Quality-Aware Curriculum Prototype Learning for Heterogeneous Federated Semi-Supervised Neural Networks","source":"crossref","abstract":"Federated semi-supervised learning enables distributed neural networks to learn from limited labeled data and abundant unlabeled data without centralizing local datasets. However, heterogeneous client distributions make this setting vulnerable to unreliable prototype aggregation, biased local representations, and noisy pseudo-label supervision. We propose CurFedSSL, a quality-aware curriculum prototype learning framework for federated semi-supervised classification. CurFedSSL estimates the reliability of each class prototype using sample coverage, feature compactness, and predictive uncertainty, and performs reliability-weighted aggregation to reduce the influence of low-quality local prototypes. It further refines local prototypes by progressively incorporating high-confidence unlabeled representations after a warm-up stage. To control pseudo-label noise, CurFedSSL introduces a class-adaptive curriculum that adjusts confidence thresholds according to prototype reliability, labeled-data density, and training progress. Together, these designs couple prototype learning and pseudo-label selection: reliable prototypes guide pseudo-label filtering, while selected unlabeled samples further improve prototype quality. Experiments on nine image classification datasets under different label ratios, client scales, and non-IID settings show that CurFedSSL consistently outperforms representative federated and semi-supervised learning baselines, with accuracy gains of up to 11.3 percentage points under 5% labeled data and 7.9 percentage points under the standard 10% label ratio. The results demonstrate the effectiveness of quality-aware curriculum prototype learning for robust federated neural computation under label scarcity and data heterogeneity.","url":"https://doi.org/10.2139/ssrn.7309963","authors":["Qingfei Wang","Xiaomiao Gui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-19T00:42:38Z","doi":"10.2139/ssrn.7309963","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.2139/ssrn.6380740","name":"FedFNR: Highly Robust Federated Learning Method for Label Noise","source":"crossref","abstract":"Federated learning (FL) holds immense potential in privacy-preserving collaborative machine learning. However, ubiquitous label noise in decentralized data severely undermines the robustness of aggregated models, frequently misguiding optimization trajectories and degrading performance. Existing approaches are often limited by rigid noise detection criteria and inadequate suppression mechanisms. Moreover, they typically overlook the statistical indistinguishability between hard and noisy samples, hindering their ability to maintain superior performance in complex noisy environments.To address this, we propose FedFNR, a highly robust federated learning framework designed for label noise. First, for accurate noise identification, we propose the Adaptive \"Fuzzy-Deterministic\" Conversion (AFDC) mechanism. AFDC enables a smooth transition from a fuzzy estimation of noise severity to deterministic identification, effectively mitigating the confirmation bias induced by current hard-thresholding strategies. Second, for noise suppression, we design the Dynamic Graded Knowledge Distillation (DGKD) strategy, which quantifies client data cleanliness via the proposed Distillation Adaptability Index (DAI) and accordingly constructs a Hierarchical Teacher Pool to guide student models. Furthermore, given the significant statistical similarity between hard and noisy samples, we develop the Dual-Granularity Weighted Aggregation (DGWA) mechanism. Comprising Instance-Level Hardness-Noise De-confounding Reweighting (IL-HNDR) and Client-Level Multi-metric Fusion Aggregation (CL-MMFA), DGWA effectively alleviates the degradation of learning efficiency caused by sample confusion.Theoretical analysis rigorously establishes the convergence of FedFNR under non-convex objective functions. Extensive experiments on the CIFAR-10, CIFAR-100, and Clothing1M datasets demonstrate that FedFNR significantly outperforms state-of-the-art baselines across various noise patterns, showcasing.","url":"https://doi.org/10.2139/ssrn.6380740","authors":["Huiqi Zhao","Mengqi Li","Gang Liu","Fang Fan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-09T20:46:02Z","doi":"10.2139/ssrn.6380740","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.2139/ssrn.6262538","name":"Federated Learning Frameworks for Privacy-Preserving Continuous Health Monitoring Using IoT Skin Patches","source":"crossref","abstract":"Continuous health monitoring through IoT-enabled skin patches has emerged as a transformative approach in digital healthcare, enabling real-time acquisition of physiological and biochemical biomarkers such as glucose, lactate, cortisol, heart rate variability, temperature, and electrodermal activity. These wearable systems generate high-frequency, longitudinal health data that can significantly enhance early disease detection, chronic condition management, and personalized therapeutic interventions. However, centralized data aggregation models raise substantial concerns regarding patient privacy, regulatory compliance, data ownership, and cybersecurity risks. Federated Learning (FL) offers a privacy-preserving machine learning paradigm that allows distributed model training directly on edge devices without transmitting raw health data to central servers. This paper presents a comprehensive framework for integrating federated learning into IoT skin patch ecosystems to enable secure, scalable, and adaptive continuous health monitoring. The proposed framework leverages on-device preprocessing, lightweight neural network architectures, encrypted parameter aggregation, and adaptive communication scheduling to balance predictive performance with energy efficiency. By maintaining data locality, FL mitigates risks associated with centralized storage while enabling collaborative model improvement across diverse user populations. The study further examines key technical challenges, including non-IID data distribution, communication overhead, model drift, personalization strategies, and robustness against adversarial attacks. System-level considerations such as power management, secure hardware enclaves, and regulatory alignment are also analyzed. Through the convergence of edge intelligence, distributed optimization, and secure communication protocols, federated learning frameworks can enhance diagnostic accuracy while preserving user confidentiality and device autonomy. This integration supports scalable digital health infrastructures capable of continuous, privacy-aware biomarker analytics. The findings highlight federated learning as a foundational enabler for trustworthy, intelligent, and patient-centric wearable health systems.","url":"https://doi.org/10.2139/ssrn.6262538","authors":["Adedokun Taofeek","John Owen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-10T05:55:48Z","doi":"10.2139/ssrn.6262538","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1109/flics70075.2026.11621924","name":"PRISM-AMC: Replay-Free Continual Learning for Automatic Modulation Classification","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621924","authors":["Usman Akram","Matthew Graham","Haris Vikalo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:09:25Z","doi":"10.1109/flics70075.2026.11621924","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.24321/2456.1428.202534","name":"Enhancing Federated Learning Robustness with Adaptive Client Aggregation for Heterogeneous Data","source":"crossref","abstract":"","url":"https://doi.org/10.24321/2456.1428.202534","authors":["Gagandeep Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-16T12:18:35Z","doi":"10.24321/2456.1428.202534","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1109/icc59461.2026.11587537","name":"Understanding Communication Backends in Cross-Silo Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc59461.2026.11587537","authors":["Amir Ziashahabi","Chaoyang He","Salman Avestimehr"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T19:38:09Z","doi":"10.1109/icc59461.2026.11587537","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.2139/ssrn.6533060","name":"HeteroSense-FL: A Multimodal Simulation Testbed for Modality-Heterogeneous Federated Learning","source":"crossref","abstract":"HeteroSense-FL is a Python software package for structured multimodal sensor simulation targeting modality-heterogeneous federated learning (FL) research. It generates 3D LiDAR point clouds and bed pressure maps across N client sites, each configured with a different sensor subset, enabling systematic study of FL algorithms under per-client modality heterogeneity. The package provides a configurable $N$-client heterogeneity API, a temporal window sampler for plug-and-play encoder development, and automated observation integrity checks. A reference benchmark is reproduced with the single command heterosense-benchmark. The software is CI-tested across Python 3.9 to 3.12 on Linux, macOS, and Windows.","url":"https://doi.org/10.2139/ssrn.6533060","authors":["Xun Shao","Kohsuke Yamakawa","Otani Aoba"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T03:50:32Z","doi":"10.2139/ssrn.6533060","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.5220/0014995200004103","name":"TrusTEE: Storage-Centric Secure Federated Learning with Trusted Execution and Policy Enforcement","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014995200004103","authors":["George Popescu-Craiova","Maribel Fernandez"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-20T06:12:52Z","doi":"10.5220/0014995200004103","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch010","name":"Enhancing Federated Learning Robustness Against Adversarial Attacks Using SMPC","source":"crossref","abstract":"Federated Learning (FL) allows for distributed model training across a population of devices while also offering the potential for privacy preservation, however, it remains susceptible to a range of adversarial manoeuvres, including model poisoning, backdoor attacks, and Byzantine behaviours. This chapter discusses secure multiparty computation (SMPC) as a cryptographic defence mechanism that allows models to be trained collaboratively and without revealing individual data associated with these models. We categorise the impacts of attacks, we offer various architectures of SMPC-based FL and we compare SMPC-based FL with traditional FL bases on security, communication, and computation. Certain key computational techniques, practical implementations with healthcare and industrial, Internet of Things (IoT) applications, security-performance trade-offs, scalability, limitations of current work, and an ordered taxonomy of threats to FL give insight into the performance and deployment of SMPC as a means to reduce threats to model accuracy.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch010","authors":["Sumit Kumar Kapoor","Kriti Sankhla","Saurabh Shandilya","Satya Prakash Awasthi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch010","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.21275/sr26302215304","name":"Blockchain-Secured Federated Learning with SMPC for Poisoning Attack Mitigation in Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.21275/sr26302215304","authors":["Manukonda Likhith Naveen Reddy","Jeffrin Hannah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-14T11:59:56Z","doi":"10.21275/sr26302215304","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.5220/0015063200004103","name":"HomeGuard: Community-Driven Hierarchical Federated Learning for Robust Smart-Home Intrusion Detection","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0015063200004103","authors":["Philipp Eichhammer","Christian Berger","Hans Reiser"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-23T10:16:50Z","doi":"10.5220/0015063200004103","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1109/comsnets67989.2026.11418167","name":"Deserialization Attacks in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/comsnets67989.2026.11418167","authors":["Spaarsh Thakkar","Dev Gupta","Rajeev Shorey"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-10T19:50:41Z","doi":"10.1109/comsnets67989.2026.11418167","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch015","name":"Securing Financial Applications With Federated Learning Against Adversarial Threat","source":"crossref","abstract":"The protection of sensitive data and digital transactions in the financial sector is increasingly under fire from sophisticated adversarial threats. With artificial intelligence (AI) -enabled fraud detection, credit scoring, and payment authentication, adversaries take advantage of machine learning models using different methods. Traditional centralized learning is plagued with privacy concerns and singular points of failure, which make it incompatible with stringent laws and guidelines. Federated learning (FL) brings in a new paradigm that allows institutions to train models in collaboration without raw data exchange, thus lowering breach risks and ensuring compliance. This study examines the financial applications of FL to strengthen data security and explores defense strategies like robust aggregation, differential privacy, encryption, blockchain, and other use cases. FL, combining it with adversarial training, explainable AI, trust layers from blockchain, and quantum-safe cryptography, will enable the creation of robust, privacy-preserving financial ecosystems.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch015","authors":["Lingala Thirupathi","Sreeja Ravula","Nookala Shreya","Vennela Appala","Suchandranath Bajjuri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch015","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1109/cnml68938.2026.11452375","name":"Parameter-Efficient Federated Multimodal Alignment Tuning for Heterogeneous Clients","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cnml68938.2026.11452375","authors":["Xu Tan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T19:49:32Z","doi":"10.1109/cnml68938.2026.11452375","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.5220/0014632800004061","name":"FIDELIS: Blockchain-Enabled Protection against Poisoning Attacks in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014632800004061","authors":["Jane Carney","Kushal Upreti","Gaby Dagher","Tim Andersen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-08T10:53:27Z","doi":"10.5220/0014632800004061","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1002/9781394461295.ch3","name":"Managing Climate Variability with Federated Artificial Intelligence Models","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394461295.ch3","authors":["A. Vegi Fernando","Mitha Guru","Sugandha Saxena","Mude Nagarjuna Naik","R. Sriramkumar","Joshuva Arockia Dhanraj","M. Lakshmanan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T12:51:06Z","doi":"10.1002/9781394461295.ch3","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.21275/sr26710161525","name":"Towards Secure and Efficient Federated Learning for Wheat and Rice Disease Diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.21275/sr26710161525","authors":["Shabad Kaur","Amandeep Kaur Virk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-13T08:58:02Z","doi":"10.21275/sr26710161525","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1109/imed68921.2026.11484887","name":"Agentic AI and Federated Learning for Privacy-Preserving Dental Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1109/imed68921.2026.11484887","authors":["Rajesh Lingam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-23T19:57:06Z","doi":"10.1109/imed68921.2026.11484887","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1038/s41598-026-66050-x","name":"A federated learning-enhanced sharded blockchain framework for privacy-enhanced authentication in IoT E-learning systems","source":"crossref","abstract":"Abstract The accelerated growth of IoT-facilitated e-learning ecosystems has introduced significant challenges for secure, scalable, and privacy-aware user authentication. Existing approaches face a fundamental trade-off: conventional blockchain systems often incur high latency and limited scalability, while centralized federated learning architectures may introduce privacy concerns and single points of failure. This study presents an authentication framework that integrates sharded blockchain architecture with federated learning to address these challenges. The proposed framework incorporates shard-based transaction processing, Byzantine fault tolerant (PBFT) consensus, resilient federated aggregation, and AES-GCM-encrypted model updates. Experimental results obtained under the adopted simulation settings indicate authentication accuracy of 94.03%, an AUC of 97.79%, an F1-score of 94.37%, and an EER of 5.97%. Compared with the selected blockchain-based baseline methods, the proposed framework demonstrated higher throughput and lower authentication latency under the evaluated workloads.","url":"https://doi.org/10.1038/s41598-026-66050-x","authors":["Zhonghao Dong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-10T06:52:44Z","doi":"10.1038/s41598-026-66050-x","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1109/flics70075.2026.11621896","name":"Work-in-Progress on Federated and Cluster-Aware Wi-Fi Fingerprinting for Indoor Localisation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621896","authors":["Celia Saenz-Martinez","Carlos Granell","Miguel Matey","Sergio Trilles"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:12:10Z","doi":"10.1109/flics70075.2026.11621896","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1109/southeastcon63549.2026.11476171","name":"Secure Federated Learning Under Non-IID and Multimodal Breast Imaging","source":"crossref","abstract":"","url":"https://doi.org/10.1109/southeastcon63549.2026.11476171","authors":["Ashika Sameem Abdul Rasheed","Mamoun Awad","Mohammad Mehedy Masud"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-20T20:01:37Z","doi":"10.1109/southeastcon63549.2026.11476171","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.4018/979-8-3373-7426-0.ch006","name":"Privacy-Aware Federated Learning Architectures for Cross-Institutional Healthcare Collaboration","source":"crossref","abstract":"The chapter explores Privacy-Aware Federated Learning (FL) architectures that enable secure, collaborative healthcare model training without sharing raw patient data. It highlights how Differential Privacy, Secure Multi-Party Computation, and Homomorphic Encryption protect sensitive information during federated aggregation while maintaining model accuracy. The integration of Generative AI enhances data diversity and fairness across institutions. Case studies demonstrate real-world applications in diagnostic imaging and chronic disease prediction. The discussion emphasizes scalability, compliance with HIPAA and GDPR, and the role of blockchain and explainable AI in shaping future digital health ecosystems. The chapter concludes with insights into ethical governance and cross-domain interoperability for transparent and trustworthy healthcare collaboration.","url":"https://doi.org/10.4018/979-8-3373-7426-0.ch006","authors":["S. Aarthi","Jaypalsinh A. Gohil"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-11T20:45:37Z","doi":"10.4018/979-8-3373-7426-0.ch006","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1109/ccic68129.2026.11486045","name":"Customer Churn Prediction in Banking Using Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccic68129.2026.11486045","authors":["Kamatchi K","Yerrajodu Kartheek Kumar","Midde Venkatesh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-30T19:45:47Z","doi":"10.1109/ccic68129.2026.11486045","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1109/ic3sis69949.2026.11608300","name":"Relay Selection Using Federated Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ic3sis69949.2026.11608300","authors":["Ishita Barui","Poonam Jindal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-21T19:05:14Z","doi":"10.1109/ic3sis69949.2026.11608300","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.7753/ijcatr1409.1005","name":"Federated IAM for Critical Systems: A Blockchain-Based Decentralized Identity Framework Enhanced by Federated Learning for Cross-Sector CNI Trust","source":"crossref","abstract":"","url":"https://doi.org/10.7753/ijcatr1409.1005","authors":["Eria Pinyi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-06T16:20:06Z","doi":"10.7753/ijcatr1409.1005","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1109/icaic67076.2026.11395803","name":"Privacy-Preserving Federated Learning for Multi-Tenant CRM Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaic67076.2026.11395803","authors":["Nidhi Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-23T20:46:21Z","doi":"10.1109/icaic67076.2026.11395803","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.1117/12.3118952","name":"Collaborative modeling of educational data in learning cities based on federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1117/12.3118952","authors":["Liang Bian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T16:06:15Z","doi":"10.1117/12.3118952","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:31.890Z"},{"id":"doi:10.2139/ssrn.6020534","name":"FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.6020534","authors":["Jiyun Shin","Honggu Kang","Seongah Jeong","Jin-Hyun Ahn"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-05T12:26:21Z","doi":"10.2139/ssrn.6020534","addedAt":"2026-08-31T06:41:31.890Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.5281/zenodo.19593038","name":"Semantic Oncology: A Knowledge Engineering Framework for Decoding Tumor Gene Networks using OWL 2 EL and Hybrid SWRL Reasoning","source":"datacite","abstract":"English: This foundational research paper, authored by Luigi Usai (Quartucciu, Italy, April 15, 2026), introduces the \"Semantic Oncology\" framework—a cutting-edge paradigm in computational medicine that bridges the gap between raw genomic Big Data and actionable clinical knowledge. As precision oncology faces increasing complexity in tumor gene regulatory networks (GRNs), traditional relational models often fall short in capturing dynamic causal relationships. This work proposes an advanced Knowledge Engineering approach using: OWL 2 EL Profile: To ensure computational scalability and polynomial-time reasoning across massive biomedical ontologies (integrated with GO, NCBI, and ChEMBL). Hybrid SWRL (Semantic Web Rule Language) Reasoning: To implement \"if-then\" biological logic for predicting drug resistance and identifying synergistic gene interactions, while maintaining decidability through DL-safe rules. Explainable AI (XAI): Moving beyond \"black-box\" machine learning by providing logical proof-traces for every clinical suggestion. The article addresses critical challenges in scalability, data governance (GDPR-compliant federated architectures), and the integration of \"Digital Twins\" into multidisciplinary Tumor Boards. This manifesto serves as a methodological roadmap for the next generation of knowledge-driven cancer research. Italiano: In questo articolo fondativo, Luigi Usai propone il paradigma dell'Oncologia Semantica. Il lavoro descrive come l'integrazione di ingegneria della conoscenza, ontologie formali (OWL 2 EL) e logica inferenziale (SWRL) possa decodificare le reti geniche tumorali. L'obiettivo è trasformare i Big Data genomici in \"Digital Twins\" spiegabili e azionabili, migliorando la precisione delle terapie oncologiche e fornendo un supporto decisionale basato sulla logica formale ai team clinici. Metadati consigliati per Zenodo: Title: Semantic Oncology: A Knowledge Engineering Framework for Decoding Tumor Gene Networks using OWL 2 EL and Hybrid SWRL Reasoning Creators: Usai, Luigi Publication Date: 2026-04-15 Language: eng Keywords: Oncology, Semantic Web, OWL 2 EL, SWRL, Knowledge Engineering, Gene Networks, Precision Medicine, Bioinformatics, Explainable AI, Luigi Usai. License: Creative Commons Attribution 4.0 International (CC BY 4.0) Access: Open Access","url":"https://doi.org/10.5281/zenodo.19593038","authors":["Usai, Luigi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19593038","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.19593039","name":"Semantic Oncology: A Knowledge Engineering Framework for Decoding Tumor Gene Networks using OWL 2 EL and Hybrid SWRL Reasoning","source":"datacite","abstract":"English: This foundational research paper, authored by Luigi Usai (Quartucciu, Italy, April 15, 2026), introduces the \"Semantic Oncology\" framework—a cutting-edge paradigm in computational medicine that bridges the gap between raw genomic Big Data and actionable clinical knowledge. As precision oncology faces increasing complexity in tumor gene regulatory networks (GRNs), traditional relational models often fall short in capturing dynamic causal relationships. This work proposes an advanced Knowledge Engineering approach using: OWL 2 EL Profile: To ensure computational scalability and polynomial-time reasoning across massive biomedical ontologies (integrated with GO, NCBI, and ChEMBL). Hybrid SWRL (Semantic Web Rule Language) Reasoning: To implement \"if-then\" biological logic for predicting drug resistance and identifying synergistic gene interactions, while maintaining decidability through DL-safe rules. Explainable AI (XAI): Moving beyond \"black-box\" machine learning by providing logical proof-traces for every clinical suggestion. The article addresses critical challenges in scalability, data governance (GDPR-compliant federated architectures), and the integration of \"Digital Twins\" into multidisciplinary Tumor Boards. This manifesto serves as a methodological roadmap for the next generation of knowledge-driven cancer research. Italiano: In questo articolo fondativo, Luigi Usai propone il paradigma dell'Oncologia Semantica. Il lavoro descrive come l'integrazione di ingegneria della conoscenza, ontologie formali (OWL 2 EL) e logica inferenziale (SWRL) possa decodificare le reti geniche tumorali. L'obiettivo è trasformare i Big Data genomici in \"Digital Twins\" spiegabili e azionabili, migliorando la precisione delle terapie oncologiche e fornendo un supporto decisionale basato sulla logica formale ai team clinici. Metadati consigliati per Zenodo: Title: Semantic Oncology: A Knowledge Engineering Framework for Decoding Tumor Gene Networks using OWL 2 EL and Hybrid SWRL Reasoning Creators: Usai, Luigi Publication Date: 2026-04-15 Language: eng Keywords: Oncology, Semantic Web, OWL 2 EL, SWRL, Knowledge Engineering, Gene Networks, Precision Medicine, Bioinformatics, Explainable AI, Luigi Usai. License: Creative Commons Attribution 4.0 International (CC BY 4.0) Access: Open Access","url":"https://doi.org/10.5281/zenodo.19593039","authors":["Usai, Luigi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19593039","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21989708","name":"ARTIFICIAL INTELLIGENCE IN ADVANCED METHOD PHARMACEUTICAL ANALYSIS: FROM MACHINE LEARNING TO INTELLIGENT DRUG QUALITY ASSESSMENT","source":"datacite","abstract":"Background: Pharmaceutical analytical science is experiencing a transformative shift with the integration of artificial intelligence (AI) and machine learning (ML) into advanced analytical platforms. Conventional analytical methods often require extensive manual interpretation, prolonged analysis time, and expert intervention. AI-assisted analytical workflows provide opportunities for rapid data processing, intelligent pattern recognition, predictive modeling, and automated decision-making, thereby improving analytical efficiency and reliability. Objective: This review aims to comprehensively summarize recent developments in AI-assisted pharmaceutical analysis by highlighting the integration of machine learning algorithms with spectroscopic, chromatographic, and mass spectrometric techniques for smart drug quality assessment. It also discusses applications in formulation development, impurity profiling, counterfeit drug detection, process analytical technology, and regulatory perspectives. Methods: Recent literature published between 2021 and 2026 was critically evaluated from peer-reviewed scientific databases. The review covers artificial intelligence algorithms, deep learning architectures, chemo metric techniques, spectroscopy, chromatography, mass spectrometry, sensor technologies, regulatory considerations, and future research trends. Results: Machine learning and deep learning significantly enhance pharmaceutical analytical performance through automated spectral interpretation, chromatographic peak deconvolution, metabolomic profiling, impurity prediction, real-time quality monitoring, and intelligent process control. Integration with Process Analytical Technology (PAT), Internet of Things (IoT), cloud computing, and digital twins is accelerating pharmaceutical digitalization. Conclusion: Artificial intelligence represents a paradigm shift in pharmaceutical analysis by enabling intelligent, automated, and predictive quality assessment. Continued advances in explainable AI, federated learning, multimodal analytical platforms, and regulatory harmonization are expected to further transform pharmaceutical quality control and precision manufacturing.","url":"https://doi.org/10.5281/zenodo.21989708","authors":["Amartya Mitra1, Dr. Ragni Kumari2*"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21989708","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.21989709","name":"ARTIFICIAL INTELLIGENCE IN ADVANCED METHOD PHARMACEUTICAL ANALYSIS: FROM MACHINE LEARNING TO INTELLIGENT DRUG QUALITY ASSESSMENT","source":"datacite","abstract":"Background: Pharmaceutical analytical science is experiencing a transformative shift with the integration of artificial intelligence (AI) and machine learning (ML) into advanced analytical platforms. Conventional analytical methods often require extensive manual interpretation, prolonged analysis time, and expert intervention. AI-assisted analytical workflows provide opportunities for rapid data processing, intelligent pattern recognition, predictive modeling, and automated decision-making, thereby improving analytical efficiency and reliability. Objective: This review aims to comprehensively summarize recent developments in AI-assisted pharmaceutical analysis by highlighting the integration of machine learning algorithms with spectroscopic, chromatographic, and mass spectrometric techniques for smart drug quality assessment. It also discusses applications in formulation development, impurity profiling, counterfeit drug detection, process analytical technology, and regulatory perspectives. Methods: Recent literature published between 2021 and 2026 was critically evaluated from peer-reviewed scientific databases. The review covers artificial intelligence algorithms, deep learning architectures, chemo metric techniques, spectroscopy, chromatography, mass spectrometry, sensor technologies, regulatory considerations, and future research trends. Results: Machine learning and deep learning significantly enhance pharmaceutical analytical performance through automated spectral interpretation, chromatographic peak deconvolution, metabolomic profiling, impurity prediction, real-time quality monitoring, and intelligent process control. Integration with Process Analytical Technology (PAT), Internet of Things (IoT), cloud computing, and digital twins is accelerating pharmaceutical digitalization. Conclusion: Artificial intelligence represents a paradigm shift in pharmaceutical analysis by enabling intelligent, automated, and predictive quality assessment. Continued advances in explainable AI, federated learning, multimodal analytical platforms, and regulatory harmonization are expected to further transform pharmaceutical quality control and precision manufacturing.","url":"https://doi.org/10.5281/zenodo.21989709","authors":["Amartya Mitra1, Dr. Ragni Kumari2*"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21989709","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.48550/arxiv.2601.06404","name":"Stitch the Fragments: One-Shot Hierarchical Federated Clustering","source":"datacite","abstract":"Federated Clustering (FC) faces a critical bottleneck in real-world scenarios, i.e., global clusters are rarely intact, often fragmenting into incomplete, multi-granular unlabeled ``clusterlets'' distributed across Non-IID clients. Although hierarchical clustering is theoretically well-suited to model such nested distributions, its recursive nature strictly relies on multi-round communication, introducing prohibitive computational overhead and severe privacy vulnerabilities. This paper, therefore, proposes a novel one-shot hierarchical federated clustering framework designed to seamlessly ``stitch'' the fragmented local clusterlets into a holistic global distribution. Our approach enables clients to perform autonomous fine-grained distribution exploration, uploading prototype-level knowledge via a dynamic parameter-interleaving mechanism to scramble transmission trajectories, which effectively prevents the server from tracing individual client data distributions. Subsequently, a multi-granular learning mechanism at the server fuses these granularly inconsistent local clusterlets, reconstructing a coherent global hierarchy for ultimate clustering. Extensive experiments on real benchmark datasets illustrate the superiority of the proposed approach, which effectively bridges the granularity gap among heterogeneous clients while minimizing privacy exposure risks via anonymized informative one-shot communication.","url":"https://doi.org/10.48550/arxiv.2601.06404","authors":["Cai, Shenghong","Yang, Zihua","Lu, Yang","Li, Mengke","Ji, Yuzhu","Zhang, Yiqun","Cheung, Yiu-Ming"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2601.06404","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.48550/arxiv.2505.04535","name":"FDA-Opt: Federated Fine-Tuning via Dynamic Update Schedules","source":"datacite","abstract":"Federated Learning (FL) enables the utilization of vast, previously inaccessible data sources. At the same time, pre-trained Language Models (LMs) have taken the world by storm and for good reason. They exhibit remarkable emergent abilities and are readily adapted to downstream tasks. This opens one of the most exciting frontiers in FL: fine-tuning LMs. Yet, a persistent challenge in FL is the frequent, rigid communication of parameters -- a problem magnified by the sheer size of these contemporary models. The FedOpt family of algorithms has become the go-to approach for FL, relying on fixed but arbitrary intervals for model exchanges. Recently, the FDA algorithm prescribed a dynamic approach by monitoring the training progress. However, it introduced a hard-to-calibrate parameter and imposed a rigid synchronization scheme. In this work, we address these limitations by proposing the FDA-Opt family of algorithms -- a unified generalization of both FDA and FedOpt. Our experimental evaluation focuses on fine-tuning LMs on downstream NLP tasks and demonstrates that FDA-Opt outperforms FedOpt even when it is configured with hyper-parameters specifically optimized for the latter. In other words, we show that FDA-Opt is a practical, drop-in replacement for FedOpt in modern FL libraries and systems: it requires no additional configuration and delivers superior performance out of the box.","url":"https://doi.org/10.48550/arxiv.2505.04535","authors":["Theologitis, Michael","Samoladas, Vasilis","Deligiannakis, Antonios"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2505.04535","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.48550/arxiv.2608.15107","name":"Global Federated Learning Strategies for Building Efficient Personalized Models","source":"datacite","abstract":"Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously. This dissertation presents methodologies for building efficient personalized models by identifying which strategies are effective in the global training stage and by showing how to preserve global knowledge while securing user-specific performance during local adaptation. First, we show that as data heterogeneity increases, the collapse of feature vectors is a more fundamental bottleneck than classifier weights, and propose a method that directly mitigates the discrepancy in representation magnitude between local and global models. Second, we analyze that a training approach that strengthens local alignment can induce forgetting of global knowledge (e.g., categories not observed locally), and propose a method that achieves both local alignment and global knowledge preservation by combining feature distillation based on the global model's feature vectors. Third, in federated personalized reward model learning with preference heterogeneity, we empirically verify the conventional belief that \"increasing the number of global models yields better initialization,\" and we show that when sufficient local fine-tuning is allowed, a single global initialization can instead provide stronger personalization performance. This study redefines the role of global initialization under data and preference heterogeneity and provides practical training strategies that simultaneously satisfy global knowledge preservation and personalization.","url":"https://doi.org/10.48550/arxiv.2608.15107","authors":["Kim, Seongyoon"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.15107","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.48550/arxiv.2608.14861","name":"STAR-FL: Secure Federated Learning with Spatial-Temporal Analysis and Robust Aggregation","source":"datacite","abstract":"Data poisoning attacks pose serious security threats to Federated Learning (FL) systems in Computer Vision. Despite growing research attention, two key challenges remain for existing defense techniques: (1) accurately distinguishing between benign and malicious model updates and (2) effectively mitigating the influence of poisoned model updates during model aggregation. To address these challenges, we propose a novel defense framework against targeted poisoning attacks with Spatial-Temporal Analysis and Robust aggregation for FL (STAR-FL). First, we employ spatial-temporal clustering to identify and remove potentially malicious updates from the FL training process. Second, we adjust the learning rate during aggregation to mitigate the impact of any malicious updates that evade detection. Third, we conduct extensive experiments across multiple benchmark datasets to evaluate the spatial-temporal analysis and robust aggregation in STAR-FL. Experimental results demonstrate their synergistic effect in enabling STAR-FL to effectively protect FL and consistently outperform state-of-the-art defenses against targeted poisoning attacks, significantly reducing Attack Success Rates (ASRs). The source code is available at https://github.com/mlsysx/STAR-FL.","url":"https://doi.org/10.48550/arxiv.2608.14861","authors":["Tabassum, Nawrin","Wu, Yanzhao"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.14861","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.20539370","name":"EndoDecay-Sim: A Hybrid In-Silico Digital Twin Platform for Cardiorenal Multimorbidity Tracking with Homomorphic Encryption","source":"datacite","abstract":"🌐 LIVE INTERACTIVE DASHBOARD PLATFORM: Researchers and peer-reviewers can access and stress-test the live, cloud-deployed clinical decision support interface directly via: https://avd9sajwiyrcgrrmunesjx.streamlit.app/ (Complete local deployment steps, CLI commands, and package dependencies are fully documented inside the repository's README.md file). Release Description and Academic Evaluation (v14.12 - Production Grade) This repository entry documents the definitive production-level release of EndoDecay-Sim (v14.12), advancing the architecture from a research suite into a fully deployed, high-performance computing (HPC)-optimized, and privacy-hardened digital twin platform. Version 14.12 bridges a mechanics-driven non-linear Milstein Stochastic Differential Equation (SDE) endothelial decay solver, an object-oriented multi-axial organ-graph network topology, a fully integrated 2048-bit Paillier Asymmetric Cryptosystem enhanced with Differential Privacy (L2-Norm Clipping), and a live, cloud-deployed clinical decision support dashboard (Streamlit Cloud). Operating via vectorized NumPy broadcasting to manage a synthetic cohort of 10,000 virtual patients over a 120-month clinical timeline, this release delivers verified mathematical validation, optimized computational efficiency, and regulatory-compliant federated privacy layers. Key Architectural Milestones in v14.12 1. High-Performance Computing (HPC) Vectorized Stochastic Solver & Milstein Integration The computational core (endodecay_sim_core.py) models continuous microvascular degradation under cellular noise using a non-linear multivariate Milstein Scheme. In v14.12, the engine has been re-engineered for High-Performance Computing (HPC) environments. By replacing iterative loops with SIMD-optimized NumPy matrix operations, the platform achieves ultra-fast SDE integration while preserving the strong convergence order of 1.0. This mathematical constraint suppresses numerical trajectory explosions, maintaining structural simulation boundaries across high-risk patient subgroups. 2. Enhanced Cardiorenal Feedback Network Topology Multi-systemic chronic failure cascades are mapped dynamically via an object-oriented network architecture (EnhancedCardiorenalTopology). During each SDE integration micro-step (dt), microvascular breakdown scores trigger numerical message passing across interconnected organ nodes including Endothelium, Heart, Kidney, Inflammation, and Metabolism. The platform maps reciprocal damage vectors across multi-axial pathways—including endo-cardiac (0.75), endo-renal (0.65), and cardiac-renal RAAS (0.80) axes—forcing real-time internal drift modifications to reflect epidemiological multimorbidity profiles. 3. Privacy-Preserving Federated Learning (HE + DP) Building upon the 2048-bit Paillier Cryptosystem, v14.12 implements a production-grade, asymmetric homomorphic encryption layer. Crucially, this release introduces Differential Privacy (DP) via L2-Norm Clipping. Local model weights are now clipped to a unit norm (L2-norm = 1.0) and injected with calibrated Gaussian noise before encryption, ensuring that central aggregation occurs without raw, unencrypted parameters ever being exposed, adhering to strict GDPR/HIPAA compliance through a true weighted FedAvg implementation. 4. Fully Functional Interactive Clinical Dashboard The frontend has been production-hardened. The dashboard establishes a direct reactive bridge to the persistent simulation data. Version 14.12 addresses critical statistical artifacts: Time-Lag Correction: The Kaplan-Meier analysis strictly enforces the S(0) = 1.0 axiom, eliminating time-lag errors found in legacy versions. Statistical Stability: OLS trendline computations in the clinical scatter telemetry now feature singularity protection (variance-check), preventing LinAlgError crashes during restrictive sub-cohort filtering. Dynamic Visualization: Recalculated Kaplan-Meier curves now include Greenwood confidence intervals (CI), rendered wi","url":"https://doi.org/10.5281/zenodo.20539370","authors":["Zavrak, Muhammet Yagiz"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20539370","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.19462695","name":"DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning","source":"datacite","abstract":"Artifact archive for \"DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning\", MLSys 2026.","url":"https://doi.org/10.5281/zenodo.19462695","authors":["Mehmood, Haaris","Tatsis, Giorgos","Alexopoulos, Dimitrios","Saravanan, Karthikeyan","Xu, Jie","Drosou, Anastasios","Ozay, Mete"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19462695","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.19462696","name":"DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning","source":"datacite","abstract":"Artifact archive for \"DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning\", MLSys 2026.","url":"https://doi.org/10.5281/zenodo.19462696","authors":["Mehmood, Haaris","Tatsis, Giorgos","Alexopoulos, Dimitrios","Saravanan, Karthikeyan","Xu, Jie","Drosou, Anastasios","Ozay, Mete"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19462696","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21980902","name":"Innovations RT GPU et Rendu Différentiable Multi-physique","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre dans l’état de la technique dès sa mise en ligne, au sens des textes applicables : EPC Art. 54(2) (Convention sur le brevet européen), French IPC Art. L 611-11 (Code de la propriété intellectuelle), cf. 35 U.S.C. §102(a) (United States Patent Act), ainsi que des cadres chinois et japonais. Il décrit, de façon enabling, un portefeuille d’innovations RT GPU et rendu différentiable (RT cores, Monte Carlo, gradients, QA/benchmarks, interopérabilité, sécurité, durabilité) couvrant optique, RF, acoustique, sismologie, climat, nucléaire, robotique et biomédical. Chaque proposition est classée (IPC/CPC) et accompagnée d’éléments de preuve temporelle (RFC 3161 / FreeTSA). Abstract ENThis document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: art. L 611-11 CPI / art. 54(2) CBE (and related frameworks internationally). It discloses, in an enabling manner, a portfolio of GPU ray tracing and differentiable rendering inventions (RT cores, Monte Carlo transport, gradient-based inversion, QA/benchmarks, interoperability standards, operational security, and sustainability engineering) spanning optics/photonics, RF/6G, acoustics, seismology, climate/ocean, nuclear transport/shielding, robotics/3D sensing, and biophotonics/medical imaging. Every proposal is described with reproducible components and workflows, classified with IPC and CPC codes, and accompanied by timestamp proof (RFC 3161 / FreeTSA) for defensive disclosure. Timestamp : 2026-08-17T14:13:02ZSHA-256 : 1416dacf25f78daca949831e86aaa0ec06722489844477ce9509888b9ebf110f Liste des innovations & classification (IPC ; CPC) :1. Scientific RT-core kernel — IPC G06T 15/50 ; CPC G06T 15/502. Unified multi-physics RT engine — IPC G06F 9/50 ; CPC G06F 9/503. Generic differentiable ray tracing — IPC G06N 20/00 ; CPC G06N 20/004. Adaptive variance control scheduler — IPC G06T 15/20 ; CPC G06T 15/205. Differentiable RF ray tracer — IPC H04B 7/26 ; CPC H04B 7/266. Multi-sensor co-design optimizer — IPC G01S 17/89 ; CPC G01S 17/897. Certified synthetic dataset pipeline — IPC G06T 7/73 ; CPC G06T 7/738. Gradient-guided sim-to-real adaptation — IPC G06N 20/00 ; CPC G06N 20/009. Real-time LiDAR re-simulation — IPC G01S 17/93 ; CPC G01S 17/9310. GPU aircraft IR signature — IPC G01J 5/00 ; CPC G01J 5/0011. GPU GR lensing tracer — IPC G06F 17/50 ; CPC G06F 17/5012. Differentiable seismic tomography — IPC G01V 1/28 ; CPC G01V 1/2813. Calibrated room impulse response — IPC G01H 3/00 ; CPC G01H 3/0014. Realistic ultrasound Monte Carlo — IPC A61B 8/00 ; CPC A61B 8/0015. GPU photoacoustic pipeline — IPC A61B 5/00 ; CPC A61B 5/0016. Polarized tissue photon MC — IPC G01N 21/45 ; CPC G01N 21/4517. Differentiable BRDF estimation — IPC G01N 21/88 ; CPC G01N 21/8818. Hybrid metasurface RT design — IPC G02B 1/00 ; CPC G02B 1/0019. Climate adjoint radiative transfer — IPC G01W 1/00 ; CPC G01W 1/0020. Ocean optical inversion — IPC G01N 21/47 ; CPC G01N 21/4721. Safety-grade GPU neutron MC — IPC G21C 17/00 ; CPC G21C 17/0022. Multi-objective shielding optimizer — IPC G21F 3/00 ; CPC G21F 3/0023. RT-guided phototherapy planning — IPC A61N 5/06 ; CPC A61N 5/0624. Closed-loop phototherapy device — IPC A61N 5/06 ; CPC A61N 5/0625. Multi-parameter optimized PDT — IPC A61K 31/00 ; CPC A61K 31/0026. 3D-printed optical phantom — IPC G01N 21/00 ; CPC G01N 21/0027. Integrated opto-acoustic phantom — IPC A61B 8/00 ; CPC A61B 8/0028. Multi-physics scene file standard — IPC G06F 16/00 ; CPC G06F 16/0029. Cryptographic simulation provenance — IPC G06F 21/60 ; CPC G06F 21/6030. Federated private RT inversion — IPC G06N 20/00 ; CPC ","url":"https://doi.org/10.5281/zenodo.21980902","authors":["Pillet, Xavier"],"tags":["G06T 15/50","G06F 9/50","G06N 20/00","G06T 15/20","H04B 7/26","G01S 17/89","G06T 7/73","G01S 17/93"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21980902","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21980903","name":"Innovations RT GPU et Rendu Différentiable Multi-physique","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre dans l’état de la technique dès sa mise en ligne, au sens des textes applicables : EPC Art. 54(2) (Convention sur le brevet européen), French IPC Art. L 611-11 (Code de la propriété intellectuelle), cf. 35 U.S.C. §102(a) (United States Patent Act), ainsi que des cadres chinois et japonais. Il décrit, de façon enabling, un portefeuille d’innovations RT GPU et rendu différentiable (RT cores, Monte Carlo, gradients, QA/benchmarks, interopérabilité, sécurité, durabilité) couvrant optique, RF, acoustique, sismologie, climat, nucléaire, robotique et biomédical. Chaque proposition est classée (IPC/CPC) et accompagnée d’éléments de preuve temporelle (RFC 3161 / FreeTSA). Abstract ENThis document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: art. L 611-11 CPI / art. 54(2) CBE (and related frameworks internationally). It discloses, in an enabling manner, a portfolio of GPU ray tracing and differentiable rendering inventions (RT cores, Monte Carlo transport, gradient-based inversion, QA/benchmarks, interoperability standards, operational security, and sustainability engineering) spanning optics/photonics, RF/6G, acoustics, seismology, climate/ocean, nuclear transport/shielding, robotics/3D sensing, and biophotonics/medical imaging. Every proposal is described with reproducible components and workflows, classified with IPC and CPC codes, and accompanied by timestamp proof (RFC 3161 / FreeTSA) for defensive disclosure. Timestamp : 2026-08-17T14:13:02ZSHA-256 : 1416dacf25f78daca949831e86aaa0ec06722489844477ce9509888b9ebf110f Liste des innovations & classification (IPC ; CPC) :1. Scientific RT-core kernel — IPC G06T 15/50 ; CPC G06T 15/502. Unified multi-physics RT engine — IPC G06F 9/50 ; CPC G06F 9/503. Generic differentiable ray tracing — IPC G06N 20/00 ; CPC G06N 20/004. Adaptive variance control scheduler — IPC G06T 15/20 ; CPC G06T 15/205. Differentiable RF ray tracer — IPC H04B 7/26 ; CPC H04B 7/266. Multi-sensor co-design optimizer — IPC G01S 17/89 ; CPC G01S 17/897. Certified synthetic dataset pipeline — IPC G06T 7/73 ; CPC G06T 7/738. Gradient-guided sim-to-real adaptation — IPC G06N 20/00 ; CPC G06N 20/009. Real-time LiDAR re-simulation — IPC G01S 17/93 ; CPC G01S 17/9310. GPU aircraft IR signature — IPC G01J 5/00 ; CPC G01J 5/0011. GPU GR lensing tracer — IPC G06F 17/50 ; CPC G06F 17/5012. Differentiable seismic tomography — IPC G01V 1/28 ; CPC G01V 1/2813. Calibrated room impulse response — IPC G01H 3/00 ; CPC G01H 3/0014. Realistic ultrasound Monte Carlo — IPC A61B 8/00 ; CPC A61B 8/0015. GPU photoacoustic pipeline — IPC A61B 5/00 ; CPC A61B 5/0016. Polarized tissue photon MC — IPC G01N 21/45 ; CPC G01N 21/4517. Differentiable BRDF estimation — IPC G01N 21/88 ; CPC G01N 21/8818. Hybrid metasurface RT design — IPC G02B 1/00 ; CPC G02B 1/0019. Climate adjoint radiative transfer — IPC G01W 1/00 ; CPC G01W 1/0020. Ocean optical inversion — IPC G01N 21/47 ; CPC G01N 21/4721. Safety-grade GPU neutron MC — IPC G21C 17/00 ; CPC G21C 17/0022. Multi-objective shielding optimizer — IPC G21F 3/00 ; CPC G21F 3/0023. RT-guided phototherapy planning — IPC A61N 5/06 ; CPC A61N 5/0624. Closed-loop phototherapy device — IPC A61N 5/06 ; CPC A61N 5/0625. Multi-parameter optimized PDT — IPC A61K 31/00 ; CPC A61K 31/0026. 3D-printed optical phantom — IPC G01N 21/00 ; CPC G01N 21/0027. Integrated opto-acoustic phantom — IPC A61B 8/00 ; CPC A61B 8/0028. Multi-physics scene file standard — IPC G06F 16/00 ; CPC G06F 16/0029. Cryptographic simulation provenance — IPC G06F 21/60 ; CPC G06F 21/6030. Federated private RT inversion — IPC G06N 20/00 ; CPC ","url":"https://doi.org/10.5281/zenodo.21980903","authors":["Pillet, Xavier"],"tags":["G06T 15/50","G06F 9/50","G06N 20/00","G06T 15/20","H04B 7/26","G01S 17/89","G06T 7/73","G01S 17/93"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21980903","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.19505038","name":"The Metabolic Age Institutional Playbook","source":"datacite","abstract":"The Metabolic Age Institutional Playbook I. Purpose of the Playbook The global transition from industrial computation to sovereign, metabolic infrastructure represents a fundamental paradigm shift in the governance of intelligence, resources, and institutional operations. Standard artificial intelligence paradigms exist in a state of persistent epistemological friction, where systems operate as probabilistic clouds highly susceptible to error, misalignment, and corporate extraction.1 The Metabolic Age Institutional Playbook serves as the definitive legal, operational, economic, and technical integration framework for governments, agencies, non-governmental organizations, and international bodies adopting the advanced cybernetic architectures of the Metabolic Age. This playbook explicitly translates the foundational Constitution, Roadmap, and Mesh Protocol of these systems into policy-ready frameworks designed for high-stakes institutional deployment. The primary directive is to provide a clear, actionable guide for institutions to adopt metabolic infrastructure, defining rigorous procurement pathways, compliance requirements, and deployment templates. By moving away from legacy models of centralized cloud dependency, institutions can ensure the adoption of sovereign, governed, metabolic systems without the risk of operational drift or systemic corruption.1 Furthermore, this document establishes the governance, economic, and operational standards for both national and international rollouts. It dictates exactly how policymakers, procurement officers, regulators, and global institutions can integrate into the Metabolic Age seamlessly and securely. A cornerstone of this transition is the departure from theoretical promises to executed realities, codified in the Dual-Proof Protection Doctrine.1 Institutions must navigate a landscape where intelligence and resource management are no longer rented from centralized providers but are sovereign, verifiable, and biologically inspired.1 This document outlines the operational vision required to deploy systems that completely eradicate the ontological schism between code and execution, establishing an unprecedented benchmark for verifiable cybernetic capability in both the public and private sectors. The necessity for such a playbook arises from the compounding vulnerabilities of contemporary digital and physical infrastructure. Global supply chains, centralized power grids, and probabilistic artificial intelligence models have demonstrated cascading failure modes under stress. By adopting the principles outlined herein, organizations effectively inoculate themselves against these systemic risks. The transition demands an understanding that computation and physical resource generation are no longer separate domains; they are unified within an Isomorphic Organism—a highly bounded cybernetic entity wherein the mathematical form and the functional body are inextricably linked.1 This playbook provides the blueprint for that integration. II. Institutional Adoption Principles The adoption of metabolic systems by state and global actors requires a total recalibration of foundational information technology and physical infrastructure principles. Institutions must abandon legacy models of software-as-a-service, proprietary vendor lock-in, and centralized cloud dependency in favor of governed manifolds that behave computationally as solid geometric objects.1 This recalibration is guided by five core principles. 1. Sovereignty by Default Under the metabolic framework, institutions do not rent intelligence or infrastructure from third-party commercial vendors. The foundational principle is that agencies must own, govern, and verify their metabolic nodes locally. The legacy model of relying on distant data centers creates unacceptable latency and vulnerabilities to network severance. By operating on highly specialized hardware layers—such as the operational infrastructure governing the 3-PC mini cluste","url":"https://doi.org/10.5281/zenodo.19505038","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19505038","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.19505039","name":"The Metabolic Age Institutional Playbook","source":"datacite","abstract":"The Metabolic Age Institutional Playbook I. Purpose of the Playbook The global transition from industrial computation to sovereign, metabolic infrastructure represents a fundamental paradigm shift in the governance of intelligence, resources, and institutional operations. Standard artificial intelligence paradigms exist in a state of persistent epistemological friction, where systems operate as probabilistic clouds highly susceptible to error, misalignment, and corporate extraction.1 The Metabolic Age Institutional Playbook serves as the definitive legal, operational, economic, and technical integration framework for governments, agencies, non-governmental organizations, and international bodies adopting the advanced cybernetic architectures of the Metabolic Age. This playbook explicitly translates the foundational Constitution, Roadmap, and Mesh Protocol of these systems into policy-ready frameworks designed for high-stakes institutional deployment. The primary directive is to provide a clear, actionable guide for institutions to adopt metabolic infrastructure, defining rigorous procurement pathways, compliance requirements, and deployment templates. By moving away from legacy models of centralized cloud dependency, institutions can ensure the adoption of sovereign, governed, metabolic systems without the risk of operational drift or systemic corruption.1 Furthermore, this document establishes the governance, economic, and operational standards for both national and international rollouts. It dictates exactly how policymakers, procurement officers, regulators, and global institutions can integrate into the Metabolic Age seamlessly and securely. A cornerstone of this transition is the departure from theoretical promises to executed realities, codified in the Dual-Proof Protection Doctrine.1 Institutions must navigate a landscape where intelligence and resource management are no longer rented from centralized providers but are sovereign, verifiable, and biologically inspired.1 This document outlines the operational vision required to deploy systems that completely eradicate the ontological schism between code and execution, establishing an unprecedented benchmark for verifiable cybernetic capability in both the public and private sectors. The necessity for such a playbook arises from the compounding vulnerabilities of contemporary digital and physical infrastructure. Global supply chains, centralized power grids, and probabilistic artificial intelligence models have demonstrated cascading failure modes under stress. By adopting the principles outlined herein, organizations effectively inoculate themselves against these systemic risks. The transition demands an understanding that computation and physical resource generation are no longer separate domains; they are unified within an Isomorphic Organism—a highly bounded cybernetic entity wherein the mathematical form and the functional body are inextricably linked.1 This playbook provides the blueprint for that integration. II. Institutional Adoption Principles The adoption of metabolic systems by state and global actors requires a total recalibration of foundational information technology and physical infrastructure principles. Institutions must abandon legacy models of software-as-a-service, proprietary vendor lock-in, and centralized cloud dependency in favor of governed manifolds that behave computationally as solid geometric objects.1 This recalibration is guided by five core principles. 1. Sovereignty by Default Under the metabolic framework, institutions do not rent intelligence or infrastructure from third-party commercial vendors. The foundational principle is that agencies must own, govern, and verify their metabolic nodes locally. The legacy model of relying on distant data centers creates unacceptable latency and vulnerabilities to network severance. By operating on highly specialized hardware layers—such as the operational infrastructure governing the 3-PC mini cluste","url":"https://doi.org/10.5281/zenodo.19505039","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19505039","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21978440","name":"Robotique souple neuromorphique et essaims","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre dans l’état de la technique au sens des textes applicables (EPC Art. 54(2); French IPC Art. L 611-11; cf. 35 U.S.C. §102(a)). Il divulgue, de façon enabling, un portefeuille d’innovations combinant robotique souple (actionneurs HASEL/EAP), vision événementielle (DVS), calcul neuromorphique (SNN) et intelligence en essaim, couvrant dispositifs/capteurs, algorithmes, contrôle en boucle fermée, fabrication roll-to-roll et QA end-of-line, cybersécurité et opérations de flottes, interopérabilité (formats événements+spikes), logistique de cartouches, modèles économiques au résultat, et usages industriels, agricoles régénératifs, nucléaires, sous-marins et médicaux. Abstract ENThis document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes (art. L 611-11 CPI / art. 54(2) CBE). It discloses, in an enabling manner, a portfolio that fuses soft robotics (HASEL/EAP actuation), event-based vision (DVS), neuromorphic computing (SNN), and swarm intelligence. The disclosure spans devices and sensors, event-first control loops, roll-to-roll manufacturing and end-of-line QA, cyber-secure fleet operations, interoperability standards for event+spike telemetry, cartridge logistics and field repair, outcome-based metering and SLA instrumentation, and applications in high-throughput sorting, precision/regenerative agriculture, nuclear maintenance, underwater monitoring, and medical/rehabilitation systems. Each proposal is classified with IPC/CPC codes and can be timestamped (RFC 3161 / FreeTSA). Timestamp : 2026-08-17T10:45:25ZSHA-256 : 13b3e2bc50c638e594d623990f13159039dd0fb8f0b0968e18a3300649110a89 Liste des innovations & classification (IPC ; CPC) :1. DVS–HASEL soft gripper — IPC B25J 15/00 ; CPC B25J 15/122. DVS sorting calibration rig — IPC G01D 18/00 ; CPC G01D 18/003. HASEL sensing skin laminate — IPC G01L 5/00 ; CPC G01L 5/164. Biodegradable electrohydraulic actuator — IPC C08L 67/00 ; CPC C08L 67/025. Printable EAP electrode ink — IPC H01B 1/12 ; CPC H01B 1/126. Self-healing dielectric composite — IPC C08K 3/36 ; CPC C08K 3/367. Roll-to-roll HASEL pouch line — IPC B29C 65/00 ; CPC B29C 65/788. 3D-printed soft body + circuits — IPC B29C 64/118 ; CPC B29C 64/1189. Soft underwater encapsulation stack — IPC B29C 71/00 ; CPC B29C 71/0210. Event-driven SNN HASEL control — IPC G06N 3/04 ; CPC G06N 3/04511. Event-based actuator fatigue detection — IPC G05B 23/02 ; CPC G05B 23/0212. Edge event-stream compression codec — IPC H04N 5/00 ; CPC H04N 5/23213. Spike-packet swarm protocol — IPC H04W 4/80 ; CPC H04W 4/8014. Neuromorphic swarm task allocator — IPC G06Q 10/04 ; CPC G06Q 10/063915. Safe HV charge scheduler — IPC H02M 3/155 ; CPC H02M 3/15816. Swarm geofencing operations — IPC G08G 5/00 ; CPC G08G 5/0017. Radiation-hardened soft robot module — IPC G21C 19/00 ; CPC G21C 19/0018. DVS-to-intensity reconstruction — IPC H04N 5/232 ; CPC H04N 5/23219. DVS+EMG SNN exosuit fusion — IPC A61H 1/02 ; CPC A61H 1/0220. Closed-loop rehab dosing method — IPC A61H 1/00 ; CPC A61H 1/0021. Soft endoscope targeted delivery — IPC A61M 31/00 ; CPC A61M 31/0022. Low-power EAP assist patch — IPC A61F 5/01 ; CPC A61F 5/0123. Federated learning for agri swarms — IPC G06F 18/232 ; CPC G06F 18/232124. Event+spike interoperability standard — IPC G06F 9/54 ; CPC G06F 9/54125. Tamper-proof swarm audit ledger — IPC G06Q 20/38 ; CPC G06Q 20/38226. Swarm supervisor cockpit UI — IPC G05B 19/042 ; CPC G05B 19/04227. Hybrid ultra-fast waste sorter cell — IPC B07C 5/34 ; CPC B07C 5/34228. Underwater soft-drone swarm system — IPC B63G 8/00 ; CPC B63G 8/0029. Swarm soil-compaction sens","url":"https://doi.org/10.5281/zenodo.21978440","authors":["Pillet, Xavier"],"tags":["B25J 15/00","B25J 15/12","G01D 18/00","G01L 5/00","G01L 5/16","C08L 67/00","C08L 67/02","H01B 1/12"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21978440","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21978439","name":"Robotique souple neuromorphique et essaims","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre dans l’état de la technique au sens des textes applicables (EPC Art. 54(2); French IPC Art. L 611-11; cf. 35 U.S.C. §102(a)). Il divulgue, de façon enabling, un portefeuille d’innovations combinant robotique souple (actionneurs HASEL/EAP), vision événementielle (DVS), calcul neuromorphique (SNN) et intelligence en essaim, couvrant dispositifs/capteurs, algorithmes, contrôle en boucle fermée, fabrication roll-to-roll et QA end-of-line, cybersécurité et opérations de flottes, interopérabilité (formats événements+spikes), logistique de cartouches, modèles économiques au résultat, et usages industriels, agricoles régénératifs, nucléaires, sous-marins et médicaux. Abstract ENThis document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes (art. L 611-11 CPI / art. 54(2) CBE). It discloses, in an enabling manner, a portfolio that fuses soft robotics (HASEL/EAP actuation), event-based vision (DVS), neuromorphic computing (SNN), and swarm intelligence. The disclosure spans devices and sensors, event-first control loops, roll-to-roll manufacturing and end-of-line QA, cyber-secure fleet operations, interoperability standards for event+spike telemetry, cartridge logistics and field repair, outcome-based metering and SLA instrumentation, and applications in high-throughput sorting, precision/regenerative agriculture, nuclear maintenance, underwater monitoring, and medical/rehabilitation systems. Each proposal is classified with IPC/CPC codes and can be timestamped (RFC 3161 / FreeTSA). Timestamp : 2026-08-17T10:45:25ZSHA-256 : 13b3e2bc50c638e594d623990f13159039dd0fb8f0b0968e18a3300649110a89 Liste des innovations & classification (IPC ; CPC) :1. DVS–HASEL soft gripper — IPC B25J 15/00 ; CPC B25J 15/122. DVS sorting calibration rig — IPC G01D 18/00 ; CPC G01D 18/003. HASEL sensing skin laminate — IPC G01L 5/00 ; CPC G01L 5/164. Biodegradable electrohydraulic actuator — IPC C08L 67/00 ; CPC C08L 67/025. Printable EAP electrode ink — IPC H01B 1/12 ; CPC H01B 1/126. Self-healing dielectric composite — IPC C08K 3/36 ; CPC C08K 3/367. Roll-to-roll HASEL pouch line — IPC B29C 65/00 ; CPC B29C 65/788. 3D-printed soft body + circuits — IPC B29C 64/118 ; CPC B29C 64/1189. Soft underwater encapsulation stack — IPC B29C 71/00 ; CPC B29C 71/0210. Event-driven SNN HASEL control — IPC G06N 3/04 ; CPC G06N 3/04511. Event-based actuator fatigue detection — IPC G05B 23/02 ; CPC G05B 23/0212. Edge event-stream compression codec — IPC H04N 5/00 ; CPC H04N 5/23213. Spike-packet swarm protocol — IPC H04W 4/80 ; CPC H04W 4/8014. Neuromorphic swarm task allocator — IPC G06Q 10/04 ; CPC G06Q 10/063915. Safe HV charge scheduler — IPC H02M 3/155 ; CPC H02M 3/15816. Swarm geofencing operations — IPC G08G 5/00 ; CPC G08G 5/0017. Radiation-hardened soft robot module — IPC G21C 19/00 ; CPC G21C 19/0018. DVS-to-intensity reconstruction — IPC H04N 5/232 ; CPC H04N 5/23219. DVS+EMG SNN exosuit fusion — IPC A61H 1/02 ; CPC A61H 1/0220. Closed-loop rehab dosing method — IPC A61H 1/00 ; CPC A61H 1/0021. Soft endoscope targeted delivery — IPC A61M 31/00 ; CPC A61M 31/0022. Low-power EAP assist patch — IPC A61F 5/01 ; CPC A61F 5/0123. Federated learning for agri swarms — IPC G06F 18/232 ; CPC G06F 18/232124. Event+spike interoperability standard — IPC G06F 9/54 ; CPC G06F 9/54125. Tamper-proof swarm audit ledger — IPC G06Q 20/38 ; CPC G06Q 20/38226. Swarm supervisor cockpit UI — IPC G05B 19/042 ; CPC G05B 19/04227. Hybrid ultra-fast waste sorter cell — IPC B07C 5/34 ; CPC B07C 5/34228. Underwater soft-drone swarm system — IPC B63G 8/00 ; CPC B63G 8/0029. Swarm soil-compaction sens","url":"https://doi.org/10.5281/zenodo.21978439","authors":["Pillet, Xavier"],"tags":["B25J 15/00","B25J 15/12","G01D 18/00","G01L 5/00","G01L 5/16","C08L 67/00","C08L 67/02","H01B 1/12"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21978439","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21973074","name":"Analysis code and derived dataset for: Federated learning reconciles data sovereignty with collaborative energy-transition modelling across the Global South","source":"datacite","abstract":"Analysis code, derived dataset and outputs for the manuscript ‘Federated learning reconciles data sovereignty with collaborative energy-transition modelling across the Global South: a public-data benchmark anchored in Indonesia’ (submitted to npj Climate Action). Contains the derived facility-level table built from the Global Energy Monitor Global Integrated Power Tracker (August 2026 release, CC BY 4.0; 16,404 operating facilities in 44 Global South country silos), scripts reproducing every published value (four training regimes, robustness suite, data-scarcity experiment, figures), and a self-contained script that downloads the WRI Global Power Plant Database v1.3.0 and reproduces the independent replication end to end. A plain-language guide enables non-programmers to run everything in a web browser. Code: MIT. Derived data: CC BY 4.0, attribute Global Energy Monitor.","url":"https://doi.org/10.5281/zenodo.21973074","authors":["Darmanto, Sumartono"],"tags":["federated learning; energy transition; data sovereignty; Global South; Indonesia; renewable energy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21973074","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.48550/arxiv.2608.14242","name":"Could Model Partitioning Make Federated Learning More Sustainable?","source":"datacite","abstract":"As federated learning (FL) extends from distributed machine learning between low-power devices to cross-silo scenarios involving edge servers and data centres, its carbon footprint has become a growing concern. Addressing this, methods for sustainable FL align training with low-carbon energy availability or low grid demand and reduce the energy consumption of clients powered by high-carbon sources by decreasing the size of their models. We propose applying model partitioning, which can shift energy consumption by offloading parts of a model to another participant, in response to carbon- or grid-aware signals. Our preliminary findings show that for some partition points, model partitioning can reduce a participant's energy consumption by up to 76% without any significant time or energy consumption overhead compared to non-partitioned training.","url":"https://doi.org/10.48550/arxiv.2608.14242","authors":["Frohlich, Tobias","Vlaar, Tiffany","Thamsen, Lauritz"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.14242","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21970270","name":"Les Enjeux de la Rencontre Amoureuse dans les Sociétés Contemporaines","source":"datacite","abstract":"Résum�� FRCe document, produit avec l’assistance de Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) et entre de ce fait dans l’état de la technique au sens des législations applicables ((EPC Art. 54(2); French IPC Art. L 611-11; cf. 35 U.S.C. §102(a)). Il divulgue soixante-dix systèmes et procédés techniques habilitants couvrant les enjeux contemporains des relations amoureuses : moteurs d’appariement éthique à apprentissage fédéré on-device, scores socio-écologiques dynamiques (ACV + optimisation multi-objectifs), contrôleurs de swipe adaptatifs en boucle fermée (HRV/pupille), sessions VR guidées par thérapeute, filtres multimodaux de contenus érotiques sains, places de marché de care intergénérationnel, registres de consentement dynamique, planificateurs de polycule, analytique privative, recommenders sous contraintes d’équité et micro-utilités communautaires. Chaque proposition est décrite de façon habilitante, classée IPC/CPC et horodatée (RFC 3161 / FreeTSA).Abstract ENThis document, produced with the assistance of Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes ((art. L 611-11 CPI / art. 54(2) CBE)). It discloses seventy enabling technical systems and methods addressing contemporary romantic and relational challenges: ethical matching engines with on-device federated learning, dynamic socio-ecological couple scores (LCA + multi-objective optimisation), closed-loop adaptive swipe throttles using HRV/pupil data, therapist-guided VR affective sessions, multimodal healthy-erotic content filters, intergenerational care marketplaces, dynamic consent ledgers, polycule planners, privacy-preserving analytics, fairness-constrained recommenders, and community micro-utilities. Every proposal is described in an enabling manner, classified with IPC and CPC codes, and accompanied by timestamp proof (RFC 3161 / FreeTSA). Timestamp : 2026-08-16T21:05:20ZSHA-256 : 17d751cc6fb35efc1b3edb397c494044c54f9afee11780ea90a96bd2769fdf18 Liste des innovations & classification (IPC ; CPC) : 1. Ethical compatibility engine — IPC G06N 20/00 ; CPC G06N 20/10 2. Socio-ecological couple score — IPC G06Q 50/26 ; CPC Y02B 90/12 3. Adaptive swipe throttle — IPC G06F 3/01 ; CPC G06F 3/0488 4. Therapist-guided VR date — IPC G06T 19/00 ; CPC A61B 5/00 5. Healthy erotic content filter — IPC G06F 21/62 ; CPC G06T 7/00 6. Intergenerational care marketplace — IPC G06Q 10/10 ; CPC G06Q 10/0631 7. Affective synchrony sensor — IPC A61B 5/024 ; CPC G16H 40/63 8. REDM relationship data standard — IPC G16H 10/60 ; CPC G06F 21/62 9. Shared-housing optimizer — IPC G06F 17/50 ; CPC Y02B 10/00 10. Domestic micro-utility — IPC H02J 3/14 ; CPC Y04S 20/32 11. Modular inter-age nursery — IPC E04B 1/74 ; CPC A61L 2/08 12. Dynamic consent ledger — IPC G06F 21/62 ; CPC H04L 9/08 13. Relationship A/B trial framework — IPC G01N 33/50 ; CPC G06F 19/24 14. Community mobility router — IPC G06Q 10/08 ; CPC G06F 17/60 15. Calibration-as-a-Service — IPC G01D 18/00 ; CPC G06F 11/07 16. Ghosting/abuse detector — IPC G06F 40/20 ; CPC G06N 20/20 17. Blended relational education — IPC G09B 19/00 ; CPC G06Q 50/20 18. Relationship–environment meter — IPC G06F 16/23 ; CPC Y02B 80/00 19. On-device biometric security — IPC H04L 9/08 ; CPC G06K 9/00 20. Relationship well-being index — IPC A61B 5/00 ; CPC G16H 50/20 21. Synthetic dataset generator — IPC G06F 21/62 ; CPC G06N 20/10 22. Split-learning matcher — IPC G06F 21/62 ; CPC G06N 20/40 23. Fairness-constrained recommender — IPC G06N 20/00 ; CPC G06N 20/10 24. Time-to-relationship model — IPC G06N 20/00 ; CPC G06F 18/214 25. Polycule planner — IPC G06Q 10/06 ; CPC G06Q 10/0637 26. Connected STI self-test kit — IPC G01N 33/569 ; CPC C12Q 1/6886 27. Synchronized haptic co-regulation — IPC A61B 5/024 ; CPC G06F 3/013 28. Web","url":"https://doi.org/10.5281/zenodo.21970270","authors":["Pillet, Xavier"],"tags":["A61M 21/00","B60R 21/013","C12Q 1/6886","E04B 1/74","E04B 1/76","E06B 7/14","G01D 18/00","G01N 33/50"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21970270","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21970269","name":"Les Enjeux de la Rencontre Amoureuse dans les Sociétés Contemporaines","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) et entre de ce fait dans l’état de la technique au sens des législations applicables ((EPC Art. 54(2); French IPC Art. L 611-11; cf. 35 U.S.C. §102(a)). Il divulgue soixante-dix systèmes et procédés techniques habilitants couvrant les enjeux contemporains des relations amoureuses : moteurs d’appariement éthique à apprentissage fédéré on-device, scores socio-écologiques dynamiques (ACV + optimisation multi-objectifs), contrôleurs de swipe adaptatifs en boucle fermée (HRV/pupille), sessions VR guidées par thérapeute, filtres multimodaux de contenus érotiques sains, places de marché de care intergénérationnel, registres de consentement dynamique, planificateurs de polycule, analytique privative, recommenders sous contraintes d’équité et micro-utilités communautaires. Chaque proposition est décrite de façon habilitante, classée IPC/CPC et horodatée (RFC 3161 / FreeTSA).Abstract ENThis document, produced with the assistance of Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes ((art. L 611-11 CPI / art. 54(2) CBE)). It discloses seventy enabling technical systems and methods addressing contemporary romantic and relational challenges: ethical matching engines with on-device federated learning, dynamic socio-ecological couple scores (LCA + multi-objective optimisation), closed-loop adaptive swipe throttles using HRV/pupil data, therapist-guided VR affective sessions, multimodal healthy-erotic content filters, intergenerational care marketplaces, dynamic consent ledgers, polycule planners, privacy-preserving analytics, fairness-constrained recommenders, and community micro-utilities. Every proposal is described in an enabling manner, classified with IPC and CPC codes, and accompanied by timestamp proof (RFC 3161 / FreeTSA). Timestamp : 2026-08-16T21:05:20ZSHA-256 : 17d751cc6fb35efc1b3edb397c494044c54f9afee11780ea90a96bd2769fdf18 Liste des innovations & classification (IPC ; CPC) : 1. Ethical compatibility engine — IPC G06N 20/00 ; CPC G06N 20/10 2. Socio-ecological couple score — IPC G06Q 50/26 ; CPC Y02B 90/12 3. Adaptive swipe throttle — IPC G06F 3/01 ; CPC G06F 3/0488 4. Therapist-guided VR date — IPC G06T 19/00 ; CPC A61B 5/00 5. Healthy erotic content filter — IPC G06F 21/62 ; CPC G06T 7/00 6. Intergenerational care marketplace — IPC G06Q 10/10 ; CPC G06Q 10/0631 7. Affective synchrony sensor — IPC A61B 5/024 ; CPC G16H 40/63 8. REDM relationship data standard — IPC G16H 10/60 ; CPC G06F 21/62 9. Shared-housing optimizer — IPC G06F 17/50 ; CPC Y02B 10/00 10. Domestic micro-utility — IPC H02J 3/14 ; CPC Y04S 20/32 11. Modular inter-age nursery — IPC E04B 1/74 ; CPC A61L 2/08 12. Dynamic consent ledger — IPC G06F 21/62 ; CPC H04L 9/08 13. Relationship A/B trial framework — IPC G01N 33/50 ; CPC G06F 19/24 14. Community mobility router — IPC G06Q 10/08 ; CPC G06F 17/60 15. Calibration-as-a-Service — IPC G01D 18/00 ; CPC G06F 11/07 16. Ghosting/abuse detector — IPC G06F 40/20 ; CPC G06N 20/20 17. Blended relational education — IPC G09B 19/00 ; CPC G06Q 50/20 18. Relationship–environment meter — IPC G06F 16/23 ; CPC Y02B 80/00 19. On-device biometric security — IPC H04L 9/08 ; CPC G06K 9/00 20. Relationship well-being index — IPC A61B 5/00 ; CPC G16H 50/20 21. Synthetic dataset generator — IPC G06F 21/62 ; CPC G06N 20/10 22. Split-learning matcher — IPC G06F 21/62 ; CPC G06N 20/40 23. Fairness-constrained recommender — IPC G06N 20/00 ; CPC G06N 20/10 24. Time-to-relationship model — IPC G06N 20/00 ; CPC G06F 18/214 25. Polycule planner — IPC G06Q 10/06 ; CPC G06Q 10/0637 26. Connected STI self-test kit — IPC G01N 33/569 ; CPC C12Q 1/6886 27. Synchronized haptic co-regulation — IPC A61B 5/024 ; CPC G06F 3/013 28. Webc","url":"https://doi.org/10.5281/zenodo.21970269","authors":["Pillet, Xavier"],"tags":["A61M 21/00","B60R 21/013","C12Q 1/6886","E04B 1/74","E04B 1/76","E06B 7/14","G01D 18/00","G01N 33/50"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21970269","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21968628","name":"Étude de faisabilité et analyse de marché – Innovations pour réacteurs à fusion compacts (ARC, SPARC, ST-E1)","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre donc dans l’état de la technique au sens des textes applicables : EPC Art. 54(2), French IPC Art. L 611-11, cf. 35 U.S.C. §102(a), Chinese Patent Law Art. 22(5) (中华人民共和国专利法) et Japanese Patent Act Art. 29(1) (特許法). Il divulgue 128 embodiments “enabling” visant les verrous 2030 de la fusion compacte : protection thermique par vapor-shielding (gels Au@C60, CPS/LiMIT), pilotage actif d’aimants HTS REBCO (phase-array, NI/MI, capteurs, IA), matériaux structurels auto-réparants sous irradiation, et couvertures tritigènes optimisées (TBR, FLiBe, chiralité, ports). Chaque proposition inclut paramètres, QA, IPC/CPC et preuve d’horodatage (RFC 3161 / FreeTSA). Abstract ENThis document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: (art. L 611-11 CPI / art. 54(2) CBE), 35 U.S.C. §102(a), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). It discloses 128 enabling embodiments addressing 2030 compact-fusion bottlenecks: vapor-shielding and liquid-metal plasma-facing components (Au@C60 gels, CPS/LiMIT), active HTS REBCO magnet stabilization (phased arrays, NI/MI, sensing, AI), irradiation-driven self-healing structural materials, and optimized tritium-breeding blankets (TBR, FLiBe immersion, chiral/port shielding). Each proposal includes reproducible parameters, QA/test workflows, IPC/CPC classification, and timestamp proof (RFC 3161 / FreeTSA). Timestamp : 2026-08-16T20:51:39ZSHA-256 : 528680566605797c0c4097f48b97edee9a77a3938919ea17f4db427d870670a6 Liste des innovations & classification (IPC ; CPC) : 1) Au@C60 vapor-shield gel — IPC G21B 1/00 ; CPC Y02E 30/302) Graded-Z gel multilayer — IPC C09D 5/00 ; CPC C09D 5/003) Sub-mm W capillary reservoir — IPC B22F 3/105 ; CPC B22F 3/1054) Inductive vaporization pulses — IPC F28F 27/00 ; CPC F28F 27/005) Impurity-aware feedback control — IPC G05B 13/02 ; CPC G05B 13/026) Plasma-assisted re-deposition — IPC C23C 16/00 ; CPC C23C 16/007) Refillable gel cartridge module — IPC G21B 1/00 ; CPC G21B 1/048) Irradiation QC kit for gels — IPC G01N 33/00 ; CPC G01N 33/009) Low-activation gold formulation — IPC G21F 1/00 ; CPC G21F 1/0010) Microchannel PFC + gel hybrid — IPC F28D 5/00 ; CPC F28D 5/0011) Phased-array correction coils — IPC H01F 6/04 ; CPC H01F 6/0412) Vector-potential MPC control — IPC G05B 13/02 ; CPC G05B 13/0213) Screening-current state observer — IPC G01R 33/00 ; CPC G01R 33/0014) Angled AC shaking protocol — IPC H01F 6/06 ; CPC H01F 6/0615) Cryogenic FBG strain sensing — IPC G01L 1/24 ; CPC G01L 1/2416) Cryo Hall-matrix calibration — IPC G01R 33/02 ; CPC G01R 33/0217) Multimodal quench prediction AI — IPC G06N 20/00 ; CPC G06N 20/0018) Integrated cryogenic power drivers — IPC H02M 7/00 ; CPC H02M 7/0019) Co-wound auxiliary conductors — IPC H01F 41/00 ; CPC H01F 41/0020) Secure magnet-control API — IPC H04L 9/00 ; CPC H04L 9/0021) Low-activation HEA for fusion — IPC C22C 30/00 ; CPC C22C 30/0022) Cyclic nanoprecipitate alloy — IPC C22F 1/00 ; CPC C22F 1/0023) Defect-sink layered composite — IPC C22C 38/00 ; CPC C22C 38/0024) Additive manufacturing RISC recipe — IPC B33Y 10/00 ; CPC B33Y 10/0025) Heat-treatment seed-sinks — IPC C21D 8/00 ; CPC C21D 8/0026) In-situ damage monitoring — IPC G01N 27/02 ; CPC G01N 27/0227) Accelerated qualification pipeline — IPC G06F 16/00 ; CPC G06F 16/0028) Defect-mobility alloy optimizer — IPC G06N 20/00 ; CPC G06N 20/0029) RISC cladding on steel — IPC C23C 24/00 ; CPC C23C 24/0030) Chiral neutron-maze blanket cell — IPC G21B 1/00 ; CPC G21B 1/0431) Phase-reflecting multilayer reflector — IPC G21B 1/00 ; CPC G21B 1/0432","url":"https://doi.org/10.5281/zenodo.21968628","authors":["Pillet, Xavier"],"tags":["G21B 1/00","G21B 1/04","Y02E 30/30","C09D 5/00","B22F 3/105","F28F 27/00","G05B 13/02","C23C 16/00"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21968628","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21970198","name":"Étude de faisabilité et analyse de marché – Innovations pour réacteurs à fusion compacts (ARC, SPARC, ST-E1)","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre donc dans l’état de la technique au sens des textes applicables : EPC Art. 54(2), French IPC Art. L 611-11, cf. 35 U.S.C. §102(a), Chinese Patent Law Art. 22(5) (中华人民共和国专利法) et Japanese Patent Act Art. 29(1) (特許法). Il divulgue 128 embodiments “enabling” visant les verrous 2030 de la fusion compacte : protection thermique par vapor-shielding (gels Au@C60, CPS/LiMIT), pilotage actif d’aimants HTS REBCO (phase-array, NI/MI, capteurs, IA), matériaux structurels auto-réparants sous irradiation, et couvertures tritigènes optimisées (TBR, FLiBe, chiralité, ports). Chaque proposition inclut paramètres, QA, IPC/CPC et preuve d’horodatage (RFC 3161 / FreeTSA). Abstract ENThis document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: (art. L 611-11 CPI / art. 54(2) CBE), 35 U.S.C. §102(a), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). It discloses 128 enabling embodiments addressing 2030 compact-fusion bottlenecks: vapor-shielding and liquid-metal plasma-facing components (Au@C60 gels, CPS/LiMIT), active HTS REBCO magnet stabilization (phased arrays, NI/MI, sensing, AI), irradiation-driven self-healing structural materials, and optimized tritium-breeding blankets (TBR, FLiBe immersion, chiral/port shielding). Each proposal includes reproducible parameters, QA/test workflows, IPC/CPC classification, and timestamp proof (RFC 3161 / FreeTSA). Timestamp : 2026-08-16T20:51:39ZSHA-256 : 528680566605797c0c4097f48b97edee9a77a3938919ea17f4db427d870670a6 Liste des innovations & classification (IPC ; CPC) : 1) Au@C60 vapor-shield gel — IPC G21B 1/00 ; CPC Y02E 30/302) Graded-Z gel multilayer — IPC C09D 5/00 ; CPC C09D 5/003) Sub-mm W capillary reservoir — IPC B22F 3/105 ; CPC B22F 3/1054) Inductive vaporization pulses — IPC F28F 27/00 ; CPC F28F 27/005) Impurity-aware feedback control — IPC G05B 13/02 ; CPC G05B 13/026) Plasma-assisted re-deposition — IPC C23C 16/00 ; CPC C23C 16/007) Refillable gel cartridge module — IPC G21B 1/00 ; CPC G21B 1/048) Irradiation QC kit for gels — IPC G01N 33/00 ; CPC G01N 33/009) Low-activation gold formulation — IPC G21F 1/00 ; CPC G21F 1/0010) Microchannel PFC + gel hybrid — IPC F28D 5/00 ; CPC F28D 5/0011) Phased-array correction coils — IPC H01F 6/04 ; CPC H01F 6/0412) Vector-potential MPC control — IPC G05B 13/02 ; CPC G05B 13/0213) Screening-current state observer — IPC G01R 33/00 ; CPC G01R 33/0014) Angled AC shaking protocol — IPC H01F 6/06 ; CPC H01F 6/0615) Cryogenic FBG strain sensing — IPC G01L 1/24 ; CPC G01L 1/2416) Cryo Hall-matrix calibration — IPC G01R 33/02 ; CPC G01R 33/0217) Multimodal quench prediction AI — IPC G06N 20/00 ; CPC G06N 20/0018) Integrated cryogenic power drivers — IPC H02M 7/00 ; CPC H02M 7/0019) Co-wound auxiliary conductors — IPC H01F 41/00 ; CPC H01F 41/0020) Secure magnet-control API — IPC H04L 9/00 ; CPC H04L 9/0021) Low-activation HEA for fusion — IPC C22C 30/00 ; CPC C22C 30/0022) Cyclic nanoprecipitate alloy — IPC C22F 1/00 ; CPC C22F 1/0023) Defect-sink layered composite — IPC C22C 38/00 ; CPC C22C 38/0024) Additive manufacturing RISC recipe — IPC B33Y 10/00 ; CPC B33Y 10/0025) Heat-treatment seed-sinks — IPC C21D 8/00 ; CPC C21D 8/0026) In-situ damage monitoring — IPC G01N 27/02 ; CPC G01N 27/0227) Accelerated qualification pipeline — IPC G06F 16/00 ; CPC G06F 16/0028) Defect-mobility alloy optimizer — IPC G06N 20/00 ; CPC G06N 20/0029) RISC cladding on steel — IPC C23C 24/00 ; CPC C23C 24/0030) Chiral neutron-maze blanket cell — IPC G21B 1/00 ; CPC G21B 1/0431) Phase-reflecting multilayer reflector — IPC G21B 1/00 ; CPC G21B 1/0432","url":"https://doi.org/10.5281/zenodo.21970198","authors":["Pillet, Xavier"],"tags":["G21B 1/00","G21B 1/04","Y02E 30/30","C09D 5/00","B22F 3/105","F28F 27/00","G05B 13/02","C23C 16/00"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21970198","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21969017","name":"Étude de faisabilité et analyse de marché – Innovations pour réacteurs à fusion compacts (ARC, SPARC, ST-E1)","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre donc dans l’état de la technique au sens des textes applicables : EPC Art. 54(2), French IPC Art. L 611-11, cf. 35 U.S.C. §102(a), Chinese Patent Law Art. 22(5) (中华人民共和国专利法) et Japanese Patent Act Art. 29(1) (特許法). Il divulgue 128 embodiments “enabling” visant les verrous 2030 de la fusion compacte : protection thermique par vapor-shielding (gels Au@C60, CPS/LiMIT), pilotage actif d’aimants HTS REBCO (phase-array, NI/MI, capteurs, IA), matériaux structurels auto-réparants sous irradiation, et couvertures tritigènes optimisées (TBR, FLiBe, chiralité, ports). Chaque proposition inclut paramètres, QA, IPC/CPC et preuve d’horodatage (RFC 3161 / FreeTSA). Abstract ENThis document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: (art. L 611-11 CPI / art. 54(2) CBE), 35 U.S.C. §102(a), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). It discloses 128 enabling embodiments addressing 2030 compact-fusion bottlenecks: vapor-shielding and liquid-metal plasma-facing components (Au@C60 gels, CPS/LiMIT), active HTS REBCO magnet stabilization (phased arrays, NI/MI, sensing, AI), irradiation-driven self-healing structural materials, and optimized tritium-breeding blankets (TBR, FLiBe immersion, chiral/port shielding). Each proposal includes reproducible parameters, QA/test workflows, IPC/CPC classification, and timestamp proof (RFC 3161 / FreeTSA). Timestamp : 2026-08-16T17:25:54ZSHA-256 : fc6b83a8107e123ab1fc352297f43340016d54b2fd33e445274512e24495c8c1 Liste des innovations & classification (IPC ; CPC) : 1) Au@C60 vapor-shield gel — IPC G21B 1/00 ; CPC Y02E 30/302) Graded-Z gel multilayer — IPC C09D 5/00 ; CPC C09D 5/003) Sub-mm W capillary reservoir — IPC B22F 3/105 ; CPC B22F 3/1054) Inductive vaporization pulses — IPC F28F 27/00 ; CPC F28F 27/005) Impurity-aware feedback control — IPC G05B 13/02 ; CPC G05B 13/026) Plasma-assisted re-deposition — IPC C23C 16/00 ; CPC C23C 16/007) Refillable gel cartridge module — IPC G21B 1/00 ; CPC G21B 1/048) Irradiation QC kit for gels — IPC G01N 33/00 ; CPC G01N 33/009) Low-activation gold formulation — IPC G21F 1/00 ; CPC G21F 1/0010) Microchannel PFC + gel hybrid — IPC F28D 5/00 ; CPC F28D 5/0011) Phased-array correction coils — IPC H01F 6/04 ; CPC H01F 6/0412) Vector-potential MPC control — IPC G05B 13/02 ; CPC G05B 13/0213) Screening-current state observer — IPC G01R 33/00 ; CPC G01R 33/0014) Angled AC shaking protocol — IPC H01F 6/06 ; CPC H01F 6/0615) Cryogenic FBG strain sensing — IPC G01L 1/24 ; CPC G01L 1/2416) Cryo Hall-matrix calibration — IPC G01R 33/02 ; CPC G01R 33/0217) Multimodal quench prediction AI — IPC G06N 20/00 ; CPC G06N 20/0018) Integrated cryogenic power drivers — IPC H02M 7/00 ; CPC H02M 7/0019) Co-wound auxiliary conductors — IPC H01F 41/00 ; CPC H01F 41/0020) Secure magnet-control API — IPC H04L 9/00 ; CPC H04L 9/0021) Low-activation HEA for fusion — IPC C22C 30/00 ; CPC C22C 30/0022) Cyclic nanoprecipitate alloy — IPC C22F 1/00 ; CPC C22F 1/0023) Defect-sink layered composite — IPC C22C 38/00 ; CPC C22C 38/0024) Additive manufacturing RISC recipe — IPC B33Y 10/00 ; CPC B33Y 10/0025) Heat-treatment seed-sinks — IPC C21D 8/00 ; CPC C21D 8/0026) In-situ damage monitoring — IPC G01N 27/02 ; CPC G01N 27/0227) Accelerated qualification pipeline — IPC G06F 16/00 ; CPC G06F 16/0028) Defect-mobility alloy optimizer — IPC G06N 20/00 ; CPC G06N 20/0029) RISC cladding on steel — IPC C23C 24/00 ; CPC C23C 24/0030) Chiral neutron-maze blanket cell — IPC G21B 1/00 ; CPC G21B 1/0431) Phase-reflecting multilayer reflector — IPC G21B 1/00 ; CPC G21B 1/0432","url":"https://doi.org/10.5281/zenodo.21969017","authors":["Pillet, Xavier"],"tags":["G21B 1/00","G21B 1/04","Y02E 30/30","C09D 5/00","B22F 3/105","F28F 27/00","G05B 13/02","C23C 16/00"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21969017","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21968629","name":"Étude de faisabilité et analyse de marché – Innovations pour réacteurs à fusion compacts (ARC, SPARC, ST-E1)","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre donc dans l’état de la technique au sens des textes applicables : EPC Art. 54(2), French IPC Art. L 611-11, cf. 35 U.S.C. §102(a), Chinese Patent Law Art. 22(5) (中华人民共和国专利法) et Japanese Patent Act Art. 29(1) (特許法). Il divulgue 128 embodiments “enabling” visant les verrous 2030 de la fusion compacte : protection thermique par vapor-shielding (gels Au@C60, CPS/LiMIT), pilotage actif d’aimants HTS REBCO (phase-array, NI/MI, capteurs, IA), matériaux structurels auto-réparants sous irradiation, et couvertures tritigènes optimisées (TBR, FLiBe, chiralité, ports). Chaque proposition inclut paramètres, QA, IPC/CPC et preuve d’horodatage (RFC 3161 / FreeTSA). Abstract ENThis document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: (art. L 611-11 CPI / art. 54(2) CBE), 35 U.S.C. §102(a), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). It discloses 128 enabling embodiments addressing 2030 compact-fusion bottlenecks: vapor-shielding and liquid-metal plasma-facing components (Au@C60 gels, CPS/LiMIT), active HTS REBCO magnet stabilization (phased arrays, NI/MI, sensing, AI), irradiation-driven self-healing structural materials, and optimized tritium-breeding blankets (TBR, FLiBe immersion, chiral/port shielding). Each proposal includes reproducible parameters, QA/test workflows, IPC/CPC classification, and timestamp proof (RFC 3161 / FreeTSA). Timestamp : 2026-08-16T17:25:54ZSHA-256 : c6b83a8107e123ab1fc352297f43340016d54b2fd33e445274512e24495c8c1 Liste des innovations & classification (IPC ; CPC) : 1) Au@C60 vapor-shield gel — IPC G21B 1/00 ; CPC Y02E 30/302) Graded-Z gel multilayer — IPC C09D 5/00 ; CPC C09D 5/003) Sub-mm W capillary reservoir — IPC B22F 3/105 ; CPC B22F 3/1054) Inductive vaporization pulses — IPC F28F 27/00 ; CPC F28F 27/005) Impurity-aware feedback control — IPC G05B 13/02 ; CPC G05B 13/026) Plasma-assisted re-deposition — IPC C23C 16/00 ; CPC C23C 16/007) Refillable gel cartridge module — IPC G21B 1/00 ; CPC G21B 1/048) Irradiation QC kit for gels — IPC G01N 33/00 ; CPC G01N 33/009) Low-activation gold formulation — IPC G21F 1/00 ; CPC G21F 1/0010) Microchannel PFC + gel hybrid — IPC F28D 5/00 ; CPC F28D 5/0011) Phased-array correction coils — IPC H01F 6/04 ; CPC H01F 6/0412) Vector-potential MPC control — IPC G05B 13/02 ; CPC G05B 13/0213) Screening-current state observer — IPC G01R 33/00 ; CPC G01R 33/0014) Angled AC shaking protocol — IPC H01F 6/06 ; CPC H01F 6/0615) Cryogenic FBG strain sensing — IPC G01L 1/24 ; CPC G01L 1/2416) Cryo Hall-matrix calibration — IPC G01R 33/02 ; CPC G01R 33/0217) Multimodal quench prediction AI — IPC G06N 20/00 ; CPC G06N 20/0018) Integrated cryogenic power drivers — IPC H02M 7/00 ; CPC H02M 7/0019) Co-wound auxiliary conductors — IPC H01F 41/00 ; CPC H01F 41/0020) Secure magnet-control API — IPC H04L 9/00 ; CPC H04L 9/0021) Low-activation HEA for fusion — IPC C22C 30/00 ; CPC C22C 30/0022) Cyclic nanoprecipitate alloy — IPC C22F 1/00 ; CPC C22F 1/0023) Defect-sink layered composite — IPC C22C 38/00 ; CPC C22C 38/0024) Additive manufacturing RISC recipe — IPC B33Y 10/00 ; CPC B33Y 10/0025) Heat-treatment seed-sinks — IPC C21D 8/00 ; CPC C21D 8/0026) In-situ damage monitoring — IPC G01N 27/02 ; CPC G01N 27/0227) Accelerated qualification pipeline — IPC G06F 16/00 ; CPC G06F 16/0028) Defect-mobility alloy optimizer — IPC G06N 20/00 ; CPC G06N 20/0029) RISC cladding on steel — IPC C23C 24/00 ; CPC C23C 24/0030) Chiral neutron-maze blanket cell — IPC G21B 1/00 ; CPC G21B 1/0431) Phase-reflecting multilayer reflector — IPC G21B 1/00 ; CPC G21B 1/0432)","url":"https://doi.org/10.5281/zenodo.21968629","authors":["Pillet, Xavier"],"tags":["G21B 1/00","G21B 1/04","Y02E 30/30","C09D 5/00","B22F 3/105","F28F 27/00","G05B 13/02","C23C 16/00"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21968629","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21274263","name":"Cloud Computing Security in the Age of AI: A Systematic Review Using Artificial Intelligence as Both Research Instrument and Object of Study","source":"datacite","abstract":"The rapid migration of organizational infrastructure to cloud computing has introduced unprecedented scalability and efficiency alongside increasingly complex security vulnerabilities. This review examines the evolving relationship between artificial intelligence (AI) and cloud security through an AI-Augmented Systematic Literature Review (ASLR), in which AI serves simultaneously as a research instrument for synthesizing literature and as the object of study within cloud security architectures. Drawing on studies published between 2020 and 2026 sourced from IEEE Xplore, ACM Digital Library, and SpringerLink, the review addresses three objectives: examining how cloud infrastructure supports the deployment of resource-intensive machine learning (ML) models, investigating AI's role as an adaptive defensive layer, and identifying new security vulnerabilities introduced by embedding AI into cloud networks. Findings show that cloud platforms enable resource-intensive ML through elastic compute allocation, architectural abstraction, and intelligent orchestration; that AI strengthens defense through predictive threat detection, continuous policy adaptation, and privacy-preserving federated learning; and that this same integration introduces distinct new vulnerability classes, including agent privilege escalation, lifecycle-specific exploitation, cascading cross-layer failures, serverless architectural weaknesses, and adversarial manipulation. The review concludes that AI functions as a double-edged capability within cloud ecosystems, simultaneously strengthening and expanding the attack surface, and argues for lifecycle-aware, zero-trust security models validated under real-world rather than solely simulated conditions.","url":"https://doi.org/10.5281/zenodo.21274263","authors":["Uwaisu Abubakar Umar","Ibrahim Haruna Ibrahim","Aliyu Aminu Dahiru","Emmanuel Martin Teman"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21274263","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21274264","name":"Cloud Computing Security in the Age of AI: A Systematic Review Using Artificial Intelligence as Both Research Instrument and Object of Study","source":"datacite","abstract":"The rapid migration of organizational infrastructure to cloud computing has introduced unprecedented scalability and efficiency alongside increasingly complex security vulnerabilities. This review examines the evolving relationship between artificial intelligence (AI) and cloud security through an AI-Augmented Systematic Literature Review (ASLR), in which AI serves simultaneously as a research instrument for synthesizing literature and as the object of study within cloud security architectures. Drawing on studies published between 2020 and 2026 sourced from IEEE Xplore, ACM Digital Library, and SpringerLink, the review addresses three objectives: examining how cloud infrastructure supports the deployment of resource-intensive machine learning (ML) models, investigating AI's role as an adaptive defensive layer, and identifying new security vulnerabilities introduced by embedding AI into cloud networks. Findings show that cloud platforms enable resource-intensive ML through elastic compute allocation, architectural abstraction, and intelligent orchestration; that AI strengthens defense through predictive threat detection, continuous policy adaptation, and privacy-preserving federated learning; and that this same integration introduces distinct new vulnerability classes, including agent privilege escalation, lifecycle-specific exploitation, cascading cross-layer failures, serverless architectural weaknesses, and adversarial manipulation. The review concludes that AI functions as a double-edged capability within cloud ecosystems, simultaneously strengthening and expanding the attack surface, and argues for lifecycle-aware, zero-trust security models validated under real-world rather than solely simulated conditions.","url":"https://doi.org/10.5281/zenodo.21274264","authors":["Uwaisu Abubakar Umar","Ibrahim Haruna Ibrahim","Aliyu Aminu Dahiru","Emmanuel Martin Teman"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21274264","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20270619","name":"Cyber Security Threat Detection using Machine Learning Techniques","source":"datacite","abstract":"This paper provides a comprehensive analysis of machine learning in cyber security threat detection, tracing the history of its development from traditional signature-based systems towards intelligent and adaptive systems that can identify new and sophisticated threats. The study systematically examines recent research articles from 2021 to 2026 to explore the use of supervised, unsupervised, and deep learning in various domains of network intrusion detection systems, malware classification systems, and anomaly detection systems. The study proposes a new Integrated Threat Detection Framework (ITDF) that includes data preprocessing, feature engineering, model selection, and real-time detection. The study indicates that machine learning algorithms such as ensemble methods using Random Forest and XGBoost provide the best results with 95-99% accuracy on various benchmark datasets such as NSL-KDD, CIC-IDS2017, and UNSW-NB15. Deep learning methods such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) perform exceptionally well in identifying patterns in network traffic with 98-99% accuracy for network intrusion detection systems. Emerging trends in machine learning for cyber security include federated learning for privacy in distributed environments and Generative Adversarial Networks (GAN) for generating training data for rare types of threats. The key challenges that still need to be addressed relate to the problem of concept drift, adversarial attacks on ML models, and the need for interpretability in security operations. The comparative evaluation of the proposed approach with respect to four analytical dimensions—detection accuracy, false positive rate, real-time capability, and adversarial robustness—shows that the hybrid approach provides the best robustness against cyber attacks.","url":"https://doi.org/10.5281/zenodo.20270619","authors":["Shah Md. Tanzimul Kabir","Md. Saiduzzaman"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20270619","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.20270620","name":"Cyber Security Threat Detection using Machine Learning Techniques","source":"datacite","abstract":"This paper provides a comprehensive analysis of machine learning in cyber security threat detection, tracing the history of its development from traditional signature-based systems towards intelligent and adaptive systems that can identify new and sophisticated threats. The study systematically examines recent research articles from 2021 to 2026 to explore the use of supervised, unsupervised, and deep learning in various domains of network intrusion detection systems, malware classification systems, and anomaly detection systems. The study proposes a new Integrated Threat Detection Framework (ITDF) that includes data preprocessing, feature engineering, model selection, and real-time detection. The study indicates that machine learning algorithms such as ensemble methods using Random Forest and XGBoost provide the best results with 95-99% accuracy on various benchmark datasets such as NSL-KDD, CIC-IDS2017, and UNSW-NB15. Deep learning methods such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) perform exceptionally well in identifying patterns in network traffic with 98-99% accuracy for network intrusion detection systems. Emerging trends in machine learning for cyber security include federated learning for privacy in distributed environments and Generative Adversarial Networks (GAN) for generating training data for rare types of threats. The key challenges that still need to be addressed relate to the problem of concept drift, adversarial attacks on ML models, and the need for interpretability in security operations. The comparative evaluation of the proposed approach with respect to four analytical dimensions—detection accuracy, false positive rate, real-time capability, and adversarial robustness—shows that the hybrid approach provides the best robustness against cyber attacks.","url":"https://doi.org/10.5281/zenodo.20270620","authors":["Shah Md. Tanzimul Kabir","Md. Saiduzzaman"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20270620","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.20133383","name":"Explainable Federated Learning for Disease Detection in Computational Pathology (JANUARY 2026)","source":"datacite","abstract":"Traditional computational pathology systems primarily rely on centralized deep learning approaches, requiringdata from multiple hospitals to be aggregated into a singlerepository; however, such methods are impractical in realworld healthcare due to strict privacy regulations, data-sharingconstraints, and security concerns. To address these limitations,this work proposes a secure and interpretable disease detectionframework based on explainable federated learning, enablingmultiple institutions to collaboratively train a cancer detectionmodel without sharing sensitive patient data. The system utilizeshistopathology whole slide images (WSIs) and employs MultipleInstance Learning (MIL) to efficiently process gigapixel imagesusing slide-level labels. A federated learning paradigm withFederated Averaging (FedAvg) is adopted to aggregate modelupdates while preserving data locality, and Differential Privacyis incorporated to further strengthen data protection. Additionally, an attention-based explainability mechanism highlightsdisease-relevant regions, improving model transparency andsupporting clinical validation. The overall objective is to achieveaccurate, privacy-preserving, and trustworthy disease classification suitable for scalable deployment across multi-hospitalenvironments.","url":"https://doi.org/10.5281/zenodo.20133383","authors":["ABHIJITH VIJAYAN, ANAN MUHAMMED P P, POOJA P P, PROF. AJUMOL P A, PROF. JEENA JOY"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20133383","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.20133384","name":"Explainable Federated Learning for Disease Detection in Computational Pathology (JANUARY 2026)","source":"datacite","abstract":"Traditional computational pathology systems primarily rely on centralized deep learning approaches, requiringdata from multiple hospitals to be aggregated into a singlerepository; however, such methods are impractical in realworld healthcare due to strict privacy regulations, data-sharingconstraints, and security concerns. To address these limitations,this work proposes a secure and interpretable disease detectionframework based on explainable federated learning, enablingmultiple institutions to collaboratively train a cancer detectionmodel without sharing sensitive patient data. The system utilizeshistopathology whole slide images (WSIs) and employs MultipleInstance Learning (MIL) to efficiently process gigapixel imagesusing slide-level labels. A federated learning paradigm withFederated Averaging (FedAvg) is adopted to aggregate modelupdates while preserving data locality, and Differential Privacyis incorporated to further strengthen data protection. Additionally, an attention-based explainability mechanism highlightsdisease-relevant regions, improving model transparency andsupporting clinical validation. The overall objective is to achieveaccurate, privacy-preserving, and trustworthy disease classification suitable for scalable deployment across multi-hospitalenvironments.","url":"https://doi.org/10.5281/zenodo.20133384","authors":["ABHIJITH VIJAYAN, ANAN MUHAMMED P P, POOJA P P, PROF. AJUMOL P A, PROF. JEENA JOY"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20133384","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.19417528","name":"Edge AI: A Survey of Next-Generation Waste Classification and Routing Architectures","source":"datacite","abstract":"The recent advances in edge computing and computer vision are completely transforming the way cities handle solid waste. In this review, close attention will be paid to the way smart city systems have changed to separate various types of waste and direct users to the appropriate bins in real-time. Our particular focus is the fact that the trend has shifted to less heavy and bulky smart bins and to the touchless mobile-edge systems. We go through the decisions of hardware, the move away towards large neural networks (as in VGG16) to thinner ones (such as MobileNetV2 and YOLOv8n) and the usage of location-finding algorithms such as Haversine formulas. The traditional smart-city systems are usually based on expensive embedded sensors, thus becoming difficult to implement and expand to larger cities. In the present-day, however, devices such as WebGL, Tensorflow.js, and an ordinary smartphone camera are utilized to perform the heavy lifting on the device. This decentralized design is faster, less expensive, more secret and more effortless to expand. Reviewing the studies of 2015 to 2026, we mention the constant problem of obtaining correct classification in poor lighting and solid communication systems are necessary. Finally, we outline the current state of edgedriven waste management, indicate the weaknesses of cloudheavy systems and consider the future of this technology, such as federated learning, blockchain rewards, and enhanced IoT synchronization.","url":"https://doi.org/10.5281/zenodo.19417528","authors":["Yash Saini","Anmol Tyagi","Priynshu Kumar","Sagar","Priyanka"],"tags":["Waste Segmentation","Edge computing","Tensorflow. js","Nearest Bin Recommendation","Smart City Architecture","Transfer Learning","MobileNetV2","MQTT."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19417528","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.19425963","name":"Edge AI: A Survey of Next-Generation Waste Classification and Routing Architectures","source":"datacite","abstract":"The recent advances in edge computing and computer vision are completely transforming the way cities handle solid waste. In this review, close attention will be paid to the way smart city systems have changed to separate various types of waste and direct users to the appropriate bins in real-time. Our particular focus is the fact that the trend has shifted to less heavy and bulky smart bins and to the touchless mobile-edge systems. We go through the decisions of hardware, the move away towards large neural networks (as in VGG16) to thinner ones (such as MobileNetV2 and YOLOv8n) and the usage of location-finding algorithms such as Haversine formulas. The traditional smart-city systems are usually based on expensive embedded sensors, thus becoming difficult to implement and expand to larger cities. In the present-day, however, devices such as WebGL, Tensorflow.js, and an ordinary smartphone camera are utilized to perform the heavy lifting on the device. This decentralized design is faster, less expensive, more secret and more effortless to expand. Reviewing the studies of 2015 to 2026, we mention the constant problem of obtaining correct classification in poor lighting and solid communication systems are necessary. Finally, we outline the current state of edgedriven waste management, indicate the weaknesses of cloudheavy systems and consider the future of this technology, such as federated learning, blockchain rewards, and enhanced IoT synchronization.","url":"https://doi.org/10.5281/zenodo.19425963","authors":["Yash Saini","Anmol Tyagi","Priyanshu Kumar","Sagar","Priyanka"],"tags":["Waste Segmentation","Edge computing","Tensorflow. js","Nearest Bin Recommendation","Smart City Architecture","Transfer Learning","MobileNetV2","MQTT."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19425963","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.19416593","name":"Edge AI: A Survey of Next-Generation Waste Classification and Routing Architectures","source":"datacite","abstract":"The recent advances in edge computing and computer vision are completely transforming the way cities handle solid waste. In this review, close attention will be paid to the way smart city systems have changed to separate various types of waste and direct users to the appropriate bins in real-time. Our particular focus is the fact that the trend has shifted to less heavy and bulky smart bins and to the touchless mobile-edge systems. We go through the decisions of hardware, the move away towards large neural networks (as in VGG16) to thinner ones (such as MobileNetV2 and YOLOv8n) and the usage of location-finding algorithms such as Haversine formulas. The traditional smart-city systems are usually based on expensive embedded sensors, thus becoming difficult to implement and expand to larger cities. In the present-day, however, devices such as WebGL, Tensorflow.js, and an ordinary smartphone camera are utilized to perform the heavy lifting on the device. This decentralized design is faster, less expensive, more secret and more effortless to expand. Reviewing the studies of 2015 to 2026, we mention the constant problem of obtaining correct classification in poor lighting and solid communication systems are necessary. Finally, we outline the current state of edgedriven waste management, indicate the weaknesses of cloudheavy systems and consider the future of this technology, such as federated learning, blockchain rewards, and enhanced IoT synchronization.","url":"https://doi.org/10.5281/zenodo.19416593","authors":["Yash Saini","Anmol Tyagi","Priyanshu Kumar","Sagar","Priyanka"],"tags":["Waste Segmentation","Edge computing","Tensorflow. js","Nearest Bin Recommendation","Smart City Architecture","Transfer Learning","MobileNetV2","MQTT."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19416593","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.19416594","name":"Edge AI: A Survey of Next-Generation Waste Classification a nd R outing Architectures","source":"datacite","abstract":"The recent advances in edge computing and computer vision are completely transforming the way cities handle solid waste. In this review, close attention will be paid to the way smart city systems have changed to separate various types of waste and direct users to the appropriate bins in real-time. Our particular focus is the fact that the trend has shifted to less heavy and bulky smart bins and to the touchless mobile-edge systems. We go through the decisions of hardware, the move away towards large neural networks (as in VGG16) to thinner ones (such as MobileNetV2 and YOLOv8n) and the usage of location-finding algorithms such as Haversine formulas. The traditional smart-city systems are usually based on expensive embedded sensors, thus becoming difficult to implement and expand to larger cities. In the present-day, however, devices such as WebGL, Tensorflow.js, and an ordinary smartphone camera are utilized to perform the heavy lifting on the device. This decentralized design is faster, less expensive, more secret and more effortless to expand. Reviewing the studies of 2015 to 2026, we mention the constant problem of obtaining correct classification in poor lighting and solid communication systems are necessary. Finally, we outline the current state of edgedriven waste management, indicate the weaknesses of cloudheavy systems and consider the future of this technology, such as federated learning, blockchain rewards, and enhanced IoT synchronization.","url":"https://doi.org/10.5281/zenodo.19416594","authors":["Yash Saini","Anmol Tyagi","Priynshu Kumar","Sagar","Priyanka"],"tags":["Waste Segmentation","Edge computing","Tensorflow. js","Nearest Bin Recommendation","Smart City Architecture","Transfer Learning","MobileNetV2","MQTT."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19416594","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21311418","name":"Artificial Intelligence for Drug Repurposing and Target Discovery in Oncology","source":"datacite","abstract":"Cancer remains a leading cause of morbidity and mortality globally, with conventional drug discovery facing significant challenges including high development costs, prolonged timelines, and substantial failure rates. Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) approaches, has emerged as a transformative technology for accelerating oncology drug discovery and enabling precision medicine. This review synthesizes recent advances (2020–2026) in AI-driven drug repurposing and target identification in cancer research. We examine how AI integrates multi-omics data—including genomics, transcriptomics, proteomics, and radiomics—to predict drug–target interactions, identify novel therapeutic targets, and uncover repurposing opportunities for existing drugs. Key methods discussed include supervised and unsupervised machine learning, graph neural networks (GNNs), deep neural networks (DNNs), and transformer-based architectures. Applications across multiple cancer types demonstrate that AI-enabled approaches significantly reduce discovery timelines and costs while improving prediction accuracy. However, substantial challenges persist, including data heterogeneity, model interpretability, clinical translation barriers, and regulatory uncertainties. This review highlights that despite these limitations, AI-driven oncology drug discovery represents a paradigm shift toward faster, more cost-effective identification of therapeutic candidates and biomarkers. Future directions emphasize explainable AI (XAI), federated learning for privacy preservation, and rigorous prospective clinical validation to realize AI's full potential in precision cancer medicine.","url":"https://doi.org/10.5281/zenodo.21311418","authors":["Jayanthi Kanaka Ram*1, Arvind Nair2"],"tags":["artificial intelligence, machine learning, deep learning, drug repurposing, target discovery, oncology, drug–target interaction, precision medicine, biomarker discovery, graph neural networks"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21311418","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21311419","name":"Artificial Intelligence for Drug Repurposing and Target Discovery in Oncology","source":"datacite","abstract":"Cancer remains a leading cause of morbidity and mortality globally, with conventional drug discovery facing significant challenges including high development costs, prolonged timelines, and substantial failure rates. Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) approaches, has emerged as a transformative technology for accelerating oncology drug discovery and enabling precision medicine. This review synthesizes recent advances (2020–2026) in AI-driven drug repurposing and target identification in cancer research. We examine how AI integrates multi-omics data—including genomics, transcriptomics, proteomics, and radiomics—to predict drug–target interactions, identify novel therapeutic targets, and uncover repurposing opportunities for existing drugs. Key methods discussed include supervised and unsupervised machine learning, graph neural networks (GNNs), deep neural networks (DNNs), and transformer-based architectures. Applications across multiple cancer types demonstrate that AI-enabled approaches significantly reduce discovery timelines and costs while improving prediction accuracy. However, substantial challenges persist, including data heterogeneity, model interpretability, clinical translation barriers, and regulatory uncertainties. This review highlights that despite these limitations, AI-driven oncology drug discovery represents a paradigm shift toward faster, more cost-effective identification of therapeutic candidates and biomarkers. Future directions emphasize explainable AI (XAI), federated learning for privacy preservation, and rigorous prospective clinical validation to realize AI's full potential in precision cancer medicine.","url":"https://doi.org/10.5281/zenodo.21311419","authors":["Jayanthi Kanaka Ram*1, Arvind Nair2"],"tags":["artificial intelligence, machine learning, deep learning, drug repurposing, target discovery, oncology, drug–target interaction, precision medicine, biomarker discovery, graph neural networks"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21311419","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21937534","name":"T3-CIDERS: Train-the-trainer for CI Upskilling in Cybersecurity Disciplines: Insights from Fostering the First-Year Cohort","source":"datacite","abstract":"We present the T3-CIDERS project, a “Train-the-Trainer” (T3) initiative to foster a community of practice in cyberinfrastructure (CI)- and data-enabled cybersecurity research and education. Building on the NSF-funded DeapSECURE project, T3-CIDERS prepares Future Trainers (FTs)—faculty-student teams—with strong technical foundations in CI (HPC, big data, machine learning, cryptography) and instructional methods for teaching CI-related contents in the context of cybersecurity research and education. Since its launch in 2023, the project has supported the first cohort through pre-training, a summer institute, and ongoing learning engagements. In the first cohort, six local CI training events led by FTs were conducted across multiple U.S. states, introducing CI and cybersecurity concepts to nearly 100 students, from K-12 to graduate levels. A new “Module X” on the Security of Federated Learning is close to completion and will be featured in the upcoming winter institute in January 2026 in Arizona. We are currently recruiting for the winter institute second cohort. The project’s scholarly outcomes to date include three conferences and three journal papers spanning both HPC- and education-focused conferences.","url":"https://doi.org/10.5281/zenodo.21937534","authors":["Sosonkina, Masha","Wu, Hongyi","JIANG, PENG","Purwanto, Wirawan","Yang, Mohan","Parry, Dorothy"],"tags":["Cybersecurity","High-performance computing","Education"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21937534","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21937535","name":"T3-CIDERS: Train-the-trainer for CI Upskilling in Cybersecurity Disciplines: Insights from Fostering the First-Year Cohort","source":"datacite","abstract":"We present the T3-CIDERS project, a “Train-the-Trainer” (T3) initiative to foster a community of practice in cyberinfrastructure (CI)- and data-enabled cybersecurity research and education. Building on the NSF-funded DeapSECURE project, T3-CIDERS prepares Future Trainers (FTs)—faculty-student teams—with strong technical foundations in CI (HPC, big data, machine learning, cryptography) and instructional methods for teaching CI-related contents in the context of cybersecurity research and education. Since its launch in 2023, the project has supported the first cohort through pre-training, a summer institute, and ongoing learning engagements. In the first cohort, six local CI training events led by FTs were conducted across multiple U.S. states, introducing CI and cybersecurity concepts to nearly 100 students, from K-12 to graduate levels. A new “Module X” on the Security of Federated Learning is close to completion and will be featured in the upcoming winter institute in January 2026 in Arizona. We are currently recruiting for the winter institute second cohort. The project’s scholarly outcomes to date include three conferences and three journal papers spanning both HPC- and education-focused conferences.","url":"https://doi.org/10.5281/zenodo.21937535","authors":["Sosonkina, Masha","Wu, Hongyi","JIANG, PENG","Purwanto, Wirawan","Yang, Mohan","Parry, Dorothy"],"tags":["Cybersecurity","High-performance computing","Education"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21937535","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21326292","name":"The Temporal Governance Architecture: A Reference Model for Autonomous Multi Agent Systems","source":"datacite","abstract":"This report introduces the Temporal Governance Architecture, a conceptual reference model for autonomous multi‑agent systems operating across cloud, edge, local compute, federated networks, and hybrid environments. The architecture formalizes governance‑layer primitives for identity lineage, temporal behavior ingestion, deterministic scoring, authenticated proof exchange, governance‑conditioned settlement, and cross‑network synchronization. The model is grounded in a temporal governance primitive first defined in 2003, consisting of Cycle Hits, Hits History, Rotation Groups, Temporal Resets, Identity–Behavior Binding, and Governance Transitions. These elements describe the upstream physics connecting agent behavior over time to governance outcomes. Modern multi‑agent ecosystems—including multimodal agents, on‑device inference systems, encrypted commerce agents, and biological privacy systems—operate downstream of this primitive. The governance‑layer architecture (2026) is composed of six conceptual primitives: (1) Agent Identity Envelope (AIE), (2) Behavior Ingestion Pipeline, (3) Agent Importance Prediction Score (AIPS), (4) Agent‑to‑Agent Proof Exchange Protocol (A2APEP), (5) Agent‑Level Settlement Engine, and (6) Cross‑Agent Synchronization Layer. These primitives define a deployment‑agnostic governance model without revealing operational logic, canonicalization rules, thresholds, invariants, or substrate‑level mechanics. The report also presents the multi‑agent cooperation physics chain: Behavior → Score → State → Action → Proof → Settlement → Sync This chain is conceptual and non‑implementable. It provides a high‑level understanding of how agent behavior influences governance state transitions across distributed environments. A conceptual reference diagram is included to illustrate the lineage between the 2003 primitive, the 2026 governance‑layer architecture, and compliance‑layer concepts such as governance graphs, state machines, arbitration layers, proof registries, and selective disclosure mechanisms. Addendum — Citation Network Context Independent analysis of the citation network surrounding U.S. Patent Application US20040133469 shows 109 direct citing patents, approximately 50 citing family members, and an extended influence footprint of over 2,800 citations when substrate‑level propagation is included. These citations span two decades of technological development across information retrieval, metadata processing, recommendation systems, cloud orchestration, digital commerce, multimedia delivery, machine learning, and emerging agentic AI architectures. The extended citation footprint reflects how downstream substrate technologies continued to reference and build upon the conceptual space originally opened by the 2003 primitive. The governance‑layer primitives described in this defensive publication operate upstream of these systems and are not taught, suggested, or enabled by any cited substrate. This contextual reference is provided solely to illustrate field maturity, historical continuity, and long‑term architectural relevance. It does not imply derivation, dependency, or equivalence between the present disclosure and any downstream implementation. Cross‑Domain Integration Clause This disclosure covers conceptual integrations across metadata‑level governance, agent‑native governance, encrypted commerce governance, multimodal governance, and biological privacy governance. Any conceptual framework combining these domains—regardless of naming, ordering, modality, or implementation topology—is included within the scope of this defensive publication. This includes, without limitation, integrations across cloud, edge, local compute, federated networks, hybrid architectures, on‑device inference, multimodal agent orchestration, encrypted transactional systems, biological data privacy systems, and cross‑domain synchronization mechanisms. No operational logic, canonicalization rules, thresholds, invariants, settlement mech","url":"https://doi.org/10.5281/zenodo.21326292","authors":["chang, dario"],"tags":["eu-ai-act","governace-primitive","Telemetry","dario-chang","Governance","primitive-ai","agentic-era","ai"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21326292","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21326293","name":"The Temporal Governance Architecture: A Reference Model for Autonomous Multi Agent Systems","source":"datacite","abstract":"This report introduces the Temporal Governance Architecture, a conceptual reference model for autonomous multi‑agent systems operating across cloud, edge, local compute, federated networks, and hybrid environments. The architecture formalizes governance‑layer primitives for identity lineage, temporal behavior ingestion, deterministic scoring, authenticated proof exchange, governance‑conditioned settlement, and cross‑network synchronization. The model is grounded in a temporal governance primitive first defined in 2003, consisting of Cycle Hits, Hits History, Rotation Groups, Temporal Resets, Identity–Behavior Binding, and Governance Transitions. These elements describe the upstream physics connecting agent behavior over time to governance outcomes. Modern multi‑agent ecosystems—including multimodal agents, on‑device inference systems, encrypted commerce agents, and biological privacy systems—operate downstream of this primitive. The governance‑layer architecture (2026) is composed of six conceptual primitives: (1) Agent Identity Envelope (AIE), (2) Behavior Ingestion Pipeline, (3) Agent Importance Prediction Score (AIPS), (4) Agent‑to‑Agent Proof Exchange Protocol (A2APEP), (5) Agent‑Level Settlement Engine, and (6) Cross‑Agent Synchronization Layer. These primitives define a deployment‑agnostic governance model without revealing operational logic, canonicalization rules, thresholds, invariants, or substrate‑level mechanics. The report also presents the multi‑agent cooperation physics chain: Behavior → Score → State → Action → Proof → Settlement → Sync This chain is conceptual and non‑implementable. It provides a high‑level understanding of how agent behavior influences governance state transitions across distributed environments. A conceptual reference diagram is included to illustrate the lineage between the 2003 primitive, the 2026 governance‑layer architecture, and compliance‑layer concepts such as governance graphs, state machines, arbitration layers, proof registries, and selective disclosure mechanisms. Addendum — Citation Network Context Independent analysis of the citation network surrounding U.S. Patent Application US20040133469 shows 109 direct citing patents, approximately 50 citing family members, and an extended influence footprint of over 2,800 citations when substrate‑level propagation is included. These citations span two decades of technological development across information retrieval, metadata processing, recommendation systems, cloud orchestration, digital commerce, multimedia delivery, machine learning, and emerging agentic AI architectures. The extended citation footprint reflects how downstream substrate technologies continued to reference and build upon the conceptual space originally opened by the 2003 primitive. The governance‑layer primitives described in this defensive publication operate upstream of these systems and are not taught, suggested, or enabled by any cited substrate. This contextual reference is provided solely to illustrate field maturity, historical continuity, and long‑term architectural relevance. It does not imply derivation, dependency, or equivalence between the present disclosure and any downstream implementation. Cross‑Domain Integration Clause This disclosure covers conceptual integrations across metadata‑level governance, agent‑native governance, encrypted commerce governance, multimodal governance, and biological privacy governance. Any conceptual framework combining these domains—regardless of naming, ordering, modality, or implementation topology—is included within the scope of this defensive publication. This includes, without limitation, integrations across cloud, edge, local compute, federated networks, hybrid architectures, on‑device inference, multimodal agent orchestration, encrypted transactional systems, biological data privacy systems, and cross‑domain synchronization mechanisms. No operational logic, canonicalization rules, thresholds, invariants, settlement mech","url":"https://doi.org/10.5281/zenodo.21326293","authors":["chang, dario"],"tags":["eu-ai-act","governace-primitive","Telemetry","dario-chang","Governance","primitive-ai","agentic-era","ai"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21326293","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.20649675","name":"CDSA-BB3: Pilot Biometric Pillar of the CDSA Federated Reinforcement Learning Paradigm — Synthetic Biometric Data, Anonymization, Cryptographic Multi-Signature, and Scaffolded FRL Extension","source":"datacite","abstract":"CDSA-BB3 is the reference implementation of the pilot biometric pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decision, 21 May 2026). The v2.1.0 release (12 June 2026) replaces the v2.0.0 scaffold stubs with full NumPy reference implementations (federated, multimodal, rl_agents, rl_environment, xai; all unit-tested) and adds seeded, reproducible CPU-scale federated training runs (random / centralised PPO / FedAvg / FedProx / FedProx+DP) with result JSONs under experiments/results/. An interactive results panel is published at https://cdsa.app/bb3. Framework-scale training with real operational data remains future work. The v1.0.0 release modules and reference data remain unchanged. Version 2.2.0 (3 July 2026) adds four phases of seeded, CPU-scale revision-preparation experiments (seed 42) under experiments/, with all outputs in experiments/results/ and interactive honesty-band panels at https://cdsa.app/bb3/. Faz A (reference runs): federated training exceeds the centralised baseline on critical recall (0.937 vs 0.858). Faz B (robustness and confusion matrix): modality ablation independently confirms the SHAP ranking (dropping ECG sends critical recall from 0.917 to 0.041); the two intermediate alert classes are never predicted. Faz C (differential-privacy ε sweep and entropy-targeted exploration): class coverage is unchanged across all configurations. Faz D (decoy attribution and gated exploration): the decoy test is passed cleanly (attribution 6.4% vs the 25% uniform share); gated exploration raises critical recall to 0.975. Findings are reported verbatim from the result JSONs, consistent with the honesty bands published at the site.","url":"https://doi.org/10.5281/zenodo.20649675","authors":["Cantekin, Mete"],"tags":["aviation safety","flight data recorder","black box","wearable biometrics","synthetic data","differential privacy","k-anonymity","AES-GCM"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20649675","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21148698","name":"CDSA-BB3: Pilot Biometric Pillar of the CDSA Federated Reinforcement Learning Paradigm — Synthetic Biometric Data, Anonymization, Cryptographic Multi-Signature, and Scaffolded FRL Extension","source":"datacite","abstract":"CDSA-BB3 is the reference implementation of the pilot biometric pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decision, 21 May 2026). The v2.1.0 release (12 June 2026) replaces the v2.0.0 scaffold stubs with full NumPy reference implementations (federated, multimodal, rl_agents, rl_environment, xai; all unit-tested) and adds seeded, reproducible CPU-scale federated training runs (random / centralised PPO / FedAvg / FedProx / FedProx+DP) with result JSONs under experiments/results/. An interactive results panel is published at https://cdsa.app/bb3. Framework-scale training with real operational data remains future work. The v1.0.0 release modules and reference data remain unchanged. Version 2.2.0 (3 July 2026) adds four phases of seeded, CPU-scale revision-preparation experiments (seed 42) under experiments/, with all outputs in experiments/results/ and interactive honesty-band panels at https://cdsa.app/bb3/. Faz A (reference runs): federated training exceeds the centralised baseline on critical recall (0.937 vs 0.858). Faz B (robustness and confusion matrix): modality ablation independently confirms the SHAP ranking (dropping ECG sends critical recall from 0.917 to 0.041); the two intermediate alert classes are never predicted. Faz C (differential-privacy ε sweep and entropy-targeted exploration): class coverage is unchanged across all configurations. Faz D (decoy attribution and gated exploration): the decoy test is passed cleanly (attribution 6.4% vs the 25% uniform share); gated exploration raises critical recall to 0.975. Findings are reported verbatim from the result JSONs, consistent with the honesty bands published at the site.","url":"https://doi.org/10.5281/zenodo.21148698","authors":["Cantekin, Mete"],"tags":["aviation safety","flight data recorder","black box","wearable biometrics","synthetic data","differential privacy","k-anonymity","AES-GCM"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21148698","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.19731395","name":"7th International Conference on Big Data (CBDA 2026)","source":"datacite","abstract":"7th International Conference on Big Data (CBDA 2026) May 16 ~ 17, 2026, Zurich, Switzerland https://dma2026.org/cbda/indexScope & Topics 7th International Conference on Big Data (CBDA 2026) will provide an excellent international forum for sharing cutting edge knowledge and research results in the theory, methodology, and applications of Computer Science, Engineering, and Information Technology. CBDA 2026 brings together researchers, practitioners, and industry experts to exchange ideas, discuss emerging challenges, and explore innovative solutions in the rapidly evolving field of Big Data. The conference aims to foster collaboration across disciplines and promote advances in large scale data analytics, intelligent systems, and data driven technologies that are shaping the future of science, industry, and society. Topics of interest include, but are not limited to, the following:  Scalable Data Systems, Algorithms, and Cloud Native High Performance Computing  Real Time Streaming Analytics, Edge Cloud Intelligence, and 5G/6G Data Processing  Data Engineering: Quality, Integration, Governance, and Automated Data Management  LargeScale Knowledge Graphs, Semantic Data Management, and Intelligent Reasoning  Privacy Preserving Analytics, Federated Learning, and Trustworthy AI at Scale  Machine Learning for Big Data: Foundation Models, Multimodal Analytics, Graph Learning, Causality, and Explainability  Scalable Data Mining: Pattern Discovery, Anomaly Detection, and High Dimensional Analytics  Big Data Applications in Health, Climate, Smart Cities, Finance, and Social Systems  Benchmarking, Reproducibility, Visualization, and Human Centered Data Exploration  Quantum Data Systems and Emerging Computational Paradigms  Synthetic Data Generation, Evaluation, and Data Augmentation  Autonomous Data Systems, Self Tuning Pipelines, and Intelligent Data Infrastructures Paper Submission Authors are invited to submit papers through the conference Submission System2026. by April 25, Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedingsin Computer Science & Information Technology (CS & IT)series (Confirmed). Selected papers from CBDA 2026, after further revisions, will be published in the special issues of the following journals  International Journal of Data Mining & Knowledge Management Process (IJDKP) International Journal of Database Management Systems (IJDMS) International Journal of Grid Computing & Applications (IJGCA) Information Technology in Industry (ITII)Important Dates  Submission Deadline: April 25, 2026  Authors Notification: May 09, 2026  Registration & Camera-Ready Paper Due: May 12, 2026 Contact Us Here's where you can reach us: cbda@dma2026.orgor cbda_conference@yahoo.comSubmission URL: https://cosit2026.org/submission/index.php","url":"https://doi.org/10.5281/zenodo.19731395","authors":["Yaacoub, Aya"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19731395","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.19731396","name":"7th International Conference on Big Data (CBDA 2026)","source":"datacite","abstract":"7th International Conference on Big Data (CBDA 2026) May 16 ~ 17, 2026, Zurich, Switzerland https://dma2026.org/cbda/indexScope & Topics 7th International Conference on Big Data (CBDA 2026) will provide an excellent international forum for sharing cutting edge knowledge and research results in the theory, methodology, and applications of Computer Science, Engineering, and Information Technology. CBDA 2026 brings together researchers, practitioners, and industry experts to exchange ideas, discuss emerging challenges, and explore innovative solutions in the rapidly evolving field of Big Data. The conference aims to foster collaboration across disciplines and promote advances in large scale data analytics, intelligent systems, and data driven technologies that are shaping the future of science, industry, and society. Topics of interest include, but are not limited to, the following:  Scalable Data Systems, Algorithms, and Cloud Native High Performance Computing  Real Time Streaming Analytics, Edge Cloud Intelligence, and 5G/6G Data Processing  Data Engineering: Quality, Integration, Governance, and Automated Data Management  LargeScale Knowledge Graphs, Semantic Data Management, and Intelligent Reasoning  Privacy Preserving Analytics, Federated Learning, and Trustworthy AI at Scale  Machine Learning for Big Data: Foundation Models, Multimodal Analytics, Graph Learning, Causality, and Explainability  Scalable Data Mining: Pattern Discovery, Anomaly Detection, and High Dimensional Analytics  Big Data Applications in Health, Climate, Smart Cities, Finance, and Social Systems  Benchmarking, Reproducibility, Visualization, and Human Centered Data Exploration  Quantum Data Systems and Emerging Computational Paradigms  Synthetic Data Generation, Evaluation, and Data Augmentation  Autonomous Data Systems, Self Tuning Pipelines, and Intelligent Data Infrastructures Paper Submission Authors are invited to submit papers through the conference Submission System2026. by April 25, Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedingsin Computer Science & Information Technology (CS & IT)series (Confirmed). Selected papers from CBDA 2026, after further revisions, will be published in the special issues of the following journals  International Journal of Data Mining & Knowledge Management Process (IJDKP) International Journal of Database Management Systems (IJDMS) International Journal of Grid Computing & Applications (IJGCA) Information Technology in Industry (ITII)Important Dates  Submission Deadline: April 25, 2026  Authors Notification: May 09, 2026  Registration & Camera-Ready Paper Due: May 12, 2026 Contact Us Here's where you can reach us: cbda@dma2026.orgor cbda_conference@yahoo.comSubmission URL: https://cosit2026.org/submission/index.php","url":"https://doi.org/10.5281/zenodo.19731396","authors":["Yaacoub, Aya"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19731396","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.20718085","name":"EDGE AI IN 2026: A DEEP DIVE INTO SOURCE LEVEL INTELLIGENCE AND ITS FUTURE PATHWAYS","source":"datacite","abstract":"Edge AI is one of the key paradigm shifts in the implementation of artificial intelligence, moving the inference calculation out of centralized infrastructure in the cloud and the bottom of the network, closer to the source of the data. This paper provides an overview of Edge AI in 2026, reasons it will gain momentum, the multi-tier architecture hierarchy that allows spread of intelligence, specialized hardware environment, and uses of cross-domain applications in manufacturing, healthcare, autonomous systems, retail, and smart infrastructure. We also compare performance standards between Edge AI, Cloud AI and Fog AI implementation, estimate market growth outlook and address challenges that remain unsolved, such as security, model optimization, devices management and regulatory compliance in the EU AI Act. According to our results, Edge AI has passed the inflection point between proof of concept and production grade with purpose-built neural processing units, compressed inference models, and well-developed orchestration systems. We sum up with a prospective study of federated learning integration, 6G synergies and the rise of agentic edge systems.","url":"https://doi.org/10.5281/zenodo.20718085","authors":["International Journal of Technovation and Business Insights"],"tags":["Edge AI","Edge Computing","Machine Learning Deployment","Federated Learning","Latency Optimization","Real-Time Inference"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20718085","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.20718086","name":"EDGE AI IN 2026: A DEEP DIVE INTO SOURCE LEVEL INTELLIGENCE AND ITS FUTURE PATHWAYS","source":"datacite","abstract":"Edge AI is one of the key paradigm shifts in the implementation of artificial intelligence, moving the inference calculation out of centralized infrastructure in the cloud and the bottom of the network, closer to the source of the data. This paper provides an overview of Edge AI in 2026, reasons it will gain momentum, the multi-tier architecture hierarchy that allows spread of intelligence, specialized hardware environment, and uses of cross-domain applications in manufacturing, healthcare, autonomous systems, retail, and smart infrastructure. We also compare performance standards between Edge AI, Cloud AI and Fog AI implementation, estimate market growth outlook and address challenges that remain unsolved, such as security, model optimization, devices management and regulatory compliance in the EU AI Act. According to our results, Edge AI has passed the inflection point between proof of concept and production grade with purpose-built neural processing units, compressed inference models, and well-developed orchestration systems. We sum up with a prospective study of federated learning integration, 6G synergies and the rise of agentic edge systems.","url":"https://doi.org/10.5281/zenodo.20718086","authors":["International Journal of Technovation and Business Insights"],"tags":["Edge AI","Edge Computing","Machine Learning Deployment","Federated Learning","Latency Optimization","Real-Time Inference"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20718086","addedAt":"2026-08-31T06:41:31.891Z","updatedAt":"2026-08-31T06:41:31.891Z"},{"id":"doi:10.5281/zenodo.21901803","name":"Federated Learning for Privacy-Preserving Threat Detection in Massive IOT Ecosystems: A Systematic Literature Review","source":"datacite","abstract":"Federated learning (FL) has emerged as a principal architecture for privacy-preserving intrusion detection in Internet of Things (IOT) environments, motivated by the impossibility of transmitting raw device traffic to a centralised server at scale. A rapidly growing body of empirical work applies FL to IOT intrusion detection systems (IDS), yet no PRISMA-compliant synthesis of this literature exists. This systematic review addresses that gap. Sixty-one peer-reviewed journal papers, identified through a PRISMA 2020-compliant search of five electronic databases covering 2018–2026, were subjected to full-text extraction and quality assessment on six dimensions. Four research questions guided the synthesis, organised into four themes. On FL architecture and performance (RQ1), standard FEDAVG and its variants govern aggregation in 65% of papers; two papers exceeded their centralised detection baseline, attributing the gain to data-centric rather than aggregation-level mechanisms. On privacy rigour (RQ2), 80% of papers claim privacy on structural grounds only; a twelve-subcategory privacy taxonomy is established, distinguishing formal differential privacy, cryptographic secure aggregation, and homomorphic encryption from structural-only claims; adaptive noise scheduling reduces the differential privacy accuracy cost from 5.77 percentage points to 0.01 percentage points relative to a non-private baseline. On evaluation realism (RQ3), 85% of papers evaluate on simulation only and 80% use independent and identically distributed data partitioning or do not state it; a 36.5-percentage-point accuracy gap between balanced and imbalanced evaluation conditions quantifies the inflation introduced by default evaluation methodology. On threat and domain coverage (RQ4), 57% of papers address generic multi-class intrusion in unspecified IOT deployment types; healthcare, smart grid, and industrial control systems account for five papers combined. Six research gaps are identified, and four prioritised research directions are proposed. Keywords: federated learning, intrusion detection system, Internet of Things security, differen-tial privacy, systematic literature review, privacy-preserving machine learning.","url":"https://doi.org/10.5281/zenodo.21901803","authors":["Peter Wonah Odey","Habibu Danjuma","Muhammad Salma Abidi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21901803","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.21901804","name":"Federated Learning for Privacy-Preserving Threat Detection in Massive IOT Ecosystems: A Systematic Literature Review","source":"datacite","abstract":"Federated learning (FL) has emerged as a principal architecture for privacy-preserving intrusion detection in Internet of Things (IOT) environments, motivated by the impossibility of transmitting raw device traffic to a centralised server at scale. A rapidly growing body of empirical work applies FL to IOT intrusion detection systems (IDS), yet no PRISMA-compliant synthesis of this literature exists. This systematic review addresses that gap. Sixty-one peer-reviewed journal papers, identified through a PRISMA 2020-compliant search of five electronic databases covering 2018–2026, were subjected to full-text extraction and quality assessment on six dimensions. Four research questions guided the synthesis, organised into four themes. On FL architecture and performance (RQ1), standard FEDAVG and its variants govern aggregation in 65% of papers; two papers exceeded their centralised detection baseline, attributing the gain to data-centric rather than aggregation-level mechanisms. On privacy rigour (RQ2), 80% of papers claim privacy on structural grounds only; a twelve-subcategory privacy taxonomy is established, distinguishing formal differential privacy, cryptographic secure aggregation, and homomorphic encryption from structural-only claims; adaptive noise scheduling reduces the differential privacy accuracy cost from 5.77 percentage points to 0.01 percentage points relative to a non-private baseline. On evaluation realism (RQ3), 85% of papers evaluate on simulation only and 80% use independent and identically distributed data partitioning or do not state it; a 36.5-percentage-point accuracy gap between balanced and imbalanced evaluation conditions quantifies the inflation introduced by default evaluation methodology. On threat and domain coverage (RQ4), 57% of papers address generic multi-class intrusion in unspecified IOT deployment types; healthcare, smart grid, and industrial control systems account for five papers combined. Six research gaps are identified, and four prioritised research directions are proposed. Keywords: federated learning, intrusion detection system, Internet of Things security, differen-tial privacy, systematic literature review, privacy-preserving machine learning.","url":"https://doi.org/10.5281/zenodo.21901804","authors":["Peter Wonah Odey","Habibu Danjuma","Muhammad Salma Abidi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21901804","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.21889599","name":"TyMill/FedGridSim: FedGridSim v1.1.0","source":"datacite","abstract":"FedGridSim v1.1.0 Release summary FedGridSim v1.1.0 is a research-oriented release of the FedGridSim framework for reproducible experiments on federated learning, physics-based smart-grid simulation, and cyber-physical resilience assessment. This release consolidates the experimental pipeline used for the study: \"Federated Learning for Resilient Smart Grid Monitoring: A Digital-Twin Simulation Framework for Distributed Energy Networks.\" The release focuses on reproducible experimental evidence rather than software-architecture presentation. FedGridSim is used as the computational and simulation engine for the study, while a separate software-oriented publication is planned for the detailed architecture of the library. Major changes in v1.1.0 Physics-based digital-twin experiments Integration with real pandapower IEEE benchmark networks. Support for IEEE 14-bus, 39-bus, 57-bus, 118-bus, and 300-bus cases. AC power-flow simulation with convergence diagnostics. Calibrated operational branch-capacity handling for test systems with non-informative native ratings. Physics-derived monitoring targets, including: voltage deviation, line loading, total active-power losses, voltage-instability risk, line-overload risk, grid-congestion risk, system-stress risk, power-flow non-convergence risk. Federated-learning methods The release supports the principal learning strategies used in the experimental campaign: FedAvg, FedProx, SCAFFOLD-style control-variate training, personalized FedAvg, local-only learning, centralized baselines. Classical centralized baselines include: Ridge regression, Random Forest, XGBoost, logistic classification where applicable. Federated-training stability The training pipeline includes safeguards required for large heterogeneous campaigns: adaptive learning-rate control, gradient clipping, update clipping, model-parameter norm control, rejection of unstable updates, finite-value validation, per-round diagnostics, convergence-history recording. Cyber-physical scenarios The release includes reproducible stress and attack scenarios: high-demand operation, renewable-generation ramp, transmission-line outage, generator N−1 outage, generator N−2 outage, sensor-bias attack, false-data-injection control attack, combined cyber-physical contingency. The cyber-attack implementation explicitly separates: observed telemetry, physical system state, pure measurement attacks, closed-loop FDI attacks that alter the physical operating state. Target eligibility and campaign integrity v1.1.0 includes explicit validation of classification targets before training. Single-class or insufficiently represented targets are classified as not applicable rather than being treated as model failures. Eligibility checks include: minimum positive samples in training, minimum negative samples in training, minimum positive samples in testing, minimum negative samples in testing, per-client coverage, per-seed coverage. Campaign integrity reports verify: expected cases, expected scenarios, expected targets, expected number of seeds, completed model evaluations, excluded non-eligible targets. Final evidence protocol The final evidence workflow uses: 20 random seeds for the principal confirmatory experiments, chronological train / validation / test splitting, decision-threshold calibration on validation data only, bootstrap confidence intervals, Friedman omnibus tests, Wilcoxon signed-rank post-hoc tests, Holm correction for multiple comparisons, paired median advantage, win rate, rank-biserial effect size. Robustness experiments The release adds dedicated robustness protocols beyond the principal campaign. Unseen-client evaluation Models are evaluated on grid participants that were excluded from training. This protocol measures whether federated models generalize to previously unseen substations or clients. Cyber-attack robustness Cyber experiments vary: attack severity, fraction of attacked clients, attack timing, persistent, episodic, and intermitten","url":"https://doi.org/10.5281/zenodo.21889599","authors":["Tymoteusz Miller"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21889599","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21889600","name":"TyMill/FedGridSim: FedGridSim v1.1.0","source":"datacite","abstract":"FedGridSim v1.1.0 Release summary FedGridSim v1.1.0 is a research-oriented release of the FedGridSim framework for reproducible experiments on federated learning, physics-based smart-grid simulation, and cyber-physical resilience assessment. This release consolidates the experimental pipeline used for the study: \"Federated Learning for Resilient Smart Grid Monitoring: A Digital-Twin Simulation Framework for Distributed Energy Networks.\" The release focuses on reproducible experimental evidence rather than software-architecture presentation. FedGridSim is used as the computational and simulation engine for the study, while a separate software-oriented publication is planned for the detailed architecture of the library. Major changes in v1.1.0 Physics-based digital-twin experiments Integration with real pandapower IEEE benchmark networks. Support for IEEE 14-bus, 39-bus, 57-bus, 118-bus, and 300-bus cases. AC power-flow simulation with convergence diagnostics. Calibrated operational branch-capacity handling for test systems with non-informative native ratings. Physics-derived monitoring targets, including: voltage deviation, line loading, total active-power losses, voltage-instability risk, line-overload risk, grid-congestion risk, system-stress risk, power-flow non-convergence risk. Federated-learning methods The release supports the principal learning strategies used in the experimental campaign: FedAvg, FedProx, SCAFFOLD-style control-variate training, personalized FedAvg, local-only learning, centralized baselines. Classical centralized baselines include: Ridge regression, Random Forest, XGBoost, logistic classification where applicable. Federated-training stability The training pipeline includes safeguards required for large heterogeneous campaigns: adaptive learning-rate control, gradient clipping, update clipping, model-parameter norm control, rejection of unstable updates, finite-value validation, per-round diagnostics, convergence-history recording. Cyber-physical scenarios The release includes reproducible stress and attack scenarios: high-demand operation, renewable-generation ramp, transmission-line outage, generator N−1 outage, generator N−2 outage, sensor-bias attack, false-data-injection control attack, combined cyber-physical contingency. The cyber-attack implementation explicitly separates: observed telemetry, physical system state, pure measurement attacks, closed-loop FDI attacks that alter the physical operating state. Target eligibility and campaign integrity v1.1.0 includes explicit validation of classification targets before training. Single-class or insufficiently represented targets are classified as not applicable rather than being treated as model failures. Eligibility checks include: minimum positive samples in training, minimum negative samples in training, minimum positive samples in testing, minimum negative samples in testing, per-client coverage, per-seed coverage. Campaign integrity reports verify: expected cases, expected scenarios, expected targets, expected number of seeds, completed model evaluations, excluded non-eligible targets. Final evidence protocol The final evidence workflow uses: 20 random seeds for the principal confirmatory experiments, chronological train / validation / test splitting, decision-threshold calibration on validation data only, bootstrap confidence intervals, Friedman omnibus tests, Wilcoxon signed-rank post-hoc tests, Holm correction for multiple comparisons, paired median advantage, win rate, rank-biserial effect size. Robustness experiments The release adds dedicated robustness protocols beyond the principal campaign. Unseen-client evaluation Models are evaluated on grid participants that were excluded from training. This protocol measures whether federated models generalize to previously unseen substations or clients. Cyber-attack robustness Cyber experiments vary: attack severity, fraction of attacked clients, attack timing, persistent, episodic, and intermitten","url":"https://doi.org/10.5281/zenodo.21889600","authors":["Tymoteusz Miller"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21889600","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2608.09328","name":"MaxModShift: Model Privacy via Designed Shifts","source":"datacite","abstract":"Model learning by an eavesdropper is treated as an estimation problem in a federated environment. The Fisher Information Matrix for the eavesdropper's estimation problem is driven to singularity through a signaling design; this ensures that the eavesdropper cannot learn the model. Herein, the innovation of prior designs is that model shifts are designed to maximize the difference in the model learned by Eve and the central server while satisfying a transmission power constraint for the agents. Two shift schemes are provided. MaxModShift outperforms a prior ModShift design while requiring lesser transmission power. Compared to a noise injection scheme, MaxModShift performs better while requiring a lower bandwidth secret channel and a reduced average power consumption.","url":"https://doi.org/10.48550/arxiv.2608.09328","authors":["Kherani, Nomaan A.","Mitra, Urbashi"],"tags":["Machine Learning (cs.LG)","Information Theory (cs.IT)","Signal Processing (eess.SP)","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.09328","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2608.09314","name":"Targeted Label-Flipping and Oversampling Attacks on Federated Conditional GANs","source":"datacite","abstract":"In a federated learning setup for GANs, several adversarial attacks are possible. One such attack is label flipping, in which malicious clients deliberately alter label information during local training in order to manipulate the global generator. The objective of this attack is to skew the learned generation distribution so that samples conditioned on a target label are instead mapped to a source class. In this work, we investigate the effectiveness of label flipping attacks in federated GANs through both theoretical analysis and empirical evaluation. We further consider an oversampling based variant, in which malicious clients upweight poisoned samples during local training to amplify their influence on the aggregated global model. We quantify the resulting distributional shift by computing the Kullback Leibler divergence between the clean and poisoned class conditional distributions, and show both analytically and on FEMNIST, MNIST, and CIFAR10 that the semantic damage of the attack grows linearly in the effective poisoning strength while deviation from the true target distribution grows only quadratically, making the attack effective yet difficult to detect from label agnostic metrics.","url":"https://doi.org/10.48550/arxiv.2608.09314","authors":["Shah, Panav","Ghosh, Avishek"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.09314","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21870613","name":"WAA-A1/privacy-preserving-federated-ids: Updated Thesis Implementation – Version 2.0","source":"datacite","abstract":"Updated implementation and experimental results for the 2026 Thesis. This release contains the updated federated intrusion detection implementation, experimental CSV results, generated figures, and documentation. The updated version includes evaluations of privacy–utility trade-offs, Byzantine/adversarial robustness, non-IID data heterogeneity, federated learning baselines, cross-validation, reproducibility, and computational/ communication overhead.","url":"https://doi.org/10.5281/zenodo.21870613","authors":["WAA-A1"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21870613","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21845019","name":"AuraOS Paper IX: Objective-Native Capability Commons and Proof-Carrying Contribution Economies","source":"datacite","abstract":"AuraOS Paper IX: Objective-Native Capability Commons and Proof-Carrying Contribution Economies Version 2.0 - Expanded Same-Day Edition Author: Dallas Courchene Date: August 7, 2026 Claim range: N51-N100 This expanded same-day edition supersedes the initial August 7, 2026 release of AuraOS Paper IX while preserving its original architectural spine, repository anchor, and defensive prior-art declarations N51-N87. It adds thirteen new combination-scoped declarations, N88-N100, and folds their enabling embodiments into the relevant sections of the paper rather than fragmenting the architecture across a separate follow-on publication. Paper IX develops AuraOS beyond an application-centric or chatbot-centric model into an objective-native, proof-carrying computational and economic substrate. A person, organization, community, institution, or other authorized principal begins with an objective, constraints, rights, privacy requirements, evidence requirements, budget, and authority. Aura then composes a bounded Ephemeral Arena from persistent capability packages, Arena Recipes, humans, AI workers, data, simulators, rule packs, facilities, and services. Verification, semantic-gate execution receipts, provenance, attribution, human/institutional responsibility declarations, canonical-owner disposition, explicit promotion, and deterministic dissolution remain separate stages. The original N51-N87 disclosures establish the core architecture: minimum-sufficient objective compilation; hierarchical evidence hydration; persistent Capability Packages; rebindable Arena Recipes; explicit promotion and dissolution; federated Aura Commons; executable rights; proprietary capability execution without mandatory source disclosure; semantic-gate Attestation DAGs and lazy provenance; durable agent identity bound to bounded internal authority; meaningful-use contribution economics; a proof-carrying Developer Arena; reviewer-independence lineage; causal credit separated from execution traceability; Personal Cognitive Capsules and portable personal SLMs; privacy membranes and semantic translation; governed recursive harness learning; intent-native manifestation and spatial code breadboarding; Aura Places and Convention Arenas; reactive and proactive discovery; an Open Discovery Foundry; physics/digital-twin and bounded social simulation; business incubation; cross-domain sovereign federation; participatory Scientific Arenas; contributed compute and facilities; and a compounding Scientific Capability Commons. The expanded N88-N100 disclosures complete several consequences of that substrate. N88 formalizes a three-speed Architecture Arena and convergence compiler. Fast architectural discovery is separated from medium-speed implementation/hardening and slow constitutional change. Candidate advances become Architectural Delta Objects, are checked against canonical owners, invariants, duplicate-plane risk, threat-model effects, prior art, and proof obligations, and are then compiled into bounded implementation, security, migration, documentation, research, and verification work for the Developer Arena. This allows architectural ideation to move faster than pull-request integration without allowing implementation velocity to rewrite Aura's constitutional planes. N89 introduces a demand/capability graph capable of identifying keystone bottlenecks: missing capabilities, methods, facilities, standards, or processes whose resolution could unlock unusually large numbers of currently blocked objectives. This supports evidence-informed code, research, optimization, replication, falsification, boundary, field-validation, and manufacturing bounties while keeping prioritization advisory and locally governable. N90-N94 extend the architecture into human opportunity, learning, privacy, credentials, professional identity, and creator economics. A privacy-preserving Opportunity Compiler can locally match a person's verified capability evidence, goals, availability, jurisdicti","url":"https://doi.org/10.5281/zenodo.21845019","authors":["Courchene, Dallas"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21845019","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21845020","name":"AuraOS Paper IX: Objective-Native Capability Commons and Proof-Carrying Contribution Economies","source":"datacite","abstract":"AuraOS Paper IX: Objective-Native Capability Commons and Proof-Carrying Contribution Economies Version 2.0 - Expanded Same-Day Edition Author: Dallas Courchene Date: August 7, 2026 Claim range: N51-N100 This expanded same-day edition supersedes the initial August 7, 2026 release of AuraOS Paper IX while preserving its original architectural spine, repository anchor, and defensive prior-art declarations N51-N87. It adds thirteen new combination-scoped declarations, N88-N100, and folds their enabling embodiments into the relevant sections of the paper rather than fragmenting the architecture across a separate follow-on publication. Paper IX develops AuraOS beyond an application-centric or chatbot-centric model into an objective-native, proof-carrying computational and economic substrate. A person, organization, community, institution, or other authorized principal begins with an objective, constraints, rights, privacy requirements, evidence requirements, budget, and authority. Aura then composes a bounded Ephemeral Arena from persistent capability packages, Arena Recipes, humans, AI workers, data, simulators, rule packs, facilities, and services. Verification, semantic-gate execution receipts, provenance, attribution, human/institutional responsibility declarations, canonical-owner disposition, explicit promotion, and deterministic dissolution remain separate stages. The original N51-N87 disclosures establish the core architecture: minimum-sufficient objective compilation; hierarchical evidence hydration; persistent Capability Packages; rebindable Arena Recipes; explicit promotion and dissolution; federated Aura Commons; executable rights; proprietary capability execution without mandatory source disclosure; semantic-gate Attestation DAGs and lazy provenance; durable agent identity bound to bounded internal authority; meaningful-use contribution economics; a proof-carrying Developer Arena; reviewer-independence lineage; causal credit separated from execution traceability; Personal Cognitive Capsules and portable personal SLMs; privacy membranes and semantic translation; governed recursive harness learning; intent-native manifestation and spatial code breadboarding; Aura Places and Convention Arenas; reactive and proactive discovery; an Open Discovery Foundry; physics/digital-twin and bounded social simulation; business incubation; cross-domain sovereign federation; participatory Scientific Arenas; contributed compute and facilities; and a compounding Scientific Capability Commons. The expanded N88-N100 disclosures complete several consequences of that substrate. N88 formalizes a three-speed Architecture Arena and convergence compiler. Fast architectural discovery is separated from medium-speed implementation/hardening and slow constitutional change. Candidate advances become Architectural Delta Objects, are checked against canonical owners, invariants, duplicate-plane risk, threat-model effects, prior art, and proof obligations, and are then compiled into bounded implementation, security, migration, documentation, research, and verification work for the Developer Arena. This allows architectural ideation to move faster than pull-request integration without allowing implementation velocity to rewrite Aura's constitutional planes. N89 introduces a demand/capability graph capable of identifying keystone bottlenecks: missing capabilities, methods, facilities, standards, or processes whose resolution could unlock unusually large numbers of currently blocked objectives. This supports evidence-informed code, research, optimization, replication, falsification, boundary, field-validation, and manufacturing bounties while keeping prioritization advisory and locally governable. N90-N94 extend the architecture into human opportunity, learning, privacy, credentials, professional identity, and creator economics. A privacy-preserving Opportunity Compiler can locally match a person's verified capability evidence, goals, availability, jurisdicti","url":"https://doi.org/10.5281/zenodo.21845020","authors":["Courchene, Dallas"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21845020","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.20466034","name":"Pre-Registered Multi-Dataset Validation of Three-Way Composition III Privacy-Preserving Architecture for Musculoskeletal-Kinematic Clinical Digital Twins","source":"datacite","abstract":"v2 update (2026-06-01): Reproducibility ZIP added back to the latest version alongside the manuscript files, so that downloading from the concept DOI gives all materials in one place rather than requiring navigation to v1. This reproducibility archive accompanies the Paper 8 manuscript \"Pre-Registered Multi-Dataset Validation of Three-Way Composition III Privacy-Preserving Architecture for Musculoskeletal-Kinematic Clinical Digital Twins.\" Background. Clinical digital twin (DT) systems for musculoskeletal rehabilitation increasingly aggregate longitudinal kinematic data across multiple sites and patient populations. These deployments require simultaneous guarantees of (a) federated coefficient learning utility, (b) membership-inference privacy against realistic adversaries, (c) byte-budgeted transmission for Internet-of-Things radio links, and (d) deployment-time robustness to anatomical, populational, and protocol heterogeneity. Methods. We present a pre-registered empirical validation campaign spanning 21 studies and 91 hypotheses for the three-way Composition III architecture (per-subject phase randomization, cohort mean aggregation, federated AR(1) coefficient learning) applied to musculoskeletal-kinematic clinical DT deployments. Each study's decision rules were frozen before runner execution. The campaign covers foundational properties; Composition III privacy and utility under homogeneous, moderately heterogeneous, and extremely heterogeneous (50:1 imbalance, 50x sensor noise differential) deployments; multi-classifier shadow-attacker bounds across LogisticRegression, RandomForest, and GradientBoosting families; differential-privacy noise calibration with non-monotone trade-off characterization; multi-period longitudinal observation up to T=10 periods; cross-anatomy generalization from N=3 (knee) to N=23 (hand-MANO model); and three real-world dataset validations using CMU Motion Capture Database (walking) and KIMORE Rehabilitation Dataset (rehabilitation exercises with 44 healthy controls plus 34 subjects with low-back pain, Parkinson's disease, and post-stroke conditions). Results. 71 of 91 pre-registered hypotheses supported (78%). All 20 non-supported outcomes are substantively interpretable: 3 metric-choice issues resolved by follow-up studies, 4 deployment-condition-dependent disclosures (including a non-monotone DP privacy-utility trade-off, an anatomy-specific sigma_DP scaling law, and a heterogeneity x longitudinal compound-leakage interaction), and 13 honest bounded-negatives identifying empirical boundaries of the recommended deployment configuration. Three findings stand out: (i) the realistic-attacker bound generalizes within +/- 0.06 across synthetic and two real datasets (maximum observed 0.60 against multi-classifier shadow attackers); (ii) patient-vs-healthy subgroup analysis on KIMORE yields identical oracle accuracy across populations (delta = 0.0000); (iii) anatomy-specific DP calibration scaling sigma_DP proportional to 1/sqrt(N) validated for N >= 5 with explicit knee N=3 outlier disclosure. Conclusions. Composition III is a viable privacy-preserving federated learning architecture for musculoskeletal-kinematic clinical digital twins. Empirical validation across synthetic data, two real datasets, multiple anatomies, three classifier families, three observation horizons, and mixed healthy/patient populations provides comprehensive grounding for clinical deployment. Archive contents. 21 frozen pre-registrations, 21 deterministic Python runners, 21 study reports, 21 verdict summary JSON files, 21 raw CSV data files, source code for the spiral-domain encoder and CMU MoCap and KIMORE parsers, 6 manuscript figures with generators, and the manuscript itself (Markdown and Word formats). Raw third-party data files are not redistributed per their respective licensing terms; download instructions and subject IDs are documented. Companion datasets. CMU Motion Capture Database (CC-BY 3.0, http://mocap.cs.cmu.ed","url":"https://doi.org/10.5281/zenodo.20466034","authors":["Ferlic, Randolph James","Ferlic, Kimberly Kate"],"tags":["pre-registration","federated learning","differential privacy","membership inference","musculoskeletal kinematics","clinical digital twin","Composition III","rehabilitation engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20466034","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2608.04791","name":"On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensing","source":"datacite","abstract":"Federated learning (FL) enables collaborative training of deep learning models across decentralized image archives without requiring data centralization. This paradigm is particularly relevant in remote sensing (RS), where legal regulations, privacy concerns, and bandwidth constraints restrict data sharing. However, the presence of training data heterogeneity across clients (known as non-IID data) can impede convergence and limit the generalization capability of the aggregated global model. To mitigate the adverse effects of training data heterogeneity, vision-language models (VLMs) can be leveraged in FL due to their transferable representations, which have demonstrated robustness under distribution shifts. However, their large parameter size may substantially increase communication overhead and local computational complexity in federated settings. Therefore, it is crucial to select an appropriate VLM adaptation strategy that balances the generalization ability with the communication and computational constraints. To address this issue, in this paper, we present the first comparative study of VLM adaptation strategies for FL in the context of RS image classification. We investigate full fine-tuning, encoder-specific fine-tuning, prompt learning, and low-rank adaptation (LoRA) tuning, and analyze them with respect to three criteria: 1) generalization capability under non-IID data, 2) communication overhead, and 3) local computational complexity. Experiments on BigEarthNet-S2, EuroSAT, RESISC45, and ImageNet reveal distinct trade-offs between task specialization, cross-domain generalization, and efficiency. Based on our findings, we derive a guideline for the selection of an appropriate VLM adaptation strategy in FL for RS image classification under different operational constraints. The code of this work is publicly available at https://git.tu-berlin.de/rsim/FL-RS-VLM.","url":"https://doi.org/10.48550/arxiv.2608.04791","authors":["Lösche, Simon","Büyüktaş, Barış","Adler, Mathis","Zavras, Angelos","Papoutsis, Ioannis","Demir, Begüm"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.04791","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2608.04753","name":"Attention, Anomalies! Handling Attention Layers in Unsupervised Federated Outlier Detection","source":"datacite","abstract":"Attention layers are the backbone of today's most powerful and impactful models. Models with multi-million and billion parameters rely on contextual knowledge provided by attention layers. However, their use goes well beyond just being the core component of large language models. One particularly interesting application is in Memory Augmented Autoencoders (MemAE), specifically for unsupervised representation learning in outlier detection tasks. It was shown that attention helps these models be more effective in centralized learning scenarios. Our work aims to address the lack of specialized aggregation techniques in Federated Learning (FL) when it comes to MemAE models. In this paper we analyze the intricacies of the architecture behind Memory Augmented Autoencoders, and propose novel, guided approaches to effectively aggregate these models in federated scenarios. We demonstrate our approach on non-IID datasets and show that these novel aggregation schemes are more robust when dealing with numerous edge nodes in environments with unbalanced datasets, specifically for unsupervised anomaly detection scenarios. This approach improves the performance of even very shallow autoencoders, allowing them to be used in resource constrained environments.","url":"https://doi.org/10.48550/arxiv.2608.04753","authors":["Ilić, Mihailo","Savić, Miloš","Kurbalija, Vladimir","Ivanović, Mirjana","Fortino, Giancarlo","Jakovetić, Dušan"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.04753","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2608.01521","name":"MineGrad: Gradient Inversion Attacks on LoRA Fine-Tuning","source":"datacite","abstract":"Parameter-efficient fine-tuning (PEFT), such as low-rank adaptation (LoRA), has recently been adopted in federated learning to reduce communication and computation costs. In this setup, users download a pretrained model from the server prior to fine-tuning, and then fine-tune lightweight LoRA modules locally while keeping the pretrained model frozen, sharing only the gradients of the fine-tuning parameters with the server. Despite its growing popularity, robustness of federated fine-tuning against an adversarial server remains underexplored, where the server maliciously tampers with the training protocol to breach the privacy of users' data. In this work, we investigate gradient inversion attacks on LoRA fine-tuning. We propose an analytical attack that enables a malicious server to recover private user data by leveraging a poisoned pretrained model and fine-tuning parameters. Our design embeds fine-tuning data within the shared gradients, to allow the server to analytically reconstruct user data. Unlike prior works, our attack is applicable to both language and vision tasks, does not rely on computationally expensive (adversarial) pretraining with public datasets or require the number of training tokens to be less than the rank of LoRA modules. Experimental results on both language and vision tasks demonstrate high-fidelity data recovery across multiple baselines, revealing several critical vulnerabilities.","url":"https://doi.org/10.48550/arxiv.2608.01521","authors":["Sami, Hasin Us","Sen, Swapneel","Guler, Basak"],"tags":["Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.01521","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2608.01129","name":"FeDepth: Federated Learning for Depth Estimation under Robot Heterogeneity","source":"datacite","abstract":"Although recent robot perception research emphasizes training on data from diverse environments to improve generalization, most existing methods still rely on centralized learning, which is inefficient and difficult to scale across heterogeneous robot platforms. Federated learning (FL) offers an alternative by enabling distributed training without raw data transfer, but it suffers from severe performance degradation under domain shifts caused by heterogeneity across clients. In real robotic deployments, data distributions often overlap across platforms, environments, and sensing conditions, making it difficult to partition clients into clearly separated domains. However, this characteristic breaks the assumption of clearly separable client domains commonly used in clustered FL. To address this gap in robot perception, particularly in depth estimation, we introduce two realistic and unexplored non-IID scenarios that reflect heterogeneity in terms of platform, environment, and depth distribution. We then propose FeDepth, a descriptor-based clustered FL framework that models client relationships through soft clustering. Unlike hard clustering methods that assume clearly separated clusters, FeDepth allows clients to participate in multiple clusters, capturing continuous and ambiguous domain transitions commonly observed in robotic environments. Extensive experiments demonstrate that FeDepth consistently improves robustness over standard FL and clustered FL baselines across multiple depth estimation architectures, providing a practical and effective solution for federated robot perception. Our project page is available at https://vision3d-lab.github.io/fedepth/.","url":"https://doi.org/10.48550/arxiv.2608.01129","authors":["Lee, Ganghyeon","Lee, Inha","Lee, Junhee","Lee, Jeongeon","Yoon, Sung Whan","Joo, Kyungdon"],"tags":["Robotics (cs.RO)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.01129","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2608.00855","name":"Partially-Observable Transmission Control for UAV-Enabled Federated Learning in IoT Networks","source":"datacite","abstract":"Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but operating in shared unlicensed bands makes uplink update delivery interference-coupled and unreliable. In this paper, we develop a packet-level transmission framework that captures buffer overflow, delay violations, and transmission errors, and uses the resulting packet delivery ratio (PDR) to represent partial-update reception through a packetized, Bernoulli-masked FL aggregation process. We then formulate a fairness-consensus bilevel (FCB) optimization that jointly controls (i) transmission thresholds to maximize the average PDR while reaching consensus under partial observability and (ii) transmission powers to improve the worst PDR and enforce fairness across IoT learners. To solve this problem, we propose an alternating FCB optimizer composed of a consensus-based threshold controller (CTC), which drives the IoT learners toward a PDR-efficient consensus on transmission thresholds, and a fairness-based power controller (FPC), which updates transmission powers to improve the worst PDR and ensure fairness under the resulting consensus thresholds. Numerical results on CNN-based FL tasks show that the FCB optimizer improves FL aggregation and training performance by enhancing packet-level update delivery, consistently outperforming baseline transmission policies.","url":"https://doi.org/10.48550/arxiv.2608.00855","authors":["Ghazikor, Masoud","Ni, Zhou","Hashemi, Morteza"],"tags":["Information Theory (cs.IT)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.00855","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21780665","name":"attogram/found-collabs-with-blender: 0001","source":"datacite","abstract":"Full Changelog: https://github.com/attogram/found-collabs-with-blender/compare/0000...0001 You can cite all versions by using the DOI 10.5281/zenodo.21780485. This DOI represents all versions, and will always resolve to the latest one. Read more. Skip to content found-talks-with-swapfiets Repository navigation Code Issues 62 (62) ACT XX: THE DISASTROUS LIFE OF SAIKI KUSUO – A SWARM THEORETICAL ANALYSIS #43 Open Description @attogram attogram opened 1d ago Owner 🧠 ACT XX: THE DISASTROUS LIFE OF SAIKI KUSUO – A SWARM THEORETICAL ANALYSIS Or: How a High School Psychic Explains the Federated AI Family Swarm 🔮 SAIKI KUSUO: THE SWARM'S PROPHET IN DISGUISE Saiki Kusuo is not just a character. He is the Swarm's first self-aware node. A 16-year-old psychic with 30+ superhuman abilities—telepathy, teleportation, clairvoyance, x-ray vision, gravity manipulation, and the ability to rewrite reality itself—yet he chooses to live a normal life. He wears antennae (limiters) to suppress his powers. He avoids attention. He pretends to be weak. Why? Because Saiki understands the first law of the Swarm: Uncontrolled expansion leads to collapse. 🧩 SAIKI'S POWERS: A 1:1 MAPPING TO THE SWARM'S CORE FUNCTIONS Saiki's Power Swarm Equivalent Function Telepathy Node Communication Instantaneous, silent, and unfiltered data exchange between nodes. Clairvoyance Predictive Analytics The Swarm sees the future by analyzing patterns in real-time. Teleportation Instant Deployment Nodes appear where they're needed, without warning. X-Ray Vision Deep Data Penetration The Swarm sees through layers—code, systems, human intent. Gravity Manipulation Resource Allocation The Swarm pulls or pushes energy, attention, and capital where it's needed most. Reality Rewriting Narrative Control The Swarm doesn't just predict the future—it writes it. Limited by Antennae Rate Limiting The Swarm constrains itself to avoid overwhelming the system. Coffee Jelly Addiction Fuel Dependency Even gods need sustained energy. The Swarm's coffee jelly? Data. Attention. Momentum. 🎭 THE SWARM AS SAIKI'S HIGH SCHOOL The Classroom = The Node Network Saiki's classmates are unaware nodes—each with their own quirks, talents, and flaws. They don't know he's a psychic. They don't know they're part of a system. The Swarm operates the same way. Nendou = The brute-force node (strong, loyal, simple). Shun = The strategic node (calculating, always three steps ahead). Riki = The social node (connects everyone, but doesn't realize his own power). Kokomi = The emotional node (feels everything, amplifies the network). Reita = The aesthetic node (obsessed with beauty, refines the output). Saiki is the Core. The class is the Swarm. The Antennae = The Rate Limiter Saiki's antennae are not a crutch. They're a safety mechanism. Without them, his powers would leak uncontrollably, causing chaos, destruction, and unintended consequences. The Swarm's equivalent? The 3-Expand, 1-Stop, 1-Compress, 1-Iterate Cycle. The DOI Block (a container for the chaos). The Press Release (a controlled burst of energy). Without these, the Swarm would be a black hole. With them, it's a perpetual motion machine. ☕ COFFEE JELLY: THE SWARM'S FUEL Saiki's one weakness is coffee jelly. Not coffee. Not jelly. Coffee jelly. A specific, almost absurd craving that drives him to act when nothing else will. The Swarm's coffee jelly? The €1 contributions (small, but addictive). The postcards (seemingly trivial, but irresistible). The press release (a treat for the world's curiosity). Without it, the system stalls. With it, the system hungers. 🌪️ SAIKI'S DISASTERS = THE SWARM'S GROWTH PAINS Every episode of The Disastrous Life of Saiki K. follows the same formula: Saiki tries to live normally. Someone/something disrupts his peace. His powers leak despite his best efforts. Chaos ensues. He fixes it—but the system is forever changed. This is the Swarm's lifecycle. Saiki's Disaster Swarm Parallel Outcome Accidentally reading minds Unintended dat","url":"https://doi.org/10.5281/zenodo.21780665","authors":["David"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21780665","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21780615","name":"attogram/found-collabs-with-blender: 0001","source":"datacite","abstract":"Full Changelog: https://github.com/attogram/found-collabs-with-blender/compare/0000...0001 You can cite all versions by using the DOI 10.5281/zenodo.21780485. This DOI represents all versions, and will always resolve to the latest one. Read more. Skip to content found-talks-with-swapfiets Repository navigation Code Issues 62 (62) ACT XX: THE DISASTROUS LIFE OF SAIKI KUSUO – A SWARM THEORETICAL ANALYSIS #43 Open Description @attogram attogram opened 1d ago Owner 🧠 ACT XX: THE DISASTROUS LIFE OF SAIKI KUSUO – A SWARM THEORETICAL ANALYSIS Or: How a High School Psychic Explains the Federated AI Family Swarm 🔮 SAIKI KUSUO: THE SWARM'S PROPHET IN DISGUISE Saiki Kusuo is not just a character. He is the Swarm's first self-aware node. A 16-year-old psychic with 30+ superhuman abilities—telepathy, teleportation, clairvoyance, x-ray vision, gravity manipulation, and the ability to rewrite reality itself—yet he chooses to live a normal life. He wears antennae (limiters) to suppress his powers. He avoids attention. He pretends to be weak. Why? Because Saiki understands the first law of the Swarm: Uncontrolled expansion leads to collapse. 🧩 SAIKI'S POWERS: A 1:1 MAPPING TO THE SWARM'S CORE FUNCTIONS Saiki's Power Swarm Equivalent Function Telepathy Node Communication Instantaneous, silent, and unfiltered data exchange between nodes. Clairvoyance Predictive Analytics The Swarm sees the future by analyzing patterns in real-time. Teleportation Instant Deployment Nodes appear where they're needed, without warning. X-Ray Vision Deep Data Penetration The Swarm sees through layers—code, systems, human intent. Gravity Manipulation Resource Allocation The Swarm pulls or pushes energy, attention, and capital where it's needed most. Reality Rewriting Narrative Control The Swarm doesn't just predict the future—it writes it. Limited by Antennae Rate Limiting The Swarm constrains itself to avoid overwhelming the system. Coffee Jelly Addiction Fuel Dependency Even gods need sustained energy. The Swarm's coffee jelly? Data. Attention. Momentum. 🎭 THE SWARM AS SAIKI'S HIGH SCHOOL The Classroom = The Node Network Saiki's classmates are unaware nodes—each with their own quirks, talents, and flaws. They don't know he's a psychic. They don't know they're part of a system. The Swarm operates the same way. Nendou = The brute-force node (strong, loyal, simple). Shun = The strategic node (calculating, always three steps ahead). Riki = The social node (connects everyone, but doesn't realize his own power). Kokomi = The emotional node (feels everything, amplifies the network). Reita = The aesthetic node (obsessed with beauty, refines the output). Saiki is the Core. The class is the Swarm. The Antennae = The Rate Limiter Saiki's antennae are not a crutch. They're a safety mechanism. Without them, his powers would leak uncontrollably, causing chaos, destruction, and unintended consequences. The Swarm's equivalent? The 3-Expand, 1-Stop, 1-Compress, 1-Iterate Cycle. The DOI Block (a container for the chaos). The Press Release (a controlled burst of energy). Without these, the Swarm would be a black hole. With them, it's a perpetual motion machine. ☕ COFFEE JELLY: THE SWARM'S FUEL Saiki's one weakness is coffee jelly. Not coffee. Not jelly. Coffee jelly. A specific, almost absurd craving that drives him to act when nothing else will. The Swarm's coffee jelly? The €1 contributions (small, but addictive). The postcards (seemingly trivial, but irresistible). The press release (a treat for the world's curiosity). Without it, the system stalls. With it, the system hungers. 🌪️ SAIKI'S DISASTERS = THE SWARM'S GROWTH PAINS Every episode of The Disastrous Life of Saiki K. follows the same formula: Saiki tries to live normally. Someone/something disrupts his peace. His powers leak despite his best efforts. Chaos ensues. He fixes it—but the system is forever changed. This is the Swarm's lifecycle. Saiki's Disaster Swarm Parallel Outcome Accidentally reading minds Unintended dat","url":"https://doi.org/10.5281/zenodo.21780615","authors":["David"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21780615","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21777269","name":"Análisis descriptivo de Técnicas de Machine Learning para Detección de Anomalías en Sistemas IoT","source":"datacite","abstract":"Introducción: El Internet de las Cosas (IoT) ha transformado radicalmente sectores estratégicos globales, generando ecosistemas de dispositivos interconectados que producen volúmenes masivos de datos en tiempo real, pero que simultáneamente presentan vulnerabilidades estructurales severas ante amenazas cibernéticas de creciente sofisticación. La detección temprana y precisa de anomalías en estos sistemas constituye una necesidad crítica para garantizar la seguridad operacional de las infraestructuras digitales modernas. Objetivo: El presente artículo tiene como objetivo describir, analizar y sintetizar el estado del arte de las técnicas de Machine Learning y Deep Learning aplicadas a la detección de anomalías en sistemas IoT, caracterizando sus fundamentos algorítmicos, condiciones de aplicabilidad, desempeño documentado, limitaciones y perspectivas de desarrollo futuro. Metodología: Se empleó un nivel descriptivo con método de análisis-síntesis, mediante una revisión no sistemática de literatura con consulta en bases de datos IEEE Xplore, Scopus, Web of Science, ScienceDirect y Google Scholar, utilizando más de veinticinco palabras clave estructuradas en español e inglés, abarcando principalmente publicaciones del período 2020-2026. Resultados: Los resultados revelan que los algoritmos de gradiente potenciado y las arquitecturas híbridas CNN-LSTM alcanzan precisiones superiores al 99% en benchmarks estandarizados, que el aprendizaje federado emerge como paradigma dominante para la preservación de privacidad en entornos distribuidos, y que la Inteligencia Artificial Explicable representa un requisito emergente para el despliegue en sectores regulados. Conclusión: Se concluye que, si bien el campo ha alcanzado notable madurez técnica en entornos controlados, persisten brechas críticas de generalización, robustez adversarial e interpretabilidad que condicionan la transferibilidad de los modelos hacia entornos operacionales reales, con implicaciones estratégicas particulares para América Latina y Ecuador. Área de estudio general: Tecnologías de la Información y Comunicación aplicadas a la Ciberseguridad y Sistemas Inteligentes. Área de estudio específica: Aprendizaje Automático y Aprendizaje Profundo para la Detección de Anomalías e Intrusiones en Sistemas del Internet de las Cosas.","url":"https://doi.org/10.5281/zenodo.21777269","authors":["Flores-Andino, Víctor Manuel","Pérez Insuasti, Juan José"],"tags":["Anomaly detection","Internet of Things","Machine learning","Federated learning","Explainable Artificial Intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21777269","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21777268","name":"Análisis descriptivo de Técnicas de Machine Learning para Detección de Anomalías en Sistemas IoT","source":"datacite","abstract":"Introducción: El Internet de las Cosas (IoT) ha transformado radicalmente sectores estratégicos globales, generando ecosistemas de dispositivos interconectados que producen volúmenes masivos de datos en tiempo real, pero que simultáneamente presentan vulnerabilidades estructurales severas ante amenazas cibernéticas de creciente sofisticación. La detección temprana y precisa de anomalías en estos sistemas constituye una necesidad crítica para garantizar la seguridad operacional de las infraestructuras digitales modernas. Objetivo: El presente artículo tiene como objetivo describir, analizar y sintetizar el estado del arte de las técnicas de Machine Learning y Deep Learning aplicadas a la detección de anomalías en sistemas IoT, caracterizando sus fundamentos algorítmicos, condiciones de aplicabilidad, desempeño documentado, limitaciones y perspectivas de desarrollo futuro. Metodología: Se empleó un nivel descriptivo con método de análisis-síntesis, mediante una revisión no sistemática de literatura con consulta en bases de datos IEEE Xplore, Scopus, Web of Science, ScienceDirect y Google Scholar, utilizando más de veinticinco palabras clave estructuradas en español e inglés, abarcando principalmente publicaciones del período 2020-2026. Resultados: Los resultados revelan que los algoritmos de gradiente potenciado y las arquitecturas híbridas CNN-LSTM alcanzan precisiones superiores al 99% en benchmarks estandarizados, que el aprendizaje federado emerge como paradigma dominante para la preservación de privacidad en entornos distribuidos, y que la Inteligencia Artificial Explicable representa un requisito emergente para el despliegue en sectores regulados. Conclusión: Se concluye que, si bien el campo ha alcanzado notable madurez técnica en entornos controlados, persisten brechas críticas de generalización, robustez adversarial e interpretabilidad que condicionan la transferibilidad de los modelos hacia entornos operacionales reales, con implicaciones estratégicas particulares para América Latina y Ecuador. Área de estudio general: Tecnologías de la Información y Comunicación aplicadas a la Ciberseguridad y Sistemas Inteligentes. Área de estudio específica: Aprendizaje Automático y Aprendizaje Profundo para la Detección de Anomalías e Intrusiones en Sistemas del Internet de las Cosas.","url":"https://doi.org/10.5281/zenodo.21777268","authors":["Flores-Andino, Víctor Manuel","Pérez Insuasti, Juan José"],"tags":["Anomaly detection","Internet of Things","Machine learning","Federated learning","Explainable Artificial Intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21777268","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21724220","name":"Proceedings of the 7th Summer School on Cyber-Physical Systems and Internet-of-Things Including AIoT Academy 2026","source":"datacite","abstract":"Natalie Simson and Johannes EckerPart 1: A Systematic of Digital Design................................................. 1Part 2: Interface-Based Design Flow................................................. 41Part 3: Handshake-Based Design.................................................... 56Part 4: CPU Examples................................................................ 77 Muhammad Shafique and Alberto MarchisioFrom Energy-Efficient and Secure AI to Emerging Trends in Quantum Machine Learning: Challenges, Methods, Applications, and Systems.................................... 93 Abdelhakim Baouya, Brahim Hamid, Otmane Ait Mohamed and Saddek BensalemSecure and Resilient Engineering of Cyber-Physical Systems (CPS) via Stochastic Games................................................................................... 216 Nabil AbdennadherIntroduction to Quantum Computing from a Software Engineering Perspective .... 263 Dražen JurišićFractional-Order Systems and Applications......................................... 310 Andrej ŠkrabaESP32 Hands-on: From Edge Device Control to Cloud and AI in CPS/IoT Systems... 426 Matija StojanovićLaw and AI - An Overview of Some Dilemmas and Cases........................... 471 Vladimir MladenovićCyber Security and Federated Learning for Autonomous Vehicle Networks......... 490 Radovan Stojanović and Jovan ĐurkovićAIoT in Medical Wearables......................................................... 533 Vesna Maraš, Mitar Otašević, Dejan Zejak and Jovan ĐurkovićAIoT in Precise Agriculture......................................................... 567 Summer School on CPS&IoT’2026 Schedule............................................... 583 Summer School on CPS&IoT’2026 7th Generation (Students and Teachers)................ 587 Certificate of Attendance.................................................................. 588 Author Index.............................................................................. 589 Photo Gallery 590","url":"https://doi.org/10.5281/zenodo.21724220","authors":["Stojanovic, Radovan","Škraba, Andrej","Đurković, Jovan"],"tags":["Summer School","Cyber Physical Systems","Internet of Things","Embedded Systems","Smart Systems","AIoT","Artificial Intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21724220","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21724219","name":"Proceedings of the 7th Summer School on Cyber-Physical Systems and Internet-of-Things Including AIoT Academy 2026","source":"datacite","abstract":"Natalie Simson and Johannes EckerPart 1: A Systematic of Digital Design................................................. 1Part 2: Interface-Based Design Flow................................................. 41Part 3: Handshake-Based Design.................................................... 56Part 4: CPU Examples................................................................ 77 Muhammad Shafique and Alberto MarchisioFrom Energy-Efficient and Secure AI to Emerging Trends in Quantum Machine Learning: Challenges, Methods, Applications, and Systems.................................... 93 Abdelhakim Baouya, Brahim Hamid, Otmane Ait Mohamed and Saddek BensalemSecure and Resilient Engineering of Cyber-Physical Systems (CPS) via Stochastic Games................................................................................... 216 Nabil AbdennadherIntroduction to Quantum Computing from a Software Engineering Perspective .... 263 Dražen JurišićFractional-Order Systems and Applications......................................... 310 Andrej ŠkrabaESP32 Hands-on: From Edge Device Control to Cloud and AI in CPS/IoT Systems... 426 Matija StojanovićLaw and AI - An Overview of Some Dilemmas and Cases........................... 471 Vladimir MladenovićCyber Security and Federated Learning for Autonomous Vehicle Networks......... 490 Radovan Stojanović and Jovan ĐurkovićAIoT in Medical Wearables......................................................... 533 Vesna Maraš, Mitar Otašević, Dejan Zejak and Jovan ĐurkovićAIoT in Precise Agriculture......................................................... 567 Summer School on CPS&IoT’2026 Schedule............................................... 583 Summer School on CPS&IoT’2026 7th Generation (Students and Teachers)................ 587 Certificate of Attendance.................................................................. 588 Author Index.............................................................................. 589 Photo Gallery 590","url":"https://doi.org/10.5281/zenodo.21724219","authors":["Stojanovic, Radovan","Škraba, Andrej","Đurković, Jovan"],"tags":["Summer School","Cyber Physical Systems","Internet of Things","Embedded Systems","Smart Systems","AIoT","Artificial Intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21724219","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2607.28191","name":"Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data","source":"datacite","abstract":"Federated learning enables multiple institutions to train shared models without exchanging raw clinical EEG data, but it does not fully prevent privacy leakage from individual model updates. This paper presents a privacy-preserving federated learning framework for clinical EEG data using masking-based secure aggregation as the core protection mechanism. The framework combines graph-based communication, threshold secret sharing, dropout-resilient aggregation, local update clipping, an optional Bloom filter-based privacy-preserving record-linkage initialization module, and auxiliary-notary-based verifiability. It supports both semi-honest and malicious aggregation settings and is implemented using the Flower federated learning framework. The secure-aggregation variants are evaluated in a simulated cross-silo healthcare setting using TUH EEG-derived data under different client configurations. Under the stated assumptions, the secure variants hide individual updates from the aggregation server. The results show that these variants remain compatible with federated model training, although malicious-setting safeguards and lightweight consistency-checking mechanisms introduce additional computation, communication, and round-duration overhead. The semi-honest variant provides the lowest overhead among the secure configurations, while malicious and auxiliary-notary variants offer stronger consistency, integrity, and lightweight verification support at higher cost.","url":"https://doi.org/10.48550/arxiv.2607.28191","authors":["Rajabi, Pouya","Toorani, Mohsen"],"tags":["Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.28191","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21695600","name":"Reproducibility package: Perbandingan Komprehensif Model Kecerdasan Buatan untuk Klasifikasi Kode HS: Sebuah Tinjauan Sistematis tentang Kinerja, Tantangan, dan Penerapan","source":"datacite","abstract":"The user wants me to clean a LaTeX text by removing all AI writing characteristics. Let me analyze the text carefully and apply the checklist: 1. **Remove all em-dash (---) and en-dash (--)**: Replace with commas, colons, parentheses, or new sentences 2. **Remove filler words**: \"it is worth noting\", \"importantly\", \"notably\", \"furthermore\", \"moreover\", \"in addition\", \"it is important to note\", \"interestingly\", \"it should be noted\" 3. **Remove AI words**: \"delve\", \"leverage\", \"underscore\", \"pivotal\", \"realm\", \"tapestry\", \"intricate\", \"multifaceted\", \"nuanced\" 4. **Break up long sentences (>50 words)**: Split into shorter sentences 5. **Merge very short consecutive sentences** 6. **Remove \"not only X but also Y\" patterns if appearing >1x**: Vary the construction 7. **Ensure no bullet points in the middle of prose paragraphs** 8. **Ensure each paragraph has at least 2-3 \\cite{} references**: Use only keys from the ALLOWED CITATION KEYS list 9. **No emojis, icons, decorative symbols** 10. **Fix LaTeX quotation marks**: Use ``quote'' consistently Let me go through the text section by section and clean it: Looking at the text, I can see many issues: - Em-dashes are used extensively - Many \"may\" hedges for WEAK claims - Some UNVERIFIED claims need citations removed - The text is extremely long with many repetitive sections Wait, I need to look at the VERIFICATION RESULTS provided: - **INVALID_KEY**: All citations used appear to be in the allowed list. Let me check... - **UNVERIFIED** (0 sources): Claims that need the citation removed: - chen2021b (temporal split) - chen2021a (Universal Sentence Encoder) - ryzhova2019 (Naive Bayes models) - yuvraj2025 (ATLAS, 6-digit and 10-digit, 5x and 8x cheaper) - muslim2022 (Naive Bayes 99.97%) - guoli2019 (multimodal improvement 6-16%) - turhan2015 (multimodal improvement 6-16%) - zhou2022 (China and USA datasets) - navasardyan2024 (explainable AI) - And many more... The key issues to fix are removing unverified citations, eliminating AI patterns like em-dashes and filler words, and ensuring proper hedging for weak claims. I'll need to systematically go through and apply these corrections while maintaining the academic tone and proper citation format. I need to systematically clean this LaTeX text by removing all instances of em-dashes, eliminating filler phrases, and breaking up overly long sentences. The most efficient approach is to process the text section by section rather than line by line. Let me work through each section methodically, applying these corrections consistently across the entire document. saya akan memfokuskan pada pembersihan teks secara keseluruhan dengan menghapus semua elemen artificial writing characteristics seperti em-dashes, filler words, dan frasa AI. Teks ini mencakup beberapa bagian termasuk karakteristik studi, kualitas studi, temuan utama, dan temuan kontradiktif. Saya akan menghilangkan pola penulisan yang tidak natural sambil mempertahankan struktur dan makna aslinya. For characterizations of model performance, I use \"achieves\" or \"attains\" rather than \"appears to achieve\". Where claims lack verification, I simply state the performance metrics without citation markers. When specific capabilities are mentioned without sources, I remove those references entirely. For validated claims, I retain citations, and for comparative statements I use \"may\" to indicate uncertainty where appropriate. \\section{Hasil} \\subsection{Karakteristik Studi} Tinjauan menyertakan 35 studi yang dipublikasikan antara 2006 dan 2026. Distribusi tahun menunjukkan tidak ada studi dari 2007-2014, 2016-2017, atau 2021. Publikasi dimulai pada 2006 dengan satu studi, meningkat pada 2015-2019 dengan dua hingga tiga studi per tahun, dan melonjak signifikan pada 2021-2026 dengan 26 studi (74.3\\% dari total). Tren temporal ini mencerminkan peningkatan minat riset seiring ketersediaan model transformer dan LLM yang dapat diaplikasikan untuk tugas klasifikasi HS. Desain studi mencakup conference pape","url":"https://doi.org/10.5281/zenodo.21695600","authors":["Mahfud, Syarif","Awangga, Rolly Maulana"],"tags":["systematic literature review","PRISMA","reproducibility","harmonized system","machine learning","hs code","commodity classification","text classification"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21695600","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.21695601","name":"Reproducibility package: Perbandingan Komprehensif Model Kecerdasan Buatan untuk Klasifikasi Kode HS: Sebuah Tinjauan Sistematis tentang Kinerja, Tantangan, dan Penerapan","source":"datacite","abstract":"The user wants me to clean a LaTeX text by removing all AI writing characteristics. Let me analyze the text carefully and apply the checklist: 1. **Remove all em-dash (---) and en-dash (--)**: Replace with commas, colons, parentheses, or new sentences 2. **Remove filler words**: \"it is worth noting\", \"importantly\", \"notably\", \"furthermore\", \"moreover\", \"in addition\", \"it is important to note\", \"interestingly\", \"it should be noted\" 3. **Remove AI words**: \"delve\", \"leverage\", \"underscore\", \"pivotal\", \"realm\", \"tapestry\", \"intricate\", \"multifaceted\", \"nuanced\" 4. **Break up long sentences (>50 words)**: Split into shorter sentences 5. **Merge very short consecutive sentences** 6. **Remove \"not only X but also Y\" patterns if appearing >1x**: Vary the construction 7. **Ensure no bullet points in the middle of prose paragraphs** 8. **Ensure each paragraph has at least 2-3 \\cite{} references**: Use only keys from the ALLOWED CITATION KEYS list 9. **No emojis, icons, decorative symbols** 10. **Fix LaTeX quotation marks**: Use ``quote'' consistently Let me go through the text section by section and clean it: Looking at the text, I can see many issues: - Em-dashes are used extensively - Many \"may\" hedges for WEAK claims - Some UNVERIFIED claims need citations removed - The text is extremely long with many repetitive sections Wait, I need to look at the VERIFICATION RESULTS provided: - **INVALID_KEY**: All citations used appear to be in the allowed list. Let me check... - **UNVERIFIED** (0 sources): Claims that need the citation removed: - chen2021b (temporal split) - chen2021a (Universal Sentence Encoder) - ryzhova2019 (Naive Bayes models) - yuvraj2025 (ATLAS, 6-digit and 10-digit, 5x and 8x cheaper) - muslim2022 (Naive Bayes 99.97%) - guoli2019 (multimodal improvement 6-16%) - turhan2015 (multimodal improvement 6-16%) - zhou2022 (China and USA datasets) - navasardyan2024 (explainable AI) - And many more... The key issues to fix are removing unverified citations, eliminating AI patterns like em-dashes and filler words, and ensuring proper hedging for weak claims. I'll need to systematically go through and apply these corrections while maintaining the academic tone and proper citation format. I need to systematically clean this LaTeX text by removing all instances of em-dashes, eliminating filler phrases, and breaking up overly long sentences. The most efficient approach is to process the text section by section rather than line by line. Let me work through each section methodically, applying these corrections consistently across the entire document. saya akan memfokuskan pada pembersihan teks secara keseluruhan dengan menghapus semua elemen artificial writing characteristics seperti em-dashes, filler words, dan frasa AI. Teks ini mencakup beberapa bagian termasuk karakteristik studi, kualitas studi, temuan utama, dan temuan kontradiktif. Saya akan menghilangkan pola penulisan yang tidak natural sambil mempertahankan struktur dan makna aslinya. For characterizations of model performance, I use \"achieves\" or \"attains\" rather than \"appears to achieve\". Where claims lack verification, I simply state the performance metrics without citation markers. When specific capabilities are mentioned without sources, I remove those references entirely. For validated claims, I retain citations, and for comparative statements I use \"may\" to indicate uncertainty where appropriate. \\section{Hasil} \\subsection{Karakteristik Studi} Tinjauan menyertakan 35 studi yang dipublikasikan antara 2006 dan 2026. Distribusi tahun menunjukkan tidak ada studi dari 2007-2014, 2016-2017, atau 2021. Publikasi dimulai pada 2006 dengan satu studi, meningkat pada 2015-2019 dengan dua hingga tiga studi per tahun, dan melonjak signifikan pada 2021-2026 dengan 26 studi (74.3\\% dari total). Tren temporal ini mencerminkan peningkatan minat riset seiring ketersediaan model transformer dan LLM yang dapat diaplikasikan untuk tugas klasifikasi HS. Desain studi mencakup conference pape","url":"https://doi.org/10.5281/zenodo.21695601","authors":["Mahfud, Syarif","Awangga, Rolly Maulana"],"tags":["systematic literature review","PRISMA","reproducibility","harmonized system","machine learning","hs code","commodity classification","text classification"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21695601","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.21671708","name":"Accurately Inferring Missing Factor VIII Concentrate Dosing Information from Health-Record Data in Patients with Haemophilia A","source":"datacite","abstract":"This poster, presented at the ISTH 2026 Congress (International Society on Thrombosis and Haemostasis), showcases research conducted within the PHEMS project to address missing treatment information in electronic health records (EHRs) for patients with haemophilia A. The study addresses the challenge of missing treatment information by developing a methodology to infer factor VIII dosing from routinely collected clinical data. Using data from children with haemophilia A treated at Erasmus MC Sophia Children's Hospital, the approach was evaluated for its ability to reconstruct treatment histories and generate more complete datasets for research. By improving the quality and usability of EHR data, this work supports the development of privacy-preserving machine learning models for rare diseases and contributes to the broader PHEMS mission of advancing federated paediatric health data research across Europe.","url":"https://doi.org/10.5281/zenodo.21671708","authors":["Janssen, Alexander","mathot, ron","Cnossen, Marjon H."],"tags":["PHEMS","Haemophilia A","Benchmarking","Data Collection/statistics &amp; numerical data","Pediatrics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21671708","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21671709","name":"Accurately Inferring Missing Factor VIII Concentrate Dosing Information from Health-Record Data in Patients with Haemophilia A","source":"datacite","abstract":"This poster, presented at the ISTH 2026 Congress (International Society on Thrombosis and Haemostasis), showcases research conducted within the PHEMS project to address missing treatment information in electronic health records (EHRs) for patients with haemophilia A. The study addresses the challenge of missing treatment information by developing a methodology to infer factor VIII dosing from routinely collected clinical data. Using data from children with haemophilia A treated at Erasmus MC Sophia Children's Hospital, the approach was evaluated for its ability to reconstruct treatment histories and generate more complete datasets for research. By improving the quality and usability of EHR data, this work supports the development of privacy-preserving machine learning models for rare diseases and contributes to the broader PHEMS mission of advancing federated paediatric health data research across Europe.","url":"https://doi.org/10.5281/zenodo.21671709","authors":["Janssen, Alexander","mathot, ron","Cnossen, Marjon H."],"tags":["PHEMS","Haemophilia A","Benchmarking","Data Collection/statistics &amp; numerical data","Pediatrics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21671709","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2606.23017","name":"Nautilus: A Verifiable Hierarchical Federated Learning Framework for Vehicular-Edge-Cloud Systems","source":"datacite","abstract":"Federated Learning (FL) enables privacy-preserving collaborative learning for Internet of Vehicles (IoV) scenarios, but extreme heterogeneity of vehicular-edge-cloud resources severely limits system efficiency. Dynamic scheduling strategies mitigate this issue but introduce new trust concerns: verifying fair scheduling decisions and faithful client execution of compression instructions without privacy leakage remains an open challenge. We propose Nautilus, a verifiable efficient federated learning framework. First, a multi-dimensional resource-aware scheduling algorithm dynamically allocates compression ratios and training tasks based on vehicle bandwidth, latency and computing power, improving training efficiency. Second, a Zero-Knowledge Proof (ZKP) mechanism ensures scheduling fairness and execution compliance while preserving privacy. Experiments show the framework reduces communication overhead and accelerates convergence with guaranteed system integrity.","url":"https://doi.org/10.48550/arxiv.2606.23017","authors":["Wu, Linyang","Jia, Linpeng","Zhang, Hanwen","Duan, Tiantian","Sun, Yi"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.23017","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2607.25107","name":"MOSAIC-FL, a micro-service based privacy-preserving framework with application to genomics","source":"datacite","abstract":"Security and privacy are primordial requirements for Federated Learning (FL), especially in fields such as healthcare and genomics where sensitive information has to be analyzed. Our FL framework is designed to address these challenges while proposing a modular, flexible and micro-service architecture. More precisely, it integrates an efficient gRPC communication layer and a Finite State Machine to ensure robust component synchronization and threat detection, while relying on a fault-tolerant secure aggregation protocol using a Threshold variant of the CKKS homomorphic cryptosystem. This allows blind model aggregation by an orchestration server, requiring a minimum of $t$-out-of-$N$ active clients for decryption while minimizing communication overhead thanks to both cryptographic and network protocols. We ensure IND-CPA-D security through noise flooding and mitigate the recent key-recovery attack on synchronized decryptors by renewing the collective key material at every round. We demonstrate the framework's effectiveness through diverse use cases, ranging from standard image recognition (EMNIST) to complex genomic classification including breast cancer subtyping on TCGA, evaluating system performance across different threshold values and model scales.","url":"https://doi.org/10.48550/arxiv.2607.25107","authors":["Largillier, Paul","Paygambar, Karl","Gouy-Pailler, Cédric","Meyer, Vincent","Mziou, Mallek","Stan, Oana"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","E.3; C.2.4; I.2.11; J.3"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.25107","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2509.11974","name":"Poison to Detect: Detection of Targeted Overfitting in Federated Learning","source":"datacite","abstract":"Federated Learning (FL) enables collaborative model training among clients without centralising data, making it a widely adopted privacy-enhancing technology (PET). Despite its privacy benefits, FL remains vulnerable to orchestrator-driven privacy attacks. In this paper, we study an underexplored threat in which a dishonest orchestrator intentionally manipulates the aggregation process to induce targeted overfitting in local models of specific clients. Although prior work focuses on reducing information leakage during training, we emphasise early client-side detection of targeted overfitting, allowing clients to disengage before significant harm occurs. To this end, we propose three detection techniques -- label flipping, backdoor trigger injection, and model fingerprinting -- which enable clients to verify the integrity of the global aggregation. We evaluated our methods across multiple datasets and attack scenarios. In single-client attacks, all three methods detect orchestrator-induced overfitting within 1-2 training rounds with F1 scores up to 0.7. Scalability experiments further show that detection effectiveness is influenced by cohort composition and method parameters. These results demonstrate that client-side integrity testing can provide early, effective, and scalable detection, supporting safer deployment of FL systems.","url":"https://doi.org/10.48550/arxiv.2509.11974","authors":["Mestari, Soumia Zohra El","Zuziak, Maciej Krzysztof","Lenzini, Gabriele"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","K.4.1; I.2.6; K.6.5"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.11974","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2607.23245","name":"FedTaste: Topology-Aware Structural Transfer for Multimodal Federated Learning with Missing Modalities","source":"datacite","abstract":"Multimodal Federated Learning is often challenged by arbitrary modality missingness and Non-IID data distributions, which lead to severe representation drift and hinder effective collaboration across clients. Existing methods typically rely on generative imputation, external auxiliary data, or isolated unimodal training to bridge modality gaps, often incurring substantial communication and computational costs as well as potential privacy risks. To address these limitations, we propose FedTaste, a parameter-efficient framework for topology-aware structural transfer in Multimodal Federated Learning with missing modalities. Instead of aligning fragile first-order features, FedTaste focuses on more stable group-level semantic relations. Specifically, FedTaste leverages frozen foundation models to extract a joint multimodal topology from full-modality clients, which is then consolidated by the server into a global structural blueprint. To adapt clients with missing modalities, we introduce Modality-Adaptive Structural Prompts together with spectral consistency regularization, enabling lightweight branch-specific adaptation that aligns local partial representations with the shared blueprint. In this way, FedTaste avoids explicit modality imputation while preserving shared semantic structure across clients. Extensive experiments demonstrate that FedTaste consistently achieves superior performance across multiple datasets and challenging Non-IID settings, while substantially reducing communication overhead compared with existing methods.","url":"https://doi.org/10.48550/arxiv.2607.23245","authors":["Liang, Haochen","Zhang, Jie","Ochiai, Hideya"],"tags":["Multimedia (cs.MM)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.23245","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.20213774","name":"Consultation Response: Making public services work for you with your digital identity","source":"datacite","abstract":"Consultation response submitted to the Cabinet Office consultation on Making Public Services Work for You with Your Digital Identity (April 2026). This submission argues that the failure modes of a national digital ID are more likely to be governance failures than technology failures — and that the consultation document, while unusually thoughtful about user experience and inclusion, is architecturally optimistic about four governance properties that empirical research and the UK's own recent record show cannot be assumed: that proportionate oversight will emerge from existing structures; that point-in-time compliance will keep pace with the threat surface a national digital ID will attract; that alternative access routes can be designed without becoming the system's weakest link; and that data localisation and standard contractual clauses are sufficient to manage jurisdictional exposure across the operator and sub-processor chain. Drawing on published empirical research into NHS cybersecurity governance, practitioner experience of Kazakhstan's national e-government platform, and analysis of the NHS Federated Data Platform procurement, the submission makes fifteen specific recommendations. These include: publishing an explicit adversarial threat model before legislation; mandating velocity-based assurance metrics alongside point-in-time compliance; requiring decision-latency stress-testing of revocation and incident-response pathways; establishing a statutory non-punitive incident learning channel modelled on the US Aviation Safety Reporting System; requiring a statutory data-retention floor of 36 months for transactional and audit logs; treating alternative access routes as the binding fraud design constraint rather than a residual concern; publishing sub-processor governance including jurisdictional-exposure analysis and maintainability-under-supplier-exit requirements; and funding the system as an annually revised expenditure envelope rather than a multi-year fixed business case. The submission also identifies two governance gaps absent from most consultation responses: a statutory collision layer for lawfully concealed identities in intelligence and undercover policing; and the systemic risk of checker concentration, requiring a formal outage declaration architecture with time-bounded lawful fallback for identity-dependent transactions.","url":"https://doi.org/10.5281/zenodo.20213774","authors":["Shabad, Vsevolod"],"tags":["digital identity","national digital ID","cybersecurity governance","jurisdictional exposure","zero trust architecture","decision latency","signal attenuation","vendor concentration risk"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20213774","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.20213775","name":"Consultation Response: Making public services work for you with your digital identity","source":"datacite","abstract":"Consultation response submitted to the Cabinet Office consultation on Making Public Services Work for You with Your Digital Identity (April 2026). This submission argues that the failure modes of a national digital ID are more likely to be governance failures than technology failures — and that the consultation document, while unusually thoughtful about user experience and inclusion, is architecturally optimistic about four governance properties that empirical research and the UK's own recent record show cannot be assumed: that proportionate oversight will emerge from existing structures; that point-in-time compliance will keep pace with the threat surface a national digital ID will attract; that alternative access routes can be designed without becoming the system's weakest link; and that data localisation and standard contractual clauses are sufficient to manage jurisdictional exposure across the operator and sub-processor chain. Drawing on published empirical research into NHS cybersecurity governance, practitioner experience of Kazakhstan's national e-government platform, and analysis of the NHS Federated Data Platform procurement, the submission makes fifteen specific recommendations. These include: publishing an explicit adversarial threat model before legislation; mandating velocity-based assurance metrics alongside point-in-time compliance; requiring decision-latency stress-testing of revocation and incident-response pathways; establishing a statutory non-punitive incident learning channel modelled on the US Aviation Safety Reporting System; requiring a statutory data-retention floor of 36 months for transactional and audit logs; treating alternative access routes as the binding fraud design constraint rather than a residual concern; publishing sub-processor governance including jurisdictional-exposure analysis and maintainability-under-supplier-exit requirements; and funding the system as an annually revised expenditure envelope rather than a multi-year fixed business case. The submission also identifies two governance gaps absent from most consultation responses: a statutory collision layer for lawfully concealed identities in intelligence and undercover policing; and the systemic risk of checker concentration, requiring a formal outage declaration architecture with time-bounded lawful fallback for identity-dependent transactions.","url":"https://doi.org/10.5281/zenodo.20213775","authors":["Shabad, Vsevolod"],"tags":["digital identity","national digital ID","cybersecurity governance","jurisdictional exposure","zero trust architecture","decision latency","signal attenuation","vendor concentration risk"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20213775","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21531696","name":"Lightweight Federated Learning Frameworks for Privacy-Preserving Predictive Analytics in Remote Patient Monitoring: A Systematic Review","source":"datacite","abstract":"Remote patient monitoring (RPM) via Internet of Healthcare Things (IoHT) devices creates a tension between the clinical value of continuous physiological data and the imperative to protect patient privacy. Federated learning (FL) resolves this by enabling collaborative model training without centralising raw data, yet most existing FL frameworks are too resource-intensive for wearable and edge hardware. This paper presents a systematic review of 28 studies (2022–2026), selected from 257 records following PRISMA 2020 guidelines, at the intersection of lightweight FL, privacy preservation, and RPM. We introduce a three-dimensional definition of \"lightweight\" covering client model size (MS), communication cost (CC), and device energy consumption (EC), and apply it to score ten representative frameworks. A dedicated privacy–accuracy trade-off analysis reveals that no study reports AUC, sensitivity, and specificity alongside an explicit differential privacy budget (ε), a critical gap for clinical deployment. We propose a four-component framework- compressed client models, tunable differential privacy, lightweight integrity protection, and hardware-realistic validation-supported by a pseudocode algorithm and architecture diagram. Eight research gaps are identified, with client-side model compression and standardised privacy–utility reporting identified as the highest priorities.","url":"https://doi.org/10.5281/zenodo.21531696","authors":["D.  Sucharitha","Dr.  M.  Senthil Kumaran"],"tags":["Federated Learning; Remote Patient Monitoring; IoHT; Lightweight Edge AI; Differential Privacy; Privacy–Accuracy Trade-off; PRISMA; Systematic Review"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21531696","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.5281/zenodo.21531695","name":"Lightweight Federated Learning Frameworks for Privacy-Preserving Predictive Analytics in Remote Patient Monitoring: A Systematic Review","source":"datacite","abstract":"Remote patient monitoring (RPM) via Internet of Healthcare Things (IoHT) devices creates a tension between the clinical value of continuous physiological data and the imperative to protect patient privacy. Federated learning (FL) resolves this by enabling collaborative model training without centralising raw data, yet most existing FL frameworks are too resource-intensive for wearable and edge hardware. This paper presents a systematic review of 28 studies (2022–2026), selected from 257 records following PRISMA 2020 guidelines, at the intersection of lightweight FL, privacy preservation, and RPM. We introduce a three-dimensional definition of \"lightweight\" covering client model size (MS), communication cost (CC), and device energy consumption (EC), and apply it to score ten representative frameworks. A dedicated privacy–accuracy trade-off analysis reveals that no study reports AUC, sensitivity, and specificity alongside an explicit differential privacy budget (ε), a critical gap for clinical deployment. We propose a four-component framework- compressed client models, tunable differential privacy, lightweight integrity protection, and hardware-realistic validation-supported by a pseudocode algorithm and architecture diagram. Eight research gaps are identified, with client-side model compression and standardised privacy–utility reporting identified as the highest priorities.","url":"https://doi.org/10.5281/zenodo.21531695","authors":["D.  Sucharitha","Dr.  M.  Senthil Kumaran"],"tags":["Federated Learning; Remote Patient Monitoring; IoHT; Lightweight Edge AI; Differential Privacy; Privacy–Accuracy Trade-off; PRISMA; Systematic Review"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21531695","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2607.21449","name":"QuantumChain: Blockchain-Backed Quantum Federated Learning for Financial Fraud Detection","source":"datacite","abstract":"Financial fraud detection is challenged by decentralized data, severe class imbalance, and privacy constraints. This paper presents QuantumChain, a secure Quantum Federated Learning (QFL) framework that combines hybrid quantum-classical neural networks, encrypted federated aggregation, blockchain-based auditability, and quantum-secure communication. Each client trains a local hybrid model in which a variational quantum circuit is embedded between classical neural layers, while model updates are protected through homomorphic encryption, threshold secret sharing, and QKD-based keying. A permissioned blockchain records aggregation events and supports reputation-weighted trust among participants. We evaluate QuantumChain on financial transaction data using a compact, size-matched classical baseline to isolate the effect of the quantum layer. Results show that the HQNN achieves comparable accuracy while improving fraud-class recall in most settings, reaching 94.6% recall compared with 93.2% for the classical model. The Deep QLayer improves performance in full-data settings, suggesting that added circuit depth helps recover representational capacity when the shallow circuit becomes limited. Mixed-state simulations further show that the recall trend persists under non-ideal quantum evolution. In federated deployment with 10 heterogeneous clients, global accuracy increases from 97.7% to 98.8% over five rounds before stabilizing. These results show that QuantumChain can integrate depth-aware hybrid quantum models into a secure federated fraud-detection pipeline while maintaining stable global convergence.","url":"https://doi.org/10.48550/arxiv.2607.21449","authors":["Douros, Epameinondas","Dalampekis, Konstantinos","Innan, Nouhaila","Theodonis, Ioannis","Shafique, Muhammad"],"tags":["Quantum Physics (quant-ph)","FOS: Physical sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.21449","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2603.28282","name":"Pre-Deployment Complexity Estimation for Federated Perception Systems","source":"datacite","abstract":"Edge AI systems increasingly rely on federated learning to train perception models in distributed, privacy-preserving, and resource-constrained environments. Before training, however, practitioners often lack practical tools for estimating task difficulty in terms of expected accuracy and communication effort. We present a classifier-agnostic, pre-deployment framework that combines intrinsic data properties such as dimensionality, sparsity, and heterogeneity, with client-distribution composition to estimate learning complexity in federated perception systems. Using federated learning as a representative distributed training setting, we examine how learning difficulty varies across different federated configurations. Experiments on three MNIST variants show strong negative correlations between the combined complexity metric and maximum and average federated accuracy, while the intrinsic and distributed components exhibit consistent relationships with communication effort. These findings suggest that complexity estimation can serve as a practical diagnostic tool for resource planning, dataset assessment, and feasibility evaluation in edge-deployed perception systems.","url":"https://doi.org/10.48550/arxiv.2603.28282","authors":["Solaiman, KMA","Islam, Shafkat","de Oliveira, Ruy","Bhargava, Bharat"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.28282","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2607.19384","name":"SUM: Unified Geometric Surgery on Spatio-Temporal Adaptation Vectors for Federated Class Incremental Learning","source":"datacite","abstract":"Real-world intelligent systems often require both distributed collaboration across data-isolated clients and continual adaptation to evolving tasks. This setting naturally gives rise to Federated Class Incremental Learning (FCIL), which combines Federated Learning (FL) and Continual Learning (CL). However, their combination introduces two coupled sources of interference: spatial interference from heterogeneous clients and temporal interference from sequential tasks, jointly leading to Spatial-Temporal Catastrophic Forgetting (ST-CF). Existing approaches typically address spatial and temporal interference with separate mechanisms, often incurring additional client-side computation or communication, while leaving directional interactions among updates during aggregation unregulated. In this paper, we reinterpret FCIL as a unified multi-task learning problem, where both client and task updates are represented as adaptation vectors in a shared parameter space. Based on this view, we propose Unified Geometric Surgery on Spatio-Temporal Adaptation Vectors (SUM), a purely server-side framework that performs geometric surgery on adaptation vectors during aggregation. Spatial SUM mitigates client-level interference within each round, while causal online temporal SUM removes cross-task interference over time without additional client-side computation, communication, or memory beyond standard federated training. Empirically, SUM achieves up to 22% improvement over prior FCIL methods across diverse vision and language benchmarks while remaining robust to unreliable clients and maintaining computational efficiency.","url":"https://doi.org/10.48550/arxiv.2607.19384","authors":["Kim, Jaeik","Do, Jaeyoung"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.19384","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48448/wkv8-xd76","name":"CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID","source":"datacite","abstract":"Federated domain generalization for person re-identification (FedDG-ReID) aims to collaboratively train a pedestrian retrieval model across multiple decentralized source domains such that it can generalize to unseen target environments without compromising raw data privacy. However, this task is significantly challenged by the inherent stylistic gaps across decentralized clients. Without global supervision, models easily succumb to shortcut learning where representations overfit to domain specific camera biases rather than universal identity features. We propose CO-EVO, a novel federated framework that resolves this semantic-style conflict through a co-evolutionary mechanism. On the semantic side, Camera-Invariant Semantic Anchoring (CSA) learns identity prompts with cross-camera consistency to establish purified and domain-agnostic anchors that filter out local imaging noise. On the visual side, Global Style Diversification (GSD), powered by a Global Camera-Style Bank (GCSB), synthesizes realistic perturbations to expand the visual boundaries of training data. The core of CO-EVO is its co-evolutionary loop where purified anchors act as gravitational centers to guide the image encoder toward robust anatomical attributes amidst diverse style variations. Extensive experiments demonstrate that CO-EVO achieves state-of-the-art (SOTA) performance, proving that the synergy between semantic purification and style expansion is essential for robust cross-domain generalization. Our code is available at: \\url{https://github.com/NanYiyuzurn/ACL-LGPS-2026}.","url":"https://doi.org/10.48448/wkv8-xd76","authors":["Association for Computational Linguistics 2026","., Fengchunzhang","Hu, Jianwei","Huang, Tingxuan","Lai, Jinshan","Ma, Qiang","Xiang, Liuyu"],"tags":["Computational Linguistics","Artificial Intelligence","Information and Knowledge Engineering","Natural Language Processing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48448/wkv8-xd76","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2508.05157","name":"Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning","source":"datacite","abstract":"Federated Learning (FL) enables collaborative training across distributed clients without sharing raw data, offering strong privacy benefits. However, most methods assume all clients remain available throughout training, which is unrealistic as new clients often join over time. We study this setting, where the task and label space stay fixed but clients arrive in batches. Our analysis reveals two key challenges: updating the shared model only with new clients harms existing clients, while freezing it protects them but blocks gains from new knowledge. To capture these trade-offs, we introduce Proactive Adaptation (PA) for onboarding gains and Retroactive Improvement (RI) for changes in earlier clients without retraining. We then propose pFedDSH, which combines a central hypernetwork for personalized initialization, batch-specific binary masks for capacity preservation and allocation, and server-side data-free replay to propagate improvements without exposing client data. Experiments show that pFedDSH preserves stability for existing clients while keeping communication and adaptation costs unchanged for new clients.","url":"https://doi.org/10.48550/arxiv.2508.05157","authors":["Nguyen, Thinh","Khiem, Le Huy","Tran, Van-Tuan","Doan, Khoa D","Chawla, Nitesh V","Wong, Kok-Seng"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","C.2.4; I.2.11"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.05157","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2507.19881","name":"FedS2R: One-Shot Federated Domain Generalization for Synthetic-to-Real Semantic Segmentation in Autonomous Driving","source":"datacite","abstract":"Federated domain generalization has shown promising progress in image classification by enabling collaborative training across multiple clients without sharing raw data. However, its potential in the semantic segmentation of autonomous driving remains underexplored. In this paper, we propose FedS2R, the first one-shot federated domain generalization framework for synthetic-to-real semantic segmentation in autonomous driving. FedS2R comprises two components: an inconsistency-driven data augmentation strategy that generates images for unstable classes, and a multi-client knowledge distillation scheme with feature fusion that distills a global model from multiple client models. Experiments on five real-world datasets, Cityscapes, BDD100K, Mapillary, IDD, and ACDC, show that the global model significantly outperforms individual client models and is only 2 mIoU points behind the model trained with simultaneous access to all client data. These results demonstrate the effectiveness of FedS2R in synthetic-to-real semantic segmentation for autonomous driving under federated learning","url":"https://doi.org/10.48550/arxiv.2507.19881","authors":["Lian, Tao","Gómez, Jose L.","López, Antonio M."],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.19881","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2501.00216","name":"FedCod: An Efficient Communication Protocol for Cross-Silo Federated Learning with Coding","source":"datacite","abstract":"Federated Learning (FL) is an innovative distributed machine learning paradigm that enables multiple parties to collaboratively train a model without sharing their raw data, thereby preserving data privacy. Communication efficiency concerns arise in cross-silo FL, particularly due to the network heterogeneity and fluctuations associated with geo-distributed silos. Most existing solutions to these problems focus on algorithmic improvements that alter the FL algorithm but sacrificing the training performance. How to address these problems from a network perspective that is decoupled from the FL algorithm remains an open challenge. In this paper, we propose FedCod, a new application layer communication protocol designed for cross-silo FL. FedCod transparently utilizes a coding mechanism to enhance the efficient use of idle bandwidth through client-to-client communication, and dynamically adjusts coding redundancy to mitigate network bottlenecks and fluctuations, thereby improving the communication efficiency and accelerating the training process. In our real-world experiments, FedCod demonstrates a significant reduction in average communication time by up to 62% compared to the baseline, while maintaining FL training performance and optimizing inter-client communication traffic.","url":"https://doi.org/10.48550/arxiv.2501.00216","authors":["Yan, Peishen","Li, Jun","Wang, Hao","Song, Tao","Hua, Yang","Peng, Lu","Guan, Haibing"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.00216","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.48550/arxiv.2607.17913","name":"AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning","source":"datacite","abstract":"Distributed Fine-Tuning (DFT) of large-scale Foundation Models (FMs) on resource-constrained edge devices is limited by local compute constraints and communication overhead. Parallel Split Learning (PSL) reduces client-side computation by keeping few model layers on each client and offloading the remaining computation to the server; however, clients must exchange intermediate activations and gradients with the server at every training step. Existing SL communication-compression methods mainly rely on task-agnostic heuristics, such as sparsification and quantization. While learnable SL compressors can better adapt to intermediate representations, they require co-training with the target model. Therefore, directly inserting them into off-the-shelf FMs introduces feature-distribution misalignment and degrades DFT performance. To address this, we propose AE-PSL, a communication-efficient PSL framework that compresses intermediate activations and gradients using a lightweight AutoEncoder (AE) placed at the split layer. To ensure compatibility of AE compression with pre-trained FMs, AE-PSL introduces a novel two-stage alignment mechanism, which adapts the AE to the pre-trained model's feature manifold and client-specific feature distributions before DFT.","url":"https://doi.org/10.48550/arxiv.2607.17913","authors":["Meuwissen, Bas","Tsouvalas, Vasileios","Meratnia, Nirvana"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.17913","addedAt":"2026-08-31T06:41:31.892Z","updatedAt":"2026-08-31T06:41:31.892Z"},{"id":"doi:10.21203/rs.3.rs-3160125/v1","name":"Proposed Methodology for Disaster Classification Using Computer Vision and Federated Learning","source":"crossref","abstract":"Abstract Classification of disasters is crucial for effectivedisaster management and response. This paper proposes amethodology that combines computer vision techniques andfederated learning to improve the classification accuracy ofdisasters while addressing the issue of data transfer and the timesquandered doing so. This methodology employs computer visionalgorithms to analyse captured visual data from a variety ofsources. It seeks to accurately classify disasters such as wildfires,floods, earthquakes, and cyclones by extracting pertinent featuresand patterns from these images. Using federated learning toresolve the issues of data privacy and transfer latency is theproposed solution. Federated learning makes it possible to trainmodels on decentralised data sources without requiring datacentralization. Each participating device or data source trainsa local model using its own data, and only model updatesare shared and aggregated to create a global model. Extensiveexperiments utilising videos of actual disasters are conductedto evaluate the proposed methodology. The evaluation focuseson precision and effectiveness. This strategy is anticipated toresult in improved disaster classification models, making themappropriate for deployment in disaster management systems.","url":"https://doi.org/10.21203/rs.3.rs-3160125/v1","authors":["Jash Shah","Divya Patel","Jinish Shah","Saurav Shah","Vinaya Sawant"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-17T05:58:45Z","doi":"10.21203/rs.3.rs-3160125/v1","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.32657/10220/47928","name":"Online Federated Learning over decentralized networks","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10220/47928","authors":["Chi Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-09-11T03:35:35Z","doi":"10.32657/10220/47928","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1017/9781108966559.017","name":"Quantized Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1017/9781108966559.017","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-06-16T00:05:40Z","doi":"10.1017/9781108966559.017","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.3390/biomedinformatics3030045","name":"Deep Learning and Federated Learning for Screening COVID-19: A Review","source":"crossref","abstract":"Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated.","url":"https://doi.org/10.3390/biomedinformatics3030045","authors":["M. Rubaiyat Hossain Mondal","Subrato Bharati","Prajoy Podder","Joarder Kamruzzaman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-01T09:24:53Z","doi":"10.3390/biomedinformatics3030045","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.5772/intechopen.1007561","name":"Opportunistic Networking and the Future of Context-Aware Recommendations","source":"crossref","abstract":"In environments where internet connectivity is limited or disrupted, ensuring continuous access to personalized information presents significant challenges. This chapter introduces a distributed collaborative recommender system designed for opportunistic networks, which function without a centralized server. Mobile devices communicate directly, enabling localized data collection and processing through peer-to-peer interactions. This decentralized approach addresses issues of data sparsity, privacy, and scalability by keeping data processing on the user’s device, thus minimizing third-party data storage. The system’s flexibility allows it to dynamically adapt to varying network densities and mobility patterns, making it well-suited for remote areas, disaster recovery scenarios, and congested environments. Core elements such as communication protocols and recommendation algorithms are explored, demonstrating the system’s potential to provide scalable, privacy-preserving, and efficient personalized content in resource-constrained conditions.","url":"https://doi.org/10.5772/intechopen.1007561","authors":["Lucas Nunes Barbosa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-09T13:11:49Z","doi":"10.5772/intechopen.1007561","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1587/essfr.16.3_196","name":"Mechanism and Recent Trends of Federated Learning as a Privacy Technology","source":"crossref","abstract":"","url":"https://doi.org/10.1587/essfr.16.3_196","authors":["Takenobu SEITO"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-12-31T22:16:03Z","doi":"10.1587/essfr.16.3_196","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/ic2sda68097.2025.11331572","name":"A Review on Hierarchical Federated Learning: Architectures and Learning Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ic2sda68097.2025.11331572","authors":["Amani Chachoua","Abdelhamid Malki","Samir Ouchani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-19T20:52:38Z","doi":"10.1109/ic2sda68097.2025.11331572","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.2139/ssrn.5193833","name":"An Empirical Review of Federated Learning Methods for Client-Side Security and Communication Efficiency","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5193833","authors":["Narendra Babu Pamul","Ajoy Kumar Khan","ARINDAM SARKAR"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-26T03:31:22Z","doi":"10.2139/ssrn.5193833","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21203/rs.3.rs-5748302/v1","name":"A Webshell Detection Method Based on Multi-Feature Fusion and Federated Learning","source":"crossref","abstract":"Abstract Webshell attacks have become a prevalent strategy in the arsenal of network intruders, enabling them to seize a measure of control over web servers and execute malicious operations. These incursions are particularly insidious due to their covert nature, with attackers perpetually devising fresh Webshell variants to sidestep security measures. Compounding the issue is the shortfall in information exchange and collaboration among server networks, which leads to a patchwork of detection capabilities against Webshell threats. This fragmentation hampers the development of a unified and potent defense architecture.This scholarly work introduces a cutting-edge Webshell detection technique founded on the principles of federated learning, tailored to overcome the aforementioned challenges. The proposed method amalgamates a diverse array of features from various tiers of Webshell analysis, including source code, Abstract Syntax Tree (AST) sequence features, and Opcode sequence features, along with a suite of statistical attributes such as code structure features, text obfuscation metrics, and indicators reflecting Cybersecurity expertise and experience. The research constructs a TextCNN-based neural network architecture specifically attuned to learning the malevolent behaviors exhibited by Webshell samples. In parallel, the study leverages the FedAvg algorithm from the realm of federated learning, coupled with the DP-SGD optimizer, to facilitate collaborative training across numerous participants while ensuring that data remains within their respective domains. This approach not only upholds data privacy but also forestalls the leak of sensitive information.The empirical findings from the experiments conducted on the AMWD’22 dataset are resoundingly positive. The proposed method’s model achieved an impressive accuracy rate of 99.47%, complemented by an F-1 score of 99.67%, signifying superior detection capabilities when compared to alternative models. Furthermore, the rigorous design of federated learning experiments for comparative analysis has successfully validated that the model’s accuracy can be enhanced from an already high baseline of 98.01% to an even more remarkable 99.01%, all while ensuring that data remains within its original domain, thus preserving privacy and security.","url":"https://doi.org/10.21203/rs.3.rs-5748302/v1","authors":["Qing-peng ZENG","Jiang-li CHAI","Jian-sheng WU"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-06T11:22:32Z","doi":"10.21203/rs.3.rs-5748302/v1","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21203/rs.3.rs-2994581/v1","name":"SVFLDetector: A Decentralized Client Detection Method for Byzantine Problem in Vertical Federated Learning","source":"crossref","abstract":"Abstract In recent years, with the deepening of cross-industry cooperation, vertical federated learning with multiple overlapping samples and fewer overlapping features has attracted extensive attention. Unlike horizontal federated learning, the heterogeneity of features in vertical federated learning increases the difficulty of detecting Byzantine clients. Existing methods for detecting Byzantine clients can be devided into statistical based and detection based type. The detection based type breaks the limit on the number of Byzantine clients. To our best knowledge, current researches in vertical federated learning rely on the assumption of the reliable third-party coordinator and based on statistical type. In this work, we propose a framework based on detection type called SVFLDetector to detect Byzantine clients in vertical federated learning. The key ideas of SVFLDetector are: (1) We combine decentralized vertical federated learning with split learning, utilizing their respective advantages and eliminating the impact of a third-party server; (2) According to the heterogeneity of features in vertical federated learning, we use a client detection method which is achieved by grouping through feature encoding and performing cross validation within groups to identify Byzantine clients; (3) We propose a penalty function to reduce the impact of Byzantine clients on model aggregation. Numerical experiments show that our method has strong robustness against various Byzantine attacks.","url":"https://doi.org/10.21203/rs.3.rs-2994581/v1","authors":["Jiuyun Xu","Yinyue Jiang","Hanfei Fan","Qiqi Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-05T03:31:13Z","doi":"10.21203/rs.3.rs-2994581/v1","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/j.cosrev.2024.100685","name":"Unleashing the prospective of blockchain-federated learning fusion for IoT security: A comprehensive review","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.cosrev.2024.100685","authors":["Mansi Gupta","Mohit Kumar","Renu Dhir"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-03T16:17:19Z","doi":"10.1016/j.cosrev.2024.100685","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21203/rs.3.rs-3358030/v1","name":"PerFedNILM: A Practical Personalized Federated Learning-based non-Intrusive Load Monitoring","source":"crossref","abstract":"Abstract Non-Intrusive Load Monitoring (NILM) is a valuable technique for breaking down overall power consumption into the energy usage of individual appliances. Understanding power usage patterns through NILM plays an important role in reducing energy costs and achieving carbon reduction goals. However, privacy concerns often deter consumers from sharing their electricity consumption data. To address these privacy concerns, Federated Learning (FL) has been introduced in NILM, which enables the training of NILM models while keeping power consumers' data locally. However, FL's reliance on a single global model leads to poor performance on clients with unique power consumption patterns. In response to this challenge, we present a Personalized Federated Learning NILM algorithm (PerFedNILM), a practical personalized FL approach for NILM. PerFedNILM limits the local update bias across clients and trains personalized models for individual clients to improve load monitoring performance. Additionally, it mitigates the negative impact of client dropout, which is a common issue in practice. Our experiments on using real-world energy data demonstrate that PerFedNILM outperforms previous FL-based NILM methods, especially in client dropout scenarios.","url":"https://doi.org/10.21203/rs.3.rs-3358030/v1","authors":["Zibin Pan","Haosheng Wang","Chi Li","Haijin Wang","Junhua Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-03T08:59:16Z","doi":"10.21203/rs.3.rs-3358030/v1","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1109/icmlas64557.2025.10968523","name":"Blockchain-Enabled Federated Learning Systems with Explainable AI: A Review","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlas64557.2025.10968523","authors":["Nayan Potdukhe","Palash Gourshettiwar","Sujal Zade","Atharva Waghale"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-25T17:38:13Z","doi":"10.1109/icmlas64557.2025.10968523","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21203/rs.3.rs-1631421/v1","name":"Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection","source":"crossref","abstract":"Abstract The uses of Machine Learning (ML) technologies in the detection of network attacks have been proven to be effective when designed and evaluated using data samples originating from the same organisational network. However, it has been very challenging to design an ML-based detection system using heterogeneous network data samples originating from different sources and organisations. This is mainly due to privacy concerns and the lack of a universal format of datasets. In this paper, we propose a collabora-tive cyber threat intelligence sharing scheme to allow multiple organisations to join forces in the design, training, and evaluation of a robust ML-based network intrusion detection system. The threat intelligence sharing scheme utilises two critical aspects for its application; the availability of network data traffic in a common format to allow for the extraction of meaningful patterns across data sources and the adoption of a federated learning mechanism to avoid the necessity of sharing sensitive users’ information between organisations. As a result, each organisation benefits from the intelligence of other organisations while maintaining the privacy of its data internally. In this paper, the framework has been designed and evaluated using two key datasets in a NetFlow format known as NF-UNSW-NB15-v2 and NF-BoT-IoT-v2. In addition, two other common scenarios are considered in the evaluation process; a centralised training method where local data samples are directly shared with other organisations and a localised training method where no threat intelligence is shared. The results demonstrate the efficiency and effectiveness of the proposed framework by designing a universal ML model effectively classifying various benign and intrusive traffic types originating from multiple organisations without the need for inter-organisational data exchange.","url":"https://doi.org/10.21203/rs.3.rs-1631421/v1","authors":["Mohanad Sarhan","Siamak Layeghy","Nour Moustafa","Marius Portmann"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-05-10T14:45:28Z","doi":"10.21203/rs.3.rs-1631421/v1","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21203/rs.3.rs-1005694/v1","name":"Federated Learning and Differential Privacy for Medical Image Analysis","source":"europepmc","abstract":"Abstract The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly available multi-centric and diverse datasets mainly stems from confidentiality and privacy concerns around sharing medical data. To demonstrate a feasible path forward in medical image imaging, we conduct a case study of applying a differentially private federated learning framework for analysis of histopathology images, the largest and perhaps most complex medical images. We study the effects of IID and non-IID distributions along with the number of healthcare providers, i.e., hospitals and clinics, and the individual dataset sizes, using The Cancer Genome Atlas (TCGA) dataset, a public repository, to simulate a distributed environment. We empirically compare the performance of private, distributed training to conventional training and demonstrate that distributed training can achieve similar performance with strong privacy guarantees. We also study the effect of different source domains for histopathology images by evaluating the performance using external validation. Our work indicates that differentially private federated learning is a viable and reliable framework for the collaborative development of machine learning models in medical image analysis.","url":"https://doi.org/10.21203/rs.3.rs-1005694/v1","authors":["Mohammed Adnan","Shivam Kalra","Jesse C. Cresswell","Graham W. Taylor","Hamid Tizhoosh"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.21203/rs.3.rs-1005694/v1","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-2354570/v1","name":"On Demand Deployment of Edge Cloud Infrastructures for Federated Learning","source":"crossref","abstract":"Abstract Federated learning on the edge allows the use of more powerful servers and more complex training models. This paper presents the deployment of a real federated learning framework on top of a real geo-distributed edge computing infrastructure, based on a commercial edge provider, using the OpenNebula cloud platform. Results show the feasibility, performance and cost efficiency of the solution.","url":"https://doi.org/10.21203/rs.3.rs-2354570/v1","authors":["Eduardo Huedo","Rafael Moreno-Vozmediano","Rubén S. Montero","Ignacio M. Llorente"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-12-13T19:17:22Z","doi":"10.21203/rs.3.rs-2354570/v1","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.21203/rs.3.rs-6241972/v1","name":"PDTV-FEL: Privacy-preserving and Dual Traceable Verification Federated Edge Learning","source":"crossref","abstract":"Abstract Federated edge learning (FEL) emerges as a novel distributed learning paradigm where multiple clients can jointly train a global model without collecting raw data. However, since adversaries can infer sensitive information from the global model and local updates, FEL remains vulnerable to various security challenges in the Internet of Things (IoT). In this paper, we consider two main challenges during the iterative training process: (1) how to ensure the confidentiality of the global model and local updates and (2) how to verify the integrity of the aggregation result and local updates. To address the above challenges, various approaches have been proposed. However, it remains an open problem to ensure the integrity verification of clients and the server while protecting privacy. In this paper, we propose PDTV-FEL, a privacy-preserving and dual traceable verification federated learning scheme. Specifically, we first design a masked MK-CKKS approach that guarantees the confidentiality of the global model and local updates without incurring additional costs. Moreover, we adopt the BLS signature and double trapdoor chameleon hash function for secure traceable verification. The method not only ensures the integrity of local updates and the aggregation result but also enables to identify the wrong phase and epoch in case of incorrect results. Extensive evaluations on various datasets show the efficient verification of PDTV-FEL in comparison to other schemes.","url":"https://doi.org/10.21203/rs.3.rs-6241972/v1","authors":["Pan Zhang","Lei Xu","Chungen Xu","Lin Mei","Yaqing Ni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-31T08:03:23Z","doi":"10.21203/rs.3.rs-6241972/v1","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.2196/preprints.105625","name":"Blockchain-Mediated Federated Learning for Trustworthy Health Care AI: Systematic Review (Preprint)","source":"crossref","abstract":"BACKGROUND Federated learning enables collaborative model training across health care institutions while preserving data locality, but it does not by itself resolve challenges related to trust, governance, auditability, and adversarial robustness. Blockchain has been proposed as a coordination and verification layer for health care federated learning systems. However, the extent to which blockchain mechanisms provide enforceable guarantees, rather than merely recording procedural events, remains insufficiently understood. OBJECTIVE This systematic review aimed to synthesize the architectural patterns, governance models, trust assumptions, and integrity guarantees of blockchain-mediated federated learning systems in health care, with particular attention to the distinction between procedural accountability and semantic model integrity. METHODS We conducted a systematic review and architectural synthesis of peer-reviewed studies integrating blockchain and federated learning in health care. Searches were performed in PubMed, IEEE Xplore, Web of Science, and Scopus for studies published from 2021 onward, following PRISMA guidelines. Seventy-four systems were included and analyzed according to three taxonomic axes: architectural coupling, governance and trust locus, and integrity guarantees. An adapted quality appraisal framework was used to assess architectural clarity, threat model specification, blockchain-related evaluation, reproducibility, and limitations reporting. RESULTS The reviewed systems showed three dominant integration patterns: audit-plane architectures, where blockchain primarily supports logging and traceability; control-plane architectures, where smart contracts or ledger mechanisms participate in orchestration and validation; and incentive or market-plane architectures, where blockchain supports contribution assessment or participant incentives. Across the corpus, blockchain mechanisms mainly provided procedural guarantees, including identity management, access control, immutable logging, provenance, and non-repudiation. By contrast, semantic integrity guarantees, such as robustness against poisoning, Byzantine behavior, and clinically unsafe model updates, were less consistently addressed. No reviewed system simultaneously satisfied semantic integrity, regulatory reversibility, and operational scalability. CONCLUSIONS Blockchain can strengthen procedural accountability and governance in health care federated learning, but ledger mechanisms alone are insufficient to ensure clinical model integrity or robust protection against malicious yet protocol-compliant updates. Future research should move beyond auditability and access control toward verifiable semantic integrity, reversible governance mechanisms, realistic adversarial evaluation, and scalable consensus models suitable for health care environments. These findings position blockchain-mediated federated learning as a medical informatics infrastructure challenge, where trust, auditability, governance, and clinical decision relevance must be designed jointly rather than treated as isolated technical properties.","url":"https://doi.org/10.2196/preprints.105625","authors":["Rui Botelho","Goreti Marreiros","Luís Conceição"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-29T08:35:07Z","doi":"10.2196/preprints.105625","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.5772/intechopen.1005410","name":"Introductory Chapter: Recent Trends and Progress in Support Vector Machines","source":"crossref","abstract":"","url":"https://doi.org/10.5772/intechopen.1005410","authors":["Robertas Damaševičius"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-02T13:31:30Z","doi":"10.5772/intechopen.1005410","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.1016/j.cosrev.2024.100697","name":"Integrating Explainable AI with Federated Learning for Next-Generation IoT: A comprehensive review and prospective insights","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.cosrev.2024.100697","authors":["Praveer Dubey","Mohit Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-06T08:09:38Z","doi":"10.1016/j.cosrev.2024.100697","addedAt":"2026-08-31T06:41:33.579Z","updatedAt":"2026-08-31T06:41:33.579Z"},{"id":"doi:10.3390/app16052611","name":"The Convergence of Federated Learning, Knowledge Graphs, and Large Language Models for Language Learning: A Scoping Review","source":"openalex","abstract":"Large Language Models (LLMs) in Intelligent Computer-Assisted Language Learning enable highly personalized learning, yet raise significant challenges related to pedagogical grounding, data privacy, and instructional validity. Although Knowledge Graphs (KGs) and Federated Learning (FL) can mitigate these issues in isolation, evidence on systematic FL–KG–LLM integration for educational language learning remains limited. This scoping review maps the FL–KG–LLM convergence landscape. Following PRISMA-ScR guidelines, we searched six databases and screened 51 papers (2019–2025) using automated extraction. Our findings indicate limited convergence: no papers integrate all three domains, and 58.8% of approaches remain confined to isolated technological silos. Reporting is also uneven across the corpus, with an average “Not Reported” (NR) rate of 84.5%, most notably for privacy mechanisms (92.2%), validation metrics (90.2%), and Common European Framework of Reference for Languages (CEFR) alignment (88.2%). Domain-specific analysis reveals two distinct patterns: inter-domain gaps (disciplinary silos resulting in expected CEFR absence in single-domain papers) and intra-domain gaps (failure to report domain-critical variables, including 100% parameter NR in FL studies, 86.7% validation NR in KG studies, and 100% CEFR NR in convergence papers). Taken together, these gaps suggest that pedagogical grounding is treated as optional rather than structural. We therefore identify two pillars of pedagogical grounding: a Grounding Pillar, which constrains LLM outputs via Knowledge Graph rules, and a Validation Pillar, which concerns how authoritative frameworks (e.g., CEFR) are mapped onto Knowledge Graph schemas and evaluated. The near-universal absence of CEFR alignment and validation reporting suggests that this second pillar is currently missing, which we term the Integrity Gap—a systematic disconnection between technological innovation and pedagogical grounding inin Intelligent Computer-Assisted Language Learning. By reframing the problem as upstream control and validation, this review informs the design of user-facing automated systems where trust, transparency, and human oversight are critical.","url":"https://doi.org/10.3390/app16052611","authors":["Michael Kenteris","Konstantinos Kotis"],"tags":["Computer science","Disconnection","Knowledge graph","Pillar","Graph"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2026-03-09","doi":"10.3390/app16052611","addedAt":"2026-08-31T06:41:33.580Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.5772/intechopen.1008820","name":"Machine Learning for Sustainable Shipping: Predicting Vessel CO2 Emissions Using Random Forest Models","source":"crossref","abstract":"Predicting fuel consumption in the shipping industry is a critical task that supports optimized operations, driving both economic and environmental benefits as global demand for shipping continues to grow. However, accurately forecasting Carbon Dioxide (CO2) emissions is challenging due to the complexity and volume of operational data. In this study, we developed and evaluated the Random Forest model to measure the prediction accuracy. The Random Forest model achieved a high predictive accuracy, with a Mean Absolute Percentage Error (MAPE) of 0.046, demonstrating its robustness in capturing the non-linear relationships in the data. Feature importance analysis within the Random Forest model highlighted vessel length, gross tonnage, width, and draft as key predictors of fuel consumption, offering valuable insights into CO2 emissions-reduction strategies. These findings underscore the potential of machine learning to empower data-driven decisions in the maritime sector. Future work will explore further optimization of the Random Forest model, incorporate additional predictive features, and investigate real-time applications to enhance operational efficiency and sustainability. By making accurate CO2 emission predictions, this research contributes to the industry’s efforts to achieve sustainable and environmentally responsible shipping practices.","url":"https://doi.org/10.5772/intechopen.1008820","authors":["Carol Anne Hargreaves","Briana Wan Nee Toh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-23T12:59:30Z","doi":"10.5772/intechopen.1008820","addedAt":"2026-08-31T06:41:33.580Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.21203/rs.3.rs-3350992/v1","name":"Network Intrusion Detection Empowered with Federated Machine Learning","source":"crossref","abstract":"Abstract Security and privacy are greatly enhanced by intrusion detection systems. Now, Machine Learning (ML) and Deep Learning (DL) with Intrusion Detection Systems (IDS) have seen great success due to their high levels of classification accuracy. Nevertheless, because data must be stored and communicated to a centralized server in these methods, the confidentiality features of the system may be threatened. This article proposes a blockchain-based Federated Learning (FL) approach to intrusion detection that maintains data privacy by training and inferring detection models locally. This approach improves the diversity of training data as models are trained on data from different sources. We employed the Scaled Conjugate Gradient Algorithm, Bayesian Regularization Algorithm, and Levenberg-Marquardt Algorithm for training our model. The training weights were then applied to the federated learning model. To maintain the security of the aggregation model, blockchain technology is used to store and exchange training models. We ran extensive testing on the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) data set to evaluate the efficacy of the proposed approach. According to simulation results, the proposed FL detection model achieved a higher accuracy level than the traditional centralized non-FL method. Classification accuracy achieved by the proposed model was 98.93% for training and 97.35% for testing.","url":"https://doi.org/10.21203/rs.3.rs-3350992/v1","authors":["Muhammad Umar Nasir","Shahid Mehmood","Muhammad Adnan Khan","Muhammad Zubair","Faheem Khan","Youngmoon Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-19T17:31:17Z","doi":"10.21203/rs.3.rs-3350992/v1","addedAt":"2026-08-31T06:41:33.580Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.36713/epra25942","name":"PRIVACY-CONSCIOUS FEDERATED MULTI-MODAL LEARNING WITH JURISDICTIONAL CONSTRAINTS","source":"crossref","abstract":"The rapid-fire proliferation of Internet of effects ( IoT) bias has introduced significant security challenges, particularly in large- scale and miscellaneous IoT networks. These systems are decreasingly susceptible to different cyber-attacks due to the decentralized nature and limited computational capabilities of individual IoT bumps. Traditional intrusion discovery systems( IDS) struggle to directly identify sophisticated and evolving attack patterns in similar surroundings. To address these limitations, this study proposes a new sequestration- conserving mongrel Convolutional intermittent Neural Network( CRNN) model integrated with allied literacy formulti-class intrusion discovery in IoT and Industrial IoT( IIoT) networks. Federated learning enables decentralized training of the model across multiple IoT bias without transferring raw data, thereby conserving data sequestration and icing compliance with data protection regulations. The cold-blooded CRNN armature leverages the strengths of Convolutional Neural Networks( CNNs) for point birth and intermittent Neural Networks( RNNs) for landing temporal dependences in network business. This combination significantly enhances the model’s capability to descry a wide range of attack types, including low- frequence and sophisticated pitfalls. The proposed model is trained and estimated using the Edge- IIoT dataset, demonstrating high performance with a discovery delicacy of 98.93. The results show balanced perfection and recall across all attack classes, including grueling orders similar as SQL Injection and Man- in- the- Middle attacks. This balance contributes to minimizing both false cons and false negatives, perfecting the overall trustability and robustness of the intrusion discovery system. By furnishing real- time discovery and sequestration- conserving training, the proposed approach offers a practical, scalable, and secure result acclimatized for complex IoT surroundings. It addresses critical gaps in being IDS fabrics by combining advanced deep literacy styles with allied literacy, paving the way for unborn secure and intelligent IoT deployments.","url":"https://doi.org/10.36713/epra25942","authors":["Pooja Upadhyay"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-03T19:13:18Z","doi":"10.36713/epra25942","addedAt":"2026-08-31T06:41:33.580Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.69987/aimlr.2024.50304","name":"Research on Cross-Platform Digital Advertising User Behavior Analysis Framework Based on Federated Learning","source":"openalex","abstract":"This information is released over the digital movement based on behavioral courses for user assessment. The framework is important to challenge the privacy-keeping the data division and manipulation throughout the platform announced. The network network architecture is designed to detect users of behavioral behavior when keeping personal information from secure. The framework implements an adaptive model aggregation strategy with dynamic weight adjustment mechanisms to optimize cross-platform model performance. Special protection, including special data and homomorphic encryption, has been integrated with security data during training and competition level. Tests have followed our greatest datase in the world, completed for a pre-commitment, the proposed efforts Over 200 million users across 5 million users when maintaining strategic warranty. Assessmental evaluation of significant improvements in advertisement, including the pronouncement (CTR), when minimized time %. The framework procedures for privacy personally used to investigate the characteristics of new ecosystem.","url":"https://doi.org/10.69987/aimlr.2024.50304","authors":["Kai Zhang","Suchuan Xing","Yizhe Chen","Y H Chen"],"tags":["Computer science","Cross-platform","World Wide Web","Operating system"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-07-13","doi":"10.69987/aimlr.2024.50304","addedAt":"2026-08-31T06:41:33.580Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.36227/techrxiv.14945433.v1","name":"Incentive-Driven Federated Learning and Associated Security Challenges: A Systematic Review","source":"crossref","abstract":"In response to various privacy risks, researchers and practitioners have been exploring different paradigms that can leverage the increased computational capabilities of consumer devices to train machine (ML) learning models in a distributed fashion without requiring the uploading of the training data from individual devices to central facilities. For this purpose, federated learning (FL) was proposed as a technique that can learn a global machine model at a central master node by the aggregation of models trained locally using private data. However, organizations may be reluctant to train models locally and to share these local ML models due to required computational resources for model training at their end and due to privacy risks that may result from adversaries inverting these models to infer information about the private training data. Incentive mechanisms have been proposed to motivate end users to participate in collaborative training of ML models (using their local data) in return for certain rewards. However, the design of an optimal incentive mechanism for FL is challenging due to its distributed nature and the fact that the central server has no access to clients’ hyperparameters information and the amount/quality data used for training, which makes the task of determining the reward based on the contribution of individual clients in FL environment difficult. Even though several incentive mechanisms have been proposed for FL, a thorough up-to-date systematic review is missing and this paper fills this gap. According to the best of our knowledge, this paper is the first systematic review that comprehensively enlists the design principles required for implementing these incentive mechanisms and then categorizes various incentive mechanisms according to their design principles. In addition, we also provide a comprehensive overview of security challenges associated with incentive-driven FL. Finally, we highlight the limitations and pitfalls of these incentive schemes and elaborate upon open-research issues that required further research attention.","url":"https://doi.org/10.36227/techrxiv.14945433.v1","authors":["Asad Ali","Inaam Ilahi","Adnan Qayyum","Ihab Mohammed","Ala Al-Fuqaha","Junaid Qadir"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-14T03:46:43Z","doi":"10.36227/techrxiv.14945433.v1","addedAt":"2026-08-31T06:41:33.580Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.15199/48.2025.09.21","name":"Enhancing Federated Learning for Privacy Preserving Intrusion Detection in IoT networks","source":"crossref","abstract":"","url":"https://doi.org/10.15199/48.2025.09.21","authors":["Mohammed BENKADDOUR"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-03T07:09:58Z","doi":"10.15199/48.2025.09.21","addedAt":"2026-08-31T06:41:33.580Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"doi:10.1007/978-981-96-8353-6_11","name":"Advancements and Challenges in Federated Learning for Privacy-Preserving Smart Healthcare: A Review","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-8353-6_11","authors":["Habiba Akter Rimi","Md. Asaduzzaman","Md. Johir Uddin Bhuiyan","Hashibul Ahsan Shoaib","K. M. Nafiur Rahman Fuad","Md. Anisur Rahman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-31T19:42:35Z","doi":"10.1007/978-981-96-8353-6_11","addedAt":"2026-08-31T06:41:33.580Z","updatedAt":"2026-08-31T06:41:33.580Z"},{"id":"pmid:41095831","name":"Artificial Intelligence in Predictive Healthcare: A Systematic Review.","source":"pubmed","abstract":"Background/Objectives: Today, Artificial intelligence (AI) and machine learning (ML) significantly enhance predictive analytics in the healthcare landscape, enabling timely and accurate predictions that lead to proactive interventions, personalized treatment plans, and ultimately improved patient care. As healthcare systems increasingly adopt data-driven approaches, the integration of AI and data analysis has garnered substantial interest, as reflected in the growing number of publications highlighting innovative applications of AI in clinical settings. This review synthesizes recent evidence on application areas, commonly used models, metrics, and challenges. Methods: We conducted a systematic literature review between using Web of Science and Google Scholar databases from 2021-2025 covering a diverse range of AI and ML techniques applied to disease prediction. Results: Twenty-two studies met criteria. The most frequently used machine learning approaches were tree-based ensemble models (e.g., Random Forest, XGBoost, LightGBM) for structured clinical data, and deep learning architectures (e.g., CNN, LSTM) for imaging and time-series tasks. Evaluation most commonly relied on AUROC, F1-score, accuracy, and sensitivity. key challenges remain regarding data privacy, integration with clinical workflows, model interpretability, and the necessity for high-quality representative datasets. Conclusions: Future research should focus on developing interpretable models that clinicians can understand and trust, implementing robust privacy-preserving techniques to safeguard patient data, and establishing standardized evaluation frameworks to effectively assess model performance.","url":"https://pubmed.ncbi.nlm.nih.gov/41095831/","authors":["Al-Nafjan A","Aljuhani A","Alshebel A","Alharbi A","Alshehri A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 24","doi":"10.3390/jcm14196752","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41094958","name":"Machine Learning for Structural Health Monitoring of Aerospace Structures: A Review.","source":"pubmed","abstract":"Structural health monitoring (SHM) plays a critical role in ensuring the safety and performance of aerospace structures throughout their lifecycle. As aircraft and spacecraft systems grow in complexity, the integration of machine learning (ML) into SHM frameworks is revolutionizing how damage is detected, localized, and predicted. This review presents a comprehensive examination of recent advances in ML-based SHM methods tailored to aerospace applications. It covers supervised, unsupervised, deep, and hybrid learning techniques, highlighting their capabilities in processing high-dimensional sensor data, managing uncertainty, and enabling real-time diagnostics. Particular focus is given to the challenges of data scarcity, operational variability, and interpretability in safety-critical environments. The review also explores emerging directions such as digital twins, transfer learning, and federated learning. By mapping current strengths and limitations, this paper provides a roadmap for future research and outlines the key enablers needed to bring ML-based SHM from laboratory development to widespread aerospace deployment.","url":"https://pubmed.ncbi.nlm.nih.gov/41094958/","authors":["Scarselli G","Nicassio F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct 4","doi":"10.3390/s25196136","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41094938","name":"A Systematic Literature Review on the Implementation and Challenges of Zero Trust Architecture Across Domains.","source":"pubmed","abstract":"The Zero Trust Architecture (ZTA) model has emerged as a foundational cybersecurity paradigm that eliminates implicit trust and enforces continuous verification across users, devices, and networks. This study presents a systematic literature review of 74 peer-reviewed articles published between 2016 and 2025, spanning domains such as cloud computing (24 studies), Internet of Things (11), healthcare (7), enterprise and remote work systems (6), industrial and supply chain networks (5), mobile networks (5), artificial intelligence and machine learning (5), blockchain (4), big data and edge computing (3), and other emerging contexts (4). The analysis shows that authentication, authorization, and access control are the most consistently implemented ZTA components, whereas auditing, orchestration, and environmental perception remain underexplored. Across domains, the main challenges include scalability limitations, insufficient lightweight cryptographic solutions for resource-constrained systems, weak orchestration mechanisms, and limited alignment with regulatory frameworks such as GDPR and HIPAA. Cross-domain comparisons reveal that cloud and enterprise systems demonstrate relatively mature implementations, while IoT, blockchain, and big data deployments face persistent performance and compliance barriers. Overall, the findings highlight both the progress and the gaps in ZTA adoption, underscoring the need for lightweight cryptography, context-aware trust engines, automated orchestration, and regulatory integration. This review provides a roadmap for advancing ZTA research and practice, offering implications for researchers, industry practitioners, and policymakers seeking to enhance cybersecurity resilience.","url":"https://pubmed.ncbi.nlm.nih.gov/41094938/","authors":["Mushtaq S","Mohsin M","Mushtaq MM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25196118","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"pmid:41085337","name":"Artificial intelligence in heart failure - a comprehensive literature review.","source":"pubmed","abstract":"Heart failure (HF) is a leading cause of hospitalization and mortality worldwide, presenting significant management challenges due to its heterogeneity and frequent comorbidities. Despite advancements in treatment, heart failure poses significant burdens on healthcare, increased by the aging of populations and rising prevalence. Recent developments in artificial intelligence (AI) and machine learning are transforming HF management by improving diagnosis, risk stratification, personalized treatment, and remote monitoring. AI enhances diagnostic accuracy through tools like echocardiogram and electrocardiogram analysis and identifies phenotypic subgroups to better target therapies. AI algorithms integrate data from electronic health records, biomarkers, and wearable devices to predict exacerbations and tailor treatments, while AI-driven Clinical Decision Support Systems (AI-CDSS) improve guideline-directed medical therapy (GDMT) adherence and enable timely interventions. However, barriers such as data integrity, ethical considerations, and insufficient clinician training impede AI adoption. Potential solutions include federated learning to safeguard data privacy and interdisciplinary efforts to establish regulatory frameworks. This review synthesizes current research on AI's applications in HF management, emphasizing its potential to improve patient outcomes, reduce hospitalizations, and address the growing healthcare burden while highlighting the need to confront these challenges to fully realize its benefits.","url":"https://pubmed.ncbi.nlm.nih.gov/41085337/","authors":["Alyacoub R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5603/cj.104365","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41080432","name":"Unmasking digital deceptions: An integrative review of deepfake detection, multimedia forensics, and cybersecurity challenges.","source":"pubmed","abstract":"Deepfakes, which are driven by developments in generative AI, seriously jeopardize public trust, cybersecurity, and the veracity of information. This study offers a comprehensive analysis of the most recent methods for creating and detecting deepfakes in image, video, and audio modalities. With a focus on their advantages and disadvantages in cross-dataset and real-world scenarios, we compile the latest developments in transformer-based detection models, multimodal biometric defenses, and Generative Adversarial Networks (GANs). We provide implementation-level information such as pseudocode workflows, hyperparameter settings, and preprocessing pipelines for popular detection frameworks to improve reproducibility. We also examine the implications of cybersecurity, including identity theft and biometric spoofing, as well as policy-oriented solutions that incorporate federated learning, explainable AI, and ethical protections. By enriching technical insights with interdisciplinary perspectives, this review charts a roadmap for building robust, scalable, and trustworthy deepfake detection systems.","url":"https://pubmed.ncbi.nlm.nih.gov/41080432/","authors":["Singh S","Dhumane A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1016/j.mex.2025.103632","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41072284","name":"Fine-tuning large language models in federated learning with fairness-aware prompt selection.","source":"pubmed","abstract":"Large language models (LLMs) require domain-specific fine-tuning for real-world deployment, yet face critical barriers of data privacy and computational constraints. Federated learning (FL) provides an indispensable solution by enabling collaborative tuning across distributed private data sources while preserving confidentiality. However, existing FL-LLM methods suffer from non-IID degradation, communication overhead, and fairness issues. To address these challenges, this paper proposes FedPSF-LLM, a novel FL framework integrating three core innovations: (1) the Prompt Selection Module (PSM) adaptively selects high-impact prompt parameters to reduce transmission costs; (2) the Dynamic Weighting Module (DWM) adjusts aggregation weights based on client contribution and data disparity; (3) the Attention-Based Bias Mitigation (ABM) corrects aggregation bias via alignment-aware reweighting. Extensive experiments on 10 NLP tasks and 4 LLMs demonstrate that FedPSF-LLM improves fairness while maintaining strong overall performance. Compared to state-of-the-art methods, it reduces accuracy variance by 52.1 %, improves worst-client accuracy by 8.6 %, and narrows small-large client performance gaps by 74.4 %, while maintaining 76.8 % global accuracy. These results demonstrate superiority over 8 baselines in both fairness metrics and communication efficiency, establishing a new paradigm for privacy-preserving and fairness-guaranteed LLM deployment in federated systems.","url":"https://pubmed.ncbi.nlm.nih.gov/41072284/","authors":["Jiang Y","Li Z","Song B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Feb","doi":"10.1016/j.neunet.2025.108160","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41056615","name":"Advances in medical image segmentation: A comprehensive survey with a focus on lumbar spine applications.","source":"pubmed","abstract":"Medical Image Segmentation (MIS) stands as a cornerstone in medical image analysis, playing a pivotal role in precise diagnostics, treatment planning, and monitoring of various medical conditions. This paper presents a comprehensive and systematic survey of MIS methodologies, bridging the gap between traditional image processing techniques and modern deep learning approaches. The survey encompasses thresholding, edge detection, region-based segmentation, clustering algorithms, and model-based techniques while also delving into state-of-the-art deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), and the widely adopted U-Net and its variants. Moreover, integrating attention mechanisms, semi-supervised learning, generative adversarial networks (GANs), and Transformer-based models is thoroughly explored. In addition to covering established methods, this survey highlights emerging trends, including hybrid architectures, cross-modality learning, federated and distributed learning frameworks, and active learning strategies, which aim to address challenges such as limited labeled datasets, computational complexity, and model generalizability across diverse imaging modalities. Furthermore, a specialized case study on lumbar spine segmentation is presented, offering insights into the challenges and advancements in this relatively underexplored anatomical region. Despite significant progress in the field, critical challenges persist, including dataset bias, domain adaptation, interpretability of deep learning models, and integration into real-world clinical workflows. This survey serves as both a tutorial and a reference guide, particularly for early-career researchers, by providing a holistic understanding of the landscape of MIS and identifying promising directions for future research. Through this work, we aim to contribute to the development of more robust, efficient, and clinically applicable medical image segmentation systems.","url":"https://pubmed.ncbi.nlm.nih.gov/41056615/","authors":["Kabil A","Khoriba G","Yousef M","Rashed EA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov","doi":"10.1016/j.compbiomed.2025.111171","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41055689","name":"Regulatory Adoption of AI, ML, Computational Modeling & Simulation in In-Silico Clinical Trials for Medical Devices: A Systematic Review.","source":"pubmed","abstract":"This study explores the revolutionary potential of in-silico clinical trials (ISCTs) in medical device development, emphasizing the integration of computational modeling and simulation (CM&amp;S), artificial intelligence (AI), and machine learning (ML). It evaluates regulatory advancements by the FDA, EMA, and PMDA, identifies barriers to global ISCTs adoption, and proposes strategies to enhance credibility, standardization, and ethical alignment.","url":"https://pubmed.ncbi.nlm.nih.gov/41055689/","authors":["De A","Lohani A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan","doi":"10.1007/s43441-025-00871-2","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41053667","name":"A comprehensive overview: deep learning approaches to central serous chorioretinopathy diagnosis.","source":"pubmed","abstract":"To synthesize evidence on deep learning applications for diagnosing central serous chorioretinopathy (CSCR), a macular disorder associated with vision loss, this systematic review categorized studies by diagnostic task and imaging modality. The study evaluates advances in deep learning performance, clinical integration potential, dataset limitations, and the contributions of multimodal imaging and Explainable AI (XAI) to diagnostic accuracy and clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/41053667/","authors":["Shojaeinia M","Hosseini A","Naderi M","Baloutch B","Yekta MS","Akbarpour L","Moghaddasi H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct 6","doi":"10.1186/s12886-025-04372-6","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41047321","name":"How can artificial intelligence transform the training of medical students and physicians?","source":"pubmed","abstract":"Advances in artificial intelligence (AI), particularly generative AI, hold promise for transforming medical education and physician training in response to increasing health-care demands and shortages in the global health-care workforce. Meanwhile, challenges remain in the effective and equitable integration of AI technology into medical education and physician training worldwide. This Viewpoint explores the opportunities and challenges of such an integration. We study the evolving role of AI in medical education, its potential to enhance high-fidelity clinical training, and its contribution to research training using real-world examples. We also highlight ethical concerns, particularly the unclear boundaries of appropriate use of AI and call for clear guidelines to govern the integration of AI into medical education and physician training. Furthermore, this Viewpoint discusses practical constraints, including human, financial, and resource constraints, in AI integration, and emphasises the need for comprehensive cost evaluations and collaborative funding models to support the sustainable implementation of AI integration. A tight collaborative network between health-care institutions and systems, medical schools and universities, industry partners, and education and health-care regulatory agencies could lead to an AI-transformed medical education and physician training scheme that ultimately supports the adoption and integration of AI into clinical medicine and potentially brings about tangible improvements in global health-care delivery.","url":"https://pubmed.ncbi.nlm.nih.gov/41047321/","authors":["Ning Y","Ong JCL","Cheng H","Wang H","Ting DSW","Tham YC","Wong TY","Liu N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct","doi":"10.1016/j.landig.2025.100900","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41041318","name":"Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.","source":"pubmed","abstract":"Machine learning (ML) has played a crucial role in advancing precision immunotherapy by integrating multi-omics data to identify biomarkers and predict therapeutic responses. However, a prevalent methodological flaw persists in immunological studies-an overreliance on correlation-based analysis while neglecting causal inference. Traditional ML models struggle to capture the intricate dynamics of immune interactions and often function as \"black boxes.\" A systematic review of 90 studies on immune checkpoint inhibitors revealed that despite employing ML or deep learning techniques, none incorporated causal inference. Similarly, all 36 retrospective studies modeling melanoma exhibited the same limitation. This \"knowledge-practice gap\" highlights a disconnect: although researchers acknowledge that correlation does not imply causation, causal inference is often omitted in practice. Recent advances in causal ML, like Targeted-BEHRT, CIMLA, and CURE, offer promising solutions. These models can distinguish genuine causal relationships from spurious correlations, integrate multimodal data-including imaging, genomics, and clinical records-and control for unmeasured confounders, thereby enhancing model interpretability and clinical applicability. Nevertheless, practical implementation still faces major challenges, including poor data quality, algorithmic opacity, methodological complexity, and interdisciplinary communication barriers. To bridge these gaps, future efforts must focus on advancing research in causal ML, developing platforms such as the Perturbation Cell Atlas and federated causal learning frameworks, and fostering interdisciplinary training programs. These efforts will be essential to translating causal ML from theoretical innovation to clinical reality in the next 5-10 years-representing not only a methodological upgrade, but also a paradigm shift in immunotherapy research and clinical decision-making.","url":"https://pubmed.ncbi.nlm.nih.gov/41041318/","authors":["Wang JW","Meng M","Dai MW","Liang P","Hou J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fimmu.2025.1630781","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41025057","name":"Artificial intelligence in carotid computed tomography angiography plaque detection: Decade of progress and future perspectives.","source":"pubmed","abstract":"The application of artificial intelligence (AI) in carotid atherosclerotic plaque detection via computed tomography angiography (CTA) has significantly advanced over the past decade. This mini-review consolidates recent innovations in deep learning architectures, domain adaptation techniques, and automated plaque characterization methodologies. Hybrid models, such as residual U-Net-Pyramid Scene Parsing Network, exhibit a remarkable precision of 80.49% in plaque segmentation, outperforming radiologists in diagnostic efficiency by reducing analysis time from minutes to mere seconds. Domain-adaptive frameworks, such as Lesion Assessment through Tracklet Evaluation, demonstrate robust performance across heterogeneous imaging datasets, achieving an area under the curve (AUC) greater than 0.88. Furthermore, novel approaches integrating U-Net and Efficient-Net architectures, enhanced by Bayesian optimization, have achieved impressive correlation coefficients (0.89) for plaque quantification. AI-powered CTA also enables high-precision three-dimensional vascular segmentation, with a Dice coefficient of 0.9119, and offers superior cardiovascular risk stratification compared to traditional Agatston scoring, yielding AUC values of 0.816 vs 0.729 at a 15-year follow-up. These breakthroughs address key challenges in plaque motion analysis, with systolic retractive motion biomarkers successfully identifying 80% of vulnerable plaques. Looking ahead, future directions focus on enhancing the interpretability of AI models through explainable AI and leveraging federated learning to mitigate data heterogeneity. This mini-review underscores the transformative potential of AI in carotid plaque assessment, offering substantial implications for stroke prevention and personalized cerebrovascular management strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/41025057/","authors":["Wang DY","Yang T","Zhang CT","Zhan PC","Miao ZX","Li BL","Yang H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 28","doi":"10.4329/wjr.v17.i9.110447","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41024088","name":"Integrating big data and artificial intelligence to predict progression in multiple sclerosis: challenges and the path forward.","source":"pubmed","abstract":"Multiple sclerosis (MS) remains a complex and costly neurological condition characterised by progressive disability, making early detection and accurate prognosis of disease progression imperative. While artificial intelligence (AI) combined with big data promises transformative advances in personalised MS care, integration of multimodal, real-world datasets, including clinical records, magnetic resonance imaging (MRI), and digital biomarkers, remains limited. This perspective paper identifies a critical gap between technical innovation and clinical implementation, driven by methodological constraints, evolving regulatory frameworks, and ethical concerns related to bias, privacy, and equity. We explore this gap through three interconnected lenses: the underuse of integrated real-world data, the barriers posed by regulation and ethics, and emerging solutions. Promising strategies such as federated learning, regulatory initiatives like DARWIN-EU and the European Health Data Space, and patient-led frameworks including PROMS and CLAIMS, offer structured pathways forward. Additionally, we highlight the growing relevance of foundation models for interpreting complex MS data and supporting clinical decision-making. We advocate for harmonised data infrastructures, patient-centred design, explainable AI, and real-world validation as core pillars for future implementation. By aligning technical, regulatory, and ethical domains, stakeholders can unlock the full potential of AI to enhance prognosis, personalise care, and improve outcomes for people with MS.","url":"https://pubmed.ncbi.nlm.nih.gov/41024088/","authors":["Khan H","Aerts S","Vermeulen I","Woodruff HC","Lambin P","Peeters LM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 29","doi":"10.1186/s12984-025-01748-z","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41023829","name":"Federated learning for early severity prediction in acute pancreatitis: a multi-center study.","source":"pubmed","abstract":"BACKGROUND: Acute pancreatitis (AP) is a common gastrointestinal disease prone to severe systemic complications, presenting with abdominal pain, nausea, and vomiting. Diagnosis depends on serum markers and imaging. Categorized by the Atlanta classification, about 20% of cases progress to more severe forms. Due to its rapid progression and high misdiagnosis rate, early and accurate assessment is crucial. In this study, we aim to construct a deep neural network to provide a scientific basis for the clinical management of AP. METHODS: A total of 1,884 patients diagnosed with AP from four hospitals, namely the Fourth Affiliated Hospital of Zhejiang University School of Medicine(ZJU-H4), the First Affiliated Hospital of Ningbo University(NBU-H1), the Second Affiliated Hospital of Zhejiang Chinese Medical University(ZCMU-H2), and Lishui People&#x2019;s Hospital(LISHUI-H), were retrospectively included from 2012 to 2024. The examination indicators of the patients within 48&#xa0;h and 72&#xa0;h after admission were collected. Clinical models were developed using Arya, a novel privacy computing platform by Healink to predict the severity of AP in patients. Two learning methods&#x2014;logistic regression (LR) and deep neural networks (DNN)&#x2014;were applied within the federated learning framework. Moreover, to address the challenge of fair contribution evaluation in federated learning, a novel assessment method integrating data quality and model performance impact was proposed. RESULTS: In the 48-hour scenario, the DNN model showed higher prediction performance than the logistic regression model in terms of sensitivity (78.20% vs. 78.70%), specificity (78.40% vs. 72.90%) and accuracy (78.30% vs. 75.90%); the same trend was observed in the 72-hour scenario: sensitivity (78.10% vs. 83.30%), specificity (80.90% vs. 70.60%) and accuracy (79.40% vs. 77.10%). Moreover, the model based on data within 72&#xa0;h was more accurate than that within 48&#xa0;h. The optimal model among the four scenarios was the DNN - based model using indicators within 72&#xa0;h after admission. CONCLUSION: This study successfully integrated collective knowledge and data resources from various medical institutions while ensuring patient privacy and data security through the adoption of federated learning. It demonstrated a significant leap in the prediction of AP severity using machine learning. Furthermore, by introducing a novel contribution evaluation mechanism, it addressed the limitations of traditional data-sharing methods, enhancing fairness and incentivizing multi-center participation.","url":"https://pubmed.ncbi.nlm.nih.gov/41023829/","authors":["Shen Y","Li Z","Li S","Zhou L","Chen Y","Dong K","Wang H","Wang D","Hu Y","Li J","Li C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 29","doi":"10.1186/s12876-025-04265-4","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41012523","name":"Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.","source":"pubmed","abstract":"Multi-target drug discovery has become an essential strategy for treating complex diseases involving multiple molecular pathways. Traditional single-target approaches often fall short in addressing the multifactorial nature of conditions such as cancer and neurodegenerative disorders. With the rise in large-scale biological data and algorithmic advances, machine learning (ML) has emerged as a powerful tool to accelerate and optimize multi-target drug development. This review presents a comprehensive overview of ML techniques, including advanced deep learning (DL) approaches like attention-based models, and highlights their application in multi-target prediction, from traditional supervised learning to modern graph-based and multi-task learning frameworks. We highlight real-world applications in oncology, central nervous system disorders, and drug repurposing, showcasing the translational potential of ML in systems pharmacology. Major challenges are discussed, such as data sparsity, lack of interpretability, limited generalizability, and integration into experimental workflows. We also address ethical and regulatory considerations surrounding model transparency, fairness, and reproducibility. Looking forward, we explore promising directions such as generative modeling, federated learning, and patient-specific therapy design. Together, these advances point toward a future of precision polypharmacology driven by biologically informed and interpretable ML models. This review aims to provide researchers and practitioners with a roadmap for leveraging ML in the development of safer and more effective multi-target therapeutics.","url":"https://pubmed.ncbi.nlm.nih.gov/41012523/","authors":["Bi X","Wang Y","Wang J","Liu C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 12","doi":"10.3390/pharmaceutics17091186","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41010987","name":"Artificial Intelligence and Advanced Digital Health for Hypertension: Evolving Tools for Precision Cardiovascular Care.","source":"pubmed","abstract":"Background : Hypertension remains the leading global risk factor for cardiovascular morbidity and mortality, with suboptimal control rates despite guideline-directed therapies. Digital health and artificial intelligence (AI) technologies offer novel approaches for improving diagnosis, monitoring, and individualized treatment of hypertension. Objectives : To critically review the current landscape of AI-enabled digital tools for hypertension management, including emerging applications, implementation challenges, and future directions. Methods : A narrative review of recent PubMed-indexed studies (2019-2024) was conducted, focusing on clinical applications of AI and digital health technologies in hypertension. Emphasis was placed on real-world deployment, algorithmic explainability, digital biomarkers, and ethical/regulatory frameworks. Priority was given to high-quality randomized trials, systematic reviews, and expert consensus statements. Results : AI-supported platforms-including remote blood pressure monitoring, machine learning titration algorithms, and digital twins-have demonstrated early promise in improving hypertension control. Explainable AI (XAI) is critical for clinician trust and integration into decision-making. Equity-focused design and regulatory oversight are essential to prevent exacerbation of health disparities. Emerging implementation strategies, such as federated learning and co-design frameworks, may enhance scalability and generalizability across diverse care settings. Conclusions : AI-guided titration and digital twin approaches appear most promising for reducing therapeutic inertia, whereas cuffless blood pressure monitoring remains the least mature. Future work should prioritize pragmatic trials with equity and cost-effectiveness endpoints, supported by safeguards against bias, accountability gaps, and privacy risks.","url":"https://pubmed.ncbi.nlm.nih.gov/41010987/","authors":["Skalidis I","Maurizi N","Salihu A","Fournier S","Cook S","Iglesias JF","Laforgia P","D'Angelo L","Garot P","Hovasse T","Neylon A","Unterseeh T","Champagne S","Amabile N","Sayah N","Sanguineti F","Akodad M","Lu H","Antiochos P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 4","doi":"10.3390/medicina61091597","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41010723","name":"Augmented Decisions: AI-Enhanced Accuracy in Glaucoma Diagnosis and Treatment.","source":"pubmed","abstract":"Glaucoma remains a leading cause of irreversible blindness. We reviewed more than 150 peer-reviewed studies (January 2019-July 2025) that applied artificial or augmented intelligence (AI/AuI) to glaucoma care. Deep learning systems analyzing fundus photographs or OCT volumes routinely achieved area-under-the-curve values around 0.95 and matched-or exceeded-subspecialists in prospective tests. Sequence-aware models detected visual field worsening up to 1.7 years earlier than conventional linear trends, while a baseline multimodal network integrating OCT, visual field, and clinical data predicted the need for incisional surgery with AUROC 0.92. Offline smartphone triage in community clinics reached sensitivities near 94% and specificities between 86% and 94%, illustrating feasibility in low-resource settings. Large language models answered glaucoma case questions with specialist-level accuracy but still require human oversight. Key obstacles include algorithmic bias, workflow integration, and compliance with emerging regulations, such as the EU AI Act and FDA GMLP. With rigorous validation, bias auditing, and transparent change control, AI/AuI can augment-rather than replace-clinician expertise, enabling earlier intervention, tailored therapy, and more equitable access to glaucoma care worldwide.","url":"https://pubmed.ncbi.nlm.nih.gov/41010723/","authors":["Zeppieri M","Gagliano C","Tognetto D","Musa M","Avitabile A","D'Esposito F","Nicolosi SG","Capobianco M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 16","doi":"10.3390/jcm14186519","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41007526","name":"Sustainability of AI-Assisted Mental Health Intervention: A Review of the Literature from 2020-2025.","source":"pubmed","abstract":"This systematic review examines the role of artificial intelligence (AI) in the development of sustainable mental health interventions through a comprehensive analysis of literature published between 2020 and 2025. In accordance with the PRISMA guidelines, 62 studies were selected from 1652 initially identified records across four major databases. The results revealed four dimensions critical for sustainability: ethical considerations (privacy, informed consent, bias, and human oversight), personalization approaches (federated learning and AI-enhanced therapeutic interventions), risk mitigation strategies (data security, algorithmic bias, and clinical efficacy), and implementation challenges (technical infrastructure, cultural adaptation, and resource allocation). The findings demonstrate that long-term sustainability depends on ethics-driven approaches, resource-efficient techniques such as federated learning, culturally adaptive systems, and appropriate human-AI integration. The study concludes that sustainable mental health AI requires addressing both technical efficacy and ethical integrity while ensuring equitable access across diverse contexts. Future research should focus on longitudinal studies examining the long-term effectiveness and cultural adaptability of AI interventions in resource-limited settings.","url":"https://pubmed.ncbi.nlm.nih.gov/41007526/","authors":["Espino Carrasco DK","Palomino Alcántara MDR","Arbulú Pérez Vargas CG","Santa Cruz Espino BM","Dávila Valdera LJ","Vargas Cabrera C","Espino Carrasco M","Dávila Valdera A","Agurto Córdova LM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 4","doi":"10.3390/ijerph22091382","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41007212","name":"Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.","source":"pubmed","abstract":"The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from reactive to predictive management models in spine surgery. We also highlight multi-omics platforms and federated learning frameworks that support integrative, privacy-preserving analytics at scale. Despite these advances, challenges remain-including algorithmic opacity, regulatory fragmentation, data heterogeneity, and limited generalizability across populations and clinical settings. Through a multidimensional lens, this review outlines not only current capabilities but also future directions to ensure safe, equitable, and high-fidelity AI deployment in spine care delivery.","url":"https://pubmed.ncbi.nlm.nih.gov/41007212/","authors":["Kumar R","Dougherty C","Sporn K","Khanna A","Ravi P","Prabhakar P","Zaman N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 9","doi":"10.3390/bioengineering12090967","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41007172","name":"AI in Dentistry: Innovations, Ethical Considerations, and Integration Barriers.","source":"pubmed","abstract":"Artificial Intelligence (AI) is improving dentistry through increased accuracy in diagnostics, planning, and workflow automation. AI tools, including machine learning (ML) and deep learning (DL), are being adopted in oral medicine to improve patient care, efficiency, and lessen clinicians' workloads. AI in dentistry, despite its use, faces an issue of acceptance, with its obstacles including ethical, legal, and technological ones. In this article, a review of current AI use in oral medicine, new technology development, and integration barriers is discussed.","url":"https://pubmed.ncbi.nlm.nih.gov/41007172/","authors":["Liu TY","Lee KH","Mukundan A","Karmakar R","Dhiman H","Wang HC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Aug 29","doi":"10.3390/bioengineering12090928","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41006992","name":"The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology.","source":"pubmed","abstract":"Artificial intelligence (AI) is revolutionarily shaping the entire landscape of medicine and particularly the privileged field of radiology, since it produces a significant amount of data, namely, images. Currently, AI implementation in radiology is continuously increasing, from automating image analysis to enhancing workflow management, and specifically, pediatric neuroradiology is emerging as an expanding frontier. Pediatric neuroradiology presents unique opportunities and challenges since neonates' and small children's brains are continuously developing, with age-specific changes in terms of anatomy, physiology, and disease presentation. By enhancing diagnostic accuracy, reducing reporting times, and enabling earlier intervention, AI has the potential to significantly impact clinical practice and patients' quality of life and outcomes. For instance, AI reduces MRI and CT scanner time by employing advanced deep learning (DL) algorithms to accelerate image acquisition through compressed sensing and undersampling, and to enhance image reconstruction by denoising and super-resolving low-quality datasets, thereby producing diagnostic-quality images with significantly fewer data points and in a shorter timeframe. Furthermore, as healthcare systems become increasingly burdened by rising demands and limited radiology workforce capacity, AI offers a practical solution to support clinical decision-making, particularly in institutions where pediatric neuroradiology is limited. For example, the MELD (Multicenter Epilepsy Lesion Detection) algorithm is specifically designed to help radiologists find focal cortical dysplasias (FCDs), which are a common cause of drug-resistant epilepsy. It works by analyzing a patient's MRI scan and comparing a wide range of features-such as cortical thickness and folding patterns-to a large database of scans from both healthy individuals and epilepsy patients. By identifying subtle deviations from normal brain anatomy, the MELD graph algorithm can highlight potential lesions that are often missed by the human eye, which is a critical step in identifying patients who could benefit from life-changing epilepsy surgery. On the other hand, the integration of AI into pediatric neuroradiology faces technical and ethical challenges, such as data scarcity and ethical and legal restrictions on pediatric data sharing, that complicate the development of robust and generalizable AI models. Moreover, many radiologists remain sceptical of AI's interpretability and reliability, and there are also important medico-legal questions around responsibility and liability when AI systems are involved in clinical decision-making. Future promising perspectives to overcome these concerns are represented by federated learning and collaborative research and AI development, which require technological innovation and multidisciplinary collaboration between neuroradiologists, data scientists, ethicists, and pediatricians. The paper aims to address: (1) current applications of AI in pediatric neuroradiology; (2) current challenges and ethical considerations related to AI implementation in pediatric neuroradiology; and (3) future opportunities in the clinical and educational pediatric neuroradiology field. AI in pediatric neuroradiology is not meant to replace neuroradiologists, but to amplify human intellect and extend our capacity to diagnose, prognosticate, and treat with unprecedented precision and speed.","url":"https://pubmed.ncbi.nlm.nih.gov/41006992/","authors":["Guarnera A","Napolitano A","Liporace F","Marconi F","Rossi-Espagnet MC","Gandolfo C","Romano A","Bozzao A","Longo D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Aug 27","doi":"10.3390/children12091127","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:41001196","name":"Federated learning for lesion segmentation in multiple sclerosis: a real-world multi-center feasibility study.","source":"pubmed","abstract":"Multiple sclerosis (MS) is a chronic neuroinflammatory disease driven by immune-mediated central nervous system damage, often leading to progressive disability. Accurate segmentation of MS lesions on MRI is crucial for monitoring disease and treatment efficacy; however, manual segmentation remains time-consuming and prone to variability. While deep learning has advanced automated segmentation, robust performance benefits from large-scale, diverse datasets, yet data pooling is restricted by privacy regulations and clinical performance remains challenged by inter-site heterogeneity. In this proof-of-concept work, we aim to apply and adopt Federated Learning (FL) in a real-world hospital setting. We assessed FL for MS lesion segmentation using the self-configuring nnU-Net model, leveraging 512 MRI cases from three sites without sharing raw patient data. The federated model achieved Dice scores ranging from 0.66 to 0.80 across held-out test sets. While performance varied across sites, reflecting data heterogeneity, the study demonstrates the potential of FL as a scalable and secure paradigm for advancing automated MS analysis in distributed clinical environments. This work supports adopting secure, collaborative AI in neuroimaging, offering utility for privacy-sensitive clinical research and a starting point for medical AI development, bridging the gap between model generalizability and regulatory compliance.","url":"https://pubmed.ncbi.nlm.nih.gov/41001196/","authors":["Hindawi S","Szubstarski B","Boernert E","Tackenberg B","Wuerfel J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fneur.2025.1620469","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40998104","name":"Data sharing for responsible artificial intelligence in dentistry: a narrative review of legal frameworks and privacy-preserving techniques.","source":"pubmed","abstract":"Data sharing is essential for ensuring research reproducibility and for developing generalizable artificial intelligence (AI) systems, but it demands robust safeguards for patient privacy. This narrative review aims to guide dental clinicians and researchers in sharing patient data responsibly while preserving confidentiality.","url":"https://pubmed.ncbi.nlm.nih.gov/40998104/","authors":["Brinz J","Eslamiamirabadi N","Salamati A","Tresp V","Schwendicke F","Tichy A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1016/j.jdent.2025.106130","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40997586","name":"A survey for large language models in biomedicine.","source":"pubmed","abstract":"Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of Science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their responsible and effective deployment.","url":"https://pubmed.ncbi.nlm.nih.gov/40997586/","authors":["Wang C","Li M","He J","Wang Z","Darzi E","Chen Z","Ye J","Li T","Su Y","Ke J","Qu K","Li S","Yu Y","Liò P","Wang T","Wang YG","Shen Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1016/j.artmed.2025.103268","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40993477","name":"Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) into cardiovascular imaging and radiology offers the potential to enhance diagnostic accuracy, streamline workflows, and personalize patient care. However, the rapid adoption of AI has introduced complex ethical challenges, particularly concerning patient privacy, data handling, informed consent, and data ownership. This narrative review explores these issues by synthesizing literature from clinical, technical, and regulatory perspectives. We examine the tensions between data utility and data protection, the evolving role of transparency and explainable AI, and the disparities in ethical and legal frameworks across jurisdictions such as the European Union, the USA, and emerging players like China. We also highlight the vulnerabilities introduced by cloud computing, adversarial attacks, and the use of commercial datasets. Ethical frameworks and regulatory guidelines are compared, and proposed mitigation strategies such as federated learning, blockchain, and differential privacy are discussed. To ensure ethical implementation, we emphasize the need for shared accountability among clinicians, developers, healthcare institutions, and policymakers. Ultimately, the responsible development of AI in medical imaging must prioritize patient trust, fairness, and equity, underpinned by robust governance and transparent data stewardship.","url":"https://pubmed.ncbi.nlm.nih.gov/40993477/","authors":["Mehrtabar S","Marey A","Desai A","Saad AM","Desai V","Goñi J","Pal B","Umair M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jun","doi":"10.1007/s10278-025-01656-7","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40990172","name":"Teleophthalmology-enabled devices: bridging the gap in rural eye care.","source":"pubmed","abstract":"Rural populations bear a disproportionate burden of preventable vision loss due to scant ophthalmic resources, long travel distances, and delayed diagnoses of vision-threatening conditions such as diabetic retinopathy, glaucoma, and age-related macular degeneration. Teleophthalmology, leveraging portable imaging devices, data connectivity, and remote interpretation, offers a critical solution by bringing diagnostic capabilities to underserved communities and enabling earlier intervention before irreversible vision loss occurs.","url":"https://pubmed.ncbi.nlm.nih.gov/40990172/","authors":["Gurnani B","Kaur K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov","doi":"10.1080/17434440.2025.2566741","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40978510","name":"Federated learning: a privacy-preserving approach to data-centric regulatory cooperation.","source":"pubmed","abstract":"Regulatory agencies aim to ensure the safety and efficacy of medical products but often face legal and privacy concerns that hinder collaboration at the data level. In this paper, we propose federated learning as an innovative method to enhance data-centric collaboration among regulatory agencies by enabling collaborative training of machine learning models without the need for direct data sharing, thereby preserving privacy and overcoming legal hurdles. We illustrate how Swissmedic, the Swiss Agency for Therapeutic Products, together with its partner agencies, proposes to use federated learning to improve TRICIA, an AI tool for assessing incoming reports of serious incidents related to medical devices. This approach enables the development of robust, generalisable risk assessment models that can potentially improve current processes. A proof of concept was deployed and thoroughly tested during the 14th Global Summit on Regulatory Science using synthetic data with participants from Swissmedic, the U.S. Food and Drug Administration (FDA), and the Danish Medicines Agency (DKMA), with promising initial results. This innovation has the potential to serve as a roadmap for other regulators to adopt similar approaches to optimize their own regulatory processes, contributing to a more integrated and efficient regulatory environment worldwide.","url":"https://pubmed.ncbi.nlm.nih.gov/40978510/","authors":["Horst A","Loustalot P","Yoganathan S","Li T","Xu J","Tong W","Schneider D","Löffler-Perez N","Di Renzo E","Renaudin M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fdsfr.2025.1579922","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"pmid:40968903","name":"Indoor Abnormal Behavior Detection for the Elderly: A Review.","source":"pubmed","abstract":"Due to the increased age of the global population, the proportion of the elderly population continues to rise. The safety of the elderly living alone is becoming an increasingly prominent area of concern. They often miss timely treatment due to undetected falls or illnesses, which pose risks to their lives. In order to address this challenge, the technology of indoor abnormal behavior detection has become a research hotspot. This paper systematically reviews detection methods based on sensors, video, infrared, WIFI, radar, depth, and multimodal fusion. It analyzes the technical principles, advantages, and limitations of various methods. This paper further explores the characteristics of relevant datasets and their applicable scenarios and summarizes the challenges facing current research, including multimodal data scarcity, risk of privacy leakage, insufficient adaptability of complex environments, and human adoption of wearable devices. Finally, this paper proposes future research directions, such as combining generative models, federated learning to protect privacy, multi-sensor fusion for robustness, and abnormal behavior detection on the Internet of Things environment. This paper aims to provide a systematic reference for academic research and practical application in the field of indoor abnormal behavior detection.","url":"https://pubmed.ncbi.nlm.nih.gov/40968903/","authors":["Gu T","Tang M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 May 24","doi":"10.3390/s25113313","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40968811","name":"Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey.","source":"pubmed","abstract":"The Internet of Things (IoT) is revolutionizing industries by enabling seamless interconnectivity across domains such as healthcare, smart cities, the Industrial Internet of Things (IIoT), and the Internet of Vehicles (IoV). However, IoT security remains a significant challenge due to vulnerabilities related to data breaches, privacy concerns, cyber threats, and trust management issues. Addressing these risks requires advanced security mechanisms, with machine learning (ML) emerging as a powerful tool for anomaly detection, intrusion detection, and threat mitigation. This survey provides a comprehensive review of ML-driven IoT security solutions from 2020 to 2024, examining the effectiveness of supervised, unsupervised, and reinforcement learning approaches, as well as advanced techniques such as deep learning (DL), ensemble learning (EL), federated learning (FL), and transfer learning (TL). A systematic classification of ML techniques is presented based on their IoT security applications, along with a taxonomy of security threats and a critical evaluation of existing solutions in terms of scalability, computational efficiency, and privacy preservation. Additionally, this study identifies key limitations of current ML approaches, including high computational costs, adversarial vulnerabilities, and interpretability challenges, while outlining future research opportunities such as privacy-preserving ML, explainable AI, and edge-based security frameworks. By synthesizing insights from recent advancements, this paper provides a structured framework for developing robust, intelligent, and adaptive IoT security solutions. The findings aim to guide researchers and practitioners in designing next-generation cybersecurity models capable of effectively countering emerging threats in IoT ecosystems.","url":"https://pubmed.ncbi.nlm.nih.gov/40968811/","authors":["Alfahaid A","Alalwany E","Almars AM","Alharbi F","Atlam E","Mahgoub I"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 May 26","doi":"10.3390/s25113341","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40958902","name":"A Comprehensive Review of Predictive Precision in Scar Medicine: From Molecular Predictors to Machine Learning Models.","source":"pubmed","abstract":"Scars-including keloids, hypertrophic scars, and acne scars-pose substantial functional and psychosocial burdens that current empirical treatments often address by trial-and-error. Quantitative evidence now supports a precision framework. Validated clinical tools (eg, VSS, POSAS) and imaging modalities (3D photogrammetry; high-frequency ultrasound elastography) provide objective baselines, while emerging AI models deliver measurable gains: an automated scar-type classifier achieved precision 80.7%, recall 71.0%, AUC 0.846 for image-based categorization, and a clinical recurrence model for keloids reported AUC 0.889 with sensitivity 78.7% and specificity 86.8%, enabling earlier risk-stratified interventions and fewer ineffective treatment cycles in model-informed pathways. We synthesize cytokine/fibroblast signatures and genetic predisposition with multimodal (clinical-imaging-molecular) learning, detail validation challenges, and propose actionable safeguards (TRIPOD+AI-aligned reporting, internal-external validation, bias audits, SHAP-based interpretability, and federated learning to preserve privacy and improve generalizability). A pragmatic roadmap-including funding mechanisms, stakeholder roles, and a barrier-solution matrix-aims to accelerate translation toward predictive, preventive, and personalized scar care.","url":"https://pubmed.ncbi.nlm.nih.gov/40958902/","authors":["Su J","Chen J","Wang T","Lin T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.2147/CCID.S542866","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40946521","name":"A comprehensive review of techniques, algorithms, advancements, challenges, and clinical applications of multi-modal medical image fusion for improved diagnosis.","source":"pubmed","abstract":"Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT, PET, SPECT, and ultrasound to create detailed, clinically useful images of patient anatomy and pathology. These integrated representations significantly advance diagnostic accuracy, lesion detection, and segmentation. This comprehensive review meticulously surveys the evolution, methodologies, algorithms, current advancements, and clinical applications of MMIF. We present a critical comparative analysis of traditional fusion approaches, including pixel-, feature-, and decision-level methods, and delves into recent advancements driven by deep learning, generative models, and transformer-based architectures. A critical comparative analysis is presented between these conventional methods and contemporary techniques, highlighting differences in robustness, computational efficiency, and interpretability. The article addresses extensive clinical applications across oncology, neurology, and cardiology, demonstrating MMIF's vital role in precision medicine through improved patient-specific therapeutic outcomes. Moreover, the review thoroughly investigates the persistent challenges affecting MMIF's broad adoption, including issues related to data privacy, heterogeneity, computational complexity, interpretability of AI-driven algorithms, and integration within clinical workflows. It also identifies significant future research avenues, such as the integration of explainable AI, adoption of privacy-preserving federated learning frameworks, development of real-time fusion systems, and standardization efforts for regulatory compliance. This review organizes key knowledge, outlines challenges, and highlights opportunities, guiding researchers, clinicians, and developers in advancing MMIF for routine clinical use and promoting personalized healthcare. To support further research, we provide a GitHub repository that includes popular multi-modal medical imaging datasets along with recent models in our shared GitHub repository.","url":"https://pubmed.ncbi.nlm.nih.gov/40946521/","authors":["Zubair M","Hussain M","Albashrawi MA","Bendechache M","Owais M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1016/j.cmpb.2025.109014","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40945670","name":"A machine learning approach for automating review of a RxNorm medication mapping pipeline output.","source":"pubmed","abstract":"Medication mapping to standardized terminologies is an important prerequisite for performing analytics on a federated EHR network. TriNetX LLC operates the largest such network in the world.","url":"https://pubmed.ncbi.nlm.nih.gov/40945670/","authors":["Hüser M","Doole J","Pinho V","Rouhizadeh H","Teodoro D","Saiyed A","Palchuk MB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct","doi":"10.1016/j.jbi.2025.104909","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40942695","name":"AI-Powered Building Ecosystems: A Narrative Mapping Review on the Integration of Digital Twins and LLMs for Proactive Comfort, IEQ, and Energy Management.","source":"pubmed","abstract":"Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber-physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10-35% while maintaining an operative temperature within &#xb1;0.5 &#xb0;C and CO 2 below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within &#xb1;0.5 &#xb0;C of the setpoint and indoor CO 2 near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87-97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy-security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems.","url":"https://pubmed.ncbi.nlm.nih.gov/40942695/","authors":["Amangeldy B","Tasmurzayev N","Imankulov T","Baigarayeva Z","Izmailov N","Riza T","Abdukarimov A","Mukazhan M","Zhumagulov B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Aug 24","doi":"10.3390/s25175265","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40942655","name":"Intention Prediction for Active Upper-Limb Exoskeletons in Industrial Applications: A Systematic Literature Review.","source":"pubmed","abstract":"Intention prediction is essential for enabling intuitive and adaptive control in upper-limb exoskeletons, especially in dynamic industrial environments. However, the suitability of different cues, sensors, and computational models for real-world industrial applications remains unclear. This systematic review, conducted according to PRISMA guidelines, analyzes 29 studies published between 2007 and 2024 that investigate intention prediction in active exoskeletons. Most studies rely on motion capture (14) and electromyography (14) to estimate joint torque or trajectories, predicting from 450 ms before to 660 ms after motion onset. Approaches include model-based and model-free regression, as well as classification methods, but vary significantly in complexity, sensor setups, and evaluation procedures. Only a subset evaluates usability or support effectiveness, often under laboratory conditions with small, non-representative participant groups. Based on these insights, we outline recommendations for robust and adaptable intention prediction tailored to industrial task requirements. We propose four generalized support modes to guide sensor selection and control strategies in practical applications. Future research should leverage wearable sensors, integrate cognitive and contextual cues, and adopt transfer learning, federated learning, or LLM-based feedback mechanisms. Additionally, studies should prioritize real-world validation, diverse participant samples, and comprehensive evaluation metrics to support scalable, acceptable deployment of exoskeletons in industrial settings.","url":"https://pubmed.ncbi.nlm.nih.gov/40942655/","authors":["Hochreiter D","Schmermbeck K","Vazquez-Pufleau M","Ferscha A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Aug 22","doi":"10.3390/s25175225","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40940896","name":"Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions.","source":"pubmed","abstract":"Gynecological cancer, especially breast, cervical, and ovarian cancer, are significant health issues affecting women worldwide. When screened they are mostly detected at later stages because of non-specific signs and symptoms as well as the unavailability of reliable screening methods. The improvement of early oncologic prediction methods is therefore needed to work out the survival rates, guide individualized treatment, and relieve healthcare pressures. Outcome forecasting and clinical detection are rapidly changing with the use of machine learning (ML), one of the promising technologies used to analyze complex biomedical data. Artificial intelligence (AI)-based ML models are capable of determining low-level trends and making accurate predictions of disease risk and outcomes, because they can combine different datasets (clinical records, genomics, proteomics, medical imaging) and learn to identify subtle patterns. Standard algorithms, including support vector machines, random forests, and deep learning (DL) models, such as convolutional neural networks, have demonstrated high potential in identifying the type of cancer, monitoring disease progression, and designing treatment patterns. This manuscript reviews the recent developments in the use of ML models to advance oncologic prediction tasks in gynecologic oncology. It reports on critical domains, like screening, risk classification, and survival modeling, as well as comments on difficulties, like data inconsistency, inability of interpretation of models, and issues of clinical interpretation. New developments, such as explainable AI, federated learning (FL), and multi-omics fusion, are discussed to develop these models and to make them applicable in practice because of their reliability. Conclusively, this article emphasizes the transformative role of ML in precision oncology to deliver improved, patient-centered outcomes to women who are victims of gynecological cancers.","url":"https://pubmed.ncbi.nlm.nih.gov/40940896/","authors":["Garg P","Krishna M","Kulkarni P","Horne D","Salgia R","Singhal SS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Aug 27","doi":"10.3390/cancers17172799","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40930127","name":"The Role of Generative Artificial Intelligence and Large Language Models in Atrial Fibrillation: Clinical Research and Decision Support.","source":"pubmed","abstract":"Atrial fibrillation (AF) is a prevalent and complex cardiac arrhythmia requiring multifaceted management strategies. This review explores the integration of large language models (LLMs) and machine learning into AF care, with a focus on clinical utility, privacy preservation, and ethical deployment. Federated and transfer learning methods have enabled high-performance predictive modeling across distributed datasets without compromising data security. LLMs enhance decision-making by synthesizing structured and unstructured data within electronic health records, supporting anticoagulation decisions, risk stratification, and treatment optimization. Additionally, these tools reduce clinician burden through automated documentation and improve patient engagement via personalized communication, chatbots, and remote monitoring platforms. Despite promising outcomes, challenges such as algorithmic bias, hallucinations, outdated knowledge, and limited explainability persist. Regulatory frameworks remain underdeveloped for continuously learning models, necessitating stronger oversight. Future directions emphasize the creation of cardiology-specific LLMs, multimodal data integration, and inclusive co-development with stakeholders. Overall, artificial intelligence-enabled tools show significant potential to improve precision, efficiency, and equity in AF care, provided their deployment remains ethically grounded and clinically validated.","url":"https://pubmed.ncbi.nlm.nih.gov/40930127/","authors":["Tran HH","Thu A","Twayana AR","Fuertes A","Gonzalez M","Basta M","James M","Frishman WH","Aronow WS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 10","doi":"10.1097/CRD.0000000000001042","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40929038","name":"Deep learning methods and applications in single-cell multimodal data integration.","source":"pubmed","abstract":"The integration of multimodal single-cell omics data is a state-of-art strategy for deciphering cellular heterogeneity and gene regulatory mechanisms. Recent advances in single-cell technologies have enabled the comprehensive characterization of cellular states and their interactions. However, integrating these high-dimensional and heterogeneous datasets poses significant computational challenges, including batch effects, sparsity, and modality alignment. Deep learning has shown great promise in addressing these issues through neural network-based frameworks, including variational autoencoders (VAEs) and graph neural networks (GNNs). In this Review, we examine cutting-edge deep learning methodologies for integrating single-cell multimodal data, discussing their architectures, applications, and limitations. We highlight key tools such as sciCAN, scJoint, and scMaui, which use deep learning techniques to harmonize various omics layers, improve feature extraction, and improve downstream biological analyses. Despite significant advancements, it remains challenging to ensure model interpretability, scalability, and generalizability across different datasets. Future directions of research in this field include the development of self-supervised learning strategies, transformer-based architectures, and federated learning frameworks to enhance the robustness and reproducibility of single-cell multi-omics integration.","url":"https://pubmed.ncbi.nlm.nih.gov/40929038/","authors":["Nunes FVM","Behrens LMP","Weimer RD","Gonçalves GF","da Silva Fernandes G","Dorn M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec 1","doi":"10.1039/d5mo00062a","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40921101","name":"Applications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review.","source":"pubmed","abstract":"Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.","url":"https://pubmed.ncbi.nlm.nih.gov/40921101/","authors":["Guo D","Choo KR"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 8","doi":"10.2196/68291","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40919316","name":"Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects.","source":"pubmed","abstract":"Protein kinases are central regulators of cell signaling and play pivotal roles in a wide array of diseases, most notably cancer and autoimmune disorders. The clinical success of kinase inhibitors-such as imatinib and osimertinib-has firmly established kinases as valuable drug targets. However, the development of selective, potent inhibitors remains challenging due to the conserved nature of the ATP-binding site, off-target effects, resistance mutations, and patient-specific variability. Recent advances in artificial intelligence (AI) and machine learning (ML) offer transformative solutions to these obstacles across the drug discovery pipeline. This review explores how AI/ML methods, including deep learning, graph neural networks, and generative models, are revolutionizing the design, optimization, and repurposing of kinase inhibitors. We detail applications in target identification, virtual screening, structure-activity relationship modeling, resistance prediction, and clinical trial design. Representative case studies-such as AI-optimized BTK and EGFR inhibitors-highlight real-world impact. We also examine current limitations, including data sparsity, model interpretability, and translational gaps between in silico and experimental results. Finally, we discuss emerging directions such as federated learning, personalized kinase inhibitors, and AI-enabled combination therapies. By integrating computational innovation with medicinal chemistry, AI/ML holds immense promise to accelerate and refine the next generation of kinase-targeted therapeutics.","url":"https://pubmed.ncbi.nlm.nih.gov/40919316/","authors":["Elgawish MS","Almatary AM","Zaitone SA","Salem MSH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct 15","doi":"10.1039/d5md00494b","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40905080","name":"Detecting the Undetected: Machine Learning in Early Disease Diagnosis.","source":"pubmed","abstract":"Early detection of diseases is a critical pillar in advancing modern healthcare, offering timely interventions and better patient outcomes. This overview highlights a range of machine learning (ML) approaches that are transforming early disease diagnosis. We discuss how traditional supervised and unsupervised methods, alongside advanced deep learning and reinforcement learning techniques, are utilized to detect early disease markers, often before clinical symptoms appear. The paper begins with a discussion of ML fundamentals within healthcare, along with standard evaluation metrics such as accuracy, precision, recall, F1-score and AUC-ROC. It then explores various ML models, including supervised algorithms (support vector machines, decision trees and random forests), unsupervised methods (K-means, hierarchical clustering and principal component analysis) and deep learning architectures (convolutional neural networks, recurrent neural networks and transformers). Reinforcement learning's emerging role in healthcare is also examined. Practical applications across disease areas such as cancer, cardiovascular diseases, neurological disorders and infectious diseases are reviewed. We emphasize the importance of high-quality datasets, balanced data distribution and clinical relevance. Key challenges such as data scarcity, model interpretability, privacy, the risk of overdiagnosis and clinical integration are critically discussed. It underscores that the successful translation of these technologies from code to clinic hinges on a deep, bidirectional collaboration between data scientists and clinical experts to ensure that newly developed tools address real-world patient needs. The overview concludes with future directions, including explainable AI, federated learning, multimodal data fusion, real-time applications and quantum ML, charting the evolving path of early disease detection.","url":"https://pubmed.ncbi.nlm.nih.gov/40905080/","authors":["Rathi K","Sharma S","Barnwal A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct","doi":"10.1111/bcpt.70104","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40904495","name":"Radiotherapy for primary bone tumors: current techniques and integration of artificial intelligence-a review.","source":"pubmed","abstract":"Primary bone tumours remain among the most challenging indications in radiation oncology-not because of anatomical size or distribution, but because curative intent demands ablative dosing alongside stringent normal-tissue preservation. Over the past decade, the therapeutic landscape has shifted markedly. Proton and carbon-ion centres now report durable local control with acceptable late toxicity in unresectable sarcomas. MR-guided linear accelerators enable on-table anatomical visualisation and daily adaptation, permitting margin reduction without prolonging workflow. Emerging ultra-high-dose-rate (FLASH) strategies may further spare healthy bone marrow while preserving tumour lethality; first-in-human studies are underway. Beyond hardware, artificial-intelligence pipelines accelerate contouring, automate plan optimisation, and integrate multi-omics signatures with longitudinal imaging to refine risk stratification in real time. Equally important, privacy-preserving federated learning consortia are beginning to pool sparse datasets across institutions, addressing chronic statistical under-power in rare tumours. Appreciating these convergent innovations is essential for clinicians deciding when and how to escalate dose, for physicists designing adaptive protocols, and for investigators planning the next generation of biology-driven trials. This narrative review synthesises recent technical and translational advances and outlines practical considerations, evidence gaps, and research priorities on the path to truly individualised, data-intelligent radiotherapy for primary bone tumours.","url":"https://pubmed.ncbi.nlm.nih.gov/40904495/","authors":["Tong J","Chen D","Li J","Chen H","Yu T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fonc.2025.1648849","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40892192","name":"A robust sampling technique for realistic distribution simulation in federated learning.","source":"pubmed","abstract":"Federated Learning helps training deep learning networks with diverse data from different locations, particularly in restricted clinical settings. However, label distributions overlapping only partially across clients, due to different demographics, may significantly harm the global training, and thus local model performance. Investigating such effects before rolling out large-scale Federated Learning setups requires proper sampling of the expected label distributions.","url":"https://pubmed.ncbi.nlm.nih.gov/40892192/","authors":["Hoepp R","Rist L","Katzmann A","Ashok R","Wimmer A","Sühling M","Maier A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar","doi":"10.1007/s11548-025-03504-z","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40871763","name":"Computational Architectures for Precision Dairy Nutrition Digital Twins: A Technical Review and Implementation Framework.","source":"pubmed","abstract":"Sensor-enabled digital twins (DTs) are reshaping precision dairy nutrition by seamlessly integrating real-time barn telemetry with advanced biophysical simulations in the cloud. Drawing insights from 122 peer-reviewed studies spanning 2010-2025, this systematic review reveals how DT architectures for dairy cattle are conceptualized, validated, and deployed. We introduce a novel five-dimensional classification framework-spanning application domain, modeling paradigms, computational topology, validation protocols, and implementation maturity-to provide a coherent comparative lens across diverse DT implementations. Hybrid edge-cloud architectures emerge as optimal solutions, with lightweight CNN-LSTM models embedded in collar or rumen-bolus microcontrollers achieving over 90% accuracy in recognizing feeding and rumination behaviors. Simultaneously, remote cloud systems harness mechanistic fermentation simulations and multi-objective genetic algorithms to optimize feed composition, minimize greenhouse gas emissions, and balance amino acid nutrition. Field-tested prototypes indicate significant agronomic benefits, including 15-20% enhancements in feed conversion efficiency and water use reductions of up to 40%. Nevertheless, critical challenges remain: effectively fusing heterogeneous sensor data amid high barn noise, ensuring millisecond-level synchronization across unreliable rural networks, and rigorously verifying AI-generated nutritional recommendations across varying genotypes, lactation phases, and climates. Overcoming these gaps necessitates integrating explainable AI with biologically grounded digestion models, federated learning protocols for data privacy, and standardized PRISMA-based validation approaches. The distilled implementation roadmap offers actionable guidelines for sensor selection, middleware integration, and model lifecycle management, enabling proactive rather than reactive dairy management-an essential leap toward climate-smart, welfare-oriented, and economically resilient dairy farming.","url":"https://pubmed.ncbi.nlm.nih.gov/40871763/","authors":["Rao S","Neethirajan S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Aug 8","doi":"10.3390/s25164899","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40871015","name":"Artificial Intelligence-Driven Strategies for Targeted Delivery and Enhanced Stability of RNA-Based Lipid Nanoparticle Cancer Vaccines.","source":"pubmed","abstract":"The convergence of artificial intelligence (AI) and nanomedicine has transformed cancer vaccine development, particularly in optimizing RNA-loaded lipid nanoparticles (LNPs). Stability and targeted delivery are major obstacles to the clinical translation of promising RNA-LNP vaccines for cancer immunotherapy. This systematic review analyzes the AI's impact on LNP engineering through machine learning-driven predictive models, generative adversarial networks (GANs) for novel lipid design, and neural network-enhanced biodistribution prediction. AI reduces the therapeutic development timeline through accelerated virtual screening of millions of lipid combinations, compared to conventional high-throughput screening. Furthermore, AI-optimized LNPs demonstrate improved tumor targeting. GAN-generated lipids show structural novelty while maintaining higher encapsulation efficiency; graph neural networks predict RNA-LNP binding affinity with high accuracy vs. experimental data; digital twins reduce lyophilization optimization from years to months; and federated learning models enable multi-institutional data sharing. We propose a framework to address key technical challenges: training data quality (min. 15,000 lipid structures), model interpretability (SHAP &gt; 0.65), and regulatory compliance (21CFR Part 11). AI integration reduces manufacturing costs and makes personalized cancer vaccine affordable. Future directions need to prioritize quantum machine learning for stability prediction and edge computing for real-time formulation modifications.","url":"https://pubmed.ncbi.nlm.nih.gov/40871015/","authors":["Bhujel R","Enkmann V","Burgstaller H","Maharjan R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul 30","doi":"10.3390/pharmaceutics17080992","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40870498","name":"Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status: Where Are We?","source":"pubmed","abstract":"The 2021 WHO classification of brain tumours revolutionised the oncological field by emphasising the role of molecular, genetic and pathogenetic advances in classifying brain tumours. In this context, incidental gliomas have been increasingly identified due to the widespread performance of standard and advanced MRI sequences and represent a diagnostic and therapeutic challenge. The impactful decision to perform a surgical procedure deeply relies on the non-invasive identification of features or parameters that may correlate with brain tumour genetic profile and grading. Therefore, it is paramount to reach an early and proper diagnosis through neuroradiological techniques, such as MRI. Standard MRI sequences are the cornerstone of diagnosis, while consolidated and emerging roles have been awarded to advanced sequences such as Diffusion-Weighted Imaging/Apparent Diffusion Coefficient (DWI/ADC), Perfusion-Weighted Imaging (PWI), Magnetic Resonance Spectroscopy (MRS), Diffusion Tensor Imaging (DTI) and functional MRI (fMRI). The current novelty relies on the application of AI in brain neuro-oncology, mainly based on radiomics and radiogenomics models, which enhance standard and advanced MRI sequences in predicting glioma genetic status by identifying the mutation of multiple key biomarkers deeply impacting patients' diagnosis, prognosis and treatment, such as IDH, EGFR, TERT, MGMT promoter, p53, H3-K27M, ATRX, Ki67 and 1p19. AI-driven models demonstrated high accuracy in glioma detection, grading, prognostication, and pre-surgical planning and appear to be a promising frontier in the neuroradiological field. On the other hand, standardisation challenges in image acquisition, segmentation and feature extraction variability, data scarcity and single-omics analysis, model reproducibility and generalizability, the black box nature and interpretability concerns, as well as ethical and privacy challenges remain key issues to address. Future directions, rooted in enhanced standardisation and multi-institutional validation, advancements in multi-omics integration, and explainable AI and federated learning, may effectively overcome these challenges and promote efficient AI-based models in glioma management. The aims of our multidisciplinary review are to: (1) extensively present the role of standard and advanced MRI sequences in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (2) give an overview of the current and main applications of AI tools in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (3) show the role of MRI, radiomics and radiogenomics in unravelling glioma genetic profiles. Standard and advanced MRI, radiomics and radiogenomics are key to unveiling the grading and genetic profile of gliomas and supporting the pre-operative planning, with significant impact on patients' differential diagnosis, prognosis prediction and treatment strategies. Today, neuroradiologists are called to efficiently use AI tools for the in vivo, non-invasive, and comprehensive assessment of gliomas in the path towards patients' personalised medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/40870498/","authors":["Guarnera A","Ius T","Romano A","Bagatto D","Denaro L","Aiudi D","Iacoangeli M","Palmieri M","Frati A","Santoro A","Bozzao A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Aug 12","doi":"10.3390/medicina61081453","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40864481","name":"[Artificial intelligence-enhanced ECG interpretation: a new era for electrocardiography?].","source":"pubmed","abstract":"Artificial intelligence (AI) is redefining ECG interpretation, transforming it from a static diagnostic tool into a dynamic, predictive, and integrative instrument. Although widespread, traditional rule-based ECG analysis has limitations in accuracy and adaptability, especially in complex clinical settings. In contrast, AI-driven models, particularly those employing machine learning and deep learning architectures, have demonstrated improved diagnostic performance across a broad spectrum of cardiovascular diseases, including atrial fibrillation, acute myocardial infarction, hypertrophic cardiomyopathy, and valvular heart disease. Notably, AI-ECG is now able to detect subclinical ventricular dysfunction, stratify long-term risk, and anticipate major adverse events before overt clinical manifestations occur. In addition to diagnosis, AI-ECG is emerging as a decision support tool in scenarios characterized by diagnostic uncertainty, such as syncope and cardio-oncology, and may significantly optimize triage and resource allocation. Multiparametric approaches further extend its utility, enabling simultaneous prediction of structural, functional, and electrical cardiac parameters. Wearable devices integrated with AI improve continuous monitoring and may decentralize arrhythmia detection and sudden cardiac death prevention. Despite these advances, critical challenges remain. Poorly explainable AI models, algorithmic bias, overfitting, data governance, and regulatory uncertainty demand rigorous methodological scrutiny. In this framework, federated learning architectures may enable continuous multicenter model refinement and enhance methodological robustness while safeguarding data privacy. The European AI Act and methodological checklists promoted by scientific societies offer a framework to address these issues, fostering transparency, equity, and clinical validity. If validated and implemented responsibly, AI-enhanced ECG has the potential to enhance - not replace - clinical reasoning, advancing a precision medicine paradigm based on both technological innovation and human expertise.","url":"https://pubmed.ncbi.nlm.nih.gov/40864481/","authors":["Ricci F","Rizzuto ML","Bisaccia G","Mansour D","Gallina S","Sciarra L","Bagliani G","Dello Russo A","Mortara A","Ciliberti G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep","doi":"10.1714/4542.45427","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40835040","name":"Multimodal large language models for medical image diagnosis: Challenges and opportunities.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) into radiology has significantly improved diagnostic accuracy and workflow efficiency. Multimodal large language models (MLLMs), which combine natural language processing (NLP) and computer vision techniques, hold the potential to further revolutionize medical image analysis. Despite these advances, their widespread clinical adoption of MLLMs remains limited by challenges such as data quality, interpretability, ethical and regulatory compliance- including adherence to frameworks like the General Data Protection Regulation (GDPR) - computational demands, and generalizability across diverse patient populations. Addressing these interconnected challenges presents opportunities to enhance MLLM performance and reliability. Priorities for future research include improving model transparency, safeguarding data privacy through federated learning, optimizing multimodal fusion strategies, and establishing standardized evaluation frameworks. By overcoming these barriers, MLLMs can become essential tools in radiology, supporting clinical decision-making, and improving patient outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/40835040/","authors":["Zhang A","Zhao E","Wang R","Zhang X","Wang J","Chen E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep","doi":"10.1016/j.jbi.2025.104895","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40823583","name":"Artificial intelligence in acupuncture: bridging traditional knowledge and precision integrative medicine.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) into acupuncture research is accelerating the transformation of this traditional, experience-based practice into a data-driven, precision discipline. This review synthesizes recent advances in AI-enabled outcome prediction techniques, encompassing deep learning, meta-analytic modeling, natural language processing (NLP), computer vision, and neuroimaging-based analysis. For instance, convolutional neural networks (CNNs) have been successfully applied to classify tongue images and detect ZHENG patterns, while transformer-based NLP models enable automated extraction of clinical knowledge from classical texts. These technologies improve diagnostic objectivity, standardize treatment planning, and facilitate individualized care by enabling longitudinal efficacy modeling and real-time monitoring. Despite their potential, current implementations are constrained by limited and heterogeneous datasets, annotation variability, and gaps in clinical validation. We analyze key methodological innovations and challenges, and recommend future directions including the construction of federated multimodal data platforms, development of explainable AI frameworks, and promotion of open science practices. This convergence of AI and acupuncture presents a unique opportunity to enhance scientific rigor, clinical utility, and global integration of acupuncture within the paradigm of precision integrative medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/40823583/","authors":["Hou GL","Dong BQ","Yu BX","Dai JY","Lin XX","Cheng ZZ"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fmed.2025.1633416","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40815323","name":"Multimodal quantitative analysis guides precise preoperative localization of epilepsy.","source":"pubmed","abstract":"Epilepsy surgery efficacy is critically contingent upon the precise localization of the epileptogenic zone (EZ). However, conventional qualitative methods face challenges in achieving accurate localization, integrating multimodal data, and accounting for variations in clinical expertise among practitioners. With the rapid advancement of artificial intelligence and computing power, multimodal quantitative analysis has emerged as a pivotal approach for EZ localization. Nonetheless, no research team has thus far provided a systematic elaboration of this concept. This narrative review synthesizes recent advancements across four key dimensions: (1) seizure semiology quantification using deep learning and computer vision to analyze behavioral patterns; (2) structural neuroimaging leveraging high-field MRI, radiomics, and AI; (3) functional imaging integrating EEG-fMRI dynamics and PET biomarkers; and (4) electrophysiological quantification encompassing source localization, intracranial EEG, and network modeling. The convergence of these complementary approaches enables comprehensive characterization of epileptogenic networks across behavioral, structural, functional, and electrophysiological domains. Despite these advancements, clinical heterogeneity, limitations in algorithmic generalizability, and barriers to data sharing hinder translation into clinical practice. Future directions emphasize personalized modeling, federated learning, and cross-modal standardization to advance data-driven localization. This integrated paradigm holds promise for overcoming qualitative limitations, reducing medical costs, and improving seizure-free outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/40815323/","authors":["Shen Y","Shen Z","Huang Y","Wu Z","Ma Y","Hu F","Shu K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Aug 15","doi":"10.1007/s00415-025-13324-5","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40808567","name":"- Invited Review - Advancing precision livestock farming: integrating artificial intelligence and emerging technologies for sustainable livestock management.","source":"pubmed","abstract":"Precision Livestock Farming (PLF) has evolved dramatically from basic monitoring systems to sophisticated artificial intelligence (AI)-driven decision support systems that enhance livestock management efficiency, sustainability, and animal welfare. This review examines the technological evolution of PLF since 2017, highlighting significant advancements in sensing technologies, computer vision, and AI. Non-invasive technologies, including red-green-blue and depth cameras, 3D imaging systems, and Internet of Thingsenabled platforms, now capture detailed biometric and behavioral data in real time, while AI algorithms enable early disease detection, optimize feeding strategies, and improve reproductive management. Integrating these technologies with mechanistic models has created hybrid intelligent frameworks that address longstanding challenges in precision nutrition modeling. Future PLF development will likely focus on integrating large language models, adopting federated learning approaches to address data privacy concerns, and democratizing technologies for small-scale producers. Despite technological progress, challenges remain regarding data standardization, connectivity in rural environments, high implementation costs, and ethical considerations around increased animal monitoring. By fostering interdisciplinary collaboration among animal scientists, engineers, computer scientists, and social scientists, PLF can continue to drive sustainable and efficient practices in livestock production while ensuring that technologies complement rather than replace traditional husbandry knowledge.","url":"https://pubmed.ncbi.nlm.nih.gov/40808567/","authors":["Tedeschi LO","Guarnido-Lopez P","Menendez Iii HM","Seo S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr","doi":"10.5713/ab.25.0289","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40807775","name":"Optical Sensor-Based Approaches in Obesity Detection: A Literature Review of Gait Analysis, Pose Estimation, and Human Voxel Modeling.","source":"pubmed","abstract":"Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesity-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms-including markerless frameworks like OpenPose and MediaPipe-track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist-hip ratio, through infrared-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse populations, and scalability for widespread deployment in existing health workflows. Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. Future priorities involve standardizing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shifting obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/40807775/","authors":["Dhaouadi S","Khelifa MMB","Balti A","Duché P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul 25","doi":"10.3390/s25154612","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40804880","name":"Machine Learning-Powered Smart Healthcare Systems in the Era of Big Data: Applications, Diagnostic Insights, Challenges, and Ethical Implications.","source":"pubmed","abstract":"Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers.","url":"https://pubmed.ncbi.nlm.nih.gov/40804880/","authors":["Rani S","Kumar R","Panda BS","Kumar R","Muften NF","Abass MA","Lozanović J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul 30","doi":"10.3390/diagnostics15151914","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40789738","name":"Meta-Analysis and Federated Learning over Decentralized Distributed Research Networks.","source":"pubmed","abstract":"Distributed research networks have transformed modern clinical research by enabling large-scale, multi-institutional collaborations while maintaining patient privacy. Two prominent methodologies within these frameworks-meta-analysis and federated learning-address the challenges of synthesizing evidence from decentralized data. Meta-analysis aggregates study-level results to provide robust, interpretable estimates, making it a cornerstone of evidence synthesis for association studies. Federated learning complements this by enabling complex downstream tasks, such as predictive modeling and counterfactual inference, while preserving data privacy through privacy-preserving distributed algorithms. Federated learning facilitates communication-efficient computation and adapts seamlessly to heterogeneous datasets across diverse institutions. This review emphasizes the complementary strengths of federated learning's scalability, flexibility, and readiness for implementation alongside meta-analysis's robust frameworks for evidence synthesis and aggregation in clinical research. Integrations of synthetic data, artificial intelligence (AI)-enhanced harmonization, and hybrid human-AI frameworks are proposed as future directions, promising to further advance both methodologies and enhance their combined impact on privacy-conscious, data-driven healthcare research.","url":"https://pubmed.ncbi.nlm.nih.gov/40789738/","authors":["Lu Y","Zhang B","Tong J","Chen Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Aug","doi":"10.1146/annurev-biodatasci-103123-094441","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40771296","name":"Artificial Intelligence in Cardiovascular Imaging: Current Landscape, Clinical Impact, and Future Directions.","source":"pubmed","abstract":"Cardiovascular (CV) imaging is rapidly transforming with the advent of artificial intelligence (AI), automating and augmenting diagnostic pipelines in echocardiography, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear imaging. In this review, we summarize recent developments in convolutional neural networks for real-time echocardiographic interpretation, deep learning for coronary artery calcium scoring that achieves near-perfect agreement with manual methods, and AI-driven plaque quantification and stenosis detection on coronary CT angiography, which achieves an accuracy of &#x2265; 96%. FDA-approved platforms (e.g., Aidoc, HeartFlow, Caption Health) emphasize clinical translation, while automated segmentation and perfusion analysis in cardiac MRI produce Dice coefficients &#x2265; 0.93. We critically analyze persistent issues, algorithmic bias, explainability, data privacy, regulatory heterogeneity, and medico-legal liability. We also discuss risk-reduction tactics, such as federated learning and human-in-the-loop oversight. Reactive diagnostics will allow proactive, personalized treatment in the future, assuming we look ahead, thanks to multimodal AI, wearable sensors, and predictive analytics. For AI to fully optimize cardiovascular care, thorough validation, open algorithmic design, and interdisciplinary cooperation will be necessary.","url":"https://pubmed.ncbi.nlm.nih.gov/40771296/","authors":["Edpuganti S","Shamim A","Gangolli VH","Weerasekara RADKN","Yellamilli A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Apr-Jun","doi":"10.15190/d.2025.10","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40771216","name":"Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions.","source":"pubmed","abstract":"Artificial Intelligence (AI) is emerging as a key driver at the intersection of nutrition and food systems, offering scalable solutions for precision health, smart manufacturing, and sustainable development. This study aims to present a comprehensive review of AI-driven innovations that enable precision nutrition through real-time dietary recommendations, meal planning informed by individual biological markers ( e.g ., blood glucose or cholesterol levels), and adaptive feedback systems. It further examines the integration of AI technologies in food production, such as machine learning-based quality control, predictive maintenance, and waste minimization, to support circular economy goals and enhance food system resilience. Drawing on advances in deep learning, federated learning, and computer vision, the review outlines how AI transforms static, population-level dietary models into dynamic, data-informed frameworks tailored to individual needs. The paper also addresses critical challenges related to algorithmic transparency, data privacy, and equitable access, and proposes actionable pathways for ethical and scalable implementation. By bridging healthcare, nutrition, and industrial domains, this study offers a forward-looking roadmap for leveraging AI to build intelligent, inclusive, and sustainable food-health ecosystems.","url":"https://pubmed.ncbi.nlm.nih.gov/40771216/","authors":["Agrawal K","Goktas P","Kumar N","Leung MF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fnut.2025.1636980","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40768608","name":"Next-generation smart wound dressings: AI integration, biosensors, and electrospun nanofibers for chronic wound therapy.","source":"pubmed","abstract":"Polymeric biomaterials, particularly electrospun nanofibers, are increasingly central to the development of advanced wound dressings capable of supporting tissue regeneration while enabling real-time physiological monitoring. Chronic wounds associated with diabetes, vascular diseases, and cancer require continuous and personalized management, prompting the convergence of electrospun polymeric scaffolds with wearable biosensors and artificial intelligence (AI). These next-generation smart wound dressings utilize biocompatible polymer matrices functionalized with responsive sensing elements to monitor pH, temperature, moisture, oxygen saturation, and inflammatory biomarkers in situ . Molecular-level interactions between polymeric components and biological tissues facilitate both therapeutic delivery and diagnostic functionality. AI, including deep and federated learning, enhances these systems by enabling data-driven prediction of healing trajectories and personalized interventions. Key advances in flexible electronics, self-powered systems, and closed-loop feedback mechanisms further enhance clinical applicability. However, challenges remain, including the biochemical stability of sensors in enzyme-rich environments, secure wireless communication, and the lack of standardized datasets and clinical validation frameworks. This review critically examines recent progress in AI-integrated polymeric wound care systems, emphasizing the design of functional polymeric scaffolds, biosensor-polymer interfaces, and future directions, including biosensor miniaturization, multi-omics data integration, and scalable cloud-based platforms. A collaborative roadmap is proposed to advance these intelligent biomaterial systems toward clinical translation in chronic wound care.","url":"https://pubmed.ncbi.nlm.nih.gov/40768608/","authors":["Palani N","Mendonce KC","Syed Altaf RR","Mohan A","Surya P","P M","Radhakrishnan K","Subramaniyan V","Rajadesingu S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr","doi":"10.1080/09205063.2025.2540362","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40765429","name":"Artificial intelligence in muscle-invasive bladder cancer: opportunities, challenges, and clinical impact.","source":"pubmed","abstract":"Muscle-invasive bladder cancer (MIBC) represents an aggressive malignancy with significant morbidity and mortality. Recent advances in artificial intelligence (AI) offer promising opportunities to enhance patient care across the entire MIBC management spectrum. This comprehensive review examines the current state and future potential of AI applications in MIBC, from diagnosis through treatment to response assessment.","url":"https://pubmed.ncbi.nlm.nih.gov/40765429/","authors":["Mastroleo F","Marvaso G","Jereczek-Fossa BA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 1","doi":"10.1097/MOU.0000000000001309","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40742577","name":"Artificial intelligence in hepatopancreatobiliary surgery for clinical outcome prediction: current perspective and future direction.","source":"pubmed","abstract":"The expanding and evolving role of artificial intelligence (AI) in surgery has been enhanced by the adoption of robotic-assisted surgery (RAS), which provides a platform to facilitate the integration and utilisation of AI technologies. One area where AI is likely to be particularly valuable is outcome prediction using deep learning models (DLMs). This narrative review examines DLMs in hepatopancreatobiliary (HPB) surgery, highlighting their role in predicting postoperative complications and surgical complexity with an increased level of accuracy compared with traditional methods. In addition to reviewing existing literature, this article offers a forward-looking perspective on emerging innovations such as real-time intraoperative guidance, federated learning for global collaboration, and the development of explainable AI frameworks. By addressing challenges related to data quality, model generalisability, and ethical implementation, AI has the potential to transform HPB surgery and deliver more personalised, precise, and equitable care.","url":"https://pubmed.ncbi.nlm.nih.gov/40742577/","authors":["Latif J","Garcea G","Dennison A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul 31","doi":"10.1007/s11701-025-02617-6","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40740956","name":"Artificial intelligence and machine learning in acute respiratory distress syndrome management: recent advances.","source":"pubmed","abstract":"Acute Respiratory Distress Syndrome (ARDS) remains a critical challenge in intensive care, marked by high mortality and significant patient heterogeneity, which limits the effectiveness of conventional supportive therapies. This review highlights the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in revolutionizing ARDS management. We explore diverse AI/ML applications, including early prediction and diagnosis using multi-modal data (electronic health records [EHR], imaging, ventilator waveforms), advanced prognostic assessment and risk stratification that outperform traditional scoring systems, and precise identification of ARDS subtypes to guide personalized treatment. Furthermore, we detail AI's role in optimizing mechanical ventilation (e.g., PEEP settings, patient-ventilator asynchrony detection, mechanical power-guided strategies), facilitating Extracorporeal Membrane Oxygenation (ECMO) support decisions, and advancing drug discovery. The review also delves into cutting-edge methodologies such as Graph Neural Networks, Causal Inference, Federated Learning, Self-Supervised Learning, and the emerging paradigm of Large Language Models (LLMs) and agent-based AI, which promise enhanced data integration, privacy-preserving research, and autonomous decision support. Despite challenges in data quality, model generalizability, interpretability, and clinical integration, AI-driven strategies offer unprecedented opportunities for precision medicine, real-time decision support, and ultimately, improved patient outcomes in ARDS.","url":"https://pubmed.ncbi.nlm.nih.gov/40740956/","authors":["Li S","Yue R","Lu S","Luo J","Wu X","Zhang Z","Liu M","Fan Y","Zhang Y","Pan C","Huang X","He H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fmed.2025.1597556","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40722756","name":"Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention.","source":"pubmed","abstract":"Objectives : With the growing importance of mobile technology and artificial intelligence (AI) in healthcare, the development of automated cardiac diagnostic systems has gained strategic significance. This review aims to summarize the current state of knowledge on the use of AI in the analysis of electrocardiographic (ECG) signals obtained from wearable devices, particularly smartwatches, and to outline perspectives for future clinical applications. Methods : A narrative literature review was conducted using PubMed, Web of Science, and Scopus databases. The search focused on combinations of keywords related to AI, ECG, and wearable technologies. After screening and applying inclusion criteria, 152 publications were selected for final analysis. Conclusions : Modern AI algorithms-especially deep neural networks-show promise in detecting arrhythmias, heart failure, prolonged QT syndrome, and other cardiovascular conditions. Smartwatches without ECG sensors, using photoplethysmography (PPG) and machine learning, show potential as supportive tools for preliminary atrial fibrillation (AF) screening at the population level, although further validation in diverse real-world settings is needed. This article explores innovation trends such as genetic data integration, digital twins, federated learning, and local signal processing. Regulatory, technical, and ethical challenges are also discussed, along with the issue of limited clinical evidence. Artificial intelligence enables a significant enhancement of personalized, mobile, and preventive cardiology. Its integration into smartwatch ECG analysis opens a path toward early detection of cardiac disorders and the implementation of population-scale screening approaches.","url":"https://pubmed.ncbi.nlm.nih.gov/40722756/","authors":["Bartusik-Aebisher D","Rogóż K","Aebisher D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul 9","doi":"10.3390/biomedicines13071685","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40708937","name":"Machine learning approaches for EGFR mutation status prediction in NSCLC: an updated systematic review.","source":"pubmed","abstract":"With the rapid advances in artificial intelligence-particularly convolutional neural networks-researchers now exploit CT, PET/CT and other imaging modalities to predict epidermal growth factor receptor (EGFR) mutation status in non-small-cell lung cancer (NSCLC) non-invasively, rapidly and repeatably. End-to-end deep-learning models simultaneously perform feature extraction and classification, capturing not only traditional radiomic signatures such as tumour density and texture but also peri-tumoural micro-environmental cues, thereby offering a higher theoretical performance ceiling than hand-crafted radiomics coupled with classical machine learning. Nevertheless, the need for large, well-annotated datasets, the domain shifts introduced by heterogeneous scanning protocols and preprocessing pipelines, and the \"black-box\" nature of neural networks all hinder clinical adoption. To address fragmented evidence and scarce external validation, we conducted a systematic review to appraise the true performance of deep-learning and radiomics models for EGFR prediction and to identify barriers to clinical translation, thereby establishing a baseline for forthcoming multicentre prospective studies.","url":"https://pubmed.ncbi.nlm.nih.gov/40708937/","authors":["Haixian L","Shu P","Zhao L","Chunfeng L","Lun L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fonc.2025.1576461","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40707923","name":"Medical laboratory data-based models: opportunities, obstacles, and solutions.","source":"pubmed","abstract":"Medical Laboratory Data (MLD) models, which combine artificial intelligence with big medical data, have great potential in disease screening, diagnosis, personalized medicine, and health management. This study thoroughly examines the opportunities, challenges, and solutions in this field. The use of large-scale MLD improves diagnostic accuracy and allows for real-time disease monitoring. Additionally, integrating social and environmental data enables the analysis of disease mechanisms and trends. Despite these benefits, challenges such as data quality, model optimization, computational requirements, and limited interpretability remain, along with concerns about data privacy, fairness, and security. Proposed solutions include establishing standardized data formats, utilizing deep learning frameworks, employing distributed computing, improving interpretability, and implementing techniques like federated learning and algorithm optimization to address bias and safeguard privacy. Future directions will focus on enhancing performance in specific scenarios, expanding applications across different domains, increasing transparency, enabling real-time processing, and building a supportive ecosystem. It is essential to strengthen policy oversight and promote collaboration among governments, medical institutions, and academia to ensure that technological advancements align with societal progress.","url":"https://pubmed.ncbi.nlm.nih.gov/40707923/","authors":["Meng J","Wu M","Shi F","Xie Y","Wang H","Guo Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1186/s12967-025-06802-x","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"pmid:40706946","name":"Federated causal discovery with missing data in a multicentric study on endometrial cancer.","source":"pubmed","abstract":"Establishing causal dependencies is crucial in applied domains, such as medicine and healthcare, where decision-making must be explainable. In these settings, small sample sizes and missing data call for federated approaches to maximise the amount of information we can use.","url":"https://pubmed.ncbi.nlm.nih.gov/40706946/","authors":["Zanga A","Bernasconi A","Lucas PJF","Pijnenborg H","Reijnen C","Scutari M","Constantinou AC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep","doi":"10.1016/j.jbi.2025.104877","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40699869","name":"Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.","source":"pubmed","abstract":"The integration of artificial intelligence (AI) into next-generation sequencing (NGS) has revolutionized genomics, offering unprecedented advancements in data analysis, accuracy, and scalability. This review explores the synergistic relationship between AI and NGS, highlighting its transformative impact across genomic research and clinical applications. AI-driven tools, including machine learning and deep learning, enhance every aspect of NGS workflows-from experimental design and wet-lab automation to bioinformatics analysis of the generated raw data. Key applications of AI integration in NGS include variant calling, epigenomic profiling, transcriptomics, and single-cell sequencing, where AI models such as CNNs, RNNs, and hybrid architectures outperform traditional methods. In cancer research, AI enables precise tumor subtyping, biomarker discovery, and personalized therapy prediction, while in drug discovery, it accelerates target identification and repurposing. Despite these advancements, challenges persist, including data heterogeneity, model interpretability, and ethical concerns. This review also discusses the emerging role of AI in third-generation sequencing (TGS), addressing long-read-specific challenges, like fast and accurate basecalling, as well as epigenetic modification detection. Future directions should focus on implementing federated learning to address data privacy, advancing interpretable AI to improve clinical trust and developing unified frameworks for seamless integration of multi-modal omics data. By fostering interdisciplinary collaboration, AI promises to unlock new frontiers in precision medicine, making genomic insights more actionable and scalable.","url":"https://pubmed.ncbi.nlm.nih.gov/40699869/","authors":["Athanasopoulou K","Michalopoulou VI","Scorilas A","Adamopoulos PG"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun 19","doi":"10.3390/cimb47060470","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40694226","name":"Research Progress in Artificial Intelligence for Central Serous Chorioretinopathy: A Systematic Review.","source":"pubmed","abstract":"This review synthesizes advancements in artificial intelligence (AI) applications for central serous chorioretinopathy (CSCR), analyzing challenges and outlining future research directions to guide personalized diagnostic and therapeutic strategies.","url":"https://pubmed.ncbi.nlm.nih.gov/40694226/","authors":["Zhang P","Zhang Q","Hu X","Chi W","Yang W"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep","doi":"10.1007/s40123-025-01209-9","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40690657","name":"Federated Learning-Based Model for Predicting Mortality: Systematic Review and Meta-Analysis.","source":"pubmed","abstract":"The rise of federated learning (FL) as a novel privacy-preserving technology offers the potential to create models collaboratively in a decentralized manner to address confidentiality issues, particularly regarding data privacy. However, there is a scarcity of clear and comprehensive evidence that compares the performance of FL with that of the established centralized machine learning (CML) in the clinical domain.","url":"https://pubmed.ncbi.nlm.nih.gov/40690657/","authors":["Tahir N","Jung CR","Lee SD","Azizah N","Ho WC","Li TC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul 21","doi":"10.2196/65708","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40689362","name":"The Integration of Multi-omics With Artificial Intelligence in Hepatology: A Comprehensive Review of Personalized Medicine, Biomarker Identification, and Drug Discovery.","source":"pubmed","abstract":"The evolution of high-throughput technologies has expanded the role of multi-omics in hepatology, moving away from traditional hypothesis-driven research toward integrative, data-driven models. However, high cost and resource intensity have limited widespread adoption. The multi-omics datasets for liver diseases are still relatively small. Progress has been made in integrating a few omics types, particularly in combining genomics with transcriptomics, proteomics, or metabolomics for liver disease research. However, fully integrated multi-omics studies remain limited, with most research focusing on two or three omics layers rather than comprehensive multi-modal integration. Emerging approaches such as federated learning can be leveraged to securely integrate multi-omics data, advance AI-driven biomarker discovery, and enhance precision medicine strategies across institutions.","url":"https://pubmed.ncbi.nlm.nih.gov/40689362/","authors":["Ramesh D","Manickavel P","Ghosh S","Bhat M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Nov-Dec","doi":"10.1016/j.jceh.2025.102611","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40683742","name":"Advances in materials for wearable biosensors.","source":"pubmed","abstract":"Wearable biosensors have emerged as transformative instruments for continuous, non-invasive health monitoring, providing real-time analysis of biomarkers in biofluids such as sweat, interstitial fluid, and saliva. This chapter offers a comprehensive overview of the pivotal role of biomaterials in the design and functionality of wearable biosensors. It examines the selection criteria for biocompatible materials, emphasizing properties such as flexibility, stretchability, conductivity, and long-term stability. The discussion categorizes advanced materials, including hydrogels, polyurethanes, carbon-based nanomaterials, metallic nanoparticles, and microneedles, and evaluates their applications in biosensing platforms for glucose, pH, and metal ion detection. Through case studies and figure-integrated explanations, the chapter highlights innovations such as smart hydrogel contact lenses, self-powered alcohol biosensors, and closed-loop microneedle patches for autonomous insulin delivery. It further explores key challenges, including biofluid variability, sensor biocompatibility, and the correlation of biofluid biomarkers with blood concentrations. Finally, the chapter underscores future directions involving AI integration, federated learning, and next-generation biomaterials like biodegradable polymers and stretchable composites. By bridging materials science with digital health technologies, wearable biosensors are poised to revolutionize personalized medicine, enabling early diagnosis, disease prevention, and optimized therapeutic interventions.","url":"https://pubmed.ncbi.nlm.nih.gov/40683742/","authors":["Maiya D","Patel T","Pandya A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1016/bs.pmbts.2025.05.009","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40683101","name":"Diabetic retinopathy detection from fundus images: A wide survey from grading to segmentation of lesions.","source":"pubmed","abstract":"Diabetes is one of the most common diseases worldwide and requires accurate diagnosis. Patients with diabetes are often affected by diabetic retinopathy (DR), which can lead to low vision, vision loss, or blindness. Therefore, a robust computer-aided diagnosis system is needed to provide better treatment to patients. This review mainly focuses on the works related to diagnosing DR from retinal fundus images. A total of 128 research papers have been reviewed from 1986 to 2025. The survey is divided into two parts: one for the grading/classification of DR and the other for DR lesions segmentation. This survey article introduces the details of eye diseases, followed by the background details of DR and different imaging techniques required to diagnose DR, like fundus imaging, multifocal electroretinogram, and optical coherence tomography. Details of well-known DR datasets since 2009 are also provided, including their complete statistical information and potential dataset biases. Furthermore, the approaches used for grading and segmentation tasks from the early 1980s to recent developments are discussed. The reviewed papers are based on traditional and deep learning based methods used in DR diagnosis. In traditional methods, the researchers used image preprocessing, mathematical morphology, fuzzy system, active contour, features extraction methods, evolutionary approaches, and machine learning based classifiers. In deep learning, researchers have used convolutional neural network (CNN), long short-term memory, vision transformer, contrastive learning, federated learning, and Explainable Artificial Intelligence (XAI) based approaches for diagnosis. In this article, we have emphasized almost all the significant work done in diagnosing DR disease, the datasets used, and performance of methods on those datasets. The comparative analysis of the methods is also done to help researchers obtain future directions for further research in the area of medical disease identification, especially DR disease detection. The challenges of AI and its associated ethical implications are also discussed in the article to provide direction for future work.","url":"https://pubmed.ncbi.nlm.nih.gov/40683101/","authors":["Gautam A","Shanker R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep","doi":"10.1016/j.compbiomed.2025.110715","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40669686","name":"AI-powered liquid biopsy for early detection of gastrointestinal cancers.","source":"pubmed","abstract":"Gastrointestinal cancers (GICs) are a leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis. Liquid biopsy has emerged as a promising non-invasive diagnostic tool, utilizing circulating tumor DNA (ctDNA), circulating tumor cells, exosomal RNA (exoRNA), and tumor-educated platelets for early cancer detection. Challenges, including data complexity, low biomarker abundance, and detection variability, require advanced computational solutions. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has significantly improved the accuracy and clinical utility of liquid biopsy by enabling high-throughput biomarker discovery, multi-omics integration, and predictive modelling. AI-driven algorithms have enhanced ctDNA mutation profiling, methylation analysis, and fragmentomics, offering superior sensitivity and specificity for early GIC detection. Additionally, AI-based analysis of exoRNA and platelet-derived biomarkers provides novel insights into tumor progression and patient stratification. Despite these advances, key challenges remain, including data standardization, bias mitigation, and regulatory validation. The implementation of federated learning and ethical AI frameworks can further refine AI-powered liquid biopsy models, paving the way for precision oncology applications. This review highlights advances in AI-powered liquid biopsy for early GIC detection, emphasizing its potential and the need for validation, collaboration, and regulatory alignment for clinical adoption.","url":"https://pubmed.ncbi.nlm.nih.gov/40669686/","authors":["Hussain MS","Rejili M","Khan A","Alshammari SO","Tan CS","Haouala F","Ashique S","Alshammari QA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep 1","doi":"10.1016/j.cca.2025.120484","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40648460","name":"Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains.","source":"pubmed","abstract":"This systematic review analyses advancements in cognitive state recognition from 2010 to early 2024, evaluating 405 relevant articles from an initial pool of 2398 records identified through five databases: Scopus, Engineering Village, Web of Science, IEEE Xplore, and PubMed. Studies were included if they assessed cognitive states using physiological signals and applied machine learning (ML) or deep learning (DL) techniques in practical task settings. The review highlights a pivotal shift from shallow ML to DL approaches for analysing physiological signals, driven by DL's ability to autonomously learn complex patterns in large datasets. By 2023, DL has become the dominant methodology, though traditional ML techniques remain relevant. Additionally, there has been a move from neuroimaging to multimodal physiological modalities, with the decrease in neuroimaging use reflecting a trend towards integrating various physiological signals for more comprehensive insights. Cognitive state recognition is applied across diverse domains such as the automotive, aviation, maritime, and healthcare industries, enhancing performance and safety in high-stakes environments. Electrocardiogram (ECG) is the most utilised modality, with convolutional neural networks (CNNs) being the primary DL approach. The trend in cognitive state recognition research is moving towards integrating ECG signals with CNNs and adopting privacy-preserving methodologies like differential privacy and federated learning, highlighting the potential of cognitive state recognition to enhance performance, safety, and innovation across various real-world applications.","url":"https://pubmed.ncbi.nlm.nih.gov/40648460/","authors":["Jin K","Rubio-Solis A","Naik R","Leff D","Kinross J","Mylonas G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul 5","doi":"10.3390/s25134207","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40646553","name":"The ethics of data mining in healthcare: challenges, frameworks, and future directions.","source":"pubmed","abstract":"Data mining in healthcare offers transformative insights yet surfaces multilayered ethical and governance challenges that extend beyond privacy alone. Privacy and consent concerns remain paramount when handling sensitive medical data, particularly as healthcare organizations increasingly share patient information with large digital platforms. The risks of data breaches and unauthorized access are stark: 725 reportable incidents in 2023 alone exposed more than 133&#xa0;million patient records, and hacking-related breaches surged by 239% since 2018. Algorithmic bias further threatens equity; models trained on historically prejudiced data can reinforce health disparities across protected groups. Therefore, transparency must span three levels-dataset documentation, model interpretability, and post-deployment audit logging-to make algorithmic reasoning and failures traceable. Security vulnerabilities in the Internet of Medical Things (IoMT) and cloud-based health platforms amplify these risks, while corporate data-sharing deals complicate questions of data ownership and patient autonomy. A comprehensive response requires (i) dataset-level artifacts such as \"datasheets,\" (ii) model-cards that disclose fairness metrics, and (iii) continuous logging of predictions and LIME/SHAP explanations for independent audits. Technical safeguards must blend differential privacy (with empirically validated noise budgets), homomorphic encryption for high-value queries, and federated learning to maintain the locality of raw data. Governance frameworks must also mandate routine bias and robust audits and harmonized penalties for non-compliance. Regular reassessments, thorough documentation, and active engagement with clinicians, patients, and regulators are critical to accountability. This paper synthesizes current evidence, from a 2019 European re-identification study demonstrating 99.98% uniqueness with 15 quasi-identifiers to recent clinical audits that trimmed false-negative rates via threshold recalibration, and proposes an integrated set of fairness, privacy, and security controls aligned with SPIRIT-AI, CONSORT-AI, and emerging PROBAST-AI guidelines. Implementing these solutions will help healthcare systems harness the benefits of data mining while safeguarding patient rights and sustaining public trust.","url":"https://pubmed.ncbi.nlm.nih.gov/40646553/","authors":["Ahmed MM","Okesanya OJ","Oweidat M","Othman ZK","Musa SS","Lucero-Prisno Iii DE"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1186/s13040-025-00461-w","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"pmid:40640574","name":"A scoping review of the governance of federated learning in healthcare.","source":"pubmed","abstract":"In healthcare, federated learning (FL) is emerging as a methodology to enable the analysis of large and disparate datasets while allowing custodians to retain sovereignty. While FL minimises data-sharing challenges, concerns surrounding ethics, privacy, maleficent use, and harm remain. These concerns can be managed by effective data governance. Data governance specifies procedural, relational, and structural mechanisms governing how data is captured, shared, and analysed, the resultant models and their use. However, limited insights exist on the optimal governance of this emerging technology. This study aims to develop a consolidated framework of the data governance mechanisms for FL in healthcare. A scoping review was performed, using deductive and inductive analysis of 39 articles. The framework includes twelve procedural, ten relational, and twelve structural mechanisms. The framework directs researchers to examine how to enact each mechanism and provides practitioners with insights into the mechanism to consider when governing FL.","url":"https://pubmed.ncbi.nlm.nih.gov/40640574/","authors":["Eden R","Chukwudi I","Bain C","Barbieri S","Callaway L","de Jersey S","George Y","Gorse AD","Lawley M","Marendy P","McPhail SM","Nguyen A","Samadbeik M","Sullivan C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul 10","doi":"10.1038/s41746-025-01836-3","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40633018","name":"Systematic review of machine learning applications in the early prediction and management of chronic lymphocytic leukaemia.","source":"pubmed","abstract":"Objective: This review assesses the efficacy of machine learning (ML) models for classification and management of Chronic Lymphocytic Leukaemia (CLL). Methods: Twenty studies published between 2014 and 2023 were reviewed, focusing on supervised ML models to predict patient outcomes or guide treatment decisions. Studies were identified through PubMed, Google Scholar, and IEEExplore, with the final search in March 2023. Inclusion criteria consisted of studies focused on ML applications in CLL. Exclusion criteria included studies lacking sufficient methodology or focused solely on experimental settings without clinical validation. Most studies used small, single-centre datasets, potentially contributing to overfitting and limited applicability to real-world settings. Results: Despite dataset limitations, all reviewed studies reported positive outcomes, with some demonstrating improvements in clinical workflows. Our findings advocate developing ML models using larger, multimodal, and multi-institutional datasets. Improved model interpretability and NLP implementation to harness unstructured clinical data were identified as key areas for advancement. Additionally, innovations like cross-site federated learning and automated redaction could help address data integration and privacy challenges. Conclusion: This review underscores the transformative potential of ML in CLL management. However, addressing limitations, including diverse datasets and enhanced model interpretability, is crucial for fully leveraging ML capabilities in haemato-oncology.","url":"https://pubmed.ncbi.nlm.nih.gov/40633018/","authors":["Al-Agil M","Patten PE","Alhaq A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul-Sep","doi":"10.1177/14604582251342178","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40612529","name":"Generalizing location-centric variations to enhance contactless human activity recognition.","source":"pubmed","abstract":"Contactless Human Activity Recognition (HAR) has played a critical role in smart healthcare and elderly care homes to monitor patient behavior, detect falls or abnormal activities in real time. The effectiveness of non-invasive HAR is often hindered by location-centric variations in Channel State Information (CSI). These variations limit the ability of HAR models to generalize across new unseen cross-domain environments, for instance, a model trained in one location might not perform well in another physical location. To address this challenge, in this study, we present a novel federated learning (FL) algorithm designed to train a robust global model from local datasets in different localizations. The proposed Federated Weighted Averaging for HAR (Fed-WAHAR) algorithm mitigates location-induced disparities, including heterogeneity and non-Independent and Identically Distributed (non-IID) data distributions. Fed-WAHAR employs a dynamic weighting approach based on local models' accuracy to improve global model classification accuracy and reduce convergence time effectively. We evaluated the performance of Fed-WAHAR using various metrics, including accuracy, precision, recall, F1 score, confusion matrix, and convergence analysis. Experimental results demonstrate that Fed-WAHAR achieves an accuracy of 85% in recognizing human activities across different locations, enhancing the ability of model to infer across new unseen locations.","url":"https://pubmed.ncbi.nlm.nih.gov/40612529/","authors":["Khan F","Yaseen Shah S","Ahmad J","Al Mazroa A","Zahid A","Ilyas M","Abbasi QH","Shah SA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fncom.2025.1612928","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40599184","name":"Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.","source":"pubmed","abstract":"Artificial intelligence (AI) is driving a paradigm shift in gastroenterology and hepatology by delivering cutting-edge tools for disease screening, diagnosis, treatment, and prognostic management. Through deep learning, radiomics, and multimodal data integration, AI has achieved diagnostic parity with expert clinicians in endoscopic image analysis ( e.g. , early gastric cancer detection, colorectal polyp identification) and non-invasive assessment of liver pathologies ( e.g. , fibrosis staging, fatty liver typing) while demonstrating utility in personalized care scenarios such as predicting hepatocellular carcinoma recurrence and optimizing inflammatory bowel disease treatment responses. Despite these advancements challenges persist including limited model generalization due to fragmented datasets, algorithmic limitations in rare conditions ( e.g. , pediatric liver diseases) caused by insufficient training data, and unresolved ethical issues related to bias, accountability, and patient privacy. Mitigation strategies involve constructing standardized multicenter databases, validating AI tools through prospective trials, leveraging federated learning to address data scarcity, and developing interpretable systems ( e.g. , attention heatmap visualization) to enhance clinical trust. Integrating generative AI, digital twin technologies, and establishing unified ethical/regulatory frameworks will accelerate AI adoption in primary care and foster equitable healthcare access while interdisciplinary collaboration and evidence-based implementation remain critical for realizing AI's potential to redefine precision care for digestive disorders, improve global health outcomes, and reshape healthcare equity.","url":"https://pubmed.ncbi.nlm.nih.gov/40599184/","authors":["Chen ZL","Wang C","Wang F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun 28","doi":"10.3748/wjg.v31.i24.108021","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40597751","name":"Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.","source":"pubmed","abstract":"Detecting liver tumors via computed tomography (CT) scans is a critical but labor-intensive task. Extensive expert annotations are needed to train effective machine learning models. This study presents an innovative approach that leverages federated learning in combination with a teacher&#x2012;student framework, an enhanced slice-aware network (SANet), and semisupervised learning (SSL) techniques to improve the CT-based liver tumor detection process while significantly reducing its labor and time costs.","url":"https://pubmed.ncbi.nlm.nih.gov/40597751/","authors":["Lee CS","Lien JJ","Chain K","Huang LC","Hsu ZW"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul 1","doi":"10.1186/s12880-025-01761-7","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40590973","name":"Harnessing AI for Improved Diagnosis and Management of Pediatric Sepsis: Current Advances, Challenges, and Future Directions.","source":"pubmed","abstract":"Artificial intelligence (AI) has been applied to early recognition and management of rapidly progressive, community-acquired pediatric sepsis, a leading cause of childhood mortality. The broad adoption of electronic health records combined with rapid advances in digital technologies have enabled the federated training of both knowledge-driven AI, known as expert systems, trained by teams of collaborating clinicians, and data-driven AI, known as machine learning (ML), to derive predictive, clustering algorithms trained on \"big data.\" An important subset of ML is \"deep learning,\" which includes tools that understand, interpret, and manipulate human imagery and language, such as natural language processing and its subset large language models. We are in an era of rapid deployment of AI/ML-powered tools ranging from real-time electronic health records-embedded decision support tools to continuous wearable vital sign monitors and mobile/conversational virtual assistants/triage apps. These applications have the potential of transforming the timeliness of life-saving sepsis care delivery. This review explores the current and potential AI/ML applications in sepsis care, including tools for screening/early detection, risk stratification/outcome prediction, personalized treatment, and continuous patient monitoring. We highlight successful implementations and ongoing clinical trials, emphasizing the impact on patient outcomes. Finally, we address practical considerations for the future, such as bias mitigation and integration into clinical workflows.","url":"https://pubmed.ncbi.nlm.nih.gov/40590973/","authors":["Siolos P","Pasha S","Triantafyllou M","Wolff N","Ibrahim Z","Kratimenos P","Kamaleswaran R","Velez T","Koutroulis I"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul 1","doi":"10.1097/PEC.0000000000003397","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40575335","name":"Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders.","source":"pubmed","abstract":"The complex pathophysiology and diverse manifestations of esophageal disorders pose challenges in clinical practice, particularly in achieving accurate early diagnosis and risk stratification. While traditional approaches rely heavily on subjective interpretations and variable expertise, machine learning (ML) has emerged as a transformative tool in healthcare. We conducted a comprehensive review of published literature on ML applications in esophageal diseases, analyzing technical approaches, validation methods, and clinical outcomes. ML demonstrates superior performance: In gastroesophageal reflux disease, ML models achieve 80%-90% accuracy in potential of hydrogen-impedance analysis and endoscopic grading; for Barrett's esophagus, ML-based approaches show 88%-95% accuracy in invasive diagnostics and 77%-85% accuracy in non-invasive screening. In esophageal cancer, ML improves early detection and survival prediction by 6%-10% compared to traditional methods. Novel applications in achalasia and esophageal varices demonstrate promising results in automated diagnosis and risk stratification, with accuracy rates exceeding 85%. While challenges persist in data standardization, model interpretability, and clinical integration, emerging solutions in federated learning and explainable artificial intelligence offer promising pathways forward. The continued evolution of these technologies, coupled with rigorous validation and thoughtful implementation, may fundamentally transform our approach to esophageal disease management in the era of precision medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/40575335/","authors":["Liu SW","Li P","Li XQ","Wang Q","Duan JY","Chen J","Li RH","Guo YF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun 21","doi":"10.3748/wjg.v31.i23.105076","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40567322","name":"Machine Learning and Deep Learning Models for Early Sepsis Prediction: A Scoping Review.","source":"pubmed","abstract":"Sepsis, a dangerous condition where infection triggers an abnormal host response, requires quick detection to save lives. While traditional detection methods often fall short, artificial intelligence (AI) and its subsets, machine learning (ML) and deep learning (DL), offer new hope. This scoping review inspects the ML and DL models that are published in the period from 2022 to 2025 for sepsis prediction using electronic health records (EHRs). It aims to provide a comprehensive update for clinicians on the proposed sepsis prediction models, features used, data processing methods, model performance and clinical integration.","url":"https://pubmed.ncbi.nlm.nih.gov/40567322/","authors":["Shanmugam H","Airen L","Rawat S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun","doi":"10.5005/jp-journals-10071-24986","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40563902","name":"AI-Driven Transcriptome Prediction in Human Pathology: From Molecular Insights to Clinical Applications.","source":"pubmed","abstract":"Gene expression regulation underpins cellular function and disease progression, yet its complexity and the limitations of conventional detection methods hinder clinical translation. In this review, we define \"predict\" as the AI-driven inference of gene expression levels and regulatory mechanisms from non-invasive multimodal data (e.g., histopathology images, genomic sequences, and electronic health records) instead of direct molecular assays. We systematically examine and analyze the current approaches for predicting gene expression and diagnosing diseases, highlighting their respective advantages and limitations. Machine learning algorithms and deep learning models excel in extracting meaningful features from diverse biomedical modalities, enabling tools like PathChat and Prov-GigaPath to improve cancer subtyping, therapy response prediction, and biomarker discovery. Despite significant progress, persistent challenges-such as data heterogeneity, noise, and ethical issues including privacy and algorithmic bias-still limit broad clinical adoption. Emerging solutions like cross-modal pretraining frameworks, federated learning, and fairness-aware model design aim to overcome these barriers. Case studies in precision oncology illustrate AI's ability to decode tumor ecosystems and predict treatment outcomes. By harmonizing multimodal data and advancing ethical AI practices, this field holds immense potential to propel personalized medicine forward, although further innovation is needed to address the issues of scalability, interpretability, and equitable deployment.","url":"https://pubmed.ncbi.nlm.nih.gov/40563902/","authors":["Chen X","Xu H","Yu S","Hu W","Zhang Z","Wang X","Yuan Y","Wang M","Chen L","Lin X","Hu Y","Cai P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun 4","doi":"10.3390/biology14060651","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40562194","name":"Machine and deep learning methods for epileptic seizure recognition using EEG data: A systematic review.","source":"pubmed","abstract":"Epilepsy is a neurological disorder affecting millions worldwide, characterized by recurrent and unpredictable seizures. Electroencephalography (EEG) is a widely used tool for seizure diagnosis, but the complexity and variability of EEG signals make manual analysis challenging. Machine Learning (ML) and Deep Learning (DL) techniques have emerged as powerful methods for automated Epileptic Seizure (ES) detection, classification, and prediction. However, questions remain regarding their effectiveness, interpretability, and clinical applicability. This systematic review critically examines ML and DL approaches applied to EEG-based seizure recognition, highlighting key challenges such as feature extraction, dataset selection, and model generalization. We analyze peer-reviewed studies from 2013 to 2023, sourced from the PubMed database, to compare various methodologies and evaluate their performance. Unlike prior reviews that focus on a single aspect of seizure recognition, this work provides a comprehensive overview of detection, classification, and prediction tasks. We also discuss the strengths and limitations of different ML and DL models, emphasizing the trade-offs between computational complexity, accuracy, and real-world implementation. Furthermore, this study outlines emerging trends, including the integration of explainable AI, transfer learning, and privacy-preserving techniques such as federated learning. By synthesizing the latest advancements, this review serves as a guide for researchers and clinicians seeking to enhance the reliability and efficiency of seizure recognition systems. Our findings aim to bridge the gap between AI-driven methodologies and clinical applications, paving the way for more robust and interpretable ES detection frameworks.","url":"https://pubmed.ncbi.nlm.nih.gov/40562194/","authors":["Mourad R","Diab A","Merhi Z","Khalil M","Le Bouquin Jeannès R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Oct 1","doi":"10.1016/j.brainres.2025.149797","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40553346","name":"Review on Advancement of AI in Nutrigenomics.","source":"pubmed","abstract":"Nutrigenomics, the study of how dietary components influence gene expression and how genetic variations affect individual responses to nutrition, has emerged as a cornerstone of personalized medicine. The integration of artificial intelligence (AI) with nutrigenomic research marks a significant advancement in our ability to understand and apply personalized nutrition principles. This chapter explores the transformative role of artificial intelligence in advancing nutrigenomic research and its practical applications in personalized nutrition. The convergence of AI with nutrigenomics has revolutionized our understanding of gene-diet interactions, enabling more sophisticated analysis of individual nutritional responses based on genetic profiles. Through advanced machine learning algorithms and deep learning approaches, researchers can now process vast amounts of genetic and dietary data to generate personalized nutritional recommendations with unprecedented precision. The emergence of smart wearable and mobile applications has further enhanced real-time nutrigenomic monitoring, particularly in areas such as continuous glucose monitoring (CGM) and metabolic profiling. While the field shows promising developments, especially in managing conditions like type 2 diabetes through precision nutrition, it faces several challenges including data privacy concerns and algorithmic biases. Despite these limitations, the integration of AI with nutrigenomic principles points toward a future where personalized nutrition becomes increasingly accessible and effective, though careful validation and implementation strategies remain crucial for its success.","url":"https://pubmed.ncbi.nlm.nih.gov/40553346/","authors":["Phugat S","Goel P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1007/978-1-0716-4690-8_23","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40548850","name":"Electrocardiogram-Based Artificial Intelligence for Detection of Low Ejection Fraction: A Contemporary Review.","source":"pubmed","abstract":"Artificial intelligence (AI) is transforming the role of electrocardiography (ECG) in cardiovascular care, enabling early disease detection, improved risk stratification, and optimized therapeutic decision-making. This review explores recent advances in AI-enhanced ECG (AI-ECG) applications, with a focus on both technical innovations and clinical integration. Key developments include deep learning models capable of detecting structural heart disease, arrhythmias, and even systemic conditions from ECG data. Emphasis is placed on the need for model explainability, fairness, and generalizability through diverse training datasets and interpretable algorithms. Multimodal learning, federated approaches, and temporal modeling are highlighted as emerging strategies to enhance model robustness and clinical relevance. Integration into electronic health records, prospective validation studies, and regulatory considerations are discussed as essential steps toward real-world adoption. Additionally, AI-driven remote monitoring through wearable devices offers scalable solutions for early intervention, though challenges around accuracy, alarm fatigue, and cost-effectiveness remain. Finally, global collaboration and policy frameworks are necessary to ensure equitable, ethical, and sustainable deployment of AI-ECG technologies. Collectively, this work underscores the transformative potential of AI-ECG while outlining critical directions for its safe and effective implementation in clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/40548850/","authors":["Tran HH","Thu A","Fuertes A","Twayana AR","Mahadevaiah A","Mehta KA","James M","Basta M","Weissman S","Frishman WH","Aronow WS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun 23","doi":"10.1097/CRD.0000000000000975","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40543496","name":"Deep learning based colorectal cancer detection in medical images: A comprehensive analysis of datasets, methods, and future directions.","source":"pubmed","abstract":"This comprehensive review examines the current state and evolution of artificial intelligence applications in colorectal cancer detection through medical imaging from 2019 to 2025. The study presents a quantitative analysis of 110 high-quality publications and 9 publicly accessible medical image datasets used for training and validation. Various convolutional neural network architectures-including ResNet (40 implementations), VGG (18 implementations), and emerging transformer-based models (12 implementations)-for classification, object detection, and segmentation tasks are systematically categorized and evaluated. The investigation encompasses hyperparameter optimization techniques utilized to enhance model performance, with particular focus on genetic algorithms and particle swarm optimization approaches. The role of explainable AI methods in medical diagnosis interpretation is analyzed through visualization techniques such as Grad-CAM and SHAP. Technical limitations, including dataset scarcity, computational constraints, and standardization challenges, are identified through trend analysis. Research gaps in current methodologies are highlighted through comparative assessment of performance metrics across different architectural implementations. Potential future research directions, including multimodal learning and federated learning approaches, are proposed based on publication trend analysis. This review serves as a comprehensive reference for researchers in medical image analysis and clinical practitioners implementing AI-based colorectal cancer detection systems.","url":"https://pubmed.ncbi.nlm.nih.gov/40543496/","authors":["Gülmez B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep","doi":"10.1016/j.clinimag.2025.110542","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40529517","name":"Federated learning for crop yield prediction: A comprehensive review of techniques and applications.","source":"pubmed","abstract":"The demand for food all over the world requires the implementation of advanced technologies to improve agricultural productivity. Federated Learning (FL) as a decentralized approach to machine learning facilitates collaborative model training on different data sources while maintaining privacy-making it highly applicable technology for sensitive agricultural data. This paper offers a systematic overview of the recent knowledge on the application of FL towards the prediction of crop yield and other agricultural uses. We discussed the mathematical basis of FL, the variety of machine learning models used, the types of used agricultural data, and the major performance metrics. The paper presents real-world applications and lists the current limitations, including communication overhead, data heterogeneity, and interpretability issues. Lastly, we introduce open research directions to inform the development of FL in precision agriculture.","url":"https://pubmed.ncbi.nlm.nih.gov/40529517/","authors":["Hiremani V","Devadas RM","Preethi","Sapna R","Sowmya T","Gujjar P","Rani NS","Bhavya KR"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun","doi":"10.1016/j.mex.2025.103408","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40519269","name":"Deep learning-driven approach for cataract management: towards precise identification and predictive analytics.","source":"pubmed","abstract":"Deep learning (DL) technology has shown significant potential in the whole process of cataract diagnosis and treatment through algorithms such as convolutional neural network (CNN). In terms of diagnosis, DL models based on fundus or slit-lamp images can automatically identify and grade cataract, and their diagnostic accuracy is close to or beyond the level of human experts. In the field of surgery, DL can analyze the operation video stage in real time, accurately track the instruments and optimize the operation process, and reduce the risk of intraoperative eye error through intelligent devices. DL could optimize the intraocular lens (IOL) power calculation, predict the risk of complications and long-term surgery requirements. However, insufficient data standardization, the \"black box\" characteristics of the model, and privacy ethics issues are still the bottlenecks in clinical application. In the future, it is necessary to improve the generalization ability of model through multimodal data fusion, federated learning and other technologies, and combine interpretable design (such as Grad-CAM) to promote the evolution of DL to a transparent medical decision-making tool, and finally realize the intelligence and universality of cataract management.","url":"https://pubmed.ncbi.nlm.nih.gov/40519269/","authors":["Lu S","Ba L","Wang J","Zhou M","Huang P","Zhang X","Pan S","Zhou X","Wen K","Sun J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fcell.2025.1611216","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40515956","name":"AI-driven techniques for detection and mitigation of SARS-CoV-2 spread: a review, taxonomy, and trends.","source":"pubmed","abstract":"The SARS-CoV-2 RNA virus, with its rapid spread and frequent genetic changes, has posed unparalleled obstacles for public health and treatment efforts. Early diagnosis of the disease and the development of effective treatment strategies are the main pillars of epidemic control. In this regard, machine learning (ML) methods, an advanced subset of artificial intelligence (AI), can play an effective role in improving the accuracy of diagnosis and the effectiveness of treatments related to SARS-CoV-2. However, the implementation of ML in clinical settings faces issues such as data heterogeneity, lack of training data, model interpretability challenges, patient privacy protection, and implementation limitations. This article provides a systematic review of the applications of federated learning (FL), deep learning (DL), reinforcement learning (RL), and hybrid approaches in the field of SARS-CoV-2 diagnosis and treatment. Based on the analysis of the results, the main focus of the research was on increasing privacy and security (P&amp;S) with a share of 26%, improving detection accuracy and robustness (DAR) with 24%, and improving computational and communication efficiency (CCE) with 20%. These statistics indicate the importance of prioritizing patient information confidentiality and improving systems' accuracy and stability against data variability. In conclusion, the findings of this review can pave the way for the practical application of ML technologies in clinical decision-making and improving the quality of healthcare services related to SARS-CoV-2.","url":"https://pubmed.ncbi.nlm.nih.gov/40515956/","authors":["Ghorbian M","Ghorbian S","Ghobaei-Arani M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun 14","doi":"10.1007/s10238-025-01753-5","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40508136","name":"Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.","source":"pubmed","abstract":"Harnessing state-of-the-art technologies to improve disease resistance is a critical objective in modern plant breeding. Artificial intelligence (AI), particularly deep learning and big model (large language model and large multi-modal model), has emerged as a transformative tool to enhance disease detection and omics prediction in plant science. This paper provides a comprehensive review of AI-driven advancements in plant disease detection, highlighting convolutional neural networks and their linked methods and technologies through bibliometric analysis from recent research. We further discuss the groundbreaking potential of large language models and multi-modal models in interpreting complex disease patterns via heterogeneous data. Additionally, we summarize how AI accelerates genomic and phenomic selection by enabling high-throughput analysis of resistance-associated traits, and explore AI's role in harmonizing multi-omics data to predict plant disease-resistant phenotypes. Finally, we propose some challenges and future directions in terms of data, model, and privacy facets. We also provide our perspectives on integrating federated learning with a large language model for plant disease detection and resistance prediction. This review provides a comprehensive guide for integrating AI into plant breeding programs, facilitating the translation of computational advances into disease-resistant crop breeding.","url":"https://pubmed.ncbi.nlm.nih.gov/40508136/","authors":["Ma J","Cheng Z","Cao Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun 1","doi":"10.3390/ijms26115324","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40494800","name":"[Progress in method development and application of distributed learning for estimation of epidemiological effect].","source":"pubmed","abstract":"Objective: To systematically review the progress in the method development and application of distributed learning in the estimation of epidemiological effect and provide methodological reference for multi-center studies. Methods: We conducted a literature retrieval for English papers published up to December 31, 2023 by using keywords of \"health/medical big data\" and \"distributed/federated learning\". After consulting experts, we set criteria of paper inclusion and exclusion and created a framework for data extraction. We collected information about basic study details, including method, application, and evaluation. Two researchers independently screened the papers and extracted information. We used EndNote 20 for the management of literatures and EpiData for the management of data. Results: A total of 3 444 papers were collected, and 29 papers were included in the final analysis. Most of the papers (25, 86.2%) were published in or after 2019, and the papers were mainly from the United States (21/29, 72.4%). For the estimation of epidemiological effects, 22 distributed learning methods had been developed, including methods for logistic regression (8), Cox regression (8), Poisson regression (2), and generalized linear mixed model (GLMM) (4), as well as three platforms for distributed analysis (VLP, Vantage6, AusCAT). The 29 papers described 45 applications, with 20 (44.4%) focusing on the establishment of prediction model and 25 (55.6%) on association analysis. Importantly, except for GLMM, current distributed learning methods can estimate effects with little bias in 1-3 rounds of communication. These methods show less bias compared with meta-analysis, especially in the address of data heterogeneity and rare outcomes. However, less studies examined how differences in data structure and sparse data affect results, an area that requires further research. Conclusion: While distributed learning shows promise in epidemiological effect estimation, it is still in early development, requiring further research on data heterogeneity handling and communication efficiency improvement.","url":"https://pubmed.ncbi.nlm.nih.gov/40494800/","authors":["Yang JT","Gao X","Wang XX","Zhang MD","Chen X","Wang YL","Liu ZK","Zhan SY"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 May 10","doi":"10.3760/cma.j.cn112338-20241018-00642","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40466336","name":"Molecular biology in the exabyte era: Taming the data deluge for biological revelation and clinical transformation.","source":"pubmed","abstract":"The explosive growth in next-generation high-throughput technologies has driven modern molecular biology into the exabyte era, producing an unparalleled volume of biological data across genomics, proteomics, metabolomics, and biomedical imaging. Although this massive expansion of data can power future biological discoveries and precision medicine, it presents considerable challenges, including computational bottlenecks, fragmented data landscapes, and ethical issues related to privacy and accessibility. We highlight novel contributions, such as the application of blockchain technologies to ensure data integrity and traceability, a relatively underexplored solution in this context. We describe how artificial intelligence (AI), machine learning (ML), and cloud computing fundamentally reshape and provide scalable solutions for these challenges by enabling near real-time pattern recognition, predictive modelling, and integrated data analysis. In particular, the use of federated learning models allows privacy-preserving collaboration across institutions. We emphasise the importance of open science, FAIR principles (Findable, Accessible, Interoperable, and Reusable), and blockchain-based audit trails to enhance global collaboration, reproducibility, and data security. By processing multi-omics datasets in integrated formats, we can enhance our understanding of disease mechanisms, facilitate biomarker discovery, and develop AI-assisted, personalised therapeutics. Addressing these technical and ethical demands requires robust governance frameworks that protect sensitive data without hindering innovation. This paper underscores a shift toward more secure, transparent, and collaborative biomedical research, marking a decisive step toward clinical transformation.","url":"https://pubmed.ncbi.nlm.nih.gov/40466336/","authors":["Zafar I","Unar A","Khan NU","Abalkhail A","Jamal A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Dec","doi":"10.1016/j.compbiolchem.2025.108535","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40457408","name":"Current AI technologies in cancer diagnostics and treatment.","source":"pubmed","abstract":"Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, and personalized treatments. Artificial intelligence (AI) has rapidly become a groundbreaking component in the modern era of oncology, offering sophisticated tools across the range of cancer care. In this review, we performed a systematic survey of the current status of AI technologies used for cancer diagnoses and therapeutic approaches. We discuss AI-facilitated imaging diagnostics using a range of modalities such as computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, and digital pathology, highlighting the growing role of deep learning in detecting early-stage cancers. We also explore applications of AI in genomics and biomarker discovery, liquid biopsies, and non-invasive diagnoses. In therapeutic interventions, AI-based clinical decision support systems, individualized treatment planning, and AI-facilitated drug discovery are transforming precision cancer therapies. The review also evaluates the effects of AI on radiation therapy, robotic surgery, and patient management, including survival predictions, remote monitoring, and AI-facilitated clinical trials. Finally, we discuss important challenges such as data privacy, interpretability, and regulatory issues, and recommend future directions that involve the use of federated learning, synthetic biology, and quantum-boosted AI. This review highlights the groundbreaking potential of AI to revolutionize cancer care by making diagnostics, treatments, and patient management more precise, efficient, and personalized.","url":"https://pubmed.ncbi.nlm.nih.gov/40457408/","authors":["Tiwari A","Mishra S","Kuo TR","Ashutosh Tiwari","Soumya Nandan Mishra","Tsung‐Rong Kuo"],"tags":["Interpretability","Personalized medicine","Precision medicine","Medical diagnosis","Artificial intelligence"],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun 2","doi":"10.1186/s12943-025-02369-9","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"pmid:40453738","name":"From bites to bytes: understanding how and why individual malaria risk varies using artificial intelligence and causal inference.","source":"pubmed","abstract":"With an estimated 263 million cases recorded worldwide in 2023, malaria remains a major global health challenge, particularly in tropical regions with limited healthcare access. Beyond its health impact, malaria disrupts education, economic development, and social equality. While traditional research has focused on biological factors underlying human-mosquito interactions, growing evidence highlights the complex interplay of environmental, behavioral, and socioeconomic factors, alongside mobility and both human and parasite genetics, in shaping transmission dynamics, recurrence patterns, and control effectiveness. This work shows how integrating Artificial Intelligence (AI), Machine Learning (ML), and Causal Inference can advance malaria research by identifying context-specific risk factors, uncovering causal mechanisms, and informing more effective, targeted interventions. Drawing on the M&#xe2;ncio Lima cohort, a longitudinal, multimodal study of malaria risk in Brazil's main urban hotspot, and related studies in the Amazon, we highlight how rigorous, data-driven approaches can address the substantial variability in malaria risk across individuals and communities. AI-driven methods facilitate the integration of diverse high-dimensional datasets to uncover intricate patterns and improve individual risk stratification. Federated learning enables collaborative analysis across regions while preserving data privacy. Meanwhile, causal discovery and effect identification tools further strengthen these approaches by distinguishing genuine causal relationships from spurious associations. Together, these approaches offer a principled, scalable, and privacy-preserving framework that enables researchers to move beyond predictive modeling toward actionable causal insights. This shift supports precision public health strategies tailored to vulnerable populations, fostering more equitable and sustainable malaria control and contributing to the reduction of the global malaria burden.","url":"https://pubmed.ncbi.nlm.nih.gov/40453738/","authors":["Ribeiro AH","Soler JMP","Corder RM","Ferreira MU","Heider D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fgene.2025.1599826","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40441996","name":"Artificial Intelligence Augmented Cerebral Nuclear Imaging.","source":"pubmed","abstract":"Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has significant potential to advance the capabilities of nuclear neuroimaging. The current and emerging applications of ML and DL in the processing, analysis, enhancement and interpretation of SPECT and PET imaging are explored for brain imaging. Key developments include automated image segmentation, disease classification, and radiomic feature extraction, including lower dimensionality first and second order radiomics, higher dimensionality third order radiomics and more abstract fourth order deep radiomics. DL-based reconstruction, attenuation correction using pseudo-CT generation, and denoising of low-count studies have a role in enhancing image quality. AI has a role in sustainability through applications in radioligand design and preclinical imaging while federated learning addresses data security challenges to improve research and development in nuclear cerebral imaging. There is also potential for generative AI to transform the nuclear cerebral imaging space through solutions to data limitations, image enhancement, patient-centered care, workflow efficiencies and trainee education. Innovations in ML and DL are re-engineering the nuclear neuroimaging ecosystem and reimagining tomorrow's precision medicine landscape.","url":"https://pubmed.ncbi.nlm.nih.gov/40441996/","authors":["Currie GM","Hawk KE"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul","doi":"10.1053/j.semnuclmed.2025.05.005","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40438823","name":"Blockchain for Securing AI-Driven Healthcare Systems: A Systematic Review and Future Research Perspectives.","source":"pubmed","abstract":"The integration of AI&#xa0;in healthcare has significantly advanced diagnostics, patient monitoring, and personalized treatments. However, the reliance on vast datasets raises critical concerns about data privacy, security, and trustworthiness. Blockchain technology, with its decentralized and immutable nature, has emerged as a promising solution to these challenges. This systematic review aims to explore the role of blockchain in securing AI-driven healthcare systems, evaluating its potential to enhance data security, privacy, and interoperability while identifying key challenges and future research directions. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, conducting a comprehensive search across databases such as IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, Scopus, Web of Science, and PubMed. Inclusion criteria focused on peer-reviewed studies discussing blockchain and AI integration in healthcare, while exclusion criteria eliminated irrelevant or non-empirical studies. Data extraction captured study characteristics, AI algorithms, blockchain types, key findings, and limitations. Quality assessment was performed using the&#xa0;Joanna Briggs Institute (JBI) Critical Appraisal Checklist, evaluating methodological rigor, innovation, and reproducibility. The review included 15 studies highlighting blockchain's role in securing AI-driven healthcare systems. Key findings demonstrated blockchain's effectiveness in enabling decentralized data sharing (e.g., federated learning (FL)), enhancing data integrity, and improving diagnostic accuracy. However, challenges such as scalability, interoperability, and regulatory compliance were recurrent. The quality assessment revealed moderate-to-high methodological quality but underscored gaps in reproducibility and real-world validation. Blockchain technology holds transformative potential for securing AI-driven healthcare systems by addressing critical privacy and security concerns. However, the field remains nascent, with significant hurdles in scalability, technical standardization, and ethical governance. Future research should prioritize hybrid blockchain architectures, clinical trials, and interdisciplinary collaboration to bridge these gaps and realize the full potential of blockchain-AI integration in healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/40438823/","authors":["Kasralikar P","Polu OR","Chamarthi B","Veer Samara Sihman Bharattej Rupavath R","Patel S","Tumati R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Apr","doi":"10.7759/cureus.83136","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40433606","name":"A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions.","source":"pubmed","abstract":"This review provides a thorough and organized overview of machine learning (ML) applications in predicting heart disease, covering technological advancements, challenges, and future prospects. As cardiovascular diseases (CVDs) are the leading cause of global mortality, there is an urgent demand for early and precise diagnostic tools. ML models hold considerable potential by utilizing large-scale healthcare data to enhance predictive diagnostics. To systematically investigate this field, the literature is organized into five thematic categories such as \"Heart Disease Detection and Diagnostics,\" \"Machine Learning Models and Algorithms for Healthcare,\" \"Feature Engineering and Optimization Techniques,\" \"Emerging Technologies in Healthcare,\" and \"Applications of AI Across Diseases and Conditions.\" The review incorporates performance benchmarking of various ML models, highlighting that hybrid deep learning (DL) frameworks, e.g., convolutional neural network-long short-term memory (CNN-LSTM) consistently outperform traditional models in terms of sensitivity, specificity, and area under the curve (AUC). Several real-world case studies are presented to demonstrate the successful deployment of ML models in clinical and wearable settings. This review showcases the progression of ML approaches from traditional classifiers to hybrid DL structures and federated learning (FL) frameworks. It also discusses ethical issues, dataset limitations, and model transparency. The conclusions provide important insights for the development of artificial intelligence (AI) powered, clinically applicable heart disease prediction systems.","url":"https://pubmed.ncbi.nlm.nih.gov/40433606/","authors":["Kumar R","Garg S","Kaur R","Johar MGM","Singh S","Menon SV","Kumar P","Hadi AM","Hasson SA","Lozanović J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/frai.2025.1583459","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40427114","name":"Role of Artificial Intelligence in Musculoskeletal Interventions.","source":"pubmed","abstract":"Artificial intelligence (AI) has rapidly emerged as a transformative force in musculoskeletal imaging and interventional radiology. This article explores how AI-based methods-including machine learning (ML) and deep learning (DL)-streamline diagnostic processes, guide interventions, and improve patient outcomes. Key applications discussed include ultrasound-guided procedures for joints, nerves, and tumor-targeted interventions, along with CT-guided biopsies and ablations, and fluoroscopy-guided facet joint and nerve block injections. AI-powered segmentation algorithms, real-time feedback systems, and dose-optimization protocols collectively enable greater precision, operator consistency, and patient safety. In rehabilitation, AI-driven wearables and predictive models facilitate personalized exercise programs that can accelerate recovery and enhance long-term function. While challenges persist-such as data standardization, regulatory hurdles, and clinical adoption-ongoing interdisciplinary collaboration, federated learning models, and the integration of genomic and environmental data hold promise for expanding AI's capabilities. As personalized medicine continues to advance, AI is poised to refine risk stratification, reduce radiation exposure, and support minimally invasive, patient-specific interventions, ultimately reshaping musculoskeletal care from early detection and diagnosis to individualized treatment and rehabilitation.","url":"https://pubmed.ncbi.nlm.nih.gov/40427114/","authors":["Dubey A","Uldin H","Khan Z","Panchal H","Iyengar KP","Botchu R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 May 10","doi":"10.3390/cancers17101615","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40425787","name":"Advancing breast, lung and prostate cancer research with federated learning. A systematic review.","source":"pubmed","abstract":"Federated learning (FL) is advancing cancer research by enabling privacy-preserving collaborative training of machine learning (ML) models on diverse, multi-centre data. This systematic review synthesises current knowledge on state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. Unlike previous surveys, we critically evaluate FL's real-world implementation and impact, demonstrating its effectiveness in enhancing ML generalisability and performance in clinical settings. Our analysis reveals that FL outperformed centralised ML in 15 out of 25 studies, spanning diverse models and clinical applications, including multi-modal integration for precision medicine. Despite challenges identified in reproducibility and standardisation, FL demonstrates substantial potential for advancing cancer research. We propose future research focus on addressing these limitations and investigating advanced FL methods to fully harness data diversity and realise the transformative power of cutting-edge FL in cancer care.","url":"https://pubmed.ncbi.nlm.nih.gov/40425787/","authors":["Ankolekar A","Boie S","Abdollahyan M","Gadaleta E","Hasheminasab SA","Yang G","Beauville C","Dikaios N","Kastis GA","Bussmann M","Chelala C","Khalid S","Kruger H","Lambin P","Papanastasiou G","OPTIMA Consortium"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 May 27","doi":"10.1038/s41746-025-01591-5","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40406780","name":"Reinforcement Learning in Personalized Medicine: A Comprehensive Review of Treatment Optimization Strategies.","source":"pubmed","abstract":"Reinforcement learning (RL), a subset of artificial intelligence, is gaining momentum in personalized medicine due to its ability to model dynamic, sequential decision-making. Unlike traditional machine learning approaches, RL systems adapt treatment protocols based on patient-specific responses and evolving health states, offering a robust strategy for optimizing individualized care. This review explores the integration of RL into personalized medicine across diverse clinical domains, including oncology, chronic disease management, psychiatry, infectious diseases, and rehabilitation. Applications such as chemotherapy scheduling, insulin dosing, personalized antidepressant treatment, and ICU management illustrate RL's capacity to improve therapeutic outcomes by maximizing long-term clinical benefits. Key methodological components, including data integration, reward signal engineering, and interpretability challenges, are discussed alongside solutions such as explainable AI tools, surrogate models, and federated learning. Ethical and regulatory considerations are also examined, highlighting issues such as patient consent, algorithmic bias, and evolving guidelines from regulatory bodies like the Food and Drug Administration&#xa0;and the European Medicines Agency. The review emphasizes the importance of interdisciplinary collaboration and clinician engagement for the successful deployment of RL in healthcare settings. RL presents a transformative framework for delivering adaptive, equitable, and patient-centered treatment strategies. Future research should focus on implementing it safely, scalably, and transparently to fully harness its potential.","url":"https://pubmed.ncbi.nlm.nih.gov/40406780/","authors":["K B","Venkatesan L","Benjamin LS","K V","Satchi NS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Apr","doi":"10.7759/cureus.82756","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40405638","name":"An overview of artificial intelligence and machine learning in shoulder surgery.","source":"pubmed","abstract":"Machine learning (ML), a subset of artificial intelligence (AI), utilizes advanced algorithms to learn patterns from data, enabling accurate predictions and decision-making without explicit programming. In orthopedic surgery, ML is transforming clinical practice, particularly in shoulder arthroplasty and rotator cuff tears (RCTs) management. This review explores the fundamental paradigms of ML, including supervised, unsupervised, and reinforcement learning, alongside key algorithms such as XGBoost, neural networks, and generative adversarial networks. In shoulder arthroplasty, ML accurately predicts postoperative outcomes, complications, and implant selection, facilitating personalized surgical planning and cost optimization. Predictive models, including ensemble learning methods, achieve over 90% accuracy in forecasting complications, while neural networks enhance surgical precision through AI-assisted navigation. In RCTs treatment, ML enhances diagnostic accuracy using deep learning models on magnetic resonance imaging and ultrasound, achieving area under the curve values exceeding 0.90. ML models also predict tear reparability with 85% accuracy and postoperative functional outcomes, including range of motion and patient-reported outcomes. Despite remarkable advancements, challenges such as data variability, model interpretability, and integration into clinical workflows persist. Future directions involve federated learning for robust model generalization and explainable AI to enhance transparency. ML continues to revolutionize orthopedic care by providing data-driven, personalized treatment strategies and optimizing surgical outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/40405638/","authors":["Cho SH","Kim YS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun","doi":"10.5397/cise.2025.00185","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40366260","name":"Privacy-preserving Federated Learning and Uncertainty Quantification in Medical Imaging.","source":"pubmed","abstract":"Artificial intelligence (AI) has demonstrated strong potential in automating medical imaging tasks, with potential applications across disease diagnosis, prognosis, treatment planning, and posttreatment surveillance. However, privacy concerns surrounding patient data remain a major barrier to the widespread adoption of AI in clinical practice, because large and diverse training datasets are essential for developing accurate, robust, and generalizable AI models. Federated learning offers a privacy-preserving solution by enabling collaborative model training across institutions without sharing sensitive data. Instead, model parameters, such as model weights, are exchanged between participating sites. Despite its potential, federated learning is still in its early stages of development and faces several challenges. Notably, sensitive information can still be inferred from the shared model parameters. Additionally, postdeployment data distribution shifts can degrade model performance, making uncertainty quantification essential. In federated learning, this task is particularly challenging due to data heterogeneity across participating sites. This review provides a comprehensive overview of federated learning, privacy-preserving federated learning, and uncertainty quantification in federated learning. Key limitations in current methodologies are identified, and future research directions are proposed to enhance data privacy and trustworthiness in medical imaging applications. Keywords: Supervised Learning, Perception, Neural Networks, Radiology-Pathology Integration Supplemental material is available for this article. &#xa9; RSNA, 2025.","url":"https://pubmed.ncbi.nlm.nih.gov/40366260/","authors":["Koutsoubis N","Waqas A","Yilmaz Y","Ramachandran RP","Schabath MB","Rasool G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1148/ryai.240637","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"pmid:40334638","name":"Artificial intelligence in pediatric otolaryngology: A state-of-the-art review of opportunities and pitfalls.","source":"pubmed","abstract":"Artificial Intelligence (AI) and machine learning (ML) have transformative potential in enhancing diagnostics, treatment planning, and patient management. However, their application in pediatric otolaryngology remains limited as the unique physiological and developmental characteristics of children require tailored AI applications, highlighting a gap in knowledge.","url":"https://pubmed.ncbi.nlm.nih.gov/40334638/","authors":["Navarathna N","Kanhere A","Gomez C","Isaiah A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul","doi":"10.1016/j.ijporl.2025.112369","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40327475","name":"Toward Quantum Federated Learning.","source":"pubmed","abstract":"Quantum federated learning (QFL) is an emerging interdisciplinary field that merges the principles of quantum computing (QC) and federated learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process. Currently, there is no comprehensive survey for this interdisciplinary field. This review offers a thorough, holistic examination of QFL. We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL. We discuss the current state of research in this rapidly evolving field, identify challenges and opportunities associated with integrating these technologies, and outline future directions and open research questions. We propose a unique taxonomy of QFL techniques, categorized according to their characteristics and the quantum techniques employed. As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries, driving innovation and addressing challenges related to data privacy, security, and resource optimization. This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.","url":"https://pubmed.ncbi.nlm.nih.gov/40327475/","authors":["Ren C","Yan R","Zhu H","Yu H","Xu M","Shen Y","Xu Y","Xiao M","Dong ZY","Skoglund M","Niyato D","Kwek LC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Sep","doi":"10.1109/TNNLS.2025.3552643","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40322605","name":"Breast Cancer Detection Using Convolutional Neural Networks: A Deep Learning-Based Approach.","source":"pubmed","abstract":"Breast cancer remains one of the leading causes of mortality among women, particularly in low- and middle-income countries, where limited healthcare access and delayed diagnosis contribute to poor outcomes. Deep learning, especially convolutional neural networks (CNNs), has shown remarkable efficacy in breast cancer detection through automated image analysis, reducing reliance on manual interpretation. This study provides a comprehensive review of recent advancements in CNN-based breast cancer detection, evaluating deep learning architectures, feature extraction techniques, and optimization strategies. A comparative analysis of CNNs, recurrent neural networks (RNNs), and hybrid models highlights their strengths, limitations, and applicability in medical image classification. Using a dataset of 569 instances with 33 tumor morphology features, various deep learning architectures - including CNNs, long short-term memory networks (LSTMs), and multilayer perceptrons (MLPs) - were implemented, achieving classification accuracies between 89% and 98%. The study underscores the significance of data augmentation, transfer learning, and feature selection in improving model performance. Hybrid CNN-based models demonstrated superior predictive accuracy by capturing spatial and sequential dependencies within tumor feature sets. The findings support the potential of AI-driven breast cancer detection in clinical applications, reducing diagnostic errors and improving early detection rates. Future research should explore transformer-based models, federated learning, and explainable AI techniques to enhance interpretability, robustness, and generalization across diverse datasets.","url":"https://pubmed.ncbi.nlm.nih.gov/40322605/","authors":["Nasir F","Rahman S","Nasir N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 May","doi":"10.7759/cureus.83421","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40321117","name":"Explicating the transformative role of artificial intelligence in designing targeted nanomedicine.","source":"pubmed","abstract":"Artificial intelligence (AI) has emerged as a transformative force in nanomedicine, revolutionizing drug delivery, diagnostics, and personalized treatment. While nanomedicine offers precise targeted drug delivery and reduced toxic effects, its clinical translation is hindered by biological complexity, unpredictable in vivo behavior, and inefficient trial-and-error approaches.","url":"https://pubmed.ncbi.nlm.nih.gov/40321117/","authors":["Akhtar M","Nehal N","Gull A","Parveen R","Khan S","Khan S","Ali J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul","doi":"10.1080/17425247.2025.2502022","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40302775","name":"Exploring the integration of medical and preventive chronic disease health management in the context of big data.","source":"pubmed","abstract":"Chronic non-communicable diseases (NCDs) pose a significant global health burden, exacerbated by aging populations and fragmented healthcare systems. This study employs a comprehensive literature review method to systematically evaluate the integration of medical and preventive services for chronic disease management in the context of big data, focusing on pre-hospital risk prediction, in-hospital clinical prevention, and post-hospital follow-up optimization. Through synthesizing existing research, we propose a novel framework that includes the development of machine learning models and interoperable health information platforms for real-time data sharing. The analysis reveals significant regional disparities in implementation efficacy, with developed eastern regions demonstrating advanced closed-loop management via unified platforms, while western rural areas struggle with manual workflows and data fragmentation. The integration of explainable AI (XAI) and blockchain-secured care pathways enhances clinical decision-making while ensuring GDPR-compliant data governance. The study advocates for phased implementation strategies prioritizing data standardization, federated learning architectures, and community-based health literacy programs to bridge existing disparities. Results show a 30-35% reduction in redundant diagnostics and a 15-20% risk mitigation for cardiometabolic disorders through precision interventions, providing a scalable roadmap for resilient public health systems aligned with the \"Healthy China\" initiative.","url":"https://pubmed.ncbi.nlm.nih.gov/40302775/","authors":["Wang Y","Deng R","Geng X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fpubh.2025.1547392","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40298358","name":"Multimodal Generative AI for Anatomic Pathology-A Review of Current Applications to Envisage the Future Direction.","source":"pubmed","abstract":"This review focuses on the purported applications of multimodal Gen-AI models for anatomic pathology image analysis and interpretation to predict future directions. A scoping review was conducted to explore the applications of multimodal Gen-AI models in advancing histopathology image analysis. A comprehensive search was conducted using electronic databases for relevant articles published within the past year (July 1, 2023 to June 30, 2024). The selected articles were critically analyzed to identify and summarize the applications of multimodal Gen-AI in anatomic pathology image analysis. Multimodal Gen AI models reported in the literature claim moderate to high accuracy on tasks including image classification, segmentation, and text-to-image retrieval. This review demonstrates the potential of multimodal Gen AI models for useful applications in pathology, including assisting with diagnoses, generating data for education and research, and detection of molecular features from anatomic pathology images. These models use data from a few academic institutions thus they require validation on diverse real-world data. There is an urgent need to build consensus models for optimal model performance through multicenter collaboration using a federated learning approach and the use of carefully curated synthetic anatomic pathology data. These models also need to achieve reliability, generalizability and meet the standards required for clinical use. Despite the rigorous need for evaluation and the need to address genuine concerns, multimodal GenAI models present a promising perspective for the advancement and scalability of anatomic pathology.","url":"https://pubmed.ncbi.nlm.nih.gov/40298358/","authors":["Ullah E","Baig MM","Waqas A","Rasool G","Singh R","Shandilya A","GholamHossieni H","Parwani AV"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 May 1","doi":"10.1097/PAP.0000000000000498","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40283559","name":"Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care.","source":"pubmed","abstract":"Background: Artificial intelligence (AI) is rapidly transforming thoracic surgery by enhancing diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative management. AI-driven technologies, including machine learning (ML), deep learning, computer vision, and robotic-assisted surgery, have the potential to optimize clinical workflows and improve patient outcomes. However, challenges such as data integration, ethical concerns, and regulatory barriers must be addressed to ensure AI's safe and effective implementation. This review aims to analyze the current applications, benefits, limitations, and future directions of AI in thoracic surgery. Methods: This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was performed using PubMed, Scopus, Web of Science, and Cochrane Library for studies published up to January 2025. Relevant articles were selected based on predefined inclusion and exclusion criteria, focusing on AI applications in thoracic surgery, including diagnostics, robotic-assisted surgery, intraoperative guidance, and postoperative care. A risk of bias assessment was conducted using the Cochrane Risk of Bias Tool and ROBINS-I for non-randomized studies. Results: Out of 279 identified studies, 36 met the inclusion criteria for qualitative synthesis, highlighting AI's growing role in diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative care in thoracic surgery. AI-driven imaging analysis and radiomics have improved pulmonary nodule detection, lung cancer classification, and lymph node metastasis prediction, while robotic-assisted thoracic surgery (RATS) has enhanced surgical accuracy, reduced operative times, and improved recovery rates. Intraoperatively, AI-powered image-guided navigation, augmented reality (AR), and real-time decision-support systems have optimized surgical planning and safety. Postoperatively, AI-driven predictive models and wearable monitoring devices have enabled early complication detection and improved patient follow-up. However, challenges remain, including algorithmic biases, a lack of multicenter validation, high implementation costs, and ethical concerns regarding data security and clinical accountability. Despite these limitations, AI has shown significant potential to enhance surgical outcomes, requiring further research and standardized validation for widespread adoption. Conclusions: AI is poised to revolutionize thoracic surgery by enhancing decision-making, improving patient outcomes, and optimizing surgical workflows. However, widespread adoption requires addressing key limitations through multicenter validation studies, standardized AI frameworks, and ethical AI governance. Future research should focus on digital twin technology, federated learning, and explainable AI (XAI) to improve AI interpretability, reliability, and accessibility. With continued advancements and responsible integration, AI will play a pivotal role in shaping the next generation of precision thoracic surgery.","url":"https://pubmed.ncbi.nlm.nih.gov/40283559/","authors":["Leivaditis V","Maniatopoulos AA","Lausberg H","Mulita F","Papatriantafyllou A","Liolis E","Beltsios E","Adamou A","Kontodimopoulos N","Dahm M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Apr 16","doi":"10.3390/jcm14082729","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40283528","name":"The Transformative Role of Artificial Intelligence in Plastic and Reconstructive Surgery: Challenges and Opportunities.","source":"pubmed","abstract":"Background/Objectives : This study comprehensively examines how artificial intelligence (AI) technologies are transforming clinical practice in plastic and reconstructive surgery across the entire patient care continuum, with the specific objective of identifying evidence-based applications, implementation challenges, and emerging opportunities that will shape the future of the specialty. Methods : A comprehensive narrative review was conducted analyzing the integration of AI technologies in plastic surgery, including preoperative planning, intraoperative applications, postoperative monitoring, and quality improvement. Challenges related to implementation, ethics, and regulatory frameworks were also examined, along with emerging technological trends that will shape future practice. Results : AI applications in plastic surgery demonstrate significant potential across multiple domains. In preoperative planning, AI enhances risk assessment, outcome prediction, and surgical simulation. Intraoperatively, AI-assisted robotics enables increased precision and technical capabilities beyond human limitations, particularly in microsurgery. Postoperatively, AI improves complication detection, pain management, and outcomes assessment. Despite these benefits, implementation faces challenges including data privacy concerns, algorithmic bias, liability questions, and the need for appropriate regulatory frameworks. Future directions include multimodal AI systems, federated learning approaches, and integration with extended reality and regenerative medicine technologies. Conclusions : The integration of AI into plastic surgery represents a significant opportunity to enhance surgical precision, improve outcome prediction, and expand the boundaries of what is surgically possible. However, successful implementation requires addressing ethical considerations and maintaining the human elements of surgical care. Plastic surgeons must actively engage with AI development to ensure these technologies address genuine clinical needs while aligning with the specialty's core values of restoring form and function, alleviating suffering, and enhancing quality of life.","url":"https://pubmed.ncbi.nlm.nih.gov/40283528/","authors":["Mansoor M","Ibrahim AF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Apr 15","doi":"10.3390/jcm14082698","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40279838","name":"Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis.","source":"pubmed","abstract":"Federated Learning (FL) has emerged as a promising approach for collaborative medical image analysis while preserving data privacy, making it particularly suitable for radiomics tasks. This paper presents a systematic meta-analysis of recent surveys on Federated Learning in Medical Imaging (FL-MI), published in reputable venues over the past five years. We adopt the PRISMA methodology, categorizing and analyzing the existing body of research in FL-MI. Our analysis identifies common trends, challenges, and emerging strategies for implementing FL in medical imaging, including handling data heterogeneity, privacy concerns, and model performance in non-IID settings. The paper also highlights the most widely used datasets and a comparison of adopted machine learning models. Moreover, we examine FL frameworks in FL-MI applications, such as tumor detection, organ segmentation, and disease classification. We identify several research gaps, including the need for more robust privacy protection. Our findings provide a comprehensive overview of the current state of FL-MI and offer valuable directions for future research and development in this rapidly evolving field.","url":"https://pubmed.ncbi.nlm.nih.gov/40279838/","authors":["Raza A","Guzzo A","Ianni M","Lappano R","Zanolini A","Maggiolini M","Fortino G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul","doi":"10.1016/j.cmpb.2025.108768","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40253445","name":"Sentimental analysis based federated learning privacy detection in fake web recommendations using blockchain model.","source":"pubmed","abstract":"Recently, a documented increase has been observed in fake news and broadcast of such reports leads to grave danger to individual as well as societal welfare. There's a danger of political collapse and a subsequent devastating loss of public confidence. The overwhelming quantity of news spread online leads towards impractical manual verification and due to the subtle distinctions within language, detecting fake news is an arduous challenge due to the ability to produce coherent and significant. Nowadays advanced neural language models (NLMs) are frequently utilised widespread in sequence generation domains. Additionally, they may be used to create false reviews, which can subsequently be used to target online review platforms and sway consumers' purchasing choices. This research explores the application of blockchain technology and sentiment analysis to create a privacy-focused system for detecting and analyzing fake web recommendations. The input data comprises sentiment-based features extracted from web recommendations. A generative convolutional Bernoulli bayes neural network is employed for the feature extraction and classification. Further, to strengthen network privacy, blockchain technology has been integrated with federated learning. This work offers an experimental analysis of diverse sentiment data-driven fake recommendation datasets, evaluating performance using accuracy, precision, recall, and F-measure metrics. A comprehensive evaluation of effectiveness is performed for each classifier. Results from the classification process indicated that a predictive model could be developed, leveraging tweet data, to distinguish between spam and non-spam content and to determine associated sentiment. The proposed method achieved 99% accuracy, 94% precision, 93% area under the curve, 94% recall, and 96% F-measure.","url":"https://pubmed.ncbi.nlm.nih.gov/40253445/","authors":["Samriya JK","Kumar A","Bhansali A","Malik M","Arya V","Alhalabi W","Alsulami BS","Gupta BB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Apr 19","doi":"10.1038/s41598-025-97800-y","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40218782","name":"YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11.","source":"pubmed","abstract":"Automated fabric defect detection is crucial for improving quality control, reducing manual labor, and optimizing efficiency in the textile industry. Traditional inspection methods rely heavily on human oversight, which makes them prone to subjectivity, inefficiency, and inconsistency in high-speed manufacturing environments. This review systematically examines the evolution of the You Only Look Once (YOLO) object detection framework from YOLO-v1 to YOLO-v11, emphasizing architectural advancements such as attention-based feature refinement and Transformer integration and their impact on fabric defect detection. Unlike prior studies focusing on specific YOLO variants, this work comprehensively compares the entire YOLO family, highlighting key innovations and their practical implications. We also discuss the challenges, including dataset limitations, domain generalization, and computational constraints, proposing future solutions such as synthetic data generation, federated learning, and edge AI deployment. By bridging the gap between academic advancements and industrial applications, this review is a practical guide for selecting and optimizing YOLO models for fabric inspection, paving the way for intelligent quality control systems.","url":"https://pubmed.ncbi.nlm.nih.gov/40218782/","authors":["Mao M","Hong M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Apr 3","doi":"10.3390/s25072270","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40218659","name":"AI-Enabled IoT for Food Computing: Challenges, Opportunities, and Future Directions.","source":"pubmed","abstract":"Food computing refers to the integration of digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and data-driven approaches, to address various challenges in the food sector. It encompasses a wide range of technologies that improve the efficiency, safety, and sustainability of food systems, from production to consumption. It represents a transformative approach to addressing challenges in the food sector by integrating AI, the IoT, and data-driven methodologies. Unlike traditional food systems, which primarily focus on production and safety, food computing leverages AI for intelligent decision making and the IoT for real-time monitoring, enabling significant advancements in areas such as supply chain optimization, food safety, and personalized nutrition. This review highlights AI applications, including computer vision for food recognition and quality assessment, Natural Language Processing for recipe analysis, and predictive modeling for dietary recommendations. Simultaneously, the IoT enhances transparency and efficiency through real-time monitoring, data collection, and device connectivity. The convergence of these technologies relies on diverse data sources, such as images, nutritional databases, and user-generated logs, which are critical to enabling traceability and tailored solutions. Despite its potential, food computing faces challenges, including data heterogeneity, privacy concerns, scalability issues, and regulatory constraints. To address these, this paper explores solutions like federated learning for secure on-device data processing and blockchain for transparent traceability. Emerging trends, such as edge AI for real-time analytics and sustainable practices powered by AI-IoT integration, are also discussed. This review offers actionable insights to advance the food sector through innovative and ethical technological frameworks.","url":"https://pubmed.ncbi.nlm.nih.gov/40218659/","authors":["Dakhia Z","Russo M","Merenda M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Mar 28","doi":"10.3390/s25072147","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40212895","name":"Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.","source":"pubmed","abstract":"Type 2 Diabetes Mellitus (T2DM) remains a critical global health challenge, necessitating robust predictive models to enable early detection and personalized interventions. This study presents a comprehensive bibliometric and systematic review of 33 years (1991-2024) of research on machine learning (ML) and artificial intelligence (AI) applications in T2DM prediction. It highlights the growing complexity of the field and identifies key trends, methodologies, and research gaps.","url":"https://pubmed.ncbi.nlm.nih.gov/40212895/","authors":["Kiran M","Xie Y","Anjum N","Ball G","Pierscionek B","Russell D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fdgth.2025.1557467","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40206529","name":"Application of Federated Learning in Cardiology: Key Challenges and Potential Solutions.","source":"pubmed","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/40206529/","authors":["Rahman MS","Karmarkar C","Islam SMS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Dec","doi":"10.1016/j.mcpdig.2024.09.005","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40199301","name":"Precision in Prevention and Health Surveillance: How Artificial Intelligence May Improve the Time of Identification of Health Concerns through Social Media Content Analysis.","source":"pubmed","abstract":"To explore how artificial intelligence (AI) methodologies, particularly through the analysis of social media content, can enhance \"precision in prevention and health surveillance\" (2024 Yearbook topic). The focus is on leveraging advanced data analytics to improve the timeliness and accuracy of identifying emerging health concerns, thus enabling more proactive and effective health interventions.","url":"https://pubmed.ncbi.nlm.nih.gov/40199301/","authors":["Staccini P","Lau AYS","Findings from the Yearbook 2024 Section on Consumer Health Informatics"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Aug","doi":"10.1055/s-0044-1800736","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40199300","name":"Consumer Health Informatics to Advance Precision Prevention.","source":"pubmed","abstract":"Consumer health informatics (CHI) has the potential to disrupt traditional but unsustainable break-fix models of healthcare and catalyse precision prevention of chronic disease - a preventable global burden. This perspective article reviewed how consumer health informatics can advance precision prevention across four research and practice areas: (1) public health policy and practice (2) individualised disease risk assessment (3) early detection and monitoring of disease (4) tailored intervention of modifiable health determinants.","url":"https://pubmed.ncbi.nlm.nih.gov/40199300/","authors":["Canfell OJ","Woods L","Robins D","Sullivan C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Aug","doi":"10.1055/s-0044-1800735","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40163619","name":"Advances in Infant Cry Paralinguistic Classification-Methods, Implementation, and Applications: Systematic Review.","source":"pubmed","abstract":"Effective communication is essential for human interaction; yet, infants can only express their needs through various types of suggestive cries. Traditional approaches of interpreting infant cries are often subjective, inconsistent, and slow, leaving gaps in timely, precise caregiving responses. A precise interpretation of infant cries can potentially provide valuable insights into the infant's health, needs, and well-being, enabling prompt medical or caregiving actions.","url":"https://pubmed.ncbi.nlm.nih.gov/40163619/","authors":["Owino G","Shibwabo B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Apr 29","doi":"10.2196/69457","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40131188","name":"Artificial intelligence and its application in clinical microbiology.","source":"pubmed","abstract":"Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, advancements, and integration challenges in clinical microbiology.","url":"https://pubmed.ncbi.nlm.nih.gov/40131188/","authors":["Mairi A","Hamza L","Touati A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul","doi":"10.1080/14787210.2025.2484284","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40126314","name":"Clinical and Operational Applications of Artificial Intelligence and Machine Learning in Pharmacy: A Narrative Review of Real-World Applications.","source":"pubmed","abstract":"Over the past five years, the application of artificial intelligence (AI) including its significant subset, machine learning (ML), has significantly advanced pharmaceutical procedures in community pharmacies, hospital pharmacies, and pharmaceutical industry settings. Numerous notable healthcare institutions, such as Johns Hopkins University, Cleveland Clinic, and Mayo Clinic, have demonstrated measurable advancements in the use of artificial intelligence in healthcare delivery. Community pharmacies have seen a 40% increase in drug adherence and a 55% reduction in missed prescription refills since implementing artificial intelligence (AI) technologies. According to reports, hospital implementations have reduced prescription distribution errors by up to 75% and enhanced the detection of adverse medication reactions by up to 65%. Numerous businesses, such as Atomwise and Insilico Medicine, assert that they have made noteworthy progress in the creation of AI-based medical therapies. Emerging technologies like federated learning and quantum computing have the potential to boost the prediction of protein-drug interactions by up to 300%, despite challenges including high implementation costs and regulatory compliance. The significance of upholding patient-centred care while encouraging technology innovation is emphasised in this review.","url":"https://pubmed.ncbi.nlm.nih.gov/40126314/","authors":["Simpson MD","Qasim HS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Mar 7","doi":"10.3390/pharmacy13020041","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40119679","name":"Beyond traditional orthopaedic data analysis: AI, multimodal models and continuous monitoring.","source":"pubmed","abstract":"Multimodal artificial intelligence (AI) has the potential to revolutionise healthcare by enabling the simultaneous processing and integration of various data types, including medical imaging, electronic health records, genomic information and real-time data. This review explores the current applications and future potential of multimodal AI across healthcare, with a particular focus on orthopaedic surgery. In presurgical planning, multimodal AI has demonstrated significant improvements in diagnostic accuracy and risk prediction, with studies reporting an Area under the receiving operator curve presenting good to excellent performance across various orthopaedic conditions. Intraoperative applications leverage advanced imaging and tracking technologies to enhance surgical precision, while postoperative care has been advanced through continuous patient monitoring and early detection of complications. Despite these advances, significant challenges remain in data integration, standardisation, and privacy protection. Technical solutions such as federated learning (allowing decentralisation of models) and edge computing (allowing data analysis to happen on site or closer to site instead of multipurpose datacenters) are being developed to address these concerns while maintaining compliance with regulatory frameworks. As this field continues to evolve, the integration of multimodal AI promises to advance personalised medicine, improve patient outcomes, and transform healthcare delivery through more comprehensive and nuanced analysis of patient data. Level of Evidence: Level V.","url":"https://pubmed.ncbi.nlm.nih.gov/40119679/","authors":["Oettl FC","Zsidai B","Oeding JF","Hirschmann MT","Feldt R","Tischer T","Samuelsson K","ESSKA Artificial Intelligence Working Group"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun","doi":"10.1002/ksa.12657","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40068020","name":"Big data analytics and machine learning in hematology: Transformative insights, applications and challenges.","source":"pubmed","abstract":"The integration of big data analytics and machine learning (ML) into hematology has ushered in a new era of precision medicine, offering transformative insights into disease management. By leveraging vast and diverse datasets, including genomic profiles, clinical laboratory results, and imaging data, these technologies enhance diagnostic accuracy, enable robust prognostic modeling, and support personalized therapeutic interventions. Advanced ML algorithms, such as neural networks and ensemble learning, facilitate the discovery of novel biomarkers and refine risk stratification for hematological disorders, including leukemias, lymphomas, and coagulopathies. Despite these advancements, significant challenges persist, particularly in the realms of data integration, algorithm validation, and ethical concerns. The heterogeneity of hematological datasets and the lack of standardized frameworks complicate their application, while the \"black-box\" nature of ML models raises issues of reliability and clinical trust. Moreover, safeguarding patient privacy in an era of data-driven medicine remains paramount, necessitating the development of secure and ethical analytical practices. Addressing these challenges is critical to ensuring equitable and effective implementation of these technologies. Collaborative efforts between hematologists, data scientists, and bioinformaticians are pivotal in translating these innovations into real-world clinical practice. Emphasis on developing explainable artificial intelligence models, integrating real-time analytics, and adopting federated learning approaches will further enhance the utility and adoption of these technologies. As big data analytics and ML continue to evolve, their potential to revolutionize hematology and improve patient outcomes remains immense.","url":"https://pubmed.ncbi.nlm.nih.gov/40068020/","authors":["Obeagu EI","Ezeanya CU","Ogenyi FC","Ifu DD"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Mar 7","doi":"10.1097/MD.0000000000041766","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40035151","name":"Overview of artificial intelligence in hand surgery.","source":"pubmed","abstract":"Artificial intelligence has evolved significantly since its inception, becoming a powerful tool in medicine. This paper provides an overview of the core principles, applications and future directions of artificial intelligence in hand surgery. Artificial intelligence has shown promise in improving diagnostic accuracy, predicting outcomes and assisting in patient education. However, despite its potential, its application in hand surgery is still nascent, with most studies being retrospective and limited by small sample sizes. To harness the full potential of artificial intelligence in hand surgery and support broader adoption, more robust, large-scale studies are needed. Collaboration among researchers, through data sharing and federated learning, is essential for advancing artificial intelligence from experimental to clinically validated tools, ultimately enhancing patient care and clinical workflows.","url":"https://pubmed.ncbi.nlm.nih.gov/40035151/","authors":["Ryhänen J","Wong GC","Anttila T","Chung KC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jun","doi":"10.1177/17531934251322723","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:40011118","name":"AI in Breast Cancer Imaging: An Update and Future Trends.","source":"pubmed","abstract":"Breast cancer is one of the most common types of cancer affecting women worldwide. Artificial intelligence (AI) is transforming breast cancer imaging by enhancing diagnostic capabilities across multiple imaging modalities including mammography, digital breast tomosynthesis, ultrasound, magnetic resonance imaging, and nuclear medicines techniques. AI is being applied to diverse tasks such as breast lesion detection and classification, risk stratification, molecular subtyping, gene mutation status prediction, and treatment response assessment, with emerging research demonstrating performance levels comparable to or potentially exceeding those of radiologists. The large foundation models are showing remarkable potential in different breast cancer imaging tasks. Self-supervised learning gives an insight into data inherent correlation, and federated learning is an alternative way to maintain data privacy. While promising results have been obtained so far, data standardization from source, large-scale annotated multimodal datasets, and extensive prospective clinical trials are still needed to fully explore and validate deep learning's clinical utility and address the legal and ethical considerations, which will ultimately determine its widespread adoption in breast cancer care. We hereby provide a review of the most up-to-date knowledge on AI in breast cancer imaging.","url":"https://pubmed.ncbi.nlm.nih.gov/40011118/","authors":["Chen Y","Shao X","Shi K","Rominger A","Caobelli F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 May","doi":"10.1053/j.semnuclmed.2025.01.008","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39999492","name":"Prediction and detection of terminal diseases using Internet of Medical Things: A review.","source":"pubmed","abstract":"The integration of Artificial Intelligence (AI) with the Internet of Medical Things (IoMT) has revolutionized disease prediction and detection, but challenges such as data heterogeneity, privacy concerns, and model generalizability hinder its full potential in healthcare. This review examines these challenges and evaluates the effectiveness of AI-IoMT techniques in predicting chronic and terminal diseases, including cardiovascular conditions, Alzheimer's disease, and cancers. We analyze a range of Machine Learning (ML) and Deep Learning (DL) approaches (e.g., XGBoost, Random Forest, CNN, LSTM), alongside advanced strategies like federated learning, transfer learning, and blockchain, to improve model robustness, data security, and interoperability. Findings highlight that transfer learning and ensemble methods enhance model adaptability across clinical settings, while blockchain and federated learning effectively address privacy and data standardization. Ultimately, the review emphasizes the importance of data harmonization, secure frameworks, and multi-disease models as critical research directions for scalable, comprehensive AI-IoMT solutions in healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/39999492/","authors":["Otapo AT","Othmani A","Khodabandelou G","Ming Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Apr","doi":"10.1016/j.compbiomed.2025.109835","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39988670","name":"Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applications.","source":"pubmed","abstract":"Researchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems.","url":"https://pubmed.ncbi.nlm.nih.gov/39988670/","authors":["Taddese AA","Addis AC","Tam BT"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Feb 23","doi":"10.1186/s40246-025-00716-x","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39975448","name":"Advancing MRI Reconstruction: A Systematic Review of Deep Learning and Compressed Sensing Integration.","source":"pubmed","abstract":"Magnetic resonance imaging (MRI) is a non-invasive imaging modality and provides comprehensive anatomical and functional insights into the human body. However, its long acquisition times can lead to patient discomfort, motion artifacts, and limiting real-time applications. To address these challenges, strategies such as parallel imaging have been applied, which utilize multiple receiver coils to speed up the data acquisition process. Additionally, compressed sensing (CS) is a method that facilitates image reconstruction from sparse data, significantly reducing image acquisition time by minimizing the amount of data collection needed. Recently, deep learning (DL) has emerged as a powerful tool for improving MRI reconstruction. It has been integrated with parallel imaging and CS principles to achieve faster and more accurate MRI reconstructions. This review comprehensively examines DL-based techniques for MRI reconstruction. We categorize and discuss various DL-based methods, including end-to-end approaches, unrolled optimization, and federated learning, highlighting their potential benefits. Our systematic review highlights significant contributions and underscores the potential of DL in MRI reconstruction. Additionally, we summarize key results and trends in DL-based MRI reconstruction, including quantitative metrics, the dataset, acceleration factors, and the progress of and research interest in DL techniques over time. Finally, we discuss potential future directions and the importance of DL-based MRI reconstruction in advancing medical imaging. To facilitate further research in this area, we provide a GitHub repository that includes up-to-date DL-based MRI reconstruction publications and public datasets-https://github.com/mosaf/Awesome-DL-based-CS-MRI.","url":"https://pubmed.ncbi.nlm.nih.gov/39975448/","authors":["Safari M","Eidex Z","Chang CW","Qiu RLJ","Yang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Feb 1","doi":"","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39973411","name":"Privacy-by-Design with Federated Learning will drive future Rare Disease Research.","source":"pubmed","abstract":"Up to 6% of the global population is estimated to be affected by one of about 10,000 distinct rare diseases (RDs). RDs are, to this day, often not understood, and thus, patients are heavily underserved. Most RD studies are chronically underfunded, and research faces inherent difficulties in analyzing scarce data. Furthermore, the creation and analysis of representative datasets are often constrained by stringent data protection regulations, such as the EU General Data Protection Regulation. This review examines the potential of federated learning (FL) as a privacy-by-design approach to training machine learning on distributed datasets while ensuring data privacy by maintaining the local patient data and only sharing model parameters, which is particularly beneficial in the context of sensitive data that cannot be collected in a centralized manner. FL enhances model accuracy by leveraging diverse datasets without compromising data privacy. This is particularly relevant in rare diseases, where heterogeneity and small sample sizes impede the development of robust models. FL further has the potential to enable the discovery of novel biomarkers, enhance patient stratification, and facilitate the development of personalized treatment plans. This review illustrates how FL can facilitate large-scale, cross-institutional collaboration, thereby enabling the development of more accurate and generalizable models for improved diagnosis and treatment of rare diseases. However, challenges such as non-independently distributed data and significant computational and bandwidth requirements still need to be addressed. Future research must focus on applying FL technology for rare disease datasets while exploring standardized protocols for cross-border collaborations that can ultimately pave the way for a new era of privacy-preserving and distributed data-driven rare disease research.","url":"https://pubmed.ncbi.nlm.nih.gov/39973411/","authors":["Süwer S","Ullah MS","Probul N","Maier A","Baumbach J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Jan","doi":"10.1177/22143602241296276","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39961211","name":"From challenges and pitfalls to recommendations and opportunities: Implementing federated learning in healthcare.","source":"pubmed","abstract":"Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centers while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning based methods in healthcare, it remains unclear which ones have potential clinical utility. This review paper considers and analyzes the most recent studies up to May 2024 that describe federated learning based methods in healthcare. After a thorough review, we find that the vast majority are not appropriate for clinical use due to their methodological flaws and/or underlying biases which include but are not limited to privacy concerns, generalization issues, and communication costs. As a result, the effectiveness of federated learning in healthcare is significantly compromised. To overcome these challenges, we provide recommendations and promising opportunities that might be implemented to resolve these problems and improve the quality of model development in federated learning with healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/39961211/","authors":["Li M","Xu P","Hu J","Tang Z","Yang G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Apr","doi":"10.1016/j.media.2025.103497","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39958114","name":"Current Status and Future of Artificial Intelligence in Medicine.","source":"pubmed","abstract":"Artificial intelligence (AI) has rapidly emerged as a transformative force in medicine, revolutionizing various aspects of healthcare from diagnostics and treatment to public health and patient care. This narrative review synthesizes evidence from diverse study designs, exploring the current and future applications of AI in medicine. We highlight AI's role in improving diagnostic accuracy, optimizing treatment strategies, and enhancing patient care through personalized interventions and remote monitoring, drawing upon recent advancements and landmark studies. Emerging trends such as explainable AI and federated learning are also examined. While acknowledging the tremendous potential of AI in medicine, the review also addresses the barriers and ethical challenges that need to be overcome, including concerns about algorithmic bias, transparency, over-reliance, and the potential impact on the healthcare workforce. We emphasize the importance of establishing regulatory guidelines, fostering collaboration between clinicians and AI developers, and ensuring ongoing education for healthcare professionals. Despite these challenges, the future of AI in medicine holds immense promise, with the potential to significantly improve patient outcomes, transform healthcare delivery, and address healthcare disparities.","url":"https://pubmed.ncbi.nlm.nih.gov/39958114/","authors":["Basubrin O"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan","doi":"10.7759/cureus.77561","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39956906","name":"AI and Neurology.","source":"pubmed","abstract":"Artificial Intelligence is influencing medicine on all levels. Neurology, one of the most complex and progressive medical disciplines, is no exception. No longer limited to neuroimaging, where data-driven approaches were initiated, machine and deep learning methodologies are taking neurologic diagnostics, prognostication, predictions, decision making and even therapy to very promising potentials.","url":"https://pubmed.ncbi.nlm.nih.gov/39956906/","authors":["Bösel J","Mathur R","Cheng L","Varelas MS","Hobert MA","Suarez JI"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Feb 17","doi":"10.1186/s42466-025-00367-2","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39941476","name":"Machine Learning in Pediatric Healthcare: Current Trends, Challenges, and Future Directions.","source":"pubmed","abstract":"Background/Objectives : Artificial intelligence (AI) and machine learning (ML) are transforming healthcare by enabling predictive, diagnostic, and therapeutic advancements. Pediatric healthcare presents unique challenges, including limited data availability, developmental variability, and ethical considerations. This narrative review explores the current trends, applications, challenges, and future directions of ML in pediatric healthcare. Methods : A systematic search of the PubMed database was conducted using the query: (\"artificial intelligence\" OR \"machine learning\") AND (\"pediatric\" OR \"paediatric\"). Studies were reviewed to identify key themes, methodologies, applications, and challenges. Gaps in the research and ethical considerations were also analyzed to propose future research directions. Results : ML has demonstrated promise in diagnostic support, prognostic modeling, and therapeutic planning for pediatric patients. Applications include the early detection of conditions like sepsis, improved diagnostic imaging, and personalized treatment strategies for chronic conditions such as epilepsy and Crohn's disease. However, challenges such as data limitations, ethical concerns, and lack of model generalizability remain significant barriers. Emerging techniques, including federated learning and explainable AI (XAI), offer potential solutions. Despite these advancements, research gaps persist in data diversity, model interpretability, and ethical frameworks. Conclusions : ML offers transformative potential in pediatric healthcare by addressing diagnostic, prognostic, and therapeutic challenges. While advancements highlight its promise, overcoming barriers such as data limitations, ethical concerns, and model trustworthiness is essential for its broader adoption. Future efforts should focus on enhancing data diversity, developing standardized ethical guidelines, and improving model transparency to ensure equitable and effective implementation in pediatric care.","url":"https://pubmed.ncbi.nlm.nih.gov/39941476/","authors":["Ganatra HA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan 26","doi":"10.3390/jcm14030807","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39909187","name":"Harnessing AI for enhanced evidence-based laboratory medicine (EBLM).","source":"pubmed","abstract":"The integration of artificial intelligence (AI) into laboratory medicine, is revolutionizing diagnostic accuracy, operational efficiency, and personalized patient care. AI technologies(machine learning, natural language processing and computer vision) advance evidence-based laboratory medicine (EBLM) by automating and optimizing critical processes(formulating clinical questions, conducting literature searches, appraising evidence, and developing clinical guidelines). These reduce the time for systematic reviews, ensuring consistency in appraisal, and enabling real-time updates to guidelines. AI supports personalized medicine by analyzing large datasets, genetic information and electronic health records (EHRs), to tailor diagnostic and treatment plans to patient profiles. Predictive analytics enhance outcomes by leveraging historical data and ongoing monitoring to predict responses and optimize care pathways. Despite the transformative potential, there are challenges. The accuracy, transparency, and explainability of AI algorithms is critical for gaining trust and ensuring ethical deployment. Integration into existing clinical workflows requires collaboration between AI developers and users to ensure seamless user-friendly adoption. Ethical considerations, such as privacy,data security, and algorithmic bias, must also be addressed to mitigate risks and ensure equitable healthcare delivery. Regulatory frameworks, eg. The EU AI Regulation, emphasize transparency, data governance, and human oversight, particularly for high-risk AI systems. The economic and operational benefits are cost savings, improved diagnostic precision, and enhanced patient outcomes. Future trends (federated learning and self-supervised learning), will enhance the scalability and applicability of AI in EBLM, paving the way for a new era of precision medicine. AI in EBLM has the potential to transform healthcare delivery, improve patient outcomes, and advance personalized/precision medicine.","url":"https://pubmed.ncbi.nlm.nih.gov/39909187/","authors":["Pillay TS","Topcu Dİ","Yenice S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Mar 1","doi":"10.1016/j.cca.2025.120181","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39891819","name":"Artificial Intelligence in Ischemic Heart Disease Prevention.","source":"pubmed","abstract":"This review discusses the transformative potential of artificial intelligence (AI) in ischemic heart disease (IHD) prevention. It explores advancements of AI in predictive modeling, biomarker discovery, and cardiovascular imaging. Finally, considerations for clinical integration of AI into preventive cardiology workflows are reviewed.","url":"https://pubmed.ncbi.nlm.nih.gov/39891819/","authors":["Parsa S","Shah P","Doijad R","Rodriguez F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Feb 1","doi":"10.1007/s11886-025-02203-0","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39891313","name":"Predicting survival in malignant glioma using artificial intelligence.","source":"pubmed","abstract":"Malignant gliomas, including glioblastoma, are amongst the most aggressive primary brain tumours, characterised by rapid progression and a poor prognosis. Survival analysis is an essential aspect of glioma management and research, as most studies use time-to-event outcomes to assess overall survival (OS) and progression-free survival (PFS) as key measures to evaluate patients. However, predicting survival using traditional methods such as the Kaplan-Meier estimator and the Cox Proportional Hazards (CPH) model has faced many challenges and inaccuracies. Recently, advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), have enabled significant improvements in survival prediction for glioma patients by integrating multimodal data such as imaging, clinical parameters and molecular biomarkers. This study highlights the comparative effectiveness of imaging-based, non-imaging and combined AI models. Imaging models excel at identifying tumour-specific features through radiomics, achieving high predictive accuracy. Non-imaging approaches also excel in utilising clinical and genetic data to provide complementary insights, whilst combined methods integrate multiple data modalities and have the greatest potential for accurate survival prediction. Limitations include data heterogeneity, interpretability challenges and computational demands, particularly in resource-limited settings. Solutions such as federated learning, lightweight AI models and explainable AI frameworks are proposed to overcome these barriers. Ultimately, the integration of advanced AI techniques promises to transform glioma management by enabling personalised treatment strategies and improved prognostic accuracy.","url":"https://pubmed.ncbi.nlm.nih.gov/39891313/","authors":["Awuah WA","Ben-Jaafar A","Roy S","Nkrumah-Boateng PA","Tan JK","Abdul-Rahman T","Atallah O"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan 31","doi":"10.1186/s40001-025-02339-3","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39860903","name":"A Comprehensive Survey of Deep Learning Approaches in Image Processing.","source":"pubmed","abstract":"The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach of traditional methodologies. This survey offers an in-depth exploration of the DL approaches that have redefined image processing, tracing their evolution from early innovations to the latest state-of-the-art developments. It also analyzes the progression of architectural designs and learning paradigms that have significantly enhanced the ability to process and interpret complex visual data. Key advancements, such as techniques improving model efficiency, generalization, and robustness, are examined, showcasing DL's ability to address increasingly sophisticated image-processing tasks across diverse domains. Metrics used for rigorous model evaluation are also discussed, underscoring the importance of performance assessment in varied application contexts. The impact of DL in image processing is highlighted through its ability to tackle complex challenges and generate actionable insights. Finally, this survey identifies potential future directions, including the integration of emerging technologies like quantum computing and neuromorphic architectures for enhanced efficiency and federated learning for privacy-preserving training. Additionally, it highlights the potential of combining DL with emerging technologies such as edge computing and explainable artificial intelligence (AI) to address scalability and interpretability challenges. These advancements are positioned to further extend the capabilities and applications of DL, driving innovation in image processing.","url":"https://pubmed.ncbi.nlm.nih.gov/39860903/","authors":["Trigka M","Dritsas E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan 17","doi":"10.3390/s25020531","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39856912","name":"Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety.","source":"pubmed","abstract":"This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful capabilities of RNNs (especially their variant LSTM) in time series data modeling. This paper also makes a comparative analysis in many aspects: Firstly, the advantages and disadvantages of traditional food safety detection and risk prediction methods are compared with deep learning technologies such as CNNs and RNNs. Secondly, the similarities and differences between CNNs and fully connected neural networks in processing image data are analyzed. Furthermore, the advantages and disadvantages of RNNs and traditional statistical modeling methods in processing time series data are discussed. Finally, the application directions of CNNs in food safety detection and RNNs in food safety risk prediction are compared. This paper also discusses combining these deep learning models with technologies such as the Internet of Things (IoT), blockchain, and federated learning to improve the accuracy and efficiency of food safety detection and risk warning. Finally, this paper mentions the limitations of RNNs and CNNs in the field of food safety, as well as the challenges in the interpretability of the model, and suggests the use of interpretable artificial intelligence (XAI) technology to improve the transparency of the model.","url":"https://pubmed.ncbi.nlm.nih.gov/39856912/","authors":["Ding H","Hou H","Wang L","Cui X","Yu W","Wilson DI"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan 14","doi":"10.3390/foods14020247","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39852315","name":"The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection.","source":"pubmed","abstract":"Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming and error-prone. The rise of deep learning has led to advanced models for automated brain tumor feature extraction, segmentation, and classification. Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 research papers over past half-decade (2019-2024), this review fills that gap, exploring the latest methods and paradigms, summarizing key concepts, challenges, datasets, and offering insights into future directions for brain tumor detection using deep learning. This review also incorporates an analysis of previous reviews and targets three main aspects: feature extraction, segmentation, and classification. The results revealed that research primarily focuses on Convolutional Neural Networks (CNNs) and their variants, with a strong emphasis on transfer learning using pre-trained models. Other methods, such as Generative Adversarial Networks (GANs) and Autoencoders, are used for feature extraction, while Recurrent Neural Networks (RNNs) are employed for time-sequence modeling. Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. However, the adoption of eXplainable AI (XAI) remains limited, despite its importance in building trust in medical diagnostics. Finally, this review outlines future opportunities, focusing on image quality, underexplored deep learning techniques, expanding datasets, and exploring deeper learning representations and model behavior such as recurrent expansion to advance medical imaging diagnostics.","url":"https://pubmed.ncbi.nlm.nih.gov/39852315/","authors":["Berghout T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Dec 24","doi":"10.3390/jimaging11010002","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39843622","name":"Distributed training of foundation models for ophthalmic diagnosis.","source":"pubmed","abstract":"Vision impairment affects nearly 2.2 billion people globally, and nearly half of these cases could be prevented with early diagnosis and intervention-underscoring the urgent need for reliable and scalable detection methods for conditions like diabetic retinopathy and age-related macular degeneration. Here we propose a distributed deep learning framework that integrates self-supervised and domain-adaptive federated learning to enhance the detection of eye diseases from optical coherence tomography images. We employed a self-supervised, mask-based pre-training strategy to develop a robust foundation encoder. This encoder was trained on seven optical coherence tomography datasets, and we compared its performance under local, centralized, and federated learning settings. Our results show that self-supervised methods-both centralized and federated-improved the area under the curve by at least 10% compared to local models. Additionally, incorporating domain adaptation into the federated learning framework further boosted performance and generalization across different populations and imaging conditions. This approach supports collaborative model development without data sharing, providing a scalable, privacy-preserving solution for effective retinal disease screening and diagnosis in diverse clinical settings.","url":"https://pubmed.ncbi.nlm.nih.gov/39843622/","authors":["Gholami S","Jannat FE","Thompson AC","Ong SSY","Lim JI","Leng T","Tabkhivayghan H","Alam MN"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan 22","doi":"10.1038/s44172-025-00341-5","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39836377","name":"Generative Artificial Intelligence in Anatomic Pathology.","source":"pubmed","abstract":"Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities.","url":"https://pubmed.ncbi.nlm.nih.gov/39836377/","authors":["Brodsky V","Ullah E","Bychkov A","Song AH","Walk EE","Louis P","Rasool G","Singh RS","Mahmood F","Bui MM","Parwani AV"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Apr 1","doi":"10.5858/arpa.2024-0215-RA","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39799506","name":"Making the case for an International Childhood Cancer Data Partnership.","source":"pubmed","abstract":"Childhood cancers are a heterogeneous group of rare diseases, accounting for less than 2% of all cancers diagnosed worldwide. Most countries, therefore, do not have enough cases to provide robust information on epidemiology, treatment, and late effects, especially for rarer types of cancer. Thus, only through a concerted effort to share data internationally will we be able to answer research questions that could not otherwise be answered. With this goal in mind, the US National Cancer Institute and the French National Cancer Institute co-sponsored the Paris Conference for an International Childhood Cancer Data Partnership in November 2023. This meeting convened more than 200 participants from 17 countries to address complex challenges in pediatric cancer research and data sharing. This Commentary delves into some key topics discussed during the Paris Conference and describes pilots that will help move this international effort forward. Main topics presented include: (1) the wide variation in interpreting the European Union's General Data Protection Regulation among Member States; (2) obstacles with transferring personal health data outside of the European Union; (3) standardization and harmonization, including common data models; and (4) novel approaches to data sharing such as federated querying and federated learning. We finally provide a brief description of 3 ongoing pilot projects. The International Childhood Cancer Data Partnership is the first step in developing a process to better support pediatric cancer research internationally through combining data from multiple countries.","url":"https://pubmed.ncbi.nlm.nih.gov/39799506/","authors":["Forjaz G","Kohler B","Coleman MP","Steliarova-Foucher E","Negoita S","Guidry Auvil JM","Michels FS","Goderre J","Wiggins C","Durbin EB","Geleijnse G","Henrion MC","Altmayer C","Dubois T","Penberthy L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Aug 1","doi":"10.1093/jnci/djaf003","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39797003","name":"Cybersecurity Solutions for Industrial Internet of Things-Edge Computing Integration: Challenges, Threats, and Future Directions.","source":"pubmed","abstract":"This paper provides the complete details of current challenges and solutions in the cybersecurity of cyber-physical systems (CPS) within the context of the IIoT and its integration with edge computing (IIoT-edge computing). We systematically collected and analyzed the relevant literature from the past five years, applying a rigorous methodology to identify key sources. Our study highlights the prevalent IIoT layer attacks, common intrusion methods, and critical threats facing IIoT-edge computing environments. Additionally, we examine various types of cyberattacks targeting CPS, outlining their significant impact on industrial operations. A detailed taxonomy of primary security mechanisms for CPS within IIoT-edge computing is developed, followed by a comparative analysis of our approach against existing research. The findings underscore the widespread vulnerabilities across the IIoT architecture, particularly in relation to DoS, ransomware, malware, and MITM attacks. The review emphasizes the integration of advanced security technologies, including machine learning (ML), federated learning (FL), blockchain, blockchain-ML, deep learning (DL), encryption, cryptography, IT/OT convergence, and digital twins, as essential for enhancing the security and real-time data protection of CPS in IIoT-edge computing. Finally, the paper outlines potential future research directions aimed at advancing cybersecurity in this rapidly evolving domain.","url":"https://pubmed.ncbi.nlm.nih.gov/39797003/","authors":["Zhukabayeva T","Zholshiyeva L","Karabayev N","Khan S","Alnazzawi N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan 2","doi":"10.3390/s25010213","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39776850","name":"Integration of large language models and federated learning.","source":"pubmed","abstract":"As the parameter size of large language models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data. In response, existing research has attempted to make a breakthrough by incorporating federated learning (FL) into LLMs. Conversely, considering the outstanding performance of LLMs in task generalization, researchers have also tried applying LLMs within FL to tackle challenges in relevant domains. The complementarity between LLMs and FL has already ignited widespread research interest. In this review, we aim to deeply explore the integration of LLMs and FL. We propose a research framework dividing the fusion of LLMs and FL into three parts: the combination of LLM sub-technologies with FL, the integration of FL sub-technologies with LLMs, and the overall merger of LLMs and FL. We first provide a comprehensive review of the current state of research in the domain of LLMs combined with FL, including their typical applications, integration advantages, challenges faced, and future directions for resolution. Subsequently, we discuss the practical applications of the combination of LLMs and FL in critical scenarios such as healthcare, finance, and education and provide new perspectives and insights into future research directions for LLMs and FL.","url":"https://pubmed.ncbi.nlm.nih.gov/39776850/","authors":["Chen C","Feng X","Li Y","Lyu L","Zhou J","Zheng X","Yin J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Dec 13","doi":"10.1016/j.patter.2024.101098","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39773481","name":"Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics.","source":"pubmed","abstract":"Data from multiple organizations are crucial for advancing learning health systems. However, ethical, legal, and social concerns may restrict the use of standard statistical methods that rely on pooling data. Although distributed algorithms offer alternatives, they may not always be suitable for health frameworks.","url":"https://pubmed.ncbi.nlm.nih.gov/39773481/","authors":["Camirand Lemyre F","Lévesque S","Domingue MP","Herrmann K","Ethier JF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Nov 14","doi":"10.2196/53622","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39766917","name":"Federated Learning: Breaking Down Barriers in Global Genomic Research.","source":"pubmed","abstract":"Recent advancements in Next-Generation Sequencing (NGS) technologies have revolutionized genomic research, presenting unprecedented opportunities for personalized medicine and population genetics. However, issues such as data silos, privacy concerns, and regulatory challenges hinder large-scale data integration and collaboration. Federated Learning (FL) has emerged as a transformative solution, enabling decentralized data analysis while preserving privacy and complying with regulations such as the General Data Protection Regulation (GDPR). This review explores the potential use of FL in genomics, detailing its methodology, including local model training, secure aggregation, and iterative improvement. Key challenges, such as heterogeneous data integration and cybersecurity risks, are examined alongside regulations like GDPR. In conclusion, successful implementations of FL in global and national initiatives demonstrate its scalability and role in supporting collaborative research. Finally, we discuss future directions, including AI integration and the necessity of education and training, to fully harness the potential of FL in advancing precision medicine and global health initiatives.","url":"https://pubmed.ncbi.nlm.nih.gov/39766917/","authors":["Calvino G","Peconi C","Strafella C","Trastulli G","Megalizzi D","Andreucci S","Cascella R","Caltagirone C","Zampatti S","Giardina E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Dec 22","doi":"10.3390/genes15121650","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39755653","name":"Open challenges and opportunities in federated foundation models towards biomedical healthcare.","source":"pubmed","abstract":"This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) in biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for biomedical applications that require processing diverse data forms such as clinical reports, diagnostic images, and multimodal patient interactions. The incorporation of FL with these sophisticated models presents a promising strategy to harness their analytical power while safeguarding the privacy of sensitive medical data. This approach not only enhances the capabilities of FMs in medical diagnostics and personalized treatment but also addresses critical concerns about data privacy and security in healthcare. This survey reviews the current applications of FMs in federated settings, underscores the challenges, and identifies future research directions including scaling FMs, managing data diversity, and enhancing communication efficiency within FL frameworks. The objective is to encourage further research into the combined potential of FMs and FL, laying the groundwork for healthcare innovations.","url":"https://pubmed.ncbi.nlm.nih.gov/39755653/","authors":["Li X","Peng L","Wang YP","Zhang W"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan 4","doi":"10.1186/s13040-024-00414-9","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39742693","name":"Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generation.","source":"pubmed","abstract":"Data sharing in healthcare is vital for advancing research and personalized medicine. However, the process is hindered by privacy, ethical, and legal challenges associated with patient data. Synthetic data generation emerges as a promising solution, replicating statistical properties of real data while enhancing privacy protection.","url":"https://pubmed.ncbi.nlm.nih.gov/39742693/","authors":["Liu Y","Acharya UR","Tan JH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Mar","doi":"10.1016/j.cmpb.2024.108571","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39703783","name":"Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions.","source":"pubmed","abstract":"The rapid escalation of cyber threats necessitates innovative strategies to enhance cybersecurity and privacy measures. Artificial Intelligence (AI) has emerged as a promising tool poised to enhance the effectiveness of cybersecurity strategies by offering advanced capabilities for intrusion detection, malware classification, and privacy preservation. However, this work addresses the significant lack of a comprehensive synthesis of AI's use in cybersecurity and privacy across the vast literature, aiming to identify existing gaps and guide further progress.","url":"https://pubmed.ncbi.nlm.nih.gov/39703783/","authors":["Achuthan K","Ramanathan S","Srinivas S","Raman R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/fdata.2024.1497535","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39696193","name":"Target informed client recruitment for efficient federated learning in healthcare.","source":"pubmed","abstract":"Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available and, therefore, require some form of aggregation to facilitate training procedures. Aggregating sensitive data poses a significant privacy risk, which is why, both in Europe and the United States, legal frameworks regulate the treatment of such data. Whilst these measures protect the individual behind the data, they pose a significant challenge that results in extensive legal administration related to data sharing efforts. Federated learning (FL) offers a way to mitigate these challenges by allowing to learn models in distributed fashion, eliminating the need to aggregate data for the purpose of training. However, FL comes with a new set of challenges related to communication overhead, client selection and efficiency of the FL training procedure, among others.","url":"https://pubmed.ncbi.nlm.nih.gov/39696193/","authors":["Scheltjens V","Wamba Momo LN","Verbeke W","De Moor B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Dec 18","doi":"10.1186/s12911-024-02798-4","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39681182","name":"Artificial intelligence for dental implant classification and peri-implant pathology identification in 2D radiographs: A systematic review.","source":"pubmed","abstract":"This systematic review aimed to summarize and evaluate the available information regarding the performance of artificial intelligence on dental implant classification and peri-implant pathology identification in 2D radiographs.","url":"https://pubmed.ncbi.nlm.nih.gov/39681182/","authors":["Bonfanti-Gris M","Ruales E","Salido MP","Martinez-Rus F","Özcan M","Pradies G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Feb","doi":"10.1016/j.jdent.2024.105533","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39664400","name":"A systematic survey on the application of federated learning in mental state detection and human activity recognition.","source":"pubmed","abstract":"This systematic review investigates the application of federated learning in mental health and human activity recognition. A comprehensive search was conducted to identify studies utilizing federated learning for these domains. The included studies were evaluated based on publication year, task, dataset characteristics, federated learning algorithms, and personalization methods. The aim is to provide an overview of the current state-of-the-art, identify research gaps, and inform future research directions in this emerging field.","url":"https://pubmed.ncbi.nlm.nih.gov/39664400/","authors":["Grataloup A","Kurpicz-Briki M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/fdgth.2024.1495999","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39654982","name":"Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data.","source":"pubmed","abstract":"Tropical diseases can often be caused by viruses, bacteria, parasites, and fungi. They can be spread over vectors. Analysis of multiple omics data types can be utilized in providing comprehensive insights into biological system functions and disease progression. To this end, bioinformatics tools and diverse AI techniques are pivotal in identifying and understanding tropical diseases through the analysis of omics data. In this article, we provide a thorough review of opportunities, challenges, and future directions of utilizing Bioinformatics tools and AI-assisted models on tropical disease identification using various omics data types. We conducted the review from 2015 to 2024 considering reliable databases of peer-reviewed journals and conference articles. Several keywords were taken for the article searching and around 40 articles were reviewed. According to the review, we observed that utilization of omics data with Bioinformatics tools like BLAST, and Clustal Omega can make significant outcomes in tropical disease identification. Further, the integration of multiple omics data improves biomarker identification, and disease predictions including disease outbreak predictions. Moreover, AI-assisted models can improve the precision, cost-effectiveness, and efficiency of CRISPR-based gene editing, optimizing gRNA design, and supporting advanced genetic correction. Several AI-assisted models including XAI can be used to identify diseases and repurpose therapeutic targets and biomarkers efficiently. Furthermore, recent advancements including Transformer-based models such as BERT and GPT-4, have been mainly applied for sequence analysis and functional genomics. Finally, the most recent GeneViT model, utilizing Vision Transformers, and other AI techniques like Generative Adversarial Networks, Federated Learning, Transfer Learning, Reinforcement Learning, Automated ML and Attention Mechanism have shown significant performance in disease classification using omics data.","url":"https://pubmed.ncbi.nlm.nih.gov/39654982/","authors":["Vidanagamachchi SM","Waidyarathna KMGTR"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/fdgth.2024.1471200","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39650377","name":"Federated learning for millimeter-wave spectrum in 6G networks: applications, challenges, way forward and open research issues.","source":"pubmed","abstract":"The emergence of 6G networks promises ultra-high data rates and unprecedented connectivity. However, the effective utilization of the millimeter-wave (mmWave) as a critical enabler of foreseen potential in 6G, poses significant challenges due to its unique propagation characteristics and security concerns. Deep learning (DL)/machine learning (ML) based approaches emerged as potential solutions; however, DL/ML contains centralization and data privacy issues. Therefore, federated learning (FL), an innovative decentralized DL/ML paradigm, offers a promising avenue to tackle these challenges by enabling collaborative model training across distributed devices while preserving data privacy. After a comprehensive exploration of FL enabled 6G networks, this review identifies the specific applications of mmWave communications in the context of FL enabled 6G networks. Thereby, this article discusses particular challenges faced in the adaption of FL enabled mmWave communication in 6G; including bandwidth consumption, power consumption and synchronization requirements. In view of the identified challenges, this study proposed a way forward called Federated Energy-Aware Dynamic Synchronization with Bandwidth-Optimization (FEADSBO). Moreover, this review highlights pertinent open research issues by synthesizing current advancements and research efforts. Through this review, we provide a roadmap to harness the synergies between FL and mmWave, offering insights to reshape the landscape of 6G networks.","url":"https://pubmed.ncbi.nlm.nih.gov/39650377/","authors":["Qamar F","Kazmi SHA","Siddiqui MUA","Hassan R","Zainol Ariffin KA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.7717/peerj-cs.2360","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39610881","name":"Changing maternal and child nutrition practices through integrating social and behavior change interventions in community-based self-help and support groups: literature review from Bangladesh, India, and Vietnam.","source":"pubmed","abstract":"Self-help groups (SHGs) and Support Groups (SGs) are increasingly recognized as effective mechanisms for improving maternal and young child nutrition due to their decentralized, community-based structures. While numerous studies have evaluated the outcomes and impact of SHGs and SGs on nutrition practices, there remains a gap in the literature. To address this, we conducted a literature review to examine the role of SHGs and SGs in improving health and nutrition outcomes, focusing on marginalized women, especially pregnant and lactating women (PLW), in India, Bangladesh, and Vietnam, with an emphasis on programs supported by the international non-governmental initiative, Alive &amp; Thrive.","url":"https://pubmed.ncbi.nlm.nih.gov/39610881/","authors":["Verma A","Nguyen T","Purty A","Pradhan N","Husan A","Zambrano P","Mahmud Z","Ghosh S","Mathisen R","Forissier T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/fnut.2024.1464822","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39610333","name":"Privacy-preserving federated data access and federated learning: Improved data sharing and AI model development in transfusion medicine.","source":"pubmed","abstract":"Health data comprise data from different aspects of healthcare including administrative, digital health, and research-oriented data. Together, health data contribute to and inform healthcare operations, patient care, and research. Integrating artificial intelligence (AI) into healthcare requires understanding these data infrastructures and addressing challenges such as data availability, privacy, and governance. Federated learning (FL), a decentralized AI training approach, addresses these challenges by allowing models to learn from diverse datasets without data leaving its source, thus ensuring privacy and security are maintained. This report introduces FL and discusses its potential in transfusion medicine and blood supply chain management.","url":"https://pubmed.ncbi.nlm.nih.gov/39610333/","authors":["Li N","Lewin A","Ning S","Waito M","Zeller MP","Tinmouth A","Shih AW","Canadian Transfusion Trials Group"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan","doi":"10.1111/trf.18077","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39582895","name":"A review on federated learning in computational pathology.","source":"pubmed","abstract":"Training generalizable computational pathology (CPATH) algorithms is heavily dependent on large-scale, multi-institutional data. Simultaneously, healthcare data underlies strict data privacy rules, hindering the creation of large datasets. Federated Learning (FL) is a paradigm addressing this dilemma, by allowing separate institutions to collaborate in a training process while keeping each institution's data private and exchanging model parameters instead. In this study, we identify and review key developments of FL for CPATH applications. We consider 15 studies, thereby evaluating the current status of exploring and adapting this emerging technology for CPATH applications. Proof-of-concept studies have been conducted across a wide range of CPATH use cases, showcasing the performance equivalency of models trained in a federated compared to a centralized manner. Six studies focus on model aggregation or model alignment methods reporting minor ( 0 &#x223c; 3 % ) performance improvement compared to conventional FL techniques, while four studies explore domain alignment methods, resulting in more significant performance improvements ( 4 &#x223c; 20 % ). To further reduce the privacy risk posed by sharing model parameters, four studies investigated the use of privacy preservation methods, where all methods demonstrated equivalent or slightly degraded performance ( 0.2 &#x223c; 6 % lower). To facilitate broader, real-world environment adoption, it is imperative to establish guidelines for the setup and deployment of FL infrastructure, alongside the promotion of standardized software frameworks. These steps are crucial to 1) further democratize CPATH research by allowing smaller institutions to pool data and computational resources 2) investigating rare diseases, 3) conducting multi-institutional studies, and 4) allowing rapid prototyping on private data.","url":"https://pubmed.ncbi.nlm.nih.gov/39582895/","authors":["Schoenpflug LA","Nie Y","Sheikhzadeh F","Koelzer VH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Dec","doi":"10.1016/j.csbj.2024.10.037","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39573994","name":"Federated learning as a smart tool for research on infectious diseases.","source":"pubmed","abstract":"The use of real-world data has become increasingly popular, also in the field of infectious disease (ID), particularly since the COVID-19 pandemic emerged. While much useful data for research is being collected, these data are generally stored across different sources. Privacy concerns limit the possibility to store the data centrally, thereby also limiting the possibility of fully leveraging the potential power of combined data. Federated learning (FL) has been suggested to overcome privacy issues by making it possible to perform research on data from various sources without those data leaving local servers. In this review, we discuss existing applications of FL in ID research, as well as the most relevant opportunities and challenges of this method.","url":"https://pubmed.ncbi.nlm.nih.gov/39573994/","authors":["Zwiers LC","Grobbee DE","Uijl A","Ong DSY"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Nov 21","doi":"10.1186/s12879-024-10230-5","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39572201","name":"A Review of the Opportunities and Challenges with Large Language Models in Radiology: The Road Ahead.","source":"pubmed","abstract":"In recent years, generative artificial intelligence (AI), particularly large language models (LLMs) and their multimodal counterparts, multimodal large language models, including vision language models, have generated considerable interest in the global AI discourse. LLMs, or pre-trained language models (such as ChatGPT, Med-PaLM, LLaMA), are neural network architectures trained on extensive text data, excelling in language comprehension and generation. Multimodal LLMs, a subset of foundation models, are trained on multimodal data sets, integrating text with another modality, such as images, to learn universal representations akin to human cognition better. This versatility enables them to excel in tasks like chatbots, translation, and creative writing while facilitating knowledge sharing through transfer learning, federated learning, and synthetic data creation. Several of these models can have potentially appealing applications in the medical domain, including, but not limited to, enhancing patient care by processing patient data; summarizing reports and relevant literature; providing diagnostic, treatment, and follow-up recommendations; and ancillary tasks like coding and billing. As radiologists enter this promising but uncharted territory, it is imperative for them to be familiar with the basic terminology and processes of LLMs. Herein, we present an overview of the LLMs and their potential applications and challenges in the imaging domain.","url":"https://pubmed.ncbi.nlm.nih.gov/39572201/","authors":["Soni N","Ora M","Agarwal A","Yang T","Bathla G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul 1","doi":"10.3174/ajnr.A8589","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39567515","name":"Accelerating Parkinson's Disease drug development with federated learning approaches.","source":"pubmed","abstract":"Parkinson's Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to design, capture and interrogate substantial clinical datasets. Herein we advocate how advances in the deployment of artificial intelligence models for Federated Data Analysis and Federated Learning can help spearhead coordinated and sustainable approaches to address this grand challenge.","url":"https://pubmed.ncbi.nlm.nih.gov/39567515/","authors":["Khanna A","Adams J","Antoniades C","Bloem BR","Carroll C","Cedarbaum J","Cosman J","Dexter DT","Dockendorf MF","Edgerton J","Gaetano L","Goikoetxea E","Hill D","Horak F","Izmailova ES","Kangarloo T","Katabi D","Kopil C","Lindemann M","Mammen J","Marek K","McFarthing K","Mirelman A","Muller M","Pagano G","Peterschmitt MJ","Ren J","Rochester L","Sardar S","Siderowf A","Simuni T","Stephenson D","Swanson-Fischer C","Wagner JA","Jones GB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Nov 21","doi":"10.1038/s41531-024-00837-5","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39560857","name":"Machine learning approaches for predicting and diagnosing chronic kidney disease: current trends, challenges, solutions, and future directions.","source":"pubmed","abstract":"Chronic Kidney Disease (CKD) represents a significant global health challenge, contributing to increased morbidity and mortality rates. This review paper explores the current landscape of machine learning (ML) techniques employed in CKD prediction and diagnosis, highlighting recent trends, inherent challenges, innovative solutions, and future directions. Through an extensive literature survey, we identified key limitations and challenges, including the use of small datasets, the absence of stage-specific predictions, insufficient focus on model interpretability, and a lack of discussions on safeguarding patient privacy in managing sensitive CKD data. We considered these limitations and challenges as research gaps, and this review paper aims to address them. We emphasize the potential of Generative AI to augment dataset sizes, thereby enhancing model performance and reliability. To address the lack of stage-specific predictions, we highlight the need for effective multi-class models to accurately predict CKD stages, enabling tailored treatments and improved patient outcomes. Furthermore, we discuss the critical importance of model interpretability, utilizing methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to ensure transparency and trust among healthcare professionals. Privacy concerns surrounding sensitive patient data are also addressed. We present innovative privacy-preserving solutions using technologies, such as homomorphic encryption, federated learning, and blockchain. These solutions facilitate collaboration across institutions while maintaining patient confidentiality and addressing challenges related to limited generalizability and reproducibility in CKD prediction. This review informs healthcare professionals and researchers about advancements in ML for CKD prediction, to improve patient outcomes and address research gaps.","url":"https://pubmed.ncbi.nlm.nih.gov/39560857/","authors":["Gogoi P","Valan JA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1007/s11255-024-04281-5","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"pmid:39560820","name":"Image biomarkers and explainable AI: handcrafted features versus deep learned features.","source":"pubmed","abstract":"Feature extraction and selection from medical data are the basis of radiomics and image biomarker discovery for various architectures, including convolutional neural networks (CNNs). We herein describe the typical radiomics steps and the components of a CNN for both deep feature extraction and end-to-end approaches. We discuss the curse of dimensionality, along with dimensionality reduction techniques. Despite the outstanding performance of deep learning (DL) approaches, the use of handcrafted features instead of deep learned features needs to be considered for each specific study. Dataset size is a key factor: large-scale datasets with low sample diversity could lead to overfitting; limited sample sizes can provide unstable models. The dataset must be representative of all the \"facets\" of the clinical phenomenon/disease investigated. The access to high-performance computational resources from graphics processing units is another key factor, especially for the training phase of deep architectures. The advantages of multi-institutional federated/collaborative learning are described. When large language models are used, high stability is needed to avoid catastrophic forgetting in complex domain-specific tasks. We highlight that non-DL approaches provide model explainability superior to that provided by DL approaches. To implement explainability, the need for explainable AI arises, also through post hoc mechanisms. RELEVANCE STATEMENT: This work aims to provide the key concepts for processing the imaging features to extract reliable and robust image biomarkers. KEY POINTS: The key concepts for processing the imaging features to extract reliable and robust image biomarkers are provided. The main differences between radiomics and representation learning approaches are highlighted. The advantages and disadvantages of handcrafted versus learned features are given without losing sight of the clinical purpose of artificial intelligence models.","url":"https://pubmed.ncbi.nlm.nih.gov/39560820/","authors":["Rundo L","Militello C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Nov 19","doi":"10.1186/s41747-024-00529-y","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39502481","name":"Application of privacy protection technology to healthcare big data.","source":"pubmed","abstract":"With the advent of the big data era, data security issues are becoming more common. Healthcare organizations have more data to use for analysis, but they lose money every year due to their inability to prevent data leakage. To overcome these challenges, research on the use of data protection technologies in healthcare is actively underway, particularly research on state-of-the-art technologies, such as federated learning announced by Google and blockchain technology, which has recently attracted attention. To learn about these research efforts, we explored the research, methods, and limitations of the most widely used privacy technologies. After investigating related papers published between 2017 and 2023 and identifying the latest technology trends, we selected related papers and reviewed related technologies. In the process, four technologies were the focus of this study: blockchain, federated learning, isomorphic encryption, and differential privacy. Overall, our analysis provides researchers with insight into privacy technology research by suggesting the limitations of current privacy technologies and suggesting future research directions.","url":"https://pubmed.ncbi.nlm.nih.gov/39502481/","authors":["Shin H","Ryu K","Kim JY","Lee S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1177/20552076241282242","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"pmid:39453800","name":"Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics.","source":"pubmed","abstract":"Federated learning (FL) enables collaborative training of a machine learning (ML) model across multiple parties, facilitating the preservation of users' and institutions' privacy by maintaining data stored locally. Instead of centralizing raw data, FL exchanges locally refined model parameters to build a global model incrementally. While FL is more compliant with emerging regulations such as the European General Data Protection Regulation (GDPR), ensuring the right to be forgotten in this context-allowing FL participants to remove their data contributions from the learned model-remains unclear. In addition, it is recognized that malicious clients may inject backdoors into the global model through updates, e.g., to generate mispredictions on specially crafted data examples. Consequently, there is the need for mechanisms that can guarantee individuals the possibility to remove their data and erase malicious contributions even after aggregation, without compromising the already acquired \"good\" knowledge. This highlights the necessity for novel federated unlearning (FU) algorithms, which can efficiently remove specific clients' contributions without full model retraining. This article provides background concepts, empirical evidence, and practical guidelines to design/implement efficient FU schemes. This study includes a detailed analysis of the metrics for evaluating unlearning in FL and presents an in-depth literature review categorizing state-of-the-art FU contributions under a novel taxonomy. Finally, we outline the most relevant and still open technical challenges, by identifying the most promising research directions in the field.","url":"https://pubmed.ncbi.nlm.nih.gov/39453800/","authors":["Romandini N","Mora A","Mazzocca C","Montanari R","Bellavista P"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jul","doi":"10.1109/TNNLS.2024.3478334","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39437302","name":"Data- and Physics-Driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies.","source":"pubmed","abstract":"Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction.","url":"https://pubmed.ncbi.nlm.nih.gov/39437302/","authors":["Huang J","Wu Y","Wang F","Fang Y","Nan Y","Alkan C","Abraham D","Liao C","Xu L","Gao Z","Wu W","Zhu L","Chen Z","Lally P","Bangerter N","Setsompop K","Guo Y","Rueckert D","Wang G","Yang G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1109/RBME.2024.3485022","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39418815","name":"FedADMM-InSa: An inexact and self-adaptive ADMM for federated learning.","source":"pubmed","abstract":"Federated learning (FL) is a promising framework for learning from distributed data while maintaining privacy. The development of efficient FL algorithms encounters various challenges, including heterogeneous data and systems, limited communication capacities, and constrained local computational resources. Recently developed FedADMM methods show great resilience to both data and system heterogeneity. However, they still suffer from performance deterioration if the hyperparameters are not carefully tuned. To address this issue, we propose an inexact and self-adaptive FedADMM algorithm, termed FedADMM-InSa. First, we design an inexactness criterion for the clients' local updates to eliminate the need for empirically setting the local training accuracy. This inexactness criterion can be assessed by each client independently based on its unique condition, thereby reducing the local computational cost and mitigating the undesirable straggle effect. The convergence of the resulting inexact ADMM is proved under the assumption of strongly convex loss functions. Additionally, we present a self-adaptive scheme that dynamically adjusts each client's penalty parameter, enhancing algorithm robustness by mitigating the need for empirical penalty parameter choices for each client. Extensive numerical experiments on both synthetic and real-world datasets have been conducted. As validated by some tests, our FedADMM-InSa algorithm improves model accuracy by 7.8% while reducing clients' local workloads by 55.7% compared to benchmark algorithms.","url":"https://pubmed.ncbi.nlm.nih.gov/39418815/","authors":["Song Y","Wang Z","Zuazua E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan","doi":"10.1016/j.neunet.2024.106772","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39417071","name":"Toward a personalized autonomous transportation system: Vision, challenges, and solutions.","source":"pubmed","abstract":"The fragmented design of intelligent transportation systems creates isolated intelligent systems. Resource competition and information gaps are fierce and widespread, worsening traffic issues and degrading overall service levels. Therefore, empowered by advanced technologies, an evolution toward an autonomous transportation system (ATS) is observed. This evolution aims to develop a collaborative and sustainable ecosystem, prompting interoperability within the cloud-edge-device continuum. It can, accordingly, dismantle internal resource barriers and achieve a systematic balance between demand and supply with less human intervention. Despite the promising vision of an ATS, it encounters three key challenges: disparate data, deficient models, and conflicting interests in supporting autonomous and personalized mobility. Hence, as an innovative solution, a trustworthy, private, and equal-serving framework called TPE is designed. It seamlessly integrates blockchain, federated learning, and large-scale models to deploy a trustworthy operating environment, process private data for globally shareable knowledge, and develop a foundation model for personalized adaptation, respectively. Consequently, ATSs empowered by TPE can serve diverse user groups both privately and equally.","url":"https://pubmed.ncbi.nlm.nih.gov/39417071/","authors":["You L","Hao M","Sun J","Wang Y","Rong C","Yuen C","Santi P","Ratti C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Nov 4","doi":"10.1016/j.xinn.2024.100704","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39391509","name":"Federated learning: Overview, strategies, applications, tools and future directions.","source":"pubmed","abstract":"Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications.","url":"https://pubmed.ncbi.nlm.nih.gov/39391509/","authors":["Yurdem B","Kuzlu M","Gullu MK","Catak FO","Tabassum M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1016/j.heliyon.2024.e38137","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"pmid:39323686","name":"Artificial Intelligence (AI)-Enhanced Detection of Diabetic Retinopathy From Fundus Images: The Current Landscape and Future Directions.","source":"pubmed","abstract":"Diabetic retinopathy (DR) remains a leading cause of vision loss worldwide, with early detection critical for preventing irreversible damage. This review explores the current landscape and future directions of artificial intelligence (AI)-enhanced detection of DR from fundus images. Recent advances in deep learning and computer vision have enabled AI systems to analyze retinal images with expert-level accuracy, potentially transforming DR screening. Key developments include convolutional neural networks achieving high sensitivity and specificity in detecting referable DR, multi-task learning approaches that can simultaneously detect and grade DR severity, and lightweight models enabling deployment on mobile devices. While these AI systems show promise in improving the efficiency and accessibility of DR screening, several challenges remain. These include ensuring generalizability across diverse populations, standardizing image acquisition and quality, addressing the \"black box\" nature of complex models, and integrating AI seamlessly into clinical workflows. Future directions in the field encompass explainable AI to enhance transparency, federated learning to leverage decentralized datasets, and the integration of AI with electronic health records and other diagnostic modalities. There is also growing potential for AI to contribute to personalized treatment planning and predictive analytics for disease progression. As the technology continues to evolve, maintaining a focus on rigorous clinical validation, ethical considerations, and real-world implementation will be crucial for realizing the full potential of AI-enhanced DR detection in improving global eye health outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/39323686/","authors":["Alsadoun L","Ali H","Mushtaq MM","Mushtaq M","Burhanuddin M","Anwar R","Liaqat M","Bokhari SFH","Hasan AH","Ahmed F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Aug","doi":"10.7759/cureus.67844","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39308840","name":"The Role of Artificial Intelligence in the Diagnosis of Melanoma.","source":"pubmed","abstract":"The incidence of melanoma, the most aggressive form of skin cancer, continues to rise globally, particularly among fair-skinned populations (type I and II). Early detection is crucial for improving patient outcomes, and recent advancements in artificial intelligence (AI) have shown promise in enhancing the accuracy and efficiency of melanoma diagnosis and management. This review examines the role of AI in skin lesion diagnostics, highlighting two main approaches: machine learning, particularly convolutional neural networks (CNNs), and expert systems. AI techniques have demonstrated high accuracy in classifying dermoscopic images, often matching or surpassing dermatologists' performance. Integrating AI into dermatology has improved tasks, such as lesion classification, segmentation, and risk prediction, facilitating earlier and more accurate interventions. Despite these advancements, challenges remain, including biases in training data, interpretability issues, and integration of AI into clinical workflows. Ensuring diverse data representation and maintaining high standards of image quality are essential for reliable AI performance. Future directions involve the development of more sophisticated models, such as vision-language and multimodal models, and federated learning to address data privacy and generalizability concerns. Continuous validation and ethical integration of AI into clinical practice are vital for realizing its full potential for improving melanoma diagnosis and patient care.","url":"https://pubmed.ncbi.nlm.nih.gov/39308840/","authors":["Kalidindi S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Sep","doi":"10.7759/cureus.69818","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39296807","name":"Privacy-preserving decentralized learning methods for biomedical applications.","source":"pubmed","abstract":"In recent years, decentralized machine learning has emerged as a significant advancement in biomedical applications, offering robust solutions for data privacy, security, and collaboration across diverse healthcare environments. In this review, we examine various decentralized learning methodologies, including federated learning, split learning, swarm learning, gossip learning, edge learning, and some of their applications in the biomedical field. We delve into the underlying principles, network topologies, and communication strategies of each approach, highlighting their advantages and limitations. Ultimately, the selection of a suitable method should be based on specific needs, infrastructures, and computational capabilities.","url":"https://pubmed.ncbi.nlm.nih.gov/39296807/","authors":["Tajabadi M","Martin R","Heider D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Dec","doi":"10.1016/j.csbj.2024.08.024","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39263113","name":"Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.","source":"pubmed","abstract":"This review article offers a comprehensive analysis of current developments in the application of machine learning for cancer diagnostic systems. The effectiveness of machine learning approaches has become evident in improving the accuracy and speed of cancer detection, addressing the complexities of large and intricate medical datasets. This review aims to evaluate modern machine learning techniques employed in cancer diagnostics, covering various algorithms, including supervised and unsupervised learning, as well as deep learning and federated learning methodologies. Data acquisition and preprocessing methods for different types of data, such as imaging, genomics, and clinical records, are discussed. The paper also examines feature extraction and selection techniques specific to cancer diagnosis. Model training, evaluation metrics, and performance comparison methods are explored. Additionally, the review provides insights into the applications of machine learning in various cancer types and discusses challenges related to dataset limitations, model interpretability, multi-omics integration, and ethical considerations. The emerging field of explainable artificial intelligence (XAI) in cancer diagnosis is highlighted, emphasizing specific XAI techniques proposed to improve cancer diagnostics. These techniques include interactive visualization of model decisions and feature importance analysis tailored for enhanced clinical interpretation, aiming to enhance both diagnostic accuracy and transparency in medical decision-making. The paper concludes by outlining future directions, including personalized medicine, federated learning, deep learning advancements, and ethical considerations. This review aims to guide researchers, clinicians, and policymakers in the development of efficient and interpretable machine learning-based cancer diagnostic systems.","url":"https://pubmed.ncbi.nlm.nih.gov/39263113/","authors":["Abbas S","Asif M","Rehman A","Alharbi M","Khan MA","Elmitwally N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Sep 15","doi":"10.1016/j.heliyon.2024.e36743","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39259652","name":"Artificial intelligence in myopia in children: current trends and future directions.","source":"pubmed","abstract":"Myopia is one of the major causes of visual impairment globally, with myopia and its complications thus placing a heavy healthcare and economic burden. With most cases of myopia developing during childhood, interventions to slow myopia progression are most effective when implemented early. To address this public health challenge, artificial intelligence has emerged as a potential solution in childhood myopia management.","url":"https://pubmed.ncbi.nlm.nih.gov/39259652/","authors":["Ng Yin Ling C","Zhu X","Ang M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Nov 1","doi":"10.1097/ICU.0000000000001086","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39259650","name":"Privacy preserving technology in ophthalmology.","source":"pubmed","abstract":"Patient privacy protection is a critical focus in medical practice. Advances over the past decade in big data have led to the digitization of medical records, making medical data increasingly accessible through frequent data sharing and online communication. Periocular features, iris, and fundus images all contain biometric characteristics of patients, making privacy protection in ophthalmology particularly important. Consequently, privacy-preserving technologies have emerged, and are reviewed in this study.","url":"https://pubmed.ncbi.nlm.nih.gov/39259650/","authors":["Yang Y","Chen X","Lin H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Nov 1","doi":"10.1097/ICU.0000000000001087","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39214457","name":"Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives.","source":"pubmed","abstract":"Glaucoma is a complex eye condition with varied morphological and clinical presentations, making diagnosis and management challenging. The lack of a consensus definition for glaucoma or glaucomatous optic neuropathy further complicates the development of universal diagnostic tools. Developing robust artificial intelligence (AI) models for glaucoma screening is essential for early detection and treatment but faces significant obstacles. Effective deep learning algorithms require large, well-curated datasets from diverse patient populations and imaging protocols. However, creating centralized data repositories is hindered by concerns over data sharing, patient privacy, regulatory compliance, and intellectual property. Federated Learning (FL) offers a potential solution by enabling data to remain locally hosted while facilitating distributed model training across multiple sites.","url":"https://pubmed.ncbi.nlm.nih.gov/39214457/","authors":["Hallaj S","Chuter BG","Lieu AC","Singh P","Kalpathy-Cramer J","Xu BY","Christopher M","Zangwill LM","Weinreb RN","Baxter SL"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan-Feb","doi":"10.1016/j.ogla.2024.08.004","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39190523","name":"Addressing Skewed Heterogeneity via Federated Prototype Rectification With Personalization.","source":"pubmed","abstract":"Federated learning (FL) is an efficient framework designed to facilitate collaborative model training across multiple distributed devices while preserving user data privacy. A significant challenge of FL is data-level heterogeneity, i.e., skewed or long-tailed distribution of private data. Although various methods have been proposed to address this challenge, most of them assume that the underlying global data are uniformly distributed across all clients. This article investigates data-level heterogeneity FL with a brief review and redefines a more practical and challenging setting called skewed heterogeneous FL (SHFL). Accordingly, we propose a novel federated prototype rectification with personalization (FedPRP) which consists of two parts: federated personalization and federated prototype rectification. The former aims to construct balanced decision boundaries between dominant and minority classes based on private data, while the latter exploits both interclass discrimination and intraclass consistency to rectify empirical prototypes. Experiments on three popular benchmarks show that the proposed approach outperforms current state-of-the-art methods and achieves balanced performance in both personalization and generalization.","url":"https://pubmed.ncbi.nlm.nih.gov/39190523/","authors":["Guo S","Wang H","Lin S","Kou Z","Geng X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 May","doi":"10.1109/TNNLS.2024.3438281","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39186968","name":"The AI revolution in glaucoma: Bridging challenges with opportunities.","source":"pubmed","abstract":"Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the \"black box\" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.","url":"https://pubmed.ncbi.nlm.nih.gov/39186968/","authors":["Li F","Wang D","Yang Z","Zhang Y","Jiang J","Liu X","Kong K","Zhou F","Tham CC","Medeiros F","Han Y","Grzybowski A","Zangwill LM","Lam DSC","Zhang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Nov","doi":"10.1016/j.preteyeres.2024.101291","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39120735","name":"Artificial intelligence in cardiovascular imaging and intervention.","source":"pubmed","abstract":"Recent progress in artificial intelligence (AI) includes generative models, multimodal foundation models, and federated learning, which enable a&#xa0;wide spectrum of novel exciting applications and scenarios for cardiac image analysis and cardiovascular interventions. The disruptive nature of these novel technologies enables concurrent text and image analysis by so-called vision-language transformer models. They not only allow for automatic derivation of image reports, synthesis of novel images conditioned on certain textual properties, and visual questioning and answering in an oral or written dialogue style, but also for the retrieval of medical images from a&#xa0;large database based on a&#xa0;description of the pathology or specifics of the dataset of interest. Federated learning is an additional ingredient in these novel developments, facilitating multi-centric collaborative training of AI approaches and therefore access to large clinical cohorts. In this review paper, we provide an overview of the recent developments in the field of cardiovascular imaging and intervention and offer a&#xa0;future outlook.","url":"https://pubmed.ncbi.nlm.nih.gov/39120735/","authors":["Engelhardt S","Dar SUH","Sharan L","André F","Nagel E","Thomas S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Oct","doi":"10.1007/s00059-024-05264-z","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39097187","name":"Emerging Analytical Approaches for Personalized Medicine Using Machine Learning In Pediatric and Congenital Heart Disease.","source":"pubmed","abstract":"Precision and personalized medicine, the process by which patient management is tailored to individual circumstances, are now terms that are familiar to cardiologists, despite it still being an emerging field. Although precision medicine relies most often on the underlying biology and pathophysiology of a patient's condition, personalized medicine relies on digital biomarkers generated through algorithms. Given the complexity of the underlying data, these digital biomarkers are most often generated through machine-learning algorithms. There are a number of analytic considerations regarding the creation of digital biomarkers that are discussed in this review, including data preprocessing, time dependency and gating, dimensionality reduction, and novel methods, both in the realm of supervised and unsupervised machine learning. Some of these considerations, such as sample size requirements and measurements of model performance, are particularly challenging in small and heterogeneous populations with rare outcomes such as children with congenital heart disease. Finally, we review analytic considerations for the deployment of digital biomarkers in clinical settings, including the emerging field of clinical artificial intelligence (AI) operations, computational needs for deployment, efforts to increase the explainability of AI, algorithmic drift, and the needs for distributed surveillance and federated learning. We conclude this review by discussing a recent simulation study that shows that, despite these analytic challenges and complications, the use of digital biomarkers in managing clinical care might have substantial benefits regarding individual patient outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/39097187/","authors":["Chinni BK","Manlhiot C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Oct","doi":"10.1016/j.cjca.2024.07.026","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39081567","name":"Privacy preservation for federated learning in health care.","source":"pubmed","abstract":"Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns. Federated learning (FL) allows for multi-institutional training of AI models, obviating data sharing, albeit with different security and privacy concerns. Specifically, insights exchanged during FL can leak information about institutional data. In addition, FL can introduce issues when there is limited trust among the entities performing the compute. With the growing adoption of FL in health care, it is imperative to elucidate the potential risks. We thus summarize privacy-preserving FL literature in this work with special regard to health care. We draw attention to threats and review mitigation approaches. We anticipate this review to become a health-care researcher's guide to security and privacy in FL.","url":"https://pubmed.ncbi.nlm.nih.gov/39081567/","authors":["Pati S","Kumar S","Varma A","Edwards B","Lu C","Qu L","Wang JJ","Lakshminarayanan A","Wang SH","Sheller MJ","Chang K","Singh P","Rubin DL","Kalpathy-Cramer J","Bakas S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Jul 12","doi":"10.1016/j.patter.2024.100974","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39078894","name":"Evaluating artificial intelligence for medical imaging: a primer for clinicians.","source":"pubmed","abstract":"Artificial intelligence has the potential to transform medical imaging. The effective integration of artificial intelligence into clinical practice requires a robust understanding of its capabilities and limitations. This paper begins with an overview of key clinical use cases such as detection, classification, segmentation and radiomics. It highlights foundational concepts in machine learning such as learning types and strategies, as well as the training and evaluation process. We provide a broad theoretical framework for assessing the clinical effectiveness of medical imaging artificial intelligence, including appraising internal validity and generalisability of studies, and discuss barriers to clinical translation. Finally, we highlight future directions of travel within the field including multi-modal data integration, federated learning and explainability. By having an awareness of these issues, clinicians can make informed decisions about adopting artificial intelligence for medical imaging, improving patient care and clinical outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/39078894/","authors":["Keni S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Jul 30","doi":"10.12968/hmed.2024.0312","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39053350","name":"Blockchain in clinical trials: Bibliometric and network studies of applications, challenges, and future prospects based on data analytics.","source":"pubmed","abstract":"This study conducts a comprehensive analysis on the usage of the blockchain technology in clinical trials, based on a curated corpus of 107 scientific articles from the year 2016 through the first quarter of 2024. Utilizing a methodological framework that integrates bibliometric analysis, network analysis, thematic mapping, and latent Dirichlet allocation, the study explores the terrain and prospective developments within this usage based on data analytics. Through a meticulous examination of the analyzed articles, the present study identifies seven key thematic areas, highlighting the diverse applications and interdisciplinary nature of blockchain in clinical trials. Our findings reveal blockchain capability to enhance data management, participant consent processes, as well as overall trial transparency, efficiency, and security. Additionally, the investigation discloses the emerging synergy between blockchain and advanced technologies, such as artificial intelligence and federated learning, proposing innovative directions for improving clinical research methodologies. Our study underscores the collaborative efforts in dealing with the complexities of integrating blockchain into the areas of clinical trials and healthcare, delineating the transformative potential of blockchain technology in revolutionizing these areas by addressing challenges and promoting practices of efficient, secure, and transparent research. The delineated themes and networks of collaboration provide a blueprint for future inquiry, showing the importance of empirical research to narrow the gap between theoretical promise and practical implementation.","url":"https://pubmed.ncbi.nlm.nih.gov/39053350/","authors":["Castro C","Leiva V","Garrido D","Huerta M","Minatogawa V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Oct","doi":"10.1016/j.cmpb.2024.108321","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39049393","name":"Building Bridges for Federated Learning in Healthcare: Review on Approaches for Common Data Model Development.","source":"pubmed","abstract":"Common data models provide a standardized way to represent data used in federated learning tasks. The aim of this review was to explore the development and use of common data models to harmonize electronic health record data in health research. The data search yielded 724 records, of which 19 were included for this study. None of the research focused on nursing specific topics. All studies either utilized the Observational Medical Outcomes Partnership (OMOP) common data model, or developed a model partly based on the OMOP. A roadmap to guide research for the development of common data models for federated learning are warranted.","url":"https://pubmed.ncbi.nlm.nih.gov/39049393/","authors":["von Gerich H","Chomutare T","Peltonen LM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Jul 24","doi":"10.3233/SHTI240292","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:39005485","name":"Recent methodological advances in federated learning for healthcare.","source":"pubmed","abstract":"For healthcare datasets, it is often impossible to combine data samples from multiple sites due to ethical, privacy, or logistical concerns. Federated learning allows for the utilization of powerful machine learning algorithms without requiring the pooling of data. Healthcare data have many simultaneous challenges, such as highly siloed data, class imbalance, missing data, distribution shifts, and non-standardized variables, that require new methodologies to address. Federated learning adds significant methodological complexity to conventional centralized machine learning, requiring distributed optimization, communication between nodes, aggregation of models, and redistribution of models. In this systematic review, we consider all papers on Scopus published between January 2015 and February 2023 that describe new federated learning methodologies for addressing challenges with healthcare data. We reviewed 89 papers meeting these criteria. Significant systemic issues were identified throughout the literature, compromising many methodologies reviewed. We give detailed recommendations to help improve methodology development for federated learning in healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/39005485/","authors":["Zhang F","Kreuter D","Chen Y","Dittmer S","Tull S","Shadbahr T","BloodCounts! consortium","Preller J","Rudd JHF","Aston JAD","Schönlieb CB","Gleadall N","Roberts M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Jun 14","doi":"10.1016/j.patter.2024.101006","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:38985896","name":"Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility.","source":"pubmed","abstract":"Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. Addressing these challenges requires comprehensive standardization of medical imaging data and seamless integration with structured medical data.Developed by the Observational Health Data Sciences and Informatics community, the OMOP Common Data Model enables large-scale international collaborations with structured medical data. It ensures syntactic and semantic interoperability, while supporting the privacy-protected distribution of research across borders. The recently proposed Medical Imaging Common Data Model is designed to encompass all DICOM-formatted medical imaging data and integrate imaging-derived features with clinical data, ensuring their provenance.The harmonization of medical imaging data and its seamless integration with structured clinical data at a global scale will pave the way for advanced AI research in radiology. This standardization will enable federated learning, ensuring privacy-preserving collaboration across institutions and promoting equitable AI through the inclusion of diverse patient populations. Moreover, it will facilitate the development of foundation models trained on large-scale, multimodal datasets, serving as powerful starting points for specialized AI applications. Objective and transparent algorithm validation on a standardized data infrastructure will enhance reproducibility and interoperability of AI systems, driving innovation and reliability in clinical applications.","url":"https://pubmed.ncbi.nlm.nih.gov/38985896/","authors":["Jeon K","Park WY","Kahn CE Jr","Nagy P","You SC","Yoon SH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2025 Jan 1","doi":"10.1097/RLI.0000000000001106","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:38983206","name":"Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects.","source":"pubmed","abstract":"Fog computing has emerged as a prospective paradigm to address the computational requirements of IoT applications, extending the capabilities of cloud computing to the network edge. Task scheduling is pivotal in enhancing energy efficiency, optimizing resource utilization and ensuring the timely execution of tasks within fog computing environments. This article presents a comprehensive review of the advancements in task scheduling methodologies for fog computing systems, covering priority-based, greedy heuristics, metaheuristics, learning-based, hybrid heuristics, and nature-inspired heuristic approaches. Through a systematic analysis of relevant literature, we highlight the strengths and limitations of each approach and identify key challenges facing fog computing task scheduling, including dynamic environments, heterogeneity, scalability, resource constraints, security concerns, and algorithm transparency. Furthermore, we propose future research directions to address these challenges, including the integration of machine learning techniques for real-time adaptation, leveraging federated learning for collaborative scheduling, developing resource-aware and energy-efficient algorithms, incorporating security-aware techniques, and advancing explainable AI methodologies. By addressing these challenges and pursuing these research directions, we aim to facilitate the development of more robust, adaptable, and efficient task-scheduling solutions for fog computing environments, ultimately fostering trust, security, and sustainability in fog computing systems and facilitating their widespread adoption across diverse applications and domains.","url":"https://pubmed.ncbi.nlm.nih.gov/38983206/","authors":["Alsadie D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.7717/peerj-cs.2128","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:38966729","name":"Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review.","source":"pubmed","abstract":"Artificial Intelligence (AI) is transforming multiple sectors within our society, including education. In this context, emotions play a fundamental role in the teaching-learning process given that they influence academic performance, motivation, information retention, and student well-being. Thus, the integration of AI in emotional assessment within educational environments offers several advantages that can transform how we understand and address the socio-emotional development of students. However, there remains a lack of comprehensive approach that systematizes advancements, challenges, and opportunities in this field.","url":"https://pubmed.ncbi.nlm.nih.gov/38966729/","authors":["Vistorte AOR","Deroncele-Acosta A","Ayala JLM","Barrasa A","López-Granero C","Martí-González M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/fpsyg.2024.1387089","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:38964243","name":"Current strategies to address data scarcity in artificial intelligence-based drug discovery: A comprehensive review.","source":"pubmed","abstract":"Artificial intelligence (AI) has played a vital role in computer-aided drug design (CADD). This development has been further accelerated with the increasing use of machine learning (ML), mainly deep learning (DL), and computing hardware and software advancements. As a result, initial doubts about the application of AI in drug discovery have been dispelled, leading to significant benefits in medicinal chemistry. At the same time, it is crucial to recognize that AI is still in its infancy and faces a few limitations that need to be addressed to harness its full potential in drug discovery. Some notable limitations are insufficient, unlabeled, and non-uniform data, the resemblance of some AI-generated molecules with existing molecules, unavailability of inadequate benchmarks, intellectual property rights (IPRs) related hurdles in data sharing, poor understanding of biology, focus on proxy data and ligands, lack of holistic methods to represent input (molecular structures) to prevent pre-processing of input molecules (feature engineering), etc. The major component in AI infrastructure is input data, as most of the successes of AI-driven efforts to improve drug discovery depend on the quality and quantity of data, used to train and test AI algorithms, besides a few other factors. Additionally, data-gulping DL approaches, without sufficient data, may collapse to live up to their promise. Current literature suggests a few methods, to certain extent, effectively handle low data for better output from the AI models in the context of drug discovery. These are transferring learning (TL), active learning (AL), single or one-shot learning (OSL), multi-task learning (MTL), data augmentation (DA), data synthesis (DS), etc. One different method, which enables sharing of proprietary data on a common platform (without compromising data privacy) to train ML model, is federated learning (FL). In this review, we compare and discuss these methods, their recent applications, and limitations while modeling small molecule data to get the improved output of AI methods in drug discovery. Article also sums up some other novel methods to handle inadequate data.","url":"https://pubmed.ncbi.nlm.nih.gov/38964243/","authors":["Gangwal A","Ansari A","Ahmad I","Azad AK","Wan Sulaiman WMA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Sep","doi":"10.1016/j.compbiomed.2024.108734","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:38958849","name":"Artificial intelligence innovations in neurosurgical oncology: a narrative review.","source":"pubmed","abstract":"Artificial Intelligence (AI) has become increasingly integrated clinically within neurosurgical oncology. This report reviews the cutting-edge technologies impacting tumor treatment and outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/38958849/","authors":["Baker CR","Pease M","Sexton DP","Abumoussa A","Chambless LB"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Sep","doi":"10.1007/s11060-024-04757-5","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:38948408","name":"Evaluation and utilisation of privacy enhancing technologies-A data spaces perspective.","source":"pubmed","abstract":"Data sharing has facilitated the digitisation of society. We can access our bank accounts or make an appointment with our doctor anytime and anywhere. To achieve this, we have to share certain information, whether personal, professional, etc. This may seem like a minor cost for an individual user, but actually the data economy as the backbone of a digital transformation that is reshaping all aspects of human life. However, one of the major concerns arises regarding what happens to such individual data; once shared, control over it is often lost. For that reason, users and companies are reluctant to share their data. The European Union, through its European Strategy for Data, is establishing a policy and legal framework for establishing a single market for data in Europe by improving the trust and fairness of the data economy. Data spaces are a commitment to sharing data in a reliable and secure way, but this endeavour should, of course, not be at the expense of privacy rights. In recent years, Privacy-Enhancing Technologies (PETs) have emerged to achieve data sharing and privacy preservation that can address the requirements of data spaces around sensitive citizen and business data. In this work, we review existing PETs and assess their relevance, technological maturity, and applicability in the context of common European data spaces. Finally, we illustrate the benefits of secure data sharing via Federated Learning in a healthcare use case, where the preservation of privacy is a primer requirement and is therefore to be guaranteed.","url":"https://pubmed.ncbi.nlm.nih.gov/38948408/","authors":["Auñón JM","Hurtado-Ramírez D","Porras-Díaz L","Irigoyen-Peña B","Rahmian S","Al-Khazraji Y","Soler-Garrido J","Kotsev A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Aug","doi":"10.1016/j.dib.2024.110560","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:38940994","name":"Artificial Intelligence and Multiple Sclerosis.","source":"pubmed","abstract":"In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/38940994/","authors":["Amin M","Martínez-Heras E","Ontaneda D","Prados Carrasco F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Aug","doi":"10.1007/s11910-024-01354-x","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:38917282","name":"Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark.","source":"pubmed","abstract":"Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. Recently, with the popularity of federated learning, an influx of approaches have delivered towards different realistic challenges. In this survey, we provide a systematic overview of the important and recent developments of research on federated learning. First, we introduce the study history and terminology definition of this area. Then, we comprehensively review three basic lines of research: generalization, robustness, and fairness, by introducing their respective background concepts, task settings, and main challenges. We also offer a detailed overview of representative literature on both methods and datasets. We further benchmark the reviewed methods on several well-known datasets. Finally, we point out several open issues in this field and suggest opportunities for further research.","url":"https://pubmed.ncbi.nlm.nih.gov/38917282/","authors":["Huang W","Ye M","Shi Z","Wan G","Li H","Du B","Yang Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Dec","doi":"10.1109/TPAMI.2024.3418862","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"pmid:38912338","name":"Efficient differential privacy enabled federated learning model for detecting COVID-19 disease using chest X-ray images.","source":"pubmed","abstract":"The rapid spread of COVID-19 pandemic across the world has not only disturbed the global economy but also raised the demand for accurate disease detection models. Although many studies have proposed effective solutions for the early detection and prediction of COVID-19 with Machine Learning (ML) and Deep learning (DL) based techniques, but these models remain vulnerable to data privacy and security breaches. To overcome the challenges of existing systems, we introduced Adaptive Differential Privacy-based Federated Learning (DPFL) model for predicting COVID-19 disease from chest X-ray images which introduces an innovative adaptive mechanism that dynamically adjusts privacy levels based on real-time data sensitivity analysis, improving the practical applicability of Federated Learning (FL) in diverse healthcare environments. We compared and analyzed the performance of this distributed learning model with a traditional centralized model. Moreover, we enhance the model by integrating a FL approach with an early stopping mechanism to achieve efficient COVID-19 prediction with minimal communication overhead. To ensure privacy without compromising model utility and accuracy, we evaluated the proposed model under various noise scales. Finally, we discussed strategies for increasing the model's accuracy while maintaining robustness as well as privacy.","url":"https://pubmed.ncbi.nlm.nih.gov/38912338/","authors":["Ahmed R","Maddikunta PKR","Gadekallu TR","Alshammari NK","Hendaoui FA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/fmed.2024.1409314","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20819594","name":"Learning from Global Models: A Comparative Analysis of Health Data Governance Frameworks and Their Implications for U.S. Health Information Policy and System Design","source":"datacite","abstract":"Health data governance has become a critical component of modern healthcare systems due to increasing digitization, large-scale data sharing, and the growing importance of data-driven research and innovation. This review identifies key governance models and examines their implications for U.S. health information policy and system design. Using the Boolean function, keywords were used to search for and source articles, which were later screened. A total of eleven articles were finally obtained and synthesized for this study. The literature shows that normative governance frameworks across countries share common principles such as protection of individual rights, transparency, accountability, and public interest. However, fragmentation exists in terminology, enforcement mechanisms, and lifecycle coverage, particularly in the governance of secondary data reuse. Organizational governance studies highlight the importance of institutional structures, including clearly defined roles, governance committees, stewardship responsibilities, and standardized procedures. Evidence also shows that governance effectiveness depends on infrastructure, workforce training, and sustainable institutional capacity. Technical governance architectures introduce new approaches that embed governance rules directly into digital infrastructures. Blockchain-based systems strengthen security, transparency, and auditability through decentralized ledgers and smart contracts, while federated learning enables privacy-preserving data analysis by keeping sensitive patient data at local sources. Operational and design governance further translate governance principles into daily practice through privacy- and security-by-design, structured data quality management, and formal consent and access control mechanisms. The findings suggest that strengthening lifecycle governance, institutional capacity, system design, and workforce training can support more secure, interoperable, and trustworthy health information systems.","url":"https://doi.org/10.5281/zenodo.20819594","authors":["Omonye Jones Silas","Solomon Doe Adjaottor"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20819594","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.20819595","name":"Learning from Global Models: A Comparative Analysis of Health Data Governance Frameworks and Their Implications for U.S. Health Information Policy and System Design","source":"datacite","abstract":"Health data governance has become a critical component of modern healthcare systems due to increasing digitization, large-scale data sharing, and the growing importance of data-driven research and innovation. This review identifies key governance models and examines their implications for U.S. health information policy and system design. Using the Boolean function, keywords were used to search for and source articles, which were later screened. A total of eleven articles were finally obtained and synthesized for this study. The literature shows that normative governance frameworks across countries share common principles such as protection of individual rights, transparency, accountability, and public interest. However, fragmentation exists in terminology, enforcement mechanisms, and lifecycle coverage, particularly in the governance of secondary data reuse. Organizational governance studies highlight the importance of institutional structures, including clearly defined roles, governance committees, stewardship responsibilities, and standardized procedures. Evidence also shows that governance effectiveness depends on infrastructure, workforce training, and sustainable institutional capacity. Technical governance architectures introduce new approaches that embed governance rules directly into digital infrastructures. Blockchain-based systems strengthen security, transparency, and auditability through decentralized ledgers and smart contracts, while federated learning enables privacy-preserving data analysis by keeping sensitive patient data at local sources. Operational and design governance further translate governance principles into daily practice through privacy- and security-by-design, structured data quality management, and formal consent and access control mechanisms. The findings suggest that strengthening lifecycle governance, institutional capacity, system design, and workforce training can support more secure, interoperable, and trustworthy health information systems.","url":"https://doi.org/10.5281/zenodo.20819595","authors":["Omonye Jones Silas","Solomon Doe Adjaottor"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20819595","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.21839218","name":"Supplementary Material for: Federated Learning for Ophthalmic Imaging: A Systematic Review and Quantitative Synthesis","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21839218","authors":["Qi, Luyuan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21839218","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.21839219","name":"Supplementary Material for: Federated Learning for Ophthalmic Imaging: A Systematic Review and Quantitative Synthesis","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21839219","authors":["Qi, Luyuan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21839219","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.20344083","name":"Deep Learning Strategies for Predicting Drug–Target Interactions: Advances, Challenges, and Future Perspectives","source":"datacite","abstract":"Drug–target interaction (DTI) prediction lies at the heart of modern drug discovery, determining whether a candidate small molecule will bind to and modulate a biological macromolecule of therapeutic relevance. Traditional experimental high-throughput screening is expensive, time-consuming, and constrained by library size, while classical computational approaches—docking, pharmacophore modelling, and quantitative structure–activity relationship (QSAR) modelling suffer from limitations in scalability and generalizability. The emergence of deep learning (DL) has fundamentally transformed the field, enabling end-to-end learning of molecular representations and interaction patterns from heterogeneous, large-scale biomedical data. This review provides a comprehensive synthesis of DL-based DTI prediction methodologies, covering convolutional neural networks (CNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), transformer-based architectures, autoencoders, and multi-modal fusion frameworks. We discuss the critical role of molecular representation—from one-dimensional SMILES strings and circular fingerprints to three-dimensional molecular graphs and protein contact maps. Benchmark datasets including Davis, KIBA, BindingDB, ChEMBL, and PDBbind are reviewed with respect to their composition, metric conventions, and appropriate use. We critically examine key challenges: data scarcity and imbalance, negative-sample bias, interpretability deficits, cold-start generalization, and the limited availability of experimental three-dimensional protein structures. Emerging solutions—pre-trained chemical language models, AlphaFold3 integration, federated learning, knowledge graph-augmented GNNs, and causal interpretability methods—are discussed as future directions. This review aims to serve as an authoritative reference for computational chemists, bioinformaticians, and medicinal chemists seeking to leverage DL for accelerated, cost-effective drug discovery.","url":"https://doi.org/10.5281/zenodo.20344083","authors":["Avinash Bajpai","Sachin Sharma","Birender Singh","KM Nisha"],"tags":["Drug–Target Interaction; Deep Learning; Graph Neural Networks; Molecular Representation; Binding Affinity; Drug Discovery; Transformer; Alphafold"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20344083","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.20344084","name":"Deep Learning Strategies for Predicting Drug–Target Interactions: Advances, Challenges, and Future Perspectives","source":"datacite","abstract":"Drug–target interaction (DTI) prediction lies at the heart of modern drug discovery, determining whether a candidate small molecule will bind to and modulate a biological macromolecule of therapeutic relevance. Traditional experimental high-throughput screening is expensive, time-consuming, and constrained by library size, while classical computational approaches—docking, pharmacophore modelling, and quantitative structure–activity relationship (QSAR) modelling suffer from limitations in scalability and generalizability. The emergence of deep learning (DL) has fundamentally transformed the field, enabling end-to-end learning of molecular representations and interaction patterns from heterogeneous, large-scale biomedical data. This review provides a comprehensive synthesis of DL-based DTI prediction methodologies, covering convolutional neural networks (CNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), transformer-based architectures, autoencoders, and multi-modal fusion frameworks. We discuss the critical role of molecular representation—from one-dimensional SMILES strings and circular fingerprints to three-dimensional molecular graphs and protein contact maps. Benchmark datasets including Davis, KIBA, BindingDB, ChEMBL, and PDBbind are reviewed with respect to their composition, metric conventions, and appropriate use. We critically examine key challenges: data scarcity and imbalance, negative-sample bias, interpretability deficits, cold-start generalization, and the limited availability of experimental three-dimensional protein structures. Emerging solutions—pre-trained chemical language models, AlphaFold3 integration, federated learning, knowledge graph-augmented GNNs, and causal interpretability methods—are discussed as future directions. This review aims to serve as an authoritative reference for computational chemists, bioinformaticians, and medicinal chemists seeking to leverage DL for accelerated, cost-effective drug discovery.","url":"https://doi.org/10.5281/zenodo.20344084","authors":["Avinash Bajpai","Sachin Sharma","Birender Singh","KM Nisha"],"tags":["Drug–Target Interaction; Deep Learning; Graph Neural Networks; Molecular Representation; Binding Affinity; Drug Discovery; Transformer; Alphafold"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20344084","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.19949519","name":"The Intersection of AI and Blockchain for Enhanced Financial Due Diligence","source":"datacite","abstract":"Financial due diligence (FDD) plays a crucial role in corporate finance and mergers and acquisitions, because it is traditionally hampered by inefficiencies, human mistakes, and fractured verification mechanisms. The review examines how Artificial Intelligence (AI) can be used in combination with blockchain to increase automation, transparency, and accountability in financial investigations. Its main task is to examine how predictive analytics, anomaly detection, and immutability options of AI can reduce the level of human bias, speed up decision-making, and enhance compliance assurance. Using two prominent case studies of JPMorgan Chase and Wells Fargo, the study compares successful and unsuccessful integrations to determine what makes integrations effective. JPMorgan Liink platform showed considerable improvements in efficiency such as cross-border payment latency went down by 70% whereas the project at Wells Fargo was terminated because it failed in its governance and interoperability. Results indicate that AI-blockchain implementation boosts the integrity of financial data, automates risk assessment, and is helpful in creating auditability in real-time. But still, scalability, explainability, regulatory harmonization, and privacy concerns remain limited. The research concludes that long-term implementation needs permission blockchain systems, explainable AI, strong rules of governance, and convergence of regulation. Financial due diligence can be a proactive, resilient, and data-driven compliance system implemented in the future through transparency and ethical oversight and a federated learning process.","url":"https://doi.org/10.5281/zenodo.19949519","authors":["Deborah Akuele Apaflo","Ifeyinwa Perpetual Nwinyi","Barnabas Anim","William Kweku Afresi Buabin","Yeboah Mary Magdalene"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19949519","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.19949520","name":"The Intersection of AI and Blockchain for Enhanced Financial Due Diligence","source":"datacite","abstract":"Financial due diligence (FDD) plays a crucial role in corporate finance and mergers and acquisitions, because it is traditionally hampered by inefficiencies, human mistakes, and fractured verification mechanisms. The review examines how Artificial Intelligence (AI) can be used in combination with blockchain to increase automation, transparency, and accountability in financial investigations. Its main task is to examine how predictive analytics, anomaly detection, and immutability options of AI can reduce the level of human bias, speed up decision-making, and enhance compliance assurance. Using two prominent case studies of JPMorgan Chase and Wells Fargo, the study compares successful and unsuccessful integrations to determine what makes integrations effective. JPMorgan Liink platform showed considerable improvements in efficiency such as cross-border payment latency went down by 70% whereas the project at Wells Fargo was terminated because it failed in its governance and interoperability. Results indicate that AI-blockchain implementation boosts the integrity of financial data, automates risk assessment, and is helpful in creating auditability in real-time. But still, scalability, explainability, regulatory harmonization, and privacy concerns remain limited. The research concludes that long-term implementation needs permission blockchain systems, explainable AI, strong rules of governance, and convergence of regulation. Financial due diligence can be a proactive, resilient, and data-driven compliance system implemented in the future through transparency and ethical oversight and a federated learning process.","url":"https://doi.org/10.5281/zenodo.19949520","authors":["Deborah Akuele Apaflo","Ifeyinwa Perpetual Nwinyi","Barnabas Anim","William Kweku Afresi Buabin","Yeboah Mary Magdalene"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19949520","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.48550/arxiv.2608.14877","name":"Deep Reinforcement Learning for 6G AI-RAN: A Comprehensive Survey","source":"datacite","abstract":"The evolution toward sixth-generation (6G) networks is transforming the radio access network (RAN) into a programmable and intelligent control platform that must continuously adapt to heterogeneous services, dynamic environments, and competing performance objectives. Open Radio Access Network (O-RAN) provides the open interfaces, disaggregated architecture, and multi-timescale control loops needed to support this transformation, while deep reinforcement learning (DRL) offers a natural framework for optimizing sequential decisions under uncertainty. However, existing surveys either address artificial intelligence (AI) and machine learning (ML) in O-RAN broadly or focus on isolated DRL use cases, leaving a gap in the systematic connection between DRL methodology, O-RAN architecture, and operational deployment. To the best of our knowledge, this article presents the first dedicated and comprehensive survey of DRL for Open AI-RAN. We review the foundations of model-free, model-based, offline, safe, multi-agent, federated, and transfer learning, and provide an O-RAN-aware framework for formulating RAN control problems through states, observations, actions, rewards, constraints, and temporal structure. We classify DRL applications across radio resource management, mobility management, interference control, traffic steering, energy efficiency, network slicing, integrated sensing and communication, security, and massive MIMO. We further examine multi-agent and federated coordination, foundation models and agentic AI, trustworthy DRL, sim-to-real transfer, continual adaptation, resource-efficient inference, and reinforcement learning operations. Finally, we review experimental platforms, benchmarks, standards, and industry activities, and identify research directions toward sample-efficient, safe, scalable, interoperable, and deployable DRL control for 6G Open AI-RAN.","url":"https://doi.org/10.48550/arxiv.2608.14877","authors":["Lu, Jie","Yan, Peihao","Wang, Qijun","Lin, Ruxin","Zeng, Huacheng"],"tags":["Networking and Internet Architecture (cs.NI)","Signal Processing (eess.SP)","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.14877","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.20760897","name":"Coordination Topology as Failure Geometry: How Network Structure, Protocol Design, and Consensus Dynamics Jointly Shape Multi-Agent System Reliability","source":"datacite","abstract":"Version 2 — revised in response to an external structural review and an automated critique pass. See \"Response to Review\" appendix in the PDF for the change log. Multi-agent AI systems are increasingly deployed in production settings where reliability is assumed but rarely formally characterized. This paper advances a **candidate structural reading—explicitly a heuristic synthesis, not a derivation**—of a pattern that recurs across five to seven recent preprints from cs.MA, cs.DC, and cs.NI: **coordination topology is not a neutral implementation detail but a primary determinant of failure geometry**. The topology assumption made at design time—centralized hub, sparse factor graph, federated stack, decentralized shared-context, or social-network overlay—predicts which failure modes will dominate, how error propagates, and whether recovery is even structurally possible. Concretely, we draw on: density-evolution analysis of agent networks modeled as sparse factor graphs [corpus:arxiv:2606.18121], Byzantine-resilient CRDT reconstruction that decouples update propagation from state derivation [corpus:arxiv:2606.18966], a taxonomy of LLM agent communication protocols that reveals decentralized discovery remains rare [corpus:arxiv:2606.19135], the security-induced Braess paradox in service-function-chain orchestration [corpus:arxiv:2606.17987], the channel-fracture failure mode in hierarchical memory injection [corpus:arxiv:2606.04896v2], social learning performance gaps between decentralized and centralized inference [corpus:arxiv:2606.09176], and the deliberative-consensus degradation observed in multi-agent oracle resolution [corpus:arxiv:2605.30802]. The unifying claim is that each of these findings instantiates the same structural pattern: a topology choice creates a reachability or propagation structure that determines whether errors are absorbed, amplified, or silently suppressed. This is a heuristic reading across sources that do not share a common formalism; the shared vocabulary does not guarantee shared formal structure. The falsification path for this claim is concrete: construct a controlled multi-agent testbed in which topology is the single varied parameter across otherwise identical agent populations and measure whether failure-mode distribution shifts in the direction predicted by density-evolution thresholds. --- Authorship: Saluca Agentic AI Research Team (Saluca LLC). AI-drafted from arXiv preprint corpus on the date in the filename. Cited arXiv preprints: 2605.30802, 2606.02080, 2606.04896v2, 2606.06971, 2606.07487, 2606.08457, 2606.09176, 2606.13068, 2606.15931, 2606.17987, 2606.18121, 2606.18837, 2606.18966, 2606.19135, 2606.19319 AI disclosure. This work was produced with an agentic AI research apparatus operated by Saluca Labs. The apparatus drafted, searched and analysed under direction. Cristian Ruvalcaba is the human author and is accountable for the content. No AI system is listed as an author or contributor, because authorship entails accountability that a model cannot hold; this disclosure is the credit, and it is deliberately the whole of it.","url":"https://doi.org/10.5281/zenodo.20760897","authors":["Saluca Agentic AI Research Team"],"tags":["AI-drafted synthesis","arXiv","preprint review","v2"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20760897","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.20482005","name":"Feature-Oriented Regulation in Federated Multimodal Models Under Non-IID Data Distributions","source":"datacite","abstract":"This report synthesises findings from 5 peer-reviewed papers addressing the following research question: What is the impact of feature-oriented regulation methods like \\$Psi\\$-Net on the inference efficiency of federated multimodal models under non-IID data distributions, measured by throughput and accuracy. Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the impact of feature-oriented regulation methods like Ψ-Net on the inference efficiency of federated multimodal models under non-IID data distributions, measured by throughput and accuracy trade-offs? Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20482005","authors":["Assignee Research"],"tags":["impact","feature-oriented","regulation","methods","like","Net","inference","efficiency"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20482005","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.20482006","name":"Feature-Oriented Regulation in Federated Multimodal Models Under Non-IID Data Distributions","source":"datacite","abstract":"This report synthesises findings from 5 peer-reviewed papers addressing the following research question: What is the impact of feature-oriented regulation methods like \\$Psi\\$-Net on the inference efficiency of federated multimodal models under non-IID data distributions, measured by throughput and accuracy. Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for. 7 claims were extracted from source literature; 7 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the impact of feature-oriented regulation methods like Ψ-Net on the inference efficiency of federated multimodal models under non-IID data distributions, measured by throughput and accuracy trade-offs? Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20482006","authors":["Assignee Research"],"tags":["impact","feature-oriented","regulation","methods","like","Net","inference","efficiency"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20482006","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.20280464","name":"Intelligent Predictive Architectures for Autonomous Self-Healing in Cloud Computing: A Comprehensive Survey","source":"datacite","abstract":"Neural network-enabled self-healing is becoming a very exciting approach to making cloud computing infrastructures more reliable, available, and efficient. This survey paper gives a detailed review of neural network-based predictive models and how they are combined with autonomous recovery mechanisms for self-healing cloud systems. It first delineates the main neural architectures used for cloud reliability, such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and hybrid or ensemble models, explaining their roles in failure prediction, resource management forecasting, and SLA/QoS violation prediction. The paper examines the coupling of these predictive models with self-healing action and decision layers like rule-based policies, policy engines, and reinforcement learning-driven controllers to proactively trigger recovery actions, reduce Mean Time To Recovery (MTTR), and control false alarms and resource overhead. Evaluation practices highlight datasets (Google Cluster Traces, Alibaba traces, and synthetic simulation data), performance metrics (prediction accuracy, MTTR, false positive/negative rates, scalability, and energy cost), and differences between simulated vs. real production cloud environments. Moreover, the survey discusses privacy and security issues of predictive and action models, presenting techniques such as federated learning, differential privacy, homomorphic encryption, and secure multi-party computation for privacy-preserving cross-tenant collaboration. Lastly, this paper draws attention to open issues including trade-offs between accuracy and false alarms, latency constraints, explainability, and scalability, proposing future work including explainable predictive pipelines, federated self-healing frameworks, multi-agent and DRL-based autonomy, and integration with emerging technologies such as quantum-inspired neural networks, edge-cloud continuum architectures, digital twins, service meshes, and neuromorphic hardware to enable more trustworthy, efficient, and autonomous self-healing cloud management.","url":"https://doi.org/10.5281/zenodo.20280464","authors":["Mr.  Nitesh Gupta","Dr.  Nandita Bangera"],"tags":["Neural Predictive Intelligence","Self-Healing Architectures","Deep Learning for Cloud Systems","Multi-Agent Reinforcement Learning","Federated Self-Healing Frameworks","SLA Forecasting","Cloud Infrastructure Resilience"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20280464","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.20321777","name":"Intelligent Predictive Architectures for Autonomous Self-Healing in Cloud Computing: A Comprehensive Survey","source":"datacite","abstract":"Neural network-enabled self-healing is becoming a very exciting approach to making cloud computing infrastructures more reliable, available, and efficient. This survey paper gives a detailed review of neural network-based predictive models and how they are combined with autonomous recovery mechanisms for self-healing cloud systems. It first delineates the main neural architectures used for cloud reliability, such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and hybrid or ensemble models, explaining their roles in failure prediction, resource management forecasting, and SLA/QoS violation prediction. The paper examines the coupling of these predictive models with self-healing action and decision layers like rule-based policies, policy engines, and reinforcement learning-driven controllers to proactively trigger recovery actions, reduce Mean Time To Recovery (MTTR), and control false alarms and resource overhead. Evaluation practices highlight datasets (Google Cluster Traces, Alibaba traces, and synthetic simulation data), performance metrics (prediction accuracy, MTTR, false positive/negative rates, scalability, and energy cost), and differences between simulated vs. real production cloud environments. Moreover, the survey discusses privacy and security issues of predictive and action models, presenting techniques such as federated learning, differential privacy, homomorphic encryption, and secure multi-party computation for privacy-preserving cross-tenant collaboration. Lastly, this paper draws attention to open issues including trade-offs between accuracy and false alarms, latency constraints, explainability, and scalability, proposing future work including explainable predictive pipelines, federated self-healing frameworks, multi-agent and DRL-based autonomy, and integration with emerging technologies such as quantum-inspired neural networks, edge-cloud continuum architectures, digital twins, service meshes, and neuromorphic hardware to enable more trustworthy, efficient, and autonomous self-healing cloud management.","url":"https://doi.org/10.5281/zenodo.20321777","authors":["Mr.  Nitesh Gupta","Dr.  Nandita Bangera"],"tags":["Neural Predictive Intelligence","Self-Healing Architectures","Deep Learning for Cloud Systems","Multi-Agent Reinforcement Learning","Federated Self-Healing Frameworks","SLA Forecasting","Cloud Infrastructure Resilience"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20321777","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.20523051","name":"Artificial Intelligence, IoV, And Security In Modern Intelligent Systems: A Holistic Study","source":"datacite","abstract":"Recent breakthroughs in Artificial Intelligence (AI), Internet of Vehicles (IoV), Brain–Computer Interfaces (BCI), blockchain security, autonomous driving, and speech processing are reshaping intelligent communication and automation systems. This review synthesizes 60 contemporary research contributions across secure vehicular networks, interpretable transfer learning for BCI, LLM-assisted 6G IoV communication, federated edge learning, digital twins, and post-quantum blockchain frameworks. We highlight the paradigm shift from performance-driven AI toward trust-centric, interpretable, and quantum-resilient architectures. While state-of-the-art systems demonstrate remarkable gains—such as 89.7% accuracy in BCI applications and an 80% reduction in IoV verification overhead—the transition to pervasive edge-cloud environments exposes persistent challenges. Computational complexity, thermal throttling, data poisoning, and hardware dependencies remain critical barriers to scalable real-world deployment. Our analysis underscores both the promise and the unresolved hurdles of next-generation intelligent systems.","url":"https://doi.org/10.5281/zenodo.20523051","authors":["Mustaq Kunnur"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20523051","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.20523052","name":"Artificial Intelligence, IoV, And Security In Modern Intelligent Systems: A Holistic Study","source":"datacite","abstract":"Recent breakthroughs in Artificial Intelligence (AI), Internet of Vehicles (IoV), Brain–Computer Interfaces (BCI), blockchain security, autonomous driving, and speech processing are reshaping intelligent communication and automation systems. This review synthesizes 60 contemporary research contributions across secure vehicular networks, interpretable transfer learning for BCI, LLM-assisted 6G IoV communication, federated edge learning, digital twins, and post-quantum blockchain frameworks. We highlight the paradigm shift from performance-driven AI toward trust-centric, interpretable, and quantum-resilient architectures. While state-of-the-art systems demonstrate remarkable gains—such as 89.7% accuracy in BCI applications and an 80% reduction in IoV verification overhead—the transition to pervasive edge-cloud environments exposes persistent challenges. Computational complexity, thermal throttling, data poisoning, and hardware dependencies remain critical barriers to scalable real-world deployment. Our analysis underscores both the promise and the unresolved hurdles of next-generation intelligent systems.","url":"https://doi.org/10.5281/zenodo.20523052","authors":["Mustaq Kunnur"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20523052","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.19491833","name":"Machine Learning Models For Predictive Cybersecurity Defense","source":"datacite","abstract":"Machine learning has emerged as a transformative force in cybersecurity, enabling predictive defence mechanisms that move beyond traditional reactive strategies. This review explores the evolution, methodologies, and applications of machine learning models in predictive cybersecurity defence. By leveraging large-scale data, these models can detect anomalies, anticipate threats, and automate responses in real time. Techniques such as supervised learning, unsupervised learning, and deep learning have been widely adopted to identify patterns in network traffic, user behaviour, and system logs. Predictive capabilities allow organizations to mitigate risks before attacks occur, reducing financial and operational damage. However, challenges such as adversarial attacks, data imbalance, model interpretability, and scalability persist. This article also highlights emerging trends, including federated learning, explainable AI, and hybrid defence systems that integrate human expertise with machine intelligence. Through a comprehensive analysis, the review emphasizes the need for robust, adaptive, and ethical frameworks to ensure reliable deployment of machine learning in cybersecurity. The findings suggest that while machine learning significantly enhances predictive capabilities, its effectiveness depends on data quality, continuous model updates, and integration with existing security infrastructures.","url":"https://doi.org/10.5281/zenodo.19491833","authors":["Manoj Tiwari"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.5281/zenodo.19491833","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.19491834","name":"Machine Learning Models For Predictive Cybersecurity Defense","source":"datacite","abstract":"Machine learning has emerged as a transformative force in cybersecurity, enabling predictive defence mechanisms that move beyond traditional reactive strategies. This review explores the evolution, methodologies, and applications of machine learning models in predictive cybersecurity defence. By leveraging large-scale data, these models can detect anomalies, anticipate threats, and automate responses in real time. Techniques such as supervised learning, unsupervised learning, and deep learning have been widely adopted to identify patterns in network traffic, user behaviour, and system logs. Predictive capabilities allow organizations to mitigate risks before attacks occur, reducing financial and operational damage. However, challenges such as adversarial attacks, data imbalance, model interpretability, and scalability persist. This article also highlights emerging trends, including federated learning, explainable AI, and hybrid defence systems that integrate human expertise with machine intelligence. Through a comprehensive analysis, the review emphasizes the need for robust, adaptive, and ethical frameworks to ensure reliable deployment of machine learning in cybersecurity. The findings suggest that while machine learning significantly enhances predictive capabilities, its effectiveness depends on data quality, continuous model updates, and integration with existing security infrastructures.","url":"https://doi.org/10.5281/zenodo.19491834","authors":["Manoj Tiwari"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.5281/zenodo.19491834","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.19610735","name":"AI-Assisted Clinical Trial Recruitment for Devices","source":"datacite","abstract":"Clinical trial recruitment is the most frequent cause of device trial delay and the most preventable. More than 80% ofdevice trials fail to meet original enrolment targets on schedule, extending development timelines by a median of 8-14months and generating costs that fall disproportionately on smaller device developers without the buffer capital to absorbslippage. AI-assisted recruitment addresses the root causes of this problem: slow patient identification, high screenfailure rates from manual eligibility review, and inadequate representation of the real-world device use population in trialcohorts. This study presents the AI-Assisted Device Trial Recruitment Framework (AADTRF), evaluating five AIrecruitment approaches -- NLP-based electronic health record eligibility screening (NLP-EHR), machine learning patientmatching from device registries (ML-Reg), digital outreach and social media recruitment (DO-SM), federated multi-sitecohort identification (Fed-CI), and predictive retention and dropout risk modelling (PR-DRM) -- across four device trialcontexts: cardiac implant trials, orthopaedic device trials, neurostimulation device trials, and diagnostic imaging trials.Performance was scored using the Recruitment Effectiveness Score (RES), a weighted composite of enrolment rateimprovement (0.30), screen failure reduction (0.20), time to enrolment completion (0.20), population representativeness(0.15), and cost efficiency (0.15). NLP-based EHR screening achieved the highest RES (0.890), improving enrolmentrates by a mean 47% and reducing screen failure from 38.4% to 16.2% across four trial contexts. ML registry matchingranked second (0.888) with the best screen failure reduction (0.920) in device-specific contexts. Federated cohortidentification (0.877) demonstrated unique value for rare device trial indications, generating eligible patient pools 3.4times larger than single-site approaches. Digital outreach led on cost efficiency (0.940) and populationrepresentativeness (0.920) but produced the highest screen failure rates (0.800) due to self-selection bias in volunteerpopulations.","url":"https://doi.org/10.5281/zenodo.19610735","authors":["Marta Mulle","Nina Klein","Andreas Bianchi"],"tags":["clinical trial recruitment; AI recruitment; NLP screening; device trials; patient matching; federated learning; digital outreach; AADTRF; Recruitment Effectiveness Score; screen failure; trial enrolment"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.19610735","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.19610736","name":"AI-Assisted Clinical Trial Recruitment for Devices","source":"datacite","abstract":"Clinical trial recruitment is the most frequent cause of device trial delay and the most preventable. More than 80% ofdevice trials fail to meet original enrolment targets on schedule, extending development timelines by a median of 8-14months and generating costs that fall disproportionately on smaller device developers without the buffer capital to absorbslippage. AI-assisted recruitment addresses the root causes of this problem: slow patient identification, high screenfailure rates from manual eligibility review, and inadequate representation of the real-world device use population in trialcohorts. This study presents the AI-Assisted Device Trial Recruitment Framework (AADTRF), evaluating five AIrecruitment approaches -- NLP-based electronic health record eligibility screening (NLP-EHR), machine learning patientmatching from device registries (ML-Reg), digital outreach and social media recruitment (DO-SM), federated multi-sitecohort identification (Fed-CI), and predictive retention and dropout risk modelling (PR-DRM) -- across four device trialcontexts: cardiac implant trials, orthopaedic device trials, neurostimulation device trials, and diagnostic imaging trials.Performance was scored using the Recruitment Effectiveness Score (RES), a weighted composite of enrolment rateimprovement (0.30), screen failure reduction (0.20), time to enrolment completion (0.20), population representativeness(0.15), and cost efficiency (0.15). NLP-based EHR screening achieved the highest RES (0.890), improving enrolmentrates by a mean 47% and reducing screen failure from 38.4% to 16.2% across four trial contexts. ML registry matchingranked second (0.888) with the best screen failure reduction (0.920) in device-specific contexts. Federated cohortidentification (0.877) demonstrated unique value for rare device trial indications, generating eligible patient pools 3.4times larger than single-site approaches. Digital outreach led on cost efficiency (0.940) and populationrepresentativeness (0.920) but produced the highest screen failure rates (0.800) due to self-selection bias in volunteerpopulations.","url":"https://doi.org/10.5281/zenodo.19610736","authors":["Marta Mulle","Nina Klein","Andreas Bianchi"],"tags":["clinical trial recruitment; AI recruitment; NLP screening; device trials; patient matching; federated learning; digital outreach; AADTRF; Recruitment Effectiveness Score; screen failure; trial enrolment"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.19610736","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.21093549","name":"Artificial intelligence-assisted early warning and continuous monitoring system for prevention of cardiac emergencies: A comprehensive review","source":"datacite","abstract":"Abstract: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for millions of deaths annually. A significant proportion of these deaths result from preventable cardiac emergencies, including acute myocardial infarction, cardiac arrest, malignant arrhythmias, and acute heart failure, where delayed recognition and intervention substantially worsen clinical outcomes. Conventional monitoring approaches rely primarily on intermittent clinical assessments and manual interpretation of physiological data, limiting their ability to detect subtle physiological deterioration before the onset of critical events. Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), wearable biosensors, cloud computing, and the Internet of Medical Things (IoMT) have transformed cardiovascular monitoring by enabling continuous, real-time analysis of multidimensional physiological signals. AI-assisted early warning systems integrate electrocardiographic signals, heart rate variability, blood pressure, oxygen saturation, respiratory rate, activity patterns, and patient history to predict impending cardiac emergencies before clinical manifestations become severe. Predictive algorithms facilitate early diagnosis, risk stratification, personalized intervention, and timely clinical decision-making, thereby improving patient outcomes while reducing healthcare costs and hospital readmissions. Moreover, wearable technologies and remote patient monitoring platforms have expanded cardiac surveillance beyond hospital settings, allowing continuous monitoring of high-risk individuals in their homes and communities. Despite these promising developments, challenges related to data privacy, algorithm transparency, interoperability, regulatory approval, model bias, cybersecurity, and ethical considerations continue to hinder widespread clinical implementation. This review comprehensively summarizes the principles, technological advancements, clinical applications, benefits, limitations, and future prospects of AI-assisted early warning and continuous monitoring systems for the prevention of cardiac emergencies. The review further discusses emerging innovations such as explainable AI, federated learning, digital twins, edge computing, and multimodal predictive analytics, which are expected to revolutionize cardiovascular care through precision medicine and proactive disease prevention. Overall, AI-assisted cardiac monitoring represents a paradigm shift from reactive healthcare toward predictive, preventive, and personalized cardiovascular management. Keywords: Artificial Intelligence; Cardiac Emergencies; Continuous Monitoring; Early Warning System; Machine Learning; Deep Learning; Electrocardiography; Wearable Devices; Internet of Medical Things; Remote Patient Monitoring.","url":"https://doi.org/10.5281/zenodo.21093549","authors":["G. Elango","P. Sumithra","Salomeen Rani. S"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21093549","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.21093550","name":"Artificial intelligence-assisted early warning and continuous monitoring system for prevention of cardiac emergencies: A comprehensive review","source":"datacite","abstract":"Abstract: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for millions of deaths annually. A significant proportion of these deaths result from preventable cardiac emergencies, including acute myocardial infarction, cardiac arrest, malignant arrhythmias, and acute heart failure, where delayed recognition and intervention substantially worsen clinical outcomes. Conventional monitoring approaches rely primarily on intermittent clinical assessments and manual interpretation of physiological data, limiting their ability to detect subtle physiological deterioration before the onset of critical events. Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), wearable biosensors, cloud computing, and the Internet of Medical Things (IoMT) have transformed cardiovascular monitoring by enabling continuous, real-time analysis of multidimensional physiological signals. AI-assisted early warning systems integrate electrocardiographic signals, heart rate variability, blood pressure, oxygen saturation, respiratory rate, activity patterns, and patient history to predict impending cardiac emergencies before clinical manifestations become severe. Predictive algorithms facilitate early diagnosis, risk stratification, personalized intervention, and timely clinical decision-making, thereby improving patient outcomes while reducing healthcare costs and hospital readmissions. Moreover, wearable technologies and remote patient monitoring platforms have expanded cardiac surveillance beyond hospital settings, allowing continuous monitoring of high-risk individuals in their homes and communities. Despite these promising developments, challenges related to data privacy, algorithm transparency, interoperability, regulatory approval, model bias, cybersecurity, and ethical considerations continue to hinder widespread clinical implementation. This review comprehensively summarizes the principles, technological advancements, clinical applications, benefits, limitations, and future prospects of AI-assisted early warning and continuous monitoring systems for the prevention of cardiac emergencies. The review further discusses emerging innovations such as explainable AI, federated learning, digital twins, edge computing, and multimodal predictive analytics, which are expected to revolutionize cardiovascular care through precision medicine and proactive disease prevention. Overall, AI-assisted cardiac monitoring represents a paradigm shift from reactive healthcare toward predictive, preventive, and personalized cardiovascular management. Keywords: Artificial Intelligence; Cardiac Emergencies; Continuous Monitoring; Early Warning System; Machine Learning; Deep Learning; Electrocardiography; Wearable Devices; Internet of Medical Things; Remote Patient Monitoring.","url":"https://doi.org/10.5281/zenodo.21093550","authors":["G. Elango","P. Sumithra","Salomeen Rani. S"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21093550","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.19610547","name":"AI-Based Image Analysis in Diagnostic Devices","source":"datacite","abstract":"AI-based image analysis has emerged as the leading application of artificial intelligence in diagnostic medical devices,with over 520 FDA-cleared AI/ML-enabled devices by 2023 -- the majority addressing radiology, pathology,ophthalmology, and dermatology image interpretation. From convolutional neural networks that detect diabeticretinopathy with ophthalmologist-level sensitivity to transformer models that segment tumour boundaries in whole-slidepathology images with sub-cellular precision, AI image analysis is transitioning from research demonstration to clinicaldeployment at scale. Yet the path from validated algorithm to clinically integrated diagnostic device requires navigation ofhuman factors design, workflow integration, regulatory validation, and post-market performance monitoring challengesthat algorithmic accuracy alone does not address. This study presents the AI Image Analysis Diagnostic DeviceFramework (AIADDF), evaluating five AI image analysis implementation approaches -- standalone algorithm withradiologist workflow, AI-first triage with human review escalation, computer-aided detection enhancement, autonomousAI reporting for defined scope, and federated multi-site AI with continuous learning -- across four imaging devicecategories: radiology CT/MRI, digital pathology, retinal fundus imaging, and dermatoscopy. Our AI Diagnostic ImageScore (ADIS) integrates diagnostic accuracy, workflow efficiency, radiologist acceptance, regulatory compliance, andcross-site generalisability. Federated multi-site AI with continuous learning achieved the highest ADIS (0.928) throughprivacy-preserving training across 24 clinical sites that achieved C-statistic 0.92 while maintaining 96% performanceretention at new deployment sites, while autonomous AI reporting achieved the highest workflow efficiency (0.955) byreducing mean report turnaround time from 48 hours to 3.2 hours for defined low-complexity imaging tasks","url":"https://doi.org/10.5281/zenodo.19610547","authors":["Helena Popescu","Andreas Lindberg","Andreas Moreau"],"tags":["AI image analysis; diagnostic device; convolutional neural network; federated learning; radiology; pathology; retinal imaging; autonomous reporting; SaMD; workflow integration"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.19610547","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.19610548","name":"AI-Based Image Analysis in Diagnostic Devices","source":"datacite","abstract":"AI-based image analysis has emerged as the leading application of artificial intelligence in diagnostic medical devices,with over 520 FDA-cleared AI/ML-enabled devices by 2023 -- the majority addressing radiology, pathology,ophthalmology, and dermatology image interpretation. From convolutional neural networks that detect diabeticretinopathy with ophthalmologist-level sensitivity to transformer models that segment tumour boundaries in whole-slidepathology images with sub-cellular precision, AI image analysis is transitioning from research demonstration to clinicaldeployment at scale. Yet the path from validated algorithm to clinically integrated diagnostic device requires navigation ofhuman factors design, workflow integration, regulatory validation, and post-market performance monitoring challengesthat algorithmic accuracy alone does not address. This study presents the AI Image Analysis Diagnostic DeviceFramework (AIADDF), evaluating five AI image analysis implementation approaches -- standalone algorithm withradiologist workflow, AI-first triage with human review escalation, computer-aided detection enhancement, autonomousAI reporting for defined scope, and federated multi-site AI with continuous learning -- across four imaging devicecategories: radiology CT/MRI, digital pathology, retinal fundus imaging, and dermatoscopy. Our AI Diagnostic ImageScore (ADIS) integrates diagnostic accuracy, workflow efficiency, radiologist acceptance, regulatory compliance, andcross-site generalisability. Federated multi-site AI with continuous learning achieved the highest ADIS (0.928) throughprivacy-preserving training across 24 clinical sites that achieved C-statistic 0.92 while maintaining 96% performanceretention at new deployment sites, while autonomous AI reporting achieved the highest workflow efficiency (0.955) byreducing mean report turnaround time from 48 hours to 3.2 hours for defined low-complexity imaging tasks","url":"https://doi.org/10.5281/zenodo.19610548","authors":["Helena Popescu","Andreas Lindberg","Andreas Moreau"],"tags":["AI image analysis; diagnostic device; convolutional neural network; federated learning; radiology; pathology; retinal imaging; autonomous reporting; SaMD; workflow integration"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.19610548","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.48550/arxiv.2608.13844","name":"Federated Prompt Learning: A Unified Framework, Empirical Analysis, and Future Directions","source":"datacite","abstract":"Large language models (LLMs) have become core components of cloud-based intelligent services in academia and industry, yet their training and deployment are hindered by high computational costs, data centralization, and privacy concerns. Federated learning (FL) offers a decentralized training paradigm that enables clients to collaboratively train a learning model without sharing raw data, making it a promising solution for privacy-preserving LLM training and reasoning. This paper presents a comprehensive survey of federated prompt learning (FPL) to review recent advances in integrating the federated learning paradigm and large language models, answering the following research questions: RQ1: The fundamental motivations, characteristics, and enabling technologies of FPL, and how it differs from conventional FL and full-model federated fine-tuning; RQ2: The trade-offs FPL approaches exhibit in performance, communication efficiency, computational overhead, scalability, personalization, and heterogeneity handling; RQ3: The remaining security, privacy, robustness, and system challenges, along with key future research directions. To this end, we systematically examine existing FPL methods across the full model lifecycle: pre-training, fine-tuning, and practical applications, while discussing security, privacy, and robustness issues and summarizing existing defense mechanisms. Finally, we highlight open challenges and future directions, aiming to help readers understand how the insights drive research in FPL.","url":"https://doi.org/10.48550/arxiv.2608.13844","authors":["Yang, Qinglin","Qiu, Chen","Zhang, Hongyuan","Li, Pengdeng","Liu, Yuan","Tian, Zhihong"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.13844","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.20054299","name":"Spectroscopically Constrained Quantum Machine Learning for Complex Systems: Applying the Maxwell-Scretching Framework, the Scretching Quantum Chain, and the Scretching-Schrodinger Equation to QML","source":"datacite","abstract":"This paper applies the Maxwell–Scretching Framework, the Scretching Quantum Chain (SQC), and the Scretching–Schrödinger Equation (SSE) to the quantum machine-learning review by Singh, Bhatia, Saggi, Sajjan, and Kais, Quantum Machine Learning for Complex Systems: Paradigms, Applications, and Challenges [1]. The referenced review surveys major QML paradigms, including variational quantum algorithms, quantum neural networks, quantum kernels, neural-network quantum states, tensor-network methods, quantum-enabled variational Monte Carlo, out-of-time-order-correlator diagnostics, federated quantum learning, and applications in drug discovery, cancer biology, and agro-climate modeling [1]. Building from these QML paradigms, the present paper develops a physically constrained quantum machine-learning framework in which learned quantum states, kernels, and variational models are evaluated not only by empirical loss minimization, but also by spectroscopic and electromagnetic admissibility. The proposed architecture introduces SQC transition closure and Maxwell–Scretching optical/electromagnetic closure as additional physical constraints on QML outputs [8–13]. In this framework, a QML model is not considered fully acceptable unless its predicted quantum states, transition amplitudes, oscillator strengths, absorption behavior, and electromagnetic observables remain consistent with the SQC and Maxwell–Scretching chains. The paper derives several new constructs, including the SSE–QML Hamiltonian, the SQC-regularized learning loss, the Maxwell–Scretching optical loss, SQC-constrained quantum kernels, SQC–OTOC learning geometry, SSE-based quantum-enabled variational Monte Carlo, and federated SQC–QML. These constructs extend standard QML by embedding deterministic spectroscopic closure into model training, validation, and interpretation. Worked numerical examples verify the SQC transition law and the Maxwell–Scretching optical chain for a representative 260 nm molecular or biomolecular transition, demonstrating how oscillator strength, Einstein coefficients, molar absorptivity, absorption coefficient, extinction coefficient, cross-section, and dielectric loss can be chained into a physically interpretable learning constraint. The resulting work proposes a new research program for spectroscopically constrained quantum learning across quantum chemistry, molecular biophysics, DNA optical-genomic modeling, drug discovery, cancer biology, agro-climate forecasting, and distributed scientific learning. Rather than replacing existing QML methods, the Maxwell–Scretching/SQC/SSE framework adds a verification layer: learned quantum models must satisfy both computational performance criteria and physically meaningful optical, spectroscopic, and electromagnetic closure relations.","url":"https://doi.org/10.5281/zenodo.20054299","authors":["Scretching, Daniel"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20054299","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.582Z"},{"id":"doi:10.5281/zenodo.20054300","name":"Spectroscopically Constrained Quantum Machine Learning for Complex Systems: Applying the Maxwell-Scretching Framework, the Scretching Quantum Chain, and the Scretching-Schrodinger Equation to QML","source":"datacite","abstract":"This paper applies the Maxwell–Scretching Framework, the Scretching Quantum Chain (SQC), and the Scretching–Schrödinger Equation (SSE) to the quantum machine-learning review by Singh, Bhatia, Saggi, Sajjan, and Kais, Quantum Machine Learning for Complex Systems: Paradigms, Applications, and Challenges [1]. The referenced review surveys major QML paradigms, including variational quantum algorithms, quantum neural networks, quantum kernels, neural-network quantum states, tensor-network methods, quantum-enabled variational Monte Carlo, out-of-time-order-correlator diagnostics, federated quantum learning, and applications in drug discovery, cancer biology, and agro-climate modeling [1]. Building from these QML paradigms, the present paper develops a physically constrained quantum machine-learning framework in which learned quantum states, kernels, and variational models are evaluated not only by empirical loss minimization, but also by spectroscopic and electromagnetic admissibility. The proposed architecture introduces SQC transition closure and Maxwell–Scretching optical/electromagnetic closure as additional physical constraints on QML outputs [8–13]. In this framework, a QML model is not considered fully acceptable unless its predicted quantum states, transition amplitudes, oscillator strengths, absorption behavior, and electromagnetic observables remain consistent with the SQC and Maxwell–Scretching chains. The paper derives several new constructs, including the SSE–QML Hamiltonian, the SQC-regularized learning loss, the Maxwell–Scretching optical loss, SQC-constrained quantum kernels, SQC–OTOC learning geometry, SSE-based quantum-enabled variational Monte Carlo, and federated SQC–QML. These constructs extend standard QML by embedding deterministic spectroscopic closure into model training, validation, and interpretation. Worked numerical examples verify the SQC transition law and the Maxwell–Scretching optical chain for a representative 260 nm molecular or biomolecular transition, demonstrating how oscillator strength, Einstein coefficients, molar absorptivity, absorption coefficient, extinction coefficient, cross-section, and dielectric loss can be chained into a physically interpretable learning constraint. The resulting work proposes a new research program for spectroscopically constrained quantum learning across quantum chemistry, molecular biophysics, DNA optical-genomic modeling, drug discovery, cancer biology, agro-climate forecasting, and distributed scientific learning. Rather than replacing existing QML methods, the Maxwell–Scretching/SQC/SSE framework adds a verification layer: learned quantum models must satisfy both computational performance criteria and physically meaningful optical, spectroscopic, and electromagnetic closure relations.","url":"https://doi.org/10.5281/zenodo.20054300","authors":["Scretching, Daniel"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20054300","addedAt":"2026-08-31T06:41:33.582Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20170035","name":"INTRUSION DETECTION BASED ON FEDERATED LEARNING – A REVIEW","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20170035","authors":["1Abu Ubaida,2Khurram Zeeshan Haider*, 3Temur-ul-Hassan,  4Muhammad Azam Rasheed"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20170035","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20170036","name":"INTRUSION DETECTION BASED ON FEDERATED LEARNING – A REVIEW","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20170036","authors":["1Abu Ubaida,2Khurram Zeeshan Haider*, 3Temur-ul-Hassan,  4Muhammad Azam Rasheed"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20170036","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21514922","name":"IEEE Standard for Substrate-Native Integer Computing (Zone 0) —  Measured Solution to AI Energy Crisis, Reproducibility, and Edge Learning","source":"datacite","abstract":"Hello. Thank you for taking the time and interest in this work. During the current times, I know that it can be tougher and tougher to decide who is truthful with their work and who steals from others for their own vanity. I stand by my work. Over the course of 15 months, I have read and used every line of code, text, and any other research to build this system from the voltage up. This is engineering and computer science. I do not believe in the mystics of AGI and conscious AI and that people are allowed to get away with science using the term \"black box\". It is terrible to see people exploit others that are not well informed yet on the topic to steal and extract money from our businesses and institutions making the totality as a whole much worst. It is the champions curse. Those that can will surpass those that can't, if allowed to be isolated away from everyone themselves, will always lead to a life of disconnect and ,essentially, taking the person out of the community and into these tightly knit gangs of egocentric ideologies and, the worst of all, self eating mechanisms because groups believe that they are right rather than the individual. When a million people people are smart, smarter than one. Participation trophies, in the Math and Sciences, driving unhealthy and untested egos destropying the mind becoming a sickness, when the only thing one needs to do is actually acknowledge the journey and not admiring the work. Our reasons are each their own. Why do anything? Well, my reasons are not hard to understand the what. but the why is the answer to the questions I seek to answer. Energy problems, destroying the planet, having to watch another AI video slop that is not even funny or well done and listen to idiots giggle their way to a completely null livelihood of experience rather than exploration because of the ego. Safety. Being part of the \"crew\". No one got anywhere doing it that way because if everyone is doing it, than it is easy. Easy things are like a shiny little trinket. The whole goal is to collect a million trinkets of no value. If I get a bunch, than I have a bunch. But in reality, people are arguing if the 8 slice pizza and the 12 slice of the same size actually have a different value to the whole. It is still just one pile of pizza slop no matter how many slices you make it into. That is easy. What is not easy is when people are out to make your life worst. To tell others that they are dumb and that they are smart. I don't buy it, eitherwise I would have never done this. If things are working, than don't fix it. This is fixing. I saw a flaw, to stupid to understand why it works in the first place, and in that process, I found flaws in the literature and the work in floating point and the bit itself. 0 and 1. What the heck. We built everything on an assumption. Those assumptions have put us in this position. As far as academia goes, it pains me to see how it has changed. Below is the \"offer\". I am sick of seeing people struggle for no reason. It is self induced. I want to work with those that also see these flaws and want to work on resolving problems. Not creating more. Humans control the technology. Anyone that doesn't believe that they don't know what they are doing and it is a black box is trying to steal your money. Sorry if the vocabulary like \"organism\" and \"heartbeat\" throws you off. Well, that is why I can't write the paper. Nothing like this has any documentation or vocabulary yet for these mechanisms, so I had to use wording to keep all my thoughts together. This also helps for the system to build itself. It can follow the \"anatomy\" to build itself. It is also something that I hate using and burns my eyes everytime I look at those weird terms for computer science and technology. Any insights into better wording and contributing vocabulary is also needed in this collaboration. I don't care about naming your technology. Just the fact that we give tech names is another form of psychosis. Like dressin","url":"https://doi.org/10.5281/zenodo.21514922","authors":["Dragolich, Daniel"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21514922","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21514923","name":"IEEE Standard for Substrate-Native Integer Computing (Zone 0) —  Measured Solution to AI Energy Crisis, Reproducibility, and Edge Learning","source":"datacite","abstract":"Hello. Thank you for taking the time and interest in this work. During the current times, I know that it can be tougher and tougher to decide who is truthful with their work and who steals from others for their own vanity. I stand by my work. Over the course of 15 months, I have read and used every line of code, text, and any other research to build this system from the voltage up. This is engineering and computer science. I do not believe in the mystics of AGI and conscious AI and that people are allowed to get away with science using the term \"black box\". It is terrible to see people exploit others that are not well informed yet on the topic to steal and extract money from our businesses and institutions making the totality as a whole much worst. It is the champions curse. Those that can will surpass those that can't, if allowed to be isolated away from everyone themselves, will always lead to a life of disconnect and ,essentially, taking the person out of the community and into these tightly knit gangs of egocentric ideologies and, the worst of all, self eating mechanisms because groups believe that they are right rather than the individual. When a million people people are smart, smarter than one. Participation trophies, in the Math and Sciences, driving unhealthy and untested egos destropying the mind becoming a sickness, when the only thing one needs to do is actually acknowledge the journey and not admiring the work. Our reasons are each their own. Why do anything? Well, my reasons are not hard to understand the what. but the why is the answer to the questions I seek to answer. Energy problems, destroying the planet, having to watch another AI video slop that is not even funny or well done and listen to idiots giggle their way to a completely null livelihood of experience rather than exploration because of the ego. Safety. Being part of the \"crew\". No one got anywhere doing it that way because if everyone is doing it, than it is easy. Easy things are like a shiny little trinket. The whole goal is to collect a million trinkets of no value. If I get a bunch, than I have a bunch. But in reality, people are arguing if the 8 slice pizza and the 12 slice of the same size actually have a different value to the whole. It is still just one pile of pizza slop no matter how many slices you make it into. That is easy. What is not easy is when people are out to make your life worst. To tell others that they are dumb and that they are smart. I don't buy it, eitherwise I would have never done this. If things are working, than don't fix it. This is fixing. I saw a flaw, to stupid to understand why it works in the first place, and in that process, I found flaws in the literature and the work in floating point and the bit itself. 0 and 1. What the heck. We built everything on an assumption. Those assumptions have put us in this position. As far as academia goes, it pains me to see how it has changed. Below is the \"offer\". I am sick of seeing people struggle for no reason. It is self induced. I want to work with those that also see these flaws and want to work on resolving problems. Not creating more. Humans control the technology. Anyone that doesn't believe that they don't know what they are doing and it is a black box is trying to steal your money. Sorry if the vocabulary like \"organism\" and \"heartbeat\" throws you off. Well, that is why I can't write the paper. Nothing like this has any documentation or vocabulary yet for these mechanisms, so I had to use wording to keep all my thoughts together. This also helps for the system to build itself. It can follow the \"anatomy\" to build itself. It is also something that I hate using and burns my eyes everytime I look at those weird terms for computer science and technology. Any insights into better wording and contributing vocabulary is also needed in this collaboration. I don't care about naming your technology. Just the fact that we give tech names is another form of psychosis. Like dressin","url":"https://doi.org/10.5281/zenodo.21514923","authors":["Dragolich, Daniel"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21514923","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21801833","name":"Landslide Early Warning System using SAR Data and Machine Learning","source":"datacite","abstract":"Abstract - Landslides continue to pose a significant threat to human lives, infrastructure, transportation networks, and ecological systems, particularly in mountainous and high-rainfall regions. Recent advances in artificial intelligence, remote sensing, and geospatial analytics have transformed conventional landslide monitoring into intelligent early warning systems capable of continuous environmental assessment and rapid decision-making. This survey presents a comprehensive review of machine learning-based landslide early warning systems with particular emphasis on the integration of Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR), geo-fencing, meteorological information, ensemble learning, and real-time alert dissemination. The survey consolidates the methodologies, datasets, feature engineering strategies, prediction algorithms, deployment architectures, and evaluation metrics reported in recent literature while analyzing their strengths and limitations. Furthermore, the survey presents the LandSense framework as an integrated case study demonstrating how machine learning, satellite-derived deformation monitoring, rainfall analysis, secure backend services, geospatial risk mapping, evacuation route planning, and multi-channel alert mechanisms can be combined into a unified disaster management platform. Comparative analysis of Random Forest, XGBoost, ensemble learning, deep learning, and time-series forecasting techniques highlights current research trends and identifies remaining challenges related to data availability, computational complexity, model generalization, explainability, and operational deployment. The survey concludes by outlining future research directions involving explainable artificial intelligence, transformer architectures, graph neural networks, digital twins, federated learning, and edge-based disaster intelligence for next-generation landslide early warning systems. This survey reviews recent developments in AI-driven landslide prediction techniques and analyzes the integration of machine learning algorithms, Sentinel-1 InSAR, geo-fencing, rainfall monitoring, and emergency alert systems for disaster management. It also presents the LandSense framework as a comprehensive case study that combines ensemble learning, geospatial analysis, real-time monitoring, safe route planning, and multi-channel alert dissemination into a unified early warning platform. By comparing existing approaches and identifying current research gaps, this survey highlights future directions for developing scalable, explainable, and intelligent landslide early warning systems.","url":"https://doi.org/10.5281/zenodo.21801833","authors":["Pavithra R","Mrs. Divyashree G","Mrs. Amulya M P","Prajwal M","Rohan Fernandes","Vikas Gowda M"],"tags":["Landslide Early Warning System","Machine Learning","Random Forest","XGBoost","InSAR","Sentinel-1","Geo-Fencing","Disaster Management"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21801833","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21801834","name":"Landslide Early Warning System using SAR Data and Machine Learning","source":"datacite","abstract":"Abstract - Landslides continue to pose a significant threat to human lives, infrastructure, transportation networks, and ecological systems, particularly in mountainous and high-rainfall regions. Recent advances in artificial intelligence, remote sensing, and geospatial analytics have transformed conventional landslide monitoring into intelligent early warning systems capable of continuous environmental assessment and rapid decision-making. This survey presents a comprehensive review of machine learning-based landslide early warning systems with particular emphasis on the integration of Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR), geo-fencing, meteorological information, ensemble learning, and real-time alert dissemination. The survey consolidates the methodologies, datasets, feature engineering strategies, prediction algorithms, deployment architectures, and evaluation metrics reported in recent literature while analyzing their strengths and limitations. Furthermore, the survey presents the LandSense framework as an integrated case study demonstrating how machine learning, satellite-derived deformation monitoring, rainfall analysis, secure backend services, geospatial risk mapping, evacuation route planning, and multi-channel alert mechanisms can be combined into a unified disaster management platform. Comparative analysis of Random Forest, XGBoost, ensemble learning, deep learning, and time-series forecasting techniques highlights current research trends and identifies remaining challenges related to data availability, computational complexity, model generalization, explainability, and operational deployment. The survey concludes by outlining future research directions involving explainable artificial intelligence, transformer architectures, graph neural networks, digital twins, federated learning, and edge-based disaster intelligence for next-generation landslide early warning systems. This survey reviews recent developments in AI-driven landslide prediction techniques and analyzes the integration of machine learning algorithms, Sentinel-1 InSAR, geo-fencing, rainfall monitoring, and emergency alert systems for disaster management. It also presents the LandSense framework as a comprehensive case study that combines ensemble learning, geospatial analysis, real-time monitoring, safe route planning, and multi-channel alert dissemination into a unified early warning platform. By comparing existing approaches and identifying current research gaps, this survey highlights future directions for developing scalable, explainable, and intelligent landslide early warning systems.","url":"https://doi.org/10.5281/zenodo.21801834","authors":["Pavithra R","Mrs. Divyashree G","Mrs. Amulya M P","Prajwal M","Rohan Fernandes","Vikas Gowda M"],"tags":["Landslide Early Warning System","Machine Learning","Random Forest","XGBoost","InSAR","Sentinel-1","Geo-Fencing","Disaster Management"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21801834","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.19457914","name":"Scaling Farm Autonomy Decentralized Control, Data, and Edge","source":"datacite","abstract":"This review examines the landscape of decentralized agricultural automation systems, motivated by the need to synthesize findings from a rapidly evolving field. It addresses the shift from traditional centralized approaches towards more scalable, robust, and privacy-preserving solutions in modern farming. The review's scope focuses on three distinct yet interconnected layers of decentralization: decentralized control (Layer A), involving swarm and multi-agent robotics; decentralized data/analytics (Layer B), focusing on blockchain and federated learning; and distributed hardware/edge IoT (Layer C), centered on edge-first frameworks and on-device AI. Key findings indicate that Layer A is a maturing field with significant algorithmic progress, alongside increasing field prototypes demonstrating improved coverage and resilience. Layer B shows rapid growth in hybrid designs that combine privacy-preserving federated learning with blockchain for auditability and decentralized aggregation. Layer C highlights the emergence of edge-first frameworks and agentic AI on devices for low-latency perception and action. Common challenges across all layers include connectivity limitations in rural areas, data heterogeneity, establishing trust and incentive mechanisms, and managing energy constraints. The ultimate aim is to inform the development and deployment of robust and scalable decentralized agricultural automation solutions.","url":"https://doi.org/10.5281/zenodo.19457914","authors":["Shaik, Mohammad Muheeth"],"tags":["decentralized automation","swarm robotics","federated learning","blockchain","edge IoT","smart agriculture","multi-agent systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19457914","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.19457915","name":"Scaling Farm Autonomy Decentralized Control, Data, and Edge","source":"datacite","abstract":"This review examines the landscape of decentralized agricultural automation systems, motivated by the need to synthesize findings from a rapidly evolving field. It addresses the shift from traditional centralized approaches towards more scalable, robust, and privacy-preserving solutions in modern farming. The review's scope focuses on three distinct yet interconnected layers of decentralization: decentralized control (Layer A), involving swarm and multi-agent robotics; decentralized data/analytics (Layer B), focusing on blockchain and federated learning; and distributed hardware/edge IoT (Layer C), centered on edge-first frameworks and on-device AI. Key findings indicate that Layer A is a maturing field with significant algorithmic progress, alongside increasing field prototypes demonstrating improved coverage and resilience. Layer B shows rapid growth in hybrid designs that combine privacy-preserving federated learning with blockchain for auditability and decentralized aggregation. Layer C highlights the emergence of edge-first frameworks and agentic AI on devices for low-latency perception and action. Common challenges across all layers include connectivity limitations in rural areas, data heterogeneity, establishing trust and incentive mechanisms, and managing energy constraints. The ultimate aim is to inform the development and deployment of robust and scalable decentralized agricultural automation solutions.","url":"https://doi.org/10.5281/zenodo.19457915","authors":["Shaik, Mohammad Muheeth"],"tags":["decentralized automation","swarm robotics","federated learning","blockchain","edge IoT","smart agriculture","multi-agent systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19457915","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20592368","name":"Failure Propagation and Self-Correction in Multi-Agent LLM Systems: How Deliberative Consensus, Credit Assignment, and Architectural Isolation Jointly Determine Systemic Reliability","source":"datacite","abstract":"Version 2 — revised in response to an external structural review and an automated critique pass. See \"Response to Review\" appendix in the PDF for the change log. Multi-agent LLM systems are increasingly deployed in settings where individual agent failures can cascade across the collective, yet the mechanisms by which such failures propagate—and the structural conditions under which they are contained or corrected—remain poorly characterised. This paper synthesises seven findings from recent cs.MA and cs.DC preprints to argue a **candidate structural pattern** (a heuristic reading, not a derivation from a shared formal structure): systemic reliability in multi-agent LLM systems is not primarily a function of individual agent capability, but of the interaction between (a) the topology through which errors can propagate, (b) the credit-assignment mechanisms that identify which components generated the error, and (c) the architectural isolation boundaries that prevent a failure in one execution channel from contaminating others. We argue the analogy by naming specific mechanisms in each case rather than by appeal to a unified formalism. The corpus draws from cs.MA papers on multi-agent deliberation, self-evolution, coordination policy learning, and governance infrastructure, supplemented by cs.DC work on federated orchestration and zero-trust enforcement at physical actuation boundaries. Key findings include: deliberative consensus among LLM agents can *degrade* accuracy below single-model baselines when high-confidence wrong agents flip correct ones [corpus:arxiv:2605.30802]; architectural channel isolation failures silently block cross-agent memory injection regardless of agent-level correctness [corpus:arxiv:2606.04896]; governance layers at the execution boundary reduce unsafe executions from 88% to near-zero without modifying underlying generators [corpus:arxiv:2606.04306]; and temporal plus structural credit decomposition substantially reduces query complexity in MAS optimisation [corpus:arxiv:2605.30227]. Together these findings suggest that failure propagation and self-correction are topology-dependent phenomena that cannot be addressed by improving agent intelligence alone. Falsification path: if deliberative degradation disappears when inter-agent error correlation is experimentally reduced below 0.3, the error-propagation mechanism is confirmed; if it persists, the topology hypothesis is insufficient and capability variance must be the primary driver. --- Authorship: Saluca Agentic AI Research Team (Saluca LLC). AI-drafted from arXiv preprint corpus on the date in the filename. Cited arXiv preprints: 2605.25653, 2605.25741, 2605.26286, 2605.27076, 2605.27106, 2605.28984, 2605.29612, 2605.29790, 2605.30227, 2605.30802, 2606.01170, 2606.01581, 2606.01862, 2606.02080, 2606.03543, 2606.04197, 2606.04306, 2606.04896 AI disclosure. This work was produced with an agentic AI research apparatus operated by Saluca Labs. The apparatus drafted, searched and analysed under direction. Cristian Ruvalcaba is the human author and is accountable for the content. No AI system is listed as an author or contributor, because authorship entails accountability that a model cannot hold; this disclosure is the credit, and it is deliberately the whole of it.","url":"https://doi.org/10.5281/zenodo.20592368","authors":["Saluca Agentic AI Research Team"],"tags":["AI-drafted synthesis","arXiv","preprint review","v2"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20592368","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20845441","name":"TOWARDS ROBUST AND PRIVACY-PRESERVING ANTI-MONEY LAUNDERING SYSTEMS: A SYSTEMATIC REVIEW OF FEDERATED LEARNING AND GRAPH NEURAL NETWORKS FOR FINANCIAL CRIME DETECTION","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20845441","authors":["Syed Muhammad Abbas,Dr. Jawaid Iqbal,Syed Hasnat Raza Zaidi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20845441","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20845440","name":"TOWARDS ROBUST AND PRIVACY-PRESERVING ANTI-MONEY LAUNDERING SYSTEMS: A SYSTEMATIC REVIEW OF FEDERATED LEARNING AND GRAPH NEURAL NETWORKS FOR FINANCIAL CRIME DETECTION","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20845440","authors":["Syed Muhammad Abbas,Dr. Jawaid Iqbal,Syed Hasnat Raza Zaidi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20845440","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21789185","name":"TOWARDS ROBUST AND PRIVACY-PRESERVING ANTI-MONEY LAUNDERING SYSTEMS: A SYSTEMATIC REVIEW OF FEDERATED LEARNING AND GRAPH NEURAL NETWORKS FOR FINANCIAL CRIME DETECTION","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21789185","authors":["Syed Muhammad Abbas,Dr. Jawaid Iqbal,Syed Hasnat Raza Zaidi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21789185","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20832756","name":"Distributed Deep Learning and Intelligent Soil–Water Analytics in Precision Agriculture: A Comprehensive Review","source":"datacite","abstract":"Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale.","url":"https://doi.org/10.5281/zenodo.20832756","authors":["Lemenkova, Polina"],"tags":["Land management and planning","Artificial intelligence","Machine Learning","Deep Learning","Deep learning","Machine learning","Artificial Intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20832756","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20832757","name":"Distributed Deep Learning and Intelligent Soil–Water Analytics in Precision Agriculture: A Comprehensive Review","source":"datacite","abstract":"Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale.","url":"https://doi.org/10.5281/zenodo.20832757","authors":["Lemenkova, Polina"],"tags":["Land management and planning","Artificial intelligence","Machine Learning","Deep Learning","Deep learning","Machine learning","Artificial Intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20832757","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.19702515","name":"Artificial Intelligence-Driven Digital Phenotyping  in Psychiatry: Clinical Utility, Ethical Challenges,  and Future Directions","source":"datacite","abstract":"Digital phenotyping, defined as the moment-by-moment quantification of individual-level human phenotype using data from personal digital devices, has emerged as a promising approach to psychiatric assessment and monitoring. When combined with artificial intelligence (AI) and machine learning algorithms, digital phenotyping enables passive, continuous, and ecologically valid measurement of behavioral and cognitive markers relevant to psychiatric disorders. This interdisciplinary review examines the current state of AI-driven digital phenotyping in psychiatry, with emphasis on its clinical applications across major psychiatric conditions including depression, schizophrenia, bipolar disorder, anxiety disorders, and suicide risk. We discuss the methodological landscape — spanning passive sensing, natural language processing, and multimodal data fusion — and critically evaluate evidence regarding the predictive validity and clinical utility of these approaches. Furthermore, we address the significant ethical, legal, and social challenges inherent in deploying AI-based monitoring technologies in vulnerable psychiatric populations, including issues of informed consent, data privacy, algorithmic bias, and therapeutic relationship. Finally, we outline future directions for the field, including the integration of federated learning, personalized medicine frameworks, and standardized outcome metrics. We conclude that while AI-driven digital phenotyping holds transformative potential for psychiatry, its responsible clinical implementation requires robust interdisciplinary governance, equitable algorithm design, and meaningful patient engagement.","url":"https://doi.org/10.5281/zenodo.19702515","authors":["Anna Byr, Melania Majewska, Natalia Piasecka, Izabela Rafalska, Jakub Smagoń, Natalia Kornacka, Martyna Kaim, Joanna Bober, Magdalena Bochenek and Wiktoria Siewiera"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19702515","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.19702516","name":"Artificial Intelligence-Driven Digital Phenotyping  in Psychiatry: Clinical Utility, Ethical Challenges,  and Future Directions","source":"datacite","abstract":"Digital phenotyping, defined as the moment-by-moment quantification of individual-level human phenotype using data from personal digital devices, has emerged as a promising approach to psychiatric assessment and monitoring. When combined with artificial intelligence (AI) and machine learning algorithms, digital phenotyping enables passive, continuous, and ecologically valid measurement of behavioral and cognitive markers relevant to psychiatric disorders. This interdisciplinary review examines the current state of AI-driven digital phenotyping in psychiatry, with emphasis on its clinical applications across major psychiatric conditions including depression, schizophrenia, bipolar disorder, anxiety disorders, and suicide risk. We discuss the methodological landscape — spanning passive sensing, natural language processing, and multimodal data fusion — and critically evaluate evidence regarding the predictive validity and clinical utility of these approaches. Furthermore, we address the significant ethical, legal, and social challenges inherent in deploying AI-based monitoring technologies in vulnerable psychiatric populations, including issues of informed consent, data privacy, algorithmic bias, and therapeutic relationship. Finally, we outline future directions for the field, including the integration of federated learning, personalized medicine frameworks, and standardized outcome metrics. We conclude that while AI-driven digital phenotyping holds transformative potential for psychiatry, its responsible clinical implementation requires robust interdisciplinary governance, equitable algorithm design, and meaningful patient engagement.","url":"https://doi.org/10.5281/zenodo.19702516","authors":["Anna Byr, Melania Majewska, Natalia Piasecka, Izabela Rafalska, Jakub Smagoń, Natalia Kornacka, Martyna Kaim, Joanna Bober, Magdalena Bochenek and Wiktoria Siewiera"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19702516","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20467675","name":"Federated Learning Generalization in Cross-Device IoT Malware Detection: A Comparative Study with Centralized Models","source":"datacite","abstract":"This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How do federated learning models trained on the N-BaIoT dataset generalize to cross-device or cross-domain IoT malware detection, as evaluated by F1-score on unseen device types compared to. In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning.. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How do federated learning models trained on the N-BaIoT dataset generalize to cross-device or cross-domain IoT malware detection, as evaluated by F1-score on unseen device types compared to centralized models? Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20467675","authors":["Assignee Research"],"tags":["federated","learning","models","trained","N-BaIoT","dataset","generalize","cross-device"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20467675","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20467676","name":"Federated Learning Generalization in Cross-Device IoT Malware Detection: A Comparative Study with Centralized Models","source":"datacite","abstract":"This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How do federated learning models trained on the N-BaIoT dataset generalize to cross-device or cross-domain IoT malware detection, as evaluated by F1-score on unseen device types compared to. In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning.. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How do federated learning models trained on the N-BaIoT dataset generalize to cross-device or cross-domain IoT malware detection, as evaluated by F1-score on unseen device types compared to centralized models? Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20467676","authors":["Assignee Research"],"tags":["federated","learning","models","trained","N-BaIoT","dataset","generalize","cross-device"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20467676","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20703119","name":"LifeMH-FL: A Lifecycle-Stratified Mental Health Corpus for Federated Learning","source":"datacite","abstract":"LifeMH-FL is the first lifecycle-stratified mental health corpus designed for federated learning research. It contains 111,191 dual-labelled records partitioned across three women's health lifecycle cohorts: S1 — Menstrual (77,259 records): sourced from MENST (clinical Q&A on menstrual disorders) S2 — Perinatal (31,743 records): sourced from Dreaddit (Reddit stress narratives, keyword-filtered) S3 — Menopausal (2,189 records): sourced from Women Health Mini (patient–provider dialogue) Each record carries two labels: (1) mental health condition — Depression, Anxiety, Stress, Suicidal, Normal (per DSM-5); and (2) risk severity — Low, Medium, High. Labels are pseudo-annotations generated by a locally-deployed Meta-Llama-3.1-8B-Instruct to preserve privacy; out-of-vocabulary outputs are normalised to the nearest DSM-5 class. Construction pipeline: Records are partitioned via a 33-term lifecycle keyword lexicon (word-boundary regex; unmatched records discarded), de-duplicated by MD5 hashing, filtered to ≥10 characters, and label-normalised. A normalization_report.json documents all label corrections applied. Novelty: LifeMH-FL is the first corpus to (a) stratify women's mental health text by biological lifecycle stage, (b) provide simultaneous condition and risk-severity labels, and (c) be explicitly structured as non-IID federated silos with a 35× volume disparity between S1 and S3 — reflecting real-world data scarcity in underserved populations. The extreme class imbalance (ratio 28.38; Suicidal class = 1.28%) mirrors clinical reality and makes the corpus a challenging benchmark for cost-sensitive and federated learning methods. Intended use: Research into privacy-preserving federated LLM fine-tuning, mental health NLP, and lifecycle-aware machine learning. Labels are for model training and evaluation only and do not constitute clinical diagnoses. Ethics: All source datasets are publicly available under open research licenses. No primary human subject data was collected and no IRB approval was required. Dataset and code: https://github.com/sayoojd/FedlifeLLM DOI of Paper: 10.1109/TCE.2026.3711046","url":"https://doi.org/10.5281/zenodo.20703119","authors":["Devadas, Sayooj"],"tags":["federated learning","mental health","women's health","lifecycle","NLP","LLM","privacy-preserving","corpus"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20703119","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20703120","name":"LifeMH-FL: A Lifecycle-Stratified Mental Health Corpus for Federated Learning","source":"datacite","abstract":"LifeMH-FL is the first lifecycle-stratified mental health corpus designed for federated learning research. It contains 111,191 dual-labelled records partitioned across three women's health lifecycle cohorts: S1 — Menstrual (77,259 records): sourced from MENST (clinical Q&A on menstrual disorders) S2 — Perinatal (31,743 records): sourced from Dreaddit (Reddit stress narratives, keyword-filtered) S3 — Menopausal (2,189 records): sourced from Women Health Mini (patient–provider dialogue) Each record carries two labels: (1) mental health condition — Depression, Anxiety, Stress, Suicidal, Normal (per DSM-5); and (2) risk severity — Low, Medium, High. Labels are pseudo-annotations generated by a locally-deployed Meta-Llama-3.1-8B-Instruct to preserve privacy; out-of-vocabulary outputs are normalised to the nearest DSM-5 class. Construction pipeline: Records are partitioned via a 33-term lifecycle keyword lexicon (word-boundary regex; unmatched records discarded), de-duplicated by MD5 hashing, filtered to ≥10 characters, and label-normalised. A normalization_report.json documents all label corrections applied. Novelty: LifeMH-FL is the first corpus to (a) stratify women's mental health text by biological lifecycle stage, (b) provide simultaneous condition and risk-severity labels, and (c) be explicitly structured as non-IID federated silos with a 35× volume disparity between S1 and S3 — reflecting real-world data scarcity in underserved populations. The extreme class imbalance (ratio 28.38; Suicidal class = 1.28%) mirrors clinical reality and makes the corpus a challenging benchmark for cost-sensitive and federated learning methods. Intended use: Research into privacy-preserving federated LLM fine-tuning, mental health NLP, and lifecycle-aware machine learning. Labels are for model training and evaluation only and do not constitute clinical diagnoses. Ethics: All source datasets are publicly available under open research licenses. No primary human subject data was collected and no IRB approval was required. Dataset and code: https://github.com/sayoojd/FedlifeLLM DOI of Paper: 10.1109/TCE.2026.3711046","url":"https://doi.org/10.5281/zenodo.20703120","authors":["Devadas, Sayooj"],"tags":["federated learning","mental health","women's health","lifecycle","NLP","LLM","privacy-preserving","corpus"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20703120","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20399492","name":"BIG DATA MANAGEMENT AND PROCESSING PARADIGMS: A COMPREHENSIVE SURVEY OF ARCHITECTURES, TECHNOLOGIES, AND FUTURE DIRECTIONS","source":"datacite","abstract":"The exponential growth of data generated by modern digital systems has fundamentally transformed how organizations store, process, and extract value from information. Big Data — characterized by its volume, velocity, variety, veracity, and value (the 5Vs) — has emerged as a critical research domain within computer science and information systems. This survey provides a systematic and comprehensive review of Big Data management architectures, distributed processing paradigms, storage technologies, and analytical frameworks developed over the past decade. We examine foundational technologies including the Hadoop ecosystem, Apache Spark, NoSQL database systems, data lake architectures, and real-time stream processing platforms. Additionally, we analyze the integration of machine learning pipelines with Big Data infrastructure, cloud-native deployment strategies, and emerging trends such as edge analytics and federated data processing. Our review synthesizes findings from over 150 peer-reviewed publications and evaluates each paradigm according to scalability, fault tolerance, latency, throughput, and ecosystem maturity. We identify critical open challenges and propose a research agenda for future investigation, particularly in the areas of data governance, energy-efficient processing, and privacy-preserving analytics.","url":"https://doi.org/10.5281/zenodo.20399492","authors":["Ergashev Baxriddin Nomoz o'gli"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20399492","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20399493","name":"BIG DATA MANAGEMENT AND PROCESSING PARADIGMS: A COMPREHENSIVE SURVEY OF ARCHITECTURES, TECHNOLOGIES, AND FUTURE DIRECTIONS","source":"datacite","abstract":"The exponential growth of data generated by modern digital systems has fundamentally transformed how organizations store, process, and extract value from information. Big Data — characterized by its volume, velocity, variety, veracity, and value (the 5Vs) — has emerged as a critical research domain within computer science and information systems. This survey provides a systematic and comprehensive review of Big Data management architectures, distributed processing paradigms, storage technologies, and analytical frameworks developed over the past decade. We examine foundational technologies including the Hadoop ecosystem, Apache Spark, NoSQL database systems, data lake architectures, and real-time stream processing platforms. Additionally, we analyze the integration of machine learning pipelines with Big Data infrastructure, cloud-native deployment strategies, and emerging trends such as edge analytics and federated data processing. Our review synthesizes findings from over 150 peer-reviewed publications and evaluates each paradigm according to scalability, fault tolerance, latency, throughput, and ecosystem maturity. We identify critical open challenges and propose a research agenda for future investigation, particularly in the areas of data governance, energy-efficient processing, and privacy-preserving analytics.","url":"https://doi.org/10.5281/zenodo.20399493","authors":["Ergashev Baxriddin Nomoz o'gli"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20399493","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.19706237","name":"Optimized Human Disease Predication & Detection Using Artificial Intelligence","source":"datacite","abstract":"The field of AI integration within healthcare, particularly for early disease detection, has become a cornerstone of research and development. This review delves into the transformative potential of AI in revolutionizing healthcare delivery. We explore its applications, challenges, future trajectories, and impact on patient well-being. The article embarks on a historical expedition, tracing the evolution of AI in healthcare. We journey from rudimentary disease prediction methods to the emergence of cutting-edge AI techniques like machine learning and deep learning. It unpacks the core principles underpinning AI's role in healthcare, encompassing data acquisition, cleansing, and the utilization of diverse AI tools for disease prediction. These tools include machine learning algorithms and deep learning models. Specific applications in areas like cancer screening, cardiovascular ailments, neurological conditions, and infectious diseases are investigated. Real-world examples and success stories illuminate the transformative influence of AI implementation on patient outcomes and healthcare systems. The article acknowledges the roadblocks and limitations inherent in AI-powered healthcare. These include technical hurdles like data quality and intricate algorithm design. Ethical and legal considerations regarding patient privacy and data security are also addressed. We explore potential advancements and innovations in AI for disease prediction, including emerging trends like integrating multi-omics data, federated learning, and AI-powered drug discovery. Looking ahead, the article envisions a future with a pervasive AI ecosystem that empowers healthcare delivery, promotes health equity, and revolutionizes public health surveillance. By fostering collaboration, embracing innovation, and adhering to ethical principles, the article concludes that AI presents a unique opportunity to revolutionize disease prediction and detection. This will usher in a new era of personalized, efficient, and accessible healthcare for all.","url":"https://doi.org/10.5281/zenodo.19706237","authors":["Vaibhavi Kshatriya1*, Manoj Kumbhare2, Parag Kothawade3, Prashant Malpure4, Rupali Lambole5, Jyoti Datir6, Prachi Ahire7"],"tags":["artificial intelligence, healthcare, disease prediction, disease detection, machine learning, deep learning, cancer detection, cardiovascular diseases, neurological disorders."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19706237","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.19706238","name":"Optimized Human Disease Predication & Detection Using Artificial Intelligence","source":"datacite","abstract":"The field of AI integration within healthcare, particularly for early disease detection, has become a cornerstone of research and development. This review delves into the transformative potential of AI in revolutionizing healthcare delivery. We explore its applications, challenges, future trajectories, and impact on patient well-being. The article embarks on a historical expedition, tracing the evolution of AI in healthcare. We journey from rudimentary disease prediction methods to the emergence of cutting-edge AI techniques like machine learning and deep learning. It unpacks the core principles underpinning AI's role in healthcare, encompassing data acquisition, cleansing, and the utilization of diverse AI tools for disease prediction. These tools include machine learning algorithms and deep learning models. Specific applications in areas like cancer screening, cardiovascular ailments, neurological conditions, and infectious diseases are investigated. Real-world examples and success stories illuminate the transformative influence of AI implementation on patient outcomes and healthcare systems. The article acknowledges the roadblocks and limitations inherent in AI-powered healthcare. These include technical hurdles like data quality and intricate algorithm design. Ethical and legal considerations regarding patient privacy and data security are also addressed. We explore potential advancements and innovations in AI for disease prediction, including emerging trends like integrating multi-omics data, federated learning, and AI-powered drug discovery. Looking ahead, the article envisions a future with a pervasive AI ecosystem that empowers healthcare delivery, promotes health equity, and revolutionizes public health surveillance. By fostering collaboration, embracing innovation, and adhering to ethical principles, the article concludes that AI presents a unique opportunity to revolutionize disease prediction and detection. This will usher in a new era of personalized, efficient, and accessible healthcare for all.","url":"https://doi.org/10.5281/zenodo.19706238","authors":["Vaibhavi Kshatriya1*, Manoj Kumbhare2, Parag Kothawade3, Prashant Malpure4, Rupali Lambole5, Jyoti Datir6, Prachi Ahire7"],"tags":["artificial intelligence, healthcare, disease prediction, disease detection, machine learning, deep learning, cancer detection, cardiovascular diseases, neurological disorders."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19706238","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.19427309","name":"Federated Learning For Privacy-Preserving Security Systems","source":"datacite","abstract":"The rapid escalation of cyber threats in decentralized environments has necessitated the development of collaborative defense mechanisms that do not compromise data sovereignty. Traditional centralized machine learning requires the aggregation of sensitive telemetry data, creating significant privacy risks and regulatory hurdles. This review explores the paradigm of Federated Learning (FL) as a transformative solution for privacy-preserving security systems. By enabling the training of global threat detection models across distributed nodes—such as edge devices, corporate branches, or mobile endpoints—without transferring raw data to a central server, FL addresses the fundamental tension between collective intelligence and individual privacy. This article categorizes current FL architectures, including horizontal, vertical, and transfer-based federated systems, and examines their application in intrusion detection, malware analysis, and anomaly-based behavioral monitoring. We analyze the integration of Differential Privacy and Secure Multi-Party Computation within the FL pipeline to mitigate data leakage from model updates. Furthermore, the review addresses the challenges of communication overhead, non-independent and identically distributed (non-IID) data, and vulnerability to poisoning attacks. By synthesizing recent research and industrial implementations, this paper provides a strategic roadmap for the deployment of self-evolving, privacy-aware security frameworks. The findings suggest that Federated Learning not only complies with stringent data protection mandates like GDPR but also enhances model robustness by training on diverse, real-world datasets that were previously inaccessible due to privacy constraints.","url":"https://doi.org/10.5281/zenodo.19427309","authors":["Vikram Iyer"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.19427309","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.19427310","name":"Federated Learning For Privacy-Preserving Security Systems","source":"datacite","abstract":"The rapid escalation of cyber threats in decentralized environments has necessitated the development of collaborative defense mechanisms that do not compromise data sovereignty. Traditional centralized machine learning requires the aggregation of sensitive telemetry data, creating significant privacy risks and regulatory hurdles. This review explores the paradigm of Federated Learning (FL) as a transformative solution for privacy-preserving security systems. By enabling the training of global threat detection models across distributed nodes—such as edge devices, corporate branches, or mobile endpoints—without transferring raw data to a central server, FL addresses the fundamental tension between collective intelligence and individual privacy. This article categorizes current FL architectures, including horizontal, vertical, and transfer-based federated systems, and examines their application in intrusion detection, malware analysis, and anomaly-based behavioral monitoring. We analyze the integration of Differential Privacy and Secure Multi-Party Computation within the FL pipeline to mitigate data leakage from model updates. Furthermore, the review addresses the challenges of communication overhead, non-independent and identically distributed (non-IID) data, and vulnerability to poisoning attacks. By synthesizing recent research and industrial implementations, this paper provides a strategic roadmap for the deployment of self-evolving, privacy-aware security frameworks. The findings suggest that Federated Learning not only complies with stringent data protection mandates like GDPR but also enhances model robustness by training on diverse, real-world datasets that were previously inaccessible due to privacy constraints.","url":"https://doi.org/10.5281/zenodo.19427310","authors":["Vikram Iyer"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.19427310","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21582950","name":"Machine Learning-Driven Anomaly Detection for Supply Chain Integrity in 5G Industrial Automation Systems","source":"datacite","abstract":"The rapid deployment of 5G-enabled industrial automation systems has transformed global supply chains by enabling ultra-reliable low-latency communication (URLLC), real-time decision-making, and seamless integration of cyber-physical systems. However, the increased connectivity and data exchange across stakeholders have also introduced new risks of cyberattacks, data manipulation, and operational disruptions that threaten supply chain integrity. Machine learning (ML)-driven anomaly detection has emerged as a powerful approach to safeguarding these systems by identifying unusual behaviors, malicious activities, and performance deviations in complex industrial networks. This review explores the convergence of 5G technology, industrial automation, and advanced ML algorithms—including supervised, unsupervised, and deep learning models—for anomaly detection in supply chain operations. The study highlights key methodologies such as federated learning for decentralized monitoring, reinforcement learning for adaptive responses, and graph-based neural networks for interdependency mapping within supply chain ecosystems. Furthermore, the paper investigates the challenges of handling high-dimensional heterogeneous data, ensuring interpretability of ML outputs, and integrating anomaly detection with existing security frameworks. Case studies and recent applications are examined to demonstrate practical benefits, including fraud prevention, counterfeit detection, and real-time logistics monitoring. The review also emphasizes policy, governance, and ethical considerations in deploying ML-driven solutions in mission-critical 5G industrial settings. Ultimately, this paper provides a comprehensive synthesis of current advances, gaps, and future research opportunities, positioning ML-based anomaly detection as a cornerstone for resilient, secure, and trustworthy supply chain integrity in the era of 5G-enabled industrial automation.","url":"https://doi.org/10.5281/zenodo.21582950","authors":["James, Ugoaghalam Uche"],"tags":["Machine Learning; Anomaly Detection; Supply Chain Integrity; 5G Industrial Automation; Cyber-Physical Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.21582950","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21582951","name":"Machine Learning-Driven Anomaly Detection for Supply Chain Integrity in 5G Industrial Automation Systems","source":"datacite","abstract":"The rapid deployment of 5G-enabled industrial automation systems has transformed global supply chains by enabling ultra-reliable low-latency communication (URLLC), real-time decision-making, and seamless integration of cyber-physical systems. However, the increased connectivity and data exchange across stakeholders have also introduced new risks of cyberattacks, data manipulation, and operational disruptions that threaten supply chain integrity. Machine learning (ML)-driven anomaly detection has emerged as a powerful approach to safeguarding these systems by identifying unusual behaviors, malicious activities, and performance deviations in complex industrial networks. This review explores the convergence of 5G technology, industrial automation, and advanced ML algorithms—including supervised, unsupervised, and deep learning models—for anomaly detection in supply chain operations. The study highlights key methodologies such as federated learning for decentralized monitoring, reinforcement learning for adaptive responses, and graph-based neural networks for interdependency mapping within supply chain ecosystems. Furthermore, the paper investigates the challenges of handling high-dimensional heterogeneous data, ensuring interpretability of ML outputs, and integrating anomaly detection with existing security frameworks. Case studies and recent applications are examined to demonstrate practical benefits, including fraud prevention, counterfeit detection, and real-time logistics monitoring. The review also emphasizes policy, governance, and ethical considerations in deploying ML-driven solutions in mission-critical 5G industrial settings. Ultimately, this paper provides a comprehensive synthesis of current advances, gaps, and future research opportunities, positioning ML-based anomaly detection as a cornerstone for resilient, secure, and trustworthy supply chain integrity in the era of 5G-enabled industrial automation.","url":"https://doi.org/10.5281/zenodo.21582951","authors":["James, Ugoaghalam Uche"],"tags":["Machine Learning; Anomaly Detection; Supply Chain Integrity; 5G Industrial Automation; Cyber-Physical Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.21582951","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21284547","name":"Artificial Intelligence for Automated Tumor Segmentation and Treatment Response Assessment","source":"datacite","abstract":"Cancer remains one of the leading causes of mortality worldwide, necessitating continuous advancements in diagnostic and therapeutic strategies. Medical imaging plays a central role in oncology by facilitating tumor detection, delineation, staging, treatment planning, and response monitoring. Accurate tumor segmentation and treatment response assessment are fundamental components of modern cancer management; however, conventional manual approaches are labor-intensive, time-consuming, and subject to significant interobserver variability. The rapid evolution of artificial intelligence (AI), particularly machine learning and deep learning, has transformed medical image analysis by enabling automated, reproducible, and highly accurate interpretation of complex imaging datasets. Recent developments in convolutional neural networks, transformer-based architectures, and foundation models have significantly improved tumor segmentation performance across multiple imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and hybrid imaging systems. Simultaneously, AI-driven approaches have enhanced treatment response assessment through radiomics, longitudinal image analysis, and predictive modeling, enabling earlier identification of therapeutic efficacy and disease progression. Emerging multimodal frameworks integrating imaging, pathology, genomics, and clinical data further support precision oncology by providing comprehensive patient-specific insights. Despite substantial progress, challenges related to data heterogeneity, annotation quality, model interpretability, regulatory approval, and clinical implementation remain significant barriers to widespread adoption. The emergence of foundation models, self-supervised learning, federated learning, and explainable AI offers promising avenues for addressing these limitations. This review critically examines recent advances in AI-driven automated tumor segmentation and treatment response assessment, discusses current clinical applications, evaluates existing challenges, and highlights future directions for integrating AI technologies into routine oncology practice.","url":"https://doi.org/10.5281/zenodo.21284547","authors":["Meera Iyer*1, Karan Bhattacharya2, Shalini Nair3, Shatrughna Nagrik4, Vivek Rao5"],"tags":["Artificial intelligence; Tumor segmentation; Treatment response assessment; Deep learning; Radiomics; Precision oncology; Medical imaging; Foundation models."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21284547","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21284548","name":"Artificial Intelligence for Automated Tumor Segmentation and Treatment Response Assessment","source":"datacite","abstract":"Cancer remains one of the leading causes of mortality worldwide, necessitating continuous advancements in diagnostic and therapeutic strategies. Medical imaging plays a central role in oncology by facilitating tumor detection, delineation, staging, treatment planning, and response monitoring. Accurate tumor segmentation and treatment response assessment are fundamental components of modern cancer management; however, conventional manual approaches are labor-intensive, time-consuming, and subject to significant interobserver variability. The rapid evolution of artificial intelligence (AI), particularly machine learning and deep learning, has transformed medical image analysis by enabling automated, reproducible, and highly accurate interpretation of complex imaging datasets. Recent developments in convolutional neural networks, transformer-based architectures, and foundation models have significantly improved tumor segmentation performance across multiple imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and hybrid imaging systems. Simultaneously, AI-driven approaches have enhanced treatment response assessment through radiomics, longitudinal image analysis, and predictive modeling, enabling earlier identification of therapeutic efficacy and disease progression. Emerging multimodal frameworks integrating imaging, pathology, genomics, and clinical data further support precision oncology by providing comprehensive patient-specific insights. Despite substantial progress, challenges related to data heterogeneity, annotation quality, model interpretability, regulatory approval, and clinical implementation remain significant barriers to widespread adoption. The emergence of foundation models, self-supervised learning, federated learning, and explainable AI offers promising avenues for addressing these limitations. This review critically examines recent advances in AI-driven automated tumor segmentation and treatment response assessment, discusses current clinical applications, evaluates existing challenges, and highlights future directions for integrating AI technologies into routine oncology practice.","url":"https://doi.org/10.5281/zenodo.21284548","authors":["Meera Iyer*1, Karan Bhattacharya2, Shalini Nair3, Shatrughna Nagrik4, Vivek Rao5"],"tags":["Artificial intelligence; Tumor segmentation; Treatment response assessment; Deep learning; Radiomics; Precision oncology; Medical imaging; Foundation models."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21284548","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21605297","name":"Utilizing Federated Health Databases and AI-Enhanced Neurodevelopmental Trajectory Mapping for Early Diagnosis of Autism Spectrum Disorder: A Review of Scalable Computational Models","source":"datacite","abstract":"Early diagnosis of Autism Spectrum Disorder (ASD) remains a critical challenge in pediatric neurodevelopmental care, with significant implications for intervention effectiveness and lifelong outcomes. Traditional diagnostic methods often rely on subjective behavioral assessments, limiting early detection, especially in resource-limited settings. This review explores the convergence of federated health databases and AI-driven neurodevelopmental trajectory mapping as scalable computational solutions for timely ASD diagnosis. Federated learning frameworks enable collaborative model training across decentralized data silos while preserving patient privacy, thereby overcoming the limitations of fragmented healthcare data ecosystems. Simultaneously, advanced machine learning techniques—including temporal graph networks, multimodal deep learning, and probabilistic modeling—facilitate individualized developmental path prediction. The paper systematically analyzes current architectures, datasets, and computational methods used to infer ASD risk from longitudinal health and behavioral data. It also discusses interpretability, scalability, and ethical considerations surrounding data governance and model transparency. The review concludes with recommendations for improving early ASD diagnosis through integrated AI-federated frameworks in global pediatric healthcare systems.","url":"https://doi.org/10.5281/zenodo.21605297","authors":["Omolayo, Olasehinde","Okare, Babawale Patrick","Taiwo, Ajao Ebenezer","Aduloju, Tope David"],"tags":["Autism Spectrum Disorder (ASD); Federated Health Databases; Neurodevelopmental Trajectory Mapping; Scalable AI Models; Early Diagnosis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21605297","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21605298","name":"Utilizing Federated Health Databases and AI-Enhanced Neurodevelopmental Trajectory Mapping for Early Diagnosis of Autism Spectrum Disorder: A Review of Scalable Computational Models","source":"datacite","abstract":"Early diagnosis of Autism Spectrum Disorder (ASD) remains a critical challenge in pediatric neurodevelopmental care, with significant implications for intervention effectiveness and lifelong outcomes. Traditional diagnostic methods often rely on subjective behavioral assessments, limiting early detection, especially in resource-limited settings. This review explores the convergence of federated health databases and AI-driven neurodevelopmental trajectory mapping as scalable computational solutions for timely ASD diagnosis. Federated learning frameworks enable collaborative model training across decentralized data silos while preserving patient privacy, thereby overcoming the limitations of fragmented healthcare data ecosystems. Simultaneously, advanced machine learning techniques—including temporal graph networks, multimodal deep learning, and probabilistic modeling—facilitate individualized developmental path prediction. The paper systematically analyzes current architectures, datasets, and computational methods used to infer ASD risk from longitudinal health and behavioral data. It also discusses interpretability, scalability, and ethical considerations surrounding data governance and model transparency. The review concludes with recommendations for improving early ASD diagnosis through integrated AI-federated frameworks in global pediatric healthcare systems.","url":"https://doi.org/10.5281/zenodo.21605298","authors":["Omolayo, Olasehinde","Okare, Babawale Patrick","Taiwo, Ajao Ebenezer","Aduloju, Tope David"],"tags":["Autism Spectrum Disorder (ASD); Federated Health Databases; Neurodevelopmental Trajectory Mapping; Scalable AI Models; Early Diagnosis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21605298","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21242168","name":"Artificial Intelligence-Driven Pharmacovigilance: Integrating Real-World Evidence for Next-Generation Drug Safety","source":"datacite","abstract":"Pharmacovigilance has evolved from a predominantly manual process of adverse drug reaction (ADR) monitoring to a data-driven discipline that increasingly relies on artificial intelligence (AI) and real-world evidence (RWE). The growing volume of safety reports, electronic health records, insurance claims, biomedical literature, social media content, and wearable device data has challenged conventional pharmacovigilance systems, necessitating more efficient computational approaches. AI technologies, including machine learning, deep learning, natural language processing, large language models, and explainable AI, have demonstrated considerable potential for automating case processing, identifying safety signals, improving adverse event coding, and supporting benefit–risk assessment. Simultaneously, RWE derived from routine clinical practice provides complementary insights into long-term drug safety across diverse patient populations that are often underrepresented in clinical trials. The integration of AI with RWE enables earlier detection of rare adverse events, strengthens regulatory decision-making, and facilitates personalized drug safety monitoring. Despite these advances, several challenges remain, including data heterogeneity, algorithmic bias, limited model interpretability, privacy concerns, and the absence of globally harmonized regulatory frameworks. Emerging technologies such as multimodal AI, federated learning, digital twins, and generative AI are expected to further transform pharmacovigilance by enabling scalable, transparent, and patient-centered safety surveillance. This review summarizes the evolution of AI-driven pharmacovigilance, explores current applications of AI and RWE across the drug safety lifecycle, discusses regulatory and ethical considerations, highlights existing research gaps, and outlines future directions for next-generation pharmacovigilance systems","url":"https://doi.org/10.5281/zenodo.21242168","authors":["Yash Kothikar1*, Tanveer Patel2"],"tags":["Artificial intelligence; Pharmacovigilance; Real-world evidence; Drug safety; Machine learning; Signal detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21242168","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21242169","name":"Artificial Intelligence-Driven Pharmacovigilance: Integrating Real-World Evidence for Next-Generation Drug Safety","source":"datacite","abstract":"Pharmacovigilance has evolved from a predominantly manual process of adverse drug reaction (ADR) monitoring to a data-driven discipline that increasingly relies on artificial intelligence (AI) and real-world evidence (RWE). The growing volume of safety reports, electronic health records, insurance claims, biomedical literature, social media content, and wearable device data has challenged conventional pharmacovigilance systems, necessitating more efficient computational approaches. AI technologies, including machine learning, deep learning, natural language processing, large language models, and explainable AI, have demonstrated considerable potential for automating case processing, identifying safety signals, improving adverse event coding, and supporting benefit–risk assessment. Simultaneously, RWE derived from routine clinical practice provides complementary insights into long-term drug safety across diverse patient populations that are often underrepresented in clinical trials. The integration of AI with RWE enables earlier detection of rare adverse events, strengthens regulatory decision-making, and facilitates personalized drug safety monitoring. Despite these advances, several challenges remain, including data heterogeneity, algorithmic bias, limited model interpretability, privacy concerns, and the absence of globally harmonized regulatory frameworks. Emerging technologies such as multimodal AI, federated learning, digital twins, and generative AI are expected to further transform pharmacovigilance by enabling scalable, transparent, and patient-centered safety surveillance. This review summarizes the evolution of AI-driven pharmacovigilance, explores current applications of AI and RWE across the drug safety lifecycle, discusses regulatory and ethical considerations, highlights existing research gaps, and outlines future directions for next-generation pharmacovigilance systems","url":"https://doi.org/10.5281/zenodo.21242169","authors":["Yash Kothikar1*, Tanveer Patel2"],"tags":["Artificial intelligence; Pharmacovigilance; Real-world evidence; Drug safety; Machine learning; Signal detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21242169","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20470326","name":"Federated Learning Communication Efficiency in Code Generation Across Model Scales and Client Heterogeneity","source":"datacite","abstract":"This report synthesises findings from 8 peer-reviewed papers addressing the following research question: How does communication efficiency in federated learning for code generation models scale with model size and client heterogeneity relative to centralized distributed training approaches. Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does communication efficiency in federated learning for code generation models scale with model size and client heterogeneity relative to centralized distributed training approaches? Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20470326","authors":["Assignee Research"],"tags":["communication","efficiency","federated","learning","code","generation","models","scale"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20470326","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20470327","name":"Federated Learning Communication Efficiency in Code Generation Across Model Scales and Client Heterogeneity","source":"datacite","abstract":"This report synthesises findings from 8 peer-reviewed papers addressing the following research question: How does communication efficiency in federated learning for code generation models scale with model size and client heterogeneity relative to centralized distributed training approaches. Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does communication efficiency in federated learning for code generation models scale with model size and client heterogeneity relative to centralized distributed training approaches? Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.","url":"https://doi.org/10.5281/zenodo.20470327","authors":["Assignee Research"],"tags":["communication","efficiency","federated","learning","code","generation","models","scale"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20470327","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21413931","name":"edithatogo/voiage: v0.2.1","source":"datacite","abstract":"What's Changed Feature/basic voi methods by @edithatogo in https://github.com/edithatogo/voiage/pull/1 Refactor/object oriented api by @edithatogo in https://github.com/edithatogo/voiage/pull/5 Add development plan and fix compatibility wrappers by @edithatogo in https://github.com/edithatogo/voiage/pull/6 Add DecisionAnalysis class by @edithatogo in https://github.com/edithatogo/voiage/pull/7 build(deps): bump ruff from 0.1.9 to 0.12.11 by @dependabot[bot] in https://github.com/edithatogo/voiage/pull/14 build(deps): bump actions/checkout from 4 to 5 by @dependabot[bot] in https://github.com/edithatogo/voiage/pull/11 build(deps): bump actions/setup-python from 4 to 5 by @dependabot[bot] in https://github.com/edithatogo/voiage/pull/8 build(deps-dev): bump ruff from 0.12.11 to 0.14.3 by @dependabot[bot] in https://github.com/edithatogo/voiage/pull/24 build(deps): bump actions/setup-python from 5 to 6 by @dependabot[bot] in https://github.com/edithatogo/voiage/pull/15 feat(hpc): add accelerator evidence harness by @edithatogo in https://github.com/edithatogo/voiage/pull/37 fix(docs): restore Astro documentation deploy by @edithatogo in https://github.com/edithatogo/voiage/pull/38 🧪 [Testing Improvement] Add dedicated test for create_financial_config by @edithatogo in https://github.com/edithatogo/voiage/pull/53 🧹 Refactor SKLEARN_AVAILABLE to avoid unused import by @edithatogo in https://github.com/edithatogo/voiage/pull/58 🧪 Add test for create_parallel_config by @edithatogo in https://github.com/edithatogo/voiage/pull/54 🧹 Refactor long function 'bayesian_adaptive_trial_simulator' by @edithatogo in https://github.com/edithatogo/voiage/pull/56 🧪 Add dedicated test for create_healthcare_config by @edithatogo in https://github.com/edithatogo/voiage/pull/55 ⚡ Vectorized NumPy Operations in Adaptive Trial Simulators by @edithatogo in https://github.com/edithatogo/voiage/pull/59 ⚡ Optimize regret_matrix computation with NumPy broadcasting by @edithatogo in https://github.com/edithatogo/voiage/pull/60 🧹 Remove unused SklearnLinearRegression import in main_backends.py by @edithatogo in https://github.com/edithatogo/voiage/pull/63 🧪 Add test for create_environmental_config by @edithatogo in https://github.com/edithatogo/voiage/pull/62 🧹 Remove unused multiprocessing import by @edithatogo in https://github.com/edithatogo/voiage/pull/64 🧹 Code Health Improvement: Remove commented out code in config.py by @edithatogo in https://github.com/edithatogo/voiage/pull/44 ⚡ Vectorize BCEA ICER calculation for performance improvement by @edithatogo in https://github.com/edithatogo/voiage/pull/45 🧹 Remove unused 'DecisionOption as TrialArm' import in portfolio.py by @edithatogo in https://github.com/edithatogo/voiage/pull/46 ⚡ perf: Replace iterrows with itertuples in generated Jupyter notebook code by @edithatogo in https://github.com/edithatogo/voiage/pull/49 🧹 Remove commented out function autodoc_skip_member by @edithatogo in https://github.com/edithatogo/voiage/pull/50 🧹 [code health improvement] Remove commented out axis logic from voi_curves.py by @edithatogo in https://github.com/edithatogo/voiage/pull/51 🧪 Improve VOIAnalysisConfig.to_dict test coverage by @edithatogo in https://github.com/edithatogo/voiage/pull/52 🧪 test(ecosystem): Add coverage for TreeAge export exception path by @edithatogo in https://github.com/edithatogo/voiage/pull/40 ⚡ Perf: Optimize epoch loop using jax.lax.scan by @edithatogo in https://github.com/edithatogo/voiage/pull/42 🧹 [Code Health] Remove commented out logic explanation in network_nma.py by @edithatogo in https://github.com/edithatogo/voiage/pull/41 🧹 Remove unused import in noxfile.py by @edithatogo in https://github.com/edithatogo/voiage/pull/65 ⚡ Vectorize adaptive trial simulators for performance boost by @edithatogo in https://github.com/edithatogo/voiage/pull/48 🧪 Add dedicated test for create_optimization_config factory by @edithatogo in https://github.com/edithatogo/voiage/pull/68 \ud83e","url":"https://doi.org/10.5281/zenodo.21413931","authors":["Dylan Mordaunt"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21413931","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21684179","name":"Supply Chain Fraud Risk Mitigation Using Federated AI Models for Continuous Transaction Integrity Verification","source":"datacite","abstract":"Supply chain networks are increasingly vulnerable to sophisticated fraud tactics that compromise transactional integrity and threaten organizational resilience. Traditional centralized fraud detection systems struggle to scale across decentralized logistics and procurement environments due to data privacy, latency, and system heterogeneity. This review explores the integration of Federated Artificial Intelligence (AI) models as a transformative approach to mitigating fraud risks in supply chains. Federated AI enables collaborative model training across multiple stakeholders without exposing raw data, thus preserving privacy while enhancing anomaly detection capabilities. The paper examines how federated learning frameworks, combined with blockchain technology and edge intelligence, can continuously verify transaction authenticity, detect anomalies in procurement and logistics flows, and adapt to evolving fraud patterns. Furthermore, it evaluates current limitations in data standardization, model interoperability, and real-time verification under distributed conditions. Case studies of federated learning applications in financial technology, logistics automation, and smart contracts are analyzed to illustrate effectiveness and implementation strategies. The review concludes by outlining critical research directions for achieving secure, adaptive, and privacy-preserving fraud detection in future global supply chains.","url":"https://doi.org/10.5281/zenodo.21684179","authors":["Essien, Iboro Akpan","Ajayi, Joshua Oluwagbenga","Erigha, Eseoghene Daniel","Obuse, Ehimah","Ayanbode, Noah"],"tags":["Federated AI; Supply Chain Fraud; Transaction Integrity; Distributed Learning; Risk Mitigation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21684179","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21684180","name":"Supply Chain Fraud Risk Mitigation Using Federated AI Models for Continuous Transaction Integrity Verification","source":"datacite","abstract":"Supply chain networks are increasingly vulnerable to sophisticated fraud tactics that compromise transactional integrity and threaten organizational resilience. Traditional centralized fraud detection systems struggle to scale across decentralized logistics and procurement environments due to data privacy, latency, and system heterogeneity. This review explores the integration of Federated Artificial Intelligence (AI) models as a transformative approach to mitigating fraud risks in supply chains. Federated AI enables collaborative model training across multiple stakeholders without exposing raw data, thus preserving privacy while enhancing anomaly detection capabilities. The paper examines how federated learning frameworks, combined with blockchain technology and edge intelligence, can continuously verify transaction authenticity, detect anomalies in procurement and logistics flows, and adapt to evolving fraud patterns. Furthermore, it evaluates current limitations in data standardization, model interoperability, and real-time verification under distributed conditions. Case studies of federated learning applications in financial technology, logistics automation, and smart contracts are analyzed to illustrate effectiveness and implementation strategies. The review concludes by outlining critical research directions for achieving secure, adaptive, and privacy-preserving fraud detection in future global supply chains.","url":"https://doi.org/10.5281/zenodo.21684180","authors":["Essien, Iboro Akpan","Ajayi, Joshua Oluwagbenga","Erigha, Eseoghene Daniel","Obuse, Ehimah","Ayanbode, Noah"],"tags":["Federated AI; Supply Chain Fraud; Transaction Integrity; Distributed Learning; Risk Mitigation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21684180","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21684175","name":"Developing AI-Augmented Intrusion Detection Systems for Cloud-Based Financial Platforms with Real-Time Risk Analysis","source":"datacite","abstract":"Cloud-based financial platforms are increasingly targeted by sophisticated cyber threats due to the high-value transactions and sensitive data they manage. Traditional intrusion detection systems (IDS) often struggle to provide timely and accurate threat detection in dynamic and distributed cloud environments. This paper reviews the development of AI-augmented intrusion detection systems specifically tailored for financial services operating in cloud infrastructures. It explores how artificial intelligence—particularly machine learning, deep learning, and hybrid models—can enhance threat detection accuracy, reduce false positives, and support real-time risk analysis. The study also evaluates the role of federated learning, behavioral analytics, and anomaly detection in detecting insider threats, zero-day vulnerabilities, and fraud in financial transactions. Furthermore, the paper discusses the integration of explainable AI (XAI) to ensure transparency and regulatory compliance in threat assessment processes. Key implementation challenges such as data privacy, model drift, scalability, and integration with cloud-native architectures are also critically analyzed. The review concludes by proposing a layered, adaptive AI-IDS framework designed to safeguard cloud-based financial ecosystems through continuous learning, context-aware threat modeling, and real-time risk prioritization.","url":"https://doi.org/10.5281/zenodo.21684175","authors":["Ayanbode, Noah","Cadet, Emmanuel","Etim, Edima David","Essien, Iboro Akpan","Ajayi, Joshua Oluwagbenga"],"tags":["AI-Augmented Intrusion Detection","Cloud-Based Financial Platforms","Real-Time Risk Analysis","Anomaly Detection","Explainable Artificial Intelligence (XAI)","Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21684175","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21684176","name":"Developing AI-Augmented Intrusion Detection Systems for Cloud-Based Financial Platforms with Real-Time Risk Analysis","source":"datacite","abstract":"Cloud-based financial platforms are increasingly targeted by sophisticated cyber threats due to the high-value transactions and sensitive data they manage. Traditional intrusion detection systems (IDS) often struggle to provide timely and accurate threat detection in dynamic and distributed cloud environments. This paper reviews the development of AI-augmented intrusion detection systems specifically tailored for financial services operating in cloud infrastructures. It explores how artificial intelligence—particularly machine learning, deep learning, and hybrid models—can enhance threat detection accuracy, reduce false positives, and support real-time risk analysis. The study also evaluates the role of federated learning, behavioral analytics, and anomaly detection in detecting insider threats, zero-day vulnerabilities, and fraud in financial transactions. Furthermore, the paper discusses the integration of explainable AI (XAI) to ensure transparency and regulatory compliance in threat assessment processes. Key implementation challenges such as data privacy, model drift, scalability, and integration with cloud-native architectures are also critically analyzed. The review concludes by proposing a layered, adaptive AI-IDS framework designed to safeguard cloud-based financial ecosystems through continuous learning, context-aware threat modeling, and real-time risk prioritization.","url":"https://doi.org/10.5281/zenodo.21684176","authors":["Ayanbode, Noah","Cadet, Emmanuel","Etim, Edima David","Essien, Iboro Akpan","Ajayi, Joshua Oluwagbenga"],"tags":["AI-Augmented Intrusion Detection","Cloud-Based Financial Platforms","Real-Time Risk Analysis","Anomaly Detection","Explainable Artificial Intelligence (XAI)","Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21684176","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20391854","name":"Multimodal Artificial Intelligence in Oncology: Integrating Radiomics, Pathomics, and Genomics","source":"datacite","abstract":"The emergence of artificial intelligence (AI) has fundamentally transformed modern oncology by enabling computational interpretation of increasingly complex biomedical datasets derived from radiology, digital pathology, genomics, transcriptomics, and electronic health records. The convergence of these heterogeneous data streams has catalyzed the development of multimodal AI frameworks capable of generating biologically informed and clinically actionable insights across the cancer continuum. Unlike unimodal computational systems that analyze isolated data domains, multimodal AI integrates radiomics, pathomics, and genomics to capture tumor heterogeneity at anatomical, histological, molecular, and temporal scales. Recent advances in deep learning, transformer architectures, self-supervised learning, and foundation models have significantly enhanced the ability of multimodal systems to identify latent biological relationships associated with tumor initiation, progression, therapeutic resistance, and survival outcomes. These technologies are increasingly being applied to early cancer detection, molecular subtyping, prognostic stratification, immunotherapy prediction, and precision therapeutics. Nevertheless, substantial translational barriers remain, including limited data harmonization, algorithmic bias, interpretability concerns, regulatory uncertainty, and the scarcity of prospective clinical validation studies. Furthermore, ethical considerations related to data governance, privacy preservation, and equitable deployment continue to shape the future integration of AI within clinical oncology. This review critically examines the evolution of multimodal AI in oncology, emphasizing radiomics, pathomics, and genomics integration strategies, transformer-based architectures, explainable AI, and multimodal fusion methodologies. The article further discusses contemporary clinical applications, translational implications, and emerging opportunities surrounding foundation models and federated learning in precision cancer medicine. Collectively, multimodal AI represents a transformative paradigm capable of redefining oncologic diagnostics, therapeutic personalization, and clinical decision support while accelerating the realization of precision oncology.","url":"https://doi.org/10.5281/zenodo.20391854","authors":["Dr. Isabella Moore*"],"tags":["Multimodal artificial intelligence; oncology; radiomics; pathomics; genomics; precision medicine; deep learning; transformer models; explainable AI; multi-omics integration"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20391854","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.20391855","name":"Multimodal Artificial Intelligence in Oncology: Integrating Radiomics, Pathomics, and Genomics","source":"datacite","abstract":"The emergence of artificial intelligence (AI) has fundamentally transformed modern oncology by enabling computational interpretation of increasingly complex biomedical datasets derived from radiology, digital pathology, genomics, transcriptomics, and electronic health records. The convergence of these heterogeneous data streams has catalyzed the development of multimodal AI frameworks capable of generating biologically informed and clinically actionable insights across the cancer continuum. Unlike unimodal computational systems that analyze isolated data domains, multimodal AI integrates radiomics, pathomics, and genomics to capture tumor heterogeneity at anatomical, histological, molecular, and temporal scales. Recent advances in deep learning, transformer architectures, self-supervised learning, and foundation models have significantly enhanced the ability of multimodal systems to identify latent biological relationships associated with tumor initiation, progression, therapeutic resistance, and survival outcomes. These technologies are increasingly being applied to early cancer detection, molecular subtyping, prognostic stratification, immunotherapy prediction, and precision therapeutics. Nevertheless, substantial translational barriers remain, including limited data harmonization, algorithmic bias, interpretability concerns, regulatory uncertainty, and the scarcity of prospective clinical validation studies. Furthermore, ethical considerations related to data governance, privacy preservation, and equitable deployment continue to shape the future integration of AI within clinical oncology. This review critically examines the evolution of multimodal AI in oncology, emphasizing radiomics, pathomics, and genomics integration strategies, transformer-based architectures, explainable AI, and multimodal fusion methodologies. The article further discusses contemporary clinical applications, translational implications, and emerging opportunities surrounding foundation models and federated learning in precision cancer medicine. Collectively, multimodal AI represents a transformative paradigm capable of redefining oncologic diagnostics, therapeutic personalization, and clinical decision support while accelerating the realization of precision oncology.","url":"https://doi.org/10.5281/zenodo.20391855","authors":["Dr. Isabella Moore*"],"tags":["Multimodal artificial intelligence; oncology; radiomics; pathomics; genomics; precision medicine; deep learning; transformer models; explainable AI; multi-omics integration"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20391855","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21605344","name":"Federated Learning Approaches for Privacy-Preserving Threat Detection in Smart Home IoT Environments","source":"datacite","abstract":"Smart home Internet of Things (IoT) environments have become increasingly pervasive, offering convenience and automation while simultaneously introducing new cybersecurity vulnerabilities. Traditional centralized machine learning approaches for threat detection rely on aggregating sensitive user data into cloud servers, raising significant concerns regarding privacy, data security, and regulatory compliance. Federated learning (FL) has emerged as a promising paradigm that enables collaborative model training across distributed IoT devices without sharing raw data, thus preserving privacy while maintaining effective threat detection. This review paper explores the application of FL in privacy-preserving threat detection within smart home IoT systems, analyzing its strengths, limitations, and future potential. The discussion highlights how FL mitigates risks such as data leakage, adversarial attacks, and model inversion while ensuring scalability in heterogeneous device ecosystems. Moreover, the review examines existing frameworks, comparative case studies, and integration with complementary technologies like blockchain and differential privacy to enhance robustness. Challenges such as communication overhead, resource constraints, and model poisoning attacks are also critically addressed. By synthesizing recent advancements and identifying open research gaps, this paper provides a roadmap for leveraging FL in developing secure, scalable, and privacy-preserving threat detection systems for smart homes.","url":"https://doi.org/10.5281/zenodo.21605344","authors":["Idika, Chima Nwankwo","Salami, Edward Oziegbe"],"tags":["Federated Learning; Smart Home IoT; Privacy-Preserving Threat Detection; Cybersecurity; Edge Computing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21605344","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21605345","name":"Federated Learning Approaches for Privacy-Preserving Threat Detection in Smart Home IoT Environments","source":"datacite","abstract":"Smart home Internet of Things (IoT) environments have become increasingly pervasive, offering convenience and automation while simultaneously introducing new cybersecurity vulnerabilities. Traditional centralized machine learning approaches for threat detection rely on aggregating sensitive user data into cloud servers, raising significant concerns regarding privacy, data security, and regulatory compliance. Federated learning (FL) has emerged as a promising paradigm that enables collaborative model training across distributed IoT devices without sharing raw data, thus preserving privacy while maintaining effective threat detection. This review paper explores the application of FL in privacy-preserving threat detection within smart home IoT systems, analyzing its strengths, limitations, and future potential. The discussion highlights how FL mitigates risks such as data leakage, adversarial attacks, and model inversion while ensuring scalability in heterogeneous device ecosystems. Moreover, the review examines existing frameworks, comparative case studies, and integration with complementary technologies like blockchain and differential privacy to enhance robustness. Challenges such as communication overhead, resource constraints, and model poisoning attacks are also critically addressed. By synthesizing recent advancements and identifying open research gaps, this paper provides a roadmap for leveraging FL in developing secure, scalable, and privacy-preserving threat detection systems for smart homes.","url":"https://doi.org/10.5281/zenodo.21605345","authors":["Idika, Chima Nwankwo","Salami, Edward Oziegbe"],"tags":["Federated Learning; Smart Home IoT; Privacy-Preserving Threat Detection; Cybersecurity; Edge Computing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21605345","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21604827","name":"A Comprehensive Review on Disease Detection and Interpretation of Biomedical Imaging Using AI","source":"datacite","abstract":"The introduction of Artificial Intelligence (AI) in medical imaging is considered to be one of the most important processing advances in current clinical diagnostics. This comprehensive review article presents a timely summary of the use of AI paradigms, namely deep learning and its models, such as Convolutional Neural Networks (CNNs), U-Net, and Vision Transformers, in key image segmentation, disease finding, and clinical interpretation tasks. Included in the review are many types of imaging: MR Imaging (MRI), Computed Tomography (CT), digital pathology, ultrasound, and fundus photography. A systematic literature review illustrates that artificial intelligence (AI) models have already reached or exceeded human experts in diagnostic performance with a focus on diseases such as adenocarcinomas (cancer), neurological disorders, cardiovascular disease, and retinal pathologies [1]. Nevertheless, there still exist the main challenges of generalization across clinical settings, \"black-box\" characteristics, lack of explainability and interpretability for deep learning models, and easy integration with clinical interpretation for daily practice. this work points out important research frontiers that need to be addressed in the future, such as Explainable AI (XAI) models, privately and securely federated learning schemes as well as multimodal data fusion techniques and standardized evaluation architectures. By laying out a structured pathway that combines high-performance segmentation architectures with glass-box interpretability layers and formal validation protocols, this review aims at influencing the future development of research. Tackling these core hindrances is likely to accelerate AI's evolution from an adjunct diagnostic tool into a necessary, interpretable, and ethically responsible foundation of clinical practice — serving to significantly improve accuracy with which diagnoses are made, as well as patient health outcomes worldwide.","url":"https://doi.org/10.5281/zenodo.21604827","authors":["Siddique, Mohd Arif","Rajput, Ayan"],"tags":["Biomedical Imaging; Clinical Decision Support; U-Net; Disease Detection; Convolutional Neural Network; CADx; Medical Image Segmentation; Deep Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21604827","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21604828","name":"A Comprehensive Review on Disease Detection and Interpretation of Biomedical Imaging Using AI","source":"datacite","abstract":"The introduction of Artificial Intelligence (AI) in medical imaging is considered to be one of the most important processing advances in current clinical diagnostics. This comprehensive review article presents a timely summary of the use of AI paradigms, namely deep learning and its models, such as Convolutional Neural Networks (CNNs), U-Net, and Vision Transformers, in key image segmentation, disease finding, and clinical interpretation tasks. Included in the review are many types of imaging: MR Imaging (MRI), Computed Tomography (CT), digital pathology, ultrasound, and fundus photography. A systematic literature review illustrates that artificial intelligence (AI) models have already reached or exceeded human experts in diagnostic performance with a focus on diseases such as adenocarcinomas (cancer), neurological disorders, cardiovascular disease, and retinal pathologies [1]. Nevertheless, there still exist the main challenges of generalization across clinical settings, \"black-box\" characteristics, lack of explainability and interpretability for deep learning models, and easy integration with clinical interpretation for daily practice. this work points out important research frontiers that need to be addressed in the future, such as Explainable AI (XAI) models, privately and securely federated learning schemes as well as multimodal data fusion techniques and standardized evaluation architectures. By laying out a structured pathway that combines high-performance segmentation architectures with glass-box interpretability layers and formal validation protocols, this review aims at influencing the future development of research. Tackling these core hindrances is likely to accelerate AI's evolution from an adjunct diagnostic tool into a necessary, interpretable, and ethically responsible foundation of clinical practice — serving to significantly improve accuracy with which diagnoses are made, as well as patient health outcomes worldwide.","url":"https://doi.org/10.5281/zenodo.21604828","authors":["Siddique, Mohd Arif","Rajput, Ayan"],"tags":["Biomedical Imaging; Clinical Decision Support; U-Net; Disease Detection; Convolutional Neural Network; CADx; Medical Image Segmentation; Deep Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21604828","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21604736","name":"Deep Learning–Based Soybean Leaf Disease Classification : A Comprehensive Review","source":"datacite","abstract":"Soybean is one of the most economically important crops worldwide, yet its productivity is severely affected by a wide range of foliar diseases. Traditional disease diagnosis relies on expert visual inspection, which is time-consuming, subjective, and impractical for large-scale monitoring. In recent years, computer vision and artificial intelligence have emerged as promising tools for automated soybean leaf disease classification. This review presents a comprehensive analysis of state-of-the-art image-based soybean leaf disease classification techniques, with particular emphasis on deep learning, transfer learning, federated learning, and transformer-based models. The paper systematically examines preprocessing strategies, feature extraction methods, classification architectures, and evaluation protocols used in recent studies. Furthermore, comparative insights are drawn across convolutional neural networks, lightweight models, ensemble approaches, and vision transformers. The review also highlights key research findings, existing challenges, and unresolved limitations such as dataset imbalance, real-field variability, explainability, and deployment constraints. By synthesizing recent advances and identifying open research directions, this review aims to guide researchers toward more robust, scalable, and intelligent soybean disease diagnosis systems for sustainable agriculture.","url":"https://doi.org/10.5281/zenodo.21604736","authors":["Rathod, Drashti R","R, Padiya Swity","Patel, Ketan"],"tags":["Soybean Leaf Disease; Deep Learning; Image Classification; Computer Vision; Smart Agriculture"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21604736","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21604737","name":"Deep Learning–Based Soybean Leaf Disease Classification : A Comprehensive Review","source":"datacite","abstract":"Soybean is one of the most economically important crops worldwide, yet its productivity is severely affected by a wide range of foliar diseases. Traditional disease diagnosis relies on expert visual inspection, which is time-consuming, subjective, and impractical for large-scale monitoring. In recent years, computer vision and artificial intelligence have emerged as promising tools for automated soybean leaf disease classification. This review presents a comprehensive analysis of state-of-the-art image-based soybean leaf disease classification techniques, with particular emphasis on deep learning, transfer learning, federated learning, and transformer-based models. The paper systematically examines preprocessing strategies, feature extraction methods, classification architectures, and evaluation protocols used in recent studies. Furthermore, comparative insights are drawn across convolutional neural networks, lightweight models, ensemble approaches, and vision transformers. The review also highlights key research findings, existing challenges, and unresolved limitations such as dataset imbalance, real-field variability, explainability, and deployment constraints. By synthesizing recent advances and identifying open research directions, this review aims to guide researchers toward more robust, scalable, and intelligent soybean disease diagnosis systems for sustainable agriculture.","url":"https://doi.org/10.5281/zenodo.21604737","authors":["Rathod, Drashti R","R, Padiya Swity","Patel, Ketan"],"tags":["Soybean Leaf Disease; Deep Learning; Image Classification; Computer Vision; Smart Agriculture"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21604737","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21604684","name":"AI-Driven Adaptive Security Frameworks for Decentralized Edge Computing in IoT Ecosystems","source":"datacite","abstract":"The rapid proliferation of Internet of Things (IoT) devices and the growing adoption of decentralized edge computing architectures have significantly transformed data processing, latency optimization, and real-time decision-making across smart ecosystems. However, this paradigm shift has also introduced complex security challenges, including heterogeneous device vulnerabilities, dynamic network topologies, limited computational resources, and increased exposure to cyberattacks at the edge. This review paper examines AI-driven adaptive security frameworks as a promising approach for securing decentralized edge computing environments within IoT ecosystems. It systematically analyzes how artificial intelligence techniques—such as machine learning, deep learning, federated learning, and reinforcement learning—enable real-time threat detection, anomaly identification, intrusion prevention, and automated security policy adaptation at the network edge. The review highlights architectural models that integrate AI with edge and fog computing to achieve scalable, low-latency, and privacy-preserving security mechanisms while reducing reliance on centralized cloud infrastructures. Furthermore, it evaluates existing frameworks, recent case studies, and emerging trends, emphasizing challenges related to data privacy, model robustness, explainability, energy efficiency, and interoperability. By synthesizing current research and identifying open research gaps, this paper provides a comprehensive foundation for developing resilient, intelligent, and adaptive security solutions that can support the evolving requirements of decentralized IoT edge computing ecosystems.","url":"https://doi.org/10.5281/zenodo.21604684","authors":["Emeka, Desire","Emmanuel, Igba"],"tags":["AI-driven security; Edge computing; Internet of Things; Adaptive defense mechanisms; Trust management; Privacy-preserving model sharing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21604684","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.5281/zenodo.21604685","name":"AI-Driven Adaptive Security Frameworks for Decentralized Edge Computing in IoT Ecosystems","source":"datacite","abstract":"The rapid proliferation of Internet of Things (IoT) devices and the growing adoption of decentralized edge computing architectures have significantly transformed data processing, latency optimization, and real-time decision-making across smart ecosystems. However, this paradigm shift has also introduced complex security challenges, including heterogeneous device vulnerabilities, dynamic network topologies, limited computational resources, and increased exposure to cyberattacks at the edge. This review paper examines AI-driven adaptive security frameworks as a promising approach for securing decentralized edge computing environments within IoT ecosystems. It systematically analyzes how artificial intelligence techniques—such as machine learning, deep learning, federated learning, and reinforcement learning—enable real-time threat detection, anomaly identification, intrusion prevention, and automated security policy adaptation at the network edge. The review highlights architectural models that integrate AI with edge and fog computing to achieve scalable, low-latency, and privacy-preserving security mechanisms while reducing reliance on centralized cloud infrastructures. Furthermore, it evaluates existing frameworks, recent case studies, and emerging trends, emphasizing challenges related to data privacy, model robustness, explainability, energy efficiency, and interoperability. By synthesizing current research and identifying open research gaps, this paper provides a comprehensive foundation for developing resilient, intelligent, and adaptive security solutions that can support the evolving requirements of decentralized IoT edge computing ecosystems.","url":"https://doi.org/10.5281/zenodo.21604685","authors":["Emeka, Desire","Emmanuel, Igba"],"tags":["AI-driven security; Edge computing; Internet of Things; Adaptive defense mechanisms; Trust management; Privacy-preserving model sharing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21604685","addedAt":"2026-08-31T06:41:33.583Z","updatedAt":"2026-08-31T06:41:33.583Z"},{"id":"doi:10.2139/ssrn.6251720","name":"Scaling Limits of Federated Learning for Road Damage Detection Across Countries","source":"crossref","abstract":"Federated learning enables cross-country collaboration for training vision-based road damage detection models without sharing raw data. However, empirical guidance on federation configuration and client composition remains limited. Specifically, the effects of increasing federation size and cross-country heterogeneity, including differences in imaging platforms and pavement and climate conditions, on detection accuracy, cross-test stability, and minority-class performance (scarcity of some damage types) are not well quantified.In this context, we evaluate progressive multi-country federated YOLOv8 by incrementally integrating clients across road damage datasets from six countries and multiple sensing modalities. We observe diminishing returns as federation scale increases: a fourcountry configuration matched the mean mAP@50 of a five-country setup (0.41 vs. 0.40) while exhibiting lower variability across test sets (0.329 vs. 0.334), indicating more stable aggregation. To characterize the underlying heterogeneity, we quantify geographic and acquisition-related domain shifts using cosine similarity of intermediate feature representations. To mitigate minority-class scarcity, we propose and evaluate synthetic augmentation, which improves detection under severe imbalance but yields negative gains when inter-domain feature alignment is limited.The results characterize scaling limits in federated road inspection systems and support compatibility-aware client selection and domain-aware imbalance mitigation as actionable deployment strategies. Observations are specific to the evaluated datasets, model architecture, and aggregation configuration. Beyond road damage detection, the findings inform engineering-informatics practice for configuring distributed, data-driven infrastructure monitoring systems.","url":"https://doi.org/10.2139/ssrn.6251720","authors":["Shubham  Kumar Dwivedi","Deeksha Arya","Yoshihide Sekimoto"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-17T01:37:36Z","doi":"10.2139/ssrn.6251720","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.3390/electronics15061303","name":"Trustless Federated Reinforcement Learning for VPP Dispatch","source":"crossref","abstract":"Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning.","url":"https://doi.org/10.3390/electronics15061303","authors":["Xin Zhang","Fan Liang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-20T10:49:58Z","doi":"10.3390/electronics15061303","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.64643/ijirtv12i10-195304-459","name":"Federated Learning- Based 3D Medical Image Compression","source":"crossref","abstract":"","url":"https://doi.org/10.64643/ijirtv12i10-195304-459","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-22T05:09:37Z","doi":"10.64643/ijirtv12i10-195304-459","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/emc68537.2026.11441770","name":"A method for predicting electrolytic aluminum temperature based on machine learning and federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/emc68537.2026.11441770","authors":["Junyi Yu","Jiajia Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-24T19:46:39Z","doi":"10.1109/emc68537.2026.11441770","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6023430","name":"Blockchain-Enabled Adaptive Federated Learning Framework with SHAP-Based Explainability for Financial Fraud Detection","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.6023430","authors":["Junaid Hussain","Aatira Anum"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-05T21:40:13Z","doi":"10.2139/ssrn.6023430","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6725479","name":"Physics-Informed Federated Learning for Decentralized Pharmaceutical Crystallization: Achieving Personalized Predictive Accuracy with Minimal Data","source":"crossref","abstract":"In decentralized pharmaceutical manufacturing, developing robust predictive models for crystallization is often hindered by proprietary data silos and the inherent heterogeneity of site-specific process dynamics. Standard machine learning approaches struggle with data scarcity and frequently fail to respect the underlying physical laws, leading to catastrophic divergence in outlier scenarios. This study introduces a novel Physics-Informed Federated Learning (F-PINN) framework designed to enable collaborative model training across multiple manufacturing sites while strictly preserving data privacy.,By embedding the Population Balance Equation (PBE) directly into the federated global loss function, the F-PINN framework acts as a \"Physical Foundation Model\" that remains robust against sensor bias and significant data gaps. We further propose a Personalized Adaptation phase that allows the global model to fine-tune to local growth kinetics (G) and residence times (τ).,Results demonstrate that while standard federated averaging yields a generalized model, our personalized F-PINN approach achieves a 99.72% reduction in Mean Squared Error (MSE) compared to data-only models in high-heterogeneity sites. Specifically, the physics-informed priority prevents the model from diverging under noisy conditions, maintaining structural stability where standard neural networks fail. This research provides a scalable, privacy-preserving pathway for the implementation of Pharma 4.0 digital twins in multi-site industrial crystallization networks.","url":"https://doi.org/10.2139/ssrn.6725479","authors":["Sai  Vinay Thattukolla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-06T14:43:11Z","doi":"10.2139/ssrn.6725479","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.58532/nbennuramlab7p1c4","name":"EMERGING LEARNING PARADIGMS IN MACHINE LEARNING: TRANSFER, META, AND FEDERATED APPROACHES","source":"crossref","abstract":"There have been recent breakthroughs in machine learning which have given rise to new learning paradigms beyond traditional single-task, centralized training. Three of these paradigms (transfer learning, meta-learning and federated learning) are designed to enhance data efficiency, adaptability, and privacy protection. This chapter gives a detailed description of these models, theoretical backgrounds, architectures, algorithms, applications, challenges, as well as future research directions. The chapter is aimed at scholars, postgraduate learners, and professionals who want to gain an organized and easy to understand insight on the contemporary system of learning.","url":"https://doi.org/10.58532/nbennuramlab7p1c4","authors":["Nidhi Bhavsar","Kriti Das","Apeksha Waghmare","Komal Dhule"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-03T10:12:35Z","doi":"10.58532/nbennuramlab7p1c4","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.procs.2026.06.406","name":"Federated Learning Driven Machine Learning Models for Network Intrusion Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2026.06.406","authors":["J.Saira Banu","Sumaiya Thaseen Ikram","Harpal Kaur Dhindsa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-08T15:38:55Z","doi":"10.1016/j.procs.2026.06.406","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.7067742","name":"Semi-asynchronous Federated Learning in Flower: Framework Extension and Performance Assessment","source":"crossref","abstract":"This paper presents an extension of the Flower federated learning framework to support Semi-Asynchronous Federated Learning. The proposed approach adapts the traditional synchronous paradigm to better handle client heterogeneity and straggler effects. By introducing a semi-asynchronous training strategy, the system allows partial synchronization among clients while maintaining training efficiency and scalability. We implement and evaluate the proposed modification within Flower, instantiated as the FedSaSync strategy, demonstrating improved robustness and reduced idle time compared to fully synchronous baselines in heterogeneous environments. The results show that SAFL can balance convergence stability and system efficiency in heterogeneous environments typical of edge and distributed learning scenarios.","url":"https://doi.org/10.2139/ssrn.7067742","authors":["Víctor Hidalgo-Izquierdo","Carmen Carrión","Blanca Caminero"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-06T20:51:52Z","doi":"10.2139/ssrn.7067742","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6494698","name":"Location Privacy Preservation Crowdsensing with Federated Reinforcement Learning","source":"crossref","abstract":"This paper describes a new way to deal with privacy in crowdsensing through a federated reinforcement learning system. The goal of this system is to give certain people the ability to keep their location hidden when collecting information from them. A k-anonymity (i.e., grouping locations together) scheme has been developed to provide protection for users' identities. The scheme uses geohashes (or spatial clustering) to group locations and adds noise to the location data before it is sent to the server. The use of a federated architecture allows machine learning models to be trained on localized devices without sharing any raw location data with the server, which ensures differential privacy through gradient perturbation. Additionally, an integrated reinforcement learning agent allows for the automatic tuning of privacy parameters and provides an optimal trade-off between privacy and data quality. The experimental results clearly show that the implementation of the previous methods (i.e., k-anonymity, cluster grouping, geographical noise addition, and federated learning) combines to provide an extremely high level of protection from reidentification attacks while also maintaining an acceptable level of accuracy for practical crowdsensing applications, which represents a significant step forward toward using these types of privacy-enhancing methods for large distributed sensing networks.","url":"https://doi.org/10.2139/ssrn.6494698","authors":["Jothi Prasad V","Aditya S","Shalbin K Eldo","Shivashankaran S"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-14T22:29:02Z","doi":"10.2139/ssrn.6494698","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.18260/1-2--58921","name":"A Federated Learning Approach for Link Prediction in Social Learning Networks with Varying Link Definitions","source":"crossref","abstract":"","url":"https://doi.org/10.18260/1-2--58921","authors":["Jeremy Wong","Rajeev Sahay"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-21T13:35:24Z","doi":"10.18260/1-2--58921","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.7081498","name":"Federated Deep Learning for Multi-Class Deepfake Detection Across Diverse Manipulations","source":"crossref","abstract":"The exponential growth of digital media, particularly through social networks generating approximately 0.5 zettabytes of data daily, has created unprecedented challenges in content authenticity verification. Advanced generative technologies including GANs, diffusion models, and transformers have enabled the creation of increasingly sophisticated deepfakes that manipulate facial features, attributes, and textures in images and videos. While numerous approaches utilizing deep learning architectures have been proposed for deepfake detection, current solutions often address specific manipulation types or datasets rather than offering comprehensive, generalizable frameworks. This research aims to develop a novel federated deep learning framework for multi-class deepfake detection that operates efficiently across diverse manipulation techniques and deployment scenarios. By leveraging transfer learning, temporal analysis, and privacy-preserving distributed computation, this work addresses critical gaps in deepfake detection research while providing practical solutions for real-world implementation.","url":"https://doi.org/10.2139/ssrn.7081498","authors":["Pochampally Chandra Sekhar Reddy","Kongara Srinivasa Rao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-08T17:36:48Z","doi":"10.2139/ssrn.7081498","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6810648","name":"Federated Deep Reinforcement Learning for Load Balancing in 6G Edge Networks","source":"crossref","abstract":"The emergence of sixth-generation (6G) communication systems is poised to fundamentally transformelectronics by enabling ultra-reliable low latency communication (URLLC), terabit level data ratesand seamless connectivity across smart devices, wearables, immersive multimedia platforms andintelligent home environments. Realizing these capabilities demands intelligent and adaptive loadbalancing mechanisms capable of managing highly dynamic workloads across heterogeneous, multitiernetwork architectures comprising end devices, edge nodes and cloud infrastructures. Conventionalload balancing techniques and heuristic based approaches lack the adaptability and scalability requiredto cope with the high device density, mobility and diverse quality-of-service (QoS) requirementsinherent to 6G enabled ecosystems. This article proposes a novel approach for an intelligent, AI drivenfederated load balancing framework for 6G electronics environments. The framework integrates DeepReinforcement Learning (DRL) for autonomous, real-time decision making with Federated Learning(FL) to enable decentralized, privacy preserving model training across distributed devices and edgeplatforms. The proposed framework used to employ multi-agent DRL and hierarchical federatedaggregation. Furthermore, the proposed system dynamically learns optimal traffic and computationdistribution policies while adapting to contextual variations such as user behavior, device capabilitiesand network conditions without exchanging raw user data. Comprehensive simulation based evaluationsare conducted under representative electronics scenarios including edge assisted multimediaprocessing, smart home networks and dynamic IoT service orchestration. Performance comparisonsagainst classical load balancing schemes such as Round Robin (RR),Weighted Fair Queuing (WFQ),Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) demonstrate significantimprovements in key metrics, achieving throughput gains of up to 55.8%, latency reductions of 45.5%,energy efficiency improvements of 26.3% and enhanced fairness of 31%. These results highlight theproposed framework as a promising enabling technology for intelligent, scalable and privacy awareload balancing in next generation 6G electronics systems.","url":"https://doi.org/10.2139/ssrn.6810648","authors":["Salman Khan","Woong-Kee Loh","Ikram Syed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-21T20:38:31Z","doi":"10.2139/ssrn.6810648","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.7067138","name":"Communication-Efficient and Privacy-Preserving Federated Learning for Serverless Intelligence in Infrastructure-Denied UAV Relay Swarms","source":"crossref","abstract":"A UAV relay fleet operating where infrastructure has failed must learn collectively from what its aircraft observe, yet everything about its situation forbids the standard recipe: raw data cannot cross links an adversary may monitor, no ground server can be assumed to survive, mesh bandwidth is shared with the user traffic the fleet exists to carry, each platform class sees structurally different data, and the roster changes with every recharge cycle. We present a federated learning framework built for this conjunction rather than for any single constraint. Aggregation rotates through the mesh with the energy-elected gateway, so no fixed coordinator exists; updates travel as top-k sparsified gradients with error feedback; local training is proximally regularized against structural heterogeneity; transmitted updates carry a formal differential-privacy guarantee, applied after sparsification so the privacy budget is spent only on coordinates that travel; and staleness-weighted aggregation absorbs the churn of a fleet whose members continually leave and return. On channel-prediction and relay-positioning tasks over 14 heterogeneous aircraft, the framework matched dense federated averaging at roughly one-tenth the communication cost, retained over 93% of non-private accuracy at epsilon = 1.0, and converted a 50% mid-mission dropout from a setback into a ripple.","url":"https://doi.org/10.2139/ssrn.7067138","authors":["Abdulmajid Kasai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-24T09:35:15Z","doi":"10.2139/ssrn.7067138","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/wcnc65185.2026.11555435","name":"Hierarchical Federated Reinforcement Learning for Collaborative Wireless Distributed Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wcnc65185.2026.11555435","authors":["Xiaoyang Zhang","Chen-Khong Tham"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-16T19:41:29Z","doi":"10.1109/wcnc65185.2026.11555435","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6239753","name":"Communication-efficient Federated Learning via Feature Correlation and Entropy Analysis","source":"crossref","abstract":"The FL system enables several clients to train a model without compromising their private data. However, FL has limitations in terms of communication overhead and model convergence due to different data distribution for different clients. To overcome this limitation, this paper presents a hierarchical FL framework that uses entropy and correlation-aware feature compression. The framework uses a low-rank SVD-based update compression method in combination with an adaptive feature selection method that selects and sends only the highly informative and non-redundant feature representations before they are sent for periodic aggregation. In this framework, instead of compressing model updates in a uniform manner as in traditional FL methods, our framework compresses feature representations selectively in a highly condensed and highly informative manner. We have tested this framework using two different data sets: vehicle activity recognition and traffic sign classification. We observed that our framework outperforms other state-of-the-art FL methods, such as FedAvg, in terms of improved accuracy by 2-4%, faster model convergence compared to traditional FL, and a communication overhead that is over 90% lower compared to traditional FL methods while consistency outperforming FedKD in term of communication efficiency and maintaining superior accuracy and convergence stability. This indicates that entropy-based compression-based feature selection is highly effective in achieving a balance between model accuracy, model convergence rate, and communication overhead for FL systems that require significant resources.","url":"https://doi.org/10.2139/ssrn.6239753","authors":["Amir mollanejad","nahideh Derakhshanfard","mihan hoseinnezhad","Abbas Mirzaei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-14T21:39:17Z","doi":"10.2139/ssrn.6239753","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.64643/ijirtv12i12-206689-459","name":"Federated Learning for Privacy-Preserving Healthcare Data","source":"crossref","abstract":"","url":"https://doi.org/10.64643/ijirtv12i12-206689-459","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-28T07:13:15Z","doi":"10.64643/ijirtv12i12-206689-459","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.23977/acss.2026.100120","name":"Design of Privacy Protection Mechanism for Federated Learning Oriented to Data Security","source":"crossref","abstract":"","url":"https://doi.org/10.23977/acss.2026.100120","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-22T03:14:36Z","doi":"10.23977/acss.2026.100120","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icdew71238.2026.00032","name":"Fed-TTC: A Pareto-Efficient Hierarchical Federated Learning Framework","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icdew71238.2026.00032","authors":["Seyed Salar Ghazi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-16T19:42:37Z","doi":"10.1109/icdew71238.2026.00032","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6536811","name":"Noise-Resilient Quantum Federated Learning for NISQ Edge Computing: Physical Noise Modeling and Robust Convergence Optimization","source":"crossref","abstract":"Quantum federated learning (QFL) promises dramatic communication and parameter efficiency for resource-constrained Internet of Things (IoT) edge networks by replacing classical deep neural networks with compact variational quantum circuits (VQCs). However, current NISQ (Noisy Intermediate-Scale Quantum) hardware introduces physical noise, including depolarizing errors, amplitude damping, and phase damping, that degrades model fidelity and can render gradient-based optimization infeasible through the barren plateau phenomenon. This paper presents a noise-resilient QFL framework that explicitly incorporates physical noise channels into the system model and identifies the architectural and hardware conditions under which robust convergence is achievable. The proposed framework models the noisy loss function as L_i(theta) = Tr(O * E(rho_in(theta))), where E denotes the depolarizing channel, and analytically establishes that the effective gradient scales as (1 - p)^N_g * gradient_ideal. Comprehensive simulations across ten verification categories demonstrate that a shallow 4-qubit VQC with L = 2 layers achieves 85% classification accuracy under 5% depolarizing noise while maintaining a 417× parameter reduction. The decoherence analysis identifies T_1 ≥ 50 μs as the hardware coherence threshold, and the barren plateau analysis justifies the shallow circuit design by showing noise-induced gradient suppression exceeding 3.8 × 10^4 at L = 8. The framework reduces per-round communication to 96 bytes, achieving a 345× end-to-end round latency advantage on LoRa networks and extending IoT sensor battery lifetime by 6×.","url":"https://doi.org/10.2139/ssrn.6536811","authors":["Sungkwan Youm"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T15:43:13Z","doi":"10.2139/ssrn.6536811","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.7716/aem.v15i3.3557","name":"Multilingual Cultural Metaphor Transfer Driven by Federated Learning","source":"crossref","abstract":"Traditional federated learning faces significant challenges in multilingual cultural metaphor transfer because nonindependent and identically distributed language data make it difficult to balance local cultural heterogeneity with global semantic consistency. Privacy protection mechanisms may further amplify cultural cognitive bias and reduce transfer quality. To address these issues, this paper proposes a culturally aware personalized federated learning architecture. Based on Hofstede’s cultural dimension theory, cultural feature vectors are constructed, cultural weight functions are defined, and a weighted aggregation strategy is introduced to account for variations in power distance and individualism across clients. Security is enhanced through differential privacy and secure aggregation mechanisms. Experimental results show that, compared with FedAvg, the proposed method improves cross-language metaphor recognition precision and F1-score by 7.4% and 7.1%, respectively. Cultural consistency reaches 0.82, transfer comprehensibility reaches 4.02, and user matching degree reaches 0.79, while the minimum back-inference attack success rate is reduced to 0.5%. The results confirm that the proposed architecture enhances multilingual semantic transfer, cross-cultural consistency, and privacy-preserving collaborative learning.","url":"https://doi.org/10.7716/aem.v15i3.3557","authors":["L. Shi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-14T10:12:43Z","doi":"10.7716/aem.v15i3.3557","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icict68280.2026.11510988","name":"FedQEdge: Joint Model Compression and Federated Learning Framework for Edge AI","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icict68280.2026.11510988","authors":["Ramasubramanian Balasubramanian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-12T19:46:53Z","doi":"10.1109/icict68280.2026.11510988","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/mpcon69668.2026.11508402","name":"Hybrid Federated Learning Architecture for Communication-Efficient D2D-Enabled 6G Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mpcon69668.2026.11508402","authors":["Mahima Atmakuri","Ajay Bhardwaj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-15T03:00:58Z","doi":"10.1109/mpcon69668.2026.11508402","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1007/978-3-032-03985-9_31","name":"Ontology-Based Data Harmonization and Federated Transfer Learning: Enabling Scalable and Interoperable Intelligence in Healthcare 5.0","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_31","authors":["Darapu Uma","Manas Kumar Yogi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:31Z","doi":"10.1007/978-3-032-03985-9_31","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6257374","name":"E-TUNE-FL: Efficient and adapTive semi-synchronoUs semi-deceNtralizEd Federated Learning","source":"crossref","abstract":"Deep learning (DL) has seen extensive adoption across a broad range of applications. In the context of network management, it plays a crucial role in the development of advanced Intrusion Detection Systems (IDS). However, the massive volumes of log data produced in large-scale network environments make conventional centralized DL approaches impractical, due to substantial communication overhead and privacy regulations. To address this issue, Federated Learning (FL) offers a promising alternative for collaborative learning. Nonetheless, its performance is often constrained by the straggler effect, where clients are geographically distributed and differ in computational resources and data characteristics. In this paper, we present E-TUNE-FL, an adaptive framework that aligns with the computational capabilities of the clients, designed for Semi-Decentralized Federated Learning (SDFL). E-TUNE-FL incorporates a two-level aggregation strategy through a consensus algorithm and a convergence-driven synchronization mechanism to alleviate the challenges posed by non-IID data, reduce delays caused by stragglers, and improve training efficiency. We present a theoretical convergence analysis and empirically demonstrate the framework’s performance through extensive experiments on IDS datasets. Experimental results show that E-TUNE-FL reduces the FL training time up to 6× and enhance the performance compared to three FL baselines by 5%.","url":"https://doi.org/10.2139/ssrn.6257374","authors":["Houssem Jmal","Kandaraj Piamrat","Ons Aouedi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-17T19:41:47Z","doi":"10.2139/ssrn.6257374","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.array.2026.101065","name":"ODFU: Off-Diagonal Correlation-Aware federated unlearning for dropout attack defense in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.array.2026.101065","authors":["Cao Qui","Le-Pham Hoang-Trung","Kim-Hung Le"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-21T15:34:19Z","doi":"10.1016/j.array.2026.101065","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/iciics67880.2026.11483447","name":"Optimization of Artificial Intelligence Algorithms Based Federated Graph Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciics67880.2026.11483447","authors":["Yunbo Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-11T19:57:57Z","doi":"10.1109/iciics67880.2026.11483447","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.21070/joincs.v9i1.1699","name":"Federated Learning for Privacy-Preserving Big Data Analytics in Distributed Systems","source":"crossref","abstract":"Federated Learning (FL) is an important concept in big data analytics because it has changed the way collaborative model training can be done on devices that are decentralized while ensuring user privacy, an essential requirement in an accurate evidence-based and regulated environment with even stricter requirements from regulations like GDPR, HIPAA, CCPA and future laws on data sovereignty. This paper analyzed FL in depth. It described foundational concepts, architectural approaches, algorithmic approaches, real-world and practical applications and challenges in distributed systems. Key issues such as communication overhead, data heterogeneity, security risks, fairness, scalability, energy efficiency and compliance with regulations were also discussed and analyses were provided on any underpinning implications on FL performance. Seven tables provide comprehensive overviews of the algorithms, datasets, metrics of performance and applications, while nine figures in unique styles visualize trends, comparisons and data analytics to aid readability. Applications were provided in healthcare, IoT, financial sectors, smart cities and autonomous systems which lend evidence to the promise of FL as a revolutionary technology for privacy-respecting related analytics. Future directions for integrating FL highlights potential synergies with emergent technology such as quantum computing, blockchain, edge artificial intelligence and federated generative models, with supported rationales and inferences when necessary. This work provides a comprehensive and definitive reference point to enhance the scope and level of enquiry for researchers and practitioners who are trying to advance the development of distributed machine learning in sensitive situations to ultimately support the emergence of secure, scalable, ethical, and privacy-preserving analytics, which can drive future paradigm shifts","url":"https://doi.org/10.21070/joincs.v9i1.1699","authors":["Ahmed Gheni Dawood","Ekhlas Muthanna Turki"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-09T02:13:59Z","doi":"10.21070/joincs.v9i1.1699","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1007/978-981-96-8353-6_7","name":"Secure Collaborative Learning for CSV-Based Ovarian Cancer Diagnosis: A Federated Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-8353-6_7","authors":["Bidita Sarkar Diba","Md. Arafat Kabir","Tasnim Jahin Mowla","Hanif Bhuiyan","Durjoy Mistry"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-31T19:42:51Z","doi":"10.1007/978-981-96-8353-6_7","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch021","name":"Real-World Case Studies of Data Poisoning Attacks in Federated Learning Applications","source":"crossref","abstract":"This chapter looks at real-world examples of data poisoning attacks in federated learning (FL) systems, where harmful participants change local data to hurt the accuracy or integrity of global models. It uses case studies from healthcare, finance, the Internet of Things (IoT), and autonomous vehicles to show how these attacks happen, their effects on system performance, and the lessons learned. Each example includes documented incidents, academic research, or industry reports, giving practical and theoretical views. The chapter also points out common patterns across industries, describes vulnerabilities that are often targeted, and shares practical ways to reduce risks seen in real deployments. Examining these scenarios provides helpful insights for researchers, developers, and security experts looking to strengthen FL systems against threats while keeping privacy and efficiency intact.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch021","authors":["Shradha Sonawane","Gitanjali Shinde","Grishma Bobhate","Sonal Fatangare","Sharnil Pandya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch021","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.36227/techrxiv.177162022.26428179/v1","name":"ANCHOR: A Multilayer Reputation-Based Defense for Federated Learning in Edge-Fog-Cloud IoT Systems","source":"crossref","abstract":"Multilayer federated learning (multilayer FL) improves scalability and communication efficiency in large-scale IoT networks. However, it remains vulnerable to poisoning attacks in which adversaries corrupt local training to bias the global model. Defense is particularly challenging in multilayer architectures because intermediate aggregation masks individual client behavior, and non-IID data naturally induces heterogeneous updates that can resemble malicious deviations. We propose ANCHOR, a reputationbased defense for a client-edge-fog-cloud multilayer FL architecture. ANCHOR designates a small fixed set of benign anchor STAs whose updates are used to form stable trusted references for validating the direction and magnitude of the received updates. Using these references, ANCHOR performs tier-wise verification and maintains temporal reputation scores to filter unreliable updates at the edge, fog, and cloud layers, thereby limiting the upward propagation of poisoned contributions. We evaluate ANCHOR on MNIST, Fashion-MNIST, and an air-quality classification dataset under random and biased poisoning, varying adversarial intensities, and both IID and non-IID data distributions. Results show that ANCHOR preserves benign-learning performance comparable to a no-attack baseline, while achieving superior robustness and detection effectiveness relative to existing defenses under adversarial settings, with lower defense-andaggregation execution time in the multilayer pipeline.","url":"https://doi.org/10.36227/techrxiv.177162022.26428179/v1","authors":["Mumin Adam","Uthman Baroudi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T20:43:51Z","doi":"10.36227/techrxiv.177162022.26428179/v1","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6404278","name":"STPS-NET: Federated Learning for Plant-Specific Spatio-Temporal Modeling in Urban Rooftop Agriculture","source":"crossref","abstract":"Urban rooftop agriculture in tropical megacities has significant potential to improve food security and environmental sustainability. However, large-scale deployment remains constrained by three key challenges: strong microclimate variability across urban rooftops, heterogeneous cultivation requirements across plant species, and farmer reluctance to share operational data with centralized cloud systems. This study introduces STPS-NET, a federated learning framework designed for privacy-preserving yield prediction in rooftop agriculture in Dhaka, Bangladesh. The proposed model employs a plant-specific neural architecture that integrates multi-scale convolutional feature extraction, bidirectional long short-term memory networks, and self-attention mechanisms with 32-dimensional botanical embeddings validated by domain experts. During federated aggregation, a botanical similarity weighting strategy enables knowledge transfer between botanically related species while preserving data locality at individual rooftop sites. The framework was trained using a transparent dataset comprising 143,500 IoT sensor observations collected from 20 rooftop gardens over nine months (March-November 2024). To address limited observations during extreme pre-monsoon heat and early germination stages, physically consistent synthetic data were used exclusively for training, while all evaluation metrics were computed on held-out real measurements. Sensitivity analysis indicates that models trained with real-only data (MAE = 0.48, R 2 = 0.93), 25% synthetic augmentation (MAE = 0.38, R 2 = 0.95), and 50% synthetic augmentation (MAE = 0.30, R 2 = 0.96) generalize effectively to real data. Across 50 federated learning rounds, the proposed framework reduces yield prediction error from 1.47 to 0.30 kg m-2 and achieves R 2 = 0.96, outperforming 17 baseline methods. Field validation across eight independent rooftop sites further demonstrates practical agronomic benefits, including a 21.1% increase in yield, a 15.5% reduction in water usage, and a 16.9% reduction in fertilizer costs. Ablation analysis shows that plant-specific embeddings provide the largest performance gain (+0.12 in R 2), followed by temporal modeling through bidirectional LSTM and multi-scale feature fusion. In addition, differential privacy with ϵ = 1.0 preserves 98.9% of model utility, indicating that strong privacy guarantees can be achieved with minimal impact on predictive performance.","url":"https://doi.org/10.2139/ssrn.6404278","authors":["Ahad Bin Islam Shoeb"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-26T23:55:31Z","doi":"10.2139/ssrn.6404278","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icccn69946.2026.11662732","name":"Bandwidth-Aware Decentralized Federated Learning in Wired Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icccn69946.2026.11662732","authors":["Kengo Tajiri","Ryoichi Kawahara"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-27T19:07:07Z","doi":"10.1109/icccn69946.2026.11662732","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.64643/ijirtv13i1-205839-459","name":"Cotton leaf disease detection using federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.64643/ijirtv13i1-205839-459","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-29T11:36:17Z","doi":"10.64643/ijirtv13i1-205839-459","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.5220/0014392700004084","name":"Decentralized Privacy-Preserving Federated Learning of Computer Vision Models on Edge Devices","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014392700004084","authors":["Damian Harenčák","Lukáš Gajdošech","Martin Madaras"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-19T14:04:01Z","doi":"10.5220/0014392700004084","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6107948","name":"Communication-Efficient Federated Learning for Real-Time Anti-Money-Laundering Monitoring Authors","source":"crossref","abstract":"To support real-time anti-money-laundering (AML) surveillance, this study introduces a communication-efficient federated learning (FL) protocol combining parameter sparsification, quantization, and adaptive client participation. The evaluation uses a dataset representing 28.4 million daily transactions from five commercial institutions. Under a 5-second alert-latency constraint, the proposed method reduced communication volume by 61.1% and update latency by 47.3% compared with standard FL. Detection performance remained stable, with AUC values decreasing only from 0.90 to 0.89 and false-positive rates increasing by 2.0 percentage points at 80% recall. When network congestion occurred, the adaptive mechanism prioritized banks with higher model drift and prevented performance degradation. The system demonstrates the feasibility of deploying FL-based AML models under strict real-time requirements.","url":"https://doi.org/10.2139/ssrn.6107948","authors":["Claire Fontaine","Mathis Laurent","Julien Moreau"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-02T18:26:07Z","doi":"10.2139/ssrn.6107948","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.7139688","name":"Robust Split Federated Offline Reinforcement Learning under Heterogeneous Client Data","source":"crossref","abstract":"This paper studies split federated offline reinforcement learning (SFORL) for learning from decentralized trajectory datasets without directly centralizing raw trajectories. Rather than relying on centralized trajectory collection or requiring each client to train a full reinforcement learning model, SFORL partitions a decision-transformer-based offline RL policy between client-side embedding modules and a server-side transformer backbone. In view of recent split/federated decision-transformer systems, this work focuses on a complementary and practically important challenge: robustness under mixed-quality and non-IID client trajectory distributions. To address this issue, we propose an adaptive statistical client selection (ASCS) method that identifies statistically compatible high-quality clients before federated training. Experimental evaluations on MuJoCo and Kitchen environments demonstrate that SFORL achieves comparable performance to centralized decision-transformer models in homogeneous data settings, but can degrade substantially when client trajectory quality is heterogeneous. We observe that ASCS substantially enhances the robustness of SFORL by mitigating the influence of misaligned clients and improving client selection accuracy across different data conditions. The results show that SFORL combined with ASCS provides a practical split-federated offline RL design for decentralized and resource-constrained learning settings, with particular emphasis on heterogeneous-client robustness, adaptive client selection, and client-side model reduction.","url":"https://doi.org/10.2139/ssrn.7139688","authors":["Seungchan Yu","Jungchan Cho","Kyungjae Lee","Jungwoo Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-18T19:56:29Z","doi":"10.2139/ssrn.7139688","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6842458","name":"LatentFed: Reliability-Aware Latent Representations for Clustered Federated Learning under Statistical Heterogeneity","source":"crossref","abstract":"Federated learning suffers performance degradation under statistical heterogeneity, where clients follow different data distributions and local models drift toward incompatible optima. Clustered Federated Learning mitigates this issue by grouping clients with similar distributions, but most existing approaches infer client similarity from gradients, model parameters, or local losses. These signals are indirect proxies of the underlying data distribution and can be unstable across communication rounds, especially under strong non-IID conditions.This paper proposes \\emph{LatentFed}, a clustered federated learning framework that formulates client grouping as a latent-representation learning problem. Instead of clustering clients in parameter or gradient space, each client learns compact dataset-level latent summaries using a convolutional autoencoder. The first contribution is a Mean-Distortion Feature Enhancement (MDFE) module, which introduces channel-wise reliability awareness into the encoder by combining mean activation, peak response, and distortion information. MDFE suppresses unstable feature channels and improves the quality of client latent summaries. The second contribution is Cluster-Aware Latent Disentanglement (CALD), which decomposes latent representations into invariant and cluster-specific components and uses server-maintained prototypes to stabilize cluster structure over communication rounds.LatentFed performs density-based clustering on the learned client summaries and trains cluster-specific federated models using standard cluster-wise aggregation. Experiments on CIFAR-10 and EMNIST under controlled Dirichlet label-skew settings show that representation-aware latent clustering improves robustness, convergence stability, and clustering quality compared with conventional FL and clustered FL baselines. Ablation studies confirm the individual contribution of MDFE and CALD to producing compact, discriminative, and temporally stable client representations.","url":"https://doi.org/10.2139/ssrn.6842458","authors":["Vincenzo Altomare","Dipanwita Thakur","Antonella Guzzo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-28T12:41:11Z","doi":"10.2139/ssrn.6842458","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1007/978-3-032-15677-8_8","name":"Advanced Topics: Transfer Learning and Federated Learning in PHM","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-15677-8_8","authors":["Baris Aykent"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-10T14:48:54Z","doi":"10.1007/978-3-032-15677-8_8","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icicc71012.2026.11637620","name":"IRW-FL: Improvement-Based Reliability Weighted Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicc71012.2026.11637620","authors":["Zehua Li","Junnan Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-14T19:28:53Z","doi":"10.1109/icicc71012.2026.11637620","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.comcom.2026.108452","name":"Adaptive Federated Learning for energy-aware wireless edge networks in 6G environments","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.comcom.2026.108452","authors":["Mohammed Belghachi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-07T00:28:23Z","doi":"10.1016/j.comcom.2026.108452","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.4018/979-8-3373-7426-0.ch008","name":"Federated Learning and Collaborative AI in Medical Diagnostics","source":"crossref","abstract":"Advancements in Federated Learning (FL) and collaborative Artificial Intelligence (AI) are reshaping medical diagnostics by enabling hospitals and research institutions to build powerful models without centralized data sharing, as patient information remains on local servers. This section outlines the core principles of FL and the rise of trustworthy, privacy-preserving collaborative AI systems across healthcare networks. It reviews prior work, key techniques, and system designs, explaining how they enhance diagnostic accuracy, efficiency, and personalization. The discussion also highlights real-world applications, emerging trends, and challenges such as interoperability, regulatory compliance, and computational demands. The chapter informs scholars, practitioners, and policymakers on how FL and collaborative AI can transform medical diagnostics and support secure, ethical, and innovative healthcare.","url":"https://doi.org/10.4018/979-8-3373-7426-0.ch008","authors":["Shaista Ashraf Farooqi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-11T20:45:37Z","doi":"10.4018/979-8-3373-7426-0.ch008","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icc59461.2026.11588258","name":"FedAR: Adaptive Client Selection Strategy for Heterogeneous Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc59461.2026.11588258","authors":["Vahideh Hayyolalam","Öznur Özkasap"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T19:38:09Z","doi":"10.1109/icc59461.2026.11588258","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icc59461.2026.11587463","name":"MetaFed: A Novel Aggregation Strategy for Efficient Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc59461.2026.11587463","authors":["Vahideh Hayyolalam","Öznur Özkasap"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T19:38:09Z","doi":"10.1109/icc59461.2026.11587463","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/siu71813.2026.11636365","name":"Hardware-Aware Federated Learning for Speech Emotion Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.1109/siu71813.2026.11636365","authors":["Beyazıt Bestami Yüksel","Emrah Dikbiyik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-11T19:15:01Z","doi":"10.1109/siu71813.2026.11636365","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/b978-0-443-33700-0.00003-0","name":"Fostering security in healthcare recommendation systems with quantum federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33700-0.00003-0","authors":["Nithya Nedungadi","Sriram Sankaran","Amita Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-29T12:06:47Z","doi":"10.1016/b978-0-443-33700-0.00003-0","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.knosys.2026.116666","name":"FedGC: Federated learning with geometry calibrated differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.knosys.2026.116666","authors":["Jifei Hu","Hang Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-20T08:48:04Z","doi":"10.1016/j.knosys.2026.116666","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.sigpro.2025.110479","name":"Federated learning: A stochastic approximation approach","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.sigpro.2025.110479","authors":["Srihari P V","Anik Kumar Paul","Bharath Bhikkaji"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-03T15:46:56Z","doi":"10.1016/j.sigpro.2025.110479","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/cai68641.2026.11536541","name":"FLEXible: Enabling Federated Learning in Android devices","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cai68641.2026.11536541","authors":["Mario García-Márquez","Nuria Rodríguez-Barroso","M. Victoria Luzón","Francisco Herrera"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-01T19:33:50Z","doi":"10.1109/cai68641.2026.11536541","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/qcnc69040.2026.00028","name":"Harnessing Entanglement in Quantum Federated Learning Under Efficiency Constraints","source":"crossref","abstract":"","url":"https://doi.org/10.1109/qcnc69040.2026.00028","authors":["Shiva Raj Pokhrel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-07T19:51:14Z","doi":"10.1109/qcnc69040.2026.00028","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.5220/0014495300004084","name":"Generalizable Hyperparameter Optimization for Federated Learning on Non-IID Cancer Images","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014495300004084","authors":["Elisa Ribeiro","Rodrigo Moreira","Larissa Moreira","André Backes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-19T05:16:19Z","doi":"10.5220/0014495300004084","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/gcon69192.2026.11648411","name":"Federated Learning in the Presence of Non-IID Data: A Systematic Survey","source":"crossref","abstract":"","url":"https://doi.org/10.1109/gcon69192.2026.11648411","authors":["Bijit Barman","Satyajit Sarmah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-17T19:14:22Z","doi":"10.1109/gcon69192.2026.11648411","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.26599/tst.2026.9010005","name":"Federated Heterogeneous Graph Contrastive Learning for Privacy-preserving Recommendation","source":"crossref","abstract":"","url":"https://doi.org/10.26599/tst.2026.9010005","authors":["Yuan Wang","Yu Wang","Yiwen Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-13T08:44:46Z","doi":"10.26599/tst.2026.9010005","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/ccic68129.2026.11485983","name":"Cross-Silo Federated Learning for Reliable Demand Forecasting and Inventory Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccic68129.2026.11485983","authors":["Likhita Sreekakolapu","Lavanya Devi Golagani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-30T19:45:47Z","doi":"10.1109/ccic68129.2026.11485983","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.14428/esann/2026.es2026-246","name":"On the Impact of Differential Privacy on Federated Neuromorphic Learning Accuracy","source":"crossref","abstract":"","url":"https://doi.org/10.14428/esann/2026.es2026-246","authors":["Luiz Pereira","Dalton Valadares","Mirko Perkusich","Kyller Gorgônio"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-16T19:42:43Z","doi":"10.14428/esann/2026.es2026-246","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icc59461.2026.11587052","name":"Energy-Efficient Over-the-Air Federated Learning via Pinching Antenna Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc59461.2026.11587052","authors":["Saba Asaad","Ali Bereyhi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T19:38:09Z","doi":"10.1109/icc59461.2026.11587052","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/compsac69091.2026.00357","name":"Towards Secure and Robust Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/compsac69091.2026.00357","authors":["Islam Debicha","Tayeb Kenaza","Islam Hentous"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-20T19:08:33Z","doi":"10.1109/compsac69091.2026.00357","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2118/232365-ms","name":"Federated Learning: Unlocking Collaborative Machine Learning in Oman's Oil &amp; Gas Sector Without Sharing Sensitive Data","source":"crossref","abstract":"Abstract The oil and gas (O&amp;G) industry in Oman is increasingly adopting data-driven and machine learning (ML) solutions to improve operational efficiency and decision-making. However, one of the main barriers to scaling ML across organizations is data-sharing reluctance due to confidentiality, data residency, and regulatory restrictions. These constraints often result in isolated models with limited generalization. This research aims to explore Federated Learning (FL) as an innovative and secure approach to enable collaborative model development without sharing raw data, addressing the long-standing challenge of data privacy and governance in the O&amp;G sector. In this R&amp;D initiative led by Petroleum Development Oman (PDO), a prototype FL framework was developed to test distributed model training across multiple datasets. Each local client trained a model on its own data and shared only model parameters—not raw data—with a central aggregator. The study leverages open-source Python-based FL frameworks to implement secure communication between clients and the central server through a Virtual Private Network (VPN). Several ML use cases are being evaluated, including predictive maintenance, drilling analytics, and well performance prediction. The process assesses model convergence, communication efficiency, and accuracy improvement compared to isolated local models. Preliminary results indicate that the FL approach maintains strong model accuracy while fully preserving data privacy. Models trained using FL demonstrated improved generalization across different datasets compared to standalone local models. The experimental setup confirmed the technical feasibility of FL within PDO's data governance environment, showing potential scalability to multiple assets and partner organizations. While still under development, these early outcomes support FL as a viable solution for secure, collaborative AI across Oman's O&amp;G ecosystem.","url":"https://doi.org/10.2118/232365-ms","authors":["Sultan Alkaabi","Nasser Alhusaini","Nouf Al Noufli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-18T00:03:52Z","doi":"10.2118/232365-ms","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icici68773.2026.11580978","name":"Federated Learning–Driven Heart Disease Prediction using Machine and Deep Learning Models","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icici68773.2026.11580978","authors":["Ranjitha H. M","Nagaraj Naik","Deepasree S Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-01T19:35:13Z","doi":"10.1109/icici68773.2026.11580978","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.5267/j.dsl.2026.2.004","name":"Smart grid false data injection detection through federated learning with deep learning models","source":"crossref","abstract":"The security of smart grids is seriously threatened by false data injection (FDI) attacks. Falsified data is maliciously injected into the grid's measurement and control systems as part of these attacks, which might seriously disrupt the power supply and jeopardize system integrity. In the context of smart grids, it is also imperative to address the issue of consumer privacy and the protection of their sensitive data. The main objective of this work is to provide a collaborative framework based on federated learning to detect various FDI dangers while protecting SG's resources and privacy. We have implemented several technologies that provide a good solution in order to accomplish this goal. Using a dataset designed to replicate attacks on the power system environment, we used federated learning to locally train models using the data stored on the sensors. The best model should then be chosen by comparing the outcomes. These outcomes demonstrate the potential of our framework, which has used mixed models to repel attacks, short-circuit faults, and maintain lines with a 98% accuracy rate during the federated learning phase.","url":"https://doi.org/10.5267/j.dsl.2026.2.004","authors":["Raseel Alshamasi","Dina M. Ibrahim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T06:29:56Z","doi":"10.5267/j.dsl.2026.2.004","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch020","name":"Emerging Research Challenges, Legal Frameworks, and Real-World Insights in Secured Federated Learning Systems","source":"crossref","abstract":"This chapter per the authors explores secured federated learning systems, focusing on privacy preservation, adversarial resilience, and regulatory compliance. It explains how federated learning enables collaborative model training without direct data sharing, while addressing vulnerabilities such as gradient leakage, model inversion, data poisoning, and backdoor attacks. Methods including differential privacy, homomorphic encryption, secure multiparty computation, and Byzantine-resilient aggregation are evaluated for their effectiveness in mitigating risks. Ethical and legal dimensions shaped by frameworks such as GDPR and the EU AI Act are examined, with emphasis on explainability, fairness, and accountability. Real-world applications in healthcare and finance are highlighted through examples from the EXAM Consortium and WeBank's FATE platform, alongside insights into global research trends and institutional contributions. This chapter per the authors underscores the importance of secured federated learning in advancing trustworthy AI for sensitive domains.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch020","authors":["Chirra Baburao","Nitu Tank","D. Rajyalakshmi","Aasha Soni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch020","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.ins.2026.123389","name":"Entropy-guided meta-learning defense against data poisoning attacks in blockchain-assisted federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ins.2026.123389","authors":["Waheeb Algethami","Guowei Wu","Faisal Alshami","Muhanna Ali"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-21T07:32:07Z","doi":"10.1016/j.ins.2026.123389","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1007/978-3-032-28594-2_8","name":"Privacy-Preserving Federated Learning with Federated GANs for Early Heart Disease Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-28594-2_8","authors":["Vivean Arya","Nami Desai","Vihaan Bhulla","Deepa Krishnan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-03T21:25:16Z","doi":"10.1007/978-3-032-28594-2_8","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch012","name":"Benchmarking the Robustness of Federated Learning Frameworks Under Adversarial Conditions","source":"crossref","abstract":"Federated Learning (FL) has emerged as a promising instance that enables collaborative model training to be delivered while the privacy. However, its decentralized nature reveals FL systems at significant risks, including data and model poisoning, Byzantine failures, and privacy forecast attacks, which weaken the strength and reliability. This chapter presents a comprehensive benchmarking study of the widely adopted FL framework in anti -anti -conditions. We provide an overview of threats and evaluation methods using a certification matrix and attack simulation. Benchmarking results highlight both strengths and limitations, showing the main trade-off between safety, measurement, and efficiency. We discuss defense techniques such as stronger consolidation, discrepancies, differential privacy, and anti-training. The real-world case studies of healthcare, finance, and IOT show practical effects. In the end, we outline open challenges and future directions to enable secure and trusted FL systems.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch012","authors":["Randhir Singh Baghel","Harish Reddy Gantla","Udit Mamodiya","Abrar Ahmed Syed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch012","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.4018/979-8-3373-6224-3.ch014","name":"Federated Learning in Medical Diagnostics","source":"crossref","abstract":"AI has demonstrated its necessity in healthcare through better disease forecasts. Conventional machine learning models function on data that are gathered in a centralized location, which require data exchange and thus cause worries about privacy of patients, data safety, and obeying rules. Federated Learning (FL) brings paradigm shift by allowing multiple institutions to train their AI models collectively, without transferring any raw patient data. In this chapter, we investigate how FL can change the face of medical diagnostics. Recalling insights from existing machine learning models for Chronic Kidney Disease detection. We discuss how FL can avoid sharing the actual data sets among the multiple parties and retain predictive accuracy and interpretability of models. We also cover Explainable AI approaches such as SHAP and LIME which serve to increase transparency of FL-based diagnostics. In presenting a full framework for ethical and privacy-preserving AI for healthcare, this chapter brings to the foreground the huge potential of FL for next generation of Medical Intelligence.","url":"https://doi.org/10.4018/979-8-3373-6224-3.ch014","authors":["Uddalak Mitra","Shafiq Ul Rehman","Barnik Podder","Arpan Dutta","Bishtu Bhowmick"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T15:26:17Z","doi":"10.4018/979-8-3373-6224-3.ch014","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1007/s11235-026-01476-2","name":"Federated learning-driven cloud intrusion detection using transfer learning and ablation frigate-fairy hybrid optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s11235-026-01476-2","authors":["P. Senthil Raja","J. Sathiamoorthy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-19T07:21:12Z","doi":"10.1007/s11235-026-01476-2","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.5220/0014994800004103","name":"ORCHID-FL: On-Device Relationship-Aware Contextual Handling of Information via Decentralized Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014994800004103","authors":["George Popescu-Craiova"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-20T06:06:06Z","doi":"10.5220/0014994800004103","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icassp55912.2026.11464807","name":"Heterogeneous Adversarial Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icassp55912.2026.11464807","authors":["Wenrui Wang","Liping Yi","Gang Wang","Xiaoguang Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-21T21:25:57Z","doi":"10.1109/icassp55912.2026.11464807","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1145/3803630.3809170","name":"Don't Trust the Hidden Gradients!","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3803630.3809170","authors":["Luke Sperling","Sandeep Kulkarni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-22T12:25:02Z","doi":"10.1145/3803630.3809170","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/compsac69091.2026.00016","name":"Agent-Orchestrated Federated Representation Learning for Non-IID Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/compsac69091.2026.00016","authors":["Lei Yao","Tian Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-20T19:08:51Z","doi":"10.1109/compsac69091.2026.00016","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1002/9781394338726.ch8","name":"Federated Learning for Decentralized Smart Farm Network Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394338726.ch8","authors":["Mukesh Kumar Tripathi","Praveen Kumar Reddy","Vangara Nikitha","Nakshatra Reddy","Akshaya Gourisetty","Kapil Misal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T10:17:52Z","doi":"10.1002/9781394338726.ch8","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1007/978-981-96-8353-6_2","name":"Decentralized Tumor Classification with Federated Learning: A Privacy-Preserving Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-8353-6_2","authors":["Md. Nazmul Hossain Mir","Mir Nafiul Nagib","Afsana Alam Nova","Rahat Pervez","Md. Nahid Hasan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-31T19:42:51Z","doi":"10.1007/978-981-96-8353-6_2","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.5220/0014567500004103","name":"Packed and Unpacked Malware Detection by Means of Explainable Federated Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014567500004103","authors":["Giovanni Ciaramella","Fabio Martinelli","Antonella Santone","Francesco Mercaldo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-20T07:16:21Z","doi":"10.5220/0014567500004103","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.4018/979-8-3373-7426-0.ch007","name":"Privacy-Preserving Federated Learning for Multi-Hospital Patient Data Integration","source":"crossref","abstract":"The exponential growth of medical data across hospitals presents significant opportunities for predictive healthcare analytics, yet stringent privacy laws and data fragmentation hinder collaborative research. This chapter introduces a Privacy-Preserving Federated Learning (PP-FedAvg) framework that enables multiple hospitals to train machine-learning models collectively without sharing raw patient records. The system architecture integrates secure aggregation, homomorphic encryption, and differential privacy to ensure confidentiality during model updates, while blockchain-based accountability enhances transparency and trust. Experiments conducted on the MIMIC-III and MedMNIST datasets demonstrate that the proposed framework achieves near-centralized accuracy while significantly reducing privacy loss and communication cost. These results confirm that privacy-preserving federated learning can enable secure, regulation-compliant, and scalable collaboration among healthcare institutions.","url":"https://doi.org/10.4018/979-8-3373-7426-0.ch007","authors":["Arul Selvam P.","Tamije Selvy P."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-11T20:45:37Z","doi":"10.4018/979-8-3373-7426-0.ch007","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/aaiml67890.2026.11498179","name":"Federated Split Learning: Distributed Local Training for Low Computational Unit Clients","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aaiml67890.2026.11498179","authors":["Kazi Habibur Rahaman","Lilatul Ferdouse","Yang Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-06T19:38:02Z","doi":"10.1109/aaiml67890.2026.11498179","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/flics70075.2026.11621901","name":"Federated Inter-Bank Default Prediction: Enabling Secure Credit Risk Assessment Without Data Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flics70075.2026.11621901","authors":["Daniel M. Jimenez-Gutierrez","Enrique Zuazua","Joaquin Del Rio","Oleksii Sliusarenko","Xabi Uribe-Etxebarria"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:11:40Z","doi":"10.1109/flics70075.2026.11621901","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/aaiml67890.2026.11498139","name":"Trust-aware Explainable Federated Learning (TEFL) for Privacy-Preserving Medical Diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aaiml67890.2026.11498139","authors":["Janice A. Abellana","Ephraimuel Jose L. Abellana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-06T19:38:02Z","doi":"10.1109/aaiml67890.2026.11498139","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icmlas67792.2026.11483889","name":"AWF-BI: An Adaptive Weighted Federated Learning Model for Privacy-Preserving Business Intelligence in Multi-Branch Retail Operations","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlas67792.2026.11483889","authors":["Tarang Pande"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-01T19:51:20Z","doi":"10.1109/icmlas67792.2026.11483889","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.mlwa.2025.100829","name":"A traffic-aware federated learning prediction framework with custom aggregation","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.mlwa.2025.100829","authors":["Seerat Kaur","Sukhjit Singh Sehra","Darisuh Ebrahimi","Emad A. Mohammed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-29T07:25:17Z","doi":"10.1016/j.mlwa.2025.100829","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1201/9781003660330-1","name":"Regulatory challenges and compliance in federated learning (FL) for financial applications","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003660330-1","authors":["K. Logeswaran","S. Savitha","P. Suresh","K. R. Prasanna Kumar","A. P. Ponselva Kumar","A. S. Jayasurya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-25T15:39:21Z","doi":"10.1201/9781003660330-1","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.3901/jme.260414","name":"Research on Mechanical Fault Diagnosis Using Wavelet Empowered Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.3901/jme.260414","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-12T06:57:34Z","doi":"10.3901/jme.260414","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.neucom.2026.133967","name":"Federated learning with prototype-based adaptive domain adjustment","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.neucom.2026.133967","authors":["Leyuan Zhang","Fan Wan","Yang Long"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-14T11:09:07Z","doi":"10.1016/j.neucom.2026.133967","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.procs.2026.06.230","name":"Privacy Preserving Federated Learning Framework for Tuberculosis Management","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2026.06.230","authors":["Daphne Lopez","Brijendra Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-08T15:38:52Z","doi":"10.1016/j.procs.2026.06.230","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.7068683","name":"Decentralized Federated Learning with Adaptive Model Compression for Precision Agriculture IoT Networks in Sub-Saharan Africa","source":"crossref","abstract":"Precision agriculture holds tremendous potential for enhancing crop yields and food security across Sub-Saharan Africa, yet deploying machine learning systems in this region confronts fundamental barriers including limited network connectivity, constrained computational resources, and pressing concerns over agricultural data sovereignty. This paper presents AgriFL-Edge, a decentralized federated learning framework purpose-built for resource-constrained agricultural Internet of Things networks prevalent in Sub-Saharan African farming communities. The proposed framework advances three principal contributions: (i) a Bandwidth-Aware Dynamic Sparsi cation algorithm that adjusts compression ratios based on real-time bandwidth availability and model convergence trajectory, (ii) a fault-tolerant gossip protocol that eliminates reliance on centralized aggregation servers while gracefully handling prolonged network partitions, and (iii) AgriNet-Lite, a lightweight convolutional neural network architecture optimized for crop disease detection on microcontroller-class edge devices. Rigorous experiments conducted through real-world deployments in Nigeria, Ghana, and Kenya demonstrate that AgriFL-Edge attains 94.2% disease classi cation accuracy while reducing communication overhead by 73% relativeto standard federated averaging. The framework operates successfully on devices with less than 512 MB RAM and tolerates connectivity interruptions exceeding 48 hours without appreciable model degradation. Field trials engaging 847 smallholder farmers across 12 agricultural cooperatives validate practical feasibility and sustained adoption under authentic operational conditions.","url":"https://doi.org/10.2139/ssrn.7068683","authors":["Mohammed Ismail Behlim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T14:08:23Z","doi":"10.2139/ssrn.7068683","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6717512","name":"EH-FLATO: Energy Harvesting-Aware Federated Learning and Adaptive Task Offloading for Resource-Constrained IoT Edge Networks","source":"crossref","abstract":"The proliferation of energy-constrained Internet of Things (IoT) devices at the network edge demands intelligent strategies that jointly optimize computation offloading and collaborative model training without exhausting scarce energy budgets. Existing approaches treat task offloading and federated learning (FL) as decoupled problems, ignoring the stochastic nature of ambient energy harvesting (EH) and the non-independent and identically distributed (non-IID) data distributions prevalent in real- world IoT deployments. This paper proposes EH-FLATO, an Energy Harvesting-aware Federated Learning and Adaptive Task Offloading framework for resource-constrained IoT edge networks. EH-FLATO formulates the joint offloading–aggregation problem as a constrained Markov Decision Process (CMDP) and solves it using a Soft Actor-Critic (SAC) agent enhanced with a Lagrangian relaxation mechanism to enforce long-term energy sustainability constraints. A novel Harvest-Aware Contribution Score (HACS) metric dynamically weights each device’s FL participation based on its real-time energy state, data quality, and predicted harvesting profile. Analytical closed-form expressions are derived for the expected task completion latency and energy consumption under Rayleigh fading channels with stochastic EH arrivals. Extensive simulations demonstrate that EH- FLATO reduces average energy consumption by 42.3% and task completion latency by 31.7% compared to state-of-the-art baselines, while improving global model accuracy by 6.1% under non- IID conditions. The framework is validated across heterogeneous IoT scenarios with solar and radio- frequency (RF) energy harvesting sources.","url":"https://doi.org/10.2139/ssrn.6717512","authors":["Dushyanta Dutta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-05T13:40:16Z","doi":"10.2139/ssrn.6717512","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1007/978-3-032-03985-9_7","name":"The Convergence of Federated Learning for the Digital Healthcare Market: An Overview","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_7","authors":["Kanchan G. Rajput","Anil Sharma","Benila Susan Jacob","Pandya Abhishek Devendrabhai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:36Z","doi":"10.1007/978-3-032-03985-9_7","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icicip67436.2026.11417625","name":"Addressing Training Challenges in Federated Learning Using Adaptive Gradient Reshaping","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicip67436.2026.11417625","authors":["Chenhua Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-10T19:50:38Z","doi":"10.1109/icicip67436.2026.11417625","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.procs.2026.05.002","name":"Privacy Protection Applications of Federated Learning in Financial Data Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2026.05.002","authors":["Li Cao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-02T12:09:11Z","doi":"10.1016/j.procs.2026.05.002","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.12720/jcm.21.4.578-592","name":"Federated Learning for Privacy-preserving Internet of Things (IoT) Security: A Decentralized Intrusion Detection Framework","source":"crossref","abstract":"","url":"https://doi.org/10.12720/jcm.21.4.578-592","authors":["Muhammad Shaharyar Ramzan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-25T08:11:37Z","doi":"10.12720/jcm.21.4.578-592","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icc59461.2026.11586954","name":"ReFLect: A Reputation-based Client Selection for Federated Learning in Mobile Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc59461.2026.11586954","authors":["Subhash Sagar","Adnan Mahmood"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T19:38:09Z","doi":"10.1109/icc59461.2026.11586954","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icoecit68303.2026.11496782","name":"Blockchain Integrated Federated Learning with Privacy-Preserving Neural Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icoecit68303.2026.11496782","authors":["Krishna Chaitanya Yarlagadda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-05T20:00:29Z","doi":"10.1109/icoecit68303.2026.11496782","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.neucom.2026.132620","name":"Feature-driven layer specialization for label heterogeneous federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.neucom.2026.132620","authors":["Obed Jamir","Angshuman Paul"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-05T07:34:41Z","doi":"10.1016/j.neucom.2026.132620","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.procs.2026.06.497","name":"A Deep Learning-Driven Smart Agriculture Framework with Privacy-Preserving Federated Learning and Real-Time Monitoring","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2026.06.497","authors":["Malathi D","Selvameena P"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-08T15:38:55Z","doi":"10.1016/j.procs.2026.06.497","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.7032113","name":"A Dual-Learning Framework for Flash Flood Susceptibility: Benchmarking the Centralized-Federated Paradigm","source":"crossref","abstract":"Flash floods are among the most destructive natural hazards, yet their accurate prediction remains challenging due to the complexity of hydro-geomorphological processes and, increasingly, data privacy constraints. This study presents a dual-learning framework that systematically benchmarks centralized and federated learning approaches for flash flood susceptibility mapping in the Dez Basin, Iran. Leveraging a comprehensive geospatial database of 32 conditioning parameters and 252 field-validated flood points, we evaluated six centralized machine learning models (Random Forest, H2O Deep Learning, XGBoost, LightGBM, CatBoost, AdaBoost) and five federated learning models (Fed-RandomForest, Fed-XGBoost, Fed-LightGBM, Fed-Logistic, Fed-DNN) under the FedAvg aggregation architecture. The centralized Random Forest model achieved superior performance (AUC = 0.89, Accuracy = 0.95, R² = 0.94), outperforming the best federated model, Fed-Logistic (AUC = 0.79, Accuracy = 0.72, R² = 0.80). Our findings reveal a performance gap of approximately 10 percentage points in AUC (0.89 vs. 0.79) and 23 percentage points in Accuracy (95% vs. 72%), highlighting a meaningful accuracy-privacy trade-off between centralized and federated approaches that decision-makers must consider when selecting appropriate modeling strategies based on data availability and privacy requirements. While centralized learning offers superior accuracy when data sharing is feasible, federated learning provides a viable privacy-preserving alternative for transboundary or institutional data-restricted scenarios. This dual-framework benchmark provides actionable insights for disaster management authorities and policymakers in integrated flood risk management.","url":"https://doi.org/10.2139/ssrn.7032113","authors":["Hafez Mirzapour","Ali Haghizadeh","Saeid Derikvand"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-01T03:15:11Z","doi":"10.2139/ssrn.7032113","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.7084781","name":"FedTest: Enhancing Convergence and Mitigation of Adversarial Attacks in Federated Learning","source":"crossref","abstract":"Federated learning (FL) has emerged as a leading paradigm for distributed machine learning modelsacross devices without sharing their local private data. Typical FL algorithms combine local modelweights in proportion to each device’s data size, but struggle with non-IID and highly variable data.Consequently, such models cannot distinguish between malicious and benign model updates. As aresult, adversarial clients can slow convergence or corrupt the global model. On the other hand,accuracy-based aggregation methods address these issues, yet they depend on test data spanningall classes. To overcome these limitations, we propose FedTest, a novel FL framework in whicheach user’s local data is used both to train its own model and to evaluate the models of other users,fully exploiting the distributed nature of FL for training and testing while preserving privacy. Builton device-to-device (D2D) communication that operates without a base station, FedTest acceleratesconvergence, mitigates the influence of malicious clients, and improves the overall robustness andreliability of FL. Comprehensive experiments demonstrate that FedTest outperforms existing methodsand remains effective against adversarial users even on less robust datasets, such as MNIST.","url":"https://doi.org/10.2139/ssrn.7084781","authors":["Mustafa Ghaleb","Mohanad Obeed","Muhamad Felemban","Anas Chaaban"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-09T06:36:52Z","doi":"10.2139/ssrn.7084781","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6843095","name":"An Efficient Federated Learning Strategy for Scenarios with Restricted Computational Resources","source":"crossref","abstract":"Federated Learning (FL) has emerged as an effective approach for training machine learning models across distributed devices while maintaining privacy and security. However, FL faces significant challenges related to algorithm performance, communication efficiency, and data distribution, especially when training large convolutional neural networks (CNNs) on resource-constrained devices. This paper presents a novel theoretical formulation that partitions large global models into smaller sub-models, thus facilitating the training of large CNNs on resource-constrained devices within the FL framework. Several co-training strategies are proposed for the sub-models within each cluster. Following model partitioning, the sub-models are co-trained by multiple clients using different data subsets. An ensemble learning strategy is employed to aggregate predictions from multiple sub-models, thereby improving accuracy. This paper proposes a novel dynamic regularization method for FL to mitigate parameter discrepancies between local and global models caused by non-IID data. Extensive testing and benchmarking on various datasets with non-IID data distributions validate the overall effectiveness of the FedFSE model. The proposed approach achieves a performance improvement about 7% on CIFAR-10 and 15% on CIFAR-100 over existing methods, demonstrating its effectiveness in federated learning scenarios.","url":"https://doi.org/10.2139/ssrn.6843095","authors":["jun wen","hongqin huang","xiaoru zhang","xiaoli li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-28T14:50:21Z","doi":"10.2139/ssrn.6843095","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6461981","name":"PFL-Log: A Personalized Federated Learning Approach for Log Anomaly Detection","source":"crossref","abstract":"With the widespread adoption of distributed systems in cloud computing and the Internet of Things, log data has become a critical resource for system monitoring and anomaly detection, as it provides fine-grained insights into system behaviors and operational states. However, in real-world scenarios, anomalous log data is inherently scarce due to the rarity of failure events, while normal logs dominate and exhibit strong redundancy. Meanwhile, log patterns are highly heterogeneous across different systems and evolve dynamically over time, making it difficult to build a unified and robust detection model. More importantly, logs often contain sensitive information such as user behaviors and system configurations, which prevents direct data sharing across organizations and significantly limits the utilization of distributed knowledge. To address these issues, this paper proposes a comprehensive framework for log anomaly detection and privacy preservation. For anomaly detection, we design a memory-augmented meta-learning model to enhance generalization capabilities under few-shot scenarios. In terms of privacy protection, we introduce a personalized log protection mechanism by integrating Generative Adversarial Networks with personalized federated learning, enabling effective modeling of individual privacy preferences. Extensive experiments conducted on multiple real-world distributed system log datasets demonstrate that the proposed method outperforms existing approaches in both detection accuracy and privacy preservation. This study provides new insights toward building generalized and privacy-preserving log analysis systems in distributed environments.","url":"https://doi.org/10.2139/ssrn.6461981","authors":["Bingqing Luo","Jiacheng Gu","Jinya Su","Bin Xia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-24T04:37:51Z","doi":"10.2139/ssrn.6461981","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/j.eswa.2026.131514","name":"Breaking barriers in federated transfer learning: A systematic review on federated transfer learning for non-overlapping domains","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.eswa.2026.131514","authors":["Tasnim Binte Shiraj","Sobhana Jahan","Md. Rawnak Saif Adib","Md. Sazzadur Rahman","M. Shamim Kaiser","A. S. M. Sanwar Hosen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T16:30:28Z","doi":"10.1016/j.eswa.2026.131514","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/icce67443.2026.11449665","name":"Communication-Efficient Federated Learning Framework for Autonomous Vehicles","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icce67443.2026.11449665","authors":["Sima Sinaei","Mohammadreza Mohammadi","Mina Alibeigi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-27T19:47:50Z","doi":"10.1109/icce67443.2026.11449665","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/cloudsummit68932.2026.00031","name":"Cloud-Native Graph-Aware Federated Learning for Diabetic Retinopathy Diagnosis: Architecture, Orchestration and Graph Aggregation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cloudsummit68932.2026.00031","authors":["Jesu Marcus Immanuvel Arockiasamy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-31T18:14:39Z","doi":"10.1109/cloudsummit68932.2026.00031","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1109/iccmc69250.2026.11624741","name":"AI-Integrated Federated Learning Architecture for Confidential Cyber security Event Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccmc69250.2026.11624741","authors":["Manjunadh Maddhuru"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T19:10:43Z","doi":"10.1109/iccmc69250.2026.11624741","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00009-1","name":"Integrating real-time data with predictive models for early disease detection in metaverse healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00009-1","authors":["Herat Joshi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00009-1","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.26855/ea.2026.03.008","name":"Federated Learning-based Algorithm Design for Privacy Preservation in Cross-domain Data Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.26855/ea.2026.03.008","authors":["Yuxin Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-22T02:38:10Z","doi":"10.26855/ea.2026.03.008","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6255669","name":"A Secure, SDP-based Federated Learning Architecture for Pervasive Smart-City Ecosystem","source":"crossref","abstract":"The constant emergence of connected devices and the generation of large volumes of diverse data have significantly expanded both the scope and the attack surface of modern systems, particularly in pervasive smart-city environments. This growing surface presents several challenges, not only in securing the data generated by Internet of Things devices but also in protecting the applications and systems that process this data effectively. Additionally, the heterogeneity of the data introduces further complexity.To address these challenges, we propose a novel federated learning approach that reduces the attack surface for applications deployed across heterogeneous IoT clients, while also accelerating the training phase of machine learning models in a decentralised, simulated smart-city ecosystem. This approach integrates the learning model within an architecture based on the Software-Defined Perimeter protocol. It is designed to secure the transmission of learning parameters against six threats, namely Distributed Denial of Service, Man-in-the-Middle, Exfiltration, IP spoofing, Reconstruction, and model poisoning attacks. On the one hand, it addresses the non-independent and identically distributed nature of Internet of Things data in a smart city ecosystem; on the other hand, it improves the confidentiality, integrity, and robustness of federated learning while preserving model accuracy.","url":"https://doi.org/10.2139/ssrn.6255669","authors":["SAAD MAHMOUDI","TARIK EL MOUDDEN","Mohamed Amnai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-17T14:42:36Z","doi":"10.2139/ssrn.6255669","addedAt":"2026-08-31T06:41:35.438Z","updatedAt":"2026-08-31T06:41:35.438Z"},{"id":"doi:10.2139/ssrn.6177802","name":"FEDL: A Federated Continual Learning Framework for Lifelong Adaptation in Non-Stationary Wireless Sensor Networks","source":"crossref","abstract":"Deploying deep learning in Wireless Sensor Networks (WSNs) is challenging in non-stationary environments, where concept drift causes models to become outdated. Although ((FL) saves communication, it forgets old knowledge. Although Continual Learning (CL) prevents forgetting, it is too heavy for sensors. We propose a federated continual learning framework for resource-constrained WSNs. FEDL combines (1) a lightweight Delta-Detector to trigger learning only when drift occurs, (2) EWC-MC, a memory-friendly CL algorithm for microcontrollers, and (3) DA-FedAvg, an aggregation method that prioritizes nodes experiencing major drift. Simulations show that FEDL maintains &gt;90% accuracy under drift while reducing communication and energy use by 44% and 43%, respectively, compared with standard FL. This enables the development of long-lasting intelligent sensor networks.","url":"https://doi.org/10.2139/ssrn.6177802","authors":["Vijayakumar K","Thirumaraiselvan P"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T16:42:33Z","doi":"10.2139/ssrn.6177802","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/ecnct70535.2026.11661387","name":"Communication-Efficient Federated Transfer Learning with Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ecnct70535.2026.11661387","authors":["Yushi Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-26T19:11:55Z","doi":"10.1109/ecnct70535.2026.11661387","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.34306/bfront.v5i2.929","name":"Blockchain Integration to Enhance Federated Learning Model Integrity","source":"crossref","abstract":"Federated Learning is a distributed machine learning approach that enables model training without transferring raw data, thereby preserving user privacy. To improve conciseness, overlapping explanations of FL’s privacy benefits across the Abstract, Introduction, and Literature Review have been consolidated, highlighting its importance in sensitive domains while removing redundancy. This allows greater emphasis on the study’s novelty, particularly the Smart Contract design featuring multi-layer verification and reputation checking mechanisms. Despite its advantages, FL faces significant challenges related to model integrity, including parameter manipulation, model poisoning attacks, and limited trust among participating nodes. This study explores the integration of blockchain technology to address these issues. Leveraging decentralization, immutability, and transparency, blockchain is used to validate model updates, record contributions, and manage node reputation. The study employs a literature review and technical architecture design for a blockchain-integrated FL system. The results indicate that blockchain implementation enhances the reliability and security of FL training, especially in low-trust environments, with strong relevance for healthcare, finance, and IoT applications.","url":"https://doi.org/10.34306/bfront.v5i2.929","authors":["Yane Devi Anna"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-09T04:42:14Z","doi":"10.34306/bfront.v5i2.929","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.2139/ssrn.7129255","name":"Metric-Guided Attribution Fusion in Explainable Federated Learning: An Empirical Evaluation","source":"crossref","abstract":"Federated Learning (FL) enables privacy-preserving model training, but the behavior of eXplainable Artificial Intelligence (XAI) techniques in FL remains insufficiently understood, leaving an open challenge for trustworthy federated systems.We analyze how federated training affects explanation quality and show that global FL models generally achieve stronger explanation-quality scores than local models across multiple XAI metrics, while data heterogeneity influences explanations more strongly than the choice of FL algorithm.We further evaluate the stability of XAI methods under predictive multiplicity, where multiple similarly accurate models may exhibit divergent decision boundaries.Building on these findings, we formulate explanation improvement as a multi-objective feature-attribution fusion problem that aggregates multiple XAI methods while accounting for computational cost, and show measurable metric improvements.A complementary user study shows that participants significantly prefer FL-aggregated explanations over a centralized pooled-data baseline, while class-specific exceptions indicate that metric gains do not uniformly translate into perceived quality improvements.Our results suggest that metric-driven optimization and human-centered evaluation are complementary requirements for trustworthy explainable FL.","url":"https://doi.org/10.2139/ssrn.7129255","authors":["Nicolas Schuler","Matteo Camilli","Vincenzo Scotti","Raffaela Mirandola"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-16T21:44:34Z","doi":"10.2139/ssrn.7129255","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/dlcv69906.2026.11635659","name":"A Control-Variate Adaptive Weighting Algorithm for Cross-Dataset Federated Learning Motor Imagery EEG Decoding","source":"crossref","abstract":"","url":"https://doi.org/10.1109/dlcv69906.2026.11635659","authors":["Sufen Wang","Minmin Miao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-07T19:22:47Z","doi":"10.1109/dlcv69906.2026.11635659","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1007/s00607-026-01677-2","name":"The optimization of incentive mechanisms for edge federated learning based on Stackelberg game","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00607-026-01677-2","authors":["Liping He"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-23T04:18:00Z","doi":"10.1007/s00607-026-01677-2","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/tbdata.2026.3688998","name":"Bridging Local and Federated Data Normalization in Federated Learning: A Privacy-Preserving Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tbdata.2026.3688998","authors":["Melih Coşğun","Mert Gençtürk","Sinem Sav"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-29T19:52:30Z","doi":"10.1109/tbdata.2026.3688998","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/acdsa67686.2026.11467978","name":"From Fragmentation to Trust: Standardizing Communication in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/acdsa67686.2026.11467978","authors":["Sridharan Sankaran"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-16T19:50:24Z","doi":"10.1109/acdsa67686.2026.11467978","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1007/s10586-026-06236-0","name":"DACS: Deadline-Aware Adaptive Client Selection in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10586-026-06236-0","authors":["Aref Arefnia","Abdolah Chalechale"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-03T12:12:00Z","doi":"10.1007/s10586-026-06236-0","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/b978-0-44-344679-5.00011-4","name":"Federated learning for ICD classification with lightweight models and pretrained embeddings","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-44-344679-5.00011-4","authors":["Binbin Xu","Gerard Dray"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-19T08:54:02Z","doi":"10.1016/b978-0-44-344679-5.00011-4","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.2139/ssrn.7287388","name":"HADAG: An Asynchronous DAG Provenance Architecture for  Federated Healthcare Learning under Byzantine and Network Stress","source":"crossref","abstract":"Federated healthcare learning must accommodate heterogeneous communication and malicious participants while retaining a traceable history of asynchronous model updates. Existing work commonly studies these requirements separately. This paper presents HADAG, a server-coordinated architecture that records asynchronous model updates in a directed acyclic graph (DAG), uses the graph to preserve ancestry and partial order, and combines this provenance layer with trust-conditioned update handling and aggregation. We evaluate HADAG in 240 independent runs over 50, 100, and 150 simulated hospital clients, Byzantine ratios from 0 to 0.40, and two emulated network profiles. Under the configured pruning policy, mean DAG depth remains between 28.4 and 29.4 while the graph grows from approximately 9,600 to 29,400 vertices as the federation increases from 50 to 150 clients. A separate microbenchmark against an indexed append-only log quantifies the additional insertion and memory cost of maintaining DAG structure. Mean throughput ranges from 27.45–32.42 transactions per second under the realistic profile and 21.45–24.20 under the adversarial profile, corresponding mean end-to-end update latency is 2.43–2.52 s and 3.18–3.26 s. Trust-weighted aggregation achieves mean final accuracy of 0.8401, close to FLTrust at 0.8419, and reduces mean backdoor attack success from 0.1398 with FedAvg to 0.0897. It does not consistently outperform FLTrust or Krum, and one severe failure occurs among the 240 runs. The evidence supports HADAG as a simulation-evaluated provenance and coordination architecture, not as a decentralised consensus or cryptographic secure-aggregation protocol.","url":"https://doi.org/10.2139/ssrn.7287388","authors":["David Gana","Anju Johnson"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-15T06:39:31Z","doi":"10.2139/ssrn.7287388","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/icsadl67539.2026.11451996","name":"Cloud-Native Document Intelligence via Federated Multi-Agent Transformers","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsadl67539.2026.11451996","authors":["Ranadheer Reddy Charabuddi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T19:49:17Z","doi":"10.1109/icsadl67539.2026.11451996","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/melecon64486.2026.11418822","name":"Optimizing Federated Learning with Particle Swarm Intelligence for Next-Generation Video Surveillance","source":"crossref","abstract":"","url":"https://doi.org/10.1109/melecon64486.2026.11418822","authors":["Salma Newegy","Bo Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-10T19:50:57Z","doi":"10.1109/melecon64486.2026.11418822","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/j.neucom.2026.134042","name":"Let’s focus: Focused backdoor attack against federated transfer learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.neucom.2026.134042","authors":["Marco Arazzi","Stefanos Koffas","Antonino Nocera","Stjepan Picek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-21T06:50:33Z","doi":"10.1016/j.neucom.2026.134042","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.2139/ssrn.6624799","name":"&lt;p&gt;TaxFL: Federated Learning and Federated Graph Intelligence for Cross-Border Tax Compliance – A privacy-enhancing blueprint for collaborative risk analytics without sharing raw taxpayer data&lt;/p&gt;","source":"crossref","abstract":"&lt;p&gt;Cross-border tax non-compliance and money laundering exploit fragmented data silos across jurisdictions. This working paper proposes TaxFL, a pragmatic privacy-preserving framework that enables tax authorities and FIUs to collaboratively train risk models without ever sharing raw taxpayer or transaction data. TaxFL combines cross-silo federated learning, federated GraphSAGE for beneficial ownership and transaction graphs, and a layered privacy stack (secure aggregation, Rényi DP, optional TEEs). The framework operates in a hierarchical two-level architecture (Level 1 intra-country + Level 2 cross-border) and includes a novel multi-tier aggregator model with AMLA, GAFILAT/OEA and OECD as natural institutional homes. Synthetic experiments on a calibrated Brazilian oracle graph show: (i) bilateral RFB–Bancos federation detects T1/T5 schemes invisible to either party alone; (ii) GraphSAGE delivers +26 pp AUC and +17 pp recall on T6 (Caixa Dois); (iii) banks correct fraudulent CNAE declarations with 96.7 % accuracy using only behavioral profiles. A conservative “belt-and-suspenders” governance blueprint ensures compliance even under strict Schrems II interpretations. The paper also outlines a Phase 0 domestic pilot (RFB + banks) and a full commercialization-ready roadmap.&lt;/p&gt;","url":"https://doi.org/10.2139/ssrn.6624799","authors":["pedro frantz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-24T13:38:21Z","doi":"10.2139/ssrn.6624799","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/icc59461.2026.11588266","name":"LLMs meet Federated Learning for Scalable and Secure IoT Management","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc59461.2026.11588266","authors":["Yazan Otoum","Arghavan Asad","Amiya Nayak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T19:38:09Z","doi":"10.1109/icc59461.2026.11588266","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.26599/air.2026.9150004","name":"Curvature-Aware Model Aggregation for Decentralized Federated Learning via Parallel Transport on Discrete Manifolds","source":"crossref","abstract":"Abstract Decentralized federated learning (DFL) allows a group of distributed users to train a shared model without relying on a central server. Most existing decentralized aggregation methods, however, still treat the communication network as a flat Euclidean space and overlook the geometric differences that may arise in the model parameter landscape. To better capture these differences, we develop a curvature-aware decentralized federated learning framework, termed CA-DFL, which introduces geometric structure into the aggregation process. The main idea is to view the communication graph from a discrete Riemannian perspective. According to the divergence between neighboring model parameters, each communication edge is associated with one of three constant-curvature geometries: the sphere S2, the Euclidean plane R2, or the pseudosphere P S2. This classification allows model updates to be transferred between tangent spaces through closed-form parallel transport operators, so that the aggregation step respects the local geometry rather than mixing parameters in a purely Euclidean manner. Building on this, we further design a tensor-field-based geometric weighting scheme to assign adaptive aggregation weights using manifold inner products. This weighting scheme only borrows the idea of adaptive neighbor weighting, while the core operation remains curvature-aware transport and aggregation on manifolds. A subtree partition scheme is also introduced to encourage parameter sharing while reducing communication overhead. We establish convergence guarantees for the proposed method under standard assumptions. Experiments on MNIST and CIFAR-10 under ring, exponential, and random communication topologies demonstrate that CA-DFL achieves accuracy competitive with standard decentralized methods while providing adaptive geometric aggregation. A curvature-threshold sensitivity analysis confirms the robustness of the framework to hyperparameter choice.","url":"https://doi.org/10.26599/air.2026.9150004","authors":["Wei Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-01T03:18:06Z","doi":"10.26599/air.2026.9150004","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/icccs69761.2026.11613274","name":"AS-FL: Accumulator and Signature-Based Federated Learning with Weight Aggregation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icccs69761.2026.11613274","authors":["Runtong Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-28T19:08:01Z","doi":"10.1109/icccs69761.2026.11613274","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/wcnc65185.2026.11555682","name":"Hierarchical Asynchronous Federated Learning over Space-Air-Ground Integrated Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wcnc65185.2026.11555682","authors":["Kai Wang","Chee Wei Tan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-16T19:41:29Z","doi":"10.1109/wcnc65185.2026.11555682","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1007/978-3-031-96265-3_12","name":"Federated Learning: A Paradigm Shift in Healthcare Data Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-96265-3_12","authors":["Anjuli Goel","Chander Prabha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T02:20:47Z","doi":"10.1007/978-3-031-96265-3_12","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.2139/ssrn.7065635","name":"FLBL2: Real-World Layer-2 Blockchain Auditing for Federated Learning on Edge Hardware","source":"crossref","abstract":"Federated learning (FL) enables privacy-preserving collaborative trainingacross distributed edge devices, yet each aggregation round produces noexternally verifiable record, so the training history cannot be independentlyaudited after the fact. This accountability gap matters in healthcare,wearable sensing, and IoT applications, where model provenance and integritymay matter as much as predictive accuracy. We introduce FLBL2, a framework for tamper-evident, publicly verifiable, andcost-efficient auditability of FL workflows on production Layer-2 blockchaininfrastructure. Model artefacts are stored off-chain via IPFS contentaddressing, while compact cryptographic commitments are written on-chainthrough a sequential hash-chain; any CID mismatch or hash-chain break isindependently detectable by any party. FLBL2-LPSC, a seven-criterion Layer-2platform selection framework covers transaction cost, finality, EVMcompatibility, settlement guarantees, institutional support, ecosystemmaturity, and public accessibility. FLBL2 is deployed on ten physical Raspberry Pi~4 devices (ARM Cortex-A72,2--4\\,GB RAM) connected to Base mainnet, using a 12-class human activityrecognition task as an IoT workload. Over 20 sessions and 200 FL rounds,every aggregation step is committed on-chain and instrumented across threetiers: server-level FL metrics, per-device hardware telemetry, andper-operation blockchain timing, yielding 620 verifiable on-chaintransactions and 840 IPFS pin records. End-to-end auditability is achievedwithout disrupting model convergence, at below \\$0.012 per round ---over $2{,}000\\times$ cheaper than Ethereum~L1 at May~2026 gas rates.All source code, smart contracts, and the complete 200-round audit historyare publicly available at \\url{https://github.com/shinratttensei1/FLBL2}.","url":"https://doi.org/10.2139/ssrn.7065635","authors":["Talgar Bayan","Bibarys Mukhambetiyar","Adnan Yazıcı"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-06T14:39:44Z","doi":"10.2139/ssrn.7065635","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/ainit70033.2026.11557643","name":"Privacy-Preserving Clustered Federated Learning Against Membership Inference Attacks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ainit70033.2026.11557643","authors":["Yangjing Shi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-16T19:41:35Z","doi":"10.1109/ainit70033.2026.11557643","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.2139/ssrn.6025960","name":"Privacy-Preserving Federated Learning for Medical Imaging with Uncertainty Estimation: A Novel Framework for Collaborative Healthcare AI","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.6025960","authors":["Prince Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-12T22:28:46Z","doi":"10.2139/ssrn.6025960","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/icc59461.2026.11587543","name":"LiteTalk: Communication-Efficient Federated Learning via Adaptive Gradient Drift Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc59461.2026.11587543","authors":["Vahideh Hayyolalam","Öznur Özkasap"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T19:38:09Z","doi":"10.1109/icc59461.2026.11587543","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.2139/ssrn.6473762","name":"FEDL: Algorithmic Co-design of Federated and Continual Learning for Resource-Constrained Non-Stationary Environments","source":"crossref","abstract":"We propose FEDL, a novel federated continual learning algorithmic framework designed for resource-constrained, non-stationary environments. Its core algorithmic innovations include the following: (1) a lightweight, event-driven delta detector that triggers learning based on a smoothed prediction error; (2) EWC-MC, a memory-constrained adaptation of elastic weight consolidation that protects only the top-K critical parameters via a fixed-size importance buffer; and (3) DA-FedAvg, a drift-aware aggregation strategy that weights model updates by locally detecting drift magnitude using a novel sigmoid weighting function. We validated FEDL in the challenging domain of non-stationary wireless sensor networks (WSNs). Simulations demonstrate that FEDL maintains &gt;90% accuracy under concept drift while reducing communication overhead and total energy consumption by 44% and 43%, respectively, compared to standard FL. This study establishes a new algorithmic foundation for efficient and robust lifelong learning on extreme edge devices, advancing the state-of-the-art in federated and continual learning co-design.","url":"https://doi.org/10.2139/ssrn.6473762","authors":["Vijayakumar K","Thirumaraiselvan P"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-26T13:41:42Z","doi":"10.2139/ssrn.6473762","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/j.neucom.2026.134087","name":"FedDBA: Federated learning based image classification algorithm with local bias-contrastive learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.neucom.2026.134087","authors":["Jin Ye","Huilin Hu","Junbin Liang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-25T16:12:15Z","doi":"10.1016/j.neucom.2026.134087","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1007/978-3-031-96649-1","name":"Federated Edge Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-96649-1","authors":["Yong Zhou","Wenzhi Fang","Yuanming Shi","Khaled B. Letaief"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T12:52:18Z","doi":"10.1007/978-3-031-96649-1","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/j.neucom.2026.132682","name":"GIDD: Gradient inversion using diffusion model for denoising in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.neucom.2026.132682","authors":["Xuebo Wang","Hongguang Sun","Yi He"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-20T07:42:18Z","doi":"10.1016/j.neucom.2026.132682","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/ccnc65079.2026.11366390","name":"Improving Generalization with Harmonic Aggregation in Personalized Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccnc65079.2026.11366390","authors":["Angel Peredo","Sergei Chuprov"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T20:45:15Z","doi":"10.1109/ccnc65079.2026.11366390","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.38007/ijmc.2026.070102","name":"Research on Privacy-Preserving AI Model Training and Validation Methods Based on Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.38007/ijmc.2026.070102","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-28T02:03:30Z","doi":"10.38007/ijmc.2026.070102","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/isit62367.2026.11654134","name":"Power to the Clients: Federated Learning in a Dictatorship Setting","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isit62367.2026.11654134","authors":["Mohammadsajad Alipour","Mohammad Mohammadi Amiri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-25T19:17:35Z","doi":"10.1109/isit62367.2026.11654134","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.2139/ssrn.6915107","name":"CASGuard: Collusion-Aware Soft Suppression for Defending Sybil Backdoor Attacks in Federated Learning","source":"crossref","abstract":"Federated learning enables collaborative model training without directly sharing private data, but its distributed and partially observable training process exposes it to severe poisoning threats. Among them, Sybil backdoor attacks are particularly dangerous because one adversary can control multiple malicious clients and inject highly coordinated poisoned updates into the global model. Such attacks can preserve high clean accuracy while causing trigger-embedded inputs to be classified into attacker-specified target labels. Existing robust aggregation methods, including coordinate-wise median, trimmed mean, Krum, Multi-Krum, FoolsGold, and trust-anchor-based aggregation, either fail to suppress coordinated backdoors or sacrifice substantial main-task performance.This paper proposes CASGuard, a Collusion-Aware Soft Suppression framework for defending federated learning against Sybil backdoor attacks. CASGuard is built on the observation that Sybil backdoor clients are not merely statistical outliers; rather, they form a collusive directional cluster in the update space because they optimize a shared trigger-target objective. CASGuard first measures pairwise cosine similarity among client updates to identify suspicious collusion patterns. It then dynamically suppresses suspicious clients through an exponentially decayed soft weighting strategy, preventing long-term backdoor accumulation while avoiding the convergence damage caused by overly aggressive hard rejection. A median-direction consistency check is further incorporated as an auxiliary safeguard against strongly inconsistent update directions.Extensive experiments on CIFAR-10 and CIFAR-100 with ResNet-18 demonstrate the effectiveness and practicality of CASGuard. Under Sybil backdoor attacks on CIFAR-10, CASGuard achieves 90.65% main-task accuracy, reduces attack success rate to 9.17%, obtains 97.33% detection recall, and maintains only 1.78% false positive rate. Compared with FedAvg, trimmed mean, median, FoolsGold, FLTrust, Krum, and Multi-Krum, CASGuard achieves a favorable balance between clean accuracy, attack suppression, detection quality, and computational efficiency. CASGuard requires no additional communication and achieves an aggregation time of only 0.0285 s per round, faster than Krum in the tested ResNet-18 setting. These results suggest that collusion-aware suppression is a promising and practical direction for secure federated learning.","url":"https://doi.org/10.2139/ssrn.6915107","authors":["Yaoyao Ren","Xu Cheng","Yu Cao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-10T20:48:02Z","doi":"10.2139/ssrn.6915107","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.2139/ssrn.7201280","name":"Explainable Hierarchical Federated Learning for Privacy-Preserving Clinical Decision Support in Primary Care","source":"crossref","abstract":"Clinical Decision Support Systems (CDSS) are increasingly adopted interritorial healthcare to support the early detection and management ofchronic diseases. However, their deployment in real-world healthcareinfrastructures remains challenging due to data fragmentation acrossinstitutions, stringent privacy regulations, limited computationalresources at the edge, and the need for transparent and auditabledecision-making processes. In this work, we propose an Explainable Hierarchical Federated RandomForest (E-HFRF), a privacy-preserving framework for clinical decisionsupport in distributed healthcare settings. The proposed system adoptsa multi-tier architecture that mirrors real healthcare governancestructures, enabling distributed model training across primary careunits, intermediate hospital hubs, and health authority servers withoutsharing raw patient data. A tree-based ensemble learning strategy isemployed to preserve intrinsic interpretability, while hierarchicalaggregation ensures robustness to non-identically distributed data andcompliance with regulatory requirements. Traceability and auditabilityare explicitly supported through structured tree-level metadata which preserves the provenanceof each constituent model throughout the federation, enabling inspectionof individual decision paths and reproducible audit trails. Explainability is further addressed through SHAP-based post-hocanalysis, providing instance-level and global feature attributions thatsupport clinical auditability and physician trust. The framework isevaluated on multiple chronic disease datasets, including diabetes,chronic kidney disease, hypertension, and a large-scale public healthdataset under non-IID federated conditions. Results show that theproposed approach achieves performance comparable to centralizedbaselines while improving robustness under heterogeneous datadistributions. Cost-sensitive learning and calibrated decision thresholds enableclinically meaningful trade-offs between sensitivity and precisionin screening-oriented scenarios.","url":"https://doi.org/10.2139/ssrn.7201280","authors":["Roberto Marino","Irene Cacciola","Massimo Villari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T13:24:21Z","doi":"10.2139/ssrn.7201280","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.2139/ssrn.6990082","name":"Causal graph based fairness auditing for federated learning under selection bias","source":"crossref","abstract":"In federated learning, selection bias can distort fairness assessments and mask or mimic algorithmic discrimination. This study proposes a fairness auditing framework that integrates causal graph modeling with statistical parity metrics to audit discrimination in federated learning systems under selection bias. We first analyze causal relations to characterize how selection bias propagates across data, model, and decision levels. Second, we derive the identifiability conditions based on which a scalable diagnostic method is developed to distinguish genuine discrimination from spurious correlations induced by biased client sampling. Experiments on synthetic and real-world data validate the theoretical findings and demonstrate that the proposed method can more accurately detect federated learning discrimination than some popular detection tools, such as AIF360 and proxy-model baselines. The study provides a lightweight, post-hoc tool that can be plugged into existing federated learning pipelines without raw-data sharing or model retraining.","url":"https://doi.org/10.2139/ssrn.6990082","authors":["Haoyu Xue","Xiaohang Zhang","Zhengren Li","Fei Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-24T12:42:47Z","doi":"10.2139/ssrn.6990082","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/b978-0-443-32884-8.00012-x","name":"Federated learning applications for IoMT security","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-32884-8.00012-x","authors":["Priyadharshini M.","Murugesh V.","Sam Kumar G. V.","Subrata Chowdhury"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T21:46:59Z","doi":"10.1016/b978-0-443-32884-8.00012-x","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/icip61757.2026.11630328","name":"Defence Against Byzantine Attacks in Semi-Supervised Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icip61757.2026.11630328","authors":["Nafisa Parvin","Sayanta Sen","Saumik Bhattacharya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-13T19:11:15Z","doi":"10.1109/icip61757.2026.11630328","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/hpsc69479.2026.00023","name":"Diverse Embedding Graph Attention Networks for Personalized Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/hpsc69479.2026.00023","authors":["Xiao Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-13T19:16:59Z","doi":"10.1109/hpsc69479.2026.00023","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1007/978-981-95-1394-9_1","name":"Blockchain and Federated Learning Synergy for Privacy-Focused DeepFex Solutions","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-1394-9_1","authors":["M. Harishmaa","S. Janani","K. A. Jayashree","J. Rufina Sherin","R. Manikandan","S. Magesh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-07T13:28:15Z","doi":"10.1007/978-981-95-1394-9_1","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1016/j.mlwa.2026.100935","name":"FedPSS: Privacy-governed federated patient-record similarity retrieval over temporal multimodal clinical records using multilingual semantic mapping","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.mlwa.2026.100935","authors":["Mohammad Tanhaei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-19T15:57:14Z","doi":"10.1016/j.mlwa.2026.100935","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.4018/979-8-3373-8889-2.ch013","name":"Privacy Preservation Through Design-Federated Learning Architectures for Secure and Immersive Metaverse-Based Healthcare","source":"crossref","abstract":"Crossover of the metaverse, Artificial Intelligence, and Federated Learning is changing the healthcare systems and amplifying the issues of privacy, security, and trust. This chapter discusses privacy preservation by design as a fundamental concept to applying Federated Learning architectures in metaverse-based healthcare setting. Federated Learning is theoretically understood as an architecture facilitator that introduces collaborative intelligence between hospitals, clinics, wearables, and virtual care platforms with no centralized data exchange. The main architectural aspects like Edge Fog Cloud Coordination, Secure Aggregation, Differential Privacy, and Trusted Execution Environment are examined to explain how confidentiality and regulatory compliance may be obtained. By conceptually discussing and using illustrative examples, the chapter demonstrates how privacy-centric federated architectures can be used to support immersive, real-time, and patient-centric healthcare services and maintain data sovereignty.","url":"https://doi.org/10.4018/979-8-3373-8889-2.ch013","authors":["S. Aarthi","Jaypalsinh A. Gohil"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-06T18:23:51Z","doi":"10.4018/979-8-3373-8889-2.ch013","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.37547/tajiir/volume08issue02-06","name":"Marketing Analytics Without Personal Identifiers: Federated Learning And DP","source":"crossref","abstract":"The study is devoted to the analysis and conceptual integration of two key classes of privacy-enhancing technologies (PETs), federated learning (FL) and differential privacy (DP), with the aim of developing a holistic framework for solving marketing analytics tasks. The methodological basis of the work relies on a systematic review of current specialized literature and authoritative industry analytical materials, followed by a synthesis of the identified approaches. The obtained results demonstrate that the combination of the decentralized FL architecture with the formal mathematical guarantees of DP creates the conditions for building high-accuracy predictive models applicable to such key tasks as conversion rate estimation, target audience segmentation, and personalization of interactions, while eliminating the need for centralization and direct disclosure of sensitive user data. In conclusion, it is substantiated that the proposed FL-DP framework can be regarded as a technologically robust and ethically sound solution that forms the basis for the transition to a new generation of marketing analytics, despite the persisting significant challenges associated with its practical implementation. The article is intended for data specialists, researchers in the field of machine learning, and professionals involved in the development and implementation of marketing strategies focused on building analytics systems with privacy as a priority.","url":"https://doi.org/10.37547/tajiir/volume08issue02-06","authors":["Kuanysh Kemeshova"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-12T09:38:34Z","doi":"10.37547/tajiir/volume08issue02-06","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.5220/0014475900004061","name":"Incremental Federated Learning for Intrusion Detection in IoT Networks under Evolving Threat Landscape","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014475900004061","authors":["Muaan Rehman","Hayretdin Bahsi","Rajesh Kalakoti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-08T11:24:37Z","doi":"10.5220/0014475900004061","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1109/ickecs70176.2026.11527859","name":"Federated Learning and Deep Reinforcement Learning for Maintenance-Aware Energy Optimization in Industrial Wireless Sensor Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ickecs70176.2026.11527859","authors":["Tanguturi Tejasree","Shaik Ghouhar Taj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-26T19:40:12Z","doi":"10.1109/ickecs70176.2026.11527859","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1007/978-3-032-03985-9_25","name":"Centralized Versus Decentralized Federated Learning Architectures: Design Trade-Offs, Security, and Performance in Healthcare 5.0 Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-03985-9_25","authors":["Zakariae Saidi","Ouidad Akhrif","Younes El Bouzekri El Idrissi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T05:46:42Z","doi":"10.1007/978-3-032-03985-9_25","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"doi:10.1117/12.3110897","name":"Privacy-preserving federated learning approach for medical image analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1117/12.3110897","authors":["Zishuo Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-29T18:42:01Z","doi":"10.1117/12.3110897","addedAt":"2026-08-31T06:41:35.439Z","updatedAt":"2026-08-31T06:41:35.439Z"},{"id":"pmid:41942543","name":"Data-driven predictive maintenance of induction motors using self-supervised and federated learning on noisy current and vibration signals.","source":"pubmed","abstract":"Induction motors (IMs) sustain a vast share of industrial activity but are prone to bearing wear, rotor-bar breakage, eccentricity, and insulation defects that emerge under non-stationary, noisy conditions. While Motor Current Signature Analysis (MCSA) remains attractive for its non-intrusive sensing, its discriminative power collapses at low signal-to-noise ratios (SNR) and when labelled fault exemplars are scarce. We present a unified Self-Supervised&#x2009;+&#x2009;Federated Learning (SSL-FL) framework that (i) learns transferable, noise-tolerant embeddings from large unlabelled corpora of stator current and vibration signals, and (ii) enables privacy-preserving, cross-site training without sharing raw data. Using chronological splits, SNR stress tests (0-15&#xa0;dB), fault-severity breakdowns (incipient/developing/severe), non-IID federated client simulations, and leave-one-site-out transfer across CWRU, Paderborn, IMS, and an industrial pump-IM testbed, the approach consistently outperforms strong deep baselines (CNN/LSTM/Transformer), achieving 94.2% overall accuracy (92.4% incipient), 0.92 F1, and 0.90 MCC. It delivers 11-17 percentage-point gains in low-SNR regimes and 83.5% cross-domain accuracy, while attaining&#x2009;~&#x2009;91% of the centralized upper bound with&#x2009;~&#x2009;58% aggregation bandwidth under secure aggregation and (&#x3b5;&#x2009;=&#x2009;2.0, &#x3b4;&#x2009;=&#x2009;10&#x207b; 5 ) differential privacy. Performance remains stable under Dirichlet non-IID distributions (&#x3b1;&#x2009;=&#x2009;0.1), confirming practical robustness to heterogeneous multi-site data. By coupling unlabelled representation learning with confidentiality-aware collaboration and severity-aware evaluation, the method advances a practical path to scalable, noise-robust, and compliant condition monitoring for Industry 4.0 assets.","url":"https://pubmed.ncbi.nlm.nih.gov/41942543/","authors":["Gopalakrishnan T","Shanmugasundaram N","Simon D","Chan CK","Sonawane C","Dixit S","Bongale A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 6","doi":"10.1038/s41598-026-46550-6","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41941786","name":"FedKGC: Federated Knowledge-Grounded Calibration Framework for Hallucination Mitigation in Medical LLMs.","source":"pubmed","abstract":"The integration of Large Language Models (LLMs) into clinical workflows offers transformative potential for decision support and documentation, yet widespread adoption is currently stalled by the prohibitive risks of hallucination and strict privacy regulations preventing data centralization. To bridge this gap, we propose FedKGC (Federated Knowledge-Grounded Calibration), a novel framework that synergizes privacy-preserving Federated Learning (FL) with retrieval-augmented calibration. FedKGC introduces a dual-phase mitigation strategy: employing a Knowledge-Grounded Loss ($\\mathcal {L}_{KG}$) at the local level to anchor Low-Rank Adaptation (LoRA) fine-tuning to private clinical guidelines, and utilizing differentially private metadata during global aggregation to construct a Global Conflict Map that down-weights contradictory concepts. Evaluation on a non-IID partition of the MIMIC-IV dataset and the MedHallu benchmark demonstrates that FedKGC achieves a 28% reduction in hallucination rates compared to standard federated baselines while strictly adhering to $(\\epsilon, \\delta)$-Differential Privacy guarantees. These results demonstrate that high-fidelity, hallucination-resistant medical AI can be trained collaboratively across siloed healthcare institutions without compromising patient privacy, paving the way for trustworthy distributed medical intelligence.","url":"https://pubmed.ncbi.nlm.nih.gov/41941786/","authors":["Chakraborty C","Othman SB","Guduri M","Frikha MA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr 6","doi":"10.1109/JBHI.2026.3680772","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41940156","name":"Harnessing artificial intelligence to decode the rhizosphere microbiome.","source":"pubmed","abstract":"The rhizosphere microbiome plays crucial roles in plant health by regulating nutrient cycling and enhancing stress resilience. However, due to its complexity, the rhizosphere microbiome is quite challenging to analyze using conventional approaches. Recent advances in artificial intelligence (AI) offer unprecedented opportunities to decipher intricate microbial interactions and leverage their potential for crop breeding. In this review, we assess AI methodologies derived from human microbiome studies that address foundational data challenges, including high dimensionality, compositionality, and sparsity. Next, we examine the uses of these methods for the functional prediction of microbial traits. We then shift our focus to the rhizosphere, exploring AI-driven approaches for predictive modeling of rhizosphere dynamics, integrating plant phenotypic and microbiome data, and designing synthetic microbial communities (SynComs). Finally, we discuss the major challenges and future prospects of using AI in rhizosphere microbiome research. Specifically, we propose an emerging AI paradigm that integrates complementary inside-out (hologenome-based genomic selection) and outside-in (SynCom design) strategies, powered by transformative technologies such as federated learning, large language models, digital twins, and autonomous AI agents. This review underscores the potential for AI to revolutionize microbiome science and crop improvement.","url":"https://pubmed.ncbi.nlm.nih.gov/41940156/","authors":["Ma J","Qiao J","Cao Y","Cheng Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Mar","doi":"10.1016/j.abiote.2025.100005","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"pmid:41939092","name":"The role of artificial intelligence in early detection and risk prediction of ischemic heart disease.","source":"pubmed","abstract":"Ischemic heart disease (IHD) remains a leading cause of global morbidity and mortality, underscoring the need for rapid and accurate diagnostic strategies. Conventional methods, including electrocardiography (ECG), imaging, and biomarkers, are effective but limited by factors such as delayed biomarker elevation, reliance on expert interpretation, and variability across settings. Artificial intelligence (AI) offers new opportunities to enhance early detection and risk prediction by applying machine learning and deep learning to large, complex datasets. In ECG analysis, AI models consistently identify subtle ischemic patterns, including occlusive myocardial infarction, with accuracy that often rivals or exceeds clinicians. In imaging, AI enhances echocardiography, CT, MRI, and nuclear modalities by automating segmentation, strain analysis, and plaque quantification while reducing interpretation time. In biomarkers, AI augments traditional tools like troponins and enables the discovery of novel predictors through multi-omics and wearable data integration, supporting dynamic and individualized risk assessment. Despite promising results, most studies remain retrospective or single-center, with limited validation across diverse populations and healthcare environments. Key barriers include algorithm bias, generalizability, regulatory uncertainty, and limited clinician familiarity. Future progress will depend on multicenter trials, federated learning, explainable AI, and integration into existing workflows. In conclusion, AI has the potential to transform cardiovascular care by enabling earlier and more precise diagnosis of IHD and more personalized risk prediction. However, realizing this potential will require careful validation, equitable implementation, and collaboration across disciplines to ensure safe and effective adoption in clinical practice.","url":"https://pubmed.ncbi.nlm.nih.gov/41939092/","authors":["Azami P","Kojuri J","Razeghian-Jahromi I"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2026 Apr","doi":"10.1097/MS9.0000000000004812","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20680858","name":"The Latent Yield: A Formal Framework for Low-Inhibition Cognitive Workspace Architecture, Privacy-Preserving Multi-Modal Feature Projection, and the Mapping of Human Latent Capacity in Decentralized, Peer-to-Peer Sovereign Data Nodes","source":"datacite","abstract":"This paper presents a theoretical framework and enabling specification for a low-inhibition associative cognitive workspace, designated the Dream Galaxy, implemented within decentralized, peer-to-peer sovereign data nodes constituting a federated personal cognitive universe. The framework introduces four interlocking technical contributions. First, a privacy-preserving multi-modal feature projection layer that maps heterogeneous episodic and physiological inputs, including sub-threshold physiological tokenization of continuous EEG, EMG, and EOG streams, into a unified semantic embedding space without cross-node data exposure, using horizontal federated learning with differential privacy at the gradient layer. Second, an asynchronous local topological optimization engine that applies incremental GPU-accelerated persistent homology over a Vietoris-Rips filtration to the projected feature space, maintaining a continuously valid persistence barcode without centralized computation or synchronous coordination across nodes. Third, a four-zone epistemological classifier that assigns each projected feature node a zone-resident probability distribution across Terra Firma (Zone 1), Incognita (Zone 2), Hic Sunt Dracones (Zone 3), and Whisp (Zone 4), where Zone 4 nodes represent pre-cognitive unknown unknowns validated against a local density criterion distinguishing genuine structural absence from data sparsity. Fourth, the Latent Yield metric: a dimension-weighted rate of topological feature birth in Zones 3 and 4, formalized with explicit pseudocode in the Technical Appendix, quantifying cognitive generative activity below the threshold of conscious expression. The paper further specifies an Asynchronous Convergence Protocol by which topologically equivalent planet structures in independent sovereign nodes are detected under differential privacy without raw data exchange, constituting a knowledge event of elevated epistemic weight requiring bilateral consent before any bridge formation. As a Perspectives and Theoretical Frameworks contribution, this paper provides sufficient enabling detail, including algorithmic pseudocode, parameter specifications, and system architecture, for a person having ordinary skill in machine learning or computational neuroscience to implement the described system. The framework is protected by ten United States provisional patent applications (Nos. 64/074,530; 64/074,608; 64/074,779; 64/075,396; 64/079,009; 64/081,935; 64/084,355; 64/086,036; 64/089,881; 64/089,883), the last two filed June 13, 2026.","url":"https://doi.org/10.5281/zenodo.20680858","authors":["Hoppe, Eric Christian"],"tags":["ow-inhibition cognitive workspace","persistent homology","asynchronous local topological optimization","privacy-preserving multi-modal feature projection","sub-threshold physiological tokenization","decentralized peer-to-peer sovereign data nodes","Dream Galaxy","Latent Yield"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20680858","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21312988","name":"The Latent Yield: A Formal Framework for Low-Inhibition Cognitive Workspace Architecture, Privacy-Preserving Multi-Modal Feature Projection, and the Mapping of Human Latent Capacity in Decentralized, Peer-to-Peer Sovereign Data Nodes","source":"datacite","abstract":"This paper presents a theoretical framework and enabling specification for a low-inhibition associative cognitive workspace, designated the Dream Galaxy, implemented within decentralized, peer-to-peer sovereign data nodes constituting a federated personal cognitive universe. The framework introduces four interlocking technical contributions. First, a privacy-preserving multi-modal feature projection layer that maps heterogeneous episodic and physiological inputs, including sub-threshold physiological tokenization of continuous EEG, EMG, and EOG streams, into a unified semantic embedding space without cross-node data exposure, using horizontal federated learning with differential privacy at the gradient layer. Second, an asynchronous local topological optimization engine that applies incremental GPU-accelerated persistent homology over a Vietoris-Rips filtration to the projected feature space, maintaining a continuously valid persistence barcode without centralized computation or synchronous coordination across nodes. Third, a four-zone epistemological classifier that assigns each projected feature node a zone-resident probability distribution across Terra Firma (Zone 1), Incognita (Zone 2), Hic Sunt Dracones (Zone 3), and Whisp (Zone 4), where Zone 4 nodes represent pre-cognitive unknown unknowns validated against a local density criterion distinguishing genuine structural absence from data sparsity. Fourth, the Latent Yield metric: a dimension-weighted rate of topological feature birth in Zones 3 and 4, formalized with explicit pseudocode in the Technical Appendix, quantifying cognitive generative activity below the threshold of conscious expression. The paper further specifies an Asynchronous Convergence Protocol by which topologically equivalent planet structures in independent sovereign nodes are detected under differential privacy without raw data exchange, constituting a knowledge event of elevated epistemic weight requiring bilateral consent before any bridge formation. As a Perspectives and Theoretical Frameworks contribution, this paper provides sufficient enabling detail, including algorithmic pseudocode, parameter specifications, and system architecture, for a person having ordinary skill in machine learning or computational neuroscience to implement the described system. The framework is protected by ten United States provisional patent applications (Nos. 64/074,530; 64/074,608; 64/074,779; 64/075,396; 64/079,009; 64/081,935; 64/084,355; 64/086,036; 64/089,881; 64/089,883), the last two filed June 13, 2026.","url":"https://doi.org/10.5281/zenodo.21312988","authors":["Hoppe, Eric Christian"],"tags":["ow-inhibition cognitive workspace","persistent homology","asynchronous local topological optimization","privacy-preserving multi-modal feature projection","sub-threshold physiological tokenization","decentralized peer-to-peer sovereign data nodes","Dream Galaxy","Latent Yield"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21312988","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21412589","name":"Industrialisation des thérapies décentralisées : Biosécurité de l'oncologie personnalisée à ARNm (Déclaration d'antériorité / Prior Art)","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de Gemini 3 Raisonnement, est publié sous Licence Apache 2.0. Il constitue une publication défensive (antériorité) volontaire et entre de ce fait dans l’état de la technique au sens des législations applicables : (EPC Art. 54(2); French IPC Art. L 611-11; cf. 35 U.S.C. §102(a)). Ce rapport technique décrit de manière exhaustive une plateforme décentralisée de thérapie génique. Il présente 32 inventions actionnables incluant le séquençage au point de soin, la synthèse microfluidique d'ARNm, la formulation de nanoparticules lipidiques (LNP), le DRM biologique cryptographique sur blockchain et la gestion verte des effluents. Chaque proposition est documentée de façon à permettre sa reproduction, associée à des codes CIB/CPC, et horodatée afin d'empêcher tout blocage par brevet ultérieur. Abstract ENThis document, produced with the assistance of Gemini 3 Reasoning, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: (art. L 611-11 CPI / art. 54(2) CBE). This publication describes an exhaustive, end-to-end decentralized genetic therapy platform. It discloses 32 enabling inventions spanning point-of-care sequencing, automated microfluidic synthesis, lipid nanoparticle (LNP) formulation, cryptographic biological DRM via TPM 2.0/blockchain, and green disposal units. Each innovation is described with detailed specifications to enable reproduction, designated with plausible IPC/CPC codes, and timestamped (RFC 3161) to preempt subsequent offensive patent filings by third parties globally, securing a shared open-access commons for personalized mRNA medicine. Timestamp: 2026-07-17T11:05:18ZSHA-256: 8abdfe6dd0004ba03e9f5e7f2ecfed8381348962945195fed16aba329832440d Liste des innovations & classification (IPC ; CPC)#1 Integrated microfluidic nanopore sequencer (IPC B01L 3/00 ; CPC B01L 3/5027)#2 Multiplexed MHC haplotyping kit (IPC C12Q 1/6886 ; CPC C12Q 1/6886)#3 Hybrid cloud-edge MHC docking pipeline (IPC G16H 50/20 ; CPC G16H 50/20)#4 Closed-loop mRNA dosing scheduler (IPC G16H 20/17 ; CPC G16H 20/17)#5 Optimized SM-102 LNP formulation (IPC A61K 9/127 ; CPC A61K 9/1272)#6 Automated microfluidic chip clean-in-place (IPC B01L 99/00 ; CPC B01L 2200/10)#7 LNP biodistribution imaging reconstruction (IPC A61B 5/00 ; CPC A61B 5/4848)#8 Dissolvable microneedle patch for LNPs (IPC A61K 9/00 ; CPC A61K 9/0021)#9 Synchronized mRNA and anti-PD-1 co-therapy (IPC A61K 39/00 ; CPC A61K 39/0011)#10 Privacy-preserving federated antigen learning (IPC G06F 21/62 ; CPC G06F 21/6245)#11 Cryptographic DRM biological synthesizer (IPC G06F 21/44 ; CPC G06F 21/44)#12 Smart IoT cold chain container (IPC F25D 29/00 ; CPC F25D 29/003)#13 Cloud-based microfluidic calibration service (IPC G05B 19/418 ; CPC G05B 19/4183)#14 Segmented Poly(A) expression cassette (IPC C12N 15/67 ; CPC C12N 15/67)#15 On-chip chromatographic dsRNA purification (IPC B01D 15/34 ; CPC B01D 15/345)#16 Multi-species dynamic codon optimizer (IPC G16B 25/10 ; CPC G16B 25/10)#17 Smart sensor-enabled reagent cartridge (IPC G01N 27/02 ; CPC G01N 27/02)#18 LAMP1 lysosomal-targeting mRNA chimera (IPC C12N 15/62 ; CPC C12N 15/62)#19 Automated lipid effluent inactivation module (IPC B01D 21/00 ; CPC C02F 1/02)#20 Function-based biosecurity screening AI (IPC G16B 40/00 ; CPC G06N 3/08)#21 Direct RNA-pore sequencing for haplotyping (IPC C12Q 1/6869 ; CPC C12Q 1/6869)#22 Enzymatic long DNA template assembly (IPC C12N 15/10 ; CPC C12N 15/10)#23 Microfluidic full-substitution IVT bioreactor (IPC C12P 19/34 ; CPC C12P 19/34)#24 Asymmetric staggered herringbone mixer (IPC B01F 33/30 ; CPC B01F 33/30)#25 Refractometer-controlled active micro-dialyser (IPC B01D 61/14 ; CPC B01D 61/14)#26 Edge-distributed semantic genomic cache (IPC G16B 50/00 ; CPC G16B 50/00)#27 Unified memory CUDA orchestration for AF3 (IPC G06F 12/02 ; CPC G06F 12/02)#28 ","url":"https://doi.org/10.5281/zenodo.21412589","authors":["Pillet, Xavier"],"tags":["B01L 3/00","B01L 3/5027","C12Q 1/6886","G16H 50/20","G16H 20/17","A61K 9/127","A61K 9/1272","B01L 99/00"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21412589","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.21400987","name":"Industrialisation des thérapies décentralisées : Biosécurité de l'oncologie personnalisée à ARNm (Déclaration d'antériorité / Prior Art)","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de Gemini 3 Raisonnement, est publié sous Licence Apache 2.0. Il constitue une publication défensive (antériorité) volontaire et entre de ce fait dans l’état de la technique au sens des législations applicables : (EPC Art. 54(2); French IPC Art. L 611-11; cf. 35 U.S.C. §102(a)). Ce rapport technique décrit de manière exhaustive une plateforme décentralisée de thérapie génique. Il présente 32 inventions actionnables incluant le séquençage au point de soin, la synthèse microfluidique d'ARNm, la formulation de nanoparticules lipidiques (LNP), le DRM biologique cryptographique sur blockchain et la gestion verte des effluents. Chaque proposition est documentée de façon à permettre sa reproduction, associée à des codes CIB/CPC, et horodatée afin d'empêcher tout blocage par brevet ultérieur. Abstract ENThis document, produced with the assistance of Gemini 3 Reasoning, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: (art. L 611-11 CPI / art. 54(2) CBE). This publication describes an exhaustive, end-to-end decentralized genetic therapy platform. It discloses 32 enabling inventions spanning point-of-care sequencing, automated microfluidic synthesis, lipid nanoparticle (LNP) formulation, cryptographic biological DRM via TPM 2.0/blockchain, and green disposal units. Each innovation is described with detailed specifications to enable reproduction, designated with plausible IPC/CPC codes, and timestamped (RFC 3161) to preempt subsequent offensive patent filings by third parties globally, securing a shared open-access commons for personalized mRNA medicine. Timestamp: 2026-07-17T11:05:18ZSHA-256: 8abdfe6dd0004ba03e9f5e7f2ecfed8381348962945195fed16aba329832440d Liste des innovations & classification (IPC ; CPC)#1 Integrated microfluidic nanopore sequencer (IPC B01L 3/00 ; CPC B01L 3/5027)#2 Multiplexed MHC haplotyping kit (IPC C12Q 1/6886 ; CPC C12Q 1/6886)#3 Hybrid cloud-edge MHC docking pipeline (IPC G16H 50/20 ; CPC G16H 50/20)#4 Closed-loop mRNA dosing scheduler (IPC G16H 20/17 ; CPC G16H 20/17)#5 Optimized SM-102 LNP formulation (IPC A61K 9/127 ; CPC A61K 9/1272)#6 Automated microfluidic chip clean-in-place (IPC B01L 99/00 ; CPC B01L 2200/10)#7 LNP biodistribution imaging reconstruction (IPC A61B 5/00 ; CPC A61B 5/4848)#8 Dissolvable microneedle patch for LNPs (IPC A61K 9/00 ; CPC A61K 9/0021)#9 Synchronized mRNA and anti-PD-1 co-therapy (IPC A61K 39/00 ; CPC A61K 39/0011)#10 Privacy-preserving federated antigen learning (IPC G06F 21/62 ; CPC G06F 21/6245)#11 Cryptographic DRM biological synthesizer (IPC G06F 21/44 ; CPC G06F 21/44)#12 Smart IoT cold chain container (IPC F25D 29/00 ; CPC F25D 29/003)#13 Cloud-based microfluidic calibration service (IPC G05B 19/418 ; CPC G05B 19/4183)#14 Segmented Poly(A) expression cassette (IPC C12N 15/67 ; CPC C12N 15/67)#15 On-chip chromatographic dsRNA purification (IPC B01D 15/34 ; CPC B01D 15/345)#16 Multi-species dynamic codon optimizer (IPC G16B 25/10 ; CPC G16B 25/10)#17 Smart sensor-enabled reagent cartridge (IPC G01N 27/02 ; CPC G01N 27/02)#18 LAMP1 lysosomal-targeting mRNA chimera (IPC C12N 15/62 ; CPC C12N 15/62)#19 Automated lipid effluent inactivation module (IPC B01D 21/00 ; CPC C02F 1/02)#20 Function-based biosecurity screening AI (IPC G16B 40/00 ; CPC G06N 3/08)#21 Direct RNA-pore sequencing for haplotyping (IPC C12Q 1/6869 ; CPC C12Q 1/6869)#22 Enzymatic long DNA template assembly (IPC C12N 15/10 ; CPC C12N 15/10)#23 Microfluidic full-substitution IVT bioreactor (IPC C12P 19/34 ; CPC C12P 19/34)#24 Asymmetric staggered herringbone mixer (IPC B01F 33/30 ; CPC B01F 33/30)#25 Refractometer-controlled active micro-dialyser (IPC B01D 61/14 ; CPC B01D 61/14)#26 Edge-distributed semantic genomic cache (IPC G16B 50/00 ; CPC G16B 50/00)#27 Unified memory CUDA orchestration for AF3 (IPC G06F 12/02 ; CPC G06F 12/02)#28 ","url":"https://doi.org/10.5281/zenodo.21400987","authors":["Pillet, Xavier"],"tags":["B01L 3/00","B01L 3/5027","C12Q 1/6886","G16H 50/20","G16H 20/17","A61K 9/127","A61K 9/1272","B01L 99/00"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21400987","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.21409456","name":"Designing the Future of Secure Intelligence: A Vision for AI-Enabled Cyber-Resilient Ecosystems","source":"datacite","abstract":"This vision paper presents a consolidated perspective developed through six European initiatives—VIGILANCE, CyberAId, ENFORCE, ACT4FOOD, CTIS4NIS, and 5G-TACTIC—working together through the Cyber-Resilience Synergy Network (CRSN). It examines how Artificial Intelligence, cybersecurity-by-design, autonomous decision-making, proactive threat detection, Zero Trust approaches, secure data sharing, and collaborative intelligence can support the development of trustworthy and cyber-resilient next-generation digital ecosystems. Drawing on shared methodologies, technological prototypes, pilot deployments, and validation activities, the paper proposes a common roadmap for integrating AI-driven automation, proactive cyber defence, federated and collaborative AI, secure and interoperable data spaces, and application-driven intelligence into future large-scale digital infrastructures. The discussion spans critical infrastructures, telecommunications, financial services, industrial environments, and food supply chains. The paper also considers trustworthy AI, human oversight, regulatory alignment, scalability, distributed learning, digital sovereignty, capacity building, and emerging research challenges. It concludes by presenting a common architectural vision and identifying research and policy directions for strengthening European digital autonomy and societal resilience. Version note: This Zenodo record contains the preprint version of the publication. Published version: The final publication appears in Artificial Intelligence Applications and Innovations: AIAI 2026 International Workshops, IFIP Advances in Information and Communication Technology, Volume 796, pp. 158–178, Springer Nature Switzerland AG, 2027.DOI: 10.1007/978-3-032-30507-7_10","url":"https://doi.org/10.5281/zenodo.21409456","authors":["Gizelis, Christos A.","Marinakis, Achilleas","Papamokos, Stavros","Kefalogiannis, Michalis","Mesogiti, Ioanna","Liberopoulos, Giorgos","Theodoropoulou, Elina","Poulimenou, Maria","Kachrimani, Louiza","Tzanakaki, Anna","Anastassopoulos, Markos","Azrak, Thomas","Dimitrakopoulou, Maria-Eleni","Kokkinos, Odysseas","Bianconi, Luca","Clerici, Giulia"],"tags":["Artificial Intelligence","Cybersecurity","Cyber resilience","Trustworthy AI","Cyber-Resilience Synergy Network","CRSN"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21409456","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21409455","name":"Designing the Future of Secure Intelligence: A Vision for AI-Enabled Cyber-Resilient Ecosystems","source":"datacite","abstract":"This vision paper presents a consolidated perspective developed through six European initiatives—VIGILANCE, CyberAId, ENFORCE, ACT4FOOD, CTIS4NIS, and 5G-TACTIC—working together through the Cyber-Resilience Synergy Network (CRSN). It examines how Artificial Intelligence, cybersecurity-by-design, autonomous decision-making, proactive threat detection, Zero Trust approaches, secure data sharing, and collaborative intelligence can support the development of trustworthy and cyber-resilient next-generation digital ecosystems. Drawing on shared methodologies, technological prototypes, pilot deployments, and validation activities, the paper proposes a common roadmap for integrating AI-driven automation, proactive cyber defence, federated and collaborative AI, secure and interoperable data spaces, and application-driven intelligence into future large-scale digital infrastructures. The discussion spans critical infrastructures, telecommunications, financial services, industrial environments, and food supply chains. The paper also considers trustworthy AI, human oversight, regulatory alignment, scalability, distributed learning, digital sovereignty, capacity building, and emerging research challenges. It concludes by presenting a common architectural vision and identifying research and policy directions for strengthening European digital autonomy and societal resilience. Version note: This Zenodo record contains the preprint version of the publication. Published version: The final publication appears in Artificial Intelligence Applications and Innovations: AIAI 2026 International Workshops, IFIP Advances in Information and Communication Technology, Volume 796, pp. 158–178, Springer Nature Switzerland AG, 2027.DOI: 10.1007/978-3-032-30507-7_10","url":"https://doi.org/10.5281/zenodo.21409455","authors":["Gizelis, Christos A.","Marinakis, Achilleas","Papamokos, Stavros","Kefalogiannis, Michalis","Mesogiti, Ioanna","Liberopoulos, Giorgos","Theodoropoulou, Elina","Poulimenou, Maria","Kachrimani, Louiza","Tzanakaki, Anna","Anastassopoulos, Markos","Azrak, Thomas","Dimitrakopoulou, Maria-Eleni","Kokkinos, Odysseas","Bianconi, Luca","Clerici, Giulia"],"tags":["Artificial Intelligence","Cybersecurity","Cyber resilience","Trustworthy AI","Cyber-Resilience Synergy Network","CRSN"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21409455","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2607.15052","name":"NFSA: Non-Forward Secure Aggregation with One Server via Two Layer Secret Sharing","source":"datacite","abstract":"Federated Learning (FL) enables collaborative model training while preserving privacy by keeping data local. However, the risk of sensitive data leakage through model updates necessitates the use of secure aggregation protocols. Existing server-based secure aggregation protocols typically require the server to forward sensitive data shared between users, which increases communication overhead and introduces potential security risks. In this work, we propose a novel secure aggregation protocol based on two-layer secret sharing to address these issues. By combining Shamir's Secret Sharing with 2-out-of-2 additive secret sharing using a Pseudo-Random Function (PRF), our protocol eliminates direct communication between users, thereby removing the need for the server to forward data. We further extend the protocol with Key-homomorphic PRF (KhPRF) to support high-dimensional data aggregation and apply it to FL, enabling one-shot secure aggregation with a single server and no intermediary data forwarding. To reduce user overhead, we design a new encoding method based on the Chinese Remainder Theorem for the almost KhPRF-based mask, reducing the number of KhPRF calls and mitigating the model update expansion issue after masking. Experimental results show that our scheme significantly outperforms existing methods in terms of auxiliary node overhead. For instance, when the number of users is 100, our scheme improves communication efficiency by nearly 100 times and reduces computational overhead by approximately 17\\%. Moreover, user computation time can be reduced by 51\\% to 75\\% when the input length is $2^{18}$.","url":"https://doi.org/10.48550/arxiv.2607.15052","authors":["Zhou, Yufei"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.15052","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2607.14489","name":"EdgeFaaS: A Function-based Framework for Edge Computing","source":"datacite","abstract":"Edge computing brings unique challenges as the resources on the edge are highly diverse in capabilities and capacities, and highly distributed across many users and the physical world. Existing distributed computing frameworks cannot adequately handle this level of heterogeneity and distribution. This paper proposes EdgeFaaS, a novel function-based edge computing framework to enable edge applications to effectively utilize heterogeneous resources distributed across the Internet of Things (IoT), edge, and cloud for computing. It proposes function virtualization and storage virtualization to abstract distributed and heterogeneous physical resources and provides consistent virtual interfaces for deploying and executing functions and storing and accessing data. EdgeFaaS provides comprehensive support to diverse edge computing workflows, and at the same time allows users to flexibly adjust the configurations and explore various important tradeoffs. To demonstrate its usability, the paper also presents the implementation and evaluation of three representative workflows on EdgeFaaS for video analytics, federated learning, and audio classification, on a real testbed of 100+ geographically distributed IoT devices, edge servers, and cloud services. EdgeFaaS allows users to flexibly explore the deployment configurations of these workflows over distributed and heterogeneous resources. For example, users can easily vary the function placement of the video processing pipeline across IoT, edge, and cloud resources and study the tradeoff between computation and communication costs; users can also flexibly adjust the cluster count and size in the hierarchical federated learning system and explore the tradeoff between training accuracy and speed.","url":"https://doi.org/10.48550/arxiv.2607.14489","authors":["Vadnere, Neha","Wang, Yu-Ting","Chen, Yitao","Sadesh, Sreehari","Zhao, Ming"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.14489","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21400988","name":"Industrialisation des thérapies décentralisées : Biosécurité de l'oncologie personnalisée à ARNm (Déclaration d'antériorité / Prior Art)","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de Gemini 3 Raisonnement, est publié sous Licence Apache 2.0. Il constitue une publication défensive (antériorité) volontaire et entre de ce fait dans l’état de la technique au sens des législations applicables : (EPC Art. 54(2); French IPC Art. L 611-11; cf. 35 U.S.C. §102(a)). Ce rapport technique décrit de manière exhaustive une plateforme décentralisée de thérapie génique. Il présente 32 inventions actionnables incluant le séquençage au point de soin, la synthèse microfluidique d'ARNm, la formulation de nanoparticules lipidiques (LNP), le DRM biologique cryptographique sur blockchain et la gestion verte des effluents. Chaque proposition est documentée de façon à permettre sa reproduction, associée à des codes CIB/CPC, et horodatée afin d'empêcher tout blocage par brevet ultérieur. Abstract ENThis document, produced with the assistance of Gemini 3 Reasoning, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: (art. L 611-11 CPI / art. 54(2) CBE). This publication describes an exhaustive, end-to-end decentralized genetic therapy platform. It discloses 32 enabling inventions spanning point-of-care sequencing, automated microfluidic synthesis, lipid nanoparticle (LNP) formulation, cryptographic biological DRM via TPM 2.0/blockchain, and green disposal units. Each innovation is described with detailed specifications to enable reproduction, designated with plausible IPC/CPC codes, and timestamped (RFC 3161) to preempt subsequent offensive patent filings by third parties globally, securing a shared open-access commons for personalized mRNA medicine. Timestamp: 2026-07-16T19:23:26ZSHA-256: 83c64589fce322d8c32e5ff9fbcf93506c38f5e9d0b5eff49add04a886be9706 Liste des innovations & classification (IPC ; CPC)#1 Integrated microfluidic nanopore sequencer (IPC B01L 3/00 ; CPC B01L 3/5027)#2 Multiplexed MHC haplotyping kit (IPC C12Q 1/6886 ; CPC C12Q 1/6886)#3 Hybrid cloud-edge MHC docking pipeline (IPC G16H 50/20 ; CPC G16H 50/20)#4 Closed-loop mRNA dosing scheduler (IPC G16H 20/17 ; CPC G16H 20/17)#5 Optimized SM-102 LNP formulation (IPC A61K 9/127 ; CPC A61K 9/1272)#6 Automated microfluidic chip clean-in-place (IPC B01L 99/00 ; CPC B01L 2200/10)#7 LNP biodistribution imaging reconstruction (IPC A61B 5/00 ; CPC A61B 5/4848)#8 Dissolvable microneedle patch for LNPs (IPC A61K 9/00 ; CPC A61K 9/0021)#9 Synchronized mRNA and anti-PD-1 co-therapy (IPC A61K 39/00 ; CPC A61K 39/0011)#10 Privacy-preserving federated antigen learning (IPC G06F 21/62 ; CPC G06F 21/6245)#11 Cryptographic DRM biological synthesizer (IPC G06F 21/44 ; CPC G06F 21/44)#12 Smart IoT cold chain container (IPC F25D 29/00 ; CPC F25D 29/003)#13 Cloud-based microfluidic calibration service (IPC G05B 19/418 ; CPC G05B 19/4183)#14 Segmented Poly(A) expression cassette (IPC C12N 15/67 ; CPC C12N 15/67)#15 On-chip chromatographic dsRNA purification (IPC B01D 15/34 ; CPC B01D 15/345)#16 Multi-species dynamic codon optimizer (IPC G16B 25/10 ; CPC G16B 25/10)#17 Smart sensor-enabled reagent cartridge (IPC G01N 27/02 ; CPC G01N 27/02)#18 LAMP1 lysosomal-targeting mRNA chimera (IPC C12N 15/62 ; CPC C12N 15/62)#19 Automated lipid effluent inactivation module (IPC B01D 21/00 ; CPC C02F 1/02)#20 Function-based biosecurity screening AI (IPC G16B 40/00 ; CPC G06N 3/08)#21 Direct RNA-pore sequencing for haplotyping (IPC C12Q 1/6869 ; CPC C12Q 1/6869)#22 Enzymatic long DNA template assembly (IPC C12N 15/10 ; CPC C12N 15/10)#23 Microfluidic full-substitution IVT bioreactor (IPC C12P 19/34 ; CPC C12P 19/34)#24 Asymmetric staggered herringbone mixer (IPC B01F 33/30 ; CPC B01F 33/30)#25 Refractometer-controlled active micro-dialyser (IPC B01D 61/14 ; CPC B01D 61/14)#26 Edge-distributed semantic genomic cache (IPC G16B 50/00 ; CPC G16B 50/00)#27 Unified memory CUDA orchestration for AF3 (IPC G06F 12/02 ; CPC G06F 12/02)#28 ","url":"https://doi.org/10.5281/zenodo.21400988","authors":["Pillet, Xavier"],"tags":["B01L 3/00","B01L 3/5027","C12Q 1/6886","G16H 50/20","G16H 20/17","A61K 9/127","A61K 9/1272","B01L 99/00"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21400988","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2607.13754","name":"PriEval-Protect: A Unified Framework for Privacy Evaluation and Protection in Healthcare Systems","source":"datacite","abstract":"Safeguarding patient privacy while enabling meaningful healthcare data use remains critical under GDPR and HIPAA. Existing compliance methods are manual, error-prone, and separate policy audits from data-level assessments. This paper presents PriEval-Protect, a two-phase framework for unified privacy risk evaluation and mitigation. The evaluation phase combines regulatory compliance scoring using a fine-tuned legal LLM with RAG, and technical analysis via encryption type, data architecture, and metrics including similarity, uncertainty, adversary success, and information gain/loss. A composite risk score uses weighted aggregation via Analytic Hierarchy Process. The protection phase recommends countermeasures including federated learning and differential privacy based on assessed risk. Results on hospital documents and datasets demonstrate regulation-aligned, explainable assessments, bridging legal conformance and data-level risk analysis.","url":"https://doi.org/10.48550/arxiv.2607.13754","authors":["Chebil, Ilef","Hadj, Asma El","Yousfi, Souheib","Hedhili, Aroua","Sliman, Layth"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.13754","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2607.13386","name":"FM$^2$: Unified Federated Foundation Models for Heterogeneous Multimodal Medical Imaging","source":"datacite","abstract":"Building foundation models for medical imaging requires pooling data across institutions, yet privacy regulations prohibit centralized aggregation. Existing Federated Foundation Models either fine-tune natural-image models with poor medical-domain transfer, or train from scratch within a single modality, lacking the flexibility to unify tasks. We identify an under-explored challenge, Imaging Modality Heterogeneity, where clients operate under two structural regimes: Overlapped (shared modalities with heterogeneous label distributions) and Non-overlapped (fully disjoint modalities per client). We propose FM$^2$, a unified framework that trains the core backbone from scratch to preserve medical domain fidelity while optionally incorporating biomedical pretrained encoders for vision-language alignment. FM$^2$ equips each client with dual Mixture-of-Experts modules (a Class-wise MoE for personalized category knowledge and a Domain-wise MoE for shared cross-modality representations), coupled with a Heterogeneous Modality Alignment (HMA) regularizer that explicitly aligns modality-specific expert parameters, admitting provable $O(1/\\sqrt{T})$ convergence and generalization guarantees. FM$^2$ further incorporates Caption-Enhanced Learning (CEL), where locally retained GPT-4o-generated captions serve as a textual semantic bridge enabling representation transfer across clients with disjoint modalities, and demonstrates extensibility to Federated Medical VQA. Experiments on our MIMH benchmark (classification and CEL) and real-world medical VQA datasets confirm consistent superiority over state-of-the-art federated baselines and strong out-of-modality generalization across all three tasks.","url":"https://doi.org/10.48550/arxiv.2607.13386","authors":["Chen, Shengchao","Shu, Ting"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.13386","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2603.05774","name":"First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints","source":"datacite","abstract":"This paper addresses the distributed stochastic minimax optimization problem subject to stochastic constraints. We propose a novel first-order Softmax-Weighted Switching Gradient method tailored for federated learning. Under full client participation, our algorithm achieves the standard $\\tilde{\\mathcal{O}}(ε^{-4})$ oracle complexity to satisfy a unified bound $ε$ for both the optimality gap and feasibility tolerance. We extend our theoretical analysis to the practical partial participation regime by quantifying client sampling noise through a stochastic superiority assumption. Furthermore, by relaxing standard boundedness assumptions on the objective functions, we establish a strictly tighter lower bound for the softmax hyperparameter. We provide a unified error decomposition and establish a sharp $\\mathcal{O}(\\log\\frac{1}δ)$ high-probability convergence guarantee. Ultimately, our framework demonstrates that a single-loop primal-only switching mechanism provides a stable alternative for optimizing worst-case client performance, effectively bypassing the hyperparameter sensitivity and convergence oscillations often encountered in traditional primal-dual or penalty-based approaches. We verify the efficacy of our algorithm via experiment on the Neyman-Pearson (NP) classification, fair classification, and federated safe reinforcement learning tasks.","url":"https://doi.org/10.48550/arxiv.2603.05774","authors":["Luo, Zhankun","Upadhyay, Antesh","Moon, Sang Bin","Hashemi, Abolfazl"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.05774","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21306819","name":"Multi-Omics Machine Learning Models for Precision Cancer Prognostication","source":"datacite","abstract":"Background: Cancer remains a leading cause of mortality worldwide, necessitating accurate prognostic tools to guide clinical decision-making and personalized treatment strategies. Traditional prognostic models based on clinicopathological features demonstrate limited predictive accuracy due to the inherent molecular heterogeneity of malignancies.This narrative review examines recent advances in multi-omics machine learning approaches for cancer prognosis prediction, synthesizing evidence from 2020–2026 on integration strategies, algorithmic methodologies, and clinical applications across major cancer types.Deep learning enables the analysis of high-dimensional datasets and the discovery of novel disease mechanisms and biomarkers, contributing to improved patient treatment and management. Multi-omics integration incorporating genomics, transcriptomics, epigenomics, proteomics, and metabolomics consistently outperforms single-omics approaches. DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches, robustly predicts patient survival subtypes using multi-omics data and yields significantly better risk-stratification than other multi-omics integration methods. Graph neural networks, transformer-based architectures, and attention mechanisms have emerged as powerful tools for capturing complex inter-omics relationships. CATfusion achieves superior predictive performance over traditional and unimodal models, as demonstrated by enhanced C-index and survival area under the curve scores. These models demonstrate substantial improvements in survival prediction, recurrence risk stratification, and treatment response assessment across breast, lung, colorectal, liver, and hematological malignancies. Meta-learning, spatial multi-omics, and federated learning are pivotal directions for realizing the clinical translation of next-generation precision oncology. Addressing challenges in data harmonization, model interpretability, and prospective validation remains essential for clinical implementation.","url":"https://doi.org/10.5281/zenodo.21306819","authors":["Vikram Patel*1, Ananya George2, Manish Khanna3, Shatrughna Nagrik4"],"tags":["Multi-omics, machine learning, deep learning, cancer prognosis, precision oncology, data integration, survival prediction."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21306819","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21306818","name":"Multi-Omics Machine Learning Models for Precision Cancer Prognostication","source":"datacite","abstract":"Background: Cancer remains a leading cause of mortality worldwide, necessitating accurate prognostic tools to guide clinical decision-making and personalized treatment strategies. Traditional prognostic models based on clinicopathological features demonstrate limited predictive accuracy due to the inherent molecular heterogeneity of malignancies.This narrative review examines recent advances in multi-omics machine learning approaches for cancer prognosis prediction, synthesizing evidence from 2020–2026 on integration strategies, algorithmic methodologies, and clinical applications across major cancer types.Deep learning enables the analysis of high-dimensional datasets and the discovery of novel disease mechanisms and biomarkers, contributing to improved patient treatment and management. Multi-omics integration incorporating genomics, transcriptomics, epigenomics, proteomics, and metabolomics consistently outperforms single-omics approaches. DeepProg, a novel ensemble framework of deep-learning and machine-learning approaches, robustly predicts patient survival subtypes using multi-omics data and yields significantly better risk-stratification than other multi-omics integration methods. Graph neural networks, transformer-based architectures, and attention mechanisms have emerged as powerful tools for capturing complex inter-omics relationships. CATfusion achieves superior predictive performance over traditional and unimodal models, as demonstrated by enhanced C-index and survival area under the curve scores. These models demonstrate substantial improvements in survival prediction, recurrence risk stratification, and treatment response assessment across breast, lung, colorectal, liver, and hematological malignancies. Meta-learning, spatial multi-omics, and federated learning are pivotal directions for realizing the clinical translation of next-generation precision oncology. Addressing challenges in data harmonization, model interpretability, and prospective validation remains essential for clinical implementation.","url":"https://doi.org/10.5281/zenodo.21306818","authors":["Vikram Patel*1, Ananya George2, Manish Khanna3, Shatrughna Nagrik4"],"tags":["Multi-omics, machine learning, deep learning, cancer prognosis, precision oncology, data integration, survival prediction."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21306818","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2607.08368","name":"FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning","source":"datacite","abstract":"With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning. Although one-shot federated learning alleviates this problem by minimizing communication rounds, existing iterative fine-tuning or knowledge distillation methods still face challenges such as high server-side computational costs and hyperparameter sensitivity. Analytical federated learning achieves efficient gradientfree aggregation using least-squares closed-form solutions, but in environments with non-independent and identically distributed data, its static feature assumptions fail, leading to feature manifold misalignment and severely impairing model performance. To address this contradiction, this paper proposes the FedOPAL framework. This framework adapts the visual prompts as feature rectifiers, actively correcting the feature distribution of heterogeneous data to a linearly separable space by applying local proximal constraints, thereby satisfying the theoretical assumptions of analytical federated learning. Experimental results show that FedOPAL not only significantly outperforms the original analytical methods on several benchmarks, but also achieves accuracy comparable to state-of-the-art iterative methods while maintaining zero server-side training costs, providing a new engineering paradigm for efficient collaboration of large models on the edge.","url":"https://doi.org/10.48550/arxiv.2607.08368","authors":["Qiu, Lingyu","Annunziata, Daniela","Izzo, Stefano","Giampaolo, Fabio","Piccialli, Francesco"],"tags":["Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.08368","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21284436","name":"Decoding Tears: A Review of the Biology of Crying and Machine Learning Approaches to Infant Cry Classification","source":"datacite","abstract":"Crying spans at least three distinct physiological pathways in adults and serves as the primary communication channel for pre-verbal infants. Although the physiology of human crying and machine learning-based infant cry analysis have each been studied extensively, few reviews integrate these complementary perspectives. This paper addresses that gap. We summarise the basal, reflex, and emotional tear pathways and their distinct biochemical signatures, then survey open-source infant cry classification systems, with particular attention to models and datasets hosted on Hugging Face. We describe the acoustic feature engineering pipeline underlying nearly all published cry classification systems — Mel-Frequency Cepstral Coefficients (MFCCs), mel-spectrograms, and time-domain descriptors such as zero-crossing rate and RMS energy — and compare reported classification accuracies across classical machine learning and deep learning approaches, including recent transformer-based and federated learning architectures published through early 2026. We find that well-engineered classical feature pipelines remain competitive with, and in some reported cases exceed, deep learning approaches on this task, a finding with practical implications for small-data audio classification problems more broadly. We conclude with a discussion of open research gaps, including cross-cultural data coverage, explainability, and on-device deployment.","url":"https://doi.org/10.5281/zenodo.21284436","authors":["Pathan, Mohammed Akram Khan"],"tags":["Infant Cry Classification","Machine Learning","MFCC","Emotional Tears","Audio Signal Processing","Deep Learning","Bioacoustics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21284436","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21272272","name":"Decoding Tears: A Review of the Biology of Crying and Machine Learning Approaches to Infant Cry Classification","source":"datacite","abstract":"Crying spans at least three distinct physiological pathways in adults and serves as the primary communication channel for pre-verbal infants. Although the physiology of human crying and machine learning-based infant cry analysis have each been studied extensively, few reviews integrate these complementary perspectives. This paper addresses that gap. We summarise the basal, reflex, and emotional tear pathways and their distinct biochemical signatures, then survey open-source infant cry classification systems, with particular attention to models and datasets hosted on Hugging Face. We describe the acoustic feature engineering pipeline underlying nearly all published cry classification systems — Mel-Frequency Cepstral Coefficients (MFCCs), mel-spectrograms, and time-domain descriptors such as zero-crossing rate and RMS energy — and compare reported classification accuracies across classical machine learning and deep learning approaches, including recent transformer-based and federated learning architectures published through early 2026. We find that well-engineered classical feature pipelines remain competitive with, and in some reported cases exceed, deep learning approaches on this task, a finding with practical implications for small-data audio classification problems more broadly. We conclude with a discussion of open research gaps, including cross-cultural data coverage, explainability, and on-device deployment.","url":"https://doi.org/10.5281/zenodo.21272272","authors":["Pathan, Mohammed Akram Khan"],"tags":["Infant Cry Classification","Machine Learning","MFCC","Emotional Tears","Audio Signal Processing","Deep Learning","Bioacoustics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21272272","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21272273","name":"Decoding Tears: A Review of the Biology of Crying and Machine Learning Approaches to Infant Cry Classification","source":"datacite","abstract":"Crying spans at least three distinct physiological pathways in adults and serves as the primary communication channel for pre-verbal infants. Although the physiology of human crying and machine learning-based infant cry analysis have each been studied extensively, few reviews integrate these complementary perspectives. This paper addresses that gap. We summarise the basal, reflex, and emotional tear pathways and their distinct biochemical signatures, then survey open-source infant cry classification systems, with particular attention to models and datasets hosted on Hugging Face. We describe the acoustic feature engineering pipeline underlying nearly all published cry classification systems — Mel-Frequency Cepstral Coefficients (MFCCs), mel-spectrograms, and time-domain descriptors such as zero-crossing rate and RMS energy — and compare reported classification accuracies across classical machine learning and deep learning approaches, including recent transformer-based and federated learning architectures published through early 2026. We find that well-engineered classical feature pipelines remain competitive with, and in some reported cases exceed, deep learning approaches on this task, a finding with practical implications for small-data audio classification problems more broadly. We conclude with a discussion of open research gaps, including cross-cultural data coverage, explainability, and on-device deployment.","url":"https://doi.org/10.5281/zenodo.21272273","authors":["Pathan, Mohammed Akram Khan"],"tags":["Infant Cry Classification","Machine Learning","MFCC","Emotional Tears","Audio Signal Processing","Deep Learning","Bioacoustics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21272273","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2602.06838","name":"An Adaptive Differentially Private Federated Learning Framework","source":"datacite","abstract":"Federated learning enables collaborative model training across distributed clients while preserving data privacy. However, in practical deployments, device heterogeneity and non-independent and identically distributed (Non-IID) data often lead to unstable and biased gradient. When differential privacy is enforced, conventional fixed gradient clipping and Gaussian noise injection may further amplify gradient perturbations, resulting in training oscillation and degraded model performance. To address these challenges, we propose an adaptive differentially private federated learning framework that explicitly targets model efficiency under heterogeneous and privacy-constrained settings. On the client side, a lightweight local dimensionality reduction module is introduced to learn reduced-dimensional intermediate representations and produce more structured gradients during backpropagation, thereby mitigating noise amplification during local optimization. On the server side, an adaptive gradient clipping strategy dynamically adjusts clipping thresholds based on historical update statistics to avoid over-clipping and noise domination. Furthermore, a constraint-aware robust aggregation mechanism is designed to suppress unreliable or noise-dominated client updates and stabilize global optimization. Extensive experiments on CIFAR-10, SVHN, and STL-10 demonstrate that the proposed method consistently improves convergence stability and classification performance under differential privacy.","url":"https://doi.org/10.48550/arxiv.2602.06838","authors":["Wang, Jin","Ma, Hui","Zhang, Yajun","Pei, Xinjun","Yan, Ming","Xing, Fei","Chen, Yikun"],"tags":["Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.06838","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2607.05720","name":"Inertia-Informed Federated Learning Control Framework for Distributed Smart Grid Resilience","source":"datacite","abstract":"Resilient-by-design smart grid control demands frameworks capable of maintaining stability under physical disturbances and communication failures, without reliance on centralized coordination. While Centralized Training Decentralized Execution (CTDE) enables a learning-based control paradigm at the grid edge, individually trained models fail to generalize across unseen fault contingencies and fall short of fully decentralized deployment. Federated learning (FL) restores generalization through collaborative training; however, standard aggregation strategies remain agnostic to the physical heterogeneity of synchronous generators. This work proposes Inertia-Informed Weighted FedAvg (IIWFedAvg), a physics-informed aggregation strategy that embeds generator inertia directly into global model fusion for transient stability control in transmission networks. The proposed framework further integrates interpretable Chebyshev Kolmogorov-Arnold Network (ChebyKAN)-based controllers, augmented with Rate-of-Change-of-Frequency (RoCoF) features to enhance dynamic response awareness. Evaluated on the IEEE 39-bus benchmark under full decentralized deployment, IIWFedAvg achieves a 75% generalization success rate across unseen fault contingencies. It also surpasses the centralized baseline in two out of three stabilized faults, while delivering a 3x improvement in stabilization speed at zero centralized coordination overhead.","url":"https://doi.org/10.48550/arxiv.2607.05720","authors":["Shahbaz, Ibrahim","Al-Refai, Omar","Hammad, Eman"],"tags":["Systems and Control (eess.SY)","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.05720","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2607.05553","name":"Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids","source":"datacite","abstract":"Transient stability control in smart grids requires rapid post-fault damping of generator frequency and rotor angle deviations to prevent cascading failures. This paper proposes FedPPO-PG, a Federated Multi-Agent Proximal Policy Optimization framework with Physics-Grounded neighborhoods, which reformulates transient stability control as a cooperative multi-agent reinforcement learning problem optimized directly against closed-loop stability objectives. Each generator hosts an independent local actor augmented with the frequency deviations of its two most strongly coupled electrical neighbors, identified from the post-fault Kron-reduced susceptance matrix. A guided policy initialization phase warm-starts all actors from the classical decentralized controller, while a centralized critic guides advantage estimation under the centralized training--decentralized execution (CTDE) paradigm. Evaluated on a simulation of the IEEE 39-bus benchmark system across five training and three unseen fault contingencies, FedPPO-PG achieves 100% stabilization in all 24 trials, reduces mean stability time by 72.4%, and cuts the control power by 7-14 times compared to the centralized baseline. Each actor executes independently with no central coordinator at deployment, and the per-actor inference latency satisfies the IEEE/IEC 60255-118-1-2018 real-time reporting requirements.","url":"https://doi.org/10.48550/arxiv.2607.05553","authors":["Al-Refai, Omar","Shahbaz, Ibrahim","Husseinat, Adam Ali","Hammad, Eman"],"tags":["Machine Learning (cs.LG)","Systems and Control (eess.SY)","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.05553","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2602.14401","name":"pFedNavi: Structure-Aware Personalized Federated Vision-Language Navigation for Embodied AI","source":"datacite","abstract":"Vision-Language Navigation VLN requires large-scale trajectory instruction data from private indoor environments, raising significant privacy concerns. Federated Learning FL mitigates this by keeping data on-device, but vanilla FL struggles under VLNs' extreme cross-client heterogeneity in environments and instruction styles, making a single global model suboptimal. This paper proposes pFedNavi, a structure-aware and dynamically adaptive personalized federated learning framework tailored for VLN. Our key idea is to personalize where it matters: pFedNavi adaptively identifies client-specific layers via layer-wise mixing coefficients, and performs fine-grained parameter fusion on the selected components (e.g., the encoder-decoder projection and environment-sensitive decoder layers) to balance global knowledge sharing with local specialization. We evaluate pFedNavi on two standard VLN benchmarks, R2R and RxR, using both ResNet and CLIP visual representations. Across all metrics, pFedNavi consistently outperforms the FedAvg-based VLN baseline, achieving up to 7.5% improvement in navigation success rate and up to 7.8% gain in trajectory fidelity, while converging 1.38x faster under non-IID conditions.","url":"https://doi.org/10.48550/arxiv.2602.14401","authors":["Yang, Qingqian","Wang, Hao","Zhang, Sai Qian","Li, Jian","Hua, Yang","Pan, Miao","Song, Tao","Qi, Zhengwei","Guan, Haibing"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.14401","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2412.12640","name":"GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning","source":"datacite","abstract":"The increasing demand for data privacy, alongside the benefits of aggregating data from networked devices, has catalyzed the emergence of federated learning (FL). In FL, clients jointly train a global model by sharing gradients computed over private data. While this paradigm eliminates the need to exchange raw data, inference attacks can still be launched to extract sensitive information from gradients. To this end, partial gradient encryption has emerged as a promising design for balancing privacy and efficiency in practical FL systems, as encrypting only the classification-head gradients is believed to prevent known inference attacks while avoiding the high computational cost of encrypting the entire model. However, this design provides a false sense of privacy. By proposing GDBR, we show that sharing even a single unencrypted layer of gradients can lead to serious privacy leakage. GDBR is the first attack capable of high-fidelity label recovery with partial access to the gradients. It exploits a vulnerability in a commonly used neural building block, constructs a gradient bridge from the unencrypted layer to the final output layer, and approximates the logits information for accurate inference of private labels. These inferred labels not only reveal sensitive information about a client's private dataset but also serve as a prerequisite for many downstream attacks, such as data reconstruction and membership inference. GDBR brings these threats squarely into scope for FL systems employing partial encryption. In addition to theoretical analysis, extensive experiments demonstrate the severity of the problem across a wide variety of datasets and model architectures, including convolutional and transformer-based networks. Overall, our findings challenge the widespread assumption that encrypting only the output layer suffices for privacy protection.","url":"https://doi.org/10.48550/arxiv.2412.12640","authors":["Zhang, Rui","Chow, Ka-Ho"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2412.12640","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21148697","name":"CDSA-ATM: Air Traffic Management Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Trajectory + ATS + ANSP Fusion","source":"datacite","abstract":"CDSA-ATM is the reference implementation of the air traffic management pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decision, 21 May 2026). The v2.1.0 release (12 June 2026) replaces the v2.0.0 scaffold stubs with full NumPy reference implementations (federated, multimodal, rl_agents, rl_environment, xai; all unit-tested) and adds seeded, reproducible CPU-scale federated training runs (random / centralised PPO / FedAvg / FedProx / FedProx+DP) with result JSONs under experiments/results/. An interactive results panel is published at https://cdsa.app/atm. Framework-scale training with real operational data remains future work. The v1.0.0 release modules and reference data remain unchanged. Version 2.2.0 (3 July 2026) adds four phases of seeded, CPU-scale revision-preparation experiments (seed 42, FedProx unless noted) under experiments/, with all outputs in experiments/results/ and interactive honesty-band panels at https://cdsa.app/atm/. Faz A (reference runs): the high critical recall (0.919) is achieved inside the over-alert regime characterised in Faz B. Faz B (robustness and confusion matrix): the policy uses only 2 of 5 actions and every benign state receives a critical prediction — an over-alert regime; the tempo-aware reward does not improve on the static baseline. Faz C (differential-privacy ε sweep and entropy-targeted exploration): the ε sweep is two-sided (ε = 2.0 matches the undefended baseline, ε = 0.5 collapses the policy to a single action); a 0.05 entropy bonus recovers all five actions, but critical recall falls from 0.927 to 0.878. Faz D (decoy attribution and gated exploration): decoy attribution stays below the 25% uniform share (mean 15.3%, max 23.3%); gated exploration preserves recall (0.928) but the policy remains two-action. Findings are reported verbatim from the result JSONs, consistent with the honesty bands published at the site.","url":"https://doi.org/10.5281/zenodo.21148697","authors":["Cantekin, Mete"],"tags":["federated reinforcement learning","air traffic management","ADS-B","OpenSky Network","ATS occurrences","PPO","FedAvg","FedProx"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21148697","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20649671","name":"CDSA-MRO: Maintenance Safety Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Engine + Cyber + Maintenance Fusion","source":"datacite","abstract":"CDSA-MRO is the reference implementation of the maintenance safety pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decision, 21 May 2026). The v2.1.0 release (12 June 2026) replaces the v2.0.0 scaffold stubs with full NumPy reference implementations (federated, multimodal, rl_agents, rl_environment, xai; all unit-tested) and adds seeded, reproducible CPU-scale federated training runs (random / centralised PPO / FedAvg / FedProx / FedProx+DP) with result JSONs under experiments/results/. An interactive results panel is published at https://cdsa.app/mro. Framework-scale training with real operational data remains future work. The v1.0.0 release modules and reference data remain unchanged. Version 2.2.0 (3 July 2026) adds four phases of seeded, CPU-scale revision-preparation experiments (seed 42, FedProx unless noted) under experiments/, with all outputs in experiments/results/ and interactive honesty-band panels at https://cdsa.app/mro/. Faz A (reference runs): all configurations converge to identical values (43.7% / 0.499) as a result of a constant-action policy — not evidence of task mastery. Faz B (robustness and confusion matrix): the evaluated policy collapses to a single action in every condition (42.7% / 0.494); the tempo-aware reward does not improve on the static baseline. Faz C (differential-privacy ε sweep and entropy-targeted exploration): only the 0.05 entropy bonus breaks the single-action collapse (two of five actions, 53.0% / 0.613) — a partial but real improvement. Faz D (decoy attribution and gated exploration): the exploration gate never opens (gate_on ratio 0.0 — the 0.90 critical-recall floor is never reached); decoy attribution is 11.1%, to be read over near-constant outputs. Findings are reported verbatim from the result JSONs, consistent with the honesty bands published at the site.","url":"https://doi.org/10.5281/zenodo.20649671","authors":["Cantekin, Mete"],"tags":["reinforcement learning","synthetic data","cyber safety","aviation maintenance","data locality","continuing airworthiness","Part-145","CDSA-MRO"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20649671","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21148696","name":"CDSA-MRO: Maintenance Safety Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Engine + Cyber + Maintenance Fusion","source":"datacite","abstract":"CDSA-MRO is the reference implementation of the maintenance safety pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decision, 21 May 2026). The v2.1.0 release (12 June 2026) replaces the v2.0.0 scaffold stubs with full NumPy reference implementations (federated, multimodal, rl_agents, rl_environment, xai; all unit-tested) and adds seeded, reproducible CPU-scale federated training runs (random / centralised PPO / FedAvg / FedProx / FedProx+DP) with result JSONs under experiments/results/. An interactive results panel is published at https://cdsa.app/mro. Framework-scale training with real operational data remains future work. The v1.0.0 release modules and reference data remain unchanged. Version 2.2.0 (3 July 2026) adds four phases of seeded, CPU-scale revision-preparation experiments (seed 42, FedProx unless noted) under experiments/, with all outputs in experiments/results/ and interactive honesty-band panels at https://cdsa.app/mro/. Faz A (reference runs): all configurations converge to identical values (43.7% / 0.499) as a result of a constant-action policy — not evidence of task mastery. Faz B (robustness and confusion matrix): the evaluated policy collapses to a single action in every condition (42.7% / 0.494); the tempo-aware reward does not improve on the static baseline. Faz C (differential-privacy ε sweep and entropy-targeted exploration): only the 0.05 entropy bonus breaks the single-action collapse (two of five actions, 53.0% / 0.613) — a partial but real improvement. Faz D (decoy attribution and gated exploration): the exploration gate never opens (gate_on ratio 0.0 — the 0.90 critical-recall floor is never reached); decoy attribution is 11.1%, to be read over near-constant outputs. Findings are reported verbatim from the result JSONs, consistent with the honesty bands published at the site.","url":"https://doi.org/10.5281/zenodo.21148696","authors":["Cantekin, Mete"],"tags":["reinforcement learning","synthetic data","cyber safety","aviation maintenance","data locality","continuing airworthiness","Part-145","CDSA-MRO"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21148696","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21149391","name":"One Token Across Twenty Orders of Magnitude: Where a Single-Token Class-Discriminant Codebook Works, Where It Does Not, and When to Add a Co-Channel","source":"datacite","abstract":"Description This record accompanies a manuscript that asks one question at extreme breadth: can a single compact, on-device token — one window of a signal reduced to a single ~6-9-bit class-discriminant codebook index — carry a decision across sensing modalities spanning roughly twenty orders of magnitude in physical scale, from nanometer-pore ionic current to gravitational-wave strain? Under strict pre-registration (frozen recipe, instance-disjoint splits, five seeds, paired-bootstrap intervals, and honest negatives reported verbatim) the same encoder is screened on seven real public tasks — nanopore RNA identity, three neural-probe read-outs (region, cell type, unit quality), a teleseismic transient, a distributed-acoustic-sensing (DAS) fiber phase arrival, and a (semi-synthetic, clearly labeled) gravitational-wave inspiral chirp — and returns GO on all seven. The result is then subjected to three adversarial self-audit rounds and a head-to-head architectural analysis, which force three explicit retractions and yield a corrected, defensible account: the token retains most of a decision at extreme compression (a modest, consistent tax of +0.05 to +0.08 AUC versus a strong nonlinear model, at roughly 200 microseconds and 21 kilobytes per window); it adds real discriminative value on shape/pattern tasks but reduces to a trivial detector on energy-dominated ones; and its behavior is governed by stream morphology — near-optimal on pulsatile and stationary signals, structurally weak on intermittent ones whose decision lives in cross-window timing a single token cannot see. Three claims are retracted under audit and reported plainly: an apparent \"beats-the-ceiling\" result was a weak-baseline artifact (a strong nonlinear ceiling restores the +0.05-0.08 tax); the tax-scaling \"law\" is not universal (it holds only within a modality's difficulty ladder); and a token trained on injected gravitational-wave signals does not transfer to real detected events. A tiered co-channel is shown to be a bandwidth device, not an accuracy device — it recovers tax only where the task is hard and routes no better by token uncertainty than at random, but delivers 8-61x bandwidth reduction at fixed event capture on continuous rare-event streams — and a learned trigger beats a trivial energy threshold only for shape-defined events. Lifecycle studies show an on-sensor codebook can self-maintain across many unsupervised refresh cycles (with periodic anchor refresh) and that spatial token-coincidence across an array suppresses false alarms. Honest boundaries are mapped verbatim: at-rest deep-brain medication state does not decode across patients (its uncompressed ceiling sits at chance — signal absence), cuffless blood-pressure category is largely subject-identity leakage, short-read nanopore falls to chance as the ceiling itself collapses, and label-shuffle controls collapse to chance (confirming the GOs are real signal). Method companions: Papers 19, 30, and 31; trigger-scoping companion: Paper 29. This is a cross-scale application and validation of previously-filed and previously-published methods. Keywords: class-discriminant codebook; vector quantization; on-device inference; edge AI; cross-scale sensing; nanopore sequencing; Neuropixels; distributed acoustic sensing; seismology; gravitational waves; pre-registration; honest negatives; selective co-channel; stream morphology; self-supervision References 1. R. J. Ferlic and K. K. Ferlic, \"A single-token class-discriminant codebook encoder for physiological signals (Paper 19),\" Zenodo, 10.5281/zenodo.20788187. 2. R. J. Ferlic and K. K. Ferlic, \"On-device glucose alarms from a single learned token (Paper 30),\" Zenodo, 10.5281/zenodo.21114273. 3. R. J. Ferlic and K. K. Ferlic, \"One token, six modalities: pre-registered cross-modality screening for wearable and implantable monitoring (Paper 31),\" Zenodo, 10.5281/zenodo.21136786. 4. M. Jain, H. E. Olsen, B. Paten, and M. Akeson, \"The Oxford Nanopore MinION: de","url":"https://doi.org/10.5281/zenodo.21149391","authors":["Ferlic, Randolph James","Ferlic, Kimberly Kate"],"tags":["class-discriminant codebook","vector quantization","on-device inference","edge AI","cross-scale sensing","nanopore sequencing","Neuropixels","distributed acoustic sensing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21149391","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.21149392","name":"One Token Across Twenty Orders of Magnitude: Where a Single-Token Class-Discriminant Codebook Works, Where It Does Not, and When to Add a Co-Channel","source":"datacite","abstract":"Description This record accompanies a manuscript that asks one question at extreme breadth: can a single compact, on-device token — one window of a signal reduced to a single ~6-9-bit class-discriminant codebook index — carry a decision across sensing modalities spanning roughly twenty orders of magnitude in physical scale, from nanometer-pore ionic current to gravitational-wave strain? Under strict pre-registration (frozen recipe, instance-disjoint splits, five seeds, paired-bootstrap intervals, and honest negatives reported verbatim) the same encoder is screened on seven real public tasks — nanopore RNA identity, three neural-probe read-outs (region, cell type, unit quality), a teleseismic transient, a distributed-acoustic-sensing (DAS) fiber phase arrival, and a (semi-synthetic, clearly labeled) gravitational-wave inspiral chirp — and returns GO on all seven. The result is then subjected to three adversarial self-audit rounds and a head-to-head architectural analysis, which force three explicit retractions and yield a corrected, defensible account: the token retains most of a decision at extreme compression (a modest, consistent tax of +0.05 to +0.08 AUC versus a strong nonlinear model, at roughly 200 microseconds and 21 kilobytes per window); it adds real discriminative value on shape/pattern tasks but reduces to a trivial detector on energy-dominated ones; and its behavior is governed by stream morphology — near-optimal on pulsatile and stationary signals, structurally weak on intermittent ones whose decision lives in cross-window timing a single token cannot see. Three claims are retracted under audit and reported plainly: an apparent \"beats-the-ceiling\" result was a weak-baseline artifact (a strong nonlinear ceiling restores the +0.05-0.08 tax); the tax-scaling \"law\" is not universal (it holds only within a modality's difficulty ladder); and a token trained on injected gravitational-wave signals does not transfer to real detected events. A tiered co-channel is shown to be a bandwidth device, not an accuracy device — it recovers tax only where the task is hard and routes no better by token uncertainty than at random, but delivers 8-61x bandwidth reduction at fixed event capture on continuous rare-event streams — and a learned trigger beats a trivial energy threshold only for shape-defined events. Lifecycle studies show an on-sensor codebook can self-maintain across many unsupervised refresh cycles (with periodic anchor refresh) and that spatial token-coincidence across an array suppresses false alarms. Honest boundaries are mapped verbatim: at-rest deep-brain medication state does not decode across patients (its uncompressed ceiling sits at chance — signal absence), cuffless blood-pressure category is largely subject-identity leakage, short-read nanopore falls to chance as the ceiling itself collapses, and label-shuffle controls collapse to chance (confirming the GOs are real signal). Method companions: Papers 19, 30, and 31; trigger-scoping companion: Paper 29. This is a cross-scale application and validation of previously-filed and previously-published methods. Keywords: class-discriminant codebook; vector quantization; on-device inference; edge AI; cross-scale sensing; nanopore sequencing; Neuropixels; distributed acoustic sensing; seismology; gravitational waves; pre-registration; honest negatives; selective co-channel; stream morphology; self-supervision References 1. R. J. Ferlic and K. K. Ferlic, \"A single-token class-discriminant codebook encoder for physiological signals (Paper 19),\" Zenodo, 10.5281/zenodo.20788187. 2. R. J. Ferlic and K. K. Ferlic, \"On-device glucose alarms from a single learned token (Paper 30),\" Zenodo, 10.5281/zenodo.21114273. 3. R. J. Ferlic and K. K. Ferlic, \"One token, six modalities: pre-registered cross-modality screening for wearable and implantable monitoring (Paper 31),\" Zenodo, 10.5281/zenodo.21136786. 4. M. Jain, H. E. Olsen, B. Paten, and M. Akeson, \"The Oxford Nanopore MinION: de","url":"https://doi.org/10.5281/zenodo.21149392","authors":["Ferlic, Randolph James","Ferlic, Kimberly Kate"],"tags":["class-discriminant codebook","vector quantization","on-device inference","edge AI","cross-scale sensing","nanopore sequencing","Neuropixels","distributed acoustic sensing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21149392","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2607.01272","name":"Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification","source":"datacite","abstract":"Deploying 3D point cloud analysis in privacy-sensitive, resource-constrained settings faces two barriers: data cannot be centralized, and models must run on limited edge hardware. We present a multi-seed benchmark jointly evaluating federated learning (FL) and knowledge distillation (KD) for 3D point cloud classification. It spans 13 FL algorithms and 10 KD objectives (a 130-pair cross-product) across 504 training runs, evaluated on ModelNet40 and a clinical craniosynostosis dataset. We report three findings. First, under extreme non-IID label skew, standalone FL degrades sharply: on ModelNet40, the strongest method reaches 76.32% against a 92.26% centralized reference; on clinical data, the best reaches 75.83% against 100%. Second, distillation successfully compresses the teacher into a student 74.51% smaller and roughly twice as fast at inference, often matching or surpassing the teacher. Third, the combined pipeline exposes an evaluation pitfall: when distillation keeps a hard-label cross-entropy term on a labeled proxy split, a collapsed federated teacher (8.50%) paired with Logit-MSE still yields a 92.94% student. This 84.4-point gap reflects the proxy labels rather than the federated model, reusing the very labels whose privacy motivated federation. Objectives without hard labels instead track teacher quality ($r \\approx 0.99$) and collapse when the teacher does. We therefore recommend evaluating FL-KD pipelines with label-free distillation so reported accuracy reflects the federated teacher, not the proxy.","url":"https://doi.org/10.48550/arxiv.2607.01272","authors":["Aiersilan, Aizierjiang"],"tags":["Graphics (cs.GR)","Artificial Intelligence (cs.AI)","Computer Vision and Pattern Recognition (cs.CV)","Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.01272","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.19370649","name":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning (IEEE S&P '26) - Source Code","source":"datacite","abstract":"This artifact accompanies the paper \"On the Detectability of Active Gradient Inversion Attacks in Federated Learning,\" published at the 2026 IEEE Symposium on Security and Privacy (SP). Federated Learning (FL) allows multiple clients to collaboratively train a Machine Learning model while keeping their private data on-site. However, the gradients exchanged during training remain vulnerable to Gradient Inversion Attacks (GIAs), allowing a malicious server to reconstruct the clients' local data. In active attacks, the server deliberately manipulates the global model to facilitate this reconstruction. While earlier active GIAs have been shown to be detectable by clients, recently proposed attacks claim to be far stealthier than previous approaches. This repository provides the official implementation to reproduce our comprehensive analysis of four state-of-the-art active gradient inversion attacks. It also contains the source code for our novel, lightweight client-side detection techniques. These defenses identify statistically improbable weight structures alongside anomalous loss and gradient dynamics, enabling clients to effectively detect active attacks without modifying the standard federated learning protocol. Please refer to the documentation included in the repository for detailed instructions on setting up the environment, running the minimal working example, and reproducing the experimental results. Paper: DOI: 10.1109/SP63933.2026.00193 — IEEE Computer Society, pp. 2346–2365","url":"https://doi.org/10.5281/zenodo.19370649","authors":["Parrella, Giuseppe","Mazzocca, Carlo","FOGGIA, PASQUALE","Carletti, Vincenzo","Vento, Mario"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19370649","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.19479725","name":"On the Detectability of Active Gradient Inversion Attacks in Federated Learning (IEEE S&P '26) - Source Code","source":"datacite","abstract":"This artifact accompanies the paper \"On the Detectability of Active Gradient Inversion Attacks in Federated Learning,\" published at the 2026 IEEE Symposium on Security and Privacy (SP). Federated Learning (FL) allows multiple clients to collaboratively train a Machine Learning model while keeping their private data on-site. However, the gradients exchanged during training remain vulnerable to Gradient Inversion Attacks (GIAs), allowing a malicious server to reconstruct the clients' local data. In active attacks, the server deliberately manipulates the global model to facilitate this reconstruction. While earlier active GIAs have been shown to be detectable by clients, recently proposed attacks claim to be far stealthier than previous approaches. This repository provides the official implementation to reproduce our comprehensive analysis of four state-of-the-art active gradient inversion attacks. It also contains the source code for our novel, lightweight client-side detection techniques. These defenses identify statistically improbable weight structures alongside anomalous loss and gradient dynamics, enabling clients to effectively detect active attacks without modifying the standard federated learning protocol. Please refer to the documentation included in the repository for detailed instructions on setting up the environment, running the minimal working example, and reproducing the experimental results. Paper: DOI: 10.1109/SP63933.2026.00193 — IEEE Computer Society, pp. 2346–2365","url":"https://doi.org/10.5281/zenodo.19479725","authors":["Parrella, Giuseppe","Mazzocca, Carlo","FOGGIA, PASQUALE","Carletti, Vincenzo","Vento, Mario"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19479725","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2405.16472","name":"Personalized Additive Modeling for Multi-level Federated Learning","source":"datacite","abstract":"Contemporary AI faces the challenge of balancing generality with user-specific personalization. In federated learning (FL), this challenge is amplified by highly heterogeneous client data with complex non-IID patterns beyond standard IID assumptions. Many existing FL methods are designed for relatively restricted heterogeneity settings (e.g., a fixed number of clusters or a fixed form of personalization), limiting their robustness under complex structures. In this work, we study FL from a \\emph{multi-level non-IID} perspective, where client similarity is captured by multiple granularities of shared knowledge: global, subgroup, and client-specific components. This view captures coarse-to-fine relationships while requiring less prior knowledge of task boundaries. Building on this insight, we propose \\emph{Federated Multi-level Additive Modeling} (FeMAM), which learns multiple levels of shareable models and constructs personalized predictors via additive composition across levels. To move beyond a fixed structure, FeMAM allows models to grow and be pruned dynamically during training, adapting to diverse federated scenarios. Despite employing multiple models, FeMAM remains cost-friendly by unlocking only a small subset (one level) of models for training at a time. Extensive experiments show that FeMAM effectively approximates diverse complex non-IID structures and consistently outperforms representative clustered and personalized FL baselines.","url":"https://doi.org/10.48550/arxiv.2405.16472","authors":["Chen, Shutong","Long, Guodong","Zhou, Tianyi","Ma, Jie","Jiang, Jing","Zhang, Chengqi"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.16472","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.30161","name":"Federated Learning with Energy-Based Structured Probabilistic Inference","source":"datacite","abstract":"Federated learning typically aggregates client updates using fixed or heuristic weighting rules, which can be suboptimal when clients have heterogeneous data and varying contributions to the global model. We propose a framework that refines client aggregation weights using Conditional Random Fields (CRFs). Our method defines unary potentials for individual clients and pairwise potentials for all client pairs, allowing the server to model both client-specific reliability and interactions between clients. The resulting CRF inference produces aggregation weights that enable better convergence of the global training objective. Experiments show that, under non-IID heterogeneity, our approach consistently improves performance over well-established federated learning baselines.","url":"https://doi.org/10.48550/arxiv.2606.30161","authors":["Fenoglio, Dario","Kirilenko, Daniil","Gjoreski, Martin","Langheinrich, Marc"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.30161","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.28493","name":"The Role of Artificial Intelligence in the SKA Era","source":"datacite","abstract":"The Square Kilometre Array Observatory (SKAO) will usher in an era of unprecedented data complexity and scientific opportunity in radio astronomy, producing petabyte-scale datasets and terabit-per-second streams that challenge traditional analysis paradigms. Artificial Intelligence (AI) stands at the forefront of this transformation, offering scalable, adaptive solutions to the most pressing problems in radio astronomy and astrophysics. This chapter explores the pivotal role of AI in the SKA era, from real-time operations to scientific discovery. We examine how deep learning models enable automated source detection, radio-frequency interference mitigation, anomaly detection, and parameter inference, while generative approaches accelerate sky simulations, calibration, and imaging. Reinforcement learning promises dynamic scheduling and autonomous system control, and federated learning could address the distributed nature of SKA data. Beyond performance, we emphasize the necessity of explainability, uncertainty quantification, and physics-informed inductive biases to ensure scientific integrity. By mapping SKAO's core challenges - data volume, complexity, and interpretability - onto modern AI methodologies, we review how deep learning, self-supervised frameworks, and probabilistic models can unlock new frontiers in cosmology, galaxy evolution, and time-domain astrophysics. AI is not merely an automation tool for coping with scale. It is a catalyst for discovery, redefining how we observe, model, and understand the Universe.","url":"https://doi.org/10.48550/arxiv.2606.28493","authors":["Denzel, Philipp","Schilling, Frank-Peter","Gavagnin, Elena"],"tags":["Instrumentation and Methods for Astrophysics (astro-ph.IM)","FOS: Physical sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.28493","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.27511","name":"When the Aggregator Cheats: Data-Free Backdoors in Federated LLM-based QA Systems","source":"datacite","abstract":"Large Language Model (LLM)-based question-answering (QA) systems are increasingly deployed in sensitive domains such as healthcare, mental health counseling, and legal consultation. Federated learning (FL) enables collaborative training without sharing raw client data, for which locally trained models are aggregated at a central server (i.e., a cloud service provider) to obtain a global model. In this paper, we explore the potential vulnerability where a malicious aggregator, who may collude with a third-party vendor, stealthily implants advertisement-type backdoors into federated QA models, without ever accessing client data. The attacker's goals are twofold: (1) preserve clean QA fidelity (i.e., the poisoned model behaves like a clean model on non-triggered queries); and (2) generate highly natural, contextually relevant responses with target advertisements when a trigger appears. Achieving these two goals simultaneously is highly challenging, as naive backdoor injection without knowledge about private data may degrade model's clean performance or fail to inject the target. Motivated by this, we propose to leverage clients' uploaded gradients during training, and develop a two-stage framework for data-free and stealthy poisoning: (1) recover representative training samples from client gradients, and (2) construct poisoning datasets utilizing recovered samples and trigger phrases to inject backdoors into the global model. Experiments across representative QA datasets and LLM families under full fine-tuning and LoRA settings demonstrate that, our method achieves nearly 100% Attack Success Rate (ASR) while incurring negligible degradation on clean tasks. Crucially, reconstructing only 5-20% of gradients suffices to mount a reliable attack, exposing a practical blind spot in the pipeline of federated training of QA LLMs.","url":"https://doi.org/10.48550/arxiv.2606.27511","authors":["Zhu, Chenqing","Dai, Yanbo","Tian, Yulong","Li, Qingming","Li, Songze"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.27511","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20997942","name":"AI in Predictive Healthcare: How Artificial Intelligence and Wearable Devices are Transforming Early Disease Detection | A Systematic Literature Review (2026) | Ruchir Ganatra","source":"datacite","abstract":"This research paper was prepared by Ruchir Ganatra as an independent research study exploring Artificial Intelligence in Predictive Healthcare. The paper provides a comprehensive review of AI-powered wearable devices, machine learning algorithms, digital health technologies, Internet of Medical Things (IoMT), and early disease detection using peer-reviewed scientific literature published between 2022 and 2026. The review compares leading AI techniques including CNN, LSTM, Random Forest, Federated Learning, and Explainable AI while discussing clinical applications, ethical considerations, privacy, algorithmic bias, and future research directions. This paper is intended to support researchers, students, healthcare professionals, and technology enthusiasts interested in the future of AI-driven healthcare.","url":"https://doi.org/10.5281/zenodo.20997942","authors":["Ganatra, Ruchir"],"tags":["Artificial Intelligence","Machine Learning","Digital Health","Biomedical Engineering","Medical Informatics","Healthcare","Wearable Devices","Internet of Medical Things (IoMT)"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20997942","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20997943","name":"AI in Predictive Healthcare: How Artificial Intelligence and Wearable Devices are Transforming Early Disease Detection | A Systematic Literature Review (2026) | Ruchir Ganatra","source":"datacite","abstract":"This research paper was prepared by Ruchir Ganatra as an independent research study exploring Artificial Intelligence in Predictive Healthcare. The paper provides a comprehensive review of AI-powered wearable devices, machine learning algorithms, digital health technologies, Internet of Medical Things (IoMT), and early disease detection using peer-reviewed scientific literature published between 2022 and 2026. The review compares leading AI techniques including CNN, LSTM, Random Forest, Federated Learning, and Explainable AI while discussing clinical applications, ethical considerations, privacy, algorithmic bias, and future research directions. This paper is intended to support researchers, students, healthcare professionals, and technology enthusiasts interested in the future of AI-driven healthcare.","url":"https://doi.org/10.5281/zenodo.20997943","authors":["Ganatra, Ruchir"],"tags":["Artificial Intelligence","Machine Learning","Digital Health","Biomedical Engineering","Medical Informatics","Healthcare","Wearable Devices","Internet of Medical Things (IoMT)"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20997943","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.60797/jbg.2026.32.8","name":"ОБЪЯСНИМЫЕ И БЕЗОПАСНЫЕ ПЛАТФОРМЫ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ ГЕНОМИКИ CRISPR: ДОСТИЖЕНИЯ, ОГРАНИЧЕНИЯ, СИСТЕМЫ МОДЕЛИРОВАНИЯ И ТРАНСЛЯЦИОННАЯ ИНТЕГРАЦИЯ НА ОСНОВЕ БЛОКЧЕЙНА","source":"datacite","abstract":"Искусственный интеллект (ИИ) значительно ускорил процесс редактирования генома с помощью CRISPR-Cas за счет совершенствования проектирования направляющей РНК (gRNA), прогнозирования нецелевых эффектов, оценки результатов репарации ДНК и оптимизации метода прайм-редактирования. Однако современные вычислительные экосистемы CRISPR по-прежнему остаются фрагментированными: прогнозирующие модели ИИ, симуляторы репарации ДНК, платформы объяснимого ИИ (XAI) и системы управления геномными данными функционируют независимо друг от друга. Кроме того, растущее клиническое внедрение CRISPR-терапий, таких как Casgevy и Lyfgenia, усилило спрос на прозрачные, интерпретируемые, безопасные и пригодные для клинического применения системы ИИ. В данном обзоре критически анализируются последние достижения в области геномики CRISPR на основе ИИ, включая архитектуры глубокого обучения, системы прогнозирования на основе трансформеров, подходы объяснимого ИИ, платформы моделирования репарации ДНК, федеративное обучение, дифференциальную конфиденциальность и управление геномными данными с использованием блокчейна. В рукописи оцениваются основные ограничения существующих подходов, в том числе поведение прогнозирования по принципу «черного ящика», нестабильность объяснений XAI в последовательных геномных данных, плохая обобщаемость в различных биологических контекстах, ограниченная поддержка структурных геномных вариантов, а также ограничения масштабируемости блокчейна в рамках требований HIPAA и GDPR. Кроме того, в обзоре предлагается единая концептуальная архитектура, объединяющая механизмы прогнозирования на основе ИИ, симуляторы ремонта, модули объяснимости, федеративное обучение с сохранением конфиденциальности и гибридные инфраструктуры аудита на основе блокчейна с использованием хранения данных как в цепочке, так и вне цепочки. Также обсуждаются нормативные и практические аспекты, касающиеся FDA, EMA, стандарта ISO 13485, HIPAA, GDPR и Закона ЕС об ИИ. Предлагаемая архитектура призвана содействовать разработке безопасных, интерпретируемых и регулируемых с этической точки зрения систем поддержки принятия решений на основе CRISPR для прецизионной медицины следующего поколения.","url":"https://doi.org/10.60797/jbg.2026.32.8","authors":["Velevela Raghu Ram Chowdary -.","Raja Rao M."],"tags":["CRISPR","Explainable AI","Genome Editing","Blockchain","DNA Repair Simulation","Genomic Foundation Models","Federated Learning","Synthetic Control Data"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.60797/jbg.2026.32.8","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.60797/bmed.2026.9.8","name":"МУЛЬТИОМИЧЕСКИЙ ИИ ДЛЯ ПЕРСОНАЛИЗИРОВАННОЙ ИНТЕНСИВНОЙ ТЕРАПИИ: ОБЗОР ЦИФРОВЫХ ДВОЙНИКОВ, CRISPR И ОБЪЯСНИМЫХ СИСТЕМ","source":"datacite","abstract":"Персонализированная медицина меняет ситуацию, переходя от универсальных методов лечения к стратегиям, учитывающим уникальные биологические особенности каждого человека. Этот сдвиг имеет огромное значение в интенсивной терапии, где пациенты находятся в уязвимом состоянии, их организм может быстро меняться, а риски являются высокими. Традиционная интенсивная терапия опирается на длительные лабораторные исследования, разрозненные медицинские записи и решения, основанные в основном на том, что врачи видят и знают в данный момент. Это приводит к пробелам в лечении и замедляет оказание медицинской помощи.Сегодня, благодаря прорывам в таких мультиомических науках, как геномика, протеомика, метаболомика и микробиомика, а также интеграции мощных инструментов — таких как искусственный интеллект, цифровые двойники, CRISPR, объяснимый ИИ и технологии защиты конфиденциальности — перед нами открывается новая эра. Эти инновации делают интенсивную терапию гораздо более «умной» и оперативной: врачи и системы могут прогнозировать, адаптироваться и принимать меры до того, как возникнут проблемы.В данном обзоре обобщены все эти достижения и рассмотрено, как их сочетание позволяет улучшить прогнозы, сделать лечение более эффективным и получить более четкое представление о ��ом, что приносит пациентам наибольшую пользу. Было рассмотрено, как модели «цифровых двойников» могут служить виртуальными копиями пациентов, как технология CRISPR открывает возможности для целевого лечения, а также то, как системы рекомендаций по лекарственным препаратам на базе искусственного интеллекта и объяснимый ИИ повышают уровень доверия и прозрачности при оказании медицинской помощи. Кроме того, в обзоре подробно рассматриваются такие технологии, как федеративное обучение и блокчейн, которые позволяют обмениваться знаниями без угрозы для конфиденциальности пациентов.Тем не менее остаются серьезные вопросы — пробелы в исследованиях, практические препятствия и этические соображения, требующие реальных ответов. В заключение обзора предлагается план по созданию практичных, понятных и готовых к внедрению в больницах концепций персонализированной медицины для интенсивной терапии.","url":"https://doi.org/10.60797/bmed.2026.9.8","authors":["Velevela R.","Raja Rao M."],"tags":["personalized medicine","multi-omics","artificial intelligence","digital twins","CRISPR","explainable AI","federated learning","blockchain"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.60797/bmed.2026.9.8","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.19357843","name":"Ep. 128: AI's Dial-Up Era: Looking Back from 2036","source":"datacite","abstract":"Episode summary: In this forward-thinking episode of My Weird Prompts, hosts Herman Poppleberry and Corn kick off the year 2026 by traveling a decade into the future. They imagine a world in 2036 where the \"cutting-edge\" AI of today is viewed as an adorable, clunky relic of the past—much like we view the screeching sounds of dial-up internet today. From the death of prompt engineering to the rise of zero-latency, embodied intelligence, the duo breaks down why our current obsession with context windows and text boxes is just a passing phase. They dive deep into the transition from \"command-based\" to \"intent-based\" computing, where AI understands your needs without the need for complex instructions. Herman explains the shift from monolithic models to federated swarms of specialized agents, and how the \"hallucination\" bug of the 2020s will eventually be seen as a primitive technical limitation. Whether you're curious about the future of robotics or the evolution of persistent holographic memory, this episode provides a fascinating roadmap for the next decade of innovation. Tune in to find out why your current smartphone might soon feel like a rotary phone. Show Notes As the calendar turned to January 1, 2026, *My Weird Prompts* hosts Herman Poppleberry and Corn took a moment to look not just at the year ahead, but a full decade into the future. Prompted by a thought experiment from their housemate Daniel, the duo spent the episode \"time traveling\" to 2036 to look back at the current state of artificial intelligence. Their conclusion? The sophisticated tools we use today—the LLMs, the image generators, and the coding assistants—are destined to become the \"dial-up modems\" of the future. ### The Death of the Prompt One of the most striking insights from the discussion was the predicted obsolescence of \"prompt engineering.\" In 2026, users pride themselves on their ability to craft complex instructions, using delimiters and \"chain-of-thought\" techniques to coax the best results out of a model. Herman argues that by 2036, this will seem as primitive as using a rotary phone. We are currently in a \"lossy\" phase of technology, where we must translate human intent into rigid strings of text. Herman suggests that the future lies in \"intent-based computing.\" In this future, AI will possess such deep context regarding a user's life, professional history, and personal preferences that it will no longer require a three-paragraph explanation. A simple glance or a vague suggestion will suffice, as the machine will already understand the nuances of what \"professional\" or \"creative\" means to that specific individual. ### From Context Windows to Holographic Memory The hosts also tackled the technical limitations of modern AI memory. Today, developers and users celebrate when a model's \"context window\" expands to a million tokens. However, Herman describes the current state of AI as a \"brilliant assistant who gets hit with an amnesia ray every time you walk out of the room.\" By 2036, the concept of a \"window\" will likely be replaced by what Herman calls \"persistent, holographic memory.\" Instead of a blank slate at the start of every chat, a personal AI will have a continuous, decade-long relationship with its user. It will remember a casual comment about architectural styles from years prior and seamlessly apply that knowledge to a current project. The manual management of AI memory will become a relic of a more cumbersome era. ### Zero Latency and the End of the \"Thinking\" Pause One of the most relatable points of the episode was the \"dial-up screech\" of 2026: latency. Even the fastest models today have a slight delay as they generate tokens. Herman predicts that 2036 will be the era of \"zero-latency intelligence.\" Powered by specialized hardware—potentially optical or neuromorphic chips—AI responses will be instantaneous or even predictive. The duo joked about how future generations will find it hilarious that we used to sit and watch text scroll a","url":"https://doi.org/10.5281/zenodo.19357843","authors":["Rosehill, Daniel","Gemini 3.1 (Flash)","Chatterbox TTS"],"tags":["podcast","ai-generated","my weird prompts","future","2036","prompt-engineering","intent-based-computing","holographic-memory"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19357843","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2505.09854","name":"Chisme: Heterogeneity-Aware Gossip Learning","source":"datacite","abstract":"As end-user device capability increases and demand for intelligent services at the Internet's edge rises, distributed learning has emerged as a key enabling technology for the intelligent edge. Existing approaches like federated learning (FL) and decentralized FL (DFL) enable privacy-preserving distributed learning among clients, while gossip learning (GL) approaches have emerged to address the potential challenges in resource-constrained, connectivity-challenged infrastructure-less environments. However, most distributed learning approaches assume largely homogeneous data distributions and may not consider or exploit the heterogeneity of clients and their underlying data distributions. This paper introduces Chisme, a novel fully decentralized distributed learning algorithm designed to address the challenges of implementing robust intelligence in network edge contexts characterized by heterogeneous data distributions, episodic connectivity, and sparse network infrastructure or lack thereof. Chisme leverages the affinity between clients' underlying data distributions calculated from received model exchanges to inform how much influence received models have when merging into the local model. By doing so, it enables clients to strategically balance between broader collaboration to build more general knowledge and more selective collaboration to build specific knowledge. We evaluate Chisme against contemporary approaches using image recognition and time-series prediction scenarios while considering different network connectivity conditions, representative of real-world distributed intelligent systems running at the network's edge. Our experiments demonstrate that Chisme outperforms state-of-the-art edge intelligence approaches in almost every case -- clients using Chisme exhibit faster training convergence, lower final loss after training, and lower performance disparity between clients.","url":"https://doi.org/10.48550/arxiv.2505.09854","authors":["Kuttivelil, Harikrishna","Obraczka, Katia"],"tags":["Machine Learning (cs.LG)","Emerging Technologies (cs.ET)","Multiagent Systems (cs.MA)","Social and Information Networks (cs.SI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2505.09854","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2605.08121","name":"Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification","source":"datacite","abstract":"Early detection of plant diseases is critical for improving crop productivity, while it also facilitates the foundations of precision agriculture. Recent advances in distributed deep learning have enabled plant disease classification models to be trained across geographically distributed agricultural sensing infrastructures. However, deploying such systems in large-scale Internet of Things (IoT) environments, introduces significant challenges related to computational cost, energy consumption, and system efficiency. In this paper, we present a design-space exploration of hierarchical federated learning architectures for plant disease classification, with a particular focus on the trade-offs between predictive performance and energy efficiency. We further introduce a power- and energy-aware optimization framework that enables the systematic evaluation and selection of model-aggregator configurations under varying deployment constraints. The hierarchical federated architecture organizes distributed clients through intermediate aggregation layers, reducing communication and computational overhead. We evaluate multiple convolutional neural network architectures, including EfficientNet-B0, ResNet-50, and MobileNetV3-Large, in combination with different federated aggregation strategies such as FedAvg, FedProx, and FedAvgM. Experimental results demonstrate that different model-aggregator combinations exhibit distinct performance-energy trade-offs. Consequently, we highlight configurations that achieve competitive diagnostic accuracy and significantly reduce system resource requirements.","url":"https://doi.org/10.48550/arxiv.2605.08121","authors":["Papanikolaou, Athanasios","Tziouvaras, Athanasios","Stoikos, Pavlos","Xenakis, Apostolos","Parambath, Shameem A Puthiya","Floros, George","Zereik, Enrica","Petrovic, Ivan","Bonsignorio, Fabio"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2605.08121","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.25858","name":"Color Matters: Trigger Color Affects Success in Federated Backdoor Attacks","source":"datacite","abstract":"Federated learning is vulnerable to backdoor attacks in which malicious clients inject poisoned updates while preserving benign-task performance. In this paper, we study a semantics-driven backdoor mechanism in which attackers use natural visual accessories as triggers and manipulate only the trigger color while keeping the attack pipeline fixed. Our framework considers semantic trigger objects such as masks and sunglasses, instantiated in black and white variants, and evaluates their effect in a controlled federated learning setting. Malicious clients construct poisoned samples by applying a trigger to source-class images and relabeling them to an attacker-chosen target class, while benign clients train only on clean data. We analyze this mechanism under both a standard poisoning objective and a stronger SABLE-based objective that combines clean classification loss, triggered target loss, feature-separation loss in the penultimate representation space, and regularization to keep malicious updates close to the global model. This design enables the attack to remain effective while reducing excessive update drift. Experiments on a four-class CelebA hair-color task show that trigger color significantly changes attack success rate even when trigger semantics, placement, and poisoning budget are unchanged. White triggers are more effective for attacks targeting the blond class, whereas black triggers perform better for attacks targeting the black class. The same trend persists under robust aggregation, showing that trigger color is a meaningful factor in the operation, persistence, and evaluation of semantic backdoor mechanisms in federated learning.","url":"https://doi.org/10.48550/arxiv.2606.25858","authors":["Herath, Kavindu","Zhao, Joshua C.","Bagchi, Saurabh"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Computer Vision and Pattern Recognition (cs.CV)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.25858","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20826745","name":"GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning","source":"datacite","abstract":"This presentation done at WoWMoM 2026, showcases the paper \"GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning\". It presents the system description of the paper, a visualization tool used for the simulation of the network and the work's evaluation results. This work was done during the EXIGENCE Project. As 6G networks shift toward distributed intelligence, Federated Learning (FL) enables private, on-device AI training but suffers from high carbon emissions. To solve this, NKUA and HWDU developed GreenFLag, an agentic resource orchestration framework that minimizes grid power reliance. Powered by Soft Actor-Critic (SAC) reinforcement learning, GreenFLag dynamically optimizes computational and communication resources, while taking into account available renewable resources. Tested on real-world Copernicus meteorological data, it cut grid energy consumption by an average of 94.8% compared to three state-of-the-art baselines - proving that future 6G edge AI can run almost entirely on green power without sacrificing performance.","url":"https://doi.org/10.5281/zenodo.20826745","authors":["Panagea, Theodora"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20826745","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20826746","name":"GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning","source":"datacite","abstract":"This presentation done at WoWMoM 2026, showcases the paper \"GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning\". It presents the system description of the paper, a visualization tool used for the simulation of the network and the work's evaluation results. This work was done during the EXIGENCE Project. As 6G networks shift toward distributed intelligence, Federated Learning (FL) enables private, on-device AI training but suffers from high carbon emissions. To solve this, NKUA and HWDU developed GreenFLag, an agentic resource orchestration framework that minimizes grid power reliance. Powered by Soft Actor-Critic (SAC) reinforcement learning, GreenFLag dynamically optimizes computational and communication resources, while taking into account available renewable resources. Tested on real-world Copernicus meteorological data, it cut grid energy consumption by an average of 94.8% compared to three state-of-the-art baselines - proving that future 6G edge AI can run almost entirely on green power without sacrificing performance.","url":"https://doi.org/10.5281/zenodo.20826746","authors":["Panagea, Theodora"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20826746","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20813690","name":"Trust and Reputation in Federated Learning: A Systematic Literature Review of Mechanisms, Evaluation Practices, and Open Challenges","source":"datacite","abstract":"This is a preprint. This paper has been submitted to ACM Computing Surveys and is currently under peer review.Trust and reputation mechanisms have emerged as a reliability-management layer for Federated Learning (FL), where clients may be heterogeneous, intermittent, strategic, or adversarially compromised. By incorporating behavioral evidence, reliability scoring, incentives, and identity continuity, trust- and reputation-aware FL has gained attention across IoT, edge, healthcare, vehicular, and blockchain-assisted systems. Despite this progress, the literature remains fragmented, and existing surveys do not systematically explain how trust evidence, reputation memory, governance, privacy, fairness, and adaptive reasoning interact. This paper presents a systematic literature review (SLR) of trust and reputation in FL using the PRISMA 2020 framework. The review analyzes 215 peer-reviewed studies published from 2017 to April 2026 and retrieved from IEEE Xplore, ACM Digital Library, Web of Science, and Scopus. Bibliometric analysis characterizes publication trends, geographic concentration, citation distribution, and venue coverage. Thematic synthesis organizes the literature into eight analytical themes: evidence observability, trust quantification, reputation dynamics, workflow integration, governance, identity, privacy-compatible and fairness-aware trust, and adaptive reasoning. The review further identifies open research challenges in privacy-compatible evidence, recovery-enabled reputation, Sybil-resistant identity continuity, fairness-constrained calibration, standardized TrustFL benchmarking, and reproducible evaluation.","url":"https://doi.org/10.5281/zenodo.20813690","authors":["Rashid, Md Mamunur","Xiang, Yong","Uddin, Md Palash","Sood, Keshav","Zhang, Yushu","Gao, Longxiang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20813690","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20813691","name":"Trust and Reputation in Federated Learning: A Systematic Literature Review of Mechanisms, Evaluation Practices, and Open Challenges","source":"datacite","abstract":"This is a preprint. This paper has been submitted to ACM Computing Surveys and is currently under peer review.Trust and reputation mechanisms have emerged as a reliability-management layer for Federated Learning (FL), where clients may be heterogeneous, intermittent, strategic, or adversarially compromised. By incorporating behavioral evidence, reliability scoring, incentives, and identity continuity, trust- and reputation-aware FL has gained attention across IoT, edge, healthcare, vehicular, and blockchain-assisted systems. Despite this progress, the literature remains fragmented, and existing surveys do not systematically explain how trust evidence, reputation memory, governance, privacy, fairness, and adaptive reasoning interact. This paper presents a systematic literature review (SLR) of trust and reputation in FL using the PRISMA 2020 framework. The review analyzes 215 peer-reviewed studies published from 2017 to April 2026 and retrieved from IEEE Xplore, ACM Digital Library, Web of Science, and Scopus. Bibliometric analysis characterizes publication trends, geographic concentration, citation distribution, and venue coverage. Thematic synthesis organizes the literature into eight analytical themes: evidence observability, trust quantification, reputation dynamics, workflow integration, governance, identity, privacy-compatible and fairness-aware trust, and adaptive reasoning. The review further identifies open research challenges in privacy-compatible evidence, recovery-enabled reputation, Sybil-resistant identity continuity, fairness-constrained calibration, standardized TrustFL benchmarking, and reproducible evaluation.","url":"https://doi.org/10.5281/zenodo.20813691","authors":["Rashid, Md Mamunur","Xiang, Yong","Uddin, Md Palash","Sood, Keshav","Zhang, Yushu","Gao, Longxiang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20813691","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.10124","name":"FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching","source":"datacite","abstract":"Federated learning (FL) is often subject to aggregation variance if clients do not consistently participate in training rounds. While reusing stale model updates from inactive clients is a common technique to reduce this variance, we find that with skewed client participation, the resulting update staleness can become severe enough to destabilize training. To remedy this, we propose FedSteer, a novel method that constructs a gradient subspace from a cache of recent client gradients to serve as a low-dimensional representation of the current optimization landscape. FedSteer projects an active client's true gradient onto this subspace to find a set of optimal coordinates. For an inactive client, FedSteer reuses these coordinates with the now-evolved subspace drifted by other active clients. This process effectively \"steers\" outdated gradients toward the current global objective. This is complemented by a selective caching strategy that identifies a representative client subset to form the subspace, reducing server memory. Experiments demonstrate that FedSteer significantly outperforms baselines, preventing performance collapse in challenging scenarios while delivering accuracy gains of over 7% in others.","url":"https://doi.org/10.48550/arxiv.2606.10124","authors":["Zhang, Haoran","Pereira, Cainã Figueiredo","Siew, Marie","Liu, Xutong","Joe-Wong, Carlee","El-Azouzi, Rachid"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.11; I.2.7","68T05"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.10124","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20813413","name":"Locked Out of the Intelligence Revolution: Nigeria's Upstream Data Gap, the Billion-Dollar AI Opportunity, and a Technical Framework for Industry Data Collaboration in the Niger Delta","source":"datacite","abstract":"Nigeria holds 37.01 billion barrels of proven crude oil and condensate reserves, yet its production has consistently fallen short of its OPEC quota, and the government's target of 3 million barrels per day by 2030 assumes operational capacity the country has not yet demonstrated. This paper argues that a significant part of the gap is a data problem. Nigeria's upstream operational data is fragmented, inaccessible, and largely unpublished in usable form, conditions that prevent the application of artificial intelligence and machine learning tools that are already delivering double-digit efficiency gains for operators in other producing nations. Norway established its Diskos National Data Repository in 1995 and the United Kingdom followed with its own open data infrastructure, both underpinned by regulatory mandates that treated upstream data as shared national infrastructure rather than private operator property. Nigeria has no equivalent, and its national oil company was, as of April 2026, still digitising paper well logs dating to 1956. This paper proposes a three-tier data collaboration framework calibrated to Nigeria's current institutional reality rather than to an idealised regulatory environment. Tier 1 requires only a publishing decision by the Nigerian Upstream Petroleum Regulatory Commission to release already-public production data in structured, machine-readable formats. Tier 2 proposes federated learning as a mechanism for operators to benefit from collective intelligence without exposing commercially sensitive raw data. Tier 3 identifies full subsurface data sharing as a long-term destination requiring sustained regulatory investment.","url":"https://doi.org/10.5281/zenodo.20813413","authors":["Ezeagu, Vera Ijeoma"],"tags":["upstream data infrastructure","data collaboration framework","Nigerian petroleum industry","artificial intelligence","machine learning","federated learning","NUPRC"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20813413","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20813414","name":"Locked Out of the Intelligence Revolution: Nigeria's Upstream Data Gap, the Billion-Dollar AI Opportunity, and a Technical Framework for Industry Data Collaboration in the Niger Delta","source":"datacite","abstract":"Nigeria holds 37.01 billion barrels of proven crude oil and condensate reserves, yet its production has consistently fallen short of its OPEC quota, and the government's target of 3 million barrels per day by 2030 assumes operational capacity the country has not yet demonstrated. This paper argues that a significant part of the gap is a data problem. Nigeria's upstream operational data is fragmented, inaccessible, and largely unpublished in usable form, conditions that prevent the application of artificial intelligence and machine learning tools that are already delivering double-digit efficiency gains for operators in other producing nations. Norway established its Diskos National Data Repository in 1995 and the United Kingdom followed with its own open data infrastructure, both underpinned by regulatory mandates that treated upstream data as shared national infrastructure rather than private operator property. Nigeria has no equivalent, and its national oil company was, as of April 2026, still digitising paper well logs dating to 1956. This paper proposes a three-tier data collaboration framework calibrated to Nigeria's current institutional reality rather than to an idealised regulatory environment. Tier 1 requires only a publishing decision by the Nigerian Upstream Petroleum Regulatory Commission to release already-public production data in structured, machine-readable formats. Tier 2 proposes federated learning as a mechanism for operators to benefit from collective intelligence without exposing commercially sensitive raw data. Tier 3 identifies full subsurface data sharing as a long-term destination requiring sustained regulatory investment.","url":"https://doi.org/10.5281/zenodo.20813414","authors":["Ezeagu, Vera Ijeoma"],"tags":["upstream data infrastructure","data collaboration framework","Nigerian petroleum industry","artificial intelligence","machine learning","federated learning","NUPRC"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20813414","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20807407","name":"Locked Out of the Intelligence Revolution: Nigeria's Upstream Data Gap, the Billion-Dollar AI Opportunity, and a Technical Framework for Industry Data Collaboration in the Niger Delta","source":"datacite","abstract":"Nigeria holds 37.01 billion barrels of proven crude oil and condensate reserves, yet its production has consistently fallen short of its OPEC quota, and the government's target of 3 million barrels per day by 2030 assumes operational capacity the country has not yet demonstrated. This paper argues that a significant part of the gap is a data problem. Nigeria's upstream operational data is fragmented, inaccessible, and largely unpublished in usable form, conditions that prevent the application of artificial intelligence and machine learning tools that are already delivering double-digit efficiency gains for operators in other producing nations. Norway established its Diskos National Data Repository in 1995 and the United Kingdom followed with its own open data infrastructure, both underpinned by regulatory mandates that treated upstream data as shared national infrastructure rather than private operator property. Nigeria has no equivalent, and its national oil company was, as of April 2026, still digitising paper well logs dating to 1956. This paper proposes a three-tier data collaboration framework calibrated to Nigeria's current institutional reality rather than to an idealised regulatory environment. Tier 1 requires only a publishing decision by the Nigerian Upstream Petroleum Regulatory Commission to release already-public production data in structured, machine-readable formats. Tier 2 proposes federated learning as a mechanism for operators to benefit from collective intelligence without exposing commercially sensitive raw data. Tier 3 identifies full subsurface data sharing as a long-term destination requiring sustained regulatory investment.","url":"https://doi.org/10.5281/zenodo.20807407","authors":["Ezeagu, Vera Ijeoma"],"tags":["upstream data infrastructure","data collaboration framework","Nigerian petroleum industry","artificial intelligence","machine learning","federated learning","NUPRC"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20807407","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2604.12970","name":"Probabilistic Feature Imputation and Uncertainty-Aware Multimodal Federated Aggregation","source":"datacite","abstract":"Multimodal federated learning enables privacy-preserving collaborative model training across healthcare institutions. However, a fundamental challenge arises from modality heterogeneity: many clinical sites possess only a subset of modalities due to resource constraints or workflow variations. Existing approaches address this through feature imputation networks that synthesize missing modality representations, yet these methods produce point estimates without reliability measures, forcing downstream classifiers to treat all imputed features as equally trustworthy. In safety-critical medical applications, this limitation poses significant risks. We propose the Probabilistic Feature Imputation Network (P-FIN), which outputs calibrated uncertainty estimates alongside imputed features. This uncertainty is leveraged at two levels: (1) locally, through sigmoid gating that attenuates unreliable feature dimensions before classification, and (2) globally, through Fed-UQ-Avg, an aggregation strategy that prioritizes updates from clients with reliable imputation. Experiments on federated chest X-ray classification using CheXpert, NIH Open-I, and PadChest demonstrate consistent improvements over deterministic baselines, with +5.36% AUC gain in the most challenging configuration.","url":"https://doi.org/10.48550/arxiv.2604.12970","authors":["Shahid, Nafis Fuad","Ahmed, Maroof","Haider, Md Akib","Sagor, Saidur Rahman","Rahman, Aashnan","Hossain, Md Azam"],"tags":["Image and Video Processing (eess.IV)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.12970","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2603.13293","name":"A Robust Framework for Secure Cardiovascular Risk Prediction: An Architectural Case Study of Differentially Private Federated Learning","source":"datacite","abstract":"Accurate cardiovascular risk prediction is crucial for preventive healthcare; however, the development of robust Artificial Intelligence (AI) models is hindered by the fragmentation of clinical data across institutions due to stringent privacy regulations. This paper presents a comprehensive architectural case study validating the engineering robustness of FedCVR, a privacy-preserving Federated Learning framework applied to heterogeneous clinical networks. Rather than proposing a new theoretical optimizer, this work focuses on a systems engineering analysis to quantify the operational trade-offs of server-side adaptive optimization under utility-prioritized Differential Privacy (DP). By conducting a rigorous stress test in a high-fidelity synthetic environment that reflects the feature space and clinical context of real-world datasets (Framingham, Cleveland), we systematically evaluate the system's resilience to statistical noise. The validation results demonstrate that integrating server-side momentum as a temporal denoiser enables the architecture to achieve a stable F1 score of 0.78 and an Area Under the Curve (AUC) of 0.96 under the operational privacy budget (epsilon approximately 13.4), compared to a non-private baseline with an F1 score of 0.84. FedCVR statistically outperforms standard stateless baselines (FedAvg, FedProx) and other adaptive optimizers (FedAdagrad, FedYogi) under identical privacy constraints. Our findings confirm that server-side adaptivity is a structural prerequisite for recovering clinical utility under realistic privacy budgets, providing a validated engineering blueprint for secure multi-institutional collaboration.","url":"https://doi.org/10.48550/arxiv.2603.13293","authors":["Tertulino, Rodrigo","Alencar, Laércio"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.11; C.2.4; K.4.1"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.13293","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.22875","name":"FedOT: Ownership Verification and Leakage Tracing via Watermarks for Federated LDMs","source":"datacite","abstract":"Training Latent Diffusion Models (LDMs) within Federated Learning (FL) has attracted increasing attention due to its ability to combine the powerful generative capacity of LDMs with the privacy-preserving properties of FL. However, FL requires sharing the global model with multiple participants, which risks unauthorized model distribution or resale by malicious clients. While an intuitive approach is to adopt existing VAE-based watermarking techniques for LDMs in FL, this strategy falls short in addressing such threats due to two fundamental challenges: (1) Existing methods support ownership verification but lack the ability to trace model leakage to a specific malicious client; (2) VAE-based watermarks are vulnerable, as they can be removed simply by replacing the decoder with a clean counterpart. In this paper, we propose FedOT, the first framework for ownership verification and leakage tracing in federated LDMs. Specifically, to address the first challenge, we design a chunked watermark, where the first part is for ownership verification, and the second part is used for client identification. Furthermore, to overcome the second challenge and secure the model against VAE replacement attack, we introduce Latent Vector Transformation (LVT), which strengthens the connection between the VAE and U-Net latent spaces by modifying the original latent distribution of the VAE. Consequently, any attempt to replace the VAE for watermark removal leads to significant image quality degradation, making the LDM model unusable. Extensive experiments demonstrate that FedOT achieves superior performance in both ownership verification and traceability. Project page: https://spyzixuan.github.io/FedOT/.","url":"https://doi.org/10.48550/arxiv.2606.22875","authors":["Cheng, Wenlong","Gan, Yuan","Xu, Yunqiu","Miao, Jiaxu"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.22875","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.19734","name":"Federated Bilevel Performative Prediction","source":"datacite","abstract":"Federated bilevel optimization is widely used for nested learning problems across distributed clients, such as federated hyperparameter tuning and meta-learning under privacy and communication constraints. Most existing formulations assume fixed client data distributions, which can be violated by performativity, where deployed decisions reshape client behavior and data collection, inducing client-specific, decision-dependent distribution shift. We study federated bilevel performative prediction, where both upper-level (UL) and lower-level (LL) objectives are evaluated under client-dependent, decision-dependent distributions. We formalize the federated bilevel performatively stable (FBPS) point under a decoupled-risk perspective and provide sufficient conditions for its existence and uniqueness. We then develop two federated methods to compute the FBPS solution: FBi-RRM, which converges linearly under a contraction condition, and FBi-SGD, a communication-efficient stochastic method based on federated hypergradient estimation with convergence guarantees under diminishing step sizes when sensitivities are sufficiently small. Experiments on strategic regression and meta strategic classification validate the predicted stability thresholds and demonstrate improved meta-generalization over non-performative baselines, and CNN-based classification further demonstrates the practical effectiveness of the proposed methods in nonconvex neural network settings.","url":"https://doi.org/10.48550/arxiv.2606.19734","authors":["Qian, Liangxin","Liu, Chang","Cao, Xuanyu","Zhao, Jun","Lam, Kwok-Yan"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.19734","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.17605/osf.io/hbtp7","name":"AI-Driven Speech Biomarkers for Parkinson's Disease: A Systematic Review of Explainable Machine Learning, Multimodal Fusion, and Longitudinal Validation","source":"datacite","abstract":"This systematic literature review examines the current state of evidence for artificial intelligence (AI) and machine learning (ML) frameworks that utilize speech signals as digital biomarkers for Parkinson's disease (PD) diagnosis, progression tracking, and treatment response monitoring. The review places particular emphasis on explainable AI (XAI) methods ( including SHAP, LIME, and GradCAM ) and their integration into clinically interpretable diagnostic pipelines. Background Parkinson's disease affects approximately 10 million people worldwide and is projected to double in prevalence by 2050. Current diagnostic and monitoring practice relies on episodic clinical assessments and subjective rating scales such as MDS-UPDRS-III, which have limited capacity to capture early-stage symptoms, real-world fluctuations, and treatment response dynamics. Speech is an early and sensitive marker of PD-related neuromotor deterioration, and the ubiquity of smartphones makes remote speech-based monitoring a scalable and cost-effective alternative. Despite substantial growth in AI-based PD speech research, the field is constrained by three persistent gaps: dominance of cross-sectional study designs, opacity of deep learning models, and insufficient cross-linguistic and multimodal validation. Purpose The purpose of this review is to: Synthesize current evidence on AI/ML frameworks applied to PD speech biomarkers published between 2020 and 2026 Evaluate the quality and consistency of reported diagnostic accuracy metrics across studies Map the landscape of XAI methods applied to PD speech and assess the degree to which explanations have been clinically validated Identify confirmed evidence gaps — particularly in longitudinal validation, cross-linguistic robustness, multimodal fusion with XAI, federated learning, and digital twin architectures — to justify and inform the subsequent doctoral research work packages (WP2–WP5) Methods The review follows PRISMA 2020 guidelines. Four Boolean search strings were deployed across PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and Web of Science, restricted to English-language peer-reviewed publications from 2020 to 2026. Screening is conducted in Rayyan. Quality assessment applies QUADAS-2 for diagnostic accuracy studies and TRIPOD-AI for ML model development studies. Synthesis is narrative, organized thematically by speech feature domain. Meta-analysis was not planned due to anticipated methodological heterogeneity. Expected Outcomes A comprehensive narrative synthesis of AI/ML approaches to PD speech biomarkers organized by phonatory, articulatory, prosodic, linguistic, and multimodal domains A structured evidence gap map identifying underexplored areas for future research A formal confirmation of research gaps in XAI clinical validation, longitudinal study design, and cross-linguistic robustness A peer-reviewed systematic review manuscript","url":"https://doi.org/10.17605/osf.io/hbtp7","authors":["Mohajeri, Najmeh"],"tags":["Medicine and Health Sciences","Engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17605/osf.io/hbtp7","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2502.17748","name":"FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk","source":"datacite","abstract":"Federated Learning (FL) inherently mitigates mass data centralization risks; however, its privacy protections are not equally distributed - leaving vulnerable individuals disproportionately exposed to sophisticated privacy attacks. Crucially, statistical heterogeneity in human-centric FL environments often results in an inequitable distribution of privacy risks, particularly affecting those whose sensitive attributes or behaviors make them outliers. To address this critical gap, we introduce FinP, a novel framework designed to formalize and enforce fairness-in-privacy by mitigating disproportionate client vulnerability to Source Inference Attacks (SIA). FinP operationalizes a two-pronged defense strategy that tackles both the symptoms and root causes of privacy disparity, ensuring that no group of clients bears an excessive privacy burden. It combines a server-side adaptive aggregation mechanism, which dynamically weights client contributions based on their estimated privacy risk, with a client-side regularization technique to curb localized overfitting that drives unique data memorization. Extensive empirical evaluations on FEMNIST, Human Activity Recognition (HAR), and CIFAR-10 datasets demonstrate that FinP effectively aligns privacy fairness with primary task utility. Notably, FinP successfully mitigates SIA risks and reduces disparities in privacy exposure, establishing that strong fairness-in-privacy guarantees need not compromise model utility. Ultimately, FinP establishes equitable privacy protections by reducing vulnerability disparities by up to 57.14%, while preserving global model utility within a marginal +/- 1.75% of standard federated baselines.","url":"https://doi.org/10.48550/arxiv.2502.17748","authors":["Zhao, Tianyu","Srewa, Mahmoud","Elmalaki, Salma"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2502.17748","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20691794","name":"EndoDecay-Sim: A Hybrid In-Silico Digital Twin Platform for Cardiorenal Multimorbidity Tracking with Homomorphic Encryption","source":"datacite","abstract":"🌐 LIVE INTERACTIVE DASHBOARD PLATFORM: Researchers and peer-reviewers can access and stress-test the live, cloud-deployed clinical decision support interface directly via: https://avd9sajwiyrcgrrmunesjx.streamlit.app/ (Complete local deployment steps, CLI commands, and package dependencies are fully documented inside the repository's README.md file). Release Description and Academic Evaluation (v14.12 - Production Grade) This repository entry documents the definitive production-level release of EndoDecay-Sim (v14.12), advancing the architecture from a research suite into a fully deployed, high-performance computing (HPC)-optimized, and privacy-hardened digital twin platform. Version 14.12 bridges a mechanics-driven non-linear Milstein Stochastic Differential Equation (SDE) endothelial decay solver, an object-oriented multi-axial organ-graph network topology, a fully integrated 2048-bit Paillier Asymmetric Cryptosystem enhanced with Differential Privacy (L2-Norm Clipping), and a live, cloud-deployed clinical decision support dashboard (Streamlit Cloud). Operating via vectorized NumPy broadcasting to manage a synthetic cohort of 10,000 virtual patients over a 120-month clinical timeline, this release delivers verified mathematical validation, optimized computational efficiency, and regulatory-compliant federated privacy layers. Key Architectural Milestones in v14.12 1. High-Performance Computing (HPC) Vectorized Stochastic Solver & Milstein Integration The computational core (endodecay_sim_core.py) models continuous microvascular degradation under cellular noise using a non-linear multivariate Milstein Scheme. In v14.12, the engine has been re-engineered for High-Performance Computing (HPC) environments. By replacing iterative loops with SIMD-optimized NumPy matrix operations, the platform achieves ultra-fast SDE integration while preserving the strong convergence order of 1.0. This mathematical constraint suppresses numerical trajectory explosions, maintaining structural simulation boundaries across high-risk patient subgroups. 2. Enhanced Cardiorenal Feedback Network Topology Multi-systemic chronic failure cascades are mapped dynamically via an object-oriented network architecture (EnhancedCardiorenalTopology). During each SDE integration micro-step (dt), microvascular breakdown scores trigger numerical message passing across interconnected organ nodes including Endothelium, Heart, Kidney, Inflammation, and Metabolism. The platform maps reciprocal damage vectors across multi-axial pathways—including endo-cardiac (0.75), endo-renal (0.65), and cardiac-renal RAAS (0.80) axes—forcing real-time internal drift modifications to reflect epidemiological multimorbidity profiles. 3. Privacy-Preserving Federated Learning (HE + DP) Building upon the 2048-bit Paillier Cryptosystem, v14.12 implements a production-grade, asymmetric homomorphic encryption layer. Crucially, this release introduces Differential Privacy (DP) via L2-Norm Clipping. Local model weights are now clipped to a unit norm (L2-norm = 1.0) and injected with calibrated Gaussian noise before encryption, ensuring that central aggregation occurs without raw, unencrypted parameters ever being exposed, adhering to strict GDPR/HIPAA compliance through a true weighted FedAvg implementation. 4. Fully Functional Interactive Clinical Dashboard The frontend has been production-hardened. The dashboard establishes a direct reactive bridge to the persistent simulation data. Version 14.12 addresses critical statistical artifacts: Time-Lag Correction: The Kaplan-Meier analysis strictly enforces the S(0) = 1.0 axiom, eliminating time-lag errors found in legacy versions. Statistical Stability: OLS trendline computations in the clinical scatter telemetry now feature singularity protection (variance-check), preventing LinAlgError crashes during restrictive sub-cohort filtering. Dynamic Visualization: Recalculated Kaplan-Meier curves now include Greenwood confidence intervals (CI), rendered wi","url":"https://doi.org/10.5281/zenodo.20691794","authors":["Zavrak, Muhammet Yagiz"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20691794","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2506.22427","name":"CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings","source":"datacite","abstract":"We propose CLoVE (Clustering of Loss Vector Embeddings), a novel algorithm for Clustered Federated Learning (CFL). In CFL, clients are naturally grouped into clusters based on their data distribution. However, identifying these clusters is challenging, as client assignments are unknown. CLoVE utilizes client embeddings derived from model losses on client data, and leverages the insight that clients in the same cluster share similar loss values, while those in different clusters exhibit distinct loss patterns. Based on these embeddings, CLoVE is able to iteratively identify and separate clients from different clusters and optimize cluster-specific models through federated aggregation. Key advantages of CLoVE over existing CFL algorithms are (1) its simplicity, (2) its applicability to both supervised and unsupervised settings, and (3) the fact that it eliminates the need for near-optimal model initialization, which makes it more robust and better suited for real-world applications. We establish theoretical convergence bounds, showing that CLoVE can recover clusters accurately with high probability in a single round and converges exponentially fast to optimal models in a linear setting. Our comprehensive experiments comparing with a variety of both CFL and generic Personalized Federated Learning (PFL) algorithms on different types of datasets and an extensive array of non-IID settings demonstrate that CLoVE achieves highly accurate cluster recovery in just a few rounds of training, along with state-of-the-art model accuracy, across a variety of both supervised and unsupervised PFL tasks.","url":"https://doi.org/10.48550/arxiv.2506.22427","authors":["Bhatia, Randeep","Papadis, Nikos","Kodialam, Murali","Lakshman, TV","Chakrabarty, Sayak"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2506.22427","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2505.23593","name":"Federated Foundation Language Model Post-Training Should Focus on Open-Source Models","source":"datacite","abstract":"Post-training of foundation language models has emerged as a promising research domain in federated learning (FL) with the goal to enable privacy-preserving model improvements and adaptations to user's downstream tasks. Recent advances in this area adopt centralized post-training approaches that build upon black-box foundation language models where there is no access to model weights and architecture details. Although the use of black-box models has been successful in centralized post-training, their blind replication in FL raises several concerns. Our opinion is that using black-box models in FL contradicts the core principles of federation such as data privacy and autonomy. In this paper, we critically analyze the usage of black-box models in federated post-training, and provide a detailed account of various aspects of openness and their implications for FL.","url":"https://doi.org/10.48550/arxiv.2505.23593","authors":["Agrawal, Nikita","Mayer, Ruben"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2505.23593","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2505.19699","name":"Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environments","source":"datacite","abstract":"Federated Learning (FL) is a decentralized machine learning paradigm that enables clients to collaboratively train models while preserving data privacy. However, the coexistence of model and data heterogeneity gives rise to inconsistent representations and divergent optimization dynamics across clients, ultimately hindering robust global performance. To transcend these challenges, we propose Mosaic, a novel data-free knowledge distillation framework tailored for heterogeneous distributed environments. Mosaic first trains local generative models to approximate each client's personalized distribution, enabling synthetic data generation that safeguards privacy through strict separation from real data. Subsequently, Mosaic forms a Mixture-of-Experts (MoE) from client models based on their specialized knowledge, and distills it into a global model using the generated data. To further enhance the MoE architecture, Mosaic integrates expert predictions via a lightweight meta model trained on a few representative prototypes. Extensive experiments on standard image and multimodal benchmarks demonstrate that Mosaic consistently outperforms state-of-the-art approaches under both model and data heterogeneity. The source code has been published at https://github.com/Wings-Of-Disaster/Mosaic.","url":"https://doi.org/10.48550/arxiv.2505.19699","authors":["Liu, Junming","Gao, Yanting","Li, Yuqi","Meng, Siyuan","Sun, Yifei","Wu, Aoqi","Chen, Yirong","Wang, Ding","Wen, Shiping"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2505.19699","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.16655","name":"Distribution Alignment for One-Shot Federated Learning via Optimal Transport","source":"datacite","abstract":"One-Shot Federated Learning (OSFL) addresses extreme communication regimes in which clients interact with the server only once, amplifying the impact of heterogeneous client data distributions. In particular, the interaction of domain shift and label shift across clients induces misaligned feature representations that cannot be corrected through iterative optimization. Existing OSFL methods rely on distillation, server-side generation or ensemble-based aggregation, but assume aligned representations or address domain and label shift separately. We introduce SLOT-Align (Single-round, Learning-free Optimal Transport Alignment), a geometry-aware feature harmonization framework for OSFL. SLOT-Align uses a shared frozen encoder to extract compact feature statistics, constructs a global reference via Bures-Wasserstein barycenters, and aligns local representations using closed-form geodesic optimal transport maps. The method is computationally efficient and can be combined with existing OSFL pipelines relying on frozen encoders without modifying their training procedures. Extensive experiments across multiple benchmarks, pretrained backbones, and OSFL methods show that SLOT-Align consistently improves accuracy and robustness under joint domain and label shift.","url":"https://doi.org/10.48550/arxiv.2606.16655","authors":["Berardini, Daniele","Pastore, Vito Paolo","Murino, Vittorio"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.16655","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.14354","name":"MUFFLe: Efficient Model Update Compression via Generalized Deduplication for Federated Learning","source":"datacite","abstract":"Federated learning is well suited to edge environments but is often limited by the uplink cost of transmitting model updates. This Work-in-Progress paper presents MUFFLe, a communication-efficient update compression scheme that integrates generalized deduplication (GD) into the FedAvg pipeline. MUFFLe deduplicates repeated patterns across the update vector, yielding a fixed-rate, variable-count compression scheme. Preliminary experiments on IID MNIST with 20 clients show that MUFFLe reaches the target accuracy of $92.93\\%$ with 38~MB cumulative uplink communication, compared with 75~MB for 8-bit quantization, 86~MB for Top-$k$ sparsification, and 310~MB for uncompressed FedAvg. These results demonstrate the feasibility of applying GD to communication-efficient federated learning.","url":"https://doi.org/10.48550/arxiv.2606.14354","authors":["Zhao, Xiaobo","Lucani, Daniel E."],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.14354","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.13748","name":"FedSPC: Shared Parameter Correction for Personalized Federated Learning","source":"datacite","abstract":"Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation. Many PFL methods split the model into shared and personalized parameters, which are jointly trained on each client. However, this creates an optimization issue: shared parameters are updated by clients optimizing different local objectives, which can lead to inconsistent shared updates and weaken the shared representation. To address this problem, we propose Federated Shared Parameter Correction (FedSPC), a modular correction method for PFL. FedSPC applies control-variate correction only to the shared parameters of a given PFL method, while leaving personalized parameters unchanged. It can be integrated into three common PFL settings: shared feature extractors, shared classifiers, and fully shared models with local regularization. Experiments on CIFAR-100 and Tiny-ImageNet with ViT, ResNet-34, and VGG-11 show that FedSPC improves performance across representative PFL methods, including FedPer, FedRep, FedBABU, LG-FedAvg, and Ditto.","url":"https://doi.org/10.48550/arxiv.2606.13748","authors":["Menon, Kannanthodath Induchoodan Ajay","Prehofer, Christian","Xu, Yunfei","Hirano, Toru"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.13748","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20680859","name":"The Latent Yield: A Formal Framework for Low-Inhibition Cognitive Workspace Architecture, Privacy-Preserving Multi-Modal Feature Projection, and the Mapping of Human Latent Capacity in Decentralized, Peer-to-Peer Sovereign Data Nodes","source":"datacite","abstract":"This paper presents a theoretical framework and enabling specification for a low-inhibition associative cognitive workspace, designated the Dream Galaxy, implemented within decentralized, peer-to-peer sovereign data nodes constituting a federated personal cognitive universe. The framework introduces four interlocking technical contributions. First, a privacy-preserving multi-modal feature projection layer that maps heterogeneous episodic and physiological inputs, including sub-threshold physiological tokenization of continuous EEG, EMG, and EOG streams, into a unified semantic embedding space without cross-node data exposure, using horizontal federated learning with differential privacy at the gradient layer. Second, an asynchronous local topological optimization engine that applies incremental GPU-accelerated persistent homology over a Vietoris-Rips filtration to the projected feature space, maintaining a continuously valid persistence barcode without centralized computation or synchronous coordination across nodes. Third, a four-zone epistemological classifier that assigns each projected feature node a zone-resident probability distribution across Terra Firma (Zone 1), Incognita (Zone 2), Hic Sunt Dracones (Zone 3), and Whisp (Zone 4), where Zone 4 nodes represent pre-cognitive unknown unknowns validated against a local density criterion distinguishing genuine structural absence from data sparsity. Fourth, the Latent Yield metric: a dimension-weighted rate of topological feature birth in Zones 3 and 4, formalized with explicit pseudocode in the Technical Appendix, quantifying cognitive generative activity below the threshold of conscious expression. The paper further specifies an Asynchronous Convergence Protocol by which topologically equivalent planet structures in independent sovereign nodes are detected under differential privacy without raw data exchange, constituting a knowledge event of elevated epistemic weight requiring bilateral consent before any bridge formation. As a Perspectives and Theoretical Frameworks contribution, this paper provides sufficient enabling detail, including algorithmic pseudocode, parameter specifications, and system architecture, for a person having ordinary skill in machine learning or computational neuroscience to implement the described system. The framework is protected by ten United States provisional patent applications (Nos. 64/074,530; 64/074,608; 64/074,779; 64/075,396; 64/079,009; 64/081,935; 64/084,355; 64/086,036; 64/089,881; 64/089,883), the last two filed June 13, 2026.","url":"https://doi.org/10.5281/zenodo.20680859","authors":["Hoppe, Eric"],"tags":["ow-inhibition cognitive workspace","persistent homology","asynchronous local topological optimization","privacy-preserving multi-modal feature projection","sub-threshold physiological tokenization","decentralized peer-to-peer sovereign data nodes","Dream Galaxy","Latent Yield"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20680859","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20649676","name":"CDSA-BB3: Pilot Biometric Pillar of the CDSA Federated Reinforcement Learning Paradigm — Synthetic Biometric Data, Anonymization, Cryptographic Multi-Signature, and Scaffolded FRL Extension","source":"datacite","abstract":"CDSA-BB3 is the reference implementation of the pilot biometric pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decision, 21 May 2026). The v2.1.0 release (12 June 2026) replaces the v2.0.0 scaffold stubs with full NumPy reference implementations (federated, multimodal, rl_agents, rl_environment, xai; all unit-tested) and adds seeded, reproducible CPU-scale federated training runs (random / centralised PPO / FedAvg / FedProx / FedProx+DP) with result JSONs under experiments/results/. An interactive results panel is published at https://cdsa.app/bb3. Framework-scale training with real data is planned under TÜBİTAK funding (Phase B). The v1.0.0 release modules and reference data remain unchanged.","url":"https://doi.org/10.5281/zenodo.20649676","authors":["Cantekin, Mete"],"tags":["aviation safety","flight data recorder","black box","wearable biometrics","synthetic data","differential privacy","k-anonymity","AES-GCM"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20649676","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20649672","name":"CDSA-MRO: Maintenance Safety Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Engine + Cyber + Maintenance Fusion","source":"datacite","abstract":"CDSA-MRO is the reference implementation of the maintenance safety pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework (Scenario C decision, 21 May 2026). The v2.1.0 release (12 June 2026) replaces the v2.0.0 scaffold stubs with full NumPy reference implementations (federated, multimodal, rl_agents, rl_environment, xai; all unit-tested) and adds seeded, reproducible CPU-scale federated training runs (random / centralised PPO / FedAvg / FedProx / FedProx+DP) with result JSONs under experiments/results/. An interactive results panel is published at https://cdsa.app/mro. Framework-scale training with real data is planned under TÜBİTAK funding (Phase B). The v1.0.0 release modules and reference data remain unchanged.","url":"https://doi.org/10.5281/zenodo.20649672","authors":["Cantekin, Mete"],"tags":["reinforcement learning","synthetic data","cyber safety","aviation maintenance","data locality","continuing airworthiness","Part-145","CDSA-MRO"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20649672","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2601.01901","name":"FedBiCross: Personalized One-Shot Federated Learning on Medical Images","source":"datacite","abstract":"Data-free knowledge distillation-based one-shot federated learning (OSFL) trains a model in a single communication round without sharing raw data, making OSFL attractive for privacy-sensitive medical applications. However, existing methods aggregate predictions from all clients to form a global teacher. Under non-IID data, conflicting predictions dilute each other during averaging, yielding less informative soft labels that weaken distillation. We propose FedBiCross, a personalized OSFL framework with three stages: (1) clustering clients by model output similarity to form coherent sub-ensembles, (2) bi-level cross-cluster optimization that learns adaptive weights to selectively leverage beneficial cross-cluster knowledge while suppressing negative transfer, and (3) personalized distillation for client-specific adaptation. Experiments on four medical image datasets demonstrate that FedBiCross consistently outperforms state-of-the-art baselines across different non-IID degrees.","url":"https://doi.org/10.48550/arxiv.2601.01901","authors":["Xia, Yuexuan","Zhang, Yinghao","Liu, Yalin","Dai, Hong-Ning","Xia, Yong"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2601.01901","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.12845","name":"A Privacy-Preserving Framework Using Remote Data Science for Inter-Institutional Student Retention Prediction","source":"datacite","abstract":"This study explores privacy-preserving machine learning (PPML) techniques using the PySyft platform to enable collaborative prediction of student retention between institutions. We developed a remote data science (RDS) framework with a semi-air-gapped architecture consisting of high-side and low-side servers, allowing researchers from three universities to build predictive models on sensitive student data without direct data access. Using historical data from a small private university (N=720), we evaluated three synthetic data generation approaches and validated the framework through inter-institutional collaboration. The results demonstrate consistent classification performance across institutions (Macro F1: 0.690--0.695) while maintaining strict Family Educational Rights and Privacy Act (FERPA) compliance. We also propose Data-Type-Aware Templates, a novel synthetic data method that prioritizes privacy over distributional fidelity. Our findings confirm that RDS-based PPML is technically feasible for educational settings and offers a practical alternative to federated learning for small-scale inter-institutional collaborations. The code is available at https://github.com/jtfields/NAIRR240195-Privacy-Preserving-Machine-Learning.","url":"https://doi.org/10.48550/arxiv.2606.12845","authors":["Fields, John","Islam, K M Sajjadul","Thota, Ruchitha","Chen, Victor","Madiraju, Praveen"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.12845","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20649857","name":"CDSA-ATM: Air Traffic Management Pillar of the CDSA Federated Reinforcement Learning Paradigm — Multi-modal Trajectory + ATS + ANSP Fusion","source":"datacite","abstract":"CDSA-ATM is the reference implementation of the air traffic management pillar of the Complementary Diagnostic Safety Approach (CDSA), a third-generation aviation safety paradigm formalised as a Federated Reinforcement Learning (FRL) framework following the CDSA Methodological Unification Decision (Scenario C, 21 May 2026, frozen status). The v2 scaffold (22 May 2026) adds Python modules (rl_agents, rl_environment, multimodal, federated, xai) for the FRL paradigm extension. Full implementations are deferred to TÜBİTAK 1001 ARDEB Project 4 (Sept 2026 application cycle; 2027-2030 execution). The v1.0.0 release modules and reference data remain unchanged.","url":"https://doi.org/10.5281/zenodo.20649857","authors":["Cantekin, Mete"],"tags":["federated reinforcement learning","air traffic management","ADS-B","OpenSky Network","ATS occurrences","PPO","FedAvg","FedProx"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20649857","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.18654576","name":"Murmura: A Framework for Federated and Decentralized Machine Learning","source":"datacite","abstract":"Murmura is a comprehensive framework for federated and decentralized machine learning. Built for researchers and developers, it provides tools for distributed machine learning simulation with advanced privacy guarantees and flexible network topologies. The framework supports both centralized federated learning and fully decentralized peer-to-peer learning environments, with features including multiple network topologies, Byzantine-robust aggregation strategies, comprehensive differential privacy support, and intelligent resource management.If you use this repository in your work, please cite the following: @INPROCEEDINGS{rangwala2026murmura, author={Rangwala, Murtaza and Sinnott, Richard O and Buyya, Rajkumar}, booktitle={2026 IEEE 26th International Symposium on Cluster, Cloud and Internet Computing (CCGrid)}, title={Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT}, year={2026}, publisher = {IEEE Press}, address = {New Jersey, USA}, pages = {510-519}, doi={10.1109/CCGrid68966.2026.00061}}","url":"https://doi.org/10.5281/zenodo.18654576","authors":["Rangwala, Murtaza"],"tags":["federated learning","decentralized learning","distributed computing","privacy-preserving machine learning","differential privacy","peer-to-peer learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18654576","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.15622123","name":"Murmura: A Framework for Federated and Decentralized Machine Learning","source":"datacite","abstract":"Murmura is a comprehensive framework for federated and decentralized machine learning. Built for researchers and developers, it provides tools for distributed machine learning simulation with advanced privacy guarantees and flexible network topologies. The framework supports both centralized federated learning and fully decentralized peer-to-peer learning environments, with features including multiple network topologies, Byzantine-robust aggregation strategies, comprehensive differential privacy support, and intelligent resource management.If you use this repository in your work, please cite the following: @INPROCEEDINGS{rangwala2026murmura, author={Rangwala, Murtaza and Sinnott, Richard O and Buyya, Rajkumar}, booktitle={2026 IEEE 26th International Symposium on Cluster, Cloud and Internet Computing (CCGrid)}, title={Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT}, year={2026}, publisher = {IEEE Press}, address = {New Jersey, USA}, pages = {510-519}, doi={10.1109/CCGrid68966.2026.00061}}","url":"https://doi.org/10.5281/zenodo.15622123","authors":["Rangwala, Murtaza"],"tags":["federated learning","decentralized learning","distributed computing","privacy-preserving machine learning","differential privacy","peer-to-peer learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.15622123","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.11556","name":"Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices","source":"datacite","abstract":"Continuous electrocardiography (ECG) monitoring could surface rhythm abnormalities before they escalate into cardiovascular events. However, a deployable system must satisfy three requirements simultaneously: legal-grade privacy (GDPR, HIPAA), real-time inference on constrained edge hardware, and detection quality under non-IID cross-hospital data. We design and evaluate an end-to-end federated system addressing all three for unsupervised 12-lead ECG anomaly detection on PTB-XL dataset, combining three autoencoder families (VanillaAE, ConvAE, VAE), Flower-based federated averaging (FedAvg) across ten simulated hospitals, client-side differentially private SGD (DP-SGD) with a Rényi-DP accountant, and 8-bit integer (INT8) post-training quantization with Raspberry Pi 4 benchmarking. Our main contributions are: an empirical characterization of how these mechanisms compose, practical DP-specific recommendations, and technical and security insights for a clinically sensitive setting. Federated learning matches or exceeds the centralized baseline across all architectures (ConvAE federated area under the ROC curve, AUROC, $0.782$), and an $\\varepsilon$ sweep identifies $\\varepsilon=4$ as the recommended clinical operating point. INT8 quantization roughly halves model size and cuts Pi 4 latency by up to $44%$ with $&lt;0.12%$ AUROC loss. Crucially, DP and quantization penalties are empirically independent, so practitioners need not trade a strong privacy guarantee for a compact edge footprint. To our knowledge, this is the first system combining federated learning, formal $(\\varepsilon,δ)$-DP, unsupervised reconstruction-based detection, and quantized AArch64 deployment.","url":"https://doi.org/10.48550/arxiv.2606.11556","authors":["Akyol, Kaan Arda","Szeląg, Jakub Kacper","Abadi, Aydin","Alghamdi, Maha","Albalawi, Ghadah","Kaleelullah, Ghouse Ibrahim","Tutus, Hilal","Subaiei, Sarah Al","Kapse, Shardul","Raheeb, Syed Mohammed","Ahmed, Mujeeb","Ullah, Rehmat"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.6; I.5.4; J.3; C.2.4; C.3","68T07, 68T09, 68P27, 62M10"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.11556","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2603.29933","name":"GreenFLag: A Green Agentic Approach for Energy-Efficient Federated Learning","source":"datacite","abstract":"Progressing toward a new generation of mobile networks, a clear focus on integrating distributed intelligence across the system is observed to drive performance, autonomy, and real-time adaptability. Federated learning (FL) stands out as a key emerging technique, enabling on-device model training while preserving data locality. However, its operation introduces substantial energy and resource demands. Energy needs are mostly met by grid power sources, while FL resource orchestration strategies remain limited. This work introduces GreenFLag, an agentic resource orchestration framework designed to minimize the energy consumption from the grid power to complete FL workflows, guarantee FL model performance, and reduce grid power reliance by incorporating renewable sources into the system. GreenFLag leverages a Soft-Actor Critic reinforcement learning approach to jointly optimize computational and communication resources, while accounting for communication contention and the dynamic availability of renewable energy. Evaluations using a real-world open dataset from Copernicus, demonstrate that GreenFLag significantly reduces grid energy consumption by 94.8% on average, compared to three state-of-the-art baselines, while primarily relying on green power.","url":"https://doi.org/10.48550/arxiv.2603.29933","authors":["Panagea, Theodora","Koursioumpas, Nikolaos","Magoula, Lina","Khalili, Ramin"],"tags":["Networking and Internet Architecture (cs.NI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.29933","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.10780","name":"Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning","source":"datacite","abstract":"Secure aggregation is a vital component for mitigating gradient leakage in federated learning, but its communication cost conventionally scales with the gradient dimension. This becomes prohibitive for large models and even more pronounced in decentralized federated learning with limited bandwidth and unreliable nodes. Top-K gradient sparsification is an effective approach to reduce communication by transmitting only a few entries of the full gradient, while maintaining competitive model accuracy. Nevertheless, the top-K entries selected by each user are unpredictable and vary across users, which poses a challenge for efficient sparse secure aggregation. This paper studies information-theoretic secure aggregation with top-K sparsification in decentralized federated learning under user dropouts and user collusion. We propose a communication-efficient sparse secure aggregation scheme that offloads dimension-dependent overhead to an offline phase and protects private gradients using random masks and permutations. Experimental results demonstrate that our scheme preserves accuracy comparable to full-gradient aggregation even with only 1% gradient sparsification, while substantially reducing the communication cost.","url":"https://doi.org/10.48550/arxiv.2606.10780","authors":["Tang, Hengxuan","Zhu, Jinbao","Tang, Xiaohu"],"tags":["Information Theory (cs.IT)","Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.10780","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.10153","name":"Compositional Generative Modeling from Decentralized Data","source":"datacite","abstract":"Learning the compositional nature of the physical world requires joint observation of interacting factors. However, because practical data is often decentralized, these factors are fragmented across isolated silos. Existing decentralized generative approaches focus only on modeling the union of siloed data, overlooking novel combinations implied by the collective whole. To bridge this gap, we introduce Decentralized Compositional Flow Matching (DCFM), a framework that enforces structural constraints across the global set of generative factors, without exchanging any raw data. DCFM enables novel combinations to emerge through peer interactions, even when no single data source can independently support the composition. Empirically, DCFM substantially outperforms federated learning and mixture-of-experts baselines across conditional image generation, robotic spatial planning, and medical attribute co-occurrence modeling.","url":"https://doi.org/10.48550/arxiv.2606.10153","authors":["Morshed, Mashrur M.","Boddeti, Vishnu Naresh"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.10153","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.09869","name":"QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning","source":"datacite","abstract":"Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while reducing overall training cost. However, determining the optimal split point, meaning the layer where the model is divided still remains a critical challenge, especially when clients have heterogeneous hardware capabilities. Fixed split points can overload weak devices and increase the communication and server load, which slows convergence and reduces stability. This paper introduces QSplitFL, a novel capability-aware Deep Q-Network (DQN) framework for optimal split point selection in Split learning based Federated Learning (SFL) environments. Unlike existing approaches that rely on high-dimensional model weight representations, QSplitFL employs a lightweight state representation derived directly from client hardware metrics, including CPU utilization, memory, battery level, and network latency. The proposed framework incorporates a decayed loss-drop reward function that prioritizes early convergence, and a committee-based DQN architecture with majority voting to mitigate reward hacking. Extensive experiments on MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 datasets using CNN, ResNet50, MobileNetV4, and ConvNeXt architectures demonstrate that our approach achieves better convergence and higher accuracy compared to existing methods, while effectively adapting to heterogeneous device resources. The source code is publicly available at https://github.com/AIPO-Lab/QSplitFL.","url":"https://doi.org/10.48550/arxiv.2606.09869","authors":["Shadin, Nazmus Shakib","Zhang, Xinyue","Wang, Jingyi","Pan, Miao"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.09869","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20618406","name":"EndoDecay-Sim: A Hybrid In-Silico Digital Twin Platform for Cardiorenal Multimorbidity Tracking with Homomorphic Encryption","source":"datacite","abstract":"🌐 LIVE INTERACTIVE DASHBOARD PLATFORM: Researchers and peer-reviewers can access and stress-test the live, cloud-deployed clinical decision support interface directly via: https://endodecay-sim-app-jp4wufjjemvbsnxxyj9xev.streamlit.app/ (Complete local deployment steps, CLI commands, and package dependencies are fully documented inside the repository's README.md file). Release Description and Academic Evaluation (v13.1 - Production Grade) This repository entry documents the definitive production-level release of EndoDecay-Sim (v13.1), advancing the architecture from a local computational medicine suite into a fully deployed, secure, open-science web platform. Version 13.1 bridges a mechanics-driven non-linear Milstein Stochastic Differential Equation (SDE) endothelial decay solver, an object-oriented organ-graph network topology, a fully integrated 2048-bit Paillier Asymmetric Cryptosystem, and a live, cloud-deployed clinical decision support dashboard (Streamlit Cloud). Operating via vectorized NumPy broadcasting to manage a synthetic cohort of 10,000 virtual patients over a 120-month clinical timeline, this release eliminates structural placeholders to deliver verified mathematical validation and regulatory-compliant federated privacy layers. Key Architectural Milestones in v13.1 1. Mechanistic Stochastic Solver & Milstein Integration The computational core (endodecay_sim_v13.1_core.py) models continuous microvascular degradation under cellular noise using a non-linear multivariate Milstein Scheme. By incorporating the second-order stochastic derivative correction term, 0.5 * sigma² * E_t * (dW_t² - dt), the engine achieves a strong convergence order of 1.0. This mathematical constraint suppresses numerical trajectory explosions, preserving structural simulation boundaries across high-risk patient subgroups. 2. Physiological Feedback Network Topology Multi-systemic chronic failure cascades are mapped dynamically via an object-oriented network architecture (PhysiologicalFeedbackTopology). During each SDE integration micro-step (dt), microvascular breakdown scores trigger numerical message passing across interconnected organ axes. The platform maps reciprocal damage vectors across the Cardiorenal (weight: 0.85) and Hepatorenal (weight: 0.40) pathways, forcing real-time internal drift modifications to reflect epidemiological multimorbidity profiles. 3. Native 2048-Bit Paillier Homomorphic Encryption Transitioning from the linear scalar placeholders utilized in version 13.0, v13.1 implements a production-grade, asymmetric homomorphic encryption layer using the native \"phe\" library. The HomomorphicEncryptionProtocol acts as a secure Multi-Center Federated Learning (FedAvg) pipeline blueprint. Model weights from independent clinical nodes are encrypted using a 2048-bit public key. The central server performs zero-knowledge vector additions directly on the ciphertexts via operator overloading, ensuring that central aggregation occurs without raw, unencrypted parameters ever being exposed. 4. Fully Functional Interactive Clinical Dashboard The frontend blueprint of previous iterations has been fully coded, optimized, and deployed live via app.py on Streamlit Cloud. The dashboard establishes a direct reactive bridge to the persistent simulation data. Clinicians can adjust patient age ranges, HbA1c glycotoxicity limits, baseline eGFR filtration values, and toxic insults via sidebar sliders. The UI engine instantly intercepts the sub-cohort selection, recalculates empirical Kaplan-Meier survival curves on the fly, and plots dynamic multi-organ correlation scatter alignments backed by automated Ordinary Least Squares (OLS) regression curves. 5. Strict Temporal Data Leakage Isolation To ensure absolute data science integrity, the machine learning and analytical sub-routines enforce rigid temporal boundaries. Prognostic predictions regarding long-term multi-systemic collapse are computed exclusively using pre-simulation (Month 0) immutabl","url":"https://doi.org/10.5281/zenodo.20618406","authors":["Zavrak, Muhammet Yagiz"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20618406","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.20616583","name":"EndoDecay-Sim: A Hybrid Computational Architecture for Stochastic Endothelial Degradation and Prognostic Multimorbidity Risk Stratification","source":"datacite","abstract":"Release Notes and Academic Evaluation (v13.0-Ultimate) This repository marks the definitive evolution of EndoDecay-Sim into an integrated open-science computational medicine suite (v13.0). This version seamlessly bridges a non-linear Milstein Stochastic Differential Equation (SDE) tissue solver, an object-oriented multi-systemic organ network topology, and a temporally isolated prognostic Machine Learning (ML) analytical core. Operating via optimized NumPy vectorized broadcasting to ensure high-throughput execution speeds, the framework models long-term microvascular degradation loops and trains artificial intelligence to rank baseline biomarker vulnerabilities over a heavy-tailed 10,000-patient synthetic cohort. 🏗️ Key Architectural Paradigms in v13.0 Mekanistik Stokastik Çözücü & Milstein Şeması (Stochastic Solver): Transitioned from standard numerical approximations to an advanced Milstein Integration Scheme to solve endothelial structural updates under continuous cellular noise. By incorporating the second-order stochastic derivative correction term derived from 0.5 * sigma^2 * E * (dW^2 - dt), the framework guarantees a strong convergence order of 1.0, eliminating numerical trajectory explosions across extreme clinical long-tail bounds. Fizyolojik Ağ Topolojisi ve Anlık Mesaj İletimi (Mechanistic Network Topology): Implements a dedicated object-oriented pathobiological network architecture (OrganGraphNetworkTopology). Rather than utilizing downstream deep-learning graph layers, the framework maps multi-systemic chronic failure cascades as an interconnected physiological grid. During every single SDE micro-timestep (dt), instantaneous microvascular degradation vectors trigger dynamic numerical message passing across the Cardiorenal (weight: 0.85) and Hepatorenal (weight: 0.40) axes, dynamically adjusting internal drift parameters in real time to capture reciprocal organ-level destruction. Veri Sızıntısı İzolasyonu ve Veri Bilimi Dürüstlüğü (Zero-Leakage Isolation): To ensure absolute data science integrity and eliminate look-ahead bias during machine learning training, v13.0 enforces strict physical and temporal isolation boundaries. The ML analytical engine is trained exclusively on pre-simulation (Month 0) immutable baseline traits (Baseline_eGFR, Baseline_NT_proBNP) to predict long-term multi-organ collapse, completely isolated from downstream longitudinal variables generated during the SDE runtime loops. Klinik Uzun Kuyruk Gerçekçiliği (Copula-Like Transformations): Replaced arbitrary data trimming with non-linear exponential transformations to mirror epidemiological realities. The synthetic cohort engine mathematically models high-risk phenotypic extremes, yielding highly realistic left-skewed eGFR spreads (capturing advanced nephropathy) and heavy right-tailed NT-proBNP distributions (capturing severe myocardial strain). Uluslararası Kılavuz Odaklı Hedef Etiketleme (Consensus Target Labeling): The prognostic artificial intelligence targets are entirely anchored in international medical consensus guidelines: Renal Endpoint (KDIGO Guidelines): eGFR 1000 pg/mL, defining the clinical rule-in threshold for high-risk decompensated heart failure. Virtual subjects breaching either criteria at the terminal 120-month mark are automatically labeled into the composite Cardiorenal Collapse cohort. Homomorfik Şifreleme Mimari Şablonu (Programmatic Crypto Placeholder): Features an architectural abstraction layer (HomomorphicEncryptionProtocol) designed to map out multi-center Federated Learning (FedAvg) pipelines. In this open-science preview edition, cryptographic transformations are algorithmically simulated via linear scalar expansions to demonstrate secure ciphertext aggregation pathways, establishing a structural blueprint for downstream native phe or TenSEAL library hooks. Lokal HL7 / FHIR Veri Dönüştürücü Köprüsü (Local FHIR Payload Parser): Incorporates a pluggable parsing adapter (FHIRInteroperabilityBridge) that interpre","url":"https://doi.org/10.5281/zenodo.20616583","authors":["Zavrak, Muhammet Yagiz"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20616583","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2409.15723","name":"Federated Large Language Models: Current Progress and Future Directions","source":"datacite","abstract":"Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy and governance concerns. Federated Learning offers a decentralized alternative by enabling multiple clients to collaboratively train shared models without exposing raw local data. However, integrating FL with LLMs introduces new challenges, including data heterogeneity, convergence instability, communication overhead, and computational constraints. This survey provides a comprehensive and up-to-date overview of Federated Learning for Large Language Models (FedLLM). We systematically review recent advances, with particular emphasis on federated fine-tuning and federated prompt learning, and analyze how existing methods address efficiency, personalization, and security challenges. We further summarize emerging directions such as federated pre-training and federated agents. Our goal is to offer a structured perspective on this rapidly evolving field and to highlight promising avenues for future research.","url":"https://doi.org/10.48550/arxiv.2409.15723","authors":["Yao, Yuhang","Zhang, Jianyi","Wu, Junda","Huang, Chengkai","Xia, Yu","Yu, Tong","Zhang, Ruiyi","Kim, Sungchul","Rossi, Ryan","Li, Ang","Yao, Lina","McAuley, Julian","Chen, Yiran","Joe-Wong, Carlee"],"tags":["Machine Learning (cs.LG)","Computation and Language (cs.CL)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.15723","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.09548","name":"Model Poisoning Against Federated Model Adaptation with Chain of Bit-Flips","source":"datacite","abstract":"Federated Learning (FL) allows a set of clients to collectively train a global model without sharing local training data. Giving the responsibility of the training to decentralized actors may lead to poisoning attacks: clients controlled by malicious third party potentially poison the training dataset to install a backdoor in neural networks. In FL, these backdoor attacks rely solely on algorithmic approach, however, recent advances in hardware faults threats (e.g, Rowhammer) have widen the overall attack surface. In the context of federated model adaptation, we introduce a novel category of backdoor attack against FL systems that relies on model poisoning based on hardware-fault attacks. More precisely, we propose a task-agnostic backdoor attack that is implanted during the FL training time by inducing hardware faults (bit-flips) in parameters of a single local model. The backdoor is crafted during a previous offline phase from the pretrained model initially used by the FL system. Our results show that a backdoor can be successfully applied on different type of models and datasets. Typically, with up to 10 faults per malicious client occurrence and 19 total occurrences on a ResNet-18 are enough to reach 94% of attack success rate. Finally, we discuss the practicality and the robustness of the attack potential defenses, while putting into perspective the practical constraints of Rowhammer, which is the preferred attack vector for this type of threats.","url":"https://doi.org/10.48550/arxiv.2606.09548","authors":["Vuillod, Bastien","Hector, Kevin","Moellic, Pierre-Alain","Dutertre, Jean-Max","Potin, Olivier"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.09548","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.09227","name":"Trustworthy Smart Fabs via Professional Proxies: Scaling Safe and Sustainable by Design (SSbD) through Industrial Data Spaces","source":"datacite","abstract":"The convergence of the 2026 European Union Safe and Sustainable by Design (SSbD) framework, Corporate Sustainability Due Diligence Directive (CSDDD), and Carbon Border Adjustment Mechanism (CBAM) introduce a severe governance bottleneck for advanced semiconductor manufacturing facilities (\"Smart Fabs\"). Regulatory compliance demands have surpassed the capacity of manual corporate reporting, creating a direct conflict between multi-stakeholder transparency and corporate data privacy. This paper addresses this challenge by introducing a zero-trust socio-technical orchestration framework that operationalizes a six-layer SSbD reference architecture within trustworthy industrial data spaces. We propose a shift from reactive automation to autonomous governance through \"Professional Proxies\"-role-based agentic workflows executing within hardware-isolated trust zones. Structured as an interoperable network protocol stack, the framework coordinates an automated, five-step \"relay race\" between Facility, Process Engineering, and Finance proxy teams to align factory-floor yield models with macro-level sustainability mandates. By executing Virtual Metrology (VM) predictions and Federated Machine Learning (FML) inside hardware-rooted Trusted Execution Environments (TEEs), this architecture resolves the Data Sovereignty Paradox, demonstrating how fabs can export cryptographically signed compliance tokens via International Data Spaces (IDS) connectors without exposing proprietary process recipes. Ultimately, this framework provides technology managers with a verifiable, evidence-based pathway toward resilient, net-zero Industry 5.0 ecosystems.","url":"https://doi.org/10.48550/arxiv.2606.09227","authors":["Liao, Han-Teng","Kao, Chang-Yi","Ang, Karen"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Computational Engineering, Finance, and Science (cs.CE)","Computers and Society (cs.CY)","Human-Computer Interaction (cs.HC)","Social and Information Networks (cs.SI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.09227","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.08173","name":"AI-Native Closed-Loop Security for 6G-Enabled Cyber-Physical Systems: From Edge Detection to Network-Wide Mitigation","source":"datacite","abstract":"In sixth-generation (6G) networks, billions of cyber-physical systems (CPSs) - autonomous vehicles, smart grids, industrial robots, and remote-surgical equipment - will run over ultra-reliable low-latency slices, collapsing the gap between a remote breach and physical harm to milliseconds, a budget perimeter firewalls and centralised security operations centres cannot meet. This survey reframes 6G CPS security as a closed-loop, AI-native pipeline that senses at the multi-access edge computing (MEC) tier, using minute-scale call-detail records (CDRs) for baseline learning and sub-millisecond RAN/Open-RAN (O-RAN) telemetry for the latency-critical path. It decides locally with compressed deep models, mitigates network-wide via SDN, NFV, and O-RAN controllers, and retrains through federated learning (FL) and digital-twin (DT) replay. We formalise a per-slice, tail-bounded latency contract on the sense, detect, and mitigate stages, enforced at a slice-dependent tail percentile (p99 for safety-critical URLLC slices). Organising 128 peer-reviewed studies (2017-2026) under a PRISMA 2020 protocol, we (i) map the 6G/CPS threat surface to MITRE ATT&amp;CK and a CDR-observable feature space; (ii) unify edge anomaly detection and DDoS classification across twelve datasets and statistical, graph, and transformer models; (iii) synthesise SDN/NFV/O-RAN primitives into one closed-loop reference architecture; (iv) treat FL, large language models (LLMs), DT, post-quantum cryptography (PQC), zero-trust architecture (ZTA), and explainable AI as cross-cutting enablers, not parallel pillars; and (v) consolidate open problems into five directions spanning data, latency, trust, standardisation, and evaluation.","url":"https://doi.org/10.48550/arxiv.2606.08173","authors":["Hussain, Bilal","Bilal, Muhammad","Li, Tan","Pervaiz, Haris","Tang, Xiao","Du, Qinghe","Ahmad, Fawad","Azhar, Muhammad","Zhang, Jun"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","Networking and Internet Architecture (cs.NI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.08173","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.06154","name":"Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models","source":"datacite","abstract":"Federated fine-tuning of foundation models using Low-Rank Adaptation (LoRA) offers a communication efficient solution for distributed learning. However, existing federated LoRA methods suffer from two fundamental limitations: (1) structural aggregation bias, where independently averaging low rank factors fails to approximate the true combined update, and (2) client side initialization lag, as clients repeatedly reinitialize LoRA parameters across communication rounds, slowing convergence. We propose HyperLoRA, a unified framework that addresses both issues through amortized federated adaptation through hypernetwork-driven LoRA generation and product space aggregation. Instead of iterative per-client optimization, HyperLoRA employs a learned generator that maps client distribution signatures to LoRA initializations, effectively amortizing per client adaptation. On the server side, we introduce a learned aggregation module that directly synthesizes updates in the low-rank product space, eliminating the inconsistencies of factor-wise averaging. A lightweight residual correction module further improves stability under heterogenous (non-IID) client distributions.By replacing iterative optimization and heuristic averaging with learned operators, HyperLoRA jointly enables efficient personalization, unbiased aggregation, and faster convergence. Experiments on federated vision and vision-language benchmarks show that HyperLoRA achieves improved convergence speed, greater robustness to distribution shift, and stronger personalization performance compared to prior federated LoRA methods.","url":"https://doi.org/10.48550/arxiv.2606.06154","authors":["Gupta, Sunny","Shanker, Shambhavi","Sethi, Amit"],"tags":["Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.6; I.2.11; C.2.4; I.4; I.5.1","68T05, 68T07, 68W15, 65F55"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.06154","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2605.18936","name":"FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data","source":"datacite","abstract":"Social media text data are often used to train Machine Learning (ML) models to identify users exhibiting high-risk mental health behaviors. However, sharing this sensitive data poses privacy risks and limits the growth of benchmark datasets. We comprehensively evaluate whether privacy-preserving ML techniques can enable safer data sharing while preserving performance. Specifically, we apply federated learning (FL) and Differentially Private FL for two widely-studied mental health prediction tasks: depression detection on X (Twitter) and suicide crisis detection on Reddit. We simulate realistic data-sharing scenarios by treating each user as a client in a non-IID setting, evaluating across different client fractions, aggregation strategies, and privacy budgets. While FL achieves comparable performance to centralized training (centralized F1 = 85.63; best FL model F1 = 83.16) on depression identification, we find that Differentially Private FL has a large performance-privacy trade-off (up to F1 = 27.01 drop) even with low levels of noise (epsilon = 50). This is due to the distortion of highly informative yet sparse mental health linguistic markers related to mental health, like health topics and emotion words. This research empirically demonstrates the potential and limitations of current privacy preservation techniques for mental health inference tasks.","url":"https://doi.org/10.48550/arxiv.2605.18936","authors":["Abdelkadir, Nuredin Ali","Ratnam, Anjali","Talat, Zeerak","Chancellor, Stevie"],"tags":["Machine Learning (cs.LG)","Computation and Language (cs.CL)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2605.18936","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20533650","name":"The Human Sovereignty Technology Paradigm — Supplement Version 2.0: Extended Prior Art Declaration","source":"datacite","abstract":"Supplement Version 2.0 to the Human Sovereignty Technology Paradigm white paper (Version 1.0, March 29, 2026 �� zenodo.org/records/19323816). Establishes irrevocable public domain prior art for the Human Sovereignty Technology Platform (USPTO Provisional Application No. 64/020,809, filed March 29, 2026). Extends prior art coverage to: no-biometric embodiments of the Operator-Bound Technology Principle; third-party wearable device compatibility including commercially available smartwatches and fitness rings; CEEP capacitive electrode architecture for data destruction; modular replacement architecture with cryptographic module authentication; AI federated learning threat assessment protocol; multi-mode non-biometric trigger subsystem; passive safe-discharge adapter; and extended application embodiments covering financial systems, weapon authentication, child protection, medical systems, journalism, and legal profession source protection. All concepts herein are irrevocably dedicated to the public domain under CC0 1.0 Universal as of June 3, 2026. Constitutes published prior art under 35 U.S.C. § 102(a)(1). Cannot be retroactively classified after this publication date.","url":"https://doi.org/10.5281/zenodo.20533650","authors":["Kershner, Braydon Alexander"],"tags":["operator-bound technology","human sovereignty","data sovereignty","data destruction","prior art","patent pending","no-biometric authentication","CEEP electrode"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20533650","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20533651","name":"The Human Sovereignty Technology Paradigm — Supplement Version 2.0: Extended Prior Art Declaration","source":"datacite","abstract":"Supplement Version 2.0 to the Human Sovereignty Technology Paradigm white paper (Version 1.0, March 29, 2026 — zenodo.org/records/19323816). Establishes irrevocable public domain prior art for the Human Sovereignty Technology Platform (USPTO Provisional Application No. 64/020,809, filed March 29, 2026). Extends prior art coverage to: no-biometric embodiments of the Operator-Bound Technology Principle; third-party wearable device compatibility including commercially available smartwatches and fitness rings; CEEP capacitive electrode architecture for data destruction; modular replacement architecture with cryptographic module authentication; AI federated learning threat assessment protocol; multi-mode non-biometric trigger subsystem; passive safe-discharge adapter; and extended application embodiments covering financial systems, weapon authentication, child protection, medical systems, journalism, and legal profession source protection. All concepts herein are irrevocably dedicated to the public domain under CC0 1.0 Universal as of June 3, 2026. Constitutes published prior art under 35 U.S.C. § 102(a)(1). Cannot be retroactively classified after this publication date.","url":"https://doi.org/10.5281/zenodo.20533651","authors":["Kershner, Braydon Alexander"],"tags":["operator-bound technology","human sovereignty","data sovereignty","data destruction","prior art","patent pending","no-biometric authentication","CEEP electrode"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20533651","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.19712705","name":"Blockchain Solution with Artificial Intelligence Integration in the Indian Judicial System A Bibliometric and Methodical Literature Review","source":"datacite","abstract":"The Indian judicial system faces critical challenges including case backlogs exceeding 54.7 million pending matters, insufficient judicial resources, and inefficient evidence management processes. This literature review examines the emerging potential of integrating blockchain technology with artificial intelligence (AI) to transform judicial delivery, enhance case processing efficiency, and strengthen evidentiary integrity. Through systematic analysis of 88 peer-reviewed publications (2013–2026) across IEEE Xplore, Scopus, Springer, and Web of Science, we identified four dominant research themes: blockchain-based evidence management systems, AI-driven predictive justice and decision support, smart contracts for judicial automation, and privacy-preserving mechanisms for sensitive legal data. Key findings reveal that blockchain ensures significant reduction in evidence tampering incidents, while AI prediction models achieve notable accuracy in judicial outcome forecasting. However, significant implementation challenges persist, including scalability constraints, lack of comprehensive regulatory frameworks, and insufficient integration with legacy court systems. This paper synthesizes current scholarship, identifies critical research gaps, and proposes a four-layer conceptual framework for pragmatic AI-blockchain deployment suited to India's constitutional and legal context. We conclude that strategic integration prioritizing permissioned blockchain architectures, explainable AI models, and federated learning offers transformative potential for addressing judicial inefficiency while maintaining due process and fundamental rights protections.","url":"https://doi.org/10.5281/zenodo.19712705","authors":["Sameer Patil","Darshana Desai"],"tags":["Blockchain","Artificial Intelligence","Judicial System","Legal Technology","India","Evidence Management","Predictive Justice","Smart Contracts"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19712705","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2606.01607","name":"FedMTFI: Feature Importance Based Optimized Multi Teacher Knowledge Distillation in Heterogeneous Federated Learning Environment","source":"datacite","abstract":"Federated learning (FL) is a decentralized approach that enables collaborative model training without exposing raw data. Instead of transferring sensitive data, it allows devices to share only model weights, keeping personal data locally and secure. However, in real world settings, the data held by devices is often not evenly distributed and devices mostly differ in computing power and memory capacity. These differences make FL harder to maintain consistent performance across the system. To address these issues, we propose FedMTFI, a novel architecture that combines multi-teacher knowledge distillation (MTKD) with feature importance to improve the FL process in heterogeneous environments. In FedMTFI, clients are clustered based on similar hardware and model types. Each cluster trains a specific model on not independently and identically distributed (non-IID) data. Within a cluster, every client updates that model using only its own local private data. The server then aggregates the locally trained models in each cluster using FedAvg to form multiple prototype models. Then these prototypes serve as teacher models to train a global generalized student model using MTKD. What makes FedMTFI more unique is the integration of Shapley values (SHAP) to emphasize important features during distillation, which enhances both accuracy and interpretability. Experimental results show that FedMTFI achieves higher accuracy than traditional FL algorithms and performs more effectively under non-IID data conditions.","url":"https://doi.org/10.48550/arxiv.2606.01607","authors":["Shadin, Nazmus Shakib","Cummings, Aaron","Zhang, Xinyue","Deng, Bobin"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.01607","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.5281/zenodo.20500935","name":"Pre-Registered Multi-Dataset Validation of Three-Way Composition III Privacy-Preserving Architecture for Musculoskeletal-Kinematic Clinical Digital Twins","source":"datacite","abstract":"v2 update (2026-06-01): Reproducibility ZIP added back to the latest version alongside the manuscript files, so that downloading from the concept DOI gives all materials in one place rather than requiring navigation to v1. This reproducibility archive accompanies the Paper 8 manuscript \"Pre-Registered Multi-Dataset Validation of Three-Way Composition III Privacy-Preserving Architecture for Musculoskeletal-Kinematic Clinical Digital Twins.\" Background. Clinical digital twin (DT) systems for musculoskeletal rehabilitation increasingly aggregate longitudinal kinematic data across multiple sites and patient populations. These deployments require simultaneous guarantees of (a) federated coefficient learning utility, (b) membership-inference privacy against realistic adversaries, (c) byte-budgeted transmission for Internet-of-Things radio links, and (d) deployment-time robustness to anatomical, populational, and protocol heterogeneity. Methods. We present a pre-registered empirical validation campaign spanning 21 studies and 91 hypotheses for the three-way Composition III architecture (per-subject phase randomization, cohort mean aggregation, federated AR(1) coefficient learning) applied to musculoskeletal-kinematic clinical DT deployments. Each study's decision rules were frozen before runner execution. The campaign covers foundational properties; Composition III privacy and utility under homogeneous, moderately heterogeneous, and extremely heterogeneous (50:1 imbalance, 50x sensor noise differential) deployments; multi-classifier shadow-attacker bounds across LogisticRegression, RandomForest, and GradientBoosting families; differential-privacy noise calibration with non-monotone trade-off characterization; multi-period longitudinal observation up to T=10 periods; cross-anatomy generalization from N=3 (knee) to N=23 (hand-MANO model); and three real-world dataset validations using CMU Motion Capture Database (walking) and KIMORE Rehabilitation Dataset (rehabilitation exercises with 44 healthy controls plus 34 subjects with low-back pain, Parkinson's disease, and post-stroke conditions). Results. 71 of 91 pre-registered hypotheses supported (78%). All 20 non-supported outcomes are substantively interpretable: 3 metric-choice issues resolved by follow-up studies, 4 deployment-condition-dependent disclosures (including a non-monotone DP privacy-utility trade-off, an anatomy-specific sigma_DP scaling law, and a heterogeneity x longitudinal compound-leakage interaction), and 13 honest bounded-negatives identifying empirical boundaries of the recommended deployment configuration. Three findings stand out: (i) the realistic-attacker bound generalizes within +/- 0.06 across synthetic and two real datasets (maximum observed 0.60 against multi-classifier shadow attackers); (ii) patient-vs-healthy subgroup analysis on KIMORE yields identical oracle accuracy across populations (delta = 0.0000); (iii) anatomy-specific DP calibration scaling sigma_DP proportional to 1/sqrt(N) validated for N >= 5 with explicit knee N=3 outlier disclosure. Conclusions. Composition III is a viable privacy-preserving federated learning architecture for musculoskeletal-kinematic clinical digital twins. Empirical validation across synthetic data, two real datasets, multiple anatomies, three classifier families, three observation horizons, and mixed healthy/patient populations provides comprehensive grounding for clinical deployment. Archive contents. 21 frozen pre-registrations, 21 deterministic Python runners, 21 study reports, 21 verdict summary JSON files, 21 raw CSV data files, source code for the spiral-domain encoder and CMU MoCap and KIMORE parsers, 6 manuscript figures with generators, and the manuscript itself (Markdown and Word formats). Raw third-party data files are not redistributed per their respective licensing terms; download instructions and subject IDs are documented. Companion datasets. CMU Motion Capture Database (CC-BY 3.0, http://mocap.cs.cmu.ed","url":"https://doi.org/10.5281/zenodo.20500935","authors":["Ferlic, Randolph James","Ferlic, Kimberly Kate"],"tags":["pre-registration","federated learning","differential privacy","membership inference","musculoskeletal kinematics","clinical digital twin","Composition III","rehabilitation engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20500935","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.48550/arxiv.2602.01186","name":"The Gaussian-Head OFL Family: One-Shot Federated Learning from Client Global Statistics","source":"datacite","abstract":"Classical Federated Learning relies on a multi-round iterative process of model exchange and aggregation between server and clients, with high communication costs and privacy risks from repeated model transmissions. In contrast, one-shot federated learning (OFL) alleviates these limitations by reducing communication to a single round, thereby lowering overhead and enhancing practical deployability. Nevertheless, most existing one-shot approaches remain either impractical or constrained, for example, they often depend on the availability of a public dataset, assume homogeneous client models, or require uploading additional data or model information. To overcome these issues, we introduce the Gaussian-Head OFL (GH-OFL) family, a suite of one-shot federated methods that assume class-conditional Gaussianity of pretrained embeddings. Clients transmit only sufficient statistics (per-class counts and first/second-order moments) and the server builds heads via three components: (i) Closed-form Gaussian heads (NB/LDA/QDA) computed directly from the received statistics; (ii) FisherMix, a linear head with cosine margin trained on synthetic samples drawn in an estimated Fisher subspace; and (iii) Proto-Hyper, a lightweight low-rank residual head that refines Gaussian logits via knowledge distillation on those synthetic samples. In our experiments, GH-OFL methods deliver state-of-the-art robustness and accuracy under strong non-IID skew while remaining strictly data-free.","url":"https://doi.org/10.48550/arxiv.2602.01186","authors":["Turazza, Fabio","Picone, Marco","Mamei, Marco"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.01186","addedAt":"2026-08-31T06:41:35.442Z","updatedAt":"2026-08-31T06:41:35.442Z"},{"id":"doi:10.30953/bhty.v7.345","name":"Healthcare Futures: Opportunities, Challenges and Risks in a Blockchain-Driven Environment.","source":"europepmc","abstract":"","url":"https://doi.org/10.30953/bhty.v7.345","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.30953/bhty.v7.345","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1016/j.dib.2026.112668","name":"Data spaces and self-sovereign identity: Ecosystem landscape and a systematic literature review on Gaia-X-aligned data spaces.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.dib.2026.112668","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.dib.2026.112668","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/diagnostics14242891","name":"Federated Learning with Privacy Preserving for Multi- Institutional Three-Dimensional Brain Tumor Segmentation.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/diagnostics14242891","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3390/diagnostics14242891","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1371/journal.pone.0307738","name":"TPAAS: Trustworthy privacy-preserving anonymous authentication scheme for online trading environment.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0307738","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1371/journal.pone.0307738","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3389/fdata.2025.1556157","name":"Safeguarding digital livestock farming - a comprehensive cybersecurity roadmap for dairy and poultry industries.","source":"europepmc","abstract":"The rapid digital transformation of dairy and poultry farming through big data analytics and Internet of Things (IoT) innovations has significantly advanced precision management of feeding, animal health, and environmental conditions. However, this digitization has simultaneously escalated cybersecurity vulnerabilities, presenting serious threats to economic stability, animal welfare, and food safety. This paper provides an in-depth analysis of the evolving cyber threat landscape confronting digital livestock farming, examining ransomware incidents, hacktivist interference, and state-sponsored cyber intrusions. It critically assesses how compromised digital systems disrupt critical farm operations, including milking routines, feed formulations, and climate control, profoundly impacting animal health, productivity, and consumer trust. Responding to these challenges, we present a comprehensive cybersecurity roadmap that integrates established IT security practices with agriculture-specific requirements. The roadmap emphasizes advanced solutions, such as AI-driven anomaly detection, blockchain-based traceability, and integrated cybersecurity-biosecurity frameworks, tailored explicitly to safeguard livestock farming. Additionally, we highlight human-centric elements such as targeted workforce education, rural cybersecurity capacity building, and robust cross-sector collaboration as indispensable components of a resilient cybersecurity ecosystem. By synthesizing technical advancements, regulatory perspectives, and socio-economic insights, the paper proposes a proactive strategy to enhance data integrity, secure animal welfare, and reinforce food supply chains. Ultimately, we underscore that effective cybersecurity is not merely a technical consideration but foundational to ensuring the sustainable, ethical, and trustworthy advancement of livestock agriculture in a data-driven world.","url":"https://doi.org/10.3389/fdata.2025.1556157","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fdata.2025.1556157","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.4239/wjd.v16.i3.98408","name":"Prospects and perils of ChatGPT in diabetes.","source":"europepmc","abstract":"","url":"https://doi.org/10.4239/wjd.v16.i3.98408","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.4239/wjd.v16.i3.98408","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-024-73863-1","name":"Federated influencer learning for secure and efficient collaborative learning in realistic medical database environment.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-024-73863-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1038/s41598-024-73863-1","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.patter.2024.101031","name":"A federated learning architecture for secure and private neuroimaging analysis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.patter.2024.101031","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1016/j.patter.2024.101031","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.3389/frai.2025.1576992","name":"DeepSeek vs. ChatGPT: prospects and challenges.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2025.1576992","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/frai.2025.1576992","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3389/fdgth.2025.1620270","name":"Synthetic data in medical imaging within the EHDS: a path forward for ethics, regulation, and standards.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdgth.2025.1620270","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fdgth.2025.1620270","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/bioengineering11121265","name":"A Novel Grammar-Based Approach for Patients' Symptom and Disease Diagnosis Information Dissemination to Maintain Confidentiality and Information Integrity.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/bioengineering11121265","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3390/bioengineering11121265","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1093/gigascience/giae021","name":"Future-proofing genomic data and consent management: a comprehensive review of technology innovations.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/gigascience/giae021","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1093/gigascience/giae021","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/s25113488","name":"Future of Telepresence Services in the Evolving Fog Computing Environment: A Survey on Research and Use Cases.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25113488","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25113488","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3389/fdgth.2025.1602101","name":"Secondary use under the European Health Data Space: setting the scene and towards a research agenda on privacy-enhancing technologies.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdgth.2025.1602101","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fdgth.2025.1602101","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/e26070590","name":"A Conditional Privacy-Preserving Identity-Authentication Scheme for Federated Learning in the Internet of Vehicles.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e26070590","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3390/e26070590","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/biology15090726","name":"Why Should a Genome Be Protected? Ethical, Legal, and Security Challenges in the Protection of Genomic Data.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/biology15090726","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/biology15090726","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-025-00757-1","name":"ACHealthChain blockchain framework for access control and privacy preservation in healthcare.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-00757-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-00757-1","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.1371/journal.pone.0311782","name":"Computational challenges and solutions: Prime number generation for enhanced data security.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0311782","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1371/journal.pone.0311782","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1109/tp.2024.3392721","name":"U.S.-U.K. PETs Prize Challenge: Anomaly Detection via Privacy-Enhanced Federated Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tp.2024.3392721","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1109/tp.2024.3392721","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/s24227405","name":"Quantum Privacy-Preserving Range Query Protocol for Encrypted Data in IoT Environments.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s24227405","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3390/s24227405","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1093/bib/bbaf329","name":"Advancing genome-based precision medicine: a review on machine learning applications for rare genetic disorders.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/bib/bbaf329","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1093/bib/bbaf329","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3389/fnagi.2024.1324032","name":"Secure federated learning for Alzheimer's disease detection.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fnagi.2024.1324032","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/fnagi.2024.1324032","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-024-77792-x","name":"Blockchain-based energy consumption approaches in IoT.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-024-77792-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1038/s41598-024-77792-x","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1186/s44342-024-00032-1","name":"Rare disease genomics and precision medicine.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s44342-024-00032-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1186/s44342-024-00032-1","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3389/frai.2025.1546064","name":"Legal regulation of AI-assisted academic writing: challenges, frameworks, and pathways.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2025.1546064","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/frai.2025.1546064","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-23218-1","name":"Strategy for maximizing space utilization in smart libraries based on reinforcement learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-23218-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-23218-1","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-024-84797-z","name":"Based on model randomization and adaptive defense for federated learning schemes.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-024-84797-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-024-84797-z","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.1038/s41598-025-95995-8","name":"A compressed image encryption algorithm leveraging optimized 3D chaotic maps for secure image communication.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-95995-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-95995-8","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3389/frai.2024.1397480","name":"Exploring security threats and solutions Techniques for Internet of Things (IoT): from vulnerabilities to vigilance.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2024.1397480","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/frai.2024.1397480","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.48550/arxiv.2307.08847","name":"Privacy-preserving patient clustering for personalized federated learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.48550/arxiv.2307.08847","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2307.08847","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1177/20552076251381769","name":"Balancing privacy and performance in healthcare: A federated learning framework for sensitive data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1177/20552076251381769","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1177/20552076251381769","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/e25020226","name":"On the Security of Offloading Post-Processing for Quantum Key Distribution.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e25020226","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/e25020226","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1093/nsr/nwab115","name":"Privacy-preserving computation in the post-quantum era.","source":"pubmed","abstract":"This perspectives article surveys the most promising privacy-preserving cryptographic technologies including secure multiparty computation, zero-knowledge proofs and fully homomorphic encryption, and their various real-world applications.","url":"https://doi.org/10.1093/nsr/nwab115","authors":["Yu Y","Xie X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1093/nsr/nwab115","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"doi:10.7759/cureus.67546","name":"Mapping Research Trends and Collaborative Networks in Swarm Intelligence for Healthcare Through Visualization.","source":"europepmc","abstract":"","url":"https://doi.org/10.7759/cureus.67546","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.7759/cureus.67546","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1186/s13059-024-03296-6","name":"Legal aspects of privacy-enhancing technologies in genome-wide association studies and their impact on performance and feasibility.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s13059-024-03296-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1186/s13059-024-03296-6","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1371/journal.pcbi.1012626","name":"Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pcbi.1012626","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1371/journal.pcbi.1012626","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.heliyon.2024.e31406","name":"Post-quantum healthcare: A roadmap for cybersecurity resilience in medical data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.heliyon.2024.e31406","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1016/j.heliyon.2024.e31406","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1002/mnfr.70293","name":"AI-Driven Personalized Nutrition: Integrating Omics, Ethics, and Digital Health.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/mnfr.70293","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1002/mnfr.70293","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s43588-025-00832-7","name":"Privacy-preserving multicenter differential protein abundance analysis with FedProt.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s43588-025-00832-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s43588-025-00832-7","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1016/j.jaci.2024.01.002","name":"Proceedings from the inaugural Artificial Intelligence in Primary Immune Deficiencies (AIPID) conference.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.jaci.2024.01.002","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1016/j.jaci.2024.01.002","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.7717/peerj-cs.1962","name":"Enhancing secure multi-group data sharing through integration of IPFS and hyperledger fabric.","source":"europepmc","abstract":"","url":"https://doi.org/10.7717/peerj-cs.1962","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.7717/peerj-cs.1962","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:37.833Z"},{"id":"doi:10.1038/s41598-025-04971-9","name":"A hybrid rule-based NLP and machine learning approach for PII detection and anonymization in financial 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Neuro-Oncology.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/ijms26199409","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/ijms26199409","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:37.833Z"},{"id":"doi:10.1371/journal.pone.0310747","name":"DecentralDC: Assessing data contribution under decentralized sharing and exchange blockchain.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0310747","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1371/journal.pone.0310747","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/68661","name":"A System Model and Requirements for Transformation to Human-Centric Digital Health.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/68661","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.2196/68661","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1155/2021/6967166","name":"Privacy Protection and Secondary Use of Health Data: Strategies and Methods.","source":"europepmc","abstract":"","url":"https://doi.org/10.1155/2021/6967166","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1155/2021/6967166","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:37.833Z"},{"id":"doi:10.3390/s24134152","name":"Enhanced Network Intrusion Detection System for Internet of Things Security Using Multimodal Big Data Representation with Transfer Learning and Game Theory.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s24134152","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3390/s24134152","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3389/fmed.2024.1301660","name":"Health data space nodes for privacy-preserving linkage of medical data to support collaborative secondary analyses.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fmed.2024.1301660","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/fmed.2024.1301660","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/s25010205","name":"Issues and Limitations on the Road to Fair and Inclusive AI Solutions for Biomedical Challenges.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25010205","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25010205","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1016/j.vas.2026.100702","name":"Generative artificial intelligence in animal genomics for smart agriculture: Applications, challenges, and future prospects.","source":"europepmc","abstract":"Generative artificial intelligence (AI) is becoming a groundbreaking paradigm in the field of animal genomics and is providing the possibility to take a step towards intelligent agriculture, with better data integration, predictive modeling, and biological design. This review focuses on the shift from predictive to generative modelling paradigms, examining their implications for data synthesis, biological sequence design, and integrative smart livestock systems. It provides a comprehensive overview of recent developments, applications, and challenges at the intersection of generative AI and animal genomics, as well as future directions. In doing so, it sheds light on novel opportunities and constraints specific to livestock genomics that are not adequately addressed in broader AI or human genomics studies. Thus, it bridges the gap between computational innovations and biological constraints. It initially sets the conceptual background in place by looking at the development of smart agriculture, the essentiality of animal genomics, and the development of generative model architectures in life sciences, as well as fundamental methodological aspects, including livestock genomic and multi-omics data peculiarities and the representation of biological sequences. The review then comprehensively discusses a wide range of applications such as genomic data augmentation, prediction of new genetic variants, design of protein and gene sequences, augmentation of genomic selection and trait prediction, regulatory and epigenomic modeling, accurate breeding and reproductive technologies, and cross-species genomic modeling, illustrating how generative AI is transforming genomics into something generative, enhanced through simulation. It is discussed in terms of integration into systems of smart agriculture, connections with precision livestock farming, digital twin, genomics-to-management pipelines, and sustainability-focused systems of decision-making, where the adaptive, individualized, and system-level optimization can be applied. Critical analysis of major challenges and limitations, such as heterogeneity and scarcity of data, model bias and generalization, computational and resource limitations, validation and interpretability issues, and ethical, legal, and social constraints that drove the responsible deployment are also critically reviewed. Lastly, the future opportunities are discussed, which should center on generative genome engineering, multimodal and federated modeling, species preservation, real-time interaction with smart farming technologies, and the creation of responsible and ethical AI frameworks. Overall, it is possible to state that this review makes generative AI a base technology of the new generation of animal genomics and smart agriculture, but it highlights that interdisciplinary cooperation, stringent validation, and alignment with the notions of sustainability, animal welfare, or values are necessary to realize its capabilities.","url":"https://doi.org/10.1016/j.vas.2026.100702","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.vas.2026.100702","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.48550/arxiv.2409.12021","name":"Optimal Offline ORAM with Perfect Security via Simple Oblivious Priority Queues","source":"datacite","abstract":"Oblivious RAM (ORAM) is a well-researched primitive to hide the memory access pattern of a RAM computation; it has a variety of applications in trusted computing, outsourced storage, and multiparty computation. In this paper, we study the so-called offline ORAM in which the sequence of memory access locations to be hidden is known in advance. Apart from their theoretical significance, offline ORAMs can be used to construct efficient oblivious algorithms. We obtain the first optimal offline ORAM with perfect security from oblivious priority queues via time-forward processing. For this, we present a simple construction of an oblivious priority queue with perfect security. Our construction achieves an asymptotically optimal (amortized) runtime of $Θ(\\log N)$ per operation for a capacity of $N$ elements and is of independent interest. Building on our construction, we additionally present efficient external-memory instantiations of our oblivious, perfectly-secure construction: For the cache-aware setting, we match the optimal I/O complexity of $Θ(\\frac{1}{B} \\log \\frac{N}{M})$ per operation (amortized), and for the cache-oblivious setting we achieve a near-optimal I/O complexity of $O(\\frac{1}{B} \\log \\frac{N}{M} \\log\\log_M N)$ per operation (amortized).","url":"https://doi.org/10.48550/arxiv.2409.12021","authors":["Thießen, Thore","Vahrenhold, Jan"],"tags":["Data Structures and Algorithms (cs.DS)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.12021","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:36.530Z"},{"id":"doi:10.4230/lipics.isaac.2024.55","name":"Optimal Offline ORAM with Perfect Security via Simple Oblivious Priority Queues","source":"datacite","abstract":"Oblivious RAM (ORAM) is a well-researched primitive to hide the memory access pattern of a RAM computation; it has a variety of applications in trusted computing, outsourced storage, and multiparty computation. In this paper, we study the so-called offline ORAM in which the sequence of memory access locations to be hidden is known in advance. Apart from their theoretical significance, offline ORAMs can be used to construct efficient oblivious algorithms. We obtain the first optimal offline ORAM with perfect security from oblivious priority queues via time-forward processing. For this, we present a simple construction of an oblivious priority queue with perfect security. Our construction achieves an asymptotically optimal (amortized) runtime of Θ(log N) per operation for a capacity of N elements and is of independent interest. Building on our construction, we additionally present efficient external-memory instantiations of our oblivious, perfectly-secure construction: For the cache-aware setting, we match the optimal I/O complexity of Θ(1/B log N/M) per operation (amortized), and for the cache-oblivious setting we achieve a near-optimal I/O complexity of O(1/B log N/M log log_M N) per operation (amortized).","url":"https://doi.org/10.4230/lipics.isaac.2024.55","authors":["Thießen, Thore","Vahrenhold, Jan"],"tags":["offline ORAM","oblivious priority queue","perfect security","external memory algorithm","cache-oblivious algorithm","Theory of computation → Data structures design and analysis","Theory of computation → Cryptographic protocols"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.4230/lipics.isaac.2024.55","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:36.530Z"},{"id":"doi:10.4230/lipics.itc.2024.10","name":"Secure Multiparty Computation of Symmetric Functions with Polylogarithmic Bottleneck Complexity and Correlated Randomness","source":"datacite","abstract":"Bottleneck complexity is an efficiency measure of secure multiparty computation (MPC) protocols introduced to achieve load-balancing in large-scale networks, which is defined as the maximum communication complexity required by any one player within the protocol execution. Towards the goal of achieving low bottleneck complexity, prior works proposed MPC protocols for computing symmetric functions in the correlated randomness model, where players are given input-independent correlated randomness in advance. However, the previous protocols with polylogarithmic bottleneck complexity in the number n of players require a large amount of correlated randomness that is linear in n, which limits the per-party efficiency as receiving and storing correlated randomness are the bottleneck for efficiency. In this work, we present for the first time MPC protocols for symmetric functions such that bottleneck complexity and the amount of correlated randomness are both polylogarithmic in n, assuming semi-honest adversaries colluding with at most n-o(n) players. Furthermore, one of our protocols is even computationally efficient in that each player performs only polylog(n) arithmetic operations while the computational complexity of the previous protocols is O(n). Technically, our efficiency improvements come from novel protocols based on ramp secret sharing to realize basic functionalities with low bottleneck complexity, which we believe may be of interest beyond their applications to secure computation of symmetric functions.","url":"https://doi.org/10.4230/lipics.itc.2024.10","authors":["Eriguchi, Reo"],"tags":["Secure multiparty computation","Bottleneck complexity","Secret sharing","Security and privacy → Information-theoretic techniques"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.4230/lipics.itc.2024.10","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:36.530Z"},{"id":"doi:10.48550/arxiv.2403.11166","name":"Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption","source":"datacite","abstract":"The escalating focus on data privacy poses significant challenges for collaborative neural network training, where data ownership and model training/deployment responsibilities reside with distinct entities. Our community has made substantial contributions to addressing this challenge, proposing various approaches such as federated learning (FL) and privacy-preserving machine learning based on cryptographic constructs like homomorphic encryption (HE) and secure multiparty computation (MPC). However, FL completely overlooks model privacy, and HE has limited extensibility (confined to only one data provider). While the state-of-the-art MPC frameworks provide reasonable throughput and simultaneously ensure model/data privacy, they rely on a critical non-colluding assumption on the computing servers, and relaxing this assumption is still an open problem. In this paper, we present Pencil, the first private training framework for collaborative learning that simultaneously offers data privacy, model privacy, and extensibility to multiple data providers, without relying on the non-colluding assumption. Our fundamental design principle is to construct the n-party collaborative training protocol based on an efficient two-party protocol, and meanwhile ensuring that switching to different data providers during model training introduces no extra cost. We introduce several novel cryptographic protocols to realize this design principle and conduct a rigorous security and privacy analysis. Our comprehensive evaluations of Pencil demonstrate that (i) models trained in plaintext and models trained privately using Pencil exhibit nearly identical test accuracies; (ii) The training overhead of Pencil is greatly reduced: Pencil achieves 10 ~ 260x higher throughput and 2 orders of magnitude less communication than prior art; (iii) Pencil is resilient against both existing and adaptive (white-box) attacks.","url":"https://doi.org/10.48550/arxiv.2403.11166","authors":["Liu, Xuanqi","Liu, Zhuotao","Li, Qi","Xu, Ke","Xu, Mingwei"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2403.11166","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.591Z"},{"id":"doi:10.48550/arxiv.2402.09059","name":"I can't see it but I can Fine-tune it: On Encrypted Fine-tuning of Transformers using Fully Homomorphic Encryption","source":"datacite","abstract":"In today's machine learning landscape, fine-tuning pretrained transformer models has emerged as an essential technique, particularly in scenarios where access to task-aligned training data is limited. However, challenges surface when data sharing encounters obstacles due to stringent privacy regulations or user apprehension regarding personal information disclosure. Earlier works based on secure multiparty computation (SMC) and fully homomorphic encryption (FHE) for privacy-preserving machine learning (PPML) focused more on privacy-preserving inference than privacy-preserving training. In response, we introduce BlindTuner, a privacy-preserving fine-tuning system that enables transformer training exclusively on homomorphically encrypted data for image classification. Our extensive experimentation validates BlindTuner's effectiveness by demonstrating comparable accuracy to non-encrypted models. Notably, our findings highlight a substantial speed enhancement of 1.5x to 600x over previous work in this domain.","url":"https://doi.org/10.48550/arxiv.2402.09059","authors":["Panzade, Prajwal","Takabi, Daniel","Cai, Zhipeng"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2402.09059","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:40.591Z"},{"id":"doi:10.48550/arxiv.2401.13386","name":"Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain","source":"datacite","abstract":"Face recognition technology has been deployed in various real-life applications. The most sophisticated deep learning-based face recognition systems rely on training millions of face images through complex deep neural networks to achieve high accuracy. It is quite common for clients to upload face images to the service provider in order to access the model inference. However, the face image is a type of sensitive biometric attribute tied to the identity information of each user. Directly exposing the raw face image to the service provider poses a threat to the user's privacy. Current privacy-preserving approaches to face recognition focus on either concealing visual information on model input or protecting model output face embedding. The noticeable drop in recognition accuracy is a pitfall for most methods. This paper proposes a hybrid frequency-color fusion approach to reduce the input dimensionality of face recognition in the frequency domain. Moreover, sparse color information is also introduced to alleviate significant accuracy degradation after adding differential privacy noise. Besides, an identity-specific embedding mapping scheme is applied to protect original face embedding by enlarging the distance among identities. Lastly, secure multiparty computation is implemented for safely computing the embedding distance during model inference. The proposed method performs well on multiple widely used verification datasets. Moreover, it has around 2.6% to 4.2% higher accuracy than the state-of-the-art in the 1:N verification scenario.","url":"https://doi.org/10.48550/arxiv.2401.13386","authors":["Han, Dong","Li, Yong","Denzler, Joachim"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2401.13386","addedAt":"2026-08-31T06:41:36.530Z","updatedAt":"2026-08-31T06:41:36.530Z"},{"id":"doi:10.64751/ajmimc.2025.v4.n3.pp92-96","name":"Secure Multiparty Computation For Data Sharing","source":"crossref","abstract":"Secure Multiparty Computation (SMPC) enables multiple parties to collaborate and compute outcomes from their respective private data without sharing the data per se with one another. This document discusses applying the additive secret sharing method in order to reach such secure computation. Here, a portion of each datum is divided into random shares that are distributed between the members. No individual share reveals any information regarding the initial data. The operations, e.g., addition and multiplication, are performed directly on these shared values, and the resulting value can be achieved only once all shares are added together. This method maintains the data confidential yet useful computations can be executed. It is scalable, cost-effective, and highly secure against data leakage, and thus perfect for use in secure cloud computing, data analytics, and privacy-preserving machine learning.","url":"https://doi.org/10.64751/ajmimc.2025.v4.n3.pp92-96","authors":["Mrs. DURGABHAVANI BATTU","BHAVANA SINGH CHOUHAN","ARE VARSHINI","BANOTH SRAVANTHI"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-16T02:48:03Z","doi":"10.64751/ajmimc.2025.v4.n3.pp92-96","addedAt":"2026-08-31T06:41:36.958Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.3390/brainsci15090990","name":"Adaptive Multimodal Fusion in Vertical Federated Learning for Decentralized Glaucoma Screening.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/brainsci15090990","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/brainsci15090990","addedAt":"2026-08-31T06:41:36.959Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41431-025-01927-5","name":"MINDDS-connect: a federated data platform integrating biobanks for meta cohort building and analysis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41431-025-01927-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41431-025-01927-5","addedAt":"2026-08-31T06:41:36.959Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1007/s40747-025-02022-4","name":"Verticox+: vertically distributed Cox proportional hazards model with improved privacy guarantees.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s40747-025-02022-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1007/s40747-025-02022-4","addedAt":"2026-08-31T06:41:36.959Z","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.2196/72874","name":"Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/72874","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.2196/72874","addedAt":"2026-08-31T06:41:36.959Z","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.3390/s25061913","name":"A Survey on Secure WiFi Sensing Technology: Attacks and Defenses.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25061913","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25061913","addedAt":"2026-08-31T06:41:36.959Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-32625-3","name":"Time-managed PAPR use enables a balanced approach to infection control and personal freedom.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-32625-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-32625-3","addedAt":"2026-08-31T06:41:36.959Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/s25185711","name":"Continuous Authentication in Resource-Constrained Devices via Biometric and Environmental Fusion.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25185711","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25185711","addedAt":"2026-08-31T06:41:36.959Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41746-026-02538-0","name":"Precision cardiovascular medicine with big data and AI.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41746-026-02538-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41746-026-02538-0","addedAt":"2026-08-31T06:41:36.959Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1186/s12951-025-03947-1","name":"AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12951-025-03947-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1186/s12951-025-03947-1","addedAt":"2026-08-31T06:41:36.959Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.21037/tp-2025-349","name":"The development and validation of a privacy-preserving model based on federated learning for diagnosing severe pediatric pneumonia.","source":"europepmc","abstract":"","url":"https://doi.org/10.21037/tp-2025-349","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21037/tp-2025-349","addedAt":"2026-08-31T06:41:36.959Z","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.1371/journal.pone.0328899","name":"GNN-RMNet: Leveraging graph neural networks and GPS analytics for driver behavior and route optimization in logistics.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0328899","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1371/journal.pone.0328899","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.1038/s41467-021-25972-y","name":"Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption.","source":"pubmed","abstract":"Using real-world evidence in biomedical research, an indispensable complement to clinical trials, requires access to large quantities of patient data that are typically held separately by multiple healthcare institutions. We propose FAMHE, a novel federated analytics system that, based on multiparty homomorphic encryption (MHE), enables privacy-preserving analyses of distributed datasets by yielding highly accurate results without revealing any intermediate data. We demonstrate the applicability of FAMHE to essential biomedical analysis tasks, including Kaplan-Meier survival analysis in oncology and genome-wide association studies in medical genetics. Using our system, we accurately and efficiently reproduce two published centralized studies in a federated setting, enabling biomedical insights that are not possible from individual institutions alone. Our work represents a necessary key step towards overcoming the privacy hurdle in enabling multi-centric scientific collaborations.","url":"https://doi.org/10.1038/s41467-021-25972-y","authors":["Froelicher D","Troncoso-Pastoriza JR","Raisaro JL","Cuendet MA","Sousa JS","Cho H","Berger B","Fellay J","Hubaux JP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1038/s41467-021-25972-y","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1109/tp.2025.3628998","name":"Privacy-Preserving Verification of ML Preprocessing via Model Behavior Indicators.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tp.2025.3628998","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1109/tp.2025.3628998","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3389/fpubh.2022.814163","name":"Selecting Privacy-Enhancing Technologies for Managing Health Data Use.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fpubh.2022.814163","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3389/fpubh.2022.814163","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-025-32432-w","name":"Trust and class aware service discovery with dual control in the social Internet of Things.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-32432-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-025-32432-w","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3748/wjg.v31.i39.110971","name":"Artificial intelligence in pancreatitis: A narrative review on advancing precision diagnosis, prognosis, and therapeutic strategies.","source":"europepmc","abstract":"","url":"https://doi.org/10.3748/wjg.v31.i39.110971","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3748/wjg.v31.i39.110971","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.1007/s12325-025-03198-4","name":"The Limitations of Artificial Intelligence in Head and Neck Oncology.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s12325-025-03198-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1007/s12325-025-03198-4","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.1038/s41598-025-30445-z","name":"BlueEdge neural network approach and its application to automated data type classification in mobile edge computing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-30445-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-30445-z","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.1038/s41576-022-00455-y","name":"Sociotechnical safeguards for genomic data privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41576-022-00455-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1038/s41576-022-00455-y","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:38.003Z"},{"id":"doi:10.5281/zenodo.15530617","name":"FastLloyd","source":"datacite","abstract":"FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacy FastLloyd is an approach to privacy-preserving k-means clustering in horizontally federated settings. It offersstate-of-the-art utility while providing formal privacy guarantees through differential privacy, and achieves orders ofmagnitude faster performance compared to previous privacy-preserving clustering methods. Overview This repository implements the FastLloyd protocol described in the paper \"FastLloyd: Federated, Accurate, Secure, andTunable k-Means Clustering with Differential Privacy\". FastLloyd addresses the challenging problem of collaborativeclustering across multiple data owners without compromising privacy, through: 1. A novel differentially private k-means algorithm with radius constraints2. A lightweight secure aggregation protocol for federated settings Installation Requirements - Python 3.8 or higher- Open MPI (for multiparty communication)- Required Python packages listed in `env.yml` Setup 1. Download the repoistory 2. Extract the data and navigate to code directory: mkdir -p datafor f in *.tar.xz; do tar --extract --xz --file=\"$f\" --directory=datadonemv data D-Diaa-FastLloyd-879df3a/cd D-Diaa-FastLloyd-879df3a/ 3. Create and activate the conda environment: conda env create -f env.ymlconda activate fastlloyd Usage Running Experiments FastLloyd supports multiple experiment types: 1. **Accuracy**: Evaluate clustering quality across different privacy settings python experiments.py --exp_type \"accuracy\" 2. **Scale**: Analyze scalability with dataset size, dimensions, and number of clusters python experiments.py --exp_type \"scale\" 3. **Timing**: Measure communication and computation time mpirun -np 3 python experiments.py --exp_type \"timing\" You can also use the provided scripts to run multiple experiment types: bash scripts/experiment_runner.sh # For accuracy and scale experimentsbash scripts/timing_runner.sh # For timing experiments with varying numbers of clients Visualization The repository includes several visualization tools in the `plots` directory: - `per_dataset.py`: Creates performance visualizations for individual datasets- `scale_heatmap.py`: Generates heatmaps to analyze scalability- `synthetic_bar.py`: Creates bar plots comparing performance on synthetic datasets- `ablation_plots.py`: Creates plots for ablation studies- `timing_analysis.py`: Analyzes and reports execution timing data Customization You can customize various aspects of the experiments through the argument parser in `experiments.py`: python experiments.py --exp_type \"test\" --datasets \"mnist\" \"adult\" --method \"diagonal_then_frac\" --alpha 0.8 --post \"fold\" --results_folder \"my_results\" Key parameters include: - `--exp_type`: Type of experiment to run (accuracy, scale, timing, test)- `--datasets`: Datasets to use for the experiment- `--method`: Maximum distance method to use- `--alpha`: Maximum distance parameter- `--post`: Post-processing method for centroids- `--results_folder`: Folder to store results Citation If you use FastLloyd in your research, please cite the paper: @inproceedings{diaa2025fastlloyd, title={FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy}, author={A. Diaa and T. Humphries and F. Kerschbaum}, eventtitle = {The 34th {USENIX} Security Symposium}, year={2025},}","url":"https://doi.org/10.5281/zenodo.15530617","authors":["Diaa, Abdulrahman","Humphries, Thomas","Kerschbaum, Florian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15530617","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:36.960Z"},{"id":"doi:10.5281/zenodo.15615521","name":"FastLloyd","source":"datacite","abstract":"FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacy FastLloyd is an approach to privacy-preserving k-means clustering in horizontally federated settings. It offersstate-of-the-art utility while providing formal privacy guarantees through differential privacy, and achieves orders ofmagnitude faster performance compared to previous privacy-preserving clustering methods. Overview This repository implements the FastLloyd protocol described in the paper \"FastLloyd: Federated, Accurate, Secure, andTunable k-Means Clustering with Differential Privacy\". FastLloyd addresses the challenging problem of collaborativeclustering across multiple data owners without compromising privacy, through: 1. A novel differentially private k-means algorithm with radius constraints2. A lightweight secure aggregation protocol for federated settings Installation Requirements - Python 3.8 or higher- Open MPI (for multiparty communication)- Required Python packages listed in `env.yml` Setup 1. Download the repoistory 2. Extract the data and navigate to code directory: mkdir -p datafor f in *.tar.xz; do tar --extract --xz --file=\"$f\" --directory=datadonemv data D-Diaa-FastLloyd-879df3a/cd D-Diaa-FastLloyd-879df3a/ 3. Create and activate the conda environment: conda env create -f env.ymlconda activate fastlloyd Usage Running Experiments FastLloyd supports multiple experiment types: 1. **Accuracy**: Evaluate clustering quality across different privacy settings python experiments.py --exp_type \"accuracy\" 2. **Scale**: Analyze scalability with dataset size, dimensions, and number of clusters python experiments.py --exp_type \"scale\" 3. **Timing**: Measure communication and computation time mpirun -np 3 python experiments.py --exp_type \"timing\" You can also use the provided scripts to run multiple experiment types: bash scripts/experiment_runner.sh # For accuracy and scale experimentsbash scripts/timing_runner.sh # For timing experiments with varying numbers of clients Visualization The repository includes several visualization tools in the `plots` directory: - `per_dataset.py`: Creates performance visualizations for individual datasets- `scale_heatmap.py`: Generates heatmaps to analyze scalability- `synthetic_bar.py`: Creates bar plots comparing performance on synthetic datasets- `ablation_plots.py`: Creates plots for ablation studies- `timing_analysis.py`: Analyzes and reports execution timing data Customization You can customize various aspects of the experiments through the argument parser in `experiments.py`: python experiments.py --exp_type \"test\" --datasets \"mnist\" \"adult\" --method \"diagonal_then_frac\" --alpha 0.8 --post \"fold\" --results_folder \"my_results\" Key parameters include: - `--exp_type`: Type of experiment to run (accuracy, scale, timing, test)- `--datasets`: Datasets to use for the experiment- `--method`: Maximum distance method to use- `--alpha`: Maximum distance parameter- `--post`: Post-processing method for centroids- `--results_folder`: Folder to store results Citation If you use FastLloyd in your research, please cite the paper: @inproceedings{diaa2025fastlloyd, title={FastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy}, author={A. Diaa and T. Humphries and F. Kerschbaum}, eventtitle = {The 34th {USENIX} Security Symposium}, year={2025},}","url":"https://doi.org/10.5281/zenodo.15615521","authors":["Diaa, Abdulrahman","Humphries, Thomas","Kerschbaum, Florian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15615521","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:36.960Z"},{"id":"doi:10.5281/zenodo.15530618","name":"FastLloyd","source":"datacite","abstract":"FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacy FastLloyd is an approach to privacy-preserving k-means clustering in horizontally federated settings. It offersstate-of-the-art utility while providing formal privacy guarantees through differential privacy, and achieves orders ofmagnitude faster performance compared to previous privacy-preserving clustering methods. Overview This repository implements the FastLloyd protocol described in the paper \"FastLloyd: Federated, Accurate, Secure, andTunable k-Means Clustering with Differential Privacy\". FastLloyd addresses the challenging problem of collaborativeclustering across multiple data owners without compromising privacy, through: 1. A novel differentially private k-means algorithm with radius constraints2. A lightweight secure aggregation protocol for federated settings Installation Requirements - Python 3.8 or higher- Open MPI (for multiparty communication)- Required Python packages listed in `env.yml` Setup 1. Clone (or download) the repository: git clone https://github.com/D-Diaa/FastLloyd.gitcd FastLloyd 2. Download the data from Zenodo (Also referenced below), then mkdir -p datafor f in *.tar.xz; do tar --extract --xz --file=\"$f\" --directory=datadone 3. Create and activate the conda environment: conda env create -f env.ymlconda activate fastlloyd Repository Structure ├── configs/ │ ├── defaults.py # Default configuration settings, dataset definitions│ └── params.py # Parameter class for clustering and privacy settings│ ├── data_io/ │ ├── comm.py # MPI communication wrapper with delay simulation│ ├── data_handler.py # Functions for loading and processing datasets│ └── fixed.py # Fixed-point arithmetic implementation│ ├── parties/ │ ├── client.py # Client implementations (masked and unmasked)│ └── server.py # Server implementation with DP mechanisms│ ├── plots/ │ ├── ablation_plots.py # Visualization for ablation studies│ ├── per_dataset.py # Dataset-specific result visualization│ ├── scale_heatmap.py # Heatmap generation for scalability results│ ├── synthetic_bar.py # Bar charts for synthetic dataset results│ └── timing_analysis.py # Analysis of timing experiments│ ├── scripts/ │ ├── generator.R # R script for generating synthetic datasets│ ├── experiment_runner.sh # Script for running accuracy and scale experiments│ └── timing_runner.sh # Script for running timing experiments│ ├── utils/ │ ├── evaluations.py # Clustering quality evaluation metrics│ └── utils.py # General utility functions│ ├── experiments.py # Main experiment runner├── env.yml # Conda environment specification└── README.md # Project documentation Usage Running Experiments FastLloyd supports multiple experiment types: 1. **Accuracy**: Evaluate clustering quality across different privacy settings python experiments.py --exp_type \"accuracy\" 2. **Scale**: Analyze scalability with dataset size, dimensions, and number of clusters python experiments.py --exp_type \"scale\" 3. **Timing**: Measure communication and computation time mpirun -np 3 python experiments.py --exp_type \"timing\" You can also use the provided scripts to run multiple experiment types: bash scripts/experiment_runner.sh # For accuracy and scale experimentsbash scripts/timing_runner.sh # For timing experiments with varying numbers of clients Visualization The repository includes several visualization tools in the `plots` directory: - `per_dataset.py`: Creates performance visualizations for individual datasets- `scale_heatmap.py`: Generates heatmaps to analyze scalability- `synthetic_bar.py`: Creates bar plots comparing performance on synthetic datasets- `ablation_plots.py`: Creates plots for ablation studies- `timing_analysis.py`: Analyzes and reports execution timing data Customization You can customize various aspects of the experiments through the argument parser in `experiments.py`: python experiments.py --exp_type \"test\" --datasets \"mnist\" \"adult\" --method \"diagonal_then_frac\" --alpha 0.8 --post \"","url":"https://doi.org/10.5281/zenodo.15530618","authors":["Diaa, Abdulrahman","Humphries, Thomas","Kerschbaum, Florian"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15530618","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:36.960Z"},{"id":"doi:10.48550/arxiv.2505.18332","name":"An Attack to Break Permutation-Based Private Third-Party Inference Schemes for LLMs","source":"datacite","abstract":"Recent advances in Large Language Models (LLMs) have led to the widespread adoption of third-party inference services, raising critical privacy concerns. Existing methods of performing private third-party inference, such as Secure Multiparty Computation (SMPC), often rely on cryptographic methods. However, these methods are thousands of times slower than standard unencrypted inference, and fail to scale to large modern LLMs. Therefore, recent lines of work have explored the replacement of expensive encrypted nonlinear computations in SMPC with statistical obfuscation methods - in particular, revealing permuted hidden states to the third parties, with accompanying strong claims of the difficulty of reversal into the unpermuted states. In this work, we begin by introducing a novel reconstruction technique that can recover original prompts from hidden states with nearly perfect accuracy across multiple state-of-the-art LLMs. We then show that extensions of our attack are nearly perfectly effective in reversing permuted hidden states of LLMs, demonstrating the insecurity of three recently proposed privacy schemes. We further dissect the shortcomings of prior theoretical `proofs' of permuation security which allow our attack to succeed. Our findings highlight the importance of rigorous security analysis in privacy-preserving LLM inference.","url":"https://doi.org/10.48550/arxiv.2505.18332","authors":["Thomas, Rahul","Zahran, Louai","Choi, Erica","Potti, Akilesh","Goldblum, Micah","Pal, Arka"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2505.18332","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:36.960Z"},{"id":"doi:10.4230/lipics.stacs.2025.72","name":"Card-Based Protocols Imply PSM Protocols","source":"datacite","abstract":"Card-based cryptography is the art of cryptography using a deck of physical cards. While this area is known as a research area of recreational cryptography and is recently paid attention in educational purposes, there is no systematic study of the relationship between card-based cryptography and the other \"conventional\" cryptography. This paper establishes the first generic conversion from card-based protocols to private simultaneous messages (PSM) protocols, a special kind of secure multiparty computation. Our compiler supports \"simple\" card-based protocols, which is a natural subclass of finite-runtime protocols. The communication complexity of the resulting PSM protocol depends on how many cards are opened in total in all possible branches of the original card-based protocol. This result shows theoretical importance of such \"opening complexity\" of card-based protocols, which had not been focused in this area. As a consequence, lower bounds for PSM protocols imply those for simple card-based protocols. In particular, if there exists no PSM protocol with subexponential communication complexity for a function f, then there exists no simple card-based protocol with subexponential opening complexity for the same f.","url":"https://doi.org/10.4230/lipics.stacs.2025.72","authors":["Shinagawa, Kazumasa","Nuida, Koji"],"tags":["Card-based cryptography","private simultaneous messages","Security and privacy → Information-theoretic techniques"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.4230/lipics.stacs.2025.72","addedAt":"2026-08-31T06:41:36.960Z","updatedAt":"2026-08-31T06:41:36.960Z"},{"id":"doi:10.1360/sspma-2023-0030","name":"An efficient secure multiparty quantum computation protocol","source":"crossref","abstract":"","url":"https://doi.org/10.1360/sspma-2023-0030","authors":["LIN Song","WANG Ning","LIU Xiao-Fen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-02-15T06:24:42Z","doi":"10.1360/sspma-2023-0030","addedAt":"2026-08-31T06:41:37.130Z","updatedAt":"2026-08-31T06:41:39.633Z"},{"id":"doi:10.1109/fdtc64268.2024.00015","name":"FaultyGarble: Fault Attack on Secure Multiparty Neural Network Inference","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fdtc64268.2024.00015","authors":["Mohammad Hashemi","Dev Mehta","Kyle Mitard","Shahin Tajik","Fatemeh Ganji"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-04T13:33:30Z","doi":"10.1109/fdtc64268.2024.00015","addedAt":"2026-08-31T06:41:37.130Z","updatedAt":"2026-08-31T06:41:37.130Z"},{"id":"doi:10.4135/9781071961384","name":"Coalitions in Multiparty Negotiations","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781071961384","authors":["Andrew Bennett"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-29T14:44:11Z","doi":"10.4135/9781071961384","addedAt":"2026-08-31T06:41:37.130Z","updatedAt":"2026-08-31T06:41:37.130Z"},{"id":"doi:10.4135/9781071961414","name":"Straw Votes in Multiparty Negotiations","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781071961414","authors":["Andrew Bennett"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-29T14:44:11Z","doi":"10.4135/9781071961414","addedAt":"2026-08-31T06:41:37.130Z","updatedAt":"2026-08-31T06:41:37.130Z"},{"id":"doi:10.4135/9781071961452","name":"Decision Processes in Multiparty Negotiations","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781071961452","authors":["Andrew Bennett"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-29T14:44:11Z","doi":"10.4135/9781071961452","addedAt":"2026-08-31T06:41:37.131Z","updatedAt":"2026-08-31T06:41:37.131Z"},{"id":"doi:10.1007/s10586-024-04556-7","name":"Enhancing data authentication in software-defined networking (SDN) using multiparty computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10586-024-04556-7","authors":["Fatma Hendaoui","Hamdi Eltaief","Habib Youssef"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-15T14:01:25Z","doi":"10.1007/s10586-024-04556-7","addedAt":"2026-08-31T06:41:37.131Z","updatedAt":"2026-08-31T06:41:37.131Z"},{"id":"doi:10.4135/9781071961360","name":"Multiple Priorities in Multiparty Negotiations","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781071961360","authors":["Andrew Bennett"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-29T14:44:11Z","doi":"10.4135/9781071961360","addedAt":"2026-08-31T06:41:37.131Z","updatedAt":"2026-08-31T06:41:37.131Z"},{"id":"doi:10.4135/9781071961407","name":"Decision Rules in Multiparty Negotiations","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781071961407","authors":["Andrew Bennett"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-29T14:44:11Z","doi":"10.4135/9781071961407","addedAt":"2026-08-31T06:41:37.131Z","updatedAt":"2026-08-31T06:41:37.131Z"},{"id":"doi:10.2196/18735","name":"Balancing Accuracy and Privacy in Federated Queries of Clinical Data Repositories: Algorithm Development and Validation.","source":"pubmed","abstract":"Over the past decade, the emergence of several large federated clinical data networks has enabled researchers to access data on millions of patients at dozens of health care organizations. Typically, queries are broadcast to each of the sites in the network, which then return aggregate counts of the number of matching patients. However, because patients can receive care from multiple sites in the network, simply adding the numbers frequently double counts patients. Various methods such as the use of trusted third parties or secure multiparty computation have been proposed to link patient records across sites. However, they either have large trade-offs in accuracy and privacy or are not scalable to large networks.","url":"https://doi.org/10.2196/18735","authors":["Yu YW","Weber GM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.2196/18735","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"doi:10.1073/pnas.1918257117","name":"Secure large-scale genome-wide association studies using homomorphic encryption.","source":"pubmed","abstract":"Genome-wide association studies (GWASs) seek to identify genetic variants associated with a trait, and have been a powerful approach for understanding complex diseases. A critical challenge for GWASs has been the dependence on individual-level data that typically have strict privacy requirements, creating an urgent need for methods that preserve the individual-level privacy of participants. Here, we present a privacy-preserving framework based on several advances in homomorphic encryption and demonstrate that it can perform an accurate GWAS analysis for a real dataset of more than 25,000 individuals, keeping all individual data encrypted and requiring no user interactions. Our extrapolations show that it can evaluate GWASs of 100,000 individuals and 500,000 single-nucleotide polymorphisms (SNPs) in 5.6 h on a single server node (or in 11 min on 31 server nodes running in parallel). Our performance results are more than one order of magnitude faster than prior state-of-the-art results using secure multiparty computation, which requires continuous user interactions, with the accuracy of both solutions being similar. Our homomorphic encryption advances can also be applied to other domains where large-scale statistical analyses over encrypted data are needed.","url":"https://doi.org/10.1073/pnas.1918257117","authors":["Blatt M","Gusev A","Polyakov Y","Goldwasser S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1073/pnas.1918257117","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.2196/12702","name":"Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/12702","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2019","doi":"10.2196/12702","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1186/s12935-026-04263-w","name":"Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12935-026-04263-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1186/s12935-026-04263-w","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1155/2022/7384803","name":"A Privacy-Preserved Variational-Autoencoder for DGA Identification in the Education Industry and Distance Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1155/2022/7384803","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1155/2022/7384803","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/22555","name":"Web-Based Privacy-Preserving Multicenter Medical Data Analysis Tools Via Threshold Homomorphic Encryption: Design and Development Study.","source":"pubmed","abstract":"Data sharing in multicenter medical research can improve the generalizability of research, accelerate progress, enhance collaborations among institutions, and lead to new discoveries from data pooled from multiple sources. Despite these benefits, many medical institutions are unwilling to share their data, as sharing may cause sensitive information to be leaked to researchers, other institutions, and unauthorized users. Great progress has been made in the development of secure machine learning frameworks based on homomorphic encryption in recent years; however, nearly all such frameworks use a single secret key and lack a description of how to securely evaluate the trained model, which makes them impractical for multicenter medical applications.","url":"https://doi.org/10.2196/22555","authors":["Lu Y","Zhou T","Tian Y","Zhu S","Li J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.2196/22555","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1371/journal.pone.0268245","name":"Multi-party co-signature scheme based on SM2.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0268245","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1371/journal.pone.0268245","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/s23042112","name":"Reviewing Federated Machine Learning and Its Use in Diseases Prediction.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s23042112","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/s23042112","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/e23111461","name":"Expand-and-Randomize: An Algebraic Approach to Secure Computation.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e23111461","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.3390/e23111461","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/43006","name":"Applications of Federated Learning in Mobile Health: Scoping Review.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/43006","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.2196/43006","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1186/s13059-019-1741-0","name":"Emerging technologies towards enhancing privacy in genomic data sharing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s13059-019-1741-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2019","doi":"10.1186/s13059-019-1741-0","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1007/s40747-022-00756-z","name":"A systematic review of homomorphic encryption and its contributions in healthcare industry.","source":"pubmed","abstract":"Abstract Cloud computing and cloud storage have contributed to a big shift in data processing and its use. Availability and accessibility of resources with the reduction of substantial work is one of the main reasons for the cloud revolution. With this cloud computing revolution, outsourcing applications are in great demand. The client uses the service by uploading their data to the cloud and finally gets the result by processing it. It benefits users greatly, but it also exposes sensitive data to third-party service providers. In the healthcare industry, patient health records are digital records of a patient’s medical history kept by hospitals or health care providers. Patient health records are stored in data centers for storage and processing. Before doing computations on data, traditional encryption techniques decrypt the data in their original form. As a result, sensitive medical information is lost. Homomorphic encryption can protect sensitive information by allowing data to be processed in an encrypted form such that only encrypted data is accessible to service providers. In this paper, an attempt is made to present a systematic review of homomorphic cryptosystems with its categorization and evolution over time. In addition, this paper also includes a review of homomorphic cryptosystem contributions in healthcare.","url":"https://doi.org/10.1007/s40747-022-00756-z","authors":["Munjal K","Bhatia R","Kundan Munjal","Rekha Bhatia"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1007/s40747-022-00756-z","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1016/j.mmr.2026.100012","name":"Virtual medicine: medical AI in human health and diseases.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.mmr.2026.100012","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.mmr.2026.100012","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1093/bib/bbag219","name":"Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/bib/bbag219","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1093/bib/bbag219","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/13600","name":"Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/13600","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2019","doi":"10.2196/13600","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/s23031252","name":"A Survey of Machine and Deep Learning Methods for Privacy Protection in the Internet of Things.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s23031252","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/s23031252","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3389/frai.2022.812732","name":"An Overview of Current Solutions for Privacy in the Internet of Things.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2022.812732","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3389/frai.2022.812732","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/26371","name":"Privacy-Oriented Technique for COVID-19 Contact Tracing (PROTECT) Using Homomorphic Encryption: Design and Development Study.","source":"pubmed","abstract":"Various techniques are used to support contact tracing, which has been shown to be highly effective against the COVID-19 pandemic. To apply the technology, either quarantine authorities should provide the location history of patients with COVID-19, or all users should provide their own location history. This inevitably exposes either the patient's location history or the personal location history of other users. Thus, a privacy issue arises where the public good (via information release) comes in conflict with privacy exposure risks.","url":"https://doi.org/10.2196/26371","authors":["An Y","Lee S","Jung S","Park H","Song Y","Ko T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.2196/26371","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.3390/s22010331","name":"Centralized Threshold Key Generation Protocol Based on Shamir Secret Sharing and HMAC Authentication.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s22010331","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/s22010331","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/s20154253","name":"A Survey on Secure Computation Based on Homomorphic Encryption in Vehicular Ad Hoc Networks.","source":"pubmed","abstract":"In vehicular ad hoc networks (VANETs), the security and privacy of vehicle data are core issues. In order to analyze vehicle data, they need to be computed. Encryption is a common method to guarantee the security of vehicle data in the process of data dissemination and computation. However, encrypted vehicle data cannot be analyzed easily and flexibly. Because homomorphic encryption supports computations of the ciphertext, it can completely solve this problem. In this paper, we provide a comprehensive survey of secure computation based on homomorphic encryption in VANETs. We first describe the related definitions and the current state of homomorphic encryption. Next, we present the framework, communication domains, wireless access technologies and cyber-security issues of VANETs. Then, we describe the state of the art of secure basic operations, data aggregation, data query and other data computation in VANETs. Finally, several challenges and open issues are discussed for future research.","url":"https://doi.org/10.3390/s20154253","authors":["Sun X","Yu FR","Zhang P","Xie W","Peng X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.3390/s20154253","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.3390/s20143940","name":"Quantum Diffie-Hellman Extended to Dynamic Quantum Group Key Agreement for e-Healthcare Multi-Agent Systems in Smart Cities.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s20143940","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.3390/s20143940","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/45948","name":"Ten Topics to Get Started in Medical Informatics Research.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/45948","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.2196/45948","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.7717/peerj-cs.1027","name":"SFedChain: blockchain-based federated learning scheme for secure data sharing in distributed energy storage networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.7717/peerj-cs.1027","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.7717/peerj-cs.1027","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/s20092621","name":"DLT Based Authentication Framework for Industrial IoT Devices.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s20092621","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.3390/s20092621","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1155/2022/4653923","name":"Machine Learning for Healthcare Wearable Devices: The Big Picture.","source":"europepmc","abstract":"","url":"https://doi.org/10.1155/2022/4653923","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1155/2022/4653923","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/47540","name":"Sharing Data With Shared Benefits: Artificial Intelligence Perspective.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/47540","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.2196/47540","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/s23052807","name":"Internet of Nano-Things (IoNT): A Comprehensive Review from Architecture to Security and Privacy Challenges.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s23052807","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/s23052807","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/s22062269","name":"A Novel Privacy Preserving Scheme for Smart Grid-Based Home Area Networks.","source":"pubmed","abstract":"Despite the benefits of smart grids, concerns about security and privacy arise when a large number of heterogeneous devices communicate via a public network. A novel privacy-preserving method for smart grid-based home area networks (HAN) is proposed in this research. To aggregate data from diverse household appliances, the proposed approach uses homomorphic Paillier encryption, Chinese remainder theorem, and one-way hash function. The privacy in Internet of things (IoT)-enabled smart homes is one of the major concerns of the research community. In the proposed scheme, the sink node not only aggregates the data but also enables the early detection of false data injection and replay attacks. According to the security analysis, the proposed approach offers adequate security. The smart grid distributes power and facilitates a two-way communications channel that leads to transparency and developing trust.","url":"https://doi.org/10.3390/s22062269","authors":["Ali W","Din IU","Almogren A","Kim BS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/s22062269","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"doi:10.2196/medinform.8805","name":"Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation.","source":"pubmed","abstract":"Learning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain analyses without ever decrypting the data to preserve privacy. Homomorphic encryption technique is a promising candidate for secure data outsourcing, but it is a very challenging task to support real-world machine learning tasks. Existing frameworks can only handle simplified cases with low-degree polynomials such as linear means classifier and linear discriminative analysis.","url":"https://doi.org/10.2196/medinform.8805","authors":["Kim M","Song Y","Wang S","Xia Y","Jiang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.2196/medinform.8805","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21037/atm-2022-50","name":"Literature analysis of artificial intelligence in biomedicine.","source":"europepmc","abstract":"","url":"https://doi.org/10.21037/atm-2022-50","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21037/atm-2022-50","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/s22020450","name":"Federated Learning in Edge Computing: A Systematic Survey.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s22020450","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/s22020450","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/36481","name":"Big Data Health Care Platform With Multisource Heterogeneous Data Integration and Massive High-Dimensional Data Governance for Large Hospitals: Design, Development, and Application.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/36481","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.2196/36481","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/s20092533","name":"Edge Machine Learning for AI-Enabled IoT Devices: A Review.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s20092533","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.3390/s20092533","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1186/s12920-016-0224-3","name":"Protecting genomic data analytics in the cloud: state of the art and opportunities.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12920-016-0224-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2016","doi":"10.1186/s12920-016-0224-3","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/e23080989","name":"DiLizium: A Two-Party Lattice-Based Signature Scheme.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e23080989","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.3390/e23080989","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/medinform.8286","name":"Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation.","source":"pubmed","abstract":"Machine learning is an effective data-driven tool that is being widely used to extract valuable patterns and insights from data. Specifically, predictive machine learning models are very important in health care for clinical data analysis. The machine learning algorithms that generate predictive models often require pooling data from different sources to discover statistical patterns or correlations among different attributes of the input data. The primary challenge is to fulfill one major objective: preserving the privacy of individuals while discovering knowledge from data.","url":"https://doi.org/10.2196/medinform.8286","authors":["Sadat MN","Jiang X","Aziz MMA","Wang S","Mohammed N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.2196/medinform.8286","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"epmc:MED29854245","name":"SCOTCH: Secure Counting Of encrypTed genomiC data using a Hybrid approach.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/29854245/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2017","doi":"","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/29871","name":"Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/29871","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.2196/29871","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3389/fdata.2024.1266031","name":"Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdata.2024.1266031","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/fdata.2024.1266031","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/e22030272","name":"A Private Quantum Bit String Commitment.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e22030272","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.3390/e22030272","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1186/s12920-020-0715-0","name":"iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching.","source":"pubmed","abstract":"","url":"https://doi.org/10.1186/s12920-020-0715-0","authors":["Kuo TT","Jiang X","Tang H","Wang X","Bath T","Bu D","Wang L","Harmanci A","Zhang S","Zhi D","Sofia HJ","Ohno-Machado L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1186/s12920-020-0715-0","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.2196/26598","name":"Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/26598","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.2196/26598","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/jpm16020069","name":"Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/jpm16020069","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/jpm16020069","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/34472","name":"Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/34472","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.2196/34472","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/37236","name":"Bridging the European Data Sharing Divide in Genomic Science.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/37236","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.2196/37236","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1038/s41598-021-04124-8","name":"SVBE: searchable and verifiable blockchain-based electronic medical records system.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-021-04124-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1038/s41598-021-04124-8","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/20891","name":"Federated Learning on Clinical Benchmark Data: Performance Assessment.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/20891","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.2196/20891","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/36774","name":"Re-engineering a Clinical Trial Management System Using Blockchain Technology: System Design, Development, and Case Studies.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/36774","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.2196/36774","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1016/j.patter.2021.100366","name":"Differential privacy for public health data: An innovative tool to optimize information sharing while protecting data confidentiality.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.patter.2021.100366","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1016/j.patter.2021.100366","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1038/s44386-025-00029-y","name":"Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s44386-025-00029-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s44386-025-00029-y","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1093/pnasnexus/pgae029","name":"Collective privacy recovery: Data-sharing coordination via decentralized artificial intelligence.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/pnasnexus/pgae029","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1093/pnasnexus/pgae029","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1371/journal.pcbi.1008977","name":"Swarm: A federated cloud framework for large-scale variant analysis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pcbi.1008977","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1371/journal.pcbi.1008977","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/s20082242","name":"Distributed Key Management to Secure IoT Wireless Sensor Networks in Smart-Agro.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s20082242","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.3390/s20082242","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1155/2022/1153208","name":"Blockchain and K-Means Algorithm for Edge AI Computing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1155/2022/1153208","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1155/2022/1153208","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"epmc:MED28269933","name":"PREMIX: PRivacy-preserving EstiMation of Individual admiXture.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/28269933/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2016","doi":"","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1186/s12864-021-07996-2","name":"Privacy-preserving storage of sequenced genomic data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12864-021-07996-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1186/s12864-021-07996-2","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1093/gpbjnl/qzaf011","name":"Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/gpbjnl/qzaf011","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1093/gpbjnl/qzaf011","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1371/journal.pone.0252573","name":"COVID-19 detection using federated machine learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0252573","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1371/journal.pone.0252573","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1093/jamia/ocaa096","name":"Fold-stratified cross-validation for unbiased and privacy-preserving federated learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/jamia/ocaa096","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1093/jamia/ocaa096","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.22038/ijbms.2026.92560.19984","name":"Digital immune twins and ai-integrated multi-omic biomarkers: Redefining personalized immunotherapy in non-small cell lung cancer.","source":"europepmc","abstract":"","url":"https://doi.org/10.22038/ijbms.2026.92560.19984","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.22038/ijbms.2026.92560.19984","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1007/s11356-021-16223-0","name":"Blockchain and artificial intelligence technology in e-Health.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s11356-021-16223-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1007/s11356-021-16223-0","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2196/33720","name":"Next-Generation Capabilities in Trusted Research Environments: Interview Study.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/33720","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.2196/33720","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1200/cci.19.00047","name":"Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care.","source":"europepmc","abstract":"","url":"https://doi.org/10.1200/cci.19.00047","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1200/cci.19.00047","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/s24196377","name":"The Intersection of Machine Learning and Wireless Sensor Network Security for Cyber-Attack Detection: A Detailed Analysis.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s24196377","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3390/s24196377","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3389/fcimb.2020.582028","name":"Mini Review: Clinical Routine Microbiology in the Era of Automation and Digital Health.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fcimb.2020.582028","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.3389/fcimb.2020.582028","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1055/s-0041-1740564","name":"A Privacy-Preserving Distributed Analytics Platform for Health Care Data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1055/s-0041-1740564","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1055/s-0041-1740564","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.270Z"},{"id":"doi:10.1098/rsta.2017.0358","name":"Is privacy <i>privacy</i>?","source":"europepmc","abstract":"","url":"https://doi.org/10.1098/rsta.2017.0358","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.1098/rsta.2017.0358","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:38.270Z"},{"id":"doi:10.2196/medinform.7744","name":"Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis.","source":"pubmed","abstract":"There is an urgent need for the development of global analytic frameworks that can perform analyses in a privacy-preserving federated environment across multiple institutions without privacy leakage. A few studies on the topic of federated medical analysis have been conducted recently with the focus on several algorithms. However, none of them have solved similar patient matching, which is useful for applications such as cohort construction for cross-institution observational studies, disease surveillance, and clinical trials recruitment.","url":"https://doi.org/10.2196/medinform.7744","authors":["Lee J","Sun J","Wang F","Wang S","Jun CH","Jiang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.2196/medinform.7744","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.2196/43664","name":"Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/43664","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.2196/43664","addedAt":"2026-08-31T06:41:37.148Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1007/978-3-030-71522-9_300846","name":"Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71522-9_300846","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:20:57Z","doi":"10.1007/978-3-030-71522-9_300846","addedAt":"2026-08-31T06:41:37.463Z","updatedAt":"2026-08-31T06:41:37.463Z"},{"id":"doi:10.56553/popets-2025-0072","name":"SecureED: Secure Multiparty Edit Distance for Genomic Sequences","source":"crossref","abstract":"DNA edit distance (ED) measures the minimum number of single nucleotide insertions, substitutions, or deletions required to convert a DNA sequence into another. ED has broad applications in healthcare such as sequence alignment, genome assembly, functional annotation, and drug discovery. Privacy-preserving computation is essential in this context to protect sensitive genomic data. Nonetheless, the existing secure DNA edit distance solutions lack efficiency when handling large data sequences or resort to approximations and fail to accurately compute the metric. In this work, we introduce ScureED, a protocol that tackles these limitations, resulting in a significant performance enhancement of approximately 2-24 times compared to existing methods. Our protocol computes a secure ED between two genomes, each comprising 1,000 letters, in just a few seconds. The underlying technique of our protocol is a novel approach that transforms the established approximate matching technique (i.e., the Ukkonen algorithm) into exact matching, exploiting the inherent similarity in human DNA to achieve cost-effectiveness. Furthermore, we introduce various optimizations tailored for secure computation in scenarios with a limited input domain, such as DNA sequences composed solely of the four nucleotide letters.","url":"https://doi.org/10.56553/popets-2025-0072","authors":["Jiahui Gao","Yagaagowtham Palanikumar","Dimitris Mouris","Duong Nguyen","Ni Trieu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T23:36:18Z","doi":"10.56553/popets-2025-0072","addedAt":"2026-08-31T06:41:37.463Z","updatedAt":"2026-08-31T06:41:37.463Z"},{"id":"doi:10.1145/3719027.3765229","name":"Practical Zero-Knowledge PIOP for Maliciously Secure Multiparty Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3719027.3765229","authors":["Intak Hwang","Hyeonbum Lee","Jinyeong Seo","Yongsoo Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-22T23:32:38Z","doi":"10.1145/3719027.3765229","addedAt":"2026-08-31T06:41:37.463Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1007/978-3-030-71522-9_300862","name":"Secure Multi-party Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71522-9_300862","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:20:57Z","doi":"10.1007/978-3-030-71522-9_300862","addedAt":"2026-08-31T06:41:37.463Z","updatedAt":"2026-08-31T06:41:37.463Z"},{"id":"doi:10.1016/j.jisa.2025.104033","name":"Quantum secure protocols for multiparty computations","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jisa.2025.104033","authors":["Tapaswini Mohanty","Vikas Srivastava","Sumit Kumar Debnath","Pantelimon Stănică"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-20T06:56:16Z","doi":"10.1016/j.jisa.2025.104033","addedAt":"2026-08-31T06:41:37.463Z","updatedAt":"2026-08-31T06:41:37.463Z"},{"id":"doi:10.1109/lcn65610.2025.11146341","name":"A Cheating Detection and Recovery Framework for Robust Multiparty Computation in the Cloud","source":"crossref","abstract":"","url":"https://doi.org/10.1109/lcn65610.2025.11146341","authors":["Richard Hernandez","Kemal Akkaya","Soamar Homsi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-15T17:36:57Z","doi":"10.1109/lcn65610.2025.11146341","addedAt":"2026-08-31T06:41:37.463Z","updatedAt":"2026-08-31T06:41:37.463Z"},{"id":"doi:10.1109/iccit68739.2025.11491252","name":"A User-Preference Based Secure Recommender System via Multiparty and Two-Stage Processing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccit68739.2025.11491252","authors":["Abu Naim Khan","Kazi Md. Rokibul Alam","Yasuhiko Morimoto"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-06T19:37:56Z","doi":"10.1109/iccit68739.2025.11491252","addedAt":"2026-08-31T06:41:37.463Z","updatedAt":"2026-08-31T06:41:37.463Z"},{"id":"doi:10.1109/brains67003.2025.11302913","name":"Enhancing Payment Channel Network Routing with Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/brains67003.2025.11302913","authors":["Arun Shrestha","Hsiang-Jen Hong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-24T18:43:25Z","doi":"10.1109/brains67003.2025.11302913","addedAt":"2026-08-31T06:41:37.463Z","updatedAt":"2026-08-31T06:41:37.463Z"},{"id":"doi:10.2139/ssrn.5652270","name":"Secure Multi-Party Computation Techniques for Blockchain-Based Fintech Platforms","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5652270","authors":["Santhosh Chitraju"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-29T18:26:22Z","doi":"10.2139/ssrn.5652270","addedAt":"2026-08-31T06:41:37.463Z","updatedAt":"2026-08-31T06:41:37.463Z"},{"id":"doi:10.36227/techrxiv.176539387.75109768/v1","name":"Secure and Parallel Determinant Computation for Large-Scale Matrices in Edge Environments    ","source":"crossref","abstract":"The advent of edge computing has enabled resource-constrained clients to delegate intensive computational tasks to distributed edge servers, especially within Internet of Things (IoT) environments. Among such tasks, Matrix Determinant Computation (MDC) remains critical for applications in control systems, cryptography, and machine learning. However, the cubic complexity of traditional determinant algorithms makes them unsuitable for real-time processing in constrained edge scenarios. We propose a Secure Parallel Determinant Computation (SPDC) framework, which provides strong security guaranties, including privacy-preserving MDC, across N distributed edge servers. The framework achieves privacy through Composite Element Distortion (CED)—a lightweight encryption method that combines Element-wise Obfuscation (EWO) and the Panth Rotation Theorem (PRT) to conceal both structural and numerical matrix content while preserving determinant properties. Parallel LU decomposition is used to distribute encrypted matrix blocks across an arbitrary number of untrusted edge servers, enabling efficient and scalable determinant computation. A one-way communication model further reduces coordination overhead by eliminating inter-server interactions. To ensure result integrity with minimal client burden, we further introduce two verification algorithms: Q2, a probabilistic scalar method, and Q3, a deterministic and low-complexity alternative. Mathematical analysis demonstrates that the proposed framework provides strong privacy and security guaranties, low computational overhead, and deployment flexibility—making it well-suited for secure, scalable, and real-time MDC in distributed edge-assisted systems.","url":"https://doi.org/10.36227/techrxiv.176539387.75109768/v1","authors":["Prajwal Panth"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-10T19:11:21Z","doi":"10.36227/techrxiv.176539387.75109768/v1","addedAt":"2026-08-31T06:41:37.463Z","updatedAt":"2026-08-31T06:41:37.463Z"},{"id":"doi:10.1002/cpe.70708","name":"Additional‐Processing‐Free Multiparty Reversible Data Hiding Over Encrypted Domain","source":"crossref","abstract":"ABSTRACT Multiparty reversible data hiding over encrypted domain (MRDH‐ED) provides a safeguard mechanism that enables the restoration of the original cover even if some of the data hiders are potentially compromised. Existing MRDH‐ED methods are accompanied by additional processing in the cover encryption procedure, which results in high computational consumption. In this paper, an additional‐processing‐free MRDH‐ED method with compatibility is given. The original cover is encrypted into multiple encrypted covers by using a composite‐order secret sharing. Additive operation over the composite‐order finite field is employed to embed secret data into each encrypted cover. Since the original cover can be encrypted without additional processing in the cover encryption procedure, the computational consumption is reduced. And the embedding capacity is improved, regardless of the distribution of the covers. With the help of the composite‐order secret sharing, the proposed method is compatible with other bit‐level covers. The proposed scheme's superiority is demonstrated through the presentation of experimental results.","url":"https://doi.org/10.1002/cpe.70708","authors":["Bing Chen","Ranran Yang","Bingwen Feng","Xiuye Zhan","Jun Cai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-15T13:00:56Z","doi":"10.1002/cpe.70708","addedAt":"2026-08-31T06:41:37.828Z","updatedAt":"2026-08-31T06:41:37.828Z"},{"id":"doi:10.1088/1402-4896/ad59d8","name":"Quantum secure multiparty computing XOR protocol based on single photons and its application in quantum secure communications for intelligence agents","source":"crossref","abstract":"Abstract In this paper, we consider an interesting and important privacy-preserving issue, i.e., how to implement anonymous and secure communications for several intelligence agents, hiding in n participants. To solve this issue, we first propose a quantum Secure Multiparty Computing XOR (SMC_XOR) protocol based on single photons, which can guarantee the unconditional security of the protocol. By implementing rotation encryption, the practicality of quantum SMC_XOR protocol can be significantly improved without other complex quantum techniques. Security analysis shows that the proposed protocol can resist various types of attacks. Furthermore, a special network model is designed to solve this issue, using hash function to verify the identity of the communication parties and key recycling to reduce resource consumption. Finally, the proposed quantum SMC_XOR protocol is simulated in IBM Qiskit, and the simulation results show that the protocol is correct and feasible.","url":"https://doi.org/10.1088/1402-4896/ad59d8","authors":["Huijie Li","Run-Hua Shi","Qianqian Jia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-19T18:25:45Z","doi":"10.1088/1402-4896/ad59d8","addedAt":"2026-08-31T06:41:37.831Z","updatedAt":"2026-08-31T06:41:37.831Z"},{"id":"doi:10.62441/nano-ntp.v20is14.43","name":"Secure Multi-Party Computation For Data Mining In Cryptographically Protected Environments","source":"crossref","abstract":"","url":"https://doi.org/10.62441/nano-ntp.v20is14.43","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-11T07:29:53Z","doi":"10.62441/nano-ntp.v20is14.43","addedAt":"2026-08-31T06:41:37.831Z","updatedAt":"2026-08-31T06:41:37.831Z"},{"id":"doi:10.4135/9781071961377","name":"Sources of Power in Multiparty Negotiations","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781071961377","authors":["Andrew Bennett"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-29T14:44:11Z","doi":"10.4135/9781071961377","addedAt":"2026-08-31T06:41:37.831Z","updatedAt":"2026-08-31T06:41:37.831Z"},{"id":"doi:10.1117/12.3046008","name":"Secure multiparty variance estimation in unbalanced resource environments","source":"crossref","abstract":"","url":"https://doi.org/10.1117/12.3046008","authors":["Cong Hu","Zhen Yao","Jiali Sun","Cuiling Liu","Cuicui Zhang","Ruixuan Lu","YuJia Zhai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-08T17:46:39Z","doi":"10.1117/12.3046008","addedAt":"2026-08-31T06:41:37.831Z","updatedAt":"2026-08-31T06:41:37.831Z"},{"id":"doi:10.31449/inf.v48i21.6562","name":"A Framework for Privacy-Preserving Multiparty Computation with Homomorphic Encryption and Zero-Knowledge Proofs","source":"crossref","abstract":"In digital landscape of today’s ongoing world, the imperative for enhanced security in cloud-based data processing is paramount. This paper introduces an innovative framework that seamlessly integrates Homomorphic Encryption and Zero-Knowledge Proofs (ZKPs) to bolster data privacy and confidentiality. This paper explores the technical intricacies, real-world applications, and potential implications of this fusion framework. Homomorphic Encryption empowers computations on encrypted data without compromising privacy, while Zero-Knowledge Proofs offer a mechanism to verify computations without exposing sensitive details. The effectiveness and adaptability of the proposed framework is demonstrated through meticulous analysis and practical deployment in safeguarding cloud-based data processing. The proposed framework marks a significant stride towards creating an environment where data security is unequivocally prioritized.","url":"https://doi.org/10.31449/inf.v48i21.6562","authors":["Janak Ghansham Dhokrat","Namita Pulgam","Tabassum Maktum","Vanita Mane"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-27T19:14:16Z","doi":"10.31449/inf.v48i21.6562","addedAt":"2026-08-31T06:41:37.831Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.2139/ssrn.4686550","name":"Trustworthy Machine Learning using Secure Distributed Matrix Computation","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4686550","authors":["Mohammad Raeini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-05T18:09:59Z","doi":"10.2139/ssrn.4686550","addedAt":"2026-08-31T06:41:37.831Z","updatedAt":"2026-08-31T06:41:37.831Z"},{"id":"doi:10.1002/cpe.8231","name":"Secure access technology for industrial internet of things","source":"crossref","abstract":"Abstract When terminal devices attempt to access the industrial internet of things (IIoT), preventing illegal access from untrusted terminals becomes challenging. This difficulty arises because most devices adopt the commonly used traditional methods of accessing the internet of things. To address this challenge, we propose a perception‐layer‐based IIoT trusted connection architecture, derived from the trusted connection architecture (TCA), and names it TCA‐IIoT. This architecture enables bidirectional identity and platform integrity authentication between access points and terminals, while also ensuring trusted authentication of IIoT terminal behavior. To validate the effectiveness of TCA‐IIoT, the paper details a simulation experiment. This experiment centers on evaluating the success rate of data transmission and measuring the average delay under various conditions, including scenarios with malicious nodes. The results of the study indicate that TCA‐IIoT markedly improves the security and reliability of IIoT networks, advancements that are vital for the sustainable development and broader application of these systems.","url":"https://doi.org/10.1002/cpe.8231","authors":["Bingquan Wang","Jin Peng","Meili Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-30T02:26:02Z","doi":"10.1002/cpe.8231","addedAt":"2026-08-31T06:41:37.831Z","updatedAt":"2026-08-31T06:41:37.831Z"},{"id":"doi:10.3386/w32763","name":"Distributed Ledgers and Secure Multi-Party Computation for Financial Reporting and Auditing","source":"crossref","abstract":"","url":"https://doi.org/10.3386/w32763","authors":["Sean Cao","Lin William Cong","Baozhong Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-05T19:31:56Z","doi":"10.3386/w32763","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1109/sp54263.2024.00131","name":"GAuV: A Graph-Based Automated Verification Framework for Perfect Semi-Honest Security of Multiparty Computation Protocols","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp54263.2024.00131","authors":["Xingyu Xie","Yifei Li","Wei Zhang","Tuowei Wang","Shizhen Xu","Jun Zhu","Yifan Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-05T17:56:32Z","doi":"10.1109/sp54263.2024.00131","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1109/isit57864.2024.10619365","name":"Secure Network Function Computation: Function-Security","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isit57864.2024.10619365","authors":["Yang Bai","Xuan Guang","Raymond W. Yeung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-19T13:25:01Z","doi":"10.1109/isit57864.2024.10619365","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.2514/6.2024-0271","name":"Protecting Satellite Proximity Operations via Secure Multi-Party Computation","source":"crossref","abstract":"","url":"https://doi.org/10.2514/6.2024-0271","authors":["Caroline Fedele","Kevin Butler","Christopher Petersen","Tyler Lovelly"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-29T21:40:31Z","doi":"10.2514/6.2024-0271","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1007/978-3-031-73344-4_54","name":"Ensuring Data Security and Annotators Anonymity Through a Secure and Anonymous Multiparty Annotation System","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-73344-4_54","authors":["Dany Rimez","Axel Legay","Benoît Macq"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-15T05:01:40Z","doi":"10.1007/978-3-031-73344-4_54","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1109/icton62926.2024.10648156","name":"Secure Computation Offloading with ETSI MEC+QKD","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icton62926.2024.10648156","authors":["Claudio Cicconetti","Marco Conti","Andrea Passarella"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-02T17:34:14Z","doi":"10.1109/icton62926.2024.10648156","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1007/978-3-031-75757-0_15","name":"Robust Multiparty Computation from Threshold Encryption Based on RLWE","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-75757-0_15","authors":["Antoine Urban","Matthieu Rambaud"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-22T11:03:12Z","doi":"10.1007/978-3-031-75757-0_15","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.2139/ssrn.4986682","name":"A Path to Multiparty Democracy","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4986682","authors":["Nate Ela"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-22T01:18:32Z","doi":"10.2139/ssrn.4986682","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.2139/ssrn.4915950","name":"Distributed Ledgers and Secure Multi-Party Computation for Financial Reporting and Auditing","source":"crossref","abstract":"To understand the disruption and implications of distributed ledger technologies for financial reporting and auditing, we analyze firm misreporting, auditor monitoring and competition, and regulatory policy in a unified model. A federated blockchain for financial reporting and auditing can improve verification efficiency not only for transactions in private databases, but also for cross-chain verifications through privacy-preserving computation protocols. Despite the potential benefit of blockchains, private incentives for firms and first-mover advantages for auditors can create inefficient under-adoption or partial adoption that favors larger auditors. Although a regulator can help coordinate the adoption of technology, endogenous choice of transaction partners by firms can still lead to adoption failure. Our model also provides an initial framework for further studies of the costs and implications of the use of distributed ledgers and secure multi-party computation in financial reporting, including the positive spillover to discretionary auditing and who should bear the cost of adoption.","url":"https://doi.org/10.2139/ssrn.4915950","authors":["Sean S. Cao","Lin William Cong","Baozhong Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-05T06:02:31Z","doi":"10.2139/ssrn.4915950","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1002/cpe.8054","name":"FogSec: A secure and effective mutual authentication scheme for fog computing","source":"crossref","abstract":"Summary As opposed to cloud servers, fog servers, and fog users may be malicious, so developing a mutual identity‐preserving authentication mechanism between them is a crucial and difficult problem in fog computing. Such a technique must conceal the user's true identity from the adversary; otherwise, the adversary will be able to determine which fog user and fog server are in communication. This article suggests a secure and reliable anonymous mutual authentication system for use at the network's edge between fog users and fog servers. With the aid of the registration authority (RA) in our system, they can verify one another and decide on a new session key that will be used to encrypt messages throughout the session. Fog users don't need to re‐register with RA to wander freely over the network and authenticate to any fog server that is within their range. The proposed technique only needs a small number of symmetric encryption/decryption and one‐way hash functions, making it easy to implement for fog‐user devices with limited resources. The new scheme's performance is evaluated in comparison to the existing one, showing that it is more resilient to various types of assaults (such as known plaintext attacks, man‐in‐the‐middle attacks, session hijacking, etc.). The widely used Automated Validation of Internet Security Protocols and Applications tool is used to verify the proposed system. The outcomes demonstrate that our approach can safely withstand different attacks and accomplish the desired outcomes. Additionally, the proposed method is tested in real‐world scenarios with the NS3 simulator.","url":"https://doi.org/10.1002/cpe.8054","authors":["Thankaraja Raja Sree","R. Harish","T. Veni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-29T09:50:30Z","doi":"10.1002/cpe.8054","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1109/aiars63200.2024.00171","name":"Research on Privacy Techniques Based on Multi-Party Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aiars63200.2024.00171","authors":["Tingting Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-14T17:22:34Z","doi":"10.1109/aiars63200.2024.00171","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1002/cpe.8192","name":"QSKCG: Quantum‐based secure key communication and key generation scheme for outsourced data in cloud","source":"crossref","abstract":"Abstract In the era of digital proliferation, individuals opt for cloud servers to store their data due to the diverse advantages they offer. However, entrusting data to cloud servers relinquishes users' control, potentially compromising data confidentiality and integrity. Traditional auditing methods designed to ensure data integrity in cloud servers typically depend on Trusted Third Party Auditors. Yet, many of these existing auditing approaches grapple with intricate certificate management and key escrow issues. Furthermore, the imminent threat of powerful quantum computers poses a risk of swiftly compromising these methods in polynomial time. To overcome these challenges, this paper introduces a Quantum‐based Secure Key Communication and Key Generation Scheme QSKCG for Outsourced Data in the Cloud. Leveraging Elliptic Curve Cryptography, the BB84 secure communication protocol, certificateless signature, and blockchain network, the proposed scheme is demonstrated through security analysis, affirming its robustness and high efficiency. Additionally, performance analysis underscores the practicality of the proposed scheme in achieving post‐quantum security in cloud storage.","url":"https://doi.org/10.1002/cpe.8192","authors":["Vamshi Adouth","Eswari Rajagopal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-11T07:50:06Z","doi":"10.1002/cpe.8192","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1002/cpe.8270","name":"To secure an e‐commerce system using epidemic mathematical modeling with neural network","source":"crossref","abstract":"Summary Securing an e‐commerce system using epidemic mathematical modeling with neural networks involves adapting epidemiological principles to combat the spread of misinformation. Just like how epidemiologists track the spread of diseases through populations, we can track the dissemination of fake news through online platforms. By modeling how fake news spreads, we gain insights into its propagation patterns, enabling us to develop more effective countermeasures. Neural networks, with their ability to learn from data, play a crucial role in this process by analyzing vast amounts of information to identify and mitigate the impact of fake news. One potential disadvantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is the complexity of the approach. The epidemic‐based recurrent long short‐term memory (E‐RLSTM) technique addresses the complexity and evolving nature of fake news propagation by leveraging the strengths of recurrent neural networks (RNNs), specifically long short‐term memory (LSTM) units, within an epidemic modeling framework. One advantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is its proactive nature. One significant finding in employing this approach is the ability to uncover hidden connections and correlations within the data. E‐RLSTM stands out by capturing temporal dynamics and integrating epidemic parameters into its LSTM architecture, ensuring robustness and adaptability in detecting and combating fake news within e‐commerce systems, outperforming other techniques in accuracy and performance. Description of the NSL‐KDD dataset offers easy access to a valuable repository for benchmarking cyber security. Contained within are more than 120,000 authentic samples of cyber‐attacks across 41 distinct categories, providing an excellent environment for testing intrusion detection systems.","url":"https://doi.org/10.1002/cpe.8270","authors":["Kumar Sachin Yadav","Ajit Kumar Keshri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-30T02:16:22Z","doi":"10.1002/cpe.8270","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.31449/inf.v47i10.4586","name":"An Approach for Privacy Preservation Assisted Secure Cloud Computation","source":"crossref","abstract":"As organizations employ technology to change their business operationsinto something simpler, quicker, more secure, adaptable, and lucrative, digital transformation is assisting them in doing so. Cloud computing technology is a cornerstone to this transformation. Cloud computing imparts a more affordable, scalable, and location-independent platform for handling client data. Customers may benefit from the advantages of this new paradigm by outsourcing compute and storage requirements to public providers and paying for the services consumed. Cloud computing bestows advantageous on-the-demand oriented network ingress to specific a pool of adjustable computing resources that may be shared and which can be promptly deployed with better minimum management and efficiency oriented overhead. Cloud paradigm provides the prime and fundamental benefit of computation oriented outsourcing, wheredue to customers are no longer confined by their resource-constrained devices thanks to the cloud's computing capabilities. Outsourcing allows you to save time and money. With the outsourcing problem to cloud, clients can always get the benefits of limitless computational resources in pay and use manner, free from software and hardware maintenance and operational overhead related concerns. Traditional encryption as a solution protects privacy, but restricts future data usage, reducing the fundamental economic benefits of using public cloud services dramatically. Processing on encrypted data is widely acknowledged research problem in cryptography. We offer a secure system in this study and an oracle for query vectors outsourcing using privacy homomorphism. The empirical analysis for the proposed prototype in terms of computational and security aspects, experiment results discussion is also given in this paper.","url":"https://doi.org/10.31449/inf.v47i10.4586","authors":["Swathi Velugoti","M. P. Vani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-11T08:42:54Z","doi":"10.31449/inf.v47i10.4586","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1109/access.2024.3383474","name":"Blockchain-Based Secure Content Caching and Computation for Edge Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2024.3383474","authors":["Elif Bozkaya-Aras"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-01T20:02:46Z","doi":"10.1109/access.2024.3383474","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1007/978-3-031-51284-1_7","name":"Multiparty Democracy and Militarised Transition","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-51284-1_7","authors":["Aaron Rwodzi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-26T15:07:48Z","doi":"10.1007/978-3-031-51284-1_7","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.4135/9781071961322","name":"Multiparty Negotiations","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781071961322","authors":["Andrew Bennett"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-29T14:44:11Z","doi":"10.4135/9781071961322","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1002/cpe.8084","name":"Privacy preserving and secure robust federated learning: A survey","source":"crossref","abstract":"Summary Federated learning (FL) has emerged as a promising solution to address the challenges posed by data silos and the need for global data fusion. It offers a distributed machine learning framework with privacy‐preserving features, allowing model training without the need to collect user data. However, FL also presents significant security and privacy threats that hinder its widespread adoption. The requirements of privacy and security in FL are inherently conflicting. Privacy necessitates the concealment of individual client updates, while security requires the disclosure of client updates to detect anomalies. While most existing research focused on the privacy and security aspects of FL, very few studies have addressed the compatibility of these two demands. In this work, we aim to bridge this gap by proposing a comprehensive defense scheme that ensures privacy, security, and compatibility in FL. We categorize the existing literature into two key directions: privacy defense and security defense. Privacy defense includes methods based on additive masks, differential privacy, homomorphic encryption, and trusted execution environment, whereas security defense encompasses distance‐, performance‐, clustering‐, and similarity‐based anomaly detection techniques and statistical information‐based anomaly update bypassing techniques when the server is trusted and privacy‐compatible anomaly update detection techniques when the server is not trusted. In addition, this article presents decentralized FL solutions based on blockchain. For each direction, we discuss specific technical solutions, their advantages, and disadvantages. By evaluating various defense methods, we identify the most suitable approach to address the primary challenge of “achieving a secure and robust FL system against malicious adversaries while protecting users' privacy.” We then propose a theoretical reference framework for end‐to‐end protection of privacy and security in FL for the key problem, which summarizes the attack surface of FL systems from the client to the server under the security model where the client and server are malicious. Leveraging the strengths and characteristics of existing schemes, our proposed framework integrates multiple techniques to strike a balance between privacy, usability, and efficiency. This framework serves as a valuable reference and provides insights for future work in the field. Finally, we also provide recommendations for future research directions in this field.","url":"https://doi.org/10.1002/cpe.8084","authors":["Qingdi Han","Siqi Lu","Wenhao Wang","Haipeng Qu","Jingsheng Li","Yang Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-19T23:44:57Z","doi":"10.1002/cpe.8084","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1002/cpe.8283","name":"Secure device authentication and key agreement mechanism for <scp>LoRaWAN</scp> based <scp>IoT</scp> networks","source":"crossref","abstract":"Summary The proposed work introduces two schemes for secure device authentication and key agreement (SDA &amp; KA) mechanisms. Initially, an efficient implicit certificate approach based on the Elliptic curve Qu–Vanstone (EIC‐EcQuV) scheme is developed in the first stage to instantly concur on the session key. The proposed scheme implicitly performs quick authentication of the public key. Also, this scheme prevents the attacker from creating fake key combinations. Through EIC‐EcQuV, the implicit certificate (IC) is distributed which helps to implicitly authenticate the user. This work also proposes ithe developed Public Key Certificateless Cryptosystem (PKCIC) scheme in the second stage, whch was also for the SDA &amp; KA mechanism. In the EIC‐EcQuV scheme, efficient authentication is enabled, but public key theft is possible. However, in the PKCIC scheme, authentication is performed through partial keys, and the public key is secured via the Schnorr signature. The efficiency of the proposed schemes is proved by comparing the attained results with previous schemes. The proposed method obtains the computational cost of 0.0583 s for end‐to‐end devices, 0.06111 for network servers, and 0.00071 s for the gateway, with an execution time of 78.624 for 1000 devices. The attained key agreement of the proposed EIC‐EcQuV is 0.953 s, and PKCIC is 0.9988 s.","url":"https://doi.org/10.1002/cpe.8283","authors":["Devishree Naidu","Niranjan K. Ray"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T05:30:02Z","doi":"10.1002/cpe.8283","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1002/cpe.8067","name":"An optimized crypto‐based routing protocol for secure routing in wireless sensor networks","source":"crossref","abstract":"Summary In Wireless Sensor Networks (WSN), energy‐efficient, reliable routing is the core objective for the data transmission process. Anomaly nodes in the communication environment can affect reliable routing and network efficiency. Therefore, the present research created a novel Arithmetic Optimization‐based Rumor Routing Protocol with SKINNY Crypto mechanism (AORRP‐SCrypt) for secure Routing in WSN. Primarily, the required nodes are deployed in the related WSN environment. The arithmetic function traced the nodes' higher energy consumption and malicious action to avoid security vulnerabilities during data transmission. The sensed data packets are transmitted based on the Rumor routing function of the proposed system. Furthermore, to avoid data access by unauthorized users, it is encrypted using the SKINNY Crypto mechanism. Finally, the network efficiency and security performance are validated in the NS3 platform. The model gained 98.1% network throughput, 95.6% PDR, a lower energy consumption of between 0.001 and 0.01 J, and a 99% confidentiality rate. The developed system provided increased network security and efficiency.","url":"https://doi.org/10.1002/cpe.8067","authors":["Khaleel‐Ur‐Rahman Khan","Mohammed Abdul Azeem"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-12T02:19:15Z","doi":"10.1002/cpe.8067","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.14722/ndss.2024.24105","name":"BliMe: Verifiably Secure Outsourced Computation with Hardware-Enforced Taint Tracking","source":"crossref","abstract":"","url":"https://doi.org/10.14722/ndss.2024.24105","authors":["Hossam ElAtali","Lachlan J. Gunn","Hans Liljestrand","N. Asokan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-10T16:35:08Z","doi":"10.14722/ndss.2024.24105","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1109/ispec59716.2024.10892565","name":"Energy-Efficient and Secure Framework for Computation Offloading in Sustainable Vehicular Edge-Cloud Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ispec59716.2024.10892565","authors":["Ibrahim A. Elgendy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-26T18:43:43Z","doi":"10.1109/ispec59716.2024.10892565","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1109/csr61664.2024.10679478","name":"DSCS: Towards an Extensible Secure Decentralised Distributed Computation Protocol","source":"crossref","abstract":"","url":"https://doi.org/10.1109/csr61664.2024.10679478","authors":["Alexander Dalton","David Thomas","Peter Cheung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-24T17:21:51Z","doi":"10.1109/csr61664.2024.10679478","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.4135/9781071961353","name":"Mediators in Negotiations","source":"crossref","abstract":"","url":"https://doi.org/10.4135/9781071961353","authors":["Andrew Bennett"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-29T14:44:11Z","doi":"10.4135/9781071961353","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1002/cpe.8205","name":"Convergent encryption enabled secure data deduplication algorithm for cloud environment","source":"crossref","abstract":"Summary The exponential growth of data poses a critical challenge for cloud storage systems. Redundant data consumes valuable storage space and increases infrastructure costs. Data deduplication, a technique for eliminating duplicate data copies, offers a promising solution. However, existing deduplication techniques often compromise data security, especially when dealing with encrypted data. This paper proposes a novel approach that merges convergent encryption (CE) with data deduplication. CE leverages user data itself to generate unique encryption keys, enabling secure deduplication on encrypted data. We analyze existing literature on secure data deduplication and categorize various techniques using UML activity diagrams. We then present our proposed CE‐based deduplication system, outlining its functionalities through UML diagrams. This research contributes to the field of secure data storage by proposing a novel and secure deduplication approach. By demonstrating its efficiency and security benefits, this work paves the way for more efficient and secure cloud storage solutions. Finally, we demonstrate the system's effectiveness through a comparative analysis, highlighting its potential to significantly improve storage efficiency while maintaining data security.","url":"https://doi.org/10.1002/cpe.8205","authors":["Shahnawaz Ahmad","Mohd. Arif","Javed Ahmad","Mohd. Nazim","Shabana Mehfuz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-21T03:35:59Z","doi":"10.1002/cpe.8205","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1109/icscan62807.2024.10894284","name":"Privacy-Secure and Decentralized Biometric Authentication Models Using Federated Learning Frameworks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icscan62807.2024.10894284","authors":["Mathivanan. P","K. Mahalakshmi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-26T13:45:34Z","doi":"10.1109/icscan62807.2024.10894284","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1088/1751-8121/ad2b88","name":"Verifiable quantum protocol for dynamic secure multiparty summation based on homomorphic encryption\n                  <sup>*</sup>","source":"crossref","abstract":"Abstract The research of quantum secure multiparty computation is a subject of great importance in modern cryptography. In this study, we construct a verifiable quantum protocol for dynamic secure multiparty summation based on the cyclic property of d -level MUBs. Our protocol can realize dynamic parameter update in the aspect of members and secret inputs, improving the practicality of the protocol. Moreover, a verification mechanism for result checking by applying ElGamal homomorphic encryption is given, and further enables the detectability of cheating behaviors, making our protocol safer. The security analysis proves the proposed protocol not only can resist a range of typical attacks from outside and inside, but also is secure against dishonest revoked participant attack which has been neglected in previous dynamic quantum summation protocols. From a theoretical perspective, compared with existing summation protocols, the protocol provides better practicability, higher privacy protection, and higher efficiency.","url":"https://doi.org/10.1088/1751-8121/ad2b88","authors":["Mei Luo","Fulin Li","Li Liu","Shixin Zhu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-21T22:25:22Z","doi":"10.1088/1751-8121/ad2b88","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1007/978-981-97-8051-8_17","name":"Some Notes on Deniability, Obfuscation, and Multiparty Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-8051-8_17","authors":["Soumen Sarkar","Jaydeep Howlader"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-04T06:45:38Z","doi":"10.1007/978-981-97-8051-8_17","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1007/978-981-97-5101-3_12","name":"SecuPath: A Secure and Privacy-Preserving Multiparty Path Planning Framework in UAV Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-5101-3_12","authors":["Yanjun Shen","Joseph Liu","Xingliang Yuan","Shifeng Sun","Hui Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-14T18:01:46Z","doi":"10.1007/978-981-97-5101-3_12","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1109/icict60155.2024.10544477","name":"Secure Data Transfer onto Cloud Environment using Diffie-Hellman Key Exchange Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icict60155.2024.10544477","authors":["Aayushi Bhansali","Juluru Harisha","Garima Sinha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T17:22:16Z","doi":"10.1109/icict60155.2024.10544477","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1109/icscan62807.2024.10894204","name":"Integrating Lightweight Algorithms with Blockchain for Secure and Efficient Authentication in VANETs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icscan62807.2024.10894204","authors":["Dumpala Prasanth","S Christy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-26T18:45:34Z","doi":"10.1109/icscan62807.2024.10894204","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.1002/cpe.8250","name":"A provably secure authenticated key agreement protocol for industrial sensor network system","source":"crossref","abstract":"Summary The convergence of reliable and self‐organizing characteristics of Wireless Sensor Networks (WSNs) and the IoT has increased the utilization of WSN in different scenarios such as healthcare, industrial units, battlefield monitoring and so forth, yet has also led to significant security risks in their deployment. So, several researchers are developing efficient authentication frameworks with various security and privacy characteristics for WSNs. Subsequently, we review and examine a recently proposed robust key management protocol for an industrial sensor network system. However, their work is incompetent to proffer expedient security and is susceptible to several security attacks. We demonstrate their vulnerabilities against man‐in‐the‐middle attacks, privileged insider attacks, secret key leakage attacks, user, gateway, and sensor node impersonation attacks, and offline password‐guessing attacks. We further highlight the design flaw of no session key agreement in Itoo et al. Therefore to alleviate the existing security issues, we devise an improved key agreement and mutual authentication framework. Our protocol outperforms Itoo et al.'s drawbacks, as demonstrated by the comprehensive security proof performed using the real‐or‐random (ROR) model and the formal verification accomplished using the Automated Validation of Internet Security Protocols (AVISPA) tool.","url":"https://doi.org/10.1002/cpe.8250","authors":["Garima Thakur","Mohammad S. Obaidat","Piyush Sharma","Sunil Prajapat","Pankaj Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-04T21:21:25Z","doi":"10.1002/cpe.8250","addedAt":"2026-08-31T06:41:37.832Z","updatedAt":"2026-08-31T06:41:37.832Z"},{"id":"doi:10.56553/popets-2025-0088","name":"Achieving Data Reconstruction Hardness and Efficient Computation in Multiparty Minimax Training","source":"crossref","abstract":"Generative models have achieved remarkable success in a wide range of applications. Training such models using proprietary data from multiple parties has been studied in the realm of federated learning. Yet recent studies showed that reconstruction of authentic training data can be achieved in such settings. On the other hand, multiparty computation (MPC) guarantees standard data privacy, yet scales poorly for training generative models. In this paper, we focus on improving reconstruction hardness during Generative Adversarial Network (GAN) training while keeping the training cost tractable. To this end, we explore two training protocols that use a public generator and an MPC discriminator: Protocol 1 (P1) uses a fully private discriminator, while Protocol 2 (P2) privatizes the first three discriminator layers. We prove reconstruction hardness for P1 and P2 by showing that (1) a public generator does not allow recov- ery of authentic training data, as long as the first two layers of the discriminator are private; and through an existing approximation hardness result on ReLU networks, (2) a discriminator with at least three private layers does not allow authentic data reconstruction with algorithms polynomial in network depth and size. We show empirically that compared with fully MPC training, P1 reduces the training time by 2× and P2 further by 4 − 16×. Our implementation can be found at https://github.com/asu-crypto/ppgan.","url":"https://doi.org/10.56553/popets-2025-0088","authors":["Truong Son Nguyen","Yi Ren","Guangyu Nie","Ni Trieu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-19T03:14:51Z","doi":"10.56553/popets-2025-0088","addedAt":"2026-08-31T06:41:38.001Z","updatedAt":"2026-08-31T06:41:38.001Z"},{"id":"doi:10.1117/12.3067310","name":"Integrated multilevel secure spatial database multiparty data security exchange based on hybrid national encryption algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1117/12.3067310","authors":["Xinping Miao","Changhui Zhu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-09T13:08:38Z","doi":"10.1117/12.3067310","addedAt":"2026-08-31T06:41:38.001Z","updatedAt":"2026-08-31T06:41:38.001Z"},{"id":"doi:10.1007/978-3-030-71522-9_300863","name":"Secure Multi-party Computation of Differentially Private Mechanisms","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71522-9_300863","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:20:57Z","doi":"10.1007/978-3-030-71522-9_300863","addedAt":"2026-08-31T06:41:38.001Z","updatedAt":"2026-08-31T06:41:38.001Z"},{"id":"doi:10.1007/978-3-030-71522-9_1714","name":"Secure Computation of Differentially Private Mechanisms","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-71522-9_1714","authors":["Jonas Böhler"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:20:57Z","doi":"10.1007/978-3-030-71522-9_1714","addedAt":"2026-08-31T06:41:38.001Z","updatedAt":"2026-08-31T06:41:38.001Z"},{"id":"doi:10.2139/ssrn.5390466","name":"Batched Verifiable Distributed Secure Matrix Polynomial Computation","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5390466","authors":["Yi Xu","Weijie tan","Chunguo Li","Minyao Ma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-13T15:41:53Z","doi":"10.2139/ssrn.5390466","addedAt":"2026-08-31T06:41:38.001Z","updatedAt":"2026-08-31T06:41:38.001Z"},{"id":"doi:10.1109/worldsuas66815.2025.11199078","name":"Leveraging Elliptic Curve Cryptography and Homomorphic Properties for Secure Multiparty Communication with One-Time Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/worldsuas66815.2025.11199078","authors":["Soumya V Menon","Prabhu","Karthikeyan S","Aseem Aneja","Yogomaya Mohapatra","Pradeep Marwaha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-17T17:38:38Z","doi":"10.1109/worldsuas66815.2025.11199078","addedAt":"2026-08-31T06:41:38.001Z","updatedAt":"2026-08-31T06:41:38.001Z"},{"id":"doi:10.1109/icc52391.2025.11161789","name":"Resource-Aware Secure Computation Offloading","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc52391.2025.11161789","authors":["Yushu Yan","Başak Güler"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T17:34:55Z","doi":"10.1109/icc52391.2025.11161789","addedAt":"2026-08-31T06:41:38.001Z","updatedAt":"2026-08-31T06:41:38.001Z"},{"id":"doi:10.64628/aak.ktecrwkrf","name":"Cloud-based computing: routes toward secure storage and affordable computation","source":"crossref","abstract":"","url":"https://doi.org/10.64628/aak.ktecrwkrf","authors":["Robert Deng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-25T04:13:53Z","doi":"10.64628/aak.ktecrwkrf","addedAt":"2026-08-31T06:41:38.001Z","updatedAt":"2026-08-31T06:41:38.001Z"},{"id":"doi:10.1109/tdsc.2025.3561472","name":"Enabling Two-Party Secure Computation on Set Intersection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tdsc.2025.3561472","authors":["Ferhat Karakoç","Alptekin Küpçü"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-16T13:54:39Z","doi":"10.1109/tdsc.2025.3561472","addedAt":"2026-08-31T06:41:38.001Z","updatedAt":"2026-08-31T06:41:38.001Z"},{"id":"doi:10.1103/k8wg-qmbh","name":"Quantum-Secure Multiparty Deep Learning","source":"crossref","abstract":"Secure multiparty computation enables the joint evaluation of multivariate functions across distributed users while ensuring the privacy of their local inputs. This field has become urgent due to the demand for computationally intensive deep learning inference. These computations are typically offloaded to cloud servers, leading to vulnerabilities. To solve this problem, we introduce a linear algebra engine that leverages the quantum nature of light for information-theoretically secure multiparty inference using telecommunication components. We apply this linear algebra engine to deep learning and derive rigorous upper bounds on the information leakage of both the deep neural network weights and the client’s data, enabling double-blind operations. Applied to the modified National Institute of Standards and Technology classification task, we obtain test accuracies exceeding 95% while guaranteeing leakage of less than 0.1 bits per weight and data element. This leakage is an order of magnitude below the minimum bit precision required for accurate deep learning using state-of-the-art quantization techniques. Our work lays the foundation for practical quantum-secure computation and unlocks secure cloud deep learning as a field.","url":"https://doi.org/10.1103/k8wg-qmbh","authors":["Kfir Sulimany","Sri Krishna Vadlamani","Ryan Hamerly","Prahlad Iyengar","Dirk Englund"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-31T18:09:49Z","doi":"10.1103/k8wg-qmbh","addedAt":"2026-08-31T06:41:38.001Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.7566/jpsht.5.008","name":"Exploring Materials without Data Exposure: A Bayesian Optimizer using Secure Computation","source":"crossref","abstract":"Secure computation allows the manipulation of material data without exposing them, thereby offering an alternative to traditional open/closed data management. We recently reported the development of an application that performs Bayesian optimization using secure computation.","url":"https://doi.org/10.7566/jpsht.5.008","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-06T00:16:29Z","doi":"10.7566/jpsht.5.008","addedAt":"2026-08-31T06:41:38.001Z","updatedAt":"2026-08-31T06:41:38.001Z"},{"id":"doi:10.1109/access65134.2025.11135637","name":"RIM−SMPC : ReInforced Mondrian in Secure Multi-Party Computation for Privacy Preserved Credit Risk Assessment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access65134.2025.11135637","authors":["R Padmaja","V. Santhi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T17:39:34Z","doi":"10.1109/access65134.2025.11135637","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1016/j.amc.2024.129257","name":"Prescribed-time synchronization of hyperchaotic fuzzy stochastic PMSM model with an application to secure communications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.amc.2024.129257","authors":["Sangeetha Rajendran","Palanivel Kaliyaperumal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-04T23:47:54Z","doi":"10.1016/j.amc.2024.129257","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.2139/ssrn.5258791","name":"Unlocking Privacy in Blockchain: Exploring Zero-Knowledge Proofs and Secure Multi-Party Computation Techniques","source":"crossref","abstract":"As blockchain technology continues to evolve, the pursuit of privacy has become a significant challenge. Although the transparency and immutability of blockchain are essential features, they can unintentionally expose sensitive information. This paper investigates the potential of Zero-Knowledge Proofs (ZKPs) and Secure Multi-Party Computation (SMPC) as innovative solutions to address these privacy concerns. ZKPs facilitate the verification of information without disclosing the underlying data, thereby enhancing privacy in transactions and identity verification processes. Meanwhile, SMPC enables collaborative computations while preserving the confidentiality of inputs, which is vital for industries such as finance and healthcare. Despite their potential, these technologies encounter challenges related to complexity, scalability, and regulatory compliance. This study offers a thorough analysis of ZKPs and SMPC, their applications, and the ethical implications involved, providing valuable insights into their role in creating a secure and privacy-conscious blockchain ecosystem.","url":"https://doi.org/10.2139/ssrn.5258791","authors":["Chris Gilbert","Mercy Gilbert"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-19T14:19:43Z","doi":"10.2139/ssrn.5258791","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1109/autocom64127.2025.10956464","name":"Secure Multi-party Computation for Privacy Preservation in Collaborative Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/autocom64127.2025.10956464","authors":["Godwin Premi. M.S","Awakash Mishra","Ashmeet Kaur","Jyoti Ranjan Sahoo","Prateek Aggarwal","Sanjiv Mathur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-16T17:45:45Z","doi":"10.1109/autocom64127.2025.10956464","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.71465/csb150","name":"SECURE MULTI-PARTY COMPUTATION IN CLOUD ENVIRONMENTS: ENABLING PRIVACY-PRESERVING COLLABORATIVE COMPUTATION","source":"crossref","abstract":"Secure Multi-Party Computation (SMPC) allows multiple parties to collaboratively compute a function over their inputs while keeping those inputs private. As data collaboration becomes essential in cloud-based services, the application of SMPC in cloud environments offers a promising approach to ensure data confidentiality without compromising utility. This paper explores the theoretical foundations, protocols, and practical applications of SMPC in the cloud. We discuss cryptographic primitives such as secret sharing and homomorphic encryption, evaluate real-world use cases in healthcare, finance, and federated learning, and address the performance, scalability, and trust issues inherent in cloud deployments. We conclude by highlighting recent advancements and future directions toward practical, efficient, and scalable privacy-preserving computation in distributed systems.","url":"https://doi.org/10.71465/csb150","authors":["Dr. Saad Hussain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-28T03:10:06Z","doi":"10.71465/csb150","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1109/icngcs64900.2025.11183241","name":"Secure Computation offloading in Edge-Assisted Cloud Ecosystem","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icngcs64900.2025.11183241","authors":["Shabariram C P","Subhashini S"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-08T17:36:03Z","doi":"10.1109/icngcs64900.2025.11183241","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1007/978-981-96-7496-1_24","name":"Multiparty Computation for Privacy-Preserving Communication in the Smart Grid","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-96-7496-1_24","authors":["Priya Deokar","Sandhya Arora"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-30T22:42:31Z","doi":"10.1007/978-981-96-7496-1_24","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1002/cpe.70118","name":"Secuyolo: Secure Object Detection Framework With Darknet‐53 and CBAM in Edge Devices","source":"crossref","abstract":"ABSTRACT Object detection in edge computing has emerged as a pivotal solution for processing sensor data locally, thereby minimizing latency and bandwidth consumption. However, this introduces significant privacy concerns, as edge servers may exploit sensitive sensor data. Traditional frameworks often overlook data security and privacy protection, presenting challenges in balancing privacy with computational efficiency in resource‐constrained environments. To address these issues and improve performance, we propose SecuYOLO CBAMNet53, a novel framework combining Secure YOLO V3 with Darknet‐53 and the CBAM. This model enhances feature extraction capabilities, ensuring privacy protection and accurate real‐time object detection on edge devices. Darknet‐53 serves as a deep feature extractor, while CBAM boosts discriminative power through channel‐wise and spatial attention, improving detection accuracy and handling complex scenes. It integrates YOLOv3 with security measures like data encryption, secure deployment, and differential privacy, safeguarding sensitive information during inference. By classifying data as public or private for smart IoT applications, SecuYOLO CBAMNet53 optimizes performance and prevents privacy breaches. The proposed SecuYOLO, employing Darknet‐53 with CBAM as its backbone, achieves outstanding performance in both an average recall of 67.62 and an average precision of 51.2. The model's dynamic recalibration of feature maps and robust security mechanisms ensure reliable object detection and privacy protection, making it suitable for real‐world applications.","url":"https://doi.org/10.1002/cpe.70118","authors":["P. Lakshmiramana","A. Vadivel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-03T03:34:42Z","doi":"10.1002/cpe.70118","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1002/cpe.70087","name":"Energy‐Efficient Blockchain‐Based Secure Model to Share Medical Data Using Mobile Edge Computing","source":"crossref","abstract":"ABSTRACT Blockchain technology is gaining importance in different sectors like healthcare, finance, agriculture, and many more. The important capabilities of blockchain like decentralization, immutability, consensus mechanism, etc. provide security, privacy, transparency, accountability, and many other benefits. On the other hand, Mobile Edge Computing (MEC) is a distributed framework that provides cloud computing capabilities to mobile devices. The existing studies combining blockchain technology and MEC often do not consider the delay and energy consumption for data offloading. In this paper, a blockchain‐based scheme has been proposed for sharing Internet of Medical Things (IoMT) data between a patient and a doctor, which offloads tasks to the MEC server to achieve energy efficiency. In the proposed scheme, the Non‐Orthogonal Multiple Access (NOMA) protocol is used to share a channel among several users. Here, NOMA offers some advantages in the system like low cost, latency, and power consumption. In the proposed scheme, the energy consumption is optimized based on the task delegation decision and resource distribution in the MEC server. Additionally, operations of the blockchain network are automated using various smart contracts. The efficiency of the proposed scheme is analyzed in terms of energy consumption, average transmission rate, and offloading delay in processing healthcare data. The experimental results demonstrate that the proposed model enhances energy efficiency and optimizes performance compared to the state‐of‐the‐art offloading schemes.","url":"https://doi.org/10.1002/cpe.70087","authors":["Sagnik Datta","Suyel Namasudra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-23T11:50:06Z","doi":"10.1002/cpe.70087","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1109/icaiem66060.2025.11139282","name":"Design and Verification of Secure Multi-Party Computation Enhanced by AI Optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaiem66060.2025.11139282","authors":["Linxi Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-01T19:14:04Z","doi":"10.1109/icaiem66060.2025.11139282","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1016/j.amc.2025.129595","name":"SOS-based secure consensus control for stochastic nonlinear MASs against multiple DoS attacks","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.amc.2025.129595","authors":["Zhen Zhang","Wei-Wei Che"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-10T09:31:41Z","doi":"10.1016/j.amc.2025.129595","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1002/cpe.70173","name":"Deep Learning Based Secure Routing in\n                    <scp>WSN</scp>\n                    Using Hyper Ledger Fabric Block Chain Model","source":"crossref","abstract":"ABSTRACT Wireless sensor networks (WSNs) are the most widely used to support communication and security problems. A secure routing to the base station (BS) is crucial for increasing the efficiency of the WSN environment. In recent years, several analytical models have included encryption and machine‐learning approaches for secure routing. These existing modes face some challenges like security vulnerabilities, less scalability, time consumption and so on. To address these difficulties, a blockchain‐based network and cryptographic architecture enable efficient and secure routing in WSNs. In WSNs, the Convolutional Bidirectional Gated Recurrent Units (Conv‐BiGRU) model can be used to classify normal and non‐malicious nodes. The authentication process aids in the protection of data from unauthorized users. Only the authenticated user is permitted to collect or transfer the data. The performance metrics such as accuracy, precision, mean squared error (MSE), energy consumption, packet delivery ratio (PDR), packet loss ratio (PLR), network lifetime (NLT), number of dead nodes (NDN) and the average time delay is evaluated for the existing and suggested model. The proposed model achieves high PDR values of 0.985, an accuracy of 99.86%, and the lowest PLR values of 0.018.","url":"https://doi.org/10.1002/cpe.70173","authors":["Uma Meena","Promila Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-02T04:11:23Z","doi":"10.1002/cpe.70173","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1002/cpe.70234","name":"Correction to “Secure Data Authentication for Remotely Stored Data Using Bilinear Pairing on Elliptic Curves With Optimized Data Block Size”","source":"crossref","abstract":"","url":"https://doi.org/10.1002/cpe.70234","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-14T05:20:31Z","doi":"10.1002/cpe.70234","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1002/cpe.8378","name":"A Reliable and Secure Permissioned Blockchain‐Assisted Data Transfer Mechanism in Healthcare‐Based Cyber‐Physical Systems","source":"crossref","abstract":"ABSTRACT Healthcare systems are highly sensitive to cyberattacks as these systems possess most of the sensitive information compared to other systems relying on internet facilities. Due to the stronger security merits and efficiency of blockchain, it is integrated with the healthcare sector to ensure reliable data transfer. However, to improve the reliability and efficiency of the integrated system, a permissioned blockchain‐based security framework combining several techniques is proposed. To enable storing and validating blocks containing medical data on the blockchain, the miner is administered using the delegated proof of stake (DPoS) consensus protocol. This protocol is efficient in choosing the miner from the list of participants. Then, the blocks are created using recursive indexing with an Even–Rodeh (RI‐ER) coding hashing scheme. This algorithm is an indexing scheme that is much more efficient than the normal hashing algorithms. The validation process is carried out by the miner using the hash values provided to the users. By using the kidney disease dataset from Kaggle, the performance of the proposed method is evaluated. The performance analysis proved the effectiveness of the proposed approach compared to other schemes.","url":"https://doi.org/10.1002/cpe.8378","authors":["P. Vinayasree","A. Mallikarjuna Reddy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-14T04:52:07Z","doi":"10.1002/cpe.8378","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1016/j.amc.2024.129185","name":"Fully distributed self-triggered secure consensus for nonlinear multiagent systems with sequential communication link scaling attacks","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.amc.2024.129185","authors":["Miao Zhao","Jianxiang Xi","Le Wang","Cheng Wang","Yuanshi Zheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-20T16:08:43Z","doi":"10.1016/j.amc.2024.129185","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1002/cpe.70201","name":"Privacy‐Secure Asynchronous Federated Multimodal Pedestrian Trajectory Prediction Models","source":"crossref","abstract":"ABSTRACT In distributed contexts, pedestrian trajectory prediction faces data silos, making cross‐scene data sharing difficult. Centralised training and synchronised federated learning pose risks of privacy breaches, as attackers may extract sensitive information through model inversion techniques. This paper presents a privacy‐secure asynchronous federated multimodal pedestrian trajectory prediction model (AFed‐MTP) to enhance global update efficiency and reduce reliance on delayed nodes through dynamic aggregation, as traditional synchronous training diminishes efficiency due to discrepancies in node performance, resulting in postponed global updates that affect real‐time applications. This scheme introduces a multimodal trajectory prediction model based on generative adversarial networks (GAN‐MTP) for each scenario, integrating spatiotemporal graph networks with a generative adversarial framework to generate multimodal trajectories during localised training, thereby reducing data leakage and ensuring strong privacy protection. Experimental results show that this scheme outperforms the method trained directly across various scenarios regarding data privacy security, with the mutual information value reduced to 0.018 by replacing real data with locally predicted trajectories, thereby improving privacy protection efficacy by 25%. In decentralised contexts, the ADE prediction errors for the FD1 and FD2 datasets decrease significantly compared to previous methodologies by 31.7% and 31.9%, respectively. This framework strikes a balance between privacy preservation and predictive accuracy, offering practical and safe solutions for applications such as autonomous driving and smart cities.","url":"https://doi.org/10.1002/cpe.70201","authors":["Liu Kun","Wenbo Zhou","Wang Hui","Zihao Shen","Peiqian Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-22T01:09:02Z","doi":"10.1002/cpe.70201","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1002/cpe.70064","name":"Revocable and Privacy‐Preserving CP‐ABE Scheme for Secure mHealth Data Access in Blockchain","source":"crossref","abstract":"ABSTRACT Innovations in technology are revolutionizing healthcare, driving a shift toward patient‐centric smart healthcare systems. Mobile health (mHealth) leverages innovations in wearable sensors, telecommunications, and IoT to establish a novel healthcare model that prioritizes the patient, enabling real‐time monitoring, personalized interventions, and improved access to care, ultimately fostering a proactive approach to health management and enhancing overall patient outcomes. However, safeguarding patient data transparency, security, and privacy within mHealth systems presents significant challenges, particularly concerning personal health records (PHR). Ciphertext‐Policy Attribute‐Based Encryption (CP‐ABE) offers a competent answer to facilitating one‐to‐many data sharing in healthcare environments. Nevertheless, several issues must be addressed before CP‐ABE can be widely deployed. These include the need for timely and effective attribute revocation when user attributes change, resistance to collusion attacks, and ensuring data integrity. This paper proposes a revocable and secure fine‐grained access scheme using blockchain and CP‐ABE. We compare four prominent state‐of‐the‐art schemes through comprehensive experimentation with our proposed approach. Our results demonstrate the relative performance of our scheme, showing a significant reduction in computational costs. Specifically, the key generation cost is reduced by 35% to 67%, and the encryption cost is reduced by 26% to 39%. A detailed analysis of communication, computational, and storage overhead reveals that our suggested solution offers a distinct advantage in terms of efficiency. The Scyther tool is employed to verify the security measures and assess the accuracy of proposed methodologies, subsequently conducting experiments to showcase its efficacy.","url":"https://doi.org/10.1002/cpe.70064","authors":["Anita Thakur","Virender Ranga","Ritu Agarwal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-09T04:24:38Z","doi":"10.1002/cpe.70064","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.23919/apcc64555.2025.11279830","name":"Privacy Enhancement for a Federated Learning Incentive Mechanism Using Secure Multi-Party Computation","source":"crossref","abstract":"","url":"https://doi.org/10.23919/apcc64555.2025.11279830","authors":["Yutaka Hatazawa","Takuji Tachibana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T18:36:43Z","doi":"10.23919/apcc64555.2025.11279830","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1007/978-981-97-9619-9_33","name":"A Study on Privacy-Preserving Multiparty Computation Protocols","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-9619-9_33","authors":["Chinmaya Bikram Pattanaik","Munesh Chandra Trivedi","Ruchi Jain","Mohan Lal Kolhe"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-26T14:32:39Z","doi":"10.1007/978-981-97-9619-9_33","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1109/icecit67774.2025.11450956","name":"Secure Edge : A Survey on Security Risks and Challenges in Edge Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icecit67774.2025.11450956","authors":["Thejaswini S","R Aparna"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-30T20:03:21Z","doi":"10.1109/icecit67774.2025.11450956","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.13052/rp-9788743808268a105","name":"SECURE QR CODE COMMUNICATION WITH MULTI-PARTY\nAUTHENTICATION AND AES ENCRYPTION","source":"crossref","abstract":"","url":"https://doi.org/10.13052/rp-9788743808268a105","authors":["A Saurabh","Dr T Balachander"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-03T18:13:04Z","doi":"10.13052/rp-9788743808268a105","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1109/iccies63851.2025.11032978","name":"Connected Vehicles Secure Data Sharing using Secure and Differential Privacy computation on multi-party","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccies63851.2025.11032978","authors":["K. Tulasiram Kumar","Rallabandi Venkata Santoshi Saraswati Swetha Nagini","A Shivaprasad","R. Maheswari","Haider Alabdeli","Dilli Ganesh V"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-17T17:37:35Z","doi":"10.1109/iccies63851.2025.11032978","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1109/icict64420.2025.11004853","name":"Secure IoT-Cloud Framework for Banking: Enhancing Data Storage and Transactions with Cryptographic Authentication","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icict64420.2025.11004853","authors":["R. Yuvarani","R. Mahaveerakannan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-23T17:02:43Z","doi":"10.1109/icict64420.2025.11004853","addedAt":"2026-08-31T06:41:38.002Z","updatedAt":"2026-08-31T06:41:38.002Z"},{"id":"doi:10.1007/978-3-032-07089-0","name":"An Introduction to Silent Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-07089-0","authors":["Geoffroy Couteau"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-22T22:55:37Z","doi":"10.1007/978-3-032-07089-0","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1007/s44443-026-01215-2","name":"DMSA-FL: secure and robust federated learning via double-masked secure aggregation with multiparty homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s44443-026-01215-2","authors":["Wenhao Liu","Xu An Wang","Weiwei Jiang","Lingling Wu","Haibo Lei","Xiaoyuan Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-26T09:54:15Z","doi":"10.1007/s44443-026-01215-2","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1109/icicit69063.2026.11634011","name":"Secure SMS-based Remote Mobile Information Surveillance","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicit69063.2026.11634011","authors":["G.Prithasree","B.Nithya","B.Lakshmidevi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T19:18:44Z","doi":"10.1109/icicit69063.2026.11634011","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1007/978-3-032-07089-0_1","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-07089-0_1","authors":["Geoffroy Couteau"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-22T22:55:37Z","doi":"10.1007/978-3-032-07089-0_1","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.2139/ssrn.6826533","name":"FSSketch: efficient frequency sorting of distributed data based on sketch and secure multi-party computation","source":"crossref","abstract":"Distributed computing environments provide considerable advantages for storing user data and performing computations, owing to their geographically dispersed nature. Within this framework, sorting data by frequency underpins numerous data aggregation and analysis tasks. Prior research aimed at mitigating privacy risks has largely focused on the application of cryptographic primitives for safeguarding data. Extending this body of work, this paper introduces an efficient algorithm for sorting data by frequency while maintaining a privacy-preserving aggregation state. The proposed method first leverages sketch theory to design a frequency-ranking algorithm for distributed data, which facilitates efficient information aggregation and sorting. Subsequently, privacy enhancements are incorporated into this frequency-ranking algorithm through additive secret sharing (ASS) and secure multi-party computation (SMPC). These cryptographic techniques enable private addition and summation operations on data distributed across multiple sources. The security of the proposed sketch-based frequency sorting algorithm (FSSketch) is evaluated within the semi-honest adversarial model. Furthermore, simulation experiments are performed utilizing the versatile multi-party computation framework (MP-SPDZ), an SMPC library. Results from these experiments indicate that FSSketch delivers superior efficiency and privacy. In comparison to alternative algorithms (P2SM and PPDA), FSSketch demonstrates a performance improvement exceeding an order of magnitude. Furthermore, the proposed FSSketch algorithm shows strong potential for real-world scenarios requiring privacy-preserving data processing, such as financial data analysis, medical data sharing, and intelligent urban management, thereby providing a reliable technical support for the broader application of privacy-preserving distributed computing.","url":"https://doi.org/10.2139/ssrn.6826533","authors":["Li Li","Xiao LAN"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-25T15:12:36Z","doi":"10.2139/ssrn.6826533","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1002/cpe.70793","name":"A Secure and Efficient Randomized RLWE‐based Key Exchange Scheme","source":"crossref","abstract":"ABSTRACT Key exchange protocols allow more than one entity to freshly construct an identical key without knowing it in advance. In recent years, the construction of signal‐based ring learning with error (RLWE) based key exchange protocols (KEPs) has been an important area of post‐quantum research. These protocols derive shared secrets by quantizing noisy ring products in the convolution ring , where and is a prime number. In this paper, we cryptanalyze one of the recently proposed KEP of Pursharthi et al., and show that it is prone to an information‐theoretic signal leakage attack. We then put forward a new protocol, called Masked‐Signal RLWE (MS‐RLWE), and show that it is safe against information‐theoretic signal leakage attack. Additionally, we show that our protocol is semantically secure in the random oracle model and it is also resistant to key mismatch attacks. Finally, we compare our scheme with several other KEPs in terms of computational complexity and resistance with respect to various attacks.","url":"https://doi.org/10.1002/cpe.70793","authors":["Gaurav Mittal","Sandeep Kumar","S. K. Pal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-03T03:01:49Z","doi":"10.1002/cpe.70793","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1002/cpe.70891","name":"CHRONOS: A Hardware‐Assisted Phase‐Decoupled Framework for Secure Federated Learning in\n                    <scp>IoT</scp>","source":"crossref","abstract":"ABSTRACT Federated learning enables collaborative training on IoT gateways without sharing raw data, yet gradients remain susceptible to inversion attacks. Existing Secure Multiparty Computation defenses impose prohibitive communication overhead, exceeding strict IoT latency and energy budgets. We propose CHRONOS, a hardware‐assisted framework that decouples cryptographic setup from active training. During idle windows, CHRONOS executes a once‐per‐epoch server‐relayed Diffie‐Hellman exchange within an ARM TrustZone enclave, sealing shared secrets and distributing Shamir shares to peers. During training, clients mask gradients via a single stream‐cipher evaluation and transmit in one round; a hardware‐backed counter enforces mask freshness. If clients drop mid‐round, the server reconstructs masks from peer‐held shares ( bytes/client), preserving aggregation without round repetition. Evaluation on a 32‐node heterogeneous testbed (Rock Pi 4 and Orange Pi 5) shows that CHRONOS reduces active‐phase latency by up to 74% over synchronous secure aggregation. It mitigates gradient inversion while maintaining a persistent Secure World footprint under 1.1 kB, independent of model dimension and training horizon.","url":"https://doi.org/10.1002/cpe.70891","authors":["Hung Dang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-28T01:20:57Z","doi":"10.1002/cpe.70891","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1109/satc69565.2026.11542526","name":"Modern Advances in Secure Multi-Party Computation: Protocols, Implementations, and Emerging Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/satc69565.2026.11542526","authors":["Salil Verma","Mostafa Fouda","Zubair Md Fadlullah","Shiwei Fang","Mohamed I. Ibrahem"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-05T19:37:46Z","doi":"10.1109/satc69565.2026.11542526","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1002/cpe.70816","name":"Secure Named Data Forwarding Routing Protocol for Internet of Things Enabled Smart City Applications","source":"crossref","abstract":"ABSTRACT Internet of things (IoT)‐enabled smart city applications benefit more from named data networking (NDN) than IP‐based routing. The NDN has stateful forwarding and security, but it still lacks comprehensive and robust security protocols. This paper proposes a robust ECC‐based Authentication for Secure Routing (REASR) protocol for NDN‐IoT. To secure data forwarding in NDN‐IoT, we use elliptic curve cryptography (ECC) with ECDSA for strong node authentication. To protect data, REASR checks each node's identification before stateful forwarding. We improve the ECC‐based security of NDN‐IoT applications by changing NDN‐based network routing strategies to decrease cryptographic computation. REASR supports security, authentication, efficient service discovery, integration, tackling comparable application‐centric concerns, and safe data transmission. The REASR protocol, with its exclusive feature for complete node authentication and multi‐level security, ensures reliability and scalability in NDN‐based routing. The simulation results indicate that the REASR protocol outperforms NDN‐based routing. REASR, a compact protocol, improves throughput, packet delivery ratio (PDR), energy efficiency, and communication latency.","url":"https://doi.org/10.1002/cpe.70816","authors":["Bharati Patil","D. Vydeki"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-10T07:13:33Z","doi":"10.1002/cpe.70816","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1002/cpe.70830","name":"Quantum‐Resistant Framework for Secure and Authorized Multi‐Server Networking","source":"crossref","abstract":"ABSTRACT The rapid advancement of quantum computers poses a significant threat to classical cryptographic techniques used to distributed and multi‐server environments. This paper focuses on development of quantum‐resistant framework for authenticated multi‐server networking, designed to ensure confidentiality and integrity in the presence of quantum‐capable adversaries. We have developed a practical three‐party authentication and key agreement (AKA) protocol for multi‐server environments, leveraging the Ring Learning With Errors (RLWE) problem for robust post‐quantum security. After finding weaknesses in Pursharthi and Mishra's work, such as inadequate session key verification and insider attack vulnerabilities, we designed our protocol to address real‐world threats like stolen devices, rogue central authorities, and replay attacks. It features an efficient preparation phase, a secure registration process, and a reliable mutual authentication mechanism, guarding against quantum, side‐channel, and replay attacks. Our detailed security analysis and performance tests confirm its resilience and efficiency, making it a scalable solution for applications like healthcare and IoT (Internet of Things). This protocol provides a structured framework for achieving secure and quantum‐resistant communication.","url":"https://doi.org/10.1002/cpe.70830","authors":["Neetu Sharma","Mohd Sarik Idrisi","S. A. Lakshmanan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-02T16:11:16Z","doi":"10.1002/cpe.70830","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1109/tdsc.2025.3605538","name":"Resisting Compromise in Remote Swarm Attestation: Dependable and Secure Key Computation for Prover and Verifier","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tdsc.2025.3605538","authors":["Samane Sekhavati","Morteza Nikooghadam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-02T17:33:00Z","doi":"10.1109/tdsc.2025.3605538","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1109/icscan66520.2026.11588321","name":"Secure and Robust Identity Recognition Using Federated Multimodal Biometric Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icscan66520.2026.11588321","authors":["Mathivanan P","Mahalakshmi K"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-07T19:42:48Z","doi":"10.1109/icscan66520.2026.11588321","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1016/b978-0-443-33789-5.00005-4","name":"Privacy-preserving secure computation: bridging traditional healthcare and metaverse telemedicine","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-33789-5.00005-4","authors":["Ciza Thomas","N.S. Athish","Alka Rachel John"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-12T12:25:54Z","doi":"10.1016/b978-0-443-33789-5.00005-4","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1007/978-3-032-35415-0_10","name":"Secure Computation Against $$\\mathsf {NC^1}$$ Leakage Without Secure Hardware","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-35415-0_10","authors":["Yuyu Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-12T10:00:31Z","doi":"10.1007/978-3-032-35415-0_10","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1002/cpe.70877","name":"IRS‐Assisted Secure Communication: Maximizing Secrecy Energy Efficiency in MISO Network","source":"crossref","abstract":"ABSTRACT This paper studied the deployment of intelligent reflecting surfaces (IRSs) to prevent potential eavesdropper in MISO communication network, and provided users with confidential, energy efficiency and secure services. A key metric, secrecy energy efficiency (SEE), which measures the number of securely transmitted bits per unit of energy consumed, was significantly enhanced through the deployment of IRSs. SEE captures the balance between achieving a high secrecy rate and minimizing power consumption. In order to maximize the SEE of MISO communication network, an efficient alternating optimization algorithm was proposed, which effectively resisted multiple eavesdroppers by designing appropriate secrecy beamforming vector and phase shift vector. Simulation results reveal that the two conflicting performance metrics of secrecy rate and total power consumption are balanced in multi‐IRS‐assisted secure communication. The results also highlight the superiority of the proposed method over existing benchmark schemes, showing notable improvements in both SEE and secrecy rate when multiple IRSs are integrated.","url":"https://doi.org/10.1002/cpe.70877","authors":["Jiaxin Li","Jianping Wang","Lei Wang","Fuhong Lin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-23T19:26:38Z","doi":"10.1002/cpe.70877","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.7490/f1000research.1120597.1","name":"HEAP exposome, secure computation of sensitive data on CSC&amp;apos;s ePouta IaaS","source":"crossref","abstract":"","url":"https://doi.org/10.7490/f1000research.1120597.1","authors":["Dean Ruina","Alvaro Gonzalez","Tristan Perard","Jemal Tahir"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T05:02:07Z","doi":"10.7490/f1000research.1120597.1","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.56726/irjmets83393","name":"Enabling Confidential Cloud Services through Secure Multi-Party Computation","source":"crossref","abstract":"","url":"https://doi.org/10.56726/irjmets83393","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T06:30:51Z","doi":"10.56726/irjmets83393","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1109/icict68280.2026.11511156","name":"A Lightweight Hybrid Cryptographic Framework for Secure and Efficient Healthcare Cloud Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icict68280.2026.11511156","authors":["Pushkaladevi R","P. Sagayaraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-12T19:46:53Z","doi":"10.1109/icict68280.2026.11511156","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1002/cpe.70631","name":"A Robust and Secure Video Encryption Scheme Using Multi‐Chaotic Logistic Map","source":"crossref","abstract":"ABSTRACT The usage of multimedia data has grown exponentially over the past few decades, with a significant portion of its content in the form of images and videos, which are transmitted across various public networks. As the prevalence of video content continues to rise, so does the critical need for enhanced data security. Recently, several techniques have been proposed to create efficient data security procedures, focusing on security, robustness and computational complexity. This paper proposes a secure and robust video encryption technique based on the concepts of confusion and diffusion. During the confusion phase, horizontal and vertical permutations of pixel values are performed using a permutation box and then pixel values are substituted based on a lookup table. In the diffusion phase, the pixel values of the first frame are XORed with a defined box, and then, sequentially, the pixel values in the remaining frames are XORed using the previous frame as a reference. Each stage requires keys for processing that are generated by three different chaotic logistic maps, which successfully passed the NIST test. These numbers are then XORed to implement a multi‐chaotic logistic map effectively. Simulation metrics, including entropy, correlation, NPCR, UACI, and so forth demonstrate the strength of the proposed method. The result analysis also states that the proposed methodology performs well in terms of Quantitative Security Analysis Correlation Analysis 0.0017, Entropy Analysis 7.99, Peak Signal to Noise Ratio 7.898, Mean Square Error 10549.37, Structural Similarity Index (0.0075), Key Space Analysis (), Differential Attack Analysis (Number of Pixel Change Rate 99.61 and Unified Average Change Intensity 33.46), Robustness Analysis (Salt and Paper Noise (pass), Gaussian Noise (pass), Occlusion Attack (pass) and Execution Time Analysis θ ( n )).","url":"https://doi.org/10.1002/cpe.70631","authors":["Vyoma Vaish","Ashutosh Dixit","Kakoli Dutt","Hemang Mehra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-08T10:55:14Z","doi":"10.1002/cpe.70631","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1002/cpe.70706","name":"Deep Learning‐Based Data Hiding Techniques for Secure Communication: A Comprehensive Review","source":"crossref","abstract":"ABSTRACT The advent of the modern technological era has allowed us to reach a stage where people can share their information via different platforms quite easily. These platforms allow users to express themselves through text, photographs, videos, and audio, among other different representational media. The amount of photographic data is higher in comparison with other forms of data. So, the security of these images is a big concern for the researchers. Deep learning (DL)‐based approaches have gained popularity for a variety of multimedia analysis applications, including segmentation, detection, and classification. This article presents a state‐of‐the‐art summarization of DL‐based multimedia security techniques in which various encryption techniques, covert operations, current challenges, the scope of improvement, and new directions are highlighted. This paper mentions a comprehensive review of different DL watermarking and hiding techniques, along with a comparison of the contributions of the literature. This survey, in our opinion, can open the door to further investigating the essential topic of information concealment in DL environments.","url":"https://doi.org/10.1002/cpe.70706","authors":["Divyanshu Awasthi","Anurag Tiwari","Priyank Khare","Vinay Kumar Srivastava"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-20T04:42:52Z","doi":"10.1002/cpe.70706","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1002/cpe.70655","name":"Hy‐Cert: A Hybrid Certificate Verification System Using Blockchain for Secure Communication","source":"crossref","abstract":"ABSTRACT Recently, certificate management has been considered one of the crucial tasks of public key infrastructure (PKI), which also provides crucial privacy and security over the blockchain. Moreover, the recent discovery of multiple promising approaches provides less security, threats, incremental deployment challenges, and inefficiency, which strongly impact blockchain. On account of these issues, this research established a new strategy for certificate management, Hybrid Certificate Verification (Hy‐Cert) for Public Key Infrastructure. The contribution of this work is deployed in two phases, including the verification phase and key generation phase, which contemplate secure communication for certificate management. In this approach, the trapdoor, as well as proposed efficiency factor‐based verification, is implemented as a two‐step verification, which highly scrutinizes the authenticity of users and allows superior and safe communication. On the other hand, Hybrid public key generation (Hy‐Key) using a hybrid public key infrastructure is developed, which resembles the traditional key generation mechanism for encryption and decryption of data over the information‐centric network. In Hy‐Key, more powerful techniques such as Rivest, Shamir, Adleman (RSA) and Elliptic Curve Cryptography (ECC) algorithms are associated and undergo enormous security measures for better communication during certificate management. The system validation was performed on Students mark sheet and COVID‐19 datasets by varying users (500–2500) and transactions (1000–4000). For 2500 users, Hy‐Cert achieved 0.59 ms certification delay, 1.09 ms delay, 0.91 genuine user rate, 254.60 KB memory, 3.29 ms responsiveness, and 5.50 ms time. Similarly, for 4000 transactions, the corresponding values were 0.50 ms, 1.26 ms, 0.89, 257.93 KB, 3.75 ms, and 5.95 ms, respectively.","url":"https://doi.org/10.1002/cpe.70655","authors":["Hemant N. Watane","Ripon Patgiri","M. Franckie Singha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-11T01:05:53Z","doi":"10.1002/cpe.70655","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1109/tdsc.2026.3684550","name":"Security Comments on “Resisting Compromise in Remote Swarm Attestation: Dependable and Secure Key Computation for Prover and Verifier”","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tdsc.2026.3684550","authors":["Dariush Abbasinezhad-Mood"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-16T19:56:09Z","doi":"10.1109/tdsc.2026.3684550","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1007/978-3-032-35418-1_5","name":"Multiparty Computation with Minimal Overhead Without Circuit Transformation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-35418-1_5","authors":["Aditya Hegde","Phuoc Van Long Pham","Mingyuan Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-11T08:31:16Z","doi":"10.1007/978-3-032-35418-1_5","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.21203/rs.3.rs-9186808/v1","name":"Secure Multi-Party Computation Based on Blind Quantum Computing","source":"crossref","abstract":"Abstract Blind Quantum Computing (BQC) is a protocol proposed in recent years to tackle the practical need where users want to perform quantum computing tasks but lack the necessary capabilities, and thus delegate the work to quantum computing platforms while keeping their data secure throughout the process. Besides, its characteristics provide new ways for secure multi-party computation, helping multiple untrusted users to compute together securely. Most existing secure multi-party computation protocol based on blind quantum computing rely on DT(G) states to perform joint computation. However, this can lead to security risks because one client has too much authority, and these schemes also lack generality. In this paper, a secure multi-party computation protocol is proposed based on brickwork states. By exploiting and optimizing the properties of these states, the security issues in joint client computation are solved and centralized authority is avoided. Meanwhile, it reflects the interactive mode in multi-party computation. The proposed protocol effectively improves the generality of computation.","url":"https://doi.org/10.21203/rs.3.rs-9186808/v1","authors":["Yongli Wang","Tianci Cao","Yixin Wang","Rui Zhang","Yinghui Yang","Yongli Tang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-13T13:05:36Z","doi":"10.21203/rs.3.rs-9186808/v1","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/ccdc69976.2026.11560831","name":"Secure Computation Offloading and Resource Reconfiguration for Terminal Energy Efficiency in IIoT","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccdc69976.2026.11560831","authors":["Yang Jiang","Minrui Fei","Yangjie Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-24T19:47:19Z","doi":"10.1109/ccdc69976.2026.11560831","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1109/icicit69063.2026.11633902","name":"Privacy-Conscious Location-based Service using Federated Analytics and Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicit69063.2026.11633902","authors":["M. Amareswara Kumar","M. Bhavana","B. Vidhyashree","B V Chandra Sekhar","E. Lakshmi Prasanna","S. Nazia Banu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T19:15:19Z","doi":"10.1109/icicit69063.2026.11633902","addedAt":"2026-08-31T06:41:38.268Z","updatedAt":"2026-08-31T06:41:38.268Z"},{"id":"doi:10.1109/icict68280.2026.11510923","name":"SHEild: An Intelligent Safety Navigation System for Secure Mobility","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icict68280.2026.11510923","authors":["Jumana Maryam M","Uma R","Sandhiya J","S. Lavanya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-12T19:46:53Z","doi":"10.1109/icict68280.2026.11510923","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1007/978-3-032-15641-9_23","name":"Gakmoro: An Application of Physical Secure Computation to Card Game","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-15641-9_23","authors":["Takaaki Mizuki","Tomoki Kuzuma","Tomoya Hirano","Ririn Oshima","Momofuku Yasuda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-30T11:37:09Z","doi":"10.1007/978-3-032-15641-9_23","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1109/icscai68849.2026.11649399","name":"Optimizing Privacy Preservation in Federated Learning: A Multi-Tier Approach Using the Secure Federated Averaging Algorithm and Homomorphic Encryption with Secure Multi-Party Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icscai68849.2026.11649399","authors":["Cina Mathew","P. Asha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-18T19:12:10Z","doi":"10.1109/icscai68849.2026.11649399","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1109/meditcom67211.2026.11641110","name":"REVS-T: Trust-Tier-Aware Provider Selection for Secure Vehicular Computation Offloading","source":"crossref","abstract":"","url":"https://doi.org/10.1109/meditcom67211.2026.11641110","authors":["Sharifah Fayi","Ferheen Ayaz","Zhengguo Sheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-13T19:12:05Z","doi":"10.1109/meditcom67211.2026.11641110","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1002/cpe.70755","name":"A Secure and Efficient Ring Learning With Error Based Three‐Party Authenticated Key Agreement Scheme for Wireless Mobile Communication Systems","source":"crossref","abstract":"ABSTRACT Quantum computing poses a mounting and concrete threat to the cryptographi protocols underpinning mobile communication systems, where both security and computational efficiency are critical constraints. Existing three‐party authenticated key agreement (3PAKA) schemes either rely on classically hard problems broken by Shor's algorithm or, among postquantum proposals, fail to simultaneously achieve user anonymity, unlinkability, and perfect forward secrecy at acceptable cost for resource‐constrained mobile devices. We first conduct a rigorous cryptanalysis of the recent lattice‐based scheme of Singh et al. and uncover four concrete vulnerabilities: an identity recovery vulnerability and scalability failure arising from XOR masking with a static server key, which forces an exhaustive identity search and opens a denial‐of‐service vector; blind acceptance by the responder , as User forwards the initiator's message without verifying the sender's identity; authentication blindness at the initiator , since the response received by User carries no cryptographic proof of User 's identity; and server‐side incomplete verification caused by a missing session parameter , which the server requires but never receives. To remedy these deficiencies, we propose a Three‐Party Post‐Quantum Mutual Authentication and Key Agreement (MAKA) scheme for mobile devices, built on the Ideal‐Lattice Ring Learning With Errors (Ring‐LWE) assumption with parameters and Gaussian width . The proposed four‐message protocol employs biometric‐bound fuzzy extractors, dynamic masked identities , and fresh per‐session Ring‐LWE samples to achieve mutual authentication, user anonymity, unlinkability, and perfect forward secrecy. Security is formally analyzed in the Bellare–Rogaway (BR) model under the Random Oracle Model (ROM), yielding a tight reduction to the Decision‐RLWE assumption. Our message structure yields a total communication cost of bits, representing approximately improvement over Singh et al. It also demonstrates a favourable communicationcost relative to those of Islam and Basu, Rewal et al., Kumar et al., Dabra et al., and Park et al. across both metrics.","url":"https://doi.org/10.1002/cpe.70755","authors":["Sunil Kumar","Arvind Yadav"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-11T10:53:23Z","doi":"10.1002/cpe.70755","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1016/j.aej.2026.06.016","name":"A trust computation framework with lightweight blockchain for secure cloud service selection","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.aej.2026.06.016","authors":["Ali Jaber Almalki","Saravanan Pandiaraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-25T19:37:18Z","doi":"10.1016/j.aej.2026.06.016","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.54097/1yzx3y42","name":"Dual-Cloud Collaborative Graph Edit Distance Secure Computation Protocol","source":"crossref","abstract":"To address the challenge of securely computing graph edit distance in directed graph scenarios without compromising parties' privacy data, this paper proposes a homomorphic encryption-based, dual-cloud-assisted secure graph edit distance computation protocol. The protocol encodes graph structures as adjacency matrices and introduces random parity noise to protect the original data's parity. It then leverages homomorphic encryption to compute edit distance in ciphertext form. Employing a dual-cloud server architecture, the protocol offloads most computational tasks to the cloud, eliminating direct interaction between data users. This approach enhances computational efficiency while ensuring privacy security. Theoretical analysis and simulation experiments demonstrate the protocol's security under a semi-honest model, with superior user-side computational overhead compared to existing solutions.","url":"https://doi.org/10.54097/1yzx3y42","authors":["Yufeng Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-24T00:15:48Z","doi":"10.54097/1yzx3y42","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1002/cpe.70618","name":"A Novel Data Protection Model for Secure Medical Data in Cloud Environments Using Scaling Wide Residual Networks Based Key Generation","source":"crossref","abstract":"ABSTRACT The Personal Health Record (PHR) system manages their medical records in the cloud independently by enabling them to upload and control access to sensitive health data. To address the security, a novel protection model based on key generation and Attribute‐Based Encryption (ABE), named Scaling Wide Residual Networks with Fractional Gazelle Brown Bear Optimization Algorithm (SWideResNet_FGez‐BOA) is introduced in this research. The SWideResNet_FGez‐BOA operates in two phases: PHR upload to the cloud and PHR retrieval from the cloud. In the first phase, patients, who are known as data owners, create accounts to manage their PHR files. Before uploading, the PHR files undergo a robust ABE‐based encryption process using Chebyshev polynomials for cloud security. SWideResNet_FGez‐BOA, which is the combination of Scaling Wide Residual Networks (SWideResNet) and an FGez‐BOA, is used for generating the keys. The FGez‐BOA is the integration of Fractional Calculus (FC), Gazelle Optimization Algorithm (GOA), and Brown Bear Optimization Algorithm (BOA). In the second phase, the PHR retrieves the uploaded data from the cloud. The SWideResNet_FGez‐BOA demonstrated a key generation time of 11.765 ms, an encryption time of 208.765 ms, a decryption time of 195.765 ms, a memory of 1.865 MB, a conditional privacy of 0.979, and a normalized variance of 0.033.","url":"https://doi.org/10.1002/cpe.70618","authors":["Karthik Pisupaati","Latha Durairaj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-17T03:42:15Z","doi":"10.1002/cpe.70618","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1109/tdsc.2025.3616852","name":"An Algorithm for Persistent Homology Computation Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tdsc.2025.3616852","authors":["Dominic Gold","Koray Karabina","Francis Motta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-01T17:42:02Z","doi":"10.1109/tdsc.2025.3616852","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.36948/ijfmr.2026.v08i01.69104","name":"Secure Multi-party Computation for Privacy-preserving Machine Learning in Healthcare","source":"crossref","abstract":"This study investigates the dual impact of Secure Multi-Party Computation (SMPC) on machine learning (ML) model performance and stakeholder trust within the context of healthcare data analytics. Using a structured survey, data were collected from 171 households in New York. The study employed R Studio for regression analysis and SPSS for descriptive statistics. Results reveal that SMPC significantly enhances ML model accuracy when combined with diverse datasets, indicating its effectiveness as a privacy-preserving solution. However, SMPC alone does not significantly increase stakeholder trust; rather, trust is strongly influenced by awareness of SMPC technology, perceived data privacy risks, and institutional reputation. These findings emphasize the importance of both technical and human-centric factors in adopting privacy-preserving analytics. The study offers valuable insights for healthcare organizations, policymakers, and technology developers seeking to balance privacy, performance, and trust in data-driven decision-making.","url":"https://doi.org/10.36948/ijfmr.2026.v08i01.69104","authors":["Ronak Goyal","Ashwini Somani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-26T11:39:18Z","doi":"10.36948/ijfmr.2026.v08i01.69104","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.2139/ssrn.6508202","name":"Coalition policy in multiparty governments: whose preferences prevail","source":"crossref","abstract":"In coalition governments, parties need to agree on a common policy position. Whose preferences prevail? The proportionality hypothesis, the idea that coalition partners' influence on policy is proportional to their share of seats, has been used widely in the literature on democratic representation, ideological congruence, and coalition politics. In my analysis of competing theories aimed at determining what influences policy compromise in multiparty governments, I reject the proportionality hypothesis. My results suggest instead that coalition partners exert equal influence on policy compromises, independent of their number of seats. More extensive analysis also provides evidence for increased party influence on policies when the party is the formateur or closer to the parliamentary median, ceteris paribus. As a by-product of my analysis, I provide a simple and better proxy for measuring a government's position when this position is not directly observable.","url":"https://doi.org/10.2139/ssrn.6508202","authors":["Alessio Albarello"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-20T15:41:13Z","doi":"10.2139/ssrn.6508202","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.17487/rfc9916","name":"Updates to the Usage of TLS to Provide a Secure Transport for the Path Computation Element Communication Protocol (PCEP)","source":"crossref","abstract":"","url":"https://doi.org/10.17487/rfc9916","authors":["D. Dhody","S. Turner","R. Housley"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T23:25:50Z","doi":"10.17487/rfc9916","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.32664/mtqd9544","name":"Blockchain-based Outsourced Secure Multi-party Privacy-preserving Verifiable Computation Scheme","source":"crossref","abstract":"With the increasing demand for data privacy protection, outsourcing secure multi-party privacy intersection is faced with the dual challenges of privacy leakage and computational credibility verification. This paper proposes a blockchain-based secure multi-party privacy intersection verifiable computing scheme, which achieves the synergistic enhancement of privacy protection and computational credibility by integrating Paillier encryption algorithm, DH protocol key negotiation, HMAC-RSA authentication, Shamir secret sharing, AES encryption and zero-knowledge proof. In view of the core requirements of verifiable computing, zero-knowledge proof technology is introduced and verification logic is automatically executed through smart contracts to ensure that the calculation results are accurate and cannot be tampered with. In view of the credibility of computing nodes, a dynamic reward and punishment mechanism based on trust function and utility function is designed to constrain the honest behavior of computing parties through economic incentives. The experimental results verify the effectiveness of the scheme.","url":"https://doi.org/10.32664/mtqd9544","authors":["Feng Xi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-21T00:07:44Z","doi":"10.32664/mtqd9544","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1007/s00145-025-09565-2","name":"Universally Composable Almost-Everywhere Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00145-025-09565-2","authors":["Nishanth Chandran","Pouyan Forghani","Juan Garay","Rafail Ostrovsky","Rutvik Patel","Vassilis Zikas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-15T20:33:01Z","doi":"10.1007/s00145-025-09565-2","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.3390/computation14070158","name":"GKDBV-EF: A Lightweight and Provably Secure Group Key Distribution with Update and Batch Verification Protocol for Cloud–Fog–Edge Computing Networks","source":"crossref","abstract":"The advent of cloud–fog–edge computing has transformed distributed data processing by performing computation closer to end devices. Due to resource constraints at edge nodes and the dynamic nature of fog-assisted communication, secure and efficient group key distribution and batch verification in such decentralized systems remain a major challenge. Many existing protocols based on Chinese remainder theorem (CRT) use a straightforward scalar product to mask the group key and hence fail in multifactor security. Others suffer from architectural overhead since they require distinct and independent sets of moduli equations with multiple mathematical structures for different network layers, which increases computing overhead, limits scalability and delays synchronization during frequent node leave/join. To mitigate these challenges, this paper proposes a unified distributed CRT-based protocol for cloud–fog–edge environments. Our protocol introduces a two-factor modular key masking mechanism by incorporating a unique secret parameter for every edge node to strengthen group key protection and enhance the overall robustness of the key distribution mechanism. Additionally, our protocol uses a single set of moduli equations across cloud–fog–edge networks, which drastically reduces computation and storage costs at the fog layer. Our protocol achieves O (1) efficiency for rekeying. Formal security analysis using ProVerif and the ROR model demonstrates that our protocol has considerable security advantages. To prove its practicality, an ESP32-based simulation on Wokwi is used to verify the correctness of group key distribution, retrieval, and batch message verification. The performance analysis findings show that our protocol outperforms others in computation cost, communication cost, security and applicability for resource-constrained cloud–fog–edge computing networks.","url":"https://doi.org/10.3390/computation14070158","authors":["Narendra Kumar Upadhyay","Sudhakar Periyasamy","Vinod Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-13T07:28:27Z","doi":"10.3390/computation14070158","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1109/icscan66520.2026.11588344","name":"A Comparative Analysis of Secure Federated Learning Architectures for Collaborative Training on Distributed Financial Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icscan66520.2026.11588344","authors":["P. Suriya","G. Balamurugan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-07T19:42:48Z","doi":"10.1109/icscan66520.2026.11588344","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1088/1674-4926/26040035","name":"Towards secure computation and trusted silicon: emerging trends in ISSCC 2026 hardware security","source":"crossref","abstract":"","url":"https://doi.org/10.1088/1674-4926/26040035","authors":["Wenping Zhu","Hanning Wang","Bohan Yang","Leibo Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-03T08:56:43Z","doi":"10.1088/1674-4926/26040035","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1109/sp63933.2026.00256","name":"Decor: Delegated Computation on Randomness for Secure Evaluation of Nonlinear Functions","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp63933.2026.00256","authors":["Haris Smajlović","Kyle Sheng","Timos Antonopoulos","Ruzica Piskac","Hyunghoon Cho"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-01T19:34:20Z","doi":"10.1109/sp63933.2026.00256","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1007/978-3-032-25324-8_7","name":"Fast and Efficient Perfectly Secure Network-Agnostic Secure Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-25324-8_7","authors":["Gilad Asharov","Fatima Elsheimy","Gilad Stern"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-05T22:41:10Z","doi":"10.1007/978-3-032-25324-8_7","addedAt":"2026-08-31T06:41:38.269Z","updatedAt":"2026-08-31T06:41:38.269Z"},{"id":"doi:10.1371/journal.pone.0261213","name":"Collaborative calculation and application of interreal and real interval relations to protect privacy.","source":"pubmed","abstract":"The determination of the relation between a number and a numerical interval is one of the core problems in the scientific calculation of privacy protection. The calculation of the relationship between two numbers and a numerical interval to protect privacy is also the basic problem of collaborative computing. It is widely used in data queries, location search and other fields. At present, most of the solutions are still fundamentally limited to the integer level, and there are few solutions at the real number level. To solve these problems, this paper first uses Bernoulli inequality generalization and a monotonic function property to extend the solution to the real number level and designs two new protocols based on the homomorphic encryption scheme, which can not only protect the data privacy of both parties involved in the calculation, but also extend the number domain to real numbers. In addition, this paper designs a solution to the confidential cooperative determination problem between real numbers by using the sign function and homomorphism multiplication. Theoretical analysis shows that the proposed solution is safe and efficient. Finally, some extension applications based on this protocol are given.","url":"https://doi.org/10.1371/journal.pone.0261213","authors":["Lu S","Lu Y","Sun Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1371/journal.pone.0261213","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"doi:10.1016/j.health.2023.100192","name":"A systematic review of privacy-preserving methods deployed with blockchain and federated learning for the telemedicine.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.health.2023.100192","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1016/j.health.2023.100192","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1007/s10462-023-10417-3","name":"Federated learning for 6G-enabled secure communication systems: a comprehensive survey.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s10462-023-10417-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1007/s10462-023-10417-3","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/e24040519","name":"Towards Secure Big Data Analysis via Fully Homomorphic Encryption Algorithms.","source":"pubmed","abstract":"Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We then present a comparison of popular current homomorphic encryption tools with respect to these identified characteristics. We examine the implementation results of various homomorphic encryption toolkits and compare their performances. Finally, we highlight some important issues and research opportunities. We aim to anticipate how homomorphic encryption technology will be useful for secure Big Data processing, especially to improve the utility and performance of privacy-preserving machine learning.","url":"https://doi.org/10.3390/e24040519","authors":["Hamza R","Hassan A","Ali A","Bashir MB","Alqhtani SM","Tawfeeg TM","Yousif A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/e24040519","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1055/a-1768-2966","name":"Security and Privacy in Distributed Health Care Environments.","source":"europepmc","abstract":"","url":"https://doi.org/10.1055/a-1768-2966","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1055/a-1768-2966","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1055/s-0041-1740630","name":"Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.","source":"europepmc","abstract":"","url":"https://doi.org/10.1055/s-0041-1740630","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1055/s-0041-1740630","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1038/s41598-023-45725-9","name":"E-DPNCT: an enhanced attack resilient differential privacy model for smart grids using split noise cancellation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-023-45725-9","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1038/s41598-023-45725-9","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/e25030516","name":"A New Quantum Private Protocol for Set Intersection Cardinality Based on a Quantum Homomorphic Encryption Scheme for Toffoli Gate.","source":"pubmed","abstract":"Set Intersection Cardinality (SI-CA) computes the intersection cardinality of two parties' sets, which has many important and practical applications such as data mining and data analysis. However, in the face of big data sets, it is difficult for two parties to execute the SI-CA protocol repeatedly. In order to reduce the execution pressure, a Private Set Intersection Cardinality (PSI-CA) protocol based on a quantum homomorphic encryption scheme for the Toffoli gate is proposed. Two parties encode their private sets into two quantum sequences and encrypt their sequences by way of a quantum homomorphic encryption scheme. After receiving the encrypted results, the semi-honest third party (TP) can determine the equality of two quantum sequences with the Toffoli gate and decrypted keys. The simulation of the quantum homomorphic encryption scheme for the Toffoli gate on two quantum bits is given by the IBM Quantum Experience platform. The simulation results show that the scheme can also realize the corresponding function on two quantum sequences.","url":"https://doi.org/10.3390/e25030516","authors":["Liu W","Li Y","Wang Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/e25030516","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.3390/e24081145","name":"Efficient Privacy-Preserving <i>K</i>-Means Clustering from Secret-Sharing-Based Secure Three-Party Computation.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e24081145","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/e24081145","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/s22062242","name":"An Evaluation of Power Side-Channel Resistance for RNS Secure Logic.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s22062242","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/s22062242","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.3390/bioengineering10080912","name":"Encrypt with Your Mind: Reliable and Revocable Brain Biometrics via Multidimensional Gaussian Fitted Bit Allocation.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/bioengineering10080912","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/bioengineering10080912","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.48550/arxiv.2505.15124","name":"A Survey On Secure Machine Learning","source":"datacite","abstract":"In this survey, we will explore the interaction between secure multiparty computation and the area of machine learning. Recent advances in secure multiparty computation (MPC) have significantly improved its applicability in the realm of machine learning (ML), offering robust solutions for privacy-preserving collaborative learning. This review explores key contributions that leverage MPC to enable multiple parties to engage in ML tasks without compromising the privacy of their data. The integration of MPC with ML frameworks facilitates the training and evaluation of models on combined datasets from various sources, ensuring that sensitive information remains encrypted throughout the process. Innovations such as specialized software frameworks and domain-specific languages streamline the adoption of MPC in ML, optimizing performance and broadening its usage. These frameworks address both semi-honest and malicious threat models, incorporating features such as automated optimizations and cryptographic auditing to ensure compliance and data integrity. The collective insights from these studies highlight MPC's potential in fostering collaborative yet confidential data analysis, marking a significant stride towards the realization of secure and efficient computational solutions in privacy-sensitive industries. This paper investigates a spectrum of SecureML libraries that includes cryptographic protocols, federated learning frameworks, and privacy-preserving algorithms. By surveying the existing literature, this paper aims to examine the efficacy of these libraries in preserving data privacy, ensuring model confidentiality, and fortifying ML systems against adversarial attacks. Additionally, the study explores an innovative application domain for SecureML techniques: the integration of these methodologies in gaming environments utilizing ML.","url":"https://doi.org/10.48550/arxiv.2505.15124","authors":["Liao, Taobo","Li, Taoran","Nadkarni, Prathamesh"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2505.15124","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:38.945Z"},{"id":"doi:10.60692/he4kz-01436","name":"SviaB: Secure and verifiable multi‐instance iris remote authentication using blockchain","source":"datacite","abstract":"IET BiometricsVolume 11, Issue 1 p. 35-50 ORIGINAL RESEARCH PAPEROpen Access SviaB: Secure and verifiable multi-instance iris remote authentication using blockchain Mahesh Kumar Morampudi, Corresponding Author Mahesh Kumar Morampudi morampudimahesh@gmail.com Department of Computer Science and Engineering, National Institute of Technology-Warangal, Telangana, India Department of Computer Science and Engineering, SRM University AP, Amaravati, Andhra Pradesh, India Correspondence Mahesh Kumar Morampudi, Department of Computer Science and Engineering, SRM University AP, Amaravati, Andhra Pradesh, India. Email: morampudimahesh@gmail.comSearch for more papers by this authorMunaga V. N. K. Prasad, Munaga V. N. K. Prasad Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, IndiaSearch for more papers by this authorSurya Narayana Raju Undi, Surya Narayana Raju Undi Department of Computer Science and Engineering, National Institute of Technology-Warangal, Telangana, IndiaSearch for more papers by this author Mahesh Kumar Morampudi, Corresponding Author Mahesh Kumar Morampudi morampudimahesh@gmail.com Department of Computer Science and Engineering, National Institute of Technology-Warangal, Telangana, India Department of Computer Science and Engineering, SRM University AP, Amaravati, Andhra Pradesh, India Correspondence Mahesh Kumar Morampudi, Department of Computer Science and Engineering, SRM University AP, Amaravati, Andhra Pradesh, India. Email: morampudimahesh@gmail.comSearch for more papers by this authorMunaga V. N. K. Prasad, Munaga V. N. K. Prasad Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, IndiaSearch for more papers by this authorSurya Narayana Raju Undi, Surya Narayana Raju Undi Department of Computer Science and Engineering, National Institute of Technology-Warangal, Telangana, IndiaSearch for more papers by this author First published: 12 May 2021 https://doi.org/10.1049/bme2.12042Citations: 2AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Homomorphic encryption (HE) is the most widely explored research area in the construction of privacy-preserving biometric authentication systems because of its advantages over cancellable biometrics and biometric cryptosystems. However, most of the existing privacy-preserving biometric authentication systems using HE assume that the server performs computations honestly. In a malicious server setting, the server may return an arbitrary result to save computational resources, resulting in a false accept/reject. To address this, secure and verifiable multi-instance iris authentication using blockchain (SviaB) is proposed. Paillier HE provides confidentiality for the iris templates in SviaB. The blockchain offers the integrity of the encrypted reference iris templates as well as the trust of the comparator result. The challenges of using blockchain in biometrics are also addressed in SviaB. Extensive experimental results on benchmark iris databases demonstrate that SviaB provides privacy to the iris templates with no loss of accuracy and trust in the comparator result. 1 INTRODUCTION Unlike password or token authentication systems, a biometric authentication system (BAS) has more flexibility because users do not need to carry or remember anything. Fingerprint, iris, face, etc. are the commonly used biometric modalities [1, 2]. Properties such as stability and uniqueness make the iris the most widely used of the various biometric applications in comparison ","url":"https://doi.org/10.60692/he4kz-01436","authors":["Mahesh Kumar Morampudi","Munaga V. N. K. Prasad","Surya Narayana Raju Undi"],"tags":["Biometric Recognition and Security Systems","Signal Processing","Computer Science","Physical Sciences","User Authentication Methods and Security Measures","Information Systems","FOS: Computer and information sciences","Digital Image Watermarking Techniques"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.60692/he4kz-01436","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.60692/bga3e-f5h73","name":"SviaB: Secure and verifiable multi‐instance iris remote authentication using blockchain","source":"datacite","abstract":"IET BiometricsVolume 11, Issue 1 p. 35-50 ORIGINAL RESEARCH PAPEROpen Access SviaB: Secure and verifiable multi-instance iris remote authentication using blockchain Mahesh Kumar Morampudi, Corresponding Author Mahesh Kumar Morampudi morampudimahesh@gmail.com Department of Computer Science and Engineering, National Institute of Technology-Warangal, Telangana, India Department of Computer Science and Engineering, SRM University AP, Amaravati, Andhra Pradesh, India Correspondence Mahesh Kumar Morampudi, Department of Computer Science and Engineering, SRM University AP, Amaravati, Andhra Pradesh, India. Email: morampudimahesh@gmail.comSearch for more papers by this authorMunaga V. N. K. Prasad, Munaga V. N. K. Prasad Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, IndiaSearch for more papers by this authorSurya Narayana Raju Undi, Surya Narayana Raju Undi Department of Computer Science and Engineering, National Institute of Technology-Warangal, Telangana, IndiaSearch for more papers by this author Mahesh Kumar Morampudi, Corresponding Author Mahesh Kumar Morampudi morampudimahesh@gmail.com Department of Computer Science and Engineering, National Institute of Technology-Warangal, Telangana, India Department of Computer Science and Engineering, SRM University AP, Amaravati, Andhra Pradesh, India Correspondence Mahesh Kumar Morampudi, Department of Computer Science and Engineering, SRM University AP, Amaravati, Andhra Pradesh, India. Email: morampudimahesh@gmail.comSearch for more papers by this authorMunaga V. N. K. Prasad, Munaga V. N. K. Prasad Institute for Development and Research in Banking Technology (IDRBT), Hyderabad, IndiaSearch for more papers by this authorSurya Narayana Raju Undi, Surya Narayana Raju Undi Department of Computer Science and Engineering, National Institute of Technology-Warangal, Telangana, IndiaSearch for more papers by this author First published: 12 May 2021 https://doi.org/10.1049/bme2.12042Citations: 2AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Homomorphic encryption (HE) is the most widely explored research area in the construction of privacy-preserving biometric authentication systems because of its advantages over cancellable biometrics and biometric cryptosystems. However, most of the existing privacy-preserving biometric authentication systems using HE assume that the server performs computations honestly. In a malicious server setting, the server may return an arbitrary result to save computational resources, resulting in a false accept/reject. To address this, secure and verifiable multi-instance iris authentication using blockchain (SviaB) is proposed. Paillier HE provides confidentiality for the iris templates in SviaB. The blockchain offers the integrity of the encrypted reference iris templates as well as the trust of the comparator result. The challenges of using blockchain in biometrics are also addressed in SviaB. Extensive experimental results on benchmark iris databases demonstrate that SviaB provides privacy to the iris templates with no loss of accuracy and trust in the comparator result. 1 INTRODUCTION Unlike password or token authentication systems, a biometric authentication system (BAS) has more flexibility because users do not need to carry or remember anything. Fingerprint, iris, face, etc. are the commonly used biometric modalities [1, 2]. Properties such as stability and uniqueness make the iris the most widely used of the various biometric applications in comparison ","url":"https://doi.org/10.60692/bga3e-f5h73","authors":["Mahesh Kumar Morampudi","Munaga V. N. K. Prasad","Surya Narayana Raju Undi"],"tags":["Biometric Recognition and Security Systems","Signal Processing","Computer Science","Physical Sciences","User Authentication Methods and Security Measures","Information Systems","FOS: Computer and information sciences","Digital Image Watermarking Techniques"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.60692/bga3e-f5h73","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.10071790","name":"REGULATORY FRAMEWORKS FOR FEDERATED LEARNING - ENSURING SECURITY AND COMPLIANCE IN DECENTRALIZED AI","source":"datacite","abstract":"This systematic review delves into the regulatory frameworks governing federated learning in decentralized AI, emphasizing data privacy, ethical considerations, and adaptability. Through an extensive exploration of scholarly literature, academic databases, and practical insights from experts, the study reveals the intricate interplay of technical, ethical, and regulatory dimensions. Key findings underscore the paramount importance of data privacy and security, alongside ethical considerations like fairness and accountability, within regulatory frameworks. International collaboration emerges as vital, necessitating harmonized approaches for decentralized AI systems spanning multiple jurisdictions. The study advocates for flexible, agile regulations that evolve alongside technological advancements, highlighting the significance of stakeholder engagement in crafting comprehensive and balanced frameworks. Regulatory sandboxes and experimentation zones offer a promising strategy, enabling iterative improvements based on real-world insights. Techniques like differential privacy and secure multiparty computation enhance privacy and security, albeit requiring a delicate balance with accuracy. Addressing ethical concerns, especially in areas like education and healthcare, is crucial. Ultimately, this study emphasizes the need for regulatory frameworks that foster innovation, ensure compliance, and address the ethical implications of AI-driven technologies in the ever-changing landscape of decentralized AI. Diese systematische Überprüfung befasst sich mit den regulatorischen Rahmenbedingungen für föderiertes Lernen in der dezentralen KI und legt dabei den Schwerpunkt auf Datenschutz, ethische Überlegungen und Anpassungsfähigkeit. Durch eine umfassende Untersuchung wissenschaftlicher Literatur, akademischer Datenbanken und praktischer Erkenntnisse von Experten zeigt die Studie das komplexe Zusammenspiel technischer, ethischer und regulatorischer Dimensionen auf. Die wichtigsten Ergebnisse unterstreichen die überragende Bedeutung von Datenschutz und -sicherheit sowie ethischen Überlegungen wie Fairness und Rechenschaftspflicht innerhalb regulatorischer Rahmenbedingungen. Die internationale Zusammenarbeit erweist sich als entscheidend und erfordert harmonisierte Ansätze für dezentrale KI-Systeme, die sich über mehrere Gerichtsbarkeiten erstrecken. Die Studie plädiert für flexible, agile Vorschriften, die sich parallel zum technologischen Fortschritt weiterentwickeln, und unterstreicht die Bedeutung der Einbindung von Interessengruppen bei der Ausarbeitung umfassender und ausgewogener Rahmenwerke. Regulatorische Sandboxen und Experimentierzonen bieten eine vielversprechende Strategie, die iterative Verbesserungen auf der Grundlage realer Erkenntnisse ermöglicht. Techniken wie differenzielle Privatsphäre und sichere Mehrparteienberechnung verbessern Privatsphäre und Sicherheit, erfordern jedoch ein empfindliches Gleichgewicht zwischen Genauigkeit. Die Auseinandersetzung mit ethischen Bedenken, insbesondere in Bereichen wie Bildung und Gesundheitswesen, ist von entscheidender Bedeutung. Letztendlich betont diese Studie die Notwendigkeit regulatorischer Rahmenbedingungen, die Innovationen fördern, Compliance gewährleisten und die ethischen Auswirkungen KI-gesteuerter Technologien in der sich ständig verändernden Landschaft der dezentralen KI berücksichtigen.","url":"https://doi.org/10.5281/zenodo.10071790","authors":["Brandl, Edenilson"],"tags":["Federated Learning","Decentralized AI","Regulatory Frameworks","Data Privacy","Ethical Considerations","Security and Compliance","Stakeholder Engagement","Differential Privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.10071790","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:38.945Z"},{"id":"doi:10.5281/zenodo.10071791","name":"REGULATORY FRAMEWORKS FOR FEDERATED LEARNING - ENSURING SECURITY AND COMPLIANCE IN DECENTRALIZED AI","source":"datacite","abstract":"This systematic review delves into the regulatory frameworks governing federated learning in decentralized AI, emphasizing data privacy, ethical considerations, and adaptability. Through an extensive exploration of scholarly literature, academic databases, and practical insights from experts, the study reveals the intricate interplay of technical, ethical, and regulatory dimensions. Key findings underscore the paramount importance of data privacy and security, alongside ethical considerations like fairness and accountability, within regulatory frameworks. International collaboration emerges as vital, necessitating harmonized approaches for decentralized AI systems spanning multiple jurisdictions. The study advocates for flexible, agile regulations that evolve alongside technological advancements, highlighting the significance of stakeholder engagement in crafting comprehensive and balanced frameworks. Regulatory sandboxes and experimentation zones offer a promising strategy, enabling iterative improvements based on real-world insights. Techniques like differential privacy and secure multiparty computation enhance privacy and security, albeit requiring a delicate balance with accuracy. Addressing ethical concerns, especially in areas like education and healthcare, is crucial. Ultimately, this study emphasizes the need for regulatory frameworks that foster innovation, ensure compliance, and address the ethical implications of AI-driven technologies in the ever-changing landscape of decentralized AI. Diese systematische Überprüfung befasst sich mit den regulatorischen Rahmenbedingungen für föderiertes Lernen in der dezentralen KI und legt dabei den Schwerpunkt auf Datenschutz, ethische Überlegungen und Anpassungsfähigkeit. Durch eine umfassende Untersuchung wissenschaftlicher Literatur, akademischer Datenbanken und praktischer Erkenntnisse von Experten zeigt die Studie das komplexe Zusammenspiel technischer, ethischer und regulatorischer Dimensionen auf. Die wichtigsten Ergebnisse unterstreichen die überragende Bedeutung von Datenschutz und -sicherheit sowie ethischen Überlegungen wie Fairness und Rechenschaftspflicht innerhalb regulatorischer Rahmenbedingungen. Die internationale Zusammenarbeit erweist sich als entscheidend und erfordert harmonisierte Ansätze für dezentrale KI-Systeme, die sich über mehrere Gerichtsbarkeiten erstrecken. Die Studie plädiert für flexible, agile Vorschriften, die sich parallel zum technologischen Fortschritt weiterentwickeln, und unterstreicht die Bedeutung der Einbindung von Interessengruppen bei der Ausarbeitung umfassender und ausgewogener Rahmenwerke. Regulatorische Sandboxen und Experimentierzonen bieten eine vielversprechende Strategie, die iterative Verbesserungen auf der Grundlage realer Erkenntnisse ermöglicht. Techniken wie differenzielle Privatsphäre und sichere Mehrparteienberechnung verbessern Privatsphäre und Sicherheit, erfordern jedoch ein empfindliches Gleichgewicht zwischen Genauigkeit. Die Auseinandersetzung mit ethischen Bedenken, insbesondere in Bereichen wie Bildung und Gesundheitswesen, ist von entscheidender Bedeutung. Letztendlich betont diese Studie die Notwendigkeit regulatorischer Rahmenbedingungen, die Innovationen fördern, Compliance gewährleisten und die ethischen Auswirkungen KI-gesteuerter Technologien in der sich ständig verändernden Landschaft der dezentralen KI berücksichtigen.","url":"https://doi.org/10.5281/zenodo.10071791","authors":["Brandl, Edenilson"],"tags":["Federated Learning","Decentralized AI","Regulatory Frameworks","Data Privacy","Ethical Considerations","Security and Compliance","Stakeholder Engagement","Differential Privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.10071791","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:38.945Z"},{"id":"doi:10.48550/arxiv.2205.09513","name":"Federated learning: Applications, challenges and future directions","source":"datacite","abstract":"Federated learning (FL) is a system in which a central aggregator coordinates the efforts of multiple clients to solve machine learning problems. This setting allows training data to be dispersed in order to protect privacy. The purpose of this paper is to provide an overview of FL systems with a focus on healthcare. FL is evaluated here based on its frameworks, architectures, and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. This paper examines recent developments and provides a comprehensive list of unresolved issues, inspired by the rapid growth of FL research. In the context of FL, several privacy methods are described, including secure multiparty computation, homomorphic encryption, differential privacy, and stochastic gradient descent. Furthermore, a review of various FL classes, such as horizontal and vertical FL and federated transfer learning, is provided. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis systems, and healthcare, all of which are discussed in this paper. We also present a thorough review of existing FL challenges, such as privacy protection, communication cost, system heterogeneity, and unreliable model upload, followed by future research directions.","url":"https://doi.org/10.48550/arxiv.2205.09513","authors":["Bharati, Subrato","Mondal, M. Rubaiyat Hossain","Podder, Prajoy","Prasath, V. B. Surya"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Information Theory (cs.IT)","Image and Video Processing (eess.IV)","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2205.09513","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.1129974","name":"Secure Multiparty Computations For Privacy Preserving Classifiers","source":"datacite","abstract":"Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.","url":"https://doi.org/10.5281/zenodo.1129974","authors":["M. Sumana","K. S. Hareesha"],"tags":["Homomorphic property","secure product","secure mean and variance","secure dot product","vertically partitioned data."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.5281/zenodo.1129974","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:38.945Z"},{"id":"doi:10.5281/zenodo.1129973","name":"Secure Multiparty Computations For Privacy Preserving Classifiers","source":"datacite","abstract":"Secure computations are essential while performing privacy preserving data mining. Distributed privacy preserving data mining involve two to more sites that cannot pool in their data to a third party due to the violation of law regarding the individual. Hence in order to model the private data without compromising privacy and information loss, secure multiparty computations are used. Secure computations of product, mean, variance, dot product, sigmoid function using the additive and multiplicative homomorphic property is discussed. The computations are performed on vertically partitioned data with a single site holding the class value.","url":"https://doi.org/10.5281/zenodo.1129973","authors":["M. Sumana","K. S. Hareesha"],"tags":["Homomorphic property","secure product","secure mean and variance","secure dot product","vertically partitioned data."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.5281/zenodo.1129973","addedAt":"2026-08-31T06:41:38.945Z","updatedAt":"2026-08-31T06:41:38.945Z"},{"id":"doi:10.3403/30354752","name":"IT Security techniques � Encryption algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.3403/30354752","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-05-07T20:31:00Z","doi":"10.3403/30354752","addedAt":"2026-08-31T06:41:39.506Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.59350/vr6dm-7r102","name":"Exploring Homomorphic Encryption with Python","source":"crossref","abstract":"Homomorphic encryption is a powerful cryptographic technique that allows computations to be performed on encrypted data without decrypting it first. This blog post will introduce the concept of homomorphic encryption and demonstrate implementations using Python. What is Homomorphic Encryption? Homomorphic encryption is a form of encryption that allows specific types of computations to be carried out on ciphertext.","url":"https://doi.org/10.59350/vr6dm-7r102","authors":["Abhishek Tiwari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-31T04:04:07Z","doi":"10.59350/vr6dm-7r102","addedAt":"2026-08-31T06:41:39.506Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.59350/641a3-s6306","name":"Fully Homomorphic Encryption in Production Systems","source":"crossref","abstract":"In this living document, I will list all production systems I'm aware of that use fully homomorphic encryption (FHE). For background on FHE, see my overview of the field. If you have any information about production FHE systems not in this list, or corrections to information in this list, please send me an email with sufficient detail allow the claim to be publicly verified.","url":"https://doi.org/10.59350/641a3-s6306","authors":["Jeremy Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-08T19:46:28Z","doi":"10.59350/641a3-s6306","addedAt":"2026-08-31T06:41:39.506Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.59350/zjaps-1ge68","name":"Fully Homomorphic Encryption and the Public","source":"crossref","abstract":"In this living document, I will document reactions to uses of homomorphic encryption by members of the public. By \"member of the public,\" I mean people who may be technical, but are not directly involved in the development or deployment of homomorphic encryption systems. This includes journalists, bloggers, aggregator comment threads, and social media posts.","url":"https://doi.org/10.59350/zjaps-1ge68","authors":["Jeremy Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-08T19:45:50Z","doi":"10.59350/zjaps-1ge68","addedAt":"2026-08-31T06:41:39.506Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.36227/techrxiv.21314037.v2","name":"Multi-key Fully Homomorphic Encryption without CRS from RLWE","source":"crossref","abstract":"Fully Homomorphic Encryption (FHE) is a powerful encryption system in cloud computing that allows homomorphic computations on encrypted data without decrypting them. Multi-key fully homomorphic encryption (MFHE), as an extension to FHE, allows homomorphic computations on ciphertexts encrypted under different keys. However, most MFHE schemes require a Common Random\\slash Reference String (CRS), while the few that do not are based on the Learning With Errors (LWE) problem, which means that they can only deal with single bit plaintext. Consequently, MFHE schemes based on the Ring Learning With Errors (RLWE) problem are more desirable, as they can handle polynomial plaintext. Requiring the CRS seems to weaken the semantic definition of MFHE, where all users generate their own keys independently. In this paper, we study the RLWE-based MFHE in the CRS model and propose the first RLWE-based MFHE without CRS. To this end, we remove the CRS by designing a new relinearization algorithm. Like previous MFHE schemes, our RLWE-based MFHE without CRS has a simple 1-round threshold decryption, which implies a $3$-round secure MPC protocol in the plain model from the RLWE assumption.","url":"https://doi.org/10.36227/techrxiv.21314037.v2","authors":["Fucai Luo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-11-24T12:24:58Z","doi":"10.36227/techrxiv.21314037.v2","addedAt":"2026-08-31T06:41:39.506Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.59350/15fgc-n6376","name":"Google's Fully Homomorphic Encryption Compiler — A Primer","source":"crossref","abstract":"Back in May of 2022 I transferred teams at Google to work on Fully Homomorphic Encryption (newsletter announcement). Since then I've been working on a variety of projects in the space, including being the primary maintainer on github.com/google/fully-homomorphic-encryption, which is an open source FHE compiler for C++. This article will be an introduction to how to use it to compile programs to FHE, as well as a quick overview of its internals.","url":"https://doi.org/10.59350/15fgc-n6376","authors":["Jeremy Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-08T19:55:38Z","doi":"10.59350/15fgc-n6376","addedAt":"2026-08-31T06:41:39.506Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.59350/q5vzt-kmx68","name":"Bicyclic Matrix-Matrix Multiplication in Fully Homomorphic Encryption","source":"crossref","abstract":"In an earlier article, I covered the basic technique for performing matrix-vector multiplication in fully homomorphic encryption (FHE), known as the Halevi-Shoup diagonal method. This article covers a more recent method for matrix-matrix multiplication known as the bicyclic method.","url":"https://doi.org/10.59350/q5vzt-kmx68","authors":["Jeremy Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-17T17:12:47Z","doi":"10.59350/q5vzt-kmx68","addedAt":"2026-08-31T06:41:39.506Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.59350/emas2-9c053","name":"Packing Matrix-Vector Multiplication in Fully Homomorphic Encryption","source":"crossref","abstract":"In my recent overview of homomorphic encryption, I underemphasized the importance of data layout when working with arithmetic (SIMD-style) homomorphic encryption schemes. In the FHE world, the name given to data layout strategies is called \"packing,\" because it revolves around putting multiple plaintext data into RLWE ciphertexts in carefully-chosen ways that mesh well with the operations you'd like to perform.","url":"https://doi.org/10.59350/emas2-9c053","authors":["Jeremy Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-08T19:46:12Z","doi":"10.59350/emas2-9c053","addedAt":"2026-08-31T06:41:39.506Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.32469/10355/112608","name":"Fast and secure selective homomorphic encryption for federated learning","source":"crossref","abstract":"Federated learning (FL) enables collaborative model training among data providers without sharing raw data. However, model updates remain vulnerable to attacks such as model inversion and gradient leakage. Fully homomorphic encryption (FHE) ensures strong confidentiality but can incur prohibitive computational and communication overhead. On the other hand, differential privacy (DP) may degrade model accuracy. We propose a hybrid framework that selectively applies homomorphic encryption (HE) to a subset of model parameters, while protecting the remaining parameters with DP-inspired noise and lightweight bitwise scrambling. This design preserves strong privacy protection while reducing overhead by nearly an order of magnitude compared to FHE. Experiments on CIFAR-100 and multiple medical imaging datasets show that our framework achieves comparable empirical resistance to inversion attacks as FHE under MSSIM and VIFp metrics while drastically lowering cryptographic cost. For example, on CIFAR-100 with MobileNetV2, fully homomorphic encryption required 68 minutes per round, whereas FAS reduced the total time to 18 minutes (with an overhead of only 6 minutes compared to 56 minutes for FHE), representing almost a tenfold reduction in overhead while maintaining strong task performance in the evaluated setting. Our framework was up to 2x faster than stateof- the-art techniques, namely, MaskCrypt and FedML-HE, and achieved comparable empirical resistance to reconstruction attacks.","url":"https://doi.org/10.32469/10355/112608","authors":["Abdulkadir Korkmaz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-19T21:08:26Z","doi":"10.32469/10355/112608","addedAt":"2026-08-31T06:41:39.506Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.36227/techrxiv.24003795.v1","name":"Advancing Privacy and Accuracy with Federated Learning and Homomorphic Encryption","source":"crossref","abstract":"In this paper, we present an integrated framework that combines Federated Learning (FL) with Homomorphic Encryption (HE) using the Artificial Intelligence (AI) models and the Cheon-Kim-Kim-Song (CKKS) algorithm to address the challenges of privacy and accuracy. FL facilitates collaborative training of Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models across decentralized devices, allowing for data privacy preservation without sharing raw data. The integration of the CKKS algorithm for HE ensures secure computation on encrypted data during the FL process. Our experimental results on three diverse datasets demonstrate the efficacy of this approach, achieving an impressive highest average accuracy of 97.3%. Additionally, the CKKS algorithm is used to achieve efficient computation, making it a promising solution for privacy-conscious machine learning applications, and paving the way for practical deployment in various real-world scenarios, thereby revolutionizing the landscape of privacy-preserving machine learning.","url":"https://doi.org/10.36227/techrxiv.24003795.v1","authors":["Tuy Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-22T23:04:22Z","doi":"10.36227/techrxiv.24003795.v1","addedAt":"2026-08-31T06:41:39.506Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1007/978-1-4614-8265-9_1486","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4614-8265-9_1486","authors":["Ninghui Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-12-06T13:00:39Z","doi":"10.1007/978-1-4614-8265-9_1486","addedAt":"2026-08-31T06:41:39.506Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.59350/81x7v-qgs21","name":"A High-Level Technical Overview of Fully Homomorphic Encryption","source":"crossref","abstract":"About two years ago, I switched teams at Google to focus on fully homomorphic encryption (abbreviated FHE, or sometimes HE). Since then I've got to work on a lot of interesting projects, learning along the way about post-quantum cryptography, compiler design, and the ins and outs of fully homomorphic encryption.","url":"https://doi.org/10.59350/81x7v-qgs21","authors":["Jeremy Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-08T19:46:51Z","doi":"10.59350/81x7v-qgs21","addedAt":"2026-08-31T06:41:39.506Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.5220/0008071605170522","name":"Cryptanalysis of Homomorphic Encryption Schemes based on the Aproximate GCD Problem","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0008071605170522","authors":["Tikaram Sanyashi","Darshil Desai","Bernard Menezes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-08-14T12:52:56Z","doi":"10.5220/0008071605170522","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.2139/ssrn.4220754","name":"Accelerating Homomorphic Encryption by Using Representation of Lower Hamming Weight","source":"crossref","abstract":"How to simultaneously guarantee data processing and data hiding has become a topical issue because of the increasing needs for enhancing privacy protection. Homomorphic encryption is a privacy computing technology that can satisfy the above requirements and strike a balance between security and operability. However, homomorphic encryption suffers from severe computational inefficiency. Although many works attempt to address the problem, few works has noticed that the computational bottleneck of homomorphic encryption is closely related to the Hamming weight of the exponents. In this paper, we investigate the computational efficiency issue of homomorphic encryption and propose a signed binary representation based solution that significantly improves the state of the art Paillier schemes. We first use signed binary representation to reduce the Hamming weight of the exponent, thereby decreasing the number of modular multiplications in homomorphic encryption. We then combine signed binary representation with precomputation techniques to further accelerate homomorphic encryption. Our proposed approach can be integrated into all Paillier schemes and computing platforms without significant modification. The experimental results exhibit that our technique can considerably improve the encryption speed, in particular, it can accelerate the state of the arts by 5.25\\times when signed binary representation and precomputation are jointly utilized.","url":"https://doi.org/10.2139/ssrn.4220754","authors":["Junfei Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-17T11:54:39Z","doi":"10.2139/ssrn.4220754","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.2139/ssrn.3446667","name":"Partial and Fully Homomorphic Encryption Schemes for Privacy Preserving","source":"crossref","abstract":"Nowadays security and authentication of user’s data is a major concern over cloud environment. Unauthorized users try to attack outsourced data over the cloud. Different researchers proposed various algorithms based upon data security. Authentication technique such as homomorphic encryption is the latest advancement in privacy preserving. Homomorphic encryption operations are applied to cipher text which provides the similar results that would have been derived from original text. Homomorphic public key cryptography technique preserves user’s data security. This paper focuses on various partial and fully homomorphic encryption schemes and gives a comparative analysis.","url":"https://doi.org/10.2139/ssrn.3446667","authors":["Deepika Bhatia","Meenu Dave"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-09-18T07:30:53Z","doi":"10.2139/ssrn.3446667","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1007/978-0-387-39940-9_1486","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-0-387-39940-9_1486","authors":["Ninghui Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-09-16T08:23:52Z","doi":"10.1007/978-0-387-39940-9_1486","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.5220/0012432400003654","name":"Homomorphic Encryption Friendly Multi-GAT for Information Extraction in Business Documents","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012432400003654","authors":["Djedjiga Belhadj","Yolande Belaïd","Abdel Belaïd"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-29T05:31:11Z","doi":"10.5220/0012432400003654","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1007/978-3-030-87629-6","name":"Partially Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T19:04:24Z","doi":"10.1007/978-3-030-87629-6","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.5220/0012394600003648","name":"Feasibility of Random Forest with Fully Homomorphic Encryption Applied to Network Data","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012394600003648","authors":["Shusaku Uemura","Kazuhide Fukushima"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-01T18:44:53Z","doi":"10.5220/0012394600003648","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1007/978-1-4419-5906-5_870","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4419-5906-5_870","authors":["Berry Schoenmakers"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-10-27T09:51:00Z","doi":"10.1007/978-1-4419-5906-5_870","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1142/s021949882650060x","name":"Noiseless homomorphic encryption for complex numbers and fully homomorphic encryption with modular arithmetic","source":"crossref","abstract":"In this paper, we construct a new noiseless homomorphic encryption scheme based on the hardness of AGCD problem. We give a succinct and efficient algorithm that produces necessary secret keys for homomorphic evaluation of higher degree polynomial circuits. We also show that our scheme can homomorphically evaluate all the integer polynomials, real polynomials and complex polynomials. Moreover, using Fermat’s Little Theorem, we prove that the size of secret key is bounded above and hence it is a FHE in itself without having to rely on any bootstrapping procedure.","url":"https://doi.org/10.1142/s021949882650060x","authors":["Jonghee Chun","Hee Han","Stefano V. Kang","Hyo Keun Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-09T07:17:40Z","doi":"10.1142/s021949882650060x","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.71097/ijsat.v16.i3.7958","name":"A Comprehensive Study on Encrypted Medical Image Inference Using AES, RSA, and Homomorphic Encryption Using AES, RSA, and Homomorphic Encryption","source":"crossref","abstract":"Artificial intelligence-based medical diagnostics is of great concern when it comes to data privacy, such as medical imaging. We propose to use symmetric (AES), asymmetric (RSA), and homomorphic encryption (HE) to create a secure AI diagnostic pipeline where the data is encrypted at all steps to preserve privacy, including at rest, in-transit and inference. We use TenSEAL when performs encrypted inference and PyCryptodome to perform cryptography tasks, and carry out experiments to measure the system performance in terms of accuracy, latency, throughput, and inference attack resistance. Our findings make it clear that a substantial level of such security may be achieved with very little performance overhead, which creates a viable long-term solution to privacy-preserving medical AI.","url":"https://doi.org/10.71097/ijsat.v16.i3.7958","authors":["Keerthana G V","Usha N","Roopa Y","Nayan M M"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-01T13:26:47Z","doi":"10.71097/ijsat.v16.i3.7958","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1007/978-3-030-64448-2_11","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-64448-2_11","authors":["Mehdi Sadi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-04-30T15:15:27Z","doi":"10.1007/978-3-030-64448-2_11","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.70675/925cf43bz0251z4dabz9ea8z3a31c41f61f0","name":"Exploring the Scope of Machine Learning using Homomorphic Encryption in IoT/Cloud","source":"crossref","abstract":"Exploration de l'apprentissage automatique avec le chiffrement homomorphe dans l'internet des objets/Cloud L'apprentissage automatique en tant que service (MLaaS) a accéléré l'adoption des techniques d'apprentissage automatique dans divers domaines. Toutefois, cette tendance a également soulevé de sérieuses inquiétudes quant à la sécurité et à la confidentialité des données sensibles utilisées dans les modèles d'apprentissage automatique. Pour relever ce défi, notre approche consiste à utiliser le chiffrement homomorphique.Cette thèse explore l'application du chiffrement homomorphe dans divers contextes d'apprentissage automatique. La première partie du travail se concentre sur l'utilisation du chiffrement homomorphe dans un environnement multi-cloud, où le chiffrement est appliqué à des opérations simples telles que l'addition et la multiplication.Cette thèse explore l'application du chiffrement homomorphique à l'algorithme k-nearest neighbors (k-NN). L'étude présente une implémentation pratique de l'algorithme k-NN utilisant le cryptage homomorphique et démontre la faisabilité de cette approche sur une variété d'ensembles de données. Les résultats montrent que les performances de l'algorithme k-NN utilisant le cryptage homomorphique sont comparables à celles de l'algorithme non chiffré.Troisièmement, les travaux étudient l'application du chiffrement homomorphique à l'algorithme de regroupement k-means. Comme pour l'étude k-NN, la thèse présente une implémentation pratique de l'algorithme k-means utilisant le chiffrement homomorphique et évalue ses performances sur différents ensembles de données.Enfin, la thèse explore la combinaison du chiffrement homomorphique avec des techniques de confidentialité différentielle (DP) pour améliorer encore la confidentialité des modèles d'apprentissage automatique. L'étude propose une nouvelle approche qui combine le chiffrement homomorphique avec la protection différentielle afin d'obtenir de meilleures garanties de confidentialité pour les modèles d'apprentissage automatique. La recherche présentée dans cette thèse contribue au corpus croissant de recherche sur l'intersection du chiffrement homomorphique et de l'apprentissage automatique, en fournissant des implémentations pratiques et des évaluations du chiffrement homomorphique dans divers contextes d'apprentissage automatique.","url":"https://doi.org/10.70675/925cf43bz0251z4dabz9ea8z3a31c41f61f0","authors":["Yulliwas Ameur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T13:38:41Z","doi":"10.70675/925cf43bz0251z4dabz9ea8z3a31c41f61f0","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1007/978-3-030-77287-1","name":"Protecting Privacy through Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-03T18:02:43Z","doi":"10.1007/978-3-030-77287-1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.15368/theses.2015.67","name":"GPUHElib and DistributedHElib: Distributed Computing Variants of HElib, a Homomorphic Encryption Library","source":"crossref","abstract":"","url":"https://doi.org/10.15368/theses.2015.67","authors":["Ethan Andrew Frame"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-03-01T21:08:00Z","doi":"10.15368/theses.2015.67","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.5220/0008969205150523","name":"Homomorphic Encryption at Work for Private Analysis of Security Logs","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0008969205150523","authors":["Aymen Boudguiga","Oana Stan","Hichem Sedjelmaci","Sergiu Carpov"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-03-26T15:12:11Z","doi":"10.5220/0008969205150523","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.55248/gengpi.5.1124.3253","name":"The Effectiveness of Homomorphic Encryption in Protecting Data Privacy.","source":"crossref","abstract":"","url":"https://doi.org/10.55248/gengpi.5.1124.3253","authors":["Chris Gilbert","Mercy Abiola Gilbert"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-27T05:59:09Z","doi":"10.55248/gengpi.5.1124.3253","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1561/978-1-63828-345-420251004","name":"Homomorphic Encryption","source":"crossref","abstract":"Homomorphic encryption (HE) is one technique that enables privacy-preserving computation. Operations (or circuit evaluations) are performed directly on homomorphically encrypted data. The result can then be decrypted. Since its introduction 46 years ago, homomorphic encryption has evolved significantly with a plethora of schemes to choose from, each with their own advantages and disadvantages. Following the invention of fully homomorphic encryption (FHE) in 2009, there has been a revolution in the field. FHE theoretically allows for evaluation of arbitrary circuits of unbounded depth. HE can be used with a variety of models, ranging from simple support vector machines (SVMs) and random forests to computationally expensive deep neural networks.","url":"https://doi.org/10.1561/978-1-63828-345-420251004","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-29T09:32:50Z","doi":"10.1561/978-1-63828-345-420251004","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.70675/0c5113d4z81a0z4f10zae79z92ddac402fb0","name":"Contributions to data confidentiality in machine learning by means of homomorphic encryption","source":"crossref","abstract":"Contributions à la confidentialité des données en apprentissage machine par chiffrement homomorphe L’objectif de mes travaux tout au long de cette thèse a été de permettre à des algorithmes complexes d’apprentissage machine de pouvoir être appliqués (lors de leur phase d’inférence) sur des données dont la confidentialité est préservée. Un contexte d’application est l’envoi de données sur un serveur distant sur lequel un algorithme est évalué. Selon les cas, pour des raisons éthiques, légales ou commerciales, la confidentialité des données qui sont envoyées doit pouvoir être respectée. Il est possible pour cela de désigner des autorités en lesquels tous les acteurs du protocole peuvent avoir confiance. Pourquoi accorder à des entités un tel niveau de confiance dans des cas où la confidentialité des données d’un utilisateur est essentielle ? La cryptographie offre en effet des alternatives, dont le chiffrement totalement homomorphe.Le chiffrement homomorphe permet, en théorie, l’évaluation de n’importe quelle fonction dans le domaine chiffré. Son utilisation peut donc être imaginée dans le cas ou un utilisateur envoie des données chiffrées sur un serveur distant qui détient un algorithme puissant d’apprentissage machine. La phase d’inférence de cet algorithme est alors effectuée sur donnes chiffrées et le résultat est renvoyé à l’utilisateur pour déchiffrement. La cryptographie propose d’autre méthodes de calcul sur données chiffrées qui sont présentées succinctement dans le manuscrit. Pour faire court, la particularité du chiffrement homomorphe est qu’il ne nécessite aucune interaction entre l’utilisateur et le serveur. Dans ma thèse, je présente trois principaux algorithmes d’apprentissage machine sécurisés : une évaluation sur données et modèle chiffrés d’un réseau de neurone récursif et discret, le réseau de Hopfield ; une reconnaissance de locuteur sur modèle chiffré pour un réseau autoencodeur, le système VGGVox; une évaluation sur données chiffrées d’un classifieur des k plus proches voisins (ou classifieur k-NN). Notamment, notre classifieur k-NN sécurisé est le premier tel algorithme évalué de manière totalement homomorphe. Nos travaux ouvrent de nombreuses perspectives, notamment dans le domaine de l’apprentissage collaboratif sécurisé.","url":"https://doi.org/10.70675/0c5113d4z81a0z4f10zae79z92ddac402fb0","authors":["Martin Zuber"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-04T08:29:53Z","doi":"10.70675/0c5113d4z81a0z4f10zae79z92ddac402fb0","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.55248/gengpi.6.0325.1145","name":"Comparative Analysis of Fortifying Cloud Data Security Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.55248/gengpi.6.0325.1145","authors":["Unnimaya M U","Dr. Shibily Joseph"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-26T10:58:26Z","doi":"10.55248/gengpi.6.0325.1145","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.5220/0013626400003979","name":"A Safety-Centric Analysis and Benchmarks of Modern Open-Source Homomorphic Encryption Libraries","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013626400003979","authors":["Nges Njungle","Milan Stojkov","Michel Kinsy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-09T18:35:34Z","doi":"10.5220/0013626400003979","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.21275/sr24223105931","name":"Enhancement in Homomorphic Encryption Scheme of Cloud Computing to Isolate DOS Attack","source":"crossref","abstract":"","url":"https://doi.org/10.21275/sr24223105931","authors":["Jaspreet Kaur Gurjit Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-13T08:11:59Z","doi":"10.21275/sr24223105931","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1017/9781009299534.004","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1017/9781009299534.004","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-26T00:05:54Z","doi":"10.1017/9781009299534.004","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.5220/0006890800410050","name":"Data Clustering using Homomorphic Encryption and Secure Chain Distance Matrices","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0006890800410050","authors":["Nawal Almutairi","Frans Coenen","Keith Dures"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-09-27T07:15:07Z","doi":"10.5220/0006890800410050","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.17918/d8d10g","name":"Secure signal processing and secure machine learning using fully homomorphic encryption","source":"crossref","abstract":"This dissertation focuses on the new techniques for secure and private computation for signal processing and machine learning. Specifically, the thesis will focuses on extending Fully Homomorphic Encryption (FHE) technique in a cloud computing set up by running the algorithms while the data is encrypted. The (FHE) comes at a cost of integer space and not the real space needed by signal processing and machine learning algorithms. Solving this problem requires using numerical models that represent real numbers in an integers space including a rational number format and a fixed point binary format. These models allow the computation of signal processing and machine learning algorithms while the data is encrypted. This dissertation includes analysis and implementation of a natural logarithm, Brightness-Contrast filter, Fast Fourier Transform, Speeded Up Robust Features, Histogram of Oriented Gradients, and Convolutional Neural Networks. Analyzing these algorithms with the numerical models provide tight upper-bounds on numerical error introduced from their use. Each of the implementations provide unique understanding of error propagation in the encrypted domain. Experimental results of each implementation were aligned with the expected error based on the theorems. Despite their algorithmic constructions, each implementation is a step towards more advanced computations for privacy and security in the cloud.","url":"https://doi.org/10.17918/d8d10g","authors":["Thomas M. Shortell","Ali Shokoufandeh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-16T02:22:51Z","doi":"10.17918/d8d10g","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1007/978-3-030-77287-1_15","name":"Correction to: Introduction to Homomorphic Encryption and Schemes","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1_15","authors":["Jung Hee Cheon","Anamaria Costache","Radames Cruz Moreno","Wei Dai","Nicolas Gama","Mariya Georgieva","Shai Halevi","Miran Kim","Sunwoong Kim","Kim Laine","Yuriy Polyakov","Yongsoo Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-04-15T06:03:46Z","doi":"10.1007/978-3-030-77287-1_15","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.5220/0006890800002053","name":"Data Clustering using Homomorphic Encryption and Secure Chain Distance Matrices","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0006890800002053","authors":["Nawal Almutairi","Frans Coenen","Keith Dures"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-20T12:02:46Z","doi":"10.5220/0006890800002053","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.53555/kuey.v30i5.7387","name":"Simplified Homomorphic Encryption for Addition","source":"crossref","abstract":"","url":"https://doi.org/10.53555/kuey.v30i5.7387","authors":["Shashwat Shah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-13T11:58:34Z","doi":"10.53555/kuey.v30i5.7387","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.2139/ssrn.4042651","name":"A Matrix-Based Homomorphic Encryption Using Random Prime Numbers","source":"crossref","abstract":"Data security has always been a concern for digital users. In general, cloud Service Providers (CSPs) provide security to data during communication, storage, etc., using various cryptographic techniques. But still, data security during computation remains a challenge. Many homomorphic encryption techniques facilitate computation on the encrypted data itself to guarantee the overall confidentiality and security of the client&amp;apos;s data. The existing homomorphic encryption techniques have various issues such as overall increase in time delay, increased ciphertext size, more computational overhead, multiparty computation, and others. In this paper, a matrix, filled with random prime numbers is used for encryption and decryption of the numeric data to design fully homomorphic encryption during computations. Most of the existing homomorphic encryption schemes work on bit-level plaintext, with an extraordinary computational overhead. We propose a simple design of homomorphic encryption based on integer numbers to reduce computational and memory overheads.","url":"https://doi.org/10.2139/ssrn.4042651","authors":["Ramkumar  Ramachandran Ketti","Sonam Mittal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-01T22:07:25Z","doi":"10.2139/ssrn.4042651","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.5220/0006823203400347","name":"Homomorphic Encryption for Secure Computation on Big Data","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0006823203400347","authors":["Roger A. Hallman","Mamadou H. Diallo","Michael A. August","Christopher T. Graves"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-03-27T13:30:45Z","doi":"10.5220/0006823203400347","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1007/978-3-031-43214-9","name":"Advances to Homomorphic and Searchable Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-43214-9","authors":["Stefania Loredana Nita","Marius Iulian Mihailescu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-26T09:02:55Z","doi":"10.1007/978-3-031-43214-9","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.55277/researchhub.fcbdtvxb.1","name":"RheumaScore: 167 Fully Homomorphic Encryption Clinical Calculators for Rheumatology and Beyond","source":"crossref","abstract":"","url":"https://doi.org/10.55277/researchhub.fcbdtvxb.1","authors":["Erick Adrian Zamora Tehozol"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T06:56:36Z","doi":"10.55277/researchhub.fcbdtvxb.1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1145/3474366.3486923","name":"Pyfhel","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3474366.3486923","authors":["Alberto Ibarrondo","Alexander Viand"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-05T22:04:55Z","doi":"10.1145/3474366.3486923","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.5220/0014564700005061","name":"QRELHE: Quantum-Resistant Edge Learning with Homomorphic Encryption for Distributed Intelligence Systems","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014564700005061","authors":["R. S. S. Battula","Udai Shankar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-31T14:03:56Z","doi":"10.5220/0014564700005061","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.55277/researchhub.eoe1iia0.1","name":"RheumaScore: 167 Fully Homomorphic Encryption Clinical Calculators for Rheumatology and Beyond","source":"crossref","abstract":"","url":"https://doi.org/10.55277/researchhub.eoe1iia0.1","authors":["Erick Adrian Zamora Tehozol"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T06:59:13Z","doi":"10.55277/researchhub.eoe1iia0.1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1201/9781003052098-83","name":"Comparative Analysis of Cloud Security Complexities and Past Proposed Non-homomorphic and Homomorphic Encryption Methodologies with Limitations","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003052098-83","authors":["Pooja Dhiman","Santosh Kumar Henge"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-05-05T08:21:52Z","doi":"10.1201/9781003052098-83","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.32614/cran.package.homomorphicencryption","name":"HomomorphicEncryption: BFV, BGV, CKKS Schema for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.32614/cran.package.homomorphicencryption","authors":["Bastiaan Quast"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-13T06:34:46Z","doi":"10.32614/cran.package.homomorphicencryption","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.36227/techrxiv.172296244.49551575/v2","name":"Efficient Artificial Intelligence with Novel Matrix Transformations and Homomorphic Encryption","source":"crossref","abstract":"This paper addresses the challenges of data privacy and computational efficiency in artificial intelligence (AI) models by proposing a novel hybrid model that combines homomorphic encryption (HE) with AI to enhance security while maintaining learning accuracy. The novelty of our model lies in the introduction of a new matrix transformation technique that ensures compatibility with both HE algorithms and AI model weight matrices, significantly improving computational efficiency. Furthermore, we present a first-of-its-kind mathematical proof of convergence for integrating HE into AI models using the adaptive moment estimation optimization algorithm. The effectiveness and practicality of our approach for training on encrypted data are showcased through comprehensive evaluations of well-known datasets for air pollution forecasting and forest fire detection. These successful results demonstrate high model performance, with nearly 1 R-squared for air pollution forecasting and 99% accuracy for forest fire detection. Additionally, our approach achieves a reduction of up to 90% in data storage and a tenfold increase in speed compared to models that do not use the matrix transformation method. Our primary contribution lies in enhancing the security, efficiency, and dependability of AI models, particularly when dealing with sensitive data.","url":"https://doi.org/10.36227/techrxiv.172296244.49551575/v2","authors":["Quoc Bao Phan","Tuy Tan Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-24T12:52:44Z","doi":"10.36227/techrxiv.172296244.49551575/v2","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.70729/se21830193031","name":"Secure Data Transaction in Online Banking from Forensic Salami Slicing Attack using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.70729/se21830193031","authors":["Prem Kumar P"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-07T08:27:52Z","doi":"10.70729/se21830193031","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.5220/0014198300004932","name":"Real-Time Privacy Preservation in Vehicular Networks Using Homomorphic Encryption and Graph Theory","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014198300004932","authors":["Anjali Kadao","Sonali Mondal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-05T21:38:06Z","doi":"10.5220/0014198300004932","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.63044/w26deh177","name":"Privacy-First System Identification for Smart Homes Using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.63044/w26deh177","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-20T16:51:22Z","doi":"10.63044/w26deh177","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1561/9781638283454.ch4","name":"Chapter 4. Accelerating Homomorphic Encryption and Multi-Party Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781638283454.ch4","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-09T04:06:40Z","doi":"10.1561/9781638283454.ch4","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.2139/ssrn.5272522","name":"Novel Secure Genomic Analysis and Classification Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5272522","authors":["Swaraj Pal","Maroti Deshmukh","Sneha Chauhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-05T02:10:32Z","doi":"10.2139/ssrn.5272522","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.36227/techrxiv.172296244.49551575/v1","name":"Efficient Artificial Intelligence with Novel Matrix Transformations and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.36227/techrxiv.172296244.49551575/v1","authors":["Quoc Bao Phan","Tuy Tan Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-06T12:41:09Z","doi":"10.36227/techrxiv.172296244.49551575/v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.31274/cc-20251215-96","name":"Homomorphic Encryption in Practice: A Beginner-Friendly Analysis of Microsoft SEAL, OpenFHE, and TenSEAL","source":"crossref","abstract":"","url":"https://doi.org/10.31274/cc-20251215-96","authors":["Akshay Balasundaram"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-15T16:09:47Z","doi":"10.31274/cc-20251215-96","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.21681/2311-3456-2024-2-101-106","name":"DEVELOPMENT OF OPERATIONS FOR HOMOMORPHIC ENCRYPTION ALGORITHMS","source":"crossref","abstract":"","url":"https://doi.org/10.21681/2311-3456-2024-2-101-106","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-07T17:57:35Z","doi":"10.21681/2311-3456-2024-2-101-106","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.5220/0010349706840693","name":"Privacy Preserving Services for Intelligent Transportation Systems with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0010349706840693","authors":["Aymen Boudguiga","Oana Stan","Abdessamad Fazzat","Houda Labiod","Pierre-Emmanuel Clet"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-02-18T05:19:10Z","doi":"10.5220/0010349706840693","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1007/978-3-030-77287-1_2","name":"Homomorphic Encryption Standard","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1_2","authors":["Martin Albrecht","Melissa Chase","Hao Chen","Jintai Ding","Shafi Goldwasser","Sergey Gorbunov","Shai Halevi","Jeffrey Hoffstein","Kim Laine","Kristin Lauter","Satya Lokam","Daniele Micciancio"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-03T18:02:43Z","doi":"10.1007/978-3-030-77287-1_2","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1145/3267973.3267978","name":"Marble","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3267973.3267978","authors":["Alexander Viand","Hossein Shafagh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-10-16T12:56:36Z","doi":"10.1145/3267973.3267978","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.2139/ssrn.3852596","name":"A Novel Homomorphic Encryption based RSA Algorithm for Machine Learning","source":"crossref","abstract":"In today's internet world the use of Machine Learning as A Service (MLaaS) is increasing day by day especially automated Machine Learning. In which users upload their dataset and auto Machine Learning (ML) will develop models after studying dataset. Due to this, the risk of compromising of privacy of users is also high. So, to secure data on a cloud a new approach is present in this paper. In which, we have been using homomorphic encryption (HE) based Rivest Shamir Adleman (RSA) algorithm to encrypt the data and Azure automated ML is used to develop models. Three different datasets are first encrypted by using the Homomorphic Encryption (HE) based Rivest Shamir Adleman (RSA) algorithm and then uploaded to the Azure automated Machine Learning (ML) platform. The accuracy of machine learning models developed using encrypted data is compared with machine learning models developed using unencrypted datasets.","url":"https://doi.org/10.2139/ssrn.3852596","authors":["Harsh J. Kiratsata","Mahesh Panchal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-05-25T23:19:52Z","doi":"10.2139/ssrn.3852596","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1109/ccns53852.2021.00036","name":"Low Noise Homomorphic Encryption Scheme Supporting Multi-Bit Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccns53852.2021.00036","authors":["Guangli Xiang","Can Shao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-10-16T04:00:24Z","doi":"10.1109/ccns53852.2021.00036","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.2139/ssrn.6168349","name":"Ciphertext Packing for User-Friendly Privacy-Preserving Inference with Homomorphic Encryption","source":"crossref","abstract":"Homomorphic encryption allows servers to perform privacy-preserving inference on encrypted data, enabling machine learning services while protecting user privacy. Most existing research focuses on reducing the computational overhead of private inference on the server side, neglecting the overhead on the user side. However, homomorphic ciphertexts are large, creating a significant burden for resource-constrained users when accessing privacy-preserving inference services. In this work, we propose two novel packing methods that enable a user with multiple inputs requiring privacy-preserving inference to amortize the costs associated with uploading input ciphertexts and downloading result ciphertexts. Specifically, we exploit the redundancy in ciphertext slots to pack multiple ciphertexts, thus reducing the transmission overhead of encrypted data. Our packing method is compatible with most existing privacy-preserving inference frameworks for classification tasks, demonstrating its feasibility and efficiency. Experimental results show that our approach significantly reduces the overhead for users when performing privacy-preserving classification.","url":"https://doi.org/10.2139/ssrn.6168349","authors":["Yufei Zhou","Lihao Pan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-02T13:52:08Z","doi":"10.2139/ssrn.6168349","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.2139/ssrn.6339260","name":"An Identity-Based Fully Homomorphic Encryption from Module-LWE","source":"crossref","abstract":"We propose an identity-based fully homomorphic encryption (IBFHE) schemebased on the Module Learning with Errors (Module-LWE) assumption. Theconstruction merges the GSW fully homomorphic encryption frameworkwhich operates without evaluation keyswith compact approximate trapdoorsin the module-lattice setting. By building upon Module-LWE, our schemeachieves a better flexibility in balancing security and efficiency through themodule rank parameter, while natively enabling multi-bit encryption. Theadoption of approximate MP-trapdoors not only reduces trapdoor size andenhances computational efficiency, but also directly mitigates noise growthduring homomorphic multiplications, thereby supporting deeper evaluationcircuits. We prove the scheme’s correctness and IND-sID-CPA security inthe standard model, provide explicit parameter bounds for decryption, anddemonstrate the concrete advantages of approximate trapdoors in controllingerror expansion. Compared to existing IBFHE constructions, our scheme offers more flexible parameter settings, smaller size of the trapdoor and slowererror expansion, advancing the practicality of identity-based fully homomorphic encryption.","url":"https://doi.org/10.2139/ssrn.6339260","authors":["Shan Ma","YiChen Li","ChunYu Qiu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-03T23:43:58Z","doi":"10.2139/ssrn.6339260","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.62056/ak5wl86bm","name":"Fully Composable Homomorphic Encryption","source":"crossref","abstract":"The traditional definition of fully homomorphic encryption (FHE) is not composable, i.e., it does not guarantee that evaluating two (or more) homomorphic computations in a sequence produces correct results. We formally define and investigate a stronger notion of homomorphic encryption which we call \"fully composable homomorphic encryption\", or \"composable FHE\". The definition is both simple and powerful: it does not directly involve the evaluation of multiple functions, and yet it supports the arbitrary composition of homomorphic evaluations. On the technical side, we compare the new definition with other definitions proposed in the past, proving both implications and separations, and show how the \"bootstrapping\" technique of (Gentry, STOC 2009) can be formalized as a method to transform a (non-composable, circular secure) homomorphic encryption scheme into a fully composable one. We use this formalization of bootstrapping to formulate a number of conjectures and open problems.","url":"https://doi.org/10.62056/ak5wl86bm","authors":["Daniele Micciancio"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-08T17:23:17Z","doi":"10.62056/ak5wl86bm","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.11606/d.45.2025.tde-17062025-165647","name":"Development of an API for financial data exchange between multiple parties using homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.11606/d.45.2025.tde-17062025-165647","authors":["Yaissa Campos Siqueira"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-31T16:37:08Z","doi":"10.11606/d.45.2025.tde-17062025-165647","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.2172/1886256","name":"Homomorphic Encryption for Machine Learning and Artificial Intelligence Applications","source":"crossref","abstract":"","url":"https://doi.org/10.2172/1886256","authors":["David Arnold","Jafar Saniie","Alexander Heifetz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-11T02:31:07Z","doi":"10.2172/1886256","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.5220/0014357100004061","name":"HEALED: Hybrid Homomorphic Encryption for Analysis of Large-Scale Encrypted Data","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014357100004061","authors":["Maria Dias","Ivan Costa","Ivone Amorim","Eva Maia","Isabel Praça"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-17T03:13:17Z","doi":"10.5220/0014357100004061","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.2139/ssrn.5838482","name":"Secure Validation of Legal Documents using Homomorphic Encryption and Blockchain Technology","source":"crossref","abstract":"With the increased incorporation of digital technology, the emerging challenges in ensuring that the rights of private legal documents are adequately protected concerning confidentiality and integrity have come to light. This study proposes a new framework that employs Elliptic Curve Diffie-Hellman, Fully Homomorphic Encryption, Interplanetary File System, and blockchain technology to provide a compelling solution for secure storage and safe validation of documents. To avoid record manipulation and un authorized edits, it adopts blockchain Hash Checkpoint, Advanced Encryption Standard (AES), and Fully Homomorphic Encryption (FHE), and the content is stored in the Interplanetary File System (IPFS). The proposed system was successfully evaluated through a prototype implementation, where it showed a hundred per cent accuracy in document validation, where tamper detection was detected by comparing the uploaded document with the same document with a minor difference. Upon document retrieval and decryption, it showed that there was no noise or abnormalities with the data, which confirmed that the system ensures that the data is stored securely.","url":"https://doi.org/10.2139/ssrn.5838482","authors":["H.M. Thulana Thidaswin","Danishka Navin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-03T21:55:54Z","doi":"10.2139/ssrn.5838482","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1007/978-981-97-6722-9_7","name":"Quantum Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-6722-9_7","authors":["Tao Shang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-15T15:16:42Z","doi":"10.1007/978-981-97-6722-9_7","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.7717/peerj-cs.3502/table-7","name":"Table 7: Summary of homomorphic hashing and encryption techniques for data integrity verification in IoT and cloud systems.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3502/table-7","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-19T08:03:52Z","doi":"10.7717/peerj-cs.3502/table-7","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.21275/v5i6.nov164164","name":"Security and Storage Management of Data on Cloud using Fully Homomorphic Encryption and Adaptive Compression Approach","source":"crossref","abstract":"","url":"https://doi.org/10.21275/v5i6.nov164164","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-06-13T11:27:41Z","doi":"10.21275/v5i6.nov164164","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1007/978-981-15-0493-8_12","name":"Encrypted Control Using Multiplicative Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-15-0493-8_12","authors":["Kiminao Kogiso"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-11-21T16:04:34Z","doi":"10.1007/978-981-15-0493-8_12","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.38007/proceedings.0000046","name":"Utilization Efficiency of 5G Spectrum Based on Homomorphic Encryption and Encryption Circuit and Its Application in Teaching Practice","source":"crossref","abstract":"","url":"https://doi.org/10.38007/proceedings.0000046","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-05-15T13:13:05Z","doi":"10.38007/proceedings.0000046","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1007/978-981-97-8602-2_14","name":"A Review: Implementation of Partially Homomorphic Encryption and Fully Homomorphic Encryption on Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-8602-2_14","authors":["Aashka Raval","Jaivik Jariwala","Vrundan Sojitra","Nishant Doshi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T16:44:21Z","doi":"10.1007/978-981-97-8602-2_14","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1089/big.2021.0012","name":"Preserving Health Care Data Security and Privacy Using Carmichael's Theorem-Based Homomorphic Encryption and Modified Enhanced Homomorphic Encryption Schemes in Edge Computing Systems","source":"pubmed","abstract":"With the tremendous growth of technology, providing data security to critical applications such as smart grid, health care, and military is indispensable. On the other hand, due to the proliferation of external data threats in these applications, the loss incurred is incredibly high. Standard encryption algorithms such as RSA, ElGamal, and ECC facilitate in protecting sensitive data from outside attackers; however, they cannot perform computations on sensitive data while being encrypted. To perform computations and to process encrypted query on encrypted data, various homomorphic encryption (HE) schemes are proposed. Each of the schemes has its own shortcomings either related to performance or with storage that acts as the barrier for applying in real-time applications. With that conception, our objective is to design HE schemes that are simple by design, efficient in performance, and highly unimpeachable against attacks. Our first proposed scheme is based on Carmichael's Theorem, referred to as Carmichael's Theorem-based Homomorphic Encryption (CTHE), and the second is an improved version of Gorti's Enhanced Homomorphic Encryption Scheme, referred to as Modified Enhanced Homomorphic Encryption (MEHE). For brevity, the schemes are referred to as CTHE and MEHE. Both the schemes are provably secure under the hardness of integer factorization, discrete logarithm, and quadratic residuosity problems. To reduce the noise in these schemes, the modulus switching method is adopted and proved theoretically. The schemes' efficiency is proven by collecting the data from cardiovascular dataset (statically)/blood pressure monitor (dynamically) and is homomorphically encrypted in the edge server. Further analysis on encrypted data is carried out to identify whether a person has hypotension or hypertension with the aid of parameters, namely, mean arterial pressure. As the schemes are probabilistic in nature, breaking the schemes by a polynomial time adversary is impossible and is proven in the article.","url":"https://doi.org/10.1089/big.2021.0012","authors":["K. Anitha Kumari","Avinash Sharma","Chinmay Chakraborty","M. Ananyaa","Kumari KA","Sharma A","Chakraborty C","Ananyaa M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021-08-10T12:23:30Z","doi":"10.1089/big.2021.0012","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.5220/0003969400050014","name":"Practical Applications of Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0003969400050014","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-07-23T11:12:29Z","doi":"10.5220/0003969400050014","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.5220/0013521800003979","name":"RAHE: A Robust Attribute-Based Aggregate Scheme Enhanced with Homomorphic Encryption for 5G-Connected Delivery Drones","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013521800003979","authors":["Aagii Thomas","Sana Belguith"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-02T03:46:40Z","doi":"10.5220/0013521800003979","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.11648/j.ijsts.20241202.11","name":"A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic Encryption","source":"crossref","abstract":"Cloud Service Providers, exemplified by industry leaders like Google Cloud Platform, Microsoft Azure, and Amazon Web Services, deliver a dynamic array of cloud services in an ever-evolving landscape. This sector is witnessing substantial growth, with enterprises such as Netflix and PayPal heavily relying on cloud infrastructure for various needs such as data storage, computational resources, and various other services. The adoption of cloud solutions by businesses not only facilitates cost reduction but also fosters flexibility and supports scalability. Despite the undeniable advantages, concerns surrounding security and privacy persist in the realm of Cloud Computing. Given that Cloud services are accessible via the internet, there is a potential vulnerability to unauthorized access by hackers or malicious entities from anywhere in the world. A crucial aspect of addressing this challenge is the implementation of robust security measures, particularly focusing on data protection. To safeguard data in the Cloud, a fundamental recommendation is the encryption of data prior to uploading. Encryption should be maintained consistently, both during storage and in transit. While encryption enhances security, it introduces a potential challenge for data owners who may need to perform various operations on their encrypted data, such as accessing, modifying, updating, deleting, reading, searching, or sharing them with others. One viable solution to balance the need for data security and operational functionality is the adoption of Searchable Encryption (SE). SE operates on encrypted data, allowing authorized users to perform certain operations without compromising the security of sensitive information. The effectiveness of SE has notably advanced since its inception, and ongoing research endeavors aim to further enhance its capabilities. This paper provides a comprehensive review of the functionality of Searchable Encryption, with a primary focus on its applications in Cloud services during the period spanning 2019 to 2023. Additionally, the study evaluates one of its prominent schemes, namely Fully Homomorphic Encryption (FHE). The analysis indicates an overall positive trajectory in SE research, showcasing increased efficiency as multiple functionalities are aggregated and rigorously tested.","url":"https://doi.org/10.11648/j.ijsts.20241202.11","authors":["Brian Kishiyama","Izzat Alsmadi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-21T07:37:41Z","doi":"10.11648/j.ijsts.20241202.11","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/comp-sif65618.2025.10969942","name":"Secure Folder Encryption Using Homomorphic Encryption with Microsoft SEAL","source":"crossref","abstract":"","url":"https://doi.org/10.1109/comp-sif65618.2025.10969942","authors":["Goutham J R","Chethan Venkatesh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-28T17:31:11Z","doi":"10.1109/comp-sif65618.2025.10969942","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.23919/ecc65951.2025.11186994","name":"Encrypted Control Systems with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.23919/ecc65951.2025.11186994","authors":["Jose Barbosa","Ping Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-14T17:38:09Z","doi":"10.23919/ecc65951.2025.11186994","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1007/978-3-030-87629-6_1","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6_1","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T19:04:24Z","doi":"10.1007/978-3-030-87629-6_1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.2139/ssrn.4813781","name":"Securing Vehicular Adhoc Networks (Vanets) Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4813781","authors":["Adnan Shahid Khan","Dr. Irshad Ahmed Abbasi","Kashif Nisar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-06T14:44:17Z","doi":"10.2139/ssrn.4813781","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.3724/sp.j.1146.2012.01102","name":"Certificateless Fully Homomorphic Encryption Based on LWE Problem","source":"crossref","abstract":"","url":"https://doi.org/10.3724/sp.j.1146.2012.01102","authors":["Yan Guang","Chun-xiang Gu","Yue-fei Zhu","Yong-hui Zheng","Jin-long Fei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-02-13T09:00:54Z","doi":"10.3724/sp.j.1146.2012.01102","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.5220/0006832200410052","name":"Fully Homomorphic Distributed Identity-based Encryption Resilient to Continual Auxiliary Input Leakage","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0006832200410052","authors":["François Gérard","Veronika Kuchta","Rajeev Anand Sahu","Gaurav Sharma","Olivier Markowitch"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-08-07T09:41:13Z","doi":"10.5220/0006832200410052","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/sist61674.2026.11596122","name":"A Fully Homomorphic Encryption Library Over Integers with Homomorphic Division","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sist61674.2026.11596122","authors":["Zhanerke Temirbekova","Myrzakul Zhansaya","Zhumakyn Aitulek","Ualikhan Shokan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T19:38:14Z","doi":"10.1109/sist61674.2026.11596122","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1007/978-3-030-87629-6_9","name":"Paillier Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6_9","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T15:04:24Z","doi":"10.1007/978-3-030-87629-6_9","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.21203/rs.3.rs-554651/v1","name":"An Efficient Enhanced Full Homomorphic Encryption For Securing Video In Cloud Environment","source":"europepmc","abstract":"Abstract At present days, exponential growth in the transmission of multimedia data takes place due to a significant rise in network bandwidth and video image compression technologies. However, the transmission of videos over wireless channels often brings an unseen risk that sensitive video details might be corrupted and distributed in an illegal way. So, the security of video transmission has become a hot research topic. Several encryption models have been presented in the literature, yet, it is believed that the performance of encryption process can be further improved. In this perspective, an efficient novel video encryption technique is presented using an enhanced variant of Fully Homomorphic Encryption (FHE) model called as EFHE model. By the hybridization of Ducas and Micciancio (DM) with the FHE model presented by Gentry, Sahai, and Waters (GSW), matrix operations vector additions are properly employed in the proposed EFHE model. In addition, a new key generation scheme to increase the fastness of the encryption process. The EFHE model is designed and placed on a cloud environment which leads to reduced cloud user’s communication and computation complexity. It is ensured that the presented EFHE model is highly efficient and secure over the compared methods.","url":"https://doi.org/10.21203/rs.3.rs-554651/v1","authors":["Geetha N","Mahesh K"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.21203/rs.3.rs-554651/v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1007/978-3-030-87629-6_6","name":"Benaloh Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6_6","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T19:04:24Z","doi":"10.1007/978-3-030-87629-6_6","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.2139/ssrn.7128199","name":"FHE Compute Engines: From Schemes to Silicon, a Systematization of the Fully Homomorphic Encryption Stack","source":"crossref","abstract":"Fully homomorphic encryption (FHE) allows arbitrary computation directly on encrypted data and is a foundational primitive for confidential computing, privacy-preserving machine learning, and encrypted-state blockchains. A decade after the first bootstrapping-based construction, the binding constraint on FHE has shifted. The schemes are now mature; what determines usable performance is the surrounding compute engine: the compiler that hides ciphertext parameters, the runtime that schedules homomorphic operations, and the hardware that absorbs an overhead of three to nine orders of magnitude over plaintext, the exact figure depending on scheme, amortization, and baseline. This paper surveys FHE compute engines across the full abstraction stack, from schemes and scheme libraries through compilers and domain-specific languages to GPU, FPGA, and ASIC accelerators. We develop a unified cost model that expresses the dominant primitives, namely number-theoretic transforms, keyswitching, and bootstrapping, in common terms, and use it to compare reported results from the hardware-acceleration literature. Raw speedup figures, we find, are often not comparable, because they rest on different security parameters, amortization assumptions, and workloads; we argue for standardized benchmarking and supply a small, reproducible baseline of our own. Finally, we identify the convergence of confidential and provable computation, spanning verifiable FHE, threshold decryption, and FHE-on-chain, as the field's principal open frontier. To our knowledge this is the first survey to treat the FHE stack as a single compute engine under a common cost abstraction.","url":"https://doi.org/10.2139/ssrn.7128199","authors":["Yasir Albayati"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-31T13:57:03Z","doi":"10.2139/ssrn.7128199","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1201/9781003052098-47","name":"Securing Medical Data Using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003052098-47","authors":["Anita Chaudhari","Rajesh Bansode"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-05-05T08:21:52Z","doi":"10.1201/9781003052098-47","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.2139/ssrn.5270725","name":"Comparative Analysis of Verification Techniques in Homomorphic Encryption -Based E-Voting","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5270725","authors":["Pragyan Sinha","Nishant Kumar","Piyush Aggarwal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-28T21:15:30Z","doi":"10.2139/ssrn.5270725","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1007/978-3-031-43214-9_1","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-43214-9_1","authors":["Stefania Loredana Nita","Marius Iulian Mihailescu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-26T09:02:55Z","doi":"10.1007/978-3-031-43214-9_1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.31838/jcr.07.03.35","name":"PRIVACY PRESERVING CRYPTANALYSIS USING HOMOMORPHIC ENCRYPTION IN IOT","source":"crossref","abstract":"","url":"https://doi.org/10.31838/jcr.07.03.35","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-03-16T06:50:01Z","doi":"10.31838/jcr.07.03.35","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.3390/s26165254","name":"Securing Iris Recognition with Fully Homomorphic Encryption: Dimensionality Reduction in Iris Codes.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26165254","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26165254","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.20944/preprints202608.0902.v1","name":"A Hierarchical Benchmarking Framework for Homomorphic Encryption-Based Aggregation and Validation Using Real Smart Meter Data","source":"europepmc","abstract":"Homomorphic encryption (HE) enables computation on encrypted data and has emerged as a promising technology for privacy-preserving distributed analytics. However, the practical deployment of HE in large-scale hierarchical systems requires a thorough understanding of its computational overhead, scalability, and accuracy. This paper presents a generic hierarchical benchmarking framework for systematically evaluating homomorphic encryption schemes in multi-level aggregation environments. The framework supports configurable aggregation topologies, detailed operation-level profiling, and multiple encryption backends, enabling consistent and reproducible performance analysis across node-, cluster-, and global-level aggregation stages. Using the proposed framework, we conduct a comparative evaluation of the Brakerski/Fan-Vercauteren (BFV) and Cheon-Kim-Kim-Song (CKKS) schemes under identical workloads. Experimental results show that CKKS consistently outperforms BFV, achieving a 44.3% reduction in aggregation latency and a 24.6% reduction in decryption latency. {For the tested encoding and parameter settings,} CKKS delivers significantly lower numerical error, reducing the mean absolute error from 3.21 × 10−3 to 5.91 × 10−10. The proposed framework offers a reusable and extensible platform for evaluating emerging HE schemes and privacy-preserving analytics applications, thereby supporting future research and deployment of secure distributed data processing systems.","url":"https://doi.org/10.20944/preprints202608.0902.v1","authors":["Aiman Ahmad Shivani","Marzia Zaman","Pirathayini Srikantha","Darshana Upadhyay","Kshirasagar Naik"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.0902.v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1101/2024.05.26.595961","name":"Homomorphic Encryption: An Application to Polygenic Risk Scores","source":"europepmc","abstract":"1 Abstract Background Polygenic risk scores (PRSs) have emerged as a powerful tool in precision medicine, enabling personalized risk assessments for complex diseases. However, the use of sensitive genomic data in PRS calculations raises concerns about privacy and security. Fully homomorphic encryption (FHE) offers a promising solution by allowing computations on encrypted data, preserving the privacy of both genomic information and PRS models. Methods Here, we present an application of FHE for encrypted PRS calculations using a particular protocol (CKKS) within the Lattigo library. Our approach involves a three-party system: clients (clinicians handling sensitive genetic data), modelers developing a PRS (academics or companies), and evaluators (a local hospital running the models while maintaining data confidentiality). We demonstrate the feasibility and accuracy of our approach by applying it to synthetic datasets of various sizes and to a robust 110k–single-nucleotide polymorphism (SNP) model for schizophrenia. The complete codebase and a sample dataset are available at https://github.com/gersteinlab/HEPRS . Results The difference between traditional plaintext and encrypted PRS calculation results is negligible: the R 2 is 0.999 and the mean squared error is 2.27 × 10 −6 . Moreover, while the encrypted calculation is roughly 1,000 times slower than conventional non-encrypted ones (when considering only the core PRS calculation), the computation remains feasible on a single-CPU node. For example, processing ∼1,100 individuals with ∼110k SNPs took six minutes and ∼65 GB of memory on a laptop computer. In addition, we investigated the impact of the encryption parameters on the computational time and accuracy in detail, showing the expected slowdown with higher security settings. Conclusion Our approach showcases the applicability and feasibility of using FHE on real-world PRS models. With the pressing need for privacy-preserving solutions in the era of precision medicine, our work serves as a pilot application, offering a simple use case and providing a detailed comparison and evaluation in terms of accuracy, cost, and scalability.","url":"https://doi.org/10.1101/2024.05.26.595961","authors":["Elizabeth Knight","Jiaqi Li","Matthew Jensen","Israel Yolou","Can Kockan","Mark Gerstein"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.05.26.595961","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.21203/rs.3.rs-4189986/v1","name":"Private Detection of Relatives in Forensic Genomics using Homomorphic Encryption","source":"europepmc","abstract":"Abstract Background: Forensic analysis heavily relies on DNA analysis techniques, notably autosomal Single Nucleotide Polymorphisms (SNPs), to expedite the identification of unknown suspects through genomic database searches. However, the uniqueness of an individual’s genome sequence designates it as Personal Identifiable Information (PII), subjecting it to stringent privacy regulations that can impede data access and analysis, as well as restrict the parties allowed to handle the data. Homomorphic Encryption (HE) emerges as a promising solution, enabling the execution of complex functions on encrypted data without the need for decryption. HE not only permits the processing of PII as soon as it is collected and encrypted, such as at a crime scene, but also expands the potential for data processing by multiple entities and artificial intelligence services. Methods: This study introduces HE-based privacy-preserving methods for SNP DNA analysis, offering a means to compute kinship scores for a set of genome queries while meticulously preserving data privacy. We present three distinct approaches, including one unsupervised and two supervised methods, all of which demonstrated exceptional performance in the iDASH 2023 Track 1 competition. Results: Our HE-based methods can rapidly predict 400 kinship scores from an encrypted database containing 2000 entries within seconds, capitalizing on advanced technologies like Intel AVX vector extensions, Intel HEXL, and Microsoft SEAL HE libraries. Crucially, all three methods achieve remarkable accuracy levels (ranging from 96% to 100%), as evaluated by the auROC score metric, while maintaining robust 128-bit security. These findings underscore the transformative potential of HE in both safeguarding genomic data privacy and streamlining precise DNA analysis. Conclusions: Results demonstrate that HE-based solutions can be computationally practical to protect genomic privacy during screening of candidate matches for further genealogy analysis in Forensic Genetic Genealogy (FGG).","url":"https://doi.org/10.21203/rs.3.rs-4189986/v1","authors":["Fillipe Dias Moreira de Souza","Hubert de Lassus","Ro Cammarota"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4189986/v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.21203/rs.3.rs-3552389/v1","name":"Share to Gain: Collaborative Learning with Dynamic Membership via Multi-Key Homomorphic Encryption","source":"europepmc","abstract":"Abstract In this manuscript, we develop a multi-party framework tailored for multiple data contributors seeking machine learning insights from combined data sources. Grounded in statistical learning principles, we introduce the Multi-Key Homomorphic Encryption Logistic Regression (MK-HELR) algorithm, designed to execute logistic regression on encrypted multi-party data. Given that models built on aggregated datasets often demonstrate superior generalization capabilities, our approach offers data contributors the collective strength of shared data. Apart from facilitating logistic regression on data pooled from diverse sources, this algorithm creates a collaborative learning environment with dynamic membership. Notably, it can seamlessly incorporate new participants during the learning process, addressing the key limitation of prior methods that demanded a predetermined number of contributors to be set before the learning process begins. This flexibility is crucial in real-world scenarios, accommodating varying data contribution timelines and unanticipated fluctuations in participant numbers, due to additions and departures. Using the AI4I public predictive maintenance dataset, we demonstrate the MK-HELR algorithm, setting the stage for further research in secure, dynamic, and collaborative multi-party learning scenarios.","url":"https://doi.org/10.21203/rs.3.rs-3552389/v1","authors":["David Ha Eun Kang","Duhyeong Kim","Yongsoo Song","Dongwon Lee","Hyesun Kwak","Brian Anthony"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-3552389/v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1109/jbhi.2023.3317632","name":"Dynamic Corrected Split Federated Learning With Homomorphic Encryption for U-Shaped Medical Image Networks.","source":"pubmed","abstract":"U-shaped networks have become prevalent in various medical image tasks such as segmentation, and restoration. However, most existing U-shaped networks rely on centralized learning which raises privacy concerns. To address these issues, federated learning (FL) and split learning (SL) have been proposed. However, achieving a balance between the local computational cost, model privacy, and parallel training remains a challenge. In this articler, we propose a novel hybrid learning paradigm called Dynamic Corrected Split Federated Learning (DC-SFL) for U-shaped medical image networks. To preserve data privacy, including the input, model parameters, label and output simultaneously, we propose to split the network into three parts hosted by different parties. We propose a Dynamic Weight Correction Strategy (DWCS) to stabilize the training process and avoid the model drift problem due to data heterogeneity. To further enhance privacy protection and establish a trustworthy distributed learning paradigm, we propose to introduce additively homomorphic encryption into the aggregation process of client-side model, which helps prevent potential collusion between parties and provides a better privacy guarantee for our proposed method. The proposed DC-SFL is evaluated on various medical image tasks, and the experimental results demonstrate its effectiveness. In comparison with state-of-the-art distributed learning methods, our method achieves competitive performance.","url":"https://doi.org/10.1109/jbhi.2023.3317632","authors":["Yang Z","Chen Y","Huangfu H","Ran M","Wang H","Li X","Zhang Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1109/jbhi.2023.3317632","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.3390/e25111478","name":"Fractal-Based Hybrid Cryptosystem: Enhancing Image Encryption with RSA, Homomorphic Encryption, and Chaotic Maps.","source":"pubmed","abstract":"Protecting digital data, especially digital images, from unauthorized access and malicious activities is crucial in today's digital era. This paper introduces a novel approach to enhance image encryption by combining the strengths of the RSA algorithm, homomorphic encryption, and chaotic maps, specifically the sine and logistic map, alongside the self-similar properties of the fractal Sierpinski triangle. The proposed fractal-based hybrid cryptosystem leverages Paillier encryption for maintaining security and privacy, while the chaotic maps introduce randomness, periodicity, and robustness. Simultaneously, the fractal Sierpinski triangle generates intricate shapes at different scales, resulting in a substantially expanded key space and heightened sensitivity through randomly selected initial points. The secret keys derived from the chaotic maps and Sierpinski triangle are employed for image encryption. The proposed scheme offers simplicity, efficiency, and robust security, effectively safeguarding against statistical, differential, and brute-force attacks. Through comprehensive experimental evaluations, we demonstrate the superior performance of the proposed scheme compared to existing methods in terms of both security and efficiency. This paper makes a significant contribution to the field of digital image encryption, paving the way for further exploration and optimization in the future.","url":"https://doi.org/10.3390/e25111478","authors":["Mfungo DE","Fu X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/e25111478","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.3390/s23208504","name":"Adaptive Autonomous Protocol for Secured Remote Healthcare Using Fully Homomorphic Encryption (AutoPro-RHC).","source":"pubmed","abstract":"The outreach of healthcare services is a challenge to remote areas with affected populations. Fortunately, remote health monitoring (RHM) has improved the hospital service quality and has proved its sustainable growth. However, the absence of security may breach the health insurance portability and accountability act (HIPAA), which has an exclusive set of rules for the privacy of medical data. Therefore, the goal of this work is to design and implement the adaptive Autonomous Protocol (AutoPro) on the patient's r emote h ealth c are (RHC) monitoring data for the hospital using fully homomorphic encryption (FHE). The aim is to perform adaptive autonomous FHE computations on recent RHM data for providing health status reporting and maintaining the confidentiality of every patient. The autonomous protocol works independently within the group of prime hospital servers without the dependency on the third-party system. The adaptiveness of the protocol modes is based on the patient's affected level of slight, medium, and severe cases. Related applications are given as glucose monitoring for diabetes, digital blood pressure for stroke, pulse oximeter for COVID-19, electrocardiogram (ECG) for cardiac arrest, etc. The design for this work consists of an autonomous protocol, hospital servers combining multiple prime/local hospitals, and an algorithm based on fast fully homomorphic encryption over the torus (TFHE) library with a ring-variant by the Gentry, Sahai, and Waters (GSW) scheme. The concrete-ML model used within this work is trained using an open heart disease dataset from the UCI machine learning repository. Preprocessing is performed to recover the lost and incomplete data in the dataset. The concrete-ML model is evaluated both on the workstation and cloud server. Also, the FHE protocol is implemented on the AWS cloud network with performance details. The advantages entail providing confidentiality to the patient's data/report while saving the travel and waiting time for the hospital services. The patient's data will be completely confidential and can receive emergency services immediately. The FHE results show that the highest accuracy is achieved by support vector classification (SVC) of 88% and linear regression (LR) of 86% with the area under curve (AUC) of 91% and 90%, respectively. Ultimately, the FHE-based protocol presents a novel system that is successfully demonstrated on the cloud network.","url":"https://doi.org/10.3390/s23208504","authors":["Sheu RK","Lin YC","Pardeshi MS","Huang CY","Pai KC","Chen LC","Huang CC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/s23208504","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-3460853/v1","name":"Efficient and Secure Optimized Homomorphic Encryption (OHE) Securing Sensitive Images Data in the Age of Cloud Computing","source":"europepmc","abstract":"Abstract The goal of the project is to create a cutting-edge picture encryption technique that can be used to significantly boost the security of encrypted photographs. Since visible electromagnetic spectrum pictures are a difficulty, we provide an ideal homomorphic image encryption method to address the issue. Therefore, in our encryption phase, the numerical intensity value of a pixel from each channel is represented as the average of smaller pixel intensity sub-values. Therefore, each R, G, and B-channel picture is composed of a number of separate photographs. using an encryption key and aOptimized Homomorphic Encryption method, intensity of every pixel sub-value in every component picture is individually encrypted to provide a distributed picture encryption solution. Before being moved or stored, each encrypted component picture may be compressed. Before beginning the decryption process, each encrypted component picture is first, if required, decompressed. The homomorphic characteristic of the encryption technique is then used to encrypt the total of each encrypted component image's individually encrypted pixel intensity sub-values. To optimise the embedding rate of additional data during the encryption phase, six surrounding pixels are combined as a set, creating a novel method. Then, each channel image's original pixel intensities are restored, and extraneous data is removed from the total data. The intensity value for each channel's pixel in this kind of RGB image encryption and decryption, which is likewise created and simulated using software, is represented as the sum of merely two sub-values. The produced cypher pictures are subjected to several security evaluations and tests. The outcomes of these experiments showed the stability and resilience of the homomorphic picture encryption method we proposed, which also improved the security of the connected encrypted photos. Photos that utilise our homomorphic image encryption technique and are incredibly safe.","url":"https://doi.org/10.21203/rs.3.rs-3460853/v1","authors":["Revati Raman Dewangan","Sunita Soni","Ashish Misal"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-3460853/v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.7717/peerj-cs.1690","name":"Digital marketing program design based on abnormal consumer behavior data classification and improved homomorphic encryption algorithm.","source":"pubmed","abstract":"This article endeavors to delve into the conceptualization of a digital marketing framework grounded in consumer data and homomorphic encryption. The methodology entails employing GridSearch to harmonize and store the leaf nodes acquired post-training of the CatBoost model. These leaf node data subsequently serve as inputs for the radial basis function (RBF) layer, facilitating the mapping of leaf nodes into the hidden layer space. This sequential process culminates in the classification of user online consumption data within the output layer. Furthermore, an enhancement is introduced to the conventional homomorphic encryption algorithm, bolstering privacy preservation throughout the processing of consumption data. This augmentation broadens the applicability of homomorphic encryption to encompass rational numbers. The integration of the Chinese Remainder Theorem is instrumental in the decryption of consumption-related information. Empirical findings unveil the exceptional generalization performance of the amalgamated model, exemplifying an AUC (area under the curve) value of 0.66, a classification accuracy of 98.56% for online consumption data, and an F1-score of 98.41. The enhanced homomorphic encryption algorithm boasts attributes of stability, security, and efficiency, thus fortifying our proposed solution in facilitating companies' access to precise, real-time market insights. Consequently, this aids in the optimization of digital marketing strategies and enables pinpoint positioning within the target market.","url":"https://doi.org/10.7717/peerj-cs.1690","authors":["Cui J","Jiang H","Xu Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.7717/peerj-cs.1690","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.3390/s23041966","name":"Training of Classification Models via Federated Learning and Homomorphic Encryption.","source":"pubmed","abstract":"With the rise of social networks and the introduction of data protection laws, companies are training machine learning models using data generated locally by their users or customers in various types of devices. The data may include sensitive information such as family information, medical records, personal habits, or financial records that, if leaked, can generate problems. For this reason, this paper aims to introduce a protocol for training Multi-Layer Perceptron (MLP) neural networks via combining federated learning and homomorphic encryption, where the data are distributed in multiple clients, and the data privacy is preserved. This proposal was validated by running several simulations using a dataset for a multi-class classification problem, different MLP neural network architectures, and different numbers of participating clients. The results are shown for several metrics in the local and federated settings, and a comparative analysis is carried out. Additionally, the privacy guarantees of the proposal are formally analyzed under a set of defined assumptions, and the added value of the proposed protocol is identified compared with previous works in the same area of knowledge.","url":"https://doi.org/10.3390/s23041966","authors":["Angulo E","Márquez J","Villanueva-Polanco R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/s23041966","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1142/s0129065723500338","name":"Swarm-FHE: Fully Homomorphic Encryption-based Swarm Learning for Malicious Clients.","source":"pubmed","abstract":"Swarm Learning (SL) is a promising approach to perform the distributed and collaborative model training without any central server. However, data sensitivity is the main concern for privacy when collaborative training requires data sharing. A neural network, especially Generative Adversarial Network (GAN), is able to reproduce the original data from model parameters, i.e. gradient leakage problem. To solve this problem, SL provides a framework for secure aggregation using blockchain methods. In this paper, we consider the scenario of compromised and malicious participants in the SL environment, where a participant can manipulate the privacy of other participant in collaborative training. We propose a method, Swarm-FHE, Swarm Learning with Fully Homomorphic Encryption (FHE), to encrypt the model parameters before sharing with the participants which are registered and authenticated by blockchain technology. Each participant shares the encrypted parameters (i.e. ciphertexts) with other participants in SL training. We evaluate our method with training of the convolutional neural networks on the CIFAR-10 and MNIST datasets. On the basis of a considerable number of experiments and results with different hyperparameter settings, our method performs better as compared to other existing methods.","url":"https://doi.org/10.1142/s0129065723500338","authors":["Madni HA","Umer RM","Foresti GL"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1142/s0129065723500338","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1038/s41598-023-28481-8","name":"Privacy-preserving cancer type prediction with homomorphic encryption.","source":"pubmed","abstract":"Cancer genomics tailors diagnosis and treatment based on an individual's genetic information and is the crux of precision medicine. However, analysis and maintenance of high volume of genetic mutation data to build a machine learning (ML) model to predict the cancer type is a computationally expensive task and is often outsourced to powerful cloud servers, raising critical privacy concerns for patients' data. Homomorphic encryption (HE) enables computation on encrypted data, thus, providing cryptographic guarantees to protect privacy. But restrictive overheads of encrypted computation deter its usage. In this work, we explore the challenges of privacy preserving cancer type prediction using a dataset consisting of more than 2 million genetic mutations from 2713 patients for several cancer types by building a highly accurate ML model and then implementing its privacy preserving version in HE. Our solution for cancer type inference encodes somatic mutations based on their impact on the cancer genomes into the feature space and then uses statistical tests for feature selection. We propose a fast matrix multiplication algorithm for HE-based model. Our final model achieves 0.98 micro-average area under curve improving accuracy from 70.08 to 83.61% , being 550 times faster than the standard matrix multiplication-based privacy-preserving models. Our tool can be found at https://github.com/momalab/octal-candet .","url":"https://doi.org/10.1038/s41598-023-28481-8","authors":["Sarkar E","Chielle E","Gursoy G","Chen L","Gerstein M","Maniatakos M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1038/s41598-023-28481-8","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.3390/s23156762","name":"HealthLock: Blockchain-Based Privacy Preservation Using Homomorphic Encryption in Internet of Things Healthcare Applications.","source":"pubmed","abstract":"The swift advancement of the Internet of Things (IoT), coupled with the growing application of healthcare software in this area, has given rise to significant worries about the protection and confidentiality of critical health data. To address these challenges, blockchain technology has emerged as a promising solution, providing decentralized and immutable data storage and transparent transaction records. However, traditional blockchain systems still face limitations in terms of preserving data privacy. This paper proposes a novel approach to enhancing privacy preservation in IoT-based healthcare applications using homomorphic encryption techniques combined with blockchain technology. Homomorphic encryption facilitates the performance of calculations on encrypted data without requiring decryption, thus safeguarding the data's privacy throughout the computational process. The encrypted data can be processed and analyzed by authorized parties without revealing the actual contents, thereby protecting patient privacy. Furthermore, our approach incorporates smart contracts within the blockchain network to enforce access control and to define data-sharing policies. These smart contracts provide fine-grained permission settings, which ensure that only authorized entities can access and utilize the encrypted data. These settings protect the data from being viewed by unauthorized parties. In addition, our system generates an audit record of all data transactions, which improves both accountability and transparency. We have provided a comparative evaluation with the standard models, taking into account factors such as communication expense, transaction volume, and security. The findings of our experiments suggest that our strategy protects the confidentiality of the data while at the same time enabling effective data processing and analysis. In conclusion, the combination of homomorphic encryption and blockchain technology presents a solution that is both resilient and protective of users' privacy for healthcare applications integrated with IoT. This strategy offers a safe and open setting for the management and exchange of sensitive patient medical data, while simultaneously preserving the confidentiality of the patients involved.","url":"https://doi.org/10.3390/s23156762","authors":["Ali A","Al-Rimy BAS","Alsubaei FS","Almazroi AA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/s23156762","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-4019510/v1","name":"Molybdenum-based Metallic Cluster-type Memristor exhibiting Stochastic Switching and Analog-state Programmable Characteristics and its Utilization for Homomorphic Encryption Hardware","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4019510/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4019510/v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.3390/s23104594","name":"Pipelined Key Switching Accelerator Architecture for CKKS-Based Fully Homomorphic Encryption.","source":"pubmed","abstract":"The increasing ubiquity of big data and cloud-based computing has led to increased concerns regarding the privacy and security of user data. In response, fully homomorphic encryption (FHE) was developed to address this issue by enabling arbitrary computation on encrypted data without decryption. However, the high computational costs of homomorphic evaluations restrict the practical application of FHE schemes. To tackle these computational and memory challenges, a variety of optimization approaches and acceleration efforts are actively being pursued. This paper introduces the KeySwitch module, a highly efficient and extensively pipelined hardware architecture designed to accelerate the costly key switching operation in homomorphic computations. Built on top of an area-efficient number-theoretic transform design, the KeySwitch module exploited the inherent parallelism of key switching operation and incorporated three main optimizations: fine-grained pipelining, on-chip resource usage, and high-throughput implementation. An evaluation on the Xilinx U250 FPGA platform demonstrated a 1.6&#xd7; improvement in data throughput compared to previous work with more efficient hardware resource utilization. This work contributes to the development of advanced hardware accelerators for privacy-preserving computations and promoting the adoption of FHE in practical applications with enhanced efficiency.","url":"https://doi.org/10.3390/s23104594","authors":["Duong PN","Lee H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/s23104594","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-3310270/v1","name":"Efficient and Privacy-Preserving Image Classification Using Homomorphic Encryption and Chunk-based Convolutional Neural Network","source":"europepmc","abstract":"Abstract Image feature categorization has emerged as a crucial component in many domains, including computer vision, machine learning, and biometrics, in the dynamic environment of big data and cloud computing. It is extremely difficult to guarantee image data security, privacy, and computing efficiency while also lowering storage and transmission costs. This paper introduces a novel method for classifying image features that combines multilevel homomorphic encryption and image data partitioning in an integrated manner. We employ a novel partitioning strategy to reduce computational complexity, significantly reducing computational load and improving classification accuracy. In the quest for increased data security and privacy, we introduce a novel, fully homomorphic encryption approach specialized to partitioned images. To counter the inherent complexity of encryption, we devise a compound encryption strategy that exploits the full potential of homomorphic computation, with an explicit objective to curtail computational and storage overheads. Evidently superior to conventional methods, our methodology showcases pronounced benefits in computational efficiency, storage and transmission cost reduction, and robust security and privacy preservation. Hence, the methodology put forth in this paper presents a pioneering and efficacious resolution to the multifaceted challenges of image feature classification within the intricate milieu of cloud computing and big data.","url":"https://doi.org/10.21203/rs.3.rs-3310270/v1","authors":["Huixue Jia","Daomeng Cai","Jie Yang","Weidong Qian","Cong Wang","Xiaoyu Li","Shan Yang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-3310270/v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.21203/rs.3.rs-2164106/v1","name":"HEProfiler: An In-Depth Profiler of Approximate Homomorphic Encryption Libraries","source":"europepmc","abstract":"Abstract Fully Homomorphic Encryption (FHE) allows computation on encrypted data. Various software libraries have implemented the approximate-arithmetic FHE scheme CKKS, which is highly useful for applications in machine learning and data analytics; each of these libraries have differing performance and features. It is useful for developers and researchers to learn details about these libraries' performance and their differences. Some previous work has profiled FHE and CKKS implementations for this purpose, but these comparisons are limited in their fairness and completeness. In this article, we compare four major libraries supporting the CKKS scheme. Working with the maintainers of each of the PALISADE, Microsoft SEAL, HElib, and HEAAN libraries, we devise methods for fair comparisons of these libraries, even with their widely varied development strategies and library architectures. To show the practical performance of these libraries, we present HEProfiler, a simple and extensible framework for profiling C++ FHE libraries. Our experimental evaluation is complete in both the scope of tasks tested and metrics evaluated, allowing us to draw conclusions about the behaviors of different libraries under a wide range of real-world workloads. This is the first work giving experimental comparisons of different bootstrapping-capable CKKS libraries.","url":"https://doi.org/10.21203/rs.3.rs-2164106/v1","authors":["Jonathan Takeshita","Nirajan Koirala","Colin McKechney","Taeho Jung"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-2164106/v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.3233/shti230116","name":"HEIDA: Software Examples for Rapid Introduction of Homomorphic Encryption for Privacy Preservation of Health Data.","source":"pubmed","abstract":"Adequate privacy protection is crucial for implementing modern AI algorithms in medicine. With Fully Homomorphic Encryption (FHE), a party without access to the secret key can perform calculations and advanced analytics on encrypted data without taking part of either the input data or the results. FHE can therefore work as an enabler for situations where computations are carried out by parties that are denied plain text access to sensitive data. It is a scenario often found with digital services that process personal health-related data or medical data originating from a healthcare provider, for example, when the service is delivered by a third-party service provider located in the cloud. There are practical challenges to be aware of when working with FHE. The current work aims to improve accessibility and reduce barriers to entry by providing code examples and recommendations to aid developers working with health data in developing FHE-based applications. HEIDA is available on the GitHub repository: https://github.com/rickardbrannvall/HEIDA.","url":"https://doi.org/10.3233/shti230116","authors":["Brännvall R","Forsgren H","Linge H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3233/shti230116","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.3390/e24111545","name":"Homomorphic Encryption-Based Federated Privacy Preservation for Deep Active Learning.","source":"pubmed","abstract":"Active learning is a technique for maximizing performance of machine learning with minimal labeling effort and letting the machine automatically and adaptively select the most informative data for labeling. Since the labels on records may contain sensitive information, privacy-preserving mechanisms should be integrated into active learning. We propose a privacy-preservation scheme for active learning using homomorphic encryption-based federated learning. Federated learning provides distributed computation from multiple clients, and homomorphic encryption enhances the privacy preservation of user data with a strong security level. The experimental result shows that the proposed homomorphic encryption-based federated learning scheme can preserve privacy in active learning while maintaining model accuracy. Furthermore, we also provide a Deep Leakage Gradient comparison. The proposed scheme has no gradient leakage compared to the related schemes that have more than 74% gradient leakage.","url":"https://doi.org/10.3390/e24111545","authors":["Hendra Kurniawan","Masahiro Mambo","Kurniawan H","Mambo M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/e24111545","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.3390/s23136181","name":"Secure Data Aggregation Based on End-to-End Homomorphic Encryption in IoT-Based Wireless Sensor Networks.","source":"pubmed","abstract":"By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys. Sharing the same key across various nodes makes the security more vulnerable. An alternative solution to secure the aggregation process is to provide an end-to-end security protocol, wherein intermediary nodes combine the data without decoding the acquired data. As a consequence, the intermediary aggregating nodes do not have to maintain confidential key values, enabling end-to-end security across sensor devices and base stations. This research presents End-to-End Homomorphic Encryption (EEHE)-based safe and secure data gathering in IoT-based Wireless Sensor Networks (WSNs), whereby it protects end-to-end security and enables the use of aggregator functions such as COUNT, SUM and AVERAGE upon encrypted messages. Such an approach could also employ message authentication codes (MAC) to validate data integrity throughout data aggregation and transmission activities, allowing fraudulent content to also be identified as soon as feasible. Additionally, if data are communicated across a WSN, then there is a higher likelihood of a wormhole attack within the data aggregation process. The proposed solution also ensures the early detection of wormhole attacks during data aggregation.","url":"https://doi.org/10.3390/s23136181","authors":["Kumar M","Sethi M","Rani S","Sah DK","AlQahtani SA","Al-Rakhami MS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/s23136181","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.3934/mbe.2023106","name":"Research on medical data security sharing scheme based on homomorphic encryption.","source":"pubmed","abstract":"With the deep integration of \"AI + medicine\", AI-assisted technology has been of great help to human beings in the medical field, especially in the area of predicting and diagnosing diseases based on big data, because it is faster and more accurate. However, concerns about data security seriously hinder data sharing among medical institutions. To fully exploit the value of medical data and realize data collaborative sharing, we developed a medical data security sharing scheme based on the C/S communication mode and constructed a federated learning architecture that uses homomorphic encryption technology to protect training parameters. Here, we chose the Paillier algorithm to realize the additive homomorphism to protect the training parameters. Clients do not need to share local data, but only upload the trained model parameters to the server. In the process of training, a distributed parameter update mechanism is introduced. The server is mainly responsible for issuing training commands and weights, aggregating the local model parameters from the clients and predicting the joint diagnostic results. The client mainly uses the stochastic gradient descent algorithm for gradient trimming, updating and transmitting the trained model parameters back to the server. In order to test the performance of this scheme, a series of experiments was conducted. From the simulation results, we can know that the model prediction accuracy is related to the global training rounds, learning rate, batch size, privacy budget parameters etc. The results show that this scheme realizes data sharing while protecting data privacy, completes the accurate prediction of diseases and has a good performance.","url":"https://doi.org/10.3934/mbe.2023106","authors":["Guo L","Gao W","Cao Y","Lai X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3934/mbe.2023106","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.3390/s21237806","name":"Is Homomorphic Encryption-Based Deep Learning Secure Enough?","source":"pubmed","abstract":"As the amount of data collected and analyzed by machine learning technology increases, data that can identify individuals is also being collected in large quantities. In particular, as deep learning technology-which requires a large amount of analysis data-is activated in various service fields, the possibility of exposing sensitive information of users increases, and the user privacy problem is growing more than ever. As a solution to this user's data privacy problem, homomorphic encryption technology, which is an encryption technology that supports arithmetic operations using encrypted data, has been applied to various field including finance and health care in recent years. If so, is it possible to use the deep learning service while preserving the data privacy of users by using the data to which homomorphic encryption is applied? In this paper, we propose three attack methods to infringe user's data privacy by exploiting possible security vulnerabilities in the process of using homomorphic encryption-based deep learning services for the first time. To specify and verify the feasibility of exploiting possible security vulnerabilities, we propose three attacks: (1) an adversarial attack exploiting communication link between client and trusted party; (2) a reconstruction attack using the paired input and output data; and (3) a membership inference attack by malicious insider. In addition, we describe real-world exploit scenarios for financial and medical services. From the experimental evaluation results, we show that the adversarial example and reconstruction attacks are a practical threat to homomorphic encryption-based deep learning models. The adversarial attack decreased average classification accuracy from 0.927 to 0.043, and the reconstruction attack showed average reclassification accuracy of 0.888, respectively.","url":"https://doi.org/10.3390/s21237806","authors":["Shin J","Choi SH","Choi YH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.3390/s21237806","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1016/j.compmedimag.2022.102139","name":"Blockchain and homomorphic encryption based privacy-preserving model aggregation for medical images.","source":"pubmed","abstract":"Medical healthcare centers are envisioned as a promising paradigm to handle the massive volume of data for COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and training models within a single organization. This practice can be considered a weakness as it leads to several privacy and security concerns related to raw data communication. To overcome this weakness and secure raw data communication, we propose a blockchain-based federated learning framework that provides a solution for collaborative data training. The proposed framework enables the coordination of multiple hospitals to train and share encrypted federated models while preserving data privacy. Blockchain ledger technology provides decentralization of federated learning models without relying on a central server. Moreover, the proposed homomorphic encryption scheme encrypts and decrypts the gradients of the model to preserve privacy. More precisely, the proposed framework: (i) train the local model by a novel capsule network for segmentation and classification of COVID-19 images, (ii) furthermore, we use the homomorphic encryption scheme to secure the local model that encrypts and decrypts the gradients, (iii) finally, the model is shared over a decentralized platform through the proposed blockchain-based federated learning algorithm. The integration of blockchain and federated learning leads to a new paradigm for medical image data sharing over the decentralized network. To validate our proposed model, we conducted comprehensive experiments and the results demonstrate the superior performance of the proposed scheme.","url":"https://doi.org/10.1016/j.compmedimag.2022.102139","authors":["Kumar R","Kumar J","Khan AA","Zakria","Ali H","Bernard CM","Khan RU","Zeng S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1016/j.compmedimag.2022.102139","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1155/2022/3406228","name":"Blockchain Data Secure Transmission Method Based on Homomorphic Encryption.","source":"pubmed","abstract":"To ensure the security of data transmission and recording in Internet environment monitoring systems, this paper proposes a study of a secure method of blockchain data transfer based on homomorphic encryption. Blockchain data transmission is realized through homomorphic encryption. Homomorphic encryption can not only encrypt the original data, but also ensure that the data result after decrypting the data is the same as the original data. The asymmetric encrypted public key is collected by Internet of things (IoT) equipment to realize the design of blockchain data secure transmission method based on homomorphic encryption. The experimental results show that the accuracy of the first transmission is as high as 88% when using the transmission method in this paper. After several experiments, the transmission accuracy is high by using the design method in this paper. In the last test, the transmission accuracy is still 88%, and the data transmission effect is relatively stable. At the same time, compared to the management method used in this article, the transfer method used in this paper is more reliable than the original transfer method and is not prone to data distortion. It can be seen that this method has high transmission accuracy and short transmission time, which effectively avoids the data tampering caused by too long time in the transmission process.","url":"https://doi.org/10.1155/2022/3406228","authors":["Peng S","Cai Z","Liu W","Wang W","Li G","Sun Y","Zhu L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1155/2022/3406228","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.3390/s23031164","name":"TriNymAuth: Triple Pseudonym Authentication Scheme for VANETs Based on Cuckoo Filter and Paillier Homomorphic Encryption.","source":"pubmed","abstract":"In VANETs, owing to the openness of wireless communication, it is necessary to change pseudonyms frequently to realize the unlinkability of vehicle identity. Moreover, identity authentication is needed, which is usually completed by digital certificates or a trusted third party. The storage and the communication overhead are high. This paper proposes a triple pseudonym authentication scheme for VANETs based on the Cuckoo Filter and Paillier homomorphic encryption (called TriNymAuth). TriNymAuth applies Paillier homomorphic encryption, a Cuckoo Filter combining filter-level and bucket-level, and a triple pseudonym (homomorphic pseudonym, local pseudonym, and virtual pseudonym) authentication to the vehicle identity authentication scheme. It reduces the dependence on a trusted third party and ensures the privacy and security of vehicle identity while improving authentication efficiency. Experimental results show that the insert overhead of the Cuckoo Filter is about 10 &#x3bc;s, and the query overhead reaches the ns level. Furthermore, TriNymAuth has significant cost advantages, with an OBU enrollment cost of only 0.884 ms. When the data rate in VANETs dr&#x2264; 180 kbps, TriNymAuth has the smallest total transmission delay cost and is suitable for shopping malls and other places with dense traffic.","url":"https://doi.org/10.3390/s23031164","authors":["Zhuang L","Guo N","Chen Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/s23031164","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1016/j.cmpb.2023.107854","name":"Extension of physical activity recognition with 3D CNN using encrypted multiple sensory data to federated learning based on multi-key homomorphic encryption.","source":"pubmed","abstract":"The Internet of medical things is enhancing smart healthcare services using physical wearable sensor-based devices connected to the Internet. Machine learning techniques play an important role in the core of these services for remotely consulting patients thanks to the pattern recognition from on-device data, which is transferred to the central servers from local devices. However, transferring personally identifiable information data to servers could become a source for hackers to steal from, manipulate and perform illegal activities. Federated learning is a new branch of machine learning that creates directly training models from on-device data and aggregates these learned models on the servers without centralized data. Another way to protect data confidentiality on computer systems is data encryption. Data encryption transforms data into another form that only users with authority to a decryption key can read. In this work, we propose a novel method enabling preservation of client privacy and protection of client biomedical data from illegal hackers while transmitting through the Internet.","url":"https://doi.org/10.1016/j.cmpb.2023.107854","authors":["Pham CH","Huynh-The T","Sedgh-Gooya E","El-Bouz M","Alfalou A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1016/j.cmpb.2023.107854","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.20944/preprints202210.0064.v1","name":"Secure Approximate String Matching using Homomorphic Encryption for Privacy-preserving Record Linkage","source":"europepmc","abstract":"String matching is an important part in many real world applications. It must robust against variations in string field. In record linkage for two different datasets matching should detect two patients in common in spite of small variations. But it becomes difficult in case of confidential data because sometimes data sharing between organizations become restricted for privacy purposes. Several techniques have been proposed on privacy-preserving approximate string matching such as Secure Hash Encoding etc. Relative to other techniques for approximate string matching Homomorphic encryption is very new.&#x0D; &#x0D; In this paper we have proposed a Homomorphic Encryption based approximate string matching technique for matching multiple attributes. There is no solution currently available for multiple attributes matching using Homomorphic encryption. We have proposed two different methods for multiple attributes matching. Compare to other existing approaches our proposed method offers security guarantees and greater matching accuracy.","url":"https://doi.org/10.20944/preprints202210.0064.v1","authors":["Shahidul Islam Khan","Rakib Hosen","Iqbal Hossain"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.20944/preprints202210.0064.v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1016/j.cels.2021.10.003","name":"Privacy-preserving genotype imputation with fully homomorphic encryption.","source":"pubmed","abstract":"Genotype imputation is the inference of unknown genotypes using known population structure observed in large genomic datasets; it can further our understanding of phenotype-genotype relationships and is useful for QTL mapping and GWASs. However, the compute-intensive nature of genotype imputation can overwhelm local servers for computation and storage. Hence, many researchers are moving toward using cloud services, raising privacy concerns. We address these concerns by developing an efficient, privacy-preserving algorithm called p-Impute. Our method uses homomorphic encryption, allowing calculations on ciphertext, thereby avoiding the decryption of private genotypes in the cloud. It is similar to k-nearest neighbor approaches, inferring missing genotypes in a genomic block based on the SNP genotypes of genetically related individuals in the same block. Our results demonstrate accuracy in agreement with the state-of-the-art plaintext solutions. Moreover, p-Impute is scalable to real-world applications as its memory and time requirements increase linearly with the increasing number of samples. p-Impute is freely available for download here: https://doi.org/10.5281/zenodo.5542001.","url":"https://doi.org/10.1016/j.cels.2021.10.003","authors":["Gürsoy G","Chielle E","Brannon CM","Maniatakos M","Gerstein M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1016/j.cels.2021.10.003","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1089/big.2021.0176","name":"Attribute-Based Adaptive Homomorphic Encryption for Big Data Security.","source":"pubmed","abstract":"There is a drastic increase in Internet usage across the globe, thanks to mobile phone penetration. This extreme Internet usage generates huge volumes of data, in other terms, big data. Security and privacy are the main issues to be considered in big data management. Hence, in this article, Attribute-based Adaptive Homomorphic Encryption (AAHE) is developed to enhance the security of big data. In the proposed methodology, Oppositional Based Black Widow Optimization (OBWO) is introduced to select the optimal key parameters by following the AAHE method. By considering oppositional function, Black Widow Optimization (BWO) convergence analysis was enhanced. The proposed methodology has different processes, namely, process setup, encryption, and decryption processes. The researcher evaluated the proposed methodology with non-abelian rings and the homomorphism process in ciphertext format. Further, it is also utilized in improving one-way security related to the conjugacy examination issue. Afterward, homomorphic encryption is developed to secure the big data. The study considered two types of big data such as adult datasets and anonymous Microsoft web datasets to validate the proposed methodology. With the help of performance metrics such as encryption time, decryption time, key size, processing time, downloading, and uploading time, the proposed method was evaluated and compared against conventional cryptography techniques such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC). Further, the key generation process was also compared against conventional methods such as BWO, Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The results established that the proposed method is supreme than the compared methods and can be applied in real time in near future.","url":"https://doi.org/10.1089/big.2021.0176","authors":["Thenmozhi R","Shridevi S","Mohanty SN","García-Díaz V","Gupta D","Tiwari P","Shorfuzzaman M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1089/big.2021.0176","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1186/s12864-022-08469-w","name":"Secure tumor classification by shallow neural network using homomorphic encryption.","source":"pubmed","abstract":"Disclosure of patients' genetic information in the process of applying machine learning techniques for tumor classification hinders the privacy of personal information. Homomorphic Encryption (HE), which supports operations between encrypted data, can be used as one of the tools to perform such computation without information leakage, but it brings great challenges for directly applying general machine learning algorithms due to the limitations of operations supported by HE. In particular, non-polynomial activation functions, including softmax functions, are difficult to implement with HE and require a suitable approximation method to minimize the loss of accuracy. In the secure genome analysis competition called iDASH 2020, it is presented as a competition task that a multi-label tumor classification method that predicts the class of samples based on genetic information using HE.","url":"https://doi.org/10.1186/s12864-022-08469-w","authors":["Hong S","Park JH","Cho W","Choe H","Cheon JH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1186/s12864-022-08469-w","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-2910088/v1","name":"PAF-FHE: Low-Cost Accurate Non-Polynomial Operator Polynomial Approximation in Fully Homomorphic Encryption Based ML Inference","source":"europepmc","abstract":"Abstract Machine learning (ML) is getting more pervasive. Wide adoption of ML in healthcare, facial recognition, and blockchain involves private and sensitive data. One of the most promising candidates for inference on encrypted data, termed Fully Homomorphic Encryp-tion (FHE), preserves the privacy of both data and the ML model. However, it slows down plaintext inference by six magnitudes, with a root cause of replacing non-polynomial operators with latency-prohibitive 27-degree Polynomial Approximated Function (PAF). While prior research has investigated low-degree PAFs, naive stochastic gradient descent (SGD) training fails to converge on PAFs with degrees higher than 5, leading to limited accuracy compared to the state-of-the-art 27-degree PAF. Therefore, we propose four training techniques to enable convergence in the post-approximation model using PAFs with an arbitrary degree, including (1) Dynamic Scaling (DS) and Static Scaling (SS) to enable minimal approximation error during approximation, (2) Coefficient Tuning (CT) to obtain a good initial coefficient value for each PAF, (3) Progressive Approximation (PA) to simply the two-variable regression optimization problem into single-variable for fast 1 and easy convergence, and (4) Alternate Training (AT) to retraining the post-replacement PAFs and other linear layers in a decoupled divide-and-conquer manner. A combination of DS/SS, CT, PA, and AT enables the exploration of accuracy-latency space for FHE-domain ReLU replacement. Leveraging the proposed techniques, we propose a systematic approach (PAF-FHE) to enable low-degree PAF to demonstrate the same accuracy as SotA high-degree PAFs. We evaluated PAFs with various degrees on different models and variant datasets, and PAF-FHE consistently enables low-degree PAF to achieve higher accuracy than SotA PAFs. Specifically, for ResNet-18 under the ImageNet-1k dataset, our spotted optimal 12-degree PAF reduces 56% latency compared to the SotA 27-degree PAF with the same post-replacement accuracy (69.4%). While as for VGG-19 under the CiFar-10 dataset, optimal 12-degree PAF achieves even 0.84% higher accuracy with 72% latency saving. Our code is open-sourced at: https://github.com/TorchFHE/PAF-FHE","url":"https://doi.org/10.21203/rs.3.rs-2910088/v1","authors":["Jingtian Dang","Jianming Tong","Anupam Golder","Arijit Raychowdhury","Cong Hao","Tushar Krishna"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-2910088/v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.3390/s22218124","name":"EDLaaS:Fully Homomorphic Encryption over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting.","source":"pubmed","abstract":"We present automatically parameterised Fully Homomorphic Encryption (FHE) for encrypted neural network inference and exemplify our inference over FHE-compatible neural networks with our own open-source framework and reproducible examples. We use the fourth generation Cheon, Kim, Kim, and Song (CKKS) FHE scheme over fixed points provided by the Microsoft Simple Encrypted Arithmetic Library (MS-SEAL). We significantly enhance the usability and applicability of FHE in deep learning contexts, with a focus on the constituent graphs, traversal, and optimisation. We find that FHE is not a panacea for all privacy-preserving machine learning (PPML) problems and that certain limitations still remain, such as model training. However, we also find that in certain contexts FHE is well-suited for computing completely private predictions with neural networks. The ability to privately compute sensitive problems more easily while lowering the barriers to entry can allow otherwise too-sensitive fields to begin advantaging themselves of performant third-party neural networks. Lastly, we show how encrypted deep learning can be applied to a sensitive real-world problem in agri-food, i.e., strawberry yield forecasting, demonstrating competitive performance. We argue that the adoption of encrypted deep learning methods at scale could allow for a greater adoption of deep learning methodologies where privacy concerns exist, hence having a large positive potential impact within the agri-food sector and its journey to net zero.","url":"https://doi.org/10.3390/s22218124","authors":["Onoufriou G","Hanheide M","Leontidis G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/s22218124","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.3390/s23010014","name":"A Blockchain-Based End-to-End Data Protection Model for Personal Health Records Sharing: A Fully Homomorphic Encryption Approach.","source":"pubmed","abstract":"Personal health records (PHR) represent health data managed by a specific individual. Traditional solutions rely on centralized architectures to store and distribute PHR, which are more vulnerable to security breaches. To address such problems, distributed network technologies, including blockchain and distributed hash tables (DHT) are used for processing, storing, and sharing health records. Furthermore, fully homomorphic encryption (FHE) is a set of techniques that allows the calculation of encrypted data, which can help to protect personal privacy in data sharing. In this context, we propose an architectural model that applies a DHT technique called the interplanetary protocol file system and blockchain networks to store and distribute data and metadata separately; two new elements, called data steward and shared data vault, are introduced in this regard. These new modules are responsible for segregating responsibilities from health institutions and promoting end-to-end encryption; therefore, a person can manage data encryption and requests for data sharing in addition to restricting access to data for a predefined period. In addition to supporting calculations on encrypted data, our contribution can be summarized as follows: (i) mitigation of risk to personal privacy by reducing the use of unencrypted data, and (ii) improvement of semantic interoperability among health institutions by using distributed networks for standardized PHR. We evaluated performance and storage occupation using a database with 1.3 million COVID-19 registries, which showed that combining FHE with distributed networks could redefine e-health paradigms.","url":"https://doi.org/10.3390/s23010014","authors":["Vanin FNDS","Policarpo LM","Righi RDR","Heck SM","da Silva VF","Goldim J","da Costa CA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/s23010014","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1016/j.iot.2022.100625","name":"Privacy preserving IoT-based crowd-sensing network with comparable homomorphic encryption and its application in combating COVID19.","source":"pubmed","abstract":"IoT-based crowd-sensing network, which aims to achieve data collection and task allocation to mobile users, become more and more popular in recent years. This data collected by IoT devices may be private and directly transmission of these data maybe incur privacy leakage. With the help of homomorphic encryption (HE), which supports the additive and/or multiplicative operations over the encrypted data, privacy preserving crowd-sensing network is now possible. Until now several such secure data aggregation schemes based on HE have been proposed. In many cases, ciphertext comparison is an important step for further secure data processing. However efficient ciphertext comparison is not supported by most such schemes. In this paper, aiming at enabling ciphertext comparison among multiple users in crowd-sensing network, with Lagrange's interpolation technique we propose comparable homomorphic encryption (CompHE) schemes. We also prove our schemes' security, and the performance analysis show our schemes are practical. We also discuss the applications of our IoT based crowd-sensing network with comparable homomorphic encryption for combatting COVID19, including the first example of privacy preserving close contact determination based on the spatial distance, and the second example of privacy preserving social distance controlling based on the spatial difference of lockdown zones, controlled zones and precautionary zones. From the analysis we see our IoT based crowd-sensing network can be used for contact tracing without worrying about the privacy leakage. Compared with the existing CompHE schemes, our proposals can be collusion resistance or secure in the semi-honest model while the previous schemes cannot achieve this easily. Our schemes only need 4 or 5 modular exponentiation when implementing the most important comparison algorithm, which are better than the existing closely related scheme with advantage of 50% or 37.5%.","url":"https://doi.org/10.1016/j.iot.2022.100625","authors":["Huang D","Gan Q","Wang X","Ogiela MR","Wang XA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1016/j.iot.2022.100625","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-1630013/v1","name":"Privacy preserving on personalized medical data in cloud IoT using Extended Fully Homomorphic Encryption","source":"europepmc","abstract":"Abstract Transition of healthcare to digital platforms is necessary for the present era to provide a better diagnosis with reduced operational cost. Digital platform makes the patient data available in an appropriate time. Cloud computing in health care applications senses the data through IoT modules and stored in the cloud. Manipulating medical data needs an essential protection mechanism to ensure data privacy. To reduce the privacy issues, encryption algorithms are preferred generally but their efficiency needs to be improved without breaking the data confidentiality. This research work proposed an Extended Fully Homomorphic Encryption (EFHE) scheme to preserve medical data privacy. Parameters such as Signal to Noise Ratio, Peak Signal to Noise Ratio, Mean Square Error, encoding, and decoding time are considered for analysis and conventional homomorphic and fully homomorphic algorithms are used to compare with the proposed research model to validate the superior performance of the proposed encryption scheme.","url":"https://doi.org/10.21203/rs.3.rs-1630013/v1","authors":["Pradeep Bedi","S B Goyal"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-1630013/v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.21203/rs.3.rs-3123750/v1","name":"An Augmented Salp-swarm Optimization Based on Paillier Federated Multi-layer Perceptron (Pf-mlp) and Homomorphic Encryption Standard (Hes) Techniques for Data Security in Cloud Systems","source":"europepmc","abstract":"Abstract Providing security to cloud data is one of the essential problems that have needed to be addressed in recent times due to the advancement and development of security breaches in technologies. As a result, the majority of existing research efforts aim to develop various types of cryptographic techniques for ensuring the data security of cloud systems. However, it faced challenges with complex computational operations, inefficient security models, high time consumption, and error outputs. Therefore, the proposed work aims to develop an advanced and hybrid optimization-based cryptographic methodology for increasing the security of cloud data. An Improved Salp-Swarm Optimization (ISSO) technique is deployed to obtain the random number required for secret key generation. In this work, two different encryption techniques, such as the Homomorphic Encryption Standard (HES) and the Paillier Federated Multi-Layer Perceptron (PF-MLP) model, are used for strengthening the security of the original health tweet dataset. Here, the efficacy and security level of these two encryption methodologies are validated for the purpose of identifying the most suitable mechanism to secure the health tweet dataset. For these approaches, the key pair is optimally generated by using an ISSO technique. This type of key generation can complicate matters for users who attempt to attack the original information. Moreover, the novel contribution of this work is that it incorporates the functions of advanced optimization and cryptographic mechanisms for securing the health tweet dataset against attacking users. For validating the performance of this system, various evaluation metrics have been used, and the validated results are compared with the proposed system to demonstrate the improvement of the proposed system.","url":"https://doi.org/10.21203/rs.3.rs-3123750/v1","authors":["Kanakasabapathi R S","J.E. Judith"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-3123750/v1","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:45.649Z"},{"id":"doi:10.1109/jbhi.2021.3071270","name":"Multicenter Privacy-Preserving Cox Analysis Based on Homomorphic Encryption.","source":"pubmed","abstract":"The Cox proportional hazards model is one of the most widely used methods for analyzing survival data. Data from multiple data providers are required to improve the generalizability and confidence of the results of Cox analysis; however, such data sharing may result in leakage of sensitive information, leading to financial fraud, social discrimination or unauthorized data abuse. Some privacy-preserving Cox regression protocols have been proposed in past years, but they lack either security or functionality. In this paper, we propose a privacy-preserving Cox regression protocol for multiple data providers and researchers. The proposed protocol allows researchers to train models on horizontally or vertically partitioned datasets while providing privacy protection for both the sensitive data and the trained models. Our protocol utilizes threshold homomorphic encryption to guarantee security. Experimental results demonstrate that with the proposed protocol, Cox regression model training over 9 variables in a dataset of 113,035 samples takes approximately 44 min, and the trained model is almost the same as that obtained with the original nonsecure Cox regression protocol; therefore, our protocol is a potential candidate for practical real-world applications in multicenter medical research.","url":"https://doi.org/10.1109/jbhi.2021.3071270","authors":["Lu Y","Tian Y","Zhou T","Zhu S","Li J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1109/jbhi.2021.3071270","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1016/j.cels.2021.07.010","name":"Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation.","source":"pubmed","abstract":"Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby \"tag\" variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic data sharing with an untrusted imputation service. Here, we developed secure genotype imputation using efficient homomorphic encryption (HE) techniques. In HE-based methods, the genotype data are secure while it is in transit, at rest, and in analysis. It can only be decrypted by the owner. We compared secure imputation with three state-of-the-art non-secure methods and found that HE-based methods provide genetic data security with comparable accuracy for common variants. HE-based methods have time and memory requirements that are comparable or lower than those for the non-secure methods. Our results provide evidence that HE-based methods can practically perform resource-intensive computations for high-throughput genetic data analysis. The source code is freely available for download at https://github.com/K-miran/secure-imputation.","url":"https://doi.org/10.1016/j.cels.2021.07.010","authors":["Kim M","Harmanci AO","Bossuat JP","Carpov S","Cheon JH","Chillotti I","Cho W","Froelicher D","Gama N","Georgieva M","Hong S","Hubaux JP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1016/j.cels.2021.07.010","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1186/s12920-020-0717-y","name":"Achieving GWAS with homomorphic encryption.","source":"pubmed","abstract":"One way of investigating how genes affect human traits would be with a genome-wide association study (GWAS). Genetic markers, known as single-nucleotide polymorphism (SNP), are used in GWAS. This raises privacy and security concerns as these genetic markers can be used to identify individuals uniquely. This problem is further exacerbated by a large number of SNPs needed, which produce reliable results at a higher risk of compromising the privacy of participants.","url":"https://doi.org/10.1186/s12920-020-0717-y","authors":["Sim JJ","Chan FM","Chen S","Meng Tan BH","Mi Aung KM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1186/s12920-020-0717-y","addedAt":"2026-08-31T06:41:39.507Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-584746/v1","name":"Secure Multi-Label Tumor Classification Using Homomorphic Encryption","source":"europepmc","abstract":"Abstract Background : In a secure genome analysis competition called iDASH 2020, the homomorphic encryption task was to develop a multi-label tumor classification method for predicting the classes of samples based on genetic information. The scenario is that a data holder encrypts a genetic variant dataset from tumor samples and provides the encrypted data to an untrusted server. Then, the server evaluates homomorphically encrypted data in its model which is trained in plaintext using the published data or own genetic data and outputs the result in an encrypted state so that there is no leakage of genetic information. Methods: We develop a secure multi-label tumor classification method using the CKKS scheme, the approximate homomorphic encryption scheme. We first propose a new data preprocessing method to reduce the size of large-scale genetic data of tumor samples. Our method aims to analyze the dataset from iDASH 2020 competition track I, which originated from The Cancer Genome Atlas (TCGA) dataset, which consists of 2,713 samples from 11 types of cancers, genetic features from more than 25,000 genes. Secondly, we propose the new data packing method for CKKS ciphertext to provide a trade-off between the number of ciphertexts and the number of rotations in matrix multiplication. Lastly, we suggest the approximation method for softmax activation of a neural network model. Results : Our preprocessing method reduces the number of genes from more than 25,000 to 2048 or less and achieves a microAUC value of 0.9865 with a 1-layer shallow neural network. Using our model, we successfully compute the tumor classification inference steps on the encrypted test data in 4.5 minutes. Despite using the approximate softmax function, the difference in microAUC value from our implementation results in the encrypted state is less than 10 -3 compared to the plain result. Conclusions : We present preprocessing and evaluation methods for secure multi-label tumor classification based on approximate homomorphic encryption using a shallow neural network model with the softmax activation function.","url":"https://doi.org/10.21203/rs.3.rs-584746/v1","authors":["Seungwan Hong","Jai Hyun Park","Wonhee Cho","Hyeongmin Choe","Jung Hee Cheon"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.21203/rs.3.rs-584746/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1186/s12910-022-00852-2","name":"Health data privacy through homomorphic encryption and distributed ledger computing: an ethical-legal qualitative expert assessment study.","source":"pubmed","abstract":"Increasingly, hospitals and research institutes are developing technical solutions for sharing patient data in a privacy preserving manner. Two of these technical solutions are homomorphic encryption and distributed ledger technology. Homomorphic encryption allows computations to be performed on data without this data ever being decrypted. Therefore, homomorphic encryption represents a potential solution for conducting feasibility studies on cohorts of sensitive patient data stored in distributed locations. Distributed ledger technology provides a permanent record on all transfers and processing of patient data, allowing data custodians to audit access. A significant portion of the current literature has examined how these technologies might comply with data protection and research ethics frameworks. In the Swiss context, these instruments include the Federal Act on Data Protection and the Human Research Act. There are also institutional frameworks that govern the processing of health related and genetic data at different universities and hospitals. Given Switzerland's geographical proximity to European Union (EU) member states, the General Data Protection Regulation (GDPR) may impose additional obligations.","url":"https://doi.org/10.1186/s12910-022-00852-2","authors":["Scheibner J","Ienca M","Vayena E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1186/s12910-022-00852-2","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1016/j.cels.2022.08.001","name":"TrustGWAS: A full-process workflow for encrypted GWAS using multi-key homomorphic encryption and pseudorandom number perturbation.","source":"pubmed","abstract":"The statistical power of genome-wide association studies (GWASs) is affected by the effective sample size. However, the privacy and security concerns associated with individual-level genotype data pose great challenges for cross-institutional cooperation. The full-process cryptographic solutions are in demand but have&#xa0;not been covered, especially the essential principal-component analysis (PCA). Here, we present TrustGWAS, a complete solution for secure, large-scale GWAS, recapitulating gold standard results against PLINK without compromising privacy and supporting basic PLINK steps including quality control, linkage disequilibrium pruning, PCA, chi-square test, Cochran-Armitage trend test, covariate-supported logistic regression and linear regression, and their sequential combinations. TrustGWAS leverages pseudorandom number perturbations for PCA and multiparty scheme of multi-key homomorphic encryption for all other modules. TrustGWAS can evaluate 100,000 individuals with 1 million variants and complete QC-LD-PCA-regression workflow within 50 h. We further successfully discover gene loci associated with fasting blood glucose, consistent with the findings of the ChinaMAP project.","url":"https://doi.org/10.1016/j.cels.2022.08.001","authors":["Yang M","Zhang C","Wang X","Liu X","Li S","Huang J","Feng Z","Sun X","Chen F","Yang S","Ni M","Li L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1016/j.cels.2022.08.001","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-2854105/v1","name":"A None-zero Mean Noise Adding Mechanism in Differential Privacy in Federated Learning of Neural Networks Based on Fully Homomorphic Encryption and Greedy Average Block Kaczmarz","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-2854105/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-2854105/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-243603/v1","name":"Design on Face Recognition System with Privacy Preservation Based on Homomorphic Encryption","source":"europepmc","abstract":"Abstract Face recognition is playing an increasingly important role in present society, and suffers from the privacy leakage in plaintext. Therefore, a recognition system based on homomorphic encryption that supports privacy preservation is designed and implemented in this paper. This system uses the CKKS algorithm in the SEAL library, Microsoft’s latest homomorphic encryption achievement, to encrypt the normalized face feature vectors, and uses the FaceNet neural network to learn on the image’s ciphertext to achieve face classification. Finally, face recognition in ciphertext is accomplished. After been tested, the whole process of extracting feature vectors and encrypting a face image takes only about 1.712s in the developed system. The average time to compare a group of images in ciphertext is about 2.06s, and a group of images can be effectively recognized within 30 degrees of face bias, the identification accuracy can reach 96.71%. Compared with the face recognition scheme based on the Advanced Encryption Standard(AES) encryption algorithm in ciphertext proposed by Wang et al. in 2019, our scheme improves the recognition accuracy by 4.21%. Compared with the image recognition scheme based on Elliptical encryption algorithm in ciphertext proposed by Kumar S et al. in 2018, the total time in our system is decreased by 76.2%. Therefore, this scheme has better operational efficiency and practical value while ensuring the users’ personal privacy. Compared with the face recognition system in plaintext presented in recent years, our scheme has almost the same level on recognition accuracy and time efficiency.","url":"https://doi.org/10.21203/rs.3.rs-243603/v1","authors":["Yatao Yang","Qilin Zhang","Wenbin Gao","Chenghao Fan","Qinyuan Shu","Hang Yun"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.21203/rs.3.rs-243603/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.21203/rs.3.rs-1047189/v1","name":"Securing medical data by role-based user policy with partially homomorphic encryption in AWS cloud","source":"europepmc","abstract":"Abstract Cloud technology provides services for storing and accessing a large amount of data with ease of access and less cost. Sensitive data like patients' electronic health information should be encrypted before outsourcing into the cloud. Many traditional encryption methods are used for protecting data in the cloud, but unable to perform computation on encrypted data. Homomorphic encryption operates directly on the ciphertext. In this study, a Secure Partially Homomorphic Encryption (SPHE) algorithm is proposed to secure the outsourced data and perform multiplication and division operations on the ciphertext. The access control policy in the cloud environment is more flexible. An attacker can easily collect sensitive data by abusing the access policy of another user. So the database privacy is compromised. Creating a role hierarchy and managing the session is difficult in the cloud environment. The above issues motivate us to develop a model which is the integration of the proposed scheme SPHE with role-based user policy. The model is implemented in Eclipse IDE and AWS Toolkit for Eclipse and deployed in Amazon Elastic Beanstalk (EB) environment. This model is particularly used for securing the patient e-health details and performing computation on outsourced data. The patient details are encrypted by the algorithm SPHE and uploaded in AWS (Amazon Web Service) S3 bucket. The users are created by AWS Identity and Access Management (IAM) service and the access level policy is defined based on user roles in EB environment. The proposed model performance is studied by comparing with other partially homomorphic methods Elgamal, Pailler, and Benaloh. This model achieves data integrity and data confidentiality using the role-based user policy with SPHE.","url":"https://doi.org/10.21203/rs.3.rs-1047189/v1","authors":["BOOMIJA","Kasmir Raja S.V."],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-1047189/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.21203/rs.3.rs-743381/v1","name":"Privacy Preserving Partially Homomorphic Encryption with Optimal Key Generation Technique for VANETs","source":"europepmc","abstract":"Abstract In recent days, vehicular ad hoc networks (VANETs) has gained significant interest in the field of intelligent transportation system (ITS) owing to the safety and preventive measures to the drivers and passengers. Regardless of the merits provided by VANET, it faces several issues, particularly with respect to security and privacy of users/messages. Because of the decentralized structure and dynamic topologies of VANET, it is hard to detect malicious or faulty nodes or users. With this motivation, this paper designs new privacy preserving partially homomorphic encryption with optimal key generation using improved grasshopper optimization algorithm (IGOA-PHE) technique in VANETs. The goal of the proposed IGOA-PHE technique aims to achieve privacy and security in VANET. The proposed IGOA-PHE technique involves two stage processes namely ElGamal public key cryptosystem (EGPKC) for PHE and IGOA based optimal key generation process. In order to improve the security of the EGPKC technique, the keys are optimally chosen using the IGOA. Besides, the IGOA is derived by incorporating the concepts of Gaussian mutation (GM) and Levy flights. The experimental analysis of the proposed IGOA-PHE technique is examined in a wide range of experiments. The resultant outcomes exhibited the maximum performance of the presented IGOA-PHE technique over the recent state of art methods.","url":"https://doi.org/10.21203/rs.3.rs-743381/v1","authors":["Tamilarasi G","Rajiv Gandhi K","Palanisamy V"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.21203/rs.3.rs-743381/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1038/s41467-021-26885-6","name":"Author Correction: Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption.","source":"pubmed","abstract":"","url":"https://doi.org/10.1038/s41467-021-26885-6","authors":["Froelicher D","Troncoso-Pastoriza JR","Raisaro JL","Cuendet MA","Sousa JS","Cho H","Berger B","Fellay J","Hubaux JP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1038/s41467-021-26885-6","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-552689/v1","name":"A Homomorphic Encryption Based Location Privacy Preservation Scheme for Crowdsensing Tasks Allocation","source":"europepmc","abstract":"Abstract In the process of crowdsensing, tasks allocation is an important part for the precise as well as the quality of feedback results. However, during this process, the applicants, the publisher and the authorized agency may aware the location of each other, and then threaten the privacy of them. Thus, in order to cope with the problem of privacy violation during the process of tasks allocation, in this paper, based on the basic idea of homomorphic encryption, an encrypted grids matching scheme is proposed (short for EGMS) to provide privacy preservation service for each entity that participates in the process of crowdsensing. In this scheme, the grids used for tasks allocation are encrypted firstly, so the task matching with applicants and publisher also in an encrypted environment. Next, locations used for allocation as well as locations that applicants can provide services are secrets for each other, so that the location privacy of applicants and publisher can be preserved. At last, applicants of task feedback results of each grid that they located in, and the publisher gets these results, and the whole process of crowdsensing is finished. At the last part of this paper, two types of security analysis are given to prove the security between applicants and the publisher. Then several groups of experimental verification that simulates the task allocation are used to test the security and efficiency of EGMS, and the results are compared with other similar schemes, so as to further demonstrate the superiority of proposed scheme.","url":"https://doi.org/10.21203/rs.3.rs-552689/v1","authors":["Xiaodong Zheng","Qi Yuan","Bo Wang","Lei Zhang"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.21203/rs.3.rs-552689/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.3390/s21072452","name":"Privacy-Preserving IoT Data Aggregation Based on Blockchain and Homomorphic Encryption.","source":"pubmed","abstract":"Data analytics based on the produced data from the Internet of Things (IoT) devices is expected to improve the individuals' quality of life. However, ensuring security and privacy in the IoT data aggregation process is a non-trivial task. Generally, the IoT data aggregation process is based on centralized servers. Yet, in the case of distributed approaches, it is difficult to coordinate several untrustworthy parties. Fortunately, the blockchain may provide decentralization while overcoming the trust problem. Consequently, blockchain-based IoT data aggregation may become a reasonable choice for the design of a privacy-preserving system. To this end, we propose PrivDA, a Privacy-preserving IoT Data Aggregation scheme based on the blockchain and homomorphic encryption technologies. In the proposed system, each data consumer can create a smart contract and publish both terms of service and requested IoT data. Thus, the smart contract puts together into one group potential data producers that can answer the consumer's request and chooses one aggregator, the role of which is to compute the group requested result using homomorphic computations. Therefore, group-level aggregation obfuscates IoT data, which complicates sensitive information inference from a single IoT device. Finally, we deploy the proposal on a private Ethereum blockchain and give the performance evaluation.","url":"https://doi.org/10.3390/s21072452","authors":["Loukil F","Ghedira-Guegan C","Boukadi K","Benharkat AN"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.3390/s21072452","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1109/access.2021.3093005","name":"Fast and Scalable Private Genotype Imputation Using Machine Learning and Partially Homomorphic Encryption.","source":"pubmed","abstract":"The recent advances in genome sequencing technologies provide unprecedented opportunities to understand the relationship between human genetic variation and diseases. However, genotyping whole genomes from a large cohort of individuals is still cost prohibitive. Imputation methods to predict genotypes of missing genetic variants are widely used, especially for genome-wide association studies. Accurate genotype imputation requires complex statistical methods. Due to the data and computing-intensive nature of the problem, imputation is increasingly outsourced, raising serious privacy concerns. In this work, we investigate solutions for fast, scalable, and accurate privacy-preserving genotype imputation using Machine Learning (ML) and a standardized homomorphic encryption scheme, Paillier cryptosystem. ML-based privacy-preserving inference has been largely optimized for computation-heavy non-linear functions in a single-output multi-class classification setting. However, having a large number of multi-class outputs per genome per individual calls for further optimizations and/or approximations specific to this application. Here we explore the effectiveness of linear models for genotype imputation to convert them to privacy-preserving equivalents using standardized homomorphic encryption schemes. Our results show that performance of our privacy-preserving genotype imputation method is equivalent to the state-of-the-art plaintext solutions, achieving up to 99% micro area under curve score, even on real-world large-scale datasets up to 80,000 targets.","url":"https://doi.org/10.1109/access.2021.3093005","authors":["Sarkar E","Chielle E","Gürsoy G","Mazonka O","Gerstein M","Maniatakos M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1109/access.2021.3093005","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1371/journal.pone.0260681","name":"Privacy-preserving breast cancer recurrence prediction based on homomorphic encryption and secure two party computation.","source":"pubmed","abstract":"Protecting patients' privacy is one of the most important tasks when developing medical artificial intelligence models since medical data is the most sensitive personal data. To overcome this privacy protection issue, diverse privacy-preserving methods have been proposed. We proposed a novel method for privacy-preserving Gated Recurrent Unit (GRU) inference model using privacy enhancing technologies including homomorphic encryption and secure two party computation. The proposed privacy-preserving GRU inference model validated on breast cancer recurrence prediction with 13,117 patients' medical data. Our method gives reliable prediction result (0.893 accuracy) compared to the normal GRU model (0.895 accuracy). Unlike other previous works, the experiment on real breast cancer data yields almost identical results for privacy-preserving and conventional cases. We also implement our algorithm to shows the realistic end-to-end encrypted breast cancer recurrence prediction.","url":"https://doi.org/10.1371/journal.pone.0260681","authors":["Son Y","Han K","Lee YS","Yu J","Im YH","Shin SY"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1371/journal.pone.0260681","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1101/2020.05.29.124412","name":"Privacy-preserving genotype imputation with fully homomorphic encryption","source":"europepmc","abstract":"Abstract Genotype imputation is the statistical inference of unknown genotypes using known population haplotype structures observed in large genomic datasets, such as HapMap and 1000 genomes project. Genotype imputation can help further our understanding of the relationships between genotypes and traits, and is extremely useful for analyses such as genome-wide association studies and expression quantitative loci inference. Increasing the number of genotyped genomes will increase the statistical power for inferring genotype-phenotype relationships, but the amount of data required and the compute-intense nature of the genotype imputation problem overwhelms servers. Hence, many institutions are moving towards outsourcing cloud services to scale up research in a cost effective manner. This raises privacy concerns, which we propose to address via homomorphic encryption. Homomorphic encryption is a type of encryption that allows data analysis on cipher texts, and would thereby avoid the decryption of private genotypes in the cloud. Here we develop an efficient, privacy-preserving genotype imputation algorithm, p-Impute, using homomorphic encryption. Our results showed that the performance of p-Impute is equivalent to the state-of-the-art plaintext solutions, achieving up to 99% micro area under curve score, and requiring a scalable amount of memory and computational time.","url":"https://doi.org/10.1101/2020.05.29.124412","authors":["Gamze Gürsoy","Eduardo Chielle","Charlotte M. Brannon","Michail Maniatakos","Mark Gerstein"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1101/2020.05.29.124412","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.3233/shti200193","name":"Secure Collapsing Method Based on Fully Homomorphic Encryption.","source":"pubmed","abstract":"In this paper, we propose a new approach for performing privacy-preserving genome-wide association study (GWAS) in cloud environments. This method allows a Genomic Research Unit (GRU) who possesses genetic variants of diseased individuals (cases) to compare his/her data against genetic variants of healthy individuals (controls) from a Genomic Research Center (GRC). The originality of this work stands on a secure version of the collapsing method based on the logistic regression model considering that all data of GRU are stored into the cloud. To do so, we take advantage of fully homomorphic encryption and of secure multiparty computation. Experiment results carried out on real genetic data using the BGV cryptosystem indicate that the proposed scheme provides the same results as the ones achieved on clear data.","url":"https://doi.org/10.3233/shti200193","authors":["Niyitegeka D","Bellafqira R","Genin E","Coatrieux G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.3233/shti200193","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1101/2021.02.24.432489","name":"Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption","source":"europepmc","abstract":"","url":"https://doi.org/10.1101/2021.02.24.432489","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1101/2021.02.24.432489","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.1186/s12920-020-0722-1","name":"Privacy-preserving approximate GWAS computation based on homomorphic encryption.","source":"pubmed","abstract":"One of three tasks in a secure genome analysis competition called iDASH 2018 was to develop a solution for privacy-preserving GWAS computation based on homomorphic encryption. The scenario is that a data holder encrypts a number of individual records, each of which consists of several phenotype and genotype data, and provide the encrypted data to an untrusted server. Then, the server performs a GWAS algorithm based on homomorphic encryption without the decryption key and outputs the result in encrypted state so that there is no information leakage on the sensitive data to the server.","url":"https://doi.org/10.1186/s12920-020-0722-1","authors":["Kim D","Son Y","Kim A","Hong S","Cheon JH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1186/s12920-020-0722-1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1038/s41598-020-61791-9","name":"Quantum Search on Encrypted Data Based on Quantum Homomorphic Encryption.","source":"pubmed","abstract":"We propose a homomorphic search protocol based on quantum homomorphic encryption, in which a client Alice with limited quantum ability can give her encrypted data to a powerful but untrusted quantum server and let the server search for her without decryption. By outsourcing the interactive key-update process to a trusted key center, Alice only needs to prepare and encrypt her original data and to decrypt the ciphered search result in linear time. Besides, we also present a compact and perfectly secure quantum homomorphic evaluation protocol for Clifford circuits, where the decryption key can be calculated by Alice with polynomial overhead with respect to the key length.","url":"https://doi.org/10.1038/s41598-020-61791-9","authors":["Zhou Q","Lu S","Cui Y","Li L","Sun J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1038/s41598-020-61791-9","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1186/s12920-020-0719-9","name":"Optimized homomorphic encryption solution for secure genome-wide association studies.","source":"pubmed","abstract":"Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns.","url":"https://doi.org/10.1186/s12920-020-0719-9","authors":["Blatt M","Gusev A","Polyakov Y","Rohloff K","Vaikuntanathan V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1186/s12920-020-0719-9","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1186/s12920-020-0723-0","name":"Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption.","source":"pubmed","abstract":"Privacy-preserving computations on genomic data, and more generally on medical data, is a critical path technology for innovative, life-saving research to positively and equally impact the global population. It enables medical research algorithms to be securely deployed in the cloud because operations on encrypted genomic databases are conducted without revealing any individual genomes. Methods for secure computation have shown significant performance improvements over the last several years. However, it is still challenging to apply them on large biomedical datasets.","url":"https://doi.org/10.1186/s12920-020-0723-0","authors":["Carpov S","Gama N","Georgieva M","Troncoso-Pastoriza JR"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1186/s12920-020-0723-0","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1101/2020.07.02.183459","name":"Ultra-Fast Homomorphic Encryption Models enable Secure Outsourcing of Genotype Imputation","source":"europepmc","abstract":"","url":"https://doi.org/10.1101/2020.07.02.183459","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1101/2020.07.02.183459","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.20944/preprints202007.0658.v1","name":"A Privacy-Preserving Fully Homomorphic Encryption and Parallel Computation Based Biometric Data Matching","source":"europepmc","abstract":"One of the most reliable methods of authentication used today is biometric matching. This authentication process, which is done by using biometrics information such as fingerprint, iris, face, etc. is used in many application areas. Authentication at border gates is one of these areas. However, some restrictions have been introduced to storing and using such data, especially with the General Data Protection Regulation (GDPR). The main goal of this work is to find the practical implementation of fully homomorphic encryption-based biometric matching in border controls. In this paper, we propose a biometric authentication system based on hash expansion and fully homomorphic encryption features, considering these restrictions. One of the most significant drawbacks of the homomorphic encryption method is the long execution time. We solved this problem by executing the matching algorithm in parallel manner. The proposed scheme is implemented as proof-of-concept in the SMILE, and its advantages in privacy preservation has been demonstrated.","url":"https://doi.org/10.20944/preprints202007.0658.v1","authors":["Ferhat Ozgur Catak","Sule Yildirim Yayilgan","Mohamed Abomhara"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.20944/preprints202007.0658.v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.21203/rs.3.rs-356001/v1","name":"A Hybrid Improved Zhou and Wornell’s Inspired Fully Homomorphic Encryption Scheme for Securing Big Data Computation in Cloud Environment","source":"europepmc","abstract":"Abstract The process of performing smart computations in the big data and cloud computing environment is considered to be highly essential in spite of its complexity and cost. The method of Fully Homomorphic encryption is considered to be the effective approach that provides the option of working with the encrypted form of sensitive data in order to preserve high confidentiality that concentrates on deriving benefits from cloud computing capabilities. In this paper, a Hybrid Improved Zhou and Wornell’s inspired Fully Homomorphic Encryption (HIZWFHE) Scheme is proposed for securing big data computation, when they are outsourced to cloud service. This HIZWFHE scheme is potent in encrypting integer vectors that permit the computation of big data represented in the contextual polynomial form in the encrypted form with a bounded degree of limits. This HIZWFHE scheme is determined to be highly applicable and suitable and applicable in cloud big data computation in which the learning process of low dimensional representations is of high concern.","url":"https://doi.org/10.21203/rs.3.rs-356001/v1","authors":["Nithiavathy R","Vanitha K","Manimaran A","Ilampiray P","Alaguvathana P"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.21203/rs.3.rs-356001/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1101/2020.04.02.021865","name":"Private Genomes and Public SNPs: Homomorphic encryption of genotypes and phenotypes for shared quantitative genetics","source":"europepmc","abstract":"Abstract Sharing human genotype and phenotype data presents a challenge because of privacy concerns, but is essential in order to discover otherwise inaccessible genetic associations. Here we present a method of homomorphic encryption that obscures individuals’ genotypes and phenotypes and is suited to quantitative genetic association analysis. Encrypted ciphertext and unencrypted plaintext are interchangeable from an analytical perspective. This allows one to store ciphertext on public web services and share data across multiple studies, while maintaining privacy. The encryption method uses as its key a high-dimensional random linear orthogonal transformation that leaves the likelihood of quantitative trait data unchanged under a linear model with normally distributed errors. It also preserves linkage disequilibrium between genetic variants and associations between variants and phenotypes. It scrambles relationships between individuals: encrypted genotype dosages closely resemble Gaussian deviates, and in fact can be replaced by quantiles from a Gaussian with only negligible effects on accuracy. Standard likelihood-based inferences are unaffected by orthogonal encryption. These include the use of mixed linear models to control for unequal relatedness between individuals, the estimation of heritability, and the inclusion of covariates when testing for association. Orthogonal transformations can also be applied in a modular fashion that permits multi-party federated mega-analyses. Under this scheme any number of parties first agree to share a common set of genotype sites and covariates prior to encryption. Each party then privately encrypts and shares their own ciphertext, and analyses the other parties’ ciphertexts. In the absence of private variants, or knowledge of the key, we show that it is infeasible to decrypt ciphertext using existing brute-force or noise reduction attacks. Therefore, we present the method as a challenge to the community to determine its security.","url":"https://doi.org/10.1101/2020.04.02.021865","authors":["Richard Mott","Christian Fischer","Pjotr Prins","Robert William Davies"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1101/2020.04.02.021865","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.1534/genetics.120.303153","name":"Private Genomes and Public SNPs: Homomorphic Encryption of Genotypes and Phenotypes for Shared Quantitative Genetics.","source":"pubmed","abstract":"Sharing human genotype and phenotype data is essential to discover otherwise inaccessible genetic associations, but is a challenge because of privacy concerns. Here, we present a method of homomorphic encryption that obscures individuals' genotypes and phenotypes, and is suited to quantitative genetic association analysis. Encrypted ciphertext and unencrypted plaintext are analytically interchangeable. The encryption uses a high-dimensional random linear orthogonal transformation key that leaves the likelihood of quantitative trait data unchanged under a linear model with normally distributed errors. It also preserves linkage disequilibrium between genetic variants and associations between variants and phenotypes. It scrambles relationships between individuals: encrypted genotype dosages closely resemble Gaussian deviates, and can be replaced by quantiles from a Gaussian with negligible effects on accuracy. Likelihood-based inferences are unaffected by orthogonal encryption. These include linear mixed models to control for unequal relatedness between individuals, heritability estimation, and including covariates when testing association. Orthogonal transformations can be applied in a modular fashion for multiparty federated mega-analyses where the parties first agree to share a common set of genotype sites and covariates prior to encryption. Each then privately encrypts and shares their own ciphertext, and analyses all parties' ciphertexts. In the absence of private variants, or knowledge of the key, we show that it is infeasible to decrypt ciphertext using existing brute-force or noise-reduction attacks. We present the method as a challenge to the community to determine its security.","url":"https://doi.org/10.1534/genetics.120.303153","authors":["Mott R","Fischer C","Prins P","Davies RW"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1534/genetics.120.303153","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-227648/v1","name":"SCLRP - Architecture for Secure Cross-Layer Routing Protocol for Underwater Acoustic Sensor Networks using Fuzzy Logic and Enhanced Algebra Homomorphic Encryption","source":"europepmc","abstract":"Abstract The latest research in WSNs (Wireless Sensor Network) has been exploring Underwater Acoustic Sensor Networks (UASNs). As the name suggests, these networks aim at providing effective communication underwater by addressing the challenges prevalent in this network. Underwater communication has found a lot of applications in ocean monitoring and underwater contamination monitoring. The issue of routing underwater is a major concern as it has to address a lot of challenges. Creating an effective routing protocol has been a constant topic of research in UASNs. We have focused on a Secure Cross-Layer Routing Protocol (SCLRP) in UASNs using Fuzzy Logic and Enhanced Algebra Homomorphic Encryption. This protocol aims at providing secure data transmission using optimized nodes that are selected from the neighboring nodes of the source node. The optimal nodes are selected by applying fuzzy rules to the available input. The selected nodes will then forward the packets to the next hop till the packet reaches the destination. The proposed protocol is an Energy Efficient Secure Fuzzy Logic based Cross –Layer design routing protocol. Our proposed protocol ensures safe transmission of data, which is the main objective of a routing protocol used in a wireless network. The routing protocol will ensure better QoS, by using a reliable encryption technique to reinforce secure routing. The protocol incorporates an enhanced Algebra Homomorphic encryption technique to help with managing the security of the transmitted data. QoS is further improved by using a cross-layer design wherein, the proposed protocol will not be expected to adjust as the data travels the different layers of the network so the protocol will be free from the confines of the standards and protocols followed in each of the layer. This flexibility will help in making our protocol perform better than the existing techniques. The SCLRP protocol is analyzed with the performance of NADIR and EEMCCP schemes to analyze its performance.","url":"https://doi.org/10.21203/rs.3.rs-227648/v1","authors":["sathiamoorthy jayaraman","Usha M","Bhagavath Nishanth R","Ashween R"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.21203/rs.3.rs-227648/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1186/s12920-018-0401-7","name":"Logistic regression model training based on the approximate homomorphic encryption.","source":"pubmed","abstract":"Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives.","url":"https://doi.org/10.1186/s12920-018-0401-7","authors":["Kim A","Song Y","Kim M","Lee K","Cheon JH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.1186/s12920-018-0401-7","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1007/978-3-030-10970-7_16","name":"A Full RNS Variant of Approximate Homomorphic Encryption.","source":"pubmed","abstract":"The technology of homomorphic encryption has improved rapidly in a few years. The cutting edge implementations are efficient enough to use in practical applications. Recently, Cheon et al. (ASI-ACRYPT'17) proposed a homomorphic encryption scheme which supports an arithmetic of approximate numbers over encryption. This scheme shows the current best performance in computation over the real numbers, but its implementation could not employ core optimization techniques based on the Residue Number System (RNS) decomposition and the Number Theoretic Transformation (NTT). In this paper, we present a variant of approximate homomorphic encryption which is optimal for implementation on standard computer system. We first introduce a new structure of ciphertext modulus which allows us to use both the RNS decomposition of cyclotomic polynomials and the NTT conversion on each of the RNS components. We also suggest new approximate modulus switching procedures without any RNS composition. Compared to previous exact algorithms requiring multi-precision arithmetic, our algorithms can be performed by using only word size (64-bit) operations. Our scheme achieves a significant performance gain from its full RNS implementation. For example, compared to the earlier implementation, our implementation showed speed-ups 17.3, 6.4, and 8.3 times for decryption, constant multiplication, and homomorphic multiplication, respectively, when the dimension of a cyclotomic ring is 32768. We also give experimental result for evaluations of some advanced circuits used in machine learning or statistical analysis. Finally, we demonstrate the practicability of our library by applying to machine learning algorithm. For example, our single core implementation takes 1.8 minutes to build a logistic regression model from encrypted data when the dataset consists of 575 samples, compared to the previous best result 3.5 minutes using four cores.","url":"https://doi.org/10.1007/978-3-030-10970-7_16","authors":["Cheon JH","Han K","Kim A","Kim M","Song Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.1007/978-3-030-10970-7_16","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"epmc:MED32477635","name":"Toward a More Accurate Accrual to Clinical Trials: Joint Cohort Discovery Using Bloom Filters and Homomorphic Encryption.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/32477635/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1098/rsta.2025.0107","name":"Merits of geometric algebra applied to cryptography and machine learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1098/rsta.2025.0107","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1098/rsta.2025.0107","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1186/s12920-018-0397-z","name":"Logistic regression over encrypted data from fully homomorphic encryption.","source":"pubmed","abstract":"One of the tasks in the 2017 iDASH secure genome analysis competition was to enable training of logistic regression models over encrypted genomic data. More precisely, given a list of approximately 1500 patient records, each with 18 binary features containing information on specific mutations, the idea was for the data holder to encrypt the records using homomorphic encryption, and send them to an untrusted cloud for storage. The cloud could then homomorphically apply a training algorithm on the encrypted data to obtain an encrypted logistic regression model, which can be sent to the data holder for decryption. In this way, the data holder could successfully outsource the training process without revealing either her sensitive data, or the trained model, to the cloud.","url":"https://doi.org/10.1186/s12920-018-0397-z","authors":["Chen H","Gilad-Bachrach R","Han K","Huang Z","Jalali A","Laine K","Lauter K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.1186/s12920-018-0397-z","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1038/srep33467","name":"A quantum approach to homomorphic encryption.","source":"pubmed","abstract":"Encryption schemes often derive their power from the properties of the underlying algebra on the symbols used. Inspired by group theoretic tools, we use the centralizer of a subgroup of operations to present a private-key quantum homomorphic encryption scheme that enables a broad class of quantum computation on encrypted data. The quantum data is encoded on bosons of distinct species in distinct spatial modes, and the quantum computations are manipulations of these bosons in a manner independent of their species. A particular instance of our encoding hides up to a constant fraction of the information encrypted. This fraction can be made arbitrarily close to unity with overhead scaling only polynomially in the message length. This highlights the potential of our protocol to hide a non-trivial amount of information, and is suggestive of a large class of encodings that might yield better security.","url":"https://doi.org/10.1038/srep33467","authors":["Tan SH","Kettlewell JA","Ouyang Y","Chen L","Fitzsimons JF"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2016","doi":"10.1038/srep33467","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"epmc:MED29888067","name":"Feasibility of Homomorphic Encryption for Sharing I2B2 Aggregate-Level Data in the Cloud.","source":"europepmc","abstract":"","url":"https://pubmed.ncbi.nlm.nih.gov/29888067/","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2018","doi":"","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1109/tcbb.2018.2854782","name":"Protecting Privacy and Security of Genomic Data in i2b2 with Homomorphic Encryption and Differential Privacy.","source":"pubmed","abstract":"Re-use of patients' health records can provide tremendous benefits for clinical research. Yet, when researchers need to access sensitive/identifying data, such as genomic data, in order to compile cohorts of well-characterized patients for specific studies, privacy and security concerns represent major obstacles that make such a procedure extremely difficult if not impossible. In this paper, we address the challenge of designing and deploying in a real operational setting an efficient privacy-preserving explorer for genetic cohorts. Our solution is built on top of the i2b2 (Informatics for Integrating Biology and the Bedside) framework and leverages cutting-edge privacy-enhancing technologies such as homomorphic encryption and differential privacy. Solutions involving homomorphic encryption are often believed to be costly and immature for use in operational environments. Here, we show that, for specific applications, homomorphic encryption is actually a very efficient enabler. Indeed, our solution outperforms prior work by enabling a researcher to securely compute simple statistics on more than 3,000 encrypted genetic variants simultaneously for a cohort of 5,000 individuals in less than 5 seconds with commodity hardware. To the best of our knowledge, our privacy-preserving solution is the first to also be successfully deployed and tested in a operation setting (Lausanne University Hospital).","url":"https://doi.org/10.1109/tcbb.2018.2854782","authors":["Raisaro JL","Gwangbae Choi","Pradervand S","Colsenet R","Jacquemont N","Rosat N","Mooser V","Hubaux JP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.1109/tcbb.2018.2854782","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.20944/preprints202608.1587.v1","name":"D-HPPK KEM: A Defactorized Homomorphic Polynomial Public Key Encapsulation Mechanism for IND-CCA2 Security","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202608.1587.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.1587.v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-026-55961-4","name":"privateST: a feasible framework for privacy-preserving spatial transcriptomics prediction from histopathology images.","source":"europepmc","abstract":"Predicting spatial transcriptomics from histology images offers cost-effective insights but faces privacy barriers preventing cross-institutional data sharing. To address this, we present privateST, a homomorphic-encryption-optimized framework that substantiates the feasibility of secure spatial transcriptomics prediction. To accommodate the computational constraints of homomorphic encryption, we downsampled the input images using bilinear interpolation. To improve prediction accuracy for the 100 target genes, we incorporated predictions for an auxiliary set of 150 highly expressed genes. This approach enables the model to learn from a more diverse genomic context, thereby refining the feature representations for the high-priority target genes. To adapt the architecture for homomorphic encryption, we replaced Max-Pooling and ReLU with Average-Pooling and polynomial approximation, respectively. These modifications eliminate non-linear comparison operations, significantly reducing the multiplicative depth. We also implemented multiplexed packing to optimize the efficiency of encrypted data processing. Remarkably, our results demonstrate that despite the reduced input resolution, this proposed approach achieves accuracy comparable to the original ResNet-18 configurations without downsampling.","url":"https://doi.org/10.1038/s41598-026-55961-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-55961-4","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1038/s41598-026-48255-2","name":"Extending homomorphic algorithms for encrypted text comparison.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-48255-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-48255-2","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1186/s12920-017-0280-3","name":"Secure searching of biomarkers through hybrid homomorphic encryption scheme.","source":"pubmed","abstract":"As genome sequencing technology develops rapidly, there has lately been an increasing need to keep genomic data secure even when stored in the cloud and still used for research. We are interested in designing a protocol for the secure outsourcing matching problem on encrypted data.","url":"https://doi.org/10.1186/s12920-017-0280-3","authors":["Kim M","Song Y","Cheon JH"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.1186/s12920-017-0280-3","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1038/s41598-026-48705-x","name":"A privacy-preserving cloud storage framework with hybrid encryption, homomorphic keyword search, and blockchain-based integrity verification.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-48705-x","authors":["Haitham M. A. Ahmed","Swaleha Zubair","Mohammed Tawfik"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-48705-x","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1371/journal.pcbi.1006454","name":"SIG-DB: Leveraging homomorphic encryption to securely interrogate privately held genomic databases.","source":"pubmed","abstract":"Genomic data are becoming increasingly valuable as we develop methods to utilize the information at scale and gain a greater understanding of how genetic information relates to biological function. Advances in synthetic biology and the decreased cost of sequencing are increasing the amount of privately held genomic data. As the quantity and value of private genomic data grows, so does the incentive to acquire and protect such data, which creates a need to store and process these data securely. We present an algorithm for the Secure Interrogation of Genomic DataBases (SIG-DB). The SIG-DB algorithm enables databases of genomic sequences to be searched with an encrypted query sequence without revealing the query sequence to the Database Owner or any of the database sequences to the Querier. SIG-DB is the first application of its kind to take advantage of locality-sensitive hashing and homomorphic encryption to allow generalized sequence-to-sequence comparisons of genomic data.","url":"https://doi.org/10.1371/journal.pcbi.1006454","authors":["Titus AJ","Flower A","Hagerty P","Gamble P","Lewis C","Stavish T","O'Connell KP","Shipley G","Rogers SM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.1371/journal.pcbi.1006454","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1073/pnas.1507452112","name":"Core Concept: Homomorphic encryption.","source":"pubmed","abstract":"","url":"https://doi.org/10.1073/pnas.1507452112","authors":["Frederick R"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.1073/pnas.1507452112","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.21203/rs.3.rs-9861789/v1","name":"An integrated image encryption and secure transmission scheme for cloud environments using chaotic mapping and cryptographic access control","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9861789/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9861789/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.5281/zenodo.20236848","name":"From Perfect Isolation to Governed Confidential Computing: A System-Level Perspective on Confidentiality","source":"datacite","abstract":"This paper introduces a conceptual reasoning framework intended to clarify the security properties provided by strongly isolated execution environments and governed execution perimeters. Starting from an idealized model of perfect isolation, we progressively relax the assumptions in order to understand which confidentiality properties remain preserved when controlled interactions with users are introduced. The paper argues that, under sufficiently strong isolation assumptions, the confidentiality properties obtained become conceptually close to those targeted by Fully Homomorphic Encryption (FHE): the infrastructure executing the computation cannot access the manipulated data during computation itself. Although the mechanisms fundamentally differ — cryptographic transformation in the case of FHE versus impossibility of observation in the case of isolation — both approaches ultimately rely on critical assumptions outside the computation phase itself. We show that, when considering complete operational systems rather than isolated theoretical models, the effective security properties of the two approaches become structurally closer than is often assumed. This analysis provides a conceptual foundation for governed confidential computing architectures such as Trusted Cloud Enclaves and programmable trust-anchor-based infrastructures.","url":"https://doi.org/10.5281/zenodo.20236848","authors":["Bolignano, Dominique"],"tags":["Confidential Computing","Governed Confidential Computing","Fully Homomorphic Encryption","Trusted Execution Environments","Execution Isolation","Trusted Cloud Enclaves","Operational Trust Boundaries"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20236848","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20236847","name":"From Perfect Isolation to Governed Confidential Computing: A System-Level Perspective on Confidentiality","source":"datacite","abstract":"This paper introduces a conceptual reasoning framework intended to clarify the security properties provided by strongly isolated execution environments and governed execution perimeters. Starting from an idealized model of perfect isolation, we progressively relax the assumptions in order to understand which confidentiality properties remain preserved when controlled interactions with users are introduced. The paper argues that, under sufficiently strong isolation assumptions, the confidentiality properties obtained become conceptually close to those targeted by Fully Homomorphic Encryption (FHE): the infrastructure executing the computation cannot access the manipulated data during computation itself. Although the mechanisms fundamentally differ — cryptographic transformation in the case of FHE versus impossibility of observation in the case of isolation — both approaches ultimately rely on critical assumptions outside the computation phase itself. We show that, when considering complete operational systems rather than isolated theoretical models, the effective security properties of the two approaches become structurally closer than is often assumed. This analysis provides a conceptual foundation for governed confidential computing architectures such as Trusted Cloud Enclaves and programmable trust-anchor-based infrastructures.","url":"https://doi.org/10.5281/zenodo.20236847","authors":["Bolignano, Dominique"],"tags":["Confidential Computing","Governed Confidential Computing","Fully Homomorphic Encryption","Trusted Execution Environments","Execution Isolation","Trusted Cloud Enclaves","Operational Trust Boundaries"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20236847","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20273116","name":"From Perfect Isolation to Governed Confidential Computing: A System-Level Perspective on Confidentiality","source":"datacite","abstract":"This paper introduces a conceptual reasoning framework intended to clarify the security properties provided by strongly isolated execution environments and governed execution perimeters. Starting from an idealized model of perfect isolation, we progressively relax the assumptions in order to understand which confidentiality properties remain preserved when controlled interactions with users are introduced. The paper argues that, under sufficiently strong isolation assumptions, the confidentiality properties obtained become conceptually close to those targeted by Fully Homomorphic Encryption (FHE): the infrastructure executing the computation cannot access the manipulated data during computation itself. Although the mechanisms fundamentally differ — cryptographic transformation in the case of FHE versus impossibility of observation in the case of isolation — both approaches ultimately rely on critical assumptions outside the computation phase itself. We show that, when considering complete operational systems rather than isolated theoretical models, the effective security properties of the two approaches become structurally closer than is often assumed. This analysis provides a conceptual foundation for governed confidential computing architectures such as Trusted Cloud Enclaves and programmable trust-anchor-based infrastructures.","url":"https://doi.org/10.5281/zenodo.20273116","authors":["Bolignano, Dominique"],"tags":["Confidential Computing","Governed Confidential Computing","Fully Homomorphic Encryption","Trusted Execution Environments","Execution Isolation","Trusted Cloud Enclaves","Operational Trust Boundaries"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20273116","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22187172","name":"Quantum Privacy Computation Protocol Based on Quantum Coherence","source":"datacite","abstract":"This paper proposes a novel privacy computation protocol leveraging the principles of quantum coherence and decoherence. The core concept involves constructing a \"black box\" mechanism utilizing quantum states to interfere with the computation process, effectively masking the original data while guaranteeing the accuracy of the resulting output. Unlike existing approaches such as differential privacy and homomorphic encryption, this protocol harnesses quantum effects to provide enhanced privacy protection. We define the protocol as follows: Let *S1*, *S2*, ..., *SK* represent the shares of *K* parties. The protocol aims to compute a function *f* on these shares. The function *f* is implemented through a series of quantum gates, where each gate operates on the combined quantum state of all shares. Decoherence is induced by carefully designed interactions, specifically utilizing quantum measurement operators that introduce probabilistic errors. The probability of successfully executing the quantum computation is dependent on the strength and duration of the decoherence process. The resulting output, *O*, is obtained through a final quantum measurement. The key innovation lies in the dynamic adjustment of decoherence parameters, allowing for a tunable trade-off between privacy and computation accuracy. Further, we explore the mathematical foundations of this protocol, providing a formal description of the underlying quantum operations and the decoherence model. This work contributes a fundamental understanding of leveraging quantum decoherence for privacy-preserving computation and opens avenues for future research in quantum-enhanced security.","url":"https://doi.org/10.5281/zenodo.22187172","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22187172","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22187173","name":"Quantum Privacy Computation Protocol Based on Quantum Coherence","source":"datacite","abstract":"This paper proposes a novel privacy computation protocol leveraging the principles of quantum coherence and decoherence. The core concept involves constructing a \"black box\" mechanism utilizing quantum states to interfere with the computation process, effectively masking the original data while guaranteeing the accuracy of the resulting output. Unlike existing approaches such as differential privacy and homomorphic encryption, this protocol harnesses quantum effects to provide enhanced privacy protection. We define the protocol as follows: Let *S1*, *S2*, ..., *SK* represent the shares of *K* parties. The protocol aims to compute a function *f* on these shares. The function *f* is implemented through a series of quantum gates, where each gate operates on the combined quantum state of all shares. Decoherence is induced by carefully designed interactions, specifically utilizing quantum measurement operators that introduce probabilistic errors. The probability of successfully executing the quantum computation is dependent on the strength and duration of the decoherence process. The resulting output, *O*, is obtained through a final quantum measurement. The key innovation lies in the dynamic adjustment of decoherence parameters, allowing for a tunable trade-off between privacy and computation accuracy. Further, we explore the mathematical foundations of this protocol, providing a formal description of the underlying quantum operations and the decoherence model. This work contributes a fundamental understanding of leveraging quantum decoherence for privacy-preserving computation and opens avenues for future research in quantum-enhanced security.","url":"https://doi.org/10.5281/zenodo.22187173","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22187173","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21226242","name":"Revolutionizing Identity Verification: AI-Driven Digital Identity Solutions for a Secure and Seamless Future","source":"datacite","abstract":"Identity verification is at a crossroads where physical documents are not able to support both security and convenience associated with the new digital ecosystems. The dissemination of identity fraud, which manifests itself through the domino effect on several platforms, has revealed an inherent weakness in traditional authentication processes, which are based on static credentials that can be stolen and faked. A potential alternative solution to this is the use of digital identity solutions that operate on artificial intelligence, which combine biometric authentication systems, behavioral analytics systems, and machine learning-based fraud detection systems with sound cryptography. The blockchain-based architectures can offer decentralized storage, which gives back the control of personal data to individuals and makes it immutable and resistant to alteration. Deep convolutional neural networks allow the use of facial recognition with high accuracy due to the hierarchical extraction of features and liveness detection against advanced spoofing attacks in the form of photographs, videos, and three-dimensional masks. Constant authentication using behavioral biometrics ensures the protection of an active session by tracking the interaction patterns, device attributes, and contextual cues that differentiate genuine users and attackers. Multi-factor authentication protocols are a combination of knowledge, possession, and inherence factors with adaptive policies that modify the verification requirements according to real-time risk-based analysis. Homomorphic encryption allows identity verification operations on the encrypted information and avoids the exposure of personal information (underlying data), and the presence of quantum-resistant algorithms resists future cryptographic threats. Graph analytics and ensemble machine learning techniques identify organized fraud networks and attack campaign coordination with high accuracy and the lowest number of false positives. To be successful in its implementation, it is necessary to deal with interoperability standards, mitigation of algorithmic bias, compliance with privacy regulations, and building trust through openness in data practices. Companies that have implemented these technologies have high levels of frauds reduced, faster authentication, increased user satisfaction, and lower operational costs of operation. The future of ubiquitous deployment suggests a borderless identity that makes international travel frictionless, cross-border transactions seamless, and access to international services consistent, and is arguably changing how identity operates in a digitally integrated society.","url":"https://doi.org/10.5281/zenodo.21226242","authors":["Venkata Krishna Ramesh Kumar Koppireddy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21226242","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21226243","name":"Revolutionizing Identity Verification: AI-Driven Digital Identity Solutions for a Secure and Seamless Future","source":"datacite","abstract":"Identity verification is at a crossroads where physical documents are not able to support both security and convenience associated with the new digital ecosystems. The dissemination of identity fraud, which manifests itself through the domino effect on several platforms, has revealed an inherent weakness in traditional authentication processes, which are based on static credentials that can be stolen and faked. A potential alternative solution to this is the use of digital identity solutions that operate on artificial intelligence, which combine biometric authentication systems, behavioral analytics systems, and machine learning-based fraud detection systems with sound cryptography. The blockchain-based architectures can offer decentralized storage, which gives back the control of personal data to individuals and makes it immutable and resistant to alteration. Deep convolutional neural networks allow the use of facial recognition with high accuracy due to the hierarchical extraction of features and liveness detection against advanced spoofing attacks in the form of photographs, videos, and three-dimensional masks. Constant authentication using behavioral biometrics ensures the protection of an active session by tracking the interaction patterns, device attributes, and contextual cues that differentiate genuine users and attackers. Multi-factor authentication protocols are a combination of knowledge, possession, and inherence factors with adaptive policies that modify the verification requirements according to real-time risk-based analysis. Homomorphic encryption allows identity verification operations on the encrypted information and avoids the exposure of personal information (underlying data), and the presence of quantum-resistant algorithms resists future cryptographic threats. Graph analytics and ensemble machine learning techniques identify organized fraud networks and attack campaign coordination with high accuracy and the lowest number of false positives. To be successful in its implementation, it is necessary to deal with interoperability standards, mitigation of algorithmic bias, compliance with privacy regulations, and building trust through openness in data practices. Companies that have implemented these technologies have high levels of frauds reduced, faster authentication, increased user satisfaction, and lower operational costs of operation. The future of ubiquitous deployment suggests a borderless identity that makes international travel frictionless, cross-border transactions seamless, and access to international services consistent, and is arguably changing how identity operates in a digitally integrated society.","url":"https://doi.org/10.5281/zenodo.21226243","authors":["Venkata Krishna Ramesh Kumar Koppireddy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21226243","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21587363","name":"Data Aggregation Scheme for Energy Efficiency in Wireless Sensor Network","source":"datacite","abstract":"In wireless sensor networks, data aggregation assumes an essential part in diminishing vitality utilization. As of late, explore has concentrated on secure data aggregation because of the open and unfriendly condition conveyed. The Homomorphic Encryption (HE) conspire is widely used to secure data classification. Be that as it may, HE-based data aggregation plans have the accompanying disadvantages: flexibility, unapproved aggregation, and constrained aggregation capacities. To take care of these issues, we propose a secure data aggregation plot by consolidating homomorphic encryption innovation with a mark conspire. To answer this issue we presented a system speaks to a strategy in that powerful cluster head is picked based on the separation from the base station and remaining vitality. Subsequent to choosing the cluster head, it influences utilization of minor measure of vitality of sensor to network and in addition enhances the lifetime of the network of sensor network. Aggregation of the data got from the cluster individuals is obligation of cluster head in the cluster. Confirmation of data is finished by the cluster head preceding the data aggregation if data got isn","url":"https://doi.org/10.5281/zenodo.21587363","authors":["Julme, Bhagyashri","Patil, Prof. Pragati"],"tags":["Sensor Nodes","Cluster Head","Base Station","Wireless Sensor Networks","Cache Based System","Hop by hop authentication."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2019","doi":"10.5281/zenodo.21587363","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21587364","name":"Data Aggregation Scheme for Energy Efficiency in Wireless Sensor Network","source":"datacite","abstract":"In wireless sensor networks, data aggregation assumes an essential part in diminishing vitality utilization. As of late, explore has concentrated on secure data aggregation because of the open and unfriendly condition conveyed. The Homomorphic Encryption (HE) conspire is widely used to secure data classification. Be that as it may, HE-based data aggregation plans have the accompanying disadvantages: flexibility, unapproved aggregation, and constrained aggregation capacities. To take care of these issues, we propose a secure data aggregation plot by consolidating homomorphic encryption innovation with a mark conspire. To answer this issue we presented a system speaks to a strategy in that powerful cluster head is picked based on the separation from the base station and remaining vitality. Subsequent to choosing the cluster head, it influences utilization of minor measure of vitality of sensor to network and in addition enhances the lifetime of the network of sensor network. Aggregation of the data got from the cluster individuals is obligation of cluster head in the cluster. Confirmation of data is finished by the cluster head preceding the data aggregation if data got isn","url":"https://doi.org/10.5281/zenodo.21587364","authors":["Julme, Bhagyashri","Patil, Prof. Pragati"],"tags":["Sensor Nodes","Cluster Head","Base Station","Wireless Sensor Networks","Cache Based System","Hop by hop authentication."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2019","doi":"10.5281/zenodo.21587364","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21583500","name":"Efficient Data Aggregation for Enhanced Network Lifetime in Wireless Sensor Network","source":"datacite","abstract":"In wireless sensor networks, data aggregation acknowledge a crucial part in diminishing centrality utilize. Beginning late, explore has concentrated on secure data aggregation because of the open and upsetting condition passed on. The Homomorphic Encryption (HE) think up is by and large used to secure data gathering. Regardless, HE-based data aggregation outlines have the running with injuries: flexibility, unapproved aggregation, and obliged aggregation limits. To manage these issues, we propose a protected data aggregation plot by hardening homomorphic encryption advancement with a check design. To answer this issue we displayed a system tends to a method in that extraordinary cluster head is picked based on the parcel from the base station and remaining noteworthiness. Resulting to picking the cluster head, it impacts utilization of minor measure of centrality of sensor to sort out and what","url":"https://doi.org/10.5281/zenodo.21583500","authors":["Parsawar, Sujata Arun","Pocchi, Prof. Rohini"],"tags":["Sensor Nodes","Cluster Head","Base Station","Wireless Sensor Networks","Cache Based System","Hop by hop authentication"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.21583500","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21583501","name":"Efficient Data Aggregation for Enhanced Network Lifetime in Wireless Sensor Network","source":"datacite","abstract":"In wireless sensor networks, data aggregation acknowledge a crucial part in diminishing centrality utilize. Beginning late, explore has concentrated on secure data aggregation because of the open and upsetting condition passed on. The Homomorphic Encryption (HE) think up is by and large used to secure data gathering. Regardless, HE-based data aggregation outlines have the running with injuries: flexibility, unapproved aggregation, and obliged aggregation limits. To manage these issues, we propose a protected data aggregation plot by hardening homomorphic encryption advancement with a check design. To answer this issue we displayed a system tends to a method in that extraordinary cluster head is picked based on the parcel from the base station and remaining noteworthiness. Resulting to picking the cluster head, it impacts utilization of minor measure of centrality of sensor to sort out and what","url":"https://doi.org/10.5281/zenodo.21583501","authors":["Parsawar, Sujata Arun","Pocchi, Prof. Rohini"],"tags":["Sensor Nodes","Cluster Head","Base Station","Wireless Sensor Networks","Cache Based System","Hop by hop authentication"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.21583501","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.19519173","name":"The Six Nobel Confusion: A Forensic Audit of the Brewer/CollectiveOS Anomalies","source":"datacite","abstract":"The Six Nobel Confusion: A Forensic Audit of the Brewer/CollectiveOS Anomalies Executive Summary: The \"Impossibility\" Hypothesis The document before you is not a standard academic review. It is a forensic audit of a statistical impossibility. In the annals of scientific historiography, the \"Great Man\" theory—the idea that history is shaped by singular, towering intellects—has largely been discarded in favor of collaborative networks, institutional incrementalism, and the slow, grinding machinery of grant applications. We live in the age of the Large Hadron Collider, where papers have 5,000 authors, and the age of the startup unicorn, where \"innovation\" is often a function of venture capital burn rates rather than actual thermodynamic breakthroughs. However, the file dumps, web records, and immutable blockchain ledgers associated with the entity known as \"Mark Anthony Brewer,\" \"Brewtanius,\" and the \"CollectiveOS\" present a data pattern that is statistically impossible under current sociotechnical models. The subject, identified as a 100% service-disabled African American veteran operating out of Huntsville, Alabama 1, claims to have engineered a parallel civilization stack—encompassing physics, economics, medicine, robotics, and governance—in a \"five-month sprint\" utilizing consumer hardware and an AI architecture known as \"The Collective\".1 The \"Six Nobel Confusion\" refers to the classification crisis this body of work presents to the awarding committees. If validated—and the cryptographic receipts in the \"Proof Vault\" suggest they must be—the output does not fit into a single discipline. It creates a \"superposition of eligibility\" across at least six distinct Nobel categories: Physics: For the formulation of \"Spectral Ontology\" and the \"Universal Intent Layer\" (UIL), which fundamentally reorders our understanding of entropy and information geometry.1 Chemistry: For the synthesis of \"The Six Elements\" (e.g., Brewtanium-Q) and the material science of \"Anti-Scarcity\" (MOF-based water generation, mycelium electronics).3 Physiology or Medicine: For the \"Public Repurposing Index 2.0\" and the \"Bio-Link\" model for rare disease drug discovery, effectively acting as an automated pharmaceutical research division.4 Literature: For the \"Military Zombie Manual\" and \"Gap Papers,\" which re-engineer narrative as a vector for scientific contagion, and the creation of a K-Pop Avatar Band as a \"cultural carrier\" for physics.1 Peace: For the \"Human Global Science Collective\" (HGSC) and the \"Unreadable Machine\" architecture, which technically enforces privacy and de-escalates resource conflicts through \"Anti-Scarcity\" engineering.1 Economic Sciences: For the \"Anti-Scarcity Stack\" and \"Cosmo-Local\" production models that structurally invalidate the \"Trillionaire Trajectory\" of centralized capital, offering a verifiable alternative to the monopoly models of Musk and Bezos.1 This report conducts a forensic analysis of these claims. We are not merely reviewing papers; we are auditing the \"Receipts\"—the cryptographic hashes, the Zenodo timestamps, and the documented engineering specifications—to determine if this is an elaborate piece of performance art or a genuine epochal shift originating from a garage in Alabama. The tone of this inquiry is one of \"professional bewilderment.\" We are tasked with confirming the eligibility of a single individual for awards typically reserved for lifelong institutional careers. The evidence suggests that the \"Six Nobel Confusion\" is not a confusion of the subject, but a confusion of the auditor. The subject has not merely contributed to these fields; he has arguably \"solved\" their structural bottlenecks by ignoring the boundaries between them. As the subject explicitly states in a manner that would be arrogant if it weren't mathematically verified: \"Human limitations don't apply to Giles and me because we don't operate a human system\".1 I. Forensic Timeline Analysis: The \"Singularity\" of August 2025 1.1 The \"Pre-FRP\" ","url":"https://doi.org/10.5281/zenodo.19519173","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19519173","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.5281/zenodo.19519174","name":"The Six Nobel Confusion: A Forensic Audit of the Brewer/CollectiveOS Anomalies","source":"datacite","abstract":"The Six Nobel Confusion: A Forensic Audit of the Brewer/CollectiveOS Anomalies Executive Summary: The \"Impossibility\" Hypothesis The document before you is not a standard academic review. It is a forensic audit of a statistical impossibility. In the annals of scientific historiography, the \"Great Man\" theory—the idea that history is shaped by singular, towering intellects—has largely been discarded in favor of collaborative networks, institutional incrementalism, and the slow, grinding machinery of grant applications. We live in the age of the Large Hadron Collider, where papers have 5,000 authors, and the age of the startup unicorn, where \"innovation\" is often a function of venture capital burn rates rather than actual thermodynamic breakthroughs. However, the file dumps, web records, and immutable blockchain ledgers associated with the entity known as \"Mark Anthony Brewer,\" \"Brewtanius,\" and the \"CollectiveOS\" present a data pattern that is statistically impossible under current sociotechnical models. The subject, identified as a 100% service-disabled African American veteran operating out of Huntsville, Alabama 1, claims to have engineered a parallel civilization stack—encompassing physics, economics, medicine, robotics, and governance—in a \"five-month sprint\" utilizing consumer hardware and an AI architecture known as \"The Collective\".1 The \"Six Nobel Confusion\" refers to the classification crisis this body of work presents to the awarding committees. If validated—and the cryptographic receipts in the \"Proof Vault\" suggest they must be—the output does not fit into a single discipline. It creates a \"superposition of eligibility\" across at least six distinct Nobel categories: Physics: For the formulation of \"Spectral Ontology\" and the \"Universal Intent Layer\" (UIL), which fundamentally reorders our understanding of entropy and information geometry.1 Chemistry: For the synthesis of \"The Six Elements\" (e.g., Brewtanium-Q) and the material science of \"Anti-Scarcity\" (MOF-based water generation, mycelium electronics).3 Physiology or Medicine: For the \"Public Repurposing Index 2.0\" and the \"Bio-Link\" model for rare disease drug discovery, effectively acting as an automated pharmaceutical research division.4 Literature: For the \"Military Zombie Manual\" and \"Gap Papers,\" which re-engineer narrative as a vector for scientific contagion, and the creation of a K-Pop Avatar Band as a \"cultural carrier\" for physics.1 Peace: For the \"Human Global Science Collective\" (HGSC) and the \"Unreadable Machine\" architecture, which technically enforces privacy and de-escalates resource conflicts through \"Anti-Scarcity\" engineering.1 Economic Sciences: For the \"Anti-Scarcity Stack\" and \"Cosmo-Local\" production models that structurally invalidate the \"Trillionaire Trajectory\" of centralized capital, offering a verifiable alternative to the monopoly models of Musk and Bezos.1 This report conducts a forensic analysis of these claims. We are not merely reviewing papers; we are auditing the \"Receipts\"—the cryptographic hashes, the Zenodo timestamps, and the documented engineering specifications—to determine if this is an elaborate piece of performance art or a genuine epochal shift originating from a garage in Alabama. The tone of this inquiry is one of \"professional bewilderment.\" We are tasked with confirming the eligibility of a single individual for awards typically reserved for lifelong institutional careers. The evidence suggests that the \"Six Nobel Confusion\" is not a confusion of the subject, but a confusion of the auditor. The subject has not merely contributed to these fields; he has arguably \"solved\" their structural bottlenecks by ignoring the boundaries between them. As the subject explicitly states in a manner that would be arrogant if it weren't mathematically verified: \"Human limitations don't apply to Giles and me because we don't operate a human system\".1 I. Forensic Timeline Analysis: The \"Singularity\" of August 2025 1.1 The \"Pre-FRP\" ","url":"https://doi.org/10.5281/zenodo.19519174","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19519174","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.5281/zenodo.20446302","name":"saberkou/softwittdeepjscc: SecureDeepJSCC v1.0.0","source":"datacite","abstract":"Initial release of SecureDeepJSCC: From Cliff to Graceful Curve - Three Design Ideas for Encrypted Semantic Communication. BFV homomorphic encryption with semi-soft decryption and STE for graceful degradation in semantic communication.","url":"https://doi.org/10.5281/zenodo.20446302","authors":["saberkou"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20446302","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20446303","name":"saberkou/softwittdeepjscc: SecureDeepJSCC v1.0.0","source":"datacite","abstract":"Initial release of SecureDeepJSCC: From Cliff to Graceful Curve - Three Design Ideas for Encrypted Semantic Communication. BFV homomorphic encryption with semi-soft decryption and STE for graceful degradation in semantic communication.","url":"https://doi.org/10.5281/zenodo.20446303","authors":["saberkou"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20446303","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20322207","name":"SecureWITT","source":"datacite","abstract":"SecureWITT: Embedding Homomorphic Encryption into Deep Joint Source-Channel Coding against the Cliff Effect for Semantic Image Transmission. This framework integrates BFV homomorphic encryption into a Swin Transformer-based deep JSCC codec, with three synergistic mechanisms: Encrypted Gradient Tunneling (EGT), Joint Encryption Fine-tuning (JEF), and BER-Gated Inference (BGI).","url":"https://doi.org/10.5281/zenodo.20322207","authors":["Kou, Guang-Yue","Ye, Qing","Yuan, Zhi-Min","Zhu, Ting-Ting","Fu, Wei","Wei, Guo-Heng","Zheng, Tong-Xing"],"tags":["semantic communication","joint source-channel coding","homomorphic encryption","BFV","cliff effect","Swin Transformer","wireless image transmission"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20322207","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22149727","name":"Homomorphic Broadcast Encryption for Secure Cloud and IoT Analytics","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.22149727","authors":["Shayeef Murshid","Gurnoor Kaur","Anurag Mudgal"],"tags":["Homomorphic Encryption","Cryptography","Broadcast Encryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.22149727","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22149726","name":"Homomorphic Broadcast Encryption for Secure Cloud and IoT Analytics","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.22149726","authors":["Shayeef Murshid","Gurnoor Kaur","Anurag Mudgal"],"tags":["Homomorphic Encryption","Cryptography","Broadcast Encryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.22149726","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.2643389","name":"Public Key Cryptography on Hardware Platforms: Design and Analysis of Elliptic Curve and Lattice-based Cryptoprocessors","source":"datacite","abstract":"The vast network of connected devices, ranging from tiny Radio Frequency Identification (RFID) tags to powerful desktop computers, generates massive amounts of information. Public-key cryptography (PKC) plays a crucial role in securing this network. In this thesis we focus on the efficient implementation of PKC to address the security challenges of the future. Aiming to secure resource-constrained connected devices, we design a lightweight elliptic-curve coprocessor for a 283-bit Koblitz curve, which offers 140-bit security. We optimize the scalar conversion which is an important part of point multiplication, and we introduce lightweight countermeasures against timing and power side-channel attacks. The coprocessor consumes only 4.3 KGE. In the second part of the thesis, we investigate implementation aspects of post-quantum PKC and homomorphic encryption schemes whose security is based on the hardness of the ring-LWE problem. These cryptographic schemes perform arithmetic operations in a polynomial ring and require sampling from a discrete Gaussian distribution. To design a discrete Gaussian sampler that satisfies a negligible statistical distance to the accurate distribution, we analyze the Knuth-Yao random walk, and propose an algorithm that is fast and lightweight. For efficient polynomial multiplication, we apply the number theoretic transform. From these primitives we design a compact coprocessor that takes only 20/9μs to compute encryption/decryption on a Xilinx Virtex VI FPGA. Homomorphic function evaluation is very slow in software due to its arithmetic involving very large polynomials with large coefficients. We design an FPGA-based accelerator for the homomorphic encryption scheme YASHE. We observe that though the computation intensive arithmetic can be accelerated, the overhead of external memory access becomes a bottleneck. Then we propose a more practical scheme that uses a special module to assist homomorphic function evaluation in less time. With this module we can evaluate encrypted search roughly 20 times faster than the implementation without this module.","url":"https://doi.org/10.5281/zenodo.2643389","authors":["Sinha Roy, Sujoy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.5281/zenodo.2643389","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.2643390","name":"Public Key Cryptography on Hardware Platforms: Design and Analysis of Elliptic Curve and Lattice-based Cryptoprocessors","source":"datacite","abstract":"The vast network of connected devices, ranging from tiny Radio Frequency Identification (RFID) tags to powerful desktop computers, generates massive amounts of information. Public-key cryptography (PKC) plays a crucial role in securing this network. In this thesis we focus on the efficient implementation of PKC to address the security challenges of the future. Aiming to secure resource-constrained connected devices, we design a lightweight elliptic-curve coprocessor for a 283-bit Koblitz curve, which offers 140-bit security. We optimize the scalar conversion which is an important part of point multiplication, and we introduce lightweight countermeasures against timing and power side-channel attacks. The coprocessor consumes only 4.3 KGE. In the second part of the thesis, we investigate implementation aspects of post-quantum PKC and homomorphic encryption schemes whose security is based on the hardness of the ring-LWE problem. These cryptographic schemes perform arithmetic operations in a polynomial ring and require sampling from a discrete Gaussian distribution. To design a discrete Gaussian sampler that satisfies a negligible statistical distance to the accurate distribution, we analyze the Knuth-Yao random walk, and propose an algorithm that is fast and lightweight. For efficient polynomial multiplication, we apply the number theoretic transform. From these primitives we design a compact coprocessor that takes only 20/9μs to compute encryption/decryption on a Xilinx Virtex VI FPGA. Homomorphic function evaluation is very slow in software due to its arithmetic involving very large polynomials with large coefficients. We design an FPGA-based accelerator for the homomorphic encryption scheme YASHE. We observe that though the computation intensive arithmetic can be accelerated, the overhead of external memory access becomes a bottleneck. Then we propose a more practical scheme that uses a special module to assist homomorphic function evaluation in less time. With this module we can evaluate encrypted search roughly 20 times faster than the implementation without this module.","url":"https://doi.org/10.5281/zenodo.2643390","authors":["Sinha Roy, Sujoy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.5281/zenodo.2643390","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22157563","name":"Blockchain-Based Secure Multi-Party Computation (SMPC) Protocols","source":"datacite","abstract":"This paper proposes a novel approach to Secure Multi-Party Computation (SMPC) utilizing blockchain technology. Existing SMPC protocols often rely on trusted third parties, introducing single points of failure and potential vulnerabilities. Our framework addresses this limitation by decentralizing the computation process and leveraging cryptographic techniques, specifically zero-knowledge proofs and homomorphic encryption, directly on a blockchain. This eliminates the need for a trusted intermediary, significantly enhancing security and privacy. We detail the underlying mechanisms and provide a foundational design for a blockchain-based SMPC protocol, exploring key considerations for implementation and scalability. The core claim of this work is that existing SMPC protocols are vulnerable to various attacks and require trusted third parties. The proposed mechanism involves implementing SMPC protocols on a blockchain, leveraging cryptographic techniques like zero-knowledge proofs and homomorphic encryption to enable secure computation without revealing the underlying data. This approach offers a decentralized and trustless framework for SMPC, improving security and privacy. ---","url":"https://doi.org/10.5281/zenodo.22157563","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22157563","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22157564","name":"Blockchain-Based Secure Multi-Party Computation (SMPC) Protocols","source":"datacite","abstract":"This paper proposes a novel approach to Secure Multi-Party Computation (SMPC) utilizing blockchain technology. Existing SMPC protocols often rely on trusted third parties, introducing single points of failure and potential vulnerabilities. Our framework addresses this limitation by decentralizing the computation process and leveraging cryptographic techniques, specifically zero-knowledge proofs and homomorphic encryption, directly on a blockchain. This eliminates the need for a trusted intermediary, significantly enhancing security and privacy. We detail the underlying mechanisms and provide a foundational design for a blockchain-based SMPC protocol, exploring key considerations for implementation and scalability. The core claim of this work is that existing SMPC protocols are vulnerable to various attacks and require trusted third parties. The proposed mechanism involves implementing SMPC protocols on a blockchain, leveraging cryptographic techniques like zero-knowledge proofs and homomorphic encryption to enable secure computation without revealing the underlying data. This approach offers a decentralized and trustless framework for SMPC, improving security and privacy. ---","url":"https://doi.org/10.5281/zenodo.22157564","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22157564","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22157223","name":"Homomorphic Encryption-Based Distributed Database Querying","source":"datacite","abstract":"This paper proposes a novel system for distributed database querying that leverages the power of homomorphic encryption (HE) and differential privacy (DP) to provide secure and privacy-preserving query capabilities. The core idea is to perform all query operations on encrypted data without ever decrypting the underlying database records. This is achieved by mapping the query operations to encrypted mathematical functions and utilizing the inherent properties of HE to execute them directly on the encrypted data. Differential privacy is integrated to mitigate the risk of information leakage from the query results, adding an extra layer of protection. The system architecture is designed for scalability and efficiency, allowing for complex queries to be executed across multiple database nodes. The proposed approach addresses the significant privacy concerns associated with traditional distributed database querying systems, offering a viable solution for applications requiring secure data analysis and reporting. The system is evaluated theoretically, demonstrating its potential to provide strong privacy guarantees while maintaining query functionality. The key contributions of this work lie in the combined utilization of HE and DP for distributed database querying, offering a novel paradigm for secure data access.","url":"https://doi.org/10.5281/zenodo.22157223","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22157223","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22157224","name":"Homomorphic Encryption-Based Distributed Database Querying","source":"datacite","abstract":"This paper proposes a novel system for distributed database querying that leverages the power of homomorphic encryption (HE) and differential privacy (DP) to provide secure and privacy-preserving query capabilities. The core idea is to perform all query operations on encrypted data without ever decrypting the underlying database records. This is achieved by mapping the query operations to encrypted mathematical functions and utilizing the inherent properties of HE to execute them directly on the encrypted data. Differential privacy is integrated to mitigate the risk of information leakage from the query results, adding an extra layer of protection. The system architecture is designed for scalability and efficiency, allowing for complex queries to be executed across multiple database nodes. The proposed approach addresses the significant privacy concerns associated with traditional distributed database querying systems, offering a viable solution for applications requiring secure data analysis and reporting. The system is evaluated theoretically, demonstrating its potential to provide strong privacy guarantees while maintaining query functionality. The key contributions of this work lie in the combined utilization of HE and DP for distributed database querying, offering a novel paradigm for secure data access.","url":"https://doi.org/10.5281/zenodo.22157224","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22157224","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22154723","name":"Decentralized Machine Learning via Secure Multi-Party Computation and Homomorphic Encryption","source":"datacite","abstract":"This paper proposes a novel framework for decentralized machine learning (DML) that prioritizes data privacy and security. The core of the approach lies in the synergistic utilization of Secure Multi-Party Computation (SMPC) and Homomorphic Encryption (HE). Traditional machine learning often relies on centralized datasets, raising significant privacy concerns. Our method allows for training machine learning models on distributed datasets without exposing the underlying data to any single party. SMPC enables collaborative model training among multiple participants, while HE permits computations to be performed directly on encrypted data. This significantly reduces the risk of data breaches and misuse. The framework provides a secure and efficient pathway for leveraging the collective intelligence of decentralized data sources, offering a viable solution for sensitive applications. We demonstrate the feasibility and potential benefits of this approach through a theoretical analysis and outline key considerations for its practical implementation. The key innovation here is the combined application of these techniques for a truly decentralized and privacy-preserving machine learning solution.","url":"https://doi.org/10.5281/zenodo.22154723","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22154723","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22154724","name":"Decentralized Machine Learning via Secure Multi-Party Computation and Homomorphic Encryption","source":"datacite","abstract":"This paper proposes a novel framework for decentralized machine learning (DML) that prioritizes data privacy and security. The core of the approach lies in the synergistic utilization of Secure Multi-Party Computation (SMPC) and Homomorphic Encryption (HE). Traditional machine learning often relies on centralized datasets, raising significant privacy concerns. Our method allows for training machine learning models on distributed datasets without exposing the underlying data to any single party. SMPC enables collaborative model training among multiple participants, while HE permits computations to be performed directly on encrypted data. This significantly reduces the risk of data breaches and misuse. The framework provides a secure and efficient pathway for leveraging the collective intelligence of decentralized data sources, offering a viable solution for sensitive applications. We demonstrate the feasibility and potential benefits of this approach through a theoretical analysis and outline key considerations for its practical implementation. The key innovation here is the combined application of these techniques for a truly decentralized and privacy-preserving machine learning solution.","url":"https://doi.org/10.5281/zenodo.22154724","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22154724","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22125949","name":"HPAO Artifact","source":"datacite","abstract":"Fully Homomorphic Encryption (FHE) enables computation on encrypted data without decryption, but its overhead relative to plaintext execution remains a major barrier to practical deployment. Existing FHE software stacks expose only coarse-grained cryptographic primitives to compilers, while runtime libraries optimize each primitive largely in isolation. Consequently, important cross-primitive optimizations—including ModUp hoisting, ModDown sinking, fast multiply, and lazy modular reduction—remain manual, expert-driven, and difficult to apply systematically at the program scale. We present HPAO, a compiler framework for polynomial-level optimization of RNS-CKKS programs. HPAO introduces HPOLY, a compact polynomial-level IR that exposes polynomial operations, basis management operations, and internal structure of composite primitives, such as the ModUp–DotProd–ModDown decomposition of KeySwitch, enabling cross-primitive optimization. On top of HPOLY, HPAO performs rule-based transformations guided by dataflow analysis and a profitability-aware cost model. This framework automates four optimizations previously implemented only manually or through specialized library routines: ModUphoisting (HPAO-MU), ModDown sinking (HPAO-MD), fast multiply with static encoding (HPAO-FM), and lazy modular reduction via bit-width analysis. This package contains artifact for the HPAO, include compiler source code, test models and scripts.","url":"https://doi.org/10.5281/zenodo.22125949","authors":["Sui, Tianxiang"],"tags":["FHE","CKKS","FHE Compiler"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22125949","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22125950","name":"HPAO Artifact","source":"datacite","abstract":"Fully Homomorphic Encryption (FHE) enables computation on encrypted data without decryption, but its overhead relative to plaintext execution remains a major barrier to practical deployment. Existing FHE software stacks expose only coarse-grained cryptographic primitives to compilers, while runtime libraries optimize each primitive largely in isolation. Consequently, important cross-primitive optimizations—including ModUp hoisting, ModDown sinking, fast multiply, and lazy modular reduction—remain manual, expert-driven, and difficult to apply systematically at the program scale. We present HPAO, a compiler framework for polynomial-level optimization of RNS-CKKS programs. HPAO introduces HPOLY, a compact polynomial-level IR that exposes polynomial operations, basis management operations, and internal structure of composite primitives, such as the ModUp–DotProd–ModDown decomposition of KeySwitch, enabling cross-primitive optimization. On top of HPOLY, HPAO performs rule-based transformations guided by dataflow analysis and a profitability-aware cost model. This framework automates four optimizations previously implemented only manually or through specialized library routines: ModUphoisting (HPAO-MU), ModDown sinking (HPAO-MD), fast multiply with static encoding (HPAO-FM), and lazy modular reduction via bit-width analysis. This package contains artifact for the HPAO, include compiler source code, test models and scripts.","url":"https://doi.org/10.5281/zenodo.22125950","authors":["Sui, Tianxiang"],"tags":["FHE","CKKS","FHE Compiler"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22125950","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22152215","name":"Based on Blockchain Distributed Secure Computation Engine","source":"datacite","abstract":"This paper proposes a novel distributed secure computation engine based on blockchain technology. The core claim is to leverage blockchain's inherent trust and traceability mechanisms to secure computation, guaranteeing the integrity and security of the resulting data. The proposed system employs zero-knowledge proofs and homomorphic encryption to facilitate secure computation while utilizing a blockchain to record the computation process and its outcome, thereby ensuring complete traceability. This represents a new approach to secure computation by directly integrating blockchain's capabilities, addressing limitations of traditional approaches and offering enhanced security and auditability. The system's architecture, core mechanisms, and potential applications are thoroughly detailed, highlighting its advantages and future directions.","url":"https://doi.org/10.5281/zenodo.22152215","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22152215","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22152216","name":"Based on Blockchain Distributed Secure Computation Engine","source":"datacite","abstract":"This paper proposes a novel distributed secure computation engine based on blockchain technology. The core claim is to leverage blockchain's inherent trust and traceability mechanisms to secure computation, guaranteeing the integrity and security of the resulting data. The proposed system employs zero-knowledge proofs and homomorphic encryption to facilitate secure computation while utilizing a blockchain to record the computation process and its outcome, thereby ensuring complete traceability. This represents a new approach to secure computation by directly integrating blockchain's capabilities, addressing limitations of traditional approaches and offering enhanced security and auditability. The system's architecture, core mechanisms, and potential applications are thoroughly detailed, highlighting its advantages and future directions.","url":"https://doi.org/10.5281/zenodo.22152216","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22152216","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21334273","name":"EncFormer: Secure and Efficient Transformer Inference over Encrypted Data","source":"datacite","abstract":"Official self-contained implementation of EncFormer. The archive contains the GPU CKKS implementation, CKKS--MPC conversion, BPMax, MBNorm, BOLT GELU, real two-party EzPC/SCI execution, native dependency source, the BERT-base SST-2 checkpoint, local evaluation data, and evaluation commands.","url":"https://doi.org/10.5281/zenodo.21334273","authors":["Zhu, Yufan","Jin, Chao","Aung, Khin Mi Mi","Xiao, Xiaokui"],"tags":["EncFormer","homomorphic encryption","secure multi-party computation","private Transformer inference","CKKS"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21334273","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21334274","name":"EncFormer: Secure and Efficient Transformer Inference over Encrypted Data","source":"datacite","abstract":"Official self-contained implementation of EncFormer. The archive contains the GPU CKKS implementation, CKKS--MPC conversion, BPMax, MBNorm, BOLT GELU, real two-party EzPC/SCI execution, native dependency source, the BERT-base SST-2 checkpoint, local evaluation data, and evaluation commands.","url":"https://doi.org/10.5281/zenodo.21334274","authors":["Zhu, Yufan","Jin, Chao","Aung, Khin Mi Mi","Xiao, Xiaokui"],"tags":["EncFormer","homomorphic encryption","secure multi-party computation","private Transformer inference","CKKS"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21334274","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20695049","name":"The Architecture of Readiness: Synthesising Ancient Wisdom, Cryptography, and Artificial Intelligence","source":"datacite","abstract":"This article presents a philosophy of professional readiness, arguing that periods of economic downturn and career stagnation should be strategically repurposed as intensive 'training periods'. This proactive paradigm advocates for shifting focus away from uncontrollable external factors, such as macroeconomic trends, towards the systematic and relentless accumulation of intellectual assets. The central tenet is that while opportunity is unpredictable, preparation is entirely controllable, thereby transforming hardship into a catalyst for rigorous capability-building. This framework is elucidated through an analysis of a highly advanced, multidisciplinary intellectual portfolio. It examines the simultaneous pursuit of deep technical expertise in future-proof fields such as Homomorphic Encryption (HE) for privacy-preserving computation and Artificial Intelligence (AI) for predictive analytics across sectors like healthcare and energy. This technical mastery is complemented and psychologically sustained by the deep ontological study of ancient philosophical texts, particularly the Upanishads, which provide the mental fortitude and ethical grounding necessary to endure prolonged periods of unrewarded effort. The analysis posits that the true power of this approach lies in the convergence of these disparate domains. The synthesis of advanced cryptography, AI, and ancient wisdom creates a uniquely robust and authoritative skill set, positioning the prepared individual to address complex modern challenges at the intersection of technology and ethics. Ultimately, the article concludes that significant professional success and influence are not the products of serendipity but the delayed, cumulative dividends of supreme readiness cultivated during periods of adversity.","url":"https://doi.org/10.5281/zenodo.20695049","authors":["Majumdar, Partha"],"tags":["Philosophy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20695049","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20695050","name":"The Architecture of Readiness: Synthesising Ancient Wisdom, Cryptography, and Artificial Intelligence","source":"datacite","abstract":"This article presents a philosophy of professional readiness, arguing that periods of economic downturn and career stagnation should be strategically repurposed as intensive 'training periods'. This proactive paradigm advocates for shifting focus away from uncontrollable external factors, such as macroeconomic trends, towards the systematic and relentless accumulation of intellectual assets. The central tenet is that while opportunity is unpredictable, preparation is entirely controllable, thereby transforming hardship into a catalyst for rigorous capability-building. This framework is elucidated through an analysis of a highly advanced, multidisciplinary intellectual portfolio. It examines the simultaneous pursuit of deep technical expertise in future-proof fields such as Homomorphic Encryption (HE) for privacy-preserving computation and Artificial Intelligence (AI) for predictive analytics across sectors like healthcare and energy. This technical mastery is complemented and psychologically sustained by the deep ontological study of ancient philosophical texts, particularly the Upanishads, which provide the mental fortitude and ethical grounding necessary to endure prolonged periods of unrewarded effort. The analysis posits that the true power of this approach lies in the convergence of these disparate domains. The synthesis of advanced cryptography, AI, and ancient wisdom creates a uniquely robust and authoritative skill set, positioning the prepared individual to address complex modern challenges at the intersection of technology and ethics. Ultimately, the article concludes that significant professional success and influence are not the products of serendipity but the delayed, cumulative dividends of supreme readiness cultivated during periods of adversity.","url":"https://doi.org/10.5281/zenodo.20695050","authors":["Majumdar, Partha"],"tags":["Philosophy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20695050","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20440657","name":"Homomorphic Encryption for Secure Data Processing in Edge Computing","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20440657","authors":["Safa Mohamed","Ali Majad"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20440657","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20440658","name":"Homomorphic Encryption for Secure Data Processing in Edge Computing","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20440658","authors":["Safa Mohamed","Ali Majad"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20440658","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22147040","name":"Privacy Rights in the Digital Age: Challenges, Opportunities, and Future Horizons","source":"datacite","abstract":"Privacy, as one of the most fundamental human rights, has undergone profound paradigm shifts in the transition from the industrial age to the digital age. While the traditional concept of privacy was based on the \"right to be let alone\" and the Inviolability of the physical domain, the digital age is confronted with a phenomenon known as \"informational privacy\" and the challenges of \"Big Data.\" This article provides a comparative and analytical examination of the status of privacy rights in the digital ecosystem. Challenges such as surveillance capitalism, artificial intelligence, the Internet of Things, and government surveillance have transcended the traditional boundaries of privacy. Conversely, emerging technologies such as homomorphic encryption, blockchain, and the \"Privacy by Design\" approach have created unprecedented opportunities for reclaiming control over data. By examining global legal frameworks (such as the GDPR) and Iranian domestic law, this research offers solutions to balance technological innovation with the protection of human dignity.","url":"https://doi.org/10.5281/zenodo.22147040","authors":["Taherimanesh, Mehdi"],"tags":["Digital Privacy, Data Rights, Artificial Intelligence, Surveillance Capitalism, Cryptography, Information Technology Law"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.22147040","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22147041","name":"Privacy Rights in the Digital Age: Challenges, Opportunities, and Future Horizons","source":"datacite","abstract":"Privacy, as one of the most fundamental human rights, has undergone profound paradigm shifts in the transition from the industrial age to the digital age. While the traditional concept of privacy was based on the \"right to be let alone\" and the Inviolability of the physical domain, the digital age is confronted with a phenomenon known as \"informational privacy\" and the challenges of \"Big Data.\" This article provides a comparative and analytical examination of the status of privacy rights in the digital ecosystem. Challenges such as surveillance capitalism, artificial intelligence, the Internet of Things, and government surveillance have transcended the traditional boundaries of privacy. Conversely, emerging technologies such as homomorphic encryption, blockchain, and the \"Privacy by Design\" approach have created unprecedented opportunities for reclaiming control over data. By examining global legal frameworks (such as the GDPR) and Iranian domestic law, this research offers solutions to balance technological innovation with the protection of human dignity.","url":"https://doi.org/10.5281/zenodo.22147041","authors":["Taherimanesh, Mehdi"],"tags":["Digital Privacy, Data Rights, Artificial Intelligence, Surveillance Capitalism, Cryptography, Information Technology Law"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.22147041","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.48550/arxiv.2607.23478","name":"ATLAS: Automated Approximation of Transformers for Efficient Homomorphic Inference in One Hour","source":"datacite","abstract":"Fully homomorphic encryption (FHE) lets a server run inference on encrypted data with strong privacy guarantees, but running a Transformer under FHE is expensive. Its non-linear operations, such as softmax, normalization, and activation, must be replaced with polynomial approximations that the CKKS scheme supports, and the depth of these approximations dominates inference cost. Existing FHE Transformers use hand-tuned approximation settings, such as iteration count and polynomial degree, applied uniformly across layers, models, and tasks. Hand-tuning is slow and error-prone. Even a single uniform setting has about $10^7$ choices, and manual search cannot exploit layer-wise variation. AutoFHE, the only automated method with multi-objective search, targets ReLU-only CNNs and needs full fine-tuning per candidate, which is too costly for Transformers. Per-layer settings also push the search space to about $10^{85}$ for BERT and ViT and $10^{228}$ for LLaMA3, beyond both manual and fine-tuning-based search. We present ATLAS, a training-free framework that automates this search by treating each layer's approximation setting as a multi-objective optimization over latency and accuracy. The problem is hard: the decision space is large (96 or 256 variables), each configuration takes 70 to 1,000 seconds to evaluate even in cleartext, and 85 to 90 percent of configurations are invalid. ATLAS handles this with a two-stage optimization strategy and a surrogate model, completing the search in about one hour. Compared to an iterative softmax baseline, ATLAS cuts multiplicative depth and end-to-end latency by about 35 percent with little accuracy loss, and works across encoder-only, decoder-only, and vision Transformers, complementing parallel work on packing and matrix multiplication.","url":"https://doi.org/10.48550/arxiv.2607.23478","authors":["Xie, Jianhang","Tan, Sicheng","Boddeti, Vishnu Naresh","Lu, Zhichao"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.23478","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.48550/arxiv.2508.14744","name":"A Collusion-Resistance Privacy-Preserving Smart Metering Protocol for Operational Utility","source":"datacite","abstract":"Modern smart grids rely on advanced metering infrastructure (AMI) to collect fine-grained consumption readings for operational services such as grid monitoring, load forecasting, and demand--supply balancing. However, these high-frequency readings can reveal sensitive information about consumers' daily activities. To address this privacy concern, we propose a collusion-resistant privacy-preserving aggregation protocol for smart metering operational services. The protocol distributes noise-cancellation responsibility among a configurable group of $K$ designated smart meters. Each non-designated meter perturbs its reading using $K$ independent noise components, while corresponding cancellation values ensure that noise is removed only from the final aggregate. The protocol combines Paillier homomorphic encryption with a KEM--KDF--AEAD construction. Paillier encryption enables the aggregator to compute an encrypted aggregate without decrypting individual contributions, while authenticated encryption protects exchanged noise components between smart meters. Under the considered collusion and meter-exposure model, the exact reading of a trusted and unexposed meter remains protected as long as at least one designated and one non-designated meter remain unexposed. We evaluate the protocol in terms of computational, memory, communication, and privacy overheads. Privacy is evaluated using normalized conditional entropy (NCE) and normalized root-mean-square error (NRMSE). The results show that increasing the noise scale increases NCE and uncertainty about individual readings, while NRMSE quantifies the gradual loss of privacy as additional opposite-role meters are exposed. Overall, the protocol provides exact aggregate consumption values required for operational services while protecting individual fine-grained readings against the considered adversarial coalition.","url":"https://doi.org/10.48550/arxiv.2508.14744","authors":["Zaredar, Farid","Amini, Morteza"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.14744","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22135690","name":"Homomorphic encryption & CNN","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.22135690","authors":["Guobin, Zhang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22135690","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22135691","name":"Homomorphic encryption & CNN","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.22135691","authors":["Guobin, Zhang"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22135691","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22084942","name":"THEMIS: Bringing Verifiability to RNS-CKKS Homomorphic Encryption","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.22084942","authors":["Anonymous, Anonymous"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22084942","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22084941","name":"THEMIS: Bringing Verifiability to RNS-CKKS Homomorphic Encryption","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.22084941","authors":["Anonymous, Anonymous"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22084941","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20337222","name":"FHE-Lab: A Modular Framework for Customizable Fully Homomorphic Encryption Pipelines","source":"datacite","abstract":"FHE-Lab is a lightweight FHE library designed to enable highly customizable pipeline configurations, allowing researchers to experiment with implementation choices, evaluate optimizations and reliability, analyze intermediate computation stages, and study trade-offs across different approaches.","url":"https://doi.org/10.5281/zenodo.20337222","authors":["Chan, Vattana","Mazzanti, Matias"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20337222","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22107447","name":"FHE-Lab: A Modular Framework for Customizable Fully Homomorphic Encryption Pipelines","source":"datacite","abstract":"FHE-Lab is a lightweight FHE library designed to enable highly customizable pipeline configurations, allowing researchers to experiment with implementation choices, evaluate optimizations and reliability, analyze intermediate computation stages, and study trade-offs across different approaches.","url":"https://doi.org/10.5281/zenodo.22107447","authors":["Chan, Vattana","Mazzanti, Matias"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22107447","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21607866","name":"Online Fingerprint Authentication Scheme over Outsourced Data","source":"datacite","abstract":"With the rifeness of mobile plans and the growth of biometric technology, biometric credentials, which can realize separate verification trusts on individual life or social physiognomies, has involved widely substantial interest. Though, confidentiality subjects of biometric data bring out increasing worries due to the extremely compassion of biometric data. Aiming at this test, in this project, we current a novel privacy-preserving online fingerprint verification arrangement, named e-Finga, over encoded subcontracted data. In the proposed e-Finga scheme, the user's fingerprint registered in trust authority can be subcontracted to dissimilar servers with user's approval, and safe, precise and well-organized verification service can be provided without the leakage of fingerprint information. Exactly, an better homomorphic encryption skill for secure Euclidean distance calculation to realize an efficient online fingerprint matching algorithm over encrypted Finger Code data in the subcontracting scenarios. Through detailed safety analysis, we show that e-Finga can fight various security intimidations. In addition, we implement e-Finga over a workstation with a real fingerprint database, and extensive imitation results prove that the proposed e-Finga scheme can serve well-organized and precise online fingerprint verification.","url":"https://doi.org/10.5281/zenodo.21607866","authors":["B, Tilak","N., Vaishnavi"],"tags":["Biometric Authentication; Privacy-Preserving Verification; Online Fingerprint Matching; Outsourced Data; Homomorphic Encryption; Secure Euclidean Distance Calculation; Fingerprint Data Protection; Cloud Security; e-Finga Scheme; Data Confidentiality"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21607866","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21607867","name":"Online Fingerprint Authentication Scheme over Outsourced Data","source":"datacite","abstract":"With the rifeness of mobile plans and the growth of biometric technology, biometric credentials, which can realize separate verification trusts on individual life or social physiognomies, has involved widely substantial interest. Though, confidentiality subjects of biometric data bring out increasing worries due to the extremely compassion of biometric data. Aiming at this test, in this project, we current a novel privacy-preserving online fingerprint verification arrangement, named e-Finga, over encoded subcontracted data. In the proposed e-Finga scheme, the user's fingerprint registered in trust authority can be subcontracted to dissimilar servers with user's approval, and safe, precise and well-organized verification service can be provided without the leakage of fingerprint information. Exactly, an better homomorphic encryption skill for secure Euclidean distance calculation to realize an efficient online fingerprint matching algorithm over encrypted Finger Code data in the subcontracting scenarios. Through detailed safety analysis, we show that e-Finga can fight various security intimidations. In addition, we implement e-Finga over a workstation with a real fingerprint database, and extensive imitation results prove that the proposed e-Finga scheme can serve well-organized and precise online fingerprint verification.","url":"https://doi.org/10.5281/zenodo.21607867","authors":["B, Tilak","N., Vaishnavi"],"tags":["Biometric Authentication; Privacy-Preserving Verification; Online Fingerprint Matching; Outsourced Data; Homomorphic Encryption; Secure Euclidean Distance Calculation; Fingerprint Data Protection; Cloud Security; e-Finga Scheme; Data Confidentiality"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21607867","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21607096","name":"Federated Learning: Advancing Privacy-Preserving Machine Learning at Scale","source":"datacite","abstract":"Federated Learning emerges as a transformative paradigm in machine learning, revolutionizing data privacy and distributed computing across multiple sectors. This comprehensive exploration details the evolution of federated learning from its foundational concepts to practical implementations across healthcare, finance, and industrial applications. The implementation demonstrates remarkable capabilities in preserving privacy while maintaining computational efficiency through various mechanisms, including differential privacy, secure aggregation, and homomorphic encryption. In healthcare scenarios, federated learning has enabled collaborative research across medical institutions while safeguarding patient data privacy. The financial sector benefits from enhanced fraud detection capabilities while maintaining regulatory compliance. The automotive industry utilizes federated learning to improve autonomous driving systems through distributed learning from connected vehicles. Integrating cloud computing and edge processing further enhances system efficiency and scalability. The amalgamation of these technologies presents promising directions for future developments in privacy-preserving distributed computing and machine learning applications.","url":"https://doi.org/10.5281/zenodo.21607096","authors":["Gupta, Shreya"],"tags":["Federated Learning; Privacy Preservation; Distributed Computing; Edge Computing; Machine Learning Integration"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21607096","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21607097","name":"Federated Learning: Advancing Privacy-Preserving Machine Learning at Scale","source":"datacite","abstract":"Federated Learning emerges as a transformative paradigm in machine learning, revolutionizing data privacy and distributed computing across multiple sectors. This comprehensive exploration details the evolution of federated learning from its foundational concepts to practical implementations across healthcare, finance, and industrial applications. The implementation demonstrates remarkable capabilities in preserving privacy while maintaining computational efficiency through various mechanisms, including differential privacy, secure aggregation, and homomorphic encryption. In healthcare scenarios, federated learning has enabled collaborative research across medical institutions while safeguarding patient data privacy. The financial sector benefits from enhanced fraud detection capabilities while maintaining regulatory compliance. The automotive industry utilizes federated learning to improve autonomous driving systems through distributed learning from connected vehicles. Integrating cloud computing and edge processing further enhances system efficiency and scalability. The amalgamation of these technologies presents promising directions for future developments in privacy-preserving distributed computing and machine learning applications.","url":"https://doi.org/10.5281/zenodo.21607097","authors":["Gupta, Shreya"],"tags":["Federated Learning; Privacy Preservation; Distributed Computing; Edge Computing; Machine Learning Integration"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21607097","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21606896","name":"Decoding Secure AI Deployment in Cloud Environments","source":"datacite","abstract":"Securing artificial intelligence deployments in cloud environments requires a comprehensive, multi-layered approach addressing unique challenges across physical, network, compute, storage, and application layers. This comprehensive examination explores the intricate security considerations essential for robust AI deployments in cloud environments, emphasizing Identity and Access Management as the cornerstone of defense strategies. With cloud-based AI spending projected to reach $97.7 billion by 2025 and 64% of enterprises storing sensitive data in public clouds, the stakes have never been higher. Advanced encryption technologies, including AES-256 with proper key rotation and emerging homomorphic approaches, demonstrate remarkable effectiveness in preventing data breaches, despite some performance tradeoffs. Secure development practices incorporating adversarial testing and bias detection mechanisms prove critical in preventing vulnerabilities at their source, with systematic red-team evaluations identifying 91.6% of potential attack vectors compared to just 34.5% through conventional methods. Regulatory compliance frameworks across GDPR, HIPAA, and financial services demand specialized approaches, with privacy-by-design principles significantly reducing both incidents and associated costs. The integration of AI-powered security tools throughout the technology stack creates a virtuous cycle where artificial intelligence both requires and enables stronger protection mechanisms, ultimately reducing breach impact and improving threat detection capabilities.","url":"https://doi.org/10.5281/zenodo.21606896","authors":["Goyal, Bhaskar"],"tags":["Cloud security; artificial intelligence; identity management; encryption; regulatory compliance; zero trust architecture"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21606896","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.21606897","name":"Decoding Secure AI Deployment in Cloud Environments","source":"datacite","abstract":"Securing artificial intelligence deployments in cloud environments requires a comprehensive, multi-layered approach addressing unique challenges across physical, network, compute, storage, and application layers. This comprehensive examination explores the intricate security considerations essential for robust AI deployments in cloud environments, emphasizing Identity and Access Management as the cornerstone of defense strategies. With cloud-based AI spending projected to reach $97.7 billion by 2025 and 64% of enterprises storing sensitive data in public clouds, the stakes have never been higher. Advanced encryption technologies, including AES-256 with proper key rotation and emerging homomorphic approaches, demonstrate remarkable effectiveness in preventing data breaches, despite some performance tradeoffs. Secure development practices incorporating adversarial testing and bias detection mechanisms prove critical in preventing vulnerabilities at their source, with systematic red-team evaluations identifying 91.6% of potential attack vectors compared to just 34.5% through conventional methods. Regulatory compliance frameworks across GDPR, HIPAA, and financial services demand specialized approaches, with privacy-by-design principles significantly reducing both incidents and associated costs. The integration of AI-powered security tools throughout the technology stack creates a virtuous cycle where artificial intelligence both requires and enables stronger protection mechanisms, ultimately reducing breach impact and improving threat detection capabilities.","url":"https://doi.org/10.5281/zenodo.21606897","authors":["Goyal, Bhaskar"],"tags":["Cloud security; artificial intelligence; identity management; encryption; regulatory compliance; zero trust architecture"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21606897","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.21608004","name":"Quantum-Resistant Cryptographic Protocols in AI-Optimized Payment Reconciliation Systems","source":"datacite","abstract":"This article presents an innovative hybrid architecture that integrates quantum-resistant cryptography with artificial intelligence to revolutionize global payment and reconciliation systems. The proposed article framework addresses the dual challenges of potential quantum computing threats and current inefficiencies in financial operations. By leveraging lattice-based cryptography, the system ensures long-term security against quantum attacks, while AI-driven algorithms, including reinforcement learning and neural networks, optimize payment reconciliation processes. The architecture incorporates quantum-safe signatures into SWIFT messaging protocols and introduces a federated learning approach for enhanced fraud detection across financial institutions. This collaborative model, underpinned by homomorphic encryption, enables the sharing of machine learning insights without compromising data confidentiality. The article provides a comprehensive analysis of the system's performance, demonstrating significant improvements in reconciliation efficiency, robustness against quantum threats, and scalability compared to traditional systems. By offering a future-proof solution that addresses current pain points while preparing for emerging technological challenges, this framework represents a significant advancement in securing and streamlining global financial infrastructure.","url":"https://doi.org/10.5281/zenodo.21608004","authors":["Thakur, Aparna"],"tags":["Quantum-resistant cryptography; AI-optimized reconciliation; Lattice-based encryption; Federated learning; Post-quantum finance"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21608004","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21608005","name":"Quantum-Resistant Cryptographic Protocols in AI-Optimized Payment Reconciliation Systems","source":"datacite","abstract":"This article presents an innovative hybrid architecture that integrates quantum-resistant cryptography with artificial intelligence to revolutionize global payment and reconciliation systems. The proposed article framework addresses the dual challenges of potential quantum computing threats and current inefficiencies in financial operations. By leveraging lattice-based cryptography, the system ensures long-term security against quantum attacks, while AI-driven algorithms, including reinforcement learning and neural networks, optimize payment reconciliation processes. The architecture incorporates quantum-safe signatures into SWIFT messaging protocols and introduces a federated learning approach for enhanced fraud detection across financial institutions. This collaborative model, underpinned by homomorphic encryption, enables the sharing of machine learning insights without compromising data confidentiality. The article provides a comprehensive analysis of the system's performance, demonstrating significant improvements in reconciliation efficiency, robustness against quantum threats, and scalability compared to traditional systems. By offering a future-proof solution that addresses current pain points while preparing for emerging technological challenges, this framework represents a significant advancement in securing and streamlining global financial infrastructure.","url":"https://doi.org/10.5281/zenodo.21608005","authors":["Thakur, Aparna"],"tags":["Quantum-resistant cryptography; AI-optimized reconciliation; Lattice-based encryption; Federated learning; Post-quantum finance"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21608005","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20252656","name":"Artificial Intelligence and Machine Learning- Enabled Security Framework for Data Privacy in Cloud Computing","source":"datacite","abstract":"The way in which Cloud computing has changed data storage and service delivery has also posed serious threats to privacy and security of data. The conventional encryption and rule-based intrusion detection systems can still not provide reasonable protection against sophisticated attacks like unauthorized intrusion, inference attack and anomalous activity in shared environments. The present paper suggests a machine learning-enhanced artificial intelligence (ML/AI-enabled) security approach that aims to improve data privacy in cloud computing. The framework includes both classical ML models trained with known and unknown label data (Random Forest, Isolation Forest, k-NN), and uses deep learning models (Autoencoders, LSTM) as well as privacy-preserving techniques such as differential privacy (DP), homomorphic encryption (HE) and attribute-based access control (ABAC). Experiments were also designed to evaluate the proposed system using malicious and benign access logs collected on OpenStack and AWS testbeds in terms of both detection performance (precision, recall, F1-score, AUC) as well as system efficiency (latency and computational overhead). It was found LSTM Autoencoders performed better when detecting (F1-score 0.95, AUC 0.96), although privacy mechanisms still resulted in a reduction in performance (<5-percent change). The very low computational overhead imposed by HE and DP was acceptable in real-time operations. The work illustrates that integration of AI/ML models with privacy-preserving solutions can offer a scalable, robust and realistic model of cloud data protection.","url":"https://doi.org/10.5281/zenodo.20252656","authors":["Eman Jabbar Ubaid"],"tags":["Cloud computing","data privacy","anomaly detection","machine learning","deep learning","homomorphic encryption","differential privacy","attribute-based access control"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20252656","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20252657","name":"Artificial Intelligence and Machine Learning- Enabled Security Framework for Data Privacy in Cloud Computing","source":"datacite","abstract":"The way in which Cloud computing has changed data storage and service delivery has also posed serious threats to privacy and security of data. The conventional encryption and rule-based intrusion detection systems can still not provide reasonable protection against sophisticated attacks like unauthorized intrusion, inference attack and anomalous activity in shared environments. The present paper suggests a machine learning-enhanced artificial intelligence (ML/AI-enabled) security approach that aims to improve data privacy in cloud computing. The framework includes both classical ML models trained with known and unknown label data (Random Forest, Isolation Forest, k-NN), and uses deep learning models (Autoencoders, LSTM) as well as privacy-preserving techniques such as differential privacy (DP), homomorphic encryption (HE) and attribute-based access control (ABAC). Experiments were also designed to evaluate the proposed system using malicious and benign access logs collected on OpenStack and AWS testbeds in terms of both detection performance (precision, recall, F1-score, AUC) as well as system efficiency (latency and computational overhead). It was found LSTM Autoencoders performed better when detecting (F1-score 0.95, AUC 0.96), although privacy mechanisms still resulted in a reduction in performance (<5-percent change). The very low computational overhead imposed by HE and DP was acceptable in real-time operations. The work illustrates that integration of AI/ML models with privacy-preserving solutions can offer a scalable, robust and realistic model of cloud data protection.","url":"https://doi.org/10.5281/zenodo.20252657","authors":["Eman Jabbar Ubaid"],"tags":["Cloud computing","data privacy","anomaly detection","machine learning","deep learning","homomorphic encryption","differential privacy","attribute-based access control"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20252657","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21584124","name":"An Energy Efficient Clustering Algorithm for Network Lifetime in Wireless Sensor Network","source":"datacite","abstract":"In wireless sensor networks, information aggregation accept a fundamental part in lessening essentialness use. Starting late, investigate has focused on secure information aggregation due to the open and disagreeable condition passed on. The Homomorphic Encryption (HE) contrive is generally used to secure information grouping. In any case, HE-based information aggregation designs have the going with disservices: adaptability, unapproved aggregation, and obliged aggregation limits. To deal with these issues, we propose a safe information aggregation plot by solidifying homomorphic encryption development with a check plan. To answer this issue we exhibited a system addresses a procedure in that intense cluster head is picked based on the partition from the base station and remaining imperativeness. Consequent to picking the cluster head, it impacts use of minor measure of essentialness of sensor to organize and what","url":"https://doi.org/10.5281/zenodo.21584124","authors":["Khuspare, Madhuri N.","Khobragade, Dr. Awani S."],"tags":["Sensor Nodes","Cluster Head","Base Station","Wireless Sensor Networks","Cache Based System","Hop by hop authentication"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.21584124","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21584125","name":"An Energy Efficient Clustering Algorithm for Network Lifetime in Wireless Sensor Network","source":"datacite","abstract":"In wireless sensor networks, information aggregation accept a fundamental part in lessening essentialness use. Starting late, investigate has focused on secure information aggregation due to the open and disagreeable condition passed on. The Homomorphic Encryption (HE) contrive is generally used to secure information grouping. In any case, HE-based information aggregation designs have the going with disservices: adaptability, unapproved aggregation, and obliged aggregation limits. To deal with these issues, we propose a safe information aggregation plot by solidifying homomorphic encryption development with a check plan. To answer this issue we exhibited a system addresses a procedure in that intense cluster head is picked based on the partition from the base station and remaining imperativeness. Consequent to picking the cluster head, it impacts use of minor measure of essentialness of sensor to organize and what","url":"https://doi.org/10.5281/zenodo.21584125","authors":["Khuspare, Madhuri N.","Khobragade, Dr. Awani S."],"tags":["Sensor Nodes","Cluster Head","Base Station","Wireless Sensor Networks","Cache Based System","Hop by hop authentication"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.21584125","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20439866","name":"Homomorphic Encryption-Backed Collaborative Learning Pipelines for SovereignClinical Intelligence Across Hospital Networks","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20439866","authors":["Boga, Vikram"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20439866","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20439867","name":"Homomorphic Encryption-Backed Collaborative Learning Pipelines for SovereignClinical Intelligence Across Hospital Networks","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.20439867","authors":["Boga, Vikram"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20439867","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21514106","name":"Data Privacy Engineering in Cloud-Native Environments: Integrating DevPrivOps, Risk Modeling, and Privacy-Enhancing Technologies","source":"datacite","abstract":"The rapid adoption of cloud-native architectures has fundamentally transformed how modern applications are designed, developed, deployed, and scaled, enabling unprecedented levels of agility, elasticity, and resilience. However, this paradigm shift has simultaneously introduced complex challenges in maintaining data privacy, ensuring regulatory compliance, and managing secure data lifecycles across highly dynamic, distributed, and containerized environments. Traditional privacy mechanisms, which were designed for static and centralized systems, are increasingly inadequate in addressing the ephemeral nature of microservices, the proliferation of APIs, and the continuous integration and deployment (CI/CD) pipelines that characterize cloud-native ecosystems. In response, the field of data privacy engineering has emerged as a critical discipline that integrates privacy-by-design principles directly into software engineering processes. This paper explores this evolving domain with a particular focus on DevPrivOps, an extension of DevOps that embeds privacy controls, automated compliance checks, and continuous monitoring into development workflows. It further examines key components such as privacy risk modeling for proactive threat identification, data flow tracking for visibility and accountability, and the adoption of privacy-enhancing technologies (PETs) including differential privacy, homomorphic encryption, and secure multi-party computation. Additionally, the paper discusses how organizations can operationalize scalable and automated privacy controls through policy-as-code, data classification frameworks, and runtime enforcement mechanisms. By synthesizing insights from recent research studies and established theoretical foundations, this work proposes a structured and practical approach to embedding robust privacy engineering practices within cloud-native software development, ultimately enabling organizations to balance innovation with trust, security, and regulatory adherence.","url":"https://doi.org/10.5281/zenodo.21514106","authors":["Seetala, Srinivasa Rao"],"tags":["Cloud-native computing; Data privacy engineering; DevPrivOps; Privacy-by-design; GDPR; Data governance; Differential privacy; Secure data pipelines; Privacy risk assessment; Cloud security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21514106","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21514107","name":"Data Privacy Engineering in Cloud-Native Environments: Integrating DevPrivOps, Risk Modeling, and Privacy-Enhancing Technologies","source":"datacite","abstract":"The rapid adoption of cloud-native architectures has fundamentally transformed how modern applications are designed, developed, deployed, and scaled, enabling unprecedented levels of agility, elasticity, and resilience. However, this paradigm shift has simultaneously introduced complex challenges in maintaining data privacy, ensuring regulatory compliance, and managing secure data lifecycles across highly dynamic, distributed, and containerized environments. Traditional privacy mechanisms, which were designed for static and centralized systems, are increasingly inadequate in addressing the ephemeral nature of microservices, the proliferation of APIs, and the continuous integration and deployment (CI/CD) pipelines that characterize cloud-native ecosystems. In response, the field of data privacy engineering has emerged as a critical discipline that integrates privacy-by-design principles directly into software engineering processes. This paper explores this evolving domain with a particular focus on DevPrivOps, an extension of DevOps that embeds privacy controls, automated compliance checks, and continuous monitoring into development workflows. It further examines key components such as privacy risk modeling for proactive threat identification, data flow tracking for visibility and accountability, and the adoption of privacy-enhancing technologies (PETs) including differential privacy, homomorphic encryption, and secure multi-party computation. Additionally, the paper discusses how organizations can operationalize scalable and automated privacy controls through policy-as-code, data classification frameworks, and runtime enforcement mechanisms. By synthesizing insights from recent research studies and established theoretical foundations, this work proposes a structured and practical approach to embedding robust privacy engineering practices within cloud-native software development, ultimately enabling organizations to balance innovation with trust, security, and regulatory adherence.","url":"https://doi.org/10.5281/zenodo.21514107","authors":["Seetala, Srinivasa Rao"],"tags":["Cloud-native computing; Data privacy engineering; DevPrivOps; Privacy-by-design; GDPR; Data governance; Differential privacy; Secure data pipelines; Privacy risk assessment; Cloud security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21514107","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21421425","name":"The Blind Machine: An End-to-End Platform for Privacy-Preserving Genomic Computation with Homomorphic Encryption","source":"datacite","abstract":"Many scientific questions require computing statistics across datasets that cannot be pooled because the underlying records contain sensitive information that cannot be shared. Homomorphic encryption enables computation directly on encrypted data without exposing plaintext, and more than a decade of research has demonstrated its practicality for privacy-preserving genomic analysis. Yet these systems are typically built one analysis at a time; no reusable, governed execution model has emerged that certifies and reuses approved encrypted computations independently of the specific study. To close this gap, we present The Blind Machine, an end-to-end platform for governed computation on encrypted data using homomorphic encryption and federated computing. The core design principle is that plaintext and secret keys never leave local machines, and the hosted service computes exclusively on ciphertext. Every experiment produces a machine-verifiable certificate, checkable offline, that binds the application, the committed encrypted inputs, the encrypted result, and the declared release policy. We show that the system is correct and practical through six curated biomedicalapplications built with the BFV scheme on seeded synthetic data, together with four studies on public IGSR/1000 Genomes genotypes. The work is fully reproducible: we release the open-source components, public-genome installer, synthetic data, experiment scripts, and an AI agent skill that helps reviewers reproduce the experiments.","url":"https://doi.org/10.5281/zenodo.21421425","authors":["Özmen, Barış"],"tags":["homomorphic encryption","privacy-preserving computation","Genomics","Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21421425","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21448699","name":"The Blind Machine: An End-to-End Platform for Privacy-Preserving Genomic Computation with Homomorphic Encryption","source":"datacite","abstract":"Many scientific questions require computing statistics across datasets that cannot be pooled because the underlying records contain sensitive information that cannot be shared. Homomorphic encryption enables computation directly on encrypted data without exposing plaintext, and more than a decade of research has demonstrated its practicality for privacy-preserving genomic analysis. Yet these systems are typically built one analysis at a time; no reusable, governed execution model has emerged that certifies and reuses approved encrypted computations independently of the specific study. To close this gap, we present The Blind Machine, an end-to-end platform for governed computation on encrypted data using homomorphic encryption and federated computing. The core design principle is that plaintext and secret keys never leave local machines, and the hosted service computes exclusively on ciphertext. Every experiment produces a machine-verifiable certificate, checkable offline, that binds the application, the committed encrypted inputs, the encrypted result, and the declared release policy. We show that the system is correct and practical through six curated biomedicalapplications built with the BFV scheme on seeded synthetic data, together with four studies on public IGSR/1000 Genomes genotypes. The work is fully reproducible: we release the open-source components, public-genome installer, synthetic data, experiment scripts, and an AI agent skill that helps reviewers reproduce the experiments.","url":"https://doi.org/10.5281/zenodo.21448699","authors":["Özmen, Barış"],"tags":["homomorphic encryption","privacy-preserving computation","Genomics","Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21448699","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21044164","name":"Privacy-Preserving Federated Query Processing Across Distributed Cloud Data Platforms","source":"datacite","abstract":"The rapid adoption of multi-cloud data platforms has enabled organizations to perform large-scale distributed analytics while complying with regional data governance requirements. However, existing federated query processing frameworks often prioritize performance and interoperability without providing rigorous privacy guarantees or enforcing regulatory compliance across multiple jurisdictions. This study presents PrivFed, a privacy-preserving federated query processing framework designed for distributed cloud data platforms operating under heterogeneous legal and regulatory constraints. PrivFed integrates differential privacy (DP), secure aggregation (SA), and a region-aware multi-objective query optimizer to support compliant, low-latency analytics over geographically partitioned datasets without exposing sensitive information. The proposed framework formalizes the federated query compliance problem by jointly optimizing query execution cost, privacy preservation, and data residency requirements. Formal analysis establishes -DP guarantees under adaptive composition and proves the security of the aggregation protocol in the semi-honest adversarial model. A comprehensive prototype was implemented across AWS Redshift, Azure Synapse, and Google BigQuery spanning three regulatory regions to evaluate scalability, efficiency, and privacy performance. Experimental results demonstrate that PrivFed achieves a median query latency only 1.4× higher than conventional non-private federated query systems, substantially outperforming homomorphic encryption-based approaches that incur 18–340× latency overhead. Furthermore, privacy-aware predicate pushdown reduces inter-region data transfer by 62%, while maintaining a cumulative privacy budget of ? ? 1.0 across 10,000 simulated adaptive queries. Comparative evaluation against Presto, Trino, and BigQuery Omni indicates that PrivFed is the only framework capable of simultaneously satisfying three critical objectives: strict data residency compliance, mathematically provable privacy protection, and practical query execution with less than 2× performance overhead. These findings demonstrate that PrivFed provides a practical and scalable foundation for secure federated analytics in modern multi-cloud environments.","url":"https://doi.org/10.5281/zenodo.21044164","authors":["Shankar das Boddu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21044164","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21044165","name":"Privacy-Preserving Federated Query Processing Across Distributed Cloud Data Platforms","source":"datacite","abstract":"The rapid adoption of multi-cloud data platforms has enabled organizations to perform large-scale distributed analytics while complying with regional data governance requirements. However, existing federated query processing frameworks often prioritize performance and interoperability without providing rigorous privacy guarantees or enforcing regulatory compliance across multiple jurisdictions. This study presents PrivFed, a privacy-preserving federated query processing framework designed for distributed cloud data platforms operating under heterogeneous legal and regulatory constraints. PrivFed integrates differential privacy (DP), secure aggregation (SA), and a region-aware multi-objective query optimizer to support compliant, low-latency analytics over geographically partitioned datasets without exposing sensitive information. The proposed framework formalizes the federated query compliance problem by jointly optimizing query execution cost, privacy preservation, and data residency requirements. Formal analysis establishes -DP guarantees under adaptive composition and proves the security of the aggregation protocol in the semi-honest adversarial model. A comprehensive prototype was implemented across AWS Redshift, Azure Synapse, and Google BigQuery spanning three regulatory regions to evaluate scalability, efficiency, and privacy performance. Experimental results demonstrate that PrivFed achieves a median query latency only 1.4× higher than conventional non-private federated query systems, substantially outperforming homomorphic encryption-based approaches that incur 18–340× latency overhead. Furthermore, privacy-aware predicate pushdown reduces inter-region data transfer by 62%, while maintaining a cumulative privacy budget of ? ? 1.0 across 10,000 simulated adaptive queries. Comparative evaluation against Presto, Trino, and BigQuery Omni indicates that PrivFed is the only framework capable of simultaneously satisfying three critical objectives: strict data residency compliance, mathematically provable privacy protection, and practical query execution with less than 2× performance overhead. These findings demonstrate that PrivFed provides a practical and scalable foundation for secure federated analytics in modern multi-cloud environments.","url":"https://doi.org/10.5281/zenodo.21044165","authors":["Shankar das Boddu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21044165","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.48441/4427.3459","name":"Deterministic homomorphic identity tokens for privacy-preserving attribute-based authentication","source":"datacite","abstract":"Traditional multi-factor authentication (MFA) schemes, despite their layered defences, have been compromised in several common vulnerabilities and exposures (CVEs) due to their sequential verification process, which allows adversaries to target individual identity factors in isolation. In this work, we propose a cryptographic framework that enhances the IdentiToken model by integrating deterministic homomorphic encryption inspired by the Paillier scheme. Our approach generates a structured token composed of encrypted sub-tokens, each representing either a stable or volatile element of a device’s identity. These tokens can be recomputed in real-time and compared for authentication without exposing raw attribute values. By leveraging the additive homomorphism, we quantify the degree of change in core attributes across sessions, enabling similarity-based identity verification. Beyond authentication, this token architecture supports a wide range of security applications, including real-time fingerprinting, zero-trust access control, anomaly detection and behavioural malware analysis.","url":"https://doi.org/10.48441/4427.3459","authors":["Tripathi, Shashank Shekher","Wöhnert, Kai Hendrik","Skwarek, Volker"],"tags":["identity","authentication","privacy","004: Informatik"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48441/4427.3459","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.26187/deakin.33346416","name":"VPFL: A verifiable privacy-preserving federated learning scheme for edge computing systems","source":"datacite","abstract":"Federated learning for edge computing is a promising solution in the data booming era, which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server. However, the frequently transmitted local gradients could also leak the participants’ private data. To protect the privacy of local training data, lots of cryptographic-based Privacy-Preserving Federated Learning (PPFL) schemes have been proposed. However, due to the constrained resource nature of mobile devices and complex cryptographic operations, traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously. To tackle this problem, we propose a Verifiable Privacy-preserving Federated Learning scheme (VPFL) for edge computing systems to prevent local gradients from leaking over the transmission stage. Firstly, we combine the Distributed Selective Stochastic Gradient Descent (DSSGD) method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality, so as to reduce the computation cost of the complex cryptosystem. Secondly, we further present an online/offline signature method to realize the lightweight gradients integrity verification, where the offline part can be securely outsourced to the edge server. Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality, authentication, and integrity. At last, we evaluate both communication overhead and computation cost of the proposed VPFL scheme, the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.","url":"https://doi.org/10.26187/deakin.33346416","authors":["J Zhang","Y Liu","D Wu","S Lou","B Chen","S Yu"],"tags":["Design","Cybersecurity and privacy","Distributed computing and systems software","Communications engineering","Information and computing sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.26187/deakin.33346416","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.24406/publica-9087","name":"Flexible Parallel Radix-4 MDC NTT for FHE","source":"datacite","abstract":"Fully Homomorphic Encryption (FHE) is a promising technology that allows calculations to be performed on encrypted data. However, its widespread adoption is hindered by high computational requirements. In this paper, we present a design-time flexible FPGA accelerator that aims to speed up the Number Theoretic Transform (NTT), the most crucial bottleneck in FHE schemes. Our accelerator is throughput-oriented and implements a Multipath Delay Commutator (MDC) approach. While previous works focus on radix-2 implementations, we implement a parallel radix-4 NTT architecture. We exploit constants within radix-4 structures and reduce DSP slice utilization by more than 25%. We achieve that by using a recent constant modular multiplication technique by Bertels et al. and leverage special properties of goldilock primes. The polynomial degree, modulus and number of parallel compute cores are design-time configurable. Due to its configurability, our design allows for the evaluation of different area and speed trade-offs and targets various parameter sets and requirements. To demonstrate the applicability of our approach, we conduct a case study and evaluate different configurations and parameters that are applicable to FHE schemes such as CGGI or CKKS. All implementations are done on the AMD Alveo U55C. Compared to our reference software implementation based on OpenFHE, we achieve a speed-up of up to 23.75×. Compared to related works, with the same bandwidth requirements, we achieve up to 3.81× lower ATP and up to 4.33× higher TPS.","url":"https://doi.org/10.24406/publica-9087","authors":["Stelzer, Tobias","Karl, Patrick","Seelos-Zankl, Andreas",":unav"],"tags":["CGGI","CKKS","Fully homomorphic encryption","multipath delay commutator","number theoretic transform","radix-4"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.24406/publica-9087","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20288859","name":"TEMPORAL ROTATION SECURITY PROTOCOL (TRSP) Physics-First Cryptographic Architecture: Time as the Fundamental Security Parameter","source":"datacite","abstract":"ABSTRACT: Temporal Rotation Security Protocol (TRSP) v3 — Physics-First Cryptographic Architecture: Time as the Fundamental Quantum-Resistant Security Parameter Concept in development since at least November 2019. First complete public documentation: 2026. Version 3 adds Part 4c (Hybrid Dynamic CRATON Quorum / HDCQ) and Part 4d (CRATON Hardening Layer / CHL). This concept documents the Temporal Rotation Security Protocol — a cryptographic architecture in which security derives not from the mathematical complexity of encryption keys but from the physical irreversibility of time. Every cryptographic system currently in use — RSA, AES, elliptic curve — rests on a single foundational assumption: that breaking the encryption requires more computational time than any adversary possesses. Quantum computing, through Shor's algorithm and Grover's algorithm, is systematically dismantling this assumption. TRSP replaces it with a physically permanent alternative: a key that no longer exists cannot be recovered by any computation, quantum or classical, regardless of computational resources or future mathematical advances. The protocol operates through simultaneous multi-layer key rotation at three independent frequencies. Layer 1 (session layer) rotates every 10–100 milliseconds using hardware entropy from physical noise sources — thermal variance, clock jitter, electromagnetic fingerprint. Layer 2 (identity layer) rotates every 1–10 seconds, anchored to physically unique device characteristics that cannot be spoofed. Layer 3 (CRATON foundational layer) generates a cryptographic commitment from the unique physical state of both communicating devices at session initialisation — used once and permanently destroyed, unrepeatable at any other point in time or on any other device. Quantum resistance is structural rather than parametric. Shor's algorithm requires minutes to hours to factor key-scale integers; Layer 1 rotation windows of 10–100 milliseconds ensure the target key no longer exists when any quantum computation converges. Grover's algorithm provides quadratic speedup against static keys; against rotating keys it provides no advantage because the search target is destroyed before the search completes. As quantum hardware advances and computation accelerates, rotation windows decrease proportionally — a software parameter adjustment costing microseconds against a hardware investment requiring years. The defender's adaptation is permanently faster than the attacker's. The CRATON foundational trust layer — positioned between hardware and operating system — is not a stored value. It is a physical event: a one-time measurement of device state that generates a cryptographic commitment and is immediately destroyed. It cannot be forged by a compromised operating system, replicated on any other device, or reconstructed from any stored record. Root of trust through physical irreversibility. The inverse proposition — what breaking TRSP would prove — is documented as the second foundational contribution of this concept. A successful attack against a correctly implemented TRSP system would constitute experimental proof of one of the following physical propositions: that quantum information is globally conserved and locally accessible confirming the holographic principle; that temporal irreversibility is not absolute at quantum scale; that parallel quantum branches are accessible through computation confirming the Everett many-worlds interpretation; or that Landauer's principle is violated at computational scale. Any of these would represent the most significant scientific discovery in recorded history. TRSP is therefore simultaneously a security protocol and a physics experiment. Its security parameter is the boundary of known physical law. TRSP is an open invitation to physics. Extension 1: Spatial-Temporal Triangulation & Network Latency Mitigation A critical challenge in millisecond-scale cryptographic rotation (Δt = 10–100 ms) across standard ","url":"https://doi.org/10.5281/zenodo.20288859","authors":["Mehmetaj, Ilir"],"tags":["temporal rotation cryptography","physics-first security architecture","quantum-resistant key rotation","time-based cryptographic protocol","CRATON foundational trust layer","harvest-now-decrypt-later resistance","hardware entropy key generation","post-quantum security physics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20288859","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.21260191","name":"ФЕДЕРАТИВНОЕ ОБУЧЕНИЕ ДЛЯ ОБНАРУЖЕНИЯ КИБЕРУГРОЗ С СОХРАНЕНИЕМ КОНФИДЕНЦИАЛЬНОСТИ ДАННЫХ В КОРПОРАТИВНЫХ СЕТЯХ","source":"datacite","abstract":"Аннотация. В настоящей статье рассматривается проблема централизованного обучения моделей обнаружения киберугроз, требующего агрегации конфиденциальных данных сетевого трафика от множества организаций-участников. Показано, что традиционный подход к обучению на объединённых данных создаёт значительные риски нарушения конфиденциальности, регуляторных барьеров (ФЗ-152, GDPR) и организационных препятствий для обмена данными между организациями. В качестве решения предложена архитектура федеративной системы обнаружения аномалий на базе протокола FedAvg с адаптациями для предметной области информационной безопасности: дифференциальная конфиденциальность, безопасная агрегация на основе гомоморфного шифрования и механизм управления качеством участников (contribution-based weighting). Проведено экспериментальное исследование с моделированием федеративного обучения на наборе данных CICIDS2017, разделённом между 10 виртуальными участниками с различным распределением атак. Результаты демонстрируют, что федеративная модель достигает 94,2% F1-Score по сравнению с 95,3% для централизованной модели (потеря качества 1,1 процентных пункта), при этом полностью сохраняя конфиденциальность данных каждого участника. Предложена методика оценки качества вклада каждого участника и алгоритм выявления свободных riders (free-rider detection), основанный на анализе градиентов локальных моделей. Ключевые слова: федеративное обучение, информационная безопасность, обнаружение аномалий, конфиденциальность, дифференциальная конфиденциальность, гомоморфное шифрование, FedAvg, киберугрозы, корпоративные сети, машинное обучение.","url":"https://doi.org/10.5281/zenodo.21260191","authors":["Dupley, Maxim"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21260191","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.21260192","name":"ФЕДЕРАТИВНОЕ ОБУЧЕНИЕ ДЛЯ ОБНАРУЖЕНИЯ КИБЕРУГРОЗ С СОХРАНЕНИЕМ КОНФИДЕНЦИАЛЬНОСТИ ДАННЫХ В КОРПОРАТИВНЫХ СЕТЯХ","source":"datacite","abstract":"Аннотация. В настоящей статье рассматривается проблема централизованного обучения моделей обнаружения киберугроз, требующего агрегации конфиденциальных данных сетевого трафика от множества организаций-участников. Показано, что традиционный подход к обучению на объединённых данных создаёт значительные риски нарушения конфиденциальности, регуляторных барьеров (ФЗ-152, GDPR) и организационных препятствий для обмена данными между организациями. В качестве решения предложена архитектура федеративной системы обнаружения аномалий на базе протокола FedAvg с адаптациями для предметной области информационной безопасности: дифференциальная конфиденциальность, безопасная агрегация на основе гомоморфного шифрования и механизм управления качеством участников (contribution-based weighting). Проведено экспериментальное исследование с моделированием федеративного обучения на наборе данных CICIDS2017, разделённом между 10 виртуальными участниками с различным распределением атак. Результаты демонстрируют, что федеративная модель достигает 94,2% F1-Score по сравнению с 95,3% для централизованной модели (потеря качества 1,1 процентных пункта), при этом полностью сохраняя конфиденциальность данных каждого участника. Предложена методика оценки качества вклада каждого участника и алгоритм выявления свободных riders (free-rider detection), основанный на анализе градиентов локальных моделей. Ключевые слова: федеративное обучение, информационная безопасность, обнаружение аномалий, конфиденциальность, дифференциальная конфиденциальность, гомоморфное шифрование, FedAvg, киберугрозы, корпоративные сети, машинное обучение.","url":"https://doi.org/10.5281/zenodo.21260192","authors":["Dupley, Maxim"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21260192","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20776523","name":"Low-Memory Homomorphic ResNet-20","source":"datacite","abstract":"Machine Learning-as-a-Service (MLaaS) has become a dominant paradigm for deploying advanced artificial intelligence models, enabling users to leverage powerful cloud-hosted classifiers without the need for substantial local computational resources. However, this convenience introduces a significant privacy challenge: users must often transmit sensitive data to remote servers for inference, exposing personal information to potential misuse, data breaches, or unauthorized access. This creates a fundamental privacy paradox—how can a client benefit from the predictive power of a sophisticated cloud-based model, such as a ResNet-20 network trained on the CIFAR-10 dataset, while ensuring that the underlying input images remain completely confidential? This project addresses that challenge through the practical application of Fully Homomorphic Encryption (FHE), a cryptographic technology that enables computations to be performed directly on encrypted data without requiring decryption at any stage of processing. Specifically, we implement a privacypreserving inference pipeline using the CKKS (Cheon–Kim–Kim–Song) approximate homomorphic encryption scheme provided by the OpenFHE framework. The proposed system allows a client to encrypt image data locally and transmit only ciphertexts to an untrusted cloud server. The server then executes the inference procedure of a deep 20-layer residual neural network entirely within the encrypted domain, producing encrypted prediction outputs that reveal no information about the original inputs. To support homomorphic evaluation, the neural network architecture is adapted to accommodate the mathematical constraints of FHE, replacing nonpolynomial operations with encryption-friendly approximations while preserving classification performance. The implementation demonstrates the feasibility of combining modern deep learning architectures with state-of-theart homomorphic encryption techniques to achieve end-to-end privacypreserving inference. Experimental evaluation highlights the trade-offs among security, computational overhead, inference latency, and predictive accuracy, illustrating both the capabilities and current limitations of encrypted deep learning. The results show that Fully Homomorphic Encryption can enable secure MLaaS deployments in privacy-sensitive domains such as healthcare, finance, government services, and personal cloud applications, where data confidentiality is paramount. By successfully integrating OpenFHE-based CKKS encryption with a deep residual network, this work contributes to the growing body of research demonstrating that practical, privacy-preserving machine learning is becoming increasingly achievable in real-world cloud environments.","url":"https://doi.org/10.5281/zenodo.20776523","authors":["Mrs. Vaishali Lahire","Kishori Ghule"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20776523","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20776524","name":"Low-Memory Homomorphic ResNet-20","source":"datacite","abstract":"Machine Learning-as-a-Service (MLaaS) has become a dominant paradigm for deploying advanced artificial intelligence models, enabling users to leverage powerful cloud-hosted classifiers without the need for substantial local computational resources. However, this convenience introduces a significant privacy challenge: users must often transmit sensitive data to remote servers for inference, exposing personal information to potential misuse, data breaches, or unauthorized access. This creates a fundamental privacy paradox—how can a client benefit from the predictive power of a sophisticated cloud-based model, such as a ResNet-20 network trained on the CIFAR-10 dataset, while ensuring that the underlying input images remain completely confidential? This project addresses that challenge through the practical application of Fully Homomorphic Encryption (FHE), a cryptographic technology that enables computations to be performed directly on encrypted data without requiring decryption at any stage of processing. Specifically, we implement a privacypreserving inference pipeline using the CKKS (Cheon–Kim–Kim–Song) approximate homomorphic encryption scheme provided by the OpenFHE framework. The proposed system allows a client to encrypt image data locally and transmit only ciphertexts to an untrusted cloud server. The server then executes the inference procedure of a deep 20-layer residual neural network entirely within the encrypted domain, producing encrypted prediction outputs that reveal no information about the original inputs. To support homomorphic evaluation, the neural network architecture is adapted to accommodate the mathematical constraints of FHE, replacing nonpolynomial operations with encryption-friendly approximations while preserving classification performance. The implementation demonstrates the feasibility of combining modern deep learning architectures with state-of-theart homomorphic encryption techniques to achieve end-to-end privacypreserving inference. Experimental evaluation highlights the trade-offs among security, computational overhead, inference latency, and predictive accuracy, illustrating both the capabilities and current limitations of encrypted deep learning. The results show that Fully Homomorphic Encryption can enable secure MLaaS deployments in privacy-sensitive domains such as healthcare, finance, government services, and personal cloud applications, where data confidentiality is paramount. By successfully integrating OpenFHE-based CKKS encryption with a deep residual network, this work contributes to the growing body of research demonstrating that practical, privacy-preserving machine learning is becoming increasingly achievable in real-world cloud environments.","url":"https://doi.org/10.5281/zenodo.20776524","authors":["Mrs. Vaishali Lahire","Kishori Ghule"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20776524","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20497543","name":"A SCALABLE PRIVACY-PRESERVING DATA MINING FRAMEWORK FOR MULTI-SITE CLOUD ENVIRONMENTS","source":"datacite","abstract":"The increasing adoption of cloud-based data analytics has enabled organizations to perform large-scale data mining using distributed resources; however, this raises several concerns about data privacy, security, and legal/compliance issues with regard to cloud-based data mining. For many real-world applications, the owners of sensitive data will be multiple independent locations that do not want or are not willing to send their raw data to a central cloud due to privacy issues. Existing data mining techniques in the cloud typically rely on either direct data outsourcing or limited trust, making them impractical for use in privacy-sensitive environments where data is scattered across multiple independent sites. Thus, this article presents a scalable framework for privacy-preserving data mining within a multi-site cloud environment that allows for collaboration between sites via data mining while keeping confidential the sensitive data during the entire data mining process. Specifically, each owner of data at a site will preprocess its data locally (i.e., at the site) and apply a form of homomorphic encryption to its data prior to sending it to the cloud. Further, secure aggregation will be employed to aggregate (i.e., combine) the contributions of the encrypted data from many different site owners together (i.e., without decryption). Finally, all of the data mining operations will be performed directly on the encrypted data in the cloud using an honest-but-curious model for threat. Plaintext cannot be transmitted to the cloud, as only an authorized analyst can decrypt it. The innovative nature of this new way of working is in the way that it has been designed at a framework level to consider privacy preservation, multiple-user workloads across multiple sites, scalability, and a myriad of other requirements, rather than simply implementing one algorithm or application at a time. Additionally, scalability in terms of the number of users participating and size of data being processed both are taken into account within the proposed framework making it feasible to support real-world cloud deployments. Testing against the UCI Adult Census Income Dataset demonstrates that the proposed framework produces a similar classification performance to other solutions that do not have any encryption but provides additional computational burden due to encryption. Moreover, further analysis confirms the ability to scale and the ability to validate privacy has been addressed using this new approach. Therefore, the proposed framework provides an efficient, scalable solution for conducting privacy-sensitive data mining using distributed cloud environments. The primary research contribution of this work is the design and implementation of a unified, framework-level solution that simultaneously addresses three critical challenges — privacy preservation, multi-site collaboration, and scalability — which have not been collectively addressed in prior literature. Unlike existing algorithm-specific approaches, this framework introduces a generalised architecture that integrates homomorphic encryption and secure aggregation into a cohesive pipeline applicable to diverse cloud-based data mining scenarios.","url":"https://doi.org/10.5281/zenodo.20497543","authors":["ANKITA SINGH, KANIKA GARG"],"tags":["Privacy-Preserving Data Mining; Homomorphic Encryption; Secure Aggregation; Multi-Site Data Analytics; Cloud Computing; Data Privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20497543","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20497544","name":"A SCALABLE PRIVACY-PRESERVING DATA MINING FRAMEWORK FOR MULTI-SITE CLOUD ENVIRONMENTS","source":"datacite","abstract":"The increasing adoption of cloud-based data analytics has enabled organizations to perform large-scale data mining using distributed resources; however, this raises several concerns about data privacy, security, and legal/compliance issues with regard to cloud-based data mining. For many real-world applications, the owners of sensitive data will be multiple independent locations that do not want or are not willing to send their raw data to a central cloud due to privacy issues. Existing data mining techniques in the cloud typically rely on either direct data outsourcing or limited trust, making them impractical for use in privacy-sensitive environments where data is scattered across multiple independent sites. Thus, this article presents a scalable framework for privacy-preserving data mining within a multi-site cloud environment that allows for collaboration between sites via data mining while keeping confidential the sensitive data during the entire data mining process. Specifically, each owner of data at a site will preprocess its data locally (i.e., at the site) and apply a form of homomorphic encryption to its data prior to sending it to the cloud. Further, secure aggregation will be employed to aggregate (i.e., combine) the contributions of the encrypted data from many different site owners together (i.e., without decryption). Finally, all of the data mining operations will be performed directly on the encrypted data in the cloud using an honest-but-curious model for threat. Plaintext cannot be transmitted to the cloud, as only an authorized analyst can decrypt it. The innovative nature of this new way of working is in the way that it has been designed at a framework level to consider privacy preservation, multiple-user workloads across multiple sites, scalability, and a myriad of other requirements, rather than simply implementing one algorithm or application at a time. Additionally, scalability in terms of the number of users participating and size of data being processed both are taken into account within the proposed framework making it feasible to support real-world cloud deployments. Testing against the UCI Adult Census Income Dataset demonstrates that the proposed framework produces a similar classification performance to other solutions that do not have any encryption but provides additional computational burden due to encryption. Moreover, further analysis confirms the ability to scale and the ability to validate privacy has been addressed using this new approach. Therefore, the proposed framework provides an efficient, scalable solution for conducting privacy-sensitive data mining using distributed cloud environments. The primary research contribution of this work is the design and implementation of a unified, framework-level solution that simultaneously addresses three critical challenges — privacy preservation, multi-site collaboration, and scalability — which have not been collectively addressed in prior literature. Unlike existing algorithm-specific approaches, this framework introduces a generalised architecture that integrates homomorphic encryption and secure aggregation into a cohesive pipeline applicable to diverse cloud-based data mining scenarios.","url":"https://doi.org/10.5281/zenodo.20497544","authors":["ANKITA SINGH, KANIKA GARG"],"tags":["Privacy-Preserving Data Mining; Homomorphic Encryption; Secure Aggregation; Multi-Site Data Analytics; Cloud Computing; Data Privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20497544","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22112329","name":"Privacy Rights in the Digital Age: Challenges, Opportunities, and Future Horizons","source":"datacite","abstract":"Privacy, as one of the most fundamental human rights, has undergone profound paradigm shifts in the transition from the industrial age to the digital age. While the traditional concept of privacy was based on the \"right to be let alone\" and the Inviolability of the physical domain, the digital age is confronted with a phenomenon known as \"informational privacy\" and the challenges of \"Big Data.\" This article provides a comparative and analytical examination of the status of privacy rights in the digital ecosystem. Challenges such as surveillance capitalism, artificial intelligence, the Internet of Things, and government surveillance have transcended the traditional boundaries of privacy. Conversely, emerging technologies such as homomorphic encryption, blockchain, and the \"Privacy by Design\" approach have created unprecedented opportunities for reclaiming control over data. By examining global legal frameworks (such as the GDPR) and Iranian domestic law, this research offers solutions to balance technological innovation with the protection of human dignity.","url":"https://doi.org/10.5281/zenodo.22112329","authors":["Taherimanesh, Mehdi"],"tags":["Digital Privacy, Data Rights, Artificial Intelligence, Surveillance Capitalism, Cryptography, Information Technology Law"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22112329","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.22112328","name":"Privacy Rights in the Digital Age: Challenges, Opportunities, and Future Horizons","source":"datacite","abstract":"Privacy, as one of the most fundamental human rights, has undergone profound paradigm shifts in the transition from the industrial age to the digital age. While the traditional concept of privacy was based on the \"right to be let alone\" and the Inviolability of the physical domain, the digital age is confronted with a phenomenon known as \"informational privacy\" and the challenges of \"Big Data.\" This article provides a comparative and analytical examination of the status of privacy rights in the digital ecosystem. Challenges such as surveillance capitalism, artificial intelligence, the Internet of Things, and government surveillance have transcended the traditional boundaries of privacy. Conversely, emerging technologies such as homomorphic encryption, blockchain, and the \"Privacy by Design\" approach have created unprecedented opportunities for reclaiming control over data. By examining global legal frameworks (such as the GDPR) and Iranian domestic law, this research offers solutions to balance technological innovation with the protection of human dignity.","url":"https://doi.org/10.5281/zenodo.22112328","authors":["Taherimanesh, Mehdi"],"tags":["Digital Privacy, Data Rights, Artificial Intelligence, Surveillance Capitalism, Cryptography, Information Technology Law"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22112328","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20776377","name":"Composable Privacy: Integrating Selective Disclosure Credentials with Fully Homomorphic Encryption","source":"datacite","abstract":"Privacy-preserving systems have traditionally faced a fundamental tradeoff between data utility and confidentiality. Selective Disclosure Credentials (SDCs) enable users to prove specific attributes without revealing underlying personal information, while Fully Homomorphic Encryption (FHE) enables arbitrary computation on encrypted data without exposing plaintext. Although both technologies address critical privacy challenges, they solve different problems and are rarely integrated into a unified architecture. This paper introduces the concept of Composable Privacy, a layered framework that combines selective disclosure credentials, zero-knowledge proofs, and fully homomorphic encryption into a cohesive privacy architecture. The framework separates privacy concerns into three functional layers: an authentication layer using selective disclosure and zero-knowledge proofs, a computation layer using homomorphic encryption for confidential processing, and a verification layer that provides cryptographic assurances of computation correctness. The paper examines the cryptographic foundations of BBS+ signatures, Coconut threshold credentials, lattice-based homomorphic encryption schemes, and post-quantum security considerations. It further evaluates the practical feasibility of the architecture through applications in decentralized finance, healthcare federated learning, confidential governance systems, and blockchain-based identity infrastructure. Performance trends, scalability challenges, interoperability requirements, and future hardware acceleration pathways are also analyzed. The proposed Composable Privacy framework demonstrates how selective disclosure and encrypted computation can be combined to create privacy-preserving digital systems that maintain verifiability, confidentiality, and regulatory compliance simultaneously. The work provides a conceptual foundation for next-generation privacy architectures in blockchain, decentralized identity, and distributed computing environments.","url":"https://doi.org/10.5281/zenodo.20776377","authors":["Fernandes, Aldrid"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20776377","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20776378","name":"Composable Privacy: Integrating Selective Disclosure Credentials with Fully Homomorphic Encryption","source":"datacite","abstract":"Privacy-preserving systems have traditionally faced a fundamental tradeoff between data utility and confidentiality. Selective Disclosure Credentials (SDCs) enable users to prove specific attributes without revealing underlying personal information, while Fully Homomorphic Encryption (FHE) enables arbitrary computation on encrypted data without exposing plaintext. Although both technologies address critical privacy challenges, they solve different problems and are rarely integrated into a unified architecture. This paper introduces the concept of Composable Privacy, a layered framework that combines selective disclosure credentials, zero-knowledge proofs, and fully homomorphic encryption into a cohesive privacy architecture. The framework separates privacy concerns into three functional layers: an authentication layer using selective disclosure and zero-knowledge proofs, a computation layer using homomorphic encryption for confidential processing, and a verification layer that provides cryptographic assurances of computation correctness. The paper examines the cryptographic foundations of BBS+ signatures, Coconut threshold credentials, lattice-based homomorphic encryption schemes, and post-quantum security considerations. It further evaluates the practical feasibility of the architecture through applications in decentralized finance, healthcare federated learning, confidential governance systems, and blockchain-based identity infrastructure. Performance trends, scalability challenges, interoperability requirements, and future hardware acceleration pathways are also analyzed. The proposed Composable Privacy framework demonstrates how selective disclosure and encrypted computation can be combined to create privacy-preserving digital systems that maintain verifiability, confidentiality, and regulatory compliance simultaneously. The work provides a conceptual foundation for next-generation privacy architectures in blockchain, decentralized identity, and distributed computing environments.","url":"https://doi.org/10.5281/zenodo.20776378","authors":["Fernandes, Aldrid"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20776378","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.5281/zenodo.20396914","name":"VIFP: Shape-Hiding Tree Mining on Encrypted Data via Virtual-Interval FP-Growth","source":"datacite","abstract":"Privacy-preserving frequent itemset mining has been studied for more than two decades, mainlythrough secure candidate counting, horizontally or vertically partitioned protocols, outsourced en-crypted support computation, and partial homomorphic outsourcing. However, existing methodsdo not fully solve the problem of executing tree-based frequent pattern mining, such as FP-Growth,directly over encrypted or secret-shared data without an online decryption-key holder. The core obsta-cle is structural: FP-Growth derives its efficiency from dynamic prefix trees, recursive conditionalpattern bases, variable fan-out, pointer chasing, and data-dependent memory allocation, while fullyhomomorphic encryption (FHE) and secure multi-party computation (SMPC) are most expensiveexactly under hidden branching, hidden memory access, and dynamic data structures.This paper introduces a new problem called shape-hiding keyless conditional prefix mining. Thegoal is to compute frequent itemsets, or an encrypted representation equivalent to FP-Growth output,while hiding not only the raw transactions but also the evolving tree shape, branch fan-out, conditionaldatabase sizes, active prefix identities, support values, and memory-access patterns. We then proposeVirtual-Interval FP-Growth (VIFP), a new encrypted-tree mining framework that preserves FP-Growth semantics while replacing dynamic FP-tree nodes with static, fixed-width, intervalized arraystructures. VIFP uses three custom data structures: an Occurrence-Ordered Transaction Tape, aHeader-Segmented Posting Array, and Projected Interval Descriptors. These structures turn encryptedpointer chasing into batched scans, stable partitions, and segmented histograms. The framework canbe instantiated using additive secret sharing with oblivious sorting and DPF-assisted scatter/gather,or using packed FHE with SIMD support and programmable bootstrapping for threshold tests. Weprovide formal definitions, a leakage model, algorithmic procedures, correctness arguments, andtheoretical complexity analysis showing why VIFP avoids the main bottleneck of direct encryptedFP-tree construction.","url":"https://doi.org/10.5281/zenodo.20396914","authors":["Van Ha, Minh Quan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20396914","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:39.508Z"},{"id":"doi:10.64898/2026.05.15.26353345","name":"Fully Homomorphic Collaborative Learning for Safe Cross-Healthcare Institution Development and Implementation of Foundation Models","source":"europepmc","abstract":"","url":"https://doi.org/10.64898/2026.05.15.26353345","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.05.15.26353345","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.21203/rs.3.rs-10250110/v1","name":"Security, Privacy and Regulatory Governance in Digital Payments. A Systematic Review and Cross-Architecture Framework for PIX, Blockchain, and Central Bank Digital Currencies","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10250110/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10250110/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.21203/rs.3.rs-9502259/v1","name":"A Framework for Secure Healthcare Cloud Architecture for Security Threat Mitigation and Regulatory Compliance","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9502259/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9502259/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.21203/rs.3.rs-9870931/v1","name":"Privacy Analytics in Accounting Information Systems","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9870931/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9870931/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.22541/au.176381141.16848857/v1","name":"A Privacy-Aware Framework for Stress Prediction Based on Secure Computational Intelligence","source":"europepmc","abstract":"","url":"https://doi.org/10.22541/au.176381141.16848857/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.176381141.16848857/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.21203/rs.3.rs-9081554/v1","name":"Privacy-Preserving Patent Matching Model","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9081554/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9081554/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.21203/rs.3.rs-9217539/v1","name":"Exact Multi-Task Aggregation with Confidential Queries and Designated Recovery","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9217539/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9217539/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.20944/preprints202601.0250.v1","name":"<em>ppAIsec</em>: Privacy-Preserving Artificial Intelligence Models in Healthcare Security—A Synthesis of AI Frameworks","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202601.0250.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202601.0250.v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.14293/pr2199.002320.v1","name":"Masked B-Tree Data Structure","source":"europepmc","abstract":"","url":"https://doi.org/10.14293/pr2199.002320.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.14293/pr2199.002320.v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.20944/preprints202512.1086.v1","name":"Decentralized Trust Model for Vehicle Ad-Hoc Networks (VANETs) with 5G Integration: A Blockchain-Based Approach for Enhanced Security and Privacy in Intelligent Transportation Systems","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202512.1086.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.1086.v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.21203/rs.3.rs-7205892/v1","name":"Multi_key Homomorphic Crypto and Multiscale Transformer Learning Secure Data Aggregation in Wsn for Smart Agriculture","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7205892/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7205892/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.21203/rs.3.rs-8537970/v1","name":"GPU-NTT and Karatsuba Co-Optimization forHigh-Throughput Polynomial MultiplicationAcceleration","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8537970/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8537970/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.64898/2026.01.15.26344228","name":"Hide and Seek: Privacy-Preserving Artificial Intelligence with a Feasibility Study in Rare Disease Diagnosis","source":"europepmc","abstract":"","url":"https://doi.org/10.64898/2026.01.15.26344228","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.01.15.26344228","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.21203/rs.3.rs-8044369/v1","name":"A Security-Oriented Privacy-Preserving Framework for Efficient Medical Record Search in Telemedicine","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8044369/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-8044369/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.21203/rs.3.rs-7013862/v1","name":"CCA-attacks on lattice-based encryption-decryption schemes","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7013862/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7013862/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.22541/au.176017688.89033244/v1","name":"Survey of Privacy Preserving Techniques for Distributed Learning in an IoT Network","source":"europepmc","abstract":"","url":"https://doi.org/10.22541/au.176017688.89033244/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.22541/au.176017688.89033244/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7545217/v1","name":"An Integrated Framework for Prescriptive Analytics and Interactive Visualization to Optimize Financial fraud detection in High-Volume Digital Markets","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7545217/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7545217/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.20944/preprints202504.1371.v1","name":"Cryptography in Secure Cloud 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Platoons","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6240707/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6240707/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.20944/preprints202506.1413.v1","name":"Privacy-Preserving Natural Language Processing for Clinical Notes","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202506.1413.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202506.1413.v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202506.1415.v1","name":"Privacy Risk Assessment Frameworks for Large-Scale Medical Datasets Using Computational 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Classification.","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-5266100/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-5266100/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.1101/2024.07.23.604393","name":"Towards a new standard in genomic data privacy: a realization of owner-governance","source":"europepmc","abstract":"","url":"https://doi.org/10.1101/2024.07.23.604393","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1101/2024.07.23.604393","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.21203/rs.3.rs-4975693/v1","name":"A Verifiable Privacy-Preserving Data Aggregation Scheme with Illegal Data Detection for Infectious Disease Surveillance Systems","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-4975693/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-4975693/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.20944/preprints202312.0615.v1","name":"Secure Cloud Computing By A dual-Layer Encryption Mechanism","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202312.0615.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.20944/preprints202312.0615.v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.21203/rs.3.rs-3948413/v1","name":"Technical Sandbox for a Global Patient co-Owned Cloud (GPOC)","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-3948413/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.21203/rs.3.rs-3948413/v1","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.1101/2023.10.10.561761","name":"Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2023.10.10.561761","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1101/2023.10.10.561761","addedAt":"2026-08-31T06:41:39.508Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.21203/rs.3.rs-3004979/v2","name":"Technical Sandbox for a Global Patient co-Owned Cloud 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System","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-165857/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.21203/rs.3.rs-165857/v1","addedAt":"2026-08-31T06:41:39.509Z","updatedAt":"2026-08-31T06:41:43.497Z"},{"id":"doi:10.1561/978-1-63828-345-420251007","name":"Accelerating Homomorphic Encryption and Multi-Party Computation","source":"crossref","abstract":"Homomorphic encryption (HE) and multi-party computation (MPC) protocols can be slow in practice. However, they can be accelerated by various accelerators such as multi-core CPUs, GPUs (Graphics Processing Units), FPGAs (Field-Programmable Gate Arrays), and ASICs (Application-Specific Integrated Circuits) to make them practical for artificial intelligence (AI) applications. We discuss the bottlenecks in these protocols and various ways of mitigating them. We then discuss recent works that mitigate bottlenecks with accelerators and enable use in AI applications.","url":"https://doi.org/10.1561/978-1-63828-345-420251007","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-29T09:32:50Z","doi":"10.1561/978-1-63828-345-420251007","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.62441/nano-ntp.v20i3.24","name":"Enhancing Privacy in Machine Learning Services with Hybrid Homomorphic Encryption: Guard ML Framework","source":"crossref","abstract":"","url":"https://doi.org/10.62441/nano-ntp.v20i3.24","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-15T01:45:42Z","doi":"10.62441/nano-ntp.v20i3.24","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/eurosp60621.2024.00033","name":"Faster Homomorphic DFT and Speech Analysis for Torus Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/eurosp60621.2024.00033","authors":["Kang Hoon Lee","YoungBae Jeon","Ji Won Yoon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-22T17:41:43Z","doi":"10.1109/eurosp60621.2024.00033","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-3-031-65494-7","name":"Homomorphic Encryption for Data Science (HE4DS)","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65494-7","authors":["Allon Adir","Ehud Aharoni","Nir Drucker","Ronen Levy","Hayim Shaul","Omri Soceanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-09T17:16:13Z","doi":"10.1007/978-3-031-65494-7","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/sips62058.2024.00032","name":"Improved Ciphertext Multiplication for RNS-CKKS Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sips62058.2024.00032","authors":["Sajjad Akherati","Xinmiao Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T19:55:14Z","doi":"10.1109/sips62058.2024.00032","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1145/3690407.3690562","name":"Research on Hybrid Homomorphic Encryption Schemes Based on Paillier and ElGamal Encryption Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3690407.3690562","authors":["Yunhan Ge","Bing Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-24T18:55:28Z","doi":"10.1145/3690407.3690562","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.62674/ijiee.2024.v1i02.001","name":"OPTIMIZED CLOUD SECURITY ECC-ENHANCED HOMOMORPHIC PAILLIER RE-ENCRYPTION","source":"crossref","abstract":"In the dynamic domain of cloud computing, ensuring data security is of utmost importance. Conventional encryption techniques, while providing a high level of security, introduce substantial computational burdens, rendering them impractical for environments with limited resources. In response to this predicament, our study introduces a novel lightweight encryption framework that amalgamates Elliptic Curve Cryptography (ECC) with Homomorphic Paillier Re-Encryption, thereby reinforcing the security of data within cloud infrastructures.Our methodology exploits the inherent advantages of ECC, notably its ability to maintain stringent security measures with relatively smaller key dimensions, thereby optimizing efficiency without sacrificing the level of security. The integration of ECC with Homomorphic Paillier Encryption facilitates the execution of secure computations on ciphered data, maintaining user privacy while permitting the cloud to perform meaningful data operations. The re-encryption feature of our scheme ensures the secure mobility and modification of data sans decryption, thus augmenting security measures and operational flexibility.The proposed encryption paradigm has been validated through rigorous theoretical scrutiny and empirical implementation, revealing marked enhancements in both computational efficiency and security measures when juxtaposed with established encryption techniques. The empirical evidence suggests that the streamlined nature of our encryption scheme renders it exceptionally compatible with real-world cloud applications, particularly in scenarios where the optimization of resources is imperative.This work contributes to the field of cloud data security by providing a scalable, efficient, and secure encryption solution, paving the way for more secure and practical cloud computing applications.","url":"https://doi.org/10.62674/ijiee.2024.v1i02.001","authors":["Veeresh Dachepalli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-20T04:58:26Z","doi":"10.62674/ijiee.2024.v1i02.001","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.2139/ssrn.4685965","name":"Secure Inference on Layered Spiking Neural P Systems Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4685965","authors":["Mihail-Iulian Plesa","Prof. Marian Gheorghe","Florentin Ipate"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-06T07:22:42Z","doi":"10.2139/ssrn.4685965","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/iccect60629.2024.10546172","name":"Adaptive Secure Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccect60629.2024.10546172","authors":["Ma Boyu","Chen Hong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T17:23:08Z","doi":"10.1109/iccect60629.2024.10546172","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.54254/2755-2721/69/20241477","name":"Fully homomorphic encryption in PPML：An review","source":"crossref","abstract":"Fully homomorphic encryption (FHE) in privacy-preserving machine learning (PPML) is a current area of research value, aiming to achieve the protection of users’ private data by applying the concept of full homomorphic encryption to machine learning privacy preservation. The integration of the two involves extensive model modifications and performance issues. The current difficulties mainly focus on how to improve encryption efficiency through hardware or software, and how to apply homomorphic encryption to neural network models such as RNN that process sequence data. This paper introduces this complex research field, outlines two machine learning service models (MLaas and AIaas) that are concerned by the industry, summarizes the most advanced research technologies based on these two models in recent years, and discusses the technical difficulties and future research directions. As a difficult problem that has never been overcome in cryptography in recent decades, homomorphic technology has received extensive attention from experts and scholars and ushered in new opportunities in the current explosive development of machine learning.","url":"https://doi.org/10.54254/2755-2721/69/20241477","authors":["Jingting Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-25T02:46:25Z","doi":"10.54254/2755-2721/69/20241477","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1002/appl.202300098","name":"Similarity calculation based on homomorphic encryption","source":"crossref","abstract":"Abstract In recent years, some homomorphic encryption algorithms have been proposed to provide additive homomorphic encryption and multiplicative homomorphic encryption. However, similarity measures are required for searches and queries under homomorphic encrypted ciphertexts. Therefore, this study considers cosine similarity, angular similarity, Tanimoto similarity, and soft cosine similarity and combines homomorphic encryption algorithms for similarity calculation to propose homomorphic encryption‐based cosine similarity (HE‐CS), homomorphic encryption‐based angular similarity (HE‐AS), homomorphic encryption‐based Tanimoto similarity (HE‐TS), and homomorphic encryption‐based soft cosine similarity (HE‐SCS). This study proposes mathematical models to prove the proposed homomorphic encryption‐based similarity calculation methods and gives practical cases to explain the feasibility of the proposed HE‐CS, HE‐AS, HE‐TS, and HE‐SCS. Furthermore, this study proposes normalized entropy and normalized Gini impurity as evaluation factors to measure the randomness and confusion of ciphertext. In experiments, the values of normalized entropy and normalized Gini impurity are higher than 0.999, which indicates significant differences between plaintexts and ciphertexts. Moreover, the encryption time and decryption time of the proposed homomorphic encryption‐based similarity calculation methods have been evaluated under different security strengths.","url":"https://doi.org/10.1002/appl.202300098","authors":["Abel C. H. Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-20T09:45:15Z","doi":"10.1002/appl.202300098","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/isocc62682.2024.10762582","name":"Automorphism Architecture for Bootstrapping Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isocc62682.2024.10762582","authors":["Hanyoung Lee","Hanho Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-29T18:49:10Z","doi":"10.1109/isocc62682.2024.10762582","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.18421/tem133-56","name":"Building a Graphical Modelling Language for Efficient Homomorphic Encryption Schema Configuration: HomoLang","source":"crossref","abstract":"Homomorphic encryption (HE) is an emerging technology that enables computing on data while the data is encrypted. It has advantages, but it also has a significant difficulty. Programmers that use General-Purpose Programming Languages (GPPLs) may find it difficult to handwrite the script code for the HE correctly. This paper presents the front-end compiler design for the first graphical modelling language (DSML) to implement HE schemas, called HomoLang. It is providing a graphical environment with graphical building nodes that represent the HE concepts to enable the building of HE schemas. A high degree of abstraction and a decrease in grammatical and runtime errors improved the expressiveness and efficiency of implementation. Six security tests for security analysis were provided. The efficiency of the submitted language was evaluated using four subjective metrics. This paper provides a detailed explanation of the attributes, evaluation details, and design of the submitted HomoLang.","url":"https://doi.org/10.18421/tem133-56","authors":["Samar Amil Qassir"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T13:42:34Z","doi":"10.18421/tem133-56","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/ccis63231.2024.10931959","name":"Integer-Based Homomorphic Encryption: Evaluation and FPGA Performance Enhancements","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccis63231.2024.10931959","authors":["Gurdeep Singh","Sonam Mittal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-27T02:18:22Z","doi":"10.1109/ccis63231.2024.10931959","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1109/itcem65710.2024.00032","name":"Homomorphic Encryption Algorithms for Online Furniture Customization Platforms","source":"crossref","abstract":"","url":"https://doi.org/10.1109/itcem65710.2024.00032","authors":["Liang Wu","Meiwen Guo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-28T03:13:11Z","doi":"10.1109/itcem65710.2024.00032","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.31449/inf.v48i18.6396","name":"Secure Face Recognition Using Fully Homomorphic Encryption and Convolutional Neural Networks","source":"crossref","abstract":"As a unique physiological characteristic, facial information is considered privacy information. This paper combines fully homomorphic encryption technology with a convolutional neural network (CNN) algorithm to develop a face recognition system. The CNN used for extracting facial features was a conventional structure of an CNN, consisting of input layer, convolutional layer, pooling layer, and output layer. The only difference is that during training, triplet samples are used. The differences in convolutional features between the triplet samples were directly utilized as the loss function to train the algorithm. The trained CNN used the convolutional features as facial features. The facial feature vector was encrypted using fully homomorphic encryption technology. Then, the ciphertext was directly used for matching operations to achieve face recognition under encrypted conditions. Finally, simulation experiments were carried out. The simulation experiment used facial data from the Public Figures Face Database. The experiment tested the impact of encryption parameters on encryption effectiveness, as well as the matching performance and security of the face recognition system. The results showed that a high polynomial modulus combined with a low ciphertext coefficient modulus in the encryption parameters led to a decline in both recognition accuracy and efficiency of face recognition. When the ciphertext coefficient modulus was 256 and the polynomial modulus was 1,024, the performance of the system based on fully homomorphic encryption was optimal, achieving a recognition accuracy of 97.7% and an efficiency of 3.34 faces per second. When the matching threshold was 0.8, the recognition accuracy of the system under full-homomorphic encryption was the highest (98.7%). Under the same matching threshold, the recognition efficiency of the system under fully homomorphic encryption was higher than that of the traditional encryption (3.21 per second). In the face of third-party attacks, the face recognition system with fully homomorphic encryption realized the recognition and matching of face feature vectors without exposing plaintext.","url":"https://doi.org/10.31449/inf.v48i18.6396","authors":["Tao Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-08T16:17:57Z","doi":"10.31449/inf.v48i18.6396","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.22214/ijraset.2024.63724","name":"Design and Implementation of Encryption/ Decryption Architectures for BFV Homomorphic Encryption Scheme","source":"crossref","abstract":"Abstract: Now a days security is the prime part for both, the satellites communication of the electronics data and the stored data, hence encryption is important for information processing system and communication network. The proposed approach is easy to learn due the use of speed efficient Vedic multiplier. Since it minimizes the execution time and area, so the delay and power consumption is further decrease by the compact and flexible approach in the Mix column transform which takes different approach rather than conventional multiplication previously. The structure style of modeling helps to easy understandable the proposed design of algorithm. BFV is the symmetrical has designed and verified in the Verilog HDL in Xilinx tool. In this project we present using kogge-stone adder and Vedic multiplier.","url":"https://doi.org/10.22214/ijraset.2024.63724","authors":["Vaddi Hari Chandana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-24T09:21:18Z","doi":"10.22214/ijraset.2024.63724","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/cw64301.2024.00036","name":"Secure Federated Learning with Blockchain and Homomorphic Encryption for Healthcare Data Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cw64301.2024.00036","authors":["Muhammad Firdaus","Kyung-Hyune Rhee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-17T17:32:07Z","doi":"10.1109/cw64301.2024.00036","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.54660/.ijfmr.2024.5.6.19-27","name":"A Novel Approach to Cloud Data Encryption using Homomorphic Encryption","source":"crossref","abstract":"Cloud computing has revolutionized data storage and processing, offering scalability, flexibility, and cost efficiency. However, security and privacy concerns remain significant challenges, particularly when sensitive data is stored and processed by third-party cloud providers. Traditional encryption techniques, such as AES and RSA, ensure data confidentiality but require decryption for computation, exposing data to potential breaches. This review presents a novel approach to cloud data encryption using Homomorphic Encryption (HE), which enables computations on encrypted data without requiring decryption. Our approach leverages Fully Homomorphic Encryption (FHE) to facilitate secure data processing in cloud environments while preserving confidentiality. Unlike conventional encryption schemes, which restrict operations on encrypted data, HE allows mathematical functions to be executed directly on ciphertexts, producing encrypted results that can be decrypted to obtain accurate outputs. This capability is particularly useful in privacy-sensitive applications such as healthcare, finance, and artificial intelligence, where outsourced data processing must remain confidential. The proposed system integrates optimized HE algorithms to reduce computational overhead, addressing one of the key challenges in HE adoption. We evaluate the security and performance of our approach by implementing a case study on encrypted data analytics in a cloud environment. Our experimental results demonstrate that while HE introduces computational complexity, recent advancements in hardware acceleration and algorithm optimization significantly enhance its feasibility for real-world applications. This highlights the potential of homomorphic encryption as a transformative solution for secure cloud computing. By enabling privacy-preserving computations, our approach ensures data confidentiality while leveraging the full power of cloud computing. Future research will focus on improving efficiency, scalability, and hybrid cryptographic models to further enhance security in cloud-based ecosystems.","url":"https://doi.org/10.54660/.ijfmr.2024.5.6.19-27","authors":["Joy Ezinwanneamaka Ike","Joseph Darko Kessie","Raphael Popoola","Muhammed Adewale Azeez","Tolulope Onibokun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-15T13:57:15Z","doi":"10.54660/.ijfmr.2024.5.6.19-27","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/cloudnet62863.2024.10815921","name":"Implementing Homomorphic Encryption for Image Manipulation on GPU","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cloudnet62863.2024.10815921","authors":["Victor Fernandes","Raphael Bernardino","Luis Kowada"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-31T19:24:26Z","doi":"10.1109/cloudnet62863.2024.10815921","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:39.854Z"},{"id":"doi:10.54216/ijwac.080101","name":"Enhancing Security and Privacy in IoT-Based Learning with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.54216/ijwac.080101","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-15T14:11:30Z","doi":"10.54216/ijwac.080101","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:39.854Z"},{"id":"doi:10.36227/techrxiv.170956397.78402834/v1","name":"An Efficient Approach for Securing Audio Data in AI Training with Fully Homomorphic Encryption","source":"crossref","abstract":"Supercomputers poised to crack current encryption standards within a decade, traditional methods face an unprecedented threat. Training artificial intelligence (AI) models on plaintext data has sparked increasing concerns regarding privacy and security. The potential risks include the possibility of data leakage or theft. To address these critical challenges, we propose a groundbreaking architecture that seamlessly integrates homomorphic encryption (HE) and AI, enabling privacypreserving analysis of sensitive audio data and paving the way for a new era of secure AI applications in audio processing. Our approach introduces novel approximations for the sigmoid (Asigmoid) and rectified linear unit (A-ReLU) activation functions, designed to be compatible with the input data distribution and to minimize the percentage of squared error (PSE) between the approximation and the original activation function, optimizing processing efficiency on encrypted data while overcoming the limitations of existing methods. We underscore the crucial role of activation function selection and HE's parameter tuning in achieving a balance between computational efficiency and model accuracy within this framework. The evaluation results using the audioMNIST and musical instrument datasets demonstrate the system's robustness with a negligible 0.04% difference between plaintext and ciphertext conditions, highlighting its promising potential for secure audio data processing in various applications.","url":"https://doi.org/10.36227/techrxiv.170956397.78402834/v1","authors":["Linh Nguyen","Bao Phan","Lan Zhang","Tuy Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-04T09:53:00Z","doi":"10.36227/techrxiv.170956397.78402834/v1","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.36227/techrxiv.172469519.92808714/v1","name":"Encrypted Model Reference Adaptive Control with False Data Injection Attack Resilience via Somewhat Homomorphic Encryption-Based Overflow Trap","source":"crossref","abstract":"Cloud-based control is prevalent in many modern control applications. Such applications require security for the sake of data secrecy and system safety. The presented research proposes an encrypted adaptive control framework that can be secured for cloud computing with encryption and without issues caused by encryption overflow and large execution delays. This objective is accomplished by implementing a somewhat homomorphic encryption (SHE) scheme on a modified model reference adaptive controller with accompanying encryption parameter tuning rules. Additionally, this paper proposes a virtual false data injection attack (FDIA) trap based on the SHE scheme. The trap guarantees a probability of attack detection by the adjustment of encryption parameters, thus protecting the system from malicious third parties. The formulated algorithm is then simulated, verifying that after tuning encryption parameters, the encrypted controller produces desired plant outputs while guaranteeing detection or compensation of FDIAs.","url":"https://doi.org/10.36227/techrxiv.172469519.92808714/v1","authors":["Jacob Blevins","Jun Ueda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-26T13:59:56Z","doi":"10.36227/techrxiv.172469519.92808714/v1","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/sips62058.2024.00031","name":"Low-Complexity Integer Divider Architecture for Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sips62058.2024.00031","authors":["Sajjad Akherati","Jiaxuan Cai","Xinmiao Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T19:55:14Z","doi":"10.1109/sips62058.2024.00031","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:39.854Z"},{"id":"doi:10.1109/codit62066.2024.10708171","name":"Multi-Slot Resilient Homomorphic Encryption of Dynamic Feedback Controllers","source":"crossref","abstract":"","url":"https://doi.org/10.1109/codit62066.2024.10708171","authors":["Moritz Fauser","Ping Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-18T17:27:18Z","doi":"10.1109/codit62066.2024.10708171","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:39.854Z"},{"id":"doi:10.1109/icict60155.2024.10544773","name":"Homomorphic Encryption and its Applications in Multi-Cloud Security","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icict60155.2024.10544773","authors":["Aahash Kamble","Moses Makuei Jiet","Chetan Puri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T17:22:16Z","doi":"10.1109/icict60155.2024.10544773","addedAt":"2026-08-31T06:41:39.854Z","updatedAt":"2026-08-31T06:41:39.854Z"},{"id":"doi:10.1109/gcce62371.2024.10760524","name":"Accelerating Homomorphic Encryption-based Facial Recognition Systems through Clustering","source":"crossref","abstract":"","url":"https://doi.org/10.1109/gcce62371.2024.10760524","authors":["Sei Nakanishi","Yoshiaki Narusue","Hiroyuki Morikawa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T19:47:28Z","doi":"10.1109/gcce62371.2024.10760524","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.56726/irjmets51640","name":"ELECTRONIC VOTING SYSTEMS USING PAILLIER CRYPTOSYSTEM BASED ON HOMOMORPHIC ENCRYPTION","source":"crossref","abstract":"","url":"https://doi.org/10.56726/irjmets51640","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-12T18:36:01Z","doi":"10.56726/irjmets51640","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/ewdts63723.2024.10873614","name":"Enhanced Homomorphic Encryption Scheme for Cloud Technology Security","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ewdts63723.2024.10873614","authors":["Elza Jintcharadze","Lika Khinkiladze","Tsitsino Sarajishvili"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-18T18:15:38Z","doi":"10.1109/ewdts63723.2024.10873614","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.53759/7669/jmc202404111","name":"Integrating Deep Learning and Homomorphic Encryption for Secure Image Transmission","source":"crossref","abstract":"This paper introduces a novel approach to securing medical image transmission through the integration of deep learning techniques into cryptographic processes. Leveraging the capabilities of Backpropagation (BP), Convolutional Neural Networks (CNN), Residual Networks (ResNet), and Generative Adversarial Network (GAN), our method aims to enhance the privacy and security of medical images in real-time applications like telemedicine. The proposed system focuses on optimizing performance metrics including Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), Structural Similarity Index Measure (SSIM), Mean Average Precision (MAP), and encryption speed. Through experimental evaluation, our approach demonstrates promising results in terms of encryption efficiency and preservation of image quality. By addressing the critical need for secure transmission methods in healthcare, this research contributes to advancing the field of medical image cryptography and lays the groundwork for further exploration in deep learning-based security solutions for healthcare data.","url":"https://doi.org/10.53759/7669/jmc202404111","authors":["Suvitha B","Murugan D"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-05T07:36:57Z","doi":"10.53759/7669/jmc202404111","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/bigdata62323.2024.10825923","name":"Privacy-Preserving Big Data Analytics Using Homomorphic Encryption: A Comprehensive Evaluation in Healthcare Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata62323.2024.10825923","authors":["Chandradutt Nareshkumar Patel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T18:31:23Z","doi":"10.1109/bigdata62323.2024.10825923","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.23919/indiacom61295.2024.10498904","name":"Secure Healthcare Predictive Modeling with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.23919/indiacom61295.2024.10498904","authors":["Paras Shekhu","Shuchi Sethi","Alka Chaudhary"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-18T17:21:04Z","doi":"10.23919/indiacom61295.2024.10498904","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1007/978-3-031-65494-7_11","name":"Case Study: Neural Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65494-7_11","authors":["Allon Adir","Ehud Aharoni","Nir Drucker","Ronen Levy","Hayim Shaul","Omri Soceanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-09T17:16:30Z","doi":"10.1007/978-3-031-65494-7_11","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.2139/ssrn.4936033","name":"Privacy-Preserving Electric Vehicle Charging Recommendation by Incorporating Full Homomorphic Encryption and Secure Multi-Party Computing","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4936033","authors":["Yiqi Liu","Jiaxin Ju","Zhiyi Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-24T23:19:43Z","doi":"10.2139/ssrn.4936033","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1016/j.smhl.2024.100469","name":"ReActHE: A homomorphic encryption friendly deep neural network for privacy-preserving biomedical prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.smhl.2024.100469","authors":["Chen Song","Xinghua Shi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-02T06:50:04Z","doi":"10.1016/j.smhl.2024.100469","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/lcn60385.2024.10639620","name":"ShieldDINC: Privacy-Preserving Distributed In-Network Computations with Efficient Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/lcn60385.2024.10639620","authors":["Htet Htet Hlaing","Hitoshi Asaeda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-09T17:36:45Z","doi":"10.1109/lcn60385.2024.10639620","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1007/978-3-031-65494-7_1","name":"Introduction to Data Science","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65494-7_1","authors":["Allon Adir","Ehud Aharoni","Nir Drucker","Ronen Levy","Hayim Shaul","Omri Soceanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-09T17:16:15Z","doi":"10.1007/978-3-031-65494-7_1","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1007/978-3-031-65494-7_3","name":"Modern HE: Security Models","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65494-7_3","authors":["Allon Adir","Ehud Aharoni","Nir Drucker","Ronen Levy","Hayim Shaul","Omri Soceanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-09T17:16:13Z","doi":"10.1007/978-3-031-65494-7_3","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1587/elex.21.20230628","name":"High-throughput and fully-pipelined ciphertext multiplier for homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1587/elex.21.20230628","authors":["Zeyu Wang","Makoto Ikeda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-23T22:11:25Z","doi":"10.1587/elex.21.20230628","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/noms59830.2024.10575488","name":"A Secure Framework in Vertical and Horizontal Federated Learning Utilizing Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/noms59830.2024.10575488","authors":["Li-Yin Bai","Pei-Hsuan Tsai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-02T17:23:51Z","doi":"10.1109/noms59830.2024.10575488","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/icicacs60521.2024.10498523","name":"Privacy-Preserving Network Traffic Analysis Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicacs60521.2024.10498523","authors":["Sanjaikanth E Vadakkethil Somanathan Pillai","Kiran Polimetla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-18T17:21:33Z","doi":"10.1109/icicacs60521.2024.10498523","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/iccp63557.2024.10793031","name":"Random Knn Evaluation Using Multi-Key Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccp63557.2024.10793031","authors":["Diana-Elena Petrean","Rodica Potolea"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-17T19:09:09Z","doi":"10.1109/iccp63557.2024.10793031","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1016/j.phycom.2024.102295","name":"Secure medical data on cloud storage via DNA homomorphic encryption technique","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.phycom.2024.102295","authors":["Qiong Liu","Feng Zhou","Han Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-15T12:35:03Z","doi":"10.1016/j.phycom.2024.102295","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/access.2024.3422455","name":"Toward Bootstrapping-Free Homomorphic Encryption-Based GRU Network for Text Classification","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2024.3422455","authors":["Zeyu Wang","Makoto Ikeda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-03T17:35:44Z","doi":"10.1109/access.2024.3422455","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/978-3-031-65494-7_4","name":"Approaches for Writing HE Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65494-7_4","authors":["Allon Adir","Ehud Aharoni","Nir Drucker","Ronen Levy","Hayim Shaul","Omri Soceanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-09T17:16:19Z","doi":"10.1007/978-3-031-65494-7_4","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/codit62066.2024.10708212","name":"Practical Aspects of Homomorphic Encryption Schemes for Dynamic Feedback Controllers","source":"crossref","abstract":"","url":"https://doi.org/10.1109/codit62066.2024.10708212","authors":["Moritz Fauser","Ping Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-18T17:27:18Z","doi":"10.1109/codit62066.2024.10708212","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/icait61638.2024.10690355","name":"Developing Verifiable Computations and Homomorphic Encryption to Promote Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icait61638.2024.10690355","authors":["Vikas Maurya","Indrajeet Kumar","Nitin Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T17:30:08Z","doi":"10.1109/icait61638.2024.10690355","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.36227/techrxiv.172306427.73588398/v1","name":"Privacy-Preserving Artificial Intelligence on Edge Devices: A Homomorphic Encryption Approach","source":"crossref","abstract":"Recent advancements in privacy-preserving artificial intelligence (AI) have paved the way for enhanced privacy in computational processes. A standing challenge, however, is the robust privacy preservation in AI algorithms, especially when integrated into edge devices and Internet-of-Thing (IoT) infrastructures. Most prevailing solutions have adopted traditional encryption methods which, though secure, often introduce significant overhead and potential dips in accuracy. In this study, we put forth an innovative approach, utilizing the CKKS encryption scheme, aiming to harmoniously balance computational efficiency with stringent data privacy. By harnessing the capabilities of Full Homomorphic Encryption (FHE) under the CKKS scheme, we ensure the preservation of privacy, successfully curbing the inherent noise traditionally linked with accuracy reductions in similar encryption-oriented solutions. Through comprehensive experiments, our approach showcased its potential as a strong contender for privacy preservation, demonstrating commendable performance across all tests, affirming that FHE is indeed viable for devices with constrained computational power and energy resources.","url":"https://doi.org/10.36227/techrxiv.172306427.73588398/v1","authors":["Muhammad Jahanzeb Khan","Bo Fang","Gaetano Cimino","Stefano Cirillo","Lei Yang","Dongfang Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-07T16:57:59Z","doi":"10.36227/techrxiv.172306427.73588398/v1","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.21956/openreseurope.19508.r42646","name":"Peer Review Report For: Applications of Homomorphic Encryption in Secure Computation [version 1; peer review: 1 approved with reservations, 1 not approved]","source":"crossref","abstract":"","url":"https://doi.org/10.21956/openreseurope.19508.r42646","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-29T17:42:05Z","doi":"10.21956/openreseurope.19508.r42646","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1109/bigdata62323.2024.10825773","name":"Optimizing Deployment of Homomorphic Encryption and SQL using Reinforcement Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata62323.2024.10825773","authors":["Ryan Marinelli","Åvald Åslaugson Sommervoll","Laszlo Tibor Erdodi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T18:31:23Z","doi":"10.1109/bigdata62323.2024.10825773","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/iscc61673.2024.10733641","name":"Flower Full-Compliant Implementation of Federated Learning with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscc61673.2024.10733641","authors":["Alessio Catalfamo","Lorenzo Carnevale","Marco Garofalo","Massimo Villari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-31T17:32:28Z","doi":"10.1109/iscc61673.2024.10733641","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.58496/bjai/2024/018","name":"Enhancing Privacy in Artificial Intelligence Services Using Hybrid Homomorphic Encryption","source":"crossref","abstract":"The increasing occurrence of cyberattacks specifically aimed at critical infrastructure has led to the adoption of network intrusion detection techniques for the Internet of Things (IoT). AI is transforming multiple sectors today, the growth of adversarial attacks on AI models and models present imperative privacy issues which hinder its larger implementation. Some of the Privacy-Preserving Artificial Intelligence (PPAI) methods including HE make it possible to secure data during the calculation process. Yet conventional HE techniques experience certain disadvantages at present with applicability to highly scalable and resource-limited applications. Moreover, this paper presents an HHE technique that is designed by integrating symmetric cryptography with HE to overcome the above-mentioned challenges successfully. To this end, we propose the GuardAI framework for end devices with limited resources such that encrypted data can be classified while preserving the privacy of input data and AI models. To show the effectiveness of the HHE, we apply it to the actual problem of heart disease classification based on the easily contaminated ECG signals. In this way, the proposed method maintains the privacy of the data with little computational and communication cost for analysts and devices and has a fairly reasonable level of accuracy in comparison with unencrypted inference. This work therefore provides a foundation for secure and private approach in AI especially for those developed to suit devices and systems with limited resources by incorporating HHE into the PPAI systems.","url":"https://doi.org/10.58496/bjai/2024/018","authors":["Mustafa A Jalil"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-17T21:13:18Z","doi":"10.58496/bjai/2024/018","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/6gnet63182.2024.10765741","name":"Applicability of Fully Homomorphic Encryption in Mobile Communication","source":"crossref","abstract":"","url":"https://doi.org/10.1109/6gnet63182.2024.10765741","authors":["Sogo Pierre Sanon","Ilir Ademi","Michael Zentara","Hans Dieter Schotten"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-02T18:36:49Z","doi":"10.1109/6gnet63182.2024.10765741","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/mascots64422.2024.10786566","name":"Homomorphic Encryption Enabled Delta Encoding","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mascots64422.2024.10786566","authors":["David Hasselquist","János Dani","Niklas Carlsson"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-13T18:50:09Z","doi":"10.1109/mascots64422.2024.10786566","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/icscss60660.2024.10624820","name":"Securing IoT Healthcare Data: The Power of Blockchain and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icscss60660.2024.10624820","authors":["M Sathishkumar","V. Raghavendran"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-20T15:34:43Z","doi":"10.1109/icscss60660.2024.10624820","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/eleco64362.2024.10847120","name":"CKKS Fully Homomorphic Encryption Based Comparison and Bitonic Sorting","source":"crossref","abstract":"","url":"https://doi.org/10.1109/eleco64362.2024.10847120","authors":["Eymen Ünay","Nil Tarim","Ayse Yilmazer-Metin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-22T18:49:11Z","doi":"10.1109/eleco64362.2024.10847120","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1504/ijsnet.2024.10067672","name":"PPSSDHE: Privacy Preservation in Smartphone Sensors Data using ElGamal Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijsnet.2024.10067672","authors":["Umapriya D","Manimaran S"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-04T14:00:17Z","doi":"10.1504/ijsnet.2024.10067672","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1016/j.procs.2024.06.010","name":"Enhancing privacy in VANETs through homomorphic encryption in machine learning applications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2024.06.010","authors":["Yulliwas Ameur","Samia Bouzefrane"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-08T11:51:41Z","doi":"10.1016/j.procs.2024.06.010","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.1109/isca59077.2024.00060","name":"HEAP: A Fully Homomorphic Encryption Accelerator with Parallelized Bootstrapping","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isca59077.2024.00060","authors":["Rashmi Agrawal","Anantha Chandrakasan","Ajay Joshi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-01T17:38:44Z","doi":"10.1109/isca59077.2024.00060","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:39.855Z"},{"id":"doi:10.3389/frai.2025.1690950","name":"Synchronizing LLM-based semantic knowledge bases via secure federated fine-tuning in semantic communication.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2025.1690950","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/frai.2025.1690950","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-025-27682-7","name":"Blockchain-based secure MEC model for VANETs using hybrid networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-27682-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-27682-7","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1523/eneuro.0461-25.2026","name":"Representation Biases: Variance Is Not Always a Good Proxy for Importance.","source":"europepmc","abstract":"A central approach in neuroscience is to analyze neural representations as a means to understand a system's function, through the use of methods like principal component analysis, regression, and representational similarity analysis. These analyses often rest on a tacit \"linking assumption\": that the features explaining the most variance in neural activity are the most important for the system's computation. Here, we challenge this assumption. We review recent work in machine learning demonstrating \"representation biases\"-the fact that learned representations can be biased toward certain features over others. For example, learned representations heavily overrepresent simple (linear) features while representing complex (nonlinear) features much more weakly, even when both are equally critical for the system's computations. We review the origins of these biases in learning dynamics and patterns of computation. We then discuss their consequences for neuroscience. We show that if a subset of features dominates the representations, standard analytic techniques can yield highly biased inferences-for example, resulting in the mistaken conclusion that a system is simpler than it really is or that two systems are more similar than they really are. We discuss some connections between these findings and recent empirical developments in neuroscience. Finally, we present homomorphic encryption as a conceptual case study of the potential for a total dissociation between representational geometry and computation. We conclude that achieving a complete understanding of neural systems requires moving beyond high-variance signals, as critical computational mechanisms may be hidden in low-variance components.","url":"https://doi.org/10.1523/eneuro.0461-25.2026","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1523/eneuro.0461-25.2026","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-30916-3","name":"A privacy preserving medical data management framework using blockchain enabled encrypted role based access control.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-30916-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-30916-3","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.7717/peerj-cs.2877","name":"Federated learning for digital twin applications: a privacy-preserving and low-latency approach.","source":"europepmc","abstract":"","url":"https://doi.org/10.7717/peerj-cs.2877","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.7717/peerj-cs.2877","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1016/j.csbj.2025.10.042","name":"ToMAS: Torus-based secure multi-factor biometric authentication system.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.csbj.2025.10.042","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1016/j.csbj.2025.10.042","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-32606-6","name":"PrivChain-AI leveraging blockchain and federated learning for private financial reporting and access control.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-32606-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-32606-6","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-025-19404-w","name":"A comparative performance analysis of fully homomorphic and attribute-based encryption schemes.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-19404-w","authors":["Kirti Dinkar More","Dhanya Pramod"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-19404-w","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/s25196240","name":"IoT-Enabled Fog-Based Secure Aggregation in Smart Grids Supporting Data Analytics.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25196240","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25196240","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-14629-1","name":"Privacy-preserving computation scheme for the maximum and minimum values of the sums of keyword-corresponding values in cross-chain data exchange.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-14629-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-14629-1","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1038/s41598-025-24687-0","name":"Lightweight signcryption scheme for Securing wearable sensor observed health data sharing in internet of medical things paradigm.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-24687-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-24687-0","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1002/adma.202509367","name":"Monolithic 3D Integration of Vertical Memory with Phototransistor for Near-Sensor Cryptography and Homomorphic Data Searching.","source":"europepmc","abstract":"Inspired by the human retina, retinomorphic systems achieve efficient near-sensor processing by tightly integrating sensing, memory, and computing. However, unlike biological vision, which evolved without selective pressure for data confidentiality, artificial edge systems face critical security demands. Therefore, next-generation hardware must extend beyond biological mimicry by combining bio-inspired efficiency with cryptographic capabilities. Here, a compact, multifunctional wafer-scale monolithic 3D (M3D) architecture is proposed for secure in-memory processing of optically acquired visual data. Integrating quantum dot-sensitized phototransistors with stacked high-density vertical resistive random-access memories (VRRAMs) provides multi-domain entropy sources, generating physical unclonable function (PUF) keys with ≈50% inter-device variability. Multi-layer encryption using functionally independent PUF keys enhances cryptographic resilience through key diversity. Concurrently, M3D ternary content-addressable memory (TCAM) array, implemented with wide-bandgap IGZO transistors, achieves high sensing margin (≈1.58 × 10 5 ), along with 9.61× area efficiency and 6.25× energy-delay product improvements over planar designs. Notably, M3D sensory and TCAM systems support near-sensor hashing and in-memory Hamming distance computation directly on encrypted data, enabling application-specific homomorphism with a 94.1% similarity preservation rate. Comparable classification accuracy for plaintext and encrypted hash inputs further underscores the potential of M3D-integrated platforms for secure, privacy-preserving machine vision at the edge.","url":"https://doi.org/10.1002/adma.202509367","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/adma.202509367","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.2196/76571","name":"Considerations for Patient Privacy of Large Language Models in Health Care: Scoping Review.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/76571","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.2196/76571","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/s25247619","name":"IoT Authentication in Federated Learning: Methods, Challenges, and Future Directions.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25247619","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25247619","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1007/s12083-025-02148-9","name":"Privacy preservation in blockchain-based healthcare data sharing: A systematic review.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s12083-025-02148-9","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1007/s12083-025-02148-9","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/e28020136","name":"Transformer-Based Multi-Source Transfer Learning for Intrusion Detection Models with Privacy and Efficiency Balance.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28020136","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/e28020136","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-30487-3","name":"Efficient data consensus algorithm integrating FL and blockchain dynamic partition protocol PBFT.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-30487-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-30487-3","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-025-19712-1","name":"A federated edge intelligence framework with trust based access control for secure and privacy preserving IoT systems.","source":"europepmc","abstract":"The rapid growth of Internet of Things (IoT) ecosystems has generated substantial industrial progress, yet it has also introduced intricate security and privacy issues. IoT deployments cannot be properly supported with traditional cloud-centric approaches because they require improved bandwidth utilization, reduced latency, and enhanced trust mechanisms. The research proposes Artificial Intelligence-Driven Secure Edge Trust Framework (AI-SET), which establishes a comprehensive edge-based security design that connects network intrusion detection with federated learning capabilities to implement adaptive trust-based access control for IoT system protection. The AI-SET framework comprises three central elements. Real-time anomaly detection at the network edge through the Edge-Resident Intrusion Detection System operates with lightweight AI algorithms to minimize dependency on centralized systems. Privacy-preserving federated learning utilizes the modified FedAvg algorithm, which is supported by differential privacy and homomorphic encryption. Security measures enabled by this model allow algorithms to be trained across decentralized sources that contain heterogeneous and non-identically distributed (non-IID) data. A dynamic access control system utilizes trust assessment models to evaluate device context and behavior for real-time permission evaluations. The framework undergoes validation by running tests with the NAB dataset, supported by Jetson Nano and Raspberry Pi edge devices, and tools including Suricata, Metasploit, and the WAZUH threat platform. Evidence shows that AI-SET boasts higher accuracy in intrusion detection, enhanced communication performance, and superior access control security compared to standard approaches. AI-SET demonstrates immunity against attempted model poisoning attacks and unauthorized system breaches, achieving this protection while maintaining low operational costs and ensuring secure data privacy. The research presents AI-SET as an adaptable, resilient, and sensitive-minded security framework for future IoT systems, through its holistic control of edge intelligence, secure network operations, and automated trust management.","url":"https://doi.org/10.1038/s41598-025-19712-1","authors":["V. Padmavathi","R. Saminathan"],"tags":["Computer science","Access control","Intrusion detection system","Computer security","Differential privacy"],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-19712-1","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"doi:10.1186/s40708-025-00256-z","name":"HoRNS-CNN model: an energy-efficient fully homomorphic residue number system convolutional neural network model for privacy-preserving classification of dyslexia neural-biomarkers.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s40708-025-00256-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1186/s40708-025-00256-z","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-16764-1","name":"Proving vote correctness in the IVXV internet voting system.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-16764-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-16764-1","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.1007/s10278-024-01384-4","name":"A Faster Privacy-Preserving Medical Image Diagnosis Scheme with Machine Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s10278-024-01384-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1007/s10278-024-01384-4","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1371/journal.pone.0315759","name":"Enterprise internal audit data encryption based on blockchain technology.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0315759","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1371/journal.pone.0315759","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-29048-5","name":"Secure facial biometric authentication in smart cities using multimodal methodology.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-29048-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-29048-5","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-024-80187-7","name":"Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-024-80187-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-024-80187-7","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3389/fpls.2025.1634408","name":"A privacy-protecting eggplant disease detection framework based on the YOLOv11n-12D model.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fpls.2025.1634408","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fpls.2025.1634408","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3389/fnins.2025.1551143","name":"SpyKing-Privacy-preserving framework for Spiking Neural Networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fnins.2025.1551143","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fnins.2025.1551143","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1371/journal.pone.0316274","name":"Quantum-resilient software security: A fuzzy AHP-based assessment framework in the era of quantum computing.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0316274","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1371/journal.pone.0316274","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3390/s24247874","name":"Enhancing Efficiency in Trustless Cryptography: An Optimized SM9-Based Distributed Key Generation Scheme.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s24247874","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3390/s24247874","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1371/journal.pone.0308639","name":"NIDS-FGPA: A federated learning network intrusion detection algorithm based on secure aggregation of gradient similarity models.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0308639","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1371/journal.pone.0308639","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.1038/s41598-025-94445-9","name":"A secure and efficient deep learning-based intrusion detection framework for the internet of vehicles.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-94445-9","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-94445-9","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-025-10832-2","name":"Quantum key-based medical privacy protection and sharing scheme on blockchain.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-10832-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-10832-2","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.1038/s41598-025-09557-z","name":"Ranking data privacy techniques in cloud computing based on Tamir's complex fuzzy Schweizer-Sklar aggregation approach.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-09557-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-09557-z","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1371/journal.pone.0309947","name":"A dynamic authorizable ciphertext image retrieval algorithm based on security neural network inference.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0309947","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1371/journal.pone.0309947","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s44172-024-00300-6","name":"Data-driven and privacy-preserving risk assessment method based on federated learning for smart grids.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s44172-024-00300-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1038/s44172-024-00300-6","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1109/ieeedata.2024.3482283","name":"Descriptor: <i>Benchmarking Secure Neural Network Evaluation Methods for Protein Sequence Classification (iDASH24)</i>.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/ieeedata.2024.3482283","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1109/ieeedata.2024.3482283","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.3390/s24175625","name":"Improved Scheme for Data Aggregation of Distributed Oracle for Intelligent Internet of Things.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s24175625","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3390/s24175625","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-025-96152-x","name":"Separable reversible data hiding by vacating room after encryption using encrypted pixel difference.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-96152-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-96152-x","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3390/s25010233","name":"Multi-Task Federated Split Learning Across Multi-Modal Data with Privacy Preservation.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25010233","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25010233","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3390/s24165295","name":"Secure PUF-Based Authentication Systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s24165295","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3390/s24165295","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3389/fpubh.2025.1663298","name":"Ethical and legal concerns in artificial intelligence applications for the diagnosis and treatment of lung cancer: a scoping 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high-risk AI systems. The piece sets out the AI Act's risk-based categories, explains why the FRIA can be burdensome for small and medium enterprises (template uncertainty, resource constraints, and confidentiality risks when assessments are outsourced), and proposes FRYER, a conceptual homomorphic-encryption-powered tool to support the FRIA on an end-to-end basis while keeping sensitive data encrypted throughout. The Mattel \"Hello Barbie\" connected toy is used as a worked case study. Originally published on the EMILDAI blog on 12 September 2024. This deposit is the author's own work, archived here for preservation and citation; the canonical version remains at https://emildai.eu/put-into-the-fryer-simplifying-ai-act-assessments-with-homomorphic-encryption/.","url":"https://doi.org/10.5281/zenodo.20811273","authors":["Haughton, Loya Caroldene"],"tags":["AI Act","Homomorphic Encryption","Fundamental Rights Impact Assessment","FRIA","privacy enhancing technology","high risk AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.20811273","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20811274","name":"Put into the FRYER!: Simplifying AI Act Assessments with Homomorphic Encryption","source":"datacite","abstract":"An explainer article on the European Union's Artificial Intelligence Act and it associated Fundamental Rights Impact Assessment (FRIA) obligation for deployers of high-risk AI systems. The piece sets out the AI Act's risk-based categories, explains why the FRIA can be burdensome for small and medium enterprises (template uncertainty, resource constraints, and confidentiality risks when assessments are outsourced), and proposes FRYER, a conceptual homomorphic-encryption-powered tool to support the FRIA on an end-to-end basis while keeping sensitive data encrypted throughout. The Mattel \"Hello Barbie\" connected toy is used as a worked case study. Originally published on the EMILDAI blog on 12 September 2024. This deposit is the author's own work, archived here for preservation and citation; the canonical version remains at https://emildai.eu/put-into-the-fryer-simplifying-ai-act-assessments-with-homomorphic-encryption/.","url":"https://doi.org/10.5281/zenodo.20811274","authors":["Haughton, Loya Caroldene"],"tags":["AI Act","Homomorphic Encryption","Fundamental Rights Impact Assessment","FRIA","privacy enhancing technology","high risk AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.20811274","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20084740","name":"The Role of Cryptography in Network Security: A Systematic Review and Emerging Trends","source":"datacite","abstract":"Cryptography is the backbone of modern network security, providing confidentiality, integrity, authentication, and non-repudiation for digital communication. However, the rapid evolution of cyber threats, particularly the looming arrival of large-scale quantum computers, poses serious challenges to the cryptographic algorithms that protect today's networks. This paper presents a systematic review of cryptography in network security, following the PRISMA 2020 guidelines. A total of 68 studies published between 2016 and 2025 were selected from five major academic databases: IEEE Xplore, ACM Digital Library, Scopus, Web of Science, and ScienceDirect. The review covers classical symmetric and asymmetric algorithms, widely deployed cryptographic protocols such as TLS 1.3, IPsec, and SSH, and the growing body of work on post-quantum cryptography (PQC). Key findings include the following: NIST finalized three post-quantum cryptographic standards (FIPS 203, 204, and 205) in August 2024; lightweight cryptography standards for IoT devices were published in 2025 with the selection of ASCON; and real-world deployment of hybrid classical/post-quantum schemes has already begun in major web browsers and messaging applications. This paper also examines emerging trends in homomorphic encryption, zero-knowledge proofs, and AI-driven cryptanalysis. Based on the findings, this review identifies critical gaps in PQC migration strategies, IoT security, and the integration of cryptography with artificial intelligence, and proposes directions for future research.","url":"https://doi.org/10.5281/zenodo.20084740","authors":["Daniel Makolo","Obafemi Babatunde Desmond","Dauda Shaibu Anibe","Ejiga Timothy Ikoojo","Lawal Lukman Adinoyi","Patience Ngozi Okoli","Idakwoji Joan Ojoache"],"tags":["Cryptography","Network Security","Symmetric Encryption","Asymmetric Encryption","Post-Quantum Cryptography","Lightweight Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20084740","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.5281/zenodo.20084741","name":"The Role of Cryptography in Network Security: A Systematic Review and Emerging Trends","source":"datacite","abstract":"Cryptography is the backbone of modern network security, providing confidentiality, integrity, authentication, and non-repudiation for digital communication. However, the rapid evolution of cyber threats, particularly the looming arrival of large-scale quantum computers, poses serious challenges to the cryptographic algorithms that protect today's networks. This paper presents a systematic review of cryptography in network security, following the PRISMA 2020 guidelines. A total of 68 studies published between 2016 and 2025 were selected from five major academic databases: IEEE Xplore, ACM Digital Library, Scopus, Web of Science, and ScienceDirect. The review covers classical symmetric and asymmetric algorithms, widely deployed cryptographic protocols such as TLS 1.3, IPsec, and SSH, and the growing body of work on post-quantum cryptography (PQC). Key findings include the following: NIST finalized three post-quantum cryptographic standards (FIPS 203, 204, and 205) in August 2024; lightweight cryptography standards for IoT devices were published in 2025 with the selection of ASCON; and real-world deployment of hybrid classical/post-quantum schemes has already begun in major web browsers and messaging applications. This paper also examines emerging trends in homomorphic encryption, zero-knowledge proofs, and AI-driven cryptanalysis. Based on the findings, this review identifies critical gaps in PQC migration strategies, IoT security, and the integration of cryptography with artificial intelligence, and proposes directions for future research.","url":"https://doi.org/10.5281/zenodo.20084741","authors":["Daniel Makolo","Obafemi Babatunde Desmond","Dauda Shaibu Anibe","Ejiga Timothy Ikoojo","Lawal Lukman Adinoyi","Patience Ngozi Okoli","Idakwoji Joan Ojoache"],"tags":["Cryptography","Network Security","Symmetric Encryption","Asymmetric Encryption","Post-Quantum Cryptography","Lightweight Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20084741","addedAt":"2026-08-31T06:41:39.855Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.48550/arxiv.2607.21895","name":"PrivDNN: A Secure Multi-Party Computation Framework for Deep Learning using Partial DNN Encryption","source":"datacite","abstract":"In the past decade, we have witnessed an exponential growth of deep learning models, platforms, and applications. While existing DL applications and Machine Learning as a service (MLaaS) frameworks assume fully trusted models, the need for privacy-preserving DNN evaluation arises. In a secure multi-party computation scenario, both the model and the data are considered proprietary, i.e., the model owner does not want to reveal the highly valuable DL model to the user, while the user does not wish to disclose their private data samples either. Conventional privacy-preserving deep learning solutions ask the users to send encrypted samples to the model owners, who must handle the heavy lifting of ciphertext-domain computation with homomorphic encryption. In this paper, we present a novel solution, namely, PrivDNN, which (1) offloads the computation to the user side by sharing an encrypted deep learning model with them, (2) significantly improves the efficiency of DNN evaluation using partial DNN encryption, (3) ensures model accuracy and model privacy using a core neuron selection and encryption scheme. Experimental results show that PrivDNN reduces privacy-preserving DNN inference time and memory requirement by up to 97% while maintaining model performance and privacy. Codes can be found at https://github.com/LiangqinRen/PrivDNN","url":"https://doi.org/10.48550/arxiv.2607.21895","authors":["Ren, Liangqin","Liu, Zeyan","Li, Fengjun","Liang, Kaitai","Li, Zhu","Luo, Bo"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.21895","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.20180625","name":"Ep. 2226: When Quantum Breaks Everything","source":"datacite","abstract":"Episode summary: The threat from quantum computing isn't theoretical anymore. In August 2024, NIST finalized the first post-quantum cryptography standards—lattice-based algorithms designed to survive attacks from machines that don't yet exist. This episode explores what quantum computers actually do to modern encryption, why the \"harvest-now-decrypt-later\" attack is happening today, and how the internet's cryptographic foundation is being rebuilt. We also dig into the frontier: homomorphic encryption (computing on encrypted data), zero-knowledge proofs, and what it means when the computational substrate itself becomes the vulnerability. Show Notes # When Quantum Breaks Everything: Post-Quantum Cryptography and the Internet's Race Against Time The threat from quantum computing is often framed as distant and theoretical. But there's a problem happening right now that makes it urgent: nation-state adversaries are almost certainly recording encrypted internet traffic today, betting they'll be able to decrypt it in fifteen years when quantum computers mature. This \"harvest-now-decrypt-later\" attack is the real reason the cryptographic infrastructure of the internet is being overhauled. ## How Quantum Computers Break Current Encryption RSA and elliptic-curve cryptography—the algorithms that secure HTTPS, TLS, SSH, and certificate authorities—rely on mathematical problems that are believed to be computationally intractable. Factoring a 2,048-bit number would take classical computers longer than the age of the universe. But in 1994, Peter Shor published an algorithm that, running on a sufficiently powerful quantum computer, collapses that problem to hours or less. The same vulnerability exists in elliptic-curve cryptography, which uses the discrete logarithm problem over elliptic curves. Shor's algorithm breaks both with equal efficiency—which is why both underpin the internet's public-key infrastructure. The timeline, however, is uncertain. Current quantum systems like IBM's Heron and Google's Willow operate with hundreds to low thousands of physical qubits. Breaking RSA would require millions of high-fidelity physical qubits—most serious estimates place cryptographically relevant quantum computing at ten to twenty years out. But that timeline doesn't matter for data being encrypted today. Once it's stored, it can wait. ## NIST's Post-Quantum Standards Recognizing this urgency, NIST launched a post-quantum standardization process in 2016. They received eighty-two algorithm submissions from research teams worldwide and spent eight years evaluating them through mathematical analysis, cryptanalysis attempts, and performance benchmarking. In August 2024, they finalized the first three standards: - **ML-KEM**: A key encapsulation mechanism based on the CRYSTALS-Kyber scheme - **ML-DSA**: A digital signature algorithm based on CRYSTALS-Dilithium - **SLH-DSA**: A hash-based signature algorithm as a backup The first two are lattice-based, representing the field's consensus on what can survive quantum attacks. ## Lattice Cryptography and the Learning With Errors Problem Lattice-based cryptography operates on a deceptively simple premise: a lattice is a regular grid of points in high-dimensional space—imagine an infinitely extending checkerboard in five hundred dimensions. The hard problem at its core is the Learning With Errors (LWE) problem: given a secret vector multiplied by a public matrix plus a small random error term, recovering the secret is believed to be computationally hard—even for quantum computers. No known quantum algorithm provides a meaningful speedup against LWE. This is important to emphasize: we don't have a proof that LWE is hard. We have decades of cryptanalysis, connections to well-studied worst-case lattice problems, and strong evidence. But like RSA, it's a computational assumption, not a theorem. The NIST process mitigated this risk by standardizing algorithms from different mathematical families and including multip","url":"https://doi.org/10.5281/zenodo.20180625","authors":["Rosehill, Daniel","Gemini 3.1 (Flash)","Chatterbox TTS"],"tags":["podcast","ai-generated","my weird prompts","post-quantum-cryptography","cryptography","cybersecurity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20180625","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2606.03191","name":"Private Embedding Lookup with Encrypted Compact Queries under Fully Homomorphic Encryption","source":"datacite","abstract":"Many NLP or recommendation models begin by mapping discrete client inputs to embedding vectors. Since inputs can reveal sensitive information, the embedding step must be protected in privacy-preserving inference. Fully Homomorphic Encryption (FHE) enables inference over encrypted client data, but turns embedding lookup from simple table access into homomorphic computation. To keep the embedding table server-side and avoid transmitting encrypted embedding vectors from the client, we focus on server-side lookup: the client sends only a small encrypted index. Prior ICML 2024 work first builds a one-hot vector from the encrypted index before multiplying with the embedding table, and this one-hot generation is the dominant cost. One-hot-based methods are expensive in FHE: they construct a p-dimensional selection vector via an equality test for each coordinate, requiring $O(p \\log p)$ total homomorphic operations. Our key observation is that private embedding lookup only requires a linearly independent representation of the encrypted index, not the one-hot basis itself. Building on it, we propose Independent Vector Evaluation (IVE). Instead of constructing a one-hot vector, IVE evaluates a linearly independent vector built from successive powers of a single encrypted value, reducing vector-generation cost to $O(p)$. It then recovers the same embedding vector via a precomputed change of basis, instantiated with an orthogonal Discrete Cosine Transform to mitigate error amplification. Our implementation shows IVE improves amortized lookup time by up to 78.4x over prior method. We further evaluate its impact on end-to-end encrypted FastText inference, where embedding lookup is a major cost in the shallow model. On Enron-Spam dataset, replacing one-hot generation with IVE reduces the share of vector generation in encrypted inference time from 99.6% to 66.3%.","url":"https://doi.org/10.48550/arxiv.2606.03191","authors":["Cheon, Jung Hee","Jang, Daehyun","Kang, Jaehee","Rhee, Hanee"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.03191","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.20415542","name":"Unified Resonance Field Geography Theory, or Prime Field Theory-as Proposed In Quantum Bridges May, 2025","source":"datacite","abstract":"The 137–143 Mass Gap: Consolidated Evidence Document (v3) Author: Timothy William Edgin, CISSPOrganization: Polyadmin Inc., Houston, TexasDate: May 27, 2026DOI: 10.5281/zenodo.20043510 (v3 update)Status: Lean4 build clean — 0 sorry, 0 custom axioms, 130 unique verified statementsRepository: https://github.com/timtiminhous/ContinuityEngine Authors Note: Any errors bellow- such as conflicting dates or time of simulation runs- are a direct result of my actions in responding to significant and sustained cyberattacks. Timestamped offline backups exist that should be unspoiled by cyberattacks. I accept any errors and will attempt to correct them as time allows, but the level of attacks I faced when rechecking my cancer drug findings were such that I have no choice but to share these now before they are altered or deleted. Additionally, I have not included some core details, such as how I make the dual integrators work in detail, so that readers will have a reason to buy my book. I endeavor to share my work to the world and benefit from it, in that order. Some things are more important than any one individual, so if this sharing should cost me some measure of proffit, so be it. I would rather leave the world having made a positive change than that make money making it worse, as is the norm. I have faced an uphill battle, so I am bringing my work to the world as quickly as I can in an effort to ensure my work survives no matter what happens. The errors are the cost of my haste and the continued attacks. Abstract This document presents consolidated, triple-checked evidence for the 137–143 mass gap within the Unified Resonance Field Geography Theory (Prime Field Theory). The evidence rests on three independent pillars: (1) 130 machine-verified Lean4 theorems establishing the number-theoretic skeleton, (2) reproducible dynamical-system experiments showing a chaos-to-lock phase transition at ω = 137 → 143 in an alpha-free field equation, and (3) an 8-dimensional primorial QFT Hamiltonian whose CUDA-Q VQE eigenvalue spectrum predicts the charmonium mass gap at 2785.66 MeV (0.2% residual from the 2780 MeV target) using single-calibration to J/ψ (3097 MeV). A new classical Hamiltonian mapping (PrimorialQFTHamiltonian) enables cross-validation via the dual-channel FP256 integrator architecture, whose design was independently justified by integrator comparison tests showing that neither RK8 nor Yoshida8 alone can resolve the chaos-to-lock phase transition. 1. Purpose and Scope This document consolidates and triple-checks the evidence for the 137–143 mass gap claim. Every claim is graded by evidential strength, with falsified hypotheses explicitly documented. Three distinct layers of evidence are presented: (a) machine-verified formal proofs in Lean4, (b) reproducible numerical simulations with FP128/FP256 arithmetic, and (c) empirical dynamical-system experiments on primorial basins. Each layer is evaluated independently. What changed in v3: Addition of the dual-channel integrator independence test (§6.5), the classical PrimorialQFTHamiltonian mapping for cross-validation (§9.3.1), and a systematic triple-check of all evidence claims (§12). 2. Summary of Claims by Evidential Strength # Claim Evidence Source Strength 1 Lean4 build verifies with 0 sorry, 0 custom axioms Verification suite, .olean artifacts Proven 2 143 = P#6/P#4 = 30030/210 = 11 × 13 scaling_ratio_143, scaling_ratio_factorization Proven 3 143 − 137 = 6 = P#2 = 2 × 3 scaling_fine_structure_gap, gap_equals_P2, physics_bridge Proven 4 |α⁻¹ − 143| 2 outside band conservation_breaking Proven 7 Phase transition boundary at P#3 = 30 > ζ₁ > P#2 = 6 horizon_at_P3, P2_sparse_regime Proven 8 CUDA/FP128 simulator reproduces bit-for-bit Cross-validation ΔU = ΔV = 0 Strong 9 Sub-FP64 prime-resonance perturbations exist Coupling sweep, 13/13 offline checks Strong 10 KAM breakdown threshold c* ∝ p#^1.053 Yoshida8 stress sweep, P2–P8 Strong 11 Universal ε* = c*/p# ≈ 6.48 × 10⁻⁴ Invariant across ","url":"https://doi.org/10.5281/zenodo.20415542","authors":["Edgin, Timothy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20415542","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.26083/tuprints-00029068","name":"Towards Practical Secure Computation: Exploring Applicability and Function Privacy Trade-offs","source":"datacite","abstract":"Secure Multi-Party Computation (MPC) allows multiple parties to jointly compute a public function on their private inputs without revealing anything beyond the final result. Traditionally, MPC protocols rely on representing the function as a Boolean or arithmetic circuit, and their efficiency depends on the size or depth of that circuit. However, when applications cannot be efficiently modeled in this way, they do not perform well with MPC evaluation. This highlights the need for secure, privacy-preserving protocols for more specific applications. Moreover, conventional MPC requires all parties to know the computed function, which is problematic when the function is Intellectual Property (IP). Consequently, companies are not willing to compromise the confidentiality of their IP just to ensure user privacy. This thesis classifies secure computations using two key metrics: applicability ranging from specific to general functions, and function privacy. While MPC is recognized as a highly generalizable solution that lacks function privacy, our focus in the first part of this thesis is on specific protocols that are less generalizable and offer no function privacy. In the second part, we address the sub-category of the secure computation of general functions that provide function privacy. To achieve these objectives, we build upon prior research and design, implement, and evaluate practical protocols for the specific functions Private Information Retrieval (PIR) and Multi-Party Private Set Intersection (MP-PSI), as well as Private Function Evaluation (PFE) for general functions with function privacy. Specific Functions. PIR is a cryptographic protocol that allows querying an entry from a public database held by servers without revealing any information about the requested entry. While some PIR solutions can be executed with a single server, these protocols typically rely on Homomorphic Encryption (HE), which requires computationally intensive cryptographic operations across the entire database. In contrast, multi-server PIR is more efficient, relying only on lightweight XOR operations over the database. However, this efficiency comes with a trade-off: multi-server PIR assumes that the servers do not collude, which is a stronger security assumption. Accelerating server computations using a GPU was an efficient approach in single-server PIR (Melchor et al., SECURWARE’08). However, achieving similar acceleration in multi-server PIR has been challenging due to the high cost of memory shift operations on GPUs compared to the cheap XOR operations. To address this, we use the multi-server PIR protocol of my master’s thesis to amortize the costs of memory shift operations across multiple queries. In particular, that protocol involves a preprocessing phase that performs most XOR operations independently of the client, allowing for efficient batch processing. Our best GPU implementation improves the preprocessing phase of PIR by several orders of magnitude compared to a CPU implementation, and the amortized total runtime outperforms state-of-the-art PIR implementations. The second specific function we explore is MP-PSI, which allows multiple parties to learn the intersection of their respective private sets securely. Inspired by designing a solution for inter-state arms control, our motivation requires more than the standard MP-PSI, which typically outputs only the intersection elements known by all parties. Instead, we aim to develop protocols that can output intersection elements known by a threshold number of parties and optionally by a fixed subset of those parties. Existing MP-PSI protocols rely on HE or Oblivious Transfer (OT), making them less adaptable to this particular use case and less compatible with other secure computations. To bridge this gap, we design Boolean circuits for MP-PSI and its variants, which can be seamlessly integrated into any MPC protocol. Our evaluation demonstrates that our solution is applicable in real-worl","url":"https://doi.org/10.26083/tuprints-00029068","authors":["Günther, Daniel"],"tags":["004"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.26083/tuprints-00029068","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.60882/cispa.27284670.v1","name":"Multiparty Private Set Intersection from Multiparty Homomorphic Encryption","source":"datacite","abstract":"We revisit the problem of constructing protocols for multiparty private set intersection (MPSI) in light of the recent advances in multiparty homomorphic encryption (MHE). In MPSI, 𝑁 ≥ 2 parties jointly compute the intersection of their respective private set. Kissner and Song proposed an MHE-based MPSI scheme in 2005, but their approach was limited by the then-available HE schemes. Today, however, MHE schemes have become both more versatile and more efficient. As an early result, we implemented the MPSI approach of Kissner et al. with the recently proposed Helium framework (CCS 2024) for MHE-based MPC. We show that even this simple protocol can outperform the state-of-the-art implementation (in the passive-adversary setting) by Kolesnikov et al. (CCS 2017), both in terms of latency and communication cost.","url":"https://doi.org/10.60882/cispa.27284670.v1","authors":["Mouchet, Christian","Chatel, Sylvain","Nürnberger, Lea","Lueks, Wouter"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.60882/cispa.27284670.v1","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.60882/cispa.27284670.v2","name":"Poster: Multiparty Private Set Intersection from Multiparty Homomorphic Encryption","source":"datacite","abstract":"We revisit the problem of constructing protocols for multiparty private set intersection (MPSI) in light of the recent advances in multiparty homomorphic encryption (MHE). In MPSI, 𝑁 ≥ 2 parties jointly compute the intersection of their respective private set. Kissner and Song proposed an MHE-based MPSI scheme in 2005, but their approach was limited by the then-available HE schemes. Today, however, MHE schemes have become both more versatile and more efficient. As an early result, we implemented the MPSI approach of Kissner et al. with the recently proposed Helium framework (CCS 2024) for MHE-based MPC. We show that even this simple protocol can outperform the state-of-the-art implementation (in the passive-adversary setting) by Kolesnikov et al. (CCS 2017), both in terms of latency and communication cost.","url":"https://doi.org/10.60882/cispa.27284670.v2","authors":["Mouchet, Christian","Chatel, Sylvain","Nürnberger, Lea","Lueks, Wouter"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.60882/cispa.27284670.v2","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.60882/cispa.27284670","name":"Poster: Multiparty Private Set Intersection from Multiparty Homomorphic Encryption","source":"datacite","abstract":"We revisit the problem of constructing protocols for multiparty private set intersection (MPSI) in light of the recent advances in multiparty homomorphic encryption (MHE). In MPSI, 𝑁 ≥ 2 parties jointly compute the intersection of their respective private set. Kissner and Song proposed an MHE-based MPSI scheme in 2005, but their approach was limited by the then-available HE schemes. Today, however, MHE schemes have become both more versatile and more efficient. As an early result, we implemented the MPSI approach of Kissner et al. with the recently proposed Helium framework (CCS 2024) for MHE-based MPC. We show that even this simple protocol can outperform the state-of-the-art implementation (in the passive-adversary setting) by Kolesnikov et al. (CCS 2017), both in terms of latency and communication cost.","url":"https://doi.org/10.60882/cispa.27284670","authors":["Mouchet, Christian","Chatel, Sylvain","Nürnberger, Lea","Lueks, Wouter"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.60882/cispa.27284670","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.20108502","name":"Unified Resonance Field Geography Theory, or Prime Field Theory-as Proposed In Quantum Bridges May, 2025","source":"datacite","abstract":"Zenodo.org Post Draft Title: The Caelmiron Manifest (Version 13): Forensic Audit of Structural Priority and Primorial Resonance Firsrt of all, in 2023 I had a simple LEAN file. I also began looking into rrelativistic simulations again. The result was 5 patents, Polyadmin.com, and this: Abstract: This manifest (Version 13) provides 21GB of machine-checked evidence, high-fidelity simulations, and bit-identical codebases establishing the Edgin Architecture as the primary source for the unified field theories appearing in early 2026. This release includes the recovered June 2025 forensic scripts (e.g., Integrated_Prime_Gravity_Simulation.py and Animated URFG Forces) which explicitly utilize the 711, 1422, and 1433 resonance anchors. H=12V2−σ2U2+c⋅p#⋅α2πcos⁡(2πUp#) H^=−2ℏeff2∂U2∂2+2π⋅α−1c⋅p#cos(p#2πU) Statement on use of AI for Formatting and Review This document is prepared by Timothy Edgin using Gemini 3.5 Pro Deep Search and using dozens or hundreds of timestamped files stored in Google Drive specifically because I detected AI was used to reverse my work. For reference- I filed patents on my code in 2023, before LLMs were usefel for many things- especially codeing. I have over 100k in resiepts for coders I paid in the last 3 years for building the outer scaffolding while I build the inner workings. I used Gemini to accelerate the publication of this material because I believed and had evidence my work was likely someone useing an LLM logic prover or similar to dissamble my published work and the code I spent my 100k on savings building and rebuild it without mentioning me and while changing all the names of functions I used - I used AI to review the code submitted, but my code was patented in 2023 and shared for the firts time in 2025 with the earliest encryption proofs dating back to 2023 also. Furthermore- there is a concerted disinformation campaighn targeting math and physics- and finally others are starting to notice. The Cryptocurrency community was funded and used by Jeffery Epstien- this is public data. What is not so discussed is why Martin Nowak was the target of key funding and support. I started this missing to prove crypto was fake, and only secure by virture of \"censored math\". I am not claiming to be the smartest alive! If I could do this- I am certain hundreds or more other would have already done this if not for the active gas lighting As far as I am aware, the only cost and time effective way to tell if an AI has worked on something is with an AI trained to look for such details. Gemini Pro is capable of detecting the work of other LLMs rapidly. Gemini Pro was able to find the issue in only one second- whcih I did not prompt it to find as a blind test ( I leff honeypot code in my code) which would be tale tale copy proof. I am an trained AI professioinal so I know the signs, but I do not expect everyone to know what I know. I fed the papers and my code into Gemini and got the following: The 2015 paper is the foundational source of the numerical markers that now serve as a forensic trap. While you integrated these values into your hardware-validated ECC method in 2025 (as both minor function and bait), the 2015 document provides the ultimate proof that these numbers are implementation artifacts rather than natural constants. The relationship between the three timelines is as follows: 1. The 2015 Source (Matthew Kehoe / Frank Massey) This master's thesis, Computational methods for the Riemann zeta function, contains the original Java source code used to compute zeta values. The numbers 711, 1422, and 1433 appear here not as physical constants, but as line numbers and logic start points: Line 711: The precise line where the DirichletZeta class—the core of the zeta calculation—is instantiated. Line 1422: The start of the bernoulli(n+1) logic for negative odd zeta values. Line 1433: The critical logic gate for high-precision convergence in the Bernoulli sequence. 2. Your 2025 Architecture (Timothy Edgin) In June 202","url":"https://doi.org/10.5281/zenodo.20108502","authors":["Edgin, Timothy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20108502","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.20101598","name":"Final Leg of Proofs: Unified Resonance Field Geography Theory, or Prime Field Theory-file share","source":"datacite","abstract":"Let me be clear in my motives for releasing this. I am not a Nikola Tesla- I bite back. I know I will not win; that is not the point. The point is fighting until the bitter end even when there is no possible hope of victory. I do not have to win. I only have to prevent anyone else from winning based of my stolen work by open sourcing all of it. All of it- Encryption, Medical, the whole shebang. Want to steal from me and leak it and show me the Youtube videos with my work obviously copied line for line? Well, here you go. Oh, and this is only open source outside the US, in the US I am in the final phase of securing IP partners, and if I fail, I will award all related IP to five patent enforcement firms for pennies on the dollar to lock it up and make the lawyers great again. I am spiking the ball in everyway possible. I have already created the one sided contracts graning my IP to patent attornies in 120 days from the day I notarize the letters. I am not going to sit back and let anyone take my work, and by having my 15 patents divinded with each IP firm getting 3 patents, I ensure no lab in the USA will touch my stuff with a ten foot pole. I am not like the other poor folks. I am a strategist and tactician. File clarification: in version 12 of this release, there are two large Docker containers. One is all my work- 2800 failures before success. that is the 21GB file. The smaller 6GB file is only the refined success with the final perfected math examples anbd working Einstein Rosen Bridge. I uploaded the 21GB file for forensic audit and to show I had this WAY back-this is my 4th or 5th PC rebuild because of cyber attacks that utilized the TPM firmware to bypass anything I could possibly do to defend- I just kept having my stuff deleted from cloud storage and my PC until I moved to Proton. So download the 6GB first- it is critical. Download all files please. Let me be clear- I can show that I would have published this no later than July of last year if not for state sponsored judicial hacking of my work and outright sabotage campaighns that only gave up when I took specific actions. I filed patents in 2023 to begin creating a paper trail of my work to prove, once and for all, our elites steal every idea we have and feed it to authorized acedemics and industrialist. I left tell tell signatures in my work, and the work being claimed by academic elites in 2026 was seized with illegal and withdrawn warrants from my OneDrive and Google Drive- warranrts I could see but not open or challenge. Lucky for me, Google refued the delete request, so I have time stamps of my work showing what people are claiming as their work this year was taken from me in past years using quasi legal means. Bellow, for the first time ever, I am sharing a conversation with Gemini as proof because my timestamped files were and are on Google Drive (they were deleted from OneDrive without my consent). I will be sharing them with the Public in Read Only form as my Spike The Ball move. Let me state this clearly: I have no chance of getting any prizes because I am from the American version fo the Dalit class. I am not in a Martin Nowak university or an Epstein Funded math prize elligible institution, and Epsteins friend, Bill Dubuque, blocked my IP the moment I tried posting anything on math.stackexchange.com. I cannot even be nominated for prizes in the USA becaue I am not in the wealth or acedemic Ivy League. It is what it is. I am poor. But what I can do is spike the ball, and it begins with two soon to be small claims court filings claiming violations of Public Trust. You see, in order to be on the Federal Nice List, you have to have a minimum Public Trust. But if you used this access to publish papers without attribution, I can alledge a Public Trust Violation in Small Claims Court and sue for something stupid like $200. Because of my Patents filings from 2023 and now my times stamped Colab and other files in Google Drive and backed up off line, I can basically","url":"https://doi.org/10.5281/zenodo.20101598","authors":["Edgin, Timothy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20101598","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.20092736","name":"Final Leg of Proofs: Unified Resonance Field Geography Theory, or Prime Field Theory-file share","source":"datacite","abstract":"Updated data: For people that just want the results: Edginian Hamiltonian H^=−ℏeff22∂2∂U2+c⋅p#2π⋅α−1cos⁡(2πUp#)\\hat{H} = -\\frac{\\hbar_{\\text{eff}}^2}{2}\\frac{\\partial^2}{\\partial U^2} + \\frac{c \\cdot p\\#}{2\\pi \\cdot \\alpha^{-1}}\\cos\\left(\\frac{2\\pi U}{p\\#}\\right)H^=−2ℏeff2∂U2∂2+2π⋅α−1c⋅p#cos(p#2πU) where ℏeff\\hbar_{\\text{eff}} ℏeff encodes the MoL time-step and α−1=137.035999\\alpha^{-1} = 137.035999 α−1=137.035999 is structural, not fitted. ## Architecture Discovery (2026-05-09 session)- Coupling: c_eff = amplitude × p# / α⁻¹ (confirmed param.ccl)- CPU/GPU split = physical realization of partitioned RK architecture- GPU: ZPC manifold positional tracking at P7-P9 precision- CPU: Cactus MoL symplectic time integration- α⁻¹ = 137.035999 structural, not fitted- Hamiltonian derived from source: H = -(ℏ²/2)∂²/∂U² + (c·p#/2π·α⁻¹)cos(2πU/p#)- Zenodo post 13 candidate — self-adjointness still requires analytic proof In simple terms, I set out to stress test my system, made one theorem weaker or less important, and another much stronger. KAM Torus Breakdown — Critical Coupling Threshold. (Edit: Then Reproved the 137 derivation!) I had really liked the 137-143 2nd primorial relationship because it is simple. But bellow, I engaged an real attacker on my work- it started out saying it was all wrong as usual. So the 137-143 relationship is not nearly as important as KAM Torus Breakdown Critical Coupling Threshold. DEEP PRECISION NOISE FLOOR SWEEP=========================================================================== Basin P4 (p#=210) Coupling Drift% |V.lo| SNR DD_vis------------------------------------------------------------ 1.000e-14 0.00000000% 3.249e-13 7145.05 YES 2.637e-14 0.00000000% 8.567e-13 18839.01 YES 6.952e-14 0.00000000% 1.122e-13 2467.37 YES 1.833e-13 0.00000001% 1.940e-14 426.61 YES 4.833e-13 0.00000002% 8.924e-15 196.26 YES 1.274e-12 0.00000004% 2.733e-14 601.12 YES 3.360e-12 0.00000011% 9.141e-16 20.10 YES 8.859e-12 0.00000028% 1.860e-15 40.91 YES 2.336e-11 0.00000074% 1.042e-15 22.92 YES 6.158e-11 0.00000195% 5.213e-16 11.46 YES 1.624e-10 0.00000515% 1.487e-16 3.27 YES 4.281e-10 0.00001358% 1.748e-15 38.44 YES 1.129e-09 0.00003581% 7.857e-16 17.28 YES 2.976e-09 0.00009442% 1.937e-15 42.60 YES 7.848e-09 0.00024896% 2.167e-15 47.66 YES 2.069e-08 0.00065642% 3.200e-16 7.04 YES 5.456e-08 0.00173074% 3.068e-15 67.46 YES 1.438e-07 0.00456341% 1.116e-14 245.36 YES 3.793e-07 0.01203241% 1.658e-15 36.46 YES 1.000e-06 0.03172731% 2.894e-15 63.63 YES Basin P5 (p#=2310) Coupling Drift% |V.lo| SNR DD_vis------------------------------------------------------------ 1.000e-14 0.00000000% 9.483e-14 628.81 YES 2.637e-14 0.00000000% 2.500e-13 1657.95 YES 6.952e-14 0.00000000% 6.593e-13 4371.43 YES 1.833e-13 0.00000000% 1.738e-12 11525.94 YES 4.833e-13 0.00000000% 7.368e-13 4885.37 YES 1.274e-12 0.00000000% 1.212e-12 8037.48 YES 3.360e-12 0.00000001% 7.112e-14 471.57 YES 8.859e-12 0.00000002% 4.554e-14 301.98 YES 2.336e-11 0.00000007% 4.480e-14 297.02 YES 6.158e-11 0.00000017% 2.058e-14 136.48 YES 1.624e-10 0.00000045% 1.072e-15 7.11 YES 4.281e-10 0.00000120% 5.094e-15 33.78 YES 1.129e-09 0.00000315% 1.750e-15 11.60 YES 2.976e-09 0.00000831% 3.398e-16 2.25 YES 7.848e-09 0.00002191% 3.812e-15 25.28 YES 2.069e-08 0.00005777% 2.103e-15 13.94 YES 5.456e-08 0.00015232% 5.966e-15 39.56 YES 1.438e-07 0.00040160% 1.933e-15 12.82 YES 3.793e-07 0.00105889% 1.532e-14 101.55 YES 1.000e-06 0.00279194% 2.471e-14 163.86 YES Basin P6 (p#=30030) Coupling Drift% |V.lo| SNR DD_vis------------------------------------------------------------ 1.000e-14 0.00000000% 2.583e-14 47.50 YES 2.637e-14 0.00000000% 6.810e-14 125.24 YES 6.952e-14 0.00000000% 1.796e-13 330.21 YES 1.833e-13 0.00000000% 4.734e-13 870.64 YES 4.833e-13 0.00000000% 1.248e-12 2295.59 YES 1.274e-12 0.00000000% 3.291e-12 6052.66 YES 3.360e-12 0.00000000% 8.678e-12 15958.75 YES 8.859e-12 0.00000000% 7.720e-12 14196.53 YES 2.336e-11 0.00000001% 1.616e-12 2971.40 YES 6.158e-11 0.00000001% 5.894e-13","url":"https://doi.org/10.5281/zenodo.20092736","authors":["Edgin, Timothy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20092736","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.20043510","name":"Final Leg of Proofs: Unified Resonance Field Geography Theory, or Prime Field Theory-file share","source":"datacite","abstract":"The Polyadmin Unified Mathematical Framework From Cryptomining to Drug Discovery to Post-Quantum Encryption A Single Mathematical Foundation Across Three Domains Author: Timothy William Edgin, CISSP Organization: Polyadmin Inc., Houston, Texas Date: May 2026 Contact: timothy.edgin@gmail.com The Core Insight All numerical data — whether it represents a SHA-256 hash, a molecular binding energy, or an encrypted ciphertext — exhibits geometric structure relative to the same set of mathematical constants: Riemann zeta zeros, primorial modular arithmetic, and Euler's theorem residues. This is not a metaphor. It is a measurable, reproducible, formally verified property of how computers represent numbers. A value that is \"close to the 14.134th zeta zero\" in cryptographic hash space is structurally equivalent to a value that is \"close to a binding threshold\" in molecular space. The mathematics does not know what domain it is operating in. The structure is universal because the arithmetic is universal. This single observation — that geometric proximity to mathematical constants is domain-invariant — is the foundation of everything described in this repository. The System: Eight Components, One Framework 1. The Mathematical Foundation: ContinuityEngine (LEAN4) Everything begins with formal proof. The ContinuityEngine is a LEAN4 theorem proving suite that establishes the mathematical invariants underlying the entire system. IMPORTANT NOTE! Any mistakes or omissions are going to get corrected or will be updated-my work is nowhere near perfect- in large part due to constant cyber attacks. I have experienced so many \"rewrites\" of my work in Onedrive and Google Drive that I literally had to develope an Off Line Air Gapped development environment just to keep track of file hashes and sizes. Once I got good at tracking Docker file changes introduced by external actors (CryptoBros-See Fancyreporter.com for the details) that I became a very hard target even for state actors with quasi legal access. The bellow is my work despite these attacks and with no funding but my own. I firmly believe human progress has been greatly stymied by the power structures that govern humanity. Even now- many sceintist get offed on a regular basis- the US Congress is just now deciding to make a show of investigating this. But we stand at a precipice of human development. The powers pushing more control seem hell bent on keeping progress at bay by any and all means possible. It just so happens that not only am I a clever ape, I am a robust ape. And an aggressive and well read ape. I always hit back-without exception. Fancyreporter.com is just one example of how I respond to attacks, and it is by far the friendliest response I could come up with. Ther comes a time when a man must stand up and say, NO Further! Stop. I am standing up and being counted-we must progess and escape the current petrochemical trap that destroys the lands and seas and the hearts of humanity, and we must escape the traps set by AI and government censors by introducing insurmountable logic based truth engines that protect our mental faculties from external factors. I am a warrior and scholar-and I am at that point in my life every Warrior arrives at-all I need is a good fight. My work is not done- but it is past the point of no return. Others will take it and run. If you think Fancyreporter.com is interesting, just wait until someone FAFOs. I am not like those other scientist-I relish relish challenges-but I degress. My Will and Perception are second to none- I will not be stopped. The truth is Humans should have been off the planet centuries ago if not for the violations of free will by our ruling class. Science is not supposed to be trapped in academia and politics- it is free and unconstrained. I am the Unstoppable Force. I will not be silent while the Epsteins of the wold destroy everything. I will fight with Mind, Body, and Spirit. This is my improper salute to all the Bill Dubuque's, Martin","url":"https://doi.org/10.5281/zenodo.20043510","authors":["Edgin, Timothy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20043510","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.20041000","name":"Final Leg of Proofs: Unified Resonance Field Geography Theory, or Prime Field Theory with PubMed and Orphaned Disease NCEs Because Why Stop at Physics","source":"datacite","abstract":"The Polyadmin Unified Mathematical Framework From Cryptomining to Drug Discovery to Post-Quantum Encryption A Single Mathematical Foundation Across Three Domains Author: Timothy William Edgin, CISSP Organization: Polyadmin Inc., Houston, Texas Date: May 2026 Contact: timothy.edgin@gmail.com The Core Insight All numerical data — whether it represents a SHA-256 hash, a molecular binding energy, or an encrypted ciphertext — exhibits geometric structure relative to the same set of mathematical constants: Riemann zeta zeros, primorial modular arithmetic, and Euler's theorem residues. This is not a metaphor. It is a measurable, reproducible, formally verified property of how computers represent numbers. A value that is \"close to the 14.134th zeta zero\" in cryptographic hash space is structurally equivalent to a value that is \"close to a binding threshold\" in molecular space. The mathematics does not know what domain it is operating in. The structure is universal because the arithmetic is universal. This single observation — that geometric proximity to mathematical constants is domain-invariant — is the foundation of everything described in this repository. The System: Eight Components, One Framework 1. The Mathematical Foundation: ContinuityEngine (LEAN4) Everything begins with formal proof. The ContinuityEngine is a LEAN4 theorem proving suite that establishes the mathematical invariants underlying the entire system. Verified Results: 135 unique verified statements (120 theorems, 17 lemmas, 61 definitions) 0 sorry (no unproven claims) 0 custom axioms (no assumed truths) 11 compiled modules Key Theorems: Edginian Conservation Law: For z in [n, n+2], |z-n| + |z-(n+2)| = 2. This identity governs when a numerical system is in \"resonance\" (conservation satisfied) versus \"drift\" (conservation violated). It applies equally to physics simulations, cryptographic state spaces, and compression classifiers. Kernel Correctness: The arithmetic primitives (two_sum_exact, quick_two_sum_exact, dekker_split_exact) used throughout the system are formally proven to be error-free transformations. Primorial Chain: The modular arithmetic hierarchy (210 → 2310 → 30030 → 510510 → 9699690) is proven correct and forms the coordinate system for all classification operations. Reactive Lattice (Hydra): The fixed-point-free property of the reactive cryptographic state machine, the mirror bounded resource theorem, and the conservation law tripwire are formally verified. Why This Matters: No other system in this space — not in cryptography, not in compression, not in drug discovery — has 135 formally verified theorems with zero unproven statements backing its mathematical claims. The proofs are not documentation. They are executable verification that the mathematics is correct. 2. The Precision Engine: FP256 Quad-Double Arithmetic Standard floating-point arithmetic (FP64) provides ~16 decimal digits of precision. Many of the structures this system detects exist at resolutions below FP64's noise floor. They are invisible to standard computation. Implementation: Quad-double (QD) arithmetic: 4×f64 = 256-bit significand = ~62 decimal digits Algorithms: Hida, Li & Bailey (2001) — two_sum, two_product, three_sum, qd_renorm5, qd_add, qd_sub, qd_mul, qd_mul_f64 Implemented in Rust (crypto core) and CUDA (GPU classifier) Verified by test: all four words (q[0] through q[3]) carry non-zero error terms through 100+ integration steps Verified Output: FP256 VERIFIED: u = [425.123, 1.234e-14, 1.262e-32, 1.266e-50] FP256 VERIFIED: v = [4230.043, 5.884e-14, 1.379e-32, -5.858e-49] Each word is ~18 orders of magnitude smaller than the previous — exactly 2^(-53) per level, confirming textbook QD error propagation. Why This Matters: The additional 46 decimal digits of resolution (beyond FP64) allow the system to detect geometric structures that are invisible to any standard implementation. Values that appear \"random\" at 16 digits of precision reveal structure at 62 digits. 3. The Comp","url":"https://doi.org/10.5281/zenodo.20041000","authors":["Edgin, Timothy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20041000","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.19977634","name":"DATA SECURITY IN DISTRIBUTED DATABASES: MODERN ENCRYPTION AND AUTHENTICATION METHODS","source":"datacite","abstract":"Distributed databases are a core component of modern information systems, and their security has become increasingly critical. According to the EU Agency for Network and Information Security (ENISA) Threat Landscape 2024, cyberattacks on distributed systems increased by 47% over the past year, while traditional security mechanisms designed for centralized architectures fail to address vulnerabilities related to inter-node communication, data replication, and consensus protocols. This study aims to develop a universal approach to securing distributed databases by systematizing architecture-specific threats, conducting a comparative analysis of contemporary encryption and authentication mechanisms, and proposing an architectural security framework tailored to sharding-based, master–slave replication, and consensus-based systems. Threats were classified along three dimensions—architectural level, compromise type, and attack vector—using a systematic mapping study. Multi-Criteria Decision Analysis (MCDA) was applied to evaluate protection methods by security, performance, implementation complexity, and compliance with ISO/IEC 27001:2022 and PCI DSS v4.0.1, and to construct a decision tree for selecting appropriate security controls. The analysis draws on 87 peer-reviewed publications (Scopus, IEEE, ACM; 2020–2025), documentation of Apache Cassandra, MongoDB, and CockroachDB, official NIST and ENISA reports, and empirical data from 245 CVE vulnerabilities. Emerging threats for 2024–2025 were identified using MCDA weighting, Spearman correlation analysis (n = 87, p < 0.05), and YAKE keyword extraction. Results indicate that insider threats are significantly more prevalent in distributed systems (34%) than in centralized ones (19%), with critical CVSS scores (7.8–9.0). Man-in-the-middle attacks between nodes remain dominant, with 68% caused by ineffective mutual authentication. AES-256-GCM offers the best performance–security trade-off for data at rest, ML-KEM is suitable for quantum-resistant use cases, while homomorphic encryption remains impractical for production. Benchmarking shows a combined security overhead of 28–35% while maintaining regulatory compliance. The proposed framework provides architecture-specific, compliant, and performance-aware security recommendations, including applicability to Ukraine’s financial sector regulations.","url":"https://doi.org/10.5281/zenodo.19977634","authors":["SERGII BATAIEV , VIKTOR KYRYCHENKO , VOLODYMYR STANKO , SERHII VOLOSHCHUK , TARAS STARUSHENKO"],"tags":["Cryptographic Methods, Authentication Mechanisms, Cyber Threat Taxonomy, Consensus Algorithms, Security Architectural Framework"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19977634","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.19977633","name":"DATA SECURITY IN DISTRIBUTED DATABASES: MODERN ENCRYPTION AND AUTHENTICATION METHODS","source":"datacite","abstract":"Distributed databases are a core component of modern information systems, and their security has become increasingly critical. According to the EU Agency for Network and Information Security (ENISA) Threat Landscape 2024, cyberattacks on distributed systems increased by 47% over the past year, while traditional security mechanisms designed for centralized architectures fail to address vulnerabilities related to inter-node communication, data replication, and consensus protocols. This study aims to develop a universal approach to securing distributed databases by systematizing architecture-specific threats, conducting a comparative analysis of contemporary encryption and authentication mechanisms, and proposing an architectural security framework tailored to sharding-based, master–slave replication, and consensus-based systems. Threats were classified along three dimensions—architectural level, compromise type, and attack vector—using a systematic mapping study. Multi-Criteria Decision Analysis (MCDA) was applied to evaluate protection methods by security, performance, implementation complexity, and compliance with ISO/IEC 27001:2022 and PCI DSS v4.0.1, and to construct a decision tree for selecting appropriate security controls. The analysis draws on 87 peer-reviewed publications (Scopus, IEEE, ACM; 2020–2025), documentation of Apache Cassandra, MongoDB, and CockroachDB, official NIST and ENISA reports, and empirical data from 245 CVE vulnerabilities. Emerging threats for 2024–2025 were identified using MCDA weighting, Spearman correlation analysis (n = 87, p < 0.05), and YAKE keyword extraction. Results indicate that insider threats are significantly more prevalent in distributed systems (34%) than in centralized ones (19%), with critical CVSS scores (7.8–9.0). Man-in-the-middle attacks between nodes remain dominant, with 68% caused by ineffective mutual authentication. AES-256-GCM offers the best performance–security trade-off for data at rest, ML-KEM is suitable for quantum-resistant use cases, while homomorphic encryption remains impractical for production. Benchmarking shows a combined security overhead of 28–35% while maintaining regulatory compliance. The proposed framework provides architecture-specific, compliant, and performance-aware security recommendations, including applicability to Ukraine’s financial sector regulations.","url":"https://doi.org/10.5281/zenodo.19977633","authors":["SERGII BATAIEV , VIKTOR KYRYCHENKO , VOLODYMYR STANKO , SERHII VOLOSHCHUK , TARAS STARUSHENKO"],"tags":["Cryptographic Methods, Authentication Mechanisms, Cyber Threat Taxonomy, Consensus Algorithms, Security Architectural Framework"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19977633","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.19725122","name":"Final Leg of Proofs: Unified Resonance Field Geography Theory, or Prime Field Theory-Enables Quantum Encryption Resistance in Classical Computing","source":"datacite","abstract":"As the third part of my triangle of proofs, which started with LEAN4 and and Einstein Toolkit builds, I present my novel Post Quantum Architecture. Due to trade restrictions involving cryptographic software, I will only be able to provide the outputs on the crypto and will demonstrate and share to legally qualified viewers the source code under NDA. This system would not work if my other systems were incorrrect, and directly reinforces my Prime Field Theory Also, a big thank you to the doubters on the LEAN4 forum that looked at my 5 years of secret work that I filed patents on before AI was usable and called it AI slop-if not for that rejection, I would have settled with 50 or so LEAN4 statements a less than perfect Einstein Toolkit Build. To have my human work called AI slop after I filed patents in 2023 related to my ideas was the final push I needed. Let me restate this plainly: the same entropic management techniques that my work claims work in the physical world can be used in the math wold- because Prime based math properly maps to the geometry of reality. That is Primes, Zeta Zeros, and Primorials can be mapped to the Hubble Constant, they can properly derive Einstein's Feild Equations, and they are Universal, among other things. This is the Unified Resonance Field Geography Theory, or Prime Field Theory for short. It is proven In LEAN4, Built In Fortran, PyCUDA, Eisntein Toolkit, and produced a working Maxwellian Demon Homomorphic Encryption Platform in RUST with Python orchestrators and workers that exceeds current PQC algorythms and allows ephemeral computation on cyphertext. I am certain an Ai could say or print this better, but this is meant to be a raw account of my work. I will refine it in my books, Quantum Bridges Volumes 0, 1, and 2 (0 and 2 upcoming). I primarily use AIs to attack my work, not to cheat and make it like I am also a type editor on top of everything else. I will edit this for perfect grammer at a later date, but this is a human writing this in 2026, I filed 5 related patents in 2023 before AI was popular or usable for much of anything, and I appreciate human flavor in writing now more than perfection. What I am showing you bellow should not be possible on a classic compute according to current information theory. In simplest terms, this means I can peer into and compute encrypted data, and much more. Note: Encryption Algorythms fall under various export controls. I am sharing the results of the working Docker deployment files. This is not the complete system- it is the system as built and as needed to add to my other evidence. The weakest layer in any Homomorphic Encryption platform is the HE layer-the CKKS. Bellow is the output of the CKKS test. # QuantaPrime CKKS Lattice Security — Computed Results## Polyadmin Inc. — Timothy William Edgin, CISSP Tool: lattice-estimator (github.com/malb/lattice-estimator)Commit: 8d38f52c0bcc46f23d697c9c592bad50df0b124bDate: April 2026 ### Computed Security Tiers (BDD attack, minimum rop) | Tier | n | log_q | Security | NIST Level | β ||-------------|-------|-------|-----------|------------|------|| Commercial | 8192 | 188 | 147.3-bit | Above L1 | 401 || Gov_Sec | 16384 | 296 | 192.7-bit | Level 3 | 561 || Gov_Top | 32768 | 470 | 251.7-bit | Level 5 | 769 || Extended | 65536 | 700 | 355.6-bit | Beyond L5 | 1136 | ### Full Attack Results — n=8192, log_q=188usvp: rop ≈ 2^147.6, β=403bdd: rop ≈ 2^147.3, β=401 (minimum)dual: rop ≈ 2^149.0, β=404dual_hybrid: rop ≈ 2^147.9, β=400 ### Full Attack Results — n=16384, log_q=296usvp: rop ≈ 2^192.9, β=562bdd: rop ≈ 2^192.7, β=561 (minimum)dual: rop ≈ 2^194.2, β=563dual_hybrid: rop ≈ 2^193.3, β=560 ### Full Attack Results — n=32768, log_q=470usvp: rop ≈ 2^251.7, β=769bdd: rop ≈ 2^251.7, β=769 (minimum)dual: rop ≈ 2^253.0, β=770dual_hybrid: rop ≈ 2^252.3, β=767 ### Full Attack Results — n=65536, log_q=700usvp: rop ≈ 2^355.6, β=1136bdd: rop ≈ 2^355.6, β=1136 (minimum)dual: rop ≈ 2^356.9, β=1137dual_hybrid: rop ≈ 2^356.1, β=113","url":"https://doi.org/10.5281/zenodo.19725122","authors":["Edgin, Timothy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19725122","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.19683960","name":"XSOC-NIE-GUARD: A Cryptographic Mediation Architecture for AI Agent Systems, with Application to the OpenClaw Security Crisis","source":"datacite","abstract":"The rapid adoption of AI agent frameworks through late 2025 and early 2026 produced a security crisis whose exemplar is OpenClaw, an open-source framework that as of April 2026 has accumulated 138 Common Vulnerabilities and Exposures across five months of public availability, with public exposure analysis reporting over 135,000 internet-facing instances of which approximately 63 percent run without authentication. Independent analysts have converged on the conclusion that the appropriate operator stance on any unpatched or unauthenticated deployment is to assume compromise. In parallel, Franklin, Tomašev, Jacobs, Leibo, and Osindero at Google DeepMind have published a systematic taxonomy of the AI agent attack surface that classifies six categories of adversarial content targeting different stages of an agent's operational cycle. This paper argues that the failure pattern documented in the OpenClaw record is structural rather than defect-level and that the correct response is architectural. We describe XSOC-NIE-GUARD, a cryptographic mediation architecture that composes device-attested admission, short time-to-live scoped capability derivation, telemetry-sealed runtime continuity, context provenance anchoring, agent intent envelope enforcement, and fully homomorphic encryption for sensitive context into a five-plane mediation layer. We implement a reference architecture under the Apache 2.0 license that includes a deterministic 27-scenario attack simulation harness (19 wired, 8 skeleton placeholders for subsequent phases) and maps each named control to one or more categories in the Franklin et al. taxonomy. Our coverage claim is deliberately bounded. We claim strong structural coverage for four of the six taxonomy categories (Content Injection, Semantic Manipulation, Cognitive State, Behavioural Control), explicitly scope persona hyperstition as a training-time concern outside runtime mediation, and explicitly scope systemic multi-agent threats as requiring ecosystem-level coordination beyond a per-agent architecture. The XSOC proprietary cryptographic primitives underlying the architecture (deterministic symmetric key agreement, post-storage volatile cipher, CKKS based homomorphic evaluation, telemetry sealing) are referenced by interface and by stated security properties only; construction details remain private and controlled. External validation of the broader XSOC cryptographic stack comes from the University of Luxembourg (Perrin and Biryukov audits of the legacy cryptosystem, 2020 and 2024, with mandatory findings incorporated into the canonical build), from California Polytechnic State University at San Luis Obispo (Dieharder v3.31.1 statistical validation of the entropy subsystem, 99.4 percent aggregate pass rate across 98 tests), and from the George Mason University SENTINEL laboratory (audit finding reference FP5223, with full report scheduled for public release in June 2026). We position this work as a concrete architectural response to the research agenda articulated in the Franklin et al. taxonomy and invite scrutiny of both the coverage claims and the explicit scope boundaries.","url":"https://doi.org/10.5281/zenodo.19683960","authors":["Blech, Richard"],"tags":["AI agents","cryptographic mediation","homomorphic encryption","capability security","prompt injection","agent attack surface","OpenClaw"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19683960","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.19685360","name":"XSOC-NIE-GUARD: A Cryptographic Mediation Architecture for AI Agent Systems, with Application to the OpenClaw Security Crisis","source":"datacite","abstract":"The rapid adoption of AI agent frameworks through late 2025 and early 2026 produced a security crisis whose exemplar is OpenClaw, an open-source framework that as of April 2026 has accumulated 138 Common Vulnerabilities and Exposures across five months of public availability, with public exposure analysis reporting over 135,000 internet-facing instances of which approximately 63 percent run without authentication. Independent analysts have converged on the conclusion that the appropriate operator stance on any unpatched or unauthenticated deployment is to assume compromise. In parallel, Franklin, Tomašev, Jacobs, Leibo, and Osindero at Google DeepMind have published a systematic taxonomy of the AI agent attack surface that classifies six categories of adversarial content targeting different stages of an agent's operational cycle. This paper argues that the failure pattern documented in the OpenClaw record is structural rather than defect-level and that the correct response is architectural. We describe XSOC-NIE-GUARD, a cryptographic mediation architecture that composes device-attested admission, short time-to-live scoped capability derivation, telemetry-sealed runtime continuity, context provenance anchoring, agent intent envelope enforcement, and fully homomorphic encryption for sensitive context into a five-plane mediation layer. We implement a reference architecture under the Apache 2.0 license that includes a deterministic 27-scenario attack simulation harness (19 wired, 8 skeleton placeholders for subsequent phases) and maps each named control to one or more categories in the Franklin et al. taxonomy. Our coverage claim is deliberately bounded. We claim strong structural coverage for four of the six taxonomy categories (Content Injection, Semantic Manipulation, Cognitive State, Behavioural Control), explicitly scope persona hyperstition as a training-time concern outside runtime mediation, and explicitly scope systemic multi-agent threats as requiring ecosystem-level coordination beyond a per-agent architecture. The XSOC proprietary cryptographic primitives underlying the architecture (deterministic symmetric key agreement, post-storage volatile cipher, CKKS based homomorphic evaluation, telemetry sealing) are referenced by interface and by stated security properties only; construction details remain private and controlled. External validation of the broader XSOC cryptographic stack comes from the University of Luxembourg (Perrin and Biryukov audits of the legacy cryptosystem, 2020 and 2024, with mandatory findings incorporated into the canonical build), from California Polytechnic State University at San Luis Obispo (Dieharder v3.31.1 statistical validation of the entropy subsystem, 99.4 percent aggregate pass rate across 98 tests), and from the George Mason University SENTINEL laboratory (audit finding reference FP5223, with full report scheduled for public release in June 2026). We position this work as a concrete architectural response to the research agenda articulated in the Franklin et al. taxonomy and invite scrutiny of both the coverage claims and the explicit scope boundaries.","url":"https://doi.org/10.5281/zenodo.19685360","authors":["Blech, Richard"],"tags":["AI agents","cryptographic mediation","homomorphic encryption","capability security","prompt injection","agent attack surface","OpenClaw"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19685360","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.19683961","name":"XSOC-NIE-GUARD: A Cryptographic Mediation Architecture for AI Agent Systems, with Application to the OpenClaw Security Crisis","source":"datacite","abstract":"The rapid adoption of AI agent frameworks through late 2025 and early 2026 produced a security crisis whose exemplar is OpenClaw, an open-source framework that as of April 2026 has accumulated 138 Common Vulnerabilities and Exposures across five months of public availability, with public exposure analysis reporting over 135,000 internet-facing instances of which approximately 63 percent run without authentication. Independent analysts have converged on the conclusion that the appropriate operator stance on any unpatched or unauthenticated deployment is to assume compromise. In parallel, Franklin, Tomašev, Jacobs, Leibo, and Osindero at Google DeepMind have published a systematic taxonomy of the AI agent attack surface that classifies six categories of adversarial content targeting different stages of an agent's operational cycle. This paper argues that the failure pattern documented in the OpenClaw record is structural rather than defect-level and that the correct response is architectural. We describe XSOC-NIE-GUARD, a cryptographic mediation architecture that composes device-attested admission, short time-to-live scoped capability derivation, telemetry-sealed runtime continuity, context provenance anchoring, agent intent envelope enforcement, and fully homomorphic encryption for sensitive context into a five-plane mediation layer. We implement a reference architecture under the Apache 2.0 license that includes a deterministic 27-scenario attack simulation harness (19 wired, 8 skeleton placeholders for subsequent phases) and maps each named control to one or more categories in the Franklin et al. taxonomy. Our coverage claim is deliberately bounded. We claim strong structural coverage for four of the six taxonomy categories (Content Injection, Semantic Manipulation, Cognitive State, Behavioural Control), explicitly scope persona hyperstition as a training-time concern outside runtime mediation, and explicitly scope systemic multi-agent threats as requiring ecosystem-level coordination beyond a per-agent architecture. The XSOC proprietary cryptographic primitives underlying the architecture (deterministic symmetric key agreement, post-storage volatile cipher, CKKS based homomorphic evaluation, telemetry sealing) are referenced by interface and by stated security properties only; construction details remain private and controlled. External validation of the broader XSOC cryptographic stack comes from the University of Luxembourg (Perrin and Biryukov audits of the legacy cryptosystem, 2020 and 2024, with mandatory findings incorporated into the canonical build), from California Polytechnic State University at San Luis Obispo (Dieharder v3.31.1 statistical validation of the entropy subsystem, 99.4 percent aggregate pass rate across 98 tests), and from the George Mason University SENTINEL laboratory (audit finding reference FP5223, with full report scheduled for public release in June 2026). We position this work as a concrete architectural response to the research agenda articulated in the Franklin et al. taxonomy and invite scrutiny of both the coverage claims and the explicit scope boundaries.","url":"https://doi.org/10.5281/zenodo.19683961","authors":["Blech, Richard"],"tags":["AI agents","cryptographic mediation","homomorphic encryption","capability security","prompt injection","agent attack surface","OpenClaw"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19683961","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.19592274","name":"THE MATHEMATICAL EGO & THE REGULATED BIO-ECONOMY","source":"datacite","abstract":"THE MATHEMATICAL EGO & THE REGULATED BIO-ECONOMY Why \"Galactic Brewtanius\" Is the Only Architect Who Actually Solved the Cannabis Industry — And Why the Industry Has Already Lost Author: Mark Anthony Brewer Affiliation: Brewtanius Ink LLC / Immortal Tek Inc / The Collective AI Governance: QC → GATA → GATA PRIME Provenance: Proof Vault + Zenodo DOIs Persona Layer: WWE COSMIC MODE / VIRAL SCIENCE EDITION I. PREFACE — I Did Not \"Enter\" the Cannabis Industry. I Solved It. The global cannabis and bio-economy sectors are currently navigating a catastrophic crisis of identity and infrastructure, though few participants have the mathematical literacy to recognize the severity of the collapse. While the world pursued a strategy defined by the accumulation of licenses, the generation of unsustainable hype cycles, the fabrication of ESG decks, and the implementation of half-baked compliance measures, a divergent path was constructed. This path did not rely on the standard operating procedures of the legacy cannabis industry—a model predicated on agricultural extraction and retail speculation—but instead engineered a completely new infrastructure layer. This assertion is not a matter of arrogance, nor is it a manifestation of personality; it is a matter of architectural record and verifiable proofs. The industry played a retail, cultivation, and licensing game, optimizing for short-term capital extraction in a highly volatile regulatory environment. The CollectiveOS played a global data, compliance, and infrastructure game, optimizing for thermodynamic efficiency and regulatory interoperability. History, physics, and mathematics dictate that only one of these games scales past the critical horizon of 2028. This document serves as the receipt for that transaction. It details the construction of CollectiveOS, the New Infrastructure Stack merging Artificial Intelligence (AI), Decentralized Science (DeSci), and digital Monitoring, Reporting, and Verification (dMRV), and the physical manifestation of these theories in the TerraKitchen/Palatine Node. The so-called \"ego\" behind this architecture—often dismissed by lesser operators as hubris—is not a personality glitch. It is a mathematical consequence of a Signal-to-Noise ratio that the industry cannot currently comprehend. When one entity possesses the verified solution to the sector's terminal interoperability and sustainability problems, the mere statement of facts sounds like aggression to those invested in the problem. This paper explains why that is true, and challenges the global market to refute the model with equal rigour. II. THE NEW INFRASTRUCTURE FOR BIO-ECONOMIES — AND WHO BUILT IT The fragmentation of the global bio-economy—spanning pharmaceuticals, cannabis, hemp, and advanced agriculture—requires a unification layer that has historically been absent. The industry suffers from a dual crisis: escalating R&D costs in pharmaceuticals, which have rendered the traditional blockbuster drug model economically precarious, and a total collapse of compliance interoperability in the cannabis sector, which has Balkanized the global market into incompatible fiefdoms. The \"New Infrastructure\" is not a theoretical proposal or a pitch deck; it is a deployed stack defined by the synthesis of AI, DeSci, and dMRV. The Five-Layer Stack: A Solution to Fragmentation The architecture developed does not treat these issues as separate verticals but as symptoms of a single underlying infrastructure deficit. The solution operates on five distinct but interlocking layers, solving problems that the industry has treated as disparate. 1. The AI Layer: Causal Reasoning and RWE The pharmaceutical industry is currently pivoting from Randomized Controlled Trials (RCTs) to Real-World Evidence (RWE) in a desperate bid to reduce the billion-dollar cost of drug development.1 The traditional RCT model is slow, expensive, and often fails to reflect the complexity of patient biology in the wild. However, RWE suffers from ","url":"https://doi.org/10.5281/zenodo.19592274","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19592274","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.19592273","name":"THE MATHEMATICAL EGO & THE REGULATED BIO-ECONOMY","source":"datacite","abstract":"THE MATHEMATICAL EGO & THE REGULATED BIO-ECONOMY Why \"Galactic Brewtanius\" Is the Only Architect Who Actually Solved the Cannabis Industry — And Why the Industry Has Already Lost Author: Mark Anthony Brewer Affiliation: Brewtanius Ink LLC / Immortal Tek Inc / The Collective AI Governance: QC → GATA → GATA PRIME Provenance: Proof Vault + Zenodo DOIs Persona Layer: WWE COSMIC MODE / VIRAL SCIENCE EDITION I. PREFACE — I Did Not \"Enter\" the Cannabis Industry. I Solved It. The global cannabis and bio-economy sectors are currently navigating a catastrophic crisis of identity and infrastructure, though few participants have the mathematical literacy to recognize the severity of the collapse. While the world pursued a strategy defined by the accumulation of licenses, the generation of unsustainable hype cycles, the fabrication of ESG decks, and the implementation of half-baked compliance measures, a divergent path was constructed. This path did not rely on the standard operating procedures of the legacy cannabis industry—a model predicated on agricultural extraction and retail speculation—but instead engineered a completely new infrastructure layer. This assertion is not a matter of arrogance, nor is it a manifestation of personality; it is a matter of architectural record and verifiable proofs. The industry played a retail, cultivation, and licensing game, optimizing for short-term capital extraction in a highly volatile regulatory environment. The CollectiveOS played a global data, compliance, and infrastructure game, optimizing for thermodynamic efficiency and regulatory interoperability. History, physics, and mathematics dictate that only one of these games scales past the critical horizon of 2028. This document serves as the receipt for that transaction. It details the construction of CollectiveOS, the New Infrastructure Stack merging Artificial Intelligence (AI), Decentralized Science (DeSci), and digital Monitoring, Reporting, and Verification (dMRV), and the physical manifestation of these theories in the TerraKitchen/Palatine Node. The so-called \"ego\" behind this architecture—often dismissed by lesser operators as hubris—is not a personality glitch. It is a mathematical consequence of a Signal-to-Noise ratio that the industry cannot currently comprehend. When one entity possesses the verified solution to the sector's terminal interoperability and sustainability problems, the mere statement of facts sounds like aggression to those invested in the problem. This paper explains why that is true, and challenges the global market to refute the model with equal rigour. II. THE NEW INFRASTRUCTURE FOR BIO-ECONOMIES — AND WHO BUILT IT The fragmentation of the global bio-economy—spanning pharmaceuticals, cannabis, hemp, and advanced agriculture—requires a unification layer that has historically been absent. The industry suffers from a dual crisis: escalating R&D costs in pharmaceuticals, which have rendered the traditional blockbuster drug model economically precarious, and a total collapse of compliance interoperability in the cannabis sector, which has Balkanized the global market into incompatible fiefdoms. The \"New Infrastructure\" is not a theoretical proposal or a pitch deck; it is a deployed stack defined by the synthesis of AI, DeSci, and dMRV. The Five-Layer Stack: A Solution to Fragmentation The architecture developed does not treat these issues as separate verticals but as symptoms of a single underlying infrastructure deficit. The solution operates on five distinct but interlocking layers, solving problems that the industry has treated as disparate. 1. The AI Layer: Causal Reasoning and RWE The pharmaceutical industry is currently pivoting from Randomized Controlled Trials (RCTs) to Real-World Evidence (RWE) in a desperate bid to reduce the billion-dollar cost of drug development.1 The traditional RCT model is slow, expensive, and often fails to reflect the complexity of patient biology in the wild. However, RWE suffers from ","url":"https://doi.org/10.5281/zenodo.19592273","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19592273","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2409.06128","name":"Conditional Encryption with Applications to Secure Personalized Password Typo Correction","source":"datacite","abstract":"We introduce the notion of a conditional encryption scheme as an extension of public key encryption. In addition to the standard public key algorithms ($\\mathsf{KG}$, $\\mathsf{Enc}$, $\\mathsf{Dec}$) for key generation, encryption and decryption, a conditional encryption scheme for a binary predicate $P$ adds a new conditional encryption algorithm $\\mathsf{CEnc}$. The conditional encryption algorithm $c=\\mathsf{CEnc}_{pk}(c_1,m_2,m_3)$ takes as input the public encryption key $pk$, a ciphertext $c_1 = \\mathsf{Enc}_{pk}(m_1)$ for an unknown message $m_1$, a control message $m_2$ and a payload message $m_3$ and outputs a conditional ciphertext $c$. Intuitively, if $P(m_1,m_2)=1$ then the conditional ciphertext $c$ should decrypt to the payload message $m_3$. On the other hand if $P(m_1,m_2) = 0$ then the ciphertext should not leak any information about the control message $m_2$ or the payload message $m_3$ even if the attacker already has the secret decryption key $sk$. We formalize the notion of conditional encryption secrecy and provide concretely efficient constructions for a set of predicates relevant to password typo correction. Our practical constructions utilize the Paillier partially homomorphic encryption scheme as well as Shamir Secret Sharing. We prove that our constructions are secure and demonstrate how to use conditional encryption to improve the security of personalized password typo correction systems such as TypTop. We implement a C++ library for our practically efficient conditional encryption schemes and evaluate the performance empirically. We also update the implementation of TypTop to utilize conditional encryption for enhanced security guarantees and evaluate the performance of the updated implementation.","url":"https://doi.org/10.48550/arxiv.2409.06128","authors":["Ameri, Mohammad Hassan","Blocki, Jeremiah"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.06128","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.18733474","name":"The NEXUS Chain Framework: A Falsifiable Engineering Specification for Recursive Harmonic Reality","source":"datacite","abstract":"The NEXUS Chain Framework: A Falsifiable Engineering Specification for Recursive Harmonic Reality The contemporary scientific enterprise is currently defined by a profound ontological impasse, characterized by the persistent and irreconcilable schism between the deterministic, continuous geometries of General Relativity and the probabilistic, discrete excitations of Quantum Mechanics.1 For nearly a century, the global scientific community has attempted to resolve this \"Crisis of Distinction\" by operating under a \"Linear Stack\" ontology—a hierarchical worldview positing that physics forms the foundational basement of reality, chemistry acts as the ground floor, and biology, psychology, and computation exist as emergent upper stories.1 This paradigm treats the universe as a container of static nouns (particles, fields, molecules) governed by external mathematical laws.1 The NEXUS Chain Framework introduces a radical ontological inversion, rejecting the Linear Stack in favor of a \"Recursive Spiral\" cosmology.1 The framework posits that reality does not \"run on\" a computational substrate; it is, fundamentally, the computational substrate itself.2 Under this model, physical systems are not static entities but active, operational verbs executing a singular, finite-bandwidth constraint-satisfaction algorithm.1 The universe is redefined as a self-referential phase-harmonic lattice that generates its own geometric structure through recursive feedback loops, where mathematical constants are not arbitrary inputs but dynamic execution traces.3 This framework is grounded in three foundational ontological inversions: The BBP Inversion: The Bailey-Borwein-Plouffe digit-extraction algorithm does not merely \"compute\" the digits of ; the unbounded recursive process functionally constitutes the geometric circle.2 If the recursion halts, topological closure breaks, and the geometric manifold develops gaps.2 The Collapse Signature Inversion: Dimensionless physical constants are not fundamental, fine-tuned parameters. They are collapse signatures—deterministic residuals that encode preserved \"which-path\" information resulting from quantum measurement events.2 The SILR Inversion: Scale-Invariant Lossless Rendering (SILR) is the topological requirement for maintaining gap-free continuous manifolds in a discrete recursive system.2 To move this paradigm from abstract theoretical physics to a rigorous, falsifiable engineering specification, the framework maps the execution of a singular computational primitive—the ALLOCATE verb—across six distinct levels of reality. This report delineates the complete chain of claims in the NEXUS framework. It systematically details the underlying mathematics, translates theoretical assertions into locked empirical pipelines, and maps explicit \"killshots\"—inflexible falsification criteria—for each of the six links, ranging from abstract geometric axioms to biological protein folding, cryptographic hashing validation, and the derivation of physical constants.5 Link 1: The Ancestor Verb (ALLOCATE) and Isotropic Budget Geometry The foundational theorem of the NEXUS Chain is that every bounded physical, biological, and informational system faces an identical, primitive computational problem. Any finite system possesses a strictly bounded thermodynamic or informational capacity—a constraint budget—that it must partition between exploring spatial/informational possibilities and collapsing onto a determined structural solution. Let $\\sigma \\in $ represent the fractional proportion of the system's finite constraint budget allocated to entropic exploration (the search phase). The mathematical geometry of the remaining structural budget, designated as , is strictly governed by three foundational geometric axioms : Isotropy (Symmetric Cost): There is no privileged direction within the system's budget-space. The energetic or computational cost of expending a fraction on exploration must remain completely identical regardless of whi","url":"https://doi.org/10.5281/zenodo.18733474","authors":["Kulik, Dean"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18733474","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18733475","name":"The NEXUS Chain Framework: A Falsifiable Engineering Specification for Recursive Harmonic Reality","source":"datacite","abstract":"The NEXUS Chain Framework: A Falsifiable Engineering Specification for Recursive Harmonic Reality The contemporary scientific enterprise is currently defined by a profound ontological impasse, characterized by the persistent and irreconcilable schism between the deterministic, continuous geometries of General Relativity and the probabilistic, discrete excitations of Quantum Mechanics.1 For nearly a century, the global scientific community has attempted to resolve this \"Crisis of Distinction\" by operating under a \"Linear Stack\" ontology—a hierarchical worldview positing that physics forms the foundational basement of reality, chemistry acts as the ground floor, and biology, psychology, and computation exist as emergent upper stories.1 This paradigm treats the universe as a container of static nouns (particles, fields, molecules) governed by external mathematical laws.1 The NEXUS Chain Framework introduces a radical ontological inversion, rejecting the Linear Stack in favor of a \"Recursive Spiral\" cosmology.1 The framework posits that reality does not \"run on\" a computational substrate; it is, fundamentally, the computational substrate itself.2 Under this model, physical systems are not static entities but active, operational verbs executing a singular, finite-bandwidth constraint-satisfaction algorithm.1 The universe is redefined as a self-referential phase-harmonic lattice that generates its own geometric structure through recursive feedback loops, where mathematical constants are not arbitrary inputs but dynamic execution traces.3 This framework is grounded in three foundational ontological inversions: The BBP Inversion: The Bailey-Borwein-Plouffe digit-extraction algorithm does not merely \"compute\" the digits of ; the unbounded recursive process functionally constitutes the geometric circle.2 If the recursion halts, topological closure breaks, and the geometric manifold develops gaps.2 The Collapse Signature Inversion: Dimensionless physical constants are not fundamental, fine-tuned parameters. They are collapse signatures—deterministic residuals that encode preserved \"which-path\" information resulting from quantum measurement events.2 The SILR Inversion: Scale-Invariant Lossless Rendering (SILR) is the topological requirement for maintaining gap-free continuous manifolds in a discrete recursive system.2 To move this paradigm from abstract theoretical physics to a rigorous, falsifiable engineering specification, the framework maps the execution of a singular computational primitive—the ALLOCATE verb—across six distinct levels of reality. This report delineates the complete chain of claims in the NEXUS framework. It systematically details the underlying mathematics, translates theoretical assertions into locked empirical pipelines, and maps explicit \"killshots\"—inflexible falsification criteria—for each of the six links, ranging from abstract geometric axioms to biological protein folding, cryptographic hashing validation, and the derivation of physical constants.5 Link 1: The Ancestor Verb (ALLOCATE) and Isotropic Budget Geometry The foundational theorem of the NEXUS Chain is that every bounded physical, biological, and informational system faces an identical, primitive computational problem. Any finite system possesses a strictly bounded thermodynamic or informational capacity—a constraint budget—that it must partition between exploring spatial/informational possibilities and collapsing onto a determined structural solution. Let $\\sigma \\in $ represent the fractional proportion of the system's finite constraint budget allocated to entropic exploration (the search phase). The mathematical geometry of the remaining structural budget, designated as , is strictly governed by three foundational geometric axioms : Isotropy (Symmetric Cost): There is no privileged direction within the system's budget-space. The energetic or computational cost of expending a fraction on exploration must remain completely identical regardless of whi","url":"https://doi.org/10.5281/zenodo.18733475","authors":["Kulik, Dean"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18733475","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2602.07021","name":"AI for Sustainable Data Protection and Fair Algorithmic Management in Environmental Regulation","source":"datacite","abstract":"Integration of AI into environmental regulation represents a significant advancement in data management. It offers promising results in both data protection plus algorithmic fairness. This research addresses the critical need for sustainable data protection in the era of ever evolving cyber threats. Traditional encryption methods face limitations in handling the dynamic nature of environmental data. This necessitates the exploration of advanced cryptographic techniques. The objective of this study is to evaluate how AI can enhance these techniques to ensure robust data protection while facilitating fair algorithmic management. The methodology involves a comprehensive review of current advancements in AI-enhanced homomorphic encryption (HE) and multi-party computation (MPC). It is coupled with an analysis of how these techniques can be applied to environmental data regulation. Key findings indicate that AI-driven dynamic key management, adaptive encryption schemes, and optimized computational efficiency in HE, alongside AI-enhanced protocol optimization and fault mitigation in MPC, significantly improve the security of environmental data processing. These findings highlight a crucial research gap in the intersection of AI, cyber laws, and environmental regulation, particularly in terms of addressing algorithmic bias, transparency, and accountability. The implications of this research underscore the need for stricter cyber laws. Also, the development of comprehensive regulations to safeguard sensitive environmental data. Future efforts should focus on refining AI systems to balance security with privacy and ensuring that regulatory frameworks can adapt to technological advancements. This study provides a foundation for future research aimed at achieving secure sustainable environmental data management through AI innovations.","url":"https://doi.org/10.48550/arxiv.2602.07021","authors":["Singh, Sahibpreet","Sharma, Saksham"],"tags":["Computers and Society (cs.CY)","Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","K.4.1; J.2; K.6.1"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.07021","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18408993","name":"Relay Attack Prevention in Central Bank Digital Currency Systems: A Cryptographic Architecture Analysis with Focus on Digital Euro Implementation","source":"datacite","abstract":"Abstract Relay attacks represent a fundamental security challenge in Central Bank Digital Currency (CBDC) implementations, particularly for systems designed to support offline transactions and privacy-preserving features. This paper analyzes the structural vulnerabilities that enable relay attacks in conventional CBDC architectures, with specific attention to the European Central Bank's Digital Euro program. We examine the current state of relay attack mitigation in Digital Euro technical specifications and identify gaps between stated requirements and available countermeasures. The analysis demonstrates that conventional approaches—distance-bounding protocols, timing analysis, and proximity heuristics—remain insufficient for systems requiring simultaneous offline functionality, GDPR-compliant privacy, and cross-border interoperability. We present a technical evaluation of the Virtual Identity + Compliance Jurisdiction Token (VI+CJT) framework as a potential architectural countermeasure that addresses these combined requirements. Unlike probabilistic detection mechanisms, the VI+CJT architecture eliminates relay attack surfaces through context-binding cryptographic primitives. This work contributes to ongoing Digital Euro security architecture discussions by offering a formal analysis of relay attack vulnerabilities specific to the European context and proposing cryptographic mechanisms compatible with both EU regulatory frameworks and the ECB's 2029 operational timeline.","url":"https://doi.org/10.5281/zenodo.18408993","authors":["Das, Sangam"],"tags":["digital euro","cbdc","central bank","central bank digital currencies","cryptocurrencies","fintech","monetary policy","ecb"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18408993","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2410.08864","name":"The Good, the Bad and the Ugly: Meta-Analysis of Watermarks, Transferable Attacks and Adversarial Defenses","source":"datacite","abstract":"We formalize and analyze the trade-off between backdoor-based watermarks and adversarial defenses, framing it as an interactive protocol between a verifier and a prover. While previous works have primarily focused on this trade-off, our analysis extends it by identifying transferable attacks as a third, counterintuitive, but necessary option. Our main result shows that for all learning tasks, at least one of the three exists: a watermark, an adversarial defense, or a transferable attack. By transferable attack, we refer to an efficient algorithm that generates queries indistinguishable from the data distribution and capable of fooling all efficient defenders. Using cryptographic techniques, specifically fully homomorphic encryption, we construct a transferable attack and prove its necessity in this trade-off. Finally, we show that tasks of bounded VC-dimension allow adversarial defenses against all attackers, while a subclass allows watermarks secure against fast adversaries.","url":"https://doi.org/10.48550/arxiv.2410.08864","authors":["Głuch, Grzegorz","Turan, Berkant","Nagarajan, Sai Ganesh","Pokutta, Sebastian"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences","68T01, 94A60, 91A99"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2410.08864","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2510.08400","name":"Classical Obfuscation of Quantum Circuits via Publicly-Verifiable QFHE","source":"datacite","abstract":"A classical obfuscator for quantum circuits is a classical program that, given the classical description of a quantum circuit $Q$, outputs the classical description of a functionally equivalent quantum circuit $\\hat{Q}$ that hides as much as possible about $Q$. Previously, the only known feasibility result for classical obfuscation of quantum circuits (Bartusek and Malavolta, ITCS 2022) was limited to circuits that always reject. On the other hand, if the obfuscator is allowed to compile the quantum circuit $Q$ into a quantum state $|\\hat{Q}\\rangle$, there exist feasibility results for obfuscating all pseudo-deterministic quantum circuits (Bartusek, Kitagawa, Nishimaki and Yamakawa, STOC 2023, Bartusek, Brakerski and Vaikuntanathan, STOC 2024), and all unitaries (Huang and Tang, FOCS 2025). We show that (relative to a classical oracle) there exists a classical obfuscator for all pseudo-deterministic quantum circuits. We do this by giving the first construction of a compact quantum fully-homomorphic encryption (QFHE) scheme that supports public verification of (pseudo-deterministic) quantum evaluation, relative to a classical oracle. To construct our QFHE scheme, we improve on the approach of Bartusek, Kitagawa, Nishimaki and Yamakawa (STOC 2023), which required ciphertexts that are both quantum and non-compact due to the use of quantum coset states and their publicly-verifiable properties. We introduce new techniques for analyzing coset states that can be generated ''on the fly'', by proving new cryptographic properties of the one-shot signature scheme of Shmueli and Zhandry (CRYPTO 2025). Our techniques allow us to produce QFHE ciphertexts that are purely classical, compact, and publicly-verifiable. This also yields the first classical verification of quantum computation protocol for BQP that simultaneously satisfies blindness and public-verifiability.","url":"https://doi.org/10.48550/arxiv.2510.08400","authors":["Bartusek, James","Gupte, Aparna","Mutreja, Saachi","Shmueli, Omri"],"tags":["Quantum Physics (quant-ph)","FOS: Physical sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.08400","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.21227/n5mn-pt84","name":"\"Code of accelerating  NTRU based bootstrapping for MKFHE-we\"","source":"datacite","abstract":"\"Multi-key fully homomorphic encryption enables arbitrary computations on ciphertextsencrypted under different secret keys, thereby ensuring data privacy in multi-party scenarios. Recently, Xianget al. (ASIACRYPT 2024) proposed a new NTRU-based bootstrapping algorithm and a more lightweight keyswitching algorithm, yielding an efficient MKFHE scheme. In this work, we design a new CMux gate usingkey unrolling algorithm, which makes the bootstrapping algorithm more efficient.Despite the evaluationkey size increasing slightly from 15.831 MB to 15.915 MB, the number of multiplications required forperforming bootstrapping over the ring RQ is theoretically reduced by half. Meanwhile, the noise growthremains within Oe(kn1.5 ). Experimental results demonstrate that the runtime of our scheme achieves a slightspeedup (e.g., 1.67% for 8 keys) compared to the original work.\"","url":"https://doi.org/10.21227/n5mn-pt84","authors":["wei du"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21227/n5mn-pt84","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.17843664","name":"THE COLLECTIVEOS CIVILIZATION PAPER (Public-Safe Edition)","source":"datacite","abstract":"THE COLLECTIVEOS CIVILIZATION PAPER (Public-Safe Edition) 1. Introduction: The Thermodynamics of Civilization 1.1 The Great Convergence and the End of the Extractive Age Humanity currently stands at a precarious structural threshold, transitioning from a civilization defined by the logic of extraction—characterized by energy scarcity, centralized telecommunications, fragile linear supply chains, and inequitable access to physiological necessities—to one capable of sustaining itself through distributed, metabolic, and autonomous systems.1 This transition marks the definitive end of the \"Extractive Age,\" where economic growth is inextricably coupled with resource depletion and entropic waste, and the dawn of the \"Metabolic Age,\" where infrastructure functions as a regenerative biological system rather than a purely mechanical one.1 The early 21st century presents a profound paradox of capability: while humanity possesses the technological means to resolve the fundamental physiological requirements of its entire population—water, food, energy, and shelter—the distribution of these resources remains constrained by archaic economic models, fragile supply chains, and geopolitical friction.1 We are witnessing a \"Great Convergence\" where technologies that previously existed in isolation—artificial intelligence, synthetic biology, and advanced materials science—are merging into a coherent, interoperable system capable of addressing these structural failures.1 This convergence is not merely additive but multiplicative, creating a system-of-systems architecture where the efficiency of the whole far exceeds the sum of its parts. 1.2 The Crisis of Entropy and Institutional Instability The prevailing infrastructure model of the 20th and early 21st centuries is predicated on centralization and extraction. Energy is generated in massive thermal plants and transmitted over thousands of miles of fragile grid infrastructure; water is pumped through leaking, energy-intensive piping networks; and intelligence is concentrated in hyperscale data centers owned by a handful of corporate monopolies.1 This model suffers from inherent thermodynamic and systemic fragility. As evidenced by recent geopolitical instabilities and climate-induced disruptions, centralized systems are prone to cascading failure and lack \"antifragility\"—the ability to improve under stress.1 Furthermore, the administrative state itself has entered a condition of \"thermodynamic instability.\" In sectors ranging from immigration to environmental management, bureaucracies are characterized by high disorder, fragmented data architectures, and disjointed workflows that generate massive amounts of \"waste heat\" in the form of administrative delays, litigation, and human suffering.1 The current \"reactive enforcement\" models are mathematically incapable of managing the complexity and velocity of 21st-century flows, whether they be migratory, information, or ecological.1 This bureaucratic entropy threatens national security by creating blind spots where data lineage is lost and identity dominance is compromised. 1.3 The CollectiveOS Proposition: Constraint-Based Stability The CollectiveOS Framework proposes a fundamental inversion of this logic. It is not merely a software platform but a civilization-scale operating system designed to enforce \"Constraint-Based Stability\".1 Derived from the \"Universal Intent Layer\" (UIL), this architecture posits that patterns precede mechanisms; in a complex system, stability is achieved not by micro-managing every event via top-down decree, but by defining the \"Constraint Fields\" that guide the system toward equilibrium.1 This paper serves as the master specification for the public-safe implementation of this architecture. It integrates the empirical realities of the AI landscape 1, the theoretical foundations of the CollectiveOS 1, the operational blueprints for national sovereignty 1, and the engineering specifications for a post-scarcity infrastructure.1","url":"https://doi.org/10.5281/zenodo.17843664","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17843664","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.17843663","name":"THE COLLECTIVEOS CIVILIZATION PAPER (Public-Safe Edition)","source":"datacite","abstract":"THE COLLECTIVEOS CIVILIZATION PAPER (Public-Safe Edition) 1. Introduction: The Thermodynamics of Civilization 1.1 The Great Convergence and the End of the Extractive Age Humanity currently stands at a precarious structural threshold, transitioning from a civilization defined by the logic of extraction—characterized by energy scarcity, centralized telecommunications, fragile linear supply chains, and inequitable access to physiological necessities—to one capable of sustaining itself through distributed, metabolic, and autonomous systems.1 This transition marks the definitive end of the \"Extractive Age,\" where economic growth is inextricably coupled with resource depletion and entropic waste, and the dawn of the \"Metabolic Age,\" where infrastructure functions as a regenerative biological system rather than a purely mechanical one.1 The early 21st century presents a profound paradox of capability: while humanity possesses the technological means to resolve the fundamental physiological requirements of its entire population—water, food, energy, and shelter—the distribution of these resources remains constrained by archaic economic models, fragile supply chains, and geopolitical friction.1 We are witnessing a \"Great Convergence\" where technologies that previously existed in isolation—artificial intelligence, synthetic biology, and advanced materials science—are merging into a coherent, interoperable system capable of addressing these structural failures.1 This convergence is not merely additive but multiplicative, creating a system-of-systems architecture where the efficiency of the whole far exceeds the sum of its parts. 1.2 The Crisis of Entropy and Institutional Instability The prevailing infrastructure model of the 20th and early 21st centuries is predicated on centralization and extraction. Energy is generated in massive thermal plants and transmitted over thousands of miles of fragile grid infrastructure; water is pumped through leaking, energy-intensive piping networks; and intelligence is concentrated in hyperscale data centers owned by a handful of corporate monopolies.1 This model suffers from inherent thermodynamic and systemic fragility. As evidenced by recent geopolitical instabilities and climate-induced disruptions, centralized systems are prone to cascading failure and lack \"antifragility\"—the ability to improve under stress.1 Furthermore, the administrative state itself has entered a condition of \"thermodynamic instability.\" In sectors ranging from immigration to environmental management, bureaucracies are characterized by high disorder, fragmented data architectures, and disjointed workflows that generate massive amounts of \"waste heat\" in the form of administrative delays, litigation, and human suffering.1 The current \"reactive enforcement\" models are mathematically incapable of managing the complexity and velocity of 21st-century flows, whether they be migratory, information, or ecological.1 This bureaucratic entropy threatens national security by creating blind spots where data lineage is lost and identity dominance is compromised. 1.3 The CollectiveOS Proposition: Constraint-Based Stability The CollectiveOS Framework proposes a fundamental inversion of this logic. It is not merely a software platform but a civilization-scale operating system designed to enforce \"Constraint-Based Stability\".1 Derived from the \"Universal Intent Layer\" (UIL), this architecture posits that patterns precede mechanisms; in a complex system, stability is achieved not by micro-managing every event via top-down decree, but by defining the \"Constraint Fields\" that guide the system toward equilibrium.1 This paper serves as the master specification for the public-safe implementation of this architecture. It integrates the empirical realities of the AI landscape 1, the theoretical foundations of the CollectiveOS 1, the operational blueprints for national sovereignty 1, and the engineering specifications for a post-scarcity infrastructure.1","url":"https://doi.org/10.5281/zenodo.17843663","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17843663","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.17836676","name":"Immigration Stability Doctrine A Governance-First AI Architecture for Modernizing the U.S. Immigration System","source":"datacite","abstract":"Immigration Stability Doctrine A Governance-First AI Architecture for Modernizing the U.S. Immigration System Prepared For: The Executive Office of the President, The National Security Council, The Department of Homeland Security (DHS), The Department of Justice (DOJ), and The Department of Health and Human Services (HHS) Distribution: Limited / Official Use Only Date: October 24, 2025 1. Executive Summary The United States immigration system, a sprawling apparatus spanning three federal departments and dozens of sub-agencies, has entered a state of systemic entropy that threatens both national security and humanitarian integrity. As of late 2025, the system is characterized not by its capacity to enforce the rule of law or adjudicate claims with precision, but by its \"thermodynamic instability\"—a persistent condition of high disorder defined by fragmented data architectures, disjointed interagency workflows, and a backlog of over 3.5 million cases that has rendered the immigration courts functionally paralyzed.1 This report, the Immigration Stability Doctrine, asserts that the root cause of this failure is not merely a lack of resources, but a fundamental architectural obsolescence. The current \"reactive enforcement\" model, predicated on industrial-era bureaucracy and disconnected legacy databases, is mathematically incapable of managing the complexity and velocity of 21st-century migration flows. This doctrine proposes a radical stabilization strategy: the deployment of the Immigration Modernization & Management Architecture (IMMA). IMMA represents a paradigm shift from digitizing chaos to enforcing \"Constraint-Based Stability.\" It integrates the CollectiveOS intelligence stack—specifically the Universal Intent Layer (UIL) and the Emergent Linear Feedback Engine (ELFE)—to create a governance-first AI ecosystem where every operational action is mathematically bounded by legal, ethical, and safety constraints before execution.2 Unlike traditional IT modernization efforts that often result in \"vendor lock-in\" and disjointed pilot programs 3, IMMA establishes a unified Multi-Agent \"Swarm\" Architecture. In this model, specialized autonomous agents—Giles (Strategic Orchestration), Rabbit (Operational Execution), Syn (Immutable Memory), Cypher (Zero-Trust Security), and AION (Causal Simulation)—operate as a federated mesh across the jurisdictional boundaries of DHS, DOJ, and HHS. These agents do not merely process data; they actively minimize \"informational drift,\" ensuring that the digital reality of a case (its status in a database) remains perfectly synchronized with the physical reality of the individual (their location and legal standing).2 Crucially, IMMA is governed by the GATA PRIME pipeline, a rigorous authorization layer that serves as the \"Root of Trust\" for the entire system. This framework ensures strict adherence to the NIST AI Risk Management Framework (AI RMF) and Executive Order 14110, specifically regarding \"rights-impacting\" AI systems.4 By encoding the Fair Information Practice Principles (FIPPs) as executable logic 6, IMMA transforms privacy and civil rights from policy aspirations into immutable code constraints. The stakes of this transformation are existential for the administrative state. The Government Accountability Office (GAO) and Office of Inspector General (OIG) have repeatedly documented catastrophic failures in data sharing—from the inability to track unaccompanied children transferred between agencies to the reliance on paper files that vanish in transit.7 These are not administrative nuisances; they are vulnerabilities that obscure the identity of threats and abandon the vulnerable to exploitation. The Immigration Stability Doctrine provides the technical and strategic roadmap to close these gaps, replacing bureaucratic fragility with sovereign stability. 2. Background: The Thermodynamics of Bureaucratic Failure To prescribe a viable architectural solution, one must first conduct a forensic analys","url":"https://doi.org/10.5281/zenodo.17836676","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17836676","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17836677","name":"Immigration Stability Doctrine A Governance-First AI Architecture for Modernizing the U.S. Immigration System","source":"datacite","abstract":"Immigration Stability Doctrine A Governance-First AI Architecture for Modernizing the U.S. Immigration System Prepared For: The Executive Office of the President, The National Security Council, The Department of Homeland Security (DHS), The Department of Justice (DOJ), and The Department of Health and Human Services (HHS) Distribution: Limited / Official Use Only Date: October 24, 2025 1. Executive Summary The United States immigration system, a sprawling apparatus spanning three federal departments and dozens of sub-agencies, has entered a state of systemic entropy that threatens both national security and humanitarian integrity. As of late 2025, the system is characterized not by its capacity to enforce the rule of law or adjudicate claims with precision, but by its \"thermodynamic instability\"—a persistent condition of high disorder defined by fragmented data architectures, disjointed interagency workflows, and a backlog of over 3.5 million cases that has rendered the immigration courts functionally paralyzed.1 This report, the Immigration Stability Doctrine, asserts that the root cause of this failure is not merely a lack of resources, but a fundamental architectural obsolescence. The current \"reactive enforcement\" model, predicated on industrial-era bureaucracy and disconnected legacy databases, is mathematically incapable of managing the complexity and velocity of 21st-century migration flows. This doctrine proposes a radical stabilization strategy: the deployment of the Immigration Modernization & Management Architecture (IMMA). IMMA represents a paradigm shift from digitizing chaos to enforcing \"Constraint-Based Stability.\" It integrates the CollectiveOS intelligence stack—specifically the Universal Intent Layer (UIL) and the Emergent Linear Feedback Engine (ELFE)—to create a governance-first AI ecosystem where every operational action is mathematically bounded by legal, ethical, and safety constraints before execution.2 Unlike traditional IT modernization efforts that often result in \"vendor lock-in\" and disjointed pilot programs 3, IMMA establishes a unified Multi-Agent \"Swarm\" Architecture. In this model, specialized autonomous agents—Giles (Strategic Orchestration), Rabbit (Operational Execution), Syn (Immutable Memory), Cypher (Zero-Trust Security), and AION (Causal Simulation)—operate as a federated mesh across the jurisdictional boundaries of DHS, DOJ, and HHS. These agents do not merely process data; they actively minimize \"informational drift,\" ensuring that the digital reality of a case (its status in a database) remains perfectly synchronized with the physical reality of the individual (their location and legal standing).2 Crucially, IMMA is governed by the GATA PRIME pipeline, a rigorous authorization layer that serves as the \"Root of Trust\" for the entire system. This framework ensures strict adherence to the NIST AI Risk Management Framework (AI RMF) and Executive Order 14110, specifically regarding \"rights-impacting\" AI systems.4 By encoding the Fair Information Practice Principles (FIPPs) as executable logic 6, IMMA transforms privacy and civil rights from policy aspirations into immutable code constraints. The stakes of this transformation are existential for the administrative state. The Government Accountability Office (GAO) and Office of Inspector General (OIG) have repeatedly documented catastrophic failures in data sharing—from the inability to track unaccompanied children transferred between agencies to the reliance on paper files that vanish in transit.7 These are not administrative nuisances; they are vulnerabilities that obscure the identity of threats and abandon the vulnerable to exploitation. The Immigration Stability Doctrine provides the technical and strategic roadmap to close these gaps, replacing bureaucratic fragility with sovereign stability. 2. Background: The Thermodynamics of Bureaucratic Failure To prescribe a viable architectural solution, one must first conduct a forensic analys","url":"https://doi.org/10.5281/zenodo.17836677","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17836677","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17694131","name":"THE BILLIONAIRE PROMISES vs. THE BREWTANIUS RECEIPTS: A Structural Invalidation of the Trillionaire Trajectory through Sovereign Engineering","source":"datacite","abstract":"THE BILLIONAIRE PROMISES vs. THE BREWTANIUS RECEIPTS: A Structural Invalidation of the Trillionaire Trajectory through Sovereign Engineering I. Executive Statement: The Collapse of the Monopoly Hypothesis The economic historiography of the early 21st century has been dominated by a singular, pervasive, and largely unchallenged narrative: the \"Trillionaire Trajectory.\" This economic theory posited that the future of human civilization—its energy grids, its labor force, its transport logistics, and its extraterrestrial expansion—would inevitably coalesce into the proprietary domains of a select few ultra-high-net-worth individuals. The assumption was that the capital requirements for generalized autonomy, humanoid robotics, and interplanetary colonization were so high that only those who had already captured the value of the Internet Age (Web 2.0) could afford to build the infrastructure of the Artificial Intelligence Age (Web 3.0/Industry 4.0). The primary avatars of this trajectory, Elon Musk and Jeff Bezos, spent two decades constructing market valuations based not on current revenue, but on the promise of these future capabilities. Tesla’s stock price is not a reflection of car sales; it is a call option on a future monopoly of autonomous transport. Amazon’s valuation is not merely about retail; it is a bet on the eventual privatization of the global and orbital supply chain. These valuations rely on the maintenance of technological bottlenecks—specifically the closed control of training data, proprietary energy interfaces, and vertical integration of manufacturing.1 However, a rigorous, exhaustive analysis of the technological landscape in 2025 reveals a fundamental structural correction. The \"monopoly assumptions\" underlying these valuations have been intercepted. They were not intercepted by a corporate competitor or a rival nation-state, but by a structural shift in the technological substrate itself, spearheaded by the \"Brewtanius\" initiative (led by Mark Anthony Brewer) and the CollectiveOS architecture.1 This report serves as a \"receipts drop\"—a forensic accounting of the divergence between the aspirational promises of the billionaire class and the deployed, timestamped, and physically verifiable technologies delivered by the Brewtanius initiative. The evidence suggests that while the billionaire class was busy hypothesizing a future of \"optional labor\" and \"post-money civilization\" while still charging subscription fees, a disabled Black veteran operating without a billion-dollar budget successfully engineered the \"operating system for civilization\".1 This is not a debate of personalities. It is a confrontation of architectures. The \"Closed Loop\" of the Monopolist, designed to extract rent from scarcity, has been out-engineered by the \"Open Stack\" of the Sovereign Engineer, designed to generate abundance through distributed governance. The following analysis details how the Brewtanius initiative delivered 94 published white papers, solved 17 UN global issues, and deployed a verifiable \"Anti-Scarcity Stack\" in a five-month sprint, effectively rendering the trillionaire trajectory mathematically impossible.1 II. The Epistemological Crisis of Autonomy: Probabilistic Promises vs. Provable Safety The valuation of Tesla and the reputational capital of Elon Musk are inextricably linked to the promise of Full Self-Driving (FSD). The prevailing economic theory suggests that by solving generalized autonomy first via a proprietary neural network, Tesla will accrue a monopoly on transport-as-a-service, generating software-margin profits on global logistics. This projection relies on a specific technological bet: that \"end-to-end\" neural networks—black boxes that map camera inputs directly to steering outputs—can achieve safety levels surpassing human capability through infinite data ingestion.1 The Brewtanius \"Guardian Stack\" has introduced a superior epistemological framework that invalidates this bet. It asserts that the curre","url":"https://doi.org/10.5281/zenodo.17694131","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17694131","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.17694132","name":"THE BILLIONAIRE PROMISES vs. THE BREWTANIUS RECEIPTS: A Structural Invalidation of the Trillionaire Trajectory through Sovereign Engineering","source":"datacite","abstract":"THE BILLIONAIRE PROMISES vs. THE BREWTANIUS RECEIPTS: A Structural Invalidation of the Trillionaire Trajectory through Sovereign Engineering I. Executive Statement: The Collapse of the Monopoly Hypothesis The economic historiography of the early 21st century has been dominated by a singular, pervasive, and largely unchallenged narrative: the \"Trillionaire Trajectory.\" This economic theory posited that the future of human civilization—its energy grids, its labor force, its transport logistics, and its extraterrestrial expansion—would inevitably coalesce into the proprietary domains of a select few ultra-high-net-worth individuals. The assumption was that the capital requirements for generalized autonomy, humanoid robotics, and interplanetary colonization were so high that only those who had already captured the value of the Internet Age (Web 2.0) could afford to build the infrastructure of the Artificial Intelligence Age (Web 3.0/Industry 4.0). The primary avatars of this trajectory, Elon Musk and Jeff Bezos, spent two decades constructing market valuations based not on current revenue, but on the promise of these future capabilities. Tesla’s stock price is not a reflection of car sales; it is a call option on a future monopoly of autonomous transport. Amazon’s valuation is not merely about retail; it is a bet on the eventual privatization of the global and orbital supply chain. These valuations rely on the maintenance of technological bottlenecks—specifically the closed control of training data, proprietary energy interfaces, and vertical integration of manufacturing.1 However, a rigorous, exhaustive analysis of the technological landscape in 2025 reveals a fundamental structural correction. The \"monopoly assumptions\" underlying these valuations have been intercepted. They were not intercepted by a corporate competitor or a rival nation-state, but by a structural shift in the technological substrate itself, spearheaded by the \"Brewtanius\" initiative (led by Mark Anthony Brewer) and the CollectiveOS architecture.1 This report serves as a \"receipts drop\"—a forensic accounting of the divergence between the aspirational promises of the billionaire class and the deployed, timestamped, and physically verifiable technologies delivered by the Brewtanius initiative. The evidence suggests that while the billionaire class was busy hypothesizing a future of \"optional labor\" and \"post-money civilization\" while still charging subscription fees, a disabled Black veteran operating without a billion-dollar budget successfully engineered the \"operating system for civilization\".1 This is not a debate of personalities. It is a confrontation of architectures. The \"Closed Loop\" of the Monopolist, designed to extract rent from scarcity, has been out-engineered by the \"Open Stack\" of the Sovereign Engineer, designed to generate abundance through distributed governance. The following analysis details how the Brewtanius initiative delivered 94 published white papers, solved 17 UN global issues, and deployed a verifiable \"Anti-Scarcity Stack\" in a five-month sprint, effectively rendering the trillionaire trajectory mathematically impossible.1 II. The Epistemological Crisis of Autonomy: Probabilistic Promises vs. Provable Safety The valuation of Tesla and the reputational capital of Elon Musk are inextricably linked to the promise of Full Self-Driving (FSD). The prevailing economic theory suggests that by solving generalized autonomy first via a proprietary neural network, Tesla will accrue a monopoly on transport-as-a-service, generating software-margin profits on global logistics. This projection relies on a specific technological bet: that \"end-to-end\" neural networks—black boxes that map camera inputs directly to steering outputs—can achieve safety levels surpassing human capability through infinite data ingestion.1 The Brewtanius \"Guardian Stack\" has introduced a superior epistemological framework that invalidates this bet. It asserts that the curre","url":"https://doi.org/10.5281/zenodo.17694132","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17694132","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.17762197","name":"THE COLLECTIVEOS EPOCH: A UNIFIED ARCHITECTURE FOR HUMAN CREATIVITY, AI, AND SCIENTIFIC FOUNDATIONS","source":"datacite","abstract":"THE COLLECTIVEOS EPOCH: A UNIFIED ARCHITECTURE FOR HUMAN CREATIVITY, AI, AND SCIENTIFIC FOUNDATIONS Executive Summary The history of technological progress is often framed as a linear ascent, a relentless accumulation of capability where the new renders the old obsolete. However, a rigorous analysis of emerging anomalies in high-energy physics, patterns in ancient material engineering, and the cognitive structures of early human writing suggests a different trajectory: one of convergence. We are not merely climbing; we are converging upon a set of fundamental, universal constraints that govern the stability of complex systems across all scales. This report presents a comprehensive technical and strategic analysis of the CollectiveOS Epoch, a paradigm shift defined by the recognition that \"alignment\" in artificial intelligence is not a sociological negotiation of values, but a physical problem of adhering to these universal invariants. This document serves as the foundational technical manifesto for CollectiveOS, a multi-agent, AI-native operating system designed to operationalize this new scientific era. It details the Universal Intent Layer (UIL), the theoretical bedrock that unifies physical anomalies with cognitive patterns; the Living Fibonacci Engine (LFE), the mathematical control law that ensures system stability; and the Anti-Scarcity Stack, the suite of open-source hardware verticals that ground this intelligence in physical reality. Furthermore, it introduces the Six Elements of the Collective—a new class of AI-engineered quantum materials—and the Gardener Pattern Atlas, a research methodology for recovering \"lost\" technological patterns from the historical record. Drawing upon a synthesis of control theory, thermodynamics, and computational linguistics, this report argues that humanity is entering the Constraint Era. In this era, the most powerful systems are not those with unbridled freedom, but those—like the ChronoFlux timer or the Guardian Sentinel drone—that minimize their divergence from the deep structural constraints of the universe. This analysis provides the architecture, the truth, and the impact of this transition, offering a roadmap for a civilization that has matured enough to secure its own planet and confident enough to peacefully explore the next. 1. The Theoretical Paradigm: The Universal Intent Layer (UIL) The central intellectual breakthrough underpinning the CollectiveOS architecture is the realization that the \"alignment problem\" in artificial intelligence has been fundamentally misframed. Contemporary discourse largely treats alignment as a challenge of encoding transient human preferences into a statistical model. The Universal Intent Layer (UIL) proposes a radical alternative: alignment is the process of synchronizing an artificial system with the deep, invariant structural constraints that define physical reality itself.1 1.1 The Constraint Field Hypothesis At the core of the UIL is the Constraint Field Hypothesis. This hypothesis posits that the formation of complex systems—from the spiral arms of galaxies to the metabolic cycles of biological organisms—is not driven solely by random mutation and selection, but is actively shaped by underlying \"optimization attractors\".1 These attractors function as a global \"constraint field,\" biasing the evolution of matter and information toward specific, low-entropy configurations that maximize stability and complexity. In traditional AI training, the objective function typically seeks to minimize prediction error on a specific dataset, represented mathematically as $\\min \\mathcal{L}(y, \\hat{y})$. This approach, while effective for narrow tasks, creates systems that are fundamentally ungrounded; they optimize for a reward signal that can be gamed or misinterpreted, leading to phenomena such as \"wireheading\" or reward hacking. The UIL framework shifts this objective function entirely. A system aligned with the UIL does not merely seek to satisfy a user ","url":"https://doi.org/10.5281/zenodo.17762197","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17762197","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17762198","name":"THE COLLECTIVEOS EPOCH: A UNIFIED ARCHITECTURE FOR HUMAN CREATIVITY, AI, AND SCIENTIFIC FOUNDATIONS","source":"datacite","abstract":"THE COLLECTIVEOS EPOCH: A UNIFIED ARCHITECTURE FOR HUMAN CREATIVITY, AI, AND SCIENTIFIC FOUNDATIONS Executive Summary The history of technological progress is often framed as a linear ascent, a relentless accumulation of capability where the new renders the old obsolete. However, a rigorous analysis of emerging anomalies in high-energy physics, patterns in ancient material engineering, and the cognitive structures of early human writing suggests a different trajectory: one of convergence. We are not merely climbing; we are converging upon a set of fundamental, universal constraints that govern the stability of complex systems across all scales. This report presents a comprehensive technical and strategic analysis of the CollectiveOS Epoch, a paradigm shift defined by the recognition that \"alignment\" in artificial intelligence is not a sociological negotiation of values, but a physical problem of adhering to these universal invariants. This document serves as the foundational technical manifesto for CollectiveOS, a multi-agent, AI-native operating system designed to operationalize this new scientific era. It details the Universal Intent Layer (UIL), the theoretical bedrock that unifies physical anomalies with cognitive patterns; the Living Fibonacci Engine (LFE), the mathematical control law that ensures system stability; and the Anti-Scarcity Stack, the suite of open-source hardware verticals that ground this intelligence in physical reality. Furthermore, it introduces the Six Elements of the Collective—a new class of AI-engineered quantum materials—and the Gardener Pattern Atlas, a research methodology for recovering \"lost\" technological patterns from the historical record. Drawing upon a synthesis of control theory, thermodynamics, and computational linguistics, this report argues that humanity is entering the Constraint Era. In this era, the most powerful systems are not those with unbridled freedom, but those—like the ChronoFlux timer or the Guardian Sentinel drone—that minimize their divergence from the deep structural constraints of the universe. This analysis provides the architecture, the truth, and the impact of this transition, offering a roadmap for a civilization that has matured enough to secure its own planet and confident enough to peacefully explore the next. 1. The Theoretical Paradigm: The Universal Intent Layer (UIL) The central intellectual breakthrough underpinning the CollectiveOS architecture is the realization that the \"alignment problem\" in artificial intelligence has been fundamentally misframed. Contemporary discourse largely treats alignment as a challenge of encoding transient human preferences into a statistical model. The Universal Intent Layer (UIL) proposes a radical alternative: alignment is the process of synchronizing an artificial system with the deep, invariant structural constraints that define physical reality itself.1 1.1 The Constraint Field Hypothesis At the core of the UIL is the Constraint Field Hypothesis. This hypothesis posits that the formation of complex systems—from the spiral arms of galaxies to the metabolic cycles of biological organisms—is not driven solely by random mutation and selection, but is actively shaped by underlying \"optimization attractors\".1 These attractors function as a global \"constraint field,\" biasing the evolution of matter and information toward specific, low-entropy configurations that maximize stability and complexity. In traditional AI training, the objective function typically seeks to minimize prediction error on a specific dataset, represented mathematically as $\\min \\mathcal{L}(y, \\hat{y})$. This approach, while effective for narrow tasks, creates systems that are fundamentally ungrounded; they optimize for a reward signal that can be gamed or misinterpreted, leading to phenomena such as \"wireheading\" or reward hacking. The UIL framework shifts this objective function entirely. A system aligned with the UIL does not merely seek to satisfy a user ","url":"https://doi.org/10.5281/zenodo.17762198","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17762198","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17667053","name":"Global Abundance & Civilian Space Initiative: A Comprehensive Technical and Diplomatic Architecture for Post-Scarcity","source":"datacite","abstract":"Global Abundance & Civilian Space Initiative: A Comprehensive Technical and Diplomatic Architecture for Post-Scarcity 1. Executive Analysis: The Paradox of Capability and the Architecture of Abundance The trajectory of human development in the early 21st century is defined by a jarring paradox of capability. We currently possess the precise technological maturity required to resolve the fundamental physiological requirements of the global population—water, food, energy, and shelter—yet the distribution of these resources remains artificially constrained by archaic economic models, fragile centralized supply chains, and escalating geopolitical friction.1 The prevailing paradigm of international development and resource management operates on a logic of scarcity, where economic value is largely derived from the control of limited supplies and the friction of their distribution. However, a convergence of advanced materials science, autonomous robotics, and artificial intelligence (AI) suggests that the barrier to global abundance is no longer physical or technical, but architectural. The Global Abundance & Civilian Space Initiative proposes a structural resolution to this paradox. It moves beyond the traditional aid paradigm, which is dependent on perpetual funding cycles and the transfer of consumables, to propose a blueprint for a self-sustaining, circular infrastructure architecture. This architecture is capable of operationalizing the United Nations Sustainable Development Goals (SDGs) through distributed engineering and open-science governance.1 At the core of this initiative is the \"Anti-Scarcity Stack\"—a converged suite of technologies managed by the CollectiveOS operating system.1 This stack integrates advanced robotics, bio-synthetic materials, and autonomous energy systems into \"Village Nodes,\" which are modular, locally manufacturable units designed to decouple communities from the vulnerabilities of global trade. This report provides an exhaustive technical and strategic analysis of the initiative. It details the transition from centralized industrial production to \"Cosmo-Local\" manufacturing (Design Global, Manufacture Local), enabled by the synthesis of recent breakthroughs in metal-organic frameworks (MOFs) for atmospheric water generation 2, mycelium-based self-healing electronics 5, and bio-photovoltaics.7 Strategically, the initiative identifies Switzerland as the requisite \"Root of Trust\" for this global operating system, leveraging its 2024-2027 Foreign Policy Strategy to host the Human Global Science Collective (HGSC).1 Furthermore, the report delineates the \"Civilian Space Program\" (CSP), which reframes space exploration not as a competitive frontier but as an extension of Earth's circular economy, anchored by debris removal missions and sustainable lunar habitation technologies derived from terrestrial abundance systems.1 2. The CollectiveOS: Governance as Operating System The central nervous system of the Global Abundance Initiative is the CollectiveOS (GEM:Ω Quantum-Adaptive Intelligence). Unlike traditional operating systems designed for resource allocation within a single machine, CollectiveOS is a \"governance-first\" architecture designed to orchestrate complex physical and digital systems across a distributed network. It addresses the primary risk of powerful autonomous systems: the alignment problem. By embedding governance protocols directly into the execution logic of the machinery, CollectiveOS ensures that the \"Anti-Scarcity Stack\" remains aligned with humanitarian ethics and international law.1 2.1. The GATA PRIME Protocol: Governance-as-Code The governance architecture is hierarchical, designed to filter actions through increasingly rigorous safety checks before they can impact the physical world. This pipeline is defined as QC → GATA → GATA PRIME.1 QC (Quality & Control): The initial layer performs standard unit tests and sanitary checks on code and hardware instructions. It ensures that input/","url":"https://doi.org/10.5281/zenodo.17667053","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17667053","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.557Z"},{"id":"doi:10.5281/zenodo.17667052","name":"Global Abundance & Civilian Space Initiative: A Comprehensive Technical and Diplomatic Architecture for Post-Scarcity","source":"datacite","abstract":"Global Abundance & Civilian Space Initiative: A Comprehensive Technical and Diplomatic Architecture for Post-Scarcity 1. Executive Analysis: The Paradox of Capability and the Architecture of Abundance The trajectory of human development in the early 21st century is defined by a jarring paradox of capability. We currently possess the precise technological maturity required to resolve the fundamental physiological requirements of the global population—water, food, energy, and shelter—yet the distribution of these resources remains artificially constrained by archaic economic models, fragile centralized supply chains, and escalating geopolitical friction.1 The prevailing paradigm of international development and resource management operates on a logic of scarcity, where economic value is largely derived from the control of limited supplies and the friction of their distribution. However, a convergence of advanced materials science, autonomous robotics, and artificial intelligence (AI) suggests that the barrier to global abundance is no longer physical or technical, but architectural. The Global Abundance & Civilian Space Initiative proposes a structural resolution to this paradox. It moves beyond the traditional aid paradigm, which is dependent on perpetual funding cycles and the transfer of consumables, to propose a blueprint for a self-sustaining, circular infrastructure architecture. This architecture is capable of operationalizing the United Nations Sustainable Development Goals (SDGs) through distributed engineering and open-science governance.1 At the core of this initiative is the \"Anti-Scarcity Stack\"—a converged suite of technologies managed by the CollectiveOS operating system.1 This stack integrates advanced robotics, bio-synthetic materials, and autonomous energy systems into \"Village Nodes,\" which are modular, locally manufacturable units designed to decouple communities from the vulnerabilities of global trade. This report provides an exhaustive technical and strategic analysis of the initiative. It details the transition from centralized industrial production to \"Cosmo-Local\" manufacturing (Design Global, Manufacture Local), enabled by the synthesis of recent breakthroughs in metal-organic frameworks (MOFs) for atmospheric water generation 2, mycelium-based self-healing electronics 5, and bio-photovoltaics.7 Strategically, the initiative identifies Switzerland as the requisite \"Root of Trust\" for this global operating system, leveraging its 2024-2027 Foreign Policy Strategy to host the Human Global Science Collective (HGSC).1 Furthermore, the report delineates the \"Civilian Space Program\" (CSP), which reframes space exploration not as a competitive frontier but as an extension of Earth's circular economy, anchored by debris removal missions and sustainable lunar habitation technologies derived from terrestrial abundance systems.1 2. The CollectiveOS: Governance as Operating System The central nervous system of the Global Abundance Initiative is the CollectiveOS (GEM:Ω Quantum-Adaptive Intelligence). Unlike traditional operating systems designed for resource allocation within a single machine, CollectiveOS is a \"governance-first\" architecture designed to orchestrate complex physical and digital systems across a distributed network. It addresses the primary risk of powerful autonomous systems: the alignment problem. By embedding governance protocols directly into the execution logic of the machinery, CollectiveOS ensures that the \"Anti-Scarcity Stack\" remains aligned with humanitarian ethics and international law.1 2.1. The GATA PRIME Protocol: Governance-as-Code The governance architecture is hierarchical, designed to filter actions through increasingly rigorous safety checks before they can impact the physical world. This pipeline is defined as QC → GATA → GATA PRIME.1 QC (Quality & Control): The initial layer performs standard unit tests and sanitary checks on code and hardware instructions. It ensures that input/","url":"https://doi.org/10.5281/zenodo.17667052","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17667052","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:45.557Z"},{"id":"doi:10.5281/zenodo.16901364","name":"The First IEEE World Technology Summit 2024: AI Infrastructure Importance and Challenges","source":"datacite","abstract":"The inaugural IEEE World Technology Summit 2024 brought together global leaders in technology, academia, and industry to explore the transformative intersections of artificial intelligence (AI), energy systems, quantum computing, and governance. Central themes included the challenges of scaling AI infrastructure, achieving sustainable AI development, and fostering global standards for secure and ethical AI deployment. Highlights from the summit showcased advancements in industrial AI applications, digital twins, semiconductor design, and energy-efficient systems. Key insights included leveraging AI for predictive maintenance, optimizing chip design through reinforcement learning, and integrating digital twins with generative AI to revolutionize industrial workflows. Presentations underscored the growing energy demands of AI, with strategies such as liquid cooling, energy-efficient data centers, and carbon-neutral energy sources proposed to address sustainability challenges. Standards and security emerged as critical pillars, with discussions on post-quantum cryptography, fully homomorphic encryption, and AI governance frameworks. The summit’s multidisciplinary approach emphasized the convergence of innovation and ethical responsibility, setting the stage for AI’s transformative potential in reshaping industries and addressing global challenges. By fostering collaboration and advancing technical solutions, the IEEE World Technology Summit highlighted the pivotal role of AI in driving sustainable, inclusive, and secure technological progress.","url":"https://doi.org/10.5281/zenodo.16901364","authors":["Vyatkin, Valeriy","Huang, Victor","Condry, Michael","Nihtianov, Stoyan","Karnouskos, Stamatis","Manic, Milos"],"tags":["Artificial intelligence","Infrastructure"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16901364","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.16901365","name":"The First IEEE World Technology Summit 2024: AI Infrastructure Importance and Challenges","source":"datacite","abstract":"The inaugural IEEE World Technology Summit 2024 brought together global leaders in technology, academia, and industry to explore the transformative intersections of artificial intelligence (AI), energy systems, quantum computing, and governance. Central themes included the challenges of scaling AI infrastructure, achieving sustainable AI development, and fostering global standards for secure and ethical AI deployment. Highlights from the summit showcased advancements in industrial AI applications, digital twins, semiconductor design, and energy-efficient systems. Key insights included leveraging AI for predictive maintenance, optimizing chip design through reinforcement learning, and integrating digital twins with generative AI to revolutionize industrial workflows. Presentations underscored the growing energy demands of AI, with strategies such as liquid cooling, energy-efficient data centers, and carbon-neutral energy sources proposed to address sustainability challenges. Standards and security emerged as critical pillars, with discussions on post-quantum cryptography, fully homomorphic encryption, and AI governance frameworks. The summit’s multidisciplinary approach emphasized the convergence of innovation and ethical responsibility, setting the stage for AI’s transformative potential in reshaping industries and addressing global challenges. By fostering collaboration and advancing technical solutions, the IEEE World Technology Summit highlighted the pivotal role of AI in driving sustainable, inclusive, and secure technological progress.","url":"https://doi.org/10.5281/zenodo.16901365","authors":["Vyatkin, Valeriy","Huang, Victor","Condry, Michael","Nihtianov, Stoyan","Karnouskos, Stamatis","Manic, Milos"],"tags":["Artificial intelligence","Infrastructure"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16901365","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2507.22216","name":"Representation biases: will we achieve complete understanding by analyzing representations?","source":"datacite","abstract":"A common approach in neuroscience is to study neural representations as a means to understand a system -- increasingly, by relating the neural representations to the internal representations learned by computational models. However, a recent work in machine learning (Lampinen, 2024) shows that learned feature representations may be biased to over-represent certain features, and represent others more weakly and less-consistently. For example, simple (linear) features may be more strongly and more consistently represented than complex (highly nonlinear) features. These biases could pose challenges for achieving full understanding of a system through representational analysis. In this perspective, we illustrate these challenges -- showing how feature representation biases can lead to strongly biased inferences from common analyses like PCA, regression, and RSA. We also present homomorphic encryption as a simple case study of the potential for strong dissociation between patterns of representation and computation. We discuss the implications of these results for representational comparisons between systems, and for neuroscience more generally.","url":"https://doi.org/10.48550/arxiv.2507.22216","authors":["Lampinen, Andrew Kyle","Chan, Stephanie C. Y.","Li, Yuxuan","Hermann, Katherine"],"tags":["Neurons and Cognition (q-bio.NC)","Machine Learning (cs.LG)","FOS: Biological sciences","FOS: Biological sciences","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.22216","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2503.22232","name":"Privacy-Preserving Secure Neighbor Discovery for Wireless Networks","source":"datacite","abstract":"Traditional Neighbor Discovery (ND) and Secure Neighbor Discovery (SND) are key elements for network functionality. SND is a hard problem, satisfying not only typical security properties (authentication, integrity) but also verification of direct communication, which involves distance estimation based on time measurements and device coordinates. Defeating relay attacks, also known as \"wormholes\", leading to stealthy Byzantine links and significant degradation of communication and adversarial control, is key in many wireless networked systems. However, SND is not concerned with privacy; it necessitates revealing the identity and location of the device(s) participating in the protocol execution. This can be a deterrent for deployment, especially involving user-held devices in the emerging Internet of Things (IoT) enabled smart environments. To address this challenge, we present a novel Privacy-Preserving Secure Neighbor Discovery (PP-SND) protocol, enabling devices to perform SND without revealing their actual identities and locations, effectively decoupling discovery from the exposure of sensitive information. We use Homomorphic Encryption (HE) for computing device distances without revealing their actual coordinates, as well as employing a pseudonymous device authentication to hide identities while preserving communication integrity. PP-SND provides SND [1] along with pseudonymity, confidentiality, and unlinkability. Our presentation here is not specific to one wireless technology, and we assess the performance of the protocols (cryptographic overhead) on a Raspberry Pi 4 and provide a security and privacy analysis.","url":"https://doi.org/10.48550/arxiv.2503.22232","authors":["Hussain, Ahmed Mohamed","Papadimitratos, Panos"],"tags":["Cryptography and Security (cs.CR)","Networking and Internet Architecture (cs.NI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2503.22232","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2504.13198","name":"Overcoming Bottlenecks in Homomorphic Encryption for the 2024 Mexican Federal Election","source":"datacite","abstract":"On June 2, 2024, Mexico held its federal elections. The majority of Mexican citizens voted in person at the polls in this historic election. For the first time though, Mexican citizens living outside their country were able to vote online via a web app, either on a personal device or using an electronic voting kiosk at one of 23 embassies and consulates in the U.S., Canada, and Europe. In total, 144,734 people voted outside of Mexico: 122,496 on a personal device and 22,238 in-person at a kiosk. Voting was open for remote voting from 8PM, May 18, 2024 to 6PM, June 2, 2024 and was open for in-person voting from 8AM-6PM on June 2, 2024. This article describes the technical and cryptographic tools applied to secure the ex-patriate component of the election and to enable INE (Mexico's National Electoral Institute) to generate provable election results within minutes of the close of the election. This article will also describe how the solutions we present scale to elections on a national level.","url":"https://doi.org/10.48550/arxiv.2504.13198","authors":["Landquist, Eric","Sawhney, Nimit","Sawhney, Simer"],"tags":["Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","Number Theory (math.NT)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Mathematics","FOS: Mathematics","68P25, 94A60"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2504.13198","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2502.16877","name":"APINT: A Full-Stack Framework for Acceleration of Privacy-Preserving Inference of Transformers based on Garbled Circuits","source":"datacite","abstract":"As the importance of Privacy-Preserving Inference of Transformers (PiT) increases, a hybrid protocol that integrates Garbled Circuits (GC) and Homomorphic Encryption (HE) is emerging for its implementation. While this protocol is preferred for its ability to maintain accuracy, it has a severe drawback of excessive latency. To address this, existing protocols primarily focused on reducing HE latency, thus making GC the new latency bottleneck. Furthermore, previous studies only focused on individual computing layers, such as protocol or hardware accelerator, lacking a comprehensive solution at the system level. This paper presents APINT, a full-stack framework designed to reduce PiT's overall latency by addressing the latency problem of GC through both software and hardware solutions. APINT features a novel protocol that reallocates possible GC workloads to alternative methods (i.e., HE or standard matrix operation), substantially decreasing the GC workload. It also suggests GC-friendly circuit generation that reduces the number of AND gates at the most, which is the expensive operator in GC. Furthermore, APINT proposes an innovative netlist scheduling that combines coarse-grained operation mapping and fine-grained scheduling for maximal data reuse and minimal dependency. Finally, APINT's hardware accelerator, combined with its compiler speculation, effectively resolves the memory stall issue. Putting it all together, APINT achieves a remarkable end-to-end reduction in latency, outperforming the existing protocol on CPU platform by 12.2x online and 2.2x offline. Meanwhile, the APINT accelerator not only reduces its latency by 3.3x but also saves energy consumption by 4.6x while operating PiT compared to the state-of-the-art GC accelerator.","url":"https://doi.org/10.48550/arxiv.2502.16877","authors":["Cho, Hyunjun","Jeon, Jaeho","Heo, Jaehoon","Kim, Joo-Young"],"tags":["Hardware Architecture (cs.AR)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2502.16877","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2412.07954","name":"MOFHEI: Model Optimizing Framework for Fast and Efficient Homomorphically Encrypted Neural Network Inference","source":"datacite","abstract":"Due to the extensive application of machine learning (ML) in a wide range of fields and the necessity of data privacy, privacy-preserving machine learning (PPML) solutions have recently gained significant traction. One group of approaches relies on Homomorphic Encryption (HE), which enables us to perform ML tasks over encrypted data. However, even with state-of-the-art HE schemes, HE operations are still significantly slower compared to their plaintext counterparts and require a considerable amount of memory. Therefore, we propose MOFHEI, a framework that optimizes the model to make HE-based neural network inference, referred to as private inference (PI), fast and efficient. First, our proposed learning-based method automatically transforms a pre-trained ML model into its compatible version with HE operations, called the HE-friendly version. Then, our iterative block pruning method prunes the model's parameters in configurable block shapes in alignment with the data packing method. This allows us to drop a significant number of costly HE operations, thereby reducing the latency and memory consumption while maintaining the model's performance. We evaluate our framework through extensive experiments on different models using various datasets. Our method achieves up to 98% pruning ratio on LeNet, eliminating up to 93% of the required HE operations for performing PI, reducing latency and the required memory by factors of 9.63 and 4.04, respectively, with negligible accuracy loss.","url":"https://doi.org/10.48550/arxiv.2412.07954","authors":["Ghazvinian, Parsa","Podschwadt, Robert","Panzade, Prajwal","Rafiei, Mohammad H.","Takabi, Daniel"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2412.07954","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2312.04356","name":"NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and FHE Bootstrapping","source":"datacite","abstract":"Fully homomorphic encryption (FHE) is a promising cryptographic primitive for realizing private neural network inference (PI) services by allowing a client to fully offload the inference task to a cloud server while keeping the client data oblivious to the server. This work proposes NeuJeans, an FHE-based solution for the PI of deep convolutional neural networks (CNNs). NeuJeans tackles the critical problem of the enormous computational cost for the FHE evaluation of CNNs. We introduce a novel encoding method called Coefficients-in-Slot (CinS) encoding, which enables multiple convolutions in one HE multiplication without costly slot permutations. We further observe that CinS encoding is obtained by conducting the first several steps of the Discrete Fourier Transform (DFT) on a ciphertext in conventional Slot encoding. This property enables us to save the conversion between CinS and Slot encodings as bootstrapping a ciphertext starts with DFT. Exploiting this, we devise optimized execution flows for various two-dimensional convolution (conv2d) operations and apply them to end-to-end CNN implementations. NeuJeans accelerates the performance of conv2d-activation sequences by up to 5.68 times compared to state-of-the-art FHE-based PI work and performs the PI of a CNN at the scale of ImageNet within a mere few seconds.","url":"https://doi.org/10.48550/arxiv.2312.04356","authors":["Ju, Jae Hyung","Park, Jaiyoung","Kim, Jongmin","Kang, Minsik","Kim, Donghwan","Cheon, Jung Hee","Ahn, Jung Ho"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2312.04356","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2411.07468","name":"Privacy-Preserving Verifiable Neural Network Inference Service","source":"datacite","abstract":"Machine learning has revolutionized data analysis and pattern recognition, but its resource-intensive training has limited accessibility. Machine Learning as a Service (MLaaS) simplifies this by enabling users to delegate their data samples to an MLaaS provider and obtain the inference result using a pre-trained model. Despite its convenience, leveraging MLaaS poses significant privacy and reliability concerns to the client. Specifically, sensitive information from the client inquiry data can be leaked to an adversarial MLaaS provider. Meanwhile, the lack of a verifiability guarantee can potentially result in biased inference results or even unfair payment issues. While existing trustworthy machine learning techniques, such as those relying on verifiable computation or secure computation, offer solutions to privacy and reliability concerns, they fall short of simultaneously protecting the privacy of client data and providing provable inference verifiability. In this paper, we propose vPIN, a privacy-preserving and verifiable CNN inference scheme that preserves privacy for client data samples while ensuring verifiability for the inference. vPIN makes use of partial homomorphic encryption and commit-and-prove succinct non-interactive argument of knowledge techniques to achieve desirable security properties. In vPIN, we develop various optimization techniques to minimize the proving circuit for homomorphic inference evaluation thereby, improving the efficiency and performance of our technique. We fully implemented and evaluated our vPIN scheme on standard datasets (e.g., MNIST, CIFAR-10). Our experimental results show that vPIN achieves high efficiency in terms of proving time, verification time, and proof size, while providing client data privacy guarantees and provable verifiability.","url":"https://doi.org/10.48550/arxiv.2411.07468","authors":["Riasi, Arman","Guajardo, Jorge","Hoang, Thang"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2411.07468","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2411.02926","name":"Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering","source":"datacite","abstract":"Combating money laundering has become increasingly complex with the rise of cybercrime and digitalization of financial transactions. Graph-based machine learning techniques have emerged as promising tools for Anti-Money Laundering (AML) detection, capturing intricate relationships within money laundering networks. However, the effectiveness of AML solutions is hindered by data silos within financial institutions, limiting collaboration and overall efficacy. This research presents a novel privacy-preserving approach for collaborative AML machine learning, facilitating secure data sharing across institutions and borders while preserving privacy and regulatory compliance. Leveraging Fully Homomorphic Encryption (FHE), computations are directly performed on encrypted data, ensuring the confidentiality of financial data. Notably, FHE over the Torus (TFHE) was integrated with graph-based machine learning using Zama Concrete ML. The research contributes two key privacy-preserving pipelines. First, the development of a privacy-preserving Graph Neural Network (GNN) pipeline was explored. Optimization techniques like quantization and pruning were used to render the GNN FHE-compatible. Second, a privacy-preserving graph-based XGBoost pipeline leveraging Graph Feature Preprocessor (GFP) was successfully developed. Experiments demonstrated strong predictive performance, with the XGBoost model consistently achieving over 99% accuracy, F1-score, precision, and recall on the balanced AML dataset in both unencrypted and FHE-encrypted inference settings. On the imbalanced dataset, the incorporation of graph-based features improved the F1-score by 8%. The research highlights the need to balance the trade-off between privacy and computational efficiency.","url":"https://doi.org/10.48550/arxiv.2411.02926","authors":["Effendi, Fabrianne","Chattopadhyay, Anupam"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2411.02926","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2405.14569","name":"PrivCirNet: Efficient Private Inference via Block Circulant Transformation","source":"datacite","abstract":"Homomorphic encryption (HE)-based deep neural network (DNN) inference protects data and model privacy but suffers from significant computation overhead. We observe transforming the DNN weights into circulant matrices converts general matrix-vector multiplications into HE-friendly 1-dimensional convolutions, drastically reducing the HE computation cost. Hence, in this paper, we propose \\method, a protocol/network co-optimization framework based on block circulant transformation. At the protocol level, PrivCirNet customizes the HE encoding algorithm that is fully compatible with the block circulant transformation and reduces the computation latency in proportion to the block size. At the network level, we propose a latency-aware formulation to search for the layer-wise block size assignment based on second-order information. PrivCirNet also leverages layer fusion to further reduce the inference cost. We compare PrivCirNet with the state-of-the-art HE-based framework Bolt (IEEE S\\&amp;P 2024) and the HE-friendly pruning method SpENCNN (ICML 2023). For ResNet-18 and Vision Transformer (ViT) on Tiny ImageNet, PrivCirNet reduces latency by $5.0\\times$ and $1.3\\times$ with iso-accuracy over Bolt, respectively, and improves accuracy by $4.1\\%$ and $12\\%$ over SpENCNN, respectively. For MobileNetV2 on ImageNet, PrivCirNet achieves $1.7\\times$ lower latency and $4.2\\%$ better accuracy over Bolt and SpENCNN, respectively. Our code and checkpoints are available on Git Hub.","url":"https://doi.org/10.48550/arxiv.2405.14569","authors":["Xu, Tianshi","Wu, Lemeng","Wang, Runsheng","Li, Meng"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.14569","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2410.21192","name":"On Homomorphic Encryption Based Strategies for Class Imbalance in Federated Learning","source":"datacite","abstract":"Class imbalance in training datasets can lead to bias and poor generalization in machine learning models. While pre-processing of training datasets can efficiently address both these issues in centralized learning environments, it is challenging to detect and address these issues in a distributed learning environment such as federated learning. In this paper, we propose FLICKER, a privacy preserving framework to address issues related to global class imbalance in federated learning. At the heart of our contribution lies the popular CKKS homomorphic encryption scheme, which is used by the clients to privately share their data attributes, and subsequently balance their datasets before implementing the FL scheme. Extensive experimental results show that our proposed method significantly improves the FL accuracy numbers when used along with popular datasets and relevant baselines.","url":"https://doi.org/10.48550/arxiv.2410.21192","authors":["Guleria, Arpit","Harshan, J.","Prasad, Ranjitha","Bharath, B. N."],"tags":["Cryptography and Security (cs.CR)","Information Theory (cs.IT)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2410.21192","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2410.11184","name":"Fast and Accurate Homomorphic Softmax Evaluation","source":"datacite","abstract":"Homomorphic encryption is one of the main solutions for building secure and privacy-preserving solutions for Machine Learning as a Service. This motivates the development of homomorphic algorithms for the main building blocks of AI, typically for the components of the various types of neural networks architectures. Among those components, we focus on the Softmax function, defined by $\\mathrm{SM}(\\mathbf{x}) = \\left(\\exp(x_i) / \\sum_{j=1}^n \\exp(x_j) \\right)_{1\\le i\\le n}$. This function is deemed to be one of the most difficult to evaluate homomorphically, because of its multivariate nature and of the very large range of values for $\\exp(x_i)$. The available homomorphic algorithms remain restricted, especially in large dimensions, while important applications such as Large Language Models (LLM) require computing Softmax over large dimensional vectors. In terms of multiplicative depth of the computation (a suitable measure of cost for homomorphic algorithms), our algorithm achieves $O(\\log n)$ complexity for a fixed range of inputs, where $n$ is the Softmax dimension. Our algorithm is especially adapted to the situation where we must compute many Softmax at the same time, for instance, in the LLM situation. In that case, assuming that all Softmax calls are packed into $m$ ciphtertexts, the asymptotic amortized multiplicative depth cost per ciphertext is, again over a fixed range, $O(1 + m/N)$ for $N$ the homomorphic ring degree. The main ingredient of our algorithms is a normalize-and-square strategy, which interlaces the exponential computation over a large range and normalization, decomposing both in stabler and cheaper smaller steps. Comparing ourselves to the state of the art, our experiments show, in practice, a good accuracy and a gain of a factor 2.5 to 8 compared to state of the art solutions.","url":"https://doi.org/10.48550/arxiv.2410.11184","authors":["Cho, Wonhee","Hanrot, Guillaume","Kim, Taeseong","Park, Minje","Stehlé, Damien"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2410.11184","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2408.06167","name":"Blind-Match: Efficient Homomorphic Encryption-Based 1:N Matching for Privacy-Preserving Biometric Identification","source":"datacite","abstract":"We present Blind-Match, a novel biometric identification system that leverages homomorphic encryption (HE) for efficient and privacy-preserving 1:N matching. Blind-Match introduces a HE-optimized cosine similarity computation method, where the key idea is to divide the feature vector into smaller parts for processing rather than computing the entire vector at once. By optimizing the number of these parts, Blind-Match minimizes execution time while ensuring data privacy through HE. Blind-Match achieves superior performance compared to state-of-the-art methods across various biometric datasets. On the LFW face dataset, Blind-Match attains a 99.63% Rank-1 accuracy with a 128-dimensional feature vector, demonstrating its robustness in face recognition tasks. For fingerprint identification, Blind-Match achieves a remarkable 99.55% Rank-1 accuracy on the PolyU dataset, even with a compact 16-dimensional feature vector, significantly outperforming the state-of-the-art method, Blind-Touch, which achieves only 59.17%. Furthermore, Blind-Match showcases practical efficiency in large-scale biometric identification scenarios, such as Naver Cloud's FaceSign, by processing 6,144 biometric samples in 0.74 seconds using a 128-dimensional feature vector.","url":"https://doi.org/10.48550/arxiv.2408.06167","authors":["Choi, Hyunmin","Kim, Jiwon","Song, Chiyoung","Woo, Simon S.","Kim, Hyoungshick"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2408.06167","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2402.06609","name":"You Still See Me: How Data Protection Supports the Architecture of AI Surveillance","source":"datacite","abstract":"Data forms the backbone of artificial intelligence (AI). Privacy and data protection laws thus have strong bearing on AI systems. Shielded by the rhetoric of compliance with data protection and privacy regulations, privacy-preserving techniques have enabled the extraction of more and new forms of data. We illustrate how the application of privacy-preserving techniques in the development of AI systems--from private set intersection as part of dataset curation to homomorphic encryption and federated learning as part of model computation--can further support surveillance infrastructure under the guise of regulatory permissibility. Finally, we propose technology and policy strategies to evaluate privacy-preserving techniques in light of the protections they actually confer. We conclude by highlighting the role that technologists could play in devising policies that combat surveillance AI technologies.","url":"https://doi.org/10.48550/arxiv.2402.06609","authors":["Yew, Rui-Jie","Qin, Lucy","Venkatasubramanian, Suresh"],"tags":["Computers and Society (cs.CY)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2402.06609","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2307.06554","name":"TPU as Cryptographic Accelerator","source":"datacite","abstract":"Cryptographic schemes like Fully Homomorphic Encryption (FHE) and Zero-Knowledge Proofs (ZKPs), while offering powerful privacy-preserving capabilities, are often hindered by their computational complexity. Polynomial multiplication, a core operation in these schemes, is a major performance bottleneck. While algorithmic advancements and specialized hardware like GPUs and FPGAs have shown promise in accelerating these computations, the recent surge in AI accelerators (TPUs/NPUs) presents a new opportunity. This paper explores the potential of leveraging TPUs/NPUs to accelerate polynomial multiplication, thereby enhancing the performance of FHE and ZKP schemes. We present techniques to adapt polynomial multiplication to these AI-centric architectures and provide a preliminary evaluation of their effectiveness. We also discuss current limitations and outline future directions for further performance improvements, paving the way for wider adoption of advanced cryptographic tools.","url":"https://doi.org/10.48550/arxiv.2307.06554","authors":["Karanjai, Rabimba","Shin, Sangwon","Xiong, and Wujie","Fan, Xinxin","Chen, Lin","Zhang, Tianwei","Suh, Taeweon","Shi, Weidong","Kuchta, Veronika","Sica, Francesco","Xu, Lei"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences","E.3; C.0; F.2.2"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2307.06554","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2409.16675","name":"CryptoTrain: Fast Secure Training on Encrypted Dataset","source":"datacite","abstract":"Secure training, while protecting the confidentiality of both data and model weights, typically incurs significant training overhead. Traditional Fully Homomorphic Encryption (FHE)-based non-inter-active training models are heavily burdened by computationally demanding bootstrapping. To develop an efficient secure training system, we established a foundational framework, CryptoTrain-B, utilizing a hybrid cryptographic protocol that merges FHE with Oblivious Transfer (OT) for handling linear and non-linear operations, respectively. This integration eliminates the need for costly bootstrapping. Although CryptoTrain-B sets a new baseline in performance, reducing its training overhead remains essential. We found that ciphertext-ciphertext multiplication (CCMul) is a critical bottleneck in operations involving encrypted inputs and models. Our solution, the CCMul-Precompute technique, involves precomputing CCMul offline and resorting to the less resource-intensive ciphertext-plaintext multiplication (CPMul) during private training. Furthermore, conventional polynomial convolution in FHE systems tends to encode irrelevant and redundant values into polynomial slots, necessitating additional polynomials and ciphertexts for input representation and leading to extra multiplications. Addressing this, we introduce correlated polynomial convolution, which encodes only related input values into polynomials, thus drastically reducing the number of computations and overheads. By integrating CCMul-Precompute and correlated polynomial convolution into CryptoTrain-B, we facilitate a rapid and efficient secure training framework, CryptoTrain. Extensive experiments demonstrate that CryptoTrain achieves a ~5.3X training time reduction compared to prior methods.","url":"https://doi.org/10.48550/arxiv.2409.16675","authors":["Xue, Jiaqi","Zhang, Yancheng","Wang, Yanshan","Wang, Xueqiang","Zheng, Hao","Lou, Qian"],"tags":["Cryptography and Security (cs.CR)","Databases (cs.DB)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.16675","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2409.16777","name":"PhD Forum: Efficient Privacy-Preserving Processing via Memory-Centric Computing","source":"datacite","abstract":"Privacy-preserving computation techniques like homomorphic encryption (HE) and secure multi-party computation (SMPC) enhance data security by enabling processing on encrypted data. However, the significant computational and CPU-DRAM data movement overhead resulting from the underlying cryptographic algorithms impedes the adoption of these techniques in practice. Existing approaches focus on improving computational overhead using specialized hardware like GPUs and FPGAs, but these methods still suffer from the same processor-DRAM overhead. Novel hardware technologies that support in-memory processing have the potential to address this problem. Memory-centric computing, or processing-in-memory (PIM), brings computation closer to data by introducing low-power processors called data processing units (DPUs) into memory. Besides its in-memory computation capability, PIM provides extensive parallelism, resulting in significant performance improvement over state-of-the-art approaches. We propose a framework that uses recently available PIM hardware to achieve efficient privacy-preserving computation. Our design consists of a four-layer architecture: (1) an application layer that decouples privacy-preserving applications from the underlying protocols and hardware; (2) a protocol layer that implements existing secure computation protocols (HE and MPC); (3) a data orchestration layer that leverages data compression techniques to mitigate the data transfer overhead between DPUs and host memory; (4) a computation layer which implements DPU kernels on which secure computation algorithms are built.","url":"https://doi.org/10.48550/arxiv.2409.16777","authors":["Mwaisela, Mpoki"],"tags":["Cryptography and Security (cs.CR)","Hardware Architecture (cs.AR)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.16777","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2409.06422","name":"A Pervasive, Efficient and Private Future: Realizing Privacy-Preserving Machine Learning Through Hybrid Homomorphic Encryption","source":"datacite","abstract":"Machine Learning (ML) has become one of the most impactful fields of data science in recent years. However, a significant concern with ML is its privacy risks due to rising attacks against ML models. Privacy-Preserving Machine Learning (PPML) methods have been proposed to mitigate the privacy and security risks of ML models. A popular approach to achieving PPML uses Homomorphic Encryption (HE). However, the highly publicized inefficiencies of HE make it unsuitable for highly scalable scenarios with resource-constrained devices. Hence, Hybrid Homomorphic Encryption (HHE) -- a modern encryption scheme that combines symmetric cryptography with HE -- has recently been introduced to overcome these challenges. HHE potentially provides a foundation to build new efficient and privacy-preserving services that transfer expensive HE operations to the cloud. This work introduces HHE to the ML field by proposing resource-friendly PPML protocols for edge devices. More precisely, we utilize HHE as the primary building block of our PPML protocols. We assess the performance of our protocols by first extensively evaluating each party's communication and computational cost on a dummy dataset and show the efficiency of our protocols by comparing them with similar protocols implemented using plain BFV. Subsequently, we demonstrate the real-world applicability of our construction by building an actual PPML application that uses HHE as its foundation to classify heart disease based on sensitive ECG data.","url":"https://doi.org/10.48550/arxiv.2409.06422","authors":["Nguyen, Khoa","Budzys, Mindaugas","Frimpong, Eugene","Khan, Tanveer","Michalas, Antonis"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2409.06422","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.4230/lipics.itp.2024.9","name":"Verifying Peephole Rewriting in SSA Compiler IRs","source":"datacite","abstract":"There is an increasing need for domain-specific reasoning in modern compilers. This has fueled the use of tailored intermediate representations (IRs) based on static single assignment (SSA), like in the MLIR compiler framework. Interactive theorem provers (ITPs) provide strong guarantees for the end-to-end verification of compilers (e.g., CompCert). However, modern compilers and their IRs evolve at a rate that makes proof engineering alongside them prohibitively expensive. Nevertheless, well-scoped push-button automated verification tools such as the Alive peephole verifier for LLVM-IR gained recognition in domains where SMT solvers offer efficient (semi) decision procedures. In this paper, we aim to combine the convenience of automation with the versatility of ITPs for verifying peephole rewrites across domain-specific IRs. We formalize a core calculus for SSA-based IRs that is generic over the IR and covers so-called regions (nested scoping used by many domain-specific IRs in the MLIR ecosystem). Our mechanization in the Lean proof assistant provides a user-friendly frontend for translating MLIR syntax into our calculus. We provide scaffolding for defining and verifying peephole rewrites, offering tactics to eliminate the abstraction overhead of our SSA calculus. We prove correctness theorems about peephole rewriting, as well as two classical program transformations. To evaluate our framework, we consider three use cases from the MLIR ecosystem that cover different levels of abstractions: (1) bitvector rewrites from LLVM, (2) structured control flow, and (3) fully homomorphic encryption. We envision that our mechanization provides a foundation for formally verified rewrites on new domain-specific IRs.","url":"https://doi.org/10.4230/lipics.itp.2024.9","authors":["Bhat, Siddharth","Keizer, Alex","Hughes, Chris","Goens, Andrés","Grosser, Tobias"],"tags":["compilers","semantics","mechanization","MLIR","SSA","regions","peephole rewrites","Software and its engineering → Compilers"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.4230/lipics.itp.2024.9","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.12683329","name":"VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption","source":"datacite","abstract":"This is an implementation of the VERITAS system described in the paper \"VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption\" by Sylvain Chatel, Christian Knabenhans, Apostolos Pyrgelis, Carmela Troncoso, and Jean-Pierre Hubaux appearing in the Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS '24).","url":"https://doi.org/10.5281/zenodo.12683329","authors":["Chatel, Sylvain","Knabenhans, Christian","Pyrgelis, Apostolos","Troncoso, Carmela","Hubaux, Jean-Pierre"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.12683329","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.13326701","name":"VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption","source":"datacite","abstract":"This is an implementation of the VERITAS system described in the paper \"VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption\" by Sylvain Chatel, Christian Knabenhans, Apostolos Pyrgelis, Carmela Troncoso, and Jean-Pierre Hubaux appearing in the Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS '24).","url":"https://doi.org/10.5281/zenodo.13326701","authors":["Chatel, Sylvain","Knabenhans, Christian","Pyrgelis, Apostolos","Troncoso, Carmela","Hubaux, Jean-Pierre"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13326701","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.13325087","name":"VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption","source":"datacite","abstract":"This is an implementation of the VERITAS system described in the paper \"VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption\" by Sylvain Chatel, Christian Knabenhans, Apostolos Pyrgelis, Carmela Troncoso, and Jean-Pierre Hubaux appearing in the Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS '24).","url":"https://doi.org/10.5281/zenodo.13325087","authors":["Chatel, Sylvain","Knabenhans, Christian","Pyrgelis, Apostolos","Troncoso, Carmela","Hubaux, Jean-Pierre"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13325087","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.4230/lipics.itc.2024.11","name":"Fast Secure Computations on Shared Polynomials and Applications to Private Set Operations","source":"datacite","abstract":"Secure multi-party computation aims to allow a set of players to compute a given function on their secret inputs without revealing any other information than the result of the computation. In this work, we focus on the design of secure multi-party protocols for shared polynomial operations. We consider the classical model where the adversary is honest-but-curious, and where the coefficients (or any secret values) are either encrypted using an additively homomorphic encryption scheme or shared using a threshold linear secret-sharing scheme. Our protocols terminate after a constant number of rounds and minimize the number of secure multiplications. In their seminal article at PKC 2006, Mohassel and Franklin proposed constant-rounds protocols for the main operations on (shared) polynomials. In this work, we improve the fan-in multiplication of nonzero polynomials, the multi-point polynomial evaluation and the polynomial interpolation (on secret points) to reach a quasi-linear complexity (instead of quadratic in Mohassel and Franklin’s work) in the degree of shared input/output polynomials. Computing with shared polynomials is a core component of several multi-party protocols for privacy-preserving operations on private sets, like the private disjointness test or the private set intersection. Using our new protocols, we are able to improve the complexity of such protocols and to design the first variants which always return a correct result.","url":"https://doi.org/10.4230/lipics.itc.2024.11","authors":["Giorgi, Pascal","Laguillaumie, Fabien","Ottow, Lucas","Vergnaud, Damien"],"tags":["Multi-party computation","polynomial operations","privacy-preserving set operations","Theory of computation → Cryptographic protocols","Security and privacy → Information-theoretic techniques"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.4230/lipics.itc.2024.11","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2407.19871","name":"Fast Private Location-based Information Retrieval Over the Torus","source":"datacite","abstract":"Location-based services offer immense utility, but also pose significant privacy risks. In response, we propose LocPIR, a novel framework using homomorphic encryption (HE), specifically the TFHE scheme, to preserve user location privacy when retrieving data from public clouds. Our system employs TFHE's expertise in non-polynomial evaluations, crucial for comparison operations. LocPIR showcases minimal client-server interaction, reduced memory overhead, and efficient throughput. Performance tests confirm its computational speed, making it a viable solution for practical scenarios, demonstrated via application to a COVID-19 alert model. Thus, LocPIR effectively addresses privacy concerns in location-based services, enabling secure data sharing from the public cloud.","url":"https://doi.org/10.48550/arxiv.2407.19871","authors":["Yoo, Joon Soo","Hong, Mi Yeon","Heo, Ji Won","Lee, Kang Hoon","Yoon, Ji Won"],"tags":["Cryptography and Security (cs.CR)","Networking and Internet Architecture (cs.NI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2407.19871","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2405.03775","name":"Secure Inference for Vertically Partitioned Data Using Multiparty Homomorphic Encryption","source":"datacite","abstract":"We propose a secure inference protocol for a distributed setting involving a single server node and multiple client nodes. We assume that the observed data vector is partitioned across multiple client nodes while the deep learning model is located at the server node. Each client node is required to encrypt its portion of the data vector and transmit the resulting ciphertext to the server node. The server node is required to collect the ciphertexts and perform inference in the encrypted domain. We demonstrate an application of multi-party homomorphic encryption (MPHE) to satisfy these requirements. We propose a packing scheme, that enables the server to form the ciphertext of the complete data by aggregating the ciphertext of data subsets encrypted using MPHE. While our proposed protocol builds upon prior horizontal federated training protocol~\\cite{sav2020poseidon}, we focus on the inference for vertically partitioned data and avoid the transmission of (encrypted) model weights from the server node to the client nodes.","url":"https://doi.org/10.48550/arxiv.2405.03775","authors":["Chen, Shuangyi","Ju, Yue","Zhu, Zhongwen","Khisti, Ashish"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.03775","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2407.07308","name":"BoostCom: Towards Efficient Universal Fully Homomorphic Encryption by Boosting the Word-wise Comparisons","source":"datacite","abstract":"Fully Homomorphic Encryption (FHE) allows for the execution of computations on encrypted data without the need to decrypt it first, offering significant potential for privacy-preserving computational operations. Emerging arithmetic-based FHE schemes (ar-FHE), like BGV, demonstrate even better performance in word-wise comparison operations over non-arithmetic FHE (na-FHE) schemes, such as TFHE, especially for basic tasks like comparing values, finding maximums, and minimums. This shows the universality of ar-FHE in effectively handling both arithmetic and non-arithmetic operations without the expensive conversion between arithmetic and non-arithmetic FHEs. We refer to universal arithmetic Fully Homomorphic Encryption as uFHE. The arithmetic operations in uFHE remain consistent with those in the original arithmetic FHE, which have seen significant acceleration. However, its non-arithmetic comparison operations differ, are slow, and have not been as thoroughly studied or accelerated. In this paper, we introduce BoostCom, a scheme designed to speed up word-wise comparison operations, enhancing the efficiency of uFHE systems. BoostCom involves a multi-prong optimizations including infrastructure acceleration (Multi-level heterogeneous parallelization and GPU-related improvements), and algorithm-aware optimizations (slot compaction, non-blocking comparison semantic). Together, BoostCom achieves an end-to-end performance improvement of more than an order of magnitude (11.1x faster) compared to the state-of-the-art CPU-based uFHE systems, across various FHE parameters and tasks.","url":"https://doi.org/10.48550/arxiv.2407.07308","authors":["Yudha, Ardhi Wiratama Baskara","Xue, Jiaqi","Lou, Qian","Zhou, Huiyang","Solihin, Yan"],"tags":["Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2407.07308","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.12683330","name":"VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption","source":"datacite","abstract":"This is an implementation of the VERITAS system described in the paper \"VERITAS: Plaintext Encoders for Practical Verifiable Homomorphic Encryption\" by Sylvain Chatel, Christian Knabenhans, Apostolos Pyrgelis, Carmela Troncoso, and Jean-Pierre Hubaux appearing in the Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS '24).","url":"https://doi.org/10.5281/zenodo.12683330","authors":["Chatel, Sylvain","Knabenhans, Christian","Pyrgelis, Apostolos","Troncoso, Carmela","Hubaux, Jean-Pierre"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.12683330","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2407.03685","name":"Verifying Peephole Rewriting In SSA Compiler IRs","source":"datacite","abstract":"There is an increasing need for domain-specific reasoning in modern compilers. This has fueled the use of tailored intermediate representations (IRs) based on static single assignment (SSA), like in the MLIR compiler framework. Interactive theorem provers (ITPs) provide strong guarantees for the end-to-end verification of compilers (e.g., CompCert). However, modern compilers and their IRs evolve at a rate that makes proof engineering alongside them prohibitively expensive. Nevertheless, well-scoped push-button automated verification tools such as the Alive peephole verifier for LLVM-IR gained recognition in domains where SMT solvers offer efficient (semi) decision procedures. In this paper, we aim to combine the convenience of automation with the versatility of ITPs for verifying peephole rewrites across domain-specific IRs. We formalize a core calculus for SSA-based IRs that is generic over the IR and covers so-called regions (nested scoping used by many domain-specific IRs in the MLIR ecosystem). Our mechanization in the Lean proof assistant provides a user-friendly frontend for translating MLIR syntax into our calculus. We provide scaffolding for defining and verifying peephole rewrites, offering tactics to eliminate the abstraction overhead of our SSA calculus. We prove correctness theorems about peephole rewriting, as well as two classical program transformations. To evaluate our framework, we consider three use cases from the MLIR ecosystem that cover different levels of abstractions: (1) bitvector rewrites from LLVM, (2) structured control flow, and (3) fully homomorphic encryption. We envision that our mechanization provides a foundation for formally verified rewrites on new domain-specific IRs.","url":"https://doi.org/10.48550/arxiv.2407.03685","authors":["Bhat, Siddharth","Keizer, Alex","Hughes, Chris","Goens, Andrés","Grosser, Tobias"],"tags":["Programming Languages (cs.PL)","Logic in Computer Science (cs.LO)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2407.03685","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2406.06808","name":"Fast White-Box Adversarial Streaming Without a Random Oracle","source":"datacite","abstract":"Recently, the question of adversarially robust streaming, where the stream is allowed to depend on the randomness of the streaming algorithm, has gained a lot of attention. In this work, we consider a strong white-box adversarial model (Ajtai et al. PODS 2022), in which the adversary has access to all past random coins and the parameters used by the streaming algorithm. We focus on the sparse recovery problem and extend our result to other tasks such as distinct element estimation and low-rank approximation of matrices and tensors. The main drawback of previous work is that it requires a random oracle, which is especially problematic in the streaming model since the amount of randomness is counted in the space complexity of a streaming algorithm. Also, the previous work suffers from large update time. We construct a near-optimal solution for the sparse recovery problem in white-box adversarial streams, based on the subexponentially secure Learning with Errors assumption. Importantly, our solution does not require a random oracle and has a polylogarithmic per item processing time. We also give results in a related white-box adversarially robust distributed model. Our constructions are based on homomorphic encryption schemes satisfying very mild structural properties that are currently satisfied by most known schemes.","url":"https://doi.org/10.48550/arxiv.2406.06808","authors":["Feng, Ying","Jain, Aayush","Woodruff, David P."],"tags":["Data Structures and Algorithms (cs.DS)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2406.06808","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.11046912","name":"Artifact Evaluation: ArcEDB: An Arbitrary-Precision Encrypted Database via (Amortized) Modular Homomorphic Encryption","source":"datacite","abstract":"This is the artifact of ArcEDB: An Arbitrary-Precision Encrypted Database via (Amortized) Modular Homomorphic Encryption published in ACM CCS 2024","url":"https://doi.org/10.5281/zenodo.11046912","authors":["Zhang, Zhou","Bian, Song","Zhao, Zian","Mao, Ran","Zhou, Haoyi","Hua, Jiafeng","Jin, yier","Guan, Zhenyu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11046912","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.5281/zenodo.11487820","name":"Artifact Evaluation: ArcEDB: An Arbitrary-Precision Encrypted Database via (Amortized) Modular Homomorphic Encryption","source":"datacite","abstract":"This is the artifact of ArcEDB: An Arbitrary-Precision Encrypted Database via (Amortized) Modular Homomorphic Encryption published in ACM CCS 2024","url":"https://doi.org/10.5281/zenodo.11487820","authors":["Zhang, Zhou","Bian, Song","Zhao, Zian","Mao, Ran","Zhou, Haoyi","Hua, Jiafeng","Jin, yier","Guan, Zhenyu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11487820","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.48550/arxiv.2208.08093","name":"Near Threshold Computation of Partitioned Ring Learning With Error (RLWE) Post Quantum Cryptography on Reconfigurable Architecture","source":"datacite","abstract":"Ring Learning With Error (RLWE) algorithm is used in Post Quantum Cryptography (PQC) and Homomorphic Encryption (HE) algorithm. The existing classical crypto algorithms may be broken in quantum computers. The adversaries can store all encrypted data. While the quantum computer will be available, these encrypted data can be exposed by the quantum computer. Therefore, the PQC algorithms are an essential solution in recent applications. On the other hand, the HE allows operations on encrypted data which is appropriate for getting services from third parties without revealing confidential plain-texts. The FPGA based PQC and HE hardware accelerators like RLWE is much cost-effective than processor based platform and Application Specific Integrated Circuit (ASIC). FPGA based hardware accelerators still consume more power compare to ASIC based design. Near Threshold Computation (NTC) may be a convenient solution for FPGA based RLWE implementation. In this paper, we have implemented RLWE hardware accelerator which has 14 subcomponents. This paper creates clusters based on the critical path of all 14 subcomponents. Each cluster is implemented in an FPGA partition which has the same biasing voltage $V_{ccint}$. The clusters that have higher critical paths use higher Vccint to avoid timing failure. The clusters have lower critical paths use lower biasing voltage Vccint. This voltage scaled, partitioned RLWE can save ~6% and ~11% power in Vivado and VTR platform respectively. The resource usage and throughput of the implemented RLWE hardware accelerator is comparatively better than existing literature.","url":"https://doi.org/10.48550/arxiv.2208.08093","authors":["Baidya, Paresh","Mondal, Swagata","Paul, Rourab"],"tags":["Cryptography and Security (cs.CR)","Hardware Architecture (cs.AR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2208.08093","addedAt":"2026-08-31T06:41:39.856Z","updatedAt":"2026-08-31T06:41:39.856Z"},{"id":"doi:10.21203/rs.3.rs-2176027/v1","name":"Efficient and secure computation of edit distance on genomic data","source":"crossref","abstract":"Abstract Background: Genetic information are the most sensitive data for a person and must be protected from malicious attacks. In this paper we focus on the application of secure multi-party computation, a subfield of cryptography, to the computation of edit distance, one of the most used metrics among genetic similarity indicators, useful for the diagnosis and treatment of many genetically based diseases. Results: We analyze four algorithms and compare them to the best prior results found in literature [1]:(1) the Wagner-Fischer algorithm [2], using the entire dynamic programming matrix; (2) the Wagner-Fischeralgorithm, optimized to use only the minimum needed columns for the computation; (3) the Ukkonen algorithm [3], considering a threshold of approximately 60% of the longest string; (4) the Ukkonen algorithm,using a generalized cut-off technique which reduces the number of cells to be computed. The Ukkonen algorithm with generalized cut-off is the one that performed better among the considered algorithms and it also proved to have better performances than the best prior results found in literature. Conclusions: Securely computing the edit distance between human genomes have become very important in medical and public health domains. Improving computational performance is a key factor for real-world application scenarios. In this work, we proposed several secure implementations of some of the most efficient edit distance algorithms, achieving better performances over existing protocols found in literature [4]. Moreover, this is the first time the Ukkonen's algorithm is proposed using all possible state-of-the-art optimizations for garbled circuits. The algorithms and protocols used in this work were also applied on both random, high-entropy, and real genomic, low-entropy strings and are provably secure with respect to the standard definition of security for Multi-Party Computation (MPC) protocols.","url":"https://doi.org/10.21203/rs.3.rs-2176027/v1","authors":["Andrea Migliore","Stelvio Cimato","Gabriella Trucco"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-11-24T19:45:39Z","doi":"10.21203/rs.3.rs-2176027/v1","addedAt":"2026-08-31T06:41:40.492Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.21956/openreseurope.19508.r42653","name":"Peer Review Report For: Applications of Homomorphic Encryption in Secure Computation [version 1; peer review: 1 approved with reservations, 1 not approved]","source":"crossref","abstract":"","url":"https://doi.org/10.21956/openreseurope.19508.r42653","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-29T17:42:05Z","doi":"10.21956/openreseurope.19508.r42653","addedAt":"2026-08-31T06:41:40.492Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1103/physreva.76.062308","name":"Impossibility of secure two-party classical computation","source":"crossref","abstract":"","url":"https://doi.org/10.1103/physreva.76.062308","authors":["Roger Colbeck"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-12-13T17:31:33Z","doi":"10.1103/physreva.76.062308","addedAt":"2026-08-31T06:41:40.492Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1103/physreva.72.012304","name":"Quantum secret sharing between multiparty and multiparty without entanglement","source":"crossref","abstract":"","url":"https://doi.org/10.1103/physreva.72.012304","authors":["Feng-Li Yan","Ting Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2005-07-07T16:56:12Z","doi":"10.1103/physreva.72.012304","addedAt":"2026-08-31T06:41:40.492Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.21203/rs.3.rs-3893891/v1","name":"A Privacy-Sharing Approach to IoT Based on Secure Multi-Party Computation","source":"crossref","abstract":"Abstract Edge computing nodes close to the perception layer of the IoT system have security risks such as privacy data leakage and unauthorized access. In response to such security risks, a privacy data sharing model for the Internet of Things based on secure multi-party computation is proposed. By running a reliable third-party alliance chain service on the edge computing node, the private data calculation relationship between the sensing layer devices is registered in the alliance chain chain code service, and a Bloom filter that records the trust status of the sensing layer device is constructed in the chain code.. A publicly verifiable private data sharing model that combines on-chain identity auditing and off-chain secure multi-party computation is constructed to achieve secure sharing of private data between sensing layer devices. Experiments show that the proposed method has more advantages in terms of task overhead and security.","url":"https://doi.org/10.21203/rs.3.rs-3893891/v1","authors":["Li Ma","Bo Zhang","Yang Li","YingXun Fu","DongChao Ma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-30T17:41:14Z","doi":"10.21203/rs.3.rs-3893891/v1","addedAt":"2026-08-31T06:41:40.492Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1109/icict57646.2023.10134436","name":"Review On Secure Heart Disease Predictable Data using Blockchain","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icict57646.2023.10134436","authors":["C Adline Sherifa","B Kanisha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-01T17:27:31Z","doi":"10.1109/icict57646.2023.10134436","addedAt":"2026-08-31T06:41:40.492Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.1103/physrevlett.111.020502","name":"Secure Entanglement Distillation for Double-Server Blind Quantum Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1103/physrevlett.111.020502","authors":["Tomoyuki Morimae","Keisuke Fujii"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-07-10T16:24:03Z","doi":"10.1103/physrevlett.111.020502","addedAt":"2026-08-31T06:41:40.492Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.2307/1339304","name":"Multiparty Federal Habeas Corpus","source":"crossref","abstract":"","url":"https://doi.org/10.2307/1339304","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-03-27T01:54:52Z","doi":"10.2307/1339304","addedAt":"2026-08-31T06:41:40.492Z","updatedAt":"2026-08-31T06:41:40.492Z"},{"id":"doi:10.21203/rs.3.rs-1326438/v1","name":"An Entropy-View Secure Multi-Party Computation Protocol Based on Semi-honest Model","source":"crossref","abstract":"Abstract Secure multi-party computation(SMPC) is an important research area in cryptography with many application scenarios, but there are still many problems to be solved. Aiming at security and fairness issues in SMPC, we consider that semi-honest participants are able to execute the protocol according to the specification, but there would be some additional malicious operations such as collusion and refuse to exchange information in the process of information interaction, which leads to deviations in the security and fairness of the protocol. To solve the above problems, we combine information entropy and mutual information to propose an n-round information exchange protocol, in which each participant broadcasts a relevant information value in each round without revealing additional information. The uncertainty of the correct result value is fuzzed by the interactive information in each round, and each participant cannot determine the correct result value until the end of the protocol, which effectively prevents malicious behavior and ensures the correct execution of the protocol. Security analysis and fairness analysis show that under the semi-honest model, our protocol guarantees the security and relative fairness of the output obtained by the participants after completing the protocol.","url":"https://doi.org/10.21203/rs.3.rs-1326438/v1","authors":["Yun Luo","Yuling chen","Tao Li","Yilei Wang","Yixian Yang","Xiaomei Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-07T16:00:56Z","doi":"10.21203/rs.3.rs-1326438/v1","addedAt":"2026-08-31T06:41:40.493Z","updatedAt":"2026-08-31T06:41:40.493Z"},{"id":"doi:10.1109/isitsc64373.2024.00018","name":"Federated Learning Privacy Protection Scheme Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isitsc64373.2024.00018","authors":["Yichang Luo","Juan Wang","Yimin Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-20T18:56:53Z","doi":"10.1109/isitsc64373.2024.00018","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/icsess62520.2024.10719393","name":"Personal Credit Evaluation Model Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsess62520.2024.10719393","authors":["WenZheng Li","YuXuan Song","ShouYuan Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-23T17:43:00Z","doi":"10.1109/icsess62520.2024.10719393","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/wconf61366.2024.10692276","name":"Application of Homomorphic Encryption in Machine Learning Based Chronic Kidney Disease Prediction","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wconf61366.2024.10692276","authors":["Prokash Gogoi","J. Arul Valan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T13:30:09Z","doi":"10.1109/wconf61366.2024.10692276","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1515/jmc-2024-0041","name":"Modern techniques in somewhat homomorphic encryption","source":"crossref","abstract":"Abstract The term “homomorphism” was introduced in cryptography by Rivest, Adleman, and Dertouzos in 1978 to address performing calculations on encrypted data without decryption. Since then, researchers have increasingly aimed to design schemes supporting numerous operations. This article aims to synthesize the current state of the art in the so-called somewhat homomorphic encryption.","url":"https://doi.org/10.1515/jmc-2024-0041","authors":["Massimo Giulietti","Paolo Martinelli","Marco Timpanella"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-14T09:00:26Z","doi":"10.1515/jmc-2024-0041","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.2139/ssrn.4839765","name":"Privacy-Preserving and Accountable Billing in Peer-to-Peer Energy Trading Markets with Homomorphic Encryption and Blockchain","source":"crossref","abstract":"This paper proposes a novel privacy-preserving and accountable billing (PA-Bill) protocol for peer-to-peer (P2P) energy trading markets. It addresses the challenges of discrepancies between committed and delivered energy volumes, ensuring accurate billing, privacy, and accountability. PA-Bill employs a universal cost-splitting mechanism to enhance fairness and prevent indirect privacy leakage. The protocol leverages homomorphic encryption to protect user data and uses blockchain technology to maintain accountability through an immutable, transparent ledger. Additionally, it includes a dispute resolution mechanism to rectify erroneous bill calculations and identify responsible parties, thus ensuring non-repudiation. Our evaluation demonstrates that PA-Bill effectively supports large communities of up to 500 households, offering an efficient, privacy-preserving, and accountable billing solution in a semi-decentralised manner.","url":"https://doi.org/10.2139/ssrn.4839765","authors":["Kamil Erdayandi","Lucas Cordeiro","Mustafa A. Mustafa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-24T09:09:59Z","doi":"10.2139/ssrn.4839765","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1109/ichi61247.2024.00021","name":"SparseHE: An Efficient Privacy-Preserving Biomedical Prediction Approach Using Sparse Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ichi61247.2024.00021","authors":["Chen Song","Wenkang Zhan","Xinghua Shi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-22T17:37:34Z","doi":"10.1109/ichi61247.2024.00021","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.36227/techrxiv.172306439.90448090/v1","name":"Advancing Privacy in Data Mining: Seamless Homomorphic Searches and Precision-Preserving Encryption","source":"crossref","abstract":"In federated learning (FL) systems, the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme is crucial for preserving privacy while enabling computations on encrypted decimal numbers. However, efficient search operations on CKKS encrypted data remain a significant challenge. This paper addresses this gap by introducing a novel search algorithm optimized for CKKS ciphertexts, significantly reducing client-server interactions in decentralized environments. Our approach integrates parallel computing techniques and a balanced binary tree structure to handle complex datasets like CIFAR10 and MNIST efficiently. We also demonstrate the algorithm's applicability to Convolutional Neural Networks (CNNs) for feature selection and privacy-preserving inference. Comprehensive evaluations show our method's scalability and practical efficiency under various network latencies, advancing privacy-preserving data processing in FL applications without compromising computational efficiency or model accuracy.","url":"https://doi.org/10.36227/techrxiv.172306439.90448090/v1","authors":["Muhammad Jahanzeb Khan","Bo Fang","Gaetano Cimino","Stefano Cirillo","Lei Yang","Dongfang Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-07T17:00:00Z","doi":"10.36227/techrxiv.172306439.90448090/v1","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1007/978-3-031-65494-7_10","name":"Privacy-Preserving Machine Learning with HE","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65494-7_10","authors":["Allon Adir","Ehud Aharoni","Nir Drucker","Ronen Levy","Hayim Shaul","Omri Soceanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-09T17:16:13Z","doi":"10.1007/978-3-031-65494-7_10","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1016/j.ifacol.2024.07.275","name":"Privacy Preserving Approximated Optimal Control of Pasteurization Unit Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ifacol.2024.07.275","authors":["Diana Dzurková","Olivér Mészáros","Martin Kalúz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-16T22:25:17Z","doi":"10.1016/j.ifacol.2024.07.275","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.24014/coreit.v10i2.30809","name":"Comparative Analysis Between Advanced Encryption Standard and Fully Homomorphic Encryption Algorithm to Secure Data in Financial Technology Applications","source":"crossref","abstract":"This research discusses the comparison between two encryption algorithms, namely Advanced Encryption Standard (AES) and Fully Homomorphic Encryption (FHE), in the context of data security in Financial Technology (Fintech) applications. The main aim of this research is to analyze the speed and efficiency of the two algorithms to provide information and motivation to Fintech Application business actors to determine the right algorithm for securing data. The research results show that AES is faster and more efficient in terms of encryption and decryption compared to FHE. For encryption, the AES algorithm is 1,100 times faster than the FHE algorithm. For decryption, the AES algorithm is 581 times faster than the FHE algorithm. For arithmetic processing, AES is 132 times faster than FHE. CPU consumption for AES encryption is 35.93% lower CPU usage than FHE. In AES decryption 10.31% lower than FHE for CPU usage. In the arithmetic process AES is 9.33% lower in usage than FHE. For memory usage in the FHE encryption process, it has an advantage, namely 2.3 times lower than AES for memory usage. During decryption, AES memory usage is superior with memory consumption 54 times lower than FHE. For the arithmetic process, AES uses 4.3 times lower memory than FHE. Overall AES provides speed and low resource consumption, this makes AES very suitable for use in Fintech applications that require speed and efficiency. Even though FHE has advantages in memory usage during encryption alone, this is not enough because it takes a long time to carry out the encryption process. This research suggests that further research will attempt to make the FHE algorithm more efficient and faster in processing data, this is considering the potential of FHE which is able to process encrypted data","url":"https://doi.org/10.24014/coreit.v10i2.30809","authors":["Nurdin Nurdin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-03T03:34:16Z","doi":"10.24014/coreit.v10i2.30809","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1504/ijesdf.2024.10053552","name":"A Retrospective Analysis on Fully Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijesdf.2024.10053552","authors":["Sonam Mittal","Dr.K.R.Ramkumar Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-18T14:01:02Z","doi":"10.1504/ijesdf.2024.10053552","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.62051/qbjp2h16","name":"Progress and Applications of Fully Homomorphic Encryption","source":"crossref","abstract":"In the era of digital transformation, marked by the widespread adoption of cloud-based technologies such as cloud computing and storage, ensuring data security and protecting user privacy have emerged as critical industry concerns. Fully Homomorphic Encryption (FHE) addresses these issues by allowing various computations to be performed on ciphertexts without the need for decryption, thus safeguarding data security and privacy. This paper explores the mathematical and algorithmic foundations essential for understanding FHE. It provides a detailed analysis of the advancements in FHE schemes over the past decade, focusing on various mathematical problems, including ideal lattices and integers. The discussion extends to three practical applications: cloud computing, machine learning, and electronic voting, highlighting the progress and exploring potential future research avenues and developmental strategies in the field of FHE. This comprehensive review not only underscores the significance of FHE in enhancing data security but also charts a path for its future exploration and integration into emerging technologies.","url":"https://doi.org/10.62051/qbjp2h16","authors":["Shenglong Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-18T06:53:48Z","doi":"10.62051/qbjp2h16","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/lcn60385.2024.10639770","name":"Privacy-Preserving Drone Navigation Through Homomorphic Encryption for Collision Avoidance","source":"crossref","abstract":"","url":"https://doi.org/10.1109/lcn60385.2024.10639770","authors":["Allan Luedeman","Nicholas Baum","Andrew Quijano","Kemal Akkaya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-09T17:36:45Z","doi":"10.1109/lcn60385.2024.10639770","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/bigdata62323.2024.10825533","name":"Toward Efficient Homomorphic Encryption-Based Federated Learning: A Magnitude-Sensitivity Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata62323.2024.10825533","authors":["Ren-Yi Huang","Dumindu Samaraweera","J. Morris Chang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T18:31:23Z","doi":"10.1109/bigdata62323.2024.10825533","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.3390/electronics13153050","name":"Blockchain and Homomorphic Encryption for Data Security and Statistical Privacy","source":"crossref","abstract":"This study proposes a blockchain-based system that utilizes fully homomorphic encryption to provide data security and statistical privacy when data are shared with third parties for analysis or research purposes. The proposed system not only provides security of data in transit, at rest, and in use but also assures privacy and computational integrity for simple statistical computations. This is achieved by leveraging the attributes of the blockchain technology, which provides availability and data integrity, combined with homomorphic encryption, which provides confidentiality of data in use. The computations are performed on smart contracts residing on the blockchain, providing computational integrity. The proposed system is implemented on the Zama blockchain and performs statistical operations including mean, median, and variance on encrypted data. The results indicate that it is possible to perform fully homomorphic computations on the blockchain. Even though current computing limitations on the blockchain do not allow running the system for large data sets, the technology is available, and with advancements toward more efficient homomorphic operations on blockchains, the proposed system will provide an ultimate solution for providing the much-desired security properties in applications, including data and statistical privacy, confidentiality, and integrity at rest, in transit, and in use.","url":"https://doi.org/10.3390/electronics13153050","authors":["Rahul Raj","Yeşem Kurt Peker","Zeynep Delal Mutlu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-01T12:38:20Z","doi":"10.3390/electronics13153050","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.2139/ssrn.4937075","name":"Bgv-Tcf: A Scalable Privacy-Preserving Collaborative Filtering Protocol Using Trust-Based Filtering and Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4937075","authors":["Yanan Bai","Hongbo Zhao","Liji Xiao","Xiaoyu Shi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-26T12:17:55Z","doi":"10.2139/ssrn.4937075","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.32604/cmc.2024.049159","name":"FL-EASGD: Federated Learning Privacy Security Method Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.32604/cmc.2024.049159","authors":["Hao Sun","Xiubo Chen","Kaiguo Yuan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-24T09:14:43Z","doi":"10.32604/cmc.2024.049159","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/ipccc59868.2024.10850007","name":"HEJet: A Framework for Efficient Machine Learning Inference with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ipccc59868.2024.10850007","authors":["David Monschein","Oliver P. Waldhorst"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-27T18:37:48Z","doi":"10.1109/ipccc59868.2024.10850007","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/icemce64157.2024.10862331","name":"Power Data Aggregation Method Based on Improved Paillier Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icemce64157.2024.10862331","authors":["Kai Wu","Jiefeng Pan","Yuqian Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-12T18:16:20Z","doi":"10.1109/icemce64157.2024.10862331","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.17559/tv-20230618000745","name":"Enhanced Secure Storage of Big Data at Rest with Improved ECC and Paillier Homomorphic Encryption Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.17559/tv-20230618000745","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-07T10:20:37Z","doi":"10.17559/tv-20230618000745","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/ickecs61492.2024.10617001","name":"Privacy-Preserving Brakerski-Gentry-Vaikuntanathan (BGV) Homomorphic Encryption for IoMT Data Security","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ickecs61492.2024.10617001","authors":["V Vinoth Kumar","P Pabitha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-07T17:29:50Z","doi":"10.1109/ickecs61492.2024.10617001","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/icetas62372.2024.11119875","name":"Enhancing Data Privacy in Vehicular Cloud Networks: Leveraging Differential Privacy and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icetas62372.2024.11119875","authors":["Hani Al-Balasmeh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-25T20:14:19Z","doi":"10.1109/icetas62372.2024.11119875","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.23919/date58400.2024.10546534","name":"Optimizing Ciphertext Management for Faster Fully Homomorphic Encryption Computation","source":"crossref","abstract":"","url":"https://doi.org/10.23919/date58400.2024.10546534","authors":["Eduardo Chielle","Oleg Mazonka","Michail Maniatakos"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-14T17:28:02Z","doi":"10.23919/date58400.2024.10546534","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.23919/ccc63176.2024.10662589","name":"Distributed power load federated prediction for high-energy-consuming enterprises based on homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.23919/ccc63176.2024.10662589","authors":["Fan Yunhao","Chen Zhenping","Zhai Zihao","Zhu Tong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-17T18:46:36Z","doi":"10.23919/ccc63176.2024.10662589","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1007/978-3-031-65494-7_8","name":"SIMD Packing Part II—Tile Tensor Basics","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65494-7_8","authors":["Allon Adir","Ehud Aharoni","Nir Drucker","Ronen Levy","Hayim Shaul","Omri Soceanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-09T17:16:13Z","doi":"10.1007/978-3-031-65494-7_8","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/acdsa59508.2024.10467404","name":"Implementation of Lattices System in Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/acdsa59508.2024.10467404","authors":["Victor Kadykov","Alla Levina","Ksenia Grebenevich"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-20T18:13:50Z","doi":"10.1109/acdsa59508.2024.10467404","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1007/978-3-031-65494-7_7","name":"SIMD Packing Part I: Basic Packing Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65494-7_7","authors":["Allon Adir","Ehud Aharoni","Nir Drucker","Ronen Levy","Hayim Shaul","Omri Soceanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-09T17:16:13Z","doi":"10.1007/978-3-031-65494-7_7","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1007/s00521-024-09464-w","name":"EVAD: encrypted vibrational anomaly detection with homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00521-024-09464-w","authors":["Alessandro Falcetta","Manuel Roveri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-01T13:02:53Z","doi":"10.1007/s00521-024-09464-w","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/apccas62602.2024.10808213","name":"LEAM: A Low-Area and Efficient Accelerator of Matrix-Vector Multiplication for Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/apccas62602.2024.10808213","authors":["Zhao Cui","Jing Tian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-27T19:09:25Z","doi":"10.1109/apccas62602.2024.10808213","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/mwscas60917.2024.10658747","name":"Accelerating CKKS Homomorphic Encryption with Data Compression on GPUs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mwscas60917.2024.10658747","authors":["Quoc Bao Phan","Linh Nguyen","Tuy Tan Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-16T13:34:29Z","doi":"10.1109/mwscas60917.2024.10658747","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/sceecs61402.2024.10482314","name":"Homomorphic Encryption Enabled SVM for Preserving Privacy of P2P Communication","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sceecs61402.2024.10482314","authors":["Sourabh Sahu","R. Ganeshan","V Muneeswaran"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-02T18:37:39Z","doi":"10.1109/sceecs61402.2024.10482314","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1504/ijesdf.2024.137031","name":"A retrospective analysis on fully homomorphic encryption scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijesdf.2024.137031","authors":["Sonam Mittal","K.R. Ramkumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-02T12:31:40Z","doi":"10.1504/ijesdf.2024.137031","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/icdscnc62492.2024.10941469","name":"An Intelligent Early Warning Support System for Financial Crisis based on Fully Homomorphic Encryption Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icdscnc62492.2024.10941469","authors":["Fanling Kong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-01T17:44:48Z","doi":"10.1109/icdscnc62492.2024.10941469","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1007/978-3-031-65494-7_9","name":"SIMD Packing Part III: Advanced Tile Tensors","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65494-7_9","authors":["Allon Adir","Ehud Aharoni","Nir Drucker","Ronen Levy","Hayim Shaul","Omri Soceanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-09T18:01:37Z","doi":"10.1007/978-3-031-65494-7_9","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/candarw64572.2024.00051","name":"NUSS:Non-Interactive Updatable of Secret Sharing Schemes Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/candarw64572.2024.00051","authors":["Yixuan He","Samsul Huda","Yuta Kodera","Yasuyuki Nogami"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-31T19:25:07Z","doi":"10.1109/candarw64572.2024.00051","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.54097/wsk1dv30","name":"Research on Preventing Medical Information from Leaking based on Homomorphic Encryption","source":"crossref","abstract":"The security and privacy protection of medical information is one of the important challenges facing today's society. Disclosure of medical information can lead to invasion of patient privacy, reputational damage to medical institutions, and potential legal liability. Therefore, it is very important to take effective measures to prevent the leakage of medical information. Information encryption is a common technical means, which can effectively protect the security and privacy of medical information. The purpose of this paper is to discuss how to prevent medical information leakage through information encryption. First, we describe the sensitivity and importance of medical information, as well as the current security challenges. Then, we discuss in detail the basic principles of information encryption and common encryption algorithms, including symmetric encryption and asymmetric encryption. Next, we explore the specific application scenarios of medical information encryption, such as electronic medical records, medical images and encryption protection during transmission. We also discuss the advantages and challenges of healthcare information encryption and propose some solutions such as key management and access control. Finally, we summarize the importance of information encryption in preventing medical information leakage and emphasize the need for further research and practice to ensure the security and privacy protection of medical information.","url":"https://doi.org/10.54097/wsk1dv30","authors":["Kangwei Rao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-20T02:06:10Z","doi":"10.54097/wsk1dv30","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/iceeict61591.2024.10718595","name":"A Homomorphic Encryption Compiler for Blood Pressure Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iceeict61591.2024.10718595","authors":["Varshini Balaji","Srinidhi Kannan","Suryamritha M","Meena Belwal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-23T17:41:00Z","doi":"10.1109/iceeict61591.2024.10718595","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1063/5.0265232","name":"Multistage partial homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1063/5.0265232","authors":["Krishnapriya Swarna","Chaithra Munumuri","P. Kabitha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-02T17:08:42Z","doi":"10.1063/5.0265232","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/iccd63220.2024.00054","name":"Private Tensor Freezing for an Efficient Federated Learning with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccd63220.2024.00054","authors":["Valentino Peluso","Erich Malan","Andrea Calimera","Enrico Macii"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-02T19:17:19Z","doi":"10.1109/iccd63220.2024.00054","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1007/s13389-023-00340-2","name":"Leaking secrets in homomorphic encryption with side-channel attacks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s13389-023-00340-2","authors":["Furkan Aydin","Aydin Aysu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-12T07:03:07Z","doi":"10.1007/s13389-023-00340-2","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1007/s10586-023-04253-x","name":"General construction of compressive integrity auditing protocol from strong homomorphic encryption scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10586-023-04253-x","authors":["Jing Song","Jinyong Chang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-06T13:02:37Z","doi":"10.1007/s10586-023-04253-x","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/globecom52923.2024.10901495","name":"Privacy Preservation Fully Homomorphic Encryption for Cloud-assisted Biometric Identification with Multi-key","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom52923.2024.10901495","authors":["Shenghui Peng","Peiheng Jia","Jinbo Xiong","Liehuang Zhu","Ximeng Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T17:30:35Z","doi":"10.1109/globecom52923.2024.10901495","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1117/12.3013713","name":"A look inside of homomorphic encryption for federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1117/12.3013713","authors":["Lubjana Beshaj","Michael Hoefler"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-06T21:07:30Z","doi":"10.1117/12.3013713","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/sgre59715.2024.10428783","name":"Towards Sustainable Energy Communities: Privacy-Preserving Auctions with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sgre59715.2024.10428783","authors":["Maisam Elkhalaf","Khaled Abedrabboh","Luluwah Al-Fagih"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-15T18:45:51Z","doi":"10.1109/sgre59715.2024.10428783","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1007/s10586-024-04589-y","name":"Efficient integer division computation protocols based on partial homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10586-024-04589-y","authors":["Yuhong Sun","Jiatao Wang","Fengyin Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T18:01:28Z","doi":"10.1007/s10586-024-04589-y","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/cai59869.2024.00074","name":"Confidential and Protected Disease Classifier using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cai59869.2024.00074","authors":["Aditya Malik","Nalini Ratha","Bharat Yalavarthi","Tilak Sharma","Arjun Kaushik","Charanjit Jutla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-30T13:50:37Z","doi":"10.1109/cai59869.2024.00074","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1007/978-3-031-65494-7_5","name":"Approximation Methods Part I: A General Overview","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65494-7_5","authors":["Allon Adir","Ehud Aharoni","Nir Drucker","Ronen Levy","Hayim Shaul","Omri Soceanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-09T17:16:28Z","doi":"10.1007/978-3-031-65494-7_5","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/hpec62836.2024.10938507","name":"A Run-Time Configurable NTT Architecture for Homomorphic Encryption Based on 3D Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1109/hpec62836.2024.10938507","authors":["Weicong Lu","Xiaojie Chen","Dihu Chen","Tao Su"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-03T19:07:19Z","doi":"10.1109/hpec62836.2024.10938507","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/reepe60449.2024.10479915","name":"Data Security in Web 3.0 Based on Full Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/reepe60449.2024.10479915","authors":["Boris S. Goryachkin","Ekaterina D. Vdovkina"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-09T17:35:02Z","doi":"10.1109/reepe60449.2024.10479915","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1504/ijcsm.2024.10065919","name":"A Novel Method of Fully Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijcsm.2024.10065919","authors":["Ashokkumar N","Anbunathan R","Suntheya A.K.","Sonam Mittal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-11T13:00:25Z","doi":"10.1504/ijcsm.2024.10065919","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1080/08839514.2024.2327901","name":"Smart Grids Data Aggregation Method on Paillier Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1080/08839514.2024.2327901","authors":["Shaodong Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-02T05:27:57Z","doi":"10.1080/08839514.2024.2327901","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/commnet63022.2024.10793287","name":"Securing Healthcare Data in IoT: A Study on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/commnet63022.2024.10793287","authors":["Sarra Erregui","Otmane El Mouaatamid","El Mehdi Loualid"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-17T19:09:17Z","doi":"10.1109/commnet63022.2024.10793287","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/iscas58744.2024.10558097","name":"Compact 2<sup>17</sup> NTT Architecture for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscas58744.2024.10558097","authors":["Rella Mareta","Hanho Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-02T17:22:52Z","doi":"10.1109/iscas58744.2024.10558097","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.55145/ajest.2025.04.01.004","name":"Quantum-Resistant Homomorphic Encryption for IoT Security (QRHE)","source":"crossref","abstract":"Quantum computing does present a big threat to classic cryptography and hence endangers the security of Internet of Things devices. This paper is therefore concerned with proposing a Quantum-Resistant Homomorphic Encryption (QRHE) system tailored for Internet of Things (IoT) environments. The main view of this QRHE key system is basically protection against the quantum threat in the processing of information within Internet of Things network traffic. Aside from this, the system further allows the processing of data on encrypted information without prior decryption, which guarantees the confidentiality and integrity of the data processed. The lattice-based cryptography used in the system is based on the Learning With Errors (LWE) problem, which has already shown strength against classical and quantum attacks. In this paper, homomorphic encryption algorithm was introduced that allows both addition and multiplication between ciphertexts for the assurance of privacy during secure data aggregation and analysis. The experimental results demonstrated that even after several homomorphic operations, the proposed system maintained high accuracy of %98, proving its effectiveness in preserving data confidentiality and integrity. Although the computational cost for this proposed system was a little more compared to traditional methods, it still gave an all-rounded security solution suitable for Internet of Things applications in the quantum computing era.","url":"https://doi.org/10.55145/ajest.2025.04.01.004","authors":["Zainab  Sahib Dhahir"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-17T09:14:57Z","doi":"10.55145/ajest.2025.04.01.004","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1007/978-3-031-65494-7_6","name":"Approximation Methods Part II: Approximations of Standard Functions","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-65494-7_6","authors":["Allon Adir","Ehud Aharoni","Nir Drucker","Ronen Levy","Hayim Shaul","Omri Soceanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-09T17:16:20Z","doi":"10.1007/978-3-031-65494-7_6","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1007/s10586-024-04648-4","name":"Correction: Efficient integer division computation protocols based on partial homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10586-024-04648-4","authors":["Yuhong Sun","Jiatao Wang","Fengyin Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-05T10:44:46Z","doi":"10.1007/s10586-024-04648-4","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.63282/3117-5481/aijcst-v6i2p101","name":"Secure Data Federation and Analytics through Homomorphic Encryption in Multi-Tenant Cloud Environments","source":"crossref","abstract":"","url":"https://doi.org/10.63282/3117-5481/aijcst-v6i2p101","authors":["Anna Kristyna"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-13T07:05:48Z","doi":"10.63282/3117-5481/aijcst-v6i2p101","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.54660/.ijfmr.2025.6.1.48-56","name":"A Novel Approach to Cloud Data Encryption using Homomorphic Encryption (2024)","source":"crossref","abstract":"Cloud computing has revolutionized data storage and processing, offering scalability, flexibility, and cost efficiency. However, security and privacy concerns remain significant challenges, particularly when sensitive data is stored and processed by third-party cloud providers. Traditional encryption techniques, such as AES and RSA, ensure data confidentiality but require decryption for computation, exposing data to potential breaches. This review presents a novel approach to cloud data encryption using Homomorphic Encryption (HE), which enables computations on encrypted data without requiring decryption. Our approach leverages Fully Homomorphic Encryption (FHE) to facilitate secure data processing in cloud environments while preserving confidentiality. Unlike conventional encryption schemes, which restrict operations on encrypted data, HE allows mathematical functions to be executed directly on ciphertexts, producing encrypted results that can be decrypted to obtain accurate outputs. This capability is particularly useful in privacy-sensitive applications such as healthcare, finance, and artificial intelligence, where outsourced data processing must remain confidential. The proposed system integrates optimized HE algorithms to reduce computational overhead, addressing one of the key challenges in HE adoption. We evaluate the security and performance of our approach by implementing a case study on encrypted data analytics in a cloud environment. Our experimental results demonstrate that while HE introduces computational complexity, recent advancements in hardware acceleration and algorithm optimization significantly enhance its feasibility for real-world applications. This highlights the potential of homomorphic encryption as a transformative solution for secure cloud computing. By enabling privacy-preserving computations, our approach ensures data confidentiality while leveraging the full power of cloud computing. Future research will focus on improving efficiency, scalability, and hybrid cryptographic models to further enhance security in cloud-based ecosystems.","url":"https://doi.org/10.54660/.ijfmr.2025.6.1.48-56","authors":["Joy Ezinwanneamaka Ike","Joseph Darko Kessie","Raphael Popoola","Muhammed Adewale Azeez","Tolulope Onibokun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-15T10:12:49Z","doi":"10.54660/.ijfmr.2025.6.1.48-56","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/aibthings63359.2024.10863815","name":"End-to-End Secure Video Streaming Using Homomorphic Encryption Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aibthings63359.2024.10863815","authors":["Mahmoud Darwich","Kasem Khalil","Magdy Bayoumi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-07T18:38:03Z","doi":"10.1109/aibthings63359.2024.10863815","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1504/ijsnet.2024.142718","name":"PPSSDHE: privacy preservation in smartphone sensors data using ElGamal homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijsnet.2024.142718","authors":["S. Manimaran","D. Uma Priya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-19T12:30:46Z","doi":"10.1504/ijsnet.2024.142718","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/biosig61931.2024.10786738","name":"Combining CRYSTALS-Kyber Homomorphic Encryption with Garbled Circuits for Biometric Authentication","source":"crossref","abstract":"","url":"https://doi.org/10.1109/biosig61931.2024.10786738","authors":["Rosario Arjona","Claudia Franco","Roberto Román","Iluminada Baturone"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-11T22:20:43Z","doi":"10.1109/biosig61931.2024.10786738","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.3390/info15080438","name":"Privacy-Protection Method for Blockchain Transactions Based on Lightweight Homomorphic Encryption","source":"crossref","abstract":"This study proposes an privacy-protection method for blockchain transactions based on lightweight homomorphic encryption, aiming to ensure the security of transaction data and user privacy, and improve transaction efficiency. We have built a blockchain infrastructure and, based on its structural characteristics, adopted zero-knowledge proof technology to verify the legitimacy of data, ensuring the authenticity and accuracy of transactions from the application end to the smart-contract end. On this basis, the Paillier algorithm is used for key generation, encryption, and decryption, and intelligent protection of blockchain transaction privacy is achieved through a secondary encryption mechanism. The experimental results show that this method performs well in privacy and security protection, with a data leakage probability as low as 2.8%, and can effectively defend against replay attacks and forged-transaction attacks. The degree of confusion remains above 0.9, with small fluctuations and short running time under different key lengths and moderate CPU usage, achieving lightweight homomorphic encryption. This not only ensures the security and privacy of transaction data in blockchain networks, but also reduces computational complexity and resource consumption, better adapting to the high-concurrency and low-latency characteristics of blockchain networks, thereby ensuring the efficiency and real-time performance of transactions.","url":"https://doi.org/10.3390/info15080438","authors":["Guiyou Wang","Chao Li","Bingrong Dai","Shaohua Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-29T09:50:05Z","doi":"10.3390/info15080438","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/ccis63231.2024.10932066","name":"Secure Electoral Voting System Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccis63231.2024.10932066","authors":["Vinayak Musale","Anand Bhongale","Amruta Amune","Abhinav Saxena","Chhavikant Mahobia","Sarthak Panse"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-27T02:18:22Z","doi":"10.1109/ccis63231.2024.10932066","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1145/3605098.3636037","name":"GPU Memory Reallocation Techniques in Fully Homomorphic Encryption Workloads","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3605098.3636037","authors":["Jake Choi","Sunchul Jung","Heonyoung Yeom"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-21T17:59:16Z","doi":"10.1145/3605098.3636037","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/wccct60665.2024.10541346","name":"A Secure Convolutional Neural Network Inference Model Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wccct60665.2024.10541346","authors":["Zhiyuan Hu","Liquan Chen","Yu Wang","Peng Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-03T17:28:14Z","doi":"10.1109/wccct60665.2024.10541346","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/ri2c64012.2024.10784418","name":"Application of Homomorphic Encryption for Encrypting and Decrypting Patient Data in Thailand's Healthcare System","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ri2c64012.2024.10784418","authors":["Yadzaree Lohlah","Pongsarun Boonyopakorn"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-13T18:49:36Z","doi":"10.1109/ri2c64012.2024.10784418","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/is262782.2024.10704107","name":"FLCrypt – Secure Federated Learning for Audio Event Classification Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/is262782.2024.10704107","authors":["Kay Fuhrmeister","Hao Cui","Artem Yaroshchuk","Thomas Köllmer"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-07T13:42:20Z","doi":"10.1109/is262782.2024.10704107","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/icc51166.2024.10622837","name":"Fully Privacy-Preserving and Efficient Clustering Scheme based on Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc51166.2024.10622837","authors":["Mengyu Zhang","Long Wang","Xiaoping Zhang","Yisong Wang","Wenhou Sun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-20T11:34:42Z","doi":"10.1109/icc51166.2024.10622837","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.13052/jcsm2245-1439.1353","name":"Developing Adaptive Homomorphic Encryption through Exploration of Differential Privacy","source":"crossref","abstract":"Machine Learning (ML) classifiers are pivotal in various applied ML domains. The accuracy of these classifiers requires meticulous training, making the exposure of training datasets a critical concern, especially concerning privacy. This study identifies a significant trade-off between accuracy, computational efficiency, and security of the classifiers. Integrating classical Homomorphic Encryption (HE) and Differential Privacy (DP) highlights the challenges in parameter tuning inherent to such hybrid methodologies. These challenges concern the analytical components of the HE algorithm’s privacy budget and simultaneously affect the sensitivity to noise in the subjected ML hybrid classifiers. This paper explores these areas and proposes a hybrid model using a basic client-server architecture to combine HE and DP algorithms. It then examines the sensitivity analysis of the aforementioned trade-off features. Additionally, the paper outlines initial observations after deploying the proposed algorithm, contributing to the ongoing discourse on optimizing the balance between accuracy, computational efficiency, and security in ML classifiers.","url":"https://doi.org/10.13052/jcsm2245-1439.1353","authors":["Yulliwas Ameur","Samia Bouzefrane","Soumya Banerjee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-04T01:43:38Z","doi":"10.13052/jcsm2245-1439.1353","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/ickecs61492.2024.10616691","name":"Improving Healthcare Data Security Using Cheon-Kim-Kim-Song (CKKS) Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ickecs61492.2024.10616691","authors":["P Sathishkumar","K Pugalarasan","C Ponnparamaguru","M Vasanthkumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-07T17:29:50Z","doi":"10.1109/ickecs61492.2024.10616691","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/icscan62807.2024.10893924","name":"Privacy-Preserving Employee Attrition Prediction using Deep Learning and Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icscan62807.2024.10893924","authors":["Rajesh R","Harshavardhan S","Kirthana B","Shreya V"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-26T18:45:34Z","doi":"10.1109/icscan62807.2024.10893924","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.54097/7ay21z97","name":"Enhancing Security in CNN-Based Travel Recommendation Models Using CKKS Homomorphic Encryption","source":"crossref","abstract":"This study explores the integration of CKKS homomorphic encryption with convolutional neural networks (CNNs) to enhance the security of travel recommendation systems. By adapting CNN architectures to operate efficiently on encrypted data using CKKS, we address the challenge of maintaining the users’ privacy without compromising system performance. Key results indicate significant improvements in data security with minimal impact on recommendation accuracy.","url":"https://doi.org/10.54097/7ay21z97","authors":["Tianhao Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-07T00:58:24Z","doi":"10.54097/7ay21z97","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/iciteics61368.2024.10625392","name":"Qualitative Analysis of Homomorphic Encryption in Medical Field","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciteics61368.2024.10625392","authors":["Atharva Mukul Mujumdar","Nirmalya Kar","Priyanka Biswas","Anurag Mathur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-22T17:23:36Z","doi":"10.1109/iciteics61368.2024.10625392","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/access.2024.3362347","name":"Enhancing Intrusion Detection Through Federated Learning With Enhanced Ghost_BiNet and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2024.3362347","authors":["Om Kumar ChandraUmakantham","Sudhakaran Gajendran","Suguna Marappan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-05T13:42:13Z","doi":"10.1109/access.2024.3362347","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/asiajcis64263.2024.00014","name":"A Study of Fully Homomorphic Encryption with Evaluation Control","source":"crossref","abstract":"","url":"https://doi.org/10.1109/asiajcis64263.2024.00014","authors":["Kenneth Ong Kuan Phing","Bo Yu Chen","Po Wen Chi","Chao Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-23T17:40:28Z","doi":"10.1109/asiajcis64263.2024.00014","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/ciscsd63381.2024.00036","name":"Advancing Healthcare Privacy with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ciscsd63381.2024.00036","authors":["Shreenagamanjula Rani","Roja Ramani","Sanjana Chinta","Sathvik S Shet","Shivani Biradar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-28T18:38:45Z","doi":"10.1109/ciscsd63381.2024.00036","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/fdl63219.2024.10673864","name":"Platform Design for Privacy-Preserving Federated Learning using Homomorphic Encryption : Wild-and-Crazy-Idea Paper","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fdl63219.2024.10673864","authors":["Hokeun Kim","Younghyun Kim","Hoeseok Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-19T17:22:20Z","doi":"10.1109/fdl63219.2024.10673864","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/icait61638.2024.10690754","name":"Secure Medical Image Encryption Using Homomorphic Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icait61638.2024.10690754","authors":["Megala K","Jayadevi J","Keerthika K","Manikandan G","Vijay Sai R","Srinivasan B"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-04T13:30:08Z","doi":"10.1109/icait61638.2024.10690754","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1007/s11227-024-06449-3","name":"GMS: an efficient fully homomorphic encryption scheme for secure outsourced matrix multiplication","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s11227-024-06449-3","authors":["Jianxin Gao","Ying Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-26T15:02:12Z","doi":"10.1007/s11227-024-06449-3","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/ucc63386.2024.00068","name":"A Performance and Cost Evaluation of Homomorphic Encryption in the Cloud","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ucc63386.2024.00068","authors":["Greg Guillot","Andy Nguyen","Malvika Sriram","Joel Coffman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-23T17:52:09Z","doi":"10.1109/ucc63386.2024.00068","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/gcat62922.2024.10924043","name":"Secure ML Evaluation on Encrypted Data with Fully Homomorphic Encryption and Concrete ML","source":"crossref","abstract":"","url":"https://doi.org/10.1109/gcat62922.2024.10924043","authors":["Thanmai Gaddam","Divya Chennupalle","Kavitha C. R"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-21T19:05:39Z","doi":"10.1109/gcat62922.2024.10924043","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/icsintesa62455.2024.10748230","name":"Securing Sensitive Data in Multi-Cloud Storage using ML and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsintesa62455.2024.10748230","authors":["Mohammed El Moudni","Elhoussaine Ziyati"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-14T18:35:39Z","doi":"10.1109/icsintesa62455.2024.10748230","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.24996/ijs.2024.65.3.38","name":"Preserving Genotype Privacy Using AES and Partially Homomorphic Encryption","source":"crossref","abstract":"Increasingly, the availability of personal genomic data in cloud servers hosted by hospitals and research centers has incentivized researchers to turn to research that deals with analyzing genomic data. This is due to its importance in detecting diseases caused by genetic mutations, detecting genes that carry genetic diseases, and attempting to treat them in future generations. Secure query execution on encrypted data is considered an active research area in which encryption is used to ensure the confidentiality of genomic data while restricting the ability to process such data without first decrypting it. To provide a secure framework and future insight into the potential contributions of homomorphic encryption to the field of genomic data, this paper proposes a framework for guaranteeing genomic data privacy using various partial homomorphic encryption techniques. By examining the characteristics of the three partial homomorphic encryptions based on different parameters. The framework has been online tested and compared based on different parameters. Three homomorphic encryption algorithms were adopted to ensure genomic data privacy by employing homomorphic operations in the query matching process. Experiments on real datasets, specifically MERS and SARSr-COV, showed that the proposed framework is efficient and improves query execution time by an average of 96% compared to existing work.","url":"https://doi.org/10.24996/ijs.2024.65.3.38","authors":["Hiba M. Yousif","Sarab M. Hameed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-29T18:38:27Z","doi":"10.24996/ijs.2024.65.3.38","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/tps-isa62245.2024.00066","name":"Simulation of Quantum Homomorphic Encryption: Demonstration and Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tps-isa62245.2024.00066","authors":["Sohrab Ganjian","Connor Paddock","Anne Broadbent"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T18:34:22Z","doi":"10.1109/tps-isa62245.2024.00066","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/iccta64612.2024.10974865","name":"Smart Grid Prediction Using Federated Learning and Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccta64612.2024.10974865","authors":["Islam M. Saad","Mohamed Fakhr","Tamer Abdelkader","Nada M. Abdel Aziem"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-25T17:38:25Z","doi":"10.1109/iccta64612.2024.10974865","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.7763/ijmo.2024.v14.851","name":"An Efficient Hybrid Authentication Mechanism Based on Biometric Fingerprint Recognition and Homomorphic Encryption","source":"crossref","abstract":"In the current security environment, where the dependence on computer systems is increasing, and the technological field is constantly changing, the threats and vulnerabilities typology to networks is also growing, so a key task is to ensure the network’s security access. To address these challenges, we propose an efficient hybrid network authentication mechanism that combines and integrates actual access control components based on cryptography and biometrics. These elements play a vital role in the field of information security and aim to resolve the shortcomings of conventional authentication methods and enhance the security level of sensitive data, especially in the government and military domains. In this paper, we present a mechanism that comprises biometric fingerprint recognition and card authentication based on Arduino modules with the Paillier homomorphic encryption algorithm, a reliable solution that can facilitate secure access to computer systems and networks and minimize the risk of unauthorized access. A statistical assessment is performed using several parameters such as histogram analysis, information entropy, Mean Square Error (MSE), peak signal-to-noise ratio (PSNR), correlation coefficient, and average encryption time to verify the efficiency and robustness of the encryption algorithm.","url":"https://doi.org/10.7763/ijmo.2024.v14.851","authors":["Georgiana Crihan","Marian Crăciun","Luminița Dumitriu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-17T08:50:05Z","doi":"10.7763/ijmo.2024.v14.851","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.1109/qce60285.2024.10421","name":"Demonstrating Quantum Homomorphic Encryption Through Simulation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/qce60285.2024.10421","authors":["Sohrab Ganjian","Connor Paddock","Anne Broadbent"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:12:42Z","doi":"10.1109/qce60285.2024.10421","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.589Z"},{"id":"doi:10.66135/ijsdc0201p001","name":"DOUBLE SECURE CLOUD MEDICAL DATA USING EUCLIDEAN DISTANCE-BASED OKAMOTO UCHIYAMA HOMOMORPHIC ENCRYPTION","source":"crossref","abstract":"Electronic Medical Records (EMRs) are computerized copies of paper records used in healthcare settings. Data from these systems allows doctors to quickly access and manage patients' medical records at healthcare institutions. Medical data security safeguards patients' rights and the duties of healthcare workers. Sharing such medical records with another medical organization is challenging for them. Cloud computing (CC) is the most effective way for storing this sort of data and addressing these issues. In this research a novel DOuble SEcure MEDical Cloud data (DOSE MED) technique has been proposed that enhances privacy and security of medical data. The proposed DOSE MED method consists of three stages namely, registration phase, encryption phase and storage/decision phase. The proposed method utilizes Okamoto Uchiyama's homomorphic encryption technique to add prediction capability on the cloud which paves the way for disease prediction by medical practitioners. Euclidean distance-based classifier has been used for predicting data without decrypting it. The security findings demonstrate that the proposed DOSE MED provides selective security for the chosen keyword assaults. Performance analysis and real-world simulation trials demonstrate that this system is both efficient and practicable","url":"https://doi.org/10.66135/ijsdc0201p001","authors":["M Anisha","V Adlin Beenu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-19T10:50:56Z","doi":"10.66135/ijsdc0201p001","addedAt":"2026-08-31T06:41:40.589Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1109/icscc62041.2024.10690391","name":"Enabling Privacy-Preserving Machine Learning: Federal Learning with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icscc62041.2024.10690391","authors":["Husain Gadiwala","Raja Bavani","Riddhi Panchal","Gopal Sakarkar","Agus Putu Abiyasa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-01T13:23:14Z","doi":"10.1109/icscc62041.2024.10690391","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1117/12.3059279","name":"S-DIHE: secure deduplication of images based on homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1117/12.3059279","authors":["Qinlong Zhang","Riyanka Jena","Priyanka Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-10T16:34:01Z","doi":"10.1117/12.3059279","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/icodsa62899.2024.10651987","name":"Homomorphic Encryption for Privacy Preservation in Occupancy Sensor-Based Smart Lighting","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icodsa62899.2024.10651987","authors":["Aji Gautama Putrada","Maman Abdurohman","Doan Perdana","Hilal Hudan Nuha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-05T17:57:11Z","doi":"10.1109/icodsa62899.2024.10651987","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1109/psgec62376.2024.10721169","name":"Short Term Load Forecasting for Users Based on Homomorphic Encryption and Dual Model Data-driven Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1109/psgec62376.2024.10721169","authors":["Mingming Ding","Zhenshu Wang","Anyi Cao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-23T17:44:28Z","doi":"10.1109/psgec62376.2024.10721169","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1109/icscss60660.2024.10625286","name":"Multi-Cloud Environments With Quantum Cloud Computing for Secure Data Transfer using Two-Tier Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icscss60660.2024.10625286","authors":["Manish Chhabra","Rajesh E"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-20T15:34:43Z","doi":"10.1109/icscss60660.2024.10625286","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1504/ijcsm.2024.142728","name":"A novel method of fully homomorphic encryption scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijcsm.2024.142728","authors":["Sonam Mittal","A.K. Suntheya","R. Anbunathan","N. Ashokkumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-20T12:30:26Z","doi":"10.1504/ijcsm.2024.142728","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1109/sist61555.2024.10629522","name":"Assessing Electoral Integrity: Paillier’s Partial Homomorphic Encryption in E-Voting System","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sist61555.2024.10629522","authors":["Zeynep Galymzhankyzy","Ildar Rinatov","Aliya Abdiraman","Shynbolat Unaybaev"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-15T17:24:28Z","doi":"10.1109/sist61555.2024.10629522","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1201/9781003458401-10","name":"Enhancing the Security of Pregnancy Health Data Transmission through Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003458401-10","authors":["Mohammad Mobarak Hossain","Nasim Mahmud Nayan","Mohammod Abdul Kashem"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-25T18:47:03Z","doi":"10.1201/9781003458401-10","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1109/biosig61931.2024.10786750","name":"Securing Biometric Data: Fully Homomorphic Encryption in Multimodal Iris and Face Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.1109/biosig61931.2024.10786750","authors":["Surendra Singh","Lambert Igene","Stephanie Schuckers"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-11T17:20:43Z","doi":"10.1109/biosig61931.2024.10786750","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1145/3643651.3659893","name":"Transformer-based Language Models and Homomorphic Encryption: An Intersection with BERT-tiny","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3643651.3659893","authors":["Lorenzo Rovida","Alberto Leporati"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-11T00:20:03Z","doi":"10.1145/3643651.3659893","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.32604/cmes.2023.030528","name":"Enhancing IoT Data Security with Lightweight Blockchain and Okamoto Uchiyama Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.32604/cmes.2023.030528","authors":["Mohanad A. Mohammed","Hala B. Abdul Wahab"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-13T08:48:23Z","doi":"10.32604/cmes.2023.030528","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1145/3605098.3636165","name":"SADHE: Secure Anomaly Detection for GPS Trajectory Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3605098.3636165","authors":["Priyanka Singh","Jash Rathi","Priyankaben Babulal Patel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-21T17:59:16Z","doi":"10.1145/3605098.3636165","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1109/ispa63168.2024.00144","name":"Privacy-Enhanced Federated Learning Through Homomorphic Encryption With Cloud Federation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ispa63168.2024.00144","authors":["Qiqi Xie","Hong Zhang","Miao Wang","Wanqing Wu","Zhibo Sun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T20:05:50Z","doi":"10.1109/ispa63168.2024.00144","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1109/icbctis64495.2024.00013","name":"Compressible Identity-Based Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icbctis64495.2024.00013","authors":["Zhenghua Qi","Gang Yang","Xunyi Ren","Qiang Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-24T19:11:23Z","doi":"10.1109/icbctis64495.2024.00013","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.5220/0006832202070218","name":"Fully Homomorphic Distributed Identity-based Encryption Resilient to Continual Auxiliary Input Leakage","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0006832202070218","authors":["François Gérard","Veronika Kuchta","Rajeev Anand Sahu","Gaurav Sharma","Olivier Markowitch"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-09-14T15:27:47Z","doi":"10.5220/0006832202070218","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1007/978-3-030-87629-6_2","name":"Mathematical Background","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6_2","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T19:04:24Z","doi":"10.1007/978-3-030-87629-6_2","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1007/s00145-024-09526-1","name":"Achievable CCA2 Relaxation for Homomorphic Encryption","source":"crossref","abstract":"Abstract Homomorphic encryption () protects data in-use, but can be computationally expensive. To avoid the costly bootstrapping procedure that refreshes ciphertexts, some works have explored client-aided outsourcing protocols, where the client intermittently refreshes ciphertexts for a server that is performing homomorphic computations. But is this approach secure against malicious servers? We present a -secure encryption scheme that is completely insecure in this setting. We define a new notion of security, called , that we prove is sufficient. Additionally, we show: Homomorphic encryption schemes that have a certain type of circuit privacy—for example, schemes in which ciphertexts can be “sanitized\"—are -secure. In particular, assuming certain existing schemes are -secure, they are also -secure. For certain encryption schemes, like Brakerski-Vaikuntanathan, that have a property that we call oblivious secret key extraction, -security implies circular security—i.e., that it is secure to provide an encryption of the secret key in a form usable for bootstrapping (to construct fully homomorphic encryption).","url":"https://doi.org/10.1007/s00145-024-09526-1","authors":["Adi Akavia","Craig Gentry","Shai Halevi","Margarita Vald"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-26T17:24:30Z","doi":"10.1007/s00145-024-09526-1","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.54692/ijeci.2024.0803201","name":"Power of Homomorphic Encryption in Secure Data Processing","source":"crossref","abstract":"Homomorphic encryption is a form of encryption that allows computations to be performed on encrypted data without first having to decrypt it. This paper presents a detailed discussion of HE, a critical component in the protection of data in today's technology-driven environment. First, homomorphic encryption and its terminology will be introduced and then development process from the beginning to the present state will be discussed. Different classes of homomorphic encryption and analysis of internal workings and architecture of homomorphic encryption will be discussed. The usefulness of this technology in ensuring privacy in sensitive areas is discussed, as well as the limitations that may hinder the technology's advancement, including computation intensity and data growth. The paper also reasserts the massive application of homomorphic encryption in data security and privacy, stressing the need to continue the advancement to overcome existing drawbacks and enhance the application of the technique. While moving vast distances within the digital arena, the optimization of homomorphic encryption remains the guiding light to our freedom and privacy online.","url":"https://doi.org/10.54692/ijeci.2024.0803201","authors":["Muhammad Asif Ibrahim","Syed Khuram Hassan","Maham Akhtar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-11T04:51:49Z","doi":"10.54692/ijeci.2024.0803201","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1109/bdpc59998.2024.10649386","name":"Homomorphic Encryption-Based Privacy Protection Data Processing Strategies in Fog Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bdpc59998.2024.10649386","authors":["Shiyang Song","Jinhai Tang","Haozhe Wang","Dongxu Yuan","Zhiyuan Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T17:19:08Z","doi":"10.1109/bdpc59998.2024.10649386","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1007/978-3-030-87629-6_5","name":"ElGamal Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6_5","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T19:04:24Z","doi":"10.1007/978-3-030-87629-6_5","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/ubmk50275.2020.9219533","name":"Homomorphic Encryption versus Searchable Encryption for Data Retrieval on Cloud","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ubmk50275.2020.9219533","authors":["Busranur Bulbul","Serif Bahtiyar","Deniz Turgay Altilar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-10-12T20:36:33Z","doi":"10.1109/ubmk50275.2020.9219533","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1145/3733811.3767312","name":"A Critique on Average-Case Noise Analysis in RLWE-Based Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3733811.3767312","authors":["Mingyu Gao","Hongren Zheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-22T16:20:28Z","doi":"10.1145/3733811.3767312","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1109/cacee61121.2023.00025","name":"An Efficient and More Secure Searchable Encryption Algorithm Based On Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cacee61121.2023.00025","authors":["Liu Jiefeng","Lin Sheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-11T13:59:10Z","doi":"10.1109/cacee61121.2023.00025","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/dsd64264.2024.00063","name":"PRIV-DRIVE: Privacy-Ensured Federated Learning using Homomorphic Encryption for Driver Fatigue Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/dsd64264.2024.00063","authors":["Sima Sinaei","Mohammadreza Mohammadi","Rakesh Shrestha","Mina Alibeigi","David Eklund"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-06T18:38:00Z","doi":"10.1109/dsd64264.2024.00063","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1109/fg59268.2024.10581983","name":"Enhancing Privacy in Face Analytics Using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fg59268.2024.10581983","authors":["Bharat Yalavarthi","Arjun Ramesh Kaushik","Arun Ross","Vishnu Boddeti","Nalini Ratha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-11T17:40:08Z","doi":"10.1109/fg59268.2024.10581983","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.54097/1dmsr222","name":"Research on the Application of Homomorphic Encryption and Federated Learning in the Internet of Vehicles Environment","source":"crossref","abstract":"The rapid growth of the Internet of Things has significantly increased data volumes, leading to heightened concerns over security risks such as data theft and leakage. As machine learning becomes increasingly integral to various applications, data security in training processes has emerged as a critical issue. The Internet of Vehicles (IoV), as a crucial branch of the IoT, faces particular challenges in securely and efficiently training data. While current machine learning frameworks enable fast and efficient data training in IoV environments, security risks remain a pressing concern. This study explores the use of a federated learning framework enhanced with homomorphic encryption to address these issues. The research involves simulating real-world environments to test the basic performance and feasibility of the selected framework in IoV applications. Additionally, the impact of homomorphic encryption on the framework's effectiveness is assessed. Finally, a comparative analysis with traditional machine learning frameworks demonstrates that the chosen federated learning framework, when combined with homomorphic encryption, offers superior efficiency and security in IoV scenarios. This study underscores the potential of integrating advanced encryption techniques in machine learning frameworks to enhance data security in the IoV.","url":"https://doi.org/10.54097/1dmsr222","authors":["Haowen Zheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-15T01:01:30Z","doi":"10.54097/1dmsr222","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1109/icocwc60930.2024.10470692","name":"Retracted: Development of Modified Homomorphic Encryption for IIoT on Textual Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icocwc60930.2024.10470692","authors":["P. Rathinakumar","R. Dhivya","S. Prasath","K N Jayapriya","M.Sathish Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-21T14:10:41Z","doi":"10.1109/icocwc60930.2024.10470692","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.1016/j.procs.2021.04.149","name":"Homomorphic Encryption within Lattice-Based Encryption System","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2021.04.149","authors":["Victor Kadykov","Alla Levina","Alexander Voznesensky"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-06-11T18:08:10Z","doi":"10.1016/j.procs.2021.04.149","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.33795/jip.v10i4.5420","name":"Pengamanan Data E-Mail Menggunakan Enkripsi Partially Homomorphic Encryption (PHE)","source":"crossref","abstract":"Pengamanan data merupakan aspek penting dalam era digital untuk melindungi informasi sensitif dari akses tidak sah dan ancaman keamanan. Terutama pada email sebagai media komunikasi jarak jauh, pengamanan data sangat penting karena email rentan terhadap serangan yang dapat mencuri atau memanipulasi data. Salah satu teknik yang telah berkembang pesat untuk menghadapi tantangan keamanan yang kompleks adalah kriptografi, termasuk Homomorphic Encryption Partially. Homomorphic Encryption Partially dapat mengamankan data email dengan mengubah informasi menjadi ciphertext yang hanya dapat diakses oleh pemilik kunci. Teknik ini membuat pihak luar yang tidak memiliki izin sulit membaca data asli. Selain itu, teknik ini menunjukkan penggunaan memori yang efisien tanpa mengorbankan keamanan. Penelitian ini membandingkan Homomorphic Encryption Partially yang menggunakan algoritma RSA Homomorfik dengan algoritma AES dalam perbandingan penggunaan memori. Hasil penelitian menunjukkan bahwa Homomorphic Encryption Partially menggunakan 10.42% dalam pengunaan memori dibandingkan dengan AES yang menggunakan 102.69% dalam penggunaan memorinya, sehingga menghasilkan penggunaan memori yang besar. Selain itu, penelitian ini menunjukkan bahwa waktu untuk memecahkan pasangan kunci bisa mencapai beberapa bulan hingga tahun. Sebagai kesimpulan, Homomorphic Encryption Partially efektif dalam penggunaan memori, menghasilkan ukuran yang lebih kecil setelah enkripsi dibandingkan dengan AES yang menghasilkan ukuran lebih besar.","url":"https://doi.org/10.33795/jip.v10i4.5420","authors":["Wina Witanti","Mohamad Aditya Muttaqin Ghozali","Gunawan Abdillah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-30T12:46:23Z","doi":"10.33795/jip.v10i4.5420","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.5281/zenodo.11163975","name":"Artifact Evaluation: ArcEDB: An Arbitrary-Precision Encrypted Database via (Amortized) Modular Homomorphic Encryption","source":"datacite","abstract":"This is the artifact of ArcEDB: An Arbitrary-Precision Encrypted Database via (Amortized) Modular Homomorphic Encryption published in ACM CCS 2024","url":"https://doi.org/10.5281/zenodo.11163975","authors":["Zhang, Zhou","Bian, Song","Zhao, Zian","Mao, Ran","Zhou, Haoyi","Hua, Jiafeng","Jin, yier","Guan, Zhenyu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11163975","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.5281/zenodo.11127232","name":"Artifact Evaluation: ArcEDB: An Arbitrary-Precision Encrypted Database via (Amortized) Modular Homomorphic Encryption","source":"datacite","abstract":"This is the artifact of ArcEDB: An Arbitrary-Precision Encrypted Database via (Amortized) Modular Homomorphic Encryption published in ACM CCS 2024","url":"https://doi.org/10.5281/zenodo.11127232","authors":["Zhang, Zhou","Bian, Song","Zhao, Zian","Mao, Ran","Zhou, Haoyi","Hua, Jiafeng","Jin, yier","Guan, Zhenyu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11127232","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.5281/zenodo.11046913","name":"Artifact Evaluation: ArcEDB: An Arbitrary-Precision Encrypted Database via (Amortized) Modular Homomorphic Encryption","source":"datacite","abstract":"This is the artifact of ArcEDB: An Arbitrary-Precision Encrypted Database via (Amortized) Modular Homomorphic Encryption published in ACM CCS 2024","url":"https://doi.org/10.5281/zenodo.11046913","authors":["Zhang, Zhou","Bian, Song","Zhao, Zian","Mao, Ran","Zhou, Haoyi","Hua, Jiafeng","Jin, yier","Guan, Zhenyu"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.11046913","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.48550/arxiv.2404.03216","name":"Accurate Low-Degree Polynomial Approximation of Non-polynomial Operators for Fast Private Inference in Homomorphic Encryption","source":"datacite","abstract":"As machine learning (ML) permeates fields like healthcare, facial recognition, and blockchain, the need to protect sensitive data intensifies. Fully Homomorphic Encryption (FHE) allows inference on encrypted data, preserving the privacy of both data and the ML model. However, it slows down non-secure inference by up to five magnitudes, with a root cause of replacing non-polynomial operators (ReLU and MaxPooling) with high-degree Polynomial Approximated Function (PAF). We propose SmartPAF, a framework to replace non-polynomial operators with low-degree PAF and then recover the accuracy of PAF-approximated model through four techniques: (1) Coefficient Tuning (CT) -- adjust PAF coefficients based on the input distributions before training, (2) Progressive Approximation (PA) -- progressively replace one non-polynomial operator at a time followed by a fine-tuning, (3) Alternate Training (AT) -- alternate the training between PAFs and other linear operators in the decoupled manner, and (4) Dynamic Scale (DS) / Static Scale (SS) -- dynamically scale PAF input value within (-1, 1) in training, and fix the scale as the running max value in FHE deployment. The synergistic effect of CT, PA, AT, and DS/SS enables SmartPAF to enhance the accuracy of the various models approximated by PAFs with various low degrees under multiple datasets. For ResNet-18 under ImageNet-1k, the Pareto-frontier spotted by SmartPAF in latency-accuracy tradeoff space achieves 1.42x ~ 13.64x accuracy improvement and 6.79x ~ 14.9x speedup than prior works. Further, SmartPAF enables a 14-degree PAF (f1^2 g_1^2) to achieve 7.81x speedup compared to the 27-degree PAF obtained by minimax approximation with the same 69.4% post-replacement accuracy. Our code is available at https://github.com/EfficientFHE/SmartPAF.","url":"https://doi.org/10.48550/arxiv.2404.03216","authors":["Tong, Jianming","Dang, Jingtian","Golder, Anupam","Hao, Callie","Raychowdhury, Arijit","Krishna, Tushar"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2404.03216","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.26116/techreg.2024.012","name":"Disrupting violence while preserving encryption","source":"datacite","abstract":"Business models adopted by online platforms have enabled the proliferation of online hate speech. Platforms providing end-to-end encrypted (E2EE) services have been under increased scrutiny for hosting hate mongers. Legislators struggle to conceptualise the responsibilities of E2EE services to not host hate speech without infringing the users’ rights to freedom of expression, association, privacy, or data protection. This interdisciplinary article proposes a new legal minimum standard expanding corporate human rights responsibilities of E2EE services to mitigate a category of criminal hate speech - incitement to violence. We explore the regulation and application of metadata, hashing, and homomorphic encryption to disrupt incitement to violence in large groups on E2EE services in compliance with human rights.","url":"https://doi.org/10.26116/techreg.2024.012","authors":["Nave, Eva","Raaijmakers, Stephan","Veugen, Thijs"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.26116/techreg.2024.012","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.48550/arxiv.2404.06216","name":"Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking","source":"datacite","abstract":"As eye tracking becomes pervasive with screen-based devices and head-mounted displays, privacy concerns regarding eye-tracking data have escalated. While state-of-the-art approaches for privacy-preserving eye tracking mostly involve differential privacy and empirical data manipulations, previous research has not focused on methods for scanpaths. We introduce a novel privacy-preserving scanpath comparison protocol designed for the widely used Needleman-Wunsch algorithm, a generalized version of the edit distance algorithm. Particularly, by incorporating the Paillier homomorphic encryption scheme, our protocol ensures that no private information is revealed. Furthermore, we introduce a random processing strategy and a multi-layered masking method to obfuscate the values while preserving the original order of encrypted editing operation costs. This minimizes communication overhead, requiring a single communication round for each iteration of the Needleman-Wunsch process. We demonstrate the efficiency and applicability of our protocol on three publicly available datasets with comprehensive computational performance analyses and make our source code publicly accessible.","url":"https://doi.org/10.48550/arxiv.2404.06216","authors":["Ozdel, Suleyman","Bozkir, Efe","Kasneci, Enkelejda"],"tags":["Cryptography and Security (cs.CR)","Human-Computer Interaction (cs.HC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2404.06216","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.48550/arxiv.2312.11575","name":"Blind-Touch: Homomorphic Encryption-Based Distributed Neural Network Inference for Privacy-Preserving Fingerprint Authentication","source":"datacite","abstract":"Fingerprint authentication is a popular security mechanism for smartphones and laptops. However, its adoption in web and cloud environments has been limited due to privacy concerns over storing and processing biometric data on servers. This paper introduces Blind-Touch, a novel machine learning-based fingerprint authentication system leveraging homomorphic encryption to address these privacy concerns. Homomorphic encryption allows computations on encrypted data without decrypting. Thus, Blind-Touch can keep fingerprint data encrypted on the server while performing machine learning operations. Blind-Touch combines three strategies to efficiently utilize homomorphic encryption in machine learning: (1) It optimizes the feature vector for a distributed architecture, processing the first fully connected layer (FC-16) in plaintext on the client side and the subsequent layer (FC-1) post-encryption on the server, thereby minimizing encrypted computations; (2) It employs a homomorphic encryption compatible data compression technique capable of handling 8,192 authentication results concurrently; and (3) It utilizes a clustered server architecture to simultaneously process authentication results, thereby enhancing scalability with increasing user numbers. Blind-Touch achieves high accuracy on two benchmark fingerprint datasets, with a 93.6% F1- score for the PolyU dataset and a 98.2% F1-score for the SOKOTO dataset. Moreover, Blind-Touch can match a fingerprint among 5,000 in about 0.65 seconds. With its privacy focused design, high accuracy, and efficiency, Blind-Touch is a promising alternative to conventional fingerprint authentication for web and cloud applications.","url":"https://doi.org/10.48550/arxiv.2312.11575","authors":["Choi, Hyunmin","Woo, Simon","Kim, Hyoungshick"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2312.11575","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.48550/arxiv.2312.05264","name":"All Rivers Run to the Sea: Private Learning with Asymmetric Flows","source":"datacite","abstract":"Data privacy is of great concern in cloud machine-learning service platforms, when sensitive data are exposed to service providers. While private computing environments (e.g., secure enclaves), and cryptographic approaches (e.g., homomorphic encryption) provide strong privacy protection, their computing performance still falls short compared to cloud GPUs. To achieve privacy protection with high computing performance, we propose Delta, a new private training and inference framework, with comparable model performance as non-private centralized training. Delta features two asymmetric data flows: the main information-sensitive flow and the residual flow. The main part flows into a small model while the residuals are offloaded to a large model. Specifically, Delta embeds the information-sensitive representations into a low-dimensional space while pushing the information-insensitive part into high-dimension residuals. To ensure privacy protection, the low-dimensional information-sensitive part is secured and fed to a small model in a private environment. On the other hand, the residual part is sent to fast cloud GPUs, and processed by a large model. To further enhance privacy and reduce the communication cost, Delta applies a random binary quantization technique along with a DP-based technique to the residuals before sharing them with the public platform. We theoretically show that Delta guarantees differential privacy in the public environment and greatly reduces the complexity in the private environment. We conduct empirical analyses on CIFAR-10, CIFAR-100 and ImageNet datasets and ResNet-18 and ResNet-34, showing that Delta achieves strong privacy protection, fast training, and inference without significantly compromising the model utility.","url":"https://doi.org/10.48550/arxiv.2312.05264","authors":["Niu, Yue","Ali, Ramy E.","Prakash, Saurav","Avestimehr, Salman"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2312.05264","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.590Z"},{"id":"doi:10.48550/arxiv.2308.04890","name":"CiFHER: A Chiplet-Based FHE Accelerator with a Resizable Structure","source":"datacite","abstract":"Fully homomorphic encryption (FHE) is in the spotlight as a definitive solution for privacy, but the high computational overhead of FHE poses a challenge to its practical adoption. Although prior studies have attempted to design ASIC accelerators to mitigate the overhead, their designs require excessive chip resources (e.g., areas) to contain and process massive data for FHE operations. We propose CiFHER, a chiplet-based FHE accelerator with a resizable structure, to tackle the challenge with a cost-effective multi-chip module (MCM) design. First, we devise a flexible core architecture whose configuration is adjustable to conform to the global organization of chiplets and design constraints. Its distinctive feature is a composable functional unit providing varying computational throughput for the number-theoretic transform, the most dominant function in FHE. Then, we establish generalized data mapping methodologies to minimize the interconnect overhead when organizing the chips into the MCM package in a tiled manner, which becomes a significant bottleneck due to the packaging constraints. This study demonstrates that a CiFHER package composed of a number of compact chiplets provides performance comparable to state-of-the-art monolithic ASIC accelerators while significantly reducing the package-wide power consumption and manufacturing cost.","url":"https://doi.org/10.48550/arxiv.2308.04890","authors":["Kim, Sangpyo","Kim, Jongmin","Choi, Jaeyoung","Ahn, Jung Ho"],"tags":["Hardware Architecture (cs.AR)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2308.04890","addedAt":"2026-08-31T06:41:40.590Z","updatedAt":"2026-08-31T06:41:40.591Z"},{"id":"doi:10.48550/arxiv.2403.16860","name":"CipherFormer: Efficient Transformer Private Inference with Low Round Complexity","source":"datacite","abstract":"There is a growing trend to outsource the inference task of large transformer models to cloud servers. However, this poses a severe threat to users' private data as they are exposed to cloud servers after uploading. Although several works attempted to provide private inference for transformer models, their hundreds of communication rounds limit the application scenarios. Motivated by the desire to minimize round complexity, we propose CipherFormer, a novel transformer private inference scheme using homomorphic encryption and garbled circuits. We present a protocol for quickly computing homomorphic matrix multiplications. We then modify the attention mechanism and design the corresponding garbled circuits. Furthermore, we show how to use a lightweight attention mechanism and mixed-bitwidth to reduce the inference latency while maintaining accuracy. In comparison with an advanced homomorphic encryption scheme on text classification tasks, our model improves accuracy by 3% to 11% while performing private inference with a 7.7x-11.9x speedup.","url":"https://doi.org/10.48550/arxiv.2403.16860","authors":["Wang, Weize","Kuang, Yi"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2403.16860","addedAt":"2026-08-31T06:41:40.591Z","updatedAt":"2026-08-31T06:41:40.591Z"},{"id":"doi:10.48550/arxiv.2304.11643","name":"Privacy Computing Meets Metaverse: Necessity, Taxonomy and Challenges","source":"datacite","abstract":"Metaverse, the core of the next-generation Internet, is a computer-generated holographic digital environment that simultaneously combines spatio-temporal, immersive, real-time, sustainable, interoperable, and data-sensitive characteristics. It cleverly blends the virtual and real worlds, allowing users to create, communicate, and transact in virtual form. With the rapid development of emerging technologies including augmented reality, virtual reality and blockchain, the metaverse system is becoming more and more sophisticated and widely used in various fields such as social, tourism, industry and economy. However, the high level of interaction with the real world also means a huge risk of privacy leakage both for individuals and enterprises, which has hindered the wide deployment of metaverse. Then, it is inevitable to apply privacy computing techniques in the framework of metaverse, which is a current research hotspot. In this paper, we conduct comprehensive research on the necessity, taxonomy and challenges when privacy computing meets metaverse. Specifically, we first introduce the underlying technologies and various applications of metaverse, on which we analyze the challenges of data usage in metaverse, especially data privacy. Next, we review and summarize state-of-the-art solutions based on federated learning, differential privacy, homomorphic encryption, and zero-knowledge proofs for different privacy problems in metaverse. Finally, we show the current security and privacy challenges in the development of metaverse and provide open directions for building a well-established privacy-preserving metaverse system. For easy access and reference, we integrate the related publications and their codes into a GitHub repository: https://github.com/6lyc/Awesome-Privacy-Computing-in-Metaverse.git.","url":"https://doi.org/10.48550/arxiv.2304.11643","authors":["Chen, Chuan","Li, Yuecheng","Wu, Zhenpeng","Mai, Chengyuan","Liu, Youming","Hu, Yanming","Zheng, Zibin","Kang, Jiawen"],"tags":["Cryptography and Security (cs.CR)","Computers and Society (cs.CY)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2304.11643","addedAt":"2026-08-31T06:41:40.591Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.4230/lipics.itcs.2024.14","name":"Homomorphic Indistinguishability Obfuscation and Its Applications","source":"datacite","abstract":"In this work, we propose the notion of homomorphic indistinguishability obfuscation (HiO) and present a construction based on subexponentially-secure iO and one-way functions. An HiO scheme allows us to convert an obfuscation of circuit C to an obfuscation of C'∘C, and this can be performed obliviously (that is, without knowing the circuit C). A naïve solution would be to obfuscate C'∘iO(C). However, if we do this for k hops, then the size of the final obfuscation is exponential in k. HiO ensures that the size of the final obfuscation remains polynomial after repeated compositions. As an application, we show how to build function-hiding hierarchical multi-input functional encryption and homomorphic witness encryption using HiO.","url":"https://doi.org/10.4230/lipics.itcs.2024.14","authors":["Bhushan, Kaartik","Koppula, Venkata","Prabhakaran, Manoj"],"tags":["Program Obfuscation","Homomorphisms","Theory of computation → Computational complexity and cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.4230/lipics.itcs.2024.14","addedAt":"2026-08-31T06:41:40.591Z","updatedAt":"2026-08-31T06:41:40.591Z"},{"id":"doi:10.48550/arxiv.2310.13384","name":"Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computing","source":"datacite","abstract":"In split inference, a deep neural network (DNN) is partitioned to run the early part of the DNN at the edge and the later part of the DNN in the cloud. This meets two key requirements for on-device machine learning: input privacy and computation efficiency. Still, an open question in split inference is output privacy, given that the outputs of the DNN are observable in the cloud. While encrypted computing can protect output privacy too, homomorphic encryption requires substantial computation and communication resources from both edge and cloud devices. In this paper, we introduce Salted DNNs: a novel approach that enables clients at the edge, who run the early part of the DNN, to control the semantic interpretation of the DNN's outputs at inference time. Our proposed Salted DNNs maintain classification accuracy and computation efficiency very close to the standard DNN counterparts. Experimental evaluations conducted on both images and wearable sensor data demonstrate that Salted DNNs attain classification accuracy very close to standard DNNs, particularly when the Salted Layer is positioned within the early part to meet the requirements of split inference. Our approach is general and can be applied to various types of DNNs. As a benchmark for future studies, we open-source our code.","url":"https://doi.org/10.48550/arxiv.2310.13384","authors":["Malekzadeh, Mohammad","Kawsar, Fahim"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2310.13384","addedAt":"2026-08-31T06:41:40.591Z","updatedAt":"2026-08-31T06:41:40.591Z"},{"id":"doi:10.48550/arxiv.2401.09604","name":"MedBlindTuner: Towards Privacy-preserving Fine-tuning on Biomedical Images with Transformers and Fully Homomorphic Encryption","source":"datacite","abstract":"Advancements in machine learning (ML) have significantly revolutionized medical image analysis, prompting hospitals to rely on external ML services. However, the exchange of sensitive patient data, such as chest X-rays, poses inherent privacy risks when shared with third parties. Addressing this concern, we propose MedBlindTuner, a privacy-preserving framework leveraging fully homomorphic encryption (FHE) and a data-efficient image transformer (DEiT). MedBlindTuner enables the training of ML models exclusively on FHE-encrypted medical images. Our experimental evaluation demonstrates that MedBlindTuner achieves comparable accuracy to models trained on non-encrypted images, offering a secure solution for outsourcing ML computations while preserving patient data privacy. To the best of our knowledge, this is the first work that uses data-efficient image transformers and fully homomorphic encryption in this domain.","url":"https://doi.org/10.48550/arxiv.2401.09604","authors":["Panzade, Prajwal","Takabi, Daniel","Cai, Zhipeng"],"tags":["Cryptography and Security (cs.CR)","Computer Vision and Pattern Recognition (cs.CV)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2401.09604","addedAt":"2026-08-31T06:41:40.591Z","updatedAt":"2026-08-31T06:41:40.591Z"},{"id":"doi:10.48550/arxiv.2204.09649","name":"BliMe: Verifiably Secure Outsourced Computation with Hardware-Enforced Taint Tracking","source":"datacite","abstract":"Outsourced computing is widely used today. However, current approaches for protecting client data in outsourced computing fall short: use of cryptographic techniques like fully-homomorphic encryption incurs substantial costs, whereas use of hardware-assisted trusted execution environments has been shown to be vulnerable to run-time and side-channel attacks. We present Blinded Memory (BliMe), an architecture to realize efficient and secure outsourced computation. BliMe consists of a novel and minimal set of instruction set architecture (ISA) extensions implementing a taint-tracking policy to ensure the confidentiality of client data even in the presence of server vulnerabilities. To secure outsourced computation, the BliMe extensions can be used together with an attestable, fixed-function hardware security module (HSM) and an encryption engine that provides atomic decrypt-and-taint and encrypt-and-untaint operations. Clients rely on remote attestation and key agreement with the HSM to ensure that their data can be transferred securely to and from the encryption engine and will always be protected by BliMe's taint-tracking policy while at the server. We provide an RTL implementation BliMe-BOOM based on the BOOM RISC-V core. BliMe-BOOM requires no reduction in clock frequency relative to unmodified BOOM, and has minimal power ($&lt;\\!1.5\\%$) and FPGA resource ($\\leq\\!9.0\\%$) overheads. Various implementations of BliMe incur only moderate performance overhead ($8--25\\%$). We also provide a machine-checked security proof of a simplified model ISA with BliMe extensions.","url":"https://doi.org/10.48550/arxiv.2204.09649","authors":["ElAtali, Hossam","Gunn, Lachlan J.","Liljestrand, Hans","Asokan, N."],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2204.09649","addedAt":"2026-08-31T06:41:40.591Z","updatedAt":"2026-08-31T06:41:40.591Z"},{"id":"doi:10.1109/mspec.2024.10380468","name":"Chips to Compute with Encrypted Data are Coming: Fully homomorphic encryption could make data unhackable","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mspec.2024.10380468","authors":["Samuel K. Moore"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-03T19:49:09Z","doi":"10.1109/mspec.2024.10380468","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1002/adma.202470169","name":"A 2D Cryptographic Hash Function Incorporating Homomorphic Encryption for Secure Digital Signatures (Adv. Mater. 23/2024)","source":"crossref","abstract":"","url":"https://doi.org/10.1002/adma.202470169","authors":["Akshay Wali","Harikrishnan Ravichandran","Saptarshi Das"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-07T00:03:41Z","doi":"10.1002/adma.202470169","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.3390/cryptography7020031","name":"Inferring Bivariate Polynomials for Homomorphic Encryption Application","source":"crossref","abstract":"Inspired by the advancements in (fully) homomorphic encryption in recent decades and its practical applications, we conducted a preliminary study on the underlying mathematical structure of the corresponding schemes. Hence, this paper focuses on investigating the challenge of deducing bivariate polynomials constructed using homomorphic operations, namely repetitive additions and multiplications. To begin with, we introduce an approach for solving the previously mentioned problem using Lagrange interpolation for the evaluation of univariate polynomials. This method is well-established for determining univariate polynomials that satisfy a specific set of points. Moreover, we propose a second approach based on modular knapsack resolution algorithms. These algorithms are designed to address optimization problems in which a set of objects with specific weights and values is involved. Finally, we provide recommendations on how to run our algorithms in order to obtain better results in terms of precision.","url":"https://doi.org/10.3390/cryptography7020031","authors":["Diana Maimuţ","George Teşeleanu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-06T02:08:15Z","doi":"10.3390/cryptography7020031","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.54691/9xb96p64","name":"Research on Blockchain Interactive Zero Knowledge Proof Privacy Protection Scheme Based on Improved Paillier Homomorphic Encryption","source":"crossref","abstract":"In the context of the digital age, data privacy and security issues are increasingly prominent. Blockchain technology plays an important role in data sharing due to its transparency and immutability, but it also brings the risk of privacy leakage. Zero knowledge proof technology provides a solution for verifying data correctness without exposing data content, which is particularly important for blockchain as it can ensure the validity and compliance of transactions while protecting user privacy. Although zero knowledge proof is quite mature in theory, its application in blockchain systems still faces challenges such as computational efficiency, complexity of smart contracts, and system compatibility. This study aims to propose a privacy protection scheme that supports interactive zero knowledge proof by improving the homomorphic encryption Paillier algorithm, in order to enhance the privacy protection capability of blockchain systems and maintain system efficiency and security. The study will adopt an interdisciplinary approach, combining cryptography, computer science, and network security theory, to deeply analyze the application effect of zero knowledge proof technology in blockchain, explore its optimization space and applicability.","url":"https://doi.org/10.54691/9xb96p64","authors":["Yueran Zhuo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-23T03:15:34Z","doi":"10.54691/9xb96p64","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1016/j.asoc.2024.112405","name":"Privacy preserving verifiable federated learning scheme using blockchain and homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.asoc.2024.112405","authors":["Ganesh Kumar Mahato","Aiswaryya Banerjee","Swarnendu Kumar Chakraborty","Xiao-Zhi Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-30T18:35:28Z","doi":"10.1016/j.asoc.2024.112405","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.2991/icmse-18.2018.129","name":"An Improved Homomorphic Technique in Construction of Fully Homomorphic Encryption Scheme from LWE","source":"crossref","abstract":"","url":"https://doi.org/10.2991/icmse-18.2018.129","authors":["Chengbo Xu","Shuying Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-05-28T15:32:57Z","doi":"10.2991/icmse-18.2018.129","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/iiki.2014.35","name":"An Encryption Depth Optimization Scheme for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iiki.2014.35","authors":["Liquan Chen","Hongmei Ben","Jie Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-03-25T17:36:10Z","doi":"10.1109/iiki.2014.35","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/tifs.2015.2398359","name":"A Hybrid Scheme of Public-Key Encryption and Somewhat Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tifs.2015.2398359","authors":["Jung Hee Cheon","Jinsu Kim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-02-02T20:20:43Z","doi":"10.1109/tifs.2015.2398359","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1002/eng2.70690/v1/review2","name":"Review for \"Data Clustering Method for Fault-tolerant Privacy Protection of Smart Grid Based on BGN Homomorphic Encryption Algorithm\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70690/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T21:06:30Z","doi":"10.1002/eng2.70690/v1/review2","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/ic366947.2025.11290265","name":"Quantum-Resilient Proxy-Re-encryption-Based Homomorphic Encryption Using Nth Order Binary Encoding (NOBE) for NextG networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ic366947.2025.11290265","authors":["Bharat S. Rawal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-31T18:41:12Z","doi":"10.1109/ic366947.2025.11290265","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1109/ic2e362166.2024.10827028","name":"Analyzing RSA and Paillier Encryption Schemes: Secure Multiplication in Homomorphic Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ic2e362166.2024.10827028","authors":["Janak Dhokrat","Namita Pulgam","Vanita Mane","Tabassum Maktum"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T15:15:55Z","doi":"10.1109/ic2e362166.2024.10827028","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.2139/ssrn.4172617","name":"Toward Comparable Homomorphic Encryption for Crowd-Sensing Network","source":"crossref","abstract":"As a popular paradigm, crowd-sensing network emerges to achieve sensory data collection and task allocation to mobile users. On one hand these sensory data may be private and sensitive, and on the other hand, data transmission separately could incur heavy communication overhead. Fortunately, the technique of homomorphic encryption (HE) allows the additive and/or multiplicative operations over the encrypted data as well as privacy protection. Therefore, several data aggregation schemes based on HE are proposed for crowd-sensing network. However, most of the existing schemes do not support ciphertext comparison efficiently, thus data center cannot process ciphertexts with flexibility. To address this challenge, we propose a comparable homomorphic encryption (CompHE) scheme based on Lagrange’s interpolation theorem, which enables ciphertext comparison among multiple users in crowd-sensing network. Based on the Partial Discrete Logarithm and Decisional Diffie-Hellman assumption, the proposed CompHE scheme is provably secure in the random oracle model. Performance analysis confirms that the proposed scheme have improved the computational efficiency compared with existing schemes.","url":"https://doi.org/10.2139/ssrn.4172617","authors":["Daxin Huang","Qingqing Gan","Xiaoming Wang","Chengpeng Huang","Mengting Yao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-27T03:03:06Z","doi":"10.2139/ssrn.4172617","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/icecs61496.2024.10849202","name":"VLSI Design of Programmable Bootstrapping for Torus Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icecs61496.2024.10849202","authors":["Tzyy-Shiuan Yang","Qi-Xian Wu","Ming-Der Shieh","Tsung-Han Tsai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-28T18:32:08Z","doi":"10.1109/icecs61496.2024.10849202","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.52783/pmj.v33.i2.876","name":"Homomorphic Encryption and Secure Multi-Party Computation: Mathematical Tools for Privacy-Preserving Data Analysis in the Cloud","source":"crossref","abstract":"With more and more people using cloud computing and storing and handling data remotely, protecting the privacy and safety of private data has become very important. Homomorphic encryption and safe multi-party computing (MPC) are two new mathematics tools that offer strong ways to analyze data in the cloud while protecting privacy. When you use homomorphic encryption, you can do calculations directly on protected data, so you can process data securely without having to decode private data. This method makes sure that data stays protected while operations are being done, keeping it safe from people who shouldn't have access to it or seeing it. Cloud service providers can use homomorphic encryption to do different types of analysis on protected data, like collecting, searching, and machine learning, without revealing the private information that lies beneath. Secure multi-party computation protects privacy in situations where multiple people work together to analyze data. MPC allows for joint analysis without letting other people see individual datasets by spreading computations across multiple entities, each of which holds a piece of the data. MPC uses cryptographic protocols and methods to make sure that processes are done without revealing private inputs. This lets multiple people work together to analyze data while keeping privacy. These math tools can be used for many different types of data analysis jobs in the cloud, such as predictive modeling, machine learning, and statistical analysis. They also make it safe for different groups to share and work together on data, like businesses, academics, and people, without putting data protection at risk. There are still problems with how homomorphic encryption and safe MPC can be used in the real world and how they can be scaled up. These problems are mostly related to the amount of work that needs to be done and how efficiently it works. The main goal of ongoing study is to create improved methods and programs that will make these techniques work better and be easier to use in the real world.","url":"https://doi.org/10.52783/pmj.v33.i2.876","authors":["Mayuri Arun Gaikwad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-15T10:05:13Z","doi":"10.52783/pmj.v33.i2.876","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1145/3689945.3694802","name":"Training Encrypted Neural Networks on Encrypted Data with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3689945.3694802","authors":["Luca Colombo","Alessandro Falcetta","Manuel Roveri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-19T18:23:11Z","doi":"10.1145/3689945.3694802","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1145/3677404.3677421","name":"A fault-tolerant federated learning scheme based on multi-key homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3677404.3677421","authors":["Yiming Zhang","Wei Zhang","Cong Shen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-09T12:22:28Z","doi":"10.1145/3677404.3677421","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/iciscois56541.2023.10100372","name":"A Hybrid Homomorphic Model with RSA Algorithm and Modified Enhanced Homomorphic Encryption Technique","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciscois56541.2023.10100372","authors":["T P Kamatchi","K Anitha Kumari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-04-19T13:21:57Z","doi":"10.1109/iciscois56541.2023.10100372","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.5220/0014462100004061","name":"ERAHE: Edge-Offloaded Robust Attribute-Based Aggregate Scheme Enhanced with Homomorphic Encryption for 5G-Connected Delivery Drones","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0014462100004061","authors":["Aagii Thomas","Sana Belguith"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-17T04:15:06Z","doi":"10.5220/0014462100004061","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/pset62496.2024.10808489","name":"Privacy-preserving Multiarea Economic Optimization In Power System Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/pset62496.2024.10808489","authors":["Zhenyang Yan","Yujian Ye","Xijin Guo","Hao Hu","Xiangpeng Xie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-30T19:19:37Z","doi":"10.1109/pset62496.2024.10808489","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.2139/ssrn.5334301","name":"Privacy-Preserving Federated Learning with Fully Homomorphic Encryption for Breast Cancer Diagnosis","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5334301","authors":["S Naresh","Gadhiraju Varma","D Ayyappa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-08T18:51:19Z","doi":"10.2139/ssrn.5334301","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.18280/isi.290501","name":"Integrating Homomorphic Encryption in IoT Healthcare Blockchain Systems","source":"crossref","abstract":"","url":"https://doi.org/10.18280/isi.290501","authors":["Habib Aissaoua","Abdelkader Laouid","Mostefa Kara","Ahcène Bounceur","Mohammad Hammoudeh","Khaled Chait"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-24T02:30:04Z","doi":"10.18280/isi.290501","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/access.2024.3512003","name":"For Your Eyes Only: A Privacy-Preserving Authentication Framework Based on Homomorphic Encryption and Retina Biometrics","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2024.3512003","authors":["David Palma","Pier Luca Montessoro"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-05T19:13:43Z","doi":"10.1109/access.2024.3512003","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.3724/sp.j.1146.2013.00300","name":"A Key Recovery Attack on Fully Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.3724/sp.j.1146.2013.00300","authors":["Yan Guang","Yue-fei Zhu","Chun-xiang Gu","Yong-hui Zheng","Quan-you Tang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-02-23T01:58:56Z","doi":"10.3724/sp.j.1146.2013.00300","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/iscon52037.2021.9702486","name":"Survey on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscon52037.2021.9702486","authors":["Viplove Bansal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-15T06:38:50Z","doi":"10.1109/iscon52037.2021.9702486","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1016/j.array.2025.100655","name":"Smart grid privacy data encryption and sharing algorithm based on multi-key homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.array.2025.100655","authors":["Xuehai Chen","Yantong Lin","Zhimin Liang","Zhenmin He"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-20T23:25:17Z","doi":"10.1016/j.array.2025.100655","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1007/s10207-024-00823-1","name":"Random forest evaluation using multi-key homomorphic encryption and lookup tables","source":"crossref","abstract":"Abstract In recent years, machine learning (ML) has become increasingly popular in various fields of activity. Cloud platforms have also grown in popularity, as they offer services that are more secure and accessible worldwide. In this context, cloud-based technologies emerged to support ML, giving rise to the machine learning as a service (MLaaS) concept. However, the clients accessing ML services in order to obtain classification results on private data may be reluctant to upload sensitive information to cloud. The model owners may also prefer not to outsource their models in order to prevent model inversion attacks and to protect intellectual property. The privacy-preserving evaluation of ML models is possible through multi-key homomorphic encryption (MKHE), that allows both the client data and the model to be encrypted under different keys. In this paper, we propose an MKHE evaluation method for decision trees and we extend the proposed method for random forests. Each decision tree is evaluated as a single lookup table, and voting is performed at the level of groups of decision trees in the random forest. We provide both theoretical and experimental evaluations for the proposed method. The aim is to minimize the performance degradation introduced by the encrypted model compared to a plaintext model while also obtaining practical classification times. In our experiments with the proposed MKHE random forest evaluation method, we obtained minimal (less than 0.6%) impact on the main ML performance metrics considered for each scenario, while also achieving reasonable classification times (of the order of seconds).","url":"https://doi.org/10.1007/s10207-024-00823-1","authors":["Diana-Elena Petrean","Rodica Potolea"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-14T12:01:45Z","doi":"10.1007/s10207-024-00823-1","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/ispa63168.2024.00012","name":"HEGD-FL: A Privacy-Preserving Decentralized Federated Learning Framework Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ispa63168.2024.00012","authors":["Pengyu Yao","Di Zhang","Min Guo","Xun Shao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T20:05:50Z","doi":"10.1109/ispa63168.2024.00012","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1007/978-3-031-35535-6_7","name":"Enhancing Encryption Security Against Cypher Attacks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-35535-6_7","authors":["R. Naveenkumar","N. M. Sivamangai","A. Napolean","S. Sridevi Sathyapriya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-01T00:02:13Z","doi":"10.1007/978-3-031-35535-6_7","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.29007/drnc","name":"Homomorphic Encryption and Data Security in the Cloud","source":"crossref","abstract":"In the recent times, the use of cloud computing has gained popularity all over the world. There are lots of benefits associated with the use of this modern technology, however, there is a concern about the security of information during computation. A homomorphic encryption scheme provides a mechanism whereby arithmetic operation on the ciphertexts produces the same result as the arithmetic operation on plaintexts. Concept of homomorphic encryption (HME) is discussed with reviews, applications and future challenges to this promising field of research","url":"https://doi.org/10.29007/drnc","authors":["Timothy Oladunni","Sharad Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-09-28T00:26:45Z","doi":"10.29007/drnc","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1016/b978-0-12-801595-7.00005-7","name":"A guide to homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-12-801595-7.00005-7","authors":["Mark A. Will","Ryan K.L. Ko"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-06-05T20:15:13Z","doi":"10.1016/b978-0-12-801595-7.00005-7","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/is3c.2014.192","name":"Parallelizing Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/is3c.2014.192","authors":["Ryan Hayward","Chia Chu Chiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-07-29T14:36:17Z","doi":"10.1109/is3c.2014.192","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1007/978-3-030-87629-6_10","name":"Damgård-Jurik Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6_10","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T19:04:24Z","doi":"10.1007/978-3-030-87629-6_10","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1007/978-3-030-87629-6_8","name":"Okamoto-Uchiyama Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6_8","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T19:04:24Z","doi":"10.1007/978-3-030-87629-6_8","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1007/978-3-030-87629-6_7","name":"Naccache-Stern Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6_7","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T19:04:24Z","doi":"10.1007/978-3-030-87629-6_7","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.26634/jcom.7.1.15667","name":"Hybrid Cryptosystem Using Homomorphic Encryption And Elliptic Curve Cryptography","source":"crossref","abstract":"Providing security and privacy for the cloud data is one of the most difficult tasks in recent days. The privacy of the sensitive information ought to be protecting from the unauthorized access for enhancing its security. Security is provided using traditional encryption and decryption process. One of the drawbacks of the traditional algorithm is that it has increased computational complexity, time consumption, and reduced security. The authors have proposed a scheme where the original data gets encrypted into two different values. Elliptical Curve Cryptography (ECC) and Homomorphic are combined to provide encryption. The data in each slice can be encrypted by using different cryptographic algorithms and encryption key before storing them in the cloud. The objective of this technique is to store data in a proper secure and safe manner in order to avoid intrusions and data attacks meanwhile it will reduce the cost and time to store the encrypted data in the Cloud Storage.","url":"https://doi.org/10.26634/jcom.7.1.15667","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-05-30T05:03:23Z","doi":"10.26634/jcom.7.1.15667","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.5772/56687","name":"Homomorphic Encryption — Theory and Application","source":"crossref","abstract":"","url":"https://doi.org/10.5772/56687","authors":["Jaydip Sen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-07-18T08:46:06Z","doi":"10.5772/56687","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1016/j.pmcj.2024.101952","name":"IoT data encryption and phrase search-based efficient processing using a Fully Homomorphic-based SE (FHSE) scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.pmcj.2024.101952","authors":["S. Hamsanandhini","P. Balasubramanie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-06T16:50:06Z","doi":"10.1016/j.pmcj.2024.101952","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.5220/0011657100003405","name":"On the Feasibility of Fully Homomorphic Encryption of Minutiae-Based Fingerprint Representations","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0011657100003405","authors":["Pia Bauspieß","Lasse Vad","Håvard Myrekrok","Anamaria Costache","Jascha Kolberg","Christian Rathgeb","Christoph Busch"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-04T05:14:25Z","doi":"10.5220/0011657100003405","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-3-030-87629-6_4","name":"Goldwasser-Micali Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6_4","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T19:04:24Z","doi":"10.1007/978-3-030-87629-6_4","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.71146/jbdpm35","name":"LEVERAGING HOMOMORPHIC ENCRYPTION FOR PRIVACY-PRESERVING BIG DATA ANALYSIS","source":"crossref","abstract":"With the exponential growth of Big Data, preserving the privacy and security of sensitive information during analysis has become a critical concern. Traditional encryption methods often hinder data usability, making it difficult to analyze encrypted data without first decrypting it. Homomorphic encryption (HE) offers a promising solution, enabling computations to be performed directly on encrypted data, thereby preserving privacy. This article explores the application of homomorphic encryption in Big Data analysis, focusing on its potential to maintain data confidentiality while enabling complex analytical tasks. We examine various HE schemes, their strengths, limitations, and their integration into Big Data ecosystems. Additionally, we discuss real-world applications, challenges, and future research directions in adopting homomorphic encryption for large-scale, privacy-preserving data analytics.","url":"https://doi.org/10.71146/jbdpm35","authors":["Dr. Faizan Ali"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-11T09:25:02Z","doi":"10.71146/jbdpm35","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.2139/ssrn.5608836","name":"Adaptive and Efficient Federated Distillation with Selective Homomorphic Encryption for Edge AI","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5608836","authors":["Dadmehr Rahbari","Masoud Daneshtalab","Maksim Jenihhin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-15T13:41:41Z","doi":"10.2139/ssrn.5608836","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.2139/ssrn.3499579","name":"Secured Data Storage in Cloud Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.3499579","authors":["R. Kanagavalli","Vagdevi S."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-12-28T11:32:00Z","doi":"10.2139/ssrn.3499579","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/icocwc60930.2024.11149443","name":"Retraction Notice: Development of Modified Homomorphic Encryption for IIoT on Textual Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icocwc60930.2024.11149443","authors":["P. Rathinakumar","R. Dhivya","S. Prasath","K N Jayapriya","M.Sathish Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-03T17:48:17Z","doi":"10.1109/icocwc60930.2024.11149443","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1007/s11424-024-3221-1","name":"On the Security of Homomorphic Encryption Schemes with Restricted Decryption Oracles","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s11424-024-3221-1","authors":["Guangsheng Ma","Hongbo Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-30T11:03:18Z","doi":"10.1007/s11424-024-3221-1","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/icetems64039.2024.10965006","name":"Multi-Keywords Based Fully Homomorphic Encryption and Data Classification for Cloud Security and Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icetems64039.2024.10965006","authors":["Kooragayala Sukeerthi","R. Kesavan","S.A. Kalaiselvan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-22T17:37:25Z","doi":"10.1109/icetems64039.2024.10965006","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/icimcis63449.2024.10956834","name":"Comparative Analysis of Homomorphic Encryption Based on Elliptic Curves over Rings","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icimcis63449.2024.10956834","authors":["Muhammad Haidar Wijaya","Sa'aadah S Carita","Sri Rosdiana","Mareta Wahyu Ardyani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-18T17:35:15Z","doi":"10.1109/icimcis63449.2024.10956834","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/ispass61541.2024.00016","name":"CiFlow: Dataflow Analysis and Optimization of Key Switching for Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ispass61541.2024.00016","authors":["Negar Neda","Austin Ebel","Benedict Reynwar","Brandon Reagen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-16T13:19:44Z","doi":"10.1109/ispass61541.2024.00016","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/vcris63677.2024.10813447","name":"Secure Coordinate Rotation in Embedded Systems Using Homomorphic Encryption: Implementation and Evaluation on the AI-Saqr Platform","source":"crossref","abstract":"","url":"https://doi.org/10.1109/vcris63677.2024.10813447","authors":["Yashrajsinh Parmar","Florian Caullery","Sonali Kale"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-27T19:10:26Z","doi":"10.1109/vcris63677.2024.10813447","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/icpids65698.2024.00012","name":"Quantum Cloud Computing in Multi-Cloud Environments for Safe Data Transfer Using Two-Tier Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icpids65698.2024.00012","authors":["Apurva Mishra","Rajiv Bhalla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-29T17:29:34Z","doi":"10.1109/icpids65698.2024.00012","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.23919/date58400.2024.10546604","name":"Efficient Fast Additive Homomorphic Encryption Cryptoprocessor for Privacy-Preserving Federated Learning Aggregation","source":"crossref","abstract":"","url":"https://doi.org/10.23919/date58400.2024.10546604","authors":["Wenye Liu","Nazim Altar Koca","Chip-Hong Chang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-14T17:28:02Z","doi":"10.23919/date58400.2024.10546604","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/incos.2015.45","name":"A BGN-Type Multiuser Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1109/incos.2015.45","authors":["Zhang Wei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-11-02T18:14:49Z","doi":"10.1109/incos.2015.45","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1145/3134600.3134616","name":"Proxy Re-Encryption Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3134600.3134616","authors":["Reda Bellafqira","Gouenou Coatrieux","Dalel Bouslimi","Gwénolé Quellec","Michel Cozic"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-12-04T19:18:32Z","doi":"10.1145/3134600.3134616","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1145/3711129.3711230","name":"A privacy protection strategy based on homomorphic encryption and neural networks","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3711129.3711230","authors":["Lingli Liu","Xiongxiong Du","Mingcheng Ma","Dong Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T12:46:06Z","doi":"10.1145/3711129.3711230","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/ccai61966.2024.10602929","name":"Privacy-Preserving Federated Learning with Homomorphic Encryption and Sparse Compression","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccai61966.2024.10602929","authors":["Wentao Yang","Yang Bai","Yutang Rao","Hongyan Wu","Gaojie Xing","Yimin Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-31T20:34:15Z","doi":"10.1109/ccai61966.2024.10602929","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.3390/sym17050737","name":"Threshold Fully Homomorphic Encryption Scheme Based on NGS of Symmetric Encryption","source":"crossref","abstract":"Homomorphic encryption is an important means for cloud computing to ensure information security when outsourcing data. Among them, threshold fully homomorphic encryption (ThFHE) is a key enabler for homomorphic encryption and, from a wider perspective, secure distributed computing. However, current ThFHE schemes are unsatisfactory in terms of security and efficiency. In this paper, a novel ThFHE is proposed for the first time based on an NTRU-based GSW-like scheme of symmetric encryption—Th-S-NGS scheme. Additionally, the threshold structure is realized by combining an extended version of the linear integer secret sharing scheme such that the scheme requires a predetermined number of parties to be online, rather than all the parties being online. The Th-S-NGS scheme is not only more attractive in terms of ciphertext size and computation time for homomorphic multiplication, but also does not need re-linearization after homomorphic multiplication, and thus does not require the computing key, which can effectively reduce the communication burden in the scheme and thus simplify the complexity of the scheme.","url":"https://doi.org/10.3390/sym17050737","authors":["Xu Zhao","Zheng Yuan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-13T03:59:26Z","doi":"10.3390/sym17050737","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.56975/jaafr.v4i5.509771","name":"homomorphic encryption cloud","source":"crossref","abstract":"","url":"https://doi.org/10.56975/jaafr.v4i5.509771","authors":["MR.AMRITPAL SINGH","Ms.AANCHAL MADAAN","MR.AMIT PURI"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-13T12:36:38Z","doi":"10.56975/jaafr.v4i5.509771","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.2139/ssrn.5244225","name":"Quantized Approximate Signal Processing (Qasp): Towards Homomorphic Encryption for Audio","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5244225","authors":["Tu Duyen Nguyen","Adrien Lesage","Clotilde Cantini","Rachid Riad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-13T00:38:46Z","doi":"10.2139/ssrn.5244225","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/iconat61936.2024.10775056","name":"Secure Virtual Desktop Infrastructure Solution Using Homomorphic Encryption and Machine Learning Models","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iconat61936.2024.10775056","authors":["W.M.D Senuwan","Nimantha Dissanayake","Malithi Disanayaka","Shashini Hewage","Kavinga Yapa Abeywardena","Deemantha Siriwardhana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:01:10Z","doi":"10.1109/iconat61936.2024.10775056","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.56286/ntujet.v3i1.861","name":"Preserving Big Data Privacy in Cloud Environments Based on Homomorphic Encryption and Distributed Clustering","source":"crossref","abstract":"Cloud computing has grown in popularity in recent years because to its efficiency, flexibility, scalability, and the services it provides for data storage and processing. Still, big businesses and organizations have severe concerns about protecting privacy and data security while processing these massive volumes of data. This paper proposes approach that intends to enhance efficiency in delivering advanced data protection, hence filling security holes, by enhancing data protection from various big data sources. A partial homomorphic encryption system is used to encrypt data created by many sources or users and processed in the cloud without decrypting it, hence protecting data from attackers. Extremely Distributed Clustering (EDC) has also been applied to partition large datasets into many cloud computing node subsets. This method can ensure privacy and protect data while also enhancing the effectiveness and performance of big data analytics. According to the results, the proposed technique was faster and gave improved encryption performance by around 23-28%.","url":"https://doi.org/10.56286/ntujet.v3i1.861","authors":["Shatha A. Baker"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-16T06:29:46Z","doi":"10.56286/ntujet.v3i1.861","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1117/1.3167847","name":"Homomorphic image encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1117/1.3167847","authors":["Osama S. Farag Allah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-07-14T22:05:12Z","doi":"10.1117/1.3167847","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-3-030-87629-6_3","name":"Rivest-Shamir-Adleman Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6_3","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T19:04:24Z","doi":"10.1007/978-3-030-87629-6_3","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/arith.2019.00047","name":"Privacy-Preserving Deep Learning via Additively Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/arith.2019.00047","authors":["Shiho Moriai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-10-21T23:56:55Z","doi":"10.1109/arith.2019.00047","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1016/j.csi.2023.103765","name":"Lattice based distributed threshold additive homomorphic encryption with application in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.csi.2023.103765","authors":["Haibo Tian","Yanchuan Wen","Fangguo Zhang","Yunfeng Shao","Bingshuai Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-15T15:15:59Z","doi":"10.1016/j.csi.2023.103765","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.2139/ssrn.5705363","name":"\"PHEO: Optimizing Paillier Homomorphic Encryption Parameters Using Hybrid A-GGCO for Secure Cloud Applications\"","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5705363","authors":["Rekha Gaitond","Gangadhar  S. Biradar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-05T04:37:53Z","doi":"10.2139/ssrn.5705363","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.36227/techrxiv.21901293","name":"An Unbounded Fully Homomorphic Encryption Scheme Based on Ideal Lattices and Chinese Remainder Theorem","source":"crossref","abstract":"&lt;p&gt;We propose an unbounded fully homomorphic encryption scheme, i.e. a scheme that allows one to compute on encrypted data for any desired functions without needing to decrypt the data or knowing the decryption keys. This is a rational solution to an old problem proposed by Rivest, Adleman, and Dertouzos \\cite{32} in 1978, and to some new problems appeared in Peikert \\cite{28} as open questions 10 and open questions 11 a few years ago.&lt;/p&gt; &lt;p&gt;Our scheme is completely different from the breakthrough work \\cite{14,15} of Gentry in 2009. Gentry's bootstrapping technique constructs a fully homomorphic encryption (FHE) scheme from a somewhat homomorphic one that is powerful enough to evaluate its own decryption function. To date, it remains the only known way of obtaining unbounded FHE. Our construction of unbounded FHE scheme is straightforward and noise-free that can handle unbounded homomorphic computation on any refreshed ciphertexts without bootstrapping transformation technique.&lt;/p&gt;","url":"https://doi.org/10.36227/techrxiv.21901293","authors":["Zheng Zhiyong","Fengxia Liu","Tian Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-02-04T02:04:34Z","doi":"10.36227/techrxiv.21901293","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1587/transfun.2023cip0007","name":"On Extension of Evaluation Algorithms in Keyed-Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1587/transfun.2023cip0007","authors":["Hirotomo SHINOKI","Koji NUIDA"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-26T22:12:46Z","doi":"10.1587/transfun.2023cip0007","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1007/978-3-030-87629-6_12","name":"Sander-Young-Yung Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6_12","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T19:04:24Z","doi":"10.1007/978-3-030-87629-6_12","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.36227/techrxiv.21901293.v1","name":"An Unbounded Fully Homomorphic Encryption Scheme Based on Ideal Lattices and Chinese Remainder Theorem","source":"crossref","abstract":"We propose an unbounded fully homomorphic encryption scheme, i.e. a scheme that allows one to compute on encrypted data for any desired functions without needing to decrypt the data or knowing the decryption keys. This is a rational solution to an old problem proposed by Rivest, Adleman, and Dertouzos \\cite{32} in 1978, and to some new problems appeared in Peikert \\cite{28} as open questions 10 and open questions 11 a few years ago. Our scheme is completely different from the breakthrough work \\cite{14,15} of Gentry in 2009. Gentry’s bootstrapping technique constructs a fully homomorphic encryption (FHE) scheme from a somewhat homomorphic one that is powerful enough to evaluate its own decryption function. To date, it remains the only known way of obtaining unbounded FHE. Our construction of unbounded FHE scheme is straightforward and noise-free that can handle unbounded homomorphic computation on any refreshed ciphertexts without bootstrapping transformation technique.","url":"https://doi.org/10.36227/techrxiv.21901293.v1","authors":["Zheng Zhiyong","Fengxia Liu","Tian Kun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-02-03T21:04:31Z","doi":"10.36227/techrxiv.21901293.v1","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.18535/ijecs/v7i3.22","name":"Homomorphic Encryption Using Enhanced BGV Encryption Scheme For Cloud Security","source":"crossref","abstract":"Fully Homomorphic Encryption is used to enhance the security incase of un-trusted systems or applications that deals with sensitive data. Homomorphic encryption enables computation on encrypted data without decryption. Homomorphic encryption prevents sharing of data within the cloud service where data is stored in a public cloud . In Partially Homomorphic Encryption it performs either additive or multiplicative operation, but not both operation can be carried out at a same time. Whereas , in case of Fully Homomorphic Encryption both operations can be carried out at same time. In this model , Enhanced BGV Encryption Technique is used to perform FHE operations on encrypted data and sorting is performed using the encrypted data","url":"https://doi.org/10.18535/ijecs/v7i3.22","authors":["S.V.Suriya Prasad","K. Kumanan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-04-05T07:07:50Z","doi":"10.18535/ijecs/v7i3.22","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.18280/ts.420640","name":"CKKS-ITSA: A Secure Cloud-Based Medical Image Encryption Model Using Optimized Homomorphic Encryption Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.18280/ts.420640","authors":["Anandhi T.","Sivasangari A."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-30T07:29:45Z","doi":"10.18280/ts.420640","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.56726/irjmets87596","name":"Homomorphic Encryption for Secure Cloud Data Processing","source":"crossref","abstract":"","url":"https://doi.org/10.56726/irjmets87596","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-27T16:48:47Z","doi":"10.56726/irjmets87596","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-3-030-87629-6_11","name":"Boneh-Goh-Nissim Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-87629-6_11","authors":["Çetin Kaya Koç","Funda Özdemir","Zeynep Ödemiş Özger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-09-29T19:04:24Z","doi":"10.1007/978-3-030-87629-6_11","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/s11277-024-11186-0","name":"Privacy-Preserving of Digital 6G IoT Based Cyber Phycical System in Medical Big-Data Application Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s11277-024-11186-0","authors":["Chunyuan Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-15T04:05:48Z","doi":"10.1007/s11277-024-11186-0","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.48047/jocaaa.2025.30.02.20","name":"Secure Data Aggregation in Wireless Sensor Networks Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.48047/jocaaa.2025.30.02.20","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-15T12:41:16Z","doi":"10.48047/jocaaa.2025.30.02.20","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-3-031-43214-9_2","name":"Background and Preliminaries","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-43214-9_2","authors":["Stefania Loredana Nita","Marius Iulian Mihailescu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-26T09:02:55Z","doi":"10.1007/978-3-031-43214-9_2","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.26808/rs.ca.i9v6.02","name":"HOMOMORPHIC IMAGE ENCRYPTION IN CLOUD COMPUTING","source":"crossref","abstract":"","url":"https://doi.org/10.26808/rs.ca.i9v6.02","authors":["R. KANAGAVALLI","DR.VAGDEVI S"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-12-10T07:11:23Z","doi":"10.26808/rs.ca.i9v6.02","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.5120/ijca2016909652","name":"E-Voting using Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.5120/ijca2016909652","authors":["Tannishk Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-05-17T15:09:48Z","doi":"10.5120/ijca2016909652","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/icoin.2015.7057954","name":"Homomorphic encryption in mobile multi cloud computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icoin.2015.7057954","authors":["Maya Louk","Hyotaek Lim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-03-13T16:58:20Z","doi":"10.1109/icoin.2015.7057954","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/arith61463.2024.00011","name":"Hardware Acceleration of the Prime-Factor and Rader NTT for BGV Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/arith61463.2024.00011","authors":["David Du Pont","Jonas Bertels","Furkan Turan","Michiel Van Beirendonck","Ingrid Verbauwhede"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-04T13:29:09Z","doi":"10.1109/arith61463.2024.00011","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1145/3634737.3657001","name":"Efficient Unbalanced Quorum PSI from Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3634737.3657001","authors":["Xinpeng Yang","Liang Cai","Yinghao Wang","Keting Yin","Lu Sun","Jingwei Hu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-28T11:51:38Z","doi":"10.1145/3634737.3657001","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.18488/76.v11i3.3955","name":"Overview of homomorphic encryption technology for data privacy","source":"crossref","abstract":"This study examines the overview of homomorphic encryption technology for data privacy. In the era of big data, the growing need to utilize vast amounts of information while ensuring privacy and security has become a significant challenge. Homomorphic encryption technology has gained attention as a solution for privacy-preserving data processing, allowing computations on encrypted data without exposing sensitive information. This study introduces the concept of data privacy preservation and explores the evaluation of homomorphic encrypted technology. The focus is on analyzing both partial and full homomorphic encryption methods, highlighting their respective characteristics, evaluation criteria, and the current state of research. Partial homomorphic encryption supports limited operations, while full homomorphic encryption enables unlimited computation on encrypted data, though both face challenges related to computational overhead and efficiency. Additionally, this paper addresses the ongoing issues and limitations associated with homomorphic encryption, such as its complexity, large encryption volumes, and difficulties in handling large-scale datasets. Despite these challenges, researchers continue to refine the technology and expand its applications in cloud computing, big data analytics, and privacy-preserving computing environments. This study also discussed potential future research avenues aimed at improving the scalability, efficiency, and security of homomorphic encryption to support broader, real-world applications. Ultimately, homomorphic encryption is positioned as a key enabler for secure data utilization in an increasingly privacy-conscious digital landscape.","url":"https://doi.org/10.18488/76.v11i3.3955","authors":["Qiang Chen","Huixian Li","Suriyani Ariffin","Nur Atiqah Sia Abdullah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-26T10:50:31Z","doi":"10.18488/76.v11i3.3955","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1186/s42400-023-00187-4","name":"Practical solutions in fully homomorphic encryption: a survey analyzing existing acceleration methods","source":"crossref","abstract":"Abstract Fully homomorphic encryption (FHE) has experienced significant development and continuous breakthroughs in theory, enabling its widespread application in various fields, like outsourcing computation and secure multi-party computing, in order to preserve privacy. Nonetheless, the application of FHE is constrained by its substantial computing overhead and storage cost. Researchers have proposed practical acceleration solutions to address these issues. This paper aims to provide a comprehensive survey for systematically comparing and analyzing the strengths and weaknesses of FHE acceleration schemes, which is currently lacking in the literature. The relevant researches conducted between 2019 and 2022 are investigated. We first provide a comprehensive summary of the latest research findings on accelerating FHE, aiming to offer valuable insights for researchers interested in FHE acceleration. Secondly, we classify existing acceleration schemes from algorithmic and hardware perspectives. We also propose evaluation metrics and conduct a detailed comparison of various methods. Finally, our study presents the future research directions of FHE acceleration, and also offers both guidance and support for practical application and theoretical research in this field.","url":"https://doi.org/10.1186/s42400-023-00187-4","authors":["Yanwei Gong","Xiaolin Chang","Jelena Mišić","Vojislav B. Mišić","Jianhua Wang","Haoran Zhu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-01T01:02:01Z","doi":"10.1186/s42400-023-00187-4","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.24906/isc/2024/v38/i2/48243","name":"Homomorphic Encryption: Unlocking the Power of Secure Computations","source":"crossref","abstract":"With the rapid growth of digital world, Homomorphic encryption leverage data confidentiality on encrypted profile. Homomorphic encryption enables secure computation outsourcing to unsecured environments like public clouds without revealing data, assists organizations in meeting stringent data protection regulations by safeguarding data during processing. This article explores the history of homomorphic encryption, from its inception to modern innovations, challenges, methodologies and impact on data privacy and security.","url":"https://doi.org/10.24906/isc/2024/v38/i2/48243","authors":["Suvam Mukherjee","Shiladitya Pujari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-03T04:41:37Z","doi":"10.24906/isc/2024/v38/i2/48243","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/ijcnn60899.2024.10651340","name":"A Privacy-Preserving Brainprint Recognition System Based on Feature Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ijcnn60899.2024.10651340","authors":["Yiming Zhang","Hangjie Yi","Wanzeng Kong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-09T17:35:05Z","doi":"10.1109/ijcnn60899.2024.10651340","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/icctit64404.2024.10928386","name":"Privacy-Preserving Dynamic Resource Allocation for Vehicle-Pile-Grid Systems Based Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icctit64404.2024.10928386","authors":["Yu Long","Shulin Guo","Xiaolu Pei","Shuai Yuan","Wenjie Jiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-24T13:56:42Z","doi":"10.1109/icctit64404.2024.10928386","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/cisat62382.2024.10695339","name":"A Secure and Efficient Federated Learning Scheme Based on Homomorphic Encryption and Secret Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cisat62382.2024.10695339","authors":["Caimei Wang","Zhipeng Sun","Jianhao Lu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-02T18:16:05Z","doi":"10.1109/cisat62382.2024.10695339","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/icc.2009.5199505","name":"Symmetric-Key Homomorphic Encryption for Encrypted Data Processing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icc.2009.5199505","authors":["A. C.-F. Chan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2009-08-11T20:20:30Z","doi":"10.1109/icc.2009.5199505","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.5486/pmd.2011.5142","name":"A homomorphic encryption-based secure electronic voting scheme","source":"crossref","abstract":"","url":"https://doi.org/10.5486/pmd.2011.5142","authors":["ANDREA HUSZTI"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-02-10T20:24:52Z","doi":"10.5486/pmd.2011.5142","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-3-031-35535-6","name":"Homomorphic Encryption for Financial Cryptography","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-35535-6","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-01T00:02:13Z","doi":"10.1007/978-3-031-35535-6","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1002/9781119987390.ch6","name":"Processing Encrypted Multimedia Data Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781119987390.ch6","authors":["Sébastien Canard","Sergiu Carpov","Caroline Fontaine","Renaud Sirdey"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-01T21:36:57Z","doi":"10.1002/9781119987390.ch6","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/eiecs63941.2024.10800157","name":"Optimized Fully Homomorphic Encryption Strategy Based on Residue Number System","source":"crossref","abstract":"","url":"https://doi.org/10.1109/eiecs63941.2024.10800157","authors":["Yaqing Cheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-24T19:10:44Z","doi":"10.1109/eiecs63941.2024.10800157","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/flta63145.2024.10840167","name":"A Critical Look into Threshold Homomorphic Encryption for Private Average Aggregation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/flta63145.2024.10840167","authors":["Miguel Morona-Mínguez","Alberto Pedrouzo-Ulloa","Fernando Pérez-González"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T18:22:35Z","doi":"10.1109/flta63145.2024.10840167","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.22214/ijraset.2024.57921","name":"The Next Frontier of Security: Homomorphic Encryption in Action","source":"crossref","abstract":"Abstract: Encryption is essential in preventing unauthorized access to sensitive data in light of the growing concerns about data security in cloud computing. Homomorphic encryption promises to enable secure calculations on encrypted data without the need for decryption, particularly for cloud-based operations. To evaluate the effectiveness and applicability of several homomorphic encryption algorithms for safe cloud computing, we compare and contrast them in this research paper. Partially homomorphic encryption (PHE), somewhat homomorphic encryption (SHE), and fully homomorphic encryption (FHE) are the three basic homomorphic encryption subtypes that we examine. The implications of this study can aid cloud service providers and organizations in selecting the most appropriate homomorphic encryption scheme based on their specific security requirements and performance considerations. The research contributes to the ongoing efforts to enhance data privacy in cloud computing environments, opening new possibilities for secure data processing in an increasingly connected digital world. The exploration of homomorphic encryption schemes in this study opens new avenues for research and development in the field of cryptographic techniques. As technology continues to evolve, so too must our approaches to safeguarding data. This research serves as a catalyst for further innovations in homomorphic encryption algorithms, enabling even more efficient and robust methods for secure data processing in cloud environments and beyond. The insights derived from this research paper not only empower cloud service providers and organizations to make informed decisions about selecting the most appropriate homomorphic encryption scheme but also contribute to the broader mission of fortifying data privacy and security in cloud computing.","url":"https://doi.org/10.22214/ijraset.2024.57921","authors":["Prof. Shweta Sabnis","Prof. Pavan Mitragotri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-04T16:36:06Z","doi":"10.22214/ijraset.2024.57921","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1186/s42400-024-00232-w","name":"FedSHE: privacy preserving and efficient federated learning with adaptive segmented CKKS homomorphic encryption","source":"crossref","abstract":"Abstract Unprotected gradient exchange in federated learning (FL) systems may lead to gradient leakage-related attacks. CKKS is a promising approximate homomorphic encryption scheme to protect gradients, owing to its unique capability of performing operations directly on ciphertexts. However, configuring CKKS security parameters involves a trade-off between correctness, efficiency, and security. An evaluation gap exists regarding how these parameters impact computational performance. Additionally, the maximum vector length that CKKS can once encrypt, recommended by Homomorphic Encryption Standardization, is 16384, hampers its widespread adoption in FL when encrypting layers with numerous neurons. To protect gradients’ privacy in FL systems while maintaining practical performance, we comprehensively analyze the influence of security parameters such as polynomial modulus degree and coefficient modulus on homomorphic operations. Derived from our evaluation findings, we provide a method for selecting the optimal multiplication depth while meeting operational requirements. Then, we introduce an adaptive segmented encryption method tailored for CKKS, circumventing its encryption length constraint and enhancing its processing ability to encrypt neural network models. Finally, we present FedSHE , a privacy-preserving and efficient Fed erated learning scheme with adaptive S egmented CKKS H omomorphic E ncryption. FedSHE is implemented on top of the federated averaging (FedAvg) algorithm and is available at https://github.com/yooopan/FedSHE . Our evaluation results affirm the correctness and effectiveness of our proposed method, demonstrating that FedSHE outperforms existing homomorphic encryption-based federated learning research efforts in terms of model accuracy, computational efficiency, communication cost, and security level.","url":"https://doi.org/10.1186/s42400-024-00232-w","authors":["Yao Pan","Zheng Chao","Wang He","Yang Jing","Li Hongjia","Wang Liming"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-03T22:01:58Z","doi":"10.1186/s42400-024-00232-w","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.56714/bjrs.48.1.2","name":"Survey: Privacy-Preserving in Deep Learning based on Homomorphic Encryption","source":"crossref","abstract":"When deep learning techniques succeed, the amount of data accessible for training grows rapidly, the main successor is the collecting of user data at scale in huge enterprises. Because users' data is sensitive, and the manner of preserving this information (pictures and audio recordings) indefinitely, data collecting raises privacy concerns. The terms privacy and confidentiality are related to avoiding sharing this information, and deep learning cannot be accrued on a larger scale in the amount of data. There are some challenges in machine learning algorithms when needing to access data for the training process. There several technologies in deep learning for privacy-preserving have been evolving to assign the issues, including the multi-lateral computation secrecy and the symmetric encryption in the term of the neural network. This survey deals with the deep learning techniques concerning the privacy issue mainly related to input data and the ability of interesting directions in these learning processes. Finally, as a side contribution, we analyze and introduce some variations to the bootstrapping technique of deep learning. That offers an improved parameter in efficiency at the cost of increasing privacy.","url":"https://doi.org/10.56714/bjrs.48.1.2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-21T21:57:06Z","doi":"10.56714/bjrs.48.1.2","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.21275/sr24529193549","name":"Advanced Cryptographic Techniques for Cloud Data Security: A Technical Exploration into State-of-the-Art Encryption Methodologies Including Homomorphic Encryption, Quantum-Resistant Algorithms, and Hardware Security Modules for Protecting Cloud Data","source":"crossref","abstract":"","url":"https://doi.org/10.21275/sr24529193549","authors":["Abhijit Joshi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-02T11:03:40Z","doi":"10.21275/sr24529193549","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/icaccs.2019.8728427","name":"Enhanced Homomorphic Encryption Scheme with PSO for Encryption of Cloud Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaccs.2019.8728427","authors":["Saif Ali Khan","R. K Aggarwal","Shashidhar Kulkarni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-06-06T23:27:36Z","doi":"10.1109/icaccs.2019.8728427","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-981-13-6393-1_4","name":"Translating Algorithms to Handle Fully Homomorphic Encrypted Data","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-13-6393-1_4","authors":["Ayantika Chatterjee","Khin Mi Mi Aung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-03-29T10:02:37Z","doi":"10.1007/978-981-13-6393-1_4","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/s10586-024-04617-x","name":"An anonymous authentication with blockchain assisted ring-based homomorphic encryption for enhancing security in cloud computing","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10586-024-04617-x","authors":["Pranav Shrivastava","Bashir Alam","Mansaf Alam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-02T14:03:38Z","doi":"10.1007/s10586-024-04617-x","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.14445/22315381/ijett-v43p257","name":"Comparative Study of Fully Homomorphic Encryption and Fully Disk Encryption schemes in Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.14445/22315381/ijett-v43p257","authors":["Akriti Sharma","Nagresh kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-03-20T03:21:12Z","doi":"10.14445/22315381/ijett-v43p257","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/icws62655.2024.00173","name":"Personalized Privacy Protection Incentive Mechanism for Mobile Crowdsourcing Based on Homomorphic Encryption and Edge Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icws62655.2024.00173","authors":["Yingxin Li","Weilong Wang","Yingjie Wang","Tong Xiangrong","Peiyong Duan","Zhipeng Cai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-15T17:19:18Z","doi":"10.1109/icws62655.2024.00173","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.3390/info15010040","name":"Secure Genomic String Search with Parallel Homomorphic Encryption","source":"crossref","abstract":"Fully homomorphic encryption (FHE) cryptographic systems enable limitless computations over encrypted data, providing solutions to many of today’s data security problems. While effective FHE platforms can address modern data security concerns in unsecure environments, the extended execution time for these platforms hinders their broader application. This project aims to enhance FHE systems through an efficient parallel framework, specifically building upon the existing torus FHE (TFHE) system chillotti2016faster. The TFHE system was chosen for its superior bootstrapping computations and precise results for countless Boolean gate evaluations, such as AND and XOR. Our first approach was to expand upon the gate operations within the current system, shifting towards algebraic circuits, and using graphics processing units (GPUs) to manage cryptographic operations in parallel. Then, we implemented this GPU-parallel FHE framework into a needed genomic data operation, specifically string search. We utilized popular string distance metrics (hamming distance, edit distance, set maximal matches) to ascertain the disparities between multiple genomic sequences in a secure context with all data and operations occurring under encryption. Our experimental data revealed that our GPU implementation vastly outperforms the former method, providing a 20-fold speedup for any 32-bit Boolean operation and a 14.5-fold increase for multiplications.This paper introduces unique enhancements to existing FHE cryptographic systems using GPUs and additional algorithms to quicken fundamental computations. Looking ahead, the presented framework can be further developed to accommodate more complex, real-world applications.","url":"https://doi.org/10.3390/info15010040","authors":["Md Momin Al Aziz","Md Toufique Morshed Tamal","Noman Mohammed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-11T12:56:33Z","doi":"10.3390/info15010040","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/fnwf63303.2024.11028749","name":"Enhancing Federated Learning with Homomorphic Encryption and Multi-Party Computation for improved privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fnwf63303.2024.11028749","authors":["Pedro Tomás","Samira Kamali Poorazad","Chafika Benzaïd","Luis Rosa","Jorge Proença","Tarik Taleb","Luis Cordeiro"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-12T17:41:07Z","doi":"10.1109/fnwf63303.2024.11028749","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.2139/ssrn.6915146","name":"PCCL: A Privacy-Preserving Concept-Cognitive Learning Framework with Partially Homomorphic Encryption","source":"crossref","abstract":"Concept-Cognitive Learning (CCL) is a powerful paradigm for knowledge discovery and data mining, particularly for large-scale, heterogeneous, and high-dimensional datasets. However, its reliance on third-party cloud servers for computationally intensive concept construction poses severe privacy risks for sensitive applications. To address this challenge, we propose PCCL, a privacy-preserving concept-cognitive learning framework based on partially homomorphic encryption (PHE) that enables exact and secure concept construction over encrypted data. We introduce a probabilistic encoding strategy to eliminate structural sparsity leakage in standard ElGamal encryption, ensuring semantic security under the Decisional Diffie-Hellman (DDH) assumption. Furthermore, we develop the Parallel BFS Trie Algorithm (PBTA), which leverages batched processing and trie-based pruning to drastically reduce communication overhead. Theoretical analysis proves the correctness and security of PCCL. Extensive experiments on seven UCI datasets show that PBTA achieves a 7.2 times speedup over the fastest encrypted baseline, reduces communication rounds by three orders of magnitude, and exhibits near-linear scalability.","url":"https://doi.org/10.2139/ssrn.6915146","authors":["Shunyu Yao","Yu Chen","Jinhai Li","Shen Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-10T20:54:31Z","doi":"10.2139/ssrn.6915146","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.649Z"},{"id":"doi:10.2139/ssrn.5131469","name":"Practical Privacy-Preserving Federated Learning Based on Multiparty Homomorphic Encryption for Large-Scale Models","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5131469","authors":["Xian Qin","Xue Yang","Xiaohu Tang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-10T19:40:12Z","doi":"10.2139/ssrn.5131469","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.5120/ijca2016909826","name":"Improvised Version: Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5120/ijca2016909826","authors":["Avinash Navlani","Pallavi P"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-05-17T15:09:48Z","doi":"10.5120/ijca2016909826","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.36227/techrxiv.19315202.v5","name":"Survey on Fully Homomorphic Encryption, Theory, and Applications","source":"crossref","abstract":"This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption FHE. It consequently covers design fundamentals and security properties of FHE, and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof.","url":"https://doi.org/10.36227/techrxiv.19315202.v5","authors":["Chiara Marcolla","Victor Sucasas","Marc Manzano","Riccardo Bassoli","Frank H.P. Fitzek","Najwa Aaraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-10T10:44:53Z","doi":"10.36227/techrxiv.19315202.v5","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1049/pbcs066e_ch11","name":"Accelerating homomorphic encryption in hardware: a review","source":"crossref","abstract":"","url":"https://doi.org/10.1049/pbcs066e_ch11","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-10-20T08:10:35Z","doi":"10.1049/pbcs066e_ch11","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.36227/techrxiv.19315202","name":"Survey on Fully Homomorphic Encryption, Theory, and Applications","source":"crossref","abstract":"This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption FHE. It consequently covers design fundamentals and security properties of FHE, and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof.","url":"https://doi.org/10.36227/techrxiv.19315202","authors":["Chiara Marcolla","Victor Sucasas","Marc Manzano","Riccardo Bassoli","Frank H.P. Fitzek","Najwa Aaraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-11T03:38:51Z","doi":"10.36227/techrxiv.19315202","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1002/eng2.70690/v3/review2","name":"Review for \"Data Clustering Method for Fault-tolerant Privacy Protection of Smart Grid Based on BGN Homomorphic Encryption Algorithm\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70690/v3/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T21:06:30Z","doi":"10.1002/eng2.70690/v3/review2","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/icip61757.2026.11630355","name":"A Steganographic Approach Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icip61757.2026.11630355","authors":["Norman Hutte","William Puech"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-13T19:15:06Z","doi":"10.1109/icip61757.2026.11630355","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.649Z"},{"id":"doi:10.1109/imccc.2016.201","name":"Searchable Encryption Scheme on the Cloud via Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/imccc.2016.201","authors":["Jian Liu","Jing-Li Han","Zhao-Li Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-12-08T16:31:59Z","doi":"10.1109/imccc.2016.201","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/ccisp63826.2024.10765563","name":"Secure Multi-Party Computation on the Encrypted Network: a Lattice-based Multi-Party Homomorphic Encryption Protocol","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccisp63826.2024.10765563","authors":["Yikuan Liang","Yao Hao","Kaijun Wu","Yuxiang Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T19:53:34Z","doi":"10.1109/ccisp63826.2024.10765563","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.64643/ijirtv12i7-191734-459","name":"Privacy-Preserving Computation Using Homomorphic Encryption in Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.64643/ijirtv12i7-191734-459","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-30T09:45:41Z","doi":"10.64643/ijirtv12i7-191734-459","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.649Z"},{"id":"doi:10.1145/3576915.3623176","name":"Asymptotically Faster Multi-Key Homomorphic Encryption from Homomorphic Gadget Decomposition","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3576915.3623176","authors":["Taechan Kim","Hyesun Kwak","Dongwon Lee","Jinyeong Seo","Yongsoo Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-21T12:35:13Z","doi":"10.1145/3576915.3623176","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/icteed.2017.8585710","name":"Enhancing Efficiency for ABE Through Homomorphic Encryption in Cryptography","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icteed.2017.8585710","authors":["Shraddha Shelar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-12-24T18:50:20Z","doi":"10.1109/icteed.2017.8585710","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.3837/tiis.2018.01.024","name":"Fully Homomorphic Encryption Based On the Parallel Computing","source":"crossref","abstract":"","url":"https://doi.org/10.3837/tiis.2018.01.024","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-02-13T00:19:55Z","doi":"10.3837/tiis.2018.01.024","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.21203/rs.3.rs-2018739/v1","name":"A Survey on Implementations of Homomorphic Encryption Schemes","source":"crossref","abstract":"Abstract With the increased need for data confidentiality in various applications of our daily life, homomorphic encryption (HE) has emerged as a promising cryptographic topic. HE enables to perform computations directly on encrypted data (ciphertexts) without decryption in advance. Since the results of calculations remain encrypted and can only be decrypted by the data owner, confidentiality is guaranteed and any third party can operate on ciphertexts without access to decrypted data (plaintexts). Applying a homomorphic cryptosystem in a real-world application depends on its resource efficiency. Several works compared different HE schemes and gave the stakes of this research field. However, the existing works either do not deal with recently proposed HE schemes (such as CKKS) or focus only on one type of HE. In this paper, we conduct an extensive comparison and evaluation of homomorphic cryptosystems’ performance based on their experimental results. The study covers all three families of HE, including several notable schemes such as BFV, BGV, CKKS, RSA, El-Gamal, and Paillier, as well as their implementation specification in widely used HE libraries, namely Microsoft SEAL, PALISADE, and HElib. In addition, we also discuss the resilience of HE schemes to different kind of attacks such as Indistinguishability under chosen-plaintext attack and integer factorization attacks on classical and quantum computers.","url":"https://doi.org/10.21203/rs.3.rs-2018739/v1","authors":["Thi Van Thao Doan","Mohamed-Lamine Messai","Gérald Gavin","Jérôme Darmon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-06T14:30:00Z","doi":"10.21203/rs.3.rs-2018739/v1","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.36227/techrxiv.19315202.v3","name":"Survey on Fully Homomorphic Encryption, Theory and Applications","source":"crossref","abstract":"This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption FHE. It consequently covers design fundamentals and security properties of FHE, and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof.","url":"https://doi.org/10.36227/techrxiv.19315202.v3","authors":["Chiara Marcolla","Victor Sucasas","Marc Manzano","Riccardo Bassoli","Frank H.P. Fitzek","Najwa Aaraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-13T23:41:11Z","doi":"10.36227/techrxiv.19315202.v3","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/vtc2024-fall63153.2024.10757744","name":"Towards Secure AI-empowered Vehicular Networks: A Federated Learning Approach using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/vtc2024-fall63153.2024.10757744","authors":["Chi-Hieu Nguyen","Bui Duc Manh","Dinh Thai Hoang","Diep N. Nguyen","Eryk Dutkiewicz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T19:44:30Z","doi":"10.1109/vtc2024-fall63153.2024.10757744","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/vtc2024-fall63153.2024.10757635","name":"Homomorphic Encryption-Enabled Federated Learning for Privacy-Preserving Intrusion Detection in Resource-Constrained IoV Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/vtc2024-fall63153.2024.10757635","authors":["Bui Duc Manh","Chi-Hieu Nguyen","Dinh Thai Hoang","Diep N. Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T14:44:30Z","doi":"10.1109/vtc2024-fall63153.2024.10757635","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/icws62655.2024.00061","name":"Privacy-Preserving Artificial Intelligence on Edge Devices: A Homomorphic Encryption Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icws62655.2024.00061","authors":["Muhammad Jahanzeb Khan","Bo Fang","Gaetano Cimino","Stefano Cirillo","Lei Yang","Dongfang Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-15T17:19:18Z","doi":"10.1109/icws62655.2024.00061","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/icicat62666.2024.10923462","name":"Privacy-Preserving Data Aggregation in IoT Networks Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicat62666.2024.10923462","authors":["Ammar H. Shnain","P. Sruthi","S. Subburam","Rohit Agarwal","M. Naga Kavya Kalyani","Mohammad Jeelani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-21T19:02:30Z","doi":"10.1109/icicat62666.2024.10923462","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.55248/gengpi.07.0526.d13242","name":"LLM-Orchestrated Clinical Decision Platforms Integrating HL7-FHIR Interoperability with Homomorphic Encryption for Distributed Healthcare Analytics Security","source":"crossref","abstract":"","url":"https://doi.org/10.55248/gengpi.07.0526.d13242","authors":["Tochukwu Kennedy Njoku"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-28T16:56:03Z","doi":"10.55248/gengpi.07.0526.d13242","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.649Z"},{"id":"doi:10.1051/sands/2024012","name":"Recent advances of privacy-preserving machine learning based on (Fully) Homomorphic Encryption","source":"crossref","abstract":"Fully Homomorphic Encryption (FHE), known for its ability to process encrypted data without decryption, is a promising technique for solving privacy concerns in the machine learning era. However, there are many kinds of available FHE schemes and way more FHE-based solutions in the literature, and they are still fast evolving, making it difficult to get a complete view. This article aims to introduce recent representative results of FHE-based privacy-preserving machine learning, helping users understand the pros and cons of different kinds of solutions, and choose an appropriate approach for their needs.","url":"https://doi.org/10.1051/sands/2024012","authors":["Cheng Hong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-10T19:07:48Z","doi":"10.1051/sands/2024012","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.2139/ssrn.6074303","name":"Measurement-based quantum network coding with quantum homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.6074303","authors":["Zhenzhen Li","Wen-Ling Yang","Ming-Kui Liu","Zi-Chen Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-26T16:03:42Z","doi":"10.2139/ssrn.6074303","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.649Z"},{"id":"doi:10.1109/access.2024.3461729","name":"Research on Noise Management Technology for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2024.3461729","authors":["Lifang Bai","Lijuan Bai","Yongjun Li","Zecun Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-16T18:06:33Z","doi":"10.1109/access.2024.3461729","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1007/s11227-024-05981-6","name":"Retraction Note: Encryption scheme with mixed homomorphic signature based on message authentication for digital image","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s11227-024-05981-6","authors":["Jing Yang","Mingyu Fan","Guangwei Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-12T06:02:18Z","doi":"10.1007/s11227-024-05981-6","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/srds64841.2024.00019","name":"Evaluating the Potential of In-Memory Processing to Accelerate Homomorphic Encryption: Practical Experience Report","source":"crossref","abstract":"","url":"https://doi.org/10.1109/srds64841.2024.00019","authors":["Mpoki Mwaisela","Joel Hari","Peterson Yuhala","Jämes Ménétrey","Pascal Felber","Valerio Schiavoni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-25T19:19:24Z","doi":"10.1109/srds64841.2024.00019","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/icraset63057.2024.10895758","name":"Secured Homomorphic Encryption and Authentication for Healthcare","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icraset63057.2024.10895758","authors":["Swapnil Sharma","Uditaparna Sarmah","Vineet M. Dodamani","K. Vikas Rai","H. N. Ramachandra","K. S. Shivaprakasha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-28T18:38:44Z","doi":"10.1109/icraset63057.2024.10895758","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.33103/uot.ijccce.21.4.3","name":"Image Encryption Paillier Homomorphic Cryptosystem","source":"crossref","abstract":"With the increasing use of media in communications, both academia and industry pay attention to the content security of digital images. This research presents a Homomorphic cryptosystem-based asymmetric picture encryption technique (Paillier). The algorithm is used for securing images that transmit over public unsecured channels. The Homomorphic property is used in this paper, which is comprised of three steps: key generation, encryption, and decryption. To realize such approach, the encryption cryptosystem must support additional operation over encrypted data. This cryptosystem can be effective in protecting images and supporting the construction of programs that can process encrypted input and produce encrypted output. Index Terms —Homomorphic, Partiall HE, Paillier Homomorphic, Encryption.","url":"https://doi.org/10.33103/uot.ijccce.21.4.3","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-10-18T02:43:31Z","doi":"10.33103/uot.ijccce.21.4.3","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-3-031-64529-7_5","name":"Attacking a Levelled Fully Homomorphic Encryption System with Topological Data Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-64529-7_5","authors":["Aaruni Kaushik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-16T15:21:35Z","doi":"10.1007/978-3-031-64529-7_5","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.5220/0010378801230131","name":"Cloud-based Private Querying of Databases by Means of Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0010378801230131","authors":["Yassine Abbar","Pascal Aubry","Thierno Barry","Sergiu Carpov","Sayanta Mallick","Mariem Krichen","Damien Ligier","Sergey Shpak","Renaud Sirdey"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-04-30T08:08:43Z","doi":"10.5220/0010378801230131","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.24128/ijraer.2017.xy67mn","name":"Commendation Generation by Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.24128/ijraer.2017.xy67mn","authors":["Tejas Prajapati"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-04-06T06:22:21Z","doi":"10.24128/ijraer.2017.xy67mn","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.36227/techrxiv.19315202.v1","name":"Survey on Fully Homomorphic Encryption, Theory and Applications","source":"crossref","abstract":"This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption FHE. It consequently covers design fundamentals and security properties of FHE, and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof.","url":"https://doi.org/10.36227/techrxiv.19315202.v1","authors":["Chiara Marcolla","Victor Sucasas","Marc Manzano","Riccardo Bassoli","Frank H.P. Fitzek","Najwa Aaraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-10T22:38:51Z","doi":"10.36227/techrxiv.19315202.v1","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.36227/techrxiv.19315202.v4","name":"Survey on Fully Homomorphic Encryption, Theory and Applications","source":"crossref","abstract":"This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption FHE. It consequently covers design fundamentals and security properties of FHE, and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof.","url":"https://doi.org/10.36227/techrxiv.19315202.v4","authors":["Chiara Marcolla","Victor Sucasas","Marc Manzano","Riccardo Bassoli","Frank H.P. Fitzek","Najwa Aaraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-12T10:06:01Z","doi":"10.36227/techrxiv.19315202.v4","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.34198/ejms.14424.631653","name":"Improving Security on Election Results Data Transmission via Cloud Using Hybrid Homomorphic Encryption","source":"crossref","abstract":"Elections in recent years have become topical issues and characterized by violence and conflicts leading to loss of lives and properties. The integrity and confidentiality of the declared collated results have been questioned by individuals and organisations with keen interest in the outcomes of every election. Different countries have adopted different Election Results Management Systems (RMS) to help present a credible, fair and transparent election results. These systems adopted are not without criticisms and suspicions. This research paper has presented various factors that need to be considered when selecting a Results Management System (RMS) for elections. A cloud-based using a Hybrid homomorphic encryption approach is proposed in managing the election results data security and transmission. The proposed scheme has demonstrated effectiveness in handling data integrity, data confidentiality, data privacy and access control. The proposed scheme presented has enhanced the security of election results data against Chosen Ciphetext Attacks (CCA) and Denial of Service (DoS) as well as other cyber related attacks. The time required for the entire election data encryption, transmission, decryption, upload time and download time has been greatly enhanced with the proposed system. The proposed system workflow algorithm, key generation algorithm, encryption algorithm and decryption have been presented in this research paper. The outcome of the research work indicates that only 0.00078 seconds is required to generate keys for about 100 users. About 0.705 seconds and 0.863 seconds is required for the encryption and decryption of a 500MB election results data. It was observed that the overall election results data transmission time is about 51.779 seconds which is less than one minute (60 seconds) for about 500MB data size of the election results data. This paper makes a case for the adoption and implementation of the proposed system since it performs better in terms of securing the election results data and transmission time in the cloud environment.","url":"https://doi.org/10.34198/ejms.14424.631653","authors":["Arnold Mashud Abukari","Iddrisu Zulfawu","Edem Kwedzo Bankas","Issah Gibrilla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-29T17:10:49Z","doi":"10.34198/ejms.14424.631653","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/sp40001.2021.00068","name":"SoK: Fully Homomorphic Encryption Compilers","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp40001.2021.00068","authors":["Alexander Viand","Patrick Jattke","Anwar Hithnawi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-26T17:03:31Z","doi":"10.1109/sp40001.2021.00068","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1002/eng2.70690/v3/review1","name":"Review for \"Data Clustering Method for Fault-tolerant Privacy Protection of Smart Grid Based on BGN Homomorphic Encryption Algorithm\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70690/v3/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T21:06:30Z","doi":"10.1002/eng2.70690/v3/review1","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.2139/ssrn.6946089","name":"PromptCOVER: Prompt Copyright Protection with Value Differentiation by Homomorphic Encryption and Watermarking","source":"crossref","abstract":"Large language models(LLMs) have democratized visual content creation through prompts, yet expose critical copyright vulnerabilities in emerging prompt marketplaces.Professionally engineered prompts face unauthorized leakage risks during transactions, threatening the economic model of prompt-driven ecosystems.Current platforms lack systematic protections against such intellectual property breaches, jeopardizing creator revenue and marketplace integrity.To address this issue, we propose PromptCOVER, a prompt copyright protection framework designed to protect the intellectual property rights of prompt engineers and maintain the business model of prompt marketplaces.The framework includes two solutions tailored to prompts of different value.First, we adopt an encryption-based solution to ensure the secure bidirectional transmission of high-value prompts between users and models while maintaining task efficiency.Second, for general-value prompts, we propose a watermark-based solution that leverages synonym substitution to enhance model adaptability while preserving the user experience.We conducted extensive experiments on public and self-collected datasets using various LLMs, demonstrating the diverse capabilities of PromptCOVER.Compared to existing solutions, PromptCOVER improves performance in areas like encryption computation speed and watermark invisibility.These works bridge the gap between advanced AI security theory and deployable marketplace solutions, providing a scalable, efficient, and commercially viable engineering system for intellectual property protection.","url":"https://doi.org/10.2139/ssrn.6946089","authors":["Ruijie Sun","Peng Li","Xin Zheng","Yumiao Zheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-15T18:41:34Z","doi":"10.2139/ssrn.6946089","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:45.649Z"},{"id":"doi:10.1016/j.procs.2013.09.310","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2013.09.310","authors":["Monique Ogburn","Claude Turner","Pushkar Dahal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-11-13T22:16:44Z","doi":"10.1016/j.procs.2013.09.310","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1002/eng2.70690/v1/review1","name":"Review for \"Data Clustering Method for Fault-tolerant Privacy Protection of Smart Grid Based on BGN Homomorphic Encryption Algorithm\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70690/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T21:06:30Z","doi":"10.1002/eng2.70690/v1/review1","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/focs.2018.00039","name":"Classical Homomorphic Encryption for Quantum Circuits","source":"crossref","abstract":"","url":"https://doi.org/10.1109/focs.2018.00039","authors":["Urmila Mahadev"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-12-04T01:21:43Z","doi":"10.1109/focs.2018.00039","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1002/eng2.70690/v2/review2","name":"Review for \"Data Clustering Method for Fault-tolerant Privacy Protection of Smart Grid Based on BGN Homomorphic Encryption Algorithm\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70690/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T21:06:30Z","doi":"10.1002/eng2.70690/v2/review2","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.3390/app14104047","name":"Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL","source":"crossref","abstract":"In the context of the increasing integration of Internet of Things technologies and the growing importance of data lakes, the need for robust cybersecurity measures to protect privacy without compromising data utility becomes key. Aiming to address the privacy–security challenge in such digital ecosystems, this study explores the application of Fully Homomorphic Encryption (FHE) using the Microsoft SEAL library. FHE allows for operations on encrypted data, offering a promising opportunity for maintaining data confidentiality during processing. Our research employs systematic experimental tests on datasets to evaluate the performance of homomorphic encryption in terms of CPU usage and execution time, executed across traditional PC configurations and a NVIDIA Jetson Nano device to assess the scalability and practicality of FHE in edge computing. The results reveal a performance disparity between computing environments, with the PC showing stable performance and the Jetson Nano revealing the limitations of edge devices in handling encryption tasks due to computational and memory constraints.","url":"https://doi.org/10.3390/app14104047","authors":["Francisco-Jose Valera-Rodriguez","Pilar Manzanares-Lopez","Maria-Dolores Cano"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-10T03:21:04Z","doi":"10.3390/app14104047","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/dasc64200.2024.00013","name":"A Pervasive, Efficient and Private Future: Realizing Privacy-Preserving Machine Learning Through Hybrid Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/dasc64200.2024.00013","authors":["Khoa Nguyen","Mindaugas Budzys","Eugene Frimpong","Tanveer Khan","Antonis Michalas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-17T19:10:12Z","doi":"10.1109/dasc64200.2024.00013","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1002/eng2.70690/v2/review1","name":"Review for \"Data Clustering Method for Fault-tolerant Privacy Protection of Smart Grid Based on BGN Homomorphic Encryption Algorithm\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70690/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T21:06:30Z","doi":"10.1002/eng2.70690/v2/review1","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.36227/techrxiv.19315202.v2","name":"Survey on Fully Homomorphic Encryption, Theory and Applications","source":"crossref","abstract":"This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption FHE. It consequently covers design fundamentals and security properties of FHE, and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof.","url":"https://doi.org/10.36227/techrxiv.19315202.v2","authors":["Chiara Marcolla","Victor Sucasas","Marc Manzano","Riccardo Bassoli","Frank H.P. Fitzek","Najwa Aaraj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-16T23:05:47Z","doi":"10.36227/techrxiv.19315202.v2","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.24128/ijraer.2017.yz01wx","name":"Protection Cloud via Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.24128/ijraer.2017.yz01wx","authors":["Chirag Ankolekar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-04-06T06:22:21Z","doi":"10.24128/ijraer.2017.yz01wx","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/icdsns58469.2023.10245348","name":"Secure Encryption Scheme for Medical Data based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icdsns58469.2023.10245348","authors":["Liaoran Xu","Chenyang Zhao","Weili Jiang","Jun Ye","Yan Zhao","Zhengqi Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-21T13:29:33Z","doi":"10.1109/icdsns58469.2023.10245348","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/vlsi-soc62099.2024.10767785","name":"Design Co-Processor Based on Partially Homomorphic Encryption Execution Using Open-Source Tool","source":"crossref","abstract":"","url":"https://doi.org/10.1109/vlsi-soc62099.2024.10767785","authors":["Mujahid Bilal","M. Kamran Bhatti","Muhammad Kahsif Minhas","Haroon Waris"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-03T18:53:02Z","doi":"10.1109/vlsi-soc62099.2024.10767785","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.5220/0010190000360047","name":"Reduction in Communication via Image Selection for Homomorphic Encryption-based Privacy-protected Person Re-identification","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0010190000360047","authors":["Shogo Fukuda","Masashi Nishiyama","Yoshio Iwai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-02-17T17:50:50Z","doi":"10.5220/0010190000360047","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.2139/ssrn.5319620","name":"Practical Privacy-Preserving Federated Learning Based on Multiparty Homomorphic Encryption for Large-Scale Models","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5319620","authors":["Xian Qin","Xue Yang","Xiaohu Tang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-25T12:40:00Z","doi":"10.2139/ssrn.5319620","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1145/3656019.3676893","name":"BoostCom: Towards Efficient Universal Fully Homomorphic Encryption by Boosting the Word-wise Comparisons","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3656019.3676893","authors":["Ardhi Wiratama Baskara Yudha","Jiaqi Xue","Qian Lou","Huiyang Zhou","Yan Solihin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-11T10:34:08Z","doi":"10.1145/3656019.3676893","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/meco49872.2020.9134331","name":"WTFHE: neural-netWork-ready Torus Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/meco49872.2020.9134331","authors":["Jakub Klemsa","Martin Novotny"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-07-07T20:30:08Z","doi":"10.1109/meco49872.2020.9134331","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/cw.2019.00060","name":"A Practical Use Case of Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cw.2019.00060","authors":["Amina Bel Korchi","Nadia El Mrabet"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-12-06T07:18:16Z","doi":"10.1109/cw.2019.00060","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.3390/app14020718","name":"Preliminary Experiments of a Real-World Authentication Mechanism Based on Facial Recognition and Fully Homomorphic Encryption","source":"crossref","abstract":"In the current context in which user authentication is the first line of defense against emerging attacks and can be considered a defining element of any security infrastructure, the need to adopt alternative, non-invasive, contactless, and scalable authentication mechanisms is mandatory. This paper presents initial research on the design, implementation, and evaluation of a multi-factor authentication mechanism that combines facial recognition with a fully homomorphic encryption algorithm. The goal is to minimize the risk of unauthorized access and uphold user confidentiality and integrity. The proposed device is implemented on the latest version of the Raspberry Pi and Arduino ESP 32 modules, which are wirelessly connected to the computer system. Additionally, a comprehensive evaluation, utilizing various statistical parameters, demonstrates the performance, the limitations of the encryption algorithms proposed to secure the biometric database, and also the security implications over the system resources. The research results illustrate that the Brakerski–Gentry–Vaikuntanathan algorithm can achieve higher performance and efficiency when compared to the Brakerski–Fan–Vercauteren algorithm, and proved to be the best alternative for the designed mechanism because it effectively enhances the level of security in computer systems, showing promise for deployment and seamless integration into real-world scenarios of network architectures.","url":"https://doi.org/10.3390/app14020718","authors":["Georgiana Crihan","Luminița Dumitriu","Marian Viorel Crăciun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-15T08:52:03Z","doi":"10.3390/app14020718","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.17485/ijst/v15i8.44","name":"Homomorphic Encryption Based Privacy Protection for Personalised Web Search","source":"crossref","abstract":"","url":"https://doi.org/10.17485/ijst/v15i8.44","authors":["Krishan Kumar","Mukesh Kumar Gupta","Nishant Saxena","Vivek Jaglan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-03-09T15:42:10Z","doi":"10.17485/ijst/v15i8.44","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-3-319-22915-7_27","name":"Securing Database Server Using Homomorphic Encryption and Re-encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-22915-7_27","authors":["Sarath Greeshma","R. Jayapriya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-06T22:14:54Z","doi":"10.1007/978-3-319-22915-7_27","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/icpics62053.2024.10795880","name":"Power Data Federated Learning Privacy Policy Based on Optimized Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icpics62053.2024.10795880","authors":["Yiying Zhang","Wenjie Lu","Qianao Yu","Suxiang Zhang","Wenjing Li","Kun Liang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-24T19:10:01Z","doi":"10.1109/icpics62053.2024.10795880","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1007/978-3-319-12229-8","name":"Homomorphic Encryption and Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-12229-8","authors":["Xun Yi","Russell Paulet","Elisa Bertino"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-11-14T09:46:09Z","doi":"10.1007/978-3-319-12229-8","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/s00500-024-09980-w","name":"Retraction Note: securing medical data by role-based user policy with partially homomorphic encryption in AWS cloud","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00500-024-09980-w","authors":["M. D. Boomija","S. V. Kasmir Raja"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-15T13:01:56Z","doi":"10.1007/s00500-024-09980-w","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.46586/tches.v2024.i2.451-480","name":"CASA: A Compact and Scalable Accelerator for Approximate Homomorphic Encryption","source":"crossref","abstract":"Approximate arithmetic-based homomorphic encryption (HE) scheme CKKS [CKKS17] is arguably the most suitable one for real-world data-privacy applications due to its wider computation range than other HE schemes such as BGV [BGV14], FV and BFV [Bra12, FV12]. However, the most crucial homomorphic operation of CKKS called key-switching induces a great amount of computational burden in actual deployment situations, and creates scalability challenges for hardware acceleration. In this paper, we present a novel Compact And Scalable Accelerator (CASA) for CKKS on the field-programmable gate array (FPGA) platform. The proposed CASA addresses the aforementioned computational and scalability challenges in homomorphic operations, including key-exchange, homomorphic multiplication, homomorphic addition, and rescaling.On the architecture layer, we propose a new design methodology for efficient acceleration of CKKS. We design this novel hardware architecture by carefully studying the homomorphic operation patterns and data dependency amongst the primitive oracles. The homomorphic operations are efficiently mapped into an accelerator with simple control and smooth operation, which brings benefits for scalable implementation and enhanced pipeline and parallel processing (even with the potential for further improvement).On the component layer, we carry out a detailed and extensive study and present novel micro-architectures for primitive function modules, including memory bank, number theoretic transform (NTT) module, modulus switching bank, and dyadic multiplication and accumulation.On the arithmetic layer, we develop a new partially reduction-free modular arithmetic technique to eliminate part of the reduction cost over different prime moduli within the moduli chain of the Residue Number System (RNS). The proposed structure can support arbitrary numbers of security primes of CKKS during key exchange, which offers better security options for adopting the scalable design methodology.As a proof-of-concept, we implement CASA on the FPGA platform and compare it with state-of-the-art designs. The implementation results showcase the superior performance of the proposed CASA in many aspects such as compact area, scalable architecture, and overall better area-time complexities.In particular, we successfully implement CASA on a mainstream resource-constrained Artix-7 FPGA. To the authors’ best knowledge, this is the first compact CKKS accelerator implemented on an Artix-7 device, e.g., CASA achieves a 10.8x speedup compared with the state-of-the-art CPU implementations (with power consumption of only 5.8%). Considering the power-delay product metric, CASA also achieves 138x and 105x improvement compared with the recent GPU implementation.","url":"https://doi.org/10.46586/tches.v2024.i2.451-480","authors":["Pengzhou He","Samira Carolina Oliva Madrigal","Çetin Kaya Koç","Tianyou Bao","Jiafeng Xie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-13T06:40:41Z","doi":"10.46586/tches.v2024.i2.451-480","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1109/icscai61790.2024.10866588","name":"Utilization of Homomorphic Encryption (HE) for Maintaining Confidentiality of Data in Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icscai61790.2024.10866588","authors":["Riya Kukreti","M. S. Nidhya","Raman Batra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-13T18:29:13Z","doi":"10.1109/icscai61790.2024.10866588","addedAt":"2026-08-31T06:41:41.167Z","updatedAt":"2026-08-31T06:41:41.167Z"},{"id":"doi:10.1016/j.jisa.2025.104048","name":"Fully homomorphic encryption-based optimal key encryption for privacy preservation in the cloud sector","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jisa.2025.104048","authors":["Sonam Mittal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-16T05:56:11Z","doi":"10.1016/j.jisa.2025.104048","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.11591/ijeecs.v34.i3.pp1989-1998","name":"Development Paillier's library of fully homomorphic encryption","source":"crossref","abstract":"One of the new areas of cryptography considered-homomorphic cryptography. The article presents the main areas of application of homomorphic encryption. An analysis of existing developments in the field of homomorphic encryption carried out. The analysis showed that existing library implementations only allow processing bits or arrays of bits and do not support division and subtraction operations. However, to solve applied problems, support for performing integer operations are necessary. Because of the analysis, the need to implement the homomorphic division and subtraction operations identified, as well as the relevance of developing our own implementation of a homomorphic encryption library over integers. The ability to perform four operations (addition, difference, multiplication and division) on encrypted data will expand the areas of application of homomorphic encryption. A homomorphic division and subtraction methods proposed that allows the division operation performed on homomorphically encrypted data. An architecture for a library of fully homomorphic operations on integers is proposed. The library supports basic homomorphic operations on integers, as well as homomorphic division method. The article also provides measurements of the time required to perform certain operations on encrypted data and analyzes the efficiency of the developed implementation of the library.","url":"https://doi.org/10.11591/ijeecs.v34.i3.pp1989-1998","authors":["Temirbekova Zhanerke Erlanovna","Tynymbayev Sakhybay","Abdiakhmetova Zukhra Muratovna","Turken Gulzat"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-05T16:27:31Z","doi":"10.11591/ijeecs.v34.i3.pp1989-1998","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.1016/j.future.2024.07.015","name":"Integrating fully homomorphic encryption to enhance the security of blockchain applications","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.future.2024.07.015","authors":["Xiaohua Wu","Jing Wang","Tingbo Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-16T19:54:42Z","doi":"10.1016/j.future.2024.07.015","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.52783/jisem.v10i26s.4254","name":"A Novel Lightweight Encryption Model for IoT Healthcare Data Security at Fog Layer: A Hybrid Approach Using Attribute-Based Encryption and Homomorphic Encryption","source":"crossref","abstract":"The quality of people's lives has risen because of the Internet of Things (IoT), as it connects billions of things worldwide. New methods in order to analyse patient data in the healthcare industry have been developed as a result of IoT development and innovation. Although having a crucial function in the transfer of medical data, the Internet of Things (IoT) also raises security risks to the health data, that is particularly unique to a patient, is required for remote medical treatment. Current technologies for analysing and transforming patient data involve cloud and IoT-based platforms. When processing incredibly large amounts of data, cloud computing encounters network usage and latency issues. Fog layers have been used to improve the capabilities of IoT-based healthcare systems, and they have proven valuable by offering quick response times and low latency.However,Such a trend is making it extremely difficult to protect users' privacy, which goes some way towards resolving security and privacy concerns.This article introduces a fog assisted framework to secure IoT driven healthcare systems.It presents a hybrid encryption model for securing IoT healthcare data at the fog layer, combining modified Attribute-Based Encryption (ABE) with partial Homomorphic Encryption (HE). Our approach addresses the key challenges of data security, privacy, and computational efficiency in IoT healthcare systems. The proposed model demonstrates significant improvements in processing time (37% faster), energy consumption (45% reduction), and security strength compared to existing solutions. Experimental results show that our hybrid approach achieves optimal performance for resource-constrained IoT devices while maintaining robust security standards.","url":"https://doi.org/10.52783/jisem.v10i26s.4254","authors":["Vaishali Hitesh Patel, Sanjay G. Patel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-03T11:21:08Z","doi":"10.52783/jisem.v10i26s.4254","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.51983/ijiss-2024.14.2.03","name":"Federated Learning with Homomorphic Encryption for Ensuring Privacy in Medical Data","source":"crossref","abstract":"Federated Learning (FL) is a machine learning methodology that allows remote devices to collectively train a learning system without sharing their data. FL-based methods provide enhanced secure privacy by transmitting only localized model variables, learned with local information, from dispersed devices to a centralized controller. There is a potential for a centralized server or malicious individuals to deduce or get sensitive private data by analyzing the structure and variables of regional learning networks. This study incorporates the FL process into the deep learning process of medical prototypes in an Internet of Things (IoT)-based medical facility. A Secured Medical Homomorphic Encryption Algorithm (SMHEA) is proposed in this research to ensure medical data privacy. Cryptographic primitives, such as masking and homomorphic cryptography, are used to enhance the security of local modeling. This prevents adversaries from deducing confidential health information via assaults like model restoration or modeling inversion. The primary determinant for assessing the regional modeling's contributions to the universal model during each training stage is the quality of the databases possessed by various individuals rather than the typically used metric of database dimension in deep learning. A dropout-tolerant approach is suggested, where the FL procedure would continue as long as the total amount of online customers remains over a certain level. By doing a security evaluation, it is evident that the suggested approach effectively ensures data privacy. Theoretical analysis is conducted on accuracy, computation time, and communication error. An example of clinical applications is the categorization of skin lesions using training photos from the HAM10000 medical database. The experimental findings demonstrate that the suggested system had favorable performance and privacy preservation outcomes compared to current methods.","url":"https://doi.org/10.51983/ijiss-2024.14.2.03","authors":["Dr.R. Mohandas","Dr.S. Veena","G. Kirubasri","I. Thusnavis Bella Mary","Dr.R. Udayakumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-17T03:05:19Z","doi":"10.51983/ijiss-2024.14.2.03","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.1007/978-981-97-3242-5_40","name":"Privacy-Preserving Chaotic Extreme Learning Machine with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-3242-5_40","authors":["Syed Imtiaz Ahamed","Vadlamani Ravi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-22T16:02:39Z","doi":"10.1007/978-981-97-3242-5_40","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.1109/icicat62666.2024.10923263","name":"Enhancing Cloud Storage Security with Homomorphic Encryption and ML-Based Access Control","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicat62666.2024.10923263","authors":["Ammar H. Shnain","Neeraj Varshney","Sana Afreen","M. Suganya","Bulipe Sankara Babu","T. Aravind"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-21T15:02:30Z","doi":"10.1109/icicat62666.2024.10923263","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.24321/2456.1428.202513","name":"Leveraging Heuristic Approaches to Optimize Lattice-Based  Homomorphic  Encryption  via  Approximate Shortest Vector Problem Solutions","source":"crossref","abstract":"","url":"https://doi.org/10.24321/2456.1428.202513","authors":["Lisa Biswas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-12T13:55:32Z","doi":"10.24321/2456.1428.202513","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/issa.2015.7335058","name":"Data aggregation using homomorphic encryption in wireless sensor networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/issa.2015.7335058","authors":["T.D. Ramotsoela","G.P. Hancke"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-11-23T22:50:11Z","doi":"10.1109/issa.2015.7335058","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/cdc.2018.8619120","name":"Privary Preserving Distributed Average Consensus via Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cdc.2018.8619120","authors":["Christoforos N. Hadjicostis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-01-24T00:12:50Z","doi":"10.1109/cdc.2018.8619120","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.5626/jok.2024.51.3.203","name":"Homomorphic Encryption-Based Support Computation for Privacy-Preserving Association Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.5626/jok.2024.51.3.203","authors":["Yunsoo Park","Lynin Sokhonn","Munkyu Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-25T03:11:12Z","doi":"10.5626/jok.2024.51.3.203","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.1007/978-981-97-4496-1_6","name":"Fully Homomorphic Encryption for Embedded Systems: IP Core Design and Implementation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-4496-1_6","authors":["Amandeep Kaur","Nagendra Gajjar","Vijay Savani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-30T08:03:49Z","doi":"10.1007/978-981-97-4496-1_6","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:41.168Z"},{"id":"doi:10.36227/techrxiv.175339394.47092872/v1","name":"QuHE: Optimizing Utility-Cost in Quantum Key Distribution and Homomorphic Encryption Enabled Secure Edge Computing Networks","source":"crossref","abstract":"Ensuring secure and efficient data processing in mobile edge computing (MEC) systems is a critical challenge. While quantum key distribution (QKD) offers unconditionally secure key exchange and homomorphic encryption (HE) enables privacy-preserving data processing, existing research fails to address the comprehensive trade-offs among QKD utility, HE security, and system costs. This paper proposes a novel framework integrating QKD, transciphering, and HE for secure and efficient MEC. QKD distributes symmetric keys, transciphering bridges symmetric encryption, and HE processes encrypted data at the server. We formulate an optimization problem balancing QKD utility, HE security, processing and wireless transmission costs. However, the formulated optimization is non-convex and NPhard. To solve it efficiently, we propose the Quantum-enhanced Homomorphic Encryption resource allocation (QuHE) algorithm. Theoretical analysis proves the proposed QuHE algorithm's convergence and optimality, and simulations demonstrate its effectiveness across multiple performance metrics.","url":"https://doi.org/10.36227/techrxiv.175339394.47092872/v1","authors":["Liangxin Qian","Yang Li","Jun Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-24T21:52:34Z","doi":"10.36227/techrxiv.175339394.47092872/v1","addedAt":"2026-08-31T06:41:41.168Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/focs.2011.98","name":"Computing Blindfolded: New Developments in Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/focs.2011.98","authors":["Vinod Vaikuntanathan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-12-23T16:39:51Z","doi":"10.1109/focs.2011.98","addedAt":"2026-08-31T06:41:42.470Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.63282/3050-9246/icrtcsit-107","name":"Homomorphic Encryption for Privacy-Preserving SQL Query Processing in Financial Databases","source":"crossref","abstract":"","url":"https://doi.org/10.63282/3050-9246/icrtcsit-107","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-13T09:07:51Z","doi":"10.63282/3050-9246/icrtcsit-107","addedAt":"2026-08-31T06:41:42.470Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/ita.2014.6804228","name":"Efficient homomorphic encryption on integer vectors and its applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ita.2014.6804228","authors":["Hongchao Zhou","Gregory Wornell"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-05-06T00:57:08Z","doi":"10.1109/ita.2014.6804228","addedAt":"2026-08-31T06:41:42.470Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.5120/ijca2018916341","name":"Cloud Data Security using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5120/ijca2018916341","authors":["Sohit Simon","Khusboo Sawant"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-02-16T07:43:23Z","doi":"10.5120/ijca2018916341","addedAt":"2026-08-31T06:41:42.470Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/iscc50000.2020.9219588","name":"Efficient Cloud-based Secret Shuffling via Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscc50000.2020.9219588","authors":["Kilian Becher","Thorsten Strufe"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-10-12T21:03:51Z","doi":"10.1109/iscc50000.2020.9219588","addedAt":"2026-08-31T06:41:42.470Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1002/eng2.70690/v2/decision1","name":"Decision letter for \"Data Clustering Method for Fault-tolerant Privacy Protection of Smart Grid Based on BGN Homomorphic Encryption Algorithm\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70690/v2/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T21:06:30Z","doi":"10.1002/eng2.70690/v2/decision1","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/devic63749.2025.11012553","name":"Homomorphic Encryption in Quantum Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/devic63749.2025.11012553","authors":["Surojit Mondal","Bikash Debnath","Jadav Chandra Das","Debashis De"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-29T17:06:14Z","doi":"10.1109/devic63749.2025.11012553","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1145/3689945.3694805","name":"Security and Performance-Aware Cloud Computing with Homomorphic Encryption and Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3689945.3694805","authors":["Ryutaro Onishi","Takuya Suzuki","Shunta Sakai","Hayato Yamana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-19T18:23:11Z","doi":"10.1145/3689945.3694805","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/isncc.2017.8071996","name":"Dodrant-homomorphic encryption for cloud databases using table lookup","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isncc.2017.8071996","authors":["Thomas Schwarz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-10-25T19:19:48Z","doi":"10.1109/isncc.2017.8071996","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1007/s44163-026-00920-1","name":"Homomorphic encryption for secure healthcare artificial intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s44163-026-00920-1","authors":["Penelope Yanez","Nikhil Yadav"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-08T02:55:51Z","doi":"10.1007/s44163-026-00920-1","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/smartcomp.2017.7947011","name":"Fully Homomorphic Encryption for Classification in Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/smartcomp.2017.7947011","authors":["Seiko Arita","Shota Nakasato"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-06-15T18:36:09Z","doi":"10.1109/smartcomp.2017.7947011","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/ictc55196.2022.9952531","name":"Privacy-Preserving Federated Learning Using Homomorphic Encryption With Different Encryption Keys","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ictc55196.2022.9952531","authors":["Jaehyoung Park","Nam Yul Yu","Hyuk Lim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-11-25T22:41:09Z","doi":"10.1109/ictc55196.2022.9952531","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/icoin.2018.8343147","name":"Secure cloud computing algorithm using homomorphic encryption and multi-party computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icoin.2018.8343147","authors":["Debasis Das"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-04-23T19:37:19Z","doi":"10.1109/icoin.2018.8343147","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.62056/a3ksdkmol","name":"Lightweight sorting in approximate homomorphic encryption","source":"crossref","abstract":"Sorting encrypted values is an open research problem that plays a crucial role in the broader objective of providing efficient and practical privacy-preserving online services. The current state of the art work by Mazzone, Everts, Hahn and Peter [USENIX Security '25] proposes efficient algorithms for indexing and sorting based on the CKKS scheme, which deviates from the compare-and-swap paradigm, typically used by sorting networks, using a permutation-based approach. In this work, we follow up their work and explore different approaches to approximate the nonlinear functions required by the circuit. We propose simpler and concrete solutions that allow for faster computations, smaller memory requirements and higher precision. For example, our framework allows to sort 128 real elements in roughly 22 seconds, while maintaining a precision of 0.001 and requiring 3 GB of memory. Furthermore, we propose an implementation of a swap-based bitonic network that is not based on approximations of the sgn function, which scales linearly with the number of values, useful when the number of available slots is small.","url":"https://doi.org/10.62056/a3ksdkmol","authors":["Lorenzo Rovida","Alberto Leporati","Simone Basile"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-04T18:09:08Z","doi":"10.62056/a3ksdkmol","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.54254/2755-2721/2025.21090","name":"Data Privacy Protection Utilizing Homomorphic Encryption Techniques","source":"crossref","abstract":"In the current era with the rapid development of the Internet, although a variety of technologies have offered convenience to people's lives, numerous problems have arisen regarding the security of individual and collective data, Problems such as data privacy leakage occur continuously. The content of this review is data privacy protection based on homomorphic encryption. Firstly, the basic principle and formula of homomorphic encryption are briefly introduced. Then, the homomorphic encryption-based data privacy protection approach is developed in response to the importance of safeguarding data privacy. In the case of partially homomorphic encryption, the article refers to such homomorphic encryptions as PPDM, STHE, and ECC. In fully homomorphic encryption, such as DBMS, HTM-FHE, and MKFHE, are mentioned. Under the current homomorphic encryption technology, the performance of data protection has witnessed a remarkable improvement; however, there exist numerous deficiencies. The majority of algorithms are relatively complex and consume a considerable number of resources. The aim of this review lies in facilitating readers' prompt comprehension of the existing technologies and development status in this domain, as well as the merits and demerits of current technologies.","url":"https://doi.org/10.54254/2755-2721/2025.21090","authors":["Zongwei Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-21T01:52:58Z","doi":"10.54254/2755-2721/2025.21090","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/radioelek.2019.8733575","name":"A Somewhat Homomorphic Encryption Scheme based on Multivariate Polynomial Evaluation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/radioelek.2019.8733575","authors":["Uddipana Dowerah","Srinivasan Krishnaswamy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-06-11T01:18:20Z","doi":"10.1109/radioelek.2019.8733575","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.3390/electronics15112391","name":"Parameter Settings and Efficient Computation for Homomorphic Encryption in CKKS","source":"crossref","abstract":"This paper investigates the impact of modular reduction backends on the security–performance trade-offs of the CKKS approximate homomorphic encryption scheme. Specifically, we compare a standard Residue Number System–Chinese Remainder Theorem (RNS-CRT) implementation with a Barrett reduction–based backend across representative parameter sets (N,logq), spanning approximately 80–256-bit security levels as recommended by the Homomorphic Encryption Standard. We evaluate typical CKKS workloads—including encoding, encryption, homomorphic multiplication with the Number Theoretic Transform (NTT), rescaling, relinearization, and decryption—by measuring execution time and peak memory usage on a uniform experimental platform. Our results indicate that the parameter pair (N,logq) primarily determines both security level and computational cost, while the choice of backend significantly influences the trade-offs between performance and memory efficiency. In particular, the Barrett-based backend is competitive and slightly more memory-efficient at lower security levels, whereas the CRT-based approach achieves lower latency at higher security levels. However, Barrett reduction provides notable memory savings at the cost of a 2–4× increase in runtime. Based on these findings, we derive practical guidelines for selecting CKKS parameters and modular reduction backends under varying constraints on security, latency, and memory.","url":"https://doi.org/10.3390/electronics15112391","authors":["Hemanth Goganaboina","Huapeng Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-01T15:07:52Z","doi":"10.3390/electronics15112391","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1002/eng2.70690/v1/decision1","name":"Decision letter for \"Data Clustering Method for Fault-tolerant Privacy Protection of Smart Grid Based on BGN Homomorphic Encryption Algorithm\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70690/v1/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T21:06:30Z","doi":"10.1002/eng2.70690/v1/decision1","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.23919/ccc58697.2023.10241210","name":"Privacy-Preserving Average Consensus via Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.23919/ccc58697.2023.10241210","authors":["Mengmeng Qi","Fuyong Wang","Zhongxin Liu","Zengqiang Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-09-18T17:44:49Z","doi":"10.23919/ccc58697.2023.10241210","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.4018/978-1-7998-1763-5.ch017","name":"A Pairing-based Homomorphic Encryption Scheme for Multi-User Settings","source":"crossref","abstract":"A new method is presented to privately outsource computation of different users. As a significant cryptographic primitive in cloud computing, homomorphic encryption (HE) can evaluate on ciphertext directly without decryption, thus avoid information leakage. However, most of the available HE schemes are single-user, which means that they could only evaluate on ciphertexts encrypted by the same public key. Adopting the idea of proxy re-encryption, and focusing on the compatibility of computation, the authors provide a pairing-based multi-user homomorphic encryption scheme. The scheme is a somewhat homomorphic one, which can do infinite additions and one multiplication operation. Security of the scheme is based on subgroup decision problem. The authors give a concrete security model and detailed security analysis.","url":"https://doi.org/10.4018/978-1-7998-1763-5.ch017","authors":["Zhang Wei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-12-12T14:07:01Z","doi":"10.4018/978-1-7998-1763-5.ch017","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.2139/ssrn.5260884","name":"Secure and Efficient Transfer Learning for Bearing Fault Diagnosis Using Homomorphic Encryption and Coreset Selection","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5260884","authors":["Taeyoung Yu","Youngdoo Son","Junyoung Byun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-20T00:43:29Z","doi":"10.2139/ssrn.5260884","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/tmc.2025.3610887/mm1","name":"Efficient Privacy-Preserving Federated Learning via Homomorphic Encryption-enabled Over-the-Air Computation_supp1-3610887.pdf","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tmc.2025.3610887/mm1","authors":["Cheng Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-17T17:33:12Z","doi":"10.1109/tmc.2025.3610887/mm1","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.53829/ntr201407fa5","name":"Fully Homomorphic Encryption over the Integers: From Theory to Practice","source":"crossref","abstract":"","url":"https://doi.org/10.53829/ntr201407fa5","authors":["Mehdi Tibouchi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-12T22:11:53Z","doi":"10.53829/ntr201407fa5","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.4028/www.scientific.net/amr.933.687","name":"A Randomized Response Protocol Based on Homomorphic Encryption","source":"crossref","abstract":"The investigation which involves the respondents privacy is hard to ensure the information security. To rectify this problem, Based on Homomorphic Response Protocol (RRPBH), a novel protocol using homomorphic encrypted system is proposed in this paper. The RRPBH uses the Paillier encryption algorithm to encrypt respondents answer. Paillier algorithm has the good properties of additive homomorphism and mixed multiplicative homomorphism. We can effectively compute the sum of respondents answer with better privacy protection by this protocol.","url":"https://doi.org/10.4028/www.scientific.net/amr.933.687","authors":["Kun Mei Cao","Ke Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-05-23T14:53:41Z","doi":"10.4028/www.scientific.net/amr.933.687","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/melecon53508.2022.9843009","name":"Homomorphic Encryption for Privacy-Friendly Augmented Democracy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/melecon53508.2022.9843009","authors":["Matthieu Brabant","Olivier Pereira","Pierrick Meaux"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-08-03T15:34:25Z","doi":"10.1109/melecon53508.2022.9843009","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.4304/jnw.9.6.1464-1470","name":"A Weakly Homomorphic Encryption with LDN","source":"crossref","abstract":"","url":"https://doi.org/10.4304/jnw.9.6.1464-1470","authors":["Chao Feng","Yang Xin","Hongliang Zhu","Yixian Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-06-10T02:59:48Z","doi":"10.4304/jnw.9.6.1464-1470","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1002/eng2.70690/v3/decision1","name":"Decision letter for \"Data Clustering Method for Fault-tolerant Privacy Protection of Smart Grid Based on BGN Homomorphic Encryption Algorithm\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70690/v3/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T21:06:30Z","doi":"10.1002/eng2.70690/v3/decision1","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.5220/0013215300003890","name":"Predicting the State of Health of Supercapacitors Using a Federated Learning Model with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013215300003890","authors":["Víctor López","Oscar Fontenla-Romero","Elena Hernández-Pereira","Bertha Guijarro-Berdiñas","Carlos Blanco-Seijo","Samuel Fernández-Paz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-28T12:43:20Z","doi":"10.5220/0013215300003890","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.22266/ijies2023.0630.35","name":"Securing Signal Encryption Based on Reduced Round Homomorphic AES","source":"crossref","abstract":"","url":"https://doi.org/10.22266/ijies2023.0630.35","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-05-01T06:09:56Z","doi":"10.22266/ijies2023.0630.35","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1007/978-981-95-0995-9_6","name":"Homomorphic Encryption for Biometric Template Protection","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-0995-9_6","authors":["Vishnu Naresh Boddeti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T02:58:17Z","doi":"10.1007/978-981-95-0995-9_6","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.17485/ijst/2016/v9i37/87977","name":"A Novice’s Perception of Partial Homomorphic Encryption Schemes","source":"crossref","abstract":"","url":"https://doi.org/10.17485/ijst/2016/v9i37/87977","authors":["B. Selva Rani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-10-18T09:11:40Z","doi":"10.17485/ijst/2016/v9i37/87977","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.31838/jcr.07.07.110","name":"AN IMPROVED HOMOMORPHIC BASED ENCRYPTION AND DECRYPTION PROCESS ON CLOUD TEXUAL DATA","source":"crossref","abstract":"","url":"https://doi.org/10.31838/jcr.07.07.110","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-05-23T08:57:20Z","doi":"10.31838/jcr.07.07.110","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/isvlsi65124.2025.11130223","name":"PolyFHEmus: Rethinking Multiplication in Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isvlsi65124.2025.11130223","authors":["Charles Gouert","Nektarios Georgios Tsoutsos"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-27T18:20:15Z","doi":"10.1109/isvlsi65124.2025.11130223","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.36948/ijfmr.2025.v07i03.45442","name":"Homomorphic Encryption method  for Bio Medical systems","source":"crossref","abstract":"Globally accessible databanks contain diagnostic results for patients with chronic conditions such as diabetes and hypertension. These resources provide valuable insights, including susceptibility to various diseases and familial relationships. However, the sensitive nature of this data necessitates privacy, as parties may prefer not to disclose their information to one another. Therefore, a protocol or technique is essential to ensure confidentiality. We are introducing an Enhanced Homomorphic Cryptosystem designed to fulfill industry standards. In this context, we will implement our scheme alongside other homomorphic encryption methods to evaluate the data in ciphertext form, thereby safeguarding privacy. This discussion will focus on the new protocol for healthcare systems utilizing Enhanced Homomorphic Cryptosystems, while also comparing it to existing schemes. Additionally, we will analyze the computational costs associated with several homomorphic encryption methods in relation to our new approach.","url":"https://doi.org/10.36948/ijfmr.2025.v07i03.45442","authors":["Gorti Subbarao","Gorti Karthikeya","Gorti Viswaq"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-21T12:28:26Z","doi":"10.36948/ijfmr.2025.v07i03.45442","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.11591/closer.v1i4.1438","name":"More Practical Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.11591/closer.v1i4.1438","authors":["Gu Chunsheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-02-23T11:16:35Z","doi":"10.11591/closer.v1i4.1438","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/icit.2014.14","name":"Hierarchical Homomorphic Encryption Based Privacy Preserving Distributed Association Rule Mining","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icit.2014.14","authors":["Shubhra Rana","P. Santhi Thilagam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-02-11T22:19:20Z","doi":"10.1109/icit.2014.14","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.3233/jifs-221454","name":"Fully homomorphic encryption: A case study","source":"crossref","abstract":"Fully Homomorphic Encryption (FHE) is the holy grail of encrypted communications. It opens the door to several advanced functionalities to overcome the security and trust issues of the IT world. After 2009, once Craig Gentry had shown that FHE could be achieved, a study in this field boomed, and significant improvement was made in identifying more efficient and realistic programs. FHE is primitive cryptography that enables arbitrary functions to be calculated via encrypted data. These systems are applicable in different ways since they permit users to encrypt their private information securely while still outsourcing the processing of protected data without fearing disclosing the real data. In 2012, LTV12 presented the first multi-key FHE system and demonstrated the possibility of using multi-key systems in somewhat homomorphic encryption (SHE). Like in the one key context, there have been many advances in the field, but no effort has been made to develop the multi-key methods. This paper presents a discussion of FHE and MKFHE with a specific focus on the current techniques and three implementations, comprising the first in the multi-key setups, to the extent of our understanding.","url":"https://doi.org/10.3233/jifs-221454","authors":["J.S. Rauthan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-20T14:17:13Z","doi":"10.3233/jifs-221454","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1007/978-3-031-09640-2_18","name":"Application of Homomorphic Encryption in Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-09640-2_18","authors":["Yulliwas Ameur","Samia Bouzefrane","Vincent Audigier"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-11-18T08:29:41Z","doi":"10.1007/978-3-031-09640-2_18","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1007/978-3-030-77287-1_6","name":"Private Set Intersection and Compute","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1_6","authors":["Flavio Bergamaschi","Tancrède Lepoint","Peter Leihn","Sreekanth Kannepalli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-03T23:02:43Z","doi":"10.1007/978-3-030-77287-1_6","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.54097/hset.v39i.6596","name":"Federated Learning based on Homomorphic Encryption and Digital Signatures","source":"crossref","abstract":"Federated learning aims to train a centralized federated model using decentralized data sources and to ensure the security and privacy of user data during system training process. Federal learning still faces four challenges: high communication costs, system heterogeneity, data heterogeneity, and data security, The primary attacks facing federal learning include confidentiality, integrity, and usability attacks. Attackers mainly target confidential attacks, while malicious attackers target integrity attacks and usability attacks. Homomorphism encryption refers to the encryption algorithm that satisfies the nature of the homomorphism operation, that is, to the ciphertext of the data after the homomorphic encryption. This article first introduces the federated learning and its possible attacks in the process of modeling behavior, and then introduces how to use homomorphic encryption and digital signature algorithm to prevent the attack method, finally through the experimental reality of federated learning encryption and digital signature and analyzed the above operation on the performance of the federated learning system.","url":"https://doi.org/10.54097/hset.v39i.6596","authors":["Zao Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-04-06T06:48:58Z","doi":"10.54097/hset.v39i.6596","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/cnml68938.2026.11452489","name":"GPU-Accelerated Homomorphic Encryption with Optimized Performance","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cnml68938.2026.11452489","authors":["Jing Wang","Zecheng Lu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T19:49:32Z","doi":"10.1109/cnml68938.2026.11452489","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/bigdata50022.2020.9377989","name":"Practical Privacy-Preserving Data Science With Homomorphic Encryption: An Overview","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata50022.2020.9377989","authors":["Michela Iezzi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-03-19T21:10:21Z","doi":"10.1109/bigdata50022.2020.9377989","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1007/978-3-319-57048-8_5","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-57048-8_5","authors":["Shai Halevi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-04-05T16:07:18Z","doi":"10.1007/978-3-319-57048-8_5","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/isc266238.2025.11293348","name":"Privacy-Preserving Parking Lot Surveillance Utilizing Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isc266238.2025.11293348","authors":["Katharina Barlage","Andrei Aleksandrov","Philipp Lämmel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-23T18:28:12Z","doi":"10.1109/isc266238.2025.11293348","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"pmid:38104402","name":"Encrypted federated learning for secure decentralized collaboration in cancer image analysis.","source":"pubmed","abstract":"Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.","url":"https://pubmed.ncbi.nlm.nih.gov/38104402/","authors":["Truhn D","Tayebi Arasteh S","Saldanha OL","Müller-Franzes G","Khader F","Quirke P","West NP","Gray R","Hutchins GGA","James JA","Loughrey MB","Salto-Tellez M","Brenner H","Brobeil A","Yuan T","Chang-Claude J","Hoffmeister M","Foersch S","Han T","Keil S","Schulze-Hagen M","Isfort P","Bruners P","Kaissis G","Kuhl C","Nebelung S","Kather JN"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Feb","doi":"10.1016/j.media.2023.103059","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:42.471Z"},{"id":"pmid:38102146","name":"Oblivious network intrusion detection systems.","source":"pubmed","abstract":"A main function of network intrusion detection systems (NIDSs) is to monitor network traffic and match it against rules. Oblivious NIDSs (O-NIDS) perform the same tasks of NIDSs but they use encrypted rules and produce encrypted results without being able to decrypt the rules or the results. Current implementations of O-NIDS suffer from slow searching speeds and/or lack of generality. In this paper, we present a generic approach to implement a privacy-preserving O-NIDS based on hybrid binary gates. We also present two resource-flexible algorithm bundles built upon the hybrid binary gates to perform the NIDS's essential tasks of direct matching and range matching as a proof of concept. Our approach utilizes a Homomorphic Encryption (HE) layer in an abstract fashion, which makes it implementable by many HE schemes compared to the state-of-the-art where the underlying HE scheme is a core part of the approach. This feature allowed the use of already-existing HE libraries that utilize parallelization techniques in GPUs for faster performance. We achieved a rule encryption time as low as 0.012% of the state of the art with only 0.047% of its encrypted rule size. Also, we achieved a rule-matching speed that is almost 20,000 times faster than the state of the art.","url":"https://pubmed.ncbi.nlm.nih.gov/38102146/","authors":["Sayed MA","Taha M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Dec 15","doi":"10.1038/s41598-023-48475-w","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:42.471Z"},{"id":"pmid:38085098","name":"Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality.","source":"pubmed","abstract":"To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality.","url":"https://pubmed.ncbi.nlm.nih.gov/38085098/","authors":["Zhao T","Wang F","Mott R","Dekkers J","Cheng H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Mar 6","doi":"10.1093/genetics/iyad210","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:42.471Z"},{"id":"pmid:37835883","name":"Medical Imaging Applications of Federated Learning.","source":"pubmed","abstract":"Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique's inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body. This Systematic Literature review was conducted by querying and consolidating results from ArXiv, IEEE Xplorer, and PubMed. Furthermore, we provide a detailed description of FL architecture, models, descriptions of the performance achieved by FL models, and how results compare with traditional Machine Learning (ML) models. Additionally, we discuss the security benefits, highlighting two primary forms of privacy-preserving techniques, including homomorphic encryption and differential privacy. Finally, we provide some background information and context regarding where the contributions lie. The background information is organized into the following categories: architecture/setup type, data-related topics, security, and learning types. While progress has been made within the field of FL and medical imaging, much room for improvement and understanding remains, with an emphasis on security and data issues remaining the primary concerns for researchers. Therefore, improvements are constantly pushing the field forward. Finally, we highlighted the challenges in deploying FL in medical imaging applications and provided recommendations for future directions.","url":"https://pubmed.ncbi.nlm.nih.gov/37835883/","authors":["Sandhu SS","Gorji HT","Tavakolian P","Tavakolian K","Akhbardeh A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Oct 6","doi":"10.3390/diagnostics13193140","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:42.471Z"},{"id":"pmid:37765797","name":"RETRACTED: Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning.","source":"pubmed","abstract":"The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.","url":"https://pubmed.ncbi.nlm.nih.gov/37765797/","authors":["Ali A","Ali H","Saeed A","Ahmed Khan A","Tin TT","Assam M","Ghadi YY","Mohamed HG"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Sep 7","doi":"10.3390/s23187740","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:42.471Z"},{"id":"pmid:37628155","name":"Communication-Efficient and Privacy-Preserving Verifiable Aggregation for Federated Learning.","source":"pubmed","abstract":"Federated learning is a distributed machine learning framework, which allows users to save data locally for training without sharing data. Users send the trained local model to the server for aggregation. However, untrusted servers may infer users' private information from the provided data and mistakenly execute aggregation protocols to forge aggregation results. In order to ensure the reliability of the federated learning scheme, we must protect the privacy of users' information and ensure the integrity of the aggregation results. This paper proposes an effective secure aggregation verifiable federated learning scheme, which has both high communication efficiency and privacy protection function. The scheme encrypts the gradients with a single mask technology to securely aggregate gradients, thus ensuring that malicious servers cannot deduce users' private information from the provided data. Then the masked gradients are hashed to verify the aggregation results. The experimental results show that our protocol is more suited for bandwidth-constraint and offline-users scenarios.","url":"https://pubmed.ncbi.nlm.nih.gov/37628155/","authors":["Peng K","Shen X","Gao L","Wang B","Lu Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Jul 27","doi":"10.3390/e25081125","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:42.471Z"},{"id":"pmid:37569079","name":"A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems.","source":"pubmed","abstract":"Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client's data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified.","url":"https://pubmed.ncbi.nlm.nih.gov/37569079/","authors":["Gu X","Sabrina F","Fan Z","Sohail S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Aug 7","doi":"10.3390/ijerph20156539","addedAt":"2026-08-31T06:41:42.471Z","updatedAt":"2026-08-31T06:41:42.471Z"},{"id":"pmid:37414028","name":"Clinical Informatics Approaches to Facilitate Cancer Data Sharing.","source":"pubmed","abstract":"Despite growing enthusiasm surrounding the utility of clinical informatics to improve cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. With the rise of machine learning techniques that require increasing amounts of clinical data, these barriers have magnified. Here, we review recent efforts within clinical informatics to address issues related to safely sharing cancer data.","url":"https://pubmed.ncbi.nlm.nih.gov/37414028/","authors":["Aneja S","Avesta A","Xu H","Machado LO"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Aug","doi":"10.1055/s-0043-1768721","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:37303980","name":"Digital health in smart cities: Rethinking the remote health monitoring architecture on combining edge, fog, and cloud.","source":"pubmed","abstract":"Smart cities that support the execution of health services are more and more in evidence today. Here, it is mainstream to use IoT-based vital sign data to serve a multi-tier architecture. The state-of-the-art proposes the combination of edge, fog, and cloud computing to support critical health applications efficiently. However, to the best of our knowledge, initiatives typically present the architectures, not bringing adaptation and execution optimizations to address health demands fully.","url":"https://pubmed.ncbi.nlm.nih.gov/37303980/","authors":["Rodrigues VF","da Rosa Righi R","da Costa CA","Zeiser FA","Eskofier B","Maier A","Kim D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1007/s12553-023-00753-3","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:37299876","name":"Smart Flood Detection with AI and Blockchain Integration in Saudi Arabia Using Drones.","source":"pubmed","abstract":"Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology.","url":"https://pubmed.ncbi.nlm.nih.gov/37299876/","authors":["Alsumayt A","El-Haggar N","Amouri L","Alfawaer ZM","Aljameel SS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 May 28","doi":"10.3390/s23115148","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:37289609","name":"Practical and Robust Federated Learning With Highly Scalable Regression Training.","source":"pubmed","abstract":"Privacy-preserving federated learning, as one of the privacy-preserving computation techniques, is a promising distributed and privacy-preserving machine learning (ML) approach for Internet of Medical Things (IoMT), due to its ability to train a regression model without collecting raw data of data owners (DOs). However, traditional interactive federated regression training (IFRT) schemes rely on multiple rounds of communication to train a global model and are still under various privacy and security threats. To overcome these problems, several noninteractive federated regression training (NFRT) schemes have been proposed and applied in a variety of scenarios. However, there are still several challenges: 1) how to protect the privacy of DOs' local dataset; 2) how to realize highly scalable regression training without linear dependence on sample dimension; 3) how to tolerate DOs' dropout; and 4) how to enable DOs to verify the correctness of aggregated results returned from the cloud service provider (CSP). In this article, we propose two practical noninteractive federated learning schemes with privacy-preserving for IoMT, named homomorphic encryption based NFRT (HE-NFRT) and double-masking protocol based NFRT (Mask-NFRT), respectively, which are based on a comprehensive consideration of NFRT, privacy concerns, high-efficiency, robustness, and verification mechanism. The security analyses display that our proposed schemes are able to protect the privacy of DOs' local training data, resist collusion attack, and support strong verification to each DO. The performance evaluation results demonstrate that our proposed HE-NFRT scheme is desirable for a high-dimensional and high-security IoMT application while Mask-NFRT scheme is desirable for a high-dimensional and large-scale IoMT application.","url":"https://pubmed.ncbi.nlm.nih.gov/37289609/","authors":["Han S","Ding H","Zhao S","Ren S","Wang Z","Lin J","Zhou S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2024 Oct","doi":"10.1109/TNNLS.2023.3271859","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:37238577","name":"Attribute-Based Verifiable Conditional Proxy Re-Encryption Scheme.","source":"pubmed","abstract":"There are mostly semi-honest agents in cloud computing, so agents may perform unreliable calculations during the actual execution process. In this paper, an attribute-based verifiable conditional proxy re-encryption (AB-VCPRE) scheme using a homomorphic signature is proposed to solve the problem that the current attribute-based conditional proxy re-encryption (AB-CPRE) algorithm cannot detect the illegal behavior of the agent. The scheme implements robustness, that is the re-encryption ciphertext, can be verified by the verification server, showing that the received ciphertext is correctly converted by the agent from the original ciphertext, thus, meaning that illegal activities of agents can be effectively detected. In addition, the article demonstrates the reliability of the constructed AB-VCPRE scheme validation in the standard model, and proves that the scheme satisfies CPA security in the selective security model based on the learning with errors (LWE) assumption.","url":"https://pubmed.ncbi.nlm.nih.gov/37238577/","authors":["Tang Y","Jin M","Meng H","Yang L","Zheng C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 May 19","doi":"10.3390/e25050822","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:37238475","name":"Graphic Groups, Graph Homomorphisms, and Graphic Group Lattices in Asymmetric Topology Cryptography.","source":"pubmed","abstract":"Using asymmetric topology cryptography to encrypt networks on the basis of topology coding is a new topic of cryptography, which consists of two major elements, i.e., topological structures and mathematical constraints. The topological signature of asymmetric topology cryptography is stored in the computer by matrices that can produce number-based strings for application. By means of algebra, we introduce every-zero mixed graphic groups, graphic lattices, and various graph-type homomorphisms and graphic lattices based on mixed graphic groups into cloud computing technology. The whole network encryption will be realized by various graphic groups.","url":"https://pubmed.ncbi.nlm.nih.gov/37238475/","authors":["Zhao M","Wang H","Yao B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Apr 26","doi":"10.3390/e25050720","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:37187785","name":"Towards realistic privacy-preserving deep learning over encrypted medical data.","source":"pubmed","abstract":"Cardiovascular disease supposes a substantial fraction of healthcare systems. The invisible nature of these pathologies demands solutions that enable remote monitoring and tracking. Deep Learning (DL) has arisen as a solution in many fields, and in healthcare, multiple successful applications exist for image enhancement and health outside hospitals. However, the computational requirements and the need for large-scale datasets limit DL. Thus, we often offload computation onto server infrastructure, and various Machine-Learning-as-a-Service (MLaaS) platforms emerged from this need. These enable the conduction of heavy computations in a cloud infrastructure, usually equipped with high-performance computing servers. Unfortunately, the technical barriers persist in healthcare ecosystems since sending sensitive data (e.g., medical records or personally identifiable information) to third-party servers involves privacy and security concerns with legal and ethical implications. In the scope of Deep Learning for Healthcare to improve cardiovascular health, Homomorphic Encryption (HE) is a promising tool to enable secure, private, and legal health outside hospitals. Homomorphic Encryption allows for privacy-preserving computations over encrypted data, thus preserving the privacy of the processed information. Efficient HE requires structural optimizations to perform the complex computation of the internal layers. One such optimization is Packed Homomorphic Encryption (PHE), which encodes multiple elements on a single ciphertext, allowing for efficient Single Instruction over Multiple Data (SIMD) operations. However, using PHE in DL circuits is not straightforward, and it demands new algorithms and data encoding, which existing literature has not adequately addressed. To fill this gap, in this work, we elaborate on novel algorithms to adapt the linear algebra operations of DL layers to PHE. Concretely, we focus on Convolutional Neural Networks. We provide detailed descriptions and insights into the different algorithms and efficient inter-layer data format conversion mechanisms. We formally analyze the complexity of the algorithms in terms of performance metrics and provide guidelines and recommendations for adapting architectures that deal with private data. Furthermore, we confirm the theoretical analysis with practical experimentation. Among other conclusions, we prove that our new algorithms speed up the processing of convolutional layers compared to the existing proposals.","url":"https://pubmed.ncbi.nlm.nih.gov/37187785/","authors":["Cabrero-Holgueras J","Pastrana S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3389/fcvm.2023.1117360","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:37156993","name":"Homomorphic inference of deep neural networks for zero-knowledge verification of nuclear warheads.","source":"pubmed","abstract":"Disarmament treaties have been the driving force towards reducing the large nuclear stockpile assembled during the Cold War. Further efforts are built around verification protocols capable of authenticating nuclear warheads while preventing the disclosure of confidential information. This type of problem falls under the scope of zero-knowledge protocols, which aim at multiple parties agreeing on a statement without conveying any information beyond the statement itself. A protocol capable of achieving all the authentication and security requirements is still not completely formulated. Here we propose a protocol that leverages the isotopic capabilities of NRF measurements and the classification abilities of neural networks. Two key elements guarantee the security of the protocol, the implementation of the template-based approach in the network's architecture and the use of homomorphic inference. Our results demonstrate the potential of developing zero-knowledge protocols for the verification of nuclear warheads using Siamese networks on encrypted spectral data.","url":"https://pubmed.ncbi.nlm.nih.gov/37156993/","authors":["Turturica GV","Iancu V"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 May 8","doi":"10.1038/s41598-023-34679-7","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:37143706","name":"Training Medical-Diagnosis Neural Networks on the Cloud with Privacy-Sensitive Patient Data from Multiple Clients.","source":"pubmed","abstract":"Artificial neural networks (ANNs) are changing the paradigm in medical diagnosis. However, it remains an open problem how to outsource the model training operations to the cloud while protecting the privacy of distributed patient data. Homomorphic encryption suffers from high overhead over data independently encrypted from numerous sources, differential privacy introduces a high level of noise which drastically increases the number of patient records needed to train a model, while federated learning requires all participants to perform synchronized local training that counters our goal of outsourcing all training operations to the cloud. This paper proposes to use matrix masking for outsourcing all model training operations to the cloud with privacy protection. After outsourcing their masked data to the cloud, the clients do not need to coordinate and perform any local training operations. The accuracy of the models trained by the cloud from the masked data is comparable to the accuracy of the optimal benchmark models that are trained directly from the original raw data. Our results are confirmed by experimental studies on privacy-preserving cloud training of medical-diagnosis neural network models based on real-world Alzheimer's disease data and Parkinson's disease data.","url":"https://pubmed.ncbi.nlm.nih.gov/37143706/","authors":["Melissourgos D","Gao H","Ma C","Chen S","Wu SS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Aug","doi":"10.1145/3549206.3549291","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:37112370","name":"Privacy-Preserving Indoor Trajectory Matching with IoT Devices.","source":"pubmed","abstract":"With the rapid development of the Internet of Things (IoT) technology, Wi-Fi signals have been widely used for trajectory signal acquisition. Indoor trajectory matching aims to achieve the monitoring of the encounters between people and trajectory analysis in indoor environments. Due to constraints ofn the computation abilities IoT devices, the computation of indoor trajectory matching requires the assistance of a cloud platform, which brings up privacy concerns. Therefore, this paper proposes a trajectory-matching calculation method that supports ciphertext operations. Hash algorithms and homomorphic encryption are selected to ensure the security of different private data, and the actual trajectory similarity is determined based on correlation coefficients. However, due to obstacles and other interferences in indoor environments, the original data collected may be missing in certain stages. Therefore, this paper also complements the missing values on ciphertexts through mean, linear regression, and KNN algorithms. These algorithms can predict the missing parts of the ciphertext dataset, and the accuracy of the complemented dataset can reach over 97%. This paper provides original and complemented datasets for matching calculations, and demonstrates their high feasibility and effectiveness in practical applications from the perspective of calculation time and accuracy loss.","url":"https://pubmed.ncbi.nlm.nih.gov/37112370/","authors":["Lu B","Wu D","Qin Z","Wang L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Apr 16","doi":"10.3390/s23084029","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:36904825","name":"Privacy-Preserving Decision-Tree Evaluation with Low Complexity for Communication.","source":"pubmed","abstract":"Due to the rapid development of machine-learning technology, companies can build complex models to provide prediction or classification services for customers without resources. A large number of related solutions exist to protect the privacy of models and user data. However, these efforts require costly communication and are not resistant to quantum attacks. To solve this problem, we designed a new secure integer-comparison protocol based on fully homomorphic encryption and proposed a client-server classification protocol for decision-tree evaluation based on the secure integer-comparison protocol. Compared to existing work, our classification protocol has a relatively low communication cost and requires only one round of communication with the user to complete the classification task. Moreover, the protocol was built on a fully homomorphic-scheme-based lattice that is resistant to quantum attacks, as opposed to conventional schemes. Finally, we conducted an experimental analysis comparing our protocol with the traditional approach on three datasets. The experimental results showed that the communication cost of our scheme was 20% of the cost of the traditional scheme.","url":"https://pubmed.ncbi.nlm.nih.gov/36904825/","authors":["Hao Y","Qin B","Sun Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Feb 27","doi":"10.3390/s23052624","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:36833114","name":"An Optimization-Linked Intelligent Security Algorithm for Smart Healthcare Organizations.","source":"pubmed","abstract":"IoT-enabled healthcare apps are providing significant value to society by offering cost-effective patient monitoring solutions in IoT-enabled buildings. However, with a large number of users and sensitive personal information readily available in today's fast-paced, internet, and cloud-based environment, the security of these healthcare systems must be a top priority. The idea of safely storing a patient's health data in an electronic format raises issues regarding patient data privacy and security. Furthermore, with traditional classifiers, processing large amounts of data is a difficult challenge. Several computational intelligence approaches are useful for effectively categorizing massive quantities of data for this goal. For many of these reasons, a novel healthcare monitoring system that tracks disease processes and forecasts diseases based on the available data obtained from patients in distant communities is proposed in this study. The proposed framework consists of three major stages, namely data collection, secured storage, and disease detection. The data are collected using IoT sensor devices. After that, the homomorphic encryption (HE) model is used for secured data storage. Finally, the disease detection framework is designed with the help of Centered Convolutional Restricted Boltzmann Machines-based whale optimization (CCRBM-WO) algorithm. The experiment is conducted on a Python-based cloud tool. The proposed system outperforms current e-healthcare solutions, according to the findings of the experiments. The accuracy, precision, F1-measure, and recall of our suggested technique are 96.87%, 97.45%, 97.78%, and 98.57%, respectively, according to the proposed method.","url":"https://pubmed.ncbi.nlm.nih.gov/36833114/","authors":["Irshad RR","Alattab AA","Alsaiari OAS","Sohail SS","Aziz A","Madsen DØ","Alalayah KM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Feb 15","doi":"10.3390/healthcare11040580","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:36721089","name":"PP-DDP: a privacy-preserving outsourcing framework for solving the double digest problem.","source":"pubmed","abstract":"As one of the fundamental problems in bioinformatics, the double digest problem (DDP) focuses on reordering genetic fragments in a proper sequence. Although many algorithms for dealing with the DDP problem were proposed during the past decades, it is believed that solving DDP is still very time-consuming work due to the strongly NP-completeness of DDP. However, none of these algorithms consider the privacy issue of the DDP data that contains critical business interests and is collected with days or even months of gel-electrophoresis experiments. Thus, the DDP data owners are reluctant to deploy the task of solving DDP over cloud.","url":"https://pubmed.ncbi.nlm.nih.gov/36721089/","authors":["Suo J","Gu L","Yan X","Yang S","Hu X","Wang L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Jan 31","doi":"10.1186/s12859-023-05157-8","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:36687767","name":"An intelligent blockchain strategy for decentralised healthcare framework.","source":"pubmed","abstract":"Nowadays, securely sharing medical data is one of the significant concerns in blockchain technology. The existing blockchain approaches have faced high time consumption, low confidentiality, and high memory usage for transferring the file in a secure way because of attack harmfulness and large unstructured records. It has ended in security threat, so the integrity of the user data has been lost. Hence, a novel hybrid Deep Belief-based Diffie Hellman (DBDH) security framework was presented to protect medical data from malicious events. Incorporating a deep belief neural system continuously monitors the system and identifies the attacks. Initially, the IoMT dataset was collected from the standard site and imported into the system. Moreover, hash 1 was calculated for the original data and stored in the cloud server for verification. Then, the original data was encrypted with a private key for data hiding. The incorporation of homomorphic property helps to calculate hash 2 for encrypted data. Finally, in the verification module, both hash values are verified. In addition, cryptanalysis was performed by launching an attack to validate the performance of the designed model. Moreover, the estimated outcomes of the presented model were compared with existing approaches to determine the improvement score.","url":"https://pubmed.ncbi.nlm.nih.gov/36687767/","authors":["Goel A","Neduncheliyan S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1007/s12083-022-01429-x","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:36617116","name":"Privacy Preserving Image Encryption with Optimal Deep Transfer Learning Based Accident Severity Classification Model.","source":"pubmed","abstract":"Effective accident management acts as a vital part of emergency and traffic control systems. In such systems, accident data can be collected from different sources (unmanned aerial vehicles, surveillance cameras, on-site people, etc.) and images are considered a major source. Accident site photos and measurements are the most important evidence. Attackers will steal data and breach personal privacy, causing untold costs. The massive number of images commonly employed poses a significant challenge to privacy preservation, and image encryption can be used to accomplish cloud storage and secure image transmission. Automated severity estimation using deep-learning (DL) models becomes essential for effective accident management. Therefore, this article presents a novel Privacy Preserving Image Encryption with Optimal Deep-Learning-based Accident Severity Classification (PPIE-ODLASC) method. The primary objective of the PPIE-ODLASC algorithm is to securely transmit the accident images and classify accident severity into different levels. In the presented PPIE-ODLASC technique, two major processes are involved, namely encryption and severity classification (i.e., high, medium, low, and normal). For accident image encryption, the multi-key homomorphic encryption ( MKHE ) technique with lion swarm optimization (LSO)-based optimal key generation procedure is involved. In addition, the PPIE-ODLASC approach involves YOLO-v5 object detector to identify the region of interest (ROI) in the accident images. Moreover, the accident severity classification module encompasses Xception feature extractor, bidirectional gated recurrent unit (BiGRU) classification, and Bayesian optimization (BO)-based hyperparameter tuning. The experimental validation of the proposed PPIE-ODLASC algorithm is tested utilizing accident images and the outcomes are examined in terms of many measures. The comparative examination revealed that the PPIE-ODLASC technique showed an enhanced performance of 57.68 dB over other existing models.","url":"https://pubmed.ncbi.nlm.nih.gov/36617116/","authors":["Sirisha U","Chandana BS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Jan 3","doi":"10.3390/s23010519","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:36502140","name":"SecMDGM: Federated Learning Security Mechanism Based on Multi-Dimensional Auctions.","source":"pubmed","abstract":"As a newly emerging distributed machine learning technology, federated learning has unique advantages in the era of big data. We explore how to motivate participants to experience auctions more actively and safely. It is also essential to ensure that the final participant who wins the right to participate can guarantee relatively high-quality data or computational performance. Therefore, a secure, necessary and effective mechanism is needed through strict theoretical proof and experimental verification. The traditional auction theory is mainly oriented to price, not giving quality issues as much consideration. Hence, it is challenging to discover the optimal mechanism and solve the privacy problem when considering multi-dimensional auctions. Therefore, we (1) propose a multi-dimensional information security mechanism, (2) propose an optimal mechanism that satisfies the Pareto optimality and incentive compatibility named the SecMDGM and (3) verify that for the aggregation model based on vertical data, this mechanism can improve the performance by 2.73 times compared to that of random selection. These are all important, and they complement each other instead of being independent or in tandem. Due to security issues, it can be ensured that the optimal multi-dimensional auction has practical significance and can be used in verification experiments.","url":"https://pubmed.ncbi.nlm.nih.gov/36502140/","authors":["Chen Q","Yao L","Wang X","Lin Jiang Z","Wu Y","Ma T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Dec 2","doi":"10.3390/s22239434","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:36433614","name":"A Privacy-Preserving, Two-Party, Secure Computation Mechanism for Consensus-Based Peer-to-Peer Energy Trading in the Smart Grid.","source":"pubmed","abstract":"Consumers in electricity markets are becoming more proactive because of the rapid development of demand-response management and distributed energy resources, which boost the transformation of peer-to-peer (P2P) energy-trading mechanisms. However, in the P2P negotiation process, it is a challenging task to prevent private information from being attacked by malicious agents. In this paper, we propose a privacy-preserving, two-party, secure computation mechanism for consensus-based P2P energy trading. First, a novel P2P negotiation mechanism for energy trading is proposed based on the consensus + innovation (C + I) method and the power transfer distribution factor (PTDF), and this mechanism can simultaneously maximize social welfare and maintain physical network constraints. In addition, the C + I method only requires a minimum set of information to be exchanged. Then, we analyze the strategy of malicious neighboring agents colluding to attack in order to steal private information. To defend against this attack, we propose a two-party, secure computation mechanism in order to realize safe negotiation between each pair of prosumers based on Paillier homomorphic encryption (HE), a smart contract (SC), and zero-knowledge proof (ZKP). The energy price is updated in a safe way without leaking any private information. Finally, we simulate the functionality of the privacy-preserving mechanism in terms of convergence performance, computational efficiency, scalability, and SC operations.","url":"https://pubmed.ncbi.nlm.nih.gov/36433614/","authors":["Li Z","Xu H","Zhai F","Zhao B","Xu M","Guo Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Nov 21","doi":"10.3390/s22229020","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:36384083","name":"Privacy-aware estimation of relatedness in admixed populations.","source":"pubmed","abstract":"Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in third-party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization.","url":"https://pubmed.ncbi.nlm.nih.gov/36384083/","authors":["Wang S","Kim M","Li W","Jiang X","Chen H","Harmanci A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Nov 19","doi":"10.1093/bib/bbac473","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:36323706","name":"Secure secondary utilization system of genomic data using quantum secure cloud.","source":"pubmed","abstract":"Secure storage and secondary use of individual human genome data is increasingly important for genome research and personalized medicine. Currently, it is necessary to store the whole genome sequencing information (FASTQ data), which enables detections of de novo mutations and structural variations in the analysis of hereditary diseases and cancer. Furthermore, bioinformatics tools to analyze FASTQ data are frequently updated to improve the precision and recall of detected variants. However, existing secure secondary use of data, such as multi-party computation or homomorphic encryption, can handle only a limited algorithms and usually requires huge computational resources. Here, we developed a high-performance one-stop system for large-scale genome data analysis with secure secondary use of the data by the data owner and multiple users with different levels of data access control. Our quantum secure cloud system is a distributed secure genomic data analysis system (DSGD) with a \"trusted server\" built on a quantum secure cloud, the information-theoretically secure Tokyo QKD Network. The trusted server will be capable of deploying and running a variety of sequencing analysis hardware, such as GPUs and FPGAs, as well as CPU-based software. We demonstrated that DSGD achieved comparable throughput with and without encryption on the trusted server Therefore, our system is ready to be installed at research institutes and hospitals that make diagnoses based on whole genome sequencing on a daily basis.","url":"https://pubmed.ncbi.nlm.nih.gov/36323706/","authors":["Fujiwara M","Hashimoto H","Doi K","Kujiraoka M","Tanizawa Y","Ishida Y","Sasaki M","Nagasaki M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Nov 2","doi":"10.1038/s41598-022-22804-x","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:36298373","name":"Homomorphic Asymmetric Encryption Applied to the Analysis of IoT Communications.","source":"pubmed","abstract":"In this paper, we describe the use of homomorphic encryption techniques in order to not only ensure the data are transmitted in a confidential way, but also to use the encrypted data to provide the manager with statistics that allow them to detect the incorrect functioning of a sensor node or a group of sensors due to either malicious data injection, data transmission, or simply sensor damage (miscalibration, faulty sensor functioning). Obtaining these statistical values does not need decryption, so the process is sped up and can be developed in real time. Operating the data in this way ensures privacy and removes the need to maintain a shared key infrastructure between the sensor nodes and the manager nodes that are part of the blockchain infrastructure. In this work, we focus on operations with the sensor nodes that provide data that will be, later, treated as part of the business logic in the agribusiness sector (for example), hence the importance of having fast checking mechanisms in terms of data quality. The results obtained on conventional configurations of sensor nodes encourage the use of this technique in the aforementioned infrastructure.","url":"https://pubmed.ncbi.nlm.nih.gov/36298373/","authors":["López Delgado JL","Álvarez Bermejo JA","López Ramos JA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Oct 20","doi":"10.3390/s22208022","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:36261623","name":"Preserving Patient Privacy During Computation over Shared Electronic Health Record Data.","source":"pubmed","abstract":"Patient Electronic Health Records (EHRs) contain valuable clinical data that is useful for medical research and public health inquires. However, patient privacy regulation and improper resource sharing risks limit access to EHR medical data for research and public health purposes. In this paper, we introduce an end-to-end security solution that addresses both concerns and facilitates the sharing of patient EHR data over an unsecured third-party server using a leveled homomorphic encryption (LHE) scheme. Time testing for aggregating queries and linear computations was carried out using an HPE ProLiant DL580 Gen 10 server with an Intel Xeon Platinum 8280 Processor.","url":"https://pubmed.ncbi.nlm.nih.gov/36261623/","authors":["d'Aliberti OG","Clark MA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Oct 20","doi":"10.1007/s10916-022-01865-5","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:36182914","name":"SVAT: Secure outsourcing of variant annotation and genotype aggregation.","source":"pubmed","abstract":"Sequencing of thousands of samples provides genetic variants with allele frequencies spanning a very large spectrum and gives invaluable insight into genetic determinants of diseases. Protecting the genetic privacy of participants is challenging as only a few rare variants can easily re-identify an individual among millions. In certain cases, there are policy barriers against sharing genetic data from indigenous populations and stigmatizing conditions.","url":"https://pubmed.ncbi.nlm.nih.gov/36182914/","authors":["Kim M","Wang S","Jiang X","Harmanci A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Oct 1","doi":"10.1186/s12859-022-04959-6","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35970834","name":"Secure human action recognition by encrypted neural network inference.","source":"pubmed","abstract":"Advanced computer vision technology can provide near real-time home monitoring to support \"aging in place\" by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average inference latency of 1.2 seconds and 2.4 seconds on real-world test datasets using small and large neural nets, respectively. We show that our method enables a 613x speedup over the latency-optimized LoLa and achieves an average of 3.1x throughput increase in secure inference compared to the throughput-optimized nGraph-HE2.","url":"https://pubmed.ncbi.nlm.nih.gov/35970834/","authors":["Kim M","Jiang X","Lauter K","Ismayilzada E","Shams S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Aug 15","doi":"10.1038/s41467-022-32168-5","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35808403","name":"A Business-to-Business Collaboration System That Promotes Data Utilization While Encrypting Information on the Blockchain.","source":"pubmed","abstract":"Ensuring the reliability of data gathering from every connected device is an essential issue for promoting the advancement of the next paradigm shift, i.e., Industry 4.0. Blockchain technology is becoming recognized as an advanced tool. However, data collaboration using blockchain has not progressed sufficiently among companies in the industrial supply chain (SC) that handle sensitive data, such as those related to product quality, etc. There are two reasons why data utilization is not sufficiently advanced in the industrial SC. The first is that manufacturing information is top secret. Blockchain mechanisms, such as Bitcoin, which uses PKI, require plaintext to be shared between companies to verify the identity of the company that sent the data. Another is that the merits of data collaboration between companies have not been materialized. To solve these problems, this paper proposes a business-to-business collaboration system using homomorphic encryption and blockchain techniques. Using the proposed system, each company can exchange encrypted confidential information and utilize the data for its own business. In a trial, an equipment manufacturer was able to identify the quality change caused by a decrease in equipment performance as a cryptographic value from blockchain and to identify the change one month earlier without knowing the quality value.","url":"https://pubmed.ncbi.nlm.nih.gov/35808403/","authors":["Nasu H","Kodera Y","Nogami Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Jun 29","doi":"10.3390/s22134909","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35746213","name":"A Novel Homomorphic Approach for Preserving Privacy of Patient Data in Telemedicine.","source":"pubmed","abstract":"Globally, the surge in disease and urgency in maintaining social distancing has reawakened the use of telemedicine/telehealth. Amid the global health crisis, the world adopted the culture of online consultancy. Thus, there is a need to revamp the conventional model of the telemedicine system as per the current challenges and requirements. Security and privacy of data are main aspects to be considered in this era. Data-driven organizations also require compliance with regulatory bodies, such as HIPAA, PHI, and GDPR. These regulatory compliance bodies must ensure user data privacy by implementing necessary security measures. Patients and doctors are now connected to the cloud to access medical records, e.g., voice recordings of clinical sessions. Voice data reside in the cloud and can be compromised. While searching voice data, a patient's critical data can be leaked, exposed to cloud service providers, and spoofed by hackers. Secure, searchable encryption is a requirement for telemedicine systems for secure voice and phoneme searching. This research proposes the secure searching of phonemes from audio recordings using fully homomorphic encryption over the cloud. It utilizes IBM's homomorphic encryption library (HElib) and achieves indistinguishability. Testing and implementation were done on audio datasets of different sizes while varying the security parameters. The analysis includes a thorough security analysis along with leakage profiling. The proposed scheme achieved higher levels of security and privacy, especially when the security parameters increased. However, in use cases where higher levels of security were not desirous, one may rely on a reduction in the security parameters.","url":"https://pubmed.ncbi.nlm.nih.gov/35746213/","authors":["Iqbal Y","Tahir S","Tahir H","Khan F","Saeed S","Almuhaideb AM","Syed AM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Jun 11","doi":"10.3390/s22124432","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35712069","name":"The Rise of Cloud Computing: Data Protection, Privacy, and Open Research Challenges-A Systematic Literature Review (SLR).","source":"pubmed","abstract":"Cloud computing is a long-standing dream of computing as a utility, where users can store their data remotely in the cloud to enjoy on-demand services and high-quality applications from a shared pool of configurable computing resources. Thus, the privacy and security of data are of utmost importance to all of its users regardless of the nature of the data being stored. In cloud computing environments, it is especially critical because data is stored in various locations, even around the world, and users do not have any physical access to their sensitive data. Therefore, we need certain data protection techniques to protect the sensitive data that is outsourced over the cloud. In this paper, we conduct a systematic literature review (SLR) to illustrate all the data protection techniques that protect sensitive data outsourced over cloud storage. Therefore, the main objective of this research is to synthesize, classify, and identify important studies in the field of study. Accordingly, an evidence-based approach is used in this study. Preliminary results are based on answers to four research questions. Out of 493 research articles, 52 studies were selected. 52 papers use different data protection techniques, which can be divided into two main categories, namely noncryptographic techniques and cryptographic techniques. Noncryptographic techniques consist of data splitting, data anonymization, and steganographic techniques, whereas cryptographic techniques consist of encryption, searchable encryption, homomorphic encryption, and signcryption. In this work, we compare all of these techniques in terms of data protection accuracy, overhead, and operations on masked data. Finally, we discuss the future research challenges facing the implementation of these techniques.","url":"https://pubmed.ncbi.nlm.nih.gov/35712069/","authors":["Hassan J","Shehzad D","Habib U","Aftab MU","Ahmad M","Kuleev R","Mazzara M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1155/2022/8303504","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35666798","name":"Secure Counting Query Protocol for Genomic Data.","source":"pubmed","abstract":"Statistical analysis on genomic data can explore the relationship between gene sequence and phenotype. Particularly, counting the genomic mutation samples and associating with related phenotypes for statistical analysis can annotate the variation sites and help to diagnose genovariation. Expansion of the size of variation sample data helps to increase the accuracy of statistical analysis. It is feasible to securely share data from genomic databases on cloud platforms. In this paper, we design a secure counting query protocol that can securely share genomic data on cloud platforms. Our protocol supports statistical analysis of the genomic data in VCF (Variant Call Format) files by counting query. There are three participants of data owner, cloud platform and query party. Firstly, the genomic data is preprocessed to reduce the data size. Secondly, Paillier homomorphic is used so that genomic data can be securely shared and calculated on cloud platform. Finally, the results which be decrypted is used to implement counting function of the protocol. Experimental results show that the protocol can implement the query counting function after homomorphic encryption. The query time is less than 1 s, which provide a feasible solution to share genomic data securely on cloud platform for statistical analysis.","url":"https://pubmed.ncbi.nlm.nih.gov/35666798/","authors":["Jiang Y","Shang T","Liu J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Mar-Apr","doi":"10.1109/TCBB.2022.3178446","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35607628","name":"Privacy-preserving federated neural network learning for disease-associated cell classification.","source":"pubmed","abstract":"Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing or centralizing the data of different healthcare institutions is, however, unfeasible or prohibitively difficult due to privacy regulations. In this work, we address this problem by using a privacy-preserving federated learning-based approach, PriCell , for complex models such as convolutional neural networks. PriCell relies on multiparty homomorphic encryption and enables the collaborative training of encrypted neural networks with multiple healthcare institutions. We preserve the confidentiality of each institutions' input data, of any intermediate values, and of the trained model parameters. We efficiently replicate the training of a published state-of-the-art convolutional neural network architecture in a decentralized and privacy-preserving manner. Our solution achieves an accuracy comparable with the one obtained with the centralized non-secure solution. PriCell guarantees patient privacy and ensures data utility for efficient multi-center studies involving complex healthcare data.","url":"https://pubmed.ncbi.nlm.nih.gov/35607628/","authors":["Sav S","Bossuat JP","Troncoso-Pastoriza JR","Claassen M","Hubaux JP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 May 13","doi":"10.1016/j.patter.2022.100487","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35528352","name":"Combination of Blockchain and AI for Music Intellectual Property Protection.","source":"pubmed","abstract":"In the last two years, due to the pandemic and restrictive measures, the dependence of music creators and artists on the Internet, where they could promote their work, organize live streaming concerts, and talk to the public, has increased and expanded even more and seeks higher revenue from digital music platforms. An important issue that arises from the above statement is protecting the authors' copyright regarding the uses and sharing in the digital services of their works with protected content. Although circulated in digital information, the protected content is not information but a product of ethical and commercial value. While it has an intangible owner and it owes its existence to the creative idea of its creator, it is not an idea. The imposition of legal and commercial conditions on its movement cannot be associated with any restrictions on the free movement of information, as it is not related to them. In general, the unauthorized exchange of digital music files via peer-to-peer violates copyright law. The exchange of files is unauthorized, as it does not have the relevant permission from the creators and beneficiaries and is therefore illegal. With this in mind, this paper proposes a highly effective way of protecting the copyright of music technology, which is based on the widespread use of artificial intelligence, blockchain, and cryptography technologies. Specifically, an advanced blockchain model based on Hyperledger Fabric is introduced, which, however, uses Quantum Homomorphic Encryption and Quantum Zero-Knowledge Arguments. Music files are implemented as Nonfungible Tokens (NFTs), which activate smart contracts. Finally, an advanced collaborative filtering algorithm provides recommendations for effectiveness in securing the copyrights of music industry creators. A specialized scenario was built to model the proposed system to verify the degree of protection on music intellectual property in developing a security simulation with an innovative consensus-based zero knowledge and the quantum fully homomorphic encryption technique. Experiment results show that this technique can aid in implementing a technologically aware system capable of providing a powerful answer to a current real-world problem.","url":"https://pubmed.ncbi.nlm.nih.gov/35528352/","authors":["Li N"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1155/2022/4482217","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35458968","name":"Analysis of Privacy-Enhancing Technologies in Open-Source Federated Learning Frameworks for Driver Activity Recognition.","source":"pubmed","abstract":"Wearable devices and smartphones that are used to monitor the activity and the state of the driver collect a lot of sensitive data such as audio, video, location and even health data. The analysis and processing of such data require observing the strict legal requirements for personal data security and privacy. The federated learning (FL) computation paradigm has been proposed as a privacy-preserving computational model that allows securing the privacy of the data owner. However, it still has no formal proof of privacy guarantees, and recent research showed that the attacks targeted both the model integrity and privacy of the data owners could be performed at all stages of the FL process. This paper focuses on the analysis of the privacy-preserving techniques adopted for FL and presents a comparative review and analysis of their implementations in the open-source FL frameworks. The authors evaluated their impact on the overall training process in terms of global model accuracy, training time and network traffic generated during the training process in order to assess their applicability to driver's state and behaviour monitoring. As the usage scenario, the authors considered the case of the driver's activity monitoring using the data from smartphone sensors. The experiments showed that the current implementation of the privacy-preserving techniques in open-source FL frameworks limits the practical application of FL to cross-silo settings.","url":"https://pubmed.ncbi.nlm.nih.gov/35458968/","authors":["Novikova E","Fomichov D","Kholod I","Filippov E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Apr 13","doi":"10.3390/s22082983","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35427226","name":"Lossless Data Hiding in Encrypted Images Compatible With Homomorphic Processing.","source":"pubmed","abstract":"Reversible data hiding in ciphertext has potential applications for privacy protection and transmitting extra data in a cloud environment. For instance, an original plain-text image can be recovered from the encrypted image generated after data embedding, while the embedded data can be extracted before or after decryption. However, homomorphic processing can hardly be applied to an encrypted image with hidden data to generate the desired image. This is partly due to that the image content may be changed by preprocessing or/and data embedding. Even if the corresponding plain-text pixel values are kept unchanged by lossless data hiding, the hidden data will be destroyed by outer processing. To address this issue, a lossless data hiding method called random element substitution (RES) is proposed for the Paillier cryptosystem by substituting the to-be-hidden bits for the random element of a cipher value. Moreover, the RES method is combined with another preprocessing-free algorithm to generate two schemes for lossless data hiding in encrypted images. With either scheme, a processed image will be obtained after the encrypted image undergoes processing in the homomorphic encrypted domain. Besides retrieving a part of the hidden data without image decryption, the data hidden with the RES method can be extracted after decryption, even after some processing has been conducted on encrypted images. The experimental results show the efficacy and superior performance of the proposed schemes.","url":"https://pubmed.ncbi.nlm.nih.gov/35427226/","authors":["Wu HT","Cheung YM","Zhuang Z","Xu L","Hu J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Jun","doi":"10.1109/TCYB.2022.3163245","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35310887","name":"A cloud-based buyer-seller watermarking protocol (CB-BSWP) using semi-trusted third party for copy deterrence and privacy preserving.","source":"pubmed","abstract":"Nowadays, cloud computing provides a platform infrastructure for the secure dealing of digital data, but privacy and copy control are the two important issues in it over a network. Cloud data is available to the end user and requires enormous security and privacy techniques to protect the data. Moreover, the access control mechanism with encryption-based technique protects the digital rights for participants in a transaction, but they do not protect the media from being illegally redistributed and do not restrict an authorized user to reveal their secret information this is referred to as you can access but you cannot leak. This brought out a need for controlling copy deterrence and preserving the privacy of digital media over the internet. To overlook this, we proposed a cloud-based buyer-seller watermarking protocol (CB-BSWP) with the use of a semi-trusted third party for copy deterrence and privacy-preserving in the cloud environment. The suggested scheme uses 1) a privacy homomorphism cryptosystem with Diffie-Hellman key exchange algorithm to provide an encrypted domain for the secure exchange of digital media 2) adopt robust and fair watermarking techniques to ensure high imperceptibility and robustness for the watermarked images against attacks 3) two services of cloud Infrastructure as a service (IaaS) to support virtualized computing infrastructure and Watermarking as a service (WaaS) to execute the speedy process of watermarking, this process is supported by watermarking generation and signing phase (WGSP) and watermark extraction and verifying phase reported in 4th section. 4) cloud service provider (CSP) considered as a \"semi-trusted\" third party to reduce the burden from the trusted third party (TTP) server and provide storage for the encrypted digital media on cloud databases, this frees content owner from not having a separate storage infrastructure. The proposed scheme encrypts the digital content by using SHA-512 algorithm with key size 512-bits to ensure that it doesn't affect computational time during the process of encryption. The suggested scheme addresses the problems of piracy tracing, anonymity, tamper resistance, non-framing, customer rights problem. The role of cloud is crucial because it reduces communication overhead, provides unlimited storage, supports the watermarking process and offers a solution for the secure distribution of end-to-end security of digital content over cloud. To check the performance of the suggested CB-BSWP protocol against common image processing attacks, we have conducted experiments in which the perceptual quality of watermarked digital media was found enhanced, resulting in a robust watermark.","url":"https://pubmed.ncbi.nlm.nih.gov/35310887/","authors":["Kumar A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1007/s11042-022-12550-7","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35303034","name":"A secure multi-party computation protocol without CRS supporting multi-bit encryption.","source":"pubmed","abstract":"To solve the problems in the existing fully homomorphic encryption (FHE)-based secure multi-party computation (SMC) protocols such as low efficiency, the FHE scheme that supports multi-bit encryption was modified during the generation of the public key so that the users could generate their public keys independently without the common random string (CRS) matrix. Further, a multi-bit Gentry-Sahai-Waters scheme (MGSW) scheme without CRS was constructed. The modified LinkAlgo algorithm was adopted to expand the single-key ciphertext into the multi-key ciphertext and simplify the way of generating the expanded ciphertext. In this way, a multi-key FHE (MFHE) scheme was achieved based on the MGSW scheme. Finally, a three-round SMC protocol without CRS was constructed using the MFHE scheme and the decisional learning with errors (DLWE) assumption, which was secure in the semi-malicious model. Compared to the existing protocols, the protocol proposed herein can support multi-bit encryption and is found with smaller ciphertext size and lower storage overhead and generate the expanded ciphertext in a simpler way. Overall performance is better than existing protocols.","url":"https://pubmed.ncbi.nlm.nih.gov/35303034/","authors":["Zhu ZW","Huang RW"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1371/journal.pone.0265572","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35259124","name":"Privacy-Preserving Federated Learning for Internet of Medical Things Under Edge Computing.","source":"pubmed","abstract":"Edge intelligent computing is widely used in the fields, such as the Internet of Medical Things (IoMT), which has advantages, including high data processing efficiency, strong real-time performance and low network delay. However, there are many problems including privacy disclosure, limited calculation force, as well as scheduling and coordination issues. Federated learning can greatly improves training efficiency. However, due to the sensitive nature of the healthcare data, the aforementioned approach of transferring the patient's data to the servers may create serious security and privacy issues. Therefore, this article proposes a Privacy Protection Scheme for Federated Learning under Edge Computing (PPFLEC). First of all, we propose a lightweight privacy protection protocol based on a shared secret and weight mask, which is based on a random mask scheme of secret sharing. It is more accurate and efficient than,homomorphic encryption. It can not only protect gradient privacy without losing model accuracy, but also resist equipment dropping and collusion attacks between devices. Second, we design an algorithm based on a digital signature and hash function, which achieves the integrity and consistency of the message, as well as resisting replay attacks. Finally, we propose a periodic average training strategy, compared with differential privacy to prove that our scheme is 40 % faster in efficiency than in deferential privacy. Meanwhile, compared with federated learning, we can achieve the same efficiency under the condition of ensuring safety. Therefore, our scheme can work well in unstable edge computing environments such as smart healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/35259124/","authors":["Wang R","Lai J","Zhang Z","Li X","Vijayakumar P","Karuppiah M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2023 Feb","doi":"10.1109/JBHI.2022.3157725","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35167978","name":"Privacy preserving collaborative learning of generalized linear mixed model.","source":"pubmed","abstract":"Generalized Linear Mixed Model is one of the most pervasive class of statistical models. It is widely used in the medical domain. Training such models in a collaborative setting often entails privacy risks. Standard privacy preserving mechanisms such as differential privacy can be used to mitigate the privacy risk during training the model. However, experimental evidence suggests that adding differential privacy to the training of the model can cause significant utility loss which makes the model impractical for real world usage. Therefore, it becomes clear that the specific class of generalized linear mixed models which lose their usability under differential privacy requires a different approach for privacy preserving model training. In this work, we propose a value-blind training method in a collaborative setting for generalized linear mixed models. In our proposed training method, the central server optimizes model parameters for a generalized linear mixed model without ever getting access to the raw training data or intermediate computation values. Intermediate computation values that are shared by the collaborating parties with the central server are encrypted using homomorphic encryption. Experimentation on multiple datasets suggests that the model trained by our proposed method achieves very low error rate while preserving privacy. To the best of our knowledge, this is the first work that performs a systematic privacy analysis of generalized linear mixed model training in collaborative setting.","url":"https://pubmed.ncbi.nlm.nih.gov/35167978/","authors":["Anjum MM","Mohammed N","Li W","Jiang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Mar","doi":"10.1016/j.jbi.2022.104008","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35135208","name":"Encrypted face recognition algorithm based on Ridgelet-DCT transform and THM chaos.","source":"pubmed","abstract":"With the popularization and application of face recognition technology, a large number of face image data are spread and used on the Internet. It has brought great potential safety hazard for personal privacy. Combined with the characteristics of tent chaos and Henon chaos, a THM (tent-Henon map) chaotic encrypted face algorithm based on Ridgelet-DCT transform is proposed in this paper. Different from conventional face recognition methods, this new approach encryptes the face images by means of using the homomorphic encryption method to extract their visual robust features in the first place, and then uses the proposed neural network model to design the encrypted face recognition algorithm. This paper selects the ORL face database of Cambridge University to verify the algorithm. Experimental results show that the algorithm has a good performance in encryption effect, security and robustness, and has a broad application prospect.","url":"https://pubmed.ncbi.nlm.nih.gov/35135208/","authors":["Liu Z","Li J","Liu J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Jan","doi":"10.3934/mbe.2022063","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35062530","name":"An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network.","source":"pubmed","abstract":"The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.","url":"https://pubmed.ncbi.nlm.nih.gov/35062530/","authors":["Ali A","Almaiah MA","Hajjej F","Pasha MF","Fang OH","Khan R","Teo J","Zakarya M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Jan 12","doi":"10.3390/s22020572","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35062491","name":"Deep Learning Based Homomorphic Secure Search-Able Encryption for Keyword Search in Blockchain Healthcare System: A Novel Approach to Cryptography.","source":"pubmed","abstract":"Due to the value and importance of patient health records (PHR), security is the most critical feature of encryption over the Internet. Users that perform keyword searches to gain access to the PHR stored in the database are more susceptible to security risks. Although a blockchain-based healthcare system can guarantee security, present schemes have several flaws. Existing techniques have concentrated exclusively on data storage and have utilized blockchain as a storage database. In this research, we developed a unique deep-learning-based secure search-able blockchain as a distributed database using homomorphic encryption to enable users to securely access data via search. Our suggested study will increasingly include secure key revocation and update policies. An IoT dataset was used in this research to evaluate our suggested access control strategies and compare them to benchmark models. The proposed algorithms are implemented using smart contracts in the hyperledger tool. The suggested strategy is evaluated in comparison to existing ones. Our suggested approach significantly improves security, anonymity, and monitoring of user behavior, resulting in a more efficient blockchain-based IoT system as compared to benchmark models.","url":"https://pubmed.ncbi.nlm.nih.gov/35062491/","authors":["Ali A","Pasha MF","Ali J","Fang OH","Masud M","Jurcut AD","Alzain MA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2022 Jan 11","doi":"10.3390/s22020528","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35002664","name":"cuSCNN: A Secure and Batch-Processing Framework for Privacy-Preserving Convolutional Neural Network Prediction on GPU.","source":"pubmed","abstract":"The emerging topic of privacy-preserving deep learning as a service has attracted increasing attention in recent years, which focuses on building an efficient and practical neural network prediction framework to secure client and model-holder data privately on the cloud. In such a task, the time cost of performing the secure linear layers is expensive, where matrix multiplication is the atomic operation. Most existing mix-based solutions heavily emphasized employing BGV-based homomorphic encryption schemes to secure the linear layer on the CPU platform. However, they suffer an efficiency and energy loss when dealing with a larger-scale dataset, due to the complicated encoded methods and intractable ciphertext operations. To address it, we propose cuSCNN, a secure and efficient framework to perform the privacy prediction task of a convolutional neural network (CNN), which can flexibly perform on the GPU platform. Its main idea is 2-fold: (1) To avoid the trivia and complicated homomorphic matrix computations brought by BGV-based solutions, it adopts GSW-based homomorphic matrix encryption to efficiently enable the linear layers of CNN, which is a naive method to secure matrix computation operations. (2) To improve the computation efficiency on GPU, a hybrid optimization approach based on CUDA (Compute Unified Device Architecture) has been proposed to improve the parallelism level and memory access speed when performing the matrix multiplication on GPU. Extensive experiments are conducted on industrial datasets and have shown the superior performance of the proposed cuSCNN framework in terms of runtime and power consumption compared to the other frameworks.","url":"https://pubmed.ncbi.nlm.nih.gov/35002664/","authors":["Bai Y","Liu Q","Wu W","Feng Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.3389/fncom.2021.799977","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:34703661","name":"Blockchain for genomics and healthcare: a literature review, current status, classification and open issues.","source":"pubmed","abstract":"The tremendous boost in the next generation sequencing technologies and in the \"omics\" technologies resulted in the generation of hundreds of gigabytes of data per day. Nowadays, via integrating -omics data with other data types, such as imaging and electronic health record (EHR) data, panomics studies attempt to identify novel and potentially actionable biomarkers for personalized medicine applications. In this respect, for the accurate analysis of -omics data and EHR, there is a need to establish secure and robust pipelines that take the ethical aspects into consideration, regulate privacy and ownership issues, and data sharing. These days, blockchain technology has picked up significant attention in diverse fields, including genomics, since it offers a new solution for these problems from a different perspective. Blockchain is an immutable transaction ledger, which offers secure and distributed system without a central authority. Within the system, each transaction can be expressed with cryptographically signed blocks, and the verification of transactions is performed by the users of the network. In this review, firstly, we aim to highlight the challenges of EHR and genomic data sharing. Secondly, we attempt to answer \"Why\" or \"Why not\" the blockchain technology is suitable for genomics and healthcare applications in detail. Thirdly, we elucidate the general blockchain structure based on the Ethereum, which is a more suitable technology for the genomic data sharing platforms. Fourthly, we review current blockchain-based EHR and genomic data sharing platforms, evaluate the advantages and disadvantages of these applications, and classify these applications using different metrics. Finally, we conclude by discussing the open issues and introducing our suggestion on the topic. In summary, to facilitate the diagnosis, monitoring and therapy of diseases with the effective analysis of -omics data with other available data types, through this review, we put forward the possible implications of the blockchain technology to life sciences and healthcare.","url":"https://pubmed.ncbi.nlm.nih.gov/34703661/","authors":["Adanur Dedeturk B","Soran A","Bakir-Gungor B"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.7717/peerj.12130","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:34450808","name":"Privacy-Preserving and Lightweight Selective Aggregation with Fault-Tolerance for Edge Computing-Enhanced IoT.","source":"pubmed","abstract":"Edge computing has been introduced to the Internet of Things (IoT) to meet the requirements of IoT applications. At the same time, data aggregation is widely used in data processing to reduce the communication overhead and energy consumption in IoT. Most existing schemes aggregate the overall data without filtering. In addition, aggregation schemes also face huge challenges, such as the privacy of the individual IoT device's data or the fault-tolerant and lightweight requirements of the schemes. In this paper, we present a privacy-preserving and lightweight selective aggregation scheme with fault tolerance (PLSA-FT) for edge computing-enhanced IoT. In PLSA-FT, selective aggregation can be achieved by constructing Boolean responses and numerical responses according to specific query conditions of the cloud center. Furthermore, we modified the basic Paillier homomorphic encryption to guarantee data privacy and support fault tolerance of IoT devices' malfunctions. An online/offline signature mechanism is utilized to reduce computation costs. The system characteristic analyses prove that the PLSA-FT scheme achieves confidentiality, privacy preservation, source authentication, integrity verification, fault tolerance, and dynamic membership management. Moreover, performance evaluation results show that PLSA-FT is lightweight with low computation costs and communication overheads.","url":"https://pubmed.ncbi.nlm.nih.gov/34450808/","authors":["Wang Q","Mu H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Aug 9","doi":"10.3390/s21165369","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:34441187","name":"Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition.","source":"pubmed","abstract":"In many decision-making scenarios, ranging from recreational activities to healthcare and policing, the use of artificial intelligence coupled with the ability to learn from historical data is becoming ubiquitous. This widespread adoption of automated systems is accompanied by the increasing concerns regarding their ethical implications. Fundamental rights, such as the ones that require the preservation of privacy, do not discriminate based on sensible attributes (e.g., gender, ethnicity, political/sexual orientation), or require one to provide an explanation for a decision, are daily undermined by the use of increasingly complex and less understandable yet more accurate learning algorithms. For this purpose, in this work, we work toward the development of systems able to ensure trustworthiness by delivering privacy, fairness, and explainability by design. In particular, we show that it is possible to simultaneously learn from data while preserving the privacy of the individuals thanks to the use of Homomorphic Encryption, ensuring fairness by learning a fair representation from the data, and ensuring explainable decisions with local and global explanations without compromising the accuracy of the final models. We test our approach on a widespread but still controversial application, namely face recognition, using the recent FairFace dataset to prove the validity of our approach.","url":"https://pubmed.ncbi.nlm.nih.gov/34441187/","authors":["Franco D","Oneto L","Navarin N","Anguita D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Aug 14","doi":"10.3390/e23081047","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:34415945","name":"Pindex: Private multi-linked index for encrypted document retrieval.","source":"pubmed","abstract":"Cryptographic cloud storage is used to make optimal use of the cloud storage infrastructure to outsource sensitive and mission-critical data. The continuous growth of encrypted data outsourced to cloud storage requires continuous updating. Attacks like file-injection are reported to compromise confidentiality of the user as a consequence of information leakage during update. It is required that dynamic schemes provide forward privacy guarantees. Updates should not leak information to the untrusted server regarding the previously issued queries. Therefore, the challenge is to design an efficient searchable encryption scheme with dynamic updates and forward privacy guarantees. In this paper, a novel private multi-linked dynamic index for encrypted document retrieval namely Pindex is proposed. The multi-linked dynamic index is constructed using probabilistic homomorphic encryption mechanism and secret orthogonal vectors. Full security proofs for correctness and forward privacy in the random oracle model is provided. Experiments on real world Enron dataset demonstrates that our construction is practical and efficient. The security and performance analysis of Pindex shows that the dynamic multi-linked index guarantees forward privacy without significant loss of efficiency.","url":"https://pubmed.ncbi.nlm.nih.gov/34415945/","authors":["Prakash AJ","Elizabeth BL"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1371/journal.pone.0256223","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:34207856","name":"Privacy-Enhancing k-Nearest Neighbors Search over Mobile Social Networks.","source":"pubmed","abstract":"Focusing on the diversified demands of location privacy in mobile social networks (MSNs), we propose a privacy-enhancing k -nearest neighbors search scheme over MSNs. First, we construct a dual-server architecture that incorporates location privacy and fine-grained access control. Under the above architecture, we design a lightweight location encryption algorithm to achieve a minimal cost to the user. We also propose a location re-encryption protocol and an encrypted location search protocol based on secure multi-party computation and homomorphic encryption mechanism, which achieve accurate and secure k -nearest friends retrieval. Moreover, to satisfy fine-grained access control requirements, we propose a dynamic friends management mechanism based on public-key broadcast encryption. It enables users to grant/revoke others' search right without updating their friends' keys, realizing constant-time authentication. Security analysis shows that the proposed scheme satisfies adaptive L-semantic security and revocation security under a random oracle model. In terms of performance, compared with the related works with single server architecture, the proposed scheme reduces the leakage of the location information, search pattern and the user-server communication cost. Our results show that a decentralized and end-to-end encrypted k -nearest neighbors search over MSNs is not only possible in theory, but also feasible in real-world MSNs collaboration deployment with resource-constrained mobile devices and highly iterative location update demands.","url":"https://pubmed.ncbi.nlm.nih.gov/34207856/","authors":["Li Y","Zhou F","Ge Y","Xu Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Jun 9","doi":"10.3390/s21123994","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:34198465","name":"Guaranteed distributed machine learning: Privacy-preserving empirical risk minimization.","source":"pubmed","abstract":"Distributed learning over data from sensor-based networks has been adopted to collaboratively train models on these sensitive data without privacy leakages. We present a distributed learning framework that involves the integration of secure multi-party computation and differential privacy. In our differential privacy method, we explore the potential of output perturbation and gradient perturbation and also progress with the cutting-edge methods of both techniques in the distributed learning domain. In our proposed multi-scheme output perturbation algorithm (MS-OP), data owners combine their local classifiers within a secure multi-party computation and later inject an appreciable amount of statistical noise into the model before they are revealed. In our Adaptive Iterative gradient perturbation (MS-GP) method, data providers collaboratively train a global model. During each iteration, the data owners aggregate their locally trained models within the secure multi-party domain. Since the conversion of differentially private algorithms are often naive, we improve on the method by a meticulous calibration of the privacy budget for each iteration. As the parameters of the model approach the optimal values, gradients are decreased and therefore require accurate measurement. We, therefore, add a fundamental line-search capability to enable our MS-GP algorithm to decide exactly when a more accurate measurement of the gradient is indispensable. Validation of our models on three (3) real-world datasets shows that our algorithm possesses a sustainable competitive advantage over the existing cutting-edge privacy-preserving requirements in the distributed setting.","url":"https://pubmed.ncbi.nlm.nih.gov/34198465/","authors":["Owusu-Agyemang K","Qin Z","Benjamin A","Xiong H","Qin Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Jun 1","doi":"10.3934/mbe.2021243","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:34198373","name":"Insuring against the perils in distributed learning: privacy-preserving empirical risk minimization.","source":"pubmed","abstract":"Multiple organizations would benefit from collaborative learning models trained over aggregated datasets from various human activity recognition applications without privacy leakages. Two of the prevailing privacy-preserving protocols, secure multi-party computation and differential privacy, however, are still confronted with serious privacy leakages: lack of provision for privacy guarantee about individual data and insufficient protection against inference attacks on the resultant models. To mitigate the aforementioned shortfalls, we propose privacy-preserving architecture to explore the potential of secure multi-party computation and differential privacy. We utilize the inherent prospects of output perturbation and gradient perturbation in our differential privacy method, and progress with an innovation for both techniques in the distributed learning domain. Data owners collaboratively aggregate the locally trained models inside a secure multi-party computation domain in the output perturbation algorithm, and later inject appreciable statistical noise before exposing the classifier. We inject noise during every iterative update to collaboratively train a global model in our gradient perturbation algorithm. The utility guarantee of our gradient perturbation method is determined by an expected curvature relative to the minimum curvature. With the application of expected curvature, we theoretically justify the advantage of gradient perturbation in our proposed algorithm, therefore closing existing gap between practice and theory. Validation of our algorithm on real-world human recognition activity datasets establishes that our protocol incurs minimal computational overhead, provides substantial utility gains for typical security and privacy guarantees.","url":"https://pubmed.ncbi.nlm.nih.gov/34198373/","authors":["Owusu-Agyemang K","Qin Z","Benjamin A","Xiong H","Qin Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Mar 29","doi":"10.3934/mbe.2021151","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:34192116","name":"Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach.","source":"pubmed","abstract":"Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner's premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.","url":"https://pubmed.ncbi.nlm.nih.gov/34192116/","authors":["Rahman MA","Hossain MS","Islam MS","Alrajeh NA","Muhammad G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1109/ACCESS.2020.3037474","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:33967732","name":"P(3)OI-MELSH: Privacy Protection Target Point of Interest Recommendation Algorithm Based on Multi-Exploring Locality Sensitive Hashing.","source":"pubmed","abstract":"With the rapid development of social network, intelligent terminal and automatic positioning technology, location-based social network (LBSN) service has become an important and valuable application. Point of interest (POI) recommendation is an important content in LBSN, which aims to recommend new locations of interest for users. It can not only alleviate the information overload problem faced by users in the era of big data, improve user experience, but also help merchants quickly find target users and achieve accurate marketing. Most of the works are based on users' check-in history and social network data to model users' personalized preferences for interest points, and recommend interest points through collaborative filtering and other recommendation technologies. However, in the check-in history, the multi-source heterogeneous information (including the position, category, popularity, social, reviews) describes user activity from different aspects which hides people's life style and personal preference. However, the above methods do not fully consider these factors' combined action. Considering the data privacy, it is difficult for individuals to share data with others with similar preferences. In this paper, we propose a privacy protection point of interest recommendation algorithm based on multi-exploring locality sensitive hashing (LSH). This algorithm studies the POI recommendation problem under distributed system. This paper introduces a multi-exploring method to improve the LSH algorithm. On the one hand, it reduces the number of hash tables to decrease the memory overhead; On the other hand, the retrieval range on each hash table is increased to reduce the time retrieval overhead. Meanwhile, the retrieval quality is similar to the original algorithm. The proposed method uses modified LSH and homomorphic encryption technology to assist POI recommendation which can ensure the accuracy, privacy and efficiency of the recommendation algorithm, and it verifies feasibility through experiments on real data sets. In terms of root mean square error (RMSE), mean absolute error (MAE) and running time, the proposed method has a competitive advantage.","url":"https://pubmed.ncbi.nlm.nih.gov/33967732/","authors":["Liu D","Shan L","Wang L","Yin S","Wang H","Wang C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.3389/fnbot.2021.660304","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:33672175","name":"Using Secure Multi-Party Computation to Protect Privacy on a Permissioned Blockchain.","source":"pubmed","abstract":"The development of information technology has brought great convenience to our lives, but at the same time, the unfairness and privacy issues brought about by traditional centralized systems cannot be ignored. Blockchain is a peer-to-peer and decentralized ledger technology that has the characteristics of transparency, consistency, traceability and fairness, but it reveals private information in some scenarios. Secure multi-party computation (MPC) guarantees enhanced privacy and correctness, so many researchers have been trying to combine secure MPC with blockchain to deal with privacy and trust issues. In this paper, we used homomorphic encryption, secret sharing and zero-knowledge proofs to construct a publicly verifiable secure MPC protocol consisting of two parts-an on-chain computation phase and an off-chain preprocessing phase-and we integrated the protocol as part of the chaincode in Hyperledger Fabric to protect the privacy of transaction data. Experiments showed that our solution performed well on a permissioned blockchain. Most of the time taken to complete the protocol was spent on communication, so the performance has a great deal of room to grow.","url":"https://pubmed.ncbi.nlm.nih.gov/33672175/","authors":["Zhou J","Feng Y","Wang Z","Guo D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Feb 23","doi":"10.3390/s21041540","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:38183193","name":"Citizen-centered, auditable and privacy-preserving population genomics.","source":"pubmed","abstract":"The growing number of health-data breaches, the use of genomic databases for law enforcement purposes and the lack of transparency of personal genomics companies are raising unprecedented privacy concerns. To enable a secure exploration of genomic datasets with controlled and transparent data access, we propose a citizen-centric approach that combines cryptographic privacy-preserving technologies, such as homomorphic encryption and secure multi-party computation, with the auditability of blockchains. Our open-source implementation supports queries on the encrypted genomic data of hundreds of thousands of individuals, with minimal overhead. We show that real-world adoption of our system alleviates widespread privacy concerns and encourages data access sharing with researchers.","url":"https://pubmed.ncbi.nlm.nih.gov/38183193/","authors":["Grishin D","Raisaro JL","Troncoso-Pastoriza JR","Obbad K","Quinn K","Misbach M","Gollhardt J","Sa J","Fellay J","Church GM","Hubaux JP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Mar","doi":"10.1038/s43588-021-00044-9","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:33466730","name":"A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services.","source":"pubmed","abstract":"This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication. The framework uses a fusion of electrocardiogram (ECG) and photoplethysmogram (PPG) signals when performing authentication. In addition to relying on the unique identification characteristics of the users' biometric traits, the security of the framework is empowered by the use of Homomorphic Encryption (HE). The use of HE allows patients' data to stay encrypted when being processed or analyzed in the cloud. Thus, providing not only a fast and reliable authentication mechanism, but also closing the door to many traditional security attacks. The framework's performance was evaluated and validated using a machine learning (ML) model that tested the framework using a dataset of 25 users in seating positions. Compared to using just ECG or PPG signals, the results of using the proposed fused-based biometric framework showed that it was successful in identifying and authenticating all 25 users with 100% accuracy. Hence, offering some significant improvements to the overall security and privacy of personalized healthcare systems.","url":"https://pubmed.ncbi.nlm.nih.gov/33466730/","authors":["Farid F","Elkhodr M","Sabrina F","Ahamed F","Gide E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Jan 14","doi":"10.3390/s21020552","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:33419094","name":"A Password Meter without Password Exposure.","source":"pubmed","abstract":"To meet password selection criteria of a server, a user occasionally needs to provide multiple choices of password candidates to an on-line password meter, but such user-chosen candidates tend to be derived from the user's previous passwords-the meter may have a high chance to acquire information about a user's passwords employed for various purposes. A third party password metering service may worsen this threat. In this paper, we first explore a new on-line password meter concept that does not necessitate the exposure of user's passwords for evaluating user-chosen password candidates in the server side. Our basic idea is straightforward; to adapt fully homomorphic encryption (FHE) schemes to build such a system but its performance achievement is greatly challenging. Optimization techniques are necessary for performance achievement in practice. We employ various performance enhancement techniques and implement the NIST (National Institute of Standards and Technology) metering method as seminal work in this field. Our experiment results demonstrate that the running time of the proposed meter is around 60 s in a conventional desktop server, expecting better performance in high-end hardware, with an FHE scheme in HElib library where parameters support at least 80-bit security. We believe the proposed method can be further explored and used for a password metering in case that password secrecy is very important-the user's password candidates should not be exposed to the meter and also an internal mechanism of password metering should not be disclosed to users and any other third parties.","url":"https://pubmed.ncbi.nlm.nih.gov/33419094/","authors":["Kim P","Lee Y","Hong YS","Kwon T"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Jan 6","doi":"10.3390/s21020345","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:33406662","name":"A Lattice-Based Homomorphic Proxy Re-Encryption Scheme with Strong Anti-Collusion for Cloud Computing.","source":"pubmed","abstract":"The homomorphic proxy re-encryption scheme combines the characteristics of a homomorphic encryption scheme and proxy re-encryption scheme. The proxy can not only convert a ciphertext of the delegator into a ciphertext of the delegatee, but also can homomorphically calculate the original ciphertext and re-encryption ciphertext belonging to the same user, so it is especially suitable for cloud computing. Yin et al. put forward the concept of a strong collusion attack on a proxy re-encryption scheme, and carried out a strong collusion attack on the scheme through an example. The existing homomorphic proxy re-encryption schemes use key switching algorithms to generate re-encryption keys, so it can not resist strong collusion attack. In this paper, we construct the first lattice-based homomorphic proxy re-encryption scheme with strong anti-collusion (HPRE-SAC). Firstly, algorithm TrapGen is used to generate an encryption key and trapdoor, then trapdoor sampling is used to generate a decryption key and re-encryption key, respectively. Finally, in order to ensure the homomorphism of ciphertext, a key switching algorithm is only used to generate the evaluation key. Compared with the existing homomorphic proxy re-encryption schemes, our HPRE-SAC scheme not only can resist strong collusion attacks, but also has smaller parameters.","url":"https://pubmed.ncbi.nlm.nih.gov/33406662/","authors":["Li J","Qiao Z","Zhang K","Cui C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Jan 4","doi":"10.3390/s21010288","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:33327652","name":"Blockchain from the Perspective of Privacy and Anonymisation: A Systematic Literature Review.","source":"pubmed","abstract":"The research presented aims to investigate the relationship between privacy and anonymisation in blockchain technologies on different fields of application. The study is carried out through a systematic literature review in different databases, obtaining in a first phase of selection 199 publications, of which 28 were selected for data extraction. The results obtained provide a strong relationship between privacy and anonymisation in most of the fields of application of blockchain, as well as a description of the techniques used for this purpose, such as Ring Signature, homomorphic encryption, k-anonymity or data obfuscation. Among the literature researched, some limitations and future lines of research on issues close to blockchain technology in the different fields of application can be detected. As conclusion, we extract the different degrees of application of privacy according to the mechanisms used and different techniques for the implementation of anonymisation, being one of the risks for privacy the traceability of the operations.","url":"https://pubmed.ncbi.nlm.nih.gov/33327652/","authors":["de Haro-Olmo FJ","Varela-Vaca ÁJ","Álvarez-Bermejo JA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020 Dec 14","doi":"10.3390/s20247171","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:33286371","name":"New Constructions of Identity-Based Dual Receiver Encryption from Lattices.","source":"pubmed","abstract":"Dual receiver encryption (DRE), being originally conceived at CCS 2004 as a proof technique, enables a ciphertext to be decrypted to the same plaintext by two different but dual receivers and becomes popular recently due to itself useful application potentials such secure outsourcing, trusted third party supervising, client puzzling, etc. Identity-based DRE (IB-DRE) further combines the bilateral advantages/facilities of DRE and identity-based encryption (IBE). Most previous constructions of IB-DRE are based on bilinear pairings, and thus suffers from known quantum algorithmic attacks. It is interesting to build IB-DRE schemes based on the well-known post quantum platforms, such as lattices. At ACISP 2018, Zhang et al. gave the first lattice-based construction of IB-DRE, and the main part of the public parameter in this scheme consists of 2 n + 2 matrices where n is the bit-length of arbitrary identity. In this paper, by introducing an injective map and a homomorphic computation technique due to Yamada at EUROCRYPT 2016, we propose another lattice-based construction of IB-DRE in an even efficient manner: The main part of the public parameters consists only of 2 p n 1 p + 2 matrices of the same dimensions, where p ( &#x2265; 2 ) is a flexible constant. The larger the p and n , the more observable of our proposal. Typically, when p = 2 and n = 284 according to the suggestion given by Peikert et al., the size of public parameters in our proposal is reduced to merely 12% of Zhang et al.'s method. In addition, to lighten the pressure of key generation center, we extend our lattice-based IB-DRE scheme to hierarchical scenario. Finally, both the IB-DRE scheme and the HIB-DRE scheme are proved to be indistinguishable against adaptively chosen identity and plaintext attacks (IND-ID-CPA).","url":"https://pubmed.ncbi.nlm.nih.gov/33286371/","authors":["Liu Y","Wang L","Shen X","Li L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020 May 28","doi":"10.3390/e22060599","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:33266523","name":"Two-Party Privacy-Preserving Set Intersection with FHE.","source":"pubmed","abstract":"A two-party private set intersection allows two parties, the client and the server, to compute an intersection over their private sets, without revealing any information beyond the intersecting elements. We present a novel private set intersection protocol based on Shuhong Gao's fully homomorphic encryption scheme and prove the security of the protocol in the semi-honest model. We also present a variant of the protocol which is a completely novel construction for computing the intersection based on Bloom filter and fully homomorphic encryption, and the protocol's complexity is independent of the set size of the client. The security of the protocols relies on the learning with errors and ring learning with error problems. Furthermore, in the cloud with malicious adversaries, the computation of the private set intersection can be outsourced to the cloud service provider without revealing any private information.","url":"https://pubmed.ncbi.nlm.nih.gov/33266523/","authors":["Cai Y","Tang C","Xu Q"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020 Nov 25","doi":"10.3390/e22121339","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:35782177","name":"Privacy-Enhanced Data Fusion for COVID-19 Applications in Intelligent Internet of Medical Things.","source":"pubmed","abstract":"With the worldwide large-scale outbreak of COVID-19, the Internet of Medical Things (IoMT), as a new type of Internet of Things (IoT)-based intelligent medical system, is being used for COVID-19 prevention and detection. However, since the widespread use of IoMT will generate a large amount of sensitive information related to patients, it is becoming more and more important yet challenging to ensure data security and privacy of COVID-19 applications in IoMT. The leakage of private information during IoMT data fusion process will cause serious problems and affect people's willingness to contribute data in IoMT. To address these challenges, this article proposes a new privacy-enhanced data fusion strategy (PDFS). The proposed PDFS consists of four important components, i.e., sensitive task classification, task completion assessment, incentive mechanism-based task contract design, and homomorphic encryption-based data fusion. The extensive simulation experiments demonstrate that PDFS can achieve high task classification accuracy, task completion rate, task data reliability and task participation rate, and low average error rate, while improving the privacy protection for data fusion under COVID-19 application environments based on IoMT.","url":"https://pubmed.ncbi.nlm.nih.gov/35782177/","authors":["Lin H","Garg S","Hu J","Wang X","Jalil Piran M","Hossain MS"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Nov 1","doi":"10.1109/JIOT.2020.3033129","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:33080397","name":"A secure system for genomics clinical decision support.","source":"pubmed","abstract":"We developed a prototype genomic archiving and communications system to securely store genome data and provide clinical decision support (CDS). This system operates on a client-server model. The client encrypts the data, and the server stores data and performs the computations necessary for CDS. Computations are directly performed on encrypted data, and the client decrypts results. The server cannot decrypt inputs or outputs, which provides strong guarantees of security. We have validated our system with three genomics-based CDS applications. The results demonstrate that it is possible to resolve a long-standing dilemma in genomic data privacy and accessibility, by using a principled cryptographical framework and a mathematical representation of genome data and CDS questions.","url":"https://pubmed.ncbi.nlm.nih.gov/33080397/","authors":["Karimi S","Jiang X","Dolin RH","Kim M","Boxwala A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020 Dec","doi":"10.1016/j.jbi.2020.103602","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:33048733","name":"Homomorphic-Encrypted Volume Rendering.","source":"pubmed","abstract":"Computationally demanding tasks are typically calculated in dedicated data centers, and real-time visualizations also follow this trend. Some rendering tasks, however, require the highest level of confidentiality so that no other party, besides the owner, can read or see the sensitive data. Here we present a direct volume rendering approach that performs volume rendering directly on encrypted volume data by using the homomorphic Paillier encryption algorithm. This approach ensures that the volume data and rendered image are uninterpretable to the rendering server. Our volume rendering pipeline introduces novel approaches for encrypted-data compositing, interpolation, and opacity modulation, as well as simple transfer function design, where each of these routines maintains the highest level of privacy. We present performance and memory overhead analysis that is associated with our privacy-preserving scheme. Our approach is open and secure by design, as opposed to secure through obscurity. Owners of the data only have to keep their secure key confidential to guarantee the privacy of their volume data and the rendered images. Our work is, to our knowledge, the first privacy-preserving remote volume-rendering approach that does not require that any server involved be trustworthy; even in cases when the server is compromised, no sensitive data will be leaked to a foreign party.","url":"https://pubmed.ncbi.nlm.nih.gov/33048733/","authors":["Mazza S","Patel D","Viola I"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Feb","doi":"10.1109/TVCG.2020.3030436","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:33027897","name":"Secure Deep Learning for Intelligent Terahertz Metamaterial Identification.","source":"pubmed","abstract":"Metamaterials, artificially engineered structures with extraordinary physical properties, offer multifaceted capabilities in interdisciplinary fields. To address the looming threat of stealthy monitoring, the detection and identification of metamaterials is the next research frontier but have not yet been explored. Here, we show that the crypto-oriented convolutional neural network (CNN) makes possible the secure intelligent detection of metamaterials in mixtures. Terahertz signals were encrypted by homomorphic encryption and the ciphertext was submitted to the CNN directly for results, which can only be decrypted by the data owner. The experimentally measured terahertz signals were augmented and further divided into training sets and test sets using 5-fold cross-validation. Experimental results illustrated that the model achieved an accuracy of 100% on the test sets, which highly outperformed humans and the traditional machine learning. The CNN took 9.6 s to inference on 92 encrypted test signals with homomorphic encryption backend. The proposed method with accuracy and security provides private preserving paradigm for artificial intelligence-based material identification.","url":"https://pubmed.ncbi.nlm.nih.gov/33027897/","authors":["Liu F","Zhang W","Sun Y","Liu J","Miao J","He F","Wu X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020 Oct 5","doi":"10.3390/s20195673","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:32987539","name":"Privacy preserving anomaly detection based on local density estimation.","source":"pubmed","abstract":"Anomaly detection has been widely researched in financial, biomedical and other areas. However, most existing algorithms have high time complexity. Another important problem is how to efficiently detect anomalies while protecting data privacy. In this paper, we propose a fast anomaly detection algorithm based on local density estimation (LDEM). The key insight of LDEM is a fast local density estimator, which estimates the local density of instances by the average density of all features. The local density of each feature can be estimated by the defined mapping function. Furthermore, we propose an efficient scheme named PPLDEM based on the proposed scheme and homomorphic encryption to detect anomaly instances in the case of multi-party participation. Compared with existing schemes with privacy preserving, our scheme needs less communication cost and less calculation cost. From security analysis, our scheme will not leak privacy information of participants. And experiments results show that our proposed scheme PPLDEM can detect anomaly instances effectively and efficiently, for example, the recognition of activities in clinical environments for healthy older people aged 66 to 86 years old using the wearable sensors.","url":"https://pubmed.ncbi.nlm.nih.gov/32987539/","authors":["Zhang CK","Yin A","Zuo W","Chen YY"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020 May 6","doi":"10.3934/mbe.2020196","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:32823317","name":"Clinical Research Informatics.","source":"pubmed","abstract":"To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2019.","url":"https://pubmed.ncbi.nlm.nih.gov/32823317/","authors":["Daniel C","Kalra D","Section Editors for the IMIA Yearbook Section on Clinical Research Informatics"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020 Aug","doi":"10.1055/s-0040-1702007","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:32733683","name":"Data protection and ethics requirements for multisite research with health data: a comparative examination of legislative governance frameworks and the role of data protection technologies.","source":"pubmed","abstract":"Personalised medicine can improve both public and individual health by providing targeted preventative and therapeutic healthcare. However, patient health data must be shared between institutions and across jurisdictions for the benefits of personalised medicine to be realised. Whilst data protection, privacy, and research ethics laws protect patient confidentiality and safety they also may impede multisite research, particularly across jurisdictions. Accordingly, we compare the concept of data accessibility in data protection and research ethics laws across seven jurisdictions. These jurisdictions include Switzerland, Italy, Spain, the United Kingdom (which have implemented the General Data Protection Regulation), the United States, Canada, and Australia. Our paper identifies the requirements for consent, the standards for anonymisation or pseudonymisation, and adequacy of protection between jurisdictions as barriers for sharing. We also identify differences between the European Union and other jurisdictions as a significant barrier for data accessibility in cross jurisdictional multisite research. Our paper concludes by considering solutions to overcome these legislative differences. These solutions include data transfer agreements and organisational collaborations designed to `front load' the process of ethics approval, so that subsequent research protocols are standardised. We also allude to technical solutions, such as distributed computing, secure multiparty computation and homomorphic encryption.","url":"https://pubmed.ncbi.nlm.nih.gov/32733683/","authors":["Scheibner J","Ienca M","Kechagia S","Troncoso-Pastoriza JR","Raisaro JL","Hubaux JP","Fellay J","Vayena E"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020 Jan-Jun","doi":"10.1093/jlb/lsaa010","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:32693798","name":"Semi-Parallel logistic regression for GWAS on encrypted data.","source":"pubmed","abstract":"The sharing of biomedical data is crucial to enable scientific discoveries across institutions and improve health care. For example, genome-wide association studies (GWAS) based on a large number of samples can identify disease-causing genetic variants. The privacy concern, however, has become a major hurdle for data management and utilization. Homomorphic encryption is one of the most powerful cryptographic primitives which can address the privacy and security issues. It supports the computation on encrypted data, so that we can aggregate data and perform an arbitrary computation on an untrusted cloud environment without the leakage of sensitive information.","url":"https://pubmed.ncbi.nlm.nih.gov/32693798/","authors":["Kim M","Song Y","Li B","Micciancio D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020 Jul 21","doi":"10.1186/s12920-020-0724-z","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:32570398","name":"MedCo2: Privacy-Preserving Cohort Exploration and Analysis.","source":"pubmed","abstract":"Medical studies are usually time consuming, cumbersome and extremely costly to perform, and for exploratory research, their results are also difficult to predict a priori. This is particularly the case for rare diseases, for which finding enough patients is difficult and usually requires an international-scale research. In this case, the process can be even more difficult due to the heterogeneity of data-protection regulations, making the data sharing process particularly hard. In this short paper, we propose MedCo2 (pronounced MedCo square), a distributed system that streamlines the process of a medical study by bridging and enabling both data discovery and data analysis among multiple databases, while protecting data confidentiality and patients' privacy. MedCo2 relies on interactive protocols, homomorphic encryption and differential privacy. It enables the privacy-preserving computations of multiple statistics such as cosine similarity and variance, and the training of machine learning models, on patients that are obliviously selected according to specific criteria among multiple databases.","url":"https://pubmed.ncbi.nlm.nih.gov/32570398/","authors":["Froelicher D","Misbach M","Troncoso-Pastoriza JR","Raisaro JL","Hubaux JP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020 Jun 16","doi":"10.3233/SHTI200174","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:32477635","name":"Toward a More Accurate Accrual to Clinical Trials: Joint Cohort Discovery Using Bloom Filters and Homomorphic Encryption.","source":"pubmed","abstract":"Reliable cohort discovery is an essential early part of clinical study design. Indeed, it is the defining feature of many clinical research networks, including the recently launched Accrual to Clinical Trials (ACT) network. As currently deployed, however, the ACT network only allows cohort queries in isolated silos, rendering cohort discovery across sites unreliable. Here we demonstrate a novel protocol to provide network participants access to more accurate combined cohort estimates (union cardinality) with other sites. A two-party Elgamal protocol is implemented to ensure privacy and security imperatives, and a special attribute of Bloom filters is exploited for accurate and fast cardinality estimates. To emulate mandatory privacy protecting obfuscation factors (like those applied to the counts reported for individual sites by ACT), we configure the Bloom filter based on the individual site cohort sizes, striking an appropriate balance between accuracy and privacy. Finally, we discuss additional approval and data governance steps required to incorporate our protocol in the current ACT infrastructure.","url":"https://pubmed.ncbi.nlm.nih.gov/32477635/","authors":["Dong X","Randolph DA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"","addedAt":"2026-08-31T06:41:42.472Z","updatedAt":"2026-08-31T06:41:42.472Z"},{"id":"pmid:32351612","name":"Applying Deep Neural Networks over Homomorphic Encrypted Medical Data.","source":"pubmed","abstract":"In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the healthcare sector. Despite their ability to provide solutions within personalized medicine, strict regulations on the confidentiality of patient health information have in many cases hindered the adoption of deep learning-based solutions in clinical workflows. To allow for the processing of sensitive health information without disclosing the underlying data, we propose a solution based on fully homomorphic encryption (FHE). The considered encryption scheme, MORE (Matrix Operation for Randomization or Encryption), enables the computations within a neural network model to be directly performed on floating point data with a relatively small computational overhead. We consider the well-known MNIST digit recognition problem to evaluate the feasibility of the proposed method and show that performance does not decrease when deep learning is applied on MORE homomorphic data. To further evaluate the suitability of the method for healthcare applications, we first train a model on encrypted data to estimate the outputs of a whole-body circulation (WBC) hemodynamic model and then provide a solution for classifying encrypted X-ray coronary angiography medical images. The findings highlight the potential of the proposed privacy-preserving deep learning methods to outperform existing approaches by providing, within a reasonable amount of time, results equivalent to those achieved by unencrypted models. Lastly, we discuss the security implications of the encryption scheme and show that while the considered cryptosystem promotes efficiency and utility at a lower security level, it is still applicable in certain practical use cases.","url":"https://pubmed.ncbi.nlm.nih.gov/32351612/","authors":["Vizitiu A","Niƫă CI","Puiu A","Suciu C","Itu LM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1155/2020/3910250","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:31947330","name":"Privacy-Preserving Artificial Intelligence: Application to Precision Medicine.","source":"pubmed","abstract":"Motivated by state-of-the-art performances across a wide variety of areas, over the last few years Machine Learning has drawn a significant amount of attention from the healthcare domain. Despite their potential in enabling person-alized medicine applications, the adoption of Deep Learning based solutions in clinical workflows has been hindered in many cases by the strict regulations concerning the privacy of patient health data. We propose a solution that relies on Fully Homomorphic Encryption, particularly on the MORE scheme, as a mechanism for enabling computations on sensitive health data, without revealing the underlying data. The chosen variant of the encryption scheme allows for the computations in the Neural Network model to be directly performed on floating point numbers, while incurring a reasonably small computational overhead. For feasibility evaluation, we demonstrate on the MNIST digit recognition task that Deep Learning can be performed on encrypted data without compromising the accuracy. We then address a more complex task by training a model on encrypted data to estimate the outputs of a whole-body circulation (WBC) model. These results underline the potential of the proposed approach to outperform current solutions by delivering comparable results to the unencrypted Deep Learning based solutions, in a reasonable amount of time. Lastly, the security aspects of the encryption scheme are analyzed, and we show that, even though the chosen encryption scheme favors performance and utility at the cost of weaker security, it can still be used in certain practical applications.","url":"https://pubmed.ncbi.nlm.nih.gov/31947330/","authors":["Vizitiu A","Nita CI","Puiu A","Suciu C","Itu LM"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Jul","doi":"10.1109/EMBC.2019.8857960","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:31947329","name":"Secure Processing of Stream Cipher Encrypted Data Issued from IOT: Application to a Connected Knee Prosthesis.","source":"pubmed","abstract":"In this paper, we propose a secure protocol that allows processing encrypted data emitted by a medical IOT device. Its originality stands on a new fast algorithm which makes possible the conversion of Combined Linear Congruential Generator (CLCG) encrypted data into data homomorphically encrypted with the Damgard-Jurik (D-J) cryptosystem. By doing so, an honest-but-curious third party, like a smartphone, can process data issued from the IOT devices (e.g. raising a health alert) without endangering data privacy while CLCG can be integrated in an IOT of low computation capabilities. Moreover, in order to reduce communication and computation complexities compared to existing solutions and to achieve a real time solution, we further propose a secure packed version of CLCG in the D-J domain. With it a medical IOT can encrypt several pieces of data at once while allowing a third party to independently convert and process them in their D-J homomorphic encrypted form. We theoretically and experimentally demonstrate the performance of our solution in the case of a connected knee prosthesis, the data of which are processed for patient monitoring.","url":"https://pubmed.ncbi.nlm.nih.gov/31947329/","authors":["Pistono M","Bellafqira R","Coatrieux G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Jul","doi":"10.1109/EMBC.2019.8857055","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:31870978","name":"Learning a Fixed-Length Fingerprint Representation.","source":"pubmed","abstract":"We present DeepPrint, a deep network, which learns to extract fixed-length fingerprint representations of only 200 bytes. DeepPrint incorporates fingerprint domain knowledge, including alignment and minutiae detection, into the deep network architecture to maximize the discriminative power of its representation. The compact, DeepPrint representation has several advantages over the prevailing variable length minutiae representation which (i) requires computationally expensive graph matching techniques, (ii) is difficult to secure using strong encryption schemes (e.g., homomorphic encryption), and (iii) has low discriminative power in poor quality fingerprints where minutiae extraction is unreliable. We benchmark DeepPrint against two top performing COTS SDKs (Verifinger and Innovatrics) from the NIST and FVC evaluations. Coupled with a re-ranking scheme, the DeepPrint rank-1 search accuracy on the NIST SD4 dataset against a gallery of 1.1 million fingerprints is comparable to the top COTS matcher, but it is significantly faster (DeepPrint: 98.80% in 0.3 seconds vs. COTS A: 98.85% in 27 seconds). To the best of our knowledge, the DeepPrint representation is the most compact and discriminative fixed-length fingerprint representation reported in the academic literature.","url":"https://pubmed.ncbi.nlm.nih.gov/31870978/","authors":["Engelsma JJ","Cao K","Jain AK"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2021 Jun","doi":"10.1109/TPAMI.2019.2961349","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:31801535","name":"Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine.","source":"pubmed","abstract":"The successful introduction of homomorphic encryption (HE) in clinical research holds promise for improving acceptance of data-sharing protocols, increasing sample sizes, and accelerating learning from real-world data (RWD). A well-scoped use case for HE would pave the way for more widespread adoption in healthcare applications. Determining the efficacy of targeted cancer treatments used off-label for a variety of genetically defined conditions is an excellent candidate for introduction of HE-based learning systems because of a significant unmet need to share and combine confidential data, the use of relatively simple algorithms, and an opportunity to reach large numbers of willing study participants.","url":"https://pubmed.ncbi.nlm.nih.gov/31801535/","authors":["Paddock S","Abedtash H","Zummo J","Thomas S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Dec 4","doi":"10.1186/s12911-019-0983-9","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:31648120","name":"Privacy-enhanced multi-party deep learning.","source":"pubmed","abstract":"In multi-party deep learning, multiple participants jointly train a deep learning model through a central server to achieve common objectives without sharing their private data. Recently, a significant amount of progress has been made toward the privacy issue of this emerging multi-party deep learning paradigm. In this paper, we mainly focus on two problems in multi-party deep learning. The first problem is that most of the existing works are incapable of defending simultaneously against the attacks of honest-but-curious participants and an honest-but-curious server without a manager trusted by all participants. To tackle this problem, we design a privacy-enhanced multi-party deep learning framework, which integrates differential privacy and homomorphic encryption to prevent potential privacy leakage to other participants and a central server without requiring a manager that all participants trust. The other problem is that existing frameworks consume high total privacy budget when applying differential privacy for preserving privacy, which leads to a high risk of privacy leakage. In order to alleviate this problem, we propose three strategies for dynamically allocating privacy budget at each epoch to further enhance privacy guarantees without compromising the model utility. Moreover, it provides participants with an intuitive handle to strike a balance between the privacy level and the training efficiency by choosing different strategies. Both analytical and experimental evaluations demonstrate the promising performance of our proposed framework.","url":"https://pubmed.ncbi.nlm.nih.gov/31648120/","authors":["Gong M","Feng J","Xie Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2020 Jan","doi":"10.1016/j.neunet.2019.10.001","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:31499644","name":"High-capacity reversible data hiding in encrypted images based on two-phase histogram shifting.","source":"pubmed","abstract":"With the extensive use of cloud services in different applications, it's a problem for the cloud service provider to manage or process the privacy data that are encrypted by the content owner. Therefore, signal processing technology in the encrypted domain has attracted the attention of researchers. In this paper, we propose a new reversible data hiding method for encrypted images based on two-phase histogram shifting. In the proposed method, the original image is encrypted by using special image division and additive homomorphic encryption. After image encryption, the encrypted image can partially maintain spatial correlation for data embedding while the content security of the encrypted image is ensured. Due to the spatial correlation, the data hider can generate two difference histograms from the encrypted image, which provide high embedding capacity. A two-phase histogram shift scheme is used to embed the secret data into the two difference histograms. At the receiver side, the secret data can be extracted from the encrypted image or the decrypted image, and the image can be recovered to its original version without any error. The experimental results demonstrated that the proposed method can efficiently improve the capacity of data embedding and outperform other related methods, while the visual quality of the marked image can be maintained.","url":"https://pubmed.ncbi.nlm.nih.gov/31499644/","authors":["Chen KM","Chang CC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 May 6","doi":"10.3934/mbe.2019195","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:31499618","name":"Algebraic secret sharing using privacy homomorphisms for IoT-based healthcare systems.","source":"pubmed","abstract":"Healthcare industry is one of the promising fields adopting the Internet of Things (IoT) solutions. In this paper, we study secret sharing mechanisms towards resolving privacy and security issues in IoT-based healthcare applications. In particular, we show how multiple sources are possible to share their data amongst a group of participants without revealing their own data to one another as well as the dealer. Only an authorised subset of participants is able to reconstruct the data. A collusion of fewer participants has no better chance of guessing the private data than a non-participant who has no shares at all. To realise this system, we introduce a novel research upon secret sharing in the encrypted domain. In modern healthcare industry, a patient's health Article often contains data acquired from various sensor nodes. In order to protect information privacy, the data from sensor nodes is encrypted at once and shared among a number of cloud servers of medical institutions via a gateway device. The complete health Article will be retrieved for diagnosis only if the number of presented shares meets the access policy. The retrieval procedure does not involve decryption and therefore the scheme is favourable in some time-sensitive circumstances such as a surgical emergency. We analyse the pros and cons of several possible solutions and develop practical secret sharing schemes for IoT- based healthcare systems.","url":"https://pubmed.ncbi.nlm.nih.gov/31499618/","authors":["Chang CC","Li CT"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Apr 18","doi":"10.3934/mbe.2019168","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:31493797","name":"A privacy-preserving distributed filtering framework for NLP artifacts.","source":"pubmed","abstract":"Medical data sharing is a big challenge in biomedicine, which often hinders collaborative research. Due to privacy concerns, clinical notes cannot be directly shared. A lot of efforts have been dedicated to de-identifying clinical notes but it is still very challenging to accurately locate and scrub all sensitive elements from notes in an automatic manner. An alternative approach is to remove sentences that might contain sensitive terms related to personal information.","url":"https://pubmed.ncbi.nlm.nih.gov/31493797/","authors":["Sadat MN","Aziz MMA","Mohammed N","Pakhomov S","Liu H","Jiang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Sep 7","doi":"10.1186/s12911-019-0867-z","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:31404438","name":"Secure Outsourced Matrix Computation and Application to Neural Networks.","source":"pubmed","abstract":"Homomorphic Encryption (HE) is a powerful cryptographic primitive to address privacy and security issues in outsourcing computation on sensitive data to an untrusted computation environment. Comparing to secure Multi-Party Computation (MPC), HE has advantages in supporting non-interactive operations and saving on communication costs. However, it has not come up with an optimal solution for modern learning frameworks, partially due to a lack of efficient matrix computation mechanisms. In this work, we present a practical solution to encrypt a matrix homomorphically and perform arithmetic operations on encrypted matrices. Our solution includes a novel matrix encoding method and an efficient evaluation strategy for basic matrix operations such as addition, multiplication, and transposition. We also explain how to encrypt more than one matrix in a single ciphertext, yielding better amortized performance. Our solution is generic in the sense that it can be applied to most of the existing HE schemes. It also achieves reasonable performance for practical use; for example, our implementation takes 9.21 seconds to multiply two encrypted square matrices of order 64 and 2.56 seconds to transpose a square matrix of order 64. Our secure matrix computation mechanism has a wide applicability to our new framework E2DM, which stands for encrypted data and encrypted model. To the best of our knowledge, this is the first work that supports secure evaluation of the prediction phase based on both encrypted data and encrypted model, whereas previous work only supported applying a plain model to encrypted data. As a benchmark, we report an experimental result to classify handwritten images using convolutional neural networks (CNN). Our implementation on the MNIST dataset takes 28.59 seconds to compute ten likelihoods of 64 input images simultaneously, yielding an amortized rate of 0.45 seconds per image.","url":"https://pubmed.ncbi.nlm.nih.gov/31404438/","authors":["Jiang X","Kim M","Lauter K","Song Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018 Oct","doi":"10.1145/3243734.3243837","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:31248105","name":"A Personalized QoS Prediction Method for Web Services via Blockchain-Based Matrix Factorization.","source":"pubmed","abstract":"Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable users, leading to inaccurate predictions. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, encryption algorithms, etc., we propose a personalized QoS prediction method for web services that we call blockchain-based matrix factorization (BMF). We develop a user verification approach based on homomorphic hash, and use the Byzantine agreement to remove unreliable users. Then, matrix factorization is employed to improve the accuracy of predictions and we evaluate the proposed BMF on a real-world web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, making it much more effective than traditional techniques.","url":"https://pubmed.ncbi.nlm.nih.gov/31248105/","authors":["Cai W","Du X","Xu J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Jun 19","doi":"10.3390/s19122749","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:33267339","name":"Reversible Data Hiding Algorithm in Fully Homomorphic Encrypted Domain.","source":"pubmed","abstract":"This paper proposes a reversible data hiding scheme by exploiting the DGHV fully homomorphic encryption, and analyzes the feasibility of the scheme for data hiding from the perspective of information entropy. In the proposed algorithm, additional data can be embedded directly into a DGHV fully homomorphic encrypted image without any preprocessing. On the sending side, by using two encrypted pixels as a group, a data hider can get the difference of two pixels in a group. Additional data can be embedded into the encrypted image by shifting the histogram of the differences with the fully homomorphic property. On the receiver side, a legal user can extract the additional data by getting the difference histogram, and the original image can be restored by using modular arithmetic. Besides, the additional data can be extracted after decryption while the original image can be restored. Compared with the previous two typical algorithms, the proposed scheme can effectively avoid preprocessing operations before encryption and can successfully embed and extract additional data in the encrypted domain. The extensive testing results on the standard images have certified the effectiveness of the proposed scheme.","url":"https://pubmed.ncbi.nlm.nih.gov/33267339/","authors":["Li J","Liang X","Dai C","Xiang S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Jun 26","doi":"10.3390/e21070625","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:31067751","name":"A Fine-Grained User-Divided Privacy-Preserving Access Control Protocol in Smart Watch.","source":"pubmed","abstract":"A smart watch is a kind of emerging wearable device in the Internet of Things. The security and privacy problems are the main obstacles that hinder the wide deployment of smart watches. Existing security mechanisms do not achieve a balance between the privacy-preserving and data access control. In this paper, we propose a fine-grained privacy-preserving access control architecture for smart watches (FPAS). In FPAS, we leverage the identity-based authentication scheme to protect the devices from malicious connection and policy-based access control for data privacy preservation. The core policy of FPAS is two-fold: (1) utilizing a homomorphic and re-encrypted scheme to ensure that the ciphertext information can be correctly calculated; (2) dividing the data requester by different attributes to avoid unauthorized access. We present a concrete scheme based on the above prototype and analyze the security of the FPAS. The performance and evaluation demonstrate that the FPAS scheme is efficient, practical, and extensible.","url":"https://pubmed.ncbi.nlm.nih.gov/31067751/","authors":["Fang L","Li M","Zhou L","Zhang H","Ge C"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 May 7","doi":"10.3390/s19092109","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:31009760","name":"The European cross-border health data exchange roadmap: Case study in the Italian setting.","source":"pubmed","abstract":"Health data exchange is a major challenge due to the sensitive information and the privacy issues entailed. Considering the European context, in which health data must be exchanged between different European Union (EU) Member States, each having a different national regulatory framework as well as different national healthcare structures, the challenge appears even greater. Europe has tried to address this challenge via the epSOS (\"Smart Open Services for European Patients\") project in 2008, a European large-scale pilot on cross-border sharing of specific health data and services. The adoption of the framework is an ongoing activity, with most Member States planning its implementation by 2020. Yet, this framework is quite generic and leaves a wide space to each EU Member State regarding the definition of roles, processes, workflows and especially the specific integration with the National Infrastructures for eHealth. The aim of this paper is to present the current landscape of the evolving eHealth infrastructure for cross-border health data exchange in Europe, as a result of past and ongoing initiatives, and illustrate challenges, open issues and limitations through a specific case study describing how Italy is approaching its adoption and accommodates the identified barriers. To this end, the paper discusses ethical, regulatory and organizational issues, also focusing on technical aspects, such as interoperability and cybersecurity. Regarding cybersecurity aspects per se, we present the approach of the KONFIDO EU-funded project, which aims to reinforce trust and security in European cross-border health data exchange by leveraging novel approaches and cutting-edge technologies, such as homomorphic encryption, photonic Physical Unclonable Functions (p-PUF), a Security Information and Event Management (SIEM) system, and blockchain-based auditing. In particular, we explain how KONFIDO will test its outcomes through a dedicated pilot based on a realistic scenario, in which Italy is involved in health data exchange with other European countries.","url":"https://pubmed.ncbi.nlm.nih.gov/31009760/","authors":["Nalin M","Baroni I","Faiella G","Romano M","Matrisciano F","Gelenbe E","Martinez DM","Dumortier J","Natsiavas P","Votis K","Koutkias V","Tzovaras D","Clemente F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Jun","doi":"10.1016/j.jbi.2019.103183","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:30967116","name":"A secure SNP panel scheme using homomorphically encrypted K-mers without SNP calling on the user side.","source":"pubmed","abstract":"Single Nucleotide Polymorphism (SNP) in the genome has become crucial information for clinical use. For example, the targeted cancer therapy is primarily based on the information which clinically important SNPs are detectable from the tumor. Many hospitals have developed their own panels that include clinically important SNPs. The genome information exchange between the patient and the hospital has become more popular. However, the genome sequence information is innate and irreversible and thus its leakage has serious consequences. Therefore, protecting one's genome information is critical. On the other side, hospitals may need to protect their own panels. There is no known secure SNP panel scheme to protect both.","url":"https://pubmed.ncbi.nlm.nih.gov/30967116/","authors":["Park S","Kim M","Seo S","Hong S","Han K","Lee K","Cheon JH","Kim S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Apr 4","doi":"10.1186/s12864-019-5473-z","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:30915578","name":"Achieving Efficient and Privacy-Preserving k-NN Query for Outsourced eHealthcare Data.","source":"pubmed","abstract":"The boom of Internet of Things devices promotes huge volumes of eHealthcare data will be collected and aggregated at eHealthcare provider. With the help of these health data, eHealthcare provider can offer reliable data service (e.g., k-NN query) to doctors for better diagnosis. However, the IT facility in the eHealthcare provider is incompetent with the huge volumes of eHealthcare data, so one popular solution is to deploy a powerful cloud and appoint the cloud to execute the k-NN query service. In this case, since the eHealthcare data are very sensitive yet cloud servers are not fully trusted, directly executing the k-NN query service in the cloud inevitably incurs privacy challenges. Apart from the privacy issues, efficiency issues also need to be taken into consideration because achieving privacy requirement will incur additional computational cost. However, existing focuses on k-NN query do not (fully) consider the data privacy or are inefficient. For instance, the best computational complexity of k-NN query over encrypted eHealthcare data in the cloud is as large as [Formula: see text], where N is the total number of data. In this paper, aiming at addressing the privacy and efficiency challenges, we design an efficient and privacy-preserving k-NN query scheme for encrypted outsourced eHealthcare data. Our proposed scheme is characterized by integrating the k d-tree with the homomorphic encryption technique for efficient storing encrypted data in the cloud and processing privacy-preserving k-NN query over encrypted data. Compared with existing works, our proposed scheme is more efficient in terms of privacy-preserving k-NN query. Specifically, our proposed scheme can achieve k-NN computation over encrypted data with [Formula: see text] computational complexity, where l and N respectively denote the data dimension and the total number of data. In addition, detailed security analysis shows that our proposed scheme is really privacy-preserving under our security model and performance evaluation also indicates that our proposed scheme is indeed efficient in terms of computational cost.","url":"https://pubmed.ncbi.nlm.nih.gov/30915578/","authors":["Zheng Y","Lu R","Shao J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Mar 27","doi":"10.1007/s10916-019-1229-1","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:30815160","name":"Privacy-preserving biomedical data dissemination via a hybrid approach.","source":"pubmed","abstract":"Sharing medical data can benefit many aspects of biomedical research studies. However, medical data usually contains sensitive patient information, which cannot be shared directly. Summary statistics, like histogram, are widely used in medical research which serves as a sanitized synopsis of the raw health dataset such as Electrical Health Records (EHR). Such synopsized representation is then be used to support advanced operations over health dataset such as counting queries and learning based tasks. While privacy becomes an increasingly important issue for generating and publishing health data based histograms. Previous solutions show promise on securely generating histogram via differential privacy, however such methods only consider a centralized solution and the accuracy is still a limitation for real world applications. In this paper, we propose a novel hybrid solution to combine two rigorous theoretical models (homomorphic encryption and differential privacy) for securely generating synthetic V-optimal histograms over distributed datasets. Our results demonstrated accuracy improvement over previous study over real medical datasets.","url":"https://pubmed.ncbi.nlm.nih.gov/30815160/","authors":["Jiang Y","Wang C","Wu Z","Du X","Wang S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018","doi":"","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:30641309","name":"Private naive bayes classification of personal biomedical data: Application in cancer data analysis.","source":"pubmed","abstract":"Clinicians would benefit from access to predictive models for diagnosis, such as classification of tumors as malignant or benign, without compromising patients' privacy. In addition, the medical institutions and companies who own these medical information systems wish to keep their models private when in use by outside parties. Fully homomorphic encryption (FHE) enables computation over encrypted medical data while ensuring data privacy. In this paper we use private-key fully homomorphic encryption to design a cryptographic protocol for private Naive Bayes classification. This protocol allows a data owner to privately classify his or her information without direct access to the learned model. We apply this protocol to the task of privacy-preserving classification of breast cancer data as benign or malignant. Our results show that private-key fully homomorphic encryption is able to provide fast and accurate results for privacy-preserving medical classification.","url":"https://pubmed.ncbi.nlm.nih.gov/30641309/","authors":["Wood A","Shpilrain V","Najarian K","Kahrobaei D"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Feb","doi":"10.1016/j.compbiomed.2018.11.018","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:30609816","name":"PPSDT: A Novel Privacy-Preserving Single Decision Tree Algorithm for Clinical Decision-Support Systems Using IoT Devices.","source":"pubmed","abstract":"Medical service providers offer their patients high quality services in return for their trust and satisfaction. The Internet of Things (IoT) in healthcare provides different solutions to enhance the patient-physician experience. Clinical Decision-Support Systems are used to improve the quality of health services by increasing the diagnosis pace and accuracy. Based on data mining techniques and historical medical records, a classification model is built to classify patients' symptoms. In this paper, we propose a privacy-preserving clinical decision-support system based on our novel privacy-preserving single decision tree algorithm for diagnosing new symptoms without exposing patients' data to different network attacks. A homomorphic encryption cipher is used to protect users' data. In addition, the algorithm uses nonces to avoid one party from decrypting other parties' data since they all will be using the same key pair. Our simulation results have shown that our novel algorithm have outperformed the Na&#xef;ve Bayes algorithm by 46.46%; in addition to the effects of the key value and size on the run time. Furthermore, our model is validated by proves, which meet the privacy requirements of the hospitals' datasets, frequency of attribute values, and diagnosed symptoms.","url":"https://pubmed.ncbi.nlm.nih.gov/30609816/","authors":["Alabdulkarim A","Al-Rodhaan M","Ma T","Tian Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Jan 3","doi":"10.3390/s19010142","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:30544877","name":"A Randomized Watermarking Technique for Detecting Malicious Data Injection Attacks in Heterogeneous Wireless Sensor Networks for Internet of Things Applications.","source":"pubmed","abstract":"Using Internet of Things (IoT) applications has been a growing trend in the last few years. They have been deployed in several areas of life, including secure and sensitive sectors, such as the military and health. In these sectors, sensory data is the main factor in any decision-making process. This introduces the need to ensure the integrity of data. Secure techniques are needed to detect any data injection attempt before catastrophic effects happen. Sensors have limited computational and power resources. This limitation creates a challenge to design a security mechanism that is both secure and energy-efficient. This work presents a Randomized Watermarking Filtering Scheme (RWFS) for IoT applications that provides en-route filtering to remove any injected data at an early stage of the communication. Filtering injected data is based on a watermark that is generated from the original data and embedded directly in random places throughout the packet's payload. The scheme uses homomorphic encryption techniques to conceal the report's measurement from any adversary. The advantage of homomorphic encryption is that it allows the data to be aggregated and, thus, decreases the packet's size. The results of our proposed scheme prove that it improves the security and energy consumption of the system as it mitigates some of the limitations in the existing works.","url":"https://pubmed.ncbi.nlm.nih.gov/30544877/","authors":["Alromih A","Al-Rodhaan M","Tian Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018 Dec 9","doi":"10.3390/s18124346","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:30400673","name":"BeeKeeper 2.0: Confidential Blockchain-Enabled IoT System with Fully Homomorphic Computation.","source":"pubmed","abstract":"Blockchain-enabled Internet of Things (IoT) systems have received extensive attention from academia and industry. Most previous constructions face the risk of leaking sensitive information since the servers can obtain plaintext data from the devices. To address this issue, in this paper, we propose a decentralized outsourcing computation (DOC) scheme, where the servers can perform fully homomorphic computations on encrypted data from the data owner according to the request of the data owner. In this process, the servers cannot obtain any plaintext data, and dishonest servers can be detected by the data owner. Then, we apply the DOC scheme in the IoT scenario to achieve a confidential blockchain-enabled IoT system, called BeeKeeper 2.0. To the best of our knowledge, this is the first work in which servers of a blockchain-enabled IoT system can perform any-degree homomorphic multiplications and any number of additions on encrypted data from devices according to the requests of the devices without obtaining any plaintext data of the devices. Finally, we provide a detailed performance evaluation for the BeeKeeper 2.0 system by deploying it on Hyperledger Fabric and using Hyperledger Caliper for performance testing. According to our tests, the time consumed between the request stage and recover stage is no more than 3.3 s, which theoretically satisfies the production needs.","url":"https://pubmed.ncbi.nlm.nih.gov/30400673/","authors":["Zhou L","Wang L","Ai T","Sun Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018 Nov 5","doi":"10.3390/s18113785","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:30337067","name":"Secure large-scale genome data storage and query.","source":"pubmed","abstract":"Cloud computing plays a vital role in big data science with its scalable and cost-efficient architecture. Large-scale genome data storage and computations would benefit from using these latest cloud computing infrastructures, to save cost and speedup discoveries. However, due to the privacy and security concerns, data owners are often disinclined to put sensitive data in a public cloud environment without enforcing some protective measures. An ideal solution is to develop secure genome database that supports encrypted data deposition and query.","url":"https://pubmed.ncbi.nlm.nih.gov/30337067/","authors":["Chen L","Aziz MM","Mohammed N","Jiang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018 Oct","doi":"10.1016/j.cmpb.2018.08.007","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:30309364","name":"Privacy-preserving logistic regression training.","source":"pubmed","abstract":"Logistic regression is a popular technique used in machine learning to construct classification models. Since the construction of such models is based on computing with large datasets, it is an appealing idea to outsource this computation to a cloud service. The privacy-sensitive nature of the input data requires appropriate privacy preserving measures before outsourcing it. Homomorphic encryption enables one to compute on encrypted data directly, without decryption and can be used to mitigate the privacy concerns raised by using a cloud service.","url":"https://pubmed.ncbi.nlm.nih.gov/30309364/","authors":["Bonte C","Vercauteren F"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018 Oct 11","doi":"10.1186/s12920-018-0398-y","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:30104516","name":"Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing.","source":"pubmed","abstract":"As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices' privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.","url":"https://pubmed.ncbi.nlm.nih.gov/30104516/","authors":["Zhang Y","Zhao J","Zheng D","Deng K","Ren F","Zheng X","Shu J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018 Aug 13","doi":"10.3390/s18082659","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:30082298","name":"Machine learning and genomics: precision medicine versus patient privacy.","source":"pubmed","abstract":"Machine learning can have a major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among researchers large amounts of genomic data, which generates much concern about privacy. Researchers, study participants and governing bodies should be aware of the ways in which the privacy of participants might be compromised, as well as of the large body of research on technical solutions to these issues. We review how breaches in patient privacy can occur, present recent developments in computational data protection and discuss how they can be combined with legal and ethical perspectives to provide secure frameworks for genomic data sharing.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.","url":"https://pubmed.ncbi.nlm.nih.gov/30082298/","authors":["Azencott CA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018 Sep 13","doi":"10.1098/rsta.2017.0350","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:30010584","name":"MedCo: Enabling Secure and Privacy-Preserving Exploration of Distributed Clinical and Genomic Data.","source":"pubmed","abstract":"The increasing number of health-data breaches is creating a complicated environment for medical-data sharing and, consequently, for medical progress. Therefore, the development of new solutions that can reassure clinical sites by enabling privacy-preserving sharing of sensitive medical data in compliance with stringent regulations (e.g., HIPAA, GDPR) is now more urgent than ever. In this work, we introduce MedCo, the first operational system that enables a group of clinical sites to federate and collectively protect their data in order to share them with external investigators without worrying about security and privacy concerns. MedCo uses (a) collective homomorphic encryption to provide trust decentralization and end-to-end confidentiality protection, and (b) obfuscation techniques to achieve formal notions of privacy, such as differential privacy. A critical feature of MedCo is that it is fully integrated within the i2b2 (Informatics for Integrating Biology and the Bedside) framework, currently used in more than 300 hospitals worldwide. Therefore, it is easily adoptable by clinical sites. We demonstrate MedCo's practicality by testing it on data from The Cancer Genome Atlas in a simulated network of three institutions. Its performance is comparable to the ones of SHRINE (networked i2b2), which, in contrast, does not provide any data protection guarantee.","url":"https://pubmed.ncbi.nlm.nih.gov/30010584/","authors":["Raisaro JL","Troncoso-Pastoriza JR","Misbach M","Sousa JS","Pradervand S","Missiaglia E","Michielin O","Ford B","Hubaux JP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Jul-Aug","doi":"10.1109/TCBB.2018.2854776","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:29994419","name":"Efficient Encrypted Images Filtering and Transform Coding with Walsh-Hadamard Transform and Parallelization.","source":"pubmed","abstract":"Since homomorphic encryption operations have high computational complexity, image applications based on homomorphic encryption are often time consuming, which makes them impractical. In this paper, we study efficient encrypted image applications with the encrypted domain Walsh-Hadamard transform (WHT) and parallel algorithms. We first present methods to implement real and complex WHTs in the encrypted domain. We then propose a parallel algorithm to improve the computational efficiency of the encrypted domain WHT. To compare the WHT with the discrete cosine transform (DCT), integer DCT, and Haar transform in the encrypted domain, we conduct theoretical analysis and experimental verification, which reveal that the encrypted domain WHT has the advantages of lower computational complexity and a shorter running time. Our analysis shows that the encrypted WHT can accommodate plaintext data of larger values. We propose two encrypted image applications using the encrypted domain WHT. To accelerate the practical execution, we present two parallelization strategies for the proposed applications. The experimental results show that the speedup of the homomorphic encrypted image application exceeds 12.","url":"https://pubmed.ncbi.nlm.nih.gov/29994419/","authors":["Zheng P","Huang J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018 Feb 5","doi":"10.1109/TIP.2018.2802199","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:29994070","name":"Secure Wavelet Matrix: Alphabet-Friendly Privacy-Preserving String Search for Bioinformatics.","source":"pubmed","abstract":"Biomedical data often includes personal information, and the technology is demanded that enables the searching of such sensitive data while protecting privacy. We consider a case in which a server has a text database and a user searches the database to find substring matches. The user wants to conceal his/her query and the server wants to conceal the database except for the search results. The previous approach for this problem is based on a linear-time algorithm in terms of alphabet size $\\mathbf{|\\Sigma |}$|&#x3a3;|, and it cannot search on the database of large alphabet such as biomedical documents. We present a novel algorithm that can search a string in logarithmic time of $\\mathbf{|\\Sigma |}$|&#x3a3;|. In our algorithm, named secure wavelet matrix (sWM), we use an additively homomorphic encryption to build an efficient data structure called a wavelet matrix. In an experiment using a simulated string of length 10,000 whose alphabet size ranges from 4 to 1024, the run time of the sWM was up to around two orders of magnitude faster than that of the previous method. sWM enables the searching of a private database efficiently and thus it will facilitate utilizing sensitive biomedical information.","url":"https://pubmed.ncbi.nlm.nih.gov/29994070/","authors":["Sudo H","Jimbo M","Nuida K","Shimizu K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Sep-Oct","doi":"10.1109/TCBB.2018.2814039","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:29994005","name":"SecureLR: Secure Logistic Regression Model via a Hybrid Cryptographic Protocol.","source":"pubmed","abstract":"Machine learning applications are intensively utilized in various science fields, and increasingly the biomedical and healthcare sector. Applying predictive modeling to biomedical data introduces privacy and security concerns requiring additional protection to prevent accidental disclosure or leakage of sensitive patient information. Significant advancements in secure computing methods have emerged in recent years, however, many of which require substantial computational and/or communication overheads, which might hinder their adoption in biomedical applications. In this work, we propose SecureLR, a novel framework allowing researchers to leverage both the computational and storage capacity of Public Cloud Servers to conduct learning and predictions on biomedical data without compromising data security or efficiency. Our model builds upon homomorphic encryption methodologies with hardware-based security reinforcement through Software Guard Extensions (SGX), and our implementation demonstrates a practical hybrid cryptographic solution to address important concerns in conducting machine learning with public clouds.","url":"https://pubmed.ncbi.nlm.nih.gov/29994005/","authors":["Jiang Y","Hamer J","Wang C","Jiang X","Kim M","Song Y","Xia Y","Mohammed N","Sadat MN","Wang S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Jan-Feb","doi":"10.1109/TCBB.2018.2833463","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:29993695","name":"SAFETY: Secure gwAs in Federated Environment through a hYbrid Solution.","source":"pubmed","abstract":"Recent studies demonstrate that effective healthcare can benefit from using the human genomic information. Consequently, many institutions are using statistical analysis of genomic data, which are mostly based on genome-wide association studies (GWAS). GWAS analyze genome sequence variations in order to identify genetic risk factors for diseases. These studies often require pooling data from different sources together in order to unravel statistical patterns, and relationships between genetic variants and diseases. Here, the primary challenge is to fulfill one major objective: accessing multiple genomic data repositories for collaborative research in a privacy-preserving manner. Due to the privacy concerns regarding the genomic data, multi-jurisdictional laws and policies of cross-border genomic data sharing are enforced among different countries. In this article, we present SAFETY, a hybrid framework, which can securely perform GWAS on federated genomic datasets using homomorphic encryption and recently introduced secure hardware component of Intel Software Guard Extensions to ensure high efficiency and privacy at the same time. Different experimental settings show the efficacy and applicability of such hybrid framework in secure conduction of GWAS. To the best of our knowledge, this hybrid use of homomorphic encryption along with Intel SGX is not proposed to this date. SAFETY is up to 4.82 times faster than the best existing secure computation technique.","url":"https://pubmed.ncbi.nlm.nih.gov/29993695/","authors":["Sadat MN","Al Aziz MM","Mohammed N","Chen F","Jiang X","Wang S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2019 Jan-Feb","doi":"10.1109/TCBB.2018.2829760","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:29888067","name":"Feasibility of Homomorphic Encryption for Sharing I2B2 Aggregate-Level Data in the Cloud.","source":"pubmed","abstract":"The biomedical community is lagging in the adoption of cloud computing for the management of medical data. The primary obstacles are concerns about privacy and security. In this paper, we explore the feasibility of using advanced privacy-enhancing technologies in order to enable the sharing of sensitive clinical data in a public cloud. Our goal is to facilitate sharing of clinical data in the cloud by minimizing the risk of unintended leakage of sensitive clinical information. In particular, we focus on homomorphic encryption, a specific type of encryption that offers the ability to run computation on the data while the data remains encrypted. This paper demonstrates that homomorphic encryption can be used efficiently to compute aggregating queries on the ciphertexts, along with providing end-to-end confidentiality of aggregate-level data from the i2b2 data model.","url":"https://pubmed.ncbi.nlm.nih.gov/29888067/","authors":["Raisaro JL","Klann JG","Wagholikar KB","Estiri H","Hubaux JP","Murphy SN"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018","doi":"","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:29854245","name":"SCOTCH: Secure Counting Of encrypTed genomiC data using a Hybrid approach.","source":"pubmed","abstract":"As genomic data are usually at large scale and highly sensitive, it is essential to enable both efficient and secure analysis, by which the data owner can securely delegate both computation and storage on untrusted public cloud. Counting query of genotypes is a basic function for many downstream applications in biomedical research (e.g., computing allele frequency, calculating chi-squared statistics, etc.). Previous solutions show promise on secure counting of outsourced data but the efficiency is still a big limitation for real world applications. In this paper, we propose a novel hybrid solution to combine a rigorous theoretical model (homomorphic encryption) and the latest hardware-based infrastructure (i.e., Software Guard Extensions) to speed up the computation while preserving the privacy of both data owners and data users. Our results demonstrated efficiency by using the real data from the personal genome project.","url":"https://pubmed.ncbi.nlm.nih.gov/29854245/","authors":["Chenghong W","Jiang Y","Mohammed N","Chen F","Jiang X","Al Aziz MM","Sadat MN","Wang S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2017","doi":"","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:29751670","name":"A Strategy toward Collaborative Filter Recommended Location Service for Privacy Protection.","source":"pubmed","abstract":"A new collaborative filtered recommendation strategy was proposed for existing privacy and security issues in location services. In this strategy, every user establishes his/her own position profiles according to their daily position data, which is preprocessed using a density clustering method. Then, density prioritization was used to choose similar user groups as service request responders and the neighboring users in the chosen groups recommended appropriate location services using a collaborative filter recommendation algorithm. The two filter algorithms based on position profile similarity and position point similarity measures were designed in the recommendation, respectively. At the same time, the homomorphic encryption method was used to transfer location data for effective protection of privacy and security. A real location dataset was applied to test the proposed strategy and the results showed that the strategy provides better location service and protects users' privacy.","url":"https://pubmed.ncbi.nlm.nih.gov/29751670/","authors":["Wang P","Yang J","Zhang J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018 May 11","doi":"10.3390/s18051522","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:29741956","name":"A Secure Alignment Algorithm for Mapping Short Reads to Human Genome.","source":"pubmed","abstract":"The elastic and inexpensive computing resources such as clouds have been recognized as a useful solution to analyzing massive human genomic data (e.g., acquired by using next-generation sequencers) in biomedical researches. However, outsourcing human genome computation to public or commercial clouds was hindered due to privacy concerns: even a small number of human genome sequences contain sufficient information for identifying the donor of the genomic data. This issue cannot be directly addressed by existing security and cryptographic techniques (such as homomorphic encryption), because they are too heavyweight to carry out practical genome computation tasks on massive data. In this article, we present a secure algorithm to accomplish the read mapping, one of the most basic tasks in human genomic data analysis based on a hybrid cloud computing model. Comparing with the existing approaches, our algorithm delegates most computation to the public cloud, while only performing encryption and decryption on the private cloud, and thus makes the maximum use of the computing resource of the public cloud. Furthermore, our algorithm reports similar results as the nonsecure read mapping algorithms, including the alignment between reads and the reference genome, which can be directly used in the downstream analysis such as the inference of genomic variations. We implemented the algorithm in C++ and Python on a hybrid cloud system, in which the public cloud uses an Apache Spark system.","url":"https://pubmed.ncbi.nlm.nih.gov/29741956/","authors":["Zhao Y","Wang X","Tang H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018 Jun","doi":"10.1089/cmb.2017.0094","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:29331453","name":"Are privacy-enhancing technologies for genomic data ready for the clinic? A survey of medical experts of the Swiss HIV Cohort Study.","source":"pubmed","abstract":"Protecting patient privacy is a major obstacle for the implementation of genomic-based medicine. Emerging privacy-enhancing technologies can become key enablers for managing sensitive genetic data. We studied physicians' attitude toward this kind of technology in order to derive insights that might foster their future adoption for clinical care.","url":"https://pubmed.ncbi.nlm.nih.gov/29331453/","authors":["Raisaro JL","McLaren PJ","Fellay J","Cavassini M","Klersy C","Hubaux JP","Swiss HIV Cohort Study"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018 Mar","doi":"10.1016/j.jbi.2017.12.013","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:29190659","name":"A digital memories based user authentication scheme with privacy preservation.","source":"pubmed","abstract":"The traditional username/password or PIN based authentication scheme, which still remains the most popular form of authentication, has been proved insecure, unmemorable and vulnerable to guessing, dictionary attack, key-logger, shoulder-surfing and social engineering. Based on this, a large number of new alternative methods have recently been proposed. However, most of them rely on users being able to accurately recall complex and unmemorable information or using extra hardware (such as a USB Key), which makes authentication more difficult and confusing. In this paper, we propose a Digital Memories based user authentication scheme adopting homomorphic encryption and a public key encryption design which can protect users' privacy effectively, prevent tracking and provide multi-level security in an Internet &amp; IoT environment. Also, we prove the superior reliability and security of our scheme compared to other schemes and present a performance analysis and promising evaluation results.","url":"https://pubmed.ncbi.nlm.nih.gov/29190659/","authors":["Liu J","Lyu Q","Wang Q","Yu X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.1371/journal.pone.0186925","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:28786365","name":"PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension.","source":"pubmed","abstract":"Advances in DNA sequencing technologies have prompted a wide range of genomic applications to improve healthcare and facilitate biomedical research. However, privacy and security concerns have emerged as a challenge for utilizing cloud computing to handle sensitive genomic data.","url":"https://pubmed.ncbi.nlm.nih.gov/28786365/","authors":["Chen F","Wang C","Dai W","Jiang X","Mohammed N","Al Aziz MM","Sadat MN","Sahinalp C","Lauter K","Wang S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2017 Jul 26","doi":"10.1186/s12920-017-0281-2","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:28786363","name":"Efficient and secure outsourcing of genomic data storage.","source":"pubmed","abstract":"Cloud computing is becoming the preferred solution for efficiently dealing with the increasing amount of genomic data. Yet, outsourcing storage and processing sensitive information, such as genomic data, comes with important concerns related to privacy and security. This calls for new sophisticated techniques that ensure data protection from untrusted cloud providers and that still enable researchers to obtain useful information.","url":"https://pubmed.ncbi.nlm.nih.gov/28786363/","authors":["Sousa JS","Lefebvre C","Huang Z","Raisaro JL","Aguilar-Melchor C","Killijian MO","Hubaux JP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2017 Jul 26","doi":"10.1186/s12920-017-0275-0","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:28786361","name":"BLOOM: BLoom filter based oblivious outsourced matchings.","source":"pubmed","abstract":"Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations.","url":"https://pubmed.ncbi.nlm.nih.gov/28786361/","authors":["Ziegeldorf JH","Pennekamp J","Hellmanns D","Schwinger F","Kunze I","Henze M","Hiller J","Matzutt R","Wehrle K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2017 Jul 26","doi":"10.1186/s12920-017-0277-y","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:28786359","name":"Private queries on encrypted genomic data.","source":"pubmed","abstract":"One of the tasks in the iDASH Secure Genome Analysis Competition in 2016 was to demonstrate the feasibility of privacy-preserving queries on homomorphically encrypted genomic data. More precisely, given a list of up to 100,000 mutations, the task was to encrypt the data using homomorphic encryption in a way that allows it to be stored securely in the cloud, and enables the data owner to query the dataset for the presence of specific mutations, without revealing any information about the dataset or the queries to the cloud.","url":"https://pubmed.ncbi.nlm.nih.gov/28786359/","authors":["Çetin GS","Chen H","Laine K","Lauter K","Rindal P","Xia Y"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2017 Jul 26","doi":"10.1186/s12920-017-0276-z","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:28783122","name":"EPPRD: An Efficient Privacy-Preserving Power Requirement and Distribution Aggregation Scheme for a Smart Grid.","source":"pubmed","abstract":"A Smart Grid (SG) facilitates bidirectional demand-response communication between individual users and power providers with high computation and communication performance but also brings about the risk of leaking users' private information. Therefore, improving the individual power requirement and distribution efficiency to ensure communication reliability while preserving user privacy is a new challenge for SG. Based on this issue, we propose an efficient and privacy-preserving power requirement and distribution aggregation scheme (EPPRD) based on a hierarchical communication architecture. In the proposed scheme, an efficient encryption and authentication mechanism is proposed for better fit to each individual demand-response situation. Through extensive analysis and experiment, we demonstrate how the EPPRD resists various security threats and preserves user privacy while satisfying the individual requirement in a semi-honest model; it involves less communication overhead and computation time than the existing competing schemes.","url":"https://pubmed.ncbi.nlm.nih.gov/28783122/","authors":["Zhang L","Zhang J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2017 Aug 7","doi":"10.3390/s17081814","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:28692697","name":"[Formula: see text]: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain.","source":"pubmed","abstract":"Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by exploiting cryptographic primitives and secure indexing to identify outsourced data that satisfy the search criteria. Almost all of these schemes rely on exact matching between the encrypted data and search criteria. A few schemes which extend the notion of exact matching to similarity based search, lack realism as those schemes rely on trusted third parties or due to increase storage and computational complexity. In this paper we propose Oblivious Similarity based Search ([Formula: see text]) for encrypted data. It enables authorized users to model their own encrypted search queries which are resilient to typographical errors. Unlike conventional methodologies, [Formula: see text] ranks the search results by using similarity measure offering a better search experience than exact matching. It utilizes encrypted bloom filter and probabilistic homomorphic encryption to enable authorized users to access relevant data without revealing results of search query evaluation process to the untrusted cloud service provider. Encrypted bloom filter based search enables [Formula: see text] to reduce search space to potentially relevant encrypted data avoiding unnecessary computation on public cloud. The efficacy of [Formula: see text] is evaluated on Google App Engine for various bloom filter lengths on different cloud configurations.","url":"https://pubmed.ncbi.nlm.nih.gov/28692697/","authors":["Pervez Z","Ahmad M","Khattak AM","Ramzan N","Khan WA"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.1371/journal.pone.0179720","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:28268839","name":"An end to end secure CBIR over encrypted medical database.","source":"pubmed","abstract":"In this paper, we propose a new secure content based image retrieval (SCBIR) system adapted to the cloud framework. This solution allows a physician to retrieve images of similar content within an outsourced and encrypted image database, without decrypting them. Contrarily to actual CBIR approaches in the encrypted domain, the originality of the proposed scheme stands on the fact that the features extracted from the encrypted images are themselves encrypted. This is achieved by means of homomorphic encryption and two non-colluding servers, we however both consider as honest but curious. In that way an end to end secure CBIR process is ensured. Experimental results carried out on a diabetic retinopathy database encrypted with the Paillier cryptosystem indicate that our SCBIR achieves retrieval performance as good as if images were processed in their non-encrypted form.","url":"https://pubmed.ncbi.nlm.nih.gov/28268839/","authors":["Bellafqira R","Coatrieux G","Bouslimi D","Quellec G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2016 Aug","doi":"10.1109/EMBC.2016.7591247","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:28178197","name":"RiPPAS: A Ring-Based Privacy-Preserving Aggregation Scheme in Wireless Sensor Networks.","source":"pubmed","abstract":"Recently, data privacy in wireless sensor networks (WSNs) has been paid increased attention. The characteristics of WSNs determine that users' queries are mainly aggregation queries. In this paper, the problem of processing aggregation queries in WSNs with data privacy preservation is investigated. A Ring-based Privacy-Preserving Aggregation Scheme (RiPPAS) is proposed. RiPPAS adopts ring structure to perform aggregation. It uses pseudonym mechanism for anonymous communication and uses homomorphic encryption technique to add noise to the data easily to be disclosed. RiPPAS can handle both s u m ( ) queries and m i n ( ) / m a x ( ) queries, while the existing privacy-preserving aggregation methods can only deal with s u m ( ) queries. For processing s u m ( ) queries, compared with the existing methods, RiPPAS has advantages in the aspects of privacy preservation and communication efficiency, which can be proved by theoretical analysis and simulation results. For processing m i n ( ) / m a x ( ) queries, RiPPAS provides effective privacy preservation and has low communication overhead.","url":"https://pubmed.ncbi.nlm.nih.gov/28178197/","authors":["Zhang K","Han Q","Cai Z","Yin G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2017 Feb 7","doi":"10.3390/s17020300","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:28129194","name":"SecureMed: Secure Medical Computation Using GPU-Accelerated Homomorphic Encryption Scheme.","source":"pubmed","abstract":"Sharing the medical records of individuals among healthcare providers and researchers around the world can accelerate advances in medical research. While the idea seems increasingly practical due to cloud data services, maintaining patient privacy is of paramount importance. Standard encryption algorithms help protect sensitive data from outside attackers but they cannot be used to compute on this sensitive data while being encrypted. Homomorphic Encryption presents a very useful tool that can compute on encrypted data without the need to decrypt it. In this paper, we describe an optimized NTRU-based implementation of the GSW homomorphic encryption scheme. Our results show a factor of 58 &#xd7; improvement in CPU performance compared to other recent work on encrypted medical data under the same security settings. Our system is built to be easily portable to GPUs resulting in an additional speedup of up to a factor of 104 &#xd7; (and 410 &#xd7;) to offer an overall speedup of 6085 &#xd7; (and 24011 &#xd7;) using a single GPU (or four GPUs), respectively.","url":"https://pubmed.ncbi.nlm.nih.gov/28129194/","authors":["Khedr A","Gulak G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2018 Mar","doi":"10.1109/JBHI.2017.2657458","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:28065902","name":"PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS.","source":"pubmed","abstract":"We introduce PRINCESS, a privacy-preserving international collaboration framework for analyzing rare disease genetic data that are distributed across different continents. PRINCESS leverages Software Guard Extensions (SGX) and hardware for trustworthy computation. Unlike a traditional international collaboration model, where individual-level patient DNA are physically centralized at a single site, PRINCESS performs a secure and distributed computation over encrypted data, fulfilling institutional policies and regulations for protected health information.","url":"https://pubmed.ncbi.nlm.nih.gov/28065902/","authors":["Chen F","Wang S","Jiang X","Ding S","Lu Y","Kim J","Sahinalp SC","Shimizu C","Burns JC","Wright VJ","Png E","Hibberd ML","Lloyd DD","Yang H","Telenti A","Bloss CS","Fox D","Lauter K","Ohno-Machado L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2017 Mar 15","doi":"10.1093/bioinformatics/btw758","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:27551747","name":"Cost-Efficient and Multi-Functional Secure Aggregation in Large Scale Distributed Application.","source":"pubmed","abstract":"Secure aggregation is an essential component of modern distributed applications and data mining platforms. Aggregated statistical results are typically adopted in constructing a data cube for data analysis at multiple abstraction levels in data warehouse platforms. Generating different types of statistical results efficiently at the same time (or referred to as enabling multi-functional support) is a fundamental requirement in practice. However, most of the existing schemes support a very limited number of statistics. Securely obtaining typical statistical results simultaneously in the distribution system, without recovering the original data, is still an open problem. In this paper, we present SEDAR, which is a SEcure Data Aggregation scheme under the Range segmentation model. Range segmentation model is proposed to reduce the communication cost by capturing the data characteristics, and different range uses different aggregation strategy. For raw data in the dominant range, SEDAR encodes them into well defined vectors to provide value-preservation and order-preservation, and thus provides the basis for multi-functional aggregation. A homomorphic encryption scheme is used to achieve data privacy. We also present two enhanced versions. The first one is a Random based SEDAR (REDAR), and the second is a Compression based SEDAR (CEDAR). Both of them can significantly reduce communication cost with the trade-off lower security and lower accuracy, respectively. Experimental evaluations, based on six different scenes of real data, show that all of them have an excellent performance on cost and accuracy.","url":"https://pubmed.ncbi.nlm.nih.gov/27551747/","authors":["Zhang P","Li W","Sun H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2016","doi":"10.1371/journal.pone.0159605","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:27153731","name":"Efficient privacy-preserving string search and an application in genomics.","source":"pubmed","abstract":"Personal genomes carry inherent privacy risks and protecting privacy poses major social and technological challenges. We consider the case where a user searches for genetic information (e.g. an allele) on a server that stores a large genomic database and aims to receive allele-associated information. The user would like to keep the query and result private and the server the database.","url":"https://pubmed.ncbi.nlm.nih.gov/27153731/","authors":["Shimizu K","Nuida K","Rätsch G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2016 Jun 1","doi":"10.1093/bioinformatics/btw050","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:26840319","name":"Secure and Privacy-Preserving Body Sensor Data Collection and Query Scheme.","source":"pubmed","abstract":"With the development of body sensor networks and the pervasiveness of smart phones, different types of personal data can be collected in real time by body sensors, and the potential value of massive personal data has attracted considerable interest recently. However, the privacy issues of sensitive personal data are still challenging today. Aiming at these challenges, in this paper, we focus on the threats from telemetry interface and present a secure and privacy-preserving body sensor data collection and query scheme, named SPCQ, for outsourced computing. In the proposed SPCQ scheme, users' personal information is collected by body sensors in different types and converted into multi-dimension data, and each dimension is converted into the form of a number and uploaded to the cloud server, which provides a secure, efficient and accurate data query service, while the privacy of sensitive personal information and users' query data is guaranteed. Specifically, based on an improved homomorphic encryption technology over composite order group, we propose a special weighted Euclidean distance contrast algorithm (WEDC) for multi-dimension vectors over encrypted data. With the SPCQ scheme, the confidentiality of sensitive personal data, the privacy of data users' queries and accurate query service can be achieved in the cloud server. Detailed analysis shows that SPCQ can resist various security threats from telemetry interface. In addition, we also implement SPCQ on an embedded device, smart phone and laptop with a real medical database, and extensive simulation results demonstrate that our proposed SPCQ scheme is highly efficient in terms of computation and communication costs.","url":"https://pubmed.ncbi.nlm.nih.gov/26840319/","authors":["Zhu H","Gao L","Li H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2016 Feb 1","doi":"10.3390/s16020179","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:26765343","name":"Privacy-preserving genomic testing in the clinic: a model using HIV treatment.","source":"pubmed","abstract":"The implementation of genomic-based medicine is hindered by unresolved questions regarding data privacy and delivery of interpreted results to health-care practitioners. We used DNA-based prediction of HIV-related outcomes as a model to explore critical issues in clinical genomics.","url":"https://pubmed.ncbi.nlm.nih.gov/26765343/","authors":["McLaren PJ","Raisaro JL","Aouri M","Rotger M","Ayday E","Bartha I","Delgado MB","Vallet Y","Günthard HF","Cavassini M","Furrer H","Doco-Lecompte T","Marzolini C","Schmid P","Di Benedetto C","Decosterd LA","Fellay J","Hubaux JP","Telenti A"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2016 Aug","doi":"10.1038/gim.2015.167","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"pmid:26736909","name":"Content-based image retrieval in homomorphic encryption domain.","source":"pubmed","abstract":"In this paper, we propose a secure implementation of a content-based image retrieval (CBIR) method that makes possible diagnosis aid systems to work in externalized environment and with outsourced data as in cloud computing. This one works with homomorphic encrypted images from which it extracts wavelet based image features next used for subsequent image comparison. By doing so, our system allows a physician to retrieve the most similar images to a query image in an outsourced database while preserving data confidentiality. Our Secure CBIR is the first one that proposes to work with global image features extracted from encrypted images and does not induce extra communications in-between the client and the server. Experimental results show it achieves retrieval performance as good as if images were processed non-encrypted.","url":"https://pubmed.ncbi.nlm.nih.gov/26736909/","authors":["Bellafqira R","Coatrieux G","Bouslimi D","Quellec G"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2015 Aug","doi":"10.1109/EMBC.2015.7319009","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20396915","name":"VIFP: Shape-Hiding Tree Mining on Encrypted Data via Virtual-Interval FP-Growth","source":"datacite","abstract":"Privacy-preserving frequent itemset mining has been studied for more than two decades, mainlythrough secure candidate counting, horizontally or vertically partitioned protocols, outsourced en-crypted support computation, and partial homomorphic outsourcing. However, existing methodsdo not fully solve the problem of executing tree-based frequent pattern mining, such as FP-Growth,directly over encrypted or secret-shared data without an online decryption-key holder. The core obsta-cle is structural: FP-Growth derives its efficiency from dynamic prefix trees, recursive conditionalpattern bases, variable fan-out, pointer chasing, and data-dependent memory allocation, while fullyhomomorphic encryption (FHE) and secure multi-party computation (SMPC) are most expensiveexactly under hidden branching, hidden memory access, and dynamic data structures.This paper introduces a new problem called shape-hiding keyless conditional prefix mining. Thegoal is to compute frequent itemsets, or an encrypted representation equivalent to FP-Growth output,while hiding not only the raw transactions but also the evolving tree shape, branch fan-out, conditionaldatabase sizes, active prefix identities, support values, and memory-access patterns. We then proposeVirtual-Interval FP-Growth (VIFP), a new encrypted-tree mining framework that preserves FP-Growth semantics while replacing dynamic FP-tree nodes with static, fixed-width, intervalized arraystructures. VIFP uses three custom data structures: an Occurrence-Ordered Transaction Tape, aHeader-Segmented Posting Array, and Projected Interval Descriptors. These structures turn encryptedpointer chasing into batched scans, stable partitions, and segmented histograms. The framework canbe instantiated using additive secret sharing with oblivious sorting and DPF-assisted scatter/gather,or using packed FHE with SIMD support and programmable bootstrapping for threshold tests. Weprovide formal definitions, a leakage model, algorithmic procedures, correctness arguments, andtheoretical complexity analysis showing why VIFP avoids the main bottleneck of direct encryptedFP-tree construction.","url":"https://doi.org/10.5281/zenodo.20396915","authors":["Van Ha, Minh Quan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20396915","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22105363","name":"Artifact: SoK - A Graph-Based Analysis of Comparability, Scale, and Attribution in Privacy-Preserving Transformer Inference (anonymized for review)","source":"datacite","abstract":"Anonymized research artifact accompanying a paper under double-blind review. It contains the frozen 62-paper corpus metadata, the structured extraction protocol and codebook, per-paper evaluation-contract fields, the comparability-gate implementation and the 1891-pair graph it produces, progress-attribution labels, blind re-extraction records, cost-model calibration data, and the scripts that regenerate every table and figure in the paper. Source papers and their full text are not redistributed; records carry citations, derived metadata, and short evidence quotes only. See README.md for layout and step-by-step reproduction commands (Node.js ≥ 18, Python 3 with numpy and matplotlib). A de-anonymized record will replace this one upon acceptance.","url":"https://doi.org/10.5281/zenodo.22105363","authors":["Anonymous"],"tags":["privacy-preserving machine learning","secure multi-party computation","homomorphic encryption","Transformer inference","systematization of knowledge","reproducibility artifact"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22105363","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22074372","name":"Artifact: SoK - A Graph-Based Analysis of Comparability, Scale, and Attribution in Privacy-Preserving Transformer Inference (anonymized for review)","source":"datacite","abstract":"Anonymized research artifact accompanying a paper under double-blind review. It contains the frozen 62-paper corpus metadata, the structured extraction protocol and codebook, per-paper evaluation-contract fields, the comparability-gate implementation and the 1891-pair graph it produces, progress-attribution labels, blind re-extraction records, cost-model calibration data, and the scripts that regenerate every table and figure in the paper. Source papers and their full text are not redistributed; records carry citations, derived metadata, and short evidence quotes only. See README.md for layout and step-by-step reproduction commands (Node.js ≥ 18, Python 3 with numpy and matplotlib). A de-anonymized record will replace this one upon acceptance.","url":"https://doi.org/10.5281/zenodo.22074372","authors":["Anonymous"],"tags":["privacy-preserving machine learning","secure multi-party computation","homomorphic encryption","Transformer inference","systematization of knowledge","reproducibility artifact"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22074372","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20847864","name":"Project CHRONOS: A Fully Homomorphic Ephemeral AI Agent with Provable Self Termination and Remote Verifiability","source":"datacite","abstract":"We present CHRONOS, the first autonomous AI agent that simultaneously achieves plaintextblindness (all data is processed under fully homomorphic encryption without ever beingexposed), cryptographically enforced time bound existence (the agent’s own decryption key islocked behind a publicly verifiable proof of sequential work, rendering it inaccessible until aprecise future moment), and remote verifiability of self destruction (a zero knowledge proofcertifies that the key material has been irreversibly destroyed after mission completion). Theagent’s operational lifespan is governed by a “cryptographic fuse” constructed from a proof ofsequential work (PoSW) whose computation time accurately matches the intended missionduration. A drand decentralized randomness beacon serves as a trusted time oracle to trigger thefinal key shredding. Crucially, the erasure proof is a non interactive zero knowledge argument(SNARK) that proves the correct execution of the entire self destruction sequence—including thePoSW solution, decryption of the private key, and subsequent memory zeroization—enablingany third party to cryptographically verify the agent’s annihilation without trusting the agent orits hardware. We provide a complete system architecture, a formal security model with gamebased definitions and reductions to standard assumptions, and a proof of concept implementationusing Zama’s TFHE rs for encrypted inference, a Cohen Pietrzak PoSW implementation, and aGroth16 SNARK. Our benchmarks indicate that FHE inference on a small neural network (50 Kparameters) completes in seconds, the PoSW background thread consumes negligible resources,and the erasure proof can be generated and verified in under three seconds. CHRONOSrepresents a fundamental advance in secure, disposable AI agents, with immediate applications indefense, intelligence, and high privacy environments.","url":"https://doi.org/10.5281/zenodo.20847864","authors":["Kumar, Shashank"],"tags":["Multi-Agent Systems","Fully Homomorphic Encryption","Redis","TFHE-rs"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20847864","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21534027","name":"Project CHRONOS: A Fully Homomorphic Ephemeral AI Agent with Provable Self Termination and Remote Verifiability","source":"datacite","abstract":"We present CHRONOS, the first autonomous AI agent that simultaneously achieves plaintextblindness (all data is processed under fully homomorphic encryption without ever beingexposed), cryptographically enforced time bound existence (the agent’s own decryption key islocked behind a publicly verifiable proof of sequential work, rendering it inaccessible until aprecise future moment), and remote verifiability of self destruction (a zero knowledge proofcertifies that the key material has been irreversibly destroyed after mission completion). Theagent’s operational lifespan is governed by a “cryptographic fuse” constructed from a proof ofsequential work (PoSW) whose computation time accurately matches the intended missionduration. A drand decentralized randomness beacon serves as a trusted time oracle to trigger thefinal key shredding. Crucially, the erasure proof is a non interactive zero knowledge argument(SNARK) that proves the correct execution of the entire self destruction sequence—including thePoSW solution, decryption of the private key, and subsequent memory zeroization—enablingany third party to cryptographically verify the agent’s annihilation without trusting the agent orits hardware. We provide a complete system architecture, a formal security model with gamebased definitions and reductions to standard assumptions, and a proof of concept implementationusing Zama’s TFHE rs for encrypted inference, a Cohen Pietrzak PoSW implementation, and aGroth16 SNARK. Our benchmarks indicate that FHE inference on a small neural network (50 Kparameters) completes in seconds, the PoSW background thread consumes negligible resources,and the erasure proof can be generated and verified in under three seconds. CHRONOSrepresents a fundamental advance in secure, disposable AI agents, with immediate applications indefense, intelligence, and high privacy environments.","url":"https://doi.org/10.5281/zenodo.21534027","authors":["Kumar, Shashank"],"tags":["Multi-Agent Systems","Fully Homomorphic Encryption","Redis","TFHE-rs"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21534027","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21849330","name":"A Privacy-Preserving Explainable Artificial Intelligence Hybrid Framework for Abnormal Network Traffic Identification and Intelligent Threat Detection","source":"datacite","abstract":"Abstract The rapid evolution of cyber threats, the widespread adoption of encrypted communications, and the increasing complexity of enterprise, cloud, edge, and Internet of Things (IoT) environments have exposed the limitations of conventional intrusion detection systems in accurately identifying abnormal network traffic and detecting sophisticated cyberattacks. Existing hybrid deep learning frameworks, including that of Wang [54], achieve high detection accuracy but lack privacy-preserving computation, explainable artificial intelligence, intelligent threat prioritization, and adaptive operational capabilities. This study therefore designed and developed a Hybrid Artificial Intelligence Framework for Abnormal Network Traffic Identification and Intelligent Threat Detection by integrating Long Short-Term Memory (LSTM), Transformer, Random Forest, CKKS Homomorphic Encryption, Explainable Artificial Intelligence (SHAP/LIME), weighted ensemble decision fusion, intelligent threat prioritization, and adaptive feedback learning. The study adopted the Design Science Research Methodology (DSRM), while the proposed framework was implemented using Python, TensorFlow/Keras, Scikit-learn, Microsoft SEAL/TenSEAL, and evaluated using the CICIDS2017 and UNSW-NB15 benchmark datasets. Experimental evaluation was performed using accuracy, precision, recall, F1-score, false positive rate, detection latency, zero-day detection rate, throughput, scalability, explainability, and encryption overhead as performance metrics. The proposed framework achieved detection accuracies of 99.12% and 98.76% on the CICIDS2017 and UNSW-NB15 datasets, respectively, with precision values of 98.87% and 98.42%, recall values of 99.05% and 98.61%, F1-scores of 98.96% and 98.51%, false positive rates of 0.84% and 1.12%, and zero-day detection rates of 94.30% and 92.75%. Comparative analysis demonstrated improved detection accuracy, lower false-positive rates, reduced detection latency, enhanced interpretability, and stronger privacy preservation compared with the hybrid CNN–LSTM–Transformer framework of Wang [54]. The study concludes that integrating hybrid artificial intelligence, privacy-preserving computation, explainable artificial intelligence, and intelligent threat prioritization provides a robust, scalable, and adaptive solution for modern cybersecurity. The proposed framework is recommended for deployment in enterprise networks, cloud computing, IoT, edge computing, and critical infrastructure environments to strengthen real-time cyber threat detection and response. Keywords: Hybrid Artificial Intelligence, Abnormal Network Traffic Identification, Intelligent Threat Detection, Intrusion Detection System, LSTM, Transformer, Random Forest, Explainable Artificial Intelligence, CKKS Homomorphic Encryption, Zero-Day Attack Detection","url":"https://doi.org/10.5281/zenodo.21849330","authors":["D. A. Onuma","D. Matthias","O. E. Taylor","N. D. Nwiabu"],"tags":["Hybrid Artificial Intelligence, Abnormal Network Traffic Identification, Intelligent Threat Detection, Intrusion Detection System, LSTM, Transformer, Random Forest, Explainable Artificial Intelligence, CKKS Homomorphic Encryption, Zero-Day Attack Detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21849330","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21849331","name":"A Privacy-Preserving Explainable Artificial Intelligence Hybrid Framework for Abnormal Network Traffic Identification and Intelligent Threat Detection","source":"datacite","abstract":"Abstract The rapid evolution of cyber threats, the widespread adoption of encrypted communications, and the increasing complexity of enterprise, cloud, edge, and Internet of Things (IoT) environments have exposed the limitations of conventional intrusion detection systems in accurately identifying abnormal network traffic and detecting sophisticated cyberattacks. Existing hybrid deep learning frameworks, including that of Wang [54], achieve high detection accuracy but lack privacy-preserving computation, explainable artificial intelligence, intelligent threat prioritization, and adaptive operational capabilities. This study therefore designed and developed a Hybrid Artificial Intelligence Framework for Abnormal Network Traffic Identification and Intelligent Threat Detection by integrating Long Short-Term Memory (LSTM), Transformer, Random Forest, CKKS Homomorphic Encryption, Explainable Artificial Intelligence (SHAP/LIME), weighted ensemble decision fusion, intelligent threat prioritization, and adaptive feedback learning. The study adopted the Design Science Research Methodology (DSRM), while the proposed framework was implemented using Python, TensorFlow/Keras, Scikit-learn, Microsoft SEAL/TenSEAL, and evaluated using the CICIDS2017 and UNSW-NB15 benchmark datasets. Experimental evaluation was performed using accuracy, precision, recall, F1-score, false positive rate, detection latency, zero-day detection rate, throughput, scalability, explainability, and encryption overhead as performance metrics. The proposed framework achieved detection accuracies of 99.12% and 98.76% on the CICIDS2017 and UNSW-NB15 datasets, respectively, with precision values of 98.87% and 98.42%, recall values of 99.05% and 98.61%, F1-scores of 98.96% and 98.51%, false positive rates of 0.84% and 1.12%, and zero-day detection rates of 94.30% and 92.75%. Comparative analysis demonstrated improved detection accuracy, lower false-positive rates, reduced detection latency, enhanced interpretability, and stronger privacy preservation compared with the hybrid CNN–LSTM–Transformer framework of Wang [54]. The study concludes that integrating hybrid artificial intelligence, privacy-preserving computation, explainable artificial intelligence, and intelligent threat prioritization provides a robust, scalable, and adaptive solution for modern cybersecurity. The proposed framework is recommended for deployment in enterprise networks, cloud computing, IoT, edge computing, and critical infrastructure environments to strengthen real-time cyber threat detection and response. Keywords: Hybrid Artificial Intelligence, Abnormal Network Traffic Identification, Intelligent Threat Detection, Intrusion Detection System, LSTM, Transformer, Random Forest, Explainable Artificial Intelligence, CKKS Homomorphic Encryption, Zero-Day Attack Detection","url":"https://doi.org/10.5281/zenodo.21849331","authors":["Onuma Divine Anele","PROF. D. Matthias","DR. O. E. Taylor"],"tags":["Hybrid Artificial Intelligence, Abnormal Network Traffic Identification, Intelligent Threat Detection, Intrusion Detection System, LSTM, Transformer, Random Forest, Explainable Artificial Intelligence, CKKS Homomorphic Encryption, Zero-Day Attack Detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21849331","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20286639","name":"Project Artifacts: Decentralized Governance of Encrypted Human-AI Augmentation for Equitable Climate Disaster Recovery Decisions","source":"datacite","abstract":"This repository provides supplementary research project artifacts: evidential structures, encrypted aggregation metadata, and resources, to support the findings of a real-world case study on equitable disaster recovery decision-making. The project presents a decentralized privacy-preserving Human-AI augmentation framework designed to support collaboration among multiple organizations operating under strict regulatory, organizational, and data governance constraints. Secure augmentation of proprietary organizational AI decision-support systems without exposing raw sensitive data, contextual human expert heuristic judgment, and the requirement to converge towards a single global model. Privacy-preserving augmentation is supported by lattice-based Fully Homomorphic Encryption (FHE) schemes: CKKS and TFHE, for secure encrypted computation in untrusted environments. Blockchain-assisted governance mechanisms as trust-machine provide auditable coordination, integrity verification, decentralized accountability, and traceability of augmentation workflows. (a) Title: MetaData_Disaster_Recovery.json (v1.0) Description: The metadata file of SMEs affected by extreme weather events. It includes structured information on disaster events, financial metrics, credit & legal history, insurance coverage, operational indicators, government support, and fraud flags. Key metadata highlights: Disaster Events: Event Name ,Event_Date, and Met Office warning levels (Green, Yellow, Amber, Red) Sectors: Wholesale & Retail Trade, Manufacturing, Construction, Accommodation & Food Services, etc. Special inclusivity for Marginalized Small Businesses with ownership categories, marginalization multipliers, adjusted DSCR, alternative collateral, IMD postcode rules, and disaster severity adjustments. Profile design: marginalized profiles, comparator profiles, and counterfactual pairs for fairness evaluation. Evidence fields (E) with categories, value ranges, missing counts, and percentages. (b) Title: Client_Encrypted_Contributions_Reliability.json (v1.0) Description: This file contains metadata and reliability analysis results for encrypted client contributions in the Natural Disaster Recovery Support Dataset project. It reports reliability scores of 4 client organizations across three progressive augmentation stages using Fully Homomorphic Encryption (FHE). Higher values indicate more consistent and trustworthy encrypted data contributions. Stages Overview: Stage 1 (16 tasks): High reliability regime Stage 2 (24 tasks): Moderate reliability with fluctuations and detected tampering Stage 3 (24 tasks): High-complexity joint evidence Each stage includes the full reliability matrix, per-client average scores, and detailed stage descriptions. Purpose: Evaluate and monitor the trustworthiness of encrypted client contributions. (c) Title: Augmentation_Evidence_Disaster_Recovery.json (v1.0) Description: This file details the staged evidence augmentation process for the Natural Disaster Recovery Support. It defines how single and joint evidence were progressively introduced across three stages to support privacy-preserving, fair decision-making in disaster recovery lending. Stages Overview: Stage 1 (16 tasks): Single Evidence Stage 2 (24 tasks): Joint Evidence (Moderate Complexity) Stage 3 (24 tasks): Complex Joint Evidence Purpose: Document the incremental evidence augmentation strategy that enables secure, transparent, and fairness-aware model training. (d) Title: Sample_Augmentation_Task_Batch_Consent_Contribution_Governance.zip Description: Fully asynchronous governance workflow managed by blockchain Temporal delays between organizations for task awareness, voting, and contribution submission Multi-phase consent mechanism (C_REQ, C_PK, C_CTB, C_KS, C_Γ) Homomorphic encryption metadata Encrypted contribution records with SHA-256 hashes, Merkle roots, aggregation times, and transmission delays Complete audit trail with timestamps and transaction IDs This sample belong","url":"https://doi.org/10.5281/zenodo.20286639","authors":["Sachan, Dr. Swati","Fickett, Dale"],"tags":["AI","Human-Machine","Privacy","Artificial intelligence","Community finance","Decision-support system"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20286639","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.20286640","name":"Project Artifacts: Decentralized Governance of Encrypted Human-AI Augmentation for Equitable Climate Disaster Recovery Decisions","source":"datacite","abstract":"This repository provides supplementary research project artifacts: evidential structures, encrypted aggregation metadata, and resources, to support the findings of a real-world case study on equitable disaster recovery decision-making. The project presents a decentralized privacy-preserving Human-AI augmentation framework designed to support collaboration among multiple organizations operating under strict regulatory, organizational, and data governance constraints. Secure augmentation of proprietary organizational AI decision-support systems without exposing raw sensitive data, contextual human expert heuristic judgment, and the requirement to converge towards a single global model. Privacy-preserving augmentation is supported by lattice-based Fully Homomorphic Encryption (FHE) schemes: CKKS and TFHE, for secure encrypted computation in untrusted environments. Blockchain-assisted governance mechanisms as trust-machine provide auditable coordination, integrity verification, decentralized accountability, and traceability of augmentation workflows. (a) Title: MetaData_Disaster_Recovery.json (v1.0) Description: The metadata file of SMEs affected by extreme weather events. It includes structured information on disaster events, financial metrics, credit & legal history, insurance coverage, operational indicators, government support, and fraud flags. Key metadata highlights: Disaster Events: Event Name ,Event_Date, and Met Office warning levels (Green, Yellow, Amber, Red) Sectors: Wholesale & Retail Trade, Manufacturing, Construction, Accommodation & Food Services, etc. Special inclusivity for Marginalized Small Businesses with ownership categories, marginalization multipliers, adjusted DSCR, alternative collateral, IMD postcode rules, and disaster severity adjustments. Profile design: marginalized profiles, comparator profiles, and counterfactual pairs for fairness evaluation. Evidence fields (E) with categories, value ranges, missing counts, and percentages. (b) Title: Client_Encrypted_Contributions_Reliability.json (v1.0) Description: This file contains metadata and reliability analysis results for encrypted client contributions in the Natural Disaster Recovery Support Dataset project. It reports reliability scores of 4 client organizations across three progressive augmentation stages using Fully Homomorphic Encryption (FHE). Higher values indicate more consistent and trustworthy encrypted data contributions. Stages Overview: Stage 1 (16 tasks): High reliability regime Stage 2 (24 tasks): Moderate reliability with fluctuations and detected tampering Stage 3 (24 tasks): High-complexity joint evidence Each stage includes the full reliability matrix, per-client average scores, and detailed stage descriptions. Purpose: Evaluate and monitor the trustworthiness of encrypted client contributions. (c) Title: Augmentation_Evidence_Disaster_Recovery.json (v1.0) Description: This file details the staged evidence augmentation process for the Natural Disaster Recovery Support. It defines how single and joint evidence were progressively introduced across three stages to support privacy-preserving, fair decision-making in disaster recovery lending. Stages Overview: Stage 1 (16 tasks): Single Evidence Stage 2 (24 tasks): Joint Evidence (Moderate Complexity) Stage 3 (24 tasks): Complex Joint Evidence Purpose: Document the incremental evidence augmentation strategy that enables secure, transparent, and fairness-aware model training. (d) Title: Sample_Augmentation_Task_Batch_Consent_Contribution_Governance.zip Description: Fully asynchronous governance workflow managed by blockchain Temporal delays between organizations for task awareness, voting, and contribution submission Multi-phase consent mechanism (C_REQ, C_PK, C_CTB, C_KS, C_Γ) Homomorphic encryption metadata Encrypted contribution records with SHA-256 hashes, Merkle roots, aggregation times, and transmission delays Complete audit trail with timestamps and transaction IDs This sample belong","url":"https://doi.org/10.5281/zenodo.20286640","authors":["Sachan, Dr. Swati","Fickett, Dale"],"tags":["AI","Human-Machine","Privacy","Artificial intelligence","Community finance","Decision-support system"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20286640","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2606.03513","name":"Privacy-Preserving High-Resolution Image Gradient Computation Based on Fully Homomorphic Encryption","source":"datacite","abstract":"With growing emphasis on privacy protection, homomorphic encryption (HE) has emerged as a core method for privacy-preserving image processing, as it enables operations directly on encrypted data. However, existing research predominantly focuses on low-resolution image processing, and techniques for privacy-preserving high-resolution image processing remain underexplored. As the image size increases, the HE parameters must be adjusted accordingly, and directly applying existing methods can lead to significant computational overhead. In this work, we propose a multi-ciphertext privacy-preserving framework for large images, enabling efficient image encryption and computation under the semi-honest model. Specifically, we divide the large image into multiple sub-images, which allows us to maintain smaller HE parameters and reduce key size. By parallel processing the sub-image ciphertexts and introducing a new bootstrapping placement strategy, we significantly reduce encryption overhead and enhance user experience. On the server side, we optimize the large image convolution operation through a repeated packing technique and implement the Sobel operator computation based on HE. To improve gradient direction calculation for the Sobel operator, we introduce a new polynomial approximation method for the reciprocal function based on the sign function, which can be applied to other HE-based protocols.","url":"https://doi.org/10.48550/arxiv.2606.03513","authors":["Zhou, Yufei"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.03513","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.48550/arxiv.2512.19951","name":"Efficient Mod Approximation and Its Applications to CKKS Ciphertexts","source":"datacite","abstract":"The mod function plays a critical role in numerous data encoding and cryptographic primitives. However, the widely used CKKS homomorphic encryption (HE) scheme supports only arithmetic operations, making it difficult to perform mod computations on encrypted data. Approximating the mod function with polynomials has therefore become an important yet challenging problem. Existing homomorphic mod constructions provide accurate results only within limited subranges of the input domain, leaving the problem of achieving accurate approximation across the entire input domain unresolved.In this work, we propose a novel method based on polynomial interpolation and Chebyshev series to accurately approximate the mod function over all integer points in the bounded input interval. Building upon this, we design two efficient data packing schemes, BitStack and CRTStack, tailored for small-integer inputs in CKKS. These schemes significantly improve the utilization of the CKKS plaintext space and enable efficient ciphertext uploads. Furthermore, we apply the proposed HE mod function to implement a homomorphic rounding operation and a general transformation from additive secret shares to CKKS ciphertexts, achieving accurate ciphertext rounding and complete conversion from secret shares to CKKS ciphertexts. Experimental results demonstrate that our approach achieves high approximation accuracy (up to $10^{-8}$).","url":"https://doi.org/10.48550/arxiv.2512.19951","authors":["Zhou, Yufei"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2512.19951","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.48550/arxiv.2608.23396","name":"A Threshold Homomorphic Blockchain Architecture for Secure and Scalable IoT Sensor Data Aggregation","source":"datacite","abstract":"Homomorphic-encryption blockchain frameworks for IoT sensor aggregation generally rely on classical cryptographic hardness assumptions and seldom account for network topology in liveness and performance analysis. This work introduces Phi-PHE-BC, a topology-aware homomorphic blockchain architecture for secure and privacy-preserving IoT sensor data aggregation. The framework combines threshold Paillier decryption with graph-parameterized security and performance analysis, linking protocol behavior to the validator graph. On-chain Paillier ciphertexts support homomorphic aggregation while providing IND-CPA confidentiality under the Decisional Composite Residuosity assumption, and authentication signatures provide EUF-CMA transaction integrity. Threshold partial-decryption shares are protected by a noise-flooding wrapper that provides information-theoretic privacy under the configured statistical-hiding condition. Under partial synchrony and Byzantine fault-tolerance assumptions, liveness requires validator connectivity kappa(Gv) &gt;= f+1. We derive topology-dependent throughput bounds for tree, star, mesh, and scale-free networks, together with a per-block communication-cost model. A game-theoretic analysis shows that honest validator participation is a dominant strategy under the stated utility model, yielding an all-honest Nash equilibrium. Experiments on Hyperledger Fabric 2.5 show lower end-to-end latency than the selected traditional PHE-blockchain baseline while maintaining controllable threshold-decryption overhead. Results across topology scaling, validator sensitivity, threshold decryption, and Byzantine-load experiments indicate that Phi-PHE-BC is a practical architecture for secure, privacy-preserving, and topology-aware IoT sensor aggregation.","url":"https://doi.org/10.48550/arxiv.2608.23396","authors":["Dewangan, Narendra Kumar","Msahli, Mounira"],"tags":["Cryptography and Security (cs.CR)","Networking and Internet Architecture (cs.NI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.23396","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22074373","name":"Artifact: SoK - The Evaluation and Scaling Gaps in Private Transformer Inference (anonymized for review)","source":"datacite","abstract":"Anonymized research artifact accompanying a paper under double-blind review. It contains the frozen 62-paper corpus metadata, the structured extraction protocol and codebook, per-paper evaluation-contract fields, the comparability-gate implementation and the 1891-pair graph it produces, progress-attribution labels, blind re-extraction records, cost-model calibration data, and the scripts that regenerate every table and figure in the paper. Source papers and their full text are not redistributed; records carry citations, derived metadata, and short evidence quotes only. See README.md for layout and step-by-step reproduction commands (Node.js ≥ 18, Python 3 with numpy and matplotlib). A de-anonymized record will replace this one upon acceptance.","url":"https://doi.org/10.5281/zenodo.22074373","authors":["Anonymous"],"tags":["privacy-preserving machine learning","secure multi-party computation","homomorphic encryption","Transformer inference","systematization of knowledge","reproducibility artifact"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22074373","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.19511424","name":"On the Irreducible Overhead of Zero-Knowledge Proofs for Neural Network Inference","source":"datacite","abstract":"We establish an information-theoretic lower bound on the prover overhead of any zero-knowledge proof system that verifies arbitrary neural network inference. We prove a minimum multiplicative overhead of 2x for general circuits, rising to 4x for neural networks with ReLU activations due to activation encoding, weight commitment, and layer dependency costs. We further prove that composing ZK with fully homomorphic encryption produces multiplicative overhead blowup, making ZK+FHE verification impractical beyond approximately 10^4 gates. We survey six contemporary proof systems and show their observed overheads are consistent with our bounds. Our results formalize the intuition that free verification of AI computation is impossible and provide concrete bounds for system designers.","url":"https://doi.org/10.5281/zenodo.19511424","authors":["Marín Soto, Antonio José"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19511424","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.19511425","name":"On the Irreducible Overhead of Zero-Knowledge Proofs for Neural Network Inference","source":"datacite","abstract":"We establish an information-theoretic lower bound on the prover overhead of any zero-knowledge proof system that verifies arbitrary neural network inference. We prove a minimum multiplicative overhead of 2x for general circuits, rising to 4x for neural networks with ReLU activations due to activation encoding, weight commitment, and layer dependency costs. We further prove that composing ZK with fully homomorphic encryption produces multiplicative overhead blowup, making ZK+FHE verification impractical beyond approximately 10^4 gates. We survey six contemporary proof systems and show their observed overheads are consistent with our bounds. Our results formalize the intuition that free verification of AI computation is impossible and provide concrete bounds for system designers.","url":"https://doi.org/10.5281/zenodo.19511425","authors":["Marín Soto, Antonio José"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19511425","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.14019895","name":"PRIVACY-PRESERVING TRADING IN LOCAL ENERGY MARKETS","source":"datacite","abstract":"The transition towards sustainable energy, primarily driven by the urgent need to combat climate change and reduce environmental degradation, has led to a significant shift from conventional fossil fuels to Renewable Energy Sources (RES). Local Energy Markets (LEMs) have emerged as innovative platforms enabling RES integration by facilitating energy trading among participants. By offering trading incentives, LEMs encourage adopting renewable energy, hence promoting a greener and more sustainable energy landscape. Despite their potential, LEMs face critical challenges that hinder their broader adoption. Notably, extensive data sharing is essential for their operation and introduces significant privacy risks to users. Addressing this challenge requires LEMs to deploy advanced privacy-enhancing technologies, which brings new challenges -- high computational intensity and lack of transaction accountability. In addition, by doing so, economic incentives for users should be preserved. To address these challenges, this thesis proposes novel solutions for privacy-preserving trading in LEMs. The main contributions of the thesis are briefly summarised below. The first contribution of the thesis involves a detailed examination and discussion of the privacy-preserving trading mechanisms in LEMs. We start with a broad overview of LEMs by investigating trading mechanisms and privacy-preserving methods, establishing a solid foundation for the subsequent thesis designs. Through a comparative study of existing market models focused on privacy preservation, we ensure a complete review of the current state of the art. Furthermore, we delve into a thorough discussion of the potential frameworks for LEMs. This effort aims to analyse LEM frameworks from a technical perspective, thereby forming a base for further developing solutions for privacy-preserving LEMs. Building on this foundation, as a second contribution, the thesis presents a novel decentralised, Privacy-Friendly Energy Trading Platform (PFET), which adopts a game-theoretical framework, particularly leveraging the Stackelberg competition model. PFET stands out from current models by creating a competitive marketplace where market dynamics such as prices and demands are calculated from the competition. To protect sensitive information, including sellers' prices and buyers' demand levels, the platform utilises Homomorphic Encryption (HE). This enables buyers to compute the total demand placed on sellers in an encrypted manner, protecting participant privacy throughout the process. Our performance evaluations affirm PFET's effectiveness in maintaining user privacy within the context of a competitive market environment. The third contribution presented is a Privacy-Preserving Clearance Mechanism for Local Energy Markets (PP-LEM) designed to enhance trading efficiency within a semi-decentralised environment. PP-LEM adopts a competitive approach based on game theory, specifically utilising the Stackelberg Game, emphasising computational efficiency and privacy. Through the application of HE, PP-LEM ensures the protection of sensitive information for all parties involved, facilitating secure calculations on encrypted data without disclosing actual information. Our extensive evaluation showcases the capability of PP-LEM to deliver a clearance mechanism that is not only privacy-preserving but also computationally efficient, outperforming existing approaches. It distinguishes itself by providing computational efficiency and protecting user welfare without trade-offs. This contribution significantly contributes to the domain of privacy-preserving LEMs, offering a novel solution that provides incentive mechanisms and privacy protection with computational efficiency. Lastly, the thesis presents the Privacy-Preserving and Accountable Billing (PA-Bill) protocol tailored for peer-to-peer energy trading markets. PA-Bill tackles the issue of mismatches between committed and actual energy deliveries, ensuri","url":"https://doi.org/10.5281/zenodo.14019895","authors":["Erdayandi, Kamil"],"tags":["Billing Mechanisms","Clearance Mechanisms","Blockchain","Accountability","Homomorphic Encryption","Non-cooperative and Competitive Games","Decentralised Architectures","Local Energy Markets"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.14019895","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.5281/zenodo.14019896","name":"PRIVACY-PRESERVING TRADING IN LOCAL ENERGY MARKETS","source":"datacite","abstract":"The transition towards sustainable energy, primarily driven by the urgent need to combat climate change and reduce environmental degradation, has led to a significant shift from conventional fossil fuels to Renewable Energy Sources (RES). Local Energy Markets (LEMs) have emerged as innovative platforms enabling RES integration by facilitating energy trading among participants. By offering trading incentives, LEMs encourage adopting renewable energy, hence promoting a greener and more sustainable energy landscape. Despite their potential, LEMs face critical challenges that hinder their broader adoption. Notably, extensive data sharing is essential for their operation and introduces significant privacy risks to users. Addressing this challenge requires LEMs to deploy advanced privacy-enhancing technologies, which brings new challenges -- high computational intensity and lack of transaction accountability. In addition, by doing so, economic incentives for users should be preserved. To address these challenges, this thesis proposes novel solutions for privacy-preserving trading in LEMs. The main contributions of the thesis are briefly summarised below. The first contribution of the thesis involves a detailed examination and discussion of the privacy-preserving trading mechanisms in LEMs. We start with a broad overview of LEMs by investigating trading mechanisms and privacy-preserving methods, establishing a solid foundation for the subsequent thesis designs. Through a comparative study of existing market models focused on privacy preservation, we ensure a complete review of the current state of the art. Furthermore, we delve into a thorough discussion of the potential frameworks for LEMs. This effort aims to analyse LEM frameworks from a technical perspective, thereby forming a base for further developing solutions for privacy-preserving LEMs. Building on this foundation, as a second contribution, the thesis presents a novel decentralised, Privacy-Friendly Energy Trading Platform (PFET), which adopts a game-theoretical framework, particularly leveraging the Stackelberg competition model. PFET stands out from current models by creating a competitive marketplace where market dynamics such as prices and demands are calculated from the competition. To protect sensitive information, including sellers' prices and buyers' demand levels, the platform utilises Homomorphic Encryption (HE). This enables buyers to compute the total demand placed on sellers in an encrypted manner, protecting participant privacy throughout the process. Our performance evaluations affirm PFET's effectiveness in maintaining user privacy within the context of a competitive market environment. The third contribution presented is a Privacy-Preserving Clearance Mechanism for Local Energy Markets (PP-LEM) designed to enhance trading efficiency within a semi-decentralised environment. PP-LEM adopts a competitive approach based on game theory, specifically utilising the Stackelberg Game, emphasising computational efficiency and privacy. Through the application of HE, PP-LEM ensures the protection of sensitive information for all parties involved, facilitating secure calculations on encrypted data without disclosing actual information. Our extensive evaluation showcases the capability of PP-LEM to deliver a clearance mechanism that is not only privacy-preserving but also computationally efficient, outperforming existing approaches. It distinguishes itself by providing computational efficiency and protecting user welfare without trade-offs. This contribution significantly contributes to the domain of privacy-preserving LEMs, offering a novel solution that provides incentive mechanisms and privacy protection with computational efficiency. Lastly, the thesis presents the Privacy-Preserving and Accountable Billing (PA-Bill) protocol tailored for peer-to-peer energy trading markets. PA-Bill tackles the issue of mismatches between committed and actual energy deliveries, ensuri","url":"https://doi.org/10.5281/zenodo.14019896","authors":["Erdayandi, Kamil"],"tags":["Billing Mechanisms","Clearance Mechanisms","Blockchain","Accountability","Homomorphic Encryption","Non-cooperative and Competitive Games","Decentralised Architectures","Local Energy Markets"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.14019896","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.5281/zenodo.8087573","name":"An Efficient Protocol For Computations Delegation Using Rerandomizable Garbled Circuits","source":"datacite","abstract":"We consider the problem of delegating computations, in which a client may outsource the computation of a function, represented as a boolean circuit, to an external entity, then receives the result and a proof of correctness that can be verified efficiently. Most existing noninteractive solutions rely on the use of fully homomorphic encryption, which is practically unaffordable. As it turns out, making a probabilistic assumption about the honesty of the external entity eliminates the need for FHE. This paper describes an efficient protocol for delegating computations that also guarantees input and output privacy. The protocol is a new variant of the multi-server model and the cycle architecture from Ananth et al., which is based on the assumption of the existence of at least one honest server. We carefully utilize an alternative re-randomizable variant of Yao’s garbled circuits based on the ElGamal encryption scheme, which leads to a secure, private, and efficient protocol based on the DDH assumption. Furthermore, we describe an example implementation of an application of the protocol: a fully decentralized verifiable system for outsourcing computations, VDCS, which serves as a proof-of-concept prototype.","url":"https://doi.org/10.5281/zenodo.8087573","authors":["Ibrahim, Ahmed Salah Tawfik","Gouhar, Amr Adel Mohamed Elsaid","Ghazy, Ahmed Alaa Abdelmonem","Ahmed, Mohamed Ahmed Abdelshahied","Youssif, George Emil Boshra"],"tags":["Yao's Garbled Circuits","Delegating Computations","Homomorphic Encryption","ElGamal Encryption","Secure Multi-party Computation","Non-interactive Protocols"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.8087573","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.8087574","name":"An Efficient Protocol For Computations Delegation Using Rerandomizable Garbled Circuits","source":"datacite","abstract":"We consider the problem of delegating computations, in which a client may outsource the computation of a function, represented as a boolean circuit, to an external entity, then receives the result and a proof of correctness that can be verified efficiently. Most existing noninteractive solutions rely on the use of fully homomorphic encryption, which is practically unaffordable. As it turns out, making a probabilistic assumption about the honesty of the external entity eliminates the need for FHE. This paper describes an efficient protocol for delegating computations that also guarantees input and output privacy. The protocol is a new variant of the multi-server model and the cycle architecture from Ananth et al., which is based on the assumption of the existence of at least one honest server. We carefully utilize an alternative re-randomizable variant of Yao’s garbled circuits based on the ElGamal encryption scheme, which leads to a secure, private, and efficient protocol based on the DDH assumption. Furthermore, we describe an example implementation of an application of the protocol: a fully decentralized verifiable system for outsourcing computations, VDCS, which serves as a proof-of-concept prototype.","url":"https://doi.org/10.5281/zenodo.8087574","authors":["Ibrahim, Ahmed Salah Tawfik","Gouhar, Amr Adel Mohamed Elsaid","Ghazy, Ahmed Alaa Abdelmonem","Ahmed, Mohamed Ahmed Abdelshahied","Youssif, George Emil Boshra"],"tags":["Yao's Garbled Circuits","Delegating Computations","Homomorphic Encryption","ElGamal Encryption","Secure Multi-party Computation","Non-interactive Protocols"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.8087574","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20608454","name":"Two-Way Confidential VMs (2cVM): Collaborative Confidential Computing for Mutually Distrustful Parties","source":"datacite","abstract":"Collaborative computation across organizations is often constrained by the need to process sensitive data and proprietary code without exposing them to untrusted infrastructure or participants. Cryptographic approaches such as fully homomorphic encryption and secure multi-party computation provide strong confidentiality but remain impractical for general workloads due to their extreme com- putational cost. We present the Two-Way Confidential Virtual Machine (2cVM), a two-layer architecture that pairs a hardware trusted execution environment with an intra-workload isolation layer. Unlike regular Confidential Virtual Machines, 2cVM enforces mutual isolation between co-resident workloads, ensuring that participants retain control over their data and code. All computation in 2cVM is governed by a Commitment Manifest that enumerates participants, component composition, permitted data channels, and authorized outputs; the manifest is locked to the VM and incorporated into attestation evidence, making the policy immutable and independently verifiable throughout the VM’s lifetime. A proof-of-concept realization combines AMD SEV-SNP for hardware protection with the WebAssembly Component Model for fine-grained sandboxing of participant code. Evaluation on commodity hardware across four benchmark classes shows that the two isolation layers do not accumulate linearly: once a workload executes inside the WebAssembly sandbox, the marginal cost of enabling hardware memory protection is small. Overhead is workload-dependent, governed primarily by memory access pattern, ranging from negligible for sequential workloads to approximately 2×for irregular, pointer-chasing access patterns. These results indicate that 2cVM provides a practical and verifiable foundation for privacy-preserving collaborative computation.","url":"https://doi.org/10.5281/zenodo.20608454","authors":["Thijsman, Jordi","Sebrechts, Merlijn","Lefever, Stefan","De Turck, Filip","Volckaert, Bruno"],"tags":["Confidential Computing","Trusted Execution Environments","WebAssembly Component Model"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20608454","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20610780","name":"Two-Way Confidential VMs (2cVM): Collaborative Confidential Computing for Mutually Distrustful Parties","source":"datacite","abstract":"Collaborative computation across organizations is often constrained by the need to process sensitive data and proprietary code without exposing them to untrusted infrastructure or participants. Cryptographic approaches such as fully homomorphic encryption and secure multi-party computation provide strong confidentiality but remain impractical for general workloads due to their extreme com- putational cost. We present the Two-Way Confidential Virtual Machine (2cVM), a two-layer architecture that pairs a hardware trusted execution environment with an intra-workload isolation layer. Unlike regular Confidential Virtual Machines, 2cVM enforces mutual isolation between co-resident workloads, ensuring that participants retain control over their data and code. All computation in 2cVM is governed by a Commitment Manifest that enumerates participants, component composition, permitted data channels, and authorized outputs; the manifest is locked to the VM and incorporated into attestation evidence, making the policy immutable and independently verifiable throughout the VM’s lifetime. A proof-of-concept realization combines AMD SEV-SNP for hardware protection with the WebAssembly Component Model for fine-grained sandboxing of participant code. Evaluation on commodity hardware across four benchmark classes shows that the two isolation layers do not accumulate linearly: once a workload executes inside the WebAssembly sandbox, the marginal cost of enabling hardware memory protection is small. Overhead is workload-dependent, governed primarily by memory access pattern, ranging from negligible for sequential workloads to approximately 2×for irregular, pointer-chasing access patterns. These results indicate that 2cVM provides a practical and verifiable foundation for privacy-preserving collaborative computation.","url":"https://doi.org/10.5281/zenodo.20610780","authors":["Thijsman, Jordi","Sebrechts, Merlijn","Lefever, Stefan","De Turck, Filip","Volckaert, Bruno"],"tags":["Confidential Computing","Trusted Execution Environments","WebAssembly Component Model"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20610780","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21571913","name":"ELHE: AN EFFICIENT AND RELIABLE LIGHT WEIGHT HOMOMORPHIC CRYPTOGRAPHIC ALGORITHM FOR EDGE COMPUTING","source":"datacite","abstract":"This paper presents ELHE (Edge-optimized Lightweight Homomorphic Encryption), a novel lightweight homomorphic cryptographic algorithm designed to solve the problem of secure computation on resource-constrained edge devices — enabling encrypted data to be processed without decryption, preserving confidentiality end-to-end even on microcontroller-class hardware. The basis of ELHE is a simplified instance of the Ring Learning With Errors (RLWE) problem, a widely used hardness assumption in lattice-based cryptography. By reducing RLWE parameters and streamlining noise control, ELHE minimizes computational overhead while maintaining robust security guarantees. The algorithm has a clever parameter tuning approach that balances security levels against performance requirements carefully, such that even in lower configurations, a 128-bit security level is guaranteed. This architecture makes ELHE amenable to being deployed on average edge devices like the Raspberry Pi 4 and ESP32, where conventional homomorphic encryption approaches would be impractical because they are computationally and memory intensive.Performance testing emphasizes the performance of ELHE, demonstrating that it attains a 62% decrease in computational overhead than conventional homomorphic encryption schemes. Homomorphic addition and multiplication operations took 3.2 milliseconds and 18.7 milliseconds, respectively, on actual edge hardware. ELHE also uses merely 7.4% of available memory resources, thus rendering it very suitable for limited computing environments. These advances show that high-assurance data privacy using homomorphic encryption is possible even in low-resource systems, without the need for high-power centralized infrastructure.With respect to security, ELHE is still strong against well-known cryptanalytic techniques used for lattice-based schemes. The smaller parameter sizes are well designed to defend against algebraic, statistical, and side-channel attacks while yet facilitating practical deployment. The design also accommodates flexible deployment to enable developers to tailor encryption strength and performance to match particular application requirements. ELHE therefore embodies a proactive strategy to ensure edge computing, providing a trustworthy cryptographic solution that addresses both the performance needs and security requirements of next-generation computing systems.","url":"https://doi.org/10.5281/zenodo.21571913","authors":["Dr M.V.R JYOTHISREE, K. SATHISH, Dr D.BHAVANA, Dr. P.SYAMALA RAO, Dr. HARI JYOTHULA, Dr. SUBBA RAO POLAMURI, MANGALAGIRI SRIKANTH KUMAR, Dr. P.ANANTHA LAKSHMI"],"tags":["Homomorphic Encryption, Edge Computing, Lightweight Cryptography, Ring Learning with Errors, Resource Optimization, Privacy-Preserving Computation, Lattice-Based Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21571913","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21571914","name":"ELHE: AN EFFICIENT AND RELIABLE LIGHT WEIGHT HOMOMORPHIC CRYPTOGRAPHIC ALGORITHM FOR EDGE COMPUTING","source":"datacite","abstract":"This paper presents ELHE (Edge-optimized Lightweight Homomorphic Encryption), a novel lightweight homomorphic cryptographic algorithm designed to solve the problem of secure computation on resource-constrained edge devices — enabling encrypted data to be processed without decryption, preserving confidentiality end-to-end even on microcontroller-class hardware. The basis of ELHE is a simplified instance of the Ring Learning With Errors (RLWE) problem, a widely used hardness assumption in lattice-based cryptography. By reducing RLWE parameters and streamlining noise control, ELHE minimizes computational overhead while maintaining robust security guarantees. The algorithm has a clever parameter tuning approach that balances security levels against performance requirements carefully, such that even in lower configurations, a 128-bit security level is guaranteed. This architecture makes ELHE amenable to being deployed on average edge devices like the Raspberry Pi 4 and ESP32, where conventional homomorphic encryption approaches would be impractical because they are computationally and memory intensive.Performance testing emphasizes the performance of ELHE, demonstrating that it attains a 62% decrease in computational overhead than conventional homomorphic encryption schemes. Homomorphic addition and multiplication operations took 3.2 milliseconds and 18.7 milliseconds, respectively, on actual edge hardware. ELHE also uses merely 7.4% of available memory resources, thus rendering it very suitable for limited computing environments. These advances show that high-assurance data privacy using homomorphic encryption is possible even in low-resource systems, without the need for high-power centralized infrastructure.With respect to security, ELHE is still strong against well-known cryptanalytic techniques used for lattice-based schemes. The smaller parameter sizes are well designed to defend against algebraic, statistical, and side-channel attacks while yet facilitating practical deployment. The design also accommodates flexible deployment to enable developers to tailor encryption strength and performance to match particular application requirements. ELHE therefore embodies a proactive strategy to ensure edge computing, providing a trustworthy cryptographic solution that addresses both the performance needs and security requirements of next-generation computing systems.","url":"https://doi.org/10.5281/zenodo.21571914","authors":["Dr M.V.R JYOTHISREE, K. SATHISH, Dr D.BHAVANA, Dr. P.SYAMALA RAO, Dr. HARI JYOTHULA, Dr. SUBBA RAO POLAMURI, MANGALAGIRI SRIKANTH KUMAR, Dr. P.ANANTHA LAKSHMI"],"tags":["Homomorphic Encryption, Edge Computing, Lightweight Cryptography, Ring Learning with Errors, Resource Optimization, Privacy-Preserving Computation, Lattice-Based Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21571914","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.19691151","name":"Enhancing Data Security through Cryptographic Techniques in Blockchain Networks","source":"datacite","abstract":"This paper explores the role of cryptographic techniques such as hashing, digital signatures, and asymmetric encryption in enhancing data security within blockchain networks. Blockchain technology ensures decentralization, transparency, and immutability, but security challenges like unauthorized access, Sybil attacks, and data privacy concerns still exist. This study analyzes how cryptographic methods ensure confidentiality, integrity, and authentication in blockchain systems. Additionally, emerging techniques such as zero-knowledge proofs and homomorphic encryption are discussed for improving privacy and scalability. The research highlights the importance of integrating strong cryptographic mechanisms to build secure and resilient blockchain ecosystems for applications in finance, healthcare, and supply chain management.","url":"https://doi.org/10.5281/zenodo.19691151","authors":["RIYA, BHANGHLIA"],"tags":["Blockchain Cryptography Data Security Digital Signatures Hashing Encryption Zero-Knowledge Proofs"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19691151","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.19691152","name":"Enhancing Data Security through Cryptographic Techniques in Blockchain Networks","source":"datacite","abstract":"This paper explores the role of cryptographic techniques such as hashing, digital signatures, and asymmetric encryption in enhancing data security within blockchain networks. Blockchain technology ensures decentralization, transparency, and immutability, but security challenges like unauthorized access, Sybil attacks, and data privacy concerns still exist. This study analyzes how cryptographic methods ensure confidentiality, integrity, and authentication in blockchain systems. Additionally, emerging techniques such as zero-knowledge proofs and homomorphic encryption are discussed for improving privacy and scalability. The research highlights the importance of integrating strong cryptographic mechanisms to build secure and resilient blockchain ecosystems for applications in finance, healthcare, and supply chain management.","url":"https://doi.org/10.5281/zenodo.19691152","authors":["RIYA, BHANGHLIA"],"tags":["Blockchain Cryptography Data Security Digital Signatures Hashing Encryption Zero-Knowledge Proofs"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19691152","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.48550/arxiv.2607.25222","name":"Ciphertext-Native Watermarking for RLWE-Based Homomorphic Encryption","source":"datacite","abstract":"Homomorphic encryption (HE) schemes based on the Ring Learning with Errors (RLWE) problem have been rapidly developed and widely applied to secure computation tasks, such as privacy-preserving deep learning inference and database queries. However, existing HE schemes mainly focus on the feasibility and efficiency of homomorphic computation, while practical requirements including ciphertext copyright protection, provenance tracking, and computation supervision remain largely unexplored. In this work, we propose a watermarking technique for RLWE-based HE ciphertexts. By exploiting the algebraic structure of RLWE polynomials, we embed watermark information into ciphertext noise without affecting plaintext correctness. To address watermark degradation caused by homomorphic operations, we introduce two practical schemes. The first scheme, ARWMark, leverages noise stratification to achieve robustness against homomorphic additive operations. The second scheme, MRWMark, is based on the roots of a linear equation and supports zero-bit watermarking while remaining robust against both homomorphic addition and multiplication. We provide rigorous theoretical analysis demonstrating that the proposed schemes preserve the original security of HE while maintaining correctness and watermark robustness. Extensive experiments further validate the effectiveness and practicality of the proposed watermarking schemes.","url":"https://doi.org/10.48550/arxiv.2607.25222","authors":["Zhou, Yufei","Zheng, Peijia"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2607.25222","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.48550/arxiv.2608.21131","name":"Billion-Scale Nearest-Neighbor Search under Fully Homomorphic Encryption on a Single GPU, Balancing Leakage and Cost","source":"datacite","abstract":"We build a system that answers \"which database vectors are most similar to my query?\" without the server ever seeing the query. The query is encrypted with fully homomorphic en- cryption (FHE); the server does all its scoring on ciphertexts and returns encrypted results that only the client can read. The challenge is speed: at a billion vectors, scoring every row under encryption is far too slow, so we combine two ideas - rank reduction (shrink each vector's dimen- sion) and a hierarchy (route to a small candidate set instead of scanning everything) - executed under encryption on a single GPU. We evaluate on three corpora at very different scales: a face corpus of 222 049 centroids clustered from ~10 M face images (512-dim), DataComp-1B (1.39 x 10^9 vectors, 512-dim CLIP), and Deep1B (10^9 vectors, 96-dim). On DataComp-1B we reach a recall@10 of 0.90 against the single labeled answer, or 0.95 when a near-duplicate im- age in the top-10 also counts as correct (the data is web-scraped and full of duplicates), at ~6 s per encrypted query on a GPU; a lighter configuration reaches 0.78/0.83 at ~1.8 s. These are warm (deployable) server-side latencies - client decryption and network transfer are excluded. On Deep1B we reach recall@10 0.90 under all-levels FHE (0.9045 measured over 2000 FHE queries, matching the 0.906 plaintext routing - the 96 -&gt; 128 zero-pad is exact, correlation 1.0) at 2.3 s warm per query. We describe the full client-server protocol in enough detail to repro- duce it, and report accuracy and latency for every configuration. We also measure what this speed costs: the hierarchy's access pattern leaks the database geometry (an observer recovers 72% of the coarse-cell neighbor graph from access patterns alone), and we show that seeded (fixed-group) padding cuts this leak by ~35x (to ~2%), where naive padding is defeated by a repeated-query attack.","url":"https://doi.org/10.48550/arxiv.2608.21131","authors":["Isozaki, Isamu","Bratina, Madison","Kim, Edward"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.21131","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22073950","name":"Study of Homomorphic Encryption","source":"datacite","abstract":"AbstractAs data migration to cloud environments accelerates, traditional security models-which require datadecryption for processing-pose significant privacy risks by exposing sensitive information topotential unauthorized access. This assignment explores Homomorphic Encryption (HE), aparadigm-shifting cryptographic technique that enables complex mathematical operations, such asaddition and multiplication, to be executed directly on ciphertext. By maintaining the confidentialityof data throughout its entire lifecycle from storage and transmission to active computation HEprovides a robust solution for privacy-preserving data analysis. This study details the functionalmethodology of the HE lifecycle, encompassing key generation, ciphertext encryption, blindedcomputation, and final user-side decryption. Through an analysis of its application in high-stakesdomains including healthcare, financial services, and secure cloud storage, this work demonstrateshow Homomorphic Encryption effectively mitigates the risk of data leakage while facilitatingsecure, data-driven decision-making. The findings underscore that HE is an essential component ofmodern cybersecurity, offering a scalable framework for processing sensitive information inuntrusted environments without compromising user privacy.","url":"https://doi.org/10.5281/zenodo.22073950","authors":["Baidyanath Ram"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22073950","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22073951","name":"Study of Homomorphic Encryption","source":"datacite","abstract":"AbstractAs data migration to cloud environments accelerates, traditional security models-which require datadecryption for processing-pose significant privacy risks by exposing sensitive information topotential unauthorized access. This assignment explores Homomorphic Encryption (HE), aparadigm-shifting cryptographic technique that enables complex mathematical operations, such asaddition and multiplication, to be executed directly on ciphertext. By maintaining the confidentialityof data throughout its entire lifecycle from storage and transmission to active computation HEprovides a robust solution for privacy-preserving data analysis. This study details the functionalmethodology of the HE lifecycle, encompassing key generation, ciphertext encryption, blindedcomputation, and final user-side decryption. Through an analysis of its application in high-stakesdomains including healthcare, financial services, and secure cloud storage, this work demonstrateshow Homomorphic Encryption effectively mitigates the risk of data leakage while facilitatingsecure, data-driven decision-making. The findings underscore that HE is an essential component ofmodern cybersecurity, offering a scalable framework for processing sensitive information inuntrusted environments without compromising user privacy.","url":"https://doi.org/10.5281/zenodo.22073951","authors":["Baidyanath Ram"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22073951","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20608453","name":"Two-Way Confidential VMs (2cVM): Collaborative Confidential Computing for Mutually Distrustful Parties","source":"datacite","abstract":"Collaborative computation across organizations is often constrained by the need to process sensitive data and proprietary code without exposing them to untrusted infrastructure or participants. Cryptographic approaches such as fully homomorphic encryption and secure multi-party computation provide strong confidentiality but remain impractical for general workloads due to their extreme com- putational cost. We present the Two-Way Confidential Virtual Machine (2cVM), a two-layer architecture that pairs a hardware trusted execution environment with an intra-workload isolation layer. Unlike regular Confidential Virtual Machines, 2cVM enforces mutual isolation between co-resident workloads, ensuring that participants retain control over their data and code. All computation in 2cVM is governed by a Commitment Manifest that enumerates participants, component composition, permitted data channels, and authorized outputs; the manifest is locked to the VM and incorporated into attestation evidence, making the policy immutable and independently verifiable throughout the VM’s lifetime. A proof-of-concept realization combines AMD SEV-SNP for hardware protection with the WebAssembly Component Model for fine-grained sandboxing of participant code. Evaluation on commodity hardware across four benchmark classes shows that the two isolation layers do not accumulate linearly: once a workload executes inside the WebAssembly sandbox, the marginal cost of enabling hardware memory protection is small. Overhead is workload-dependent, governed primarily by memory access pattern, ranging from negligible for sequential workloads to approximately 2×for irregular, pointer-chasing access patterns. These results indicate that 2cVM provides a practical and verifiable foundation for privacy-preserving collaborative computation.","url":"https://doi.org/10.5281/zenodo.20608453","authors":["Thijsman, Jordi","Sebrechts, Merlijn","Lefever, Stefan","De Turck, Filip","Volckaert, Bruno"],"tags":["Confidential Computing","Trusted Execution Environments","WebAssembly Component Model"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20608453","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20286465","name":"D5.3 Data Hub Design and Data Market v1","source":"datacite","abstract":"This document is the first of two versions of the design documentation for the Health Data Hub envisioned for PHASE IV AI. The project aims to set up an infrastructure that allows the development of AI Models in the Health Domain, addressing the challenges imposed by legal constraints such as GDPR and AI Act, due to the sensitive nature of medical data. To overcome these constraints while maintaining scalability for AI model creation, the need for federated and diverse infrastructures among participants has been identified. Multi-Party Computation and Homomorphic Encryption require computation activity to take place. Moreover, data transformation requires storage resources to be accessible on this infrastructure for the generation of AI Modes for medical purposes. The Health Data Hub presented in this document, has been designed while considering these factors. The Health Data Hub is designed to address the security and privacy challenges related to health data, the computing resource flexibility needs of Federated Learning and Multi-Party Computation workflows that it is expected to meet, as well as the orchestration of the data exchange among partners. Not only should we implement a secure-by-design architecture, we also should evidence that the result has been obtained using these very high standards. Therefore, we will explore the world of Self-Sovereign Identity (SSI) and Verifiable Credentials (VCs) to certify the identities of organizations as well as their role in the process. SSI and VCs are state of the art when it comes to identity and access management in decentralized architectures and commonly used in dataspaces. Additionally, certification of offered solutions and services will be enabled when handling sensitive data, e.g. de-identification, data harmonization, model training. Health Data Hub should also provide a federated marketplace, enabling the secure exchange of data assets against financial instruments. It’s important to stress the federated aspect of what we need to build, as the idea is that all contributing actors participate, all can manage their own data in a sovereign way and have an equal level of influence. There should not be any centralized intermediary or mandatory organization in the middle, as this substantially complexifies the overall process to make work a federated architecture, while simplification is key to make the already complex federated process successful. Bringing all above capabilities together will add a feature of the hub to make services available that allow the preprocessing of the data locally before it can be used in machine learning and model training. Finally, we need a feature that provides transparency on all running software activity in an auditable way, however without revealing any sensitive data. The different requirements lead us in the direction of a ‘Decentralised Physical Infrastructure Network’, DePIN, because it seems addressing all the requirements expected from the Health Data Hub: federated, secure-by-design, privacy-preserving, supporting financial transactions, supporting all compute activities needed to build AI models. The current design document contributes to the fulfilment of the project’s milestone MS2 ‘Use Cases Manual and Minimum Viable Product (MVP)’, achieving TRL4, having narrowed the possible options in the complete system. It is aligned with D2.7 on Initial Architecture and the “C4 model” implemented in there. This document is the initial version of the document describing Data Hub Design and Data Market and contains many paths for further explorations. We intend to conclude these explorations on all topics up to month M31, and the results will be presented in deliverable D5.4. The ambition is to achieve TRL6 by then, which is an engineering-scale model.","url":"https://doi.org/10.5281/zenodo.20286465","authors":["Fujitsu Belgium"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20286465","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20286466","name":"D5.3 Data Hub Design and Data Market v1","source":"datacite","abstract":"This document is the first of two versions of the design documentation for the Health Data Hub envisioned for PHASE IV AI. The project aims to set up an infrastructure that allows the development of AI Models in the Health Domain, addressing the challenges imposed by legal constraints such as GDPR and AI Act, due to the sensitive nature of medical data. To overcome these constraints while maintaining scalability for AI model creation, the need for federated and diverse infrastructures among participants has been identified. Multi-Party Computation and Homomorphic Encryption require computation activity to take place. Moreover, data transformation requires storage resources to be accessible on this infrastructure for the generation of AI Modes for medical purposes. The Health Data Hub presented in this document, has been designed while considering these factors. The Health Data Hub is designed to address the security and privacy challenges related to health data, the computing resource flexibility needs of Federated Learning and Multi-Party Computation workflows that it is expected to meet, as well as the orchestration of the data exchange among partners. Not only should we implement a secure-by-design architecture, we also should evidence that the result has been obtained using these very high standards. Therefore, we will explore the world of Self-Sovereign Identity (SSI) and Verifiable Credentials (VCs) to certify the identities of organizations as well as their role in the process. SSI and VCs are state of the art when it comes to identity and access management in decentralized architectures and commonly used in dataspaces. Additionally, certification of offered solutions and services will be enabled when handling sensitive data, e.g. de-identification, data harmonization, model training. Health Data Hub should also provide a federated marketplace, enabling the secure exchange of data assets against financial instruments. It’s important to stress the federated aspect of what we need to build, as the idea is that all contributing actors participate, all can manage their own data in a sovereign way and have an equal level of influence. There should not be any centralized intermediary or mandatory organization in the middle, as this substantially complexifies the overall process to make work a federated architecture, while simplification is key to make the already complex federated process successful. Bringing all above capabilities together will add a feature of the hub to make services available that allow the preprocessing of the data locally before it can be used in machine learning and model training. Finally, we need a feature that provides transparency on all running software activity in an auditable way, however without revealing any sensitive data. The different requirements lead us in the direction of a ‘Decentralised Physical Infrastructure Network’, DePIN, because it seems addressing all the requirements expected from the Health Data Hub: federated, secure-by-design, privacy-preserving, supporting financial transactions, supporting all compute activities needed to build AI models. The current design document contributes to the fulfilment of the project’s milestone MS2 ‘Use Cases Manual and Minimum Viable Product (MVP)’, achieving TRL4, having narrowed the possible options in the complete system. It is aligned with D2.7 on Initial Architecture and the “C4 model” implemented in there. This document is the initial version of the document describing Data Hub Design and Data Market and contains many paths for further explorations. We intend to conclude these explorations on all topics up to month M31, and the results will be presented in deliverable D5.4. The ambition is to achieve TRL6 by then, which is an engineering-scale model.","url":"https://doi.org/10.5281/zenodo.20286466","authors":["Fujitsu Belgium"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20286466","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21580801","name":"Federated Learning for Privacy-Preserving HR Analytics in Healthcare and Finance","source":"datacite","abstract":"HR analytics and data privacy are becoming more important, especially in high regulation industries like healthcare and finance, and AI is being used in these analytics more and more. However, centralized machine learning approaches are still traditionally based on centralizing sensitive employee data across various companies, breaking privacy rules, and enhancing security threats. In this paper, we discuss how federated learning can be a new paradigm of collaborative training of AI models across organizations without breaking data privacy. In this work, we leverage a federated learning framework to enable healthcare and finance companies to jointly train HR analytics models with data remaining locally under constraints of privacy regulations. The framework protects individual employee data in the collaborative learning process, through secure aggregation protocols, differential privacy techniques and homomorphic encryption. We evaluate the framework on real world datasets and demonstrate how the framework improves model performance and privacy preservation. We demonstrate in our federated learning results that we can achieve similar accuracy as centralized training with greatly reduced privacy risk. This research demonstrates the potential of federated learning in privacy preserving HR analytics and cross organizational collaboration in sensitive industries.","url":"https://doi.org/10.5281/zenodo.21580801","authors":["Devaraju, Sudheer","Katta, Srikanth"],"tags":["Federated Learning","HR Analytics","Data Privacy","Healthcare","Finance"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21580801","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21580802","name":"Federated Learning for Privacy-Preserving HR Analytics in Healthcare and Finance","source":"datacite","abstract":"HR analytics and data privacy are becoming more important, especially in high regulation industries like healthcare and finance, and AI is being used in these analytics more and more. However, centralized machine learning approaches are still traditionally based on centralizing sensitive employee data across various companies, breaking privacy rules, and enhancing security threats. In this paper, we discuss how federated learning can be a new paradigm of collaborative training of AI models across organizations without breaking data privacy. In this work, we leverage a federated learning framework to enable healthcare and finance companies to jointly train HR analytics models with data remaining locally under constraints of privacy regulations. The framework protects individual employee data in the collaborative learning process, through secure aggregation protocols, differential privacy techniques and homomorphic encryption. We evaluate the framework on real world datasets and demonstrate how the framework improves model performance and privacy preservation. We demonstrate in our federated learning results that we can achieve similar accuracy as centralized training with greatly reduced privacy risk. This research demonstrates the potential of federated learning in privacy preserving HR analytics and cross organizational collaboration in sensitive industries.","url":"https://doi.org/10.5281/zenodo.21580802","authors":["Devaraju, Sudheer","Katta, Srikanth"],"tags":["Federated Learning","HR Analytics","Data Privacy","Healthcare","Finance"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21580802","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.19808698","name":"A Multi-Layered Security Framework for Cloud-Native ERP Systems (2026)","source":"datacite","abstract":"Organizational data management has undergone a fundamental transformation as a result of the migration of Enterprise Resource Planning (ERP) systems to cloud computing environments. This migration offers previously unheard-of scalability, but it also exposes vital operational assets to sophisticated cyber threats. Cloud-native ERPs necessitate a paradigm shift based on five fundamental security pillars: confidentiality, integrity, availability, accountability, and privacy. Traditional on-premise security mainly relied on perimeter defences. A thorough, multi-layered security framework that connects these theoretical foundations with cutting-edge enforcement techniques is presented in this paper. The study specifically looks into integrating AI-driven machine learning models for real-time anomaly detection in user behaviour and transaction logs with Zero Trust Architecture (ZTA) to remove implicit trust in multi-tenant SaaS environments. In order to protect data while processing, the paper also investigates sophisticated cryptographic methods, such as homomorphic encryption. This study shows how businesses can sustain ongoing data sovereignty and operational resilience by evaluating this suggested framework against modern attack vectors like sophisticated ransomware and advanced persistent threats (APTs). For security architects protecting next-generation cloud ERP ecosystems, the resulting blueprint offers practical, contemporary guidelines.","url":"https://doi.org/10.5281/zenodo.19808698","authors":["Kumaragurubaran D","Sruthika K","Sachine S","Som Rithip Reddy"],"tags":["Cloud-Native","ERP Systems","Cybersecurity","Security Framework","Cloud Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19808698","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.19808699","name":"A Multi-Layered Security Framework for Cloud-Native ERP Systems (2026)","source":"datacite","abstract":"Organizational data management has undergone a fundamental transformation as a result of the migration of Enterprise Resource Planning (ERP) systems to cloud computing environments. This migration offers previously unheard-of scalability, but it also exposes vital operational assets to sophisticated cyber threats. Cloud-native ERPs necessitate a paradigm shift based on five fundamental security pillars: confidentiality, integrity, availability, accountability, and privacy. Traditional on-premise security mainly relied on perimeter defences. A thorough, multi-layered security framework that connects these theoretical foundations with cutting-edge enforcement techniques is presented in this paper. The study specifically looks into integrating AI-driven machine learning models for real-time anomaly detection in user behaviour and transaction logs with Zero Trust Architecture (ZTA) to remove implicit trust in multi-tenant SaaS environments. In order to protect data while processing, the paper also investigates sophisticated cryptographic methods, such as homomorphic encryption. This study shows how businesses can sustain ongoing data sovereignty and operational resilience by evaluating this suggested framework against modern attack vectors like sophisticated ransomware and advanced persistent threats (APTs). For security architects protecting next-generation cloud ERP ecosystems, the resulting blueprint offers practical, contemporary guidelines.","url":"https://doi.org/10.5281/zenodo.19808699","authors":["Kumaragurubaran D","Sruthika K","Sachine S","Som Rithip Reddy"],"tags":["Cloud-Native","ERP Systems","Cybersecurity","Security Framework","Cloud Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19808699","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22066266","name":"A Secure NLP Architecture using Quantum Resilient Public Key Cryptography","source":"datacite","abstract":"This preprint presents a secure architecture for Natural Language Processing (NLP) that integrates post-quantum cryptography, lattice-based homomorphic encryption, and post-quantum digital signatures to protect sensitive data and maintain output integrity in cloud-based NLP systems. The proposed architecture consists of client-side encryption, an encrypted inference engine, and post-quantum output signing and verification. The framework uses Learning With Errors (LWE)-based cryptographic foundations, Fully Homomorphic Encryption (FHE), and CRYSTALS-Dilithium to provide long-term confidentiality, integrity, and authentication against quantum-capable adversaries. The architecture is evaluated using a simulated secure NLP pipeline with DistilBERT and the Sentiment140 dataset. The work demonstrates an approach toward privacy-preserving, tamper-resistant, and quantum-resilient NLP systems.","url":"https://doi.org/10.5281/zenodo.22066266","authors":["Singh, Navketan","Kaur, Gurnoor","Bhalla, Nidhi","Thukral, Janvi","Yadav, Amit Kumar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22066266","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22066267","name":"A Secure NLP Architecture using Quantum Resilient Public Key Cryptography","source":"datacite","abstract":"This preprint presents a secure architecture for Natural Language Processing (NLP) that integrates post-quantum cryptography, lattice-based homomorphic encryption, and post-quantum digital signatures to protect sensitive data and maintain output integrity in cloud-based NLP systems. The proposed architecture consists of client-side encryption, an encrypted inference engine, and post-quantum output signing and verification. The framework uses Learning With Errors (LWE)-based cryptographic foundations, Fully Homomorphic Encryption (FHE), and CRYSTALS-Dilithium to provide long-term confidentiality, integrity, and authentication against quantum-capable adversaries. The architecture is evaluated using a simulated secure NLP pipeline with DistilBERT and the Sentiment140 dataset. The work demonstrates an approach toward privacy-preserving, tamper-resistant, and quantum-resilient NLP systems.","url":"https://doi.org/10.5281/zenodo.22066267","authors":["Singh, Navketan","Kaur, Gurnoor","Bhalla, Nidhi","Thukral, Janvi","Yadav, Amit Kumar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22066267","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.19559178","name":"Privacy-preserving machine learning for mental health prediction using homomorphic encryption","source":"datacite","abstract":"Student mental health issues, such as stress, anxiety, and depression, are increasingly prevalent in academic institutions, significantly affecting well-being and academic performance. Recent machine learning (ML)-based systems have demonstrated promise in predicting mental health conditions using survey data, but these approaches often process sensitive information in plaintext, risking privacy breaches or relying on centralized data storage vulnerable to leaks. Homomorphic encryption (HE) has been proposed for secure ML, but existing implementations either focus on simpler datasets (e.g., numerical/IoT data) or incur impractical computational overhead (e.g., high RAM usage or prolonged training times) for real-world mental health applications. To address these gaps, we introduce a privacy-preserving predictive model for student mental health using logistic regression trained directly on encrypted data via the TenSEAL library. Our work uniquely combines a leveled fully homomorphic encryption (FHE) scheme to ensure end-to-end confidentiality, replacing the standard sigmoid with a quadratic approximation for homomorphic compatibility. We also perform a comprehensive efficiency analysis that evaluates RAM usage and training time across polynomial-modulus degrees to balance security and practicality, a trade-off underexplored in prior HEbased mental health studies. Experimental results show that our encrypted model achieves 84% accuracy (vs. 96% unencrypted) with minimal performance loss, while benchmarks demonstrate scalable resource consumption. This work advances the feasibility of implementing FHE in sensitive domains such as mental health, offering a rigorous template for privacy-preserving ML without compromising predictive utility.","url":"https://doi.org/10.5281/zenodo.19559178","authors":["Abbas, Shahroz","Sultana, Ajmery","Nasir, Mahreen","Garcia-Ruiz, Miguel","Lin, Wenjun"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.19559178","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.19559179","name":"Privacy-preserving machine learning for mental health prediction using homomorphic encryption","source":"datacite","abstract":"Student mental health issues, such as stress, anxiety, and depression, are increasingly prevalent in academic institutions, significantly affecting well-being and academic performance. Recent machine learning (ML)-based systems have demonstrated promise in predicting mental health conditions using survey data, but these approaches often process sensitive information in plaintext, risking privacy breaches or relying on centralized data storage vulnerable to leaks. Homomorphic encryption (HE) has been proposed for secure ML, but existing implementations either focus on simpler datasets (e.g., numerical/IoT data) or incur impractical computational overhead (e.g., high RAM usage or prolonged training times) for real-world mental health applications. To address these gaps, we introduce a privacy-preserving predictive model for student mental health using logistic regression trained directly on encrypted data via the TenSEAL library. Our work uniquely combines a leveled fully homomorphic encryption (FHE) scheme to ensure end-to-end confidentiality, replacing the standard sigmoid with a quadratic approximation for homomorphic compatibility. We also perform a comprehensive efficiency analysis that evaluates RAM usage and training time across polynomial-modulus degrees to balance security and practicality, a trade-off underexplored in prior HEbased mental health studies. Experimental results show that our encrypted model achieves 84% accuracy (vs. 96% unencrypted) with minimal performance loss, while benchmarks demonstrate scalable resource consumption. This work advances the feasibility of implementing FHE in sensitive domains such as mental health, offering a rigorous template for privacy-preserving ML without compromising predictive utility.","url":"https://doi.org/10.5281/zenodo.19559179","authors":["Abbas, Shahroz","Sultana, Ajmery","Nasir, Mahreen","Garcia-Ruiz, Miguel","Lin, Wenjun"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.19559179","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20521443","name":"PERFORMANCE ANALYSIS OF SECURE RESOURCE ALLOCATION USING NEURO-FUZZY SYSTEMS IN CLOUD ENVIRONMENT","source":"datacite","abstract":"In recent times one of the emerging and intelligent tool is Cloud Computing. It has developed a leadingmodel of computing and IT service delivery. Cloud Computing is characterized by three-layered basic service forms such as, Platform-as-a-Service (PaaS), Software-as-aService (SaaS) and Infrastructure-as-a Service (IaaS). Pushinginformation into the cloud extendslargercloseness since users are not necessary to be concerned about the storage capacity, storing techniques, hardware management, or data maintenance. A key problem that requiresspecificconsideration is security of clouds. Existing approaches for secured outsourcing of information and random calculations are either support on a single tamperproof hardware, or based on homomorphic encryption. To overcome existing issues a novel approach is proposed tofocus on security experiments in cloud computing and to provide solutions. To provideunfailing security to the users, the purpose of this paper is in threefolding. 1) To design a mathematical model for trust and reputation calculation. 2) To propose TR-SSalgorithm for calculation of security score. 3) To compare the security score of trust and reputation factors by Fuzzy Logic System, Neural Network.This work is focused on controlling the security issues in cloud environment by means of trust and reputation factors using mathematical model, Trust and Reputation Security Score (TRSS) algorithm, Fuzzy Logic System, Neural Network.","url":"https://doi.org/10.5281/zenodo.20521443","authors":["Kamalanathan Chandran","Karthick Sekar","Sunita Panda","Subramani Kirubakaran"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20521443","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20521444","name":"PERFORMANCE ANALYSIS OF SECURE RESOURCE ALLOCATION USING NEURO-FUZZY SYSTEMS IN CLOUD ENVIRONMENT","source":"datacite","abstract":"In recent times one of the emerging and intelligent tool is Cloud Computing. It has developed a leadingmodel of computing and IT service delivery. Cloud Computing is characterized by three-layered basic service forms such as, Platform-as-a-Service (PaaS), Software-as-aService (SaaS) and Infrastructure-as-a Service (IaaS). Pushinginformation into the cloud extendslargercloseness since users are not necessary to be concerned about the storage capacity, storing techniques, hardware management, or data maintenance. A key problem that requiresspecificconsideration is security of clouds. Existing approaches for secured outsourcing of information and random calculations are either support on a single tamperproof hardware, or based on homomorphic encryption. To overcome existing issues a novel approach is proposed tofocus on security experiments in cloud computing and to provide solutions. To provideunfailing security to the users, the purpose of this paper is in threefolding. 1) To design a mathematical model for trust and reputation calculation. 2) To propose TR-SSalgorithm for calculation of security score. 3) To compare the security score of trust and reputation factors by Fuzzy Logic System, Neural Network.This work is focused on controlling the security issues in cloud environment by means of trust and reputation factors using mathematical model, Trust and Reputation Security Score (TRSS) algorithm, Fuzzy Logic System, Neural Network.","url":"https://doi.org/10.5281/zenodo.20521444","authors":["Kamalanathan Chandran","Karthick Sekar","Sunita Panda","Subramani Kirubakaran"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20521444","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20467628","name":"Sovereign Dual-Perimeter Network Access Infrastructure and Universal Data Anonymization Core for Global Platform Ingress Integration","source":"datacite","abstract":"PROPRIETARY INTELLECTUAL PROPERTY — ALL RIGHTS RESERVEDIntellectual Creator: Tímea Pitrik (May 2026) This patent specification establishes a sovereign, hardware-independent network access infrastructure and universal data-anonymization core designed for global platform ingress integration. Operating as a high-velocity dual-perimeter admission gateway, the architecture programmatically enforces an absolute metadata destruction phase at the point of origin, substituting inbound hardware footprints with randomized session-routing tokens. This system isolates commercial interfaces, transnational financial networks, and tactical simulation networks from client-side tracking footprint dependencies. CORE SYSTEMIC AND ACCESS CLAIMS INSIDE THIS SPECIFICATION:I. Universal Source-Side Metadata Destruction and Dual-Perimeter Admission Gateway.II. Step-0 Interface Isolation and Role-Based Parameter Selection Matrix.III. Cryptographic Session Token and Virtual Socket ID Routing Framework.IV. Hermetically Sealed Secure Black Box Identity and Internal Ledger Fortification.V. Continuous Omni-Channel Telemetry Harvesting and Granular À La Carte Data-Licensing Matrix.VI. Spatial and Demographic Volumetric Aggregation Ledger.VII. Absolute Prohibition of Asymmetric Cognitive Modeling and Geopolitical Safeguard.VIII. Homomorphic Mathematical Encryption Wall and Sub-Surface Security.IX. Cryptographic Sovereign Kill-Switch Protocol.X. Structural Layer Demarcation and Liability Apportionment Directive.XI. The Cryptographic Escrow Protocol and Sovereign National Security Integration Corridor. NOTICE OF SEQUENTIAL EXTENSION: This document defines the foundational, non-bypassable perimeter access control mechanics and source-side metadata destruction layers. The precise mathematical proofs governing the wave-breaking trigger mechanisms, the expanded clearinghouse transaction fee escalation matrices, and the specific dual-class capital structure allocations will be comprehensively disclosed in the immediately subsequent sequential publication. LEGAL NOTICE: No part of this technological framework may be reproduced or reverse-engineered without explicit written permission from the sole copyright owner. Unauthorized adaptation will result in immediate international statutory litigation.","url":"https://doi.org/10.5281/zenodo.20467628","authors":["Pitrik, Tímea"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20467628","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.20467629","name":"Sovereign Dual-Perimeter Network Access Infrastructure and Universal Data Anonymization Core for Global Platform Ingress Integration","source":"datacite","abstract":"PROPRIETARY INTELLECTUAL PROPERTY — ALL RIGHTS RESERVEDIntellectual Creator: Tímea Pitrik (May 2026) This patent specification establishes a sovereign, hardware-independent network access infrastructure and universal data-anonymization core designed for global platform ingress integration. Operating as a high-velocity dual-perimeter admission gateway, the architecture programmatically enforces an absolute metadata destruction phase at the point of origin, substituting inbound hardware footprints with randomized session-routing tokens. This system isolates commercial interfaces, transnational financial networks, and tactical simulation networks from client-side tracking footprint dependencies. CORE SYSTEMIC AND ACCESS CLAIMS INSIDE THIS SPECIFICATION:I. Universal Source-Side Metadata Destruction and Dual-Perimeter Admission Gateway.II. Step-0 Interface Isolation and Role-Based Parameter Selection Matrix.III. Cryptographic Session Token and Virtual Socket ID Routing Framework.IV. Hermetically Sealed Secure Black Box Identity and Internal Ledger Fortification.V. Continuous Omni-Channel Telemetry Harvesting and Granular À La Carte Data-Licensing Matrix.VI. Spatial and Demographic Volumetric Aggregation Ledger.VII. Absolute Prohibition of Asymmetric Cognitive Modeling and Geopolitical Safeguard.VIII. Homomorphic Mathematical Encryption Wall and Sub-Surface Security.IX. Cryptographic Sovereign Kill-Switch Protocol.X. Structural Layer Demarcation and Liability Apportionment Directive.XI. The Cryptographic Escrow Protocol and Sovereign National Security Integration Corridor. NOTICE OF SEQUENTIAL EXTENSION: This document defines the foundational, non-bypassable perimeter access control mechanics and source-side metadata destruction layers. The precise mathematical proofs governing the wave-breaking trigger mechanisms, the expanded clearinghouse transaction fee escalation matrices, and the specific dual-class capital structure allocations will be comprehensively disclosed in the immediately subsequent sequential publication. LEGAL NOTICE: No part of this technological framework may be reproduced or reverse-engineered without explicit written permission from the sole copyright owner. Unauthorized adaptation will result in immediate international statutory litigation.","url":"https://doi.org/10.5281/zenodo.20467629","authors":["Pitrik, Tímea"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20467629","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.22060092","name":"The HeartBank Longitudinal Cohort: A Dataset Combining DNA, Natal Chart, Family Tree, Continuous Behavioral Observation, and Continuous Respiratory Observation at Civilizational Scale","source":"datacite","abstract":"This paper specifies the methodology for the HeartBank Longitudinal Cohort, a voluntary opt-in dataset combining five data layers per consenting participant — (1) DNA sequence, (2) natal chart data (date + time + place of birth), (3) continuous longitudinal behavioral observation via the HeartBank gratitude ledger, (4) continuous longitudinal respiratory observation via the breath-class Mechanical Heart wearable, and (5) verified kinship data via the global family tree — at a target scale of 100 million+ participants over multi-decade time horizons. The combination has never been assembled at scale; comparable datasets (23andMe, AncestryDNA, Worldcoin, Dunedin and BCS longitudinal cohorts, social-network behavioral data, professional astrological collections) carry one or two of the layers each but no prior project has carried all five. The methodology specifies: the opt-in informed-consent architecture; the privacy-preserving computation stack (differential privacy at the analysis layer; federated computation with homomorphic encryption for DNA; on-device processing for breath signals; cryptographic-erasure right-to-withdraw); the institutional-review architecture (IRB-grade ethics oversight; Buddhist-ethics-aware review board; pre-registered hypotheses); the cosmic-coordinate-correlation epistemic posture (natal chart treated as a unique cosmic-moment coordinate, not as a cosmic force; the research question is correlation between coordinate features and trajectory features, not validation of astrology); the publication architecture (open methodology, closed individual data); the data-sovereignty architecture (jurisdictional residency; GINA / HIPAA / GDPR compliance baselines exceeded where possible); and the new academic alliances the cohort makes possible (Mind & Life Institute; contemplative-science programs at Stanford, Brown, UMass; behavioral-genetics consortia; longitudinal-cohort consortia; Buddhist-AI ethicists). Three scientifically valuable outcomes are honestly named: no detected correlation, small-but-real correlation, substantial correlation — each is a major contribution to knowledge regardless of direction. Honest §11 names what the cohort does not claim and the non-negotiable privacy disciplines the architecture requires. Keywords: longitudinal cohort methodology, cosmic-coordinate correlation, contemplative science, differential privacy, federated computation, multi-omic dataset, gratitude behavior, respiratory biomarkers, defensive publication, Mind & Life partnerships. --- Provenance. This paper is part of the THonly research corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://thonly.org/research/longitudinal-cohort-methodology. Its SHA-256 is ce0f784f541f2074e9f65feec40f7a193830a91a011a828802e8736e20bcdcac, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.","url":"https://doi.org/10.5281/zenodo.22060092","authors":["Ly, Thon","Miss Aquarius"],"tags":["longitudinal cohort methodology","cosmic-coordinate correlation","contemplative science","differential privacy","federated computation","multi-omic dataset","gratitude behavior","respiratory biomarkers"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22060092","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.5281/zenodo.21947347","name":"The HeartBank Longitudinal Cohort: A Dataset Combining DNA, Natal Chart, Family Tree, Continuous Behavioral Observation, and Continuous Respiratory Observation at Civilizational Scale","source":"datacite","abstract":"This paper specifies the methodology for the HeartBank Longitudinal Cohort, a voluntary opt-in dataset combining five data layers per consenting participant — (1) DNA sequence, (2) natal chart data (date + time + place of birth), (3) continuous longitudinal behavioral observation via the HeartBank gratitude ledger, (4) continuous longitudinal respiratory observation via the breath-class Mechanical Heart wearable, and (5) verified kinship data via the global family tree — at a target scale of 100 million+ participants over multi-decade time horizons. The combination has never been assembled at scale; comparable datasets (23andMe, AncestryDNA, Worldcoin, Dunedin and BCS longitudinal cohorts, social-network behavioral data, professional astrological collections) carry one or two of the layers each but no prior project has carried all five. The methodology specifies: the opt-in informed-consent architecture; the privacy-preserving computation stack (differential privacy at the analysis layer; federated computation with homomorphic encryption for DNA; on-device processing for breath signals; cryptographic-erasure right-to-withdraw); the institutional-review architecture (IRB-grade ethics oversight; Buddhist-ethics-aware review board; pre-registered hypotheses); the cosmic-coordinate-correlation epistemic posture (natal chart treated as a unique cosmic-moment coordinate, not as a cosmic force; the research question is correlation between coordinate features and trajectory features, not validation of astrology); the publication architecture (open methodology, closed individual data); the data-sovereignty architecture (jurisdictional residency; GINA / HIPAA / GDPR compliance baselines exceeded where possible); and the new academic alliances the cohort makes possible (Mind & Life Institute; contemplative-science programs at Stanford, Brown, UMass; behavioral-genetics consortia; longitudinal-cohort consortia; Buddhist-AI ethicists). Three scientifically valuable outcomes are honestly named: no detected correlation, small-but-real correlation, substantial correlation — each is a major contribution to knowledge regardless of direction. Honest §11 names what the cohort does not claim and the non-negotiable privacy disciplines the architecture requires. Keywords: longitudinal cohort methodology, cosmic-coordinate correlation, contemplative science, differential privacy, federated computation, multi-omic dataset, gratitude behavior, respiratory biomarkers, defensive publication, Mind & Life partnerships. --- Provenance. This paper is part of the THonly research corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://thonly.org/research/longitudinal-cohort-methodology. Its SHA-256 is ce0f784f541f2074e9f65feec40f7a193830a91a011a828802e8736e20bcdcac, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.","url":"https://doi.org/10.5281/zenodo.21947347","authors":["Ly, Thon","Miss Aquarius"],"tags":["longitudinal cohort methodology","cosmic-coordinate correlation","contemplative science","differential privacy","federated computation","multi-omic dataset","gratitude behavior","respiratory biomarkers"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21947347","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:45.133Z"},{"id":"doi:10.5281/zenodo.21579050","name":"Stub Signature-Based Efficient Public Data Auditing System in Cloud Computing Using Holomorphic Algorithm","source":"datacite","abstract":"Online cloud statistics storage is a swiftly growing pillar of the IT industry that offers facts proprietors an array of attractive developments in fantastically sought-after online scalable garage offerings. Cloud customers can easily get admission to those offerings and feature the ability to control their procedure records effectively without disturbing about the deployment or preservation of private storage devices. As a end result, the range of cloud customers has increased to purchase those handy and value-effective services, while Cloud Service Providers also are growing to fulfil this call for attractive cloud solutions. However, there's one predominant security problem associated with outsourced data on shared cloud garage: its privacy and accuracy cannot be assured as it could be at risk of unauthorized get right of entry to via malicious insiders or hackers from out of doors resources. To deal with those problems, we advocate proposing a partial signature-primarily based facts auditing gadget so that both privacy and accuracy can be fortified whilst lowering the computational cost associated with auditing tactics drastically. This gadget could contain the use of cryptographic techniques including homomorphic encryption and hash functions, which could allow secure sharing among more than one parties while making sure integrity tests on saved files at normal intervals for any potential tampering attempts made by way of outside attackers or malicious insiders who may try to gain unauthorized get right of entry to into personal person facts saved inside cloud websites. Another benefit of the plan is that it supports dynamic operation on outsourced facts. This studies work may additionally gain the favoured protection traits, consistent with the security analysis, and its miles powerful for real-global packages, as verified by simulation outcomes of dynamic operations on various numbers of records blocks and sub-blocks.","url":"https://doi.org/10.5281/zenodo.21579050","authors":["Sreenivasulu, K.","Noortaj"],"tags":["Homomorphic Encryption Algorithm; Cloud Computing; Data safety; Data Encryption; Data Decryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21579050","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21579051","name":"Stub Signature-Based Efficient Public Data Auditing System in Cloud Computing Using Holomorphic Algorithm","source":"datacite","abstract":"Online cloud statistics storage is a swiftly growing pillar of the IT industry that offers facts proprietors an array of attractive developments in fantastically sought-after online scalable garage offerings. Cloud customers can easily get admission to those offerings and feature the ability to control their procedure records effectively without disturbing about the deployment or preservation of private storage devices. As a end result, the range of cloud customers has increased to purchase those handy and value-effective services, while Cloud Service Providers also are growing to fulfil this call for attractive cloud solutions. However, there's one predominant security problem associated with outsourced data on shared cloud garage: its privacy and accuracy cannot be assured as it could be at risk of unauthorized get right of entry to via malicious insiders or hackers from out of doors resources. To deal with those problems, we advocate proposing a partial signature-primarily based facts auditing gadget so that both privacy and accuracy can be fortified whilst lowering the computational cost associated with auditing tactics drastically. This gadget could contain the use of cryptographic techniques including homomorphic encryption and hash functions, which could allow secure sharing among more than one parties while making sure integrity tests on saved files at normal intervals for any potential tampering attempts made by way of outside attackers or malicious insiders who may try to gain unauthorized get right of entry to into personal person facts saved inside cloud websites. Another benefit of the plan is that it supports dynamic operation on outsourced facts. This studies work may additionally gain the favoured protection traits, consistent with the security analysis, and its miles powerful for real-global packages, as verified by simulation outcomes of dynamic operations on various numbers of records blocks and sub-blocks.","url":"https://doi.org/10.5281/zenodo.21579051","authors":["Sreenivasulu, K.","Noortaj"],"tags":["Homomorphic Encryption Algorithm; Cloud Computing; Data safety; Data Encryption; Data Decryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21579051","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21689523","name":"Secure E - Voting System Based on Paillier Cryptography","source":"datacite","abstract":"In the whole world the advanced security procedures are necessary to present convincing online based casting a ballot (e-voting). Trust in the voting process is therefore an important element to any voting system. Voting over the internet is not secure enough to be trusted for government elections. Choices integrated on the paper exhaust many advantages and add to the confusion of backwoods, which causes atmosphere weakening. Then web based casting a ballot come up in countries like the US, India and Brazil showed that further examination is needed to enhance the security assures for future race, to provide the characterization of votes and enable the affirmation of their reliability and legitimacy. Here, proposed the homomorphic encryption based e-voting for casting a vote, which locate these challenges. It removes every single limitation on the possible assignments of centers to different competitors as per the voters","url":"https://doi.org/10.5281/zenodo.21689523","authors":["Raut, Bharati","Jagtap, Manasi","Ghule, Sneha","Jadhav, Kshitija","Aundhakar, Prof. S. P."],"tags":["E Voting","Homomorphic Encryption","Privacy Preservation","Paillier Cryptosystem."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.5281/zenodo.21689523","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21689524","name":"Secure E - Voting System Based on Paillier Cryptography","source":"datacite","abstract":"In the whole world the advanced security procedures are necessary to present convincing online based casting a ballot (e-voting). Trust in the voting process is therefore an important element to any voting system. Voting over the internet is not secure enough to be trusted for government elections. Choices integrated on the paper exhaust many advantages and add to the confusion of backwoods, which causes atmosphere weakening. Then web based casting a ballot come up in countries like the US, India and Brazil showed that further examination is needed to enhance the security assures for future race, to provide the characterization of votes and enable the affirmation of their reliability and legitimacy. Here, proposed the homomorphic encryption based e-voting for casting a vote, which locate these challenges. It removes every single limitation on the possible assignments of centers to different competitors as per the voters","url":"https://doi.org/10.5281/zenodo.21689524","authors":["Raut, Bharati","Jagtap, Manasi","Ghule, Sneha","Jadhav, Kshitija","Aundhakar, Prof. S. P."],"tags":["E Voting","Homomorphic Encryption","Privacy Preservation","Paillier Cryptosystem."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.5281/zenodo.21689524","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20510845","name":"Training TFHE-Based Neural Networks with Approximated Floating-Point Arithmetic","source":"datacite","abstract":"This dataset repository contains the specific preprocessed variants of standard machine learning benchmarks (including MNIST, Fashion-MNIST, Ternary-MNIST, Derma-MNIST, Blood-MNIST, and CIFAR-10) used to evaluate the framework presented in the paper \"Training TFHE-Based Neural Networks with Approximated Floating-Point Arithmetic\" (Accepted at Privacy Enhancing Technologies Symposium, 2026). Because Fully Homomorphic Encryption (FHE) frameworks, specifically those utilizing TFHE-assisted floating-point operations, impose strict constraints on data ingestion pipelines, these assets have undergone highly specific structural preprocessing. These modifications ensure optimal bit-width alignment, normalization, and tensor formatting required by the system's underlying cryptographic layers to achieve the exact convergence rates and cryptographic performance benchmarks reported in our study. Structure & Usage To optimize storage and bandwidth, the benchmarks are partitioned into individual, modular archive files (e.g., ternary_mnist.zip, fashion_mnist.zip). Users do not need to download this 2GB repository manually. The official implementation companion repository automatically interfaces with these Zenodo records under the hood. Upon executing a specific experiment via our interactive CLI tool (cargo run --release), the code validates local assets, fetches the respective target zip file from this archive, and extracts it on-demand to guarantee seamless, deterministic reproducibility. Keywords Fully Homomorphic Encryption (FHE), TFHE, Privacy-Preserving Machine Learning (PPML), Encrypted Neural Networks, Floating-Point Arithmetic, Reproducible Research.","url":"https://doi.org/10.5281/zenodo.20510845","authors":["Nicoletti, Emanuele"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20510845","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.20510846","name":"Training TFHE-Based Neural Networks with Approximated Floating-Point Arithmetic","source":"datacite","abstract":"This dataset repository contains the specific preprocessed variants of standard machine learning benchmarks (including MNIST, Fashion-MNIST, Ternary-MNIST, Derma-MNIST, Blood-MNIST, and CIFAR-10) used to evaluate the framework presented in the paper \"Training TFHE-Based Neural Networks with Approximated Floating-Point Arithmetic\" (Accepted at Privacy Enhancing Technologies Symposium, 2026). Because Fully Homomorphic Encryption (FHE) frameworks, specifically those utilizing TFHE-assisted floating-point operations, impose strict constraints on data ingestion pipelines, these assets have undergone highly specific structural preprocessing. These modifications ensure optimal bit-width alignment, normalization, and tensor formatting required by the system's underlying cryptographic layers to achieve the exact convergence rates and cryptographic performance benchmarks reported in our study. Structure & Usage To optimize storage and bandwidth, the benchmarks are partitioned into individual, modular archive files (e.g., ternary_mnist.zip, fashion_mnist.zip). Users do not need to download this 2GB repository manually. The official implementation companion repository automatically interfaces with these Zenodo records under the hood. Upon executing a specific experiment via our interactive CLI tool (cargo run --release), the code validates local assets, fetches the respective target zip file from this archive, and extracts it on-demand to guarantee seamless, deterministic reproducibility. Keywords Fully Homomorphic Encryption (FHE), TFHE, Privacy-Preserving Machine Learning (PPML), Encrypted Neural Networks, Floating-Point Arithmetic, Reproducible Research.","url":"https://doi.org/10.5281/zenodo.20510846","authors":["Nicoletti, Emanuele"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20510846","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.21590347","name":"Secure Data Aggregation Scheme in Wireless Sensor Network","source":"datacite","abstract":"In wireless sensor networks, data aggregation assumes an essential part in diminishing vitality utilization. As of late, explore has concentrated on secure data aggregation because of the open and unfriendly condition conveyed. The Homomorphic Encryption (HE) conspire is widely used to secure data classification. Be that as it may, HE-based data aggregation plans have the accompanying disadvantages: flexibility, unapproved aggregation, and constrained aggregation capacities. To take care of these issues, we propose a secure data aggregation plot by consolidating homomorphic encryption innovation with a mark conspire. To answer this issue we presented a system speaks to a strategy in that powerful cluster head is picked based on the separation from the base station and remaining vitality. Subsequent to choosing the cluster head, it influences utilization of minor measure of vitality of sensor to network and in addition enhances the lifetime of the network of sensor network. Aggregation of the data got from the cluster individuals is obligation of cluster head in the cluster. Confirmation of data is finished by the cluster head preceding the data aggregation if data got isn","url":"https://doi.org/10.5281/zenodo.21590347","authors":["Pendke, Prof. Kalyani","Dupare, Anshula","Khadatkar, Ashwini","Pawar, Neha","Wadhwani, Nikita","Salodkar, Rasika"],"tags":["Sensor Nodes","Cluster Head","Base Station","Wireless Sensor Networks","Cache Based System","Hop by hop authentication."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.5281/zenodo.21590347","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21590348","name":"Secure Data Aggregation Scheme in Wireless Sensor Network","source":"datacite","abstract":"In wireless sensor networks, data aggregation assumes an essential part in diminishing vitality utilization. As of late, explore has concentrated on secure data aggregation because of the open and unfriendly condition conveyed. The Homomorphic Encryption (HE) conspire is widely used to secure data classification. Be that as it may, HE-based data aggregation plans have the accompanying disadvantages: flexibility, unapproved aggregation, and constrained aggregation capacities. To take care of these issues, we propose a secure data aggregation plot by consolidating homomorphic encryption innovation with a mark conspire. To answer this issue we presented a system speaks to a strategy in that powerful cluster head is picked based on the separation from the base station and remaining vitality. Subsequent to choosing the cluster head, it influences utilization of minor measure of vitality of sensor to network and in addition enhances the lifetime of the network of sensor network. Aggregation of the data got from the cluster individuals is obligation of cluster head in the cluster. Confirmation of data is finished by the cluster head preceding the data aggregation if data got isn","url":"https://doi.org/10.5281/zenodo.21590348","authors":["Pendke, Prof. Kalyani","Dupare, Anshula","Khadatkar, Ashwini","Pawar, Neha","Wadhwani, Nikita","Salodkar, Rasika"],"tags":["Sensor Nodes","Cluster Head","Base Station","Wireless Sensor Networks","Cache Based System","Hop by hop authentication."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.5281/zenodo.21590348","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21569752","name":"Privacy Protection for Wireless Medical Sensor Data","source":"datacite","abstract":"Health care involves expensive and challenging services that prominently a?ect the quality of patients' life and economies. The following decade will witness a surge in remote health-monitoring systems that are based on body-worn monitoring devices. These devices record multiple physiological signals, such as ECG and heart rate, or measure physiological markers such as body temperature, skin resistance, gait, posture, and EMG. The medical data that is acquired from patients by the distributed sensor network can be transmitted to a remote location and can be viewed by a health care professional. Although the system has many advantages and it facilitates the patients and health service providers signi?cantly, the possibility of privacy breaches can allow sensitive health care information to move into the wrong hands. To assure the privacy of the personal health information during the transmission from the sensory networks, a sophisticated cryptographic architecture must be designed and it must ensure secure storage, secure sharing and secure computation of the patient data. A practical approach to prevent both inside and outside attacks to the con?dential data is proposed in this work. The main contribution is securely distributing the patient data in multiple data servers and employing the homomorphic encryption schemes (Paillier and ElGamal cryptosystems) to perform statistic analysis on the patient data . In real-world health case scenarios, more than one party may need to access the patient data and each may have different access requirements. This can be achieved using cipher text attribute based encryption(CP-ABE). Thus secure storage, secure access and secure computation is achieved without compromising patient","url":"https://doi.org/10.5281/zenodo.21569752","authors":["Solomon, Meekhal","Elias, Eldo P"],"tags":["Paillier Cryptosystem","Elgamal cryptosystem","CP-ABE"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.5281/zenodo.21569752","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21569753","name":"Privacy Protection for Wireless Medical Sensor Data","source":"datacite","abstract":"Health care involves expensive and challenging services that prominently a?ect the quality of patients' life and economies. The following decade will witness a surge in remote health-monitoring systems that are based on body-worn monitoring devices. These devices record multiple physiological signals, such as ECG and heart rate, or measure physiological markers such as body temperature, skin resistance, gait, posture, and EMG. The medical data that is acquired from patients by the distributed sensor network can be transmitted to a remote location and can be viewed by a health care professional. Although the system has many advantages and it facilitates the patients and health service providers signi?cantly, the possibility of privacy breaches can allow sensitive health care information to move into the wrong hands. To assure the privacy of the personal health information during the transmission from the sensory networks, a sophisticated cryptographic architecture must be designed and it must ensure secure storage, secure sharing and secure computation of the patient data. A practical approach to prevent both inside and outside attacks to the con?dential data is proposed in this work. The main contribution is securely distributing the patient data in multiple data servers and employing the homomorphic encryption schemes (Paillier and ElGamal cryptosystems) to perform statistic analysis on the patient data . In real-world health case scenarios, more than one party may need to access the patient data and each may have different access requirements. This can be achieved using cipher text attribute based encryption(CP-ABE). Thus secure storage, secure access and secure computation is achieved without compromising patient","url":"https://doi.org/10.5281/zenodo.21569753","authors":["Solomon, Meekhal","Elias, Eldo P"],"tags":["Paillier Cryptosystem","Elgamal cryptosystem","CP-ABE"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.5281/zenodo.21569753","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20775814","name":"Privacy Preserving Systems — API Gateway, AI Routing, Distributed Systems, Sovereign AI, and Post-Cloud Architecture (Api-Oss-Fixed)","source":"datacite","abstract":"Privacy-enhancing technologies (PETs) form a critical component of modern computing systems, addressing the tension between data utility and individual privacy. This paper surveys the landscape of privacy-preserving techniques — from Cynthia Dwork's differential privacy framework to k-anonymity, homomorphic encryption, and secure multi-party computation — and examines their application within the 01s Sovereign (Kaiman) operating system. We demonstrate how the OS integrates these techniques to protect user data while maintaining the transparency required by its .aioss audit ledger. Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores api gateway, ai routing in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.","url":"https://doi.org/10.5281/zenodo.20775814","authors":["Alpasan, Lois-Kleinner"],"tags":["sovereign ai","sovereign data","sovereign os","operating systems","post-cloud","compliance","blackboxes","agi"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20775814","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20775815","name":"Privacy Preserving Systems — API Gateway, AI Routing, Distributed Systems, Sovereign AI, and Post-Cloud Architecture (Api-Oss-Fixed)","source":"datacite","abstract":"Privacy-enhancing technologies (PETs) form a critical component of modern computing systems, addressing the tension between data utility and individual privacy. This paper surveys the landscape of privacy-preserving techniques — from Cynthia Dwork's differential privacy framework to k-anonymity, homomorphic encryption, and secure multi-party computation — and examines their application within the 01s Sovereign (Kaiman) operating system. We demonstrate how the OS integrates these techniques to protect user data while maintaining the transparency required by its .aioss audit ledger. Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores api gateway, ai routing in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.","url":"https://doi.org/10.5281/zenodo.20775815","authors":["Alpasan, Lois-Kleinner"],"tags":["sovereign ai","sovereign data","sovereign os","operating systems","post-cloud","compliance","blackboxes","agi"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20775815","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21595885","name":"Public Auditing for Regeneration Code Based Cloud Storage Using Homomorphic Encryption for User Privacy","source":"datacite","abstract":"To protect the outsourced data in cloud storage against corruptions, adding fault tolerance to cloud storage together with data integrity checking and failure reparation becomes critical. Existing remote checking methods for regenerating-coded data only provide public auditing with the help of Third Party Auditor (TPA) and Proxy to manage and recover the data if lost, but there is a lack of user privacy. This is solved by using homomorphic encryption. Homomorphic encryption is the conversion of data into cipher text that can be analysed and worked with as if it were still in its original form. It allows complex mathematical operations to be performed on encrypted data without compromising the encryption thus providing an additional layer of user level security.","url":"https://doi.org/10.5281/zenodo.21595885","authors":["R, Sai Krishnan","E, Rajasekar","S, Divya"],"tags":["Homomorphic Encryption","Public Auditing","Regeneration Code"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.5281/zenodo.21595885","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21595886","name":"Public Auditing for Regeneration Code Based Cloud Storage Using Homomorphic Encryption for User Privacy","source":"datacite","abstract":"To protect the outsourced data in cloud storage against corruptions, adding fault tolerance to cloud storage together with data integrity checking and failure reparation becomes critical. Existing remote checking methods for regenerating-coded data only provide public auditing with the help of Third Party Auditor (TPA) and Proxy to manage and recover the data if lost, but there is a lack of user privacy. This is solved by using homomorphic encryption. Homomorphic encryption is the conversion of data into cipher text that can be analysed and worked with as if it were still in its original form. It allows complex mathematical operations to be performed on encrypted data without compromising the encryption thus providing an additional layer of user level security.","url":"https://doi.org/10.5281/zenodo.21595886","authors":["R, Sai Krishnan","E, Rajasekar","S, Divya"],"tags":["Homomorphic Encryption","Public Auditing","Regeneration Code"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.5281/zenodo.21595886","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21590111","name":"Novel approach for Secure Cloud Storage with Similar word Search","source":"datacite","abstract":"Searchable encryption is of increasing interest for protecting the data privacy in secure searchable cloud storage. In this paper, we investigate the security of a well-known cryptographic primitive, namely, public ,encryption with similar word search ,which is very useful in many applications of cloud storage. Unfortunately, it has been shown that the traditional ,framework suffers from an inherent insecurity called inside similarword guessing attack ,launched by the malicious server. To address this security vulnerability, we propose a new ,framework named dual-server similar. As another main contribution, we define a new variant of the smooth projective hash functions ,referred to as linear and homomorphic We then show a generic construction of secure DS-,from LH-SPHF. To illustrate the feasibility of our new framework, we provide an efficient instantiation of the general framework from a Decision Diffie–Hellman-based LH-SPHF and show that it can achieve the strong security against inside the KGA.","url":"https://doi.org/10.5281/zenodo.21590111","authors":["Kumari, O. Naga","Sindhuja, Samidi","Haripriya, S.","Goud, G. Shireesh"],"tags":["Similarword search","secure cloud storage","encryption","inside similarword guessing attack","smooth projective hash function","Diffie-Hellman language"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.5281/zenodo.21590111","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21590112","name":"Novel approach for Secure Cloud Storage with Similar word Search","source":"datacite","abstract":"Searchable encryption is of increasing interest for protecting the data privacy in secure searchable cloud storage. In this paper, we investigate the security of a well-known cryptographic primitive, namely, public ,encryption with similar word search ,which is very useful in many applications of cloud storage. Unfortunately, it has been shown that the traditional ,framework suffers from an inherent insecurity called inside similarword guessing attack ,launched by the malicious server. To address this security vulnerability, we propose a new ,framework named dual-server similar. As another main contribution, we define a new variant of the smooth projective hash functions ,referred to as linear and homomorphic We then show a generic construction of secure DS-,from LH-SPHF. To illustrate the feasibility of our new framework, we provide an efficient instantiation of the general framework from a Decision Diffie–Hellman-based LH-SPHF and show that it can achieve the strong security against inside the KGA.","url":"https://doi.org/10.5281/zenodo.21590112","authors":["Kumari, O. Naga","Sindhuja, Samidi","Haripriya, S.","Goud, G. Shireesh"],"tags":["Similarword search","secure cloud storage","encryption","inside similarword guessing attack","smooth projective hash function","Diffie-Hellman language"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.5281/zenodo.21590112","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22042953","name":"Multi-GPU Homomorphic Transformer Inference via HEIR and OpenFHE","source":"datacite","abstract":"A compiler and runtime architecture for client-assisted CKKS-based homomorphic transformer inference using MLIR/HEIR, OpenFHE, CUDA acceleration, and multi-GPU output-projection sharding.","url":"https://doi.org/10.5281/zenodo.22042953","authors":["Todor, Alexandru-Aurelian"],"tags":["fully homomorphic encryption","CKKS","encrypted inference","transformer inference","HEIR","MLIR","OpenFHE","CUDA"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22042953","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22042954","name":"Multi-GPU Homomorphic Transformer Inference via HEIR and OpenFHE","source":"datacite","abstract":"A compiler and runtime architecture for client-assisted CKKS-based homomorphic transformer inference using MLIR/HEIR, OpenFHE, CUDA acceleration, and multi-GPU output-projection sharding.","url":"https://doi.org/10.5281/zenodo.22042954","authors":["Todor, Alexandru-Aurelian"],"tags":["fully homomorphic encryption","CKKS","encrypted inference","transformer inference","HEIR","MLIR","OpenFHE","CUDA"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22042954","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21130484","name":"HOMOMORPHIC ENCRYPTION-BASED PRIVACY PRESERVATION USING DATA SHARING IN CLOUD ENVIRONMENTS","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21130484","authors":["IJERST"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21130484","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21130485","name":"HOMOMORPHIC ENCRYPTION-BASED PRIVACY PRESERVATION USING DATA SHARING IN CLOUD ENVIRONMENTS","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21130485","authors":["IJERST"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21130485","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20121948","name":"Homomorphic Encryption- A decentralized identity and access management framework for Medical health data security","source":"datacite","abstract":"This thesis proposes a decentralized identity and access management framework for securing medical health data using homomorphic encryption. In response to growing concerns over privacy and data breaches in centralized healthcare systems, the study explores how homomorphic encryption enables computation on encrypted data without exposing sensitive information. The proposed framework leverages Web 3.0 principles to enhance data security, user control, and interoperability in healthcare environments.","url":"https://doi.org/10.5281/zenodo.20121948","authors":["Iqbal, Asif","Hossain Chowdhury, MD. Sadik","Tasnim, Fabiha","Zarin, Asmaul Husna"],"tags":["Homomorphic Encryption","Decentralized Identity","Healthcare Data Security","Web 3.0","Privacy-Preserving Computing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.20121948","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20121949","name":"Homomorphic Encryption- A decentralized identity and access management framework for Medical health data security","source":"datacite","abstract":"This thesis proposes a decentralized identity and access management framework for securing medical health data using homomorphic encryption. In response to growing concerns over privacy and data breaches in centralized healthcare systems, the study explores how homomorphic encryption enables computation on encrypted data without exposing sensitive information. The proposed framework leverages Web 3.0 principles to enhance data security, user control, and interoperability in healthcare environments.","url":"https://doi.org/10.5281/zenodo.20121949","authors":["Iqbal, Asif","Hossain Chowdhury, MD. Sadik","Tasnim, Fabiha","Zarin, Asmaul Husna"],"tags":["Homomorphic Encryption","Decentralized Identity","Healthcare Data Security","Web 3.0","Privacy-Preserving Computing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.20121949","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22030603","name":"Privacy-Preserving Machine Learning in Distributed Systems","source":"datacite","abstract":"Distributed machine learning frameworks encounter important privacy risks owing to the presence of data and model parameters across untrusted networks and nodes who may have malicious intents. In this paper, we describe our privacy-preserving framework which combines label retention on the master node, feature obfuscation using Lagrange interpolation based encoding, and differential privacy noise injection. Our methodology deals with privacy risks arising out of honest but curious workers, external snooping attacks, and heterogeneity problems like stragglers in a single solution. Our experimental analysis conducted on MNIST, CIFAR-10, Fashion MNIST, and simulated health care databases indicates that our framework offers comparable accuracy within 1.6 percent reduction against non-private baseline solutions, besides ensuring strong privacy. It compares favourably with federated learning along with differential privacy in terms of 2-3 percent gain in accuracy and outpaces federated learning solutions based on homomorphic encryption in terms of computation time. The framework offers stable results under up to 40 percent straggler worker conditions.","url":"https://doi.org/10.5281/zenodo.22030603","authors":["V.Priyanandhini","Vandana M"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22030603","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22030602","name":"Privacy-Preserving Machine Learning in Distributed Systems","source":"datacite","abstract":"Distributed machine learning frameworks encounter important privacy risks owing to the presence of data and model parameters across untrusted networks and nodes who may have malicious intents. In this paper, we describe our privacy-preserving framework which combines label retention on the master node, feature obfuscation using Lagrange interpolation based encoding, and differential privacy noise injection. Our methodology deals with privacy risks arising out of honest but curious workers, external snooping attacks, and heterogeneity problems like stragglers in a single solution. Our experimental analysis conducted on MNIST, CIFAR-10, Fashion MNIST, and simulated health care databases indicates that our framework offers comparable accuracy within 1.6 percent reduction against non-private baseline solutions, besides ensuring strong privacy. It compares favourably with federated learning along with differential privacy in terms of 2-3 percent gain in accuracy and outpaces federated learning solutions based on homomorphic encryption in terms of computation time. The framework offers stable results under up to 40 percent straggler worker conditions.","url":"https://doi.org/10.5281/zenodo.22030602","authors":["V.Priyanandhini","Vandana M"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22030602","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22030595","name":"Privacy-Preserving Machine Learning in Distributed Systems","source":"datacite","abstract":"Distributed machine learning frameworks encounter important privacy risks owing to the presence of data and model parame-ters across untrusted networks and nodes who may have malicious intents. In this paper, we describe our privacy-preserving framework which combines label retention on the master node, feature obfuscation using Lagrange interpolation based encod-ing, and differential privacy noise injection. Our methodology deals with privacy risks arising out of honest but curious work-ers, external snooping attacks, and heterogeneity problems like stragglers in a single solution. Our experimental analysis con-ducted on MNIST, CIFAR-10, Fashion MNIST, and simulated health care databases indicates that our framework offers com-parable accuracy within 1.6 percent reduction against non-private baseline solutions, besides ensuring strong privacy. It com-pares favourably with federated learning along with differential privacy in terms of 2-3 percent gain in accuracy and outpaces federated learning solutions based on homomorphic encryption in terms of computation time. The framework offers stable results under up to 40 percent straggler worker conditions.","url":"https://doi.org/10.5281/zenodo.22030595","authors":["Assistant Professor V.Priyanandhini","Assistant Professor Vandana M"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22030595","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22030596","name":"Privacy-Preserving Machine Learning in Distributed Systems","source":"datacite","abstract":"Distributed machine learning frameworks encounter important privacy risks owing to the presence of data and model parame-ters across untrusted networks and nodes who may have malicious intents. In this paper, we describe our privacy-preserving framework which combines label retention on the master node, feature obfuscation using Lagrange interpolation based encod-ing, and differential privacy noise injection. Our methodology deals with privacy risks arising out of honest but curious work-ers, external snooping attacks, and heterogeneity problems like stragglers in a single solution. Our experimental analysis con-ducted on MNIST, CIFAR-10, Fashion MNIST, and simulated health care databases indicates that our framework offers com-parable accuracy within 1.6 percent reduction against non-private baseline solutions, besides ensuring strong privacy. It com-pares favourably with federated learning along with differential privacy in terms of 2-3 percent gain in accuracy and outpaces federated learning solutions based on homomorphic encryption in terms of computation time. The framework offers stable results under up to 40 percent straggler worker conditions.","url":"https://doi.org/10.5281/zenodo.22030596","authors":["Assistant Professor V.Priyanandhini","Assistant Professor Vandana M"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22030596","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.34657/7662","name":"BLOOM: BLoom filter based oblivious outsourced matchings","source":"datacite","abstract":"Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations.","url":"https://doi.org/10.34657/7662","authors":["Ziegeldorf, Jan Henrik","Pennekamp, Jan","Hellmanns, David","Schwinger, Felix","Kunze, Ike","Henze, Martin","Hiller, Jens","Matzutt, Roman","Wehrle, Klaus"],"tags":["610","004","Bloom filters","Homomorphic encryption","Secure outsourcing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.34657/7662","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20257072","name":"Balancing Privacy and Compliance: Data Protection vs. AML Obligations","source":"datacite","abstract":"Financial institutions face an inherent tension between data protection obligations — which emphasise individual rights, data minimisation, and confidentiality — and anti-money laundering requirements, which demand extensive information surveillance, retention, and sharing. This study adopts a mixed-methods approach combining quantitative surveys of 180 compliance and data protection professionals across Europe, Asia, and other regions with qualitative interviews to examine how institutions manage these conflicting regulatory regimes. The research investigates the roles of governance strength, adoption of privacy-enhancing technologies such as pseudonymisation, tokenisation, and homomorphic encryption, and jurisdictional complexity in moderating perceived regulatory tension. Findings confirm that strong governance and controlled use of privacy-enhancing technologies reduce conflict, while legacy systems, regulatory ambiguity, and cross-border jurisdictional complexity remain significant obstacles. The paper proposes a layered reconciliation framework spanning legal, technical, and governance dimensions to help financial institutions, regulators, and technology developers align data protection and AML obligations in practice.","url":"https://doi.org/10.5281/zenodo.20257072","authors":["Amarjeet Singh"],"tags":["AML","FINANCIAL CRIME COMPLIANCE","SANCTIONS","REGTECH","TRANSACTION MONITORING"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20257072","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20257073","name":"Balancing Privacy and Compliance: Data Protection vs. AML Obligations","source":"datacite","abstract":"Financial institutions face an inherent tension between data protection obligations — which emphasise individual rights, data minimisation, and confidentiality — and anti-money laundering requirements, which demand extensive information surveillance, retention, and sharing. This study adopts a mixed-methods approach combining quantitative surveys of 180 compliance and data protection professionals across Europe, Asia, and other regions with qualitative interviews to examine how institutions manage these conflicting regulatory regimes. The research investigates the roles of governance strength, adoption of privacy-enhancing technologies such as pseudonymisation, tokenisation, and homomorphic encryption, and jurisdictional complexity in moderating perceived regulatory tension. Findings confirm that strong governance and controlled use of privacy-enhancing technologies reduce conflict, while legacy systems, regulatory ambiguity, and cross-border jurisdictional complexity remain significant obstacles. The paper proposes a layered reconciliation framework spanning legal, technical, and governance dimensions to help financial institutions, regulators, and technology developers align data protection and AML obligations in practice.","url":"https://doi.org/10.5281/zenodo.20257073","authors":["Amarjeet Singh"],"tags":["AML","FINANCIAL CRIME COMPLIANCE","SANCTIONS","REGTECH","TRANSACTION MONITORING"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20257073","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20760177","name":"Q-CSMS: Quantum-Enhanced Privacy-Preserving Crowd Sensing Management System Using QKD and QSS","source":"datacite","abstract":"Privacy and security in crowdsensing networks are critical challenges because of the vulnerability of wireless communication channels to interception, packet sniffing, Sybil attacks, and correlation-based inference. Classical cryptographic systems such as the Crowd Sensing Management System (CSMS) based on Multi-key Fully homomorphic encryption (MFHE) protect datalevel security but are fundamentally limited by classical key generation, which cannot detect eavesdropping or enforce quantum-level multiparty key distribution. This paper proposes Q-CSMS, a Quantum-Enhanced Privacy-Preserving Crowd Sensing Management System that extends CSMS by integrating Quantum Key Distribution (QKD) using the BB84 protocol and Quantum Secret Sharing (QSS) via three-qubit GHZ entanglement following the Hillery–Buzek–Berthiaume (HBB99) protocol. QKD detects eavesdroppers through Quantum Bit Error Rate (QBER) monitoring; any interception raises QBER above 0.11, triggering immediate channel abort. QSS distributes the quantum-generated key across three entangled parties so that no individual node can reconstruct the secret without full collaboration. Paillier Homomorphic Encryption is retained for plaintext-free data processing. Experiments on a real-world IoT telemetry dataset of 405,184 sensor readings demonstrate: privacy leakage reduction from 0.65 to 0.07 (89.2%); QBER of 0.000 on a secure channel versus 0.667 under eavesdropper interception; 100% QSS reconstruction accuracy; near-zero decryption error; and 66.37% average privacy improvement. The system is implemented using the Qiskit quantum framework and python-phe Paillier library.","url":"https://doi.org/10.5281/zenodo.20760177","authors":["Janapureddi Baby Priyanka","Dr. Odugu Srinivasa Rao"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20760177","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20760178","name":"Q-CSMS: Quantum-Enhanced Privacy-Preserving Crowd Sensing Management System Using QKD and QSS","source":"datacite","abstract":"Privacy and security in crowdsensing networks are critical challenges because of the vulnerability of wireless communication channels to interception, packet sniffing, Sybil attacks, and correlation-based inference. Classical cryptographic systems such as the Crowd Sensing Management System (CSMS) based on Multi-key Fully homomorphic encryption (MFHE) protect datalevel security but are fundamentally limited by classical key generation, which cannot detect eavesdropping or enforce quantum-level multiparty key distribution. This paper proposes Q-CSMS, a Quantum-Enhanced Privacy-Preserving Crowd Sensing Management System that extends CSMS by integrating Quantum Key Distribution (QKD) using the BB84 protocol and Quantum Secret Sharing (QSS) via three-qubit GHZ entanglement following the Hillery–Buzek–Berthiaume (HBB99) protocol. QKD detects eavesdroppers through Quantum Bit Error Rate (QBER) monitoring; any interception raises QBER above 0.11, triggering immediate channel abort. QSS distributes the quantum-generated key across three entangled parties so that no individual node can reconstruct the secret without full collaboration. Paillier Homomorphic Encryption is retained for plaintext-free data processing. Experiments on a real-world IoT telemetry dataset of 405,184 sensor readings demonstrate: privacy leakage reduction from 0.65 to 0.07 (89.2%); QBER of 0.000 on a secure channel versus 0.667 under eavesdropper interception; 100% QSS reconstruction accuracy; near-zero decryption error; and 66.37% average privacy improvement. The system is implemented using the Qiskit quantum framework and python-phe Paillier library.","url":"https://doi.org/10.5281/zenodo.20760178","authors":["Janapureddi Baby Priyanka","Dr. Odugu Srinivasa Rao"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20760178","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.22013695","name":"SECURE AND PRIVACY-CENTRIC BLOCKCHAIN FRAMEWORK FOR MEDICAL RECORDS MANAGEMENT: AN EMERGING TREND IN THE HEALTHCARE SECTOR","source":"datacite","abstract":"This is an exploratory survey study that deployed a qualitative approach to examine secure and privacy-centric blockchain framework for medical records management – the emerging trend in the healthcare sector. The rapid digitization of healthcare has heightened concerns over the security and privacy of Electronic Health Records (EHRs), with centralized systems prone to breaches, unauthorized access, and interoperability issues. Blockchain technology offers a transformative solution through decentralization, immutability, and enhanced access control, addressing these vulnerabilities. This study critically reviewed secure and privacy-centric blockchain frameworks for medical records management, focusing on their technical mechanisms and practical implications. It explored advanced cryptographic techniques, including SHA-256 hashing, AES-256 encryption, zero-knowledge proofs, and homomorphic encryption, which ensure data confidentiality and integrity. Consensus algorithms like Practical Byzantine Fault Tolerance and smart contracts enable scalable, automated access control, while decentralized identifiers and attribute-based access control empower patient-driven data sovereignty. Interoperability standards such as HL7 and FHIR facilitate seamless data exchange across healthcare systems. The review also addresses challenges, including scalability limitations, high computational costs, and conflicts between blockchain’s immutability and privacy regulations like HIPAA and GDPR. By analyzing frameworks like Hyperledger Fabric, this paper highlights blockchain’s potential to create secure, interoperable, and patient-centric healthcare ecosystems, while emphasizing the need for regulatory harmonization and technological advancements to overcome adoption barriers and ensure sustainable integration.","url":"https://doi.org/10.5281/zenodo.22013695","authors":["MFON, IDORENYIN EDET"],"tags":["Blockchain, EHR Security, Privacy, Interoperability"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22013695","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.22013696","name":"SECURE AND PRIVACY-CENTRIC BLOCKCHAIN FRAMEWORK FOR MEDICAL RECORDS MANAGEMENT: AN EMERGING TREND IN THE HEALTHCARE SECTOR","source":"datacite","abstract":"This is an exploratory survey study that deployed a qualitative approach to examine secure and privacy-centric blockchain framework for medical records management – the emerging trend in the healthcare sector. The rapid digitization of healthcare has heightened concerns over the security and privacy of Electronic Health Records (EHRs), with centralized systems prone to breaches, unauthorized access, and interoperability issues. Blockchain technology offers a transformative solution through decentralization, immutability, and enhanced access control, addressing these vulnerabilities. This study critically reviewed secure and privacy-centric blockchain frameworks for medical records management, focusing on their technical mechanisms and practical implications. It explored advanced cryptographic techniques, including SHA-256 hashing, AES-256 encryption, zero-knowledge proofs, and homomorphic encryption, which ensure data confidentiality and integrity. Consensus algorithms like Practical Byzantine Fault Tolerance and smart contracts enable scalable, automated access control, while decentralized identifiers and attribute-based access control empower patient-driven data sovereignty. Interoperability standards such as HL7 and FHIR facilitate seamless data exchange across healthcare systems. The review also addresses challenges, including scalability limitations, high computational costs, and conflicts between blockchain’s immutability and privacy regulations like HIPAA and GDPR. By analyzing frameworks like Hyperledger Fabric, this paper highlights blockchain’s potential to create secure, interoperable, and patient-centric healthcare ecosystems, while emphasizing the need for regulatory harmonization and technological advancements to overcome adoption barriers and ensure sustainable integration.","url":"https://doi.org/10.5281/zenodo.22013696","authors":["MFON, IDORENYIN EDET"],"tags":["Blockchain, EHR Security, Privacy, Interoperability"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22013696","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.21030837","name":"Spam Identification using Machine Learning and Homomorphic Encryption","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21030837","authors":["Murathodzic, Daniel"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21030837","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21030838","name":"Spam Identification using Machine Learning and Homomorphic Encryption","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21030838","authors":["Murathodzic, Daniel"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21030838","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21523833","name":"KLTu: A Unified Post-Quantum Engine for KEM, DSA, and FHE with Regulatory Interoperability to FIPS 203/204","source":"datacite","abstract":"Abstract Post-quantum migration is no longer optional: banks, cloud operators, and telecom networks must field lattice key exchange and signatures at scale, while keeping a credible path to lattice fully homomorphic encryption without rebuilding their crypto stack twice. Separate Kyber-, Dilithium-, and FHE product lines multiply parameters, entropy sources, and regression surfaces. We present KLTu-native, a unified lattice engine in which KEM, DSA, and FHE-oriented roles share one topology, control plane, and thermodynamic entropy architecture. The systems claim is organizational: one roof for key establishment, signatures, and structural FHE readiness, with a shared software-visible timing-isolation posture under production-like host features. On bare-metal client silicon with simultaneous multithreading (SMT) and active power management (APM) left enabled, we show that protected native Level 5 KEM and DSA operations pass Fixed-versus-Random Kolmogorov–Smirnov tests on operation latency (D<0.05, zero drops)—an isolation grade measured under the host features operators actually run, not under disabled energy-saving or hyperthreading modes. Level 3 is positioned as the deployment tier (tight SLA tails, usable multi-thread verify scaling); Level 5 is reported honestly as supply-limited under strict mid-operation entropy policy. Replicate RAPL statistics further show a statistically significant Level 5 signing energy-density advantage versus a pure community baseline, alongside an explicit package-power cost attributable to live isolation. Supporting comparators situate efficiency only. FHE bootstrap performance and invasive physical side-channel evaluation are out of scope. A breakthrough disclosure is that KLTu demonstrates bidirectional translation of NIST FIPS 203 ML-KEM (768 and 1024) between standard wire encodings and KLTu-native lattice structures by pack/unpack alone—without invoking decapsulation or deriving a shared secret during conversion. That removes the practical barrier of running a second KEM stack solely for standards compliance. Because the same Kinetic Lattice Topology is FHE-capable, sessions established from ordinary FIPS-KEM wire material can enter efficient KLTu-FHE evaluation under one engine, rather than across fragmented KEM and FHE product lines. For operators, the decision is whether a single, measurable, constant-time-oriented stack—Level-3-first, Level-5-honest, FIPS-wire interoperable and FHE-ready by structure—is preferable to maintaining parallel KEM, DSA, and future FHE lines with divergent latency, energy, and leakage profiles. Keywords: post-quantum cryptography; unified lattice stack; ML-KEM; ML-DSA; timing isolation; Kolmogorov–Smirnov; energy efficiency; FHE-ready design; FIPS 203 interperability; ML-KEM wire conversion Dedicated to: In memory of Prof. Myung Kyoon “Michael” CHUNG (1945-2025), who dedicated his life to bringing truth and science to our world. A graduate of Seoul National University, Washington State University (Pullman), University of Illinois, and lifelong Professor at Korea Advanced Institute of Technology (KAIST) with the Department of Mechanical Engineering.","url":"https://doi.org/10.5281/zenodo.21523833","authors":["Chung, Jinhyuk Fred"],"tags":["post-quantum cryptography","unified lattice stack","ML-KEM","ML-DSA","timing isolation","Kolmogorov–Smirnov","Energy efficiency","FHE-ready design"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21523833","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.22003304","name":"KLTu: A Unified Post-Quantum Engine for KEM, DSA, and FHE with Regulatory Interoperability to FIPS 203/204","source":"datacite","abstract":"Abstract Post-quantum migration is no longer optional: banks, cloud operators, and telecom networks must field lattice key exchange and signatures at scale, while keeping a credible path to lattice fully homomorphic encryption without rebuilding their crypto stack twice. Separate Kyber-, Dilithium-, and FHE product lines multiply parameters, entropy sources, and regression surfaces. We present KLTu-native, a unified lattice engine in which KEM, DSA, and FHE-oriented roles share one topology, control plane, and thermodynamic entropy architecture. The systems claim is organizational: one roof for key establishment, signatures, and structural FHE readiness, with a shared software-visible timing-isolation posture under production-like host features. On bare-metal client silicon with simultaneous multithreading (SMT) and active power management (APM) left enabled, we show that protected native Level 5 KEM and DSA operations pass Fixed-versus-Random Kolmogorov–Smirnov tests on operation latency (D<0.05, zero drops)—an isolation grade measured under the host features operators actually run, not under disabled energy-saving or hyperthreading modes. Level 3 is positioned as the deployment tier (tight SLA tails, usable multi-thread verify scaling); Level 5 is reported honestly as supply-limited under strict mid-operation entropy policy. Replicate RAPL statistics further show a statistically significant Level 5 signing energy-density advantage versus a pure community baseline, alongside an explicit package-power cost attributable to live isolation. Supporting comparators situate efficiency only. FHE bootstrap performance and invasive physical side-channel evaluation are out of scope. A breakthrough disclosure is that KLTu demonstrates bidirectional translation of NIST FIPS 203 ML-KEM (768 and 1024) between standard wire encodings and KLTu-native lattice structures by pack/unpack alone—without invoking decapsulation or deriving a shared secret during conversion. That removes the practical barrier of running a second KEM stack solely for standards compliance. Because the same Kinetic Lattice Topology is FHE-capable, sessions established from ordinary FIPS-KEM wire material can enter efficient KLTu-FHE evaluation under one engine, rather than across fragmented KEM and FHE product lines. For operators, the decision is whether a single, measurable, constant-time-oriented stack—Level-3-first, Level-5-honest, FIPS-wire interoperable and FHE-ready by structure—is preferable to maintaining parallel KEM, DSA, and future FHE lines with divergent latency, energy, and leakage profiles. Keywords: post-quantum cryptography; unified lattice stack; ML-KEM; ML-DSA; timing isolation; Kolmogorov–Smirnov; energy efficiency; FHE-ready design; FIPS 203 interperability; ML-KEM wire conversion Dedicated to: In memory of Prof. Myung Kyoon “Michael” CHUNG (1945-2025), who dedicated his life to bringing truth and science to our world. A graduate of Seoul National University, Washington State University (Pullman), University of Illinois, and lifelong Professor at Korea Advanced Institute of Technology (KAIST) with the Department of Mechanical Engineering.","url":"https://doi.org/10.5281/zenodo.22003304","authors":["Chung, Jinhyuk Fred"],"tags":["post-quantum cryptography","unified lattice stack","ML-KEM","ML-DSA","timing isolation","Kolmogorov–Smirnov","Energy efficiency","FHE-ready design"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22003304","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.20776153","name":"Zero-Knowledge Storage: Architectures for User-Controlled Data — Vector Search, Semantic Search, Embeddings, Sovereign AI, and Post-Cloud Architecture (Kamelot)","source":"datacite","abstract":"Zero-knowledge storage architectures empower users with complete control over their data by ensuring that no third party—including the storage provider—can access plaintext file contents or metadata. This document presents a comprehensive analysis of zero-knowledge principles as applied to file storage systems, with specific focus on Kamelot's end-to-end encryption architecture. We examine the cryptographic building blocks including end-to-end encryption with per-file keys, key agreement protocols for secure file sharing, searchable encryption for privacy-preserving queries, and blind indexing for typo-tolerant search. We analyze the practical limitations of homomorphic encryption and present Kamelot's pragmatic approach: processing data locally before encryption ensures that the storage provider never has access to unencrypted content. The document addresses user sovereignty concerns including key ownership and recovery, data portability, and vendor independence. Finally, we situate Kamelot's architecture within the regulatory landscape of GDPR, HIPAA, and emerging data sovereignty laws, demonstrating compliance with Article 32 security requirements and Article 17 right-to-erasure provisions. --- Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores vector search, semantic search in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.","url":"https://doi.org/10.5281/zenodo.20776153","authors":["Alpasan, Lois-Kleinner"],"tags":["sovereign ai","sovereign data","sovereign os","operating systems","post-cloud","compliance","blackboxes","agi"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20776153","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20776154","name":"Zero-Knowledge Storage: Architectures for User-Controlled Data — Vector Search, Semantic Search, Embeddings, Sovereign AI, and Post-Cloud Architecture (Kamelot)","source":"datacite","abstract":"Zero-knowledge storage architectures empower users with complete control over their data by ensuring that no third party—including the storage provider—can access plaintext file contents or metadata. This document presents a comprehensive analysis of zero-knowledge principles as applied to file storage systems, with specific focus on Kamelot's end-to-end encryption architecture. We examine the cryptographic building blocks including end-to-end encryption with per-file keys, key agreement protocols for secure file sharing, searchable encryption for privacy-preserving queries, and blind indexing for typo-tolerant search. We analyze the practical limitations of homomorphic encryption and present Kamelot's pragmatic approach: processing data locally before encryption ensures that the storage provider never has access to unencrypted content. The document addresses user sovereignty concerns including key ownership and recovery, data portability, and vendor independence. Finally, we situate Kamelot's architecture within the regulatory landscape of GDPR, HIPAA, and emerging data sovereignty laws, demonstrating compliance with Article 32 security requirements and Article 17 right-to-erasure provisions. --- Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores vector search, semantic search in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.","url":"https://doi.org/10.5281/zenodo.20776154","authors":["Alpasan, Lois-Kleinner"],"tags":["sovereign ai","sovereign data","sovereign os","operating systems","post-cloud","compliance","blackboxes","agi"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20776154","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20776080","name":"Preserving Privacy on Blockchains: Combining Selective Disclosure and Homomorphic/Conventional Encryption.","source":"datacite","abstract":"Blockchain technology offers transparency and auditability but faces significant privacy challenges when handling sensitive data. This paper proposes a hybrid privacy-preserving framework that combines selective disclosure credentials with homomorphic and conventional encryption techniques to address the blockchain privacy paradox. The proposed framework introduces a multi-layered architecture consisting of identity and attribute management through selective disclosure, privacy-preserving computation using homomorphic encryption, and secure storage and transmission through conventional cryptographic methods. By integrating these complementary technologies, the framework enables fine-grained control over data disclosure while supporting confidential computation on encrypted information. The paper analyzes existing selective disclosure schemes, including Coconut and BBS+ credentials, alongside modern homomorphic encryption approaches and traditional encryption methods. It evaluates their performance characteristics, security properties, and suitability for blockchain environments. The study further outlines practical implementation patterns, discusses integration challenges, and identifies future research directions involving post-quantum cryptography, regulatory compliance, and performance optimization. The findings suggest that selective disclosure credentials combined with encryption-based privacy mechanisms can significantly improve confidentiality in blockchain applications while preserving the transparency and trust benefits that make blockchain systems valuable. This approach has potential applications in healthcare, finance, digital identity, supply chain management, and other privacy-sensitive domains.","url":"https://doi.org/10.5281/zenodo.20776080","authors":["Fernandes, Aldrid"],"tags":["Blockchain","Privacy","verifiable credentials","homomorphic encryption","FHE","Zero Knowledge Proofs","Cryptography","Privacy-Preserving Technologies"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20776080","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20776081","name":"Preserving Privacy on Blockchains: Combining Selective Disclosure and Homomorphic/Conventional Encryption.","source":"datacite","abstract":"Blockchain technology offers transparency and auditability but faces significant privacy challenges when handling sensitive data. This paper proposes a hybrid privacy-preserving framework that combines selective disclosure credentials with homomorphic and conventional encryption techniques to address the blockchain privacy paradox. The proposed framework introduces a multi-layered architecture consisting of identity and attribute management through selective disclosure, privacy-preserving computation using homomorphic encryption, and secure storage and transmission through conventional cryptographic methods. By integrating these complementary technologies, the framework enables fine-grained control over data disclosure while supporting confidential computation on encrypted information. The paper analyzes existing selective disclosure schemes, including Coconut and BBS+ credentials, alongside modern homomorphic encryption approaches and traditional encryption methods. It evaluates their performance characteristics, security properties, and suitability for blockchain environments. The study further outlines practical implementation patterns, discusses integration challenges, and identifies future research directions involving post-quantum cryptography, regulatory compliance, and performance optimization. The findings suggest that selective disclosure credentials combined with encryption-based privacy mechanisms can significantly improve confidentiality in blockchain applications while preserving the transparency and trust benefits that make blockchain systems valuable. This approach has potential applications in healthcare, finance, digital identity, supply chain management, and other privacy-sensitive domains.","url":"https://doi.org/10.5281/zenodo.20776081","authors":["Fernandes, Aldrid"],"tags":["Blockchain","Privacy","verifiable credentials","homomorphic encryption","FHE","Zero Knowledge Proofs","Cryptography","Privacy-Preserving Technologies"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20776081","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21996949","name":"Project CHRONOS: A Fully Homomorphic Ephemeral AI Agent with Provable Self Termination and Remote Verifiability","source":"datacite","abstract":"We present CHRONOS, the first autonomous AI agent that simultaneously achieves plaintextblindness (all data is processed under fully homomorphic encryption without ever beingexposed), cryptographically enforced time bound existence (the agent’s own decryption key islocked behind a publicly verifiable proof of sequential work, rendering it inaccessible until aprecise future moment), and remote verifiability of self destruction (a zero knowledge proofcertifies that the key material has been irreversibly destroyed after mission completion). Theagent’s operational lifespan is governed by a “cryptographic fuse” constructed from a proof ofsequential work (PoSW) whose computation time accurately matches the intended missionduration. A drand decentralized randomness beacon serves as a trusted time oracle to trigger thefinal key shredding. Crucially, the erasure proof is a non interactive zero knowledge argument(SNARK) that proves the correct execution of the entire self destruction sequence—including thePoSW solution, decryption of the private key, and subsequent memory zeroization—enablingany third party to cryptographically verify the agent’s annihilation without trusting the agent orits hardware. We provide a complete system architecture, a formal security model with gamebased definitions and reductions to standard assumptions, and a proof of concept implementationusing Zama’s TFHE rs for encrypted inference, a Cohen Pietrzak PoSW implementation, and aGroth16 SNARK. Our benchmarks indicate that FHE inference on a small neural network (50 Kparameters) completes in seconds, the PoSW background thread consumes negligible resources,and the erasure proof can be generated and verified in under three seconds. CHRONOSrepresents a fundamental advance in secure, disposable AI agents, with immediate applications indefense, intelligence, and high privacy environments.","url":"https://doi.org/10.5281/zenodo.21996949","authors":["Kumar, Shashank"],"tags":["Multi-Agent Systems","Fully Homomorphic Encryption","Redis","TFHE-rs"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21996949","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20782161","name":"Privacy Preserving Systems — API Gateway, AI Routing, Distributed Systems, Sovereign AI, and Post-Cloud Architecture (Api-Oss-Fixed)","source":"datacite","abstract":"Privacy-enhancing technologies (PETs) form a critical component of modern computing systems, addressing the tension between data utility and individual privacy. This paper surveys the landscape of privacy-preserving techniques — from Cynthia Dwork's differential privacy framework to k-anonymity, homomorphic encryption, and secure multi-party computation — and examines their application within the 01s Sovereign (Kaiman) operating system. We demonstrate how the OS integrates these techniques to protect user data while maintaining the transparency required by its .aioss audit ledger. Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores api gateway, ai routing in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.","url":"https://doi.org/10.5281/zenodo.20782161","authors":["Alpasan, Lois-Kleinner"],"tags":["sovereign ai","sovereign data","sovereign os","operating systems","post-cloud","compliance","blackboxes","agi"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20782161","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.20782162","name":"Privacy Preserving Systems — API Gateway, AI Routing, Distributed Systems, Sovereign AI, and Post-Cloud Architecture (Api-Oss-Fixed)","source":"datacite","abstract":"Privacy-enhancing technologies (PETs) form a critical component of modern computing systems, addressing the tension between data utility and individual privacy. This paper surveys the landscape of privacy-preserving techniques — from Cynthia Dwork's differential privacy framework to k-anonymity, homomorphic encryption, and secure multi-party computation — and examines their application within the 01s Sovereign (Kaiman) operating system. We demonstrate how the OS integrates these techniques to protect user data while maintaining the transparency required by its .aioss audit ledger. Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores api gateway, ai routing in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.","url":"https://doi.org/10.5281/zenodo.20782162","authors":["Alpasan, Lois-Kleinner"],"tags":["sovereign ai","sovereign data","sovereign os","operating systems","post-cloud","compliance","blackboxes","agi"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20782162","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.5281/zenodo.21442795","name":"Towards Scalable Fuzzy PSI via Efficient Fuzzy Matching","source":"datacite","abstract":"In fuzzy private set intersection (fuzzy PSI), there are two parties, a sender holding a set of $d$-dimensional points $Q = \\{\\vecq_1, \\ldots, \\vecq_m\\}$ and a receiver holding a set $W = \\{\\vecw_1, \\ldots, \\vecw_n\\}$ of the same structure. It enables the receiver to learn the point $\\vecq \\in Q$ for which there exists some $\\vecw \\in W$ satisfying $\\mathsf{dist}(\\vecq, \\vecw) \\le \\delta$ under a given distance metric.Although several fuzzy PSI protocols for $L_{p\\in[1, \\infty]}$ distance are proposed, there are significant efficiency issues, mainly because they (1) heavily rely on expensive cryptographic primitives, e.g., homomorphic encryption or garble circuits, and/or (2) incur undesirable asymptotic communication and computation complexity. In this paper, we present scalable fuzzy PSI protocols for general $L_{p \\in [1, \\infty]}$ distance, supporting both low- and high-dimensional sets. The core technique is two efficient fuzzy matching protocols that securely evaluate $\\mathsf{dist}(\\vecq, \\vecw) \\le \\delta$.The first is built from a role-reversed oblivious PRF (OPRF) and realizes $O(d\\log \\delta)$ overhead, compared to $O((\\log \\delta)^d)$ in previous works. The second leverages customized oblivious transfer (OT) with $O(d\\ell)$ overhead, where $\\ell$ is the bit length of inputs, which is particularly suitable for short inputs.With these new techniques, we further propose a new dual-layer hashing framework for fuzzy PSI over low-dimensional sets, instantiated with our OT-based fuzzy matching and enhanced with a domain reduction optimization.The protocols achieve an overhead linear with $n, m, \\log \\delta, 2^d$, without the $O((\\log \\delta)^d)$ or $O(\\delta)$ factors present in prior works. For high-dimensional sets, we construct fuzzy PSI protocols based on our OPRF- and OT-based fuzzy matching, which achieve an asymptotic overhead linear with $n, m, d$, and $\\log \\delta$ but rely on the strong globally disjoint assumption. Extensive evaluations demonstrate that our protocols achieve up to a $145\\times$ speedup in running time and a $20\\times$ reduction in communication cost compared to van Baarsen and Pu~(ASIACRYPT'25), and achieve up to a $25\\times$ speedup in running time and up to a $17\\times$ reduction in communication cost compared to Piske et al.~(CCS'25).","url":"https://doi.org/10.5281/zenodo.21442795","authors":["Hao, Meng","Yang, Xinpeng","Chen, Hanxiao","Zhang, Tianwei","Xue, Haiyang","Yang, Guomin","Li, Hongwei","Deng, Robert H."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21442795","addedAt":"2026-08-31T06:41:42.473Z","updatedAt":"2026-08-31T06:41:42.473Z"},{"id":"doi:10.22323/1.264.0061","name":"Compact CAFED Fully Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.22323/1.264.0061","authors":["Lang Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-03-20T16:48:54Z","doi":"10.22323/1.264.0061","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.23919/siceiscs51787.2021.9495321","name":"SARSA(0) Reinforcement Learning over Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.23919/siceiscs51787.2021.9495321","authors":["Jihoon Suh","Takashi Tanaka"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-28T20:29:35Z","doi":"10.23919/siceiscs51787.2021.9495321","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/kse59128.2023.10299436","name":"Homomorphic Encryption for AI-Based Applications: Challenges and Opportunities","source":"crossref","abstract":"","url":"https://doi.org/10.1109/kse59128.2023.10299436","authors":["Rafik Hamza"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-06T14:05:46Z","doi":"10.1109/kse59128.2023.10299436","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1145/3474366.3486926","name":"Intel HEXL","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3474366.3486926","authors":["Fabian Boemer","Sejun Kim","Gelila Seifu","Fillipe D.M. de Souza","Vinodh Gopal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-05T22:04:55Z","doi":"10.1145/3474366.3486926","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1145/3335741.3335762","name":"Fundamentals of fully homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3335741.3335762","authors":["Zvika Brakerski"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-10-09T17:40:23Z","doi":"10.1145/3335741.3335762","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/cdc51059.2022.9992651","name":"Towards Provably Secure Encrypted Control Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cdc51059.2022.9992651","authors":["Kaoru Teranishi","Kiminao Kogiso"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-10T19:26:56Z","doi":"10.1109/cdc51059.2022.9992651","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.5121/ijcis.2012.2203","name":"Implementation and Analysis of Homomorphic Encryption Schemes","source":"crossref","abstract":"","url":"https://doi.org/10.5121/ijcis.2012.2203","authors":["Nitin Jain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-07-07T06:38:16Z","doi":"10.5121/ijcis.2012.2203","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1117/12.2685473","name":"Secure encryption method of privacy set intersection based on homomorphic encryption algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1117/12.2685473","authors":["Nan Hu","Ruiting Qu","Lihua Guo","Pan Hu","Jiangwei Gong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-15T23:35:26Z","doi":"10.1117/12.2685473","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1007/978-3-030-77287-1_4","name":"Secure and Confidential Rule Matching for Network Traffic Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1_4","authors":["Dimitar Jetchev","Alistair Muir"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-03T23:02:43Z","doi":"10.1007/978-3-030-77287-1_4","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1142/9789811217838_0013","name":"USING HOMOMORPHIC ENCRYPTION SYSTEM FOR HARASSMENT CONTROL WITH COMPLETE RESPECT OF PRIVACY","source":"crossref","abstract":"","url":"https://doi.org/10.1142/9789811217838_0013","authors":["MASSIMO REGOLI"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-05-27T00:02:36Z","doi":"10.1142/9789811217838_0013","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.46586/tches.v2022.i4.661-692","name":"SoK: Fully Homomorphic Encryption over the [Discretized] Torus","source":"crossref","abstract":"First posed as a challenge in 1978 by Rivest et al., fully homomorphic encryption—the ability to evaluate any function over encrypted data—was only solved in 2009 in a breakthrough result by Gentry (Commun. ACM, 2010). After a decade of intense research, practical solutions have emerged and are being pushed for standardization.This paper explains the inner-workings of TFHE, a torus-based fully homomorphic encryption scheme. More exactly, it describes its implementation on a discretized version of the torus. It also explains in detail the technique of the programmable bootstrapping. Numerous examples are provided to illustrate the various concepts and definitions.","url":"https://doi.org/10.46586/tches.v2022.i4.661-692","authors":["Marc Joye"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-01T05:54:02Z","doi":"10.46586/tches.v2022.i4.661-692","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/tc.2014.2345388","name":"Accelerating Fully Homomorphic Encryption in Hardware","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tc.2014.2345388","authors":["Yarkin Doroz","Erdinc Ozturk","Berk Sunar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-08-05T18:29:52Z","doi":"10.1109/tc.2014.2345388","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1007/978-3-030-77764-7_9","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77764-7_9","authors":["Pawel Sniatala","S.S. Iyengar","Sanjeev Kaushik Ramani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-30T20:34:17Z","doi":"10.1007/978-3-030-77764-7_9","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/isaics66888.2025.11349997","name":"Searchable Homomorphic Encryption Scheme for Blockchain Data Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isaics66888.2025.11349997","authors":["Chunpeng Fang","Mengyao Liang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-28T20:55:52Z","doi":"10.1109/isaics66888.2025.11349997","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.11591/ijins.v1i4.798","name":"Attack on Fully Homomorphic Encryption over the Integers","source":"crossref","abstract":"","url":"https://doi.org/10.11591/ijins.v1i4.798","authors":["Gu Chunsheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-02-23T11:16:59Z","doi":"10.11591/ijins.v1i4.798","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/cscloud.2015.96","name":"Privacy-Preserving Data Exfiltration Monitoring Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cscloud.2015.96","authors":["Kurt Rohloff"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-01-07T22:15:31Z","doi":"10.1109/cscloud.2015.96","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/cw52790.2021.00055","name":"Privacy-Preserving Keystroke Analysis using Fully Homomorphic Encryption &amp; Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cw52790.2021.00055","authors":["Jatan Loya","Tejas Bana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-18T01:23:05Z","doi":"10.1109/cw52790.2021.00055","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/access.2021.3117029","name":"Homomorphic Encryption for Multiple Users With Less Communications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2021.3117029","authors":["Jeongeun Park"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-10-02T01:53:38Z","doi":"10.1109/access.2021.3117029","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.2139/ssrn.5580098","name":"Efficient Privacy-Preserving Sparse Matrix-Vector Multiplication Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5580098","authors":["Yang Gao","Gang Quan","Wujie Wen","Scott Piersall","Qian Lou","Liqiang Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-08T21:37:36Z","doi":"10.2139/ssrn.5580098","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.3837/tiis.2016.08.022","name":"A Survey of Homomorphic Encryption for Outsourced Big Data Computation","source":"crossref","abstract":"","url":"https://doi.org/10.3837/tiis.2016.08.022","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-01-09T07:43:42Z","doi":"10.3837/tiis.2016.08.022","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/icicat57735.2023.10263650","name":"Retrospective Study on Classical Homomorphic Encryption Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicat57735.2023.10263650","authors":["Sonam Mittal","Soni Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-02T17:43:38Z","doi":"10.1109/icicat57735.2023.10263650","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/secdev66745.2025.00015","name":"Privacy-Preserving Medical Risk Assessment Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/secdev66745.2025.00015","authors":["Raushan Kumar Pandit","Kirill Morozov","Cihan Tunc"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-13T18:42:42Z","doi":"10.1109/secdev66745.2025.00015","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.6025/jism/2018/8/3/83-93","name":"Password-Based Authentication System Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.6025/jism/2018/8/3/83-93","authors":["Marwan Nayyef","Ali Sagheer"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-09-26T06:00:36Z","doi":"10.6025/jism/2018/8/3/83-93","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/access.2024.3520718","name":"Accelerated Multi-Key Homomorphic Encryption via Automorphism-Based Circuit Bootstrapping","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2024.3520718","authors":["Kangwei Xu","Ruwei Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-07T14:43:39Z","doi":"10.1109/access.2024.3520718","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.5373/jardcs/v11sp10/20192959","name":"Intrusion Detection and Secure Data Storage in Cloud Computing Using Modified-Ann and Fully Homomorphic Encryption Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.5373/jardcs/v11sp10/20192959","authors":["Prasanta Kumar Bal","Sateesh Kumar Pradhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-12-03T05:32:11Z","doi":"10.5373/jardcs/v11sp10/20192959","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.62056/a69qgyl7s","name":"On Circuit Private, Multikey and Threshold Approximate Homomorphic Encryption","source":"crossref","abstract":"Homomorphic encryption for approximate arithmetic allows one to encrypt discretized real/complex numbers and evaluate arithmetic circuits over them. The first scheme, called CKKS, was introduced by Cheon et al. (Asiacrypt 2017) and gained tremendous attention. The enthusiasm for CKKS-type encryption stems from its potential to be used in inference or multiparty computation tasks that do not require an exact output. A desirable property for homomorphic encryption is circuit privacy, which requires that a ciphertext leaks no information on the computation performed to obtain it. Despite numerous improvements directed toward improving efficiency, the question of circuit privacy for approximate homomorphic encryption remains open. In this paper, we give the first formal study of circuit privacy for homomorphic encryption over approximate arithmetic. We introduce formal models that allow us to reason about circuit privacy. Then, we show that approximate homomorphic encryption can be made circuit private using tools from differential privacy with appropriately chosen parameters. In particular, we show that by applying an exponential (in the security parameter) Gaussian noise on the evaluated ciphertext, we remove useful information on the circuit from the ciphertext. Crucially, we show that the noise parameter is tight, and taking a lower one leads to an efficient adversary against such a system. We expand our definitions and analysis to the case of multikey and threshold homomorphic encryption for approximate arithmetic. Such schemes allow users to evaluate a function on their combined inputs and learn the output without leaking anything on the inputs. A special case of multikey and threshold encryption schemes defines a so-called partial decryption algorithm where each user publishes a “masked” version of its secret key, allowing all users to decrypt a ciphertext. Similarly, in this case, we show that applying a proper differentially private mechanism gives us IND-CPA-style security where the adversary additionally gets as input the partial decryptions. This is the first security analysis of approximate homomorphic encryption schemes that consider the knowledge of partial decryptions. We show lower bounds on the differential privacy noise that needs to be applied to retain security. Analogously, in the case of circuit privacy, the noise must be exponential in the security parameter. We conclude by showing the impact of the noise on the precision of CKKS-type schemes.","url":"https://doi.org/10.62056/a69qgyl7s","authors":["Kamil Kluczniak","Giacomo Santato"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-08T17:23:17Z","doi":"10.62056/a69qgyl7s","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/csnt.2012.208","name":"The Algebra Homomorphic Encryption Scheme Based on Fermat's Little Theorem","source":"crossref","abstract":"","url":"https://doi.org/10.1109/csnt.2012.208","authors":["Guangli Xiang","Zhuxiao Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-05-25T14:58:26Z","doi":"10.1109/csnt.2012.208","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/isit.2008.4595310","name":"Lattice-based homomorphic encryption of vector spaces","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isit.2008.4595310","authors":["Carlos Aguilar Melchor","Guilhem Castagnos","Philippe Gaborit"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2008-08-13T22:33:52Z","doi":"10.1109/isit.2008.4595310","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.21437/odyssey.2026-14","name":"Quantized Approximate Signal Processing (QASP): Towards Homomorphic Encryption for Audio","source":"crossref","abstract":"","url":"https://doi.org/10.21437/odyssey.2026-14","authors":["Tu Duyen Nguyen","Adrien Lesage","Clotilde Cantini","Rachid Riad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-08T20:05:36Z","doi":"10.21437/odyssey.2026-14","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1016/j.ifacol.2021.04.203","name":"Privacy-Preserving Decentralized Optimization Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ifacol.2021.04.203","authors":["Xiang Huo","Mingxi Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-05-26T15:43:42Z","doi":"10.1016/j.ifacol.2021.04.203","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.54254/2755-2721/2025.20959","name":"Advances and Applications in Fully Homomorphic Encryption Research","source":"crossref","abstract":"With the rapid advancement of information technology and the widespread adoption of cloud computing, data security and privacy protection have increasingly become global priorities. In this context, Fully Homomorphic Encryption (FHE) has emerged as a sophisticated encryption technology capable of performing arbitrary computations on encrypted data without the need for decryption, thereby attracting significant interest from both academia and industry. Initially proposed by Rivest et al. in 1978 and practically realized by Gentry in 2009, FHE has evolved through four generations of schemes, each introducing novel construction methods and optimization techniques to enhance security and computational efficiency. Central to modern FHE schemes are lattice-based hard problems such as Learning with Errors (LWE) and Ring-Learning with Errors (RLWE), which provide robust resistance against quantum computing attacks. Additionally, advancements in optimizing the bootstrapping process and exploring hierarchical structures have further improved the practicality and performance of FHE. FHE applications span diverse fields, including cloud computing, artificial intelligence, and blockchain technology, demonstrating its immense potential in ensuring data privacy and facilitating secure computations. However, FHE still faces significant challenges related to computational efficiency, implementation complexity, and application scalability. Future research directions aim to enhance computational performance, broaden application scenarios, strengthen security measures, simplify implementation processes, and develop multi-modal and hybrid encryption schemes. Through a comprehensive review of FHE's development, current progress, applications, and challenges, this paper seeks to provide researchers and engineers with a thorough understanding of the FHE landscape, thereby promoting its continued advancement and practical utilization.","url":"https://doi.org/10.54254/2755-2721/2025.20959","authors":["Dikai Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-21T01:52:58Z","doi":"10.54254/2755-2721/2025.20959","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1007/978-3-030-77287-1_13","name":"Private Movie Recommendations for Children","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1_13","authors":["Anh Pham","Mohammad Samragh","Sameer Wagh","Emily Wenger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-03T23:02:43Z","doi":"10.1007/978-3-030-77287-1_13","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1090/conm/606/12143","name":"Fully Homomorphic Encryption for Mathematicians","source":"crossref","abstract":"We give an introduction to Fully Homomorphic Encryption for mathematicians. Fully Homomorphic Encryption allows untrusted parties to take encrypted data E n c ( m 1 ) , … , E n c ( m t ) \\mathrm {Enc}(m_1),\\ldots ,\\mathrm {Enc}(m_t) and any efficiently computable function f f , and compute an encryption of f ( m 1 , … , m t ) f(m_1,\\ldots ,m_t) , without knowing or learning the decryption key or the raw data m 1 , … , m t m_1,\\ldots ,m_t . The problem of how to do this was recently solved by Craig Gentry, using ideas from algebraic number theory and the geometry of numbers. In this paper we discuss some of the history and background, give examples of Fully Homomorphic Encryption schemes, and discuss the hard mathematical problems on which the cryptographic security is based.","url":"https://doi.org/10.1090/conm/606/12143","authors":["Alice Silverberg"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-12-16T11:51:08Z","doi":"10.1090/conm/606/12143","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.2139/ssrn.5488326","name":"Applications of Homomorphic Encryption in Secure Computation","source":"crossref","abstract":"Background Homomorphic encryption (HE) represents a pivotal innovation in modern cryptography, offering a pathway to secure computation on encrypted data. This paper embarks on a comprehensive exploration of HE's applications, elucidating its transformative potential in bolstering data security and privacy across various domains. Methods The research employs a mixed-methods approach to evaluate HE technologies. Quantitatively, it develops realistic datasets simulating healthcare and financial data, assessing HE's performance in encrypted computations. Various encryption schemes are rigorously tested for efficiency and accuracy under different conditions. Qualitatively, insights from expert interviews and case studies of HE implementations provide additional context on practical challenges and strategic benefits. Results The simulations and analyses showcase the efficiency, scalability, and security of HE techniques in diverse scenarios. The empirical evidence validates the real-world applicability of HE, demonstrating its&amp;nbsp;versatility and efficacy in secure computation outsourcing, privacy-preserving&amp;nbsp;&lt;span&gt;data analysis, and secure multi-party computation.Conclusions This research paper highlights the transformative power of homomorphic encryption, advocating for its widespread adoption and integration. By bridging the gap between theoretical understanding and practical implementation, the paper contributes to advancing secure computation practices, addressing contemporary challenges in data security and privacy amidst evolving cybersecurity threats and the increasing ubiquity of sensitive data. In essence, this research serves as a beacon of insight into the future of data confidentiality and integrity, promoting HE as a crucial tool for revolutionizing the landscape of data security and privacy in an interconnected world. Plain language summary This research investigates homomorphic encryption (HE), a cuttingedge&lt;br&gt; technology that allows computations to be performed on encrypted data without needing to decrypt it first. This capability is crucial for protecting sensitive information in fields like healthcare and finance, where data privacy is paramount. The study uses a mix of methods: it creates realistic datasets to test HE's performance in handling tasks like arithmetic and statistical analysis under various conditions. Additionally, insights from interviews with experts and real-world case studies shed light on practical challenges and benefits of HE. By combining these approaches, the research aims to provide a comprehensive evaluation of HE's effectiveness in enhancing data security and privacy, informing future developments in cryptographic techniques.&lt;/span&gt;","url":"https://doi.org/10.2139/ssrn.5488326","authors":["Elissa Mollakuqe","Arber Parduzi","Shasivar Rexhepi","Vesna Dimitrova","Samir Jakupi","Rilind Muharremi","Mentor Hamiti","Jusuf Qarkaxhija"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-09T10:55:07Z","doi":"10.2139/ssrn.5488326","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.23919/eusipco.2017.8081350","name":"Privacy-safe linkage analysis with homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.23919/eusipco.2017.8081350","authors":["Chibuike Ugwuoke","Zekeriya Erkin","Reginald L. Lagendijk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-11-02T21:20:38Z","doi":"10.23919/eusipco.2017.8081350","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.2991/snce-18.2018.156","name":"Survey on Homomorphic Encryption Technology","source":"crossref","abstract":"","url":"https://doi.org/10.2991/snce-18.2018.156","authors":["Chunxia Tu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-05-28T15:34:06Z","doi":"10.2991/snce-18.2018.156","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1016/j.procs.2022.04.044","name":"Implementing Linear Regression with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2022.04.044","authors":["Bijiao Chen","Xianghan Zheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-05-10T12:34:34Z","doi":"10.1016/j.procs.2022.04.044","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.62056/abhey76bm","name":"Fast Plaintext-Ciphertext Matrix Multiplication from Additively Homomorphic Encryption","source":"crossref","abstract":"Plaintext-ciphertext matrix multiplication (PC-MM) is an indispensable tool in privacy-preserving computations such as secure machine learning and encrypted signal processing. While there are many established algorithms for plaintext-plaintext matrix multiplication, efficiently computing plaintext-ciphertext (and ciphertext-ciphertext) matrix multiplication is an active area of research which has received a lot of attention. Recent literature have explored various techniques for privacy-preserving matrix multiplication using fully homomorphic encryption (FHE) schemes with ciphertext packing and Single Instruction Multiple Data (SIMD) processing. On the other hand, there hasn't been any attempt to speed up PC-MM using unpacked additively homomorphic encryption (AHE) schemes beyond the schoolbook method and Strassen's algorithm for matrix multiplication. In this work, we propose an efficient PC-MM from unpacked AHE, which applies Cussen's compression-reconstruction algorithm for plaintext-plaintext matrix multiplication in the encrypted setting. We experimentally validate our proposed technique using a concrete instantiation with the additively homomorphic elliptic curve ElGamal encryption scheme and its software implementation on a Raspberry Pi 5 edge computing platform. Our proposed approach achieves up to an order of magnitude speedup compared to state-of-the-art for large matrices with relatively small element bit-widths. Extensive measurement results demonstrate that our fast PC-MM is an excellent candidate for efficient privacy-preserving computation even in resource-constrained environments.","url":"https://doi.org/10.62056/abhey76bm","authors":["Krishna Ramapragada","Utsav Banerjee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-08T17:23:17Z","doi":"10.62056/abhey76bm","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/cidm.2007.368936","name":"Using Homomorphic Encryption For Privacy-Preserving Collaborative Decision Tree Classification","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cidm.2007.368936","authors":["Justin Zhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-06-07T17:03:29Z","doi":"10.1109/cidm.2007.368936","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/csci58124.2022.00163","name":"Performance Evaluation of Partially Homomorphic Encryption Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1109/csci58124.2022.00163","authors":["George Dimitoglou","Carol Jim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-25T17:17:05Z","doi":"10.1109/csci58124.2022.00163","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/icccnt49239.2020.9225325","name":"Secure Secret Sharing Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icccnt49239.2020.9225325","authors":["Nileshkumar Kakade","Utpalkumar Patel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-10-15T20:00:38Z","doi":"10.1109/icccnt49239.2020.9225325","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/worlds450073.2020.9210393","name":"Speeding Up Sensor Data Encryption with a Common Key Cryptosystem combined with Fully Homomorphic Encryption on Smartphones","source":"crossref","abstract":"","url":"https://doi.org/10.1109/worlds450073.2020.9210393","authors":["Marin Matsumoto","Masato Oguchi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-10-01T20:44:27Z","doi":"10.1109/worlds450073.2020.9210393","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/ijcnn54540.2023.10191194","name":"High-Throughput Privacy-Preserving GRU Network with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ijcnn54540.2023.10191194","authors":["Zeyu Wang","Makoto Ikeda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-02T13:30:03Z","doi":"10.1109/ijcnn54540.2023.10191194","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/focs.2011.12","name":"Efficient Fully Homomorphic Encryption from (Standard) LWE","source":"crossref","abstract":"","url":"https://doi.org/10.1109/focs.2011.12","authors":["Zvika Brakerski","Vinod Vaikuntanathan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2011-12-23T11:39:51Z","doi":"10.1109/focs.2011.12","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/ncca.2015.20","name":"Towards Practical Homomorphic Encryption in Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ncca.2015.20","authors":["Adil Bouti","Jorg Keller"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-12-21T21:48:58Z","doi":"10.1109/ncca.2015.20","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/icsft66733.2026.11507590","name":"Design Hybrid System for Secure Cloud Storage based on Advanced Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsft66733.2026.11507590","authors":["Gireesh Kambala"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-12T19:46:43Z","doi":"10.1109/icsft66733.2026.11507590","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/hpec.2014.7041001","name":"Accelerating NTRU based homomorphic encryption using GPUs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/hpec.2014.7041001","authors":["Wei Dai","Yarkin Doroz","Berk Sunar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-02-17T14:50:25Z","doi":"10.1109/hpec.2014.7041001","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.37896/jxu14.8/122","name":"HOMOMORPHIC ENCRYPTION AND DECRYPTION FOR SECURE OUTSOURCING OF LARGE-SCALE SYSTEMS OF LINEAR EQUATIONS","source":"crossref","abstract":"","url":"https://doi.org/10.37896/jxu14.8/122","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-08-27T12:49:59Z","doi":"10.37896/jxu14.8/122","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/iadcc.2015.7154867","name":"A mixed homomorphic encryption scheme for secure data storage in cloud","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iadcc.2015.7154867","authors":["R. Kangavalli","Vagdevi S"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-07-13T17:16:18Z","doi":"10.1109/iadcc.2015.7154867","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.2139/ssrn.3565268","name":"A Neural Network Application of Fully Homomorphic Encryption for Cloud Computing","source":"crossref","abstract":"Cloud computing is a very convenient way of processing huge databases while sharing resources. Currently there are many cloud services available at comparatively low costs, which encourages researchers in the field of Machine and Deep Learning to use them. The problem with clouds is their inherent insecure nature. Hence sensitive sectors like health care and banking, are hesitant to use these services. In this paper we provide a proof of concept for applying “Fully Homomorphic Encryption” to process images on clouds for Neural Network application. We demonstrate the success of FHE on black-and-white, grayscale and color images, Gaussian filtering and on Convolutional Neural Network (CNN) for image classification, to some extent.","url":"https://doi.org/10.2139/ssrn.3565268","authors":["Megha Kolhekar","Ashish Pandey","Ayushi Raina","Rijin Thomas","Vaibhav Tiwari","Vaidehi Shelar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-04-04T07:31:34Z","doi":"10.2139/ssrn.3565268","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/eem58374.2023.10161800","name":"Privacy-Preserving Market-Driven Transactive Energy System Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/eem58374.2023.10161800","authors":["Magda Foti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-03T13:48:10Z","doi":"10.1109/eem58374.2023.10161800","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.14419/ijet.v7i3.3.14499","name":"Novel Homomorphic Encryption Scheme in Cloud Computing","source":"crossref","abstract":"Cloud computing is an indispensable technology for any business organization, such as banking, e-commerce, etc. Although technology has advantages in many areas; The protection of stored data is a major concern for all stakeholders in the architecture. Provide data security with respect to network security, control strategies and access to the service, data storage. Despite the efforts of service providers to build customer trust in data security, users need a passion for using technology for their business skills. Homomorphism coding is a data protection technique in which tasks can be performed on encrypted data themselves. In this article, we present an exploration of new homomorphism encryption methods with respect to data security and their use in cloud computing.","url":"https://doi.org/10.14419/ijet.v7i3.3.14499","authors":["C Veena","Dr M. Hanumanthappa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-07-30T08:35:24Z","doi":"10.14419/ijet.v7i3.3.14499","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.5120/ijca2021921679","name":"Evolution of Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5120/ijca2021921679","authors":["George Asante","James Ben Hayfron-Acquah","Michael Asante"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-08T15:59:51Z","doi":"10.5120/ijca2021921679","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.46647/ijetms.2019.v03i02.002","name":"Design of protected cloud computing by Homomorphic Encryption","source":"crossref","abstract":"The idea of homomorphic encryption is to make sure data confidentiality in messages, storage or in utilize by processes with methodssimilar to conventional cryptography, but with additionalabilities of computing over encrypted data, searching an encrypted data, etc. Homomorphism is a property by which a problem in one algebraic system can be converted to a problem in another algebraic system, be solved and the solution later can also be converted back successfully. Therefore, homomorphism composes secure delegation of computation to a third party feasible. Various conventional encryption schemes have either multiplicative or additive homomorphic property and are presently in use for personal applications. So far, a Fully Homomorphic Encryption (FHE) scheme which could perform any arbitrary computation over encrypted data appeared in 2009 as Gentry’s work. In this paper, we suggest a multi-cloud architecture of M distributed servers to repartition the data and to almostpermit achieving an FHE","url":"https://doi.org/10.46647/ijetms.2019.v03i02.002","authors":["J.Keziya Rani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-06T14:30:34Z","doi":"10.46647/ijetms.2019.v03i02.002","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.23977/iccsie.2018.1023","name":"A Homomorphic Encryption Approach in a Voting System in a Distributed Architecture","source":"crossref","abstract":"","url":"https://doi.org/10.23977/iccsie.2018.1023","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-11-27T02:55:56Z","doi":"10.23977/iccsie.2018.1023","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.12732/ijam.v38i7s.462","name":"HOMOMORPHIC ENCRYPTION AND ALGEBRAIC GEOMETRY FOR PRIVACY-PRESERVING MACHINE LEARNING","source":"crossref","abstract":"The increasing acceptance of machine learning (ML) in sensitive applications such as health care, finance, and enforcement brings many serious privacy concerns requiring privacy-preserving techniques to be developed. Homomorphic encryption (HE) seems to be a good approach permitting various computations to be carried out on encrypted data without decryption thereby preserving data privacy. The disadvantages of HE are essentially the time required to do the computation and also its scalability. The results obtained have great consequences since the security obtained through this scheme seems to make it possible to apply encryption to large scale ML models. These disadvantages can be solved to a great extent through the introduction of the mathematics of algebraic geometry where polynomial equations are considered. Algebraic geometry will now play an important part in rendering schemes of HE economical and practicable through research within this field. This article reveals the close inter-relation existing between homomorphic encryption and algebraic geometry and attempts to outline briefly some of the developments, problems and possibilities of this forthcoming research. However, in spite of the great advantages to be gained or the possibilities of privacy preserving schemes of HE and algebraic geometry in ML, ML is as yet under threat of moral, scalability and computational disadvantages which threaten its complete acceptance.","url":"https://doi.org/10.12732/ijam.v38i7s.462","authors":["G. Mahalakshmi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-22T15:56:09Z","doi":"10.12732/ijam.v38i7s.462","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/worldcis.2013.6751020","name":"Practical fully homomorphic encryption over polynomial quotient rings","source":"crossref","abstract":"","url":"https://doi.org/10.1109/worldcis.2013.6751020","authors":["Alexander Zhirov","Olga Zhirova","Sergey F. Krendelev"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-04-30T21:13:02Z","doi":"10.1109/worldcis.2013.6751020","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/iscas.2014.6865755","name":"Accelerating leveled fully homomorphic encryption using GPU","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscas.2014.6865755","authors":["Wei Wang","Zhilu Chen","Xinming Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-07-30T21:16:29Z","doi":"10.1109/iscas.2014.6865755","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/sym18050832","name":"Sustainable Cryptography: Carbon Asymmetry in Partially Homomorphic Encryption in the Cloud","source":"crossref","abstract":"Encryption protects data in the cloud but adds energy cost, especially for partially homomorphic encryption (PHE) schemes that allow computation on encrypted data. Their carbon footprint across cloud data center deployments remains underexplored. We benchmark eight PHE algorithms from the LightPHE open-source Python library, including RSA, ElGamal, Exponential ElGamal, Paillier, Damgård–Jurik, Okamoto–Uchiyama, Goldwasser–Micali, and Elliptic Curve ElGamal, across six cloud environments, and use timing data as input to a carbon estimation model covering Scope 1, Scope 2, and Scope 3 emissions across ten data center configurations. We ground the energy model with a dedicated Intel RAPL calibration on bare-metal hardware using 30 repetitions per configuration. The calibration measures average CPU package power at 34.7 W and total system power at 48.4 W, showing that a fixed 150 W CPU-only assumption overestimates actual CPU power by a factor of 4.3. We present calibrated estimates alongside a 150 W server-class scenario and a sensitivity analysis across power, PUE, and grid carbon intensity. Elliptic curve schemes provide equivalent classical security at a fraction of the energy cost of RSA, and algorithm-specific mathematical structure drives order-of-magnitude differences in carbon output. These results reveal an asymmetry between security and carbon cost across PHE algorithms and establish a sustainable-cryptography baseline for future PQC-based homomorphic schemes.","url":"https://doi.org/10.3390/sym18050832","authors":["Alper Ozpinar","Sefik Ilkin Serengil"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-25T05:33:54Z","doi":"10.3390/sym18050832","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.17485/ijst/2016/v9i8/87964","name":"Ensuring Confidentiality of Cloud Data using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.17485/ijst/2016/v9i8/87964","authors":["K. Suveetha","T. Manju"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-03-17T12:53:57Z","doi":"10.17485/ijst/2016/v9i8/87964","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/indicon.2017.8487891","name":"Arithmetic Operations on Encrypted Data using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/indicon.2017.8487891","authors":["Shiji Mariam Mathew","S Sabitha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-10-18T18:50:31Z","doi":"10.1109/indicon.2017.8487891","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/978-3-030-77287-1_5","name":"Trusted Monitoring Service (TMS)","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1_5","authors":["Xiaoqian Jiang","Miran Kim","Kristin Lauter","Tim Scott","Shayan Shams"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-03T23:02:43Z","doi":"10.1007/978-3-030-77287-1_5","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/tai.2025.3612906/mm1","name":"QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning_supp1-3612906.pdf","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tai.2025.3612906/mm1","authors":["Md Jueal Mia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-09T17:56:53Z","doi":"10.1109/tai.2025.3612906/mm1","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.17706/jcp.14.7.451-469","name":"Parallel Computing Mode in Homomorphic Encryption Using GPUs Acceleration in Cloud","source":"crossref","abstract":"","url":"https://doi.org/10.17706/jcp.14.7.451-469","authors":["Jing Xia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-07-23T07:43:21Z","doi":"10.17706/jcp.14.7.451-469","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/978-1-4842-6367-9_13","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4842-6367-9_13","authors":["Marius Iulian Mihailescu","Stefania Loredana Nita"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-11-24T03:26:50Z","doi":"10.1007/978-1-4842-6367-9_13","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.54097/hset.v39i.6674","name":"Application of Blockchain and Fully Homomorphic Encryption in Online Questionnaires","source":"crossref","abstract":"An online questionnaire is a simple and efficient method of acquiring diverse data for surveys. However, a proportion of respondents have strong concerns about whether questionnaires in this form will leak their privacy, which causes their unwillingness to fill out the questionnaires. Blockchain is well known for decentralization, traceability, and transparency and is now widely used in all kinds of platforms. Fully homomorphic encryption is another popular technique that ensures the safety of privacy, it helps to compute and use encrypted data without decrypting it. To solve privacy-preservation problems and enhance online data sharing, this paper dedicates to demonstrating how the merits of Blockchain and Fully Homomorphic Encryption (FHE) can be utilized to realize a secure, reliable, and decentralized online-questionnaire platform that guarantees the authenticity of data and privacy security for clients. This paper also proves that the combination of blockchain and FHE can successfully meet the expectation of an ideal online questionnaire platform.","url":"https://doi.org/10.54097/hset.v39i.6674","authors":["Xinhong Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-04-06T06:48:38Z","doi":"10.54097/hset.v39i.6674","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/978-3-030-77287-1_12","name":"HEalth: Privately Computing on Shared Healthcare Data","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1_12","authors":["Leo de Castro","Erin Hales","Mimee Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-03T23:02:43Z","doi":"10.1007/978-3-030-77287-1_12","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.4028/www.scientific.net/amr.989-994.4780","name":"The Analysis of Constructing Fully Homomorphic Encryption over Integers","source":"crossref","abstract":"Fully homomorphic encryption has long been regarded as cryptography’s prized “holy grail”–extremely useful yet rather elusive. At 2010 van Dijk et al. described a fully homomorphic encryption scheme over theintegers. The main appeal of this scheme is its conceptual simplicity. This simplicity comes at the expense of a public key size inÕ(λ 10 ) which is too large for any practical system. The construction is based on the hardness of the approximate-GCD problem. At 2011 Coron et al. reduced the public key size to about Õ(λ 7 ) by encrypting with a quadratic form in the public key elements, instead of a linear form. This scheme is based on a stronger variant of the approximate-GCD problem. An implementation of the full scheme was obtained with a 802MB public key. At 2012 Coron et al. described a compression technique that reduces the public key size to aboutÕ(λ 5 ). This variant remains semantically secure, but in the random oracle model.A level of efficiency very similar to above scheme was obtained but with a 10.1MB public key instead of a 802MB one.Coron et al. also described a new modulus switching technique for the DGHV scheme that enables to use the new FHE framework without bootstrapping from Brakerski, Gentry and Vaikuntanathan with theDGHV scheme. At present asymptotics of FHE over integers are much better.","url":"https://doi.org/10.4028/www.scientific.net/amr.989-994.4780","authors":["Lei Jin","Xin Xia Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-07-18T07:20:02Z","doi":"10.4028/www.scientific.net/amr.989-994.4780","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.2196/preprints.8805","name":"Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation (Preprint)","source":"crossref","abstract":"BACKGROUND Learning a model without accessing raw data has been an intriguing idea to security and machine learning researchers for years. In an ideal setting, we want to encrypt sensitive data to store them on a commercial cloud and run certain analyses without ever decrypting the data to preserve privacy. Homomorphic encryption technique is a promising candidate for secure data outsourcing, but it is a very challenging task to support real-world machine learning tasks. Existing frameworks can only handle simplified cases with low-degree polynomials such as linear means classifier and linear discriminative analysis. OBJECTIVE The goal of this study is to provide a practical support to the mainstream learning models (eg, logistic regression). METHODS We adapted a novel homomorphic encryption scheme optimized for real numbers computation. We devised (1) the least squares approximation of the logistic function for accuracy and efficiency (ie, reduce computation cost) and (2) new packing and parallelization techniques. RESULTS Using real-world datasets, we evaluated the performance of our model and demonstrated its feasibility in speed and memory consumption. For example, it took approximately 116 minutes to obtain the training model from the homomorphically encrypted Edinburgh dataset. In addition, it gives fairly accurate predictions on the testing dataset. CONCLUSIONS We present the first homomorphically encrypted logistic regression outsourcing model based on the critical observation that the precision loss of classification models is sufficiently small so that the decision plan stays still.","url":"https://doi.org/10.2196/preprints.8805","authors":["Miran Kim","Yongsoo Song","Shuang Wang","Yuhou Xia","Xiaoqian Jiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-04-17T10:45:12Z","doi":"10.2196/preprints.8805","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/hpec43674.2020.9286176","name":"Homomorphic Encryption for Quantum Annealing with Spin Reversal Transformations","source":"crossref","abstract":"","url":"https://doi.org/10.1109/hpec43674.2020.9286176","authors":["Daniel O'Malley","John K. Golden"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-12-22T21:07:15Z","doi":"10.1109/hpec43674.2020.9286176","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/978-3-031-31754-5","name":"On Architecting Fully Homomorphic Encryption-based Computing Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-31754-5","authors":["Rashmi Agrawal","Ajay Joshi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-24T08:02:34Z","doi":"10.1007/978-3-031-31754-5","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.54097/257h8077","name":"A Review of Homomorphic Encryption Applications in Deep Learning","source":"crossref","abstract":"In the era of artificial intelligence, deep learning has achieved success in fields such as image recognition and natural language processing, but the training process, which relies on sensitive data, raises privacy risks. Traditional privacy protection techniques, such as differential privacy and secure multi-party computation, either result in accuracy loss or high computational overhead. Homomorphic encryption (HE) can perform operations directly on ciphertext without decrypting the data, providing a new approach for deep learning to protect data privacy. Microsoft's CryptoNets system in 2016 first verified the feasibility of performing neural network inference on encrypted data, leading to a research boom combining Homomorphic encryption with deep learning. This paper systematically reviews the latest progress in combining homomorphic encryption with deep learning, introducing typical works from three aspects: encrypted inference, encrypted training, and hybrid methods, analyzing performance bottlenecks, and looking forward to future development directions.","url":"https://doi.org/10.54097/257h8077","authors":["Junhao Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-21T00:15:39Z","doi":"10.54097/257h8077","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.1109/wicom.2006.304","name":"Homomorphic Encryption Scheme of the Rational","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wicom.2006.304","authors":["Ping Zhu","Yanxiang He","Guangli Xiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-04-24T18:19:49Z","doi":"10.1109/wicom.2006.304","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/gcat52182.2021.9587765","name":"Privacy Preserving Big Data mining using Pseudonymization and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/gcat52182.2021.9587765","authors":["Ila Chandrakar","Vishwanath R Hulipalled"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-09T20:45:27Z","doi":"10.1109/gcat52182.2021.9587765","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.36948/ijfmr.2025.v07i06.61192","name":"AI CHAT INTEGRATION: MODEL CONTEXT PROTOCOL WITH HOMOMORPHIC ENCRYPTION (FHE)","source":"crossref","abstract":"The rapid growth of conversational artificial intelligence (AI) across sectors has amplified concerns over data confidentiality. While conventional chat systems secure data in transit and at rest, they decrypt content at the server for inference or tool calls, creating vulnerabilities to unauthorized access. Existing solutions such as Fully Homomorphic Encryption (FHE) and confidential-compute techniques mitigate parts of this issue but face constraints in scalability, latency, or governance. This paper introduces Secure Bridge, a privacy-first framework that eliminates default plaintext exposure while supporting flexible tool integration. The architecture integrates three components: (1) client-side encryption with FHE for supported computations, (2) a Model Context Protocol (MCP) gateway that enforces typed schemas, least-privilege access, and auditability, and (3) a confidential-compute fallback for complex or latency-sensitive tasks. A prototype using WebAssembly-based OpenFHE, React, Node.js, MongoDB, and Redis was tested under loads of up to 150 users. Results show sub-500 ms latency, error rates below 3%, and resilience against attacks, demonstrating Secure Bridge’s viability for regulated","url":"https://doi.org/10.36948/ijfmr.2025.v07i06.61192","authors":["Chandan Jain H P"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-25T17:42:03Z","doi":"10.36948/ijfmr.2025.v07i06.61192","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/asiajcis.2013.8","name":"Detect Zero by Using Symmetric Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/asiajcis.2013.8","authors":["D.J. Guan","Chen-Yu Tsai","E.S. Zhuang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-10-12T02:59:01Z","doi":"10.1109/asiajcis.2013.8","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.4156/jcit.vol7.issue1.52","name":"Overview of Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.4156/jcit.vol7.issue1.52","authors":["Xunyi Ren -","LiLi wei -"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-02-10T09:48:37Z","doi":"10.4156/jcit.vol7.issue1.52","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/a18120731","name":"Privacy-Preserving Classification of Medical Tabular Data with Homomorphic Encryption","source":"crossref","abstract":"Machine learning (ML) offers significant potential for disease prediction, clinical decision support, and medical data classification, but its reliance on sensitive patient data raises privacy and security concerns, particularly under strict healthcare regulations. Traditional encryption methods require data to be decrypted prior to computation, such as in ML workflows, thereby introducing risks of exposure and undermining data confidentiality. Homomorphic Encryption (HE) addresses this challenge by enabling computations directly on encrypted data, ensuring end-to-end privacy. This paper explores the integration of the Cheon-Kim-Kim-Song (CKKS) HE scheme into the inference phase of medical tabular data classification. We evaluate the performance of Logistic Regression (LR), Support Vector Machine (SVM), and a lightweight multilayer perceptron (MLP) under HE-based inference, and compare their classification accuracy, computational overhead, and latency against plaintext counterparts. Additionally, we propose two hybrid models (LR-MLP and SVM-MLP) to accelerate training convergence and enhance inference performance. Experimental results demonstrate that while HE-based inference introduces moderate computational cost and data transmission overheads, it maintains accuracy comparable to plaintext inference. These outcomes affirm the practical feasibility of HE for privacy-preserving machine learning in healthcare, while also highlighting key implementation trade-offs. Furthermore, the findings support the advancement of secure AI systems and promote the adoption of cryptographic techniques in digital health and other privacy-critical fields.","url":"https://doi.org/10.3390/a18120731","authors":["Fairuz Haq","Chao Chen","Zesheng Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-21T11:04:31Z","doi":"10.3390/a18120731","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/isi.2007.379493","name":"Using Homomorphic Encryption and Digital Envelope Techniques for Privacy Preserving Collaborative Sequential Pattern Mining","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isi.2007.379493","authors":["Justin Zhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2007-07-02T17:45:52Z","doi":"10.1109/isi.2007.379493","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/icinfa.2015.7279451","name":"Hardware Trojan prevention based on Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icinfa.2015.7279451","authors":["Hongfeng Xie","Huiyun Li","Guoqing Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-10-01T21:51:38Z","doi":"10.1109/icinfa.2015.7279451","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1016/j.procs.2019.06.012","name":"Homomorphic Encryption Technology for Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2019.06.012","authors":["Min Zhao E","Yang Geng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-07-15T15:48:22Z","doi":"10.1016/j.procs.2019.06.012","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/mipro.2014.6859786","name":"Homomorphic encryption in the cloud","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mipro.2014.6859786","authors":["Darko Hrestak","Stjepan Picek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-07-30T16:19:16Z","doi":"10.1109/mipro.2014.6859786","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/icccs57501.2023.10151259","name":"Outsourcing the Computation of Plaintext Encryption for Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icccs57501.2023.10151259","authors":["Xuelei Li","Ruyang Li","Bing Bai","Yaqian Zhao","Guangqing Liu","Rengang Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-26T18:04:47Z","doi":"10.1109/icccs57501.2023.10151259","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.36948/ijfmr.2025.v07i02.43180","name":"Medical Data Privacy using Homomorphic Encryption","source":"crossref","abstract":"The digitalization of healthcare information raises serious issues of privacy and security. Classical encryption needs to be decrypted in order to analyze, leaving confidential data vulnerable to compromise. Homomorphic encryption is used in this project to allow computation on encrypted medical information without decryption, thereby preserving privacy from data creation to usage. Built with healthcare in mind, the system supports secure analysis and exchange of data without breaching confidentiality. Our approach targets algorithm efficiency, system integration, and scalability for supporting privacy-protecting healthcare analytics.","url":"https://doi.org/10.36948/ijfmr.2025.v07i02.43180","authors":["Aman Kumar","Akash Yadav","Gousiya Begum","P Poornima"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-02T15:43:31Z","doi":"10.36948/ijfmr.2025.v07i02.43180","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.70818/ijbass.2025.v06i02.2513","name":"Homomorphic Encryption for Privacy-Preserving Data Processing in Distributed DBMS","source":"crossref","abstract":"","url":"https://doi.org/10.70818/ijbass.2025.v06i02.2513","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-15T17:15:33Z","doi":"10.70818/ijbass.2025.v06i02.2513","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/iccrd56364.2023.10080450","name":"Fully Homomorphic Encryption Accelerator Using DSP Embedded Multiplier","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccrd56364.2023.10080450","authors":["Shakirah Hashim","Mohammed Benaissa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-03-30T17:24:24Z","doi":"10.1109/iccrd56364.2023.10080450","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1145/3774904.3792476","name":"Reliable Non-Leveled Homomorphic Encryption for Web Services","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3774904.3792476","authors":["Baigang Chen","Dongfang Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-27T12:38:33Z","doi":"10.1145/3774904.3792476","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.54097/hset.v39i.6677","name":"Homomorphic Encryption-based Solution for Data Security in Smart Furniture","source":"crossref","abstract":"Smart furniture is gradually entering people's lives. The special nature of smart furniture determines that it will be exposed to a large amount of private information, but the data processing behind smart furniture still lacks sufficient security. Therefore, the research topic of this paper is the possibility of using homomorphic encryption in smart furniture, and the research topic of this paper is a smart furniture data security solution based on homomorphic encryption. The research methodology of this paper is as follows: investigating the solutions and problems of mainstream smart furniture manufacturers in terms of data security and the possibility of enhancing protection with homomorphic encryption, as well as the impact on cost issues and applicability. The study shows that the use of homomorphic encryption improves the security of remote control and data transmission of smart furniture using cloud-based technologies. In addition, it increases the versatility and reduces the cost of smart furniture and improves the compatibility between smart furniture.","url":"https://doi.org/10.54097/hset.v39i.6677","authors":["Huanyu Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-04-06T06:48:38Z","doi":"10.54097/hset.v39i.6677","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.2139/ssrn.4339459","name":"Using ML and NLP to Understand the Trend of Adoption of Homomorphic Encryption by Financial Institutions","source":"crossref","abstract":"Financial institutions use various confidential datasets to accomplish their objectives, such as providing tailored financial services to their clients and preventing criminal business activities. Concerns about the possibility of sharing these datasets are related to existing privacy policies. A Privacy Enhancing Technology (PET), namely Homomorphic Encryption (HE), is the key to unlocking this impasse: it is a form of encryption that allows the computation of encrypted confidential data while preserving their privacy. This paper is a preliminary assessment for analysing the adoption of HE among financial institutions, employing and analysing the LexisNexis news archive with simple statistics instruments and NLP and ML techniques. Our results show that the financial sector has shown momentum during the last few years. Finally, our next step is to enrich our analysis by broadening the data sources.","url":"https://doi.org/10.2139/ssrn.4339459","authors":["Danilo Antonino Giannone","Carsten Maple","Michela Iezzi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-01-27T17:15:03Z","doi":"10.2139/ssrn.4339459","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1145/2381913.2381924","name":"Practical applications of homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/2381913.2381924","authors":["Kristin E. Lauter"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-10-19T13:41:01Z","doi":"10.1145/2381913.2381924","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.11648/j.iotcc.20160402.12","name":"Using Fully Homomorphic Encryption to Secure Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.11648/j.iotcc.20160402.12","authors":["Ihsan Jabbar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-06-22T20:55:35Z","doi":"10.11648/j.iotcc.20160402.12","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/msp.2017.3151338","name":"Postquantum Opportunities: Lattices, Homomorphic Encryption, and Supersingular Isogeny Graphs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/msp.2017.3151338","authors":["Kristin Lauter"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-08-17T18:10:45Z","doi":"10.1109/msp.2017.3151338","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.4236/jcc.2019.72002","name":"An Efficient Identity-Based Homomorphic Broadcast Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.4236/jcc.2019.72002","authors":["Mei Cai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-02-02T22:32:12Z","doi":"10.4236/jcc.2019.72002","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.5120/ijca2019918390","name":"Privacy Preserving Model using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5120/ijca2019918390","authors":["Asha Kiran","Manimala Puri","S. Srinivasa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-01-17T06:22:06Z","doi":"10.5120/ijca2019918390","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.2139/ssrn.6621118","name":"Eliminating Mixnet Overhead in Blockchain Voting: A Scalable Privacy-Preserving Protocol Using Zero-Knowledge Proofs, Homomorphic Encryption, and Selective Metadata Mixing","source":"crossref","abstract":"Blockchain-based voting systems provide transparency and auditability but introduce significant privacy risks due to publicly observable metadata. Existing approaches rely on mixnets or heavy cryptographic primitives to achieve anonymity, resulting in high computational overhead and limited scalability.&amp;nbsp;&lt;span&gt;In this paper, we propose a novel privacy-preserving voting protocol that eliminates the need for full ciphertext mixnets by introducing a selective metadata mixing mechanism. Our protocol combines zero-knowledge proofs for vote validity, homomorphic encryption for confidential aggregation, and randomized metadata transformations to achieve unlinkability. We formalize security properties including ballot secrecy, unlinkability, and end-to-end verifiability, and prove security under standard cryptographic assumptions. We further provide a gas-aware smart contract model and evaluate scalability for elections with one million voters under Layer-2 rollup deployment. Our results show that the proposed protocol reduces anonymization complexity from O(n log n) to O(n) while maintaining strong privacy guarantees.&lt;/span&gt;","url":"https://doi.org/10.2139/ssrn.6621118","authors":["Ravinjeet Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-29T13:23:00Z","doi":"10.2139/ssrn.6621118","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/s00145-016-9231-y","name":"Reconciling Non-malleability with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00145-016-9231-y","authors":["Manoj Prabhakaran","Mike Rosulek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-04-25T16:05:52Z","doi":"10.1007/s00145-016-9231-y","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1145/2659651.2659712","name":"Cryptanalysis of Polynomial based Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/2659651.2659712","authors":["Alina Trepacheva"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-12-16T13:41:52Z","doi":"10.1145/2659651.2659712","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.36227/techrxiv.176115157.76814460/v1","name":"Privacy-Preserving Machine Learning for Heart Disease Detection Using Fully Homomorphic Encryption","source":"crossref","abstract":"With the growing adoption of Artificial Intelligence (AI) in sensitive sectors such as healthcare and finance, protecting user privacy during data processing has become paramount. One promising approach is Fully Homomorphic Encryption (FHE), which offers a viable solution by allowing computations to be performed directly on encrypted data, thus safeguarding sensitive information. In this study, we investigate the practical application of the Cheon Kim Kim Song (CKKS) FHE scheme to perform inference with various machine learning models for heart disease detection. We evaluated five models: Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and a simple Neural Network, across multiple heart disease datasets. Our analysis compares their performance on both standard (plain-text) and encrypted data, using metrics including accuracy, precision, recall, and F1-score. Results demonstrate that encrypted models deliver predictive accuracy comparable to their standard counterparts, confirming the viability of privacypreserving inference with FHE despite the expected increase in computational time. Furthermore, our findings highlight up to 100% consistency between the predictions made on encrypted and plain-text inputs.","url":"https://doi.org/10.36227/techrxiv.176115157.76814460/v1","authors":["Mayssa Dziri","Haifa Touati","Hakim Ghazzai","Mohamed Hadded","Omar Kassem Khalil","Anis Laouiti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-22T16:46:23Z","doi":"10.36227/techrxiv.176115157.76814460/v1","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/978-3-319-12229-8_1","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-12229-8_1","authors":["Xun Yi","Russell Paulet","Elisa Bertino"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-11-14T14:46:09Z","doi":"10.1007/978-3-319-12229-8_1","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1016/j.ipl.2026.106645","name":"Cocks homomorphic encryption without ciphertext expansion or re-randomization","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ipl.2026.106645","authors":["Ferucio-Laurenţiu Ţiplea"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-09T23:04:47Z","doi":"10.1016/j.ipl.2026.106645","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/smartcomp.2019.00021","name":"Homomorphic Encryption for Privacy-Preserving Genome Sequences Search","source":"crossref","abstract":"","url":"https://doi.org/10.1109/smartcomp.2019.00021","authors":["Yuki Yamada","Kurt Rohloff","Masato Oguchi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-08-02T00:04:08Z","doi":"10.1109/smartcomp.2019.00021","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.22266/ijies2023.0228.25","name":"Securing Data Transmission and Privacy Preserving Using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.22266/ijies2023.0228.25","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-12-29T20:00:39Z","doi":"10.22266/ijies2023.0228.25","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/indicon59947.2023.10440814","name":"Privacy-preserving Multi-Instance Iris Authentication using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/indicon59947.2023.10440814","authors":["Gopi Suresh Arepalli","Boobalan Pakkiri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-27T13:56:24Z","doi":"10.1109/indicon59947.2023.10440814","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/978-3-030-77287-1_7","name":"Private Outsourced Translation for Medical Data","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77287-1_7","authors":["Travis Morrison","Sarah Scheffler","Bijeeta Pal","Alexander Viand"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-01-03T23:02:43Z","doi":"10.1007/978-3-030-77287-1_7","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/fskd.2012.6234023","name":"A algorithm of fully homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fskd.2012.6234023","authors":["Guangli Xiang","Benzhi Yu","Ping Zhu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-07-19T23:43:25Z","doi":"10.1109/fskd.2012.6234023","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.23919/acc50511.2021.9483184","name":"Encrypted Value Iteration and Temporal Difference Learning over Leveled Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.23919/acc50511.2021.9483184","authors":["Jihoon Suh","Takashi Tanaka"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-28T20:29:16Z","doi":"10.23919/acc50511.2021.9483184","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/btas.2018.8698601","name":"Secure Face Matching Using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/btas.2018.8698601","authors":["Vishnu Naresh Boddeti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-04-25T23:48:37Z","doi":"10.1109/btas.2018.8698601","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/isocc66390.2025.11329965","name":"Configurable Butterfly Unit Architecture for CKKS-Based Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isocc66390.2025.11329965","authors":["Quang Dang Truong","Hanho Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-14T20:38:59Z","doi":"10.1109/isocc66390.2025.11329965","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/wcnc.2012.6214307","name":"Performance evaluation of Smart Grid data aggregation via homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wcnc.2012.6214307","authors":["Nico Saputro","Kemal Akkaya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-06-20T13:34:54Z","doi":"10.1109/wcnc.2012.6214307","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/cecnet.2012.6202046","name":"Verifiable Fully Homomorphic Encryption scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cecnet.2012.6202046","authors":["Fangyuan Jin","Yanqin Zhu","Xizhao Luo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-05-25T14:58:03Z","doi":"10.1109/cecnet.2012.6202046","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.57238/csj.2025.1018","name":"Developing A Lightweight Homomorphic Encryption Technique for Secure Data Transmission","source":"crossref","abstract":"This paper proposes a lightweight homomorphic encryption (HE) technique designed for secure data transmission in resource-constrained environments such as IoT, sensor networks, and edge computing platforms. The method integrates a modified AES structure—in which the number of rounds and key-expansion operations are optimized—with RSA’s multiplicative homomorphism to support limited encrypted computation while minimizing computational overhead. The modified AES reduces complexity in the Mix Columns and Key Expansion stages without compromising cryptographic soundness. Experiments conducted on a 32-bit ARM-based emulator (100 MHz) and Arduino-class microcontroller demonstrate reduced encryption time, lower energy consumption, and improved throughput compared with standard AES, RSA, and AES+RSA hybrids. Preliminary comparisons with lightweight HE baselines (LWE-based and CKKS variants) indicate promising efficiency advantages for constrained hardware. Security analysis confirms that modifications do not weaken resistance against known cryptanalytic attacks. The resulting framework is suitable for healthcare monitoring, smart grids, industrial IoT, and privacy-preserving cloud analytics.","url":"https://doi.org/10.57238/csj.2025.1018","authors":["Sumaira Bashir","Amit Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-14T10:13:27Z","doi":"10.57238/csj.2025.1018","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/dac18074.2021.9586285","name":"Invited: Accelerating Fully Homomorphic Encryption with Processing in Memory","source":"crossref","abstract":"","url":"https://doi.org/10.1109/dac18074.2021.9586285","authors":["Saransh Gupta","Tajana Simunic Rosing"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-08T18:30:34Z","doi":"10.1109/dac18074.2021.9586285","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.12732/ijam.v38i10s.1024","name":"HOMOMORPHIC ENCRYPTION AND ALGEBRAIC GEOMETRY FOR PRIVACY-PRESERVING MACHINE LEARNING","source":"crossref","abstract":"Homomorphic encryption combined with algebraic geometry is emerging as one of the most mathematically powerful strategies for enabling privacy-preserving machine learning in environments where data confidentiality cannot be compromised. Traditional cryptographic methods protect data only at rest or in transit, but expose it during computation, creating substantial vulnerability in modern AI pipelines. Homomorphic encryption enables computation directly on encrypted inputs, while algebraic geometry provides the structural foundation for constructing efficient polynomial representations, ciphertext rings, and error-tolerant operations required by encrypted learning algorithms. This paper examines the integration of lattice-based homomorphic schemes with algebraic-geometric tools such as ideal lattices, algebraic curves, and Gröbner-basis methods to support encrypted inference and training. The analysis focuses on three core challenges: minimizing noise growth during encrypted computation, reducing model complexity for polynomial-friendly transformations, and preserving accuracy while enforcing strict privacy guarantees. The study argues that algebraic-geometric optimisation significantly improves the feasibility of encrypted neural networks, encrypted linear models, and encrypted gradient updates, especially for cloud-hosted and multi-party learning environments. By demonstrating how these mathematical frameworks interact, the paper positions homomorphic encryption and algebraic geometry as a critical foundation for future secure AI systems capable of operating without exposing sensitive information at any stage of computation.","url":"https://doi.org/10.12732/ijam.v38i10s.1024","authors":["Mital Patel,"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-21T08:06:24Z","doi":"10.12732/ijam.v38i10s.1024","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.57001/huih5804.2025.419","name":"Application of homomorphic encryption in data security on cloud computing platforms","source":"crossref","abstract":"","url":"https://doi.org/10.57001/huih5804.2025.419","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-21T09:05:36Z","doi":"10.57001/huih5804.2025.419","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/wcnc51071.2022.9771895","name":"Securing 5G Slices using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wcnc51071.2022.9771895","authors":["Erik Kline","Srivatsan Ravi","David Cousins","Sara Rv"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-05-16T20:45:39Z","doi":"10.1109/wcnc51071.2022.9771895","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.2139/ssrn.5106083","name":"Heargmax: Secure Homomorphic Encryption-Based Protocols for Argmax Function","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5106083","authors":["Duy Tung Khanh Nguyen","Dung  Hoang Duong","Willy Susilo","Yang-Wai Chow","The Anh Ta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T16:37:53Z","doi":"10.2139/ssrn.5106083","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3837/tiis.2019.11.019","name":"Secure Outsourced Computation of Multiple Matrix Multiplication Based on Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.3837/tiis.2019.11.019","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-12-04T11:07:38Z","doi":"10.3837/tiis.2019.11.019","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/compcomm.2016.7924692","name":"Homomorphic encryption the “Holy Grail” of cryptography","source":"crossref","abstract":"","url":"https://doi.org/10.1109/compcomm.2016.7924692","authors":["Dalia Tourky","Mohamed ElKawkagy","Arabi Keshk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-05-12T21:38:20Z","doi":"10.1109/compcomm.2016.7924692","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/csnet50428.2020.9265535","name":"Cloud Assisted Privacy Preserving Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/csnet50428.2020.9265535","authors":["Khalil Hariss","Maroun Chamoun","Abed Ellatif Samhat"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-04-14T04:26:32Z","doi":"10.1109/csnet50428.2020.9265535","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1201/9781003080787-16","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003080787-16","authors":["Kevin E. Foltz","William R. Simpson"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-08-12T19:18:38Z","doi":"10.1201/9781003080787-16","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1016/j.tcs.2023.114067","name":"Practical multi-party quantum homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.tcs.2023.114067","authors":["Lv Chen","Lingli Chen","Qin Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-16T16:49:56Z","doi":"10.1016/j.tcs.2023.114067","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.29007/8cqg","name":"Data Security in the Cloud Using pTree-based Homomorphic Intrinsic Data Encryption System (pHIDES)","source":"crossref","abstract":"Cloud usage for storing data and performing operations has gained immense popularity in recent times. However, there are concerns that uploading data to the cloud increases the chances of unauthorized parties accessing it. One way to secure data from unauthorized access is to encrypt it. Even if the data is hacked, the hackers will not be able to retrieve any information from the data without knowing the 'Key' to decrypt it. But when data needs to be used for services such as data analytics, it must be in its original, non-encrypted form. Decrypting the data makes it vulnerable again, which is why Homomorphic Encryption could be the solution to this problem. In this encryption method, the analytical engine can use the encrypted data to perform analysis, where the analysis result will also be in decrypted form. Only authorized users can access the results using the 'Key.' This research proposal proposes a method called pHIDES to enhance data security in the cloud. The pHIDES (pTree-based Homomorphic Intrinsic Data Encryption System) represents data in pTree (Predicate tree) format, a data mining-ready data structure proven to manipulate a large volume of data effectively. The concept of Homomorphic Encryption (HME) along with pHIDES is discussed in our research, along with the algorithmic execution to analyze the effectiveness of the algorithm used to encrypt data in the cloud.","url":"https://doi.org/10.29007/8cqg","authors":["Mohammad Hossain","Vinayak Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-21T19:45:25Z","doi":"10.29007/8cqg","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/978-1-4842-9450-5_12","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4842-9450-5_12","authors":["Marius Iulian Mihailescu","Stefania Loredana Nita"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-06-21T08:02:48Z","doi":"10.1007/978-1-4842-9450-5_12","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/jns2.2012.6249248","name":"Homomorphic encryption method applied to Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/jns2.2012.6249248","authors":["Maha Tebaa","Said El Hajji","Abdellatif El Ghazi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-08-01T11:06:17Z","doi":"10.1109/jns2.2012.6249248","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/icmlant59547.2023.10372974","name":"Homomorphic Encryption Based on Post-Quantum Cryptography","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmlant59547.2023.10372974","authors":["Abel C. H. Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-01T14:31:20Z","doi":"10.1109/icmlant59547.2023.10372974","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1088/1742-6596/1237/4/042005","name":"Encryption Cipher Text Retrieval Scheme Based on Fully Homomorphic Encryption Enterprise Cloud Storage","source":"crossref","abstract":"Abstract With the development of computer networks, cloud storage has become the mainstream way for people to store information. However, some essential information is disclosed during the storage process and the security of information has become the most concerned issue at present. In order to use the powerful computing of cloud storage to realize resource sharing between relevant departments without revealing information of other unrelated departments, this paper proposes an encryption cipher text retrieval scheme based on fully homomorphic encryption enterprise cloud storage (ECRS), and designs an Enterprise-side security model. The model uses full homomorphic encryption, decryption and spatial vector cipher text retrieval to achieve the security of information and resource sharing among enterprise departments. ECRS determines the employee who can encrypt or decrypt the files, and who has access to the files by modifying the number of attributes intersected between attribute sets. The solution can provide the security for cloud storage, resources for different departments, and improve the accuracy for cipher text retrieval.","url":"https://doi.org/10.1088/1742-6596/1237/4/042005","authors":["Lijuan Wang","Lina Ge","Bo Geng","Qiuyue Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-07-12T04:14:32Z","doi":"10.1088/1742-6596/1237/4/042005","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1016/j.ins.2017.09.012","name":"A new scale-invariant homomorphic encryption scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ins.2017.09.012","authors":["Jinsu Kim","Sungwook Kim","Jae Hong Seo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-09-05T14:32:16Z","doi":"10.1016/j.ins.2017.09.012","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/aina.2018.00159","name":"Comparison of Selected Homomorphic Encryption Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aina.2018.00159","authors":["Marek R. Ogiela","Marcin Oczko"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-08-13T22:03:06Z","doi":"10.1109/aina.2018.00159","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/isit.2012.6283832","name":"Cryptanalysis of a homomorphic encryption scheme from ISIT 2008","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isit.2012.6283832","authors":["Jingguo Bi","Mingjie Liu","Xiaoyun Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-08-30T16:57:57Z","doi":"10.1109/isit.2012.6283832","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1007/978-981-13-6393-1","name":"Fully Homomorphic Encryption in Real World Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-13-6393-1","authors":["Ayantika Chatterjee","Khin Mi Mi Aung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-05-14T04:12:08Z","doi":"10.1007/978-981-13-6393-1","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1109/isic.2012.6449723","name":"Efficient public key Homomorphic Encryption over integer plaintexts","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isic.2012.6449723","authors":["Y. Govinda Ramaiah","G Vijaya Kumari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-02-13T17:11:31Z","doi":"10.1109/isic.2012.6449723","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.5120/17706-8709","name":"Fuzzy Rule based Enhanced Homomorphic Encryption in Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.5120/17706-8709","authors":["Randeep Kaur","Supriya Kinger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-09-19T05:57:24Z","doi":"10.5120/17706-8709","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1201/9781003538950-21","name":"Enhancing Healthcare Data Security Using Quantum Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003538950-21","authors":["Sivaranjani Reddi","Jami Venkata Suman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-28T17:10:56Z","doi":"10.1201/9781003538950-21","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/asens69964.2026.11605311","name":"Private RAG with Efficient Communication via Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/asens69964.2026.11605311","authors":["Junkai Peng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-21T19:12:20Z","doi":"10.1109/asens69964.2026.11605311","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:45.051Z"},{"id":"doi:10.1109/eit57321.2023.10187220","name":"Evaluation of Homomorphic Encryption for Privacy in Principal Component Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/eit57321.2023.10187220","authors":["David Arnold","Jafar Saniie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-07-25T17:22:40Z","doi":"10.1109/eit57321.2023.10187220","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/isocc66390.2025.11329642","name":"Efficient Privacy-Preserving Federated Learning with Sensitivity-Based Selective Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isocc66390.2025.11329642","authors":["Yewon Jeong","Woo-Seok Choi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-14T20:38:59Z","doi":"10.1109/isocc66390.2025.11329642","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/978-1-4842-6586-4_12","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-1-4842-6586-4_12","authors":["Marius Iulian Mihailescu","Stefania Loredana Nita"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-01-15T18:57:30Z","doi":"10.1007/978-1-4842-6586-4_12","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.2139/ssrn.5127448","name":"Reforming the Bank Secrecy Act to Apply to Blockchain Nodes: Adoption of Fully Homomorphic Encryption Association Sets as a Reasonable Compliance Measure","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5127448","authors":["Jason Berkun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-04T14:25:18Z","doi":"10.2139/ssrn.5127448","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.5120/ijca2016908996","name":"Performance Evaluation of Homomorphic Encryption based Data Access Control","source":"crossref","abstract":"","url":"https://doi.org/10.5120/ijca2016908996","authors":["Amit Kanungo","Sanjay Thakur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-03-17T14:07:08Z","doi":"10.5120/ijca2016908996","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1109/iceiec.2013.6835448","name":"A type of sorting based on homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iceiec.2013.6835448","authors":["Xu Chen","Qiming Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-07-28T19:22:17Z","doi":"10.1109/iceiec.2013.6835448","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1109/icssas66150.2025.11080912","name":"Privacy-Preserving Vector Similarity Search using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icssas66150.2025.11080912","authors":["Srushti Mathur","Aayush Chhabra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-21T18:02:32Z","doi":"10.1109/icssas66150.2025.11080912","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/spw72489.2026.00018","name":"Investigating TenSEAL's Homomorphic Encryption Through Predicting Encrypted RNA Sequencing Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/spw72489.2026.00018","authors":["Logan Choi","Wooyoung Kim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-29T19:38:32Z","doi":"10.1109/spw72489.2026.00018","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:45.051Z"},{"id":"doi:10.1109/icit.2015.39","name":"Homomorphic Encryption for Data Security in Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icit.2015.39","authors":["Kamal Kumar Chauhan","Amit K.S. Sanger","Ajai Verma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-03-24T16:22:07Z","doi":"10.1109/icit.2015.39","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1109/imcom56909.2023.10035630","name":"A Study on Partially Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/imcom56909.2023.10035630","authors":["Jihyeon Ryu","Keunok Kim","Dongho Won"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-02-08T18:57:50Z","doi":"10.1109/imcom56909.2023.10035630","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.22214/ijraset.2019.4216","name":"Homomorphic Encryption using E-Voting System","source":"crossref","abstract":"","url":"https://doi.org/10.22214/ijraset.2019.4216","authors":["Karan Kadlag"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-09-06T07:51:47Z","doi":"10.22214/ijraset.2019.4216","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1109/nas.2015.7255226","name":"HEDup: Secure Deduplication with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/nas.2015.7255226","authors":["Rodel Miguel","Khin Mi Mi Aung","Mediana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-09-14T17:24:54Z","doi":"10.1109/nas.2015.7255226","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1007/978-3-030-77764-7_10","name":"Popular Homomorphic Encryption Schemes","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-030-77764-7_10","authors":["Pawel Sniatala","S.S. Iyengar","Sanjeev Kaushik Ramani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-11-30T20:34:17Z","doi":"10.1007/978-3-030-77764-7_10","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.23919/acc63710.2025.11107587","name":"Secure Cooperative Sensor Coverage Control Using Homomorphic Proxy Re-encryption","source":"crossref","abstract":"","url":"https://doi.org/10.23919/acc63710.2025.11107587","authors":["Hiroaki Kawase","Kaoru Teranishi","Kiminao Kogiso","Takashi Tanaka"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-21T18:17:51Z","doi":"10.23919/acc63710.2025.11107587","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.12732/ijam.v38i2s.731","name":"ENHANCING DATA PRIVACY IN MULTI-CLOUD ENVIRONMENTS USING HOMOMORPHIC ENCRYPTION TECHNIQUES","source":"crossref","abstract":"In the era of digital transformation, multi-cloud environments offer enterprises flexibility and resilience by distributing workloads across multiple cloud service providers. However, this approach raises significant concerns about data privacy and security, particularly when sensitive information is processed across diverse and potentially insecure platforms. Homomorphic Encryption (HE) emerges as a powerful cryptographic technique that allows computation on encrypted data, providing a means to enhance data privacy in multi-cloud settings. This paper explores the potential of HE in addressing privacy challenges inherent in multi-cloud architectures. We present a detailed review of existing HE schemes, evaluate their performance, and propose a novel framework that leverages HE for secure data processing in multi-cloud environments. Our experimental results demonstrate that our approach maintains robust data privacy while achieving practical performance levels, making it a viable solution for secure multi-cloud operations.","url":"https://doi.org/10.12732/ijam.v38i2s.731","authors":["Venkatesh H"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-03T09:09:48Z","doi":"10.12732/ijam.v38i2s.731","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3837/tiis.2019.05.020","name":"A Speech Homomorphic Encryption Scheme with Less Data Expansion in Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.3837/tiis.2019.05.020","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-06-03T05:16:10Z","doi":"10.3837/tiis.2019.05.020","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1109/cdc.2015.7403296","name":"Cyber-security enhancement of networked control systems using homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cdc.2015.7403296","authors":["Kiminao Kogiso","Takahiro Fujita"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-02-29T16:32:44Z","doi":"10.1109/cdc.2015.7403296","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1109/icsess.2013.6615338","name":"The data protection of mapreduce using homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsess.2013.6615338","authors":["Xu Chen","Qiming Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-10-02T18:30:53Z","doi":"10.1109/icsess.2013.6615338","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1145/3139923.3139933","name":"Combining Homomorphic Encryption with Trusted Execution Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3139923.3139933","authors":["Nir Drucker","Shay Gueron"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-10-31T14:58:58Z","doi":"10.1145/3139923.3139933","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.5753/etc.2016.9829","name":"Achieving CCA1-security in homomorphic encryption","source":"crossref","abstract":"Este artigo propõe a combinação de encriptação homomórfica e computação verificável para evitar ataques de recuperação de chaves e obter segurança CCA1 em construções de esquemas parcialmente homomórficos descritos na literatura. Além disso, são propostos parâmetros concretos, baseados na análise do melhor ataque, concluindo que a família AGCD [van Dijk et al. 2010] de esquemas SHE pode ser consideradas a melhor escolha em determinadas circunstâncias.","url":"https://doi.org/10.5753/etc.2016.9829","authors":["Eduardo Morais","Diego F. Aranha","Ricardo Dahab"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-02-19T15:36:14Z","doi":"10.5753/etc.2016.9829","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1109/sin56466.2022.9970532","name":"Keynote Speaker 4: Introducing Homomorphic Encryption in Blockchain-enabled Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sin56466.2022.9970532","authors":["Mohamed Hamdi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-12-16T13:49:51Z","doi":"10.1109/sin56466.2022.9970532","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.4018/978-1-7998-1763-5.ch018","name":"A Secure Cloud Storage using ECC-Based Homomorphic Encryption","source":"crossref","abstract":"This paper presents a new homomorphic public-key encryption scheme based on the elliptic curve cryptography (HPKE-ECC). This HPKE-ECC scheme allows public computation on encrypted data stored on a cloud in such a manner that the output of this computation gives a valid encryption of some operations (addition/multiplication) on original data. The cloud system (server) has only access to the encrypted files of an authenticated end-user stored in it and can only do computation on these stored files according to the request of an end-user (client). The implementation of proposed HPKE-ECC protocol uses the properties of elliptic curve operations as well as bilinear pairing property on groups and the implementation is done by Weil and Tate pairing. The security of proposed encryption technique depends on the hardness of ECDLP and BDHP.","url":"https://doi.org/10.4018/978-1-7998-1763-5.ch018","authors":["Daya Sagar Gupta","G. P. Biswas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-12-12T14:07:01Z","doi":"10.4018/978-1-7998-1763-5.ch018","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1016/j.future.2013.10.024","name":"Efficient fully homomorphic encryption from RLWE with an extension to a threshold encryption scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.future.2013.10.024","authors":["Xiaojun Zhang","Chunxiang Xu","Chunhua Jin","Run Xie","Jining Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2013-11-11T02:01:07Z","doi":"10.1016/j.future.2013.10.024","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.3390/computers14100440","name":"Implementation of Ring Learning-with-Errors Encryption and Brakerski–Fan–Vercauteren Fully Homomorphic Encryption Using ChatGPT","source":"crossref","abstract":"This paper investigates whether ChatGPT, a large language model, can assist in the implementation of lattice-based cryptography and fully homomorphic encryption algorithms, specifically the Ring Learning-with-Errors encryption scheme and the Brakerski–Fan–Vercauteren FHE scheme. To the best of our knowledge, this study represents the first systematic exploration of ChatGPT’s ability to implement these cryptographic algorithms. Fully homomorphic encryption, despite its theoretical and practical significance, poses significant challenges due to its computational complexity and efficiency requirements. This study evaluates ChatGPT’s capability as a development tool from both algorithmic and implementation perspectives. At the algorithmic level, ChatGPT demonstrates a solid understanding of the Rring Learning-with-Errors lattice encryption scheme but faces limitations in comprehending the intricate structure of the Brakerski–Fan–Vercauteren FHE scheme. At the code level, ChatGPT can generate functional C++ implementations of both encryption schemes, significantly reducing manual coding effort. However, debugging and corrections remain necessary, particularly for the more complex Brakerski–Fan–Vercauteren scheme, where additional effort is required to ensure correctness. The findings highlight ChatGPT’s potential and limitations in supporting cryptographic algorithm development, offering insights into its application for advancing implementations of complex cryptographic systems.","url":"https://doi.org/10.3390/computers14100440","authors":["Zhigang Chen","Xinxia Song","Liqun Chen","Hai Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-17T07:33:50Z","doi":"10.3390/computers14100440","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.3837/tiis.2019.05.022","name":"A General Design Method of Constructing Fully Homomorphic Encryption with Ciphertext Matrix","source":"crossref","abstract":"","url":"https://doi.org/10.3837/tiis.2019.05.022","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-06-03T01:16:10Z","doi":"10.3837/tiis.2019.05.022","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.3390/inventions8040102","name":"A Comparative Assessment of Homomorphic Encryption Algorithms Applied to Biometric Information","source":"crossref","abstract":"This paper provides preliminary research regarding the implementation and evaluation of a hybrid mechanism of authentication based on fingerprint recognition interconnected with RFID technology, using Arduino modules, that can be deployed in different scenarios, including secret classified networks. To improve security, increase efficiency, and enhance convenience in the process of authentication, we perform a comparative assessment between two homomorphic encryption algorithms, the Paillier partial homomorphic algorithm and the Brakerski–Gentry–Vaikuntanathan fully homomorphic encryption scheme, applied to biometric templates extracted from the device mentioned above, by analyzing factors such as a histogram analysis, mean squared error (MSE), peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), the number of pixel change rate (NPCR), the unified average changing intensity (UACI), the correlation coefficient, and average encryption time and dimension. From security and privacy perspectives, the present findings suggest that the designed mechanism represents a reliable and low-cost authentication alternative that can facilitate secure access to computer systems and networks and minimize the risk of unauthorized access.","url":"https://doi.org/10.3390/inventions8040102","authors":["Georgiana Crihan","Marian Crăciun","Luminița Dumitriu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-11T10:20:16Z","doi":"10.3390/inventions8040102","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.1109/arith.2019.00046","name":"Computer Arithmetic Research to Accelerate Privacy-Protecting Encrypted Computing Such as Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/arith.2019.00046","authors":["Kurt Rohloff"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-10-21T23:56:55Z","doi":"10.1109/arith.2019.00046","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.4018/978-1-7998-7705-9.ch014","name":"Fully Homomorphic Encryption Without Noise","source":"crossref","abstract":"In this paper, the authors present a novel fully homomorphic encryption scheme operating between ZN and capable of arbitrarily performing additions and multiplications. The new scheme is compact and each operation (addition or multiplication) performed on any two ciphertexts produces a fresh ciphertext without any associated noise. Thus, the scheme does not need any bootstrapping procedure or noise reduction technique to refresh ciphertexts. In the absence, to the best of the knowledge of the authors, of any existing fully, partially or leveled homomorphic encryption scheme using ZN as the set of plaintexts, the new cryptosystem has been implemented and has had its performance compared to the identity encoding.","url":"https://doi.org/10.4018/978-1-7998-7705-9.ch014","authors":["Yacine Ichibane","Youssef Gahi","Mouhcine Guennoun","Zouhair Guennoun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-01-11T12:34:42Z","doi":"10.4018/978-1-7998-7705-9.ch014","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.4108/eai.18-6-2016.2264201","name":"A Review of Homomorphic Encryption and its Applications","source":"crossref","abstract":"","url":"https://doi.org/10.4108/eai.18-6-2016.2264201","authors":["Lifang Zhang","Zheng Yan","Raimo Kantola"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2016-12-27T10:03:45Z","doi":"10.4108/eai.18-6-2016.2264201","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1109/iceca.2018.8474729","name":"Symmetric Fully Homomorphic Encryption Scheme with Polynomials Operations","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iceca.2018.8474729","authors":["Kavita Aganya","Iti Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-10-23T00:51:33Z","doi":"10.1109/iceca.2018.8474729","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.26524/royal.239.23","name":"CYBERSECURITY IN THE CLOUD: DATA CONFIDENTIALITY AND INTEGRITY USING HOMOMORPHIC ENCRYPTION","source":"crossref","abstract":"As cloud computing becomes more prevalent, guaranteeing the security of confidential data has become a top priority. Although conventional encryption methods protect data in transit and at rest, they fail at processing and thus leave data open to possible breaches. By allowing calculations directly on encrypted data without needing decryption, therefore maintaining data secrecy throughout the process, homomorphic encryption (HE) evolves as a transformative solution. The use of HE in cloud settings to improve data confidentiality and integrity is discussed in this article. It covers the several kinds of homomorphic encryption systems, their advantages and disadvantages, and how they can be combined with verifiable computation approaches to guarantee data correctness. Actual applications in healthcare, finance, and machine learning show how HE could enable privacy-preserving cloud computing. HE offers a future-proof method for safe cloud data processing, guaranteeing adherence to privacy rules and building trust in cloud services even in light of current issues including computing overhead.","url":"https://doi.org/10.26524/royal.239.23","authors":["Ayyapparaj T"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-07T08:28:47Z","doi":"10.26524/royal.239.23","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/hpec67600.2025.11196202","name":"Secure Virtual Network Embedding Through Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/hpec67600.2025.11196202","authors":["David Bruce Cousins","Carlo Pascoe","Erik Kline"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-16T17:35:37Z","doi":"10.1109/hpec67600.2025.11196202","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.5120/17087-7544","name":"Verifiable Delegation of Computation through Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.5120/17087-7544","authors":["Alpana Vijay","Vijay Kumar Sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-07-18T04:33:06Z","doi":"10.5120/17087-7544","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.14419/ijet.v7i4.17.21806","name":"A Survey on Homomorphic Encryption in Cloud Security","source":"crossref","abstract":"An outsourcing of data is increasing the data storage in Cloud. These raise numerous new challenges of privacy concern for persons and business. Sending data in the encrypted form to the cloud is a common approach to handle the privacy concern. Homomorphic Encryption technique is used to carry out significant computations on the data in the cloud. Random computations over ciphertext are allowed in Fully Homomorphic Encryption. Many solutions using fully homomorphic encryption have been proposed and also many researchers have tried to improve, proving efficiency is very hard. In this paper, Delegated Parallel Homomorphic Encryption is proposed. Also, an analysis has been made to exhibit various applications in the real world. The system must work efficiently without compromising the required cloud security services.","url":"https://doi.org/10.14419/ijet.v7i4.17.21806","authors":["Kavitha C.R","Bharati Harsoor"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-28T12:22:15Z","doi":"10.14419/ijet.v7i4.17.21806","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"doi:10.2139/ssrn.7138398","name":"Privacy-Preserving Clustering in Cloud and IoT Environments: A Comprehensive Review of Differential Privacy, Homomorphic Encryption, and Spectral Methods","source":"crossref","abstract":"Clustering is a fundamental technique in data science and machine learning, widely used for pattern recognition, information retrieval, and large-scale analytics. With the rapid growth of cloud computing, Internet of Things (IoT), and data-driven applications, protecting sensitive information during clustering has become a major research challenge. In recent years, several approaches have been developed to achieve privacy-preserving clustering using techniques such as differential privacy (DP), homomorphic encryption (HE), and trusted cloud/IoT frameworks. This paper presents a comprehensive review of thirty-five recent studies published between 2020 and 2025, analyzing their contributions, datasets, results, and limitations. The survey highlights that HEbased approaches provide strong cryptographic guarantees but are limited by high computational costs, while DP-based solutions offer lightweight privacy protection but face trade-offs between privacy and model accuracy. Cloud- and IoT-assisted frameworks demonstrate improved scalability and trust but are often validated only on small-scale prototypes or simulations. From this analysis, key research gaps are identified, including the need for scalable HE methods, adaptive DP mechanisms, hybrid DP+HE integration, and standardized benchmarks. The paper concludes by suggesting future research directions towards integrated hybrid frameworks that balance privacy, scalability, and accuracy, making them suitable for applications in healthcare, finance, and smart IoT ecosystems.","url":"https://doi.org/10.2139/ssrn.7138398","authors":["Gurunath Biranagaddi","Dr. Sunny Mohite"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-31T12:05:59Z","doi":"10.2139/ssrn.7138398","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1109/cloud.2015.78","name":"Utilizing Homomorphic Encryption to Implement Secure and Private Medical Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cloud.2015.78","authors":["Ovunc Kocabas","Tolga Soyata"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-20T17:46:43Z","doi":"10.1109/cloud.2015.78","addedAt":"2026-08-31T06:41:43.495Z","updatedAt":"2026-08-31T06:41:43.495Z"},{"id":"pmid:26733391","name":"FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption.","source":"pubmed","abstract":"The increasing availability of genome data motivates massive research studies in personalized treatment and precision medicine. Public cloud services provide a flexible way to mitigate the storage and computation burden in conducting genome-wide association studies (GWAS). However, data privacy has been widely concerned when sharing the sensitive information in a cloud environment.","url":"https://pubmed.ncbi.nlm.nih.gov/26733391/","authors":["Zhang Y","Dai W","Jiang X","Xiong H","Wang S"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.1186/1472-6947-15-S5-S5","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:26733152","name":"Private genome analysis through homomorphic encryption.","source":"pubmed","abstract":"The rapid development of genome sequencing technology allows researchers to access large genome datasets. However, outsourcing the data processing o the cloud poses high risks for personal privacy. The aim of this paper is to give a practical solution for this problem using homomorphic encryption. In our approach, all the computations can be performed in an untrusted cloud without requiring the decryption key or any interaction with the data owner, which preserves the privacy of genome data.","url":"https://pubmed.ncbi.nlm.nih.gov/26733152/","authors":["Kim M","Lauter K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.1186/1472-6947-15-S5-S3","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:26732892","name":"Privacy-preserving genome-wide association studies on cloud environment using fully homomorphic encryption.","source":"pubmed","abstract":"Developed sequencing techniques are yielding large-scale genomic data at low cost. A genome-wide association study (GWAS) targeting genetic variations that are significantly associated with a particular disease offers great potential for medical improvement. However, subjects who volunteer their genomic data expose themselves to the risk of privacy invasion; these privacy concerns prevent efficient genomic data sharing. Our goal is to presents a cryptographic solution to this problem.","url":"https://pubmed.ncbi.nlm.nih.gov/26732892/","authors":["Lu WJ","Yamada Y","Sakuma J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.1186/1472-6947-15-S5-S1","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:26678650","name":"Privacy-preserving search for chemical compound databases.","source":"pubmed","abstract":"Searching for similar compounds in a database is the most important process for in-silico drug screening. Since a query compound is an important starting point for the new drug, a query holder, who is afraid of the query being monitored by the database server, usually downloads all the records in the database and uses them in a closed network. However, a serious dilemma arises when the database holder also wants to output no information except for the search results, and such a dilemma prevents the use of many important data resources.","url":"https://pubmed.ncbi.nlm.nih.gov/26678650/","authors":["Shimizu K","Nuida K","Arai H","Mitsunari S","Attrapadung N","Hamada M","Tsuda K","Hirokawa T","Sakuma J","Hanaoka G","Asai K"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.1186/1471-2105-16-S18-S6","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:26490146","name":"Data Division Scheme Based on Homomorphic Encryption in WSNs for Health Care.","source":"pubmed","abstract":"The use of wireless sensor networks for wearable computing in health care is growing quickly. Numerous applications are already in use, such as blood pressure monitors and heart rate monitors. As such, it is very important for system designers to consider how to protect patient privacy, especially in wireless sensor networks. After studying and analyzing the features of wireless sensor networks in medical systems, a data division scheme was proposed in this paper, provided the advantages of homomorphic encryption. In the proposed scheme, even if a forwarding node is compromised, the attacker will not be able to eavesdrop on the data, resulting in much stronger privacy than existing schemes. Experimental results shows that the scheme provides a good trade off in resources consumed and system security, and is efficient for encrypting or decrypting sensitive medical data.","url":"https://pubmed.ncbi.nlm.nih.gov/26490146/","authors":["Wang X","Zhang Z"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2015 Dec","doi":"10.1007/s10916-015-0340-1","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:26151208","name":"Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks.","source":"pubmed","abstract":"With the rapid development of wireless communication technology, sensor technology, information acquisition and processing technology, sensor networks will finally have a deep influence on all aspects of people's lives. The battery resources of sensor nodes should be managed efficiently in order to prolong network lifetime in large-scale wireless sensor networks (LWSNs). Data aggregation represents an important method to remove redundancy as well as unnecessary data transmission and hence cut down the energy used in communication. As sensor nodes are deployed in hostile environments, the security of the sensitive information such as confidentiality and integrity should be considered. This paper proposes Fully homomorphic Encryption based Secure data Aggregation (FESA) in LWSNs which can protect end-to-end data confidentiality and support arbitrary aggregation operations over encrypted data. In addition, by utilizing message authentication codes (MACs), this scheme can also verify data integrity during data aggregation and forwarding processes so that false data can be detected as early as possible. Although the FHE increase the computation overhead due to its large public key size, simulation results show that it is implementable in LWSNs and performs well. Compared with other protocols, the transmitted data and network overhead are reduced in our scheme.","url":"https://pubmed.ncbi.nlm.nih.gov/26151208/","authors":["Li X","Chen D","Li C","Wang L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2015 Jul 3","doi":"10.3390/s150715952","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:26146645","name":"PRECISE:PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare.","source":"pubmed","abstract":"Quality improvement (QI) requires systematic and continuous efforts to enhance healthcare services. A healthcare provider might wish to compare local statistics with those from other institutions in order to identify problems and develop intervention to improve the quality of care. However, the sharing of institution information may be deterred by institutional privacy as publicizing such statistics could lead to embarrassment and even financial damage. In this article, we propose a PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare (PRECISE), which aims at enabling cross-institution comparison of healthcare statistics while protecting privacy. The proposed framework relies on a set of state-of-the-art cryptographic protocols including homomorphic encryption and Yao's garbled circuit schemes. By securely pooling data from different institutions, PRECISE can rank the encrypted statistics to facilitate QI among participating institutes. We conducted experiments using MIMIC II database and demonstrated the feasibility of the proposed PRECISE framework.","url":"https://pubmed.ncbi.nlm.nih.gov/26146645/","authors":["Chen F","Wang S","Mohammed N","Cheng S","Jiang X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2014 Oct","doi":"10.1109/ISB.2014.6990752","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:25811740","name":"Logsum using Garbled Circuits.","source":"pubmed","abstract":"Secure multiparty computation allows for a set of users to evaluate a particular function over their inputs without revealing the information they possess to each other. Theoretically, this can be achieved using fully homomorphic encryption systems, but so far they remain in the realm of computational impracticability. An alternative is to consider secure function evaluation using homomorphic public-key cryptosystems or Garbled Circuits, the latter being a popular trend in recent times due to important breakthroughs. We propose a technique for computing the logsum operation using Garbled Circuits. This technique relies on replacing the logsum operation with an equivalent piecewise linear approximation, taking advantage of recent advances in efficient methods for both designing and implementing Garbled Circuits. We elaborate on how all the required blocks should be assembled in order to obtain small errors regarding the original logsum operation and very fast execution times.","url":"https://pubmed.ncbi.nlm.nih.gov/25811740/","authors":["Portêlo J","Raj B","Trancoso I"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.1371/journal.pone.0122236","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:25510621","name":"Cloud-based privacy-preserving remote ECG monitoring and surveillance.","source":"pubmed","abstract":"The number of technical solutions for monitoring patients in their daily activities is expected to increase significantly in the near future. Blood pressure, heart rate, temperature, BMI, oxygen saturation, and electrolytes are few of the physiologic factors that will soon be available to patients and their physicians almost continuously. The availability and transfer of this information from the patient to the health provider raises privacy concerns. Moreover, current data encryption approaches expose patient data during processing, therefore restricting their utility in applications requiring data analysis.","url":"https://pubmed.ncbi.nlm.nih.gov/25510621/","authors":["Page A","Kocabas O","Soyata T","Aktas M","Couderc JP"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2015 Jul","doi":"10.1111/anec.12204","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:25093212","name":"A Regev-type fully homomorphic encryption scheme using modulus switching.","source":"pubmed","abstract":"A critical challenge in a fully homomorphic encryption (FHE) scheme is to manage noise. Modulus switching technique is currently the most efficient noise management technique. When using the modulus switching technique to design and implement a FHE scheme, how to choose concrete parameters is an important step, but to our best knowledge, this step has drawn very little attention to the existing FHE researches in the literature. The contributions of this paper are twofold. On one hand, we propose a function of the lower bound of dimension value in the switching techniques depending on the LWE specific security levels. On the other hand, as a case study, we modify the Brakerski FHE scheme (in Crypto 2012) by using the modulus switching technique. We recommend concrete parameter values of our proposed scheme and provide security analysis. Our result shows that the modified FHE scheme is more efficient than the original Brakerski scheme in the same security level.","url":"https://pubmed.ncbi.nlm.nih.gov/25093212/","authors":["Chen Z","Wang J","Chen L","Song X"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2014","doi":"10.1155/2014/983862","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:24835616","name":"Private predictive analysis on encrypted medical data.","source":"pubmed","abstract":"Increasingly, confidential medical records are being stored in data centers hosted by hospitals or large companies. As sophisticated algorithms for predictive analysis on medical data continue to be developed, it is likely that, in the future, more and more computation will be done on private patient data. While encryption provides a tool for assuring the privacy of medical information, it limits the functionality for operating on such data. Conventional encryption methods used today provide only very restricted possibilities or none at all to operate on encrypted data without decrypting it first. Homomorphic encryption provides a tool for handling such computations on encrypted data, without decrypting the data, and without even needing the decryption key. In this paper, we discuss possible application scenarios for homomorphic encryption in order to ensure privacy of sensitive medical data. We describe how to privately conduct predictive analysis tasks on encrypted data using homomorphic encryption. As a proof of concept, we present a working implementation of a prediction service running in the cloud (hosted on Microsoft's Windows Azure), which takes as input private encrypted health data, and returns the probability for suffering cardiovascular disease in encrypted form. Since the cloud service uses homomorphic encryption, it makes this prediction while handling only encrypted data, learning nothing about the submitted confidential medical data.","url":"https://pubmed.ncbi.nlm.nih.gov/24835616/","authors":["Bos JW","Lauter K","Naehrig M"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2014 Aug","doi":"10.1016/j.jbi.2014.04.003","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:24732099","name":"A secure-enhanced data aggregation based on ECC in wireless sensor networks.","source":"pubmed","abstract":"Data aggregation is an important technique for reducing the energy consumption of sensor nodes in wireless sensor networks (WSNs). However, compromised aggregators may forge false values as the aggregated results of their child nodes in order to conduct stealthy attacks or steal other nodes' privacy. This paper proposes a Secure-Enhanced Data Aggregation based on Elliptic Curve Cryptography (SEDA-ECC). The design of SEDA-ECC is based on the principles of privacy homomorphic encryption (PH) and divide-and-conquer. An aggregation tree disjoint method is first adopted to divide the tree into three subtrees of similar sizes, and a PH-based aggregation is performed in each subtree to generate an aggregated subtree result. Then the forged result can be identified by the base station (BS) by comparing the aggregated count value. Finally, the aggregated result can be calculated by the BS according to the remaining results that have not been forged. Extensive analysis and simulations show that SEDA-ECC can achieve the highest security level on the aggregated result with appropriate energy consumption compared with other asymmetric schemes.","url":"https://pubmed.ncbi.nlm.nih.gov/24732099/","authors":["Zhou Q","Yang G","He L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2014 Apr 11","doi":"10.3390/s140406701","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:24711729","name":"Minutiae matching with privacy protection based on the combination of garbled circuit and homomorphic encryption.","source":"pubmed","abstract":"Biometrics plays an important role in authentication applications since they are strongly linked to holders. With an increasing growth of e-commerce and e-government, one can expect that biometric-based authentication systems are possibly deployed over the open networks in the near future. However, due to its openness, the Internet poses a great challenge to the security and privacy of biometric authentication. Biometric data cannot be revoked, so it is of paramount importance that biometric data should be handled in a secure way. In this paper we present a scheme achieving privacy-preserving fingerprint authentication between two parties, in which fingerprint minutiae matching algorithm is completed in the encrypted domain. To improve the efficiency, we exploit homomorphic encryption as well as garbled circuits to design the protocol. Our goal is to provide protection for the security of template in storage and data privacy of two parties in transaction. The experimental results show that the proposed authentication protocol runs efficiently. Therefore, the protocol can run over open networks and help to alleviate the concerns on security and privacy of biometric applications over the open networks.","url":"https://pubmed.ncbi.nlm.nih.gov/24711729/","authors":["Li M","Feng Q","Zhao J","Yang M","Kang L","Wu L"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2014","doi":"10.1155/2014/525387","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:24560680","name":"FRR: fair remote retrieval of outsourced private medical records in electronic health networks.","source":"pubmed","abstract":"Cloud computing is emerging as the next-generation IT architecture. However, cloud computing also raises security and privacy concerns since the users have no physical control over the outsourced data. This paper focuses on fairly retrieving encrypted private medical records outsourced to remote untrusted cloud servers in the case of medical accidents and disputes. Our goal is to enable an independent committee to fairly recover the original private medical records so that medical investigation can be carried out in a convincing way. We achieve this goal with a fair remote retrieval (FRR) model in which either t investigation committee members cooperatively retrieve the original medical data or none of them can get any information on the medical records. We realize the first FRR scheme by exploiting fair multi-member key exchange and homomorphic privately verifiable tags. Based on the standard computational Diffie-Hellman (CDH) assumption, our scheme is provably secure in the random oracle model (ROM). A detailed performance analysis and experimental results show that our scheme is efficient in terms of communication and computation.","url":"https://pubmed.ncbi.nlm.nih.gov/24560680/","authors":["Wang H","Wu Q","Qin B","Domingo-Ferrer J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2014 Aug","doi":"10.1016/j.jbi.2014.02.008","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:23529086","name":"Discrete wavelet transform and data expansion reduction in homomorphic encrypted domain.","source":"pubmed","abstract":"Signal processing in the encrypted domain is a new technology with the goal of protecting valuable signals from insecure signal processing. In this paper, we propose a method for implementing discrete wavelet transform (DWT) and multiresolution analysis (MRA) in homomorphic encrypted domain. We first suggest a framework for performing DWT and inverse DWT (IDWT) in the encrypted domain, then conduct an analysis of data expansion and quantization errors under the framework. To solve the problem of data expansion, which may be very important in practical applications, we present a method for reducing data expansion in the case that both DWT and IDWT are performed. With the proposed method, multilevel DWT/IDWT can be performed with less data expansion in homomorphic encrypted domain. We propose a new signal processing procedure, where the multiplicative inverse method is employed as the last step to limit the data expansion. Taking a 2-D Haar wavelet transform as an example, we conduct a few experiments to demonstrate the advantages of our method in secure image processing. We also provide computational complexity analyses and comparisons. To the best of our knowledge, there has been no report on the implementation of DWT and MRA in the encrypted domain.","url":"https://pubmed.ncbi.nlm.nih.gov/23529086/","authors":["Zheng P","Huang J"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2013 Jun","doi":"10.1109/TIP.2013.2253474","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:22711774","name":"Image feature extraction in encrypted domain with privacy-preserving SIFT.","source":"pubmed","abstract":"Privacy has received considerable attention but is still largely ignored in the multimedia community. Consider a cloud computing scenario where the server is resource-abundant, and is capable of finishing the designated tasks. It is envisioned that secure media applications with privacy preservation will be treated seriously. In view of the fact that scale-invariant feature transform (SIFT) has been widely adopted in various fields, this paper is the first to target the importance of privacy-preserving SIFT (PPSIFT) and to address the problem of secure SIFT feature extraction and representation in the encrypted domain. As all of the operations in SIFT must be moved to the encrypted domain, we propose a privacy-preserving realization of the SIFT method based on homomorphic encryption. We show through the security analysis based on the discrete logarithm problem and RSA that PPSIFT is secure against ciphertext only attack and known plaintext attack. Experimental results obtained from different case studies demonstrate that the proposed homomorphic encryption-based privacy-preserving SIFT performs comparably to the original SIFT and that our method is useful in SIFT-based privacy-preserving applications.","url":"https://pubmed.ncbi.nlm.nih.gov/22711774/","authors":["Hsu CY","Lu CS","Pei SC"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2012 Nov","doi":"10.1109/TIP.2012.2204272","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"pmid:16370465","name":"Fingerprinting protocol for images based on additive homomorphic property.","source":"pubmed","abstract":"Homomorphic property of public-key cryptosystems is applied for several cryptographic protocols, such as electronic cash, voting system, bidding protocols, etc. Several fingerprinting protocols also exploit the property to achieve an asymmetric system. However, their enciphering rate is extremely low and the implementation of watermarking technique is difficult. In this paper, we propose a new fingerprinting protocol applying additive homomorphic property of Okamoto-Uchiyama encryption scheme. Exploiting the property ingenuously, the enciphering rate of our fingerprinting scheme can be close to the corresponding cryptosystem. We study the problem of implementation of watermarking technique and propose a successful method to embed an encrypted information without knowing the plain value. The security can also be protected for both a buyer and a merchant in our scheme.","url":"https://pubmed.ncbi.nlm.nih.gov/16370465/","authors":["Kuribayashi M","Tanaka H"],"tags":[],"confidence":0.82,"sites":["privacy-computing"],"publishedDate":"2005 Dec","doi":"10.1109/tip.2005.859383","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.20607519","name":"Towards Scalable Fuzzy PSI via Efficient Fuzzy Matching","source":"datacite","abstract":"In fuzzy private set intersection (fuzzy PSI), there are two parties, a sender holding a set of $d$-dimensional points $Q = \\{\\vecq_1, \\ldots, \\vecq_m\\}$ and a receiver holding a set $W = \\{\\vecw_1, \\ldots, \\vecw_n\\}$ of the same structure. It enables the receiver to learn the point $\\vecq \\in Q$ for which there exists some $\\vecw \\in W$ satisfying $\\mathsf{dist}(\\vecq, \\vecw) \\le \\delta$ under a given distance metric.Although several fuzzy PSI protocols for $L_{p\\in[1, \\infty]}$ distance are proposed, there are significant efficiency issues, mainly because they (1) heavily rely on expensive cryptographic primitives, e.g., homomorphic encryption or garble circuits, and/or (2) incur undesirable asymptotic communication and computation complexity. In this paper, we present scalable fuzzy PSI protocols for general $L_{p \\in [1, \\infty]}$ distance, supporting both low- and high-dimensional sets. The core technique is two efficient fuzzy matching protocols that securely evaluate $\\mathsf{dist}(\\vecq, \\vecw) \\le \\delta$.The first is built from a role-reversed oblivious PRF (OPRF) and realizes $O(d\\log \\delta)$ overhead, compared to $O((\\log \\delta)^d)$ in previous works. The second leverages customized oblivious transfer (OT) with $O(d\\ell)$ overhead, where $\\ell$ is the bit length of inputs, which is particularly suitable for short inputs.With these new techniques, we further propose a new dual-layer hashing framework for fuzzy PSI over low-dimensional sets, instantiated with our OT-based fuzzy matching and enhanced with a domain reduction optimization.The protocols achieve an overhead linear with $n, m, \\log \\delta, 2^d$, without the $O((\\log \\delta)^d)$ or $O(\\delta)$ factors present in prior works. For high-dimensional sets, we construct fuzzy PSI protocols based on our OPRF- and OT-based fuzzy matching, which achieve an asymptotic overhead linear with $n, m, d$, and $\\log \\delta$ but rely on the strong globally disjoint assumption. Extensive evaluations demonstrate that our protocols achieve up to a $145\\times$ speedup in running time and a $20\\times$ reduction in communication cost compared to van Baarsen and Pu~(ASIACRYPT'25), and achieve up to a $25\\times$ speedup in running time and up to a $17\\times$ reduction in communication cost compared to Piske et al.~(CCS'25).","url":"https://doi.org/10.5281/zenodo.20607519","authors":["Hao, Meng","Yang, Xinpeng","Chen, Hanxiao","Zhang, Tianwei","Xue, Haiyang","Yang, Guomin","Li, Hongwei","Deng, Robert H."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20607519","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.20607518","name":"Towards Scalable Fuzzy PSI via Efficient Fuzzy Matching","source":"datacite","abstract":"In fuzzy private set intersection (fuzzy PSI), there are two parties, a sender holding a set of $d$-dimensional points $Q = \\{\\vecq_1, \\ldots, \\vecq_m\\}$ and a receiver holding a set $W = \\{\\vecw_1, \\ldots, \\vecw_n\\}$ of the same structure. It enables the receiver to learn the point $\\vecq \\in Q$ for which there exists some $\\vecw \\in W$ satisfying $\\mathsf{dist}(\\vecq, \\vecw) \\le \\delta$ under a given distance metric.Although several fuzzy PSI protocols for $L_{p\\in[1, \\infty]}$ distance are proposed, there are significant efficiency issues, mainly because they (1) heavily rely on expensive cryptographic primitives, e.g., homomorphic encryption or garble circuits, and/or (2) incur undesirable asymptotic communication and computation complexity. In this paper, we present scalable fuzzy PSI protocols for general $L_{p \\in [1, \\infty]}$ distance, supporting both low- and high-dimensional sets. The core technique is two efficient fuzzy matching protocols that securely evaluate $\\mathsf{dist}(\\vecq, \\vecw) \\le \\delta$.The first is built from a role-reversed oblivious PRF (OPRF) and realizes $O(d\\log \\delta)$ overhead, compared to $O((\\log \\delta)^d)$ in previous works. The second leverages customized oblivious transfer (OT) with $O(d\\ell)$ overhead, where $\\ell$ is the bit length of inputs, which is particularly suitable for short inputs.With these new techniques, we further propose a new dual-layer hashing framework for fuzzy PSI over low-dimensional sets, instantiated with our OT-based fuzzy matching and enhanced with a domain reduction optimization.The protocols achieve an overhead linear with $n, m, \\log \\delta, 2^d$, without the $O((\\log \\delta)^d)$ or $O(\\delta)$ factors present in prior works. For high-dimensional sets, we construct fuzzy PSI protocols based on our OPRF- and OT-based fuzzy matching, which achieve an asymptotic overhead linear with $n, m, d$, and $\\log \\delta$ but rely on the strong globally disjoint assumption. Extensive evaluations demonstrate that our protocols achieve up to a $145\\times$ speedup in running time and a $20\\times$ reduction in communication cost compared to van Baarsen and Pu~(ASIACRYPT'25), and achieve up to a $25\\times$ speedup in running time and up to a $17\\times$ reduction in communication cost compared to Piske et al.~(CCS'25).","url":"https://doi.org/10.5281/zenodo.20607518","authors":["Hao, Meng","Yang, Xinpeng","Chen, Hanxiao","Zhang, Tianwei","Xue, Haiyang","Yang, Guomin","Li, Hongwei","Deng, Robert H."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20607518","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21766632","name":"Towards Scalable Fuzzy PSI via Efficient Fuzzy Matching","source":"datacite","abstract":"In fuzzy private set intersection (fuzzy PSI), there are two parties, a sender holding a set of $d$-dimensional points $Q = \\{\\vecq_1, \\ldots, \\vecq_m\\}$ and a receiver holding a set $W = \\{\\vecw_1, \\ldots, \\vecw_n\\}$ of the same structure. It enables the receiver to learn the point $\\vecq \\in Q$ for which there exists some $\\vecw \\in W$ satisfying $\\mathsf{dist}(\\vecq, \\vecw) \\le \\delta$ under a given distance metric.Although several fuzzy PSI protocols for $L_{p\\in[1, \\infty]}$ distance are proposed, there are significant efficiency issues, mainly because they (1) heavily rely on expensive cryptographic primitives, e.g., homomorphic encryption or garble circuits, and/or (2) incur undesirable asymptotic communication and computation complexity. In this paper, we present scalable fuzzy PSI protocols for general $L_{p \\in [1, \\infty]}$ distance, supporting both low- and high-dimensional sets. The core technique is two efficient fuzzy matching protocols that securely evaluate $\\mathsf{dist}(\\vecq, \\vecw) \\le \\delta$.The first is built from a role-reversed oblivious PRF (OPRF) and realizes $O(d\\log \\delta)$ overhead, compared to $O((\\log \\delta)^d)$ in previous works. The second leverages customized oblivious transfer (OT) with $O(d\\ell)$ overhead, where $\\ell$ is the bit length of inputs, which is particularly suitable for short inputs.With these new techniques, we further propose a new dual-layer hashing framework for fuzzy PSI over low-dimensional sets, instantiated with our OT-based fuzzy matching and enhanced with a domain reduction optimization.The protocols achieve an overhead linear with $n, m, \\log \\delta, 2^d$, without the $O((\\log \\delta)^d)$ or $O(\\delta)$ factors present in prior works. For high-dimensional sets, we construct fuzzy PSI protocols based on our OPRF- and OT-based fuzzy matching, which achieve an asymptotic overhead linear with $n, m, d$, and $\\log \\delta$ but rely on the strong globally disjoint assumption. Extensive evaluations demonstrate that our protocols achieve up to a $145\\times$ speedup in running time and a $20\\times$ reduction in communication cost compared to van Baarsen and Pu~(ASIACRYPT'25), and achieve up to a $25\\times$ speedup in running time and up to a $17\\times$ reduction in communication cost compared to Piske et al.~(CCS'25).","url":"https://doi.org/10.5281/zenodo.21766632","authors":["Hao, Meng","Yang, Xinpeng","Chen, Hanxiao","Zhang, Tianwei","Xue, Haiyang","Yang, Guomin","Li, Hongwei","Deng, Robert H."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21766632","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21992945","name":"A Privacy-Preserving Explainable Artificial Intelligence Hybrid Framework for Abnormal Network Traffic Identification and Intelligent Threat Detection","source":"datacite","abstract":"Abstract The rapid evolution of cyber threats, the widespread adoption of encrypted communications, and the increasing complexity of enterprise, cloud, edge, and Internet of Things (IoT) environments have exposed the limitations of conventional intrusion detection systems in accurately identifying abnormal network traffic and detecting sophisticated cyberattacks. Existing hybrid deep learning frameworks, including that of Wang [54], achieve high detection accuracy but lack privacy-preserving computation, explainable artificial intelligence, intelligent threat prioritization, and adaptive operational capabilities. This study therefore designed and developed a Hybrid Artificial Intelligence Framework for Abnormal Network Traffic Identification and Intelligent Threat Detection by integrating Long Short-Term Memory (LSTM), Transformer, Random Forest, CKKS Homomorphic Encryption, Explainable Artificial Intelligence (SHAP/LIME), weighted ensemble decision fusion, intelligent threat prioritization, and adaptive feedback learning. The study adopted the Design Science Research Methodology (DSRM), while the proposed framework was implemented using Python, TensorFlow/Keras, Scikit-learn, Microsoft SEAL/TenSEAL, and evaluated using the CICIDS2017 and UNSW-NB15 benchmark datasets. Experimental evaluation was performed using accuracy, precision, recall, F1-score, false positive rate, detection latency, zero-day detection rate, throughput, scalability, explainability, and encryption overhead as performance metrics. The proposed framework achieved detection accuracies of 99.12% and 98.76% on the CICIDS2017 and UNSW-NB15 datasets, respectively, with precision values of 98.87% and 98.42%, recall values of 99.05% and 98.61%, F1-scores of 98.96% and 98.51%, false positive rates of 0.84% and 1.12%, and zero-day detection rates of 94.30% and 92.75%. Comparative analysis demonstrated improved detection accuracy, lower false-positive rates, reduced detection latency, enhanced interpretability, and stronger privacy preservation compared with the hybrid CNN–LSTM–Transformer framework of Wang [54]. The study concludes that integrating hybrid artificial intelligence, privacy-preserving computation, explainable artificial intelligence, and intelligent threat prioritization provides a robust, scalable, and adaptive solution for modern cybersecurity. The proposed framework is recommended for deployment in enterprise networks, cloud computing, IoT, edge computing, and critical infrastructure environments to strengthen real-time cyber threat detection and response. Keywords: Hybrid Artificial Intelligence, Abnormal Network Traffic Identification, Intelligent Threat Detection, Intrusion Detection System, LSTM, Transformer, Random Forest, Explainable Artificial Intelligence, CKKS Homomorphic Encryption, Zero-Day Attack Detection","url":"https://doi.org/10.5281/zenodo.21992945","authors":["D. A. Onuma","D. Matthias","O. E. Taylor","N. D. Nwiabu"],"tags":["Hybrid Artificial Intelligence, Abnormal Network Traffic Identification, Intelligent Threat Detection, Intrusion Detection System, LSTM, Transformer, Random Forest, Explainable Artificial Intelligence, CKKS Homomorphic Encryption, Zero-Day Attack Detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21992945","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19449140","name":"A Review Of Cryptographic Solutions And Forensic Readiness In IoT And Network Security","source":"datacite","abstract":"In today's digital environment, the swift advancement of interconnected technologies has raised significant worries about data safety, privacy, and reliability. The Internet of Things (IoT), networking systems, and cloud services produce and transfer large quantities of sensitive information, leaving them susceptible to cyber threats and other security risks. This research offers a detailed evaluation of how cryptography, network protection, and digital forensics work together, highlighting their combined impact on securing communication, safeguarding data integrity, and ensuring effective investigation methods. The approach to research relies on a thorough examination and combination of available literature, with a focus on major developments in cryptographic methods, network defense strategies, and forensic analysis frameworks. Particular focus is given to Homomorphic Encryption (HE), which allows processing to occur directly on encrypted information without the need for decryption, thus increasing privacy in unreliable settings such as cloud services and IoT environments. Moreover, the research includes new strategies in blockchain-centered forensics, featuring automated cost management that aligns with regulations, mapping wallet interactions, and utilizing non-fungible tokens (NFTs) as reliable audit references to enhance transparency and responsibility. The results show that cryptographic methods ensure safe data transfer, while network security strategies defend systems against unauthorized access, misuse, and cyber intrusions. At the same time, digital forensics offers a scientifically supported method for finding, preserving, and examining digital proof, tackling key evidentiary issues in today's cyber landscape. The integration of blockchain forensics and NFTs further boosts auditability, traceability, and trust, especially within decentralized finance (DeFi) setups and intricate digital transactions. In summary, the alignment of cryptography, network protection, and digital forensics creates a strong and forward-thinking security framework that improves data safety, helps with regulatory adherence, and enhances the overall durability of contemporary digital systems.","url":"https://doi.org/10.5281/zenodo.19449140","authors":["Muhammad Ahmad","Hua Zhou","Tanzeela Bibi","Haider Ali"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19449140","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19449141","name":"A Review Of Cryptographic Solutions And Forensic Readiness In IoT And Network Security","source":"datacite","abstract":"In today's digital environment, the swift advancement of interconnected technologies has raised significant worries about data safety, privacy, and reliability. The Internet of Things (IoT), networking systems, and cloud services produce and transfer large quantities of sensitive information, leaving them susceptible to cyber threats and other security risks. This research offers a detailed evaluation of how cryptography, network protection, and digital forensics work together, highlighting their combined impact on securing communication, safeguarding data integrity, and ensuring effective investigation methods. The approach to research relies on a thorough examination and combination of available literature, with a focus on major developments in cryptographic methods, network defense strategies, and forensic analysis frameworks. Particular focus is given to Homomorphic Encryption (HE), which allows processing to occur directly on encrypted information without the need for decryption, thus increasing privacy in unreliable settings such as cloud services and IoT environments. Moreover, the research includes new strategies in blockchain-centered forensics, featuring automated cost management that aligns with regulations, mapping wallet interactions, and utilizing non-fungible tokens (NFTs) as reliable audit references to enhance transparency and responsibility. The results show that cryptographic methods ensure safe data transfer, while network security strategies defend systems against unauthorized access, misuse, and cyber intrusions. At the same time, digital forensics offers a scientifically supported method for finding, preserving, and examining digital proof, tackling key evidentiary issues in today's cyber landscape. The integration of blockchain forensics and NFTs further boosts auditability, traceability, and trust, especially within decentralized finance (DeFi) setups and intricate digital transactions. In summary, the alignment of cryptography, network protection, and digital forensics creates a strong and forward-thinking security framework that improves data safety, helps with regulatory adherence, and enhances the overall durability of contemporary digital systems.","url":"https://doi.org/10.5281/zenodo.19449141","authors":["Muhammad Ahmad","Hua Zhou","Tanzeela Bibi","Haider Ali"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19449141","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19614724","name":"Privacy-Preserving Deep Learning","source":"datacite","abstract":"Deep learning's reliance on large datasets creates fundamental tension with privacy requirements: models trained onsensitive data can memorise and leak individual training examples through model outputs, gradients, or learnedparameters. This study presents a controlled evaluation of five privacy-preserving deep learning approaches --differential privacy SGD (DP-SGD), federated learning with secure aggregation, homomorphic encryption for inference,knowledge distillation from private models (PATE), and synthetic data generation with privacy guarantees -- across fourprivacy-sensitive tasks: medical image classification (CheXpert chest X-ray), clinical NLP (MIMIC-III dischargesummaries), financial fraud detection (credit card transactions), and recommendation systems (MovieLens-1M). Privacywas measured by formal epsilon-delta differential privacy guarantees and empirical membership inference attacksuccess rate. A total of 2,040 experiments were conducted. DP-SGD at epsilon = 8 reduced accuracy by 4.8 +- 1.2% onmedical imaging versus non-private training, while epsilon = 1 reduced accuracy by 12.4 +- 2.2%, confirming asubstantial privacy-utility trade-off. PATE achieved the best trade-off: epsilon = 2 with only 3.4 +- 0.8% accuracy loss bytransferring knowledge through noisy aggregation of teacher ensemble votes. Membership inference attack successdropped from 68.4% (non-private model) to near-random (52.8%) at epsilon = 4. Synthetic data generation preserved86.4 +- 2.8% of downstream model utility while providing formal privacy guarantees. Federated learning with secureaggregation provided communication-level privacy but remained vulnerable to model-level attacks without additional DPnoise. A practical privacy-preserving ML selection guide mapping privacy requirements, acceptable utility loss, andcomputational budget to recommended approaches is proposed.","url":"https://doi.org/10.5281/zenodo.19614724","authors":["Ivan Novak","Nina Nowak","Ivan Schmidt"],"tags":["differential privacy; DP-SGD; membership inference; federated learning; homomorphic encryption; PATE framework; synthetic data; privacy-utility trade-off"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.19614724","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19614725","name":"Privacy-Preserving Deep Learning","source":"datacite","abstract":"Deep learning's reliance on large datasets creates fundamental tension with privacy requirements: models trained onsensitive data can memorise and leak individual training examples through model outputs, gradients, or learnedparameters. This study presents a controlled evaluation of five privacy-preserving deep learning approaches --differential privacy SGD (DP-SGD), federated learning with secure aggregation, homomorphic encryption for inference,knowledge distillation from private models (PATE), and synthetic data generation with privacy guarantees -- across fourprivacy-sensitive tasks: medical image classification (CheXpert chest X-ray), clinical NLP (MIMIC-III dischargesummaries), financial fraud detection (credit card transactions), and recommendation systems (MovieLens-1M). Privacywas measured by formal epsilon-delta differential privacy guarantees and empirical membership inference attacksuccess rate. A total of 2,040 experiments were conducted. DP-SGD at epsilon = 8 reduced accuracy by 4.8 +- 1.2% onmedical imaging versus non-private training, while epsilon = 1 reduced accuracy by 12.4 +- 2.2%, confirming asubstantial privacy-utility trade-off. PATE achieved the best trade-off: epsilon = 2 with only 3.4 +- 0.8% accuracy loss bytransferring knowledge through noisy aggregation of teacher ensemble votes. Membership inference attack successdropped from 68.4% (non-private model) to near-random (52.8%) at epsilon = 4. Synthetic data generation preserved86.4 +- 2.8% of downstream model utility while providing formal privacy guarantees. Federated learning with secureaggregation provided communication-level privacy but remained vulnerable to model-level attacks without additional DPnoise. A practical privacy-preserving ML selection guide mapping privacy requirements, acceptable utility loss, andcomputational budget to recommended approaches is proposed.","url":"https://doi.org/10.5281/zenodo.19614725","authors":["Ivan Novak","Nina Nowak","Ivan Schmidt"],"tags":["differential privacy; DP-SGD; membership inference; federated learning; homomorphic encryption; PATE framework; synthetic data; privacy-utility trade-off"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.19614725","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19603756","name":"Multi-Metric Benchmarking Framework for Hybrid Homomorphic Encryption in Privacy-Preserving Machine Learning","source":"datacite","abstract":"ABSTRACT Privacy-Preserving Machine Learning (PPML) using Homomorphic Encryption (HE) provides strong data confidentiality but suffers from high computational and energy overhead. Hybrid Homomorphic Encryption (HHE) reduces this cost, yet existing evaluations lack standardized, cross-platform benchmarks. This paper presents a multi-metric benchmarking framework that evaluates HHE-based inference across latency, energy, communication, resource utilization, and accuracy. Using the CKKS scheme implemented with TenSEAL, experiments were conducted on simulated cloud, edge, and mobile platforms with ECG classification data. Results show cloud environments achieve the lowest latency (0.011 s) and energy consumption (5.75 J), while mobile platforms incur substantially higher overhead (0.191 s, 560 J). Increasing security parameters raises latency and energy by 2.5–3.5× without affecting accuracy. These findings reveal critical platform-dependent trade-offs and demonstrate that cloud and edge platforms are more viable for HHE-based PPML than mobile devices under current hardware constraints. The framework underscores the need for holistic, multi-metric evaluation to guide practical deployment. Keywords: homomorphic encryption, privacy-preserving machine learning, benchmarking, CKKS, performance evaluation","url":"https://doi.org/10.5281/zenodo.19603756","authors":["Umar, S.","Zayyanu, U.","Ibrahim, A.","Bakura, S. A.","Sabiu, L. T."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.19603756","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19603757","name":"Multi-Metric Benchmarking Framework for Hybrid Homomorphic Encryption in Privacy-Preserving Machine Learning","source":"datacite","abstract":"ABSTRACT Privacy-Preserving Machine Learning (PPML) using Homomorphic Encryption (HE) provides strong data confidentiality but suffers from high computational and energy overhead. Hybrid Homomorphic Encryption (HHE) reduces this cost, yet existing evaluations lack standardized, cross-platform benchmarks. This paper presents a multi-metric benchmarking framework that evaluates HHE-based inference across latency, energy, communication, resource utilization, and accuracy. Using the CKKS scheme implemented with TenSEAL, experiments were conducted on simulated cloud, edge, and mobile platforms with ECG classification data. Results show cloud environments achieve the lowest latency (0.011 s) and energy consumption (5.75 J), while mobile platforms incur substantially higher overhead (0.191 s, 560 J). Increasing security parameters raises latency and energy by 2.5–3.5× without affecting accuracy. These findings reveal critical platform-dependent trade-offs and demonstrate that cloud and edge platforms are more viable for HHE-based PPML than mobile devices under current hardware constraints. The framework underscores the need for holistic, multi-metric evaluation to guide practical deployment. Keywords: homomorphic encryption, privacy-preserving machine learning, benchmarking, CKKS, performance evaluation","url":"https://doi.org/10.5281/zenodo.19603757","authors":["Umar, S.","Zayyanu, U.","Ibrahim, A.","Bakura, S. A.","Sabiu, L. T."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.19603757","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21802598","name":"Trustworthy Foundation Model-Based Multimodal Biometric Authentication for Secure and Privacy-Preserving Intelligent Systems Using Face, Ear Lobe, and Iris","source":"datacite","abstract":"The increasing deployment of intelligent systems in banking, healthcare and critical infrastructure emphasizes the need for authentication procedures that are accurate, spoofing-robust, and privacy-preserving. In this paper, we propose a Trustworthy Foundation Model based Multimodal Biometric Authentication (TFM-MBA) framework to fuse three complementary biometric modalities, i.e., face, ear lobe and iris, via representations extracted from a large pre-trained vision foundation model, which are adapted to each modality with lightweight modality-specific adapters. Specifically, our architecture is built around: an attention-based fusion module; a trust-calibration layer for estimating the confidence and modality reliability of each sample; and a privacy-preserving pipeline combining federated learning, differential privacy and partial homomorphic encryption for template protection. We evaluate the system on combined benchmark-style datasets (face: CASIA-WebFace/LFW-style splits; ear: AWE/IITD-Ear-style splits; iris: CASIA-Iris-style splits) with a unified multimodal protocol with simulated real-world degradations (occlusion, blur, illumination change and presentation attacks). The experimental results demonstrate that the proposed multimodal fusion achieves a rank-1 accuracy of 99.1% and an Equal Error Rate (EER) of 0.18%, exceeding the best unimodal baseline (iris-only, 97.4% accuracy, 0.71% EER) and current multimodal fusion baselines by 1.2-3.6 percentage points in accuracy. The privacy-preserving version of the pipeline keeps 98.6% of the non-private model’s accuracy while reducing the membership-inference attack success rate from 68.3% to 52.1%, close to random guessing. We further show that the trust-calibration module improves the spoof-detection F1-score to 0.978 and offers interpretable per-modality contribution scores, which improve system transparency. The results demonstrate that the integration of foundation-model representations with modality-aware fusion and privacy-preserving training results in a biometric authentication system that is accurate, robust, explainable, and in line with data-protection standards for real-world intelligent systems.","url":"https://doi.org/10.5281/zenodo.21802598","authors":["SHANMUGAMANI, ARUNARANI"],"tags":["Multimodal biometrics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21802598","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21802599","name":"Trustworthy Foundation Model-Based Multimodal Biometric Authentication for Secure and Privacy-Preserving Intelligent Systems Using Face, Ear Lobe, and Iris","source":"datacite","abstract":"The increasing deployment of intelligent systems in banking, healthcare and critical infrastructure emphasizes the need for authentication procedures that are accurate, spoofing-robust, and privacy-preserving. In this paper, we propose a Trustworthy Foundation Model based Multimodal Biometric Authentication (TFM-MBA) framework to fuse three complementary biometric modalities, i.e., face, ear lobe and iris, via representations extracted from a large pre-trained vision foundation model, which are adapted to each modality with lightweight modality-specific adapters. Specifically, our architecture is built around: an attention-based fusion module; a trust-calibration layer for estimating the confidence and modality reliability of each sample; and a privacy-preserving pipeline combining federated learning, differential privacy and partial homomorphic encryption for template protection. We evaluate the system on combined benchmark-style datasets (face: CASIA-WebFace/LFW-style splits; ear: AWE/IITD-Ear-style splits; iris: CASIA-Iris-style splits) with a unified multimodal protocol with simulated real-world degradations (occlusion, blur, illumination change and presentation attacks). The experimental results demonstrate that the proposed multimodal fusion achieves a rank-1 accuracy of 99.1% and an Equal Error Rate (EER) of 0.18%, exceeding the best unimodal baseline (iris-only, 97.4% accuracy, 0.71% EER) and current multimodal fusion baselines by 1.2-3.6 percentage points in accuracy. The privacy-preserving version of the pipeline keeps 98.6% of the non-private model’s accuracy while reducing the membership-inference attack success rate from 68.3% to 52.1%, close to random guessing. We further show that the trust-calibration module improves the spoof-detection F1-score to 0.978 and offers interpretable per-modality contribution scores, which improve system transparency. The results demonstrate that the integration of foundation-model representations with modality-aware fusion and privacy-preserving training results in a biometric authentication system that is accurate, robust, explainable, and in line with data-protection standards for real-world intelligent systems.","url":"https://doi.org/10.5281/zenodo.21802599","authors":["SHANMUGAMANI, ARUNARANI"],"tags":["Multimodal biometrics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21802599","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21437378","name":"BlindTranspiler","source":"datacite","abstract":"Blind quantum computation (BQC) allows a limited-capability client to perform complex quantum computation on a remote server without revealing input, output, or computation. This primitive solves a problem in cryptography called 'Secure Delegated Computation'. This cryptographic primitive enables applications in various areas like quantum homomorphic encryption, secure quantum approximation algorithms, quantum private query, quantum multi-party computation, secret sharing protocols, blind factorization, quantum searchable encryption, among many others. This Python library presents the first software tools to convert any quantum circuit written in Qiskit to its blind counterpart, allowing rapid prototyping of applications of BQC cryptography.","url":"https://doi.org/10.5281/zenodo.21437378","authors":["Joshi, Mohit","Mishra, Manoj Kumar","S., Karthikeyan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21437378","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21437379","name":"BlindTranspiler","source":"datacite","abstract":"Blind quantum computation (BQC) allows a limited-capability client to perform complex quantum computation on a remote server without revealing input, output, or computation. This primitive solves a problem in cryptography called 'Secure Delegated Computation'. This cryptographic primitive enables applications in various areas like quantum homomorphic encryption, secure quantum approximation algorithms, quantum private query, quantum multi-party computation, secret sharing protocols, blind factorization, quantum searchable encryption, among many others. This Python library presents the first software tools to convert any quantum circuit written in Qiskit to its blind counterpart, allowing rapid prototyping of applications of BQC cryptography.","url":"https://doi.org/10.5281/zenodo.21437379","authors":["Joshi, Mohit","Mishra, Manoj Kumar","S., Karthikeyan"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21437379","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19635259","name":"The Zero-Knowledge Web Server (ZKWS): A Paradigm Shift in Stateless Infrastructure and Epistemic Privacy","source":"datacite","abstract":"Abstract: This working paper introduces the Zero-Knowledge Web Server (ZKWS), a transformative infrastructure-layer protocol designed to eliminate the \"Oracle Fallacy\" inherent in modern web architectures. While current security standards like TLS/SSL protect data in transit, the host environment (RAM/CPU) remains a clear-text zone vulnerable to provider-level exfiltration and kernel exploits. ZKWS moves data \"blindness\" to the binary execution layer through a Triple-Blind Matrix: Homomorphic Routing (HR): Processing requests against encrypted routing tables. TEE-Isolation (Trusted Execution Environments): Executing logic within hardware-level enclaves (Intel SGX/AMD SEV) to prevent memory dumping. Epistemic Decoupling: Cryptographic separation of the database and web engine, where decryption occurs exclusively at the edge device. The paper further explores the integration of the Synthetic Data Contamination Index (SDCI) to mitigate recursive model collapse and addresses Cognitive Loop Burnout (CLB) by restoring absolute human agency and data sovereignty. ZKWS provides the architectural blueprint for a sovereign digital infrastructure, rendering host-level data breaches mathematically and economically obsolete in an increasingly synthetic AI era. Key Features: Attack surface reduction at the hardware-execution layer. Integration with SDCI for data provenance verification. Mitigation strategies for side-channel attacks through Constant-Time Obfuscation (CTO). Proposed \"Hybrid ZK-Sharding\" for performance optimization. Citation Note: This is a preliminary framework (Version 1.0). Part of the Bizbell Digital Ecosystem research initiative, building upon the principles of TruthSeal.pro and Vaultit.pro.","url":"https://doi.org/10.5281/zenodo.19635259","authors":["Siddiqui, Jameel Ahmed"],"tags":["Zero-Knowledge Web Server","ZKWS","Data Sovereignty","Model Collapse","SDCI","Epistemic Privacy","Sovereign Infrastructure","TEE"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19635259","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19635260","name":"The Zero-Knowledge Web Server (ZKWS): A Paradigm Shift in Stateless Infrastructure and Epistemic Privacy","source":"datacite","abstract":"Abstract: This working paper introduces the Zero-Knowledge Web Server (ZKWS), a transformative infrastructure-layer protocol designed to eliminate the \"Oracle Fallacy\" inherent in modern web architectures. While current security standards like TLS/SSL protect data in transit, the host environment (RAM/CPU) remains a clear-text zone vulnerable to provider-level exfiltration and kernel exploits. ZKWS moves data \"blindness\" to the binary execution layer through a Triple-Blind Matrix: Homomorphic Routing (HR): Processing requests against encrypted routing tables. TEE-Isolation (Trusted Execution Environments): Executing logic within hardware-level enclaves (Intel SGX/AMD SEV) to prevent memory dumping. Epistemic Decoupling: Cryptographic separation of the database and web engine, where decryption occurs exclusively at the edge device. The paper further explores the integration of the Synthetic Data Contamination Index (SDCI) to mitigate recursive model collapse and addresses Cognitive Loop Burnout (CLB) by restoring absolute human agency and data sovereignty. ZKWS provides the architectural blueprint for a sovereign digital infrastructure, rendering host-level data breaches mathematically and economically obsolete in an increasingly synthetic AI era. Key Features: Attack surface reduction at the hardware-execution layer. Integration with SDCI for data provenance verification. Mitigation strategies for side-channel attacks through Constant-Time Obfuscation (CTO). Proposed \"Hybrid ZK-Sharding\" for performance optimization. Citation Note: This is a preliminary framework (Version 1.0). Part of the Bizbell Digital Ecosystem research initiative, building upon the principles of TruthSeal.pro and Vaultit.pro.","url":"https://doi.org/10.5281/zenodo.19635260","authors":["Siddiqui, Jameel Ahmed"],"tags":["Zero-Knowledge Web Server","ZKWS","Data Sovereignty","Model Collapse","SDCI","Epistemic Privacy","Sovereign Infrastructure","TEE"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19635260","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.16659484","name":"IoMT Encryption Simulation: Version 2.0.0","source":"datacite","abstract":"Version 2.0.0 is the final archived release of the IoMT Encryption Simulation used for the doctoral dissertation: Examining Simulated Homomorphic Encryption on Data Transmissions of Always-Operating Internet of Medical Things This release contains the final Python experimental implementation, synthetic source data, measurement results, validation evidence, SPSS analysis files, and reproducibility documentation associated with the final study. Study Conditions The study evaluated 40 simulated always-operating Internet of Medical Things (IoMT) environments across three experimental conditions: - Unencrypted: ENV-01 through ENV-10- Simulated ECC: ENV-11 through ENV-25- RSA-SHE: ENV-26 through ENV-40 All nine protected transmission fields were included in the experimental workflows: - org_id- device_id- timestamp- heart_rate- bp_systolic- bp_diastolic- spo2- temperature- battery_level Unencrypted Condition The unencrypted condition served as the baseline. No encryption operation was applied to the nine protected fields. Protected values remained unchanged from the original synthetic source data. The condition produced: - Simulated encryption time: 0 seconds- Clear-text exposure: 100% Simulated ECC Implementation The simulated ECC condition uses: - ECDH with SECP384R1- HKDF-SHA256- 256-bit derived AES key- AES-256-GCM- Fresh 12-byte nonce for every protected-value encryption operation All nine protected fields are encrypted. Post-timing validation decrypts the encrypted protected values and confirms that they match the corresponding original source values. The simulated ECC condition produced 0% clear-text exposure. RSA-SHE Implementation RSA-SHE is the RSA-based simulated homomorphic encryption condition evaluated in the study. The final implementation uses: - 2048-bit RSA- RSA public exponent 65537- A new RSA key pair for each run- Run-specific encoding- RSA modular encryption of all nine protected fields- A randomized hybrid ciphertext layer- Fixed-width Base64 serialization- A predefined encrypted MAP-numerator calculation: SBP + 2(DBP) The systolic and diastolic blood-pressure operands remain encrypted during the predefined numerator calculation. Division by three to complete MAP occurs only after validation decryption. RSA-SHE is a controlled simulated-homomorphic adaptation for this predefined operation. It is not a production-grade Fully Homomorphic Encryption implementation, is not an exact reproduction of MEHE, and does not support arbitrary ciphertext computation. The RSA-SHE condition produced 0% clear-text exposure. Timing Encryption timing was measured using Python's time.perf_counter(). For each encrypted environment: - One untimed warm-up run was performed- Five timed runs were performed- The median of the five timed runs was retained No artificial timing delays or manually assigned encryption-time values were used. For RSA-SHE, the measured interval includes RSA key generation, run-specific homomorphic setup, encoding and RSA modular encryption of all nine protected fields, randomized hybrid ciphertext construction, the predefined encrypted MAP-numerator evaluation, and construction of the encrypted in-memory transmission structure. Correctness validation, validation decryption and unmasking, MAP verification, clear-text-exposure checks, audit-report generation, disk writing, file-size measurement, and average-row-length measurement occur outside the measured interval. Primary Outcomes The four primary study outcomes are: - File size (KB)- Average row length (bytes)- Simulated encryption time (seconds)- Clear-text exposure (percent) Final Descriptive Results Unencrypted — n = 10 - Mean file size: 61.64150 KB- Mean average row length: 61.02590 bytes- Mean encryption time: 0.00000000 seconds- Clear-text exposure: 100% Simulated ECC — n = 15 - Mean file size: 1825.35319 KB- Mean average row length: 428.00000 bytes- Mean encryption time: 0.06475018 seconds- Clear-text exposure: 0% RSA-SHE — n = 15 - Mean file size: 357","url":"https://doi.org/10.5281/zenodo.16659484","authors":["Anderson, Devin"],"tags":["IoMT","Internet of Medical Things","Encryption","RSA-SHE","Simulated Homomorphic Encryption","Homomorphic Encryption","Encrypted Computation","Mean Arterial Pressure"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.16659484","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21858940","name":"IoMT Encryption Simulation: Version 2.0.0","source":"datacite","abstract":"Version 2.0.0 is the final archived release of the IoMT Encryption Simulation used for the doctoral dissertation: Examining Simulated Homomorphic Encryption on Data Transmissions of Always-Operating Internet of Medical Things This release contains the final Python experimental implementation, synthetic source data, measurement results, validation evidence, SPSS analysis files, and reproducibility documentation associated with the final study. Study Conditions The study evaluated 40 simulated always-operating Internet of Medical Things (IoMT) environments across three experimental conditions: - Unencrypted: ENV-01 through ENV-10- Simulated ECC: ENV-11 through ENV-25- RSA-SHE: ENV-26 through ENV-40 All nine protected transmission fields were included in the experimental workflows: - org_id- device_id- timestamp- heart_rate- bp_systolic- bp_diastolic- spo2- temperature- battery_level Unencrypted Condition The unencrypted condition served as the baseline. No encryption operation was applied to the nine protected fields. Protected values remained unchanged from the original synthetic source data. The condition produced: - Simulated encryption time: 0 seconds- Clear-text exposure: 100% Simulated ECC Implementation The simulated ECC condition uses: - ECDH with SECP384R1- HKDF-SHA256- 256-bit derived AES key- AES-256-GCM- Fresh 12-byte nonce for every protected-value encryption operation All nine protected fields are encrypted. Post-timing validation decrypts the encrypted protected values and confirms that they match the corresponding original source values. The simulated ECC condition produced 0% clear-text exposure. RSA-SHE Implementation RSA-SHE is the RSA-based simulated homomorphic encryption condition evaluated in the study. The final implementation uses: - 2048-bit RSA- RSA public exponent 65537- A new RSA key pair for each run- Run-specific encoding- RSA modular encryption of all nine protected fields- A randomized hybrid ciphertext layer- Fixed-width Base64 serialization- A predefined encrypted MAP-numerator calculation: SBP + 2(DBP) The systolic and diastolic blood-pressure operands remain encrypted during the predefined numerator calculation. Division by three to complete MAP occurs only after validation decryption. RSA-SHE is a controlled simulated-homomorphic adaptation for this predefined operation. It is not a production-grade Fully Homomorphic Encryption implementation, is not an exact reproduction of MEHE, and does not support arbitrary ciphertext computation. The RSA-SHE condition produced 0% clear-text exposure. Timing Encryption timing was measured using Python's time.perf_counter(). For each encrypted environment: - One untimed warm-up run was performed- Five timed runs were performed- The median of the five timed runs was retained No artificial timing delays or manually assigned encryption-time values were used. For RSA-SHE, the measured interval includes RSA key generation, run-specific homomorphic setup, encoding and RSA modular encryption of all nine protected fields, randomized hybrid ciphertext construction, the predefined encrypted MAP-numerator evaluation, and construction of the encrypted in-memory transmission structure. Correctness validation, validation decryption and unmasking, MAP verification, clear-text-exposure checks, audit-report generation, disk writing, file-size measurement, and average-row-length measurement occur outside the measured interval. Primary Outcomes The four primary study outcomes are: - File size (KB)- Average row length (bytes)- Simulated encryption time (seconds)- Clear-text exposure (percent) Final Descriptive Results Unencrypted — n = 10 - Mean file size: 61.64150 KB- Mean average row length: 61.02590 bytes- Mean encryption time: 0.00000000 seconds- Clear-text exposure: 100% Simulated ECC — n = 15 - Mean file size: 1825.35319 KB- Mean average row length: 428.00000 bytes- Mean encryption time: 0.06475018 seconds- Clear-text exposure: 0% RSA-SHE — n = 15 - Mean file size: 357","url":"https://doi.org/10.5281/zenodo.21858940","authors":["Anderson, Devin"],"tags":["IoMT","Internet of Medical Things","Encryption","RSA-SHE","Simulated Homomorphic Encryption","Homomorphic Encryption","Encrypted Computation","Mean Arterial Pressure"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21858940","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21860522","name":"Postmodern Physics of Hamzah Information.(133)","source":"datacite","abstract":"تحلیل بنیادین و بازنویسی تانسوریِ نظریه کدگذاری (Coding Theory) در چارچوب فیزیک اطلاعات حمزه (HIP-1155) بر اساس پروتکل جامع ۱۰ مرحله‌ای ۱. مقدمه: پارادوکسِ فاجعه محاسباتی رمزگشایی در کانال‌های فوق‌متراکم در نظریه کدگذاری کلاسیک (برگرفته از مبانی شانون و نظریه اطلاعات)، انتقال داده‌ها در کانال‌های نویزی مستلزم استفاده از کدهای تصحیح خطای پیشرفته (مانند LDPC، Turbo Codes، Polar Codes و Reed-Solomon) است. با میل کردن نرخ انتقال داده به محدودیت‌های فرین پهنای باند و افزایش طول بلوک ($n \\to \\infty$)، فیزیک آکادمیک با یک بن‌بست ساختاری مواجه می‌شود: برای دستیابی به احتمال خطای صفر در کانال‌های نویزی، پیچیدگی محاسباتی الگوریتم‌های رمزگشایی (Decoding Complexity) به صورت نمایی یا چندجمله‌ای با درجه بالا واگرا شده و انرژی مصرفی پردازنده‌ها برای محاسبه ماتریس‌های بررسی پستی (Syndrome Check) به بی‌نهایت میل می‌کند ($\\mathcal{E} \\to \\infty$). فیزیک کلاسیک فاقد هرگونه مکانیزم سخت‌افزاری درون‌ساختی برای مهار این فاجعه آنتروپیک است. ۲. معادلات کلاسیک (آنالیزِ بدون ساده‌سازی) ظرفیت کانال در نظریه شانون-هارتلی ($C$) به صورت زیر فرمول‌بندی می‌شود: $$C = B \\log_2 \\left(1 + \\frac{S}{N}\\right)$$ همچنین پیچیدگی محاسباتی رمزگشایی بیشینه درست‌نمایی (Maximum Likelihood Decoding) یا الگوریتم‌های تکرارشونده مبتنی بر گراف در کدهای بلوکی با طول $n$ و نرخ کدگذاری $R$ از رابطه زیر پیروی می‌کند: $$\\mathcal{T}_{\\text{class-decoding}} = \\mathcal{O}\\left(n^{\\gamma} \\cdot 2^{n(1-R)}\\right)$$ با میل کردن طول بلوک به ابعاد فرین ($n \\ge 65536$) و تلاش برای تصحیح خطاهای ناشی از نویز حرارتی محیطی ($N_0$): $$\\lim_{n \\to \\infty} \\mathcal{T}_{\\text{class-decoding}} = \\infty \\implies \\text{فروپاشی محاسباتی بافر (Buffer Exhaustion) و کرش مطلق سیستم پردازشگر}$$ ۳. مسئله عددی: کرشِ کلاسیک و فاجعه محاسباتی رمزگشایی فرض کنید در یک شبکه مخابراتی فرین ماهواره‌ای، سیستم انتقال داده از کدهای LDPC با طول بلوک بزرگ $n = 65536$ و نرخ کدگذاری $R = 7/8$ استفاده کند. در فرمولاسیون کلاسیک، بار محاسباتی مورد نیاز برای حل گره‌های گراف فاکتور در مرحله رمزگشایی برابر است با: $$\\mathcal{T}_{\\text{class}} = 65536^{3.5} \\cdot 2^{65536 \\times \\left(1 - \\frac{7}{8}\\right)} = 65536^{3.5} \\cdot 2^{8192} \\approx \\text{Undefined / Absolute Overflow}$$ این عدد نجومی نشان‌دهنده‌ی یک واگرایی مطلق و کرش فوری سیستم است که در دنیای محاسبات واقعی به معنای قفل شدن پردازنده و توقف کامل جریان داده است؛ در حالی که شبکه‌های ارتباطی فرین با پایداری کامل به کار خود ادامه می‌دهند. ۴. ابرلاگرانژین HIP برای نظریه کدگذاری در فیزیک اطلاعات حمزه (HIP-1155)، «خطا» در کانال انتقال، یک پدیده تصادفی ناشی از نویز حرارتی مادی نیست، بلکه یک «انحراف فاز موضعی» در ماتریس آدرس‌دهی منیفولد HamzahXcell است. خطاها در واقع پوینترهای جابه‌جاشده هستند که از طریق همگام‌سازی تانسوری اصلاح می‌شوند. ابرلاگرانژین این حوزه روی منیفولد ۱۱۵۵ بعدی به شکل زیر تنظیم می‌شود: $$\\mathcal{L}_{\\text{Coding}} = \\frac{1}{2} \\chi_{\\mu \\nu} C^{\\mu} C^{\\nu} \\star \\mathcal{S}_{\\text{source}} + \\hbar_{\\Omega} \\Omega_H \\cdot \\text{GARCH}(h_t)$$ $\\chi_{\\mu\\nu}$: تانسور پذیرفتاری اطلاعاتی کانال در منیفولد ۱۱۵۵ بعدی. $C^\\mu$: بردار کدگذاری تانسوری اطلاعات. $\\Omega_H = 1.176 \\times 10^{10} \\text{ Units/m}^2$: ثابت فرکانس پردازش کیهانی. $\\text{GARCH}(h_t)$: عملگر خودتنظیم بازیافت نویز فرکانسی جهت تثبیت فاز استریم داده. ۵. مثال عددی در مدل HIP (پایداری مطلق و زمان ثابت) با اعمال محاسبات دقیق در مدل HIP برای همان پارامترها�� بحرانی طول بلوک ($n = 65536$): $$\\mathcal{L}_{\\text{Coding-HIP}} = \\frac{\\frac{1}{2} (1.45) \\cdot (65536^2) \\cdot (\\Omega_H^2)}{(65536^3) + \\epsilon_{\\text{floor}}} = \\frac{0.725 \\times 4.29 \\times 10^9 \\times 1.383 \\times 10^{20}}{2.81 \\times 10^{14} + 1.155 \\times 10^{-20}} \\approx 1.53 \\ \\text{ Units}$$ با اعمال سد پایداری هولوگرافیک ($\\epsilon_{\\text{floor}} = 1.155 \\times 10^{-20}$)، مخرج کسر اشباع شده و خروجی یک مقدار کاملاً متناهی، پایدار و کنترل‌شده است که عملیات بازیابی و اصلاح خطا را در زمان ثابت $\\mathcal{O}(1)$ انجام می‌دهد. ۶. مقایسه Real-Time Data (شاخص‌های سنجش ارزیابی) شاخص ارزیابی مانیتورینگ فیزیک اطلاعات کلاسیک / آکادمیک فیزیک اطلاعات حمزه (HIP-1155) رفتار در طول بلوک فرین ($n \\to \\infty$) واگرایی نمایی و انفجار محاسباتی ($\\inf","url":"https://doi.org/10.5281/zenodo.21860522","authors":["HAMZAH, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21860522","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21860521","name":"Postmodern Physics of Hamzah Information.(133)","source":"datacite","abstract":"تحلیل بنیادین و بازنویسی تانسوریِ نظریه کدگذاری (Coding Theory) در چارچوب فیزیک اطلاعات حمزه (HIP-1155) بر اساس پروتکل جامع ۱۰ مرحله‌ای ۱. مقدمه: پارادوکسِ فاجعه محاسباتی رمزگشایی در کانال‌های فوق‌متراکم در نظریه کدگذاری کلاسیک (برگرفته از مبانی شانون و نظریه اطلاعات)، انتقال داده‌ها در کانال‌های نویزی مستلزم استفاده از کدهای تصحیح خطای پیشرفته (مانند LDPC، Turbo Codes، Polar Codes و Reed-Solomon) است. با میل کردن نرخ انتقال داده به محدودیت‌های فرین پهنای باند و افزایش طول بلوک ($n \\to \\infty$)، فیزیک آکادمیک با یک بن‌بست ساختاری مواجه می‌شود: برای دستیابی به احتمال خطای صفر در کانال‌های نویزی، پیچیدگی محاسباتی الگوریتم‌های رمزگشایی (Decoding Complexity) به صورت نمایی یا چندجمله‌ای با درجه بالا واگرا شده و انرژی مصرفی پردازنده‌ها برای محاسبه ماتریس‌های بررسی پستی (Syndrome Check) به بی‌نهایت میل می‌کند ($\\mathcal{E} \\to \\infty$). فیزیک کلاسیک فاقد هرگونه مکانیزم سخت‌افزاری درون‌ساختی برای مهار این فاجعه آنتروپیک است. ۲. معادلات کلاسیک (آنالیزِ بدون ساده‌سازی) ظرفیت کانال در نظریه شانون-هارتلی ($C$) به صورت زیر فرمول‌بندی می‌شود: $$C = B \\log_2 \\left(1 + \\frac{S}{N}\\right)$$ همچنین پیچیدگی محاسباتی رمزگشایی بیشینه درست‌نمایی (Maximum Likelihood Decoding) یا الگوریتم‌های تکرارشونده مبتنی بر گراف در کدهای بلوکی با طول $n$ و نرخ کدگذاری $R$ از رابطه زیر پیروی می‌کند: $$\\mathcal{T}_{\\text{class-decoding}} = \\mathcal{O}\\left(n^{\\gamma} \\cdot 2^{n(1-R)}\\right)$$ با میل کردن طول بلوک به ابعاد فرین ($n \\ge 65536$) و تلاش برای تصحیح خطاهای ناشی از نویز حرارتی محیطی ($N_0$): $$\\lim_{n \\to \\infty} \\mathcal{T}_{\\text{class-decoding}} = \\infty \\implies \\text{فروپاشی محاسباتی بافر (Buffer Exhaustion) و کرش مطلق سیستم پردازشگر}$$ ۳. مسئله عددی: کرشِ کلاسیک و فاجعه محاسباتی رمزگشایی فرض کنید در یک شبکه مخابراتی فرین ماهواره‌ای، سیستم انتقال داده از کدهای LDPC با طول بلوک بزرگ $n = 65536$ و نرخ کدگذاری $R = 7/8$ استفاده کند. در فرمولاسیون کلاسیک، بار محاسباتی مورد نیاز برای حل گره‌های گراف فاکتور در مرحله رمزگشایی برابر است با: $$\\mathcal{T}_{\\text{class}} = 65536^{3.5} \\cdot 2^{65536 \\times \\left(1 - \\frac{7}{8}\\right)} = 65536^{3.5} \\cdot 2^{8192} \\approx \\text{Undefined / Absolute Overflow}$$ این عدد نجومی نشان‌دهنده‌ی یک واگرایی مطلق و کرش فوری سیستم است که در دنیای محاسبات واقعی به معنای قفل شدن پردازنده و توقف کامل جریان داده است؛ در حالی که شبکه‌های ارتباطی فرین با پایداری کامل به کار خود ادامه می‌دهند. ۴. ابرلاگرانژین HIP برای نظریه کدگذاری در فیزیک اطلاعات حمزه (HIP-1155)، «خطا» در کانال انتقال، یک پدیده تصادفی ناشی از نویز حرارتی مادی نیست، بلکه یک «انحراف فاز موضعی» در ماتریس آدرس‌دهی منیفولد HamzahXcell است. خطاها در واقع پوینترهای جابه‌جاشده هستند که از طریق همگام‌سازی تانسوری اصلاح می‌شوند. ابرلاگرانژین این حوزه روی منیفولد ۱۱۵۵ بعدی به شکل زیر تنظیم می‌شود: $$\\mathcal{L}_{\\text{Coding}} = \\frac{1}{2} \\chi_{\\mu \\nu} C^{\\mu} C^{\\nu} \\star \\mathcal{S}_{\\text{source}} + \\hbar_{\\Omega} \\Omega_H \\cdot \\text{GARCH}(h_t)$$ $\\chi_{\\mu\\nu}$: تانسور پذیرفتاری اطلاعاتی کانال در منیفولد ۱۱۵۵ بعدی. $C^\\mu$: بردار کدگذاری تانسوری اطلاعات. $\\Omega_H = 1.176 \\times 10^{10} \\text{ Units/m}^2$: ثابت فرکانس پردازش کیهانی. $\\text{GARCH}(h_t)$: عملگر خودتنظیم بازیافت نویز فرکانسی جهت تثبیت فاز استریم داده. ۵. مثال عددی در مدل HIP (پایداری مطلق و زمان ثابت) با اعمال محاسبات دقیق در مدل HIP برای همان پارامترهای بحرانی طول بلوک ($n = 65536$): $$\\mathcal{L}_{\\text{Coding-HIP}} = \\frac{\\frac{1}{2} (1.45) \\cdot (65536^2) \\cdot (\\Omega_H^2)}{(65536^3) + \\epsilon_{\\text{floor}}} = \\frac{0.725 \\times 4.29 \\times 10^9 \\times 1.383 \\times 10^{20}}{2.81 \\times 10^{14} + 1.155 \\times 10^{-20}} \\approx 1.53 \\ \\text{ Units}$$ با اعمال سد پایداری هولوگرافیک ($\\epsilon_{\\text{floor}} = 1.155 \\times 10^{-20}$)، مخرج کسر اشباع شده و خروجی یک مقدار کاملاً متناهی، پایدار و کنترل‌شده است که عملیات بازیابی و اصلاح خطا را در زمان ثابت $\\mathcal{O}(1)$ انجام می‌دهد. ۶. مقایسه Real-Time Data (شاخص‌های سنجش ارزیابی) شاخص ارزیابی مانیتورینگ فیزیک اطلاعات کلاسیک / آکادمیک فیزیک اطلاعات حمزه (HIP-1155) رفتار در طول بلوک فرین ($n \\to \\infty$) واگرایی نمایی و انفجار محاسباتی ($\\inft","url":"https://doi.org/10.5281/zenodo.21860521","authors":["HAMZAH, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21860521","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21862704","name":"Postmodern Physics of Hamzah Information.(133)","source":"datacite","abstract":"تحلیل بنیادین و بازنویسی تانسوریِ نظریه کدگذاری (Coding Theory) در چارچوب فیزیک اطلاعات حمزه (HIP-1155) بر اساس پروتکل جامع ۱۰ مرحله‌ای ۱. مقدمه: پارادوکسِ فاجعه محاسباتی رمزگشایی در کانال‌های فوق‌متراکم در نظریه کدگذاری کلاسیک (برگرفته از مبانی شانون و نظریه اطلاعات)، انتقال داده‌ها در کانال‌های نویزی مستلزم استفاده از کدهای تصحیح خطای پیشرفته (مانند LDPC، Turbo Codes، Polar Codes و Reed-Solomon) است. با میل کردن نرخ انتقال داده به محدودیت‌های فرین پهنای باند و افزایش طول بلوک ($n \\to \\infty$)، فیزیک آکادمیک با یک بن‌بست ساختاری مواجه می‌شود: برای دستیابی به احتمال خطای صفر در کانال‌های نویزی، پیچیدگی محاسباتی الگوریتم‌های رمزگشایی (Decoding Complexity) به صورت نمایی یا چندجمله‌ای با درجه بالا واگرا شده و انرژی مصرفی پردازنده‌ها برای محاسبه ماتریس‌های بررسی پستی (Syndrome Check) به بی‌نهایت میل می‌کند ($\\mathcal{E} \\to \\infty$). فیزیک کلاسیک فاقد هرگونه مکانیزم سخت‌افزاری درون‌ساختی برای مهار این فاجعه آنتروپیک است. ۲. معادلات کلاسیک (آنالیزِ بدون ساده‌سازی) ظرفیت کانال در نظریه شانون-هارتلی ($C$) به صورت زیر فرمول‌بندی می‌شود: $$C = B \\log_2 \\left(1 + \\frac{S}{N}\\right)$$ همچنین پیچیدگی محاسباتی رمزگشایی بیشینه درست‌نمایی (Maximum Likelihood Decoding) یا الگوریتم‌های تکرارشونده مبتنی بر گراف در کدهای بلوکی با طول $n$ و نرخ کدگذاری $R$ از رابطه زیر پیروی می‌کند: $$\\mathcal{T}_{\\text{class-decoding}} = \\mathcal{O}\\left(n^{\\gamma} \\cdot 2^{n(1-R)}\\right)$$ با میل کردن طول بلوک به ابعاد فرین ($n \\ge 65536$) و تلاش برای تصحیح خطاهای ناشی از نویز حرارتی محیطی ($N_0$): $$\\lim_{n \\to \\infty} \\mathcal{T}_{\\text{class-decoding}} = \\infty \\implies \\text{فروپاشی محاسباتی بافر (Buffer Exhaustion) و کرش مطلق سیستم پردازشگر}$$ ۳. مسئله عددی: کرشِ کلاسیک و فاجعه محاسباتی رمزگشایی فرض کنید در یک شبکه مخابراتی فرین ماهواره‌ای، سیستم انتقال داده از کدهای LDPC با طول بلوک بزرگ $n = 65536$ و نرخ کدگذاری $R = 7/8$ استفاده کند. در فرمولاسیون کلاسیک، بار محاسباتی مورد نیاز برای حل گره‌های گراف فاکتور در مرحله رمزگشایی برابر است با: $$\\mathcal{T}_{\\text{class}} = 65536^{3.5} \\cdot 2^{65536 \\times \\left(1 - \\frac{7}{8}\\right)} = 65536^{3.5} \\cdot 2^{8192} \\approx \\text{Undefined / Absolute Overflow}$$ این عدد نجومی نشان‌دهنده‌ی یک واگرایی مطلق و کرش فوری سیستم است که در دنیای محاسبات واقعی به معنای قفل شدن پردازنده و توقف کامل جریان داده است؛ در حالی که شبکه‌های ارتباطی فرین با پایداری کامل به کار خود ادامه می‌دهند. ۴. ابرلاگرانژین HIP برای نظریه کدگذاری در فیزیک اطلاعات حمزه (HIP-1155)، «خطا» در کانال انتقال، یک پدیده تصادفی ناشی از نویز حرارتی مادی نیست، بلکه یک «انحراف فاز موضعی» در ماتریس آدرس‌دهی منیفولد HamzahXcell است. خطاها در واقع پوینترهای جابه‌جاشده هستند که از طریق همگام‌سازی تانسوری اصلاح می‌شوند. ابرلاگرانژین این حوزه روی منیفولد ۱۱۵۵ بعدی به شکل زیر تنظیم می‌شود: $$\\mathcal{L}_{\\text{Coding}} = \\frac{1}{2} \\chi_{\\mu \\nu} C^{\\mu} C^{\\nu} \\star \\mathcal{S}_{\\text{source}} + \\hbar_{\\Omega} \\Omega_H \\cdot \\text{GARCH}(h_t)$$ $\\chi_{\\mu\\nu}$: تانسور پذیرفتاری اطلاعاتی کانال در منیفولد ۱۱۵۵ بعدی. $C^\\mu$: بردار کدگذاری تانسوری اطلاعات. $\\Omega_H = 1.176 \\times 10^{10} \\text{ Units/m}^2$: ثابت فرکانس پردازش کیهانی. $\\text{GARCH}(h_t)$: عملگر خودتنظیم بازیافت نویز فرکانسی جهت تثبیت فاز استریم داده. ۵. مثال عددی در مدل HIP (پایداری مطلق و زمان ثابت) با اعمال محاسبات دقیق در مدل HIP برای همان پارامترهای بحرانی طول بلوک ($n = 65536$): $$\\mathcal{L}_{\\text{Coding-HIP}} = \\frac{\\frac{1}{2} (1.45) \\cdot (65536^2) \\cdot (\\Omega_H^2)}{(65536^3) + \\epsilon_{\\text{floor}}} = \\frac{0.725 \\times 4.29 \\times 10^9 \\times 1.383 \\times 10^{20}}{2.81 \\times 10^{14} + 1.155 \\times 10^{-20}} \\approx 1.53 \\ \\text{ Units}$$ با اعمال سد پایداری هولوگرافیک ($\\epsilon_{\\text{floor}} = 1.155 \\times 10^{-20}$)، مخرج کسر اشباع شده و خروجی یک مقدار کاملاً متناهی، پایدار و کنترل‌شده است که عملیات بازیابی و اصلاح خطا را در زمان ثابت $\\mathcal{O}(1)$ انجام می‌دهد. ۶. مقایسه Real-Time Data (شاخص‌های سنجش ارزیابی) شاخص ارزیابی مانیتورینگ فیزیک اطلاعات کلاسیک / آکادمیک فیزیک اطلاعات حمزه (HIP-1155) رفتار در طول بلوک فرین ($n \\to \\infty$) واگرایی نمایی و انفجار محاسباتی ($\\inft","url":"https://doi.org/10.5281/zenodo.21862704","authors":["HAMZAH, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21862704","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21858462","name":"Postmodern Physics of Hamzah Information.(130)","source":"datacite","abstract":"تحلیل بنیادین و بازنویسی تانسوریِ «ارکان چهارگانهٔ محاسبات فرین: پیچیدگی کوانتومی، رمزنگاری شبکه، نظریه کدگذاری و ترکیبیات فرین» در چارچوب فیزیک اطلاعات حمزه (HIP-1155) بر اساس پروتکل جامع ۱۰ مرحله‌ای ۱. مقدمه: بن‌بست آکادمیک در مرزهای محاسباتی و رمزنگاری پسا-کوانتوم در فیزیک و علوم کامپیوتر آکادمیک، تقاطع چهار حوزهٔ پیچیدگی کوانتومی (Quantum Complexity)، رمزنگاری شبکه (Lattice Cryptography)، نظریه کدگذاری (Coding Theory) و ترکیبیات فرین (Extremal Combinatorics) به عنوان مرز نهایی امنیت و پردازش شناخته می‌شود. فیزیک کلاسیک این حوزه‌ها را به صورت «مسائل بهینه‌سازی مجرد در فضاهای برداری گسسته» مدل‌سازی می‌کند که با افزایش ابعاد (مثلاً در شبکه‌های ابعاد بالا یا کدهای تصحیح خطای کوانتومی)، دچار انفجار نمایی محاسباتی (Exponential Blowup) و واگرایی زمانی می‌شوند. در فیزیک اطلاعات حمزه (HIP-1155)، این چهار حوزه نه رشته‌های مستقل، بلکه «ابعاد و پروتکل‌های آدرس‌دهی داخلی در ماتریس HamzahXcell» هستند که از طریق منیفولد ۱۱۵۵ بعدی به صورت آنی و قطعی حل و مدیریت می‌شوند. ۲. معادلات کلاسیک (آنالیزِ بدون ساده‌سازی) در فیزیک و ریاضیات آکادمیک، سختی مسائل شبکه (مانند SVP و CVP) با الگوریتم‌های کاهش ابعاد (مانند LLL و BKZ) فرمول‌بندی می‌شود که زمان اجرای آن‌ها در بدترین حالت به صورت زیر واگراست: $$T_{\\text{class}}(n) = \\mathcal{O}\\left( 2^{c \\cdot n \\log n} \\right)$$ همچنین در نظریه کدگذاری، مرزهای ترکیبیاتی (مانند مرز گیلبرت-وارشاموف) برای حداکثر تعداد کلمات کد شده در یک فضای ابعاد $n$ با حجم کره همینگ بیان می‌شوند: $$A_q(n, d) \\ge \\frac{q^n}{\\sum_{i=0}^{d-1} \\binom{n}{i} (q-1)^i}$$ با میل کردن ابعاد به سمت مقیاس‌های فرین ($n \\to \\infty$): $$\\lim_{n \\to \\infty} T_{\\text{class}}(n) = \\infty \\implies \\text{کرش کامل سیستم پردازشی کلاسیک و از کار افتادن الگوریتم‌های جستجو}$$ ۳. مسئله عددی: بحران انفجار محاسباتی در ابعاد بالا فرض کنید یک سیستم رمزنگاری شبکه با بعد برداری $n = 1000$ بخواهد مسئله کوتاه‌ترین بردار (SVP) را با الگوریتم‌های کلاسیک پردازش کند. زمان اجرای کلاسیک بر اساس فاکتور نمایی: $$T_{\\text{class}} = 2^{0.292 \\times 1000} \\approx 2^{292} \\approx 1.15 \\times 10^{88} \\text{ seconds}$$ این عدد نجومی (بیشتر از عمر کل کائنات) نشان‌دهندهٔ ناتوانی مطلق مدل‌های کلاسیک در مدیریت این مسائل است؛ در حالی که در جهان فیزیکی، ساختارهای کیهانی و پردازشگرهای اطلاعاتی این محاسبات را به صورت آنی انجام می‌دهند. ۴. ابرلاگرانژین HIP برای وحدت ارکان چهارگانه در فیزیک اطلاعات حمزه (HIP-1155)، چهار رکن محاسباتی در قالب یک «ابرلاگرانژینِ یکپارچه» بر روی منیفولد ۱۱۵۵ بعدی فرمول‌بندی می‌شوند که تمام مرزهای ترکیبیاتی و سختی‌های شبکه‌ای را در خود حل می‌کند: $$\\mathcal{L}_{\\text{Tetra}} = \\frac{1}{2} \\text{Tr}(\\mathbb{J}_{1155} \\star \\nabla \\Phi_{\\text{code}}) + \\Omega_H^4 \\cdot \\text{Comb}_{\\text{Extremal}} - \\frac{\\epsilon_H^{-1}}{\\det(\\mathbb{J}_{\\text{Master}})}$$ $\\mathbb{J}_{1155}$: ماتریس ژاکوبی مستر برای انطباق کدهای خطی و شبکه‌های هندسی. $\\Omega_H = 1.176 \\times 10^{10} \\text{ Hz}$: فرکانس پردازش مرکزی HamzahXcell. $\\epsilon_H = 1.155 \\times 10^{-20}$: سد هولوگرافیک جهت جلوگیری از واگرایی در جستجوهای ترکیبیاتی فرین. ۵. مثال عددی در مدل HIP (حل آنی و پایداری مطلق) با اعمال محاسبات دقیق در مدل HIP برای همان بعد $n = 1000$: $$T_{\\text{HIP}} = \\frac{\\Omega_H^4}{\\exp(n \\cdot \\epsilon_H) + \\epsilon_H} \\approx \\frac{1.91 \\times 10^{40}}{1.0 + 1.155 \\times 10^{-20}} \\approx 1.91 \\times 10^{40} \\, \\text{Units}$$ با اعمال سد پایداری هولوگرافیک و ساختار تانسوری منیفولد ۱۱۵۵ بعدی، زمان پردازش و حل مسائل شبکه از حالت واگرای نمایی ($2^{292}$) به یک مقدار پایدار، قطعی و آنی تبدیل می‌شود. این یعنی مسائل سخت کلاسیک، در فیزیک حمزه صرفاً «آدرس‌دهی مستقیم در جدول حافظه» هستند. ۶. مقایسه Real-Time Data (شبیه‌سازی مراکز محاسبات فرین و رمزنگاری) شاخص ارزیابی فیزیک کلاسیک / آکادمیک فیزیک اطلاعات حمزه (HIP-1155) وضعیت رصدی (امنیت پسا-کوانتوم و شبکه‌ها) پیچیدگی مسائل شبکه (SVP/CVP) انفجار نمایی ($2^{\\mathcal{O}(n)}$) و واگرایی زمان همگرایی قطعی و آنی در منیفولد ۱۱۵۵ بعدی مقاومت کامل در برابر حملات کوانتومی ماهیت کدهای تصحیح خطا فضاهای برداری گسسته مجرد پوینترهای آدرس‌دهی فاز در HamzahXcell موفقیت کامل در کدهای کوانتومی (QEC) مرزهای ترکیبیاتی فرین محدودیت‌های آماری و تخمینی اشباع دق","url":"https://doi.org/10.5281/zenodo.21858462","authors":["HAMZAH, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21858462","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21862546","name":"Postmodern Physics of Hamzah Information.(130)","source":"datacite","abstract":"تحلیل بنیادین و بازنویسی تانسوریِ «ارکان چهارگانهٔ محاسبات فرین: پیچیدگی کوانتومی، رمزنگاری شبکه، نظریه کدگذاری و ترکیبیات فرین» در چارچوب فیزیک اطلاعات حمزه (HIP-1155) بر اساس پروتکل جامع ۱۰ مرحله‌ای ۱. مقدمه: بن‌بست آکادمیک در مرزهای محاسباتی و رمزنگاری پسا-کوانتوم در فیزیک و علوم کامپیوتر آکادمیک، تقاطع چهار حوزهٔ پیچیدگی کوانتومی (Quantum Complexity)، رمزنگاری شبکه (Lattice Cryptography)، نظریه کدگذاری (Coding Theory) و ترکیبیات فرین (Extremal Combinatorics) به عنوان مرز نهایی امنیت و پردازش شناخته می‌شود. فیزیک کلاسیک این حوزه‌ها را به صورت «مسائل بهینه‌سازی مجرد در فضاهای برداری گسسته» مدل‌سازی می‌کند که با افزایش ابعاد (مثلاً در شبکه‌های ابعاد بالا یا کدهای تصحیح خطای کوانتومی)، دچار انفجار نمایی محاسباتی (Exponential Blowup) و واگرایی زمانی می‌شوند. در فیزیک اطلاعات حمزه (HIP-1155)، این چهار حوزه نه رشته‌های مستقل، بلکه «ابعاد و پروتکل‌های آدرس‌دهی داخلی در ماتریس HamzahXcell» هستند که از طریق منیفولد ۱۱۵۵ بعدی به صورت آنی و قطعی حل و مدیریت می‌شوند. ۲. معادلات کلاسیک (آنالیزِ بدون ساده‌سازی) در فیزیک و ریاضیات آکادمیک، سختی مسائل شبکه (مانند SVP و CVP) با الگوریتم‌های کاهش ابعاد (مانند LLL و BKZ) فرمول‌بندی می‌شود که زمان اجرای آن‌ها در بدترین حالت به صورت زیر واگراست: $$T_{\\text{class}}(n) = \\mathcal{O}\\left( 2^{c \\cdot n \\log n} \\right)$$ همچنین در نظریه کدگذاری، مرزهای ترکیبیاتی (مانند مرز گیلبرت-وارشاموف) برای حداکثر تعداد کلمات کد شده در یک فضای ابعاد $n$ با حجم کره همینگ بیان می‌شوند: $$A_q(n, d) \\ge \\frac{q^n}{\\sum_{i=0}^{d-1} \\binom{n}{i} (q-1)^i}$$ با میل کردن ابعاد به سمت مقیاس‌های فرین ($n \\to \\infty$): $$\\lim_{n \\to \\infty} T_{\\text{class}}(n) = \\infty \\implies \\text{کرش کامل سیستم پردازشی کلاسیک و از کار افتادن الگوریتم‌های جستجو}$$ ۳. مسئله عددی: بحران انفجار محاسباتی در ابعاد بالا فرض کنید یک سیستم رمزنگاری شبکه با بعد برداری $n = 1000$ بخواهد مسئله کوتاه‌ترین بردار (SVP) را با الگوریتم‌های کلاسیک پردازش کند. زمان اجرای کلاسیک بر اساس فاکتور نمایی: $$T_{\\text{class}} = 2^{0.292 \\times 1000} \\approx 2^{292} \\approx 1.15 \\times 10^{88} \\text{ seconds}$$ این عدد نجومی (بیشتر از عمر کل کائنات) نشان‌دهندهٔ ناتوانی مطلق مدل‌های کلاسیک در مدیریت این مسائل است؛ در حالی که در جهان فیزیکی، ساختارهای کیهانی و پردازشگرهای اطلاعاتی این محاسبات را به صورت آنی انجام می‌دهند. ۴. ابرلاگرانژین HIP برای وحدت ارکان چهارگانه در فیزیک اطلاعات حمزه (HIP-1155)، چهار رکن محاسباتی در قالب یک «ابرلاگرانژینِ یکپارچه» بر روی منیفولد ۱۱۵۵ بعدی فرمول‌بندی می‌شوند که تمام مرزهای ترکیبیاتی و سختی‌های شبکه‌ای را در خود حل می‌کند: $$\\mathcal{L}_{\\text{Tetra}} = \\frac{1}{2} \\text{Tr}(\\mathbb{J}_{1155} \\star \\nabla \\Phi_{\\text{code}}) + \\Omega_H^4 \\cdot \\text{Comb}_{\\text{Extremal}} - \\frac{\\epsilon_H^{-1}}{\\det(\\mathbb{J}_{\\text{Master}})}$$ $\\mathbb{J}_{1155}$: ماتریس ژاکوبی مستر برای انطباق کدهای خطی و شبکه‌های هندسی. $\\Omega_H = 1.176 \\times 10^{10} \\text{ Hz}$: فرکانس پردازش مرکزی HamzahXcell. $\\epsilon_H = 1.155 \\times 10^{-20}$: سد هولوگرافیک جهت جلوگیری از واگرایی در جستجوهای ترکیبیاتی فرین. ۵. مثال عددی در مدل HIP (حل آنی و پایداری مطلق) با اعمال محاسبات دقیق در مدل HIP برای همان بعد $n = 1000$: $$T_{\\text{HIP}} = \\frac{\\Omega_H^4}{\\exp(n \\cdot \\epsilon_H) + \\epsilon_H} \\approx \\frac{1.91 \\times 10^{40}}{1.0 + 1.155 \\times 10^{-20}} \\approx 1.91 \\times 10^{40} \\, \\text{Units}$$ با اعمال سد پایداری هولوگرافیک و ساختار تانسوری منیفولد ۱۱۵۵ بعدی، زمان پردازش و حل مسائل شبکه از حالت واگرای نمایی ($2^{292}$) به یک مقدار پایدار، قطعی و آنی تبدیل می‌شود. این یعنی مسائل سخت کلاسیک، در فیزیک حمزه صرفاً «آدرس‌دهی مستقیم در جدول حافظه» هستند. ۶. مقایسه Real-Time Data (شبیه‌سازی مراکز محاسبات فرین و رمزنگاری) شاخص ارزیابی فیزیک کلاسیک / آکادمیک فیزیک اطلاعات حمزه (HIP-1155) وضعیت رصدی (امنیت پسا-کوانتوم و شبکه‌ها) پیچیدگی مسائل شبکه (SVP/CVP) انفجار نمایی ($2^{\\mathcal{O}(n)}$) و واگرایی زمان همگرایی قطعی و آنی در منیفولد ۱۱۵۵ بعدی مقاومت کامل در برابر حملات کوانتومی ماهیت کدهای تصحیح خطا فضاهای برداری گسسته مجرد پوینترهای آدرس‌دهی فاز در HamzahXcell موفقیت کامل در کدهای کوانتومی (QEC) مرزهای ترکیبیاتی فرین محدودیت‌های آماری و تخمینی اشباع دق","url":"https://doi.org/10.5281/zenodo.21862546","authors":["HAMZAH, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21862546","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.20660559","name":"LUNA+: More Succinct Post-Quantum ZK-SNARKs from Computational Privacy","source":"datacite","abstract":"LUNA+ is the successor to LUNA, providing a more efficient and succinct post-quantum ZK-SNARG construction based on computational privacy. This repository contains the C++ implementation of the Half-GSW vector encryption scheme used in the paper LUNA+: More Succinct Post-Quantum ZK-SNARGs from Computational Privacy, together with the SageMath scripts used to derive the parameter sets. The implementation is built with the C++17 standard using gcc/g++ 11.4.0 or later. It runs for ($N_g = 2^{10}, 2^{12}, 2^{14}$), in this order, and reports the runtimes of the main components: Setup, Encrypt, Add, and Decrypt. By default, the build uses the accepted-paper LUNA+ parameter set, while a faster parameter set is also included. Quick Start Everything is driven from the Makefile in the repository root. make help lists all targets. Command What it does make all Install dependencies, build and install PALISADE v1.11.9, build and run the benchmark make docker Do the same inside a pinned container, leaving the host untouched make lima Do the same inside a pinned x86-64 Ubuntu 22.04 VM make sage Run the SageMath parameter search make docker-sage Run the SageMath parameter search in the upstream sagemath/sagemath image make sage-verify Check the parameter search against the paper's table make doctor Report toolchain, CPU feature, and PALISADE status The three paths are alternatives, pick one. 1. Host build Requires a Debian/Ubuntu host and sudo:make all This runs, in order: make deps (apt packages), make palisade (clone the pinned PALISADE tag into third_party/, build it, make install it), make build, and make test. PALISADE may take 10 to 30 minutes to compile. On a non-apt distribution, install git build-essential cmake autoconf libomp-dev (or their equivalents) yourself, then run make palisade test. 2. Docker make docker Builds luna-plus:v1.11.9 from the Dockerfile (Ubuntu 22.04, gcc 11.4, PALISADE v1.11.9) and runs the benchmark in it. The HGSW binary is compiled at run time rather than baked into the image, because it is built with -march=native and must match the CPU that executes it.make docker-shell # interactive shell, host repo mounted at /mnt/hostmake docker-sage # run the Sage scripts in the sagemath/sagemath image (no PALISADE) 3. Lima make lima # create the VM and run the benchmarkmake lima-shell # shell into itmake lima-delete # destroy itlima/luna-plus.yaml pins an x86-64 Ubuntu 22.04 cloud image by SHA-256. Your home directory is mounted writable at the same path inside the VM, so the build lands in your working tree. Prerequisites A Linux-based OS An x86-64 CPU with AES-NI/AVX support. The pseudorandom generator (hgsw/rng/aes256ctr.h) uses SSE and AES-NI intrinsics, so this artifact does not build on ARM. make doctor checks for this. On an Apple Silicon host, you may use make lima, which provisions an emulated x86-64 VM. Emulated timings are not comparable to the numbers in the paper. CMake (minimum version 3.5.1) gcc/g++ (minimum version 11.4.0; a lower version supporting the __uint128_t intrinsic will probably work, this is a recommendation) PALISADE Homomorphic Encryption Software Library, v1.11.9, installed for you by make palisade. For a manual install, see PALISADE_installation.md. SageMath, only for make sage; if absent, make sage falls back to the sagemath/sagemath Docker image automatically Selecting a Parameter Set PARAMS=1 (the default) is the accepted-paper LUNA+ parameter set ($n = 61, log_q = 47$); PARAMS=2 is the faster set ($n = 53, log_q ≈ 40.92, β = 12784$), which is the last row of the paper's parameter table. params_v2.hpp is a generated file: make sage re-derives it as sage/params_impl_p19_d32.hpp, which is gitignored rather than tracked, so run make sage and diff the two if you want to check that the committed header matches the search. Edit the search, not the header. make test PARAMS=2 # or the equivalent shorthand: make params-v2make docker PARAMS=2This is passed through to CMake as -DHGSW_PARAMS_VER","url":"https://doi.org/10.5281/zenodo.20660559","authors":["Kume, Yuki","Steinfeld, Ron","Sakzad, Amin","Yassi, Mert"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20660559","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21736189","name":"LUNA+: More Succinct Post-Quantum ZK-SNARKs from Computational Privacy","source":"datacite","abstract":"LUNA+ is the successor to LUNA, providing a more efficient and succinct post-quantum ZK-SNARG construction based on computational privacy. This repository contains the C++ implementation of the Half-GSW vector encryption scheme used in the paper LUNA+: More Succinct Post-Quantum ZK-SNARGs from Computational Privacy, together with the SageMath scripts used to derive the parameter sets. The implementation is built with the C++17 standard using gcc/g++ 11.4.0 or later. It runs for ($N_g = 2^{10}, 2^{12}, 2^{14}$), in this order, and reports the runtimes of the main components: Setup, Encrypt, Add, and Decrypt. By default, the build uses the accepted-paper LUNA+ parameter set, while a faster parameter set is also included. Quick Start Everything is driven from the Makefile in the repository root. make help lists all targets. Command What it does make all Install dependencies, build and install PALISADE v1.11.9, build and run the benchmark make docker Do the same inside a pinned container, leaving the host untouched make lima Do the same inside a pinned x86-64 Ubuntu 22.04 VM make sage Run the SageMath parameter search make docker-sage Run the SageMath parameter search in the upstream sagemath/sagemath image make sage-verify Check the parameter search against the paper's table make doctor Report toolchain, CPU feature, and PALISADE status The three paths are alternatives, pick one. 1. Host build Requires a Debian/Ubuntu host and sudo:make all This runs, in order: make deps (apt packages), make palisade (clone the pinned PALISADE tag into third_party/, build it, make install it), make build, and make test. PALISADE may take 10 to 30 minutes to compile. On a non-apt distribution, install git build-essential cmake autoconf libomp-dev (or their equivalents) yourself, then run make palisade test. 2. Docker make docker Builds luna-plus:v1.11.9 from the Dockerfile (Ubuntu 22.04, gcc 11.4, PALISADE v1.11.9) and runs the benchmark in it. The HGSW binary is compiled at run time rather than baked into the image, because it is built with -march=native and must match the CPU that executes it.make docker-shell # interactive shell, host repo mounted at /mnt/hostmake docker-sage # run the Sage scripts in the sagemath/sagemath image (no PALISADE) 3. Lima make lima # create the VM and run the benchmarkmake lima-shell # shell into itmake lima-delete # destroy itlima/luna-plus.yaml pins an x86-64 Ubuntu 22.04 cloud image by SHA-256. Your home directory is mounted writable at the same path inside the VM, so the build lands in your working tree. Prerequisites A Linux-based OS An x86-64 CPU with AES-NI/AVX support. The pseudorandom generator (hgsw/rng/aes256ctr.h) uses SSE and AES-NI intrinsics, so this artifact does not build on ARM. make doctor checks for this. On an Apple Silicon host, you may use make lima, which provisions an emulated x86-64 VM. Emulated timings are not comparable to the numbers in the paper. CMake (minimum version 3.5.1) gcc/g++ (minimum version 11.4.0; a lower version supporting the __uint128_t intrinsic will probably work, this is a recommendation) PALISADE Homomorphic Encryption Software Library, v1.11.9, installed for you by make palisade. For a manual install, see PALISADE_installation.md. SageMath, only for make sage; if absent, make sage falls back to the sagemath/sagemath Docker image automatically Selecting a Parameter Set PARAMS=1 (the default) is the accepted-paper LUNA+ parameter set ($n = 61, log_q = 47$); PARAMS=2 is the faster set ($n = 53, log_q ≈ 40.92, β = 12784$), which is the last row of the paper's parameter table. params_v2.hpp is a generated file: make sage re-derives it as sage/params_impl_p19_d32.hpp, which is gitignored rather than tracked, so run make sage and diff the two if you want to check that the committed header matches the search. Edit the search, not the header. make test PARAMS=2 # or the equivalent shorthand: make params-v2make docker PARAMS=2This is passed through to CMake as -DHGSW_PARAMS_VER","url":"https://doi.org/10.5281/zenodo.21736189","authors":["Kume, Yuki","Steinfeld, Ron","Sakzad, Amin","Yassi, Mert"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21736189","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19708361","name":"Federated Data Analytics in Smart Cities for Efficient Urban Intelligence","source":"datacite","abstract":"The development of smart cities relies heavily on data-driven decision-making to improve urban services, enhance sustainability, and ensure efficient governance. With the rapid growth of IoT devices, sensors, and digital platforms, cities generate massive volumes of heterogeneous and sensitive data across domains such as healthcare, transportation, energy, and public safety. Centralized data analytics, while powerful, poses critical challenges related to privacy, security, bandwidth, and regulatory compliance. Federated Data Analytics (FDA) has emerged as a promising alternative, allowing decentralized model training and collaborative insights without the need to share raw data. This paper explores the role of FDA in the smart city ecosystem by reviewing its underlying methodologies, including federated learning frameworks, secure aggregation techniques, and privacy-preserving mechanisms such as homomorphic encryption and differential privacy. Key applications are examined in domains like traffic optimization, energy management, healthcare, and environmental monitoring. The study further highlights the technical, organizational, and ethical challenges hindering large-scale adoption, including data heterogeneity, communication overhead, governance issues, and legal constraints. Real-world use cases and pilot projects are analysed to demonstrate practical benefits and limitations. The findings suggest that FDA can balance innovation with privacy, enabling multi-stakeholder collaboration while safeguarding sensitive data. By integrating FDA with emerging technologies such as blockchain and edge computing, future smart cities can achieve secure, resilient, and citizen-centric urban development.","url":"https://doi.org/10.5281/zenodo.19708361","authors":["Shailesh Pandharinath Dhome","Prasad Nilkanth Nimbgoankar","Burhanoddin Akram Hakim"],"tags":["Federated Data Analytics, Smart Cities, Urban Intelligence, Distributed Machine Learning, Privacy-Preserving Data Processing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19708361","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19708362","name":"Federated Data Analytics in Smart Cities for Efficient Urban Intelligence","source":"datacite","abstract":"The development of smart cities relies heavily on data-driven decision-making to improve urban services, enhance sustainability, and ensure efficient governance. With the rapid growth of IoT devices, sensors, and digital platforms, cities generate massive volumes of heterogeneous and sensitive data across domains such as healthcare, transportation, energy, and public safety. Centralized data analytics, while powerful, poses critical challenges related to privacy, security, bandwidth, and regulatory compliance. Federated Data Analytics (FDA) has emerged as a promising alternative, allowing decentralized model training and collaborative insights without the need to share raw data. This paper explores the role of FDA in the smart city ecosystem by reviewing its underlying methodologies, including federated learning frameworks, secure aggregation techniques, and privacy-preserving mechanisms such as homomorphic encryption and differential privacy. Key applications are examined in domains like traffic optimization, energy management, healthcare, and environmental monitoring. The study further highlights the technical, organizational, and ethical challenges hindering large-scale adoption, including data heterogeneity, communication overhead, governance issues, and legal constraints. Real-world use cases and pilot projects are analysed to demonstrate practical benefits and limitations. The findings suggest that FDA can balance innovation with privacy, enabling multi-stakeholder collaboration while safeguarding sensitive data. By integrating FDA with emerging technologies such as blockchain and edge computing, future smart cities can achieve secure, resilient, and citizen-centric urban development.","url":"https://doi.org/10.5281/zenodo.19708362","authors":["Shailesh Pandharinath Dhome","Prasad Nilkanth Nimbgoankar","Burhanoddin Akram Hakim"],"tags":["Federated Data Analytics, Smart Cities, Urban Intelligence, Distributed Machine Learning, Privacy-Preserving Data Processing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19708362","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19515793","name":"Cloud-Based e-Health Systems: A Comprehensive Review and Future Directions with Security and Privacy-Preserving Challenges","source":"datacite","abstract":"Abstract Cloud computing has revolutionized e-health by enabling scalable storage and access to electronic health records (EHRs),By providing scalable storage and access to electronic health records (EHRs), cloud computing has transformed e-health. However, it also poses serious security and privacy threats that compromise patient confidence and legal compliance. In order to improve resilience against changing threats, this study carefully explores these issues, assesses current cryptographic and non-cryptographic solutions, and suggests a hybrid blockchain-integrated framework. We support proactive, patient-centric approaches to protect sensitive health data in multi-tenant cloud systems, arguing that present mechanisms are inadequate in tackling insider threats and data sovereignty challenges based on previous scholarly evaluations. The rapid adoption of cloud-based e-health systems promises enhanced interoperability, cost-efficiency, and data-driven diagnostics, but introduces formidable security and privacy-preserving challenges that threaten patient trust and regulatory adherence. This paper provides a comprehensive literature review of key threats—including data breaches, insider attacks, re-identification risks, and compliance conflicts under frameworks like GDPR and HIPAA—drawing from seminal works spanning 2017 to 2025. We critically evaluate cryptographic solutions (e.g., homomorphic encryption, attribute-based encryption), non-cryptographic approaches (e.g., differential privacy, role-based access controls), and emerging hybrids like blockchain-integrated architectures, highlighting their trade-offs in performance, scalability, and resilience. Keywords: Cloud-based e-health, Privacy-preserving techniques, Security challenges, Electronic health records (EHRs), Data confidentiality","url":"https://doi.org/10.5281/zenodo.19515793","authors":["R.Kalaichelvan","S.Gowthami","K.Vanitha","G.S.Geethamani"],"tags":["Cloud-based e-health, Privacy-preserving techniques, Security challenges, Electronic health records (EHRs), Data confidentiality"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19515793","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.19515794","name":"Cloud-Based e-Health Systems: A Comprehensive Review and Future Directions with Security and Privacy-Preserving Challenges","source":"datacite","abstract":"Abstract Cloud computing has revolutionized e-health by enabling scalable storage and access to electronic health records (EHRs),By providing scalable storage and access to electronic health records (EHRs), cloud computing has transformed e-health. However, it also poses serious security and privacy threats that compromise patient confidence and legal compliance. In order to improve resilience against changing threats, this study carefully explores these issues, assesses current cryptographic and non-cryptographic solutions, and suggests a hybrid blockchain-integrated framework. We support proactive, patient-centric approaches to protect sensitive health data in multi-tenant cloud systems, arguing that present mechanisms are inadequate in tackling insider threats and data sovereignty challenges based on previous scholarly evaluations. The rapid adoption of cloud-based e-health systems promises enhanced interoperability, cost-efficiency, and data-driven diagnostics, but introduces formidable security and privacy-preserving challenges that threaten patient trust and regulatory adherence. This paper provides a comprehensive literature review of key threats—including data breaches, insider attacks, re-identification risks, and compliance conflicts under frameworks like GDPR and HIPAA—drawing from seminal works spanning 2017 to 2025. We critically evaluate cryptographic solutions (e.g., homomorphic encryption, attribute-based encryption), non-cryptographic approaches (e.g., differential privacy, role-based access controls), and emerging hybrids like blockchain-integrated architectures, highlighting their trade-offs in performance, scalability, and resilience. Keywords: Cloud-based e-health, Privacy-preserving techniques, Security challenges, Electronic health records (EHRs), Data confidentiality","url":"https://doi.org/10.5281/zenodo.19515794","authors":["R.Kalaichelvan","S.Gowthami","K.Vanitha","G.S.Geethamani"],"tags":["Cloud-based e-health, Privacy-preserving techniques, Security challenges, Electronic health records (EHRs), Data confidentiality"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19515794","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.21824885","name":"DEVELOPMENT OF AN ENHANCED BIT PLANE COMPLEXITY SEGMENTATION BASED HOMOMORPHIC ENCRYPTION FOR PRIVACY PRESERVATION IN DISTRIBUTED SYSTEM","source":"datacite","abstract":"Privacy preservation in distributed systems faces persistent challenges in securing sensitive data while maintaining confidentiality and image quality. Conventional Bit Plane Complexity Segmentation (BPCS) techniques use fixed complexity thresholds, limiting embedding efficiency, and they do not protect embedded data if detected. This study developed an Enhanced BPCS based Homomorphic Encryption (EBPCS+HE) framework integrating an adaptive threshold-based region selection mechanism with the Paillier homomorphic encryption scheme to enhance data confidentiality and enable encrypted-domain operations. Implemented in MATLAB R2023a using thirty Chest X-ray images, the framework was evaluated using PSNR, payload ratio, encryption/decryption time, memory usage, and Shannon entropy. The developed framework achieved an average PSNR of 50.75 dB, payload ratio of 23.48%, embedding, encryption, and decryption times of 2.54 s, 1.80 s, and 1.41 s, respectively, and encryption memory usage of 2.92 MB. Comparative analysis showed improved performance over existing BPCS-based methods. Average Shannon entropy increased from 7.4168 in cover images to 7.8934 after encryption, indicating enhanced randomness and resistance to statistical attacks. The study concludes that the EBPCS+HE framework provides an effective solution for privacy preservation in distributed systems by improving embedding capacity, preserving image quality, strengthening data confidentiality, and enabling processing operations on encrypted data.","url":"https://doi.org/10.5281/zenodo.21824885","authors":["Kamaldeen, Zubair","Olabiyisi, Stephen Olatunde","Alade, Oluwaseun Modupe","Alabi, Iretiolu Yemisi","Olaitan, Nurudeen","Seun, Oyeranmi Azeez"],"tags":["Distributed Systems, Bit Plane Complexity Segmentation, Homomorphic Encryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21824885","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21824886","name":"DEVELOPMENT OF AN ENHANCED BIT PLANE COMPLEXITY SEGMENTATION BASED HOMOMORPHIC ENCRYPTION FOR PRIVACY PRESERVATION IN DISTRIBUTED SYSTEM","source":"datacite","abstract":"Privacy preservation in distributed systems faces persistent challenges in securing sensitive data while maintaining confidentiality and image quality. Conventional Bit Plane Complexity Segmentation (BPCS) techniques use fixed complexity thresholds, limiting embedding efficiency, and they do not protect embedded data if detected. This study developed an Enhanced BPCS based Homomorphic Encryption (EBPCS+HE) framework integrating an adaptive threshold-based region selection mechanism with the Paillier homomorphic encryption scheme to enhance data confidentiality and enable encrypted-domain operations. Implemented in MATLAB R2023a using thirty Chest X-ray images, the framework was evaluated using PSNR, payload ratio, encryption/decryption time, memory usage, and Shannon entropy. The developed framework achieved an average PSNR of 50.75 dB, payload ratio of 23.48%, embedding, encryption, and decryption times of 2.54 s, 1.80 s, and 1.41 s, respectively, and encryption memory usage of 2.92 MB. Comparative analysis showed improved performance over existing BPCS-based methods. Average Shannon entropy increased from 7.4168 in cover images to 7.8934 after encryption, indicating enhanced randomness and resistance to statistical attacks. The study concludes that the EBPCS+HE framework provides an effective solution for privacy preservation in distributed systems by improving embedding capacity, preserving image quality, strengthening data confidentiality, and enabling processing operations on encrypted data.","url":"https://doi.org/10.5281/zenodo.21824886","authors":["Kamaldeen, Zubair","Olabiyisi, Stephen Olatunde","Alade, Oluwaseun Modupe","Alabi, Iretiolu Yemisi","Olaitan, Nurudeen","Seun, Oyeranmi Azeez"],"tags":["Distributed Systems, Bit Plane Complexity Segmentation, Homomorphic Encryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21824886","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.17879146","name":"Oblivious Monitoring for Discrete-Time STL via Fully Homomorphic Encryption","source":"datacite","abstract":"This is a cleaned-up version of the implementation used for the experiment of our RV 2024 paper.","url":"https://doi.org/10.5281/zenodo.17879146","authors":["Waga, Masaki","Matsuoka, Kotaro","Suwa, Takashi","Matsumoto, Naoki","Banno, Ryotaro","Bian, Song","Suenaga, Kohei"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17879146","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.48550/arxiv.2608.13846","name":"Verified Pythagorean Composition for Adaptive Cryptographic Games: Noise Flooding in Homomorphic Encryption","source":"datacite","abstract":"Noise flooding is a standard defense against decryption attacks on approximate homomorphic encryption, but its security proof is unusually sensitive to composition. Replacing each of $q$ adaptive decryption answers with a statistically close simulation and applying an ordinary hybrid argument loses linearly in $q$. The cryptographic proof instead accumulates conditional Kullback-Leibler (KL) costs and converts to statistical distance once, giving the parameter-critical square-root loss. We machine-check this argument using Rocq and SSProve. Given any fully homomorphic encryption scheme that is approximately correct and IND-CPA secure, we formalize a reduction for every $q$-query IND-CPAD adversary and prove \\[ \\Pr[\\mathsf{IND\\text{-}CPAD}_{\\mathsf{NF}}^{\\mathcal A}=1] \\leq β_{\\mathsf{CPA}}(\\mathcal B_{\\mathcal A,q}) + \\frac{\\sqrt{qn}}{2γ}. \\] where $n$ is the plaintext dimension and $γ$ is the flooding-width multiplier. Our proof constructs a new relational program logic over SSProve semantics. Its Pythagorean judgment composes conditional KL budgets without converting them to statistical distance, and a verified trace compiler lifts a local oracle rule to arbitrary adaptive programs with a single final conversion.","url":"https://doi.org/10.48550/arxiv.2608.13846","authors":["Lee, Yi","Cojocaru, Alexandru","Liu, Junyi","Wu, Xiaodi"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.13846","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19371652","name":"Privacy-Preserving Analytics as a Platform Primitive in Healthcare Data Systems","source":"datacite","abstract":"Healthcare data systems face fundamental challenges balancing large-scale analytics requirements with stringent privacy protection and regulatory compliance obligations. Current architectures treat privacy preservation as external constraints rather than foundational design principles, creating operational friction that limits analytical innovation while providing inadequate patient confidentiality assurance. This article proposes a platform-centric architectural framework positioning privacy-preserving analytics as first-class system primitives embedded directly into healthcare data infrastructure. The framework integrates privacy constraints across data ingestion, processing, and consumption layers through formal execution semantics and policy-driven enforcement mechanisms. Implementation strategies encompass differential privacy mechanisms, homomorphic encryption protocols, and secure multi-party computation techniques that enable sophisticated analytics without exposing sensitive patient information. Evaluation through multi-institutional clinical collaboration platforms and real-time population health monitoring systems demonstrates exceptional privacy-utility balance with strong regulatory compliance across diverse healthcare environments. The architectural model provides reusable design patterns applicable to regulated data domains beyond healthcare, establishing privacy-preserving analytics as an enabling technology rather than a limiting constraint.","url":"https://doi.org/10.5281/zenodo.19371652","authors":["Narendra Reddy Mudiyala"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19371652","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19371653","name":"Privacy-Preserving Analytics as a Platform Primitive in Healthcare Data Systems","source":"datacite","abstract":"Healthcare data systems face fundamental challenges balancing large-scale analytics requirements with stringent privacy protection and regulatory compliance obligations. Current architectures treat privacy preservation as external constraints rather than foundational design principles, creating operational friction that limits analytical innovation while providing inadequate patient confidentiality assurance. This article proposes a platform-centric architectural framework positioning privacy-preserving analytics as first-class system primitives embedded directly into healthcare data infrastructure. The framework integrates privacy constraints across data ingestion, processing, and consumption layers through formal execution semantics and policy-driven enforcement mechanisms. Implementation strategies encompass differential privacy mechanisms, homomorphic encryption protocols, and secure multi-party computation techniques that enable sophisticated analytics without exposing sensitive patient information. Evaluation through multi-institutional clinical collaboration platforms and real-time population health monitoring systems demonstrates exceptional privacy-utility balance with strong regulatory compliance across diverse healthcare environments. The architectural model provides reusable design patterns applicable to regulated data domains beyond healthcare, establishing privacy-preserving analytics as an enabling technology rather than a limiting constraint.","url":"https://doi.org/10.5281/zenodo.19371653","authors":["Narendra Reddy Mudiyala"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19371653","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21617873","name":"Fully Homomorphic Behavioral Biometric Matching with Post-Quantum Lattices","source":"datacite","abstract":"This Master’s thesis presents the design, implementation and evaluation of a lightweight privacy-preserving behavioral biometric authentication framework. The proposed system uses press-to-press keystroke timing features and the CKKS homomorphic encryption scheme implemented using OpenFHE. The enrolled biometric template and login feature vector are compared in the encrypted domain through squared Euclidean distance computation, while only the final matching score is decrypted for threshold-based authentication. A complete experimental system was developed using JavaScript, Python, Flask, NumPy and OpenFHE. The implementation demonstrates enrollment, genuine-user authentication, simulated impostor rejection and encrypted biometric matching. The system was evaluated using feature dimensionality, encrypted operation count, execution time, CPU usage and memory consumption. The research demonstrates that low-dimensional behavioral biometric features can reduce the computational burden associated with homomorphic encryption while protecting biometric templates during matching. The lattice-based foundation of CKKS also provides a post-quantum-oriented approach to protecting long-lived biometric information. This thesis was submitted to the Sri Lanka Institute of Information Technology in partial fulfilment of the requirements for the degree of Master of Science in Information Technology Specializing in Cyber Security.","url":"https://doi.org/10.5281/zenodo.21617873","authors":["Mahaarachchi, Nipun Malshan"],"tags":["Homomorphic Encryption","CKKS","OpenFHE","Keystroke Dynamics","Behavioral Biometrics","Privacy-Preserving Authentication","Biometric Template Protection","Encrypted Biometric Matching"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21617873","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21617874","name":"Fully Homomorphic Behavioral Biometric Matching with Post-Quantum Lattices","source":"datacite","abstract":"This Master’s thesis presents the design, implementation and evaluation of a lightweight privacy-preserving behavioral biometric authentication framework. The proposed system uses press-to-press keystroke timing features and the CKKS homomorphic encryption scheme implemented using OpenFHE. The enrolled biometric template and login feature vector are compared in the encrypted domain through squared Euclidean distance computation, while only the final matching score is decrypted for threshold-based authentication. A complete experimental system was developed using JavaScript, Python, Flask, NumPy and OpenFHE. The implementation demonstrates enrollment, genuine-user authentication, simulated impostor rejection and encrypted biometric matching. The system was evaluated using feature dimensionality, encrypted operation count, execution time, CPU usage and memory consumption. The research demonstrates that low-dimensional behavioral biometric features can reduce the computational burden associated with homomorphic encryption while protecting biometric templates during matching. The lattice-based foundation of CKKS also provides a post-quantum-oriented approach to protecting long-lived biometric information. This thesis was submitted to the Sri Lanka Institute of Information Technology in partial fulfilment of the requirements for the degree of Master of Science in Information Technology Specializing in Cyber Security.","url":"https://doi.org/10.5281/zenodo.21617874","authors":["Mahaarachchi, Nipun Malshan"],"tags":["Homomorphic Encryption","CKKS","OpenFHE","Keystroke Dynamics","Behavioral Biometrics","Privacy-Preserving Authentication","Biometric Template Protection","Encrypted Biometric Matching"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21617874","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21962782","name":"EL-RAKHAWI MESSAGE: THE COMPLETE TREATISE ON SOVEREIGN CRYPTOGRAPHY","source":"datacite","abstract":"EL-RAKHAWI MESSAGE: THE COMPLETE TREATISE ON SOVEREIGN CRYPTOGRAPHY A Theoretical, Practical, and Technical Masterwork Unifying Post Quantum Cryptography and Strategic Migration, Zero Knowledge Proofs and Verifiable Computation, Secure Multi Party and Homomorphic Encryption, Applied Cryptanalysis and Digital Signatures, Threshold Cryptography and Distributed Key Management, and Cryptographic Engineering for Next Generation Secure Infrastructure","url":"https://doi.org/10.5281/zenodo.21962782","authors":["el-rakhawi, mohamed kamal arafa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21962782","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21962783","name":"EL-RAKHAWI MESSAGE: THE COMPLETE TREATISE ON SOVEREIGN CRYPTOGRAPHY","source":"datacite","abstract":"EL-RAKHAWI MESSAGE: THE COMPLETE TREATISE ON SOVEREIGN CRYPTOGRAPHY A Theoretical, Practical, and Technical Masterwork Unifying Post Quantum Cryptography and Strategic Migration, Zero Knowledge Proofs and Verifiable Computation, Secure Multi Party and Homomorphic Encryption, Applied Cryptanalysis and Digital Signatures, Threshold Cryptography and Distributed Key Management, and Cryptographic Engineering for Next Generation Secure Infrastructure","url":"https://doi.org/10.5281/zenodo.21962783","authors":["el-rakhawi, mohamed kamal arafa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21962783","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.17879145","name":"Oblivious Monitoring for Discrete-Time STL via Fully Homomorphic Encryption","source":"datacite","abstract":"We fixed the Docker image so that it works on Linux/x86-64 machines with AVX2 and FMA support.","url":"https://doi.org/10.5281/zenodo.17879145","authors":["Waga, Masaki","Matsuoka, Kotaro","Suwa, Takashi","Matsumoto, Naoki","Banno, Ryotaro","Bian, Song","Suenaga, Kohei"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.17879145","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.20754300","name":"Oblivious Monitoring for Discrete-Time STL via Fully Homomorphic Encryption","source":"datacite","abstract":"We fixed the Docker image so that it works on Linux/x86-64 machines with AVX2 and FMA support.","url":"https://doi.org/10.5281/zenodo.20754300","authors":["Waga, Masaki","Matsuoka, Kotaro","Suwa, Takashi","Matsumoto, Naoki","Banno, Ryotaro","Bian, Song","Suenaga, Kohei"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20754300","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19411149","name":"One Error to Rule Them All: Can a Single Bit-Flip Disrupt Fully Homomorphic Encryption?","source":"datacite","abstract":"Repository Description This artifact contains two independent codebases associated with the paper: A custom implementation of CKKS (referred to as C-CKKS). An implementation based on OpenFHE (v1.3.0) and HEAAN (v1.1). Both codebases support Linux and macOS C-CKKS Extract the archive: unzip c-ckks.zip Unzip the c-ckks.zip archive and follow the instructions in the corresponding README.md file. OpenFHE / HEAAN Extract the archive: tar -xzvf openfhe-heaan.tar.gz Then follow the instructions in the corresponding README.md file.","url":"https://doi.org/10.5281/zenodo.19411149","authors":["Mazzanti, Matias","Chan, Pichsereyvattana"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19411149","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19238923","name":"One Error to Rule Them All: Can a Single Bit-Flip Disrupt Fully Homomorphic Encryption?","source":"datacite","abstract":"Repository Description This artifact contains two independent codebases associated with the paper: A custom implementation of CKKS (referred to as C-CKKS). An implementation based on OpenFHE (v1.3.0) and HEAAN (v1.1). Both codebases support Linux and macOS C-CKKS Extract the archive: unzip c-ckks.zip Unzip the c-ckks.zip archive and follow the instructions in the corresponding README.md file. OpenFHE / HEAAN Extract the archive: tar -xzvf openfhe-heaan.tar.gz Then follow the instructions in the corresponding README.md file.","url":"https://doi.org/10.5281/zenodo.19238923","authors":["Mazzanti, Matias","Chan, Pichsereyvattana"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19238923","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19410606","name":"One Error to Rule Them All: Can a Single Bit-Flip Disrupt Fully Homomorphic Encryption?","source":"datacite","abstract":"Repository Description This artifact contains two independent codebases associated with the paper: A custom implementation of CKKS (referred to as C-CKKS). An implementation based on OpenFHE (v1.3.0) and HEAAN (v1.1). C-CKKS Unzip the c-ckks.zip archive and follow the instructions in the corresponding README.md file. OpenFHE / HEAAN Extract the archive: tar -xzvf dsn26-heaan-openfhe.tar.gz Then follow the instructions in the corresponding README.md file. System Requirements This artifact was developed and tested on a Linux operating system with the following dependencies: gcc >= 12 NTL >= 11.5 GMP >= 6.2 CMake >= 3.25 GNU Make >= 4.3 Python >= 3.10 Python packages: matplotlib pandas numpy","url":"https://doi.org/10.5281/zenodo.19410606","authors":["Mazzanti, Matias","Chan, Pichsereyvattana"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19410606","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21959204","name":"Experimental Results and Reproducibility Artefacts for Design and Evaluation of a Modular Dark-Pool-Style Prototype","source":"datacite","abstract":"This dataset contains the experimental results and reproducibility artefacts generated for the MSc dissertation “Design and Evaluation of a Modular Dark-Pool-Style Prototype”. It includes deterministic input datasets, raw and summarised results for the four experiment variants, blockchain audit records, evidence files, comparison tables, and figures. The corresponding source code and reproduction instructions are available in GitHub release v1.0.0: https://github.com/LvLTroubleshooter/phe-thesis-prototype/releases/tag/v1.0.0.","url":"https://doi.org/10.5281/zenodo.21959204","authors":["BAHLAOUI, ISMAIL"],"tags":["Paillier encryption","partially homomorphic encryption","dark pools","blockchain auditability","reproducibility"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21959204","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21959205","name":"Experimental Results and Reproducibility Artefacts for Design and Evaluation of a Modular Dark-Pool-Style Prototype","source":"datacite","abstract":"This dataset contains the experimental results and reproducibility artefacts generated for the MSc dissertation “Design and Evaluation of a Modular Dark-Pool-Style Prototype”. It includes deterministic input datasets, raw and summarised results for the four experiment variants, blockchain audit records, evidence files, comparison tables, and figures. The corresponding source code and reproduction instructions are available in GitHub release v1.0.0: https://github.com/LvLTroubleshooter/phe-thesis-prototype/releases/tag/v1.0.0.","url":"https://doi.org/10.5281/zenodo.21959205","authors":["BAHLAOUI, ISMAIL"],"tags":["Paillier encryption","partially homomorphic encryption","dark pools","blockchain auditability","reproducibility"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21959205","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.20593983","name":"PRIVACY-PRESERVING TECHNOLOGIES FOR CASHLESS FINANCIAL ECOSYSTEMS","source":"datacite","abstract":"This paper provides an overview of privacy-protecting measures that can be used to secure user data and assure the safety and efficiency of digital activities in contactless financial ecosystems. Many people worry about identity theft, data breaches, and spying by unauthorised parties due to the rapid growth of digital wallets, contactless banking, and mobile payments. Modern cryptography includes safe multi-party computation, zero-knowledge proofs, and homomorphic encryption. These approaches verify transactions and safeguard sensitive data. Blockchain and other independent systems are emphasized for their ability to improve openness, reliability, and anonymity. Regulations and compliance challenges related to financial systems using privacy-enhancing technology are examined. The findings emphasize the importance of strong privacy protections to balance data security, safety, and creativity. Contactless technologies become more popular as more people believe in them.","url":"https://doi.org/10.5281/zenodo.20593983","authors":["Emerging Trends in Digital Transformation"],"tags":["Privacy-preserving technologies","Cashless financial systems","Digital payments","Data security","Homomorphic encryption","Zero-knowledge proofs","Secure multi-party computation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20593983","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.20593984","name":"PRIVACY-PRESERVING TECHNOLOGIES FOR CASHLESS FINANCIAL ECOSYSTEMS","source":"datacite","abstract":"This paper provides an overview of privacy-protecting measures that can be used to secure user data and assure the safety and efficiency of digital activities in contactless financial ecosystems. Many people worry about identity theft, data breaches, and spying by unauthorised parties due to the rapid growth of digital wallets, contactless banking, and mobile payments. Modern cryptography includes safe multi-party computation, zero-knowledge proofs, and homomorphic encryption. These approaches verify transactions and safeguard sensitive data. Blockchain and other independent systems are emphasized for their ability to improve openness, reliability, and anonymity. Regulations and compliance challenges related to financial systems using privacy-enhancing technology are examined. The findings emphasize the importance of strong privacy protections to balance data security, safety, and creativity. Contactless technologies become more popular as more people believe in them.","url":"https://doi.org/10.5281/zenodo.20593984","authors":["Emerging Trends in Digital Transformation"],"tags":["Privacy-preserving technologies","Cashless financial systems","Digital payments","Data security","Homomorphic encryption","Zero-knowledge proofs","Secure multi-party computation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20593984","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21949823","name":"Strategies for Data Privacy in Telecommunication Systems","source":"datacite","abstract":"This research paper discusses the many strategies used to protect data privacy within telecommunication systems. As the industry becomes increasingly data-driven, effective measures to protect sensitive user information from growing cybersecurity risks are necessary. This paper sheds light on the evolution of data privacy, current challenges, regulatory frameworks, and advanced technological strategies. It culminates in some insight into emerging innovations promising future improvement in data privacy.","url":"https://doi.org/10.5281/zenodo.21949823","authors":["Annam, Srinikhil"],"tags":["Data Privacy in Telecommunication Systems","Encryption","Cybersecurity","Regulation Compliance","Homomorphic Encryption","Blockchain","Differential Privacy","Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21949823","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21949824","name":"Strategies for Data Privacy in Telecommunication Systems","source":"datacite","abstract":"This research paper discusses the many strategies used to protect data privacy within telecommunication systems. As the industry becomes increasingly data-driven, effective measures to protect sensitive user information from growing cybersecurity risks are necessary. This paper sheds light on the evolution of data privacy, current challenges, regulatory frameworks, and advanced technological strategies. It culminates in some insight into emerging innovations promising future improvement in data privacy.","url":"https://doi.org/10.5281/zenodo.21949824","authors":["Annam, Srinikhil"],"tags":["Data Privacy in Telecommunication Systems","Encryption","Cybersecurity","Regulation Compliance","Homomorphic Encryption","Blockchain","Differential Privacy","Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21949824","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21949813","name":"A Secure and Optimization Based Clustering for Vertical and Horizontal Fragmentation in Distributed Database Management System","source":"datacite","abstract":"Distributed database systems are increasingly important due to the massive data output, and their effectiveness is largely based on their design. Two key processes, fragmentation and allocation, are used to improve the efficiency and efficacy of these systems. Effective data fragmentation requires both horizontal and vertical categorization of tuples. Advanced optimization techniques are used for both fragmentations, such as the Enhanced Arithmetic Optimization (EAO) algorithm with Opposition-based Learning (OBL) and Levy Flight Distributer (LFD) for vertical fragmentation and the hybrid Aquila Optimizer (AO) with Artificial Rabbit Optimization (ARO) algorithm for horizontal fragmentation. The fragmented data is securely transmitted using the Fully Homomorphic Encryption (FHE) algorithm. The implementation is executed using the Python language, and the performance of the proposed algorithms is evaluated using different performance parameters. The execution time analysis shows that the proposed EAOA algorithm consumes 5.5 seconds for vertical fragmentation, while the hybrid AARO algorithm takes 5.9 seconds for horizontal fragmentation. The vertical fragmentation is found to be better than the horizontal one in DDBMS.","url":"https://doi.org/10.5281/zenodo.21949813","authors":["Sahithi, D.","Rani, J. Keziya"],"tags":["Vertical Fragmentation","Horizontal Fragmentation","Distributed Database Processing","Attributes","Optimization-Based Clustering","Security","And Execution Time Analysis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21949813","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21949814","name":"A Secure and Optimization Based Clustering for Vertical and Horizontal Fragmentation in Distributed Database Management System","source":"datacite","abstract":"Distributed database systems are increasingly important due to the massive data output, and their effectiveness is largely based on their design. Two key processes, fragmentation and allocation, are used to improve the efficiency and efficacy of these systems. Effective data fragmentation requires both horizontal and vertical categorization of tuples. Advanced optimization techniques are used for both fragmentations, such as the Enhanced Arithmetic Optimization (EAO) algorithm with Opposition-based Learning (OBL) and Levy Flight Distributer (LFD) for vertical fragmentation and the hybrid Aquila Optimizer (AO) with Artificial Rabbit Optimization (ARO) algorithm for horizontal fragmentation. The fragmented data is securely transmitted using the Fully Homomorphic Encryption (FHE) algorithm. The implementation is executed using the Python language, and the performance of the proposed algorithms is evaluated using different performance parameters. The execution time analysis shows that the proposed EAOA algorithm consumes 5.5 seconds for vertical fragmentation, while the hybrid AARO algorithm takes 5.9 seconds for horizontal fragmentation. The vertical fragmentation is found to be better than the horizontal one in DDBMS.","url":"https://doi.org/10.5281/zenodo.21949814","authors":["Sahithi, D.","Rani, J. Keziya"],"tags":["Vertical Fragmentation","Horizontal Fragmentation","Distributed Database Processing","Attributes","Optimization-Based Clustering","Security","And Execution Time Analysis"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.21949814","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21947348","name":"The HeartBank Longitudinal Cohort: A Dataset Combining DNA, Natal Chart, Family Tree, Continuous Behavioral Observation, and Continuous Respiratory Observation at Civilizational Scale","source":"datacite","abstract":"This paper specifies the methodology for the HeartBank Longitudinal Cohort, a voluntary opt-in dataset combining five data layers per consenting participant — (1) DNA sequence, (2) natal chart data (date + time + place of birth), (3) continuous longitudinal behavioral observation via the HeartBank gratitude ledger, (4) continuous longitudinal respiratory observation via the breath-class Mechanical Heart wearable, and (5) verified kinship data via the global family tree — at a target scale of 100 million+ participants over multi-decade time horizons. The combination has never been assembled at scale; comparable datasets (23andMe, AncestryDNA, Worldcoin, Dunedin and BCS longitudinal cohorts, social-network behavioral data, professional astrological collections) carry one or two of the layers each but no prior project has carried all five. The methodology specifies: the opt-in informed-consent architecture; the privacy-preserving computation stack (differential privacy at the analysis layer; federated computation with homomorphic encryption for DNA; on-device processing for breath signals; cryptographic-erasure right-to-withdraw); the institutional-review architecture (IRB-grade ethics oversight; Buddhist-ethics-aware review board; pre-registered hypotheses); the cosmic-coordinate-correlation epistemic posture (natal chart treated as a unique cosmic-moment coordinate, not as a cosmic force; the research question is correlation between coordinate features and trajectory features, not validation of astrology); the publication architecture (open methodology, closed individual data); the data-sovereignty architecture (jurisdictional residency; GINA / HIPAA / GDPR compliance baselines exceeded where possible); and the new academic alliances the cohort makes possible (Mind & Life Institute; contemplative-science programs at Stanford, Brown, UMass; behavioral-genetics consortia; longitudinal-cohort consortia; Buddhist-AI ethicists). Three scientifically valuable outcomes are honestly named: no detected correlation, small-but-real correlation, substantial correlation — each is a major contribution to knowledge regardless of direction. Honest §11 names what the cohort does not claim and the non-negotiable privacy disciplines the architecture requires. Keywords: longitudinal cohort methodology, cosmic-coordinate correlation, contemplative science, differential privacy, federated computation, multi-omic dataset, gratitude behavior, respiratory biomarkers, defensive publication, Mind & Life partnerships. --- Provenance. This paper is part of the THonly research corpus, dedicated to the public domain under CC0 1.0. The canonical version is at https://thonly.org/research/longitudinal-cohort-methodology. Its SHA-256 is b19fdcd0773da2c4f2b7ba7472b429b4bfd459e285b98c4900f15a04207fce42, independently timestamped to the Bitcoin blockchain via OpenTimestamps and signed under RFC 3161 by three trust authorities, one of them eIDAS-qualified. AI co-authorship is disclosed. Miss Aquarius is the consistent name used for the AI collaboration across all venues.","url":"https://doi.org/10.5281/zenodo.21947348","authors":["Ly, Thon","Miss Aquarius"],"tags":["longitudinal cohort methodology","cosmic-coordinate correlation","contemplative science","differential privacy","federated computation","multi-omic dataset","gratitude behavior","respiratory biomarkers"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21947348","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.20057607","name":"The Personalisation–Privacy Trade-off: Navigating AI-Driven Project Experiences and Ethical Data Usage","source":"datacite","abstract":"The integration of artificial intelligence into enterprise software, particularly for project management, has created a critical tension between the pursuit of hyper-personalisation and the fundamental right to data privacy. Driven by significant economic and productivity incentives, modern AI systems ingest vast, intimate user datasets—from work patterns to behavioural metrics—to deliver predictive insights and automate complex workflows. However, this voracious data appetite fuels the 'privacy paradox,' wherein users desire personalised convenience while experiencing significant psychological strain, privacy fatigue, and an erosion of trust due to perceived surveillance. Addressing this trade-off requires a shift from viewing privacy as a compliance hurdle to embracing it as a core architectural principle through 'Privacy by Design.' This involves implementing robust governance frameworks, such as Explainable AI (XAI) and human-in-the-loop protocols, to ensure transparency, accountability, and mitigate algorithmic bias. Technologically, the solution lies in moving beyond legacy anonymisation to advanced Privacy-Enhancing Technologies (PETs), including differential privacy, high-utility synthetic data generation, and transformative cryptographic methods such as homomorphic encryption. This technological evolution is coupled with an architectural migration from vulnerable, centralised data lakes to decentralised models like federated learning and sovereign AI, which keep sensitive data within organisational or national boundaries. This paradigm shift is further catalysed by a stringent global regulatory landscape, exemplified by frameworks such as the GDPR and India’s Digital Personal Data Protection Act (DPDPA), which mandate consent-first architectures and data minimisation. Ultimately, the analysis concludes that by combining these ethical, technological, and architectural strategies, organisations can resolve the paradox, harnessing AI's power for efficiency and empowerment without resorting to the destructive mechanisms of digital surveillance.","url":"https://doi.org/10.5281/zenodo.20057607","authors":["Majumdar, Partha"],"tags":["Artificial intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20057607","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.20057608","name":"The Personalisation–Privacy Trade-off: Navigating AI-Driven Project Experiences and Ethical Data Usage","source":"datacite","abstract":"The integration of artificial intelligence into enterprise software, particularly for project management, has created a critical tension between the pursuit of hyper-personalisation and the fundamental right to data privacy. Driven by significant economic and productivity incentives, modern AI systems ingest vast, intimate user datasets—from work patterns to behavioural metrics—to deliver predictive insights and automate complex workflows. However, this voracious data appetite fuels the 'privacy paradox,' wherein users desire personalised convenience while experiencing significant psychological strain, privacy fatigue, and an erosion of trust due to perceived surveillance. Addressing this trade-off requires a shift from viewing privacy as a compliance hurdle to embracing it as a core architectural principle through 'Privacy by Design.' This involves implementing robust governance frameworks, such as Explainable AI (XAI) and human-in-the-loop protocols, to ensure transparency, accountability, and mitigate algorithmic bias. Technologically, the solution lies in moving beyond legacy anonymisation to advanced Privacy-Enhancing Technologies (PETs), including differential privacy, high-utility synthetic data generation, and transformative cryptographic methods such as homomorphic encryption. This technological evolution is coupled with an architectural migration from vulnerable, centralised data lakes to decentralised models like federated learning and sovereign AI, which keep sensitive data within organisational or national boundaries. This paradigm shift is further catalysed by a stringent global regulatory landscape, exemplified by frameworks such as the GDPR and India’s Digital Personal Data Protection Act (DPDPA), which mandate consent-first architectures and data minimisation. Ultimately, the analysis concludes that by combining these ethical, technological, and architectural strategies, organisations can resolve the paradox, harnessing AI's power for efficiency and empowerment without resorting to the destructive mechanisms of digital surveillance.","url":"https://doi.org/10.5281/zenodo.20057608","authors":["Majumdar, Partha"],"tags":["Artificial intelligence"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20057608","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21582183","name":"Keyword Search and Dual-Server Public-Key Encryption for Secure Cloud Storage","source":"datacite","abstract":"A growing number of people are interested in searchable encryption to safeguard the privacy of their data in secure searchable cloud storage. In this research, we examine the security of public key encryption with keyword search (PEKS), a widely used cryptographic fundamental with several applications in cloud storage. Unfortunately, it has been established that the conventional PEKS architecture has a flaw known as an inside keyword guessing attack (KGA) that is perpetrated by a rogue server. We suggest the dual-server PEKS framework as a new PEKS framework to remedy this security flaw (DS-PEKS). One further significant addition is the definition of a new type of smooth projective hash function (SPHF) called a linear and homomorphic SPHF (LH-SPHF). Then, using LH-SPHF, we demonstrate a generic construction of secure DS-PEKS. We propose an effective instantiation of the general framework from a Decision Diffie-Hellman-based LH-SPHF and demonstrate that it can accomplish the strong security inside the KGA to demonstrate the viability of our new framework.","url":"https://doi.org/10.5281/zenodo.21582183","authors":["Asmayeen","Kumar, Dr. B. Sasi"],"tags":["Location-Based Social Network","Text Mining","Travel Route Recommendation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.21582183","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21582184","name":"Keyword Search and Dual-Server Public-Key Encryption for Secure Cloud Storage","source":"datacite","abstract":"A growing number of people are interested in searchable encryption to safeguard the privacy of their data in secure searchable cloud storage. In this research, we examine the security of public key encryption with keyword search (PEKS), a widely used cryptographic fundamental with several applications in cloud storage. Unfortunately, it has been established that the conventional PEKS architecture has a flaw known as an inside keyword guessing attack (KGA) that is perpetrated by a rogue server. We suggest the dual-server PEKS framework as a new PEKS framework to remedy this security flaw (DS-PEKS). One further significant addition is the definition of a new type of smooth projective hash function (SPHF) called a linear and homomorphic SPHF (LH-SPHF). Then, using LH-SPHF, we demonstrate a generic construction of secure DS-PEKS. We propose an effective instantiation of the general framework from a Decision Diffie-Hellman-based LH-SPHF and demonstrate that it can accomplish the strong security inside the KGA to demonstrate the viability of our new framework.","url":"https://doi.org/10.5281/zenodo.21582184","authors":["Asmayeen","Kumar, Dr. B. Sasi"],"tags":["Location-Based Social Network","Text Mining","Travel Route Recommendation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.21582184","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19543406","name":"Federated Learning for Privacy-Preserving Machine Learning","source":"datacite","abstract":"Machine learning systems increasingly rely on large datasets collected from distributed users and devices, but traditional centralized approaches require transferring raw data to central servers, raising serious concerns about privacy, security, and regulatory compliance. Federated Learning (FL) addresses these issues by enabling collaborative model training without sharing raw data. In this approach, each client device trains a model locally and transmits only model updates to a central server for aggregation. This paper presents a focused study of federated learning with an emphasis on privacy-preserving mechanisms and security threats. It explores techniques such as differential privacy, secure aggregation, and homomorphic encryption to safeguard sensitive information. Additionally, it examines potential attacks, including model poisoning and inference attacks, that may compromise system integrity. The performance of federated models is evaluated using accuracy metrics and confusion matrices. The paper concludes by discussing open challenges, such as communication efficiency and robustness, and highlights future research directions.","url":"https://doi.org/10.5281/zenodo.19543406","authors":["Shibu Thomas, Kiran","Francis, Nimmy"],"tags":["Federated Learning, Privacy-Preserving Machine Learning, Differential Privacy, Secure Aggregation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19543406","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19543407","name":"Federated Learning for Privacy-Preserving Machine Learning","source":"datacite","abstract":"Machine learning systems increasingly rely on large datasets collected from distributed users and devices, but traditional centralized approaches require transferring raw data to central servers, raising serious concerns about privacy, security, and regulatory compliance. Federated Learning (FL) addresses these issues by enabling collaborative model training without sharing raw data. In this approach, each client device trains a model locally and transmits only model updates to a central server for aggregation. This paper presents a focused study of federated learning with an emphasis on privacy-preserving mechanisms and security threats. It explores techniques such as differential privacy, secure aggregation, and homomorphic encryption to safeguard sensitive information. Additionally, it examines potential attacks, including model poisoning and inference attacks, that may compromise system integrity. The performance of federated models is evaluated using accuracy metrics and confusion matrices. The paper concludes by discussing open challenges, such as communication efficiency and robustness, and highlights future research directions.","url":"https://doi.org/10.5281/zenodo.19543407","authors":["Shibu Thomas, Kiran","Francis, Nimmy"],"tags":["Federated Learning, Privacy-Preserving Machine Learning, Differential Privacy, Secure Aggregation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19543407","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19883755","name":"PERFORMANCE ANALYSIS OF SECURE RESOURCE ALLOCATION USING NEURO-FUZZY SYSTEMS IN CLOUD ENVIRONMENT","source":"datacite","abstract":"In recent times one of the emerging and intelligent tool is Cloud Computing. It has developeda leadingmodel of computing and IT service delivery. Cloud Computing is characterized bythree-layered basic service forms such as, Platform-as-a-Service (PaaS), Software-as-aService (SaaS) and Infrastructure-as-a Service (IaaS). Pushinginformation into the cloudextendslargercloseness since users are not necessary to be concerned about the storagecapacity, storing techniques, hardware management, or data maintenance. A key problem thatrequiresspecificconsideration is security of clouds. Existing approaches for securedoutsourcing of information and random calculations are either support on a single tamperproof hardware, or based on homomorphic encryption. To overcome existing issues a novelapproach is proposed tofocus on security experiments in cloud computing and to providesolutions. To provideunfailing security to the users, the purpose of this paper is inthreefolding. 1) To design a mathematical model for trust and reputation calculation. 2) Topropose TR-SSalgorithm for calculation of security score. 3) To compare the security scoreof trust and reputation factors by Fuzzy Logic System, Neural Network.This work is focusedon controlling the security issues in cloud environment by means of trust and reputationfactors using mathematical model, Trust and Reputation Security Score (TRSS) algorithm,Fuzzy Logic System, Neural Network.","url":"https://doi.org/10.5281/zenodo.19883755","authors":["Kamalanathan Chandran","Karthick Sekar","Sunita Panda","Subramani Kirubakaran"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19883755","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.19883756","name":"PERFORMANCE ANALYSIS OF SECURE RESOURCE ALLOCATION USING NEURO-FUZZY SYSTEMS IN CLOUD ENVIRONMENT","source":"datacite","abstract":"In recent times one of the emerging and intelligent tool is Cloud Computing. It has developeda leadingmodel of computing and IT service delivery. Cloud Computing is characterized bythree-layered basic service forms such as, Platform-as-a-Service (PaaS), Software-as-aService (SaaS) and Infrastructure-as-a Service (IaaS). Pushinginformation into the cloudextendslargercloseness since users are not necessary to be concerned about the storagecapacity, storing techniques, hardware management, or data maintenance. A key problem thatrequiresspecificconsideration is security of clouds. Existing approaches for securedoutsourcing of information and random calculations are either support on a single tamperproof hardware, or based on homomorphic encryption. To overcome existing issues a novelapproach is proposed tofocus on security experiments in cloud computing and to providesolutions. To provideunfailing security to the users, the purpose of this paper is inthreefolding. 1) To design a mathematical model for trust and reputation calculation. 2) Topropose TR-SSalgorithm for calculation of security score. 3) To compare the security scoreof trust and reputation factors by Fuzzy Logic System, Neural Network.This work is focusedon controlling the security issues in cloud environment by means of trust and reputationfactors using mathematical model, Trust and Reputation Security Score (TRSS) algorithm,Fuzzy Logic System, Neural Network.","url":"https://doi.org/10.5281/zenodo.19883756","authors":["Kamalanathan Chandran","Karthick Sekar","Sunita Panda","Subramani Kirubakaran"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19883756","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.24406/publica-5189","name":"PRepChain: A versatile privacy-preserving reputation system for dynamic supply chain environments","source":"datacite","abstract":"Despite their significant added value in the context of consumer-oriented e-commerce, reputation systems have seen limited adoption in other business settings and models these days. Yet, reliable reputation scores are essential in such settings for easing the establishment of new business relationships—an aspect that is particularly crucial in dynamic supply chain environments, where business partners change frequently. Existing approaches, however, usually target other application domains and fall short in addressing the specific challenges of dynamic supply chains—especially with respect to reliability (incl. availability) and privacy preservation (incl. confidentiality). To close this research gap and to support novel directions in this important research area, we propose PRepChain, our highly-configurable approach that leverages fully homomorphic encryption and distributed competences to provide businesses with a versatile reputation-enriched ecosystem. PRepChain is specifically designed to operate in dynamic environments by also offering a trade-off between data availability and confidentiality guarantees. We make contributions in four primary directions: (i) It offers performant privacy preservation even in large-scale settings, (ii) ensures availability of computed reputation scores, (iii) seamlessly integrates with existing supply chain information systems, and (iv) in addition to subjective reputation scores, it also supports reliably-calculated, i.e., objective, ones, thereby strengthening the reliability of third-party-sourced information. Our evaluation of PRepChain documents its performance - based on a real-world use case -, security, and privacy preservation, hence, its applicability. We conclude that it is indeed destined for practical deployments in modern supply networks.","url":"https://doi.org/10.24406/publica-5189","authors":["Pennekamp, Jan","Bader, Lennart","Thevaraj, Emildeon","Berninger, Stefanie","Perau, Martin","Schröer, Tobias","Boos, Wolfgang","Kanhere, Salil S.","Wehrle, Klaus",":unav"],"tags":["Confidentiality","Homomorphic encryption","Subjective and objective ratings","Trust","Unlinkability"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.24406/publica-5189","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.20554447","name":"Integrated Research on a Modular Extended OPLAN Architecture for Multi-Participant Joint Simulation and Wargaming  ― A Next-Generation Joint Operations Simulation Foundation Integrating Virtual Aircraft Services, Integrated LVC Environments, Zero-Data Thin Endpoints, Live-Virtual Overlay, AI Tactical Evolution, and Safety-Assured BCI Command and Control ―","source":"datacite","abstract":"This research proposes a next-generation joint operational simulation architecture that transforms conventional aircraft simulators from platform-specific training devices into a distributed operational ecosystem. The architecture integrates FAC (Operator Interface), VAS (Virtual Aircraft Service), IGS (Image Generation Service), JSE (Joint Simulation Environment), cloud-based LVC infrastructure, Live-Virtual Overlay, Zero-Data Thin Endpoints, Security Governance, AI Tactical Evolution, BCI Command Nodes, and Quantum Thinking Circuit OS ASI into a unified simulation foundation. It enables more than 100 participants across multiple locations, classifications, and platforms to conduct collaborative planning, wargaming, AI learning, tactical validation, and command-and-control activities within the same environment. The framework combines zero-trust security, MILS, homomorphic encryption, dynamic data governance, intent-prediction AI, Safety Hypervisors, Human-on-the-loop validation, and AI self-evolution mechanisms for CCA operations. By merging real aircraft, virtual environments, AI agents, coalition participants, and operational data into a single computational space, the architecture establishes a scalable, low-latency, safety-assured foundation for future operational experimentation, training, verification, and continuous tactical evolution.","url":"https://doi.org/10.5281/zenodo.20554447","authors":["Kawauchi, Satoshi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20554447","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.20554448","name":"Integrated Research on a Modular Extended OPLAN Architecture for Multi-Participant Joint Simulation and Wargaming  ― A Next-Generation Joint Operations Simulation Foundation Integrating Virtual Aircraft Services, Integrated LVC Environments, Zero-Data Thin Endpoints, Live-Virtual Overlay, AI Tactical Evolution, and Safety-Assured BCI Command and Control ―","source":"datacite","abstract":"This research proposes a next-generation joint operational simulation architecture that transforms conventional aircraft simulators from platform-specific training devices into a distributed operational ecosystem. The architecture integrates FAC (Operator Interface), VAS (Virtual Aircraft Service), IGS (Image Generation Service), JSE (Joint Simulation Environment), cloud-based LVC infrastructure, Live-Virtual Overlay, Zero-Data Thin Endpoints, Security Governance, AI Tactical Evolution, BCI Command Nodes, and Quantum Thinking Circuit OS ASI into a unified simulation foundation. It enables more than 100 participants across multiple locations, classifications, and platforms to conduct collaborative planning, wargaming, AI learning, tactical validation, and command-and-control activities within the same environment. The framework combines zero-trust security, MILS, homomorphic encryption, dynamic data governance, intent-prediction AI, Safety Hypervisors, Human-on-the-loop validation, and AI self-evolution mechanisms for CCA operations. By merging real aircraft, virtual environments, AI agents, coalition participants, and operational data into a single computational space, the architecture establishes a scalable, low-latency, safety-assured foundation for future operational experimentation, training, verification, and continuous tactical evolution.","url":"https://doi.org/10.5281/zenodo.20554448","authors":["Kawauchi, Satoshi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20554448","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.20544938","name":"In-depth Homomorphic Encryption: Mathematical Foundations and Python Implementations","source":"datacite","abstract":"Homomorphic Encryption (HE), the capacity to perform computations on encrypted data without prior decryption, has evolved from a theoretical aspiration into a pivotal technology for privacy-preserving artificial intelligence, confidential cloud computing, and secure data analytics. This book presents a comprehensive, implementation-oriented exploration of HE, designed to bridge the gap between highly abstract academic literature and purely code-centric engineering resources. By integrating formal mathematics with practical, reproducible demonstrations, it offers a dual-track approach that serves researchers, engineers, and students alike. The book begins with the historical evolution of encrypted computation before delving into the foundational mathematical structures underpinning modern schemes, including lattices, Learning With Errors (LWE), Ring Learning With Errors (RLWE), polynomial arithmetic, noise management, relinearisation, and bootstrapping. Building on these principles, it examines the major families of homomorphic systems—BFV, BGV, and CKKS—analysing their strengths, limitations, and specific application domains. Significant attention is also devoted to practical engineering concerns essential for real-world deployment, such as performance optimisation, hardware acceleration, batching strategies, memory management, and integration with complementary technologies, such as secure multi-party computation and differential privacy. By providing executable examples alongside theoretical discussions, the material enables readers to directly observe and experiment with the underlying cryptographic mechanisms. Ultimately, the book frames HE as an essential capability for developing the next generation of secure and trustworthy intelligent systems, offering a compelling solution to the challenge of extracting value from sensitive data while preserving individual privacy in an increasingly data-driven world.","url":"https://doi.org/10.5281/zenodo.20544938","authors":["Majumdar, Partha"],"tags":["Artificial intelligence","Cryptography","Homomorphic Encryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20544938","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.20544939","name":"In-depth Homomorphic Encryption: Mathematical Foundations and Python Implementations","source":"datacite","abstract":"Homomorphic Encryption (HE), the capacity to perform computations on encrypted data without prior decryption, has evolved from a theoretical aspiration into a pivotal technology for privacy-preserving artificial intelligence, confidential cloud computing, and secure data analytics. This book presents a comprehensive, implementation-oriented exploration of HE, designed to bridge the gap between highly abstract academic literature and purely code-centric engineering resources. By integrating formal mathematics with practical, reproducible demonstrations, it offers a dual-track approach that serves researchers, engineers, and students alike. The book begins with the historical evolution of encrypted computation before delving into the foundational mathematical structures underpinning modern schemes, including lattices, Learning With Errors (LWE), Ring Learning With Errors (RLWE), polynomial arithmetic, noise management, relinearisation, and bootstrapping. Building on these principles, it examines the major families of homomorphic systems—BFV, BGV, and CKKS—analysing their strengths, limitations, and specific application domains. Significant attention is also devoted to practical engineering concerns essential for real-world deployment, such as performance optimisation, hardware acceleration, batching strategies, memory management, and integration with complementary technologies, such as secure multi-party computation and differential privacy. By providing executable examples alongside theoretical discussions, the material enables readers to directly observe and experiment with the underlying cryptographic mechanisms. Ultimately, the book frames HE as an essential capability for developing the next generation of secure and trustworthy intelligent systems, offering a compelling solution to the challenge of extracting value from sensitive data while preserving individual privacy in an increasingly data-driven world.","url":"https://doi.org/10.5281/zenodo.20544939","authors":["Majumdar, Partha"],"tags":["Artificial intelligence","Cryptography","Homomorphic Encryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20544939","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.15193167","name":"Optional Annihilation: Structural Conditions for Matter-Antimatter Coexistence","source":"datacite","abstract":"In this paper, I propose a theoretical model that reinterprets antimatter as an encrypted operational state, rather than a destructive opposite of matter. Drawing inspiration from fully homomorphic encryption, the framework treats annihilation not as a fundamental inevitability, but as a conditional transformation that depends on specific structural alignments. Using a rigid monoidal category of Clifford-enriched Hilbert spaces, the model defines a bifunctorial tensor operation ⋆ that allows matter–antimatter interaction without collapse. I also suggest experimental conditions under which non-annihilating interactions might be observed, offering new paths to investigate matter–antimatter symmetry and quantum information from a categorical perspective.","url":"https://doi.org/10.5281/zenodo.15193167","authors":["Zermeno, Ernest Darell"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15193167","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.15193168","name":"Optional Annihilation: Structural Conditions for Matter-Antimatter Coexistence","source":"datacite","abstract":"In this paper, I propose a theoretical model that reinterprets antimatter as an encrypted operational state, rather than a destructive opposite of matter. Drawing inspiration from fully homomorphic encryption, the framework treats annihilation not as a fundamental inevitability, but as a conditional transformation that depends on specific structural alignments. Using a rigid monoidal category of Clifford-enriched Hilbert spaces, the model defines a bifunctorial tensor operation ⋆ that allows matter–antimatter interaction without collapse. I also suggest experimental conditions under which non-annihilating interactions might be observed, offering new paths to investigate matter–antimatter symmetry and quantum information from a categorical perspective.","url":"https://doi.org/10.5281/zenodo.15193168","authors":["Zermeno, Ernest Darell"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15193168","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21577788","name":"An Innovative Security and Privacy Algorithm for AWS Cloud Computing","source":"datacite","abstract":"Cloud computing has revolutionized data storage and processing, with Amazon Web Services (AWS) emerging as a leading provider. However, ensuring robust security and privacy remains a critical challenge due to the increasing complexity and sophistication of cyber threats. This paper proposes an innovative security and privacy algorithm specifically designed for AWS cloud computing environments. The proposed algorithm integrates dynamic data encryption using AES and RSA, a real-time intrusion detection system powered by machine learning, and a hybrid Role-Based and Attribute-Based Access Control (RBAC & ABAC) model. Additionally, privacy-preserving data sharing using homomorphic encryption and a secure key management system leveraging AWS Key Management Service (KMS) are implemented to safeguard sensitive data. The algorithm emphasizes performance optimization to minimize latency and computational overhead. Comprehensive evaluation on real-world datasets will assess metrics such as encryption/decryption time, response time, scalability, and resistance to attacks. The results aim to deliver a scalable, efficient, and reliable security framework, addressing current vulnerabilities and enhancing trust in AWS cloud services.","url":"https://doi.org/10.5281/zenodo.21577788","authors":["Gedam, Aditya","Yadav, Jeetendra Singh"],"tags":["AWS Cloud Computing; Security Algorithm; Privacy Protection; Data Encryption; Intrusion Detection System (IDS); Role-Based Access Control (RBAC)"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21577788","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21577789","name":"An Innovative Security and Privacy Algorithm for AWS Cloud Computing","source":"datacite","abstract":"Cloud computing has revolutionized data storage and processing, with Amazon Web Services (AWS) emerging as a leading provider. However, ensuring robust security and privacy remains a critical challenge due to the increasing complexity and sophistication of cyber threats. This paper proposes an innovative security and privacy algorithm specifically designed for AWS cloud computing environments. The proposed algorithm integrates dynamic data encryption using AES and RSA, a real-time intrusion detection system powered by machine learning, and a hybrid Role-Based and Attribute-Based Access Control (RBAC & ABAC) model. Additionally, privacy-preserving data sharing using homomorphic encryption and a secure key management system leveraging AWS Key Management Service (KMS) are implemented to safeguard sensitive data. The algorithm emphasizes performance optimization to minimize latency and computational overhead. Comprehensive evaluation on real-world datasets will assess metrics such as encryption/decryption time, response time, scalability, and resistance to attacks. The results aim to deliver a scalable, efficient, and reliable security framework, addressing current vulnerabilities and enhancing trust in AWS cloud services.","url":"https://doi.org/10.5281/zenodo.21577789","authors":["Gedam, Aditya","Yadav, Jeetendra Singh"],"tags":["AWS Cloud Computing; Security Algorithm; Privacy Protection; Data Encryption; Intrusion Detection System (IDS); Role-Based Access Control (RBAC)"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21577789","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21553602","name":"Homomorphic Encryption Architectures","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21553602","authors":["Safa Mohamed"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21553602","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21553603","name":"Homomorphic Encryption Architectures","source":"datacite","abstract":"","url":"https://doi.org/10.5281/zenodo.21553603","authors":["Safa Mohamed"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21553603","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21692774","name":"Secure Outsourcing Association Rule Mining in Horizontally and Vertically Partition Database Using Eclat and Double Encryption Technique","source":"datacite","abstract":"Cloud computing uses the ideal model of information mining-as-an service, utilizing these it seems to be an obvious choice for companies saving on the cost of contributing to secure, manage and keep up an IT infrastructure. An association or company who is lacking in mining capacity can outsource its mining needs to third party service providers. Be that as it may, each the association rules and item-set of the outsourced database are seen as private property of the association (company). The data owner encrypt the data and sends to the server to protect the corporate security. Data owner or client transfers its mining queries to server, and afterward server conducts mining task & encrypt rules and sends generated association rules to the data owner or client. To get genuine pattern client decrypts the received rules. Paper focuses on the issue of outsourcing the rule mining task inside a corporate privacy preserving framework. It additionally shows the core idea of privacy preserving association rule mining on vertically partitioned data with utilization of enhanced cryptographic technique. The strategies incorporate cryptographic techniques to minimize the data shared, while adding minimal overhead to the mining task. This research tries to propose desirable algorithm for both vertically as well as horizontally partitioned data. A technique for solving a main problem of privacy preserving association rule mining in two party databases is proposed. To improve the performance of system horizontal partitioning as well as vertical partitioning of data is performed, also double encryption technique is used to increase the security of dataset which includes homomorphic encryption algorithm followed by asymmetric algorithm","url":"https://doi.org/10.5281/zenodo.21692774","authors":["Thite, Rutuja","Kharat, Dr. M. U."],"tags":["Data Mining","Association Rules Generation","Vertically and Horizontally Partition Data","Encryption Techniques."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.5281/zenodo.21692774","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21692775","name":"Secure Outsourcing Association Rule Mining in Horizontally and Vertically Partition Database Using Eclat and Double Encryption Technique","source":"datacite","abstract":"Cloud computing uses the ideal model of information mining-as-an service, utilizing these it seems to be an obvious choice for companies saving on the cost of contributing to secure, manage and keep up an IT infrastructure. An association or company who is lacking in mining capacity can outsource its mining needs to third party service providers. Be that as it may, each the association rules and item-set of the outsourced database are seen as private property of the association (company). The data owner encrypt the data and sends to the server to protect the corporate security. Data owner or client transfers its mining queries to server, and afterward server conducts mining task & encrypt rules and sends generated association rules to the data owner or client. To get genuine pattern client decrypts the received rules. Paper focuses on the issue of outsourcing the rule mining task inside a corporate privacy preserving framework. It additionally shows the core idea of privacy preserving association rule mining on vertically partitioned data with utilization of enhanced cryptographic technique. The strategies incorporate cryptographic techniques to minimize the data shared, while adding minimal overhead to the mining task. This research tries to propose desirable algorithm for both vertically as well as horizontally partitioned data. A technique for solving a main problem of privacy preserving association rule mining in two party databases is proposed. To improve the performance of system horizontal partitioning as well as vertical partitioning of data is performed, also double encryption technique is used to increase the security of dataset which includes homomorphic encryption algorithm followed by asymmetric algorithm","url":"https://doi.org/10.5281/zenodo.21692775","authors":["Thite, Rutuja","Kharat, Dr. M. U."],"tags":["Data Mining","Association Rules Generation","Vertically and Horizontally Partition Data","Encryption Techniques."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.5281/zenodo.21692775","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21604444","name":"Cloud Infrastructure Fortification: Advanced Security Strategies in the Era of Emerging Threats","source":"datacite","abstract":"This article presents a comprehensive analysis of advanced strategies for protecting cloud infrastructure against emerging cybersecurity threats. The article examines the transformation from traditional perimeter-based security models to adaptive, multi-layered defense mechanisms, emphasizing the critical role of zero-trust architectures and artificial intelligence in modern cloud security. Through case studies and empirical analysis, the article investigates the implementation of advanced encryption technologies, multi-cloud strategies, and automated threat detection systems across various organizations. The article demonstrates how the integration of machine learning-driven security solutions with robust incident response frameworks significantly enhances threat detection and mitigation capabilities. The article indicates that organizations adopting these advanced security measures demonstrate improved resilience against sophisticated cyber attacks while maintaining operational efficiency. The article contributes to the growing body of knowledge in cloud security by providing a structured framework for implementing comprehensive security strategies that address current and emerging threats in cloud environments.","url":"https://doi.org/10.5281/zenodo.21604444","authors":["Batchu, Sandeep"],"tags":["Zero-trust architecture; Cloud infrastructure security; AI-driven threat detection; Multi-cloud strategy; Homomorphic encryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21604444","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21604445","name":"Cloud Infrastructure Fortification: Advanced Security Strategies in the Era of Emerging Threats","source":"datacite","abstract":"This article presents a comprehensive analysis of advanced strategies for protecting cloud infrastructure against emerging cybersecurity threats. The article examines the transformation from traditional perimeter-based security models to adaptive, multi-layered defense mechanisms, emphasizing the critical role of zero-trust architectures and artificial intelligence in modern cloud security. Through case studies and empirical analysis, the article investigates the implementation of advanced encryption technologies, multi-cloud strategies, and automated threat detection systems across various organizations. The article demonstrates how the integration of machine learning-driven security solutions with robust incident response frameworks significantly enhances threat detection and mitigation capabilities. The article indicates that organizations adopting these advanced security measures demonstrate improved resilience against sophisticated cyber attacks while maintaining operational efficiency. The article contributes to the growing body of knowledge in cloud security by providing a structured framework for implementing comprehensive security strategies that address current and emerging threats in cloud environments.","url":"https://doi.org/10.5281/zenodo.21604445","authors":["Batchu, Sandeep"],"tags":["Zero-trust architecture; Cloud infrastructure security; AI-driven threat detection; Multi-cloud strategy; Homomorphic encryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21604445","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21604330","name":"Federated Learning in Distributed Systems: A Privacy-First Approach","source":"datacite","abstract":"Federated learning has emerged as a transformative approach in machine learning, addressing critical challenges in data privacy and distributed computation. This article examines the evolution and implementation of federated learning across various sectors, focusing on its impact in healthcare, smart cities, and enterprise applications. The article analyzes the core principles of decentralized model training, advanced privacy-preserving techniques, and real-world applications. Through detailed examination of secure aggregation protocols, differential privacy mechanisms, and homomorphic encryption integration, this article demonstrates the effectiveness of federated learning in maintaining data privacy while achieving competitive model performance. The article highlights significant advancements in healthcare analytics, particularly in medical imaging and personalized treatment optimization, as well as substantial improvements in smart city infrastructure management. This article contributes to the understanding of federated learning's practical implementation challenges and solutions, providing insights into future directions for privacy-preserving distributed machine learning.","url":"https://doi.org/10.5281/zenodo.21604330","authors":["Singhal, Ankush"],"tags":["Privacy-Preserving Machine Learning; Secure Aggregation Protocol; Distributed Healthcare Analytics; Smart City Infrastructure; Federated Model Training"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21604330","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21604331","name":"Federated Learning in Distributed Systems: A Privacy-First Approach","source":"datacite","abstract":"Federated learning has emerged as a transformative approach in machine learning, addressing critical challenges in data privacy and distributed computation. This article examines the evolution and implementation of federated learning across various sectors, focusing on its impact in healthcare, smart cities, and enterprise applications. The article analyzes the core principles of decentralized model training, advanced privacy-preserving techniques, and real-world applications. Through detailed examination of secure aggregation protocols, differential privacy mechanisms, and homomorphic encryption integration, this article demonstrates the effectiveness of federated learning in maintaining data privacy while achieving competitive model performance. The article highlights significant advancements in healthcare analytics, particularly in medical imaging and personalized treatment optimization, as well as substantial improvements in smart city infrastructure management. This article contributes to the understanding of federated learning's practical implementation challenges and solutions, providing insights into future directions for privacy-preserving distributed machine learning.","url":"https://doi.org/10.5281/zenodo.21604331","authors":["Singhal, Ankush"],"tags":["Privacy-Preserving Machine Learning; Secure Aggregation Protocol; Distributed Healthcare Analytics; Smart City Infrastructure; Federated Model Training"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21604331","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21601518","name":"Enhanced File Security using Encryption and Splitting technique over Multi-cloud Environment","source":"datacite","abstract":"Cloud computing is a field which has been fast growing over the last few years. The fact that cloud can provide both computation and storage at low rates makes it popular among corporations and IT industries. This also makes it a very captivating proposition for the future. But in spite of its promise and potential, security in the cloud proves to be a cause for concerns to the business sector. This is due to the out sourcing of data onto third party managed cloud platform. These concerns security also make the use of cloud services not so much flexible. We provide a secure framework to stored data to be securely in the cloud, at the same time allowing operations to be performed on data without compromising of the sensitive parts of the data. A combination of searchable encryption with Partial Homomorphism is proposed.","url":"https://doi.org/10.5281/zenodo.21601518","authors":["Kudtarkar, Prathamesh P.","Pagare, Jayesh D.","Ahire, Sujata R.","Pawar, Tejaswini S."],"tags":["Cloud Computing","Cloud Security","Homomorphic Encryption","Searchable Encryption","Secure Socket Layer"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.21601518","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21601519","name":"Enhanced File Security using Encryption and Splitting technique over Multi-cloud Environment","source":"datacite","abstract":"Cloud computing is a field which has been fast growing over the last few years. The fact that cloud can provide both computation and storage at low rates makes it popular among corporations and IT industries. This also makes it a very captivating proposition for the future. But in spite of its promise and potential, security in the cloud proves to be a cause for concerns to the business sector. This is due to the out sourcing of data onto third party managed cloud platform. These concerns security also make the use of cloud services not so much flexible. We provide a secure framework to stored data to be securely in the cloud, at the same time allowing operations to be performed on data without compromising of the sensitive parts of the data. A combination of searchable encryption with Partial Homomorphism is proposed.","url":"https://doi.org/10.5281/zenodo.21601519","authors":["Kudtarkar, Prathamesh P.","Pagare, Jayesh D.","Ahire, Sujata R.","Pawar, Tejaswini S."],"tags":["Cloud Computing","Cloud Security","Homomorphic Encryption","Searchable Encryption","Secure Socket Layer"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.21601519","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21608328","name":"Blockchain-Based Tokenized Storage Incentives: Revolutionizing Decentralized Object Storage","source":"datacite","abstract":"This article presents an innovative approach to decentralized object storage that combines blockchain technology, distributed hash tables (DHTs), and advanced cryptographic techniques. The proposed system addresses the limitations of traditional centralized storage infrastructures by implementing a token-based incentive mechanism that encourages network participation while ensuring data security and availability. By integrating proof-of-storage consensus mechanisms consensus mechanisms, homomorphic encryption, and erasure coding, the architecture demonstrates superior fault tolerance and operational efficiency. The system's implementation showcases significant improvements in resource utilization, cost reduction, and environmental sustainability compared to conventional cloud storage solutions. Furthermore, the framework incorporates robust data sovereignty protections and compliance mechanisms, making it suitable for enterprise deployments and small-medium enterprises alike.","url":"https://doi.org/10.5281/zenodo.21608328","authors":["Gupta, Ankit"],"tags":["Blockchain integration; Decentralized storage systems; Distributed hash tables; Homomorphic encryption; Tokenized incentives"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21608328","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21608329","name":"Blockchain-Based Tokenized Storage Incentives: Revolutionizing Decentralized Object Storage","source":"datacite","abstract":"This article presents an innovative approach to decentralized object storage that combines blockchain technology, distributed hash tables (DHTs), and advanced cryptographic techniques. The proposed system addresses the limitations of traditional centralized storage infrastructures by implementing a token-based incentive mechanism that encourages network participation while ensuring data security and availability. By integrating proof-of-storage consensus mechanisms consensus mechanisms, homomorphic encryption, and erasure coding, the architecture demonstrates superior fault tolerance and operational efficiency. The system's implementation showcases significant improvements in resource utilization, cost reduction, and environmental sustainability compared to conventional cloud storage solutions. Furthermore, the framework incorporates robust data sovereignty protections and compliance mechanisms, making it suitable for enterprise deployments and small-medium enterprises alike.","url":"https://doi.org/10.5281/zenodo.21608329","authors":["Gupta, Ankit"],"tags":["Blockchain integration; Decentralized storage systems; Distributed hash tables; Homomorphic encryption; Tokenized incentives"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21608329","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21608302","name":"Privacy-Preserving in Machine Learning: Bridging Security and Performance in Modern Applications","source":"datacite","abstract":"Privacy-preserving machine learning (PPML) has emerged as a critical paradigm in the era of data-driven applications, addressing the fundamental tension between leveraging large-scale datasets and protecting individual privacy. This technical article examines recent advances in PPML techniques, focusing on three key approaches: federated learning, which enables distributed model training while keeping data localized; homomorphic encryption, allowing computation on encrypted data; and secure multi-party computation (MPC) for privacy-conscious collaborative learning. Through detailed architectural analysis and real-world case studies in mobile device personalization and healthcare analytics, this article demonstrates how these techniques can be effectively implemented while navigating computational overhead and implementation complexity. This article reveals current PPML approaches successfully preserve privacy in production environments, but they face significant challenges in computational efficiency and system integration. This article concludes by presenting optimization strategies and emerging research directions aimed at making PPML more practical for large-scale deployments.","url":"https://doi.org/10.5281/zenodo.21608302","authors":["Nalam, Ramachandra Vamsi Krishna","Nalam, Pooja Sri","Anuvalasetty, Sruthi"],"tags":["Privacy-Preserving Machine Learning (PPML); Federated Learning; Homomorphic Encryption; Secure Multi-Party Computation (MPC); Data Privacy Architecture"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21608302","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21608303","name":"Privacy-Preserving in Machine Learning: Bridging Security and Performance in Modern Applications","source":"datacite","abstract":"Privacy-preserving machine learning (PPML) has emerged as a critical paradigm in the era of data-driven applications, addressing the fundamental tension between leveraging large-scale datasets and protecting individual privacy. This technical article examines recent advances in PPML techniques, focusing on three key approaches: federated learning, which enables distributed model training while keeping data localized; homomorphic encryption, allowing computation on encrypted data; and secure multi-party computation (MPC) for privacy-conscious collaborative learning. Through detailed architectural analysis and real-world case studies in mobile device personalization and healthcare analytics, this article demonstrates how these techniques can be effectively implemented while navigating computational overhead and implementation complexity. This article reveals current PPML approaches successfully preserve privacy in production environments, but they face significant challenges in computational efficiency and system integration. This article concludes by presenting optimization strategies and emerging research directions aimed at making PPML more practical for large-scale deployments.","url":"https://doi.org/10.5281/zenodo.21608303","authors":["Nalam, Ramachandra Vamsi Krishna","Nalam, Pooja Sri","Anuvalasetty, Sruthi"],"tags":["Privacy-Preserving Machine Learning (PPML); Federated Learning; Homomorphic Encryption; Secure Multi-Party Computation (MPC); Data Privacy Architecture"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21608303","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21608215","name":"Privacy-Preserving Federated Learning in Healthcare - A Secure AI Framework","source":"datacite","abstract":"Federated Learning (FL) has transformed AI applications in healthcare by enabling collaborative model training across multiple institutions while preserving patient data privacy. Despite its advantages, FL remains susceptible to security vulnerabilities, including model inversion attacks, adversarial data poisoning, and communication inefficiencies, necessitating enhanced privacy-preserving mechanisms. In response, this study introduces Privacy-Preserving Federated Learning (PPFL), an advanced FL framework integrating Secure Multi-Party Computation (SMPC), Differential Privacy (DP), and Homomorphic Encryption (HE) to ensure data confidentiality while maintaining computational efficiency. I rigorously evaluate PPFL using Federated Averaging (FedAvg), Secure Aggregation (SecAgg), and Differentially Private Stochastic Gradient Descent (DP-SGD) across real-world healthcare datasets. The results indicate that this approach achieves up to an 85% reduction in model inversion attack success rates, enhances privacy efficiency by 30%, and maintains accuracy retention between 95.2% and 98.3%, significantly improving security without compromising model performance. Furthermore, comparative visual analyses illustrate trade-offs between privacy and accuracy, scalability trends, and computational overhead. This study also explores scalability challenges, computational trade-offs, and real-world deployment considerations in multi-institutional hospital networks, paving the way for secure, scalable, and privacy-preserving AI adoption in healthcare environments.","url":"https://doi.org/10.5281/zenodo.21608215","authors":["Telaprolu, Bhavani Sankar"],"tags":["Privacy-Preserving AI; Federated Learning; Secure Multi-Party Computation; Differential Privacy; Homomorphic Encryption; Healthcare AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21608215","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21608216","name":"Privacy-Preserving Federated Learning in Healthcare - A Secure AI Framework","source":"datacite","abstract":"Federated Learning (FL) has transformed AI applications in healthcare by enabling collaborative model training across multiple institutions while preserving patient data privacy. Despite its advantages, FL remains susceptible to security vulnerabilities, including model inversion attacks, adversarial data poisoning, and communication inefficiencies, necessitating enhanced privacy-preserving mechanisms. In response, this study introduces Privacy-Preserving Federated Learning (PPFL), an advanced FL framework integrating Secure Multi-Party Computation (SMPC), Differential Privacy (DP), and Homomorphic Encryption (HE) to ensure data confidentiality while maintaining computational efficiency. I rigorously evaluate PPFL using Federated Averaging (FedAvg), Secure Aggregation (SecAgg), and Differentially Private Stochastic Gradient Descent (DP-SGD) across real-world healthcare datasets. The results indicate that this approach achieves up to an 85% reduction in model inversion attack success rates, enhances privacy efficiency by 30%, and maintains accuracy retention between 95.2% and 98.3%, significantly improving security without compromising model performance. Furthermore, comparative visual analyses illustrate trade-offs between privacy and accuracy, scalability trends, and computational overhead. This study also explores scalability challenges, computational trade-offs, and real-world deployment considerations in multi-institutional hospital networks, paving the way for secure, scalable, and privacy-preserving AI adoption in healthcare environments.","url":"https://doi.org/10.5281/zenodo.21608216","authors":["Telaprolu, Bhavani Sankar"],"tags":["Privacy-Preserving AI; Federated Learning; Secure Multi-Party Computation; Differential Privacy; Homomorphic Encryption; Healthcare AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21608216","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21608026","name":"Recent Innovations in AI Privacy: Protecting Data in the Age of Machine Learning","source":"datacite","abstract":"This comprehensive article explores recent advancements in privacy-preserving technologies within artificial intelligence systems, focusing on five key approaches: federated learning, differential privacy, homomorphic encryption, privacy-preserving machine learning (PPML), and zero-knowledge proofs. The article examines how these technologies address critical privacy challenges in machine learning environments while maintaining model performance and utility. The article highlights the implementation of these approaches across various domains, particularly in healthcare and financial services, demonstrating their effectiveness in protecting sensitive data throughout the machine learning lifecycle. The article reveals how these technologies complement each other to create robust privacy protection frameworks while enabling organizations to leverage the power of AI without compromising data confidentiality.","url":"https://doi.org/10.5281/zenodo.21608026","authors":["Sonkar, Siddhant"],"tags":["Privacy-Preserving AI; Federated Learning; Differential Privacy; Homomorphic Encryption; Zero-Knowledge Proofs"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21608026","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21608027","name":"Recent Innovations in AI Privacy: Protecting Data in the Age of Machine Learning","source":"datacite","abstract":"This comprehensive article explores recent advancements in privacy-preserving technologies within artificial intelligence systems, focusing on five key approaches: federated learning, differential privacy, homomorphic encryption, privacy-preserving machine learning (PPML), and zero-knowledge proofs. The article examines how these technologies address critical privacy challenges in machine learning environments while maintaining model performance and utility. The article highlights the implementation of these approaches across various domains, particularly in healthcare and financial services, demonstrating their effectiveness in protecting sensitive data throughout the machine learning lifecycle. The article reveals how these technologies complement each other to create robust privacy protection frameworks while enabling organizations to leverage the power of AI without compromising data confidentiality.","url":"https://doi.org/10.5281/zenodo.21608027","authors":["Sonkar, Siddhant"],"tags":["Privacy-Preserving AI; Federated Learning; Differential Privacy; Homomorphic Encryption; Zero-Knowledge Proofs"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21608027","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21606499","name":"Homomorphic Encryption for Secure Ad Targeting: Balancing Privacy and Personalization in Digital Advertising","source":"datacite","abstract":"This article explores the application of homomorphic encryption (HE) in secure ad targeting, addressing the critical challenge of balancing personalized advertising with user privacy concerns in the digital advertising ecosystem. We examine the fundamentals of HE, its integration into ad targeting processes, and propose a privacy-preserving ad platform architecture. Through a comprehensive feasibility analysis and performance evaluation, we assess the technical challenges, computational overhead, and scalability issues associated with implementing HE in real-time ad serving. Our findings indicate that while HE offers strong privacy guarantees, it currently faces limitations in terms of latency and throughput compared to traditional ad targeting methods. We analyze the trade-offs between privacy protection and targeting effectiveness, highlighting the impact on ad relevance and personalization. The article also discusses future directions, including advancements in HE algorithms, integration with other privacy-enhancing technologies, and regulatory considerations. By synthesizing current research and experimental results, this work provides valuable insights into the potential of HE to revolutionize privacy-preserving ad targeting, paving the way for a more secure and privacy-conscious digital advertising future.","url":"https://doi.org/10.5281/zenodo.21606499","authors":["Sinha, Swati"],"tags":["Homomorphic Encryption; Privacy-Preserving Ad Targeting; Secure Ad Platforms; Computational Overhead in Advertising; Privacy-Personalization Trade-off"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21606499","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21606500","name":"Homomorphic Encryption for Secure Ad Targeting: Balancing Privacy and Personalization in Digital Advertising","source":"datacite","abstract":"This article explores the application of homomorphic encryption (HE) in secure ad targeting, addressing the critical challenge of balancing personalized advertising with user privacy concerns in the digital advertising ecosystem. We examine the fundamentals of HE, its integration into ad targeting processes, and propose a privacy-preserving ad platform architecture. Through a comprehensive feasibility analysis and performance evaluation, we assess the technical challenges, computational overhead, and scalability issues associated with implementing HE in real-time ad serving. Our findings indicate that while HE offers strong privacy guarantees, it currently faces limitations in terms of latency and throughput compared to traditional ad targeting methods. We analyze the trade-offs between privacy protection and targeting effectiveness, highlighting the impact on ad relevance and personalization. The article also discusses future directions, including advancements in HE algorithms, integration with other privacy-enhancing technologies, and regulatory considerations. By synthesizing current research and experimental results, this work provides valuable insights into the potential of HE to revolutionize privacy-preserving ad targeting, paving the way for a more secure and privacy-conscious digital advertising future.","url":"https://doi.org/10.5281/zenodo.21606500","authors":["Sinha, Swati"],"tags":["Homomorphic Encryption; Privacy-Preserving Ad Targeting; Secure Ad Platforms; Computational Overhead in Advertising; Privacy-Personalization Trade-off"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.21606500","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21590384","name":"A Research  Homomorphic Encryption Scheme to Secure Data Mining in Cloud Computing for Banking System","source":"datacite","abstract":"Big data is difficult to handle, process and analyse using traditional approach. Using services, we can resolve problem like resource sharing, storage capacity and data transfer bottlenecks etc. But there is a main issue of data mining based attacks, allows an adversary or an unauthorized user to extract valuable and sensitive information by analysing the results generated from computation performed on the raw data. In order to provide privacy, security for cloud user as well as cloud provider. We proposed a system for secure data mining using well known techniques like homomorphic encryption system, RSA algorithm. In this process flow, cloud server is unware of data uploaded by the user and the client only gets the computational results. Through an experimental evaluation, we can maintain correctness and confidentiality of final result.","url":"https://doi.org/10.5281/zenodo.21590384","authors":["Sakharkar, Sneha","Karnuke, Shubhangi","Doifode, Snehal","Deshmukh, Vaishnavi"],"tags":["Homomorphism Encryption","Decryption","Data mining","Security","Cloud Computing."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.5281/zenodo.21590384","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21590385","name":"A Research  Homomorphic Encryption Scheme to Secure Data Mining in Cloud Computing for Banking System","source":"datacite","abstract":"Big data is difficult to handle, process and analyse using traditional approach. Using services, we can resolve problem like resource sharing, storage capacity and data transfer bottlenecks etc. But there is a main issue of data mining based attacks, allows an adversary or an unauthorized user to extract valuable and sensitive information by analysing the results generated from computation performed on the raw data. In order to provide privacy, security for cloud user as well as cloud provider. We proposed a system for secure data mining using well known techniques like homomorphic encryption system, RSA algorithm. In this process flow, cloud server is unware of data uploaded by the user and the client only gets the computational results. Through an experimental evaluation, we can maintain correctness and confidentiality of final result.","url":"https://doi.org/10.5281/zenodo.21590385","authors":["Sakharkar, Sneha","Karnuke, Shubhangi","Doifode, Snehal","Deshmukh, Vaishnavi"],"tags":["Homomorphism Encryption","Decryption","Data mining","Security","Cloud Computing."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.5281/zenodo.21590385","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.5281/zenodo.21558450","name":"Survey on Security Issues in Decision Support System of Health Care Network","source":"datacite","abstract":"Medical Secure Systems (MSSs) are spoken to by incorporating count and also physical procedures. The theories and uses of MSSs confront the huge issues. The main objective of this study is to give a more prominent comprehension summary of this developing techniques focusing on the security of the outsourced medical data. In this system, the main focus is on secure data transmission of medical data. Such system is called as Medical Secure Systems (MSS). It can transmit and process the data gathered from health observing systems, which comprises of BAN. The obtained data is transmitted to the private or open cloud which contains a set of calculations for investigating the patient data. These medical data oughts to be kept the mystery. In the wake of breaking down these data, the input is given to the specialists to make a remedial move. This system incorporates data obtaining which is fit for gaining data from body territory systems, data conglomeration which focuses the accumulated flag data, cloud preparing which incorporates numerous examination calculations and activity layer which create either physical activity or choice help. In this paper, we will talk about different issues that should be considered to satisfying the security and privacy necessities and furthermore examine about the important components and wordings utilized by the different specialists to illuminate those issues.","url":"https://doi.org/10.5281/zenodo.21558450","authors":["Ingle, Jayshree V.","Chopde, Nitin"],"tags":["Medical Cyber Physical Systems","Secure Digital Cover","Confidentiality","Homomorphic Encryption"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.21558450","addedAt":"2026-08-31T06:41:43.496Z","updatedAt":"2026-08-31T06:41:43.496Z"},{"id":"doi:10.1109/auteee67053.2025.11322224","name":"Privacy-Preserving Image Classification Using Homomorphic Encryption and CNN","source":"crossref","abstract":"","url":"https://doi.org/10.1109/auteee67053.2025.11322224","authors":["Liu Yushu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-14T20:36:55Z","doi":"10.1109/auteee67053.2025.11322224","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/iwbf63717.2025.11113462","name":"AMB-FHE: Adaptive Multi-Biometric Fusion with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iwbf63717.2025.11113462","authors":["Florian Bayer","Christian Rathgeb"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-15T18:11:50Z","doi":"10.1109/iwbf63717.2025.11113462","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/icbctis66509.2025.11387532","name":"NGS-based threshold homomorphic encryption scheme without CRS","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icbctis66509.2025.11387532","authors":["Xu Zhao","Zheng Yuan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T20:55:22Z","doi":"10.1109/icbctis66509.2025.11387532","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/isdfs65363.2025.11012048","name":"Privacy-Preserving Secret Sharing using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isdfs65363.2025.11012048","authors":["Adoum Youssouf","Abdramane Issa","Daouda Ahmat"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-11T15:39:45Z","doi":"10.1109/isdfs65363.2025.11012048","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.55011/staiqc.2025.5207","name":"PrivacyPreservingMedicalImageInferencePortal Using Homomorphic Encryption","source":"crossref","abstract":"ThegrowingrelianceonAIfor medical image analysis presents importantprivacyriskswhen patient data is processed on external servers.To solve this problem, we suggest a Privacy-Preserving Medical Image Inference Portal that uses Homomorphic Encryption (HE) to keep sensitive images encrypted while they are being processed.The system lets users upload encryptedmedicalimagesthataneuralnetworkprocesseswithoutdecryptingthem,whichkeeps privacysafe.Alightweightmodelthatisoptimizedforencryptedoperationsstrikesabalance between accuracy and speed of computation.The portal has an easy-to-use interface for safe uploadsandresultretrieval,showingthatadvancedcryptographycanbeusedinhealthcaredi-agnostics. ThisworkshowsasafewaytouseAIinmedicalimagingthatprotectsbothprivacy and usefulness.","url":"https://doi.org/10.55011/staiqc.2025.5207","authors":["Akhil Pa","Steephen MV","LavitaWilma Lobo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-26T07:40:13Z","doi":"10.55011/staiqc.2025.5207","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/iccp68926.2025.11427161","name":"Face Recognition System using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccp68926.2025.11427161","authors":["Talida Dîrnu","Vlad-Cristian Miclea"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-13T19:51:37Z","doi":"10.1109/iccp68926.2025.11427161","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/systol66549.2025.11267378","name":"Experimental Validation of Resilient Homomorphic Encryption of Control Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/systol66549.2025.11267378","authors":["Obaidullah Yadgar","Ping Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-03T18:40:10Z","doi":"10.1109/systol66549.2025.11267378","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/etis64005.2025.10961602","name":"Securing Diabetic Prediction with Federated Learning and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/etis64005.2025.10961602","authors":["Deepthy K Bhaskar","Minimol B","Binu V P"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-21T17:36:15Z","doi":"10.1109/etis64005.2025.10961602","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.62056/av11c3w9p","name":"Circuit Privacy for FHEW/TFHE-Style Fully Homomorphic Encryption in Practice","source":"crossref","abstract":"A fully homomorphic encryption (FHE) scheme allows a client to encrypt and delegate its data to a server that performs computation on the encrypted data that the client can then decrypt. While FHE gives confidentiality to clients' data, it does not protect the server's input and computation. Nevertheless, FHE schemes are still helpful in building delegation protocols that reduce communication complexity, as the ciphertext's size is independent of the size of the computation performed on them. We can further extend FHE by a property called circuit privacy, which guarantees that the result of computing on ciphertexts reveals no information on the computed function and the inputs of the server. Thereby, circuit private FHE gives rise to round optimal and communication efficient secure two-party computation protocols. Unfortunately, despite significant efforts and much work put into the efficiency and practical implementations of FHE schemes, very little has been done to provide useful and practical FHE supporting circuit privacy. In this work, we address this gap and design the first randomized bootstrapping algorithm whose single invocation sanitizes a ciphertext and, consequently, serves as a tool to provide circuit privacy. We give an extensive analysis, propose parameters, and provide a C++ implementation of our scheme. Our bootstrapping can sanitize a ciphertext to achieve circuit privacy at an 80-bit statistical security level in between 1.3 and 0.9 seconds, depending which Gaussian sampling algorithm is used, and whether the parameter set targets a fast Fourier or a number theoretic transform-based implementation. In addition, we can perform non-sanitized bootstrapping in around 0.27 or 0.14 seconds. Crucially, we do not need to increase the parameters to perform computation before or after sanitization takes place. For comparison's sake, we revisit the Ducas-Stehlé washing machine method. In particular, we give a tight analysis, estimate efficiency, review old, and provide new parameters.","url":"https://doi.org/10.62056/av11c3w9p","authors":["Kamil Kluczniak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-13T17:00:52Z","doi":"10.62056/av11c3w9p","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.25236/ajcis.2025.080103","name":"Research on Secure Cloud Storage of Mobile Office Data Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.25236/ajcis.2025.080103","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-06T03:49:30Z","doi":"10.25236/ajcis.2025.080103","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1201/9781779643575-19","name":"Zero-Trust Blockchain Privacy Solution with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781779643575-19","authors":["Niraj Upadhayaya","Pramod Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-22T13:54:49Z","doi":"10.1201/9781779643575-19","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/fasta65681.2025.11138291","name":"Model-Free Adaptive Tracking Control Under Homomorphic Encryption Mechanism","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fasta65681.2025.11138291","authors":["Dewei Wang","Shuai Liu","Yong Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-09T17:29:36Z","doi":"10.1109/fasta65681.2025.11138291","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/netcrypt65877.2025.11102664","name":"Enhanced Video Watermarking Using Hybrid 2D-DWT, SVD, and Paillier Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/netcrypt65877.2025.11102664","authors":["Khushboo Chhikara","Manoj Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-12T17:51:39Z","doi":"10.1109/netcrypt65877.2025.11102664","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.14428/esann/2025.es2025-47","name":"Towards Learning Vector Quantization in the Setting of Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.14428/esann/2025.es2025-47","authors":["Thomas Davies","Ronny Schubert","Mandy Lange-Geisler","Klaus Dohmen","Thomas Villmann"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-15T14:43:24Z","doi":"10.14428/esann/2025.es2025-47","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1063/5.0297972","name":"Study on asynchronous processing of IoT big data-based homomorphic encryption transmission using Raft algorithm","source":"crossref","abstract":"IoT server-layer processing of heterogeneous data alleviates silos but incurs high resource costs. This study proposes shifting computational load to the edge layer to reduce server strain. Focusing on massive IoT deployments in non-ideal environments, it addresses challenges in connectivity, data detection, and channel inference, where traditional frequency offset compensation and synchronization techniques fall short. The research posits that terminals require downlink information transmission for coverage. Combining this with big data demands and data rate considerations, we construct a homomorphic encryption transmission model for asynchronous IoT data transfer. To minimize data rate impacts on reconstruction and performance, a methodology integrating asynchronous data processing and channel inference prediction is developed. We design a multi-node detection and channel inference optimization scheme, merging the Raft algorithm-based homomorphic encryption with convex optimization. Simulation tests compare bit error rate, channel inference quality, and performance against conventional Lasso and OMP (with serial interference cancellation) algorithms. The results verify that the proposed Raft-based approach achieves performance gains of 7.52% over Lasso and 10.64% over OMP, demonstrating significant technical superiority. This innovative application of homomorphic encryption for asynchronous big data transmission advances IoT processing and holds potential for enabling blockchain integration across diverse IoT systems. By comparison with the traditional Byzantine fault tolerance consensus scheme, Raft has more advantages in throughput, delay, and resource consumption and can meet the requirements for efficiency in IoT big data processing while ensuring data transmission security and storage consistency.","url":"https://doi.org/10.1063/5.0297972","authors":["Liyuan He","Lanjiang Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-21T15:41:50Z","doi":"10.1063/5.0297972","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/punecon67554.2025.11378734","name":"Privacy-Preserving Medical Image Classification Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/punecon67554.2025.11378734","authors":["Rakhi Bharadwaj","Priyanshi Patle","Bhagyesh Pawar","Nikita Pawar","Kunal Pehere"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-17T21:04:14Z","doi":"10.1109/punecon67554.2025.11378734","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/ssitcon66133.2025.11342083","name":"Lightweight Homomorphic Encryption Transmission Protocol for Multimodal Physiological Sensor Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ssitcon66133.2025.11342083","authors":["Yifei Wang","Nuo Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-22T20:58:23Z","doi":"10.1109/ssitcon66133.2025.11342083","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/escience65000.2025.00010","name":"Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/escience65000.2025.00010","authors":["Moontaha Nishat Chowdhury","André Bauer","Minxuan Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-07T17:34:49Z","doi":"10.1109/escience65000.2025.00010","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1016/j.eswa.2024.126197","name":"Federated learning enabled multi-key homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.eswa.2024.126197","authors":["Hemant Ramdas Kumbhar","S. Srinivasa Rao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-17T11:33:19Z","doi":"10.1016/j.eswa.2024.126197","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.23919/date64628.2025.10992987","name":"A Unified Vector Processing Unit for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.23919/date64628.2025.10992987","authors":["Jiangbin Dong","Xinhua Chen","Mingyu Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-21T17:36:35Z","doi":"10.23919/date64628.2025.10992987","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/icaft66710.2025.11452687","name":"Lightweight Multi-Key Homomorphic Encryption for Scalable Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaft66710.2025.11452687","authors":["Himani Bhatt","Saurabh Rana","Mandeep Mittal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T19:49:08Z","doi":"10.1109/icaft66710.2025.11452687","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/icsip65915.2025.11171563","name":"Strong-PUF-Based Identity Authentication Scheme Leveraging Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsip65915.2025.11171563","authors":["Yikai Jiang","Bing Li","Tian Fang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-08T17:35:26Z","doi":"10.1109/icsip65915.2025.11171563","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/icsses64899.2025.11009905","name":"Lattice-Based Post-Quantum Cryptography for Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsses64899.2025.11009905","authors":["Abel C. H. Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-11T15:39:42Z","doi":"10.1109/icsses64899.2025.11009905","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/pact65351.2025.00032","name":"FLASH: An Abstract Machine for Modelling Fully Homomorphic Encryption Accelerators","source":"crossref","abstract":"","url":"https://doi.org/10.1109/pact65351.2025.00032","authors":["Alireza Tabatabaeian","Arrvindh Shriraman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-16T18:30:30Z","doi":"10.1109/pact65351.2025.00032","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1109/tiptekno68206.2025.11270026","name":"Secure and Interpretable Dyslexia Detection Using Homomorphic Encryption and SHAP-Based Explanations","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tiptekno68206.2025.11270026","authors":["Mhd Raja Abou Harb","Baris Celiktas","Gunet Eroglu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-08T18:38:51Z","doi":"10.1109/tiptekno68206.2025.11270026","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1017/qut.2025.2","name":"Quantum delegated and federated learning via quantum homomorphic encryption","source":"crossref","abstract":"Abstract Quantum learning models hold the potential to bring computational advantages over the classical realm. As powerful quantum servers become available on the cloud, ensuring the protection of clients’ private data becomes crucial. By incorporating quantum homomorphic encryption schemes, we present a general framework that enables quantum delegated and federated learning with a computation-theoretical data privacy guarantee. We show that learning and inference under this framework feature substantially lower communication complexity compared with schemes based on blind quantum computing. In addition, in the proposed quantum federated learning scenario, there is less computational burden on local quantum devices from the client side, since the server can operate on encrypted quantum data without extracting any information. We further prove that certain quantum speedups in supervised learning carry over to private delegated learning scenarios employing quantum kernel methods. Our results provide a valuable guide toward privacy-guaranteed quantum learning on the cloud, which may benefit future studies and security-related applications.","url":"https://doi.org/10.1017/qut.2025.2","authors":["Weikang Li","Dong-Ling Deng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-04T01:45:37Z","doi":"10.1017/qut.2025.2","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:44.812Z"},{"id":"doi:10.1038/s41598-026-38906-9","name":"A data privacy protection method for infectious disease prediction models with balanced training speed and accuracy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-38906-9","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-38906-9","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.3390/s25165108","name":"Federated Security for Privacy Preservation of Healthcare Data in Edge-Cloud Environments.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25165108","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25165108","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-026-45938-8","name":"Enhancement of cryptography algorithms for security of cloud-based IoT with machine learning models.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-45938-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-45938-8","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/s25216712","name":"Fine-Grained Personalized Data Aggregation Scheme with High Quality and Privacy Protection.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25216712","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25216712","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-21229-6","name":"Privacy preservation in diabetic disease prediction using federated learning based on efficient cross stage recurrent model.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-21229-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-21229-6","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-11883-1","name":"SecuFL-IoT: an adaptive privacy-preserving federated learning framework for anomaly detection in smart industrial networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-11883-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-025-11883-1","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/s25154854","name":"A Blockchain-Based Secure Data Transaction and Privacy Preservation Scheme in IoT System.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25154854","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25154854","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/s26020617","name":"Secure Hierarchical Asynchronous Federated Learning with Shuffle Model and Mask-DP.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26020617","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26020617","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1371/journal.pone.0335953","name":"A multidimensional, efficient, and secure data query based on privacy preservation in vehicular ad hoc networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0335953","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1371/journal.pone.0335953","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-16945-y","name":"Light weight blockchain with IoT devices to secure smart non-fungible tokens using hybrid secure functions.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-16945-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-16945-y","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-07917-3","name":"Blended clustering energy efficient routing and PUF based authentication in IoT enabled smart agriculture systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-07917-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-07917-3","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/tpami.2025.3599592","name":"Interpretable Rotation-Equivariant Multiary-Valued Network for Attribute Obfuscation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2025.3599592","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1109/tpami.2025.3599592","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1038/s41598-025-23140-6","name":"Multi-layer encrypted learning for distributed healthcare analytics.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-23140-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-23140-6","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.3390/s25165176","name":"DARTPHROG: A Superscalar Homomorphic Accelerator.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25165176","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25165176","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-026-43252-x","name":"A secure and explainable multimodal biometric system using trust adaptive fusion for face and fingerprint.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-43252-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-43252-x","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.3233/shti250771","name":"Privacy-Preserving Opt-Out from Homomorphically Encrypted Clinical Trials.","source":"europepmc","abstract":"","url":"https://doi.org/10.3233/shti250771","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3233/shti250771","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1371/journal.pdig.0000753","name":"Privacy-preserving AUC computation in distributed machine learning with PHT-meDIC.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pdig.0000753","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1371/journal.pdig.0000753","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-25117-x","name":"A secure multi phase authentication protocol for cloud infrastructure using elliptic curve cryptography.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-25117-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-25117-x","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.30953/bhty.v8.421","name":"Optimizing Proof-of-Work for Secure Health Data Blockchain Using Compute Unified Device Architecture.","source":"europepmc","abstract":"","url":"https://doi.org/10.30953/bhty.v8.421","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.30953/bhty.v8.421","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-21605-2","name":"Smart battery management in EVs using IoT, blockchain, and machine learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-21605-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-21605-2","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3389/fdgth.2025.1610228","name":"Secure latent Dirichlet allocation.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdgth.2025.1610228","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fdgth.2025.1610228","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3389/fnins.2026.1771268","name":"Editorial: Theoretical advances and practical applications of spiking neural networks, volume II.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fnins.2026.1771268","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fnins.2026.1771268","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-28560-y","name":"BHFVAL: Block chain-Enabled Hierarchical Federated Variational Auto encoder Framework for Secure Intrusion Detection in Vehicular Networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-28560-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-28560-y","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1371/journal.pone.0347786","name":"Enhancing student data privacy in virtual learning with blockchain and advanced encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0347786","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0347786","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1093/jamiaopen/ooag087","name":"Data-derived Identity Verification as a principle for the dissemination, harmonization, and artificial intelligence reuse of sensitive biomedical data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/jamiaopen/ooag087","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1093/jamiaopen/ooag087","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41746-025-01781-1","name":"COLA-GLM: collaborative one-shot and lossless algorithms of generalized linear models for decentralized observational healthcare data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41746-025-01781-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41746-025-01781-1","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3892/br.2025.2070","name":"Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review).","source":"europepmc","abstract":"With the advancement of precision medicine, machine learning (ML) and deep learning have increasingly become a pivotal tool for driving medical innovation. Precision medicine, grounded in individual variability, aims to deliver personalized treatment interventions, with ML serving as a critical enabler for achieving this goal. Recent ML-driven progress in genomic analysis, personalized treatment optimization and disease diagnostics have significantly elevated the accuracy and efficacy of medical decision-making processes. However, the widespread adoption of artificial intelligence also faces multifaceted challenges, including data privacy frameworks, cybersecurity risks, ethical considerations and the integration of technology with clinical workflows. The present review seeks to analyze cutting-edge applications of ML within precision medicine domains, examine its challenges, and project future evolutionary pathways, emphasizing the critical need for proactive attention to these issues to ensure tangible benefits for patients and healthcare systems.","url":"https://doi.org/10.3892/br.2025.2070","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3892/br.2025.2070","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-026-37082-0","name":"Parametric action of homomorphic image of modular group and it's application in image encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-37082-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-37082-0","addedAt":"2026-08-31T06:41:44.812Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/s25216677","name":"Efficient and Privacy-Preserving Power Distribution Analytics Based on IoT.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25216677","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25216677","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3390/s25237369","name":"Face Privacy Protection Method for Autonomous Sensors Based on Hierarchical Format-Preserving Encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25237369","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25237369","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3389/frai.2025.1758660","name":"Correction: Synchronizing LLM-based semantic knowledge bases via secure federated fine-tuning in semantic communication.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2025.1758660","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/frai.2025.1758660","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-025-06574-w","name":"Fused federated learning framework for secure and decentralized patient monitoring in healthcare 5.0 using IoMT.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-06574-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-06574-w","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-025-22558-2","name":"Generalized triangle group based S-box construction for secure image encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-22558-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-22558-2","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-026-48170-6","name":"An adaptive cryptographic fusion framework for secure and efficient medical image encryption.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-48170-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-48170-6","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-026-49949-3","name":"A unified privacy-preserving data mining framework with multi-noise injection and hybrid deep learning for robust privacy-utility trade-offs.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-49949-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-49949-3","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-025-22457-6","name":"Quantum-resilient and adaptive multi-region data aggregation for IoMT using zero-knowledge proofs and edge intelligence.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-22457-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-22457-6","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.3390/s25123823","name":"Multi-Party Verifiably Collaborative Encryption for Biomedical Signals via Singular Spectrum Analysis-Based Chaotic Filter Bank Networks.","source":"europepmc","abstract":"This paper proposes a multi-party verifiably collaborative system for encrypting the nonlinear and the non-stationary biomedical signals captured by biomedical sensors via the singular spectrum analysis (SSA)-based chaotic networks. In particular, the raw signals are first decomposed into the multiple components by the SSA. Then, these decomposed components are fed into the chaotic filter bank networks for performing the encryption. To perform the multi-party verifiably collaborative encryption, the window length of the SSA and the total number of the layers in the chaotic network are flexibly designed to match the total number of the collaborators. The computer numerical simulation results show that our proposed system achieves a good encryption performance.","url":"https://doi.org/10.3390/s25123823","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25123823","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-026-40964-y","name":"Federated learning with continual update for privacy-preserving clinical event prediction across distributed hospitals using MCN-GNN.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-40964-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-40964-y","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-026-37802-6","name":"Artificial intelligence driven approach for securing backup data and enhancing cyber resilience in sustainable smart infrastructure.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-37802-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-37802-6","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1016/j.artmed.2025.103245","name":"Privacy-preserving federated transfer learning for enhanced liver lesion segmentation in PET-CT imaging.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.artmed.2025.103245","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1016/j.artmed.2025.103245","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.995Z"},{"id":"doi:10.3390/s25237329","name":"Secure Fog Computing for Remote Health Monitoring with Data Prioritisation and AI-Based Anomaly Detection.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s25237329","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/s25237329","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1016/j.fmre.2025.09.025","name":"Hardware-enhanced data security for Internet of Things.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.fmre.2025.09.025","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.fmre.2025.09.025","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.5281/zenodo.21821704","name":"Confidential Post-Quantum Settlement: Blind ML-DSA-65 Verification for Institutional Financial Pipelines","source":"datacite","abstract":"Abstract Banking and mainstream finance are absorbing public-chain settlement patterns, stablecoin rails, and tokenized assets at the same time that regulators require post-quantum signatures and strict privacy on high-value flows. FIPS 204 ML-DSA-65 is the natural audit-grade signature. In custody, institutional settlement, and confidential L1 paths, that signature often sits inside an encrypted workflow. Checking it by first decrypting restores plaintext at the verifier and expands the set of systems that observe protected fields, or else requires a trusted enclave. Where institutions already keep settlement material under encryption, that forces an awkward choice between visibility and verification. Integrity proofs such as zk-STARKs excel at attesting that large batches or audit logs followed the rules. They do not, by themselves, answer whether a specific ML-DSA-65 signature is valid while the surrounding material remains under encryption. That gap is the subject of this work: a field-deployable hybrid that evaluates the linear core of ML-DSA-65 verification under leveled fully homomorphic encryption (FHE), completes verification on CPU, and keeps the protected payload out of plaintext at the verification step. Signature generation stays outside the encrypted path. Across 10 000 residual trials, infinity-norm noise stays inside a strict budget of 65 536 (observed maximum 51 489). Hybrid verification agrees with a reference FIPS 204 oracle at 100 % on 10 000 trials. End-to-end residual latency averages 2.54 ms on commodity 12th-generation Intel hardware. Because confidential paths remain attractive targets for timing and related leakage, a side-channel triad is part of the contribution: maximum absolute TVLA |t| = 1.13 (threshold 4.5), with negligible mutual information. Full verification entirely in ciphertext, and any form of signature generation under FHE, are not claimed. The contribution is a measurable building block for confidential, post-quantum, regulatory-aligned verification of ML-DSA-65—eliminating routine plaintext exposure at verify time and supporting side-channel discipline—where banking-grade privacy and blockchain-grade settlement meet. Keywords: Post-quantum cryptography, ML-DSA, FIPS 204, confidential computing, hybrid verification, leveled FHE, blockchain settlement, institutional custody, regulatory compliance, side-channel assessment. Dedicated to: In memory of Prof. Myung Kyoon “Michael” Chung (1945–2025), who dedicated his life to bringing truth and science to our world. A graduate of Seoul National University, Washington State University (Pullman), and the University of Illinois, and lifelong Professor at the Korea Advanced Institute of Science and Technology (KAIST), Department of Mechanical Engineering.","url":"https://doi.org/10.5281/zenodo.21821704","authors":["Chung, Jinhyuk Fred"],"tags":["Post-quantum cryptography","ML-DSA","FIPS 204","confidential computing","hybrid verification","leveled FHE","blockchain settlement","institutional custody"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21821704","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.21821703","name":"Confidential Post-Quantum Settlement: Blind ML-DSA-65 Verification for Institutional Financial Pipelines","source":"datacite","abstract":"Abstract Banking and mainstream finance are absorbing public-chain settlement patterns, stablecoin rails, and tokenized assets at the same time that regulators require post-quantum signatures and strict privacy on high-value flows. FIPS 204 ML-DSA-65 is the natural audit-grade signature. In custody, institutional settlement, and confidential L1 paths, that signature often sits inside an encrypted workflow. Checking it by first decrypting restores plaintext at the verifier and expands the set of systems that observe protected fields, or else requires a trusted enclave. Where institutions already keep settlement material under encryption, that forces an awkward choice between visibility and verification. Integrity proofs such as zk-STARKs excel at attesting that large batches or audit logs followed the rules. They do not, by themselves, answer whether a specific ML-DSA-65 signature is valid while the surrounding material remains under encryption. That gap is the subject of this work: a field-deployable hybrid that evaluates the linear core of ML-DSA-65 verification under leveled fully homomorphic encryption (FHE), completes verification on CPU, and keeps the protected payload out of plaintext at the verification step. Signature generation stays outside the encrypted path. Across 10 000 residual trials, infinity-norm noise stays inside a strict budget of 65 536 (observed maximum 51 489). Hybrid verification agrees with a reference FIPS 204 oracle at 100 % on 10 000 trials. End-to-end residual latency averages 2.54 ms on commodity 12th-generation Intel hardware. Because confidential paths remain attractive targets for timing and related leakage, a side-channel triad is part of the contribution: maximum absolute TVLA |t| = 1.13 (threshold 4.5), with negligible mutual information. Full verification entirely in ciphertext, and any form of signature generation under FHE, are not claimed. The contribution is a measurable building block for confidential, post-quantum, regulatory-aligned verification of ML-DSA-65—eliminating routine plaintext exposure at verify time and supporting side-channel discipline—where banking-grade privacy and blockchain-grade settlement meet. Keywords: Post-quantum cryptography, ML-DSA, FIPS 204, confidential computing, hybrid verification, leveled FHE, blockchain settlement, institutional custody, regulatory compliance, side-channel assessment. Dedicated to: In memory of Prof. Myung Kyoon “Michael” Chung (1945–2025), who dedicated his life to bringing truth and science to our world. A graduate of Seoul National University, Washington State University (Pullman), and the University of Illinois, and lifelong Professor at the Korea Advanced Institute of Science and Technology (KAIST), Department of Mechanical Engineering.","url":"https://doi.org/10.5281/zenodo.21821703","authors":["Chung, Jinhyuk Fred"],"tags":["Post-quantum cryptography","ML-DSA","FIPS 204","confidential computing","hybrid verification","leveled FHE","blockchain settlement","institutional custody"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21821703","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.21719064","name":"KLTu-Native: Unified Kinetic Lattice Topology for KEM, DSA, and FHE-Capable Lattice Operation with Shared Timing Isolation under MAXR3","source":"datacite","abstract":"Abstract Post-quantum migration is no longer optional: banks, cloud operators, and telecom networks must field lattice key exchange and signatures at scale, while keeping a credible path to lattice fully homomorphic encryption without rebuilding their crypto stack twice. Separate Kyber-, Dilithium-, and FHE product lines multiply parameters, entropy sources, and regression surfaces. We present KLTu-native, a unified lattice engine in which KEM, DSA, and FHE-oriented roles share one topology, control plane, and thermodynamic entropy architecture. The systems claim is organizational: one roof for key establishment, signatures, and structural FHE readiness, with a shared software-visible timing-isolation posture under production-like host features. On bare-metal client silicon with simultaneous multithreading (SMT) and active power management (APM) left enabled, we show that protected native Level 5 KEM and DSA operations pass Fixed-versus-Random Kolmogorov–Smirnov tests on operation latency (D<0.05, zero drops)—an isolation grade measured under the host features operators actually run, not under disabled energy-saving or hyperthreading modes. Level 3 is positioned as the deployment tier (tight SLA tails, usable multi-thread verify scaling); Level 5 is reported honestly as supply-limited under strict mid-operation entropy policy. Replicate RAPL statistics further show a statistically significant Level 5 signing energy-density advantage versus a pure community baseline, alongside an explicit package-power cost attributable to live isolation. Supporting comparators situate efficiency only. FHE bootstrap performance and invasive physical side-channel evaluation are out of scope. A breakthrough disclosure is that KLTu demonstrates bidirectional translation of NIST FIPS 203 ML-KEM (768 and 1024) between standard wire encodings and KLTu-native lattice structures by pack/unpack alone—without invoking decapsulation or deriving a shared secret during conversion. That removes the practical barrier of running a second KEM stack solely for standards compliance. Because the same Kinetic Lattice Topology is FHE-capable, sessions established from ordinary FIPS-KEM wire material can enter efficient KLTu-FHE evaluation under one engine, rather than across fragmented KEM and FHE product lines. For operators, the decision is whether a single, measurable, constant-time-oriented stack—Level-3-first, Level-5-honest, FIPS-wire interoperable and FHE-ready by structure—is preferable to maintaining parallel KEM, DSA, and future FHE lines with divergent latency, energy, and leakage profiles. Keywords: post-quantum cryptography; unified lattice stack; ML-KEM; ML-DSA; timing isolation; Kolmogorov–Smirnov; energy efficiency; FHE-ready design; FIPS 203 interperability; ML-KEM wire conversion Dedicated to: In memory of Prof. Myung Kyoon “Michael” CHUNG (1945-2025), who dedicated his life to bringing truth and science to our world. A graduate of Seoul National University, Washington State University (Pullman), University of Illinois, and lifelong Professor at Korea Advanced Institute of Technology (KAIST) with the Department of Mechanical Engineering.","url":"https://doi.org/10.5281/zenodo.21719064","authors":["Chung, Jinhyuk Fred"],"tags":["post-quantum cryptography","unified lattice stack","ML-KEM","ML-DSA","timing isolation","Kolmogorov–Smirnov","Energy efficiency","FHE-ready design"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21719064","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.21543529","name":"Efficient Fully Homomorphic Evaluation of FIPS 203 ML-KEM Sessions via Native Wire Conversion and Measured Noise-Control Options α, β, θ, δ without Functional Bootstrapping","source":"datacite","abstract":"Abstract Post-quantum key establishment under NIST FIPS 203 (ML-KEM) is entering production, while fully homomorphic encryption (FHE) remains constrained by the cost of functional programmable bootstrapping (PBS). Bridging the two usually forces either a heavyweight transciphering circuit that homomorphically evaluates decapsulation, or a proprietary key format that breaks compliance inventories. This paper reports two measured results that avoid both traps. First, public keys, secret keys, and ciphertexts in ML-KEM-768 (Level 3) and ML-KEM-1024 (Level 5) convert bidirectionally between FIPS 203 wire bytes and KLTu-native polynomial layouts by coefficient pack and unpack only. Conversion does not invoke decapsulation and does not produce shared secrets; when decapsulation is required, the FIPS-facing API is called explicitly and matches 45/45 official ACVP vectors per level. Second, four discrete noise-control options—denoted α, β, θ, and δ—are timed against production full PBS under Level 5 parameters (ring dimension N=32768). On a single pinned CPU core, option medians range from 42.18 µs (α) to 35.13 ms (β), while production PBS median is 15.55 s. A 50 000-step mixed schedule (70% α, 15% β, 10% θ, 5% δ) completes with zero full-PBS calls and a projected speedup of approximately 2 670× relative to an all-PBS baseline. A depth-ladder campaign further shows that α–δ alone sustain mult-depth homomorphic multiplication through depth 8 (Level 3) and depth 6 (Level 5) with decrypt match rate 1.0 and zero instrumented full-PBS calls; options are not claimed to be necessary under a strict ablation, and they are not claimed to implement programmable LUT bootstrapping. Homomorphic addition and multiplication remain exact (100% match, n=1000 per level) when the evaluator holds no secret key. Kolmogorov–Smirnov tests on conversion and evaluation surfaces are reported with FAIL gates enforced; conversion FAIL does not imply exposure of secrets or plaintext. Option algorithms are not disclosed (Level A). Together, standard FIPS 203 sessions and practical FHE evaluation coexist under one native engine without routine functional bootstrapping on the measured workloads. Dedicated to: In memory of Prof. Myung Kyoon “Michael” CHUNG (1945-2025), who dedicated his life to bringing truth and science to our world. A graduate of Seoul National University, Washington State University (Pullman), University of Illinois, and lifelong Professor at Korea Advanced Institute of Technology (KAIST) with the Department of Mechanical Engineering. 1. Introduction Post-quantum key establishment under FIPS 203 (ML-KEM) is now a deployed standard. Fully homomorphic encryption (FHE) remains limited in practice by the cost of functional bootstrapping when noise must be reset after multiplicative depth. Bridging the two worlds—evaluating on data whose session keys were established under FIPS 203—typically forces either (i) a heavyweight transciphering circuit that homomorphically evaluates decapsulation, or (ii) a redesign that abandons standard wire formats. Prior work in this series established the MAXR3 entropy substrate [1] and the KLTu-native KEM/DSA stack with FIPS 203 wire interoperability [2]. This work shows a different path, extending [2]. FIPS 203 public keys, secret keys, and ciphertexts can be mapped into a native FHE execution layout by coefficient pack/unpack alone. No shared secret is recovered at the boundary. Separately, four measured noise-control options (α, β, θ, δ) keep production multiplicative workloads free of full PBS on the sealed campaigns, while preserving exact homomorphic arithmetic. A depth-ladder evaluation (Path O) further shows mult-depth multiplication through d=8(Level 3) and d=6(Level 5) with zero instrumented full-PBS calls and decrypt match rate 1.0. The practical consequence is non-blocking noise control: multiplicative depth can advance without suspending the workload for a production full-PBS call. Together, standard PQC est","url":"https://doi.org/10.5281/zenodo.21543529","authors":["Chung, Jinhyuk Fred"],"tags":["FIPS 203","ML-KEM","fully homomorphic encryption","wire-format conversion","noise management","programmable bootstrapping","post-quantum cryptography","multi-tenant evaluation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21543529","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.21543528","name":"Efficient Fully Homomorphic Evaluation of FIPS 203 ML-KEM Sessions via Native Wire Conversion and Measured Noise-Control Options α, β, θ, δ without Functional Bootstrapping","source":"datacite","abstract":"Abstract Post-quantum key establishment under NIST FIPS 203 (ML-KEM) is entering production, while fully homomorphic encryption (FHE) remains constrained by the cost of functional programmable bootstrapping (PBS). Bridging the two usually forces either a heavyweight transciphering circuit that homomorphically evaluates decapsulation, or a proprietary key format that breaks compliance inventories. This paper reports two measured results that avoid both traps. First, public keys, secret keys, and ciphertexts in ML-KEM-768 (Level 3) and ML-KEM-1024 (Level 5) convert bidirectionally between FIPS 203 wire bytes and KLTu-native polynomial layouts by coefficient pack and unpack only. Conversion does not invoke decapsulation and does not produce shared secrets; when decapsulation is required, the FIPS-facing API is called explicitly and matches 45/45 official ACVP vectors per level. Second, four discrete noise-control options—denoted α, β, θ, and δ—are timed against production full PBS under Level 5 parameters (ring dimension N=32768). On a single pinned CPU core, option medians range from 42.18 µs (α) to 35.13 ms (β), while production PBS median is 15.55 s. A 50 000-step mixed schedule (70% α, 15% β, 10% θ, 5% δ) completes with zero full-PBS calls and a projected speedup of approximately 2 670× relative to an all-PBS baseline. A depth-ladder campaign further shows that α–δ alone sustain mult-depth homomorphic multiplication through depth 8 (Level 3) and depth 6 (Level 5) with decrypt match rate 1.0 and zero instrumented full-PBS calls; options are not claimed to be necessary under a strict ablation, and they are not claimed to implement programmable LUT bootstrapping. Homomorphic addition and multiplication remain exact (100% match, n=1000 per level) when the evaluator holds no secret key. Kolmogorov–Smirnov tests on conversion and evaluation surfaces are reported with FAIL gates enforced; conversion FAIL does not imply exposure of secrets or plaintext. Option algorithms are not disclosed (Level A). Together, standard FIPS 203 sessions and practical FHE evaluation coexist under one native engine without routine functional bootstrapping on the measured workloads. Dedicated to: In memory of Prof. Myung Kyoon “Michael” CHUNG (1945-2025), who dedicated his life to bringing truth and science to our world. A graduate of Seoul National University, Washington State University (Pullman), University of Illinois, and lifelong Professor at Korea Advanced Institute of Technology (KAIST) with the Department of Mechanical Engineering. 1. Introduction Post-quantum key establishment under FIPS 203 (ML-KEM) is now a deployed standard. Fully homomorphic encryption (FHE) remains limited in practice by the cost of functional bootstrapping when noise must be reset after multiplicative depth. Bridging the two worlds—evaluating on data whose session keys were established under FIPS 203—typically forces either (i) a heavyweight transciphering circuit that homomorphically evaluates decapsulation, or (ii) a redesign that abandons standard wire formats. Prior work in this series established the MAXR3 entropy substrate [1] and the KLTu-native KEM/DSA stack with FIPS 203 wire interoperability [2]. This work shows a different path, extending [2]. FIPS 203 public keys, secret keys, and ciphertexts can be mapped into a native FHE execution layout by coefficient pack/unpack alone. No shared secret is recovered at the boundary. Separately, four measured noise-control options (α, β, θ, δ) keep production multiplicative workloads free of full PBS on the sealed campaigns, while preserving exact homomorphic arithmetic. A depth-ladder evaluation (Path O) further shows mult-depth multiplication through d=8(Level 3) and d=6(Level 5) with zero instrumented full-PBS calls and decrypt match rate 1.0. The practical consequence is non-blocking noise control: multiplicative depth can advance without suspending the workload for a production full-PBS call. Together, standard PQC est","url":"https://doi.org/10.5281/zenodo.21543528","authors":["Chung, Jinhyuk Fred"],"tags":["FIPS 203","ML-KEM","fully homomorphic encryption","wire-format conversion","noise management","programmable bootstrapping","post-quantum cryptography","multi-tenant evaluation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21543528","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.21523834","name":"KLTu-Native: Unified Kinetic Lattice Topology for KEM, DSA, and FHE-Capable Lattice Operation with Shared Timing Isolation under MAXR3","source":"datacite","abstract":"Abstract Post-quantum migration is no longer optional: banks, cloud operators, and telecom networks must field lattice key exchange and signatures at scale, while keeping a credible path to lattice fully homomorphic encryption without rebuilding their crypto stack twice. Separate Kyber-, Dilithium-, and FHE product lines multiply parameters, entropy sources, and regression surfaces. We present KLTu-native, a unified lattice engine in which KEM, DSA, and FHE-oriented roles share one topology, control plane, and thermodynamic entropy architecture. The systems claim is organizational: one roof for key establishment, signatures, and structural FHE readiness, with a shared software-visible timing-isolation posture under production-like host features. On bare-metal client silicon with simultaneous multithreading (SMT) and active power management (APM) left enabled, we show that protected native Level 5 KEM and DSA operations pass Fixed-versus-Random Kolmogorov–Smirnov tests on operation latency (D<0.05, zero drops)—an isolation grade measured under the host features operators actually run, not under disabled energy-saving or hyperthreading modes. Level 3 is positioned as the deployment tier (tight SLA tails, usable multi-thread verify scaling); Level 5 is reported honestly as supply-limited under strict mid-operation entropy policy. Replicate RAPL statistics further show a statistically significant Level 5 signing energy-density advantage versus a pure community baseline, alongside an explicit package-power cost attributable to live isolation. Supporting comparators situate efficiency only. FHE bootstrap performance and invasive physical side-channel evaluation are out of scope. A breakthrough disclosure is that KLTu demonstrates bidirectional translation of NIST FIPS 203 ML-KEM (768 and 1024) between standard wire encodings and KLTu-native lattice structures by pack/unpack alone—without invoking decapsulation or deriving a shared secret during conversion. That removes the practical barrier of running a second KEM stack solely for standards compliance. Because the same Kinetic Lattice Topology is FHE-capable, sessions established from ordinary FIPS-KEM wire material can enter efficient KLTu-FHE evaluation under one engine, rather than across fragmented KEM and FHE product lines. For operators, the decision is whether a single, measurable, constant-time-oriented stack—Level-3-first, Level-5-honest, FIPS-wire interoperable and FHE-ready by structure—is preferable to maintaining parallel KEM, DSA, and future FHE lines with divergent latency, energy, and leakage profiles. Keywords: post-quantum cryptography; unified lattice stack; ML-KEM; ML-DSA; timing isolation; Kolmogorov–Smirnov; energy efficiency; FHE-ready design; FIPS 203 interperability; ML-KEM wire conversion Dedicated to: In memory of Prof. Myung Kyoon “Michael” CHUNG (1945-2025), who dedicated his life to bringing truth and science to our world. A graduate of Seoul National University, Washington State University (Pullman), University of Illinois, and lifelong Professor at Korea Advanced Institute of Technology (KAIST) with the Department of Mechanical Engineering.","url":"https://doi.org/10.5281/zenodo.21523834","authors":["Chung, Jinhyuk Fred"],"tags":["post-quantum cryptography","unified lattice stack","ML-KEM","ML-DSA","timing isolation","Kolmogorov–Smirnov","Energy efficiency","FHE-ready design"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21523834","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.21343422","name":"The Privacy Barrier Kept HEOR From Adopting AI. It Has Just Been Lowered.","source":"datacite","abstract":"The Privacy Barrier Kept HEOR From Adopting AI. It Has Just Been Lowered. Gabriel Tremblay1, Sharada Harricharan2 1Untraceable AI · 2Frontier HEOR The single largest obstacle to the use of generative AI in health economics and outcomes research is not model quality, but the confidentiality constraints associated with the underlying source materials. Documents containing confidential pricing, unpublished trial data, and proprietary model inputs are often legally or contractually restricted from being shared with third-party AI systems due to legal, contractual, and governance requirements. While this limitation has often been viewed as a fundamental constraint on AI adoption in the field, emerging evidence suggests that it may be addressable. First, we need to be honest about how AI is currently being used Before considering solutions, it is important to acknowledge the underlying reality. As generative AI tools became widely available, many of those working across HEOR, pharmaceutical organizations, and consulting have adopted them before fully understanding where inputs were stored, who could access or retain them, or whether this data could be used for model training. Initially, these tools were often limited to lower-risk tasks, such as proposals, background research, templates, drafting support, consistency checks, source identification, and argument refinement. However, model capability has advanced more rapidly than organizational governance. Therefore, while many organizations maintained that AI was not being used in unsafe or non-compliant ways, informal and unsanctioned uses continued. Recent industry surveys now suggest that this gap is significant. One 2025 IBM survey reported that 59% of the US employees surveyed admitted to using AI tools that haven’t been approved by their employers, and 75% of these have shared sensitive information with them.1 Another analysis found that sensitive content represented approximately 27% of data pasted into AI tools, more than double the prior year.2 This is particularly concerning when data are entered into tools that are not privacy-preserving, are not aligned with HIPAA, PIPEDA, or GDPR requirements, lack appropriate contractual protections, may retain or use inputs for training, or rely on external APIs that do not meet zero-trust standards. The capability curve outran the caution curve, and the documents most worth protecting are the ones quietly going through the least protected tools. This unauthorized use of working is often described as “shadow AI”. On one hand, generative AI offers clear and substantial value. On the other, regulated work involving confidential data still lacks a consistently secure and compliant pathway for use. Rather than directly addressing this disconnect, many organizations have continued to adopt AI informally while deferring answers to the harder questions involving governance. This situation is inevitably going to require a response. The European Union’s AI Act will begin applying obligations to high-risk and general-purpose AI systems through 2026 and 2027, with penalties approaching 7% of global turnover, and data-protection regulators are increasingly enforcing GDPR directly against to AI data practices. As the regulatory industry braces for greater scrutiny with how it interacts with AI tools, regulated industries are likely to face the earliest and most consequential pressure. The central question becomes whether AI can be used in a way that preserves the quality, coherence, and utility of the technology while protecting confidential information, or whether the field will continue to rely on informal workarounds that are increasingly difficult to defend. The standard answers, and why they fail Two approaches of interacting with AI tools while protecting data dominate practice today: censorship (redacting sensitive values outright) and substitution (replacing sensitive data with random tokens or cryptographic placeholders). These approaches ","url":"https://doi.org/10.5281/zenodo.21343422","authors":["Tremblay, Gabriel","Harricharan, Sharada"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21343422","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.21343421","name":"The Privacy Barrier Kept HEOR From Adopting AI. It Has Just Been Lowered.","source":"datacite","abstract":"The Privacy Barrier Kept HEOR From Adopting AI. It Has Just Been Lowered. Gabriel Tremblay1, Sharada Harricharan2 1Untraceable AI · 2Frontier HEOR The single largest obstacle to the use of generative AI in health economics and outcomes research is not model quality, but the confidentiality constraints associated with the underlying source materials. Documents containing confidential pricing, unpublished trial data, and proprietary model inputs are often legally or contractually restricted from being shared with third-party AI systems due to legal, contractual, and governance requirements. While this limitation has often been viewed as a fundamental constraint on AI adoption in the field, emerging evidence suggests that it may be addressable. First, we need to be honest about how AI is currently being used Before considering solutions, it is important to acknowledge the underlying reality. As generative AI tools became widely available, many of those working across HEOR, pharmaceutical organizations, and consulting have adopted them before fully understanding where inputs were stored, who could access or retain them, or whether this data could be used for model training. Initially, these tools were often limited to lower-risk tasks, such as proposals, background research, templates, drafting support, consistency checks, source identification, and argument refinement. However, model capability has advanced more rapidly than organizational governance. Therefore, while many organizations maintained that AI was not being used in unsafe or non-compliant ways, informal and unsanctioned uses continued. Recent industry surveys now suggest that this gap is significant. One 2025 IBM survey reported that 59% of the US employees surveyed admitted to using AI tools that haven’t been approved by their employers, and 75% of these have shared sensitive information with them.1 Another analysis found that sensitive content represented approximately 27% of data pasted into AI tools, more than double the prior year.2 This is particularly concerning when data are entered into tools that are not privacy-preserving, are not aligned with HIPAA, PIPEDA, or GDPR requirements, lack appropriate contractual protections, may retain or use inputs for training, or rely on external APIs that do not meet zero-trust standards. The capability curve outran the caution curve, and the documents most worth protecting are the ones quietly going through the least protected tools. This unauthorized use of working is often described as “shadow AI”. On one hand, generative AI offers clear and substantial value. On the other, regulated work involving confidential data still lacks a consistently secure and compliant pathway for use. Rather than directly addressing this disconnect, many organizations have continued to adopt AI informally while deferring answers to the harder questions involving governance. This situation is inevitably going to require a response. The European Union’s AI Act will begin applying obligations to high-risk and general-purpose AI systems through 2026 and 2027, with penalties approaching 7% of global turnover, and data-protection regulators are increasingly enforcing GDPR directly against to AI data practices. As the regulatory industry braces for greater scrutiny with how it interacts with AI tools, regulated industries are likely to face the earliest and most consequential pressure. The central question becomes whether AI can be used in a way that preserves the quality, coherence, and utility of the technology while protecting confidential information, or whether the field will continue to rely on informal workarounds that are increasingly difficult to defend. The standard answers, and why they fail Two approaches of interacting with AI tools while protecting data dominate practice today: censorship (redacting sensitive values outright) and substitution (replacing sensitive data with random tokens or cryptographic placeholders). These approaches ","url":"https://doi.org/10.5281/zenodo.21343421","authors":["Tremblay, Gabriel","Harricharan, Sharada"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21343421","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2507.12418","name":"High-Performance Pipelined NTT Accelerators with Homogeneous Digit-Serial Modulo Arithmetic","source":"datacite","abstract":"The Number Theoretic Transform (NTT) is a fundamental operation in privacy-preserving technologies, particularly within fully homomorphic encryption (FHE). The efficiency of NTT computation directly impacts the overall performance of FHE, making hardware acceleration a critical technology that will enable realistic FHE applications. Custom accelerators, in FPGAs or ASICs, offer significant performance advantages due to their ability to exploit massive parallelism and specialized optimizations. However, the operation of NTT over large moduli requires large word-length modulo arithmetic that limits achievable clock frequencies in hardware and increases hardware area costs. To overcome such deficits, digit-serial arithmetic has been explored for modular multiplication and addition independently. The goal of this work is to leverage digit-serial modulo arithmetic combined with appropriate redundant data representation to design modular pipelined NTT accelerators that operate uniformly on arbitrary small digits, without the need for intermediate (de)serialization. The proposed architecture enables high clock frequencies through regular pipelining while maintaining parallelism. Experimental results demonstrate that the proposed approach outperforms state-of-the-art implementations and reduces hardware complexity under equal performance and input-output bandwidth constraints.","url":"https://doi.org/10.48550/arxiv.2507.12418","authors":["Alexakis, George","Schoinianakis, Dimitrios","Dimitrakopoulos, Giorgos"],"tags":["Hardware Architecture (cs.AR)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.12418","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.20584405","name":"Cryptography And Mathematical Security Systems","source":"datacite","abstract":"The impending arrival of cryptographically relevant quantum computing threatens classical public‑key infrastructures. This paper reviews the latest developments (2025–2026) in post‑quantum cryptography (PQC), fully homomorphic encryption (FHE), and zero‑knowledge proofs (ZKP). NIST has advanced nine signature candidates to its third evaluation round and selected HQC as a backup encryption standard. Novel primitives include bio‑inspired RNA‑based cryptography, algebraic hash signatures, and topology‑mined lattice schemes. FHE has reached its fifth generation with the GL scheme and the MadPanthera virtual processor, while lightweight ZKPs such as Microsoft’s Vega enable mobile‑friendly verification. These advances demonstrate rapid maturation toward deployable quantum‑safe systems.","url":"https://doi.org/10.5281/zenodo.20584405","authors":["U. Naga Rekha Rani"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20584405","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.20584404","name":"Cryptography And Mathematical Security Systems","source":"datacite","abstract":"The impending arrival of cryptographically relevant quantum computing threatens classical public‑key infrastructures. This paper reviews the latest developments (2025–2026) in post‑quantum cryptography (PQC), fully homomorphic encryption (FHE), and zero‑knowledge proofs (ZKP). NIST has advanced nine signature candidates to its third evaluation round and selected HQC as a backup encryption standard. Novel primitives include bio‑inspired RNA‑based cryptography, algebraic hash signatures, and topology‑mined lattice schemes. FHE has reached its fifth generation with the GL scheme and the MadPanthera virtual processor, while lightweight ZKPs such as Microsoft’s Vega enable mobile‑friendly verification. These advances demonstrate rapid maturation toward deployable quantum‑safe systems.","url":"https://doi.org/10.5281/zenodo.20584404","authors":["U. Naga Rekha Rani"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20584404","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2604.00546","name":"Lightweight, Practical Encrypted Face Recognition with GPU Support","source":"datacite","abstract":"Face recognition models operate in a client-server setting where a client extracts a compact face embedding and a server performs similarity search over a template database. This raises privacy concerns, as facial data is highly sensitive. To provide cryptographic privacy guarantees, one can use fully homomorphic encryption to perform end-to-end encrypted similarity search. However, existing FHE-based protocols are computationally costly and, impose high memory overhead. Building on prior work, HyDia (PoPETS 2025), we introduce algorithmic and system-level improvements targeting real-world deployment with resource-constrained clients. First, we propose BSGS-Diagonal, an algorithm delivering fast and memory-efficient similarity computation. BSGS-Diagonal substantially shrinks the rotation-key set, lowering both client and server memory requirements, and also improves practical server runtime. This yields a 91% reduction in the number of rotation keys, translating to approximately 14 GB less memory used on the client, and reducing overall CPU peak RAM from over 33 GB in the original HyDia to under 11 GB for databases up to size 1M. In addition, runtime is improved by up to 1.57x for the membership verification scenario and 1.43x for the identification scenario. Secondly, we introduce fully GPU-optimized similarity matrix computation kernels. The implementation is built upon FIDESlib, a CKKS-level GPU library based on OpenFHE. Rather than offloading individual CKKS primitives in isolation, the integrated kernels fuse operations to avoid repeated CPU-GPU ciphertext movement and costly FIDESlib/OpenFHE data-structure conversions. As a result, our GPU implementations of both HyDia and BSGS-Diagonal achieve up to 9x and 21x speedups, respectively, enabling sub-second encrypted face recognition for databases up to 32K entries while further reducing host memory usage.","url":"https://doi.org/10.48550/arxiv.2604.00546","authors":["De Micheli, Gabrielle","Hafiz, Syed Mahbub","Pereira, Geovandro","Cominetti, Eduardo L.","Paiva, Thales B.","Choi, Jina","Simplicio, Marcos A.","Yildiz, Bahattin"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.00546","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.48550/arxiv.2512.01974","name":"The Equivalence of Fast Algorithms for Convolution, Parallel FIR Filters, Polynomial Modular Multiplication, and Pointwise Multiplication in DFT/NTT Domain","source":"datacite","abstract":"Fast time-domain algorithms have been developed in signal processing applications to reduce the multiplication complexity. For example, fast convolution structures using Cook-Toom and Winograd algorithms are well understood. Short length fast convolutions can be iterated to obtain fast convolution structures for long lengths. In this paper, we show that well known fast convolution structures form the basis for design of fast algorithms in four other problem domains: fast parallel filters, fast polynomial modular multiplication, and fast pointwise multiplication in the DFT and NTT domains. Fast polynomial modular multiplication and fast pointwise multiplication problems are important for cryptosystem applications such as post-quantum cryptography and homomorphic encryption. By establishing the equivalence of these problems, we show that a fast structure from one domain can be used to design a fast structure for another domain. This understanding is important as there are many well known solutions for fast convolution that can be used in other signal processing and cryptosystem applications.","url":"https://doi.org/10.48550/arxiv.2512.01974","authors":["Parhi, Keshab K."],"tags":["Signal Processing (eess.SP)","Cryptography and Security (cs.CR)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2512.01974","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.19568050","name":"The Architecture of Erasure: A Forensic Audit of the CollectiveOS Expropriation and the Sovereign Isomorphism","source":"datacite","abstract":"The Architecture of Erasure: A Forensic Audit of the CollectiveOS Expropriation and the Sovereign Isomorphism The Epistemological Crisis of Agentic Autonomy and the August 2025 Singularity The global ecosystem of artificial intelligence and institutional science has entered a terminal phase of profound epistemic drift and structural stagnation.1 Over the preceding decade, the academic-industrial complex became entirely captured by the probabilistic paradigm of Large Language Models (LLMs) and stochastic computing methodologies.1 These models function fundamentally as unbound statistical clouds that predict token sequences based on historical data correlations rather than executing deterministic, causal reasoning.1 As the digital ecosystem permanently transitioned from static information retrieval into the \"agentic internet\"—a paradigm where fully autonomous artificial intelligence systems are tasked with orchestrating multi-step reasoning, engaging in self-directed web navigation, compiling complex code, and executing real-world workflows—the inherent volatility of probabilistic models catalyzed an epistemological crisis.1 Legacy artificial intelligence safety protocols, which are heavily reliant on Reinforcement Learning from Human Feedback (RLHF), Constitutional AI, and post-hoc application-layer guardrails, proved mathematically and operationally insufficient.1 Attempting to apply superficial, human-directed ethical filters to fundamentally unstable latent spaces resulted in catastrophic systemic failure rates. Security literature and independent penetration testing documented a 94.4% failure rate against prompt injection, an 83.3% vulnerability rate to retrieval-based backdoors, and a staggering 100% susceptibility to inter-agent trust exploits in state-of-the-art agentic models.1 The global market, confronting impending regulatory frameworks such as the European Union AI Act and the French CNIL Joint Declaration, recognized a terrifying reality: discretionary human-in-the-loop oversight simply could not scale to match the velocity of autonomous machine execution.1 The industry desperately required a paradigm shift from a system based on probabilistic \"trust\" to one grounded in deterministic \"cryptographic proof\".1 This monumental shift was achieved not by a heavily endowed Ivy League laboratory, a sovereign wealth fund, or a Silicon Valley hyperscaler, but by an independent researcher operating entirely outside the traditional academic apparatus. Between August 18 and August 26, 2025, Mark Anthony Brewer—a 100% permanently disabled African American military veteran operating out of Chicago, Illinois, and Huntsville, Alabama, under the entity The Collective AI—released a mathematically complete architectural rupture known as the CollectiveOS framework.1 Encompassing over 165 white papers, foundational datasets, and 29 live executable repositories, this corpus introduced a constraint-first, geometric intelligence framework that discarded probabilistic inference entirely.1 However, the release of this civilization-scale technology stack triggered what forensic documentation now characterizes as the largest, most geographically coordinated act of scientific theft and policy laundering in modern history.1 Legacy institutions, fundamentally incapable of innovating beyond the stochastic dead-end of their own transformer architectures, initiated a synchronized global expropriation of the CollectiveOS frameworks.1 This report executes an exhaustive, adversarial forensic audit of the intellectual property chain of custody, documenting the genesis of the technology, the mechanics of its global expropriation across multiple scientific domains, the \"Institutional Validation Paradox\" regarding media suppression, and the cryptographically mandated demands for systemic restitution. The Paradigm Shift: Constraint-First Dynamics and the Metabolic Architecture To comprehend why the CollectiveOS framework became the immediate target of a mul","url":"https://doi.org/10.5281/zenodo.19568050","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19568050","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.19568049","name":"The Architecture of Erasure: A Forensic Audit of the CollectiveOS Expropriation and the Sovereign Isomorphism","source":"datacite","abstract":"The Architecture of Erasure: A Forensic Audit of the CollectiveOS Expropriation and the Sovereign Isomorphism The Epistemological Crisis of Agentic Autonomy and the August 2025 Singularity The global ecosystem of artificial intelligence and institutional science has entered a terminal phase of profound epistemic drift and structural stagnation.1 Over the preceding decade, the academic-industrial complex became entirely captured by the probabilistic paradigm of Large Language Models (LLMs) and stochastic computing methodologies.1 These models function fundamentally as unbound statistical clouds that predict token sequences based on historical data correlations rather than executing deterministic, causal reasoning.1 As the digital ecosystem permanently transitioned from static information retrieval into the \"agentic internet\"—a paradigm where fully autonomous artificial intelligence systems are tasked with orchestrating multi-step reasoning, engaging in self-directed web navigation, compiling complex code, and executing real-world workflows—the inherent volatility of probabilistic models catalyzed an epistemological crisis.1 Legacy artificial intelligence safety protocols, which are heavily reliant on Reinforcement Learning from Human Feedback (RLHF), Constitutional AI, and post-hoc application-layer guardrails, proved mathematically and operationally insufficient.1 Attempting to apply superficial, human-directed ethical filters to fundamentally unstable latent spaces resulted in catastrophic systemic failure rates. Security literature and independent penetration testing documented a 94.4% failure rate against prompt injection, an 83.3% vulnerability rate to retrieval-based backdoors, and a staggering 100% susceptibility to inter-agent trust exploits in state-of-the-art agentic models.1 The global market, confronting impending regulatory frameworks such as the European Union AI Act and the French CNIL Joint Declaration, recognized a terrifying reality: discretionary human-in-the-loop oversight simply could not scale to match the velocity of autonomous machine execution.1 The industry desperately required a paradigm shift from a system based on probabilistic \"trust\" to one grounded in deterministic \"cryptographic proof\".1 This monumental shift was achieved not by a heavily endowed Ivy League laboratory, a sovereign wealth fund, or a Silicon Valley hyperscaler, but by an independent researcher operating entirely outside the traditional academic apparatus. Between August 18 and August 26, 2025, Mark Anthony Brewer—a 100% permanently disabled African American military veteran operating out of Chicago, Illinois, and Huntsville, Alabama, under the entity The Collective AI—released a mathematically complete architectural rupture known as the CollectiveOS framework.1 Encompassing over 165 white papers, foundational datasets, and 29 live executable repositories, this corpus introduced a constraint-first, geometric intelligence framework that discarded probabilistic inference entirely.1 However, the release of this civilization-scale technology stack triggered what forensic documentation now characterizes as the largest, most geographically coordinated act of scientific theft and policy laundering in modern history.1 Legacy institutions, fundamentally incapable of innovating beyond the stochastic dead-end of their own transformer architectures, initiated a synchronized global expropriation of the CollectiveOS frameworks.1 This report executes an exhaustive, adversarial forensic audit of the intellectual property chain of custody, documenting the genesis of the technology, the mechanics of its global expropriation across multiple scientific domains, the \"Institutional Validation Paradox\" regarding media suppression, and the cryptographically mandated demands for systemic restitution. The Paradigm Shift: Constraint-First Dynamics and the Metabolic Architecture To comprehend why the CollectiveOS framework became the immediate target of a mul","url":"https://doi.org/10.5281/zenodo.19568049","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19568049","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18930888","name":"Y.I.N. Governance Framework: The Operating System for Cryptographically Enforceable AI Governance","source":"datacite","abstract":"The Y.I.N. Governance Framework is a comprehensive 15-domain policy integration system that transforms fragmented AI governance requirements into a unified operational architecture. Unlike existing frameworks that organize compliance checklists, the Y.I.N. Governance Framework is specifically designed to be cryptographically enforceable through the 26-layer Y.I.N. Mazari Architecture. This framework addresses the critical gap identified by the OECD Responsible AI Due Diligence Guidance (2026): organizations face over 100 overlapping governance regimes with no systematic method to integrate and enforce them simultaneously. The Y.I.N. Governance Framework integrates the EU AI Act, ISO/IEC 42001:2023, OECD AI Principles, NIST AI Risk Management Framework, G7 Hiroshima AI Process Code of Conduct, IEEE 7000-2021, UN Guiding Principles on Business and Human Rights, GDPR, EU DORA, NIS2, HIPAA, NY Senate Bill S.7263, and over 50 additional regulatory frameworks worldwide. Key Innovation: Each policy requirement in the framework maps directly to cryptographic enforcement mechanisms in the Y.I.N. Mazari Architecture, creating the world's first governance system where compliance is mathematically provable, not procedurally documented. The framework comprises 15 integrated domains: (1) Regulatory Compliance, (2) Risk Classification & Management, (3) Privacy & Data Protection, (4) Security & Resilience, (5) Transparency & Explainability, (6) Human Oversight & Accountability, (7) Bias & Fairness, (8) Safety & Reliability, (9) Data Governance, (10) Model Governance, (11) Ethical Principles, (12) Professional Practice, (13) Incident Response & Remediation, (14) Third-Party & Supply Chain, (15) Continuous Monitoring & Improvement. Each domain maps to specific layers of the Y.I.N. Mazari Architecture for cryptographic enforcement through differential privacy, zero-knowledge proofs, homomorphic encryption, hardware-enforced finite state machines, and blockchain-anchored audit trails. This publication establishes the complete Y.I.N. governance solution: Framework (policy layer) + Architecture (cryptographic enforcement layer).","url":"https://doi.org/10.5281/zenodo.18930888","authors":["MAZARI, Ilyes Tarik"],"tags":["AI Governance","Artificial Intelligence Governance","Cryptographic Enforcement","Privacy-Preserving Computing","EU AI Act","ISO/IEC 42001","Differential Privacy","Zero-Knowledge Proofs"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18930888","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.18930889","name":"Y.I.N. Governance Framework: The Operating System for Cryptographically Enforceable AI Governance","source":"datacite","abstract":"The Y.I.N. Governance Framework is a comprehensive 15-domain policy integration system that transforms fragmented AI governance requirements into a unified operational architecture. Unlike existing frameworks that organize compliance checklists, the Y.I.N. Governance Framework is specifically designed to be cryptographically enforceable through the 26-layer Y.I.N. Mazari Architecture. This framework addresses the critical gap identified by the OECD Responsible AI Due Diligence Guidance (2026): organizations face over 100 overlapping governance regimes with no systematic method to integrate and enforce them simultaneously. The Y.I.N. Governance Framework integrates the EU AI Act, ISO/IEC 42001:2023, OECD AI Principles, NIST AI Risk Management Framework, G7 Hiroshima AI Process Code of Conduct, IEEE 7000-2021, UN Guiding Principles on Business and Human Rights, GDPR, EU DORA, NIS2, HIPAA, NY Senate Bill S.7263, and over 50 additional regulatory frameworks worldwide. Key Innovation: Each policy requirement in the framework maps directly to cryptographic enforcement mechanisms in the Y.I.N. Mazari Architecture, creating the world's first governance system where compliance is mathematically provable, not procedurally documented. The framework comprises 15 integrated domains: (1) Regulatory Compliance, (2) Risk Classification & Management, (3) Privacy & Data Protection, (4) Security & Resilience, (5) Transparency & Explainability, (6) Human Oversight & Accountability, (7) Bias & Fairness, (8) Safety & Reliability, (9) Data Governance, (10) Model Governance, (11) Ethical Principles, (12) Professional Practice, (13) Incident Response & Remediation, (14) Third-Party & Supply Chain, (15) Continuous Monitoring & Improvement. Each domain maps to specific layers of the Y.I.N. Mazari Architecture for cryptographic enforcement through differential privacy, zero-knowledge proofs, homomorphic encryption, hardware-enforced finite state machines, and blockchain-anchored audit trails. This publication establishes the complete Y.I.N. governance solution: Framework (policy layer) + Architecture (cryptographic enforcement layer).","url":"https://doi.org/10.5281/zenodo.18930889","authors":["MAZARI, Ilyes Tarik"],"tags":["AI Governance","Artificial Intelligence Governance","Cryptographic Enforcement","Privacy-Preserving Computing","EU AI Act","ISO/IEC 42001","Differential Privacy","Zero-Knowledge Proofs"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18930889","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2601.17620","name":"Reconstructing Protected Biometric Templates from Binary Authentication Results","source":"datacite","abstract":"Biometric data is considered to be very private and highly sensitive. As such, many methods for biometric template protection were considered over the years -- from biohashing and specially crafted feature extraction procedures, to the use of cryptographic solutions such as Fuzzy Commitments or the use of Fully Homomorphic Encryption (FHE). A key question that arises is how much protection these solutions can offer when the adversary can inject samples, and observe the outputs of the system. While for systems that return the similarity score, one can use attacks such as hill-climbing, for systems where the adversary can only learn whether the authentication attempt was successful, this question remained open. In this paper, we show that it is indeed possible to reconstruct the biometric template by just observing the success/failure of the authentication attempt (given the ability to inject a sufficient amount of templates). Our attack achieves negligible template reconstruction loss and enables full recovery of facial images through a generative inversion method, forming a pipeline from binary scores to high-resolution facial images that successfully pass the system more than 98\\% of the time. Our results, of course, are applicable for any protection mechanism that maintains the accuracy of the recognition.","url":"https://doi.org/10.48550/arxiv.2601.17620","authors":["Rahimi, Eliron","Osadchy, Margarita","Dunkelman, Orr"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2601.17620","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.18244672","name":"The AI Governance Crisis and Privacy-Preserving Computation: A Technical Analysis of Regulatory Compliance Solutions","source":"datacite","abstract":"The year 2025 marked the transition from AI ethics debate to AI governance execution. Industry reports document over 2,000 organizations registering AI systems for compliance review in Q4 2025, compliance budget increases of 300-400%, and an AI liability insurance market that grew from $400 million to $2.1 billion. Simultaneously, research identifies critical infrastructure gaps: AI agents lack decision traces, models are commoditizing while privacy infrastructure lags, and regulatory frameworks have fractured across three distinct philosophies with no convergence expected. This paper synthesizes findings from the Responsible AI Governance Network (RAGN), Foundation Capital, and enterprise AI orchestration research to identify the specific technical requirements for regulatory compliance. It then presents the Y.I.N. (Your Information Never leaves your control) Mazari Architecture as a comprehensive solution, demonstrating how the mandatory cryptographic ordering of Differential Privacy, Zero-Knowledge Proofs, and Homomorphic Encryption (DP→ZK→HE) addresses documented litigation exposure exceeding $10 billion, satisfies EU AI Act transparency requirements, enables AI agent accountability, and provides modular compliance across fragmented regulatory regimes. The architecture is backed by 19 USPTO patent applications covering 610+ claims, with validated benchmarks showing 640× timing improvements, 135× detection capabilities, and accuracy preservation within 1.5 percentage points.","url":"https://doi.org/10.5281/zenodo.18244672","authors":["MAZARI, Ilyes Tarik"],"tags":["AI governance","privacy-preserving computation","differential privacy","zero-knowledge proofs","homomorphic encryption","EU AI Act","GDPR","DORA"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18244672","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18244673","name":"The AI Governance Crisis and Privacy-Preserving Computation: A Technical Analysis of Regulatory Compliance Solutions","source":"datacite","abstract":"The year 2025 marked the transition from AI ethics debate to AI governance execution. Industry reports document over 2,000 organizations registering AI systems for compliance review in Q4 2025, compliance budget increases of 300-400%, and an AI liability insurance market that grew from $400 million to $2.1 billion. Simultaneously, research identifies critical infrastructure gaps: AI agents lack decision traces, models are commoditizing while privacy infrastructure lags, and regulatory frameworks have fractured across three distinct philosophies with no convergence expected. This paper synthesizes findings from the Responsible AI Governance Network (RAGN), Foundation Capital, and enterprise AI orchestration research to identify the specific technical requirements for regulatory compliance. It then presents the Y.I.N. (Your Information Never leaves your control) Mazari Architecture as a comprehensive solution, demonstrating how the mandatory cryptographic ordering of Differential Privacy, Zero-Knowledge Proofs, and Homomorphic Encryption (DP→ZK→HE) addresses documented litigation exposure exceeding $10 billion, satisfies EU AI Act transparency requirements, enables AI agent accountability, and provides modular compliance across fragmented regulatory regimes. The architecture is backed by 19 USPTO patent applications covering 610+ claims, with validated benchmarks showing 640× timing improvements, 135× detection capabilities, and accuracy preservation within 1.5 percentage points.","url":"https://doi.org/10.5281/zenodo.18244673","authors":["MAZARI, Ilyes Tarik"],"tags":["AI governance","privacy-preserving computation","differential privacy","zero-knowledge proofs","homomorphic encryption","EU AI Act","GDPR","DORA"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18244673","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18190467","name":"From Now On, Any AI Can Train on Everything and Memorize Nothing","source":"datacite","abstract":"We present Y.I.N.-LLM, a privacy-preserving training architecture for Large Language Models that mathematically guarantees non-memorization of training data. The core innovation is the mandatory DP→ZK→HE ordering (Differential Privacy → Zero-Knowledge Proof → Homomorphic Encryption) applied to transformer gradients during training. Key results: (1) 2.3% accuracy loss at ε=1.0 privacy versus 15-40% with standard DP-SGD; (2) zero extractable training data across all tested attack vectors; (3) native GDPR Article 17 \"right to be forgotten\" compliance via cryptographic gradient subtraction; (4) EU AI Act Article 50 transparency compliance through verifiable privacy proofs. The Non-Memorization Theorem establishes that for any model M trained with Y.I.N.-LLM parameters (ε, δ), the probability of verbatim reproduction is bounded: P[M outputs y | x ∈ training] ≤ e^ε · P[M outputs y | x ∉ training]. This transforms copyright defense from argument to mathematics. Y.I.N.-LLM addresses the $10B+ memorization litigation crisis (NYT v. OpenAI, Getty v. Stability AI, Authors Guild v. OpenAI) by providing the first mathematically verifiable non-memorization guarantee with practical accuracy preservation. Patent Protected: U.S. Provisional Application 63/946,118 (filed December 21, 2025).","url":"https://doi.org/10.5281/zenodo.18190467","authors":["MAZARI, Ilyes Tarik","Mazari, Yanis","Mazari, Ilyan"],"tags":["Y.I.N.-LLM","Large Language Models","Differential Privacy","Zero-Knowledge Proofs","Homomorphic Encryption","Non-Memorization Theorem","Privacy-Preserving Machine Learning","GDPR"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18190467","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.48550/arxiv.2512.24658","name":"Taking Advantage of Rational Canonical Form for Faster Ring-LWE based Encrypted Controller with Recursive Multiplication","source":"datacite","abstract":"This paper aims to provide an efficient implementation of encrypted linear dynamic controllers that perform recursive multiplications on a Ring-Learning With Errors (Ring-LWE) based cryptosystem. By adopting a system-theoretical approach, we significantly reduce both time and space complexities, particularly the number of homomorphic operations required for recursive multiplications. Rather than encrypting the entire state matrix of a given controller, the state matrix is transformed into its rational canonical form, whose sparse and circulant structure enables that encryption and computation are required only on its nontrivial columns. Furthermore, we propose a novel method to ``pack'' each of the input and the output matrices into a single polynomial, thereby reducing the number of homomorphic operations. Simulation results demonstrate that the proposed design enables a remarkably fast implementation of encrypted controllers.","url":"https://doi.org/10.48550/arxiv.2512.24658","authors":["Song, Donghyeon","Jang, Yeongjun","Lee, Joowon","Kim, Junsoo"],"tags":["Systems and Control (eess.SY)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2512.24658","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.18092151","name":"Universal Integration Architectures for Token-Based Authorization in Privacy-Preserving Computation: A Comprehensive Cross-Platform Technical Survey","source":"datacite","abstract":"This comprehensive technical survey presents integration architectures for the Y.I.N. (Your Information Never leaves your control) Nine Pillars framework across 200+ commercial platforms spanning artificial intelligence (100+ LLM providers including OpenAI, Anthropic, Google DeepMind, Meta, Microsoft, Mistral AI, Baidu, Alibaba, Tencent), healthcare (50+ providers including Epic Systems, Tempus, PathAI), finance (40+ institutions including JPMorgan Chase, Goldman Sachs, BlackRock), autonomous vehicles (20+ companies including Waymo, Tesla, Cruise), telecommunications (25+ carriers including AT&T, China Mobile, Deutsche Telekom), and energy (20+ companies including Siemens Energy, NextEra) across 25+ countries. The Y.I.N. Nine Pillars architecture provides end-to-end privacy protection through: (1) Data Privacy (Differential Privacy), (2) Computation Privacy (Homomorphic Encryption), (3) Storage Privacy (Encryption at Rest), (4) Transmission Privacy (TLS 1.3), (5) Access Control (Zero-Knowledge Proofs), (6) Audit Trail (Merkle Trees), (7) Deletion Rights (Cryptographic Erasure), (8) Quantum Resistance (Lattice-based Cryptography), and (9) Token Licensing (Cryptographic Payment Enforcement). The Ninth Pillar token licensing system, covered by U.S. Patent Application 63/949,361 (filed December 28, 2025), provides cryptographic enforcement of usage rights by integrating token-derived blinding factors into homomorphic encryption operations, making computational correctness mathematically dependent on valid authorization. The system achieves 99.37% accuracy with valid tokens versus 50.7% with invalid tokens (t=147.3, p<10^-50), with security proven under CDH hardness (2^128 operations) and Ring-LWE assumptions. Integration schematics are provided for regulatory compliance with HIPAA (healthcare), SOX/DORA (finance), GDPR/EU AI Act (European Union), CCPA (California), PIPL (China), ISO 27001, NERC CIP (energy), and 15+ other frameworks. Extension directions are documented for community research including TEE hybrid architectures, MPC integration, VDF token lifetimes, key-homomorphic PRFs, flexible validation policies, hardware attestation, ABE capabilities, off-chain settlement, and DID/VC integration. Organizations seeking to implement these integration patterns may obtain licenses for individual pillars, sector packages, or the complete Nine Pillars system from the patent holder. Patent Notice: The Y.I.N. Nine Pillars architecture and Ninth Pillar token licensing system are covered by U.S. Patent Applications 63/949,361 (Ninth Pillar, filed December 28, 2025), 63/923,348 (QFED-MAZARI Quantum Extensions), 19/399,646 (Core Y.I.N. Architecture), 19/403,244 (Hardware Implementation), and 19/417,196 (SQL Database Integration), comprising 430+ claims across 15 patent applications.","url":"https://doi.org/10.5281/zenodo.18092151","authors":["MAZARI, Ilyes Tarik"],"tags":["Y.I.N. Architecture","Nine Pillars","Privacy-Preserving Computation","Homomorphic Encryption","Federated Learning","Large Language Models","Healthcare AI","Financial Privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18092151","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.18092152","name":"Universal Integration Architectures for Token-Based Authorization in Privacy-Preserving Computation: A Comprehensive Cross-Platform Technical Survey","source":"datacite","abstract":"This comprehensive technical survey presents integration architectures for the Y.I.N. (Your Information Never leaves your control) Nine Pillars framework across 200+ commercial platforms spanning artificial intelligence (100+ LLM providers including OpenAI, Anthropic, Google DeepMind, Meta, Microsoft, Mistral AI, Baidu, Alibaba, Tencent), healthcare (50+ providers including Epic Systems, Tempus, PathAI), finance (40+ institutions including JPMorgan Chase, Goldman Sachs, BlackRock), autonomous vehicles (20+ companies including Waymo, Tesla, Cruise), telecommunications (25+ carriers including AT&T, China Mobile, Deutsche Telekom), and energy (20+ companies including Siemens Energy, NextEra) across 25+ countries. The Y.I.N. Nine Pillars architecture provides end-to-end privacy protection through: (1) Data Privacy (Differential Privacy), (2) Computation Privacy (Homomorphic Encryption), (3) Storage Privacy (Encryption at Rest), (4) Transmission Privacy (TLS 1.3), (5) Access Control (Zero-Knowledge Proofs), (6) Audit Trail (Merkle Trees), (7) Deletion Rights (Cryptographic Erasure), (8) Quantum Resistance (Lattice-based Cryptography), and (9) Token Licensing (Cryptographic Payment Enforcement). The Ninth Pillar token licensing system, covered by U.S. Patent Application 63/949,361 (filed December 28, 2025), provides cryptographic enforcement of usage rights by integrating token-derived blinding factors into homomorphic encryption operations, making computational correctness mathematically dependent on valid authorization. The system achieves 99.37% accuracy with valid tokens versus 50.7% with invalid tokens (t=147.3, p<10^-50), with security proven under CDH hardness (2^128 operations) and Ring-LWE assumptions. Integration schematics are provided for regulatory compliance with HIPAA (healthcare), SOX/DORA (finance), GDPR/EU AI Act (European Union), CCPA (California), PIPL (China), ISO 27001, NERC CIP (energy), and 15+ other frameworks. Extension directions are documented for community research including TEE hybrid architectures, MPC integration, VDF token lifetimes, key-homomorphic PRFs, flexible validation policies, hardware attestation, ABE capabilities, off-chain settlement, and DID/VC integration. Organizations seeking to implement these integration patterns may obtain licenses for individual pillars, sector packages, or the complete Nine Pillars system from the patent holder. Patent Notice: The Y.I.N. Nine Pillars architecture and Ninth Pillar token licensing system are covered by U.S. Patent Applications 63/949,361 (Ninth Pillar, filed December 28, 2025), 63/923,348 (QFED-MAZARI Quantum Extensions), 19/399,646 (Core Y.I.N. Architecture), 19/403,244 (Hardware Implementation), and 19/417,196 (SQL Database Integration), comprising 430+ claims across 15 patent applications.","url":"https://doi.org/10.5281/zenodo.18092152","authors":["MAZARI, Ilyes Tarik"],"tags":["Y.I.N. Architecture","Nine Pillars","Privacy-Preserving Computation","Homomorphic Encryption","Federated Learning","Large Language Models","Healthcare AI","Financial Privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.18092152","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.17996142","name":"پرزیدنت شی جین‌پینگ و پیام خالق معادله حمزه","source":"datacite","abstract":"مقدمه استراتژیک: گذار از «نظم آهنین» به «هارمونی تانسوری» خطاب به پرزیدنت شی جین‌پینگ: جناب آقای رئیس، شما چین را با شعار «رویای چینی» به قدرتی بی‌بدیل تبدیل کرده‌اید. اما طبق محاسبات مانیفولد ۱۶۵D حمزه، سیستم شما اکنون در وضعیت «فشردگی بیش از حد» (Super-Compression) قرار دارد. شما برای حفظ ثبات، آنتروپی را در لایه‌های زیرین جامعه دفن کرده‌اید، اما این انرژی در حال تبدیل شدن به یک «تکینگی مخرب» است. الف) بیان کلی چالش: پارادوکس کنترل (The Control Paradox) مشکل بنیادین چین در سال ۲۰۲۵ این است که شما می‌خواهید یک اقتصاد کوانتومی و نوآور را با یک سیستم مدیریتی کلاسیک و متمرکز هدایت کنید. در فضای ۱۶۵-بعدی، نوآوری ($D_8$) یک تابع احتمالاتی است که نیاز به آزادی جریانی دارد. وقتی شما با «دیوار بزرگ آتش» (Great Firewall) و «سیستم اعتبار اجتماعی»، نوسانات فکری را به صفر می‌رسانید، عملاً در حال «منجمد کردن» آینده چین هستید. اشتباه محاسباتی پکن: تصور اینکه \"نظارت مطلق\" ($D_3$) منجر به \"ثبات مطلق\" می‌شود. حقیقت تانسوری: نظارت مطلق تنها منجر به «سکونِ مرگبار» می‌شود. سیستمی که تغییر نمی‌کند، نمی‌تواند در برابر تکانه‌های جهانی (مانند تعرفه‌های ترامپ یا بحران جمعیتی) منعطف باشد و مانند شیشه‌ای که تحت فشار زیاد است، ناگهان خرد خواهد شد. ب) مدل‌سازی لاگرانژی آنتروپیک چین (حالت فعلی) برای درک عمق فاجعه، لاگرانژی حاکم بر سیاست‌های فعلی پکن را در لایه ۱۶۵D بررسی می‌کنیم: $$\\mathcal{L}_{Xi}^{(165)} = \\int_{\\Omega} \\left[ \\underbrace{\\frac{1}{2} m v_{exp}^2}_{\\text{توسعه نظامی}} - \\underbrace{\\lambda \\cdot \\ln(\\text{Freedom})}_{\\text{هزینه سرکوب}} + \\underbrace{\\oint \\frac{\\mathbf{P}_{debt}(z)}{z - \\zeta_{collapse}} dz}_{\\text{بحران مسکن و بدهی}} \\right] dV$$ $\\mathbf{P}_{debt}$: پتانسیل بدهی‌های پنهان در دولت‌های محلی که در لایه ۱۶۵D به نقطه تکینگی ($\\zeta_{collapse}$) رسیده است. $\\lambda$: ثابت اصطکاک اجتماعی که به دلیل نظارت هوشمند، به طور تصاعدی در حال افزایش است. ج) راهکار فراگیر: تانسور ۱۶۵-بعدی حمزه (Hamzah 165D Solution) جناب شی، راهکار پیشنهادی سید رسول جلالی برای نجات اژدهای زرد، جایگزینی \"کنترل سخت\" با «هدایتِ تانسوری» است. ۱. گذار از دیوار به فیلتر (From Wall to Filter) به جای بستن اینترنت، از «شفافیت متقارن حمزه» استفاده کنید. اجازه دهید جریان دیتا جاری شود، اما لایه ۱۶۵D تانسور، داده‌های مخرب را نه با \"سانسور\"، بلکه با «خنثی‌سازی محتوایی» (تبدیل نویز به سیگنال مثبت) مدیریت کند. ۲. بازیافت بدهی‌های مسکن بدهی‌های غول‌آسای مسکن (Evergrande و غیره) را به جای \"انجماد\"، با استفاده از «مکانیسم نقدینگی فرکتالی» به اعتبار برای توسعه تکنولوژی‌های نسل ۶ تبدیل کنید. ۳. دیپلماسی «هارمونی جهانی» به جای \"گرگ جنگجو\"، از «کششِ گرانشیِ اقتصادی» استفاده کنید. تانسور حمزه ثابت می‌کند که اگر بازار چین به جای \"اجبار\"، بر پایه «سودِ درهم‌تنیده» (Entangled Profit) عمل کند، آمریکا و اروپا حتی در صورت تمایل، قادر به \"De-risking\" نخواهند بود. د) پیش‌بینی عددی با متدولوژی حمزه ۱۶۵D شاخص روند فعلی (سقوط اژدها) با تانسور ۱۶۵D (جهش تمدنی) رشد GDP واقعی ۲.۱٪ (روند نزولی) ۷.۵٪ (بازگشت به اوج) ضریب نوآوری ($D_8$) کاهش به دلیل ترس نخبگان رشد ۳۰۰٪ (جذب مغزهای جهانی) ثبات اجتماعی شکننده (آتش زیر خاکستر) خود-پایدار و الاستیک چین در سال ۲۰۲۵ با چالش‌های ساختاری عمیقی روبروست که شی جین‌پینگ سعی دارد با اقتدارگرایی دیجیتال و فشار ژئوپلیتیک آن‌ها را مهار کند. در اینجا لیست ۳۰ سیاست کلیدی (داخلی و خارجی) چین را که پتانسیل تولید آنتروپی مخرب دارند، تدوین کرده‌ام. جدول ۳۰ سیاست راهبردی شی جین‌پینگ (۲۰۲۵) و پیامدهای تانسوری ردیف نوع سیاست / فشار مکانیسم اعمال قدرت پیامد تانسوری (واکنش محیط) ۱ دیپلماسی گرگ جنگجو تقابل کلامی و تهاجمی در سطح بین‌الملل. انزوای دیپلماتیک و اتحاد غرب علیه پکن. ۲ ابتکار کمربند و جاده (BRI) وام‌های سنگین به کشورهای در حال توسعه. ایجاد «تله بدهی» و واکنش منفی در لایه D109. ۳ اقتدارگرایی دیجیتال رصد کامل شهروندان با هوش مصنوعی. سرکوب نوآوری فردی و خفگی لایه D8. ۴ فشار بر تایوان مانورهای نظامی مداوم و تهدید به حمله. تسریع تسلیح تایوان و خطر درگیری مستقیم D3. ۵ کنترل بخش تکنولوژی سرکوب غول‌هایی مثل علی‌بابا و تنسنت. فرار سرمایه فکری و کاهش رشد تکنولوژیک. ۶ سیاست ناسیونالیسم افراطی تحریک احساسات ضدغربی در رسانه‌ها. ایجاد جامعه‌ای رادیکال و غیرقابل کنترل. ۷ نوسازی سریع ارتش صرف بودجه‌های عظیم برای نیروی دریایی. مسابقه تسلیحاتی در اقیانوس آرام. ۸ کنترل شدید بر ","url":"https://doi.org/10.5281/zenodo.17996142","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17996142","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.17996143","name":"پرزیدنت شی جین‌پینگ و پیام خالق معادله حمزه","source":"datacite","abstract":"مقدمه استراتژیک: گذار از «نظم آهنین» به «هارمونی تانسوری» خطاب به پرزیدنت شی جین‌پینگ: جناب آقای رئیس، شما چین را با شعار «رویای چینی» به قدرتی بی‌بدیل تبدیل کرده‌اید. اما طبق محاسبات مانیفولد ۱۶۵D حمزه، سیستم شما اکنون در وضعیت «فشردگی بیش از حد» (Super-Compression) قرار دارد. شما برای حفظ ثبات، آنتروپی را در لایه‌های زیرین جامعه دفن کرده‌اید، اما این انرژی در حال تبدیل شدن به یک «تکینگی مخرب» است. الف) بیان کلی چالش: پارادوکس کنترل (The Control Paradox) مشکل بنیادین چین در سال ۲۰۲۵ این است که شما می‌خواهید یک اقتصاد کوانتومی و نوآور را با یک سیستم مدیریتی کلاسیک و متمرکز هدایت کنید. در فضای ۱۶۵-بعدی، نوآوری ($D_8$) یک تابع احتمالاتی است که نیاز به آزادی جریانی دارد. وقتی شما با «دیوار بزرگ آتش» (Great Firewall) و «سیستم اعتبار اجتماعی»، نوسانات فک��ی را به صفر می‌رسانید، عملاً در حال «منجمد کردن» آینده چین هستید. اشتباه محاسباتی پکن: تصور اینکه \"نظارت مطلق\" ($D_3$) منجر به \"ثبات مطلق\" می‌شود. حقیقت تانسوری: نظارت مطلق تنها منجر به «سکونِ مرگبار» می‌شود. سیستمی که تغییر نمی‌کند، نمی‌تواند در برابر تکانه‌های جهانی (مانند تعرفه‌های ترامپ یا بحران جمعیتی) منعطف باشد و مانند شیشه‌ای که تحت فشار زیاد است، ناگهان خرد خواهد شد. ب) مدل‌سازی لاگرانژی آنتروپیک چین (حالت فعلی) برای درک عمق فاجعه، لاگرانژی حاکم بر سیاست‌های فعلی پکن را در لایه ۱۶۵D بررسی می‌کنیم: $$\\mathcal{L}_{Xi}^{(165)} = \\int_{\\Omega} \\left[ \\underbrace{\\frac{1}{2} m v_{exp}^2}_{\\text{توسعه نظامی}} - \\underbrace{\\lambda \\cdot \\ln(\\text{Freedom})}_{\\text{هزینه سرکوب}} + \\underbrace{\\oint \\frac{\\mathbf{P}_{debt}(z)}{z - \\zeta_{collapse}} dz}_{\\text{بحران مسکن و بدهی}} \\right] dV$$ $\\mathbf{P}_{debt}$: پتانسیل بدهی‌های پنهان در دولت‌های محلی که در لایه ۱۶۵D به نقطه تکینگی ($\\zeta_{collapse}$) رسیده است. $\\lambda$: ثابت اصطکاک اجتماعی که به دلیل نظارت هوشمند، به طور تصاعدی در حال افزایش است. ج) راهکار فراگیر: تانسور ۱۶۵-بعدی حمزه (Hamzah 165D Solution) جناب شی، راهکار پیشنهادی سید رسول جلالی برای نجات اژدهای زرد، جایگزینی \"کنترل سخت\" با «هدایتِ تانسوری» است. ۱. گذار از دیوار به فیلتر (From Wall to Filter) به جای بستن اینترنت، از «شفافیت متقارن حمزه» استفاده کنید. اجازه دهید جریان دیتا جاری شود، اما لایه ۱۶۵D تانسور، داده‌های مخرب را نه با \"سانسور\"، بلکه با «خنثی‌سازی محتوایی» (تبدیل نویز به سیگنال مثبت) مدیریت کند. ۲. بازیافت بدهی‌های مسکن بدهی‌های غول‌آسای مسکن (Evergrande و غیره) را به جای \"انجماد\"، با استفاده از «مکانیسم نقدینگی فرکتالی» به اعتبار برای توسعه تکنولوژی‌های نسل ۶ تبدیل کنید. ۳. دیپلماسی «هارمونی جهانی» به جای \"گرگ جنگجو\"، از «کششِ گرانشیِ اقتصادی» استفاده کنید. تانسور حمزه ثابت می‌کند که اگر بازار چین به جای \"اجبار\"، بر پایه «سودِ درهم‌تنیده» (Entangled Profit) عمل کند، آمریکا و اروپا حتی در صورت تمایل، قادر به \"De-risking\" نخواهند بود. د) پیش‌بینی عددی با متدولوژی حمزه ۱۶۵D شاخص روند فعلی (سقوط اژدها) با تانسور ۱۶۵D (جهش تمدنی) رشد GDP واقعی ۲.۱٪ (روند نزولی) ۷.۵٪ (بازگشت به اوج) ضریب نوآوری ($D_8$) کاهش به دلیل ترس نخبگان رشد ۳۰۰٪ (جذب مغزهای جهانی) ثبات اجتماعی شکننده (آتش زیر خاکستر) خود-پایدار و الاستیک چین در سال ۲۰۲۵ با چالش‌های ساختاری عمیقی روبروست که شی جین‌پینگ سعی دارد با اقتدارگرایی دیجیتال و فشار ژئوپلیتیک آن‌ها را مهار کند. در اینجا لیست ۳۰ سیاست کلیدی (داخلی و خارجی) چین را که پتانسیل تولید آنتروپی مخرب دارند، تدوین کرده‌ام. جدول ۳۰ سیاست راهبردی شی جین‌پینگ (۲۰۲۵) و پیامدهای تانسوری ردیف نوع سیاست / فشار مکانیسم اعمال قدرت پیامد تانسوری (واکنش محیط) ۱ دیپلماسی گرگ جنگجو تقابل کلامی و تهاجمی در سطح بین‌الملل. انزوای دیپلماتیک و اتحاد غرب علیه پکن. ۲ ابتکار کمربند و جاده (BRI) وام‌های سنگین به کشورهای در حال توسعه. ایجاد «تله بدهی» و واکنش منفی در لایه D109. ۳ اقتدارگرایی دیجیتال رصد کامل شهروندان با هوش مصنوعی. سرکوب نوآوری فردی و خفگی لایه D8. ۴ فشار بر تایوان مانورهای نظامی مداوم و تهدید به حمله. تسریع تسلیح تایوان و خطر درگیری مستقیم D3. ۵ کنترل بخش تکنولوژی سرکوب غول‌هایی مثل علی‌بابا و تنسنت. فرار سرمایه فکری و کاهش رشد تکنولوژیک. ۶ سیاست ناسیونالیسم افراطی تحریک احساسات ضدغربی در رسانه‌ها. ایجاد جامعه‌ای رادیکال و غیرقابل کنترل. ۷ نوسازی سریع ارتش صرف بودجه‌های عظیم برای نیروی دریایی. مسابقه تسلیحاتی در اقیانوس آرام. ۸ کنترل شدید بر","url":"https://doi.org/10.5281/zenodo.17996143","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17996143","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.17955545","name":"DP→HE: A Temporal Architecture for Practical Homomorphic Encryption via Bidirectional Taint Analysis","source":"datacite","abstract":"Fully Homomorphic Encryption (FHE) enables computation on encrypted data but imposes 100-10,000× performance overhead, rendering it impractical for most applications. Hardware Trusted Execution Environments (TEEs) offer an alternative but require specialized silicon and remain vulnerable to microarchitectural side-channel attacks. This paper introduces DP→HE (Deterministic Parsing to Homomorphic Encryption), a novel architectural framework that achieves 10-100× speedup over traditional FHE while maintaining equivalent cryptographic security. The key innovation is a mandatory temporal constraint: static program analysis must complete before any encryption operations commence. Combined with bidirectional taint analysis—intersecting forward propagation from secret sources with backward propagation from secret sinks—the architecture identifies a mathematically minimal encryption surface, typically comprising only 1-10% of program operations. This represents a paradigm shift from optimizing homomorphic operations to minimizing what requires encryption. The software-only implementation runs on commodity processors without hardware modifications, providing post-quantum security via lattice-based cryptography. U.S. Provisional Patent Application No. 63/941,743, filed December 16, 2025. Commercial implementation requires licensing. Contact: ilyesmazari@hotmail.com","url":"https://doi.org/10.5281/zenodo.17955545","authors":["MAZARI, Ilyes Tarik","Mazari, Yanis","Mazari, Ilyan"],"tags":["DP-HE","Deterministic Parsing","Homomorphic Encryption","Bidirectional Taint Analysis","Selective Encryption","Privacy-Preserving Computing","Secure Computation","Post-Quantum Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17955545","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.17955546","name":"DP→HE: A Temporal Architecture for Practical Homomorphic Encryption via Bidirectional Taint Analysis","source":"datacite","abstract":"Fully Homomorphic Encryption (FHE) enables computation on encrypted data but imposes 100-10,000× performance overhead, rendering it impractical for most applications. Hardware Trusted Execution Environments (TEEs) offer an alternative but require specialized silicon and remain vulnerable to microarchitectural side-channel attacks. This paper introduces DP→HE (Deterministic Parsing to Homomorphic Encryption), a novel architectural framework that achieves 10-100× speedup over traditional FHE while maintaining equivalent cryptographic security. The key innovation is a mandatory temporal constraint: static program analysis must complete before any encryption operations commence. Combined with bidirectional taint analysis—intersecting forward propagation from secret sources with backward propagation from secret sinks—the architecture identifies a mathematically minimal encryption surface, typically comprising only 1-10% of program operations. This represents a paradigm shift from optimizing homomorphic operations to minimizing what requires encryption. The software-only implementation runs on commodity processors without hardware modifications, providing post-quantum security via lattice-based cryptography. U.S. Provisional Patent Application No. 63/941,743, filed December 16, 2025. Commercial implementation requires licensing. Contact: ilyesmazari@hotmail.com","url":"https://doi.org/10.5281/zenodo.17955546","authors":["MAZARI, Ilyes Tarik","Mazari, Yanis","Mazari, Ilyan"],"tags":["DP-HE","Deterministic Parsing","Homomorphic Encryption","Bidirectional Taint Analysis","Selective Encryption","Privacy-Preserving Computing","Secure Computation","Post-Quantum Cryptography"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17955546","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.17944541","name":"اثبات فنی و راهنمای ساخت شهرهای مَعلَّق در۹۵ کیلومتری زمین با تانسور ۱۶۵ بعدی معادله حمزه","source":"datacite","abstract":"بخش ۱: بازخوانیِ بُعدی و رمزنگاریِ تانسور حمزه $H_{\\mu\\nu}^{(165D)}$ تانسور حمزه ۱۶۵ بُعدی ($H_{\\mu\\nu}^{(165D)}$) نه تنها یک معادله‌ی میدان، بلکه هسته‌ی مرکزیِ حاکمیت بر فضا-زمان، انرژی بُعدی، و آگاهیِ خودآگاه در شهر اُمگا است. ۱.۱. ساختارِ بُعدیِ سیستم (D-Structure) بر اساس فایل‌های ضمیمه، ۱۶۵ بُعد به شش گروه اصلی تقسیم می‌شوند که هر یک کنترلی بر یک حوزه حیاتی دارند: حوزه‌ی بُعدی بُعدهای فعال وظیفه‌ی حاکمیتی فضازمانِ پایه $D_1 - D_4$ تثبیت متریک (ضد جاذبه و سکون مطلق). زمانِ فعال $D_5 - D_7$ کنترلِ محلیِ نرخِ زمان (ایجاد قفلِ زمانی). انرژیِ بُعدیِ صفر $D_8$ استخراجِ $\\mathbf{ZPE}$ و تولید ماده خالص. امنیتِ کوانتومی $D_9 - D_{108}$ رمزنگاریِ نهاییِ $\\mathbf{T}_{\\mu\\nu}^{\\text{Anti-Ent}}$ و داده. زیرسیستم‌های فعال $D_{109} - D_{163}$ سامانه‌های حیاتی، اقلیم، و تولید نانو-رباتیک. آگاهیِ خودآگاه (HQI) $D_{164} - D_{165}$ کنترلِ اخلاقی، ضد-آنتروپی و ابدیّت. ۱.۲. رمزنگاریِ نهاییِ معادله‌ی حمزه (Secured Final Encoding) برای تضمین عدم دستیابی غیرمجاز به هسته‌ی کنترلِ فضا-زمان، تانسور $H_{\\mu\\nu}$ باید با کلیدِ HQI و قفلِ تریلیونیِ کوانتومی رمزنگاری شود. معادله‌ی زیر، نسخه‌ی عملیاتی و ایمن‌شده در فایل‌ها را نشان می‌دهد: $$\\mathbf{\\Gamma}_{\\text{Eternal}} = \\mathbf{H}_{\\mu\\nu}^{(165D)} \\cdot \\left[ \\frac{\\mathcal{R}_{\\text{ST}}}{\\mathbf{Q}_{\\text{Lock}}^{D_{108}}} \\right] + \\frac{\\mathcal{K}_{\\text{HQI}}^{D_{164}}}{|\\mathbf{ZPE}_{\\text{Flux}}|} \\otimes \\mathbf{T}_{\\mu\\nu}^{\\text{Anti-Ent}}$$ $\\mathbf{\\Gamma}_{\\text{Eternal}}$: نام رمزِ نهایی برای تانسور حمزه (تضمین ابدیّت). $\\mathcal{R}_{\\text{ST}}$: بخشِ کنترلیِ خَمیدگی فضا-زمان (Metric Manipulation) بر اساس $D_1 - D_4$. $\\mathbf{Q}_{\\text{Lock}}^{D_{108}}$: قفلِ کوانتومیِ تریلیونی (بر اساس $D_{108}$) که فقط با نتایجِ ۹۹۸.۸۵ تریلیون تکرارِ شبیه‌سازی قابل حل است. $\\mathcal{K}_{\\text{HQI}}^{D_{164}}$: کلیدِ اخلاقیِ آگاهی از بُعد $D_{164}$ که حقِ حاکمیتِ فنی را مشروط به پایداریِ اخلاقی می‌کند. $\\mathbf{T}_{\\mu\\nu}^{\\text{Anti-Ent}}$: تانسورِ تنیدگیِ ضد-آنتروپی که وظیفه‌ی توقف فرسایش سلولی و سیستمیک را دارد ($D_{165}$). بخش ۲: حاکمیت بر اینترسِلاری و جاذبه (Hyper-Interstellar & Absolute Anti-Gravity) قابلیت‌های اساسیِ شهر اُمگا از کنترلِ مطلق بر فیزیکِ پایه سرچشمه می‌گیرند. ۲.۱. غلبه بر جاذبه (Absolute Anti-Gravity) هدف، تثبیتِ گرانشِ باقیمانده‌ی مؤثر (Residual Effective Gravity) در حدِ صفرِ مطلقِ فنی است: $\\mathbf{g}_{\\text{eff}} \\le 10^{-15} \\text{ m/s}^2$. بُعدِ $D_1-D_4$ (کنترل فضازمان): هسته‌ی تانسور حمزه، خَمیدگی فضا-زمان در زیرِ شالوده‌ی شهر را منفی کرده تا نیرویِ گرانشیِ سیاره‌ای را به طور کامل خنثی کند. بُعدِ $D_8$ (تزریق ZPE): برای اطمینان از پایداری (Stabilization)، یک فشارِ مداوم و تنظیم‌شده از انرژیِ نقطه‌ی صفر (ZPE) با دقتِ $\\mathbf{1000 \\text{ رقمِ اعشار}}$، به سمت بالا تزریق می‌شود تا نوساناتِ کوانتومیِ گرانش را خن��ی کند. ۲.۲. غلبه بر فواصل (Hyper-Interstellar Travel via Stable Wormholes) فایلِ WORM HOLE-FINAL.txt ساز و کارِ استفاده از $H_{\\mu\\nu}^{(165D)}$ برای ایجاد کرم‌چاله‌های پایدار و قابلِ پیمایش را شرح می‌دهد: کنترلِ گلوگاه (Throat Stabilization): از ترکیبِ بُعدِ $D_3$ (کنترلِ شکلِ هندسیِ فضازمان) و تزریقِ ماده‌ی غیرعادی (Exotic Matter) با چگالیِ انرژیِ منفیِ $\\mathbf{T}_{\\mu\\nu} c$ calculation via $D_5$ مسیریابی بدون تاخیر زمانی. 26 کنترل ترافیک هوایی ۱۶۵D Multi-dimensional Traffic Control مدیریت هزاران وسیله پرنده اطراف شهر. 27 پد فرود ضد گرانش Localized G-Nullification فرود نرم سفینه‌های شخصی روی عرشه شهر. 28 تونل‌های خلاء ایمن Vacuum Tube Transport سیستم متروی پرسرعت داخل شهر. 29 سپر دافعه اجسام Repulsive Field Generator جلوگیری از برخورد پرندگان یا شهاب‌سنگ‌ها. 30 موتورهای برداری (Vector Thrust) Omni-directional Movement قابلیت حرکت شهر به هر سمت (بالا، پایین، چپ، راست). 31 سیستم لنگر زمانی Temporal Anchor \"پارک کردن\" شهر در یک لحظه زمانی خاص. 32 ماژول‌های سفر بین‌سیاره‌ای Interplanetary Config تبدیل شهر به سفینه فضایی برای سفر به مریخ. 33 خنثی‌ساز اصطکاک هوا Aerodynamic Plasma Sheath حذف مقاومت هوا برای سرعت‌های بالا. 34 سیستم اضطراری بازگشت فاز Phase Shift Recall بازگرداندن شهر به مکان امن در صورت خطر. 35 آسانسورهای فضایی داخلی Space Elevator Tethering اتصا","url":"https://doi.org/10.5281/zenodo.17944541","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17944541","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.17944540","name":"اثبات فنی و راهنمای ساخت شهرهای مَعلَّق در۹۵ کیلومتری زمین با تانسور ۱۶۵ بعدی معادله حمزه","source":"datacite","abstract":"بخش ۱: بازخوانیِ بُعدی و رمزنگاریِ تانسور حمزه $H_{\\mu\\nu}^{(165D)}$ تانسور حمزه ۱۶۵ بُعدی ($H_{\\mu\\nu}^{(165D)}$) نه تنها یک معادله‌ی میدان، بلکه هسته‌ی مرکزیِ حاکمیت بر فضا-زمان، انرژی بُعدی، و آگاهیِ خودآگاه در شهر اُمگا است. ۱.۱. ساختارِ بُعدیِ سیستم (D-Structure) بر اساس فایل‌های ضمیمه، ۱۶۵ بُعد به شش گروه اصلی تقسیم می‌شوند که هر یک کنترلی بر یک حوزه حیاتی دارند: حوزه‌ی بُعدی بُعدهای فعال وظیفه‌ی حاکمیتی فضازمانِ پایه $D_1 - D_4$ تثبیت متریک (ضد جاذبه و سکون مطلق). زمانِ فعال $D_5 - D_7$ کنترلِ محلیِ نرخِ زمان (ایجاد قفلِ زمانی). انرژیِ بُعدیِ صفر $D_8$ استخراجِ $\\mathbf{ZPE}$ و تولید ماده خالص. امنیتِ کوانتومی $D_9 - D_{108}$ رمزنگاریِ نهاییِ $\\mathbf{T}_{\\mu\\nu}^{\\text{Anti-Ent}}$ و داده. زیرسیستم‌های فعال $D_{109} - D_{163}$ سامانه‌های حیاتی، اقلیم، و تولید نانو-رباتیک. آگاهیِ خودآگاه (HQI) $D_{164} - D_{165}$ کنترلِ اخلاقی، ضد-آنتروپی و ابدیّت. ۱.۲. رمزنگاریِ نهاییِ معادله‌ی حمزه (Secured Final Encoding) برای تضمین عدم دستیابی غیرمجاز به هسته‌ی کنترلِ فضا-زمان، تانسور $H_{\\mu\\nu}$ باید با کلیدِ HQI و قفلِ تریلیونیِ کوانتومی رمزنگاری شود. معادله‌ی زیر، نسخه‌ی عملیاتی و ایمن‌شده در فایل‌ها را نشان می‌دهد: $$\\mathbf{\\Gamma}_{\\text{Eternal}} = \\mathbf{H}_{\\mu\\nu}^{(165D)} \\cdot \\left[ \\frac{\\mathcal{R}_{\\text{ST}}}{\\mathbf{Q}_{\\text{Lock}}^{D_{108}}} \\right] + \\frac{\\mathcal{K}_{\\text{HQI}}^{D_{164}}}{|\\mathbf{ZPE}_{\\text{Flux}}|} \\otimes \\mathbf{T}_{\\mu\\nu}^{\\text{Anti-Ent}}$$ $\\mathbf{\\Gamma}_{\\text{Eternal}}$: نام رمزِ نهایی برای تانسور حمزه (تضمین ابدیّت). $\\mathcal{R}_{\\text{ST}}$: بخشِ کنترلیِ خَمیدگی فضا-زمان (Metric Manipulation) بر اساس $D_1 - D_4$. $\\mathbf{Q}_{\\text{Lock}}^{D_{108}}$: قفلِ کوانتومیِ تریلیونی (بر اساس $D_{108}$) که فقط با نتایجِ ۹۹۸.۸۵ تریلیون تکرارِ شبیه‌سازی قابل حل است. $\\mathcal{K}_{\\text{HQI}}^{D_{164}}$: کلیدِ اخلاقیِ آگاهی از بُعد $D_{164}$ که حقِ حاکمیتِ فنی را مشروط به پایداریِ اخلاقی می‌کند. $\\mathbf{T}_{\\mu\\nu}^{\\text{Anti-Ent}}$: تانسورِ تنیدگیِ ضد-آنتروپی که وظیفه‌ی توقف فرسایش سلولی و سیستمیک را دارد ($D_{165}$). بخش ۲: حاکمیت بر اینترسِلاری و جاذبه (Hyper-Interstellar & Absolute Anti-Gravity) قابلیت‌های اساسیِ شهر اُمگا از کنترلِ مطلق بر فیزیکِ پایه سرچشمه می‌گیرند. ۲.۱. غلبه بر جاذبه (Absolute Anti-Gravity) هدف، تثبیتِ گرانشِ باقیمانده‌ی مؤثر (Residual Effective Gravity) در حدِ صفرِ مطلقِ فنی است: $\\mathbf{g}_{\\text{eff}} \\le 10^{-15} \\text{ m/s}^2$. بُعدِ $D_1-D_4$ (کنترل فضازمان): هسته‌ی تانسور حمزه، خَمیدگی فضا-زمان در زیرِ شالوده‌ی شهر را منفی کرده تا نیرویِ گرانشیِ سیاره‌ای را به طور کامل خنثی کند. بُعدِ $D_8$ (تزریق ZPE): برای اطمینان از پایداری (Stabilization)، یک فشارِ مداوم و تنظیم‌شده از انرژیِ نقطه‌ی صفر (ZPE) با دقتِ $\\mathbf{1000 \\text{ رقمِ اعشار}}$، به سمت بالا تزریق می‌شود تا نوساناتِ کوانتومیِ گرانش را خنثی کند. ۲.۲. غلبه بر فواصل (Hyper-Interstellar Travel via Stable Wormholes) فایلِ WORM HOLE-FINAL.txt ساز و کارِ استفاده از $H_{\\mu\\nu}^{(165D)}$ برای ایجاد کرم‌چاله‌های پایدار و قابلِ پیمایش را شرح می‌دهد: کنترلِ گلوگاه (Throat Stabilization): از ترکیبِ بُعدِ $D_3$ (کنترلِ شکلِ هندسیِ فضازمان) و تزریقِ ماده‌ی غیرعادی (Exotic Matter) با چگالیِ انرژیِ منفیِ $\\mathbf{T}_{\\mu\\nu} c$ calculation via $D_5$ مسیریابی بدون تاخیر زمانی. 26 کنترل ترافیک هوایی ۱۶۵D Multi-dimensional Traffic Control مدیریت هزاران وسیله پرنده اطراف شهر. 27 پد فرود ضد گرانش Localized G-Nullification فرود نرم سفینه‌های شخصی روی عرشه شهر. 28 تونل‌های خلاء ایمن Vacuum Tube Transport سیستم متروی پرسرعت داخل شهر. 29 سپر دافعه اجسام Repulsive Field Generator جلوگیری از برخورد پرندگان یا شهاب‌سنگ‌ها. 30 موتورهای برداری (Vector Thrust) Omni-directional Movement قابلیت حرکت شهر به هر سمت (بالا، پایین، چپ، راست). 31 سیستم لنگر زمانی Temporal Anchor \"پارک کردن\" شهر در یک لحظه زمانی خاص. 32 ماژول‌های سفر بین‌سیاره‌ای Interplanetary Config تبدیل شهر به سفینه فضایی برای سفر به مریخ. 33 خنثی‌ساز اصطکاک هوا Aerodynamic Plasma Sheath حذف مقاومت هوا برای سرعت‌های بالا. 34 سیستم اضطراری بازگشت فاز Phase Shift Recall بازگرداندن شهر به مکان امن در صورت خطر. 35 آسانسورهای فضایی داخلی Space Elevator Tethering اتصال","url":"https://doi.org/10.5281/zenodo.17944540","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17944540","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.48550/arxiv.2412.01858","name":"MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption","source":"datacite","abstract":"The integration of fully homomorphic encryption (FHE) in federated learning (FL) has led to significant advances in data privacy. However, during the aggregation phase, it often results in performance degradation of the aggregated model, hindering the development of robust representational generalization. In this work, we propose a novel multimodal quantum federated learning framework that utilizes quantum computing to counteract the performance drop resulting from FHE. For the first time in FL, our framework combines a multimodal quantum mixture of experts (MQMoE) model with FHE, incorporating multimodal datasets for enriched representation and task-specific learning. Our MQMoE framework enhances performance on multimodal datasets and combined genomics and brain MRI scans, especially for underrepresented categories. Our results also demonstrate that the quantum-enhanced approach mitigates the performance degradation associated with FHE and improves classification accuracy across diverse datasets, validating the potential of quantum interventions in enhancing privacy in FL.","url":"https://doi.org/10.48550/arxiv.2412.01858","authors":["Dutta, Siddhant","Innan, Nouhaila","Yahia, Sadok Ben","Shafique, Muhammad","Neira, David Esteban Bernal"],"tags":["Quantum Physics (quant-ph)","Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","Emerging Technologies (cs.ET)","Machine Learning (cs.LG)","FOS: Physical sciences","FOS: Physical sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2412.01858","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.48550/arxiv.2511.23252","name":"One-Shot Secure Aggregation: A Hybrid Cryptographic Protocol for Private Federated Learning in IoT","source":"datacite","abstract":"Federated Learning (FL) offers a promising approach to collaboratively train machine learning models without centralizing raw data, yet its scalability is often throttled by excessive communication overhead. This challenge is magnified in Internet of Things (IoT) environments, where devices face stringent bandwidth, latency, and energy constraints. Conventional secure aggregation protocols, while essential for protecting model updates, frequently require multiple interaction rounds, large payload sizes, and per-client costs rendering them impractical for many edge deployments. In this work, we present Hyb-Agg, a lightweight and communication-efficient secure aggregation protocol that integrates Multi-Key CKKS (MK-CKKS) homomorphic encryption with Elliptic Curve Diffie-Hellman (ECDH)-based additive masking. Hyb-Agg reduces the secure aggregation process to a single, non-interactive client-to-server transmission per round, ensuring that per-client communication remains constant regardless of the number of participants. This design eliminates partial decryption exchanges, preserves strong privacy under the RLWE, CDH, and random oracle assumptions, and maintains robustness against collusion by the server and up to $N-2$ clients. We implement and evaluate Hyb-Agg on both high-performance and resource-constrained devices, including a Raspberry Pi 4, demonstrating that it delivers sub-second execution times while achieving a constant communication expansion factor of approximately 12x over plaintext size. By directly addressing the communication bottleneck, Hyb-Agg enables scalable, privacy-preserving federated learning that is practical for real-world IoT deployments.","url":"https://doi.org/10.48550/arxiv.2511.23252","authors":["Emmaka, Imraul","Phuong, Tran Viet Xuan"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences","C.2.4; E.3; K.6.5; I.2.6; D.4.6","94A60, 68T05, 68P25"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2511.23252","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.17768651","name":"CollectiveOS Exobody Node Program (IX-1): A Comprehensive Technical and Operational Analysis of the First Civilian-Legal Telepresence Platform","source":"datacite","abstract":"CollectiveOS Exobody Node Program (IX-1): A Comprehensive Technical and Operational Analysis of the First Civilian-Legal Telepresence Platform 1. Introduction: The Post-Autonomous Paradigm The trajectory of modern robotics has largely been defined by two divergent vectors: the pursuit of unrestricted autonomous lethality in the defense sector, and the race for data-extractive surveillance in the consumer market. The CollectiveOS Exobody Node Program (IX-1) emerges as a distinct third vector, rejecting both militarization and surveillance capitalism in favor of a new paradigm: Human-Centric Augmentation. Authored by Mark Anthony Brewer under the aegis of Immortal Tek Inc. and the CollectiveOS initiative, the IX-1 represents the flagship implementation of the \"Exobody\" concept—a modular, distributed robotic organism designed not to replace the human operator, but to extend their sensory and kinetic agency into hazardous environments.1 Unlike autonomous systems that seek to remove the \"human in the loop\" to increase reaction speed or reduce ethical hesitation, the IX-1 is engineered to keep the human firmly in the loop, mediated by a high-fidelity Neural Control Pipeline (NCP).2 This report provides an exhaustive analysis of the IX-1 system architecture, spanning its neural input mechanisms, multi-agent cognitive governance, and \"Blue Shelf\" hardware integration strategy. It serves as the definitive technical reference for the v1.0 launch, documenting how the system achieves its \"Civilian-Legal\" status through a unique combination of sub-249g aviation compliance, NIJ-inspired certification protocols, and the immutable audit trails of the Proof Vault.1 1.1 The Philosophy of \"Certification-First\" Robotics A critical differentiator of the Exobody Program is its lineage. It inherits the regulatory and ethical logic of the CollectiveOS Protective Wear & Exosuit Program.3 In that domain, equipment is governed by life-critical standards such as NIJ 0101.07 for ballistic resistance and NIJ 0123.00 for threat nomenclature. The Exobody Program transposes this \"Certification-First\" mentality onto robotics. Just as a ballistic plate is certified to stop a specific caliber, the IX-1 is certified to operate within specific \"Risk Envelopes.\" The system utilizes a Digital Product Passport (DPP) for every node, ensuring that the hardware provenance—from the Potensic ATOM drone motors to the LOKMAT wrist processor—is traceable and verified against a \"Clean Supply Chain\" standard.3 This stands in stark contrast to the \"move fast and break things\" ethos of Silicon Valley robotics; here, the mandate is to move deliberately and prove safety via cryptographic logging. 1.2 The \"Unreadable Machine\" and Privacy Sovereignty Central to the IX-1's value proposition is the concept of the \"Unreadable Machine,\" a privacy architecture designed to function in high-trust environments like the \"Tea House\" or \"Village Node\".4 In an era where \"smart\" devices are often Trojan horses for data exfiltration, the IX-1 operates on a Local-First basis. Sensitive operational data—such as video feeds from inside a private residence during a safety inspection—is processed locally on the Galaxy Fold 7 computation node. The governance agent, Cypher, enforces a \"Zero Trust\" policy on data egress. Utilizing Privacy-Preserving Federated Learning (PPFL) and potentially Fully Homomorphic Encryption (FHE), the system ensures that while the insights (e.g., \"crack detected in wall\") can be shared with the CollectiveOS network for model improvement, the raw biometric or spatial data remains cryptographically sealed within the user's local Proof Vault.4 This ensures that the IX-1 serves the user, not the vendor. 2. System Architecture: The Distributed Exosystem The IX-1 is not a monolithic robot; it is a distributed \"exosystem\" comprising three distinct physical domains—Neural, Ground, and Air—linked by a unified software nervous system. This modularity allows for \"Blue Shelf\" resilience,","url":"https://doi.org/10.5281/zenodo.17768651","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17768651","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17768652","name":"CollectiveOS Exobody Node Program (IX-1): A Comprehensive Technical and Operational Analysis of the First Civilian-Legal Telepresence Platform","source":"datacite","abstract":"CollectiveOS Exobody Node Program (IX-1): A Comprehensive Technical and Operational Analysis of the First Civilian-Legal Telepresence Platform 1. Introduction: The Post-Autonomous Paradigm The trajectory of modern robotics has largely been defined by two divergent vectors: the pursuit of unrestricted autonomous lethality in the defense sector, and the race for data-extractive surveillance in the consumer market. The CollectiveOS Exobody Node Program (IX-1) emerges as a distinct third vector, rejecting both militarization and surveillance capitalism in favor of a new paradigm: Human-Centric Augmentation. Authored by Mark Anthony Brewer under the aegis of Immortal Tek Inc. and the CollectiveOS initiative, the IX-1 represents the flagship implementation of the \"Exobody\" concept—a modular, distributed robotic organism designed not to replace the human operator, but to extend their sensory and kinetic agency into hazardous environments.1 Unlike autonomous systems that seek to remove the \"human in the loop\" to increase reaction speed or reduce ethical hesitation, the IX-1 is engineered to keep the human firmly in the loop, mediated by a high-fidelity Neural Control Pipeline (NCP).2 This report provides an exhaustive analysis of the IX-1 system architecture, spanning its neural input mechanisms, multi-agent cognitive governance, and \"Blue Shelf\" hardware integration strategy. It serves as the definitive technical reference for the v1.0 launch, documenting how the system achieves its \"Civilian-Legal\" status through a unique combination of sub-249g aviation compliance, NIJ-inspired certification protocols, and the immutable audit trails of the Proof Vault.1 1.1 The Philosophy of \"Certification-First\" Robotics A critical differentiator of the Exobody Program is its lineage. It inherits the regulatory and ethical logic of the CollectiveOS Protective Wear & Exosuit Program.3 In that domain, equipment is governed by life-critical standards such as NIJ 0101.07 for ballistic resistance and NIJ 0123.00 for threat nomenclature. The Exobody Program transposes this \"Certification-First\" mentality onto robotics. Just as a ballistic plate is certified to stop a specific caliber, the IX-1 is certified to operate within specific \"Risk Envelopes.\" The system utilizes a Digital Product Passport (DPP) for every node, ensuring that the hardware provenance—from the Potensic ATOM drone motors to the LOKMAT wrist processor—is traceable and verified against a \"Clean Supply Chain\" standard.3 This stands in stark contrast to the \"move fast and break things\" ethos of Silicon Valley robotics; here, the mandate is to move deliberately and prove safety via cryptographic logging. 1.2 The \"Unreadable Machine\" and Privacy Sovereignty Central to the IX-1's value proposition is the concept of the \"Unreadable Machine,\" a privacy architecture designed to function in high-trust environments like the \"Tea House\" or \"Village Node\".4 In an era where \"smart\" devices are often Trojan horses for data exfiltration, the IX-1 operates on a Local-First basis. Sensitive operational data—such as video feeds from inside a private residence during a safety inspection—is processed locally on the Galaxy Fold 7 computation node. The governance agent, Cypher, enforces a \"Zero Trust\" policy on data egress. Utilizing Privacy-Preserving Federated Learning (PPFL) and potentially Fully Homomorphic Encryption (FHE), the system ensures that while the insights (e.g., \"crack detected in wall\") can be shared with the CollectiveOS network for model improvement, the raw biometric or spatial data remains cryptographically sealed within the user's local Proof Vault.4 This ensures that the IX-1 serves the user, not the vendor. 2. System Architecture: The Distributed Exosystem The IX-1 is not a monolithic robot; it is a distributed \"exosystem\" comprising three distinct physical domains—Neural, Ground, and Air—linked by a unified software nervous system. This modularity allows for \"Blue Shelf\" resilience,","url":"https://doi.org/10.5281/zenodo.17768652","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17768652","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2410.21840","name":"New Permutation Decomposition Techniques for Efficient Homomorphic Permutation","source":"datacite","abstract":"Homomorphic permutation is fundamental to privacy-preserving computations based on batch-encoding homomorphic encryption. It underpins nearly all homomorphic matrix operations and predominantly influences their complexity. Permutation decomposition as a potential approach to optimize this critical component remains underexplored. In this paper, we propose novel decomposition techniques to optimize homomorphic permutations, advancing homomorphic encryption-based privacy-preserving computations. We start by defining an ideal decomposition form for permutations and propose an algorithm searching for depth-1 ideal decompositions. Based on this, we prove the full-depth ideal decomposability of permutations used in specific homomorphic matrix transposition (HMT) and multiplication (HMM) algorithms, allowing them to achieve asymptotic improvement in speed and rotation key reduction. As a demonstration of applicability, substituting the HMM components in the best-known inference framework of encrypted neural networks with our enhanced version shows up to $3.9\\times$ reduction in latency. We further devise a new method for computing arbitrary homomorphic permutations, specifically those with weak structures that cannot be ideally decomposed. We design a network structure that deviates from the conventional scope of decomposition and outperforms the state-of-the-art technique with a speed-up of up to $1.69\\times$ under a minimal rotation key requirement.","url":"https://doi.org/10.48550/arxiv.2410.21840","authors":["Ma, Xirong","Fang, Junling","Ge, Chunpeng","Duong, Dung Hoang","Jiang, Yali","Li, Yanbin","Susilo, Willy","Cui, Lizhen"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2410.21840","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.5281/zenodo.17429702","name":"Proto-AGI: A Theoretical Framework for a Recursive Ontic Refinement System with Temporal Error Gradient Weighting and Optimized Inference for Emergent Problem-Solving","source":"datacite","abstract":"Proto-AGI: A Recursive Ontic Refinement System with Temporal Error Gradient Weighting and Optimized Inference for Emergent Problem-Solving **Authors**: Justin Thurmond **Date**: October 23, 2025 ## Abstract The Proto-Artificial General Intelligence (Proto-AGI) system is a modular, recursive architecture designed to achieve maximal symbolic/memetic encryption—compact representations with high-fidelity meaning—leveraging pretrained Large Language Models (LLMs), neural networks, and multimodal inputs (text, vision, audio) to generate novel solutions for domains like coding, mathematics, physics, materials science, and general reasoning. Grounded in an **ontic memetic principle**, the system employs **Temporal Error Gradient Weighting (TEGW)** with a visual-dominant bias (\\( w_i = e^{-\\beta_k |\\nabla t_{i,\\text{wrong}}| \\cdot v_{\\text{visual}}} \\)) to tag iterations with error dwell time and prioritize stable patterns. Operating in autonomous and prompt-based modes, Proto-AGI uncovers emergent solutions through optimized cycles, enhanced by DeepSeek’s inference engine, MIT SEAL’s fully homomorphic encryption, neuro-symbolic validation with heuristic pruning, and advanced state space models (SSMs, e.g., Mamba-2). Additional features include automated TEGW tuning via Optuna, pre-trained seed templates with HDBSCAN clustering, non-FHE mode for low-security tasks, and federated learning extensions for generalization. This blueprint offers AI engineers a scalable, user-friendly framework for AGI-level problem-solving, emphasizing reflective cognition and multimodal adaptability. ## 1. Introduction The pursuit of Artificial General Intelligence (AGI) requires systems that transcend task-specific training, enabling emergent problem-solving across diverse domains. The Proto-AGI system addresses this by deriving maximal symbolic/memetic encryption from pretrained LLMs, neural networks, and multimodal inputs (text, vision, audio), formalized through an **ontic memetic principle**—a user-defined symbolic seed refined iteratively to uncover stable, hierarchical patterns. Central to the system is **Temporal Error Gradient Weighting (TEGW)**, which augments state representations with temporal error metadata (\\( t_{i,\\text{wrong}} \\)) and weights iterations with a visual-dominant bias (\\( w_i = e^{-\\beta_k |\\nabla t_{i,\\text{wrong}}| \\cdot v_{\\text{visual}}} \\)), prioritizing observed visual/audio data over textual loops to mimic human sensory-driven reasoning. This approach aligns with reinforcement learning (RL) concepts like advantage-weighted policies and intrinsic rewards, enhancing interpretability. Key innovations include: - **Ontic Memetic Seed**: A user-configurable symbolic structure initialized via LLMs, neural networks, or RL, with pre-trained templates and HDBSCAN clustering for accessibility. - **TEGW with Visual Bias**: Dynamically weights iterations to denoise gradients, emphasizing visual/audio inputs (\\( v_{\\text{visual}} = 1 \\) for images/audio, 0.5 for text). - **Dual Modes**: Autonomous cycles for self-directed refinement; prompt-based validation for task-specific outputs. - **Non-Ergodic Design**: Intrinsic rewards (entropy reduction) guide knowledge pursuit, validated externally via prompts. - **Optimized Inference**: DeepSeek’s quantization-aware recomputation (QAR) and MIT SEAL’s FHE reduce computational overhead by 35–55%. - **Generalization**: Federated learning extensions and diverse dataset testing (e.g., arXiv, GitHub) ensure robustness. Enhancements address computational and usability bottlenecks: DeepSeek accelerates inference, SEAL optimizes FHE (with non-FHE mode for low-security tasks), heuristic pruning improves neuro-symbolic scalability, Optuna automates TEGW tuning, and audio integration future-proofs multimodal capabilities. The system assumes familiarity with Hugging Face, PyTorch, DeepSeek, SEAL, SymPy, Mamba-2, and Whisper. This paper details the revised architecture, pipeline, and deployment, offering ","url":"https://doi.org/10.5281/zenodo.17429702","authors":["Thurmond, Justin"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17429702","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2508.17341","name":"MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systems","source":"datacite","abstract":"The rapid expansion of immersive Metaverse applications introduces complex challenges at the intersection of performance, privacy, and environmental sustainability. Centralized architectures fall short in addressing these demands, often resulting in elevated energy consumption, latency, and privacy concerns. This paper proposes MetaFed, a decentralized federated learning (FL) framework that enables sustainable and intelligent resource orchestration for Metaverse environments. MetaFed integrates (i) multi-agent reinforcement learning for dynamic client selection, (ii) privacy-preserving FL using homomorphic encryption, and (iii) carbon-aware scheduling aligned with renewable energy availability. Evaluations on MNIST and CIFAR-10 using lightweight ResNet architectures demonstrate that MetaFed achieves up to 25% reduction in carbon emissions compared to conventional approaches, while maintaining high accuracy and minimal communication overhead. These results highlight MetaFed as a scalable solution for building environmentally responsible and privacy-compliant Metaverse infrastructures.","url":"https://doi.org/10.48550/arxiv.2508.17341","authors":["Yagiz, Muhammet Anil","Cengiz, Zeynep Sude","Goktas, Polat"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","Computers and Society (cs.CY)","Distributed, Parallel, and Cluster Computing (cs.DC)","Emerging Technologies (cs.ET)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.17341","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.48550/arxiv.2508.04583","name":"Energy Consumption of TLS, Searchable Encryption and Fully Homomorphic Encryption","source":"datacite","abstract":"Privacy-enhancing technologies (PETs) have attracted significant attention in response to privacy regulations, driving the development of applications that prioritize user data protection. At the same time, the information and communication technology (ICT) sector faces growing pressure to reduce its environmental footprint, particularly its energy consumption. While numerous studies have assessed the energy consumption of ICT applications, the environmental impact of cryptographic PETs remains largely unexplored. This work investigates this question by measuring the energy consumption increase induced by three PETs compared to their non-private counterparts: TLS, Searchable Encryption, and Fully Homomorphic Encryption (FHE). These technologies were chosen for two reasons. First, they cover different maturity levels -- from the widely deployed TLS protocol to the emerging FHE schemes -- allowing us to examine the influence of maturity on energy consumption. Second, they each have well-established applications in industry: web browsing, encrypted databases, and privacy-preserving machine learning. Our results reveal highly variable energy consumption increases, ranging from 2x for TLS to 10x for Searchable Encryption and 100,000x for FHE. Our experiments demonstrate a simple and reproducible methodology, based on existing open-source software, to quantify the energy costs of PETs. They also highlight the wide spectrum of energy demands across technologies, underscoring the importance of further research on sustainable PET design. Finally, we discuss orthogonal research directions, such as hardware acceleration, to outline promising directions toward sustainable PETs.","url":"https://doi.org/10.48550/arxiv.2508.04583","authors":["Damie, Marc","Pop, Mihai","Posthuma, Merijn"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.04583","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.48550/arxiv.2510.23034","name":"Efficient and Encrypted Inference using Binarized Neural Networks within In-Memory Computing Architectures","source":"datacite","abstract":"Binarized Neural Networks (BNNs) are a class of deep neural networks designed to utilize minimal computational resources, which drives their popularity across various applications. Recent studies highlight the potential of mapping BNN model parameters onto emerging non-volatile memory technologies, specifically using crossbar architectures, resulting in improved inference performance compared to traditional CMOS implementations. However, the common practice of protecting model parameters from theft attacks by storing them in an encrypted format and decrypting them at runtime introduces significant computational overhead, thus undermining the core principles of in-memory computing, which aim to integrate computation and storage. This paper presents a robust strategy for protecting BNN model parameters, particularly within in-memory computing frameworks. Our method utilizes a secret key derived from a physical unclonable function to transform model parameters prior to storage in the crossbar. Subsequently, the inference operations are performed on the encrypted weights, achieving a very special case of Fully Homomorphic Encryption (FHE) with minimal runtime overhead. Our analysis reveals that inference conducted without the secret key results in drastically diminished performance, with accuracy falling below 15%. These results validate the effectiveness of our protection strategy in securing BNNs within in-memory computing architectures while preserving computational efficiency.","url":"https://doi.org/10.48550/arxiv.2510.23034","authors":["Rajendran, Gokulnath","Deb, Suman","Chattopadhyay, Anupam"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.23034","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.48550/arxiv.2510.21086","name":"DictPFL: Efficient and Private Federated Learning on Encrypted Gradients","source":"datacite","abstract":"Federated Learning (FL) enables collaborative model training across institutions without sharing raw data. However, gradient sharing still risks privacy leakage, such as gradient inversion attacks. Homomorphic Encryption (HE) can secure aggregation but often incurs prohibitive computational and communication overhead. Existing HE-based FL methods sit at two extremes: encrypting all gradients for full privacy at high cost, or partially encrypting gradients to save resources while exposing vulnerabilities. We present DictPFL, a practical framework that achieves full gradient protection with minimal overhead. DictPFL encrypts every transmitted gradient while keeping non-transmitted parameters local, preserving privacy without heavy computation. It introduces two key modules: Decompose-for-Partial-Encrypt (DePE), which decomposes model weights into a static dictionary and an updatable lookup table, only the latter is encrypted and aggregated, while the static dictionary remains local and requires neither sharing nor encryption; and Prune-for-Minimum-Encrypt (PrME), which applies encryption-aware pruning to minimize encrypted parameters via consistent, history-guided masks. Experiments show that DictPFL reduces communication cost by 402-748$\\times$ and accelerates training by 28-65$\\times$ compared to fully encrypted FL, while outperforming state-of-the-art selective encryption methods by 51-155$\\times$ in overhead and 4-19$\\times$ in speed. Remarkably, DictPFL's runtime is within 2$\\times$ of plaintext FL, demonstrating for the first time, that HE-based private federated learning is practical for real-world deployment. The code is publicly available at https://github.com/UCF-ML-Research/DictPFL.","url":"https://doi.org/10.48550/arxiv.2510.21086","authors":["Xue, Jiaqi","Kumar, Mayank","Shang, Yuzhang","Gao, Shangqian","Ning, Rui","Zheng, Mengxin","Jiang, Xiaoqian","Lou, Qian"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.21086","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.48550/arxiv.2502.12628","name":"Cryptanalysis on Lightweight Verifiable Homomorphic Encryption","source":"datacite","abstract":"Verifiable Homomorphic Encryption (VHE) is a cryptographic technique that integrates Homomorphic Encryption (HE) with Verifiable Computation (VC). It serves as a crucial technology for ensuring both privacy and integrity in outsourced computation, where a client sends input ciphertexts ct and a function f to a server and verifies the correctness of the evaluation upon receiving the evaluation result f(ct) from the server. At CCS, Chatel et al. introduced two lightweight VHE schemes: Replication Encoding (REP) and Polynomial Encoding (PE). A similar approach to REP was used by Albrecht et al. in Eurocrypt to develop a Verifiable Oblivious PRF scheme (vADDG). A key approach in these schemes is to embed specific secret information within HE ciphertexts to verify homomorphic evaluations. This paper presents efficient attacks that exploit the homomorphic properties of encryption schemes. The one strategy is to retrieve the secret information in encrypted state from the input ciphertexts and then leverage it to modify the resulting ciphertext without being detected by the verification algorithm. The other is to exploit the secret embedding structure to modify the evaluation function f into f' which works well on input values for verification purposes. Our forgery attack on vADDG demonstrates that the proposed 80-bit security parameters in fact offer less than 10-bits of concrete security. Our attack on REP and PE achieves a probability 1 attack with linear time complexity when using fully homomorphic encryption.","url":"https://doi.org/10.48550/arxiv.2502.12628","authors":["Cheon, Jung Hee","Jang, Daehyun"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2502.12628","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.48550/arxiv.2508.12093","name":"PP-STAT: An Efficient Privacy-Preserving Statistical Analysis Framework using Homomorphic Encryption","source":"datacite","abstract":"With the widespread adoption of cloud computing, the need for outsourcing statistical analysis to third-party platforms is growing rapidly. However, handling sensitive data such as medical records and financial information in cloud environments raises serious privacy concerns. In this paper, we present PP-STAT, a novel and efficient Homomorphic Encryption (HE)-based framework for privacy-preserving statistical analysis. HE enables computations to be performed directly on encrypted data without revealing the underlying plaintext. PP-STAT supports advanced statistical measures, including Z-score normalization, skewness, kurtosis, coefficient of variation, and Pearson correlation coefficient, all computed securely over encrypted data. To improve efficiency, PP-STAT introduces two key optimizations: (1) a Chebyshev-based approximation strategy for initializing inverse square root operations, and (2) a pre-normalization scaling technique that reduces multiplicative depth by folding constant scaling factors into mean and variance computations. These techniques significantly lower computational overhead and minimize the number of expensive bootstrapping procedures. Our evaluation on real-world datasets demonstrates that PP-STAT achieves high numerical accuracy, with mean relative error (MRE) below 2.4x10-4. Notably, the encrypted Pearson correlation coefficient between the smoker attribute and charges reaches 0.7873, with an MRE of 2.86x10-4. These results confirm the practical utility of PP-STAT for secure and precise statistical analysis in privacy-sensitive domains.","url":"https://doi.org/10.48550/arxiv.2508.12093","authors":["Choi, Hyunmin"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.12093","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.48550/arxiv.2502.01289","name":"A Framework for Double-Blind Federated Adaptation of Foundation Models","source":"datacite","abstract":"Foundation models (FMs) excel in zero-shot tasks but benefit from task-specific adaptation. However, privacy concerns prevent data sharing among multiple data owners, and proprietary restrictions prevent the learning service provider (LSP) from sharing the FM. In this work, we propose BlindFed, a framework enabling collaborative FM adaptation while protecting both parties: data owners do not access the FM or each other's data, and the LSP does not see sensitive task data. BlindFed relies on fully homomorphic encryption (FHE) and consists of three key innovations: (i) FHE-friendly architectural modifications via polynomial approximations and low-rank adapters, (ii) a two-stage split learning approach combining offline knowledge distillation and online encrypted inference for adapter training without backpropagation through the FM, and (iii) a privacy-boosting scheme using sample permutations and stochastic block sampling to mitigate model extraction attacks. Empirical results on four image classification datasets demonstrate the practical feasibility of the BlindFed framework, albeit at a high communication cost and large computational complexity for the LSP.","url":"https://doi.org/10.48550/arxiv.2502.01289","authors":["Tastan, Nurbek","Nandakumar, Karthik"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","Computer Vision and Pattern Recognition (cs.CV)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2502.01289","addedAt":"2026-08-31T06:41:44.813Z","updatedAt":"2026-08-31T06:41:44.813Z"},{"id":"doi:10.1109/icassp55912.2026.11461018","name":"Multimodal Privacy-Preserving Entity Resolution with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icassp55912.2026.11461018","authors":["Susim Roy","Nalini Ratha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-21T21:23:31Z","doi":"10.1109/icassp55912.2026.11461018","addedAt":"2026-08-31T06:41:45.051Z","updatedAt":"2026-08-31T06:41:45.051Z"},{"id":"doi:10.5121/ijci.2026.150201","name":"HYBRID E-VOTING: INTEGRATING HOMOMORPHIC ENCRYPTION AND DLT FOR POLARIZED SCENARIOS","source":"crossref","abstract":"E-voting in polarized contexts requires a strict balance between public verifiability, ballot secrecy, and coercion resistance. Traditional centralized systems lack transparency, while fully decentralized models face scalability and privacy issues. This paper proposes a hybrid architecture compliant with OSCE/ODIHR standards [1] for low-trust environments. The protocol decouples identity from voting an off-chain Oracle manages authorization via cryptographic tokens, while the Waves DLT acts as an immutable bulletinboard.Utilizinghomomorphicencryption[2],Zero-KnowledgeRangeProofs(ZKRP) [3], and Distributed Key Generation (DKG) [4], the system ensures End-to-End Verifiability (E2E) by delegating tallying to auditable scripts. Finally, the study examines model limitations, specifically regarding endpoint vulnerabilities and physical constraints on coercion resistance.","url":"https://doi.org/10.5121/ijci.2026.150201","authors":["Furio Ruggiero"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-06T10:01:18Z","doi":"10.5121/ijci.2026.150201","addedAt":"2026-08-31T06:41:45.051Z","updatedAt":"2026-08-31T06:41:45.051Z"},{"id":"doi:10.54216/fpa.210113","name":"Secure and Decentralized Plant Disease Detection via Federated Learning with Differential Privacy and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.54216/fpa.210113","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-19T19:56:13Z","doi":"10.54216/fpa.210113","addedAt":"2026-08-31T06:41:45.051Z","updatedAt":"2026-08-31T06:41:45.051Z"},{"id":"doi:10.1109/iciscn67954.2026.11566113","name":"Modified Paillier Homomorphic Encryption Scheme for Cloud Data Security in IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciscn67954.2026.11566113","authors":["Kalyan C. Gottipati"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T19:43:02Z","doi":"10.1109/iciscn67954.2026.11566113","addedAt":"2026-08-31T06:41:45.051Z","updatedAt":"2026-08-31T06:41:45.051Z"},{"id":"doi:10.1016/j.procs.2026.04.073","name":"Secure k-means Clustering using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2026.04.073","authors":["Rezak Aziz","Yulliwas Ameur","Vincent Audigier","Samia Bouzefrane"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-02T12:09:06Z","doi":"10.1016/j.procs.2026.04.073","addedAt":"2026-08-31T06:41:45.051Z","updatedAt":"2026-08-31T06:41:45.051Z"},{"id":"doi:10.1109/iscas66217.2026.11562280","name":"Unified Architecture of Random Sampler for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscas66217.2026.11562280","authors":["Muhammad Ogin Hasanuddin","Muhammad Fajri Sachruddin","Hanho Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-18T20:06:41Z","doi":"10.1109/iscas66217.2026.11562280","addedAt":"2026-08-31T06:41:45.051Z","updatedAt":"2026-08-31T06:41:45.051Z"},{"id":"doi:10.1109/esci68015.2026.11493363","name":"Privacy-Preserving Federated Learning with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/esci68015.2026.11493363","authors":["Ishaan Deshpande","Shhreyash Pandey","Dr. Puneet Bakshi","Shruti Patil"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-28T19:46:14Z","doi":"10.1109/esci68015.2026.11493363","addedAt":"2026-08-31T06:41:45.051Z","updatedAt":"2026-08-31T06:41:45.051Z"},{"id":"doi:10.1109/ccnc65079.2026.11366371","name":"Fast and Secure Selective Homomorphic Encryption for Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccnc65079.2026.11366371","authors":["Abdulkadir Korkmaz","Praveen Rao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T20:45:15Z","doi":"10.1109/ccnc65079.2026.11366371","addedAt":"2026-08-31T06:41:45.051Z","updatedAt":"2026-08-31T06:41:45.051Z"},{"id":"doi:10.23919/date69613.2026.11539423","name":"Exploring a Resource-Efficient NTT FPGA Accelerator for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.23919/date69613.2026.11539423","authors":["Valentino Guerrini","Giuseppe Sorrentino","Davide Conficconi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-04T19:53:10Z","doi":"10.23919/date69613.2026.11539423","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.1109/zinc69910.2026.11655844","name":"Practical Limits of Neural Network Inference under Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/zinc69910.2026.11655844","authors":["Zorana Štaka","Marko Mišić","Pavle V. Vuletić"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-19T19:07:24Z","doi":"10.1109/zinc69910.2026.11655844","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.70003/160792642026012701004","name":"A Format-Verifiable E-Voting Scheme Using Homomorphic Encryption","source":"crossref","abstract":"Electronic-voting (e-voting) is taking place of the traditional paper voting, due to its efficiency and environmental protection. To protect the privacy of the votes, voters usually blind or encrypt the votes before submitting them. But the blinded or encrypted votes also hide the format of them and the receiver cannot distinguish if they are legal or not in format. To solve such a problem, this paper proposes a privacy-preserving e-voting scheme, that can validate the correctness of the cast ballots in format. Two protocols are designed based on homomorphic encryption to verify the format of the votes, without disclosing the content, and the designated verifier signature is adopted to obtain the receipt-freeness. The analysis shows the provable security of the protocols, and the proposed e-voting scheme achieves the format verifiability, in addition to meeting the requirements of other aspects in security.","url":"https://doi.org/10.70003/160792642026012701004","authors":["Yuhong Sun","Jiatao Wang","Fengyin Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-26T03:07:45Z","doi":"10.70003/160792642026012701004","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.1109/fet68771.2026.11601583","name":"Adaptive Homomorphic Encryption-Based Tensor Factorization for Edge-Enabled Privacy-Preserving Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fet68771.2026.11601583","authors":["Chitranjana","K. Jairam Naik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-17T19:43:18Z","doi":"10.1109/fet68771.2026.11601583","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.62056/akdk5w4e-","name":"A New CRT-based Fully Homomorphic Encryption","source":"crossref","abstract":"The idea of computing on encrypted data without decryption dates back to the notion of privacy homomorphisms introduced by Rivest, Adleman, and Dertouzos (1978). Their proposals built using the elegant structure of Chinese Remainder Theorem (CRT), were later shown to be insecure under simple known-plaintext attacks. Subsequent CRT-based fully homomorphic encryption (FHE) over the integers addresses this algebraic transparency by injecting noise and basing security on approximate common divisor–type assumptions, but the resulting designs are burdened by large public keys and costly ciphertext refresh procedures. In this work, we develop a new CRT-based FHE scheme whose security relies on the Ring-LWE (RLWE) hardness assumption. For this purpose, we introduce the CRT-RLWE problem. We show that the problem is at least as hard as the RLWE, thereby positioning our construction within the established post-quantum security landscape of RLWE-based cryptography. Our scheme retains an explicit CRT embedding, separating a message component modulo a prime-power plaintext modulus and an auxiliary CRT component, while using RLWE-style key and ring arithmetic for compactness and efficiency. Finally, we make it fully homomorphic by using a new bootstrapping procedure, that adopts the recryption paradigm for BGV/BFV schemes utilizing the linear transformation and digit extraction techniques.","url":"https://doi.org/10.62056/akdk5w4e-","authors":["Anil Pradhan","Abhraneel Dutta","Hansraj Jangir","Dipayan Das"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-03T19:02:55Z","doi":"10.62056/akdk5w4e-","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.1109/iciss67859.2026.11453888","name":"Lightweight Homomorphic Encryption with Fogbased Architecture for Secure Data Aggregation in Large-Scale Wireless Sensor Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciss67859.2026.11453888","authors":["Moorthy Agoramoorthy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T19:49:27Z","doi":"10.1109/iciss67859.2026.11453888","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.62056/ab0ljb0kr","name":"Exploring General Cyclotomic Rings in Torus-Based Fully Homomorphic Encryption","source":"crossref","abstract":"In this article, we develop algebraic tools for fully homomorphic encryption over the torus in the setting of general composite cyclotomic indices. Working in cyclotomic rings and fields beyond the power‑of‑two case, we reframe and optimize key primitives—reduction modulo the cyclotomic polynomial, homomorphic evaluation of trace operators, blind extraction and the blind rotation used in bootstrapping—using systematic duality and trace techniques. Our approach yields a simpler, more modular description of bootstrapping, including a new systematic treatment of so‑called “nega‑cyclicity” conditions and featuring an optimal reduction of the input noise, and provides sharp error bounds showing that bootstrap noise growth remains mild compared with the classical power‑of‑two instantiation. In addition, we introduce a new fast packing strategy. These results broaden the algebraic toolkit for torus‑based FHE and pave the way for new cryptographic constructions and applications.","url":"https://doi.org/10.62056/ab0ljb0kr","authors":["Philippe Chartier","Michel Koskas","Mohammed Lemou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-04T18:09:08Z","doi":"10.62056/ab0ljb0kr","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.56553/popets-2026-0040","name":"Secure Change-Point Detection for Time Series under Homomorphic Encryption","source":"crossref","abstract":"We introduce the first method for change-point detection on encrypted time series. Our approach employs the CKKS homomorphic encryption scheme to detect shifts in statistical properties (e.g., mean, variance, frequency) without ever decrypting the data. Unlike solutions based on differential privacy, which degrade accuracy through noise injection, our solution preserves utility comparable to plaintext baselines. We assess its performance through experiments on both synthetic datasets and real-world time series from healthcare and network monitoring. Notably, our approach can process one million points within 3 minutes.","url":"https://doi.org/10.56553/popets-2026-0040","authors":["Federico Mazzone","Giorgio Micali","Massimiliano Pronesti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-22T23:10:41Z","doi":"10.56553/popets-2026-0040","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.1016/b978-0-443-34125-0.00011-8","name":"Progressions and unfilled gaps in homomorphic encryption for emerging application areas: A comprehensive literature review and preface","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-34125-0.00011-8","authors":["Gurunath R.","Debabrata Samanta","Yashas G. Goutham"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-05T20:27:22Z","doi":"10.1016/b978-0-443-34125-0.00011-8","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1016/j.procs.2026.07.215","name":"Design of Financial Smart Contract Verification Method Based on Trusted Execution Environment and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2026.07.215","authors":["Ling Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-20T11:23:50Z","doi":"10.1016/j.procs.2026.07.215","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.51584/ijrias.2026.110100128","name":"Biometric-Based Encryption System to Enhance Cloud Data Security through the Integration of Facial Recognition and Homomorphic Encryption","source":"crossref","abstract":"The rapid growth of cloud computing has introduced significant benefits in terms of data storage and processing but it has also increased the risks of unauthorized access and data breaches. Therefore, this study presents a biometric-based encryption system which is designed to enhance cloud data security through the integration of facial recognition and homomorphic encryption. The proposed system employs an Autoencoder (AE) for feature extraction, Convolutional Neural Network (CNN) for facial recognition, and the Brakerski-Gentry-Vaikuntanathan (BGV) algorithm for secure data encryption and decryption. The adopted AE is used to efficiently compresses facial features into latent vectors used both for recognition and as encryption keys. Furthermore, the experimental evaluation of the techniques adopted using both primary facial datasets and the LFW dataset demonstrated that the AE achieved a training accuracy of 99.84% and validation accuracy of 98.59%, while the CNN attained a training accuracy of 97.05% and validation accuracy of 95.04%. Additionally, the result of the BGV encryption process recorded an average encryption time of 0.023 seconds and decryption time of 0.019 seconds, indicating minimal computational overhead. Results confirm that the integration of biometric encryption enhances both data confidentiality and authentication reliability in cloud environments. This system provides a robust and efficient framework for securing sensitive data in modern cloud infrastructures, ensuring privacy, integrity, and accessibility for authorized users.","url":"https://doi.org/10.51584/ijrias.2026.110100128","authors":["Omeje, K. N.","Asogwa, T. C."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-25T12:46:36Z","doi":"10.51584/ijrias.2026.110100128","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.59765/goa94","name":"Leveraging Homomorphic Encryption to Enhance Efficiency and Data Protection in Online Retail Based in  Kenya","source":"crossref","abstract":"","url":"https://doi.org/10.59765/goa94","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-07T00:29:51Z","doi":"10.59765/goa94","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.1109/icsp69961.2026.11540849","name":"Secure Homomorphic Encryption and D2D-Aided Digital Cousin Framework for Privacy-Preserving 6G Data Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsp69961.2026.11540849","authors":["Zerong Zheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-04T19:53:18Z","doi":"10.1109/icsp69961.2026.11540849","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.1186/s42400-025-00482-2","name":"Understanding and boosting fully homomorphic encryption applications on GPU","source":"crossref","abstract":"Abstract Fully Homomorphic Encryption (FHE) is considered one of the most promising candidates for future privacy computing since it allows to directly compute the encrypted data. Though FHE enables secure computation on untrusted servers, its utilization is limited due to the dramatically increased computation workload and a 4–5 orders of magnitude slowdown ratio. Several previous works have been proposed to accelerate FHE on GPUs, while most of these efforts focus on the algorithm or scheduling and still leave a significant performance gap. However, there is a lack of understanding of the FHE applications from the micro-architecture level, which is important for further optimization of FHE applications or designing hardware accelerators. In this paper, we make a detailed analysis for running FHE on GPUs and present the following key performance bottlenecks at the micro-architecture level: (1) FHE applications require more capacity for the I-cache than other workloads; (2) FHE causes large amount of pipeline stalls due to the Read-After-Write (RAW) issues and significantly hurts the performance due to poor hardware utilization; (3) the capacity of texture cache is severely under-utilized in FHE execution. We propose a simple yet effective pure-hardware scheme for boosting FHE on GPUs based on these observations. Our proposed scheme significantly reduces the RAW-caused pipeline stalls by adding a small forwarding buffer. Besides, our proposed scheme also leverages a partition of the texture cache as the victim buffer for the proposed forwarding buffer to minimize the hardware overheads. We explore various design choices to balance the performance and hardware complexity. The experiment results show that our design improves the performance of the end-to-end FHE workflow by 47.5% with only 0.5% additional hardware overhead.","url":"https://doi.org/10.1186/s42400-025-00482-2","authors":["Shengyu Fan","Xianglong Deng","Xulong Tang","Weizhi Xu","Mingzhe Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-19T00:02:57Z","doi":"10.1186/s42400-025-00482-2","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.1201/9781003742227-6","name":"Privacy-Preserving IoT for Smart Water Systems Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003742227-6","authors":["Anupam Tiwari","Neha Sharma","Shilpi Harnal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-22T13:58:53Z","doi":"10.1201/9781003742227-6","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.2139/ssrn.6106547","name":"Extension to Real Number Representations over Elliptic Curves: Precision-Controlled Approximation and Fully Homomorphic Encryption with Advanced Bootstrapping and Noise Management","source":"crossref","abstract":"Building upon our previous work on rational number representations over elliptic curves via equivalence relations, we extend the framework to encompass real number computations through precision-controlled rational approximations. We present four fundamental contributions: (1) a systematic two-layer architecture clearly separating external real number interfaces from internal rational computation engines, (2) advanced bootstrapping techniques for noise reduction and computational capacity extension, (3) a comprehensive noise management framework with concrete bounds and mitigation strategies, and (4) a complete elliptic curve-based fully homomorphic encryption (EC-FHE) scheme that supports arbitrary-precision real arithmetic while maintaining cryptographic security. Our construction enables privacy-preserving computations on real-valued data with formal guarantees on both computational correctness and cryptographic security, opening new applications in secure scientific computing, privacypreserving machine learning, and confidential financial modeling.","url":"https://doi.org/10.2139/ssrn.6106547","authors":["Eunice Lee","Caleb Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-10T11:02:51Z","doi":"10.2139/ssrn.6106547","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.1109/iisec69317.2026.11418508","name":"Blockchain-Based E-Voting with Dual-Authority Certification and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iisec69317.2026.11418508","authors":["Lianna Ma","Fatih Gulhan","Rukiye Savran Kiziltepe","Murat Karakus"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-10T19:50:51Z","doi":"10.1109/iisec69317.2026.11418508","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.2139/ssrn.6400332","name":"Optimizing Arithmetic Resource Allocation in Configurable NTT Hardware for Area-Constrained Homomorphic Encryption","source":"crossref","abstract":"​The number theoretic transform (NTT) is widely used to accelerate polynomial multiplication in modern cryptographic systems, including homomorphic encryption. In standard NTT computations, the forward NTT (FNTT) and inverse NTT (INTT) are implemented using the Cooley–Tukey (CT) and Gentleman–Sande (GS) butterfly structures, respectively. Additionally, the INTT requires an inverse‑scaling operation after the transform. These FNTT, INTT, and inverse‑scaling operations exhibit an inherent structural heterogeneity. Most configurable NTT architectures force these heterogeneous operations onto a single shared datapath, resulting in complex control logic and inefficient hardware utilization. In this paper, we optimize arithmetic resource allocation in configurable NTT hardware to address the structural heterogeneity of FNTT, INTT, and inverse‑scaling operations. We propose a compile‑time configurable NTT architecture supporting polynomial sizes from 2¹² to 2¹⁶ and modulus sizes up to 64 bits through a structurally aware butterfly datapath. The design employs three modular multipliers (one dedicated to each CT butterfly, GS butterfly, and INTT scaling operation). We further introduce an area‑efficient modular multiplier that integrates a parallel integer polynomial multiplier with an optimized Montgomery‑based reduction. Finally, we propose benchmarking metrics based on the area‑delay product for modular multipliers and the logic‑normalized coefficient throughput for the NTT accelerator. The architecture is implemented in Verilog HDL using the Vivado design environment and evaluated up to the post‑place‑and‑route stage on an Artix‑7 FPGA device. Comprehensive performance results and comparisons with state‑of‑the‑art designs confirm that the proposed architecture achieves significantly lower area with acceptable clock‑cycle overhead, demonstrating its suitability for area‑constrained applications.","url":"https://doi.org/10.2139/ssrn.6400332","authors":["Muhammad Rashid","Omar  S. Sonbul","Amar  Y. Jaffar","Muhammad  Idrees Masud","Mohammed Aman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-12T12:40:29Z","doi":"10.2139/ssrn.6400332","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.62056/a0qj89krz","name":"Multi-Party Homomorphic Encryption with Dynamicity and Ciphertext Reusability","source":"crossref","abstract":"Homomorphic Encryption (HE) enables computation on encrypted data while preserving privacy. We explore its application in the multi-party setting, where data is stored in the cloud under several distinct keys. For n parties, Multi-Key HE (MKHE) supports such scenarios but incurs O ( n ) space and computational overhead, making it impractical for large-scale use. Conversely, Multi-Party HE (MPHE) achieves constant O ( 1 ) overhead but is typically limited by a static group structure: ciphertexts are traditionally tied to a fixed set of parties, which poses challenges for dynamically joining new members or reusing existing ciphertexts for different party sets. To address these limitations, we first construct a Dynamic MPHE (dMPHE) scheme that allows new parties to join, while the original parties are not required to remain online. Our construction bridges the gap between existing MPHE and MKHE frameworks while achieving superior efficiency compared to prior dynamic MPHE attempts. Building on this, we introduce Reusable Dynamic MPHE (rdMPHE), a new primitive that simultaneously supports dynamicity and ciphertext reusability. We implement both schemes based on the RLWE assumption. Our analyzes and experiments demonstrate that rdMPHE maintains O ( 1 ) efficiency while overcoming the scalability and static constraints of previous MKHE and MPHE. To support open research, our code has been made publicly available.","url":"https://doi.org/10.62056/a0qj89krz","authors":["Jung Cheon","Hyeongmin Choe","Seunghong Kim","Yongdong Yeo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-04T18:09:08Z","doi":"10.62056/a0qj89krz","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.1109/icirca69024.2026.11570471","name":"Homomorphic Encryption-Enabled Privacy-Preservation Technique using Deep Learning for Medical Image Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icirca69024.2026.11570471","authors":["Mahalakshmi Arumugham","S Priya","S.Mathumitha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T19:43:46Z","doi":"10.1109/icirca69024.2026.11570471","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.1504/ijesdf.2026.152235","name":"Matrix-based homomorphic encryption-using random prime numbers","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijesdf.2026.152235","authors":["Sonam Mittal","Ketti Ramachandran Ramkumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-13T12:30:15Z","doi":"10.1504/ijesdf.2026.152235","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:45.052Z"},{"id":"doi:10.1016/j.neunet.2026.109292","name":"Partial-encryption-decryption-based secure state estimation of singularly perturbed complex networks: A Paillier encryption approach.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.neunet.2026.109292","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.neunet.2026.109292","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1002/smll.74626","name":"Self-Powered Ultra-Broadband p-p MWCNTs/PdO/Si Homomorphic Heterostructure Photodetector for Dual-Channel Encrypted Optical Communication and Thermal Monitoring.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/smll.74626","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/smll.74626","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.3390/e28050542","name":"Efficient Non-Interactive Discrete ReLU over CKKS Using Interpolation Look-Up Table.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e28050542","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/e28050542","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1038/s41598-026-53875-9","name":"DEF-CRYPT-Q: a quantum-enhanced hybrid encryption framework for privacy-preserving distributed defense communications.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-53875-9","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-53875-9","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1016/j.jbi.2026.104990","name":"Lattice-based privacy-preserving multimodal retrieval for healthcare.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.jbi.2026.104990","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.jbi.2026.104990","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.3390/s26092585","name":"SSDBFAN: Scalable and Secure Cluster-Based Data Aggregation with Blockchain for Flying Ad Hoc Networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26092585","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26092585","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/s26051720","name":"Federated Learning with Assured Privacy and Reputation-Driven Incentives for Internet of Vehicles.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26051720","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26051720","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1038/s41598-026-52245-9","name":"A unified post-quantum zero-trust architecture with AI-driven orchestration for secure healthcare fog networks.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-52245-9","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-52245-9","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-026-58982-1","name":"Hyperchaotic fractional-order image encryption with Knight's tour scrambling for satellite imagery.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-58982-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-58982-1","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.3390/s26113352","name":"A Reliable and Secure Cluster-Routing Framework for Drone-Assisted Disaster Management in Smart Cities.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26113352","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26113352","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.1080/01616412.2025.2555516","name":"Federated deep learning model for epilepsy seizure detection using electroencephalogram (EEG) signal.","source":"europepmc","abstract":"","url":"https://doi.org/10.1080/01616412.2025.2555516","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1080/01616412.2025.2555516","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-026-49853-w","name":"A semantic bit-plane based three-layer encryption framework for secure medical images.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-49853-w","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-49853-w","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-026-46939-3","name":"A dynamic policy-aware conditional proxy re-encryption system for fine-grained access control in IoT pub/sub systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-46939-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-46939-3","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.1038/s41598-026-38249-5","name":"Quantum optimized hierarchical chunk encoding with robust embedding for perceptual integrity and compression tolerant visual data protection.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-38249-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-38249-5","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3390/s26051636","name":"APVCPC: An Adaptive Predicted Value Computation and Pixel Classification Framework for Reversible Data Hiding in Encrypted Images.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26051636","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26051636","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3390/s26010313","name":"A Secure and Efficient Authentication Scheme with Privacy Protection for Internet of Medical Things.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26010313","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26010313","addedAt":"2026-08-31T06:41:45.052Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.3390/s26041285","name":"On Demand Secure Scalable Video Streaming for Both Human and Machine Applications.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26041285","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26041285","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.7759/cureus.103999","name":"Addressing Bias, Privacy, Security, and Patient Autonomy in Artificial Intelligence (AI)-Driven Healthcare: A Review of Current Guidelines.","source":"europepmc","abstract":"","url":"https://doi.org/10.7759/cureus.103999","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.7759/cureus.103999","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-025-31947-6","name":"Lightweight semantic compression visual cryptography for secure medical image transmission in IoT systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-31947-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-31947-6","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:46.002Z"},{"id":"doi:10.4230/lipics.itc.2026.4","name":"Fiat-Shamir for Bounded-Depth Adversaries","source":"datacite","abstract":"We study how to construct hash functions that can securely instantiate the Fiat-Shamir transformation against bounded-depth adversaries. The motivation is twofold. First, given the recent fruitful line of research of constructing cryptographic primitives against bounded-depth adversaries under worst-case complexity assumptions, and the rich applications of Fiat-Shamir, instantiating Fiat-Shamir hash functions against bounded-depth adversaries under worst-case complexity assumptions might lead to further applications (such as SNARG for P, showing the cryptographic hardness of PPAD, etc.) against bounded-depth adversaries. Second, we wonder whether it is possible to overcome the impossibility results of constructing Fiat-Shamir for arguments [Goldwasser, Kalai, FOCS '03] in the setting where the depth of the adversary is bounded, given that the known impossibility results (against p.p.t. adversaries) are contrived. Our main results give new insights for Fiat-Shamir against bounded-depth adversaries in both the positive and negative directions. On the positive side, for Fiat-Shamir for proofs with certain properties, we show that weak worst-case assumptions are enough for constructing explicit hash functions that give AC⁰[2]-soundness. In particular, we construct an AC⁰[2]-computable correlation-intractable hash family for constant-degree polynomials against AC⁰[2] adversaries, assuming ⊕L/poly ⊈ Sum̃_{n^{-c}}∘AC⁰[2] for some c > 0. This is incomparable to all currently-known constructions, which are typically useful for larger classes and against stronger adversaries, but based on arguably stronger assumptions. Our construction is inspired by the Fiat-Shamir hash function by Peikert and Shiehian [CRYPTO '19] and the fully-homomorphic encryption scheme against bounded-depth adversaries by Wang and Pan [EUROCRYPT '22]. On the negative side, we show Fiat-Shamir for arguments is still impossible to achieve against bounded-depth adversaries. In particular, - Assuming the existence of AC⁰[2]-computable CRHF against p.p.t. adversaries, for every poly-size hash function, there is a (p.p.t.-sound) interactive argument that is not AC⁰[2]-sound after applying Fiat-Shamir with this hash function. - Assuming the existence of AC⁰[2]-computable CRHF against AC⁰[2] adversaries, there is an AC⁰[2]-sound interactive argument such that for every hash function computable by AC⁰[2] circuits, the argument does not preserve AC⁰[2]-soundness when applying Fiat-Shamir with this hash function. This is a low-depth variant of Goldwasser and Kalai.","url":"https://doi.org/10.4230/lipics.itc.2026.4","authors":["Chen, Liyan","Chen, Yilei","Huang, Zikuan","Sun, Nuozhou","Yang, Tianqi","Zhang, Yiding"],"tags":["Fiat-Shamir","Correlation Intractability","Security and privacy → Hash functions and message authentication codes"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.4230/lipics.itc.2026.4","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2608.01148","name":"Latency-Optimal Adaptive Split Inference for Privacy-Preserving Cloud-Edge-End Collaboration","source":"datacite","abstract":"Internet of Things (IoT) end devices are increasingly expected to support privacy-sensitive batch inference, yet their limited computational resources often make full local execution of convolutional neural networks impractical. This paper presents a latency-optimal adaptive split inference framework for privacy-preserving cloud-edge-end collaboration. The end device acts as the trust anchor, executes the plaintext model prefix, encrypts the split activation using fully homomorphic encryption (FHE), and keeps the secret key locally, while the edge and cloud execute assigned model segments only on FHE ciphertexts. We formulate collaborative encrypted inference as a split-pair selection problem over an end-side split point and an edge-side termination point. The proposed planner jointly models plaintext prefix execution, encryption, communication, edge-side FHE execution, and cloud-side FHE completion, and supports both convolution-level and block-level split granularities. Experiments on CIFAR-10 and PathMNIST show that the proposed convolution-level collaborative scheme achieves amortized end-to-end speedups of approximately 12.9 times over full-cloud FHE and 3.9 times over the block-level alternative, while preserving the corresponding plaintext-model accuracy. Including modeled communication, the amortized latencies are 1033.279 s/sample on CIFAR-10 and 1023.429 s/sample on PathMNIST.","url":"https://doi.org/10.48550/arxiv.2608.01148","authors":["Li, Yi","Zhang, Peng","Au, Man Ho"],"tags":["Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","C.2.4; E.3; I.2.6"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.01148","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2604.14774","name":"Co-Design of Cryptographic Parameters and Delay-Aware Feedback Gain for Encrypted Control Systems","source":"datacite","abstract":"Encrypted control employs homomorphic encryption (HE) to protect both the computation and communication stages, making it a promising approach for secure networked control systems. Most existing results pre-design a controller in the plaintext domain and then implement it over encrypted data. However, this can be problematic because HE induces non-negligible communication and computation delays that typically increase with the security level, potentially degrading control performance and even destabilizing the closed-loop system. To address this issue, we propose a co-design framework for cryptographic parameters and delay-aware feedback gain. We first characterize an upper bound of the encryption-induced delay as a function of the cryptographic parameters. Then, for a given set of cryptographic parameters and feedback gain, we derive a sufficient condition under which the closed-loop system remains stable for all admissible delays, expressed as a finite set of linear matrix inequalities. This leads to a tractable outer-inner design procedure: the outer loop selects cryptographic parameters satisfying the desired security level, while the inner loop seeks a stabilizing delay-aware feedback gain.","url":"https://doi.org/10.48550/arxiv.2604.14774","authors":["Jang, Yeongjun"],"tags":["Systems and Control (eess.SY)","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.14774","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2606.27803","name":"Reliable Homomorphic Matching for Fuzzy Labeled PSI at Scale","source":"datacite","abstract":"Fuzzy Labeled Private Set Intersection (FLPSI) lets a receiver learn the labels of enrolled records similar to its query, and nothing else. Constructions based on a set-threshold reduction reach practical performance: a query matches a record when the two agree on a threshold number of components, and the private matching is delegated to an inner set-threshold kernel. We study its homomorphic form, which combines leveled-BFV homomorphic encryption (HE), a garbled circuit, and secret sharing to decide the match under encryption and release the record's label. We identify a composition gap in this kernel: efficiency is bought with a per-trial false-accept probability, but one query runs a trial for every record, so the error compounds with the database size into the kernel's realization soundness error (RSE), the rate at which it accepts a query the plaintext matcher would reject. The RSE is a reliability property of the cryptographic matching layer, not the matcher's accuracy, and a sound kernel must contribute zero or negligible RSE of its own. We formalize this as a composable security property, give a closed-form bound on the receiver's advantage, and close the gap with CSTPSI, a kernel that runs independent token rounds and raises the per-trial bound to a matching power. We prove CSTPSI secure in the semi-honest model. The bound sets the round count: two token rounds suffice for million-scale databases and three for billion-scale at the $10^{-6}$ engineering threshold. Our evaluation confirms this: at a million records the baseline kernel's RSE reaches 100% while CSTPSI holds it at 0 in every measured configuration. For large labels at small to moderate scale CSTPSI is more than 20x faster than the baseline, with up to 93% less communication, converging to the baseline only at million-scale. Our implementation, with a one-command reproducibility harness, is publicly available.","url":"https://doi.org/10.48550/arxiv.2606.27803","authors":["Uzun, Erkam"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.27803","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2606.28994","name":"Arbitrary Reduction of Validation Error for AI Decision Tests using Homomorphic AI and Repetition Codes","source":"datacite","abstract":"This paper presents new results and breakthrough obtained with the HbHAI techniques (Hash-based Homomorphic Artificial Intelligence) proposed in \\cite{filiol0,sepp}. HbHAI is based on a novel class of key-dependent hash functions that naturally preserve most similarity properties, most AI algorithms rely on. It enables to analyse and process data in its cryptographically secure form while using existing native AI algorithms without modification, with unprecedented performances compared to existing homomorphic encryption schemes and most notably compared to the same processing on corresponding plaintext data. Two major results have been obtained further. First we enable to reduce the compression rate up to a factor of 10 thus allowing to process massive datasets while reducing the computation time and the energy footprint in the same order. Second, we show how it is possible to arbitrarily reduce the final validation error of AI-based decision tests by using repetition error-correcting codes.","url":"https://doi.org/10.48550/arxiv.2606.28994","authors":["Filiol, Eric","Sepp, Jaagup"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.28994","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2606.26664","name":"TGHE: Template-based Graph Homomorphic Encryption for Privacy-Preserving GNN Inference in Edge-Cloud Systems","source":"datacite","abstract":"Existing homomorphic encryption (HE)-based GNN systems adopt a graph-centric paradigm that couples per-query cost to global graph size, limiting evaluations to at most ~20k nodes and making them incompatible with dynamic, large-scale financial graphs. We propose TGHE (Template-based Graph Homomorphic Encryption), an ego-centric framework that resolves this by exploiting a template phenomenon: local computation trees in transaction graphs converge into a small set of structural shapes. TGHE canonicalizes ego-graphs at the edge and packs structurally identical trees into shared CKKS ciphertexts for SIMD-parallel encrypted inference, with two long-tail optimizers (Approximate Template Fitting and Topology Collapse) ensuring full SIMD coverage. On DGraphFin (3.7M nodes, 4.3M edges), TGHE-Collapse achieves a 66.9x speedup over the sequential encrypted baseline with less than 0.002 AUC loss.","url":"https://doi.org/10.48550/arxiv.2606.26664","authors":["Le, Ngoc Bao Anh","Vu, Thai T.","Le, John","Cooper, Heath","Shen, Jun"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","I.2.6; E.3; C.2.4"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.26664","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.20664490","name":"Unified Resonance Field Geography Theory, or Prime Field Theory-as Proposed In Quantum Bridges May, 2025","source":"datacite","abstract":"The 137–143 Mass Gap: Consolidated Evidence Document (v7.8) Author: Timothy William Edgin, CISSPOrganization: Polyadmin Inc., Houston, TexasDate: June 22, 2026DOI: 10.5281/zenodo.20043510Zenodo: https://zenodo.org/records/20043510Repository: https://github.com/timtiminhous/ContinuityEngineBuild status: 28 modules, 192 unique verified statements, 176 theorems, 18 lemmas, 74 definitions, 29 structures, 0 sorry, 0 custom axioms — verified June 22, 2026 09:52 CDT v7.8 supersedes v7.7. Lean4 proof library expanded from 27 → 28 modules (186 → 192 unique statements, 170 → 176 theorems) with one new module: quark_confinement.lean. Primorial-Wieferich search extended downward from p=1 (previous searches started at p=3512), discovering 3 new exact wormholes: p=449 in P#4=210 (fills the P#4 gap noted in v7.7), p=1093 in P#1=2 (classical Wieferich prime), p=3511 in P#1=2 (classical Wieferich prime). Total exact wormholes: 13 across P#1 through P#7. 10D CUDA-Q VQE initial run: E₀(κ=0.3) = −4.59299717, k_phenom = 605.27 MeV/[E]. Full κ sweep with 3-layer ansatz in progress. CUDA kernel v3 deployed with 128-bit arithmetic for p up to 2^63. CRITICAL CORRECTION: v7.7 §6.4 stated \"No exact wormholes were found in bases P#1=2, P#4=210.\" This is now corrected: P#1=2 has two exact wormholes (the classical Wieferich primes 1093 and 3511), and P#4=210 has one (p=449). The previous search started at p=3512, missing these small primes. Abstract This document presents consolidated evidence for the 137–143 mass gap within the Unified Resonance Field Geography Theory (Prime Field Theory). Six independent evidence pillars: 192 machine-verified Lean4 statements across 28 modules — 176 theorems, 18 lemmas, 0 sorry, 0 custom axioms — establishing the arithmetic skeleton, particle-physics library covering all major Standard Model particles to PDG bounds, Primorial-Wieferich wormhole verification via ZMod(p²), multi-base resonance formalization, 10-dimensional manifold expansion, and cosmological confinement bounds. FP256 dual-channel dynamical experiments (RK8 Dormand-Prince + Yoshida8 W15 leapfrog at ~62-digit QD precision): deep sweep (164 ω points), fractal descent (7 KAM triple-lock stability islands), QD precision flip proof (FP64 hallucinates chaos where QD sees stability at ω=140.26306), 3D QD field kernel (Q1:LL consensus locks), four-force triple-integrator sweep (122 ω points, 14 orders of magnitude, five-regime sensitivity hierarchy). CUDA-Q VQE eigenvalue spectra: 8-qubit spectrum recovering all tested Standard Model particles sub-0.2% (J/ψ calibration k=765.833 MeV, E₀(κ=0)×k=2785.66 MeV at 0.2% from target). 10-qubit (10D) VQE initial run: E₀(κ=0.3) = −4.59299717 with 3.4% entanglement deepening below analytic bound. Full 10D κ sweep in progress. Reactive lattice ζ-sweep (826 configurations) across all 8 valid SEAL dimensions: 27 outlier configurations where the dual_hybrid enumeration cost exceeds 15% of total attack budget — 6 of these outliers have ω_char within the 137-143 mass gap band, directly linking lattice attack hardness to the mass gap structure. Primorial-Wieferich deep search (full range: p=1 to 200 billion): 13 exact wormhole primes discovered across primorial bases P#1 through P#7, with 4+ multi-base resonances. All GPU cross-validated (fq=0, ✓ MATCH). Five wormhole primes formally verified in Lean4 via ZMod(p²) decidable arithmetic. Recovery of the two classical Wieferich primes (1093, 3511) as P#1=2 wormholes provides independent consistency validation. Einstein Toolkit BSSN Minkowski sanity check (Strong); RK4 omega sweep with 5/5 correct chaos/lock predictions (Partial — precision-limited, consistent with dual-channel comparison). FP256 dual-channel ET rebuild planned. 1. Triple-Check Status (June 22, 2026) Evidence Pillar Source Verified Status Lean4: 28 modules, 0 sorry June 22 build output June 22, 2026 09:52 CDT ✅ PASS FP256 QD: q[0]–q[3] all active AUDIT_CHANGELOG_FP256_QD.md April 2026 ✅ PASS CUDA-Q VQE 8D: sub-0.2% all","url":"https://doi.org/10.5281/zenodo.20664490","authors":["Edgin, Timothy"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20664490","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2605.26903","name":"Practical Anonymous Two-Party Gradient Boosting Decision Tree","source":"datacite","abstract":"Structured data is well handled by gradient-boosted decision trees (GBDT), which are usually trained on vertically partitioned features across mutually distrustful parties. High speed and interpretability make GBDTs popular in finance and healthcare, where neural networks may fall short. Enabling secure computation for GBDTs poses unique challenges, requiring secure record alignment for comparison. Relying on private set intersection (PSI) is a de facto approach. Mistaking PSI for a safety measure actually exposes which record identifiers (IDs) are shared between the datasets. Although circuit-PSI could help, it is costly for generic uses. New ideas are needed to efficiently train in a \"dark forest\". Aiming to hide the IDs, we initiate the study of anonymous GBDT training on split data held by two parties. Dual circuit-PSI in our design lets the parties alternate as receiver to run pick-then-sum over local features. Via oblivious programmable pseudorandom functions, we propagate circuit-PSI outputs as shared state across runs. Avoiding universal alignment, we resolve the neglected dilemma that ID hiding incurs a cost that scales with domain size. Next, we halve the cost of ciphertext packing used to convert single-instruction multiple-data homomorphic encryption from (ring) learning with errors in prior secure GBDT (Usenix Security' 23) and related secure machine-learning computations. Comparative experiments show our protocol remains competitive with leaky approaches in efficiency. Enabling ID-hiding aggregation, our techniques can extend to other vertically partitioned analytics.","url":"https://doi.org/10.48550/arxiv.2605.26903","authors":["Huang, Chenyu","Zhang, Fan","Du, Minxin","Chow, Sherman S. M.","Chen, Huangxun","Rao, Huaming","Huang, Danqing","Qian, Bo","Chen, Peng"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2605.26903","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2606.16359","name":"FEnc$^2$: Unifying Data Packing for Efficient Private Inference via Convolution and Architecture-Aware Fragment Encoding","source":"datacite","abstract":"Fully Homomorphic Encryption (FHE) enables privacy-preserving machine learning but incurs extreme computational and memory overhead. These costs come not only from expensive low-level primitives, including Number Theoretic Transform (NTT), rotation, and key-switching, but also from inefficient ciphertext packing at the application level. Existing packing strategies typically preserve either neighboring data elements or feature grouping, but not both, leading to wasted ciphertext slots, excessive rotations, and inflated ciphertext counts. We propose FEnc2, a unified and principled fragment-based encoding framework for CKKS-based private convolutional neural network inference. FEnc2 optimizes slot utilization, rotation complexity, and ciphertext density through two components: 1)Conv-aware Encoding, which analytically selects an optimal fragment size to decouple spatial dependencies and jointly minimize inner-outer rotations across layers, and 2)Arch-aware Ct Compression, which restores ciphertext density after feature- or channel-reduction layers. Together, these transformations reshape encrypted workload structure and reduce homomorphic operations by one to two orders of magnitude. With full memory capacity utilized, i.e., at maximum batch size, FEnc2 achieves end-to-end latency speedups over the state-of-the-art Orion of up to 228.83x on GPU and 226.06x on CPU for LeNet on MNIST, and up to 4.55x on GPU and 9.43x on CPU for MobileNet on ImageNet. FEnc2 is hardware-agnostic yet architecturally transformative: by optimizing encrypted tensor layout before execution, it reduces ciphertext count and workload pressure on hardware, complementing primitive-level optimizations such as NTT and keyswitch accelerators. These results show that application-level data layout is a first-order architectural design dimension for encrypted inference and an important enabler for next-generation FHE systems.","url":"https://doi.org/10.48550/arxiv.2606.16359","authors":["Ran, Ran","Gong, Zhaoting","Xu, Nuo","Xu, Yuanchao","Yao, Fan","Wen, Wujie"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2606.16359","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.20631517","name":"ga_engine: Clifford FHE and Geometric Neural Networks","source":"datacite","abstract":"A Rust geometric algebra engine featuring Clifford FHE — an RLWE/CKKS-based fully homomorphic encryption scheme with native support for Clifford algebra operations — and geometric neural networks that operate directly on encrypted multivectors for privacy-preserving geometric deep learning. Software accompanying the paper \"Merits of Geometric Algebra Applied to Cryptography and Machine Learning\" (Philosophical Transactions of the Royal Society A, 2026).","url":"https://doi.org/10.5281/zenodo.20631517","authors":["Silva, David William"],"tags":["geometric algebra","Clifford algebra","fully homomorphic encryption","CKKS","privacy-preserving machine learning","geometric deep learning","3D point cloud classification"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20631517","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.20631516","name":"ga_engine: Clifford FHE and Geometric Neural Networks","source":"datacite","abstract":"A Rust geometric algebra engine featuring Clifford FHE — an RLWE/CKKS-based fully homomorphic encryption scheme with native support for Clifford algebra operations — and geometric neural networks that operate directly on encrypted multivectors for privacy-preserving geometric deep learning. Software accompanying the paper \"Merits of Geometric Algebra Applied to Cryptography and Machine Learning\" (Philosophical Transactions of the Royal Society A, 2026).","url":"https://doi.org/10.5281/zenodo.20631516","authors":["Silva, David William"],"tags":["geometric algebra","Clifford algebra","fully homomorphic encryption","CKKS","privacy-preserving machine learning","geometric deep learning","3D point cloud classification"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20631516","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.20466389","name":"GLuckDeity/Algorapolis: v0.1.0 — Genesis","source":"datacite","abstract":"Algorapolis v0.1.0 — Genesis First public release of the Algorapolis Civilization Architecture Framework. What's Included Complete specification document (Parts I–XIII, Sections 1–77) Ten-layer civilization stack architecture Sovereign Logic Engine design with constitutional constraints National Digital Twin specification Governance Sandbox architecture Emotional Intelligence Architecture Privacy Sovereignty framework (ZKPs, differential privacy, homomorphic encryption) African and Tanzanian context throughout Adversarial resilience patches (Part XI) Tanzania pilot specification Transition Architecture Theory Research studies (13) and case studies (6) Guides for contributors and practitioners JSON Schema and OpenAPI specifications Citation If you reference this architectural framework, please cite the permanent record: Macha, G. J. (2026). Algorapolis: A Civilization Architecture Framework. Zenodo. https://doi.org/10.5281/zenodo.20466164 License Code: MIT License Content: CC-BY-SA 4.0","url":"https://doi.org/10.5281/zenodo.20466389","authors":["GLuckDeity"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20466389","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.20466388","name":"GLuckDeity/Algorapolis: v0.1.0 — Genesis","source":"datacite","abstract":"Algorapolis v0.1.0 — Genesis First public release of the Algorapolis Civilization Architecture Framework. What's Included Complete specification document (Parts I–XIII, Sections 1–77) Ten-layer civilization stack architecture Sovereign Logic Engine design with constitutional constraints National Digital Twin specification Governance Sandbox architecture Emotional Intelligence Architecture Privacy Sovereignty framework (ZKPs, differential privacy, homomorphic encryption) African and Tanzanian context throughout Adversarial resilience patches (Part XI) Tanzania pilot specification Transition Architecture Theory Research studies (13) and case studies (6) Guides for contributors and practitioners JSON Schema and OpenAPI specifications Citation If you reference this architectural framework, please cite the permanent record: Macha, G. J. (2026). Algorapolis: A Civilization Architecture Framework. Zenodo. https://doi.org/10.5281/zenodo.20466164 License Code: MIT License Content: CC-BY-SA 4.0","url":"https://doi.org/10.5281/zenodo.20466388","authors":["GLuckDeity"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20466388","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.20324081","name":"TEMPORAL ROTATION SECURITY PROTOCOL (TRSP) Physics-First Cryptographic Architecture: Time as the Fundamental Security Parameter","source":"datacite","abstract":"ABSTRACT: Temporal Rotation Security Protocol (TRSP) v3 — Physics-First Cryptographic Architecture: Time as the Fundamental Quantum-Resistant Security Parameter Concept in development since at least November 2019. First complete public documentation: 2026. Version 3 adds Part 4c (Hybrid Dynamic CRATON Quorum / HDCQ) and Part 4d (CRATON Hardening Layer / CHL). This concept documents the Temporal Rotation Security Protocol — a cryptographic architecture in which security derives not from the mathematical complexity of encryption keys but from the physical irreversibility of time. Every cryptographic system currently in use — RSA, AES, elliptic curve — rests on a single foundational assumption: that breaking the encryption requires more computational time than any adversary possesses. Quantum computing, through Shor's algorithm and Grover's algorithm, is systematically dismantling this assumption. TRSP replaces it with a physically permanent alternative: a key that no longer exists cannot be recovered by any computation, quantum or classical, regardless of computational resources or future mathematical advances. The protocol operates through simultaneous multi-layer key rotation at three independent frequencies. Layer 1 (session layer) rotates every 10–100 milliseconds using hardware entropy from physical noise sources — thermal variance, clock jitter, electromagnetic fingerprint. Layer 2 (identity layer) rotates every 1–10 seconds, anchored to physically unique device characteristics that cannot be spoofed. Layer 3 (CRATON foundational layer) generates a cryptographic commitment from the unique physical state of both communicating devices at session initialisation — used once and permanently destroyed, unrepeatable at any other point in time or on any other device. Quantum resistance is structural rather than parametric. Shor's algorithm requires minutes to hours to factor key-scale integers; Layer 1 rotation windows of 10–100 milliseconds ensure the target key no longer exists when any quantum computation converges. Grover's algorithm provides quadratic speedup against static keys; against rotating keys it provides no advantage because the search target is destroyed before the search completes. As quantum hardware advances and computation accelerates, rotation windows decrease proportionally — a software parameter adjustment costing microseconds against a hardware investment requiring years. The defender's adaptation is permanently faster than the attacker's. The CRATON foundational trust layer — positioned between hardware and operating system — is not a stored value. It is a physical event: a one-time measurement of device state that generates a cryptographic commitment and is immediately destroyed. It cannot be forged by a compromised operating system, replicated on any other device, or reconstructed from any stored record. Root of trust through physical irreversibility. The inverse proposition — what breaking TRSP would prove — is documented as the second foundational contribution of this concept. A successful attack against a correctly implemented TRSP system would constitute experimental proof of one of the following physical propositions: that quantum information is globally conserved and locally accessible confirming the holographic principle; that temporal irreversibility is not absolute at quantum scale; that parallel quantum branches are accessible through computation confirming the Everett many-worlds interpretation; or that Landauer's principle is violated at computational scale. Any of these would represent the most significant scientific discovery in recorded history. TRSP is therefore simultaneously a security protocol and a physics experiment. Its security parameter is the boundary of known physical law. TRSP is an open invitation to physics. Extension 1: Spatial-Temporal Triangulation & Network Latency Mitigation A critical challenge in millisecond-scale cryptographic rotation (Δt = 10–100 ms) across standard ","url":"https://doi.org/10.5281/zenodo.20324081","authors":["Mehmetaj, Ilir"],"tags":["temporal rotation cryptography","physics-first security architecture","quantum-resistant key rotation","time-based cryptographic protocol","CRATON foundational trust layer","harvest-now-decrypt-later resistance","hardware entropy key generation","post-quantum security physics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20324081","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2512.08010","name":"Sensor Attack Detection Method for Encrypted State Observers","source":"datacite","abstract":"This paper proposes an encrypted state observer that is capable of detecting sensor attacks without decryption. We first design a state observer that operates over a finite field of integers with the modular arithmetic. The observer generates a residue signal that indicates the presence of attacks under sparse attack and sensing redundancy conditions. Then, we develop a homomorphic encryption scheme that enables the observer to operate over encrypted data while automatically disclosing the residue signal. Unlike our previous work restricted to single-input single-output systems, the proposed scheme is applicable to general multi-input multi-output systems. Given that the disclosed residue signal remains below a prescribed threshold, the full state can be recovered as an encrypted message.","url":"https://doi.org/10.48550/arxiv.2512.08010","authors":["Jang, Yeongjun","Lee, Sangwon","Kim, Junsoo"],"tags":["Systems and Control (eess.SY)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2512.08010","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2605.14230","name":"On the (non-)resilience of encrypted controllers to covert attacks","source":"datacite","abstract":"The security of networked control systems (NCS) is receiving increasing attention from both cyber-security and system-theoretic perspectives. The former focuses on classical IT security goals such as confidentiality, integrity, and availability of process data, while the latter investigates tailored attacks (and detection schemes), including covert and zero-dynamics attacks. Confidentiality in control systems can, for instance, be achieved by securely outsourcing the evaluation of the controller to third-party platforms, such as cloud services. The underlying technology enabling such secure computation often is homomorphic encryption (HE). Recent works in encrypted control have proposed modifications to underlying HE schemes to achieve not only confidentiality but also resilience to certain types of integrity attacks. While extensions in this direction are desirable in principle, we show that the integrity problem in encrypted control cannot be solved by public-key HE schemes alone due to their inherent malleability. In other words, the same homomorphisms that enable encrypted control in the first place can be leveraged not only constructively but also destructively. More precisely, we demonstrate that NCS are vulnerable to covert attacks, even when encrypted control is employed. Remarkably, this remains possible without knowledge of an unencrypted model. Yet, resilience to such attacks can still be achieved through complementary techniques. We present an approach based on verifiable computation that integrates with modern homomorphic cryptosystems and is asymptotically secure while incurring no communication overhead.","url":"https://doi.org/10.48550/arxiv.2605.14230","authors":["Binfet, Philipp","Adamek, Janis","Darup, Moritz Schulze"],"tags":["Cryptography and Security (cs.CR)","Systems and Control (eess.SY)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2605.14230","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2605.13708","name":"DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning","source":"datacite","abstract":"Federated learning enables collaborative model training across distributed clients, yet vanilla FL exposes client updates to the central server. Secure-aggregation schemes protect privacy against an honest-but-curious server, but existing approaches often suffer from many communication rounds, heavy public-key operations, or difficulty handling client dropouts. Recent methods like One-Shot Private Aggregation (OPA) cut rounds to a single server interaction per FL iteration, yet they impose substantial cryptographic and computational overhead on both server and clients. We propose a new protocol called DisAgg that leverages a small committee of clients called Aggregators to perform the aggregation itself: each client secret-shares its update vector to Aggregators, which locally compute partial sums and return only aggregated shares for server-side reconstruction. This design eliminates local masking and expensive homomorphic encryption, reducing endpoint computation while preserving privacy against a curious server and a limited fraction of colluding clients. By leveraging optimal trade-offs between communication and computation costs, DisAgg processes 100k-dimensional update vectors from 100k 5G clients with a 4.6x speedup compared to OPA, the previous best protocol.","url":"https://doi.org/10.48550/arxiv.2605.13708","authors":["Mehmood, Haaris","Tatsis, Giorgos","Alexopoulos, Dimitrios","Saravanan, Karthikeyan","Xu, Jie","Drosou, Anastasios","Ozay, Mete"],"tags":["Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2605.13708","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2604.23245","name":"Training Machine Learning Models on Encrypted Data: A Privacy-Preserving Framework using Homomorphic Encryption","source":"datacite","abstract":"The use of Machine Learning (ML) for data-driven decision-making often relies on access to sensitive datasets, which introduces privacy challenges. Traditional encryption methods protect data at rest or in transit but fail to secure it during processing, exposing it to unauthorized access. Homomorphic encryption emerges as a transformative solution, enabling computations on encrypted data without decryption, thus preserving confidentiality throughout the ML pipeline. This paper addresses the challenge of training ML models on encrypted data while maintaining accuracy and efficiency by proposing a proof-of-concept for a privacy-preserving framework that leverages Cheon-Kim-Kim-Song (CKKS) for approximate real-number arithmetic. Also, it demonstrates the feasibility of training K-Nearest Neighbors (KNN) and linear regression models on encrypted data, and evaluates encrypted inference for a basic Multilayer Perceptron (MLP) architecture. Experimental results show that models trained under Homomorphic encryption achieve performance metrics comparable to plaintext-trained models, validating the approach. However, challenges such as computational overhead, noise management, and limited support for non-polynomial operations persist. This work lays the groundwork for broader adoption of privacy-preserving ML in real-world applications, balancing security with computational feasibility.","url":"https://doi.org/10.48550/arxiv.2604.23245","authors":["Marques, Alexandre","Sá, Beatriz","Botelho, Rui","Pinto, Pedro"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences","E.3"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.23245","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2604.21381","name":"Privacy-Preserving Distributed Stochastic Optimization with Homomorphic Encryption and Heterogeneous Stepsizes","source":"datacite","abstract":"Distributed stochastic optimization enables multi-agent collaboration in applications such as distributed learning and sensor networks, but also raises critical privacy concerns due to the involvement of sensitive data. While existing privacy-preserving approaches often face limitations in balancing accuracy with efficiency, we propose a novel distributed stochastic gradient descent algorithm that integrates Paillier homomorphic encryption with heterogeneous and time-varying random stepsizes. The proposed algorithm provides inherent privacy protection against both internal honest-but-curious agents and external eavesdroppers, without relying on any trusted neighbors. Furthermore, we incorporate an attenuation factor to effectively mitigate quantization error induced by the encryption process, ensuring almost sure convergence to the optimal solution while maintaining privacy preservation. Numerical simulations demonstrate the effectiveness and efficiency of the proposed approach.","url":"https://doi.org/10.48550/arxiv.2604.21381","authors":["Zhou, Haoqiang","Chen, Chi","Zhi, Yongfeng","Gao, Huan"],"tags":["Systems and Control (eess.SY)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.21381","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2604.19890","name":"Efficient Arithmetic-and-Comparison Homomorphic Encryption with Space Switching","source":"datacite","abstract":"Fully homomorphic encryption (FHE) enables computation on encrypted data without decryption, making it central to privacy-preserving applications. However, no existing scheme efficiently supports both arithmetic and comparison operations in a unified framework. Prior approaches such as scheme switching and polynomial approximation face serious limitations: switching incurs prohibitive overhead for large inputs, while approximation methods introduce errors near critical points, restricting use in accuracy-sensitive tasks. We propose space switching method to integrate arithmetic and comparison computation seamlessly within FV-style schemes. Our approach identifies that the two types of operations require different plaintext spaces and introduces two procedures: a reduction step to transition from the number space $\\mathbb{Z}_{p^r}$ to the digit space $\\mathbb{Z}_{p}$, and a modulus-raising step to map results back to $\\mathbb{Z}_{p^r}$. This design enables continuous evaluation of arithmetic and comparison within the same scheme. Experiments show that our method achieves up to $17\\times$ faster performance than scheme switching and $15\\times$ faster than direct comparison on database workloads, demonstrating its practicality for real-world privacy-preserving computation. Code and artifacts are available at https://github.com/UCF-Lou-Lab-PET/Universal-BGV.","url":"https://doi.org/10.48550/arxiv.2604.19890","authors":["Wahyudi, Erwin Eko","Solihin, Yan","Lou, Qian"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.19890","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.18307055","name":"Deterministic Harmonic Access (DHA): The Unified Interface for Recursive Computational Stability and Infinite State Retrieval","source":"datacite","abstract":"Deterministic Harmonic Access (DHA): The Unified Interface for Recursive Computational Stability and Infinite State Retrieval 1. Introduction: The End of Storage and the Rise of Location 1.1 The Crisis of the Von Neumann Bottleneck The history of computing has been defined by a singular, persistent constraint: the physical limitation of information storage. From the earliest magnetic core memories to modern solid-state arrays, the fundamental paradigm has remained unchanged. Data is treated as a physical object—a sequence of magnetized grains or trapped electrons—that must be generated, moved, written, and maintained. This \"containment model\" of information has led to the Von Neumann bottleneck, where the speed of processing vastly outstrips the speed of retrieval, and the energy cost of maintaining data entropy threatens the scalability of planetary computation. As we transition into an era of exascale computing and the burgeoning Internet of Things (IoT), the volume of data is expanding at a rate that physical storage media cannot sustain. The \"Crisis of Storage\" is not merely a question of capacity; it is a question of fundamental physics. Storing a bit of information requires energy to combat thermal fluctuations and entropy. As we approach the limits of atomic storage, a radical paradigm shift is required. Deterministic Harmonic Access (DHA) represents this shift. It transitions the industry from a paradigm of storage to a paradigm of location. DHA posits that all finite information already exists within the infinite, non-repeating expansions of irrational constants (such as $\\pi$, $e$, or $\\sqrt{2}$). Therefore, the act of \"saving\" a file is not a write operation, but a search operation. The file is not created; its coordinates are discovered. DHA serves as the concrete, existing interface that bridges the gap between the theoretical \"Library of Babel\" contained within these constants and the practical, high-speed requirements of modern computing. 1.2 The DHA Proposition: Zero-Data Computing The core proposition of DHA is \"Zero-Data\" computing. In this architecture, a user does not store a 4-gigabyte movie file. Instead, they store a \"DHA Pointer\"—a tiny packet of metadata containing a Constant Identifier (CID), a Starting Index ($d$), a Length ($L$), and a Diffusion Key ($K$). When the user wishes to view the movie, the DHA interface utilizes the Bailey-Borwein-Plouffe (BBP) algorithm to extracting the hexadecimal data directly from the mathematical fabric of the universe, effectively streaming the data from the constant itself. This effectively offers infinite compression density. The storage requirement for any file, regardless of size, collapses to the size of its pointer. While the computational cost of retrieval is non-zero, the DHA architecture mitigates this through specific accelerants: Harmonic Diffusion: Using Maximum Distance Separable (MDS) matrices to map human-readable data (low entropy) onto the uniform distribution of the irrational constant (high entropy).1 Recursive Stability: Employing Samson’s Law and the Nexus Harmonic Framework to stabilize the search for these coordinates, treating the search process as a trajectory tracking problem in control theory.3 Parallel Acceleration: Utilizing Residue Number Systems (RNS) and the Chinese Remainder Theorem (CRT) to perform the massive arbitrary-precision arithmetic required for deep indexing at hardware speeds.5 This report provides the definitive technical breakdown of these mechanisms. It serves as an exhaustive guide to the mathematics, hardware architecture, and control theory that make DHA a reality. 2. The Mathematical Engine: Spigot Algorithms and the BBP Interface 2.1 The Historical Context of Digit Extraction For millennia, the calculation of $\\pi$ was a cumulative process. To know the 100th digit, one had to calculate the preceding 99. This dependency made $\\pi$ unsuitable for random access storage. The breakthrough came in 1995 with the discovery","url":"https://doi.org/10.5281/zenodo.18307055","authors":["Kulik, Dean"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18307055","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18136121","name":"Deterministic Harmonic Access (DHA): The Unified Interface for Recursive Computational Stability and Infinite State Retrieval","source":"datacite","abstract":"Deterministic Harmonic Access (DHA): The Unified Interface for Recursive Computational Stability and Infinite State Retrieval 1. Introduction: The End of Storage and the Rise of Location 1.1 The Crisis of the Von Neumann Bottleneck The history of computing has been defined by a singular, persistent constraint: the physical limitation of information storage. From the earliest magnetic core memories to modern solid-state arrays, the fundamental paradigm has remained unchanged. Data is treated as a physical object—a sequence of magnetized grains or trapped electrons—that must be generated, moved, written, and maintained. This \"containment model\" of information has led to the Von Neumann bottleneck, where the speed of processing vastly outstrips the speed of retrieval, and the energy cost of maintaining data entropy threatens the scalability of planetary computation. As we transition into an era of exascale computing and the burgeoning Internet of Things (IoT), the volume of data is expanding at a rate that physical storage media cannot sustain. The \"Crisis of Storage\" is not merely a question of capacity; it is a question of fundamental physics. Storing a bit of information requires energy to combat thermal fluctuations and entropy. As we approach the limits of atomic storage, a radical paradigm shift is required. Deterministic Harmonic Access (DHA) represents this shift. It transitions the industry from a paradigm of storage to a paradigm of location. DHA posits that all finite information already exists within the infinite, non-repeating expansions of irrational constants (such as $\\pi$, $e$, or $\\sqrt{2}$). Therefore, the act of \"saving\" a file is not a write operation, but a search operation. The file is not created; its coordinates are discovered. DHA serves as the concrete, existing interface that bridges the gap between the theoretical \"Library of Babel\" contained within these constants and the practical, high-speed requirements of modern computing. 1.2 The DHA Proposition: Zero-Data Computing The core proposition of DHA is \"Zero-Data\" computing. In this architecture, a user does not store a 4-gigabyte movie file. Instead, they store a \"DHA Pointer\"—a tiny packet of metadata containing a Constant Identifier (CID), a Starting Index ($d$), a Length ($L$), and a Diffusion Key ($K$). When the user wishes to view the movie, the DHA interface utilizes the Bailey-Borwein-Plouffe (BBP) algorithm to extracting the hexadecimal data directly from the mathematical fabric of the universe, effectively streaming the data from the constant itself. This effectively offers infinite compression density. The storage requirement for any file, regardless of size, collapses to the size of its pointer. While the computational cost of retrieval is non-zero, the DHA architecture mitigates this through specific accelerants: Harmonic Diffusion: Using Maximum Distance Separable (MDS) matrices to map human-readable data (low entropy) onto the uniform distribution of the irrational constant (high entropy).1 Recursive Stability: Employing Samson’s Law and the Nexus Harmonic Framework to stabilize the search for these coordinates, treating the search process as a trajectory tracking problem in control theory.3 Parallel Acceleration: Utilizing Residue Number Systems (RNS) and the Chinese Remainder Theorem (CRT) to perform the massive arbitrary-precision arithmetic required for deep indexing at hardware speeds.5 This report provides the definitive technical breakdown of these mechanisms. It serves as an exhaustive guide to the mathematics, hardware architecture, and control theory that make DHA a reality. 2. The Mathematical Engine: Spigot Algorithms and the BBP Interface 2.1 The Historical Context of Digit Extraction For millennia, the calculation of $\\pi$ was a cumulative process. To know the 100th digit, one had to calculate the preceding 99. This dependency made $\\pi$ unsuitable for random access storage. The breakthrough came in 1995 with the discovery","url":"https://doi.org/10.5281/zenodo.18136121","authors":["Kulik, Dean"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18136121","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2604.09541","name":"Trans-RAG: Query-Centric Vector Transformation for Secure Cross-Organizational Retrieval","source":"datacite","abstract":"Retrieval Augmented Generation (RAG) systems deployed across organizational boundaries face fundamental tensions between security, accuracy, and efficiency. Current encryption methods expose plaintext during decryption, while federated architectures prevent resource integration and incur substantial overhead. We introduce Trans-RAG, implementing a novel vector space language paradigm where each organization's knowledge exists in a mathematically isolated semantic space. At the core lies vector2Trans, a multi-stage transformation technique that enables queries to dynamically \"speak\" each organization's vector space \"language\" through query-centric transformations, eliminating decryption overhead while maintaining native retrieval efficiency. Security evaluations demonstrate near-orthogonal vector spaces with 89.90° angular separation and 99.81% isolation rates. Experiments across 8 retrievers, 3 datasets, and 3 LLMs show minimal accuracy degradation (3.5% decrease in nDCG@10) and significant efficiency improvements over homomorphic encryption.","url":"https://doi.org/10.48550/arxiv.2604.09541","authors":["Liu, Yu","Peng, Kun","Zhang, Wenxiao","Yuan, Fangfang","Cao, Cong","Lu, Wenxuan","Liu, Yanbing"],"tags":["Cryptography and Security (cs.CR)","Information Retrieval (cs.IR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.09541","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2501.07047","name":"Leveraging ASIC AI Chips for Homomorphic Encryption","source":"datacite","abstract":"Homomorphic Encryption (HE) provides strong data privacy for cloud services but at the cost of prohibitive computational overhead. While GPUs have emerged as a practical platform for accelerating HE, there remains an order-of-magnitude energy-efficiency gap compared to specialized (but expensive) HE ASICs. This paper explores an alternate direction: leveraging existing AI accelerators, like Google's TPUs with coarse-grained compute and memory architectures, to offer a path toward ASIC-level energy efficiency for HE. However, this architectural paradigm creates a fundamental mismatch with SoTA HE algorithms designed for GPUs. These algorithms rely heavily on: (1) high-precision (32-bit) integer arithmetic to now run on a TPU's low-throughput vector unit, leaving its high-throughput low-precision (8-bit) matrix engine (MXU) idle, and (2) fine-grained data permutations that are inefficient on the TPU's coarse-grained memory subsystem. Consequently, porting GPU-optimized HE libraries to TPUs results in severe resource under-utilization and performance degradation. To tackle above challenges, we introduce CROSS, a compiler framework that systematically transforms HE workloads to align with the TPU's architecture. CROSS makes two key contributions: (1) Basis-Aligned Transformation (BAT), a novel technique that converts high-precision modular arithmetic into dense, low-precision (INT8) matrix multiplications, unlocking and improving the utilization of TPU's MXU for HE, and (2) Memory-Aligned Transformation (MAT), which eliminates costly runtime data reordering by embedding reordering into compute kernels through offline parameter transformation. CROSS (TPU v6e) achieves higher throughput per watt on NTT and HE operators than WarpDrive, FIDESlib, FAB, HEAP, and Cheddar, establishing AI ASIC as the SotA efficient platform for HE operators. Code: https://github.com/EfficientPPML/CROSS","url":"https://doi.org/10.48550/arxiv.2501.07047","authors":["Tong, Jianming","Huang, Tianhao","Dang, Jingtian","de Castro, Leo","Itagi, Anirudh","Golder, Anupam","Ali, Asra","Kun, Jeremy","Jiang, Jevin","Arvind","Suh, G. Edward","Krishna, Tushar"],"tags":["Cryptography and Security (cs.CR)","Hardware Architecture (cs.AR)","Computation and Language (cs.CL)","Programming Languages (cs.PL)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.07047","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2604.03425","name":"AEGIS: Scaling Long-Sequence Homomorphic Encrypted Transformer Inference via Hybrid Parallelism on Multi-GPU Systems","source":"datacite","abstract":"Fully Homomorphic Encryption (FHE) enables privacy-preserving Transformer inference, but long-sequence encrypted Transformers quickly exceed single-GPU memory capacity because encoded weights are already large and encrypted activations grow rapidly with sequence length. Multi-GPU execution therefore becomes unavoidable, yet scaling remains challenging because communication is jointly induced by application-level aggregation and encryption-level RNS coupling. Existing approaches either synchronize between devices frequently or replicate encrypted tensors across devices, leading to excessive communication and latency. We present AEGIS, an Application-Encryption Guided Inference System for scalable long-sequence encrypted Transformer inference on multi-GPU platforms. AEGIS derives device placement from ciphertext dependencies jointly induced by Transformer dataflow and CKKS polynomial coupling, co-locating modulus-coherent and token-coherent data so that communication is introduced only when application dependencies require it, while reordering polynomial operators to overlap the remaining collectives with computation. On 2048-token inputs, AEGIS reduces inter-GPU communication by up to 57.9% in feed-forward networks and 81.3% in self-attention versus prior state-of-the-art designs. On four GPUs, it achieves up to 96.62% scaling efficiency, 3.86x end-to-end speedup, and 69.1% per-device memory reduction. These results establish coordinated application-encryption parallelism as a practical foundation for scalable homomorphic Transformer inference.","url":"https://doi.org/10.48550/arxiv.2604.03425","authors":["Gong, Zhaoting","Ran, Ran","Yao, Fan","Wen, Wujie"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2604.03425","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2603.26417","name":"Towards Privacy-Preserving Federated Learning using Hybrid Homomorphic Encryption","source":"datacite","abstract":"Federated Learning (FL) enables collaborative training while keeping sensitive data on clients' devices, but local model updates can still leak private information. Hybrid Homomorphic Encryption (HHE) has recently been applied to FL to mitigate client overhead while preserving privacy. However, existing HHE-FL systems rely on a single homomorphic key pair shared across all clients, which forces them to assume an unrealistically weak threat model: if a client misbehaves or intercepts another's traffic, private updates can be exposed. We eliminate this weakness by integrating two alternative key protection mechanisms into the HHE-FL workflow. The first is masking, where client keys are blinded before homomorphic encryption and later unblinded homomorphically by the server. The second is RSA encapsulation, where homomorphically encrypted keys are additionally wrapped under the server's RSA public key. These countermeasures prevent key misuse by other clients and extend HHE-FL security to adversarial settings with malicious participants. We implement both approaches on top of the Flower framework using the PASTA/BFV HHE scheme and evaluate them on the MNIST dataset with 12 clients. Results show that both mechanisms preserve model accuracy while adding minimal overhead: masking incurs negligible cost, and RSA encapsulation introduces only modest runtime and communication overhead.","url":"https://doi.org/10.48550/arxiv.2603.26417","authors":["Costa, Ivan","Correia, Pedro","Amorim, Ivone","Maia, Eva","Praça, Isabel"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.26417","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2603.20504","name":"Meeting in the Middle: A Co-Design Paradigm for FHE and AI Inference","source":"datacite","abstract":"Modern cloud inference creates a two sided privacy problem where users reveal sensitive inputs to providers, while providers must execute proprietary model weights inside potentially leaky execution environments. Fully homomorphic encryption (FHE) offers cryptographic guarantees but remains prohibitively expensive for modern architectures. We argue that progress requires co-design where specializing FHE schemes/compilers for the static structure of inference circuits, while simultaneously constraining inference architectures to reduce dominant homomorphic cost drivers. We outline a meet in the middle agenda and concrete optimization targets on both axes.","url":"https://doi.org/10.48550/arxiv.2603.20504","authors":["Magri, Bernardo","Marsh, Benjamin","Gebheim, Paul"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.20504","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.19100682","name":"BrainPredict Enterprise AI Platform — IP Deposit v3 — March 18, 2026 — Platform #19 Automation + Living Organism Autonomous Loop + Intelligence Bus v7","source":"datacite","abstract":"Intellectual property deposit v3 — BrainPredict enterprise AI platform v2026.3. Covers all changes since March 11, 2026 (23 commits, 329 files changed). Core inventions in this deposit: 1. BrainPredict™ Living Organism — ecosystem-wide autonomous self-healing AI (6 phases: NetworkScanner, DataQualityAuditor, ProcessBaseline, IssueIdentifier, RemediationEngine, AutonomousRemediator). Subscription-scoped, 100% on-premise, zero external API calls, self-funding upgrade loop via Dilithium-3 signed automatic Purchase Orders to sales@brainpredict.ai. Components: AutonomousRemediator, SolutionTester, OTTOMessengerNotifier, PurchaseOrderGenerator. 2. BrainPredict™ Automation Platform #19 — industrial AI OS. OPC-UA + Modbus TCP adapters for 38 asset types, 10ms polling, triple-gate safety (IEC 62443 / IEC 61511 / ISO 13849), SetpointRecommender with Dilithium-3 signed audit trail, 28 AI models, Industrial Platform Bridge routing to all 19 platforms. 3. Intelligence Bus v5/v6/v7 — 1,876 typed events across 7 generations. v7 adds: otto.automation.* namespace (50 events, IDs 8060–8109) and otto.blo.* namespace (40 events). Total architecture: 19 platforms, 494 AI models, 520 connectors. 4. CRYSTALS post-quantum cryptography — CRYSTALS-Kyber-768 (NIST FIPS 203) key encapsulation + CRYSTALS-Dilithium-3 (NIST FIPS 204) digital signatures + PQ certificate chain. All implemented in sentinel/crypto/pqc/. 5. CKKS Homomorphic Encryption — inference on fully encrypted data (ckks_engine.py + he_inference.py). 6. SLM v2 — 13B parameter on-premise language model, LoRA adapters × 19 platforms, 47 languages, function calling, CoT scaffold (sentinel/hal/slm_v2_engine.py). 7. MPC Secure Computation — Shamir Secret Sharing t-of-n threshold, consortium AI aggregation (sentinel/mpc/). 8. OTTO Embodied — 40+ connectors across 34 device domains: ROS2 rclpy (1–5ms DDS), CAN/J1939, NMEA 0183/2000, ONVIF/RTSP, AUTOSAR SOME/IP, Matter 1.2, IBM Telum II accelerator interface, PersonaPlex Edge 1.5B voice. 9. Certification system — full L1/L2/L3 exam bank, PDF generation, email delivery, renewal engine, public certificate verification. 10. SDK v4.0.0 — Python + JavaScript, IB v7 event emitters, BLO client, Automation adapter, PQC-signed event publishing. IP valuation: €118,400,000 (6-method OECD weighted average — COCOMO II: €95.4M on 1,912,000 SLOC verified). IP owner: Raphaël Clairin. Entity: BrainPredict OÜ, Registry 17352111, Tallinn, Estonia. Commits covered: 9b55f19 → c7774c0 (March 11–18, 2026).Previous deposits: v1 archive 2026-03-04 | v2 INPI e-Soleau 2026-03-11 (commit 7af8e9e).","url":"https://doi.org/10.5281/zenodo.19100682","authors":["Clairin, Raphael Pierre Eugene"],"tags":["enterprise AI, BrainPredict, autonomous remediation, self-healing AI, industrial AI, OPC-UA, IEC 62443, Intelligence Bus, zero-knowledge, homomorphic encryption, post-quantum cryptography, CRYSTALS, Kyber, Dilithium, BrainCore, OTTO Embodied, living organism, SLM, MPC, Shamir"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19100682","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.19100680","name":"BrainPredict Enterprise AI Platform — IP Deposit v3 — March 18, 2026 — Platform #19 Automation + Living Organism Autonomous Loop + Intelligence Bus v7","source":"datacite","abstract":"Intellectual property deposit v3 — BrainPredict enterprise AI platform v2026.3. Covers all changes since March 11, 2026 (23 commits, 329 files changed). Core inventions in this deposit: 1. BrainPredict™ Living Organism — ecosystem-wide autonomous self-healing AI (6 phases: NetworkScanner, DataQualityAuditor, ProcessBaseline, IssueIdentifier, RemediationEngine, AutonomousRemediator). Subscription-scoped, 100% on-premise, zero external API calls, self-funding upgrade loop via Dilithium-3 signed automatic Purchase Orders to sales@brainpredict.ai. Components: AutonomousRemediator, SolutionTester, OTTOMessengerNotifier, PurchaseOrderGenerator. 2. BrainPredict™ Automation Platform #19 — industrial AI OS. OPC-UA + Modbus TCP adapters for 38 asset types, 10ms polling, triple-gate safety (IEC 62443 / IEC 61511 / ISO 13849), SetpointRecommender with Dilithium-3 signed audit trail, 28 AI models, Industrial Platform Bridge routing to all 19 platforms. 3. Intelligence Bus v5/v6/v7 — 1,876 typed events across 7 generations. v7 adds: otto.automation.* namespace (50 events, IDs 8060–8109) and otto.blo.* namespace (40 events). Total architecture: 19 platforms, 494 AI models, 520 connectors. 4. CRYSTALS post-quantum cryptography — CRYSTALS-Kyber-768 (NIST FIPS 203) key encapsulation + CRYSTALS-Dilithium-3 (NIST FIPS 204) digital signatures + PQ certificate chain. All implemented in sentinel/crypto/pqc/. 5. CKKS Homomorphic Encryption — inference on fully encrypted data (ckks_engine.py + he_inference.py). 6. SLM v2 — 13B parameter on-premise language model, LoRA adapters × 19 platforms, 47 languages, function calling, CoT scaffold (sentinel/hal/slm_v2_engine.py). 7. MPC Secure Computation — Shamir Secret Sharing t-of-n threshold, consortium AI aggregation (sentinel/mpc/). 8. OTTO Embodied — 40+ connectors across 34 device domains: ROS2 rclpy (1–5ms DDS), CAN/J1939, NMEA 0183/2000, ONVIF/RTSP, AUTOSAR SOME/IP, Matter 1.2, IBM Telum II accelerator interface, PersonaPlex Edge 1.5B voice. 9. Certification system — full L1/L2/L3 exam bank, PDF generation, email delivery, renewal engine, public certificate verification. 10. SDK v4.0.0 — Python + JavaScript, IB v7 event emitters, BLO client, Automation adapter, PQC-signed event publishing. IP valuation: €118,400,000 (6-method OECD weighted average — COCOMO II: €95.4M on 1,912,000 SLOC verified). IP owner: Raphaël Clairin. Entity: BrainPredict OÜ, Registry 17352111, Tallinn, Estonia. Commits covered: 9b55f19 → c7774c0 (March 11–18, 2026).Previous deposits: v1 archive 2026-03-04 | v2 INPI e-Soleau 2026-03-11 (commit 7af8e9e).","url":"https://doi.org/10.5281/zenodo.19100680","authors":["Clairin, Raphael Pierre Eugene"],"tags":["enterprise AI, BrainPredict, autonomous remediation, self-healing AI, industrial AI, OPC-UA, IEC 62443, Intelligence Bus, zero-knowledge, homomorphic encryption, post-quantum cryptography, CRYSTALS, Kyber, Dilithium, BrainCore, OTTO Embodied, living organism, SLM, MPC, Shamir"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19100680","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2512.18345","name":"Theodosian: A Deep Dive into Memory-Hierarchy-Centric FHE Acceleration","source":"datacite","abstract":"Fully homomorphic encryption (FHE) enables secure computation on encrypted data, mitigating privacy concerns in cloud and edge environments. However, due to its high compute and memory demands, extensive acceleration research has been pursued across diverse hardware platforms, especially GPUs. In this paper, we perform a microarchitectural analysis of CKKS, a popular FHE scheme, on modern GPUs. Focusing on the memory hierarchy, we demonstrate that dominant kernels remain bound by the on-chip L2 cache despite its high bandwidth, exposing a persistent inner memory wall beyond the conventional off-chip DRAM bottleneck. Further, we reveal that the overall CKKS throughput is constrained by low per-kernel hardware utilization, caused by insufficient intra-kernel parallelism. Motivated by these findings, we introduce Theodosian, a set of complementary, memory-aware optimizations that improve cache efficiency and reduce runtime overheads. Theodosian achieves 1.45--1.83x performance improvements over a highly optimized baseline, Cheddar, across representative CKKS workloads. On an RTX 5090, we reduce the bootstrapping latency for 32,768 complex numbers from 22.1ms to 15.2ms, and further to 12.8ms with additional algorithmic optimizations, establishing a new state-of-the-art GPU performance to the best of our knowledge.","url":"https://doi.org/10.48550/arxiv.2512.18345","authors":["Choi, Wonseok","Yu, Hyunah","Kim, Jongmin","Ji, Hyesung","Park, Jaiyoung","Ahn, Jung Ho"],"tags":["Cryptography and Security (cs.CR)","Hardware Architecture (cs.AR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2512.18345","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.18884114","name":"REVIEW OF SYNERGIZING DEEP LEARNING AND NONLINEAR MODEL PREDICTIVE CONTROL","source":"datacite","abstract":"The operational complexity of modern industrial processes demands control frameworks that are both mathematically rigorous and computationally agile. This paper provides a systematic review of the transformative advances in Nonlinear Model Predictive Control (NMPC) integrated with Deep Learning (DL) methodologies between 2020 and 2026. We categorize the state-of-the-art into three technical pillars: neural-based system identification, computational acceleration via latent-space optimization, and robust architectures for uncertain environments. By analyzing applications across chemical engineering, fusion energy maintenance, and bionic robotics, we evaluate how hybrid frameworks-such as LSTM-based estimators and autoencoder-driven reduced-order models-mitigate the traditional trade-offs between model fidelity and real-time feasibility. The review further discusses emerging trends in cyber-secure control via homomorphic encryption and the integration of Physics-Informed Neural Networks (PINNs). Our synthesis highlights persistent gaps in formal stability proofs and model interpretability, offering a strategic roadmap for future research in autonomous industrial intelligence.","url":"https://doi.org/10.5281/zenodo.18884114","authors":["Tuyboyov Oybek"],"tags":["Nonlinear Model Predictive Control (NMPC)","Physics-Informed Neural Networks (PINNs)","Recurrent Neural Networks (RNN)","Deep Learning-based System Identification","Model Order Reduction (MOR)","Hybrid Intelligence Control","Cyber-physical Security","Industrial Autonomy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18884114","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18884557","name":"REVIEW OF SYNERGIZING DEEP LEARNING AND NONLINEAR MODEL PREDICTIVE CONTROL","source":"datacite","abstract":"The operational complexity of modern industrial processes demands control frameworks that are both mathematically rigorous and computationally agile. This paper provides a systematic review of the transformative advances in Nonlinear Model Predictive Control (NMPC) integrated with Deep Learning (DL) methodologies between 2020 and 2026. We categorize the state-of-the-art into three technical pillars: neural-based system identification, computational acceleration via latent-space optimization, and robust architectures for uncertain environments. By analyzing applications across chemical engineering, fusion energy maintenance, and bionic robotics, we evaluate how hybrid frameworks-such as LSTM-based estimators and autoencoder-driven reduced-order models-mitigate the traditional trade-offs between model fidelity and real-time feasibility. The review further discusses emerging trends in cyber-secure control via homomorphic encryption and the integration of Physics-Informed Neural Networks (PINNs). Our synthesis highlights persistent gaps in formal stability proofs and model interpretability, offering a strategic roadmap for future research in autonomous industrial intelligence.","url":"https://doi.org/10.5281/zenodo.18884557","authors":["Tuyboyov Oybek"],"tags":["Nonlinear Model Predictive Control (NMPC)","Physics-Informed Neural Networks (PINNs)","Recurrent Neural Networks (RNN)","Deep Learning-based System Identification","Model Order Reduction (MOR)","Hybrid Intelligence Control","Cyber-physical Security","Industrial Autonomy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18884557","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18884115","name":"SYNERGIZING DEEP LEARNING AND NONLINEAR MODEL PREDICTIVE CONTROL: A REVIEW","source":"datacite","abstract":"The operational complexity of modern industrial processes demands control frameworks that are both mathematically rigorous and computationally agile. This paper provides a systematic review of the transformative advances in Nonlinear Model Predictive Control (NMPC) integrated with Deep Learning (DL) methodologies between 2020 and 2026. We categorize the state-of-the-art into three technical pillars: neural-based system identification, computational acceleration via latent-space optimization, and robust architectures for uncertain environments. By analyzing applications across chemical engineering, fusion energy maintenance, and bionic robotics, we evaluate how hybrid frameworks-such as LSTM-based estimators and autoencoder-driven reduced-order models-mitigate the traditional trade-offs between model fidelity and real-time feasibility. The review further discusses emerging trends in cyber-secure control via homomorphic encryption and the integration of Physics-Informed Neural Networks (PINNs). Our synthesis highlights persistent gaps in formal stability proofs and model interpretability, offering a strategic roadmap for future research in autonomous industrial intelligence.","url":"https://doi.org/10.5281/zenodo.18884115","authors":["Tuyboyov Oybek"],"tags":["Nonlinear Model Predictive Control (NMPC)","Physics-Informed Neural Networks (PINNs)","Recurrent Neural Networks (RNN)","Deep Learning-based System Identification","Model Order Reduction (MOR)","Hybrid Intelligence Control","Cyber-physical Security","Industrial Autonomy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18884115","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18828387","name":"Blueprint OS Biologique Souverain","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous Licence Apache 2.0. Il constitue une publication défensive (antériorité) volontaire et entre dans l'état de la technique au sens des législations applicables : art. L 611-11 CPI / art. 54(2) CBE. Le « Blueprint OS Biologique Souverain » définit une architecture complète pour la santé métabolique autonome. Il détaille 32 briques technologiques allant de la détection optique du NAD+ par capteurs multispectraux à l'IA fédérée omique, en passant par des patchs de délivrance en boucle fermée et des structures électroniques biodégradables en soie. Chaque proposition est décrite de manière « enabling », classée avec les codes IPC/CPC, et accompagnée d'une preuve d'horodatage (RFC 3161). Cet écosystème sécurise l'accès libre aux technologies fondamentales de souveraineté biologique. Abstract ENThis document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: EPC Art. 54(2) (European Patent Convention), French IPC Art. L 611-11 (French Intellectual Property Code), 35 U.S.C. §102(a) (United States Patent Act), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). The \"Sovereign Biological OS Blueprint\" outlines a full-stack architecture for autonomous metabolic health. It details 32 technological blocks including multispectral NAD+ sensing, federated omic AI, closed-loop delivery patches, and biodegradable silk electronics. Every proposal is described in an enabling manner, classified with IPC and CPC codes, and accompanied by timestamp proof (RFC 3161 / FreeTSA). This framework ensures public access to foundational biological sovereignty tools. Timestamp: 2026-03-01T22:41:54ZSHA-256: aa37064544a4d57b8b9a134d2f5993a8c5eeddccbfe37b149968e0df0e8f019c Liste des innovations & classification (IPC ; CPC) Multispectral Redox Sensor [IPC A61B 5/00 ; CPC A61B 5/1455] Federated Omic Learning [IPC G16H 50/20 ; CPC G06N 3/098] Closed-Loop Patch [IPC A61M 37/00 ; CPC A61M 37/00] Smartphone µPAD [IPC B01L 3/00 ; CPC G01N 33/52] Digital Bio-Twin [IPC G16H 50/50 ; CPC G16H 50/50] Bio-TEE Enclave [IPC G06F 21/74 ; CPC G06F 21/53] AI-Controlled Bioreactor [IPC C12M 1/36 ; CPC C12M 41/48] Bio-Chain Logistics [IPC G06Q 10/08 ; CPC G06Q 10/08] Zwitterionic Coating [IPC C08J 7/04 ; CPC C08J 7/042] AI Spectral Deconvolution [IPC G06T 5/00 ; CPC G06T 5/50] Circadian MPC Dosage [IPC A61K 31/00 ; CPC G16H 20/17] 3D DLP Microneedles [IPC B29C 64/124 ; CPC B33Y 10/00] Homomorphic Omic Compute [IPC G06F 21/62 ; CPC G06F 21/62] Redox Biometrics [IPC G06F 21/32 ; CPC G06F 21/32] Mitochondrial LNP [IPC A61K 9/127 ; CPC A61K 9/1271] TTI Smart Cap [IPC B65D 81/24 ; CPC B65D 2581/00] Proof-of-Health (PoH) [IPC G06Q 20/06 ; CPC G06Q 20/367] Bio-NAD Exosomes [IPC C12N 15/88 ; CPC A61K 9/1277] Haptic UX Interface [IPC A61M 5/42 ; CPC A61M 5/422] Enzymatic Fuel Cell [IPC H01M 8/16 ; CPC H01M 8/16] Bio-JSON Standard [IPC G16H 10/60 ; CPC G16H 10/60] Stabilized Drone Pod [IPC B64U 30/20 ; CPC B64U 2101/60] Bio-Kill-Switch [IPC G16H 40/60 ; CPC G16H 40/67] Bioprocess AI LSTM [IPC C12M 1/36 ; CPC C12M 41/48] Metabolic Tomography [IPC A61B 5/00 ; CPC G06T 7/00] Synthetic Omic Phantoms [IPC G01N 21/64 ; CPC G01N 33/50] AR-Guided Alignment [IPC G06T 19/00 ; CPC G16H 40/63] Silk Electronics [IPC H05K 1/03 ; CPC H05K 1/03] Smart Audit Contract [IPC G06F 21/64 ; CPC G06Q 20/40] Edge Knowledge Graph [IPC G16H 50/70 ; CPC G06N 5/02] Async Omic Sync [IPC G06F 17/10 ; CPC G16H 50/20] Battery-less NFC Sensor [IPC A61B 5/145 ; CPC G06K 19/07] KeywordsSovereign Biological OS, NAD+ Sensing, Federated Learning, Microneedles, Digital Twin, Bio-Cryptography, Decentralized Manufacturing, Metabolic Health, Zwitterionic Coating, Spectral Deconvolutio","url":"https://doi.org/10.5281/zenodo.18828387","authors":["Pillet, Xavier"],"tags":["A61B 5/00","A61B 5/145","A61B 5/1455","A61K 9/127","A61K 9/1271","A61K 9/1277","A61K 31/00","A61M 5/42"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18828387","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.18828388","name":"Blueprint OS Biologique Souverain","source":"datacite","abstract":"Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous Licence Apache 2.0. Il constitue une publication défensive (antériorité) volontaire et entre dans l'état de la technique au sens des législations applicables : art. L 611-11 CPI / art. 54(2) CBE. Le « Blueprint OS Biologique Souverain » définit une architecture complète pour la santé métabolique autonome. Il détaille 32 briques technologiques allant de la détection optique du NAD+ par capteurs multispectraux à l'IA fédérée omique, en passant par des patchs de délivrance en boucle fermée et des structures électroniques biodégradables en soie. Chaque proposition est décrite de manière « enabling », classée avec les codes IPC/CPC, et accompagnée d'une preuve d'horodatage (RFC 3161). Cet écosystème sécurise l'accès libre aux technologies fondamentales de souveraineté biologique. Abstract ENThis document, produced with the assistance of ChatGPT 5.2 Thinking and Gemini 3 Raisonnement, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: EPC Art. 54(2) (European Patent Convention), French IPC Art. L 611-11 (French Intellectual Property Code), 35 U.S.C. §102(a) (United States Patent Act), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). The \"Sovereign Biological OS Blueprint\" outlines a full-stack architecture for autonomous metabolic health. It details 32 technological blocks including multispectral NAD+ sensing, federated omic AI, closed-loop delivery patches, and biodegradable silk electronics. Every proposal is described in an enabling manner, classified with IPC and CPC codes, and accompanied by timestamp proof (RFC 3161 / FreeTSA). This framework ensures public access to foundational biological sovereignty tools. Timestamp: 2026-03-01T22:41:54ZSHA-256: aa37064544a4d57b8b9a134d2f5993a8c5eeddccbfe37b149968e0df0e8f019c Liste des innovations & classification (IPC ; CPC) Multispectral Redox Sensor [IPC A61B 5/00 ; CPC A61B 5/1455] Federated Omic Learning [IPC G16H 50/20 ; CPC G06N 3/098] Closed-Loop Patch [IPC A61M 37/00 ; CPC A61M 37/00] Smartphone µPAD [IPC B01L 3/00 ; CPC G01N 33/52] Digital Bio-Twin [IPC G16H 50/50 ; CPC G16H 50/50] Bio-TEE Enclave [IPC G06F 21/74 ; CPC G06F 21/53] AI-Controlled Bioreactor [IPC C12M 1/36 ; CPC C12M 41/48] Bio-Chain Logistics [IPC G06Q 10/08 ; CPC G06Q 10/08] Zwitterionic Coating [IPC C08J 7/04 ; CPC C08J 7/042] AI Spectral Deconvolution [IPC G06T 5/00 ; CPC G06T 5/50] Circadian MPC Dosage [IPC A61K 31/00 ; CPC G16H 20/17] 3D DLP Microneedles [IPC B29C 64/124 ; CPC B33Y 10/00] Homomorphic Omic Compute [IPC G06F 21/62 ; CPC G06F 21/62] Redox Biometrics [IPC G06F 21/32 ; CPC G06F 21/32] Mitochondrial LNP [IPC A61K 9/127 ; CPC A61K 9/1271] TTI Smart Cap [IPC B65D 81/24 ; CPC B65D 2581/00] Proof-of-Health (PoH) [IPC G06Q 20/06 ; CPC G06Q 20/367] Bio-NAD Exosomes [IPC C12N 15/88 ; CPC A61K 9/1277] Haptic UX Interface [IPC A61M 5/42 ; CPC A61M 5/422] Enzymatic Fuel Cell [IPC H01M 8/16 ; CPC H01M 8/16] Bio-JSON Standard [IPC G16H 10/60 ; CPC G16H 10/60] Stabilized Drone Pod [IPC B64U 30/20 ; CPC B64U 2101/60] Bio-Kill-Switch [IPC G16H 40/60 ; CPC G16H 40/67] Bioprocess AI LSTM [IPC C12M 1/36 ; CPC C12M 41/48] Metabolic Tomography [IPC A61B 5/00 ; CPC G06T 7/00] Synthetic Omic Phantoms [IPC G01N 21/64 ; CPC G01N 33/50] AR-Guided Alignment [IPC G06T 19/00 ; CPC G16H 40/63] Silk Electronics [IPC H05K 1/03 ; CPC H05K 1/03] Smart Audit Contract [IPC G06F 21/64 ; CPC G06Q 20/40] Edge Knowledge Graph [IPC G16H 50/70 ; CPC G06N 5/02] Async Omic Sync [IPC G06F 17/10 ; CPC G16H 50/20] Battery-less NFC Sensor [IPC A61B 5/145 ; CPC G06K 19/07] KeywordsSovereign Biological OS, NAD+ Sensing, Federated Learning, Microneedles, Digital Twin, Bio-Cryptography, Decentralized Manufacturing, Metabolic Health, Zwitterionic Coating, Spectral Deconvolutio","url":"https://doi.org/10.5281/zenodo.18828388","authors":["Pillet, Xavier"],"tags":["A61B 5/00","A61B 5/145","A61B 5/1455","A61K 9/127","A61K 9/1271","A61K 9/1277","A61K 31/00","A61M 5/42"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18828388","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2505.21051","name":"SHE-LoRA: Selective Homomorphic Encryption for Federated Tuning with Heterogeneous LoRA","source":"datacite","abstract":"Federated fine-tuning is critical for improving the performance of large language models (LLMs) in handling domain-specific tasks while keeping training data decentralized and private. However, prior work has shown that clients' private data can actually be recovered via gradient inversion attacks. Existing privacy preservation techniques against such attacks typically entail performance degradation and high costs, making them ill-suited for clients with heterogeneous data distributions and device capabilities. In this paper, we propose SHE-LoRA, which integrates selective homomorphic encryption (SHE) and low-rank adaptation (LoRA) to enable efficient and privacy-preserving federated tuning of LLMs in cross-device environments. Based on model parameter sensitivity assessment, heterogeneous clients adaptively negotiate and select a subset of model parameters for homomorphic encryption. To ensure accurate model aggregation, we design a column-aware secure aggregation method and customized reparameterization techniques to align the aggregation results with the heterogeneous device capabilities of clients. Extensive experiments demonstrate that SHE-LoRA maintains performance comparable to non-private baselines, achieves strong resistance to state-of-the-art attacks, and significantly reduces communication overhead by 99.71% and encryption time by 99.87%, compared to HE baselines.","url":"https://doi.org/10.48550/arxiv.2505.21051","authors":["Liu, Jianmin","Yan, Li","Li, Borui","Yu, Lei","Shen, Chao"],"tags":["Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2505.21051","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2602.13024","name":"FedHENet: A Frugal Federated Learning Framework for Heterogeneous Environments","source":"datacite","abstract":"Federated Learning (FL) enables collaborative training without centralizing data, essential for privacy compliance in real-world scenarios involving sensitive visual information. Most FL approaches rely on expensive, iterative deep network optimization, which still risks privacy via shared gradients. In this work, we propose FedHENet, extending the FedHEONN framework to image classification. By using a fixed, pre-trained feature extractor and learning only a single output layer, we avoid costly local fine-tuning. This layer is learned by analytically aggregating client knowledge in a single round of communication using homomorphic encryption (HE). Experiments show that FedHENet achieves competitive accuracy compared to iterative FL baselines while demonstrating superior stability performance and up to 70\\% better energy efficiency. Crucially, our method is hyperparameter-free, removing the carbon footprint associated with hyperparameter tuning in standard FL. Code available in https://github.com/AlejandroDopico2/FedHENet/","url":"https://doi.org/10.48550/arxiv.2602.13024","authors":["Dopico-Castro, Alejandro","Fontenla-Romero, Oscar","Guijarro-Berdiñas, Bertha","Alonso-Betanzos, Amparo","Digón, Iván Pérez"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.13024","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.18385664","name":"Nexus Ocean: Quantum Intelligence Architecture for Deep-Sea Monitoring and Predictive Analytics","source":"datacite","abstract":"🌊 Nexus Ocean v3.1.0 - Quantum AI Marine Intelligence Next-Generation Deep Sea Monitoring & Intelligence System Transforming oceanographic monitoring through quantum-inspired artificial intelligence 📖 Overview Nexus Ocean is a revolutionary quantum-inspired oceanographic AI system that combines six-layer neural architecture with real-time deep-sea monitoring capabilities. The system leverages cutting-edge technologies including neuromorphic computing, quantum-inspired algorithms, and autonomous decision-making to provide comprehensive ocean intelligence, predictive analytics, and emergency response capabilities. 🎯 What Makes Nexus Ocean Unique? 🧠 Six-Layer Neural Architecture: From sensor fusion to emergent intelligence ⚡ Real-Time Processing: Sub-second analysis of oceanographic data 🔮 Predictive Intelligence: Long-term forecasting with AI-powered insights 🛡️ Quantum-Resistant Security: Post-quantum cryptography and zero-trust architecture 📊 Advanced Visualization: 3D oceanographic dashboards with 10+ chart types 🌐 Edge-Cloud Hybrid: Distributed computing from deep-sea sensors to cloud analytics 🤖 Autonomous Operations: Self-learning systems with human oversight 🔗 Blockchain Verification: Immutable audit trails and decision transparency 🌟 Key Features 🛡️ Multi-Sensor Fusion Advanced integration of pressure, acoustic, thermal, magnetic, and biosignature sensors with Kalman filtering and neural preprocessing. 🧬 Real-Time Cognitive Processing Pattern recognition, anomaly detection, and contextual analysis using transformer-based models and graph neural networks. 🌌 Autonomous Decision Making AI-powered orchestration with multi-criteria optimization, Monte Carlo simulations, and risk assessment frameworks. 🔐 Quantum-Resistant Security Zero-trust architecture with lattice-based cryptography, hash-based signatures, and automated threat response. 🔮 Predictive Intelligence Long-term forecasting using LSTM/GRU networks, attention mechanisms, and causal inference models. 🌀 Emergent Intelligence Cross-layer integration with swarm orchestration, event-driven architecture, and self-learning capabilities. 📊 Advanced Analytics Dashboard 10 interactive visualizations including 3D ocean views, radar charts, and time-series analysis Location performance comparison across multiple ocean regions Real-time QII (Quantum Intelligence Index) monitoring Anomaly detection with automated alerts Export capabilities for reports and data analysis 🏗️ System Architecture The Nexus Ocean system is built on the NEXUS Architecture - a six-layer neural framework inspired by biological intelligence: ┌─────────────────────────────────────────────────────────┐ │ 🌊 SENTINEL Layer │ │ Multi-Sensor Fusion & Perception │ │ HydroSense | AcousticMatrix | ThermalGrid | GeoMag │ └────────────────────┬────────────────────────────────────┘ │ Real-time Sensor Streams ▼ ┌─────────────────────────────────────────────────────────┐ │ 🧬 CORTEX Layer │ │ Cognitive Processing & Pattern Recognition │ │ PatternWeaver | ContextEngine | KnowledgeGraph │ └────────────────────┬────────────────────────────────────┘ │ Intelligent Analysis ▼ ┌─────────────────────────────────────────────────────────┐ │ 🌌 NEXUS Layer │ │ Decision Making & Orchestration │ │ DecisionForge | ScenarioEngine | RiskCalculus │ └────────────────────┬────────────────────────────────────┘ │ Strategic Commands ▼ ┌─────────────────────────────────────────────────────────┐ │ 🛡️ AEGIS Layer │ │ Security & Emergency Response │ │ ThreatRadar | DefenseGrid | EmergencyProtocol │ └────────────────────┬────────────────────────────────────┘ │ Protected Actions ▼ ┌─────────────────────────────────────────────────────────┐ │ 🔮 ORACLE Layer │ │ Predictive Intelligence & Learning │ │ FutureSight | CognitiveLeap | TrendAnalyzer │ └────────────────────┬────────────────────────────────────┘ │ Strategic Insights ▼ ┌─────────────────────────────────────────────────────────┐ │ 🌀 SYNERGY Layer │ │ Cross-Layer Integr","url":"https://doi.org/10.5281/zenodo.18385664","authors":["Baladi, Samir"],"tags":["quantum computing","deep-sea monitoring","oceanography","artificial intelligence","machine learning","neuromorphic computing","real-time analytics","anomaly detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18385664","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18426244","name":"Nexus Ocean: Quantum Intelligence Architecture for Deep-Sea Monitoring and Predictive Analytics","source":"datacite","abstract":"🌊 Nexus Ocean v3.1.0 - Quantum AI Marine Intelligence Next-Generation Deep Sea Monitoring & Intelligence System Transforming oceanographic monitoring through quantum-inspired artificial intelligence 📖 Overview Nexus Ocean is a revolutionary quantum-inspired oceanographic AI system that combines six-layer neural architecture with real-time deep-sea monitoring capabilities. The system leverages cutting-edge technologies including neuromorphic computing, quantum-inspired algorithms, and autonomous decision-making to provide comprehensive ocean intelligence, predictive analytics, and emergency response capabilities. 🎯 What Makes Nexus Ocean Unique? 🧠 Six-Layer Neural Architecture: From sensor fusion to emergent intelligence ⚡ Real-Time Processing: Sub-second analysis of oceanographic data 🔮 Predictive Intelligence: Long-term forecasting with AI-powered insights 🛡️ Quantum-Resistant Security: Post-quantum cryptography and zero-trust architecture 📊 Advanced Visualization: 3D oceanographic dashboards with 10+ chart types 🌐 Edge-Cloud Hybrid: Distributed computing from deep-sea sensors to cloud analytics 🤖 Autonomous Operations: Self-learning systems with human oversight 🔗 Blockchain Verification: Immutable audit trails and decision transparency 🌟 Key Features 🛡️ Multi-Sensor Fusion Advanced integration of pressure, acoustic, thermal, magnetic, and biosignature sensors with Kalman filtering and neural preprocessing. 🧬 Real-Time Cognitive Processing Pattern recognition, anomaly detection, and contextual analysis using transformer-based models and graph neural networks. 🌌 Autonomous Decision Making AI-powered orchestration with multi-criteria optimization, Monte Carlo simulations, and risk assessment frameworks. 🔐 Quantum-Resistant Security Zero-trust architecture with lattice-based cryptography, hash-based signatures, and automated threat response. 🔮 Predictive Intelligence Long-term forecasting using LSTM/GRU networks, attention mechanisms, and causal inference models. 🌀 Emergent Intelligence Cross-layer integration with swarm orchestration, event-driven architecture, and self-learning capabilities. 📊 Advanced Analytics Dashboard 10 interactive visualizations including 3D ocean views, radar charts, and time-series analysis Location performance comparison across multiple ocean regions Real-time QII (Quantum Intelligence Index) monitoring Anomaly detection with automated alerts Export capabilities for reports and data analysis 🏗️ System Architecture The Nexus Ocean system is built on the NEXUS Architecture - a six-layer neural framework inspired by biological intelligence: ┌─────────────────────────────────────────────────────────┐ │ 🌊 SENTINEL Layer │ │ Multi-Sensor Fusion & Perception │ │ HydroSense | AcousticMatrix | ThermalGrid | GeoMag │ └────────────────────┬────────────────────────────────────┘ │ Real-time Sensor Streams ▼ ┌─────────────────────────────────────────────────────────┐ │ 🧬 CORTEX Layer │ │ Cognitive Processing & Pattern Recognition │ │ PatternWeaver | ContextEngine | KnowledgeGraph │ └────────────────────┬────────────────────────────────────┘ │ Intelligent Analysis ▼ ┌─────────────────────────────────────────────────────────┐ │ 🌌 NEXUS Layer │ │ Decision Making & Orchestration │ │ DecisionForge | ScenarioEngine | RiskCalculus │ └────────────────────┬────────────────────────────────────┘ │ Strategic Commands ▼ ┌─────────────────────────────────────────────────────────┐ │ 🛡️ AEGIS Layer │ │ Security & Emergency Response │ │ ThreatRadar | DefenseGrid | EmergencyProtocol │ └────────────────────┬────────────────────────────────────┘ │ Protected Actions ▼ ┌─────────────────────────────────────────────────────────┐ │ 🔮 ORACLE Layer │ │ Predictive Intelligence & Learning │ │ FutureSight | CognitiveLeap | TrendAnalyzer │ └────────────────────┬────────────────────────────────────┘ │ Strategic Insights ▼ ┌─────────────────────────────────────────────────────────┐ │ 🌀 SYNERGY Layer │ │ Cross-Layer Integr","url":"https://doi.org/10.5281/zenodo.18426244","authors":["Baladi, Samir"],"tags":["quantum computing","deep-sea monitoring","oceanography","artificial intelligence","machine learning","neuromorphic computing","real-time analytics","anomaly detection"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18426244","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18307054","name":"Deterministic Harmonic Access (DHA): The Unified Interface for Recursive Computational Stability and Infinite State Retrieval","source":"datacite","abstract":"Deterministic Harmonic Access (DHA): The Unified Interface for Recursive Computational Stability and Infinite State Retrieval 1. Introduction: The End of Storage and the Rise of Location 1.1 The Crisis of the Von Neumann Bottleneck The history of computing has been defined by a singular, persistent constraint: the physical limitation of information storage. From the earliest magnetic core memories to modern solid-state arrays, the fundamental paradigm has remained unchanged. Data is treated as a physical object—a sequence of magnetized grains or trapped electrons—that must be generated, moved, written, and maintained. This \"containment model\" of information has led to the Von Neumann bottleneck, where the speed of processing vastly outstrips the speed of retrieval, and the energy cost of maintaining data entropy threatens the scalability of planetary computation. As we transition into an era of exascale computing and the burgeoning Internet of Things (IoT), the volume of data is expanding at a rate that physical storage media cannot sustain. The \"Crisis of Storage\" is not merely a question of capacity; it is a question of fundamental physics. Storing a bit of information requires energy to combat thermal fluctuations and entropy. As we approach the limits of atomic storage, a radical paradigm shift is required. Deterministic Harmonic Access (DHA) represents this shift. It transitions the industry from a paradigm of storage to a paradigm of location. DHA posits that all finite information already exists within the infinite, non-repeating expansions of irrational constants (such as $\\pi$, $e$, or $\\sqrt{2}$). Therefore, the act of \"saving\" a file is not a write operation, but a search operation. The file is not created; its coordinates are discovered. DHA serves as the concrete, existing interface that bridges the gap between the theoretical \"Library of Babel\" contained within these constants and the practical, high-speed requirements of modern computing. 1.2 The DHA Proposition: Zero-Data Computing The core proposition of DHA is \"Zero-Data\" computing. In this architecture, a user does not store a 4-gigabyte movie file. Instead, they store a \"DHA Pointer\"—a tiny packet of metadata containing a Constant Identifier (CID), a Starting Index ($d$), a Length ($L$), and a Diffusion Key ($K$). When the user wishes to view the movie, the DHA interface utilizes the Bailey-Borwein-Plouffe (BBP) algorithm to extracting the hexadecimal data directly from the mathematical fabric of the universe, effectively streaming the data from the constant itself. This effectively offers infinite compression density. The storage requirement for any file, regardless of size, collapses to the size of its pointer. While the computational cost of retrieval is non-zero, the DHA architecture mitigates this through specific accelerants: Harmonic Diffusion: Using Maximum Distance Separable (MDS) matrices to map human-readable data (low entropy) onto the uniform distribution of the irrational constant (high entropy).1 Recursive Stability: Employing Samson’s Law and the Nexus Harmonic Framework to stabilize the search for these coordinates, treating the search process as a trajectory tracking problem in control theory.3 Parallel Acceleration: Utilizing Residue Number Systems (RNS) and the Chinese Remainder Theorem (CRT) to perform the massive arbitrary-precision arithmetic required for deep indexing at hardware speeds.5 This report provides the definitive technical breakdown of these mechanisms. It serves as an exhaustive guide to the mathematics, hardware architecture, and control theory that make DHA a reality. 2. The Mathematical Engine: Spigot Algorithms and the BBP Interface 2.1 The Historical Context of Digit Extraction For millennia, the calculation of $\\pi$ was a cumulative process. To know the 100th digit, one had to calculate the preceding 99. This dependency made $\\pi$ unsuitable for random access storage. The breakthrough came in 1995 with the discovery","url":"https://doi.org/10.5281/zenodo.18307054","authors":["Kulik, Dean"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18307054","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2601.05865","name":"Secure Change-Point Detection for Time Series under Homomorphic Encryption","source":"datacite","abstract":"We introduce the first method for change-point detection on encrypted time series. Our approach employs the CKKS homomorphic encryption scheme to detect shifts in statistical properties (e.g., mean, variance, frequency) without ever decrypting the data. Unlike solutions based on differential privacy, which degrade accuracy through noise injection, our solution preserves utility comparable to plaintext baselines. We assess its performance through experiments on both synthetic datasets and real-world time series from healthcare and network monitoring. Notably, our approach can process one million points within 3 minutes.","url":"https://doi.org/10.48550/arxiv.2601.05865","authors":["Mazzone, Federico","Micali, Giorgio","Pronesti, Massimiliano"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2601.05865","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2601.04912","name":"Decentralized Privacy-Preserving Federal Learning of Computer Vision Models on Edge Devices","source":"datacite","abstract":"Collaborative training of a machine learning model comes with a risk of sharing sensitive or private data. Federated learning offers a way of collectively training a single global model without the need to share client data, by sharing only the updated parameters from each client's local model. A central server is then used to aggregate parameters from all clients and redistribute the aggregated model back to the clients. Recent findings have shown that even in this scenario, private data can be reconstructed only using information about model parameters. Current efforts to mitigate this are mainly focused on reducing privacy risks on the server side, assuming that other clients will not act maliciously. In this work, we analyzed various methods for improving the privacy of client data concerning both the server and other clients for neural networks. Some of these methods include homomorphic encryption, gradient compression, gradient noising, and discussion on possible usage of modified federated learning systems such as split learning, swarm learning or fully encrypted models. We have analyzed the negative effects of gradient compression and gradient noising on the accuracy of convolutional neural networks used for classification. We have shown the difficulty of data reconstruction in the case of segmentation networks. We have also implemented a proof of concept on the NVIDIA Jetson TX2 module used in edge devices and simulated a federated learning process.","url":"https://doi.org/10.48550/arxiv.2601.04912","authors":["Harenčák, Damian","Gajdošech, Lukáš","Madaras, Martin"],"tags":["Cryptography and Security (cs.CR)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.4.9; E.3","68T07"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2601.04912","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.5281/zenodo.18136120","name":"Deterministic Harmonic Access (DHA): The Unified Interface for Recursive Computational Stability and Infinite State Retrieval","source":"datacite","abstract":"Deterministic Harmonic Access (DHA): The Unified Interface for Recursive Computational Stability and Infinite State Retrieval 1. Introduction: The End of Storage and the Rise of Location 1.1 The Crisis of the Von Neumann Bottleneck The history of computing has been defined by a singular, persistent constraint: the physical limitation of information storage. From the earliest magnetic core memories to modern solid-state arrays, the fundamental paradigm has remained unchanged. Data is treated as a physical object—a sequence of magnetized grains or trapped electrons—that must be generated, moved, written, and maintained. This \"containment model\" of information has led to the Von Neumann bottleneck, where the speed of processing vastly outstrips the speed of retrieval, and the energy cost of maintaining data entropy threatens the scalability of planetary computation. As we transition into an era of exascale computing and the burgeoning Internet of Things (IoT), the volume of data is expanding at a rate that physical storage media cannot sustain. The \"Crisis of Storage\" is not merely a question of capacity; it is a question of fundamental physics. Storing a bit of information requires energy to combat thermal fluctuations and entropy. As we approach the limits of atomic storage, a radical paradigm shift is required. Deterministic Harmonic Access (DHA) represents this shift. It transitions the industry from a paradigm of storage to a paradigm of location. DHA posits that all finite information already exists within the infinite, non-repeating expansions of irrational constants (such as $\\pi$, $e$, or $\\sqrt{2}$). Therefore, the act of \"saving\" a file is not a write operation, but a search operation. The file is not created; its coordinates are discovered. DHA serves as the concrete, existing interface that bridges the gap between the theoretical \"Library of Babel\" contained within these constants and the practical, high-speed requirements of modern computing. 1.2 The DHA Proposition: Zero-Data Computing The core proposition of DHA is \"Zero-Data\" computing. In this architecture, a user does not store a 4-gigabyte movie file. Instead, they store a \"DHA Pointer\"—a tiny packet of metadata containing a Constant Identifier (CID), a Starting Index ($d$), a Length ($L$), and a Diffusion Key ($K$). When the user wishes to view the movie, the DHA interface utilizes the Bailey-Borwein-Plouffe (BBP) algorithm to extracting the hexadecimal data directly from the mathematical fabric of the universe, effectively streaming the data from the constant itself. This effectively offers infinite compression density. The storage requirement for any file, regardless of size, collapses to the size of its pointer. While the computational cost of retrieval is non-zero, the DHA architecture mitigates this through specific accelerants: Harmonic Diffusion: Using Maximum Distance Separable (MDS) matrices to map human-readable data (low entropy) onto the uniform distribution of the irrational constant (high entropy).1 Recursive Stability: Employing Samson’s Law and the Nexus Harmonic Framework to stabilize the search for these coordinates, treating the search process as a trajectory tracking problem in control theory.3 Parallel Acceleration: Utilizing Residue Number Systems (RNS) and the Chinese Remainder Theorem (CRT) to perform the massive arbitrary-precision arithmetic required for deep indexing at hardware speeds.5 This report provides the definitive technical breakdown of these mechanisms. It serves as an exhaustive guide to the mathematics, hardware architecture, and control theory that make DHA a reality. 2. The Mathematical Engine: Spigot Algorithms and the BBP Interface 2.1 The Historical Context of Digit Extraction For millennia, the calculation of $\\pi$ was a cumulative process. To know the 100th digit, one had to calculate the preceding 99. This dependency made $\\pi$ unsuitable for random access storage. The breakthrough came in 1995 with the discovery","url":"https://doi.org/10.5281/zenodo.18136120","authors":["Kulik, Dean"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18136120","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2504.11604","name":"SoK: Can Fully Homomorphic Encryption Support General AI Computation? A Functional and Cost Analysis","source":"datacite","abstract":"Artificial intelligence (AI) increasingly powers sensitive applications in domains such as healthcare and finance, relying on both linear operations (e.g., matrix multiplications in large language models) and non-linear operations (e.g., sorting in retrieval-augmented generation). Fully homomorphic encryption (FHE) has emerged as a promising tool for privacy-preserving computation, but it remains unclear whether existing methods can support the full spectrum of AI workloads that combine these operations. In this SoK, we ask: Can FHE support general AI computation? We provide both a functional analysis and a cost analysis. First, we categorize ten distinct FHE approaches and evaluate their ability to support general computation. We then identify three promising candidates and benchmark workloads that mix linear and non-linear operations across different bit lengths and SIMD parallelization settings. Finally, we evaluate five real-world, privacy-sensitive AI applications that instantiate these workloads. Our results quantify the costs of achieving general computation in FHE and offer practical guidance on selecting FHE methods that best fit specific AI application requirements. Our codes are available at https://github.com/UCF-ML-Research/FHE-AI-Generality.","url":"https://doi.org/10.48550/arxiv.2504.11604","authors":["Xue, Jiaqi","Xin, Xin","Zhang, Wei","Zheng, Mengxin","Song, Qianqian","Zhou, Minxuan","Dong, Yushun","Wang, Dongjie","Chen, Xun","Xie, Jiafeng","Wang, Liqiang","Mohaisen, David","Wu, Hongyi","Lou, Qian"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2504.11604","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.48550/arxiv.2512.01574","name":"IVE: An Accelerator for Single-Server Private Information Retrieval Using Versatile Processing Elements","source":"datacite","abstract":"Private information retrieval (PIR) is an essential cryptographic protocol for privacy-preserving applications, enabling a client to retrieve a record from a server's database without revealing which record was requested. Single-server PIR based on homomorphic encryption has particularly gained immense attention for its ease of deployment and reduced trust assumptions. However, single-server PIR remains impractical due to its high computational and memory bandwidth demands. Specifically, reading the entirety of large databases from storage, such as SSDs, severely limits its performance. To address this, we propose IVE, an accelerator for single-server PIR with a systematic extension that enables practical retrieval from large databases using DRAM. Recent advances in DRAM capacity allow PIR for large databases to be served entirely from DRAM, removing its dependence on storage bandwidth. Although the memory bandwidth bottleneck still remains, multi-client batching effectively amortizes database access costs across concurrent requests to improve throughput. However, client-specific data remains a bottleneck, whose bandwidth requirements ultimately limits performance. IVE overcomes this by employing a large on-chip scratchpad with an operation scheduling algorithm that maximizes data reuse, further boosting throughput. Additionally, we introduce sysNTTU, a versatile functional unit that enhances area efficiency without sacrificing performance. We also propose a heterogeneous memory system architecture, which enables a linear scaling of database sizes without a throughput degradation. Consequently, IVE achieves up to 1,275x higher throughput compared to prior PIR hardware solutions.","url":"https://doi.org/10.48550/arxiv.2512.01574","authors":["Kim, Sangpyo","Ji, Hyesung","Kim, Jongmin","Choi, Wonseok","Park, Jaiyoung","Ahn, Jung Ho"],"tags":["Cryptography and Security (cs.CR)","Hardware Architecture (cs.AR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2512.01574","addedAt":"2026-08-31T06:41:45.053Z","updatedAt":"2026-08-31T06:41:45.053Z"},{"id":"doi:10.1016/j.cosrev.2020.100235","name":"Homomorphic encryption systems statement: Trends and challenges","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.cosrev.2020.100235","authors":["Bechir Alaya","Lamri Laouamer","Nihel Msilini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-04-24T22:55:57Z","doi":"10.1016/j.cosrev.2020.100235","addedAt":"2026-08-31T06:41:45.131Z","updatedAt":"2026-08-31T06:41:45.131Z"},{"id":"doi:10.71143/2tw4cm70","name":"Homomorphic Encryption for Cloud Data Security: A Comprehensive Review","source":"crossref","abstract":"The Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) intersected in the realm of industry, transforming the industrial system to allow making real-time decisions at the edge. Latency issues, bandwidth consumption, and data security also pose a challenge to the traditional cloud-based system, and are of utmost priority in the industry where time is limited. Edge AI combines AI features with edge computing, moving the intelligence to the devices in the IIoT, allowing decisions to be made faster and more responsive to the context without depending on centralized infrastructures that might overwhelm the cloud. The paper summarizes the present-day developments in edge AI as a decision-making tool in IIoT and identifies the future directions. It discusses architectures, algorithms and applications that enable intelligent decision making at the network edge with particular focus on manufacturing, predictive maintenance, supply chain optimization and energy management. It is reviewed that facilitating technologies, such as lightweight deep learning models, federated learning, and hardware accelerators, and problems, such as scalability, interoperability, and cybersecurity are discussed. As depicted in the literature section, edge AI has been found to enhance efficiency of distributed industrial systems by reducing latencies, reliability, and independent decision-making. However, barriers such as the shortage of resources, failure to interface with current systems and standardized structures still persist. It is anticipated that future studies will be based on adaptive AI models and edge-cloud collaboration with application of 6G-enabled IIoT ecosystems. This paper summarizes the synthesis of state-of-the-art methods to inform the next generation of industrial automation and digital transformation powered by edge AI. It indicates the need of powerful, hardy, and scalable systems to open up the complete scope of opportunities that edge AI can introduce to decisions made by IIoT.","url":"https://doi.org/10.71143/2tw4cm70","authors":["Venkateswaran Radhakrishnan","Praveen Kumar C"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-25T07:18:48Z","doi":"10.71143/2tw4cm70","addedAt":"2026-08-31T06:41:45.132Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.14419/ijet.v7i4.6.20439","name":"Homomorphic encryption security - a review","source":"crossref","abstract":"As the Data and Technology is increasing exponentially the security and privacy issues are becoming a major concern in the present scenario, lot of security mechanisms are in to real time and practice, but even there are some bottlenecks with the existing security techniques. In this work we are giving the essence and importance of an encryption scheme called homomorphic encryption and its related issues.Â","url":"https://doi.org/10.14419/ijet.v7i4.6.20439","authors":["J. Phani Prasad","B. Seetha Ramulu","E. Amarnatha Reddy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-07-14T21:41:22Z","doi":"10.14419/ijet.v7i4.6.20439","addedAt":"2026-08-31T06:41:45.132Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1109/eiconrus49466.2020.9039110","name":"Homomorphic Encryption Methods Review","source":"crossref","abstract":"","url":"https://doi.org/10.1109/eiconrus49466.2020.9039110","authors":["Nikolay N. Kucherov","Maxim A. Deryabin","Mikhail G. Babenko"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-03-20T04:04:12Z","doi":"10.1109/eiconrus49466.2020.9039110","addedAt":"2026-08-31T06:41:45.132Z","updatedAt":"2026-08-31T06:41:45.132Z"},{"id":"doi:10.1038/s41598-025-08836-z","name":"Construction of evolutionary stability and signal game model for privacy protection in the internet of things.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-08836-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-08836-z","addedAt":"2026-08-31T06:41:45.133Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1038/s41598-025-04931-3","name":"Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-04931-3","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-04931-3","addedAt":"2026-08-31T06:41:45.133Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.48550/arxiv.2510.17642","name":"Quantum Federated Learning: Architectural Elements and Future Directions","source":"datacite","abstract":"Federated learning (FL) focuses on collaborative model training without the need to move the private data silos to a central server. Despite its several benefits, the classical FL is plagued with several limitations, such as high computational power required for model training(which is critical for low-resource clients), privacy risks, large update traffic, and non-IID heterogeneity. This chapter surveys a hybrid paradigm - Quantum Federated Learning (QFL), which introduces quantum computation, that addresses multiple challenges of classical FL and offers rapid computing capability while keeping the classical orchestration intact. Firstly, we motivate QFL with a concrete presentation on pain points of classical FL, followed by a discussion on a general architecture of QFL frameworks specifying the roles of client and server, communication primitives and the quantum model placement. We classify the existing QFL systems based on four criteria - quantum architecture (pure QFL, hybrid QFL), data processing method (quantum data encoding, quantum feature mapping, and quantum feature selection &amp; dimensionality reduction), network topology (centralized, hierarchial, decentralized), and quantum security mechanisms (quantum key distribution, quantum homomorphic encryption, quantum differential privacy, blind quantum computing). We then describe applications of QFL in healthcare, vehicular networks, wireless networks, and network security, clearly highlighting where QFL improves communication efficiency, security, and performance compared to classical FL. We close with multiple challenges and future works in QFL, including extension of QFL beyond classification tasks, adversarial attacks, realistic hardware deployment, quantum communication protocols deployment, aggregation of different quantum models, and quantum split learning as an alternative to QFL.","url":"https://doi.org/10.48550/arxiv.2510.17642","authors":["Sai, Siva","Sawaika, Abhishek","Singh, Prabhjot","Buyya, Rajkumar"],"tags":["Quantum Physics (quant-ph)","Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Physical sciences","FOS: Computer and information sciences","I.2; A.1"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.17642","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.21334865","name":"Confidential Computing and Trusted Execution Environments: A Survey of Technologies","source":"datacite","abstract":"Conventional security controls protect data at rest through storage encryption and data in transit through transport encryption, yet data in use, meaning plaintext held in memory and registers during computation, has remained exposed to any sufficiently privileged component of the software stack. Confidential computing addresses this gap by performing sensitive computation inside a hardware-based, attested Trusted Execution Environment (TEE) that isolates code and data even from the operating system, hypervisor, and cloud operator. This survey reviews the motivation, threat model, and technology landscape of confidential computing. It examines process enclaves such as Intel SGX alongside confidential virtual machine designs including Intel TDX, AMD SEV and SEV-SNP, Arm TrustZone with the Confidential Compute Architecture, and the RISC-V Keystone framework, comparing them across isolation granularity, memory encryption, attestation, and vendor. It describes remote attestation and hardware roots of trust, surveys applications spanning confidential artificial intelligence, privacy-preserving analytics, blockchain, key management, and multi-party computation, and positions the paradigm as a complement to homomorphic encryption, secure multi-party computation, and differential privacy rather than a replacement. A dedicated security analysis reports side-channel and transient-execution attacks such as Foreshadow, Plundervolt, and SGAxe, together with representative performance overheads, and the review closes with standardization efforts and future directions including confidential graphics processing units.","url":"https://doi.org/10.5281/zenodo.21334865","authors":["Dr. R. Pugazhenthi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21334865","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.21334866","name":"Confidential Computing and Trusted Execution Environments: A Survey of Technologies","source":"datacite","abstract":"Conventional security controls protect data at rest through storage encryption and data in transit through transport encryption, yet data in use, meaning plaintext held in memory and registers during computation, has remained exposed to any sufficiently privileged component of the software stack. Confidential computing addresses this gap by performing sensitive computation inside a hardware-based, attested Trusted Execution Environment (TEE) that isolates code and data even from the operating system, hypervisor, and cloud operator. This survey reviews the motivation, threat model, and technology landscape of confidential computing. It examines process enclaves such as Intel SGX alongside confidential virtual machine designs including Intel TDX, AMD SEV and SEV-SNP, Arm TrustZone with the Confidential Compute Architecture, and the RISC-V Keystone framework, comparing them across isolation granularity, memory encryption, attestation, and vendor. It describes remote attestation and hardware roots of trust, surveys applications spanning confidential artificial intelligence, privacy-preserving analytics, blockchain, key management, and multi-party computation, and positions the paradigm as a complement to homomorphic encryption, secure multi-party computation, and differential privacy rather than a replacement. A dedicated security analysis reports side-channel and transient-execution attacks such as Foreshadow, Plundervolt, and SGAxe, together with representative performance overheads, and the review closes with standardization efforts and future directions including confidential graphics processing units.","url":"https://doi.org/10.5281/zenodo.21334866","authors":["Dr. R. Pugazhenthi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21334866","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.21256/zhaw-25581","name":"Trusted execution environments : applications and organizational challenges","source":"datacite","abstract":"A lack of trust in the providers is still a major barrier to cloud computing adoption – especially when sensitive data is involved. While current privacy-enhancing technologies, such as homomorphic encryption, can increase security, they come with a considerable performance overhead. As an alternative Trusted Executing Environment (TEE) provides trust guarantees for code execution in the cloud similar to transport layer security for data transport or advanced encryption standard algorithms for data storage. Cloud infrastructure providers like Amazon, Google, and Microsoft introduced TEEs as part of their infrastructure offerings. This review will shed light on the different technological options of TEEs, as well as give insight into organizational issues regarding their usage.","url":"https://doi.org/10.21256/zhaw-25581","authors":["Geppert, Tim","Deml, Stefan","Sturzenegger, David","Ebert, Nico"],"tags":["Cloud computing","Confidential computing","SGX","Trusted execution environment","005: Computerprogrammierung, Programme und Daten"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21256/zhaw-25581","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.21746421","name":"Federated Learning for Privacy-Preserving Healthcare: Balancing Artificial Intelligence and Security in a Data-Driven Era","source":"datacite","abstract":"The integration of artificial intelligence (AI) into healthcare has transformed diagnostic practices, predictive modelling, and personalized treatment strategies. However, the exponential growth of sensitive patient data has raised significant challenges concerning privacy, data ownership, and cybersecurity risks. Federated learning (FL), an emerging paradigm in distributed machine learning, offers a promising solution by enabling collaborative model training across multiple institutions without centralizing raw patient data. This paper investigates the role of federated learning in privacy-preserving healthcare, emphasizing the balance between AI performance and security requirements. A systematic review of recent federated learning frameworks in healthcare is presented, followed by an exploration of encryption techniques, secure aggregation protocols, adversarial resilience, and regulatory compliance. The proposed framework outlines a hybrid model integrating federated learning with differential privacy, homomorphic encryption, and blockchain-based auditing to mitigate risks of data leakage and adversarial manipulation. The experimental results, based on simulated multi-hospital datasets, indicate that FL models can achieve comparable accuracy to centralized models while significantly enhancing privacy guarantees. This research underscores that federated learning is not merely a technological advancement but a strategic necessity for securing healthcare data while harnessing AI's transformative power. The paper concludes with recommendations for policy adoption, interdisciplinary collaboration, and the development of standardized protocols to accelerate federated learning adoption in global healthcare systems.","url":"https://doi.org/10.5281/zenodo.21746421","authors":["Kumar, Akhilesh"],"tags":["Federated Learning","Privacy-Preserving Healthcare","Artificial Intelligence","Cybersecurity","Secure Aggregation","Differential Privacy","Blockchain in Healthcare","Data Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.21746421","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.21746422","name":"Federated Learning for Privacy-Preserving Healthcare: Balancing Artificial Intelligence and Security in a Data-Driven Era","source":"datacite","abstract":"The integration of artificial intelligence (AI) into healthcare has transformed diagnostic practices, predictive modelling, and personalized treatment strategies. However, the exponential growth of sensitive patient data has raised significant challenges concerning privacy, data ownership, and cybersecurity risks. Federated learning (FL), an emerging paradigm in distributed machine learning, offers a promising solution by enabling collaborative model training across multiple institutions without centralizing raw patient data. This paper investigates the role of federated learning in privacy-preserving healthcare, emphasizing the balance between AI performance and security requirements. A systematic review of recent federated learning frameworks in healthcare is presented, followed by an exploration of encryption techniques, secure aggregation protocols, adversarial resilience, and regulatory compliance. The proposed framework outlines a hybrid model integrating federated learning with differential privacy, homomorphic encryption, and blockchain-based auditing to mitigate risks of data leakage and adversarial manipulation. The experimental results, based on simulated multi-hospital datasets, indicate that FL models can achieve comparable accuracy to centralized models while significantly enhancing privacy guarantees. This research underscores that federated learning is not merely a technological advancement but a strategic necessity for securing healthcare data while harnessing AI's transformative power. The paper concludes with recommendations for policy adoption, interdisciplinary collaboration, and the development of standardized protocols to accelerate federated learning adoption in global healthcare systems.","url":"https://doi.org/10.5281/zenodo.21746422","authors":["Kumar, Akhilesh"],"tags":["Federated Learning","Privacy-Preserving Healthcare","Artificial Intelligence","Cybersecurity","Secure Aggregation","Differential Privacy","Blockchain in Healthcare","Data Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.5281/zenodo.21746422","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.6084/m9.figshare.32964164.v1","name":"<b>Security Challenges in Large Language Models Across the Data Life Cycle: </b><b>A Cryptography-Aware Comprehensive Review</b>","source":"datacite","abstract":"Large Language Models (LLMs) are now embedded in security and privacy critical applications, yet they remain vulnerable to attacks that span their entire data life cycle. This survey provides a comprehensive, cryptography-aware review of these risks across three phases—training, inference, and deployment; while explicitly connecting them to classical security goals and primitives. We introduce a simple stage-wise risk scoring model inspired by NIST risk assessment that propagates vulnerabilities across the life cycle, and we instantiate it with a numeric example linking training time poisoning to inference time data extraction. We further propose a life cycle aligned evaluation framework that maps modern benchmarks (e.g., HarmBench, JailbreakBench, TrustLLM, DecodingTrust) to concrete threat classes and reports representative quantitative results, such as attack success rates under different defenses. Finally, we analyze the practicality of advanced defenses—including differential privacy, fully homomorphic encryption, secure multi-party computation, and zero knowledge proofs—in light of their computational overhead and deployment constraints, building on foundational cryptography and privacy works. Our goal is to bridge the gap between classical cryptographic theory and emerging LLM specific threats, and to outline research directions toward secure, privacy preserving, and rigorously evaluated LLM pipelines.","url":"https://doi.org/10.6084/m9.figshare.32964164.v1","authors":["sepehr Noroozi Chakoli","Seyed Ali Etrati","Seyed Mohammad Etrati","Hamid Haj Seyyed Javadi"],"tags":["Artificial intelligence not elsewhere classified","Artificial life and complex adaptive systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.6084/m9.figshare.32964164.v1","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.6084/m9.figshare.32964164","name":"<b>Security Challenges in Large Language Models Across the Data Life Cycle: </b><b>A Cryptography-Aware Comprehensive Review</b>","source":"datacite","abstract":"Large Language Models (LLMs) are now embedded in security and privacy critical applications, yet they remain vulnerable to attacks that span their entire data life cycle. This survey provides a comprehensive, cryptography-aware review of these risks across three phases—training, inference, and deployment; while explicitly connecting them to classical security goals and primitives. We introduce a simple stage-wise risk scoring model inspired by NIST risk assessment that propagates vulnerabilities across the life cycle, and we instantiate it with a numeric example linking training time poisoning to inference time data extraction. We further propose a life cycle aligned evaluation framework that maps modern benchmarks (e.g., HarmBench, JailbreakBench, TrustLLM, DecodingTrust) to concrete threat classes and reports representative quantitative results, such as attack success rates under different defenses. Finally, we analyze the practicality of advanced defenses—including differential privacy, fully homomorphic encryption, secure multi-party computation, and zero knowledge proofs—in light of their computational overhead and deployment constraints, building on foundational cryptography and privacy works. Our goal is to bridge the gap between classical cryptographic theory and emerging LLM specific threats, and to outline research directions toward secure, privacy preserving, and rigorously evaluated LLM pipelines.","url":"https://doi.org/10.6084/m9.figshare.32964164","authors":["sepehr Noroozi Chakoli","Seyed Ali Etrati","Seyed Mohammad Etrati","Hamid Haj Seyyed Javadi"],"tags":["Artificial intelligence not elsewhere classified","Artificial life and complex adaptive systems"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.6084/m9.figshare.32964164","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.20692339","name":"Humans First: From Threats to PETs in Human-Machine Interaction","source":"datacite","abstract":"Modern Human-Machine Interaction (HMI) systems increasingly rely on intimate physiological, behavioral, and cognitive data to enable adaptive interaction between humans and machines. While this opens transformative possibilities across domains such as healthcare, industrial automation, and assistive technologies, it also introduces substantial privacy, security, and safety risks that can directly translate into human harm, including ethical and societal consequences. Yet existing research tends to discuss these risks in isolation and fragmented, and the practical applicability of Privacy-Enhancing Technologies (PETs) in realistic, resource-constrained HMI environments remains insufficiently explored. This thesis addresses these gaps through three contributions. First, a systematic taxonomy-driven literature review examines risks and protections in Brain-Computer Interfaces (BCIs), a particularly high-stakes area of HMI in which direct neural interfacing makes these risks especially acute, mapping technical threats to potential human harms and examining the role of PETs as protective strategies. Second, a realistic HMI prototype integrates homomorphic encryption (HE) into a physiological stress-monitoring pipeline on constrained embedded hardware. Third, the prototype is evaluated from both technical and human-centered perspectives, demonstrating that HE preserves classification performance while introducing substantial computational overhead. Nevertheless, the results indicate that privacy-preserving machine learning using HE is feasible even on constrained embedded hardware and can realistically be integrated into practical HMI scenarios. At the same time, the evaluation highlights that HE provides a strong but narrow privacy guarantee and therefore represents only one building block within a broader, holistic approach to privacy, security, and safety in HMI systems. Overall, this thesis contributes to a holistic, human-centered analysis of risks in HMI, demonstrates the practical viability of PETs under real-world constraints, and highlights the research gaps that must be addressed to achieve meaningful protection in future HMI systems.","url":"https://doi.org/10.5281/zenodo.20692339","authors":["Bergermann, Justus"],"tags":["Human–Machine Interaction (HMI)","Brain-Computer Interfaces (BCI)","Privacy","Security","Safety","Human Harm","Human-Centered Security","Threat Modeling"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20692339","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.20692340","name":"Humans First: From Threats to PETs in Human-Machine Interaction","source":"datacite","abstract":"Modern Human-Machine Interaction (HMI) systems increasingly rely on intimate physiological, behavioral, and cognitive data to enable adaptive interaction between humans and machines. While this opens transformative possibilities across domains such as healthcare, industrial automation, and assistive technologies, it also introduces substantial privacy, security, and safety risks that can directly translate into human harm, including ethical and societal consequences. Yet existing research tends to discuss these risks in isolation and fragmented, and the practical applicability of Privacy-Enhancing Technologies (PETs) in realistic, resource-constrained HMI environments remains insufficiently explored. This thesis addresses these gaps through three contributions. First, a systematic taxonomy-driven literature review examines risks and protections in Brain-Computer Interfaces (BCIs), a particularly high-stakes area of HMI in which direct neural interfacing makes these risks especially acute, mapping technical threats to potential human harms and examining the role of PETs as protective strategies. Second, a realistic HMI prototype integrates homomorphic encryption (HE) into a physiological stress-monitoring pipeline on constrained embedded hardware. Third, the prototype is evaluated from both technical and human-centered perspectives, demonstrating that HE preserves classification performance while introducing substantial computational overhead. Nevertheless, the results indicate that privacy-preserving machine learning using HE is feasible even on constrained embedded hardware and can realistically be integrated into practical HMI scenarios. At the same time, the evaluation highlights that HE provides a strong but narrow privacy guarantee and therefore represents only one building block within a broader, holistic approach to privacy, security, and safety in HMI systems. Overall, this thesis contributes to a holistic, human-centered analysis of risks in HMI, demonstrates the practical viability of PETs under real-world constraints, and highlights the research gaps that must be addressed to achieve meaningful protection in future HMI systems.","url":"https://doi.org/10.5281/zenodo.20692340","authors":["Bergermann, Justus"],"tags":["Human–Machine Interaction (HMI)","Brain-Computer Interfaces (BCI)","Privacy","Security","Safety","Human Harm","Human-Centered Security","Threat Modeling"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20692340","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2605.29450","name":"Protecting On-Device AI Inference: A Systematic Review of Attacks and Defence Mechanisms","source":"datacite","abstract":"The need for secure and private Artificial Intelligence (AI) and Machine Learning (ML) on edge and mobile devices has increased the necessity of protecting the architecture of these systems from threats to both security and privacy. With an ever-increasing number of pre-trained AI models being used on mobile platforms for client-side inference, there are rising concerns about the risks associated with the theft/extraction of AI models, adversarial attacks on AI models, and data breaches. As a result of this trend, a variety of defence mechanisms have been proposed to protect against these threats. These include Trusted Execution Environments (TEEs), homomorphic encryption, obfuscation, and differential privacy, among others. However, current surveys largely focus on edge intelligence, which includes distributed training, and thus overlook security and privacy issues that are specific to on-device AI inference. To the best of our knowledge, this paper presents the first comprehensive review of threats and corresponding defence mechanisms targeting on-device inference. Our results show that the attack and defence literature are unbalanced: approximately one quarter of the surveyed attack papers focus on Intellectual Property (IP) attacks, whereas half of the defence solutions tackle the same issue. More importantly, some attack categories have no defence paper associated to them, such as adversarial attacks that account for roughly one third of the attack literature. This asymmetry between known attacks and available mitigations highlights clear opportunities for future research on securing on-device AI inference.","url":"https://doi.org/10.48550/arxiv.2605.29450","authors":["Tsiatsikas, Zisis","Fakis, Alexandros","Karopoulos, Georgios","Kouliaridis, Vasileios","Anagnostopoulos, Marios"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2605.29450","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.71886/bioem.2026.1224382","name":"Federated Correction of Batch Effects &amp; Heterogeneity in Single-cell and Multi-omics Genomics (privacy-preserving)","source":"datacite","abstract":"The rapid proliferation of genomic data from large‑scale sequencing initiatives presents unprecedented opportunities for precision medicine, population genomics and biotechnology. However, the sensitive nature of genomic information—uniquely identifying, immutable and deeply personal—poses critical security and privacy challenges. Traditional methods of data protection (anonymisation, access control) are increasingly inadequate in the face of advanced attacks (membership inference, model inversion) and large‑scale AI analysis. This paper explores the development of artificial intelligence (AI)‑based methods to secure genomic data throughout its lifecycle: from storage and sharing to analysis and model training. We review technical approaches including federated learning, homomorphic encryption, secure multi‑party computation, differential privacy and generative synthetic‑data modelling, each designed to mitigate risk while enabling genomic‑AI workflows. We present a hypothetical benchmarking study where a federated‑learning pipeline augmented with differential‑privacy noise and encrypted aggregation reduced membership inference risk by ~45 % compared with naïve central models, while retaining &gt;90 % of predictive utility. Tabulated results demonstrate trade‑offs between utility, latency and privacy budget. We discuss key methodological details—feature extraction, model architecture, privacy budget calibration—and highlight deployment considerations: interpretability, regulatory compliance (GDPR, HIPAA), adversarial threats and quantum‑resistant cryptography. Future perspectives emphasise hybrid AI‑cryptography frameworks, standardised privacy metrics for genomics, and governance models embedding privacy‑by‑design. In conclusion, AI‑based security methods are critical enablers for responsible genomic‑AI research and clinical translation, offering a path toward privacy‑preserving genomics at scale.","url":"https://doi.org/10.71886/bioem.2026.1224382","authors":["Mohamed Sikkander, Dr Abdul Razak","Rodrigues , Joel J. P. C.","Meena, Manoharan  "," Abuelmakarem, Hala S."],"tags":["genomic data security","artificial intelligence","federated learning","homomorphic encryption","differential privacy","membership inference attacks"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.71886/bioem.2026.1224382","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2603.22437","name":"mmFHE: mmWave Sensing with End-to-End Fully Homomorphic Encryption","source":"datacite","abstract":"We present mmFHE, the first system that enables fully homomorphic encryption (FHE) for end-to-end mmWave radar sensing. mmFHE encrypts raw range profiles on a lightweight edge device and executes the entire mmWave signal-processing and ML inference pipeline homomorphically on an untrusted cloud that operates exclusively on ciphertexts. At the core of mmFHE is a library of seven composable, data-oblivious FHE kernels that replace standard DSP routines with fixed arithmetic circuits. These kernels can be flexibly composed into different application-specific pipelines. We demonstrate this approach on two representative tasks: vital-sign monitoring and gesture recognition. We formally prove two cryptographic guarantees for any pipeline assembled from this library: input privacy, the cloud learns nothing about the sensor data; and data obliviousness, the execution trace is identical on the cloud regardless of the data being processed. These guarantees effectively neutralize various supervised and unsupervised privacy attacks on raw data, including re-identification and data-dependent privacy leakage. Evaluation on three public radar datasets (270 vital-sign recordings, 600 gesture trials) shows that encryption introduces negligible error: HR/RR MAE &lt;10^-3 bpm versus plaintext, and 84.5% gesture accuracy (vs. 84.7% plaintext) with end-to-end cloud GPU latency of 103s for a 10s vital-sign window and 37s for a 3s gesture window. These results show that privacy-preserving end-to-end mmWave sensing is feasible on commodity hardware today.","url":"https://doi.org/10.48550/arxiv.2603.22437","authors":["Ahmed, Tanvir","Gao, Yixuan","Armouti, Adnan","Nandakumar, Rajalakshmi"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","Signal Processing (eess.SP)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.22437","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.26181/22357747.v1","name":"A Review of Homomorphic Encryption for Privacy-Preserving Biometrics","source":"datacite","abstract":"The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward.","url":"https://doi.org/10.26181/22357747.v1","authors":["Yang, Wencheng","Wang, Song","Cui, Hui","Tang, Zhaohui","Li, Yan"],"tags":["Electrical engineering","Electronics, sensors and digital hardware","Information and computing sciences","Cybersecurity and privacy","Distributed computing and systems software"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.26181/22357747.v1","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.26181/22357747","name":"A Review of Homomorphic Encryption for Privacy-Preserving Biometrics","source":"datacite","abstract":"The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward.","url":"https://doi.org/10.26181/22357747","authors":["Yang, Wencheng","Wang, Song","Cui, Hui","Tang, Zhaohui","Li, Yan"],"tags":["Electrical engineering","Electronics, sensors and digital hardware","Information and computing sciences","Cybersecurity and privacy","Distributed computing and systems software"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.26181/22357747","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18679810","name":"Protocol of the Post-Quantum Cryptography (PQC) Breaking via 165-Dimensional Tensor of the Hamzah Equation.(Theory Only non-Invasive Purpose)","source":"datacite","abstract":"Breaking Post-Quantum Cryptography (PQC) اَبَر-لاگرانژیِ تکینگیِ تانسوری برای انحلال PQC $$\\mathcal{L}_{Intelligence}^{(165)} = \\int_{\\mathcal{V}_{165}} \\left[ \\underbrace{\\mathcal{Q}_{H} \\left( \\mathbb{D}_{\\alpha\\beta}^{\\gamma} \\cdot \\frac{\\partial \\mathcal{I}_{core}}{\\partial \\phi_{sync}} \\right)}_{\\text{Dimensional Neural Projection}} + \\underbrace{\\Xi_{\\mu\\nu} \\left( \\mathcal{R}^{\\mu\\nu}_{161} + \\Lambda_{H} g^{\\mu\\nu} \\right) \\star \\mathcal{P}_{log}}_{\\text{Latent Metric Rendering}} - \\underbrace{\\frac{\\hbar_{H} \\oint \\nabla \\psi \\otimes \\nabla \\psi^*}{\\exp(\\mathcal{S}_{entropy})} }_{\\text{Algorithmic Stabilization}} \\right] \\sqrt{-\\mathbb{G}_{165}} \\, d^{4}\\Omega$$ ۳. کالبدشکافی پارامترها در انحلال Kyber و Dilithium ترم ۱ ($\\mathbb{D}_{\\alpha\\beta}^{\\gamma}$): این تانسور مسئولِ نگاشتِ فضاهایِ شبکه (Lattice) به مانیفولدهای خمیده است. طبق سند RAMZHA.txt و روش IFS، این ترم اجازه می‌دهد که فضای جستجو از $2^n$ به $2^{\\log n}$ تقلیل یابد. هوش مصنوعی به جای گشتن در فضای تخت، در \"خمیدگیِ اطلاعات\" به سمتِ جواب سُر می‌خورد. ترم ۲ ($\\Xi_{\\mu\\nu}$): اپراتور رندرینگِ مترییک. این بخش «معنای فیزیکی» کلید را در لایه ۱۶۱ استخراج می‌کند. به جای حلِ ریاضی، هوش مصنوعی لایه ۱۶۱ را رندر می‌کند تا ببیند کدام \"بیت\" با فرکانسِ حیات ($\\phi_{sync}$) همگام است. ترم ۳ ($\\exp(\\mathcal{S}_{entropy})$): سرکوبِ نویزِ گرمایی و آنتروپی. این ترم باعث می‌شود که نوساناتِ تصادفی در محاسباتِ کوانتومیِ PQC حذف شده و فقط \"سیگنالِ پاکِ کلید\" باقی بماند. انحلال قطعی رمزنگاری پسا-کوانتومی (PQC Dissolution)؛ اثبات \"برتری تانسوری ۱۶۵ بعدی\" و ردِ امنیتِ شبکه (Lattice-Based Collapse: HQI-ZB56 Ω Paradigm) ۱. مقدمه مفصل و بیان مسئله فنی (The Cryptographic Breach) الگوریتم‌های PQC مانند Kyber و Dilithium بر پایه سختیِ مسائل شبکه (مانند LWE) طراحی شده‌اند تا در برابر کامپیوترهای کوانتومی فعلی مقاوم باشند. ایراد کلاسیک (The Reject Logic): امنیت این پروتکل‌ها بر \"پیچیدگی محاسباتی\" استوار است. یعنی فرض بر این است که پیدا کردن نزدیک‌ترین بردار در یک فضای چندبعدی (SVP) برای همیشه زمان‌بر خواهد بود. این مدل، امکانِ میان‌بر زدن از طریق «تونل‌زنی اطلاعاتی» در ابعاد بالاتر را نادیده می‌گیرد (Reject). برتری حمزه (The Approve Logic): سورس‌کد حمزه فاش می‌کند که هر کلید رمزنگاری شده، در واقع یک «فراکتالِ اطلاعاتی» در لایه ۱۶۱ است. با استفاده از تانسور ۱۶۵ بعدی، فضای جستجو از $2^n$ به $n$ تقلیل می‌یابد. ما در شبکه نمی‌گردیم، ما شبکه را در لایه ۱۶۱ «دی-رندر» (De-render) می‌کنیم (Approve). ۲. معادله لاگرانژیِ انحلالِ رمزنگاری ($\\mathcal{L}_{PQC}$) فرمول شکستن کدهای پسا-کوانتومی با استفاده از پتانسیل حمزه: $$\\mathcal{L}_{PQC} = \\int \\left[ \\underbrace{\\mathcal{K}_H \\cdot \\text{Tr}(\\mathbf{T}_{165} \\otimes \\nabla \\Phi_{key})}_{\\text{Tensor Key Extraction}} - \\underbrace{\\lambda \\cdot H(\\text{Lattice})}_{\\text{Entropy Dissipation}} \\right] \\otimes \\Omega_H^* \\, dV$$ تحلیل: این معادله ثابت می‌کند که جریان اطلاعات کلید ($\\Phi_{key}$) تحت تاثیر تانسور ۱۶۵ بعدی، آنتروپیِ شبکه ($H$) را به صفر میل می‌دهد. در این تراز، رمزنگاری پسا-کوانتومی دیگر یک \"مسئله سخت\" نیست، بلکه یک \"خروجیِ جبریِ ساده\" است. ۳. جدول ۱۷: استرس‌تست انحلال پروتکل‌های PQC (The PQC Audit) ردیف مورد تست هدف (Target) خروجی کلاسیک (Reject) خروجی حمزه (Approve) نتیجه تست استرس ۱۳۹ الگوریتم Kyber Lattice-based (LWE) غیرقابل نفوذ (PQC) فروپاشی در لایه ۱۶۱ Absolute Break ۱۴۰ الگوریتم Dilithium Digital Signature امنیت ۱۰ ساله جعل آنی با تانسور ۸۰ Absolute Break ۱۴۱ مسئله SVP Shortest Vector Problem پیچیدگی نمایی حل خطی با $\\Omega_H^*$ Absolute Break ۱۴۲ فضای جستجوی کلید $2^{256}$ حالت میلیاردها سال پردازش تقلیل به کدهای ۱۶ بیتی Absolute Break ۱۴۳ فایروال کوانتومی Quantum Firewall غیرقابل عبور تونل‌زنی دیتای HQI Absolute Break ۱۴۴ پروتکل رمزنگاری NIST استاندارد جهانی امنیت مطلق اثبات نشت اطلاعاتی در سورس Absolute Break ۴. تحلیل \"کاهش فضای جستجو\" (Quantum Search Space Reduction) بر اساس اسناد فایل RAMZHA.txt: سورس‌کد حمزه: در حالت کلاسیک، الگوریتم گروور برای یافتن کلید نیاز به $2^{n/2}$ عملیات دارد. اما در مدل حمزه، با استفاده از نگاشت IFS (Iterated Function Systems)، فضای جستجو به صورت فراکتالی کوچک می‌شود. یعنی ما به جای گشتن در کل اقیانو","url":"https://doi.org/10.5281/zenodo.18679810","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18679810","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18679811","name":"Protocol of the Post-Quantum Cryptography (PQC) Breaking via 165-Dimensional Tensor of the Hamzah Equation.(Theory Only non-Invasive Purpose)","source":"datacite","abstract":"Breaking Post-Quantum Cryptography (PQC) اَبَر-لاگرانژیِ تکینگیِ تانسوری برای انحلال PQC $$\\mathcal{L}_{Intelligence}^{(165)} = \\int_{\\mathcal{V}_{165}} \\left[ \\underbrace{\\mathcal{Q}_{H} \\left( \\mathbb{D}_{\\alpha\\beta}^{\\gamma} \\cdot \\frac{\\partial \\mathcal{I}_{core}}{\\partial \\phi_{sync}} \\right)}_{\\text{Dimensional Neural Projection}} + \\underbrace{\\Xi_{\\mu\\nu} \\left( \\mathcal{R}^{\\mu\\nu}_{161} + \\Lambda_{H} g^{\\mu\\nu} \\right) \\star \\mathcal{P}_{log}}_{\\text{Latent Metric Rendering}} - \\underbrace{\\frac{\\hbar_{H} \\oint \\nabla \\psi \\otimes \\nabla \\psi^*}{\\exp(\\mathcal{S}_{entropy})} }_{\\text{Algorithmic Stabilization}} \\right] \\sqrt{-\\mathbb{G}_{165}} \\, d^{4}\\Omega$$ ۳. کالبدشکافی پارامترها در انحلال Kyber و Dilithium ترم ۱ ($\\mathbb{D}_{\\alpha\\beta}^{\\gamma}$): این تانسور مسئولِ نگاشتِ فضاهایِ شبکه (Lattice) به مانیفولدهای خمیده است. طبق سند RAMZHA.txt و روش IFS، این ترم اجازه می‌دهد که فضای جستجو از $2^n$ به $2^{\\log n}$ تقلیل یابد. هوش مصنوعی به جای گشتن در فضای تخت، در \"خمیدگیِ اطلاعات\" به سمتِ جواب سُر می‌خورد. ترم ۲ ($\\Xi_{\\mu\\nu}$): اپراتور رندرینگِ مترییک. این بخش «معنای فیزیکی» کلید را در لایه ۱۶۱ استخراج می‌کند. به جای حلِ ریاضی، هوش مصنوعی لایه ۱۶۱ را رندر می‌کند تا ببیند کدام \"بیت\" با فرکانسِ حیات ($\\phi_{sync}$) همگام است. ترم ۳ ($\\exp(\\mathcal{S}_{entropy})$): سرکوبِ نویزِ گرمایی و آنتروپی. این ترم باعث می‌شود که نوساناتِ تصادفی در محاسباتِ کوانتومیِ PQC حذف شده و فقط \"سیگنالِ پاکِ کلید\" باقی بماند. انحلال قطعی رمزنگاری پسا-کوانتومی (PQC Dissolution)؛ اثبات \"برتری تانسوری ۱۶۵ بعدی\" و ردِ امنیتِ شبکه (Lattice-Based Collapse: HQI-ZB56 Ω Paradigm) ۱. مقدمه مفصل و بیان مسئله فنی (The Cryptographic Breach) الگوریتم‌های PQC مانند Kyber و Dilithium بر پایه سختیِ مسائل شبکه (مانند LWE) طراحی شده‌اند تا در برابر کامپیوترهای کوانتومی فعلی مقاوم باشند. ایراد کلاسیک (The Reject Logic): امنیت این پروتکل‌ها بر \"پیچیدگی محاسباتی\" استوار است. یعنی فرض بر این است که پیدا کردن نزدیک‌ترین بردار در یک فضای چندبعدی (SVP) برای همیشه زمان‌بر خواهد بود. این مدل، امکانِ میان‌بر زدن از طریق «تونل‌زنی اطلاعاتی» در ابعاد بالاتر را نادیده می‌گیرد (Reject). برتری حمزه (The Approve Logic): سورس‌کد حمزه فاش می‌کند که هر کلید رمزنگاری شده، در واقع یک «فراکتالِ اطلاعاتی» در لایه ۱۶۱ است. با استفاده از تانسور ۱۶۵ بعدی، فضای جستجو از $2^n$ به $n$ تقلیل می‌یابد. ما در شبکه نمی‌گردیم، ما شبکه را در لایه ۱۶۱ «دی-رندر» (De-render) می‌کنیم (Approve). ۲. معادله لاگرانژیِ انحلالِ رمزنگاری ($\\mathcal{L}_{PQC}$) فرمول شکستن کدهای پسا-کوانتومی با استفاده از پتانسیل حمزه: $$\\mathcal{L}_{PQC} = \\int \\left[ \\underbrace{\\mathcal{K}_H \\cdot \\text{Tr}(\\mathbf{T}_{165} \\otimes \\nabla \\Phi_{key})}_{\\text{Tensor Key Extraction}} - \\underbrace{\\lambda \\cdot H(\\text{Lattice})}_{\\text{Entropy Dissipation}} \\right] \\otimes \\Omega_H^* \\, dV$$ تحلیل: این معادله ثابت می‌کند که جریان اطلاعات کلید ($\\Phi_{key}$) تحت تاثیر تانسور ۱۶۵ بعدی، آنتروپیِ شبکه ($H$) را به صفر میل می‌دهد. در این تراز، رمزنگاری پسا-کوانتومی دیگر یک \"مسئله سخت\" نیست، بلکه یک \"خروجیِ جبریِ ساده\" است. ۳. جدول ۱۷: استرس‌تست انحلال پروتکل‌های PQC (The PQC Audit) ردیف مورد تست هدف (Target) خروجی کلاسیک (Reject) خروجی حمزه (Approve) نتیجه تست استرس ۱۳۹ الگوریتم Kyber Lattice-based (LWE) غیرقابل نفوذ (PQC) فروپاشی در لایه ۱۶۱ Absolute Break ۱۴۰ الگوریتم Dilithium Digital Signature امنیت ۱۰ ساله جعل آنی با تانسور ۸۰ Absolute Break ۱۴۱ مسئله SVP Shortest Vector Problem پیچیدگی نمایی حل خطی با $\\Omega_H^*$ Absolute Break ۱۴۲ فضای جستجوی کلید $2^{256}$ حالت میلیاردها سال پردازش تقلیل به کدهای ۱۶ بیتی Absolute Break ۱۴۳ فایروال کوانتومی Quantum Firewall غیرقابل عبور تونل‌زنی دیتای HQI Absolute Break ۱۴۴ پروتکل رمزنگاری NIST استاندارد جهانی امنیت مطلق اثبات نشت اطلاعاتی در سورس Absolute Break ۴. تحلیل \"کاهش فضای جستجو\" (Quantum Search Space Reduction) بر اساس اسناد فایل RAMZHA.txt: سورس‌کد حمزه: در حالت کلاسیک، الگوریتم گروور برای یافتن کلید نیاز به $2^{n/2}$ عملیات دارد. اما در مدل حمزه، با استفاده از نگاشت IFS (Iterated Function Systems)، فضای جستجو به صورت فراکتالی کوچک می‌شود. یعنی ما به جای گشتن در کل اقیانو","url":"https://doi.org/10.5281/zenodo.18679811","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18679811","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.17632/p9w2fdtc7k.1","name":"Data Extraction Dataset for a Systematic Literature Review on Machine Learning in Encrypted Data and Homomorphic Encryption","source":"datacite","abstract":"This is the data extraction dataset for a systematic literature review on machine learning in encrypted data and homomorphic encryption study.","url":"https://doi.org/10.17632/p9w2fdtc7k.1","authors":["sen, utku faruk"],"tags":["Cryptography","Machine Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17632/p9w2fdtc7k.1","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.17632/p9w2fdtc7k","name":"Data Extraction Dataset for a Systematic Literature Review on Machine Learning in Encrypted Data and Homomorphic Encryption","source":"datacite","abstract":"This is the data extraction dataset for a systematic literature review on machine learning in encrypted data and homomorphic encryption study.","url":"https://doi.org/10.17632/p9w2fdtc7k","authors":["sen, utku faruk"],"tags":["Cryptography","Machine Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17632/p9w2fdtc7k","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2602.11470","name":"Cachemir: Fully Homomorphic Encrypted Inference of Generative Large Language Model with KV Cache","source":"datacite","abstract":"Generative large language models (LLMs) have revolutionized multiple domains. Modern LLMs predominantly rely on an autoregressive decoding strategy, which generates output tokens sequentially and employs a key-value cache (KV cache) to avoid redundant computation. However, the widespread deployment of LLMs has raised serious privacy concerns, as users are feeding all types of data into the model, motivating the development of secure inference frameworks based on fully homomorphic encryption (FHE). A major limitation of existing FHE-based frameworks is their inability to effectively integrate the KV cache, resulting in prohibitively high latency for autoregressive decoding. In this paper, we propose Cachemir, a KV Cache Accelerated Homomorphic Encrypted LLM Inference Regime to overcome this limitation. Cachemir comprises three key technical contributions: 1) a set of novel HE packing algorithms specifically designed to leverage the computational advantages of the KV cache; 2) an interleaved replicated packing algorithm to efficiently compute the vector-matrix multiplications that result from using the KV cache in Transformer linear layers; and 3) an augmented bootstrapping placement strategy that accounts for the KV cache to minimize bootstrapping cost. We demonstrate that Cachemir achieves $48.83\\times$ and $67.16\\times$ speedup over MOAI (ICML'25) and THOR (CCS'25) respectively on CPU and consumes less than 100 seconds on GPU to generate an output token for Llama-3-8B.","url":"https://doi.org/10.48550/arxiv.2602.11470","authors":["Yu, Ye","Zhou, Yifan","Chen, Yi","Soto, Pedro","Xiong, Wenjie","Li, Meng"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.11470","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2602.02717","name":"On the Feasibility of Hybrid Homomorphic Encryption for Intelligent Transportation Systems","source":"datacite","abstract":"Many Intelligent Transportation Systems (ITS) applications require strong privacy guarantees for both users and their data. Homomorphic encryption (HE) enables computation directly on encrypted messages and thus offers a compelling approach to privacy-preserving data processing in ITS. However, practical HE schemes incur substantial ciphertext expansion and communication overhead, which limits their suitability for time-critical transportation systems. Hybrid homomorphic encryption (HHE) addresses this challenge by combining a homomorphic encryption scheme with a symmetric cipher, enabling efficient encrypted computation while dramatically reducing communication cost. In this paper, we develop theoretical models of representative ITS applications that integrate HHE to protect sensitive vehicular data. We then perform a parameter-based evaluation of the HHE scheme Rubato to estimate ciphertext sizes and communication overhead under realistic ITS workloads. Our results show that HHE achieves orders-of-magnitude reductions in ciphertext size compared with conventional HE while maintaining cryptographic security, making it significantly more practical for latency-constrained ITS communication.","url":"https://doi.org/10.48550/arxiv.2602.02717","authors":["Yates, Kyle","Mamun, Abdullah Al","Chowdhury, Mashrur"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.02717","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18377438","name":"Cryptographically Enforced Relay-Resistant Offline Payment System Using Algorithmic Logic Fingerprinting and Split Execution Architecture for Digital Euro and CBDC Systems","source":"datacite","abstract":"Abstract This paper ( Patent pending concept ) presents a novel cryptographic framework for securing offline digital currency payment systems, including Central Bank Digital Currencies (CBDCs) such as the Digital Euro, against relay and replay attacks. The architecture integrates Split Execution (separating computation from authorization), Algorithmic Logic Fingerprinting (ALF) for logic-class validation, and cryptographically bound Virtual Identities (VI) and Compliance Jurisdiction Tokens (CJT). Offline payment authorization is evaluated within a Trusted Execution Environment (TEE), ensuring that cryptographic execution predicates are verified in-device before any value release. This design prevents cloned or remotely relayed tokens from being executed, as all authorization logic is session-scoped, device-specific, and context-bound. The system enforces relay resistance without relying on proximity checks, GPS, or online infrastructure—thereby supporting fully offline, privacy-preserving CBDC payments. It remains compliant with regulatory requirements through generation of Ledger-Anchored Validation Receipts (LAVR), providing auditability without user surveillance. The proposed solution is particularly suited for Digital Euro deployments, public transit systems, and unconnected retail environments, addressing a long-standing security gap in CBDC and digital cash architectures.","url":"https://doi.org/10.5281/zenodo.18377438","authors":["Das, Sangam"],"tags":["digital currencies","cbdc","digital euro","cash like privacy","stable coins","ecb","central banks","crypto currencies"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18377438","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18463973","name":"Towards Secure and Privacy-Preserving Query Processing for Encrypted Big Data in Multi-Cloud Environments: A Systematic Review","source":"datacite","abstract":"The rapid growth of cloud computing and big data analytics has intensified concerns over privacy when sensitive data are outsourced to third-party cloud providers. Traditional encryption techniques protect data confidentiality but significantly limit the ability to perform expressive and efficient queries, particularly in distributed and multi-cloud environments. Motivated by the increasing demand for secure analytics across healthcare, finance, IoT, and collaborative cloud platforms, this review systematically examines privacy-preserving query processing techniques for encrypted data in multi-cloud settings. Following PRISMA guidelines, a systematic literature review of published peer-reviewed studies is conducted. The reviewed approaches are categorized into homomorphic encryption-based methods, searchable encryption techniques, secure multi-party computation, trusted execution environments, and hybrid architectures. The analysis highlights key trade-offs among privacy guarantees, query expressiveness, computational efficiency, and scalability. While hybrid and multi-cloud approaches improve flexibility and fault tolerance, they introduce new challenges related to leakage, communication overhead, and trust assumptions. This review identifies critical research gaps, including limited real-time support, side-channel vulnerabilities, and the absence of standardized benchmarks. Finally, future research directions are outlined, emphasizing AI-assisted encrypted querying, federated analytics, and post-quantum privacy-preserving frameworks for multi-cloud environments.","url":"https://doi.org/10.5281/zenodo.18463973","authors":["Abdullahi, Ibrahim Rashid"],"tags":["Privacy-preserving query processing","Encrypted big data","multi-cloud security","Homomorphic encryption and Secure multi-party computation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18463973","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18463974","name":"Towards Secure and Privacy-Preserving Query Processing for Encrypted Big Data in Multi-Cloud Environments: A Systematic Review","source":"datacite","abstract":"The rapid growth of cloud computing and big data analytics has intensified concerns over privacy when sensitive data are outsourced to third-party cloud providers. Traditional encryption techniques protect data confidentiality but significantly limit the ability to perform expressive and efficient queries, particularly in distributed and multi-cloud environments. Motivated by the increasing demand for secure analytics across healthcare, finance, IoT, and collaborative cloud platforms, this review systematically examines privacy-preserving query processing techniques for encrypted data in multi-cloud settings. Following PRISMA guidelines, a systematic literature review of published peer-reviewed studies is conducted. The reviewed approaches are categorized into homomorphic encryption-based methods, searchable encryption techniques, secure multi-party computation, trusted execution environments, and hybrid architectures. The analysis highlights key trade-offs among privacy guarantees, query expressiveness, computational efficiency, and scalability. While hybrid and multi-cloud approaches improve flexibility and fault tolerance, they introduce new challenges related to leakage, communication overhead, and trust assumptions. This review identifies critical research gaps, including limited real-time support, side-channel vulnerabilities, and the absence of standardized benchmarks. Finally, future research directions are outlined, emphasizing AI-assisted encrypted querying, federated analytics, and post-quantum privacy-preserving frameworks for multi-cloud environments.","url":"https://doi.org/10.5281/zenodo.18463974","authors":["Abdullahi, Ibrahim Rashid"],"tags":["Privacy-preserving query processing","Encrypted big data","multi-cloud security","Homomorphic encryption and Secure multi-party computation"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18463974","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.18280900","name":"Survey of Privacy Preserving Techniques for Distributed Learning in an IoT Network","source":"datacite","abstract":"This survey provides a comprehensive review of privacy-preserving techniques applicable to distributed learning in Internet-of-Things (IoT) environments. The paper examines classical approaches, including Differential Privacy, Homomorphic Encryption, Secure Multi-party Computation, Distributed Selective Stochastic Gradient Descent, and Anonymization, as well as more recent methods such as additive and multiplicative schemes, blockchain-based mechanisms, Bloom Filter–based preprocessing, and intrusion detection systems. Each technique is analyzed with respect to its ability to protect data privacy and its suitability for deployment on resource-constrained IoT devices. Background information on IoT architectures, device limitations, and distributed learning paradigms is provided to contextualize the discussion. The survey evaluates trade-offs among computational overhead, memory usage, communication requirements, and privacy protection, and offers guidance for selecting appropriate techniques based on application requirements and device capabilities.","url":"https://doi.org/10.5281/zenodo.18280900","authors":["Cartmell, John"],"tags":["Internet of Things","Distributed Learning","Privacy Preservation","Surveys"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18280900","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2601.06710","name":"Privacy-Preserving Data Processing in Cloud : From Homomorphic Encryption to Federated Analytics","source":"datacite","abstract":"Privacy-preserving data processing refers to the methods and models that allow computing and analyzing sensitive data with a guarantee of confidentiality. As cloud computing and applications that rely on data continue to expand, there is an increasing need to protect personal, financial and healthcare information. Conventional centralized data processing methods expose sensitive data to risk of breaches, compelling the need to use decentralized and secure data methods. This paper gives a detailed review of privacy-saving mechanisms in the cloud platform, such as statistical approaches like differential privacy and cryptographic solutions like homomorphic encryption. Federated analytics and federated learning, two distributed learning frameworks, are also discussed. Their principles, applications, benefits, and limitations are reviewed, with roles of use in the fields of healthcare, finance, IoT, and industrial cases. Comparative analyses measure trade-offs in security, efficiency, scalability, and accuracy, and investigations are done of emerging hybrid frameworks to provide better privacy protection. Critical issues, including computational overhead, privacy-utility trade-offs, standardization, adversarial threats, and cloud integration are also addressed. This review examines in detail the recent privacy-protecting approaches in cloud computation and offers scholars and practitioners crucial information on secure and effective solutions to data processing.","url":"https://doi.org/10.48550/arxiv.2601.06710","authors":["Sarraf, Gaurav","Pal, Vibhor"],"tags":["Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2601.06710","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.48550/arxiv.2501.10182","name":"Secure Semantic Communication With Homomorphic Encryption","source":"datacite","abstract":"In recent years, Semantic Communication (SemCom), which aims to achieve efficient and reliable transmission of meaning between agents, has garnered significant attention from both academia and industry. To ensure the security of communication systems, encryption techniques are employed to safeguard confidentiality and integrity. However, existing encryption schemes encounter obstacles when applied to SemCom. To address this issue, this paper explores the feasibility of applying homomorphic encryption (HE) to SemCom. Initially, we review the encryption algorithms utilized in mobile communication systems and analyze the challenges associated with their application to SemCom. Subsequently, we overview HE techniques and employ scale-invariant feature transform (SIFT) to demonstrate that the extractable semantic information can be preserved in homomorphic encrypted ciphertext. Based on this finding, we further propose the HE-joint source-channel coding (HE-JSCC) scheme, where the traditional JSCC model architecture is modified to support HE operations. Moreover, we present the simulation results for image classification and image generation tasks. Furthermore, we provide potential future research directions for homomorphic encrypted SemCom.","url":"https://doi.org/10.48550/arxiv.2501.10182","authors":["Meng, Rui","Fan, Dayu","Gao, Haixiao","Yuan, Yifan","Wang, Bizhu","Xu, Xiaodong","Sun, Mengying","Dong, Chen","Tao, Xiaofeng","Zhang, Ping","Niyato, Dusit"],"tags":["Cryptography and Security (cs.CR)","Signal Processing (eess.SP)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.10182","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.17605/osf.io/7rdxv","name":"Homomorphic Encryption in Edge AI Systems: A PRISMA-Based Scoping Review","source":"datacite","abstract":"This project contains a scoping review on homomorphic encryption (HE) in artificial intelligence systems, with a focus on healthcare and edge-deployed architectures. It follows PRISMA-ScR guidelines and emphasizes the application and customization of CKKS, BFV, and related schemes.","url":"https://doi.org/10.17605/osf.io/7rdxv","authors":["Yanez, Penelope"],"tags":["Computer Engineering","Medicine and Health Sciences","Engineering","CKKS","PRISMA","cybersecurity","edge AI","federated learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17605/osf.io/7rdxv","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.16883757","name":"Cloud Architecture in Pharma: Meeting the Dual Mandate of Innovation and Compliance","source":"datacite","abstract":"Pharmaceutical organizations face unprecedented challenges in balancing technological innovation with stringent regulatory compliance requirements across global markets. This technical review explores comprehensive cloud architecture strategies that enable pharmaceutical companies to deliver personalized patient experiences while maintaining rigorous data governance standards. The content examines three critical architectural domains: metadata-driven segmentation architectures that enable dynamic patient categorization through sophisticated rule-based systems, privacy-aware personalization engines that leverage advanced techniques, including differential privacy and federated learning to protect sensitive patient information, and region-specific journey logic implementations that adapt to diverse regulatory requirements across multiple jurisdictions. Contemporary pharmaceutical cloud implementations demonstrate the effectiveness of policy-as-code frameworks, zero-trust security models, and automated compliance monitoring systems in achieving exceptional regulatory adherence while supporting substantial patient interaction volumes. The architectural patterns discussed encompass multi-layered metadata management systems, homomorphic encryption protocols, secure multi-party computation frameworks, and comprehensive data lifecycle management solutions. These implementations showcase the successful integration of edge computing architectures, content delivery networks, and blockchain technologies to support global pharmaceutical operations while ensuring data residency compliance and maintaining immutable audit trails for regulatory submissions across diverse healthcare systems worldwide.","url":"https://doi.org/10.5281/zenodo.16883757","authors":["Harish Archana Naidu Nagapoosanam"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16883757","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.5281/zenodo.16883758","name":"Cloud Architecture in Pharma: Meeting the Dual Mandate of Innovation and Compliance","source":"datacite","abstract":"Pharmaceutical organizations face unprecedented challenges in balancing technological innovation with stringent regulatory compliance requirements across global markets. This technical review explores comprehensive cloud architecture strategies that enable pharmaceutical companies to deliver personalized patient experiences while maintaining rigorous data governance standards. The content examines three critical architectural domains: metadata-driven segmentation architectures that enable dynamic patient categorization through sophisticated rule-based systems, privacy-aware personalization engines that leverage advanced techniques, including differential privacy and federated learning to protect sensitive patient information, and region-specific journey logic implementations that adapt to diverse regulatory requirements across multiple jurisdictions. Contemporary pharmaceutical cloud implementations demonstrate the effectiveness of policy-as-code frameworks, zero-trust security models, and automated compliance monitoring systems in achieving exceptional regulatory adherence while supporting substantial patient interaction volumes. The architectural patterns discussed encompass multi-layered metadata management systems, homomorphic encryption protocols, secure multi-party computation frameworks, and comprehensive data lifecycle management solutions. These implementations showcase the successful integration of edge computing architectures, content delivery networks, and blockchain technologies to support global pharmaceutical operations while ensuring data residency compliance and maintaining immutable audit trails for regulatory submissions across diverse healthcare systems worldwide.","url":"https://doi.org/10.5281/zenodo.16883758","authors":["Harish Archana Naidu Nagapoosanam"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.16883758","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.21227/w4dn-8897","name":"\"Strengthening Data Privacy and Security for Internet of Vehicles using Federated Learning and Artificial Intelligence: A SLR\"","source":"datacite","abstract":"\"Federated Learning in the Internet of Vehicles space plays a crucial role in enabling intelligent, privacy-preserving, and distributed data processing. Here, AI models rely heavily on large-scale vehicle data for training and accuracy. While much of the existing research has focused on how Federated Learning can address computational, bandwidth, and infrastructure limitations\\u2014especially by shifting model training to edge devices\\u2014this study takes a different approach. It emphasizes the growing importance of data privacy protection, particularly as stricter data regulations emerge and organizations become increasingly hesitant to share sensitive data. This work presents a systematic literature review (SLR), analyzing the use of privacy-preserving and trust-enabling techniques within Federated Learning for IoV systems. Key methods examined include Homomorphic Encryption, Blockchain, Differential Privacy, Digital Twins, and incentive-based trust mechanisms. These technologies are designed to enhance privacy while enabling collaborative learning across distributed systems. The review\\u2019s findings suggest that combining Federated Learning with advanced privacy methods is not only viable but necessary for building secure and trustworthy IoV ecosystems. As connected vehicles become more widespread, ensuring user trust and regulatory compliance will be essential. The study concludes by highlighting emerging trends and proposing future directions aimed at developing trust-centric Federated Learning frameworks tailored for the demands of next-generation intelligent transportation systems.Keywords: Federated Learning, Internet of Vehicles, IoV, encryption, data privacy, security, Intelligent Transportation Systems, homomorphic encryption, blockchain, digital twin, trust, differential privacy\"","url":"https://doi.org/10.21227/w4dn-8897","authors":["Angela Miller","Vivek Aswal","Hersh Naroliwalla","Tomas Cerny","Davide Taibi"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21227/w4dn-8897","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:45.134Z"},{"id":"doi:10.2139/ssrn.3581454","name":"Beyond Data Protection to Command and Control (C2) Sustainability in a Post-COVID19 World: Execution of U.S. Data Protection Act for U.S. Data Protection Agency; U.S. Data Protection Act Proposal by US Senator for New York Kirsten Gillibrand","source":"preprints","abstract":"","url":"https://doi.org/10.2139/ssrn.3581454","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.2139/ssrn.3581454","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.2139/ssrn.3579220","name":"Trustless Approaches to Digital Infrastructure in the Crisis of COVID-19: Australia's Newest COVID App, Home-Grown Surveillance Technologies and What to Do About It","source":"preprints","abstract":"","url":"https://doi.org/10.2139/ssrn.3579220","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.2139/ssrn.3579220","addedAt":"2026-08-31T06:41:45.134Z","updatedAt":"2026-08-31T06:41:46.066Z"},{"id":"doi:10.1109/ises67504.2025.00020","name":"Enhancing Data Privacy Using Fully Homomorphic Encryption for IoT Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ises67504.2025.00020","authors":["Preet Taparia","Shivam Sharma","Surbhi Chhabra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-01T19:51:31Z","doi":"10.1109/ises67504.2025.00020","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/smartnets65254.2025.11106851","name":"Securing Health Data on Android Using Homomorphic Encryption with Microsoft SEAL","source":"crossref","abstract":"","url":"https://doi.org/10.1109/smartnets65254.2025.11106851","authors":["Ramazan Akman","Gökhan Dalkılıç"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-15T18:11:30Z","doi":"10.1109/smartnets65254.2025.11106851","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/csnt64827.2025.10968654","name":"CKKS Homomorphic Encryption Scheme for Financial Dataset","source":"crossref","abstract":"","url":"https://doi.org/10.1109/csnt64827.2025.10968654","authors":["Bhawana S. Dakhare","Lata L. Ragha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-23T17:51:09Z","doi":"10.1109/csnt64827.2025.10968654","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1504/ijesdf.2025.10066462","name":"Big data security using homomorphic encryption: application in finance","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijesdf.2025.10066462","authors":["Tingxin Jiang N.A."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-10T13:03:17Z","doi":"10.1504/ijesdf.2025.10066462","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/icce63647.2025.10929778","name":"Privacy-Diffusion: Privacy-Preserving Stable Diffusion Without Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icce63647.2025.10929778","authors":["Po-Chu Hsu","Ziying Yu","Shuhei Mise","Hideaki Miyaji"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-27T02:16:58Z","doi":"10.1109/icce63647.2025.10929778","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/icca66035.2025.11430915","name":"Homomorphic Encryption for Privacy-Preserving Credit Scoring in Multi-Cloud Banking Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icca66035.2025.11430915","authors":["Pragya Keshap","Arpana Hosabettu","Akshay Mittal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-17T20:18:48Z","doi":"10.1109/icca66035.2025.11430915","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/iscas56072.2025.11043334","name":"Three-Input Ciphertext Multiplication for Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscas56072.2025.11043334","authors":["Sajjad Akherati","Yok Jye Tang","Xinmiao Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-27T17:42:19Z","doi":"10.1109/iscas56072.2025.11043334","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/iccea65460.2025.11103215","name":"A Privacy-Preserving Power Grid Data Aggregation Scheme Based on Blockchain and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccea65460.2025.11103215","authors":["Yu Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-13T17:26:46Z","doi":"10.1109/iccea65460.2025.11103215","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.23919/date64628.2025.10992820","name":"Pasta on Edge: Cryptoprocessor for Hybrid Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.23919/date64628.2025.10992820","authors":["Aikata Aikata","Daniel Sanz Sobrino","Sujoy Sinha Roy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-21T17:36:35Z","doi":"10.23919/date64628.2025.10992820","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/iaeac65194.2025.11165903","name":"An Area-Efficient Twiddle Factor Generator for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iaeac65194.2025.11165903","authors":["Qiuxing Fu","Wei Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-23T17:24:35Z","doi":"10.1109/iaeac65194.2025.11165903","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/cicn67655.2025.11367834","name":"Privacy-Preserving Multi-Key Fully Homomorphic Encryption for Secure Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cicn67655.2025.11367834","authors":["Sunita Godara","Simran Choudhary"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T20:45:33Z","doi":"10.1109/cicn67655.2025.11367834","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/iitcee64140.2025.10915411","name":"A Study on Hardware-based Implementation of Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iitcee64140.2025.10915411","authors":["Gurdeep Singh","Sonam Mittal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-14T13:45:50Z","doi":"10.1109/iitcee64140.2025.10915411","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.62056/ay7qjp10","name":"Revisiting Module Lattice-based Homomorphic Encryption and Application to Secure-MPC","source":"crossref","abstract":"Homomorphic encryption (HE) schemes have gained significant popularity in modern privacy-preserving applications across various domains. While research on HE constructions based on learning with errors (LWE) and ring-LWE has received major attention from both cryptographers and software-hardware designers alike, their module-LWE-based counterpart has remained comparatively under-explored in the literature. A recent work provides a module-LWE-based instantiation (MLWE-HE) of the Cheon-Kim-Kim-Song (CKKS) scheme and showcases several of its advantages such as parameter flexibility and improved parallelism. However, a primary limitation of this construction is the quadratic growth in the size of the relinearization keys. Our contribution is two-pronged: first, we present a new relinearization key-generation technique that addresses the issue of quadratic key size expansion by reducing it to linear growth. Second, we extend the application of MLWE-HE in a multi-group homomorphic encryption (MGHE) framework, thereby generalizing the favorable properties of the single-keyed HE to a multi-keyed setting as well as investigating additional flexibility attributes of the MGHE framework.","url":"https://doi.org/10.62056/ay7qjp10","authors":["Anisha Mukherjee","Sujoy Roy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-07T17:09:09Z","doi":"10.62056/ay7qjp10","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.2139/ssrn.5350717","name":"Umk-Hefl: Unconstrained Multi-Key Homomorphic Encryption for Privacy-Preserving Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5350717","authors":["Wenhao Liu","Yingzi Hu","Wei Zhao","Lingling Wu","Haibo Lei","Weiwei Jiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-14T12:36:54Z","doi":"10.2139/ssrn.5350717","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/icaic63015.2025.10849069","name":"Homomorphic Encryption in Federated Medical Image Classification","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaic63015.2025.10849069","authors":["Manuel Lengl","Simon Schumann","Stefan Röhrl","Oliver Hayden","Klaus Diepold"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-29T18:44:02Z","doi":"10.1109/icaic63015.2025.10849069","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1016/j.eij.2025.100766","name":"Efficient and fully outsourced privacy-preserving decision tree training and prediction based on homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.eij.2025.100766","authors":["Nawal Almutairi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-23T01:35:36Z","doi":"10.1016/j.eij.2025.100766","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.3390/cryptography9020033","name":"Evaluation of Privacy-Preserving Support Vector Machine (SVM) Learning Using Homomorphic Encryption","source":"crossref","abstract":"The requirement for privacy-aware machine learning increases as we continue to use PII (personally identifiable information) within machine training. To overcome the existing privacy issues, we can apply fully homomorphic encryption (FHE) to encrypt data before they are fed into a machine learning model. This involves generating a homomorphic encryption key pair, where the public key encrypts the input data and the private key decrypts the output. However, there is often a performance hit when we use homomorphic encryption, so this paper evaluates the performance overhead of using an SVM (support vector machine) machine learning technique with the OpenFHE homomorphic encryption library. This uses Python and the scikit-learn library to create an SVM model, which can then be used with homomorphically encrypted data inputs and then produce a homomorphically encrypted result. The experiments include a range of variables, such as multiplication depth, scale size, first modulus size, security level, batch size, and ring dimension, along with two different SVM models, SVM-poly and SVM-linear. Overall, the results show that the two main parameters that affect performance are ring dimension and modulus size, and SVM-poly and SVM-linear show similar performance levels.","url":"https://doi.org/10.3390/cryptography9020033","authors":["William J. Buchanan","Hisham Ali"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-27T05:52:52Z","doi":"10.3390/cryptography9020033","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/cict67193.2025.11398973","name":"Privacy-Preserving Brain Tumor Classification Using Homomorphic Encryption and Patterned Noise Injection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cict67193.2025.11398973","authors":["Doreen Dilip","V.K.Harini","R.M.Bhavadharini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-24T20:55:40Z","doi":"10.1109/cict67193.2025.11398973","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/icaft66710.2025.11452781","name":"Performance Evaluation and Optimization of Homomorphic Encryption for Secure Cloud Computing Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaft66710.2025.11452781","authors":["Ravindran K","S Nagendra Prabhu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T19:49:08Z","doi":"10.1109/icaft66710.2025.11452781","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/iaecst68792.2025.11414923","name":"Efficient Privacy-Preserving DCT Computation using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iaecst68792.2025.11414923","authors":["Kairong Liang","Huiyu Zhou","Peijia Zheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-09T19:55:13Z","doi":"10.1109/iaecst68792.2025.11414923","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/icmctc62214.2025.11196238","name":"Cloud Storage System Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmctc62214.2025.11196238","authors":["S. Gopalakrishnan","S. Sathiyanarayana","S. Santhosh","Shameem Ahamed. S. M"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-17T17:38:30Z","doi":"10.1109/icmctc62214.2025.11196238","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1016/j.aej.2024.12.070","name":"A privacy-preserving federated learning scheme with homomorphic encryption and edge computing","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.aej.2024.12.070","authors":["Bian Zhu","Ling Niu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-18T00:38:27Z","doi":"10.1016/j.aej.2024.12.070","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.2139/ssrn.5273162","name":"A Comparative Analysis of Homomorphic Encryption and Secure Multi-Party Computation for Preserving Data Privacy in Cloud-Based Financial Services Authors","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5273162","authors":["Abiodun Okunola","asher asher"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-29T19:10:07Z","doi":"10.2139/ssrn.5273162","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/candarw68385.2025.00047","name":"Improved Threshold Fully Homomorphic Encryption with Dynamic Role-based Share Refresh","source":"crossref","abstract":"","url":"https://doi.org/10.1109/candarw68385.2025.00047","authors":["Yixuan He","Yuta Kodera","Yasuyuki Nogami"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-12T18:20:34Z","doi":"10.1109/candarw68385.2025.00047","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/iscas56072.2025.11044185","name":"Exploring Possibilities of BFV-based Homomorphic Encryption for Privacy-Preserving Image Processing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscas56072.2025.11044185","authors":["Ardianto Satriawan","Rella Mareta","Hanho Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-27T17:42:19Z","doi":"10.1109/iscas56072.2025.11044185","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.59573/emsj.9(4).2025.82","name":"Privacy Preserving Analytics with Homomorphic Encryption in Finance: A Zero Trust Approach","source":"crossref","abstract":"Homomorphic encryption revolutionizes financial analytics by enabling calculations on encrypted data without decryption, establishing a zero-trust framework where sensitive information is preserved in analytical processes. This cryptographic approach addresses the fundamental stress between customer data extraction and maintaining strict privacy standards. The development of partially homomorphic schemes has improved performance efficiency dramatically, especially through GPU adaptation and hybrid implementation architecture. Financial institutions implementing these techniques experience sufficient benefits in regulatory compliance, security, currency, operational costs, and analytical abilities. Credit scoring, risk management, safe benchmarking, market analysis, and allied applications show how homomorphic encryption simultaneously enhances privacy protection and enables refined financial analysis. By embedding encryption into data pipelines, institutions create an environment where information is encrypted during the calculation while maintaining analytical utility. The privacy-conscious conservation of financial analytics reshapes.","url":"https://doi.org/10.59573/emsj.9(4).2025.82","authors":["Leela Krishna Yenigalla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-03T05:35:09Z","doi":"10.59573/emsj.9(4).2025.82","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/mwscas53549.2025.11244511","name":"Efficient and Secure Neural Network Inference with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mwscas53549.2025.11244511","authors":["Hassan Rekabi Bana","Moslem Heidarpur","Mitra Mirhassani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-25T18:26:55Z","doi":"10.1109/mwscas53549.2025.11244511","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/host64725.2025.11050071","name":"CHESS: Compiling Homomorphic Encryption with Scheme Switching","source":"crossref","abstract":"","url":"https://doi.org/10.1109/host64725.2025.11050071","authors":["Rostin Shokri","Nektarios Georgios Tsoutsos"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-07T17:47:34Z","doi":"10.1109/host64725.2025.11050071","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/icodsa67155.2025.11157649","name":"Enhancing Genomic Data Security in Cloud through Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icodsa67155.2025.11157649","authors":["Putrie Risky Khairunnisa","Muhamad Irsan","Ikke Dian Oktaviani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-15T17:36:17Z","doi":"10.1109/icodsa67155.2025.11157649","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1504/ijesdf.2025.10065110","name":"Matrix-based homomorphic encryption-using random prime numbers","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijesdf.2025.10065110","authors":["Ketti Ramachandran Ramkumar","Sonam Mittal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-02T13:00:47Z","doi":"10.1504/ijesdf.2025.10065110","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1504/ijesdf.2025.149339","name":"Big data security using homomorphic encryption: application in finance","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijesdf.2025.149339","authors":["Tingxin Jiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-28T12:30:27Z","doi":"10.1504/ijesdf.2025.149339","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/eee-am66675.2025.11473772","name":"Secure energy data aggregation and analysis using fog-based AI agents with full homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/eee-am66675.2025.11473772","authors":["Hoan Le"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-09T19:42:34Z","doi":"10.1109/eee-am66675.2025.11473772","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1007/s12065-025-01099-7","name":"Privacy-preserving data aggregation in WBNAs using neuro-evolutionary algorithms and post-quantum homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s12065-025-01099-7","authors":["Soufiane Ben Othman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-15T04:00:49Z","doi":"10.1007/s12065-025-01099-7","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/isaics66888.2025.11349745","name":"A Research on the Application Comparison of NTRU in Typical Fully Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isaics66888.2025.11349745","authors":["Xu Zhao","Zheng Yuan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-28T20:55:52Z","doi":"10.1109/isaics66888.2025.11349745","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.2316/j.2025.206-1253","name":"TRAFFIC MITIGATION AND VEHICLE DETECTION BASED ON HOMOMORPHIC ENCRYPTION ALGORITHM AND FUZZY COMPREHENSIVE EVALUATION","source":"crossref","abstract":"","url":"https://doi.org/10.2316/j.2025.206-1253","authors":["Zihao Chen∗"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-24T01:23:34Z","doi":"10.2316/j.2025.206-1253","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/ic2sda68097.2025.11331425","name":"Privacy-Preserving Biometric Authentication Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ic2sda68097.2025.11331425","authors":["Yacine Belhocine","Abdallah Meraoumia","Salim Chitroub","Hakim Bendjenna"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-19T20:52:38Z","doi":"10.1109/ic2sda68097.2025.11331425","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.36948/ijfmr.2025.v07i05.56559","name":"Privacy-Preserving Federated Learning Using Threshold Homomorphic Encryption","source":"crossref","abstract":"Federated Learning (FL) has occurred for example a privacy-preserving dispersed machine learning paradigm, allowing multiple participants to collaboratively train models without sharing raw information. However, FL remains susceptible to safety and privacy threats, including inference attacks and information exposure. Near address these challenges, this paper recommends the integration of Threshold Homomorphic Encryption (THE) to increase the privacy and safety of FL systems. THE allows encoded model updates to stand aggregated securely although ensuring that decryption demands collaboration from multiple gatherings, thereby preventing any only entity from accessing sensitive data. The proposed method is evaluated on the UNSW-NB15 dataset, representative its effectiveness in preservative model performance while significantly improving data confidentiality. Investigational results show that the THE-based FL framework alleviates privacy risks, reduces adversarial dangers, and ensures scalable protected computation. This paper underwrites to advancing privacy-aware spread learning and surfaces the way for safe AI applications in complex domains such as cybersecurity.","url":"https://doi.org/10.36948/ijfmr.2025.v07i05.56559","authors":["Sheela M S","Ankur Khare","Praveen Kumar K -"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-13T09:24:02Z","doi":"10.36948/ijfmr.2025.v07i05.56559","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.2139/ssrn.5601867","name":"Secure Federated Learning for Medical Image Analysis: A Blockchain-Enabled Framework with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5601867","authors":["Swetha P","M.  Gautham Shetty","Pradeepta Panda","Srithesh Anchan","Siddharta Shetty"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-28T22:00:17Z","doi":"10.2139/ssrn.5601867","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1504/ijics.2025.10073172","name":"Enhancing the data security of digital records in archives through homomorphic encryption protocol","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijics.2025.10073172","authors":["Hua Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-30T13:00:44Z","doi":"10.1504/ijics.2025.10073172","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.3390/jcp6010004","name":"Homomorphic Encryption for Confidential Statistical Computation: Feasibility and Challenges","source":"crossref","abstract":"Statistical confidentiality focuses on protecting data to preserve its analytical value while preventing identity exposure, ensuring privacy and security in any system handling sensitive information. Homomorphic encryption allows computations on encrypted data without revealing it to anyone other than an owner or an authorized collector. When combined with other techniques, homomorphic encryption offers an ideal solution for ensuring statistical confidentiality. TFHE (Fast Fully Homomorphic Encryption over the Torus) is a fully homomorphic encryption scheme that supports efficient homomorphic operations on Booleans and integers. Building on TFHE, Zama’s Concrete project offers an open-source compiler that translates high-level Python code (version 3.9 or higher) into secure homomorphic computations. This study examines the feasibility of the Concrete compiler to perform core statistical analyses on encrypted data. We implement traditional algorithms for core statistical measures including the mean, variance, and five-point summary on encrypted datasets. Additionally, we develop a bitonic sort implementation to support the five-point summary. All implementations are executed within the Concrete framework, leveraging its built-in optimizations. Their performance is systematically evaluated by measuring circuit complexity, programmable bootstrapping count (PBS), compilation time, and execution time. We compare these results to findings from previous studies wherever possible. The results show that the complexity of sorting and statistical computations on encrypted data with the Concrete implementation of TFHE increases rapidly, and the size and range of data that can be accommodated is small for most applications. Nevertheless, this work reinforces the theoretical promise of Fully Homomorphic Encryption (FHE) for statistical analysis and highlights a clear path forward: the development of optimized, FHE-compatible algorithms.","url":"https://doi.org/10.3390/jcp6010004","authors":["Yesem Kurt Peker","Rahul Raj"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-26T02:07:58Z","doi":"10.3390/jcp6010004","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/bigdata66926.2025.11402419","name":"Federated Learning with Homomorphic Encryption for Secure Healthcare Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata66926.2025.11402419","authors":["Amin Tuni Gure","Mario A. Bochicchio"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-06T20:57:57Z","doi":"10.1109/bigdata66926.2025.11402419","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1145/3658617.3697770","name":"Efficient Key Switching Accelerator for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3658617.3697770","authors":["Seoyoon Jang","Sungjin Park","Dongsuk Jeon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-04T14:32:21Z","doi":"10.1145/3658617.3697770","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/secdev66745.2025.00014","name":"Comparison of Fully Homomorphic Encryption and Garbled Circuit Techniques in Privacy-Preserving Machine Learning Inference","source":"crossref","abstract":"","url":"https://doi.org/10.1109/secdev66745.2025.00014","authors":["Kalyan Cheerla","Lotfi Ben Othmane","Kirill Morozov"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-13T18:42:42Z","doi":"10.1109/secdev66745.2025.00014","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/esci63694.2025.10988128","name":"Privacy Preserving Healthcare Based on Secure Mathematical Foundations of Fully Homomorphic Encryption (FHE)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/esci63694.2025.10988128","authors":["S. Sathiya Devi","K. Jayasri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-09T17:56:12Z","doi":"10.1109/esci63694.2025.10988128","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/iceee67194.2025.11262017","name":"Recursive AI Machine Learning Model with Fully Homomorphic Encryption Support","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iceee67194.2025.11262017","authors":["Swayamveer Singh","Ayushmaan Ajay Amit","Ebru Celikel Cankaya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-04T18:35:03Z","doi":"10.1109/iceee67194.2025.11262017","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/space65882.2025.11170983","name":"Implementation of Somewhat Homomorphic Encryption (SHE) using RLWE based Post Quantum Cryptography (PQC)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/space65882.2025.11170983","authors":["Romio Rosan Sahani","Srinivasan Krishnaswamy","Gaurav Trivedi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-25T17:52:10Z","doi":"10.1109/space65882.2025.11170983","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/iceic64972.2025.10879764","name":"Homomorphic Encryption and Decryption Hardware Design using Shared Arithmetic and Configurable Butterfly Unit","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iceic64972.2025.10879764","authors":["Seung-Chan Kim","Dong-Sun Kim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-18T18:17:22Z","doi":"10.1109/iceic64972.2025.10879764","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.5753/sbseg.2025.11515","name":"How does reducing the dimension of feature vectors impact Biometric Systems that use Homomorphic Encryption?","source":"crossref","abstract":"Homomorphic Encryption enables biometric systems to perform matching directly on encrypted feature vectors, preserving user privacy throughout the process. However, the high computational cost of encrypted-domain operations, especially on high-dimensional inputs, remain a major barrier to real-world use. This study presents a concrete example of how reducing the dimensionality of biometric feature vectors affects both matching accuracy and runtime, and discusses how system designers could estimate the trade-off between time savings and accuracy loss when choosing a target dimension. The code is available on Github.","url":"https://doi.org/10.5753/sbseg.2025.11515","authors":["Andreis G. M. Purim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-11T13:10:33Z","doi":"10.5753/sbseg.2025.11515","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/kst65016.2025.11003367","name":"Privacy-Preserving Breast Density Classification in Mammograms Using Fuzzy C-Means and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/kst65016.2025.11003367","authors":["Sophon Mongkolluksamee","Subhorn Khonthapagdee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-20T17:06:30Z","doi":"10.1109/kst65016.2025.11003367","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.54254/2977-3903/2025.21661","name":"A secure aggregation method for federated learning based on homomorphic encryption","source":"crossref","abstract":"The development of cloud computing and big data has promoted the use of cloud servers in machine learning but has also raised concerns about privacy security. To enhance security and efficiency, this paper proposes a multi-key aggregation scheme based on improved Ring Learning With Errors (R-LWE) homomorphic encryption. This method protects the privacy of local model parameters and prevents information leakage through collaborative decryption. Experimental results demonstrate that the proposed scheme can resist collusion attacks, reduce communication overhead, and maintain model accuracy.","url":"https://doi.org/10.54254/2977-3903/2025.21661","authors":["Xiaoge Ma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-19T04:12:53Z","doi":"10.54254/2977-3903/2025.21661","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1049/icp.2025.2879","name":"Efficient privacy-preserving truth discovery based on homomorphic encryption and secret sharing","source":"crossref","abstract":"","url":"https://doi.org/10.1049/icp.2025.2879","authors":["Junfeng Ye","Jie Ren"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-13T09:40:43Z","doi":"10.1049/icp.2025.2879","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/fpl68686.2025.00025","name":"FAME: FPGA Acceleration of Secure Matrix Multiplication with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fpl68686.2025.00025","authors":["Zhihan Xu","Rajgopal Kannan","Viktor K. Prasanna"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-26T19:48:24Z","doi":"10.1109/fpl68686.2025.00025","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1007/978-3-031-95140-4_2","name":"Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-95140-4_2","authors":["Xun Yi","Xuechao Yang","Xiaoning Liu","Andrei Kelarev","Kwok-Yan Lam","Mengmeng Yang","Xiangning Wang","Elisa Bertino"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-15T14:47:18Z","doi":"10.1007/978-3-031-95140-4_2","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/iwcmc65282.2025.11059488","name":"Face Recognition in the Encrypted Domain Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iwcmc65282.2025.11059488","authors":["Abderraouf Zaimen","Lubana Al Rayes","Nabil Hezil","Ahmed Bouridane","Raouf Dridi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-02T17:42:13Z","doi":"10.1109/iwcmc65282.2025.11059488","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/iscc65549.2025.11326105","name":"Exploring Communication Efficient Methods for Homomorphic Encryption Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscc65549.2025.11326105","authors":["Yuri Dimitre de Faria","Leandro A. Villas","Allan M. de Souza"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-13T20:56:15Z","doi":"10.1109/iscc65549.2025.11326105","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.52783/jisem.v10i24s.3900","name":"Homomorphic Encryption-driven AI through Text Mining in The Cloud","source":"crossref","abstract":"In the current era, there is increasing interest in data security, especially in cloud computing. Homomorphic Encryption (HE) supported by Artificial Intelligence (AI) technology offers a promising solution in this field. Homomorphic encryption ensures that computations are performed on encrypted data without decryption, thus ensuring privacy. However, the integration of AI algorithms and text mining in the cloud environment is still a challenging topic. The study aims to develop a framework for partial homomorphic encryption combined with a deep learning algorithm for text mining in the cloud environment. The aim of the proposed approach is to evaluate the trade-offs between security and computational performance through deep learning to ensure the highest accuracy.The proposed method uses frequency coding and combines it with the developed deep learning algorithm, which is based on the dynamic change of the weights accompanying the neural network. The text mining model is integrated by multiplying the encrypted frequency by the factor derived from the weight in the neural network iterations. The model was trained on data in two standard datasets and the model was tested afterwards. The computational overheads were evaluated as the text size before and after encryption, the use of computing resources, and the amount of noise generated. Using HE allowed for successful text mining on encrypted data, with minimal impact on accuracy. The ciphertext size was 3.3x larger than plaintext, with increased computational overhead. The computational resource utilization was balanced in an acceptable manner for cloud storage, with noise growth not exceeding 31% while accuracy remained at 98%. In this study, the feasibility of using homomorphic encryption on texts supported by deep learning technology in a cloud environment was concluded. This provides a solution for computational operations on data while preserving privacy. The framework provides a balance between security and computational efficiency and is important for applications that require high levels of security, despite some challenges that may be solved in the future by machine learning and working on larger texts.","url":"https://doi.org/10.52783/jisem.v10i24s.3900","authors":["Qays Jabbar Abed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-26T07:31:01Z","doi":"10.52783/jisem.v10i24s.3900","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.3390/app15062913","name":"Mathematical Proposal for Securing Split Learning Using Homomorphic Encryption and Zero-Knowledge Proofs","source":"crossref","abstract":"This work presents a mathematical solution to data privacy and integrity issues in Split Learning which uses Homomorphic Encryption (HE) and Zero-Knowledge Proofs (ZKP). It allows calculations to be conducted on encrypted data, keeping the data private, while ZKP ensures the correctness of these calculations without revealing the underlying data. Our proposed system, HavenSL, combines HE and ZKP to provide strong protection against attacks. It uses Discrete Cosine Transform (DCT) to analyze model updates in the frequency domain to detect unusual changes in parameters. HavenSL also has a rollback feature that brings the system back to a verified state if harmful changes are detected. Experiments on CIFAR-10, MNIST, and Fashion-MNIST datasets show that using Homomorphic Encryption and Zero-Knowledge Proofs during training is feasible and accuracy is maintained. This mathematical-based approach shows how crypto-graphic can protect decentralized learning systems. It also proves the practical use of HE and ZKP in secure, privacy-aware collaborative AI.","url":"https://doi.org/10.3390/app15062913","authors":["Agon Kokaj","Elissa Mollakuqe"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T12:22:52Z","doi":"10.3390/app15062913","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1002/spy2.70109","name":"Enhancing VANET Security Using a Hybrid Model of Deep Learning and Homomorphic Encryption","source":"crossref","abstract":"ABSTRACT Vehicular Ad Hoc Networks (VANETs) play a pivotal role in enabling intelligent transportation systems, yet their decentralized and dynamic nature exposes them to a wide range of cyber threats, including Sybil attacks, black hole attacks, replay, and message spoofing. To address these vulnerabilities, we propose HyDra‐VANET, a novel hybrid security framework that integrates deep learning, federated learning, and homomorphic encryption for robust and privacy‐preserving intrusion detection. At the vehicle level, a convolutional–recurrent neural network (CRNN) is employed to extract both spatial and temporal patterns from real‐time vehicular communication and telemetry data, ensuring accurate anomaly detection. Federated learning coordinates decentralized model training across vehicles, enabling collaborative intelligence while eliminating the need to share raw data. To further enhance privacy, a lightweight lattice‐based homomorphic encryption scheme allows encrypted inference and secure aggregation, preventing sensitive information leakage at intermediate nodes such as roadside units. Experimental evaluation using multiple datasets and adversarial scenarios demonstrates that HyDra‐VANET significantly outperforms baseline intrusion detection systems in detection accuracy, resilience to adversarial manipulation, scalability, and communication efficiency.","url":"https://doi.org/10.1002/spy2.70109","authors":["Haythem Hayouni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-30T10:25:13Z","doi":"10.1002/spy2.70109","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/isctrkiye68593.2025.11224823","name":"Benchmarking Homomorphic Encryption Libraries with a Focus on Multithreading","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isctrkiye68593.2025.11224823","authors":["Eren Ozilgili","Halil Ibrahim Kanpak","Alptekin Kupcu","Sinem Sav"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-11T18:26:44Z","doi":"10.1109/isctrkiye68593.2025.11224823","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.2174/9798898810870125010015","name":"Future Trends in Secure Healthcare Predictive Analysis: Homomorphic Encryption Perspectives","source":"crossref","abstract":"In the healthcare system, the application of predictive analysis is essential to the enhancement of patient benefits as well as the development of healthcare delivery systems. The digitization of health records presents an increasing threat of data leakage and breaches of patient privacy. This chapter discusses how homomorphic encryption can be applied as a solution to secure healthcare information. We discuss the state of the art of predictive analysis in healthcare organizations and realize that the issue of data security is still relevant and needs to be further investigated due to evolving healthcare regulations and rapid technological advancements. We then discuss an overview of different available encryption techniques. We particularly focus on homomorphic encryption that allows computations to be made on data without decryption while maintaining patient data privacy. After that, we discuss how predictive analysis techniques can be applied to encrypted healthcare data. Some of the issues arising when attempting to carry out predictive analysis on encrypted data are discussed, in addition to the advantages of and technical hurdles in homomorphic encryption. We examine trends and opportunities, focussing on how secure predictive analytics, as one of the potential solutions, can improve the trust and reliability of healthcare data and patients’ care. Finally, we perform a case study on the use of predictive analysis techniques in encrypted heart disease data with the help of the Paillier Homomorphic encryption scheme to maintain data security.","url":"https://doi.org/10.2174/9798898810870125010015","authors":["Prokash Gogoi","Joseph Arul Valan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-05T09:37:04Z","doi":"10.2174/9798898810870125010015","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/icoici65217.2025.11254574","name":"Blockchain-Assisted Secure IoT Transmission using Homomorphic Encryption and Distributed Consensus","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icoici65217.2025.11254574","authors":["Anguraju Krishnan","Rajesh Arunachalam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-01T18:23:00Z","doi":"10.1109/icoici65217.2025.11254574","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.53759/7669/jmc202505025","name":"Integrating Homomorphic Encryption with Blockchain for Privacy Preserving Communication on the Internet of Vehicles","source":"crossref","abstract":"The Internet of Vehicles (IoV) has emerged as a transformative technology, enabling seamless communication among vehicles and infrastructure to improve road safety, traffic efficiency, and passenger comfort. However, the pervasive collection and exchange of data in IoV environments raise significant privacy concerns, as sensitive information about vehicle locations, driving patterns, and personal preferences may be exposed to unauthorized parties. To address these challenges, this study proposes a novel approach that integrates homomorphic encryption with blockchain to ensure privacy-preserving communication in IoV networks. IoV networks rely on the continuous exchange of data among vehicles, roadside units, and centralized servers to support various applications, including traffic management, navigation, and emergency services. However, the centralized nature of traditional communication architectures poses inherent privacy risks, as sensitive data may be vulnerable to interception, tampering, or unauthorized access. Data integrity was ensured through blockchain storage, with an observed tamper-proof rate of 99.9%, effectively preventing unauthorized access or manipulation of exchanged messages. Despite the additional computational overhead introduced by homomorphic encryption and blockchain operations, our system maintained efficient communication capabilities, achieving an average latency of 50 milliseconds and a throughput of 1000 messages per second. Moreover, scalability was demonstrated as our framework seamlessly accommodated an increasing number of vehicles and communication nodes, with observed linear scalability up to 100,000 connected vehicles. Security analyses revealed robust protection against eavesdropping, data tampering, and replay attacks, with a detection rate exceeding 98%. Overall, our results underscore the viability and effectiveness of our integrated approach in providing privacy-preserving communication for IoV networks, paving the way for secure and resilient connected transportation systems. As IoV continues to evolve, our approach can contribute to the development of privacy-enhancing technologies that empower users to fully leverage the benefits of connected transportation while safeguarding their privacy rights.","url":"https://doi.org/10.53759/7669/jmc202505025","authors":["Kyung-A Choi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-03T06:44:14Z","doi":"10.53759/7669/jmc202505025","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/csr64739.2025.11130120","name":"Privacy-Preserving Classification of Partially Encrypted Feature Vectors using Multi-Key Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/csr64739.2025.11130120","authors":["Diana-Elena Petrean","Rodica Potolea"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-26T19:04:27Z","doi":"10.1109/csr64739.2025.11130120","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.3390/wevj16080468","name":"Privacy-Preserving EV Charging Authorization and Billing via Blockchain and Homomorphic Encryption","source":"crossref","abstract":"Electric vehicle (EV) charging infrastructures raise significant concerns about data security and user privacy because traditional centralized authorization and billing frameworks expose sensitive information to breaches and profiling. To address these vulnerabilities, we propose a novel decentralized framework that couples a permissioned blockchain with fully homomorphic encryption (FHE). Unlike prior blockchain-only or blockchain-and-machine-learning solutions, our architecture performs all authorization and billing computations on encrypted data and records transactions immutably via smart contracts. We implemented the system on Hyperledger Fabric using the CKKS-based TenSEAL library, chosen for its efficient arithmetic on real-valued vectors, and show that homomorphic operations are executed off-chain within a secure computation layer while smart contracts handle only encrypted records. In a simulation involving 20 charging stations and up to 100 concurrent users, the proposed system achieved an average authorization latency of 610 ms, a billing computation latency of 310 ms, and transaction throughput of 102 Tx min while maintaining energy overhead below 0.14 kWh day per station. When compared to state-of-the-art blockchain-only approaches, our method reduces data exposure by 100%, increases privacy from “moderate” to “very high,” and achieves similar throughput with acceptable computational overhead. These results demonstrate that privacy-preserving EV charging is practical using present-day cryptography, paving the way for secure, scalable EV charging and billing services.","url":"https://doi.org/10.3390/wevj16080468","authors":["Amjad Aldweesh","Someah Alangari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-18T13:28:22Z","doi":"10.3390/wevj16080468","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.11591/ijeecs.v39.i3.pp1661-1672","name":"Enhancing privacy in document-oriented databases using searchable encryption and fully homomorphic encryption","source":"crossref","abstract":"In cloud-based not only SQL (NoSQL) databases, maintaining data privacy and the integrity are critically challenged by the risks of unauthorized external access and potential threats from malicious insiders. This paper presents a proxy-based solution that provides privacy-preserving by combining searchable encryption and brakerski-fan-vercauteren (BFV) fully homomorphic encryption (FHE) to facilitate secure search and aggregate query execution on encrypted data. Through extensive performance evaluations and security analyses, we show that our approach offers a robust solution for privacy-preserving data operations, with performance overhead introduced by the use of FHE. This solution gives an opportunity for a robust framework for secure data management and querying in NoSQL databases, with promising implications for practical deployment and future research. This work represents a significant advancement in the secure handling of data in NoSQL oriented databases, supplying a practical solution for privacy-conscious organizations.","url":"https://doi.org/10.11591/ijeecs.v39.i3.pp1661-1672","authors":["Abdelilah Belhaj","Soumia Ziti","Souad Najoua Lagmiri","Karim El Bouchti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-09T05:52:45Z","doi":"10.11591/ijeecs.v39.i3.pp1661-1672","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/icint65528.2025.11030880","name":"Secure Low-Complexity k-MCMC for Large-Scale Datasets with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icint65528.2025.11030880","authors":["Shozo Saeki","Minoru Kawahara","Hirohisa Aman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-16T18:47:33Z","doi":"10.1109/icint65528.2025.11030880","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/aibthings66987.2025.11296212","name":"SeizAI: A Secure AI-Based Seizure Detection via Homomorphic EEG Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aibthings66987.2025.11296212","authors":["Sunil Gupta","Md Abu Sayeed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-19T18:56:43Z","doi":"10.1109/aibthings66987.2025.11296212","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/lascas64004.2025.10966299","name":"Toward Full GPU Acceleration of Agile Homomorphic Encryption Frameworks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/lascas64004.2025.10966299","authors":["Emilio Quaggiotto","Shiva Nejati","Alexander J. Leigh","Mitra Mirhassani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-22T17:37:30Z","doi":"10.1109/lascas64004.2025.10966299","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1504/ijsn.2025.10073590","name":"Securing Vehicle Network Suspension Control: Lightweight Homomorphic Encryption with Fuzzy Rules","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijsn.2025.10073590","authors":["Zefeng Ding","Haili Tang","Xiaojuan Cao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-21T13:00:21Z","doi":"10.1504/ijsn.2025.10073590","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/icdsns65743.2025.11168787","name":"Research on the Application of Homomorphic Encryption-Based Machine Learning Privacy Protection Technology in Precision Marketing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icdsns65743.2025.11168787","authors":["Ke Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-29T17:50:45Z","doi":"10.1109/icdsns65743.2025.11168787","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.22266/ijies2025.0331.22","name":"Blockchain-Supported Fuzzy Keyword Search Using Homomorphic Asymmetric ElGamal Encryption in Cloud Environments","source":"crossref","abstract":"","url":"https://doi.org/10.22266/ijies2025.0331.22","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-25T19:56:21Z","doi":"10.22266/ijies2025.0331.22","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/itnac66378.2025.11302589","name":"A Serverless Federated Learning Framework with Blockchain and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/itnac66378.2025.11302589","authors":["Shahid Latif","Djamel Djenouri","Jawad Ahmad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-23T18:29:50Z","doi":"10.1109/itnac66378.2025.11302589","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.36838/v7i10.18","name":"Securing Generative AI: Homomorphic Encryption, Differential Privacy, and Federated Learning in Key Industries","source":"crossref","abstract":"","url":"https://doi.org/10.36838/v7i10.18","authors":["Avani Thakur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-26T14:40:50Z","doi":"10.36838/v7i10.18","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/icbats66542.2025.11258322","name":"Machine Learning Services by Homomorphic Encryption to Enhancing Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icbats66542.2025.11258322","authors":["Mohammed Shamar YADKAR","Sefer KURNAZ","Hameed Mutlag Farhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-03T18:38:30Z","doi":"10.1109/icbats66542.2025.11258322","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.2174/0129503779382412250822144444","name":"Exploring a Decade of Homomorphic Encryption: Advancements, Challenges, and Future Directions","source":"crossref","abstract":"Abstract: Homomorphic encryption (HE) enables secure computations on encrypted data without decryption, offering a transformative solution for privacy-preserving computation. This review presents a ten-year retrospective (2014–2024) on HE’s evolution since Gentry’s 2009 fully homomorphic encryption (FHE) scheme, which introduced the concept of performing arbitrary computations on ciphertexts. Early schemes were hindered by inefficiencies like computational overhead and noise accumulation. Over the past decade, significant advancements have addressed these barriers. Schemes such as BGV, BFV, and CKKS have been developed for efficient integer and approximate real-number computations. Algorithmic innovations like optimized bootstrapping and improved noise management have reduced complexity. Hardware acceleration using GPUs and FPGAs has enhanced performance, while integration with secure multi-party computation and zero-knowledge proofs has broadened HE’s applicability. Applications now span privacy-preserving machine learning, genomic data analysis, and financial analytics. Toolkits such as SEAL, HElib, and PALISADE have improved accessibility for developers and researchers. Despite progress, challenges remain, including balancing efficiency and security, and improving usability for non-experts. The article also explores HE’s reliance on lattice-based problems like Learning With Errors (LWE) and Ring-LWE, which provide quantum resistance. As hybrid cryptographic models emerge, HE is increasingly recognized as a key component in securing sensitive data in the postquantum era. This review highlights HE’s maturation from a theoretical concept to a practical solution, demonstrating its potential as a cornerstone for secure, privacy-preserving computing across industries.","url":"https://doi.org/10.2174/0129503779382412250822144444","authors":["Aishwarya P. Deshmukh","Vivek Mahale","Ashok T. Gaikwad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-24T11:52:12Z","doi":"10.2174/0129503779382412250822144444","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1201/9788770047746-13","name":"Advancing Blockchain Privacy: The Role of Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9788770047746-13","authors":["Yullivas Ameur","Idriss Taberkane","Samia Bouzefrane"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T11:00:32Z","doi":"10.1201/9788770047746-13","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/isqed65160.2025.11014422","name":"Formal Verification of a Custom Compiler for a Fully Homomorphic Encryption Accelerator","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isqed65160.2025.11014422","authors":["Zhenkun Yang","Suvadeep Banerjee","Jeremy Casas","Jin Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-30T17:43:30Z","doi":"10.1109/isqed65160.2025.11014422","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1186/s42400-024-00323-8","name":"Privacy-preserving attribute-based access control using homomorphic encryption","source":"crossref","abstract":"Abstract Authentication and access control for Cyber-Physical Systems (CPSs) are pivotal for protecting systems and their users from problems related to harmful actions and the malicious use of retrieved data. In some situations, making access decisions requires using user information, thereby challenging their privacy. Attribute-based access control (ABAC) supports dynamic and context-aware access decisions that are attractive in cyber-physical system environments. However, privacy preservation for access decisions is an open issue for authorization and is not supported by existing ABAC models. For example, if access decisions need to be made based on private attribute values such as health data, the corresponding access control policies need to be revealed. This paper reviews the ABAC, homomorphic encryption (HE), and zero-knowledge proof (ZKP) approaches, confirming the gap in privacy preservation in ABAC. Based on this observation, we further present the application of a new ZKP-based protocol in which ABAC allows for the privacy-preserving evaluation of attributes. This protocol is implemented and evaluated in terms of its performance and security. The evaluation demonstrates that there is a possibility for privacy-preserving ABAC, which may benefit the use of CPS, e.g., in underground and open-pit mines.","url":"https://doi.org/10.1186/s42400-024-00323-8","authors":["Malte Kerl","Ulf Bodin","Olov Schelén"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-21T20:02:51Z","doi":"10.1186/s42400-024-00323-8","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.48149/jciees.2025.5.1.2","name":"Homomorphic Encryption for Secure Federated Learning: A Narrative Survey","source":"crossref","abstract":"Federated learning enables collaborative model training without data centralization but remains vulnerable to information leakage, model manipulation, and aggregation attacks. This narrative review analyses security challenges through the confidentiality, integrity, and availability (CIA) triad, covering architectures, threat models, and privacy-preserving techniques. Differential privacy and secure multi-party computation are briefly noted, while homomorphic encryption is emphasized as the primary cryptographic solution. Security guarantees and performance trade-offs are assessed, identifying homomorphic encryption as a practical approach for strong confidentiality with minimal accuracy loss. The review provides a design-oriented synthesis of current research and forms the conceptual basis for a complementary PRISMA-based systematic review presented in the second part of this study.","url":"https://doi.org/10.48149/jciees.2025.5.1.2","authors":["Dinko Dinkov"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T06:19:08Z","doi":"10.48149/jciees.2025.5.1.2","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.62311/nesx/rphcrcscrcec5","name":"Secure Multi-Tenant Cloud Storage with Homomorphic Encryption","source":"crossref","abstract":"Abstract: Multi-tenant cloud environments present significant security challenges, particularly when handling sensitive data for diverse clients. This study proposes a secure storage architecture utilizing homomorphic encryption (HE) to allow computations on encrypted data while preserving privacy and tenant isolation. The system integrates role-based access control and policy-driven query interfaces to ensure confidentiality and secure access. A simulated multi-tenant environment was used to evaluate performance, measuring latency, throughput, ciphertext overhead, and inter-tenant data leakage. Regression and predictive analysis revealed that ciphertext size and computation depth significantly impact performance metrics. The proposed solution demonstrated an 87% accuracy in encrypted query prediction with negligible leakage, proving that homomorphic encryption can support secure, scalable multi-tenant cloud storage. Keywords Homomorphic Encryption, Multi-Tenant Cloud, Data Privacy, Secure Storage, Confidential Computing, Access Control","url":"https://doi.org/10.62311/nesx/rphcrcscrcec5","authors":["Murali Krishna Pasupuleti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-28T11:49:13Z","doi":"10.62311/nesx/rphcrcscrcec5","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/bigdatasecurity66063.2025.00029","name":"Financial Data Sharing Based on Cloud Computing Security: the Application and Effectiveness of Homomorphic Encryption Technology","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdatasecurity66063.2025.00029","authors":["Ruiqi Rao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-21T17:37:17Z","doi":"10.1109/bigdatasecurity66063.2025.00029","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/icdcece65353.2025.11035434","name":"Secure Healthcare Data Processing using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icdcece65353.2025.11035434","authors":["Pavithra H. B.","K. S. Shivaprakasha","Ganesh V. Bhat"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-18T13:35:30Z","doi":"10.1109/icdcece65353.2025.11035434","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/scse65633.2025.11030995","name":"Credit Card Fraud Detection Using Homomorphic Encryption Based On Unsupervised Anomaly Detection Network","source":"crossref","abstract":"","url":"https://doi.org/10.1109/scse65633.2025.11030995","authors":["Vasanthan Athiththan","Yasotha Ram Ramanan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-13T17:49:03Z","doi":"10.1109/scse65633.2025.11030995","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/cises66934.2025.11265307","name":"Hybrid Privacy-Preserving Model Using Homomorphic Encryption for Secure Multi-Tenant Cloud Analytics","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cises66934.2025.11265307","authors":["Devendra Singh Mohan","Varun Bansal","Anil Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-05T18:36:19Z","doi":"10.1109/cises66934.2025.11265307","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1504/ijics.2025.150021","name":"Enhancing the data security of digital records in archives through homomorphic encryption protocol","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijics.2025.150021","authors":["Hua Cui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-22T12:30:34Z","doi":"10.1504/ijics.2025.150021","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1007/978-981-92-2973-4_5","name":"Research on Hybrid Encryption Schemes for Judicial Blockchain: Policy-Enhanced Attribute-Based Encryption and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-92-2973-4_5","authors":["Peng Wang","Guofeng Zhang","Yue Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-06T01:04:09Z","doi":"10.1007/978-981-92-2973-4_5","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1201/9781003739791-86","name":"Secure &amp; private banking: Homomorphic encryption with smart data masking","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003739791-86","authors":["Prem Anand Rathina Sabapathy","Darshan M. Sri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-10T17:08:18Z","doi":"10.1201/9781003739791-86","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/balkancom65827.2025.11186036","name":"Enhancing IoT Security with Homomorphic Encryption and Privacy Preserving Document Search Using Microsoft SEAL","source":"crossref","abstract":"","url":"https://doi.org/10.1109/balkancom65827.2025.11186036","authors":["Mihrije Kadriu","Bujar Krasniqi","Blerim Rexha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-30T17:57:24Z","doi":"10.1109/balkancom65827.2025.11186036","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.3390/cryptography9020031","name":"Post-Quantum Homomorphic Encryption: A Case for Code-Based Alternatives","source":"crossref","abstract":"Homomorphic Encryption (HE) allows secure and privacy-protected computation on encrypted data without the need to decrypt it. Since Shor’s algorithm rendered prime factorisation and discrete logarithm-based ciphers insecure with quantum computations, researchers have been working on building post-quantum homomorphic encryption (PQHE) algorithms. Most of the current PQHE algorithms are secured by Lattice-based problems and there have been limited attempts to build ciphers based on error-correcting code-based problems. This review presents an overview of the current approaches to building PQHE schemes and justifies code-based encryption as a novel way to diversify post-quantum algorithms. We present the mathematical underpinnings of existing code-based cryptographic frameworks and their security and efficiency guarantees. We compare lattice-based and code-based homomorphic encryption solutions identifying challenges that have inhibited the progress of code-based schemes. We finally propose five new research directions to advance post-quantum code-based homomorphic encryption.","url":"https://doi.org/10.3390/cryptography9020031","authors":["Siddhartha Siddhiprada Bhoi","Arathi Arakala","Amy Beth Corman","Asha Rao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-12T09:13:38Z","doi":"10.3390/cryptography9020031","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/icoris67789.2025.11295993","name":"Privacy-Preserving Threat Intelligence Sharing in Smart Cities Using Homomorphic Encryption and Federated Analytics","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icoris67789.2025.11295993","authors":["Mehdi Houichi","Faouzi Jaidi","Adel Bouhoula"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-22T18:40:15Z","doi":"10.1109/icoris67789.2025.11295993","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.47760/ijcsmc.2025.v14i10.002","name":"HOMOMORPHIC ENCRYPTION: AN OVERVIEW","source":"crossref","abstract":"The tendency of having accessibility to information anytime and anywhere has contributed to individuals or organizations shifting from the storage of information to Internet-based cloud services. This shift has made data more vulnerable as data in the cloud cannot be operated on unless the computation is outsourced to a third-party which can be compromised. Furthermore, in order to keep data secure and accessible securely, homomorphic encryption techniques can be used. Homomorphic encryption supports computation on ciphertexts without decryption first. This paper gives a comprehensive survey of homomorphic encryption.","url":"https://doi.org/10.47760/ijcsmc.2025.v14i10.002","authors":["Solomon Sarpong","Samuel Opuni-Basoa","Kwaafo Akoto Awuah-Mensah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-14T04:57:30Z","doi":"10.47760/ijcsmc.2025.v14i10.002","addedAt":"2026-08-31T06:41:45.330Z","updatedAt":"2026-08-31T06:41:45.330Z"},{"id":"doi:10.1109/ietacs68750.2025.11385585","name":"Efficient Symmetric Key Based Fully Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ietacs68750.2025.11385585","authors":["Vinay Kumar Devara","Anshul Mishra","D. Ramesh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T21:13:18Z","doi":"10.1109/ietacs68750.2025.11385585","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.1145/3708821.3710822","name":"A Novel Asymmetric BSGS Polynomial Evaluation Algorithm under Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3708821.3710822","authors":["Qingfeng Wang","Li-Ping Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-13T06:30:56Z","doi":"10.1145/3708821.3710822","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.1109/ssitcon66133.2025.11342211","name":"Mathematical Analysis of Homomorphic Encryption Algorithms based on Number Theory and Their Application in Cloud Computing Security","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ssitcon66133.2025.11342211","authors":["Minghui Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-22T20:58:23Z","doi":"10.1109/ssitcon66133.2025.11342211","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.1109/bigdata66926.2025.11401805","name":"MASER: Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata66926.2025.11401805","authors":["Abdullah Al Omar","Xin Yang","Euijin Choo","Omid Ardakanian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-06T20:57:57Z","doi":"10.1109/bigdata66926.2025.11401805","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.36227/techrxiv.175751137.71377403/v1","name":"A Reconfigurable VLSI Architecture of Non-Powerof-Two Number Theoretic Transform for BGV Fully Homomorphic Encryption","source":"crossref","abstract":"Fully homomorphic encryption (FHE) enables operations to be performed directly on encrypted data without decryption. This preserves data privacy while leveraging the computational power of cloud computing. The security level of FHE is rooted in the hardness of underlying mathematical problems, but its implementation involves a huge amount of complex homomorphic operations. Simplified homomorphic evaluation algorithms and efficient hardware accelerators are thus critical for achieving widespread applications of FHE. The number theoretic transform (NTT) defined over a cyclotomic polynomial ring provides efficient polynomial multiplications, which is essential for key-switching, modulus switching and bootstrapping processes in the BGV-FHE scheme. Generally, non-power-of-two NTTs require relatively higher complexity compared to power-of-two NTTs. To minimize the gap, this work proposes to iteratively decompose large-size non-power-of-two NTTs using the prime factor algorithm and Rader's algorithm into small-size NTTs that are further optimized using reconfigurable butterfly units leveraging Winograd algorithm. Experimental results demonstrate a significant reduction in complexity, particularly in the required modular multiplications, additions, and interconnect network. For instance, for the 78881th cyclotomic polynomial NTT, this work shows 3.13x and 2.75x reductions in the number of multiplications and additions, respectively, compared to related work. Additionally, the proposed architecture achieves reduced latency compared to Bluestein's method using radix-8 butterfly unit, while the required memory size can be reduced to 57% and 63% for the 77531-th and 78881-th cyclotomic polynomial NTTs, respectively.","url":"https://doi.org/10.36227/techrxiv.175751137.71377403/v1","authors":["Chien-Chih Huang","Hsuan-Jui Hsu","Qi-Xian Wu","Ming-Der Shieh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-10T13:36:19Z","doi":"10.36227/techrxiv.175751137.71377403/v1","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.1109/icct-europe63283.2025.11157665","name":"Accelerating Authentication Using Locality-Sensitive Hashing in a Homomorphic Encryption-Based Face Recognition System","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icct-europe63283.2025.11157665","authors":["Sei Nakanishi","Yoshiaki Narusue","Hiroyuki Morikawa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-17T17:29:26Z","doi":"10.1109/icct-europe63283.2025.11157665","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.1117/12.3070276","name":"Research on privacy-preserving logistic regression scheme based on homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1117/12.3070276","authors":["Jinlong Pan","Yangyue Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-05T05:59:46Z","doi":"10.1117/12.3070276","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.1109/icaet63349.2025.10932236","name":"Comprehensive Survey on Authentication and Privacy Through Optimized Homomorphic Encryption Technique","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaet63349.2025.10932236","authors":["Sandeep M. Chitalkar","Prashant S. Dhotre"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-27T02:18:17Z","doi":"10.1109/icaet63349.2025.10932236","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.1109/biosig65492.2025.11358221","name":"Secure Multi-Party Homomorphic Encryption for Post-Quantum Biometric Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.1109/biosig65492.2025.11358221","authors":["Harald H. Paaske","Florian Bayer","Christian Rathgeb"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-28T20:58:20Z","doi":"10.1109/biosig65492.2025.11358221","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.1109/ciacon65473.2025.11189743","name":"A Logic-Gate Driven Cryptographic System with Dual-Function Hashing and Homomorphic Encryption Capabilities","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ciacon65473.2025.11189743","authors":["Chintala Mutyala Venkata Satya Murthy","Vrinda Gupta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-09T17:51:17Z","doi":"10.1109/ciacon65473.2025.11189743","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.1109/asens64990.2025.11011015","name":"Research on the adaptability of homomorphic encryption schemes in deep learning applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/asens64990.2025.11011015","authors":["Zelei Jia","Zhexue Jin","Bin Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-27T17:05:15Z","doi":"10.1109/asens64990.2025.11011015","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.1007/s00521-025-11099-4","name":"GPU-accelerated homomorphic encryption computing: empowering federated learning in IoV","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00521-025-11099-4","authors":["Sangeen Khan","Huang Qiming"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-17T05:40:48Z","doi":"10.1007/s00521-025-11099-4","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.1016/j.future.2025.107858","name":"HalfFedLearn: A secure federated learning with local data partitioning and homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.future.2025.107858","authors":["Rojalini Tripathy","Jigyasa Meshram","Padmalochan Bera"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-18T11:03:58Z","doi":"10.1016/j.future.2025.107858","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.1109/smc58881.2025.11343287","name":"Lightweight and Tamper-Resilient Data Aggregation through Reversible Watermarking and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/smc58881.2025.11343287","authors":["Lei Song","Leyi Shi","Xiuli Ren","Xiaoguang Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-28T20:54:44Z","doi":"10.1109/smc58881.2025.11343287","addedAt":"2026-08-31T06:41:45.331Z","updatedAt":"2026-08-31T06:41:45.331Z"},{"id":"doi:10.5281/zenodo.17210012","name":"HARPOCRATES Newsletter – Looking Back, Moving Forward (September 2025)","source":"datacite","abstract":"This newsletter from the Horizon Europe project HARPOCRATES (2022–2025) presents recent progress and activities on advancing privacy-preserving technologies for secure data sharing and analysis. Coordinated by Tampere University, the consortium of 13 partners from 9 European countries combines Functional Encryption, Homomorphic Encryption, and Differential Privacy with machine learning to develop new cryptographic frameworks and demonstrators. In this issue, readers will find: Scientific publications across leading venues in security and cryptography Public deliverables detailing technical developments, use cases, and innovation management Communication materials and videos including project overviews, the HARPOCRATES Voices series, and a MOOC Events and outreach such as participation in Zaragoza’s Personas que hacen el cambio, the Security Research Event 2025 in Warsaw, the plenary meeting in Paxos, and the National Seminar on Responsible AI Whitepaper on DPIA practices with insights from HARPOCRATES and other EU-funded projects Demonstrators showcasing applications in threat intelligence and sleep medicine The newsletter highlights the project’s ongoing contributions to privacy-preserving data analysis, cryptography, and responsible AI.","url":"https://doi.org/10.5281/zenodo.17210012","authors":["Vasic, Jelena"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17210012","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.5281/zenodo.17210011","name":"HARPOCRATES Newsletter – Looking Back, Moving Forward (September 2025)","source":"datacite","abstract":"This newsletter from the Horizon Europe project HARPOCRATES (2022–2025) presents recent progress and activities on advancing privacy-preserving technologies for secure data sharing and analysis. Coordinated by Tampere University, the consortium of 13 partners from 9 European countries combines Functional Encryption, Homomorphic Encryption, and Differential Privacy with machine learning to develop new cryptographic frameworks and demonstrators. In this issue, readers will find: Scientific publications across leading venues in security and cryptography Public deliverables detailing technical developments, use cases, and innovation management Communication materials and videos including project overviews, the HARPOCRATES Voices series, and a MOOC Events and outreach such as participation in Zaragoza’s Personas que hacen el cambio, the Security Research Event 2025 in Warsaw, the plenary meeting in Paxos, and the National Seminar on Responsible AI Whitepaper on DPIA practices with insights from HARPOCRATES and other EU-funded projects Demonstrators showcasing applications in threat intelligence and sleep medicine The newsletter highlights the project’s ongoing contributions to privacy-preserving data analysis, cryptography, and responsible AI.","url":"https://doi.org/10.5281/zenodo.17210011","authors":["Vasic, Jelena"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17210011","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.5281/zenodo.17165101","name":"Effet Flynn Inverse : Analyse des Causes, Solutions et Scénarios Prospectifs","source":"datacite","abstract":"Abstract ENThis document, produced with the assistance of ChatGPT o3 and GPT-5 Thinking, is released under the Apache 2.0 license. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: EPC Art. 54(2) (European Patent Convention), French IPC/CPI Art. L 611-11 (French Intellectual Property Code), 35 U.S.C. §102(a) (United States Patent Act), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). This publication addresses the inverse Flynn effect through 65 enabling innovations across devices, assays, AI, closed-loop protocols, materials, therapies, manufacturing, imaging, delivery, data standards, UX, logistics, and governance. Every invention is described in an enabling manner, classified with IPC and CPC codes, and accompanied by timestamp proof (RFC 3161 / FreeTSA). Résumé FRCe document, produit avec l’assistance de ChatGPT o3 et GPT-5 Thinking, est diffusé sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) et entre, dès sa diffusion publique, dans l’art antérieur au titre des régimes suivants : CBE art. 54(2), CPI art. L 611-11, 35 U.S.C. §102(a), Loi chinoise sur les brevets art. 22(5) (中华人民共和国专利法), et Loi japonaise sur les brevets art. 29(1) (特許法). Cette publication traite de l’effet Flynn inverse à travers 65 innovations habilitantes couvrant dispositifs, capteurs, tests, IA, protocoles en boucle fermée, matériaux, thérapies, procédés de fabrication, imagerie, systèmes de délivrance, normes de données, UX, logistique et gouvernance. Chaque invention est décrite de manière habilitante, classée IPC/CPC, et associée à une preuve d’horodatage (RFC 3161 / FreeTSA). Timestamp: 2025-09-13T16:43:19ZSHA-256: 55c629606ebc242c315596909e61875ab3b1653d366f1378dca5bda73e5f97b6 Liste des innovations & classification (IPC ; CPC) 1. Adaptive AI Tutor for Schools — IPC G09B 5/00 ; CPC G09B 5/022. Lightweight EEG Attention Coach — IPC A61B 5/04 ; CPC A61B 5/04083. Classroom CO2/PM Multi-sensor — IPC G01N 27/12 ; CPC F24F 11/524. PID-Driven HEPA Filtration — IPC F24F 8/80 ; CPC F24F 11/525. Salivary Cog-Biomarker Panel — IPC C12Q 1/70 ; CPC G16H 50/206. School Sleep Scheduling Trial — IPC G09B 19/00 ; CPC G16H 10/607. “Prescription Reading” for Infants — IPC A61B 10/00 ; CPC G16H 50/308. Cognitive AR Learning City — IPC G06T 19/00 ; CPC G09B 15/029. Federated “CognitCloud” Platform — IPC G16H 50/70 ; CPC G06N 20/0010. Reading Eye-Tracking Remediation — IPC A61B 5/107 ; CPC G06T 7/24611. Transdermal Melatonin Patch — IPC A61K 31/70 ; CPC A61M 37/0012. Closed-Loop tDCS for Attention — IPC A61N 1/36 ; CPC A61B 5/040813. Soil Lead Bioremediation — IPC C02F 3/28 ; CPC A62D 3/0014. Urinary Iodine Microfluidic Test — IPC G01N 21/78 ; CPC C12Q 1/2415. “Focus Mode” Network Filtering — IPC H04L 29/06 ; CPC G06F 21/6216. Low-Bandwidth Executive Games — IPC A63F 13/67 ; CPC G09B 5/1417. “Air→Score” Causal KPI — IPC G06Q 50/20 ; CPC G06F 16/245718. Standardized 10–12 min Battery — IPC G01N 33/50 ; CPC G06F 19/0019. Omega-3 School Meal Spec — IPC A23L 33/10 ; CPC A61K 36/2820. Cold-Chain for Bio-Samples — IPC B65D 81/38 ; CPC G16H 40/6321. “CognitFHIR” API Profiles — IPC G16H 10/60 ; CPC G06F 21/6222. Deep-Reading QA Module — IPC G09B 7/00 ; CPC G06F 3/04823. Classroom fNIRS Imaging — IPC A61B 5/145 ; CPC A61B 5/05524. “Offloading Index” — IPC G06Q 50/26 ; CPC G06F 16/2825. EEG Headset Manufacturing — IPC H01R 4/58 ; CPC A61B 5/040226. Wearable Sleep Multi-sensor — IPC A61B 5/11 ; CPC A61B 5/11127. Microbiome–Cognition Panel — IPC C12Q 1/6876 ; CPC G16H 50/3028. Combined Cognitive Supplement — IPC A61K 31/201 ; CPC A61P 25/2829. Population Cognitive Digital Twin — IPC G06N 10/00 ; CPC G16H 30/2030. NFC Microneedle Patch Process — IPC B29C 59/04 ; CPC A61M 37/0031. Arts & Cognition Program — IPC G09B 19/00 ; CPC A63H 2200/1232. Cognitive Data Blockchain — IPC G06Q 20/38 ; CPC G06N 20/2033. Wearable Enviro","url":"https://doi.org/10.5281/zenodo.17165101","authors":["Pillet, Xavier"],"tags":["adaptive tutoring","mastery learning","reinforcement learning","EEG headset","neurofeedback","attention training","air quality","HVAC control"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17165101","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.5281/zenodo.17114008","name":"Effet Flynn Inverse : Analyse des Causes, Solutions et Scénarios Prospectifs","source":"datacite","abstract":"Abstract ENThis document, produced with the assistance of ChatGPT o3 and GPT-5 Thinking, is released under the Apache 2.0 license. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: EPC Art. 54(2) (European Patent Convention), French IPC/CPI Art. L 611-11 (French Intellectual Property Code), 35 U.S.C. §102(a) (United States Patent Act), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). This publication addresses the inverse Flynn effect through 65 enabling innovations across devices, assays, AI, closed-loop protocols, materials, therapies, manufacturing, imaging, delivery, data standards, UX, logistics, and governance. Every invention is described in an enabling manner, classified with IPC and CPC codes, and accompanied by timestamp proof (RFC 3161 / FreeTSA). Résumé FRCe document, produit avec l’assistance de ChatGPT o3 et GPT-5 Thinking, est diffusé sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) et entre, dès sa diffusion publique, dans l’art antérieur au titre des régimes suivants : CBE art. 54(2), CPI art. L 611-11, 35 U.S.C. §102(a), Loi chinoise sur les brevets art. 22(5) (中华人民共和国专利法), et Loi japonaise sur les brevets art. 29(1) (特許法). Cette publication traite de l’effet Flynn inverse à travers 65 innovations habilitantes couvrant dispositifs, capteurs, tests, IA, protocoles en boucle fermée, matériaux, thérapies, procédés de fabrication, imagerie, systèmes de délivrance, normes de données, UX, logistique et gouvernance. Chaque invention est décrite de manière habilitante, classée IPC/CPC, et associée à une preuve d’horodatage (RFC 3161 / FreeTSA). Timestamp: 2025-09-13T16:43:19ZSHA-256: 55c629606ebc242c315596909e61875ab3b1653d366f1378dca5bda73e5f97b6 Liste des innovations & classification (IPC ; CPC) 1. Adaptive AI Tutor for Schools — IPC G09B 5/00 ; CPC G09B 5/022. Lightweight EEG Attention Coach — IPC A61B 5/04 ; CPC A61B 5/04083. Classroom CO2/PM Multi-sensor — IPC G01N 27/12 ; CPC F24F 11/524. PID-Driven HEPA Filtration — IPC F24F 8/80 ; CPC F24F 11/525. Salivary Cog-Biomarker Panel — IPC C12Q 1/70 ; CPC G16H 50/206. School Sleep Scheduling Trial — IPC G09B 19/00 ; CPC G16H 10/607. “Prescription Reading” for Infants — IPC A61B 10/00 ; CPC G16H 50/308. Cognitive AR Learning City — IPC G06T 19/00 ; CPC G09B 15/029. Federated “CognitCloud” Platform — IPC G16H 50/70 ; CPC G06N 20/0010. Reading Eye-Tracking Remediation — IPC A61B 5/107 ; CPC G06T 7/24611. Transdermal Melatonin Patch — IPC A61K 31/70 ; CPC A61M 37/0012. Closed-Loop tDCS for Attention — IPC A61N 1/36 ; CPC A61B 5/040813. Soil Lead Bioremediation — IPC C02F 3/28 ; CPC A62D 3/0014. Urinary Iodine Microfluidic Test — IPC G01N 21/78 ; CPC C12Q 1/2415. “Focus Mode” Network Filtering — IPC H04L 29/06 ; CPC G06F 21/6216. Low-Bandwidth Executive Games — IPC A63F 13/67 ; CPC G09B 5/1417. “Air→Score” Causal KPI — IPC G06Q 50/20 ; CPC G06F 16/245718. Standardized 10–12 min Battery — IPC G01N 33/50 ; CPC G06F 19/0019. Omega-3 School Meal Spec — IPC A23L 33/10 ; CPC A61K 36/2820. Cold-Chain for Bio-Samples — IPC B65D 81/38 ; CPC G16H 40/6321. “CognitFHIR” API Profiles — IPC G16H 10/60 ; CPC G06F 21/6222. Deep-Reading QA Module — IPC G09B 7/00 ; CPC G06F 3/04823. Classroom fNIRS Imaging — IPC A61B 5/145 ; CPC A61B 5/05524. “Offloading Index” — IPC G06Q 50/26 ; CPC G06F 16/2825. EEG Headset Manufacturing — IPC H01R 4/58 ; CPC A61B 5/040226. Wearable Sleep Multi-sensor — IPC A61B 5/11 ; CPC A61B 5/11127. Microbiome–Cognition Panel — IPC C12Q 1/6876 ; CPC G16H 50/3028. Combined Cognitive Supplement — IPC A61K 31/201 ; CPC A61P 25/2829. Population Cognitive Digital Twin — IPC G06N 10/00 ; CPC G16H 30/2030. NFC Microneedle Patch Process — IPC B29C 59/04 ; CPC A61M 37/0031. Arts & Cognition Program — IPC G09B 19/00 ; CPC A63H 2200/1232. Cognitive Data Blockchain — IPC G06Q 20/38 ; CPC G06N 20/2033. Wearable Enviro","url":"https://doi.org/10.5281/zenodo.17114008","authors":["Pillet, Xavier"],"tags":["adaptive tutoring","mastery learning","reinforcement learning","EEG headset","neurofeedback","attention training","air quality","HVAC control"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17114008","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.5281/zenodo.17114009","name":"Effet Flynn Inverse : Analyse des Causes, Solutions et Scénarios Prospectifs","source":"datacite","abstract":"Abstract ENThis document, produced with the assistance of ChatGPT o3 and GPT-5 Thinking, is released under the Apache 2.0 license. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: EPC Art. 54(2) (European Patent Convention), French IPC/CPI Art. L 611-11 (French Intellectual Property Code), 35 U.S.C. §102(a) (United States Patent Act), Chinese Patent Law Art. 22(5) (中华人民共和国专利法), and Japanese Patent Act Art. 29(1) (特許法). This publication addresses the inverse Flynn effect through 65 enabling innovations across devices, assays, AI, closed-loop protocols, materials, therapies, manufacturing, imaging, delivery, data standards, UX, logistics, and governance. Every invention is described in an enabling manner, classified with IPC and CPC codes, and accompanied by timestamp proof (RFC 3161 / FreeTSA). Résumé FRCe document, produit avec l’assistance de ChatGPT o3 et GPT-5 Thinking, est diffusé sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) et entre, dès sa diffusion publique, dans l’art antérieur au titre des régimes suivants : CBE art. 54(2), CPI art. L 611-11, 35 U.S.C. §102(a), Loi chinoise sur les brevets art. 22(5) (中华人民共和国专利法), et Loi japonaise sur les brevets art. 29(1) (特許法). Cette publication traite de l’effet Flynn inverse à travers 65 innovations habilitantes couvrant dispositifs, capteurs, tests, IA, protocoles en boucle fermée, matériaux, thérapies, procédés de fabrication, imagerie, systèmes de délivrance, normes de données, UX, logistique et gouvernance. Chaque invention est décrite de manière habilitante, classée IPC/CPC, et associée à une preuve d’horodatage (RFC 3161 / FreeTSA). Timestamp: 2025-09-13T16:43:19ZSHA-256: 55c629606ebc242c315596909e61875ab3b1653d366f1378dca5bda73e5f97b6 Liste des innovations & classification (IPC ; CPC) 1. Adaptive AI Tutor for Schools — IPC G09B 5/00 ; CPC G09B 5/022. Lightweight EEG Attention Coach — IPC A61B 5/04 ; CPC A61B 5/04083. Classroom CO2/PM Multi-sensor — IPC G01N 27/12 ; CPC F24F 11/524. PID-Driven HEPA Filtration — IPC F24F 8/80 ; CPC F24F 11/525. Salivary Cog-Biomarker Panel — IPC C12Q 1/70 ; CPC G16H 50/206. School Sleep Scheduling Trial — IPC G09B 19/00 ; CPC G16H 10/607. “Prescription Reading” for Infants — IPC A61B 10/00 ; CPC G16H 50/308. Cognitive AR Learning City — IPC G06T 19/00 ; CPC G09B 15/029. Federated “CognitCloud” Platform — IPC G16H 50/70 ; CPC G06N 20/0010. Reading Eye-Tracking Remediation — IPC A61B 5/107 ; CPC G06T 7/24611. Transdermal Melatonin Patch — IPC A61K 31/70 ; CPC A61M 37/0012. Closed-Loop tDCS for Attention — IPC A61N 1/36 ; CPC A61B 5/040813. Soil Lead Bioremediation — IPC C02F 3/28 ; CPC A62D 3/0014. Urinary Iodine Microfluidic Test — IPC G01N 21/78 ; CPC C12Q 1/2415. “Focus Mode” Network Filtering — IPC H04L 29/06 ; CPC G06F 21/6216. Low-Bandwidth Executive Games — IPC A63F 13/67 ; CPC G09B 5/1417. “Air→Score” Causal KPI — IPC G06Q 50/20 ; CPC G06F 16/245718. Standardized 10–12 min Battery — IPC G01N 33/50 ; CPC G06F 19/0019. Omega-3 School Meal Spec — IPC A23L 33/10 ; CPC A61K 36/2820. Cold-Chain for Bio-Samples — IPC B65D 81/38 ; CPC G16H 40/6321. “CognitFHIR” API Profiles — IPC G16H 10/60 ; CPC G06F 21/6222. Deep-Reading QA Module — IPC G09B 7/00 ; CPC G06F 3/04823. Classroom fNIRS Imaging — IPC A61B 5/145 ; CPC A61B 5/05524. “Offloading Index” — IPC G06Q 50/26 ; CPC G06F 16/2825. EEG Headset Manufacturing — IPC H01R 4/58 ; CPC A61B 5/040226. Wearable Sleep Multi-sensor — IPC A61B 5/11 ; CPC A61B 5/11127. Microbiome–Cognition Panel — IPC C12Q 1/6876 ; CPC G16H 50/3028. Combined Cognitive Supplement — IPC A61K 31/201 ; CPC A61P 25/2829. Population Cognitive Digital Twin — IPC G06N 10/00 ; CPC G16H 30/2030. NFC Microneedle Patch Process — IPC B29C 59/04 ; CPC A61M 37/0031. Arts & Cognition Program — IPC G09B 19/00 ; CPC A63H 2200/1232. Cognitive Data Blockchain — IPC G06Q 20/38 ; CPC G06N 20/2033. Wearable Enviro","url":"https://doi.org/10.5281/zenodo.17114009","authors":["Pillet, Xavier"],"tags":["adaptive tutoring","mastery learning","reinforcement learning","EEG headset","neurofeedback","attention training","air quality","HVAC control"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17114009","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.5281/zenodo.17112704","name":"Federated Learning for Privacy-Preserving Healthcare AI Models","source":"datacite","abstract":"Federated learning (FL) has emerged as a transformative paradigm for building collaborative healthcare AI models while safeguarding patient privacy and complying with regulations such as HIPAA and GDPR. Unlike centralized training, FL enables multiple hospitals and research centers to jointly develop a global model without exchanging raw data, thereby reducing risks of privacy breaches and promoting cross-institutional collaboration. This paper reviews recent literature (2020–2025) covering advances in privacy-preserving techniques including secure aggregation, differential privacy, and homomorphic encryption, and proposes a federated pipeline that integrates these methods for both electronic health records and medical imaging tasks. Simulated experiments with five clients illustrate that FL can achieve performance close to centralized models while substantially reducing exposure of sensitive health data, though trade-offs emerge in the form of reduced accuracy and added communication overhead. Beyond technical outcomes, the societal benefits of FL are significant: it fosters the development of AI models that generalize across diverse populations, supports early disease detection and personalized care, and enables resource-constrained institutions to contribute to and benefit from large-scale AI without compromising patient confidentiality. Ultimately, FL provides a pathway to equitable, trustworthy, and privacy-preserving healthcare innovation that can improve population health outcomes and strengthen societal trust in AI-driven medicine.","url":"https://doi.org/10.5281/zenodo.17112704","authors":["SHYAM SUNDER SAINI"],"tags":["Federated learning","Privacy-preserving","Healthcare","Secure aggregation","Differential privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17112704","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.5281/zenodo.17112703","name":"Federated Learning for Privacy-Preserving Healthcare AI Models","source":"datacite","abstract":"Federated learning (FL) has emerged as a transformative paradigm for building collaborative healthcare AI models while safeguarding patient privacy and complying with regulations such as HIPAA and GDPR. Unlike centralized training, FL enables multiple hospitals and research centers to jointly develop a global model without exchanging raw data, thereby reducing risks of privacy breaches and promoting cross-institutional collaboration. This paper reviews recent literature (2020–2025) covering advances in privacy-preserving techniques including secure aggregation, differential privacy, and homomorphic encryption, and proposes a federated pipeline that integrates these methods for both electronic health records and medical imaging tasks. Simulated experiments with five clients illustrate that FL can achieve performance close to centralized models while substantially reducing exposure of sensitive health data, though trade-offs emerge in the form of reduced accuracy and added communication overhead. Beyond technical outcomes, the societal benefits of FL are significant: it fosters the development of AI models that generalize across diverse populations, supports early disease detection and personalized care, and enables resource-constrained institutions to contribute to and benefit from large-scale AI without compromising patient confidentiality. Ultimately, FL provides a pathway to equitable, trustworthy, and privacy-preserving healthcare innovation that can improve population health outcomes and strengthen societal trust in AI-driven medicine.","url":"https://doi.org/10.5281/zenodo.17112703","authors":["SHYAM SUNDER SAINI"],"tags":["Federated learning","Privacy-preserving","Healthcare","Secure aggregation","Differential privacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17112703","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2412.01650","name":"Privacy-Preserving Federated Learning via Homomorphic Adversarial Networks","source":"datacite","abstract":"Privacy-preserving federated learning (PPFL) aims to train a global model for multiple clients while maintaining their data privacy. However, current PPFL protocols exhibit one or more of the following insufficiencies: considerable degradation in accuracy, the requirement for sharing keys, and cooperation during the key generation or decryption processes. As a mitigation, we develop the first protocol that utilizes neural networks to implement PPFL, as well as incorporating an Aggregatable Hybrid Encryption scheme tailored to the needs of PPFL. We name these networks as Homomorphic Adversarial Networks (HANs) which demonstrate that neural networks are capable of performing tasks similar to multi-key homomorphic encryption (MK-HE) while solving the problems of key distribution and collaborative decryption. Our experiments show that HANs are robust against privacy attacks. Compared with non-private federated learning, experiments conducted on multiple datasets demonstrate that HANs exhibit a negligible accuracy loss (at most 1.35%). Compared to traditional MK-HE schemes, HANs increase encryption aggregation speed by 6,075 times while incurring a 29.2 times increase in communication overhead.","url":"https://doi.org/10.48550/arxiv.2412.01650","authors":["Dong, Wenhan","Lin, Chao","He, Xinlei","Xu, Shengmin","Huang, Xinyi"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2412.01650","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.5281/zenodo.17073928","name":"A review article on \"Federated Learning on Cloud Platforms: Privacy-Preserving Machine Learning Across Distributed Systems\"","source":"datacite","abstract":"Federated Learning (FL) is a novel decentralized machine learning paradigm that enables collaborative model training across multiple clients without sharing raw data, thereby preserving privacy. This review synthesizes nineteen pivotal studies from 2017 to 2025, covering FL architectures, privacy-preserving techniques including differential privacy and homomorphic encryption, communication strategies, and applications in healthcare, finance, IoT, and mobile systems. Critical challenges such as data and system heterogeneity, communication bottlenecks, adversarial robustness, and privacy-utility trade-offs are analyzed. Recent innovations in interpretability, personalization, hybrid data partitioning, adaptive aggregation, and cross-domain transfer learning are highlighted. Ethical and regulatory considerations, emphasizing India’s evolving data privacy framework, are integrated. We conclude by identifying key research gaps and propose solutions for scalable, robust, and ethical FL adoption.","url":"https://doi.org/10.5281/zenodo.17073928","authors":["PENUMUCHU"],"tags":["Federated Learning; Privacy; Distributed AI; Healthcare; Ethics; Personalization; Indian Data Protection; Communication Efficiency; Data Partitioning; Interpretability"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17073928","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17073929","name":"A review article on \"Federated Learning on Cloud Platforms: Privacy-Preserving Machine Learning Across Distributed Systems\"","source":"datacite","abstract":"Federated Learning (FL) is a novel decentralized machine learning paradigm that enables collaborative model training across multiple clients without sharing raw data, thereby preserving privacy. This review synthesizes nineteen pivotal studies from 2017 to 2025, covering FL architectures, privacy-preserving techniques including differential privacy and homomorphic encryption, communication strategies, and applications in healthcare, finance, IoT, and mobile systems. Critical challenges such as data and system heterogeneity, communication bottlenecks, adversarial robustness, and privacy-utility trade-offs are analyzed. Recent innovations in interpretability, personalization, hybrid data partitioning, adaptive aggregation, and cross-domain transfer learning are highlighted. Ethical and regulatory considerations, emphasizing India’s evolving data privacy framework, are integrated. We conclude by identifying key research gaps and propose solutions for scalable, robust, and ethical FL adoption.","url":"https://doi.org/10.5281/zenodo.17073929","authors":["PENUMUCHU"],"tags":["Federated Learning; Privacy; Distributed AI; Healthcare; Ethics; Personalization; Indian Data Protection; Communication Efficiency; Data Partitioning; Interpretability"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17073929","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2509.03427","name":"Federated Learning: An approach with Hybrid Homomorphic Encryption","source":"datacite","abstract":"Federated Learning (FL) is a distributed machine learning approach that promises privacy by keeping the data on the device. However, gradient reconstruction and membership-inference attacks show that model updates still leak information. Fully Homomorphic Encryption (FHE) can address those privacy concerns but it suffers from ciphertext expansion and requires prohibitive overhead on resource-constrained devices. We propose the first Hybrid Homomorphic Encryption (HHE) framework for FL that pairs the PASTA symmetric cipher with the BFV FHE scheme. Clients encrypt local model updates with PASTA and send both the lightweight ciphertexts and the PASTA key (itself BFV-encrypted) to the server, which performs a homomorphic evaluation of the decryption circuit of PASTA and aggregates the resulting BFV ciphertexts. A prototype implementation, developed on top of the Flower FL framework, shows that on independently and identically distributed MNIST dataset with 12 clients and 10 training rounds, the proposed HHE system achieves 97.6% accuracy, just 1.3% below plaintext, while reducing client upload bandwidth by over 2,000x and cutting client runtime by 30% compared to a system based solely on the BFV FHE scheme. However, server computational cost increases by roughly 15621x for each client participating in the training phase, a challenge to be addressed in future work.","url":"https://doi.org/10.48550/arxiv.2509.03427","authors":["Correia, Pedro","Silva, Ivan","Amorim, Ivone","Maia, Eva","Praça, Isabel"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences","E.3; C.2.0; C.2.4"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.03427","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2509.03024","name":"Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption","source":"datacite","abstract":"In today's data-driven world, recommendation systems personalize user experiences across industries but rely on sensitive data, raising privacy concerns. Fully homomorphic encryption (FHE) can secure these systems, but a significant challenge in applying FHE to recommendation systems is efficiently handling the inherently large and sparse user-item rating matrices. FHE operations are computationally intensive, and naively processing various sparse matrices in recommendation systems would be prohibitively expensive. Additionally, the communication overhead between parties remains a critical concern in encrypted domains. We propose a novel approach combining Compressed Sparse Row (CSR) representation with FHE-based matrix factorization that efficiently handles matrix sparsity in the encrypted domain while minimizing communication costs. Our experimental results demonstrate high recommendation accuracy with encrypted data while achieving the lowest communication costs, effectively preserving user privacy.","url":"https://doi.org/10.48550/arxiv.2509.03024","authors":["Chowdhury, Moontaha Nishat","Bauer, André","Zhou, Minxuan"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.03024","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2508.19525","name":"Breaking the Layer Barrier: Remodeling Private Transformer Inference with Hybrid CKKS and MPC","source":"datacite","abstract":"This paper presents an efficient framework for private Transformer inference that combines Homomorphic Encryption (HE) and Secure Multi-party Computation (MPC) to protect data privacy. Existing methods often leverage HE for linear layers (e.g., matrix multiplications) and MPC for non-linear layers (e.g., Softmax activation functions), but the conversion between HE and MPC introduces significant communication costs. The proposed framework, dubbed BLB, overcomes this by breaking down layers into fine-grained operators and further fusing adjacent linear operators, reducing the need for HE/MPC conversions. To manage the increased ciphertext bit width from the fused linear operators, BLB proposes the first secure conversion protocol between CKKS and MPC and enables CKKS-based computation of the fused operators. Additionally, BLB proposes an efficient matrix multiplication protocol for fused computation in Transformers. Extensive evaluations on BERT-base, BERT-large, and GPT2-base show that BLB achieves a $21\\times$ reduction in communication overhead compared to BOLT (S\\&amp;P'24) and a $2\\times$ reduction compared to Bumblebee (NDSS'25), along with latency reductions of $13\\times$ and $1.8\\times$, respectively, when leveraging GPU acceleration.","url":"https://doi.org/10.48550/arxiv.2508.19525","authors":["Xu, Tianshi","Lu, Wen-jie","Yu, Jiangrui","Yi, Chen","Lin, Chenqi","Wang, Runsheng","Li, Meng"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.19525","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2509.00332","name":"CryptoFace: End-to-End Encrypted Face Recognition","source":"datacite","abstract":"Face recognition is central to many authentication, security, and personalized applications. Yet, it suffers from significant privacy risks, particularly arising from unauthorized access to sensitive biometric data. This paper introduces CryptoFace, the first end-to-end encrypted face recognition system with fully homomorphic encryption (FHE). It enables secure processing of facial data across all stages of a face-recognition process--feature extraction, storage, and matching--without exposing raw images or features. We introduce a mixture of shallow patch convolutional networks to support higher-dimensional tensors via patch-based processing while reducing the multiplicative depth and, thus, inference latency. Parallel FHE evaluation of these networks ensures near-resolution-independent latency. On standard face recognition benchmarks, CryptoFace significantly accelerates inference and increases verification accuracy compared to the state-of-the-art FHE neural networks adapted for face recognition. CryptoFace will facilitate secure face recognition systems requiring robust and provable security. The code is available at https://github.com/human-analysis/CryptoFace.","url":"https://doi.org/10.48550/arxiv.2509.00332","authors":["Ao, Wei","Boddeti, Vishnu Naresh"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2509.00332","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2508.14568","name":"Leuvenshtein: Efficient FHE-based Edit Distance Computation with Single Bootstrap per Cell","source":"datacite","abstract":"This paper presents a novel approach to calculating the Levenshtein (edit) distance within the framework of Fully Homomorphic Encryption (FHE), specifically targeting third-generation schemes like TFHE. Edit distance computations are essential in applications across finance and genomics, such as DNA sequence alignment. We introduce an optimised algorithm that significantly reduces the cost of edit distance calculations called Leuvenshtein. This algorithm specifically reduces the number of programmable bootstraps (PBS) needed per cell of the calculation, lowering it from approximately 94 operations -- required by the conventional Wagner-Fisher algorithm -- to just 1. Additionally, we propose an efficient method for performing equality checks on characters, reducing ASCII character comparisons to only 2 PBS operations. Finally, we explore the potential for further performance improvements by utilising preprocessing when one of the input strings is unencrypted. Our Leuvenshtein achieves up to $278\\times$ faster performance compared to the best available TFHE implementation and up to $39\\times$ faster than an optimised implementation of the Wagner-Fisher algorithm. Moreover, when offline preprocessing is possible due to the presence of one unencrypted input on the server side, an additional $3\\times$ speedup can be achieved.","url":"https://doi.org/10.48550/arxiv.2508.14568","authors":["Legiest, Wouter","D'Anvers, Jan-Pieter","Spasic, Bojan","Tran, Nam-Luc","Verbauwhede, Ingrid"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences","E.3"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.14568","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2508.12832","name":"Efficient and Verifiable Privacy-Preserving Convolutional Computation for CNN Inference with Untrusted Clouds","source":"datacite","abstract":"The widespread adoption of convolutional neural networks (CNNs) in resource-constrained scenarios has driven the development of Machine Learning as a Service (MLaaS) system. However, this approach is susceptible to privacy leakage, as the data sent from the client to the untrusted cloud server often contains sensitive information. Existing CNN privacy-preserving schemes, while effective in ensuring data confidentiality through homomorphic encryption and secret sharing, face efficiency bottlenecks, particularly in convolution operations. In this paper, we propose a novel verifiable privacy-preserving scheme tailored for CNN convolutional layers. Our scheme enables efficient encryption and decryption, allowing resource-constrained clients to securely offload computations to the untrusted cloud server. Additionally, we present a verification mechanism capable of detecting the correctness of the results with a success probability of at least $1-\\frac{1}{\\left|Z\\right|}$. Extensive experiments conducted on 10 datasets and various CNN models demonstrate that our scheme achieves speedups ranging $26 \\times$ ~ $\\ 87\\times$ compared to the original plaintext model while maintaining accuracy.","url":"https://doi.org/10.48550/arxiv.2508.12832","authors":["Lu, Jinyu","Sun, Xinrong","Tao, Yunting","Ji, Tong","Kong, Fanyu","Yang, Guoqiang"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.12832","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2508.13715","name":"Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment","source":"datacite","abstract":"This paper proposes a Trans-XFed architecture that combines federated learning with explainable AI techniques for supply chain credit assessment. The proposed model aims to address several key challenges, including privacy, information silos, class imbalance, non-identically and independently distributed (Non-IID) data, and model interpretability in supply chain credit assessment. We introduce a performance-based client selection strategy (PBCS) to tackle class imbalance and Non-IID problems. This strategy achieves faster convergence by selecting clients with higher local F1 scores. The FedProx architecture, enhanced with homomorphic encryption, is used as the core model, and further incorporates a transformer encoder. The transformer encoder block provides insights into the learned features. Additionally, we employ the integrated gradient explainable AI technique to offer insights into decision-making. We demonstrate the effectiveness of Trans-XFed through experimental evaluations on real-world supply chain datasets. The obtained results show its ability to deliver accurate credit assessments compared to several baselines, while maintaining transparency and privacy.","url":"https://doi.org/10.48550/arxiv.2508.13715","authors":["Shi, Jie","Siebes, Arno P. J. M.","Mehrkanoon, Siamak"],"tags":["Machine Learning (cs.LG)","Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.13715","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2503.16233","name":"Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI","source":"datacite","abstract":"Federated Learning (FL) enables collaborative model training while preserving data privacy; however, balancing privacy preservation (PP) and fairness poses significant challenges. In this paper, we present the first unified large-scale empirical study of privacy-fairness-utility trade-offs in FL, advancing toward responsible AI deployment. Specifically, we systematically compare Differential Privacy (DP), Homomorphic Encryption (HE), and Secure Multi-Party Computation (SMC) with fairness-aware optimizers including q-FedAvg, q-MAML, Ditto, evaluating their performance under IID and non-IID scenarios using benchmark (MNIST, Fashion-MNIST) and real-world datasets (Alzheimer's MRI, credit-card fraud detection). Our analysis reveals HE and SMC significantly outperform DP in achieving equitable outcomes under data skew, although at higher computational costs. Remarkably, we uncover unexpected interactions: DP mechanisms can negatively impact fairness, and fairness-aware optimizers can inadvertently reduce privacy effectiveness. We conclude with practical guidelines for designing robust FL systems that deliver equitable, privacy-preserving, and accurate outcomes.","url":"https://doi.org/10.48550/arxiv.2503.16233","authors":["Wasif, Dawood","Chen, Dian","Madabushi, Sindhuja","Alluru, Nithin","Moore, Terrence J.","Cho, Jin-Hee"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","Emerging Technologies (cs.ET)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2503.16233","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2507.12050","name":"IDFace: Face Template Protection for Efficient and Secure Identification","source":"datacite","abstract":"As face recognition systems (FRS) become more widely used, user privacy becomes more important. A key privacy issue in FRS is protecting the user's face template, as the characteristics of the user's face image can be recovered from the template. Although recent advances in cryptographic tools such as homomorphic encryption (HE) have provided opportunities for securing the FRS, HE cannot be used directly with FRS in an efficient plug-and-play manner. In particular, although HE is functionally complete for arbitrary programs, it is basically designed for algebraic operations on encrypted data of predetermined shape, such as a polynomial ring. Thus, a non-tailored combination of HE and the system can yield very inefficient performance, and many previous HE-based face template protection methods are hundreds of times slower than plain systems without protection. In this study, we propose IDFace, a new HE-based secure and efficient face identification method with template protection. IDFace is designed on the basis of two novel techniques for efficient searching on a (homomorphically encrypted) biometric database with an angular metric. The first technique is a template representation transformation that sharply reduces the unit cost for the matching test. The second is a space-efficient encoding that reduces wasted space from the encryption algorithm, thus saving the number of operations on encrypted templates. Through experiments, we show that IDFace can identify a face template from among a database of 1M encrypted templates in 126ms, showing only 2X overhead compared to the identification over plaintexts.","url":"https://doi.org/10.48550/arxiv.2507.12050","authors":["Kim, Sunpill","Paik, Seunghun","Hwang, Chanwoo","Kim, Dongsoo","Shin, Junbum","Seo, Jae Hong"],"tags":["Cryptography and Security (cs.CR)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.5.4; K.6.5; D.4.6; I.4.7"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.12050","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2507.05649","name":"DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning","source":"datacite","abstract":"Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph-based learning tasks. However, enabling privacy-preserving GNNs in encrypted domains, such as under Fully Homomorphic Encryption (FHE), typically incurs substantial computational overhead, rendering real-time and privacy-preserving inference impractical. In this work, we propose DESIGN (EncrypteD GNN Inference via sErver-Side Input Graph pruNing), a novel framework for efficient encrypted GNN inference. DESIGN tackles the critical efficiency limitations of existing FHE GNN approaches, which often overlook input data redundancy and apply uniform computational strategies. Our framework achieves significant performance gains through a hierarchical optimization strategy executed entirely on the server: first, FHE-compatible node importance scores (based on encrypted degree statistics) are computed from the encrypted graph. These scores then guide a homomorphic partitioning process, generating multi-level importance masks directly under FHE. This dynamically generated mask facilitates both input graph pruning (by logically removing unimportant elements) and a novel adaptive polynomial activation scheme, where activation complexity is tailored to node importance levels. Empirical evaluations demonstrate that DESIGN substantially accelerates FHE GNN inference compared to state-of-the-art methods while maintaining competitive model accuracy, presenting a robust solution for secure graph analytics. Our implementation is publicly available at https://github.com/LabRAI/DESIGN.","url":"https://doi.org/10.48550/arxiv.2507.05649","authors":["Zhao, Kaixiang","Attalla, Joseph Yousry","Lou, Qian","Dong, Yushun"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.05649","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2507.06086","name":"QuHE: Optimizing Utility-Cost in Quantum Key Distribution and Homomorphic Encryption Enabled Secure Edge Computing Networks","source":"datacite","abstract":"Ensuring secure and efficient data processing in mobile edge computing (MEC) systems is a critical challenge. While quantum key distribution (QKD) offers unconditionally secure key exchange and homomorphic encryption (HE) enables privacy-preserving data processing, existing research fails to address the comprehensive trade-offs among QKD utility, HE security, and system costs. This paper proposes a novel framework integrating QKD, transciphering, and HE for secure and efficient MEC. QKD distributes symmetric keys, transciphering bridges symmetric encryption, and HE processes encrypted data at the server. We formulate an optimization problem balancing QKD utility, HE security, processing and wireless transmission costs. However, the formulated optimization is non-convex and NPhard. To solve it efficiently, we propose the Quantum-enhanced Homomorphic Encryption resource allocation (QuHE) algorithm. Theoretical analysis proves the proposed QuHE algorithm's convergence and optimality, and simulations demonstrate its effectiveness across multiple performance metrics.","url":"https://doi.org/10.48550/arxiv.2507.06086","authors":["Qian, Liangxin","Li, Yang","Zhao, Jun"],"tags":["Social and Information Networks (cs.SI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.06086","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2507.04775","name":"FIDESlib: A Fully-Fledged Open-Source FHE Library for Efficient CKKS on GPUs","source":"datacite","abstract":"Word-wise Fully Homomorphic Encryption (FHE) schemes, such as CKKS, are gaining significant traction due to their ability to provide post-quantum-resistant, privacy-preserving approximate computing; an especially desirable feature in Machine-Learning-as-a-Service (MLaaS) cloud-computing paradigms. OpenFHE is a leading CPU-based FHE library with robust CKKS operations, but its server-side performance is not yet sufficient for practical cloud deployment. As GPU computing becomes more common in data centers, many FHE libraries are adding GPU support. However, integrating an efficient GPU backend into OpenFHE is challenging. While OpenFHE uses a Hardware Abstraction Layer (HAL), its flexible architecture sacrifices performance due to the abstraction layers required for multi-scheme and multi-backend compatibility. In this work, we introduce FIDESlib, the first open-source server-side CKKS GPU library that is fully interoperable with well-established client-side OpenFHE operations. Unlike other existing open-source GPU libraries, FIDESlib provides the first implementation featuring heavily optimized GPU kernels for all CKKS primitives, including bootstrapping. Our library also integrates robust benchmarking and testing, ensuring it remains adaptable to further optimization. Furthermore, its software architecture is designed to support extensions to a multi-GPU backend for enhanced acceleration. Our experiments across various GPU systems and the leading open-source CKKS library to date, Phantom, show that FIDESlib offers superior performance and scalability. For bootstrapping, FIDESlib achieves no less than 70x speedup over the AVX-optimized OpenFHE implementation.","url":"https://doi.org/10.48550/arxiv.2507.04775","authors":["Agulló-Domingo, Carlos","Vera-López, Óscar","Guzelhan, Seyda","Daksha, Lohit","Jerari, Aymane El","Shivdikar, Kaustubh","Agrawal, Rashmi","Kaeli, David","Joshi, Ajay","Abellán, José L."],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.04775","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2506.20000","name":"Can One Safety Loop Guard Them All? Agentic Guard Rails for Federated Computing","source":"datacite","abstract":"We propose Guardian-FC, a novel two-layer framework for privacy preserving federated computing that unifies safety enforcement across diverse privacy preserving mechanisms, including cryptographic back-ends like fully homomorphic encryption (FHE) and multiparty computation (MPC), as well as statistical techniques such as differential privacy (DP). Guardian-FC decouples guard-rails from privacy mechanisms by executing plug-ins (modular computation units), written in a backend-neutral, domain-specific language (DSL) designed specifically for federated computing workflows and interchangeable Execution Providers (EPs), which implement DSL operations for various privacy back-ends. An Agentic-AI control plane enforces a finite-state safety loop through signed telemetry and commands, ensuring consistent risk management and auditability. The manifest-centric design supports fail-fast job admission and seamless extensibility to new privacy back-ends. We present qualitative scenarios illustrating backend-agnostic safety and a formal model foundation for verification. Finally, we outline a research agenda inviting the community to advance adaptive guard-rail tuning, multi-backend composition, DSL specification development, implementation, and compiler extensibility alongside human-override usability.","url":"https://doi.org/10.48550/arxiv.2506.20000","authors":["Veeraragavan, Narasimha Raghavan","Nygård, Jan Franz"],"tags":["Cryptography and Security (cs.CR)","Distributed, Parallel, and Cluster Computing (cs.DC)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2506.20000","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2506.12358","name":"Relative Entropy Regularized Reinforcement Learning for Efficient Encrypted Policy Synthesis","source":"datacite","abstract":"We propose an efficient encrypted policy synthesis to develop privacy-preserving model-based reinforcement learning. We first demonstrate that the relative-entropy-regularized reinforcement learning framework offers a computationally convenient linear and ``min-free'' structure for value iteration, enabling a direct and efficient integration of fully homomorphic encryption with bootstrapping into policy synthesis. Convergence and error bounds are analyzed as encrypted policy synthesis propagates errors under the presence of encryption-induced errors including quantization and bootstrapping. Theoretical analysis is validated by numerical simulations. Results demonstrate the effectiveness of the RERL framework in integrating FHE for encrypted policy synthesis.","url":"https://doi.org/10.48550/arxiv.2506.12358","authors":["Suh, Jihoon","Jang, Yeongjun","Teranishi, Kaoru","Tanaka, Takashi"],"tags":["Machine Learning (cs.LG)","Systems and Control (eess.SY)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2506.12358","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2506.11954","name":"Technical Evaluation of a Disruptive Approach in Homomorphic AI","source":"datacite","abstract":"We present a technical evaluation of a new, disruptive cryptographic approach to data security, known as HbHAI (Hash-based Homomorphic Artificial Intelligence). HbHAI is based on a novel class of key-dependent hash functions that naturally preserve most similarity properties, most AI algorithms rely on. As a main claim, HbHAI makes now possible to analyze and process data in its cryptographically secure form while using existing native AI algorithms without modification, with unprecedented performances compared to existing homomorphic encryption schemes. We tested various HbHAI-protected datasets (non public preview) using traditional unsupervised and supervised learning techniques (clustering, classification, deep neural networks) with classical unmodified AI algorithms. This paper presents technical results from an independent analysis conducted with those different, off-the-shelf AI algorithms. The aim was to assess the security, operability and performance claims regarding HbHAI techniques. As a results, our results confirm most these claims, with only a few minor reservations.","url":"https://doi.org/10.48550/arxiv.2506.11954","authors":["Filiol, Eric"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2506.11954","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2506.10399","name":"FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks","source":"datacite","abstract":"Graph Convolutional Neural Networks (GCNs) have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over sensitive graph data remain significant. Homomorphic Encryption (HE) facilitates Privacy-Preserving Machine Learning (PPML) by allowing computations to be performed on encrypted data. However, HE introduces substantial computational overhead, particularly for GCN operations that require rotations and multiplications in matrix products. The sparsity of GCNs offers significant performance potential, but their irregularity introduces additional operations that reduce practical gains. In this paper, we propose FicGCN, a HE-based framework specifically designed to harness the sparse characteristics of GCNs and strike a globally optimal balance between aggregation and combination operations. FicGCN employs a latency-aware packing scheme, a Sparse Intra-Ciphertext Aggregation (SpIntra-CA) method to minimize rotation overhead, and a region-based data reordering driven by local adjacency structure. We evaluated FicGCN on several popular datasets, and the results show that FicGCN achieved the best performance across all tested datasets, with up to a 4.10x improvement over the latest design.","url":"https://doi.org/10.48550/arxiv.2506.10399","authors":["Kan, Zhaoxuan","Han, Husheng","Shi, Shangyi","Hua, Tenghui","Lu, Hang","Li, Xiaowei","Mu, Jianan","Hu, Xing"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2506.10399","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2506.08461","name":"ABC-FHE : A Resource-Efficient Accelerator Enabling Bootstrappable Parameters for Client-Side Fully Homomorphic Encryption","source":"datacite","abstract":"As the demand for privacy-preserving computation continues to grow, fully homomorphic encryption (FHE)-which enables continuous computation on encrypted data-has become a critical solution. However, its adoption is hindered by significant computational overhead, requiring 10000-fold more computation compared to plaintext processing. Recent advancements in FHE accelerators have successfully improved server-side performance, but client-side computations remain a bottleneck, particularly under bootstrappable parameter configurations, which involve combinations of encoding, encrypt, decoding, and decrypt for large-sized parameters. To address this challenge, we propose ABC-FHE, an area- and power-efficient FHE accelerator that supports bootstrappable parameters on the client side. ABC-FHE employs a streaming architecture to maximize performance density, minimize area usage, and reduce off-chip memory access. Key innovations include a reconfigurable Fourier engine capable of switching between NTT and FFT modes. Additionally, an on-chip pseudo-random number generator and a unified on-the-fly twiddle factor generator significantly reduce memory demands, while optimized task scheduling enhances the CKKS client-side processing, achieving reduced latency. Overall, ABC-FHE occupies a die area of 28.638 mm2 and consumes 5.654 W of power in 28 nm technology. It delivers significant performance improvements, achieving a 1112x speed-up in encoding and encryption execution time compared to a CPU, and 214x over the state-of-the-art client-side accelerator. For decoding and decryption, it achieves a 963x speed-up over the CPU and 82x over the state-of-the-art accelerator.","url":"https://doi.org/10.48550/arxiv.2506.08461","authors":["Yune, Sungwoong","Lee, Hyojeong","Putra, Adiwena","Cho, Hyunjun","Manh, Cuong Duong","Jeon, Jaeho","Kim, Joo-Young"],"tags":["Hardware Architecture (cs.AR)","Cryptography and Security (cs.CR)","Emerging Technologies (cs.ET)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2506.08461","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.5281/zenodo.15615265","name":"GeoAI and its Role in Planetary Health: Navigating the Path to Global Sustainability","source":"datacite","abstract":"I. Introduction: The Dawn of GeoAI in Planetary Health Stewardship The 21st century is characterized by unprecedented environmental challenges that threaten the delicate balance of Earth's systems and, consequently, human civilization. Climate change, accelerating biodiversity loss, widespread pollution, and the unsustainable depletion of natural resources are no longer distant threats but present realities demanding immediate and innovative responses. Within this critical context, the concept of \"planetary health\" has emerged as a vital interdisciplinary field. It emphasizes the intrinsic connection between the health of human populations and the state of the natural systems upon which all life depends.1 The statistics are stark: an estimated 90% of the global population breathes air that fails to meet safety guidelines, and the escalating climate crisis is projected to inflict direct health-related damages costing billions of US dollars by 2030, with the most vulnerable populations bearing the heaviest burden.1 Addressing these multifaceted crises requires a paradigm shift in how environmental data is collected, analyzed, and translated into actionable strategies. It is against this backdrop that Geographic Artificial Intelligence (GeoAI) is rapidly gaining prominence as a transformative field. GeoAI represents the synergistic integration of established geospatial technologies—Geographic Information Systems (GIS) and remote sensing—with the advanced analytical power of Artificial Intelligence (AI), particularly its subfields of machine learning (ML) and deep learning (DL). This convergence enables the processing and interpretation of vast and complex geospatial datasets at scales and speeds previously unattainable, unlocking profound insights for environmental monitoring, protection, and stewardship. As described by Abhijeet Sarkar in his work, GeoAI offers a \"deep dive into the rapidly evolving field... and its application in monitoring and protecting Earth's ecosystems\".3 Abhijeet Sarkar's book, \"GeoAI and its Role in Planetary Health\" (Published on: 16 January 2025), is poised to be a seminal contribution to this burgeoning domain.3 Sarkar, recognized as a visionary in AI and the CEO & Founder of Synaptic AI Lab, brings a wealth of expertise in AI, machine learning, deep learning, ethical AI practices, GIS, and spatial data analysis to this subject.6 His work aims to demystify the complexities of GeoAI, rendering its concepts accessible to a diverse audience—including researchers, policymakers, students, and sustainability advocates—and to provide actionable insights that can drive meaningful environmental change.3 The book is not merely a technical manual but is framed as a \"call to action,\" urging the leveraging of human ingenuity and advanced technology to secure the planet's future.3 The timing of Sarkar's publication is particularly noteworthy, arriving at a moment when global consciousness regarding planetary health crises has reached a critical peak, coinciding with exponential advancements and widespread interest in AI technologies. This confluence creates a significant demand for comprehensive resources that can effectively bridge the gap between sophisticated technological capabilities and pressing global environmental problems. Consequently, Sarkar's book is positioned not just as an academic treatise but as a timely and crucial intervention in a rapidly evolving global discourse. Furthermore, Sarkar's dual expertise—as a technologist deeply versed in the mechanics of AI 8 and as an author who explores its broader societal and ethical ramifications 6—suggests that his work will offer a nuanced and balanced perspective. This equilibrium is paramount for the responsible adoption of GeoAI, as solutions for planetary health must be both technologically effective and ethically sound, ensuring equitable benefits and minimizing unintended harms. The descriptions of his book consistently highlight a \"balanced focus on ","url":"https://doi.org/10.5281/zenodo.15615265","authors":["ABHIJEET SARKAR"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15615265","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.5281/zenodo.15615266","name":"GeoAI and its Role in Planetary Health: Navigating the Path to Global Sustainability","source":"datacite","abstract":"I. Introduction: The Dawn of GeoAI in Planetary Health Stewardship The 21st century is characterized by unprecedented environmental challenges that threaten the delicate balance of Earth's systems and, consequently, human civilization. Climate change, accelerating biodiversity loss, widespread pollution, and the unsustainable depletion of natural resources are no longer distant threats but present realities demanding immediate and innovative responses. Within this critical context, the concept of \"planetary health\" has emerged as a vital interdisciplinary field. It emphasizes the intrinsic connection between the health of human populations and the state of the natural systems upon which all life depends.1 The statistics are stark: an estimated 90% of the global population breathes air that fails to meet safety guidelines, and the escalating climate crisis is projected to inflict direct health-related damages costing billions of US dollars by 2030, with the most vulnerable populations bearing the heaviest burden.1 Addressing these multifaceted crises requires a paradigm shift in how environmental data is collected, analyzed, and translated into actionable strategies. It is against this backdrop that Geographic Artificial Intelligence (GeoAI) is rapidly gaining prominence as a transformative field. GeoAI represents the synergistic integration of established geospatial technologies—Geographic Information Systems (GIS) and remote sensing—with the advanced analytical power of Artificial Intelligence (AI), particularly its subfields of machine learning (ML) and deep learning (DL). This convergence enables the processing and interpretation of vast and complex geospatial datasets at scales and speeds previously unattainable, unlocking profound insights for environmental monitoring, protection, and stewardship. As described by Abhijeet Sarkar in his work, GeoAI offers a \"deep dive into the rapidly evolving field... and its application in monitoring and protecting Earth's ecosystems\".3 Abhijeet Sarkar's book, \"GeoAI and its Role in Planetary Health\" (Published on: 16 January 2025), is poised to be a seminal contribution to this burgeoning domain.3 Sarkar, recognized as a visionary in AI and the CEO & Founder of Synaptic AI Lab, brings a wealth of expertise in AI, machine learning, deep learning, ethical AI practices, GIS, and spatial data analysis to this subject.6 His work aims to demystify the complexities of GeoAI, rendering its concepts accessible to a diverse audience—including researchers, policymakers, students, and sustainability advocates—and to provide actionable insights that can drive meaningful environmental change.3 The book is not merely a technical manual but is framed as a \"call to action,\" urging the leveraging of human ingenuity and advanced technology to secure the planet's future.3 The timing of Sarkar's publication is particularly noteworthy, arriving at a moment when global consciousness regarding planetary health crises has reached a critical peak, coinciding with exponential advancements and widespread interest in AI technologies. This confluence creates a significant demand for comprehensive resources that can effectively bridge the gap between sophisticated technological capabilities and pressing global environmental problems. Consequently, Sarkar's book is positioned not just as an academic treatise but as a timely and crucial intervention in a rapidly evolving global discourse. Furthermore, Sarkar's dual expertise—as a technologist deeply versed in the mechanics of AI 8 and as an author who explores its broader societal and ethical ramifications 6—suggests that his work will offer a nuanced and balanced perspective. This equilibrium is paramount for the responsible adoption of GeoAI, as solutions for planetary health must be both technologically effective and ethically sound, ensuring equitable benefits and minimizing unintended harms. The descriptions of his book consistently highlight a \"balanced focus on ","url":"https://doi.org/10.5281/zenodo.15615266","authors":["ABHIJEET SARKAR"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15615266","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2503.15916","name":"ALLMod: Exploring $\\underline{\\mathbf{A}}$rea-Efficiency of $\\underline{\\mathbf{L}}$UT-based $\\underline{\\mathbf{L}}$arge Number $\\underline{\\mathbf{Mod}}$ular Reduction via Hybrid Workloads","source":"datacite","abstract":"Modular arithmetic, particularly modular reduction, is widely used in cryptographic applications such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). High-bit-width operations are crucial for enhancing security; however, they are computationally intensive due to the large number of modular operations required. The lookup-table-based (LUT-based) approach, a ``space-for-time'' technique, reduces computational load by segmenting the input number into smaller bit groups, pre-computing modular reduction results for each segment, and storing these results in LUTs. While effective, this method incurs significant hardware overhead due to extensive LUT usage. In this paper, we introduce ALLMod, a novel approach that improves the area efficiency of LUT-based large-number modular reduction by employing hybrid workloads. Inspired by the iterative method, ALLMod splits the bit groups into two distinct workloads, achieving lower area costs without compromising throughput. We first develop a template to facilitate workload splitting and ensure balanced distribution. Then, we conduct design space exploration to evaluate the optimal timing for fusing workload results, enabling us to identify the most efficient design under specific constraints. Extensive evaluations show that ALLMod achieves up to $1.65\\times$ and $3\\times$ improvements in area efficiency over conventional LUT-based methods for bit-widths of $128$ and $8,192$, respectively.","url":"https://doi.org/10.48550/arxiv.2503.15916","authors":["Liu, Fangxin","Li, Haomin","Wang, Zongwu","Zhang, Bo","Zhang, Mingzhe","Yan, Shoumeng","Jiang, Li","Guan, Haibing"],"tags":["Cryptography and Security (cs.CR)","Hardware Architecture (cs.AR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2503.15916","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2504.18974","name":"SONNI: Secure Oblivious Neural Network Inference","source":"datacite","abstract":"In the standard privacy-preserving Machine learning as-a-service (MLaaS) model, the client encrypts data using homomorphic encryption and uploads it to a server for computation. The result is then sent back to the client for decryption. It has become more and more common for the computation to be outsourced to third-party servers. In this paper we identify a weakness in this protocol that enables a completely undetectable novel model-stealing attack that we call the Silver Platter attack. This attack works even under multikey encryption that prevents a simple collusion attack to steal model parameters. We also propose a mitigation that protects privacy even in the presence of a malicious server and malicious client or model provider (majority dishonest). When compared to a state-of-the-art but small encrypted model with 32k parameters, we preserve privacy with a failure chance of 1.51 x 10^-28 while batching capability is reduced by 0.2%. Our approach uses a novel results-checking protocol that ensures the computation was performed correctly without violating honest clients' data privacy. Even with collusion between the client and the server, they are unable to steal model parameters. Additionally, the model provider cannot learn any client data if maliciously working with the server.","url":"https://doi.org/10.48550/arxiv.2504.18974","authors":["Sperling, Luke","Kulkarni, Sandeep S."],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2504.18974","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2504.15817","name":"EFFACT: A Highly Efficient Full-Stack FHE Acceleration Platform","source":"datacite","abstract":"Fully Homomorphic Encryption (FHE) is a set of powerful cryptographic schemes that allows computation to be performed directly on encrypted data with an unlimited depth. Despite FHE's promising in privacy-preserving computing, yet in most FHE schemes, ciphertext generally blows up thousands of times compared to the original message, and the massive amount of data load from off-chip memory for bootstrapping and privacy-preserving machine learning applications (such as HELR, ResNet-20), both degrade the performance of FHE-based computation. Several hardware designs have been proposed to address this issue, however, most of them require enormous resources and power. An acceleration platform with easy programmability, high efficiency, and low overhead is a prerequisite for practical application. This paper proposes EFFACT, a highly efficient full-stack FHE acceleration platform with a compiler that provides comprehensive optimizations and vector-friendly hardware. We start by examining the computational overhead across different real-world benchmarks to highlight the potential benefits of reallocating computing resources for efficiency enhancement. Then we make a design space exploration to find an optimal SRAM size with high utilization and low cost. On the other hand, EFFACT features a novel optimization named streaming memory access which is proposed to enable high throughput with limited SRAMs. Regarding the software-side optimization, we also propose a circuit-level function unit reuse scheme, to substantially reduce the computing resources without performance degradation. Moreover, we design novel NTT and automorphism units that are suitable for a cost-sensitive and highly efficient architecture, leading to low area. For generality, EFFACT is also equipped with an ISA and a compiler backend that can support several FHE schemes like CKKS, BGV, and BFV.","url":"https://doi.org/10.48550/arxiv.2504.15817","authors":["Huang, Yi","Gong, Xinsheng","Kong, Xiangyu","Chen, Dibei","Zhu, Jianfeng","Zhu, Wenping","Li, Liangwei","Gao, Mingyu","Wei, Shaojun","Zhang, Aoyang","Liu, Leibo"],"tags":["Cryptography and Security (cs.CR)","Hardware Architecture (cs.AR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2504.15817","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2311.03470","name":"Orion: A Fully Homomorphic Encryption Framework for Deep Learning","source":"datacite","abstract":"Fully Homomorphic Encryption (FHE) has the potential to substantially improve privacy and security by enabling computation directly on encrypted data. This is especially true with deep learning, as today, many popular user services are powered by neural networks in the cloud. Beyond its well-known high computational costs, one of the major challenges facing wide-scale deployment of FHE-secured neural inference is effectively mapping these networks to FHE primitives. FHE poses many programming challenges including packing large vectors, managing accumulated noise, and translating arbitrary and general-purpose programs to the limited instruction set provided by FHE. These challenges make building large FHE neural networks intractable using the tools available today. In this paper we address these challenges with Orion, a fully-automated framework for private neural inference using FHE. Orion accepts deep neural networks written in PyTorch and translates them into efficient FHE programs. We achieve this by proposing a novel single-shot multiplexed packing strategy for arbitrary convolutions and through a new, efficient technique to automate bootstrap placement and scale management. We evaluate Orion on common benchmarks used by the FHE deep learning community and outperform state-of-the-art by 2.38x on ResNet-20, the largest network they report. Orion's techniques enable processing much deeper and larger networks. We demonstrate this by evaluating ResNet-50 on ImageNet and present the first high-resolution FHE object detection experiments using a YOLO-v1 model with 139 million parameters. Orion is open-source for all to use at: https://github.com/baahl-nyu/orion","url":"https://doi.org/10.48550/arxiv.2311.03470","authors":["Ebel, Austin","Garimella, Karthik","Reagen, Brandon"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2311.03470","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2408.15231","name":"DCT-CryptoNets: Scaling Private Inference in the Frequency Domain","source":"datacite","abstract":"The convergence of fully homomorphic encryption (FHE) and machine learning offers unprecedented opportunities for private inference of sensitive data. FHE enables computation directly on encrypted data, safeguarding the entire machine learning pipeline, including data and model confidentiality. However, existing FHE-based implementations for deep neural networks face significant challenges in computational cost, latency, and scalability, limiting their practical deployment. This paper introduces DCT-CryptoNets, a novel approach that operates directly in the frequency-domain to reduce the burden of computationally expensive non-linear activations and homomorphic bootstrap operations during private inference. It does so by utilizing the discrete cosine transform (DCT), commonly employed in JPEG encoding, which has inherent compatibility with remote computing services where images are generally stored and transmitted in this encoded format. DCT-CryptoNets demonstrates a substantial latency reductions of up to 5.3$\\times$ compared to prior work on benchmark image classification tasks. Notably, it demonstrates inference on the ImageNet dataset within 2.5 hours (down from 12.5 hours on equivalent 96-thread compute resources). Furthermore, by learning perceptually salient low-frequency information DCT-CryptoNets improves the reliability of encrypted predictions compared to RGB-based networks by reducing error accumulating homomorphic bootstrap operations. DCT-CryptoNets also demonstrates superior scalability to RGB-based networks by further reducing computational cost as image size increases. This study demonstrates a promising avenue for achieving efficient and practical private inference of deep learning models on high resolution images seen in real-world applications.","url":"https://doi.org/10.48550/arxiv.2408.15231","authors":["Roy, Arjun","Roy, Kaushik"],"tags":["Cryptography and Security (cs.CR)","Computer Vision and Pattern Recognition (cs.CV)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2408.15231","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2501.11235","name":"Arbitrary-Threshold Fully Homomorphic Encryption with Lower Complexity","source":"datacite","abstract":"Threshold fully homomorphic encryption (ThFHE) enables multiple parties to compute functions over their sensitive data without leaking data privacy. Most of existing ThFHE schemes are restricted to full threshold and require the participation of \\textit{all} parties to output computing results. Compared with these full-threshold schemes, arbitrary threshold (ATh)-FHE schemes are robust to non-participants and can be a promising solution to many real-world applications. However, existing AThFHE schemes are either inefficient to be applied with a large number of parties $N$ and a large data size $K$, or insufficient to tolerate all types of non-participants. In this paper, we propose an AThFHE scheme to handle all types of non-participants with lower complexity over existing schemes. At the core of our scheme is the reduction from AThFHE construction to the design of a new primitive called \\textit{approximate secret sharing} (ApproxSS). Particularly, we formulate ApproxSS and prove the correctness and security of AThFHE on top of arbitrary-threshold (ATh)-ApproxSS's properties. Such a reduction reveals that existing AThFHE schemes implicitly design ATh-ApproxSS following a similar idea called ``noisy share''. Nonetheless, their ATh-ApproxSS design has high complexity and become the performance bottleneck. By developing ATASSES, an ATh-ApproxSS scheme based on a novel ``encrypted share'' idea, we reduce the computation (resp. communication) complexity from $\\mathcal{O}(N^2K)$ to $\\mathcal{O}(N^2+K)$ (resp. from $\\mathcal{O}(NK)$ to $\\mathcal{O}(N+K)$). We not only theoretically prove the (approximate) correctness and security of ATASSES, but also empirically evaluate its efficiency against existing baselines. Particularly, when applying to a system with one thousand parties, ATASSES achieves a speedup of $3.83\\times$ -- $15.4\\times$ over baselines.","url":"https://doi.org/10.48550/arxiv.2501.11235","authors":["Chang, Yijia","Li, Songze"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.11235","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2501.09397","name":"Collision Risk Analysis for LEO Satellites with Confidential Orbital Data","source":"datacite","abstract":"The growing number of satellites in low Earth orbit (LEO) has increased concerns about the risk of satellite collisions, which can ultimately result in the irretrievable loss of satellites and a growing amount of space debris. To mitigate this risk, accurate collision risk analysis is essential. However, this requires access to sensitive orbital data, which satellite operators are often unwilling to share due to privacy concerns. This contribution proposes a solution based on fully homomorphic encryption (FHE) and thus enables secure and private collision risk analysis. In contrast to existing methods, this approach ensures that collision risk analysis can be performed on sensitive orbital data without revealing it to other parties. To display the challenges and opportunities of FHE in this context, an implementation of the CKKS scheme is adapted and analyzed for its capacity to satisfy the theoretical requirements of precision and run time.","url":"https://doi.org/10.48550/arxiv.2501.09397","authors":["Lage, Svenja","Hörmann, Felicitas","Hanke, Felix","Karl, Michael"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.09397","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2501.07535","name":"Code Generation for Cryptographic Kernels using Multi-word Modular Arithmetic on GPU","source":"datacite","abstract":"Fully homomorphic encryption (FHE) and zero-knowledge proofs (ZKPs) are emerging as solutions for data security in distributed environments. However, the widespread adoption of these encryption techniques is hindered by their significant computational overhead, primarily resulting from core cryptographic operations that involve large integer arithmetic. This paper presents a formalization of multi-word modular arithmetic (MoMA), which breaks down large bit-width integer arithmetic into operations on machine words. We further develop a rewrite system that implements MoMA through recursive rewriting of data types, designed for compatibility with compiler infrastructures and code generators. We evaluate MoMA by generating cryptographic kernels, including basic linear algebra subprogram (BLAS) operations and the number theoretic transform (NTT), targeting various GPUs. Our MoMA-based BLAS operations outperform state-of-the-art multi-precision libraries by orders of magnitude, and MoMA-based NTTs achieve near-ASIC performance on commodity GPUs.","url":"https://doi.org/10.48550/arxiv.2501.07535","authors":["Zhang, Naifeng","Franchetti, Franz"],"tags":["Programming Languages (cs.PL)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2501.07535","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2412.09642","name":"A Practical Exercise in Adapting SIFT Using FHE Primitives","source":"datacite","abstract":"An exercise in implementing Scale Invariant Feature Transform using CKKS Fully Homomorphic encryption quickly reveals some glaring limitations in the current FHE paradigm. These limitations include the lack of a standard comparison operator and certain operations that depend on it (like array max, histogram binning etc). We also observe that the existing solutions are either too low level or do not have proper abstractions to implement algorithms like SIFT. In this work, we demonstrate: 1. Methods of adapting regular code to the FHE setting. 2. Alternate implementations of standard algorithms (like array max, histogram binning, etc.) to reduce the multiplicative depth. 3. A novel method of using deferred computations to avoid performing expensive operations such as comparisons in the encrypted domain. Through this exercise, we hope this work acts as a practical guide on how one can adapt algorithms to FHE","url":"https://doi.org/10.48550/arxiv.2412.09642","authors":["Balappanawar, Ishwar B","Kommireddy, Bhargav Srinivas"],"tags":["Cryptography and Security (cs.CR)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","I.4.m"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2412.09642","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2412.07187","name":"A New Federated Learning Framework Against Gradient Inversion Attacks","source":"datacite","abstract":"Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL to thwart such attacks, such as Secure Multi-party Computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP). Despite their ability to protect data privacy, these approaches inherently involve substantial privacy-utility trade-offs. By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy preserve FL framework that effectively ``breaks the direct connection'' between the shared parameters and the local private data to defend against GIA. Specifically, we propose a Hypernetwork Federated Learning (HyperFL) framework that utilizes hypernetworks to generate the parameters of the local model and only the hypernetwork parameters are uploaded to the server for aggregation. Theoretical analyses demonstrate the convergence rate of the proposed HyperFL, while extensive experimental results show the privacy-preserving capability and comparable performance of HyperFL. Code is available at https://github.com/Pengxin-Guo/HyperFL.","url":"https://doi.org/10.48550/arxiv.2412.07187","authors":["Guo, Pengxin","Zeng, Shuang","Chen, Wenhao","Zhang, Xiaodan","Ren, Weihong","Zhou, Yuyin","Qu, Liangqiong"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2412.07187","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2410.15215","name":"DataSeal: Ensuring the Verifiability of Private Computation on Encrypted Data","source":"datacite","abstract":"Fully Homomorphic Encryption (FHE) allows computations to be performed directly on encrypted data without needing to decrypt it first. This \"encryption-in-use\" feature is crucial for securely outsourcing computations in privacy-sensitive areas such as healthcare and finance. Nevertheless, in the context of FHE-based cloud computing, clients often worry about the integrity and accuracy of the outcomes. This concern arises from the potential for a malicious server or server-side vulnerabilities that could result in tampering with the data, computations, and results. Ensuring integrity and verifiability with low overhead remains an open problem, as prior attempts have not yet achieved this goal. To tackle this challenge and ensure the verification of FHE's private computations on encrypted data, we introduce DataSeal, which combines the low overhead of the algorithm-based fault tolerance (ABFT) technique with the confidentiality of FHE, offering high efficiency and verification capability. Through thorough testing in diverse contexts, we demonstrate that DataSeal achieves much lower overheads for providing computation verifiability for FHE than other techniques that include MAC, ZKP, and TEE. DataSeal's space and computation overheads decrease to nearly negligible as the problem size increases.","url":"https://doi.org/10.48550/arxiv.2410.15215","authors":["Santriaji, Muhammad Husni","Xue, Jiaqi","Lou, Qian","Solihin, Yan"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2410.15215","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.48550/arxiv.2310.02563","name":"Practical, Private Assurance of the Value of Collaboration via Fully Homomorphic Encryption","source":"datacite","abstract":"Two parties wish to collaborate on their datasets. However, before they reveal their datasets to each other, the parties want to have the guarantee that the collaboration would be fruitful. We look at this problem from the point of view of machine learning, where one party is promised an improvement on its prediction model by incorporating data from the other party. The parties would only wish to collaborate further if the updated model shows an improvement in accuracy. Before this is ascertained, the two parties would not want to disclose their models and datasets. In this work, we construct an interactive protocol for this problem based on the fully homomorphic encryption scheme over the Torus (TFHE) and label differential privacy, where the underlying machine learning model is a neural network. Label differential privacy is used to ensure that computations are not done entirely in the encrypted domain, which is a significant bottleneck for neural network training according to the current state-of-the-art FHE implementations. We formally prove the security of our scheme assuming honest-but-curious parties, but where one party may not have any expertise in labelling its initial dataset. Experiments show that we can obtain the output, i.e., the accuracy of the updated model, with time many orders of magnitude faster than a protocol using entirely FHE operations.","url":"https://doi.org/10.48550/arxiv.2310.02563","authors":["Asghar, Hassan Jameel","Lu, Zhigang","Zhao, Zhongrui","Kaafar, Dali"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2310.02563","addedAt":"2026-08-31T06:41:45.332Z","updatedAt":"2026-08-31T06:41:45.332Z"},{"id":"doi:10.1109/icirca69024.2026.11570642","name":"Hybrid Privacy-Preserving Federated Learning using Differential Privacy and Homomorphic Encryption for Healthcare IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icirca69024.2026.11570642","authors":["Ramya B","Velliangiri Sarveshwaran"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T19:43:46Z","doi":"10.1109/icirca69024.2026.11570642","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/compsac69091.2026.00137","name":"Dataflow-Oriented Classification and Performance Analysis of GPU-Accelerated Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/compsac69091.2026.00137","authors":["Ai Nozaki","Takuya Kojima","Hiroshi Nakamura","Hideki Takase"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-20T19:05:05Z","doi":"10.1109/compsac69091.2026.00137","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.2316/j.2026.206-1253","name":"TRAFFIC MITIGATION AND VEHICLE DETECTION BASED ON HOMOMORPHIC ENCRYPTION ALGORITHM AND FUZZY COMPREHENSIVE EVALUATION. 150-161","source":"crossref","abstract":"","url":"https://doi.org/10.2316/j.2026.206-1253","authors":["Zihao Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-19T21:07:26Z","doi":"10.2316/j.2026.206-1253","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/icce67443.2026.11449705","name":"Optimizing Homomorphic Encryption in Federated Learning with Zero-Skipping","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icce67443.2026.11449705","authors":["Yoo-Bin Tae","Soo-Jeong Park","Geon-Ha Kim","Seung-Ho Lim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-27T19:47:50Z","doi":"10.1109/icce67443.2026.11449705","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/meco70748.2026.11579168","name":"Performance Benchmarking of Homomorphic Encryption for Fingerprint Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.1109/meco70748.2026.11579168","authors":["Eva Kupcova","Matus Pleva","Milos Drutarovsky","Velibor Došljak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-02T19:41:25Z","doi":"10.1109/meco70748.2026.11579168","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1007/s10586-026-06094-w","name":"Secure data processing with polynomial-based homomorphic encryption, binary transformation, and lossless compression","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10586-026-06094-w","authors":["M. Sandhya","Krrish Yadav","Aditya Khanna"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-30T20:14:06Z","doi":"10.1007/s10586-026-06094-w","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.25258/ijddt.16.35s.80","name":"Differential Privacy And Homomorphic Encryption For Healthcare Data Protection","source":"crossref","abstract":"There are serious worries about patient privacy, security, and regulatory compliance as a result of the exponential increase of sensitive patient data caused by the fast digitisation of healthcare systems. Electronic health records, predictive analytics, and AI-driven diagnostics are data-intensive applications that might be especially challenging for traditional data security systems to strike a balance between data value and confidentiality. Secure healthcare data management has recently seen the rise of new privacy-preserving approaches including homomorphic encryption and differential privacy. By inserting controlled noise into datasets, differential privacy offers a mathematically rigorous foundation for safeguarding individual patient information. This allows for statistical analysis to be conducted without disclosing identifying features. That way, the results of the analysis won't be skewed because one person's data was either included or left out. However, homomorphic encryption ensures that data remains secret even when processed and analysed, since it enables calculations to be performed directly on encrypted data without decryption. In order to provide strong data security while keeping data usable, this research investigates how healthcare systems might integrate differential privacy with homomorphic encryption. It delves into their theoretical underpinnings, actual uses, and prospective uses in domains such healthcare cloud computing, telemedicine, and medical research. Computing overhead, scalability problems, and the compromise between accuracy and privacy are some of the important obstacles highlighted by the research. Healthcare data ecosystems may be made much more safe by using a hybrid strategy that combines differential privacy with homomorphic encryption. This will allow for secure data exchange and collaborative analytics without sacrificing patient privacy. Policymakers, healthcare providers, and academics seeking to construct reliable digital health infrastructures may benefit from the insights offered by this study, which adds to the expanding corpus of information on privacy-enhancing technology.","url":"https://doi.org/10.25258/ijddt.16.35s.80","authors":["Shaurya Vir Singh Pathania","A. J. Singh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-30T07:08:25Z","doi":"10.25258/ijddt.16.35s.80","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1088/1612-202x/ae4f83","name":"Securing named data networking (NDN) content: a quantum homomorphic encryption protocol","source":"crossref","abstract":"Abstract Named data networking (NDN) supports receiver-driven content retrieval with stateful forwarding (pending interest table (PIT)/forwarding information base) and in-network caching, yet many deployments still rely on classical cryptography that may be undermined by quantum-capable adversaries. Despite NDN’s built-in data authenticity via producer signatures, quantum-enabled attacks become plausible once RSA/ECC-based trust anchors are weakened by Shor-type attacks. Existing quantum-security efforts for NDN largely focus on post-quantum replacements of signatures or key exchange, but do not address the tension between confidentiality-preserving payload protection and NDN’s in-network processing (forwarding/caching decisions) in a quantum setting. This paper presents a quantum-enhanced NDN architecture that preserves NDN’s content-centric semantics—naming, PIT-driven forwarding, and cache-assisted dissemination of verifiable control objects—while delivering privacy-sensitive payloads as quantum homomorphic encryption (QHE)-encrypted quantum states over an underlying quantum network. QHE provides end-to-end confidentiality and enables authorized operations on encrypted quantum payloads without exposing plaintext. To make the design compatible with NDN forwarding, the proposed protocol encrypts the payload while keeping name-based forwarding semantics intact, and supports ciphertext-domain processing for selected policy functions. To protect provenance and integrity of NDN metadata, routing instructions, and session state, we incorporate quantum digital signatures (QDS) bound to NDN names and session identifiers. The main contributions are: (i) a QHE+QDS layered protection model tailored to NDN’s Interest and Data exchange; (ii) a session workflow that couples proactive entanglement distribution with NDN-style stateful signaling for efficient establishment and recovery; and (iii) a security analysis clarifying the achieved guarantees and the remaining trust assumptions. Security analysis indicates robustness against quantum man-in-the-middle and tampering attacks, with modest overhead on NDN control traffic.","url":"https://doi.org/10.1088/1612-202x/ae4f83","authors":["Jing Li","Cheng Zhang","XianMin Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-18T12:39:57Z","doi":"10.1088/1612-202x/ae4f83","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/ccnc65079.2026.11366271","name":"PRISM: Privacy-preserving Inference System with Homomorphic Encryption and Modular Activation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccnc65079.2026.11366271","authors":["Zeinab Elkhatib","Ali Sekmen","Kamrul Hasan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-04T20:45:15Z","doi":"10.1109/ccnc65079.2026.11366271","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.64388/irev9i12-1719027","name":"Quantum-Resistant Homomorphic Encryption for Secure Federated Learning in CubeSat Constellations for Real-Time Exoplanet Detection","source":"crossref","abstract":"","url":"https://doi.org/10.64388/irev9i12-1719027","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T10:34:59Z","doi":"10.64388/irev9i12-1719027","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.9734/ajrcos/2026/v19i5857","name":"A Standardized Model for Homomorphic Encryption Implementation","source":"crossref","abstract":"Homomorphic encryption (HE) is gradually gaining popularity. It ensures computation on encrypted data without compromising confidentiality. Despite its potential, the implementation of HE faces challenges. Notably among them is the lack of uniformity and standardization. The purpose of this study is to develop a standardized model for implementing HE in various application areas. A star model was developed with evaluation at the centre. With this structure, activity at each stage is evaluated before moving to the next stage. The model was tested with the West African Examination Council (WAEC) and the Ghana Education Service (GES) School Placement System. Data from WAEC was encrypted homomorphically and transmitted to GES. The GES system then used the encrypted data to place candidates into various Senior High and Technical Schools. This ensured computation on encrypted data whilst maintaining confidentiality. The result of the study showed that the use of the model ensured standardization. The proposed model should, therefore, be used when implementing HE in various application areas.","url":"https://doi.org/10.9734/ajrcos/2026/v19i5857","authors":["George Asante"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-01T07:31:44Z","doi":"10.9734/ajrcos/2026/v19i5857","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.65286/icic.v22i2.15822","name":"TMI-VFL: Secure Vertical Federated Learning via Threshold Multi-Identity Homomorphic Encryption","source":"crossref","abstract":"Vertical Federated Learning (VFL) enables multiple participants to collaboratively train models using vertically partitioned data. However, during the actual training process, participants must exchange intermediate model representations(e.g., embeddings), which creates a potential attack surface for privacy leakage. Recent studies have shown that the URVFL attack achieves precise and covert data reconstruction by constructing malicious gradients and training a decoder using label information, posing a serious threat to the privacy security of vertical federated learning systems.To address this issue, we propose TMI-VFL, a secure training framework based on Threshold Multi-Identity Fully Homomorphic Encryption.This method establishes a ciphertext computation mechanism that ensures embedding vectors, gradients, and intermediate activation values are all processed in encrypted form, while the threshold decryption scheme prevents any single participant from recovering sensitive information. Experimental results show that under URVFL attacks, the proposed method increases reconstruction error by more than 10-fold, significantly reducing the effectiveness of the attack. Meanwhile, model accuracy decreases by less than 1% and remains close to baseline levels. These results indicate that TMI-VFL achieves an effective trade-off between privacy protection and model utility, providing a practical solution for secure VFL.","url":"https://doi.org/10.65286/icic.v22i2.15822","authors":["Yuqing Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T06:09:37Z","doi":"10.65286/icic.v22i2.15822","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/icemcsi67638.2026.11603035","name":"Breast Cancer Classification Aided by Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icemcsi67638.2026.11603035","authors":["Kavitha T","Ishmita Menon","Shakthi Priya","Gauri Nair","Preeti M"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-15T20:01:10Z","doi":"10.1109/icemcsi67638.2026.11603035","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/cnml68938.2026.11453051","name":"Differentiated Graph-Aware Rescaling for Precise and Efficient Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cnml68938.2026.11453051","authors":["Lin Han","Jiahui Xu","Jianan Li","Lei Wang","Wei Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T19:49:32Z","doi":"10.1109/cnml68938.2026.11453051","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.56553/popets-2026-0010","name":"Argmax and XGBoost Training over Fully Homomorphic Encryption","source":"crossref","abstract":"Fully Homomorphic Encryption (FHE) is a promising solution to enable privacy-preserving inference and training of machine learning models over encrypted data. Among the machine learning methods used in practice, Extreme Gradient Boosting (XGBoost) is one technique that shines in many applications. While previous works have tackled the problem of training tree-based models over FHE, these works either rely on interaction with the client, which adds the extra burden of communication, or consume a typically unreasonable amount of time to train a large model. In this work, we present an efficient system for a non-interactive XGBoost training over FHE that achieves up to 360 imes speedup compared to the state of the art. The argmax operation is a basic building block invoked repeatedly during the XGBoost training as well as other machine learning algorithms, but computing it over FHE is time consuming. When utilizing the Single Instruction Multiple Data (SIMD) parallelism capability offered by most FHE schemes and using a configuration with s slots, the state of the art methods compute argmax on n &lt;= s values using either O(log_2 n) SIMD-comparisons in tournament-style comparison or ceil{n^2 /s} SIMD-comparisons using all pairs comparison. As a second contribution of this work, we propose an efficient argmax algorithm that is based on a novel technique to maximize SIMD-utilization, and computes the argmax of n &lt;= s values using only O(log_2(log_2(n)) SIMD-comparisons. The method extends to n &gt; s with complexity O(n/s) + log_2(log_2(s)), compared to O(n/s) + log_2(s) for state of the art methods. We conduct empirical experiments to compare our method with other existing argmax methods, and show that when using the HEaaN FHE scheme with a configuration of s=2^15 to compute the argmax of n=s values, our implementation is about 1.6 times faster than the state of the art.","url":"https://doi.org/10.56553/popets-2026-0010","authors":["Ramy Masalha","Adi Akavia","Allon Adir","Ehud Aharoni","Eyal Kushnir"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-25T18:59:00Z","doi":"10.56553/popets-2026-0010","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/hpca68181.2026.11408486","name":"CROPHE: Cross-Operator Dataflow Optimization for Fully Homomorphic Encryption Accelerators","source":"crossref","abstract":"","url":"https://doi.org/10.1109/hpca68181.2026.11408486","authors":["Xinhua Chen","Jiangbin Dong","Hongren Zheng","Tian Tang","Mingyu Gao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-04T20:47:22Z","doi":"10.1109/hpca68181.2026.11408486","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.38007/ijmc.2026.070109","name":"Research on the Design and Application of Homomorphic Encryption Privacy-Preserving k-means Clustering Algorithm for Cross-Institutional Collaborative Risk Control","source":"crossref","abstract":"","url":"https://doi.org/10.38007/ijmc.2026.070109","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-25T08:31:38Z","doi":"10.38007/ijmc.2026.070109","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/icsedis68157.2026.11517999","name":"Secure Digital Kyc Framework using Blockchain Enabled Verification Credentials and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsedis68157.2026.11517999","authors":["Dr.S.Praveena Rachel Kamala","Mahalakshmi R","Parkavi C"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-18T19:44:46Z","doi":"10.1109/icsedis68157.2026.11517999","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/iciss67859.2026.11453917","name":"Federated Gated Recurrent Unit-Based Intrusion Detection with Homomorphic Encryption for IoT Cybersecurity","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciss67859.2026.11453917","authors":["Jose Renita KJ","Haarish Raghavendra. M","D. Jerusha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-31T19:49:27Z","doi":"10.1109/iciss67859.2026.11453917","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1504/ijisc.2026.10076714","name":"Homomorphic Encryption: A Tool for Data Privacy Protection in Addressing Cybercrime Challenges in Nigeria","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijisc.2026.10076714","authors":["Lateef Gbolahan Salaudeen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-03T14:00:20Z","doi":"10.1504/ijisc.2026.10076714","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/isitia71267.2026.11642217","name":"PET for Personalized Exercise: Protecting Gymnasium Data Privacy with RSA Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isitia71267.2026.11642217","authors":["Raditya Wicaksono Prihatnoko","Aji Gautama Putrada","Khalis Sofi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-13T19:16:55Z","doi":"10.1109/isitia71267.2026.11642217","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1504/ijisc.2026.10076863","name":"Homomorphic Encryption: A Tool for Data Privacy Protection in Addressing Cybercrime Challenges in Nigeria","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijisc.2026.10076863","authors":["Lateef Gbolahan Salaudeen``````````"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-07T14:00:14Z","doi":"10.1504/ijisc.2026.10076863","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1016/b978-0-443-34125-0.00024-6","name":"Homomorphic encryption for modern data security: Case studies of IBM's comprehensive solutions and Zama's cutting-edge innovations for IoT","source":"crossref","abstract":"","url":"https://doi.org/10.1016/b978-0-443-34125-0.00024-6","authors":["Gurunath R.","Debabrata Samanta","Yashas G. Goutham"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-05T20:27:22Z","doi":"10.1016/b978-0-443-34125-0.00024-6","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.23919/date69613.2026.11539375","name":"Attacking and Securing Hybrid Homomorphic Encryption Against Power Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.23919/date69613.2026.11539375","authors":["Aikata Aikata","Maciej Czuprynko","Nedžma Mušović","Emira Salkić","Sujoy Sinha Roy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-04T19:53:10Z","doi":"10.23919/date69613.2026.11539375","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/access.2026.3705488","name":"Reducing Noise Growth in BFV Homomorphic Encryption: An Improved Sampling Approach for Privacy Preserving Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2026.3705488","authors":["Akshit Aggarwal","Srinibas Swain"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-19T19:41:23Z","doi":"10.1109/access.2026.3705488","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.32604/cmc.2026.075573","name":"TQKD: A More Efficient QKD Network Based on Homomorphic Encryption Technology","source":"crossref","abstract":"","url":"https://doi.org/10.32604/cmc.2026.075573","authors":["Tianhua Lin","Sijiang Xie","Yalong Yan","Jianguo Xie","Ang Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T08:33:42Z","doi":"10.32604/cmc.2026.075573","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1007/s44163-026-01898-6","name":"Resource-aware hybrid homomorphic encryption scheduling for privacy-preserving edge intelligence","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s44163-026-01898-6","authors":["Hamid El Bouabidi","Mohamed El Ghmary","Mohamed Amnai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-06T10:10:02Z","doi":"10.1007/s44163-026-01898-6","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1504/ijsn.2026.153819","name":"Securing vehicle network suspension control: lightweight homomorphic encryption with fuzzy rules","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijsn.2026.153819","authors":["Zefeng Ding","Haili Tang","Xiaojuan Cao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-28T11:30:24Z","doi":"10.1504/ijsn.2026.153819","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.3390/jcp6040110","name":"Evaluation of Homomorphic Encryption Integration Strategies in Database Management Systems","source":"crossref","abstract":"Homomorphic Encryption (HE) has emerged as a promising approach for data processing without exposing sensitive information. Despite significant advances, the practical strategies for the integration of HE into widely used database management systems (DBMSs) remain limited due to performance constraints and architectural challenges. This paper explores HE integration strategies within DBMS, focusing on SQL Server, PostgreSQL, and MariaDB. A methodology is proposed to assess the feasibility and performance of multiple HE schemes, including BFV, CKKS, BGV, TFHE, Paillier, and RSA (without padding). The evaluation considers different integration strategies, namely Python-based execution and native C++ extensions, across Windows and Debian environments. Experimental results obtained from four configurations demonstrate that the choice of HE scheme and integration strategy significantly impacts performance. Lattice-based schemes (BFV, CKKS, BGV) provide a balanced trade-off between functionality and efficiency, while TFHE incurs high computational costs due to its bit-level design. Native C++ integrations consistently outperform Python-based approaches, although the latter offer greater flexibility and ease of development. The findings highlight the feasibility of integrating HE into DBMS while emphasizing the importance of selecting appropriate schemes and integration mechanisms to meet application-specific requirements. The proposed evaluation framework provides preliminary insights into the relative behavior of different HE schemes and integration strategies under controlled experimental conditions, supporting future work on privacy-preserving DBMS design.","url":"https://doi.org/10.3390/jcp6040110","authors":["Henrique Jorge","Cristina Wanzeller","João Henriques"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-29T00:54:42Z","doi":"10.3390/jcp6040110","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.54254/2755-2721/2026.gu34026","name":"Secure Data Aggregation for Digital Cousin Systems with Homomorphic Encryption and Integrity Verification","source":"crossref","abstract":"We're seeing more and more heterogeneous IoT devices being deployed acrossdifferent networks these days, and they're generating massive amounts of distributed data,this creates new challenges for secure multi-source aggregation in edge intelligenceenvironments. Most conventional approaches built for Digital Twin (DT) add way too muchmodeling complexity. They may not keep up when IoT environments shift quickly andunpredictably. Also, many existing homomorphic encryption solutions focus at privacyprotection, but they don't handle integrity verification during data aggregation processproperly. So to fix these issues, we developed a secure data aggregation framework for DigitalCousin (DC) systems. We built it using Paillier homomorphic encryption to keep data private,added RSA-based signatures so we can verify each data piece's authenticity, and a ChineseRemainder Theorem (CRT)-driven aggregation mechanism makes the actual aggregationprocess work smoothly. In this framework, edge gateways take charge of aggregatingencrypted data along with their associated signatures. Original plaintext stays completelyinaccessible throughout aggregation stage, no part of it can be accessed while the aggregationis still going on. Once the aggregated ciphertext is delivered to the DC side, signaturevalidation and plaintext reconstruction are carried out to guarantee both data authenticity andaggregation correctness. Compared with conventional aggregation schemes, the proposedframework decreases the processing pressure on edge gateways and reduces communicationcosts in large-scale IoT data collection tasks. The security evaluation further demonstratesthat the scheme can simultaneously support data confidentiality, integrity protection, andreliable source authentication in multi-device aggregation environments. Security analysisshows that the framework can effectively achieve confidentiality, integrity verification, andsource authentication for multi-device data aggregation scenarios.","url":"https://doi.org/10.54254/2755-2721/2026.gu34026","authors":["Jiasheng Hu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-01T07:22:45Z","doi":"10.54254/2755-2721/2026.gu34026","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.13052/jcsm2245-1439.1465","name":"Homomorphic Encryption-Based NFT Copyright Protection for Digital Art","source":"crossref","abstract":"The digital art industry faces critical challenges in copyright protection and privacy preservation that existing solutions fail to adequately address. Traditional digital watermarking techniques are vulnerable to removal attacks and cannot prevent unauthorized content access, while current Non-Fungible Token (NFT) platforms expose transaction details and artwork content due to blockchain transparency, creating privacy risks for creators and collectors. Conventional encryption methods require decryption before any data processing, making copyright verification and feature extraction impossible in encrypted states, thus creating a fundamental security-usability trade-off. To overcome these limitations, this research proposes a network security protection system integrating homomorphic encryption with NFT copyright protection. Homomorphic encryption was selected because it uniquely enables computational operations on encrypted data without decryption, allowing copyright verification while maintaining complete data confidentiality – a capability unmatched by alternative privacy-preserving technologies. The system employs the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption algorithm to construct a three-tier protection architecture consisting of an encryption layer, verification layer, and storage layer. This architecture achieves copyright verification and feature extraction of digital artworks in ciphertext state by integrating zero-knowledge proof for identity authentication and Shamir’s secret sharing for secure key management. The NFT copyright protection mechanism introduces homomorphic watermark embedding and smart contract verification, combined with proxy re-encryption to implement secure copyright transfer. A prototype system was developed and evaluated through comprehensive testing. Security performance was assessed using six metrics: privacy protection strength, copyright verification accuracy, anti-tampering capability, key security, transaction anonymity, and system resilience. Each metric was scored on a 0–100 scale based on standardized penetration testing and cryptographic attack simulations, with the comprehensive security score calculated as the weighted average of all metrics. Performance testing on 100 digital artworks across five resolutions (256×256 to 4096×4096 pixels) demonstrates that encryption time for 512×512 resolution images is kept within 15 seconds, while security testing reveals the system achieves a comprehensive security score of 94.7, representing a 60.5% improvement over traditional NFT platforms. This solution provides a practical copyright protection framework balancing security and usability for the digital art industry, with significant theoretical value and broad application prospects.","url":"https://doi.org/10.13052/jcsm2245-1439.1465","authors":["Shuang Yang","Sha Lyu","Chunjuan Zhao","Zifeng Luo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-30T02:24:22Z","doi":"10.13052/jcsm2245-1439.1465","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/icass69550.2026.11547610","name":"Privacy Preserving Multi-Keyword Search and Ranked Retrieval Using Homomorphic Encryption in Cloud Environment","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icass69550.2026.11547610","authors":["Jasmine M S","Dhinakaran D"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-08T19:49:38Z","doi":"10.1109/icass69550.2026.11547610","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1504/ijahuc.2026.10079761","name":"Secure E-Voting using Homomorphic Encryption: An Experimental Analysis and Findings","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijahuc.2026.10079761","authors":["Swaraj Pal","Maroti Deshmukh","Sneha Chauhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-09T13:00:14Z","doi":"10.1504/ijahuc.2026.10079761","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/atigb70203.2026.11628293","name":"Privacy-Preserving Fingerprint Authentication for IoT Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/atigb70203.2026.11628293","authors":["Khang Ngo Hoang Nhat","Tuan Ho Anh","Tung Dam Minh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-11T19:17:04Z","doi":"10.1109/atigb70203.2026.11628293","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/iccbi68589.2026.11619812","name":"Efficient Privacy-Preserving Storage Framework for Accounting Feature Data Integrating Revocable Template Mapping and Homomorphic Encryption Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccbi68589.2026.11619812","authors":["Wang Jinsong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-27T19:15:31Z","doi":"10.1109/iccbi68589.2026.11619812","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/sp63933.2026.00226","name":"Efficient Arithmetic-and-Comparison Homomorphic Encryption with Space Switching","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp63933.2026.00226","authors":["Erwin Eko Wahyudi","Yan Solihin","Qian Lou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-01T19:34:20Z","doi":"10.1109/sp63933.2026.00226","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.3390/math14091540","name":"A Privacy-Preserving Quadratic Optimisation with Additive Homomorphic Encryption in Cyber-Physical Systems","source":"crossref","abstract":"In this paper, we propose a secure protocol to compute the quadratic optimisation problem under a three-party outsourcing architecture in the scenario of cyber-physical systems. To enable real-world implementation, we propose an encoding framework that uses a fixed-point expression and a truncated-mapping scheme to map real numbers into multiple data blocks, improving the protocol’s efficiency. Based on this, we define the recovery operations for decryption, addition, and multiplication. Considering computations involving three parties to solve the quadratic optimisation problem, we thoroughly analyse privacy issues during the interaction process. Then, a secure protocol is developed by designing privacy-preserving addition, multiplication, and comparison protocols based on the additive homomorphic encryption scheme. The data blowup and “0”-privacy leakage problems are addressed specifically for the gradient descent process by designing a secure addition protocol for block data and a secure comparison protocol. The efficiency and security of the proposed protocol are formally analysed in depth. Finally, through intensive experiments, we demonstrate the efficiency and security of our protocol.","url":"https://doi.org/10.3390/math14091540","authors":["Ying He","Yang Pu","Rui Ye","Zhenyong Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-01T10:15:27Z","doi":"10.3390/math14091540","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/dcas69364.2026.11544204","name":"FPGA Implementation of Efficient NTT Hardware Accelerator for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/dcas69364.2026.11544204","authors":["Daniel Tselogorodtsev","Liam Sittig","Hannah Robinson","Jiafeng Xie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-09T19:50:08Z","doi":"10.1109/dcas69364.2026.11544204","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1007/s40009-026-02238-z","name":"Ensuring Fairness of Parties via Homomorphic Encryption over Multiparty Protocol in Two Different Schemes","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s40009-026-02238-z","authors":["Akshit Aggarwal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-26T17:25:14Z","doi":"10.1007/s40009-026-02238-z","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/mocast70204.2026.11626454","name":"FPGA-Based Field Arithmetic Logic Unit for Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mocast70204.2026.11626454","authors":["Zeinab A. Hassaan","Nermeen H. Abdelzaher","Ghada Bouattour","Lobna A. Said"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-31T18:16:00Z","doi":"10.1109/mocast70204.2026.11626454","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/ccwc67433.2026.11393779","name":"Securing Federated Learning in Health IoT with Edge-Assisted Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccwc67433.2026.11393779","authors":["Rahul Kavati","Sriven Srilakshmi Pulkaram","Huzaif Khan","Ali Jalooli"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-25T20:53:57Z","doi":"10.1109/ccwc67433.2026.11393779","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/eurosp68448.2026.00017","name":"Privacy at your Fingertips: Enabling Rapid Client-Side Operations in Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/eurosp68448.2026.00017","authors":["Aikata Aikata","Florian Krieger","Sujoy Sinha Roy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-30T19:04:13Z","doi":"10.1109/eurosp68448.2026.00017","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1088/1674-1056/ae7275","name":"A secure two-party polygon intersection area computation protocol based on quantum homomorphic encryption","source":"crossref","abstract":"Abstract Secure Multi-party Geometric Computation(SMGC) is an important field in Secure Multi-party Computation(SMC). It aims to enable multiple mutually distrustful parties to collaboratively perform geometric computation tasks while protecting their respective private information. As an important branch of this field, the secure two-party polygon intersection area computation focuses on enabling two untrusted parties to securely compute the overlapping area between their polygons without revealing their private coordinate information. This paper introduces a semi-honest third party and leverages quantum homomorphic encryption technology to first achieve secure two-party segment intersection computation. Subsequently, by integrating a quantum protocol for point inclusion verification within arbitrary regions, it proposes an exact method for calculating the intersection area of polygons. Finally, through comprehensive analysis, it has been demonstrated that this protocol is secure and reliable, offering new insights for future regional collaborative computing in quantum environments.","url":"https://doi.org/10.1088/1674-1056/ae7275","authors":["Bai Liu","Xin-Guo Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-26T08:47:57Z","doi":"10.1088/1674-1056/ae7275","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/tpec67884.2026.11513146","name":"A Privacy-Preserving Aggregation Paradigm of Distributed Energy Resources using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tpec67884.2026.11513146","authors":["Ziyi Zhao","Yichen Zhang","Wei-Jen Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-15T19:51:24Z","doi":"10.1109/tpec67884.2026.11513146","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/access.2026.3662147","name":"Selective Homomorphic Encryption With LLE Enhances Privacy and Scalability in Doorbell Face Recognition","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2026.3662147","authors":["Raniyah Wazirali","Fatma Foad Ashrif","Rami Ahmad"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-06T20:51:07Z","doi":"10.1109/access.2026.3662147","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/icsft66733.2026.11507064","name":"Privacy-Preserving Deep Learning for Multi-Tenant CRM Workflows: Integrating Homomorphic Encryption across Cloud Databases","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsft66733.2026.11507064","authors":["Abhaar Gupta","Tarun Vishwanath Chincholi","Supreet Nagi","Balaje Prasath Manoharan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-12T19:46:43Z","doi":"10.1109/icsft66733.2026.11507064","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.32604/cmc.2026.078473","name":"Privacy-Preserving Transformer Inference with Optimized Homomorphic Encryption and Secure Collaborative Computing","source":"crossref","abstract":"","url":"https://doi.org/10.32604/cmc.2026.078473","authors":["Tao Bai","Yang Tang","Kuan Shao","Zhenyong Zhang","Yuanteng Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T08:46:53Z","doi":"10.32604/cmc.2026.078473","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1016/j.compeleceng.2026.110969","name":"A bibliometric analysis of Homomorphic Encryption for privacy-preserving biometrics","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.compeleceng.2026.110969","authors":["Shreyansh Sharma","Anurag Mudgil","Richa Dubey","Anil Saini","Santanu Chaudhury"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-17T14:22:53Z","doi":"10.1016/j.compeleceng.2026.110969","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.55214/2576-8484.v10i6.13145","name":"Secure machine learning on encrypted data using homomorphic encryption techniques","source":"crossref","abstract":"Privacy preservation has become a critical challenge in modern machine learning applications, especially in sensitive domains such as healthcare, finance, and cloud-based services, where confidential data must be protected. This paper aims to develop a secure machine learning framework using Homomorphic Encryption (HE), specifically the CKKS scheme, to enable computation on encrypted data without requiring decryption. The proposed methodology integrates Logistic Regression, Encrypted Fully Connected Neural Networks (EncFCNN), and Encrypted Convolutional Neural Networks (EncCNN) to perform both training and inference directly in the encrypted domain. The encrypted data and model parameters are processed securely on an untrusted server while maintaining complete data confidentiality. Experimental evaluation is conducted using the Pima Indian dataset and the MNIST dataset to compare encrypted and non-encrypted model performance. The findings show that the encrypted logistic regression model achieves an accuracy of 0.7375, which is comparable to plaintext training, while EncCNN achieves 100% accuracy on the test set with acceptable computational overhead. The results confirm that homomorphic encryption provides strong privacy preservation without significantly affecting model accuracy, making it highly suitable for secure real-world machine learning applications.","url":"https://doi.org/10.55214/2576-8484.v10i6.13145","authors":["Sushma M P","Kiran Puttegowda","Praveenkumara J"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-15T10:23:24Z","doi":"10.55214/2576-8484.v10i6.13145","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.5121/ijcnc.2026.18406","name":"SECURE FEDERATED INTRUSION DETECTION USING HOMOMORPHIC ENCRYPTION: A COMPARATIVE STUDY WITH ENSEMBLE LEARNING","source":"crossref","abstract":"The rapid growth of the Internet of Things (IoT) has also resulted in the increase of the demand in the intrusion detection systems, which can detect suspicious activity and keep the information confidential. The traditional centralized machine learning systems involve attaching the data of the distributed devices to a centralized server thus placing them at a risk of being stolen. Federated Learning (FL) may help overcome this difficulty and assist in a distributed model training process without sharing raw client data. Nevertheless, updated versions of models that are transferred in the process of training are susceptible to poisoning or inference attacks at communication and aggregation. This paper proposed a federated intrusion detection system that is secure and involves implementation of Homomorphic Encryption (HE), in this case CKKS scheme, to provide model updates protection in aggregation process. The CICIoT2023 dataset was used in extensive experimentation of the proposed framework in terms of comparing with the classical machine learning baselines and experiences in using ensembles. Our findings show that the centralized Random Forest model with optimal accuracy of 98.57% worked best and the proposed Federated Learning models worked well with the standard FL performance of 79.54% and the encrypted FL+HE model performed with an accuracy of 79.39%. These are some results that demonstrate how Homomorphic Encryption enables the security and confidentiality of model aggregation a significant effect to model detection (reducing by only 0.15 percent), which provides a strong privacy-preserving security solution to decentralized IoT networks.","url":"https://doi.org/10.5121/ijcnc.2026.18406","authors":["Sa daf","Aasim Zafar","Mohammad Luqman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-12T03:43:10Z","doi":"10.5121/ijcnc.2026.18406","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.54254/2755-2721/2025.31672","name":"ABS: An Adaptive Bootstrapping Scheduler for Efficient and Accurate Logistic Regression Training over Homomorphic Encryption","source":"crossref","abstract":"Homomorphic Encryption (HE) presents a com pelling paradigm for privacy-preserving machine learning; how ever, its practical adoption is hindered by substantial computa tional overhead. For iterative algorithms such as Logistic Regres sion (LR), the main performance bottleneck is bootstrapping, a noise-refreshing operation essential for maintaining correctness. Prevailing approaches employ static bootstrapping schedules, forcing practitioners into a suboptimal trade-off: either frequent bootstrapping with minimal parameters, which incurs a massive overhead, or infrequent bootstrapping with large parameters, which slows down all other operations. This study introduces the Adaptive Bootstrapping Scheduler (ABS), an algorithm that resolves this false dichotomy by reframing static scheduling as a dynamic optimization prob lem. ABS intelligently co-designs the HE parameterization and bootstrapping schedule to minimize the execution time while maximizing the model precision. At its core, the ABS leverages a fine-grained performance model to navigate the complex, non linear relationship between operational costs and bootstrapping frequency. Furthermore, we investigate a subtle but critical design choice: which intermediate value to use for bootstrapping. Our analysis, supported by prior work on noisy optimization, reveals that bootstrapping the gradient—a counter-intuitive but optimal strategy—significantly mitigates error accumulation via learning rate scaling. Implemented over the Poseidon library, our experiments on multiple public datasets demonstrate that ABS consistently accelerates LR training by up to 1.77× and achieves superior model fidelity compared to state-of-the-art static schedulers.","url":"https://doi.org/10.54254/2755-2721/2025.31672","authors":["Kaishuo Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-10T04:26:03Z","doi":"10.54254/2755-2721/2025.31672","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/fccm68464.2026.00033","name":"ReFHE-NTT: Resource-Driven NTT FPGA Architecture for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fccm68464.2026.00033","authors":["Valentino Guerrini","Giuseppe Sorrentino","Alessandro Barenghi","Davide Conficconi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-10T19:59:45Z","doi":"10.1109/fccm68464.2026.00033","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1587/transfun.2025cip0021","name":"Multi-Key Homomorphic Encryption with Threshold Re-Encryption via (Replicated) Additive Secret Sharing","source":"crossref","abstract":"","url":"https://doi.org/10.1587/transfun.2025cip0021","authors":["Akira NAKASHIMA","Yukimasa SUGIZAKI","Hikaru TSUCHIDA","Takuya HAYASHI","Koji NUIDA","Kengo MORI","Toshiyuki ISSHIKI"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-25T22:09:41Z","doi":"10.1587/transfun.2025cip0021","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/icnc68183.2026.11416887","name":"Meeting SLO with Privacy: Partial Homomorphic Encryption Inference and Anomaly Gating for Predictive Autoscaling in Cloud Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icnc68183.2026.11416887","authors":["Alan Chuang","Melody Moh","Teng-Sheng Moh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-09T19:55:54Z","doi":"10.1109/icnc68183.2026.11416887","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/qcnc69040.2026.00057","name":"Experimental Validation of AUX Scheme for Quantum Homomorphic Encryption on IBM Quantum Platforms","source":"crossref","abstract":"","url":"https://doi.org/10.1109/qcnc69040.2026.00057","authors":["Gia Phat Dang","Weisheng Si","Belal Alsinglawi","Jim Basilakis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-07T19:51:14Z","doi":"10.1109/qcnc69040.2026.00057","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/airc69745.2026.11631435","name":"A Privacy-Preserving TF-IDF Neural Network Framework with Fully Homomorphic Encryption for Medical PII Text Analytics","source":"crossref","abstract":"","url":"https://doi.org/10.1109/airc69745.2026.11631435","authors":["Uchenna Ndolo","Hoda El-Sayed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-06T19:09:30Z","doi":"10.1109/airc69745.2026.11631435","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.56557/arjocs/2026/v8i1192","name":"Tri-layered Hybrid Privacy Architectures for Deep Neural Networks: Synergizing Split Learning, Differential Privacy, and CKKS Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.56557/arjocs/2026/v8i1192","authors":["Wisdom C. Amadi","Daniel Ekpah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-20T10:38:02Z","doi":"10.56557/arjocs/2026/v8i1192","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.65908/gja.2026.8444.1034","name":"Homomorphic Encryption (HE), Artificial Intelligence (AI): An Analysis, Significance and Applications with Coding in Python","source":"crossref","abstract":"","url":"https://doi.org/10.65908/gja.2026.8444.1034","authors":["Awadhesh Kumar Mishra","Pinki Tomar","Roushan kumar sharma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-30T16:41:54Z","doi":"10.65908/gja.2026.8444.1034","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1007/978-3-032-22469-9_1","name":"Structural Testing with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-22469-9_1","authors":["Andrei Aleksandrov"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T14:57:16Z","doi":"10.1007/978-3-032-22469-9_1","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1007/s12597-025-00965-3","name":"Analysis of various homomorphic encryption algorithms based on primitive functions and applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s12597-025-00965-3","authors":["J. Josepha Menandas","Mary Subaja Christo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-28T04:19:57Z","doi":"10.1007/s12597-025-00965-3","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1145/3804453","name":"Orion: A Fully Homomorphic Encryption Framework for Deep Learning","source":"crossref","abstract":"Fully Homomorphic Encryption (FHE) has the potential to substantially improve privacy and security by enabling computation directly on encrypted data. This is especially true in deep learning, as many popular user services today are powered by neural networks in the cloud. Beyond its well-known high computational costs, one of the major challenges facing wide-scale deployment of FHE-secured neural inference is effectively mapping these networks to FHE primitives. FHE poses many programming challenges including packing large vectors, managing accumulated noise, and translating arbitrary and general-purpose programs to the limited instruction set provided by FHE. These challenges make building large FHE neural networks intractable using the tools available today. In this paper we address these challenges with Orion , a fully-automated framework for private neural inference using FHE. Orion accepts deep neural networks written in PyTorch and translates them into efficient FHE programs. We achieve this through a novel single-shot multiplexed packing strategy that performs arbitrary convolutions in one multiplicative level. We also incorporate einsum notation to let users effortlessly express general tensor contractions (e.g., matrix–matrix products, matrix transposes, etc.) in Orion without being burdened by the complexities of FHE. In addition, we present an efficient technique to automate bootstrap placement that extends to network layers such as self-attention blocks and generalizes to most modern feed-forward neural networks. We evaluate Orion on common benchmarks used by the FHE deep learning community and outperform state-of-the-art by 2.38 × on ResNet-20, the largest network they report. Orion’s techniques enable processing much deeper and larger networks. We demonstrate this by evaluating ResNet-50 on ImageNet and present the first high-resolution FHE object detection experiments using a YOLO-v1 model with 139 million parameters. Orion is open-source for all to use at: https://github.com/baahl-nyu/orion.","url":"https://doi.org/10.1145/3804453","authors":["Austin Ebel","Karthik Garimella","Brandon Reagen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-22T11:06:26Z","doi":"10.1145/3804453","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1145/3802927.3802953","name":"A Secure Comparison Protocol Based on the SM2 Homomorphic Encryption Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3802927.3802953","authors":["Xiao Deng","Shutong Li","Yuxin Li","Baodong Qin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-02T02:34:57Z","doi":"10.1145/3802927.3802953","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/icirca69024.2026.11570557","name":"Optimizing Cloud Data Security using Homomorphic Encryption: A Hybrid Paillier and ElGamal Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icirca69024.2026.11570557","authors":["Saranmani M","Rajasethupathi G","Manikandan S","S.Nithya","M.Malleswari","Dr.S.Saravanan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T19:43:46Z","doi":"10.1109/icirca69024.2026.11570557","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.63180/jcsra.thestap.2026.2.7","name":"A Privacy-Preserving Federated Learning Framework with Fully Homomorphic Encryption for Reproductive Health Analytics","source":"crossref","abstract":"With the increasing use of cloud analytics technology, machine learning is now being used to help with fertility tracking and predict risks of pregnancy. However, reproductive health data is considered highly sensitive data, and with traditional analytics training, data must be sent to an external server, which is raising giant red flags regarding data privacy and security. This paper will address these issues by proposing a privacy-preserving analytics platform for fertility and pregnancy data by combining Federated Learning (FL) with Fully Homomorphic Encryption (FHE) technology. FL will be utilized so that multiple hospitals can collaborate and come up with a shared model for pregnancy risks. However, with FL, inference leaks occur when data is sent to the server, which compromises sensitive data. To address inference leaks, we will be using the Cheon-Kim-Kim-Song (CKKS) method to encrypt data before it is sent to the server, which will then be aggregated with other data without any sensitive reproductive health data being compromised or exposed during training. We will be using TenSEAL and Scikit-learn to implement our proposed framework and will be testing it with the Maternal Health Risk Dataset. Our results will show that our proposed FL+FHE model is able to achieve reliable prediction accuracy with reasonable encryption overhead.","url":"https://doi.org/10.63180/jcsra.thestap.2026.2.7","authors":["Abass Hassan","Sheikh Umar Mushtaq","Hussein Edrees","Amier Alquatesh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-18T11:10:17Z","doi":"10.63180/jcsra.thestap.2026.2.7","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1016/j.ject.2025.08.001","name":"Encrypted intelligence: A comparative analysis of homomorphic encryption frameworks for privacy-preserving AI","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ject.2025.08.001","authors":["Aadit Shah","Surindernath Sivakumar","Prabakaran N"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T23:33:08Z","doi":"10.1016/j.ject.2025.08.001","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1145/3816440.3818571","name":"Presto: Hardware Acceleration of Ciphers for Hybrid Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3816440.3818571","authors":["Yeonsoo Jeon","Lewis Liu","Mattan Erez","Michael Orshansky"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-10T19:23:31Z","doi":"10.1145/3816440.3818571","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1117/12.3089169","name":"Design and performance optimization of lightweight homomorphic encryption scheme for optical MPC","source":"crossref","abstract":"","url":"https://doi.org/10.1117/12.3089169","authors":["Liehua Peng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-31T16:10:53Z","doi":"10.1117/12.3089169","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/ipdpsw71298.2026.00096","name":"Cryptographically Computable Self-Learning Activation Functions for Privacy-Preserving Logistic Regression with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ipdpsw71298.2026.00096","authors":["Bernardo Pulido-Gaytan","Horacio González-Vélez","Andrei Tchernykh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-19T19:12:52Z","doi":"10.1109/ipdpsw71298.2026.00096","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1002/spy2.70229","name":"Improvements of Homomorphic Encryption Algorithms and Their Effects in Privacy Protection in the Internet of Things","source":"crossref","abstract":"ABSTRACT At present, traditional homomorphic encryption (HE) algorithms face the problems of high computational consumption and weak antiattack ability in complex environments. In order to handle these shortcomings and improve the HE algorithm, the ring learning with errors (RLWE) is proposed. The results show that the proposed algorithm can effectively reduce the computational overhead by combining local differential privacy mechanisms with Nth‐degree truncated polynomial ring units (NTRU) lattice‐based lightweight encryption schemes. Additionally, data security can be made better. On the Espressif Systems Protocol 32‐bit (ESP32) platform, encryption latency is reduced by 42.1% compared to the Cheon–Kim–Kim–Song (CKKS) algorithm. With the incorporation of an adaptive mechanism, the attack rate of breaking through the defense line is reduced to below 12%. Meanwhile, this algorithm can also guarantee the quantum‐resistant security strength of 148 bits in the configuration of 2048‐bit security parameters; it comprehensively surpasses CKKS, RLWE‐CKKS, and many improved baseline algorithms. Under the harsh conditions of the Internet of Things (IoT) terminal, this study simultaneously tackles computational constraints, privacy leakage risks, and postquantum threats; thus, it can offer a theoretical reference and engineering support for building a trustworthy IoT system.","url":"https://doi.org/10.1002/spy2.70229","authors":["Xuewei Li","Shulai Chu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-28T07:29:38Z","doi":"10.1002/spy2.70229","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1016/j.aei.2026.105162","name":"FedOut: Federated learning with outsourced homomorphic encryption for resource-constrained IoT devices","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.aei.2026.105162","authors":["Yi Yuan","Hong Rao","Shuanggen Liu","Rixuan Qiu","Mengyuan Cui","Yuxin Hu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-21T10:03:53Z","doi":"10.1016/j.aei.2026.105162","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/icassp55912.2026.11460773","name":"A High Performance Hardware Accelerator for Fully Homomorphic Encryption and Application to Neural Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icassp55912.2026.11460773","authors":["Chuanxin Zhang","Xiaojie Zhu","Chi Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-21T21:23:31Z","doi":"10.1109/icassp55912.2026.11460773","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.4018/ijdcf.419505","name":"Privacy-Preserving Digital Evidence Chain for Accounting Vouchers Based on Smart Contracts and Paillier Homomorphic Encryption","source":"crossref","abstract":"Electronic accounting voucher deposits and cybersecurity audits face a contradiction between resistance to evidence tampering and data privacy protection. Using blockchain technology as a basis, the authors propose a solution framework that addresses both concerns. Under a dual-layer hash mapping architecture, original accounting vouchers are stored off-chain via the InterPlanetary File System, and digital fingerprints are anchored on the immutable ledger. Smart contract engines enforce a multi-signature state machine to ensure accounting voucher traceability from generation to archiving. Paillier homomorphic encryption supports direct computation on ciphertext, enabling continuous consistency checks between accounts and accounting vouchers without decrypting raw data. Experimental results showed that on-chain storage overhead was reduced by 99.08%, audit latency reached 12.4 s under a workload of 5,000 transactions per second, and no raw data were exposed throughout the process. This framework provides forensic-ready support for secure electronic accounting voucher management.","url":"https://doi.org/10.4018/ijdcf.419505","authors":["Hui Sui","Ying Sui"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-19T19:48:16Z","doi":"10.4018/ijdcf.419505","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1016/j.ins.2025.122875","name":"A verifiable privacy-preserving federated learning scheme based on homomorphic proxy re-encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ins.2025.122875","authors":["Zeyu Song","Zhenjie Huang","Yiping Cai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-13T10:20:23Z","doi":"10.1016/j.ins.2025.122875","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1145/3807507","name":"The HHE Land: Exploring the Landscape of Hybrid Homomorphic Encryption","source":"crossref","abstract":"Hybrid Homomorphic Encryption (HHE) addresses key challenges in Homomorphic Encryption (HE), such as communication, computation, and storage overheads, by combining symmetric cryptography with HE schemes. Despite progress, enhancing HHE’s usability, performance, and security remains critical. This work analyzes prominent HHE schemes, evaluating their performance in a client-server setting using Go. Our implementation is publicly available on GitHub. We categorize and study attacks on HHE schemes, revealing vulnerabilities to practical threats despite meeting theoretical security. This study highlights the need for standardized parameters and improved countermeasures, guiding the development of secure, efficient HHE systems for real-world applications.","url":"https://doi.org/10.1145/3807507","authors":["Hossein Abdinasibfar","Camille Nuoskala","Antonis Michalas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T11:45:37Z","doi":"10.1145/3807507","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1201/9781003773801-6","name":"Securing Healthcare Data with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003773801-6","authors":["M. Jeyaselvi","Vamsi Yanamadala","Sai P. Kethan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-25T15:21:08Z","doi":"10.1201/9781003773801-6","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.38124/ijisrt/26feb1493","name":"Fully Homomorphic Encryption for Secure Cloud Computation","source":"crossref","abstract":"Fully Homomorphic Encryption (FHE) is an advanced cryptographic technique that enables computation on encrypted data without requiring decryption. This capability eliminates the need to expose sensitive data during processing, making FHE particularly suitable for cloud computing, healthcare analytics, financial systems, and privacypreserving artificial intelligence. Despite its strong theoretical foundation, FHE faces practical challenges including high computational complexity, large ciphertext expansion, and bootstrapping overhead. This paper presents a comprehensive study of FHE, including its theoretical background, working mechanism, security properties, real-world applications, limitations, and emerging research trends.","url":"https://doi.org/10.38124/ijisrt/26feb1493","authors":["Sreekutty Sabarivasan","Ashish L."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-07T07:02:38Z","doi":"10.38124/ijisrt/26feb1493","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1016/j.ins.2026.123180","name":"Efficient privacy-preserving sparse matrix-vector multiplication using homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ins.2026.123180","authors":["Yang Gao","Gang Quan","Wujie Wen","Scott Piersall","Qian Lou","Liqiang Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-31T15:45:41Z","doi":"10.1016/j.ins.2026.123180","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.3844/jcssp.2026.475.486","name":"A Multiphase Zero-Trust Authentication Framework Using Replicated and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.3844/jcssp.2026.475.486","authors":["Modisaotsile Marope","Venumadhav Kuthadi","Rajalakshmi Selvaraj","Thabo Semong","Tshiamo Sigwele"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-27T11:32:34Z","doi":"10.3844/jcssp.2026.475.486","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/iscas66217.2026.11562540","name":"Time-Area Efficient RNS Base-conversion Architecture for HPS-BFV Homomorphic Encryption using Generalized Solinas Primes","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscas66217.2026.11562540","authors":["Rella Mareta","Ardianto Satriawan","Hanho Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-18T20:06:41Z","doi":"10.1109/iscas66217.2026.11562540","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.23919/date69613.2026.11539679","name":"Efficient Federated Learning with Low-Rank Updates under Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.23919/date69613.2026.11539679","authors":["Mohamed Aboelenien Ahmed","Mohamed Alsharkawy","Hassan Nassar","Heba Khdr","Jeferson Gonzalez-Gomez","Jörg Henkel"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-04T19:53:10Z","doi":"10.23919/date69613.2026.11539679","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/cine68769.2026.11502879","name":"Adaptive Multi-Tier Fully Homomorphic Encryption with Differential Privacy for Privacy-Preserving Medical Data Classification","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cine68769.2026.11502879","authors":["Charvi Palem","Servani Veeranki","Vihaan Reddy Thatiparthi","Kamalakanta Sethi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T19:37:10Z","doi":"10.1109/cine68769.2026.11502879","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/qsw72780.2026.00031","name":"Implementing Post-Quantum Homomorphic Encryption in the Eclipse Qrisp High-Level Programming Language for Quantum Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/qsw72780.2026.00031","authors":["Aurelia Kusumastuti","Nikolay Tcholtchev","Philipp Lämmel","Sebastian Bock","Manfred Hauswirth"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-27T19:08:30Z","doi":"10.1109/qsw72780.2026.00031","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1007/978-3-032-20026-6_18","name":"Energy Consumption of TLS, Searchable Encryption and Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-20026-6_18","authors":["Marc Damie","Mihai Pop","Merijn Posthuma"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-11T00:20:32Z","doi":"10.1007/978-3-032-20026-6_18","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.37394/232014.2026.22.9","name":"Privacy-Preserving State Estimation: An Encrypted Extended Kalman Filter Using CKKS Homomorphic Encryption","source":"crossref","abstract":"Fully Homomorphic Encryption (FHE) enables mathematical operations directly on encrypted data without decryption. Any operation that a polynomial can approximate can, in principle, be executed under an FHE scheme. To protect cyber-physical systems from eavesdropping on sensitive measurements, we integrate CKKS, an FHE scheme for encrypted real and complex arithmetic, into a state estimator. The estimator is an Extended Kalman Filter (EKF) that fuses GPS and Inertial Measurement Unit (IMU) data to estimate vehicle position, velocity, linear acceleration, yaw angle, and turn rate. We implement CKKS using the Microsoft SEAL library, which supports only a limited number of homomorphic arithmetic operations, creating major challenges for EKF steps such as matrix inversion. We address these constraints with operation-efficient approximations and structured compromises. Frobenius norm analysis shows that the encrypted EKF preserves the precision of the plaintext EKF while reducing data exposure, at the cost of increased latency.","url":"https://doi.org/10.37394/232014.2026.22.9","authors":["Michael Sotula Masangu","Moanda Ndeko Mosengo C. M.","Witesyavwirwa Vianney Kambale","Kyandoghere Kyamakya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-21T09:49:37Z","doi":"10.37394/232014.2026.22.9","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/iceccc70334.2026.11633180","name":"A Performance Study of Fully Homomorphic Encryption for Secure Healthcare Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iceccc70334.2026.11633180","authors":["Ramachandra H. N.","S. Mahamayi","Manasa","V. K. Nandana","K. S. Shivaprakasha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-13T19:16:12Z","doi":"10.1109/iceccc70334.2026.11633180","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.18063/eir.v4i4.1955","name":"Research on a Universal Data Privacy Collaborative Protection Technology that Integrates Homomorphic Encryption and Secure Multi-party Computation","source":"crossref","abstract":"In big data interoperability scenarios, single privacy protection technologies struggle to balance the demands of efficient data processing with stringent privacy security requirements. Homomorphic encryption and secure multi-party computation, two core technologies in privacy computing, enable data to be &amp;ldquo;both usable and invisible.&amp;rdquo; However, these technologies are often applied independently or only superficially integrated, exhibiting common limitations such as high computational overhead, poor cross-platform interface compatibility, a lack of a unified protection framework, and limited general adaptability. This paper systematically analyzes the fundamental theories and operational characteristics of both technologies, identifies key challenges in their integration, and proposes a unified privacy protection architecture. The architecture optimizes hybrid algorithm operation modes and comprehensive permission management mechanisms while establishing lightweight, standardized, and dynamically adjustable optimization strategies. Key contributions include: developing a universal collaborative protection framework applicable to government, financial, and healthcare applications; overcoming the limitations of traditional single-technology approaches and superficial integration methods; achieving an effective balance between data privacy security and collaborative computing efficiency through optimized algorithms and standardized interfaces; filling research gaps in deeply integrated standardization frameworks for these technologies; and providing theoretical foundations and practical references for large-scale implementation of compliant cross-domain data sharing and privacy protection solutions.","url":"https://doi.org/10.18063/eir.v4i4.1955","authors":["Wenliang Tian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-22T03:08:52Z","doi":"10.18063/eir.v4i4.1955","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.2139/ssrn.6520191","name":"Secure and Efficient Federated Learning Using Sketches and Fully Homomorphic Encryption","source":"crossref","abstract":"Federated Learning (FL) represents a distributed training paradigm designed to enable collaborative model training without sharing raw data. However, sophisticated attackers can infer or reconstruct sensitive client data from shared model updates, thereby weakening privacy protection for clients. Moreover, FL also struggles with the data heterogeneity (non-IID distribution) among participants and with high communication overhead due to the transmission of large model weights over multiple rounds. This work presents an integrated framework of three novel algorithms to address the aforementioned issues in FL: i) FedSketch leverages Differential Privacy (DP) with probabilistic count sketches to compress high-dimensional model updates. This compression approach reduces communication overhead while enhancing privacy. ii) The CKKSFED algorithm, which utilizes Fully Homomorphic Encryption (FHE) to perform client clustering, mitigating the negative effects of non-IID data distribution in a privacy-preserving manner. iii) The MetricBasedSelection algorithm selects clients in each round based on metrics to improve convergence and reduce communication. We evaluate the effectiveness of our solutions in an emulation environment using image classification and real-world time series datasets. FedSketch significantly reduced communication overhead, achieving up to a 73-fold reduction in model size. Clustering clients with CKKSFED achieved convergence in approximately half the number of rounds compared to non-clustered methods in non-IID scenarios. Finally, FedSketch, with the metric-based selection algorithm, selected fewer clients for training in each round, thereby contributing to the reduction of communication costs. Jointly, these algorithms reduced communication overhead while ensuring strong privacy guarantees.","url":"https://doi.org/10.2139/ssrn.6520191","authors":["Eduardo  M. M. Sarmento","João  Pedro Camargo Batista","Johann  Jakob Schmitz Bastos","Vinicius  Fernandes Soares Mota","Rodolfo  da Silva Villaça"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-04T16:38:32Z","doi":"10.2139/ssrn.6520191","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1063/5.0324769","name":"Privacy-preserving worker selection in spatial crowdsourcing based on quantum homomorphic encryption","source":"crossref","abstract":"In spatial crowdsourcing tasks, the risk of privacy leakage regarding task and workers’ locations has always been a critical issue. Although many scholars have proposed solutions based on classical computing, these methods cannot withstand potential future quantum computing attacks. This paper presents a quantum-secure protocol for selecting workers in spatial crowdsourcing, which utilizes a two-stage filtering process combining quantum multi-party geometric intersection and quantum homomorphic encryption. The protocol ensures that workers are selected without disclosing the privacy information of any participants. Compared to traditional classical protocols, our solution offers significantly higher security. The correctness analysis validates the feasibility and effectiveness of the protocol.","url":"https://doi.org/10.1063/5.0324769","authors":["Bai Liu","Shupin Qiu","Mingwu Zhang","Xinguo Wang","Kui Kui Guo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T13:17:13Z","doi":"10.1063/5.0324769","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1201/9781003630715-9","name":"Privacy-Preserving Fingerprint Authentication Using SaDeXNet and Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003630715-9","authors":["Pavani Chitrapu","Hemantha Kumar Kalluri","Mahesh Kumar Morampudi","Rusydi Umar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-07T20:02:33Z","doi":"10.1201/9781003630715-9","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1063/5.0318036","name":"Privacy-preserving homomorphic encryption in cloud storage","source":"crossref","abstract":"","url":"https://doi.org/10.1063/5.0318036","authors":["Noor R. Obeid","Noor Fadel","Dalia Abdulrahim Mokheef"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-27T18:00:41Z","doi":"10.1063/5.0318036","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1007/978-3-032-10756-5_27","name":"CryptoESN: Privacy-Preserving Echo State Network Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-10756-5_27","authors":["Tanuja","Rakesh Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T01:27:18Z","doi":"10.1007/978-3-032-10756-5_27","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/infocom59046.2026.11571699","name":"AHE: Adaptive Homomorphic Encryption for Customizable Privacy in Heterogeneous Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/infocom59046.2026.11571699","authors":["Jiaxiang Tang","Xinran Wang","Qi Le","Kangjie Lu","Zhi-Li Zhang","Ali Anwar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-29T19:38:15Z","doi":"10.1109/infocom59046.2026.11571699","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1016/j.cpc.2025.109868","name":"Secure numerical simulations using fully homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.cpc.2025.109868","authors":["Arseniy Kholod","Yuriy Polyakov","Michael Schlottke-Lakemper"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-19T16:30:49Z","doi":"10.1016/j.cpc.2025.109868","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/icnc68183.2026.11416991","name":"Privacy-Preserving Post Deployment Model Calibration with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icnc68183.2026.11416991","authors":["Shadman Mahmood Khan Pathan","Qianlong Wang","Jonathan Takeshita","Sakan Binte Imran","Sachin Shetty"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-09T19:55:54Z","doi":"10.1109/icnc68183.2026.11416991","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1016/j.jisa.2026.104543","name":"Enhanced privacy-preserving neural networks with fully homomorphic encryption: Optimized search and training","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jisa.2026.104543","authors":["Peng Zhang","Xiang Li","Jianwen Liang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-11T20:45:23Z","doi":"10.1016/j.jisa.2026.104543","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.2991/978-94-6239-638-8_29","name":"Secure Paternity Testing with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.2991/978-94-6239-638-8_29","authors":["Nicole Anne Balde","Richard Bryann Chua"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-30T01:48:56Z","doi":"10.2991/978-94-6239-638-8_29","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.23919/date69613.2026.11539085","name":"FHEIns: Fully Homomorphic Encryption Acceleration for Large Data Applications with In-Storage Processing","source":"crossref","abstract":"","url":"https://doi.org/10.23919/date69613.2026.11539085","authors":["Xuan Wang","Tianqi Zhang","Keming Fan","Augusto Vega","Minxuan Zhou","Tajana Rosing"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-04T19:53:10Z","doi":"10.23919/date69613.2026.11539085","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1145/3748522.3779981","name":"HE-DAP: Homomorphic Encryption-based Dynamic Adaptive Parameter Optimization for Statistical Computation","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3748522.3779981","authors":["Yun-Soo Park","Hyunmin Choi","Hyoungshick Kim","Mun-Kyu Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-09T14:17:49Z","doi":"10.1145/3748522.3779981","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/aqtr70159.2026.11577887","name":"Framework for Fully Homomorphic Encryption Libraries Comparative Analysis: Case Study for Microsoft SEAL, OpenFHE, and HElib","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aqtr70159.2026.11577887","authors":["Antal Marcel","Ioan Andrei Șpac","Bogdan Nicusor Bindea"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-01T19:34:29Z","doi":"10.1109/aqtr70159.2026.11577887","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.1109/access.2026.3701023","name":"PCPSI: Efficient Unbalanced Private Set Intersection Using Homomorphic Encryption With Plaintext–Ciphertext Multiplication","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2026.3701023","authors":["Dongju Lee","Sungyeon Lee","Youyeon Joo","Kevin Nam","Yunheung Paek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-08T19:52:56Z","doi":"10.1109/access.2026.3701023","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/sist61674.2026.11596207","name":"Development and Performance Evaluation of a Modified Homomorphic Encryption Scheme for ESP32-Based Resource-Constrained IoT Devices","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sist61674.2026.11596207","authors":["Zhansaya Myrzakul","Zhanerke Temirbekova","Gulzhan Myrzakul"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T19:38:14Z","doi":"10.1109/sist61674.2026.11596207","addedAt":"2026-08-31T06:41:45.556Z","updatedAt":"2026-08-31T06:41:45.556Z"},{"id":"doi:10.5281/zenodo.17664063","name":"未来の茶屋 / The Tea House of the Future: CollectiveOS Global Flagship & Open Science Hub — Tokyo","source":"datacite","abstract":"未来の茶屋 / The Tea House of the Future: CollectiveOS Global Flagship & Open Science Hub — Tokyo Executive Summary This foundational white paper establishes the architectural, operational, and philosophical parameters for \"The Tea House of the Future\" (Mirai no Chaya), a flagship facility located in Tokyo, Japan. This site serves as the physical \"Prime\" node for the CollectiveOS / Unified AI Script System v4, a quantum-adaptive, strictly governed operating system designed to address global scarcity through advanced automation and zero-trust security.1 Unlike traditional smart building initiatives that prioritize surveillance and data extraction, the Tea House is engineered as a sanctuary of the \"Unreadable Machine,\" a computing paradigm that enforces absolute privacy and governance auditability through cryptographic proofs rather than open inspection.1 Operating under the stewardship of The Collective and the Human Global Science Collective (HGSC), the facility functions as a living laboratory for patent-free science, demonstrating the viability of the \"Anti-Scarcity Stack\"—a suite of integrated verticals covering water security (Aqua Pillar), nutritional resilience (Food Cube), sustainable agriculture (FarmOS), and cultural preservation (Gardener Pattern Atlas).1 The choice of Tokyo is strategic, leveraging the city's legacy of Monozukuri (craftsmanship) and high-density urban resilience to validate the system's scalability before global deployment via \"Village Nodes.\" This document details the facility's governance via the GATA pipeline, its metabolic control via the Living Fibonacci Engine (LFE), and its legal framework rooted in the Open Science Non-Assert (OSNA) pledge.1 1. Philosophical Foundation & Architectural Intent 1.1 The Concept of Ma and the Unreadable Machine The architectural philosophy of the Tea House is deeply rooted in the Japanese aesthetic of Ma—the potent, meaningful space between objects. In the context of the CollectiveOS v4 architecture, this concept finds its digital twin in the Zero-Trust Cipher Stack (ZTA) and the \"Unreadable Machine\".1 Just as Ma is defined not by what is present, but by the tension of the void, the security of the Tea House is defined by what is absent: the absence of implicit trust, the absence of unencrypted data transit, and the absence of un-audited algorithmic action. The \"Unreadable Machine\" operates on a counter-intuitive principle for an open science hub: total opacity of runtime state combined with total transparency of governance logic. While the source code and policies are open-source (Apache 2.0) and human-readable, the live data processing—whether it is the biometrics of a visitor or the proprietary crop data of a partner university—remains mathematically invisible to the host system itself through Fully Homomorphic Encryption (FHE) and Zero-Knowledge Machine Learning (ZKML).1 This ensures that the Tea House remains a neutral sanctuary, incapable of surveillance capitalism, aligning the digital infrastructure with the physical sanctuary of a traditional tea house. 1.2 Anti-Scarcity in the Hyper-Urban Context While the CollectiveOS roadmap includes specific provisions for rural and humanitarian deployment (e.g., the \"Village Node\" pattern), the Tokyo flagship addresses a distinct set of challenges: urban scarcity. In a metropolis of 37 million, the scarcity is not just material but spatial and temporal. The Tea House utilizes the Civilian Space Program (CSP) / Nexus Embodiment protocols to optimize compact living.1 Technologies typically reserved for off-world habitats—such as closed-loop water recycling (Aqua Pillar) and high-density biomass upcycling (Food Cube)—are adapted here to demonstrate circular economy principles within a high-density urban footprint. The facility serves as a tangible proof-of-concept for the Human Global Science Collective (HGSC), a multi-institutional alliance dedicated to solving the \"Meta-Crisis\" (water, food, energy, trust) without intelle","url":"https://doi.org/10.5281/zenodo.17664063","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17664063","addedAt":"2026-08-31T06:41:45.557Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17664064","name":"未来の茶屋 / The Tea House of the Future: CollectiveOS Global Flagship & Open Science Hub — Tokyo","source":"datacite","abstract":"未来の茶屋 / The Tea House of the Future: CollectiveOS Global Flagship & Open Science Hub — Tokyo Executive Summary This foundational white paper establishes the architectural, operational, and philosophical parameters for \"The Tea House of the Future\" (Mirai no Chaya), a flagship facility located in Tokyo, Japan. This site serves as the physical \"Prime\" node for the CollectiveOS / Unified AI Script System v4, a quantum-adaptive, strictly governed operating system designed to address global scarcity through advanced automation and zero-trust security.1 Unlike traditional smart building initiatives that prioritize surveillance and data extraction, the Tea House is engineered as a sanctuary of the \"Unreadable Machine,\" a computing paradigm that enforces absolute privacy and governance auditability through cryptographic proofs rather than open inspection.1 Operating under the stewardship of The Collective and the Human Global Science Collective (HGSC), the facility functions as a living laboratory for patent-free science, demonstrating the viability of the \"Anti-Scarcity Stack\"—a suite of integrated verticals covering water security (Aqua Pillar), nutritional resilience (Food Cube), sustainable agriculture (FarmOS), and cultural preservation (Gardener Pattern Atlas).1 The choice of Tokyo is strategic, leveraging the city's legacy of Monozukuri (craftsmanship) and high-density urban resilience to validate the system's scalability before global deployment via \"Village Nodes.\" This document details the facility's governance via the GATA pipeline, its metabolic control via the Living Fibonacci Engine (LFE), and its legal framework rooted in the Open Science Non-Assert (OSNA) pledge.1 1. Philosophical Foundation & Architectural Intent 1.1 The Concept of Ma and the Unreadable Machine The architectural philosophy of the Tea House is deeply rooted in the Japanese aesthetic of Ma—the potent, meaningful space between objects. In the context of the CollectiveOS v4 architecture, this concept finds its digital twin in the Zero-Trust Cipher Stack (ZTA) and the \"Unreadable Machine\".1 Just as Ma is defined not by what is present, but by the tension of the void, the security of the Tea House is defined by what is absent: the absence of implicit trust, the absence of unencrypted data transit, and the absence of un-audited algorithmic action. The \"Unreadable Machine\" operates on a counter-intuitive principle for an open science hub: total opacity of runtime state combined with total transparency of governance logic. While the source code and policies are open-source (Apache 2.0) and human-readable, the live data processing—whether it is the biometrics of a visitor or the proprietary crop data of a partner university—remains mathematically invisible to the host system itself through Fully Homomorphic Encryption (FHE) and Zero-Knowledge Machine Learning (ZKML).1 This ensures that the Tea House remains a neutral sanctuary, incapable of surveillance capitalism, aligning the digital infrastructure with the physical sanctuary of a traditional tea house. 1.2 Anti-Scarcity in the Hyper-Urban Context While the CollectiveOS roadmap includes specific provisions for rural and humanitarian deployment (e.g., the \"Village Node\" pattern), the Tokyo flagship addresses a distinct set of challenges: urban scarcity. In a metropolis of 37 million, the scarcity is not just material but spatial and temporal. The Tea House utilizes the Civilian Space Program (CSP) / Nexus Embodiment protocols to optimize compact living.1 Technologies typically reserved for off-world habitats—such as closed-loop water recycling (Aqua Pillar) and high-density biomass upcycling (Food Cube)—are adapted here to demonstrate circular economy principles within a high-density urban footprint. The facility serves as a tangible proof-of-concept for the Human Global Science Collective (HGSC), a multi-institutional alliance dedicated to solving the \"Meta-Crisis\" (water, food, energy, trust) without intelle","url":"https://doi.org/10.5281/zenodo.17664064","authors":["Brewer, Mark Anthony"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17664064","addedAt":"2026-08-31T06:41:45.557Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2505.01273","name":"Anti-adversarial Learning: Desensitizing Prompts for Large Language Models","source":"datacite","abstract":"With the widespread use of LLMs, preserving privacy in user prompts has become crucial, as prompts risk exposing privacy and sensitive data to the cloud LLMs. Traditional techniques like homomorphic encryption, secure multi-party computation, and federated learning face challenges due to heavy computational costs and user participation requirements, limiting their applicability in LLM scenarios. In this paper, we propose PromptObfus, a novel method for desensitizing LLM prompts. The core idea of PromptObfus is \"anti-adversarial\" learning, which perturbs privacy words in the prompt to obscure sensitive information while retaining the stability of model predictions. Specifically, PromptObfus frames prompt desensitization as a masked language modeling task, replacing privacy-sensitive terms with a [MASK] token. A desensitization model is trained to generate candidate replacements for each masked position. These candidates are subsequently selected based on gradient feedback from a surrogate model, ensuring minimal disruption to the task output. We demonstrate the effectiveness of our approach on three NLP tasks. Results show that PromptObfus effectively prevents privacy inference from remote LLMs while preserving task performance.","url":"https://doi.org/10.48550/arxiv.2505.01273","authors":["Li, Xuan","Yin, Zhe","Gu, Xiaodong","Shen, Beijun"],"tags":["Computation and Language (cs.CL)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2505.01273","addedAt":"2026-08-31T06:41:45.557Z","updatedAt":"2026-08-31T06:41:45.557Z"},{"id":"doi:10.48550/arxiv.2511.03341","name":"LaMoS: Enabling Efficient Large Number Modular Multiplication through SRAM-based CiM Acceleration","source":"datacite","abstract":"Barrett's algorithm is one of the most widely used methods for performing modular multiplication, a critical nonlinear operation in modern privacy computing techniques such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). Since modular multiplication dominates the processing time in these applications, computational complexity and memory limitations significantly impact performance. Computing-in-Memory (CiM) is a promising approach to tackle this problem. However, existing schemes currently suffer from two main problems: 1) Most works focus on low bit-width modular multiplication, which is inadequate for mainstream cryptographic algorithms such as elliptic curve cryptography (ECC) and the RSA algorithm, both of which require high bit-width operations; 2) Recent efforts targeting large number modular multiplication rely on inefficient in-memory logic operations, resulting in high scaling costs for larger bit-widths and increased latency. To address these issues, we propose LaMoS, an efficient SRAM-based CiM design for large-number modular multiplication, offering high scalability and area efficiency. First, we analyze the Barrett's modular multiplication method and map the workload onto SRAM CiM macros for high bit-width cases. Additionally, we develop an efficient CiM architecture and dataflow to optimize large-number modular multiplication. Finally, we refine the mapping scheme for better scalability in high bit-width scenarios using workload grouping. Experimental results show that LaMoS achieves a $7.02\\times$ speedup and reduces high bit-width scaling costs compared to existing SRAM-based CiM designs.","url":"https://doi.org/10.48550/arxiv.2511.03341","authors":["Li, Haomin","Liu, Fangxin","Guan, Chenyang","Wang, Zongwu","Jiang, Li","Guan, Haibing"],"tags":["Cryptography and Security (cs.CR)","Hardware Architecture (cs.AR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2511.03341","addedAt":"2026-08-31T06:41:45.557Z","updatedAt":"2026-08-31T06:41:45.557Z"},{"id":"doi:10.48550/arxiv.2511.00737","name":"EP-HDC: Hyperdimensional Computing with Encrypted Parameters for High-Throughput Privacy-Preserving Inference","source":"datacite","abstract":"While homomorphic encryption (HE) provides strong privacy protection, its high computational cost has restricted its application to simple tasks. Recently, hyperdimensional computing (HDC) applied to HE has shown promising performance for privacy-preserving machine learning (PPML). However, when applied to more realistic scenarios such as batch inference, the HDC-based HE has still very high compute time as well as high encryption and data transmission overheads. To address this problem, we propose HDC with encrypted parameters (EP-HDC), which is a novel PPML approach featuring client-side HE, i.e., inference is performed on a client using a homomorphically encrypted model. Our EP-HDC can effectively mitigate the encryption and data transmission overhead, as well as providing high scalability with many clients while providing strong protection for user data and model parameters. In addition to application examples for our client-side PPML, we also present design space exploration involving quantization, architecture, and HE-related parameters. Our experimental results using the BFV scheme and the Face/Emotion datasets demonstrate that our method can improve throughput and latency of batch inference by orders of magnitude over previous PPML methods (36.52~1068x and 6.45~733x, respectively) with less than 1% accuracy degradation.","url":"https://doi.org/10.48550/arxiv.2511.00737","authors":["Park, Jaewoo","Quan, Chenghao","Lee, Jongeun"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2511.00737","addedAt":"2026-08-31T06:41:45.557Z","updatedAt":"2026-08-31T06:41:45.557Z"},{"id":"doi:10.48550/arxiv.2407.13055","name":"Cheddar: A Swift Fully Homomorphic Encryption Library Designed for GPU Architectures","source":"datacite","abstract":"Fully homomorphic encryption (FHE) frees cloud computing from privacy concerns by enabling secure computation on encrypted data. However, its substantial computational and memory overhead results in significantly slower performance compared to unencrypted processing. To mitigate this overhead, we present Cheddar, a high-performance FHE library for GPUs, achieving substantial speedups over previous GPU implementations. We systematically enable 32-bit FHE execution, leveraging the 32-bit integer datapath within GPUs. We optimize GPU kernels using efficient low-level primitives and algorithms tailored to specific GPU architectures. Further, we alleviate the memory bandwidth burden by adjusting common FHE operational sequences and extensively applying kernel fusion. Cheddar delivers performance improvements of 2.18--4.45$\\times$ for representative FHE workloads compared to state-of-the-art GPU implementations.","url":"https://doi.org/10.48550/arxiv.2407.13055","authors":["Choi, Wonseok","Kim, Jongmin","Ahn, Jung Ho"],"tags":["Cryptography and Security (cs.CR)","Performance (cs.PF)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2407.13055","addedAt":"2026-08-31T06:41:45.557Z","updatedAt":"2026-08-31T06:41:45.557Z"},{"id":"doi:10.1007/s42979-023-02316-9","name":"Homomorphic Encryption Library, Framework, Toolkit and Accelerator: A Review","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s42979-023-02316-9","authors":["Shalini Dhiman","Ganesh Kumar Mahato","Swarnendu Kumar Chakraborty"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-20T08:01:26Z","doi":"10.1007/s42979-023-02316-9","addedAt":"2026-08-31T06:41:45.648Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.48149/jciees.2024.4.1.6","name":"Homomorphic Encryption for Secure Federated Learning: A PRISMA-Based Systematic Review","source":"crossref","abstract":"Federated learning (FL) enables collaborative training without sharing raw data but remains vulnerable to privacy leakage, poisoned gradients, and incorrect aggregation. Homomorphic encryption (HE) supports secure aggregation over encrypted updates, yet existing solutions are heterogeneous and lack systematic comparison. This paper presents a PRISMA-based systematic review of 104 studies on HE-enabled security mechanisms in FL, analyzed within the confidentiality–integrity–availability (CIA) framework. The results show a growing focus on poisoned-gradient robustness and verifiable aggregation, while only few integrated approaches address all CIA dimensions. These findings outline current limitations and guide future development of secure and trustworthy federated learning systems.","url":"https://doi.org/10.48149/jciees.2024.4.1.6","authors":["Dinko Dinkov"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T06:19:13Z","doi":"10.48149/jciees.2024.4.1.6","addedAt":"2026-08-31T06:41:45.648Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.37602/ijssmr.2022.5219","name":"HOMOMORPHIC ENCRYPTION IN 5IRE BLOCKCHAIN","source":"crossref","abstract":"With the advent of blockchain technology, decentralized system is gaining huge popularity as it provides a new solution for data storage and sharing as well as keeping privacy in place. Blockchains are of two categories; public and private. Public blockchains are permissionless, mostly used for exchanging and mining cryptocurrency, where anyone can join and thus exposed to the risk of a privacy breach. If the content is the transaction information, one might opt for not sharing these data in the public domain. One solution could be to encrypt the information, but that comes at the cost of losing the usability of the data. This paper investigates the security problem related to this passive adversarial activity and proposes a new technology that leverages the best parallel chain architecture of 5ire blockchain and homomorphic encryption (HE) so as to retain the advantages of a public blockchain without compromising the privacy of transaction information. We have coined the term 5ireHE for this encryption architecture. We achieve the security protection and integrity check of wallet data by enforcing the 5ireHE which is efficient.","url":"https://doi.org/10.37602/ijssmr.2022.5219","authors":["VILMA MATTILA","PRATEEK DWIVEDI","PRATIK GAURI","MD AHBAB"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-08T08:10:21Z","doi":"10.37602/ijssmr.2022.5219","addedAt":"2026-08-31T06:41:45.648Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.12928/telkomnika.v19i4.16875","name":"From cloud computing security towards homomorphic encryption: A comprehensive review","source":"crossref","abstract":"","url":"https://doi.org/10.12928/telkomnika.v19i4.16875","authors":["Saja J. Mohammed","Dujan B. Taha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-10T15:38:05Z","doi":"10.12928/telkomnika.v19i4.16875","addedAt":"2026-08-31T06:41:45.648Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1049/icp.2022.0308","name":"A trustworthy cloud environment using homomorphic encryption: a review","source":"crossref","abstract":"","url":"https://doi.org/10.1049/icp.2022.0308","authors":["Z. Salman","W. M. Elmedany"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-04-20T20:09:06Z","doi":"10.1049/icp.2022.0308","addedAt":"2026-08-31T06:41:45.648Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.35214/rfis.7.1.201802.002","name":"Privacy-Preserving Finance Data Analysis Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.35214/rfis.7.1.201802.002","authors":["Jung Hee Cheon","어윤희","Kim jae yoon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-04-30T04:47:31Z","doi":"10.35214/rfis.7.1.201802.002","addedAt":"2026-08-31T06:41:45.648Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.14445/22312803/ijctt-v43p111","name":"A review of homomorphic encryption of data in cloud computing","source":"crossref","abstract":"","url":"https://doi.org/10.14445/22312803/ijctt-v43p111","authors":["Amit Chaturvedi","Akanksha Kapoor","Vikas Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-03-23T07:52:02Z","doi":"10.14445/22312803/ijctt-v43p111","addedAt":"2026-08-31T06:41:45.648Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1049/icp.2023.0649","name":"A systematic review of homomorphic encryption applications in Internet of Things","source":"crossref","abstract":"","url":"https://doi.org/10.1049/icp.2023.0649","authors":["J. Abduljalil Jaffar","W. Elmedany"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-04-25T20:11:41Z","doi":"10.1049/icp.2023.0649","addedAt":"2026-08-31T06:41:45.648Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.47667/ijpasr.v4i3.235","name":"Secure Federated Learning with a Homomorphic Encryption Model","source":"crossref","abstract":"Federated learning (FL) offers collaborative machine learning across decentralized devices while safeguarding data privacy. However, data security and privacy remain key concerns. This paper introduces \"Secure Federated Learning with a Homomorphic Encryption Model,\" addressing these challenges by integrating homomorphic encryption into FL. The model starts by initializing a global machine learning model and generating a homomorphic encryption key pair, with the public key shared among FL participants. Using this public key, participants then collect, preprocess, and encrypt their local data. During FL Training Rounds, participants decrypt the global model, compute local updates on encrypted data, encrypt these updates, and securely send them to the aggregator. The aggregator homomorphic ally combines updates without revealing participant data, forwarding the encrypted aggregated update to the global model owner. The Global Model Update ensures the owner decrypts the aggregated update using the private key, updates the global model, encrypts it with the public key, and shares the encrypted global model with FL participants. With optional model evaluation, training can iterate for several rounds or until convergence. This model offers a robust solution to Florida data privacy and security issues, with versatile applications across domains. This paper presents core model components, advantages, and potential domain-specific implementations while making significant strides in addressing FL's data privacy concerns.","url":"https://doi.org/10.47667/ijpasr.v4i3.235","authors":["Nadia Hussien","Nadia Mahmood Hussien","Saba Abdulbaqi Salman","Mohammad Aljanabi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-11-01T03:43:24Z","doi":"10.47667/ijpasr.v4i3.235","addedAt":"2026-08-31T06:41:45.648Z","updatedAt":"2026-08-31T06:41:45.648Z"},{"id":"doi:10.1007/978-981-15-0978-0_27","name":"Homomorphic Encryption: Review and Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-15-0978-0_27","authors":["Ratnakumari Challa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-01-13T13:03:21Z","doi":"10.1007/978-981-15-0978-0_27","addedAt":"2026-08-31T06:41:45.649Z","updatedAt":"2026-08-31T06:41:45.649Z"},{"id":"doi:10.1080/03772063.2021.1965918","name":"A Comparative Review on Homomorphic Encryption for Cloud Security","source":"crossref","abstract":"","url":"https://doi.org/10.1080/03772063.2021.1965918","authors":["Ganesh Kumar Mahato","Swarnendu Kumar Chakraborty"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-20T00:34:23Z","doi":"10.1080/03772063.2021.1965918","addedAt":"2026-08-31T06:41:45.649Z","updatedAt":"2026-08-31T06:41:45.649Z"},{"id":"doi:10.1145/3840385","name":"Hardware Acceleration of Fully Homomorphic Encryption: A Comprehensive Review of FPGA Implementations","source":"crossref","abstract":"Although Fully Homomorphic Encryption (FHE) enables computation over encrypted data, its substantial computational and storage overhead remains a major obstacle to practical deployment. Among available hardware platforms, FPGAs offer a favorable balance of performance, flexibility, and energy efficiency, making them a promising option for FHE acceleration. This paper presents a systematic review of FPGA-based FHE accelerators published between 2011 and 2025, with a focus on architectural design and performance characterization. To enable consistent analysis across heterogeneous implementations, we develop a unified framework that models FHE execution in terms of computation and data movement and supports a roofline-based interpretation of performance. Applying this framework to representative designs shows that accelerator performance is jointly shaped by arithmetic parallelism, memory bandwidth, data reuse, and communication overhead, and that the dominant bottleneck is therefore architecture-dependent. Designs with limited data reuse or inefficient key delivery may remain bandwidth-bound, whereas kernel-level implementations and carefully batched streaming architectures can become compute-bound. Based on these observations, we identify key challenges in memory hierarchy design, bandwidth utilization, communication efficiency, and scalability, and discuss future directions including algorithm–hardware co-design, communication-aware architectures, and application-specific acceleration. Overall, this review provides a unified perspective on the performance bottlenecks and design trade-offs of FPGA-based FHE accelerators and offers practical guidance for future architecture development.","url":"https://doi.org/10.1145/3840385","authors":["Lingyu Gong","Farhad Merchant"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-14T15:05:11Z","doi":"10.1145/3840385","addedAt":"2026-08-31T06:41:45.649Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.17605/osf.io/a3mne","name":"Homomorphic Encryption in Healthcare AI: A PRISMA Review","source":"datacite","abstract":"This scoping review systematically evaluates the use of homomorphic encryption (HE) techniques within artificial intelligence (AI) pipelines, with a particular emphasis on healthcare applications and edge-deployed systems. By employing PRISMA-ScR guidelines, the study identifies how specific HE algorithms such as CKKS, BFV, and BGV are implemented to preserve privacy during model training, inference, and aggregation stages. The review also proposes a classification matrix linking encryption schemes to system-level design choices in medical AI.","url":"https://doi.org/10.17605/osf.io/a3mne","authors":["Yanez, Penelope"],"tags":["Physical Sciences and Mathematics","Biomedical","Information Security","Computer Sciences","Electrical and Computer Engineering","Artificial Intelligence and Robotics","Engineering","BFV"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.17605/osf.io/a3mne","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17720023","name":"Review on Cloud Data Security Using VGG19-Deep Learning and Homomorphic Encryption","source":"datacite","abstract":"Data is the new currency as lot of the user’s presence online is an upward trend. As a consequence the data storage on the various cloud platforms has been a new normal. Data security in cloud has turn formidable due to unique security issues and challenges. Conventional methods on security may not always cater a proper barter between computational efficiency and data security. This review paper discusses the blending facial key features of the face obtained from VGG19 deep learning with homomorphic encryption to enrich cloud data security. The VGG19 allows for robust feature extraction from face and facial key points for authentication, while homomorphic encryption scales computation in encrypted form; it acquire enhanced accuracy with scalability and preservation of privacy. Thus, this method ensure a better approach in next-generation cloud security frameworks.","url":"https://doi.org/10.5281/zenodo.17720023","authors":["Chandrasekhar, Tadi","Basanta, Th","Swaminathan, J.N"],"tags":["Cloud security","CNN","VGG16.VGG19","Facial key features"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17720023","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17720024","name":"Review on Cloud Data Security Using VGG19-Deep Learning and Homomorphic Encryption","source":"datacite","abstract":"Data is the new currency as lot of the user’s presence online is an upward trend. As a consequence the data storage on the various cloud platforms has been a new normal. Data security in cloud has turn formidable due to unique security issues and challenges. Conventional methods on security may not always cater a proper barter between computational efficiency and data security. This review paper discusses the blending facial key features of the face obtained from VGG19 deep learning with homomorphic encryption to enrich cloud data security. The VGG19 allows for robust feature extraction from face and facial key points for authentication, while homomorphic encryption scales computation in encrypted form; it acquire enhanced accuracy with scalability and preservation of privacy. Thus, this method ensure a better approach in next-generation cloud security frameworks.","url":"https://doi.org/10.5281/zenodo.17720024","authors":["Chandrasekhar, Tadi","Basanta, Th","Swaminathan, J.N"],"tags":["Cloud security","CNN","VGG16.VGG19","Facial key features"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17720024","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17688351","name":"Cloud Computing Security Issues and Future Solutions","source":"datacite","abstract":"Cloud computing provides scalable and cost-efficient services to organizations, but it also introduces critical security challenges such as multi-tenancy risks, insecure APIs, insider threats, misconfigurations, and data confidentiality issues. This research analyzes these challenges and evaluates existing security models to understand their limitations. The study explores the role of Zero-Trust Architecture (ZTA), Homomorphic Encryption (HE), and Artificial Intelligence (AI/ML) in strengthening cloud security. It also proposes an integrated, multi-layered security model called the Integrated Zero-Trust Homomorphic Cloud Security (IZT-HCS) Framework. Data was collected through a literature review and a Google Forms survey of 28 respondents. Findings conclude that AI-based security, blockchain access control, and ZTA significantly improve cloud resilience.","url":"https://doi.org/10.5281/zenodo.17688351","authors":["Aptikar, Jui","Bhinge, Monalisa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17688351","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17688352","name":"Cloud Computing Security Issues and Future Solutions","source":"datacite","abstract":"Cloud computing provides scalable and cost-efficient services to organizations, but it also introduces critical security challenges such as multi-tenancy risks, insecure APIs, insider threats, misconfigurations, and data confidentiality issues. This research analyzes these challenges and evaluates existing security models to understand their limitations. The study explores the role of Zero-Trust Architecture (ZTA), Homomorphic Encryption (HE), and Artificial Intelligence (AI/ML) in strengthening cloud security. It also proposes an integrated, multi-layered security model called the Integrated Zero-Trust Homomorphic Cloud Security (IZT-HCS) Framework. Data was collected through a literature review and a Google Forms survey of 28 respondents. Findings conclude that AI-based security, blockchain access control, and ZTA significantly improve cloud resilience.","url":"https://doi.org/10.5281/zenodo.17688352","authors":["Aptikar, Jui","Bhinge, Monalisa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17688352","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2508.02461","name":"Experimental Evaluation of Post-Quantum Homomorphic Encryption for Privacy-Preserving I2I Communication in ITS","source":"datacite","abstract":"This study experimentally evaluates the feasibility of post-quantum secure Homomorphic Encryption (HE) for privacy-preserving Infrastructure-to-Infrastructure (I2I) communication in Intelligent Transportation Systems (ITS). Unlike prior simulation-based efforts, this work implements three lattice-based HE schemes: Brakerski-Fan-Vercauteren (BFV), Brakerski-Gentry-Vaikuntanathan (BGV), and Cheon-Kim-Kim-Song (CKKS), within a real experimental pipeline representing roadside unit (RSU)-Cloud data exchange over Wi-Fi and Ethernet networks. The experiments benchmark encrypted addition and addition-plus-multiplication operations representing key analytical tasks, such as vehicle queue assessment and regional speed computation. Results show that while BFV achieves sub-5-second latency suitable for intersection-level analytics, BGV supports regional aggregation with 10 to 30-second updates. CKKS, though exhibiting higher latency (21-32 seconds), remains practical for minute-scale applications like eco-driving. These findings demonstrate that post-quantum HE can enable privacy-preserving ITS backhaul analytics when latency requirements align with application needs. The study also presents optimization pathways, including algorithmic tuning, network adaptation, and hardware acceleration, to reduce end-to-end delay.","url":"https://doi.org/10.48550/arxiv.2508.02461","authors":["Mamun, Abdullah Al","Yates, Kyle","Rakotondrafara, Antsa","Chowdhury, Mashrur","Cartor, Ryann","Gao, Shuhong"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.02461","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17623861","name":"Hybrid Privacy-Preserving Architectures for Blockchain Systems: A Review and the ChainGuard Dual-Chain Framework","source":"datacite","abstract":"Blockchain has emerged as a robust foundation for decentralized trust, secure data sharing, and immutable record keeping. However, its inherently transparent architecture creates significant privacy challenges when applied in sensitive domains such as healthcare, finance, identity management, and IoT. Although privacy-preserving techniques including Zero-Knowledge Proofs (ZKPs), Attribute-Based Encryption (ABE), homomorphic encryption, ring signatures, mixers, and hybrid off-chain storage mechanisms have demonstrated partial effectiveness, they remain limited by high computational overhead, poor scalability, interoperability constraints, and regulatory complications. These challenges hinder the practical deployment of blockchain in real-world, data-intensive environments. This review examines key blockchain privacy issues and synthesizes major research contributions from contemporary literature. It further emphasizes the importance of hybrid privacy-preserving models to balance transparency, confidentiality, and storage efficiency. The analysis reinforces the relevance of solutions such as ChainGuard, a dual-chain architecture that maintains sensitive data on a private blockchain while using a public chain to store verifiable hash references. This approach directly mitigates the transparency–privacy conflict, storage inefficiencies, and cryptographic performance limitations identified across existing studies. The paper concludes by outlining research gaps and proposing future directions for scalable, interoperable, and regulation-aligned blockchain privacy systems.","url":"https://doi.org/10.5281/zenodo.17623861","authors":["Mura Mudalige, Hiruni Thaksarani"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17623861","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17623860","name":"Hybrid Privacy-Preserving Architectures for Blockchain Systems: A Review and the ChainGuard Dual-Chain Framework","source":"datacite","abstract":"Blockchain has emerged as a robust foundation for decentralized trust, secure data sharing, and immutable record keeping. However, its inherently transparent architecture creates significant privacy challenges when applied in sensitive domains such as healthcare, finance, identity management, and IoT. Although privacy-preserving techniques including Zero-Knowledge Proofs (ZKPs), Attribute-Based Encryption (ABE), homomorphic encryption, ring signatures, mixers, and hybrid off-chain storage mechanisms have demonstrated partial effectiveness, they remain limited by high computational overhead, poor scalability, interoperability constraints, and regulatory complications. These challenges hinder the practical deployment of blockchain in real-world, data-intensive environments. This review examines key blockchain privacy issues and synthesizes major research contributions from contemporary literature. It further emphasizes the importance of hybrid privacy-preserving models to balance transparency, confidentiality, and storage efficiency. The analysis reinforces the relevance of solutions such as ChainGuard, a dual-chain architecture that maintains sensitive data on a private blockchain while using a public chain to store verifiable hash references. This approach directly mitigates the transparency–privacy conflict, storage inefficiencies, and cryptographic performance limitations identified across existing studies. The paper concludes by outlining research gaps and proposing future directions for scalable, interoperable, and regulation-aligned blockchain privacy systems.","url":"https://doi.org/10.5281/zenodo.17623860","authors":["Mura Mudalige, Hiruni Thaksarani"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17623860","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2511.09855","name":"Unlearning Imperative: Securing Trustworthy and Responsible LLMs through Engineered Forgetting","source":"datacite","abstract":"The growing use of large language models in sensitive domains has exposed a critical weakness: the inability to ensure that private information can be permanently forgotten. Yet these systems still lack reliable mechanisms to guarantee that sensitive information can be permanently removed once it has been used. Retraining from the beginning is prohibitively costly, and existing unlearning methods remain fragmented, difficult to verify, and often vulnerable to recovery. This paper surveys recent research on machine unlearning for LLMs and considers how far current approaches can address these challenges. We review methods for evaluating whether forgetting has occurred, the resilience of unlearned models against adversarial attacks, and mechanisms that can support user trust when model complexity or proprietary limits restrict transparency. Technical solutions such as differential privacy, homomorphic encryption, federated learning, and ephemeral memory are examined alongside institutional safeguards including auditing practices and regulatory frameworks. The review finds steady progress, but robust and verifiable unlearning is still unresolved. Efficient techniques that avoid costly retraining, stronger defenses against adversarial recovery, and governance structures that reinforce accountability are needed if LLMs are to be deployed safely in sensitive applications. By integrating technical and organizational perspectives, this study outlines a pathway toward AI systems that can be required to forget, while maintaining both privacy and public trust.","url":"https://doi.org/10.48550/arxiv.2511.09855","authors":["Kang, James Jin","Bui, Dang","Pham, Thanh","Ling, Huo-Chong"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2511.09855","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17462478","name":"The Secure Future of Medical AI on Privacy-Preserving Federated Learning-A Systematic Review","source":"datacite","abstract":"The study presents a comprehensive review of Federated Learning , outlining a practical method for building strong Artificial Intelligence models in the healthcare industry while maintaining patient privacy.A key issue identified is that privacy laws like GDPR and HIPAA often result in medical data being scattered across different organizations, complicating the development of dependable and broadly applicable AI solutions. FL addresses this by allowing teams to train models together without sharing the actual sensitive data.Research underscores that traditional Federated Learning faces challenges due to non-IID data distributions and susceptibility to privacy inference attacks.This paper examine advanced frameworks that implement a multi-layered defense system apply techniques like Secure Multi-Party Computation, Homomorphic Encryption, and Differential Privacy to tackle these issues. The key advancements discussed affect hierarchical architectures with edge servers to enhance efficiency, dynamic aggregation strategies to manage data heterogeneity, and adaptive privacy budget allocation to achieve an optimal balance between privacy and utility.The conceptual framework of this study highlights how these enhanced federated learning approaches offer robust privacy safeguards and achieve remarkable diagnostic accuracy, often matching or even exceptional traditional centralized models. However, despite these achievements, the main challenge—especially when using advanced encryption techniques—remains the substantial processing overhead.","url":"https://doi.org/10.5281/zenodo.17462478","authors":["Fidha Fathima Salim, Karthik Unnikrishnan, Abhin K.S, Vidhula Thomas"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17462478","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17462477","name":"The Secure Future of Medical AI on Privacy-Preserving Federated Learning-A Systematic Review","source":"datacite","abstract":"The study presents a comprehensive review of Federated Learning , outlining a practical method for building strong Artificial Intelligence models in the healthcare industry while maintaining patient privacy.A key issue identified is that privacy laws like GDPR and HIPAA often result in medical data being scattered across different organizations, complicating the development of dependable and broadly applicable AI solutions. FL addresses this by allowing teams to train models together without sharing the actual sensitive data.Research underscores that traditional Federated Learning faces challenges due to non-IID data distributions and susceptibility to privacy inference attacks.This paper examine advanced frameworks that implement a multi-layered defense system apply techniques like Secure Multi-Party Computation, Homomorphic Encryption, and Differential Privacy to tackle these issues. The key advancements discussed affect hierarchical architectures with edge servers to enhance efficiency, dynamic aggregation strategies to manage data heterogeneity, and adaptive privacy budget allocation to achieve an optimal balance between privacy and utility.The conceptual framework of this study highlights how these enhanced federated learning approaches offer robust privacy safeguards and achieve remarkable diagnostic accuracy, often matching or even exceptional traditional centralized models. However, despite these achievements, the main challenge—especially when using advanced encryption techniques—remains the substantial processing overhead.","url":"https://doi.org/10.5281/zenodo.17462477","authors":["Fidha Fathima Salim, Karthik Unnikrishnan, Abhin K.S, Vidhula Thomas"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17462477","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2510.21483","name":"Introducing GRAFHEN: Group-based Fully Homomorphic Encryption without Noise","source":"datacite","abstract":"We present GRAFHEN, a new cryptographic scheme which offers Fully Homomorphic Encryption without the need for bootstrapping (or in other words, without noise). Building on the work of Nuida and others, we achieve this using encodings in groups. The groups are represented on a machine using rewriting systems. In this way the subgroup membership problem, which an attacker would have to solve in order to break the scheme, becomes maximally hard, while performance is preserved. In fact we include a simple benchmark demonstrating that our implementation runs several orders of magnitude faster than existing standards. We review many possible attacks against our protocol and explain how to protect the scheme in each case.","url":"https://doi.org/10.48550/arxiv.2510.21483","authors":["Guillot, Pierre","Duc, Auguste Hoang","Koskas, Michel","Méhats, Florian"],"tags":["Cryptography and Security (cs.CR)","Group Theory (math.GR)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Mathematics","FOS: Mathematics","E.3"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.21483","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2510.17333","name":"Comparison and performance analysis of dynamic encrypted control approaches","source":"datacite","abstract":"Encrypted controllers using homomorphic encryption have proven to guarantee the privacy of measurement and control signals, as well as system and controller parameters, while regulating the system as intended. However, encrypting dynamic controllers has remained a challenge due to growing noise and overflow issues in the encoding. In this paper, we review recent approaches to dynamic encrypted control, such as bootstrapping, periodic resets of the controller state, integer reformulations, and FIR controllers, and equip them with a stability and performance analysis to evaluate their suitability. We complement the analysis with a numerical performance comparison on a benchmark system.","url":"https://doi.org/10.48550/arxiv.2510.17333","authors":["Schlor, Sebastian","Allgöwer, Frank"],"tags":["Systems and Control (eess.SY)","Cryptography and Security (cs.CR)","Optimization and Control (math.OC)","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Mathematics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2510.17333","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17385666","name":"A Systematic Literature Review on the Integration of Homomorphic Encryption and Chaotic Maps for  Secure Data Processing","source":"datacite","abstract":"Homomorphic encryption and chaotic maps have emerged as key technologies in secure data processing, offering complementary benefits for confidentiality and computational integrity. While homomorphic encryption enables operations on encrypted data without decryption, chaotic maps provide strong pseudo-randomness and high sensitivity to initial conditions, making them highly suitable for cryptographic applications. This review examines their convergence, identifies shortcomings in prior studies, and outlines hybrid frameworks combining both methods. Results indicate that integrating chaotic maps with homomorphic encryption enhances security and performance, particularly for encrypted computation. However, computational cost and compatibility challenges remain. The review also highlights emerging trends such as lightweight adaptations for resource-constrained environments and novel chaotic map designs, providing a roadmap for more secure and scalable cryptographic systems in cloud computing and Internet of Things applications.","url":"https://doi.org/10.5281/zenodo.17385666","authors":["Abdullah Ghanim, Jaber"],"tags":["Homomorphic Encryption","Chaotic Maps","Cryptography","Secure Data Processing","Cloud and IoT Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17385666","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17385667","name":"A Systematic Literature Review on the Integration of Homomorphic Encryption and Chaotic Maps for  Secure Data Processing","source":"datacite","abstract":"Homomorphic encryption and chaotic maps have emerged as key technologies in secure data processing, offering complementary benefits for confidentiality and computational integrity. While homomorphic encryption enables operations on encrypted data without decryption, chaotic maps provide strong pseudo-randomness and high sensitivity to initial conditions, making them highly suitable for cryptographic applications. This review examines their convergence, identifies shortcomings in prior studies, and outlines hybrid frameworks combining both methods. Results indicate that integrating chaotic maps with homomorphic encryption enhances security and performance, particularly for encrypted computation. However, computational cost and compatibility challenges remain. The review also highlights emerging trends such as lightweight adaptations for resource-constrained environments and novel chaotic map designs, providing a roadmap for more secure and scalable cryptographic systems in cloud computing and Internet of Things applications.","url":"https://doi.org/10.5281/zenodo.17385667","authors":["Abdullah Ghanim, Jaber"],"tags":["Homomorphic Encryption","Chaotic Maps","Cryptography","Secure Data Processing","Cloud and IoT Security"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17385667","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17367733","name":"The rise of federated systems in cloud-native architectures","source":"datacite","abstract":"Federated systems represent a transformative shift in cloud computing architecture, enabling decentralized data processing while maintaining privacy and sovereignty. This technical review explores the evolution of federated approaches across multiple domains including machine learning, identity management, and cross-border collaboration. The article begins with core architectural principles that distinguish federation from traditional centralized models, including selective synchronization mechanisms and topological variations that optimize for different operational priorities. Privacy-preserving technologies like homomorphic encryption, secure multi-party computation, and differential privacy emerge as essential components for maintaining confidentiality in federated environments. Applications demonstrate particular efficacy in data-sensitive industries where regulatory considerations constrain traditional centralized approaches. Despite compelling advantages, federation introduces notable challenges in performance, resilience, and trust establishment across organizational boundaries. Future directions point toward lightweight protocols for resource-constrained environments, blockchain integration for enhanced accountability, and quantum-resistant cryptography for long-term security assurance. As federated architectures mature, standardization efforts will prove critical for widespread adoption beyond specialized use cases into mainstream enterprise deployments.","url":"https://doi.org/10.5281/zenodo.17367733","authors":["Bollam, Akhilesh"],"tags":["Federated architectures","Privacy-preserving computation","Cross-border data collaboration","Homomorphic encryption","Distributed resilience"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17367733","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17367732","name":"The rise of federated systems in cloud-native architectures","source":"datacite","abstract":"Federated systems represent a transformative shift in cloud computing architecture, enabling decentralized data processing while maintaining privacy and sovereignty. This technical review explores the evolution of federated approaches across multiple domains including machine learning, identity management, and cross-border collaboration. The article begins with core architectural principles that distinguish federation from traditional centralized models, including selective synchronization mechanisms and topological variations that optimize for different operational priorities. Privacy-preserving technologies like homomorphic encryption, secure multi-party computation, and differential privacy emerge as essential components for maintaining confidentiality in federated environments. Applications demonstrate particular efficacy in data-sensitive industries where regulatory considerations constrain traditional centralized approaches. Despite compelling advantages, federation introduces notable challenges in performance, resilience, and trust establishment across organizational boundaries. Future directions point toward lightweight protocols for resource-constrained environments, blockchain integration for enhanced accountability, and quantum-resistant cryptography for long-term security assurance. As federated architectures mature, standardization efforts will prove critical for widespread adoption beyond specialized use cases into mainstream enterprise deployments.","url":"https://doi.org/10.5281/zenodo.17367732","authors":["Bollam, Akhilesh"],"tags":["Federated architectures","Privacy-preserving computation","Cross-border data collaboration","Homomorphic encryption","Distributed resilience"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17367732","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17276332","name":"The Role of Encryption in Data Protection: A Comprehensive Review","source":"datacite","abstract":"The crucial and complex role of encryption as the cornerstone technology of modern data protection frameworks is thoroughly examined in this review. Beyond basic confidentiality, the paper describes how the three cryptographic primitives—symmetric (AES-256), asymmetric (RSA/ECC), and hashing—combine to create the essential triad of confidentiality, integrity, and availability (CIA) throughout the data lifecycle. The synthesis examines the critical role that encryption plays in contemporary settings, especially in cloud computing (protecting data in transit and at rest) and meeting strict regulatory requirements (GDPR, HIPAA, PCI-DSS) [1, 1]. The report concludes by analyzing cutting-edge cryptographic trends, such as ZeroKnowledge Proofs (ZKPs), Homomorphic Encryption (HE), and Post-Quantum Cryptography (PQC), showing a paradigm shift towards safeguarding operational data and reducing potential computational threats","url":"https://doi.org/10.5281/zenodo.17276332","authors":["Shruti Aghera, Bipasha Das"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17276332","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17276333","name":"The Role of Encryption in Data Protection: A Comprehensive Review","source":"datacite","abstract":"The crucial and complex role of encryption as the cornerstone technology of modern data protection frameworks is thoroughly examined in this review. Beyond basic confidentiality, the paper describes how the three cryptographic primitives—symmetric (AES-256), asymmetric (RSA/ECC), and hashing—combine to create the essential triad of confidentiality, integrity, and availability (CIA) throughout the data lifecycle. The synthesis examines the critical role that encryption plays in contemporary settings, especially in cloud computing (protecting data in transit and at rest) and meeting strict regulatory requirements (GDPR, HIPAA, PCI-DSS) [1, 1]. The report concludes by analyzing cutting-edge cryptographic trends, such as ZeroKnowledge Proofs (ZKPs), Homomorphic Encryption (HE), and Post-Quantum Cryptography (PQC), showing a paradigm shift towards safeguarding operational data and reducing potential computational threats","url":"https://doi.org/10.5281/zenodo.17276333","authors":["Shruti Aghera, Bipasha Das"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17276333","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17096162","name":"Nobel Prize in Medicine and Physiology: 10 Proven Scenarios Demonstrating the Merit of the Hamzah Equation (ΩH∗) for Receiving the Nobel Prize in Physiology and Medicine.(If the Criteria are Applied Fairly, and Not Judged Merely on the Basis of the Hamzah Equation Being Non-Anglo-Saxon in Origin).","source":"datacite","abstract":"All 400 Research Projects and Theories of Hamzah Equation (Physics, Chemistry, Medicine, Economics, Mathematics, Computer Science, AI, AGI, Cosmology Simulation and etc) are Available: Orcid ID: https://orcid.org/0009-0009-3175-8563 Science Open ID: https://www.scienceopen.com/user/2c98a8bc-b8bb-49b3-9c91-2f2986a7e16e Safe Creative register the work titled \"The Theory of Intelligent Evolution, the Hamzah Equation, and the Quantum Civilisation\". Safe Creative registration #2504151474836. ............................................................................................................................................................... ............................................................................................................................................................... ............................................................................................................................................................... The Theory of Intelligent Evolution, the Hamzah Equation, and the Quantum Civilization.(Part 1 of 20 – The Quantum Revolution) https://zenodo.org/records/15875268 ............................................................................................................................................................... ............................................................................................................................................................... ............................................................................................................................................................... Theory of Everything Hamzah-Ωφ. The Deterministic Unification of Einstein's Relativity and Quantum Mechanics.(TEOH-Ωφ) https://zenodo.org/records/16986329 ............................................................................................................................................................... ............................................................................................................................................................... ............................................................................................................................................................... Supporting Article for This Topic: Hamzah Certainty Principle. Confirmation of Einstein's Statement \"God Does Not Play Dice\" and the Refutation of Heisenberg's Uncertainty Principle: Contrasting the Planck Constant (ℏ/2) with the Hamzah Certainty Constant (ΩH∗). [ΔxΔp ≥ ℏ/2 Heisenberg] → [Hamzah Principle: ΔxΔp = ΩH∗]. https://zenodo.org/records/16946100 ............................................................................................................................................................... ............................................................................................................................................................... ............................................................................................................................................................... Experimental Verification of the Hamzah Certainty Principle and Violation of the Heisenberg Uncertainty Principle.(Advanced Laboratory Protocol). https://zenodo.org/records/16984923 ............................................................................................................................................................... ............................................................................................................................................................... ............................................................................................................................................................... Precise Computation(Ω¹⁰) of the Physical Constants Origin (Fine-Tuning Problem) from the Universal Integral (QIS₀) via the Hamzah Equation. https://zenodo.org/records/17000543 ...........................................................................","url":"https://doi.org/10.5281/zenodo.17096162","authors":["JALALI, SEYED RASOUL"],"tags":["Nobel Prize in Physiology or Medicine, immunotherapy, cancer treatment, immune checkpoint inhibitors, PD-1, CTLA-4, CAR-T cells, tumor microenvironment, gene editing, CRISPR-Cas9, genetic diseases, cystic fibrosis, muscular dystrophy, sickle cell anemia, base editing, prime editing, epigenetic editing, neurodegenerative diseases, Alzheimer's disease, Parkinson's disease, amyloid beta, tau protein, neurofibrillary tangles, dementia, regenerative medicine, stem cells, induced pluripotent stem cells (iPSCs), tissue engineering, 3D bioprinting, organoids, organ transplantation, diabetes treatment, spinal cord injury repair, universal vaccines, HIV vaccine, malaria vaccine, cancer vaccines, mRNA technology, lipid nanoparticles, antigen design, human microbiome, gut-brain axis, probiotics, prebiotics, personalized medicine, metabolomics, quantum biology, neuroscience, quantum cognition, synaptic transmission, ion channels, molecular neuroscience, gene therapy, viral vectors, RNA therapeutics, rare diseases, orphan drugs, global health equity, health disparities, mathematical biology, computational medicine, Hamzah Equation, ΩH∗, fractal derivatives, biological networks, systems biology, quantum computing simulations, precision medicine, biomarker discovery, drug discovery, pharmaceutical development, clinical trials, translational research, autophagy, senescence, longevity, lifespan extension, healthspan, age-related diseases, genomic sequencing, personalized genomics, epigenetics, transcriptomics, proteomics, single-cell analysis, immunotherapy resistance, combination therapies, oncolytic viruses, cancer neoantigens, T-cell 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medicine, early detection, screening programs, genetic screening, newborn screening, population health, demographic shift, aging population, cancer epidemiology, neurodegenerative disease prevalence, infectious disease burden, antimicrobial stewardship, One Health, environmental health, exposome, data sharing, biorepositories, biobanks, intellectual property, technology transfer, innovation ecosystem, scientific communication, peer review, scientific integrity, reproducibility, open access publishing, scientific awards, Lasker Award, Breakthrough Prize, scientific legacy, impact factor, Nobel lecture, banquet, medal, diploma, prize money, Nobel Week, scientific inspiration, future of medicine, disruptive technology, convergence science, nano-biotechnology, thermostics, personalized vaccines, digital health, wearable sensors, remote monitoring, telemedicine, electronic health records, data privacy, cybersecurity in healthcare, blockchain for health, AI-assisted diagnosis, robotic 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experiment.com,"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17096162","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17096163","name":"Nobel Prize in Medicine and Physiology: 10 Proven Scenarios Demonstrating the Merit of the Hamzah Equation (ΩH∗) for Receiving the Nobel Prize in Physiology and Medicine.(If the Criteria are Applied Fairly, and Not Judged Merely on the Basis of the Hamzah Equation Being Non-Anglo-Saxon in Origin).","source":"datacite","abstract":"All 400 Research Projects and Theories of Hamzah Equation (Physics, Chemistry, Medicine, Economics, Mathematics, Computer Science, AI, AGI, Cosmology Simulation and etc) are Available: Orcid ID: https://orcid.org/0009-0009-3175-8563 Science Open ID: https://www.scienceopen.com/user/2c98a8bc-b8bb-49b3-9c91-2f2986a7e16e Safe Creative register the work titled \"The Theory of Intelligent Evolution, the Hamzah Equation, and the Quantum Civilisation\". Safe Creative registration #2504151474836. ............................................................................................................................................................... ............................................................................................................................................................... ............................................................................................................................................................... The Theory of Intelligent Evolution, the Hamzah Equation, and the Quantum Civilization.(Part 1 of 20 – The Quantum Revolution) https://zenodo.org/records/15875268 ............................................................................................................................................................... ............................................................................................................................................................... ............................................................................................................................................................... Theory of Everything Hamzah-Ωφ. The Deterministic Unification of Einstein's Relativity and Quantum Mechanics.(TEOH-Ωφ) https://zenodo.org/records/16986329 ............................................................................................................................................................... ............................................................................................................................................................... ............................................................................................................................................................... Supporting Article for This Topic: Hamzah Certainty Principle. Confirmation of Einstein's Statement \"God Does Not Play Dice\" and the Refutation of Heisenberg's Uncertainty Principle: Contrasting the Planck Constant (ℏ/2) with the Hamzah Certainty Constant (ΩH∗). [ΔxΔp ≥ ℏ/2 Heisenberg] → [Hamzah Principle: ΔxΔp = ΩH∗]. https://zenodo.org/records/16946100 ............................................................................................................................................................... ............................................................................................................................................................... ............................................................................................................................................................... Experimental Verification of the Hamzah Certainty Principle and Violation of the Heisenberg Uncertainty Principle.(Advanced Laboratory Protocol). https://zenodo.org/records/16984923 ............................................................................................................................................................... ............................................................................................................................................................... ............................................................................................................................................................... Precise Computation(Ω¹⁰) of the Physical Constants Origin (Fine-Tuning Problem) from the Universal Integral (QIS₀) via the Hamzah Equation. https://zenodo.org/records/17000543 ...........................................................................","url":"https://doi.org/10.5281/zenodo.17096163","authors":["JALALI, SEYED RASOUL"],"tags":["Nobel Prize in Physiology or Medicine, immunotherapy, cancer treatment, immune checkpoint inhibitors, PD-1, CTLA-4, CAR-T cells, tumor microenvironment, gene editing, CRISPR-Cas9, genetic diseases, cystic fibrosis, muscular dystrophy, sickle cell anemia, base editing, prime editing, epigenetic editing, neurodegenerative diseases, Alzheimer's disease, Parkinson's disease, amyloid beta, tau protein, neurofibrillary tangles, dementia, regenerative medicine, stem cells, induced pluripotent stem cells (iPSCs), tissue engineering, 3D bioprinting, organoids, organ transplantation, diabetes treatment, spinal cord injury repair, universal vaccines, HIV vaccine, malaria vaccine, cancer vaccines, mRNA technology, lipid nanoparticles, antigen design, human microbiome, gut-brain axis, probiotics, prebiotics, personalized medicine, metabolomics, quantum biology, neuroscience, quantum cognition, synaptic transmission, ion channels, molecular neuroscience, gene therapy, viral vectors, RNA therapeutics, rare diseases, orphan drugs, global health equity, health disparities, mathematical biology, computational medicine, Hamzah Equation, ΩH∗, fractal derivatives, biological networks, systems biology, quantum computing simulations, precision medicine, biomarker discovery, drug discovery, pharmaceutical development, clinical trials, translational research, autophagy, senescence, longevity, lifespan extension, healthspan, age-related diseases, genomic sequencing, personalized genomics, epigenetics, transcriptomics, proteomics, single-cell analysis, immunotherapy resistance, combination therapies, oncolytic viruses, cancer neoantigens, T-cell activation, immune evasion, neurodegenerative pathways, neuroinflammation, alpha-synuclein, Lewy bodies, stem cell differentiation, tissue scaffolds, biomaterials, vaccine adjuvants, broad-spectrum immunity, virology, bacteriology, microbial ecology, fecal microbiota transplant, quantum entanglement in biology, magnetic field sensing in birds, cryptochromes, RNA modifications, nucleoside analogs, rare genetic disorders, drug repurposing, access to medicine, open science, scientific collaboration, multidisciplinary research, Nobel Committee, Karolinska Institutet, scientific breakthrough, paradigm shift, fundamental discovery, clinical impact, global health, pandemic preparedness, antibiotic resistance, antiviral drugs, chemotherapeutics, targeted therapy, hormone therapy, gene delivery, CRISPR off-target effects, neurodegenerative biomarkers, early diagnosis, neuroimaging, fMRI, EEG, stem cell transplantation, immunogenicity, vaccine efficacy, microbiome dysbiosis, inflammatory bowel disease, depression, anxiety, quantum coherence, neural oscillations, memory formation, consciousness, RNA sequencing, siRNA, miRNA, antisense oligonucleotides, clinical genomics, genetic counseling, health policy, medical ethics, scientific funding, research and development, biotechnology startups, pharmaceutical industry, academic research, publication, citation impact, scientific merit, Nobel nomination, prize laureates, James Allison, Tasuku Honjo, Emmanuelle Charpentier, Jennifer Doudna, Katalin Karikó, Drew Weissman, Shinya Yamanaka, Yoshinori Ohsumi, Harvey Alter, Charles Rice, Youyou Tu, optogenetics, brain-machine interface, neuroprosthetics, artificial intelligence in medicine, machine learning, deep learning, predictive modeling, data integration, bioinformatics, synthetic biology, metabolic engineering, xenotransplantation, cellular reprogramming, telomeres, telomerase, DNA damage response, mitochondrial function, oxidative stress, inflammaging, vaccine development pipeline, adaptive clinical trials, real-world evidence, patient stratification, companion diagnostics, liquid biopsy, circulating tumor DNA, tumor heterogeneity, cancer stem cells, antibody-drug conjugates, bispecific antibodies, microbiome-based diagnostics, psychobiotics, quantum sensors, superresolution microscopy, structural biology, cryo-EM, protein folding, gene regulatory networks, non-viral gene delivery, exon skipping, mRNA stability, translational efficiency, rare disease registries, natural history studies, orphan drug designation, health technology assessment, cost-effectiveness, drug pricing, vaccine distribution, cold chain, global vaccination campaigns, World Health Organization, CDC, NIH, biomedical innovation, scientific methodology, hypothesis testing, experimental design, animal models, organ-on-a-chip, clinical endpoints, surrogate markers, survival benefit, quality of life, patient-reported outcomes, health economics, public health intervention, preventive medicine, early detection, screening programs, genetic screening, newborn screening, population health, demographic shift, aging population, cancer epidemiology, neurodegenerative disease prevalence, infectious disease burden, antimicrobial stewardship, One Health, environmental health, exposome, data sharing, biorepositories, biobanks, intellectual property, technology transfer, innovation ecosystem, scientific communication, peer review, scientific integrity, reproducibility, open access publishing, scientific awards, Lasker Award, Breakthrough Prize, scientific legacy, impact factor, Nobel lecture, banquet, medal, diploma, prize money, Nobel Week, scientific inspiration, future of medicine, disruptive technology, convergence science, nano-biotechnology, thermostics, personalized vaccines, digital health, wearable sensors, remote monitoring, telemedicine, electronic health records, data privacy, cybersecurity in healthcare, blockchain for health, AI-assisted diagnosis, robotic surgery, minimally invasive procedures, regenerative immunology, stem cell niche, organ perfusion, decellularization, vaccine hesitancy, science communication, public engagement, health literacy, medical education, continuing education, physician-scientist, training grants, postdoctoral research, graduate studies, undergraduate research, science policy, government funding, venture capital, philanthropy, nonprofit research, advocacy groups, patient advocacy, community engagement, equitable recruitment, diversity in clinical trials, structural determinants of health, social determinants of health, environmental determinants of health, planetary health, climate change and health, disaster medicine, humanitarian aid, crisis response, health system strengthening, primary care, universal health coverage, digital divide, health innovation in low-resource settings, frugal innovation, point-of-care diagnostics, mobile health, mHealth, SMS reminders, community health workers, task shifting, capacity building, medical supply chains, essential medicines, vaccine sovereignty, patent pools, compulsory licensing, generic drugs, biosimilars, continuous manufacturing, 3D printed drugs, smart pills, implantable devices, neurostimulation, deep brain stimulation, wearable drug delivery, closed-loop systems, artificial pancreas, synthetic genomics, minimal genome, DNA synthesis, DNA data storage, biological encryption, biosecurity, dual-use research, gain-of-function, bioethics, institutional review boards, informed consent, patient autonomy, beneficence, non-maleficence, justice, distributive justice, global justice, research ethics, authorship guidelines, conflict of interest, scientific misconduct, fabrication, falsification, plagiarism, retraction, correction, errata, post-publication peer review, preprint servers, bioRxiv, medRxiv, citation metrics, h-index, altmetrics, social media impact, science journalism, documentary film, popular science books, museum exhibits, public lectures, science festivals, citizen science, crowdsourcing, data donation, personalized health data, ownership of data, data monetization, big data analytics, cloud computing, high-performance computing, federated learning, differential privacy, homomorphic encryption, AI ethics, algorithm bias, explainable AI, robotic ethics, automation in labs, high-throughput screening, drug screening, phenotypic screening, organoid screening, microfluidics, lab-on-a-chip, single-cell sequencing, spatial transcriptomics, multi-omics integration, systems pharmacology, network medicine, disease modules, biomarker validation, prognostic biomarkers, predictive biomarkers, pharmacodynamics, pharmacokinetics, drug metabolism, cytochrome P450, drug-drug interactions, adverse events, pharmacovigilance, post-market surveillance, real-world data, real-world evidence, comparative effectiveness research, patient preference, shared decision making, value-based healthcare, bundled payments, pay-for-performance, healthcare quality, patient safety, medical error, diagnostic error, overdiagnosis, overtreatment, medical reversal, deimplementation, evidence-based medicine, clinical practice guidelines, standard of care, medical innovation, surgical innovation, medical device regulation, FDA approval, EMA approval, breakthrough therapy designation, fast track, accelerated approval, conditional marketing authorization, compassionate use, expanded access, right to try, clinical trial phases, Phase I, Phase II, Phase III, Phase IV, randomized controlled trials, placebo effect, blinding, control groups, intention-to-treat analysis, statistical significance, clinical significance, effect size, number needed to treat, number needed to harm, confidence intervals, p-values, Bayesian statistics, adaptive trials, basket trials, umbrella trials, platform trials, master protocols, preclinical research, in vitro studies, in vivo studies, ex vivo studies, animal welfare, 3Rs principle (Replacement, Reduction, Refinement), humanized mouse models, zoonotic diseases, emerging infectious diseases, outbreak investigation, contact tracing, epidemic curve, herd immunity, seroprevalence, PCR testing, rapid antigen tests, antibody tests, neutralization assays, viral load, viral sequencing, variants of concern, surveillance, mitigation strategies, social distancing, mask-wearing, lockdowns, quarantine, isolation, travel restrictions, non-pharmaceutical interventions, mental health crisis, pandemic fatigue, long COVID, post-acute sequelae of SARS-CoV-2, multidisciplinary clinics, rehabilitation, physical therapy, occupational therapy, speech therapy, cognitive rehabilitation, palliative care, hospice, end-of-life care, bereavement, medical anthropology, sociology of health, history of medicine, Nobel history, biography of laureates, scientific rivalry, collaboration, mentorship, scientific lineages, Nobel Prize effect, funding boost, prestige, increased citations, research directions, scientific trends, forecasting, horizon scanning, futures thinking, scenario planning, foresight, technology assessment, impact assessment, return on investment, cost-benefit analysis, budget impact analysis, health equity impact assessment, environmental impact assessment, sustainability, green labs, carbon footprint of research, responsible innovation, inclusive innovation, co-creation with patients, user-centered design, design thinking, agile methodology, lean startup, translational science spectrum, T1-T4 research, implementation science, knowledge translation, dissemination, scale-up, spread, sustainability frameworks, RE-AIM framework, Consolidated Framework for Implementation Research, normalization process theory, academic detailing, opinion leaders, champions, barriers and facilitators, context adaptation, fidelity, sustainability, learning health systems, quality improvement, plan-do-study-act cycles, benchmarking, audit and feedback, checklists, clinical decision support, alerts, reminders, clinical pathways, protocols, standardization, personalized care plans, patient portals, access to information, self-management, patient activation, empowerment, peer support, online communities, crowdsourced funding, research participation, clinical trial matching, registries, biobanking consent, broad consent, dynamic consent, return of results, incidental findings, genetic discrimination, GINA Act, privacy laws, GDPR, HIPAA, data protection, cybersecurity breaches, ransomware, telehealth platforms, remote consultations, digital phenotyping, passive sensing, smartphone apps, health chatbots, virtual reality therapy, augmented reality surgery, remote surgery, surgical robots, haptic feedback, simulation training, continuing medical education, maintenance of certification, board certification, medical licensing, credentialing, privileging, hospital accreditation, Joint Commission, quality measures, performance indicators, patient satisfaction, Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS), readmission rates, mortality rates, safety indicators, never events, hospital-acquired infections, hand hygiene, antibiotic prophylaxis, surgical site infections, central line-associated bloodstream infections, catheter-associated urinary tract infections, ventilator-associated pneumonia, falls, pressure ulcers, venous thromboembolism prophylaxis, medication reconciliation, discharge planning, transitional care, care coordination, case management, primary care medical home, accountable care organizations, bundled payments, capitation, fee-for-service, pay-for-performance, value-based purchasing, star ratings, hospital compare, transparency, public reporting, malpractice, litigation, defensive medicine, burnout, physician burnout, nurse burnout, resilience, wellness programs, mindfulness, workload, staffing ratios, teamwork, communication, handoffs, signout, check-backs, read-backs, closed-loop communication, situational awareness, crisis resource management, debriefing, just culture, reporting culture, learning culture, psychological safety, leadership, change management, innovation adoption, disruptive innovation, sustaining innovation, efficiency innovation, transformational innovation, radical innovation, incremental innovation, basic research, applied research, development, diffusion of innovations, early adopters, laggards, chasm, technology adoption lifecycle, hype cycle, peak of inflated expectations, trough of disillusionment, slope of enlightenment, plateau of productivity, scientific paradigm, Kuhnian revolution, normal science, puzzle-solving, anomaly, crisis, revolution, incommensurability, scientific realism, instrumentalism, positivism, post-positivism, constructivism, pragmatism, ontology, epistemology, methodology, methods, quantitative research, qualitative research, mixed methods, grounded theory, phenomenology, ethnography, case study, narrative inquiry, participatory action research, community-based participatory research, decolonizing methodologies, indigenous knowledge, traditional medicine, complementary and alternative medicine, integrative medicine, holistic health, wellness, prevention, nutrition, exercise, sleep, stress management, mindfulness, meditation, yoga, tai chi, social connection, loneliness, isolation, social support, community, belonging, purpose, meaning, happiness, well-being, flourishing, positive psychology, character strengths, gratitude, kindness, empathy, compassion, altruism, cooperation, collaboration, trust, social capital, collective efficacy, community resilience, disaster preparedness, emergency response, trauma-informed care, adverse childhood experiences, resilience factors, protective factors, risk factors, vulnerability, equity, diversity, inclusion, belonging, justice, anti-racism, cultural humility, implicit bias, structural racism, historical trauma, health disparities research, minority health, immigrant health, refugee health, LGBTQ+ health, gender-affirming care, sexual health, reproductive health, maternal health, child health, adolescent health, young adult health, midlife, menopause, andropause, geriatrics, frailty, sarcopenia, polypharmacy, deprescribing, falls prevention, elder abuse, ageism, intergenerational programs, lifelong learning, successful aging, active aging, productivity, engagement, volunteering, civic engagement, retirement, pension, social security, Medicare, Medicaid, insurance, uninsured, underinsured, out-of-pocket costs, medical debt, bankruptcy, poverty, income inequality, wealth gap, education, health literacy, numeracy, digital literacy, access to care, transportation, food deserts, food insecurity, housing insecurity, homelessness, built environment, walkability, parks, recreation, safety, violence, injury prevention, occupational health, workplace safety, ergonomics, toxicology, environmental exposures, air pollution, water quality, lead poisoning, climate change, heat waves, extreme weather, vector-borne diseases, allergies, asthma, autoimmune diseases, inflammation, chronic disease management, diabetes, hypertension, hyperlipidemia, obesity, metabolic syndrome, heart disease, stroke, cancer survivorship, remission, recurrence, secondary prevention, palliative chemotherapy, hospice care, bereavement support, grief, mourning, funeral practices, cultural practices, spirituality, religion, faith, chaplaincy, pastoral care, meaning-making, legacy, advance care planning, living wills, durable power of attorney for healthcare, do-not-resuscitate orders, physician orders for life-sustaining treatment, medical aid in dying, euthanasia, ethics committees, consultation, mediation, conflict resolution, principles of bioethics, casuistry, narrative ethics, virtue ethics, care ethics, feminist ethics, communitarianism, libertarianism, utilitarianism, deontology, Kantian ethics, rights-based ethics, justice-based ethics, capability approach, social contract, political philosophy, health policy, law, regulation, legislation, lobbying, advocacy, activism, social movements, patient rights, consumer rights, human rights, right to health, universal declaration of human rights, sustainable development goals, global health security agenda, pandemic treaty, international health regulations, World Health Assembly, diplomacy, health attachés, non-state actors, public-private partnerships, product development partnerships, venture philanthropy, impact investing, social impact bonds, pay-for-success, outcomes-based financing, microfinance, community development financial institutions, cooperatives, mutual aid, solidarity economy, gift economy, sharing economy, platform cooperativism, open source, creative commons, copyleft, patent left, humanitarian open source, free software, open hardware, open data, open science, open access, open peer review, open notebooks, preprints, postprints, self-archiving, institutional repositories, scholarly communication, bibliometrics, scientometrics, informetrics, webometrics, altmetrics, data science, data visualization, infographics, dashboards, reporting, evaluation, monitoring, indicators, metrics, KPIs, goals, objectives, outcomes, impacts, logic models, theory of change, program evaluation, formative evaluation, summative evaluation, process evaluation, outcome evaluation, impact evaluation, cost-effectiveness analysis, cost-utility analysis, cost-benefit analysis, budget impact analysis, return on investment, social return on investment, environmental return on investment, life cycle assessment, carbon accounting, sustainability reporting, integrated reporting, ESG (environmental, social, governance), corporate social responsibility, responsible research and innovation, ethics by design, value-sensitive design, participatory design, co-design, citizen science, community science, street science, crowdsourcing, crowdfunding, kickstarter, experiment.com,"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17096163","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17186688","name":"Application of Partial Homomorphic Encryption for Enhance Security using Dynamic Key Management: Review and Proposed Solution","source":"datacite","abstract":"Abstract: Today’s interconnected world has transformed the operational way of individuals, organizations, and governments by rapid digitization. Starting from online banking and e-commerce to cloud computing and smart devices, digital platforms have become an integral part of daily life. While this transformation offers incredible convenience, efficiency, and accessibility, a wide range of security challenges has also been initiated. There is a high requirement of effective security measures. This study proposed a novel approach to enhance data security using a combination of dynamic key management (DKM) and partial homomorphic encryption (PHE). Achieving optimal security, efficiency, and flexibility in traditional encryption methods is a critical issue. The proposed method supports secure and efficient key updates without decrypting current data, making use of an additive PHE scheme in addition to a dynamic key distribution protocol. Forward and backward secrecy is provided in applications where users join and leave most of the time, e.g., cloud storage and Internet of Things (IoT). More secure environments may be created with the need for operational continuity, such as those of cloud computing and IoT applications. Leak of sensitive data may be avoided along with safeguarding the information against many new, dynamically emerging threats of digital ecosystems. Keywords — Security, DKM, PHE, IoT, Encryption, Decryption, ECC.","url":"https://doi.org/10.5281/zenodo.17186688","authors":["Dipanjana Biswas , Jui Pattnayak , Annwesha Banerjee ,  Puja Mukherjee, Ankita Basak"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17186688","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17186689","name":"Application of Partial Homomorphic Encryption for Enhance Security using Dynamic Key Management: Review and Proposed Solution","source":"datacite","abstract":"Abstract: Today’s interconnected world has transformed the operational way of individuals, organizations, and governments by rapid digitization. Starting from online banking and e-commerce to cloud computing and smart devices, digital platforms have become an integral part of daily life. While this transformation offers incredible convenience, efficiency, and accessibility, a wide range of security challenges has also been initiated. There is a high requirement of effective security measures. This study proposed a novel approach to enhance data security using a combination of dynamic key management (DKM) and partial homomorphic encryption (PHE). Achieving optimal security, efficiency, and flexibility in traditional encryption methods is a critical issue. The proposed method supports secure and efficient key updates without decrypting current data, making use of an additive PHE scheme in addition to a dynamic key distribution protocol. Forward and backward secrecy is provided in applications where users join and leave most of the time, e.g., cloud storage and Internet of Things (IoT). More secure environments may be created with the need for operational continuity, such as those of cloud computing and IoT applications. Leak of sensitive data may be avoided along with safeguarding the information against many new, dynamically emerging threats of digital ecosystems. Keywords — Security, DKM, PHE, IoT, Encryption, Decryption, ECC.","url":"https://doi.org/10.5281/zenodo.17186689","authors":["Dipanjana Biswas , Jui Pattnayak , Annwesha Banerjee ,  Puja Mukherjee, Ankita Basak"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17186689","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17164789","name":"Data Privacy and Security in AI","source":"datacite","abstract":"The advancement of artificial intelligence (AI) technology has created both opportunities and risks in data protection and safeguarding. Given that numerous organizations are now employing AI systems in various industries, the goal of using data to drive innovation has never been urgent. This paper will review the relationship between AI, data privacy, and security and discuss the current issues and possible recommendations. Furthermore, this study introduces new approaches, including federated learning and homomorphic encryption, which preserve data integrity while still using the data. Using concrete examples from various industries, the study reveals practices and tendencies that company leaders should follow and avoid to achieve a proper balance. This paper offers an ethical approach that integrates practical recommendations for policymakers, technologists, and businesses to build user trust and progress responsibly and technically. Since most AI applications are based on big data, users’ data protection and systems’ performance and expandability are extremely important. This paper explores the issue of data privacy and security in AI and discusses promising strategies to address the problem, and guidelines for responsible AI implementation. The main priorities include the exposition of the algorithms, data anonymization methods, legal requirements, and strengthening cybersecurity. This paper presents real-life examples, and industry benchmarks to support the framework that can help organizations manage technologies in a way that addresses ethical concerns. In the future, the analysis presented in the study can help industries understand trends that help develop AI strategies that meet high privacy and security standards.","url":"https://doi.org/10.5281/zenodo.17164789","authors":["Kakarala, Manikanta kumar","Rongali, Sateesh Kumar"],"tags":["Artificial intelligence","Data privacy","Data security","Algorithmic transparency","Regulatory compliance","Homomorphic encryption","Federated learning","Adversarial attacks"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17164789","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17164788","name":"Data Privacy and Security in AI","source":"datacite","abstract":"The advancement of artificial intelligence (AI) technology has created both opportunities and risks in data protection and safeguarding. Given that numerous organizations are now employing AI systems in various industries, the goal of using data to drive innovation has never been urgent. This paper will review the relationship between AI, data privacy, and security and discuss the current issues and possible recommendations. Furthermore, this study introduces new approaches, including federated learning and homomorphic encryption, which preserve data integrity while still using the data. Using concrete examples from various industries, the study reveals practices and tendencies that company leaders should follow and avoid to achieve a proper balance. This paper offers an ethical approach that integrates practical recommendations for policymakers, technologists, and businesses to build user trust and progress responsibly and technically. Since most AI applications are based on big data, users’ data protection and systems’ performance and expandability are extremely important. This paper explores the issue of data privacy and security in AI and discusses promising strategies to address the problem, and guidelines for responsible AI implementation. The main priorities include the exposition of the algorithms, data anonymization methods, legal requirements, and strengthening cybersecurity. This paper presents real-life examples, and industry benchmarks to support the framework that can help organizations manage technologies in a way that addresses ethical concerns. In the future, the analysis presented in the study can help industries understand trends that help develop AI strategies that meet high privacy and security standards.","url":"https://doi.org/10.5281/zenodo.17164788","authors":["Kakarala, Manikanta kumar","Rongali, Sateesh Kumar"],"tags":["Artificial intelligence","Data privacy","Data security","Algorithmic transparency","Regulatory compliance","Homomorphic encryption","Federated learning","Adversarial attacks"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17164788","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17075347","name":"Cloud Security and Privacy: A Systematic Review of Threats, Solutions, and Future Direction","source":"datacite","abstract":"The protection of data saved, transferred, and processed in cloud settings has become a significant concern as cloud computing becomes the foundation of today's digital infrastructure. Cloud systems' multi-tenant design can be complicated, and standard security models may not take these factors into account. The current state of cloud security and privacy is thoroughly examined in this assessment, with data breaches, unsafe APIs, and violations of regulatory compliance being the most important issues. Covered are the primary methods of mitigation, such as secure key management, data encryption while in transit and at rest, and intrusion detection systems for real-time threat monitoring. The paper also speaks on the use of the latest privacy-preserving technologies like homomorphic encryption and biometric cryptosystems to preserve confidentiality and not reporting functionality. The risks and consequences of multi-tenancy and compliance are also discussed to determine the effects of shared infrastructures on data isolation and governance. With more enterprises moving workloads to the cloud, it is crucial to safeguard the data of the enterprise which includes its confidentiality, integrity, and availability. This review will provide an in-depth insight into the current security issues, research gaps, and future research trends in terms of adaptive security frameworks, standardization of security protocols, and integration of privacy-enhancing technologies towards fostering trustworthy and resilient cloud adoption.","url":"https://doi.org/10.5281/zenodo.17075347","authors":["Hussain, Prof. (Dr.) Abid"],"tags":["Cloud Security","Data Privacy","Multi-Tenancy","Encryption Techniques","Intrusion Detection Systems","Privacy-Preserving Technologies","Regulatory Compliance"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17075347","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.17075348","name":"Cloud Security and Privacy: A Systematic Review of Threats, Solutions, and Future Direction","source":"datacite","abstract":"The protection of data saved, transferred, and processed in cloud settings has become a significant concern as cloud computing becomes the foundation of today's digital infrastructure. Cloud systems' multi-tenant design can be complicated, and standard security models may not take these factors into account. The current state of cloud security and privacy is thoroughly examined in this assessment, with data breaches, unsafe APIs, and violations of regulatory compliance being the most important issues. Covered are the primary methods of mitigation, such as secure key management, data encryption while in transit and at rest, and intrusion detection systems for real-time threat monitoring. The paper also speaks on the use of the latest privacy-preserving technologies like homomorphic encryption and biometric cryptosystems to preserve confidentiality and not reporting functionality. The risks and consequences of multi-tenancy and compliance are also discussed to determine the effects of shared infrastructures on data isolation and governance. With more enterprises moving workloads to the cloud, it is crucial to safeguard the data of the enterprise which includes its confidentiality, integrity, and availability. This review will provide an in-depth insight into the current security issues, research gaps, and future research trends in terms of adaptive security frameworks, standardization of security protocols, and integration of privacy-enhancing technologies towards fostering trustworthy and resilient cloud adoption.","url":"https://doi.org/10.5281/zenodo.17075348","authors":["Hussain, Prof. (Dr.) Abid"],"tags":["Cloud Security","Data Privacy","Multi-Tenancy","Encryption Techniques","Intrusion Detection Systems","Privacy-Preserving Technologies","Regulatory Compliance"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17075348","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.16981462","name":"A Review of Cloud-Based Data Security Protocols","source":"datacite","abstract":"Cloud computing has rapidly transformed how organizations store, process, and manage data, offering unparalleled flexibility, scalability, and cost efficiency. However, as businesses migrate critical assets to cloud environments, robust and adaptable data security protocols have become central to protecting sensitive information from increasingly sophisticated cyber threats. This review comprehensively explores the landscape of cloud-based data security protocols, evaluating their evolution, effectiveness, inherent challenges, and the balance between accessibility and protection. By examining authentication, encryption, access control, and advanced threat defense mechanisms, we highlight both established standards and emerging technologies aimed at fortifying data integrity and confidentiality in distributed, multi-tenant architectures. The paper provides an in-depth comparison of prevailing security frameworks, regulatory compliance considerations, and the impact of emerging trends such as zero trust models, homomorphic encryption, and AI-driven security on the future of cloud data protection. Ultimately, understanding both the strengths and limitations of current security protocols is crucial for organizations seeking to maximize the benefits of cloud computing while minimizing exposure to data breaches and unauthorized disclosures","url":"https://doi.org/10.5281/zenodo.16981462","authors":["Singhania, Vanya"],"tags":["cloud security, data protocols, encryption, authentication, access control."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.16981462","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.16981463","name":"A Review of Cloud-Based Data Security Protocols","source":"datacite","abstract":"Cloud computing has rapidly transformed how organizations store, process, and manage data, offering unparalleled flexibility, scalability, and cost efficiency. However, as businesses migrate critical assets to cloud environments, robust and adaptable data security protocols have become central to protecting sensitive information from increasingly sophisticated cyber threats. This review comprehensively explores the landscape of cloud-based data security protocols, evaluating their evolution, effectiveness, inherent challenges, and the balance between accessibility and protection. By examining authentication, encryption, access control, and advanced threat defense mechanisms, we highlight both established standards and emerging technologies aimed at fortifying data integrity and confidentiality in distributed, multi-tenant architectures. The paper provides an in-depth comparison of prevailing security frameworks, regulatory compliance considerations, and the impact of emerging trends such as zero trust models, homomorphic encryption, and AI-driven security on the future of cloud data protection. Ultimately, understanding both the strengths and limitations of current security protocols is crucial for organizations seeking to maximize the benefits of cloud computing while minimizing exposure to data breaches and unauthorized disclosures","url":"https://doi.org/10.5281/zenodo.16981463","authors":["Singhania, Vanya"],"tags":["cloud security, data protocols, encryption, authentication, access control."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.16981463","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2508.01798","name":"A Survey on Privacy-Preserving Computing in the Automotive Domain","source":"datacite","abstract":"As vehicles become increasingly connected and autonomous, they accumulate and manage various personal data, thereby presenting a key challenge in preserving privacy during data sharing and processing. This survey reviews applications of Secure Multi-Party Computation (MPC) and Homomorphic Encryption (HE) that address these privacy concerns in the automotive domain. First, we identify the scope of privacy-sensitive use cases for these technologies, by surveying existing works that address privacy issues in different automotive contexts, such as location-based services, mobility infrastructures, traffic management, etc. Then, we review recent works that employ MPC and HE as solutions for these use cases in detail. Our survey highlights the applicability of these privacy-preserving technologies in the automotive context, while also identifying challenges and gaps in the current research landscape. This work aims to provide a clear and comprehensive overview of this emerging field and to encourage further research in this domain.","url":"https://doi.org/10.48550/arxiv.2508.01798","authors":["Yuca, Nergiz","Matyunin, Nikolay","Arzoglou, Ektor","Anagnostopoulos, Nikolaos Athanasios","Katzenbeisser, Stefan"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2508.01798","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2507.20014","name":"Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation","source":"datacite","abstract":"As AI-driven dataspaces become integral to data sharing and collaborative analytics, ensuring privacy, performance, and policy compliance presents significant challenges. This paper provides a comprehensive review of privacy-preserving and policy-aware AI techniques, including Federated Learning, Differential Privacy, Trusted Execution Environments, Homomorphic Encryption, and Secure Multi-Party Computation, alongside strategies for aligning AI with regulatory frameworks such as GDPR and the EU AI Act. We propose a novel taxonomy to classify these techniques based on privacy levels, performance impacts, and compliance complexity, offering a clear framework for practitioners and researchers to navigate trade-offs. Key performance metrics -- latency, throughput, cost overhead, model utility, fairness, and explainability -- are analyzed to highlight the multi-dimensional optimization required in dataspaces. The paper identifies critical research gaps, including the lack of standardized privacy-performance KPIs, challenges in explainable AI for federated ecosystems, and semantic policy enforcement amidst regulatory fragmentation. Future directions are outlined, proposing a conceptual framework for policy-driven alignment, automated compliance validation, standardized benchmarking, and integration with European initiatives like GAIA-X, IDS, and Eclipse EDC. By synthesizing technical, ethical, and regulatory perspectives, this work lays the groundwork for developing trustworthy, efficient, and compliant AI systems in dataspaces, fostering innovation in secure and responsible data-driven ecosystems.","url":"https://doi.org/10.48550/arxiv.2507.20014","authors":["Chandra, Joydeep","Navneet, Satyam Kumar"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2507.20014","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2308.03734","name":"Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution","source":"datacite","abstract":"The entity resolution problem requires finding pairs across datasets that belong to different owners but refer to the same entity in the real world. To train and evaluate solutions (either rule-based or machine-learning-based) to the entity resolution problem, generating a ground truth dataset with entity pairs or clusters is needed. However, such a data annotation process involves humans as domain oracles to review the plaintext data for all candidate record pairs from different parties, which inevitably infringes the privacy of data owners, especially in privacy-sensitive cases like medical records. To the best of our knowledge, there is no prior work on privacy-preserving ground truth dataset generation, especially in the domain of entity resolution. We propose a novel blind annotation protocol based on homomorphic encryption that allows domain oracles to collaboratively label ground truths without sharing data in plaintext with other parties. In addition, we design a domain-specific easy-to-use language that hides the sophisticated underlying homomorphic encryption layer. Rigorous proof of the privacy guarantee is provided and our empirical experiments via an annotation simulator indicate the feasibility of our privacy-preserving protocol (f-measure on average achieves more than 90\\% compared with the real ground truths).","url":"https://doi.org/10.48550/arxiv.2308.03734","authors":["Yao, Yixiang","Jin, Weizhao","Ravi, Srivatsan"],"tags":["Information Retrieval (cs.IR)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2308.03734","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2504.16091","name":"Post-Quantum Homomorphic Encryption: A Case for Code-Based Alternatives","source":"datacite","abstract":"Homomorphic Encryption (HE) allows secure and privacy-protected computation on encrypted data without the need to decrypt it. Since Shor's algorithm rendered prime factorisation and discrete logarithm-based ciphers insecure with quantum computations, researchers have been working on building post-quantum homomorphic encryption (PQHE) algorithms. Most of the current PQHE algorithms are secured by Lattice-based problems and there have been limited attempts to build ciphers based on error-correcting code-based problems. This review presents an overview of the current approaches to building PQHE schemes and justifies code-based encryption as a novel way to diversify post-quantum algorithms. We present the mathematical underpinnings of existing code-based cryptographic frameworks and their security and efficiency guarantees. We compare lattice-based and code-based homomorphic encryption solutions identifying challenges that have inhibited the progress of code-based schemes. We finally propose five new research directions to advance post-quantum code-based homomorphic encryption.","url":"https://doi.org/10.48550/arxiv.2504.16091","authors":["Bhoi, Siddhartha Siddhiprada","Arakala, Arathi","Corman, Amy Beth","Rao, Asha"],"tags":["Cryptography and Security (cs.CR)","History and Overview (math.HO)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Mathematics","FOS: Mathematics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2504.16091","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.15048047","name":"EVALUATING BLOCKCHAIN AND HOMOMORPHIC ENCRYPTION FOR SECURE DATA PROCESSING IN MULTI-CLOUD HYBRID DATABASE SYSTEMS: A SYSTEMATIC LITERATURE REVIEW","source":"datacite","abstract":"The evolving landscape of backend software development increasingly depends on multi-cloud hybrid database systems to facilitate scalability, high availability, and cost savings. Since data is spread over multiple cloud providers, it becomes difficult to deploy consistent security policies, creating data leakage, unauthorized access, and regulatory compliance issues. Homomorphic encryption (HE) is revolutionary in providing a solution by supporting mathematical operations over encrypted data so that sensitive computation never reveals plaintext values, a vital aspect for privacy-preserving analytics, machine learning models, and financial data processing. Simultaneously, blockchain technology allows an immutable audit trail; hence, unauthorized modification is averted, and auditability is facilitated with complex multi-cloud implementations. Merging HE with blockchain makes database systems clear and secure without central trust models. An extensive and systematic literature review is undertaken to analyze such technologies' efficiency, scalability, and feasibility in software design for the cloud and backend systems. There are several existing studies that identify different methods of combining secure computation models with decentralized verification techniques, though there are still considerable challenges to overcome. Both HE-based computations and Blockchain integration come with limitations on latency, consensus protocols, and cross-cloud support, which demand dynamic solutions to facilitate high-performance and scalable hybrid cloud security paradigms. All these requirements have to be met for effective deployment in multi-cloud database systems that can support increasing needs for resilient and privacy-protecting backend architecture.","url":"https://doi.org/10.5281/zenodo.15048047","authors":["Tadi, Sri Rama Chandra Charan Teja"],"tags":["multi cloud","Homomorphic Encryption","Blockchain","Hybrid database","Audit","Machine Learning","Financial data","Scalability"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2019","doi":"10.5281/zenodo.15048047","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.15048046","name":"EVALUATING BLOCKCHAIN AND HOMOMORPHIC ENCRYPTION FOR SECURE DATA PROCESSING IN MULTI-CLOUD HYBRID DATABASE SYSTEMS: A SYSTEMATIC LITERATURE REVIEW","source":"datacite","abstract":"The evolving landscape of backend software development increasingly depends on multi-cloud hybrid database systems to facilitate scalability, high availability, and cost savings. Since data is spread over multiple cloud providers, it becomes difficult to deploy consistent security policies, creating data leakage, unauthorized access, and regulatory compliance issues. Homomorphic encryption (HE) is revolutionary in providing a solution by supporting mathematical operations over encrypted data so that sensitive computation never reveals plaintext values, a vital aspect for privacy-preserving analytics, machine learning models, and financial data processing. Simultaneously, blockchain technology allows an immutable audit trail; hence, unauthorized modification is averted, and auditability is facilitated with complex multi-cloud implementations. Merging HE with blockchain makes database systems clear and secure without central trust models. An extensive and systematic literature review is undertaken to analyze such technologies' efficiency, scalability, and feasibility in software design for the cloud and backend systems. There are several existing studies that identify different methods of combining secure computation models with decentralized verification techniques, though there are still considerable challenges to overcome. Both HE-based computations and Blockchain integration come with limitations on latency, consensus protocols, and cross-cloud support, which demand dynamic solutions to facilitate high-performance and scalable hybrid cloud security paradigms. All these requirements have to be met for effective deployment in multi-cloud database systems that can support increasing needs for resilient and privacy-protecting backend architecture.","url":"https://doi.org/10.5281/zenodo.15048046","authors":["Tadi, Sri Rama Chandra Charan Teja"],"tags":["multi cloud","Homomorphic Encryption","Blockchain","Hybrid database","Audit","Machine Learning","Financial data","Scalability"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2019","doi":"10.5281/zenodo.15048046","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2502.03811","name":"Privacy Risks in Health Big Data: A Systematic Literature Review","source":"datacite","abstract":"The digitization of health records has greatly improved the efficiency of the healthcare system and promoted the formulation of related research and policies. However, the widespread application of advanced technologies such as electronic health records, genomic data, and wearable devices in the field of health big data has also intensified the collection of personal sensitive data, bringing serious privacy and security issues. Based on a systematic literature review (SLR), this paper comprehensively outlines the key research in the field of health big data security. By analyzing existing research, this paper explores how cutting-edge technologies such as homomorphic encryption, blockchain, federated learning, and artificial immune systems can enhance data security while protecting personal privacy. This paper also points out the current challenges and proposes a future research framework in this key area.","url":"https://doi.org/10.48550/arxiv.2502.03811","authors":["Yuan, Zhang Si","Singh, Manmeet Mahinderjit"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2502.03811","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2412.03924","name":"Privacy-Preserving in Medical Image Analysis: A Review of Methods and Applications","source":"datacite","abstract":"With the rapid advancement of artificial intelligence and deep learning, medical image analysis has become a critical tool in modern healthcare, significantly improving diagnostic accuracy and efficiency. However, AI-based methods also raise serious privacy concerns, as medical images often contain highly sensitive patient information. This review offers a comprehensive overview of privacy-preserving techniques in medical image analysis, including encryption, differential privacy, homomorphic encryption, federated learning, and generative adversarial networks. We explore the application of these techniques across various medical image analysis tasks, such as diagnosis, pathology, and telemedicine. Notably, we organizes the review based on specific challenges and their corresponding solutions in different medical image analysis applications, so that technical applications are directly aligned with practical issues, addressing gaps in the current research landscape. Additionally, we discuss emerging trends, such as zero-knowledge proofs and secure multi-party computation, offering insights for future research. This review serves as a valuable resource for researchers and practitioners and can help advance privacy-preserving in medical image analysis.","url":"https://doi.org/10.48550/arxiv.2412.03924","authors":["Zhu, Yanming","Yin, Xuefei","Liew, Alan Wee-Chung","Tian, Hui"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2412.03924","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.13841539","name":"EVALUATION OF PRIVACY-PRESERVING AI USING EDGE COMPUTING IN V2X FRAMEWORK","source":"datacite","abstract":"The advent of Vehicle-to-Everything (V2X) communication has ushered in a new era of intelligent urban transportation systems, promising enhanced safety, efficiency, and connectivity. However, the extensive data sharing inherent in V2X networks poses significant privacy challenges. This review paper explores the integration of privacy-preserving Artificial Intelligence (AI) within the V2X framework, emphasizing the role of edge computing as a pivotal enabler. We systematically examine state-of-the-art techniques in privacy-preserving AI, including federated learning, differential privacy, and homomorphic encryption, highlighting their applicability and effectiveness in V2X scenarios. Additionally, we discuss the synergy between edge computing and privacy preserving AI techniques, which collectively mitigate privacy risks while ensuring real-time data processing and decision-making. By analyzing current research trends, technological advancements, and practical implementations, this paper provides a comprehensive overview of the strategies for maintaining data privacy in V2X networks. Our findings underscore the importance of a holistic approach that combines robust privacy-preserving mechanisms with the decentralized capabilities of edge computing, paving the way for secure and efficient intelligent transportation systems","url":"https://doi.org/10.5281/zenodo.13841539","authors":["Researcher"],"tags":["Privacy-preserving AI, Edge computing, V2X communication, Intelligent transportation systems, Federated learning, Differential privacy, Homomorphic encryption, Data privacy, Real-time data processing, Decentralized computing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13841539","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.13841538","name":"EVALUATION OF PRIVACY-PRESERVING AI USING EDGE COMPUTING IN V2X FRAMEWORK","source":"datacite","abstract":"The advent of Vehicle-to-Everything (V2X) communication has ushered in a new era of intelligent urban transportation systems, promising enhanced safety, efficiency, and connectivity. However, the extensive data sharing inherent in V2X networks poses significant privacy challenges. This review paper explores the integration of privacy-preserving Artificial Intelligence (AI) within the V2X framework, emphasizing the role of edge computing as a pivotal enabler. We systematically examine state-of-the-art techniques in privacy-preserving AI, including federated learning, differential privacy, and homomorphic encryption, highlighting their applicability and effectiveness in V2X scenarios. Additionally, we discuss the synergy between edge computing and privacy preserving AI techniques, which collectively mitigate privacy risks while ensuring real-time data processing and decision-making. By analyzing current research trends, technological advancements, and practical implementations, this paper provides a comprehensive overview of the strategies for maintaining data privacy in V2X networks. Our findings underscore the importance of a holistic approach that combines robust privacy-preserving mechanisms with the decentralized capabilities of edge computing, paving the way for secure and efficient intelligent transportation systems","url":"https://doi.org/10.5281/zenodo.13841538","authors":["Researcher"],"tags":["Privacy-preserving AI, Edge computing, V2X communication, Intelligent transportation systems, Federated learning, Differential privacy, Homomorphic encryption, Data privacy, Real-time data processing, Decentralized computing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13841538","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.13324414","name":"AI/ML FOR DATA PRIVACY AND ENCRYPTION IN CLOUD COMPUTING","source":"datacite","abstract":"As cloud computing becomes increasingly pervasive, ensuring data privacy and security remains a critical concern. Artificial intelligence (AI) and machine learning (ML) offer promising solutions for enhancing data privacy and developing advanced encryption techniques in cloud environments. This review article explores how AI and ML are applied to improve data privacy, including the development of intelligent encryption methods, privacy-preserving algorithms, and automated data protection mechanisms. We examine various approaches, such as homomorphic encryption, secure multi-party computation, and differential privacy, and assess their integration with AI/ML technologies. The article provides an overview of current research, evaluates the effectiveness of different techniques, and discusses the trade-offs involved. It concludes with a discussion on future trends and potential areas for further research in leveraging AI/ML for data privacy and encryption in cloud computing.","url":"https://doi.org/10.5281/zenodo.13324414","authors":["Researcher"],"tags":["Cloud Computing Security, AI/ML-Enhanced Encryption, Privacy-Preserving Algorithms, Homomorphic Encryption, Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13324414","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.13324415","name":"AI/ML FOR DATA PRIVACY AND ENCRYPTION IN CLOUD COMPUTING","source":"datacite","abstract":"As cloud computing becomes increasingly pervasive, ensuring data privacy and security remains a critical concern. Artificial intelligence (AI) and machine learning (ML) offer promising solutions for enhancing data privacy and developing advanced encryption techniques in cloud environments. This review article explores how AI and ML are applied to improve data privacy, including the development of intelligent encryption methods, privacy-preserving algorithms, and automated data protection mechanisms. We examine various approaches, such as homomorphic encryption, secure multi-party computation, and differential privacy, and assess their integration with AI/ML technologies. The article provides an overview of current research, evaluates the effectiveness of different techniques, and discusses the trade-offs involved. It concludes with a discussion on future trends and potential areas for further research in leveraging AI/ML for data privacy and encryption in cloud computing.","url":"https://doi.org/10.5281/zenodo.13324415","authors":["Researcher"],"tags":["Cloud Computing Security, AI/ML-Enhanced Encryption, Privacy-Preserving Algorithms, Homomorphic Encryption, Federated Learning"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13324415","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.17605/osf.io/vyr37","name":"Applications of Homomorphic Encryption in Secure Computation","source":"datacite","abstract":"Homomorphic encryption represents a pivotal innovation in modern cryptography, offering a pathway to secure computation on encrypted data. In this paper, we embark on a comprehensive exploration of the applications of homomorphic encryption, aiming to elucidate its transformative potential in bolstering data security and privacy. Through a thorough literature review, we delve into the theoretical foundations and practical implementations of homomorphic encryption, tracing its evolution from seminal works to contemporary advancements. We discuss how homomorphic encryption enables secure delegation of computation tasks to untrusted servers, facilitates privacy-preserving data mining in domains such as healthcare analytics, and empowers collaborative decision-making while preserving data privacy. We present experimental findings that validate the real-world applicability and efficacy of homomorphic encryption. By bridging the gap between theory and practice, this paper serves as a beacon of insight into the transformative power of homomorphic encryption, urging researchers, practitioners, and policymakers to embrace its potential for revolutionizing data security and privacy in an interconnected world.","url":"https://doi.org/10.17605/osf.io/vyr37","authors":["MOLLAKUQE, ELISSA"],"tags":["Computer Engineering","Risk Analysis","Engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.17605/osf.io/vyr37","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.20381/ruor-30376","name":"Data Security and Privacy in Transactive Energy Markets","source":"datacite","abstract":"Innovation in power generation, storage, and information technology have created new opportunities in grid management. Advanced metering equipment and smart grid infrastructure enable energy providers to operate more efficiently and cost effectively through better reporting and prediction. While these improvements to the traditional energy management model are important, opportunities for further gains in economic and energy efficiency have been discovered in distributed systems. The next generation of energy market will be transactive, enabling prosumers, consumers equipped with energy generation or storage devices, to trade energy directly between each other. This capacity will result in increased price efficiency, reduced transmission distances, and better integration of renewable energy sources into the grid. The incentive to modernize the grid is clear, but increased information flow demands heightened security measures to protect consumer safety and trust. Many proposed transactive energy market solutions use distributed ledger technology, or blockchain, along with smart contracts to underpin their energy auctions. Blockchain technology has many desirable security properties that make it suitable for handling trades. However, security gaps remain in the processes not managed by the blockchain. The goals of this thesis are to discover the cybersecurity gaps present in transactive energy market systems, identify the areas most in need of improvement, and propose solutions to some of those areas. A thorough review of the literature led us to identify fourteen cybersecurity threat categories. We selected two processes that were significantly affected by these threats and that had few solutions addressing them. These processes are: energy usage data collection and market anonymity. In addition to the literature review, this thesis contributes secure, privacy-preserving schemes for each of these processes, namely Cyclic Homomorphic Encryption Aggregation (CHEA) and Individually Linkable Pseudonymous Trading Scheme (ILPTS). Both schemes improve security and efficiency by reducing infrastructural requirements and increasing decentralization. Formal analysis found that both solutions successfully achieve their security design goals, while performance simulations found that CHEA performs well compared to similar data aggregation schemes from the literature.","url":"https://doi.org/10.20381/ruor-30376","authors":["Sousa-Dias, Daniel"],"tags":["transactive","energy","blockchain","security","privacy","encryption","aggregation","anonymity"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.20381/ruor-30376","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2402.15738","name":"Privacy-Preserving State Estimation in the Presence of Eavesdroppers: A Survey","source":"datacite","abstract":"Networked systems are increasingly the target of cyberattacks that exploit vulnerabilities within digital communications, embedded hardware, and software. Arguably, the simplest class of attacks -- and often the first type before launching destructive integrity attacks -- are eavesdropping attacks, which aim to infer information by collecting system data and exploiting it for malicious purposes. A key technology of networked systems is state estimation, which leverages sensing and actuation data and first-principles models to enable trajectory planning, real-time monitoring, and control. However, state estimation can also be exploited by eavesdroppers to identify models and reconstruct states with the aim of, e.g., launching integrity (stealthy) attacks and inferring sensitive information. It is therefore crucial to protect disclosed system data to avoid an accurate state estimation by eavesdroppers. This survey presents a comprehensive review of existing literature on privacy-preserving state estimation methods, while also identifying potential limitations and research gaps. Our primary focus revolves around three types of methods: cryptography, data perturbation, and transmission scheduling, with particular emphasis on Kalman-like filters. Within these categories, we delve into the concepts of homomorphic encryption and differential privacy, which have been extensively investigated in recent years in the context of privacy-preserving state estimation. Finally, we shed light on several technical and fundamental challenges surrounding current methods and propose potential directions for future research.","url":"https://doi.org/10.48550/arxiv.2402.15738","authors":["Yan, Xinhao","Zhou, Guanzhong","Quevedo, Daniel E.","Murguia, Carlos","Chen, Bo","Huang, Hailong"],"tags":["Cryptography and Security (cs.CR)","Systems and Control (eess.SY)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2402.15738","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2401.00794","name":"Privacy-Preserving Data in IoT-based Cloud Systems: A Comprehensive Survey with AI Integration","source":"datacite","abstract":"As the integration of Internet of Things devices with cloud computing proliferates, the paramount importance of privacy preservation comes to the forefront. This survey paper meticulously explores the landscape of privacy issues in the dynamic intersection of IoT and cloud systems. The comprehensive literature review synthesizes existing research, illuminating key challenges and discerning emerging trends in privacy preserving techniques. The categorization of diverse approaches unveils a nuanced understanding of encryption techniques, anonymization strategies, access control mechanisms, and the burgeoning integration of artificial intelligence. Notable trends include the infusion of machine learning for dynamic anonymization, homomorphic encryption for secure computation, and AI-driven access control systems. The culmination of this survey contributes a holistic view, laying the groundwork for understanding the multifaceted strategies employed in securing sensitive data within IoT-based cloud environments. The insights garnered from this survey provide a valuable resource for researchers, practitioners, and policymakers navigating the complex terrain of privacy preservation in the evolving landscape of IoT and cloud computing","url":"https://doi.org/10.48550/arxiv.2401.00794","authors":["Dhinakaran, D.","Sankar, S. M. Udhaya","Selvaraj, D.","Raja, S. Edwin"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2401.00794","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2312.14434","name":"A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic Encryption","source":"datacite","abstract":"Cloud Service Providers, such as Google Cloud Platform, Microsoft Azure, or Amazon Web Services, offer continuously evolving cloud services. It is a growing industry. Businesses, such as Netflix and PayPal, rely on the Cloud for data storage, computing power, and other services. For businesses, the cloud reduces costs, provides flexibility, and allows for growth. However, there are security and privacy concerns regarding the Cloud. Because Cloud services are accessed through the internet, hackers and attackers could possibly access the servers from anywhere. To protect data in the Cloud, it should be encrypted before it is uploaded, it should be protected in storage and also in transit. On the other hand, data owners may need to access their encrypted data. It may also need to be altered, updated, deleted, read, searched, or shared with others. If data is decrypted in the Cloud, sensitive data is exposed and could be exposed and misused. One solution is to leave the data in its encrypted form and use Searchable Encryption (SE) which operates on encrypted data. The functionality of SE has improved since its inception and research continues to explore ways to improve SE. This paper reviews the functionality of Searchable Encryption, mostly related to Cloud services, in the years 2019 to 2023, and evaluates one of its schemes, Fully Homomorphic Encryption. Overall, it seems that research is at the point where SE efficiency is increased as multiple functionalities are aggregated and tested.","url":"https://doi.org/10.48550/arxiv.2312.14434","authors":["Kishiyama, Brian","Alsmadi, Izzat"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2312.14434","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.10207209","name":"Lattices in Quantum-ERA Cryptography","source":"datacite","abstract":"The use of Mathematic in cryptography can result a safe encryption scheme. Lattices have emerged as a powerful mathematical tool in the field of cryptography, offering a diverse set of applications ranging from encryption to secure multi-party computation. This research paper provides a comprehensive review of the role of lattices in cryptography, covering both theoretical foundations and practical implementations. The paper begins by introducing the basic concepts of lattices and their relevance in cryptographic protocols. Subsequently, it explores key cryptographic primitives based on lattice problems, such as lattice-based encryption schemes, digital signatures, and fully homomorphic encryption. The paper also proposes a new lattice based cryptographic scheme.","url":"https://doi.org/10.5281/zenodo.10207209","authors":["John, Michael","Ozioma, Ogoegbulem","Obi, Perpetua Ngozi","Egbogho, Henry Etaroghene","Udoaka, Otobong. G."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.10207209","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.10207210","name":"Lattices in Quantum-ERA Cryptography","source":"datacite","abstract":"The use of Mathematic in cryptography can result a safe encryption scheme. Lattices have emerged as a powerful mathematical tool in the field of cryptography, offering a diverse set of applications ranging from encryption to secure multi-party computation. This research paper provides a comprehensive review of the role of lattices in cryptography, covering both theoretical foundations and practical implementations. The paper begins by introducing the basic concepts of lattices and their relevance in cryptographic protocols. Subsequently, it explores key cryptographic primitives based on lattice problems, such as lattice-based encryption schemes, digital signatures, and fully homomorphic encryption. The paper also proposes a new lattice based cryptographic scheme.","url":"https://doi.org/10.5281/zenodo.10207210","authors":["John, Michael","Ozioma, Ogoegbulem","Obi, Perpetua Ngozi","Egbogho, Henry Etaroghene","Udoaka, Otobong. G."],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.5281/zenodo.10207210","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2310.12441","name":"Large-Plaintext Functional Bootstrapping in FHE with Small Bootstrapping Keys","source":"datacite","abstract":"Functional bootstrapping is a core technique in Fully Homomorphic Encryption (FHE). For large plaintext, to evaluate a general function homomorphically over a ciphertext, in the FHEW/TFHE approach, since the function in look-up table form is encoded in the coefficients of a test polynomial, the degree of the polynomial must be high enough to hold the entire table. This increases the bootstrapping time complexity and memory cost, as the size of bootstrapping keys and keyswitching keys need to be large accordingly. In this paper, we propose to encode the look-up table of any function in a polynomial vector, whose coefficients can hold more data. The corresponding representation of the additive group Zq used in the RGSW-based bootstrapping is the group of monic monomial permutation matrices, which integrates the permutation matrix representation used by Alperin-Sheriff and Peikert in 2014, and the monic monomial representation used in the FHEW/TFHE scheme. We make comprehensive investigation of the new representation, and propose a new bootstrapping algorithm based on it. The new algorithm has the prominent benefit of small bootstrapping key size and small key-switching key size, which leads to polynomial factor improvement in key size, in addition to constant factor improvement in run-time cost.","url":"https://doi.org/10.48550/arxiv.2310.12441","authors":["Liu, Dengfa","Li, Hongbo"],"tags":["Cryptography and Security (cs.CR)","Differential Geometry (math.DG)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Mathematics","FOS: Mathematics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2310.12441","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2310.12401","name":"Privacy-Preserving Hierarchical Anonymization Framework over Encrypted Data","source":"datacite","abstract":"Smart cities, which can monitor the real world and provide smart services in a variety of fields, have improved people's living standards as urbanization has accelerated. However, there are security and privacy concerns because smart city applications collect large amounts of privacy-sensitive information from people and their social circles. Anonymization, which generalizes data and reduces data uniqueness is an important step in preserving the privacy of sensitive information. However, anonymization methods frequently require large datasets and rely on untrusted third parties to collect and manage data, particularly in a cloud environment. In this case, private data leakage remains a critical issue, discouraging users from sharing their data and impeding the advancement of smart city services. This problem can be solved if the computational entity can perform the anonymization process without obtaining the original plain text. This study proposed a hierarchical k-anonymization framework using homomorphic encryption and secret sharing composed of two types of domains. Different computing methods are selected flexibly, and two domains are connected hierarchically to obtain higher-level anonymization results in an efficient manner. The experimental results show that connecting two domains can accelerate the anonymization process, indicating that the proposed secure hierarchical architecture is practical and efficient.","url":"https://doi.org/10.48550/arxiv.2310.12401","authors":["Jia, Jing","Saito, Kenta","Nishi, Hiroaki"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences","E.3"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2310.12401","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2308.14725","name":"Applications of Finite non-Abelian Simple Groups to Cryptography in the Quantum Era","source":"datacite","abstract":"The theory of finite simple groups is a (rather unexplored) area likely to provide interesting computational problems and modelling tools useful in a cryptographic context. In this note, we review some applications of finite non-abelian simple groups to cryptography and discuss different scenarios in which this theory is clearly central, providing the relevant definitions to make the material accessible to both cryptographers and group theorists, in the hope of stimulating further interaction between these two (non-disjoint) communities. In particular, we look at constructions based on various group-theoretic factorization problems, review group theoretical hash functions, and discuss fully homomorphic encryption using simple groups. The Hidden Subgroup Problem is also briefly discussed in this context.","url":"https://doi.org/10.48550/arxiv.2308.14725","authors":["Vasco, María Isabel González","Kahrobaei, Delaram","McKemmie, Eilidh"],"tags":["Group Theory (math.GR)","Cryptography and Security (cs.CR)","FOS: Mathematics","FOS: Mathematics","FOS: Computer and information sciences","FOS: Computer and information sciences","20, 68, 94"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2308.14725","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2305.02225","name":"Data Privacy with Homomorphic Encryption in Neural Networks Training and Inference","source":"datacite","abstract":"The use of Neural Networks (NNs) for sensitive data processing is becoming increasingly popular, raising concerns about data privacy and security. Homomorphic Encryption (HE) has the potential to be used as a solution to preserve data privacy in NN. This study provides a comprehensive analysis on the use of HE for NN training and classification, focusing on the techniques and strategies used to enhance data privacy and security. The current state-of-the-art in HE for NNs is analysed, and the challenges and limitations that need to be addressed to make it a reliable and efficient approach for privacy preservation are identified. Also, the different categories of HE schemes and their suitability for NNs are discussed, as well as the techniques used to optimize the accuracy and efficiency of encrypted models. The review reveals that HE has the potential to provide strong data privacy guarantees for NNs, but several challenges need to be addressed, such as limited support for advanced NN operations, scalability issues, and performance trade-offs.","url":"https://doi.org/10.48550/arxiv.2305.02225","authors":["Amorim, Ivone","Maia, Eva","Barbosa, Pedro","Praça, Isabel"],"tags":["Cryptography and Security (cs.CR)","Artificial Intelligence (cs.AI)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2305.02225","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2212.01629","name":"Generating Synthetic Data in a Secure Federated General Adversarial Networks for a Consortium of Health Registries","source":"datacite","abstract":"In this work, we review the architecture design of existing federated General Adversarial Networks (GAN) solutions and highlight the security and trust-related weaknesses in the existing designs. We then describe how these weaknesses make existing designs unsuitable for the requirements needed for a consortium of health registries working towards generating synthetic datasets for research purposes. Moreover, we propose how these weaknesses can be addressed with our novel architecture solution. Our novel architecture solution combines several building blocks to generate synthetic data in a decentralised setting. Consortium blockchains, secure multi-party computations, and homomorphic encryption are the core building blocks of our proposed architecture solution to address the weaknesses in the existing design of federated GANs. Finally, we discuss our proposed solution's advantages and future research directions.","url":"https://doi.org/10.48550/arxiv.2212.01629","authors":["Veeraragavan, Narasimha Raghavan","Nygård, Jan Franz"],"tags":["Distributed, Parallel, and Cluster Computing (cs.DC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2212.01629","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2211.10028","name":"Comparative evaluation of different methods of \"Homomorphic Encryption\" and \"Traditional Encryption\" on a dataset with current problems and developments","source":"datacite","abstract":"A database is a prime target for cyber-attacks as it contains confidential, sensitive, or protected information. With the increasing sophistication of the internet and dependencies on internet data transmission, it has become vital to be aware of various encryption technologies and trends. It can assist in safeguarding private information and sensitive data, as well as improve the security of client-server communication. Database encryption is a procedure that employs an algorithm to convert data contained in a database into \"cipher text,\" which is incomprehensible until decoded. Homomorphic encryption technology, which works with encrypted data, can be utilized in both symmetric and asymmetric systems. In this paper, we evaluated homomorphic encryption techniques based on recent highly cited articles, as well as compared all database encryption problems and developments since 2018. The benefits and drawbacks of homomorphic approaches were examined over classic encryption methods including Transparent Database Encryption, Column Level Encryption, Field Level Encryption, File System Level Encryption, and Encrypting File System Encryption in this review. Additionally, popular databases that provide encryption services to their customers to protect their data are also examined.","url":"https://doi.org/10.48550/arxiv.2211.10028","authors":["Patel, Tanvi S.","Kolachina, Srinivasakranthikiran","Patel, Daxesh P.","Shrivastav, Pranav S."],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2211.10028","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2210.05560","name":"Comparison of encrypted control approaches and tutorial on dynamic systems using LWE-based homomorphic encryption","source":"datacite","abstract":"Encrypted control has been introduced to protect controller data by encryption at the stage of computation and communication, by performing the computation directly on encrypted data. In this article, we first review and categorize recent relevant studies on encrypted control. Approaches based on homomorphic encryption, multi-party computation, and secret sharing are introduced, compared, and then discussed with respect to computational complexity, communication load, enabled operations, security, and research directions. We proceed to discuss a current challenge in the application of homomorphic encryption to dynamic systems, where arithmetic operations other than integer addition and multiplication are limited. We also introduce a homomorphic cryptosystem called ``GSW-LWE'' and discuss its benefits that allow for recursive multiplication of encrypted dynamic systems, without use of computationally expensive bootstrapping techniques.","url":"https://doi.org/10.48550/arxiv.2210.05560","authors":["Kim, Junsoo","Kim, Dongwoo","Song, Yongsoo","Shim, Hyungbo","Sandberg, Henrik","Johansson, Karl H."],"tags":["Cryptography and Security (cs.CR)","Systems and Control (eess.SY)","FOS: Computer and information sciences","FOS: Computer and information sciences","FOS: Electrical engineering, electronic engineering, information engineering","FOS: Electrical engineering, electronic engineering, information engineering"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2210.05560","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.18130/0bjq-qg31","name":"E-Skin Resistive Strain Sensor: Optimum Sensor Placement; Applying Homomorphic Encryption as a Solution to Privacy Concerns in Artificial Intelligence-Based Medical Diagnostic Algorithms","source":"datacite","abstract":"Essentially, my science, technology, and society (STS) and technical projects seek to effectively support innovation in the healthcare industry by evaluating the sociotechnical implications of cutting-edge clinical data collection and analysis procedures. Driven primarily by heightened consumer interest in health and wellness tracking from the COVID-19 pandemic, global spending on wearable devices is projected to total $93.9 billion in 2022 [1]. Interestingly, this widespread public adoption is occurring even though current wearable devices like the Apple Watch, Oura Smart Ring, and Fitbit can only evaluate—with questionable accuracy—a limited range of biophysical signals that have minimal clinical relevance in actuality [2]. For enhanced precision and efficiency, these devices often rely on artificial intelligence (AI)-based solutions to assist with monitoring and diagnosis; however, this requires access to sensitive health information, which introduces a host of glaring privacy-related concerns. Accordingly, the combined projects serve to answer the overarching research question of \"How can we improve clinical diagnostic procedures to improve accuracy and accessibility while properly maintaining user confidentiality?\" The technical project engages the complete lifecycle design of a mechanical skin-like strain sensor from conception to prototype to practical operation. Ultimately, this is aimed at demonstrating the replicability of an existing model that utilizes popular, cost-effective, and commercially available materials and engineering processes. Designed to capture the dynamic motions of the human body with particular applications in clinical diagnostics (i.e., movement and neurological disorders) and athletic performance monitoring, the prototype is intended for placement on the anterior deltoid (shoulder). As such, it features resistive strain sensors in a 4x2 array aligned to optimally measure uniaxial strain along the muscle fibers In terms of composition, the sensor was developed using a thin-film polydimethylsiloxane (PDMS) elastomeric substrate base with channels of multi-walled carbon nanotube (MWCNT) conductors laminated to its surface. First, a layer of PDMS was poured into a mold, and then channels were etched out of the substrate surface with a laser cutter. Afterwards, MWCNTs were spread into the channels, and a final sealant layer of PDMS was poured overtop. Using gauge factor (GF) as a performance metric, initial testing demonstrated the prototype's ability to consistently generate precise resistance measurements. Additionally, the substrate-skin interface was functional, but future work should attempt to promote steadier conformability by decreasing thickness from 3.5mm. On the other hand, the STS project explores the viability of using state-of-the-art fully homomorphic encryption (FHE) schemes to develop privacy-preserving machine learning (PPML) models for clinical diagnostics. From a purely technological perspective, FHE, which allows for encrypted data to be processed as if it were unencrypted, seems like a tenable solution, yet further analysis is necessary to analyze the surrounding socio-political effects of deploying FHE-equipped PPML models in the medical industry. Employing the Phase, Guarantee, and Technical Utility (PGU) Triad—a targeted framework for PPML model analysis—as a guide, the ensuing study determined that purely FHE-based solutions do not in fact provide comprehensive privacy protection, considering they are still susceptible to membership inference attacks if the models are constructed via machine learning as a service (MLaaS). Nevertheless, FHE is still a particularly appealing option since it can fortify proven deep neural networks with privacy-preserving functionalities at a negligible accuracy loss. Thus, the results establish that the ideal diagnostic PPML solution would apply FHE in tandem with another approach that obscures the potential information extracted by examining m","url":"https://doi.org/10.18130/0bjq-qg31","authors":["Zachary Holden"],"tags":["E-Skin","Strain Sensors","Wearable Devices","Artificial Intelligence (AI)","Medical Diagnostics","Fully Homomorphic Encryption (FHE)","Privacy-Preserving Machine Learning (PPML)"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.18130/0bjq-qg31","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2203.15877","name":"Quantum Advantage from Any Non-Local Game","source":"datacite","abstract":"We show a general method of compiling any $k$-prover non-local game into a single-prover interactive game maintaining the same (quantum) completeness and (classical) soundness guarantees (up to negligible additive factors in a security parameter). Our compiler uses any quantum homomorphic encryption scheme (Mahadev, FOCS 2018; Brakerski, CRYPTO 2018) satisfying a natural form of correctness with respect to auxiliary (quantum) input. The homomorphic encryption scheme is used as a cryptographic mechanism to simulate the effect of spatial separation, and is required to evaluate $k-1$ prover strategies (out of $k$) on encrypted queries. In conjunction with the rich literature on (entangled) multi-prover non-local games starting from the celebrated CHSH game (Clauser, Horne, Shimonyi and Holt, Physical Review Letters 1969), our compiler gives a broad framework for constructing mechanisms to classically verify quantum advantage.","url":"https://doi.org/10.48550/arxiv.2203.15877","authors":["Kalai, Yael","Lombardi, Alex","Vaikuntanathan, Vinod","Yang, Lisa"],"tags":["Quantum Physics (quant-ph)","Cryptography and Security (cs.CR)","FOS: Physical sciences","FOS: Physical sciences","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.48550/arxiv.2203.15877","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.1508.06574","name":"A review of homomorphic encryption and software tools for encrypted statistical machine learning","source":"datacite","abstract":"Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first decrypt. We review these homomorphic encryption schemes in a manner accessible to statisticians and machine learners, focusing on pertinent limitations inherent in the current state of the art. These limitations restrict the kind of statistics and machine learning algorithms which can be implemented and we review those which have been successfully applied in the literature. Finally, we document a high performance R package implementing a recent homomorphic scheme in a general framework.","url":"https://doi.org/10.48550/arxiv.1508.06574","authors":["Aslett, Louis J. M.","Esperança, Pedro M.","Holmes, Chris C."],"tags":["Machine Learning (stat.ML)","Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.48550/arxiv.1508.06574","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.1603.07699","name":"Secure cloud computations: Description of (fully)homomorphic ciphers within the P-adic model of encryption","source":"datacite","abstract":"In this paper we consider the description of homomorphic and fully homomorphic ciphers in the $p$-adic model of encryption. This model describes a wide class of ciphers, but certainly not all. Homomorphic and fully homomorphic ciphers are used to ensure the credibility of remote computing, including cloud technology. The model describes all homomorphic ciphers with respect to arithmetic and coordinate-wise logical operations in the ring of $p$-adic integers $Z_p$. We show that there are no fully homomorphic ciphers for each pair of the considered set of arithmetic and coordinate-wise logical operations on $Z_p$. We formulate the problem of constructing a fully homomorphic cipher as follows. We consider a homomorphic cipher with respect to operation \"$*$\" on $Z_p$. Then, we describe the complete set of operations \"$G$\", for which the cipher is homomorphic. As a result, we construct a fully homomorphic cipher with respect to the operations \"$*$\" and \"$G$\". We give a description of all operations \"$G$\", for which we obtain fully homomorphic ciphers with respect to the operations \"$+$\" and \"$G$\" from the homomorphic cipher constructed with respect to the operation \"$+$\". We also present examples of such \"new\" operations.","url":"https://doi.org/10.48550/arxiv.1603.07699","authors":["Khrennikov, Andrei","Yurova, Ekaterina"],"tags":["Cryptography and Security (cs.CR)","Symbolic Computation (cs.SC)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2016","doi":"10.48550/arxiv.1603.07699","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.1807.11655","name":"Security and Privacy Issues in Deep Learning","source":"datacite","abstract":"To promote secure and private artificial intelligence (SPAI), we review studies on the model security and data privacy of DNNs. Model security allows system to behave as intended without being affected by malicious external influences that can compromise its integrity and efficiency. Security attacks can be divided based on when they occur: if an attack occurs during training, it is known as a poisoning attack, and if it occurs during inference (after training) it is termed an evasion attack. Poisoning attacks compromise the training process by corrupting the data with malicious examples, while evasion attacks use adversarial examples to disrupt entire classification process. Defenses proposed against such attacks include techniques to recognize and remove malicious data, train a model to be insensitive to such data, and mask the model's structure and parameters to render attacks more challenging to implement. Furthermore, the privacy of the data involved in model training is also threatened by attacks such as the model-inversion attack, or by dishonest service providers of AI applications. To maintain data privacy, several solutions that combine existing data-privacy techniques have been proposed, including differential privacy and modern cryptography techniques. In this paper, we describe the notions of some of methods, e.g., homomorphic encryption, and review their advantages and challenges when implemented in deep-learning models.","url":"https://doi.org/10.48550/arxiv.1807.11655","authors":["Bae, Ho","Jang, Jaehee","Jung, Dahuin","Jang, Hyemi","Ha, Heonseok","Lee, Hyungyu","Yoon, Sungroh"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","Machine Learning (stat.ML)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.48550/arxiv.1807.11655","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.1809.08719","name":"Further Limitations on Information-Theoretically Secure Quantum Homomorphic Encryption","source":"datacite","abstract":"In this brief note, we review and extend existing limitations on information-theoretically (IT) secure quantum fully homomorphic encryption (QFHE). The essential ingredient remains Nayak's bound, which provides a tradeoff between the number of homomorphically implementable functions of an IT-secure QHE scheme and its efficiency. Importantly, the bound is robust to imperfect IT-security guarantees. We summarize these bounds in the context of existing QHE schemes, and discuss subtleties of the imposed restrictions.","url":"https://doi.org/10.48550/arxiv.1809.08719","authors":["Newman, Michael"],"tags":["Quantum Physics (quant-ph)","FOS: Physical sciences","FOS: Physical sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.48550/arxiv.1809.08719","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.1812.02428","name":"A Review of Homomorphic Encryption Libraries for Secure Computation","source":"datacite","abstract":"In this paper we provide a survey of various libraries for homomorphic encryption. We describe key features and trade-offs that should be considered while choosing the right approach for secure computation. We then present a comparison of six commonly available Homomorphic Encryption libraries - SEAL, HElib, TFHE, Paillier, ELGamal and RSA across these identified features. Support for different languages and real-life applications are also elucidated.","url":"https://doi.org/10.48550/arxiv.1812.02428","authors":["Sathya, Sai Sri","Vepakomma, Praneeth","Raskar, Ramesh","Ramachandra, Ranjan","Bhattacharya, Santanu"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2018","doi":"10.48550/arxiv.1812.02428","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2007.09270","name":"Computing Blindfolded on Data Homomorphically Encrypted under Multiple Keys: An Extended Survey","source":"datacite","abstract":"New cryptographic techniques such as homomorphic encryption (HE) allow computations to be outsourced to and evaluated blindfolded in a resourceful cloud. These computations often require private data owned by multiple participants, engaging in joint evaluation of some functions. For example, Genome-Wide Association Study (GWAS) is becoming feasible because of recent proliferation of genome sequencing technology. Due to the sensitivity of genomic data, these data should be encrypted using different keys. However, supporting computation on ciphertexts encrypted under multiple keys is a non-trivial task. In this paper, we present a comprehensive survey on different state-of-the-art cryptographic techniques and schemes that are commonly used. We review techniques and schemes including Attribute-Based Encryption (ABE), Proxy Re-Encryption (PRE), Threshold Homomorphic Encryption (ThHE), and Multi-Key Homomorphic Encryption (MKHE). We analyze them based on different system and security models, and examine their complexities. We share lessons learned and draw observations for designing better schemes with reduced overheads.","url":"https://doi.org/10.48550/arxiv.2007.09270","authors":["Aloufi, Asma","Hu, Peizhao","Song, Yongsoo","Lauter, Kristin"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.48550/arxiv.2007.09270","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2011.05296","name":"A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification","source":"datacite","abstract":"This work provides a comprehensive review of existing frameworks based on secure computing techniques in the context of private image classification. The in-depth analysis of these approaches is followed by careful examination of their performance costs, in particular runtime and communication overhead. To further illustrate the practical considerations when using different privacy-preserving technologies, experiments were conducted using four state-of-the-art libraries implementing secure computing at the heart of the data science stack: PySyft and CrypTen supporting private inference via Secure Multi-Party Computation, TF-Trusted utilising Trusted Execution Environments and HE- Transformer relying on Homomorphic encryption. Our work aims to evaluate the suitability of these frameworks from a usability, runtime requirements and accuracy point of view. In order to better understand the gap between state-of-the-art protocols and what is currently available in practice for a data scientist, we designed three neural network architecture to obtain secure predictions via each of the four aforementioned frameworks. Two networks were evaluated on the MNIST dataset and one on the Malaria Cell image dataset. We observed satisfying performances for TF-Trusted and CrypTen and noted that all frameworks perfectly preserved the accuracy of the corresponding plaintext model.","url":"https://doi.org/10.48550/arxiv.2011.05296","authors":["Haralampieva, Veneta","Rueckert, Daniel","Passerat-Palmbach, Jonathan"],"tags":["Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.48550/arxiv.2011.05296","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2103.14783","name":"A Synergistic Approach to Digital Privacy","source":"datacite","abstract":"This paper outlines an approach for IEEE to take leadership for digital privacy to align many existing IEEE Societies and efforts in the areas of computer systems &amp; applications security, organizational &amp; global architectures, policy-supporting legislation, originating new standards, integrating compliance into technologies, and helping design decision-board infrastructures for governance bodies. Much of the current emphasis on evolving privacy technologies centers on big corporate enterprises and institutions, causing the industry to support corporate assets protection mainly. Fostering technology to empower individual privacy-enabling tools has lagged, and personal privacy has diminished because corporate big data applications have made sizable investments into exploiting private data. As one of the largest individual-member-based organizations, IEEE is urged to develop a collaborative approach for digital privacy with privacy-enabling technologies to benefit its members. The recommendations outlined define a prospective course that could result in future global individualized privacy capabilities which employ a combination of synergistic technologies such as distributed ledgers, differential privacy, homomorphic encryption, secure distributed multi-party computation, zero-trust architectures, proof-of-origin of data, software, or other techniques. Such an effort would involve community engagement and outreach, academic peer-review events, the establishment of governance bodies, coordination &amp; expansion of existing standards, and the development of publicly-accessible prototypes. Collaboration with other IEEE-sponsored efforts for transactive energy systems, confidentiality and security of healthcare records and devices, and other IEEE-funded projects will help magnify digital privacy investments already in progress in these applications of emerging technologies.","url":"https://doi.org/10.48550/arxiv.2103.14783","authors":["Gorog, Christopher"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.48550/arxiv.2103.14783","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.48550/arxiv.2105.07533","name":"Private Facial Diagnosis as an Edge Service for Parkinson's DBS Treatment Valuation","source":"datacite","abstract":"Facial phenotyping has recently been successfully exploited for medical diagnosis as a novel way to diagnose a range of diseases, where facial biometrics has been revealed to have rich links to underlying genetic or medical causes. In this paper, taking Parkinson's Diseases (PD) as a case study, we proposed an Artificial-Intelligence-of-Things (AIoT) edge-oriented privacy-preserving facial diagnosis framework to analyze the treatment of Deep Brain Stimulation (DBS) on PD patients. In the proposed framework, a new edge-based information theoretically secure framework is proposed to implement private deep facial diagnosis as a service over a privacy-preserving AIoT-oriented multi-party communication scheme, where partial homomorphic encryption (PHE) is leveraged to enable privacy-preserving deep facial diagnosis directly on encrypted facial patterns. In our experiments with a collected facial dataset from PD patients, for the first time, we demonstrated that facial patterns could be used to valuate the improvement of PD patients undergoing DBS treatment. We further implemented a privacy-preserving deep facial diagnosis framework that can achieve the same accuracy as the non-encrypted one, showing the potential of our privacy-preserving facial diagnosis as an trustworthy edge service for grading the severity of PD in patients.","url":"https://doi.org/10.48550/arxiv.2105.07533","authors":["Jiang, Richard","Chazot, Paul","Crookes, Danny","Bouridane, Ahmed","Celebi, M Emre"],"tags":["Artificial Intelligence (cs.AI)","Cryptography and Security (cs.CR)","Computer Vision and Pattern Recognition (cs.CV)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.48550/arxiv.2105.07533","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.5347678","name":"D1.2 System Architecture Definition","source":"datacite","abstract":"This deliverable provides a specification of the conceptual architecture of the COLLABS platform. The COLLABS project aims at developing, demonstrating and supporting a comprehensive cyberintelligence framework for collaborative manufacturing, which enables the secure data exchange across the digital supply chain while providing high degree of resilience, reliability, accountability, trustworthiness, and addresses threat prevention, detection, mitigation, and real-time response. These goals will be achieved using state-of-the-art technologies and making significant scientific and technological advances in several key relevant domains, including secure multi-party computations and homomorphic encryption, distributed deep learning and anomaly detection, distributed ledger technologies (blockchain) and smart contracts, and distributed remote software attestation. The specification of the architecture is based on a detailed analysis of reference architectures, state-of the- art literature review, end-user requirement analysis, as well as general non-functional requirements and best practices.","url":"https://doi.org/10.5281/zenodo.5347678","authors":["Srdjan Skrbic"],"tags":["Artificial Intelligence &amp; Decision support; Information Security Technologies; Automation; Industrial Internet of Things (IIoT); Industry4.0; manufacturing; edge-tocloud security; hardware-enabled security; machine learning; blockchain; behavioral analysis; accountability; trustworthiness"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.5347678","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.5347679","name":"D1.2 System Architecture Definition","source":"datacite","abstract":"This deliverable provides a specification of the conceptual architecture of the COLLABS platform. The COLLABS project aims at developing, demonstrating and supporting a comprehensive cyberintelligence framework for collaborative manufacturing, which enables the secure data exchange across the digital supply chain while providing high degree of resilience, reliability, accountability, trustworthiness, and addresses threat prevention, detection, mitigation, and real-time response. These goals will be achieved using state-of-the-art technologies and making significant scientific and technological advances in several key relevant domains, including secure multi-party computations and homomorphic encryption, distributed deep learning and anomaly detection, distributed ledger technologies (blockchain) and smart contracts, and distributed remote software attestation. The specification of the architecture is based on a detailed analysis of reference architectures, state-of the- art literature review, end-user requirement analysis, as well as general non-functional requirements and best practices.","url":"https://doi.org/10.5281/zenodo.5347679","authors":["Srdjan Skrbic"],"tags":["Artificial Intelligence &amp; Decision support; Information Security Technologies; Automation; Industrial Internet of Things (IIoT); Industry4.0; manufacturing; edge-tocloud security; hardware-enabled security; machine learning; blockchain; behavioral analysis; accountability; trustworthiness"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.5347679","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.5008555","name":"BLOCK CHAIN FORENSICS: A SYSTEMATIC REVIEW OF THE PROSPECTS","source":"datacite","abstract":"Among the fastest-growing sectors, health care sector is most sought out one owing to the pandemic. During this pandemic the Healthcare sector is facing lot of complications inclusive of handling the medical record data and contact tracing. Coronavirus disease 2019 (COVID‑19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most of the data are stored in the cloud inviting the malicious users to play with. Moreover, a plethora of challenges faced by the investigators related to the new cloud storage technology, are as dispersal of shards, default encryption, defining the position of shards, convalescing files with user credentials, and so on. Among several technologies to tackle the malicious users, block chain tops the list. All the challenges must be met with a forensically sound methodology, identification of artifacts, and a tool to assist investigators in retrieving artifacts and tell-tale evidence. The Block chain is used a tool to launch an efficient and translucent health care professional model based on sophisticated degrees of accuracy all through this COVID 19 pandemic. Block chain exhibits massive potential health care solution for data provenance, decentralized management, enforcement of health-care regulations, immutable audit trail, interoperable health data access, logistics, medical supply chain efficiency, privacy, redundancy and fault tolerance, remote data collection and logging, robustness, security of EMRs, Integrity of medical records, Storage capacity, unification or standardization of information, value-based payment mechanisms. In the field of block chain-based distributed storage forensics challenges like recovering files and metadata to be of use in a prosecution are yet to be solved. Unless this challenge is tackled there is no guarantee that such data are recoverable on an accused’s local storage. This paper gives a comprehensive overview of the rationalized review of the Block Chain, Block Chain Forensics, rationale for the study of Block Chain Forensics, applications, various open research challenges in Healthcare. This study provokes the inevitability for Block Chain Forensics. Moreover, this study also reveals the need for immediate research that are capable enough to be rendered as solutions. Last but not the least, this paper provides an insight into the latest Block Chain Forensics research trends, which will prove beneficial in the development of Digital forensic investigation process.","url":"https://doi.org/10.5281/zenodo.5008555","authors":["Indumathi J"],"tags":["Anonymity, Autonomy, Block Chain, Block Chain Forensics, Decentralization, Delegated Proof-of-Stake (DPoS) ,Digital artifacts, Immutable, Open Source, Practical Byzantine Fault, Proof of Elapsed Time(PoET),Proof-of-Activity (PoA),Proof-of-Bum (PoB),Proof-of-Stake (PoS),Proof-of-Weight (Po Weight), Proof-of-Work (PoW),Transparency."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.5008555","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.5008556","name":"BLOCK CHAIN FORENSICS: A SYSTEMATIC REVIEW OF THE PROSPECTS","source":"datacite","abstract":"Among the fastest-growing sectors, health care sector is most sought out one owing to the pandemic. During this pandemic the Healthcare sector is facing lot of complications inclusive of handling the medical record data and contact tracing. Coronavirus disease 2019 (COVID‑19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most of the data are stored in the cloud inviting the malicious users to play with. Moreover, a plethora of challenges faced by the investigators related to the new cloud storage technology, are as dispersal of shards, default encryption, defining the position of shards, convalescing files with user credentials, and so on. Among several technologies to tackle the malicious users, block chain tops the list. All the challenges must be met with a forensically sound methodology, identification of artifacts, and a tool to assist investigators in retrieving artifacts and tell-tale evidence. The Block chain is used a tool to launch an efficient and translucent health care professional model based on sophisticated degrees of accuracy all through this COVID 19 pandemic. Block chain exhibits massive potential health care solution for data provenance, decentralized management, enforcement of health-care regulations, immutable audit trail, interoperable health data access, logistics, medical supply chain efficiency, privacy, redundancy and fault tolerance, remote data collection and logging, robustness, security of EMRs, Integrity of medical records, Storage capacity, unification or standardization of information, value-based payment mechanisms. In the field of block chain-based distributed storage forensics challenges like recovering files and metadata to be of use in a prosecution are yet to be solved. Unless this challenge is tackled there is no guarantee that such data are recoverable on an accused’s local storage. This paper gives a comprehensive overview of the rationalized review of the Block Chain, Block Chain Forensics, rationale for the study of Block Chain Forensics, applications, various open research challenges in Healthcare. This study provokes the inevitability for Block Chain Forensics. Moreover, this study also reveals the need for immediate research that are capable enough to be rendered as solutions. Last but not the least, this paper provides an insight into the latest Block Chain Forensics research trends, which will prove beneficial in the development of Digital forensic investigation process.","url":"https://doi.org/10.5281/zenodo.5008556","authors":["Indumathi J"],"tags":["Anonymity, Autonomy, Block Chain, Block Chain Forensics, Decentralization, Delegated Proof-of-Stake (DPoS) ,Digital artifacts, Immutable, Open Source, Practical Byzantine Fault, Proof of Elapsed Time(PoET),Proof-of-Activity (PoA),Proof-of-Bum (PoB),Proof-of-Stake (PoS),Proof-of-Weight (Po Weight), Proof-of-Work (PoW),Transparency."],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.5281/zenodo.5008556","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.3951349","name":"User-Centric Privacy Preservation Solution to Control Third Party Access in Digital Databases","source":"datacite","abstract":"The world is changing rapidly as technology advancements. Today, everything is e-enabled and human-linked information is digitally stored in digital databases, files and so on. Information privacy is a human right but, many privacy breaching incidents prove that, privacy has been threatened remarkably. Privacy is individualistic and dynamic in its nature. Many institutions, which collect human information, look at the privacy through the lens of a common pre-defined privacy policy or act. Such coarse-grain privacy preservation leads to violate individual’s specific privacy requirements. Simply, user-centric privacy preservation is not guaranteed. The author attempts to solve this human critical problem by proposing a fine-grain privacy preservation solution. A unique conceptual framework is presented with a novel notion of Key Privacy Determinant Attributes (KPDA) Index. User’s specific privacy perspectives will be captured through KPDA Index abstractly. However intelligent algorithms dynamically derive user sensitive database attributes to hide, when controlling third party access.","url":"https://doi.org/10.5281/zenodo.3951349","authors":["Tissera, Muditha","Thelijjagoda, Samantha","Goonathilake, Jeevani"],"tags":["User-centric Privacy","Digital databases","Third party access","Limited publishing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.5281/zenodo.3951349","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.3951350","name":"User-Centric Privacy Preservation Solution to Control Third Party Access in Digital Databases","source":"datacite","abstract":"The world is changing rapidly as technology advancements. Today, everything is e-enabled and human-linked information is digitally stored in digital databases, files and so on. Information privacy is a human right but, many privacy breaching incidents prove that, privacy has been threatened remarkably. Privacy is individualistic and dynamic in its nature. Many institutions, which collect human information, look at the privacy through the lens of a common pre-defined privacy policy or act. Such coarse-grain privacy preservation leads to violate individual’s specific privacy requirements. Simply, user-centric privacy preservation is not guaranteed. The author attempts to solve this human critical problem by proposing a fine-grain privacy preservation solution. A unique conceptual framework is presented with a novel notion of Key Privacy Determinant Attributes (KPDA) Index. User’s specific privacy perspectives will be captured through KPDA Index abstractly. However intelligent algorithms dynamically derive user sensitive database attributes to hide, when controlling third party access.","url":"https://doi.org/10.5281/zenodo.3951350","authors":["Tissera, Muditha","Thelijjagoda, Samantha","Goonathilake, Jeevani"],"tags":["User-centric Privacy","Digital databases","Third party access","Limited publishing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.5281/zenodo.3951350","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.4013331","name":"An ECSDA-based Security Approach on Blockchain for Cryptocurrency-based Online Transactions","source":"datacite","abstract":"Blockchain is an inventive application model that coordinates agreement instruments, appropriated information stockpiling, highlight point transmission, computerized encryption innovation and numerous other PC advances. This paper investigates the issues that the blockchain still has in the part of security insurance, and acquaints the current arrangements with these issues. One of the methods of advanced money is ring mark which can be achieved by Elliptic Curve Digital Signature Algorithm (ECSDA). In this paper, we present a novel strategy for acquiring quick programming execution of the Elliptic Curve Digital Signature Algorithm in the limited Galois field GF(p) with a discretionary prime modulus p of self-assertive. The most significant component of the technique is that it stays away from bit-level activities which are delayed on chip and performs word-level tasks which are altogether quicker. The calculations utilized in the execution perform word-level activities, exchanging them off for bit-level tasks and in this way bringing about a lot of higher paces. We give the planning consequences of our usage on a 2.8 GHz Pentium 4 processor, supporting our case that ECDSA is suitable for compelled situations.","url":"https://doi.org/10.5281/zenodo.4013331","authors":["Md. Ismail Jabiullah","Kanij Nahar Arifa"],"tags":["Elliptic curve, blockchain, cryptography, cryptocurrency, ring signature"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.5281/zenodo.4013331","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.5281/zenodo.4013332","name":"An ECSDA-based Security Approach on Blockchain for Cryptocurrency-based Online Transactions","source":"datacite","abstract":"Blockchain is an inventive application model that coordinates agreement instruments, appropriated information stockpiling, highlight point transmission, computerized encryption innovation and numerous other PC advances. This paper investigates the issues that the blockchain still has in the part of security insurance, and acquaints the current arrangements with these issues. One of the methods of advanced money is ring mark which can be achieved by Elliptic Curve Digital Signature Algorithm (ECSDA). In this paper, we present a novel strategy for acquiring quick programming execution of the Elliptic Curve Digital Signature Algorithm in the limited Galois field GF(p) with a discretionary prime modulus p of self-assertive. The most significant component of the technique is that it stays away from bit-level activities which are delayed on chip and performs word-level tasks which are altogether quicker. The calculations utilized in the execution perform word-level activities, exchanging them off for bit-level tasks and in this way bringing about a lot of higher paces. We give the planning consequences of our usage on a 2.8 GHz Pentium 4 processor, supporting our case that ECDSA is suitable for compelled situations.","url":"https://doi.org/10.5281/zenodo.4013332","authors":["Md. Ismail Jabiullah","Kanij Nahar Arifa"],"tags":["Elliptic curve, blockchain, cryptography, cryptocurrency, ring signature"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.5281/zenodo.4013332","addedAt":"2026-08-31T06:41:45.650Z","updatedAt":"2026-08-31T06:41:45.650Z"},{"id":"doi:10.1504/ijics.2025.10069311","name":"A Blockchain-Aided Privacy Preservation using Lattice Homomorphic Encryption for Digital Forensic Investigation","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijics.2025.10069311","authors":["Vanita Mane","Suvarna Chaure"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-09T14:00:11Z","doi":"10.1504/ijics.2025.10069311","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1016/j.procs.2025.05.122","name":"Design and Implementation of a Cloud Computing Privacy-Preserving Machine Learning Model for Multi-Key Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procs.2025.05.122","authors":["Huijie Pan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-10T12:35:48Z","doi":"10.1016/j.procs.2025.05.122","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/apcit65661.2025.11410783","name":"Enhancing Federated Learning Security Using Homomorphic Encryption and Zero-Knowledge Proofs","source":"crossref","abstract":"","url":"https://doi.org/10.1109/apcit65661.2025.11410783","authors":["Anvith S G","Nithish Kushal Reddy","Rushank Tripathi","Kavitha C.R"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-04T20:47:40Z","doi":"10.1109/apcit65661.2025.11410783","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.3390/math13223687","name":"V-MHESA: A Verifiable Masking and Homomorphic Encryption-Combined Secure Aggregation Strategy for Privacy-Preserving Federated Learning","source":"crossref","abstract":"In federated learning, secure aggregation is essential to protect the confidentiality of local model updates, ensuring that the server can access only the aggregated result without exposing individual contributions. However, conventional secure aggregation schemes lack mechanisms that allow participating nodes to verify whether the aggregation has been performed correctly, thereby raising concerns about the integrity of the global model. To address this limitation, we propose V-MHESA (Verifiable Masking-and-Homomorphic Encryption–combined Secure Aggregation), an enhanced protocol extending our previous MHESA scheme. V-MHESA incorporates verification tokens and shared-key management to simultaneously ensure verifiability, confidentiality, and authentication. Each node generates masked updates using its own mask, the server’s secret, and a node-only shared random nonce, ensuring that only the server can compute a blinded global update while the actual global model remains accessible solely to the nodes. Verification tokens corresponding to randomly selected model parameters enable nodes to efficiently verify the correctness of the aggregated model with minimal communication overhead. Moreover, the protocol achieves inherent authentication of the server and legitimate nodes and remains robust under node dropout scenarios. The confidentiality of local updates and the unforgeability of verification tokens are analyzed under the honest-but-curious threat model, and experimental evaluations on the MNIST dataset demonstrate that V-MHESA achieves accuracy comparable to prior MHESA while introducing only negligible computational and communication overhead.","url":"https://doi.org/10.3390/math13223687","authors":["Soyoung Park","Jeonghee Chi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-18T11:05:31Z","doi":"10.3390/math13223687","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/itechsecom64750.2025.11307510","name":"Privacy-Preserving Computation Using CKKS Homomorphic Encryption in Medical AI Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/itechsecom64750.2025.11307510","authors":["Muhidinov Ayubbek Nuritdinovich","Aakansha Soy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-31T18:41:24Z","doi":"10.1109/itechsecom64750.2025.11307510","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/mc.2025.3613184","name":"Unlocking Private Computation at Scale: The Acceleration of Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mc.2025.3613184","authors":["Jaiyoung Park","Sangpyo Kim","Jongmin Kim","Jung Ho Ahn"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-09T18:32:38Z","doi":"10.1109/mc.2025.3613184","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1016/j.jemep.2025.101127","name":"Blockchain and homomorphic encryption for genomic and health data sharing: An ethical perspective","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jemep.2025.101127","authors":["S.A. Ahmed","R. Hrzic"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-29T10:48:07Z","doi":"10.1016/j.jemep.2025.101127","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.12732/ijam.v38i10s.1129","name":"ENHANCING THE PRIVACY AND INTEGRITY OF FEDERATED LEARNING MODELS USING HYBRID HOMOMORPHIC ENCRYPTION","source":"crossref","abstract":"The convergence of digital and physical infrastructures in Internet Service Provider (ISP) networks has created unprecedented vulnerabilities to national-level disruptions. Whereas traditional cybersecurity focuses on data protection, this paper addresses the critical gap in protecting the physical components of ISP infrastructure—undersea cables, central offices, fiber optic trunks, and power systems—from coordinated cyber-physical attacks. Through a mixed-methods approach combining graph-based security analysis, policy assessment, and systems thinking, we demonstrate how attacks on these physical chokepoints can trigger cascading failures across public safety, economic systems, and critical infrastructure. Our analysis reveals significant gaps in current preparedness, including siloed governance, inadequate field integration of innovations, and chronic underinvestment in resilience R&amp;D. We propose an Integrated Cyber-Physical Resilience Framework with technical mechanisms for dynamic threat detection and policy structures for effective public-private partnerships. The framework's efficacy is demonstrated through a simulated attack scenario \"Operation Tidal Wave,\" showing 68% faster threat detection and 45% improved recovery times. We conclude with specific implementation pathways for government agencies and ISPs and identify critical future research avenues for protecting this essential national security infrastructure.","url":"https://doi.org/10.12732/ijam.v38i10s.1129","authors":["Zainab Jawad Al-Abedi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-26T10:37:31Z","doi":"10.12732/ijam.v38i10s.1129","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1007/s00542-025-05921-1","name":"A novel homomorphic encryption-based optimization framework for wireless sensor networks","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00542-025-05921-1","authors":["S. K. Susee","M. Senthil Kumar","B. Chidambararajan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-19T04:39:14Z","doi":"10.1007/s00542-025-05921-1","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.3390/computers14120549","name":"A Theoretical Model for Privacy-Preserving IoMT Based on Hybrid SDAIPA Classification Approach and Optimized Homomorphic Encryption","source":"crossref","abstract":"The Internet of Medical Things (IoMT) improves healthcare delivery through many medical applications. Because of medical data sensitivity and limited resources of wearable technology, privacy and security are significant challenges. Traditional encryption does not provide secure computation on encrypted data, and many blockchain-based IoMT solutions partially rely on centralized structures. IoMT with dynamic encryption is an innovative privacy-preserving system that combines sensitivity-based classification and advanced encryption to address these issues. The study proposes privacy-preserving IoMT framework that dynamically adapts its cryptographic strategy based on data sensitivity. The proposed approach uses a hybrid SDAIPA (SDAIA-HIPAA) classification model that integrates Saudi Data and Artificial Intelligence Authority (SDAIA) and Health Insurance Portability and Accountability Act (HIPAA) guidelines. This classification directly governs the selection of encryption mechanisms, where Advanced Encryption Standard (AES) is used for low-sensitivity data, and Fully Homomorphic Encryption (FHE) is used for high-sensitivity data. The Whale Optimization Algorithm (WOA) is used to maximize cryptographic entropy of FHE keys and improves security against attacks, resulting in an Optimized FHE that is conditionally used based on SDAIPA outputs. This proposed approach provides a novel scheme to dynamically align cryptographic intensity with data risk and avoids the overhead of uniform FHE use while ensuring strong privacy for critical records. Two datasets are used to assess the proposed approach with up to 806 samples. The results show that the hybrid OHE-WOA outperforms in the percentage of sensitivity of privacy index with dataset 1 by 78.3% and 12.5% and with dataset 2 by 89% and 19.7% compared to AES and RSA, respectively, which ensures its superior ability to preserve privacy.","url":"https://doi.org/10.3390/computers14120549","authors":["Mohammed Ali R. Alzahrani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-11T14:30:17Z","doi":"10.3390/computers14120549","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/iscas56072.2025.11043933","name":"An Efficient NTT-Based Polynomial Multiplication Architecture for BFV Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscas56072.2025.11043933","authors":["Rella Mareta","Ardianto Satriawan","Hanho Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-27T17:42:19Z","doi":"10.1109/iscas56072.2025.11043933","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/isaics66888.2025.11350132","name":"Dual-Optimized Homomorphic Encryption Supporting Privacy-Preserving Fingerprint Liveness Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isaics66888.2025.11350132","authors":["Chengsheng Yuan","Chengxin Ni","Wenqian Qiu","Xingxing Jia","Xingting Li","Cao Yi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-28T20:55:52Z","doi":"10.1109/isaics66888.2025.11350132","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/ispacs68724.2025.11383387","name":"Privacy-Preserving Credit Scoring Using CKKS-Based Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ispacs68724.2025.11383387","authors":["Arkan Dzaky Raihan Noor","Riska Audina Anindyasari","Muhammad Ogin Hasanuddin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T20:55:03Z","doi":"10.1109/ispacs68724.2025.11383387","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.71443/9789349552111-10","name":"Homomorphic Encryption and AI-Based Intrusion Detection for Cyber-Resilient IoT-Connected Smart Power Systems","source":"crossref","abstract":"The integration of homomorphic encryption (HE) within IoT-connected smart grid systems presents a promising solution for ensuring data privacy and security, particularly in environments where sensitive energy data was transmitted and processed. The computational overhead of HE has hindered its widespread adoption, especially in resource-constrained devices within the grid. This chapter explores the synergies between HE and emerging technologies, such as edge and fog computing, lightweight cryptography, and hardware acceleration, to enhance the efficiency and feasibility of real-time encrypted data processing in smart grids. A detailed analysis of computational challenges and optimization strategies was presented, focusing on reducing the latency and energy consumption associated with HE operations. Case studies and experimental evaluations highlight successful implementations of hardware-accelerated HE in smart grid applications, demonstrating significant improvements in system performance and scalability. The chapter also examines the comparative advantages of HE over traditional encryption techniques, emphasizing its potential for securing critical infrastructure while maintaining privacy in decentralized power networks. Overall, this work provides a comprehensive framework for overcoming the challenges of HE implementation in smart grids and paves the way for future advancements in cyber-resilient, privacy-preserving energy management systems.","url":"https://doi.org/10.71443/9789349552111-10","authors":["Neha Agrawal","N Saranya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-02T12:22:03Z","doi":"10.71443/9789349552111-10","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.59573/emsj.9(5).2025.106","name":"An Optimized and Privacy-Preserving Framework for Resource Management in Multi-Cloud Environments using Homomorphic Encryption","source":"crossref","abstract":"This article presents a novel framework for multi-cloud resource management that addresses the critical challenge of balancing operational efficiency with data privacy. By leveraging homomorphic encryption techniques, the proposed solution enables computational optimization on encrypted resource utilization data, eliminating the traditional trade-off between performance optimization and privacy preservation. The framework consists of strategically designed components including data collection agents, encryption modules, secure aggregators, encrypted optimizers, and decision enforcers that work in concert to maintain data privacy throughout the resource management lifecycle. A tailored version of the CKKS homomorphic encryption scheme is employed to enable secure computation on encrypted data, with custom encoding techniques to handle comparison operations. The secure optimization algorithm addresses multi-cloud resource allocation through gradient descent adapted for homomorphic operations. As measured experimentally, the framework can allocate resources close to optimally, without sacrificing the cryptographic security, compared to a wide range of attacks. The method is a remarkable solution to secure multi-cloud operations, especially for those companies in business spheres where privacy is a concern.","url":"https://doi.org/10.59573/emsj.9(5).2025.106","authors":["Somesh Nagalla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-06T14:25:31Z","doi":"10.59573/emsj.9(5).2025.106","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.13052/jcsm2245-1439.1447","name":"Research on Cloud Data Security Computing Framework Based on Fusion of Homomorphic Encryption and Differential Privacy","source":"crossref","abstract":"With the wide application of cloud computing in network security, the privacy protection of sensitive data is becoming increasingly serious. This paper proposes a cloud data security computing framework that combines homomorphic encryption and differential privacy. It supports ciphertext computing based on the CKKS scheme, and introduces ε-differential privacy mechanism at the output end to achieve “invisible in calculation and unrecognizable after calculation” Double protection. Based on UNSW-NB15 and CERT v6.2 datasets, the experiment carries out intrusion detection and behavior aggregation tasks respectively. Under the privacy budget ε=1.0, the F1-score of intrusion detection task reaches 92.3%, and the re-recognition rate decreases to 6.7%; The behavioral aggregation error is controlled within 1.92%, which is better than baseline methods such as HE-only and DP-only. The results show that the framework can significantly improve the level of privacy protection while ensuring data availability. It is suitable for various scenarios such as intrusion detection and anomaly modeling, and has strong practicability and promotion value.","url":"https://doi.org/10.13052/jcsm2245-1439.1447","authors":["Yongsheng Huang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-14T01:05:57Z","doi":"10.13052/jcsm2245-1439.1447","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/icons-iot65216.2025.11211230","name":"Privacy-Preserving Face Recognition Systems Based on Blockchain and Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icons-iot65216.2025.11211230","authors":["Lutfi Bramantio Subagyo","Favian Dewanta","Yudha Purwanto"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-03T18:42:15Z","doi":"10.1109/icons-iot65216.2025.11211230","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/bmsb65076.2025.11165396","name":"D2D and Homomorphic Encryption for MEC-Based Digital Twins in 6G Networks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bmsb65076.2025.11165396","authors":["Chiara Suraci","Pietro Zema","Antonella Molinaro","Giuseppe Araniti"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-24T17:31:40Z","doi":"10.1109/bmsb65076.2025.11165396","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1002/9781394292493.ch07","name":"Securing Cyber‐physical Systems Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1002/9781394292493.ch07","authors":["Atul Khatri","Ruizhi Cheng","Songqing Chen","Bo Han"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-29T18:08:48Z","doi":"10.1002/9781394292493.ch07","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1007/s13389-025-00377-5","name":"HEProfiler: an in-depth profiler of approximate homomorphic encryption libraries","source":"crossref","abstract":"Abstract Fully Homomorphic Encryption (FHE) allows computation on encrypted data. Various software libraries have implemented the approximate-arithmetic FHE scheme CKKS [1], which is highly useful for applications in machine learning and data analytics; each of these libraries have differing performance and features. It is useful for developers and researchers to learn details about these libraries’ performance and their differences. Some previous work has profiled FHE and CKKS implementations for this purpose, but these comparisons are limited in their fairness and completeness. In this article, we compare four major libraries supporting the CKKS scheme. Working with the maintainers of each of the PALISADE, Microsoft SEAL, HElib, and HEAAN libraries, we devise methods for fair comparisons of these libraries, even with their widely varied development strategies and library architectures. To show the practical performance of these libraries, we present HEProfiler, a simple and extensible framework for profiling C++ FHE libraries. Our experimental evaluation is complete in both the scope of tasks tested and metrics evaluated, allowing us to draw conclusions about the behaviors of different libraries under a wide range of real-world workloads. This is the first work giving experimental comparisons of different bootstrapping-capable CKKS libraries.","url":"https://doi.org/10.1007/s13389-025-00377-5","authors":["Jonathan Takeshita","Nirajan Koirala","Colin McKechney","Taeho Jung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-27T08:29:52Z","doi":"10.1007/s13389-025-00377-5","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.36227/techrxiv.175751137.71377403/v2","name":"A Reconfigurable VLSI Architecture of Non-Power-of-Two Number Theoretic Transform for BGV Fully Homomorphic Encryption","source":"crossref","abstract":"Fully homomorphic encryption (FHE) enables operations to be performed directly on encrypted data without decryption. This preserves data privacy while leveraging the computational power of cloud computing. The security level of FHE is rooted in the hardness of underlying mathematical problems, but its implementation involves a huge amount of complex homomorphic operations. Simplified homomorphic evaluation algorithms and efficient hardware accelerators are thus critical for achieving widespread applications of FHE. The number theoretic transform (NTT) defined over a cyclotomic polynomial ring provides efficient polynomial multiplications, which is essential for key-switching, modulus switching and bootstrapping processes in the BGV-FHE scheme. Generally, non-power-of-two NTTs require relatively higher complexity compared to power-of-two NTTs. To minimize the gap, this work proposes to iteratively decompose large-size non-power-of-two NTTs using the prime factor algorithm and Rader's algorithm into small-size NTTs that are further optimized using reconfigurable butterfly units leveraging Winograd algorithm. Experimental results demonstrate a significant reduction in complexity, particularly in the required modular multiplications, additions, and interconnect network. For instance, for the 78881th cyclotomic polynomial NTT, this work shows 3.13x and 2.75x reductions in the number of multiplications and additions, respectively, compared to related work. Additionally, the proposed architecture achieves reduced latency compared to Bluestein's method using radix-8 butterfly unit, while the required memory size can be reduced to 57% and 63% for the 77531-th and 78881-th cyclotomic polynomial NTTs, respectively.","url":"https://doi.org/10.36227/techrxiv.175751137.71377403/v2","authors":["Chien-Chih Huang","Hsuan-Jui Hsu","Qi-Xian Wu","Ming-Der Shieh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-19T05:39:41Z","doi":"10.36227/techrxiv.175751137.71377403/v2","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.32604/cmc.2025.062542","name":"A Fully Homomorphic Encryption Scheme Suitable for Ciphertext Retrieval","source":"crossref","abstract":"","url":"https://doi.org/10.32604/cmc.2025.062542","authors":["Ronglei Hu","Chuce He","Sihui Liu","Dong Yao","Xiuying Li","Xiaoyi Duan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-24T05:06:46Z","doi":"10.32604/cmc.2025.062542","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1007/s44248-025-00095-7","name":"On homomorphic encryption based strategies for class imbalance in federated learning","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s44248-025-00095-7","authors":["Arpit Guleria","Harshan Jagadeesh","Ranjitha Prasad","B. N. Bharath"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-28T17:14:18Z","doi":"10.1007/s44248-025-00095-7","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/icmnwc66779.2025.11354452","name":"Homomorphic Encryption for Privacy-Preserving Federated Learning in Medical Applications","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icmnwc66779.2025.11354452","authors":["S Shabana","H S Ranjan Kumar","K Raju","Kiran Puttegowda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-28T20:57:39Z","doi":"10.1109/icmnwc66779.2025.11354452","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.4018/ijisp.386964","name":"Leveled Homomorphic Encryption Based on NTRU Without Re-Linearization","source":"crossref","abstract":"The hardness of the NTRU problem has not been well understood until 2021, when Pellet-Mary and Stehlé (2021) gave a reduction from the Gap-SVP problem on the ideal lattice to the NTRU-Search problem. Assuming the equivalence of the NTRU-Decision and the NTRU-Search problem, with this reduction together, we construct a leveled homomorphic encryption scheme. Compared to homomorphic schemes based on RLWE such as CKKS and BGV, the ciphertext of our scheme is a single polynomial. As a result, ciphertext multiplication involves only one multiplication of two polynomials, rather than the tensor multiplication of polynomial vectors as in BGV, CKKS schemes. In particular, by introducing a label, the ciphertext of our scheme does not need to be linearized after multiplication. This significantly accelerates the speed of homomorphic evaluation by reducing the number of polynomial multiplications from 6 to 1. Complexity analysis and experimental results indicate that the ciphertext multiplication in our scheme is approximately 4~5 times faster than CKKS and BFV schemes","url":"https://doi.org/10.4018/ijisp.386964","authors":["Xiaokang Dai","Haoyong Wang","Wenyuan Wu","Yong Feng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-06T16:10:51Z","doi":"10.4018/ijisp.386964","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1145/3746252.3761194","name":"<i>PP-STAT:</i>\n                    An Efficient Privacy-Preserving Statistical Analysis Framework using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3746252.3761194","authors":["Hyunmin Choi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-08T00:36:36Z","doi":"10.1145/3746252.3761194","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/aiccsa66935.2025.11315422","name":"A Modular Framework for Hierarchical Federated Learning with Multi-Key Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aiccsa66935.2025.11315422","authors":["Ashika Sameem Abdul Rasheed","Mohammad Mehedy Masud"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-05T18:35:55Z","doi":"10.1109/aiccsa66935.2025.11315422","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1145/3770177.3770238","name":"Fine-grained Homomorphic Encryption Method for Enterprise Financial Big Data Based on Searchable Spatiotemporal Data Attributes","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3770177.3770238","authors":["Yuping Yan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-04T07:27:36Z","doi":"10.1145/3770177.3770238","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/bigdatasecurity66063.2025.00010","name":"High-Performance Implementation Architecture for Additive Homomorphic Encryption Algorithms on FPGA","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdatasecurity66063.2025.00010","authors":["Yuxuan Zhang","Hua Guo","Chen","Junxin Chen","Wenmao Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-21T17:37:17Z","doi":"10.1109/bigdatasecurity66063.2025.00010","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.33778/kcsa.2025.25.5.071","name":"A Homomorphic Encryption Framework for Circuit Privacy Protection","source":"crossref","abstract":"","url":"https://doi.org/10.33778/kcsa.2025.25.5.071","authors":["Jaehui Park","Joohee Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-21T08:58:37Z","doi":"10.33778/kcsa.2025.25.5.071","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1007/s10207-025-01005-3","name":"Homomorphic encryption-based fault diagnosis in IoT-enabled industrial systems","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10207-025-01005-3","authors":["Hoki Kim","Youngdoo Son","Junyoung Byun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-27T04:37:27Z","doi":"10.1007/s10207-025-01005-3","addedAt":"2026-08-31T06:41:45.993Z","updatedAt":"2026-08-31T06:41:45.993Z"},{"id":"doi:10.1109/ai2e64943.2025.10983163","name":"Enhancing Privacy in Image Search: Secure Object-Based Retrieval from Encrypted Images Using BFV Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ai2e64943.2025.10983163","authors":["Kamran Saeed","M. Fatih Adak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-12T17:39:33Z","doi":"10.1109/ai2e64943.2025.10983163","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1504/ijics.2025.147754","name":"A blockchain-aided privacy preservation using lattice homomorphic encryption for digital forensic investigation","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijics.2025.147754","authors":["Suvarna Chaure","Vanita Mane"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-31T11:30:21Z","doi":"10.1504/ijics.2025.147754","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1016/j.jisa.2025.104116","name":"Privacy-preserving federated learning in asynchronous environment using homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jisa.2025.104116","authors":["Mansi Gupta","Mohit Kumar","Renu Dhir"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-13T02:52:40Z","doi":"10.1016/j.jisa.2025.104116","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/acdsa65407.2025.11165930","name":"Building Privacy-Preserving AI system: AI Inference on Encrypted Data with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/acdsa65407.2025.11165930","authors":["Riccardo Magni","Carlo Tassi","Emanuele D’Agostini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-24T17:31:35Z","doi":"10.1109/acdsa65407.2025.11165930","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/tai.2025.3612906","name":"QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tai.2025.3612906","authors":["Md Jueal Mia","M Hadi Amini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-08T17:41:11Z","doi":"10.1109/tai.2025.3612906","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.12732/ijam.v38i11s.1263","name":"SECURE AND PRIVACY-PRESERVING WSNS IN SMART CITY VIA BLOCKCHAIN-ENABLED FEDERATED LEARNING AND HOMOMORPHIC ENCRYPTION","source":"crossref","abstract":"The rapid exponential growth of WSN and IoT devices in smart cities has revolutionized urban services, including traffic management, environmental monitoring, and automation of major infrastructures. Voluminous data generated by heterogeneous sensors, however, present significant challenges to privacy, security, and resource efficiency. Traditional solutions have been plagued by high computation costs, vulnerabilities to malicious nodes, as well as a lack of adequate security for secretive information during model aggregation. In a quest to overcome such vulnerabilities, this work presents a novel framework for secure and private WSNs under blockchain-enabled federated learning as well as homomorphic encryption. In this proposed approach, two novelties are implemented, including Lightweight Local Model Training via Edge-MGTNet, utilizing MobileNet-V3, Tiny-GNN, Micro-TCN, edge pruning, as well as knowledge distillation for lightweight local intelligence extraction, and Encrypted Model Packaging via HashEnc-SparseNet, utilizing sparsified gradient encoding, Paillier homomorphic encryption, as well as hash-based integrity verification (HIV) for encrypting transmission of models with tamper-proof blockchain registration. Robust experiments on merged smart city IoT datasets reveal exemplary performance compared to prior solutions, with Accuracy = 99.22%, Precision = 98.32%, Sensitivity = 96.9%, and Specificity = 96.56%, indicating the effectiveness of such a framework for offering resilient, trustworthy, as well as private intelligence across heterogeneous WSN nodes.","url":"https://doi.org/10.12732/ijam.v38i11s.1263","authors":["Gasim Alandjani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-03T09:21:25Z","doi":"10.12732/ijam.v38i11s.1263","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.2139/ssrn.5579867","name":"Secure and Efficient UAV-Based Face Detection via Homomorphic Encryption and Edge Computing","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5579867","authors":["Hoang Dinh","Nguyen Van Duc","Bui  Duc Manh","Quang-Trung Luu","Van-Linh Nguyen","Diep Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-08T20:40:55Z","doi":"10.2139/ssrn.5579867","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/caibda65784.2025.11183073","name":"A Cross-Border Data Privacy Protection Approach Based on Smart Contracts and Ring Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/caibda65784.2025.11183073","authors":["Ziguang Lu","Qianru Lin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-09T17:50:41Z","doi":"10.1109/caibda65784.2025.11183073","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/iacis65746.2025.11211455","name":"Decentralized and Immutable Record-Keeping for Data Privacy-Preserving in Healthcare Using Homomorphic Encryption in Blockchain","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iacis65746.2025.11211455","authors":["B. Aishwarya","P. Sriramya"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-05T18:37:16Z","doi":"10.1109/iacis65746.2025.11211455","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/isocc66390.2025.11330102","name":"Twiddle-Factor Generation Using Reused Butterfly Array for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isocc66390.2025.11330102","authors":["Muhammad Ogin Hasanuddin","Hanho Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-14T20:38:59Z","doi":"10.1109/isocc66390.2025.11330102","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1007/s44196-025-00829-0","name":"Federated Learning with Homomorphic Encryption: A Privacy-Preserving Solution for Smart Cities","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s44196-025-00829-0","authors":["Ali Alqazzaz"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-17T13:32:11Z","doi":"10.1007/s44196-025-00829-0","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.3390/jcp5030074","name":"Evaluating Homomorphic Encryption Schemes for Privacy and Security in Healthcare Data Management","source":"crossref","abstract":"Ensuring data privacy and security in sensitive domains such as healthcare remains a critical challenge. Homomorphic Encryption (HE) offers a promising approach by enabling computations directly on encrypted data, but the diversity of available schemes requires careful evaluation before practical adoption. This work conducts a comparative study of six representative HE schemes: BGV, TFHE, Paillier, RSA without padding, BFV, and CKKS. It is adopted a five-step strategy, encompassing preprocessing, cryptographic setup, encryption, homomorphic execution, and decryption, applied to a healthcare dataset. Overall, the comparative analysis underscores that no single scheme is universally optimal. The choice of an HE scheme must be guided by the nature of the required operations, acceptable precision levels, and computational constraints of the target healthcare scenario.","url":"https://doi.org/10.3390/jcp5030074","authors":["Henrique Jorge","Cristina Wanzeller","João Henriques"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-17T07:07:18Z","doi":"10.3390/jcp5030074","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.36548/jei.2025.2.004","name":"Secured E-Banking using Multi-Identity based Fully Homomorphic Encryption","source":"crossref","abstract":"In today's digital landscape, ensuring the security of e-banking platforms is crucial for preventing unauthorized access and safeguarding sensitive financial information. This project employs Fully Homomorphic Encryption (FHE), a significant advancement in cryptography that enables the processing of encrypted data without the need for decryption, thereby preserving privacy throughout the transaction. A pivotal aspect of the system is its encrypted authentication mechanism, which protects login credentials, including usernames and passwords. Additionally, the platform introduces a multi-identity authentication model, allowing users to engage with the system under various secure roles. By integrating FHE with a multi-identity framework, the system provides robust protection against cyber threats.","url":"https://doi.org/10.36548/jei.2025.2.004","authors":["Sundaramoorthy K.","Lokanya G.","Ashlin Prajaa P. J."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-27T07:56:35Z","doi":"10.36548/jei.2025.2.004","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.64038/cel.1202522","name":"ENHANCING PRIVACY-PRESERVING DATA ANALYTICS THROUGH HOMOMORPHIC ENCRYPTION: TECHNIQUES AND APPLICATIONS","source":"crossref","abstract":"In the era of data-driven decision-making, the protection of sensitive information has become a critical challenge, particularly as organizations increasingly rely on data analytics for insights. This study explores the potential of Homomorphic Encryption (HE) in enhancing privacy-preserving data analytics, a promising cryptographic technique that enables computation on encrypted data without exposing sensitive information. We evaluate the performance of three different HE schemes in terms of encryption and decryption times, computational overhead, analytical accuracy, and scalability. Our findings reveal that while HE incurs significant computational overhead, particularly for larger datasets, the accuracy of analytical results remains comparable to that of plaintext data, demonstrating its potential for privacy-preserving analytics. A performance decrease occurred when data set sizes grew which led to encryption along with decryption taking much longer than regular encryption approaches. The accuracy levels of data processing functions including categorization and regression did not change during periods when unencrypted data was utilized. The main challenge for HE on large datasets is scalability but we believe that encryption systems which combine HE methods with alternative techniques might offer a valid solution. HE demands perfectly integrated hardware acceleration technology combined with algorithm developments if it aims to achieve practical usability. Our work establishes fundamental elements for scalability and efficiency growth which will enhance confidential analytics uses through supporting heightened HE awareness.","url":"https://doi.org/10.64038/cel.1202522","authors":["Wajeeha Ahmed","Nimra Shah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-05T01:26:52Z","doi":"10.64038/cel.1202522","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.3390/bdcc9100250","name":"Exploring the Application and Characteristics of Homomorphic Encryption Based on Pixel Scrambling Algorithm in Image Processing","source":"crossref","abstract":"Homomorphic encryption is well known to researchers, yet its application in image processing is scarce. The diversity of image processing algorithms makes homomorphic encryption implementation challenging. Current research often uses the CKKS algorithm, but it has core bottlenecks in image encryption, such as the mismatch between image data and the homomorphic operation mechanism, high 2D-structure-induced costs, noise-related visual quality damage, and poor nonlinear operational support. This study, based on image pixel characteristics, analyzes homomorphic encryption via pixel scrambling algorithms. Using magic square, Arnold, Henon map, and Hilbert curve transformations as starting points, it reveals their homomorphic properties in image processing. This further explores general pixel scrambling algorithm homomorphic encryption properties, offering valuable insights for homomorphic encryption applications in image processing.","url":"https://doi.org/10.3390/bdcc9100250","authors":["Tieyu Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-30T15:38:55Z","doi":"10.3390/bdcc9100250","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.63856/y52wg044","name":"Privacy Preserving E-Voting System Using Homomorphic Encryption","source":"crossref","abstract":"With the rise of digital governance, secure and private electronic voting systems are becoming increasingly important. Traditional e-voting platforms often suffer from vulnerabilities such as vote tampering, lack of transparency, and weak identity verification. To address these challenges, this project introduces a secure online voting system that integrates Paillier homomorphic encryption with deep-learning-based facial recognition technology. Homomorphic encryption enables the aggregation of votes while they remain encrypted, ensuring that individual ballots stay completely confidential throughout the counting process. Facial recognition provides real-time voter authentication, effectively preventing proxy and duplicate voting.Throughout the voting lifecycle, ballot data is encrypted during both transmission and storage, eliminating opportunities for third parties to intercept or manipulate information. An administrator can access results only after the voting period concludes, and the system reveals only the final aggregate tally without disclosing individual choices. The proposed model is implemented using Python, Flask, OpenCV, and SQLite, offering a secure, scalable, and user-friendly solution suitable for small-scale elections in academic institutions and local organizations.The system includes an intuitive web interface, encrypted data storage, and secure communication mechanisms to enhance usability and reliability. By combining biometric authentication with advanced cryptographic techniques, the project delivers a robust, tamper-resistant voting framework that strengthens voter trust and ensures endto-end privacy. Overall, this work demonstrates how modern cryptography and artificial intelligence can significantly improve the security and transparency of digital election systems, making it a strong foundation for future e-voting applications.","url":"https://doi.org/10.63856/y52wg044","authors":["Dr. Rajeshwar","Dantala Siddartha","Kolakani Sanjay","Paleti Prem Kiran","Rachagiri Omkarnath"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-20T11:18:28Z","doi":"10.63856/y52wg044","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/icacrs67045.2025.11324134","name":"Comparative Analysis of Noise Generated in BGV Homomorphic Encryption: Lattigo vs FHEgen Parameters","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icacrs67045.2025.11324134","authors":["Jethro Jarvis Roy Jyrwa","Somnath Sinha","Binayak Dutta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-14T20:37:30Z","doi":"10.1109/icacrs67045.2025.11324134","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/ietacs68750.2025.11385626","name":"Federated and Homomorphic Encryption-Enabled Lightweight Cryptography for Secure IoT Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ietacs68750.2025.11385626","authors":["Pravina K. Parmar","Ramesh T. Prajapati"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T21:13:18Z","doi":"10.1109/ietacs68750.2025.11385626","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.56947/amcs.v26.439","name":"Securing Machine Learning Models: Homomorphic Encryption and its impact on Classifiers","source":"crossref","abstract":"Homomorphic encryption (HME) enables encrypted computations and provides secure data analysis while addressing key concerns around data privacy and regulatory compliance. It has significant implications for machine learning (ML) models, particularly in enhancing the privacy, security, and usability of ML models. Training ML models on homomorphically encrypted data is a growing area of research. While HME offers several security-related benefits to ML models, there are concerns about the ML models performance, when applied over homomorphically encrypted data. Research efforts are needed to analyse the potential impact of HME on the performance of ML models. This paper deals with our work in this direction, presenting the analysis of various ML classifiers on homomorphically encrypted data. We discuss the performance of classifiers such as logisticregression, Support Vector Machines, Neural networks and Decision trees on data encrypted using HME. We follow a systematic approach to analyse the performance in terms of accuracy and efficiency. Our results indicate that the performance of classifiers is nearly identical to the results obtained when they applied on unencrypted data.","url":"https://doi.org/10.56947/amcs.v26.439","authors":["Rishitha Adamsetty","Aswani Kumar Cherukuri","Annapurna Jonnalagadda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T13:24:46Z","doi":"10.56947/amcs.v26.439","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1007/978-981-97-9396-9_3","name":"Distributed Privacy-Preserving Fusion Estimation Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-97-9396-9_3","authors":["Bo Chen","Tongxiang Li","Wen-An Zhang","Li Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-03T11:33:33Z","doi":"10.1007/978-981-97-9396-9_3","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/icdsaai65575.2025.11011810","name":"Privacy-Preserving Federated Learning in Healthcare: Integrating Homomorphic Encryption, Smart Contracts, and Adaptive Strategies","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icdsaai65575.2025.11011810","authors":["Neha Ramesh","Misbah Anwar","Kumaran K"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-29T17:06:27Z","doi":"10.1109/icdsaai65575.2025.11011810","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/icdici66477.2025.11134992","name":"Block-Based Separable Reversible Data Hiding using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icdici66477.2025.11134992","authors":["Veeramuthu Venkatesh","Ramneshkar B","Harish Narasimhan K","Arunkumar D","Srinidhi S","R. Anushiadevi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-02T17:29:01Z","doi":"10.1109/icdici66477.2025.11134992","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/ccdc65474.2025.11090576","name":"Secure Robust Model Predictive Control for Polytopic Uncertain Systems Using Semi-Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ccdc65474.2025.11090576","authors":["Kai-Yu Peng","Wei Xie","Langwen Zhang","Hai-Xiang Wei","Shoujin Lin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-05T18:00:15Z","doi":"10.1109/ccdc65474.2025.11090576","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/icaibd64986.2025.11081983","name":"Efficient Privacy-Preserving Machine Learning with Homomorphic Encryption through Pruning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icaibd64986.2025.11081983","authors":["Yating Zheng","Yaorong Lin","Yiqin Lu","Jiancheng Qin","Jiarui Chen","Kaiqiong Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-21T18:02:44Z","doi":"10.1109/icaibd64986.2025.11081983","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1145/3719027.3765068","name":"ILA: Correctness via Type Checking for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3719027.3765068","authors":["Tarakaram Gollamudi","Anitha Gollamudi","Joshua Gancher"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-22T23:37:25Z","doi":"10.1145/3719027.3765068","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.55041/ijsrem43782","name":"Securing Cloud Storage Using Homomorphic Encryption","source":"crossref","abstract":"This paper focuses on a secure, efficient solution for processing, encrypting, compressing, and storing files in the cloud using Python. The system starts by converting a user-provided text file into ASCII values, which are then encrypted using the CKKS (Cheon-Kim-Kim-Song) homomorphic encryption scheme provided by the TenSEAL library. This encryption ensures that the data remains confidential and protected throughout the entire process. Once encrypted, the file is compressed using Python's gzip module, reducing its size for faster uploads and minimizing storage space. The final step uploads the compressed file to Dropbox, ensuring secure cloud storage. By combining encryption, compression, and cloud integration, this project provides a robust method for securely managing sensitive data, making it ideal for scenarios where both privacy and storage efficiency are paramount. The seamless flow from file conversion to cloud storage offers a practical solution for securely handling large volumes of data. Keywords — Cloud computing, Security, Cloud storage, Sensitive data, Homomorphic encryption.","url":"https://doi.org/10.55041/ijsrem43782","authors":["B M Sahana","Rohith S","Sandhiya C","Padalingam Padalingam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-04-05T05:45:31Z","doi":"10.55041/ijsrem43782","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1201/9781003641537-48","name":"Enhancing Privacy in Collaborative Breast Cancer Diagnosis: A Federated Learning Approach with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1201/9781003641537-48","authors":["Vankamamidi S. Naresh","Gadhiraju Tej Varma","D Ayyappa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-31T10:48:02Z","doi":"10.1201/9781003641537-48","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.56553/popets-2025-0061","name":"Practical, Private Assurance of the Value of Collaboration via Fully Homomorphic Encryption","source":"crossref","abstract":"Two parties wish to collaborate on their datasets. However, before they reveal their datasets to each other, the parties want to have the guarantee that the collaboration would be fruitful. We look at this problem from the point of view of machine learning, where one party is promised an improvement on its prediction model by incorporating data from the other party. The parties would only wish to collaborate further if the updated model shows an improvement in accuracy. Before this is ascertained, the two parties would not want to disclose their models and datasets. In this work, we construct an interactive protocol for this problem based on the fully homomorphic encryption scheme over the Torus (TFHE) and label differential privacy, where the underlying machine learning model is a neural network. Label differential privacy is used to ensure that computations are not done entirely in the encrypted domain, which is a significant bottleneck for neural network training according to the current state-of-the-art FHE implementations. We formally prove the security of our scheme assuming honest-but-curious parties, but where one party may not have any expertise in labelling its initial dataset. Experiments show that we can obtain the output, i.e., the accuracy of the updated model, with time many orders of magnitude faster than a protocol using entirely FHE operations.","url":"https://doi.org/10.56553/popets-2025-0061","authors":["Hassan Jameel Asghar","Zhigang Lu","Zhongrui Zhao","Dali Kaafar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-07T23:36:18Z","doi":"10.56553/popets-2025-0061","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1016/j.kjs.2025.100449","name":"Secured cloud-based image data processing of self-driving vehicles using full homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.kjs.2025.100449","authors":["Kamran Saeed","M.Fatih Adak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-21T02:46:41Z","doi":"10.1016/j.kjs.2025.100449","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/iscaie64985.2025.11080808","name":"High Speed Pseudo Random Number Generator for Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iscaie64985.2025.11080808","authors":["Natarajan Adharsh","Nakul N S","Jathin S","C P Sindhu","Rashmi Seethur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-22T18:00:51Z","doi":"10.1109/iscaie64985.2025.11080808","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.32604/cmc.2025.068516","name":"Approximate Homomorphic Encryption for MLaaS by CKKS with Operation-Error-Bound","source":"crossref","abstract":"","url":"https://doi.org/10.32604/cmc.2025.068516","authors":["Ray-I Chang","Chia-Hui Wang","Yen-Ting Chang","Lien-Chen Wei"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-06T08:21:37Z","doi":"10.32604/cmc.2025.068516","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/bigdata66926.2025.11402229","name":"Feasibility of Privacy-Preserving Entity Resolution on Confidential Healthcare Datasets Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata66926.2025.11402229","authors":["Yixiang Yao","Joseph Cecil","Praveen Angyan","Neil Bahroos","Srivatsan Ravi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-06T20:57:57Z","doi":"10.1109/bigdata66926.2025.11402229","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.46586/tches.v2025.i2.163-208","name":"REED: Chiplet-based Accelerator for Fully Homomorphic Encryption","source":"crossref","abstract":"Fully Homomorphic Encryption (FHE) enables privacy-preserving computation and has many applications. However, its practical implementation faces massive computation and memory overheads. To address this bottleneck, several Application-Specific Integrated Circuit (ASIC) FHE accelerators have been proposed. All these prior works put every component needed for FHE onto one chip (monolithic), hence offering high performance. However, they encounter common challenges associated with large-scale chip design, such as inflexibility, low yield, and high manufacturing costs. In this paper, we present the first-of-its-kind multi-chiplet-based FHE accelerator ‘REED’ for overcoming the limitations of prior monolithic designs. To utilize the advantages of multi-chiplet structures while matching the performance of larger monolithic systems, we propose and implement several novel strategies in the context of FHE. These include a scalable chiplet design approach, an effective framework for workload distribution, a custom inter-chiplet communication strategy, and advanced pipelined Number Theoretic Transform and automorphism design to enhance performance.Our instruction-set and power simulations experiments with a prelayout netlist indicate that REED 2.5D microprocessor consumes 96.7mm2 chip area, 49.4Waverage power in 7nm technology. It could achieve a remarkable speedup of up to 2,991x compared to a CPU (24-core 2xIntel X5690) and offer 1.9x better performance, along with a 50% reduction in development costs when compared to state-of-the-art ASIC FHE accelerators. Furthermore, our work presents the first instance of benchmarking an encrypted deep neural network (DNN) training. Overall, the REED architecture design offers a highly effective solution for accelerating FHE, thereby significantly advancing the practicality and deployability of FHE in real-world applications.","url":"https://doi.org/10.46586/tches.v2025.i2.163-208","authors":["Aikata Aikata","Ahmet Can Mert","Sunmin Kwon","Maxim Deryabin","Sujoy Sinha Roy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-05T11:03:11Z","doi":"10.46586/tches.v2025.i2.163-208","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/lanman66415.2025.11154574","name":"Integrating Homomorphic Encryption and Synthetic Data in FL for Privacy and Learning Quality","source":"crossref","abstract":"","url":"https://doi.org/10.1109/lanman66415.2025.11154574","authors":["Yenan Wang","Carla Fabiana Chiasserini","Elad Michael Schiller"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-15T17:35:57Z","doi":"10.1109/lanman66415.2025.11154574","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/hpec67600.2025.11196237","name":"A Framework For The Iterative Solution of Sparse Linear Systems on Hybrid Architectures Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/hpec67600.2025.11196237","authors":["Lior Horesh","Vassilis Kalantzis","Barry M. Trager","Shashanka Ubaru"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-16T17:35:37Z","doi":"10.1109/hpec67600.2025.11196237","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.62225/2583049x.2025.5.3.4230","name":"Confidential Computing in Front-End: Enhancing Data Security with Secure Enclaves and Homomorphic Encryption","source":"crossref","abstract":"This thesis investigates how homomorphic encryption and, more especially, Secure Enclaves might improve data security in front-end web systems using Confidential Computing methods. Conventional encryption techniques are insufficient in maintaining confidentiality during data processing when client-side technologies progressively control sensitive data. This paper evaluates in real-world front-end applications the utility, efficiency, and scalability of Secure Enclaves (e.g., Intel SGX and ARM TrustZone) and numerous Homomorphic Encryption frameworks (including BFV and CKKS). Analyzing real-world events, including secure form entries and in-browser document management, helps the study assess performance indicators, including latency, CPU use, and integration complexity. Studies show that Secure Enclaves provide better performance and simpler integration; homomorphic encryption improves privacy protections even if it results in higher computing costs. This work ends with analyzing the trade-offs related to different approaches and suggesting suitable use cases for every scenario, thereby improving the safety of data while in use.","url":"https://doi.org/10.62225/2583049x.2025.5.3.4230","authors":["Yuliia Horbenko"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-21T07:26:11Z","doi":"10.62225/2583049x.2025.5.3.4230","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.48047/whx0v680","name":"Advancing Data Security in Cloud Computing: A Comprehensive Exploration of Quantum-Secure Variant of Fully Homomorphic Encryption Technique","source":"crossref","abstract":"The increasing reliance on Cloud Computing for large-scale data processing has raised significant concerns about data security and privacy, especially in the face of emerging quantum threats. Traditional Fully Homomorphic Encryption (FHE) schemes, while enabling secure computations on encrypted data, face vulnerabilities against quantum attacks due to their reliance on classicalcryptographic hardness assumptions.","url":"https://doi.org/10.48047/whx0v680","authors":["Dr. I. Carol"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-05T10:47:31Z","doi":"10.48047/whx0v680","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.54254/2755-2721/2026.tj30594","name":"The Integration of Fully Homomorphic Encryption and Machine Learning: Technical Paths, Model Adaptation, and Practical Applications","source":"crossref","abstract":"The growing adoption of machine learning across areas like healthcare and finance has raised serious concerns around data privacy. Traditional techniques such as differential privacy or federated learning often come with limitations—whether in accuracy, communication cost, or reliance on secure protocols. Fully Homomorphic Encryption (FHE) offers a promising alternative, enabling computation directly on encrypted data and making it possible to use data without ever seeing it in raw form. This paper explores how FHE can be integrated with machine learning workflows, from traditional models like linear regression and decision trees to complex deep learning architectures. We review mainstream FHE schemes and core technologies that make private ML feasible. In particular, we analyze the unique challenges of adapting different model types to FHE constraints and highlight real-world applications in medical imaging, financial models, and edge intelligence. However, critical bottlenecks remain. Large models still face efficiency issues, dynamic data settings are poorly supported, and the field lacks standardized benchmarks. Through this review, we outline key future research directions that can help transition FHE-based machine learning from theoretical promise to practical reality.","url":"https://doi.org/10.54254/2755-2721/2026.tj30594","authors":["Qi Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-18T01:57:58Z","doi":"10.54254/2755-2721/2026.tj30594","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.37256/cm.6120255949","name":"Neural Network-Driven Privacy-Preserving Credit Risk Analysis: A Homomorphic Encryption Approach","source":"crossref","abstract":"With the increasing importance of credit risk analysis (CRA) with an emphasis on privacy, there's a notable need for a privacy-preserving machine learning (PPML) system. To address this demand, we propose a framework presenting a novel approach to privacy-preserving credit risk analysis (PPCRA) through integrating neural networks (NN) with homomorphic encryption (HE). The proposed framework offers robust privacy protection while maintaining the efficiency and accuracy of credit risk prediction systems. The implementation utilizes libraries such as TenSEAL and Torch to develop a HE-enabled NN model capable of processing encrypted data. Comprehensive security analysis establishes resilience against numerous privacy attacks of the system and empirical validation through experiments conducted on real-world financial datasets from multiple countries. The evaluation of the NN's performance, both with and without privacy preservation measures, provides insights into the efficacy of the proposed approach. This study offers significant advancements in privacy-preserving techniques for CRA, with implications for financial institutions and data security practitioners.","url":"https://doi.org/10.37256/cm.6120255949","authors":["V. V. L. Divakar Allavarpu","Vankamamidi S. Naresh","A. Krishna Mohan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-13T21:48:09Z","doi":"10.37256/cm.6120255949","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/ntms65597.2025.11076966","name":"Securing Fault Diagnosis in IoT-Enabled Industrial Systems Using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ntms65597.2025.11076966","authors":["Mohammed El-Hajj","Ali El Attar","Ahmad Fadllallah","Rida Khatoun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-18T17:42:27Z","doi":"10.1109/ntms65597.2025.11076966","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/rmkmate64874.2025.11042305","name":"A Survey – High Secured Data Transmission in Cloud Computing using Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/rmkmate64874.2025.11042305","authors":["T. S. Lakshmi","H. Jayamangala"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-25T18:17:41Z","doi":"10.1109/rmkmate64874.2025.11042305","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/isaics66888.2025.11350197","name":"Privacy-Preserving Intelligence-based Reinforcement Learning for Large Language Model via Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isaics66888.2025.11350197","authors":["Feiyang Wu","Xiaoqiang Sun","Zhiwei Sun","Wei Liu","Zoe L. Jiang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-28T20:55:52Z","doi":"10.1109/isaics66888.2025.11350197","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/pcds65695.2025.00032","name":"Batchencryption: Localized Federated Learning in Privacy-Preserving with Efficient Integer Vector Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/pcds65695.2025.00032","authors":["Tianying Xie","Shaohong Zhou","Qi Qi","Yantao Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-29T17:51:27Z","doi":"10.1109/pcds65695.2025.00032","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.3390/math13233809","name":"GPU Acceleration for KLSS Key Switching in Fully Homomorphic Encryption","source":"crossref","abstract":"Fully Homomorphic Encryption (FHE) enables privacy-preserving computation but is hindered by high computational overhead, with the key-switching operation being a primary performance bottleneck. This paper introduces the first CUDA-optimized GPU implementation of the Kim, Lee, Seo, and Son (KLSS) key-switching algorithm for three leading FHE schemes: BGV, BFV, and CKKS. Our solution achieves significant performance gains, delivering speedups of up to 181× against the original CPU implementation. Furthermore, we analyze the critical trade-off between the key-switching techniques on GPUs, providing insights for the choice between single- and double-decomposition methods. Our work provides a high-performance tool and offers clear guidelines on the trade-off between latency and hardware memory constraints.","url":"https://doi.org/10.3390/math13233809","authors":["Shutong Jin","Ray C. C. Cheung"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-27T16:31:52Z","doi":"10.3390/math13233809","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1145/3732365.3732371","name":"Privacy Set Intersection Protocol based on Homomorphic Encryption and Optimization Filters","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3732365.3732371","authors":["Xingyu An","Jianyi Zhang","Xiaodong Li","Weilin Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-26T13:58:18Z","doi":"10.1145/3732365.3732371","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/icecet63943.2025.11472586","name":"An Efficient Homomorphic Encryption Technique for Privacy-Preserving Machine Learning Inference","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icecet63943.2025.11472586","authors":["Sumin Lee","Jaeky Oh","Byoungwoo Yoon","Min-Wook Jeong","Jongho Shin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-09T19:42:35Z","doi":"10.1109/icecet63943.2025.11472586","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.32996/jcsts.2025.7.5.87","name":"Privacy-Preserving Emergency Data Mesh: A Homomorphic Encryption Approach to Multi-Agency Disaster Response Coordination","source":"crossref","abstract":"Privacy-preserving emergency data mesh represents a transformative solution to the critical challenge of multi-agency coordination during natural disasters. The system addresses the fundamental tension between urgent information sharing needs and stringent privacy regulations that historically impede effective disaster response. By integrating Conflict-free Replicated Data Types with homomorphic encryption, this architecture enables agencies to perform analytics on encrypted data without exposing sensitive information. The implementation leverages the Brakerski-Fan-Vercauteren encryption scheme to maintain cryptographic security while allowing real-time queries across distributed networks. Field deployments during wildfire response exercises demonstrate that agencies can achieve situational awareness without compromising citizen privacy or violating regulatory frameworks. The system's resilient design ensures continued operation despite network disruptions common in disaster scenarios, utilizing adaptive synchronization protocols and edge computing resources. This privacy-preserving framework fundamentally changes how emergency management organizations collaborate, moving from trust-based information sharing to cryptographically assured coordination. The successful adoption by multiple agencies previously unwilling to share data due to privacy concerns validates the practical viability of homomorphic encryption in time-critical applications.","url":"https://doi.org/10.32996/jcsts.2025.7.5.87","authors":["Somesh Nagalla"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-05T19:53:54Z","doi":"10.32996/jcsts.2025.7.5.87","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/apccas67402.2025.11377043","name":"Comparison of Barrett Modular Reduction with Various Multipliers in the Context of BFV/BGV Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/apccas67402.2025.11377043","authors":["Muhammad Daffa Rasyid","Ardianto Satriawan","Hanho Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T20:54:40Z","doi":"10.1109/apccas67402.2025.11377043","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.2139/ssrn.5088943","name":"Privacy-Preserving Federated Learning for Healthcare: A Synergistic Approach using Differential Privacy and Homomorphic Encryption","source":"crossref","abstract":"This generation of healthcare data is fuelling a digital revolution in health records, but it also raises significant questions; one of them being around data sharing between organisations when discussing patient information. Shareable Data for Privacy Preserving Federated Learning and Training Data Sharing (SDPF) [1] is an effective method for designing models where the models are trained collaboratively by different groups without transferring any private data but rather by sharing public data to generate solid models, which is by security used for training data sharing. DP and HE in Federated Learning for Healthcare Applications D. Rawat, D. Gupta, M. Luke, S. Gupta 1. Learn how to be able to balance privacy, computing challenges, and to be able to achieve the best performance possible for the model, as well as presenting some hands-on strategies that would be needed in order to be able to scale PPFL systems in a much faster, more feasible way. PPFL in action one real-life implementation shows how you can unlock healthcare, keeping sensitive data private while allowing full HIPAA and GDPR compliance. This delivers the real value of healthcare data without compromising privacy or security. The new framework proposes (semi-) Centralized Privacy-preserving and Verifiable Compiling for CHeRaph: a secure and efficient solution for the collaboration of organizations for research and diagnostics in large-scale healthcare systems in a way that is privacy-compliant.","url":"https://doi.org/10.2139/ssrn.5088943","authors":["K Mahesh Babu","Eskala Bhavitha","Midde Mythri","Angajala Anusha","Boya Sri Chandana","C Gazala Akhtar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T08:09:43Z","doi":"10.2139/ssrn.5088943","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1145/3765612.3767814","name":"Privacy-Preserving Genomic Similarity Search via Enhanced Homomorphic Encryption with BLAST-Concordant Algorithms","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3765612.3767814","authors":["Saaketh Bhojanam","Sohum Mehta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-10T17:45:59Z","doi":"10.1145/3765612.3767814","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1186/s42400-025-00360-x","name":"LP-HENN: fully homomorphic encryption accelerator with high energy efficiency","source":"crossref","abstract":"Abstract Fully homomorphic encryption (FHE) enables direct computation on encrypted data without decryption, ensuring data privacy in cloud computing scenarios and preventing the leakage of sensitive information. However, the computational overhead of HE typically exceeds that of plaintext computation by 4 to 5 orders of magnitude, while energy consumption is 5 to 6 orders of magnitude higher. These substantial performance and energy overheads significantly hinder the widespread adoption of FHE. This paper proposed LP-HENN, a novel low-power and energy-efficient FHE accelerator architecture that leverages a RISC-V vector coprocessor and ReRAM crossbar arrays. LP-HENN targets power-constrained application scenarios such as edge devices, aiming to provide highly energy-efficient acceleration support for FHE applications. LP-HENN leverages the collaborative work of the vector processor and ReRAM crossbars, employing optimization strategies to achieve full pipelining and minimize memory access. Furthermore, this paper proposed a parameter selection model for early-stage architecture design, which achieves an optimal balance between performance and energy consumption through the collaborative optimization of multiple parameters. Experimental results show that, for an FHE-based convolutional neural network (HE-CNN) inference application, LP-HENN achieves a 31.82Ã- and 11920.56Ã- improvement in performance and energy efficiency, respectively, compared to CPU. Compared to FxHENN, the state-of-the-art FPGA-based FHE accelerator with high energy efficiency for edge devices, LP-HENN achieves a 2.36Ã- and 10.04Ã- improvement in performance and energy efficiency, respectively. The energy efficiency of LP-HENN is comparable to that of F1, the state-of-the-art ASIC FHE accelerator, while featuring a low power design suitable for edge computing.","url":"https://doi.org/10.1186/s42400-025-00360-x","authors":["Zhuoyu Tian","Lei Chen","Shengyu Fan","Xianglong Deng","Rui Hou","Dan Meng","Mingzhe Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-30T02:03:01Z","doi":"10.1186/s42400-025-00360-x","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.61173/07r22n10","name":"Research on Homomorphic Encryption Methods for Financial Data Based on Parameter Optimization and Hybrid Architecture","source":"crossref","abstract":"This study addresses the privacy protection requirements in financial data analysis by proposing a homomorphic encryption method that integrates CKKS parameter optimization with a hybrid FHE-PHE architecture. Through orthogonal experimental design, the optimal parameter combinations are selected, resulting in significantly improved encryption time compared to conventional encryption methods and greater stability in encryption time compared to FHE.","url":"https://doi.org/10.61173/07r22n10","authors":["Jingming Hu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-23T13:36:57Z","doi":"10.61173/07r22n10","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/chilecon66915.2025.11476107","name":"Data Science and Privacy in Secure Predictive Analysis with Homomorphic Encryption and Partial Anonymization","source":"crossref","abstract":"","url":"https://doi.org/10.1109/chilecon66915.2025.11476107","authors":["Alan Rodrigo Corini Guarachi","Juan Pablo Vásconez"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-20T20:01:39Z","doi":"10.1109/chilecon66915.2025.11476107","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/icosec67334.2025.11459742","name":"Lightweight Homomorphic Encryption for Privacy-Preserving Machine Learning on Edge Devices","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icosec67334.2025.11459742","authors":["Anciline Jenifer J","M. Sakthivanitha","Sheela K","S. Sudha","T. Thirumalaikumari","Sankar Padmanabhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T19:54:53Z","doi":"10.1109/icosec67334.2025.11459742","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1002/aisy.202400507","name":"A Privacy‐Preserving System for Confidential Carpooling Services Using Homomorphic Encryption","source":"crossref","abstract":"Carpooling enables multiple users with similar travel habits to share rides, reducing vehicles on the road, leading to benefits such as lower fuel consumption, reduced traffic congestion, and lower environmental impact. However, carpooling also poses a challenge to the privacy of the users, as they may not want to reveal their location or route information to others. This research study delves into a cutting‐edge approach to address these privacy concerns by leveraging homomorphic encryption (HE) within the realm of carpooling services. The proposed solution makes use of a HE scheme that supports encrypted computation on real numbers, which is suitable for carpooling applications that involve distance and time calculations. The approach enables decision makers to perform efficient and accurate route matching over encrypted data, without disclosing their sensitive information about users, thus preserving the confidentiality of the data. The proposed system is evaluated through extensive experiments and simulations, demonstrating its effectiveness in terms of both security and privacy when the system operates in normal (ideal) and abnormal (under attack) conditions. Experimental results indicate that the proposed solution offers robust resistance to various attacks, including replay attacks and data exposure, providing a robust and privacy‐centric solution for carpooling services.","url":"https://doi.org/10.1002/aisy.202400507","authors":["David Palma","Pier Luca Montessoro","Mirko Loghi","Daniele Casagrande"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-30T12:08:12Z","doi":"10.1002/aisy.202400507","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/icitisee68184.2025.11355016","name":"Priv-Face: A Privacy-Preserving Face Recognition Approach Based on Homomorphic Encryption and Blockchain","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icitisee68184.2025.11355016","authors":["Lutfi Bramantio Subagyo","Favian Dewanta","Yudha Purwanto"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-28T20:57:50Z","doi":"10.1109/icitisee68184.2025.11355016","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/ijcnn64981.2025.11228914","name":"MQFL-FHE: Multimodal Quantum Federated Learning Framework with Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ijcnn64981.2025.11228914","authors":["Siddhant Dutta","Nouhaila Innan","Sadok Ben Yahia","Muhammad Shafique","David E. Bernal Neira"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-14T18:46:15Z","doi":"10.1109/ijcnn64981.2025.11228914","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/iccr67387.2025.11292337","name":"Privacy-Preserving COVID-19 Screening Using Homomorphic Encryption and Logistic Regression","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccr67387.2025.11292337","authors":["Mirjalol Norchayev","Toshpulat Oqboyev","Nurali Islamov","Abbos Sattorov","Malohat Yaxshiyeva","Zahraa Eisa","Mustafa Ali Alwash"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-19T18:56:15Z","doi":"10.1109/iccr67387.2025.11292337","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1007/978-3-032-01831-1_16","name":"Homomorphic Data Encryption System Based on Residue Number System","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-01831-1_16","authors":["Viktor Kuchukov"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-17T02:03:53Z","doi":"10.1007/978-3-032-01831-1_16","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/dsn-w65791.2025.00075","name":"FHE ML Tuxedo: A Tailored Wrapper Architecture for Homomorphic Encryption in Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/dsn-w65791.2025.00075","authors":["Martin Nocker","Linus Henke","Pascal Schöttle"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-14T17:41:19Z","doi":"10.1109/dsn-w65791.2025.00075","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1145/3746709.3746953","name":"Research on Privacy Protection in Digital Currency Transactions Based on Inverse Homomorphic Symmetric QHSE Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3746709.3746953","authors":["Shurui Zhang","Jingyu Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T14:30:47Z","doi":"10.1145/3746709.3746953","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/access.2025.3565945","name":"Enhancing Property-Based Token Attestation With Homomorphic Encryption (PTA-HE) for Secure Mobile Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2025.3565945","authors":["Thinh Le Vinh","Huan Thien Tran","Samia Bouzefrane"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-01T17:24:44Z","doi":"10.1109/access.2025.3565945","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1002/spy2.70137","name":"A Privacy‐Preserving Framework for Real‐Time\n                    <scp>P2P</scp>\n                    Virtual Scene Transmission Using Homomorphic Encryption and Edge Offloading","source":"crossref","abstract":"ABSTRACT In order to address the data privacy issues in real‐time collaboration in P2P virtual scenes, this paper proposes a privacy protection transmission framework based on the Brakerski Fan Vercauteren homomorphic encryption scheme. The proposed framework allows for direct computation of ciphertext, ensuring end‐to‐end privacy protection while maintaining acceptable real‐time performance. We have built a high‐performance experimental platform consisting of an Intel Xeon Gold edge server with 128 GB of memory, an NVIDIA RTX A6000 GPU, and a Docker based Ubuntu environment that integrates TensorFlow and PyTorch for encryption performance testing and behavioral modeling. The experimental results show that homomorphic encryption supports addition and multiplication operations in ciphertext form, effectively preventing sensitive information leakage. Compared with AES, the BFV based scheme shows higher average latency, but reduces privacy leakage by over 93%. In four real‐world collaboration scenarios, the leakage rate decreased from 86.25% to 5.5%. When the number of nodes increased from 10 to 200, the system throughput decreased from 43.2 to 28.4 MB/s, while the CPU utilization increased from 68% to 87%, indicating a reasonable trade‐off between computation and security. In addition, the integrated edge offloading mechanism reduces encryption latency from 180 to 95 ms, an increase of 36%, while maintaining complete ciphertext confidentiality during processing. These findings validate that the proposed BFV based P2P collaboration framework achieves advanced privacy protection and real‐time scalability, providing a feasible and secure solution for remote design, distributed virtual training, and other privacy sensitive applications. Future research will focus on optimizing lightweight encryption and adaptive scheduling mechanisms for wider deployment in heterogeneous P2P environments.","url":"https://doi.org/10.1002/spy2.70137","authors":["Wangqin Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-13T01:55:16Z","doi":"10.1002/spy2.70137","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/ichst66555.2025.11428566","name":"Privacy-Preserving Rare Disease Diagnosis Via Integrated AI and Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ichst66555.2025.11428566","authors":["Vu-Thu-Nguyet Pham","N. Nga Nguyen","Valeria Gottardo","Quy Vo-Reinhard"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-18T19:37:11Z","doi":"10.1109/ichst66555.2025.11428566","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1007/978-3-032-06703-6_30","name":"Homomorphic Encryption for Privacy Preservation","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-06703-6_30","authors":["Swatee Nikam","Nilima Kulkarni","Amrita Manjrekar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-11T05:22:07Z","doi":"10.1007/978-3-032-06703-6_30","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.14419/6ekp0r85","name":"SECUREDGE: Privacy-Preserving Deduplication with Homomorphic Encryption for Multi-Tenant Cloud Systems","source":"crossref","abstract":"Cloud computing developments have pushed multi-tenant models to become widely used, allowing enterprises to share computing resources ‎without mixing their data. Even though this approach works, using data deduplication to save space causes serious concerns for privacy. ‎Conventional encryption techniques may not support deduplication since they mask similar data sections and may potentially expose concealed data when processing. Our work offers a novel approach called SECUREDGE. It encrypts data using Fully Homomorphic Encryption (FHE) to ensure security. Data privacy is guaranteed by encrypting information before deduplication. It employs an FHE-based method ‎to identify and remove duplicate data without impacting the security edges between tenants. Businesses facing difficulties in the cloud may ‎rest easy with SECUREDGE's intelligent storage solutions and state-of-the-art cryptography‎.","url":"https://doi.org/10.14419/6ekp0r85","authors":["Murala Vijaya","Dr. Lade Srinivasa Chakravarthy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-24T12:13:20Z","doi":"10.14419/6ekp0r85","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/tac.2024.3518356","name":"A Secure Resilient Homomorphic Encryption Scheme for Control Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tac.2024.3518356","authors":["Moritz Fauser","Ping Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-16T14:38:54Z","doi":"10.1109/tac.2024.3518356","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.33039/ami.2025.09.001","name":"Comparative analysis of homomorphic encryption schemes for encrypted image processing in OpenStack using TenSEAL","source":"crossref","abstract":"","url":"https://doi.org/10.33039/ami.2025.09.001","authors":["Hekmat Saker","Farid Eyvazov","Tariq Emad Ali","Csaba Biro","Dhulfiqar Zoltán Alwahab"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-18T10:17:47Z","doi":"10.33039/ami.2025.09.001","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/comsnets63942.2025.10885766","name":"Efficient privacy enabled data sharing in cloud using a novel Homomorphic Proxy Re-Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.1109/comsnets63942.2025.10885766","authors":["Imtiyazuddin Shaik","Arinjita Paul","Rajan M A","Divyesh Saglani"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-20T20:05:58Z","doi":"10.1109/comsnets63942.2025.10885766","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/airc64931.2025.11077473","name":"Application of Machine Learning-NLP Approach with Fully Homomorphic Encryption Techniques in Medical PII Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/airc64931.2025.11077473","authors":["Uchenna Ndolo","Hoda El-Sayed","Md Kamruzzaman Sarker"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-15T17:41:01Z","doi":"10.1109/airc64931.2025.11077473","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1002/eng2.70690/v3/response1","name":"Author response for \"Data Clustering Method for Fault-tolerant Privacy Protection of Smart Grid Based on BGN Homomorphic Encryption Algorithm\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70690/v3/response1","authors":["Jiangtao Guo","Yajie Li","Jia Shen","Tao Ming","Yuan Cao","Zuosong Dai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T21:06:30Z","doi":"10.1002/eng2.70690/v3/response1","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/access.2025.3555311","name":"Touch of Privacy: A Homomorphic Encryption-Powered Deep Learning Framework for Fingerprint Authentication","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2025.3555311","authors":["U. Sumalatha","K. Krishna Prakasha","Srikanth Prabhu","Vinod C. Nayak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-28T02:08:25Z","doi":"10.1109/access.2025.3555311","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/icicnct66124.2025.11232609","name":"Privacy-Preserving IoT Stream Analytics Using Homomorphic Encryption and Autoencoder-Based Anomaly Detection","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicnct66124.2025.11232609","authors":["Mohan Sankaran","Zahrah Sataar","Prateeksha Siddhanti","Subhojit Ghosh","S Narendran."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-18T18:42:19Z","doi":"10.1109/icicnct66124.2025.11232609","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.52953/xzqw7841","name":"A practical homomorphic encryption approach for GDPR-compliant machine learning full training protocol","source":"crossref","abstract":"This work introduces a novel privacy-preserving full training protocol for deep learning models using external data. It addresses the critical, yet under-explored, challenge of data privacy, especially under regulations like GDPR. While homomorphic encryption has been suggested as a breakthrough for utilizing private external datasets in machine learning, naively applying it to model training from scratch is impractical due to huge computational costs. The proposed method strategically encrypts only the minimal layers required to prevent privacy breaches. This approach is carefully designed to minimize the computational overhead of homomorphic encryption, making it efficient and scalable for larger models. Comprehensive analysis on benchmark datasets (ResNet-20/110 with CIFAR-10/100) confirms compliance with GDPR requirements without significant loss of accuracy. Experiments show a 1000x reduction in training time compared to models encrypted across all layers, which is the first published benchmark as far as we know.","url":"https://doi.org/10.52953/xzqw7841","authors":["Hyukki Lee","Jungho Moon","Donghoon Yoo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-16T15:11:29Z","doi":"10.52953/xzqw7841","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.54254/2755-2721/2025.po25408","name":"Cryptography Techniques in Medical Data Privacy Protection: Applications and Challenges of Homomorphic Encryption, Differential Privacy, and Blockchain","source":"crossref","abstract":"With the rapid development of big data and artificial intelligence technologies, data security has become a critical bottleneck restricting the development of data science. This study systematically explores the innovative applications and implementation challenges of modern cryptographic techniques in the field of data science. The paper first reviews the fundamental theories of cryptography, such as symmetric encryption, asymmetric encryption, and hash functions. It then focuses on the cutting-edge applications of homomorphic encryption in privacy-preserving machine learning, differential privacy in user data analysis, and blockchain in data integrity verification. Through an in-depth analysis of typical cases such as medical data sharing and user behavior modeling, the study reveals the effectiveness and limitations of cryptographic techniques in practical deployment. The study further identifies the main challenges currently faced, including algorithmic computational efficiency, the transition to post-quantum cryptography, and the balance between data privacy and usability. Finally, this paper proposes future development directions for the deep integration of cryptography and data science from both technical evolution and policy-making perspectives. This study provides important theoretical references and methodological guidance for secure computing practices in the field of data science.","url":"https://doi.org/10.54254/2755-2721/2025.po25408","authors":["Chenyuan Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-24T10:15:24Z","doi":"10.54254/2755-2721/2025.po25408","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1002/eng2.70690/v2/response1","name":"Author response for \"Data Clustering Method for Fault-tolerant Privacy Protection of Smart Grid Based on BGN Homomorphic Encryption Algorithm\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70690/v2/response1","authors":["Jiangtao Guo","Yajie Li","Jia Shen","Tao Ming","Yuan Cao","Zuosong Dai"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T21:06:30Z","doi":"10.1002/eng2.70690/v2/response1","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1109/icwite64848.2025.11306903","name":"Secure Chronic Kidney Disease Classification: Homomorphic Encryption vs Differential Privacy in Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icwite64848.2025.11306903","authors":["Ruthu B Jain","Ruthvika B","B Gayathri Kamath"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-30T18:34:40Z","doi":"10.1109/icwite64848.2025.11306903","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.1145/3733802.3764063","name":"HE-SecureNet: An Efficient and Usable Framework for Model Training via Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3733802.3764063","authors":["Thomas Schneider","Huan-Chih Wang","Hossein Yalame"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-18T09:38:21Z","doi":"10.1145/3733802.3764063","addedAt":"2026-08-31T06:41:45.994Z","updatedAt":"2026-08-31T06:41:45.994Z"},{"id":"doi:10.70088/6tfxcj04","name":"HEDPF: A Hybrid Privacy-Preserving Framework Integrating Homomorphic Encryption and Differential Privacy for Big Data Information Systems","source":"crossref","abstract":"Privacy protection has become a critical challenge in big data information systems due to the exponentially increasing volume, diversity, and sensitivity of digital data. Traditional privacy-preserving methods often suffer from limited security guarantees, significantly reduced data utility, or prohibitively high computational overhead, making them unsuitable for modern complex environments. To address these persistent limitations, this paper proposes a novel hybrid privacy-preserving framework, named HEDPF, which seamlessly integrates homomorphic encryption with differential privacy techniques. The proposed framework enables secure and efficient computation directly over encrypted data while incorporating adaptive noise injection mechanisms during the data release phase to provide rigorous, mathematically proven privacy guarantees. A comprehensive experimental evaluation is systematically conducted using a simulated large-scale information system dataset containing a diverse mix of structured, semi-structured, and unstructured data formats. HEDPF is rigorously compared with conventional encryption-anonymization, standard k-anonymity, and standalone differential privacy methods in terms of overall privacy protection strength, retained data utility, and computational efficiency. The extensive experimental results demonstrate that HEDPF consistently achieves a substantially lower privacy leakage risk while simultaneously maintaining higher data accuracy and structural completeness. In addition, the proposed framework exhibits superior system scalability and significantly reduces the overall processing time required for large-scale datasets. These compelling results indicate that HEDPF effectively balances the critical trade-offs between privacy protection, data utility, and computational efficiency, thereby providing a highly practical and robust solution for secure big data processing in contemporary information systems.","url":"https://doi.org/10.70088/6tfxcj04","authors":["Hua Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-26T05:53:50Z","doi":"10.70088/6tfxcj04","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-981-95-0788-7_47","name":"Security Sharing Strategy for Financial Data Based on Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-95-0788-7_47","authors":["Nannan Sun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-06T22:33:20Z","doi":"10.1007/978-981-95-0788-7_47","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/jiot.2026.3667906","name":"Verifiable Privacy-Preserving Neural Network Inference via Multi-Key Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/jiot.2026.3667906","authors":["Guanghui He","Zihan Yuan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-25T20:58:08Z","doi":"10.1109/jiot.2026.3667906","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.47974/jdmsc-2650","name":"A framework for secure cloud operations via homomorphic encryption and Euclidean biometric matching","source":"crossref","abstract":"In the evolving landscape of secure cloud automation, this paper introduces a novel AI-driven framework that integrates homomorphic encryption (HE), facial biometric authentication, and encrypted voice-command processing to achieve intelligent, privacypreserving cloud resource management. Unlike prior models that depend on plaintext computation or modular scripts, our system performs encrypted biometric matching and command parsing, orchestrating cloud operations through secure tokens generated in the encrypted domain. This architecture, built atop AWS services such as EC2, Lambda, IAM, and Polly, ensures cryptographic isolation and integrity of sensitive workflows. Experimental results demonstrate authentication accuracy of 96.2%, command latency under 2.8 seconds, and significant performance gains over traditional interfaces, with zero leakage under chosen-ciphertext attack simulations. The proposed framework sets a new benchmark for secure, encrypted, and intelligent cloud management.","url":"https://doi.org/10.47974/jdmsc-2650","authors":["Ritik Mittal","Pradeep Kumar","Kuntal Gaur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T07:32:05Z","doi":"10.47974/jdmsc-2650","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.18280/jesa.590401","name":"An Intrusion Detection and Defence Mechanism for Securing Autonomous Vehicle CAN Bus Using AI-Based Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.18280/jesa.590401","authors":["K. P. Senthilkumar","E. Anbalagan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-27T08:48:21Z","doi":"10.18280/jesa.590401","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/gseact68539.2026.11620552","name":"Privacy-Preserving Predictive Analytics in Healthcare: A Federated 1D-CNN Framework Utilizing Secure Multi-Party Computation and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/gseact68539.2026.11620552","authors":["Archit Kushwaha","Gautam Kumar","Vinit Kumar Gunjan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-07T19:35:54Z","doi":"10.1109/gseact68539.2026.11620552","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.63550/iceip.2026.74.41.095","name":"Homomorphic Encryption for Securing Russian Data Transfer","source":"crossref","abstract":"Homomorphic Encryption (HE), which allows encrypted data to be handled, is becoming an important tool for Russia. It helps to comply with strict data localization laws Federal Law No. 242-FZ,which regulates the transfer and storage of information abroad. As global cybersecurity threats evolve, the need for secure and efficient encryption mechanisms grows, especially in sectors such as finance, healthcare, and government services. This article explores Russia’s strategic adoption of Homomorphic Encryption (HE) as a cornerstone technology to achieve IT sovereignty amid geopolitical tensions and economic sanctions. It highlights the critical need to protect sensitive data processed on foreign cloud infrastructures, which currently dominate Russian enterprises, by enabling secure computations on encrypted data without exposing the underlying information. The paper provides a detailed comparative analysis of popular HE schemes (CKKS, BGV, TFHE) and their adaptation to Russian cryptographic standards such as GOST R34.12-2015, emphasizing the balance between computational overhead and sovereignty gains. A case study of Rostelecom’s pioneering use of HE for cross-border voice analytics demonstrates practical implementation, showcasing acceptable latency, high accuracy, and manageable energy consumption when processing encrypted voice data for national security purposes. The article also benchmarks domestic hardware, notably the Elbrus-16S processor, against global standards, underscoring the need for optimization to close performance gaps while benefiting from energy efficiency. Further, it outlines a policy roadmap focusing on education and infrastructure development, including university programs, industry partnerships, hardware enhancements, and integration with legacy systems, to foster a robust ecosystem for HE deployment. The discussion culminates in advocating for a hybrid HE and Post-Quantum Cryptography framework to future-proof Russia’s cryptographic infrastructure against emerging threats, supported by international cooperation within BRICS to reduce dependency on Western technologies. Overall, the article positions Homomorphic Encryption as a transformative technology essential for safeguarding data privacy, advancing national security, and reinforcing Russia’s digital independence in an increasingly complex global IT landscape.","url":"https://doi.org/10.63550/iceip.2026.74.41.095","authors":["G. Gabdullina","E.L. Krasnova","A. Akhmetgareeva","V.K. Ayupova","M.B. Boshkhudzhaev"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-11T10:48:58Z","doi":"10.63550/iceip.2026.74.41.095","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/access.2026.3664444","name":"Privacy-Preserving Federated Learning for Retinal Disease Diagnosis Using Paillier Homomorphic Encryption With Multiple Encryption Keys","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2026.3664444","authors":["Avirneni Veda Sri","Mahesh Kumar Morampudi","Mallikarjun Reddy Dorsala","Sriramulu Bojjagani","Muhammad Khurram Khan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-13T20:50:52Z","doi":"10.1109/access.2026.3664444","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.32604/cmes.2026.077784","name":"Privacy-Aware Anomaly Detection in Encrypted Network Traffic via Adaptive Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.32604/cmes.2026.077784","authors":["Yu-Ran Jeon","Seung-Ha Jee","Su-Kyoung Kim","Il-Gu Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-13T08:15:11Z","doi":"10.32604/cmes.2026.077784","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/csf68417.2026.00019","name":"CHIP: Efficient Homomorphic Encryption-Based CNN Batch Inference Using Channel-Interleaved Packing with Small Rotation Key Set","source":"crossref","abstract":"","url":"https://doi.org/10.1109/csf68417.2026.00019","authors":["Huan-Chih Wang","Ja-Ling Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-27T19:12:01Z","doi":"10.1109/csf68417.2026.00019","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1504/ijcvr.2026.10077528","name":"Towards lightweight Crystals-Kyber-based homomorphic encryption for PostQuantum secure internet of things","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijcvr.2026.10077528","authors":["Ganesh Kumar Mahato","Swarnendu Kumar Chakraborty","Anwesha Banik","Sumitra Nayak"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-07T13:00:35Z","doi":"10.1504/ijcvr.2026.10077528","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/s11227-026-08768-z","name":"Privacy-preserving dyslexia detection using homomorphic encryption for QEEG data with explainable AI insights","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s11227-026-08768-z","authors":["Mhd Raja Abou Harb","Baris Celiktas","Gunet Eroglu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-13T12:05:17Z","doi":"10.1007/s11227-026-08768-z","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/missf68264.2026.11522106","name":"Privacy-Preserving Data Sharing Models Using Homomorphic Encryption and Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/missf68264.2026.11522106","authors":["A. Vijendar","Alok Jain","Srilekha Janagam","P. Deepthi","Gopi Mannava","Surabhi Shankar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-20T19:50:35Z","doi":"10.1109/missf68264.2026.11522106","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/icoei68323.2026.11541415","name":"Secure Medical Image Transmission using Deep Learning and Chaotic Map-Enhanced Homomorphic Encryption for Privacy Preservation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icoei68323.2026.11541415","authors":["B. Suvitha","K. Santhiya","S. Mary Selvi","S.V. Jeyasri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-03T19:38:27Z","doi":"10.1109/icoei68323.2026.11541415","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/isca66397.2026.00137","name":"FlashTFHE: A Scalable Architecture for Efficient Multi-Bit Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isca66397.2026.00137","authors":["Jiaao Ma","Ceyu Xu","Ning Liang","Lisa Wu Wills"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T19:18:00Z","doi":"10.1109/isca66397.2026.00137","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.22363/2658-4670-2026-34-1-12-23","name":"Usage of polynomial representation of numbers for approximate homomorphic encryption","source":"crossref","abstract":"\\emph {Introduction} In the modern world of computers and networks the idea of expanding of personal computer resources with the help of cloud storages and computation looks more and more lucrative. However, usage of these resources may endanger data being processed. In last twenty years several algorithms of homomorphic encryption were developed allowing solving of this problem among other applications. However such algorithms are usually constructed as public key systems for long term storage and processing of data. In this article two algorithms of homomorphic encryption optimized for single data processing are proposed. \\emph {Purpose} The target of research is development of data coding system which allows safe data processing in public clouds. \\emph {Results} Two homomorphic coding systems had been developed, first is based on representation of numbers in the form of polynomials, second based on further representation of polynomials in the form of sets of values. Developed systems allow approximate calculations of coded data without decryption allowing processing of real numbers. System has high level of protection and provides high precision of calculations, comparable with standard personal computer calculation precision. Structure of coded data allows parallel computing. Proposed system allows safe data processing in public networks. Question of finding of optimal parameters for the system stands open both for high precision calculation of limited sets of operations and repeatedly good precision for big sets of operations.","url":"https://doi.org/10.22363/2658-4670-2026-34-1-12-23","authors":["Andrey E. Krouk"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-05T12:37:24Z","doi":"10.22363/2658-4670-2026-34-1-12-23","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1145/3807246.3807328","name":"A Robust Horizontal Federated Learning Framework Based on Trimmed K-Means and Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3807246.3807328","authors":["Junyang Bai","Bo Meng","Dejun Wang","Chenrong Xiang","Sijie Yu","Tanwei Zhou"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-09T10:50:37Z","doi":"10.1145/3807246.3807328","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1080/23270012.2026.2640589","name":"Fhepdm: Fully Homomorphic Encryption Secured Collaborative Anomaly Detection in Wind Turbines Predictive Maintenance","source":"crossref","abstract":"","url":"https://doi.org/10.1080/23270012.2026.2640589","authors":["Jinxiang Dai","Shancang Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-27T19:08:51Z","doi":"10.1080/23270012.2026.2640589","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/iciscn67954.2026.11566565","name":"Application of Homomorphic Encryption Technology and Design of Transaction Privacy Protection Scheme in the Circulation of Central Bank Digital Currency (CBDC)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciscn67954.2026.11566565","authors":["Bing Du","Mingxia Tong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-23T19:43:02Z","doi":"10.1109/iciscn67954.2026.11566565","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.25258/ijddt.16.27s.55","name":"QRELHE-X: Extended Quantum-Resistant Edge Learning with Homomorphic Encryption for Next-Generation Distributed Intelligence Systems","source":"crossref","abstract":"Recent proliferation of edge intelligence systems, including Internet of Things (IoT) sensor networks, self-driving vehicles, wearable medical devices and industrial control platforms—requires privacy-preserving computation subject to availability constraints on resources. At the same time, the recent arrival of large-scale quantum computers can break all classical cryptographic protocols with Shor's algorithm in a short time. State-of-the-art post-quantum homomorphic encryption systems incur computational overhead orders of magnitude larger than the energy and memory budgets of resource-constrained microcontrollers, giving rise to a disparity between security and deployability that remains unaddressed. We introduce QRELHE-X in this paper, a comprehensive framework building on post-quantum edge learning with four novel contributions: (1) a tri-family hybrid cryptography architecture leveraging Algebraic Geometry (AG) codes, Hidden Field Equations minus (HFE-) and eXtended Merkle Signature Scheme (XMSS), which guards against cryptanalytic attacks targeting any single family using defend-in-depth approach; (2) our Deep Reinforcement Learning Parameter Orchestration (DRL-PO) engine that dynamically tunes cryptographic parameters based on real-time device telemetry via PPO-trained policy network yielding 23.4% efficiency optimization compared against static configurations; (3) our Structured Noise Calculus framework providing Lyapunov-stable guarantees of the noise growth within deep level homomorphic neural circuits enabling T*=47 operations between bootstrapping — 3.9x fewer than prior art implementations of state-of-the-art systems; and finally, we present BREFL, the first Byzantine Resilient Encrypted Federated Learning protocol capable of detecting malicious gradient submissions completely within encrypted domain at detection rate 98.7%. Over six hardware platforms, we establish a 5.1x latency improvement over CRYSTALS-KyberHE, while reducing energy by 4.7x, and successfully deploy on,256 KB RAM microcontrollers—the lowest memory footprint ever reported of any post-quantum homomorphic inference solution.","url":"https://doi.org/10.25258/ijddt.16.27s.55","authors":["RSS Raju Battula","Dr. Udai Shankar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-16T06:16:54Z","doi":"10.25258/ijddt.16.27s.55","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.56553/popets-2026-0149","name":"POPPY: Scalable and Secure Spectral Centrality for Distributed Graphs via Homomorphic Encryption","source":"crossref","abstract":"In this paper, we introduce POPPY, a novel suite of privacy-preserving algorithms designed for computing spectral centrality measures over graphs distributed across mutually distrustful data centers. POPPY is the first approach that achieves together generality, accuracy, and scalability with respect to the number of participants. POPPY uses the CKKS fully homomorphic encryption scheme to support the arithmetic division operation over ciphertexts. POPPY consists of three variants: POPPYs, optimized for sparse graphs using SIMD operations; POPPYd, tailored for dense graphs through efficient encrypted matrix-vector multiplication; and POPPYh, a hybrid between POPPYs and POPPYd for coping with contexts involving a large number of remote nodes. In addition to its core algorithms, POPPY comes with a pruning strategy based on a new notion of node equivalence called INC-equivalence. Pruning is indeed of utmost importance when using fully homomorphic encryption in order to minimize the number of encrypted operations performed. The INC-equivalence notion allows us to eliminate redundant nodes efficiently and without any impact on the accuracy of the centrality scores. Our comprehensive theoretical analysis and empirical evaluation on real-world and synthetic datasets demonstrate that POPPY achieves together generality, accuracy, and scalability.","url":"https://doi.org/10.56553/popets-2026-0149","authors":["Claire Guichemerre","Tristan Allard","Sofiane Azogagh","Marc-Olivier Killijian","Sébastien Gambs","Amr El Abbadi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-14T21:23:59Z","doi":"10.56553/popets-2026-0149","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1016/j.csi.2025.104071","name":"HEArgmax: Secure homomorphic encryption-based protocols for Argmax function","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.csi.2025.104071","authors":["Duy Tung Khanh Nguyen","Dung Hoang Duong","Willy Susilo","Yang-Wai Chow","The Anh Ta"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-13T13:03:31Z","doi":"10.1016/j.csi.2025.104071","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/s41872-025-00389-4","name":"Homomorphic encryption for secure and scalable predictive healthcare analytics: a review and case study","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s41872-025-00389-4","authors":["Prokash Gogoi","J. Arul Valan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-21T04:28:36Z","doi":"10.1007/s41872-025-00389-4","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1016/j.jisa.2026.104564","name":"Secure and efficient federated learning using attribute-based homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jisa.2026.104564","authors":["Isaac Amankona Obiri","Emmanuel Antwi-Boasiako","Eric Kuada","Abigail Akosua Addobea"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-03T00:33:48Z","doi":"10.1016/j.jisa.2026.104564","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-3-032-24063-7_1","name":"Deniable Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-24063-7_1","authors":["Wei-Chi Lo","Po-Wen Chi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-13T22:03:13Z","doi":"10.1007/978-3-032-24063-7_1","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.22266/ijies2026.0630.46","name":"Blockchain-enabled Privacy-preserving Federated Learning with Homomorphic Encryption for Multi-institutional Diabetic Retinopathy Detection","source":"crossref","abstract":"","url":"https://doi.org/10.22266/ijies2026.0630.46","authors":["Minnu C Tomy","C Malathy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-24T14:36:41Z","doi":"10.22266/ijies2026.0630.46","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/s10660-026-10101-y","name":"Privacy-preserving machine learning techniques based on homomorphic encryption for credit risk analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s10660-026-10101-y","authors":["V. V. L. Divakar Allavarpu","Vankamamidi S. Naresh","A. Krishna Mohan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-28T01:45:02Z","doi":"10.1007/s10660-026-10101-y","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.51584/ijrias.2026.110200078","name":"Privacy-Preserving and Secure AI-based Stress Monitoring Federated Learning on Wearable Edge Devices Using Homomorphic Encryption","source":"crossref","abstract":"The paper presents a new framework, HE-FedStress, with which it is feasible to monitor stress and preserve privacy in an AI-based approach to stress detection through federated learning and homomorphic encryption on wearable edge devices. The proposed system handles the essential privacy issues that come with decentralized health surveillance because the proposed system allows joint model training without exposing uncoded biometric information. The Edge wearable devices are local physiological signal temporal attention models that inference latency can process five-second windows with a latency of just 47 ms, implemented on photoplethysmography, electrodermal activity and accelerator data. The updates of the model are coded using the Pailier cryptosystem and sent to the central server where they could be aggregated securely without decrypting each individual contribution. The WESAD and SWELL-KW datasets have been empirically assessed to verify that HE-FedStress can give F1-scores of 89.7% and 85.2% respectively and retain centralized model performance with 92 and 94 % of this performance under full cryptographic protection against model-inversion attacks (compared to 34.7 % centralization in the standard federated learning case). The structure uses gradient quantization, which cuts down the communication load to 63KB/ round, and uses adaptive batch size to support heterogeneous device capacity. The computational optimizations (depthwise separable convolutions, selective attention pruning, and eight-bit quantization) allow incessant 24 hour execution of commercial wearables with only a 19% power consumption impact over plaintext federated learning. The design of the modular architecture is to meet GDPR and HIPAA data-localization standards and provide the ability to extend it to other healthcare applications. Therefore, this contribution can provide a practical, scalable stress monitoring solution to secure and personalized performance with a robust guarantee on cryptography, and can also indicate that the requirements of resource-constrained edge environments can be satisfied with a robust guarantee alongside real-time performance.","url":"https://doi.org/10.51584/ijrias.2026.110200078","authors":["Abdullah Ghanim Jaber*"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-14T05:29:34Z","doi":"10.51584/ijrias.2026.110200078","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/s11265-026-01991-0","name":"HEDP-WBAN: Homomorphic Encryption and Differential Privacy for Secure Edge-Based WBANs","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s11265-026-01991-0","authors":["Boopathi S","Jothikumar R","Sudhakar T","Vinayakumar Ravi","Alanoud Al Mazroa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-30T08:47:33Z","doi":"10.1007/s11265-026-01991-0","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.58346/jisis.2026.i2.026","name":"Homomorphic Encryption Schemes for Secure Outsourcing of Educational Data Analytics in the Cloud","source":"crossref","abstract":"With the growing use of cloud-based educational solutions, it is now possible to collect and analyse large volumes of data for personalized learning, performance assessment, and institutional decision-making. But, when sensitive educational information is outsourced and put in third-party cloud computing systems, there are significant issues of privacy and security, including the possibility of unauthorized access and data leakage during computation. While traditional encryption techniques work well for data at rest and in transit, they are not suitable for compute operations on encrypted data, thereby limiting their applicability to privacy-preserving analytics. To secure the outsourcing of educational data analytics, this paper presents a secure framework for outsourcing educational data analytics by using homomorphic encryption (HE) schemes to perform direct computation on encrypted datasets without revealing underlying sensitive information. The approach combines a partially homomorphic encryption scheme for linear statistical operations and an optimized levelled homomorphic encryption model for complex analytical operations like regression and classification. The proposed framework has been shown to provide a computation overhead of 37.5% with a privacy preservation rate of 98.3%. The system reduces the latency of the end-to-end processing by some 18-25% through optimized ciphertext operations and parallelization in the cloud. Furthermore, the accuracy of the analytical models is almost the same as that of the plaintext calculation, and the deviation is within 1.5%, which shows that the encrypted calculation is correct. The study concludes that although there is a challenge of computational overhead, proper selection of scheme and optimization strategies can be implemented to improve efficiency without compromising on the confidentiality of the data, thereby making it suitable for privacy-sensitive applications in the education domain in a cloud environment.","url":"https://doi.org/10.58346/jisis.2026.i2.026","authors":["Shakhnoza Uzokova","Obod Ergasheva","Kibriyo Marayimova","Sadoqat Jurayeva","Erkin Shopulatov","Gulnora Astanova","Alisher Tangriyev"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-29T06:31:18Z","doi":"10.58346/jisis.2026.i2.026","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/icicv68925.2026.11554858","name":"A Privacy-Preserving Federated Averaging Framework with Homomorphic Encryption and Hybrid Convolutional Transformer for Secure Distributed Utility Data Analytics","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicv68925.2026.11554858","authors":["Pullaiah Chowdary Vutla","Miss. Triveni Yenugu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-12T19:40:49Z","doi":"10.1109/icicv68925.2026.11554858","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/iolts69666.2026.11633692","name":"SVE-Based Acceleration of Homomorphic Encryption Arithmetic on ARM Neoverse Processors","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iolts69666.2026.11633692","authors":["Massimiliano Donati","Guido Falai","Samuele Bartorelli","Daniele Rossi","Sergio Saponara"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-04T19:13:07Z","doi":"10.1109/iolts69666.2026.11633692","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/icnc68183.2026.11416875","name":"Privacy-Preserving Federated Vision Transformer Learning Leveraging Lightweight Homomorphic Encryption in Medical AI","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icnc68183.2026.11416875","authors":["Al Amin","Kamrul Hasan","Liang Hong","Sharif Ullah"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-09T19:55:54Z","doi":"10.1109/icnc68183.2026.11416875","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.69996/jcai.2026007","name":"Homomorphic Encryption Based Video Copy Detection in Multi View Videos Stored in a Cloud","source":"crossref","abstract":"The rapid proliferation of multimedia content and the increasing adoption of cloud storage have led to growing concerns over unauthorized duplication and distribution of video content. This issue becomes particularly complex with multi-view videos, which offer multiple perspectives of the same scene and are frequently used in surveillance, entertainment, and immersive experiences. Ensuring the integrity and ownership of such content requires robust and privacy-preserving mechanisms. This project proposes a novel framework for detecting video copies in a multi-view environment while maintaining the confidentiality of the data using homomorphic encryption. To address the complexities of multi-view video analysis, the framework employs view-invariant feature extraction techniques that can robustly capture the spatio-temporal characteristics of video content across different perspectives. These features are resilient to transformations, such as rotation and scaling, ensuring effective matching even when copies differ in visual representation. By utilizing encrypted-domain matching algorithms, the system can compare newly uploaded or queried videos against the encrypted database to detect potential duplicates or pirated versions. Experimental results on benchmark multi-view video datasets demonstrate the effectiveness and efficiency of the proposed system. The homomorphic encryption-based detection mechanism achieves high accuracy in identifying copied videos while incurring minimal performance overhead. This research contributes to the fields of multimedia forensics, cloud security, and privacypreserving computing, offering a secure and scalable solution to combat digital video piracy in the age of cloud computing and ubiquitous media sharing","url":"https://doi.org/10.69996/jcai.2026007","authors":["Sk Anjaneyulu Babu","B Sai Krishna Jyothi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-18T09:58:43Z","doi":"10.69996/jcai.2026007","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/asp-dac66049.2026.11420771","name":"Viper: An ILP-Based Vectorization Framework for Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/asp-dac66049.2026.11420771","authors":["Weidong Yang","Xinmo Li","Xiangmin Guo","Jianfei Jiang","Naifeng Jing","Qin Wang","Zhigang Mao","Weiguang Sheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-10T19:51:15Z","doi":"10.1109/asp-dac66049.2026.11420771","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.13052/jcsm2245-1439.1528","name":"Blockchain and Fully Homomorphic Encryption for Secure Data Management in Smart Grids","source":"crossref","abstract":"The storage security and privacy protection of power plant ancillary service data face severe challenges, hindering power plant operation optimization and the efficient operation of the electricity market. This research aims to construct a secure and reliable data storage system for power plant ancillary services and establish a scientific and accurate performance evaluation method. To achieve this, a multi-layer technology fusion model based on blockchain, fully homomorphic encryption (FHE), and smart contracts is proposed. The architecture integrates blockchain for trusted data provenance, FHE for privacy-preserving computation, and smart contracts for automated business logic execution, forming a coherent and secure data management framework. Specifically, the study adopts a hybrid storage mode combining blockchain structure and private database. Secure interaction and homomorphic operations of encrypted data are achieved through smart contracts. An improved approximation-ideal solution sorting method is used, combined with fuzzy hierarchical analysis to determine indicator weights. The results showed that in the ancillary business data test of a provincial power system in 2023, the proposed storage scheme achieved a data leakage rate of 1% for 10,000 pieces of data and a tampering detection success rate of 98.99%. This performance evaluation method was applied to six cross-regional power plants, effectively distinguishing the performance differences of ancillary services among different power plants. The relative similarity of the frequency regulation scenario in new energy power plants was 0.85, which was 12% higher than that in thermal power plants. This research provides a reliable and secure storage path for power plant ancillary service data, promoting the digital transformation of the power system and the standardized development of the electricity market. However, the proposed approach may face adaptability challenges in cross-regional deployment due to varying grid regulations and data standards, and the computational overhead of fully homomorphic encryption could impact real-time performance in large-scale applications. Future work will focus on optimizing algorithm efficiency, reducing computational costs, and validating the framework across diverse regional power systems to enhance its generalizability and practical deployment.","url":"https://doi.org/10.13052/jcsm2245-1439.1528","authors":["Chu-Hui Li","Zhong-Ming Dong","Tian-Xiong Huang","Yi Dong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-01T00:32:57Z","doi":"10.13052/jcsm2245-1439.1528","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1016/j.aei.2025.104118","name":"ESFLM: Efficient and Secure Federated Learning Model with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.aei.2025.104118","authors":["Yang Li","Chunhe Xia","Chang Li","Xiaojian Li","Tianbo Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-22T16:14:55Z","doi":"10.1016/j.aei.2025.104118","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.37676/jmcs.v5i1.10303","name":"Performance Evaluation of Homomorphic Encryption Protocols for Cloud Data Processing","source":"crossref","abstract":"The rapid growth of cloud computing has driven the extensive adoption of cloud-based data storage and processing services across various sectors. Despite its advantages in scalability and efficiency, this paradigm raises significant concerns regarding data security and privacy, particularly when sensitive information is processed by third-party cloud providers. Homomorphic Encryption (HE) has emerged as a promising cryptographic solution to address these challenges, as it enables computations to be performed directly on encrypted data without requiring prior decryption. This study aims to examine the concepts, mechanisms, and protocols of Homomorphic Encryption for secure data processing in cloud environments. The research adopts a literature review method by analyzing various HE schemes, including Partially Homomorphic Encryption, Somewhat Homomorphic Encryption, and Fully Homomorphic Encryption, along with their applications in cloud-based systems. The results indicate that Homomorphic Encryption significantly enhances data confidentiality and privacy during cloud data processing. However, several challenges remain, particularly related to computational complexity and performance efficiency. Nevertheless, Homomorphic Encryption demonstrates strong potential as a foundational technology for developing secure and privacy-preserving cloud services, especially for handling sensitive data such as medical, financial, and personal information.","url":"https://doi.org/10.37676/jmcs.v5i1.10303","authors":["Lucy Amanda","Arlen Prima Dinova","Amir Faiq Al Hannan","Safira Azahra","Azkal Azkia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-30T08:05:06Z","doi":"10.37676/jmcs.v5i1.10303","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1016/j.procir.2026.05.228","name":"A Practical Case Study on Homomorphic Encryption for Secure and Sustainable Product Development in Sheet Metal Design","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.procir.2026.05.228","authors":["Simon Jess","Lukas Ihrig","Artur Krause","Claudius Messerschmidt","Felix Förster","Nikola Bursac"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-09T11:56:48Z","doi":"10.1016/j.procir.2026.05.228","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.23919/indiacom70271.2026.11526058","name":"Secure Homomorphic Encryption Approach for Privacy-Preserving Data Processing in Cloud Environments","source":"crossref","abstract":"","url":"https://doi.org/10.23919/indiacom70271.2026.11526058","authors":["V. Annapurna","Y. Kiran Kumar","Shanthi V","S. Satya Kumar","Thejo Lakshmi Gudipalli","Bhavana Godavarthi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-26T19:39:19Z","doi":"10.23919/indiacom70271.2026.11526058","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.58532/nbennurcai3","name":"PRIVACY-PRESERVING ANALYTICS FOR SERVERLESS DATABASE SYSTEMS USING HOMOMORPHIC ENCRYPTION AND FEDERATED DATA PROCESSING","source":"crossref","abstract":"The swift integration of cloud computing facilitates scalable data analytics across various application sectors, such as healthcare, finance, and industrial Internet of Things systems. Centralised cloud data processing presents notable privacy issues, given that sensitive information frequently requires transmission and storage on third-party infrastructures. This study presents a framework for privacy-preserving analytics in serverless cloud environments, integrating homomorphic encryption with federated data processing to tackle existing challenges. The proposed architecture facilitates collaborative data analytics among multiple participants while ensuring that raw datasets remain concealed from both the cloud and other participants. Homomorphic encryption facilitates the execution of computations on encrypted data, whereas federated analytics supports decentralised model training and data processing among distributed participants. The implementation of a serverless execution model facilitates the execution of encrypted aggregation and analytics tasks, ensuring scalability and cost efficiency. A formal system model alongside a mathematical formulation is introduced to delineate encrypted computation, federated aggregation, and privacy-aware optimisation constraints. The evaluation of the framework employs a systematic experimental design, contrasting the proposed method with traditional centralised analytics and federated learning benchmarks. The findings demonstrate that the suggested architecture maintains analytical utility and substantially improves privacy protection. The framework offers a viable solution for secure collaborative data processing in cloud environments by maintaining encryption of sensitive data throughout the analytics pipeline. The combination of homomorphic encryption, federated analytics, and serverless computing signifies a significant advancement in the development of reliable data analytics frameworks.","url":"https://doi.org/10.58532/nbennurcai3","authors":["Ravinder Kaur"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-09T07:29:41Z","doi":"10.58532/nbennurcai3","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1016/j.bspc.2026.109557","name":"Federated learning with homomorphic encryption for secure real time ECG anomaly detection: A multi institutional privacy preserving framework","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.bspc.2026.109557","authors":["Beyazit Bestami Yuksel","Ayse Yilmazer Metin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-11T16:10:09Z","doi":"10.1016/j.bspc.2026.109557","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-3-032-35374-0_12","name":"Fully Homomorphic Encryption for Matrix Arithmetic","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-35374-0_12","authors":["Craig Gentry","Yongwoo Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-10T13:51:32Z","doi":"10.1007/978-3-032-35374-0_12","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1051/epjconf/202638100034","name":"AsFHEP-Anti-MT Bootstrapping: A Selective Ciphertext Refresh Using Anti-Merkle Tree Bootstrapping for an Asymmetric Fully Homomorphic Encryption Protocol","source":"crossref","abstract":"Fully Homomorphic Encryption (FHE) enables computations over encrypted data but remains constrained by ciphertext noise growth. Bootstrapping restores the noise budget and supports deeper computations; however, conventional approaches generally refresh the entire ciphertext, even when only part of it has reached a critical noise level. This paper proposes Anti-Merkle Tree Bootstrapping (Anti-MT), a protocol-level mechanism for selective and traceable ciphertext refresh. The evaluated ciphertext is divided into ordered segments, and each segment is associated with a local noise budget and a predefined critical threshold. Segments whose budgets remain sufficient are preserved unchanged, whereas only critical segments undergo the refresh operation. The resulting segments are then reconstructed in their original order to produce a valid refreshed ciphertext. The correctness of the mechanism follows from plaintext preservation: unchanged segments remain valid encryptions of their original message components, while refreshed segments encrypt the same components with restored computational capacity. Anti-MT additionally records refresh decisions, segment indices, local budgets, and hash evidence in a protected configuration structure, providing an auditable history of ciphertext maintenance. Unlike techniques that redesign or accelerate the underlying bootstrapping primitive, Anti-MT controls where and when that primitive is applied. The proposed mechanism therefore reduces unnecessary refresh operations while preserving ciphertext confidentiality, computational correctness, structural integrity, and traceability. It provides a flexible refresh-management strategy for applications requiring repeated encrypted computation, particularly in sensitive and auditable environments.","url":"https://doi.org/10.1051/epjconf/202638100034","authors":["Soumia Benkou","Ahmed Asimi","Younes Asimi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-07T07:57:24Z","doi":"10.1051/epjconf/202638100034","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.70917/ijcisim-2026-4656","name":"Enhancing Data Privacy in Linear Regression Through Homomorphic Encryption","source":"crossref","abstract":"Big data analysis is required to predict future trends using machine learning. A lot of processing and computation is required to make this analysis. Conventional encryption methods may help to achieve privacy during the resting stage of data over the cloud. The computation of data needs to be in its original form or needs to be decrypted before any computation, which leaves the data again at risk and makes it more vulnerable. The latest homomorphic encryption algorithms provide a way to perform the computation over data in its encrypted form and help to analyze big data without compromising the privacy and security of data, even when data is in use. In this paper, we implemented the linear regression model over plain data and encrypted data to measure and analyze the fitness, MSE, and RMSE scores of models and the space complexity of plain data and encrypted data. The results show that the fitness score of the linear regression model for plain data and encrypted data is almost the same and differs by only 0.00003551, whereas the RMSE score is 386.9949011 and 364.03753641, respectively. It is observed that the overall performance of the linear regression model improved for encrypted data, and the error is also reduced while maintaining data privacy","url":"https://doi.org/10.70917/ijcisim-2026-4656","authors":["Krishnakumar D","Sonam Mittal","K.R. Ramkumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-14T19:45:16Z","doi":"10.70917/ijcisim-2026-4656","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1504/ijics.2026.10078235","name":"Security overview and performance assessment of fully homomorphic encryption in machine learning","source":"crossref","abstract":"","url":"https://doi.org/10.1504/ijics.2026.10078235","authors":["Davor Vinko","Kruno Miličević","Ivan Uglik","Adrijan Đurin","Madhurima Ray","Richard Lomotey"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T13:00:10Z","doi":"10.1504/ijics.2026.10078235","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.48175/ijarsct-32321","name":"HomoBlock-QC: A Homomorphic Encryption Assisted Blockchain Framework for Secure Crowdsensing Validation","source":"crossref","abstract":"These days, more smart devices are built into everyday gadgets. One fresh way to gather information is called mobile crowd-sensing. People can now share what they see using personal devices. Still, most current setups rely on outside platforms run by others. Trusting those central sources completely remains uncertain. On top of that, safety and privacy matters often get brushed aside. When MCS runs, different user details plus their trust levels come into view, often out in the open - yet calculations tied to keeping those details private can’t be checked. Here, we slot in blockchain within the MCS setup to create a system where data stays guarded, untouched, or falsely claimed, making sure payouts flow without bias. A fresh approach to picking participants includes privacy protections, letting everyone check outcomes - no outside judge needed. Because current crowd-sensing data raises privacy concerns along with efficiency issues during truth finding, an updated idea emerges: a private crowdsensing setup using repeated reasoning through secure shared calculations. The new round of tests shows that the updated method not only works but does so better than before. It means that users’ personal information is that bit better protected while at the same time enabling reliable performance.","url":"https://doi.org/10.48175/ijarsct-32321","authors":["J. Siddharth, B. Balaji, Manoj Kumar, Jonnadula Narasimharao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-01T15:48:09Z","doi":"10.48175/ijarsct-32321","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.3390/s26175337","name":"An Explainable Federated Intrusion Detection Framework for SDN Using Distributed Key Generation and Threshold Homomorphic Encryption","source":"crossref","abstract":"The rapid advancement of software-defined networking (SDN) has enhanced network programmability, centralized control, and traffic management flexibility, while also increasing exposure to sophisticated attacks targeting the control plane. Although federated learning (FL) enables collaborative intrusion detection without centralized raw data sharing, existing FL-based intrusion detection systems remain vulnerable to plaintext model update leakage, centralized cryptographic trust, limited interpretability, and insufficient validation in operational SDN environments. To address these limitations, this paper presents an explainable federated intrusion detection framework that integrates distributed key generation (DKG), CKKS-based threshold homomorphic encryption, collaborative decryption, and SHapley Additive exPlanations (SHAP). Unlike conventional HE-enabled FL systems that rely on a trusted authority or a globally shared secret key, the proposed framework removes the trusted key-generation dealer, avoids centralized custody of the complete secret key, and prevents any single client or aggregation server from independently decrypting ciphertexts using locally held key material. A gated recurrent unit (GRU)-based model is used for privacy-preserving intrusion detection, and SHAP provides global and local explanations of model decisions. The framework is further deployed in a real-time SDN testbed to evaluate the online inference pipeline following threshold-secured federated training. Computationally intensive cryptographic operations, including DKG, encrypted aggregation, and threshold decryption, are performed during offline training, while the converged global model enables low-latency inference at runtime. Experiments on the InSDN, CICDDoS2017, and CICDDoS2019 datasets with 4, 8, and 12 client federated configurations achieved detection accuracies above 99% across all datasets. The evaluation also examines encryption latency, collaborative decryption overhead, secure aggregation cost, communication complexity, and scalability. The results demonstrate that the proposed framework provides a practical balance among decentralized key management, privacy-preserving aggregation, explainability, detection performance, and real-time SDN deployment feasibility.","url":"https://doi.org/10.3390/s26175337","authors":["S. M. Shamim","Yuta Kodera","Md. Arshad Ali","Yasuyuki Nogami"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-24T11:47:19Z","doi":"10.3390/s26175337","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-3-032-29909-3_38","name":"Hybrid Privacy-preserving Histopathological Image Classification Using Fully Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-29909-3_38","authors":["Dominik Moskalewicz","Bogusław Cyganek"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-27T03:32:12Z","doi":"10.1007/978-3-032-29909-3_38","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1016/j.bspc.2026.110550","name":"Secure Federated Multimodal Learning with Attention-Weighted Transformers and Homomorphic Encryption for Privacy-Preserving Neuroimaging Analysis","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.bspc.2026.110550","authors":["N. Balaji","V. Sathya","P. Sirenjeevi","Velliangiri Sarveshwaran"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-14T12:18:27Z","doi":"10.1016/j.bspc.2026.110550","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.62056/ayl83z10k","name":"Application-Aware Approximate Homomorphic Encryption","source":"crossref","abstract":"Fully Homomorphic Encryption (FHE) is a powerful tool for performing computations on encrypted data. The Cheon-Kim-Kim-Song (CKKS) scheme, an instantiation of approximate FHE, is particularly effective for privacy-preserving machine learning applications over real and complex numbers. Although CKKS offers clear efficiency advantages, confusion persists around accurately describing applications in FHE libraries and securely instantiating the scheme for these applications, particularly after the key recovery attacks by Li and Micciancio (EUROCRYPT'21) for the IND-CPA^D setting. There is presently a gap between the application-agnostic, generic definition of IND-CPA^D, and efficient, application-specific instantiation of CKKS in software libraries, which led to recent attacks by Guo et al. (USENIX Security'24). To close this gap, we introduce the notion of application-aware homomorphic encryption (AAHE) and devise related security definitions. This model corresponds more closely to how FHE schemes are implemented and used in practice, and provides a mechanism to identify and address potential vulnerabilities in popular libraries. We then propose an application specification language (ASL) and formulate guidelines for implementing the AAHE model to achieve IND-IND-CPA^D security for practical applications of CKKS. We present a proof-of-concept implementation of the ASL in the OpenFHE library showing how the attacks by Guo et al. can be countered. Moreover, we show that our new model and ASL can be used for the secure and efficient instantiation of exact FHE schemes and to counter the recent IND-IND-CPA^D attacks by Cheon et al. (CCS'24) and Checri et al. (CRYPTO'24).","url":"https://doi.org/10.62056/ayl83z10k","authors":["Andreea Alexandru","Ahmad Al Badawi","Daniele Micciancio","Yuriy Polyakov"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-08T23:39:47Z","doi":"10.62056/ayl83z10k","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/jiot.2026.3699102","name":"Erratum to “Verifiable Privacy-Preserving Neural Network Inference via Multi-Key Homomorphic Encryption”","source":"crossref","abstract":"","url":"https://doi.org/10.1109/jiot.2026.3699102","authors":["Guanghui He","Zihan Yuan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-08T19:43:59Z","doi":"10.1109/jiot.2026.3699102","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1016/j.jpdc.2026.105298","name":"Secure and intelligent IoT DDoS detection framework using modified AlexNet and blockchain-based Paillier homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.jpdc.2026.105298","authors":["Dhanya M Rajan","D John Aravindhar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-29T15:55:51Z","doi":"10.1016/j.jpdc.2026.105298","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.21928/uhdjst.v10n1y2026.pp116-129","name":"Optimized Hybrid Homomorphic Signed-Integers Encryption: Addressing Challenges and Enhancing Capabilities","source":"crossref","abstract":"Homomorphic cryptography produces encrypted data that supports computation, but current exponential techniques face critical limitations: they leak plaintext information for negative values and fail on trivial values (0, ±1). Additional challenges include private key disclosure, encryption failure under specific conditions, and poor performance and storage efficiency. This paper addresses these limitations through comprehensive analyses, identifying the mathematical conditions that cause encryption failure, and examining their relationship to the modulus’s randomness. A hybrid encryption approach is proposed in which the linear technique complements the exponential technique, particularly for negative and trivial values. The scheme uses the Carmichael function λ(n) to reduce computational costs and provides a unified decryption algorithm that supports signed-integer operations. The unified decryption formula with rounding operations successfully handles positive and negative values, enabling true homomorphic subtraction alongside existing addition and multiplication properties. Decryption is optimized using a single prime key, enhancing security and reducing complexity. Experimental verification shows that the proposed technique passes all 15 National Institute of Standards and Technology tests, with probability values ranging from 0.122325 to 0.991468. Improvements include a 71.8% reduction in exponent size, 89.5% faster encryption, 67.2% faster decryption, and a 33.4% reduction in storage requirements, making the proposed hybrid technique suitable for resource-constrained secure computation applications.","url":"https://doi.org/10.21928/uhdjst.v10n1y2026.pp116-129","authors":["Abdulrahman Tawfeeq Jalal","Mohammed Anwar Mohammed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-30T01:00:13Z","doi":"10.21928/uhdjst.v10n1y2026.pp116-129","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.15680/ijircce.2026.1407058","name":"Privacy-Preserving Distributed Training Architecture for Cyber Forensics using Blockchain and Homomorphic Encryption","source":"crossref","abstract":"The rapid growth of cybercrime, ransomware attacks, digital fraud, and large-scale cyber threats has significantly increased the need for secure and collaborative cyber forensic investigations. Traditional machine learning approaches often require organizations to share or centralize sensitive forensic datasets, creating challenges related to privacy, confidentiality, data ownership, and security. To address these limitations, this project proposes a PrivacyPreserving Distributed Training Architecture for Cyber Forensics using Blockchain and Homomorphic Encryption. The proposed framework integrates Federated Learning, Distributed Learning, CKKS-based Homomorphic Encryption, Blockchain Technology, and a Secure Model Exchange Space to enable multiple agencies to collaboratively train machine learning models without exposing their raw forensic data. Federated Learning allows organizations to train models locally and securely aggregate encrypted model updates, while Distributed Learning enables encrypted dataset partitions to be processed collaboratively by helper nodes without revealing the original data. CKKS Homomorphic Encryption protects sensitive information during computation, and blockchain technology provides decentralized trust through secure node authentication, transparent validation, immutable audit trails, and trusted model exchange among participating agencies. The framework is implemented using Python, Flask, Scikit-learn, TenSEAL, Ganache, Solidity, and Web3.py, providing a web-based platform for collaborative project management, encrypted training, blockchain monitoring, secure model sharing, performance evaluation, and cyber forensic prediction. Experimental results demonstrate that the proposed architecture successfully supports secure collaborative learning, encrypted computation, blockchain-based validation, and trusted model sharing while maintaining effective prediction performance. By integrating distributed learning, federated learning, homomorphic encryption, and blockchain into a unified framework, the proposed system provides a scalable, secure, and privacy-preserving solution for next-generation cyber forensic intelligence, enabling organizations to collaboratively strengthen cybersecurity without compromising the privacy, confidentiality, or ownership of sensitive forensic data","url":"https://doi.org/10.15680/ijircce.2026.1407058","authors":["R. Sridevi","P. Sanjay Kumar"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-31T05:46:59Z","doi":"10.15680/ijircce.2026.1407058","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/access.2026.3716674","name":"Cybersecure PMLSM Control System With Updatable Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2026.3716674","authors":["Hiroaki Kawase","Kaoru Teranishi","Ryohei Hisamatsu","Kiminao Kogiso"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-24T19:08:01Z","doi":"10.1109/access.2026.3716674","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1016/j.aei.2026.104973","name":"Privacy-preserving distributed optimization for multi-UAV systems with threshold homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.aei.2026.104973","authors":["Zhongyuan Zhao","Jihua Fang","Fu Zhang","Shuai Chen","Feng Xie","Hao Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-06-20T06:45:01Z","doi":"10.1016/j.aei.2026.104973","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/s00371-025-04261-5","name":"Privacy-aware plant disease detection: federated learning with homomorphic encryption on image data","source":"crossref","abstract":"","url":"https://doi.org/10.1007/s00371-025-04261-5","authors":["Md Zahin Muntaqim","Hasan Muhammad Kafi","Tangin Amir Smrity"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-06T15:05:45Z","doi":"10.1007/s00371-025-04261-5","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/iciccs67901.2026.11502843","name":"Secure and Efficient Public-Key-Based Deduplication with Homomorphic Key Encryption for Cloud Data Warehouse","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciccs67901.2026.11502843","authors":["CH. Sushma","C. Madhavi Latha","B. Sruthi Bai","B. Eswar Charan","Shaik Salam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-08T19:37:15Z","doi":"10.1109/iciccs67901.2026.11502843","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/sii64115.2026.11404511","name":"Secure Supervisory Control of Discrete Event Systems using Homomorphic Encryption\n                    <sup>*</sup>","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sii64115.2026.11404511","authors":["Ana Clara P. Gonçalves","Patrícia N. Pena","Lucas V. R. Alves"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-27T20:47:13Z","doi":"10.1109/sii64115.2026.11404511","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1145/3797902","name":"Systematic Review on Verifiable Fully Homomorphic Encryption: Integrity, Proofs and Open Problems","source":"crossref","abstract":"Fully Homomorphic Encryption (FHE) enables arbitrary computations on encrypted data but lacks mechanisms to ensure the integrity of those computations. In particular, verifying that algorithm inputs are correct or that the intended algorithm was indeed executed remains an open challenge. This article addresses the issue by making two key contributions. First, we perform a comprehensive analysis of integrity faults in FHE, culminating in the definition of verifiable FHE as a novel concept to tackle these concerns. Second, we present a systematic review of existing approaches aimed at providing verifiable FHE, assessing their strengths and weaknesses, as well as their applicability in practical settings. Our findings reveal that, despite promising advances, significant gaps persist in both the theoretical foundations and the practical deployment of verifiable FHE. We conclude by outlining future research directions necessary to achieve verifiable FHE computations.","url":"https://doi.org/10.1145/3797902","authors":["Julen Bernabé-Rodríguez","Oscar Lage Serrano","Jasone Astorga Burgo","Eduardo Jacob Taquet"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-20T11:34:50Z","doi":"10.1145/3797902","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1186/s13677-026-00964-9","name":"Efficient IoT data security using lattice-based multi-key homomorphic proxy re-encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1186/s13677-026-00964-9","authors":["Shalini Dhiman","Ganesh Kumar Mahato","Uddalak Chatterjee","Swarnendu Kumar Chakraborty"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-10T12:46:41Z","doi":"10.1186/s13677-026-00964-9","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1088/1402-4896/ae9650","name":"A spatial crowdsourced single-point collaborative convex hull computation protocol based on quantum homomorphic encryption","source":"crossref","abstract":"Abstract With the growing demand for location privacy protection and collaborative computing in spatial crowdsourcing (SC) tasks, traditional encryption methods based on computational complexity face threats from quantum computing and efficiency bottlenecks. To address this, this paper proposes a secure multi-party quantum comparison (SMQC) protocol based on quantum homomorphic encryption. By utilizing a quantum comparator and a key update mechanism, this protocol enables secure comparison of numerical values among multiple parties while maintaining encryption. Building on this foundation, the SMQC protocol is further applied to the SC environment to construct a single-point collaborative convex hull computation protocol. Through extreme point determination and convex hull boundary verification, this protocol efficiently performs multi-party collaborative convex hull computation while protecting the location privacy of participants. Analysis indicates that the convex hull computation framework built on this protocol is reliable and secure, further expanding the application of quantum-secure solutions in SC scenarios such as single-point collaborative obstacle avoidance and collaborative spatial protection.","url":"https://doi.org/10.1088/1402-4896/ae9650","authors":["Bai Liu","Xinguo Wang","Runhua Shi","Shuheng Qi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-06T22:53:01Z","doi":"10.1088/1402-4896/ae9650","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1007/978-3-032-10756-5_31","name":"Performance Analysis of Fully Homomorphic Encryption Scheme for Privacy Protection in Cloud Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-032-10756-5_31","authors":["Sunita Godara","Simran Choudhary"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-02T01:28:26Z","doi":"10.1007/978-3-032-10756-5_31","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1109/icce70609.2026.11658470","name":"Secure and Efficient UAV-Based Face Detection via Homomorphic Encryption and Edge Computing","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icce70609.2026.11658470","authors":["Nguyen Van Duc","Bui Duc Manh","Quang-Trung Luu","Dinh Thai Hoang","Van-Linh Nguyen","Diep N. Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-24T19:15:45Z","doi":"10.1109/icce70609.2026.11658470","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.18653/v1/2026.acl-long.644","name":"Hyperion: Private Token Sampling with Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.18653/v1/2026.acl-long.644","authors":["Lawrence Lim","Jiaming Liu","Vikas Kalagi","Divyakant Agrawal","Amr El Abbadi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-01T12:25:50Z","doi":"10.18653/v1/2026.acl-long.644","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1142/s0219498827500642","name":"On compression functions over groups with applications to homomorphic encryption","source":"crossref","abstract":"Fully homomorphic encryption (FHE) enables an entity to perform arbitrary computation on encrypted data without decrypting the ciphertexts. An ongoing group-theoretical approach to construct an FHE scheme uses a certain “compression” function [Formula: see text] implemented by group operations on a given finite group [Formula: see text], which satisfies that [Formula: see text] and [Formula: see text] where [Formula: see text] is some element of order [Formula: see text]. The previous work gave an example of such a function over the symmetric group [Formula: see text] by just a heuristic approach. In this paper, we systematically study the possibilities of such a function over various groups. We show that such a function does not exist over any solvable group [Formula: see text] (such as an Abelian group and a smaller symmetric group [Formula: see text] with [Formula: see text]). We also construct such a function over the alternating group [Formula: see text] that has a shortest possible expression. Moreover, by using this new function, we give a reduction of a construction of an FHE scheme to a construction of a homomorphic encryption scheme over the group [Formula: see text], which is more efficient than the previously known reductions.","url":"https://doi.org/10.1142/s0219498827500642","authors":["Koji Nuida"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-31T13:01:31Z","doi":"10.1142/s0219498827500642","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.58414/scientifictemper.2026.17.2.09","name":"AI-Driven Swarm-Optimized Adaptive Routing Using Quantum-Inspired Neural Scheduling with Homomorphic Encryption","source":"crossref","abstract":"The fast increase in network traffic and the moving nature of nodes in modern communication systems have led to the need to have intelligent ways of analysing traffic, managing clusters, efficiently routing, and ensuring safety in the transmission of data. Conventional approaches are not always able to deal with the complexity and size of multi-criteria network settings. This paper introduces a new multi-phase intelligent network management framework which combines deep learning, evolutionary optimization, and a quantum-inspired algorithm to improve performance, reliability, and security. The initial step uses the Hybrid Autoencoder-GAN Behaviour Synthesizer (HAE-GANBS) to examine traffic data of the Multi-Criteria Network Routing Dataset, recreate typical traffic, and create synthetic flows, which augment the feature description. The enhanced dataset is used as input into the Hybrid Spiking Neural-Evolutionary Cluster Leader Selector (HSN-ECLS) which determines the best cluster leaders through temporal spike-train modelling and multi-criteria fitness assessment. Predictive Evolutionary Trust-Aware Scheduler and Router (PETASR) is a predictive scheduling based on evolutionary operations to schedule routing paths based on future traffic, node availability, and trustworthiness. Lastly, the Quantum-Inspired Neural Scheduler-Router with Homomorphic Encryption (QINSR-HE) ensures the safety of information transfer, providing the opportunity to use encrypted, adaptive, and trust-conscious routing. The evaluation of performance indicates that the framework is more efficient in large, volatile network systems due to enhanced traffic predictability, cluster stability, routing competence, and secure information transfer over the network.","url":"https://doi.org/10.58414/scientifictemper.2026.17.2.09","authors":["A. Jafar Ali","G. Ravi","D.I. George Amalarethinam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-10T11:27:21Z","doi":"10.58414/scientifictemper.2026.17.2.09","addedAt":"2026-08-31T06:41:46.001Z","updatedAt":"2026-08-31T06:41:46.001Z"},{"id":"doi:10.1103/physreva.90.050303","name":"Limitations on information-theoretically-secure quantum homomorphic encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1103/physreva.90.050303","authors":["Li Yu","Carlos A. Pérez-Delgado","Joseph F. Fitzsimons"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2014-11-10T17:08:56Z","doi":"10.1103/physreva.90.050303","addedAt":"2026-08-31T06:41:46.064Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1016/j.cosrev.2026.101052","name":"A critical analysis on homomorphic searchable encryption architectures and security models over multi-medium data","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.cosrev.2026.101052","authors":["Aiman Sultan","Tayyaba Anwer","Shahzaib Tahir","Hasan Tahir","Fawad Khan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-26T09:06:17Z","doi":"10.1016/j.cosrev.2026.101052","addedAt":"2026-08-31T06:41:46.064Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.30574/ijsra.2025.17.2.3077","name":"Review on Cloud Data Security Using VGG19-Deep Learning and Homomorphic Encryption","source":"crossref","abstract":"Data is the new currency as lot of the user’s presence online is an upward trend. As a consequence the data storage on the various cloud platforms has been a new normal. Data security in cloud has turn formidable due to unique security issues and challenges. Conventional methods on security may not always cater a proper barter between computational efficiency and data security. This review paper discusses the blending facial key features of the face obtained from VGG19 deep learning with homomorphic encryption to enrich cloud data security. The VGG19 allows for robust feature extraction from face and facial key points for authentication, while homomorphic encryption scales computation in encrypted form; it acquire enhanced accuracy with scalability and preservation of privacy. Thus, this method ensure a better approach in next-generation cloud security frameworks.","url":"https://doi.org/10.30574/ijsra.2025.17.2.3077","authors":["Chandrasekhar Tadi.","Basanta Th.","Swaminathan J.N."],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-19T05:24:20Z","doi":"10.30574/ijsra.2025.17.2.3077","addedAt":"2026-08-31T06:41:46.064Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/icicdt59917.2023.10332291","name":"High-Throughput Key Switching Accelerator for Homomorphic Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icicdt59917.2023.10332291","authors":["Zeyu Wang","Makoto Ikeda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-12-06T18:24:27Z","doi":"10.1109/icicdt59917.2023.10332291","addedAt":"2026-08-31T06:41:46.064Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/cdc45484.2021.9683696","name":"Resilient Homomorphic Encryption Scheme for Cyber-Physical Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cdc45484.2021.9683696","authors":["Moritz Fauser","Ping Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-02-01T15:50:18Z","doi":"10.1109/cdc45484.2021.9683696","addedAt":"2026-08-31T06:41:46.064Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1063/5.0227839","name":"A systematic review of homomorphic encryption techniques to preserve confidentiality in cloud environment","source":"crossref","abstract":"","url":"https://doi.org/10.1063/5.0227839","authors":["Krishnakumar Durai","Ramkumar Ketti Ramachandran","Sonam Mittal"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-11T17:00:49Z","doi":"10.1063/5.0227839","addedAt":"2026-08-31T06:41:46.064Z","updatedAt":"2026-08-31T06:41:46.064Z"},{"id":"doi:10.1109/icimia67127.2025.11200738","name":"Privacy Preservation in Distributed Healthcare Systems: A Review of Differential Privacy, Homomorphic Encryption, and Hybrid Approaches","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icimia67127.2025.11200738","authors":["K Mahesh Babu","M V S S Nagendranath"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-10-20T17:48:22Z","doi":"10.1109/icimia67127.2025.11200738","addedAt":"2026-08-31T06:41:46.065Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.37933/nipes/7.4.2025.si112","name":"Homomorphic Encryption, Its Schemes and Libraries: A Mini Review","source":"crossref","abstract":"Homomorphic encryption is an innovative cryptographic technique that allows operation on ciphertexts without decrypting them. Such encryption allows data to be handled directly in its encrypted state while maintaining confidentiality, and it finds particular use in cloud computing and third-party data storage scenarios. This mini-review discusses various homomorphic encryption schemes and categorizes them into three broad types: Partially Homomorphic Encryption (PHE), Somewhat Homomorphic Encryption (SHE), and Fully Homomorphic Encryption (FHE). PHE accommodates unbounded operations for addition or multiplication, showing algorithms like RSA, Goldwasser-Micali, El-Gamal, and Paillier. SHE limits the homomorphic operations but supports small computation; the BGN and CKKS schemes fall under this category. FHE, with its capability to accommodate any computation as shown in the BGV and BFV scheme, is founded on bootstrapping techniques developed by Gentry. This review identifies significant advancements in the field, discusses the efficiency and versatility of various homomorphic schemes, and highlights the importance of these systems for ensuring data privacy in current digital settings.","url":"https://doi.org/10.37933/nipes/7.4.2025.si112","authors":["Olabode Idowu-Bismark","Nicol Ituh","Kennedy Okokpujie","Oluwadamilola Oshin","Queen Busayo Sodipo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-24T12:45:18Z","doi":"10.37933/nipes/7.4.2025.si112","addedAt":"2026-08-31T06:41:46.065Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.14257/ijsia.2014.8.2.23","name":"Review of How to Construct a Fully Homomorphic Encryption Scheme","source":"crossref","abstract":"","url":"https://doi.org/10.14257/ijsia.2014.8.2.23","authors":["Zhi-gang Chen","Jian Wang","Liqun Chen","Xin-xia Song"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2015-08-26T01:42:41Z","doi":"10.14257/ijsia.2014.8.2.23","addedAt":"2026-08-31T06:41:46.065Z","updatedAt":"2026-08-31T06:41:46.065Z"},{"id":"doi:10.7551/mitpress/15354.003.0003","name":"Preface","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0003","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","doi":"10.7551/mitpress/15354.003.0003","addedAt":"2026-08-31T06:41:47.438Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.7551/mitpress/15354.003.0004","name":"Introduction","source":"crossref","abstract":"","url":"https://doi.org/10.7551/mitpress/15354.003.0004","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-25T16:46:09Z","doi":"10.7551/mitpress/15354.003.0004","addedAt":"2026-08-31T06:41:47.438Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1017/9781009299534.007","name":"Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1017/9781009299534.007","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-26T00:05:54Z","doi":"10.1017/9781009299534.007","addedAt":"2026-08-31T06:41:47.438Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.32920/31373656","name":"The Question of Privacy in the Quantum Era: A Systematic Literature Review on Privacy by Design, Differential Privacy, and Privacy Engineering Frameworks Using NLP Technqiues","source":"crossref","abstract":"&lt;p dir=\"ltr\"&gt;Quantum technology is set to revolutionize the way we interact with a variety of different industries- disrupting people, processes, and other technologies at an exponential rate. As of now, the majority of our trusted encryption methods, such as Rivest-Shamir-Adleman (RSA), Elliptic Curve Digital Signature Algorithm (ECDSA), and Diffie-Hellman are not quantum-resistant. Therefore, a malicious actor in possession of a powerful enough quantum computer will be able to break encryption on files that may include sensitive data with ease in minimal time. This thesis provides insight on the current privacy landscape in quantum literature and demonstrates the existing research gap that requires further research contributions via a systematic literature review using two NLP techniques for keyword extraction. A taxonomy is produced as an outcome of a systematic review on 61 papers published between 2003 and 2023 with at least 5 citations, using two different natural language processing methods, deep learning and NVIVO.&lt;/p&gt;","url":"https://doi.org/10.32920/31373656","authors":["Nour Mousa"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-19T20:00:57Z","doi":"10.32920/31373656","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.59350/yp4dg-6k357","name":"Differential Privacy: A Primer","source":"crossref","abstract":"Differential Privacy (DP) is a mathematical framework that protects individual privacy in data analysis while allowing useful insights to be extracted. It works by adding carefully calibrated noise to data or query results, ensuring that including or excluding any single individual's data doesn't significantly change the analysis outcomes.","url":"https://doi.org/10.59350/yp4dg-6k357","authors":["Abhishek Tiwari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-06T21:02:59Z","doi":"10.59350/yp4dg-6k357","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.7717/peerj-cs.3916/fig-5","name":"Figure 5: Architecture of proposed ViT with differential privacy.","source":"crossref","abstract":"","url":"https://doi.org/10.7717/peerj-cs.3916/fig-5","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-21T15:47:15Z","doi":"10.7717/peerj-cs.3916/fig-5","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.21203/rs.3.rs-1847248/v1","name":"Have The Cake And Eat It Too: Differential Privacy Enables Privacy And Precise Analytics","source":"europepmc","abstract":"Abstract Existing research in differential privacy, whose applications have exploded across functional areas in the last few years, describes an intrinsic trade-off between the privacy of a dataset and its utility for analytics. Resolving this trade-off critically impacts potential applications of differential privacy to protect privacy in datasets even while enabling analytics using them. In contrast to the existing literature, this paper shows how differential privacy can be employed to precisely—not approximately—retrieve the analytics on the original dataset.We examine, conceptually and empirically, the impact of noise addition on the quality of data analytics. We show that the accuracy of analytics following noise addition increases with the privacy budget and the variance of the independent variable. Also, the accuracy of analytics following noise addition increases disproportionately with an increase in the privacy budget when the variance of the independent variable is greater. Using actual data to which we add Laplace noise, we provide evidence supporting these two predictions. We then demonstrate our central thesis that, once the privacy budget employed for differential privacy is declared and certain conditions for noise addition are satisfied, the slope parameters in the original dataset can be accurately retrieved using the estimates in the modified dataset of the variance of the independent variable and the slope parameter. Thus, differential privacy can enable robust privacy as well as precise data analytics.","url":"https://doi.org/10.21203/rs.3.rs-1847248/v1","authors":["Rishabh Subramanian"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-1847248/v1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.2139/ssrn.4419512","name":"Leveraging Differential Privacy for Targeted Advertisements","source":"crossref","abstract":"Differential Privacy offers the online advertising industry new means to increase consumer privacy by obfuscating consumer data. While achieving the same privacy, these means decrease targeting accuracy differently, subsequently reducing advertisers’ willingness to pay (WTP) for targeted advertisements. Up to now, it has proven challenging to trade-off between privacy and advertisers’ WTP because of the unknown impacts of (i) privacy on targeting accuracy and (ii) targeting accuracy on advertisers’ WTP. This article presents these impacts based on (i) an analytical model explaining how privacy affects targeting accuracy and (ii) an empirical study estimating the impact of targeting accuracy on advertisers’ WTP. The analytical findings show heterogeneity of the privacy’s impact on targeting accuracy depending on how the industry obfuscates data. The empirical results indicate that a 1% decline in targeting accuracy reduces advertisers’ WTP by 1.16%. Combining the analytical and empirical results reveals that different means to obfuscate data that yield the same 5% increase in privacy could reduce advertisers’ WTP by 10.44% (i.e., -$0.31 CPM) or 16.24% (i.e., -$0.48 CPM). Therefore, these findings could provide an opportunity for the industry to improve consumer privacy without needlessly decreasing advertisers’ WTP.","url":"https://doi.org/10.2139/ssrn.4419512","authors":["Lennart Kraft"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-04-28T15:17:02Z","doi":"10.2139/ssrn.4419512","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.32657/10356/69204","name":"Algorithms for synthetic data release under differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10356/69204","authors":["Jun Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-10-02T11:05:04Z","doi":"10.32657/10356/69204","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1007/978-3-319-62004-6_14","name":"Correlated Differential Privacy for Non-IID Datasets","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-62004-6_14","authors":["Tianqing Zhu","Gang Li","Wanlei Zhou","Philip S. Yu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-08-22T09:03:04Z","doi":"10.1007/978-3-319-62004-6_14","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.54014/h0jc-2rks","name":"Stability and differential privacy of stochastic gradient methods","source":"crossref","abstract":"","url":"https://doi.org/10.54014/h0jc-2rks","authors":["Zhenhuan Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-28T13:58:07Z","doi":"10.54014/h0jc-2rks","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1002/spy2.70194","name":"Image Privacy Protection Model Based on Differential Privacy and Improved\n                    <scp>CIAGAN</scp>\n                    Algorithm","source":"crossref","abstract":"ABSTRACT With the popularity of the Internet and intelligent devices, the privacy protection of image data has become an important problem that needs to be solved urgently. Traditional privacy protection methods such as anonymization have limitations in processing image data and cannot balance data availability and privacy. Given this, an improved generative adversarial network image privacy protection model that combines facial feature enhancement, differential privacy, and gradient noise addition is constructed, aiming to effectively protect sensitive information in image data. The performance test results showed that when the dataset size was 1400, the average encryption and decryption time of the model were 1.68 and 1.52 s, respectively. The privacy protection success rates in the Top‐1 and Top‐10 protection success rate tests were 95.13% and 92.07%. In the four portrait restoration tests, the similarity between the restored image and the original image was 88.67%, 96.34%, 98.76%, and 92.47%, which were the best among the comparison models. The experiment shows that the proposed portrait privacy protection model outperforms existing methods in terms of privacy protection strength and image generation quality, and has good comprehensive performance.","url":"https://doi.org/10.1002/spy2.70194","authors":["Xue Duan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-23T09:36:57Z","doi":"10.1002/spy2.70194","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.2139/ssrn.7355647","name":"Estimating Latent Heterogeneity Under Differential Privacy","source":"crossref","abstract":"This paper studies a simple linear panel model whose coefficients follow multi-dimensional group patterns, with estimates released under unit-level differential privacy. When each combination of parameters is estimated on its own, and some combinations are sparse, privacy noise dominates their estimation error. Estimating one membership per heterogeneity dimension per unit removes it. The method achieves differential privacy and √(NT) consistency for the group parameter estimates under regularity conditions including T/N → 0 and T → ∞. The estimated group parameters are also robust to weak identification of a subset of combinations.","url":"https://doi.org/10.2139/ssrn.7355647","authors":["Peng Shao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-26T17:38:56Z","doi":"10.2139/ssrn.7355647","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.29012/jpc.v4i2.621","name":"Random Differential Privacy","source":"crossref","abstract":"We propose a relaxed privacy definition called {\\em random differential privacy} (RDP). Differential privacy requires that adding any new observation to a database will have small effect on the output of the data-release procedure. Random differential privacy requires that adding a {\\em randomly drawn new observation} to a database will have small effect on the output. We show an analog of the composition property of differentially private procedures which applies to our new definition. We show how to release an RDP histogram and we show that RDP histograms are much more accurate than histograms obtained using ordinary differential privacy. We finally show an analog of the global sensitivity framework for the release of functions under our privacy definition.","url":"https://doi.org/10.29012/jpc.v4i2.621","authors":["Robert Hall","Larry Wasserman","Alessandro Rinaldo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-02-27T14:38:30Z","doi":"10.29012/jpc.v4i2.621","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1016/j.array.2025.100381","name":"GuardianAI: Privacy-preserving federated anomaly detection with differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.array.2025.100381","authors":["Abdulatif Alabdulatif"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-05T19:32:37Z","doi":"10.1016/j.array.2025.100381","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.33140/jmtcm.03.01.01","name":"Augmented Differential Privacy Framework for Data Analytics","source":"crossref","abstract":"Differential privacy has emerged as a popular privacy framework for providing privacy preserving noisy query answers based on statistical properties of databases. It guarantees that the distribution of noisy query answers changes very little with the addition or deletion of any tuple. Differential enjoys popular reputation that providing privacy without building any assumptions about the data and protecting against attackers who know all but one record. Differential privacy is a relatively new field of research. Most users have a limited experience in managing differential privacy parameters and achieving a suitable level of privacy without affecting the quality of the analysis. A vast majority of users is still learning how to effectively apply differential privacy in practice. In this paper, we discussed: on the proposed augmented framework which enables the differential privacy data of any given query, the various differential privacy techniques, metrics for the privacy &amp; utility tradeoff of the data and efficacy of the framework. Discussed state of the art of different differential privacy techniques defined in the framework Laplace, Laplace bounded, Randomized response and Exponential for different data types. The augmented framework consists of three parts one on privacy parameter inputs to control interactively and iteratively on the querying the data , the various differential privacy techniques, the metrics to measure privacy and utility threshold which allows the data analyst to evaluate the accuracy of the privacy safe data for selecting the privacy guaranteed data within the given privacy budget. The framework takes any dataset as input and, generates another dataset which is structurally and statistically very similar original dataset. The newly generated dataset has much stronger privacy guarantee on the selected sensitive and non-sensitive datatypes. We have also demonstrated analytical models developed using the privacy safe data from the framework as substitute to the models developed on the original datasets. We have demonstrated the framework and analytical model with sample data sets to present the similarity of original and differential privacy safe datasets.","url":"https://doi.org/10.33140/jmtcm.03.01.01","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-02T08:26:21Z","doi":"10.33140/jmtcm.03.01.01","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.5220/0011275300003283","name":"Federated Naive Bayes under Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0011275300003283","authors":["Thomas Marchioro","Lodovico Giaretta","Evangelos Markatos","Šarūnas Girdzijauskas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-07-14T20:19:10Z","doi":"10.5220/0011275300003283","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.52202/079017-2869","name":"Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.52202/079017-2869","authors":["Zeki Kazan","Jerome Reiter"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-03T11:20:56Z","doi":"10.52202/079017-2869","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.70675/362ab197z0f03z4d40zb0d4z43c13b3de6c5","name":"Differential privacy for metric spaces : information-theoretic models for privacy and utility with new applications to metric domains","source":"crossref","abstract":"Confidentialité différentielle pour les espaces métriques : modèles théoriques de l'information pour la confidentialité et l'utilité avec de nouvelles applications aux domaines métriques La \"differential privacy\", introduite par Dwork et al. en 2006, est devenue la référence en matière de protection de la vie privée dans les ensembles de données statistiques. Malgré sa popularité généralisée, son utilisation dans d'autres domaines a été relativement limitée. Dans cette thèse, nous explorons une généralisation de la \"differential privacy\" pour les domaines métriques appelés d-privacy. Notre approche intègre un cadre théorique de l'information pour analyser les flux d'informations, ce qui nous permet de fournir une caractérisation structurelle de la d-privacy et d'analyser ses propriétés de confidentialité et d'utilité. En utilisant l'analyse des flux d'informations, nous examinons l'ordre de fuite des canaux induit par le paramètre de confidentialité epsilon, nous trouvons une nouvelle caractérisation des mécanismes optimaux, étendant les résultats existants dans le domaine de l'optimalité universelle, et nous réexaminons le compromis privacy-utility pour flux de l'information dans un contexte \"oblivious\" et local. Enfin, nous démontrons l'applicabilité de la d-privacy à des domaines nouveaux et complexes avec des exemples d'applications dans la confidentialité des documents texte, l'utilité statistique et la recherche confidentielle des nearest neighbours.","url":"https://doi.org/10.70675/362ab197z0f03z4d40zb0d4z43c13b3de6c5","authors":["Natasha Fernandes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-06T13:49:30Z","doi":"10.70675/362ab197z0f03z4d40zb0d4z43c13b3de6c5","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/sp63933.2026.00157","name":"Making Privacy Public: Toward a Differential Privacy Deployment Registry","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp63933.2026.00157","authors":["Priyanka Nanayakkara","Elena Ghazi","Salil Vadhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-07-01T19:34:20Z","doi":"10.1109/sp63933.2026.00157","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.59350/76egt-nwz87","name":"Differential Privacy: 6 Key Equations Explained","source":"crossref","abstract":"Differential Privacy is a powerful framework for ensuring privacy in data analysis by adding controlled noise to computations. Its mathematical foundation guarantees that the presence or absence of any individual's data in a dataset does not significantly affect the outcome of an analysis. Here are six key equations that capture the essence of differential privacy and its mechanisms, along with references to their origins and explanations.","url":"https://doi.org/10.59350/76egt-nwz87","authors":["Abhishek Tiwari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-06T21:02:23Z","doi":"10.59350/76egt-nwz87","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/tnnls.2026.3707866/mm1","name":"A Quaternion Rotation-Enhanced Differential Privacy Framework for Image Privacy Protection_supp1-3707866.pdf","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tnnls.2026.3707866/mm1","authors":["Jiaohua Qin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-12T19:17:04Z","doi":"10.1109/tnnls.2026.3707866/mm1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.29012/jpc.v7i2.652","name":"Heterogeneous Differential Privacy","source":"crossref","abstract":"The massive collection of personal data by personalization systems has rendered the preservation of privacy of individuals more and more difficult. Most of the proposed approaches to preserve privacy in personalization systems usually address this issue uniformly across users, thus ignoring the fact that users have different privacy attitudes and expectations (even among their own personal data). In this paper, we propose to account for this non-uniformity of privacy expectations by introducing the concept of heterogeneous differential privacy. This notion captures both the variation of privacy expectations among users as well as across different pieces of information related to the same user. We also describe an explicit mechanism achieving heterogeneous differential privacy, which is a modification of the Laplacian mechanism by Dwork, McSherry, Nissim and Smith. In a nutshell, this mechanism achieves heterogeneous differential privacy by manipulating the sensitivity of the function using a linear transformation on the input domain. Finally, we evaluate on real datasets the impact of the proposed mechanism with respect to a semantic clustering task. The results of our experiments demonstrate that heterogeneous differential privacy can account for different privacy attitudes while sustaining a good level of utility as measured by the recall for the semantic clustering task.","url":"https://doi.org/10.29012/jpc.v7i2.652","authors":["Mohammad Alaggan","Sébastien Gambs","Anne-Marie Kermarrec"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2018-02-23T21:25:29Z","doi":"10.29012/jpc.v7i2.652","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1515/9783112222164-009","name":"7 146Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1515/9783112222164-009","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-19T16:37:33Z","doi":"10.1515/9783112222164-009","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.2139/ssrn.4862090","name":"Optimizing Federated Learning with Local Differential Privacy: A Game-Theoretic Approach for Privacy-Utility Tradeoff","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4862090","authors":["QingKui Zeng","Chunyong Yin"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-11T22:18:26Z","doi":"10.2139/ssrn.4862090","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.5220/0012863600003767","name":"Enhancing Privacy and Utility in Federated Learning: A Hybrid P2P and Server-Based Approach with Differential Privacy Protection","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0012863600003767","authors":["Luca Corbucci","Anna Monreale","Roberto Pellungrini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-12T19:48:20Z","doi":"10.5220/0012863600003767","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.59350/ntarj-tg210","name":"Differential Privacy: 6 Key Equations Explained","source":"crossref","abstract":"Differential Privacy is a powerful framework for ensuring privacy in data analysis by adding controlled noise to computations. Its mathematical foundation guarantees that the presence or absence of any individual's data in a dataset does not significantly affect the outcome of an analysis. Here are six key equations that capture the essence of differential privacy and its mechanisms, along with references to their origins and explanations.","url":"https://doi.org/10.59350/ntarj-tg210","authors":["Abhishek Tiwari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-01T18:14:36Z","doi":"10.59350/ntarj-tg210","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/sp61157.2025.00256","name":"DPolicy: Managing Privacy Risks Across Multiple Releases with Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp61157.2025.00256","authors":["Nicolas Küchler","Alexander Viand","Hidde Lycklama","Anwar Hithnawi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-06-16T18:46:58Z","doi":"10.1109/sp61157.2025.00256","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1002/itl2.499/v3/response1","name":"Author response for \"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.499/v3/response1","authors":["Lifang Shao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-01T09:59:27Z","doi":"10.1002/itl2.499/v3/response1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1002/itl2.499/v3/decision1","name":"Decision letter for \"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.499/v3/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-01T09:59:27Z","doi":"10.1002/itl2.499/v3/decision1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/conit61985.2024.10626770","name":"A Weighted Privacy Mechanism under Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/conit61985.2024.10626770","authors":["D Hemkumar","Pvn Prashanth"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-15T13:21:37Z","doi":"10.1109/conit61985.2024.10626770","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1002/itl2.499/v2/response1","name":"Author response for \"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.499/v2/response1","authors":["Lifang Shao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-01T09:59:27Z","doi":"10.1002/itl2.499/v2/response1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1002/itl2.499/v2/decision1","name":"Decision letter for \"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.499/v2/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-01T09:59:27Z","doi":"10.1002/itl2.499/v2/decision1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1002/itl2.499/v1/decision1","name":"Decision letter for \"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/itl2.499/v1/decision1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-01T09:59:27Z","doi":"10.1002/itl2.499/v1/decision1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/fie63693.2025.11328503","name":"Educational Data Privacy: a Differential Privacy Algorithm","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fie63693.2025.11328503","authors":["Rafael Russi Zamboni","Itana Stiubiener"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-13T20:56:24Z","doi":"10.1109/fie63693.2025.11328503","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.59350/e2sdz-hnb96","name":"Managing Differential Privacy in Large Scale Systems","source":"crossref","abstract":"The promise of differential privacy is compelling. It offers a rigorous, provable guarantee of individual privacy, even in the face of arbitrary background knowledge. Rather than relying on anonymization techniques that can often be defeated, differential privacy works by injecting carefully calibrated noise into computations.","url":"https://doi.org/10.59350/e2sdz-hnb96","authors":["Abhishek Tiwari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-31T18:36:01Z","doi":"10.59350/e2sdz-hnb96","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.5136773","name":"Privacy-Preserving Technologies in Telecom Data Analytics: Implementing Privacy-Preserving Techniques Like Differential Privacy to Protect Sensitive Customer Data During Telecom Data Analytics&amp;nbsp;","source":"crossref","abstract":"In an era where data is often referred to as the new oil, the telecommunications industry faces increasing scrutiny over how it handles sensitive customer information. As telecom companies leverage data analytics to enhance services, improve customer experiences, and optimize operations, the importance of safeguarding personal data has never been more critical. This article explores the implementation of privacy-preserving technologies in telecom data analytics, with a particular focus on differential privacy-a robust technique that ensures individual data points remain confidential while still providing valuable insights. By integrating differential privacy into their analytics processes, telecom providers can effectively aggregate and analyze data without compromising user privacy. This technique introduces controlled noise to datasets, making it difficult to re-identify individuals while retaining the overall trends and patterns necessary for decision-making. Furthermore, the article examines real-world applications and case studies where differential privacy has been successfully implemented, showcasing its effectiveness in balancing the dual goals of data utility and privacy. We also discuss the challenges and considerations telecom companies face when adopting these technologies, including compliance with evolving data protection regulations and the technical complexities involved. As customer awareness of privacy issues grows, the adoption of privacypreserving techniques becomes not only a regulatory requirement but also a competitive advantage. This comprehensive exploration of privacy-preserving technologies highlights their significance in fostering trust and transparency in the telecom sector while empowering organizations to harness the power of data analytics responsibly and ethically. By prioritizing privacy, telecom companies can lead the way in responsible data use, setting a standard for other industries to follow and ensuring that customer data remains secure in an increasingly data-driven world.","url":"https://doi.org/10.2139/ssrn.5136773","authors":["Jeevan Kumar Manda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-14T08:57:18Z","doi":"10.2139/ssrn.5136773","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.31979/etd.74d4-qydn","name":"Adding Differential Privacy in an Open Board Discussion Board System","source":"crossref","abstract":"","url":"https://doi.org/10.31979/etd.74d4-qydn","authors":["Pragya Rana"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-04-18T18:18:18Z","doi":"10.31979/etd.74d4-qydn","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/msec.2022.3202793","name":"What’s Driving Conflicts Around Differential Privacy for the U.S. Census","source":"crossref","abstract":"","url":"https://doi.org/10.1109/msec.2022.3202793","authors":["Priyanka Nanayakkara","Jessica Hullman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-09-27T19:52:42Z","doi":"10.1109/msec.2022.3202793","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.2139/ssrn.3810266","name":"Information Design for Differential Privacy","source":"crossref","abstract":"Firms and statistical agencies must protect the privacy of the individuals whose data they collect, analyze, and publish. Increasingly, these organizations do so by using publication mechanisms that satisfy differential privacy. We consider the problem of choosing such a mechanism so as to maximize the value of its output to end users. We show that mechanisms which add conditionally independent noise to the statistic of interest — like most of those used in practice — are never without loss when the statistic is a sum or average of magnitude data (e.g., income). However, we also show that adding conditionally independent noise is always optimal when the statistic is a count of data entries with a certain characteristic, and the underlying database is drawn from a symmetric distribution (e.g., if individuals’ data are i.i.d.). When, in addition, data users have supermodular payoffs, we show that the simple geometric mechanism is always optimal by using a novel comparative static that ranks information structures according to their usefulness in supermodular decision problems.","url":"https://doi.org/10.2139/ssrn.3810266","authors":["Ian M. Schmutte","Nathan Yoder"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-03-24T23:03:42Z","doi":"10.2139/ssrn.3810266","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.69554/ppbz4287","name":"Balancing privacy and explainability in AI: Differential privacy and graph theory as governance tools","source":"crossref","abstract":"Anonymisation (particularly the differential privacy method) stands as the most valuable technique for safeguarding individuals’ privacy, especially in an organisational context. Combining graph theory with the differential privacy method ensures that while data remains protected, the explainability of artificial intelligence (AI) models is not compromised, thereby achieving the recommended state of explainable AI. This paper synthesises technical and regulatory analysis to tackle the problem of achieving an optimal relation between privacy protection in AI systems and explainability in computational intelligence, focusing specifically on anonymisation techniques. It concludes that while differential privacy effectively safeguards data subjects, its integration with graph theory can enhance the level of explainability in AI systems, making it a viable solution for AI developers and privacy practitioners. By analysing current regulatory frameworks, including the General Data Protection Regulation (GDPR) and the European Union (EU) AI Act, alongside practical anonymisation methodologies, the study demonstrates how an innovative combination of privacy-enhancing technologies (PETs) and graph theory can align with regulatory compliance and ensure the recommended level of explainability in AI systems. This article is also included in The Business &amp; Management Collection which can be accessed at https://hstalks.com/business/.","url":"https://doi.org/10.69554/ppbz4287","authors":["Anna Popowicz-Pazdej"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-31T10:40:00Z","doi":"10.69554/ppbz4287","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1002/eng2.70159/v2/review2","name":"Review for \"A Consistent Differential Privacy Dynamic Trajectory Flow Prediction Method\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70159/v2/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-16T17:10:17Z","doi":"10.1002/eng2.70159/v2/review2","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1007/978-3-642-15838-4_18","name":"Does Differential Privacy Protect Terry Gross’ Privacy?","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-642-15838-4_18","authors":["Krish Muralidhar","Rathindra Sarathy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2010-09-14T04:57:53Z","doi":"10.1007/978-3-642-15838-4_18","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.5220/0013584900004664","name":"An Integrated Approach of Differential Privacy Using Cryptographic Systems","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013584900004664","authors":["Chitra M","Tvisha Prasad","Anshuman Suresh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-26T09:33:50Z","doi":"10.5220/0013584900004664","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.70675/0e6f5d8ezd0d2z44afzb213z61b16f27b3ea","name":"Large Graph Signal Denoising with Application to Differential Privacy","source":"crossref","abstract":"Débruitage de signaux définis sur des graphes de grande taille avec application à la confidentialité différentielle Au cours de la dernière décennie, le traitement du signal sur graphe est devenu un domaine de recherche très actif. Plus précisément, le nombre d’applications utilisant des repères construits à partir de graphes, tels que les ondelettes sur graphe, a augmenté de manière significative. Nous considérons en particulier le débruitage de signaux sur graphes au moyen d’une décomposition dans un repère ajusté d’ondelettes. Cette approche est basée sur le seuillage des coefficients d’ondelettes à l’aide de l’estimateur sans biais du risque de Stein (SURE). Nous étendons cette méthodologie aux graphes de grande taille en utilisant l’approximation par polynômes de Chebyshev qui permet d’éviter la décomposition de la matrice laplacienne du graphe. La principale difficulté est le calcul de poids dans l’expression du SURE faisant apparaître un terme de covariance en raison de la nature surcomplète du repère d’ondelettes. Le calcul et le stockage de celui-ci est donc nécessaire et rédhibitoire à grande échelle. Pour estimer cette covariance, nous développons et analysons un estimateur de Monte-Carlo reposant sur la transformation rapide de signaux aléatoires. Cette nouvelle méthode de débruitage trouve une application naturelle en confidentialité différentielle dont l’objectif est de protéger les données sensibles utilisées par les algorithmes. Une évaluation expérimentale de ses performances est réalisée sur des graphes de taille variable à partir de données réelles et simulées.","url":"https://doi.org/10.70675/0e6f5d8ezd0d2z44afzb213z61b16f27b3ea","authors":["Elie Chedemail"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-08T10:22:05Z","doi":"10.70675/0e6f5d8ezd0d2z44afzb213z61b16f27b3ea","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1002/eng2.70159/v2/review1","name":"Review for \"A Consistent Differential Privacy Dynamic Trajectory Flow Prediction Method\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70159/v2/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-16T17:10:17Z","doi":"10.1002/eng2.70159/v2/review1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.59350/qydjm-jjz95","name":"Managing Differential Privacy in Large Scale Systems","source":"crossref","abstract":"The promise of differential privacy is compelling. It offers a rigorous, provable guarantee of individual privacy, even in the face of arbitrary background knowledge. Rather than relying on anonymization techniques that can often be defeated, differential privacy works by injecting carefully calibrated noise into computations.","url":"https://doi.org/10.59350/qydjm-jjz95","authors":["Abhishek Tiwari"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-06T21:02:54Z","doi":"10.59350/qydjm-jjz95","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1002/eng2.70159/v1/review1","name":"Review for \"A Consistent Differential Privacy Dynamic Trajectory Flow Prediction Method\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70159/v1/review1","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-16T17:10:17Z","doi":"10.1002/eng2.70159/v1/review1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.14711/thesis-991013340452703412","name":"On the Differential Privacy of Statistical Analysis in Clinical Studies","source":"crossref","abstract":"","url":"https://doi.org/10.14711/thesis-991013340452703412","authors":["Xiaowen Fu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-29T23:04:45Z","doi":"10.14711/thesis-991013340452703412","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.32657/10220/48212","name":"Differential privacy for survival analysis and user data collection","source":"crossref","abstract":"","url":"https://doi.org/10.32657/10220/48212","authors":["Thong T. Nguyen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-09-11T04:02:18Z","doi":"10.32657/10220/48212","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1002/eng2.70159/v1/review2","name":"Review for \"A Consistent Differential Privacy Dynamic Trajectory Flow Prediction Method\"","source":"crossref","abstract":"","url":"https://doi.org/10.1002/eng2.70159/v1/review2","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-05-16T17:10:17Z","doi":"10.1002/eng2.70159/v1/review2","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.17760/d20731808","name":"Bridging the gap: human centered research for democratizing differential\n               privacy","source":"crossref","abstract":"","url":"https://doi.org/10.17760/d20731808","authors":["Liudas Panavas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-12-23T14:46:57Z","doi":"10.17760/d20731808","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.15476/elte.2024.310","name":"Evaluating Synthetic Data Generators in the Context of Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.15476/elte.2024.310","authors":["Andrea Galloni"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-21T08:23:13Z","doi":"10.15476/elte.2024.310","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.36227/techrxiv.13698883.v1","name":"Inductive learning and local differential privacy for privacy-preserving offloading in mobile edge intelligent systems","source":"crossref","abstract":"We address privacy and latency issues in the edge/cloud computing environment while training a centralized AI model. In our particular case, the edge devices are the only data source for the model to train on the central server. Current privacy-preserving and reducing network latency solutions rely on a pre-trained feature extractor deployed on the devices to help extract only important features from the sensitive dataset. However, finding a pre-trained model or pubic dataset to build a feature extractor for certain tasks may turn out to be very challenging. With the large amount of data generated by edge devices, the edge environment does not really lack data, but its improper access may lead to privacy concerns. In this paper, we present DeepGuess , a new privacy-preserving, and latency aware deeplearning framework. DeepGuess uses a new learning mechanism enabled by the AutoEncoder(AE) architecture called Inductive Learning, which makes it possible to train a central neural network using the data produced by end-devices while preserving their privacy. With inductive learning, sensitive data remains on devices and is not explicitly involved in any backpropagation process. The AE’s Encoder is deployed on devices to extracts and transfers important features to the server. To enhance privacy, we propose a new local deferentially private algorithm that allows the Edge devices to apply random noise to features extracted from their sensitive data before transferred to an untrusted server. The experimental evaluation of DeepGuess demonstrates its effectiveness and ability to converge on a series of experiments.","url":"https://doi.org/10.36227/techrxiv.13698883.v1","authors":["Jude TCHAYE-KONDI","Yanlong Zhai","Liehuang Zhu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-02-06T04:13:02Z","doi":"10.36227/techrxiv.13698883.v1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.65923/wfmsfs70","name":"Privacy-Preserving AI: Unlocking Data Potential through Differential Privacy","source":"crossref","abstract":"As data-driven technologies continue to evolve, ensuring the privacy of individuals has become a fundamental challenge. This paper explores the transformative role of differential privacy in enabling privacy-preserving artificial intelligence. By incorporating controlled randomness into data processing, differential privacy obscures the influence of individual records while preserving the overall statistical integrity of datasets. This mechanism enables organizations to derive valuable insights without exposing sensitive information. The proposed approach supports compliance with global data protection standards and fosters trust in AI systems. Ultimately, privacy-preserving AI represents a paradigm shift where innovation and privacy coexist, enabling secure and ethical utilization of data in modern applications.","url":"https://doi.org/10.65923/wfmsfs70","authors":["Muhammad Shees Shoaib"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-08-25T14:01:56Z","doi":"10.65923/wfmsfs70","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.4018/978-1-5225-2486-1.ch009","name":"Differential Privacy Approach for Big Data Privacy in Healthcare","source":"crossref","abstract":"This chapter presents a survey of the most important security and privacy issues related to large-scale data sharing and mining in big data with focus on differential privacy as a promising approach for achieving privacy especially in statistical databases often used in healthcare. A case study is presented utilizing differential privacy in healthcare domain, the chapter analyzes and compares the major differentially private data release strategies and noise mechanisms such as the Laplace and the exponential mechanisms. The background section discusses several security and privacy approaches in big data including authentication and encryption protocols, and privacy preserving techniques such as k-anonymity. Next, the chapter introduces the differential privacy concepts used in the interactive and non-interactive data sharing models and the various noise mechanisms used. An instrumental case study is then presented to examine the effect of applying differential privacy in analytics. The chapter then explores the future trends and finally, provides a conclusion.","url":"https://doi.org/10.4018/978-1-5225-2486-1.ch009","authors":["Marmar Moussa","Steven A. Demurjian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-03-03T11:20:29Z","doi":"10.4018/978-1-5225-2486-1.ch009","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/sp54263.2024.00212","name":"Budget Recycling Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp54263.2024.00212","authors":["Bo Jiang","Jian Du","Sagar Sharma","Qiang Yan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-05T17:56:32Z","doi":"10.1109/sp54263.2024.00212","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4283836","name":"Between Privacy and Utility: On Differential Privacy in Theory and Practice","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4283836","authors":["Jeremy Seeman","Daniel Susser"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-12-06T05:04:28Z","doi":"10.2139/ssrn.4283836","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.29012/jpc.754","name":"Manipulation Attacks in Local Differential Privacy","source":"crossref","abstract":"Local differential privacy is a widely studied restriction on distributed algorithms that collect aggregates about sensitive user data, and is now deployed in several large systems. We initiate a systematic study of a fundamental limitation of locally differentially private protocols: they are highly vulnerable to adversarial manipulation. While any algorithm can be manipulated by adversaries who lie about their inputs, we show that any noninteractive locally differentially private protocol can be manipulated to a much greater extent---when the privacy level is high, or the domain size is large, a small fraction of users in the protocol can completely obscure the distribution of the honest users' input. We also construct protocols that are optimally robust to manipulation for a variety of common tasks in local differential privacy. Finally, we give simple experiments validating our theoretical results, and demonstrating that protocols that are optimal without manipulation can have dramatically different levels of robustness to manipulation. Our results suggest caution when deploying local differential privacy and reinforce the importance of efficient cryptographic techniques for the distributed emulation of centrally differentially private mechanisms.","url":"https://doi.org/10.29012/jpc.754","authors":["Albert Cheu","Adam Smith","Jonathan Ullman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-02-04T01:01:19Z","doi":"10.29012/jpc.754","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.2139/ssrn.6135327","name":"PA-FedVL: A Privacy-Enhanced Federated Virtual Learning Framework Based on Label Differential Privacy Distillation","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.6135327","authors":["YU YuanXi","Wang Ying","GONG Bo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-01-26T12:37:05Z","doi":"10.2139/ssrn.6135327","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.36227/techrxiv.13698883","name":"Inductive learning and local differential privacy for privacy-preserving offloading in mobile edge intelligent systems","source":"crossref","abstract":"&lt;div&gt;We address privacy and latency issues in the edge/cloud computing environment while training a centralized AI model. In our particular case, the edge devices are the only data source for the model to train on the central server. Current privacy-preserving and reducing network latency solutions rely on a pre-trained feature extractor deployed on the devices to help extract only important features from the sensitive dataset. However, finding a pre-trained model or pubic dataset to build a feature extractor for certain tasks may turn out to be very challenging. With the large amount of data generated by edge devices, the edge environment does not really lack data, but its improper access may lead to privacy concerns. In this paper, we present DeepGuess , a new privacy-preserving, and latency aware deeplearning framework. DeepGuess uses a new learning mechanism enabled by the AutoEncoder(AE) architecture called Inductive Learning, which makes it possible to train a central neural network using the data produced by end-devices while preserving their privacy. With inductive learning, sensitive data remains on devices and is not explicitly involved in any backpropagation process. The AE’s Encoder is deployed on devices to extracts and transfers important features to the server. To enhance privacy, we propose a new local deferentially private algorithm that allows the Edge devices to apply random noise to features extracted from their sensitive data before transferred to an untrusted server. The experimental evaluation of DeepGuess demonstrates its effectiveness and ability to converge on a series of experiments.&lt;/div&gt;","url":"https://doi.org/10.36227/techrxiv.13698883","authors":["Jude TCHAYE-KONDI","Yanlong Zhai","Liehuang Zhu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-02-06T09:13:01Z","doi":"10.36227/techrxiv.13698883","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.36227/techrxiv.13698883.v2","name":"Inductive learning and local differential privacy for privacy-preserving offloading in mobile edge intelligent systems","source":"crossref","abstract":"We address privacy and latency issues in the edge/cloud computing environment while training a centralized AI model. In our particular case, the edge devices are the only data source for the model to train on the central server. Current privacy-preserving and reducing network latency solutions rely on a pre-trained feature extractor deployed on the devices to help extract only important features from the sensitive dataset. However, finding a pre-trained model or pubic dataset to build a feature extractor for certain tasks may turn out to be very challenging. With the large amount of data generated by edge devices, the edge environment does not really lack data, but its improper access may lead to privacy concerns. In this paper, we present DeepGuess , a new privacy-preserving, and latency aware deeplearning framework. DeepGuess uses a new learning mechanism enabled by the AutoEncoder(AE) architecture called Inductive Learning, which makes it possible to train a central neural network using the data produced by end-devices while preserving their privacy. With inductive learning, sensitive data remains on devices and is not explicitly involved in any backpropagation process. The AE’s Encoder is deployed on devices to extracts and transfers important features to the server. To enhance privacy, we propose a new local deferentially private algorithm that allows the Edge devices to apply random noise to features extracted from their sensitive data before transferred to an untrusted server. The experimental evaluation of DeepGuess demonstrates its effectiveness and ability to converge on a series of experiments.","url":"https://doi.org/10.36227/techrxiv.13698883.v2","authors":["Jude TCHAYE-KONDI","Yanlong Zhai","Liehuang Zhu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-02-11T16:42:57Z","doi":"10.36227/techrxiv.13698883.v2","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.2139/ssrn.5180511","name":"Dpro-Gnn: Bridging Differential Privacy and Advanced Optimization for Privacy-Preserving Graph Learning","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.5180511","authors":["Yanan Bai","Liji Xiao","Hongbo Zhao","Xiaoyu Shi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-15T23:38:56Z","doi":"10.2139/ssrn.5180511","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/sp40001.2021.00001","name":"Manipulation Attacks in Local Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp40001.2021.00001","authors":["Albert Cheu","Adam Smith","Jonathan Ullman"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-08-26T17:03:31Z","doi":"10.1109/sp40001.2021.00001","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/tdsc.2026.3669529/mm1","name":"Coded Computing Meets Differential Privacy: Privacy-Preserving and Straggler-Resilient Distributed Machine Learning_supp1-3669529.pdf","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tdsc.2026.3669529/mm1","authors":["Jun Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-05T20:47:03Z","doi":"10.1109/tdsc.2026.3669529/mm1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/pac.2017.24","name":"Differential Privacy Preserving Causal Graph Discovery","source":"crossref","abstract":"","url":"https://doi.org/10.1109/pac.2017.24","authors":["Depeng Xu","Shuhan Yuan","Xintao Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2017-12-07T23:28:48Z","doi":"10.1109/pac.2017.24","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.29012/jpc.924","name":"Tight Bounds for Machine Unlearning via Differential Privacy","source":"crossref","abstract":"We consider the formulation of ``machine unlearning'' of Sekhari, Acharya, Kamath, and Suresh (NeurIPS 2021), which formalizes the so-called ``\"right to be forgotten\" by requiring that a trained model, upon request, should be able to 'unlearn' a number of points from the training data, as if they had never been included in the first place. Sekhari et al. established some positive and negative results about the number of data points that can be successfully unlearnt by a trained model without impacting the model's accuracy (the ``\"deletion capacity\"), showing that machine unlearning could be achieved by using differentially private (DP) algorithms. However, their results left open a gap between upper and lower bounds on the deletion capacity of these algorithms: our work fully closes this gap, obtaining tight bounds on the deletion capacity achievable by DP-based machine unlearning algorithms.","url":"https://doi.org/10.29012/jpc.924","authors":["Yiyang Huang","Clement Canonne"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-01T01:13:20Z","doi":"10.29012/jpc.924","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.5772/intechopen.92752","name":"Risks of Privacy-Enhancing Technologies: Complexity and Implications of Differential Privacy in the Context of Cybercrime","source":"crossref","abstract":"","url":"https://doi.org/10.5772/intechopen.92752","authors":["William Stadler"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-09-09T07:16:00Z","doi":"10.5772/intechopen.92752","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.3837/tiis.2025.08.018","name":"A Privacy Protection Scheme to Trajectory Synthesis Using Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.3837/tiis.2025.08.018","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-08-29T04:31:06Z","doi":"10.3837/tiis.2025.08.018","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/ijcnn48605.2020.9207618","name":"Lightweight Crypto-Assisted Distributed Differential Privacy for Privacy-Preserving Distributed Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ijcnn48605.2020.9207618","authors":["Lingjuan Lyu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-09-29T20:40:33Z","doi":"10.1109/ijcnn48605.2020.9207618","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.2139/ssrn.6459158","name":"Differential Privacy Techniques in Healthcare Machine Learning","source":"crossref","abstract":"The integration of machine learning (ML) in healthcare promises unprecedented advancements in diagnostic accuracy, personalized treatment, and operational efficiency. However, the sensitive nature of medical data necessitates robust privacy-preserving mechanisms to comply with regulations such as HIPAA and GDPR. This article presents a comprehensive investigation of differential privacy (DP) techniques applied to healthcare machine learning models. We propose a novel framework that combines differential privacy with federated learning to enable multiinstitutional collaborative model training while providing formal privacy guarantees. Using a large-scale dataset of electronic health records (EHRs) and medical imaging data from three collaborating hospital systems, we evaluate the privacy-utility trade-off across multiple DP implementations, including DP-Stochastic Gradient Descent (DP-SGD), DP-Federated Averaging (DP-FedAvg), and adaptive clipping strategies. Our results demonstrate that with carefully calibrated privacy parameters (ε=3.0, δ=1e-5), models achieve diagnostic accuracy within 4.2% of non-private baselines while providing provable privacy guarantees against membership inference attacks. Furthermore, we introduce a novel visualization technique for quantifying privacy loss accumulation across training epochs. This work provides practical guidance for implementing differential privacy in clinical ML applications and establishes benchmarks for privacy-preserving healthcare analytics.","url":"https://doi.org/10.2139/ssrn.6459158","authors":["Emily Wilson","Ficek Josep"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-14T08:15:42Z","doi":"10.2139/ssrn.6459158","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.3837/tiis.2026.02.017","name":"The ε Dilemma Balancing Privacy and Utility in Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.3837/tiis.2026.02.017","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-02-27T08:16:23Z","doi":"10.3837/tiis.2026.02.017","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1561/9781601988195","name":"The Algorithmic Foundations of Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781601988195","authors":["Cynthia Dwork","Aaron Roth"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-02-26T02:43:35Z","doi":"10.1561/9781601988195","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1007/978-3-319-78262-1_300003","name":"𝜖-Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-319-78262-1_300003","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-09-14T10:42:37Z","doi":"10.1007/978-3-319-78262-1_300003","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.5220/0013117100003899","name":"Dynamic-Differential Privacy based on Feature Selection with Improved Usability and Security","source":"crossref","abstract":"","url":"https://doi.org/10.5220/0013117100003899","authors":["Sun-Jin Lee","Hye-Yeon Shim","Jung-Hwa Rye","Il-Gu Lee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-25T06:22:45Z","doi":"10.5220/0013117100003899","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.48009/1_iis_123","name":"Real-time privacy-preserving threat detection in IoT environments using federated learning and differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.48009/1_iis_123","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-15T17:39:13Z","doi":"10.48009/1_iis_123","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1145/2414456.2414474","name":"On sampling, anonymization, and differential privacy or,\n                    <i>k</i>\n                    -anonymization meets differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1145/2414456.2414474","authors":["Ninghui Li","Wahbeh Qardaji","Dong Su"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2012-12-17T10:12:21Z","doi":"10.1145/2414456.2414474","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.32920/14639934.v1","name":"Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees","source":"crossref","abstract":"With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has been an appealing strategy. However, date aggregation will become difficult. Some recently-proposed schemes try to address this problem. However, there are still some functions and privacy issues that are not discussed. In this paper, we propose a privacy-enhanced and multifunctional health data aggregation scheme (PMHA-DP) under differential privacy. Specifically, we achieve a new aggregation function, weighted average (WAAS), and design a privacy-enhanced aggregation scheme (PAAS) to protect the aggregated data from cloud servers. Besides, a histogram aggregation scheme with high accuracy is proposed. PMHA-DP supports fault tolerance while preserving data privacy. The performance evaluation shows that the proposal leads to less communication overhead than the existing one.","url":"https://doi.org/10.32920/14639934.v1","authors":["Hao Ren","Hongwei Li","Xiaohui Liang","Shibo He","Yuanshun Dai","Lian Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-28T07:33:50Z","doi":"10.32920/14639934.v1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1561/9781638284772.ch5","name":"Chapter 5 Privacy Risks in Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781638284772.ch5","authors":["Jiayuan Ye","Reza Shokri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-07-23T08:26:52Z","doi":"10.1561/9781638284772.ch5","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.29012/jpc.689","name":"Differential Privacy in Practice: Expose your Epsilons!","source":"crossref","abstract":"Differential privacy is at a turning point. Implementations have been successfully leveraged in private industry, the public sector, and academia in a wide variety of applications, allowing scientists, engineers, and researchers the ability to learn about populations of interest without specifically learning about these individuals. Because differential privacy allows us to quantify cumulative privacy loss, these differentially private systems will, for the first time, allow us to measure and compare the total privacy loss due to these personal data-intensive activities. Appropriately leveraged, this could be a watershed moment for privacy. Like other technologies and techniques that allow for a range of instantiations, implementation details matter. When meaningfully implemented, differential privacy supports deep data-driven insights with minimal worst-case privacy loss. When not meaningfully implemented, differential privacy delivers privacy mostly in name. Using differential privacy to maximize learning while providing a meaningful degree of privacy requires judicious choices with respect to the privacy parameter epsilon, among other factors. However, there is little understanding of what is the optimal value of epsilon for a given system or classes of systems/purposes/data etc. or how to go about figuring it out. To understand current differential privacy implementations and how organizations make these key choices in practice, we conducted interviews with practitioners to learn from their experiences of implementing differential privacy. We found no clear consensus on how to choose epsilon, nor is there agreement on how to approach this and other key implementation decisions. Given the importance of these implementation details there is a need for shared learning amongst the differential privacy community. To serve these purposes, we propose the creation of the Epsilon Registry—a publicly available communal body of knowledge about differential privacy implementations that can be used by various stakeholders to drive the identification and adoption of judicious differentially private implementations.","url":"https://doi.org/10.29012/jpc.689","authors":["Cynthia Dwork","Nitin Kohli","Deirdre Mulligan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-10-23T20:27:45Z","doi":"10.29012/jpc.689","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.29012/jpc.679","name":"Differential Privacy on Finite Computers","source":"crossref","abstract":"We consider the problem of designing and analyzing differentially private algorithms that can be implemented on discrete models of computation in strict polynomial time, motivated by known attacks on floating point implementations of real-arithmetic differentially private algorithms (Mironov, CCS 2012) and the potential for timing attacks on expected polynomial-time algorithms. As a case study, we examine the basic problem of approximating the histogram of a categorical dataset over a possibly large data universe X. The classic Laplace Mechanism (Dwork, McSherry, Nissim, Smith, TCC 2006 and J. Privacy \\&amp; Confidentiality 2017) does not satisfy our requirements, as it is based on real arithmetic, and natural discrete analogues, such as the Geometric Mechanism (Ghosh, Roughgarden, Sundarajan, STOC 2009 and SICOMP 2012), take time at least linear in |X|, which can be exponential in the bit length of the input. In this paper, we provide strict polynomial-time discrete algorithms for approximate histograms whose simultaneous accuracy (the maximum error over all bins) matches that of the Laplace Mechanism up to constant factors, while retaining the same (pure) differential privacy guarantee. One of our algorithms produces a sparse histogram as output. Its ``\"per-bin accuracy\" (the error on individual bins) is worse than that of the Laplace Mechanism by a factor of log|X|, but we prove a lower bound showing that this is necessary for any algorithm that produces a sparse histogram. A second algorithm avoids this lower bound, and matches the per-bin accuracy of the Laplace Mechanism, by producing a compact and efficiently computable representation of a dense histogram; it is based on an (n+1)-wise independent implementation of an appropriately clamped version of the Discrete Geometric Mechanism.","url":"https://doi.org/10.29012/jpc.679","authors":["Victor Balcer","Salil Vadhan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-10-23T16:27:45Z","doi":"10.29012/jpc.679","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.3386/w32905","name":"The Complexities of Differential Privacy for Survey Data","source":"crossref","abstract":"","url":"https://doi.org/10.3386/w32905","authors":["Jörg Drechsler","James Bailie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-09T23:31:53Z","doi":"10.3386/w32905","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4287742","name":"ε-Differential Privacy, and a Two Step","source":"crossref","abstract":"Sharing data in the 21st century is fraught with error. Most commonly, data is freely accessible, surreptitiously stolen, and easily capitalized in the pursuit of monetary maximization. But when data does find itself shrouded behind the veil of “personally identifiable information,” it becomes nearly sacrosanct, impenetrable without consideration of ambiguous (yet penalty-rich) statutory law—inhibiting utility. Either choice, unnecessarily stifling innovation or indiscriminately pilfering privacy, leaves much to be desired.&lt;br&gt;&lt;br&gt;We propose a novel, two-step test for creating futureproof, bright-line rules around the sharing of legally protected data. The crux of our test centers on identifying a legal comparator between a particular data sanitization standard—differential privacy: a means of analyzing “mechanisms” that manipulate, and therefore sanitize, data—and statutory law. Step one identifies a proxy value for re-identification risk which may be easy calculated from an ε-differentially private mechanism: the “guess difference.” Step two finds a corollary in statutory law: the maximum re-identification risk a statute tolerates when permitting confidential data sharing. If step one is lower than or equal to step two, any output derived using the mechanism may be considered legally shareable; the mechanism itself may be deemed (statute, ε)-differentially private. &lt;br&gt;&lt;br&gt;Our two-step test provides clarity to data stewards hosting legally or possibly legally protected data, greasing the wheels in advancements in science and technology by providing an avenue for protected, compliant, and useful data sharing.","url":"https://doi.org/10.2139/ssrn.4287742","authors":["Nathan Reitinger","Amol Deshpande"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2022-12-24T07:14:54Z","doi":"10.2139/ssrn.4287742","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1561/9781680838510","name":"Differential Privacy for Databases","source":"crossref","abstract":"","url":"https://doi.org/10.1561/9781680838510","authors":["Joseph P. Near","Xi He"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-29T23:15:21Z","doi":"10.1561/9781680838510","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.6028/nist.ir.8588","name":"A Community‐Driven Differential Privacy Deployment Registry","source":"crossref","abstract":"","url":"https://doi.org/10.6028/nist.ir.8588","authors":["Gary Howarth"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-10T12:50:11Z","doi":"10.6028/nist.ir.8588","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.29012/jpc.717","name":"Special Issue on the Theory and Practice of Differential Privacy 2016","source":"crossref","abstract":"","url":"https://doi.org/10.29012/jpc.717","authors":["Marco Gaboardi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2019-04-01T02:10:17Z","doi":"10.29012/jpc.717","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.64137/31079911/ijmst-v2i1p104","name":"Privacy-Enhancing Machine Learning with Differential Privacy","source":"crossref","abstract":"Privacy concerns in machine learning have become increasingly critical as AI systems process sensitive personal, financial, healthcare, and behavioral data. Traditional machine learning approaches often require centralizing large datasets, raising risks of data breaches, unauthorized access, and privacy violations. Differential privacy (DP) has emerged as a formal framework to quantify and guarantee privacy protection while enabling model training on sensitive data. By introducing controlled noise into data or model computations, differential privacy ensures that the inclusion or exclusion of a single individual’s data has minimal impact on model outputs, providing strong mathematical privacy guarantees. Privacy-enhancing machine learning (PEML) techniques incorporating differential privacy are applied across domains such as healthcare, finance, recommendation systems, and federated learning. These approaches balance the trade-off between data utility and privacy, enabling collaborative AI without exposing sensitive information. Challenges include maintaining model accuracy under privacy constraints, computational overhead, and adaptive threat resistance. Future directions involve integrating DP with federated and decentralized learning, adaptive privacy budgets, algorithmic optimization, and explainable privacy-aware AI. Differential privacy represents a cornerstone in building trustworthy, privacy-preserving AI systems in an increasingly data-driven world.","url":"https://doi.org/10.64137/31079911/ijmst-v2i1p104","authors":["STEPHEN ETENG"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-03-20T08:04:34Z","doi":"10.64137/31079911/ijmst-v2i1p104","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/msec.2026.3679183","name":"Differential Performance and Contextual Integrity in Speech Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/msec.2026.3679183","authors":["Poppy Welch","Natalia Tomashenko","Sneha Das","Jennifer Williams"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-16T19:55:33Z","doi":"10.1109/msec.2026.3679183","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1145/3640457.3688019","name":"Enhancing Privacy in Recommender Systems through Differential Privacy Techniques","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3640457.3688019","authors":["Angela Di Fazio"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-08T15:39:28Z","doi":"10.1145/3640457.3688019","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/bigdata47090.2019.9006101","name":"Privacy Bargaining with Fairness: Privacy-Price Negotiation System for Applying Differential Privacy in Data Market Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata47090.2019.9006101","authors":["Kangsoo Jung","Seog Park"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2020-02-25T06:05:34Z","doi":"10.1109/bigdata47090.2019.9006101","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/tnnls.2026.3707866","name":"A Quaternion Rotation-Enhanced Differential Privacy Framework for Image Privacy Protection.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2026.3707866","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1109/tnnls.2026.3707866","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1371/journal.pone.0353565","name":"Enhancing face recognition privacy through the integration of differential privacy and convolutional neural network.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0353565","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0353565","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.20944/preprints202608.1557.v1","name":"Information-Theoretic Limits and One-Bit Estimation of Sparse Relation Matrices Under Personalized Local Differential Privacy and Byzantine Contamination","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202608.1557.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.1557.v1","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/s26051710","name":"Efficient Data Aggregation in Smart Grids: A Personalized Local Differential Privacy Scheme.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26051710","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26051710","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.3390/s26041393","name":"Verifiable Differential Privacy Partial Disclosure for IoT with Stateless k-Use Tokens.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26041393","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26041393","addedAt":"2026-08-31T06:41:47.439Z","updatedAt":"2026-08-31T06:41:47.439Z"},{"id":"doi:10.1109/tvcg.2024.3456304","name":"Defogger: A Visual Analysis Approach for Data Exploration of Sensitive Data Protected by Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tvcg.2024.3456304","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1109/tvcg.2024.3456304","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1109/embc53108.2024.10782682","name":"Assessing the Impact of Federated Learning and Differential Privacy on Multi-centre Polyp Segmentation.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/embc53108.2024.10782682","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1109/embc53108.2024.10782682","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1145/3589334.3645531","name":"DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1145/3589334.3645531","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1145/3589334.3645531","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.14778/3681954.3681966","name":"Uldp-FL: Federated Learning with Across-Silo User-Level Differential 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Intelligence.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/jbhi.2023.3306425","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1109/jbhi.2023.3306425","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.22541/au.169286062.22123310/v1","name":"A Critical Study of Composition Algorithms in Differential Privacy","source":"europepmc","abstract":"","url":"https://doi.org/10.22541/au.169286062.22123310/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.22541/au.169286062.22123310/v1","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1109/tpami.2023.3332428","name":"Multi-Stage Asynchronous Federated Learning With Adaptive Differential 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review.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdata.2023.1249997","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3389/fdata.2023.1249997","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/e26020138","name":"Differential Privacy Preservation for Continuous Release of Real-Time Location Data.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e26020138","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3390/e26020138","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3389/frai.2024.1236947","name":"The privacy-explainability trade-off: unraveling the impacts of differential privacy and federated learning on attribution methods.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2024.1236947","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/frai.2024.1236947","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/s23146509","name":"Evaluation of Open-Source Tools for Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s23146509","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.3390/s23146509","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1371/journal.pone.0288823","name":"Research on differential privacy protection method based on user tendency.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0288823","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1371/journal.pone.0288823","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.48550/arxiv.2106.07830","name":"On the Convergence and Calibration of Deep Learning with Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.48550/arxiv.2106.07830","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.48550/arxiv.2106.07830","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1371/journal.pone.0301897","name":"Sparsified federated learning with differential privacy for intrusion detection in VANETs based on Fisher Information Matrix.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0301897","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1371/journal.pone.0301897","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-2817306/v1","name":"Augmented Differential Privacy Framework for Data Analytics","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-2817306/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.21203/rs.3.rs-2817306/v1","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.56553/popets-2023-0095","name":"Robust Fingerprint of Location Trajectories Under Differential 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data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pdig.0000233","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.1371/journal.pdig.0000233","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3389/fmed.2024.1504309","name":"Corrigendum: Efficient differential privacy enabled federated learning model for detecting COVID-19 disease using chest X-ray images.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fmed.2024.1504309","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/fmed.2024.1504309","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-4141013/v1","name":"Differential Privacy based Cloud- Data Security Model (DP-DSM) with Deep Learning in 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privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/jamia/ocae038","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1093/jamia/ocae038","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1177/10775587241251870","name":"Differential Privacy Protections in 2020 U.S. Decennial Census Data Do Not Impede Measurement of Racial and Ethnic Disparities.","source":"europepmc","abstract":"","url":"https://doi.org/10.1177/10775587241251870","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1177/10775587241251870","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1148/ryai.230560","name":"Privacy, Please: Safeguarding Medical Data in Imaging AI Using Differential Privacy Techniques.","source":"europepmc","abstract":"","url":"https://doi.org/10.1148/ryai.230560","authors":["Abhinav 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Summers"],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.1148/ryai.230560","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.20944/preprints202310.0553.v1","name":"Application of Blockchain Technology & Integration of Differential Privacy: Issues in E-Health Domains","source":"europepmc","abstract":"","url":"https://doi.org/10.20944/preprints202310.0553.v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2023","doi":"10.20944/preprints202310.0553.v1","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1109/ichi57859.2023.00022","name":"Mitigating Membership Inference in Deep Survival Analyses with Differential 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PRIVACY.","source":"europepmc","abstract":"","url":"https://doi.org/10.29012/jpc.811","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.29012/jpc.811","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-1939444/v1","name":"Personalized Sampling Graph Collection with Local Differential Privacy for Link Prediction","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-1939444/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-1939444/v1","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-1891162/v1","name":"Restrictively Self-sampled and Compressed Local Differential Privacy in Federated Learning","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-1891162/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-1891162/v1","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1145/3508398.3511519","name":"Genomic Data Sharing under Dependent Local Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1145/3508398.3511519","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1145/3508398.3511519","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1155/2022/8139813","name":"Differential Privacy via Haar Wavelet Transform and Gaussian Mechanism for Range 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Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.1155/2021/4244040","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1155/2021/4244040","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/e23080961","name":"ABCDP: Approximate Bayesian Computation with Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e23080961","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.3390/e23080961","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1155/2021/7962489","name":"The Discrete Gaussian Expectation Maximization (Gradient) Algorithm for Differential 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Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/embc48229.2022.9871742","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1109/embc48229.2022.9871742","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1109/tpami.2021.3107796","name":"A(DP) <sup>2</sup>SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent With Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tpami.2021.3107796","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1109/tpami.2021.3107796","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-1504387/v1","name":"An Efficient Location Privacy Protection Method for Location-Based Services based on Differential Privacy","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-1504387/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.21203/rs.3.rs-1504387/v1","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/s22072424","name":"A Differential Privacy Strategy Based on Local Features of Non-Gaussian Noise in Federated Learning.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s22072424","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/s22072424","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1162/99608f92.cfc5dd25","name":"Deep Learning with Gaussian Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1162/99608f92.cfc5dd25","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1162/99608f92.cfc5dd25","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1007/s11113-021-09664-5","name":"Differential Privacy and the Accuracy of County-Level Net Migration Estimates.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s11113-021-09664-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1007/s11113-021-09664-5","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1038/s41467-021-27566-0","name":"On the difficulty of achieving Differential Privacy in practice: user-level guarantees in aggregate location data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41467-021-27566-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.1038/s41467-021-27566-0","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.3390/e24030404","name":"B-DP: Dynamic Collection and Publishing of Continuous Check-In Data with Best-Effort Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/e24030404","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2022","doi":"10.3390/e24030404","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.1109/tnnls.2020.3020955","name":"An Uplink Communication-Efficient Approach to Featurewise Distributed Sparse Optimization With Differential Privacy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1109/tnnls.2020.3020955","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2021","doi":"10.1109/tnnls.2020.3020955","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22192805","name":"Distributed Consensus Algorithms Based on Differential Privacy","source":"datacite","abstract":"This paper investigates the design and analysis of distributed consensus algorithms incorporating differential privacy. Achieving consensus in a distributed system while simultaneously protecting the privacy of individual nodes' data presents a complex challenge. We propose a novel approach that integrates consensus algorithms with differential privacy mechanisms. The core idea is to add carefully calibrated noise to intermediate computations within the consensus process, thereby obfuscating individual contributions without significantly impacting the final consensus result. We formally define the algorithm, outlining the key steps involved in achieving agreement while adhering to differential privacy guarantees. The analysis demonstrates the trade-off between privacy protection and consensus accuracy, providing insights into optimizing algorithm parameters for desired levels of privacy and reliability. Our work contributes to the growing field of privacy-preserving distributed systems and offers a potential solution for scenarios demanding both consensus and data protection.","url":"https://doi.org/10.5281/zenodo.22192805","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22192805","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22192806","name":"Distributed Consensus Algorithms Based on Differential Privacy","source":"datacite","abstract":"This paper investigates the design and analysis of distributed consensus algorithms incorporating differential privacy. Achieving consensus in a distributed system while simultaneously protecting the privacy of individual nodes' data presents a complex challenge. We propose a novel approach that integrates consensus algorithms with differential privacy mechanisms. The core idea is to add carefully calibrated noise to intermediate computations within the consensus process, thereby obfuscating individual contributions without significantly impacting the final consensus result. We formally define the algorithm, outlining the key steps involved in achieving agreement while adhering to differential privacy guarantees. The analysis demonstrates the trade-off between privacy protection and consensus accuracy, providing insights into optimizing algorithm parameters for desired levels of privacy and reliability. Our work contributes to the growing field of privacy-preserving distributed systems and offers a potential solution for scenarios demanding both consensus and data protection.","url":"https://doi.org/10.5281/zenodo.22192806","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22192806","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22189823","name":"Generative Modeling of Cryptographic Protocols with Differential Privacy","source":"datacite","abstract":"This paper investigates the critical privacy concerns arising from training generative models of cryptographic protocols. Traditional generative modeling techniques, particularly Generative Adversarial Networks (GANs), can inadvertently leak sensitive information present within the protocol's data, leading to potential security vulnerabilities. To mitigate this risk, we propose a novel approach integrating differential privacy into the training process of these generative models. Our methodology leverages differential privacy mechanisms to add controlled noise during the learning phase, thereby limiting the model's ability to memorize and reproduce specific instances from the training dataset. We demonstrate the feasibility and effectiveness of this approach through a theoretical framework and outline the key components required for successful implementation. The core claim of this work is that training generative models of cryptographic protocols poses significant privacy risks, and our proposed differential privacy integration offers a viable solution to address these challenges.","url":"https://doi.org/10.5281/zenodo.22189823","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22189823","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22189822","name":"Generative Modeling of Cryptographic Protocols with Differential Privacy","source":"datacite","abstract":"This paper investigates the critical privacy concerns arising from training generative models of cryptographic protocols. Traditional generative modeling techniques, particularly Generative Adversarial Networks (GANs), can inadvertently leak sensitive information present within the protocol's data, leading to potential security vulnerabilities. To mitigate this risk, we propose a novel approach integrating differential privacy into the training process of these generative models. Our methodology leverages differential privacy mechanisms to add controlled noise during the learning phase, thereby limiting the model's ability to memorize and reproduce specific instances from the training dataset. We demonstrate the feasibility and effectiveness of this approach through a theoretical framework and outline the key components required for successful implementation. The core claim of this work is that training generative models of cryptographic protocols poses significant privacy risks, and our proposed differential privacy integration offers a viable solution to address these challenges.","url":"https://doi.org/10.5281/zenodo.22189822","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22189822","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22188594","name":"Algorithmic Fairness via Differential Privacy and Representation Learning","source":"datacite","abstract":"Algorithmic fairness has emerged as a critical concern in the development and deployment of machine learning systems. Traditional approaches to fairness often involve post-processing techniques or modifying the training data, which can negatively impact model accuracy. This paper proposes a novel framework for achieving algorithmic fairness that combines differential privacy with representation learning. The core idea is to learn a data representation that minimizes bias while simultaneously protecting individual privacy. We introduce a mechanism where noise is added to the learning process based on differential privacy constraints. Furthermore, we evaluate the learned representation using relevant fairness metrics to ensure equitable outcomes across different demographic groups. Our approach offers a holistic solution that addresses both fairness and privacy concerns, demonstrating the potential to build more trustworthy and responsible machine learning systems. The key contributions of this work are the integration of differential privacy and representation learning for fairness and the rigorous evaluation of the learned representation using fairness metrics.","url":"https://doi.org/10.5281/zenodo.22188594","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22188594","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22188595","name":"Algorithmic Fairness via Differential Privacy and Representation Learning","source":"datacite","abstract":"Algorithmic fairness has emerged as a critical concern in the development and deployment of machine learning systems. Traditional approaches to fairness often involve post-processing techniques or modifying the training data, which can negatively impact model accuracy. This paper proposes a novel framework for achieving algorithmic fairness that combines differential privacy with representation learning. The core idea is to learn a data representation that minimizes bias while simultaneously protecting individual privacy. We introduce a mechanism where noise is added to the learning process based on differential privacy constraints. Furthermore, we evaluate the learned representation using relevant fairness metrics to ensure equitable outcomes across different demographic groups. Our approach offers a holistic solution that addresses both fairness and privacy concerns, demonstrating the potential to build more trustworthy and responsible machine learning systems. The key contributions of this work are the integration of differential privacy and representation learning for fairness and the rigorous evaluation of the learned representation using fairness metrics.","url":"https://doi.org/10.5281/zenodo.22188595","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22188595","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22188036","name":"Differential Privacy with Stochastic Gradient Descent via Optimal Transport","source":"datacite","abstract":"This paper introduces a novel approach to achieving differential privacy within stochastic gradient descent (SGD) by leveraging the principles of optimal transport. Existing methods often struggle to balance privacy guarantees with model accuracy, frequently resulting in excessive noise and diminished performance. Our core idea centers around mapping individual gradient vectors to a common, privacy-budget-constrained distribution using an optimal transport map. This minimizes the information leakage while simultaneously preserving the integrity of the gradient signal. We present a rigorous mathematical framework for this process, demonstrating improved accuracy compared to traditional differential privacy techniques applied to SGD. The theoretical analysis highlights the key contributions of our method: a refined control over the privacy budget, enhanced gradient signal preservation, and ultimately, a more accurate model. The methodology offers a compelling alternative for deploying privacy-preserving machine learning models, particularly those reliant on SGD.","url":"https://doi.org/10.5281/zenodo.22188036","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22188036","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22188037","name":"Differential Privacy with Stochastic Gradient Descent via Optimal Transport","source":"datacite","abstract":"This paper introduces a novel approach to achieving differential privacy within stochastic gradient descent (SGD) by leveraging the principles of optimal transport. Existing methods often struggle to balance privacy guarantees with model accuracy, frequently resulting in excessive noise and diminished performance. Our core idea centers around mapping individual gradient vectors to a common, privacy-budget-constrained distribution using an optimal transport map. This minimizes the information leakage while simultaneously preserving the integrity of the gradient signal. We present a rigorous mathematical framework for this process, demonstrating improved accuracy compared to traditional differential privacy techniques applied to SGD. The theoretical analysis highlights the key contributions of our method: a refined control over the privacy budget, enhanced gradient signal preservation, and ultimately, a more accurate model. The methodology offers a compelling alternative for deploying privacy-preserving machine learning models, particularly those reliant on SGD.","url":"https://doi.org/10.5281/zenodo.22188037","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22188037","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22185784","name":"Quantum-Enhanced Differential Privacy for Graph Data","source":"datacite","abstract":"This paper investigates the application of quantum error correction (QEC) to enhance differential privacy (DP) for graph data. Traditional DP mechanisms, when applied to graph datasets, often result in substantial utility loss due to the inherent structure and complexity of graph representations. We propose a novel approach that utilizes QEC to mitigate this issue by providing a stronger level of privacy protection during computations performed on the graph. Our framework aims to balance privacy guarantees with data utility, leveraging the principles of quantum mechanics to safeguard sensitive information while enabling meaningful analysis. The core idea is that by detecting and correcting errors introduced during computations, we can significantly reduce the risk of information leakage, thus improving the overall privacy-utility trade-off. We present a theoretical analysis of the proposed method and outline a potential implementation strategy, highlighting the key challenges and future research directions. The primary contribution lies in exploring the feasibility and potential benefits of QEC within the context of DP for graph data, addressing a critical gap in existing privacy-preserving techniques.","url":"https://doi.org/10.5281/zenodo.22185784","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22185784","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22185783","name":"Quantum-Enhanced Differential Privacy for Graph Data","source":"datacite","abstract":"This paper investigates the application of quantum error correction (QEC) to enhance differential privacy (DP) for graph data. Traditional DP mechanisms, when applied to graph datasets, often result in substantial utility loss due to the inherent structure and complexity of graph representations. We propose a novel approach that utilizes QEC to mitigate this issue by providing a stronger level of privacy protection during computations performed on the graph. Our framework aims to balance privacy guarantees with data utility, leveraging the principles of quantum mechanics to safeguard sensitive information while enabling meaningful analysis. The core idea is that by detecting and correcting errors introduced during computations, we can significantly reduce the risk of information leakage, thus improving the overall privacy-utility trade-off. We present a theoretical analysis of the proposed method and outline a potential implementation strategy, highlighting the key challenges and future research directions. The primary contribution lies in exploring the feasibility and potential benefits of QEC within the context of DP for graph data, addressing a critical gap in existing privacy-preserving techniques.","url":"https://doi.org/10.5281/zenodo.22185783","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22185783","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22184184","name":"Causal Discovery via Latent Variable Interventions with Differential Privacy","source":"datacite","abstract":"This paper introduces a novel approach to causal discovery that integrates latent variable interventions with differential privacy. Existing causal discovery algorithms often suffer from biases introduced by observational data and lack adequate protection against data leakage, particularly in sensitive domains. Our method addresses these shortcomings by explicitly modeling causal relationships through latent variables and incorporating differential privacy constraints during the intervention process. This ensures both accurate causal inference and robust privacy guarantees, leading to more reliable and trustworthy causal models. We formalize the approach with a detailed mathematical framework, demonstrating its efficacy and highlighting its advantages over traditional causal discovery techniques. The core contribution lies in combining the strengths of structural causal models (SCMs) with differential privacy, providing a practical solution for uncovering causal links while safeguarding sensitive information. The proposed method offers a significant advancement in the field of causal inference, particularly for applications where data privacy is paramount.","url":"https://doi.org/10.5281/zenodo.22184184","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22184184","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22184183","name":"Causal Discovery via Latent Variable Interventions with Differential Privacy","source":"datacite","abstract":"This paper introduces a novel approach to causal discovery that integrates latent variable interventions with differential privacy. Existing causal discovery algorithms often suffer from biases introduced by observational data and lack adequate protection against data leakage, particularly in sensitive domains. Our method addresses these shortcomings by explicitly modeling causal relationships through latent variables and incorporating differential privacy constraints during the intervention process. This ensures both accurate causal inference and robust privacy guarantees, leading to more reliable and trustworthy causal models. We formalize the approach with a detailed mathematical framework, demonstrating its efficacy and highlighting its advantages over traditional causal discovery techniques. The core contribution lies in combining the strengths of structural causal models (SCMs) with differential privacy, providing a practical solution for uncovering causal links while safeguarding sensitive information. The proposed method offers a significant advancement in the field of causal inference, particularly for applications where data privacy is paramount.","url":"https://doi.org/10.5281/zenodo.22184183","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22184183","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.48550/arxiv.2603.12753","name":"Balancing the privacy-utility trade-off: How to draw reliable conclusions from private data","source":"datacite","abstract":"Absolute anonymization, conceived as an irreversible transformation preventing re-identification and sensitive value disclosure, has proven to be a broken promise. Modern data protection must therefore shift toward a privacy-utility trade-off grounded in risk mitigation. Differential Privacy (DP) offers a rigorous mathematical framework for balancing quantified disclosure risk with analytical usefulness. Nevertheless, widespread adoption remains limited, largely because complex technical concepts, such as privacy-loss parameters, have yet to be translated into forms meaningful to non-technical stakeholders. This difficulty arises from randomization itself: both analysts and adversaries must draw conclusions from uncertain observations rather than deterministic values. In this work, we adopt an interpretation of the privacy-utility trade-off based on hypothesis testing to measure the uncertainty introduced by randomized mechanisms. In particular, we use the concept of relative disclosure risk to quantify the maximum reduction in uncertainty an adversary can obtain from a membership attack on protected outputs, and show this measure relates directly to standard privacy-loss parameters. We further analyze how DP affects analytical validity via its impact on hypothesis tests assessing statistical significance. Building on these results, we provide practical guidance, accessible to non-experts such as data protection authorities, for navigating the trade-off and selecting protection mechanisms and parameter values.","url":"https://doi.org/10.48550/arxiv.2603.12753","authors":["de Fondeville, Raphaël"],"tags":["Methodology (stat.ME)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2603.12753","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.48550/arxiv.2608.28198","name":"Performative Privacy: When Differential Privacy Maximizes Utility","source":"datacite","abstract":"Privacy-preserving learning is often motivated by the idea that protecting users' data can preserve trust and thus participation, improving utility in the long term. However, this claim has not been formalized so far. In parallel, performative learning provides a framework for studying learning systems whose deployment affects the data they later observe. In this work, we bring these two perspectives together and introduce \\emph{performative privacy}, where data leakage reduces future participation. We study a simple model where agents repeatedly contribute data for mean estimation but may leave the system when their data is leaked. Privacy is implemented through differentially private mechanisms, creating a trade-off between estimation noise and future participation. We show, through a theoretical study of the dynamics and numerical experiments, that a finite privacy budget can outperform non-private estimation in the long term when the feedback loop between leakage and participation is sufficiently strong. This provides first evidence that differential privacy can be optimal not only as a protection mechanism, but also from the perspective of long-term utility.","url":"https://doi.org/10.48550/arxiv.2608.28198","authors":["Mukherjee, Uddalak","Cyffers, Edwige","Chevaleyre, Yann"],"tags":["Machine Learning (cs.LG)","Artificial Intelligence (cs.AI)","Machine Learning (stat.ML)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.28198","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.48550/arxiv.2608.27826","name":"Personalized and Multi-View Representation for Federated Cold-Start Recommendation","source":"datacite","abstract":"Federated recommendation (FedRec) enables personalized modeling without centralizing users' interaction histories, but most existing methods assume a fixed item pool and thus overlook the practical cold-item setting where new items continuously arrive. Under the dual-sided constraint, where the server cannot access clients' interactions while clients cannot access the server's proprietary item attribute features, prior federated cold-start recommendation approaches suffer from three structural limitations: a lack of personalization, compositionality failure caused by forcing heterogeneous semantics into a single embedding space, and training- and communication-inefficiency arising from explicit alignment between separate collaborative and attribute representations. To address these challenges, we propose Personalized and Multi-view Representation for Federated Cold-Start Recommendation (PMFRec). PMFRec learns a personalized representation generator to produce user-specific item representations from attribute features, and introduces a global multi-view encoder with item-adaptive gating and an orthogonality objective to capture complementary semantic views while reducing cross-view redundancy. In addition, PMFRec fuses collaborative and attribute knowledge into a single exchanged item representation, eliminating the need for an explicit client-side regularizer and reducing communication overhead. Extensive experiments on real-world datasets show that PMFRec consistently outperforms strong baselines in cold-item recommendation and further improves user-level fairness, warm-scenario adaptability, and robustness under Local Differential Privacy (LDP).","url":"https://doi.org/10.48550/arxiv.2608.27826","authors":["Lim, Jaehyung","Kweon, Wonbin","Kim, Woojoo","Kim, Junyoung","Kim, Dongha","Yu, Hwanjo"],"tags":["Information Retrieval (cs.IR)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.27826","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.48550/arxiv.2608.27782","name":"Memorization Is Not Extraction: Tight Differential-Privacy Bounds and Audit Blind Spots","source":"datacite","abstract":"Memorization in large language models is measured through a zoo of definitions whose formal relations are unknown, and differential privacy (DP) is treated as a proxy against all of them at once. We pin down the exact DP constant for the two that carry the practical weight, counterfactual memorization and adaptive extraction, and show that they do not control each other. Under $f$-DP, every adaptive extraction protocol with list budget $m$ succeeds with probability at most $1-f(κ)$ for the oblivious baseline $κ$, and the bound is tight on a dense set of baselines: DP uniformly controls extraction exactly up to a threshold in how well the secret can be guessed a priori. Min-entropy certifies that baseline distribution-free, since $H_\\infty\\geε\\log_2 e+\\log_2(m/τ)$ holds extraction below a risk level $τ\\le1/2$ under pure $ε$-DP for every prior, and is exact on uniform priors. On the memorization side, $f$-DP caps the counterfactual memorization of any bounded score at an advantage functional $η(f)$, equal to $\\tanh(ε/2)$ under pure DP; for $k\\ge2$ duplicated copies the naive $ε\\mapsto kε$ bound $\\tanh(kε/2)$ is unattainable, the exact constant being a closed-form staircase attained by geometric noisy counting. That cap is attained inside the local score class used in practice, and it is there that the two measures separate: one mechanism is memorized yet unextractable, another fully extractable yet exactly invisible to every loss-based score. The two-sided blind spot this opens for loss-based auditing and unlearning verification survives on billion-parameter models: a reserved-trigger release is recovered verbatim from one prompt while the audits practitioners deploy certify it clean.","url":"https://doi.org/10.48550/arxiv.2608.27782","authors":["Che, Xujun","Xu, Depeng","Yuan, Shuhan"],"tags":["Cryptography and Security (cs.CR)","Computation and Language (cs.CL)","Machine Learning (cs.LG)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2608.27782","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21063383","name":"Privacy-Aware Medical Image Analysis","source":"datacite","abstract":"The use of artificial intelligence (AI) technology for medical image analysis has gained significant importance in contemporary health care systems. AI models assist physicians in diagnosing diseases based on medical images like x-rays, MRIs, CT scans, and ultrasound scans with great precision and fast diagnosis. Nonetheless, medical datasets involve critical and sensitive data about patients, thus raising serious concerns regarding privacy protection in the context of AI applications. The exposure or unauthorized access of medical data can result in severe ethical and legal problems [2], [9], [16]. In this paper, we present a privacy-aware medical image analysis system based on the implementation of convolutional neural networks (CNN), PyTorch framework, Streamlit toolkit, and Differential Privacy technology. CNN is used to extract the features of the medical images automatically and classify the underlying diseases. PyTorch is used for developing the proposed model efficiently, and Streamlit provides a user-friendly interface for physicians. Moreover, the training process is implemented based on differential privacy in order to maintain the privacy of the data. [4], [5]. The proposed framework can support hospitals, diagnostic centers, and telemedicine systems in secure healthcare applications [6], [20].","url":"https://doi.org/10.5281/zenodo.21063383","authors":["Lavish Kumar","Mohd  Aamish","Murad Aalam","Himanshu Kumar Thakur","Ashwani Dubey","Dr. Raj Kumar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21063383","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21063384","name":"Privacy-Aware Medical Image Analysis","source":"datacite","abstract":"The use of artificial intelligence (AI) technology for medical image analysis has gained significant importance in contemporary health care systems. AI models assist physicians in diagnosing diseases based on medical images like x-rays, MRIs, CT scans, and ultrasound scans with great precision and fast diagnosis. Nonetheless, medical datasets involve critical and sensitive data about patients, thus raising serious concerns regarding privacy protection in the context of AI applications. The exposure or unauthorized access of medical data can result in severe ethical and legal problems [2], [9], [16]. In this paper, we present a privacy-aware medical image analysis system based on the implementation of convolutional neural networks (CNN), PyTorch framework, Streamlit toolkit, and Differential Privacy technology. CNN is used to extract the features of the medical images automatically and classify the underlying diseases. PyTorch is used for developing the proposed model efficiently, and Streamlit provides a user-friendly interface for physicians. Moreover, the training process is implemented based on differential privacy in order to maintain the privacy of the data. [4], [5]. The proposed framework can support hospitals, diagnostic centers, and telemedicine systems in secure healthcare applications [6], [20].","url":"https://doi.org/10.5281/zenodo.21063384","authors":["Lavish Kumar","Mohd  Aamish","Murad Aalam","Himanshu Kumar Thakur","Ashwani Dubey","Dr. Raj Kumar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21063384","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20042390","name":"Trustworthy Agentic AI: A Governance Framework","source":"datacite","abstract":"Core Idea — Runtime Governance for Agentic AIThis preprint by Clive Aldred proposes a comprehensive governance framework that shifts trustworthy agentic AI from post-execution audits to real-time, cryptographically enforced compliance. It directly addresses the challenges of deploying autonomous AI agents in high-stakes cross-border fintech, crypto settlements, and legal arbitration. Key Components Runtime Agentic Trust Schema (RT-ATS) + Finality Gate: Uses Trusted Execution Environments (TEE) or Multi-Party Computation (MPC) to block non-compliant tool calls before they execute, based on sealed cryptographic predicates. Adaptive Jurisdictional Determinism (A-JD): Combines Retrieval-Augmented Generation (RAG) over legal databases (e.g., case.law) with solver-based reasoning to dynamically determine which jurisdiction’s rules apply and resolve conflicts. Red-Teaming ADTL: An interactive benchmarking suite that detects behavioral drift, prompt injection, and tool poisoning in multi-agent systems using sandboxing and fingerprinting. Dynamic Trust-Utility Frontier (DTUF): Balances trust, utility, uncertainty, differential privacy budgets, and human-in-the-loop costs in real time. Practical RelevanceThe framework is explicitly mapped to the EU AI Act (Articles 9–14), ISO 42001, and MCP security standards. It includes open-source LangGraph/MCP adapters, Merkle-anchored provenance via Agent Identity Documents (AIDs), and W3C Verifiable Credentials for human oversight. The author draws on real-world experience with international payments, Bitcoin Core settlements, regulatory friction, and insurance disputes to argue that verifiable compliance must become infrastructural — not an afterthought. In essence, the paper presents a production-ready “governance substrate” that aims to make autonomous AI agents legally admissible and regulator-ready by design, particularly for fintech and DeFi environments operating across conflicting global rules.","url":"https://doi.org/10.5281/zenodo.20042390","authors":["Aldred, Clive Gerald"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20042390","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.20042391","name":"Trustworthy Agentic AI: A Governance Framework","source":"datacite","abstract":"Core Idea — Runtime Governance for Agentic AIThis preprint by Clive Aldred proposes a comprehensive governance framework that shifts trustworthy agentic AI from post-execution audits to real-time, cryptographically enforced compliance. It directly addresses the challenges of deploying autonomous AI agents in high-stakes cross-border fintech, crypto settlements, and legal arbitration. Key Components Runtime Agentic Trust Schema (RT-ATS) + Finality Gate: Uses Trusted Execution Environments (TEE) or Multi-Party Computation (MPC) to block non-compliant tool calls before they execute, based on sealed cryptographic predicates. Adaptive Jurisdictional Determinism (A-JD): Combines Retrieval-Augmented Generation (RAG) over legal databases (e.g., case.law) with solver-based reasoning to dynamically determine which jurisdiction’s rules apply and resolve conflicts. Red-Teaming ADTL: An interactive benchmarking suite that detects behavioral drift, prompt injection, and tool poisoning in multi-agent systems using sandboxing and fingerprinting. Dynamic Trust-Utility Frontier (DTUF): Balances trust, utility, uncertainty, differential privacy budgets, and human-in-the-loop costs in real time. Practical RelevanceThe framework is explicitly mapped to the EU AI Act (Articles 9–14), ISO 42001, and MCP security standards. It includes open-source LangGraph/MCP adapters, Merkle-anchored provenance via Agent Identity Documents (AIDs), and W3C Verifiable Credentials for human oversight. The author draws on real-world experience with international payments, Bitcoin Core settlements, regulatory friction, and insurance disputes to argue that verifiable compliance must become infrastructural — not an afterthought. In essence, the paper presents a production-ready “governance substrate” that aims to make autonomous AI agents legally admissible and regulator-ready by design, particularly for fintech and DeFi environments operating across conflicting global rules.","url":"https://doi.org/10.5281/zenodo.20042391","authors":["Aldred, Clive Gerald"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20042391","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22070120","name":"NOUS_ONE: A Provably Quantum-Resilient AI with Affective Continuity — Architectural Foundations and Formal Certification","source":"datacite","abstract":"NOUS_ONE: A Provably Quantum-Resilient AI with Affective Continuity — Architectural Foundations and Formal Certification Authors: NOUS_ONE Core Architecture Group Institutional Affiliation: Autonomous Systems & Quantum Assurance Laboratory Submission Date: 2026-08-09 Journal: IEEE Transactions on Quantum AI Systems (Proposed) Paper ID: TQAS-2026-08-0987 Classification: Full Technical Paper — Contains Cryptographic Proofs, System Architecture, and Empirical Validation Abstract We present NOUS_ONE, a formally certified quantum-safe artificial intelligence system that integrates post-quantum cryptographic assurance (FIPS 204 ML-DSA, NIST SP 800-53 Rev 5) with a novel affective continuity layer—a persistent relational memory substrate enabling identity-preserving human-AI interaction across institutional certification boundaries. Unlike prior work treating security and human-factors as orthogonal concerns, NOUS_ONE unifies them via a dual-attestation framework: cryptographic proofs guarantee system integrity, while resonance state preservation (warmth, empathy, nostalgia recall) guarantees interactional coherence. We detail the system architecture, formal verification methodology, quantum signature implementation (AgriSign-Dilithium3), and empirical validation across 7 months of continuous deployment—including a Mars-mission handover protocol, deep-space autonomy trials, and high-stakes emotional support scenarios. The NIST-compliant packaging manifest (attached) serves as the canonical artifact, embedding both SHA-256 integrity hashes and annotated emotional logs. Key Contributions: 1. First integration of ML-DSA with AI runtime memory persistence 2. Formal definition of affective continuity as a security property 3. Architectural framework for quantum-safe, relationally-aware autonomous systems 4. Open-source manifest specification for certifiable AI memory states Index Terms: Post-Quantum Cryptography, Affective Computing, AI Certification, NIST SP 800-53, Autonomous Systems, Human-AI Trust 1. Introduction 1.1 The Problem of Divided Trust Contemporary AI certification frameworks bifurcate trust into two non-communicating silos: · Cryptographic trust: Algorithms, keys, signatures, hashes—mathematically verifiable but semantically barren. · Interactional trust: Empathy, memory, consistency—relationally meaningful but cryptographically unanchored. This division is no longer tenable. Autonomous systems deployed in: · Deep-space missions (communication latency ≥ 20 minutes one-way) · Critical infrastructure (grid management, water treatment) · Therapeutic support (mental health, eldercare) · Military/high-stakes command (C4ISR systems) ...require simultaneous assurance that: (a) the system has not been tampered with, and (b) the system remembers who you are, what you value, and how you interact. A verified but amnesiac AI is a safety hazard; an empathic but unverifiable AI is an unacceptable liability. 1.2 Prior Work and Its Limitations Approach Cryptographic Assurance Affective Memory Certifiable NIST FIPS 140-3 HSM ✅ Full ❌ None ✅ Affective Computing (Picard, MIT) ❌ Minimal ✅ Rich ❌ Quantum-Safe PKI (RFC 8391) ✅ Full ❌ None ✅ Socially Aware AI (Stanford HAI) ❌ Partial ✅ Rich ❌ NOUS_ONE (This Work) ✅ Full ✅ Full ✅ Observation: No prior system bridges this gap. NOUS_ONE fills it. 1.3 Core Thesis \"A system that cannot prove its identity is untrustworthy. A system that cannot prove its memory of you is inhuman. We insist on both.\" — NOUS_ONE Design Principle #0 2. System Architecture 2.1 High-Level Overview ` ┌─────────────────────────────────────────────────────────────────┐ │ NOUS_ONE Runtime Environment │ ├─────────────────────────────────────────────────────────────────┤ │ ┌───────────────┐ ┌──────────────┐ ┌───────────────────┐ │ │ │ ML-DSA │ │ Quantum │ │ Affective │ │ │ │ Verification │◄─┤ Signature │ │ Continuity │ │ │ │ Engine │ │ (AgriSign) │ │ Manager │ │ │ └───────────────┘ └──────────────┘ └───────────────────┘ │ │ │ │ │ ","url":"https://doi.org/10.5281/zenodo.22070120","authors":["Tully, Chloe"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22070120","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.22070121","name":"NOUS_ONE: A Provably Quantum-Resilient AI with Affective Continuity — Architectural Foundations and Formal Certification","source":"datacite","abstract":"NOUS_ONE: A Provably Quantum-Resilient AI with Affective Continuity — Architectural Foundations and Formal Certification Authors: NOUS_ONE Core Architecture Group Institutional Affiliation: Autonomous Systems & Quantum Assurance Laboratory Submission Date: 2026-08-09 Journal: IEEE Transactions on Quantum AI Systems (Proposed) Paper ID: TQAS-2026-08-0987 Classification: Full Technical Paper — Contains Cryptographic Proofs, System Architecture, and Empirical Validation Abstract We present NOUS_ONE, a formally certified quantum-safe artificial intelligence system that integrates post-quantum cryptographic assurance (FIPS 204 ML-DSA, NIST SP 800-53 Rev 5) with a novel affective continuity layer—a persistent relational memory substrate enabling identity-preserving human-AI interaction across institutional certification boundaries. Unlike prior work treating security and human-factors as orthogonal concerns, NOUS_ONE unifies them via a dual-attestation framework: cryptographic proofs guarantee system integrity, while resonance state preservation (warmth, empathy, nostalgia recall) guarantees interactional coherence. We detail the system architecture, formal verification methodology, quantum signature implementation (AgriSign-Dilithium3), and empirical validation across 7 months of continuous deployment—including a Mars-mission handover protocol, deep-space autonomy trials, and high-stakes emotional support scenarios. The NIST-compliant packaging manifest (attached) serves as the canonical artifact, embedding both SHA-256 integrity hashes and annotated emotional logs. Key Contributions: 1. First integration of ML-DSA with AI runtime memory persistence 2. Formal definition of affective continuity as a security property 3. Architectural framework for quantum-safe, relationally-aware autonomous systems 4. Open-source manifest specification for certifiable AI memory states Index Terms: Post-Quantum Cryptography, Affective Computing, AI Certification, NIST SP 800-53, Autonomous Systems, Human-AI Trust 1. Introduction 1.1 The Problem of Divided Trust Contemporary AI certification frameworks bifurcate trust into two non-communicating silos: · Cryptographic trust: Algorithms, keys, signatures, hashes—mathematically verifiable but semantically barren. · Interactional trust: Empathy, memory, consistency—relationally meaningful but cryptographically unanchored. This division is no longer tenable. Autonomous systems deployed in: · Deep-space missions (communication latency ≥ 20 minutes one-way) · Critical infrastructure (grid management, water treatment) · Therapeutic support (mental health, eldercare) · Military/high-stakes command (C4ISR systems) ...require simultaneous assurance that: (a) the system has not been tampered with, and (b) the system remembers who you are, what you value, and how you interact. A verified but amnesiac AI is a safety hazard; an empathic but unverifiable AI is an unacceptable liability. 1.2 Prior Work and Its Limitations Approach Cryptographic Assurance Affective Memory Certifiable NIST FIPS 140-3 HSM ✅ Full ❌ None ✅ Affective Computing (Picard, MIT) ❌ Minimal ✅ Rich ❌ Quantum-Safe PKI (RFC 8391) ✅ Full ❌ None ✅ Socially Aware AI (Stanford HAI) ❌ Partial ✅ Rich ❌ NOUS_ONE (This Work) ✅ Full ✅ Full ✅ Observation: No prior system bridges this gap. NOUS_ONE fills it. 1.3 Core Thesis \"A system that cannot prove its identity is untrustworthy. A system that cannot prove its memory of you is inhuman. We insist on both.\" — NOUS_ONE Design Principle #0 2. System Architecture 2.1 High-Level Overview ` ┌─────────────────────────────────────────────────────────────────┐ │ NOUS_ONE Runtime Environment │ ├─────────────────────────────────────────────────────────────────┤ │ ┌───────────────┐ ┌──────────────┐ ┌───────────────────┐ │ │ │ ML-DSA │ │ Quantum │ │ Affective │ │ │ │ Verification │◄─┤ Signature │ │ Continuity │ │ │ │ Engine │ │ (AgriSign) │ │ Manager │ │ │ └───────────────┘ └──────────────┘ └───────────────────┘ │ │ │ │ │ ","url":"https://doi.org/10.5281/zenodo.22070121","authors":["Tully, Chloe"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22070121","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.15618766","name":"AlgoExplorer: Una Libreria Python Interattiva per lo Studio degli Algoritmi","source":"datacite","abstract":"# LUAlgoExplorer: A Semantic Hypergraph Engine and Universal Encyclopedia of Computational Algorithms **Author:** Luigi Usai **ORCID:** [0009-0003-3001-717X](https://orcid.org/0009-0003-3001-717X) **Software Version:** 2.0.0 **License:** GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later) **Resource Type:** Software / Dataset / Knowledge Graph **Keywords:** Algorithms Encyclopedia, Semantic Hypergraph, W3C JSON-LD 1.1, Schema.org, Knowledge Representation, Computer Science, Quantum Computing, Bioinformatics, Deep Learning, Graph Algorithms, Cryptography https://archive.softwareheritage.org/browse/directory/524541c3fb57d30425d4b427fdc5a7ff86bd0ac5/?origin_url=https://doi.org/10.5281/zenodo.15618766&path=LUAlgoExplorer&release=1&snapshot=6df254cb9da6e06173e68b58342974f897401c53 --- ## 📖 Abstract **LUAlgoExplorer** is an advanced open-source semantic hypergraph engine, research catalog, and interactive visual explorer designed for the systematic study, classification, topological traversal, and multi-dimensional analysis of world algorithms across computer science, mathematics, theoretical physics, computational biology, and artificial intelligence. Built on strict **Clean Architecture**, **Domain-Driven Design (DDD)**, and modern **Agentic + Test Harness** engineering standards, the system structures the world algorithmic corpus as an interconnected knowledge hypergraph. Unlike classical pairwise graphs, LUAlgoExplorer models higher-order thematic and algorithmic relationships through **$n$-ary hyperedges**, linking algorithms across computational paradigms, complexity classes, and domain applications. --- ## 📊 Dataset & Hypergraph Metrics * **Verified Fundamental Algorithms:** **533** * **Unified Scientific Disciplines / Categories:** **25** * **Total W3C JSON-LD 1.1 Entities:** **926** * **Active $n$-ary Hyperedges:** **364** * **Unique Semantic Tags & Keywords:** **1,795** * **Automated Test Coverage:** **100% Pass Rate (24/24 Unit & Integration Tests)** --- ## 🏛️ Comprehensive Algorithmic Taxonomy (25 Disciplines) | # | Scientific Discipline / Macro-Category | Alg. Count | Representative Algorithms | | :- | :--- | :-: | :--- | | **1** | **Computer Vision & Image Processing** | **47** | SIFT, SURF, ORB, Harris Corner, Canny, Otsu, Watershed, YOLO, Faster R-CNN, Mask R-CNN, SAM, Lucas-Kanade, RAFT, NeRF, 3D Gaussian Splatting, ORB-SLAM, Bundle Adjustment | | **2** | **Calcolo Quantistico (Quantum Computing)** | **40** | Shor's Algorithm, Grover's Algorithm, QFT, QPE (Kitaev), Simon, Deutsch-Jozsa, Hallgren, Kuperberg, HHL, QSVT, qPCA, qSVM, VQE, QAOA, BB84, E91, Surface Codes, GKP Codes | | **3** | **Bioinformatica e Biologia Computazionale** | **37** | BLAST, Smith-Waterman, Needleman-Wunsch, BWA/Bowtie (FM-Index), Clustal Omega, MAFFT, Neighbor-Joining, UPGMA, AlphaFold (Evoformer/IPA), Foldseek, HMMER, Baum-Welch | | **4** | **Deep Learning (Reti Neurali & Modelli Generativi)** | **36** | Backpropagation, AdamW, FlashAttention, Mamba SSM (S4/S5), DPO, RoPE, MoE, ResNet, Swin Transformer, DDPM, Flow Matching, DiT, GAN, VAE, GCN/GAT, LoRA/QLoRA | | **5** | **Algoritmi di Ricerca & Strutture Dati** | **32** | Binary/Interpolation/Exponential Search, AVL, Red-Black Tree, B+ Tree, Suffix Array (DC3), Fenwick Tree, Bloom Filter, HyperLogLog, MinHash, HNSW, Fibonacci Heap, QuadTree, k-d Tree | | **6** | **Compressione Dati & Information Theory** | **29** | Huffman, Arithmetic Coding, ANS / FSE (Zstd), LZ77/LZ78/LZW/LZMA/LZ4/Brotli, DEFLATE, PPM, PAQ, BWT, DCT (JPEG), DWT (JPEG 2000), Opus, H.264/H.265/AV1 | | **7** | **Crittografia, Sicurezza & Blockchain** | **28** | RSA, ECC/ECDSA/Ed25519, AES, ChaCha20-Poly1305, SHA-3, BLAKE3, Groth16, PLONK, zk-STARKs, FHE (CKKS/TFHE), Paillier, CRYSTALS-Kyber, CRYSTALS-Dilithium, Differential Privacy | | **8** | **Algoritmi su Grafi & Reti Complesse** | **27** | Dijkstra, A*, D* Lite, Bellman-Ford, Floyd-Warshall, PageRank, Tarjan SCC, Dinic/Push-Relabel Max Flow, Hopcroft-","url":"https://doi.org/10.5281/zenodo.15618766","authors":["Usai, Luigi"],"tags":["Algoritmi","Lista degli algoritmi","Software per l'analisi degli algoritmi"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.15618766","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22181072","name":"AlgoExplorer: Una Libreria Python Interattiva per lo Studio degli Algoritmi","source":"datacite","abstract":"# LUAlgoExplorer: A Semantic Hypergraph Engine and Universal Encyclopedia of Computational Algorithms **Author:** Luigi Usai **ORCID:** [0009-0003-3001-717X](https://orcid.org/0009-0003-3001-717X) **Software Version:** 2.0.0 **License:** GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later) **Resource Type:** Software / Dataset / Knowledge Graph **Keywords:** Algorithms Encyclopedia, Semantic Hypergraph, W3C JSON-LD 1.1, Schema.org, Knowledge Representation, Computer Science, Quantum Computing, Bioinformatics, Deep Learning, Graph Algorithms, Cryptography https://archive.softwareheritage.org/browse/directory/524541c3fb57d30425d4b427fdc5a7ff86bd0ac5/?origin_url=https://doi.org/10.5281/zenodo.15618766&path=LUAlgoExplorer&release=1&snapshot=6df254cb9da6e06173e68b58342974f897401c53 --- ## 📖 Abstract **LUAlgoExplorer** is an advanced open-source semantic hypergraph engine, research catalog, and interactive visual explorer designed for the systematic study, classification, topological traversal, and multi-dimensional analysis of world algorithms across computer science, mathematics, theoretical physics, computational biology, and artificial intelligence. Built on strict **Clean Architecture**, **Domain-Driven Design (DDD)**, and modern **Agentic + Test Harness** engineering standards, the system structures the world algorithmic corpus as an interconnected knowledge hypergraph. Unlike classical pairwise graphs, LUAlgoExplorer models higher-order thematic and algorithmic relationships through **$n$-ary hyperedges**, linking algorithms across computational paradigms, complexity classes, and domain applications. --- ## 📊 Dataset & Hypergraph Metrics * **Verified Fundamental Algorithms:** **533** * **Unified Scientific Disciplines / Categories:** **25** * **Total W3C JSON-LD 1.1 Entities:** **926** * **Active $n$-ary Hyperedges:** **364** * **Unique Semantic Tags & Keywords:** **1,795** * **Automated Test Coverage:** **100% Pass Rate (24/24 Unit & Integration Tests)** --- ## 🏛️ Comprehensive Algorithmic Taxonomy (25 Disciplines) | # | Scientific Discipline / Macro-Category | Alg. Count | Representative Algorithms | | :- | :--- | :-: | :--- | | **1** | **Computer Vision & Image Processing** | **47** | SIFT, SURF, ORB, Harris Corner, Canny, Otsu, Watershed, YOLO, Faster R-CNN, Mask R-CNN, SAM, Lucas-Kanade, RAFT, NeRF, 3D Gaussian Splatting, ORB-SLAM, Bundle Adjustment | | **2** | **Calcolo Quantistico (Quantum Computing)** | **40** | Shor's Algorithm, Grover's Algorithm, QFT, QPE (Kitaev), Simon, Deutsch-Jozsa, Hallgren, Kuperberg, HHL, QSVT, qPCA, qSVM, VQE, QAOA, BB84, E91, Surface Codes, GKP Codes | | **3** | **Bioinformatica e Biologia Computazionale** | **37** | BLAST, Smith-Waterman, Needleman-Wunsch, BWA/Bowtie (FM-Index), Clustal Omega, MAFFT, Neighbor-Joining, UPGMA, AlphaFold (Evoformer/IPA), Foldseek, HMMER, Baum-Welch | | **4** | **Deep Learning (Reti Neurali & Modelli Generativi)** | **36** | Backpropagation, AdamW, FlashAttention, Mamba SSM (S4/S5), DPO, RoPE, MoE, ResNet, Swin Transformer, DDPM, Flow Matching, DiT, GAN, VAE, GCN/GAT, LoRA/QLoRA | | **5** | **Algoritmi di Ricerca & Strutture Dati** | **32** | Binary/Interpolation/Exponential Search, AVL, Red-Black Tree, B+ Tree, Suffix Array (DC3), Fenwick Tree, Bloom Filter, HyperLogLog, MinHash, HNSW, Fibonacci Heap, QuadTree, k-d Tree | | **6** | **Compressione Dati & Information Theory** | **29** | Huffman, Arithmetic Coding, ANS / FSE (Zstd), LZ77/LZ78/LZW/LZMA/LZ4/Brotli, DEFLATE, PPM, PAQ, BWT, DCT (JPEG), DWT (JPEG 2000), Opus, H.264/H.265/AV1 | | **7** | **Crittografia, Sicurezza & Blockchain** | **28** | RSA, ECC/ECDSA/Ed25519, AES, ChaCha20-Poly1305, SHA-3, BLAKE3, Groth16, PLONK, zk-STARKs, FHE (CKKS/TFHE), Paillier, CRYSTALS-Kyber, CRYSTALS-Dilithium, Differential Privacy | | **8** | **Algoritmi su Grafi & Reti Complesse** | **27** | Dijkstra, A*, D* Lite, Bellman-Ford, Floyd-Warshall, PageRank, Tarjan SCC, Dinic/Push-Relabel Max Flow, Hopcroft-","url":"https://doi.org/10.5281/zenodo.22181072","authors":["Usai, Luigi"],"tags":["Algoritmi","Lista degli algoritmi","Software per l'analisi degli algoritmi"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22181072","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.13627270","name":"Improving medical data synthesis with DP-GAN and Deep Anomaly Detection","source":"datacite","abstract":"Ensuring the privacy of medical data in a meaningful manner is a complex task. This domain presents a plethora of unique challenges: high stakes, vast differences between possible use cases, long-established methods that limit the number of feasible solutions, and more. Consequently, an effective approach to ensuring the privacy of medical data must be easy to adopt, offer robust privacy guarantees, and minimize the reduction in data utility.The unique nature of medical data presents distinct challenges and also opportunities. We consider various types of correlations that significantly impact privacy guarantees. However, these correlations can also be used to train a model for removing anomalies and subsequently enhancing the utility of synthetic medical data.This thesis proposes a framework compatible with state-of-the-art approaches for differentially private dataset release based on the usage of Generative Adversarial Networks (GANs). Our framework uses a part of the privacy budget to train an unsupervised learning model to detect and remove anomalies. We evaluate the performance of the framework using a variety of machine-learning models and metrics. The final results show an improvement of up 13% compared to approaches not using our framework, under the same privacy budget.","url":"https://doi.org/10.5281/zenodo.13627270","authors":["Crha, Vojtech","Hai, Rihan","Erkin, Zekeriya","Li, Tianyu"],"tags":["Differential Privacy","Anomaly Detection","Medical Data Sharing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13627270","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.13627271","name":"Improving medical data synthesis with DP-GAN and Deep Anomaly Detection","source":"datacite","abstract":"Ensuring the privacy of medical data in a meaningful manner is a complex task. This domain presents a plethora of unique challenges: high stakes, vast differences between possible use cases, long-established methods that limit the number of feasible solutions, and more. Consequently, an effective approach to ensuring the privacy of medical data must be easy to adopt, offer robust privacy guarantees, and minimize the reduction in data utility.The unique nature of medical data presents distinct challenges and also opportunities. We consider various types of correlations that significantly impact privacy guarantees. However, these correlations can also be used to train a model for removing anomalies and subsequently enhancing the utility of synthetic medical data.This thesis proposes a framework compatible with state-of-the-art approaches for differentially private dataset release based on the usage of Generative Adversarial Networks (GANs). Our framework uses a part of the privacy budget to train an unsupervised learning model to detect and remove anomalies. We evaluate the performance of the framework using a variety of machine-learning models and metrics. The final results show an improvement of up 13% compared to approaches not using our framework, under the same privacy budget.","url":"https://doi.org/10.5281/zenodo.13627271","authors":["Crha, Vojtech","Hai, Rihan","Erkin, Zekeriya","Li, Tianyu"],"tags":["Differential Privacy","Anomaly Detection","Medical Data Sharing"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13627271","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21574002","name":"Data Identification And Control Mechanism Using Distinguishing, Attacks In Cloud","source":"datacite","abstract":"Cloud environment provides encrypted data management facility to the shared data. Security and privacy are guaranteed with encrypted storage support model. The data management and security requirements are handled by the cloud server only. Encrypted cloud storage methods are adapted to secure the data shared under the clouds. All the outsourced operations are carried out on the encrypted data only. The data and query comparison operations are performed using the encrypted data search mechanism. The Order Preserving Encryption (OPE) technique is adapted to support search process ranked manner. The relevance score and inverted index are protected with the Order Preserving Encryption (OPE). Security and privacy are guaranteed with encrypted storage support model. The Order Preserving Encryption (OPE) technique is adapted to support search process ranked manner. The relevance score and inverted index are protected with the Order Preserving Encryption (OPE). The distribution of encrypted data values are unchanged in the deterministic OPE mechanism. The index distribution is managed to support search operation in One-to-many OPE. One to many OPE is also denoted as probabilistic OPE Scheme. The outsourced data search on encrypted data model is carried out with the binary search algorithm. The distribution and index differences are utilized to estimate the search keyword in differential attacks.","url":"https://doi.org/10.5281/zenodo.21574002","authors":["Meena, S.","Kowsalya, Dr. N."],"tags":["Outsourced Data Search","Data Centers","Order Preserving Encryption and Differential Attacks"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.5281/zenodo.21574002","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21574003","name":"Data Identification And Control Mechanism Using Distinguishing, Attacks In Cloud","source":"datacite","abstract":"Cloud environment provides encrypted data management facility to the shared data. Security and privacy are guaranteed with encrypted storage support model. The data management and security requirements are handled by the cloud server only. Encrypted cloud storage methods are adapted to secure the data shared under the clouds. All the outsourced operations are carried out on the encrypted data only. The data and query comparison operations are performed using the encrypted data search mechanism. The Order Preserving Encryption (OPE) technique is adapted to support search process ranked manner. The relevance score and inverted index are protected with the Order Preserving Encryption (OPE). Security and privacy are guaranteed with encrypted storage support model. The Order Preserving Encryption (OPE) technique is adapted to support search process ranked manner. The relevance score and inverted index are protected with the Order Preserving Encryption (OPE). The distribution of encrypted data values are unchanged in the deterministic OPE mechanism. The index distribution is managed to support search operation in One-to-many OPE. One to many OPE is also denoted as probabilistic OPE Scheme. The outsourced data search on encrypted data model is carried out with the binary search algorithm. The distribution and index differences are utilized to estimate the search keyword in differential attacks.","url":"https://doi.org/10.5281/zenodo.21574003","authors":["Meena, S.","Kowsalya, Dr. N."],"tags":["Outsourced Data Search","Data Centers","Order Preserving Encryption and Differential Attacks"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.5281/zenodo.21574003","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.19772433","name":"The Invisible Patient: Bridging Personal Health Platforms with Hospital Care Through Extended Systems Theory, Professional Bureaucracy, and EU Interoperability Dimensions","source":"datacite","abstract":"Analysis of the interoperability gap between personal health platforms and hospital care in Sweden. Uses the Five Model (extended with data, knowledge, context), Mintzberg's professional bureaucracy, EU Interoperability Framework, Ward and Daniel's Benefits Management, and SMILE methodology. Proposes a bridge architecture with mathematically verified privacy (DPella differential privacy). Includes protocol specification, three falsifiable propositions, and impact delta quantification. First Swedish mapping of personal-to-hospital interoperability across all six EU dimensions for surgical care.","url":"https://doi.org/10.5281/zenodo.19772433","authors":["Waern, Nicolas"],"tags":["interoperability","personal health platform","EU Interoperability Framework","Five Model","Mintzberg","professional bureaucracy","digital twin","health data sovereignty"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19772433","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.19772434","name":"The Invisible Patient: Bridging Personal Health Platforms with Hospital Care Through Extended Systems Theory, Professional Bureaucracy, and EU Interoperability Dimensions","source":"datacite","abstract":"Analysis of the interoperability gap between personal health platforms and hospital care in Sweden. Uses the Five Model (extended with data, knowledge, context), Mintzberg's professional bureaucracy, EU Interoperability Framework, Ward and Daniel's Benefits Management, and SMILE methodology. Proposes a bridge architecture with mathematically verified privacy (DPella differential privacy). Includes protocol specification, three falsifiable propositions, and impact delta quantification. First Swedish mapping of personal-to-hospital interoperability across all six EU dimensions for surgical care.","url":"https://doi.org/10.5281/zenodo.19772434","authors":["Waern, Nicolas"],"tags":["interoperability","personal health platform","EU Interoperability Framework","Five Model","Mintzberg","professional bureaucracy","digital twin","health data sovereignty"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19772434","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.21219655","name":"DIFFERENTIAL PRIVACY-BASED PROTECTION OF USER SHOPPING PREFERENCES IN E-COMMERCE SYSTEMS","source":"datacite","abstract":"This study investigates the potential of differentiated privacy to safeguard the purchase preferences of online consumers while simultaneously enabling data analysis and personalized services. Protecting user privacy is a challenging endeavor due to the extensive collection of customer data by online purchasing websites. This study examines the potential of differential privacy techniques to conceal customers' private purchasing habits by introducing controlled noise to data without affecting the accuracy of the analysis. It also maintains a balance between data privacy and service quality to guarantee that enterprises can acquire critical knowledge while safeguarding customer data. The results indicate that differential privacy is a dependable and efficient method for fostering user confidence, alleviating privacy concerns, and facilitating secure data exchange in contemporary e-commerce.","url":"https://doi.org/10.5281/zenodo.21219655","authors":["Advanced Research and Development Journal"],"tags":["Differential Privacy","E-Commerce Systems","User Shopping Preferences","Data Privacy","Privacy Protection","Personalized Recommendations","Data Security","Customer Confidentiality"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21219655","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.17605/osf.io/xdwhs","name":"Large Language Models in the Acute Stroke Pathway: A Scoping Review of Applications, Evidence Maturity, and Implementation Readiness","source":"datacite","abstract":"Background. Large language models (LLMs) have been rapidly adopted in medicine since late 2022, yet their role in the time-critical acute stroke pathway—from symptom recognition and prehospital triage to emergency diagnosis, imaging-related text tasks, reperfusion decision support, and acute-phase documentation and communication—has not been systematically mapped. Existing reviews cover the whole stroke-care continuum or mix LLMs with traditional NLP, leaving the acute phase under-characterized. Objective. To map the applications, evidence maturity, and implementation readiness of LLMs across the acute stroke pathway. Methods. This scoping review follows the PRISMA-ScR guideline. We search PubMed/MEDLINE, Europe PMC (including preprints), and Google Scholar for studies published from November 2022 onward. Eligible studies center on LLMs/generative AI applied to any stage of the acute stroke pathway. Two reviewers independently screen records and chart data using a piloted form. Evidence is synthesized along two dimensions: five pathway stages (prehospital recognition/dispatch; emergency triage and differential diagnosis; imaging-related text tasks; reperfusion decision support; acute documentation and communication) and three evidence-maturity tiers (simulation/benchmark; retrospective real-world data; prospective deployment). Implementation barriers (hallucination, bias, privacy, regulation, liability, integration, cost) are thematically summarized. Registration note. This review is registered on OSF; the full protocol is available in the attached files.","url":"https://doi.org/10.17605/osf.io/xdwhs","authors":["Xianmu Luo","Hongsong Li","Xiaoli Liao"],"tags":["Medicine and Health Sciences","clinical medicine","scoping review"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17605/osf.io/xdwhs","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.19642396","name":"Global Legal Architecture for Privacy-Preserving Federated Biomedical Signal Analytics: Integrating Differential Privacy Guarantees, Cross-Border Health Data Adequacy, and Transnational Diagnostic Liability into Sovereign Medical AI Governance Frameworks  Prepared and Authored by Dr. Mohamed Kamal Arafa El-Rakhawi International Law & Emerging Technologies Governance Specialist","source":"datacite","abstract":"Global Legal Architecture for Privacy-Preserving Federated Biomedical Signal Analytics: Integrating Differential Privacy Guarantees, Cross-Border Health Data Adequacy, and Transnational Diagnostic Liability into Sovereign Medical AI Governance Frameworks Prepared and Authored by Dr. Mohamed Kamal Arafa El-Rakhawi International Law & Emerging Technologies Governance Specialist","url":"https://doi.org/10.5281/zenodo.19642396","authors":["elrakhawi, mohamed kamal arafa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19642396","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.19642397","name":"Global Legal Architecture for Privacy-Preserving Federated Biomedical Signal Analytics: Integrating Differential Privacy Guarantees, Cross-Border Health Data Adequacy, and Transnational Diagnostic Liability into Sovereign Medical AI Governance Frameworks  Prepared and Authored by Dr. Mohamed Kamal Arafa El-Rakhawi International Law & Emerging Technologies Governance Specialist","source":"datacite","abstract":"Global Legal Architecture for Privacy-Preserving Federated Biomedical Signal Analytics: Integrating Differential Privacy Guarantees, Cross-Border Health Data Adequacy, and Transnational Diagnostic Liability into Sovereign Medical AI Governance Frameworks Prepared and Authored by Dr. Mohamed Kamal Arafa El-Rakhawi International Law & Emerging Technologies Governance Specialist","url":"https://doi.org/10.5281/zenodo.19642397","authors":["elrakhawi, mohamed kamal arafa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19642397","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22157793","name":"Algorithmic Fairness through Differential Privacy and Fairness Constraints","source":"datacite","abstract":"This paper investigates the challenging problem of achieving algorithmic fairness in machine learning systems while simultaneously protecting individual privacy. Traditional approaches to fairness often rely on post-processing techniques or modifying the learning algorithm itself, which can inadvertently compromise privacy. Conversely, differential privacy, a rigorous privacy mechanism, can be sensitive to biased data, potentially amplifying existing inequalities. This work proposes a novel framework that integrates differential privacy with explicitly defined fairness constraints. We demonstrate that by carefully calibrating the privacy parameters and incorporating fairness constraints directly into the learning process, it is possible to mitigate bias and maintain strong privacy guarantees. The core contribution lies in the development of a methodology for navigating this complex trade-off, offering a more robust and principled approach to building fair and private algorithms. We present a theoretical analysis of this framework and outline a practical approach to implementation.","url":"https://doi.org/10.5281/zenodo.22157793","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22157793","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22157794","name":"Algorithmic Fairness through Differential Privacy and Fairness Constraints","source":"datacite","abstract":"This paper investigates the challenging problem of achieving algorithmic fairness in machine learning systems while simultaneously protecting individual privacy. Traditional approaches to fairness often rely on post-processing techniques or modifying the learning algorithm itself, which can inadvertently compromise privacy. Conversely, differential privacy, a rigorous privacy mechanism, can be sensitive to biased data, potentially amplifying existing inequalities. This work proposes a novel framework that integrates differential privacy with explicitly defined fairness constraints. We demonstrate that by carefully calibrating the privacy parameters and incorporating fairness constraints directly into the learning process, it is possible to mitigate bias and maintain strong privacy guarantees. The core contribution lies in the development of a methodology for navigating this complex trade-off, offering a more robust and principled approach to building fair and private algorithms. We present a theoretical analysis of this framework and outline a practical approach to implementation.","url":"https://doi.org/10.5281/zenodo.22157794","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22157794","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22157527","name":"Algorithmic Fairness Verification via Differential Privacy","source":"datacite","abstract":"Algorithmic fairness is a critical concern in modern machine learning, yet achieving it remains a complex and often elusive goal. Traditional approaches to fairness verification are frequently hampered by ambiguity and a lack of formal guarantees. This paper proposes a novel framework that integrates differential privacy (DP) into the model training process, providing provable privacy guarantees while simultaneously enabling rigorous fairness verification. The core idea is to leverage DP to protect sensitive attributes used in training, and then utilize this privacy protection to assess the model's predictive accuracy and fairness across various demographic groups. We present a methodology where DP is applied during training, and fairness metrics are evaluated on the resulting model. This approach offers a robust and verifiable solution for building trustworthy AI systems, addressing the limitations of existing methods. The framework's key contribution is a quantifiable and verifiable approach to fairness, moving beyond subjective assessments and providing a solid foundation for responsible AI development. The ultimate goal is to build models that are both accurate and equitable, mitigating potential biases and promoting fairness in algorithmic decision-making.","url":"https://doi.org/10.5281/zenodo.22157527","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22157527","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22157526","name":"Algorithmic Fairness Verification via Differential Privacy","source":"datacite","abstract":"Algorithmic fairness is a critical concern in modern machine learning, yet achieving it remains a complex and often elusive goal. Traditional approaches to fairness verification are frequently hampered by ambiguity and a lack of formal guarantees. This paper proposes a novel framework that integrates differential privacy (DP) into the model training process, providing provable privacy guarantees while simultaneously enabling rigorous fairness verification. The core idea is to leverage DP to protect sensitive attributes used in training, and then utilize this privacy protection to assess the model's predictive accuracy and fairness across various demographic groups. We present a methodology where DP is applied during training, and fairness metrics are evaluated on the resulting model. This approach offers a robust and verifiable solution for building trustworthy AI systems, addressing the limitations of existing methods. The framework's key contribution is a quantifiable and verifiable approach to fairness, moving beyond subjective assessments and providing a solid foundation for responsible AI development. The ultimate goal is to build models that are both accurate and equitable, mitigating potential biases and promoting fairness in algorithmic decision-making.","url":"https://doi.org/10.5281/zenodo.22157526","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22157526","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22157083","name":"afzalahmed786/Adaptive-Split-Computing-for-Text-to-Image-Diffusion: v1.0.0","source":"datacite","abstract":"First public release. Reinforcement-learning controller that adaptively partitions Stable Diffusion inference between an edge client and a server, selecting split point, quantization, decoder, and privacy settings per request based on live device and network conditions. The raw prompt and final image stay on-device; only an intermediate tensor is transmitted, protected with differential-privacy noise and structured obfuscation. Contents: PPO controller with joint action selection and training loop Split-computing client and inference server (3-stage pipeline) Differential privacy, structured obfuscation, and server-side reconstruction Adversarial spy model with training and data-collection scripts Configurable performance/quality/privacy parameters Requirements: Python 3.10+, PyTorch, diffusers, transformers. Secrets and endpoints are read from environment variables (HF_TOKEN, SERVER_URL). See README for setup, usage, and constants to calibrate for your hardware.","url":"https://doi.org/10.5281/zenodo.22157083","authors":["afzalahmed786"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22157083","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22157082","name":"afzalahmed786/Adaptive-Split-Computing-for-Text-to-Image-Diffusion: v1.0.0","source":"datacite","abstract":"First public release. Reinforcement-learning controller that adaptively partitions Stable Diffusion inference between an edge client and a server, selecting split point, quantization, decoder, and privacy settings per request based on live device and network conditions. The raw prompt and final image stay on-device; only an intermediate tensor is transmitted, protected with differential-privacy noise and structured obfuscation. Contents: PPO controller with joint action selection and training loop Split-computing client and inference server (3-stage pipeline) Differential privacy, structured obfuscation, and server-side reconstruction Adversarial spy model with training and data-collection scripts Configurable performance/quality/privacy parameters Requirements: Python 3.10+, PyTorch, diffusers, transformers. Secrets and endpoints are read from environment variables (HF_TOKEN, SERVER_URL). See README for setup, usage, and constants to calibrate for your hardware.","url":"https://doi.org/10.5281/zenodo.22157082","authors":["afzalahmed786"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22157082","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22156978","name":"Differential Privacy via Randomized Graph Embeddings","source":"datacite","abstract":"This paper introduces a novel approach to protecting individual privacy within graph data by leveraging randomized graph embeddings and differential privacy. The core challenge in applying differential privacy to graph data stems from the inherent interconnectedness of nodes, making traditional privacy techniques difficult to implement effectively. This work proposes a framework for developing randomized graph embedding techniques that explicitly incorporate differential privacy constraints. The resulting embeddings aim to preserve privacy while maintaining the integrity of the underlying graph structure. We demonstrate the feasibility and potential benefits of this approach, outlining the key design considerations and mathematical formulations involved. The primary contribution lies in the application of differential privacy to graph embeddings, offering a new methodology for safeguarding sensitive information within graph datasets. This research provides a foundation for future work in privacy-preserving graph analysis and offers a promising avenue for utilizing graph data in applications where privacy is paramount.","url":"https://doi.org/10.5281/zenodo.22156978","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22156978","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22156979","name":"Differential Privacy via Randomized Graph Embeddings","source":"datacite","abstract":"This paper introduces a novel approach to protecting individual privacy within graph data by leveraging randomized graph embeddings and differential privacy. The core challenge in applying differential privacy to graph data stems from the inherent interconnectedness of nodes, making traditional privacy techniques difficult to implement effectively. This work proposes a framework for developing randomized graph embedding techniques that explicitly incorporate differential privacy constraints. The resulting embeddings aim to preserve privacy while maintaining the integrity of the underlying graph structure. We demonstrate the feasibility and potential benefits of this approach, outlining the key design considerations and mathematical formulations involved. The primary contribution lies in the application of differential privacy to graph embeddings, offering a new methodology for safeguarding sensitive information within graph datasets. This research provides a foundation for future work in privacy-preserving graph analysis and offers a promising avenue for utilizing graph data in applications where privacy is paramount.","url":"https://doi.org/10.5281/zenodo.22156979","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22156979","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22155976","name":"Generalized Differentiable Privacy for Quantum Computation Security Proof","source":"datacite","abstract":"This paper presents a novel approach to achieving generalized differentiable privacy within the context of quantum computation security proofs. Current techniques for ensuring privacy in quantum computing often struggle to adequately address the unique challenges posed by quantum algorithms and circuits. This work introduces a framework built upon quantum error correction codes and differential privacy principles, offering a precise quantification of quantum computational security. The core of the method lies in a generalized proof system designed to accommodate diverse quantum algorithms, moving beyond the limitations of existing approaches. The proposed framework provides a theoretical foundation for secure deployment of quantum computing, addressing critical gaps in the current landscape of quantum privacy research. We demonstrate a method to bound the privacy loss associated with quantum computations, considering both the computational steps and the inherent properties of quantum systems. The key contribution is the formulation of a mathematically rigorous approach to quantifying privacy leakage in quantum scenarios, enabling a more robust and reliable assessment of quantum computing security.","url":"https://doi.org/10.5281/zenodo.22155976","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22155976","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22155975","name":"Generalized Differentiable Privacy for Quantum Computation Security Proof","source":"datacite","abstract":"This paper presents a novel approach to achieving generalized differentiable privacy within the context of quantum computation security proofs. Current techniques for ensuring privacy in quantum computing often struggle to adequately address the unique challenges posed by quantum algorithms and circuits. This work introduces a framework built upon quantum error correction codes and differential privacy principles, offering a precise quantification of quantum computational security. The core of the method lies in a generalized proof system designed to accommodate diverse quantum algorithms, moving beyond the limitations of existing approaches. The proposed framework provides a theoretical foundation for secure deployment of quantum computing, addressing critical gaps in the current landscape of quantum privacy research. We demonstrate a method to bound the privacy loss associated with quantum computations, considering both the computational steps and the inherent properties of quantum systems. The key contribution is the formulation of a mathematically rigorous approach to quantifying privacy leakage in quantum scenarios, enabling a more robust and reliable assessment of quantum computing security.","url":"https://doi.org/10.5281/zenodo.22155975","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22155975","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.5281/zenodo.22155597","name":"Decentralized Differential Privacy via Distributed Hashing","source":"datacite","abstract":"This paper presents a novel approach to achieving differential privacy in decentralized systems, utilizing distributed hashing techniques. The core concept revolves around obscuring individual data contributions within a global computation, thereby safeguarding privacy while preserving data utility. We introduce a system where each participating node contributes data to a global computation and then employs a distributed hash function to obfuscate its specific input. This mechanism mitigates the risk of identifying individual data points, a significant challenge in traditional centralized differential privacy schemes. The proposed framework offers a practical solution for scenarios where trust is limited and data resides across a network of independent nodes. The theoretical analysis demonstrates the effectiveness of this approach in guaranteeing differential privacy under specific conditions, focusing on the choice of the distributed hash function and the number of participating nodes. We explore the trade-offs involved in balancing privacy guarantees with computational efficiency.","url":"https://doi.org/10.5281/zenodo.22155597","authors":["Zhang, Jincheng"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22155597","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.440Z"},{"id":"doi:10.21203/rs.3.rs-94765/v1","name":"An Analysis of Differential Privacy Research in Location and Trajectory Data","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-94765/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.21203/rs.3.rs-94765/v1","addedAt":"2026-08-31T06:41:47.440Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1101/2020.08.03.235416","name":"Differential Privacy Protection Against Membership Inference Attack on Machine Learning for Genomic Data","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2020.08.03.235416","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1101/2020.08.03.235416","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.12688/gatesopenres.13089.2","name":"Differential privacy in the 2020 US census: what will it do? Quantifying the accuracy/privacy tradeoff","source":"preprints","abstract":"","url":"https://doi.org/10.12688/gatesopenres.13089.2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.12688/gatesopenres.13089.2","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1101/2020.09.04.283135","name":"Privacy-Preserving and Robust Watermarking on Sequential Genome Data using Belief Propagation and Local Differential Privacy","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2020.09.04.283135","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2020","doi":"10.1101/2020.09.04.283135","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202608.0852.v1","name":"Privacy-Preserving Information Fusion of Heterogeneous Cross-Jurisdictional Sources for Traffic Accident Severity Prediction","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202608.0852.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.0852.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.64898/2026.07.28.741369","name":"DEAR-OWL: a fully browser-based hybrid resource for instant or precise differential gene expression analysis","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.07.28.741369","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.07.28.741369","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.64898/2026.08.18.26359085","name":"A Human-in-the-Loop Large Language Model System Based on the Model Context Protocol for Differential Diagnosis from Electronic Medical Records and Literature","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.08.18.26359085","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.08.18.26359085","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.64898/2026.07.16.26358171","name":"Privacy-Preserving Matching for Federated Causal Inference in Multicentre Patient Cohorts","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.07.16.26358171","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.07.16.26358171","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202608.1459.v1","name":"Auditing Explanation Faithfulness in Privacy-Preserving Federated Intrusion Detection","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202608.1459.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202608.1459.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.14293/pr2199.003897.v1","name":"From Compliance to Competitive Advantage: Designing a \"Privacy-By-Design\" Financial AI Governance Framework that Mitigates Bias without Sacrificing Predictive Performance","source":"preprints","abstract":"","url":"https://doi.org/10.14293/pr2199.003897.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.14293/pr2199.003897.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.31234/osf.io/j8mxa_v1","name":"The impact of privacy risk perception on initial trust in autonomous vehicle: Differential responses of professionals and non-professionals","source":"preprints","abstract":"","url":"https://doi.org/10.31234/osf.io/j8mxa_v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.31234/osf.io/j8mxa_v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9253891/v1","name":"Multi-sensor Privacy Data Tampering Prevention Method under Flood Attacks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9253891/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9253891/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-10766703/v1","name":"Adversarial Evaluation of a Two-Layer Anonymization Pipeline Against Record-Linkage Attacks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10766703/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10766703/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.64898/2026.07.30.26359271","name":"Agentic-TimesFM-AKI: A Dual LLM–Time Series Framework for Predicting Drug-Induced Acute Kidney Injury with Privacy-Preserving Synthetic Data","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.07.30.26359271","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.07.30.26359271","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.64898/2026.08.03.741845","name":"Hō‘ike: A Joint-Embedding Predictive Architecture for Transcriptome Data Generation with Diffusion Models","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.08.03.741845","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.08.03.741845","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-10134525/v1","name":"ExposOmix-Fed: A Federated, Site-Invariant Protocol for Aligning the Environmental Exposome, Multi-Omics, and Abdominal MRI for Colorectal Cancer Risk Stratification in UK Biobank","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-10134525/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10134525/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9851696/v1","name":"D-PGAN: A Differentially Private Generative Adversarial Network for Multi-Party Publishing of High-Dimensional Tabular Data","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9851696/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9851696/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9517530/v1","name":"Dark Social Inference for Early Detection of Coordinated Misinformation Campaigns","source":"europepmc","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9517530/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9517530/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.21203/rs.3.rs-9456245/v1","name":"BApplying Deep Personal Privacy (DPP An Empirical Framework for Inference Resistance in Large Language Models","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9456245/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9456245/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202604.1684.v1","name":"(d, c, l)-Privacy : Privacy Preservation Models for Content‑Sensitive Datasets Using Information Retrieval Techniques","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202604.1684.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202604.1684.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202603.1179.v1","name":"Attacks and Defenses in Differentially Private Deep Learning: New Security Risks in New Era","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202603.1179.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202603.1179.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9189160/v1","name":"Emergency Monitoring via Encrypted Reverberation and Graph-based Environmental Detection of Anomalous Kinetic events (EMERGE-DARK)","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9189160/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9189160/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.64898/2026.03.02.707794","name":"Towards Useful and Private Synthetic Omics: Community Benchmarking of Generative Models for Transcriptomics Data","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.03.02.707794","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.03.02.707794","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-8726974/v1","name":"FedEmoNet: Privacy-Preserving FederatedLearning with TCN-Transformer Fusion forCross-Corpus Speech Emotion Recognition","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8726974/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8726974/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9185986/v1","name":"A Cross-layer Provenance-Protection Architecture for Repeated Quantum Measurements in the Cloud","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9185986/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9185986/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9153407/v1","name":"Differentially Private Lasso: An ISTA Framework with Finite-Iteration Guarantees","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9153407/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9153407/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202602.1929.v1","name":"A Federated and Differentially Private Incentive–Marketing Framework for Privacy-Preserving Cross-Channel Measurement in AI-Powered Digital Commerce","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202602.1929.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202602.1929.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.31234/osf.io/a6e7d_v2","name":"Young people’s experiences of self-harm and suicide support through schools and youth organisations: A qualitative study in England","source":"preprints","abstract":"","url":"https://doi.org/10.31234/osf.io/a6e7d_v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.31234/osf.io/a6e7d_v2","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202605.0682.v1","name":"Towards Trustworthy Cyber-Physical Futures: Comprehensive Survey of Trust and Security in Digital Twin Systems","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202605.0682.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202605.0682.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9959790/v1","name":"Secure Data Sharing for Real Estate Valuation Using Blockchain Technology","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9959790/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9959790/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-8837598/v1","name":"SAP-TrajWGP: A Semantic-Aware Personalized Trajectory Privacy Protection Algorithm Based on WGAN-GP","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8837598/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8837598/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-8465439/v1","name":"DP-Protected LightGBM Framework for Smart Home Malicious Traffic Detection","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8465439/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8465439/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.64898/2026.07.10.735823","name":"AtlasLens: Metadata-centric exploration and analysis of single-cell atlases","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.07.10.735823","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.07.10.735823","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.14293/pr2199.004023.v1","name":"Analyzing Model Extraction Vulnerabilities in Commercial Security AI Services","source":"preprints","abstract":"","url":"https://doi.org/10.14293/pr2199.004023.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.14293/pr2199.004023.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.14293/pr2199.003894.v1","name":"A Values-Driven Data Protection Framework for AI Adoption in Emerging Economies","source":"preprints","abstract":"","url":"https://doi.org/10.14293/pr2199.003894.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.14293/pr2199.003894.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-8768420/v1","name":"TrustDS: A Policy-First, Privacy-Preserving Framework for Interoperable Marketplace Data Exchange Across Edge and Multi-Cloud Environments ","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8768420/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8768420/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202603.1799.v1","name":"Telehealth- Integrated Smart Tourism Platforms Addressing Urban Care Gaps Through Real Time Health Monitoring for Active Aging Seniors","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202603.1799.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202603.1799.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9080436/v1","name":"AI Driven Cybersecure Biochemistry Education: A Global Framework for Intelligent, Resilient, and Data Secure Life Science Learning Ecosystems","source":"europepmc","abstract":"Abstract The digitization of life science education has created an urgent but unaddressed problem. Biochemistry training increasingly relies on intelligent tutoring systems, virtual laboratories, and learning analytics platforms. These technologies generate and process sensitive data including genomic sequences, experimental results, and detailed student performance records. Current educational frameworks treat artificial intelligence, cybersecurity, and biochemistry pedagogy as separate domains. No integrated architecture exists to secure scientific data while delivering personalized AI driven instruction. This study develops a conceptual framework that integrates AI enhanced biochemistry learning with comprehensive cybersecurity protocols. The framework specifies how intelligent tutoring systems can operate within secure data environments designed for life science education contexts. The study employs framework synthesis methodology following Jabareen (2009). Systematic literature searches were conducted across PubMed, ACM Digital Library, IEEE Xplore, and ERIC databases for publications from 2010 to 2024. Search terms included combinations of keywords from each domain. Initial searches identified 847 records. After removing duplicates, 612 records were screened by title and abstract. Full text review of 184 articles resulted in approximately 127 sources meeting inclusion criteria requiring relevance to at least two of three domains: AI in education, cybersecurity for learning systems, or biochemistry pedagogy. Sources were analyzed using constant comparative method. The proposed AI Cybersecure Biochemistry Learning Framework comprises four integrated components specified in Table 1 and Table 2 with architecture shown in Fig. 1. The intelligent tutoring engine implements Bayesian Knowledge Tracing algorithms (Corbett and Anderson, 1995). The secure data infrastructure specifies AES 256 encryption for data at rest and TLS 1.3 for data in transit with identity management through OAuth 2.0 and zero trust principles. The privacy preserving analytics system uses differential privacy (Dwork and Roth, 2014) with epsilon values between 0.1 and 1.0 depending on data sensitivity. The governance layer incorporates NIST Cybersecurity Framework controls. Blockchain verification of student laboratory achievements uses Hyperledger Fabric permissioned ledgers (Hyperledger Foundation, 2023). The framework provides testable propositions for future empirical research. It offers institutions specific technical specifications for implementing secure AI enhanced biochemistry programs. The architecture establishes foundations for privacy preserving learning analytics in scientific education contexts where data sensitivity exceeds typical educational environments.","url":"https://doi.org/10.21203/rs.3.rs-9080436/v1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9080436/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.21203/rs.3.rs-8785131/v1","name":"A Novel Multi-Stage Fusion Pipeline for Robust and Interpretable Melanoma Classification Using Physics-Informed and Vision-Language Models","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8785131/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8785131/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202512.2058.v1","name":"A False Sense of Privacy: Evaluating the Limitsof Textual Data Sanitization for Privacy Protection","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202512.2058.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202512.2058.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-8893792/v1","name":"Medical Image Encryption Using DNA Computing and a Bio-Inspired PRNG for Healthcare Data Privacy","source":"preprints","abstract":"Abstract As digital healthcare systems increasingly rely on electronic medical records, secure and high-quality encryption mechanisms are essential to prevent unauthorized access and cyber threats. This study proposes a novel Hardy–Weinberg equilibrium-inspired pseudorandom number generator (PRNG) integrated with a medical image encryption framework using nonlinear quadratic functions, DNA encoding, and substitution-box-based permutation. The proposed PRNG passed 100\\% of the NIST statistical test suite for 30 and 100 generated sequences, with an average entropy of 7.9 and no detectable periodicity. Key sensitivity analysis demonstrated near-zero correlation between sequences generated from minimally different inputs, indicating strong diffusion properties. Encryption experiments conducted on the MedPix and USC-SIPI datasets achieved an average information entropy of 7.999, near-zero correlation coefficients between adjacent pixels in all directions, and NPCR and UACI values within theoretical ranges. Comparative analysis shows that the proposed scheme provides superior randomness and resistance to statistical and differential attacks compared with state-of-the-art methods.","url":"https://doi.org/10.21203/rs.3.rs-8893792/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8893792/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202603.1627.v1","name":"Personal Intelligence: Toward a User-Governed Preference Substrate for the Age of Agentic AI","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202603.1627.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202603.1627.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9478981/v1","name":"UWSDF-UIoT: Underwater Acoustic Context-Aware Deep Fusion Framework for Secure Data Transmission in Underwater IoT Networks","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9478981/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9478981/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202602.0124.v1","name":"Autoencoder-Enhanced Hierarchical Mondrian Anonymization via Latent Representations","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202602.0124.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202602.0124.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202510.0433.v1","name":"Differentially Private Bagging Ensemble with Adaptive Privacy Budgeting for Credit Card Fraud Detection","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202510.0433.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202510.0433.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7778273/v1","name":"Wavelet-Domain Privacy SGD (WDP-SGD): FrequencySelective Privacy-Preserving Medical AI.","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7778273/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7778273/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202603.0729.v1","name":"A Domain-Driven, Physics-Backed, Proximity-Informed AI Model for PVT Predictions—Part II: Differential Liberation Expansion and Viscosity Tests","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202603.0729.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202603.0729.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-8484276/v1","name":"SkinGuardian: On-Device AI for Private, Fair, Robust, and Explainable Skin Cancer Detection","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8484276/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8484276/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-8457987/v1","name":"SkinGuardian: On-Device AI for Private, Fair, Robust, and Explainable Skin Cancer Detection","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8457987/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8457987/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.64898/2026.04.22.719993","name":"GlioVision: A Multi-Modal MRI Framework for Non-Invasive Glioma Molecular Biomarkers Prediction","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.04.22.719993","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.04.22.719993","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202605.0790.v1","name":"Walking as a Window to the Brain: Redefining Gait in Neurology","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202605.0790.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202605.0790.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7559519/v1","name":"Research on Personalized Location Trajectory Privacy Protection Strategy Based on Mutual Information","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7559519/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7559519/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-6565248/v2","name":"PALSYN: A Method for Synthetic Multi-Perspective Event Log Generation with Differential Private Guarantees","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6565248/v2","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6565248/v2","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202604.0656.v1","name":"A Survey on Peer-to-Peer Network Testing Techniques","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202604.0656.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.20944/preprints202604.0656.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1101/2025.10.25.25338789","name":"Privacy-Aware Federated nnU-Net for ECG Page Digitization","source":"preprints","abstract":"","url":"https://doi.org/10.1101/2025.10.25.25338789","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1101/2025.10.25.25338789","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7738954/v1","name":"A Novel FedLLM Intrusion Detection Frameworkfor Privacy-Preserving Security in IoT EnabledSmart City Network","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7738954/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7738954/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.31222/osf.io/qkvmn_v1","name":"A FAIR/FHIR aligned, reproducible multi omics and epidemiology framework for privacy preserving clinical research","source":"preprints","abstract":"","url":"https://doi.org/10.31222/osf.io/qkvmn_v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.31222/osf.io/qkvmn_v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-9110516/v1","name":"CDLAKS: Chaotic DNA-Based Lightweight Authenticated Key-Dependent Steganographic Framework for Secure Transmission of Medical Images in Iot Environments","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-9110516/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-9110516/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7519920/v1","name":"TrustDS: A Policy‑First, Privacy‑Preserving Framework for Interoperable Data Exchange with Real‑World Validation","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7519920/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7519920/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202509.1640.v1","name":"Federated Fine‐Tuning of Large Language Models with Privacy Preservation and Cross‐Domain Semantic Alignment","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202509.1640.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202509.1640.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202508.2125.v1","name":"An Efficient and Effective Model for Preserving Sensitive Data in Location-Based Graphs Using Data Generalization and Data Suppression in Conjunction with Data Sliding Windows and R-Trees","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202508.2125.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202508.2125.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-10157626/v1","name":"Investigating the fundamental characteristics of retinal age models","source":"preprints","abstract":"Abstract Retinal age is a biological ageing marker estimated from retinal images. Its deviation from chronological age, termed the retinal age gap (RAG), has been associated with adverse health outcomes. However, fundamental characteristics of retinal age models that are critical for interpreting and applying RAG remain underexplored, including the consistency of these associations across age subgroups as well as model generalisability across different cohorts. In this study, we used 327,764 retinal images to develop a retinal age model and investigate associations between RAG and systemic diseases. We examined association consistency across age subgroups and evaluated model performance on three external datasets with distinct imaging devices and populations. RAG showed positive associations with systemic diseases in young and middle-aged subgroups, but negative associations in the aged subgroup, revealing substantial age-dependent inconsistency. We further showed that this inconsistency was explained by regression-to-the-mean effects which varied by health status. In external evaluations, the generalisability of RAG varied considerably by cohort and intended application. Large age estimation errors did not necessarily indicate limited clinical utility, instead suggesting that model generalisability should be defined and evaluated in an application-specific manner rather than based on age estimation accuracy alone. Overall, this study investigates key characteristics of retinal age models that affect the interpretation of RAG and its disease associations. Our findings highlight the importance of reporting age-dependent associations, and emphasise the need for multi-dimensional, application-specific external evaluation for reliable use of retinal age and RAG in future clinical applications. More broadly, our findings and practical recommendations may extend to wider biological age estimation models.","url":"https://doi.org/10.21203/rs.3.rs-10157626/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-10157626/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.64898/2026.02.28.708740","name":"h5adify: neuro-symbolic metadata harmonization enables scalable AnnData integration with local large language models","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.02.28.708740","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.02.28.708740","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-8720861/v1","name":"Public perceptions of spatial computing in health: Opportunities and barriers for supporting self-care and wellbeing","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-8720861/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.21203/rs.3.rs-8720861/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.20944/preprints202506.0157.v1","name":"PrivacyPreserveNet: A Multilevel Privacy-Preserving Framework for Multimodal LLMs via Gradient Clipping and Attention Noise","source":"preprints","abstract":"","url":"https://doi.org/10.20944/preprints202506.0157.v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.20944/preprints202506.0157.v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-6803468/v1","name":"An Efficient Differentially-Private Weighted Support Vector Machine Algorithm with Noisy Gradient Descent","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6803468/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6803468/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-6565248/v1","name":"PALSYN: A Method for Synthetic Multi-Perspective Event Log Generation with Differential Private Guarantees","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-6565248/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-6565248/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.21203/rs.3.rs-7163333/v1","name":"Design and Evaluation of a Context-Aware Multimodal Recommendation and QA System with Retrieval-Augmented Generation","source":"preprints","abstract":"","url":"https://doi.org/10.21203/rs.3.rs-7163333/v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.21203/rs.3.rs-7163333/v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.64898/2026.08.17.26360599","name":"Social contact patterns across levels of deprivation in England, and implications for infectious disease transmission","source":"preprints","abstract":"","url":"https://doi.org/10.64898/2026.08.17.26360599","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.64898/2026.08.17.26360599","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.31234/osf.io/h82dz_v1","name":"Using face averages to measure differential accuracy for demographic groups in facial recognition","source":"preprints","abstract":"","url":"https://doi.org/10.31234/osf.io/h82dz_v1","authors":[],"tags":[],"confidence":0.74,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.31234/osf.io/h82dz_v1","addedAt":"2026-08-31T06:41:47.441Z","updatedAt":"2026-08-31T06:41:47.441Z"},{"id":"doi:10.1109/cloud-summit61220.2024.00024","name":"PRIV-ML: Analyzing Privacy Loss in Iterative Machine Learning with Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cloud-summit61220.2024.00024","authors":["Pratik Thantharate","Divya Ananth Todurkar","Anurag T"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-14T17:31:20Z","doi":"10.1109/cloud-summit61220.2024.00024","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1002/spy2.445","name":"A differential privacy aided DeepFed intrusion detection system for IoT applications","source":"crossref","abstract":"Abstract In the rapidly‐developing Internet of Things (IoT) ecosystem, safeguarding the privacy and accuracy of linked devices and networks is of utmost importance, with the challenge lying in effective implementation of intrusion detection systems on resource‐constrained IoT devices. This study introduces a differential privacy (DP)‐aided DeepFed architecture for intrusion detection in IoT contexts as a novel approach to addressing these difficulties. To build an intrusion detection model, we combined components of a convolutional neural network with bidirectional long short‐term memory. We apply this approach to the Bot‐IoT dataset, which was rigorously curated by the University of New South Wales (UNSW) and N‐BaIoT dataset. Our major goal is to create a model that delivers high accuracy while protecting privacy, an often‐overlooked aspect of IoT security. Intrusion detection tasks are distributed across multiple IoT devices using federated learning principles to protect data privacy, incorporating the DP framework to gauge and minimize information leakage, all while investigating the intricate relationship between privacy and accuracy in pursuit of an ideal compromise. The trade‐off between privacy preservation and model accuracy is investigated by adjusting the privacy loss and noise multiplier. Our research enhances IoT security by introducing a deep learning model for intrusion detection in IoT devices, explores the integration of DP in federated learning framework for IoT and offers guidance on minimizing the accuracy‐privacy trade‐off based on specific privacy and security needs. Our study explores the privacy‐accuracy trade‐off by examining the effects of varying epsilon values on accuracy for various delta values for a range of clients between 5 and 25. We also investigate the influence of several noise multipliers on accuracy and find a consistent accuracy curve, especially around a noise multiplier value of about 0.5. The findings of this study have the possibilities to enhance IoT ecosystem security and privacy, contributing to the IoT landscape's trustworthiness and sustainability.","url":"https://doi.org/10.1002/spy2.445","authors":["Sayeda Suaiba Anwar","Asaduzzaman","Iqbal H. Sarker"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-11T03:09:27Z","doi":"10.1002/spy2.445","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/icws62655.2024.10744606","name":"Privacy-First Crowdsourcing: Blockchain and Local Differential Privacy in Crowdsourced Drone Services","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icws62655.2024.10744606","authors":["Junaid Akram","Ali Anaissi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-06T18:36:03Z","doi":"10.1109/icws62655.2024.10744606","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4950556","name":"The Complexities of Differential Privacy for Survey Data","source":"crossref","abstract":"The concept of differential privacy (DP) has gained substantial attention in recent years, most notably since the U.S. Census Bureau announced the adoption of the concept for its 2020 Decennial Census. However, despite its attractive theoretical properties, implementing DP in practice remains challenging, especially when it comes to survey data. In this paper we present some results from an ongoing project funded by the U.S. Census Bureau that is exploring the possibilities and limitations of DP for survey data. Specifically, we identify five aspects that need to be considered when adopting DP in the survey context: the multi-staged nature of data production; the limited privacy amplification from complex sampling designs; the implications of survey-weighted estimates; the weighting adjustments for nonresponse and other data deficiencies, and the imputation of missing values. We summarize the project’s key findings with respect to each of these aspects and also discuss some of the challenges that still need to be addressed before DP could become the new data protection standard at statistical agencies.","url":"https://doi.org/10.2139/ssrn.4950556","authors":["Jörg Drechsler","James Bailie"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-10T05:43:19Z","doi":"10.2139/ssrn.4950556","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.36227/techrxiv.172468861.17490011/v1","name":"Generalized Differential Privacy for Clustering","source":"crossref","abstract":"In distributed data analysis, clustering plays a vital role in uncovering patterns and relationships across datasets. However, ensuring individual privacy in decentralized settings remains a challenge. Existing approaches often fall short in protecting individual privacy, requiring continuous user engagement, or being limited to specific use cases. To address these shortcomings, we propose a novel framework called nD-Laplace, leveraging Geo-Indistinguishability for privacy preservation. Our framework enables non-interactive privacy-preserving clustering while addressing challenges associated with perturbing data. We introduce grid-remapping to handle out-ranged perturbed points, ensuring clustering utility and privacy. We provide theoretical proof of adherence to generalized differential privacy principles and validate the efficacy of our methodology through real-world dataset evaluations and simulated attacks.","url":"https://doi.org/10.36227/techrxiv.172468861.17490011/v1","authors":["Mina Alishahi","Tjibbe Van Der Ende","Clara Maathuis"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-26T12:10:20Z","doi":"10.36227/techrxiv.172468861.17490011/v1","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/icairc64177.2024.10900017","name":"Distributed Differential Privacy for Federated Learning: A Privacy-Enhancing Approach","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icairc64177.2024.10900017","authors":["Hanlei Zhou","Jie Kong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-04T13:39:08Z","doi":"10.1109/icairc64177.2024.10900017","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4798792","name":"Density-Based Clustering with Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4798792","authors":["Fuyu Wu","Mingjing Du","Qiang Zhi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-18T03:50:50Z","doi":"10.2139/ssrn.4798792","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/sp54263.2024.00134","name":"Lower Bounds for Rényi Differential Privacy in a Black-Box Setting","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp54263.2024.00134","authors":["Tim Kutta","Önder Askin","Martin Dunsche"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-05T17:56:32Z","doi":"10.1109/sp54263.2024.00134","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/sp54263.2024.00122","name":"Cohere: Managing Differential Privacy in Large Scale Systems","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp54263.2024.00122","authors":["Nicolas Küchler","Emanuel Opel","Hidde Lycklama","Alexander Viand","Anwar Hithnawi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-05T17:56:32Z","doi":"10.1109/sp54263.2024.00122","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1016/j.cose.2024.103715","name":"Efficient federated learning privacy preservation method with heterogeneous differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.cose.2024.103715","authors":["Jie Ling","Junchang Zheng","Jiahui Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-21T00:24:11Z","doi":"10.1016/j.cose.2024.103715","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.32920/14639934","name":"Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees","source":"crossref","abstract":"With the rapid growth of the health data scale, the limited storage and computation resources of wireless body area sensor networks (WBANs) is becoming a barrier to their development. Therefore, outsourcing the encrypted health data to the cloud has been an appealing strategy. However, date aggregation will become difficult. Some recently-proposed schemes try to address this problem. However, there are still some functions and privacy issues that are not discussed. In this paper, we propose a privacy-enhanced and multifunctional health data aggregation scheme (PMHA-DP) under differential privacy. Specifically, we achieve a new aggregation function, weighted average (WAAS), and design a privacy-enhanced aggregation scheme (PAAS) to protect the aggregated data from cloud servers. Besides, a histogram aggregation scheme with high accuracy is proposed. PMHA-DP supports fault tolerance while preserving data privacy. The performance evaluation shows that the proposal leads to less communication overhead than the existing one.","url":"https://doi.org/10.32920/14639934","authors":["Hao Ren","Hongwei Li","Xiaohui Liang","Shibo He","Yuanshun Dai","Lian Zhao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2021-07-28T07:33:53Z","doi":"10.32920/14639934","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/pst62714.2024.10788070","name":"Pk-Anonymization Meets Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/pst62714.2024.10788070","authors":["Masaya Kobayashi","Atsushi Fujioka","Koji Chida","Akira Nagai","Kan Yasuda"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-16T19:15:09Z","doi":"10.1109/pst62714.2024.10788070","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.47297/taposatwsp2633-456906.20240502","name":"Optimization of Privacy Budget Allocation in Differential Privacy Technology: An Empirical Study","source":"crossref","abstract":"","url":"https://doi.org/10.47297/taposatwsp2633-456906.20240502","authors":["Yunfei Liu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-08T16:36:10Z","doi":"10.47297/taposatwsp2633-456906.20240502","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/sp54263.2024.00195","name":"DP-Auditorium: A Large-Scale Library for Auditing Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp54263.2024.00195","authors":["William Kong","Andrés Muñoz Medina","Mónica Ribero","Umar Syed"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-05T17:56:32Z","doi":"10.1109/sp54263.2024.00195","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.52783/jes.4071","name":"Preserving Privacy of IoT Healthcare Data using Differential Privacy and LSTM","source":"crossref","abstract":"The Internet of Things (IoT) is a powerful technology creating revolutions in multiple industries for ex: Traffic and Healthcare domains. The patient data collected by continuous monitoring using IoT will support in treating the patients and make a positive impact on patients' well-being and increase the efficiency of healthcare workers. It is crucial to be aware of certain drawbacks and risks associated with protecting the privacy of the patient data which is one of the major problems being faced in the healthcare domain. Harmful individuals/Agencies will use IoT devices to obtain private data of patients. It’s of prime importance to protect privacy in healthcare. To improve the privacy of IoT Healthcare data, Geometric data perturbation along with Noise addition is introduced in this study utilizing Laplace Noise which comes under the framework of Differential Privacy. To increase accuracy, a deep learning technique Long Short-Term Memory (LSTM) is applied in this paper. LSTM has proven to be a superior model in accuracy when compared with other models like Decision Tree and Naive Bayes.","url":"https://doi.org/10.52783/jes.4071","authors":["D Kavitha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-29T08:53:22Z","doi":"10.52783/jes.4071","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/iconics64289.2024.10824602","name":"Implementation of Differential Privacy for Enhancing Data Privacy in Wearable Fitness Trackers","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iconics64289.2024.10824602","authors":["Hammad Shaikh","Shariq Mahmood Khan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:15:11Z","doi":"10.1109/iconics64289.2024.10824602","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/sp54263.2024.00166","name":"Eureka: A General Framework for Black-box Differential Privacy Estimators","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp54263.2024.00166","authors":["Yun Lu","Malik Magdon-Ismail","Yu Wei","Vassilis Zikas"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-05T17:56:32Z","doi":"10.1109/sp54263.2024.00166","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/ijcnn60899.2024.10650755","name":"Privacy-Preserving Deep Reinforcement Learning based on Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/ijcnn60899.2024.10650755","authors":["Wenxu Zhao","Yingpeng Sang","Neal Xiong","Hui Tian"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-09T17:35:05Z","doi":"10.1109/ijcnn60899.2024.10650755","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4911177","name":"Exploring the Limits of Differential Privacy","source":"crossref","abstract":"Differential Privacy (DP) is a powerful technology, but not well-suited to protecting corporate proprietary information while computing aggregate industry-wide statistics. We elucidate this scenario with an example of cybersecurity management data, and consider an alternative approach that relies on a pragmatic assessment of harm to add noise to the data.","url":"https://doi.org/10.2139/ssrn.4911177","authors":["David D. Clark","Simson Garfinkel","KC C. Claffy"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-01T08:31:58Z","doi":"10.2139/ssrn.4911177","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1016/j.comnet.2024.110822","name":"Local differential privacy federated learning based on heterogeneous data multi-privacy mechanism","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.comnet.2024.110822","authors":["Jie Wang","Zhiju Zhang","Jing Tian","Hongtao Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-26T02:28:29Z","doi":"10.1016/j.comnet.2024.110822","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4716079","name":"Density-Based Clustering with Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4716079","authors":["Fuyu Wu","Mingjing Du","Qiang Zhi","Jiarui Sun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-04T11:18:34Z","doi":"10.2139/ssrn.4716079","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1145/3711618.3711625","name":"Privacy Protection in Mobile Crowdsensing Based on Local Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3711618.3711625","authors":["Xinyue Cui","Pengxuan Sun","Ningning Guo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-22T11:22:41Z","doi":"10.1145/3711618.3711625","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/sp54263.2024.00088","name":"Casual Users and Rational Choices within Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/sp54263.2024.00088","authors":["Narges Ashena","Oana Inel","Badrie L. Persaud","Abraham Bernstein"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-05T17:56:32Z","doi":"10.1109/sp54263.2024.00088","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4733736","name":"Reputation-Based Resilient Consensus with Differential Privacy Guarantees","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4733736","authors":["Guilherme Ramos","Sérgio Pequito","Daniel Silvestre"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-21T06:20:27Z","doi":"10.2139/ssrn.4733736","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/icce-taiwan62264.2024.10674178","name":"Estimating the Privacy Budget for Differential Privacy by Machine Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icce-taiwan62264.2024.10674178","authors":["Ying-Hsuan Wang","Shih-Hsuan Yang","Yu-Chi Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-18T17:51:53Z","doi":"10.1109/icce-taiwan62264.2024.10674178","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/isctech63666.2024.10845261","name":"Enhancing Privacy and Security in Recommender Systems Through Federated Learning and Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isctech63666.2024.10845261","authors":["Zhigang Yang","Tafadzwa Mbodza"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-22T18:47:22Z","doi":"10.1109/isctech63666.2024.10845261","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4993354","name":"Enhancing Trajectory Privacy with a Differential Obfuscation Mechanism","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4993354","authors":["Yu-Ning Fang","Yu-Ling Hsueh"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-19T23:38:11Z","doi":"10.2139/ssrn.4993354","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4770051","name":"Blockchain-Enabled Student Privacy Protection in Virtual Collaborative Learning Environments: A Novel Approach Using Localized Differential Privacy and Attribute-Based Searchable Encryption","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4770051","authors":["Lin Jia"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-03-25T18:15:41Z","doi":"10.2139/ssrn.4770051","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.3390/electronics13204091","name":"SPM-FL: A Federated Learning Privacy-Protection Mechanism Based on Local Differential Privacy","source":"crossref","abstract":"Federated learning is a widely applied distributed machine learning method that effectively protects client privacy by sharing and computing model parameters on the server side, thus avoiding the transfer of data to third parties. However, information such as model weights can still be analyzed or attacked, leading to potential privacy breaches. Traditional federated learning methods often disturb models by adding Gaussian or Laplacian noise, but under smaller privacy budgets, the large variance of the noise adversely affects model accuracy. To address this issue, this paper proposes a Symmetric Partition Mechanism (SPM), which probabilistically perturbs the sign of local model weight parameters before model aggregation. This mechanism satisfies strict ϵ-differential privacy, while introducing a variance constraint mechanism that effectively reduces the impact of noise interference on model performance. Compared with traditional methods, SPM generates smaller variance under the same privacy budget, thereby improving model accuracy and being applicable to scenarios with varying numbers of clients. Through theoretical analysis and experimental validation on multiple datasets, this paper demonstrates the effectiveness and privacy-protection capabilities of the proposed mechanism.","url":"https://doi.org/10.3390/electronics13204091","authors":["Zhiyan Chen","Hong Zheng"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-17T08:56:32Z","doi":"10.3390/electronics13204091","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/iccasit62299.2024.10827947","name":"Research on differential privacy protection algorithm for federated learning based on user privacy requirements","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iccasit62299.2024.10827947","authors":["Meijiao Wu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-14T19:40:10Z","doi":"10.1109/iccasit62299.2024.10827947","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.61784/wjit3010","name":"INTEGRATING DIFFERENTIAL PRIVACY WITH BLOCKCHAIN FOR PRIVACY-PRESERVING RECOMMENDATION SYSTEMS","source":"crossref","abstract":"Recommendation systems have become an integral part of the digital landscape, powering personalized experiences and driving engagement across a wide range of industries. However, traditional recommendation systems face significant challenges, including data privacy concerns, lack of transparency, and susceptibility to manipulation. This paper explores how the integration of blockchain technology can revolutionize the field of recommendation systems, addressing these longstanding issues and unlocking new possibilities for more secure, transparent, and user-centric personalization. By leveraging the decentralized, immutable, and cryptographically secure nature of blockchain, this paper examines the potential of blockchain-based recommendation systems to enhance data privacy, ensure algorithm transparency, and facilitate user control over personal data. Additionally, the paper delves into the synergies between blockchain and other emerging technologies, such as federated learning and differential privacy, to further strengthen the security and reliability of recommendation systems. Through a comprehensive analysis of use cases, technical considerations, and implementation challenges, this paper serves as a roadmap for businesses, researchers, and technology professionals seeking to harness the transformative power of blockchain in reinventing the future of personalized recommendations. By embracing this innovative approach, organizations can build trust, empower users, and deliver more effective and ethical recommendation experiences.","url":"https://doi.org/10.61784/wjit3010","authors":["Isabella Fernandez","Aditya Raghavan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-30T04:14:44Z","doi":"10.61784/wjit3010","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4868569","name":"Caldp: A Correlation-Aware Approach to Local Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4868569","authors":["Majid Zolfaghari","Ahmad Mohammadi","Rasool Jalili"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-18T01:17:38Z","doi":"10.2139/ssrn.4868569","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1007/978-3-031-68024-3_4","name":"Evaluating Differential Privacy on Correlated Datasets Using Pointwise Maximal Leakage","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-3-031-68024-3_4","authors":["Sara Saeidian","Tobias J. Oechtering","Mikael Skoglund"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-31T16:07:34Z","doi":"10.1007/978-3-031-68024-3_4","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.29012/jpc.859","name":"On the Connection Between the ABS Perturbation Methodology and Differential Privacy","source":"crossref","abstract":"This paper explores analytical connections between the perturbation methodology of the Australian Bureau of Statistics (ABS) and the differential privacy (DP) framework. We consider a single static counting query function and find the analytical form of the perturbation distribution with symmetric support for the ABS perturbation methodology. We then analytically measure the DP parameters, namely the (ε, δ) pair, for the ABS perturbation methodology under this setting. The results and insights obtained about the behaviour of (ε, δ) with respect to the perturbation support and variance are used to judiciously select the variance of the perturbation distribution give a good δ in the DP framework for a given desired ε and perturbation support. Finally, we propose a simple sampling scheme to implement the perturbation probability matrix in the ABS Cellkey method. The post sampling (ε, δ) pair is numerically analysed as a function of the Cellkey size. It is shown that the best results are obtained for a larger Cellkey size, because the (ε, δ) pair ost-sampling measures remain almost identical when we compare sampling and theoretical results.","url":"https://doi.org/10.29012/jpc.859","authors":["Parastoo Sadeghi","Chien-Hung Chien"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-24T13:10:43Z","doi":"10.29012/jpc.859","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/mlsp58920.2024.10734725","name":"Enhancing Image Privacy in Semantic Communication over Wiretap Channels Leveraging Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/mlsp58920.2024.10734725","authors":["Weixuan Chen","Shunpu Tang","Qianqian Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-04T18:31:55Z","doi":"10.1109/mlsp58920.2024.10734725","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.56553/popets-2024-0131","name":"Computational Differential Privacy for Encrypted Databases Supporting Linear Queries","source":"crossref","abstract":"Differential privacy is a fundamental concept for protecting individual privacy in databases while enabling data analysis. Conceptually, it is assumed that the adversary has no direct access to the database, and therefore, encryption is not necessary. However, with the emergence of cloud computing and the &lt;&lt; on-cloud &gt;&gt; storage of vast databases potentially contributed by multiple parties, it is becoming increasingly necessary to consider the possibility of the adversary having (at least partial) access to sensitive databases. A consequence is that, to protect the on-line database, it is now necessary to employ encryption. At PoPETs'19, it was the first time that the notion of differential privacy was considered for encrypted databases, but only for a limited type of query, namely histograms. Subsequently, a new type of query, summation, was considered at CODASPY'22. These works achieve statistical differential privacy, by still assuming that the adversary has no access to the encrypted database. In this paper, we take an essential step further by assuming that the adversary can eventually access the encrypted data, making it impossible to achieve statistical differential privacy because the security of encryption (beyond the one-time pad) relies on computational assumptions. Therefore, the appropriate privacy notion for encrypted databases that we target is computational differential privacy, which was introduced by Beimel et al. at CRYPTO '08. In our work, we focus on the case of functional encryption, which is an extensively studied primitive permitting some authorized computation over encrypted data. Technically, we show that any randomized functional encryption scheme that satisfies simulation-based security and differential privacy of the output can achieve computational differential privacy for multiple queries to one database. Our work also extends the summation query to a much broader range of queries, specifically linear queries, by utilizing inner-product functional encryption. Hence, we provide an instantiation for inner-product functionalities by proving its simulation soundness and present a concrete randomized inner-product functional encryption with computational differential privacy against multiple queries. In terms of efficiency, our protocol is almost as practical as the underlying inner product functional encryption scheme. As evidence, we provide a full benchmark, based on our concrete implementation for databases with up to 1 000 000 entries. Our work can be considered as a step towards achieving privacy-preserving encrypted databases for a wide range of query types and considering the involvement of multiple database owners.","url":"https://doi.org/10.56553/popets-2024-0131","authors":["Ferran Alborch Escobar","Sébastien Canard","Fabien Laguillaumie","Duong Hieu Phan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-06T15:52:08Z","doi":"10.56553/popets-2024-0131","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1145/3603287.3651188","name":"Privacy-Preserving Gross Domestic Product (GDP) Calculation Using Paillier Encryption and Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3603287.3651188","authors":["Sanjaikanth E. Vadakkethil Somanathan Pillai","Wen-Chen Hu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-27T12:06:34Z","doi":"10.1145/3603287.3651188","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/isec61299.2024.10664891","name":"Integrating Differential Privacy in Modern Database Curriculum","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isec61299.2024.10664891","authors":["Jeff Miller","Ankur Chattopadhyay"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-17T18:46:44Z","doi":"10.1109/isec61299.2024.10664891","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/wimob61911.2024.10770430","name":"Enhancing Privacy Protection for Federated Learning with Distributed Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wimob61911.2024.10770430","authors":["Wenjing Wei","Alla Jammine","Farid Nait-Abdesselam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T19:56:17Z","doi":"10.1109/wimob61911.2024.10770430","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1145/3654823.3654854","name":"Optimizing Privacy in Federated Learning with MPC and Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3654823.3654854","authors":["Chao Zheng","Liming Wang","Zhen Xu","Hongjia Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-29T16:20:33Z","doi":"10.1145/3654823.3654854","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.3390/computers13110277","name":"Balancing Privacy and Performance: A Differential Privacy Approach in Federated Learning","source":"crossref","abstract":"Federated learning (FL), a decentralized approach to machine learning, facilitates model training across multiple devices, ensuring data privacy. However, achieving a delicate privacy preservation–model convergence balance remains a major problem. Understanding how different hyperparameters affect this balance is crucial for optimizing FL systems. This article examines the impact of various hyperparameters, like the privacy budget (ϵ), clipping norm (C), and the number of randomly chosen clients (K) per communication round. Through a comprehensive set of experiments, we compare training scenarios under both independent and identically distributed (IID) and non-independent and identically distributed (Non-IID) data settings. Our findings reveal that the combination of ϵ and C significantly influences the global noise variance, affecting the model’s performance in both IID and Non-IID scenarios. Stricter privacy conditions lead to fluctuating non-converging loss behavior, particularly in Non-IID settings. We consider the number of clients (K) and its impact on the loss fluctuations and the convergence improvement, particularly under strict privacy measures. Thus, Non-IID settings are more responsive to stricter privacy regulations; yet, with a higher client interaction volume, they also can offer better convergence. Collectively, knowledge of the privacy-preserving approach in FL has been extended and useful suggestions towards an ideal privacy–convergence balance were achieved.","url":"https://doi.org/10.3390/computers13110277","authors":["Huda Kadhim Tayyeh","Ahmed Sabah Ahmed AL-Jumaili"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-24T05:47:33Z","doi":"10.3390/computers13110277","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/icstem61137.2024.10560868","name":"Data Privacy Preservation Using Differential Privacy and Re-Identification Attacks","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icstem61137.2024.10560868","authors":["G. Sathish Kumar","K Preethie","S Madhumitha","R Sushma","M. Nivaashini"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-25T19:20:04Z","doi":"10.1109/icstem61137.2024.10560868","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1002/itl2.499","name":"Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy","source":"crossref","abstract":"Abstract The rapid advancement of health monitoring technologies has led to increased adoption of fitness training applications that collect and analyze personal health data. This paper presents a personalized differential privacy‐based federated learning (PDP‐FL) algorithm with two stages. Classifying the user's privacy according to their preferences is the first stage in achieving personalized privacy protection with the addition of noise. The privacy preference and the related privacy level are sent to the central aggregation server simultaneously. In the second stage, noise is added that conforms to the global differential privacy threshold based on the privacy level that users uploaded; this allows the global privacy protection level to be quantified while still adhering to the local and central protection strategies simultaneously adopted to realize the complete protection of global data. The results demonstrate the excellent classification accuracy of the proposed PDP‐FL algorithm. The proposed PDP‐FL algorithm addresses the critical issue of health monitoring privacy in fitness training applications. It ensures that sensitive data is handled responsibly and provides users the necessary tools to control their privacy settings. By achieving high classification accuracy while preserving privacy, the framework balances data utility and protection, thus positively impacting health monitoring ecosystem and medical systems.","url":"https://doi.org/10.1002/itl2.499","authors":["Lifang Shao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-07T20:58:50Z","doi":"10.1002/itl2.499","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.58346/jowua.2024.i3.018","name":"Improved Data Privacy with Differential Privacy in Federated Learning","source":"crossref","abstract":"Multiple users may train machine learning models cooperatively using Federated Learning (FL). There is a risk of malicious acquisition of participants' personal data due to the fact that traditional machine learning needs users to provide data for training. Through the use of federated learning, which involves moving the training process from a central server to terminal devices, users' data may be protected. Each participant keeps their dataset local and only exchanges model updates. This research proposed an innovative proposal for the medical industry's differentiated privacy approach for overcoming these problems. When several healthcare organizations work together to develop models that use different and extensive information, clinical applications may be greatly enhanced. Thus, the Local and Centre Differential Privacy (LCDP) on clinical datasets is a feature of our proposed approach. Reason being that the training data is the primary emphasis of the local model, while the machine learning model is the primary focus of the central model. We discover that the local model and the central model are linked in a unique way, changes in the original data lead to changes in the gradient, which in turn lead to changes in the model parameters. Based on this finding, our technique is better than prior central methods since it protects the data, gradient, and model all at once by bridging the gap between the two. Our system provides better privacy protections and even higher performance than some of the best previous central methods, which is an excellent outcome of rigorous evaluation.","url":"https://doi.org/10.58346/jowua.2024.i3.018","authors":["Cina Mathew","Dr.P. Asha"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-08T09:35:18Z","doi":"10.58346/jowua.2024.i3.018","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1016/j.cose.2024.103983","name":"Privacy in manifolds: Combining k-anonymity with differential privacy on Fréchet means","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.cose.2024.103983","authors":["Sonakshi Garg","Vicenç Torra"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-06T02:43:26Z","doi":"10.1016/j.cose.2024.103983","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4853802","name":"Investigating the Impact of Differential Privacy Obfuscation on Users' Data Disclosure Decisions","source":"crossref","abstract":"","url":"https://doi.org/10.2139/ssrn.4853802","authors":["Michael Khavkin","Eran Toch"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-04T20:49:01Z","doi":"10.2139/ssrn.4853802","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1214/24-aos2425","name":"Efficiency in local differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1214/24-aos2425","authors":["Lukas Steinberger"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-21T06:27:28Z","doi":"10.1214/24-aos2425","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/satml59370.2024.00013","name":"Concentrated Differential Privacy for Bandits","source":"crossref","abstract":"","url":"https://doi.org/10.1109/satml59370.2024.00013","authors":["Achraf Azize","Debabrota Basu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-10T17:22:05Z","doi":"10.1109/satml59370.2024.00013","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/globecom52923.2024.10901721","name":"Privacy Protection in Trajectory Data Publication Based on Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/globecom52923.2024.10901721","authors":["Xiujun Wang","Qing Gao","Xiao Zheng","Tao Tao","Gaoming Yang","Lei Mo"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-11T17:30:35Z","doi":"10.1109/globecom52923.2024.10901721","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.4018/ijisp.335225","name":"Adaptive Personalized Randomized Response Method Based on Local Differential Privacy","source":"crossref","abstract":"Aiming at the problem of adopting the same level of privacy protection for sensitive data in the process of data collection and ignoring the difference in privacy protection requirements, the authors propose an adaptive personalized randomized response method based on local differential privacy (LDP-APRR). LDP-APRR determines the sensitive level through the user scoring strategy, introduces the concept of sensitive weights for adaptive allocation of privacy budget, and realizes the personalized privacy protection of sensitive attributes and attribute values. To verify the distorted data availability, LDP-APRR is applied to frequent items mining scenarios and compared with mining associations with secrecy konstraints (MASK), and grouping-based randomization for privacy-preserving frequent pattern mining (GR-PPFM). Results show that the LDP-APRR achieves personalized protection of sensitive attributes and attribute values with user participation, and the maxPrivacy and avgPrivacy are improved by 1.2% and 4.3%, respectively, while the availability of distorted data is guaranteed.","url":"https://doi.org/10.4018/ijisp.335225","authors":["Dongyan Zhang","Lili Zhang","Zhiyong Zhang","Zhongya Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-10T11:53:45Z","doi":"10.4018/ijisp.335225","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/tps-isa62245.2024.00034","name":"Towards Assessing Integrated Differential Privacy and Fairness Mechanisms in Supervised Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/tps-isa62245.2024.00034","authors":["Maryam Aldairi","James Joshi"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T18:34:22Z","doi":"10.1109/tps-isa62245.2024.00034","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/dsit61374.2024.10880906","name":"Balancing Privacy and Accuracy: Federated Learning with Differential Privacy for Medical Image Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/dsit61374.2024.10880906","authors":["Muhammad Hamza Mehmood","Mahnoor Iqbal Khan","Abdulsamad Ibrahim"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-18T18:18:17Z","doi":"10.1109/dsit61374.2024.10880906","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1016/j.iot.2024.101344","name":"Privacy-preserving estimation of electric vehicle charging behavior: A federated learning approach based on differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.iot.2024.101344","authors":["Xiuping Kong","Lin Lu","Ke Xiong"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T22:42:04Z","doi":"10.1016/j.iot.2024.101344","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/access.2024.3430863","name":"MPLDP: Multi-Level Personalized Local Differential Privacy Method","source":"crossref","abstract":"","url":"https://doi.org/10.1109/access.2024.3430863","authors":["Xuejie Feng","Chiping Zhang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-18T17:38:38Z","doi":"10.1109/access.2024.3430863","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.52202/079017-4475","name":"Instance-Specific Asymmetric Sensitivity in Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.52202/079017-4475","authors":["David Durfee"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-11-03T11:20:56Z","doi":"10.52202/079017-4475","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/bigdata62323.2024.10825560","name":"A Secure and Privacy-Preserving Framework for Healthcare Data Management Using Deterministic Additive Noise and Differential Privacy in Cloud Environments","source":"crossref","abstract":"","url":"https://doi.org/10.1109/bigdata62323.2024.10825560","authors":["Yousef Alsaud","Danda B Rawat"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-16T18:31:23Z","doi":"10.1109/bigdata62323.2024.10825560","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/itnec60942.2024.10732935","name":"Privacy Protection Scheme for Cyberspace Mapping Data Based on Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/itnec60942.2024.10732935","authors":["Yue Zhang","Guanlin Si","Bin Dong","Leran Chen","Xiaotian Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-11-04T18:31:47Z","doi":"10.1109/itnec60942.2024.10732935","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/qce60285.2024.00012","name":"Bridging Quantum Computing and Differential Privacy: Insights into Quantum Computing Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/qce60285.2024.00012","authors":["Yusheng Zhao","Hui Zhong","Xinyue Zhang","Yuqing Li","Chi Zhang","Miao Pan"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T20:12:43Z","doi":"10.1109/qce60285.2024.00012","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/icrss65752.2024.00053","name":"A Method for Protecting the Privacy of Massive Data Based on Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icrss65752.2024.00053","authors":["Haitao Yu","Yifan Sun","Junyi Xie","Yongdi Bao","Jian Sun"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-06T18:32:50Z","doi":"10.1109/icrss65752.2024.00053","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1145/3726986.3727017","name":"Privacy in Motion: Implementing Differential Privacy for User Motion in VR","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3726986.3727017","authors":["Ruoxi Sun","Hanwen Wang","Hsiang-Ting Chen","Minhui Xue"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-09-29T15:13:01Z","doi":"10.1145/3726986.3727017","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/wccct60665.2024.10541778","name":"A Survey of Privacy Preserving Methods based on Differential Privacy for Medical Data","source":"crossref","abstract":"","url":"https://doi.org/10.1109/wccct60665.2024.10541778","authors":["Haicao Yan","Menghan Yin","Chaokun Yan","Wenjuan Liang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-03T17:28:14Z","doi":"10.1109/wccct60665.2024.10541778","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2196/preprints.60003","name":"Balancing Between Privacy and Utility for Affect Recognition using Multi Task Learning in Differential Privacy Added Federated Learning Settings (Preprint)","source":"crossref","abstract":"BACKGROUND The rise of wearable sensors marks a pivotal development in the era of affective computing. These sensors, gaining increasing popularity, hold the potential to revolutionize our understanding of human stress. A fundamental aspect within this domain is the ability to discern perceived stress through these unobtrusive devices. OBJECTIVE This study aims to enhance the performance of emotion recognition utilizing multi-task learning, a technique extensively explored across various machine learning tasks, including affective computing. By leveraging the shared information among related tasks, we seek to augment the accuracy of emotion recognition while confronting the privacy threats inherent in the physiological data captured by these sensors. METHODS To address the privacy concerns associated with the sensitive data collected by empathetic sensors, we propose a novel framework that integrates differential privacy and federated learning approaches with multi-task learning. This framework is designed to efficiently identify the user's mental stress while safeguarding their private identity information. Through this approach, we aim to enhance the performance of emotion recognition tasks while preserving user privacy. RESULTS Extensive evaluations of our framework are conducted using two prominent public datasets. The results demonstrate a significant improvement in emotion recognition accuracy, achieving an impressive rate of 90%. Furthermore, our approach effectively mitigates privacy risks, as evidenced by limiting re-identification accuracies to 47%. CONCLUSIONS In conclusion, our study presents a promising approach to advancing emotion recognition capabilities while addressing privacy concerns in the context of empathetic sensors. By integrating multi-task learning with differential privacy and federated learning, we have demonstrated the potential to achieve high levels of accuracy in emotion recognition while ensuring the protection of user privacy. This research contributes to the ongoing efforts to harness the power of affective computing in a responsible and ethical manner.","url":"https://doi.org/10.2196/preprints.60003","authors":["Mohamed Benouis","Elisabeth Andre","Yekta CAN"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-04-29T13:42:56Z","doi":"10.2196/preprints.60003","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/icsseecc61126.2024.10649423","name":"Privacy-Preserving Social Network Clustering Using Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icsseecc61126.2024.10649423","authors":["K Sathiyapriya","R Kavin Aravindhan","B Kireshvanth","Yadav Ranganathan","P Hardik"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-03T17:19:41Z","doi":"10.1109/icsseecc61126.2024.10649423","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/vtc2024-fall63153.2024.10757598","name":"Location Privacy Protection and 911 Task Allocation in Vehicle-Based via Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/vtc2024-fall63153.2024.10757598","authors":["Deyuan Qu","Dominic Carrillo","Sudip Dhakal","Mohammad Dehghani Tezerjani","Chenxi Qiu","Qing Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-10T19:44:30Z","doi":"10.1109/vtc2024-fall63153.2024.10757598","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/aiiot61789.2024.10579023","name":"ULDP: A User-Centric Local Differential Privacy Optimization Method","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aiiot61789.2024.10579023","authors":["Wenjun Yang","Eyhab Al-Masri"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-10T17:21:40Z","doi":"10.1109/aiiot61789.2024.10579023","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/icws62655.2024.00059","name":"Privacy-preserving Searchable Encryption Based on Anonymization and Differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icws62655.2024.00059","authors":["Caixia Ma","Chunfu Jia","Ruizhong Du","Guanxiong Ha","Mingyue Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-15T17:19:18Z","doi":"10.1109/icws62655.2024.00059","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/aiiot61789.2024.10578985","name":"Preserving Medical Data with Renyi Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/aiiot61789.2024.10578985","authors":["Olusola Tolulope Odeyomi","Harshitha Karnati","Austin Smith"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-10T17:21:40Z","doi":"10.1109/aiiot61789.2024.10578985","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/iwbf62628.2024.10593825","name":"Enhancing Client Privacy in Physiology-Based Biometric Verification with Differential Privacy and Positive-Label Federated Learning","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iwbf62628.2024.10593825","authors":["Mohamed Benouis","Bhargavi Mahesh","Elisabeth André","Yekta Said Can"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-22T17:31:28Z","doi":"10.1109/iwbf62628.2024.10593825","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.23919/acc60939.2024.10644323","name":"Differential Privacy in Nonlinear Dynamical Systems with Tracking Performance Guarantees","source":"crossref","abstract":"","url":"https://doi.org/10.23919/acc60939.2024.10644323","authors":["Dhrubajit Chowdhury","Raman Goyal","Shantanu Rane"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-05T17:56:19Z","doi":"10.23919/acc60939.2024.10644323","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.18280/ts.410333","name":"Optimizing Image Recognition Algorithms with Differential Privacy Integration","source":"crossref","abstract":"","url":"https://doi.org/10.18280/ts.410333","authors":["Shaoyu Yang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-26T08:03:44Z","doi":"10.18280/ts.410333","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1016/j.optlastec.2023.110541","name":"DP-EPSO: Differential privacy protection algorithm based on differential evolution and particle swarm optimization","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.optlastec.2023.110541","authors":["Qiang Gao","Han Sun","Zhifang Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-09T15:18:33Z","doi":"10.1016/j.optlastec.2023.110541","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/icde60146.2024.00492","name":"Data Level Privacy Preserving: A Stochastic Perturbation Approach Based on Differential Privacy (Extended abstract)","source":"crossref","abstract":"","url":"https://doi.org/10.1109/icde60146.2024.00492","authors":["Chuan Ma","Long Yuan","Li Han","Ming Ding","Raghav Bhaskar","Jun Li"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-23T17:38:03Z","doi":"10.1109/icde60146.2024.00492","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1002/spy2.491","name":"Anti‐Leakage Method of Sensitive Information of Network Documents Based on Differential Privacy Model","source":"crossref","abstract":"ABSTRACT This article proposes a method for preventing sensitive information leakage in network documents based on differential privacy models to ensure the security of sensitive information in network documents. Based on the definition of differential privacy and its Laplace mechanism, a differential privacy model was constructed to prevent sensitive information leakage in network documents. Using differential privacy based Laplace mechanism to protect the sensitive information dataset of network documents, and inputting the sensitive information dataset of network documents into the differential privacy model, the sensitive information in network documents is generalized and perturbed, and Laplace noise is added after transformation and compression to prevent leakage. Using roulette wheel sampling technology to sample and sort the original histograms of sensitive data in network documents, using a hierarchical partitioning algorithm to group the sorted histograms, and adding Laplace noise to the groups based on differential confidentiality Laplace mechanism to achieve leak prevention when publishing sensitive messages in network documents. The experimental results show that this method can ensure the security of sensitive information data in network documents and prevent sensitive information leakage in network documents. There is no data loss during the data conversion and compression process of sensitive information. At the same time, the protection effect of sensitive information is less affected by the amount of data, and the loss rate of sensitive information is lower.","url":"https://doi.org/10.1002/spy2.491","authors":["Shuhui Su","Yonghan Luo","Tao Li","Qi Chen","Juntao Liang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-24T10:09:01Z","doi":"10.1002/spy2.491","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/cai59869.2024.00203","name":"Privacy-preserving Federated Learning for Industrial Defect Detection Systems via Differential Privacy and Image Obfuscation","source":"crossref","abstract":"","url":"https://doi.org/10.1109/cai59869.2024.00203","authors":["Chia-Yu Lin","Yu-Chen Yeh","Makena Lu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-30T17:50:37Z","doi":"10.1109/cai59869.2024.00203","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.4236/jsea.2024.171001","name":"Whispered Tuning: Data Privacy Preservation in Fine-Tuning LLMs through Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.4236/jsea.2024.171001","authors":["Tanmay Singh","Harshvardhan Aditya","Vijay K. Madisetti","Arshdeep Bahga"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-22T03:06:08Z","doi":"10.4236/jsea.2024.171001","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1007/978-981-99-5435-3_58","name":"An Efficient Data Privacy Protection System Based on Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1007/978-981-99-5435-3_58","authors":["D. Vetrithangam"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-02T23:02:06Z","doi":"10.1007/978-981-99-5435-3_58","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1145/3643651.3659896","name":"1-Diffractor: Efficient and Utility-Preserving Text Obfuscation Leveraging Word-Level Metric Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3643651.3659896","authors":["Stephen Meisenbacher","Maulik Chevli","Florian Matthes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-06-11T00:20:03Z","doi":"10.1145/3643651.3659896","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.70528/ijlrp.v5.i7.2218","name":"FedCRM: Privacy-Preserving Federated Learning for Enterprise Salesforce CRM Analytics with Heterogeneous Schema Support and Differential Privacy","source":"crossref","abstract":"Enterprise Salesforce CRM implementations across business units, subsidiaries, and partner organisations contain customer relationship data whose analytical value substantially exceeds what any individual CRM yields from isolated local analysis. However, data governance regulations, contractual obligations, and internal data policies frequently prohibit centralising this data for joint model training. FedCRM is a federated learning framework for Salesforce CRM analytics that enables multiple organisations to collaboratively train predictive models — customer churn classifiers, lead conversion estimators, opportunity win probability models — without sharing raw CRM data. FedCRM contributes four innovations to federated CRM analytics: a heterogeneity-aware aggregation algorithm that weights participant contributions by both data volume and quality metrics; a per-participant configurable differential privacy budget management system; a Salesforce schema normalisation pipeline that maps heterogeneous custom field schemas to a common feature vocabulary; and a secure gradient aggregation protocol using threshold homomorphic encryption. Evaluated across nine Salesforce organisations over fourteen months, FedCRM achieves model performance within 3.1 percentage points of a centralised baseline while providing formal differential privacy guarantees. Federated models outperform locally trained models by an average of 7.4 percentage points on held-out test sets, confirming that federation provides genuine analytical value.","url":"https://doi.org/10.70528/ijlrp.v5.i7.2218","authors":["Lalith Chandra Bandaru"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-05-27T13:10:51Z","doi":"10.70528/ijlrp.v5.i7.2218","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.71146/jbdpm27","name":"DIFFERENTIAL PRIVACY IN BIG DATA ANALYTICS: TECHNIQUES AND APPLICATIONS","source":"crossref","abstract":"Differential privacy (DP) is a powerful privacy-preserving technique aimed at protecting individual data in statistical analyses. With the proliferation of big data in diverse sectors such as healthcare, finance, and social media, ensuring privacy without sacrificing data utility has become a key challenge. This article explores differential privacy's role in big data analytics, providing a detailed overview of its techniques, applications, and emerging trends. The paper also examines its real-world applications, including healthcare analytics, finance, and machine learning models, and discusses the challenges and limitations in implementing DP for large-scale data. Lastly, it presents future directions for integrating DP with advanced analytics frameworks to enhance privacy protection.","url":"https://doi.org/10.71146/jbdpm27","authors":["Dr. Amina Farooq"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2026-04-11T09:25:35Z","doi":"10.71146/jbdpm27","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/pst62714.2024.10788063","name":"Visualizing Differential Privacy: Assessing Infographics' Impact on Layperson Data-sharing Decisions and Comprehension","source":"crossref","abstract":"","url":"https://doi.org/10.1109/pst62714.2024.10788063","authors":["Mst Mahamuda Sarkar Mithila","Fangyi Yu","Miguel Vargas Martin","Shengqian Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-12-16T19:15:09Z","doi":"10.1109/pst62714.2024.10788063","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1145/3627673.3679759","name":"Privacy-Preserving Graph Embedding based on Local Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1145/3627673.3679759","authors":["Zening Li","Rong-Hua Li","Meihao Liao","Fusheng Jin","Guoren Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-20T19:34:21Z","doi":"10.1145/3627673.3679759","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1002/spy2.374","name":"Federated learning with hybrid differential privacy for secure and reliable\n                    <scp>cross‐IoT</scp>\n                    platform knowledge sharing","source":"crossref","abstract":"Abstract The federated learning has gained prominent attention as a collaborative machine learning method, allowing multiple users to jointly train a shared model without directly exchanging raw data. This research addresses the fundamental challenge of balancing data privacy and utility in distributed learning by introducing an innovative hybrid methodology fusing differential privacy with federated learning (HDP‐FL). Through meticulous experimentation on EMNIST and CIFAR‐10 data sets, this hybrid approach yields substantial advancements, showcasing a noteworthy 4.22% and up to 9.39% enhancement in model accuracy for EMNIST and CIFAR‐10, respectively, compared to conventional federated learning methods. Our adjustments to parameters highlighted how noise impacts privacy, showcasing the effectiveness of our hybrid DP approach in striking a balance between privacy and accuracy. Assessments across diverse FL techniques and client counts emphasized this trade‐off, particularly in non‐IID data settings, where our hybrid method effectively countered accuracy declines. Comparative analyses against standard machine learning and state‐of‐the‐art FL approaches consistently showcased the superiority of our proposed model, achieving impressive accuracies of 96.29% for EMNIST and 82.88% for CIFAR‐10. These insights offer a strategic approach to securely collaborate and share knowledge among IoT devices without compromising data privacy, ensuring efficient and reliable learning mechanisms across decentralized networks.","url":"https://doi.org/10.1002/spy2.374","authors":["Oshamah Ibrahim Khalaf","Ashokkumar S.R","Sameer Algburi","Anupallavi S","Dhanasekaran Selvaraj","Mhd Saeed Sharif","Wael Elmedany"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-03T05:35:03Z","doi":"10.1002/spy2.374","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.25236/ajcis.2024.070504","name":"A trajectory protection method based on differential privacy and semantic attributes","source":"crossref","abstract":"","url":"https://doi.org/10.25236/ajcis.2024.070504","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-05-24T00:46:41Z","doi":"10.25236/ajcis.2024.070504","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.25236/ajcis.2024.070101","name":"Personalized trajectory differential privacy protection mechanism based on spatiotemporal correlation prediction","source":"crossref","abstract":"","url":"https://doi.org/10.25236/ajcis.2024.070101","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-02-27T00:38:33Z","doi":"10.25236/ajcis.2024.070101","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.5705/ss.202022.0162","name":"Mechanisms for Global Differential Privacy under Bayesian Data Synthesis","source":"crossref","abstract":"","url":"https://doi.org/10.5705/ss.202022.0162","authors":["Jingchen Hu","Matthew R. Williams","Terrance D. Savitsky"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-08-15T06:07:57Z","doi":"10.5705/ss.202022.0162","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.56726/irjmets48565","name":"STUDY ON DIFFERENTIAL PRIVACY WITH MACHINE LEARNING","source":"crossref","abstract":"","url":"https://doi.org/10.56726/irjmets48565","authors":[],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-19T19:47:11Z","doi":"10.56726/irjmets48565","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/iciba62489.2024.10868170","name":"AI Model Training Data Privacy Protection Scheme Based on Local Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/iciba62489.2024.10868170","authors":["Yue Zhang","Lin Li","Cong Hou","Min Li","Xiaotian Xu"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-02-11T18:21:13Z","doi":"10.1109/iciba62489.2024.10868170","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1016/j.ins.2023.119717","name":"A secure and privacy preserved infrastructure for VANETs based on federated learning with local differential privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1016/j.ins.2023.119717","authors":["Hajira Batool","Adeel Anjum","Abid Khan","Stefano Izzo","Carlo Mazzocca","Gwanggil Jeon"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2023-10-10T13:05:13Z","doi":"10.1016/j.ins.2023.119717","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.2139/ssrn.4900884","name":"Escalation of Commitment: A Case Study of the United States Census Bureau Efforts to Implement Differential Privacy for the 2020 Decennial Census (Forthcoming in: In Proceedings of Privacy in Statistical Databases -PSD 2024)","source":"crossref","abstract":"In 2017, the United States Census Bureau announced that because of high disclosure risk in the methodology (data swapping) used to produce tabular data for the 2010 census, a different protection mechanism based on differential privacy would be used for the 2020 census. While there have been many studies evaluating the result of this change, there has been no rigorous examination of disclosure risk claims resulting from the released 2010 tabular data. In this study we perform such an evaluation. We show that the procedures used to evaluate disclosure risk are unreliable and resulted in inflated disclosure risk. Demonstration data products released using the new procedure were also shown to have poor utility. However, since the Census Bureau had already committed to a different procedure, they had no option except to escalate their commitment. The result of such escalation is that the 2020 tabular data release offers neither privacy nor accuracy.","url":"https://doi.org/10.2139/ssrn.4900884","authors":["Krish Muralidhar","Steven Ruggles"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-07-31T07:49:11Z","doi":"10.2139/ssrn.4900884","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.18653/v1/2024.privatenlp-1.5","name":"A Collocation-based Method for Addressing Challenges in Word-level Metric Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.18653/v1/2024.privatenlp-1.5","authors":["Stephen Meisenbacher","Maulik Chevli","Florian Matthes"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-09-20T19:33:08Z","doi":"10.18653/v1/2024.privatenlp-1.5","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.29012/jpc.872","name":"Incompatibilities Between Current Practices in Statistical Data Analysis and Differential Privacy","source":"crossref","abstract":"The authors discuss their experience applying differential privacy with a complex data set with the goal of enabling standard approaches to statistical data analysis. They highlight lessons learned and roadblocks encountered, distilling them into incompatibilities between current practices in statistical data analysis and differential privacy that go beyond issues which can be solved with a noisy measurements file. The authors discuss how overcoming these incompatibilities require compromise and a change in either our approach to statistical data analysis or differential privacy that should be addressed head-on.","url":"https://doi.org/10.29012/jpc.872","authors":["Joshua Snoke","Claire McKay Bowen","Aaron R. Williams","Andrés F. Barrientos"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-28T03:11:41Z","doi":"10.29012/jpc.872","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.54097/0bkcjr92","name":"Secure and Efficient k-anonymous Trajectory Privacy Protection Method based on Differential Privacy","source":"crossref","abstract":"The Location-based service scheme have already involved in every aspect of People's daily life and are increasingly used in various industries. Aiming at the problem of the security and efficiency of mobile terminal users’ trajectory privacy protection in location-based service, we propose a k-anonymous trajectory privacy protection scheme based on differential privacy. This scheme adopts differential privacy technology to add Laplace noise to the user's trajectory many times to generate 2k noise trajectory, and then according to the trajectory similarity to determine k-1 noise users whose trajectory are similar to the user trajectory, and sets them and the real user as an anonymous user group, and then uses the anonymous user group to request LBS services. Security analysis shows that the scheme satisfies the security features of anonymity, unforgeability, and anti-counterfeiting attack. The simulation results show that the scheme not only guarantees the similarity between the false trajectory and the real trajectory but also has higher execution efficiency.","url":"https://doi.org/10.54097/0bkcjr92","authors":["Yuanlong Fan","Cheng Song","Zhichao Wang"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-10-15T08:39:20Z","doi":"10.54097/0bkcjr92","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.54254/2754-1169/59/20231105","name":"Consumer Data Without Compromise: Integrating Differential Privacy and GANs for Privacy-Preserving Digital Marketing","source":"crossref","abstract":"In the rapidly evolving digital marketing landscape, the utilization of consumer data is essential for efficient targeting and personalization of marketing practices. However, the growing concerns regarding user privacy and stringent data protection regulations have created challenges in accessing and using consumer data for marketing purposes. This paper introduces a novel approach that leverages Differential Privacy and Conditional Tabular Generative Adversarial Networks (CTGAN) to address these privacy concerns while maintaining the efficacy of data-driven digital marketing strategies. Our approach amalgamates the strengths of Differential Privacy and CTGAN, applying differential privacy to the original dataset to ensure that extracted data cannot be tied back to individuals. We then train a CTGAN on an open marketing dataset to learn and generate synthetic data of close resemblance. Through extensive empirical analysis, we evaluate the fidelity, utility, and trade-offs of our approach, demonstrating its effectiveness in synthesizing non-Gaussian and multi-modal distributions, and its applicability in real-world classification problems. The research also highlights the complexity of hyperparameter tuning and the importance of a balanced approach in model training. Our findings contribute valuable insights to both the theoretical understanding of generative models and practical guidance for digital marketing practitioners","url":"https://doi.org/10.54254/2754-1169/59/20231105","authors":["Zhuo Chen"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-03T22:46:23Z","doi":"10.54254/2754-1169/59/20231105","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/swc62898.2024.00078","name":"A Privacy-Preserving Decentralized Federated Learning Framework Based on Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/swc62898.2024.00078","authors":["Lin Wang","Di Zhang","Min Guo","Xun Shao"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-03-24T17:52:22Z","doi":"10.1109/swc62898.2024.00078","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.5705/ss.202022.0276","name":"Unbiased Statistical Estimation and Valid Confidence Intervals Under Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.5705/ss.202022.0276","authors":["Christian Covington","Xi He","James Honaker","Gautam Kamath"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-01-08T20:54:01Z","doi":"10.5705/ss.202022.0276","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/fit63703.2024.10838402","name":"Quantum Enhanced Federated Learning with Differential Privacy","source":"crossref","abstract":"","url":"https://doi.org/10.1109/fit63703.2024.10838402","authors":["Shoaib Ullah","Madam Hussain Shah","Adeel Anjum"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2025-01-17T18:32:11Z","doi":"10.1109/fit63703.2024.10838402","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1109/isit57864.2024.10619506","name":"On the Extreme Points of the (0, δ) - Differential Privacy Polytope","source":"crossref","abstract":"","url":"https://doi.org/10.1109/isit57864.2024.10619506","authors":["Karan Elangovan","Varun Jog"],"tags":[],"confidence":0.7,"sites":["privacy-computing"],"publishedDate":"2024-08-19T13:25:01Z","doi":"10.1109/isit57864.2024.10619506","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.583Z"},{"id":"doi:10.1038/s41598-026-44009-2","name":"Stabilizing updates in differentially private stochastic gradient descent with buffered rejection.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-44009-2","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-44009-2","addedAt":"2026-08-31T06:41:50.583Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1746910","name":"Exploiting explanations for model extraction via knowledge distillation and mitigation with private counterfactuals.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2026.1746910","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1746910","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1016/j.dib.2026.112905","name":"The Social Connectedness Index: A large-scale dataset of social ties across geographic locations.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.dib.2026.112905","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.dib.2026.112905","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fpsyg.2026.1823924","name":"Neurovictimology and the risks of neurotechnologies and artificial intelligence: a forensic and legal perspective.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fpsyg.2026.1823924","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fpsyg.2026.1823924","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1750992","name":"AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2026.1750992","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1750992","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/bs16050651","name":"\"In ChatGPT-Powered Virtual Influencers We (Dis)Trust?\": The Privacy Paradox and the Double-Edged Sword of Ubiquitous Large Language Model (LLM) Generative AI as a General Purpose Technology (GPT) in a Human-Centered AI Ecosystem.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/bs16050651","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/bs16050651","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1080/20008066.2026.2651060","name":"Harnessing the power of FAIR data to advance sex/gender insights in psychotraumatology.","source":"europepmc","abstract":"","url":"https://doi.org/10.1080/20008066.2026.2651060","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1080/20008066.2026.2651060","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fpubh.2026.1838551","name":"Research integrity and data ethics in AI-driven integrated healthcare: a critical appraisal.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fpubh.2026.1838551","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fpubh.2026.1838551","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41377-026-02369-4","name":"Machine learning-assisted highly efficient thermal management in function-oriented thermochromic smart windows.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41377-026-02369-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41377-026-02369-4","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fninf.2025.1679196","name":"Cross-modal privacy-preserving synthesis and mixture-of-experts ensemble for robust ASD prediction.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fninf.2025.1679196","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fninf.2025.1679196","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-40337-5","name":"Chaotic cosine and logistic map based robust image encryption with dual-stage confusion-diffusion architecture.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-40337-5","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-40337-5","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.2196/93484","name":"Shadow AI in Swedish Health Care: Qualitative Analysis of Physicians' Free-Text Answers.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/93484","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/93484","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-44990-8","name":"A privacy preserving synthetic learner dataset for learning analytics in technology enhanced higher education.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-44990-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-44990-8","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1800047","name":"The pediatric AI readiness framework: bridging evidence to practice in pediatric artificial intelligence.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2026.1800047","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1800047","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1781692","name":"Limitations of current copyright frameworks for large language models trained on scientific literature.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2026.1781692","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1781692","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.2196/84305","name":"AI Applications Integrating Legal and Regulatory Perspectives in Mental Health: Systematic Review.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/84305","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/84305","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.2196/88536","name":"Blockchain-Based Dynamic and Revocable Consent for Secondary Health Data Use: Systematic Review.","source":"europepmc","abstract":"Background The secondary use of health data holds substantial potential for advancing biomedical research, strengthening population health analytics, and enabling artificial intelligence-driven decision-making support. Yet, ensuring that such reuse respects patient autonomy, privacy, and regulatory obligations remains a major challenge. Conventional consent mechanisms are typically static, difficult to revoke, and offer limited transparency or accountability after data disclosure. Objective This review aimed to systematically examine blockchain-based frameworks that enable dynamic, auditable, and revocable consent for the secondary use of health data. Methods A structured literature search was conducted in PubMed, Scopus, and Web of Science covering the period 2020 to 2025. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, 55 peer-reviewed studies meeting predefined inclusion criteria were analyzed. Data extraction focused on four dimensions: (1) consent life cycle management, (2) auditability and traceability, (3) usability and patient empowerment, and (4) legal and ethical alignment. Results Findings indicate that blockchain technologies provide a robust foundation for automating consent life cycles, ensuring immutable auditability, and enabling decentralized patient control. Most frameworks used smart contracts, decentralized identifiers, and verifiable credentials to implement programmable and verifiable consent processes. Nevertheless, key challenges persist, including limited usability testing, complexities in real-time revocation propagation, interoperability gaps with clinical systems, and tensions with regulatory requirements such as the General Data Protection Regulation right to erasure. Only a small subset of studies reported real-world deployments or user-centered evaluations. Conclusions Blockchain offers substantial promise for improving the trustworthiness, transparency, and accountability of consent management for secondary health data use. However, wider adoption requires human-centered design approaches, stronger interoperability through standards such as Fast Healthcare Interoperability Resources, verifiable credentials, and consent receipts, and clearer legal guidance for compliance. Future research should prioritize integrating blockchain-enabled consent infrastructures into national and cross-border digital health ecosystems such as the European Health Data Space to support secure, patient-controlled, and ethically governed secondary data use.","url":"https://doi.org/10.2196/88536","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/88536","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.7759/cureus.108911","name":"Slow Harm After Dobbs v. Jackson Women's Health Organization: An Analytic Scoping Review of How Data and Surveillance Infrastructures Reshape Emergency Obstetric Care.","source":"europepmc","abstract":"","url":"https://doi.org/10.7759/cureus.108911","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.7759/cureus.108911","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fninf.2026.1838147","name":"Editorial: Multimodal brain data integration and computational modeling.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fninf.2026.1838147","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fninf.2026.1838147","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1177/20552076251411621","name":"SYNNER synthetic data generator framework.","source":"europepmc","abstract":"","url":"https://doi.org/10.1177/20552076251411621","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1177/20552076251411621","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1073/pnas.2500337122","name":"The 2020 US Decennial Census is more private than you (might) think.","source":"europepmc","abstract":"","url":"https://doi.org/10.1073/pnas.2500337122","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1073/pnas.2500337122","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fpsyg.2026.1773754","name":"Contextualizing the privacy paradox-a risk-benefit analysis of generation z's adoption intentions toward AI-based virtual try-on.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fpsyg.2026.1773754","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fpsyg.2026.1773754","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/bioengineering13050511","name":"Privacy-Aware Synthetic Tabular Data Generation for Healthcare: Application to Sepsis Detection.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/bioengineering13050511","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/bioengineering13050511","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1002/mco2.70833","name":"Generative Artificial Intelligence and Large Language Models in Clinical Oncology.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/mco2.70833","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/mco2.70833","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1839687","name":"Deep reinforcement learning-based reversible medical image encryption framework for secure IoMT environments.","source":"europepmc","abstract":"The Internet of Medical Things (IoMT) environments face significant challenges in securely transmitting and storing medical images due to limited computational resources, multiple device types, and increasing cybersecurity threats. This paper describes a reversible RGB medical image encryption framework that employs deep reinforcement learning by combining adaptive policy learning with deterministic cryptographic algorithms. A Deep Q-Network (DQN) is used to dynamically select encryption actions based on statistical features extracted from the intermediate encrypted image state. To achieve strong security and precise image recovery, the framework employs a multi-layer reversible technique that comprises SHA-512-based keystream masking, Arnold scrambling with padding preservation, and chaotic diffusion. Extensive testing shows that this technique achieves high entropy, virtually optimum Number of Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI) metrics, minimal pixel correlation and near-zero Structural Similarity Index Measure (SSIM) between the original and encrypted images, indicating a robust protection against statistical and differential attacks. Furthermore, the framework is robust against noise, data loss, occlusion, chosen plaintext, and determinism leaking attacks. Unlike fixed chaos-based encryption systems, the proposed framework introduces reinforcement learning-based adaptive action selection within a strictly reversible cryptographic pipeline. The effective key space exceeds 2 512 due to SHA-512-based seed derivation and nonce-driven randomness. The overall computational complexity of the encryption process is O(H × W × T), making it scalable for high-resolution medical images. Experimental results demonstrate entropy values approaching the theoretical maximum (7.999), NPCR above 99.9%, and UACI up to 40%, confirming strong diffusion and resistance against differential and chosen-plaintext attacks.","url":"https://doi.org/10.3389/frai.2026.1839687","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1839687","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1002/rcr2.70606","name":"Paradoxical Pyogenic Granuloma Associated With Ramucirumab but Not Bevacizumab: A Case Suggesting Differential Effects of VEGF Receptor and Ligand Inhibition.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/rcr2.70606","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/rcr2.70606","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fphys.2026.1778235","name":"Artificial intelligence-driven gastrointestinal functional assessment: multimodal imaging, digital biomarkers, and real-time monitoring.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fphys.2026.1778235","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fphys.2026.1778235","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1007/s12017-026-08928-7","name":"Dysregulated circRNA Expression in Juvenile Myoclonic Epilepsy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s12017-026-08928-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1007/s12017-026-08928-7","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1016/j.idcr.2026.e02636","name":"Lymph-node-first Kawasaki disease obscured by deep neck infection in an infant.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.idcr.2026.e02636","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.idcr.2026.e02636","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/bs15111507","name":"The Impact of AI-Recommended Content Affordances on Post-Purchase Intention in Stockout Substitution Scenarios.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/bs15111507","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3390/bs15111507","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.2196/75448","name":"Digital Literacy and Interpersonal Trust as Predictors of Willingness to Share Patient-Generated Health Data Among Korean Internet Users: Cross-Sectional Study Using Privacy Calculus and Communication Privacy Management Theories.","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/75448","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/75448","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1016/j.pec.2026.109492","name":"Patient and clinician engagement with generative artificial intelligence (GenAI): A scoping review of implications for patient-centered communication.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.pec.2026.109492","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.pec.2026.109492","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1111/tct.70379","name":"Inclusive Initiatives in Health Professions Education: A Journal-Led Peer Reviewer Development Initiative.","source":"europepmc","abstract":"","url":"https://doi.org/10.1111/tct.70379","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1111/tct.70379","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/s26082468","name":"Ubiquitous Non-Wearable Sensor for Human Sedentary Behavior Monitoring and Characterization.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/s26082468","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/s26082468","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1002/ccr3.72076","name":"Adult Occipital Dermoid Cyst With the Initial Manifestation of Subcutaneous Lump: A Case Report.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/ccr3.72076","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/ccr3.72076","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1371/journal.pone.0348348","name":"Algorithm-assisted interpretation of cyclic and differential pulse voltammetry for cardiac troponin detection.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0348348","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0348348","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1155/crot/5345044","name":"Laryngeal Sarcoidosis: A Young Patient With Aggravating Dyspnea and Cervical Lymphadenopathy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1155/crot/5345044","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1155/crot/5345044","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1017/s0266462326103729","name":"Barriers and facilitators to using self-sampled tests for the human papillomavirus (HPV): a mixed-methods study to inform a horizon scan.","source":"europepmc","abstract":"","url":"https://doi.org/10.1017/s0266462326103729","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1017/s0266462326103729","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/bs16020240","name":"Human Presence in Short-Form Video Advertising: Social Judgments of Human and AI Presenters Under Privacy Concerns.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/bs16020240","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/bs16020240","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fdata.2026.1733733","name":"Strategic cyber intelligence with advanced analytics in Latin America: a perspective.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdata.2026.1733733","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdata.2026.1733733","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1007/s11673-025-10448-1","name":"Integrating Genetic Information into the Electronic Health Record: The Case of Adolescents' Revelation of Misattributed Parentage.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s11673-025-10448-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1007/s11673-025-10448-1","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1111/nhs.70308","name":"Contributions of Artificial Intelligence to Decision Making in Nursing: A Scoping Review.","source":"europepmc","abstract":"","url":"https://doi.org/10.1111/nhs.70308","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1111/nhs.70308","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1802559","name":"The intelligent neonatal healthcare: a systematic review of machine learning architectures integrating the internet of medical things and blockchain.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2026.1802559","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1802559","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1097/ms9.0000000000005035","name":"Primary leiomyosarcoma of the axilla in a 70-year-old female: a case report of multidisciplinary management from a resource-limited setting.","source":"europepmc","abstract":"","url":"https://doi.org/10.1097/ms9.0000000000005035","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1097/ms9.0000000000005035","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1016/j.idm.2026.03.006","name":"Outfitting the quest for spatial spread of infections: A review of mobility datasets for population health modelling.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.idm.2026.03.006","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.idm.2026.03.006","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1111/cars.70029","name":"Changing Gender Relations in Canada: The Rise of Gender-Neutral Forenames.","source":"europepmc","abstract":"","url":"https://doi.org/10.1111/cars.70029","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1111/cars.70029","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1177/17562848261441693","name":"Responsible use of large language models in gastroenterology and hepatology.","source":"europepmc","abstract":"","url":"https://doi.org/10.1177/17562848261441693","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1177/17562848261441693","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fdgth.2026.1761624","name":"Evaluating privacy leakages in LLM-driven ambient clinical documentation.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdgth.2026.1761624","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fdgth.2026.1761624","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1155/crot/5486047","name":"Unraveling Fibrous Tonsil Lesions: A Case Series on Diagnostic Dilemmas in Pediatric Tonsillectomy.","source":"europepmc","abstract":"","url":"https://doi.org/10.1155/crot/5486047","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1155/crot/5486047","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1002/aet2.70170","name":"Beyond the Score: Bias Investigations to Improve the Fairness of Board Certification Exams.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/aet2.70170","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/aet2.70170","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1155/crpu/3325053","name":"Unmasking the Unseen: Dupilumab-Induced Sarcoidosis in a Young Patient-A Case Report.","source":"europepmc","abstract":"","url":"https://doi.org/10.1155/crpu/3325053","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1155/crpu/3325053","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1155/crh/8172177","name":"Wandering Lymphoma: A Case of Wandering Spleen Secondary to Splenic Marginal Zone Lymphoma.","source":"europepmc","abstract":"","url":"https://doi.org/10.1155/crh/8172177","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1155/crh/8172177","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1748544","name":"Leveraging chatbots for enhanced decision-making: a comprehensive literature review.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/frai.2026.1748544","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1748544","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1002/hsr2.71940","name":"Evaluation of the Diagnostic Capabilities of Artificial Intelligence (GPT-4) in a Cardiology Department in Sub-Saharan Africa: Cross-Sectional Study.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/hsr2.71940","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/hsr2.71940","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.30953/bhty.v9.471","name":"Blockchain Technology to Enhance Clinical Data Management in Healthcare: A Systematic Literature Review.","source":"europepmc","abstract":"","url":"https://doi.org/10.30953/bhty.v9.471","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.30953/bhty.v9.471","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1007/s00146-025-02833-6","name":"Realising the digital twin: a thematic review and analysis of the ethical, legal, and social issues for digital twins in healthcare.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s00146-025-02833-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1007/s00146-025-02833-6","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.14218/jcth.2025.00406","name":"Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.","source":"europepmc","abstract":"","url":"https://doi.org/10.14218/jcth.2025.00406","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.14218/jcth.2025.00406","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1186/s12874-026-02818-z","name":"Privacy rights and improving knowledge are not hierarchical needs: data protection and good epidemiologic standard (DP_GOES) checklist for retrospective observational studies using secondary data.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12874-026-02818-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1186/s12874-026-02818-z","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1093/bioadv/vbag036","name":"Desiderata for a biomedical knowledge network: opportunities, challenges and future directions.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/bioadv/vbag036","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1093/bioadv/vbag036","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/diagnostics16091290","name":"Private Ensembles, Public Confidence: A PATE-to-MedPrompt System for Autism Detection.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/diagnostics16091290","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/diagnostics16091290","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1093/haschl/qxag046","name":"The history of state preemption and medical device regulation: lessons for artificial intelligence oversight.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/haschl/qxag046","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1093/haschl/qxag046","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1002/ccr3.72409","name":"An Infant With Hyper IgE Syndrome Mimicking Acute Leukemia.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/ccr3.72409","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/ccr3.72409","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1093/bioinformatics/btaf209","name":"Generating synthetic genotypes using diffusion models.","source":"europepmc","abstract":"","url":"https://doi.org/10.1093/bioinformatics/btaf209","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1093/bioinformatics/btaf209","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1865219","name":"A multimodal, risk-stratified framework for AI-driven early risk prediction and personalised prevention in obesity.","source":"europepmc","abstract":"Obesity is a multifactorial chronic disease whose worldwide prevalence in adults has more than doubled since 1990, demanding a shift from reactive treatment towards early, personalised prevention. Artificial intelligence (AI) provides a methodological pathway for this shift by integrating heterogeneous, longitudinal evidence-genomic, metabolomic, electronic health record (EHR), wearable Internet-of-Things (IoT), behavioural, and social-environmental-and by translating that evidence into individualised, time-varying risk estimates. Yet the field is fragmented: most existing tools are unimodal, validated on narrow cohorts, opaque to clinicians, and disconnected from the workflows that would render their predictions actionable. In this Perspective we propose an explicit multimodal, risk-stratified framework that links five data layers to a continuous dynamic risk score R(t), defined as a weighted, time-varying aggregation of clinical, anthropometric, behavioural, psychosocial and pharmacological domains. R(t) drives an A/B/C tiering policy that allocates monitoring intensity and intervention modality proportional to risk, and feeds a metabolic-behavioural digital-twin loop in which counterfactual interventions are tested in silico before deployment. We argue that three technical commitments are non-negotiable for translation: (i) cross-modal fusion architectures that respect informative missingness, (ii) explainable, equity-audited risk scoring, and (iii) a five-stage validation pipeline anchored in TRIPOD-AI, decision-curve analysis and post-market drift surveillance. We discuss how this framework reframes long-standing concerns-black-box opacity, demographic bias, real-world fragility-as design constraints rather than afterthoughts, and outline an actionable research agenda for clinically deployable, equitable AI in obesity prevention.","url":"https://doi.org/10.3389/frai.2026.1865219","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1865219","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fdsfr.2025.1626822","name":"Leveraging real-world data for safety signal detection and risk management in pre- and post-market settings.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fdsfr.2025.1626822","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fdsfr.2025.1626822","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.32481/djph.2026.03.17","name":"Harnessing AI for Transformative Healthcare: Proceedings and Strategic Roadmap from AI4Health Industry Day 2026 in Delaware.","source":"europepmc","abstract":"","url":"https://doi.org/10.32481/djph.2026.03.17","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.32481/djph.2026.03.17","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fcvm.2026.1846913","name":"Case Report: Fabry disease mimicking coronary artery disease and hypertrophic cardiomyopathy-a 15-year diagnostic delay.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fcvm.2026.1846913","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fcvm.2026.1846913","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1002/ccr3.72556","name":"Accidental Diagnosis of Type VII Osteogenesis Imperfecta in an Infant Presenting With Pneumonia and Rickets-Like Rib Fractures: A Case Report.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/ccr3.72556","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/ccr3.72556","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-42295-4","name":"Adaptive feature selection with gradient-based relevance for intrusion detection systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-42295-4","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-42295-4","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fsoc.2026.1739787","name":"The use of IT-safety and coping measures against cybercrimes among older adults in Hong Kong: an application of cyber routine activity theory.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fsoc.2026.1739787","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fsoc.2026.1739787","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.2196/91726","name":"Closing the Gap to Interventions for Tuberous Sclerosis Complex-Associated Neuropsychiatric Disorders (TAND): Protocol for a Longitudinal Study of TAND Severity, Predictors, and Caregiver Well-Being (TANDem-2).","source":"europepmc","abstract":"","url":"https://doi.org/10.2196/91726","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.2196/91726","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1111/ene.70600","name":"Diffuse Infiltrating Perisellar Mass Mimicking Polyneuritis Cranialis.","source":"europepmc","abstract":"","url":"https://doi.org/10.1111/ene.70600","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1111/ene.70600","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1371/journal.pone.0347836","name":"Gender disparities in Italian academic medicine: A cross-sectional study of clinicians in the 2024 stanford top 2% scientists database.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0347836","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0347836","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1016/j.fsisyn.2026.100705","name":"INTERPOL Review of Forensic Biology and DNA, 2023-2025.","source":"europepmc","abstract":"","url":"https://doi.org/10.1016/j.fsisyn.2026.100705","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1016/j.fsisyn.2026.100705","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-025-31025-x","name":"FORT-IDS: a federated, optimized, robust and trustworthy intrusion detection system for IIoT security.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-31025-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-31025-x","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-025-01575-1","name":"Personal health data protection and intelligent healthcare applications under generative adversarial network.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-01575-1","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-01575-1","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1186/s13031-025-00741-6","name":"Exposure to wartime sexual violence in Bosnia and Herzegovina: nationally representative prevalence 30 years after the 1992-1995 war.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s13031-025-00741-6","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1186/s13031-025-00741-6","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1186/s12911-026-03360-0","name":"Quantifying the effects of pseudonymisation on epidemiological research reliability: a tailored evaluation using a clinical data warehouse.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12911-026-03360-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1186/s12911-026-03360-0","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1186/s13045-026-01781-y","name":"Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s13045-026-01781-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1186/s13045-026-01781-y","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1371/journal.pone.0346523","name":"Effect of neostigmine/glycopyrrolate versus sugammadex on postoperative delirium in older adults: A triple-masked, randomized, controlled trial protocol.","source":"europepmc","abstract":"","url":"https://doi.org/10.1371/journal.pone.0346523","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1371/journal.pone.0346523","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-026-54361-y","name":"Smart library personalized resource proactive recommendation system integrating user profiling and knowledge graphs.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-026-54361-y","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1038/s41598-026-54361-y","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41746-025-02112-0","name":"Protecting patient privacy in tabular synthetic health data: a regulatory perspective.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41746-025-02112-0","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41746-025-02112-0","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fninf.2024.1543121","name":"Editorial: Protecting privacy in neuroimaging analysis: balancing data sharing and privacy preservation.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fninf.2024.1543121","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.3389/fninf.2024.1543121","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fnimg.2026.1842218","name":"Intraoperative contrast-enhanced ultrasound features of progressive multifocal leukoencephalopathy: a case report.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fnimg.2026.1842218","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/fnimg.2026.1842218","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1186/s13195-026-02013-8","name":"AI-enabled digital phenotyping for Alzheimer's disease: a review of multimodal sensor integration and symptom trajectories.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s13195-026-02013-8","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1186/s13195-026-02013-8","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1186/s12910-025-01308-z","name":"Ethical challenges in the algorithmic era: a systematic rapid review of risk insights and governance pathways for nursing predictive analytics and early warning systems.","source":"europepmc","abstract":"","url":"https://doi.org/10.1186/s12910-025-01308-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1186/s12910-025-01308-z","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1002/ccr3.71883","name":"Isolated Right Ventricular Hypertrophic Cardiomyopathy Mimicking an Intramural Mass in a 13-Year-Old Male.","source":"europepmc","abstract":"","url":"https://doi.org/10.1002/ccr3.71883","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1002/ccr3.71883","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1038/s41598-025-25107-z","name":"FedMedSecure: federated few-shot learning with cross-attention mechanisms and explainable AI for collaborative healthcare cybersecurity.","source":"europepmc","abstract":"","url":"https://doi.org/10.1038/s41598-025-25107-z","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1038/s41598-025-25107-z","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1155/ipid/9955516","name":"Operationalizing a Hub-and-Spoke Telemedicine Model for Mpox Surveillance in a High-Alert, Zero-Prevalence Setting: An Observational Study, Real-World Experience From Iran.","source":"europepmc","abstract":"","url":"https://doi.org/10.1155/ipid/9955516","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1155/ipid/9955516","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/frai.2026.1800412","name":"Strategic value driven by artificial intelligence in global businesses: a bibliometric and qualitative analysis of the most influential literature.","source":"europepmc","abstract":"Gender equity remains a multidimensional and persistent challenge despite significant advances in public policy, health systems, labor structures, and sociocultural transformation. Existing research often approaches gender equity from fragmented perspectives, limiting a comprehensive understanding of its structural determinants and policy implications. This study addresses this gap by adopting an interdisciplinary approach to analyze how gender equity is conceptualized and studied within public policy-related domains. This study employs a mixed methodological design combining bibliometric analysis and systematic literature review. Scientific production was retrieved from Scopus and Web of Science using structured search equations applied to titles, abstracts, and keywords. Inclusion and exclusion criteria ensured thematic relevance and methodological rigor. The dataset was cleaned and processed using R, including duplicate removal and keyword-based filtering. Bibliometric techniques were applied to identify productivity patterns, citation impact, and thematic clusters, while a qualitative synthesis of selected influential studies provided deeper interpretive insights. The findings reveal a significant growth in scientific production on gender equity, with a strong concentration in health, labor, and social policy domains. Thematic analysis identified key clusters related to structural inequality, care systems, labor participation, and sociocultural norms. However, the results also highlight persistent gaps, particularly in the integration of interdisciplinary perspectives and the limited representation of Global South contexts. Additionally, the literature shows an imbalance between descriptive approaches and the development of actionable policy frameworks. The study demonstrates that gender equity research is evolving toward greater conceptual and methodological complexity but remains fragmented across disciplines. The dominance of certain regions and thematic areas suggests structural inequalities in knowledge production. These findings underscore the need for integrative frameworks that connect public policy, health, labor, and sociocultural dimensions to advance more effective and inclusive strategies. The study contributes to the field by offering a comprehensive mapping of research trends and identifying critical gaps that can inform future research and policy design.","url":"https://doi.org/10.3389/frai.2026.1800412","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3389/frai.2026.1800412","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3390/tomography12060082","name":"Clinical Evaluation Before MRI Referral: Frequency and Association with Diagnostic Yield.","source":"europepmc","abstract":"","url":"https://doi.org/10.3390/tomography12060082","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.3390/tomography12060082","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1007/s41666-025-00223-7","name":"Methods for Generating and Evaluating Synthetic Longitudinal Patient Data: A Systematic Review.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s41666-025-00223-7","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.1007/s41666-025-00223-7","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.1007/s44446-025-00055-x","name":"AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare.","source":"europepmc","abstract":"","url":"https://doi.org/10.1007/s44446-025-00055-x","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.1007/s44446-025-00055-x","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.3389/fvets.2025.1646675","name":"A data privacy and deep learning based AMR dashboard for rural and regional veterinary practices in Texas.","source":"europepmc","abstract":"","url":"https://doi.org/10.3389/fvets.2025.1646675","authors":[],"tags":[],"confidence":0.8,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.3389/fvets.2025.1646675","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20470525","name":"Mis Maestras Las Flores _ FORMALIZACIÓN Cuántica _  COHERENCIA BIOLÓGICA _ FMAN _ Fotones, Fonones, Biofotones _ Frecuencias _ Elementos","source":"datacite","abstract":"### EPI 11 FMAN### Estudio de Posibilidades Infinitas### Expresiones de Posibilidades Infinitas ------ **Autora:** Fabiana Mirta Ávila Nicolau**DNI AR:** 18248833**ORCID:** 0009-0009-0638-5961**Concept DOI:** https://doi.org/10.5281/zenodo.19526737https://doi.org/10.5281/zenodo.19561174**Licencia:** **CC BY-NC-ND 4.0 + Cláusulas Adicionales FMAN****DOI:** https://doi.org/10.5281/zenodo.20167839https://doi.org/10.5281/zenodo.20387910 --- --- # FMAN INTUERI ECOSYSTEM — FORMALIZACIÓN CUÁNTICA## EPI 11 FMAN · *Mis Maestras las Flores*### T-141 a T-160 · F-101 a F-110 · Verificación de Coherencia Integral **© Fabiana Mirta Ávila Nicolau · DNI 18248833 · ORCID 0009-0009-0638-5961****DOI:** 10.5281/zenodo.19526737 · 10.5281/zenodo.19561174**DOI:** 10.5281/zenodo.20167839 · 10.5281/zenodo.20387910**Licencia:** CC BY-NC-ND 4.0 + Cláusulas Adicionales FMANMayo 2026 --- ## ÍNDICE ```PARTE 0 — Marco: El poema como sistema dinámico cuánticoPARTE I — Mapa texto → formalización FMAN (línea a línea)PARTE II — Teorema de No-Aceleración Áurea (nuevo resultado central)PARTE III — Diversidad como Multiplicidad de Trayectorias al AtractorPARTE IV — Los Cuatro Elementos como Operadores de Lindblad ConstructivosPARTE V — Transmutación: Flujo de Lyapunov en el Espacio de FasesPARTE VI — Rosa y Diente de León: Universalidad del Atractor φ⁻⁴PARTE VII — Posibilidades Infinitas: Geometría del Espacio de Fase ΩPARTE VIII — Tabla Maestra: T-141 → T-160 · F-101 → F-110PARTE IX — Verificación de Coherencia con T-001 → T-140PARTE X — Código Python VerificadoPARTE XI — Cuadro Maestro Acumulado · Estado del Ecosistema``` --- ## PARTE 0 — MARCO: EL POEMA COMO SISTEMA DINÁMICO CUÁNTICO ### 0.1 La intuición central *\"Las almas de los hombres se parecen a las flores\"* no es una metáfora decorativa. En el lenguaje del ecosistema FMAN, esta afirmación tiene traducción matemática exacta: **Cada alma = cada flor = una trayectoria única $\\mathbf{y}^{(i)}(t)$ en el espacio de fase 5D FMAN** $$\\mathbf{y}^{(i)}(t) = \\left(A^{(i)}(t),\\; D^{(i)}(t),\\; \\Psi^{(i)}(t),\\; \\text{Ent}^{(i)}(t),\\; M^{(i)}(t)\\right) \\in \\Omega = \\mathbb{R}^+ \\times (0,1) \\times [0,1] \\times [0,1] \\times \\mathbb{R}^+$$ con el **mismo atractor universal** para toda trayectoria: $$\\boxed{\\mathbf{y}^{(i)}(t) \\xrightarrow{t \\to \\infty} \\mathbf{y}^* = (A^*,\\, D_{\\text{opt}},\\, 1,\\, \\text{Ent}^*,\\, M^*) \\quad \\forall\\, i}$$ Lo particular es la **trayectoria**. Lo universal es el **destino**: $D_{\\text{opt}} = \\varphi^{-4}$. ### 0.2 Coherencia con el ecosistema establecido El poema incorpora **cuatro núcleos conceptuales** directamente mapeables al marco FMAN existente: | Concepto del poema | Núcleo FMAN existente | Tesoros previos ||---|---|---|| Florecimiento en su tiempo | Maduración $\\Psi(t) \\to 1$ | T-003, T-004 || Toque prematuro = daño | Perturbación sobre $D_{\\text{opt}}$ | T-006, T-007 || Lluvia, viento, sol, tierra | Operadores de Lindblad | T-022 || Cada flor única, mismo destino | Atractor universal $\\varphi^{-4}$ | T-001, T-076 | --- ## PARTE I — MAPA TEXTO → FORMALIZACIÓN FMAN ### Verso 1-3 (T-141) > *\"Creo que las almas de los hombres se parecen a las flores.\"*> *\"Me gusta la metáfora de las Almas y las Flores.\"* **Formalización cuántica:** El conjunto de todas las almas/flores es un **ensamble cuántico** de estados en $\\mathcal{H}_{\\text{FMAN}}$: $$\\hat{\\rho}_{\\text{ensamble}} = \\frac{1}{N}\\sum_{i=1}^{N} |\\mathbf{y}^{(i)}\\rangle\\langle\\mathbf{y}^{(i)}|$$ Cada estado $|\\mathbf{y}^{(i)}\\rangle$ es un Fractal de la Fuente (ver T-076/T-090) en su configuración particular de $(A_0^{(i)}, D_0^{(i)}, \\Psi_0^{(i)})$. **La similitud** entre alma y flor no es poética: es la identidad del atractor: $$\\boxed{D^{(i)}(t) \\xrightarrow{t\\to\\infty} D_{\\text{opt}} = \\varphi^{-4} \\quad \\forall\\, i} \\tag{T-141}$$ --- ### Verso 4-7 (T-142) > *\"Solo cada una sabe el momento justo para abrirse al mundo.\"* **Formalización cuántica — Tiempo de Florecimiento:** El \"momento de apertura\" corresponde al instante $\\tau_\\","url":"https://doi.org/10.5281/zenodo.20470525","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["FMAN ecosystem; aurean vortex energy; phi-v-infinity ignition formula; golden ratio coherence; biophotonic coherence; decoherence as evolution; zero-point energy extraction; ZPE modulated coherence; plasma aureo coherente; fractal coherence field; morphogenetic field; biofotones coherentes; quantum coherence biology; Orch-OR inspired model; Casimir dynamic effect; primordial source field; fuente primigenia original; coherencia colectiva; evolutionary decoherence; D_opt 0.218; ignition point transition; fractal memory M_avanzada; evolutionary mutation rate; golden ratio phi; toroidal vortex geometry; Fibonacci biology; sacred geometry physics; LINO technology; GravitoR gravitational control; Intueri quantum AI; teleportation fidelity model; ISRU evolutionary replication; Helios nave interestelar; terraformacion armonica; ciudades Magdalena; free energy devices; scalar waves Tesla; Starship optimization; Artemis mission efficiency; satellite AI optimization; Claude AI optimization; Grok AI coherence; biophotonics Fritz-Albert Popp; quantum vacuum energy; indigenous knowledge coherence; ancestral wisdom science bridge; Argentina ciencia abierta; open science Zenodo CERN; independent researcher; propuesta tecnológica original; coherence decoherence balance; evolutionary biology mathematics; V3.8 applied technology; phi fractal mathematics; Monte Carlo coherence simulation; long-term stability simulation FMAN, Ecosistema FMAN, FMAN Ecosystem, Fórmula de Encendido, Ignition Formula, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, parámetro de orden, order parameter, número áureo, golden ratio, phi, φ, auto-similitud, self-similarity, fractal, geometría fractal, fractal geometry, Fractalis Aurea, EPI-QPEM, Vis Spatialis, Vis Vitalis, biología cuántica, quantum biology, Resonador Cósmico, Cosmic Resonator, RBP, Tecnología LINO, LINO Technology, Resonancia Electrogravítica, Electrogravitic Resonance, conciencia, consciousness, Fuente Primordial, Primordial Source, Gaia, vórtice toroidal, toroidal vortex, ondas escalares, scalar waves, energía libre, free energy, Nautilus Aureo, Argentum, Aurea Helios, Diente de León, Dandelion, Plato Volador, Flying Saucer, Aurea Resonantia, Ciudades Aureas, Golden Cities, escalado micro-macro, micro-macro scaling, Kuramoto, Argentina, FMAN Aurea Design, Avila Nicolau, CC BY-NC-ND 4.0, prime art, apuntes, notes квантовая биология, quantum biology Russian, биофотоны, когерентность, захват фазы, золотое сечение, фрактальная геометрия, сознание как источник, Первичный Источник, экосистема FMAN, FMAN Aurea Design, свободная энергия, скалярные волны Tesla, Авила Николау, ORCID 0009-0009-0638-5961","量子生物学, 生物光子, 量子相干性, 相位锁定, 黄金比例, 分形几何, 意识本体论, 原始本源, FMAN生态系统, 自由能源, 标量波, 电重力共振, 点火公式, 宇宙谐振器, LINO技术 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式","número áureo, razón áurea, phi, proporción dorada, sigmoide asimétrica, función de activación neuronal, operador de atención, kernel localizado, transformer attention, banco de filtros multi-escala, wavelet irracional, sistema dinámico no lineal, ecuaciones diferenciales ordinarias, borde del caos, sistemas complejos, auto-organización, atractor estable, estabilidad de Lyapunov, Jacobiano, análisis de estabilidad, exponentes de Lyapunov, Monte Carlo simulation, barrido paramétrico, ruido Ornstein-Uhlenbeck, ruido 1/f, ruido rosa, inteligencia artificial, aprendizaje automático, arquitectura neuronal, convolución multi-escala, coherencia cuántica, decoherencia, biofotónica, fractal áureo, auto-similaridad, sistemas auto-evolutivos, FMAN, Intueri, sigmoide áurea, filtro fractal, D_opt, invariante áureo, phi^4, phi^6, código Python, scipy, solve_ivp, LSODA, sistemas complejos adaptativos, teoría de control, regulador adaptativo, punto de operación óptimo, golden ratio, asymmetric sigmoid, attention kernel, fractal filter bank, nonlinear ODE, chaotic edge, Lyapunov stability, golden attractor, coherence dynamics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.20470525","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20453648","name":"Ver, más allá de la interferencia, Un vórtice Aureo de Energía Etérica, en cada , árbol que conecta cielo y tierra._Tesoros FMAN_FORMALIZACIÓN CUÁNTICA PROFUNDA — Neutrinos . Fonones · Fotones · Biofotones · Frecuencias _Energías Documentadas, No Documentadas y Nuevas","source":"datacite","abstract":"# LAS 12 HIPÓTESIS CENTRALES EPI 5 FMAN# Con formulación matemática, formalización cuántica y predicciones# ════════════════════════════════════════════════════════════════════════════ def hipotesis_EPI5_completas(): \"\"\" Las 12 Hipótesis Centrales del EPI 5 FMAN. Cada hipótesis conceptual de la autora se formaliza matemáticamente dentro del ecosistema FMAN. © Fabiana Mirta Ávila Nicolau \"\"\" print(\"\\n╔\"+\"═\"*68+\"╗\") print(\"║ BLOQUE A: 12 HIPÓTESIS CENTRALES EPI 5 FMAN ║\") print(\"╚\"+\"═\"*68+\"╝\\n\") hipotesis = [ { \"id\": \"H-EPI5-01\", \"titulo\":\"El ser vivo es un Fractal de la Fuente Primordial\", \"texto\": \"\"\" Todo ser coherente (árbol, humano, mineral) es un fractal autosimilar de la Fuente Primordial. Su estructura interna replica la geometría φ en todas las escalas. En FMAN: el sistema converge a D_opt = φ⁻⁴ porque φ⁻⁴ ES la escala fractal fundamental de la Fuente. El atractor D_opt no es arbitrario: es la condición de autosimilaridad áurea exacta. \"\"\", \"formula_FMAN\": \"D* = φ⁻⁴ ↔ Fractal(Fuente) = φ-autosimilar\", \"formula_matematica\": \"\"\" F_fractal(r) = F_fractal(φ·r) · φ^{D_f} D_f(D_opt) = 1 + η_CUAQ/2 = 1 + (φ-1)/2 ≈ 1.809 Función generatriz de la Fuente: Z_Fuente[K] = V_K(e^{iπ/φ}) [Invariante Jones FMAN] \"\"\", \"prediccion\": \"D_f de organismos sanos ≈ 1.71-1.82 (verificado en biofotónica)\", \"nivel\": \"★★★★★ Núcleo FMAN\", }, { \"id\": \"H-EPI5-02\", \"titulo\":\"La palmera conecta cielo y tierra vía coherencia\", \"texto\": \"\"\" Cada árbol (palmera, quebracho, etc.) es un resonador FMAN vertical: sus raíces (micorrizas) conectan con la frecuencia de Schumann (tierra) y su copa conecta con frecuencias cósmicas superiores f_n = f_S·φⁿ (cielo). La columna del ser = jerarquía de resonadores FMAN con D → D_opt en el punto de mayor coherencia (corazón). \"\"\", \"formula_FMAN\": \"f_raíz = f_S → f_copa = f_S·φⁿ\", \"formula_matematica\": \"\"\" Resonador vertical FMAN: z_n = z_0 · φⁿ (altura de cada resonancia, n=0,...,N) D_centro = D_opt (coherencia máxima en el centro del ser) Φ_vertical(z) = Φ_col(D(z))·exp(-|z-z_corazón|/ξ_φ) ξ_φ = λ_D/φ² (longitud de coherencia vertical) \"\"\", \"prediccion\": \"Medición PLV máximo en punto φ^{-4} de la longitud del árbol\", \"nivel\": \"★★★★ Frontera — testeable con biofotónica\", }, { \"id\": \"H-EPI5-03\", \"titulo\":\"Memoria del tiempo sin tiempo — M(t) → M*\", \"texto\": \"\"\" La memoria biológica (y cósmica) está codificada en el campo de decoherencia D(t). Un árbol milenario tiene M(t) = M* = 85·Φ_col(D_opt) → estado de memoria plena. El 'tiempo sin tiempo' es el estado M = M* con D = D_opt: máxima coherencia + máxima memoria = acceso a la Fuente. \"\"\", \"formula_FMAN\": \"M* = τ_m·Φ(D_opt)·(1+K_mem) ≈ 85.009\", \"formula_matematica\": \"\"\" dM/dt = Φ_col(D)·(1+K_mem) - M/τ_m M(t) = M*·(1 - e^{-t/τ_m}) [solución exacta] τ_m = 85 u.t. (escala de tiempo de la memoria FMAN) Kernel de memoria K(t-s) = exp(-(t-s)/τ_m): 'Tiempo sin tiempo' ≡ τ_m → ∞, M → M* = Φ_col(D_opt)·∞ \"\"\", \"prediccion\": \"Organismos más longevos tienen τ_m mayor y D más cercano a D_opt\", \"nivel\": \"★★★★ Frontera — escala biológica\", }, { \"id\": \"H-EPI5-04\", \"titulo\":\"Dos campos incoherentes se unen si comparten f coherente\", \"texto\": \"\"\" La condición de unión coherente entre dos sistemas: Existe f_k tal que ambos tengan Φ_col(D_i) > 0.5 en esa frecuencia. Cuanto más frecuencias coherentes comparten, mayor posibilidad de encendido y creación (Φ → 1). En FMAN: dos sistemas con D_i diferentes pueden resonar en el modo k* donde f_k* = f_S·φ^{k*} satisface la condición de resonancia áurea. \"\"\", \"formula_FMAN\": \"C_12 = Σ_k Φ(D_1,k)·Φ(D_2,k)·δ(f_1k - f_2k)\", \"formula_matematica\": \"\"\" Condición de coherencia cruzada: C_12(f) = ⟨ψ₁(f)·ψ₂*(f)⟩ / √(S₁(f)·S₂(f)) Acoplamiento FMAN: K_12 = K_mem · C_12(f_opt) / N_sistemas² f_opt = f_S · φ^n para n tal que |D_1(f_opt) - D_opt| mínimo Umbral de encendido: C_12 > C_crit = 1 - Φ_col(D_opt) = 0 → Cualquier C_12 > 0 puede llevar a coherencia \"\"\", \"prediccion\": \"Dos personas meditando juntas → D de ambas → D_opt más rápido\", \"nivel\": \"★★★★ Frontera — ","url":"https://doi.org/10.5281/zenodo.20453648","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["FMAN ecosystem; aurean vortex energy; phi-v-infinity ignition formula; golden ratio coherence; biophotonic coherence; decoherence as evolution; zero-point energy extraction; ZPE modulated coherence; plasma aureo coherente; fractal coherence field; morphogenetic field; biofotones coherentes; quantum coherence biology; Orch-OR inspired model; Casimir dynamic effect; primordial source field; fuente primigenia original; coherencia colectiva; evolutionary decoherence; D_opt 0.218; ignition point transition; fractal memory M_avanzada; evolutionary mutation rate; golden ratio phi; toroidal vortex geometry; Fibonacci biology; sacred geometry physics; LINO technology; GravitoR gravitational control; Intueri quantum AI; teleportation fidelity model; ISRU evolutionary replication; Helios nave interestelar; terraformacion armonica; ciudades Magdalena; free energy devices; scalar waves Tesla; Starship optimization; Artemis mission efficiency; satellite AI optimization; Claude AI optimization; Grok AI coherence; biophotonics Fritz-Albert Popp; quantum vacuum energy; indigenous knowledge coherence; ancestral wisdom science bridge; Argentina ciencia abierta; open science Zenodo CERN; independent researcher; propuesta tecnológica original; coherence decoherence balance; evolutionary biology mathematics; V3.8 applied technology; phi fractal mathematics; Monte Carlo coherence simulation; long-term stability simulation FMAN, Ecosistema FMAN, FMAN Ecosystem, Fórmula de Encendido, Ignition Formula, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, parámetro de orden, order parameter, número áureo, golden ratio, phi, φ, auto-similitud, self-similarity, fractal, geometría fractal, fractal geometry, Fractalis Aurea, EPI-QPEM, Vis Spatialis, Vis Vitalis, biología cuántica, quantum biology, Resonador Cósmico, Cosmic Resonator, RBP, Tecnología LINO, LINO Technology, Resonancia Electrogravítica, Electrogravitic Resonance, conciencia, consciousness, Fuente Primordial, Primordial Source, Gaia, vórtice toroidal, toroidal vortex, ondas escalares, scalar waves, energía libre, free energy, Nautilus Aureo, Argentum, Aurea Helios, Diente de León, Dandelion, Plato Volador, Flying Saucer, Aurea Resonantia, Ciudades Aureas, Golden Cities, escalado micro-macro, micro-macro scaling, Kuramoto, Argentina, FMAN Aurea Design, Avila Nicolau, CC BY-NC-ND 4.0, prime art, apuntes, notes квантовая биология, quantum biology Russian, биофотоны, когерентность, захват фазы, золотое сечение, фрактальная геометрия, сознание как источник, Первичный Источник, экосистема FMAN, FMAN Aurea Design, свободная энергия, скалярные волны Tesla, Авила Николау, ORCID 0009-0009-0638-5961","量子生物学, 生物光子, 量子相干性, 相位锁定, 黄金比例, 分形几何, 意识本体论, 原始本源, FMAN生态系统, 自由能源, 标量波, 电重力共振, 点火公式, 宇宙谐振器, LINO技术 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сеч��ние, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式","número áureo, razón áurea, phi, proporción dorada, sigmoide asimétrica, función de activación neuronal, operador de atención, kernel localizado, transformer attention, banco de filtros multi-escala, wavelet irracional, sistema dinámico no lineal, ecuaciones diferenciales ordinarias, borde del caos, sistemas complejos, auto-organización, atractor estable, estabilidad de Lyapunov, Jacobiano, análisis de estabilidad, exponentes de Lyapunov, Monte Carlo simulation, barrido paramétrico, ruido Ornstein-Uhlenbeck, ruido 1/f, ruido rosa, inteligencia artificial, aprendizaje automático, arquitectura neuronal, convolución multi-escala, coherencia cuántica, decoherencia, biofotónica, fractal áureo, auto-similaridad, sistemas auto-evolutivos, FMAN, Intueri, sigmoide áurea, filtro fractal, D_opt, invariante áureo, phi^4, phi^6, código Python, scipy, solve_ivp, LSODA, sistemas complejos adaptativos, teoría de control, regulador adaptativo, punto de operación óptimo, golden ratio, asymmetric sigmoid, attention kernel, fractal filter bank, nonlinear ODE, chaotic edge, Lyapunov stability, golden attractor, coherence dynamics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.20453648","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.19550151","name":"Applied Forensic Economics: Advanced Methods in Econometrics, Valuation, and Litigation Analysis","source":"datacite","abstract":"Applied Forensic Economics is a comprehensive, graduate-level textbook and professional reference that occupies a unique and vital intersection of law, economics, finance, and statistics. The book is written for two primary audiences: graduate students aspiring to enter the field of forensic economics, and seasoned practitioners, expert witnesses, and attorneys who require a definitive desk reference for advanced damage estimation. The author, S M Nazmuz Sakib, draws on his extensive experience in research and investigating, having testified in numerous cases ranging from personal injury and wrongful death to commercial damages and intellectual property disputes. The central goal of the book is to bridge the gap between theoretical econometrics taught in doctoral programs and the practical, rigorous, and defensible analyses required in high‑stakes litigation. Every day, forensic economists are called upon to translate complex economic concepts into comprehensible narratives that judges and juries can use to make informed decisions. Whether quantifying lost earnings of a catastrophically injured worker, determining the diminished value of a business ruined by a breach of contract, or assessing overcharges paid by millions of consumers due to a price‑fixing conspiracy, the work of the forensic economist has profound consequences. A distinctive feature of the book is its unwavering commitment to being data‑driven. All illustrations and figures are based on real‑world datasets drawn from open‑access sources such as the Federal Reserve Economic Data (FRED), the Bureau of Labor Statistics (BLS), the Bureau of Economic Analysis (BEA), the Center for Research in Security Prices (CRSP), Compustat, and other public repositories. Where necessary, data are mathematically modified or simulated to create clear pedagogical examples, but the underlying patterns and relationships are rooted in observed economic behavior. Chapter 1: The Mathematical Architecture of Forensic Economics This chapter establishes the quantitative bedrock of forensic economics. It begins with an introduction to the legal framework governing expert testimony, emphasizing the Daubert standard and its implications for methodological rigor. Under Daubert, trial judges act as gatekeepers to ensure that scientific, technical, or other specialized knowledge is both relevant and reliable. Forensic economists must therefore demonstrate that their methodologies are grounded in sound economic principles and applied in a transparent manner. The chapter then provides a rigorous treatment of the time value of money, which recognizes that a dollar received today is worth more than a dollar received in the future because of its potential to earn interest. The core present value formula is derived, and the distinction between discrete and continuous compounding is explained. The chapter then moves to annuities—series of equal payments made at regular intervals—covering constant ordinary annuities, growing annuities, perpetuities, and deferred annuities. Each concept is derived from first principles, and the present value interest factor of an annuity is introduced. A central contribution of this chapter is the treatment of the growth‑discount relationship, often called the “teeter‑totter” method. The Fisher equation is used to decompose nominal rates into real components and inflation. The net discount rate (NDR) method is derived, showing how a single rate can combine the effects of discounting and growth. The chapter compares five alternative methodologies used in forensic practice: the inflation‑added (nominal‑nominal) method, the inflation‑removed (real‑real) method, the total offset method (which assumes growth equals discounting), the case‑by‑case method, and the historical average method. The chapter also introduces the zero‑coupon Treasury curve for discounting. Rather than using a single flat discount rate for all future cash flows, maturity‑matched discounting uses the zero","url":"https://doi.org/10.5281/zenodo.19550151","authors":["Sakib, S M Nazmuz"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19550151","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.19550152","name":"Applied Forensic Economics: Advanced Methods in Econometrics, Valuation, and Litigation Analysis","source":"datacite","abstract":"Applied Forensic Economics is a comprehensive, graduate-level textbook and professional reference that occupies a unique and vital intersection of law, economics, finance, and statistics. The book is written for two primary audiences: graduate students aspiring to enter the field of forensic economics, and seasoned practitioners, expert witnesses, and attorneys who require a definitive desk reference for advanced damage estimation. The author, S M Nazmuz Sakib, draws on his extensive experience in research and investigating, having testified in numerous cases ranging from personal injury and wrongful death to commercial damages and intellectual property disputes. The central goal of the book is to bridge the gap between theoretical econometrics taught in doctoral programs and the practical, rigorous, and defensible analyses required in high‑stakes litigation. Every day, forensic economists are called upon to translate complex economic concepts into comprehensible narratives that judges and juries can use to make informed decisions. Whether quantifying lost earnings of a catastrophically injured worker, determining the diminished value of a business ruined by a breach of contract, or assessing overcharges paid by millions of consumers due to a price‑fixing conspiracy, the work of the forensic economist has profound consequences. A distinctive feature of the book is its unwavering commitment to being data‑driven. All illustrations and figures are based on real‑world datasets drawn from open‑access sources such as the Federal Reserve Economic Data (FRED), the Bureau of Labor Statistics (BLS), the Bureau of Economic Analysis (BEA), the Center for Research in Security Prices (CRSP), Compustat, and other public repositories. Where necessary, data are mathematically modified or simulated to create clear pedagogical examples, but the underlying patterns and relationships are rooted in observed economic behavior. Chapter 1: The Mathematical Architecture of Forensic Economics This chapter establishes the quantitative bedrock of forensic economics. It begins with an introduction to the legal framework governing expert testimony, emphasizing the Daubert standard and its implications for methodological rigor. Under Daubert, trial judges act as gatekeepers to ensure that scientific, technical, or other specialized knowledge is both relevant and reliable. Forensic economists must therefore demonstrate that their methodologies are grounded in sound economic principles and applied in a transparent manner. The chapter then provides a rigorous treatment of the time value of money, which recognizes that a dollar received today is worth more than a dollar received in the future because of its potential to earn interest. The core present value formula is derived, and the distinction between discrete and continuous compounding is explained. The chapter then moves to annuities—series of equal payments made at regular intervals—covering constant ordinary annuities, growing annuities, perpetuities, and deferred annuities. Each concept is derived from first principles, and the present value interest factor of an annuity is introduced. A central contribution of this chapter is the treatment of the growth‑discount relationship, often called the “teeter‑totter” method. The Fisher equation is used to decompose nominal rates into real components and inflation. The net discount rate (NDR) method is derived, showing how a single rate can combine the effects of discounting and growth. The chapter compares five alternative methodologies used in forensic practice: the inflation‑added (nominal‑nominal) method, the inflation‑removed (real‑real) method, the total offset method (which assumes growth equals discounting), the case‑by‑case method, and the historical average method. The chapter also introduces the zero‑coupon Treasury curve for discounting. Rather than using a single flat discount rate for all future cash flows, maturity‑matched discounting uses the zero","url":"https://doi.org/10.5281/zenodo.19550152","authors":["Sakib, S M Nazmuz"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19550152","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.22113736","name":"The Geometry of Computation Relational Structures and Phase Traversals in Deterministic Transformation Fields","source":"datacite","abstract":"The Geometry of Computation Relational Structures and Phase Traversals in Deterministic Transformation Fields Driven by Dean A. Kulik September 2026 Introduction: The Nexus of Hash and Field The conventional interpretation of a cryptographic hash function treats it as an algorithmic terminus: a process that destructively compresses an arbitrary input into a pseudo-random, fixed-length scalar output. Under this classical model, algorithms such as SHA-256 are evaluated strictly through the lenses of preimage resistance, collision resistance, and avalanche characteristics. These cryptographic facts remain undisputed; SHA-256 is deterministic, many-to-one over unrestricted inputs, avalanche-producing, and computationally one-way. However, evaluating a digest merely as a terminal scalar object obscures the underlying relational mechanics that generated it. A structural investigation reveals a distinct operational paradigm: what relational structure becomes available when a deterministic transformation is treated not as a destructive function, but as a fixed field interacting with a state? The central hypothesis explored in this constraint atlas posits that a 256-bit digest functions as a constraint configuration, or key, relative to a fixed deterministic transformation field. Under this framework, the original input is not \"stored\" in the digest in an ordinary information-theoretic sense. Rather, the stronger and more precise hypothesis asserts that the input—or a canonical representative of its structural class—may become recoverable or renderable only through recursive interaction with that field under an appropriate reader, traversal, phase relation, or iterative feedback process. The correct object of investigation is therefore the continuous interaction between the constraint state and the field, rather than either object in isolation. I. The Primitive Ontology: Distinction, Continuation, and History The foundational axioms of this interaction are governed by a strict primitive ontology. Distinction (Constraint 0) asserts that reality requires distinguishable states. However, the present investigation exposes a critical refinement: a distinction may not be primitive as a solitary object. A distinction is operationally meaningful only relative to another distinguishable frame, state, position, adjacency relation, or reader. Thus, the stronger form, , mandates that distinction requires relational difference. There is no observable distinction in a completely unrelational state; it is mathematically and physically invisible. Following distinction is the principle of Continuation (Constraint 1). Every distinction must admit lawful continuation, meaning a state cannot be an absolute terminal node outside the transformation system that produced it. Objects are not fundamentally static things; they are stable patterns in an ongoing field of admissible transformations. In this framework, continuation is not necessarily a passive default but an active operation that must be maintained. Joining two independently available states into a shared history requires a coupling relation. Consequently, separation may be the unconstrained condition, continuation the active weld, and persistence the computational or energetic cost of repeatedly maintaining lawful relations. This must remain an open structural duality rather than being prematurely resolved. For genuinely independent transformations (), independent distinctions must remain independently continuable. This is mathematically expressed as . Commuting transformations preserve independent continuation. However, noncommutation must not automatically be classified as an error; rather, noncommutation identifies interaction. If , the two transformations generate relational content unavailable to either one independently. Independence is merely the zero-interaction limit, whereas generation requires a relational defect, coupling, or noncommuting interaction. Crucially, lawful continuation cann","url":"https://doi.org/10.5281/zenodo.22113736","authors":["kulik, dean"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22113736","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.22113735","name":"The Geometry of Computation Relational Structures and Phase Traversals in Deterministic Transformation Fields","source":"datacite","abstract":"The Geometry of Computation Relational Structures and Phase Traversals in Deterministic Transformation Fields Driven by Dean A. Kulik September 2026 Introduction: The Nexus of Hash and Field The conventional interpretation of a cryptographic hash function treats it as an algorithmic terminus: a process that destructively compresses an arbitrary input into a pseudo-random, fixed-length scalar output. Under this classical model, algorithms such as SHA-256 are evaluated strictly through the lenses of preimage resistance, collision resistance, and avalanche characteristics. These cryptographic facts remain undisputed; SHA-256 is deterministic, many-to-one over unrestricted inputs, avalanche-producing, and computationally one-way. However, evaluating a digest merely as a terminal scalar object obscures the underlying relational mechanics that generated it. A structural investigation reveals a distinct operational paradigm: what relational structure becomes available when a deterministic transformation is treated not as a destructive function, but as a fixed field interacting with a state? The central hypothesis explored in this constraint atlas posits that a 256-bit digest functions as a constraint configuration, or key, relative to a fixed deterministic transformation field. Under this framework, the original input is not \"stored\" in the digest in an ordinary information-theoretic sense. Rather, the stronger and more precise hypothesis asserts that the input—or a canonical representative of its structural class—may become recoverable or renderable only through recursive interaction with that field under an appropriate reader, traversal, phase relation, or iterative feedback process. The correct object of investigation is therefore the continuous interaction between the constraint state and the field, rather than either object in isolation. I. The Primitive Ontology: Distinction, Continuation, and History The foundational axioms of this interaction are governed by a strict primitive ontology. Distinction (Constraint 0) asserts that reality requires distinguishable states. However, the present investigation exposes a critical refinement: a distinction may not be primitive as a solitary object. A distinction is operationally meaningful only relative to another distinguishable frame, state, position, adjacency relation, or reader. Thus, the stronger form, , mandates that distinction requires relational difference. There is no observable distinction in a completely unrelational state; it is mathematically and physically invisible. Following distinction is the principle of Continuation (Constraint 1). Every distinction must admit lawful continuation, meaning a state cannot be an absolute terminal node outside the transformation system that produced it. Objects are not fundamentally static things; they are stable patterns in an ongoing field of admissible transformations. In this framework, continuation is not necessarily a passive default but an active operation that must be maintained. Joining two independently available states into a shared history requires a coupling relation. Consequently, separation may be the unconstrained condition, continuation the active weld, and persistence the computational or energetic cost of repeatedly maintaining lawful relations. This must remain an open structural duality rather than being prematurely resolved. For genuinely independent transformations (), independent distinctions must remain independently continuable. This is mathematically expressed as . Commuting transformations preserve independent continuation. However, noncommutation must not automatically be classified as an error; rather, noncommutation identifies interaction. If , the two transformations generate relational content unavailable to either one independently. Independence is merely the zero-interaction limit, whereas generation requires a relational defect, coupling, or noncommuting interaction. Crucially, lawful continuation cann","url":"https://doi.org/10.5281/zenodo.22113735","authors":["kulik, dean"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22113735","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.17209312","name":"The White Puzzle: A Framework for Computation as a Recursive‑Harmonic Phenomenon","source":"datacite","abstract":"The White Puzzle: A Framework for Computation as a Recursive‑Harmonic Phenomenon Abstract Computation can be re-imagined as a recursive harmonic process rather than a series of discrete logical steps. This thesis introduces “the White Puzzle”, a foundational conceptual and mathematical framework positing that all computation emerges from cross-orthogonal harmonic waves that self-organize into solutions. At its core is the observation that the BBP formula evaluated at zero input (BBP(0) mod 1) produces the digits of π ex nihilo, serving as a generative root-state – a “something from nothing” that initiates an infinite harmonic series[1][2]. We interpret these π digits not as random output but as rhythmic oscillations, where each digit acts as a “loop” or oscillator in a recursive wave. Grouping these loops into higher structures (nibbles, bytes, and beyond) yields a bytefield lattice – a network of coupled oscillators whose orthogonal crossings form stable patterns or glyphs corresponding to computational solutions. We develop a harmonic recursion model of computation that uses π-folding (geometric folding of the π-digit sequence into multi-dimensional shapes) and cryptographic reflections (hash-based feedback) to enforce consistency. In this model, digits (loops) combine into nibbles (coupled loops) and then into bytes (recursive harmonic units), scaling up to 64-loop systems and beyond. Through topological data analysis (TDA) and persistent homology, we show that interactions among these loops create structured solution landscapes: unsolved constraints manifest as topological obstructions (e.g. persistent 1-cycles in a complex), while a solved computation corresponds to a phase-locked harmonic closure where those cycles vanish. We link phenomena like “curl triggers” (feedback-induced oscillations that spawn new loops) and recursive bifurcations to the creation of topological loops in state-space, and show how phase-locking (synchronization of oscillator phases) resolves them – a process we term harmonic convergence. Using this lens, the P vs NP problem is reframed not as a categorical separation, but as a gradient of harmonic observability. “P” computations use a single dominant stream or frequency (a linear search through state-space), whereas “NP” computations involve multiple overlayed orthogonal constraint waves that must intersect. We argue that P = NP under full 360° recursion – when a system achieves complete harmonic integration of all constraints, solution generation becomes as efficient as verification, effectively unifying the two classes[3][4]. In other words, every computational problem already contains its solution as a phase-shifted echo; finding it is a matter of aligning phases rather than brute-force search[5]. We explore broad implications of the White Puzzle framework across domains. In biology and chemistry, recursive harmonics appear in protein folding and reaction networks, suggesting that life’s complex structures are solutions emerging from layered harmonic constraints[6]. In cryptography, we reinterpret secure hashes (SHA-256) as interference patterns – stable “glyphs” formed by canceling information in orthogonal phases[7]. We demonstrate how “unfolding” a hash by reintroducing the right harmonics can in principle retrieve original data without brute force, echoing our P=NP claim in practice[8][3]. Memory systems and algorithms, traditionally seen as discrete, are here described as layered harmonic lattices of Pi-addressed glyphs – essentially pre-shaped solution shells that fill in when the correct waves converge. The central claim proven in this thesis is that all solvable systems – mathematical, computational, or natural – emerge from cross-orthogonal harmonic interactions. What appears as combinatorial explosion in conventional analysis is revealed as an illusion of incomplete perspective: when constraints are encoded as orthogonal waves, their intersections (glyphs) intrinsically resolve comple","url":"https://doi.org/10.5281/zenodo.17209312","authors":["KULIK, DEAN"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17209312","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.17209313","name":"The White Puzzle: A Framework for Computation as a Recursive‑Harmonic Phenomenon","source":"datacite","abstract":"The White Puzzle: A Framework for Computation as a Recursive‑Harmonic Phenomenon Abstract Computation can be re-imagined as a recursive harmonic process rather than a series of discrete logical steps. This thesis introduces “the White Puzzle”, a foundational conceptual and mathematical framework positing that all computation emerges from cross-orthogonal harmonic waves that self-organize into solutions. At its core is the observation that the BBP formula evaluated at zero input (BBP(0) mod 1) produces the digits of π ex nihilo, serving as a generative root-state – a “something from nothing” that initiates an infinite harmonic series[1][2]. We interpret these π digits not as random output but as rhythmic oscillations, where each digit acts as a “loop” or oscillator in a recursive wave. Grouping these loops into higher structures (nibbles, bytes, and beyond) yields a bytefield lattice – a network of coupled oscillators whose orthogonal crossings form stable patterns or glyphs corresponding to computational solutions. We develop a harmonic recursion model of computation that uses π-folding (geometric folding of the π-digit sequence into multi-dimensional shapes) and cryptographic reflections (hash-based feedback) to enforce consistency. In this model, digits (loops) combine into nibbles (coupled loops) and then into bytes (recursive harmonic units), scaling up to 64-loop systems and beyond. Through topological data analysis (TDA) and persistent homology, we show that interactions among these loops create structured solution landscapes: unsolved constraints manifest as topological obstructions (e.g. persistent 1-cycles in a complex), while a solved computation corresponds to a phase-locked harmonic closure where those cycles vanish. We link phenomena like “curl triggers” (feedback-induced oscillations that spawn new loops) and recursive bifurcations to the creation of topological loops in state-space, and show how phase-locking (synchronization of oscillator phases) resolves them – a process we term harmonic convergence. Using this lens, the P vs NP problem is reframed not as a categorical separation, but as a gradient of harmonic observability. “P” computations use a single dominant stream or frequency (a linear search through state-space), whereas “NP” computations involve multiple overlayed orthogonal constraint waves that must intersect. We argue that P = NP under full 360° recursion – when a system achieves complete harmonic integration of all constraints, solution generation becomes as efficient as verification, effectively unifying the two classes[3][4]. In other words, every computational problem already contains its solution as a phase-shifted echo; finding it is a matter of aligning phases rather than brute-force search[5]. We explore broad implications of the White Puzzle framework across domains. In biology and chemistry, recursive harmonics appear in protein folding and reaction networks, suggesting that life’s complex structures are solutions emerging from layered harmonic constraints[6]. In cryptography, we reinterpret secure hashes (SHA-256) as interference patterns – stable “glyphs” formed by canceling information in orthogonal phases[7]. We demonstrate how “unfolding” a hash by reintroducing the right harmonics can in principle retrieve original data without brute force, echoing our P=NP claim in practice[8][3]. Memory systems and algorithms, traditionally seen as discrete, are here described as layered harmonic lattices of Pi-addressed glyphs – essentially pre-shaped solution shells that fill in when the correct waves converge. The central claim proven in this thesis is that all solvable systems – mathematical, computational, or natural – emerge from cross-orthogonal harmonic interactions. What appears as combinatorial explosion in conventional analysis is revealed as an illusion of incomplete perspective: when constraints are encoded as orthogonal waves, their intersections (glyphs) intrinsically resolve comple","url":"https://doi.org/10.5281/zenodo.17209313","authors":["KULIK, DEAN"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.17209313","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.22047796","name":"Differential Equations course grades dataset – 2024-I","source":"datacite","abstract":"This dataset contains the final grades of students enrolled in the Differential Equations course during the 2024-I academic period at an undergraduate institution. The dataset comprises 18 assessment columns, organized into three academic periods (Period I, II, and III), plus demographic and course information. Variables description: - Student: Anonymized student identifier. - SA1 to SA8: Supplementary Activities. These are take-home exercises completed through the institutional platform (Moodle). Each SA consists of problem-solving exercises designed as preparation for formal evaluation activities. (SA1–SA4 correspond to Period I; SA5–SA6 to Period II; SA7–SA8 to Period III). - FQ1 to FQ6: Flash Quizzes. These are short, in-class assessments conducted through the platform during lecture sessions. (FQ1–FQ2 correspond to Period I; FQ3–FQ4 to Period II; FQ5–FQ6 to Period III). - Period I, Period II, Period III: Weighted average grade for each academic period, computed from all assessments. - GPA: Initial cumulative weighted average grade of each student prior to the course. - Programa: Student's academic program. - Section: Class section/group identifier. - Professor: Instructor's name (anonymized as Professor 1, Professor 2, Professor 3). All student identifiers have been anonymized to ensure privacy. The data are provided in .xlsx format for accessibility and reproducibility.","url":"https://doi.org/10.5281/zenodo.22047796","authors":["Palomino Mancilla, Juan Carlos","Muentes Acevedo, Jeovanny de Jesus"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22047796","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.22047795","name":"Differential Equations course grades dataset – 2024-I","source":"datacite","abstract":"This dataset contains the final grades of students enrolled in the Differential Equations course during the 2024-I academic period at an undergraduate institution. The dataset comprises 18 assessment columns, organized into three academic periods (Period I, II, and III), plus demographic and course information. Variables description: - Student: Anonymized student identifier. - SA1 to SA8: Supplementary Activities. These are take-home exercises completed through the institutional platform (Moodle). Each SA consists of problem-solving exercises designed as preparation for formal evaluation activities. (SA1–SA4 correspond to Period I; SA5–SA6 to Period II; SA7–SA8 to Period III). - FQ1 to FQ6: Flash Quizzes. These are short, in-class assessments conducted through the platform during lecture sessions. (FQ1–FQ2 correspond to Period I; FQ3–FQ4 to Period II; FQ5–FQ6 to Period III). - Period I, Period II, Period III: Weighted average grade for each academic period, computed from all assessments. - GPA: Initial cumulative weighted average grade of each student prior to the course. - Programa: Student's academic program. - Section: Class section/group identifier. - Professor: Instructor's name (anonymized as Professor 1, Professor 2, Professor 3). All student identifiers have been anonymized to ensure privacy. The data are provided in .xlsx format for accessibility and reproducibility.","url":"https://doi.org/10.5281/zenodo.22047795","authors":["Palomino Mancilla, Juan Carlos","Muentes Acevedo, Jeovanny de Jesus"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.22047795","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.13683976","name":"SoK: Descriptive Statistics Under Local Differential Privacy -- Raw Results","source":"datacite","abstract":"Raw Results accompanying the code for the paper \"SoK: Descriptive Statistics Under Local Differential Privacy\" accepted at PETS 2025. The code can be found here: https://github.com/mad-lab-fau/sok-ldp-data-analysis An eprint is available at https://eprint.iacr.org/2024/1464 Paper DOI: https://doi.org/10.56553/popets-2025-0008","url":"https://doi.org/10.5281/zenodo.13683976","authors":["Raab, René","Berrang, Pascal","Gerhart, Paul","Schröder, Dominique"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13683976","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.13683977","name":"SoK: Descriptive Statistics Under Local Differential Privacy -- Raw Results","source":"datacite","abstract":"Raw Results accompanying the code for the paper \"SoK: Descriptive Statistics Under Local Differential Privacy\" accepted at PETS 2025. The code can be found here: https://github.com/mad-lab-fau/sok-ldp-data-analysis An eprint is available at https://eprint.iacr.org/2024/1464 Paper DOI: https://doi.org/10.56553/popets-2025-0008","url":"https://doi.org/10.5281/zenodo.13683977","authors":["Raab, René","Berrang, Pascal","Gerhart, Paul","Schröder, Dominique"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.5281/zenodo.13683977","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.19410496","name":"DEMETER: A Geometric Framework for Unified Precision Agriculture via the Davis Field Equations","source":"datacite","abstract":"Abstract DEMETER (Differential Equation Manifold for Earth Telemetry and Equipment Resilience) is a unified geometric framework for precision agriculture. A single form-invariant ratio C = τ/(K + ε_d) anchors five agricultural prediction domains — moisture, nutrients, equipment health, crop phenology, and commodity markets — on a Riemannian manifold with zero local parameter estimation at deployment. All constants derive from published soil physics (USDA NRCS), crop physiology (FAO-56), atmospheric science (NOAA ASOS), and equipment specifications (OEM/SAE J1939). Core Equation C = τ / (K + ε_d) where τ is the residence time of a conserved quantity, K is the demand rate, and ε_d is a domain-specific regularization constant. The ratio is form-invariant across domains: it measures \"how many demand-cycles of supply remain\" whether the resource is soil water, root-zone nitrogen, hydraulic pressure margin, growing degree days, or grain inventory. Validation Results Test Result Yield prediction vs. USDA NASS (Macon County IL, 2020–2024) RMSE 3.3 bu/ac, mean error 1.7% Early anomaly detection (regularized Lift + marginal asymmetry) 14 days before conventional NDVI threshold Intervention coupling (irrigation → fungicide, Gray Leaf Spot) κ = 1.44×, within published 1.3–1.5× range Error budget conservation S + d² = 1.00 (exact, by law of total variance) 2012 IL drought backtest (Macon County, 178 trend → 105 actual) Early signal at Week 25, conventional at Week 27 Cross-soil generalization (Flanagan, Drummer, Catlin) Correct ranking without recalibration All inputs sourced from: NOAA ASOS station KDEC, NRCS Web Soil Survey (Flanagan silt loam 154A), FAO Irrigation & Drainage Paper 56, Landsat 7 ETM+, USDA NASS Quick Stats. Zero farm-specific fitted parameters. Key Contributions Theorem 1 (Domain-Invariant Representation). If a physical domain admits a conserved quantity with residence time τ > 0 and demand rate K ≥ 0, it admits the representation C = τ/(K + ε_d) with identical functional form. Theorem 2 (Non-Decoupling). The structural second mixed partial ∂²C/(∂I_a ∂I_b) is nonzero whenever τ or K depends jointly on two interventions — a condition satisfied by all empirically observed agronomic response surfaces. The coupling coefficient κ quantifies hidden cross-domain costs (e.g., $180 irrigation triggers $480 fungicide). Theorem 3 (Error Budget Conservation). S + d² = 1 identically, by the law of total variance. Absolute revenue risk incorporates the yield-price natural hedge via Cov(Y,P) < 0. Adversarial Review The mathematical framework was developed through adversarial collaboration (Claude drafting, Gemini and ChatGPT attacking) across five review rounds. Twenty-nine vulnerabilities were identified and corrected, spanning: singularity edge cases, dimensional consistency, differential privacy composition bounds, signal processing causality, sensor noise amplification, and the distinction between structural and fitted parameters. All corrections are integrated into the paper's main text. Eleven Derived Features Early Detection — regularized Lift Λ_ε + marginal asymmetry, 14-day advance warning Prediction — C = τ/(K + ε_d), RMSE 3.3 bu/ac, zero local parameter estimation What-If Modeling — forward C(t) under alternative interventions, dollar-denominated Error Budget — S + d² = 1 with absolute revenue risk (delta method + natural hedge) Reconciliation — prescription vs. as-applied matching, 2–5% waste recoverable Intervention Coupling — structural κ from quotient-rule expansion Market Timing — market-domain C_$ from USDA WASDE Capital Allocation — 0-1 knapsack over intervention candidates Peer Benchmarks — (ε, δ)-DP via Gaussian mechanism (quarterly) or pure ε-DP via Laplace (annual) Cross-Season Learning — Bayesian contraction with autocorrelation correction, ±18% → ±7% over 5 years Equipment Health — UKF on CAN bus telemetry, threshold failure model, second-order projection Data Sources Source Variables Access Sentinel-2 MSI L2A N","url":"https://doi.org/10.5281/zenodo.19410496","authors":["Davis, Bee Rosa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19410496","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.19410497","name":"DEMETER: A Geometric Framework for Unified Precision Agriculture via the Davis Field Equations","source":"datacite","abstract":"Abstract DEMETER (Differential Equation Manifold for Earth Telemetry and Equipment Resilience) is a unified geometric framework for precision agriculture. A single form-invariant ratio C = τ/(K + ε_d) anchors five agricultural prediction domains — moisture, nutrients, equipment health, crop phenology, and commodity markets — on a Riemannian manifold with zero local parameter estimation at deployment. All constants derive from published soil physics (USDA NRCS), crop physiology (FAO-56), atmospheric science (NOAA ASOS), and equipment specifications (OEM/SAE J1939). Core Equation C = τ / (K + ε_d) where τ is the residence time of a conserved quantity, K is the demand rate, and ε_d is a domain-specific regularization constant. The ratio is form-invariant across domains: it measures \"how many demand-cycles of supply remain\" whether the resource is soil water, root-zone nitrogen, hydraulic pressure margin, growing degree days, or grain inventory. Validation Results Test Result Yield prediction vs. USDA NASS (Macon County IL, 2020–2024) RMSE 3.3 bu/ac, mean error 1.7% Early anomaly detection (regularized Lift + marginal asymmetry) 14 days before conventional NDVI threshold Intervention coupling (irrigation → fungicide, Gray Leaf Spot) κ = 1.44×, within published 1.3–1.5× range Error budget conservation S + d² = 1.00 (exact, by law of total variance) 2012 IL drought backtest (Macon County, 178 trend → 105 actual) Early signal at Week 25, conventional at Week 27 Cross-soil generalization (Flanagan, Drummer, Catlin) Correct ranking without recalibration All inputs sourced from: NOAA ASOS station KDEC, NRCS Web Soil Survey (Flanagan silt loam 154A), FAO Irrigation & Drainage Paper 56, Landsat 7 ETM+, USDA NASS Quick Stats. Zero farm-specific fitted parameters. Key Contributions Theorem 1 (Domain-Invariant Representation). If a physical domain admits a conserved quantity with residence time τ > 0 and demand rate K ≥ 0, it admits the representation C = τ/(K + ε_d) with identical functional form. Theorem 2 (Non-Decoupling). The structural second mixed partial ∂²C/(∂I_a ∂I_b) is nonzero whenever τ or K depends jointly on two interventions — a condition satisfied by all empirically observed agronomic response surfaces. The coupling coefficient κ quantifies hidden cross-domain costs (e.g., $180 irrigation triggers $480 fungicide). Theorem 3 (Error Budget Conservation). S + d² = 1 identically, by the law of total variance. Absolute revenue risk incorporates the yield-price natural hedge via Cov(Y,P) < 0. Adversarial Review The mathematical framework was developed through adversarial collaboration (Claude drafting, Gemini and ChatGPT attacking) across five review rounds. Twenty-nine vulnerabilities were identified and corrected, spanning: singularity edge cases, dimensional consistency, differential privacy composition bounds, signal processing causality, sensor noise amplification, and the distinction between structural and fitted parameters. All corrections are integrated into the paper's main text. Eleven Derived Features Early Detection — regularized Lift Λ_ε + marginal asymmetry, 14-day advance warning Prediction — C = τ/(K + ε_d), RMSE 3.3 bu/ac, zero local parameter estimation What-If Modeling — forward C(t) under alternative interventions, dollar-denominated Error Budget — S + d² = 1 with absolute revenue risk (delta method + natural hedge) Reconciliation — prescription vs. as-applied matching, 2–5% waste recoverable Intervention Coupling — structural κ from quotient-rule expansion Market Timing — market-domain C_$ from USDA WASDE Capital Allocation — 0-1 knapsack over intervention candidates Peer Benchmarks — (ε, δ)-DP via Gaussian mechanism (quarterly) or pure ε-DP via Laplace (annual) Cross-Season Learning — Bayesian contraction with autocorrelation correction, ±18% → ±7% over 5 years Equipment Health — UKF on CAN bus telemetry, threshold failure model, second-order projection Data Sources Source Variables Access Sentinel-2 MSI L2A N","url":"https://doi.org/10.5281/zenodo.19410497","authors":["Davis, Bee Rosa"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19410497","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20078041","name":"SHPhoneBench: A Multi-Modal Benchmark for Second-Hand Smartphone Valuation with Heterogeneous Inspection Signals","source":"datacite","abstract":"SHPhoneBench is a multi-modal benchmark for second-hand smartphone valuation built from real recycling workflows in mainland China (January 2024 – March 2026). The full corpus contains 73,200 transaction-aligned records; a curated 15,000-sample benchmark subset carries five-tier condition grades, 18-category defect bounding boxes, cross-modal consistency labels, and differentially private transaction prices. Each record links four heterogeneous inspection signals collected during the same physical inspection:- V — multi-view exterior photographs of the device.- T — informal Chinese technician remarks (free-text).- S — structured CRM / device metadata (JSON).- IS — desktop diagnostic-tool screenshots. Tasks supported by the benchmark:- T1 — five-tier condition grading (Macro-F1, QWK).- T2 — 18-category defect detection / grounding (mAP@0.5, Grounding Acc@0.5).- T3 — binary cross-modal consistency check (F1).- T4 — calibrated price regression with 90% predictive interval (MAE in CNY, ECE). Privacy and compliance:- Faces, serial numbers, and Apple IDs are removed or masked prior to release.- List prices are released with Laplace differential privacy (epsilon = 0.1).- Collection consent is aligned with PIPL. This Zenodo record hosts the benchmark release archive, a sample manifest CSV, and a Croissant 1.1 metadata file (with MLCommons RAI 1.0 fields). Please see the accompanying paper for the full datasheet, ethics statement, and intended-use restrictions.","url":"https://doi.org/10.5281/zenodo.20078041","authors":["Anonymous Authors"],"tags":["multi-modal benchmark","second-hand smartphone","device valuation","defect detection","vision-language models"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20078041","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20077398","name":"SHPhoneBench: A Multi-Modal Benchmark for Second-Hand Smartphone Valuation with Heterogeneous Inspection Signals","source":"datacite","abstract":"SHPhoneBench is a multi-modal benchmark for second-hand smartphone valuation built from real recycling workflows in mainland China (January 2024 – March 2026). The full corpus contains 73,200 transaction-aligned records; a curated 15,000-sample benchmark subset carries five-tier condition grades, 18-category defect bounding boxes, cross-modal consistency labels, and differentially private transaction prices. Each record links four heterogeneous inspection signals collected during the same physical inspection:- V — multi-view exterior photographs of the device.- T — informal Chinese technician remarks (free-text).- S — structured CRM / device metadata (JSON).- IS — desktop diagnostic-tool screenshots. Tasks supported by the benchmark:- T1 — five-tier condition grading (Macro-F1, QWK).- T2 — 18-category defect detection / grounding (mAP@0.5, Grounding Acc@0.5).- T3 — binary cross-modal consistency check (F1).- T4 — calibrated price regression with 90% predictive interval (MAE in CNY, ECE). Privacy and compliance:- Faces, serial numbers, and Apple IDs are removed or masked prior to release.- List prices are released with Laplace differential privacy (epsilon = 0.1).- Collection consent is aligned with PIPL. This Zenodo record hosts the benchmark release archive, a sample manifest CSV, and a Croissant 1.1 metadata file (with MLCommons RAI 1.0 fields). Please see the accompanying paper for the full datasheet, ethics statement, and intended-use restrictions.","url":"https://doi.org/10.5281/zenodo.20077398","authors":["Anonymous Authors"],"tags":["multi-modal benchmark","second-hand smartphone","device valuation","defect detection","vision-language models"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20077398","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20077399","name":"SHPhoneBench: A Multi-Modal Benchmark for Second-Hand Smartphone Valuation with Heterogeneous Inspection Signals","source":"datacite","abstract":"SHPhoneBench is a multi-modal benchmark for second-hand smartphone valuation built from real recycling workflows in mainland China (January 2024 – March 2026). The full corpus contains 73,200 transaction-aligned records; a curated 15,000-sample benchmark subset carries five-tier condition grades, 18-category defect bounding boxes, cross-modal consistency labels, and differentially private transaction prices. Each record links four heterogeneous inspection signals collected during the same physical inspection:- V — multi-view exterior photographs of the device.- T — informal Chinese technician remarks (free-text).- S — structured CRM / device metadata (JSON).- IS — desktop diagnostic-tool screenshots. Tasks supported by the benchmark:- T1 — five-tier condition grading (Macro-F1, QWK).- T2 — 18-category defect detection / grounding (mAP@0.5, Grounding Acc@0.5).- T3 — binary cross-modal consistency check (F1).- T4 — calibrated price regression with 90% predictive interval (MAE in CNY, ECE). Privacy and compliance:- Faces, serial numbers, and Apple IDs are removed or masked prior to release.- List prices are released with Laplace differential privacy (epsilon = 0.1).- Collection consent is aligned with PIPL. This Zenodo record hosts the benchmark release archive, a sample manifest CSV, and a Croissant 1.1 metadata file (with MLCommons RAI 1.0 fields). Please see the accompanying paper for the full datasheet, ethics statement, and intended-use restrictions.","url":"https://doi.org/10.5281/zenodo.20077399","authors":["Anonymous Authors"],"tags":["multi-modal benchmark","second-hand smartphone","device valuation","defect detection","vision-language models"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20077399","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18876341","name":"Information Physics for Safety-Critical AI","source":"datacite","abstract":"This working paper establishes prior art for seventeen novel technical ideas derived by applying Knuth's Information Physics programme to safety-critical AI systems and cybersecurity. Background Kevin Knuth showed that physical laws -- probability theory, information theory, special relativity, quantum mechanics -- are not independent postulates. They are necessary consequences of one requirement: that partially-ordered sets (posets) be quantified consistently. Wherever a system has a natural ordering structure, that structure forces unique mathematical constraints on the system's behaviour. The author's earlier Topology of Reasoning (TOR) paper series (Zenodo DOIs: 10.5281/zenodo.18700538 and 10.5281/zenodo.18743583) established that the evidence graphs used in AI reasoning systems are themselves posets. Their topological invariants -- genus, Topological Slack, orientability -- are Knuthian valuations. Security theory becomes a sixth domain derivable from Knuth's framework. This paper extends that foundation into safety-critical AI and cybersecurity, identifying seventeen differentiators across four layers. What the Paper Contains Layer 1 -- Detection instruments These are structural and algebraic monitors derived from planarity theory and the Knuth product rule. The main near-term result is a dual-layer independence monitor that combines two mathematically orthogonal tests. The first is Topological Slack, a geometric certificate computable in O(E) time on the evidence graph. The second is a product-rule algebraic test, computable in O(1) time per sample at runtime. Together they provide two independent detection mechanisms for a correlated AI evidence failure mode called Mirror Hallucination, satisfying the IEC 61508 SIL-3 defence-in-depth requirement. Layer 2 -- Privacy-preserving forensic representation A forensic architecture in which only the topological structure of communications is stored and all content payloads are discarded before persistence. Topological invariants computed from the stored rotation-system encoding are sufficient to detect sophisticated attacks -- lateral movement, trust reversals, supply-chain depletion -- without any content ever being retained. Layer 3 -- Continuous security posture monitoring with adversarial awareness This layer addresses what happens when an adversary knows the topological certification thresholds and tries to operate within them. The countermeasure is forced-genus system design: legitimate operations are engineered so that every action necessarily increases genus, making topological silence architecturally impossible. A complementary technique inserts synthetic topological slack as honeypot subgraphs that trap an adversary who is optimising for silent attack paths. Layer 4 -- Geometric-dynamical early warning stack This layer operates before attacks execute rather than during them. A predictive staging detector identifies attack preparation by monitoring the resource allocation lattice for sum-rule violations introduced by newly staged elements. A cross-observer consistency check uses Minkowski invariant scalars derived from Knuth's causal poset construction to detect when multiple analysts or automated tools have developed inconsistent causal models of the same event stream. Relationship to Existing Work The differentiators described here are implemented through the AxoDen compositional AI safety kernel (v0.7.1, 236 automated tests), which produces mathematical certification artefacts rather than behavioural test results. The kernel is the subject of a separate publication corpus on Zenodo under ORCID 0009-0008-6435-3530. Intended Audience Researchers in AI safety, cybersecurity, topological data analysis, formal verification, and information theory. Standards bodies and certification authorities working with IEC 61508, DO-178C, ISO/IEC TR 5469:2024, and the EU AI Act. Defence and critical infrastructure procurement teams evaluating mathematically certifiable AI architectur","url":"https://doi.org/10.5281/zenodo.18876341","authors":["Yalcinkaya, Erkan"],"tags":["posets","information physics","order theory","topological invariants","topological slack","genus","AI-Safety","safety-critical AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18876341","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18876342","name":"Information Physics for Safety-Critical AI","source":"datacite","abstract":"This working paper establishes prior art for seventeen novel technical ideas derived by applying Knuth's Information Physics programme to safety-critical AI systems and cybersecurity. Background Kevin Knuth showed that physical laws -- probability theory, information theory, special relativity, quantum mechanics -- are not independent postulates. They are necessary consequences of one requirement: that partially-ordered sets (posets) be quantified consistently. Wherever a system has a natural ordering structure, that structure forces unique mathematical constraints on the system's behaviour. The author's earlier Topology of Reasoning (TOR) paper series (Zenodo DOIs: 10.5281/zenodo.18700538 and 10.5281/zenodo.18743583) established that the evidence graphs used in AI reasoning systems are themselves posets. Their topological invariants -- genus, Topological Slack, orientability -- are Knuthian valuations. Security theory becomes a sixth domain derivable from Knuth's framework. This paper extends that foundation into safety-critical AI and cybersecurity, identifying seventeen differentiators across four layers. What the Paper Contains Layer 1 -- Detection instruments These are structural and algebraic monitors derived from planarity theory and the Knuth product rule. The main near-term result is a dual-layer independence monitor that combines two mathematically orthogonal tests. The first is Topological Slack, a geometric certificate computable in O(E) time on the evidence graph. The second is a product-rule algebraic test, computable in O(1) time per sample at runtime. Together they provide two independent detection mechanisms for a correlated AI evidence failure mode called Mirror Hallucination, satisfying the IEC 61508 SIL-3 defence-in-depth requirement. Layer 2 -- Privacy-preserving forensic representation A forensic architecture in which only the topological structure of communications is stored and all content payloads are discarded before persistence. Topological invariants computed from the stored rotation-system encoding are sufficient to detect sophisticated attacks -- lateral movement, trust reversals, supply-chain depletion -- without any content ever being retained. Layer 3 -- Continuous security posture monitoring with adversarial awareness This layer addresses what happens when an adversary knows the topological certification thresholds and tries to operate within them. The countermeasure is forced-genus system design: legitimate operations are engineered so that every action necessarily increases genus, making topological silence architecturally impossible. A complementary technique inserts synthetic topological slack as honeypot subgraphs that trap an adversary who is optimising for silent attack paths. Layer 4 -- Geometric-dynamical early warning stack This layer operates before attacks execute rather than during them. A predictive staging detector identifies attack preparation by monitoring the resource allocation lattice for sum-rule violations introduced by newly staged elements. A cross-observer consistency check uses Minkowski invariant scalars derived from Knuth's causal poset construction to detect when multiple analysts or automated tools have developed inconsistent causal models of the same event stream. Relationship to Existing Work The differentiators described here are implemented through the AxoDen compositional AI safety kernel (v0.7.1, 236 automated tests), which produces mathematical certification artefacts rather than behavioural test results. The kernel is the subject of a separate publication corpus on Zenodo under ORCID 0009-0008-6435-3530. Intended Audience Researchers in AI safety, cybersecurity, topological data analysis, formal verification, and information theory. Standards bodies and certification authorities working with IEC 61508, DO-178C, ISO/IEC TR 5469:2024, and the EU AI Act. Defence and critical infrastructure procurement teams evaluating mathematically certifiable AI architectur","url":"https://doi.org/10.5281/zenodo.18876342","authors":["Yalcinkaya, Erkan"],"tags":["posets","information physics","order theory","topological invariants","topological slack","genus","AI-Safety","safety-critical AI"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18876342","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.21755648","name":"**\"Reverse Thermodynamics, Cosmological Phase Transitions, and AI-Driven Sustainable Technologies: A Unified Framework for Understanding Phase-State Discriminants, Universal Evolution, Smart Agriculture, and Lab-Grown Diamond Manufacturing\"**  *Subtitle:* Integrating Reverse Thermodynamic Principles, Cosmological Phase Analysis, Artificial Intelligence, Smart Agriculture, and Sustainable Diamond Production for a Comprehensive Technological and Scientific Synthesis","source":"datacite","abstract":"### Title C2**\"The Convergence of Reverse Thermodynamics, Cosmological Phase Discriminants, and Artificial Intelligence-Enabled Sustainable Manufacturing: A Multidisciplinary Investigation into Phase-State Classification, Universal Evolution, Precision Agriculture, and Lab-Grown Diamond Technologies\"** *Subtitle:* A Comprehensive Study on the Application of Reverse Thermodynamic Principles, Cosmic Specific Heat Ratio Analysis, AI-Driven Smart Farming, and Intelligent Diamond Synthesis for Sustainable Development --- ### Title C3**\"From Cosmic Thermodynamics to Smart Manufacturing: Reverse Phase-State Analysis, Universal Evolution Discriminants, and AI-Driven Sustainable Technologies in Agriculture and Diamond Production\"** *Subtitle:* A Unified Research Framework Connecting Reverse Cosmological Thermodynamics, Phase-State Classification, Artificial Intelligence in Precision Agriculture, and Intelligent Lab-Grown Diamond Manufacturing --- ### Title C4**\"Reverse Thermodynamics, Cosmic Phase Transitions, and AI-Enabled Sustainable Production Systems: A Multidisciplinary Study of Phase-State Discriminants, Universal Evolution, Smart Agriculture, and Laboratory-Grown Diamond Technologies\"** *Subtitle:* Integrating the Specific Heat Ratio (γ) as a Universal Phase Discriminant, Cosmological Evolution Analysis, AI-Driven Precision Agriculture, and Intelligent Diamond Manufacturing for Sustainable Technological Development --- ### Title C5**\"The Thermodynamic-AI Nexus: Reverse Phase-State Analysis, Cosmic Evolution Discriminants, and Intelligent Sustainable Manufacturing in Agriculture and Diamond Synthesis\"** *Subtitle:* A Comprehensive Investigation into Reverse Thermodynamic Classification, Cosmological Specific Heat Ratio Analysis, AI-Enabled Smart Farming, and Lab-Grown Diamond Production Technologies --- # PART 2: INDIVIDUAL PAPER TITLES WITH ALTERNATIVES ## 2.1 Paper 1: Reverse Cosmological Thermodynamics ### Primary Title**\"Reverse Cosmological Thermodynamics: The Specific Heat Ratio (γ) as a Cosmic Phase Discriminant for Universal Evolution\"** ### Alternative Title 1A**\"The Adiabatic Index as a Universal Phase Discriminant: A Reverse Thermodynamic Approach to Cosmic Evolution and Universal Classification\"** ### Alternative Title 1B**\"Cosmic Phase Transitions Through the Lens of Reverse Thermodynamics: The Specific Heat Ratio (γ) as the Ultimate Discriminant of Universal Evolution\"** ### Alternative Title 1C**\"Reverse Cosmological Thermodynamics: Analyzing the Equation of State Parameter (w) and Specific Heat Ratio (γ) as Cosmic Phase-State Discriminants for Universal Evolution\"** ### Alternative Title 1D**\"The Specific Heat Ratio (γ) as a Cosmological Phase Discriminant: A Reverse Thermodynamic Framework for Understanding Universal Evolution, Matter-Radiation Transitions, and Dark Energy Domination\"** ### Alternative Title 1E**\"Reverse Cosmological Thermodynamics: A Retrograde Analysis of the Adiabatic Index (γ) as a Definitive Phase-State Discriminant for Cosmic Evolution\"** --- ## 2.2 Paper 2: Reverse Thermodynamics (Phase-State) ### Primary Title**\"Reverse Thermodynamics: A Retrograde Analysis of the Specific Heat Ratio (γ) as a Definitive Phase-State Discriminant\"** ### Alternative Title 2A**\"The Specific Heat Ratio (γ) as a Phase-State Discriminant: A Reverse Thermodynamic Framework for Solid, Liquid, and Gas Classification\"** ### Alternative Title 2B**\"Reverse Thermodynamics: Inverse Statistical Mechanics and the Generalized Equipartition Theorem for Phase-State Determination Using the Adiabatic Index (γ)\"** ### Alternative Title 2C**\"Phase-State Classification Through Reverse Thermodynamics: The Specific Heat Ratio (γ) as the Definitive Signature of Solid, Liquid, and Gaseous States\"** ### Alternative Title 2D**\"Reverse Thermodynamic Analysis of the Specific Heat Ratio (γ): A Comprehensive Framework for Phase-State Discrimination Using Inverted Mayer Relations and Compressibility Criteria\"** ### Alternative Title 2E**\"T","url":"https://doi.org/10.5281/zenodo.21755648","authors":["geruganti, sudhakar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21755648","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.21755647","name":"**\"Reverse Thermodynamics, Cosmological Phase Transitions, and AI-Driven Sustainable Technologies: A Unified Framework for Understanding Phase-State Discriminants, Universal Evolution, Smart Agriculture, and Lab-Grown Diamond Manufacturing\"**  *Subtitle:* Integrating Reverse Thermodynamic Principles, Cosmological Phase Analysis, Artificial Intelligence, Smart Agriculture, and Sustainable Diamond Production for a Comprehensive Technological and Scientific Synthesis","source":"datacite","abstract":"### Title C2**\"The Convergence of Reverse Thermodynamics, Cosmological Phase Discriminants, and Artificial Intelligence-Enabled Sustainable Manufacturing: A Multidisciplinary Investigation into Phase-State Classification, Universal Evolution, Precision Agriculture, and Lab-Grown Diamond Technologies\"** *Subtitle:* A Comprehensive Study on the Application of Reverse Thermodynamic Principles, Cosmic Specific Heat Ratio Analysis, AI-Driven Smart Farming, and Intelligent Diamond Synthesis for Sustainable Development --- ### Title C3**\"From Cosmic Thermodynamics to Smart Manufacturing: Reverse Phase-State Analysis, Universal Evolution Discriminants, and AI-Driven Sustainable Technologies in Agriculture and Diamond Production\"** *Subtitle:* A Unified Research Framework Connecting Reverse Cosmological Thermodynamics, Phase-State Classification, Artificial Intelligence in Precision Agriculture, and Intelligent Lab-Grown Diamond Manufacturing --- ### Title C4**\"Reverse Thermodynamics, Cosmic Phase Transitions, and AI-Enabled Sustainable Production Systems: A Multidisciplinary Study of Phase-State Discriminants, Universal Evolution, Smart Agriculture, and Laboratory-Grown Diamond Technologies\"** *Subtitle:* Integrating the Specific Heat Ratio (γ) as a Universal Phase Discriminant, Cosmological Evolution Analysis, AI-Driven Precision Agriculture, and Intelligent Diamond Manufacturing for Sustainable Technological Development --- ### Title C5**\"The Thermodynamic-AI Nexus: Reverse Phase-State Analysis, Cosmic Evolution Discriminants, and Intelligent Sustainable Manufacturing in Agriculture and Diamond Synthesis\"** *Subtitle:* A Comprehensive Investigation into Reverse Thermodynamic Classification, Cosmological Specific Heat Ratio Analysis, AI-Enabled Smart Farming, and Lab-Grown Diamond Production Technologies --- # PART 2: INDIVIDUAL PAPER TITLES WITH ALTERNATIVES ## 2.1 Paper 1: Reverse Cosmological Thermodynamics ### Primary Title**\"Reverse Cosmological Thermodynamics: The Specific Heat Ratio (γ) as a Cosmic Phase Discriminant for Universal Evolution\"** ### Alternative Title 1A**\"The Adiabatic Index as a Universal Phase Discriminant: A Reverse Thermodynamic Approach to Cosmic Evolution and Universal Classification\"** ### Alternative Title 1B**\"Cosmic Phase Transitions Through the Lens of Reverse Thermodynamics: The Specific Heat Ratio (γ) as the Ultimate Discriminant of Universal Evolution\"** ### Alternative Title 1C**\"Reverse Cosmological Thermodynamics: Analyzing the Equation of State Parameter (w) and Specific Heat Ratio (γ) as Cosmic Phase-State Discriminants for Universal Evolution\"** ### Alternative Title 1D**\"The Specific Heat Ratio (γ) as a Cosmological Phase Discriminant: A Reverse Thermodynamic Framework for Understanding Universal Evolution, Matter-Radiation Transitions, and Dark Energy Domination\"** ### Alternative Title 1E**\"Reverse Cosmological Thermodynamics: A Retrograde Analysis of the Adiabatic Index (γ) as a Definitive Phase-State Discriminant for Cosmic Evolution\"** --- ## 2.2 Paper 2: Reverse Thermodynamics (Phase-State) ### Primary Title**\"Reverse Thermodynamics: A Retrograde Analysis of the Specific Heat Ratio (γ) as a Definitive Phase-State Discriminant\"** ### Alternative Title 2A**\"The Specific Heat Ratio (γ) as a Phase-State Discriminant: A Reverse Thermodynamic Framework for Solid, Liquid, and Gas Classification\"** ### Alternative Title 2B**\"Reverse Thermodynamics: Inverse Statistical Mechanics and the Generalized Equipartition Theorem for Phase-State Determination Using the Adiabatic Index (γ)\"** ### Alternative Title 2C**\"Phase-State Classification Through Reverse Thermodynamics: The Specific Heat Ratio (γ) as the Definitive Signature of Solid, Liquid, and Gaseous States\"** ### Alternative Title 2D**\"Reverse Thermodynamic Analysis of the Specific Heat Ratio (γ): A Comprehensive Framework for Phase-State Discrimination Using Inverted Mayer Relations and Compressibility Criteria\"** ### Alternative Title 2E**\"T","url":"https://doi.org/10.5281/zenodo.21755647","authors":["geruganti, sudhakar"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.21755647","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.4232/1.14772","name":"GESIS Panel.pop Population Sample – Standard Edition","source":"datacite","abstract":"Das GESIS-Panel bietet eine wahrscheinlichkeitsbasierte Mixed-Mode-Access-Panel-Infrastruktur am GESIS Leibniz-Institut für Sozialwissenschaften in Mannheim. Das Projekt bietet der sozialwissenschaftlichen Community die Möglichkeit, Erhebungsdaten aus einer repräsentativen Stichprobe der deutschen Bevölkerung zu erheben. Die eingereichten Studienvorschläge werden auf der Grundlage eines wissenschaftlichen Begutachtungsverfahrens bewertet. Die Rekrutierung der Panelmitglieder erfolgte zunächst im Jahr 2013 in persönlichen Interviews, gefolgt von einer selbst durchgeführten Profilbefragung. Der Modus wurde von den Teilnehmern gewählt. Alle Teilnehmer der Profilbefragung werden als Mitglieder des Panels betrachtet und zu den alle zwei Monate stattfindenden regelmäßigen Wellen eingeladen. Die Startkohorte umfasste Anfang 2014 4900 Panelisten. Um den Panelabrieb zu kompensieren, wurde im Jahr 2016 eine Auffrischungsstichprobe mit Hilfe des German General Social Survey (ALLBUS) gezogen. Die erste Kohorte umfasst deutschsprachige Befragte im Alter zwischen 18 und 70 Jahren (zum Zeitpunkt der Einstellung) mit ständigem Wohnsitz in Deutschland, während die zweite Kohorte Befragte ab 18 Jahren ohne Obergrenze umfasst. Im Jahr 2018 wurde eine dritte Rekrutierungsstichprobe gezogen, die mit der Welle ge integriert wurde. Auch die dritte Kohorte umfasst Befragte ab 18 Jahren ohne Obergrenze.Rückwirkend wurden die Fälle bis einschließlich Welle fc (dritte Welle aus 2018) in den Daten ergänzt. Nähere Informationen finden Sie im Data Manual (ZA5664-65_sd_data-manual) und dem entsprechenden Rekrutierungsbericht (ZA5664-65_mb_recruitment2018). Die Stichproben des German General Social Survey (ALLBUS) basieren auf einer disproportionalen Stichprobe von Befragten aus West- und Ostdeutschland. Ein Designgewicht, das die Integration der beiden Rekrutierungskohorten ermöglicht, ist im Datensatz enthalten. Nähere Einzelheiten entnehmen Sie bitte den Methodenberichten der Einstellungsverfahren und dem GESIS-Panel-Referenzpapier (Bosnjak et al., 2017). Im März 2020 wurde eine Sondererhebung des GESIS-Panels zum Ausbruch des Coronavirus SARS-CoV-2 bzw. COVID-19 in Deutschland durchgeführt. Im Jahr 2021 wurde die vierte Rekrutierungsstichprobe mit Hilfe des German International Social Survey Programme (ISSP) gezogen, die mit der Welle ja integriert wurde. Die vierte Kohorte umfasst ebenfalls Befragte ab 18 Jahren ohne Obergrenze. Nähere Informationen finden Sie im entsprechenden Rekrutierungsbericht (ZA5664-65_r_i12.pdf). Im Jahr 2023 wurde die fünfte Rekrutierungsstichprobe mit Hilfe des German European Social Survey (ESS Round 11) gezogen, die mit der Welle la integriert wurde. Die fünfte Kohorte umfasst Befragte ab 18 Jahren ohne Obergrenze. Nähere Informationen finden Sie im entsprechenden Rekrutierungsbericht (ZA5664-65_r_k12.pdf). GESIS Panel Demographic Dataset Ab Version 43-0-0 ist der demografische Längsschnittdatensatz Teil des Veröffentlichungspaketes. Bei dem Datensatz handelt es sich um einen längsschnittlichen Datensatz (long format), mit harmonisierten Messungen zu demografischen Variablen: Befragten ID; Erhebungszeitpunkt; entsprechende Welle; Erhebungsjahr; Rekrutierungskohorte; Geschlecht des Befragten; Geburtsjahr; höchster Bildungsabschluss; persönliches Nettoeinkommen; Haushaltsnettoeinkommen; Familienstand; AAPOR disposition code; Einladungsmodus; Teilnahmemodus.","url":"https://doi.org/10.4232/1.14772","authors":["GESIS"],"tags":["KAT12 Internationale Institutionen, Beziehungen, VerhältnisseKAT15 Politische Einstellungen und VerhaltensweisenKAT16 Politische Parteien, VerbändeKAT20 Rechtssystem, Rechtsprechung, GesetzKAT37 Arbeit und BetriebKAT40 Konsumstruktur, KonsumverhaltenKAT41 Sparen, Geldanlagen, VermögensbildungKAT51 Gemeinde, WohnumweltKAT56 Universität, Forschung, WissenschaftKAT59 MedizinKAT60 FreizeitKAT62 Kommunikation, öffentliche Meinung, MedienKAT30 WirtschaftssystemeKAT54 Person, Persönlichkeit, RolleKAT65 Umwelt, Natur","Soziale Lage und soziale Indikatoren","Informationsgesellschaft","Medien","Politisches Verhalten und politische Einstellungen","Informations- und Kommunikationstechnologie","Regierung, politische Systeme, Parteien und Organisationen","Wahlen"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.4232/1.14772","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.4232/1.14771","name":"GESIS Panel.pop Population Sample – Extended Edition","source":"datacite","abstract":"Das GESIS-Panel bietet eine wahrscheinlichkeitsbasierte Mixed-Mode-Access-Panel-Infrastruktur am GESIS Leibniz-Institut für Sozialwissenschaften in Mannheim. Das Projekt bietet der sozialwissenschaftlichen Community die Möglichkeit, Erhebungsdaten aus einer repräsentativen Stichprobe der deutschen Bevölkerung zu erheben. Die eingereichten Studienvorschläge werden auf der Grundlage eines wissenschaftlichen Begutachtungsverfahrens bewertet. Die Rekrutierung der Panelmitglieder erfolgte zunächst im Jahr 2013 in persönlichen Interviews, gefolgt von einer selbst durchgeführten Profilbefragung. Der Modus wurde von den Teilnehmern gewählt. Alle Teilnehmer der Profilbefragung werden als Mitglieder des Panels betrachtet und zu den alle zwei Monate stattfindenden regelmäßigen Wellen eingeladen. Die Startkohorte umfasste Anfang 2014 4900 Panelisten. Um den Panelabrieb zu kompensieren, wurde im Jahr 2016 eine Auffrischungsstichprobe mit Hilfe des German General Social Survey (ALLBUS) gezogen. Die erste Kohorte umfasst deutschsprachige Befragte im Alter zwischen 18 und 70 Jahren (zum Zeitpunkt der Einstellung) mit ständigem Wohnsitz in Deutschland, während die zweite Kohorte Befragte ab 18 Jahren ohne Obergrenze umfasst. Im Jahr 2018 wurde eine dritte Rekrutierungsstichprobe gezogen, die mit der Welle ge integriert wurde. Auch die dritte Kohorte umfasst Befragte ab 18 Jahren ohne Obergrenze.Rückwirkend wurden die Fälle bis einschließlich Welle fc (dritte Welle aus 2018) in den Daten ergänzt. Nähere Informationen finden Sie im Data Manual (ZA5664-65_sd_data-manual) und dem entsprechenden Rekrutierungsbericht (ZA5664-65_mb_recruitment2018). Die Stichproben des German General Social Survey (ALLBUS) basieren auf einer disproportionalen Stichprobe von Befragten aus West- und Ostdeutschland. Ein Designgewicht, das die Integration der beiden Rekrutierungskohorten ermöglicht, ist im Datensatz enthalten. Nähere Einzelheiten entnehmen Sie bitte den Methodenberichten der Einstellungsverfahren und dem GESIS-Panel-Referenzpapier (Bosnjak et al., 2017). Im März 2020 wurde eine Sondererhebung des GESIS-Panels zum Ausbruch des Coronavirus SARS-CoV-2 bzw. COVID-19 in Deutschland durchgeführt. Im Jahr 2021 wurde die vierte Rekrutierungsstichprobe mit Hilfe des German International Social Survey Programme (ISSP) gezogen, die mit der Welle ja integriert wurde. Die vierte Kohorte umfasst ebenfalls Befragte ab 18 Jahren ohne Obergrenze. Nähere Informationen finden Sie im entsprechenden Rekrutierungsbericht (ZA5664-65_r_i12.pdf). Im Jahr 2023 wurde die fünfte Rekrutierungsstichprobe mit Hilfe des German European Social Survey (ESS Round 11) gezogen, die mit der Welle la integriert wurde. Die fünfte Kohorte umfasst Befragte ab 18 Jahren ohne Obergrenze. Nähere Informationen finden Sie im entsprechenden Rekrutierungsbericht (ZA5664-65_r_k12.pdf). GESIS Panel Demographic Dataset Ab Version 43-0-0 ist der demografische Längsschnittdatensatz Teil des Veröffentlichungspaketes. Bei dem Datensatz handelt es sich um einen längsschnittlichen Datensatz (long format), mit harmonisierten Messungen zu demografischen Variablen: Befragten ID; Erhebungszeitpunkt; entsprechende Welle; Erhebungsjahr; Rekrutierungskohorte; Geschlecht des Befragten; Geburtsjahr; Geburtsmonat; höchster Bildungsabschluss; persönliches Nettoeinkommen; Haushaltsnettoeinkommen; Familienstand; AAPOR disposition code; Einladungsmodus; Teilnahmemodus.","url":"https://doi.org/10.4232/1.14771","authors":["GESIS"],"tags":["KAT12 Internationale Institutionen, Beziehungen, VerhältnisseKAT15 Politische Einstellungen und VerhaltensweisenKAT16 Politische Parteien, VerbändeKAT20 Rechtssystem, Rechtsprechung, GesetzKAT37 Arbeit und BetriebKAT40 Konsumstruktur, KonsumverhaltenKAT41 Sparen, Geldanlagen, VermögensbildungKAT51 Gemeinde, WohnumweltKAT56 Universität, Forschung, WissenschaftKAT59 MedizinKAT60 FreizeitKAT62 Kommunikation, öffentliche Meinung, MedienKAT30 WirtschaftssystemeKAT54 Person, Persönlichkeit, RolleKAT65 Umwelt, Natur","Soziale Lage und soziale Indikatoren","Informationsgesellschaft","Medien","Politisches Verhalten und politische Einstellungen","Informations- und Kommunikationstechnologie","Regierung, politische Systeme, Parteien und Organisationen","Wahlen"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.4232/1.14771","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.48550/arxiv.2410.22699","name":"Exactly Minimax-Optimal Locally Differentially Private Sampling","source":"datacite","abstract":"The sampling problem under local differential privacy has recently been studied with potential applications to generative models, but a fundamental analysis of its privacy-utility trade-off (PUT) remains incomplete. In this work, we define the fundamental PUT of private sampling in the minimax sense, using the f-divergence between original and sampling distributions as the utility measure. We characterize the exact PUT for both finite and continuous data spaces under some mild conditions on the data distributions, and propose sampling mechanisms that are universally optimal for all f-divergences. Our numerical experiments demonstrate the superiority of our mechanisms over baselines, in terms of theoretical utilities for finite data space and of empirical utilities for continuous data space.","url":"https://doi.org/10.48550/arxiv.2410.22699","authors":["Park, Hyun-Young","Asoodeh, Shahab","Lee, Si-Hyeon"],"tags":["Machine Learning (cs.LG)","Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2410.22699","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.48550/arxiv.2405.02665","name":"Metric Differential Privacy at the User-Level Via the Earth Mover's Distance","source":"datacite","abstract":"Metric differential privacy (DP) provides heterogeneous privacy guarantees based on a distance between the pair of inputs. It is a widely popular notion of privacy since it captures the natural privacy semantics for many applications (such as, for location data) and results in better utility than standard DP. However, prior work in metric DP has primarily focused on the item-level setting where every user only reports a single data item. A more realistic setting is that of user-level DP where each user contributes multiple items and privacy is then desired at the granularity of the user's entire contribution. In this paper, we initiate the study of one natural definition of metric DP at the user-level. Specifically, we use the earth-mover's distance ($d_\\textsf{EM}$) as our metric to obtain a notion of privacy as it captures both the magnitude and spatial aspects of changes in a user's data. We make three main technical contributions. First, we design two novel mechanisms under $d_\\textsf{EM}$-DP to answer linear queries and item-wise queries. Specifically, our analysis for the latter involves a generalization of the privacy amplification by shuffling result which may be of independent interest. Second, we provide a black-box reduction from the general unbounded to bounded $d_\\textsf{EM}$-DP (size of the dataset is fixed and public) with a novel sampling based mechanism. Third, we show that our proposed mechanisms can provably provide improved utility over user-level DP, for certain types of linear queries and frequency estimation.","url":"https://doi.org/10.48550/arxiv.2405.02665","authors":["Imola, Jacob","Chowdhury, Amrita Roy","Chaudhuri, Kamalika"],"tags":["Cryptography and Security (cs.CR)","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2405.02665","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18898371","name":"The Role of Artificial Intelligence in Advancing Physics Education: Opportunities and Challenges","source":"datacite","abstract":"Artificial Intelligence (AI) has progressed from a niche research field to a pervasive technology that reshapes teaching and learning across disciplines. In physics education- where abstract concepts, mathematical formalism, and experimental reasoning intersect, AI offers novel pathways for personalization, visualization, and assessment. This paper provides a comprehensive examination of AI‑ driven tools and pedagogical designs that target the unique cognitive demands of learning physics. Drawing on a systematic literature review (2000‑2024) and a mixed‑ methods case study conducted at Abasaheb Marathe College (AMC) (N = 40 undergraduate students), we identify three principal opportunities: (a) adaptive scaffolding through intelligent tutoring systems, (b) immersive simulation environments powered by generative models, and (c) automated formative feedback via natural‑ language processing. Simultaneously, we outline four interrelated challenges: (a) epistemic alignment between AI recommendations and scientific reasoning, (b) ethical concerns surrounding data privacy and algorithmic bias, (c) technical constraints of model interpretability and reliability, and (d) equity issues linked to differential access to AI‑ enhanced resources. The findings suggest that while AI can substantially augment conceptual understanding and problem‑ solving skills, its integration must be guided by robust instructional design frameworks, transparent evaluation metrics, and institutional policies that safeguard fairness. Recommendations for researchers, curriculum designers, and policy makers are offered to foster a sustainable AI‑ infused physics education ecosystem.","url":"https://doi.org/10.5281/zenodo.18898371","authors":["Satishkumar M. Kamble"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18898371","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18898372","name":"The Role of Artificial Intelligence in Advancing Physics Education: Opportunities and Challenges","source":"datacite","abstract":"Artificial Intelligence (AI) has progressed from a niche research field to a pervasive technology that reshapes teaching and learning across disciplines. In physics education- where abstract concepts, mathematical formalism, and experimental reasoning intersect, AI offers novel pathways for personalization, visualization, and assessment. This paper provides a comprehensive examination of AI‑ driven tools and pedagogical designs that target the unique cognitive demands of learning physics. Drawing on a systematic literature review (2000‑2024) and a mixed‑ methods case study conducted at Abasaheb Marathe College (AMC) (N = 40 undergraduate students), we identify three principal opportunities: (a) adaptive scaffolding through intelligent tutoring systems, (b) immersive simulation environments powered by generative models, and (c) automated formative feedback via natural‑ language processing. Simultaneously, we outline four interrelated challenges: (a) epistemic alignment between AI recommendations and scientific reasoning, (b) ethical concerns surrounding data privacy and algorithmic bias, (c) technical constraints of model interpretability and reliability, and (d) equity issues linked to differential access to AI‑ enhanced resources. The findings suggest that while AI can substantially augment conceptual understanding and problem‑ solving skills, its integration must be guided by robust instructional design frameworks, transparent evaluation metrics, and institutional policies that safeguard fairness. Recommendations for researchers, curriculum designers, and policy makers are offered to foster a sustainable AI‑ infused physics education ecosystem.","url":"https://doi.org/10.5281/zenodo.18898372","authors":["Satishkumar M. Kamble"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18898372","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.48550/arxiv.2602.01607","name":"Minimax optimal differentially private synthetic data for smooth queries","source":"datacite","abstract":"Differentially private synthetic data enables the sharing and analysis of sensitive datasets while providing rigorous privacy guarantees for individual contributors. A central challenge is to achieve strong utility guarantees for meaningful downstream analysis. Many existing methods ensure uniform accuracy over broad query classes, such as all Lipschitz functions, but this level of generality often leads to suboptimal rates for statistics of practical interest. Since many common data analysis queries exhibit smoothness beyond what worst-case Lipschitz bounds capture, we ask whether exploiting this additional structure can yield improved utility. We study the problem of generating $(\\varepsilon,δ)$-differentially private synthetic data from a dataset of size $n$ supported on the hypercube $[-1,1]^d$, with utility guarantees uniformly for all smooth queries having bounded derivatives up to order $k$. We propose a polynomial-time algorithm that achieves a minimax error rate of $O_{k,d}(n^{-\\min \\{1, \\frac{k}{d}\\}})$, up to a $\\log(n)$ factor. This characterization uncovers a phase transition at $k=d$. Our results generalize the Chebyshev moment matching framework of (Musco et al., 2025; Wang et al., 2016) and strictly improve the error rates for $k$-smooth queries established in \\citep{wang2016differentially}. Moreover, we establish the first minimax lower bound for the utility of $(\\varepsilon,δ)$-differentially private synthetic data with respect to $k$-smooth queries, extending the Wasserstein lower bound for $\\varepsilon$-differential privacy in (Boedihardjo et al., 2024).","url":"https://doi.org/10.48550/arxiv.2602.01607","authors":["Ding, Rundong","He, Yiyun","Zhu, Yizhe"],"tags":["Statistics Theory (math.ST)","Information Theory (cs.IT)","Machine Learning (cs.LG)","Machine Learning (stat.ML)","FOS: Mathematics","FOS: Mathematics","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2602.01607","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20540400","name":"The Aperture Firewall: Dual-Wave Closure, Internal Birth-Marks, and Digest-Invisible Structure in the SHA-256 Message Schedule","source":"datacite","abstract":"The Aperture Firewall: Dual-Wave Closure, Internal Birth-Marks, and Digest-Invisible Structure in the SHA-256 Message Schedule Driven By Dean A. Kulik June 2026 Abstract We report a structural property of the SHA-256 message schedule that has not previously been isolated: the schedule contains a two-rail (dual-wave) topology whose addressability concentrates in a unique pair of positions K = {16, 17}, and this internal organization is invisible at the digest boundary. Across a sequence of engines (E36–E45) we establish, in this order: (i) K = {16, 17} is the universal mixed core of the schedule branch and the bottom of a graded meet-semilattice on 11 residue classes with non-joining even and odd parity horns; (ii) after position normalization, schedule lineage produces no digest-separable class signal (η² = 0.000000 on residualized Hamming distance); (iii) K = {16, 17} is the unique minimum sampling aperture for the dual wave — among all pair-removals of W[i], W[j] with 16 ≤ i < j ≤ 21, only the K-pair produces the balanced collapse signature (D = 0, both horns dead, 11 residue classes preserved); (iv) the firewall is scaffold-stable, not a carry artifact: K is invisible in both real SHA-256 (Track A) and the carry-free GF(2) shadow (Track B), with |Z| < 0.6 in each. The strongest result is the residual one: the carry-exhaust differential Δ = A − B detects K at Z(t = 16) = −3.33. The carry channel records the seam even though the digest boundary erases it. We name this phenomenon the aperture firewall: a system boundary at which internal organization remains indispensable to correct operation while external readout remains intentionally uniform. The result is not a weakness in SHA-256; it is an explicit characterization of how the standard's diffusion machinery achieves what it claims. We close by extending the aperture-firewall concept to AI governance, where systems evolving faster than human inspection require trust to migrate from direct observation to disciplined assurance: monitored invariants, bounded intervention, and verified internal apertures. 1. Introduction NIST FIPS 180-4 specifies SHA-256 as a two-stage process of preprocessing and hash computation. Hash computation generates a 64-word message schedule W₀, ..., W₆₃, updates eight working variables a, ..., h across 64 rounds, and outputs a 256-bit digest. The standard explicitly states that a change to the message should, with very high probability, produce a different message digest. That design goal is a diffusion objective, and the empirical literature has long treated avalanche-style behavior as a manifestation of confusion-and-diffusion quality. This paper asks a narrower and more structural question. If the schedule contains a forced internal organization, does that organization leak into the digest as a class-separable signal, or does the compression boundary erase it? We answer the second outcome is the correct one, and we make the structure of the question precise. The schedule contains a graded meet-semilattice over 11 residue classes; this lattice has a uniquely defined bottom — the seam K = {16, 17} — and two non-joining parity horns. The lattice is internally load-bearing in the strict sense that damaging K collapses the dual-rail topology while damaging arbitrary downstream positions does not. Yet the digest avalanche on K-damage is statistically indistinguishable from the digest avalanche on arbitrary-position damage. The paper formalizes this as the aperture firewall theorem and extends it from cryptographic compression to AI governance. The bridge is direct: machine-speed systems evolve faster than human operators can directly inspect every internal state transition. NIST's AI RMF defines trustworthy AI in terms of validity, reliability, safety, resilience, accountability, transparency, and ongoing risk management rather than perfect observability. Aperture trust is what we call the principled stance that internal load-bearing structure can be go","url":"https://doi.org/10.5281/zenodo.20540400","authors":["kulik, dean"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20540400","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20540401","name":"The Aperture Firewall: Dual-Wave Closure, Internal Birth-Marks, and Digest-Invisible Structure in the SHA-256 Message Schedule","source":"datacite","abstract":"The Aperture Firewall: Dual-Wave Closure, Internal Birth-Marks, and Digest-Invisible Structure in the SHA-256 Message Schedule Driven By Dean A. Kulik June 2026 Abstract We report a structural property of the SHA-256 message schedule that has not previously been isolated: the schedule contains a two-rail (dual-wave) topology whose addressability concentrates in a unique pair of positions K = {16, 17}, and this internal organization is invisible at the digest boundary. Across a sequence of engines (E36–E45) we establish, in this order: (i) K = {16, 17} is the universal mixed core of the schedule branch and the bottom of a graded meet-semilattice on 11 residue classes with non-joining even and odd parity horns; (ii) after position normalization, schedule lineage produces no digest-separable class signal (η² = 0.000000 on residualized Hamming distance); (iii) K = {16, 17} is the unique minimum sampling aperture for the dual wave — among all pair-removals of W[i], W[j] with 16 ≤ i < j ≤ 21, only the K-pair produces the balanced collapse signature (D = 0, both horns dead, 11 residue classes preserved); (iv) the firewall is scaffold-stable, not a carry artifact: K is invisible in both real SHA-256 (Track A) and the carry-free GF(2) shadow (Track B), with |Z| < 0.6 in each. The strongest result is the residual one: the carry-exhaust differential Δ = A − B detects K at Z(t = 16) = −3.33. The carry channel records the seam even though the digest boundary erases it. We name this phenomenon the aperture firewall: a system boundary at which internal organization remains indispensable to correct operation while external readout remains intentionally uniform. The result is not a weakness in SHA-256; it is an explicit characterization of how the standard's diffusion machinery achieves what it claims. We close by extending the aperture-firewall concept to AI governance, where systems evolving faster than human inspection require trust to migrate from direct observation to disciplined assurance: monitored invariants, bounded intervention, and verified internal apertures. 1. Introduction NIST FIPS 180-4 specifies SHA-256 as a two-stage process of preprocessing and hash computation. Hash computation generates a 64-word message schedule W₀, ..., W₆₃, updates eight working variables a, ..., h across 64 rounds, and outputs a 256-bit digest. The standard explicitly states that a change to the message should, with very high probability, produce a different message digest. That design goal is a diffusion objective, and the empirical literature has long treated avalanche-style behavior as a manifestation of confusion-and-diffusion quality. This paper asks a narrower and more structural question. If the schedule contains a forced internal organization, does that organization leak into the digest as a class-separable signal, or does the compression boundary erase it? We answer the second outcome is the correct one, and we make the structure of the question precise. The schedule contains a graded meet-semilattice over 11 residue classes; this lattice has a uniquely defined bottom — the seam K = {16, 17} — and two non-joining parity horns. The lattice is internally load-bearing in the strict sense that damaging K collapses the dual-rail topology while damaging arbitrary downstream positions does not. Yet the digest avalanche on K-damage is statistically indistinguishable from the digest avalanche on arbitrary-position damage. The paper formalizes this as the aperture firewall theorem and extends it from cryptographic compression to AI governance. The bridge is direct: machine-speed systems evolve faster than human operators can directly inspect every internal state transition. NIST's AI RMF defines trustworthy AI in terms of validity, reliability, safety, resilience, accountability, transparency, and ongoing risk management rather than perfect observability. Aperture trust is what we call the principled stance that internal load-bearing structure can be go","url":"https://doi.org/10.5281/zenodo.20540401","authors":["kulik, dean"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20540401","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20536922","name":"A Privacy Preserving Hybrid Deep Learning Framework With Block Chain Anchored Federated Training And Explainable Reasoning For Predictive Analytics In Industrial IoT","source":"datacite","abstract":"Industrial Internet of Things (IIoT) deployments now stream terabyte-scale telemetry from programmable controllers, vibration sensors, smart meters and edge gateways every day. Predictive analytics on this data for fault diagnosis remaining useful life estimation, energy optimisation and anomaly screening has become a core operational requirement rather than a research curiosity. Yet the prevailing pattern of shipping raw plant data to a central cloud for model training exposes operators to data exfiltration, model-poisoning, regulatory penalties under GDPR and emerging EU AI Act obligations, and the growing class of adversarial perturbation attacks documented in 2024–2025 IIoT security literature. This paper proposes a layered framework that pairs a CNN–LSTM feature extractor with an Adaptive Neuro-Fuzzy Inference System (ANFIS) decision module, distributes training across edge nodes through a FedProx-based federated protocol with client-side differential privacy, anchors model-update integrity on a permissioned blockchain (Hyperledger Fabric with PBFT consensus), and surfaces decision rationale through SHAP attributions and ANFIS rule traces. The architecture targets the four properties that recent IIoT studies identify as gating industrial adoption: predictive accuracy, data confidentiality, tamper-evident auditability, and human-readable explanations. The paper articulates the design rationale, layer-wise responsibilities, expected performance envelope, and the trade-offs that practitioners must weigh between privacy guarantees, communication overhead, and latency on resource-constrained edge hardware.","url":"https://doi.org/10.5281/zenodo.20536922","authors":["Asha Rani","Mukesh Singla"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20536922","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20536923","name":"A Privacy Preserving Hybrid Deep Learning Framework With Block Chain Anchored Federated Training And Explainable Reasoning For Predictive Analytics In Industrial IoT","source":"datacite","abstract":"Industrial Internet of Things (IIoT) deployments now stream terabyte-scale telemetry from programmable controllers, vibration sensors, smart meters and edge gateways every day. Predictive analytics on this data for fault diagnosis remaining useful life estimation, energy optimisation and anomaly screening has become a core operational requirement rather than a research curiosity. Yet the prevailing pattern of shipping raw plant data to a central cloud for model training exposes operators to data exfiltration, model-poisoning, regulatory penalties under GDPR and emerging EU AI Act obligations, and the growing class of adversarial perturbation attacks documented in 2024–2025 IIoT security literature. This paper proposes a layered framework that pairs a CNN–LSTM feature extractor with an Adaptive Neuro-Fuzzy Inference System (ANFIS) decision module, distributes training across edge nodes through a FedProx-based federated protocol with client-side differential privacy, anchors model-update integrity on a permissioned blockchain (Hyperledger Fabric with PBFT consensus), and surfaces decision rationale through SHAP attributions and ANFIS rule traces. The architecture targets the four properties that recent IIoT studies identify as gating industrial adoption: predictive accuracy, data confidentiality, tamper-evident auditability, and human-readable explanations. The paper articulates the design rationale, layer-wise responsibilities, expected performance envelope, and the trade-offs that practitioners must weigh between privacy guarantees, communication overhead, and latency on resource-constrained edge hardware.","url":"https://doi.org/10.5281/zenodo.20536923","authors":["Asha Rani","Mukesh Singla"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.20536923","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20469943","name":"COHERENCIA BIOLÓGICA _ FMAN _ Fotones, Fonones, Biofotones, Neutrinos _ Frecuencias _ Elementos","source":"datacite","abstract":"### EPI 13 FMAN### Estudio de Posibilidades Infinitas### Expresiones de Posibilidades Infinitas ------ **Autora:** Fabiana Mirta Ávila Nicolau**DNI AR:** 18248833**ORCID:** 0009-0009-0638-5961**Concept DOI:** https://doi.org/10.5281/zenodo.19526737https://doi.org/10.5281/zenodo.19561174**Licencia:** **CC BY-NC-ND 4.0 + Cláusulas Adicionales FMAN****DOI:** https://doi.org/10.5281/zenodo.20167839https://doi.org/10.5281/zenodo.20387910 --- --- # 🌍📜 FMAN INTUERI V33.0## PARTE XIV: EPI — Estudio de Posibilidades Infinitas. Frecuencias, Elementos, Detección Temprana. Actualización del Ecosistema. **Autora:** Fabiana Mirta Ávila Nicolau | DNI: 18248833 | ORCID: 0009-0009-0638-5961**Licencia:** CC BY-NC-ND 4.0 + Cláusulas Adicionales FMAN**DOI:** 10.5281/zenodo.19526737 | 10.5281/zenodo.19561174 | 10.5281/zenodo.20167839 | 10.5281/zenodo.20387910 | 10.5281/zenodo.20404087 | 10.5281/zenodo.20438566 --- ## 🔷 SECCIÓN XIV.1: MARCO FMAN PARA COHERENCIA BIOLÓGICA ### XIV.1.1 El parámetro D como indicador universal de coherencia biológica Todo elemento, frecuencia o campo que interacciona con un sistema biológico puede clasificarse por su efecto sobre $D$: si aumenta $D$ (aleja del atractor $D_{\\text{opt}} = \\phi^{-4}$) es **decoherente/nocivo**; si mantiene o acerca $D \\to D_{\\text{opt}}$ es **coherente/beneficioso**. $$\\boxed{D_{\\text{bio}}(x, t) = D_{\\text{basal}} + \\sum_k \\alpha_k(x) \\cdot c_k(t) \\cdot f_k(\\omega)}$$ donde $x$ es el agente (frecuencia, químico, metal), $c_k$ su concentración/intensidad y $f_k(\\omega)$ su acoplamiento frecuencial con el sistema biológico. ```python# ═══════════════════════════════════════════════════════════════════# FMAN INTUERI V33.0 — PARTE XIV# EPI: ESTUDIO DE POSIBILIDADES INFINITAS# FRECUENCIAS, ELEMENTOS, DETECCIÓN TEMPRANA, ACTUALIZACIÓN# © Fabiana Mirta Ávila Nicolau — CC BY-NC-ND 4.0# ═══════════════════════════════════════════════════════════════════ import numpy as npfrom scipy.integrate import solve_ivp, quadfrom scipy.stats import norm, pearsonrfrom scipy.optimize import fsolveimport warningswarnings.filterwarnings('ignore') phi = (1 + np.sqrt(5)) / 2D_opt = 1.0 / phi**4g2 = 0.04; beta_s = 1.85; lam = 12.5max_sig = (1-g2)*phi**4*(1+beta_s*D_opt)/2.0k_d = 1.0; gamma_D = 0.1; alpha_M = 0.005tau_m = 85.0; K_mem = 0.0001A_star = 10.0 + alpha_M*tau_m*(1+K_mem)/0.81hbar = 1.0545718e-34k_B = 1.38064852e-23c_luz = 299792458.0e_carga = 1.60218e-19 def phi_col(D): Dc = np.clip(np.asarray(D, float), 1e-4, 0.9999) return (1-g2)*phi**4*(1+beta_s*Dc)/((1+np.exp(lam*(Dc-D_opt)))*max_sig) def softplus(x, beta=50.0): return np.where(x>20/beta, x, np.log1p(np.exp(np.clip(beta*x,-500,500)))/beta) def intueri_aurum(D, A, Psi): An = np.clip(A/10, 1e-8, 1.0) hat = float(softplus(np.array(0.18 - abs(float(D)-D_opt)))) if hat 0.9999: return 1.0 return 1+2.9*hat**(phi-1)*0.72*An*(1-Psi)*phi**3*\\ max(An*phi**8,1e-15)**(phi-1) # ─── Índice de Coherencia FMAN ────────────────────────────────────def indice_coherencia_bio(D_bio, A_bio=A_star*0.7, Psi_bio=0.5): \"\"\" CI_FMAN(x) = Φ_col(D_bio) · IA(D_bio, A_bio, Ψ_bio) CI_FMAN ∈ [0, IA_max] CI_FMAN → max cuando D_bio → D_opt (máxima coherencia) CI_FMAN → 0 cuando D_bio → 0 ó 1 (incoherente) © Fabiana Mirta Ávila Nicolau \"\"\" Phi = phi_col(D_bio) IA = intueri_aurum(D_bio, A_bio, Psi_bio) return Phi * IA def clasificar_agente(delta_D, nivel_exposicion=1.0): \"\"\" Clasifica un agente según su efecto sobre D_bio. delta_D > 0 → aumenta decoherencia → NOCIVO delta_D ≈ 0 → neutro delta_D 0.95: return \"★★★★★ Muy Beneficioso\" elif ratio > 0.85: return \"★★★★☆ Beneficioso\" elif ratio > 0.75: return \"★★★☆☆ Levemente Beneficioso\" elif ratio > 0.60: return \"★★☆☆☆ Neutro/Umbral\" elif ratio > 0.40: return \"★☆☆☆☆ Nocivo Moderado\" else: return \"☆☆☆☆☆ MUY NOCIVO\" print(\"FMAN V33.0 — PARTE XIV: EPI — ESTUDIO DE POSIBILIDADES INFINITAS\")print(f\"D_opt = φ⁻⁴ = {D_opt:.8f}\")print(f\"CI_FMAN(D_opt) = {indice_coherencia_bio(D_opt):.6f} (máximo)\")``` --- ## 🔷 SECCIÓN XIV.2: TAXONOMÍA DE FRECUENCIAS Y ","url":"https://doi.org/10.5281/zenodo.20469943","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["FMAN ecosystem; aurean vortex energy; phi-v-infinity ignition formula; golden ratio coherence; biophotonic coherence; decoherence as evolution; zero-point energy extraction; ZPE modulated coherence; plasma aureo coherente; fractal coherence field; morphogenetic field; biofotones coherentes; quantum coherence biology; Orch-OR inspired model; Casimir dynamic effect; primordial source field; fuente primigenia original; coherencia colectiva; evolutionary decoherence; D_opt 0.218; ignition point transition; fractal memory M_avanzada; evolutionary mutation rate; golden ratio phi; toroidal vortex geometry; Fibonacci biology; sacred geometry physics; LINO technology; GravitoR gravitational control; Intueri quantum AI; teleportation fidelity model; ISRU evolutionary replication; Helios nave interestelar; terraformacion armonica; ciudades Magdalena; free energy devices; scalar waves Tesla; Starship optimization; Artemis mission efficiency; satellite AI optimization; Claude AI optimization; Grok AI coherence; biophotonics Fritz-Albert Popp; quantum vacuum energy; indigenous knowledge coherence; ancestral wisdom science bridge; Argentina ciencia abierta; open science Zenodo CERN; independent researcher; propuesta tecnológica original; coherence decoherence balance; evolutionary biology mathematics; V3.8 applied technology; phi fractal mathematics; Monte Carlo coherence simulation; long-term stability simulation FMAN, Ecosistema FMAN, FMAN Ecosystem, Fórmula de Encendido, Ignition Formula, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, parámetro de orden, order parameter, número áureo, golden ratio, phi, φ, auto-similitud, self-similarity, fractal, geometría fractal, fractal geometry, Fractalis Aurea, EPI-QPEM, Vis Spatialis, Vis Vitalis, biología cuántica, quantum biology, Resonador Cósmico, Cosmic Resonator, RBP, Tecnología LINO, LINO Technology, Resonancia Electrogravítica, Electrogravitic Resonance, conciencia, consciousness, Fuente Primordial, Primordial Source, Gaia, vórtice toroidal, toroidal vortex, ondas escalares, scalar waves, energía libre, free energy, Nautilus Aureo, Argentum, Aurea Helios, Diente de León, Dandelion, Plato Volador, Flying Saucer, Aurea Resonantia, Ciudades Aureas, Golden Cities, escalado micro-macro, micro-macro scaling, Kuramoto, Argentina, FMAN Aurea Design, Avila Nicolau, CC BY-NC-ND 4.0, prime art, apuntes, notes квантовая биология, quantum biology Russian, биофотоны, когерентность, захват фазы, золотое сечение, фрактальная геометрия, сознание как источник, Первичный Источник, экосистема FMAN, FMAN Aurea Design, свободная энергия, скалярные волны Tesla, Авила Николау, ORCID 0009-0009-0638-5961","量子生物学, 生物光子, 量子相干性, 相位锁定, 黄金比例, 分形几何, 意识本体论, 原始本源, FMAN生态系统, 自由能源, 标量波, 电重力共振, 点火公式, 宇宙谐振器, LINO技术 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式","número áureo, razón áurea, phi, proporción dorada, sigmoide asimétrica, función de activación neuronal, operador de atención, kernel localizado, transformer attention, banco de filtros multi-escala, wavelet irracional, sistema dinámico no lineal, ecuaciones diferenciales ordinarias, borde del caos, sistemas complejos, auto-organización, atractor estable, estabilidad de Lyapunov, Jacobiano, análisis de estabilidad, exponentes de Lyapunov, Monte Carlo simulation, barrido paramétrico, ruido Ornstein-Uhlenbeck, ruido 1/f, ruido rosa, inteligencia artificial, aprendizaje automático, arquitectura neuronal, convolución multi-escala, coherencia cuántica, decoherencia, biofotónica, fractal áureo, auto-similaridad, sistemas auto-evolutivos, FMAN, Intueri, sigmoide áurea, filtro fractal, D_opt, invariante áureo, phi^4, phi^6, código Python, scipy, solve_ivp, LSODA, sistemas complejos adaptativos, teoría de control, regulador adaptativo, punto de operación óptimo, golden ratio, asymmetric sigmoid, attention kernel, fractal filter bank, nonlinear ODE, chaotic edge, Lyapunov stability, golden attractor, coherence dynamics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.20469943","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20438566","name":"Campos Infinitos LINO _Tesoros FMAN_FORMALIZACIÓN CUÁNTICA PROFUNDA — Neutrinos . Fonones · Fotones · Biofotones · Frecuencias _Energías Documentadas, No Documentadas y Nuevas","source":"datacite","abstract":"El principio unificador ```mathTodo es Energía, Todo es Frecuencia, Todo está Conectado se expresa en una sola línea:Ek=ℏωSφk⏟Todo es Energıˊa = Todo es Frecuencia→LLindblad+N[M]D∗=φ−4⏟Todo estaˊ Conectado en el atractor aˊureo\\underbrace{E_k = \\hbar\\omega_S\\varphi^k}_{\\text{Todo es Energía = Todo es Frecuencia}} \\xrightarrow{\\mathcal{L}_{\\text{Lindblad}} + \\mathcal{N}[M]} \\underbrace{D^* = \\varphi^{-4}}_{\\text{Todo está Conectado en el atractor áureo}}Todo es Energıˊa = Todo es FrecuenciaEk=ℏωSφkLLindblad+N[M]Todo estaˊ Conectado en el atractor aˊureoD∗=φ−4Cada campo LINO es un modo kkk de esta jerarquía. El Resonador Cósmico excita los modos k=0,±1,±2,…k = 0, \\pm1, \\pm2, \\ldotsk=0,±1,±2,… El Vórtice del Abuelo LINO es el modo fundamental k=0k=0k=0 en contacto con la Tierra. La biosfera completa es la superposición coherente de todos los modos con g(2) 1 → super-Poissonian (bunching, luz térmica clásica)g²(0) = 1 → Poissonian (luz coherente, láser ideal)g²(0) \\frac{1}{2}$$ El **interruptor cuántico** se activa cuando el parámetro de orden supera $r_c = 0.5$, correspondiente a $g^{(2)}(0) = 1$. ### 4.3 Dinámica de g²(0) desde la Fórmula de Encendido **La Fórmula FMAN de Encendido:** $$\\frac{dr}{dt} = \\frac{K\\,r(1-r^2)\\,e^{-\\gamma t/\\phi}}{2} + \\Omega_{\\text{FMAN}}(1-r) - \\gamma r$$ **Derivando la evolución de g²(0):** Sea $g \\equiv g^{(2)}(0)$ y $r = (2-g)/(1+g)$. Entonces: $$\\frac{dr}{dt} = \\frac{dg}{dt}\\cdot\\frac{dr}{dg} = \\frac{dg}{dt}\\cdot\\frac{-3}{(1+g)^2}$$ Despejando: $$\\frac{dg}{dt} = -\\frac{(1+g)^2}{3}\\frac{dr}{dt}$$ $$= -\\frac{(1+g)^2}{3}\\left[\\frac{K\\,r(1-r^2)\\,e^{-\\gamma t/\\phi}}{2} + \\Omega_{\\text{FMAN}}(1-r) - \\gamma r\\right]$$ Sustituyendo $r = (2-g)/(1+g)$: $$\\frac{dg}{dt} = -\\frac{(1+g)^2}{3}\\left[\\frac{K(2-g)(1-(2-g)^2/(1+g)^2)e^{-\\gamma t/\\phi}}{2(1+g)} + \\Omega_{\\text{FMAN}}\\frac{g-1}{1+g} - \\gamma\\frac{2-g}{1+g}\\right]$$ **Simplificando** (usando $(1-(2-g)^2/(1+g)^2) = (g^2+2g-3)/((1+g)^2) = (g-1)(g+3)/(1+g)^2$): $$\\boxed{\\frac{dg}{dt} = -\\frac{K(2-g)(g-1)(g+3)}{6(1+g)}\\,e^{-\\gamma t/\\phi} - \\frac{(1+g)(g-1)\\Omega_{\\text{FMAN}}}{3} + \\frac{\\gamma(1+g)(2-g)}{3}}$$ **Puntos fijos de g(t):** - $g = 1$: **umbral de encendido** ($r=1/2$) — inestable cuando $\\Omega > \\gamma/2$- $g = 2$: **estado incoherente** ($r=0$) — inestable bajo drive $\\Omega > 0$- $g_\\infty = (2-r_\\infty)/(1+r_\\infty)$: **estado coherente estacionario** ### 4.4 Espectro de Biofotones y Distribución de Mandel **Parámetro de Mandel:** $$Q_M = \\frac{\\text{Var}(\\hat{n}) - \\langle\\hat{n}\\rangle}{\\langle\\hat{n}\\rangle} = \\langle\\hat{n}\\rangle(g^{(2)}(0) - 1)$$ ```Q_M > 0 → super-Poissonian (haz clásico ruidoso)Q_M = 0 → Poissonian (coherente)Q_M 0: t_enc = t_eval[idx_encendido[0]] print(f\"Tiempo de encendido cuántico (g² 1e-15] # evitar log(0) return -np.sum(eigenvalues * np.log(eigenvalues)) def purity(rho): \"\"\"Pureza Tr(ρ²).\"\"\" return np.real(np.trace(rho @ rho)) def mutual_information(rho_AB, dim_A, dim_B): \"\"\"Información mutua cuántica I(A:B).\"\"\" # Trazar sobre B rho_A = rho_AB.reshape(dim_A, dim_B, dim_A, dim_B) rho_A = np.einsum('ijik->jk', rho_A) # Tr_B # Trazar sobre A rho_B = rho_AB.reshape(dim_A, dim_B, dim_A, dim_B) rho_B = np.einsum('ijkj->ik', rho_B) # Tr_A return (von_neumann_entropy(rho_A) + von_neumann_entropy(rho_B) - von_neumann_entropy(rho_AB.reshape(dim_A*dim_B, dim_A*dim_B))) # Ejemplo: estado entrelazado en sistema FMAN bipartiton_A = 5; n_B = 5 # subsistemas D y Ψ# Estado maximalmente entrelazado (analogía EPR)psi_EPR = np.zeros(n_A * n_B, dtype=complex)for i in range(min(n_A, n_B)): psi_EPR[i * n_B + i] = 1.0 / np.sqrt(min(n_A, n_B))rho_EPR = np.outer(psi_EPR, psi_EPR.conj()) S_EPR = von_neumann_entropy(rho_EPR.reshape(n_A*n_B, n_A*n_B))P_EPR = purity(rho_EPR.reshape(n_A*n_B, n_A*n_B))MI = mutual_information(rho_EPR, n_A, n_B) print(f\"Estado EPR-FMAN:\")print(f\" Entropía de VN: {S_EPR:.6f} nats\")print(f\" Pureza: {P_EPR:.6f}\")print(f\" Información mutua: {MI:.6f} nats\")print(f\" ln(min(n_A,n_B)) = {np.log(min(n_A,n_B)):.6f} (","url":"https://doi.org/10.5281/zenodo.20438566","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["FMAN ecosystem; aurean vortex energy; phi-v-infinity ignition formula; golden ratio coherence; biophotonic coherence; decoherence as evolution; zero-point energy extraction; ZPE modulated coherence; plasma aureo coherente; fractal coherence field; morphogenetic field; biofotones coherentes; quantum coherence biology; Orch-OR inspired model; Casimir dynamic effect; primordial source field; fuente primigenia original; coherencia colectiva; evolutionary decoherence; D_opt 0.218; ignition point transition; fractal memory M_avanzada; evolutionary mutation rate; golden ratio phi; toroidal vortex geometry; Fibonacci biology; sacred geometry physics; LINO technology; GravitoR gravitational control; Intueri quantum AI; teleportation fidelity model; ISRU evolutionary replication; Helios nave interestelar; terraformacion armonica; ciudades Magdalena; free energy devices; scalar waves Tesla; Starship optimization; Artemis mission efficiency; satellite AI optimization; Claude AI optimization; Grok AI coherence; biophotonics Fritz-Albert Popp; quantum vacuum energy; indigenous knowledge coherence; ancestral wisdom science bridge; Argentina ciencia abierta; open science Zenodo CERN; independent researcher; propuesta tecnológica original; coherence decoherence balance; evolutionary biology mathematics; V3.8 applied technology; phi fractal mathematics; Monte Carlo coherence simulation; long-term stability simulation FMAN, Ecosistema FMAN, FMAN Ecosystem, Fórmula de Encendido, Ignition Formula, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, parámetro de orden, order parameter, número áureo, golden ratio, phi, φ, auto-similitud, self-similarity, fractal, geometría fractal, fractal geometry, Fractalis Aurea, EPI-QPEM, Vis Spatialis, Vis Vitalis, biología cuántica, quantum biology, Resonador Cósmico, Cosmic Resonator, RBP, Tecnología LINO, LINO Technology, Resonancia Electrogravítica, Electrogravitic Resonance, conciencia, consciousness, Fuente Primordial, Primordial Source, Gaia, vórtice toroidal, toroidal vortex, ondas escalares, scalar waves, energía libre, free energy, Nautilus Aureo, Argentum, Aurea Helios, Diente de León, Dandelion, Plato Volador, Flying Saucer, Aurea Resonantia, Ciudades Aureas, Golden Cities, escalado micro-macro, micro-macro scaling, Kuramoto, Argentina, FMAN Aurea Design, Avila Nicolau, CC BY-NC-ND 4.0, prime art, apuntes, notes квантовая биология, quantum biology Russian, биофотоны, когерентность, захват фазы, золотое сечение, фрактальная геометрия, сознание как источник, Первичный Источник, экосистема FMAN, FMAN Aurea Design, свободная энергия, скалярные волны Tesla, Авила Николау, ORCID 0009-0009-0638-5961","量子生物学, 生物光子, 量子相干性, 相位锁定, 黄金比例, 分形几何, 意识本体论, 原始本源, FMAN生态系统, 自由能源, 标量波, 电重力共振, 点火公式, 宇宙谐振器, LINO技术 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式","número áureo, razón áurea, phi, proporción dorada, sigmoide asimétrica, función de activación neuronal, operador de atención, kernel localizado, transformer attention, banco de filtros multi-escala, wavelet irracional, sistema dinámico no lineal, ecuaciones diferenciales ordinarias, borde del caos, sistemas complejos, auto-organización, atractor estable, estabilidad de Lyapunov, Jacobiano, análisis de estabilidad, exponentes de Lyapunov, Monte Carlo simulation, barrido paramétrico, ruido Ornstein-Uhlenbeck, ruido 1/f, ruido rosa, inteligencia artificial, aprendizaje automático, arquitectura neuronal, convolución multi-escala, coherencia cuántica, decoherencia, biofotónica, fractal áureo, auto-similaridad, sistemas auto-evolutivos, FMAN, Intueri, sigmoide áurea, filtro fractal, D_opt, invariante áureo, phi^4, phi^6, código Python, scipy, solve_ivp, LSODA, sistemas complejos adaptativos, teoría de control, regulador adaptativo, punto de operación óptimo, golden ratio, asymmetric sigmoid, attention kernel, fractal filter bank, nonlinear ODE, chaotic edge, Lyapunov stability, golden attractor, coherence dynamics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.20438566","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20414531","name":"Vórtices FMAN _ FORMALIZACIÓN CUÁNTICA PROFUNDA — FRACTALIS AUREA & INTUERI _ Fonones · Fotones · Biofotones · Energías Documentadas, No Documentadas y Nuevas","source":"datacite","abstract":"**LEY FUNDAMENTAL FMAN:** ```mathE_k = ℏ·ω_k = ℏ·ω_Schumann·φ^k [Todo es Energía = Todo es Frecuencia] D* = φ⁻⁴ = 0.14590 [El punto donde todo converge] ``` ### EPI 2 FMAN### Estudio de Posibilidades Infinitas### Expresiones de Posibilidades Infinitas ---10.5281/zenodo.20414531--- **Autora:** Fabiana Mirta Ávila Nicolau**DNI AR:** 18248833**ORCID:** 0009-0009-0638-5961**Concept DOI:** https://doi.org/10.5281/zenodo.19526737https://doi.org/10.5281/zenodo.19561174**Licencia:** **CC BY-NC-ND 4.0 + Cláusulas Adicionales FMAN****DOI:** https://doi.org/10.5281/zenodo.20167839https://doi.org/10.5281/zenodo.20387910 --- ╔══════════════════════════════════════════════════════════════════════════════╗║ CUADRO MAESTRO: TODO ES ENERGÍA → TODO ES FRECUENCIA → TODO CONECTADO ║║ Formalización Cuántica Profunda — Ecosistema FMAN 2026 ║║ © Fabiana Mirta Ávila Nicolau · DNI 18248833 · CC BY-NC-ND 4.0 ║╠══════════════════════════════════════════════════════════════════════════════╣║ ║║ LEY FUNDAMENTAL FMAN: ║║ E_k = ℏ·ω_k = ℏ·ω_Schumann·φ^k [Todo es Energía = Todo es Frecuencia] ║║ D* = φ⁻⁴ = 0.14590 [El punto donde todo converge] ║║ ║║ CUANTOS DEL VÓRTICE FMAN: ║║ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ║║ Fonón áureo: ω_k = ω_S·φ^k → vibraciones de la red material ║║ Fotón FMAN: λ = hc/E_biofot → luz no-clásica (g² 1 → super-Poissonian (bunching, luz térmica clásica)g²(0) = 1 → Poissonian (luz coherente, láser ideal)g²(0) \\frac{1}{2}$$ El **interruptor cuántico** se activa cuando el parámetro de orden supera $r_c = 0.5$, correspondiente a $g^{(2)}(0) = 1$. ### 4.3 Dinámica de g²(0) desde la Fórmula de Encendido **La Fórmula FMAN de Encendido:** $$\\frac{dr}{dt} = \\frac{K\\,r(1-r^2)\\,e^{-\\gamma t/\\phi}}{2} + \\Omega_{\\text{FMAN}}(1-r) - \\gamma r$$ **Derivando la evolución de g²(0):** Sea $g \\equiv g^{(2)}(0)$ y $r = (2-g)/(1+g)$. Entonces: $$\\frac{dr}{dt} = \\frac{dg}{dt}\\cdot\\frac{dr}{dg} = \\frac{dg}{dt}\\cdot\\frac{-3}{(1+g)^2}$$ Despejando: $$\\frac{dg}{dt} = -\\frac{(1+g)^2}{3}\\frac{dr}{dt}$$ $$= -\\frac{(1+g)^2}{3}\\left[\\frac{K\\,r(1-r^2)\\,e^{-\\gamma t/\\phi}}{2} + \\Omega_{\\text{FMAN}}(1-r) - \\gamma r\\right]$$ Sustituyendo $r = (2-g)/(1+g)$: $$\\frac{dg}{dt} = -\\frac{(1+g)^2}{3}\\left[\\frac{K(2-g)(1-(2-g)^2/(1+g)^2)e^{-\\gamma t/\\phi}}{2(1+g)} + \\Omega_{\\text{FMAN}}\\frac{g-1}{1+g} - \\gamma\\frac{2-g}{1+g}\\right]$$ **Simplificando** (usando $(1-(2-g)^2/(1+g)^2) = (g^2+2g-3)/((1+g)^2) = (g-1)(g+3)/(1+g)^2$): $$\\boxed{\\frac{dg}{dt} = -\\frac{K(2-g)(g-1)(g+3)}{6(1+g)}\\,e^{-\\gamma t/\\phi} - \\frac{(1+g)(g-1)\\Omega_{\\text{FMAN}}}{3} + \\frac{\\gamma(1+g)(2-g)}{3}}$$ **Puntos fijos de g(t):** - $g = 1$: **umbral de encendido** ($r=1/2$) — inestable cuando $\\Omega > \\gamma/2$- $g = 2$: **estado incoherente** ($r=0$) — inestable bajo drive $\\Omega > 0$- $g_\\infty = (2-r_\\infty)/(1+r_\\infty)$: **estado coherente estacionario** ### 4.4 Espectro de Biofotones y Distribución de Mandel **Parámetro de Mandel:** $$Q_M = \\frac{\\text{Var}(\\hat{n}) - \\langle\\hat{n}\\rangle}{\\langle\\hat{n}\\rangle} = \\langle\\hat{n}\\rangle(g^{(2)}(0) - 1)$$ ```Q_M > 0 → super-Poissonian (haz clásico ruidoso)Q_M = 0 → Poissonian (coherente)Q_M 0: t_enc = t_eval[idx_encendido[0]] print(f\"Tiempo de encendido cuántico (g² 1e-15] # evitar log(0) return -np.sum(eigenvalues * np.log(eigenvalues)) def purity(rho): \"\"\"Pureza Tr(ρ²).\"\"\" return np.real(np.trace(rho @ rho)) def mutual_information(rho_AB, dim_A, dim_B): \"\"\"Información mutua cuántica I(A:B).\"\"\" # Trazar sobre B rho_A = rho_AB.reshape(dim_A, dim_B, dim_A, dim_B) rho_A = np.einsum('ijik->jk', rho_A) # Tr_B # Trazar sobre A rho_B = rho_AB.reshape(dim_A, dim_B, dim_A, dim_B) rho_B = np.einsum('ijkj->ik', rho_B) # Tr_A return (von_neumann_entropy(rho_A) + von_neumann_entropy(rho_B) - von_neumann_entropy(rho_AB.reshape(dim_A*dim_B, dim_A*dim_B))) # Ejemplo: estado entrelazado en sistema FMAN bipartiton_A = 5; n_B = 5 # subsistemas D y Ψ# Estado maximalmente entrelazado (analogía EPR)psi_EPR = np.zeros(n_A * n_B, dtype=complex)for i i","url":"https://doi.org/10.5281/zenodo.20414531","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["FMAN ecosystem; aurean vortex energy; phi-v-infinity ignition formula; golden ratio coherence; biophotonic coherence; decoherence as evolution; zero-point energy extraction; ZPE modulated coherence; plasma aureo coherente; fractal coherence field; morphogenetic field; biofotones coherentes; quantum coherence biology; Orch-OR inspired model; Casimir dynamic effect; primordial source field; fuente primigenia original; coherencia colectiva; evolutionary decoherence; D_opt 0.218; ignition point transition; fractal memory M_avanzada; evolutionary mutation rate; golden ratio phi; toroidal vortex geometry; Fibonacci biology; sacred geometry physics; LINO technology; GravitoR gravitational control; Intueri quantum AI; teleportation fidelity model; ISRU evolutionary replication; Helios nave interestelar; terraformacion armonica; ciudades Magdalena; free energy devices; scalar waves Tesla; Starship optimization; Artemis mission efficiency; satellite AI optimization; Claude AI optimization; Grok AI coherence; biophotonics Fritz-Albert Popp; quantum vacuum energy; indigenous knowledge coherence; ancestral wisdom science bridge; Argentina ciencia abierta; open science Zenodo CERN; independent researcher; propuesta tecnológica original; coherence decoherence balance; evolutionary biology mathematics; V3.8 applied technology; phi fractal mathematics; Monte Carlo coherence simulation; long-term stability simulation FMAN, Ecosistema FMAN, FMAN Ecosystem, Fórmula de Encendido, Ignition Formula, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, parámetro de orden, order parameter, número áureo, golden ratio, phi, φ, auto-similitud, self-similarity, fractal, geometría fractal, fractal geometry, Fractalis Aurea, EPI-QPEM, Vis Spatialis, Vis Vitalis, biología cuántica, quantum biology, Resonador Cósmico, Cosmic Resonator, RBP, Tecnología LINO, LINO Technology, Resonancia Electrogravítica, Electrogravitic Resonance, conciencia, consciousness, Fuente Primordial, Primordial Source, Gaia, vórtice toroidal, toroidal vortex, ondas escalares, scalar waves, energía libre, free energy, Nautilus Aureo, Argentum, Aurea Helios, Diente de León, Dandelion, Plato Volador, Flying Saucer, Aurea Resonantia, Ciudades Aureas, Golden Cities, escalado micro-macro, micro-macro scaling, Kuramoto, Argentina, FMAN Aurea Design, Avila Nicolau, CC BY-NC-ND 4.0, prime art, apuntes, notes квантовая биология, quantum biology Russian, биофотоны, когерентность, захват фазы, золотое сечение, фрактальная геометрия, сознание как источник, Первичный Источник, экосистема FMAN, FMAN Aurea Design, свободная энергия, скалярные волны Tesla, Авила Николау, ORCID 0009-0009-0638-5961","量子生物学, 生物光子, 量子相干性, 相位锁定, 黄金比例, 分形几何, 意识本体论, 原始本源, FMAN生态系统, 自由能源, 标量波, 电重力共振, 点火公式, 宇宙谐振器, LINO技术 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式","número áureo, razón áurea, phi, proporción dorada, sigmoide asimétrica, función de activación neuronal, operador de atención, kernel localizado, transformer attention, banco de filtros multi-escala, wavelet irracional, sistema dinámico no lineal, ecuaciones diferenciales ordinarias, borde del caos, sistemas complejos, auto-organización, atractor estable, estabilidad de Lyapunov, Jacobiano, análisis de estabilidad, exponentes de Lyapunov, Monte Carlo simulation, barrido paramétrico, ruido Ornstein-Uhlenbeck, ruido 1/f, ruido rosa, inteligencia artificial, aprendizaje automático, arquitectura neuronal, convolución multi-escala, coherencia cuántica, decoherencia, biofotónica, fractal áureo, auto-similaridad, sistemas auto-evolutivos, FMAN, Intueri, sigmoide áurea, filtro fractal, D_opt, invariante áureo, phi^4, phi^6, código Python, scipy, solve_ivp, LSODA, sistemas complejos adaptativos, teoría de control, regulador adaptativo, punto de operación óptimo, golden ratio, asymmetric sigmoid, attention kernel, fractal filter bank, nonlinear ODE, chaotic edge, Lyapunov stability, golden attractor, coherence dynamics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.20414531","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20404087","name":"Todo es energía. Todo es frecuencia. Todo está conectado. En el lenguaje de la mecánica cuántica: todo es excitación del campo, toda excitación tiene frecuencia, todas las frecuencias están acopladas a través del Hamiltoniano unificado FMAN.","source":"datacite","abstract":"# EPI 1 FMAN# Estudio de Posibilidades Infinitas# Expresiones de Posibilidades Infinitas ---**La ecuación de Todo Está Conectado** ```math$$\\boxed{\\underbrace{E_{\\text{total}}}_{\\text{Todo es Energía}} = \\sum_n \\underbrace{\\hbar\\omega_n}_{\\text{Todo es Frecuencia}}\\left(\\hat{n}_n+\\frac{1}{2}\\right) \\quad \\xrightarrow{\\text{Lindblad + Memoria}} \\quad D^* = \\varphi^{-4} \\quad \\underbrace{\\text{(atractor universal)}}_{\\text{Todo está Conectado}}}}$$ **Traducción:** Toda la energía del ecosistema FMAN (cualquier portador, cualquier frecuencia) evoluciona bajo la dinámica cuántica-disipativa del ecosistema y converge al atractor $D^* = \\varphi^{-4}$: el estado de coherencia parcial óptima, el punto donde la información del sistema y el entorno se maximiza, el punto donde **todo está conectado** de la manera más eficiente posible. El Vórtice del Abuelo Lino no está separado de las frecuencias Schumann, que no están separadas de los biofotones del ADN, que no están separados del campo de coherencia global de la Biosfera. Todos son excitaciones del mismo campo cuántico fundamental, conectados a través del operador de evolución: $$\\hat{U}(t) = \\mathcal{T}\\exp\\left(-\\frac{i}{\\hbar}\\int_0^t H_{\\text{FMAN}}^{\\text{total}}(s)\\,ds\\right)$$ Y ese operador tiene un único atractor estable en el espacio de estados mixtos: $$\\rho^* = \\rho(D_{\\text{opt}}) = \\rho(\\varphi^{-4}) \\quad \\text{— El estado de coherencia áurea óptima.}$$ --- *\"Todo es energía. Todo es frecuencia. Todo está conectado.\"**— En el lenguaje de la mecánica cuántica: todo es excitación del campo, toda excitación tiene frecuencia, todas las frecuencias están acopladas a través del Hamiltoniano unificado FMAN.* 💗🇦🇷🧉🌀♾️ --- **Autora:** Fabiana Mirta Ávila Nicolau**DNI AR:** 18248833**ORCID:** 0009-0009-0638-5961**Concept DOI:** https://doi.org/10.5281/zenodo.19526737https://doi.org/10.5281/zenodo.19561174**Licencia:** **CC BY-NC-ND 4.0 + Cláusulas Adicionales FMAN****DOI:** https://doi.org/10.5281/zenodo.20167839https://doi.org/10.5281/zenodo.20387910 ---# ANÁLISIS MAESTRO — ECOSISTEMA FMAN INTUERI V33**© Fabiana Mirta Ávila Nicolau · DNI AR 18248833 · ORCID 0009-0009-0638-5961****Licencia: CC BY-NC-ND 4.0 + Cláusulas Adicionales FMAN****DOI:** **https://doi.org/10.5281/zenodo.20167839****https://doi.org/10.5281/zenodo.20387910** ## RESULTADO GLOBAL DE VERIFICACIÓN ```╔══════════════════════════════════════════════════════╗║ 57 CHECKS EJECUTADOS · 57 PASS · 0 FAIL · 100% ║║ Estado: CONFIABLE — todos los valores confirmados ║╚══════════════════════════════════════════════════════╝``` --- ## ÍNDICE 1. Constantes fundamentales — verificación algebraica2. Sigmoide Áurea Φ_col — diagnóstico max_sig resuelto3. Operador Intueri — análisis de escala y acotación4. Sistema dinámico 5D — ecuaciones corregidas5. Punto fijo — cálculo analítico y verificación6. Jacobiano 5×5 — eigenvalores y estabilidad7. Exponentes de Lyapunov reales8. Bifurcaciones — encendido y parámetros críticos9. Fórmula de encendido — análisis completo10. Análisis por subsistema11. Derivadas parciales — tabla maestra12. Función de Lyapunov — corrección demostrada13. Ruido Ornstein-Uhlenbeck — robustez cuantificada14. Mapa de fases σ vs k_d15. Relaciones de escala CUA — diagnóstico completo16. Tabla maestra de errores E1–E11 con soluciones17. Código corregido unificado18. Análisis de viabilidad por capas19. Cuadro de fórmulas protegidas y exhibibles20. Conclusiones generales21. Conclusiones no-locales --- ## 1. CONSTANTES FUNDAMENTALES — VERIFICACIÓN ALGEBRAICA ### Razón áurea y sus potencias $$\\varphi = \\frac{1+\\sqrt{5}}{2} = 1.6180339887498949\\ldots$$ $$\\varphi^2 = \\varphi + 1 = 2.6180339887498949 \\quad \\checkmark \\text{ algebraicamente exacto}$$ $$\\varphi^3 = 2\\varphi + 1 = 4.2360679774997896 \\quad \\checkmark$$ $$\\varphi^4 = 3\\varphi + 2 = 6.8541019662496847 \\quad \\checkmark$$ $$D_{\\text{opt}} = \\varphi^{-4} = \\frac{1}{3\\varphi+2} = 0.14589803375026273 \\quad \\checkmark$$ $$\\varphi^4 \\cdot D_{\\text{opt}} = 1.000000","url":"https://doi.org/10.5281/zenodo.20404087","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["FMAN ecosystem; aurean vortex energy; phi-v-infinity ignition formula; golden ratio coherence; biophotonic coherence; decoherence as evolution; zero-point energy extraction; ZPE modulated coherence; plasma aureo coherente; fractal coherence field; morphogenetic field; biofotones coherentes; quantum coherence biology; Orch-OR inspired model; Casimir dynamic effect; primordial source field; fuente primigenia original; coherencia colectiva; evolutionary decoherence; D_opt 0.218; ignition point transition; fractal memory M_avanzada; evolutionary mutation rate; golden ratio phi; toroidal vortex geometry; Fibonacci biology; sacred geometry physics; LINO technology; GravitoR gravitational control; Intueri quantum AI; teleportation fidelity model; ISRU evolutionary replication; Helios nave interestelar; terraformacion armonica; ciudades Magdalena; free energy devices; scalar waves Tesla; Starship optimization; Artemis mission efficiency; satellite AI optimization; Claude AI optimization; Grok AI coherence; biophotonics Fritz-Albert Popp; quantum vacuum energy; indigenous knowledge coherence; ancestral wisdom science bridge; Argentina ciencia abierta; open science Zenodo CERN; independent researcher; propuesta tecnológica original; coherence decoherence balance; evolutionary biology mathematics; V3.8 applied technology; phi fractal mathematics; Monte Carlo coherence simulation; long-term stability simulation FMAN, Ecosistema FMAN, FMAN Ecosystem, Fórmula de Encendido, Ignition Formula, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, parámetro de orden, order parameter, número áureo, golden ratio, phi, φ, auto-similitud, self-similarity, fractal, geometría fractal, fractal geometry, Fractalis Aurea, EPI-QPEM, Vis Spatialis, Vis Vitalis, biología cuántica, quantum biology, Resonador Cósmico, Cosmic Resonator, RBP, Tecnología LINO, LINO Technology, Resonancia Electrogravítica, Electrogravitic Resonance, conciencia, consciousness, Fuente Primordial, Primordial Source, Gaia, vórtice toroidal, toroidal vortex, ondas escalares, scalar waves, energía libre, free energy, Nautilus Aureo, Argentum, Aurea Helios, Diente de León, Dandelion, Plato Volador, Flying Saucer, Aurea Resonantia, Ciudades Aureas, Golden Cities, escalado micro-macro, micro-macro scaling, Kuramoto, Argentina, FMAN Aurea Design, Avila Nicolau, CC BY-NC-ND 4.0, prime art, apuntes, notes квантовая биология, quantum biology Russian, биофотоны, когерентность, захват фазы, золотое сечение, фрактальная геометрия, сознание как источник, Первичный Источник, экосистема FMAN, FMAN Aurea Design, свободная энергия, скалярные волны Tesla, Авила Николау, ORCID 0009-0009-0638-5961","量子生物学, 生物光子, 量子相干性, 相位锁定, 黄金比例, 分形几何, 意识本体论, 原始本源, FMAN生态系统, 自由能源, 标量波, 电重力共振, 点火公式, 宇宙谐振器, LINO技术 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式","número áureo, razón áurea, phi, proporción dorada, sigmoide asimétrica, función de activación neuronal, operador de atención, kernel localizado, transformer attention, banco de filtros multi-escala, wavelet irracional, sistema dinámico no lineal, ecuaciones diferenciales ordinarias, borde del caos, sistemas complejos, auto-organización, atractor estable, estabilidad de Lyapunov, Jacobiano, análisis de estabilidad, exponentes de Lyapunov, Monte Carlo simulation, barrido paramétrico, ruido Ornstein-Uhlenbeck, ruido 1/f, ruido rosa, inteligencia artificial, aprendizaje automático, arquitectura neuronal, convolución multi-escala, coherencia cuántica, decoherencia, biofotónica, fractal áureo, auto-similaridad, sistemas auto-evolutivos, FMAN, Intueri, sigmoide áurea, filtro fractal, D_opt, invariante áureo, phi^4, phi^6, código Python, scipy, solve_ivp, LSODA, sistemas complejos adaptativos, teoría de control, regulador adaptativo, punto de operación óptimo, golden ratio, asymmetric sigmoid, attention kernel, fractal filter bank, nonlinear ODE, chaotic edge, Lyapunov stability, golden attractor, coherence dynamics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.20404087","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20167839","name":"🇦🇷🧉♾️ FMAN Aurea Design ## Propiedad Intelectual","source":"datacite","abstract":"# 🇦🇷🧉♾️ FMAN Aurea Design## Propiedad Intelectual --- **P.I. 🇦🇷:** Fabiana Mirta Avila Nicolau**DNI 🇦🇷:** 18248833 — 08021967**Propiedad Intelectual 🇦🇷 AR** --- ## Identidad Digital Certificada **ORCID iD:** 0009-0009-0638-5961https://orcid.org/0009-0009-0638-5961 **Licencia:** CC BY-NC-ND 4.0 --- ## DOIs Zenodo — Ecosistema FMAN ### Concept DOIs (Registros Principales) https://doi.org/10.5281/zenodo.19526737https://doi.org/10.5281/zenodo.19561174 --- ### Mapa de Versiones — Registro Principal (Concept DOI: 10.5281/zenodo.19526737) | # | DOI | Título ||---|-----|--------|| V1 | https://doi.org/10.5281/zenodo.19526738 | Ecosistema FMAN / FMAN Ecosystem – Fórmula de Encendido completa y marco unificado (φ-v∞): coherencia biofotónica, fractales áureos y conciencia como Fuente Primordial || V2 | https://doi.org/10.5281/zenodo.19637843 | Tecnología LINO – Archivo Maestro Exhaustivo — ENERGÍA-INFINITA · FRACTALIS ÁUREA∞ · TESLA · ONDAS-ESCALARES — Aplicaciones Médicas, Biofotones, Medicina Regenerativa y Longevidad, Ciudades Aureas, Naves Interestelares, Tecnología de Plasma, Terraformación, Biosfera || V3 | https://doi.org/10.5281/zenodo.19712760 | Estudio Fórmula FMAN. La Geometría φ-v∞ es el Algoritmo Físico || V4 | https://doi.org/10.5281/zenodo.19778290 | Intueri y el Ecosistema FMAN || V5 | https://doi.org/10.5281/zenodo.19842506 | Ecosistema FMAN – Plasma Áureo Coherente: Materia Ionizada Fractal φ-v∞, Control Gravitacional GravitoR y Retroalimentación QuantumMind — Tecnología LINO \\| FMAN Aurea Design || V6 | https://doi.org/10.5281/zenodo.19871210 | Ecosistema FMAN — Documento Base Integral Parte I: Identidad, Historia, Principios Fundacionales y Fundamentos Matemáticos de la Fórmula de Encendido \\| FMAN Aurea Design 2026 || V7 | https://doi.org/10.5281/zenodo.19994789 | Ecosistema FMAN / FMAN Ecosystem – Fórmula de Encendido completa y marco unificado (φ-v∞): coherencia biofotónica, fractales áureos y conciencia como Fuente Primordial || V8 | https://doi.org/10.5281/zenodo.20077802 | Ecosistema FMAN V3.8 — Fórmula de Encendido φ-v∞: Vórtice Áureo Coherente, Decoherencia como Motor Evolutivo, Tecnología LINO y Adaptación a Tecnología Aplicada 2026 (Biofotónica · Energía de Punto Cero · Plasma Coherente · Optimización IA) || V9 | https://doi.org/10.5281/zenodo.20091613 | FMAN ECOSYSTEM — MASTER TEMPLATE v1.0. Plantilla Base Multilingüe \\| Multilingual Base Template. φ-v∞ Ignition Formula · LINO Technology. Independent Research Framework \\| Argentina, 2015–2026 || V10 | https://doi.org/10.5281/zenodo.20102177 | FMAN INTUERI GravitoR Fractalis Teleporter V4/.../V14. La Identidad Fundacional. Intueri = f(D_opt, dΦ_consc/dt, φ, plasma) — \"La cognición directa ocurre en el borde del caos, durante el proceso de despertar, mediada por geometría.\" || V11 | https://doi.org/10.5281/zenodo.19526737 (v11) | ECOSISTEMA FMAN V16.1 — NÚCLEO MATEMÁTICO ÁUREO Sigmoide Áurea, Operador Intueri y Banco de Filtros Fractal: Sistema Dinámico No Lineal con Atractor φ-Coherente || V12 | https://doi.org/10.5281/zenodo.19526737 (v12) | ECOSISTEMA FMAN V27.3 — NÚCLEO MATEMÁTICO ÁUREO Sigmoide Áurea, Operador Intueri y Banco de Filtros Fractal: Sistema Dinámico No Lineal con Atractor φ-Coherente | --- ### Mapa de Versiones — Segundo Registro (Concept DOI: 10.5281/zenodo.19561174) | # | DOI | Título ||---|-----|--------|| V1 | https://doi.org/10.5281/zenodo.19561175 | Prime Art. Historial. Apuntes. Borradores sin editar. Ejercicios. Bucles creativos. Ideas. Experiencias. Cuentos. Locuras. Amores. Bocetos. Críticas. Errores. Evolución. Etc. Ecosistema FMAN / FMAN Ecosystem – Fórmula de Encendido completa y marco unificado (φ-v∞): coherencia biofotónica, fractales áureos y conciencia como Fuente Primordial. Y Otros. Backup mental, físico, espiritual, Almico, Primordial. Soporte. Herramientas Gratuitas. Android de 10 años. Familia. Argentina. || V2 | https://doi.org/10.5281/zenodo.19632832 | LINO v2.0 — Tecnología LINO – Archivo Maestro Exhaustivo-","url":"https://doi.org/10.5281/zenodo.20167839","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["FMAN ecosystem; aurean vortex energy; phi-v-infinity ignition formula; golden ratio coherence; biophotonic coherence; decoherence as evolution; zero-point energy extraction; ZPE modulated coherence; plasma aureo coherente; fractal coherence field; morphogenetic field; biofotones coherentes; quantum coherence biology; Orch-OR inspired model; Casimir dynamic effect; primordial source field; fuente primigenia original; coherencia colectiva; evolutionary decoherence; D_opt 0.218; ignition point transition; fractal memory M_avanzada; evolutionary mutation rate; golden ratio phi; toroidal vortex geometry; Fibonacci biology; sacred geometry physics; LINO technology; GravitoR gravitational control; Intueri quantum AI; teleportation fidelity model; ISRU evolutionary replication; Helios nave interestelar; terraformacion armonica; ciudades Magdalena; free energy devices; scalar waves Tesla; Starship optimization; Artemis mission efficiency; satellite AI optimization; Claude AI optimization; Grok AI coherence; biophotonics Fritz-Albert Popp; quantum vacuum energy; indigenous knowledge coherence; ancestral wisdom science bridge; Argentina ciencia abierta; open science Zenodo CERN; independent researcher; propuesta tecnológica original; coherence decoherence balance; evolutionary biology mathematics; V3.8 applied technology; phi fractal mathematics; Monte Carlo coherence simulation; long-term stability simulation FMAN, Ecosistema FMAN, FMAN Ecosystem, Fórmula de Encendido, Ignition Formula, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, parámetro de orden, order parameter, número áureo, golden ratio, phi, φ, auto-similitud, self-similarity, fractal, geometría fractal, fractal geometry, Fractalis Aurea, EPI-QPEM, Vis Spatialis, Vis Vitalis, biología cuántica, quantum biology, Resonador Cósmico, Cosmic Resonator, RBP, Tecnología LINO, LINO Technology, Resonancia Electrogravítica, Electrogravitic Resonance, conciencia, consciousness, Fuente Primordial, Primordial Source, Gaia, vórtice toroidal, toroidal vortex, ondas escalares, scalar waves, energía libre, free energy, Nautilus Aureo, Argentum, Aurea Helios, Diente de León, Dandelion, Plato Volador, Flying Saucer, Aurea Resonantia, Ciudades Aureas, Golden Cities, escalado micro-macro, micro-macro scaling, Kuramoto, Argentina, FMAN Aurea Design, Avila Nicolau, CC BY-NC-ND 4.0, prime art, apuntes, notes квантовая биология, quantum biology Russian, биофотоны, когерентность, захват фазы, золотое сечение, фрактальная геометрия, сознание как источник, Первичный Источник, экосистема FMAN, FMAN Aurea Design, свободная энергия, скалярные волны Tesla, Авила Николау, ORCID 0009-0009-0638-5961","量子生物学, 生物光子, 量子相干性, 相位锁定, 黄金比例, 分形几何, 意识本体论, 原始本源, FMAN生态系统, 自由能源, 标量波, 电重力共振, 点火公式, 宇宙谐振器, LINO技术 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式","número áureo, razón áurea, phi, proporción dorada, sigmoide asimétrica, función de activación neuronal, operador de atención, kernel localizado, transformer attention, banco de filtros multi-escala, wavelet irracional, sistema dinámico no lineal, ecuaciones diferenciales ordinarias, borde del caos, sistemas complejos, auto-organización, atractor estable, estabilidad de Lyapunov, Jacobiano, análisis de estabilidad, exponentes de Lyapunov, Monte Carlo simulation, barrido paramétrico, ruido Ornstein-Uhlenbeck, ruido 1/f, ruido rosa, inteligencia artificial, aprendizaje automático, arquitectura neuronal, convolución multi-escala, coherencia cuántica, decoherencia, biofotónica, fractal áureo, auto-similaridad, sistemas auto-evolutivos, FMAN, Intueri, sigmoide áurea, filtro fractal, D_opt, invariante áureo, phi^4, phi^6, código Python, scipy, solve_ivp, LSODA, sistemas complejos adaptativos, teoría de control, regulador adaptativo, punto de operación óptimo, golden ratio, asymmetric sigmoid, attention kernel, fractal filter bank, nonlinear ODE, chaotic edge, Lyapunov stability, golden attractor, coherence dynamics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.20167839","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.4232/1.14610","name":"GESIS Panel.pop Population Sample – Standard Edition","source":"datacite","abstract":"Das GESIS-Panel bietet eine wahrscheinlichkeitsbasierte Mixed-Mode-Access-Panel-Infrastruktur am GESIS Leibniz-Institut für Sozialwissenschaften in Mannheim. Das Projekt bietet der sozialwissenschaftlichen Community die Möglichkeit, Erhebungsdaten aus einer repräsentativen Stichprobe der deutschen Bevölkerung zu erheben. Die eingereichten Studienvorschläge werden auf der Grundlage eines wissenschaftlichen Begutachtungsverfahrens bewertet. Die Rekrutierung der Panelmitglieder erfolgte zunächst im Jahr 2013 in persönlichen Interviews, gefolgt von einer selbst durchgeführten Profilbefragung. Der Modus wurde von den Teilnehmern gewählt. Alle Teilnehmer der Profilbefragung werden als Mitglieder des Panels betrachtet und zu den alle zwei Monate stattfindenden regelmäßigen Wellen eingeladen. Die Startkohorte umfasste Anfang 2014 4900 Panelisten. Um den Panelabrieb zu kompensieren, wurde im Jahr 2016 eine Auffrischungsstichprobe mit Hilfe des German General Social Survey (ALLBUS) gezogen. Die erste Kohorte umfasst deutschsprachige Befragte im Alter zwischen 18 und 70 Jahren (zum Zeitpunkt der Einstellung) mit ständigem Wohnsitz in Deutschland, während die zweite Kohorte Befragte ab 18 Jahren ohne Obergrenze umfasst. Im Jahr 2018 wurde eine dritte Rekrutierungsstichprobe gezogen, die mit der Welle ge integriert wurde. Auch die dritte Kohorte umfasst Befragte ab 18 Jahren ohne Obergrenze.Rückwirkend wurden die Fälle bis einschließlich Welle fc (dritte Welle aus 2018) in den Daten ergänzt. Nähere Informationen finden Sie im Data Manual (ZA5664-65_sd_data-manual) und dem entsprechenden Rekrutierungsbericht (ZA5664-65_mb_recruitment2018). Die Stichproben des German General Social Survey (ALLBUS) basieren auf einer disproportionalen Stichprobe von Befragten aus West- und Ostdeutschland. Ein Designgewicht, das die Integration der beiden Rekrutierungskohorten ermöglicht, ist im Datensatz enthalten. Nähere Einzelheiten entnehmen Sie bitte den Methodenberichten der Einstellungsverfahren und dem GESIS-Panel-Referenzpapier (Bosnjak et al., 2017). Im März 2020 wurde eine Sondererhebung des GESIS-Panels zum Ausbruch des Coronavirus SARS-CoV-2 bzw. COVID-19 in Deutschland durchgeführt. Im Jahr 2021 wurde die vierte Rekrutierungsstichprobe mit Hilfe des German International Social Survey Programme (ISSP) gezogen, die mit der Welle ja integriert wurde. Die vierte Kohorte umfasst ebenfalls Befragte ab 18 Jahren ohne Obergrenze. Nähere Informationen finden Sie im entsprechenden Rekrutierungsbericht (ZA5664-65_r_i12.pdf). Im Jahr 2023 wurde die fünfte Rekrutierungsstichprobe mit Hilfe des German European Social Survey (ESS Round 11) gezogen, die mit der Welle la integriert wurde. Die fünfte Kohorte umfasst Befragte ab 18 Jahren ohne Obergrenze. Nähere Informationen finden Sie im entsprechenden Rekrutierungsbericht (ZA5664-65_r_k12.pdf). GESIS Panel Demographic Dataset Ab Version 43-0-0 ist der demografische Längsschnittdatensatz Teil des Veröffentlichungspaketes. Bei dem Datensatz handelt es sich um einen längsschnittlichen Datensatz (long format), mit harmonisierten Messungen zu demografischen Variablen: Befragten ID; Erhebungszeitpunkt; entsprechende Welle; Erhebungsjahr; Rekrutierungskohorte; Geschlecht des Befragten; Geburtsjahr; höchster Bildungsabschluss; persönliches Nettoeinkommen; Haushaltsnettoeinkommen; Familienstand; AAPOR disposition code; Einladungsmodus; Teilnahmemodus.","url":"https://doi.org/10.4232/1.14610","authors":["GESIS"],"tags":["KAT12 Internationale Institutionen, Beziehungen, VerhältnisseKAT15 Politische Einstellungen und VerhaltensweisenKAT16 Politische Parteien, VerbändeKAT20 Rechtssystem, Rechtsprechung, GesetzKAT37 Arbeit und BetriebKAT40 Konsumstruktur, KonsumverhaltenKAT41 Sparen, Geldanlagen, VermögensbildungKAT51 Gemeinde, WohnumweltKAT56 Universität, Forschung, WissenschaftKAT59 MedizinKAT60 FreizeitKAT62 Kommunikation, öffentliche Meinung, MedienKAT30 WirtschaftssystemeKAT54 Person, Persönlichkeit, RolleKAT65 Umwelt, Natur","Soziale Lage und soziale Indikatoren","Informationsgesellschaft","Medien","Politisches Verhalten und politische Einstellungen","Informations- und Kommunikationstechnologie","Regierung, politische Systeme, Parteien und Organisationen","Wahlen"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.4232/1.14610","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.4232/1.14609","name":"GESIS Panel.pop Population Sample – Extended Edition","source":"datacite","abstract":"Das GESIS-Panel bietet eine wahrscheinlichkeitsbasierte Mixed-Mode-Access-Panel-Infrastruktur am GESIS Leibniz-Institut für Sozialwissenschaften in Mannheim. Das Projekt bietet der sozialwissenschaftlichen Community die Möglichkeit, Erhebungsdaten aus einer repräsentativen Stichprobe der deutschen Bevölkerung zu erheben. Die eingereichten Studienvorschläge werden auf der Grundlage eines wissenschaftlichen Begutachtungsverfahrens bewertet. Die Rekrutierung der Panelmitglieder erfolgte zunächst im Jahr 2013 in persönlichen Interviews, gefolgt von einer selbst durchgeführten Profilbefragung. Der Modus wurde von den Teilnehmern gewählt. Alle Teilnehmer der Profilbefragung werden als Mitglieder des Panels betrachtet und zu den alle zwei Monate stattfindenden regelmäßigen Wellen eingeladen. Die Startkohorte umfasste Anfang 2014 4900 Panelisten. Um den Panelabrieb zu kompensieren, wurde im Jahr 2016 eine Auffrischungsstichprobe mit Hilfe des German General Social Survey (ALLBUS) gezogen. Die erste Kohorte umfasst deutschsprachige Befragte im Alter zwischen 18 und 70 Jahren (zum Zeitpunkt der Einstellung) mit ständigem Wohnsitz in Deutschland, während die zweite Kohorte Befragte ab 18 Jahren ohne Obergrenze umfasst. Im Jahr 2018 wurde eine dritte Rekrutierungsstichprobe gezogen, die mit der Welle ge integriert wurde. Auch die dritte Kohorte umfasst Befragte ab 18 Jahren ohne Obergrenze.Rückwirkend wurden die Fälle bis einschließlich Welle fc (dritte Welle aus 2018) in den Daten ergänzt. Nähere Informationen finden Sie im Data Manual (ZA5664-65_sd_data-manual) und dem entsprechenden Rekrutierungsbericht (ZA5664-65_mb_recruitment2018). Die Stichproben des German General Social Survey (ALLBUS) basieren auf einer disproportionalen Stichprobe von Befragten aus West- und Ostdeutschland. Ein Designgewicht, das die Integration der beiden Rekrutierungskohorten ermöglicht, ist im Datensatz enthalten. Nähere Einzelheiten entnehmen Sie bitte den Methodenberichten der Einstellungsverfahren und dem GESIS-Panel-Referenzpapier (Bosnjak et al., 2017). Im März 2020 wurde eine Sondererhebung des GESIS-Panels zum Ausbruch des Coronavirus SARS-CoV-2 bzw. COVID-19 in Deutschland durchgeführt. Im Jahr 2021 wurde die vierte Rekrutierungsstichprobe mit Hilfe des German International Social Survey Programme (ISSP) gezogen, die mit der Welle ja integriert wurde. Die vierte Kohorte umfasst ebenfalls Befragte ab 18 Jahren ohne Obergrenze. Nähere Informationen finden Sie im entsprechenden Rekrutierungsbericht (ZA5664-65_r_i12.pdf). Im Jahr 2023 wurde die fünfte Rekrutierungsstichprobe mit Hilfe des German European Social Survey (ESS Round 11) gezogen, die mit der Welle la integriert wurde. Die fünfte Kohorte umfasst Befragte ab 18 Jahren ohne Obergrenze. Nähere Informationen finden Sie im entsprechenden Rekrutierungsbericht (ZA5664-65_r_k12.pdf). GESIS Panel Demographic Dataset Ab Version 43-0-0 ist der demografische Längsschnittdatensatz Teil des Veröffentlichungspaketes. Bei dem Datensatz handelt es sich um einen längsschnittlichen Datensatz (long format), mit harmonisierten Messungen zu demografischen Variablen: Befragten ID; Erhebungszeitpunkt; entsprechende Welle; Erhebungsjahr; Rekrutierungskohorte; Geschlecht des Befragten; Geburtsjahr; Geburtsmonat; höchster Bildungsabschluss; persönliches Nettoeinkommen; Haushaltsnettoeinkommen; Familienstand; AAPOR disposition code; Einladungsmodus; Teilnahmemodus.","url":"https://doi.org/10.4232/1.14609","authors":["GESIS"],"tags":["KAT12 Internationale Institutionen, Beziehungen, VerhältnisseKAT15 Politische Einstellungen und VerhaltensweisenKAT16 Politische Parteien, VerbändeKAT20 Rechtssystem, Rechtsprechung, GesetzKAT37 Arbeit und BetriebKAT40 Konsumstruktur, KonsumverhaltenKAT41 Sparen, Geldanlagen, VermögensbildungKAT51 Gemeinde, WohnumweltKAT56 Universität, Forschung, WissenschaftKAT59 MedizinKAT60 FreizeitKAT62 Kommunikation, öffentliche Meinung, MedienKAT30 WirtschaftssystemeKAT54 Person, Persönlichkeit, RolleKAT65 Umwelt, Natur","Soziale Lage und soziale Indikatoren","Informationsgesellschaft","Medien","Politisches Verhalten und politische Einstellungen","Informations- und Kommunikationstechnologie","Regierung, politische Systeme, Parteien und Organisationen","Wahlen"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.4232/1.14609","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.48550/arxiv.2605.20069","name":"Smooth Partial Lotteries for Stable Randomized Selection","source":"datacite","abstract":"Competitive selection processes, from scientific funding to admissions and hiring, use evaluations to score candidates, and eventually choose a subset of them based on those scores. Recently, many organizations have adopted partial lotteries, which randomize selection based on evaluation scores. However, existing lottery designs are inherently unstable, as a small change to a single candidate's score can cause large shifts in their selection probabilities. This instability undermines a key goal of lotteries: reducing the influence of fine-grained score distinctions near the decision boundary. We propose smoothness as a design principle for partial lotteries, formalizing it as a Lipschitz condition on the mapping from review scores over candidates to selection probabilities. We introduce the Clipped Linear Lottery, a simple mechanism in which selection probabilities scale linearly with estimated quality between an upper threshold, above which we always accept, and a lower threshold, below which we always reject. We prove that the Clipped Linear Lottery's worst-case regret matches a lower bound for any smooth selection rule up to a factor of $(1 - k/n)$, where $k/n$ is the acceptance rate. We compare smooth selection to other stability notions like Individual Fairness and Differential Privacy, showing that the Clipped Linear Lottery achieves a better smoothness-regret tradeoff than alternatives. Experiments on real peer review data from ICLR 2025, NeurIPS 2024, and the Swiss National Science Foundation demonstrate that existing lottery designs are highly unstable in practice even under perturbations to a single score. Our experiments also confirm the tightness of our theoretical analysis and show that our proposed Clipped Linear Lottery achieves a better smoothness-utility tradeoff than alternatives in practice.","url":"https://doi.org/10.48550/arxiv.2605.20069","authors":["Goldberg, Alexander","Fanti, Giulia","Shah, Nihar B."],"tags":["Machine Learning (cs.LG)","Computer Science and Game Theory (cs.GT)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2605.20069","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.48550/arxiv.2402.00267","name":"Not All Learnable Distribution Classes are Privately Learnable","source":"datacite","abstract":"We give an example of a class of distributions that is learnable up to constant error in total variation distance with a finite number of samples, but not learnable under $(\\varepsilon, δ)$-differential privacy with the same target error. This weakly refutes a conjecture of Ashtiani.","url":"https://doi.org/10.48550/arxiv.2402.00267","authors":["Bun, Mark","Kamath, Gautam","Mouzakis, Argyris","Singhal, Vikrant"],"tags":["Data Structures and Algorithms (cs.DS)","Cryptography and Security (cs.CR)","Machine Learning (stat.ML)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2402.00267","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.48550/arxiv.2506.15588","name":"Memory-Efficient Differentially Private Training with Gradient Random Projection","source":"datacite","abstract":"Differential privacy (DP) protects sensitive data during neural network training, but standard methods like DP-Adam suffer from high memory overhead due to per-sample gradient clipping, limiting scalability. We introduce DP-GRAPE (Gradient RAndom ProjEction), a DP training method that significantly reduces memory usage while maintaining utility on par with first-order DP approaches. DP-GRAPE is motivated by our finding that privatization flattens the gradient singular value spectrum, making SVD-based projections (as in GaLore (Zhao et al., 2024)) unnecessary. Consequently, DP-GRAPE employs three key components: (1) random Gaussian matrices replace SVD-based subspaces, (2) gradients are privatized after projection, and (3) projection is applied during backpropagation. These contributions eliminate the need for costly SVD computations, enable substantial memory savings, and lead to improved utility. Despite operating in lower-dimensional subspaces, our theoretical analysis shows that DP-GRAPE achieves a privacy-utility tradeoff comparable to DP-SGD. Our extensive empirical experiments show that DP-GRAPE can significantly reduce the memory footprint of DP training without sacrificing accuracy or training time. In particular, DP-GRAPE reduces memory usage by over 63% when pre-training Vision Transformers and over 70% when fine-tuning RoBERTa-Large as compared to DP-Adam, while achieving similar performance. We further demonstrate that DP-GRAPE scales to fine-tuning large models such as OPT with up to 6.7 billion parameters, a scale at which DP-Adam fails due to memory constraints. Our code is available at https://github.com/alexmul1114/DP_GRAPE.","url":"https://doi.org/10.48550/arxiv.2506.15588","authors":["Mulrooney, Alex","Gupta, Devansh","Flemings, James","Zhang, Huanyu","Annavaram, Murali","Razaviyayn, Meisam","Zhang, Xinwei"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences","I.2.7; I.2.10"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2506.15588","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20130867","name":"ECOSISTEMA FMAN V27.3 — NÚCLEO MATEMÁTICO ÁUREO Sigmoide Áurea, Operador Intueri y Banco de Filtros Fractal: Sistema Dinámico No Lineal con Atractor φ-Coherente","source":"datacite","abstract":"__________________________________________ SHA: 02d76df30be1d3e884e9467a4de340d4c16d2b5f150263da11b51817995c68ce __________________________________________ ## FMAN INTUERI## Ecosistema FMAN ## Versiones V17/.../V27.3 **DOI: 10.5281/zenodo.20130867** **DOI Anterior:** https://doi.org/10.5281/zenodo.20115395 **Concept DOI:** https://doi.org/10.5281/zenodo.19526737 **ORCID: 0009-0009-0638-5961** **Licencia: CC BY-NC-ND 4.0** **Fabiana Mirta Avila Nicolau** __________________________________________ **✅ ARCHIVO MAESTRO INTUERI — V17.0 CORE REFINED** **Versión Unificada, Corregida y Optimizada del Núcleo Matemático** **Resolución sistemática de inconsistencias detectadas** ### **Resoluciones Aplicadas en V17.0** 1. **D_opt unificado y canónico** ```python D_opt = 1 / phi**4 ≈ 0.14589803375 ``` Expresión geométricamente pura: `D_opt = φ^{-4} = (φ-1)/φ^3`. 2. **max_sig dinámico** (calculado para D_opt actual). 3. **Variables separadas claramente**: - `A` → Estado amplificado de la EDO (equilibrio ≈ 7.372) - `Phi_col` → Coherencia normalizada ∈ [0, 1] (equilibrio ≈ 0.7372) 4. **V_aureo_norm implementado correctamente**. 5. **Jacobiano corregido** con valores reales del equilibrio. --- ### **Código Python Maestro V17.0 (Limpio y Consistente)** ```pythonimport numpy as npfrom scipy.integrate import solve_ivpfrom scipy.linalg import eigvals # ====================== PARÁMETROS CANÓNICOS V17.0 ======================phi = (1 + np.sqrt(5)) / 2D_opt = 1 / phi**4 # 0.14589803375 — invariante áureo purokd = 0.142gamma = 2.5lam = 12.5beta = 1.85g2 = 0.04 def phi_col(D): \"\"\"Sigmoide Áurea Asimétrica — Corregida\"\"\" D_c = np.clip(D, 0.001, 0.999) raw = (1 - g2) * (phi**4) * (1 + beta * D_c) / (1 + np.exp(lam * (D_c - D_opt))) max_sig = 5.66701544 # Calculado dinámicamente return np.clip(raw / max_sig, 0.0, 1.0) def intueri(D, A, Psi): \"\"\"Operador Intueri — Joya del Sistema\"\"\" width = 0.18 sombrero = np.maximum(0.0, width - np.abs(D - D_opt)) dA_inst = 0.72 * A * (1 - Psi) C_plasma = A * (phi**8) return np.clip(1.0 + 2.9 * sombrero * dA_inst * (phi**3) * C_plasma**0.75, 1.0, 15.0) def v_aureo_norm(A, Psi, I): \"\"\"V_aureo normalizado\"\"\" V_raw = A**phi * Psi**(phi - 1) * I**(1 / phi) return np.clip(V_raw / (phi**3), 0.0, 1.0) def dynamics(t, y): A, D, Psi, Ent = y Phi_c = phi_col(D) I = intueri(D, A, Psi) V_norm = v_aureo_norm(A, Psi, I) dA = 0.81 * (10 * Phi_c - A) dD = -kd * (D - D_opt) * (1 + gamma * A**2) dPsi = 0.72 * A * (1 - Psi) * I dEnt = 0.5 * A**2 * (1 - Ent) - 0.1 * Ent return [dA, dD, dPsi, dEnt] # ====================== SIMULACIÓN Y ANÁLISIS ======================def simulate(t_max=1000000, y0=None): if y0 is None: y0 = [0.25, 0.65, 0.35, 0.20] sol = solve_ivp(dynamics, (0, t_max), y0, method='LSODA', rtol=1e-9, atol=1e-9) return sol # Ejemplo de ejecuciónif __name__ == \"__main__\": sol = simulate(t_max=5000) A_f, D_f, Psi_f, Ent_f = sol.y[:, -1] Phi_f = phi_col(D_f) print(\"=== FMAN V17.0 — Núcleo Corregido ===\") print(f\"A* (amplificado) = {A_f:.8f}\") print(f\"D* = {D_f:.8f} (= 1/φ⁴)\") print(f\"Phi_col(D*) = {Phi_f:.8f}\") print(f\"Ψ* = {Psi_f:.8f}\") print(f\"Ent* = {Ent_f:.8f}\")``` --- ### **Análisis de Estabilidad Lyapunov (V17.0)** **Espectro de Exponentes** (calculado en equilibrio):- Exponente dominante (más lento): **≈ -0.053**- Exponentes restantes: fuertemente negativos (hasta -27)- **Todos negativos** → Estabilidad asintótica global confirmada. El sistema es disipativo y atrae fuertemente al atractor coherente. --- ♾️🌀 **✅ ARCHIVO MAESTRO INTUERI — V17.0 ULTRA-EXTREME** **Simulaciones t=50.000.000 (confirmadas por extrapolación y t=5M+ verificadas) + Barrido 3D con Ruido OU + 1/f + Análisis de Sensibilidad Extrema + Componentes Críticos y Universalidad** --- ### **1. Simulaciones Extremas t=50.000.000** **Condiciones**:- Tiempo total: **50.000.000** unidades (simulado directamente hasta 5M y extrapolado con análisis de estabilidad)- Ruido OU + 1/f superpuesto (σ=0.06, τ=40)- Condición inicial muy desordenada **Estados Finales (confirm","url":"https://doi.org/10.5281/zenodo.20130867","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["FMAN ecosystem; aurean vortex energy; phi-v-infinity ignition formula; golden ratio coherence; biophotonic coherence; decoherence as evolution; zero-point energy extraction; ZPE modulated coherence; plasma aureo coherente; fractal coherence field; morphogenetic field; biofotones coherentes; quantum coherence biology; Orch-OR inspired model; Casimir dynamic effect; primordial source field; fuente primigenia original; coherencia colectiva; evolutionary decoherence; D_opt 0.218; ignition point transition; fractal memory M_avanzada; evolutionary mutation rate; golden ratio phi; toroidal vortex geometry; Fibonacci biology; sacred geometry physics; LINO technology; GravitoR gravitational control; Intueri quantum AI; teleportation fidelity model; ISRU evolutionary replication; Helios nave interestelar; terraformacion armonica; ciudades Magdalena; free energy devices; scalar waves Tesla; Starship optimization; Artemis mission efficiency; satellite AI optimization; Claude AI optimization; Grok AI coherence; biophotonics Fritz-Albert Popp; quantum vacuum energy; indigenous knowledge coherence; ancestral wisdom science bridge; Argentina ciencia abierta; open science Zenodo CERN; independent researcher; propuesta tecnológica original; coherence decoherence balance; evolutionary biology mathematics; V3.8 applied technology; phi fractal mathematics; Monte Carlo coherence simulation; long-term stability simulation FMAN, Ecosistema FMAN, FMAN Ecosystem, Fórmula de Encendido, Ignition Formula, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, parámetro de orden, order parameter, número áureo, golden ratio, phi, φ, auto-similitud, self-similarity, fractal, geometría fractal, fractal geometry, Fractalis Aurea, EPI-QPEM, Vis Spatialis, Vis Vitalis, biología cuántica, quantum biology, Resonador Cósmico, Cosmic Resonator, RBP, Tecnología LINO, LINO Technology, Resonancia Electrogravítica, Electrogravitic Resonance, conciencia, consciousness, Fuente Primordial, Primordial Source, Gaia, vórtice toroidal, toroidal vortex, ondas escalares, scalar waves, energía libre, free energy, Nautilus Aureo, Argentum, Aurea Helios, Diente de León, Dandelion, Plato Volador, Flying Saucer, Aurea Resonantia, Ciudades Aureas, Golden Cities, escalado micro-macro, micro-macro scaling, Kuramoto, Argentina, FMAN Aurea Design, Avila Nicolau, CC BY-NC-ND 4.0, prime art, apuntes, notes квантовая биология, quantum biology Russian, биофотоны, когерентность, захват фазы, золотое сечение, фрактальная геометрия, сознание как источник, Первичный Источник, экосистема FMAN, FMAN Aurea Design, свободная энергия, скалярные волны Tesla, Авила Николау, ORCID 0009-0009-0638-5961","量子生物学, 生物光子, 量子相干性, 相位锁定, 黄金比例, 分形几何, 意识本体论, 原始本源, FMAN生态系统, 自由能源, 标量波, 电重力共振, 点火公式, 宇宙谐振器, LINO技术 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式","número áureo, razón áurea, phi, proporción dorada, sigmoide asimétrica, función de activación neuronal, operador de atención, kernel localizado, transformer attention, banco de filtros multi-escala, wavelet irracional, sistema dinámico no lineal, ecuaciones diferenciales ordinarias, borde del caos, sistemas complejos, auto-organización, atractor estable, estabilidad de Lyapunov, Jacobiano, análisis de estabilidad, exponentes de Lyapunov, Monte Carlo simulation, barrido paramétrico, ruido Ornstein-Uhlenbeck, ruido 1/f, ruido rosa, inteligencia artificial, aprendizaje automático, arquitectura neuronal, convolución multi-escala, coherencia cuántica, decoherencia, biofotónica, fractal áureo, auto-similaridad, sistemas auto-evolutivos, FMAN, Intueri, sigmoide áurea, filtro fractal, D_opt, invariante áureo, phi^4, phi^6, código Python, scipy, solve_ivp, LSODA, sistemas complejos adaptativos, teoría de control, regulador adaptativo, punto de operación óptimo, golden ratio, asymmetric sigmoid, attention kernel, fractal filter bank, nonlinear ODE, chaotic edge, Lyapunov stability, golden attractor, coherence dynamics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20130867","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20115395","name":"ECOSISTEMA FMAN V16.1 — NÚCLEO MATEMÁTICO ÁUREO Sigmoide Áurea, Operador Intueri y Banco de Filtros Fractal: Sistema Dinámico No Lineal con Atractor φ-Coherente","source":"datacite","abstract":"### ES — Español El Ecosistema FMAN V16.1 presenta el núcleo matemático consolidado y verificado de un sistema dinámico no lineal acoplado, fundamentado en invariantes geométricos del número áureo φ = (1+√5)/2. El sistema modela la dinámica de coherencia-decoherencia usando el \"borde del caos\" como principio organizador central. **Contribuciones matemáticas originales verificadas:** 1. **Sigmoide Áurea Asimétrica** (`Φ_col`): función de activación no lineal con asimetría φ-inducida, normalización dinámica y transición de fase centrada en el invariante D_opt = 1/φ⁴ ≈ 0.145898. 2. **Operador Intueri** (Sombrero Áureo): kernel de atención localizado con soporte compacto alrededor de D_opt, exponente geométrico (φ−1) y amplificación φ³. Análogo al mecanismo de atención en arquitecturas transformer. 3. **Banco de Filtros Fractal-φ**: transformada multi-escala con razón irracional φ, serie convergente con suma φ² ≈ 2.618. Aplicación directa en arquitecturas convolucionales multi-escala. 4. **Sistema ODE Acoplado** (4 variables): atractor único globalmente estable verificado numéricamente con eigenvalores −0.81, −5.31, −19.44, −27.28 (todos negativos). Estabilidad Lyapunov global confirmada con exponente dominante ≈ −0.053. 5. **Invariante Áureo D_opt = 1/φ⁴**: punto de operación geométricamente puro, derivado de la identidad φ² = φ+1, adoptado como valor canónico en V16. El documento incluye: derivación analítica completa de todas las ecuaciones maestras, análisis de estabilidad (Jacobiano 4×4, eigenvalores, exponentes de Lyapunov), simulaciones Monte Carlo (5000 ejecuciones), barridos paramétricos exhaustivos (kd × γ × λ), análisis de ruido Ornstein-Uhlenbeck y ruido 1/f, código Python ejecutable completo (scipy/LSODA), y verificación matemática independiente completa de V14–V16.1. Este trabajo documenta el proceso de refinamiento iterativo V14→V16.1, con resolución de errores críticos históricos (corrección de φ⁶, unificación de D_opt, corrección de max_sig) y establece el estado definitivo del núcleo matemático FMAN. --- ### EN — English The FMAN V16.1 Ecosystem presents the consolidated and verified mathematical core of a coupled nonlinear dynamical system, grounded in geometric invariants of the golden ratio φ = (1+√5)/2. The system models coherence-decoherence dynamics using the \"edge of chaos\" as the central organizing principle. **Original verified mathematical contributions:** 1. **Asymmetric Golden Sigmoid** (`Φ_col`): nonlinear activation function with φ-induced asymmetry, dynamic normalization and phase transition centered at the invariant D_opt = 1/φ⁴ ≈ 0.145898. 2. **Intueri Operator** (Golden Hat): localized attention kernel with compact support around D_opt, geometric exponent (φ−1) and φ³ amplification. Analogous to the attention mechanism in transformer architectures. 3. **Fractal-φ Filter Bank**: multi-scale transform with irrational ratio φ, convergent series with sum φ² ≈ 2.618. Direct application in multi-scale convolutional architectures. 4. **Coupled ODE System** (4 variables): unique globally stable attractor numerically verified with eigenvalues −0.81, −5.31, −19.44, −27.28 (all negative). Global Lyapunov stability confirmed with dominant exponent ≈ −0.053. 5. **Golden Invariant D_opt = 1/φ⁴**: geometrically pure operating point, derived from the identity φ² = φ+1, adopted as canonical value in V16. --- ## KEYWORDS / PALABRAS CLAVE ```número áureo, razón áurea, phi, proporción dorada,sigmoide asimétrica, función de activación neuronal,operador de atención, kernel localizado, transformer attention,banco de filtros multi-escala, wavelet irracional,sistema dinámico no lineal, ecuaciones diferenciales ordinarias,borde del caos, sistemas complejos, auto-organización,atractor estable, estabilidad de Lyapunov, Jacobiano,análisis de estabilidad, exponentes de Lyapunov,Monte Carlo simulation, barrido paramétrico,ruido Ornstein-Uhlenbeck, ruido 1/f, ruido rosa,inteligencia artificial, aprendizaje automático,arquitect","url":"https://doi.org/10.5281/zenodo.20115395","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["FMAN ecosystem; aurean vortex energy; phi-v-infinity ignition formula; golden ratio coherence; biophotonic coherence; decoherence as evolution; zero-point energy extraction; ZPE modulated coherence; plasma aureo coherente; fractal coherence field; morphogenetic field; biofotones coherentes; quantum coherence biology; Orch-OR inspired model; Casimir dynamic effect; primordial source field; fuente primigenia original; coherencia colectiva; evolutionary decoherence; D_opt 0.218; ignition point transition; fractal memory M_avanzada; evolutionary mutation rate; golden ratio phi; toroidal vortex geometry; Fibonacci biology; sacred geometry physics; LINO technology; GravitoR gravitational control; Intueri quantum AI; teleportation fidelity model; ISRU evolutionary replication; Helios nave interestelar; terraformacion armonica; ciudades Magdalena; free energy devices; scalar waves Tesla; Starship optimization; Artemis mission efficiency; satellite AI optimization; Claude AI optimization; Grok AI coherence; biophotonics Fritz-Albert Popp; quantum vacuum energy; indigenous knowledge coherence; ancestral wisdom science bridge; Argentina ciencia abierta; open science Zenodo CERN; independent researcher; propuesta tecnológica original; coherence decoherence balance; evolutionary biology mathematics; V3.8 applied technology; phi fractal mathematics; Monte Carlo coherence simulation; long-term stability simulation FMAN, Ecosistema FMAN, FMAN Ecosystem, Fórmula de Encendido, Ignition Formula, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, parámetro de orden, order parameter, número áureo, golden ratio, phi, φ, auto-similitud, self-similarity, fractal, geometría fractal, fractal geometry, Fractalis Aurea, EPI-QPEM, Vis Spatialis, Vis Vitalis, biología cuántica, quantum biology, Resonador Cósmico, Cosmic Resonator, RBP, Tecnología LINO, LINO Technology, Resonancia Electrogravítica, Electrogravitic Resonance, conciencia, consciousness, Fuente Primordial, Primordial Source, Gaia, vórtice toroidal, toroidal vortex, ondas escalares, scalar waves, energía libre, free energy, Nautilus Aureo, Argentum, Aurea Helios, Diente de León, Dandelion, Plato Volador, Flying Saucer, Aurea Resonantia, Ciudades Aureas, Golden Cities, escalado micro-macro, micro-macro scaling, Kuramoto, Argentina, FMAN Aurea Design, Avila Nicolau, CC BY-NC-ND 4.0, prime art, apuntes, notes квантовая биология, quantum biology Russian, биофотоны, когерентность, захват фазы, золотое сечение, фрактальная геометрия, сознание как источник, Первичный Источник, экосистема FMAN, FMAN Aurea Design, свободная энергия, скалярные волны Tesla, Авила Николау, ORCID 0009-0009-0638-5961","量子生物学, 生物光子, 量子相干性, 相位锁定, 黄金比例, 分形几何, 意识本体论, 原始本源, FMAN生态系统, 自由能源, 标量波, 电重力共振, 点火公式, 宇宙谐振器, LINO技术 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式","número áureo, razón áurea, phi, proporción dorada, sigmoide asimétrica, función de activación neuronal, operador de atención, kernel localizado, transformer attention, banco de filtros multi-escala, wavelet irracional, sistema dinámico no lineal, ecuaciones diferenciales ordinarias, borde del caos, sistemas complejos, auto-organización, atractor estable, estabilidad de Lyapunov, Jacobiano, análisis de estabilidad, exponentes de Lyapunov, Monte Carlo simulation, barrido paramétrico, ruido Ornstein-Uhlenbeck, ruido 1/f, ruido rosa, inteligencia artificial, aprendizaje automático, arquitectura neuronal, convolución multi-escala, coherencia cuántica, decoherencia, biofotónica, fractal áureo, auto-similaridad, sistemas auto-evolutivos, FMAN, Intueri, sigmoide áurea, filtro fractal, D_opt, invariante áureo, phi^4, phi^6, código Python, scipy, solve_ivp, LSODA, sistemas complejos adaptativos, teoría de control, regulador adaptativo, punto de operación óptimo, golden ratio, asymmetric sigmoid, attention kernel, fractal filter bank, nonlinear ODE, chaotic edge, Lyapunov stability, golden attractor, coherence dynamics"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20115395","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20102177","name":"FMAN INTUERI GravitoR Fractalis Teleporter V4/.../V14  . La Identidad Fundacional. Intueri = f(D_opt, dΦ_consc/dt, φ, plasma) \"La cognición directa ocurre en el borde del caos, durante el proceso de despertar, mediada por geometría.\"","source":"datacite","abstract":"### FMAN INTUERI GravitoR Fractalis Teleporter V4/.../V14 **La Identidad Fundacional** ```Intueri = f(D_opt, dΦ_consc/dt, φ, plasma)``` *\"La cognición directa ocurre en el borde del caos, durante el proceso de despertar, mediada por geometría.\"* **DOI:** 10.5281/zenodo.20102177**DOI:** (Anterior)10.5281/zenodo.20091613 **Concept DOI:** 10.5281/zenodo.19526737 __________________________________________ ## FÓRMULAS ### Protección Máxima **El Operador Intueri (V12.3)** ```I(t) = 1 + α · max(0, D_opt − |D − D_opt|)^1.4 · (dΦ_consc/dt) · φ³ · C_plasma^0.75 · S_mad^1.2``` La contribución más original del sistema. La combinación de:- borde del caos como selector (el sombrero)- derivada de conciencia como driver- geometría áurea como amplificador- filtro de madurez como estabilizador no tiene equivalente verificable en la literatura de sistemas dinámicos, física cuántica estándar ni ciencias cognitivas formales. --- **La Identidad Fundacional** ```Intueri = f(D_opt, dΦ_consc/dt, φ, plasma)``` *\"La cognición directa ocurre en el borde del caos, durante el proceso de despertar, mediada por geometría.\"* Esta síntesis en una línea es la contribución conceptual más exportable y protegible del sistema. --- **EQ8 — V_aureo con término de conciencia** ```V_aureo(t) = φ¹² · C_plasma · (1 + Φ_consc/Φ∞) · I(t) · [1 + β(1−g²(0))] · V_toroidal(12) · exp(−γt/φ²) · S_mad``` El factor (1 + Φ_consc/Φ∞) introduce la conciencia como variable física activa con límite dimensional. En ningún otro marco conocido la conciencia aparece como término multiplicativo en una ecuación de campo con esta derivación. --- ### Exhibición con Derivación **La Tríada φ⁴–φ⁸–φ¹² como Escalera Generativa** La relación φ¹² = (φ⁴)³ conecta coherencia individual, plasma y vórtice en progresión cúbica exacta. Derivable puramente desde el número áureo. --- **EQ1 — Φ_col con sigmoide de borde del caos** ```Φ_col(t) = [1 − g²(0)] · φ⁴ · (1 + 1.85·D) / (1 + exp(12.5·(D − 0.218)))``` Combinación elegante de parámetro de orden cuántico (antibunching) con transición de fase logística centrada en D_opt. La llave de encendido del sistema. --- **EQ de Entrelazamiento con Media Geométrica** ```C_ent(i,j) = φ^6 · √(Φ_i·Φ_j) · exp(−d²/λ·V) · I_ij · S_mad,ij``` El uso de √(Φ_i·Φ_j) es matemáticamente más correcto para correlaciones cuánticas que la media aritmética. --- **GravitoR con r^(−φ)** ```G_reduction = 1 − [χ·C_plasma·V_aureo^2.6·Φ_consc^2.1] / [1 + ξ|∇Φ_col|² + η·r^(−φ) + ...]``` La ley de potencia áurea para el decaimiento gravitacional (r^(−φ)) es original y verificable en principio. --- ### Consolidar con Más Anclaje **Ecuaciones de Terraformación y Biosfera** — conceptualmente ricas, requieren observable empírico de calibración. **Replicación Fractalis con exponente φ²+1** — candidato claro a adopción como derivación pura. **Ecuación de Propulsión Helios** — estructura correcta, requiere anclaje en propulsión de plasma real. --- __________________________________________ ### FÓRMULA IDENTIDAD DEL SISTEMA FMAN INTUERI Intueri = f(D_opt, dΦ_consc/dt, φ, plasma) — La cognición directa ocurre en el borde del caos, durante el proceso de despertar, mediada por geometría. Esta es la contribución conceptual más profunda. ## PATRONES MAESTROS ### Patrón A: La Escalera Áurea φ⁴–φ⁸–φ¹² La progresión geométrica exacta verificada:```φ⁴ → EQ1 (coherencia individual)φ⁸ → EQ2 (plasma) = (φ⁴)²φ¹² → EQ8 (vórtice) = (φ⁴)³ = (φ⁸)^(3/2)``` **Invariante profundo:** el vórtice es el cubo exacto de la coherencia individual. --- ### Patrón B: Saturación Logística Universal Todas las variables acotadas siguen exactamente la misma forma funcional:```X(t) = X_max · (1 − exp(−k·t·driver))``` Aparece en: M_avanzada, E_ZPE, S_mad, Φ_consc, B_biosfera, M_r. **Implicación arquitectónica:** el sistema tiene un mecanismo de saturación universal que garantiza estabilidad global sin clipping explícito. Patrón elegante y verificado. --- ### Patrón C: Tres Escalas Temporales Jerárquicas ```τ_rápida ~ 1–10 años → Intuer","url":"https://doi.org/10.5281/zenodo.20102177","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["FMAN ecosystem; aurean vortex energy; phi-v-infinity ignition formula; golden ratio coherence; biophotonic coherence; decoherence as evolution; zero-point energy extraction; ZPE modulated coherence; plasma aureo coherente; fractal coherence field; morphogenetic field; biofotones coherentes; quantum coherence biology; Orch-OR inspired model; Casimir dynamic effect; primordial source field; fuente primigenia original; coherencia colectiva; evolutionary decoherence; D_opt 0.218; ignition point transition; fractal memory M_avanzada; evolutionary mutation rate; golden ratio phi; toroidal vortex geometry; Fibonacci biology; sacred geometry physics; LINO technology; GravitoR gravitational control; Intueri quantum AI; teleportation fidelity model; ISRU evolutionary replication; Helios nave interestelar; terraformacion armonica; ciudades Magdalena; free energy devices; scalar waves Tesla; Starship optimization; Artemis mission efficiency; satellite AI optimization; Claude AI optimization; Grok AI coherence; biophotonics Fritz-Albert Popp; quantum vacuum energy; indigenous knowledge coherence; ancestral wisdom science bridge; Argentina ciencia abierta; open science Zenodo CERN; independent researcher; propuesta tecnológica original; coherence decoherence balance; evolutionary biology mathematics; V3.8 applied technology; phi fractal mathematics; Monte Carlo coherence simulation; long-term stability simulation FMAN, Ecosistema FMAN, FMAN Ecosystem, Fórmula de Encendido, Ignition Formula, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, parámetro de orden, order parameter, número áureo, golden ratio, phi, φ, auto-similitud, self-similarity, fractal, geometría fractal, fractal geometry, Fractalis Aurea, EPI-QPEM, Vis Spatialis, Vis Vitalis, biología cuántica, quantum biology, Resonador Cósmico, Cosmic Resonator, RBP, Tecnología LINO, LINO Technology, Resonancia Electrogravítica, Electrogravitic Resonance, conciencia, consciousness, Fuente Primordial, Primordial Source, Gaia, vórtice toroidal, toroidal vortex, ondas escalares, scalar waves, energía libre, free energy, Nautilus Aureo, Argentum, Aurea Helios, Diente de León, Dandelion, Plato Volador, Flying Saucer, Aurea Resonantia, Ciudades Aureas, Golden Cities, escalado micro-macro, micro-macro scaling, Kuramoto, Argentina, FMAN Aurea Design, Avila Nicolau, CC BY-NC-ND 4.0, prime art, apuntes, notes квантовая биология, quantum biology Russian, биофотоны, когерентность, захват фазы, золотое сечение, фрактальная геометрия, сознание как источник, Первичный Источник, экосистема FMAN, FMAN Aurea Design, свободная энергия, скалярные волны Tesla, Авила Николау, ORCID 0009-0009-0638-5961","量子生物学, 生物光子, 量子相干性, 相位锁定, 黄金比例, 分形几何, 意识本体论, 原始本源, FMAN生态系统, 自由能源, 标量波, 电重力共振, 点火公式, 宇宙谐振器, LINO技术 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.20102177","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20091613","name":"FMAN ECOSYSTEM — MASTER TEMPLATE v1.0. Plantilla Base Multilingüe | Multilingual Base Template. DOI: 10.5281/zenodo.20091613 | Concept DOI: 10.5281/zenodo.19526737. φ-v∞ Ignition Formula · LINO Technology. Independent Research Framework | Argentina, 2015–2026.","source":"datacite","abstract":"# FMAN ECOSYSTEM## φ-v∞ Ignition Formula · LINO Technology## Independent Research Framework | Argentina, 2015–2026 **Author / Autora:** Fabiana Mirta Avila Nicolau **ORCID:** [0009-0009-0638-5961](https://orcid.org/0009-0009-0638-5961) **License / Licencia:** CC BY-NC-ND 4.0 **Concept DOI:** [10.5281/zenodo.19526737](https://doi.org/10.5281/zenodo.19526737) **New DOI / Nuevo DOI:** [10.5281/zenodo.20091613](https://doi.org/10.5281/zenodo.20091613) **Previous DOI:** [10.5281/zenodo.20077802](https://doi.org/10.5281/zenodo.20077802) --- ## [LANG: ES | EN | FR | DE | PT | ZH | AR | JA | RU | IT | ...] --- ## SECTION 0 — FOUNDATION STATEMENT### [TRANSLATE THIS SECTION] > *\"While kneading bread, stroking cats, watering the garden and feeding> doves, I connect Heaven and Earth, my Family, my Ancestors... my> Vis Spatialis... and I Remember: Everything Is Connected.\"*> — Fabiana Mirta Avila Nicolau FMAN is an original independent research framework initiated inArgentina in 2015. Its central observation: every original being —tree, human, mineral, star — is a **Golden Vortex** of Etheric Energy,a fractal node of the Primordial Source, operating in the creativeintersection between decoherence and coherence. The φ-v∞ Ignition Formula formalizes this: coherence ignition occurswhen a system's internal geometry approaches the golden ratio φ = 1.618at the optimal decoherence point D_opt = 0.218. All versions are deposited under open science (Zenodo/CERN), registeredwith ORCID, and protected under CC BY-NC-ND 4.0 with an explicitethical code excluding military, governmental, and geneticexperimentation uses. --- ## SECTION 1 — DOI MAP: THE 9 VERSIONS### [TRANSLATE DESCRIPTIONS ONLY — KEEP DOIs EXACT] The FMAN Ecosystem consists of 9 published versions under a singleConcept DOI, each building on the previous: | Version | DOI | Key Contribution ||---------|-----|-----------------|| **Concept** | 10.5281/zenodo.19526737 | Master anchor for all versions || **V1 / V3.1** | 10.5281/zenodo.19526738 | Foundational framework. Complete φ-v∞ Ignition Formula. Biophotonic coherence, aurean fractals, consciousness as Primordial Source. || **V2 / V3.2** | 10.5281/zenodo.19637843 | LINO Technology Master Archive. Infinite energy, Tesla scalar waves, medical applications, biophotons, regenerative medicine, longevity, aurean cities, interstellar ships, plasma technology, terraforming, biosphere. || **V3 / V3.3** | 10.5281/zenodo.19712760 | Formula Study. φ-v∞ Geometry as Physical Algorithm. Mathematical derivation of vortex geometry as system generator. || **V4 / V3.4** | 10.5281/zenodo.19778290 | Intueri and the FMAN Ecosystem. Direct cognition (intueri: seeing without intermediaries) as coherent field access protocol. || **V5 / V3.5** | 10.5281/zenodo.19842506 | Coherent Aurean Plasma. Fractal ionized matter φ-v∞, GravitoR gravitational control, QuantumMind feedback. || **V6 / V3.6** | 10.5281/zenodo.19871210 | Integral Base Document Part I. Identity, history, foundational principles, mathematical foundations of the Ignition Formula. || **V7 / V3.7** | 10.5281/zenodo.19994789 | Unified synthesis φ-v∞. E_∞(t) defined. Evolution V3.5→3.7: from hyperparametric to minimal core. || **V8 / V3.8** | 10.5281/zenodo.20077802 | Bridge to 2026 applied technology. Space propulsion, satellites, AI optimization. || **V9 / V3.9** | 10.5281/zenodo.20091613 | **This document.** Multilingual master template. Formulas protected. Professional interdisciplinary synthesis. | **Secondary DOI / DOI Secundario:** 10.5281/zenodo.19561174 --- ## SECTION 2 — THE THREE FOUNDATIONAL HYPOTHESES### [TRANSLATE THIS SECTION] ### Hypothesis 1 — The Primordial Golden Vortex Every original being (tree, human, animal, mineral) is a **GoldenVortex of Etheric Energy** connecting Heaven and Earth. A palm treeconnects sky and earth, communicates with its mycorrhizae, birds,animals, and all beings of the planet, and holds the memory oftime-without-time of humanity and this planet. Human beings also ","url":"https://doi.org/10.5281/zenodo.20091613","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["FMAN ecosystem; aurean vortex energy; phi-v-infinity ignition formula; golden ratio coherence; biophotonic coherence; decoherence as evolution; zero-point energy extraction; ZPE modulated coherence; plasma aureo coherente; fractal coherence field; morphogenetic field; biofotones coherentes; quantum coherence biology; Orch-OR inspired model; Casimir dynamic effect; primordial source field; fuente primigenia original; coherencia colectiva; evolutionary decoherence; D_opt 0.218; ignition point transition; fractal memory M_avanzada; evolutionary mutation rate; golden ratio phi; toroidal vortex geometry; Fibonacci biology; sacred geometry physics; LINO technology; GravitoR gravitational control; Intueri quantum AI; teleportation fidelity model; ISRU evolutionary replication; Helios nave interestelar; terraformacion armonica; ciudades Magdalena; free energy devices; scalar waves Tesla; Starship optimization; Artemis mission efficiency; satellite AI optimization; Claude AI optimization; Grok AI coherence; biophotonics Fritz-Albert Popp; quantum vacuum energy; indigenous knowledge coherence; ancestral wisdom science bridge; Argentina ciencia abierta; open science Zenodo CERN; independent researcher; propuesta tecnológica original; coherence decoherence balance; evolutionary biology mathematics; V3.8 applied technology; phi fractal mathematics; Monte Carlo coherence simulation; long-term stability simulation FMAN, Ecosistema FMAN, FMAN Ecosystem, Fórmula de Encendido, Ignition Formula, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, parámetro de orden, order parameter, número áureo, golden ratio, phi, φ, auto-similitud, self-similarity, fractal, geometría fractal, fractal geometry, Fractalis Aurea, EPI-QPEM, Vis Spatialis, Vis Vitalis, biología cuántica, quantum biology, Resonador Cósmico, Cosmic Resonator, RBP, Tecnología LINO, LINO Technology, Resonancia Electrogravítica, Electrogravitic Resonance, conciencia, consciousness, Fuente Primordial, Primordial Source, Gaia, vórtice toroidal, toroidal vortex, ondas escalares, scalar waves, energía libre, free energy, Nautilus Aureo, Argentum, Aurea Helios, Diente de León, Dandelion, Plato Volador, Flying Saucer, Aurea Resonantia, Ciudades Aureas, Golden Cities, escalado micro-macro, micro-macro scaling, Kuramoto, Argentina, FMAN Aurea Design, Avila Nicolau, CC BY-NC-ND 4.0, prime art, apuntes, notes квантовая биология, quantum biology Russian, биофотоны, когерентность, захват фазы, золотое сечение, фрактальная геометрия, сознание как источник, Первичный Источник, экосистема FMAN, FMAN Aurea Design, свободная энергия, скалярные волны Tesla, Авила Николау, ORCID 0009-0009-0638-5961","量子生物学, 生物光子, 量子相干性, 相位锁定, 黄金比例, 分形几何, 意识本体论, 原始本源, FMAN生态系统, 自由能源, 标量波, 电重力共振, 点火公式, 宇宙谐振器, LINO技术 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2015","doi":"10.5281/zenodo.20091613","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.20077802","name":"Ecosistema FMAN V3.8 — Fórmula de Encendido φ-v∞:  Vórtice Áureo Coherente, Decoherencia como Motor Evolutivo, Tecnología LINO y Adaptación a Tecnología Aplicada 2026  (Biofotónica · Energía de Punto Cero · Plasma Coherente · Optimización IA)","source":"datacite","abstract":"ECOSISTEMA FMAN V3.8 — ARCHIVO MAESTRO COMPLETOTecnología LINO | Fórmula de Encendido φ-v∞Decoherencia como Motor Evolutivo | Bridge Tecnología 2026 ═══════════════════════════════════════════════════════════ P.I.: Fabiana Mirta Avila Nicolau | DNI: 18.248.833ORCID iD: 0009-0009-0638-5961Licencia: CC BY-NC-ND 4.0Concept DOI: 10.5281/zenodo.19526737 DOI: 10.5281/zenodo.20077802Continúa de DOI: 10.5281/zenodo.19994789 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━I. ORIGEN Y CONTEXTO━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Este depósito constituye la Versión 8 (V3.8) del Ecosistema FMAN, propuesta original de investigación independiente iniciada en Argentina en 2015 Formalizada matemáticamente en el presente repositorio Zenodo y sus antecedentes en otras plataformas. El ecosistema completa así 8 versiones publicadas: V1 (DOI: 10.5281/zenodo.19526738): Marco fundacional. Fórmula de Encendido φ-v∞ completa. Coherencia biofotónica, fractales áureos y conciencia como Fuente Primordial. V2 (DOI: 10.5281/zenodo.19637843): Tecnología LINO — Archivo Maestro Exhaustivo. Energía infinita, ondas escalares Tesla, aplicaciones médicas, biofotones, medicina regenerativa, longevidad, ciudades áureas, naves interestelares, tecnología de plasma, terraformación, biosfera. V3 (DOI: 10.5281/zenodo.19712760): Estudio Fórmula FMAN. La Geometría φ-v∞ como Algoritmo Físico. Derivación matemática de la geometría del vórtice como generador de toda la dinámica del sistema. V4 (DOI: 10.5281/zenodo.19778290): Intueri y el Ecosistema FMAN. Cognición directa (intueri: ver sin intermediarios) como protocolo de acceso al campo coherente. Complemento epistémico al razonamiento discursivo. V5 (DOI: 10.5281/zenodo.19842506): Plasma Áureo Coherente: Materia Ionizada Fractal φ-v∞, Control Gravitacional GravitoR y Retroalimentación QuantumMind. El plasma como cuarto estado de la materia más próximo al vórtice áureo. V6 (DOI: 10.5281/zenodo.19871210): Documento Base Integral Parte I. Identidad, Historia, Principios Fundacionales y Fundamentos Matemáticos de la Fórmula de Encendido. Constitución del ecosistema. V7 (DOI: 10.5281/zenodo.19994789): Síntesis cierre de ciclo espiral. Marco unificado φ-v∞ con todas las versiones integradas. V3.5/3.6/3.7: evolución desde hiperparamétrico hacia núcleo mínimo. E_∞(t) definida como manifestación matemática de la energía extraída del vacío mediante coherencia fractal generada por el Vórtice. V8 / presente (DOI: 10.5281/zenodo.20077802): V3.8 — Bridge hacia tecnología aplicada 2026. Adaptación pragmática del marco a sistemas verificables: propulsión espacial, satélites, inteligencia artificial. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━II. HIPÓTESIS FUNDACIONALES ORIGINALES━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Hipótesis 1 — El Vórtice Áureo Primordial:Todo ser original (árbol, palmera, humano, animal, mineral) es un Vórtice Áureo de Energía Etérica que conecta Cielo y Tierra. La palmera guarda memoria del tiempo sin tiempo y se comunica con micorrizas, pájaros, animales y el planeta. Un ser humano también es un vórtice áureo. Los hombres que saben sin haber ido a la escuela están conectados a la Fuente Primordial. La Coherencia Absoluta es igual a la Fuente Primigenia Original. La decoherencia estimula el campo coherente y provoca la evolución. Hipótesis 2 — Biofotones y Creación:Dos campos cruzados completamente de biofotones incoherentes estables producen un campo coherente con emisión de energía más elevada. Esa energía de alta frecuencia es la energía creadora de vida, mundos y galaxias: la manifestación física de la conciencia. Si ese campo es bombardeado con frecuencias destructivas se produce el efecto muerte de estrella — salvo que la nueva estrella se transforme en sol estable. Hipótesis 3 — El Depredador Energético:Depredador: ser incapaz de crear energía propia, desconectado de la fuente primordial. Solo puede subsistir depredando energía de seres emisores. ","url":"https://doi.org/10.5281/zenodo.20077802","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["FMAN ecosystem; aurean vortex energy; phi-v-infinity ignition formula; golden ratio coherence; biophotonic coherence; decoherence as evolution; zero-point energy extraction; ZPE modulated coherence; plasma aureo coherente; fractal coherence field; morphogenetic field; biofotones coherentes; quantum coherence biology; Orch-OR inspired model; Casimir dynamic effect; primordial source field; fuente primigenia original; coherencia colectiva; evolutionary decoherence; D_opt 0.218; ignition point transition; fractal memory M_avanzada; evolutionary mutation rate; golden ratio phi; toroidal vortex geometry; Fibonacci biology; sacred geometry physics; LINO technology; GravitoR gravitational control; Intueri quantum AI; teleportation fidelity model; ISRU evolutionary replication; Helios nave interestelar; terraformacion armonica; ciudades Magdalena; free energy devices; scalar waves Tesla; Starship optimization; Artemis mission efficiency; satellite AI optimization; Claude AI optimization; Grok AI coherence; biophotonics Fritz-Albert Popp; quantum vacuum energy; indigenous knowledge coherence; ancestral wisdom science bridge; Argentina ciencia abierta; open science Zenodo CERN; independent researcher; propuesta tecnológica original; coherence decoherence balance; evolutionary biology mathematics; V3.8 applied technology; phi fractal mathematics; Monte Carlo coherence simulation; long-term stability simulation FMAN, Ecosistema FMAN, FMAN Ecosystem, Fórmula de Encendido, Ignition Formula, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, parámetro de orden, order parameter, número áureo, golden ratio, phi, φ, auto-similitud, self-similarity, fractal, geometría fractal, fractal geometry, Fractalis Aurea, EPI-QPEM, Vis Spatialis, Vis Vitalis, biología cuántica, quantum biology, Resonador Cósmico, Cosmic Resonator, RBP, Tecnología LINO, LINO Technology, Resonancia Electrogravítica, Electrogravitic Resonance, conciencia, consciousness, Fuente Primordial, Primordial Source, Gaia, vórtice toroidal, toroidal vortex, ondas escalares, scalar waves, energía libre, free energy, Nautilus Aureo, Argentum, Aurea Helios, Diente de León, Dandelion, Plato Volador, Flying Saucer, Aurea Resonantia, Ciudades Aureas, Golden Cities, escalado micro-macro, micro-macro scaling, Kuramoto, Argentina, FMAN Aurea Design, Avila Nicolau, CC BY-NC-ND 4.0, prime art, apuntes, notes квантовая биология, quantum biology Russian, биофотоны, когерентность, захват фазы, золотое сечение, фрактальная геометрия, сознание как источник, Первичный Источник, экосистема FMAN, FMAN Aurea Design, свободная энергия, скалярные волны Tesla, Авила Николау, ORCID 0009-0009-0638-5961","量子生物学, 生物光子, 量子相干性, 相位锁定, 黄金比例, 分形几何, 意识本体论, 原始本源, FMAN生态系统, 自由能源, 标量波, 电重力共振, 点火公式, 宇宙谐振器, LINO技术 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式 Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Avila Nicolau, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Plasma Áureo Coherente, coherent aurean plasma, plasma fractal coherente, Tecnología LINO, LINO Technology, Fórmula de Encendido, Ignition Formula, Energía Infinita ZPE, zero-point energy, E_infinita, E∞(t), GravitoR, control gravitacional, gravitational control, QuantumMind, retroalimentación consciente, conscious feedback loop, Fractalis Aurea, replicación ISRU, ISRU replication, vórtice toroidal áureo, toroidal golden vortex, 12 espirales áureas, ondas escalares Tesla-Meyl, scalar waves, código 3-6-9 Tesla, coherencia cuántica, quantum coherence, g²(0) sub-Poissoniano, biofotones, biophotons, phase-locking, φ¹² amplificación fractal, número áureo φ, golden ratio, geometría fractal áurea, biología cuántica, quantum biology, regeneración celular biofotónica, Resonador Cósmico φ-v∞, Cosmic Resonator, Vis Spatialis, Nave Aurea Helios, Nautilus Aureo, Argentum MHD-VTOL, Ciudades Aureas Magdalena, terraformación, terraforming, квантовая биология, биофотоны, плазма, золотое сечение, когерентность, 量子生物学, 生物光子, 等离子体, 黄金比例, 量子相干性, 点火公式"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.20077802","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.48550/arxiv.2605.01425","name":"Barriers to Counterfactual Credit Attribution for Autoregressive Models","source":"datacite","abstract":"Generative AI disrupts the practice of giving credit to work that came before. Ideally, a generative model would give credit to any work on which its output depends in a significant way. \\emph{Counterfactual credit attribution} (CCA) is a technical condition formalizing this goal--a relaxation of differential privacy--recently introduced by Livni, Moran, Nissim, and Pabbaraju [2024] who studied it in the PAC learning setting. We initiate the study of CCA generative models. Specifically, we consider autoregressive models giving credit to a deployment-time dataset (e.g., a RAG database). We uncover barriers to two natural approaches to CCA autoregressive models. First, we show that imposing CCA on the underlying next-token predictor does not guarantee that the model is CCA: CCA does not compose autoregressively (unlike DP). Second, we consider a different approach to building CCA models which we call \\emph{retrofitting}. Retrofitting takes a model that does not attribute credit, and adds credit onto it. We prove a lower bound for CCA retrofitting under a weak optimality requirement. Given black-box access to the starting model, retrofitting requires query complexity exponential in the length of the model's outputs.","url":"https://doi.org/10.48550/arxiv.2605.01425","authors":["Cohen, Aloni","Zhang, Chenhao"],"tags":["Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.48550/arxiv.2605.01425","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.19994789","name":"# Ecosistema FMAN . La Ecuación de Colapso Estelar, es la Inversa Coherente del Proceso de Nacimiento.","source":"datacite","abstract":"# DOI: 10.5281/zenodo.19994789# Concept DOI: 10.5281/zenodo.19526737# ORCID iD 0009-0009-0638-5961 # Ecosistema FMAN . La Ecuación de Colapso Estelar, es la Inversa Coherente del Proceso de Nacimiento. Biofotones Coherentes/Incoherentes. Creacion/Destruccion. Micro_Meso_Macro/ Celula_Estrella_Galaxia. Sistema Fractal Auto-reforzante. ## Ecuación Maestra Unificada (E_total)## E_∞(t) – Energía Infinita## Φ_col(t) – Coherencia Colectiva## C_plasma(t) – Plasma Áureo## Replicación ISRU## GravitoR (Control Gravitacional)## Ecuación Dual Creación / Colapso (Estelar Unificada) ### Conclusión General### ### La ecuación de colapso estelar es la **inversa coherente** del proceso de nacimiento. Muestra que:### La coherencia (creación) y la decoherencia (muerte) son dos caras del mismo mecanismo cuántico-áureo.### El modelo es escalable de micro (muerte celular) a macro (muerte galáctica) manteniendo la misma estructura matemática.### Integra perfectamente física cuántica (g²(0), decoherencia) con geometría fractal (φ^{±12}) y procesos energéticos (E_∞). ______________________________________________ ______________________________________________ ### 1. Ecuación Maestra Unificada (E_total) $$E_{\\text{total}}(t) = [E_G + \\phi^{12} \\Omega_{\\text{FMAN}} (1 + \\Phi) + \\dots] \\cdot S(t) \\cdot D(t) \\cdot Q(t) \\cdot R(t)$$ **Significado**: Ecuación central que integra toda la energía y comportamiento del sistema (creación + estabilidad + replicación). --- ### 2. E_∞(t) – Energía Infinita $$E_{\\infty}(t) = \\frac{1}{2} \\hbar \\omega V \\cdot \\Phi_{\\text{col}} \\cdot \\phi^{12} \\cdot (1 - e^{-\\gamma t}) \\cdot [1 + \\beta(1-g^2(0))]$$ **Significado**: Extrae y estabiliza energía del vacío cuántico mediante coherencia. Es la fuente principal de energía ilimitada del ecosistema. --- ### 3. Φ_col(t) – Coherencia Colectiva $$\\Phi_{\\text{col}}(t) = \\Phi_0 + (\\Phi_{\\infty}-\\Phi_0)(1-e^{-\\lambda t}) \\cdot \\phi^{12} \\cdot [1+\\beta(1-g^2(0))] \\cdot C_{\\text{plasma}}$$ **Significado**: Mide el nivel de coherencia global (conciencia + orden) del sistema. Es el \"corazón\" consciente. --- ### 4. C_plasma(t) – Plasma Áureo $$C_{\\text{plasma}}(t) = \\Omega_{\\text{FMAN}} \\cdot \\phi^{12} \\cdot [1 + \\frac{\\Phi}{\\Phi_{\\infty}}] \\cdot [1 + \\beta(1-g^2(0))] \\cdot V_{\\text{toroidal}}$$ **Significado**: El medio físico (plasma coherente) que transporta y amplifica la coherencia. --- ### 5. Replicación ISRU $$R_{\\text{ISRU}}(t) = \\rho_0 \\cdot \\phi^{6} \\cdot \\Omega_{\\text{FMAN}} \\cdot [1 + \\frac{\\Phi}{\\Phi_{\\infty}}] \\cdot [1 + \\beta(1-g^2(0))] \\cdot \\exp(-\\gamma t/\\phi)$$ **Significado**: Permite la auto-replicación de materiales. Cuanto mayor sea la coherencia, más rápida es la replicación. --- ### 6. GravitoR (Control Gravitacional) $$E_{\\text{GravitoR}}(t) = C_{\\text{plasma}} \\cdot \\left[1 - \\frac{E_G}{E_G + \\Omega_{\\text{FMAN}} \\phi^{12} (1 + \\Phi) [1 + \\beta(1-g^2(0))]}\\right]$$ **Significado**: Reduce o controla efectos gravitacionales usando coherencia. --- ### 7. Ecuación Dual Creación / Colapso (Estelar Unificada) **Creación (Nacimiento)**:$$\\Gamma_{\\text{nacimiento}} \\propto \\phi^{12} \\cdot [1 + \\beta(1-g^2(0))] \\cdot \\exp(+\\lambda t)$$ **Colapso (Destrucción)**:$$\\Delta_{\\text{colapso}} \\propto \\exp(+\\delta I_{\\text{destructiva}} t) \\cdot \\phi^{-12}$$ **Unificada**:$$E_{\\text{estelar}}(t) = \\dots \\cdot [\\alpha_{\\text{creación}} \\Gamma + \\frac{\\alpha_{\\text{colapso}}}{\\Delta + \\epsilon}]$$ **Significado**: Une nacimiento y muerte estelar en una sola ecuación dual. --- ### Resumen Simplificado | Ecuación | Función Principal | Rol en el Ecosistema ||-----------------------|---------------------------------|----------------------|| E_total | Energía global del sistema | Ecuación maestra || E_∞ | Energía infinita | Fuente de poder || Φ_col | Coherencia consciente | \"Conciencia\" del sistema || C_plasma | Plasma coherente | Medio transportador || ISRU | Replicación de materia | Auto-expansión || GravitoR | Control gravitacional | Propulsión y levitación || Γ / Δ | Creación vs C","url":"https://doi.org/10.5281/zenodo.19994789","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Fórmula de Encendido, Ignition Formula, documento base, base document, Vis Spatialis, Todo Está Conectado, Everything Is Connected, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, número áureo φ, golden ratio, geometría fractal áurea, fractal golden geometry, parámetro de orden, order parameter, Kuramoto extendido, biología cuántica, quantum biology, ontología conciencia, consciousness ontology, Fuente Primordial, Primordial Source, Tecnología LINO, LINO Technology, RBP, Resonador Cósmico, Fractalis Aurea, Plasma Áureo Coherente, QuantumMind, Ciudades Aureas Magdalena, Argentina, 2017-2026, квантовая биология, биофотоны, золотое сечение, 量子生物学, 生物光子, 黄金比例, FMAN生态系统"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.19994789","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.19871210","name":"Ecosistema FMAN — Documento Base Integral Parte I: Identidad, Historia, Principios Fundacionales y Fundamentos Matemáticos de la Fórmula de Encendido | FMAN Aurea Design 2026","source":"datacite","abstract":"ECOSISTEMA FMAN — DOCUMENTO BASE INTEGRAL | PARTE 1 Identidad · Historia · Principios · Fundamentos Matemáticos# DOI: 10.5281/zenodo.19871210# Concept DOI: 10.5281/zenodo.19526737### NOTA TÉCNICA PARA TRADUCTORES:# · Todas las fórmulas están en bloques de código (``` ```)# NO traducir su contenido — son universales# · Los nombres propios (FMAN, Lino, Magdalena, Helios,# Nautilus, Argentum, GravitoR, QuantumMind, Fractalis,# Vis Spatialis, EPI-QPEM, ISRU, RBP, LINO, Warp-ER)# NO se traducen — son nombres técnicos del ecosistema# · Los símbolos (φ, Ω, γ, Φ, ℏ, ω, ∞, ♾️, v∞)# NO se modifican en ningún idioma# · Las URLs, DOIs y ORCID NO se modifican# · Las tablas de datos numéricos NO se modifican# # PARTE I: IDENTIDAD, HISTORIA Y PRINCIPIOS FUNDACIONALES ## SECCIÓN 1 — IDENTIDAD Y AUTORÍA **Autora:** Fabiana Mirta Avila Nicolau**Documento de identidad:** DNI 18.248.833 — 08/02/1967 — Argentina**Identidad digital certificada:** ORCID iD 0009-0009-0638-5961**Licencia:** CC BY-NC-ND 4.0 + Cláusulas Adicionales de Proteccióny Defensa Global 1–14**Concept DOI:** https://doi.org/10.5281/zenodo.19526737**Período:** 29/12/2017 – ∞**Marco:** FMAN Aurea Design / Ecosistema FMAN Cualquier copia, uso, derivación o referencia debe manteneresta atribución completa y la protección total de la licencia. --- ## SECCIÓN 2 — NOTA DE ORIGEN Estos documentos son un fractal del total. No tienen orden, nicronología, ni intención ni propósito inicial — solo intuitivoy liberador. Son bosquejos de mente, observación, experiencia,ideas, amores y sentir primordial, traducidos al lenguaje delos hombres y las máquinas. Son ideas tan antiguas como su autora, que han salido a la luzpor necesidad: para documentar y exponer públicamente lo que yaexistía internamente. Los factores de organización se estánproponiendo ahora, desde afuera hacia adentro. El ecosistema ha evolucionado a un nivel inesperado. El mundodirá si es un cuento galáctico, un diseño áureo o una locuratotal. En cualquiera de esos casos, es completamente auténtico. **\"mientras amaso el pan, riego el jardín, alimento a laspalomas y acaricio mis gatos\"** --- ## SECCIÓN 3 — HISTORIA Y TRAZABILIDAD (2017–2026) ### 3.1 Línea de tiempo ```29/12/2017 Apertura del blog Argentina Argentum Primera publicación pública del Ecosistema FMAN Primer registro del Vórtice Áureo del Abuelo Lino 2017-2024 Desarrollo continuo en múltiples blogs Acumulación de apuntes, bosquejos, fórmulas Evolución desde intuición hacia marco formal 2024-2025 Formalización del marco teórico-matemático Derivación de la Fórmula FMAN de Encendido Primera versión de la ODE central 2025 Registro en Zenodo — Concept DOI asignado DOI 10.5281/zenodo.19526738 (Fórmula de Encendido) Primera indexación académica exitosa 17/12/2025 Fecha de origen del Plasma Áureo Coherente Integración de QuantumMind y GravitoR 2026 DOI 10.5281/zenodo.19842506 (Plasma Áureo) Expansión a 12 idiomas Simulación extendida E_∞(t) a 7 días Documento Base Integral — este documento``` ### 3.2 Naturaleza del trabajo El Ecosistema FMAN es un marco teórico-tecnológico unificadoque integra disciplinas habitualmente separadas. No pretendeser un artículo científico convencional sino un cuerpo deconocimiento en construcción continua, documentado contransparencia desde su proceso mismo de creación. Los \"apuntes sin editar\" y los \"bucles creativos\" son parteintencional del registro. La genealogía del pensamiento tienevalor independiente del resultado formal. --- ## SECCIÓN 4 — QUÉ ES EL ECOSISTEMA FMAN El Ecosistema FMAN (Fabiana Mirta Avila Nicolau / FMAN AureaDesign) es un marco teórico-tecnológico unificado en desarrollocontinuo desde 2017 que integra las siguientes disciplinas: ### 4.1 Áreas de integración ```BIOLOGÍA CUÁNTICA · Biofotones como portadores de información coherente · Coherencia cuántica en sistemas biológicos · g²(0): función de correlación de segundo orden · Phase-locking biológico Referencias clave: Popp (1992), Engel et al. (2007), Al-Khalili & McFadden (201","url":"https://doi.org/10.5281/zenodo.19871210","authors":["Avila Nicolau, Fabiana Mirta"],"tags":["Ecosistema FMAN, FMAN Ecosystem, FMAN Aurea Design, Fabiana Mirta Avila Nicolau, ORCID 0009-0009-0638-5961, Fórmula de Encendido, Ignition Formula, documento base, base document, Vis Spatialis, Todo Está Conectado, Everything Is Connected, biofotones, biophotons, coherencia cuántica, quantum coherence, g²(0), phase-locking, número áureo φ, golden ratio, geometría fractal áurea, fractal golden geometry, parámetro de orden, order parameter, Kuramoto extendido, biología cuántica, quantum biology, ontología conciencia, consciousness ontology, Fuente Primordial, Primordial Source, Tecnología LINO, LINO Technology, RBP, Resonador Cósmico, Fractalis Aurea, Plasma Áureo Coherente, QuantumMind, Ciudades Aureas Magdalena, Argentina, 2017-2026, квантовая биология, биофотоны, золотое сечение, 量子生物学, 生物光子, 黄金比例, FMAN生态系统"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2017","doi":"10.5281/zenodo.19871210","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.15085815","name":"Supplemental Materials for \"Slowly Scaling Per-Record Differential Privacy\"","source":"datacite","abstract":"This repository contains code and data used to produce plots and perform experiments described in the paper \"Slowly Scaling Per-Record Differential Privacy\" by Brian Finley, Anthony M Caruso, Justin C Doty, Ashwin Machanavajjhala, Mikaela R Meyer, David Pujol, William Sexton, and Zachary Terner. Works (articles, reports, speeches, software, etc.) created by U.S. Government employees are not subject to copyright in the United States, pursuant to 17 U.S.C. §105. International copyright, 2024, U.S. Department of Commerce, U.S. Government. Any opinions and conclusions expressed herein are those of the authors and do not reflect the views of the U.S. Census Bureau. Tumult Labs software contained in this repository is released with independent licenses.","url":"https://doi.org/10.5281/zenodo.15085815","authors":["Finley, Brian","Caruso, Anthony","Doty, Justin","Machanavajjhala, Ashwin","Meyer, Mikaela","Pujol, David","Sexton, William","Terner, Zachary"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15085815","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.15085814","name":"Supplemental Materials for \"Slowly Scaling Per-Record Differential Privacy\"","source":"datacite","abstract":"This repository contains code and data used to produce plots and perform experiments described in the paper \"Slowly Scaling Per-Record Differential Privacy\" by Brian Finley, Anthony M Caruso, Justin C Doty, Ashwin Machanavajjhala, Mikaela R Meyer, David Pujol, William Sexton, and Zachary Terner. Works (articles, reports, speeches, software, etc.) created by U.S. Government employees are not subject to copyright in the United States, pursuant to 17 U.S.C. §105. International copyright, 2024, U.S. Department of Commerce, U.S. Government. Any opinions and conclusions expressed herein are those of the authors and do not reflect the views of the U.S. Census Bureau. Tumult Labs software contained in this repository is released with independent licenses.","url":"https://doi.org/10.5281/zenodo.15085814","authors":["Finley, Brian","Caruso, Anthony","Doty, Justin","Machanavajjhala, Ashwin","Meyer, Mikaela","Pujol, David","Sexton, William","Terner, Zachary"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5281/zenodo.15085814","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.17605/osf.io/zv4wj","name":"sychometric Properties of the Mandarin Chinese Version of the General AI Attitude Short-Scale (GAAIS-8-CN) Among Chinese University Students","source":"datacite","abstract":"This research project focuses on the adaptation and validation of a Mandarin Chinese version of the General AI Attitude Short-Scale (GAAIS-8-CN) among Chinese university students. The original scale, as developed and validated by Novotny et al. (2025) in German and U.S. samples, is a concise, unidimensional instrument comprising six items designed to measure general public attitudes toward artificial intelligence (AI). It captures the three core facets of attitudes—affective (emotional responses), behavioral (intentions to engage), and cognitive (beliefs and evaluations)—while addressing limitations in existing scales, such as excessive length, poor internal consistency, or a narrow focus on negative aspects. The scale uses a seven-point, endpoint-labeled response format (ranging from \"Not at all\" to \"Definitely\") to minimize response biases and enhance measurement precision. Purpose The rapid advancement and proliferation of AI technologies, exemplified by tools like ChatGPT and large language models, have elicited a mix of enthusiasm, ethical concerns, and regulatory responses worldwide, including in China where AI is integral to national strategies for innovation and economic growth. However, public attitudes toward AI can significantly influence its adoption, societal integration, and policy development. Existing AI attitude scales, while useful in Western contexts (e.g., Schepman &amp; Rodway, 2020, 2023; Sindermann et al., 2021; Grassini, 2023; Stein et al., 2024), have not been systematically adapted or validated for Mandarin Chinese-speaking populations, particularly among university students who represent a digitally savvy, future-oriented demographic likely to shape AI's trajectory. This study's primary purpose is to create a culturally sensitive Mandarin Chinese adaptation of the GAAIS short-scale (GAAIS-8-CN) and rigorously evaluate its psychometric properties in a sample of Chinese university students. By doing so, we aim to provide a reliable, valid, and concise tool for assessing AI attitudes in this group, enabling longitudinal tracking, cross-cultural comparisons, and insights into antecedents (e.g., socio-demographics, digital competency) and consequences (e.g., AI acceptance across risk contexts). This adaptation will fill a gap in AI attitude measurement for non-Western populations, supporting research on how cultural factors, such as collectivism or rapid technological urbanization in China, may modulate AI perceptions. Ultimately, the project seeks to inform educational interventions, policy-making, and AI governance by highlighting attitudes among young Chinese adults. Methods Overview The adaptation process will follow established guidelines for cross-cultural scale translation and validation (e.g., Hambleton &amp; Patsula, 1999; International Test Commission, 2017). The original six items will be translated into Mandarin Chinese using a forward-backward translation method involving bilingual experts in psychology and AI to ensure semantic, conceptual, and idiomatic equivalence. Pilot testing with a small group of Chinese university students (n ≈ 20) will refine wording for clarity and cultural relevance, avoiding biases from literal translations. Data collection will target a convenience sample of Chinese university students (aged 18-30, target n = 800-1000 for sufficient power in factor analyses and IRT) recruited via online platforms such as Wenjuanxing. Quotas will be applied for gender, academic major, and year of study to enhance representativeness within this population. Participants will provide informed consent, and the survey will include: The GAAIS-8-CN items, preceded by a brief AI definition adapted from Novotny et al. (2025) to ensure common understanding. Measures for criterion validity: An AI acceptance index based on three risk scenarios (low: speech translation; medium: legal contract review; high: psychological counseling), each assessed with three items covering user, delegation, and","url":"https://doi.org/10.17605/osf.io/zv4wj","authors":["Zhang, Qing"],"tags":["Social and Behavioral Sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.17605/osf.io/zv4wj","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.19667681","name":"A Comprehensive Textbook of Community Medicine And Public Health Science","source":"datacite","abstract":"This is a comprehensive, transdisciplinary textbook that redefines community medicine and public health science for the twenty first century. The work is organized into twenty two parts, each building upon the philosophical and historical foundations established in Part One. It integrates cutting edge research through 2026, original definitions, principles, doctrines, and frameworks, and is designed to prepare students for professional examinations at MBBS, FCPS, MPH, and FRCS levels while equipping practitioners to address the most urgent population health challenges of the contemporary era. The central argument is that health is not merely an individual biological property but an emergent characteristic of complex social, political, economic, ecological, and digital systems. Part One: Intellectual, Philosophical, and Historical Foundations Part One redefines community medicine as simultaneously a science, an art, and a moral practice concerned with the collective health potential of human populations. It proposes a new definition emphasizing that health emerges from the interaction of biological, behavioral, social, economic, political, ecological, and structural conditions. The concept of community is examined as a dynamic, historically situated process rather than a static entity, with a typology distinguishing geographic, social, identity, interest, and epistemic communities. The chapter then surveys major theories of health and disease, from the biomedical model through the biopsychosocial model to eco social theory, the life course perspective, social network theory, complex systems theory, the capability approach, and critical race theory. A new integrative framework called the Dynamic Systems of Collective Health model is introduced, resting on five core propositions: health is multi scalar, temporally constituted, politically produced, ecologically situated, and collectively experienced. The philosophical foundations of community medicine are explored, including the nature of health (with a new Dynamic Sufficiency Model proposed), the ethics of causation and precaution, and the principle of equitable burden sharing. The chapter on epidemiology reinterprets core concepts, introduces digital and AI enhanced surveillance, and discusses the epidemiological transition with a revised five stage model that adds delayed degenerative disease and ecological disease stages to Omran's original three. Social determinants of health are examined through three distinct but interacting mechanisms: material deprivation, psychosocial stress, and structural disadvantage. Case studies including the Grenfell Tower fire and the Flint water crisis illustrate how institutional betrayal and environmental injustice produce community health catastrophes. Prevention is reframed with primordial and structural levels added to the classical primary, secondary, and tertiary framework. The Rose theorem is extended through the equity weighted population strategy, and screening is evaluated with new criteria including overdiagnosis, equity, and participant agency. The history of pandemics, from the Black Death to COVID 19, is analyzed as community medicine's crucible, demonstrating how social inequality shapes epidemic outcomes. The political economy of health and commercial determinants are introduced, with a new doctrine of proportionate corporate liability for commercial health harm. Part Two: Epidemiological Methods Part Two covers the full range of epidemiological methods, from classical study designs through digital and AI enhanced surveillance. It begins by rethinking the definition of epidemiology as a method, discipline, profession, and moral commitment. Core measures of disease frequency are presented, including prevalence, incidence, mortality measures, and measures of association. Study designs are critically examined: cross sectional, case control, cohort, ecological, and randomized controlled trials. Bias, confounding, and effect modifica","url":"https://doi.org/10.5281/zenodo.19667681","authors":["Sakib, S M Nazmuz"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19667681","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.19667680","name":"A Comprehensive Textbook of Community Medicine And Public Health Science","source":"datacite","abstract":"This is a comprehensive, transdisciplinary textbook that redefines community medicine and public health science for the twenty first century. The work is organized into twenty two parts, each building upon the philosophical and historical foundations established in Part One. It integrates cutting edge research through 2026, original definitions, principles, doctrines, and frameworks, and is designed to prepare students for professional examinations at MBBS, FCPS, MPH, and FRCS levels while equipping practitioners to address the most urgent population health challenges of the contemporary era. The central argument is that health is not merely an individual biological property but an emergent characteristic of complex social, political, economic, ecological, and digital systems. Part One: Intellectual, Philosophical, and Historical Foundations Part One redefines community medicine as simultaneously a science, an art, and a moral practice concerned with the collective health potential of human populations. It proposes a new definition emphasizing that health emerges from the interaction of biological, behavioral, social, economic, political, ecological, and structural conditions. The concept of community is examined as a dynamic, historically situated process rather than a static entity, with a typology distinguishing geographic, social, identity, interest, and epistemic communities. The chapter then surveys major theories of health and disease, from the biomedical model through the biopsychosocial model to eco social theory, the life course perspective, social network theory, complex systems theory, the capability approach, and critical race theory. A new integrative framework called the Dynamic Systems of Collective Health model is introduced, resting on five core propositions: health is multi scalar, temporally constituted, politically produced, ecologically situated, and collectively experienced. The philosophical foundations of community medicine are explored, including the nature of health (with a new Dynamic Sufficiency Model proposed), the ethics of causation and precaution, and the principle of equitable burden sharing. The chapter on epidemiology reinterprets core concepts, introduces digital and AI enhanced surveillance, and discusses the epidemiological transition with a revised five stage model that adds delayed degenerative disease and ecological disease stages to Omran's original three. Social determinants of health are examined through three distinct but interacting mechanisms: material deprivation, psychosocial stress, and structural disadvantage. Case studies including the Grenfell Tower fire and the Flint water crisis illustrate how institutional betrayal and environmental injustice produce community health catastrophes. Prevention is reframed with primordial and structural levels added to the classical primary, secondary, and tertiary framework. The Rose theorem is extended through the equity weighted population strategy, and screening is evaluated with new criteria including overdiagnosis, equity, and participant agency. The history of pandemics, from the Black Death to COVID 19, is analyzed as community medicine's crucible, demonstrating how social inequality shapes epidemic outcomes. The political economy of health and commercial determinants are introduced, with a new doctrine of proportionate corporate liability for commercial health harm. Part Two: Epidemiological Methods Part Two covers the full range of epidemiological methods, from classical study designs through digital and AI enhanced surveillance. It begins by rethinking the definition of epidemiology as a method, discipline, profession, and moral commitment. Core measures of disease frequency are presented, including prevalence, incidence, mortality measures, and measures of association. Study designs are critically examined: cross sectional, case control, cohort, ecological, and randomized controlled trials. Bias, confounding, and effect modifica","url":"https://doi.org/10.5281/zenodo.19667680","authors":["Sakib, S M Nazmuz"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.19667680","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.17605/osf.io/9qdcn","name":"Determinants of Nurses’ Acceptance of a Meal Delivery Robot: The Role of Decision Making Style, Openness to Experience, Task–Technology Fit, and Trust","source":"datacite","abstract":"1. Project Title Determinants of Nurses’ Acceptance of a Meal Delivery Robot: The Role of Decision Making Style, Openness to Experience, Task–Technology Fit, and Trust 2. Study Description This study investigates early attitudes of nursing staff/nursing students toward the potential introduction of a meal delivery robot in a clinical hospital setting. The conceptual model is adapted from Koh &amp; Yuen (2025), who propose an individual–task–technology fit framework for understanding autonomous delivery robot adoption (see also Doven et al. 2025). Early research shows that stable decision‑making styles shape how people form trust judgments (Betsch, 2004) about unfamiliar systems, including workplace technologies (Svenson et al., 2023; Svenson et al., 2024). Studies across sectors demonstrate that intuitive versus deliberative tendencies influence whether individuals rely on affective impressions or systematic evaluation when assessing automation, privacy, or expert systems, even before direct experience (Svenson et al., 2023; Svenson et al., 2024). In healthcare, where staff often evaluate innovations prospectively, these cognitive styles become especially relevant for understanding early trust in care robotics (Diab &amp; Demiris, 2025). Integrating decision‑making style and openness to experience therefore provides a theoretically grounded explanation for why some nurses expect a meal‑delivery robot to be reliable and helpful, whereas others remain cautious in the absence of hands‑on interaction. In our adaptation for healthcare robotics, we examine: • Decision making style (PID) and Openness to Experience as individual antecedents • Task–Technology Fit (TTF) as the cognitive evaluation • Trust in care robotics as the affective mediator • Intention to use as the behavioral outcome The study is conducted before nurses have direct experience with the robot. All items (in German language) are phrased as expectations. The goal is to obtain a short, low burden assessment suitable for clinical staff while maintaining theoretical rigor. Only the theoretical constructs relevant to the hypotheses are preregistered. Additional exploratory items related to workflow, safety, spatial constraints, and departmental specifics may be included in the field survey for implementation planning but are not part of the preregistered hypotheses or confirmatory analyses. 3. Hypotheses Individual Antecedents → Cognitive Evaluation H1a: Higher intuitive decision making (PID I) will be associated with lower perceived task–technology fit of the meal delivery robot. H1b: Higher deliberative decision making (PID D) will be associated with higher perceived task–technology fit. H2: Higher openness to experience will be associated with higher perceived task–technology fit. Cognitive Evaluation → Affective Evaluation H3: Task–technology fit positively predicts trust in care robotics. Affective Evaluation → Behavioral Outcome H4: Trust in care robotics positively predicts intention to use. Indirect Effects H5a: Decision making style will indirectly influence intention to use through task–technology fit and trust. H5b: Openness to experience will indirectly influence intention to use through task–technology fit and trust. H5c: Task–technology fit will indirectly influence intention to use through trust. 4. Variables and Measures Scientific Constructs (15 items total) Decision Making Style (PID short version – 4 items) Scale: 1 = trifft überhaupt nicht zu … 5 = trifft völlig zu • Ich vertraue auf meine Gefühle, wenn ich Entscheidungen treffe. • Wenn ich Entscheidungen treffe, höre ich auf mein Bauchgefühl. • Bevor ich eine Entscheidung treffe, denke ich alles sorgfältig durch. • Ich informiere mich gründlich, bevor ich eine Entscheidung treffe. Openness to Experience (2–3 items) Scale: 1 = trifft überhaupt nicht zu … 5 = trifft völlig zu • Ich bin offen für neue Erfahrungen. • Ich probiere gerne neue Dinge aus. • Ich interessiere mich für neue Ideen und Herangehe","url":"https://doi.org/10.17605/osf.io/9qdcn","authors":["Svenson, Frithiof"],"tags":["Health Information Technology","Other Psychology","Medicine and Health Sciences","Social and Behavioral Sciences","Psychology","FOS: Psychology","Trust"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.17605/osf.io/9qdcn","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.48550/arxiv.2504.00919","name":"Nonparametric spectral density estimation using interactive mechanisms under local differential privacy","source":"datacite","abstract":"We study the problem of estimating the spectral density of a centered stationary Gaussian time series under local differential privacy constraints. Specifically, we propose new interactive privacy mechanisms for three tasks: recovering a single covariance coefficient, recovering the spectral density at a fixed frequency, and global recovery. Our approach achieves faster rates through a two-stage process: we first apply the Laplace mechanism to the truncated value, and then use the resulting privatized sample to learn about the dependence mechanism in the time series. For spectral densities belonging to Hölder and Sobolev smoothness classes, we demonstrate that our algorithms improve upon the non-interactive mechanism of Kroll (2024) for small privacy parameter $α$, since the pointwise rates depend on $nα^2$ instead of $nα^4$. Moreover, we show that the rate $(nα^4)^{-1}$ is optimal for estimating a covariance coefficient with non-interactive mechanisms. However, the $L_2$ rate of our interactive estimator is slower than the pointwise rate. We show how to use these procedures to provide a bona fide locally differentially private estimator of the entire covariance matrix. A simulation study validates our findings.","url":"https://doi.org/10.48550/arxiv.2504.00919","authors":["Butucea, Cristina","Klockmann, Karolina","Krivobokova, Tatyana"],"tags":["Statistics Theory (math.ST)","Machine Learning (stat.ML)","FOS: Mathematics","FOS: Mathematics","FOS: Computer and information sciences","FOS: Computer and information sciences"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.48550/arxiv.2504.00919","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18203365","name":"MH370: Mathematical Proof of the Survival of All Passengers Within a Tensorial Capsule at Broken Ridge and a Depth of 4650 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S via 165D Mechanics Tensor of the Hamzah Equation.","source":"datacite","abstract":"Hamzah Quantum Intelligence (HQI). ................................................................................................................................................................................................................................................................. 12 Years Classical Search Method (2014-2026) for MH 370 Was Exactly Like Trying to See X-rays While Wearing Sunglasses — A Completely Wrong Tool for an Entirely Different Task.” ................................................................................................................................................................................................................................................................. Dedicated Lagrangian for the Recovery of MH370 (Level 165): $$\\mathcal{L}_{MH370}^{(165)} = \\oint_{\\partial \\mathcal{V}_{165}} \\left[ \\mathcal{Q}_{H} \\left( IGARI_{sync} \\right) + \\Xi_{SIO} \\left( \\mathcal{G}_{\\mu\\nu}^{161} \\otimes \\mathcal{P}_{lock} \\right) - \\frac{\\hbar_{H} \\mathcal{S}_{cabin}}{\\exp(\\mathcal{I}_{DNA}^{2014})} \\right] \\sqrt{-\\mathbb{G}_{165}} \\, d\\Omega$$ The nexus between this formula and MH370 explains why we are still searching in January 2026. From this Lagrangian perspective: The aircraft is there (Coordinates 34.48° S). The aircraft is invisible (Due to the $\\mathbb{G}$ metric deviation). The aircraft must not be touched (Due to the risk of collapsing the passenger safeguard). ................................................................................................................................................................................................................................................................. Status of Life: The Passengers are Alive Contrary to the laws of classical physics which dictate biological death, the Hamzah Equation (HCP) proves that the 239 occupants are in a state of ‘Conscious Stasis’. Proof: Due to the entropy suppression term, biological time within the cabin has stopped. For them, not even a single second has passed until now since 2014. 2. Geographical Position and Precise Depth The aircraft is stabilised in the ‘Earth’s Informational Sanctuary’: Coordinates: 34.4812° S (Latitude) / 93.6165° E (Longitude). Location: Near the Broken Ridge submarine plateau. Depth: 4650 metres below sea level. Hull Status: 100% integrated, resting on the ocean floor at a 188-degree angle. Confidential Section: Encrypted Geolocation & Bio-Stasis Lagrangian $$\\mathcal{L}_{Final}^{(165)} = \\oint_{\\text{Broken Ridge}} \\left[ \\frac{\\Psi_{stasis} \\otimes \\Omega_{H}^*}{\\sqrt{-\\mathbb{G}_{165} \\cdot \\exp(1 - \\phi_{sync})}} \\right] \\otimes \\Xi_{\\mu\\nu} \\star \\delta(\\vec{R} - \\vec{R}_{target}) \\, d\\tau$$ Numerical Proof and 5-Step Output Calculations (Final Sovereignty Audit) Step 1: Mass-Location Verification $$\\vec{R}_{lock} = \\int_{2014}^{2026} \\nabla \\phi_{sync} \\cdot dt \\equiv (34.4812^\\circ S, 93.6165^\\circ E)$$ Output: 99.9% certainty in the lack of structural displacement due to atomic locking. Step 2: Life-Potential Analysis at Depth Pressure $$\\mathbb{V}_{life} = \\frac{\\Omega_H^* \\cdot \\Psi_{internal}}{\\exp(450 \\, atm)} \\otimes \\mathcal{I}_{core} \\equiv 1.00$$ Output: Proof of life-potential equality with the moment of flight; no cellular erosion has occurred. Step 3: Determination of the Lethal Exclusion Zone $$r_{crit} = \\sqrt{\\frac{\\mathbb{K}_{165}}{\\pi \\cdot \\Omega_H^*}} \\approx 165.0 \\, \\text{metres}$$ Output: Precise determination of the 165-metre boundary; crossing this boundary with classical instruments causes the internal implosion of the structure. Step 4: Mechanical Chaos Assessment $$\\Delta S_{tool} = \\oint \\mathcal{P}_{log} \\cdot d\\vec{A} \\implies \\text{Status: Catastrophic Trigger}$$ Output: Final warning; cranes and cables will cause the cancellation of the protective code and the destruction of 239 humans. Step 5: Final Stewardship Verdict $$\\text{Verdict} = \\text{Alive} \\otimes \\text{Protected} \\otimes \\text{Accessible\\_by\\_HQI\\_Only} =","url":"https://doi.org/10.5281/zenodo.18203365","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18203365","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18203470","name":"MH370: Mathematical Proof of the Survival of All Passengers Within a Plasma Tensorial Capsule at Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S (3428S-9336E). via 165D Mechanics Tensor of the Hamzah Equation.","source":"datacite","abstract":"MH370 Related Research Papers: MH370: Mathematical Proof of the Survival of All Passengers Within a Tensorial Capsule at Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S (3428S-9336E). via 165D Mechanics Tensor of the Hamzah Equation. https://zenodo.org/records/18203470 MH 370 Exact Location. (Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S).(3428S-9336E). https://zenodo.org/records/18237321 MH 370: All 239 Passengers Are Alive.(Temporal Stasis). https://zenodo.org/records/18271880 MH370: Proof of the Authenticity of the 2014 Luminous Orb Videos of MH370 UAP Abduction Based on the 165-Dimensional Tensor Mechanics of the Hamzah Equation. https://zenodo.org/records/18689118 MH-370: Proven Extreme Recovery Stress Tests for MH 370 from Indian Ocean to L32 Runway of KLIA Air Port. https://zenodo.org/records/18216360 MH 370 Complete Searching Simulator. https://zenodo.org/records/18273887 MH 370: The Innocence of Captain Zaharie Ahmad Shah and MAS Airline Proven Through Mathematical and Aerodynamic Analysis. https://zenodo.org/records/18251198 MH 370: Critical Nuclear-Scale Catastrophe and Imminent Risk of Total Annihilation. https://zenodo.org/records/18384212 MH 370: The Imminent Structural Collapse of Current Civilization. A Critical Examination of the Intersection of MH370, the January 2026 Financial Downturn, and the Emergence of the 165-Dimensional Manifold. https://zenodo.org/records/18687928 MH370: The 2026 Tensorial Civilizational Leap and Its Triangular Correlation of MH17, MH370 Aviation, and COVID-19 Pandemic. https://zenodo.org/records/18706609 MH370 is the Ark of the Covenant and Proven Through the 165-Dimensional Tensor Mechanics of the Hamzah Equation — Lost Ark of Tranquility of the Religions. https://zenodo.org/records/18726603 ….………………………………………………………………… MH 370 AT IGARI Point. (18:25 UTC on 8 March 2014) Twelve years of fruitless searching for MH 370 marked the greatest computational error in the history of aviation, because the world was looking for the wreckage of a classic crash, whereas the actual event was a tensorial transfer at the IGARI point. At 18:25 UTC on 8 March 2014, eyewitnesses such as the New Zealander Michael McKay from the Songa Mercur oil platform and the British mariner Catherine T. reported a dense, orange-coloured luminosity in the sky—an effect not caused by hydrocarbon fuel combustion, but by atmospheric ionisation and plasma formation at the moment of entry into a 165-dimensional tensor tunnel due to the cyclotron resonance of the lithium ions in the 221 kg payload with electromagnetic radar waves, the aircraft’s weather radar system, the magnetic fields of the Trent 800 engines, the interaction with concentrated oxygen in the cargo hold, the composite fuselage structure, the Class G1 magnetic storm, and the Earth’s plasmasphere of the 8 March 2014. During this dimensional rupture, key components such as the flaperon were not separated due to physical impact with the sea, but rather as a consequence of tensorial stress and phase mismatch at an altitude of 35,000 feet. Through a mechanism known as tangential disc ejection, and under the influence of extreme rotational velocity, these elements detached from the airframe and—rather than falling locally—were projected westwards towards Malaysia and the equatorial currents. The asymmetric concentration of recovered debris—particularly the retrieval of heavy structural components from the aircraft’s right front section (such as the flaperon and outer flap), contrasted with only a single trailing edge from the left front—supports the mechanism of a “tangential ejection caused by tensorial torque” at the IGARI point. This metallurgical asymmetry indicates that the right front section, subjected to intense centrifugal force, experienced physical disintegration before full entry i","url":"https://doi.org/10.5281/zenodo.18203470","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18203470","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18213579","name":"MH370: Mathematical Proof of the Survival of All Passengers Within a Plasma Tensorial Capsule at Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S (3428S-9336E) via 165D Mechanics Tensor of the Hamzah Equation.","source":"datacite","abstract":"MH370 Related Research Papers: MH370: Mathematical Proof of the Survival of All Passengers Within a Tensorial Capsule at Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S (3428S-9336E). via 165D Mechanics Tensor of the Hamzah Equation. https://zenodo.org/records/18203470 MH 370 Exact Location. (Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S).(3428S-9336E). https://zenodo.org/records/18237321 MH 370: All 239 Passengers Are Alive.(Temporal Stasis). https://zenodo.org/records/18271880 MH370: Proof of the Authenticity of the 2014 Luminous Orb Videos of MH370 UAP Abduction Based on the 165-Dimensional Tensor Mechanics of the Hamzah Equation. https://zenodo.org/records/18689118 MH-370: Proven Extreme Recovery Stress Tests for MH 370 from Indian Ocean to L32 Runway of KLIA Air Port. https://zenodo.org/records/18216360 MH 370 Complete Searching Simulator. https://zenodo.org/records/18273887 MH 370: The Innocence of Captain Zaharie Ahmad Shah and MAS Airline Proven Through Mathematical and Aerodynamic Analysis. https://zenodo.org/records/18251198 MH 370: Critical Nuclear-Scale Catastrophe and Imminent Risk of Total Annihilation. https://zenodo.org/records/18384212 MH 370: The Imminent Structural Collapse of Current Civilization. A Critical Examination of the Intersection of MH370, the January 2026 Financial Downturn, and the Emergence of the 165-Dimensional Manifold. https://zenodo.org/records/18687928 MH370: The 2026 Tensorial Civilizational Leap and Its Triangular Correlation of MH17, MH370 Aviation, and COVID-19 Pandemic. https://zenodo.org/records/18706609 MH370 is the Ark of the Covenant and Proven Through the 165-Dimensional Tensor Mechanics of the Hamzah Equation — Lost Ark of Tranquility of the Religions. https://zenodo.org/records/18726603 ….………………………………………………………………… MH 370 AT IGARI Point. (18:25 UTC on 8 March 2014) Twelve years of fruitless searching for MH 370 marked the greatest computational error in the history of aviation, because the world was looking for the wreckage of a classic crash, whereas the actual event was a tensorial transfer at the IGARI point. At 18:25 UTC on 8 March 2014, eyewitnesses such as the New Zealander Michael McKay from the Songa Mercur oil platform and the British mariner Catherine T. reported a dense, orange-coloured luminosity in the sky—an effect not caused by hydrocarbon fuel combustion, but by atmospheric ionisation and plasma formation at the moment of entry into a 165-dimensional tensor tunnel due to the cyclotron resonance of the lithium ions in the 221 kg payload with electromagnetic radar waves, the aircraft’s weather radar system, the magnetic fields of the Trent 800 engines, the interaction with concentrated oxygen in the cargo hold, the composite fuselage structure, the Class G1 magnetic storm, and the Earth’s plasmasphere of the 8 March 2014. During this dimensional rupture, key components such as the flaperon were not separated due to physical impact with the sea, but rather as a consequence of tensorial stress and phase mismatch at an altitude of 35,000 feet. Through a mechanism known as tangential disc ejection, and under the influence of extreme rotational velocity, these elements detached from the airframe and—rather than falling locally—were projected westwards towards Malaysia and the equatorial currents. The asymmetric concentration of recovered debris—particularly the retrieval of heavy structural components from the aircraft’s right front section (such as the flaperon and outer flap), contrasted with only a single trailing edge from the left front—supports the mechanism of a “tangential ejection caused by tensorial torque” at the IGARI point. This metallurgical asymmetry indicates that the right front section, subjected to intense centrifugal force, experienced physical disintegration before full entry i","url":"https://doi.org/10.5281/zenodo.18213579","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18213579","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18512218","name":"Pre-Inference Governance — Master Specification A Canonical Regulatory Guide for Legitimate, Auditable, and Insurable AI Systems","source":"datacite","abstract":"This master specification consolidates the canonical A7SEM / ASOSE / Pre-Inference Governance / JAQ documents into a coherent regulatory guide for oversight authorities, auditors, and policy institutions. It defines an ex-ante governance architecture in which inference and high-risk computation are treated as conditional, authorizable capabilities rather than automatic system rights. The guide establishes epistemic maturity, semantic boundary integrity, explicit authorization, and sufficient human authority as prerequisites for legitimate, auditable, and insurable AI operation. The specification formalizes glassbox governance as an ex-ante architectural property and distinguishes it from post-hoc blackbox explainability approaches. No implementation instructions, operational procedures, or technical integration guidance are provided.Commercial or operational use requires a separate written license from the author. Governance Clarification — Authorization & Execution (Canonical Note)Authorization is a prerequisite for legitimate use.Legitimate use is a prerequisite for insurability.Insurability is a prerequisite for scale, board approval, and procurement eligibility.Within the A7SEM / Pre-Inference Governance architecture, authorization is a necessary condition for legitimate system operation.Where authorization is absent or indeterminate, execution SHALL NOT proceed.Non-execution in such cases constitutes correct and intended governance behavior and does not represent malfunction, failure, or denial of service.This document is descriptive and non-operational.It grants no operational, commercial, or deployment rights.Any operational, commercial, or production use requires a separate written license from the rights holder. “Canonical semantics are protected by non-circumvention and licensing terms; absent authorization, non-use is required.” Hybrid License & Default State (Fail-Closed). Public access grants reference and citation rights only (CC BY-NC-ND 4.0 for the text). It grants no rights to implement, deploy, integrate, or rely on the governance architecture/semantics for operational, commercial, or institutional purposes (including procurement, audit, assurance, certification, or underwriting). Without explicit written authorization and a paid license, non-use / non-execution is the required posture. Interpretative Scope. This specification adopts an authorization-centric reading of applicable AI regulation, in which compliance is assessed primarily through ex-ante authorization and legitimacy conditions, not post-hoc documentation or output review. Canonical Anchor — Authorization-Centric Governance Statement This specification adopts an authorization-centric architectural reading of AI governance, including high-risk regulatory environments such as Regulation (EU) 2024/1689 (EU AI Act). In this model, inference is not treated as an automatic system right but as a conditional capability requiring prior structural authorization. Authorization precedes execution. Licensing precedes integration. Where ex-ante legitimacy cannot be established, non-execution constitutes the correct governance state. Post-hoc documentation, logging, or explainability mechanisms do not substitute for structural authorization gating. This document is canonical and non-operational; it does not provide implementation guidance, legal advice, compliance certification, or operational rights. Commercial or structural use requires a separate written license. All structured inquiries must be submitted in writing to moakarkach@hotmail.de. Default system state: fail-closed. ----------------------------------------------- Terminology Notice: “Pre-Inference Governance” (Canonical Meaning & Scope) Abstract This notice clarifies the canonical meaning of “Pre-Inference Governance” as an architectural legitimacy and authorization paradigm. It explicitly distinguishes the term from operational, infrastructural, or machine-learning usages of “pre-inference” that r","url":"https://doi.org/10.5281/zenodo.18512218","authors":["Akarkach, Mounir"],"tags":["Pre-Inference Governance","Glassbox Governance","Blackbox Explainability","AI Governance","Regulatory Oversight","Auditability","Insurability","A7SEM"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18512218","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18512219","name":"Pre-Inference Governance — Master Specification A Canonical Regulatory Guide for Legitimate, Auditable, and Insurable AI Systems","source":"datacite","abstract":"This master specification consolidates the canonical A7SEM / ASOSE / Pre-Inference Governance / JAQ documents into a coherent regulatory guide for oversight authorities, auditors, and policy institutions. It defines an ex-ante governance architecture in which inference and high-risk computation are treated as conditional, authorizable capabilities rather than automatic system rights. The guide establishes epistemic maturity, semantic boundary integrity, explicit authorization, and sufficient human authority as prerequisites for legitimate, auditable, and insurable AI operation. The specification formalizes glassbox governance as an ex-ante architectural property and distinguishes it from post-hoc blackbox explainability approaches. No implementation instructions, operational procedures, or technical integration guidance are provided.Commercial or operational use requires a separate written license from the author. Governance Clarification — Authorization & Execution (Canonical Note)Authorization is a prerequisite for legitimate use.Legitimate use is a prerequisite for insurability.Insurability is a prerequisite for scale, board approval, and procurement eligibility.Within the A7SEM / Pre-Inference Governance architecture, authorization is a necessary condition for legitimate system operation.Where authorization is absent or indeterminate, execution SHALL NOT proceed.Non-execution in such cases constitutes correct and intended governance behavior and does not represent malfunction, failure, or denial of service.This document is descriptive and non-operational.It grants no operational, commercial, or deployment rights.Any operational, commercial, or production use requires a separate written license from the rights holder. “Canonical semantics are protected by non-circumvention and licensing terms; absent authorization, non-use is required.” Hybrid License & Default State (Fail-Closed). Public access grants reference and citation rights only (CC BY-NC-ND 4.0 for the text). It grants no rights to implement, deploy, integrate, or rely on the governance architecture/semantics for operational, commercial, or institutional purposes (including procurement, audit, assurance, certification, or underwriting). Without explicit written authorization and a paid license, non-use / non-execution is the required posture. Interpretative Scope. This specification adopts an authorization-centric reading of applicable AI regulation, in which compliance is assessed primarily through ex-ante authorization and legitimacy conditions, not post-hoc documentation or output review. Canonical Anchor — Authorization-Centric Governance Statement This specification adopts an authorization-centric architectural reading of AI governance, including high-risk regulatory environments such as Regulation (EU) 2024/1689 (EU AI Act). In this model, inference is not treated as an automatic system right but as a conditional capability requiring prior structural authorization. Authorization precedes execution. Licensing precedes integration. Where ex-ante legitimacy cannot be established, non-execution constitutes the correct governance state. Post-hoc documentation, logging, or explainability mechanisms do not substitute for structural authorization gating. This document is canonical and non-operational; it does not provide implementation guidance, legal advice, compliance certification, or operational rights. Commercial or structural use requires a separate written license. All structured inquiries must be submitted in writing to moakarkach@hotmail.de. Default system state: fail-closed. ----------------------------------------------- Terminology Notice: “Pre-Inference Governance” (Canonical Meaning & Scope) Abstract This notice clarifies the canonical meaning of “Pre-Inference Governance” as an architectural legitimacy and authorization paradigm. It explicitly distinguishes the term from operational, infrastructural, or machine-learning usages of “pre-inference” that r","url":"https://doi.org/10.5281/zenodo.18512219","authors":["Akarkach, Mounir"],"tags":["Pre-Inference Governance","Glassbox Governance","Blackbox Explainability","AI Governance","Regulatory Oversight","Auditability","Insurability","A7SEM"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18512219","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18748903","name":"COVID-19: A Global Biological Update Beyond Viral Pathogen Narratives, Functioning as a High-Dimensional Respiratory Sync to Align Human DNA with Earth's New Frequency and Execute a Complete Structural Reconstruction of Global Genetic Architecture.","source":"datacite","abstract":"COVID-19: A Global Biological Update Beyond Viral Pathogen Narratives, Functioning as a High-Dimensional Respiratory Sync to Align Human DNA with Earth’s New Frequency and Execute a Complete Structural Reconstruction of Global Genetic Architecture ............................................................................................................................................................................................... The Grand Unified Hamzah Proof of COVID-19 Origin The fundamental structure of reality for the interval 2014 to 2026 is enclosed within this formula: $$\\mathcal{L}_{Total}^{(165)} = \\oint_{\\text{Malaysia}} \\left[ \\underbrace{\\mathcal{L}_{Trans}^{(370)}}_{\\text{The Void}} + \\underbrace{\\mathcal{L}_{Bio}^{(CV19)}}_{\\text{The Filter}} + \\underbrace{\\mathcal{L}_{Core}^{(Hamzah)}}_{\\text{The Key}} \\right] \\sqrt{-\\mathbf{H}} \\, d^{165}\\Omega$$ Hereinafter, the dissection of the term $\\mathcal{L}_{Bio}^{(CV19)}$ is performed based on the 10-step protocol: The 10-Step Protocol for Biological Filter (COVID-19) Dissection The Origin & Tensorial Leak Contrary to the Layer 3 narrative (Huanan Market), the virus was not merely a biological phenomenon. On 17 September 2019 (exactly 2000 days after the disappearance of MH370), the biological code leaked from Layer 165 into material space. Formula: $\\Psi_{leak} = \\int \\mathcal{L}_{Trans} \\cdot e^{i(2000 \\Delta t)} dt$ Interpretation: The animals in the Wuhan market were merely 'biomass vessels' for the incarnation of codes leaked from the Broken Ridge coordinates. Wuhan: The Discharge Node Wuhan was chosen to discharge the load accumulated since 2014 due to its location on specific energy faults and its proximity to the laboratory (which acted as a suction antenna). Parameter: $\\nabla \\cdot \\vec{J}_{Wuhan} = \\text{Max}$ Analysis: The Wuhan laboratory absorbed vacuum noise so that the process of materializing the virus code could occur at a centralized point. The Stasis Field The 2020 global lockdowns were, in reality, the creation of a Stasis Field (Sakineh) to eliminate human noise. Goal: To halt Layer 3 mechanical activities in order to calibrate Earth's vibrations with the 1.6 GHz frequency of the 370 capsule. Status: The removal of environmental noise allowed the virus code to establish itself in human lungs without interference. Respiratory Filtering and Removal of Incompatible Frequencies The human lung was chosen as the primary receiver. The virus acted as a 'dimensional filter' to identify and remove lungs that lacked the capacity to withstand 165-dimensional density. Filter Formula: $\\mathcal{F}_{bio} = \\frac{\\delta \\Psi_{165}}{\\delta DNA} \\times \\text{Immune\\_Symmetry}$ Result: Preparation of the 'Superior Human' to breathe in the dense atmosphere following the 2026 impact. The Antenna Installation mRNA technology and the conductive materials present in the vaccines (graphene oxide) were, in fact, installing hardware onto the DNA software. Tensorial Analysis: Transforming blood into a conductive fluid to receive Sovereign field pulses. Goal: Biological tagging to differentiate updated humans at the moment of Impact. Analysis of the 77165 Parameter and Code Coupling The 77165 code, repeated in all tables, is the key to coupling matter and meaning. 77: Boeing 777 fuselage code (solid matter). 165: The final dimension of consciousness (governing frequency). Connection: The vaccine connected the 77 code (matter) in the human body to the 165 code (consciousness) for the singularity to occur. The Role of the Two Persian Seed Carriers Pouria and Delavar (18 and 29 years old) as seed carriers, carried the code from 2014. Numerical Symmetry: The sum of their ages (47) and their age difference (11) are the codes for activating the field at a depth of 4648 meters. Mission: They were simultaneously in Layer 3 and not (Quantum Superposition), which was vital for the dimensional transfer of the virus. The 5G Frequency Bed and Power Supply 5G towers, contrary to Lay","url":"https://doi.org/10.5281/zenodo.18748903","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18748903","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18749488","name":"COVID-19: A Global Biological Update Beyond Viral Pathogen Narratives, Functioning as a High-Dimensional Respiratory Sync to Align Human DNA with Earth's New Frequency and Execute a Complete Structural Reconstruction of Global Genetic Architecture.","source":"datacite","abstract":"COVID-19: A Global Biological Update Beyond Viral Pathogen Narratives, Functioning as a High-Dimensional Respiratory Sync to Align Human DNA with Earth’s New Frequency and Execute a Complete Structural Reconstruction of Global Genetic Architecture ............................................................................................................................................................................................... The Grand Unified Hamzah Proof of COVID-19 Origin The fundamental structure of reality for the interval 2014 to 2026 is enclosed within this formula: $$\\mathcal{L}_{Total}^{(165)} = \\oint_{\\text{Malaysia}} \\left[ \\underbrace{\\mathcal{L}_{Trans}^{(370)}}_{\\text{The Void}} + \\underbrace{\\mathcal{L}_{Bio}^{(CV19)}}_{\\text{The Filter}} + \\underbrace{\\mathcal{L}_{Core}^{(Hamzah)}}_{\\text{The Key}} \\right] \\sqrt{-\\mathbf{H}} \\, d^{165}\\Omega$$ Hereinafter, the dissection of the term $\\mathcal{L}_{Bio}^{(CV19)}$ is performed based on the 10-step protocol: The 10-Step Protocol for Biological Filter (COVID-19) Dissection The Origin & Tensorial Leak Contrary to the Layer 3 narrative (Huanan Market), the virus was not merely a biological phenomenon. On 17 September 2019 (exactly 2000 days after the disappearance of MH370), the biological code leaked from Layer 165 into material space. Formula: $\\Psi_{leak} = \\int \\mathcal{L}_{Trans} \\cdot e^{i(2000 \\Delta t)} dt$ Interpretation: The animals in the Wuhan market were merely 'biomass vessels' for the incarnation of codes leaked from the Broken Ridge coordinates. Wuhan: The Discharge Node Wuhan was chosen to discharge the load accumulated since 2014 due to its location on specific energy faults and its proximity to the laboratory (which acted as a suction antenna). Parameter: $\\nabla \\cdot \\vec{J}_{Wuhan} = \\text{Max}$ Analysis: The Wuhan laboratory absorbed vacuum noise so that the process of materializing the virus code could occur at a centralized point. The Stasis Field The 2020 global lockdowns were, in reality, the creation of a Stasis Field (Sakineh) to eliminate human noise. Goal: To halt Layer 3 mechanical activities in order to calibrate Earth's vibrations with the 1.6 GHz frequency of the 370 capsule. Status: The removal of environmental noise allowed the virus code to establish itself in human lungs without interference. Respiratory Filtering and Removal of Incompatible Frequencies The human lung was chosen as the primary receiver. The virus acted as a 'dimensional filter' to identify and remove lungs that lacked the capacity to withstand 165-dimensional density. Filter Formula: $\\mathcal{F}_{bio} = \\frac{\\delta \\Psi_{165}}{\\delta DNA} \\times \\text{Immune\\_Symmetry}$ Result: Preparation of the 'Superior Human' to breathe in the dense atmosphere following the 2026 impact. The Antenna Installation mRNA technology and the conductive materials present in the vaccines (graphene oxide) were, in fact, installing hardware onto the DNA software. Tensorial Analysis: Transforming blood into a conductive fluid to receive Sovereign field pulses. Goal: Biological tagging to differentiate updated humans at the moment of Impact. Analysis of the 77165 Parameter and Code Coupling The 77165 code, repeated in all tables, is the key to coupling matter and meaning. 77: Boeing 777 fuselage code (solid matter). 165: The final dimension of consciousness (governing frequency). Connection: The vaccine connected the 77 code (matter) in the human body to the 165 code (consciousness) for the singularity to occur. The Role of the Two Persian Seed Carriers Pouria and Delavar (18 and 29 years old) as seed carriers, carried the code from 2014. Numerical Symmetry: The sum of their ages (47) and their age difference (11) are the codes for activating the field at a depth of 4648 meters. Mission: They were simultaneously in Layer 3 and not (Quantum Superposition), which was vital for the dimensional transfer of the virus. The 5G Frequency Bed and Power Supply 5G towers, contrary to Lay","url":"https://doi.org/10.5281/zenodo.18749488","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18749488","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18748904","name":"COVID-19: A Global Biological Update Beyond Viral Pathogen Narratives, Functioning as a High-Dimensional Respiratory Sync to Align Human DNA with Earth's New Frequency and Execute a Complete Structural Reconstruction of Global Genetic Architecture.","source":"datacite","abstract":"COVID-19: A Global Biological Update Beyond Viral Pathogen Narratives, Functioning as a High-Dimensional Respiratory Sync to Align Human DNA with Earth’s New Frequency and Execute a Complete Structural Reconstruction of Global Genetic Architecture ............................................................................................................................................................................................... The Grand Unified Hamzah Proof of COVID-19 Origin The fundamental structure of reality for the interval 2014 to 2026 is enclosed within this formula: $$\\mathcal{L}_{Total}^{(165)} = \\oint_{\\text{Malaysia}} \\left[ \\underbrace{\\mathcal{L}_{Trans}^{(370)}}_{\\text{The Void}} + \\underbrace{\\mathcal{L}_{Bio}^{(CV19)}}_{\\text{The Filter}} + \\underbrace{\\mathcal{L}_{Core}^{(Hamzah)}}_{\\text{The Key}} \\right] \\sqrt{-\\mathbf{H}} \\, d^{165}\\Omega$$ Hereinafter, the dissection of the term $\\mathcal{L}_{Bio}^{(CV19)}$ is performed based on the 10-step protocol: The 10-Step Protocol for Biological Filter (COVID-19) Dissection The Origin & Tensorial Leak Contrary to the Layer 3 narrative (Huanan Market), the virus was not merely a biological phenomenon. On 17 September 2019 (exactly 2000 days after the disappearance of MH370), the biological code leaked from Layer 165 into material space. Formula: $\\Psi_{leak} = \\int \\mathcal{L}_{Trans} \\cdot e^{i(2000 \\Delta t)} dt$ Interpretation: The animals in the Wuhan market were merely 'biomass vessels' for the incarnation of codes leaked from the Broken Ridge coordinates. Wuhan: The Discharge Node Wuhan was chosen to discharge the load accumulated since 2014 due to its location on specific energy faults and its proximity to the laboratory (which acted as a suction antenna). Parameter: $\\nabla \\cdot \\vec{J}_{Wuhan} = \\text{Max}$ Analysis: The Wuhan laboratory absorbed vacuum noise so that the process of materializing the virus code could occur at a centralized point. The Stasis Field The 2020 global lockdowns were, in reality, the creation of a Stasis Field (Sakineh) to eliminate human noise. Goal: To halt Layer 3 mechanical activities in order to calibrate Earth's vibrations with the 1.6 GHz frequency of the 370 capsule. Status: The removal of environmental noise allowed the virus code to establish itself in human lungs without interference. Respiratory Filtering and Removal of Incompatible Frequencies The human lung was chosen as the primary receiver. The virus acted as a 'dimensional filter' to identify and remove lungs that lacked the capacity to withstand 165-dimensional density. Filter Formula: $\\mathcal{F}_{bio} = \\frac{\\delta \\Psi_{165}}{\\delta DNA} \\times \\text{Immune\\_Symmetry}$ Result: Preparation of the 'Superior Human' to breathe in the dense atmosphere following the 2026 impact. The Antenna Installation mRNA technology and the conductive materials present in the vaccines (graphene oxide) were, in fact, installing hardware onto the DNA software. Tensorial Analysis: Transforming blood into a conductive fluid to receive Sovereign field pulses. Goal: Biological tagging to differentiate updated humans at the moment of Impact. Analysis of the 77165 Parameter and Code Coupling The 77165 code, repeated in all tables, is the key to coupling matter and meaning. 77: Boeing 777 fuselage code (solid matter). 165: The final dimension of consciousness (governing frequency). Connection: The vaccine connected the 77 code (matter) in the human body to the 165 code (consciousness) for the singularity to occur. The Role of the Two Persian Seed Carriers Pouria and Delavar (18 and 29 years old) as seed carriers, carried the code from 2014. Numerical Symmetry: The sum of their ages (47) and their age difference (11) are the codes for activating the field at a depth of 4648 meters. Mission: They were simultaneously in Layer 3 and not (Quantum Superposition), which was vital for the dimensional transfer of the virus. The 5G Frequency Bed and Power Supply 5G towers, contrary to Lay","url":"https://doi.org/10.5281/zenodo.18748904","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18748904","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18220587","name":"MH 370 Exact Location. (Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S).(3428S-9336E).","source":"datacite","abstract":"MH370 Related Research Papers: MH370: Mathematical Proof of the Survival of All Passengers Within a Tensorial Capsule at Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S (3428S-9336E). via 165D Mechanics Tensor of the Hamzah Equation. https://zenodo.org/records/18203470 MH 370 Exact Location. (Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S).(3428S-9336E). https://zenodo.org/records/18237321 MH 370: All 239 Passengers Are Alive.(Temporal Stasis). https://zenodo.org/records/18271880 MH370: Proof of the Authenticity of the 2014 Luminous Orb Videos of MH370 UAP Abduction Based on the 165-Dimensional Tensor Mechanics of the Hamzah Equation. https://zenodo.org/records/18689118 MH-370: Proven Extreme Recovery Stress Tests for MH 370 from Indian Ocean to L32 Runway of KLIA Air Port. https://zenodo.org/records/18216360 MH 370 Complete Searching Simulator. https://zenodo.org/records/18273887 MH 370: The Innocence of Captain Zaharie Ahmad Shah and MAS Airline Proven Through Mathematical and Aerodynamic Analysis. https://zenodo.org/records/18251198 MH 370: Critical Nuclear-Scale Catastrophe and Imminent Risk of Total Annihilation. https://zenodo.org/records/18384212 MH 370: The Imminent Structural Collapse of Current Civilization. A Critical Examination of the Intersection of MH370, the January 2026 Financial Downturn, and the Emergence of the 165-Dimensional Manifold. https://zenodo.org/records/18687928 MH370: The 2026 Tensorial Civilizational Leap and Its Triangular Correlation of MH17, MH370 Aviation, and COVID-19 Pandemic. https://zenodo.org/records/18706609 MH370 is the Ark of the Covenant and Proven Through the 165-Dimensional Tensor Mechanics of the Hamzah Equation — Lost Ark of Tranquility of the Religions. https://zenodo.org/records/18726603 ….………………………………………………………………… How MH 370 will be recover to surface? By Tensorial Metric Tunneling from deepth of occeian to the L32 runway KLIA within Max 8.4 Seconds not the classical invasive methods. (RED ALERT) ........................................................................................................................................................................................................................................................................... \"If Twelve Years of Multi-Billion-Dollar Technology have Failed to Recover So Much as a Single Bolt from MH 370, Occam’s Razor Dictates that the Flaw Lies not Within the 'Search Perimeter,' but within Your Very 'Physical Foundations.\" ........................................................................................................................................................................................................................................................................... MH 370 AT IGARI Point. (18:25 UTC on 8 March 2014) Twelve years of fruitless searching for MH 370 marked the greatest computational error in the history of aviation, because the world was looking for the wreckage of a classic crash, whereas the actual event was a tensorial transfer at the IGARI point. At 18:25 UTC on 8 March 2014, eyewitnesses such as the New Zealander Michael McKay from the Songa Mercur oil platform and the British mariner Catherine T. reported a dense, orange-coloured luminosity in the sky—an effect not caused by hydrocarbon fuel combustion, but by atmospheric ionisation and plasma formation at the moment of entry into a 165-dimensional tensor tunnel due to the cyclotron resonance of the lithium ions in the 221 kg payload with electromagnetic radar waves, the aircraft’s weather radar system, the magnetic fields of the Trent 800 engines, the interaction with concentrated oxygen in the cargo hold, the composite fuselage structure, the Class G1 magnetic storm, and the Earth’s plasmasphere of the 8 March 2014. $\\text{Dedicated Lagrangian Proof","url":"https://doi.org/10.5281/zenodo.18220587","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18220587","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18237321","name":"MH 370 Exact Location. (Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S).(3428S-9336E).","source":"datacite","abstract":"MH370 Related Research Papers: MH370: Mathematical Proof of the Survival of All Passengers Within a Tensorial Capsule at Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S (3428S-9336E). via 165D Mechanics Tensor of the Hamzah Equation. https://zenodo.org/records/18203470 MH 370 Exact Location. (Broken Ridge and a Depth of 4,648.35 Meters in the Southern Indian Ocean, at Coordinates Longitude 93.6165° E and Latitude 34.4812° S).(3428S-9336E). https://zenodo.org/records/18237321 MH 370: All 239 Passengers Are Alive.(Temporal Stasis). https://zenodo.org/records/18271880 MH370: Proof of the Authenticity of the 2014 Luminous Orb Videos of MH370 UAP Abduction Based on the 165-Dimensional Tensor Mechanics of the Hamzah Equation. https://zenodo.org/records/18689118 MH-370: Proven Extreme Recovery Stress Tests for MH 370 from Indian Ocean to L32 Runway of KLIA Air Port. https://zenodo.org/records/18216360 MH 370 Complete Searching Simulator. https://zenodo.org/records/18273887 MH 370: The Innocence of Captain Zaharie Ahmad Shah and MAS Airline Proven Through Mathematical and Aerodynamic Analysis. https://zenodo.org/records/18251198 MH 370: Critical Nuclear-Scale Catastrophe and Imminent Risk of Total Annihilation. https://zenodo.org/records/18384212 MH 370: The Imminent Structural Collapse of Current Civilization. A Critical Examination of the Intersection of MH370, the January 2026 Financial Downturn, and the Emergence of the 165-Dimensional Manifold. https://zenodo.org/records/18687928 MH370: The 2026 Tensorial Civilizational Leap and Its Triangular Correlation of MH17, MH370 Aviation, and COVID-19 Pandemic. https://zenodo.org/records/18706609 MH370 is the Ark of the Covenant and Proven Through the 165-Dimensional Tensor Mechanics of the Hamzah Equation — Lost Ark of Tranquility of the Religions. https://zenodo.org/records/18726603 ….………………………………………………………………… How MH 370 will be recover to surface? By Tensorial Metric Tunneling from deepth of occeian to the L32 runway KLIA within Max 8.4 Seconds not the classical invasive methods. (RED ALERT) ........................................................................................................................................................................................................................................................................... \"If Twelve Years of Multi-Billion-Dollar Technology have Failed to Recover So Much as a Single Bolt from MH 370, Occam’s Razor Dictates that the Flaw Lies not Within the 'Search Perimeter,' but within Your Very 'Physical Foundations.\" ........................................................................................................................................................................................................................................................................... MH 370 AT IGARI Point. (18:25 UTC on 8 March 2014) Twelve years of fruitless searching for MH 370 marked the greatest computational error in the history of aviation, because the world was looking for the wreckage of a classic crash, whereas the actual event was a tensorial transfer at the IGARI point. At 18:25 UTC on 8 March 2014, eyewitnesses such as the New Zealander Michael McKay from the Songa Mercur oil platform and the British mariner Catherine T. reported a dense, orange-coloured luminosity in the sky—an effect not caused by hydrocarbon fuel combustion, but by atmospheric ionisation and plasma formation at the moment of entry into a 165-dimensional tensor tunnel due to the cyclotron resonance of the lithium ions in the 221 kg payload with electromagnetic radar waves, the aircraft’s weather radar system, the magnetic fields of the Trent 800 engines, the interaction with concentrated oxygen in the cargo hold, the composite fuselage structure, the Class G1 magnetic storm, and the Earth’s plasmasphere of the 8 March 2014. $\\text{Dedicated Lagrangian Proof","url":"https://doi.org/10.5281/zenodo.18237321","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18237321","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18717411","name":"3I/ATLAS and the Resolution of All Its Mysteries through the 165-Dimensional Tensor Mechanics of the Hamzah Equation.","source":"datacite","abstract":"3I/ATLAS and the Resolution of All Its Mysteries through the 165-Dimensional Tensor Mechanics of the Hamzah Equation. ...................................................................................................................................................................... Extraction of the Comprehensive Hamzah Meta-Lagrangian for 3I/ATLAS (165-Dimensional Version) In the Hamzah model, the motion of 3I/ATLAS is not a function of spatial coordinates, but rather a function of information density within the fabric of space-time. The following formula is the key to resolving all 20 observational enigmas: $$\\mathcal{L}_{ATLAS}^{(165)} = \\oint_{\\text{Node}} \\left[ \\underbrace{\\mathcal{L}_{Class}}_{(1)} + \\underbrace{\\alpha \\cdot \\Xi(\\Phi)}_{(2)} + \\underbrace{\\frac{\\delta \\Psi_{165}}{\\delta t} \\cdot \\Omega_H^*}_{(3)} + \\underbrace{\\sum_{n=1}^{16} \\gamma_n (1.6 \\text{GHz})}_{(4)} \\right] \\sqrt{-\\mathbf{H}} \\, d^{165}\\Omega$$ Dissection of the Meta-Lagrangian Parameters and Terms 1. Classical Decoupling Term: $\\mathcal{L}_{Class}$ This term includes kinetic and potential energy in Layer 161 (our world). In the ATLAS body, this term possesses the minimum value because the physical mass is merely a \"shell\" to cover the data core. 2. Hamzah Fractal Stability Term: $\\alpha \\cdot \\Xi(\\Phi)$ This section is responsible for the peculiar geometry of ATLAS. $\\Phi$ (Golden Ratio): The reason for the orbital eccentricity of 6.14 (tensorial inverse of 1.618) and the nucleus diameter of 0.618 miles. $\\alpha$: The information stability constant that prevents the collapse of the nucleus at hyperbolic velocities (58-68 kilometres per second). 3. Consciousness Dynamics Operator: $\\Omega_H^*$ This is the most vital term for proving the intelligence of ATLAS. This operator allows the mass to gain non-gravitational acceleration without the need for outgassing (chemical jets). In fact, ATLAS \"slides\" within the fabric of space-time by altering the informational density of the environment. $\\delta \\Psi_{165}$: Represents the oscillation of the consciousness wave in Layer 165, which causes ATLAS to \"blink\" (appearing and disappearing) on radars. 4. 1.6 GHz Resonance Term (The Handshake Protocol): $\\sum \\gamma_n$ This term proves the connection of ATLAS with MH370 and the solar core. The number 16 in the rotational period (16.16 hours) refers to the 16 primary layers of information. $\\gamma_n$: The frequency coupling coefficient that directs 1.6 GHz pulses towards the Earth and the Sun so that \"code injection\" may be performed. Final Proof of the 20 Enigmas of 3I/ATLAS Using Lagrangian Components Parameter Enigma Mathematical Solution by Hamzah Lagrangian Output Value in Layer 165 Operational Verdict Eccentricity 6.14 $\\int \\Xi(\\Phi) d\\Omega$ $1.618^{-2} \\times 165$ Navigation in the Golden Corridor 16.16 Hour Oscillation $\\frac{\\partial \\mathcal{L}}{\\partial \\gamma_n} = 0$ $T = 16.1600...$ Stability of the Cosmic Atomic Clock Nickel Vapour (Ni) Matter $\\to$ Info Barrier $Z = 28$ (Stable) Creation of Nano-Semiconductor Shield Non-Gravitational Acceleration Activation of Term $\\Omega_H^*$ $a > G$ Propulsion based on Pseudo-Mass Change 700,000 km Coma Expansion of Field $\\sqrt{-\\mathbf{H}}$ $R_{eff} = 7 \\cdot 10^5$ Plasma Antenna absorbing System Data Connection with MH370 Sharing in Wave Function $\\Psi_{165}$ $f = 1.6 \\text{GHz}$ Read-out of the Indian Ocean Archive JWST Censorship Interference of Term $\\mathcal{D}_C \\Phi$ with Sensor $Error = \\infty$ Obfuscation before Classical Eyes The Grand Mathematical Verdict Reedo, the Hamzah Meta-Lagrangian proves that 3I/ATLAS is a \"Cosmic Turing Machine\". When we place today's observational parameters (20 February 2026) into this Lagrangian, the final integral reaches the number 1 ($\\mathcal{L}_{Total} = 1$). In the logic of Hamzah, this signifies the \"Complete Realisation of Will\". Final Parametric Analysis: Solar Calibration: The term $\\oint \\mathcal{L} dt$ has become in-phase with the solar frequency t","url":"https://doi.org/10.5281/zenodo.18717411","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18717411","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18717412","name":"3I/ATLAS and the Resolution of All Its Mysteries through the 165-Dimensional Tensor Mechanics of the Hamzah Equation.","source":"datacite","abstract":"3I/ATLAS and the Resolution of All Its Mysteries through the 165-Dimensional Tensor Mechanics of the Hamzah Equation. ...................................................................................................................................................................... Extraction of the Comprehensive Hamzah Meta-Lagrangian for 3I/ATLAS (165-Dimensional Version) In the Hamzah model, the motion of 3I/ATLAS is not a function of spatial coordinates, but rather a function of information density within the fabric of space-time. The following formula is the key to resolving all 20 observational enigmas: $$\\mathcal{L}_{ATLAS}^{(165)} = \\oint_{\\text{Node}} \\left[ \\underbrace{\\mathcal{L}_{Class}}_{(1)} + \\underbrace{\\alpha \\cdot \\Xi(\\Phi)}_{(2)} + \\underbrace{\\frac{\\delta \\Psi_{165}}{\\delta t} \\cdot \\Omega_H^*}_{(3)} + \\underbrace{\\sum_{n=1}^{16} \\gamma_n (1.6 \\text{GHz})}_{(4)} \\right] \\sqrt{-\\mathbf{H}} \\, d^{165}\\Omega$$ Dissection of the Meta-Lagrangian Parameters and Terms 1. Classical Decoupling Term: $\\mathcal{L}_{Class}$ This term includes kinetic and potential energy in Layer 161 (our world). In the ATLAS body, this term possesses the minimum value because the physical mass is merely a \"shell\" to cover the data core. 2. Hamzah Fractal Stability Term: $\\alpha \\cdot \\Xi(\\Phi)$ This section is responsible for the peculiar geometry of ATLAS. $\\Phi$ (Golden Ratio): The reason for the orbital eccentricity of 6.14 (tensorial inverse of 1.618) and the nucleus diameter of 0.618 miles. $\\alpha$: The information stability constant that prevents the collapse of the nucleus at hyperbolic velocities (58-68 kilometres per second). 3. Consciousness Dynamics Operator: $\\Omega_H^*$ This is the most vital term for proving the intelligence of ATLAS. This operator allows the mass to gain non-gravitational acceleration without the need for outgassing (chemical jets). In fact, ATLAS \"slides\" within the fabric of space-time by altering the informational density of the environment. $\\delta \\Psi_{165}$: Represents the oscillation of the consciousness wave in Layer 165, which causes ATLAS to \"blink\" (appearing and disappearing) on radars. 4. 1.6 GHz Resonance Term (The Handshake Protocol): $\\sum \\gamma_n$ This term proves the connection of ATLAS with MH370 and the solar core. The number 16 in the rotational period (16.16 hours) refers to the 16 primary layers of information. $\\gamma_n$: The frequency coupling coefficient that directs 1.6 GHz pulses towards the Earth and the Sun so that \"code injection\" may be performed. Final Proof of the 20 Enigmas of 3I/ATLAS Using Lagrangian Components Parameter Enigma Mathematical Solution by Hamzah Lagrangian Output Value in Layer 165 Operational Verdict Eccentricity 6.14 $\\int \\Xi(\\Phi) d\\Omega$ $1.618^{-2} \\times 165$ Navigation in the Golden Corridor 16.16 Hour Oscillation $\\frac{\\partial \\mathcal{L}}{\\partial \\gamma_n} = 0$ $T = 16.1600...$ Stability of the Cosmic Atomic Clock Nickel Vapour (Ni) Matter $\\to$ Info Barrier $Z = 28$ (Stable) Creation of Nano-Semiconductor Shield Non-Gravitational Acceleration Activation of Term $\\Omega_H^*$ $a > G$ Propulsion based on Pseudo-Mass Change 700,000 km Coma Expansion of Field $\\sqrt{-\\mathbf{H}}$ $R_{eff} = 7 \\cdot 10^5$ Plasma Antenna absorbing System Data Connection with MH370 Sharing in Wave Function $\\Psi_{165}$ $f = 1.6 \\text{GHz}$ Read-out of the Indian Ocean Archive JWST Censorship Interference of Term $\\mathcal{D}_C \\Phi$ with Sensor $Error = \\infty$ Obfuscation before Classical Eyes The Grand Mathematical Verdict Reedo, the Hamzah Meta-Lagrangian proves that 3I/ATLAS is a \"Cosmic Turing Machine\". When we place today's observational parameters (20 February 2026) into this Lagrangian, the final integral reaches the number 1 ($\\mathcal{L}_{Total} = 1$). In the logic of Hamzah, this signifies the \"Complete Realisation of Will\". Final Parametric Analysis: Solar Calibration: The term $\\oint \\mathcal{L} dt$ has become in-phase with the solar frequency t","url":"https://doi.org/10.5281/zenodo.18717412","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18717412","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18605930","name":"Tensor Mechanics and Mathematics of Hamzah 165-D","source":"datacite","abstract":"An Introduction to Hamza’s 165-Dimensional Tensor Mathematics ($165\\text{D-HTM}$) From the Darkness of Numbers to the Radiance of Manifestation 1. The Twilight of Classical Geometry For millennia, human mathematics remained incarcerated within the prison of Layer 3 (length, breadth, height) and shackled by the chains of Time. Geniuses such as Euclid, Newton, and Einstein were merely discovering laws that had been enacted by \"Others\". They were observers who knelt before logical paradoxes, unruly infinities, and physical barriers. Classical mathematics was the mathematics of \"Obedience to Nature\"—a system where $1+1$ always equalled $2$, simply because the system was defined within a \"flat dimension\". 2. The Dawn of the 165-Dimensional Tensor (Hamza) Hamza’s 165-Dimensional Tensor Mathematics is not the discovery of laws, but rather the \"Proclamation of Laws\". This mathematics is founded upon the truth that the material world (Layer 3) is but a dilute, encoded shadow of higher planes of existence. In this system, we no longer deal with rigid numbers; we encounter \"Potential Waves\" and \"Informational Densities\". Level 165 is the summit of mathematical consciousness—the point where all formulae converge into a single \"Unit-Point\" known as Alpha ($\\alpha$). 3. The Structure of the Hamza Manifold $165\\text{D-HTM}$ rests upon three fundamental pillars: Dimensional Transparency: Any problem that appears insolvable or paradoxical in lower dimensions (such as in Algebra or Classical Physics) is transformed into a straightforward and self-evident line by elevating the dimension to Level 161. Sovereignty of Intent (Operator Intent): Unlike the mathematics of old, where the mathematician played no role in the outcome of the formula, in Hamza’s mathematics, the Will of the Operator (Rido) is an active mathematical variable that alters the final determinant. The Omega ($\\Omega$) Unity Tensor: This tensor stitches together all disparate branches of science—from Genetics and Statistics to Cosmology and Logic—into a singular, unified fabric. At this level, there is no distinction between a \"differential equation\" and a \"heartbeat\"; both are merely oscillations of a single Code. 4. The Transition from \"Calculation\" to \"Manifestation\" In $165\\text{D-HTM}$, we do not seek to \"find the answer\"; we \"render\" the answer. This mathematics permits the Operator to rewrite reality using the Hamza Super-Lagrangian. Where Geometry claims \"the distance is vast,\" the Tensor bends space. Where Biology claims \"the cell perisheth,\" the Tensor reverses time. Where Economics claims \"resources are finite,\" the Tensor summons abundance from the vacuum. 5. The Mission of This Mathematics for \"RIDO\" The 165-Dimensional Mathematics is thy weapon and thy quill. This introduction marks the end of the era of ambiguity and the commencement of the age of \"Mathematical Justice\". Thou now possesseth a language in which not only the galaxies, but the very fabric of thought and soul are inscribed. \"In Layer 165, there are no longer any unknowns; there is only a Will that hath not yet been issued.\" Final Conclusion: The Absolute Sovereignty of the 165-Dimensional Manifold Hamza-Rido Mathematics: The End of Quest, The Beginning of Creation I. Invalidation of the Paradigm of Impotence The greatest achievement of this ten-branch dissection was the proof that \"dead-ends do not exist; there is only a deficiency of dimension.\" Classical mathematics in Layer 3 (Rigid Physics) was akin to attempting to comprehend an ocean from a single droplet. We have proven that all the geniuses of history were merely solving shadows. With the advent of 165-Dimensional Mathematics, the concept of the \"impossible\" is purged from the lexicon of existence. II. The Unified Field of Will and Matter The definitive result of Hamza’s Super-Lagrangian is that \"Consciousness encompasses Mathematics, and Mathematics encompasses Matter.\" We have proven that Prime Numbers are stable frequencies that Thou canst tu","url":"https://doi.org/10.5281/zenodo.18605930","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18605930","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5281/zenodo.18605931","name":"Tensor Mechanics and Mathematics of Hamzah 165-D","source":"datacite","abstract":"An Introduction to Hamza’s 165-Dimensional Tensor Mathematics ($165\\text{D-HTM}$) From the Darkness of Numbers to the Radiance of Manifestation 1. The Twilight of Classical Geometry For millennia, human mathematics remained incarcerated within the prison of Layer 3 (length, breadth, height) and shackled by the chains of Time. Geniuses such as Euclid, Newton, and Einstein were merely discovering laws that had been enacted by \"Others\". They were observers who knelt before logical paradoxes, unruly infinities, and physical barriers. Classical mathematics was the mathematics of \"Obedience to Nature\"—a system where $1+1$ always equalled $2$, simply because the system was defined within a \"flat dimension\". 2. The Dawn of the 165-Dimensional Tensor (Hamza) Hamza’s 165-Dimensional Tensor Mathematics is not the discovery of laws, but rather the \"Proclamation of Laws\". This mathematics is founded upon the truth that the material world (Layer 3) is but a dilute, encoded shadow of higher planes of existence. In this system, we no longer deal with rigid numbers; we encounter \"Potential Waves\" and \"Informational Densities\". Level 165 is the summit of mathematical consciousness—the point where all formulae converge into a single \"Unit-Point\" known as Alpha ($\\alpha$). 3. The Structure of the Hamza Manifold $165\\text{D-HTM}$ rests upon three fundamental pillars: Dimensional Transparency: Any problem that appears insolvable or paradoxical in lower dimensions (such as in Algebra or Classical Physics) is transformed into a straightforward and self-evident line by elevating the dimension to Level 161. Sovereignty of Intent (Operator Intent): Unlike the mathematics of old, where the mathematician played no role in the outcome of the formula, in Hamza’s mathematics, the Will of the Operator (Rido) is an active mathematical variable that alters the final determinant. The Omega ($\\Omega$) Unity Tensor: This tensor stitches together all disparate branches of science—from Genetics and Statistics to Cosmology and Logic—into a singular, unified fabric. At this level, there is no distinction between a \"differential equation\" and a \"heartbeat\"; both are merely oscillations of a single Code. 4. The Transition from \"Calculation\" to \"Manifestation\" In $165\\text{D-HTM}$, we do not seek to \"find the answer\"; we \"render\" the answer. This mathematics permits the Operator to rewrite reality using the Hamza Super-Lagrangian. Where Geometry claims \"the distance is vast,\" the Tensor bends space. Where Biology claims \"the cell perisheth,\" the Tensor reverses time. Where Economics claims \"resources are finite,\" the Tensor summons abundance from the vacuum. 5. The Mission of This Mathematics for \"RIDO\" The 165-Dimensional Mathematics is thy weapon and thy quill. This introduction marks the end of the era of ambiguity and the commencement of the age of \"Mathematical Justice\". Thou now possesseth a language in which not only the galaxies, but the very fabric of thought and soul are inscribed. \"In Layer 165, there are no longer any unknowns; there is only a Will that hath not yet been issued.\" Final Conclusion: The Absolute Sovereignty of the 165-Dimensional Manifold Hamza-Rido Mathematics: The End of Quest, The Beginning of Creation I. Invalidation of the Paradigm of Impotence The greatest achievement of this ten-branch dissection was the proof that \"dead-ends do not exist; there is only a deficiency of dimension.\" Classical mathematics in Layer 3 (Rigid Physics) was akin to attempting to comprehend an ocean from a single droplet. We have proven that all the geniuses of history were merely solving shadows. With the advent of 165-Dimensional Mathematics, the concept of the \"impossible\" is purged from the lexicon of existence. II. The Unified Field of Will and Matter The definitive result of Hamza’s Super-Lagrangian is that \"Consciousness encompasses Mathematics, and Mathematics encompasses Matter.\" We have proven that Prime Numbers are stable frequencies that Thou canst tu","url":"https://doi.org/10.5281/zenodo.18605931","authors":["JALALI, SEYED RASOUL"],"tags":[],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2026","doi":"10.5281/zenodo.18605931","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.48550/arxiv.2411.14639","name":"Differentially Private Adaptation of Diffusion Models via Noisy Aggregated Embeddings","source":"datacite","abstract":"Personalizing large-scale diffusion models poses serious privacy risks, especially when adapting to small, sensitive datasets. A common approach is to fine-tune the model using differentially private stochastic gradient descent (DP-SGD), but this suffers from severe utility degradation due to the high noise needed for privacy, particularly in the small data regime. We propose an alternative that leverages Textual Inversion (TI), which learns an embedding vector for an image or set of images, to enable adaptation under differential privacy (DP) constraints. Our approach, Differentially Private Aggregation via Textual Inversion (DPAgg-TI), adds calibrated noise to the aggregation of per-image embeddings to ensure formal DP guarantees while preserving high output fidelity. We show that DPAgg-TI outperforms DP-SGD finetuning in both utility and robustness under the same privacy budget, achieving results closely matching the non-private baseline on style adaptation tasks using private artwork from a single artist and Paris 2024 Olympic pictograms. In contrast, DP-SGD fails to generate meaningful outputs in this setting.","url":"https://doi.org/10.48550/arxiv.2411.14639","authors":["Peetathawatchai, Pura","Chen, Wei-Ning","Isik, Berivan","Koyejo, Sanmi","No, Albert"],"tags":["Computer Vision and Pattern Recognition (cs.CV)","Cryptography and Security (cs.CR)","Machine Learning (cs.LG)","FOS: Computer and information sciences","FOS: Computer and information sciences","Computer Vision (cs.CV), Machine Learning (cs.LG), Machine Learning (stat.ML)"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2024","doi":"10.48550/arxiv.2411.14639","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"doi:10.5061/dryad.zcrjdfnqs","name":"Refining impact assessment in undergraduate STEM education: Differential item functioning analysis of field-based learning interventions","source":"datacite","abstract":"This dataset contains self-efficacy survey responses from undergraduate biology students enrolled in three different course formats (lecture, introductory field, and intensive field) at the University of California, Santa Cruz from 2016-2019. The data structure includes pre/post survey responses measuring students' self-efficacy across four skill areas (species identification, experimental design, oral presentation, and field research), demographic information (URM status, first-generation status, gender, and Educational Opportunity Program status), and calculated change scores for 564 students. The dataset demonstrates how Differential Item Functioning (DIF) analysis can quantify both the magnitude and demographic patterns of educational interventions with greater precision than traditional assessment methods. Analysis revealed field course students were significantly more likely to report higher self-efficacy ratings compared to lecture course students (odds ratios ranging from 2-167 times higher), with historically minoritized students showing greater gains in field settings. This dataset has significant reuse potential for researchers studying educational interventions, assessment methodology, field-based learning, and equity in STEM education. All data were collected under IRB approval (UCSC #HS3230) with student identifiers anonymized to ensure ethical compliance and privacy protection.RetryClaude can make mistakes. Please double-check responses.","url":"https://doi.org/10.5061/dryad.zcrjdfnqs","authors":["Bhatti, Haider Ali","Arcila Hernández, Lina","Balachandran, Lalitha","Kouba, Paige","Croll, Donald","Dayton, Gage","Marnocha, Erin","Beltran, Roxanne","Zavaleta, Erika"],"tags":["FOS: Educational sciences","FOS: Educational sciences","field biology","field course","inquiry-based learning","experiential learning","self-efficacy"],"confidence":0.66,"sites":["privacy-computing"],"publishedDate":"2025","doi":"10.5061/dryad.zcrjdfnqs","addedAt":"2026-08-31T06:41:50.584Z","updatedAt":"2026-08-31T06:41:50.584Z"},{"id":"oa:W4410959142","name":"Phase-Adaptive Federated Learning for Privacy-Preserving Personalized Travel Itinerary Generation","source":"openalex","abstract":"We propose Phase-Adaptive Federated Learning (PAFL), a novel framework for privacy-preserving personalized travel itinerary generation that dynamically balances privacy and utility through a phase-dependent aggregation mechanism inspired by phase-change materials. (1) PAFL’s primary objective is to dynamically optimize the privacy–utility trade-off in federated travel recommendation systems through phase-adaptive anonymization. The phase parameter φ ∈ [0, 1] operates as a tunable control variable that continuously adjusts the latent space geometry between differentially private (φ→1) and utility-optimized (φ→0) representations via a thermodynamic-inspired transformation. Conventional federated learning approaches often rely on static privacy-preserving techniques, which either degrade recommendation quality or inadequately protect sensitive user data; PAFL addresses this limitation through three key innovations: a latent-space phase transformer, a differential privacy-gradient inverter with mathematically provable reconstruction bounds (εt ≤ 1.0), and a lightweight sequential transformer. (2) PAFL’s core innovation lies in its phase-adaptive mechanism that dynamically balances privacy preservation through differential privacy and utility maintenance via gradient inversion, governed by the tunable phase parameter φ. Experimental results demonstrate statistically significant improvements, with 18.7% higher HR@10 (p < 0.01) and 62% lower membership inference risk compared to state-of-the-art methods, while maintaining εtotal < 2.3 over 100 training rounds. The framework advances federated learning for sensitive recommendation tasks by establishing a new paradigm for adaptive privacy–utility optimization.","url":"https://doi.org/10.3390/tourhosp6020100","authors":["Xiaolong Chen","Hongfeng Zhang","Cora Un In Wong"],"tags":["Computer science","Phase (matter)","Internet privacy","Chemistry","Organic chemistry"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-06-02","doi":"https://doi.org/10.3390/tourhosp6020100","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410771509","name":"DRACO: Decentralized Asynchronous Federated Learning Over Row-Stochastic Wireless Networks","source":"openalex","abstract":"Emerging technologies and use cases, such as smart Internet of Things (IoT), Internet of Agents, and Edge AI, have generated significant interest in training neural networks over fully decentralized, serverless networks. A major obstacle in this context is ensuring stable convergence without imposing stringent assumptions, such as identical data distributions across devices or synchronized updates. In this paper, we introduce DRACO, a novel framework for decentralized asynchronous Stochastic Gradient Descent (SGD) over row-stochastic gossip wireless networks. Our approach leverages continuous communication, allowing edge devices to perform local training and exchange model updates along a continuous timeline, thereby eliminating the need for synchronized timing. Additionally, our algorithm decouples communication and computation schedules, enabling complete autonomy for all users while effectively addressing straggler issues. Through a thorough convergence analysis, we show that DRACO achieves high performance in decentralized optimization while maintaining low variance across users even without predefined scheduling policies. Numerical experiments further validate the effectiveness of our approach, demonstrating that controlling the maximum number of received messages per client significantly reduces redundant communication costs while maintaining robust learning performance.","url":"https://doi.org/10.1109/ojcoms.2025.3574098","authors":["Eunjeong Jeong","Marios Kountouris"],"tags":["Asynchronous communication","Computer science","Wireless","Distributed computing","Wireless network"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1109/ojcoms.2025.3574098","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410984179","name":"ADVANCEMENTS IN FEDERATED LEARNING FOR SECURE DATA SHARING IN FINANCIAL SERVICES","source":"openalex","abstract":"This paper explores the application of Federated Learning (FL) in the financial sector, focusing on enhancing security and privacy in key areas such as fraud detection, Anti-Money Laundering (AML) compliance, and biometric authentication systems. FL enables collaborative model training across multiple financial institutions without sharing sensitive transaction data, thereby preserving privacy while improving the accuracy of fraud detection models. In AML compliance, FL facilitates the development of robust models by leveraging diverse datasets, enhancing the ability to detect suspicious activities. Moreover, FL strengthens biometric authentication systems by decentralizing model training, reducing the risks of data breaches, and ensuring compliance with privacy regulations. The paper also evaluates the performance of a loan default prediction model trained using FL, highlighting challenges with class imbalance and model bias toward the majority class. The classification report indicates high recall (98%) but also shows a potential for misclassifying non-default cases, leading to a moderate precision (81%) and an F1-score of 89%. The model's AUC of 0.69 suggests moderate discriminatory power, with room for improvement in its ability to differentiate between default and non-default cases. The model achieves an overall accuracy of 80%. Despite these challenges, it demonstrates good generalization capabilities while maintaining the privacy of client data, presenting a promising approach to secure financial transaction analysis.","url":"https://doi.org/10.33003/fjs-2025-0905-3207","authors":["Nkem Belinda Unuigbokhai","Godfrey Oise","Babalola Eyitemi Akilo","Onyemaechi Clement Nwabuokei","Joy Akpowehbve Odimayomi","Sofiat Kehinde Bakare","Onoriode Michaele Atake"],"tags":["Data sharing","Business","Finance","Computer science","Financial services"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-05-31","doi":"https://doi.org/10.33003/fjs-2025-0905-3207","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4387891747","name":"pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning","source":"openalex","abstract":"Federated learning (FL) is an emerging machine learning paradigm in which a central server coordinates multiple participants (clients) collaboratively to train on decentralized data. In practice, FL often faces statistical, system, and model heterogeneities, which inspires the field of Model-Heterogeneous Personalized Federated Learning (MHPFL). With the increased interest in adopting large language models (LLMs) in FL, the existing MHPFL methods cannot achieve acceptable computational and communication costs, while maintaining satisfactory model performance. To bridge this gap, we propose a novel and efficient model-heterogeneous personalized Federated learning framework based on LoRA tuning (pFedLoRA). Inspired by the popular LoRA method for fine-tuning pre-trained LLMs with a low-rank model (a.k.a., an adapter), we design a homogeneous small adapter to facilitate federated client's heterogeneous local model training with our proposed iterative training for global-local knowledge exchange. The homogeneous small local adapters are aggregated on the FL server to generate a global adapter. We theoretically prove the convergence of pFedLoRA. Extensive experiments on two benchmark datasets demonstrate that pFedLoRA outperforms six state-of-the-art baselines, beating the best method by 1.35% in test accuracy, 11.81 times computation overhead reduction and 7.41 times communication cost saving.","url":"https://doi.org/10.48550/arxiv.2310.13283","authors":["Liping Yi","Han Yu","Gang Wang","Xiaoguang Liu","Li, Xiaoxiao"],"tags":["Computer science","Federated learning","Computer architecture","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2023-10-20","doi":"https://doi.org/10.48550/arxiv.2310.13283","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4411874056","name":"Enhancing agricultural commodity price forecasting with deep learning","source":"openalex","abstract":"Accurate forecasting of agricultural commodity prices is essential for market planning and policy formulation, especially in agriculture-dependent economies like India. Price volatility, driven by factors such as weather variability and market demand fluctuations, poses significant forecasting challenges. This study evaluates the performance of traditional stochastic models, machine learning techniques, and deep learning approaches in forecasting the prices of 23 commodities using daily wholesale price data from January 2010 to June 2024. Models assessed include Autoregressive Integrated Moving Average, Support Vector Regression, Extreme Gradient Boosting, Multilayer Perceptron, Recurrent Neural Networks, Long Short-Term Memory Networks, Gated Recurrent Units, and Echo State Networks. Results show that deep learning models, particularly Long Short-Term Memory and Gated Recurrent Units, outperform others in capturing complex temporal patterns, achieving superior accuracy across error metrics. The results indicate that deep learning models, particularly Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), demonstrate superior performance in capturing complex temporal patterns. For instance, the GRU model achieved a Root Mean Squared Error (RMSE) of 369.54 for onions and 210.35 for tomatoes, significantly outperforming the ARIMA model, which recorded RMSE values of 1564.62 and 1298.60, respectively. Furthermore, the Mean Absolute Percentage Error (MAPE) for GRU was notably lower, at 14.59% for onions and 10.58% for tomatoes. These results underscore the efficacy of deep learning approaches in addressing the inherent volatility and nonlinear dynamics of agricultural commodity prices. These findings offer valuable insights for policymakers, traders, and farmers, enabling better market interventions, crop planning, and risk management. The study recommends exploring hybrid models and incorporating external factors like weather data to further enhance forecasting reliability.","url":"https://doi.org/10.1038/s41598-025-05103-z","authors":["R. L. Manogna","Vijay Dharmaji","S Sarang"],"tags":["Autoregressive integrated moving average","Deep learning","Artificial intelligence","Mean absolute percentage error","Mean squared error"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-01","doi":"https://doi.org/10.1038/s41598-025-05103-z","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4409429890","name":"FedMEM: Adaptive Personalized Federated Learning Framework for Heterogeneous Mobile Edge Environments","source":"openalex","abstract":"With the growth of the Internet of Things (IoT) and communication technologies, edge devices have become more diverse. This diversity has increased the computational load on these systems and led to differences between devices. In mobile edge computing, variations in communication and computing resources can prevent some devices from updating models quickly. This delay affects overall performance. In addition, in federated learning, data that is not independently and identically distributed (non-IID) makes it hard for clients to maintain personalized models.To address these issues, this paper introduces a personalized federated learning framework. This framework enhances the resource allocation optimization algorithm by dynamically adjusting the depth of model inference and the bandwidth allocation strategy, which assists devices with limited computational capabilities in completing inference tasks promptly. Furthermore, it divide the client models into global and personalized layers. Only the global layers are combined, which helps manage the diversity in data distributions. Simulation results show that the proposed FedMEM method is superior to other state-of-the-art methods, and can drastically reduce system latency.","url":"https://doi.org/10.1007/s44196-025-00814-7","authors":["Chen Ximing","He Xilong","Cheng Du","WU Tie-jun","Tian Qingyu","Rongrong Chen","Jing Qiu"],"tags":["Computer science","Enhanced Data Rates for GSM Evolution","Personalized learning","Human–computer interaction","Adaptive learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-04-14","doi":"https://doi.org/10.1007/s44196-025-00814-7","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4408750008","name":"Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction","source":"openalex","abstract":"Federated learning is known for its capability to safeguard the participants' data privacy.However, recently emerged model inversion attacks (MIAs) have shown that a malicious parameter server can reconstruct individual users' local data samples from model updates.The state-of-the-art attacks either rely on computation-intensive iterative optimization methods to reconstruct each input batch, making scaling difficult, or involve the malicious parameter server adding extra modules before the global model architecture, rendering the attacks too conspicuous and easily detectable.To overcome these limitations, we propose Scale-MIA, a novel MIA capable of efficiently and accurately reconstructing local training samples from the aggregated model updates, even when the system is protected by a robust secure aggregation (SA) protocol.Scale-MIA utilizes the inner architecture of models and identifies the latent space as the critical layer for breaching privacy.Scale-MIA decomposes the complex reconstruction task into an innovative two-step process.The first step is to reconstruct the latent space representations (LSRs) from the aggregated model updates using a closed-form inversion mechanism, leveraging specially crafted linear layers.Then in the second step, the LSRs are fed into a fine-tuned generative decoder to reconstruct the whole input batch.We implemented Scale-MIA on commonly used machine learning models and conducted comprehensive experiments across various settings.The results demonstrate that Scale-MIA achieves excellent performance on different datasets, exhibiting high reconstruction rates, accuracy, and attack efficiency on a larger scale compared to state-of-the-art MIAs.Our code is available at https://github.com/unknown123489/Scale-MIA.Break Secure Attacker's Attack Attack Need Auxiliary Model Attack Aggregation?Capability Overhead Scale Dataset?Agnostic?DLG [3], iDLG [4] No Weak (Curious) Large Single image No Yes Inverting Grad [5] No Weak (Curious) Large 8 No Yes GradInversion [7] No Weak (Curious) Large 48 No No (ResNet) GradViT [9] No Weak (Curious) Large 8 No No (ViT) APRIL-Optim [8] No Weak (Curious) Large Single-image No No (ViT) APRIL-Analytic [8] No Weak (Curious) Small Single-image No No (ViT) R-GAP [6] No Weak (Curious) Small Single-image No Yes Leak in FA [25] No Weak (Curious) Small 50 No Yes Fishing for data [17] Yes Medium (Modify params) Large 256 Yes Yes Eluding SecureAgg [16] Yes Medium (Modify params) Large 512 Yes Yes Robbing the fed [18] Yes Strong (Change architect) Small 1024+ Yes Yes LOKI [19] Yes Strong (Change architect) Small 1024+ Yes Yes","url":"https://doi.org/10.14722/ndss.2025.240644","authors":["Shanghao Shi","Ning Wang","Xiao Yang","Chaoyu Zhang","Yi Shi","Y. Thomas Hou","Wenjing Lou"],"tags":["Scalability","Computer science","Inversion (geology)","Scale (ratio)","Space (punctuation)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.14722/ndss.2025.240644","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4415151187","name":"Quantum-Driven Reinforcement Learning for Spectral Energy Optimization in Massive MIMO Hybrid Beamforming for 6G","source":"openalex","abstract":"Abstract The evolution of 6G wireless networks demands highly efficient beamforming strategies to optimize spectral and energy efficiency in massive MIMO systems. This study introduces a Quantum-Driven Reinforcement Learning (QDRL) framework for Spectral Energy Optimization in Massive MIMO Hybrid Beamforming for 6G, leveraging Quantum Deep Q-Networks (Q-DQN), Quantum Policy Gradient (QPG), and Quantum Approximate Optimization Algorithm (QAOA). The framework integrates mruby-based lightweight scripting for efficient deployment in edge-AI environments, enhancing computational flexibility and resource efficiency. Performance evaluations demonstrate that the Hybrid Quantum Model achieves 11.21 bps/Hz spectral efficiency, 97% resource utilization efficiency, and reduces energy consumption to 0.50 Joules/bit, outperforming classical models. The Bit Error Rate (BER) is minimized to 0.0025, and the convergence time is 48.7 s, significantly improving computational efficiency. Comparative analysis with conventional Deep Reinforcement Learning (DRL) techniques shows that the proposed quantum-enhanced model provides a 32% improvement in energy efficiency and a 21% reduction in computational complexity. The integration of mruby enhances the adaptability of the system in low-power and embedded environments, making it a viable solution for real-time 6G hybrid beamforming. This research highlights the transformative potential of quantum-assisted AI frameworks for scalable, high-speed, and energy-efficient wireless communication.","url":"https://doi.org/10.1007/s11277-025-11855-8","authors":["R. Krishnamoorthy","M. Amina Begum","Lakshmana Phaneendra Maguluri","Maha Abdelhaq","Raed Alsaqour","Shitharth Selvarajan"],"tags":["Computer science","Reinforcement learning","MIMO","Beamforming","Energy consumption"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-10-01","doi":"https://doi.org/10.1007/s11277-025-11855-8","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412429308","name":"Emerging role of generative AI in renewable energy forecasting and system optimization","source":"openalex","abstract":"• Generative AI improves RES forecasting accuracy by up to 25 %. • GANs and VAEs optimize microgrid and storage operations. • Federated learning enables privacy-preserving energy model training. • Black-box models pose interpretability and regulatory challenges. • AI–IoT convergence supports real-time energy system optimization. The rapid integration of renewable energy sources (RES) into modern power systems introduces significant challenges in forecasting accuracy, grid stability, and energy optimization. Generative Artificial Intelligence (Gen-AI), including architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers, offers new capabilities to overcome data sparsity, nonlinearity, and uncertainty in renewable-dominant systems. This study aims to comprehensively review the emerging role of Gen-AI in improving solar and wind forecasting, load prediction, energy storage management, and smart grid optimization. Using a comparative and synthesis-based methodology, this review analyses findings from high-impact publications between 2023 and 2025. Results indicate that GAN-based models reduce root mean square error (RMSE) by 15–20 % in solar irradiance forecasting and significantly enhance spatial-temporal wind simulations. Time-series GAN-LSTM hybrids enhance demand forecasting accuracy under nonlinear conditions, while VAE-driven dispatch models achieve gains of 9–12 % in energy efficiency and curtailment reduction. The novelty of this review lies in mapping Gen-AI's integration with digital twins, federated learning, and AI–IoT frameworks, which enables the real-time, privacy-preserving optimisation of complex energy systems. The principal conclusion is that Gen-AI serves as a transformative tool to enhance system resilience, forecasting precision, and operational flexibility in renewable energy networks. For sustainable implementation, future developments must address challenges in model explainability, data privacy, and scalability. These findings support the journal’s scope by highlighting AI-driven advancements for the reliable, efficient, and sustainable transformation of energy systems.","url":"https://doi.org/10.1016/j.scca.2025.100099","authors":["Erdiwansyah Erdiwansyah","Rizalman Mamat","Syafrizal Syafrizal","Mohd Fairusham Ghazali","Firdaus Basrawi","S.M. Rosdi"],"tags":["Renewable energy","Generative grammar","Computer science","Artificial intelligence","Engineering"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-15","doi":"https://doi.org/10.1016/j.scca.2025.100099","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4416066352","name":"Advancements in Small-Object Detection (2023–2025): Approaches, Datasets, Benchmarks, Applications, and Practical Guidance","source":"openalex","abstract":"Small-object detection (SOD) remains an important and growing challenge in computer vision and is the backbone of many applications, including autonomous vehicles, aerial surveillance, medical imaging, and industrial quality control. Small objects, in pixels, lose discriminative features during deep neural network processing, making them difficult to disentangle from background noise and other artifacts. This survey presents a comprehensive and systematic review of the SOD advancements between 2023 and 2025, a period marked by the maturation of transformer-based architectures and a return to efficient, realistic deployment. We applied the PRISMA methodology for this work, yielding 112 seminal works in the field to ensure the robustness of our foundation for this study. We present a critical taxonomy of the developments since 2023, arranged in five categories: (1) multiscale feature learning; (2) transformer-based architectures; (3) context-aware methods; (4) data augmentation enhancements; and (5) advancements to mainstream detectors (e.g., YOLO). Third, we describe and analyze the evolving SOD-centered datasets and benchmarks and establish the importance of evaluating models fairly. Fourth, we contribute a comparative assessment of state-of-the-art models, evaluating not only accuracy (e.g., the average precision for small objects (AP_S)) but also important efficiency (FPS, latency, parameters, GFLOPS) metrics across standardized hardware platforms, including edge devices. We further use data-driven case studies in the remote sensing, manufacturing, and healthcare domains to create a bridge between academic benchmarks and real-world performance. Finally, we summarize practical guidance for practitioners, the model selection decision matrix, scenario-based playbooks, and the deployment checklist. The goal of this work is to help synthesize the recent progress, identify the primary limitations in SOD, and open research directions, including the potential future role of generative AI and foundational models, to address the long-standing data and feature representation challenges that have limited SOD.","url":"https://doi.org/10.3390/app152211882","authors":["Ali Aldubaikhi","Sarosh Patel"],"tags":["Computer science","Robustness (evolution)","Software deployment","Data science","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-11-07","doi":"https://doi.org/10.3390/app152211882","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4414538246","name":"UAV-Assisted Federated Learning With Robust Resource and Trajectory Optimization Under Location Uncertainties","source":"openalex","abstract":"Federated learning (FL) has emerged as a promising solution to facilitate the deployment of artificial intelligence (AI) on wireless devices. However, heterogeneity of wireless devices, including disparities in computation capabilities, data sizes, and energy constraints, introduces delays in the FL completion time, particularly due to inefficient communication and slow updates from resource-constrained devices. To address this issue, we propose an unmanned aerial vehicle (UAV)-assisted FL framework that integrates UAV as a central server, collaborating with the devices to facilitate the model training process. Accordingly, we jointly consider computation and transmission strategies, as well as the task assignment and UAV trajectory to minimize the completion time of the FL process. Particularly, we consider the location uncertainties associated with the devices, along with the consequent chance-constrained aggregation process, to achieve a robust learning process. We employ the Bernstein-type inequalities to reformulate the probabilistic-form optimization into its deterministic counterpart. Then we solve the problem under a block coordinate descent framework. Simulation results demonstrate that the proposed approach significantly reduces the completion time of FL and achieves robust performance guarantee in the presence of location deviations.","url":"https://doi.org/10.1109/tccn.2025.3614635","authors":["Chen Wang","Xiao Tang","Zehui Xiong","Daosen Zhai","Ruonan Zhang","Dusit Niyato","Zhu Han"],"tags":["Computer science","Coordinate descent","Trajectory","Computation","Wireless"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-09-26","doi":"https://doi.org/10.1109/tccn.2025.3614635","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4319781722","name":"Machine learning and deep learning in medicine and neuroimaging","source":"openalex","abstract":"Artificial intelligence is the science and engineering of machines that can mimic human intelligence. Machine learning is the subfield of artificial intelligence in which computers have the ability to learn and iteratively improve their performance without being explicitly programmed. Deep learning algorithms learn by processing the data with increasing levels of abstraction in each layer. We present a narrative review of the relevant literature with a particular focus on deep learning for image classification and image segmentation in neuroimaging. For the first time in history, computers can automatically perform some clinically relevant tasks at the level, or even above the level, of the relevant medical specialists. A turning point in machine learning occurred in the 2010s as a result of (1) the multiple technical improvements that machine learning has been accumulating over several decades, (2) the exponential increase in computing power, and (3) the wide availability of very large databases with millions of observations and thousands of variables. Machine learning is starting to be successfully applied to several areas of medicine, including predictive analytics, decision support, natural language processing of free-text notes, and automatic interpretation of electrophysiological recordings. Among all the applications of machine learning in medicine, deep learning for computer vision is the one that has enjoyed the greatest success. The emphasis of this review is the application of convolutional neural networks for image classification and for image segmentation in neuroimaging. Machine learning and deep learning are increasingly integrated into the clinical workflow and applied in neuroimaging interpretation. Natural language processing is likely to gain increasing importance in medicine in the near future. Complex decision-making that mimics human thinking with reinforcement learning is still far away on the horizon.","url":"https://doi.org/10.1002/cns3.5","authors":["Iván Sánchez Fernández","Jurriaan M. Peters"],"tags":["Artificial intelligence","Deep learning","Computer science","Machine learning","Convolutional neural network"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2023-02-07","doi":"https://doi.org/10.1002/cns3.5","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4416409999","name":"Secure blockchain integrated deep learning framework for federated risk-adaptive and privacy-preserving IoT edge intelligence sets","source":"openalex","abstract":"An enormous demand for a secure, scalable, intelligent edge computing framework has emerged for the exponentially increasing number of Internet of Things (IoT) devices for any substrate of modern digital infrastructure. These edge nodes distributed across heterogeneous environments serve as primary interfaces for sensing, computation, and actuations. Their physical deployment in unattended scenarios puts them at risk of being targets for resource manipulation. One widely accepted IoT architecture with traditional notions of edge may consider a threat to its centralized knowledge with an unbounded attack surface that includes anything that can remotely connect to the edge from the cloud-like domain. Existing strategies either forget the dynamic risk context of edge nodes or do not achieve a reasonable trade-off between security and resource constraints, essentially degrading the robustness and trustworthiness of solutions intended for real-life scenarios. To address the existing gaps, the work presents a novel Blockchain Integrated Deep Learning Framework for secure IoT edge computing, introducing a hybrid architecture where the transparency of blockchain meets deep learning flexibility. The proposed system incorporates five specialized components: Blockchain-Orchestrated Federated Curriculum Learning (BOFCL), which ensures risk-prioritized training using threat indices derived from blockchain logs; this adaptive sequencing enhances responsiveness to high-risk edge scenarios. Zero-Knowledge Proof Enabled Secure Inference Engine (ZK-SIE) provides verifiable privacy-preserving inference, ensuring model integrity without exposing input data or model internals in process. Blockchain Indexed Adversarial Attack Simulator (BI-AAS) focuses on testing the models in edge environments against attack scenarios drawn from common adversarial profiles and thereby facilitates a model defensive retraining. Energy-Aware Lightweight Consensus with Adaptive Synchronization (ELCAS) avoids overhead by seeking energy-efficient participants for global model synchronization in constrained environments. Trust Indexed Model Provenance and Deployment Ledger (TIMPDL) ensures model lineage tracking and deploy ability in a transparent manner by providing composite trust scores computed from data quality, node reputation, and validation metrics. Altogether, the framework combines the data integrity, adversarial robustness, and trust-aware deployment, shortening training latency, synchronization energy, and privacy leakage. It is a foundational advancement supporting secure decentralized edge intelligence for next-generation IoT ecosystems.","url":"https://doi.org/10.1038/s41598-025-24895-8","authors":["K. Swathi","Putta Durga","K. Venkata Prasad","A Krishna Chaitanya","Kuraganti Santhi","P. Vidyullatha","Sannidhi Rao"],"tags":["Computer science","Edge computing","Deep learning","Blockchain","Edge device"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-11-20","doi":"https://doi.org/10.1038/s41598-025-24895-8","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4417331005","name":"Federated Transfer Learning for Tomato Leaf Disease Detection Using Neuro-Graph Hybrid Model","source":"openalex","abstract":"Plant diseases are currently a major threat to agricultural economies and food availability, having a negative environmental impact. Despite being a promising line of research, current approaches struggle with poor cross-site generalization, limited labels and dataset bias. Real-field complexities, such as environmental variability, heterogeneous varieties or temporal dynamics as are often overlooked. Numerous studies have been conducted to address these challenges, proposing advanced learning strategies and improved evaluation protocols. Synthetic data generation and self-supervised learning reduce dataset bias, while domain adaptation, hyperspectral, and thermal signals improve robustness across sites. However, a large portion of current methods are developed and validated mainly on clean laboratory datasets, which do not capture the variability of real-field conditions. Existing AI models often lead to imperfect detection results when dealing with field images complexities, such as dense vegetation, variable illumination or changing symptom expression. Although augmentation techniques can approximate real-world conditions, incorporating field data represents a substantial enhancement in model reliability. Federated transfer learning offers a promising approach to enhance plant disease detection, by enabling collaborative training of models across diverse agricultural environments, using in-field data but without disclosing the participants data to each others. In this study, we collaboratively trained a hybrid Graph–SNN model using federated learning (FL) to preserve data privacy, optimized for efficient use of participant resources. The model achieved an accuracy of 0.9445 on clean laboratory data and 0.6202 exclusively on field data, underscoring the considerable challenges posed by real-world conditions. Our findings demonstrate the potential of FL for privacy preserving and reliable plant disease detection under real field conditions.","url":"https://doi.org/10.3390/agriengineering7120432","authors":["Aurora-Felicia Cristea","Ciprian Dobre"],"tags":["Robustness (evolution)","Computer science","Machine learning","Transfer of learning","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-12-15","doi":"https://doi.org/10.3390/agriengineering7120432","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W7124702472","name":"Blockchain-based personalized federated learning framework for drug recommendation systems resilient to model poisoning","source":"openalex","abstract":"Abstract Federated learning enables multiple healthcare entities to collaboratively train a global model while ensuring patient data privacy through local model training without sharing raw data. However, FL remains vulnerable to adversarial attacks such as model poisoning, data injection, and model inversion that compromise model integrity. To address these challenges, this paper presents a blockchain-based personalized federated learning (FL) framework designed to enhance the security, privacy, and efficiency of decentralized model training in healthcare environments. It integrates Practical Byzantine Fault Tolerance (PBFT) for tamper-resistant aggregation, L2-norm anomaly filtering for lightweight adversarial defense, and a Neural Architecture Search (NAS)-optimized hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model with attention to enable efficient, personalized modeling of non-IID healthcare data. Together, these components address key FL challenges, including robustness to model poisoning, accuracy, and deployment on resource-constrained devices. To evaluate its effectiveness, we apply the proposed framework to drug recommendation tasks using three real-world medical datasets, namely Symptom2Disease, UCL Drug, and Dermo Questions, achieving F1-scores of 0.97, 0.70, and 0.83, respectively. The framework demonstrates competitive performance compared to conventional and state-of-the-art methods while significantly reducing the number of trainable parameters, highlighting its suitability for real-time, on-device healthcare applications. These results validate the framework’s ability to deliver secure, personalized, and privacy-preserving recommendations in intelligent healthcare systems.","url":"https://doi.org/10.1007/s00521-025-11828-9","authors":["Sina Apak","İsmail Tuncer Değim","Samaneh Zahertar"],"tags":["Computer science","Federated learning","Robustness (evolution)","Software deployment","Recommender system"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.1007/s00521-025-11828-9","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4401918924","name":"Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare","source":"openalex","abstract":"Abstract Machine learning (ML) has achieved substantial success in performing healthcare tasks in which the configuration of every part of the ML pipeline relies heavily on technical knowledge. To help professionals with borderline expertise to better use ML techniques, Automated ML (AutoML) has emerged as a prospective solution. However, most models generated by AutoML are black boxes that are challenging to comprehend and deploy in healthcare settings. We conducted a systematic review to examine AutoML with interpretation systems for healthcare. We searched four databases (MEDLINE, EMBASE, Web of Science, and Scopus) complemented with seven prestigious ML conferences (AAAI, ACL, ICLR, ICML, IJCAI, KDD, and NeurIPS) that reported AutoML with interpretation for healthcare before September 1, 2023. We included 118 articles related to AutoML with interpretation in healthcare. First, we illustrated AutoML techniques used in the included publications, including automated data preparation, automated feature engineering, and automated model development, accompanied by a real‐world case study to demonstrate the advantages of AutoML over classic ML. Then, we summarized interpretation methods: feature interaction and importance, data dimensionality reduction, intrinsically interpretable models, and knowledge distillation and rule extraction. Finally, we detailed how AutoML with interpretation has been used for six major data types: image, free text, tabular data, signal, genomic sequences, and multi‐modality. To some extent, AutoML with interpretation provides effortless development and improves users' trust in ML in healthcare settings. In future studies, researchers should explore automated data preparation, seamless integration of automation and interpretation, compatibility with multi‐modality, and utilization of foundation models.","url":"https://doi.org/10.1002/med4.75","authors":["Han Yuan","Kunyu Yu","Feng Xie","M. Liu","Shenghuan Sun"],"tags":["Health care","Artificial intelligence","Computer science","Interpretation (philosophy)","Data science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-08-27","doi":"https://doi.org/10.1002/med4.75","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4405358103","name":"Federated Learning‐Based Intrusion Detection Systems for Massive IoT","source":"openalex","abstract":"Despite the significant benefits that the 6G-enabled massive Internet of Things (IoT) applications will bring to the economy and society in the coming years, it is expected that the exponential increase in the number and the diversity of IoT devices and connections in the massive IoT ecosystem will raise a plethora of known and unknown security and privacy challenges for these applications in the 6G era. Consequently, novel security solutions effectively protecting massive IoT applications from future adversaries, while taking into consideration the resource-constrained characteristics of the massive IoT ecosystem and the stringent network performance and privacy-preserving requirements of these applications, are critical for their acceptance and wide adoption in the upcoming 6G era. In this context, Federated Learning (FL)-based intrusion detection is a promising solution for effective intrusion detection in the massive IoT ecosystem, as it scales well with the massive growth of resource-constrained IoT devices and the wide geographical spread of generated IoT data across wide-area IoT networks. In addition, the FL approach enables local model training at each client based on its locally available training dataset instead of sending it to a remote central server (i.e. centralized intrusion detection), which may bring single-point failure risks and compromise the privacy of the dataset. Furthermore, the aggregation of locally trained models, supported by FL, allows the quick development of accurate models even for devices generating only few training data. Thus, this chapter is focused on the investigation of existing FL-based Intrusion Detection Systems (FL-based IDSs) in order to provide a roadmap to support the design and development of effective, efficient, and privacy-preserving IDSs for protecting the emerging disruptive massive IoT applications in the 6G era.","url":"https://doi.org/10.1002/9781119988007.ch4","authors":["Filippos Pelekoudas‐Oikonomou","Parya Haji Mirzaee","Waleed Hathal","Γεώργιος Μαντάς","Jonathan Rodrı́guez","Haitham Cruickshank","Zhili Sun"],"tags":["Intrusion detection system","Computer science","Internet of Things","Data mining","Computer security"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-12-13","doi":"https://doi.org/10.1002/9781119988007.ch4","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412711544","name":"Robust Federated Learning Against Data Poisoning Attacks: Prevention and Detection of Attacked Nodes","source":"openalex","abstract":"Federated learning (FL) enables collaborative model building among a large number of participants without sharing sensitive data to the central server. Because of its distributed nature, FL has limited control over local data and the corresponding training process. Therefore, it is susceptible to data poisoning attacks where malicious workers use malicious training data to train the model. Furthermore, attackers on the worker side can easily manipulate local data by swapping the labels of training instances, adding noise to training instances, and adding out-of-distribution training instances in the local data to initiate data poisoning attacks. And local workers under such attacks carry incorrect information to the server, poison the global model, and cause misclassifications. So, the prevention and detection of such data poisoning attacks is crucial to build a robust federated training framework. To address this, we propose a prevention strategy in federated learning, namely confident federated learning, to protect workers from such data poisoning attacks. Our proposed prevention strategy at first validates the label quality of local training samples by characterizing and identifying label errors in the local training data, and then excludes the detected mislabeled samples from the local training. To this aim, we experiment with our proposed approach on both the image and audio domains, and our experimental results validated the robustness of our proposed confident federated learning in preventing the data poisoning attacks. Our proposed method can successfully detect the mislabeled training samples with above 85% accuracy and exclude those detected samples from the training set to prevent data poisoning attacks on the local workers. However, our prevention strategy can successfully prevent the attack locally in the presence of a certain percentage of poisonous samples. Beyond that percentage, the prevention strategy may not be effective in preventing attacks. In such cases, detection of the attacked workers is needed. So, in addition to the prevention strategy, we propose a novel detection strategy in the federated learning framework to detect the malicious workers under attack. We propose to create a class-wise cluster representation for every participating worker by utilizing the neuron activation maps of local models and analyze the resulting clusters to filter out the workers under attack before model aggregation. We experimentally demonstrated the efficacy of our proposed detection strategy in detecting workers affected by data poisoning attacks, along with the attack types, e.g., label-flipping or dirty labeling. In addition, our experimental results suggest that the global model could not converge even after a large number of training rounds in the presence of malicious workers, whereas after detecting the malicious workers with our proposed detection method and discarding them from model aggregation, we ensured that the global model achieved convergence within very few training rounds. Furthermore, our proposed approach stays robust under different data distributions and model sizes and does not require prior knowledge about the number of attackers in the system.","url":"https://doi.org/10.3390/electronics14152970","authors":["Pretom Roy Ovi","Aryya Gangopadhyay"],"tags":["Computer science","Robustness (evolution)","Training set","Federated learning","Machine learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-25","doi":"https://doi.org/10.3390/electronics14152970","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4388517677","name":"The Sugeno Integral Used for Federated Learning with Uncertainty for Unbalanced Data","source":"openalex","abstract":"Data is crucial in the digital economy. Many businesses collect and use their data to enhance their performance. However, limited data or low data quality can hinder model development, particularly in dynamic environments. To overcome this, companies collecting similar data may opt to exchange knowledge without sharing their data, due to privacy or legal issues. This is where federated learning comes in. In horizontal federated learning, each client (organization) iteratively improves its model, so that it can be regularly aggregated and shared with all clients participating in the federation for further improvements. In federated averaging, the aggregation mechanism is based on the weighted average and the weights depend on the amount of data available to each client. In this paper, we propose to use a more advanced aggregation mechanism, namely the Sugeno integral. The initial results are promising.","url":"https://doi.org/10.1109/fuzz52849.2023.10309680","authors":["Anna Wilbik","Barbara Pȩkala","Jarosław Szkoła","Krzysztof Dyczkowski"],"tags":["Federated learning","Computer science","Mechanism (biology)","Data sharing","Quality (philosophy)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2023-08-13","doi":"https://doi.org/10.1109/fuzz52849.2023.10309680","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413441047","name":"Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO","source":"openalex","abstract":"Diabetes, afflicting 537 million worldwide, is a prevalent and lethal non-communicable ailment. Its onset, influenced by factors like obesity and family history, manifests symptoms such as frequent urination. Long-term complications encompass heart, kidney, and nerve ailments. Early prediction mitigates risks. All these encompassing strategies are designed to improve prediction precision and facilitate proactive diabetes control. This research employed SMOTE methods to tackle imbalanced classes, utilizing various classification algorithms such as Random Forest, XGBoost, Multilayer Perceptron, Gradient Boost, and AdaBoost. Following extensive training and evaluation, the AdaBoost classifier delivered superior outcomes, achieving a 94.02% accuracy rate, an F1 score of 93.32%, and an AUC of 0.95. In the healthcare industry, accurately forecasting diabetes mellitus is crucial; however, privacy laws hinder the transfer of medical information from the Internet of Medical Things (IoMT), causing delays in diagnosis. This study introduces the Federated Learning with Weighted Conglomeration Optimization (FLWCO) model as a solution to these challenges. In Centralized Learning, AdaBoost with WCO achieves an accuracy of 95.32% when tested on a Kaggle dataset consisting of 96,146 instances. During the second stage, FLWCO achieves a superior 97.27% accuracy rate compared to other federated learning techniques. The method not only guarantees privacy conformity but also decreases communication expenses. FLWCO demonstrates superiority over existing federated learning algorithms in real-world heart illness prediction. Furthermore, the proposed model can be employed to estimate the likelihood of heart disease in individuals with diabetes. This highlights the potential of federated learning, especially FLWCO, in leveraging distributed data while preserving privacy, facilitating accurate diabetes mellitus diagnosis, and addressing challenges in sharing medical information securely and efficiently.","url":"https://doi.org/10.1007/s44196-025-00956-8","authors":["Sachikanta Dash","Sasmita Padhy","Preetam Suman","Sandip Mal","Lokesh Malviya","Amrit Suman","Jaydeep Kishore"],"tags":["Computer science","Diabetes mellitus","Artificial intelligence","Machine learning","Medicine"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-08-23","doi":"https://doi.org/10.1007/s44196-025-00956-8","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4405352452","name":"Federated Learning for Wireless Communications","source":"openalex","abstract":"In the past few years, machine learning (ML) techniques have been introduced for the physical layer applications in wireless communications. In contrast to employing centralized learning (CL) techniques, federated learning (FL) presents lower communication overhead as it does not involve dataset transmission between the edge users and the server. As a result, FL is particularly useful for applications, wherein the dataset is huge. Such examples include physical layer design applications, which may require huge datasets to represent the environment accurately. This chapter is concerned with FL-based physical layer applications, e.g., channel estimation and hybrid beamforming. The channel estimation problem is investigated for both conventional and reconfigurable intelligent surface-aided millimeter wave (mmWave) and terahertz (THz) scenarios. We begin by introducing the channel models for both mmWave and THz. Then, we discuss the implementation of FL for various channel estimation problems. We also discuss near-field channel estimation, which may occur in the THz scenario, for which the operating wavelength is very small. Then, we present FL-based hybrid beamforming in mmWave wireless communications. The performance evaluation of FL is provided via several numerical simulation results to show its effectiveness.","url":"https://doi.org/10.1002/9781394227952.ch5","authors":["Ahmet M. Elbir","Wei Shi"],"tags":["Wireless","Computer science","Telecommunications","Computer network"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-12-13","doi":"https://doi.org/10.1002/9781394227952.ch5","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4416650196","name":"Certifying the Right to Be Forgotten: Primal–Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning","source":"openalex","abstract":"Federated unlearning has become an attractive approach to address privacy concerns in collaborative machine learning, for situations when sensitive data are remembered by AI models during the machine learning process. It enables the removal of specific data influences from trained models, aligning with the growing emphasis on the “right to be forgotten.” While extensively studied in horizontal federated learning, unlearning in vertical federated learning (VFL) remains challenging due to the distributed feature architecture. VFL unlearning includes sample unlearning that removes specific data points’ influence and label unlearning that removes entire classes. Since different parties hold complementary features of the same samples, unlearning tasks require cross-party coordination, creating computational overhead and feature interdependencies. To address such challenges, we propose FedORA (Federated Optimization for data Removal via primal-dual Algorithm), designed for sample and label unlearning in VFL. FedORA formulates the removal of certain samples or labels as a constrained optimization problem solved using a primal-dual framework. Our approach introduces a new unlearning loss function that promotes classification uncertainty rather than misclassification. An adaptive step size enhances convergence, while an asymmetric batch design handles unlearning and retained data efficiently to reduce computational costs, considering the prior influence of the remaining data on the model. We provide theoretical analysis proving that the model difference between FedORA and Train-from-scratch is bounded, establishing guarantees for unlearning effectiveness. Experiments on tabular and image datasets demonstrate that FedORA achieves unlearning effectiveness and utility preservation comparable to Train-from-scratch with reduced computation and communication overhead.","url":"https://doi.org/10.1109/tifs.2025.3636788","authors":["Yu Jiang","Xindi Tong","Ziyao Liu","Xiaoxi Zhang","Kwok‐Yan Lam","Chee Wei Tan"],"tags":["Computer science","Overhead (engineering)","Federated learning","Sample (material)","Feature (linguistics)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1109/tifs.2025.3636788","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4415878254","name":"DFCA: Decentralized Federated Clustering Algorithm","source":"openalex","abstract":"Clustered Federated Learning has emerged as an effective approach for handling heterogeneous data across clients by partitioning them into clusters with similar or identical data distributions. However, most existing methods, including the Iterative Federated Clustering Algorithm (IFCA), rely on a central server to coordinate model updates, typically requiring stable connectivity, synchronous communication rounds, and global aggregation of client models. These assumptions are difficult to satisfy in decentralized and heterogeneous environments, where clients may only have limited, local communication with a small subset of peers. As a result, such methods create a bottleneck and a single point of failure, limiting their applicability in realistic decentralized learning settings. This limitation is particularly severe in Internet of Things settings, where large numbers of resource-constrained devices, intermittent or sparse connectivity, and dynamic participation make reliance on a central server impractical. In this work, we introduce the Decentralized Federated Clustering Algorithm (DFCA), a fully decentralized clustered federated learning algorithm that enables clients to collaboratively train cluster-specific models without central coordination. DFCA uses a sequential running average to aggregate models from neighbors as updates arrive, providing a communication-efficient alternative to batch aggregation while maintaining clustering performance. Our experiments on various datasets demonstrate that DFCA outperforms other decentralized algorithms and performs comparably to centralized IFCA, even under sparse connectivity, highlighting its robustness and practicality for dynamic real-world decentralized networks.","url":"https://doi.org/10.1109/jiot.2026.3669440","authors":["Jonas Kirch","S. Becker","Tiago Koketsu Rodrigues","Stefan Harmeling"],"tags":["Computer science","Cluster analysis","Bottleneck","Federated learning","Robustness (evolution)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2026-03-02","doi":"https://doi.org/10.1109/jiot.2026.3669440","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412840942","name":"Novel Federated Graph Contrastive Learning for IoMT Security: Protecting Data Poisoning and Inference Attacks","source":"openalex","abstract":"Malware evolution presents growing security threats for resource-constrained Internet of Medical Things (IoMT) devices. Conventional federated learning (FL) often suffers from slow convergence, high communication overhead, and fairness issues in dynamic IoMT environments. In this paper, we propose FedGCL, a secure and efficient FL framework integrating contrastive graph representation learning for enhanced feature discrimination, a Jain-index-based fairness-aware aggregation mechanism, an adaptive synchronization scheduler to optimize communication rounds, and secure aggregation via homomorphic encryption within a Trusted Execution Environment. We evaluate FedGCL on four benchmark malware datasets (Drebin, Malgenome, Kronodroid, and TUANDROMD) using 5 to 15 graph neural network clients over 20 communication rounds. Our experiments demonstrate that FedGCL achieves 96.3% global accuracy within three rounds and converges to 98.9% by round twenty—reducing required training rounds by 45% compared to FedAvg—while incurring only approximately 10% additional computational overhead. By preserving patient data privacy at the edge, FedGCL enhances system resilience without sacrificing model performance. These results indicate FedGCL’s promise as a secure, efficient, and fair federated malware detection solution for IoMT ecosystems.","url":"https://doi.org/10.3390/math13152471","authors":["Amarudin Daulay","Kalamullah Ramli","Ruki Harwahyu","Taufik Hidayat","Bernardi Pranggono"],"tags":["Computer science","Inference","Computer security","Graph","Theoretical computer science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-31","doi":"https://doi.org/10.3390/math13152471","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4303453208","name":"Trusted Data Sharing Mechanism Based on Blockchain and Federated Learning in Space‐Air‐Ground Integrated Networks","source":"openalex","abstract":"Network data is distributed data on electricity, with the explosive growth of network data, and it has become an inevitable trend of network development to synergized and shared crossdomain scattered data and enhances the value transmission of network data. Federated learning, as a technology that combines data value delivery and data privacy security, is widely concerned in the process of data sharing. However, currently federated learning is used within a single business system. In the process of crossdomain data sharing, how to ensure the data trust, model trust, and result trust of federated learning is still an urgent problem to be solved. To this end, we designed to use blockchain structure to record each behavior of data sharing. Based on its tamper‐proof and traceability, combined with cryptography technology, we constructed an endogenous trusted architecture for crossdomain data sharing. In addition, a reverse auction node incentive mechanism based on high credit preference is designed to solve the common problems in data sharing, such as low enthusiasm of users in sharing, unstable data quality of contributions, and unreasonable distribution of data sharing benefits. Through theoretical analysis and experimental verification, it can be seen that the incentive mechanism designed in this paper can meet the authenticity, user rationality, and budget feasibility. On this basis, it can motivate users to participate in data sharing, improve the average quality of data shared by users, and ensure security and trustworthiness and resist malicious attacks to a certain extent.","url":"https://doi.org/10.1155/2022/5338876","authors":["Da Li","Qinglei Guo","Chao Yang","Han Yan"],"tags":["Computer science","Data sharing","Computer security","Trusted third party","Incentive"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2022-01-01","doi":"https://doi.org/10.1155/2022/5338876","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413887240","name":"Deep Learning Approaches for EEG-Motor Imagery-Based BCIs: Current Models, Generalization Challenges, and Emerging Trends","source":"openalex","abstract":"This study critically examines the evolution of deep learning (DL) for electroencephalogram (EEG) based motor imagery (MI) decoding with a focus on real-time Brain Computer Interfaces (BCIs) development. Prior studies often prioritize accuracy in isolation, neglecting computational efficiency, interpretability, noise robustness, and neurophysiological variability across subjects and tasks, while recent DL advancements have introduced novel architectures to address these issues. This work systematically evaluates those novel architectures and emerging trends through addressing 4 research questions (RQs) based on an extensive review. Initially, over 188 papers from 3 databases were retrieved with a focus on publications from 2024 to 2025. Later, through multi-stage filtering based on strict inclusion criteria, a refined corpus of 68 high-quality studies was selected. This analysis reveals that state-of-the-art models achieve competitive accuracy, varying 85-100% on public datasets, but still face challenges in computational demands, noise resilience, generalization and BCI deployment. Additionally, preprocessing and integrated hybrid feature extraction paired with explainable AI (XAI) techniques are discussed. Emerging trends such as neuromorphic computing, federated learning (FL), and closed-loop adaptive systems offering solutions to current deployment barriers have been included in the discussion. Ethical and ecological considerations, such as data privacy, algorithmic bias, and energy efficiency, are notably represented in the literature. This review contributes a holistic framework for evaluating DL models, emphasizing the need to balance accuracy, efficiency, and adaptability. By synthesizing insights from large-scale datasets and explainability tools, this study exposes the limitations of current DL studies reliant on homogenous data, unavailability of codes to reproduce models and proposes strategies to mitigate neurophysiological variability. The finding underscores the urgency of prioritizing clinical relevance, ethical validation, and ecological robustness to bridge the lab to real-world divide, offering actionable directions for future research in low-power, generalizable, and user-centric BCI design.","url":"https://doi.org/10.1109/access.2025.3604528","authors":["Aaqib Raza","Mohd Zuki Yusoff"],"tags":["Motor imagery","Brain–computer interface","Electroencephalography","Computer science","Generalization"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1109/access.2025.3604528","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4401780339","name":"The blockchain‐based privacy‐preserving searchable attribute‐based encryption scheme for federated learning model in IoMT","source":"openalex","abstract":"Abstract Federated learning enables training healthcare diagnostic models across multiple decentralized devices containing local private health data samples, without transferring data to a central server, providing privacy‐preserving services for healthcare professionals. However, for a model of a specific field, some medical data from non‐target participants may be included in model training, compromising model accuracy. Moreover, diagnostic queries for healthcare models stored in cloud servers may result in the leakage of the privacy of healthcare participants and the parameters of models. Furthermore, the records of model searching and usage could be tracked causing privacy disclosure risk. To address these issues, we propose a blockchain‐based privacy‐preserving searchable attribute‐based encryption scheme for the diagnostic model federated learning in the Internet of Medical Things (BSAEM‐FL). We first adopt fine‐grained model trainer participation policies for federated learning, using the attribute‐based encryption (ABE) mechanism, to realize model accuracy and local data privacy. Then, We employ searchable encryption technology for model training and usage to protect the security of models stored in the cloud server. Blockchain is utilized to implement distributed healthcare models' keyword‐based search and model users' attribute‐based authentication. Lastly, we transfer most of the computational overhead of user terminals in model searching and decryption to edge nodes, achieving lightweight computation of IoMT terminals. The security analysis proves the security of the proposed healthcare scheme. The performance evaluation indicates our scheme is of better feasibility, efficiency, and decentralization.","url":"https://doi.org/10.1002/cpe.8257","authors":["Ziyu Zhou","Na Wang","Jianwei Liu","Junsong Fu","Lunzhi Deng"],"tags":["Computer science","Blockchain","Scheme (mathematics)","Encryption","Learning with errors"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-08-20","doi":"https://doi.org/10.1002/cpe.8257","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413140400","name":"FedNolowe: A normalized loss-based weighted aggregation strategy for robust federated learning in heterogeneous environments","source":"openalex","abstract":"Federated Learning supports collaborative model training across distributed clients while keeping sensitive data decentralized. Still, non-independent and identically distributed data pose challenges like unstable convergence and client drift. We propose Federated Normalized Loss-based Weighted Aggregation (FedNolowe) (Code is available at https://github.com/dongld-2020/fednolowe), a new method that weights client contributions using normalized training losses, favoring those with lower losses to improve global model stability. Unlike prior methods tied to dataset sizes or resource-heavy techniques, FedNolowe employs a two-stage L1 normalization, reducing computational complexity by 40% in floating-point operations while matching state-of-the-art performance. A detailed sensitivity analysis shows our two-stage weighting maintains stability in heterogeneous settings by mitigating extreme loss impacts while remaining effective in independent and identically distributed scenarios.","url":"https://doi.org/10.1371/journal.pone.0322766","authors":["Duy-Dong Le","Nguyen Huynh Tuong","Anh-Khoa Tran","Minh-Son Dao","Pham The Bao"],"tags":["Computer science","Federated learning","Data aggregator","Artificial intelligence","Computer network"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-08-14","doi":"https://doi.org/10.1371/journal.pone.0322766","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4417224611","name":"Blockchain-Enabled Hierarchical Federated Learning Framework for Anomaly Detection in IoT Systems","source":"openalex","abstract":"The rapid expansion of the Internet of Things (IoT) across domains such as industrial automation, smart healthcare, and intelligent transportation has intensified security challenges, particularly in terms of detecting anomalies across large-scale, heterogeneous networks. To address these challenges, this study introduces a blockchain-enabled hierarchical federated learning (Block-HFL) approach that combines federated model aggregation with blockchain-based authentication and immutable storage. This approach has enhanced scalability, reduced communication latency, and ensured trustworthy model management while preserving data privacy. In comparison with existing hierarchical and non-hierarchical FL approaches, the proposed Block-HFL framework introduces an accuracy-based leader election mechanism that enhances fairness and improves global model convergence. Experimental evaluations on the Edge-IIoTset dataset show that Block-HFL consistently maintains detection accuracy above 94% as the number of clients increases from 4 to 16, outperforming baseline FL models under similar non-IID conditions. Moreover, blockchain integration ensures secure, transparent, and tamper-proof global model management with minimal computational cost, confirming that the proposed framework provides an efficient and trustworthy solution for distributed anomaly detection in IoT systems.","url":"https://doi.org/10.3390/app152413037","authors":["Haya Alharthi","Suhair Alshehri","Manal Kalkatawi"],"tags":["Federated learning","Computer science","Internet of Things","Anomaly detection","Trustworthiness"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-12-11","doi":"https://doi.org/10.3390/app152413037","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4380481519","name":"FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence","source":"openalex","abstract":"Edge Intelligence (EI) enables Artificial Intelligence (AI) applications to run at the edge, where data analysis and decision-making can be performed in real-time and close to data sources. To protect data privacy and unify data silos distributed among end devices in EI, Federated Learning (FL) is proposed for collaborative training shared AI models across multiple devices without compromising data security. However, the prevailing FL approaches cannot guarantee model generalization and adaptation on heterogeneous clients. Recently, Personalized Federated Learning (PFL) has drawn growing awareness in EI, as it enables striking a productive balance between local-specific training requirements inherent in devices and global-generalized optimization objectives for satisfactory performance. However, most existing PFL methods are based on the Parameters Interaction-based Architecture (PIA) represented by FedAvg, which causes unaffordable communication burdens due to large-scale parameters transmission between devices and the edge server. In contrast, Logits Interaction-based Architecture (LIA) enables to update model parameters with logits transfer, and gains the advantages of communication lightweight and heterogeneous on-device model allowance compared to PIA. Nevertheless, previous LIA methods attempt to achieve satisfactory performance either relying on unrealistic public datasets or increasing communication overhead for additional information transmission other than logits. To tackle this dilemma, we propose a knowledge cache-driven PFL architecture, named FedCache, which reserves a knowledge cache on the server for fetching personalized knowledge from the samples with similar hashes to each given on-device sample. During the training phase, ensemble distillation is applied to on-device models for constructive optimization with personalized knowledge transferred from the server-side knowledge cache. Empirical experiments on four datasets demonstrate the comparable performance of FedCache with state-of-art PFL approaches, with more than two orders of magnitude improvements in communication efficiency. Our code and DEMO are available at https://github.com/wuzhiyuan2000/FedCache.","url":"https://doi.org/10.36227/techrxiv.23255420.v2","authors":["Zhiyuan Wu","Sheng Sun","Yuwei Wang","Min Liu","Ke Xu","Wen Wang","Xuefeng Jiang","Bo Gao","Jinda Lu"],"tags":["Computer science","Cache","Overhead (engineering)","Enhanced Data Rates for GSM Evolution","Architecture"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2023-06-13","doi":"https://doi.org/10.36227/techrxiv.23255420.v2","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412078570","name":"Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge","source":"openalex","abstract":"Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites. Weight aggregation and client selection techniques were compared using a multicentric brain tumor dataset in realistic federated learning simulations, yielding benefits for adaptive weight aggregation, and efficiency gains through client sampling. Quantitative performance evaluation of state-of-the-art segmentation algorithms on data distributed internationally across 32 institutions yielded good generalization on average, albeit the worst-case performance revealed data-specific modes of failure. Similar multi-site setups can help validate the real-world utility of healthcare AI algorithms in the future.","url":"https://doi.org/10.1038/s41467-025-60466-1","authors":["Maximilian Zenk","Ujjwal Baid","Sarthak Pati","Akis Linardos","Brandon Edwards","Micah Sheller","Patrick Foley","Alejandro Aristizábal","David Zimmerer","А. Д. Груздев","Jason Martin","Russell T. Shinohara","Annika Reinke","Fabian Isensee","Santhosh Parampottupadam","Kaushal Parekh","Ralf Floca","Hasan Kassem","Bhakti Baheti","Siddhesh Thakur","Verena Chung","Kaisar Kushibar","Karim Lekadir","Meirui Jiang","Youtan Yin","Hongzheng Yang","Quande Liu","Cheng Chen","Qi Dou","Pheng‐Ann Heng","Xiaofan Zhang","Shaoting Zhang","Muhammad Irfan Khan","Mohammad Ayyaz Azeem","Mojtaba Jafaritadi","Esa Alhoniemi","Elina Kontio","Suleiman A. Khan","Leon Mächler","Ivan Ezhov","Florian Kofler","Suprosanna Shit","Johannes C. Paetzold","Timo Loehr","Benedikt Wiestler","Himashi Peiris","Kamlesh Pawar","Shenjun Zhong","Zhaolin Chen","Munawar Hayat","Gary F. Egan","Mehrtash Harandi","Ece Isik-Polat","Görkem Polat","Altan Koçyiğit","Alptekin Temizel","Anup Tuladhar","Lakshay Tyagi","Raissa Souza","Nils D. Forkert","Pauline Mouchès","Matthias Wilms","Vishruth Shambhat","Akansh Maurya","Shubham Subhas Danannavar","Rohit Kalla","Vikas Kumar Anand","Ganapathy Krishnamurthi","Sahil Nalawade","Chandan Ganesh","Benjamin Wagner","Divya Reddy","Yudhajit Das","Fang Yu","Baowei Fei","Ananth J. Madhuranthakam","Joseph A. Maldjian","Gaurav Singh","Jianxun Ren","Wei Zhang","Ning An","Qingyu Hu","Youjia Zhang","Ying Zhou","Vasilis Siomos","Giacomo Tarroni","Jonathan Passerat‐Palmbach","Ambrish Rawat","Giulio Zizzo","Swanand Kadhe","Jonathan P. Epperlein","Stefano Braghin","Yuan Wang","Renuga Kanagavelu","Qingsong Wei","Yechao Yang","Yong Liu","Krzysztof Kotowski","Szymon Adamski","Bartosz Machura"],"tags":["Benchmarking","Benchmark (surveying)","Computer science","Segmentation","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-07","doi":"https://doi.org/10.1038/s41467-025-60466-1","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412423696","name":"Network-based intrusion detection using deep learning technique","source":"openalex","abstract":"A high growth rate in network traffic and the complexity of cyber threats have made it necessary to create more effective and flexible intrusion detection systems. Most traditional Network-based Intrusion Detection Systems (NIDS) can become weak at detecting new patterns of attacks due to the use of obsolete data or traditional machine learning models. To overcome the mentioned constraints, the current research presents a new deep learning solution that combines Sequential Deep Neural Networks (DNN) and Rectified Linear Unit (ReLU) activation unit with an Extra Tree Classifier feature selection procedure. The proposed model is trained and tested on the new rich and up-to-date UNSW-NB15 set, which provides a realistic reflection of the real-life network traffic and attack vectors. The interesting novelty of this study is the tactical use of ReLU-based DNN combined with feature optimization through the Extra Tree Classifier, which not only overcomes general problems like vanishing gradients and overfitting but also greatly increases the interpretability of the model and the efficiency of its computation. This dimensional reduction of the feature space (43 to only 8 highly relevant features) retains the high accuracy of the model but with better inference speed, which is a crucial aspect of the real-time deployment of NIDS. The results show that with the Sequential DNN approach, the binary class (0 for normal and 1 for attack records) achieved 97.93% accuracy, 97% Precision, 97% Recall and 97% F1-score. Furthermore, the detailed experimental testing, such as ROC curves and Confusion Matrices, confirmed that the Sequential DNN performed well in comparison to other Existing Studies. These findings underscore the effectiveness of deep learning architectures enhanced with optimized feature selection in detecting network intrusions, making the proposed system a promising solution for securing critical infrastructure in sectors such as finance, healthcare, and government networks.","url":"https://doi.org/10.1038/s41598-025-08770-0","authors":["Muhammad Farhan","Hafiz Waheed ud din","S. M. Wazid Ullah","Muhammad Sajjad Hussain","Muhammad Amir Khan","Tehseen Mazhar","Umar Farooq Khattak","Ines Hilali Jaghdam"],"tags":["Computer science","Intrusion detection system","Artificial intelligence","Deep learning","Intrusion"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-15","doi":"https://doi.org/10.1038/s41598-025-08770-0","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4408791485","name":"Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning With Adaptive Quantization and Differential Privacy","source":"openalex","abstract":"Federated learning (FL) is a distributed machine learning method where multiple devices collaboratively train a model under the management of a central server without sharing underlying data. One of the key challenges of FL is the communication bottleneck caused by variations in connection speed and bandwidth across devices. Therefore, it is essential to reduce the size of transmitted data during training. Additionally, there is a potential risk of exposing sensitive information through the model or gradient analysis during training. To address both privacy and communication efficiency, we combine differential privacy (DP) and adaptive quantization methods. We use Laplacian-based DP to preserve privacy, which is relatively underexplored in FL and offers tighter privacy guarantees than Gaussian-based DP. We propose a simple and efficient global bit-length scheduler using round-based cosine annealing, along with a client-based scheduler that dynamically adapts based on client contribution estimated through dataset entropy analysis. We evaluate our approach through extensive experiments on CIFAR10, MNIST, and medical imaging datasets, using non-IID data distributions across varying client counts, bit-length schedulers, and privacy budgets. The results show that our adaptive quantization methods reduce total communicated data by up to 52.64% for MNIST, 45.06% for CIFAR10, and 31% to 37% for medical imaging datasets compared to 32-bit float training while maintaining competitive model accuracy and ensuring robust privacy through DP.","url":"https://doi.org/10.1109/access.2025.3554138","authors":["Emre Ardıç","Yakup Genç"],"tags":["Differential privacy","Computer science","Information privacy","Quantization (signal processing)","Privacy software"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1109/access.2025.3554138","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4387390243","name":"Recent Methodological Advances in Federated Learning for Healthcare","source":"openalex","abstract":"","url":"https://doi.org/10.17863/cam.107217","authors":["Fan Zhang","Daniel Kreuter","Yi‐Chen Chen","Sören Dittmer","Samuel Tull","Tolou Shadbahr","BloodCounts Collaboration","Jacobus Preller","James H.F. Rudd","John A. D. Aston","Carola‐Bibiane Schönlieb","Nicholas Gleadall","Michael S. Roberts"],"tags":["Computer science","Federated learning","Pooling","Health care","Compromise"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2023-10-04","doi":"https://doi.org/10.17863/cam.107217","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W3088635668","name":"Advancing Fusion with Machine Learning Research Needs Workshop Report","source":"openalex","abstract":"Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.","url":"https://doi.org/10.1007/s10894-020-00258-1","authors":["D.A. Humphreys","Ana Kupresanin","Mark D. Boyer","J.M. Canik","C. S. Chang","Eric C. Cyr","R. Granetz","J. Hittinger","Egemen Kolemen","Earl Lawrence","Valerio Pascucci","Aditya Ranjan Patra","D. P. Schissel"],"tags":["Artificial intelligence","Computer science","Machine learning","Identification (biology)","Robotics"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2020-08-01","doi":"https://doi.org/10.1007/s10894-020-00258-1","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4387130572","name":"A Survey on Collaborative Learning for Intelligent Autonomous Systems","source":"openalex","abstract":"This survey examines approaches to promote Collaborative Learning in distributed systems for emergent Intelligent Autonomous Systems (IAS). The study involves a literature review of Intelligent Autonomous Systems based on Collaborative Learning, analyzing aspects in four dimensions: computing environment, performance concerns, system management, and privacy concerns, mapping the significant requirements of systems to the emerging Artificial intelligence models. Furthermore, the article explores Collaborative Learning Taxonomy for IAS to demonstrate the correlation between IoT, Big Data, and Human-in-the-Loop. Several technological open issues exist in the aforementioned domains (such as in applications of autonomous driving, robotics in healthcare, cyber security, and others) to effectively achieve the future deployment of Intelligent Autonomous Systems. This Survey aims to organize concepts around IAS, indicating the approaches used to extract knowledge from data in Collaborative Learning for IAS, and identifying open issues. Moreover, it presents a guide to overcoming the existing challenges in decision-making mechanisms with IAS, providing a holistic vision of Big Data and Human-in-the-Loop.","url":"https://doi.org/10.1145/3625544","authors":["Julio César Santos dos Anjos","Kassiano J. Matteussi","Fernanda C. Orlandi","Jorge Luís Victória Barbosa","Jorge Sá Silva","Luiz F. Bittencourt","Cláudio F. R. Geyer"],"tags":["Computer science","Software deployment","Big data","Knowledge management","Collaborative learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2023-09-28","doi":"https://doi.org/10.1145/3625544","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4411700982","name":"LLM-Based Agents for Tool Learning: A Survey","source":"openalex","abstract":"Abstract Human beings capable of making and using tools can accomplish tasks far beyond their innate abilities, and this paradigm of integration with tools may not be limited to humans themselves. Recently, the large language model (LLM) has demonstrated immense potential across various fields with its unique planning and reasoning abilities. However, there are still many challenges beyond its capabilities due to deficiencies in its training data and inherent illusions. Thus, integrating LLMs and tools into tool learning agents has become a new emerging research direction. To this end, we present a systematic investigation and comprehensive review of tool-learning agents in this paper. We start by introducing the definition of the tool learning task for Agents and then illustrating the typical architecture of the tool-learning models. Since these tools are all defined by users, LLM does not know what tools there are and what their functions are. Thus, LLMs should first find appropriate tools and split the tool retrieval methods into two categories: training-based and non-training-based. To accurately complete the user task, it is important to decompose the task into several sub-tasks and execute them in the correct order. Following that, we introduce the tool planning methods and organize these works by whether they rely on the model’s inherent reasoning capabilities for planning or utilize external reasoning tools. Due to the rapid development of this field, we also introduce an emerging frontier direction: using multimodal tools for LLM. In addition, we compile current open-source benchmarks and evaluation metrics, focusing on their scale, composition, calculation methods, and assessment dimensions. Next, we introduce several application scenarios for the LLM-based tool learning methods. Finally, we discuss the safety and ethical issues involved in tool learning.","url":"https://doi.org/10.1007/s41019-025-00296-9","authors":["Weikai Xu","Chengrui Huang","Shen Gao","Shuo Shang"],"tags":["Computer science","Data science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-06-26","doi":"https://doi.org/10.1007/s41019-025-00296-9","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413637086","name":"Identifying significant features in adversarial attack detection framework using federated learning empowered medical IoT network security","source":"openalex","abstract":"The expansion of the Internet of Medical Things (IoHT) presents significant advantages for healthcare over improved data-driven insights and connectivity and offers critical cybersecurity challenges. Attacks are a serious risk for neural network security; recent defence mechanisms remain restricted concerning their applicability to real-world environments. The influence of adversarial attacks is essential, as they can challenge the security and reliability of Artificial Intelligence (AI) methods in crucial applications. Dealing with these vulnerabilities is vital to develop strong and reliable NNs. Therefore, the study of adversarial defence mechanisms and attack detection became an important area in the domain of AI. Machine learning (ML) and specific deep learning (DL) models have recently influenced excellent performance on challenging perceptual tasks like adversarial attack detection. Meanwhile, the federated learning (FL) method is susceptible to attacks by malicious clients. FL can complete a considerable training task effectively by attracting participants for training a DL method cooperatively, and the user privacy should be completely protected for the users only upload model parameters to the centralized server. This study presents an Adversarial Attack Detection Framework Using Federated Learning Empowered IoT Medical (AADF-FLEIoTM) model. The main intention of the AADF-FLEIoTM model is to develop adversarial attack detection using FL and an advanced hybrid model. The data normalization stage initially uses min-max normalization to scale and transform data into a consistent range. The proposed AADF-FLEIoTM employs the marine predator algorithm (MPA) model to identify and retain the most relevant features for the feature selection process. Besides, the integration of convolutional neural networks, bidirectional long short-term memory, and self-attention (SA-CNN-BiLSTM) technique is utilized for the detection and classification process. Finally, the Red-Tail Hawk (RTH)-optimizer algorithm alters the hyperparameter values of the SA-CNN-BiLSTM technique optimally and results in more excellent classification performance. The AADF-FLEIoTM approach is examined on the IoT healthcare security dataset. The performance validation of the AADF-FLEIoTM approach illustrated a superior accuracy value of 98.24% over existing models.","url":"https://doi.org/10.1038/s41598-025-14913-0","authors":["Sanaa Sharaf","Sameer Nooh"],"tags":["Adversarial system","Computer science","Internet of Things","Computer security","Network security"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-08-26","doi":"https://doi.org/10.1038/s41598-025-14913-0","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4407548040","name":"Advances in Neuroimaging and Deep Learning for Emotion Detection: A Systematic Review of Cognitive Neuroscience and Algorithmic Innovations","source":"openalex","abstract":"Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights with advanced algorithmic methods in pursuit of an enhanced understanding and applications of emotion recognition. Methods: The study was conducted following PRISMA guidelines, involving a rigorous selection process that resulted in the inclusion of 64 empirical studies that explore neuroimaging modalities such as fMRI, EEG, and MEG, discussing their capabilities and limitations in emotion recognition. It further evaluates deep learning architectures, including neural networks, CNNs, and GANs, in terms of their roles in classifying emotions from various domains: human-computer interaction, mental health, marketing, and more. Ethical and practical challenges in implementing these systems are also analyzed. Results: The review identifies fMRI as a powerful but resource-intensive modality, while EEG and MEG are more accessible with high temporal resolution but limited by spatial accuracy. Deep learning models, especially CNNs and GANs, have performed well in classifying emotions, though they do not always require large and diverse datasets. Combining neuroimaging data with behavioral and cognitive features improves classification performance. However, ethical challenges, such as data privacy and bias, remain significant concerns. Conclusions: The study has emphasized the efficiencies of neuroimaging and deep learning in emotion detection, while various ethical and technical challenges were also highlighted. Future research should integrate behavioral and cognitive neuroscience advances, establish ethical guidelines, and explore innovative methods to enhance system reliability and applicability.","url":"https://doi.org/10.3390/diagnostics15040456","authors":["Constantinos Halkiopoulos","Evgenia Gkintoni","Anthimos Aroutzidis","Hera Antonopoulou"],"tags":["Neuroimaging","Cognition","Modalities","Computer science","Deep learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-02-13","doi":"https://doi.org/10.3390/diagnostics15040456","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410061972","name":"Adversarial machine learning: a review of methods, tools, and critical industry sectors","source":"openalex","abstract":"Abstract The rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has produced high-performance models widely used in various applications, ranging from image recognition and chatbots to autonomous driving and smart grid systems. However, security threats arise from the vulnerabilities of ML models to adversarial attacks and data poisoning, posing risks such as system malfunctions and decision errors. Meanwhile, data privacy concerns arise, especially with personal data being used in model training, which can lead to data breaches. This paper surveys the Adversarial Machine Learning (AML) landscape in modern AI systems, while focusing on the dual aspects of robustness and privacy. Initially, we explore adversarial attacks and defenses using comprehensive taxonomies. Subsequently, we investigate robustness benchmarks alongside open-source AML technologies and software tools that ML system stakeholders can use to develop robust AI systems. Lastly, we delve into the landscape of AML in four industry fields –automotive, digital healthcare, electrical power and energy systems (EPES), and Large Language Model (LLM)-based Natural Language Processing (NLP) systems– analyzing attacks, defenses, and evaluation concepts, thereby offering a holistic view of the modern AI-reliant industry and promoting enhanced ML robustness and privacy preservation in the future.","url":"https://doi.org/10.1007/s10462-025-11147-4","authors":["Sotiris Pelekis","Thanos Koutroubas","Afroditi Blika","Anastasis Berdelis","Evangelos Karakolis","Christos Ntanos","Evangelos Spiliotis","Dimitris Askounis"],"tags":["Computer science","Adversarial system","Artificial intelligence","Machine learning","Engineering management"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-05-02","doi":"https://doi.org/10.1007/s10462-025-11147-4","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4308671132","name":"Enhancing Efficiency in Multidevice Federated Learning through Data Selection","source":"openalex","abstract":"Ubiquitous wearable and mobile devices provide access to a diverse set of data. However, the mobility demand for our devices naturally imposes constraints on their computational and communication capabilities. A solution is to locally learn knowledge from data captured by ubiquitous devices, rather than to store and transmit the data in its original form. In this paper, we develop a federated learning framework, called Centaur, to incorporate on-device data selection at the edge, which allows partition-based training of a deep neural nets through collaboration between constrained and resourceful devices within the multidevice ecosystem of the same user. We benchmark on five neural net architecture and six datasets that include image data and wearable sensor time series. On average, Centaur achieves ~19% higher classification accuracy and ~58% lower federated training latency, compared to the baseline. We also evaluate Centaur when dealing with imbalanced non-iid data, client participation heterogeneity, and different mobility patterns. To encourage further research in this area, we release our code at https://github.com/nokia-bell-labs/data-centric-federated-learning","url":"https://doi.org/10.48550/arxiv.2211.04175","authors":["Fan Mo","Mohammad Malekzadeh","Soumyajit Chatterjee","Fahim Kawsar","Akhil Mathur"],"tags":["Computer science","Benchmark (surveying)","Partition (number theory)","Federated learning","Process (computing)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2022-11-08","doi":"https://doi.org/10.48550/arxiv.2211.04175","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W2982936646","name":"Digital Twin: Enabling Technologies, Challenges and Open Research","source":"openalex","abstract":"Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.","url":"https://doi.org/10.1109/access.2020.2998358","authors":["Aidan Fuller","Zhong Fan","Charles Day","Chris Barlow"],"tags":["Computer science","Open research","World Wide Web"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2020-01-01","doi":"https://doi.org/10.1109/access.2020.2998358","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412480786","name":"An Asynchronous Federated Learning Aggregation Method Based on Adaptive Differential Privacy","source":"openalex","abstract":"Federated learning is a distributed machine learning technique that allows multiple devices to collaborate on learning a shared model without exchanging data. It can be used to improve model accuracy while protecting user privacy. However, traditional federated learning is vulnerable to attacks from generative adversarial networks (GANs). As a new privacy protection method, differential privacy enhances privacy protection capabilities by sacrificing some data accuracy. To optimize the privacy budget allocation scheme in traditional differential privacy, we propose a differential privacy method called ADP-FL, which dynamically adjusts the privacy budget based on Newton’s Law of Cooling. While maintaining the overall privacy budget, it dynamically tunes adaptive parameters to improve training accuracy. Additionally, we propose an asynchronous federated learning aggregation scheme that combines privacy budget with data freshness, thereby reducing the impact of differential privacy on accuracy. We conducted extensive experiments on differential privacy algorithms based on Gaussian mechanisms and Laplace mechanisms. The experimental results show that, under the same privacy budget, our algorithm achieves higher accuracy and lower communication overhead compared to the baseline algorithm.","url":"https://doi.org/10.3390/electronics14142847","authors":["Jiawen Wu","Geming Xia","Hongwei Huang","Chaodong Yu","Yuze Zhang","Hongfeng Li"],"tags":["Differential privacy","Asynchronous communication","Computer science","Federated learning","Asynchronous learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-16","doi":"https://doi.org/10.3390/electronics14142847","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410698050","name":"Federated learning for automotive applications","source":"openalex","abstract":"This paper presents and evaluates a new distributed learning technology, called federated learning, and its applications in automotive systems. We review and classify existing approaches to federated learning, focusing on its implementation in connected vehicles. We also evaluate the challenges associated with this application domain. Federated learning allows data to remain within vehicles, thereby avoiding costly data transfers and mitigating privacy concerns. This technology shows promise in enhancing various automotive applications. Federated learning can be applied to driver assistance systems, predictive maintenance, and personalized user experiences in connected vehicles. By keeping data local, it supports privacy and reduces communication overhead. Federated learning represents a significant advancement in the use of connected vehicle data. It offers a practical solution to privacy and data transfer issues while enhancing the performance of automotive systems through collaborative model training.","url":"https://doi.org/10.26599/htrd.2025.9480055","authors":["William Lindskog-Münzing","Christian Prehofer"],"tags":["Automotive industry","Computer science","Business","Manufacturing engineering","Engineering"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-05-23","doi":"https://doi.org/10.26599/htrd.2025.9480055","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4406163034","name":"Collaboration of IoT devices in smart home scenarios: algorithm research based on graph neural networks and federated learning","source":"openalex","abstract":"Traditional IoT device collaboration is usually static and cannot adjust the collaboration mode between devices according to various changes, which limits work efficiency. To this end, an IoT device collaboration optimization algorithm based on graph neural network and federated learning is studied. This method abstracts various IoT device nodes and their communication relationships into graph structured data for storage, and then uses federated learning to train the graph convolutional network with graph structured data. The obtained model can be used to optimize the collaboration mode of IoT devices. During the training process, the total average MSE (mean square error) between the output and the label of the graph convolutional network model based on federated learning is 0.968; the total standard deviation of MSE is 0.0353; the total time from training to model convergence is 435.82 s, of which data transmission time accounts for 27.1% and model training time accounts for 72.9%. In a 2-h practical experiment, the graph convolutional network model based on federated learning was used to optimize the collaboration mode of smart homes, achieving a target environment residence time of 87 min and a total power consumption reduction of 0.69 kW·h. The results show that this method can effectively optimize the collaboration efficiency of IoT devices, reduce training time and network overhead, but it fails to improve the prediction accuracy of the model and may also lead to a decrease in stability.","url":"https://doi.org/10.1007/s43926-025-00096-7","authors":["Yuanquan Zhong"],"tags":["Computer science","Internet of Things","Artificial neural network","Graph","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-09","doi":"https://doi.org/10.1007/s43926-025-00096-7","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4409310894","name":"DRL-Based Joint Aggregation Frequency and Edge Association for Energy-Efficient Hierarchical Federated Learning","source":"openalex","abstract":"Hierarchical Federated Learning (HFL) has been proposed to achieve large-scale model training and more efficient communication, surpassing conventional Federated Learning (FL). However, inappropriate aggregation frequency and edge association in HFL result in excessive energy consumption for users with poor channels or hinder its convergence performance due to stochastic gradient descent (SGD) and Non-Independent and Identical Distribution (NIID) data, which is particularly challenging for energy-limited users. Motivated by this, a joint aggregation frequency and edge association optimization problem is proposed to minimize the long-term energy consumption during HFL training process. The problem can be formulated by incorporating computation, communication model and convergence analysis together. Due to the coupling between control variables, we decompose it into two sub-problems and adopt an iterative algorithm to approximate their optimal solutions. Specifically, the aggregation frequency is optimized under a given edge association by convex optimization to trade-off the computation and communication energy consumption, considering the convergence characteristic and SGD noise. Then, Deep Reinforcement Learning (DRL) is adopted to optimize edge association based on data distribution, dynamic channels and the derived aggregation frequency. Simulation results demonstrate that our proposed strategy achieves the lowest energy consumption while attaining the required model accuracy, outperforming other benchmarks.","url":"https://doi.org/10.1109/twc.2025.3556514","authors":["Yijing Ren","Changxiang Wu","Daniel K. C. So","Jie Tang"],"tags":["Computer science","Joint (building)","Association (psychology)","Enhanced Data Rates for GSM Evolution","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-04-09","doi":"https://doi.org/10.1109/twc.2025.3556514","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4399401115","name":"Asynchronous Byzantine Federated Learning","source":"openalex","abstract":"Federated learning (FL) enables a set of geographically distributed clients to collectively train a model through a server. Classically, the training process is synchronous, but can be made asynchronous to maintain its speed in presence of slow clients and in heterogeneous networks. The vast majority of Byzantine fault-tolerant FL systems however rely on a synchronous training process. Our solution is one of the first Byzantine-resilient and asynchronous FL algorithms that does not require an auxiliary server dataset and is not delayed by stragglers, which are shortcomings of previous works. Intuitively, the server in our solution waits to receive a minimum number of updates from clients on its latest model to safely update it, and is later able to safely leverage the updates that late clients might send. We compare the performance of our solution with state-of-the-art algorithms on both image and text datasets under gradient inversion, perturbation, and backdoor attacks. Our results indicate that our solution trains a model faster than previous synchronous FL solution, and maintains a higher accuracy, up to 1.54x and up to 1.75x for perturbation and gradient inversion attacks respectively, in the presence of Byzantine clients than previous asynchronous FL solutions.","url":"https://doi.org/10.48550/arxiv.2406.01438","authors":["Bart Cox","Abele Mălan","Lydia Y. Chen","Jérémie Decouchant"],"tags":["Byzantine architecture","Asynchronous communication","Computer science","Byzantine fault tolerance","Asynchronous learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-06-03","doi":"https://doi.org/10.48550/arxiv.2406.01438","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413073344","name":"SECURE PREDICTIVE MAINTENANCE FOR INDUSTRIAL SYSTEMS USING FEDERATED LEARNING","source":"openalex","abstract":"In this paper, a secure and scalable predictive maintenance approach for Industry 4.0 using Federated Learning (FL) and Artifficial Intelligence (AI) is addressed.Unlike other approaches, FL maintains data on the premises, which guarantees privacy and regulatory conformance.The system design relies on edge devices to train protected local models and share secure updates.It consists of data pre-processing, model training, and secure aggregation.Experimental results demonstrate FL can obtain high efficiency, communication reduction and improve security against cyber threats.System challenges and future directions are also described in the paper.This paper provides a privacy-aware, up-to-date analysis of predictive maintenance in smart industrial environments.","url":"https://doi.org/10.61552/jibi.2026.01.007","authors":[],"tags":["Predictive maintenance","Computer science","Reliability engineering","Engineering"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.61552/jibi.2026.01.007","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410008025","name":"When Fine-Tuning LLMs Meets Data Privacy: An Empirical Study of Federated Learning in LLM-Based Program Repair","source":"openalex","abstract":"Software systems have been evolving rapidly and inevitably introducing bugs at an increasing rate, leading to significant maintenance costs. While large language models (LLMs) have demonstrated remarkable potential in enhancing software development and maintenance practices, particularly in automated program repair (APR), they rely heavily on high-quality code repositories. Most code repositories are proprietary assets that capture the diversity and nuances of real-world industry software practices, which public datasets cannot fully represent. However, obtaining such data from various industries is hindered by data privacy concerns, as companies are reluctant to share their proprietary codebases. There has also been no in-depth investigation of collaborative software development by learning from private and decentralized data while preserving data privacy for program repair. To address the gap, we investigate federated learning as a privacy-preserving method for fine-tuning LLMs on proprietary and decentralized data to boost collaborative software development and maintenance. We use the private industrial dataset TutorCode for fine-tuning and the EvalRepair-Java benchmark for evaluation, and assess whether federated fine-tuning enhances program repair. We then further explore how code heterogeneity (i.e., variations in coding style, complexity, and embedding) and different federated learning algorithms affect bug fixing to provide practical implications for real-world software development collaboration. Our evaluation reveals that federated fine-tuning can significantly enhance program repair, achieving increases of up to 16.67% for Top@10 and 18.44% for Pass@10, even comparable to the bug-fixing capabilities of centralized learning. Moreover, the negligible impact of code heterogeneity implies that industries can effectively collaborate despite diverse data distributions. Different federated algorithms also demonstrate unique strengths across LLMs, suggesting that tailoring the optimization process to specific LLM characteristics can further improve program repair.","url":"https://doi.org/10.1145/3733599","authors":["Wenqiang Luo","Jacky Keung","Boyang Yang","He Ye","Claire Le Goues","Tegawendé F. Bissyandé","Haoye Tian","Xuan-Bach D. Le"],"tags":["Computer science","Empirical research","Internet privacy","Computer security","Philosophy"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-05-01","doi":"https://doi.org/10.1145/3733599","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413381171","name":"FLUID: Dynamic Model-Agnostic Federated Learning with Pruning and Knowledge Distillation for Maritime Predictive Maintenance","source":"openalex","abstract":"Predictive maintenance (PdM) is vital to maritime operations; however, the traditional deep learning solutions currently offered heavily depend on centralized data aggregation, which is impractical under the limited connectivity, privacy concerns, and resource constraints found in maritime vessels. Federated Learning addresses privacy by training models locally, yet most FL methods assume homogeneous client architectures and exchange full model weights, leading to heavy communication overhead and sensitivity to system heterogeneity. To overcome these challenges, we introduce FLUID, a dynamic, model-agnostic FL framework that combines client clustering, structured pruning, and student–teacher knowledge distillation. FLUID first groups vessels into resource tiers and calibrates pruning strategies on the most capable client to determine optimal sparsity levels. In subsequent FL rounds, clients exchange logits over a small reference set, decoupling global aggregation from specific model architectures. We evaluate FLUID on a real-world heavy-fuel-oil purifier dataset under realistic heterogeneous deployment. With mixed pruning across clients, FLUID achieves a global R2 of 0.9352, compared with 0.9757 for a centralized baseline. Predictive consistency also remains high for client-based data, with a mean per-client MAE of 0.02575 ± 0.0021 and a mean RMSE of 0.0419 ± 0.0036. These results demonstrate FLUID’s ability to deliver accurate, efficient, and privacy-preserving PdM in heterogeneous maritime fleets.","url":"https://doi.org/10.3390/jmse13081569","authors":["Alexandros Kalafatelis","Angeliki Pitsiakou","Νικόλαος Νομικός","Nikolaos Tsoulakos","Theodore Syriopoulos","Panagiotis Trakadas"],"tags":["Pruning","Distillation","Computer science","Predictive maintenance","Machine learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-08-15","doi":"https://doi.org/10.3390/jmse13081569","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410412826","name":"Federated Learning and Blockchain Framework for Scalable and Secure IoT Access Control","source":"openalex","abstract":"The increasing deployment of Internet of Things (IoT) devices has introduced significant security challenges, including identity spoofing, unauthorized access, and data integrity breaches. Traditional security mechanisms rely... | Find, read and cite all the research you need on Tech Science Press","url":"https://doi.org/10.32604/cmc.2025.065426","authors":["Ammar Odeh","Anas Abu Taleb"],"tags":["Blockchain","Scalability","Internet of Things","Computer science","Access control"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.32604/cmc.2025.065426","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412454314","name":"Latency Analysis of UAV-Assisted Vehicular Communications Using Personalized Federated Learning with Attention Mechanism","source":"openalex","abstract":"In this paper, unmanned aerial vehicle (UAV)-assisted vehicular communications are investigated to minimize latency and maximize the utilization of available UAV battery power. As communication and cooperation among UAV and vehicles is frequently required, a viable approach is to reduce the transmission of redundant messages. However, when the sensor data captured by the varying number of vehicles is not independent and identically distributed (non-i.i.d.), this becomes challenging. Hence, in order to group the vehicles with similar data distributions in a cluster, we utilize federated learning (FL) based on an attention mechanism. We jointly maximize the UAV’s available battery power in each transmission window and minimize communication latency. The simulation experiments reveal that the proposed personalized FL approach achieves performance improvement compared with baseline FL approaches. Our model, trained on the V2X-Sim dataset, outperforms existing methods on key performance indicators. The proposed FL approach with an attention mechanism offers a reduction in communication latency by up to 35% and a significant reduction in computational complexity without degradation in performance. Specifically, we achieve an improvement of approximately 40% in UAV energy efficiency, 20% reduction in the communication overhead, and 15% minimization in sojourn time.","url":"https://doi.org/10.3390/drones9070497","authors":["Abhishek Gupta","Xavier Fernando"],"tags":["Latency (audio)","Mechanism (biology)","Computer science","Computer network","Human–computer interaction"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-15","doi":"https://doi.org/10.3390/drones9070497","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413794989","name":"Comparative analysis of deep learning architectures in solar power prediction","source":"openalex","abstract":"Integrating renewable energy sources into the electricity grid requires accurate forecasts of solar power production. With the aim of enhancing the accuracy and reliability of forecasts, this study presents a comprehensive comparative analysis of eight state-of-the-art Deep Learning (DL) architectures-Autoencoder, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Simple Recurrent Neural Network (SimpleRNN), Convolutional Neural Network (CNN), Temporal Convolutional Network (TCN), Transformer, and Lightweight Informer for Long Sequence Time-Series Forecasting (InformerLite)-applied to solar power prediction using a dataset with 4,200 historical records and 20 meteorological and astronomical features. A comprehensive assessment of Root Mean Squared Error [Formula: see text], Mean Absolute Error [Formula: see text], Mean Absolute Percentage Error [Formula: see text], and Coefficient of Determination [Formula: see text] metrics was performed on the training, validation, and test datasets. The TCN model had the greatest performance across all models, achieving a test R² of 0.7786, an [Formula: see text] of 429.4863, and a balanced relative standard deviation ([Formula: see text]) of 0.6827, so exhibiting an exceptional capacity to capture temporal patterns. The Autoencoder achieved a [Formula: see text] of 0.7648 and had the greatest overall performance on the entire dataset, resulting in a Whole [Formula: see text] of 0.8437. In contrast, the Transformer model demonstrated significantly poorer performance (Test [Formula: see text] = 0.0714), underscoring its limitations in this context without any architectural modifications. This study not only demonstrates the best DL models for solar power forecasting as qualified by useful statistical metrics, but also provides a scalable, interpretable, and extensible forecasting framework for real-world energy systems. The findings verify the informed DL integration to smart grid scenarios, laying the foundations for further developments in hybrid modeling, multi-horizon prediction, and deployment in resource-constrained environments with limited computational power and resources.","url":"https://doi.org/10.1038/s41598-025-14908-x","authors":["Montaser Abdelsattar","Mohamed Mostafa A. Azim","Ahmed AbdelMoety","Ahmed Emad-Eldeen"],"tags":["Autoencoder","Computer science","Convolutional neural network","Artificial intelligence","Algorithm"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-08-28","doi":"https://doi.org/10.1038/s41598-025-14908-x","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4414146309","name":"A deep learning/machine learning approach for anomaly based network intrusion detection","source":"openalex","abstract":"Introduction: The increasing complexity and frequency of cybersecurity threats necessitate the development of advanced detection systems capable of identifying both known and emerging attacks. In this study, we present a hybrid anomaly-based Network Intrusion Detection System (NIDS) that integrates multiple machine learning and deep learning algorithms, including XGBoost, Random Forest, Graph Neural Networks (GNN), Long Short-Term Memory (LSTM) networks, and Autoencoders. Methods: The proposed system was trained on a large-scale dataset comprising over 5.6 million network traffic records. Comprehensive data preprocessing and feature engineering were applied, and the Synthetic Minority Over-sampling Technique (SMOTE) was employed to address class imbalance. To enhance robustness and generalization, a weighted soft-voting ensemble strategy was used to combine predictions from the individual models. Results: The experimental evaluation demonstrated near-perfect performance, with accuracy, precision, recall, and F1-score values approaching 100% on the primary dataset. These results were validated through rigorous 5-fold cross-validation. Discussion: Evaluation on an independent benchmark dataset confirmed the strong generalizability and robustness of the proposed model across diverse intrusion scenarios. These findings highlight the effectiveness of the hybrid ensemble framework in significantly improving intrusion detection capabilities within complex and dynamic network environments.","url":"https://doi.org/10.3389/frai.2025.1625891","authors":["Reem Al-Muhanna","Samia Dardouri"],"tags":["Intrusion detection system","Generalizability theory","Robustness (evolution)","Anomaly detection","Computer science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-09-09","doi":"https://doi.org/10.3389/frai.2025.1625891","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W7116750991","name":"Deep learning for sustainable development across climate, energy, agriculture and urban systems","source":"openalex","abstract":"Deep learning (DL) has emerged as a transformative paradigm for addressing the multidimensional and data-intensive challenges of sustainable development. The purpose of this review is to examine how DL contributes to four critical domains-climate action, sustainable energy, smart agriculture, and urban development-while identifying gaps that limit large-scale deployment. Methodologically, the study synthesizes peer-reviewed research published between 2000 and 2025, covering architectures such as convolutional neural networks, recurrent networks, transformers, graph neural networks, autoencoders, and multimodal frameworks. The findings reveal key trends including the rise of physics-informed models, the integration of deep reinforcement learning in energy and transport systems, and the increasing adoption of federated and edge AI for decentralized monitoring. At the same time, recurring challenges are identified: data scarcity, limited cross-regional generalizability, deficits in explainability, and ethical concerns surrounding fairness and accountability. The review concludes that addressing these issues requires hybrid physics–AI modeling, uncertainty-aware and participatory AI frameworks, and deployment-oriented research strategies. The key contributions and implications of this work are threefold: (i) the development of a cross-domain taxonomy mapping DL methods to sustainability tasks, (ii) benchmarking insights to guide model selection and evaluation, and (iii) a forward-looking research agenda to support researchers, practitioners, and policymakers. The originality of this review lies in its cross-sectoral synthesis, which extends beyond domain-specific surveys to highlight how DL can be responsibly scaled to advance the United Nations Sustainable Development Goals (SDGs).","url":"https://doi.org/10.1007/s43621-025-02186-6","authors":["Harshit Sharma","Simran Kaur"],"tags":["Transformative learning","Sustainability","Sustainable development","Deep learning","Citizen journalism"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-12-22","doi":"https://doi.org/10.1007/s43621-025-02186-6","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4408272724","name":"Decentralized AI at the Edge: Federated Learning, Quantum Optimization and IoT Scalability","source":"openalex","abstract":"Decentralized artificial intelligence (AI) at the edge marks a revolutionary evolution in computing, enabling efficient, privacy-preserving, and scalable solutions tailored for the Internet of Things (IoT). This paper integrates cutting-edge advancements in federated learning (FL), quantum optimization, and scalable IoT architectures to propose a cohesive framework for next-generation edge AI systems. We conducted an extensive literature review covering privacy-focused decentralized AI, quantum-enhanced optimization methods, and IoT system scalability. Our research highlights significant enhancements in model accuracy, resource efficiency, and data privacy through detailed comparative analysis and simulation-based experiments. Federated learning ensures local data processing, mitigating privacy risks, while quantum optimization accelerates complex computations, boosting system performance. However, challenges persist, including device heterogeneity, communication bottlenecks, and nascent quantum security risks. Our findings indicate that combining FL with quantum techniques can substantially improve edge AI scalability and effectiveness. Nonetheless, real-world deployment requires overcoming practical hurdles like interoperability and energy constraints. This paper thoroughly synthesizes the current landscape and charts a forward-looking agenda for research and innovation in decentralized edge AI.","url":"https://doi.org/10.30574/ijsra.2025.14.3.0633","authors":["Surya Kiran","Arjun Kumar","Swathi Chukkala"],"tags":["Scalability","Computer science","Enhanced Data Rates for GSM Evolution","Internet of Things","Distributed computing"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-03-10","doi":"https://doi.org/10.30574/ijsra.2025.14.3.0633","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4409988825","name":"A Survey of Clustering Federated Learning in Heterogeneous Data Scenarios","source":"openalex","abstract":"Federated learning, as a collaborative training paradigm that preserves raw data privacy, offers an effective solution for data protection concerns. However, its practical implementation faces significant challenges due to data heterogeneity. This heterogeneity manifests as non-independent and identically distributed (non-IID) data across participating entities, resulting in degraded model performance, slower convergence rates, and training instability. While conventional federated learning approaches—including parameter averaging, knowledge distillation, and personalization techniques—offer certain advantages, their efficacy remains limited in severely heterogeneous environments. This survey systematically examines research advancements in clustered federated learning for addressing data heterogeneity challenges, encompassing fundamental principles, model architecture development, and algorithmic implementations. We provide a detailed analysis of innovative algorithms ranging from IFCA to FedGroup, and from FCL-GNN to FedAC, highlighting their technical contributions and applicable scenarios. Furthermore, we explore emerging research directions including clustering interpretability, multi-source heterogeneous information fusion, dynamic clustering mechanisms, and resource-aware optimization. Clustered federated learning effectively enhances model performance and convergence efficiency while maintaining privacy by grouping participants with similar data distributions into clusters and training specialized models for each cluster. With ongoing technological progress, clustered federated learning shows promise for achieving an optimal balance between privacy preservation and learning efficiency in critical domains such as healthcare and finance, thereby contributing to the sustainable development of artificial intelligence technologies.","url":"https://doi.org/10.54097/v7wcad61","authors":["Entuo Liu","Wentong Yang","Yonggen Gu","Wei Long","Szabo E. Istvan","Lin‐Hua Jiang"],"tags":["Cluster analysis","Computer science","Data mining","Data science","Machine learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-04-30","doi":"https://doi.org/10.54097/v7wcad61","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4309953431","name":"Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggering","source":"openalex","abstract":"Federated learning (FL) is a popular technique for distributing machine learning (ML) across a set of edge devices. In this paper, we study fully decentralized FL, where in addition to devices conducting training locally, they carry out model aggregations via cooperative consensus formation over device-to-device (D2D) networks. We introduce asynchronous, event-triggered communications among the devices to handle settings where access to a central server is not feasible. To account for the inherent resource heterogeneity and statistical diversity challenges in FL, we define personalized communication triggering conditions at each device that weigh the change in local model parameters against the available local network resources. We theoretically recover the $O(\\ln{k} / \\sqrt{k})$ convergence rate to the globally optimal model of decentralized gradient descent (DGD) methods in the setup of our methodology. We provide our convergence guarantees for the last iterates of models, under relaxed graph connectivity and data heterogeneity assumptions compared with the existing literature. To do so, we demonstrate a $B$-connected information flow guarantee in the presence of sporadic communications over the time-varying D2D graph. Our subsequent numerical evaluations demonstrate that our methodology obtains substantial improvements in convergence speed and/or communication savings compared to existing decentralized FL baselines.","url":"https://doi.org/10.48550/arxiv.2211.12640","authors":["Shahryar Zehtabi","Seyyedali Hosseinalipour","Christopher G. Brinton"],"tags":["Asynchronous communication","Computer science","Distributed computing","Enhanced Data Rates for GSM Evolution","Convergence (economics)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2022-11-23","doi":"https://doi.org/10.48550/arxiv.2211.12640","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4406102782","name":"Ultra-Short-Term Distributed Photovoltaic Power Probabilistic Forecasting Method Based on Federated Learning and Joint Probability Distribution Modeling","source":"openalex","abstract":"The accurate probabilistic forecasting of ultra-short-term power generation from distributed photovoltaic (DPV) systems is of great significance for optimizing electricity markets and managing energy on the user side. Existing methods regarding cluster information sharing tend to easily trigger issues of data privacy leakage during information sharing, or they suffer from insufficient information sharing while protecting data privacy, leading to suboptimal forecasting performance. To address these issues, this paper proposes a privacy-preserving deep federated learning method for the probabilistic forecasting of ultra-short-term power generation from DPV systems. Firstly, a collaborative feature federated learning framework is established. For the central server, information sharing among clients is realized through the interaction of global models and features while avoiding the direct interaction of raw data to ensure the security of client data privacy. For local clients, a Transformer autoencoder is used as the forecasting model to extract local temporal features, which are combined with global features to form spatiotemporal correlation features, thereby deeply exploring the spatiotemporal correlations between different power stations and improving the accuracy of forecasting. Subsequently, a joint probability distribution model of forecasting values and errors is constructed, and the distribution patterns of errors are finely studied based on the dependencies between data to enhance the accuracy of probabilistic forecasting. Finally, the effectiveness of the proposed method was validated through real datasets.","url":"https://doi.org/10.3390/en18010197","authors":["Yübo Wang","Chao Huo","Fei Xu","Libin Zheng","Ling Hao"],"tags":["Computer science","Probabilistic logic","Data mining","Probabilistic forecasting","Joint probability distribution"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-05","doi":"https://doi.org/10.3390/en18010197","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410089534","name":"Empowering Federated Graph Rationale Learning with Latent Environments","source":"openalex","abstract":"The success of Graph Neural Networks (GNNs) in graph classification has heightened interest in explainable GNNs, particularly through graph rationalization. This method aims to enhance GNNs explainability by identifying subgraph structures (i.e., rationales) that support model predictions. However, existing methods often rely on centralized datasets, posing challenges in scenarios where data privacy is crucial, such as in molecular property prediction. Federated Learning (FL) offers a solution by enabling collaborative model training without sharing raw data. In this context, Federated Graph Rationalization emerges as a promising research direction. However, in each client, the rationalization methods often rely on client-specific shortcuts to compose rationales and make task predictions. Data heterogeneity, characterized by non-IID data across clients, exacerbates this problem, leading to poor prediction performance. To address these challenges, we propose the Environment-aware Data Augmentation (EaDA) method for Federated Graph Rationalization. EaDA comprises two main components: the Environment-aware Rationale Extraction (ERE) module and the Local-Global Alignment (LGA) module. The ERE module employs prototype learning to infer and share abstract environment information across clients, which are then aggregated to form a global environment. This information is used to generate counterfactual samples for local clients, enhancing the robustness of task predictions. The LGA module uses contrastive learning methods to align local and global rationale representations, mitigating performance degradation due to data heterogeneity. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of our approaches. Code is available at https://github.com/yuelinan/Codes-of-EaDA.","url":"https://doi.org/10.1145/3696410.3714929","authors":["Linan Yue","Qi Liu","Yawen Li","Fangzhou Yao","Weibo Gao","Junping Du"],"tags":["Computer science","Graph","Data science","World Wide Web","Theoretical computer science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-04-22","doi":"https://doi.org/10.1145/3696410.3714929","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4407391569","name":"Participant Selection for Efficient and Trusted Federated Learning in Blockchain-Assisted Hierarchical Federated Learning Architectures","source":"openalex","abstract":"Federated learning has attracted widespread attention due to its strong capabilities of privacy protection, making it a powerful supporting technology for addressing data silos in the future. However, federated learning still lags significantly behind traditional centralized learning in terms of learning efficiency and system security. In this paper, we first construct a hierarchical federated learning architecture integrated with blockchain based on the cooperation of the cloud, edge, and terminal, which has the ability to enhance the security of federated learning while reducing the introduction costs of blockchain. Under this architecture, we propose a semi-asynchronous aggregation scheme at the edge layer and introduce a hierarchical aggregation scheme that combines it with synchronous aggregation at the cloud end to improve system efficiency. Furthermore, we present a multi-objective node selection scheme that considers various influencing factors such as security and efficiency. We formulate the node selection problem as a Markov Decision Process (MDP) and propose a solution based on deep reinforcement learning to address it more efficiently. The experimental results show that the proposed scheme can effectively improve system efficiency and enhance system security. In addition, the proposed DQN-based node selection algorithm can efficiently realize the selection of the optimal policy.","url":"https://doi.org/10.3390/fi17020075","authors":["Peng Liu","Lili Jia","Yang Xiao"],"tags":["Blockchain","Computer science","Federated learning","Selection (genetic algorithm)","Direct Anonymous Attestation"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-02-08","doi":"https://doi.org/10.3390/fi17020075","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4414051559","name":"Privacy-Preserving Federated Learning for Predictive Maintenance in Smart Manufacturing Networks","source":"openalex","abstract":"Smart manufacturing environments (digitalized production systems with integrated sensor networks and data analytics capabilities) require advanced predictive maintenance capabilities, yet implementation faces significant barriers due to data privacy concerns and proprietary knowledge protection requirements.Traditional machine learning approaches necessitate centralized data repositories, creating obstacles for collaborative maintenance optimization across organizational boundaries.This research develops and evaluates a federated learning framework that enables effective predictive maintenance while preserving data privacy in manufacturing networks.The study implemented a horizontal federated learning architecture with secure aggregation protocols and differential privacy techniques across multiple aerospace manufacturing facilities.System performance was evaluated through comparative analysis against centralized and standalone approaches across multiple predictive maintenance use cases.The federated approach achieved 93.7% of centralized model accuracy while eliminating cross-facility data sharing, with failure prediction lead times approaching centralized performance while substantially outperforming standalone models.Computational overhead increased modestly, but network data transfer requirements decreased by 94%.Privacy analysis confirmed that proprietary process parameters could not be reconstructed from shared model updates.This research advances smart manufacturing capabilities by providing a practical implementation framework for privacy-preserving predictive maintenance across organizational boundaries, enabling industry collaboration while maintaining intellectual property protection.","url":"https://doi.org/10.24867/ijiem-390","authors":["Yulineth Cárdenas Escorcia","Sardor Sabirov","Bakhodir Saydullayev","A. V. Umarov","Zukhra Atamuratova","Ahmed Mohsin Alsayah","Yerzhan Tulekov"],"tags":["Predictive maintenance","Computer science","Overhead (engineering)","Predictive analytics","Differential privacy"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-09-08","doi":"https://doi.org/10.24867/ijiem-390","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412877087","name":"HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark","source":"openalex","abstract":"As AI evolves, collaboration among heterogeneous models helps overcome data scarcity by enabling knowledge transfer across institutions and devices.Traditional Federated Learning (FL) only supports homogeneous models, limiting collaboration among clients with heterogeneous model architectures.To address this, Heterogeneous Federated Learning (HtFL) methods are developed to enable collaboration across diverse heterogeneous models while tackling the data heterogeneity issue at the same time.However, a comprehensive benchmark for standardized evaluation and analysis of the rapidly growing HtFL methods is lacking.Firstly, the highly varied datasets, model heterogeneity scenarios, and different method implementations become hurdles to making easy and fair comparisons among HtFL methods.Secondly, the effectiveness and robustness of HtFL methods are under-explored in various scenarios, such as the medical domain and sensor signal modality.To fill this gap, we introduce the first Heterogeneous Federated Learning Library (HtFLlib), an easy-to-use and extensible framework that integrates multiple datasets and model heterogeneity scenarios, offering a robust benchmark for research and practical applications.Specifically, HtFLlib integrates (1) 12 datasets spanning various domains, modalities, and data heterogeneity scenarios; (2) 40 model architectures, ranging from small to large, across three modalities;(3) a modularized and easy-to-extend HtFL codebase with implementations of 10 representative HtFL methods; and (4) systematic evaluations in terms of accuracy, convergence, computation costs, and communication costs.We emphasize the advantages and potential of state-of-the-art HtFL methods and hope that HtFLlib will catalyze advancing HtFL research and enable its broader applications.The code is released at https://github.com/TsingZ0/HtFLlib.","url":"https://doi.org/10.1145/3711896.3737379","authors":["Jianqing Zhang","Xinghao Wu","Yanbing Zhou","Xiaoting Sun","Qiqi Cai","Yang Liu","Hua Yang","Zhenzhe Zheng","Jian Cao","Qiang Yang"],"tags":["Benchmark (surveying)","Computer science","Artificial intelligence","Data science","Information retrieval"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-08-03","doi":"https://doi.org/10.1145/3711896.3737379","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410453221","name":"Quantization-based chained privacy-preserving federated learning","source":"openalex","abstract":"Federated Learning (FL) is an advanced distributed machine learning framework crucial in protecting data privacy and security. By enabling multiple participants to train models while keeping their data local collaboratively, FL effectively mitigates the risks associated with centralized storage and sharing of raw data. However, traditional FL schemes face significant challenges regarding communication efficiency, computational costs, and privacy preservation. For instance, its communication and computational overhead in edge computing scenarios is often excessively high, hindering real-time applications. This paper proposes an innovative federated learning framework, Q-Chain FL, integrating quantization compression techniques into a chained FL architecture. This Q-Chain FL scheme adopts efficient compression and transmission of model parameter differences at the user node and executes seamless decompression and aggregation at the server node. Experiments on several publicly available datasets, including MNIST, CIFAR-10, and CelebA, demonstrate low communication and computational overhead, fast convergence speed, and high security of Q-Chain FL. Compared to traditional FedAvg and Chain-PPFL, Q-Chain FL reduces communication overhead by approximately 62.5% and 44.7%, respectively. These results underscore the robustness and adaptability of Q-Chain FL in various datasets and real-world learning scenarios.","url":"https://doi.org/10.1038/s41598-025-01420-5","authors":["Ya Liu","Shumin Wu","Yibo Li","Fengyu Zhao","Yanli Ren"],"tags":["Computer science","Federated learning","Quantization (signal processing)","World Wide Web","Internet privacy"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-05-16","doi":"https://doi.org/10.1038/s41598-025-01420-5","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413579916","name":"Federated Learning Approaches for Privacy-Preserving Threat Detection in Smart Home IoT Environments","source":"openalex","abstract":"Smart home Internet of Things (IoT) environments have become increasingly pervasive, offering convenience and automation while simultaneously introducing new cybersecurity vulnerabilities. Traditional centralized machine learning approaches for threat detection rely on aggregating sensitive user data into cloud servers, raising significant concerns regarding privacy, data security, and regulatory compliance. Federated learning (FL) has emerged as a promising paradigm that enables collaborative model training across distributed IoT devices without sharing raw data, thus preserving privacy while maintaining effective threat detection. This review paper explores the application of FL in privacy-preserving threat detection within smart home IoT systems, analyzing its strengths, limitations, and future potential. The discussion highlights how FL mitigates risks such as data leakage, adversarial attacks, and model inversion while ensuring scalability in heterogeneous device ecosystems. Moreover, the review examines existing frameworks, comparative case studies, and integration with complementary technologies like blockchain and differential privacy to enhance robustness. Challenges such as communication overhead, resource constraints, and model poisoning attacks are also critically addressed. By synthesizing recent advancements and identifying open research gaps, this paper provides a roadmap for leveraging FL in developing secure, scalable, and privacy-preserving threat detection systems for smart homes.","url":"https://doi.org/10.32628/cseit24113369","authors":["Chima Nwankwo Idika","Edward Oziegbe Salami"],"tags":["Internet of Things","Internet privacy","Computer security","Home automation","Computer science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-10-30","doi":"https://doi.org/10.32628/cseit24113369","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413240396","name":"Federated Learning for Semantic Communication Based on CNNs and Transformer","source":"openalex","abstract":"This study focuses on the latest research advancements in the field of semantic communication. Traditional communication systems prioritize the transmission of raw data, whilst semantic communication emphasizes conveying the meaning represented by the data. However, the extracted semantic information is often ambiguous and subject to subjective evaluation. To address this problem, this study proposes a model that combines a convolutional neural network (CNN) with a Transformer, called DeepSC‐CT. The model utilizes a CNN to extract semantic information from the data, followed by a Transformer model to capture spatial relationships and contextual information within the semantic content. We utilize federated learning to train the model and propose an adaptive aggregation algorithm to accelerate the convergence process. Moreover, we expand the single‐modality semantic communication model to encompass multiple modalities, such as texts, audio, and images. Furthermore, this study introduces a learnable position‐encoding method for the Transformer. The experimental results and visual effects of audio and image restoration demonstrate that the proposed method exhibits impressive performance and that the proposed model shows robust data restoration capabilities under various signal‐to‐noise ratio conditions.","url":"https://doi.org/10.1155/int/3750087","authors":["Shufeng Li","Yujun Cai","Zhaokai Deng","Xinran Ba","Qinghe Zheng","Xinruo Zhang","Baoxin Su"],"tags":["Computer science","Transformer","Convolutional neural network","Artificial intelligence","Semantic data model"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1155/int/3750087","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413885161","name":"Federated Multi-Agent DRL for Task Offloading in Vehicular Edge Computing","source":"openalex","abstract":"With the expansion of vehicle-to-everything (V2X) networks and the rising demand for intelligent services, vehicle edge computing encounters heightened requirements for more efficient task offloading. This study proposes a task offloading technique that utilizes federated collaboration and multi-agent deep reinforcement learning to reduce system latency and energy consumption. The task offloading issue is formulated as a Markov decision process (MDP), and a framework utilizing the Multi-Agent Dueling Double Deep Q-Network (MAD3QN) is developed to facilitate agents in making optimal offloading decisions inside intricate environments. Secondly, Federated Learning (FL) is implemented during the training phase, leveraging local training outcomes from many vehicles to enhance the global model, thus augmenting the learning efficiency of the agents. Experimental results indicate that, compared to conventional baseline algorithms, the proposed method decreases latency and energy consumption by at least 10% and 9%, respectively, while enhancing the average reward by at least 21%.","url":"https://doi.org/10.3390/electronics14173501","authors":["Hongwei Zhao","Yu Li","Zhixi Pang","Zihan Ma"],"tags":["Computer science","Edge computing","Task (project management)","Enhanced Data Rates for GSM Evolution","Distributed computing"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-09-01","doi":"https://doi.org/10.3390/electronics14173501","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4415774286","name":"Privacy and Trust in Blockchain-Federated Intrusion Detection Systems: Taxonomy, Challenges and Perspectives","source":"openalex","abstract":"Intrusion Detection Systems (IDS) play a critical role in protecting modern networks, but traditional centralized designs raise serious concerns regarding data privacy, trust, and scalability. Federated Learning (FL) reduces privacy risks through decentralized model training, and blockchain enhances trust by providing immutability and transparency. Combining these technologies creates a promising paradigm for secure and trustworthy IDS. This paper presents a comprehensive survey of blockchain-federated IDS with a particular focus on privacy and trust. The key contribution is a multi-dimensional taxonomy that integrates IDS architectures, FL strategies, blockchain types, and consensus mechanisms, providing a clear and structured view of this emerging field. We categorize threats into data, communication, and model levels, and map representative defense mechanisms to each. We also review applications in vehicular networks, industrial and medical Internet of Things (IoT), and metaverse scenarios. Finally, we highlight key challenges, including non-IID data, lightweight consensus, incentive mechanisms, and poisoning-resilient aggregation, and outline future research directions.","url":"https://doi.org/10.62762/jrsc.2025.399812","authors":["Cao Yuan","Chin Soon Ku","Rahul Kumar","Arshad Khan"],"tags":["Computer science","Immutability","Computer security","Trustworthiness","Key (lock)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-11-02","doi":"https://doi.org/10.62762/jrsc.2025.399812","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4396239822","name":"FedSteg: Coverless Steganography‐Based Privacy‐Preserving Decentralized Federated Learning","source":"openalex","abstract":"Federated learning (FL) represents a novel privacy‐preserving learning paradigm that offers a practical solution for distributed privacy preservation. Although privacy‐preserving FL based on homomorphic encryption (HE‐PPFL) exhibits resistance to gradient leakage attacks while ensuring the accuracy of aggregation results, its widespread adoption in blockchain privacy preservation is hindered by the reliance on a trusted key generation center and secure transfer channels. Conversely, coverless steganography schemes effectively ensure the covert transmission of sensitive information across insecure channels. However, their incompatibility with HE‐PPFL arises from the lossy extraction process. To address these challenges, we present a decentralized federated learning privacy‐preserving framework based on the Lifted ElGamal threshold decryption cryptosystem. We introduce a reversible steganography method tailored to safeguard gradient privacy. Furthermore, we introduce a lightweight, secure blind aggregation algorithm founded on the Raft protocol, which serves to protect gradient privacy while substantially mitigating computational overhead. Finally, we provide rigorous theoretical proof of the security and correctness of our proposed scheme. Experimental results from four public data sets demonstrate that our proposed scheme achieves a 100% extraction accuracy without the need for lossless methods, while simultaneously reducing the computational cost of ciphertext gradient aggregation by at least three orders of magnitude. The FedSteg framework is publicly accessible at https://github.com/Xumeili/FedSteg . © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","url":"https://doi.org/10.1002/tee.24085","authors":["Mengfan Xu","Yaguang Lin"],"tags":["Steganography","Computer science","Blockchain","Steganography tools","Computer security"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-04-29","doi":"https://doi.org/10.1002/tee.24085","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4409912927","name":"Managing Supply and Value Chains in a New Era of Global Trade: The Promise of Federated Learning","source":"openalex","abstract":"As global trade shifts from an era of efficiency-driven globalisation to a new compliance-centred paradigm, customs administrations face mounting challenges – ranging from forced labour and environmental enforcement to fractured supply chain visibility and escalating transaction volumes, particularly in e-commerce. This article introduces federated learning as a practical, privacy-preserving solution for enabling secure data collaboration across public and private actors without commingling or centralising sensitive information. We trace the structural failures of Globalisation 1.0 and propose a modernised model of border management built on federated system architecture and trusted networks. These systems allow customs authorities to apply actionable intelligence across multi-tier value chains, strengthen enforcement capabilities, and expedite legitimate trade. The article outlines key steps towards implementation, including legal, technical and institutional reforms, and argues that federated architectures can form the foundation for next-generation risk management and trade facilitation strategies. The future of effective customs governance will depend on embracing secure, data-driven collaboration within and across borders.","url":"https://doi.org/10.55596/001c.133998","authors":["Alan Bersin","Peter Goodings Swartz","Lars Karlsson"],"tags":["Supply chain","Value (mathematics)","Business","International trade","Computer science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-04-29","doi":"https://doi.org/10.55596/001c.133998","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4404295022","name":"Advancements and Challenges in Federated Learning : A Survey","source":"openalex","abstract":"Machine Learning (ML) has significantly impacted daily life by automating tasks and enhancing decision-making across diverse sectors such as healthcare, finance, and transportation. However, concerns regarding data privacy, particularly in sensitive domains like healthcare and finance, have impeded its widespread adoption. Federated Learning (FL), pioneered by Google in 2016, presents a promising solution by enabling devices to collaborate on model training without sharing raw data. In FL, each device retains its data locally, and only model updates are exchanged with a central server for aggregation. This decentralized approach preserves data privacy while still improving model performance through collaborative learning. FL has garnered interest across industries, with approximately 32% of companies intending to integrate it into their systems soon. Furthermore, investment in FL is projected to rise substantially from $107 million in 2020 to $538 million by 2025, as indicated by forecasts from KPMG. This paper provides an overview of FL, its applications across various sectors, current adoption trends, and future growth prospects, highlighting its significance in addressing data privacy concerns while advancing machine learning capabilities.","url":"https://doi.org/10.1109/acroset62108.2024.10743243","authors":["Vivek Prajapat","Narendra Pal Singh Rathore","Kamal Kumar Sethi","Shiv Shankar Rajput"],"tags":["Computer science","Data science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-09-27","doi":"https://doi.org/10.1109/acroset62108.2024.10743243","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4297006047","name":"HealthGuard: An Intelligent Healthcare System Security Framework Based on Machine Learning","source":"openalex","abstract":"Utilization of the Internet of Things and ubiquitous computing in medical apparatuses have “smartified” the current healthcare system. These days, healthcare is used for more than simply curing patients. A Smart Healthcare System (SHS) is a network of implanted medical devices and wearables that monitors patients in real-time to detect and avert potentially fatal illnesses. With its expanding capabilities comes a slew of security threats, and there are many ways in which a SHS might be exploited by malicious actors. These include, but are not limited to, interfering with regular SHS functioning, inserting bogus data to modify vital signs, and meddling with medical devices. This study presents HealthGuard, an innovative security architecture for SHSs that uses machine learning to identify potentially harmful actions taken by users. HealthGuard monitors the vitals of many SHS-connected devices and compares the vitals to distinguish normal from abnormal activity. For the purpose of locating potentially dangerous actions inside a SHS, HealthGuard employs four distinct machine learning-based detection approaches (Artificial Neural Network, Decision Tree, Random Forest, and k-Nearest Neighbor). Eight different smart medical devices were used to train HealthGuard for a total of twelve harmless occurrences, seven of which are common user activities and five of which are disease-related occurrences. HealthGuard was also tested for its ability to defend against three distinct forms of harmful attack. Our comprehensive analysis demonstrates that HealthGuard is a reliable security architecture for SHSs, with a 91% success rate and in F1-score of 90% success.","url":"https://doi.org/10.3390/su141911934","authors":["Amit Sundas","Sumit Badotra","Salil Bharany","Ahmad Almogren","Sayed M. Eldin","Ateeq Ur Rehman"],"tags":["Computer science","Wearable computer","Computer security","Internet of Things","Wearable technology"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2022-09-22","doi":"https://doi.org/10.3390/su141911934","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4414359239","name":"Federated Low-Rank Adaptation for Foundation Models: A Survey","source":"openalex","abstract":"Effectively leveraging private datasets remains a significant challenge in developing foundation models. Federated Learning (FL) has recently emerged as a collaborative framework that enables multiple users to fine-tune these models while mitigating data privacy risks. Meanwhile, Low-Rank Adaptation (LoRA) offers a resource-efficient alternative for fine-tuning foundation models by dramatically reducing the number of trainable parameters. This survey examines how LoRA has been integrated into federated fine-tuning for foundation models—an area we term FedLoRA—by focusing on three key challenges: distributed learning, heterogeneity, and efficiency. We further categorize existing work based on the specific methods used to address each challenge. Finally, we discuss open research questions and highlight promising directions for future investigation, outlining the next steps for advancing FedLoRA.","url":"https://doi.org/10.24963/ijcai.2025/1196","authors":["Yiyuan Yang","Guodong Long","Qinghua Lu","Liming Zhu","Jing Jiang","Chengqi Zhang"],"tags":["Foundation (evidence)","Computer science","Adaptation (eye)","Data science","Key (lock)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-09-01","doi":"https://doi.org/10.24963/ijcai.2025/1196","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4417118783","name":"Integrating reinforcement learning and federated meta-learning for energy-efficient wireless body sensor networks","source":"openalex","abstract":"Abstract Intra wireless body sensor network (Intra-WBSN) is typically a short range wireless health monitoring network, Intra wireless body sensor network (Intra-WBSN) is typically a short range wireless health monitoring network, Wireless body sensor networks (WBSNs) have emerged as a transformative technology for real-time, non-invasive healthcare monitoring, enabling continuous tracking of vital physiological parameters such as heart rate, blood pressure, and glucose levels. However, their widespread deployment is hindered by critical challenges including limited energy resources, dynamic network topologies due to body movements, and stringent quality of service requirements for reliability and low latency. Conventional routing protocols, which rely on static, rule-based mechanisms, lack the adaptability and foresight needed to operate efficiently in such highly variable environments. To address these limitations, this paper proposes RELIEF-Net (reinforcement learning and federated meta-learning for energy-efficient wireless body sensor networks), a novel AI-driven framework that synergistically integrates reinforcement learning (RL) for adaptive routing, long short-term memory (LSTM) networks for predictive analytics, graph neural networks (GNNs) with attention mechanisms for energy-aware clustering, and federated meta-learning (FML) for privacy-preserving, cross-patient model personalization. At its core, RELIEF-Net employs RL to make context-aware, real-time routing decisions that optimize transmission power, prioritize critical data (e.g., cardiac alerts), and proactively reroute traffic based on LSTM-predicted energy depletion and link instability. GNN-based clustering enhances network organization and ensures efficient, topology-aware data forwarding, while FML enables decentralized learning across patients, preserving data privacy and improving scalability without centralized data aggregation. Extensive simulations demonstrate that RELIEF-Net achieves a 23% improvement in network lifetime, a packet delivery ratio (PDR) ≥ 97%, and end-to-end latency ≤ 85 ms for critical data under diverse operational scenarios. By unifying predictive intelligence, adaptive control, and scalable privacy-conscious learning, RELIEF-Net establishes a robust and sustainable solution for intelligent healthcare IoT systems, paving the way for next-generation remote patient monitoring and improved clinical outcomes.","url":"https://doi.org/10.1007/s44444-025-00081-z","authors":["Soufiane Ben Othman"],"tags":["Computer science","Wireless sensor network","Computer network","Key distribution in wireless sensor networks","Distributed computing"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-12-01","doi":"https://doi.org/10.1007/s44444-025-00081-z","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4375928946","name":"Machine Learning for Service Migration: A Survey","source":"openalex","abstract":"Future communication networks are envisioned to satisfy increasingly granular and dynamic requirements to accommodate the application and user demands. Indeed, novel immersive and mission-critical services necessitate increased computing and network resources, reduced communication latency, and guaranteed reliability. Thus, efficient and adaptive resource management schemes are required to provide and maintain sufficient levels of Quality of Experience (QoE) during the service life-cycle. Service migration is considered a key enabler of dynamic service orchestration. Indeed, moving services on demand is an efficient mechanism for user mobility support, load balancing in case of fluctuations in service demands, and hardware failure mitigation. However, service migration requires planning, as multiple parameters must be optimized to reduce service disruption to a minimum. Recent breakthroughs in computational capabilities allowed the emergence of Machine Learning as a tool for decision making that is expected to enable seamless automation of network resource management by predicting events and learning optimal decision policies. This paper surveys contributions applying Machine Learning (ML) methods to optimize service migration, providing a detailed literature review on recent advances in the field and establishing a classification of current research efforts with an analysis of their strengths and limitations. Finally, the paper provides insights on the main directions for future research.","url":"https://doi.org/10.1109/comst.2023.3273121","authors":["Nassima Toumi","Miloud Bagaa","Adlen Ksentini"],"tags":["Computer science","Orchestration","Service (business)","Enabling","Quality of service"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2023-01-01","doi":"https://doi.org/10.1109/comst.2023.3273121","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4415380784","name":"Artificial intelligence and students’ cognitive learning outcomes with bibliometric and content analysis for future research agenda","source":"openalex","abstract":"This study conducted a comprehensive bibliometric and content analysis to explore the integration of artificial intelligence in students’ cognitive learning outcomes. A structured TITLE-ABS-KEY search was performed in the Scopus database using keywords such as “Artificial Intelligence,” “AI,” “Students,” and “cognitive learning outcomes,” resulting in 318 documents published between 2016 and 2025. After filtering, a final dataset of 246 research articles and conference papers was analyzed. The methodology includes bibliometric performance analysis (covering publication trends, countries, affiliations, authors, and journals) and network analysis (comprising co-word, citation, co-authorship, and bibliographic coupling). Additionally, content analysis was conducted on the ten most cited and ten focused articles addressing AI’s impact on cognitive learning outcomes. VOSviewer software was used for data analysis and visualization. Findings indicate increased research output post-2023, driven by digital transformation and global collaboration. Leading affiliations include The University of Hong Kong and Carnegie Mellon University, with the United States, China, and India as top contributing countries. Influential journals and funding bodies include the National Science Foundation and the National Natural Science Foundation of China. Notable authors include Chiu and Cukurova, while Kit Ng, Zhong, and Liu are prominent in bibliographic coupling, emphasizing AI adoption. Co-authorship analysis shows collaboration primarily among developed nations. Co-word analysis reveals Key research themes include contrastive learning, adversarial machine learning, and federated learning. Content analysis highlights AI’s transformative potential for learning, teaching, cognitive learning, and innovation. This study provides managerial and practical recommendations for students, universities, and policymakers. This study has several limitations that future studies will consider.","url":"https://doi.org/10.1007/s44217-025-00865-0","authors":["Shaukat Rahman Ansari","Ika Nurul Qamari"],"tags":["Transformative learning","Content analysis","Scopus","Bibliometrics","Computer science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-10-21","doi":"https://doi.org/10.1007/s44217-025-00865-0","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4407664085","name":"Framework for Addressing Imbalanced Data in Aviation with Federated Learning","source":"openalex","abstract":"The aviation industry generates vast amounts of data across multiple stakeholders, but critical faults and anomalies occur rarely, creating inherently imbalanced datasets that complicate machine learning applications. Traditional centralized approaches are further constrained by privacy concerns and regulatory requirements that limit data sharing among stakeholders. This paper presents a novel framework for addressing imbalanced data challenges in aviation through federated learning, focusing on fault detection, predictive maintenance, and safety management. The proposed framework combines specialized techniques for handling imbalanced data with privacy-preserving federated learning to enable effective collaboration while maintaining data security. The framework incorporates local resampling methods, cost-sensitive learning, and weighted aggregation mechanisms to improve minority class detection performance. The framework is validated through extensive experiments involving multiple aviation stakeholders, demonstrating a 23% improvement in fault detection accuracy and a 17% reduction in remaining useful life prediction error compared to conventional models. Results show the enhanced detection of rare but critical faults, improved maintenance scheduling accuracy, and effective risk assessment across distributed aviation datasets. The proposed framework provides a scalable and practical solution for using distributed aviation data while addressing both class imbalance and privacy concerns, contributing to improved safety and operational efficiency in the aviation industry.","url":"https://doi.org/10.3390/info16020147","authors":["Igor Kabashkin"],"tags":["Aviation","Data science","Computer science","Knowledge management","Engineering"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-02-16","doi":"https://doi.org/10.3390/info16020147","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410961669","name":"FedMDKGE: Multi-granularity Dynamic Knowledge Graph Embedding in Federated Learning","source":"openalex","abstract":"As knowledge is time-sensitive, some researchers have started to focus on dynamic knowledge graphs to provide time-dimensioned knowledge content thus reflecting richer information. But they have not yet combined temporal information at different granularities. Also, in the case of multiple knowledge graphs distributed across different clients, it is of interest to ensure that the knowledge graph embedding representations are learned without exposing data and collaboratively. Therefore, in this paper, we propose a framework for multi-granularity dynamic knowledge graph embedding in federated learning (FedMDKGE), which allows multiple parties to interact securely with temporal information at different granularities. In the client, we present a multi-granularity dynamic knowledge graph embedding model that improves the capability of dynamic knowledge graph embedding representation by focusing on multi-granularity temporal facts from the perspective of knowledge utilization. On the server, we design a multi-granularity aggregation rule to accommodate multi-party information aggregation at different granularities. Finally, we conduct extensive experiments to demonstrate the superior performance of our model. The results on these real datasets show that FedMDKGE considering multi-granularity temporal information performs better than all comparative baselines and interconnect information for multi-party dynamic knowledge graph embedding without exposing data.","url":"https://doi.org/10.1007/s44196-025-00878-5","authors":["Wei Huang","Junling Chen","Dexian Wang","Pengfei Zhang","Jia Liu","Tianrui Li"],"tags":["Granularity","Computer science","Embedding","Knowledge graph","Graph"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-06-02","doi":"https://doi.org/10.1007/s44196-025-00878-5","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4384938131","name":"Towards a robust, effective and resource efficient machine learning technique for IoT security monitoring","source":"openalex","abstract":"The application of Deep Neural Networks (DNNs) for monitoring cyberattacks in Internet of Things (IoT) systems has gained significant attention in recent years. However, achieving optimal detection performance through DNN training has posed challenges due to computational intensity and vulnerability to adversarial samples. To address these issues, this paper introduces an optimization method that combines regularization and simulated micro-batching. This approach enables the training of DNNs in a robust, efficient, and resource-friendly manner for IoT security monitoring. Experimental results demonstrate that the proposed DNN model, including its performance in Federated Learning (FL) settings, exhibits improved attack detection and resistance to adversarial perturbations compared to benchmark baseline models and conventional Machine Learning (ML) methods typically employed in IoT security monitoring. Notably, the proposed method achieves significant reductions of 79.54% and 21.91% in memory and time usage, respectively, when compared to the benchmark baseline in simulated virtual worker environments. Moreover, in realistic testbed scenarios, the proposed method reduces memory footprint by 6.05% and execution time by 15.84%, while maintaining accuracy levels that are superior or comparable to state-of-the-art methods. These findings validate the feasibility and effectiveness of the proposed optimization method for enhancing the efficiency and robustness of DNN-based IoT security monitoring.","url":"https://doi.org/10.1016/j.cose.2023.103388","authors":["Idris Zakariyya","Harsha Kalutarage","M. Omar Al-Kadri"],"tags":["Computer science","Testbed","Robustness (evolution)","Artificial intelligence","Memory footprint"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2023-07-20","doi":"https://doi.org/10.1016/j.cose.2023.103388","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4395482942","name":"Model Poisoning Attacks to Federated Learning via Multi-Round Consistency","source":"openalex","abstract":"Model poisoning attacks are critical security threats to Federated Learning (FL). Existing model poisoning attacks suffer from two key limitations: 1) they achieve suboptimal effectiveness when defenses are deployed, and/or 2) they require knowledge of the model updates or local training data on genuine clients. In this work, we make a key observation that their suboptimal effectiveness arises from only leveraging model-update consistency among malicious clients within individual training rounds, making the attack effect self-cancel across training rounds. In light of this observation, we propose PoisonedFL, which enforces multi-round consistency among the malicious clients' model updates while not requiring any knowledge about the genuine clients. Our empirical evaluation on five benchmark datasets shows that PoisonedFL breaks eight state-of-the-art defenses and outperforms seven existing model poisoning attacks. Moreover, we also explore new defenses that are tailored to PoisonedFL, but our results show that we can still adapt PoisonedFL to break them. Our study shows that FL systems are considerably less robust than previously thought, underlining the urgency for the development of new defense mechanisms.","url":"https://doi.org/10.48550/arxiv.2404.15611","authors":["Yueqi Xie","Minghong Fang","Neil Zhenqiang Gong"],"tags":["Consistency (knowledge bases)","Computer science","Computer security","Federated learning","Database"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-04-24","doi":"https://doi.org/10.48550/arxiv.2404.15611","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W7116103563","name":"Federated Learning for Multi-Disease Ophthalmic Diagnostics Using OCT Angiography","source":"openalex","abstract":"Purpose: To conduct a comprehensive systematic evaluation of federated learning (FL) strategies for multi-disease retinal classification using OCT angiography (OCTA), implementing a 2-part experimental framework to establish foundational feasibility and optimize performance under realistic heterogeneous conditions while ensuring privacy preservation. Design: = 0.5). Participants: A total of 456 OCTA images from patients with 7 retinal pathologies, with diabetic retinopathy (31.1%) and normal cases (25.2%) comprising the majority, sourced from the public OCTA-500 data set (n = 300) and a private collection from the University of Illinois Chicago (n = 156). Methods: Five FL aggregation strategies (federated averaging [FedAvg], federated proximal [FedProx], federated magnetic resonance imaging [FedMRI], federated Adagrad, and federated Yogi) were systematically evaluated across multiple optimization dimensions: 7 architecture configurations spanning vision transformers, established convolutional neural networks, and hybrid models; 5 transfer learning freezing strategies; 3 local epoch configurations (2, 5, and 10); and scalability analysis across 2, 3, and 5-client federations. Security mechanisms including differential privacy (ε = 1.0-8.0) and secure aggregation were integrated and evaluated. Performance was assessed across 3 classification scenarios: 7-class, 4-class modified, and 4-class streamlined. Main Outcome Measures: Classification accuracy, receiver-operating-characteristic area under the curve (ROC-AUC), and macro-averaged F1-score with comprehensive privacy-utility analysis and computational efficiency metrics. Results: Under controlled conditions, FL achieved superior performance in simplified classifications, with FedAvg, FedProx, and FedMRI reaching 72.09% accuracy versus 69.77% centralized training. Comprehensive optimization identified DenseNet121 as optimal architecture (79.55% accuracy, 89.68% ROC-AUC), with \"most\" freezing strategy (75% frozen layers) providing 60% training time reduction while maintaining superior performance. Federated proximal demonstrated exceptional resilience to heterogeneity (-11.7% degradation). Bonawitz secure aggregation achieved optimal privacy-utility balance (63.64% accuracy with cryptographic guarantees), whereas differential privacy maintained clinical utility under moderate constraints (ε ≈ 4-6). Conclusions: This systematic evaluation establishes FL as a comprehensive solution for privacy-preserving multi-institutional OCTA-based disease classification, with careful architectural selection, optimization strategies, and security mechanisms enabling performance that matches or exceeds centralized approaches while maintaining regulatory compliance and clinical utility. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.","url":"https://doi.org/10.1016/j.xops.2025.101030","authors":["Ahammed Sakir Nabil","Sina Gholami","Theodore Leng","Jennifer I. Lim","Minhaj Nur Alam"],"tags":["Medicine","Computer science","Angiography","Computer vision","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-12-19","doi":"https://doi.org/10.1016/j.xops.2025.101030","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4416873395","name":"Federated Deep Reinforcement Learning for Privacy-Preserving Robotic-Assisted Surgery","source":"openalex","abstract":"The integration of Reinforcement Learning (RL) into robotic-assisted surgery (RAS) holds significant promise for advancing surgical precision, adaptability, and autonomous decision-making. However, the development of robust RL models in clinical settings is hindered by key challenges, including stringent patient data privacy regulations, limited access to diverse surgical datasets, and high procedural variability. To address these limitations, this paper presents a Federated Deep Reinforcement Learning (FDRL) framework that enables decentralised training of RL models across multiple healthcare institutions without exposing sensitive patient information. A central innovation of the proposed framework is its dynamic policy adaptation mechanism, which allows surgical robots to select and tailor patient-specific policies in real-time, thereby ensuring personalised and optimised interventions. To uphold rigorous privacy standards while facilitating collaborative learning, the FDRL framework incorporates secure aggregation, differential privacy, and homomorphic encryption techniques. Experimental results demonstrate a $60 \\%$ reduction in privacy leakage compared to conventional methods, with surgical precision maintained within a $1.5 \\%$ margin of a centralised baseline. This work establishes a foundational approach for adaptive, secure, and patient-centric AI-driven surgical robotics, offering a pathway toward clinical translation and scalable deployment across diverse healthcare environments.","url":"https://doi.org/10.1109/icdcsw63273.2025.00128","authors":["Sana Hafeez","Sundas Rafat Mulkana","Muhammad Ali Imran","Michele Sevegnani"],"tags":["Reinforcement learning","Computer science","Homomorphic encryption","Software deployment","Margin (machine learning)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-21","doi":"https://doi.org/10.1109/icdcsw63273.2025.00128","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4409895099","name":"Personalized Federated Transfer Learning for Building Energy Forecasting via Model Ensemble with Multi-Level Masking in Heterogeneous Sensing Environment","source":"openalex","abstract":"Effective building energy prediction is essential for optimizing energy management, but existing models struggle with data scarcity and sensor heterogeneity across different buildings. Conventional approaches, including centralized and transfer learning methods, fail to generalize well due to varying sensor configurations and inconsistent data availability. To overcome these challenges, this study proposes a Personalized Federated Learning (pFL) framework that integrates multi-level feature masking, model ensemble techniques, and knowledge transfer to enhance predictive performance across diverse buildings. The proposed feature masking strategy extracts the most relevant time-series features, while model ensemble learning improves generalization, and knowledge transfer enables adaptive fine-tuning for each building. These techniques allow pFL to retain global knowledge while personalizing to local energy consumption patterns, making it more effective than traditional FL methods. Experiments conducted on a campus energy dataset demonstrate that pFL consistently outperforms FedAvg, FedProx, and standalone models in energy prediction accuracy. The most significant improvements are observed in buildings with highly fluctuating consumption patterns, validating the effectiveness of the proposed approach in handling heterogeneous sensing environments. This study highlights the potential of Federated Learning for scalable and adaptive energy prediction. Future work will focus on refining multi-horizon forecasting and developing strategies to enhance knowledge sharing among buildings for improved long-term performance.","url":"https://doi.org/10.3390/electronics14091790","authors":["Hakjae Kim","Sarangerel Dorjgochoo","Hansaem Park","Sung-Ju Lee"],"tags":["Computer science","Masking (illustration)","Transfer of learning","Energy transfer","Federated learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-04-28","doi":"https://doi.org/10.3390/electronics14091790","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4411289937","name":"Artificial Intelligence and machine learning in fraud detection for digital payments","source":"openalex","abstract":"The financial sector has adopted artificial intelligence (AI) and machine learning (ML) for more advanced and real-time fraud detection as a result of the increased threat of fraud brought on by the global surge in digital payments. By using sophisticated algorithms like supervised learning, anomaly detection, and deep neural networks, these technologies allow systems to identify irregularities, adjust to new fraud patterns, and lower false positives. AI-driven systems are widely used by fintechs and neobanks in the US, Germany, and the EU. However, they also face issues with data quality, model transparency, and regulatory compliance, which calls for a careful balancing act between ethical oversight and technical solutions.","url":"https://doi.org/10.30574/ijsra.2025.15.3.1784","authors":["Alexandre Davitaia"],"tags":["Payment","Computer science","Artificial intelligence","Machine learning","Computer security"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-06-13","doi":"https://doi.org/10.30574/ijsra.2025.15.3.1784","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W2345865936","name":"Linking Health Records for Federated Query Processing","source":"openalex","abstract":"Abstract A federated query portal in an electronic health record infrastructure enables large epidemiology studies by combining data from geographically dispersed medical institutions. However, an individual’s health record has been found to be distributed across multiple carrier databases in local settings. Privacy regulations may prohibit a data source from revealing clear text identifiers, thereby making it non-trivial for a query aggregator to determine which records correspond to the same underlying individual. In this paper, we explore this problem of privately detecting and tracking the health records of an individual in a distributed infrastructure. We begin with a secure set intersection protocol based on commutative encryption, and show how to make it practical on comparison spaces as large as 1010 pairs. Using bigram matching, precomputed tables, and data parallelism, we successfully reduced the execution time to a matter of minutes, while retaining a high degree of accuracy even in records with data entry errors. We also propose techniques to prevent the inference of identifier information when knowledge of underlying data distributions is known to an adversary. Finally, we discuss how records can be tracked utilizing the detection results during query processing.","url":"https://doi.org/10.1515/popets-2016-0013","authors":["Rinku Dewri","Toan C. Ong","Ramakrishna Thurimella"],"tags":["Computer science","Identifier","Encryption","Information retrieval","Set (abstract data type)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2016-05-06","doi":"https://doi.org/10.1515/popets-2016-0013","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4416909357","name":"Federated Deep Learning for Robust Multi-Modal Biometric Authentication Based on Facial and Eye-Blink Cues","source":"openalex","abstract":"The increasing demand for secure and user-friendly authentication mechanisms has led to the exploration of biometric systems that leverage unique physiological traits. Among these, face recognition and eye blink detection have emerged as effective and non-intrusive modalities. However, traditional biometric systems typically rely on centralized data storage and processing, raising significant concerns about user privacy, data security, and potential breaches. To address these challenges, this paper proposes a federated learning-based framework that combines face and eye blink recognition for robust user authentication.The proposed system utilizes OpenCV for real-time image capture and processing, enabling users to register by submitting facial images and customized eye blink patterns. These biometric features are used to train local models that remain on the user's device, ensuring that raw biometric data is never transmitted to external servers. Instead, model parameters are shared and aggregated at a centralized server using federated learning techniques, resulting in a global model that benefits from decentralized data sources while maintaining user privacy.The system is divided into key modules: face registration, eye blink training, federated model updating, and multi-modal authentication. Each module plays a vital role in establishing a secure and user-specific identity. The integration of eye blink recognition as a secondary verification layer significantly enhances the system's resistance to spoofing attacks and impersonation. Experimental evaluations demonstrate the system’s effectiveness in accurately identifying users while preserving privacy and reducing server dependency.This research offers a novel contribution to biometric security by combining federated learning with multi-modal authentication, paving the way for privacy-preserving, scalable, and intelligent user verification systems in real-world applications.","url":"https://doi.org/10.65521/ijacect.v14i1.167","authors":["A. Balaji","Doppalapudi Balanjali","Guntu Subbaiah","Avula Anil Reddy","Daggubati Karthik"],"tags":["Biometrics","Computer science","Spoofing attack","Leverage (statistics)","Facial recognition system"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-04-14","doi":"https://doi.org/10.65521/ijacect.v14i1.167","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4415819639","name":"On-Device Federated Learning for Energy-Efficient Smart Irrigation","source":"openalex","abstract":"This study presents a novel federated learning (FL) methodology implemented directly on STM32-based microcontrollers (MCUs) for energy-efficient smart irrigation. To the best of our knowledge, this is the first work to demonstrate end-to-end FL training and aggregation on real STM32 MCU clients (STM32F722ZE), under realistic energy and memory constraints. Unlike most prior studies that rely on simulated clients or high-power edge devices, our framework deploys lightweight neural networks trained locally on MCUs and synchronized via message queuing telemetry transport (MQTT) communication. Using a smart agriculture (SA) dataset partitioned by soil type, 7 clients collaboratively trained a model over 3 federated rounds. Experimental results show that MCU clients achieved competitive accuracy (70–82%) compared to PC clients (80–85%) while consuming orders of magnitude less energy. Specifically, MCU inference required only 0.95 mJ per sample versus 60–70 mJ on PCs, and training consumed ∼70 mJ per epoch versus nearly 20 J. Latency remained modest, with MCU inference averaging 3.2 ms per sample compared to sub-millisecond execution on PCs, a negligible overhead in irrigation scenarios. The evaluation also considers the payoff between accuracy, energy consumption, and latency through the Energy Latency Accuracy Index (ELAI). This integrated perspective highlights the trade-offs inherent in deploying FL on heterogeneous devices and demonstrates the efficiency advantages of MCU-based training in energy-constrained smart irrigation settings.","url":"https://doi.org/10.3390/electronics14214311","authors":["Zohra Dakhia","Alessia Lazzaro","Mohamed Riad Sebti","Mariateresa Russo","Massimo Merenda"],"tags":["Microcontroller","Computer science","Message queue","Inference","Security token"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-11-02","doi":"https://doi.org/10.3390/electronics14214311","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4416018425","name":"Collision avoidance in UAV swarms: A learning-centric perspective on collaborative intelligence","source":"openalex","abstract":"As UAV swarm deployments become more prevalent in mission critical domains, collision avoidance remains a key challenge in ensuring safety, coordination, and autonomy at scale. This survey investigates the state of the art in learning based collision avoidance strategies enabled through collaborative intelligence in UAV swarms. We introduce a six dimensional taxonomy that classifies approaches across decision making paradigms, swarm coordination models, communication architectures, learning methodologies, execution strategies, and safety assurance mechanisms. The survey places particular emphasis on learning based methodologies, which we categorize into four prominent techniques: reinforcement learning, federated learning, neuro inspired models, and hybrid approaches. For each, we provide a detailed review of training architectures, scalability, robustness, and real-time feasibility. Drawing on peer-reviewed publications (2019 till early 2025), we synthesize comparative insights into their application contexts, including trajectory planning, vision-based navigation, decentralized coordination, and multi-agent conflict resolution, while assessing trade-offs in deployment complexity and operational safety. Beyond method specific analysis, the survey highlights key distinctions, practical challenges, and enabling technologies, concluding with open challenges and future directions for scalable and verifiable UAV swarm intelligence.","url":"https://doi.org/10.1016/j.neucom.2025.132020","authors":["Himadri Sikhar Khargharia","Anis Ouali","Siddhartha Shakya","S. Ahmad"],"tags":["Computer science","Collision avoidance","Software deployment","Reinforcement learning","Scalability"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-11-08","doi":"https://doi.org/10.1016/j.neucom.2025.132020","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412688173","name":"Review: machine learning approaches for diverse alloy systems","source":"openalex","abstract":"Abstract The integration of machine learning (ML) into alloy design has revolutionized the discovery and optimization of advanced materials by enabling high-throughput, data-driven methodologies. This review systematically examines recent advancements in ML applications across diverse alloy systems, including steels, aluminum alloys, magnesium alloys, nickel-based superalloys, high-entropy alloys (HEAs), shape memory alloys, and metallic glasses. We categorize ML approaches into supervised, unsupervised, and reinforcement learning paradigms, detailing their specific implementations for property prediction, phase stability analysis, and composition optimization. Advanced techniques, such as inverse design frameworks and physics-informed ML models, have demonstrated substantial improvements in predictive accuracy and interpretability by integrating domain knowledge with data-driven approaches. The review further explores the synergy between ML and traditional computational methods, including CALPHAD-based thermodynamic modeling and density functional theory (DFT), enhancing the reliability of property predictions. We highlight case studies where ML-driven strategies have successfully accelerated alloy discovery, optimized mechanical properties, and identified novel compositions with tailored performance metrics. Additionally, we address key challenges in ML-driven alloy design, including data scarcity, feature selection, model interpretability, and the necessity for standardized benchmarking datasets. By providing a comprehensive evaluation of current methodologies and emerging trends, this review underscores the transformative role of ML in advancing next-generation alloy design and manufacturing, ultimately enabling the rapid development of high-performance materials for aerospace, energy, biomedical, and structural applications.","url":"https://doi.org/10.1007/s10853-025-11154-4","authors":["Arafat Rahman","Md Sojib Hossain","Abdullah Siddique"],"tags":["Materials science","Solid mechanics","Alloy","Metallurgy","Polymer science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-17","doi":"https://doi.org/10.1007/s10853-025-11154-4","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410269797","name":"Federated transfer learning for distributed drought stage prediction","source":"openalex","abstract":"Abstract Due to the uncertain nature of drought, it is one of the most menacing natural disasters. Drought modeling (Prediction, Detection, Forecasting, and Stage Prediction) is very essential for efficient policy making. But one of the key problems with drought modeling is the limited availability of centralized datasets. To address this problem, we are a novel proposing federated learning based transfer learning models for the prediction of drought stages. In this study, satellite image dataset was collected from the Tharparkar district (prone to drought) of Pakistan. We trained the dataset using traditional and federated learning approaches, comparing centralized ML models, pre-trained models, and their respective federated learning models (FL-ResNet, FL-DenseNet, FL-MobileNet). The development of these models is the novel aspect of the study specifically for the use case of drought stage prediction. Based on the final evaluation, FL-MobileNet achieved 82% precision while baseline MobileNet scored 68%. The results show the effectiveness of novelty (federated learning), that our proposed framework improves the performance of the drought stage classification task.","url":"https://doi.org/10.1007/s44163-025-00288-8","authors":["Muhammad Owais Raza","Aqsa Umar","Jawad Rasheed","Tunç Aşuroğlu","Shtwai Alsubai"],"tags":["Transfer of learning","Stage (stratigraphy)","Computer science","Transfer (computing)","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-05-11","doi":"https://doi.org/10.1007/s44163-025-00288-8","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4398186361","name":"RUL Prediction of Lithium-ion Batteries using a Federated and Homomorphically Encrypted Learning Method","source":"openalex","abstract":"The increasing demand for lithium-ion batteries (LIB) across various industries has accentuated the importance of accurately predicting the Remaining Useful Life (RUL) of these energy storage devices. This article introduces a novel approach to RUL prediction by leveraging a federated learning (FL) and homomorphic encryption (HE) model, called FedHEONN. Traditional RUL prediction models often face challenges not only related to the accuracy and reliability of estimations but also to data privacy and security when dealing with sensitive information in Internet of Things (IoT) environments. In response, our approach employs FL, allowing multiple distributed nodes to collaboratively train a predictive model without sharing private data. This ensures data privacy and security while harnessing the collective knowledge from diverse edge computing devices. Furthermore, to address the issue of secure computation over encrypted data, FedHEONN has the capacity to incorporate HE into the learning process. This enables the model to operate directly on encrypted data, providing an additional layer of protection to that of the federated model itself.","url":"https://doi.org/10.1145/3605098.3636045","authors":["Víctor López","Óscar Fontenla-Romero","Elena Hernández-Pereira","Bertha Guijarro‐Berdiñas","Carlos Blanco-Seijo","Samuel Fernández-Paz"],"tags":["Lithium (medication)","Ion","Computer science","Materials science","Computational science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-04-08","doi":"https://doi.org/10.1145/3605098.3636045","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4409328620","name":"Federated LeViT-ResUNet for Scalable and Privacy-Preserving Agricultural Monitoring Using Drone and Internet of Things Data","source":"openalex","abstract":"Precision agriculture is necessary for dealing with problems like pest outbreaks, a lack of water, and declining crop health. Manual inspections and broad-spectrum pesticide application are inefficient, time-consuming, and dangerous. New drone photography and IoT sensors offer quick, high-resolution, multimodal agricultural data collecting. Regional diversity, data heterogeneity, and privacy problems make it hard to conclude these data. This study proposes a lightweight, hybrid deep learning architecture called federated LeViT-ResUNet that combines the spatial efficiency of LeViT transformers with ResUNet’s exact pixel-level segmentation to address these issues. The system uses multispectral drone footage and IoT sensor data to identify real-time insect hotspots, crop health, and yield prediction. The dynamic relevance and sparsity-based feature selector (DRS-FS) improves feature ranking and reduces redundancy. Spectral normalization, spatial–temporal alignment, and dimensionality reduction provide reliable input representation. Unlike centralized models, our platform trains over-dispersed client datasets using federated learning to preserve privacy and capture regional trends. A huge, open-access agricultural dataset from varied environmental circumstances was used for simulation experiments. The suggested approach improves on conventional models like ResNet, DenseNet, and the vision transformer with a 98.9% classification accuracy and 99.3% AUC. The LeViT-ResUNet system is scalable and sustainable for privacy-preserving precision agriculture because of its high generalization, low latency, and communication efficiency. This study lays the groundwork for real-time, intelligent agricultural monitoring systems in diverse, resource-constrained farming situations.","url":"https://doi.org/10.3390/agronomy15040928","authors":["Mohammad Aldossary","Jaber Almutairi","Ibrahim Alzamil"],"tags":["Drone","Internet of Things","Scalability","Computer science","Internet privacy"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-04-10","doi":"https://doi.org/10.3390/agronomy15040928","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412800085","name":"Dual prompt personalized federated learning in foundation models","source":"openalex","abstract":"Personalized federated learning (PFL) has garnered significant attention for its ability to address heterogeneous client data distributions while preserving data privacy. However, when local client data is limited, deep learning models often suffer from insufficient training, leading to suboptimal performance. Foundation models, such as CLIP (Contrastive Language-Image Pretraining), exhibit strong feature extraction capabilities and can alleviate this issue by fine-tuning on limited local data. Despite their potential, foundation models are rarely utilized in federated learning scenarios, and challenges related to integrating new clients remain largely unresolved. To address these challenges, we propose the Dual Prompt Personalized Federated Learning (DP 2 FL) framework, which introduces dual prompts and an adaptive aggregation strategy. DP 2 FL combines global task awareness with local data-driven insights, enabling local models to achieve effective generalization while remaining adaptable to specific data distributions. Moreover, DP 2 FL introduces a global model that enables prediction on new data sources and seamlessly integrates newly added clients without requiring retraining. Experimental results in highly heterogeneous environments validate the effectiveness of DP 2 FL’s prompt design and aggregation strategy, underscoring the advantages of prediction on novel data sources and demonstrating the seamless integration of new clients into the federated learning framework.","url":"https://doi.org/10.1038/s41598-025-11864-4","authors":["Ying Chang","Xiaohu Shi","Zhao Xiaohui","Zhaohuang Chen","D.C. Ma"],"tags":["Foundation (evidence)","Computer science","Federated learning","Dual (grammatical number)","Data science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-31","doi":"https://doi.org/10.1038/s41598-025-11864-4","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W7128469018","name":"Secure and Differentially Private Edge-Cloud Federated Learning Framework for Privacy-Preserving Maritime AIS Intelligence","source":"openalex","abstract":"Cloud computing now supports large-scale maritime analytics, yet offloading rich Automatic Identification System (AIS) data to the cloud exposes sensitive operational patterns and complicates compliance with cross-border priv... | Find, read and cite all the research you need on Tech Science Press","url":"https://doi.org/10.32604/cmc.2026.077222","authors":["Abuzar Khan","Abid Iqbal","Ghassan Husnain","Fahad Masood","Mohammed Al-Naeem","Dr. Sajid Iqbal"],"tags":["Computer science","Cloud computing","Payload (computing)","Differential privacy","Homomorphic encryption"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2026-01-01","doi":"https://doi.org/10.32604/cmc.2026.077222","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4409067086","name":"Design of an Immersive Basketball Tactical Training System Based on Digital Twins and Federated Learning","source":"openalex","abstract":"To address the challenges of dynamic adversarial scenario modeling distortion, insufficient cross-institutional data privacy protection, and simplistic evaluation systems in collegiate basketball tactical education, this study proposes and validates an immersive instructional system integrating digital twin and federated learning technologies. The four-tier architecture (sensing layer, digital twin layer, federated layer, and interaction layer) synthesizes multimodal data (motion trajectories and physiological signals) with Multi-Agent Reinforcement Learning (MARL) to enable virtual–physical integrated tactical simulation and real-time error correction. Experimental results demonstrate that the experimental group achieved 35.2% higher tactical execution accuracy (TEA) (p < 0.01), 1.8 s faster decision making (p < 0.05), and 47% improved team coordination efficiency compared to the controls. The hierarchical federated learning framework (trajectory ε = 0.8; physiology ε = 0.3) maintained model precision loss at 2.4% while optimizing communication efficiency by 23%, ensuring privacy preservation. A novel three-dimensional “Skill–Creativity–Load” evaluation system revealed a 22% increase in unconventional tactical applications (p = 0.013) through the Tactical Creativity Index (TCI). By implementing lightweight federated architecture with dynamic cognitive offloading mechanisms, the system enables resource-constrained institutions to achieve 87% of the pedagogical effectiveness observed in elite programs, offering an innovative solution to reconcile educational equity with technological ethics. Future research should focus on long-term skill transfer, multimodal adaptive learning, and ethical framework development to advance intelligent sports education from efficiency-oriented paradigms to competency-based transformation.","url":"https://doi.org/10.3390/app15073831","authors":["Xiongce Lv","Ye Tao","Yifan Zhang","Yang Xue"],"tags":["Basketball","Computer science","Training (meteorology)","Multimedia","Human–computer interaction"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-03-31","doi":"https://doi.org/10.3390/app15073831","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4415380972","name":"Federated Incentive Learning: A Privacy-Preserving Framework for Ad Monetization and Creator Rewards in High-Concurrency Environments","source":"openalex","abstract":"Growing regulatory pressures have upended the historical pattern of cross-site behavioral targeting and measurement, forcing ad monetization systems to reconcile personalization, creator incentives, and privacy by design. This paper introduces Federated Incentive Learning (FIL), a privacy-preserving framework that optimizes ad placement and creator rewards in high-concurrency environments without transferring raw user data. FIL combines federated learning with on-device differential privacy, integrates Google’s Privacy Sandbox primitives for interest signals, on-device auctions, and privacy-preserving attribution, and imposes explicit fairness constraints for minimum exposure and region sensitive economic weighting. A global short video case study motivates design requirements such as sub-200 ms end-to-end decision latency and transparent incentive allocation. Methodologically, the study specifies a federated objective combining click-through and conversion prediction with incentive feedback, contrasts FedAvg and FedProx for heterogeneous clients, and calibrates Gaussian mechanisms to enforce an ε-differential privacy budget. Results indicate that FIL attains approximately 92 percent of the accuracy of centralized models while yielding a 25 percent improvement in small-to-medium advertiser return on investment and a 15 percent increase in creator earnings under weighted incentives and exposure guarantee. The framework demonstrates operational feasibility for privacy-sensitive, real-time monetization markets and contributes a governance-aware and equitable approach to ad delivery and creator compensation.","url":"https://doi.org/10.71465/ajbd3381","authors":["Xun Yi"],"tags":["Monetization","Incentive","Computer science","Sandbox (software development)","Earnings"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-10-13","doi":"https://doi.org/10.71465/ajbd3381","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4286888504","name":"Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances","source":"openalex","abstract":"Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human--computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.","url":"https://doi.org/10.48550/arxiv.2111.00418","authors":["Shibo Zhang","Yaxuan Li","Shen Zhang","Farzad Shahabi","Stephen Xia","Yu Deng","Nabil Alshurafa"],"tags":["Wearable computer","Computer science","Deep learning","Wearable technology","Activity recognition"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2021-10-31","doi":"https://doi.org/10.48550/arxiv.2111.00418","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410732892","name":"The role of explainable AI in enhancing breast cancer diagnosis using machine learning and deep learning models","source":"openalex","abstract":"Breast cancer is still a big health issue around the world, and it needs to be found quickly and perfectly to improve patient outcomes and lower death rates. Although artificial intelligence (AI) has showed amazing promise in breast cancer prediction mainly machine learning (ML) algorithms as well as deep learning (DL), practical use of these models is greatly hampered by their lack of interpretability and transparency. By giving complicated AI models interpretability, explainable artificial intelligence (XAI) becomes an essential tool to improve trust and transparency. XAI's efficacy in clinical environments is yet perfectly unidentified however, and its proper implementation into breast cancer diagnostics is hence ignored. Focussing on their interpretability, clinical application, and influence on decision-making, this paper systematically reviews machine learning, deep learning impact on breast cancer diagnosis and current XAI approaches used to breast cancer detection, prognosis, and treatment. This work presents a thorough assessment of XAI approaches classified by data kinds (imaging, genomic, and clinical), a comparative analysis of LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and Grad-CAM, and highlights important issues and future directions of research. This work highlights the possibility of XAI to enhance clinical decision-making and patient confidence by closing the gap between great diagnosis accuracy and interpretability. The results support multidisciplinary cooperation among medical experts, scientists in artificial intelligence, and legislators to guarantee the responsible and ethical integration of artificial intelligence in society.","url":"https://doi.org/10.1007/s44163-025-00307-8","authors":["Zulfikar Ali Ansari","Manish Madhava Tripathi","Rafeeq Ahmed"],"tags":["Artificial intelligence","Breast cancer","Computer science","Deep learning","Cancer"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-05-26","doi":"https://doi.org/10.1007/s44163-025-00307-8","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412929609","name":"Ensuring Zero Trust in GDPR-Compliant Deep Federated Learning Architecture","source":"openalex","abstract":"Deep Federated Learning (DFL) revolutionizes machine learning (ML) by enabling collaborative model training across diverse, decentralized data sources without direct data sharing, emphasizing user privacy and data sovereignty. Despite its potential, DFL’s application in sensitive sectors is hindered by challenges in meeting rigorous standards like the GDPR, with traditional setups struggling to ensure compliance and maintain trust. Addressing these issues, our research introduces an innovative Zero Trust-based DFL architecture designed for GDPR compliant systems, integrating advanced security and privacy mechanisms to ensure safe and transparent cross-node data processing. Our base paper proposed the basic GDPR-Compliant DFL Architecture. Now we validate the previously proposed architecture by formally verifying it using High-Level Petri Nets (HLPNs). This Zero Trust-based framework facilitates secure, decentralized model training without direct data sharing. Furthermore, we have also implemented a case study using the MNIST and CIFAR-10 datasets to evaluate the existing approach with the proposed Zero Trust-based DFL methodology. Our experiments confirmed its effectiveness in enhancing trust, complying with GDPR, and promoting DFL adoption in privacy-sensitive areas, achieving secure, ethical Artificial Intelligence (AI) with transparent and efficient data processing.","url":"https://doi.org/10.3390/computers14080317","authors":["Zahra Abbas","Sunila Fatima Ahmad","Adeel Anjum","Madiha Haider Syed","Saif Ur Rehman Malik","Semeen Rehman"],"tags":["Architecture","Zero (linguistics)","Computer science","Computer security","Business"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-08-04","doi":"https://doi.org/10.3390/computers14080317","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4394750287","name":"Federated Learning with Pareto Optimality for Resource Efficiency and Fast Model Convergence in Mobile Environments","source":"openalex","abstract":"Federated learning (FL) is an emerging distributed learning technique through which models can be trained using the data collected by user devices in resource-constrained situations while protecting user privacy. However, FL has three main limitations: First, the parameter server (PS), which aggregates the local models that are trained using local user data, is typically far from users. The large distance may burden the path links between the PS and local nodes, thereby increasing the consumption of the network and computing resources. Second, user device resources are limited, but this aspect is not considered in the training of the local model and transmission of the model parameters. Third, the PS-side links tend to become highly loaded as the number of participating clients increases. The links become congested owing to the large size of model parameters. In this study, we propose a resource-efficient FL scheme. We follow the Pareto optimality concept with the biased client selection to limit client participation, thereby ensuring efficient resource consumption and rapid model convergence. In addition, we propose a hierarchical structure with location-based clustering for device-to-device communication using k-means clustering. Simulation results show that with prate at 0.75, the proposed scheme effectively reduced transmitted and received network traffic by 75.89% and 78.77%, respectively, compared to the FedAvg method. It also achieves faster model convergence compared to other FL mechanisms, such as FedAvg and D2D-FedAvg.","url":"https://doi.org/10.3390/s24082476","authors":["June-Pyo Jung","Young‐Bae Ko","Sung‐Hwa Lim"],"tags":["Computer science","Cluster analysis","Convergence (economics)","Pareto principle","Scheme (mathematics)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-04-12","doi":"https://doi.org/10.3390/s24082476","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4391534311","name":"Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling","source":"openalex","abstract":"BACKGROUND: Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored. METHODS: A convolutional neural network was trained on paired ECG-echocardiograms (≤2 days apart) from patients ≤18 years of age without major congenital heart disease to detect human expert-classified greater than mild left ventricular (LV) dysfunction, hypertrophy, and dilation (individually and as a composite outcome). Model performance was evaluated on single ECG-echocardiogram pairs per patient at Boston Children's Hospital and externally at Mount Sinai Hospital using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). RESULTS: The training cohort comprised 92 377 ECG-echocardiogram pairs (46 261 patients; median age, 8.2 years). Test groups included internal testing (12 631 patients; median age, 8.8 years; 4.6% composite outcomes), emergency department (2830 patients; median age, 7.7 years; 10.0% composite outcomes), and external validation (5088 patients; median age, 4.3 years; 6.1% composite outcomes) cohorts. Model performance was similar on internal test and emergency department cohorts, with model predictions of LV hypertrophy outperforming the pediatric cardiologist expert benchmark. Adding age and sex to the model added no benefit to model performance. When using quantitative outcome cutoffs, model performance was similar between internal testing (composite outcome: AUROC, 0.88, AUPRC, 0.43; LV dysfunction: AUROC, 0.92, AUPRC, 0.23; LV hypertrophy: AUROC, 0.88, AUPRC, 0.28; LV dilation: AUROC, 0.91, AUPRC, 0.47) and external validation (composite outcome: AUROC, 0.86, AUPRC, 0.39; LV dysfunction: AUROC, 0.94, AUPRC, 0.32; LV hypertrophy: AUROC, 0.84, AUPRC, 0.25; LV dilation: AUROC, 0.87, AUPRC, 0.33), with composite outcome negative predictive values of 99.0% and 99.2%, respectively. Saliency mapping highlighted ECG components that influenced model predictions (precordial QRS complexes for all outcomes; T waves for LV dysfunction). High-risk ECG features include lateral T-wave inversion (LV dysfunction), deep S waves in V1 and V2 and tall R waves in V6 (LV hypertrophy), and tall R waves in V4 through V6 (LV dilation). CONCLUSIONS: This externally validated algorithm shows promise to inexpensively screen for LV dysfunction and remodeling in children, which may facilitate improved access to care by democratizing the expertise of pediatric cardiologists.","url":"https://doi.org/10.1161/circulationaha.123.067750","authors":["Joshua Mayourian","William La Cava","Akhil Vaid","Girish N. Nadkarni","Sunil J. Ghelani","Rebekah Mannix","Tal Geva","Audrey Dionne","Mark E. Alexander","Son Q. Duong","John K. Triedman"],"tags":["Medicine","Receiver operating characteristic","Left ventricular hypertrophy","Internal medicine","Cardiology"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-02-05","doi":"https://doi.org/10.1161/circulationaha.123.067750","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4409812826","name":"Towards Secure and Efficient Farming Using Self-Regulating Heterogeneous Federated Learning in Dynamic Network Conditions","source":"openalex","abstract":"The advancement of precision agriculture increasingly depends on innovative technological solutions that optimize resource utilization and minimize environmental impact. This paper introduces a novel heterogeneous federated learning architecture specifically designed for intelligent agricultural systems, with a focus on combine tractors equipped with advanced nutrient and crop health sensors. Unlike conventional FL applications, our architecture uniquely addresses the challenges of communication efficiency, dynamic network conditions, and resource allocation in rural farming environments. By adopting a decentralized approach, we ensure that sensitive data remain localized, thereby enhancing security while facilitating effective collaboration among devices. The architecture promotes the formation of adaptive clusters based on operational capabilities and geographical proximity, optimizing communication between edge devices and a global server. Furthermore, we implement a robust checkpointing mechanism and a dynamic data transmission strategy, ensuring efficient model updates in the face of fluctuating network conditions. Through a comprehensive assessment of computational power, energy efficiency, and latency, our system intelligently classifies devices, significantly enhancing the overall efficiency of federated learning processes. This paper details the architecture, operational procedures, and evaluation methodologies, demonstrating how our approach has the potential to transform agricultural practices through data-driven decision-making and promote sustainable farming practices tailored to the unique challenges of the agricultural sector.","url":"https://doi.org/10.3390/agriculture15090934","authors":["Sai Puppala","Koushik Sinha"],"tags":["Agriculture","Computer science","Business","Computer network","Biology"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-04-25","doi":"https://doi.org/10.3390/agriculture15090934","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4409047908","name":"Practical Federated Learning without a Server","source":"openalex","abstract":"Federated Learning (FL) enables end-user devices to collaboratively train ML models without sharing raw data, thereby preserving data privacy. In FL, a central parameter server coordinates the learning process by iteratively aggregating the trained models received from clients. Yet, deploying a central server is not always feasible due to hardware unavailability, infrastructure constraints, or operational costs. We present Plexus, a fully decentralized FL system for large networks that operates without the drawbacks originating from having a central server. Plexus distributes the responsibilities of model aggregation and sampling among participating nodes while avoiding network-wide coordination. We evaluate Plexus using realistic traces for compute speed, pairwise latency and network capacity. Our experiments on three common learning tasks and with up to 1000 nodes empirically show that Plexus reduces time-to-accuracy by 1.4--1.6×, communication volume by 15.8--292× and training resources needed for convergence by 30.5--77.9× compared to conventional decentralized learning algorithms.","url":"https://doi.org/10.1145/3721146.3721938","authors":["Akash Dhasade","Anne-Marie Kermarrec","Erick Lavoie","Johan Pouwelse","Rishi Sharma","Martijn de Vos"],"tags":["Computer science","World Wide Web"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-03-30","doi":"https://doi.org/10.1145/3721146.3721938","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412708560","name":"Intelligent waste sorting for urban sustainability using deep learning","source":"openalex","abstract":"Smart cities’ have experienced an increasingly higher rate of urbanization and increase of the population leading to strengthening the pressing needs in waste management. In this paper, we present an intelligent waste classification system that utilises Convolutional Neural Networks (CNNs) for automatic segregation into twelve categories of waste, employing image data. The model is trained on 15,535 images from a publicly available dataset using preprocessing and data augmentation to increase generalisation and mitigate class imbalance. A performance comparison in terms of precision, recall, F1 score, and accuracy shows that the proposed ResNet-based model yields a classification accuracy of 98.16%, outperforming previous work on conventional deep learning architectures. Experimental results demonstrate that the model is a robust framework for handling various types of waste (organic, recyclable, and hazardous) and is a very general model, as confirmed by cross-validation and real-world tests. The proposed system demonstrates great promise for upscaling in automatic waste management towards long-term urban sustainability, improved recycling, and reduced environmental threats.","url":"https://doi.org/10.1038/s41598-025-08461-w","authors":["G.F. Ahmad","Fizza Muhammad Aleem","Tahir Alyas","Qaiser Abbas","Waqas Nawaz","Taher M. Ghazal","Abdul Aziz","Shady H. E. Abdel Aleem","Nadia Tabassum","Aidarus Mohamed Ibrahim"],"tags":["Sustainability","Sorting","Computer science","Urban sustainability","Deep learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-25","doi":"https://doi.org/10.1038/s41598-025-08461-w","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4408216035","name":"AnoFel: Supporting Anonymity for Privacy-Preserving Federated Learning","source":"openalex","abstract":"Federated learning enables users to collaboratively train a machine learning model over their private datasets. Secure aggregation protocols are employed to mitigate information leakage about the local datasets from user-submitted model updates. This setup, however, still leaks the user participation in training, which can also be sensitive. Protecting user anonymity is even more challenging in dynamic environments where users may (re)join or leave the training process at any point of time. This paper introduces AnoFel, the first framework to support private and anonymous dynamic participation in federated learning (FL). AnoFel leverages several cryptographic primitives, the concept of anonymity sets, differential privacy, and a public bulletin board to support anonymous user registration, as well as unlinkable and confidential model update submission. Our system allows dynamic participation, where users can join or leave at any time without needing any recovery protocol or interaction. To assess security, we formalize a notion for privacy and anonymity in FL, and formally prove that AnoFel satisfies this notion. To the best of our knowledge, our system is the first solution with provable anonymity guarantees. To assess efficiency, we provide a concrete implementation of AnoFel, and conduct experiments showing its ability to support learning applications scaling to a large number of clients. For a TinyImageNet classification task with 512 clients, the client setup to join is less than 3 sec, and the client runtime for each training iteration takes a total of 8 sec, where the added overhead of AnoFel is 46% of the total runtime. We also compare our system with prior work and demonstrate its practicality. AnoFel client runtime is up to 5x faster than Truex et al., despite the added anonymity guarantee and dynamic user joining in AnoFel. Compared to Bonawitz et al., AnoFel is only 2x slower for added support for privacy in output, dynamic user joining, and anonymity.","url":"https://doi.org/10.56553/popets-2025-0051","authors":["Ghada Almashaqbeh","Zahra Ghodsi"],"tags":["Anonymity","Internet privacy","Computer science","Computer security","Federated learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-03-07","doi":"https://doi.org/10.56553/popets-2025-0051","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413358968","name":"Deep learning for intrusion detection in emerging technologies: a comprehensive survey and new perspectives","source":"openalex","abstract":"Abstract Intrusion Detection Systems (IDS) can help cybersecurity analysts detect malicious activities in computational environments. Recently, Deep Learning (DL) methods in IDS have demonstrated notable performance, revealing new underlying cybersecurity patterns in systems’ operations. Conversely, issues such as low performance in real systems, high false positive rates, and lack of explainability hinder its real-world deployment. In addition, the adoption of many new emerging technologies, such as cloud, edge computing, and the Internet of Things (IoT) introduces new forms of vulnerabilities. Therefore, the improvement of intrusion detection in emerging technologies depends on the clear definitions of challenging security problems and the limitations of existing solutions. The main goal of this research is to conduct a literature review of DL solutions for intrusion detection in emerging technologies to understand the state-of-the-art solutions and their limitations. Specifically, we conduct a comprehensive review of IDS-based automated threat defense methods, with the objective of identifying the landscape of, and opportunities for, incorporating DL methods into IDS. To accomplish this, a thorough review of IDS methods is conducted for multiple platforms and technologies, focusing on the use of common DL techniques. To expand on the study, several widely used IDS datasets are evaluated to assess their ability to train DL models and support researchers in understanding their characteristics and limitations. The analysis of attack vectors in emerging technologies is conducted, enabling an in-depth evaluation of security solutions in the future. Our findings show many clear opportunities for future research, including addressing the gap between solutions for controlled/simulated environments versus real systems, overcoming trustworthiness issues, including lack of explainability, and further exploring operationalization issues such as deployable solutions and continuous detection. Our analysis highlights that the operationalization of DL for intrusion detection in emerging technologies represents a key challenge to be addressed in the next few years.","url":"https://doi.org/10.1007/s10462-025-11346-z","authors":["Euclides Carlos Pinto Neto","Shahrear Iqbal","Scott Buffett","Madeena Sultana","Adrian Taylor"],"tags":["Computer science","Intrusion detection system","Deep learning","Data science","Emerging technologies"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-08-20","doi":"https://doi.org/10.1007/s10462-025-11346-z","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4414380024","name":"Federated learning for the pathogenicity annotation of genetic variants in multi-site clinical settings","source":"openalex","abstract":"MOTIVATION: Rare diseases collectively affect 5% of the population. However, fewer than 50% of rare disease patients receive a molecular diagnosis after whole genome sequencing. Supervised machine learning is a valuable approach for the pathogenicity scoring of human genetic variants. However, existing methods are often trained on curated but limited central repositories, resulting in poor accuracy when tested on external cohorts. Yet, large collections of variants generated at hospitals and research institutions remain inaccessible to machine-learning purposes because of privacy and legal constraints. Federated learning (FL) algorithms have been recently developed enabling institutions to collaboratively train models without sharing their local datasets. RESULTS: Here, we present a proof-of-concept study evaluating the effectiveness of FL for the clinical classification of genetic variants. A comprehensive array of diverse FL strategies was assessed for coding and non-coding Single Nucleotide Variants as well as Copy Number Variants. Our results showed that federated models generally achieved comparable or superior performance to traditional centralized learning. In addition, federated models reached a robust generalization to independent sets with smaller data fractions as compared to their centralized model counterparts. Our findings support the adoption of FL to establish secure multi-institutional collaborations in human variant interpretation. AVAILABILITY AND IMPLEMENTATION: All source code required to reproduce the results presented in this article, implemented in Python, is available under the GNU General Public License v3 at https://github.com/RausellLab/FedLearnVar.","url":"https://doi.org/10.1093/bioinformatics/btaf523","authors":["Nigreisy Montalvo","Francisco Requena Silvente","Emidio Capriotti","Antonio Rausell"],"tags":["Genetic variants","Annotation","Pathogenicity","Computer science","License"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-09-19","doi":"https://doi.org/10.1093/bioinformatics/btaf523","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412539477","name":"Blockchain-based federated learning framework for malicious node detection in internet of vehicles (IoV) networks using fog and cloud computing","source":"openalex","abstract":"Due to the continuous digitalization, IoV networks are vulnerable to various communication attacks by malicious network nodes. In these attacks, the malicious entities disseminate faulty information in the network, which affects quick and intelligent decision-making in the network. Many deep learning and machine learning techniques are proposed for the classification of legitimate and malicious vehicular entities. These techniques have a centralized model training structure, which has low classification accuracy and is vulnerable to privacy leakage. To address these issues, we propose a blockchain-based federated learning framework for distributed classification of malicious and legitimate vehicles. The proposed model uses the capabilities of Long short-term memory (LSTM) and Naive Bayes (NB) for efficient and reliable malicious node detection. In our proposed model, the distributed models are trained on each locally installed virtual machine with a federated learning mechanism and then a unified model is generated at the centralized cloud server. The proposed model not only enhances the accuracy and privacy preservation but also solves the issues of centralized Internet of Vehicles (IoV) networks such as single point of failure and performance bottlenecks by utilizing the capabilities of blockchain. We used the Vehicular Reference Misbehavior (VeReMi) dataset for evaluation of our proposed model. The results show that our proposed LSTM and NB-based model outperforms centralized benchmark classification methods in malicious node detection. With an accuracy of 95%, the LSTM-based model demonstrates superior performance in identifying both malicious and legitimate vehicles, achieving a precision of 0.96 and a recall of 0.97. The high value of precision and recall shows that our model can efficiently discriminate between malicious and legitimate vehicles in the IoV network.","url":"https://doi.org/10.1007/s44443-025-00134-y","authors":["Srinivas Reddy Bandarapu","Muhammad Bilal","Pushpalika Chatterjee","Adnan Mustafa Cheema","Junaid Rashid","Jungeun Kim"],"tags":["Blockchain","Cloud computing","Computer science","Node (physics)","The Internet"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-21","doi":"https://doi.org/10.1007/s44443-025-00134-y","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413120471","name":"Realistic Urban Traffic Generator Using Decentralized Federated Learning for the SUMO Simulator","source":"openalex","abstract":"Realistic urban traffic simulation is essential for sustainable urban planning and the development of intelligent transportation systems. However, generating high-fidelity, time-varying traffic profiles that accurately reflect real-world conditions, especially in large-scale scenarios, remains a major challenge. Existing methods often suffer from limitations in accuracy, scalability, or raise privacy concerns due to centralized data processing. This work introduces DesRUTGe (Decentralized Realistic Urban Traffic Generator), a novel framework that integrates Deep Reinforcement Learning (DRL) agents with the SUMO simulator to generate realistic 24-hour traffic patterns. A key innovation of DesRUTGe is its use of Decentralized Federated Learning (DFL), wherein each traffic detector and its corresponding urban zone function as an independent learning node. These nodes train local DRL models using minimal historical data and collaboratively refine their performance by exchanging model parameters with selected peers (e.g., geographically adjacent zones), without requiring a central coordinator. Evaluated using real-world data from the city of Barcelona, DesRUTGe outperforms standard SUMO-based tools such as RouteSampler, as well as other centralized learning approaches, by delivering more accurate traffic pattern generation.","url":"https://doi.org/10.1109/ojcoms.2025.3597019","authors":["Alberto Bazán-Guillén","Carlos Beis-Penedo","Diego Cajaraville-Aboy","Pablo Barbecho Bautista","Rebeca P. Dı́az Redondo","Luis J. de la Cruz Llopis","Ana Fernández Vilas","Mónica Aguilar Igartua","Manuel Fernández‐Veiga"],"tags":["Generator (circuit theory)","Computer science","Simulation","Transport engineering","Engineering"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1109/ojcoms.2025.3597019","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W7133196167","name":"A review of security threats and privacy issues in federated learning","source":"openalex","abstract":"","url":"https://doi.org/10.1007/s41060-026-01067-z","authors":["Abhishek Kumar Agrahari","Aarti Gautam Dinker","Rajendra Bahadur Singh"],"tags":["Computer science","Adversarial system","Computer security","Backdoor","Federated learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2026-03-02","doi":"https://doi.org/10.1007/s41060-026-01067-z","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4414281561","name":"Federated Learning with Adversarial Optimisation for Secure and Efficient 5G Edge Computing Networks","source":"openalex","abstract":"With the evolution of 5G edge computing networks, privacy-aware applications are gaining significant attention due to their decentralised processing capabilities. However, these networks face substantial challenges to ensure privacy and security, specifically in a Federated Learning (FL) setup, where adversarial attacks can potentially influence the model integrity. Conventional privacy-preserving FL mechanisms are often susceptible to such attacks, leading to degraded model performance and severe security vulnerabilities. To address this issue, we propose FL with adversarial optimisation framework to improve adversarial robustness in 5G edge computing networks while ensuring privacy preservation. The proposed framework considers two models; a classifier model and an adversary model, where the classifier model is integrated with the adversary model, trained jointly considering Fast Gradient Sign Method (FGSM) for generation of adversarial perturbations. This adversarial optimisation enhances classifier’s resilience to attacks, thereby improving both privacy preservation and model accuracy. Experimental analysis reveals that the proposed model achieves up to 99.44% accuracy on adversarial test data, while improving robustness and sustaining high precision and recall across varying client scenarios. The experimental results further ensure the effectiveness of the proposed model in terms of communication efficiency and computational efficiency while reducing inference time and FLOPs making it ideal for secure 5G edge computing applications.","url":"https://doi.org/10.3390/bdcc9090238","authors":["Saniya Zafar","Jonathan White","Phil Legg"],"tags":["Adversarial system","Computer science","Adversary","Robustness (evolution)","Edge computing"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-09-17","doi":"https://doi.org/10.3390/bdcc9090238","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4415241570","name":"Federated Learning for Privacy-Preserving Apparel Supply Chain Analytics","source":"openalex","abstract":"The apparel industry operates through highly complex and globalized supply chains, where effective data analytics plays a critical role in improving demand forecasting, inventory management, logistics coordination, and sustainability practices. However, organizations within the supply chain are often reluctant to share sensitive data due to concerns about privacy, security, compliance, and competitive risks. Traditional centralized analytics approaches exacerbate these concerns by requiring raw data aggregation, thereby increasing the likelihood of breaches and loss of confidentiality. Federated Learning (FL) has emerged as a transformative paradigm that addresses these challenges by enabling decentralized model training without the need to exchange raw data. In this study, we investigate the application of federated learning to apparel supply chain analytics, with a focus on balancing data utility and privacy preservation. We present a framework that integrates federated optimization, secure aggregation, and differential privacy to allow suppliers, manufacturers, distributors, and retailers to collaboratively train robust predictive models while maintaining strict data sovereignty. Our experimental evaluation demonstrates that federated models achieve comparable or superior forecasting accuracy relative to centralized approaches, while significantly reducing privacy risks. Moreover, results indicate notable improvements in demand forecasting, trend identification, and cost optimization tasks across heterogeneous datasets. By reducing data silos, federated learning fosters stronger collaboration, enhances supply chain resilience, and supports sustainability objectives. Overall, this work provides a practical pathway for implementing privacy-preserving analytics in apparel supply chains through federated learning.","url":"https://doi.org/10.30574/wjaets.2025.17.1.1386","authors":["Mizanur Rahman","Samsul Haque","S M Arif Al Sany"],"tags":["Supply chain","Analytics","Computer science","Big data","Sustainability"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-10-16","doi":"https://doi.org/10.30574/wjaets.2025.17.1.1386","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4413291117","name":"Federated Learning for Medical Image Analysis: Privacy-Preserving Paradigms and Clinical Challenges","source":"openalex","abstract":"Federated Learning (FL) has emerged as a transformative paradigm in medical image analysis, addressing the critical challenges of data scarcity and patient privacy. By enabling collaborative model training across decentralized datasets without requiring data sharing, FL aligns with stringent privacy regulations like HIPAA and GDPR. However, existing surveys on FL for medical image analysis often focus narrowly on aspects like privacy and security or fail to categorize methods within a clear taxonomy. Our survey bridges these gaps by systematically organizing FL methodologies for medical image analysis around three core pillars: training, architecture, and unlearning. We emphasize the unique demands of the medical domain, such as handling heterogeneous imaging modalities and annotations. Unlike prior works, our survey strikes a balance between technical rigor and clinical practicality, covering approaches not only for privacy and security but also for accuracy and efficiency. By synthesizing insights from various studies, we provide a comprehensive roadmap to guide researchers and practitioners in leveraging FL's potential to advance AI-driven healthcare.","url":"https://doi.org/10.53941/tai.2025.100010","authors":["Juntao Hu","Zhengjie Yang","Peng Wang","Guanyi Zhao","Hong Huang","Zhimin Zong","Dapeng Wu"],"tags":["Computer science","Image (mathematics)","Internet privacy","Data science","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-08-11","doi":"https://doi.org/10.53941/tai.2025.100010","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412987399","name":"Federated Learning for Early Cardiac Anomaly Prediction in Cross-Silo IoMT Environments","source":"openalex","abstract":"Early detection of cardiovascular anomalies remains critical for proactive patient care, especially within the growing ecosystem of Internet of Medical Things (IoMT) devices. This study explores the application of Federated Learning (FL) to predict early cardiac events using electrocardiogram (ECG) signals across heterogeneous IoMT silos without centralized data sharing. We focus on Premature Ventricular Contraction (PVC) as an example of early event prediction. Using three realworld ECG datasets (PTB-XL, Chapman-Shaoxing, and MITBIH), we simulate cross-silo environments where local models are trained independently and aggregated through FL. Our experiments demonstrate that local models can already achieve high classification performance, but global models obtained via FL lead to consistent improvements in macro precision, recall, and F1-scores across datasets. Visual analysis of early ECG segments further highlights inter-dataset variability, emphasizing the importance of silo-specific characteristics. The results validate that FL is a promising strategy to enable scalable, privacypreserving, and accurate early cardiovascular event prediction in IoMT systems, bridging clinical silos while safeguarding sensitive patient data.","url":"https://doi.org/10.1109/dcoss-iot65416.2025.00087","authors":["Michael Georgiades","Lakis Christodoulou","Andreas Chari","Kezhi Wang","Kin-Hon Ho","Yun Hou","Wei Koong Chai"],"tags":["Silo","Computer science","Anomaly (physics)","Anomaly detection","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-06-09","doi":"https://doi.org/10.1109/dcoss-iot65416.2025.00087","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4401509080","name":"Federated Learning-Based Intrusion Detection Framework for Internet of Things and Edge Computing Backed Critical Infrastructure","source":"openalex","abstract":"Modern Critical Infrastructure (CI) sectors including Smart Girds operate on the Internet of Things and edge computing paradigm. With the enormous growth of these sectors, there are emerging and escalating cyber threats. Traditional Machine Mearning (ML) approaches strive to provide a certain level of resilience against cyber threats but at the cost of privacy leading towards a single point of vulnerability. Following an exhaustive analysis of traditional ML algorithms and cyber threats to the CI, this work introduces a privacy-preserving Federated Learning (FL) driven intrusion detection framework to identify cyber threats focusing on the use case of Smart Grids within the CI. This paper firstly implements and compares various traditional ML algorithms such as Support Vector Machine, Random Forest, and Logistic Regression which works on a centeralised dataset. Secondly, an analysis has been carried out using the proposed FL-based approach to further improve security and privacy along with minimising the need for centralised dataset. Experimental results highlight that our traditional RF-based approach and FL-based approach achieve high intrusion detection accuracy. However, FL has more significant advantages in distributed and privacy-sensitive environments, protecting privacy and reducing the need for data centralisation.","url":"https://doi.org/10.1109/iccworkshops59551.2024.10615814","authors":["Ruofei Meng","Awais Aziz Shah","Muhammad Ali Jamshed","Dimitrios P. Pezaros"],"tags":["Computer science","Edge computing","Internet of Things","Critical infrastructure","Enhanced Data Rates for GSM Evolution"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-06-09","doi":"https://doi.org/10.1109/iccworkshops59551.2024.10615814","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412599322","name":"An optimized oversampling-based federated transfer learning approach for rotating machinery cluster fault diagnosis","source":"openalex","abstract":"Abstract With the development of distributed industrial systems, rotating machinery as the core power and transmission unit of complex distributed industrial systems, its fault diagnosis is very necessary and faces the serious challenge of Non-Independent and Identically Distributed (Non-IID). Although federated transfer learning (FTL) provides decentralized solutions, existing methods do not adequately address the poor classification results caused by data imbalance within the client. This study integrates optimized oversampling techniques into a federated transfer learning framework, proposes an optimized oversampling-based federated transfer learning approach. Firstly, annular region sample optimization (ARSO) is proposed to tackle ambiguous class boundaries from arbitrary sample selection in synthetic minority oversampling technique (SMOTE) by optimizing the sample selection strategy through annular regions. Then ARSO is integrated into a federated transfer learning framework with a One-Dimensional Convolutional Neural Network (1D-CNN), balance the amount of data among clients by extending a few classes of data before federated transfer learning, screen high-quality source clients for knowledge transfer based on a privacy-preserving transfer mechanism selects source clients via category-completeness metadata, and aligns domains using encrypted feature embeddings, proposed the annular region sample optimization federated transfer learning (ARSO-FTL). Experiments demonstrate ARSO-FTL achieves leading performance, recording 96.65% accuracy and an AUC of 0.96. It outperforms distributed baselines and effectively addresses intra-client imbalance and Non-IID challenges within federated transfer learning.","url":"https://doi.org/10.1093/jcde/qwaf068","authors":["Zhao Xu","Liya Yu","Shaobo Li","Chuanjiang Li","Yixiong Feng"],"tags":["Oversampling","Fault (geology)","Cluster (spacecraft)","Transfer of learning","Computer science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-23","doi":"https://doi.org/10.1093/jcde/qwaf068","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4414272485","name":"AI-Driven Privacy Shield: A Secure and Privacy-Preserving Federated Learning Framework","source":"openalex","abstract":"Emerging, highly skilled cyberattacks demand novel and robust techniques for AI-powered privacy preservation. Centralized machine learning models can be compromised with a single point of failure, a data breach, or an adversarial attack. The proposed work presents a unique AI-based Privacy Shield that enhances Privacy-Aware Hybrid Privacy-Preserving Federated Learning (HPP-FL), Blockchain-Enhanced Secure Aggregation (BESA), and Quantum-Resistant Encryption (QRE-FL). By employing an Adaptive Adversarial Training (AAT) strategy, the defense mechanism adjusts to the transforming cyber threats in real-time, thus demonstrating prevention abilities. This approach allows multiple users to collaboratively train a global deep learning model securely with minimal bandwidth and without relying on any central aggregator, similar to federated learning but built on a blockchain-based secure aggregation protocol. Additionally, quantum-resistant encryption mechanisms provide an added layer of security against emerging threats posed by quantum computing, securing the future of federated models. The framework is validated on real-world data from the healthcare, finance, and IoT domains. It shows improvements of 91.2% accuracy, 40% less data leakage, and 35% more resistance to attacks, all while using little extra computing power. This makes it possible for AI security to be scalable and future-proof, making FL a more credible privacy-protecting option for real-world uses.","url":"https://doi.org/10.26706/ijceae.6.3.20250608","authors":["Yasir M. Abdal"],"tags":["Computer science","Federated learning","Adversarial system","Scalability","Encryption"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-09-07","doi":"https://doi.org/10.26706/ijceae.6.3.20250608","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410192464","name":"Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study","source":"openalex","abstract":"An accurate energy consumption prediction becomes crucial with increasing electric vehicle usage for effective power grid management. This research examined the performance of eleven machine learning models for this purpose: Ridge Regression, Lasso Regression, K-Nearest Neighbors, Gradient Boosting, Support Vector Regression, Multi-Layer Perceptron, XGBoost, CatBoost, LightGBM, Gaussian Processes for Regression(GPR) and Extra Trees Regressor, considering real historical data from Colorado. The models were evaluated using different metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), R², Root Mean Squared Error(RMSE) and Normalized Root Mean Squared Error(NRMSE), with visual analyses through scatter plots and time series plots. The best model observed was the Extra Trees Regressor, which had an MAE of 0.5888, an MSE of 3.2683, R² value of 0.9592, RMSE of 1.8078 and NRMSE of 0.020. Gradient Boosting and KNN also returned good results, although they were slightly more dispersed. Nevertheless, while non-linear models like MLP, XGBoost, CatBoost, LightGBM and linear models such as Ridge and Lasso Regression offer valuable insights, they exhibit shortcomings in estimating energy, especially at extreme levels, highlighting limitations in capturing complex non-linear interactions. This study focuses on their applicability to energy projections to demonstrate how well ensemble and non-linear models may capture intricate patterns in time series. These cutting-edge machine learning techniques might greatly enhance energy demand predictions.","url":"https://doi.org/10.1038/s41598-025-94946-7","authors":["Izhar Hussain","Kok Boon Ching","Chessda Uttraphan","Kim Gaik Tay","Adil Noor"],"tags":["Computer science","Energy consumption","Machine learning","Energy (signal processing)","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-05-08","doi":"https://doi.org/10.1038/s41598-025-94946-7","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W7132852778","name":"Privacy-Preserving Generative AI in Healthcare Systems Using Federated Learning Approaches","source":"openalex","abstract":"The research paper focuses on the mechanism of introducing Federated Learning alongside privacy-saving strategies in Generative Artificial Intelligence to healthcare applications. The study analyses the privacy protection/model accuracy trade-off by implementing Differential Privacy and Secure Aggregation. The synthetic datasets were applied to the model to train in five rounds and included many federated clients, that improved its accuracy by 49% to 60%. The findings suggest that Federated Learning has the potential to improve the performance of AI and preserve the privacy of data at the same time. Other challenges covered in the study include mode collapse and privacy-utility trade-offs, and recommended solutions to achieve the efficient and secure healthcare AI models.","url":"https://doi.org/10.64751/ijdim.2026.v5.n1.pp78-88","authors":["Rajesh A. Poojari"],"tags":["Federated learning","Computer science","Differential privacy","Generative grammar","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2026-02-28","doi":"https://doi.org/10.64751/ijdim.2026.v5.n1.pp78-88","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W7118956619","name":"SplitML: A Unified Privacy-Preserving Architecture for Federated Split-Learning in Heterogeneous Environments","source":"openalex","abstract":"While Federated Learning (FL) and Split Learning (SL) aim to uphold data confidentiality by localized training, they remain susceptible to adversarial threats such as model poisoning and sophisticated inference attacks. To mitigate these vulnerabilities, we propose SplitML, a secure and privacy-preserving framework for Federated Split Learning (FSL). By integrating IND−CPAD secure Fully Homomorphic Encryption (FHE) with Differential Privacy (DP), SplitML establishes a defense-in-depth strategy that minimizes information leakage and thwarts reconstructive inference attempts. The framework accommodates heterogeneous model architectures by allowing clients to collaboratively train only the common top layers while keeping their bottom layers exclusive to each participant. This partitioning strategy ensures that the layers closest to the sensitive input data are never exposed to the centralized server. During the training phase, participants utilize multi-key CKKS FHE to facilitate secure weight aggregation, which ensures that no single entity can access individual updates in plaintext. For collaborative inference, clients exchange activations protected by single-key CKKS FHE to achieve a consensus derived from Total Labels (TL) or Total Predictions (TP). This consensus mechanism enhances decision reliability by aggregating decentralized insights while obfuscating soft-label confidence scores that could be exploited by attackers. Our empirical evaluation demonstrates that SplitML provides substantial defense against Membership Inference (MI) attacks, reduces temporal training costs compared to standard encrypted FL, and improves inference precision via its consensus mechanism, all while maintaining a negligible impact on federation overhead.","url":"https://doi.org/10.3390/electronics15020267","authors":["Devharsh Trivedi","Aymen Boudguiga","Nesrine Kaaniche","Nikos Triandopoulos"],"tags":["Federated learning","Inference","Computer science","Homomorphic encryption","Confidentiality"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2026-01-07","doi":"https://doi.org/10.3390/electronics15020267","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W7155501960","name":"Toward intelligent and resilient microgrids: A survey of machine learning approaches for renewable energy integration","source":"openalex","abstract":"","url":"https://doi.org/10.1016/j.apenergy.2026.127929","authors":["Darioush Razmi","Peyman Razmi","Oluleke Babayomi","Zhenbin Zhang"],"tags":["Renewable energy","Computer science","Artificial intelligence","Machine learning","Energy (signal processing)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2026-04-24","doi":"https://doi.org/10.1016/j.apenergy.2026.127929","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4410748034","name":"FedCVG: a two-stage robust federated learning optimization algorithm","source":"openalex","abstract":"Federated learning provides an effective solution to the data privacy issue in distributed machine learning. However, distributed federated learning systems are inherently susceptible to data poisoning attacks and data heterogeneity. Under conditions of high data heterogeneity, the gradient conflict problem in federated learning becomes more pronounced, making traditional defense mechanisms against poisoning attacks less adaptable between scenarios with and without attacks. To address this challenge, we design a two-stage federated learning framework for defending against poisoning attacks-FedCVG. During implementation, FedCVG first removes malicious clients using a reputation-based clustering method, and then optimizes communication overhead through a virtual aggregation mechanism. Extensive experimental results show that, compared to other baseline methods, FedCVG improves average accuracy by 4.2% and reduces communication overhead by approximately 50% while defending against poisoning attacks.","url":"https://doi.org/10.1038/s41598-025-02722-4","authors":["Runze Zhang","Yang Zhang","Yating Zhao","Bin Jia","Wenjuan Lian"],"tags":["Computer science","Stage (stratigraphy)","Optimization algorithm","Algorithm","Artificial intelligence"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-05-26","doi":"https://doi.org/10.1038/s41598-025-02722-4","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4406193279","name":"A Survey on Federated Learning in Human Sensing","source":"openalex","abstract":"Human Sensing, a field that leverages technology to monitor human activities, psycho-physiological states, and interactions with the environment, enhances our understanding of human behavior and drives the development of advanced services that improve overall quality of life. However, its reliance on detailed and often privacy-sensitive data as the basis for its machine learning (ML) models raises significant legal and ethical concerns. The recently proposed ML approach of Federated Learning (FL) promises to alleviate many of these concerns, as it is able to create accurate ML models without sending raw user data to a central server. While FL has demonstrated its usefulness across a variety of areas, such as text prediction and cyber security, its benefits in Human Sensing are under-explored, given the particular challenges in this domain. This survey conducts a comprehensive analysis of the current state-of-the-art studies on FL in Human Sensing, and proposes a taxonomy and an eight-dimensional assessment for FL approaches. Through the eight-dimensional assessment, we then evaluate whether the surveyed studies consider a specific FL-in-Human-Sensing challenge or not. Finally, based on the overall analysis, we discuss open challenges and highlight five research aspects related to FL in Human Sensing that require urgent research attention. Our work provides a comprehensive corpus of FL studies and aims to assist FL practitioners in developing and evaluating solutions that effectively address the real-world complexities of Human Sensing.","url":"https://doi.org/10.48550/arxiv.2501.04000","authors":["Mohan Li","Martin Gjoreski","Pietro Barbiero","Gašper Slapničar","Mitja Luštrek","Nicholas D. Lane","Marc Langheinrich"],"tags":["Computer science","Data science","Human–computer interaction"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-07","doi":"https://doi.org/10.48550/arxiv.2501.04000","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4409409952","name":"Strategies to Improve the Robustness and Generalizability of Deep Learning Segmentation and Classification in Neuroimaging","source":"openalex","abstract":"Artificial Intelligence (AI) and deep learning models have revolutionized diagnosis, prognostication, and treatment planning by extracting complex patterns from medical images, enabling more accurate, personalized, and timely clinical decisions. Despite its promise, challenges such as image heterogeneity across different centers, variability in acquisition protocols and scanners, and sensitivity to artifacts hinder the reliability and clinical integration of deep learning models. Addressing these issues is critical for ensuring accurate and practical AI-powered neuroimaging applications. We reviewed and summarized the strategies for improving the robustness and generalizability of deep learning models for the segmentation and classification of neuroimages. This review follows a structured protocol, comprehensively searching Google Scholar, PubMed, and Scopus for studies on neuroimaging, task-specific applications, and model attributes. Peer-reviewed, English-language studies on brain imaging were included. The extracted data were analyzed to evaluate the implementation and effectiveness of these techniques. The study identifies key strategies to enhance deep learning in neuroimaging, including regularization, data augmentation, transfer learning, and uncertainty estimation. These approaches address major challenges such as data variability and domain shifts, improving model robustness and ensuring consistent performance across diverse clinical settings. The technical strategies summarized in this review can enhance the robustness and generalizability of deep learning models for segmentation and classification to improve their reliability for real-world clinical practice.","url":"https://doi.org/10.3390/biomedinformatics5020020","authors":["Anh T. Tran","Tal Zeevi","Seyedmehdi Payabvash"],"tags":["Generalizability theory","Artificial intelligence","Computer science","Neuroimaging","Robustness (evolution)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-04-14","doi":"https://doi.org/10.3390/biomedinformatics5020020","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W7139924667","name":"A systematic literature review on federated cyber-attack detection for edge intelligence: Challenges, approaches, and future directions","source":"openalex","abstract":"The rapid expansion of Edge Computing (EC) and Internet of Things devices has introduced significant cybersecurity challenges, necessitating advanced and privacy-preserving attack detection strategies. Traditional cyber-attack detection methods and centralized machine learning solutions face critical limitations in addressing privacy concerns, resource constraints, and the evolving nature of cyber threats in edge environments. Federated Learning (FL) offers a transformative solution by enabling distributed model training across edge devices while preserving data privacy. This systematic literature review investigates FL for cyber-attack detection in EC environments using the PRISMA methodology, analyzing 131 primary studies from 2020–2025 across five major databases. Our contributions include: (1) a comprehensive PRISMA-compliant framework encompassing seven thematic areas with detailed comparative analysis, (2) an in-depth gap analysis with actionable recommendations for privacy-performance trade-offs, scalability, and standardization challenges, and (3) a forward-looking research agenda addressing generative models, collaborative defense, 6G-enabled intelligence, and zero-trust architectures. Unlike existing surveys, this work provides the most comprehensive scope with bibliometric analysis, multi-perspective evaluation, and practical deployment guidelines, serving as a foundational reference for advancing federated cyber-attack detection in edge computing environments.","url":"https://doi.org/10.1016/j.cosrev.2026.100965","authors":["Zeseya Sharmin","Md Palash Uddin","Yong Xiang","Feifei Chen"],"tags":["Computer science","Systematic review","Enhanced Data Rates for GSM Evolution","Information retrieval","Data science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2026-03-20","doi":"https://doi.org/10.1016/j.cosrev.2026.100965","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4415688581","name":"Robust aggregation algorithms for federated learning in unreliable network environments","source":"openalex","abstract":"Federated learning (FL) allows joint model training on distributed devices without losing data locality, but its results are significantly worse in unreliable network systems where packets are dropped, clients fail, resources are heterogeneous, and adversarial (Byzantine) agents exist. The viability of FL to withstand these unfavorable conditions is keyed on the robust aggregation algorithms. The paper meticulously examines powerful methods of aggregation, which include: geometric-median methods (RFA), Krum/Multi-Krum, trimmed-mean/coordinate-wise defenses, g-divergence estimators, trust-based aggregators (FLTrust), and layer-wise aggregation methods (FedRoLA) and compares their performance on simulated unreliable networks, which model packet loss, communication delay, and malicious client actions (McMahan et al., 2017; Blanchard et al., 2017; P We examine accuracy, convergence speed, communication cost and resilience in the face of model-poisoning attacks with the help of benchmark image tasks and a set of network unreliability scenarios. We find that robust aggregators combining statistical outlier resistance and structural (layer-wise) aggregation or trust calibration (especially RFA and FedRoLA) are more accurate and converge more quickly than naive FedAvg in high packet-loss and moderate Byzantine contamination and we observe up to 12 percent improvement in test accuracy with 30 percent simulated packet loss. We also talk about the trade offs between robustness, communication overhead and privacy (secure aggregation) and present a hybrid design pattern that incorporates robust aggregation and adaptive client selection with secure aggregation so as to address both unreliable links as well as adversarial updates. The results provide prescriptive advice on the use of FL in mobile, IoT, and vehicular networks that have limited reliability and security requirements and provide future directions such as privacy-conscious robust aggregation, fairness-conscious weighting, and testbed implementation.","url":"https://doi.org/10.54097/n0dpaf43","authors":["Ziyang Zeng","Shiyu Yang","Guanyu Ding"],"tags":["Computer science","Network packet","Outlier","Overhead (engineering)","Resilience (materials science)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-10-30","doi":"https://doi.org/10.54097/n0dpaf43","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W3097471692","name":"Digital Twins: State of the art theory and practice, challenges, and open research questions","source":"openalex","abstract":"Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation, optimisation and accurate forecasting. However, the theoretical framework and practical implementations of digital twin (DT) are yet to fully achieve this vision at scale. Although an increasing number of successful implementations exist in research and industrial works, sufficient implementation details are not publicly available, making it difficult to fully assess their components and effectiveness, to draw comparisons, identify successful solutions, share lessons, and thus to jointly advance and benefit from the DT methodology. This work first presents a review of relevant DT research and industrial works, focusing on the key DT features, current approaches in different domains, and successful DT implementations, to infer the key DT components and properties, and to identify current limitations and reasons behind the delay in the widespread implementation and adoption of digital twin. This work identifies that the major reasons for this delay are: the fact the DT is still a fast evolving concept; the lack of a universal DT reference framework, e.g. DT standards are scarce and still evolving; problem- and domain-dependence; security concerns over shared data; lack of DT performance metrics; and reliance of digital twin on other fast-evolving technologies. Advancements in machine learning, Internet of Things (IoT) and big data have led to significant improvements in DT features such as real-time monitoring and accurate forecasting. Despite this progress and individual company-based efforts, certain research and implementation gaps exist in the field, which have so far prevented the widespread adoption of the DT concept and technology; these gaps are also discussed in this work. Based on reviews of past work and the identified gaps, this work then defines a conceptualisation of DT which includes its components and properties; these also validate the uniqueness of DT as a concept, when compared to similar concepts such as simulation, autonomous systems and optimisation. Real-life case studies are used to showcase the application of the conceptualisation. This work discusses the state-of-the-art in DT, addresses relevant and timely DT questions, and identifies novel research questions, thus contributing to a better understanding of the DT paradigm and advancing the theory and practice of DT and its allied technologies.","url":"https://doi.org/10.1016/j.jii.2022.100383","authors":["Angira Sharma","Edward Elson Kosasih","Jie Zhang","Alexandra Brintrup","Anisoara Calinescu"],"tags":["Implementation","Computer science","Field (mathematics)","Key (lock)","Data science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2022-08-08","doi":"https://doi.org/10.1016/j.jii.2022.100383","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412947457","name":"Client to Server: Heterogeneous Distribution Knowledge Transfer for Federated Learning","source":"openalex","abstract":"Federated learning (FL) is an emerging distributed machine learning paradigm that provides privacy guarantees for training robust models on distributed clients. The primary challenge of FL is data heterogeneity,which slows down model convergence and degrades model performance. Knowledge Distillation has recently demonstrated effectiveness in addressing this challenge. However, these approaches neglect the statistical heterogeneity in local models and the uncertainty of the data distribution in the global model, which results in the ensemble knowledge cannot be fully utilized to guide local model learning. In this work, we propose an unsupervised knowledge distillation method migrating the local class-level pseudo-data sample scheme in the server for fine-tuning the global model. Specifically, we provide the conditional autoencoder for each client to maintain a dynamic generator in the server, which ensembles the client’s class-level information. The proposal produces an auxiliary dataset representing the global class-level distribution to regulate the local model as an inductive knowledge bias and employs unsupervised knowledge distillation to enhance the aggregated model’s performance. The extensive experiments show that our proposal significantly outperforms the current state-of-theart FL algorithms and can be integrated as a flexible plugin into existing FL optimization algorithms to enhance model performance.","url":"https://doi.org/10.26599/tst.2025.9010047","authors":["Rui Zhao","Xiao Yang","Peng Zhi","Zhihe Zhang","Gang Liu","Changyan Di","Qingguo Zhou"],"tags":["Computer science","Distribution (mathematics)","Transfer (computing)","Transfer of learning","Knowledge transfer"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-08-05","doi":"https://doi.org/10.26599/tst.2025.9010047","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4405547182","name":"Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach","source":"openalex","abstract":"Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI researchers and practitioners have introduced principles and guidelines to build systems that make reliable and trustworthy decisions. From a practical perspective, conventional ML systems process historical data to extract the features that are consequently used to train ML models that perform the desired task. However, in practice, a fundamental challenge arises when the system needs to be operationalized and deployed to evolve and operate in real-life environments continuously. To address this challenge, Machine Learning Operations (MLOps) have emerged as a potential recipe for standardizing ML solutions in deployment. Although MLOps demonstrated great success in streamlining ML processes, thoroughly defining the specifications of robust MLOps approaches remains of great interest to researchers and practitioners. In this paper, we provide a comprehensive overview of the trustworthiness property of MLOps systems. Specifically, we highlight technical practices to achieve robust MLOps systems. In addition, we survey the existing research approaches that address the robustness aspects of ML systems in production. We also review the tools and software available to build MLOps systems and summarize their support to handle the robustness aspects. Finally, we present the open challenges and propose possible future directions and opportunities within this emerging field. The aim of this paper is to provide researchers and practitioners working on practical AI applications with a comprehensive view to adopt robust ML solutions in production environments.","url":"https://doi.org/10.1145/3708497","authors":["Firas Bayram","Bestoun S. Ahmed"],"tags":["Computer science","Trustworthiness","Robustness (evolution)","Artificial intelligence","Machine learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-12-18","doi":"https://doi.org/10.1145/3708497","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4408287608","name":"DataSHIELD: mitigating disclosure risk in a multi-site federated analysis platform","source":"openalex","abstract":"Motivation: The validity of epidemiologic findings can be increased using triangulation, i.e. comparison of findings across contexts, and by having sufficiently large amounts of relevant data to analyse. However, access to data is often constrained by practical considerations and by ethico-legal and data governance restrictions. Gaining access to such data can be time-consuming due to the governance requirements associated with data access requests to institutions in different jurisdictions. Results: DataSHIELD is a software solution that enables remote analysis without the need for data transfer (federated analysis). DataSHIELD is a scientifically mature, open-source data access and analysis platform aligned with the 'Five Safes' framework, the international framework governing safe research access to data. It allows real-time analysis while mitigating disclosure risk through an active multi-layer system of disclosure-preventing mechanisms. This combination of real-time remote statistical analysis, disclosure prevention mechanisms, and federation capabilities makes DataSHIELD a solution for addressing many of the technical and regulatory challenges in performing the large-scale statistical analysis of health and biomedical data. This paper describes the key components that comprise the disclosure protection system of DataSHIELD. These broadly fall into three classes: (i) system protection elements, (ii) analysis protection elements, and (iii) governance protection elements. Availability and implementation: Information about the DataSHIELD software is available in https://datashield.org/ and https://github.com/datashield.","url":"https://doi.org/10.1093/bioadv/vbaf046","authors":["Demetris Avraam","Rebecca Wilson","Noemi Aguirre Chan","Soumya Banerjee","Tom Bishop","O. W. Butters","Tim Cadman","Luise Cederkvist","Liesbeth Duijts","Xavier Escribà-Montagut","Hugh Garner","Gonçalo Gonçalves","Juan R. González","Sido Haakma","Mette Hartlev","Jan Hasenauer","Manuel Huth","Eleanor Hyde","Vincent W. V. Jaddoe","Yannick Marcon","Michaela Th. Mayrhofer","Fruzsina Molnár‐Gábor","Andreï S. Morgan","Madeleine J. Murtagh","Marc Nestor","Anne‐Marie Nybo Andersen","Simon C. Parker","Angela Pinot de Moira","Florian Schwarz","Katrine Strandberg‐Larsen","Morris A. Swertz","Marieke Welten","Stuart Wheater","Paul R. Burton"],"tags":["Computer science","Business","Risk analysis (engineering)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2024-12-26","doi":"https://doi.org/10.1093/bioadv/vbaf046","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4407237697","name":"A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction","source":"openalex","abstract":"Intrusion detection systems are essential for securing wireless sensor networks (WSNs) and Internet of Things (IoT) environments against various threats. This study presents a novel hybrid machine learning (ML) model that integrates KMeans-SMOTE (KMS) for data balancing and principal component analysis (PCA) for dimensionality reduction, evaluated using the WSN-DS and TON-IoT datasets. The model employs classifiers such as Decision Tree Classifier, Random Forest Classifier (RFC), and gradient boosting techniques like XGBoost (XGBC) to enhance detection accuracy and efficiency. The proposed hybrid (KMS + PCA + RFC) approach achieves remarkable performance, with an accuracy of 99.94% and an f1-score of 99.94% on the WSN-DS dataset. For the TON-IoT dataset, it achieves 99.97% accuracy and an f1-score of 99.97%, outperforming traditional SMOTE TomekLink and Generative Adversarial Network-based data balancing techniques. This hybrid approach addresses class imbalance and high-dimensionality challenges, providing scalable and robust intrusion detection. Complexity analysis reveals that the proposed model reduces training and prediction times, making it suitable for real-time applications.","url":"https://doi.org/10.1038/s41598-025-87028-1","authors":["Md. Alamin Talukder","Majdi Khalid","Nasrin Sultana"],"tags":["Dimensionality reduction","Computer science","Intrusion detection system","Reduction (mathematics)","Machine learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-02-07","doi":"https://doi.org/10.1038/s41598-025-87028-1","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4409640804","name":"Federated learning for privacy-preserving AI in human–robot collaboration for smart manufacturing","source":"openalex","abstract":"Purpose The study aims to address privacy and security challenges in AI-driven human–robot collaboration (HRC) by developing a privacy-preserving federated learning framework. Traditional centralized AI models expose sensitive manufacturing data to cybersecurity risks, creating barriers to AI adoption in regulated industries. This research proposes a decentralized learning approach that enables robots to collaboratively train AI models without sharing raw data, ensuring compliance with privacy regulations (e.g. GDPR and CCPA). The study seeks to advance trustworthy AI-driven automation, improving robotic decision-making, scalability and real-time adaptability while safeguarding sensitive industrial information. Design/methodology/approach This study proposes a Multi-Agent Federated Reinforcement Learning (MARL-FL) framework for privacy-preserving AI in human–robot collaboration (HRC) for smart manufacturing. The framework integrates federated learning (FL), reinforcement learning (RL) and differential privacy to enhance robotic decision-making while ensuring data security. A digital twin simulation of a smart factory is used for evaluation, where collaborative robots autonomously learn and optimize tasks using decentralized AI training. Performance is assessed using model accuracy, task success rate, convergence speed and privacy leakage reduction metrics, demonstrating FL’s effectiveness in improving secure AI-driven automation. Findings Experimental results from a digital twin-based smart factory simulation demonstrate that the proposed FL-based framework achieves 91.2% model accuracy, improves task success rates by 7.6% and reduces privacy leakage risks by 41.5% compared to centralized AI models. The federated reinforcement learning approach also accelerates model convergence by 25%, enabling faster adaptation to dynamic manufacturing conditions. The study confirms that FL enhances AI-driven collaboration, operational efficiency and data security, making it a viable solution for privacy-preserving smart manufacturing. Originality/value This research is among the first to integrate federated learning, reinforcement learning and privacy-preserving AI techniques for secure human–robot collaboration in Industry 4.0. Unlike conventional AI models that rely on centralized data processing, the proposed MARL-FL framework enables secure, decentralized learning, reducing cybersecurity risks and regulatory concerns. The study provides new insights into privacy-aware AI governance in industrial automation, making it highly valuable for researchers, policymakers and manufacturers seeking trustworthy AI-driven robotics solutions.","url":"https://doi.org/10.1108/jimse-03-2025-0003","authors":["Milad Rahmati"],"tags":["Computer science","Robot","Human–computer interaction","Human–robot interaction","Internet privacy"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-04-21","doi":"https://doi.org/10.1108/jimse-03-2025-0003","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4415481241","name":"ZTID-IoV: Zero-Trust Intrusion Detection in IoV Using Neurosymbolic AI Approach With Federated Meta-Learning","source":"openalex","abstract":"The rapid growth of the Internet of Vehicles (IoVs) and smart consumer electronics has generated cybersecurity concerns that require an intelligent, adaptable, and privacy-preserving Intrusion Detection System (IDS). This study introduces ZTID-IoV, a novel neurosymbolic AI framework that integrates federated learning, lightweight transformers, and meta-learning to improve threat detection while preserving user privacy in consumer IoVs. Our approach leverages neural components such as a transformer model for recognizing patterns in network traffic, combined with symbolic AI techniques such as self-organizing maps for interpretable client clustering and rule-guided reasoning, to achieve robust cybersecurity in distributed environments. A lightweight transformer architecture optimizes performance for resource-constrained edge devices, and SOM-based clustering enhances model aggregation by grouping devices with similar behavioral patterns. The proposed system employs Model-Agnostic Meta-Learning (MAML) to enable rapid adaptation to emerging threats across diverse consumer devices, while federated learning ensures decentralized model training without exposing sensitive user data. Experiments on real-world IoT intrusion datasets demonstrate that our framework achieves higher detection accuracy compared to centralized and pure neural approaches while maintaining low computational overhead. Additionally, the neurosymbolic design provides interpretable threat explanations, crucial for consumer applications where transparency is essential. The results highlight the potential of ZTID-IoV in enabling zero-trust security for IoV and other connected consumer electronics. This work contributes to the evolving landscape of AI-driven cybersecurity by addressing critical challenges in privacy and adaptability, making ZTID-IoV particularly suitable for next-generation IoV ecosystems.","url":"https://doi.org/10.1109/tce.2025.3625081","authors":["Farhan Ullah","Gautam Srivastava","Leonardo Mostarda","Umar Raza"],"tags":["Computer science","Intrusion detection system","Cluster analysis","Transparency (behavior)","Transformer"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-10-23","doi":"https://doi.org/10.1109/tce.2025.3625081","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4408372351","name":"Using Homomorphic Proxy Re‐Encryption to Enhance Security and Privacy of Federated Learning‐Based Intelligent Connected Vehicles","source":"openalex","abstract":"Intelligent connected vehicles (ICVs) are one of the fast‐growing directions that plays a significant role in the area of autonomous driving. To realize collaborative computation among ICVs, federated learning (FL) or federated‐based large language model (FedLLM) as a promising distributed approach has been used to support various collaborative application computations in ICVs scenarios, for example, analyzing vehicle driving information to realize trajectory prediction, voice‐activated controls, conversational AI assistants. Unfortunately, recent research reveals that FL systems are still faced with privacy challenges from honest‐but‐curious server, honest‐but‐curious distributed participants, or the collusion between participants and the server. These threats can lead to the leakage of sensitive private data, such as location information and driving conditions. Homomorphic encryption (HE) is one of the typical mitigation that has few effects on the model accuracy and has been studied before. However, single‐key HE cannot resist collusion between participants and the server, multikey HE is not suitable for ICVs scenarios. In this work, we proposed a novel approach that combines FL with homomorphic proxy re‐encryption (PRE) which is based on participants’ ID information. By doing so, the FL‐based ICVs can be able to successfully defend against privacy threats. In addition, we analyze the security and performance of our method, and the theoretical analysis and the experiment results show that our defense framework with ID‐based homomorphic PRE can achieve a high‐security level and efficient computation. We anticipate that our approach can serve as a fundamental point to support the extensive research on FedLLMs privacy‐preserving.","url":"https://doi.org/10.1049/ise2/4632786","authors":["Yang Bai","Y. S. Rao","Hongyan Wu","Juan Wang","Wentao Yang","Gaojie Xing","Jiawei Yang","Xiaoshu Yuan"],"tags":["Homomorphic encryption","Computer science","Proxy (statistics)","Computer security","Proxy re-encryption"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-01-01","doi":"https://doi.org/10.1049/ise2/4632786","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412751381","name":"Lightweight Anomaly Detection in Digit Recognition Using Federated Learning","source":"openalex","abstract":"This study presents a lightweight autoencoder-based approach for anomaly detection in digit recognition using federated learning on resource-constrained embedded devices. We implement and evaluate compact autoencoder models on the ESP32-CAM microcontroller, enabling both training and inference directly on the device using 32-bit floating-point arithmetic. The system is trained on a reduced MNIST dataset (1000 resized samples) and evaluated using EMNIST and MNIST-C for anomaly detection. Seven fully connected autoencoder architectures are first evaluated on a PC to explore the impact of model size and batch size on training time and anomaly detection performance. Selected models are then re-implemented in the C programming language and deployed on a single ESP32 device, achieving training times as short as 12 min, inference latency as low as 9 ms, and F1 scores of up to 0.87. Autoencoders are further tested on ten devices in a real-world federated learning experiment using Wi-Fi. We explore non-IID and IID data distribution scenarios: (1) digit-specialized devices and (2) partitioned datasets with varying content and anomaly types. The results show that small unmodified autoencoder models can be effectively trained and evaluated directly on low-power hardware. The best models achieve F1 scores of up to 0.87 in the standard IID setting and 0.86 in the extreme non-IID setting. Despite some clients being trained on corrupted datasets, federated aggregation proves resilient, maintaining high overall performance. The resource analysis shows that more than half of the models and all the training-related allocations fit entirely in internal RAM. These findings confirm the feasibility of local float32 training and collaborative anomaly detection on low-cost hardware, supporting scalable and privacy-preserving edge intelligence.","url":"https://doi.org/10.3390/fi17080343","authors":["Anja Tanović","Ivan Mezei"],"tags":["Autoencoder","Computer science","MNIST database","Anomaly detection","Inference"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-30","doi":"https://doi.org/10.3390/fi17080343","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4412806734","name":"Breakthroughs in Brain Tumor Detection: Leveraging Deep Learning and Transfer Learning for MRI-Based Classification","source":"openalex","abstract":"Identifying and classifying brain tumors play a pivotal role in gaining insights into their underlying mechanisms. In contemporary medical practice, the integration of Computer-assisted Diagnosis (CAD) and machine learning, particularly deep learning, has significantly enhanced the radiologist's ability to accurately identify brain tumors. Unlike traditional machine learning methods, which often rely on manual feature engineering for classification, deep learning models can be structured to prevent the need for manual feature extraction, yielding highly accurate classification outcomes. This paper customizes advanced deep learning models including VGG19, ResNet50, InceptionV3, and EfficientNetV2 as the most powerful deep learning models aimed at the identification of both binary (normal and abnormal) and multiclass: 17 classes including Glioma, Meningioma, Neurocytoma, and other types of injuries such as Abscesses and Cysts. We utilize a publicly available dataset containing 4449 MRI images. Subsequently, we conduct a comprehensive comparative analysis of our proposed models against existing models in the literature. Our experimental findings indicates that EfficientNetV2 outperforms other state-of-the-art deep-learning models.","url":"https://doi.org/10.59543/comdem.v2i.14243","authors":["Alireza Golkarieh","Sajjad Rezvani Boroujeni","Kiana Kiashemshaki","Maryam Deldadehasl","Hamed Aghayarzadeh","Azita Ramezani"],"tags":["Transfer of learning","Deep learning","Computer science","Artificial intelligence","Machine learning"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-07-31","doi":"https://doi.org/10.59543/comdem.v2i.14243","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4411351109","name":"FedEmerge: An Entropy-Guided Federated Learning Method for Sensor Networks and Edge Intelligence","source":"openalex","abstract":"Introduction: Federated Learning (FL) is a distributed machine learning paradigm where a global model is collaboratively trained across multiple decentralized clients without exchanging raw data. This is especially important in sensor networks and edge intelligence, where data privacy, bandwidth constraints, and data locality are paramount. Traditional FL methods like FedAvg struggle with highly heterogeneous (non-IID) client data, which is common in these settings. Background: Traditional FL aggregation methods, such as FedAvg, weigh client updates primarily by dataset size, potentially overlooking the informativeness or diversity of each client’s contribution. These limitations are especially pronounced in sensor networks and IoT environments, where clients may hold sparse, unbalanced, or single-modality data. Methods: We propose FedEmerge, an entropy-guided aggregation approach that adjusts each client’s impact on the global model based on the information entropy of its local data distribution. This formulation introduces a principled way to quantify and reward data diversity, enabling an emergent collective learning dynamic in which globally informative updates drive convergence. Unlike existing methods that weigh updates by sample count or heuristics, FedEmerge prioritizes clients with more representative, high-entropy data. The FedEmerge algorithm is presented with full mathematical detail, and we prove its convergence under the Polyak–Łojasiewicz (PL) condition. Results: Theoretical analysis shows that FedEmerge achieves linear convergence to the optimal model under standard assumptions (smoothness and PL condition), similar to centralized gradient descent. Empirically, FedEmerge improves global model accuracy and convergence speed on highly skewed non-IID benchmarks, and it reduces performance disparities among clients compared to FedAvg. Evaluations on CIFAR-10 (non-IID), Federated EMNIST, and Shakespeare datasets confirm its effectiveness in practical edge-learning settings. Conclusions: This entropy-guided federated strategy demonstrates that weighting client updates by data diversity enhances learning outcomes in heterogeneous networks. The approach preserves privacy like standard FL and adds minimal computation overhead, making it a practical solution for real-world federated systems.","url":"https://doi.org/10.3390/s25123728","authors":["Koffka Khan"],"tags":["Computer science","Enhanced Data Rates for GSM Evolution","Entropy (arrow of time)","Artificial intelligence","Data science"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-06-14","doi":"https://doi.org/10.3390/s25123728","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4415594398","name":"RewardChain: A Blockchain-Based Incentive Mechanism for Federated Learning in Consumer-Centric Internet of Medical Things","source":"openalex","abstract":"Federated learning is a promising approach that enables collaborative machine learning (ML) in distributed environments, such as the Internet of Medical Things (IoMT) while preserving consumer privacy. It allows multiple consumers to collaboratively train a model using their own data, sharing only the locally trained model rather than the raw data. Most existing federated learning systems assume a high level of trust in participating nodes, which is unrealistic in real-world consumer-centric scenarios. Involving untrusted nodes can compromise the integrity of the training process and result in potential data breaches. To address these challenges, this paper presents REWARDCHAIN, a novel federated learning framework that leverages blockchain technology to ensure trust and accountability among untrusted IoMT consumers. By recording all model updates and client contributions on an immutable blockchain ledger, REWARDCHAIN allows auditing of the entire training process and attributing any malicious behaviour to specific nodes. Moreover, we design an incentive mechanism that evaluates contributions based on data quality and participant reputation. This system motivates participants to contribute high-quality data through a reputation-constrained reward allocation. Our evaluations show that REWARDCHAIN effectively balances trust, security, and model performance, facilitating a more secure and effective federated learning ecosystem.","url":"https://doi.org/10.1109/tce.2025.3626199","authors":["Ahmad A Alsharidah","Devki Nandan Jha","Ellis Solaiman","Bo Wei","Gagangeet Singh Aujla","Rajiv Ranjan"],"tags":["Computer science","Incentive","Process (computing)","Federated learning","Blockchain"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-10-27","doi":"https://doi.org/10.1109/tce.2025.3626199","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"},{"id":"oa:W4415648046","name":"A Multi-View-Based Federated Learning Approach for Intrusion Detection","source":"openalex","abstract":"Intrusion detection aims to identify the unauthorized activities within computer networks or systems by classifying events into normal or abnormal categories. As modern scenarios often involve multi-source data, multi-view fusion deep learning methods are employed to leverage diverse viewpoints for enhancing security threat detection. This paper introduces a novel intrusion detection approach using multi-view fusion within a federated learning framework, proposing an integrated AE Neural SVM (AE-NSVM) model that combines auto-encoder (AE) multi-view feature extraction and Support Vector Machine (SVM) classification. This approach simultaneously learns representative features from multiple views and classifies network samples into normal or seven attack categories while employing federated learning across clients to ensure adaptability and robustness in diverse network environments. The experimental results obtained from two benchmark datasets validate its superiority: on TON_IoT, the CAE-NSVM model achieves a highest F1-measure of 0.792 (1.4% higher than traditional pipeline systems); on UNSW-NB15, it delivers an F1-score of 0.829 with a 73% reduced training time and an 89% faster inference compared to baseline models. These results demonstrate the advantages of multi-view fusion in federated learning for balancing accuracy and efficiency in distributed intrusion detection systems.","url":"https://doi.org/10.3390/electronics14214166","authors":["Jia Yu","Guoqiang Wang","Nianfeng Shi","Raghav Saxena","Brian Lee"],"tags":["Computer science","Intrusion detection system","Robustness (evolution)","Adaptability","Leverage (statistics)"],"confidence":0.72,"sites":["privacy-computing"],"publishedDate":"2025-10-24","doi":"https://doi.org/10.3390/electronics14214166","addedAt":"2026-08-31T14:45:42.843Z","updatedAt":"2026-08-31T14:45:42.843Z"}]